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Marta miniprojekt + raport(2)

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Marta Roszak 2020-05-10 18:57:52 +02:00
parent c025d1b5bc
commit d63fc89ed4
320 changed files with 183407 additions and 8 deletions
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23
Restaurant/Marta/.idea/workspace.xml
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Restaurant/Marta/venv/Lib/site-packages/joblib-0.14.1.dist-info/INSTALLER Normal file
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pip

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Restaurant/Marta/venv/Lib/site-packages/joblib-0.14.1.dist-info/LICENSE.txt Normal file
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BSD 3-Clause License
Copyright (c) 2008-2016, The joblib developers.
All rights reserved.
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 in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder 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
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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.

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Restaurant/Marta/venv/Lib/site-packages/joblib-0.14.1.dist-info/METADATA Normal file
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Metadata-Version: 2.1
Name: joblib
Version: 0.14.1
Summary: Lightweight pipelining: using Python functions as pipeline jobs.
Home-page: https://joblib.readthedocs.io
Author: Gael Varoquaux
Author-email: gael.varoquaux@normalesup.org
License: BSD
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Classifier: Topic :: Software Development :: Libraries
Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular:
1. transparent disk-caching of functions and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
Joblib is optimized to be **fast** and **robust** on large
data in particular and has specific optimizations for `numpy` arrays. It is
**BSD-licensed**.
==================== ===============================================
**Documentation:** https://joblib.readthedocs.io
**Download:** https://pypi.python.org/pypi/joblib#downloads
**Source code:** https://github.com/joblib/joblib
**Report issues:** https://github.com/joblib/joblib/issues
==================== ===============================================
Vision
--------
The vision is to provide tools to easily achieve better performance and
reproducibility when working with long running jobs.
* **Avoid computing the same thing twice**: code is often rerun again and
again, for instance when prototyping computational-heavy jobs (as in
scientific development), but hand-crafted solutions to alleviate this
issue are error-prone and often lead to unreproducible results.
* **Persist to disk transparently**: efficiently persisting
arbitrary objects containing large data is hard. Using
joblib's caching mechanism avoids hand-written persistence and
implicitly links the file on disk to the execution context of
the original Python object. As a result, joblib's persistence is
good for resuming an application status or computational job, eg
after a crash.
Joblib addresses these problems while **leaving your code and your flow
control as unmodified as possible** (no framework, no new paradigms).
Main features
------------------
1) **Transparent and fast disk-caching of output value:** a memoize or
make-like functionality for Python functions that works well for
arbitrary Python objects, including very large numpy arrays. Separate
persistence and flow-execution logic from domain logic or algorithmic
code by writing the operations as a set of steps with well-defined
inputs and outputs: Python functions. Joblib can save their
computation to disk and rerun it only if necessary::
>>> from joblib import Memory
>>> cachedir = 'your_cache_dir_goes_here'
>>> mem = Memory(cachedir)
>>> import numpy as np
>>> a = np.vander(np.arange(3)).astype(np.float)
>>> square = mem.cache(np.square)
>>> b = square(a) # doctest: +ELLIPSIS
________________________________________________________________________________
[Memory] Calling square...
square(array([[0., 0., 1.],
[1., 1., 1.],
[4., 2., 1.]]))
___________________________________________________________square - 0...s, 0.0min
>>> c = square(a)
>>> # The above call did not trigger an evaluation
2) **Embarrassingly parallel helper:** to make it easy to write readable
parallel code and debug it quickly::
>>> from joblib import Parallel, delayed
>>> from math import sqrt
>>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
3) **Fast compressed Persistence**: a replacement for pickle to work
efficiently on Python objects containing large data (
*joblib.dump* & *joblib.load* ).
..
>>> import shutil ; shutil.rmtree(cachedir)

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distribute the following files with your program in your program’s
application local folder or by deploying them into the Global
Assembly Cache (GAC):
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
use when redistributing VC++ runtime files. Subject to the license
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(unmodified) in the application local folder as a sub-folder with
no change to the folder name. You may also redistribute all the
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\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
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GCC RUNTIME LIBRARY EXCEPTION
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Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
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----
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15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
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
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program 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.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
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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# 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': ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_info'], 'libraries': ['openblas_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
blas_opt_info={'library_dirs': ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_info'], 'libraries': ['openblas_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
lapack_mkl_info={}
openblas_lapack_info={'library_dirs': ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_lapack_info'], 'libraries': ['openblas_lapack_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
lapack_opt_info={'library_dirs': ['C:\\projects\\numpy-wheels\\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():
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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"""
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.
"""
from __future__ import division, absolute_import, print_function
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
# FIXME: why have numpy.lib if everything is imported here??
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 *
from .compat import long
# Make these accessible from numpy name-space
# but not imported in from numpy import *
# TODO[gh-6103]: Deprecate these
if sys.version_info[0] >= 3:
from builtins import bool, int, float, complex, object, str
unicode = str
else:
from __builtin__ import bool, int, float, complex, object, unicode, str
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

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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
from ctypes import WinDLL
import glob
if os.name == 'nt':
# convention for storing / loading the DLL from
# numpy/.libs/, if present
try:
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.
"""
from __future__ import division, absolute_import, print_function
__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(object):
"""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 explicily 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.
"""
from __future__ import division, absolute_import, print_function
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(object):
"""
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
#FIXME This is no longer needed? Assume it was for use in tests.
# cap verbosity at 3, which is equivalent to the pytest '-vv' option
#from . import utils
#verbose = min(int(verbose), 3)
#utils.verbose = verbose
#
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",
]
# Ignore python2.7 -3 warnings
pytest_args += [
r"-W ignore:sys\.exc_clear\(\) not supported in 3\.x:DeprecationWarning",
r"-W ignore:in 3\.x, __setslice__:DeprecationWarning",
r"-W ignore:in 3\.x, __getslice__:DeprecationWarning",
r"-W ignore:buffer\(\) not supported in 3\.x:DeprecationWarning",
r"-W ignore:CObject type is not supported in 3\.x:DeprecationWarning",
r"-W ignore:comparing unequal types not supported in 3\.x:DeprecationWarning",
r"-W ignore:the commands module has been removed in Python 3\.0:DeprecationWarning",
r"-W ignore:The 'new' module has been removed in Python 3\.0:DeprecationWarning",
]
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":
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)
# 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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"""
Pytest configuration and fixtures for the Numpy test suite.
"""
from __future__ import division, absolute_import, print_function
import os
import pytest
import numpy
from numpy.core._multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
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.")
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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"""
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 __future__ import division, absolute_import, print_function
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 sys
if sys.version_info[0] >= 3:
import copyreg
else:
import copy_reg as copyreg
copyreg.pickle(ufunc, _ufunc_reduce, _ufunc_reconstruct)
# Unclutter namespace (must keep _ufunc_reconstruct for unpickling)
del copyreg
del sys
del _ufunc_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

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"""
Functions in the ``as*array`` family that promote array-likes into arrays.
`require` fits this category despite its name not matching this pattern.
"""
from __future__ import division, absolute_import, print_function
from .overrides import set_module
from .multiarray import array
__all__ = [
"asarray", "asanyarray", "ascontiguousarray", "asfortranarray", "require",
]
@set_module('numpy')
def asarray(a, dtype=None, order=None):
"""Convert the input to an array.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes lists, lists of tuples, tuples, tuples of tuples, tuples
of lists and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
Whether to use row-major (C-style) or
column-major (Fortran-style) memory representation.
Defaults to 'C'.
Returns
-------
out : ndarray
Array interpretation of `a`. No copy is performed if the input
is already an ndarray with matching dtype and order. If `a` is a
subclass of ndarray, a base class ndarray is returned.
See Also
--------
asanyarray : Similar function which passes through subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asarray(a)
array([1, 2])
Existing arrays are not copied:
>>> a = np.array([1, 2])
>>> np.asarray(a) is a
True
If `dtype` is set, array is copied only if dtype does not match:
>>> a = np.array([1, 2], dtype=np.float32)
>>> np.asarray(a, dtype=np.float32) is a
True
>>> np.asarray(a, dtype=np.float64) is a
False
Contrary to `asanyarray`, ndarray subclasses are not passed through:
>>> issubclass(np.recarray, np.ndarray)
True
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True
"""
return array(a, dtype, copy=False, order=order)
@set_module('numpy')
def asanyarray(a, dtype=None, order=None):
"""Convert the input to an ndarray, but pass ndarray subclasses through.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes scalars, lists, lists of tuples, tuples, tuples of tuples,
tuples of lists, and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
Whether to use row-major (C-style) or column-major
(Fortran-style) memory representation. Defaults to 'C'.
Returns
-------
out : ndarray or an ndarray subclass
Array interpretation of `a`. If `a` is an ndarray or a subclass
of ndarray, it is returned as-is and no copy is performed.
See Also
--------
asarray : Similar function which always returns ndarrays.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and
Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asanyarray(a)
array([1, 2])
Instances of `ndarray` subclasses are passed through as-is:
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asanyarray(a) is a
True
"""
return array(a, dtype, copy=False, order=order, subok=True)
@set_module('numpy')
def ascontiguousarray(a, dtype=None):
"""
Return a contiguous array (ndim >= 1) in memory (C order).
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
Data-type of returned array.
Returns
-------
out : ndarray
Contiguous array of same shape and content as `a`, with type `dtype`
if specified.
See Also
--------
asfortranarray : Convert input to an ndarray with column-major
memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> np.ascontiguousarray(x, dtype=np.float32)
array([[0., 1., 2.],
[3., 4., 5.]], dtype=float32)
>>> x.flags['C_CONTIGUOUS']
True
Note: This function returns an array with at least one-dimension (1-d)
so it will not preserve 0-d arrays.
"""
return array(a, dtype, copy=False, order='C', ndmin=1)
@set_module('numpy')
def asfortranarray(a, dtype=None):
"""
Return an array (ndim >= 1) laid out in Fortran order in memory.
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
By default, the data-type is inferred from the input data.
Returns
-------
out : ndarray
The input `a` in Fortran, or column-major, order.
See Also
--------
ascontiguousarray : Convert input to a contiguous (C order) array.
asanyarray : Convert input to an ndarray with either row or
column-major memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> y = np.asfortranarray(x)
>>> x.flags['F_CONTIGUOUS']
False
>>> y.flags['F_CONTIGUOUS']
True
Note: This function returns an array with at least one-dimension (1-d)
so it will not preserve 0-d arrays.
"""
return array(a, dtype, copy=False, order='F', ndmin=1)
@set_module('numpy')
def require(a, dtype=None, requirements=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or list of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
"""
possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'}
if not requirements:
return asanyarray(a, dtype=dtype)
else:
requirements = {possible_flags[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
arr = arr.copy(order)
break
return arr

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"""
A place for code to be called from the implementation of np.dtype
String handling is much easier to do correctly in python.
"""
from __future__ import division, absolute_import, print_function
import sys
import numpy as np
_kind_to_stem = {
'u': 'uint',
'i': 'int',
'c': 'complex',
'f': 'float',
'b': 'bool',
'V': 'void',
'O': 'object',
'M': 'datetime',
'm': 'timedelta'
}
if sys.version_info[0] >= 3:
_kind_to_stem.update({
'S': 'bytes',
'U': 'str'
})
else:
_kind_to_stem.update({
'S': 'string',
'U': 'unicode'
})
def _kind_name(dtype):
try:
return _kind_to_stem[dtype.kind]
except KeyError:
raise RuntimeError(
"internal dtype error, unknown kind {!r}"
.format(dtype.kind)
)
def __str__(dtype):
if dtype.fields is not None:
return _struct_str(dtype, include_align=True)
elif dtype.subdtype:
return _subarray_str(dtype)
elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
return dtype.str
else:
return dtype.name
def __repr__(dtype):
arg_str = _construction_repr(dtype, include_align=False)
if dtype.isalignedstruct:
arg_str = arg_str + ", align=True"
return "dtype({})".format(arg_str)
def _unpack_field(dtype, offset, title=None):
"""
Helper function to normalize the items in dtype.fields.
Call as:
dtype, offset, title = _unpack_field(*dtype.fields[name])
"""
return dtype, offset, title
def _isunsized(dtype):
# PyDataType_ISUNSIZED
return dtype.itemsize == 0
def _construction_repr(dtype, include_align=False, short=False):
"""
Creates a string repr of the dtype, excluding the 'dtype()' part
surrounding the object. This object may be a string, a list, or
a dict depending on the nature of the dtype. This
is the object passed as the first parameter to the dtype
constructor, and if no additional constructor parameters are
given, will reproduce the exact memory layout.
Parameters
----------
short : bool
If true, this creates a shorter repr using 'kind' and 'itemsize', instead
of the longer type name.
include_align : bool
If true, this includes the 'align=True' parameter
inside the struct dtype construction dict when needed. Use this flag
if you want a proper repr string without the 'dtype()' part around it.
If false, this does not preserve the
'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
struct arrays like the regular repr does, because the 'align'
flag is not part of first dtype constructor parameter. This
mode is intended for a full 'repr', where the 'align=True' is
provided as the second parameter.
"""
if dtype.fields is not None:
return _struct_str(dtype, include_align=include_align)
elif dtype.subdtype:
return _subarray_str(dtype)
else:
return _scalar_str(dtype, short=short)
def _scalar_str(dtype, short):
byteorder = _byte_order_str(dtype)
if dtype.type == np.bool_:
if short:
return "'?'"
else:
return "'bool'"
elif dtype.type == np.object_:
# The object reference may be different sizes on different
# platforms, so it should never include the itemsize here.
return "'O'"
elif dtype.type == np.string_:
if _isunsized(dtype):
return "'S'"
else:
return "'S%d'" % dtype.itemsize
elif dtype.type == np.unicode_:
if _isunsized(dtype):
return "'%sU'" % byteorder
else:
return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
# unlike the other types, subclasses of void are preserved - but
# historically the repr does not actually reveal the subclass
elif issubclass(dtype.type, np.void):
if _isunsized(dtype):
return "'V'"
else:
return "'V%d'" % dtype.itemsize
elif dtype.type == np.datetime64:
return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif dtype.type == np.timedelta64:
return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif np.issubdtype(dtype, np.number):
# Short repr with endianness, like '<f8'
if short or dtype.byteorder not in ('=', '|'):
return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
# Longer repr, like 'float64'
else:
return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
elif dtype.isbuiltin == 2:
return dtype.type.__name__
else:
raise RuntimeError(
"Internal error: NumPy dtype unrecognized type number")
def _byte_order_str(dtype):
""" Normalize byteorder to '<' or '>' """
# hack to obtain the native and swapped byte order characters
swapped = np.dtype(int).newbyteorder('s')
native = swapped.newbyteorder('s')
byteorder = dtype.byteorder
if byteorder == '=':
return native.byteorder
if byteorder == 's':
# TODO: this path can never be reached
return swapped.byteorder
elif byteorder == '|':
return ''
else:
return byteorder
def _datetime_metadata_str(dtype):
# TODO: this duplicates the C append_metastr_to_string
unit, count = np.datetime_data(dtype)
if unit == 'generic':
return ''
elif count == 1:
return '[{}]'.format(unit)
else:
return '[{}{}]'.format(count, unit)
def _struct_dict_str(dtype, includealignedflag):
# unpack the fields dictionary into ls
names = dtype.names
fld_dtypes = []
offsets = []
titles = []
for name in names:
fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
fld_dtypes.append(fld_dtype)
offsets.append(offset)
titles.append(title)
# Build up a string to make the dictionary
# First, the names
ret = "{'names':["
ret += ",".join(repr(name) for name in names)
# Second, the formats
ret += "], 'formats':["
ret += ",".join(
_construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
# Third, the offsets
ret += "], 'offsets':["
ret += ",".join("%d" % offset for offset in offsets)
# Fourth, the titles
if any(title is not None for title in titles):
ret += "], 'titles':["
ret += ",".join(repr(title) for title in titles)
# Fifth, the itemsize
ret += "], 'itemsize':%d" % dtype.itemsize
if (includealignedflag and dtype.isalignedstruct):
# Finally, the aligned flag
ret += ", 'aligned':True}"
else:
ret += "}"
return ret
def _is_packed(dtype):
"""
Checks whether the structured data type in 'dtype'
has a simple layout, where all the fields are in order,
and follow each other with no alignment padding.
When this returns true, the dtype can be reconstructed
from a list of the field names and dtypes with no additional
dtype parameters.
Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
"""
total_offset = 0
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
if fld_offset != total_offset:
return False
total_offset += fld_dtype.itemsize
if total_offset != dtype.itemsize:
return False
return True
def _struct_list_str(dtype):
items = []
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
item = "("
if title is not None:
item += "({!r}, {!r}), ".format(title, name)
else:
item += "{!r}, ".format(name)
# Special case subarray handling here
if fld_dtype.subdtype is not None:
base, shape = fld_dtype.subdtype
item += "{}, {}".format(
_construction_repr(base, short=True),
shape
)
else:
item += _construction_repr(fld_dtype, short=True)
item += ")"
items.append(item)
return "[" + ", ".join(items) + "]"
def _struct_str(dtype, include_align):
# The list str representation can't include the 'align=' flag,
# so if it is requested and the struct has the aligned flag set,
# we must use the dict str instead.
if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
sub = _struct_list_str(dtype)
else:
sub = _struct_dict_str(dtype, include_align)
# If the data type isn't the default, void, show it
if dtype.type != np.void:
return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
else:
return sub
def _subarray_str(dtype):
base, shape = dtype.subdtype
return "({}, {})".format(
_construction_repr(base, short=True),
shape
)
def _name_includes_bit_suffix(dtype):
if dtype.type == np.object_:
# pointer size varies by system, best to omit it
return False
elif dtype.type == np.bool_:
# implied
return False
elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
# unspecified
return False
else:
return True
def _name_get(dtype):
# provides dtype.name.__get__, documented as returning a "bit name"
if dtype.isbuiltin == 2:
# user dtypes don't promise to do anything special
return dtype.type.__name__
if issubclass(dtype.type, np.void):
# historically, void subclasses preserve their name, eg `record64`
name = dtype.type.__name__
else:
name = _kind_name(dtype)
# append bit counts
if _name_includes_bit_suffix(dtype):
name += "{}".format(dtype.itemsize * 8)
# append metadata to datetimes
if dtype.type in (np.datetime64, np.timedelta64):
name += _datetime_metadata_str(dtype)
return name

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"""
Conversion from ctypes to dtype.
In an ideal world, we could achieve this through the PEP3118 buffer protocol,
something like::
def dtype_from_ctypes_type(t):
# needed to ensure that the shape of `t` is within memoryview.format
class DummyStruct(ctypes.Structure):
_fields_ = [('a', t)]
# empty to avoid memory allocation
ctype_0 = (DummyStruct * 0)()
mv = memoryview(ctype_0)
# convert the struct, and slice back out the field
return _dtype_from_pep3118(mv.format)['a']
Unfortunately, this fails because:
* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
* PEP3118 cannot represent unions, but both numpy and ctypes can
* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
"""
import _ctypes
import ctypes
import numpy as np
def _from_ctypes_array(t):
return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
def _from_ctypes_structure(t):
for item in t._fields_:
if len(item) > 2:
raise TypeError(
"ctypes bitfields have no dtype equivalent")
if hasattr(t, "_pack_"):
formats = []
offsets = []
names = []
current_offset = 0
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
# Each type has a default offset, this is platform dependent for some types.
effective_pack = min(t._pack_, ctypes.alignment(ftyp))
current_offset = ((current_offset + effective_pack - 1) // effective_pack) * effective_pack
offsets.append(current_offset)
current_offset += ctypes.sizeof(ftyp)
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
else:
fields = []
for fname, ftyp in t._fields_:
fields.append((fname, dtype_from_ctypes_type(ftyp)))
# by default, ctypes structs are aligned
return np.dtype(fields, align=True)
def _from_ctypes_scalar(t):
"""
Return the dtype type with endianness included if it's the case
"""
if getattr(t, '__ctype_be__', None) is t:
return np.dtype('>' + t._type_)
elif getattr(t, '__ctype_le__', None) is t:
return np.dtype('<' + t._type_)
else:
return np.dtype(t._type_)
def _from_ctypes_union(t):
formats = []
offsets = []
names = []
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
offsets.append(0) # Union fields are offset to 0
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
def dtype_from_ctypes_type(t):
"""
Construct a dtype object from a ctypes type
"""
if issubclass(t, _ctypes.Array):
return _from_ctypes_array(t)
elif issubclass(t, _ctypes._Pointer):
raise TypeError("ctypes pointers have no dtype equivalent")
elif issubclass(t, _ctypes.Structure):
return _from_ctypes_structure(t)
elif issubclass(t, _ctypes.Union):
return _from_ctypes_union(t)
elif isinstance(getattr(t, '_type_', None), str):
return _from_ctypes_scalar(t)
else:
raise NotImplementedError(
"Unknown ctypes type {}".format(t.__name__))

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"""
Various richly-typed exceptions, that also help us deal with string formatting
in python where it's easier.
By putting the formatting in `__str__`, we also avoid paying the cost for
users who silence the exceptions.
"""
from numpy.core.overrides import set_module
def _unpack_tuple(tup):
if len(tup) == 1:
return tup[0]
else:
return tup
def _display_as_base(cls):
"""
A decorator that makes an exception class look like its base.
We use this to hide subclasses that are implementation details - the user
should catch the base type, which is what the traceback will show them.
Classes decorated with this decorator are subject to removal without a
deprecation warning.
"""
assert issubclass(cls, Exception)
cls.__name__ = cls.__base__.__name__
cls.__qualname__ = cls.__base__.__qualname__
set_module(cls.__base__.__module__)(cls)
return cls
class UFuncTypeError(TypeError):
""" Base class for all ufunc exceptions """
def __init__(self, ufunc):
self.ufunc = ufunc
@_display_as_base
class _UFuncBinaryResolutionError(UFuncTypeError):
""" Thrown when a binary resolution fails """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
assert len(self.dtypes) == 2
def __str__(self):
return (
"ufunc {!r} cannot use operands with types {!r} and {!r}"
).format(
self.ufunc.__name__, *self.dtypes
)
@_display_as_base
class _UFuncNoLoopError(UFuncTypeError):
""" Thrown when a ufunc loop cannot be found """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
def __str__(self):
return (
"ufunc {!r} did not contain a loop with signature matching types "
"{!r} -> {!r}"
).format(
self.ufunc.__name__,
_unpack_tuple(self.dtypes[:self.ufunc.nin]),
_unpack_tuple(self.dtypes[self.ufunc.nin:])
)
@_display_as_base
class _UFuncCastingError(UFuncTypeError):
def __init__(self, ufunc, casting, from_, to):
super().__init__(ufunc)
self.casting = casting
self.from_ = from_
self.to = to
@_display_as_base
class _UFuncInputCastingError(_UFuncCastingError):
""" Thrown when a ufunc input cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.in_i = i
def __str__(self):
# only show the number if more than one input exists
i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
return (
"Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
@_display_as_base
class _UFuncOutputCastingError(_UFuncCastingError):
""" Thrown when a ufunc output cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.out_i = i
def __str__(self):
# only show the number if more than one output exists
i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
return (
"Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
# Exception used in shares_memory()
@set_module('numpy')
class TooHardError(RuntimeError):
pass
@set_module('numpy')
class AxisError(ValueError, IndexError):
""" Axis supplied was invalid. """
def __init__(self, axis, ndim=None, msg_prefix=None):
# single-argument form just delegates to base class
if ndim is None and msg_prefix is None:
msg = axis
# do the string formatting here, to save work in the C code
else:
msg = ("axis {} is out of bounds for array of dimension {}"
.format(axis, ndim))
if msg_prefix is not None:
msg = "{}: {}".format(msg_prefix, msg)
super(AxisError, self).__init__(msg)
@_display_as_base
class _ArrayMemoryError(MemoryError):
""" Thrown when an array cannot be allocated"""
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
@property
def _total_size(self):
num_bytes = self.dtype.itemsize
for dim in self.shape:
num_bytes *= dim
return num_bytes
@staticmethod
def _size_to_string(num_bytes):
""" Convert a number of bytes into a binary size string """
import math
# https://en.wikipedia.org/wiki/Binary_prefix
LOG2_STEP = 10
STEP = 1024
units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
unit_val = 1 << (unit_i * LOG2_STEP)
n_units = num_bytes / unit_val
del unit_val
# ensure we pick a unit that is correct after rounding
if round(n_units) == STEP:
unit_i += 1
n_units /= STEP
# deal with sizes so large that we don't have units for them
if unit_i >= len(units):
new_unit_i = len(units) - 1
n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
unit_i = new_unit_i
unit_name = units[unit_i]
# format with a sensible number of digits
if unit_i == 0:
# no decimal point on bytes
return '{:.0f} {}'.format(n_units, unit_name)
elif round(n_units) < 1000:
# 3 significant figures, if none are dropped to the left of the .
return '{:#.3g} {}'.format(n_units, unit_name)
else:
# just give all the digits otherwise
return '{:#.0f} {}'.format(n_units, unit_name)
def __str__(self):
size_str = self._size_to_string(self._total_size)
return (
"Unable to allocate {} for an array with shape {} and data type {}"
.format(size_str, self.shape, self.dtype)
)

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"""
A place for internal code
Some things are more easily handled Python.
"""
from __future__ import division, absolute_import, print_function
import re
import sys
import platform
from numpy.compat import unicode
from .multiarray import dtype, array, ndarray
try:
import ctypes
except ImportError:
ctypes = None
IS_PYPY = platform.python_implementation() == 'PyPy'
if (sys.byteorder == 'little'):
_nbo = b'<'
else:
_nbo = b'>'
def _makenames_list(adict, align):
allfields = []
fnames = list(adict.keys())
for fname in fnames:
obj = adict[fname]
n = len(obj)
if not isinstance(obj, tuple) or n not in [2, 3]:
raise ValueError("entry not a 2- or 3- tuple")
if (n > 2) and (obj[2] == fname):
continue
num = int(obj[1])
if (num < 0):
raise ValueError("invalid offset.")
format = dtype(obj[0], align=align)
if (n > 2):
title = obj[2]
else:
title = None
allfields.append((fname, format, num, title))
# sort by offsets
allfields.sort(key=lambda x: x[2])
names = [x[0] for x in allfields]
formats = [x[1] for x in allfields]
offsets = [x[2] for x in allfields]
titles = [x[3] for x in allfields]
return names, formats, offsets, titles
# Called in PyArray_DescrConverter function when
# a dictionary without "names" and "formats"
# fields is used as a data-type descriptor.
def _usefields(adict, align):
try:
names = adict[-1]
except KeyError:
names = None
if names is None:
names, formats, offsets, titles = _makenames_list(adict, align)
else:
formats = []
offsets = []
titles = []
for name in names:
res = adict[name]
formats.append(res[0])
offsets.append(res[1])
if (len(res) > 2):
titles.append(res[2])
else:
titles.append(None)
return dtype({"names": names,
"formats": formats,
"offsets": offsets,
"titles": titles}, align)
# construct an array_protocol descriptor list
# from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
# a descriptor that has no fields and then return
# a simple typestring
def _array_descr(descriptor):
fields = descriptor.fields
if fields is None:
subdtype = descriptor.subdtype
if subdtype is None:
if descriptor.metadata is None:
return descriptor.str
else:
new = descriptor.metadata.copy()
if new:
return (descriptor.str, new)
else:
return descriptor.str
else:
return (_array_descr(subdtype[0]), subdtype[1])
names = descriptor.names
ordered_fields = [fields[x] + (x,) for x in names]
result = []
offset = 0
for field in ordered_fields:
if field[1] > offset:
num = field[1] - offset
result.append(('', '|V%d' % num))
offset += num
elif field[1] < offset:
raise ValueError(
"dtype.descr is not defined for types with overlapping or "
"out-of-order fields")
if len(field) > 3:
name = (field[2], field[3])
else:
name = field[2]
if field[0].subdtype:
tup = (name, _array_descr(field[0].subdtype[0]),
field[0].subdtype[1])
else:
tup = (name, _array_descr(field[0]))
offset += field[0].itemsize
result.append(tup)
if descriptor.itemsize > offset:
num = descriptor.itemsize - offset
result.append(('', '|V%d' % num))
return result
# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibility.
def _reconstruct(subtype, shape, dtype):
return ndarray.__new__(subtype, shape, dtype)
# format_re was originally from numarray by J. Todd Miller
format_re = re.compile(br'(?P<order1>[<>|=]?)'
br'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
br'(?P<order2>[<>|=]?)'
br'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(br'\s*,\s*')
space_re = re.compile(br'\s+$')
# astr is a string (perhaps comma separated)
_convorder = {b'=': _nbo}
def _commastring(astr):
startindex = 0
result = []
while startindex < len(astr):
mo = format_re.match(astr, pos=startindex)
try:
(order1, repeats, order2, dtype) = mo.groups()
except (TypeError, AttributeError):
raise ValueError('format number %d of "%s" is not recognized' %
(len(result)+1, astr))
startindex = mo.end()
# Separator or ending padding
if startindex < len(astr):
if space_re.match(astr, pos=startindex):
startindex = len(astr)
else:
mo = sep_re.match(astr, pos=startindex)
if not mo:
raise ValueError(
'format number %d of "%s" is not recognized' %
(len(result)+1, astr))
startindex = mo.end()
if order2 == b'':
order = order1
elif order1 == b'':
order = order2
else:
order1 = _convorder.get(order1, order1)
order2 = _convorder.get(order2, order2)
if (order1 != order2):
raise ValueError(
'inconsistent byte-order specification %s and %s' %
(order1, order2))
order = order1
if order in [b'|', b'=', _nbo]:
order = b''
dtype = order + dtype
if (repeats == b''):
newitem = dtype
else:
newitem = (dtype, eval(repeats))
result.append(newitem)
return result
class dummy_ctype(object):
def __init__(self, cls):
self._cls = cls
def __mul__(self, other):
return self
def __call__(self, *other):
return self._cls(other)
def __eq__(self, other):
return self._cls == other._cls
def __ne__(self, other):
return self._cls != other._cls
def _getintp_ctype():
val = _getintp_ctype.cache
if val is not None:
return val
if ctypes is None:
import numpy as np
val = dummy_ctype(np.intp)
else:
char = dtype('p').char
if (char == 'i'):
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
val = ctypes.c_long
_getintp_ctype.cache = val
return val
_getintp_ctype.cache = None
# Used for .ctypes attribute of ndarray
class _missing_ctypes(object):
def cast(self, num, obj):
return num.value
class c_void_p(object):
def __init__(self, ptr):
self.value = ptr
class _ctypes(object):
def __init__(self, array, ptr=None):
self._arr = array
if ctypes:
self._ctypes = ctypes
self._data = self._ctypes.c_void_p(ptr)
else:
# fake a pointer-like object that holds onto the reference
self._ctypes = _missing_ctypes()
self._data = self._ctypes.c_void_p(ptr)
self._data._objects = array
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
"""
Return the data pointer cast to a particular c-types object.
For example, calling ``self._as_parameter_`` is equivalent to
``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
pointer to a ctypes array of floating-point data:
``self.data_as(ctypes.POINTER(ctypes.c_double))``.
The returned pointer will keep a reference to the array.
"""
# _ctypes.cast function causes a circular reference of self._data in
# self._data._objects. Attributes of self._data cannot be released
# until gc.collect is called. Make a copy of the pointer first then let
# it hold the array reference. This is a workaround to circumvent the
# CPython bug https://bugs.python.org/issue12836
ptr = self._ctypes.cast(self._data, obj)
ptr._arr = self._arr
return ptr
def shape_as(self, obj):
"""
Return the shape tuple as an array of some other c-types
type. For example: ``self.shape_as(ctypes.c_short)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
"""
Return the strides tuple as an array of some other
c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
@property
def data(self):
"""
A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byte-order. The memory area may not even be writeable. The array
flags and data-type of this array should be respected when passing this
attribute to arbitrary C-code to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as ``self._array_interface_['data'][0]``.
Note that unlike ``data_as``, a reference will not be kept to the array:
code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
pointer to a deallocated array, and should be spelt
``(a + b).ctypes.data_as(ctypes.c_void_p)``
"""
return self._data.value
@property
def shape(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to ``dtype('p')`` on this
platform. This base-type could be `ctypes.c_int`, `ctypes.c_long`, or
`ctypes.c_longlong` depending on the platform.
The c_intp type is defined accordingly in `numpy.ctypeslib`.
The ctypes array contains the shape of the underlying array.
"""
return self.shape_as(_getintp_ctype())
@property
def strides(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes array
contains the strides information from the underlying array. This strides
information is important for showing how many bytes must be jumped to
get to the next element in the array.
"""
return self.strides_as(_getintp_ctype())
@property
def _as_parameter_(self):
"""
Overrides the ctypes semi-magic method
Enables `c_func(some_array.ctypes)`
"""
return self.data_as(ctypes.c_void_p)
# kept for compatibility
get_data = data.fget
get_shape = shape.fget
get_strides = strides.fget
get_as_parameter = _as_parameter_.fget
def _newnames(datatype, order):
"""
Given a datatype and an order object, return a new names tuple, with the
order indicated
"""
oldnames = datatype.names
nameslist = list(oldnames)
if isinstance(order, (str, unicode)):
order = [order]
seen = set()
if isinstance(order, (list, tuple)):
for name in order:
try:
nameslist.remove(name)
except ValueError:
if name in seen:
raise ValueError("duplicate field name: %s" % (name,))
else:
raise ValueError("unknown field name: %s" % (name,))
seen.add(name)
return tuple(list(order) + nameslist)
raise ValueError("unsupported order value: %s" % (order,))
def _copy_fields(ary):
"""Return copy of structured array with padding between fields removed.
Parameters
----------
ary : ndarray
Structured array from which to remove padding bytes
Returns
-------
ary_copy : ndarray
Copy of ary with padding bytes removed
"""
dt = ary.dtype
copy_dtype = {'names': dt.names,
'formats': [dt.fields[name][0] for name in dt.names]}
return array(ary, dtype=copy_dtype, copy=True)
def _getfield_is_safe(oldtype, newtype, offset):
""" Checks safety of getfield for object arrays.
As in _view_is_safe, we need to check that memory containing objects is not
reinterpreted as a non-object datatype and vice versa.
Parameters
----------
oldtype : data-type
Data type of the original ndarray.
newtype : data-type
Data type of the field being accessed by ndarray.getfield
offset : int
Offset of the field being accessed by ndarray.getfield
Raises
------
TypeError
If the field access is invalid
"""
if newtype.hasobject or oldtype.hasobject:
if offset == 0 and newtype == oldtype:
return
if oldtype.names is not None:
for name in oldtype.names:
if (oldtype.fields[name][1] == offset and
oldtype.fields[name][0] == newtype):
return
raise TypeError("Cannot get/set field of an object array")
return
def _view_is_safe(oldtype, newtype):
""" Checks safety of a view involving object arrays, for example when
doing::
np.zeros(10, dtype=oldtype).view(newtype)
Parameters
----------
oldtype : data-type
Data type of original ndarray
newtype : data-type
Data type of the view
Raises
------
TypeError
If the new type is incompatible with the old type.
"""
# if the types are equivalent, there is no problem.
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
if oldtype == newtype:
return
if newtype.hasobject or oldtype.hasobject:
raise TypeError("Cannot change data-type for object array.")
return
# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'h',
'H': 'H',
'i': 'i',
'I': 'I',
'l': 'l',
'L': 'L',
'q': 'q',
'Q': 'Q',
'e': 'e',
'f': 'f',
'd': 'd',
'g': 'g',
'Zf': 'F',
'Zd': 'D',
'Zg': 'G',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
_pep3118_standard_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'i2',
'H': 'u2',
'i': 'i4',
'I': 'u4',
'l': 'i4',
'L': 'u4',
'q': 'i8',
'Q': 'u8',
'e': 'f2',
'f': 'f',
'd': 'd',
'Zf': 'F',
'Zd': 'D',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
_pep3118_unsupported_map = {
'u': 'UCS-2 strings',
'&': 'pointers',
't': 'bitfields',
'X': 'function pointers',
}
class _Stream(object):
def __init__(self, s):
self.s = s
self.byteorder = '@'
def advance(self, n):
res = self.s[:n]
self.s = self.s[n:]
return res
def consume(self, c):
if self.s[:len(c)] == c:
self.advance(len(c))
return True
return False
def consume_until(self, c):
if callable(c):
i = 0
while i < len(self.s) and not c(self.s[i]):
i = i + 1
return self.advance(i)
else:
i = self.s.index(c)
res = self.advance(i)
self.advance(len(c))
return res
@property
def next(self):
return self.s[0]
def __bool__(self):
return bool(self.s)
__nonzero__ = __bool__
def _dtype_from_pep3118(spec):
stream = _Stream(spec)
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
return dtype
def __dtype_from_pep3118(stream, is_subdtype):
field_spec = dict(
names=[],
formats=[],
offsets=[],
itemsize=0
)
offset = 0
common_alignment = 1
is_padding = False
# Parse spec
while stream:
value = None
# End of structure, bail out to upper level
if stream.consume('}'):
break
# Sub-arrays (1)
shape = None
if stream.consume('('):
shape = stream.consume_until(')')
shape = tuple(map(int, shape.split(',')))
# Byte order
if stream.next in ('@', '=', '<', '>', '^', '!'):
byteorder = stream.advance(1)
if byteorder == '!':
byteorder = '>'
stream.byteorder = byteorder
# Byte order characters also control native vs. standard type sizes
if stream.byteorder in ('@', '^'):
type_map = _pep3118_native_map
type_map_chars = _pep3118_native_typechars
else:
type_map = _pep3118_standard_map
type_map_chars = _pep3118_standard_typechars
# Item sizes
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
if itemsize_str:
itemsize = int(itemsize_str)
else:
itemsize = 1
# Data types
is_padding = False
if stream.consume('T{'):
value, align = __dtype_from_pep3118(
stream, is_subdtype=True)
elif stream.next in type_map_chars:
if stream.next == 'Z':
typechar = stream.advance(2)
else:
typechar = stream.advance(1)
is_padding = (typechar == 'x')
dtypechar = type_map[typechar]
if dtypechar in 'USV':
dtypechar += '%d' % itemsize
itemsize = 1
numpy_byteorder = {'@': '=', '^': '='}.get(
stream.byteorder, stream.byteorder)
value = dtype(numpy_byteorder + dtypechar)
align = value.alignment
elif stream.next in _pep3118_unsupported_map:
desc = _pep3118_unsupported_map[stream.next]
raise NotImplementedError(
"Unrepresentable PEP 3118 data type {!r} ({})"
.format(stream.next, desc))
else:
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
#
# Native alignment may require padding
#
# Here we assume that the presence of a '@' character implicitly implies
# that the start of the array is *already* aligned.
#
extra_offset = 0
if stream.byteorder == '@':
start_padding = (-offset) % align
intra_padding = (-value.itemsize) % align
offset += start_padding
if intra_padding != 0:
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
# Inject internal padding to the end of the sub-item
value = _add_trailing_padding(value, intra_padding)
else:
# We can postpone the injection of internal padding,
# as the item appears at most once
extra_offset += intra_padding
# Update common alignment
common_alignment = _lcm(align, common_alignment)
# Convert itemsize to sub-array
if itemsize != 1:
value = dtype((value, (itemsize,)))
# Sub-arrays (2)
if shape is not None:
value = dtype((value, shape))
# Field name
if stream.consume(':'):
name = stream.consume_until(':')
else:
name = None
if not (is_padding and name is None):
if name is not None and name in field_spec['names']:
raise RuntimeError("Duplicate field name '%s' in PEP3118 format"
% name)
field_spec['names'].append(name)
field_spec['formats'].append(value)
field_spec['offsets'].append(offset)
offset += value.itemsize
offset += extra_offset
field_spec['itemsize'] = offset
# extra final padding for aligned types
if stream.byteorder == '@':
field_spec['itemsize'] += (-offset) % common_alignment
# Check if this was a simple 1-item type, and unwrap it
if (field_spec['names'] == [None]
and field_spec['offsets'][0] == 0
and field_spec['itemsize'] == field_spec['formats'][0].itemsize
and not is_subdtype):
ret = field_spec['formats'][0]
else:
_fix_names(field_spec)
ret = dtype(field_spec)
# Finished
return ret, common_alignment
def _fix_names(field_spec):
""" Replace names which are None with the next unused f%d name """
names = field_spec['names']
for i, name in enumerate(names):
if name is not None:
continue
j = 0
while True:
name = 'f{}'.format(j)
if name not in names:
break
j = j + 1
names[i] = name
def _add_trailing_padding(value, padding):
"""Inject the specified number of padding bytes at the end of a dtype"""
if value.fields is None:
field_spec = dict(
names=['f0'],
formats=[value],
offsets=[0],
itemsize=value.itemsize
)
else:
fields = value.fields
names = value.names
field_spec = dict(
names=names,
formats=[fields[name][0] for name in names],
offsets=[fields[name][1] for name in names],
itemsize=value.itemsize
)
field_spec['itemsize'] += padding
return dtype(field_spec)
def _prod(a):
p = 1
for x in a:
p *= x
return p
def _gcd(a, b):
"""Calculate the greatest common divisor of a and b"""
while b:
a, b = b, a % b
return a
def _lcm(a, b):
return a // _gcd(a, b) * b
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
""" Format the error message for when __array_ufunc__ gives up. """
args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
['{}={!r}'.format(k, v)
for k, v in kwargs.items()])
args = inputs + kwargs.get('out', ())
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
return ('operand type(s) all returned NotImplemented from '
'__array_ufunc__({!r}, {!r}, {}): {}'
.format(ufunc, method, args_string, types_string))
def array_function_errmsg_formatter(public_api, types):
""" Format the error message for when __array_ufunc__ gives up. """
func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
return ("no implementation found for '{}' on types that implement "
'__array_function__: {}'.format(func_name, list(types)))
def _ufunc_doc_signature_formatter(ufunc):
"""
Builds a signature string which resembles PEP 457
This is used to construct the first line of the docstring
"""
# input arguments are simple
if ufunc.nin == 1:
in_args = 'x'
else:
in_args = ', '.join('x{}'.format(i+1) for i in range(ufunc.nin))
# output arguments are both keyword or positional
if ufunc.nout == 0:
out_args = ', /, out=()'
elif ufunc.nout == 1:
out_args = ', /, out=None'
else:
out_args = '[, {positional}], / [, out={default}]'.format(
positional=', '.join(
'out{}'.format(i+1) for i in range(ufunc.nout)),
default=repr((None,)*ufunc.nout)
)
# keyword only args depend on whether this is a gufunc
kwargs = (
", casting='same_kind'"
", order='K'"
", dtype=None"
", subok=True"
"[, signature"
", extobj]"
)
if ufunc.signature is None:
kwargs = ", where=True" + kwargs
# join all the parts together
return '{name}({in_args}{out_args}, *{kwargs})'.format(
name=ufunc.__name__,
in_args=in_args,
out_args=out_args,
kwargs=kwargs
)
def npy_ctypes_check(cls):
# determine if a class comes from ctypes, in order to work around
# a bug in the buffer protocol for those objects, bpo-10746
try:
# ctypes class are new-style, so have an __mro__. This probably fails
# for ctypes classes with multiple inheritance.
if IS_PYPY:
# (..., _ctypes.basics._CData, Bufferable, object)
ctype_base = cls.__mro__[-3]
else:
# # (..., _ctypes._CData, object)
ctype_base = cls.__mro__[-2]
# right now, they're part of the _ctypes module
return 'ctypes' in ctype_base.__module__
except Exception:
return False
class recursive(object):
'''
A decorator class for recursive nested functions.
Naive recursive nested functions hold a reference to themselves:
def outer(*args):
def stringify_leaky(arg0, *arg1):
if len(arg1) > 0:
return stringify_leaky(*arg1) # <- HERE
return str(arg0)
stringify_leaky(*args)
This design pattern creates a reference cycle that is difficult for a
garbage collector to resolve. The decorator class prevents the
cycle by passing the nested function in as an argument `self`:
def outer(*args):
@recursive
def stringify(self, arg0, *arg1):
if len(arg1) > 0:
return self(*arg1)
return str(arg0)
stringify(*args)
'''
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
return self.func(self, *args, **kwargs)

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"""
Array methods which are called by both the C-code for the method
and the Python code for the NumPy-namespace function
"""
from __future__ import division, absolute_import, print_function
import warnings
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core._asarray import asanyarray
from numpy.core import numerictypes as nt
from numpy.core import _exceptions
from numpy._globals import _NoValue
from numpy.compat import pickle, os_fspath, contextlib_nullcontext
# save those O(100) nanoseconds!
umr_maximum = um.maximum.reduce
umr_minimum = um.minimum.reduce
umr_sum = um.add.reduce
umr_prod = um.multiply.reduce
umr_any = um.logical_or.reduce
umr_all = um.logical_and.reduce
# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
# small reductions
def _amax(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_maximum(a, axis, None, out, keepdims, initial, where)
def _amin(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_minimum(a, axis, None, out, keepdims, initial, where)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_prod(a, axis, dtype, out, keepdims, initial, where)
def _any(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_any(a, axis, dtype, out, keepdims)
def _all(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_all(a, axis, dtype, out, keepdims)
def _count_reduce_items(arr, axis):
if axis is None:
axis = tuple(range(arr.ndim))
if not isinstance(axis, tuple):
axis = (axis,)
items = 1
for ax in axis:
items *= arr.shape[ax]
return items
# Numpy 1.17.0, 2019-02-24
# Various clip behavior deprecations, marked with _clip_dep as a prefix.
def _clip_dep_is_scalar_nan(a):
# guarded to protect circular imports
from numpy.core.fromnumeric import ndim
if ndim(a) != 0:
return False
try:
return um.isnan(a)
except TypeError:
return False
def _clip_dep_is_byte_swapped(a):
if isinstance(a, mu.ndarray):
return not a.dtype.isnative
return False
def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
# normal path
if casting is not None:
return ufunc(*args, out=out, casting=casting, **kwargs)
# try to deal with broken casting rules
try:
return ufunc(*args, out=out, **kwargs)
except _exceptions._UFuncOutputCastingError as e:
# Numpy 1.17.0, 2019-02-24
warnings.warn(
"Converting the output of clip from {!r} to {!r} is deprecated. "
"Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
"correct the type of the variables.".format(e.from_, e.to),
DeprecationWarning,
stacklevel=2
)
return ufunc(*args, out=out, casting="unsafe", **kwargs)
def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
if min is None and max is None:
raise ValueError("One of max or min must be given")
# Numpy 1.17.0, 2019-02-24
# This deprecation probably incurs a substantial slowdown for small arrays,
# it will be good to get rid of it.
if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
using_deprecated_nan = False
if _clip_dep_is_scalar_nan(min):
min = -float('inf')
using_deprecated_nan = True
if _clip_dep_is_scalar_nan(max):
max = float('inf')
using_deprecated_nan = True
if using_deprecated_nan:
warnings.warn(
"Passing `np.nan` to mean no clipping in np.clip has always "
"been unreliable, and is now deprecated. "
"In future, this will always return nan, like it already does "
"when min or max are arrays that contain nan. "
"To skip a bound, pass either None or an np.inf of an "
"appropriate sign.",
DeprecationWarning,
stacklevel=2
)
if min is None:
return _clip_dep_invoke_with_casting(
um.minimum, a, max, out=out, casting=casting, **kwargs)
elif max is None:
return _clip_dep_invoke_with_casting(
um.maximum, a, min, out=out, casting=casting, **kwargs)
else:
return _clip_dep_invoke_with_casting(
um.clip, a, min, max, out=out, casting=casting, **kwargs)
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
elif issubclass(arr.dtype.type, nt.float16):
dtype = mu.dtype('f4')
is_float16_result = True
ret = umr_sum(arr, axis, dtype, out, keepdims)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = arr.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis)
# Make this warning show up on top.
if ddof >= rcount:
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = umr_sum(arr, axis, dtype, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(
arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean.dtype.type(arrmean / rcount)
# Compute sum of squared deviations from mean
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
x = um.multiply(x, x, out=x)
else:
x = um.multiply(x, um.conjugate(x), out=x).real
ret = umr_sum(x, axis, dtype, out, keepdims)
# Compute degrees of freedom and make sure it is not negative.
rcount = max([rcount - ddof, 0])
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
def _ptp(a, axis=None, out=None, keepdims=False):
return um.subtract(
umr_maximum(a, axis, None, out, keepdims),
umr_minimum(a, axis, None, None, keepdims),
out
)
def _dump(self, file, protocol=2):
if hasattr(file, 'write'):
ctx = contextlib_nullcontext(file)
else:
ctx = open(os_fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def _dumps(self, protocol=2):
return pickle.dumps(self, protocol=protocol)

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"""
String-handling utilities to avoid locale-dependence.
Used primarily to generate type name aliases.
"""
# "import string" is costly to import!
# Construct the translation tables directly
# "A" = chr(65), "a" = chr(97)
_all_chars = [chr(_m) for _m in range(256)]
_ascii_upper = _all_chars[65:65+26]
_ascii_lower = _all_chars[97:97+26]
LOWER_TABLE = "".join(_all_chars[:65] + _ascii_lower + _all_chars[65+26:])
UPPER_TABLE = "".join(_all_chars[:97] + _ascii_upper + _all_chars[97+26:])
def english_lower(s):
""" Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy.core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
"""
lowered = s.translate(LOWER_TABLE)
return lowered
def english_upper(s):
""" Apply English case rules to convert ASCII strings to all upper case.
This is an internal utility function to replace calls to str.upper() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
uppered : str
Examples
--------
>>> from numpy.core.numerictypes import english_upper
>>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
>>> english_upper('')
''
"""
uppered = s.translate(UPPER_TABLE)
return uppered
def english_capitalize(s):
""" Apply English case rules to convert the first character of an ASCII
string to upper case.
This is an internal utility function to replace calls to str.capitalize()
such that we can avoid changing behavior with changing locales.
Parameters
----------
s : str
Returns
-------
capitalized : str
Examples
--------
>>> from numpy.core.numerictypes import english_capitalize
>>> english_capitalize('int8')
'Int8'
>>> english_capitalize('Int8')
'Int8'
>>> english_capitalize('')
''
"""
if s:
return english_upper(s[0]) + s[1:]
else:
return s

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"""
Due to compatibility, numpy has a very large number of different naming
conventions for the scalar types (those subclassing from `numpy.generic`).
This file produces a convoluted set of dictionaries mapping names to types,
and sometimes other mappings too.
.. data:: allTypes
A dictionary of names to types that will be exposed as attributes through
``np.core.numerictypes.*``
.. data:: sctypeDict
Similar to `allTypes`, but maps a broader set of aliases to their types.
.. data:: sctypeNA
NumArray-compatible names for the scalar types. Contains not only
``name: type`` mappings, but ``char: name`` mappings too.
.. deprecated:: 1.16
.. data:: sctypes
A dictionary keyed by a "type group" string, providing a list of types
under that group.
"""
import warnings
import sys
from numpy.compat import unicode
from numpy._globals import VisibleDeprecationWarning
from numpy.core._string_helpers import english_lower, english_capitalize
from numpy.core.multiarray import typeinfo, dtype
from numpy.core._dtype import _kind_name
sctypeDict = {} # Contains all leaf-node scalar types with aliases
class TypeNADict(dict):
def __getitem__(self, key):
# 2018-06-24, 1.16
warnings.warn('sctypeNA and typeNA will be removed in v1.18 '
'of numpy', VisibleDeprecationWarning, stacklevel=2)
return dict.__getitem__(self, key)
def get(self, key, default=None):
# 2018-06-24, 1.16
warnings.warn('sctypeNA and typeNA will be removed in v1.18 '
'of numpy', VisibleDeprecationWarning, stacklevel=2)
return dict.get(self, key, default)
sctypeNA = TypeNADict() # Contails all leaf-node types -> numarray type equivalences
allTypes = {} # Collect the types we will add to the module
# separate the actual type info from the abstract base classes
_abstract_types = {}
_concrete_typeinfo = {}
for k, v in typeinfo.items():
# make all the keys lowercase too
k = english_lower(k)
if isinstance(v, type):
_abstract_types[k] = v
else:
_concrete_typeinfo[k] = v
_concrete_types = {v.type for k, v in _concrete_typeinfo.items()}
def _bits_of(obj):
try:
info = next(v for v in _concrete_typeinfo.values() if v.type is obj)
except StopIteration:
if obj in _abstract_types.values():
raise ValueError("Cannot count the bits of an abstract type")
# some third-party type - make a best-guess
return dtype(obj).itemsize * 8
else:
return info.bits
def bitname(obj):
"""Return a bit-width name for a given type object"""
bits = _bits_of(obj)
dt = dtype(obj)
char = dt.kind
base = _kind_name(dt)
if base == 'object':
bits = 0
if bits != 0:
char = "%s%d" % (char, bits // 8)
return base, bits, char
def _add_types():
for name, info in _concrete_typeinfo.items():
# define C-name and insert typenum and typechar references also
allTypes[name] = info.type
sctypeDict[name] = info.type
sctypeDict[info.char] = info.type
sctypeDict[info.num] = info.type
for name, cls in _abstract_types.items():
allTypes[name] = cls
_add_types()
# This is the priority order used to assign the bit-sized NPY_INTxx names, which
# must match the order in npy_common.h in order for NPY_INTxx and np.intxx to be
# consistent.
# If two C types have the same size, then the earliest one in this list is used
# as the sized name.
_int_ctypes = ['long', 'longlong', 'int', 'short', 'byte']
_uint_ctypes = list('u' + t for t in _int_ctypes)
def _add_aliases():
for name, info in _concrete_typeinfo.items():
# these are handled by _add_integer_aliases
if name in _int_ctypes or name in _uint_ctypes:
continue
# insert bit-width version for this class (if relevant)
base, bit, char = bitname(info.type)
myname = "%s%d" % (base, bit)
# ensure that (c)longdouble does not overwrite the aliases assigned to
# (c)double
if name in ('longdouble', 'clongdouble') and myname in allTypes:
continue
base_capitalize = english_capitalize(base)
if base == 'complex':
na_name = '%s%d' % (base_capitalize, bit//2)
elif base == 'bool':
na_name = base_capitalize
else:
na_name = "%s%d" % (base_capitalize, bit)
allTypes[myname] = info.type
# add mapping for both the bit name and the numarray name
sctypeDict[myname] = info.type
sctypeDict[na_name] = info.type
# add forward, reverse, and string mapping to numarray
sctypeNA[na_name] = info.type
sctypeNA[info.type] = na_name
sctypeNA[info.char] = na_name
sctypeDict[char] = info.type
sctypeNA[char] = na_name
_add_aliases()
def _add_integer_aliases():
seen_bits = set()
for i_ctype, u_ctype in zip(_int_ctypes, _uint_ctypes):
i_info = _concrete_typeinfo[i_ctype]
u_info = _concrete_typeinfo[u_ctype]
bits = i_info.bits # same for both
for info, charname, intname, Intname in [
(i_info,'i%d' % (bits//8,), 'int%d' % bits, 'Int%d' % bits),
(u_info,'u%d' % (bits//8,), 'uint%d' % bits, 'UInt%d' % bits)]:
if bits not in seen_bits:
# sometimes two different types have the same number of bits
# if so, the one iterated over first takes precedence
allTypes[intname] = info.type
sctypeDict[intname] = info.type
sctypeDict[Intname] = info.type
sctypeDict[charname] = info.type
sctypeNA[Intname] = info.type
sctypeNA[charname] = info.type
sctypeNA[info.type] = Intname
sctypeNA[info.char] = Intname
seen_bits.add(bits)
_add_integer_aliases()
# We use these later
void = allTypes['void']
#
# Rework the Python names (so that float and complex and int are consistent
# with Python usage)
#
def _set_up_aliases():
type_pairs = [('complex_', 'cdouble'),
('int0', 'intp'),
('uint0', 'uintp'),
('single', 'float'),
('csingle', 'cfloat'),
('singlecomplex', 'cfloat'),
('float_', 'double'),
('intc', 'int'),
('uintc', 'uint'),
('int_', 'long'),
('uint', 'ulong'),
('cfloat', 'cdouble'),
('longfloat', 'longdouble'),
('clongfloat', 'clongdouble'),
('longcomplex', 'clongdouble'),
('bool_', 'bool'),
('bytes_', 'string'),
('string_', 'string'),
('unicode_', 'unicode'),
('object_', 'object')]
if sys.version_info[0] >= 3:
type_pairs.extend([('str_', 'unicode')])
else:
type_pairs.extend([('str_', 'string')])
for alias, t in type_pairs:
allTypes[alias] = allTypes[t]
sctypeDict[alias] = sctypeDict[t]
# Remove aliases overriding python types and modules
to_remove = ['ulong', 'object', 'int', 'float',
'complex', 'bool', 'string', 'datetime', 'timedelta']
if sys.version_info[0] >= 3:
to_remove.extend(['bytes', 'str'])
else:
to_remove.extend(['unicode', 'long'])
for t in to_remove:
try:
del allTypes[t]
del sctypeDict[t]
except KeyError:
pass
_set_up_aliases()
sctypes = {'int': [],
'uint':[],
'float':[],
'complex':[],
'others':[bool, object, bytes, unicode, void]}
def _add_array_type(typename, bits):
try:
t = allTypes['%s%d' % (typename, bits)]
except KeyError:
pass
else:
sctypes[typename].append(t)
def _set_array_types():
ibytes = [1, 2, 4, 8, 16, 32, 64]
fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
for bytes in ibytes:
bits = 8*bytes
_add_array_type('int', bits)
_add_array_type('uint', bits)
for bytes in fbytes:
bits = 8*bytes
_add_array_type('float', bits)
_add_array_type('complex', 2*bits)
_gi = dtype('p')
if _gi.type not in sctypes['int']:
indx = 0
sz = _gi.itemsize
_lst = sctypes['int']
while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
indx += 1
sctypes['int'].insert(indx, _gi.type)
sctypes['uint'].insert(indx, dtype('P').type)
_set_array_types()
# Add additional strings to the sctypeDict
_toadd = ['int', 'float', 'complex', 'bool', 'object']
if sys.version_info[0] >= 3:
_toadd.extend(['str', 'bytes', ('a', 'bytes_')])
else:
_toadd.extend(['string', ('str', 'string_'), 'unicode', ('a', 'string_')])
for name in _toadd:
if isinstance(name, tuple):
sctypeDict[name[0]] = allTypes[name[1]]
else:
sctypeDict[name] = allTypes['%s_' % name]
del _toadd, name

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"""
Functions for changing global ufunc configuration
This provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`
"""
from __future__ import division, absolute_import, print_function
try:
# Accessing collections abstract classes from collections
# has been deprecated since Python 3.3
import collections.abc as collections_abc
except ImportError:
import collections as collections_abc
import contextlib
from .overrides import set_module
from .umath import (
UFUNC_BUFSIZE_DEFAULT,
ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,
)
from . import umath
__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate",
]
_errdict = {"ignore": ERR_IGNORE,
"warn": ERR_WARN,
"raise": ERR_RAISE,
"call": ERR_CALL,
"print": ERR_PRINT,
"log": ERR_LOG}
_errdict_rev = {value: key for key, value in _errdict.items()}
@set_module('numpy')
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
Set how floating-point errors are handled.
Note that operations on integer scalar types (such as `int16`) are
handled like floating point, and are affected by these settings.
Parameters
----------
all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Set treatment for all types of floating-point errors at once:
- ignore: Take no action when the exception occurs.
- warn: Print a `RuntimeWarning` (via the Python `warnings` module).
- raise: Raise a `FloatingPointError`.
- call: Call a function specified using the `seterrcall` function.
- print: Print a warning directly to ``stdout``.
- log: Record error in a Log object specified by `seterrcall`.
The default is not to change the current behavior.
divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for division by zero.
over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point overflow.
under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point underflow.
invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for invalid floating-point operation.
Returns
-------
old_settings : dict
Dictionary containing the old settings.
See also
--------
seterrcall : Set a callback function for the 'call' mode.
geterr, geterrcall, errstate
Notes
-----
The floating-point exceptions are defined in the IEEE 754 standard [1]_:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
- Underflow: result so close to zero that some precision
was lost.
- Invalid operation: result is not an expressible number, typically
indicates that a NaN was produced.
.. [1] https://en.wikipedia.org/wiki/IEEE_754
Examples
--------
>>> old_settings = np.seterr(all='ignore') #seterr to known value
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.seterr(**old_settings) # reset to default
{'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in short_scalars
>>> from collections import OrderedDict
>>> old_settings = np.seterr(all='print')
>>> OrderedDict(np.geterr())
OrderedDict([('divide', 'print'), ('over', 'print'), ('under', 'print'), ('invalid', 'print')])
>>> np.int16(32000) * np.int16(3)
30464
"""
pyvals = umath.geterrobj()
old = geterr()
if divide is None:
divide = all or old['divide']
if over is None:
over = all or old['over']
if under is None:
under = all or old['under']
if invalid is None:
invalid = all or old['invalid']
maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
(_errdict[over] << SHIFT_OVERFLOW) +
(_errdict[under] << SHIFT_UNDERFLOW) +
(_errdict[invalid] << SHIFT_INVALID))
pyvals[1] = maskvalue
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterr():
"""
Get the current way of handling floating-point errors.
Returns
-------
res : dict
A dictionary with keys "divide", "over", "under", and "invalid",
whose values are from the strings "ignore", "print", "log", "warn",
"raise", and "call". The keys represent possible floating-point
exceptions, and the values define how these exceptions are handled.
See Also
--------
geterrcall, seterr, seterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> from collections import OrderedDict
>>> sorted(np.geterr().items())
[('divide', 'warn'), ('invalid', 'warn'), ('over', 'warn'), ('under', 'ignore')]
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> OrderedDict(sorted(np.geterr().items()))
OrderedDict([('divide', 'warn'), ('invalid', 'warn'), ('over', 'raise'), ('under', 'warn')])
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
"""
maskvalue = umath.geterrobj()[1]
mask = 7
res = {}
val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
res['divide'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_OVERFLOW) & mask
res['over'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_UNDERFLOW) & mask
res['under'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_INVALID) & mask
res['invalid'] = _errdict_rev[val]
return res
@set_module('numpy')
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
Parameters
----------
size : int
Size of buffer.
"""
if size > 10e6:
raise ValueError("Buffer size, %s, is too big." % size)
if size < 5:
raise ValueError("Buffer size, %s, is too small." % size)
if size % 16 != 0:
raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
pyvals = umath.geterrobj()
old = getbufsize()
pyvals[0] = size
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
"""
return umath.geterrobj()[0]
@set_module('numpy')
def seterrcall(func):
"""
Set the floating-point error callback function or log object.
There are two ways to capture floating-point error messages. The first
is to set the error-handler to 'call', using `seterr`. Then, set
the function to call using this function.
The second is to set the error-handler to 'log', using `seterr`.
Floating-point errors then trigger a call to the 'write' method of
the provided object.
Parameters
----------
func : callable f(err, flag) or object with write method
Function to call upon floating-point errors ('call'-mode) or
object whose 'write' method is used to log such message ('log'-mode).
The call function takes two arguments. The first is a string describing
the type of error (such as "divide by zero", "overflow", "underflow",
or "invalid value"), and the second is the status flag. The flag is a
byte, whose four least-significant bits indicate the type of error, one
of "divide", "over", "under", "invalid"::
[0 0 0 0 divide over under invalid]
In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
If an object is provided, its write method should take one argument,
a string.
Returns
-------
h : callable, log instance or None
The old error handler.
See Also
--------
seterr, geterr, geterrcall
Examples
--------
Callback upon error:
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')
>>> from collections import OrderedDict
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<function err_handler at 0x...>
>>> OrderedDict(sorted(np.seterr(**save_err).items()))
OrderedDict([('divide', 'call'), ('invalid', 'call'), ('over', 'call'), ('under', 'call')])
Log error message:
>>> class Log(object):
... def write(self, msg):
... print("LOG: %s" % msg)
...
>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')
>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in true_divide
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<numpy.core.numeric.Log object at 0x...>
>>> OrderedDict(sorted(np.seterr(**save_err).items()))
OrderedDict([('divide', 'log'), ('invalid', 'log'), ('over', 'log'), ('under', 'log')])
"""
if func is not None and not isinstance(func, collections_abc.Callable):
if not hasattr(func, 'write') or not isinstance(func.write, collections_abc.Callable):
raise ValueError("Only callable can be used as callback")
pyvals = umath.geterrobj()
old = geterrcall()
pyvals[2] = func
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
Returns
-------
errobj : callable, log instance or None
The current error handler. If no handler was set through `seterrcall`,
``None`` is returned.
See Also
--------
seterrcall, seterr, geterr
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrcall() # we did not yet set a handler, returns None
>>> oldsettings = np.seterr(all='call')
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
>>> oldhandler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True
"""
return umath.geterrobj()[2]
class _unspecified(object):
pass
_Unspecified = _unspecified()
@set_module('numpy')
class errstate(contextlib.ContextDecorator):
"""
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
.. versionchanged:: 1.17.0
`errstate` is also usable as a function decorator, saving
a level of indentation if an entire function is wrapped.
See :py:class:`contextlib.ContextDecorator` for more information.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> from collections import OrderedDict
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([nan, inf, inf])
>>> with np.errstate(divide='warn'):
... np.arange(3) / 0.
array([nan, inf, inf])
>>> np.sqrt(-1)
nan
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> OrderedDict(sorted(np.geterr().items()))
OrderedDict([('divide', 'ignore'), ('invalid', 'ignore'), ('over', 'ignore'), ('under', 'ignore')])
"""
# Note that we don't want to run the above doctests because they will fail
# without a from __future__ import with_statement
def __init__(self, **kwargs):
self.call = kwargs.pop('call', _Unspecified)
self.kwargs = kwargs
def __enter__(self):
self.oldstate = seterr(**self.kwargs)
if self.call is not _Unspecified:
self.oldcall = seterrcall(self.call)
def __exit__(self, *exc_info):
seterr(**self.oldstate)
if self.call is not _Unspecified:
seterrcall(self.oldcall)
def _setdef():
defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]
umath.seterrobj(defval)
# set the default values
_setdef()

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"""Simple script to compute the api hash of the current API.
The API has is defined by numpy_api_order and ufunc_api_order.
"""
from __future__ import division, absolute_import, print_function
from os.path import dirname
from code_generators.genapi import fullapi_hash
from code_generators.numpy_api import full_api
if __name__ == '__main__':
curdir = dirname(__file__)
print(fullapi_hash(full_api))

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from __future__ import division, absolute_import, print_function
import functools
import warnings
import operator
import types
from . import numeric as _nx
from .numeric import (result_type, NaN, shares_memory, MAY_SHARE_BOUNDS,
TooHardError, asanyarray, ndim)
from numpy.core.multiarray import add_docstring
from numpy.core import overrides
__all__ = ['logspace', 'linspace', 'geomspace']
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None,
dtype=None, axis=None):
return (start, stop)
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
try:
num = operator.index(num)
except TypeError:
raise TypeError(
"object of type {} cannot be safely interpreted as an integer."
.format(type(num)))
if num < 0:
raise ValueError("Number of samples, %s, must be non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an __array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
# In-place multiplication y *= delta/div is faster, but prevents the multiplicant
# from overriding what class is produced, and thus prevents, e.g. use of Quantities,
# see gh-7142. Hence, we multiply in place only for standard scalar types.
_mult_inplace = _nx.isscalar(delta)
if div > 0:
step = delta / div
if _nx.any(step == 0):
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
else:
if _mult_inplace:
y *= step
else:
y = y * step
else:
# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
# have an undefined step
step = NaN
# Multiply with delta to allow possible override of output class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
dtype=None, axis=None):
return (start, stop)
@array_function_dispatch(_logspace_dispatcher)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
axis=0):
"""
Return numbers spaced evenly on a log scale.
In linear space, the sequence starts at ``base ** start``
(`base` to the power of `start`) and ends with ``base ** stop``
(see `endpoint` below).
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
``base ** start`` is the starting value of the sequence.
stop : array_like
``base ** stop`` is the final value of the sequence, unless `endpoint`
is False. In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
base : float, optional
The base of the log space. The step size between the elements in
``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
Default is 10.0.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
arange : Similar to linspace, with the step size specified instead of the
number of samples. Note that, when used with a float endpoint, the
endpoint may or may not be included.
linspace : Similar to logspace, but with the samples uniformly distributed
in linear space, instead of log space.
geomspace : Similar to logspace, but with endpoints specified directly.
Notes
-----
Logspace is equivalent to the code
>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP
Examples
--------
>>> np.logspace(2.0, 3.0, num=4)
array([ 100. , 215.443469 , 464.15888336, 1000. ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100. , 177.827941 , 316.22776602, 562.34132519])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4. , 5.0396842 , 6.34960421, 8. ])
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> x1 = np.logspace(0.1, 1, N, endpoint=True)
>>> x2 = np.logspace(0.1, 1, N, endpoint=False)
>>> y = np.zeros(N)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
if dtype is None:
return _nx.power(base, y)
return _nx.power(base, y).astype(dtype, copy=False)
def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None,
axis=None):
return (start, stop)
@array_function_dispatch(_geomspace_dispatcher)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
"""
Return numbers spaced evenly on a log scale (a geometric progression).
This is similar to `logspace`, but with endpoints specified directly.
Each output sample is a constant multiple of the previous.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The final value of the sequence, unless `endpoint` is False.
In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
logspace : Similar to geomspace, but with endpoints specified using log
and base.
linspace : Similar to geomspace, but with arithmetic instead of geometric
progression.
arange : Similar to linspace, with the step size specified instead of the
number of samples.
Notes
-----
If the inputs or dtype are complex, the output will follow a logarithmic
spiral in the complex plane. (There are an infinite number of spirals
passing through two points; the output will follow the shortest such path.)
Examples
--------
>>> np.geomspace(1, 1000, num=4)
array([ 1., 10., 100., 1000.])
>>> np.geomspace(1, 1000, num=3, endpoint=False)
array([ 1., 10., 100.])
>>> np.geomspace(1, 1000, num=4, endpoint=False)
array([ 1. , 5.62341325, 31.6227766 , 177.827941 ])
>>> np.geomspace(1, 256, num=9)
array([ 1., 2., 4., 8., 16., 32., 64., 128., 256.])
Note that the above may not produce exact integers:
>>> np.geomspace(1, 256, num=9, dtype=int)
array([ 1, 2, 4, 7, 16, 32, 63, 127, 256])
>>> np.around(np.geomspace(1, 256, num=9)).astype(int)
array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])
Negative, decreasing, and complex inputs are allowed:
>>> np.geomspace(1000, 1, num=4)
array([1000., 100., 10., 1.])
>>> np.geomspace(-1000, -1, num=4)
array([-1000., -100., -10., -1.])
>>> np.geomspace(1j, 1000j, num=4) # Straight line
array([0. +1.j, 0. +10.j, 0. +100.j, 0.+1000.j])
>>> np.geomspace(-1+0j, 1+0j, num=5) # Circle
array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
6.12323400e-17+1.00000000e+00j, 7.07106781e-01+7.07106781e-01j,
1.00000000e+00+0.00000000e+00j])
Graphical illustration of ``endpoint`` parameter:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> y = np.zeros(N)
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.axis([0.5, 2000, 0, 3])
[0.5, 2000, 0, 3]
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()
"""
start = asanyarray(start)
stop = asanyarray(stop)
if _nx.any(start == 0) or _nx.any(stop == 0):
raise ValueError('Geometric sequence cannot include zero')
dt = result_type(start, stop, float(num), _nx.zeros((), dtype))
if dtype is None:
dtype = dt
else:
# complex to dtype('complex128'), for instance
dtype = _nx.dtype(dtype)
# Promote both arguments to the same dtype in case, for instance, one is
# complex and another is negative and log would produce NaN otherwise.
# Copy since we may change things in-place further down.
start = start.astype(dt, copy=True)
stop = stop.astype(dt, copy=True)
out_sign = _nx.ones(_nx.broadcast(start, stop).shape, dt)
# Avoid negligible real or imaginary parts in output by rotating to
# positive real, calculating, then undoing rotation
if _nx.issubdtype(dt, _nx.complexfloating):
all_imag = (start.real == 0.) & (stop.real == 0.)
if _nx.any(all_imag):
start[all_imag] = start[all_imag].imag
stop[all_imag] = stop[all_imag].imag
out_sign[all_imag] = 1j
both_negative = (_nx.sign(start) == -1) & (_nx.sign(stop) == -1)
if _nx.any(both_negative):
_nx.negative(start, out=start, where=both_negative)
_nx.negative(stop, out=stop, where=both_negative)
_nx.negative(out_sign, out=out_sign, where=both_negative)
log_start = _nx.log10(start)
log_stop = _nx.log10(stop)
result = out_sign * logspace(log_start, log_stop, num=num,
endpoint=endpoint, base=10.0, dtype=dtype)
if axis != 0:
result = _nx.moveaxis(result, 0, axis)
return result.astype(dtype, copy=False)
def _needs_add_docstring(obj):
"""
Returns true if the only way to set the docstring of `obj` from python is
via add_docstring.
This function errs on the side of being overly conservative.
"""
Py_TPFLAGS_HEAPTYPE = 1 << 9
if isinstance(obj, (types.FunctionType, types.MethodType, property)):
return False
if isinstance(obj, type) and obj.__flags__ & Py_TPFLAGS_HEAPTYPE:
return False
return True
def _add_docstring(obj, doc, warn_on_python):
if warn_on_python and not _needs_add_docstring(obj):
warnings.warn(
"add_newdoc was used on a pure-python object {}. "
"Prefer to attach it directly to the source."
.format(obj),
UserWarning,
stacklevel=3)
try:
add_docstring(obj, doc)
except Exception:
pass
def add_newdoc(place, obj, doc, warn_on_python=True):
"""
Add documentation to an existing object, typically one defined in C
The purpose is to allow easier editing of the docstrings without requiring
a re-compile. This exists primarily for internal use within numpy itself.
Parameters
----------
place : str
The absolute name of the module to import from
obj : str
The name of the object to add documentation to, typically a class or
function name
doc : {str, Tuple[str, str], List[Tuple[str, str]]}
If a string, the documentation to apply to `obj`
If a tuple, then the first element is interpreted as an attribute of
`obj` and the second as the docstring to apply - ``(method, docstring)``
If a list, then each element of the list should be a tuple of length
two - ``[(method1, docstring1), (method2, docstring2), ...]``
warn_on_python : bool
If True, the default, emit `UserWarning` if this is used to attach
documentation to a pure-python object.
Notes
-----
This routine never raises an error if the docstring can't be written, but
will raise an error if the object being documented does not exist.
This routine cannot modify read-only docstrings, as appear
in new-style classes or built-in functions. Because this
routine never raises an error the caller must check manually
that the docstrings were changed.
Since this function grabs the ``char *`` from a c-level str object and puts
it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
C-API best-practices, by:
- modifying a `PyTypeObject` after calling `PyType_Ready`
- calling `Py_INCREF` on the str and losing the reference, so the str
will never be released
If possible it should be avoided.
"""
new = getattr(__import__(place, globals(), {}, [obj]), obj)
if isinstance(doc, str):
_add_docstring(new, doc.strip(), warn_on_python)
elif isinstance(doc, tuple):
attr, docstring = doc
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
elif isinstance(doc, list):
for attr, docstring in doc:
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)

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from __future__ import division, print_function
import os
import genapi
from genapi import \
TypeApi, GlobalVarApi, FunctionApi, BoolValuesApi
import numpy_api
# use annotated api when running under cpychecker
h_template = r"""
#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE)
typedef struct {
PyObject_HEAD
npy_bool obval;
} PyBoolScalarObject;
extern NPY_NO_EXPORT PyTypeObject PyArrayMapIter_Type;
extern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type;
extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];
%s
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
#define PyArray_API PY_ARRAY_UNIQUE_SYMBOL
#endif
#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)
extern void **PyArray_API;
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
void **PyArray_API;
#else
static void **PyArray_API=NULL;
#endif
#endif
%s
#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)
static int
_import_array(void)
{
int st;
PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
PyObject *c_api = NULL;
if (numpy == NULL) {
return -1;
}
c_api = PyObject_GetAttrString(numpy, "_ARRAY_API");
Py_DECREF(numpy);
if (c_api == NULL) {
PyErr_SetString(PyExc_AttributeError, "_ARRAY_API not found");
return -1;
}
#if PY_VERSION_HEX >= 0x03000000
if (!PyCapsule_CheckExact(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object");
Py_DECREF(c_api);
return -1;
}
PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL);
#else
if (!PyCObject_Check(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCObject object");
Py_DECREF(c_api);
return -1;
}
PyArray_API = (void **)PyCObject_AsVoidPtr(c_api);
#endif
Py_DECREF(c_api);
if (PyArray_API == NULL) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer");
return -1;
}
/* Perform runtime check of C API version */
if (NPY_VERSION != PyArray_GetNDArrayCVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"ABI version 0x%%x but this version of numpy is 0x%%x", \
(int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion());
return -1;
}
if (NPY_FEATURE_VERSION > PyArray_GetNDArrayCFeatureVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"API version 0x%%x but this version of numpy is 0x%%x", \
(int) NPY_FEATURE_VERSION, (int) PyArray_GetNDArrayCFeatureVersion());
return -1;
}
/*
* Perform runtime check of endianness and check it matches the one set by
* the headers (npy_endian.h) as a safeguard
*/
st = PyArray_GetEndianness();
if (st == NPY_CPU_UNKNOWN_ENDIAN) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as unknown endian");
return -1;
}
#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
if (st != NPY_CPU_BIG) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"big endian, but detected different endianness at runtime");
return -1;
}
#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
if (st != NPY_CPU_LITTLE) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"little endian, but detected different endianness at runtime");
return -1;
}
#endif
return 0;
}
#if PY_VERSION_HEX >= 0x03000000
#define NUMPY_IMPORT_ARRAY_RETVAL NULL
#else
#define NUMPY_IMPORT_ARRAY_RETVAL
#endif
#define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NUMPY_IMPORT_ARRAY_RETVAL; } }
#define import_array1(ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return ret; } }
#define import_array2(msg, ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, msg); return ret; } }
#endif
#endif
"""
c_template = r"""
/* These pointers will be stored in the C-object for use in other
extension modules
*/
void *PyArray_API[] = {
%s
};
"""
c_api_header = """
===========
NumPy C-API
===========
"""
def generate_api(output_dir, force=False):
basename = 'multiarray_api'
h_file = os.path.join(output_dir, '__%s.h' % basename)
c_file = os.path.join(output_dir, '__%s.c' % basename)
d_file = os.path.join(output_dir, '%s.txt' % basename)
targets = (h_file, c_file, d_file)
sources = numpy_api.multiarray_api
if (not force and not genapi.should_rebuild(targets, [numpy_api.__file__, __file__])):
return targets
else:
do_generate_api(targets, sources)
return targets
def do_generate_api(targets, sources):
header_file = targets[0]
c_file = targets[1]
doc_file = targets[2]
global_vars = sources[0]
scalar_bool_values = sources[1]
types_api = sources[2]
multiarray_funcs = sources[3]
multiarray_api = sources[:]
module_list = []
extension_list = []
init_list = []
# Check multiarray api indexes
multiarray_api_index = genapi.merge_api_dicts(multiarray_api)
genapi.check_api_dict(multiarray_api_index)
numpyapi_list = genapi.get_api_functions('NUMPY_API',
multiarray_funcs)
# FIXME: ordered_funcs_api is unused
ordered_funcs_api = genapi.order_dict(multiarray_funcs)
# Create dict name -> *Api instance
api_name = 'PyArray_API'
multiarray_api_dict = {}
for f in numpyapi_list:
name = f.name
index = multiarray_funcs[name][0]
annotations = multiarray_funcs[name][1:]
multiarray_api_dict[f.name] = FunctionApi(f.name, index, annotations,
f.return_type,
f.args, api_name)
for name, val in global_vars.items():
index, type = val
multiarray_api_dict[name] = GlobalVarApi(name, index, type, api_name)
for name, val in scalar_bool_values.items():
index = val[0]
multiarray_api_dict[name] = BoolValuesApi(name, index, api_name)
for name, val in types_api.items():
index = val[0]
multiarray_api_dict[name] = TypeApi(name, index, 'PyTypeObject', api_name)
if len(multiarray_api_dict) != len(multiarray_api_index):
keys_dict = set(multiarray_api_dict.keys())
keys_index = set(multiarray_api_index.keys())
raise AssertionError(
"Multiarray API size mismatch - "
"index has extra keys {}, dict has extra keys {}"
.format(keys_index - keys_dict, keys_dict - keys_index)
)
extension_list = []
for name, index in genapi.order_dict(multiarray_api_index):
api_item = multiarray_api_dict[name]
extension_list.append(api_item.define_from_array_api_string())
init_list.append(api_item.array_api_define())
module_list.append(api_item.internal_define())
# Write to header
s = h_template % ('\n'.join(module_list), '\n'.join(extension_list))
genapi.write_file(header_file, s)
# Write to c-code
s = c_template % ',\n'.join(init_list)
genapi.write_file(c_file, s)
# write to documentation
s = c_api_header
for func in numpyapi_list:
s += func.to_ReST()
s += '\n\n'
genapi.write_file(doc_file, s)
return targets

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"""Machine limits for Float32 and Float64 and (long double) if available...
"""
from __future__ import division, absolute_import, print_function
__all__ = ['finfo', 'iinfo']
import warnings
from .machar import MachAr
from .overrides import set_module
from . import numeric
from . import numerictypes as ntypes
from .numeric import array, inf
from .umath import log10, exp2
from . import umath
def _fr0(a):
"""fix rank-0 --> rank-1"""
if a.ndim == 0:
a = a.copy()
a.shape = (1,)
return a
def _fr1(a):
"""fix rank > 0 --> rank-0"""
if a.size == 1:
a = a.copy()
a.shape = ()
return a
class MachArLike(object):
""" Object to simulate MachAr instance """
def __init__(self,
ftype,
**kwargs):
params = _MACHAR_PARAMS[ftype]
float_conv = lambda v: array([v], ftype)
float_to_float = lambda v : _fr1(float_conv(v))
float_to_str = lambda v: (params['fmt'] % array(_fr0(v)[0], ftype))
self.title = params['title']
# Parameter types same as for discovered MachAr object.
self.epsilon = self.eps = float_to_float(kwargs.pop('eps'))
self.epsneg = float_to_float(kwargs.pop('epsneg'))
self.xmax = self.huge = float_to_float(kwargs.pop('huge'))
self.xmin = self.tiny = float_to_float(kwargs.pop('tiny'))
self.ibeta = params['itype'](kwargs.pop('ibeta'))
self.__dict__.update(kwargs)
self.precision = int(-log10(self.eps))
self.resolution = float_to_float(float_conv(10) ** (-self.precision))
self._str_eps = float_to_str(self.eps)
self._str_epsneg = float_to_str(self.epsneg)
self._str_xmin = float_to_str(self.xmin)
self._str_xmax = float_to_str(self.xmax)
self._str_resolution = float_to_str(self.resolution)
_convert_to_float = {
ntypes.csingle: ntypes.single,
ntypes.complex_: ntypes.float_,
ntypes.clongfloat: ntypes.longfloat
}
# Parameters for creating MachAr / MachAr-like objects
_title_fmt = 'numpy {} precision floating point number'
_MACHAR_PARAMS = {
ntypes.double: dict(
itype = ntypes.int64,
fmt = '%24.16e',
title = _title_fmt.format('double')),
ntypes.single: dict(
itype = ntypes.int32,
fmt = '%15.7e',
title = _title_fmt.format('single')),
ntypes.longdouble: dict(
itype = ntypes.longlong,
fmt = '%s',
title = _title_fmt.format('long double')),
ntypes.half: dict(
itype = ntypes.int16,
fmt = '%12.5e',
title = _title_fmt.format('half'))}
# Key to identify the floating point type. Key is result of
# ftype('-0.1').newbyteorder('<').tobytes()
# See:
# https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
_KNOWN_TYPES = {}
def _register_type(machar, bytepat):
_KNOWN_TYPES[bytepat] = machar
_float_ma = {}
def _register_known_types():
# Known parameters for float16
# See docstring of MachAr class for description of parameters.
f16 = ntypes.float16
float16_ma = MachArLike(f16,
machep=-10,
negep=-11,
minexp=-14,
maxexp=16,
it=10,
iexp=5,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(f16(-10)),
epsneg=exp2(f16(-11)),
huge=f16(65504),
tiny=f16(2 ** -14))
_register_type(float16_ma, b'f\xae')
_float_ma[16] = float16_ma
# Known parameters for float32
f32 = ntypes.float32
float32_ma = MachArLike(f32,
machep=-23,
negep=-24,
minexp=-126,
maxexp=128,
it=23,
iexp=8,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(f32(-23)),
epsneg=exp2(f32(-24)),
huge=f32((1 - 2 ** -24) * 2**128),
tiny=exp2(f32(-126)))
_register_type(float32_ma, b'\xcd\xcc\xcc\xbd')
_float_ma[32] = float32_ma
# Known parameters for float64
f64 = ntypes.float64
epsneg_f64 = 2.0 ** -53.0
tiny_f64 = 2.0 ** -1022.0
float64_ma = MachArLike(f64,
machep=-52,
negep=-53,
minexp=-1022,
maxexp=1024,
it=52,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=2.0 ** -52.0,
epsneg=epsneg_f64,
huge=(1.0 - epsneg_f64) / tiny_f64 * f64(4),
tiny=tiny_f64)
_register_type(float64_ma, b'\x9a\x99\x99\x99\x99\x99\xb9\xbf')
_float_ma[64] = float64_ma
# Known parameters for IEEE 754 128-bit binary float
ld = ntypes.longdouble
epsneg_f128 = exp2(ld(-113))
tiny_f128 = exp2(ld(-16382))
# Ignore runtime error when this is not f128
with numeric.errstate(all='ignore'):
huge_f128 = (ld(1) - epsneg_f128) / tiny_f128 * ld(4)
float128_ma = MachArLike(ld,
machep=-112,
negep=-113,
minexp=-16382,
maxexp=16384,
it=112,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-112)),
epsneg=epsneg_f128,
huge=huge_f128,
tiny=tiny_f128)
# IEEE 754 128-bit binary float
_register_type(float128_ma,
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
_register_type(float128_ma,
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
_float_ma[128] = float128_ma
# Known parameters for float80 (Intel 80-bit extended precision)
epsneg_f80 = exp2(ld(-64))
tiny_f80 = exp2(ld(-16382))
# Ignore runtime error when this is not f80
with numeric.errstate(all='ignore'):
huge_f80 = (ld(1) - epsneg_f80) / tiny_f80 * ld(4)
float80_ma = MachArLike(ld,
machep=-63,
negep=-64,
minexp=-16382,
maxexp=16384,
it=63,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-63)),
epsneg=epsneg_f80,
huge=huge_f80,
tiny=tiny_f80)
# float80, first 10 bytes containing actual storage
_register_type(float80_ma, b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf')
_float_ma[80] = float80_ma
# Guessed / known parameters for double double; see:
# https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
# These numbers have the same exponent range as float64, but extended number of
# digits in the significand.
huge_dd = (umath.nextafter(ld(inf), ld(0))
if hasattr(umath, 'nextafter') # Missing on some platforms?
else float64_ma.huge)
float_dd_ma = MachArLike(ld,
machep=-105,
negep=-106,
minexp=-1022,
maxexp=1024,
it=105,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-105)),
epsneg= exp2(ld(-106)),
huge=huge_dd,
tiny=exp2(ld(-1022)))
# double double; low, high order (e.g. PPC 64)
_register_type(float_dd_ma,
b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf')
# double double; high, low order (e.g. PPC 64 le)
_register_type(float_dd_ma,
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<')
_float_ma['dd'] = float_dd_ma
def _get_machar(ftype):
""" Get MachAr instance or MachAr-like instance
Get parameters for floating point type, by first trying signatures of
various known floating point types, then, if none match, attempting to
identify parameters by analysis.
Parameters
----------
ftype : class
Numpy floating point type class (e.g. ``np.float64``)
Returns
-------
ma_like : instance of :class:`MachAr` or :class:`MachArLike`
Object giving floating point parameters for `ftype`.
Warns
-----
UserWarning
If the binary signature of the float type is not in the dictionary of
known float types.
"""
params = _MACHAR_PARAMS.get(ftype)
if params is None:
raise ValueError(repr(ftype))
# Detect known / suspected types
key = ftype('-0.1').newbyteorder('<').tobytes()
ma_like = _KNOWN_TYPES.get(key)
# Could be 80 bit == 10 byte extended precision, where last bytes can be
# random garbage. Try comparing first 10 bytes to pattern.
if ma_like is None and ftype == ntypes.longdouble:
ma_like = _KNOWN_TYPES.get(key[:10])
if ma_like is not None:
return ma_like
# Fall back to parameter discovery
warnings.warn(
'Signature {} for {} does not match any known type: '
'falling back to type probe function'.format(key, ftype),
UserWarning, stacklevel=2)
return _discovered_machar(ftype)
def _discovered_machar(ftype):
""" Create MachAr instance with found information on float types
"""
params = _MACHAR_PARAMS[ftype]
return MachAr(lambda v: array([v], ftype),
lambda v:_fr0(v.astype(params['itype']))[0],
lambda v:array(_fr0(v)[0], ftype),
lambda v: params['fmt'] % array(_fr0(v)[0], ftype),
params['title'])
@set_module('numpy')
class finfo(object):
"""
finfo(dtype)
Machine limits for floating point types.
Attributes
----------
bits : int
The number of bits occupied by the type.
eps : float
The smallest representable positive number such that
``1.0 + eps != 1.0``. Type of `eps` is an appropriate floating
point type.
epsneg : floating point number of the appropriate type
The smallest representable positive number such that
``1.0 - epsneg != 1.0``.
iexp : int
The number of bits in the exponent portion of the floating point
representation.
machar : MachAr
The object which calculated these parameters and holds more
detailed information.
machep : int
The exponent that yields `eps`.
max : floating point number of the appropriate type
The largest representable number.
maxexp : int
The smallest positive power of the base (2) that causes overflow.
min : floating point number of the appropriate type
The smallest representable number, typically ``-max``.
minexp : int
The most negative power of the base (2) consistent with there
being no leading 0's in the mantissa.
negep : int
The exponent that yields `epsneg`.
nexp : int
The number of bits in the exponent including its sign and bias.
nmant : int
The number of bits in the mantissa.
precision : int
The approximate number of decimal digits to which this kind of
float is precise.
resolution : floating point number of the appropriate type
The approximate decimal resolution of this type, i.e.,
``10**-precision``.
tiny : float
The smallest positive usable number. Type of `tiny` is an
appropriate floating point type.
Parameters
----------
dtype : float, dtype, or instance
Kind of floating point data-type about which to get information.
See Also
--------
MachAr : The implementation of the tests that produce this information.
iinfo : The equivalent for integer data types.
Notes
-----
For developers of NumPy: do not instantiate this at the module level.
The initial calculation of these parameters is expensive and negatively
impacts import times. These objects are cached, so calling ``finfo()``
repeatedly inside your functions is not a problem.
"""
_finfo_cache = {}
def __new__(cls, dtype):
try:
dtype = numeric.dtype(dtype)
except TypeError:
# In case a float instance was given
dtype = numeric.dtype(type(dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError("data type %r not inexact" % (dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
machar = _get_machar(dtype)
for word in ['precision', 'iexp',
'maxexp', 'minexp', 'negep',
'machep']:
setattr(self, word, getattr(machar, word))
for word in ['tiny', 'resolution', 'epsneg']:
setattr(self, word, getattr(machar, word).flat[0])
self.bits = self.dtype.itemsize * 8
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self.machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
return self
def __str__(self):
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'precision = %(precision)3s resolution = %(_str_resolution)s\n'
'machep = %(machep)6s eps = %(_str_eps)s\n'
'negep = %(negep)6s epsneg = %(_str_epsneg)s\n'
'minexp = %(minexp)6s tiny = %(_str_tiny)s\n'
'maxexp = %(maxexp)6s max = %(_str_max)s\n'
'nexp = %(nexp)6s min = -max\n'
'---------------------------------------------------------------\n'
)
return fmt % self.__dict__
def __repr__(self):
c = self.__class__.__name__
d = self.__dict__.copy()
d['klass'] = c
return (("%(klass)s(resolution=%(resolution)s, min=-%(_str_max)s,"
" max=%(_str_max)s, dtype=%(dtype)s)") % d)
@set_module('numpy')
class iinfo(object):
"""
iinfo(type)
Machine limits for integer types.
Attributes
----------
bits : int
The number of bits occupied by the type.
min : int
The smallest integer expressible by the type.
max : int
The largest integer expressible by the type.
Parameters
----------
int_type : integer type, dtype, or instance
The kind of integer data type to get information about.
See Also
--------
finfo : The equivalent for floating point data types.
Examples
--------
With types:
>>> ii16 = np.iinfo(np.int16)
>>> ii16.min
-32768
>>> ii16.max
32767
>>> ii32 = np.iinfo(np.int32)
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
With instances:
>>> ii32 = np.iinfo(np.int32(10))
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
"""
_min_vals = {}
_max_vals = {}
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
self.dtype = numeric.dtype(type(int_type))
self.kind = self.dtype.kind
self.bits = self.dtype.itemsize * 8
self.key = "%s%d" % (self.kind, self.bits)
if self.kind not in 'iu':
raise ValueError("Invalid integer data type %r." % (self.kind,))
@property
def min(self):
"""Minimum value of given dtype."""
if self.kind == 'u':
return 0
else:
try:
val = iinfo._min_vals[self.key]
except KeyError:
val = int(-(1 << (self.bits-1)))
iinfo._min_vals[self.key] = val
return val
@property
def max(self):
"""Maximum value of given dtype."""
try:
val = iinfo._max_vals[self.key]
except KeyError:
if self.kind == 'u':
val = int((1 << self.bits) - 1)
else:
val = int((1 << (self.bits-1)) - 1)
iinfo._max_vals[self.key] = val
return val
def __str__(self):
"""String representation."""
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'min = %(min)s\n'
'max = %(max)s\n'
'---------------------------------------------------------------\n'
)
return fmt % {'dtype': self.dtype, 'min': self.min, 'max': self.max}
def __repr__(self):
return "%s(min=%s, max=%s, dtype=%s)" % (self.__class__.__name__,
self.min, self.max, self.dtype)

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#ifdef _UMATHMODULE
extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;
extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;
NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndData \
(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int);
NPY_NO_EXPORT int PyUFunc_RegisterLoopForType \
(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *);
NPY_NO_EXPORT int PyUFunc_GenericFunction \
(PyUFuncObject *, PyObject *, PyObject *, PyArrayObject **);
NPY_NO_EXPORT void PyUFunc_f_f_As_d_d \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_d_d \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_f_f \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_g_g \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_F_F_As_D_D \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_F_F \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_D_D \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_G_G \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_O_O \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_ff_f_As_dd_d \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_ff_f \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_dd_d \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_gg_g \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_FF_F_As_DD_D \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_DD_D \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_FF_F \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_GG_G \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_OO_O \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_O_O_method \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_OO_O_method \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_On_Om \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT int PyUFunc_GetPyValues \
(char *, int *, int *, PyObject **);
NPY_NO_EXPORT int PyUFunc_checkfperr \
(int, PyObject *, int *);
NPY_NO_EXPORT void PyUFunc_clearfperr \
(void);
NPY_NO_EXPORT int PyUFunc_getfperr \
(void);
NPY_NO_EXPORT int PyUFunc_handlefperr \
(int, PyObject *, int, int *);
NPY_NO_EXPORT int PyUFunc_ReplaceLoopBySignature \
(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *);
NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignature \
(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *);
NPY_NO_EXPORT int PyUFunc_SetUsesArraysAsData \
(void **, size_t);
NPY_NO_EXPORT void PyUFunc_e_e \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_e_e_As_f_f \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_e_e_As_d_d \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_ee_e \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_ee_e_As_ff_f \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT void PyUFunc_ee_e_As_dd_d \
(char **, npy_intp *, npy_intp *, void *);
NPY_NO_EXPORT int PyUFunc_DefaultTypeResolver \
(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **);
NPY_NO_EXPORT int PyUFunc_ValidateCasting \
(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **);
NPY_NO_EXPORT int PyUFunc_RegisterLoopForDescr \
(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *);
NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *);
#else
#if defined(PY_UFUNC_UNIQUE_SYMBOL)
#define PyUFunc_API PY_UFUNC_UNIQUE_SYMBOL
#endif
#if defined(NO_IMPORT) || defined(NO_IMPORT_UFUNC)
extern void **PyUFunc_API;
#else
#if defined(PY_UFUNC_UNIQUE_SYMBOL)
void **PyUFunc_API;
#else
static void **PyUFunc_API=NULL;
#endif
#endif
#define PyUFunc_Type (*(PyTypeObject *)PyUFunc_API[0])
#define PyUFunc_FromFuncAndData \
(*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int)) \
PyUFunc_API[1])
#define PyUFunc_RegisterLoopForType \
(*(int (*)(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *)) \
PyUFunc_API[2])
#define PyUFunc_GenericFunction \
(*(int (*)(PyUFuncObject *, PyObject *, PyObject *, PyArrayObject **)) \
PyUFunc_API[3])
#define PyUFunc_f_f_As_d_d \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[4])
#define PyUFunc_d_d \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[5])
#define PyUFunc_f_f \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[6])
#define PyUFunc_g_g \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[7])
#define PyUFunc_F_F_As_D_D \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[8])
#define PyUFunc_F_F \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[9])
#define PyUFunc_D_D \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[10])
#define PyUFunc_G_G \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[11])
#define PyUFunc_O_O \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[12])
#define PyUFunc_ff_f_As_dd_d \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[13])
#define PyUFunc_ff_f \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[14])
#define PyUFunc_dd_d \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[15])
#define PyUFunc_gg_g \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[16])
#define PyUFunc_FF_F_As_DD_D \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[17])
#define PyUFunc_DD_D \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[18])
#define PyUFunc_FF_F \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[19])
#define PyUFunc_GG_G \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[20])
#define PyUFunc_OO_O \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[21])
#define PyUFunc_O_O_method \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[22])
#define PyUFunc_OO_O_method \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[23])
#define PyUFunc_On_Om \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[24])
#define PyUFunc_GetPyValues \
(*(int (*)(char *, int *, int *, PyObject **)) \
PyUFunc_API[25])
#define PyUFunc_checkfperr \
(*(int (*)(int, PyObject *, int *)) \
PyUFunc_API[26])
#define PyUFunc_clearfperr \
(*(void (*)(void)) \
PyUFunc_API[27])
#define PyUFunc_getfperr \
(*(int (*)(void)) \
PyUFunc_API[28])
#define PyUFunc_handlefperr \
(*(int (*)(int, PyObject *, int, int *)) \
PyUFunc_API[29])
#define PyUFunc_ReplaceLoopBySignature \
(*(int (*)(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *)) \
PyUFunc_API[30])
#define PyUFunc_FromFuncAndDataAndSignature \
(*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *)) \
PyUFunc_API[31])
#define PyUFunc_SetUsesArraysAsData \
(*(int (*)(void **, size_t)) \
PyUFunc_API[32])
#define PyUFunc_e_e \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[33])
#define PyUFunc_e_e_As_f_f \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[34])
#define PyUFunc_e_e_As_d_d \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[35])
#define PyUFunc_ee_e \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[36])
#define PyUFunc_ee_e_As_ff_f \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[37])
#define PyUFunc_ee_e_As_dd_d \
(*(void (*)(char **, npy_intp *, npy_intp *, void *)) \
PyUFunc_API[38])
#define PyUFunc_DefaultTypeResolver \
(*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **)) \
PyUFunc_API[39])
#define PyUFunc_ValidateCasting \
(*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **)) \
PyUFunc_API[40])
#define PyUFunc_RegisterLoopForDescr \
(*(int (*)(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *)) \
PyUFunc_API[41])
#define PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
(*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *)) \
PyUFunc_API[42])
static NPY_INLINE int
_import_umath(void)
{
PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
PyObject *c_api = NULL;
if (numpy == NULL) {
PyErr_SetString(PyExc_ImportError,
"numpy.core._multiarray_umath failed to import");
return -1;
}
c_api = PyObject_GetAttrString(numpy, "_UFUNC_API");
Py_DECREF(numpy);
if (c_api == NULL) {
PyErr_SetString(PyExc_AttributeError, "_UFUNC_API not found");
return -1;
}
#if PY_VERSION_HEX >= 0x03000000
if (!PyCapsule_CheckExact(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is not PyCapsule object");
Py_DECREF(c_api);
return -1;
}
PyUFunc_API = (void **)PyCapsule_GetPointer(c_api, NULL);
#else
if (!PyCObject_Check(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is not PyCObject object");
Py_DECREF(c_api);
return -1;
}
PyUFunc_API = (void **)PyCObject_AsVoidPtr(c_api);
#endif
Py_DECREF(c_api);
if (PyUFunc_API == NULL) {
PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is NULL pointer");
return -1;
}
return 0;
}
#if PY_VERSION_HEX >= 0x03000000
#define NUMPY_IMPORT_UMATH_RETVAL NULL
#else
#define NUMPY_IMPORT_UMATH_RETVAL
#endif
#define import_umath() \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError,\
"numpy.core.umath failed to import");\
return NUMPY_IMPORT_UMATH_RETVAL;\
}\
} while(0)
#define import_umath1(ret) \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError,\
"numpy.core.umath failed to import");\
return ret;\
}\
} while(0)
#define import_umath2(ret, msg) \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError, msg);\
return ret;\
}\
} while(0)
#define import_ufunc() \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError,\
"numpy.core.umath failed to import");\
}\
} while(0)
#endif

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#ifndef _NPY_INCLUDE_NEIGHBORHOOD_IMP
#error You should not include this header directly
#endif
/*
* Private API (here for inline)
*/
static NPY_INLINE int
_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);
/*
* Update to next item of the iterator
*
* Note: this simply increment the coordinates vector, last dimension
* incremented first , i.e, for dimension 3
* ...
* -1, -1, -1
* -1, -1, 0
* -1, -1, 1
* ....
* -1, 0, -1
* -1, 0, 0
* ....
* 0, -1, -1
* 0, -1, 0
* ....
*/
#define _UPDATE_COORD_ITER(c) \
wb = iter->coordinates[c] < iter->bounds[c][1]; \
if (wb) { \
iter->coordinates[c] += 1; \
return 0; \
} \
else { \
iter->coordinates[c] = iter->bounds[c][0]; \
}
static NPY_INLINE int
_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)
{
npy_intp i, wb;
for (i = iter->nd - 1; i >= 0; --i) {
_UPDATE_COORD_ITER(i)
}
return 0;
}
/*
* Version optimized for 2d arrays, manual loop unrolling
*/
static NPY_INLINE int
_PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)
{
npy_intp wb;
_UPDATE_COORD_ITER(1)
_UPDATE_COORD_ITER(0)
return 0;
}
#undef _UPDATE_COORD_ITER
/*
* Advance to the next neighbour
*/
static NPY_INLINE int
PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)
{
_PyArrayNeighborhoodIter_IncrCoord (iter);
iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
return 0;
}
/*
* Reset functions
*/
static NPY_INLINE int
PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter)
{
npy_intp i;
for (i = 0; i < iter->nd; ++i) {
iter->coordinates[i] = iter->bounds[i][0];
}
iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
return 0;
}

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#define NPY_SIZEOF_SHORT SIZEOF_SHORT
#define NPY_SIZEOF_INT SIZEOF_INT
#define NPY_SIZEOF_LONG SIZEOF_LONG
#define NPY_SIZEOF_FLOAT 4
#define NPY_SIZEOF_COMPLEX_FLOAT 8
#define NPY_SIZEOF_DOUBLE 8
#define NPY_SIZEOF_COMPLEX_DOUBLE 16
#define NPY_SIZEOF_LONGDOUBLE 8
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16
#define NPY_SIZEOF_PY_INTPTR_T 8
#define NPY_SIZEOF_OFF_T 4
#define NPY_SIZEOF_PY_LONG_LONG 8
#define NPY_SIZEOF_LONGLONG 8
#define NPY_NO_SIGNAL 1
#define NPY_NO_SMP 0
#define NPY_HAVE_DECL_ISNAN
#define NPY_HAVE_DECL_ISINF
#define NPY_HAVE_DECL_SIGNBIT
#define NPY_HAVE_DECL_ISFINITE
#define NPY_USE_C99_COMPLEX 1
#define NPY_RELAXED_STRIDES_CHECKING 1
#define NPY_USE_C99_FORMATS 1
#define NPY_VISIBILITY_HIDDEN
#define NPY_ABI_VERSION 0x01000009
#define NPY_API_VERSION 0x0000000D
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif

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#ifndef Py_ARRAYOBJECT_H
#define Py_ARRAYOBJECT_H
#include "ndarrayobject.h"
#include "npy_interrupt.h"
#ifdef NPY_NO_PREFIX
#include "noprefix.h"
#endif
#endif

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#ifndef _NPY_ARRAYSCALARS_H_
#define _NPY_ARRAYSCALARS_H_
#ifndef _MULTIARRAYMODULE
typedef struct {
PyObject_HEAD
npy_bool obval;
} PyBoolScalarObject;
#endif
typedef struct {
PyObject_HEAD
signed char obval;
} PyByteScalarObject;
typedef struct {
PyObject_HEAD
short obval;
} PyShortScalarObject;
typedef struct {
PyObject_HEAD
int obval;
} PyIntScalarObject;
typedef struct {
PyObject_HEAD
long obval;
} PyLongScalarObject;
typedef struct {
PyObject_HEAD
npy_longlong obval;
} PyLongLongScalarObject;
typedef struct {
PyObject_HEAD
unsigned char obval;
} PyUByteScalarObject;
typedef struct {
PyObject_HEAD
unsigned short obval;
} PyUShortScalarObject;
typedef struct {
PyObject_HEAD
unsigned int obval;
} PyUIntScalarObject;
typedef struct {
PyObject_HEAD
unsigned long obval;
} PyULongScalarObject;
typedef struct {
PyObject_HEAD
npy_ulonglong obval;
} PyULongLongScalarObject;
typedef struct {
PyObject_HEAD
npy_half obval;
} PyHalfScalarObject;
typedef struct {
PyObject_HEAD
float obval;
} PyFloatScalarObject;
typedef struct {
PyObject_HEAD
double obval;
} PyDoubleScalarObject;
typedef struct {
PyObject_HEAD
npy_longdouble obval;
} PyLongDoubleScalarObject;
typedef struct {
PyObject_HEAD
npy_cfloat obval;
} PyCFloatScalarObject;
typedef struct {
PyObject_HEAD
npy_cdouble obval;
} PyCDoubleScalarObject;
typedef struct {
PyObject_HEAD
npy_clongdouble obval;
} PyCLongDoubleScalarObject;
typedef struct {
PyObject_HEAD
PyObject * obval;
} PyObjectScalarObject;
typedef struct {
PyObject_HEAD
npy_datetime obval;
PyArray_DatetimeMetaData obmeta;
} PyDatetimeScalarObject;
typedef struct {
PyObject_HEAD
npy_timedelta obval;
PyArray_DatetimeMetaData obmeta;
} PyTimedeltaScalarObject;
typedef struct {
PyObject_HEAD
char obval;
} PyScalarObject;
#define PyStringScalarObject PyStringObject
#define PyUnicodeScalarObject PyUnicodeObject
typedef struct {
PyObject_VAR_HEAD
char *obval;
PyArray_Descr *descr;
int flags;
PyObject *base;
} PyVoidScalarObject;
/* Macros
Py<Cls><bitsize>ScalarObject
Py<Cls><bitsize>ArrType_Type
are defined in ndarrayobject.h
*/
#define PyArrayScalar_False ((PyObject *)(&(_PyArrayScalar_BoolValues[0])))
#define PyArrayScalar_True ((PyObject *)(&(_PyArrayScalar_BoolValues[1])))
#define PyArrayScalar_FromLong(i) \
((PyObject *)(&(_PyArrayScalar_BoolValues[((i)!=0)])))
#define PyArrayScalar_RETURN_BOOL_FROM_LONG(i) \
return Py_INCREF(PyArrayScalar_FromLong(i)), \
PyArrayScalar_FromLong(i)
#define PyArrayScalar_RETURN_FALSE \
return Py_INCREF(PyArrayScalar_False), \
PyArrayScalar_False
#define PyArrayScalar_RETURN_TRUE \
return Py_INCREF(PyArrayScalar_True), \
PyArrayScalar_True
#define PyArrayScalar_New(cls) \
Py##cls##ArrType_Type.tp_alloc(&Py##cls##ArrType_Type, 0)
#define PyArrayScalar_VAL(obj, cls) \
((Py##cls##ScalarObject *)obj)->obval
#define PyArrayScalar_ASSIGN(obj, cls, val) \
PyArrayScalar_VAL(obj, cls) = val
#endif

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#ifndef __NPY_HALFFLOAT_H__
#define __NPY_HALFFLOAT_H__
#include <Python.h>
#include <numpy/npy_math.h>
#ifdef __cplusplus
extern "C" {
#endif
/*
* Half-precision routines
*/
/* Conversions */
float npy_half_to_float(npy_half h);
double npy_half_to_double(npy_half h);
npy_half npy_float_to_half(float f);
npy_half npy_double_to_half(double d);
/* Comparisons */
int npy_half_eq(npy_half h1, npy_half h2);
int npy_half_ne(npy_half h1, npy_half h2);
int npy_half_le(npy_half h1, npy_half h2);
int npy_half_lt(npy_half h1, npy_half h2);
int npy_half_ge(npy_half h1, npy_half h2);
int npy_half_gt(npy_half h1, npy_half h2);
/* faster *_nonan variants for when you know h1 and h2 are not NaN */
int npy_half_eq_nonan(npy_half h1, npy_half h2);
int npy_half_lt_nonan(npy_half h1, npy_half h2);
int npy_half_le_nonan(npy_half h1, npy_half h2);
/* Miscellaneous functions */
int npy_half_iszero(npy_half h);
int npy_half_isnan(npy_half h);
int npy_half_isinf(npy_half h);
int npy_half_isfinite(npy_half h);
int npy_half_signbit(npy_half h);
npy_half npy_half_copysign(npy_half x, npy_half y);
npy_half npy_half_spacing(npy_half h);
npy_half npy_half_nextafter(npy_half x, npy_half y);
npy_half npy_half_divmod(npy_half x, npy_half y, npy_half *modulus);
/*
* Half-precision constants
*/
#define NPY_HALF_ZERO (0x0000u)
#define NPY_HALF_PZERO (0x0000u)
#define NPY_HALF_NZERO (0x8000u)
#define NPY_HALF_ONE (0x3c00u)
#define NPY_HALF_NEGONE (0xbc00u)
#define NPY_HALF_PINF (0x7c00u)
#define NPY_HALF_NINF (0xfc00u)
#define NPY_HALF_NAN (0x7e00u)
#define NPY_MAX_HALF (0x7bffu)
/*
* Bit-level conversions
*/
npy_uint16 npy_floatbits_to_halfbits(npy_uint32 f);
npy_uint16 npy_doublebits_to_halfbits(npy_uint64 d);
npy_uint32 npy_halfbits_to_floatbits(npy_uint16 h);
npy_uint64 npy_halfbits_to_doublebits(npy_uint16 h);
#ifdef __cplusplus
}
#endif
#endif

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/*
* DON'T INCLUDE THIS DIRECTLY.
*/
#ifndef NPY_NDARRAYOBJECT_H
#define NPY_NDARRAYOBJECT_H
#ifdef __cplusplus
extern "C" {
#endif
#include <Python.h>
#include "ndarraytypes.h"
/* Includes the "function" C-API -- these are all stored in a
list of pointers --- one for each file
The two lists are concatenated into one in multiarray.
They are available as import_array()
*/
#include "__multiarray_api.h"
/* C-API that requires previous API to be defined */
#define PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type)
#define PyArray_Check(op) PyObject_TypeCheck(op, &PyArray_Type)
#define PyArray_CheckExact(op) (((PyObject*)(op))->ob_type == &PyArray_Type)
#define PyArray_HasArrayInterfaceType(op, type, context, out) \
((((out)=PyArray_FromStructInterface(op)) != Py_NotImplemented) || \
(((out)=PyArray_FromInterface(op)) != Py_NotImplemented) || \
(((out)=PyArray_FromArrayAttr(op, type, context)) != \
Py_NotImplemented))
#define PyArray_HasArrayInterface(op, out) \
PyArray_HasArrayInterfaceType(op, NULL, NULL, out)
#define PyArray_IsZeroDim(op) (PyArray_Check(op) && \
(PyArray_NDIM((PyArrayObject *)op) == 0))
#define PyArray_IsScalar(obj, cls) \
(PyObject_TypeCheck(obj, &Py##cls##ArrType_Type))
#define PyArray_CheckScalar(m) (PyArray_IsScalar(m, Generic) || \
PyArray_IsZeroDim(m))
#if PY_MAJOR_VERSION >= 3
#define PyArray_IsPythonNumber(obj) \
(PyFloat_Check(obj) || PyComplex_Check(obj) || \
PyLong_Check(obj) || PyBool_Check(obj))
#define PyArray_IsIntegerScalar(obj) (PyLong_Check(obj) \
|| PyArray_IsScalar((obj), Integer))
#define PyArray_IsPythonScalar(obj) \
(PyArray_IsPythonNumber(obj) || PyBytes_Check(obj) || \
PyUnicode_Check(obj))
#else
#define PyArray_IsPythonNumber(obj) \
(PyInt_Check(obj) || PyFloat_Check(obj) || PyComplex_Check(obj) || \
PyLong_Check(obj) || PyBool_Check(obj))
#define PyArray_IsIntegerScalar(obj) (PyInt_Check(obj) \
|| PyLong_Check(obj) \
|| PyArray_IsScalar((obj), Integer))
#define PyArray_IsPythonScalar(obj) \
(PyArray_IsPythonNumber(obj) || PyString_Check(obj) || \
PyUnicode_Check(obj))
#endif
#define PyArray_IsAnyScalar(obj) \
(PyArray_IsScalar(obj, Generic) || PyArray_IsPythonScalar(obj))
#define PyArray_CheckAnyScalar(obj) (PyArray_IsPythonScalar(obj) || \
PyArray_CheckScalar(obj))
#define PyArray_GETCONTIGUOUS(m) (PyArray_ISCONTIGUOUS(m) ? \
Py_INCREF(m), (m) : \
(PyArrayObject *)(PyArray_Copy(m)))
#define PyArray_SAMESHAPE(a1,a2) ((PyArray_NDIM(a1) == PyArray_NDIM(a2)) && \
PyArray_CompareLists(PyArray_DIMS(a1), \
PyArray_DIMS(a2), \
PyArray_NDIM(a1)))
#define PyArray_SIZE(m) PyArray_MultiplyList(PyArray_DIMS(m), PyArray_NDIM(m))
#define PyArray_NBYTES(m) (PyArray_ITEMSIZE(m) * PyArray_SIZE(m))
#define PyArray_FROM_O(m) PyArray_FromAny(m, NULL, 0, 0, 0, NULL)
#define PyArray_FROM_OF(m,flags) PyArray_CheckFromAny(m, NULL, 0, 0, flags, \
NULL)
#define PyArray_FROM_OT(m,type) PyArray_FromAny(m, \
PyArray_DescrFromType(type), 0, 0, 0, NULL)
#define PyArray_FROM_OTF(m, type, flags) \
PyArray_FromAny(m, PyArray_DescrFromType(type), 0, 0, \
(((flags) & NPY_ARRAY_ENSURECOPY) ? \
((flags) | NPY_ARRAY_DEFAULT) : (flags)), NULL)
#define PyArray_FROMANY(m, type, min, max, flags) \
PyArray_FromAny(m, PyArray_DescrFromType(type), min, max, \
(((flags) & NPY_ARRAY_ENSURECOPY) ? \
(flags) | NPY_ARRAY_DEFAULT : (flags)), NULL)
#define PyArray_ZEROS(m, dims, type, is_f_order) \
PyArray_Zeros(m, dims, PyArray_DescrFromType(type), is_f_order)
#define PyArray_EMPTY(m, dims, type, is_f_order) \
PyArray_Empty(m, dims, PyArray_DescrFromType(type), is_f_order)
#define PyArray_FILLWBYTE(obj, val) memset(PyArray_DATA(obj), val, \
PyArray_NBYTES(obj))
#ifndef PYPY_VERSION
#define PyArray_REFCOUNT(obj) (((PyObject *)(obj))->ob_refcnt)
#define NPY_REFCOUNT PyArray_REFCOUNT
#endif
#define NPY_MAX_ELSIZE (2 * NPY_SIZEOF_LONGDOUBLE)
#define PyArray_ContiguousFromAny(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_DEFAULT, NULL)
#define PyArray_EquivArrTypes(a1, a2) \
PyArray_EquivTypes(PyArray_DESCR(a1), PyArray_DESCR(a2))
#define PyArray_EquivByteorders(b1, b2) \
(((b1) == (b2)) || (PyArray_ISNBO(b1) == PyArray_ISNBO(b2)))
#define PyArray_SimpleNew(nd, dims, typenum) \
PyArray_New(&PyArray_Type, nd, dims, typenum, NULL, NULL, 0, 0, NULL)
#define PyArray_SimpleNewFromData(nd, dims, typenum, data) \
PyArray_New(&PyArray_Type, nd, dims, typenum, NULL, \
data, 0, NPY_ARRAY_CARRAY, NULL)
#define PyArray_SimpleNewFromDescr(nd, dims, descr) \
PyArray_NewFromDescr(&PyArray_Type, descr, nd, dims, \
NULL, NULL, 0, NULL)
#define PyArray_ToScalar(data, arr) \
PyArray_Scalar(data, PyArray_DESCR(arr), (PyObject *)arr)
/* These might be faster without the dereferencing of obj
going on inside -- of course an optimizing compiler should
inline the constants inside a for loop making it a moot point
*/
#define PyArray_GETPTR1(obj, i) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0]))
#define PyArray_GETPTR2(obj, i, j) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0] + \
(j)*PyArray_STRIDES(obj)[1]))
#define PyArray_GETPTR3(obj, i, j, k) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0] + \
(j)*PyArray_STRIDES(obj)[1] + \
(k)*PyArray_STRIDES(obj)[2]))
#define PyArray_GETPTR4(obj, i, j, k, l) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0] + \
(j)*PyArray_STRIDES(obj)[1] + \
(k)*PyArray_STRIDES(obj)[2] + \
(l)*PyArray_STRIDES(obj)[3]))
/* Move to arrayobject.c once PyArray_XDECREF_ERR is removed */
static NPY_INLINE void
PyArray_DiscardWritebackIfCopy(PyArrayObject *arr)
{
PyArrayObject_fields *fa = (PyArrayObject_fields *)arr;
if (fa && fa->base) {
if ((fa->flags & NPY_ARRAY_UPDATEIFCOPY) ||
(fa->flags & NPY_ARRAY_WRITEBACKIFCOPY)) {
PyArray_ENABLEFLAGS((PyArrayObject*)fa->base, NPY_ARRAY_WRITEABLE);
Py_DECREF(fa->base);
fa->base = NULL;
PyArray_CLEARFLAGS(arr, NPY_ARRAY_WRITEBACKIFCOPY);
PyArray_CLEARFLAGS(arr, NPY_ARRAY_UPDATEIFCOPY);
}
}
}
#define PyArray_DESCR_REPLACE(descr) do { \
PyArray_Descr *_new_; \
_new_ = PyArray_DescrNew(descr); \
Py_XDECREF(descr); \
descr = _new_; \
} while(0)
/* Copy should always return contiguous array */
#define PyArray_Copy(obj) PyArray_NewCopy(obj, NPY_CORDER)
#define PyArray_FromObject(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_BEHAVED | \
NPY_ARRAY_ENSUREARRAY, NULL)
#define PyArray_ContiguousFromObject(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_DEFAULT | \
NPY_ARRAY_ENSUREARRAY, NULL)
#define PyArray_CopyFromObject(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_ENSURECOPY | \
NPY_ARRAY_DEFAULT | \
NPY_ARRAY_ENSUREARRAY, NULL)
#define PyArray_Cast(mp, type_num) \
PyArray_CastToType(mp, PyArray_DescrFromType(type_num), 0)
#define PyArray_Take(ap, items, axis) \
PyArray_TakeFrom(ap, items, axis, NULL, NPY_RAISE)
#define PyArray_Put(ap, items, values) \
PyArray_PutTo(ap, items, values, NPY_RAISE)
/* Compatibility with old Numeric stuff -- don't use in new code */
#define PyArray_FromDimsAndData(nd, d, type, data) \
PyArray_FromDimsAndDataAndDescr(nd, d, PyArray_DescrFromType(type), \
data)
/*
Check to see if this key in the dictionary is the "title"
entry of the tuple (i.e. a duplicate dictionary entry in the fields
dict.
*/
static NPY_INLINE int
NPY_TITLE_KEY_check(PyObject *key, PyObject *value)
{
PyObject *title;
if (PyTuple_Size(value) != 3) {
return 0;
}
title = PyTuple_GetItem(value, 2);
if (key == title) {
return 1;
}
#ifdef PYPY_VERSION
/*
* On PyPy, dictionary keys do not always preserve object identity.
* Fall back to comparison by value.
*/
if (PyUnicode_Check(title) && PyUnicode_Check(key)) {
return PyUnicode_Compare(title, key) == 0 ? 1 : 0;
}
#if PY_VERSION_HEX < 0x03000000
if (PyString_Check(title) && PyString_Check(key)) {
return PyObject_Compare(title, key) == 0 ? 1 : 0;
}
#endif
#endif
return 0;
}
/* Macro, for backward compat with "if NPY_TITLE_KEY(key, value) { ..." */
#define NPY_TITLE_KEY(key, value) (NPY_TITLE_KEY_check((key), (value)))
#define DEPRECATE(msg) PyErr_WarnEx(PyExc_DeprecationWarning,msg,1)
#define DEPRECATE_FUTUREWARNING(msg) PyErr_WarnEx(PyExc_FutureWarning,msg,1)
#if !defined(NPY_NO_DEPRECATED_API) || \
(NPY_NO_DEPRECATED_API < NPY_1_14_API_VERSION)
static NPY_INLINE void
PyArray_XDECREF_ERR(PyArrayObject *arr)
{
/* 2017-Nov-10 1.14 */
DEPRECATE("PyArray_XDECREF_ERR is deprecated, call "
"PyArray_DiscardWritebackIfCopy then Py_XDECREF instead");
PyArray_DiscardWritebackIfCopy(arr);
Py_XDECREF(arr);
}
#endif
#ifdef __cplusplus
}
#endif
#endif /* NPY_NDARRAYOBJECT_H */

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#ifndef NPY_NOPREFIX_H
#define NPY_NOPREFIX_H
/*
* You can directly include noprefix.h as a backward
* compatibility measure
*/
#ifndef NPY_NO_PREFIX
#include "ndarrayobject.h"
#include "npy_interrupt.h"
#endif
#define SIGSETJMP NPY_SIGSETJMP
#define SIGLONGJMP NPY_SIGLONGJMP
#define SIGJMP_BUF NPY_SIGJMP_BUF
#define MAX_DIMS NPY_MAXDIMS
#define longlong npy_longlong
#define ulonglong npy_ulonglong
#define Bool npy_bool
#define longdouble npy_longdouble
#define byte npy_byte
#ifndef _BSD_SOURCE
#define ushort npy_ushort
#define uint npy_uint
#define ulong npy_ulong
#endif
#define ubyte npy_ubyte
#define ushort npy_ushort
#define uint npy_uint
#define ulong npy_ulong
#define cfloat npy_cfloat
#define cdouble npy_cdouble
#define clongdouble npy_clongdouble
#define Int8 npy_int8
#define UInt8 npy_uint8
#define Int16 npy_int16
#define UInt16 npy_uint16
#define Int32 npy_int32
#define UInt32 npy_uint32
#define Int64 npy_int64
#define UInt64 npy_uint64
#define Int128 npy_int128
#define UInt128 npy_uint128
#define Int256 npy_int256
#define UInt256 npy_uint256
#define Float16 npy_float16
#define Complex32 npy_complex32
#define Float32 npy_float32
#define Complex64 npy_complex64
#define Float64 npy_float64
#define Complex128 npy_complex128
#define Float80 npy_float80
#define Complex160 npy_complex160
#define Float96 npy_float96
#define Complex192 npy_complex192
#define Float128 npy_float128
#define Complex256 npy_complex256
#define intp npy_intp
#define uintp npy_uintp
#define datetime npy_datetime
#define timedelta npy_timedelta
#define SIZEOF_LONGLONG NPY_SIZEOF_LONGLONG
#define SIZEOF_INTP NPY_SIZEOF_INTP
#define SIZEOF_UINTP NPY_SIZEOF_UINTP
#define SIZEOF_HALF NPY_SIZEOF_HALF
#define SIZEOF_LONGDOUBLE NPY_SIZEOF_LONGDOUBLE
#define SIZEOF_DATETIME NPY_SIZEOF_DATETIME
#define SIZEOF_TIMEDELTA NPY_SIZEOF_TIMEDELTA
#define LONGLONG_FMT NPY_LONGLONG_FMT
#define ULONGLONG_FMT NPY_ULONGLONG_FMT
#define LONGLONG_SUFFIX NPY_LONGLONG_SUFFIX
#define ULONGLONG_SUFFIX NPY_ULONGLONG_SUFFIX
#define MAX_INT8 127
#define MIN_INT8 -128
#define MAX_UINT8 255
#define MAX_INT16 32767
#define MIN_INT16 -32768
#define MAX_UINT16 65535
#define MAX_INT32 2147483647
#define MIN_INT32 (-MAX_INT32 - 1)
#define MAX_UINT32 4294967295U
#define MAX_INT64 LONGLONG_SUFFIX(9223372036854775807)
#define MIN_INT64 (-MAX_INT64 - LONGLONG_SUFFIX(1))
#define MAX_UINT64 ULONGLONG_SUFFIX(18446744073709551615)
#define MAX_INT128 LONGLONG_SUFFIX(85070591730234615865843651857942052864)
#define MIN_INT128 (-MAX_INT128 - LONGLONG_SUFFIX(1))
#define MAX_UINT128 ULONGLONG_SUFFIX(170141183460469231731687303715884105728)
#define MAX_INT256 LONGLONG_SUFFIX(57896044618658097711785492504343953926634992332820282019728792003956564819967)
#define MIN_INT256 (-MAX_INT256 - LONGLONG_SUFFIX(1))
#define MAX_UINT256 ULONGLONG_SUFFIX(115792089237316195423570985008687907853269984665640564039457584007913129639935)
#define MAX_BYTE NPY_MAX_BYTE
#define MIN_BYTE NPY_MIN_BYTE
#define MAX_UBYTE NPY_MAX_UBYTE
#define MAX_SHORT NPY_MAX_SHORT
#define MIN_SHORT NPY_MIN_SHORT
#define MAX_USHORT NPY_MAX_USHORT
#define MAX_INT NPY_MAX_INT
#define MIN_INT NPY_MIN_INT
#define MAX_UINT NPY_MAX_UINT
#define MAX_LONG NPY_MAX_LONG
#define MIN_LONG NPY_MIN_LONG
#define MAX_ULONG NPY_MAX_ULONG
#define MAX_LONGLONG NPY_MAX_LONGLONG
#define MIN_LONGLONG NPY_MIN_LONGLONG
#define MAX_ULONGLONG NPY_MAX_ULONGLONG
#define MIN_DATETIME NPY_MIN_DATETIME
#define MAX_DATETIME NPY_MAX_DATETIME
#define MIN_TIMEDELTA NPY_MIN_TIMEDELTA
#define MAX_TIMEDELTA NPY_MAX_TIMEDELTA
#define BITSOF_BOOL NPY_BITSOF_BOOL
#define BITSOF_CHAR NPY_BITSOF_CHAR
#define BITSOF_SHORT NPY_BITSOF_SHORT
#define BITSOF_INT NPY_BITSOF_INT
#define BITSOF_LONG NPY_BITSOF_LONG
#define BITSOF_LONGLONG NPY_BITSOF_LONGLONG
#define BITSOF_HALF NPY_BITSOF_HALF
#define BITSOF_FLOAT NPY_BITSOF_FLOAT
#define BITSOF_DOUBLE NPY_BITSOF_DOUBLE
#define BITSOF_LONGDOUBLE NPY_BITSOF_LONGDOUBLE
#define BITSOF_DATETIME NPY_BITSOF_DATETIME
#define BITSOF_TIMEDELTA NPY_BITSOF_TIMEDELTA
#define _pya_malloc PyArray_malloc
#define _pya_free PyArray_free
#define _pya_realloc PyArray_realloc
#define BEGIN_THREADS_DEF NPY_BEGIN_THREADS_DEF
#define BEGIN_THREADS NPY_BEGIN_THREADS
#define END_THREADS NPY_END_THREADS
#define ALLOW_C_API_DEF NPY_ALLOW_C_API_DEF
#define ALLOW_C_API NPY_ALLOW_C_API
#define DISABLE_C_API NPY_DISABLE_C_API
#define PY_FAIL NPY_FAIL
#define PY_SUCCEED NPY_SUCCEED
#ifndef TRUE
#define TRUE NPY_TRUE
#endif
#ifndef FALSE
#define FALSE NPY_FALSE
#endif
#define LONGDOUBLE_FMT NPY_LONGDOUBLE_FMT
#define CONTIGUOUS NPY_CONTIGUOUS
#define C_CONTIGUOUS NPY_C_CONTIGUOUS
#define FORTRAN NPY_FORTRAN
#define F_CONTIGUOUS NPY_F_CONTIGUOUS
#define OWNDATA NPY_OWNDATA
#define FORCECAST NPY_FORCECAST
#define ENSURECOPY NPY_ENSURECOPY
#define ENSUREARRAY NPY_ENSUREARRAY
#define ELEMENTSTRIDES NPY_ELEMENTSTRIDES
#define ALIGNED NPY_ALIGNED
#define NOTSWAPPED NPY_NOTSWAPPED
#define WRITEABLE NPY_WRITEABLE
#define UPDATEIFCOPY NPY_UPDATEIFCOPY
#define WRITEBACKIFCOPY NPY_ARRAY_WRITEBACKIFCOPY
#define ARR_HAS_DESCR NPY_ARR_HAS_DESCR
#define BEHAVED NPY_BEHAVED
#define BEHAVED_NS NPY_BEHAVED_NS
#define CARRAY NPY_CARRAY
#define CARRAY_RO NPY_CARRAY_RO
#define FARRAY NPY_FARRAY
#define FARRAY_RO NPY_FARRAY_RO
#define DEFAULT NPY_DEFAULT
#define IN_ARRAY NPY_IN_ARRAY
#define OUT_ARRAY NPY_OUT_ARRAY
#define INOUT_ARRAY NPY_INOUT_ARRAY
#define IN_FARRAY NPY_IN_FARRAY
#define OUT_FARRAY NPY_OUT_FARRAY
#define INOUT_FARRAY NPY_INOUT_FARRAY
#define UPDATE_ALL NPY_UPDATE_ALL
#define OWN_DATA NPY_OWNDATA
#define BEHAVED_FLAGS NPY_BEHAVED
#define BEHAVED_FLAGS_NS NPY_BEHAVED_NS
#define CARRAY_FLAGS_RO NPY_CARRAY_RO
#define CARRAY_FLAGS NPY_CARRAY
#define FARRAY_FLAGS NPY_FARRAY
#define FARRAY_FLAGS_RO NPY_FARRAY_RO
#define DEFAULT_FLAGS NPY_DEFAULT
#define UPDATE_ALL_FLAGS NPY_UPDATE_ALL_FLAGS
#ifndef MIN
#define MIN PyArray_MIN
#endif
#ifndef MAX
#define MAX PyArray_MAX
#endif
#define MAX_INTP NPY_MAX_INTP
#define MIN_INTP NPY_MIN_INTP
#define MAX_UINTP NPY_MAX_UINTP
#define INTP_FMT NPY_INTP_FMT
#ifndef PYPY_VERSION
#define REFCOUNT PyArray_REFCOUNT
#define MAX_ELSIZE NPY_MAX_ELSIZE
#endif
#endif

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#ifndef _NPY_1_7_DEPRECATED_API_H
#define _NPY_1_7_DEPRECATED_API_H
#ifndef NPY_DEPRECATED_INCLUDES
#error "Should never include npy_*_*_deprecated_api directly."
#endif
/* Emit a warning if the user did not specifically request the old API */
#ifndef NPY_NO_DEPRECATED_API
#if defined(_WIN32)
#define _WARN___STR2__(x) #x
#define _WARN___STR1__(x) _WARN___STR2__(x)
#define _WARN___LOC__ __FILE__ "(" _WARN___STR1__(__LINE__) ") : Warning Msg: "
#pragma message(_WARN___LOC__"Using deprecated NumPy API, disable it with " \
"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION")
#elif defined(__GNUC__)
#warning "Using deprecated NumPy API, disable it with " \
"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
#endif
/* TODO: How to do this warning message for other compilers? */
#endif
/*
* This header exists to collect all dangerous/deprecated NumPy API
* as of NumPy 1.7.
*
* This is an attempt to remove bad API, the proliferation of macros,
* and namespace pollution currently produced by the NumPy headers.
*/
/* These array flags are deprecated as of NumPy 1.7 */
#define NPY_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS
#define NPY_FORTRAN NPY_ARRAY_F_CONTIGUOUS
/*
* The consistent NPY_ARRAY_* names which don't pollute the NPY_*
* namespace were added in NumPy 1.7.
*
* These versions of the carray flags are deprecated, but
* probably should only be removed after two releases instead of one.
*/
#define NPY_C_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS
#define NPY_F_CONTIGUOUS NPY_ARRAY_F_CONTIGUOUS
#define NPY_OWNDATA NPY_ARRAY_OWNDATA
#define NPY_FORCECAST NPY_ARRAY_FORCECAST
#define NPY_ENSURECOPY NPY_ARRAY_ENSURECOPY
#define NPY_ENSUREARRAY NPY_ARRAY_ENSUREARRAY
#define NPY_ELEMENTSTRIDES NPY_ARRAY_ELEMENTSTRIDES
#define NPY_ALIGNED NPY_ARRAY_ALIGNED
#define NPY_NOTSWAPPED NPY_ARRAY_NOTSWAPPED
#define NPY_WRITEABLE NPY_ARRAY_WRITEABLE
#define NPY_UPDATEIFCOPY NPY_ARRAY_UPDATEIFCOPY
#define NPY_BEHAVED NPY_ARRAY_BEHAVED
#define NPY_BEHAVED_NS NPY_ARRAY_BEHAVED_NS
#define NPY_CARRAY NPY_ARRAY_CARRAY
#define NPY_CARRAY_RO NPY_ARRAY_CARRAY_RO
#define NPY_FARRAY NPY_ARRAY_FARRAY
#define NPY_FARRAY_RO NPY_ARRAY_FARRAY_RO
#define NPY_DEFAULT NPY_ARRAY_DEFAULT
#define NPY_IN_ARRAY NPY_ARRAY_IN_ARRAY
#define NPY_OUT_ARRAY NPY_ARRAY_OUT_ARRAY
#define NPY_INOUT_ARRAY NPY_ARRAY_INOUT_ARRAY
#define NPY_IN_FARRAY NPY_ARRAY_IN_FARRAY
#define NPY_OUT_FARRAY NPY_ARRAY_OUT_FARRAY
#define NPY_INOUT_FARRAY NPY_ARRAY_INOUT_FARRAY
#define NPY_UPDATE_ALL NPY_ARRAY_UPDATE_ALL
/* This way of accessing the default type is deprecated as of NumPy 1.7 */
#define PyArray_DEFAULT NPY_DEFAULT_TYPE
/* These DATETIME bits aren't used internally */
#if PY_VERSION_HEX >= 0x03000000
#define PyDataType_GetDatetimeMetaData(descr) \
((descr->metadata == NULL) ? NULL : \
((PyArray_DatetimeMetaData *)(PyCapsule_GetPointer( \
PyDict_GetItemString( \
descr->metadata, NPY_METADATA_DTSTR), NULL))))
#else
#define PyDataType_GetDatetimeMetaData(descr) \
((descr->metadata == NULL) ? NULL : \
((PyArray_DatetimeMetaData *)(PyCObject_AsVoidPtr( \
PyDict_GetItemString(descr->metadata, NPY_METADATA_DTSTR)))))
#endif
/*
* Deprecated as of NumPy 1.7, this kind of shortcut doesn't
* belong in the public API.
*/
#define NPY_AO PyArrayObject
/*
* Deprecated as of NumPy 1.7, an all-lowercase macro doesn't
* belong in the public API.
*/
#define fortran fortran_
/*
* Deprecated as of NumPy 1.7, as it is a namespace-polluting
* macro.
*/
#define FORTRAN_IF PyArray_FORTRAN_IF
/* Deprecated as of NumPy 1.7, datetime64 uses c_metadata instead */
#define NPY_METADATA_DTSTR "__timeunit__"
/*
* Deprecated as of NumPy 1.7.
* The reasoning:
* - These are for datetime, but there's no datetime "namespace".
* - They just turn NPY_STR_<x> into "<x>", which is just
* making something simple be indirected.
*/
#define NPY_STR_Y "Y"
#define NPY_STR_M "M"
#define NPY_STR_W "W"
#define NPY_STR_D "D"
#define NPY_STR_h "h"
#define NPY_STR_m "m"
#define NPY_STR_s "s"
#define NPY_STR_ms "ms"
#define NPY_STR_us "us"
#define NPY_STR_ns "ns"
#define NPY_STR_ps "ps"
#define NPY_STR_fs "fs"
#define NPY_STR_as "as"
/*
* The macros in old_defines.h are Deprecated as of NumPy 1.7 and will be
* removed in the next major release.
*/
#include "old_defines.h"
#endif

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/*
* This is a convenience header file providing compatibility utilities
* for supporting Python 2 and Python 3 in the same code base.
*
* If you want to use this for your own projects, it's recommended to make a
* copy of it. Although the stuff below is unlikely to change, we don't provide
* strong backwards compatibility guarantees at the moment.
*/
#ifndef _NPY_3KCOMPAT_H_
#define _NPY_3KCOMPAT_H_
#include <Python.h>
#include <stdio.h>
#if PY_VERSION_HEX >= 0x03000000
#ifndef NPY_PY3K
#define NPY_PY3K 1
#endif
#endif
#include "numpy/npy_common.h"
#include "numpy/ndarrayobject.h"
#ifdef __cplusplus
extern "C" {
#endif
/*
* PyInt -> PyLong
*/
#if defined(NPY_PY3K)
/* Return True only if the long fits in a C long */
static NPY_INLINE int PyInt_Check(PyObject *op) {
int overflow = 0;
if (!PyLong_Check(op)) {
return 0;
}
PyLong_AsLongAndOverflow(op, &overflow);
return (overflow == 0);
}
#define PyInt_FromLong PyLong_FromLong
#define PyInt_AsLong PyLong_AsLong
#define PyInt_AS_LONG PyLong_AsLong
#define PyInt_AsSsize_t PyLong_AsSsize_t
/* NOTE:
*
* Since the PyLong type is very different from the fixed-range PyInt,
* we don't define PyInt_Type -> PyLong_Type.
*/
#endif /* NPY_PY3K */
/* Py3 changes PySlice_GetIndicesEx' first argument's type to PyObject* */
#ifdef NPY_PY3K
# define NpySlice_GetIndicesEx PySlice_GetIndicesEx
#else
# define NpySlice_GetIndicesEx(op, nop, start, end, step, slicelength) \
PySlice_GetIndicesEx((PySliceObject *)op, nop, start, end, step, slicelength)
#endif
/* <2.7.11 and <3.4.4 have the wrong argument type for Py_EnterRecursiveCall */
#if (PY_VERSION_HEX < 0x02070B00) || \
((0x03000000 <= PY_VERSION_HEX) && (PY_VERSION_HEX < 0x03040400))
#define Npy_EnterRecursiveCall(x) Py_EnterRecursiveCall((char *)(x))
#else
#define Npy_EnterRecursiveCall(x) Py_EnterRecursiveCall(x)
#endif
/* Py_SETREF was added in 3.5.2, and only if Py_LIMITED_API is absent */
#if PY_VERSION_HEX < 0x03050200
#define Py_SETREF(op, op2) \
do { \
PyObject *_py_tmp = (PyObject *)(op); \
(op) = (op2); \
Py_DECREF(_py_tmp); \
} while (0)
#endif
/*
* PyString -> PyBytes
*/
#if defined(NPY_PY3K)
#define PyString_Type PyBytes_Type
#define PyString_Check PyBytes_Check
#define PyStringObject PyBytesObject
#define PyString_FromString PyBytes_FromString
#define PyString_FromStringAndSize PyBytes_FromStringAndSize
#define PyString_AS_STRING PyBytes_AS_STRING
#define PyString_AsStringAndSize PyBytes_AsStringAndSize
#define PyString_FromFormat PyBytes_FromFormat
#define PyString_Concat PyBytes_Concat
#define PyString_ConcatAndDel PyBytes_ConcatAndDel
#define PyString_AsString PyBytes_AsString
#define PyString_GET_SIZE PyBytes_GET_SIZE
#define PyString_Size PyBytes_Size
#define PyUString_Type PyUnicode_Type
#define PyUString_Check PyUnicode_Check
#define PyUStringObject PyUnicodeObject
#define PyUString_FromString PyUnicode_FromString
#define PyUString_FromStringAndSize PyUnicode_FromStringAndSize
#define PyUString_FromFormat PyUnicode_FromFormat
#define PyUString_Concat PyUnicode_Concat2
#define PyUString_ConcatAndDel PyUnicode_ConcatAndDel
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
#define PyUString_Size PyUnicode_Size
#define PyUString_InternFromString PyUnicode_InternFromString
#define PyUString_Format PyUnicode_Format
#define PyBaseString_Check(obj) (PyUnicode_Check(obj))
#else
#define PyBytes_Type PyString_Type
#define PyBytes_Check PyString_Check
#define PyBytesObject PyStringObject
#define PyBytes_FromString PyString_FromString
#define PyBytes_FromStringAndSize PyString_FromStringAndSize
#define PyBytes_AS_STRING PyString_AS_STRING
#define PyBytes_AsStringAndSize PyString_AsStringAndSize
#define PyBytes_FromFormat PyString_FromFormat
#define PyBytes_Concat PyString_Concat
#define PyBytes_ConcatAndDel PyString_ConcatAndDel
#define PyBytes_AsString PyString_AsString
#define PyBytes_GET_SIZE PyString_GET_SIZE
#define PyBytes_Size PyString_Size
#define PyUString_Type PyString_Type
#define PyUString_Check PyString_Check
#define PyUStringObject PyStringObject
#define PyUString_FromString PyString_FromString
#define PyUString_FromStringAndSize PyString_FromStringAndSize
#define PyUString_FromFormat PyString_FromFormat
#define PyUString_Concat PyString_Concat
#define PyUString_ConcatAndDel PyString_ConcatAndDel
#define PyUString_GET_SIZE PyString_GET_SIZE
#define PyUString_Size PyString_Size
#define PyUString_InternFromString PyString_InternFromString
#define PyUString_Format PyString_Format
#define PyBaseString_Check(obj) (PyBytes_Check(obj) || PyUnicode_Check(obj))
#endif /* NPY_PY3K */
static NPY_INLINE void
PyUnicode_ConcatAndDel(PyObject **left, PyObject *right)
{
Py_SETREF(*left, PyUnicode_Concat(*left, right));
Py_DECREF(right);
}
static NPY_INLINE void
PyUnicode_Concat2(PyObject **left, PyObject *right)
{
Py_SETREF(*left, PyUnicode_Concat(*left, right));
}
/*
* PyFile_* compatibility
*/
/*
* Get a FILE* handle to the file represented by the Python object
*/
static NPY_INLINE FILE*
npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
{
int fd, fd2, unbuf;
PyObject *ret, *os, *io, *io_raw;
npy_off_t pos;
FILE *handle;
/* For Python 2 PyFileObject, use PyFile_AsFile */
#if !defined(NPY_PY3K)
if (PyFile_Check(file)) {
return PyFile_AsFile(file);
}
#endif
/* Flush first to ensure things end up in the file in the correct order */
ret = PyObject_CallMethod(file, "flush", "");
if (ret == NULL) {
return NULL;
}
Py_DECREF(ret);
fd = PyObject_AsFileDescriptor(file);
if (fd == -1) {
return NULL;
}
/*
* The handle needs to be dup'd because we have to call fclose
* at the end
*/
os = PyImport_ImportModule("os");
if (os == NULL) {
return NULL;
}
ret = PyObject_CallMethod(os, "dup", "i", fd);
Py_DECREF(os);
if (ret == NULL) {
return NULL;
}
fd2 = PyNumber_AsSsize_t(ret, NULL);
Py_DECREF(ret);
/* Convert to FILE* handle */
#ifdef _WIN32
handle = _fdopen(fd2, mode);
#else
handle = fdopen(fd2, mode);
#endif
if (handle == NULL) {
PyErr_SetString(PyExc_IOError,
"Getting a FILE* from a Python file object failed");
return NULL;
}
/* Record the original raw file handle position */
*orig_pos = npy_ftell(handle);
if (*orig_pos == -1) {
/* The io module is needed to determine if buffering is used */
io = PyImport_ImportModule("io");
if (io == NULL) {
fclose(handle);
return NULL;
}
/* File object instances of RawIOBase are unbuffered */
io_raw = PyObject_GetAttrString(io, "RawIOBase");
Py_DECREF(io);
if (io_raw == NULL) {
fclose(handle);
return NULL;
}
unbuf = PyObject_IsInstance(file, io_raw);
Py_DECREF(io_raw);
if (unbuf == 1) {
/* Succeed if the IO is unbuffered */
return handle;
}
else {
PyErr_SetString(PyExc_IOError, "obtaining file position failed");
fclose(handle);
return NULL;
}
}
/* Seek raw handle to the Python-side position */
ret = PyObject_CallMethod(file, "tell", "");
if (ret == NULL) {
fclose(handle);
return NULL;
}
pos = PyLong_AsLongLong(ret);
Py_DECREF(ret);
if (PyErr_Occurred()) {
fclose(handle);
return NULL;
}
if (npy_fseek(handle, pos, SEEK_SET) == -1) {
PyErr_SetString(PyExc_IOError, "seeking file failed");
fclose(handle);
return NULL;
}
return handle;
}
/*
* Close the dup-ed file handle, and seek the Python one to the current position
*/
static NPY_INLINE int
npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos)
{
int fd, unbuf;
PyObject *ret, *io, *io_raw;
npy_off_t position;
/* For Python 2 PyFileObject, do nothing */
#if !defined(NPY_PY3K)
if (PyFile_Check(file)) {
return 0;
}
#endif
position = npy_ftell(handle);
/* Close the FILE* handle */
fclose(handle);
/*
* Restore original file handle position, in order to not confuse
* Python-side data structures
*/
fd = PyObject_AsFileDescriptor(file);
if (fd == -1) {
return -1;
}
if (npy_lseek(fd, orig_pos, SEEK_SET) == -1) {
/* The io module is needed to determine if buffering is used */
io = PyImport_ImportModule("io");
if (io == NULL) {
return -1;
}
/* File object instances of RawIOBase are unbuffered */
io_raw = PyObject_GetAttrString(io, "RawIOBase");
Py_DECREF(io);
if (io_raw == NULL) {
return -1;
}
unbuf = PyObject_IsInstance(file, io_raw);
Py_DECREF(io_raw);
if (unbuf == 1) {
/* Succeed if the IO is unbuffered */
return 0;
}
else {
PyErr_SetString(PyExc_IOError, "seeking file failed");
return -1;
}
}
if (position == -1) {
PyErr_SetString(PyExc_IOError, "obtaining file position failed");
return -1;
}
/* Seek Python-side handle to the FILE* handle position */
ret = PyObject_CallMethod(file, "seek", NPY_OFF_T_PYFMT "i", position, 0);
if (ret == NULL) {
return -1;
}
Py_DECREF(ret);
return 0;
}
static NPY_INLINE int
npy_PyFile_Check(PyObject *file)
{
int fd;
/* For Python 2, check if it is a PyFileObject */
#if !defined(NPY_PY3K)
if (PyFile_Check(file)) {
return 1;
}
#endif
fd = PyObject_AsFileDescriptor(file);
if (fd == -1) {
PyErr_Clear();
return 0;
}
return 1;
}
static NPY_INLINE PyObject*
npy_PyFile_OpenFile(PyObject *filename, const char *mode)
{
PyObject *open;
open = PyDict_GetItemString(PyEval_GetBuiltins(), "open");
if (open == NULL) {
return NULL;
}
return PyObject_CallFunction(open, "Os", filename, mode);
}
static NPY_INLINE int
npy_PyFile_CloseFile(PyObject *file)
{
PyObject *ret;
ret = PyObject_CallMethod(file, "close", NULL);
if (ret == NULL) {
return -1;
}
Py_DECREF(ret);
return 0;
}
/* This is a copy of _PyErr_ChainExceptions
*/
static NPY_INLINE void
npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb)
{
if (exc == NULL)
return;
if (PyErr_Occurred()) {
/* only py3 supports this anyway */
#ifdef NPY_PY3K
PyObject *exc2, *val2, *tb2;
PyErr_Fetch(&exc2, &val2, &tb2);
PyErr_NormalizeException(&exc, &val, &tb);
if (tb != NULL) {
PyException_SetTraceback(val, tb);
Py_DECREF(tb);
}
Py_DECREF(exc);
PyErr_NormalizeException(&exc2, &val2, &tb2);
PyException_SetContext(val2, val);
PyErr_Restore(exc2, val2, tb2);
#endif
}
else {
PyErr_Restore(exc, val, tb);
}
}
/* This is a copy of _PyErr_ChainExceptions, with:
* - a minimal implementation for python 2
* - __cause__ used instead of __context__
*/
static NPY_INLINE void
npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb)
{
if (exc == NULL)
return;
if (PyErr_Occurred()) {
/* only py3 supports this anyway */
#ifdef NPY_PY3K
PyObject *exc2, *val2, *tb2;
PyErr_Fetch(&exc2, &val2, &tb2);
PyErr_NormalizeException(&exc, &val, &tb);
if (tb != NULL) {
PyException_SetTraceback(val, tb);
Py_DECREF(tb);
}
Py_DECREF(exc);
PyErr_NormalizeException(&exc2, &val2, &tb2);
PyException_SetCause(val2, val);
PyErr_Restore(exc2, val2, tb2);
#endif
}
else {
PyErr_Restore(exc, val, tb);
}
}
/*
* PyObject_Cmp
*/
#if defined(NPY_PY3K)
static NPY_INLINE int
PyObject_Cmp(PyObject *i1, PyObject *i2, int *cmp)
{
int v;
v = PyObject_RichCompareBool(i1, i2, Py_LT);
if (v == 1) {
*cmp = -1;
return 1;
}
else if (v == -1) {
return -1;
}
v = PyObject_RichCompareBool(i1, i2, Py_GT);
if (v == 1) {
*cmp = 1;
return 1;
}
else if (v == -1) {
return -1;
}
v = PyObject_RichCompareBool(i1, i2, Py_EQ);
if (v == 1) {
*cmp = 0;
return 1;
}
else {
*cmp = 0;
return -1;
}
}
#endif
/*
* PyCObject functions adapted to PyCapsules.
*
* The main job here is to get rid of the improved error handling
* of PyCapsules. It's a shame...
*/
#if PY_VERSION_HEX >= 0x03000000
static NPY_INLINE PyObject *
NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
{
PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
if (ret == NULL) {
PyErr_Clear();
}
return ret;
}
static NPY_INLINE PyObject *
NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *))
{
PyObject *ret = NpyCapsule_FromVoidPtr(ptr, dtor);
if (ret != NULL && PyCapsule_SetContext(ret, context) != 0) {
PyErr_Clear();
Py_DECREF(ret);
ret = NULL;
}
return ret;
}
static NPY_INLINE void *
NpyCapsule_AsVoidPtr(PyObject *obj)
{
void *ret = PyCapsule_GetPointer(obj, NULL);
if (ret == NULL) {
PyErr_Clear();
}
return ret;
}
static NPY_INLINE void *
NpyCapsule_GetDesc(PyObject *obj)
{
return PyCapsule_GetContext(obj);
}
static NPY_INLINE int
NpyCapsule_Check(PyObject *ptr)
{
return PyCapsule_CheckExact(ptr);
}
#else
static NPY_INLINE PyObject *
NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(void *))
{
return PyCObject_FromVoidPtr(ptr, dtor);
}
static NPY_INLINE PyObject *
NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context,
void (*dtor)(void *, void *))
{
return PyCObject_FromVoidPtrAndDesc(ptr, context, dtor);
}
static NPY_INLINE void *
NpyCapsule_AsVoidPtr(PyObject *ptr)
{
return PyCObject_AsVoidPtr(ptr);
}
static NPY_INLINE void *
NpyCapsule_GetDesc(PyObject *obj)
{
return PyCObject_GetDesc(obj);
}
static NPY_INLINE int
NpyCapsule_Check(PyObject *ptr)
{
return PyCObject_Check(ptr);
}
#endif
#ifdef __cplusplus
}
#endif
#endif /* _NPY_3KCOMPAT_H_ */

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/*
* This set (target) cpu specific macros:
* - Possible values:
* NPY_CPU_X86
* NPY_CPU_AMD64
* NPY_CPU_PPC
* NPY_CPU_PPC64
* NPY_CPU_PPC64LE
* NPY_CPU_SPARC
* NPY_CPU_S390
* NPY_CPU_IA64
* NPY_CPU_HPPA
* NPY_CPU_ALPHA
* NPY_CPU_ARMEL
* NPY_CPU_ARMEB
* NPY_CPU_SH_LE
* NPY_CPU_SH_BE
* NPY_CPU_ARCEL
* NPY_CPU_ARCEB
* NPY_CPU_RISCV64
*/
#ifndef _NPY_CPUARCH_H_
#define _NPY_CPUARCH_H_
#include "numpyconfig.h"
#include <string.h> /* for memcpy */
#if defined( __i386__ ) || defined(i386) || defined(_M_IX86)
/*
* __i386__ is defined by gcc and Intel compiler on Linux,
* _M_IX86 by VS compiler,
* i386 by Sun compilers on opensolaris at least
*/
#define NPY_CPU_X86
#elif defined(__x86_64__) || defined(__amd64__) || defined(__x86_64) || defined(_M_AMD64)
/*
* both __x86_64__ and __amd64__ are defined by gcc
* __x86_64 defined by sun compiler on opensolaris at least
* _M_AMD64 defined by MS compiler
*/
#define NPY_CPU_AMD64
#elif defined(__powerpc64__) && defined(__LITTLE_ENDIAN__)
#define NPY_CPU_PPC64LE
#elif defined(__powerpc64__) && defined(__BIG_ENDIAN__)
#define NPY_CPU_PPC64
#elif defined(__ppc__) || defined(__powerpc__) || defined(_ARCH_PPC)
/*
* __ppc__ is defined by gcc, I remember having seen __powerpc__ once,
* but can't find it ATM
* _ARCH_PPC is used by at least gcc on AIX
* As __powerpc__ and _ARCH_PPC are also defined by PPC64 check
* for those specifically first before defaulting to ppc
*/
#define NPY_CPU_PPC
#elif defined(__sparc__) || defined(__sparc)
/* __sparc__ is defined by gcc and Forte (e.g. Sun) compilers */
#define NPY_CPU_SPARC
#elif defined(__s390__)
#define NPY_CPU_S390
#elif defined(__ia64)
#define NPY_CPU_IA64
#elif defined(__hppa)
#define NPY_CPU_HPPA
#elif defined(__alpha__)
#define NPY_CPU_ALPHA
#elif defined(__arm__) || defined(__aarch64__)
#if defined(__ARMEB__) || defined(__AARCH64EB__)
#if defined(__ARM_32BIT_STATE)
#define NPY_CPU_ARMEB_AARCH32
#elif defined(__ARM_64BIT_STATE)
#define NPY_CPU_ARMEB_AARCH64
#else
#define NPY_CPU_ARMEB
#endif
#elif defined(__ARMEL__) || defined(__AARCH64EL__)
#if defined(__ARM_32BIT_STATE)
#define NPY_CPU_ARMEL_AARCH32
#elif defined(__ARM_64BIT_STATE)
#define NPY_CPU_ARMEL_AARCH64
#else
#define NPY_CPU_ARMEL
#endif
#else
# error Unknown ARM CPU, please report this to numpy maintainers with \
information about your platform (OS, CPU and compiler)
#endif
#elif defined(__sh__) && defined(__LITTLE_ENDIAN__)
#define NPY_CPU_SH_LE
#elif defined(__sh__) && defined(__BIG_ENDIAN__)
#define NPY_CPU_SH_BE
#elif defined(__MIPSEL__)
#define NPY_CPU_MIPSEL
#elif defined(__MIPSEB__)
#define NPY_CPU_MIPSEB
#elif defined(__or1k__)
#define NPY_CPU_OR1K
#elif defined(__mc68000__)
#define NPY_CPU_M68K
#elif defined(__arc__) && defined(__LITTLE_ENDIAN__)
#define NPY_CPU_ARCEL
#elif defined(__arc__) && defined(__BIG_ENDIAN__)
#define NPY_CPU_ARCEB
#elif defined(__riscv) && defined(__riscv_xlen) && __riscv_xlen == 64
#define NPY_CPU_RISCV64
#else
#error Unknown CPU, please report this to numpy maintainers with \
information about your platform (OS, CPU and compiler)
#endif
#define NPY_COPY_PYOBJECT_PTR(dst, src) memcpy(dst, src, sizeof(PyObject *))
#if (defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64))
#define NPY_CPU_HAVE_UNALIGNED_ACCESS 1
#else
#define NPY_CPU_HAVE_UNALIGNED_ACCESS 0
#endif
#endif

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#ifndef _NPY_ENDIAN_H_
#define _NPY_ENDIAN_H_
/*
* NPY_BYTE_ORDER is set to the same value as BYTE_ORDER set by glibc in
* endian.h
*/
#if defined(NPY_HAVE_ENDIAN_H) || defined(NPY_HAVE_SYS_ENDIAN_H)
/* Use endian.h if available */
#if defined(NPY_HAVE_ENDIAN_H)
#include <endian.h>
#elif defined(NPY_HAVE_SYS_ENDIAN_H)
#include <sys/endian.h>
#endif
#if defined(BYTE_ORDER) && defined(BIG_ENDIAN) && defined(LITTLE_ENDIAN)
#define NPY_BYTE_ORDER BYTE_ORDER
#define NPY_LITTLE_ENDIAN LITTLE_ENDIAN
#define NPY_BIG_ENDIAN BIG_ENDIAN
#elif defined(_BYTE_ORDER) && defined(_BIG_ENDIAN) && defined(_LITTLE_ENDIAN)
#define NPY_BYTE_ORDER _BYTE_ORDER
#define NPY_LITTLE_ENDIAN _LITTLE_ENDIAN
#define NPY_BIG_ENDIAN _BIG_ENDIAN
#elif defined(__BYTE_ORDER) && defined(__BIG_ENDIAN) && defined(__LITTLE_ENDIAN)
#define NPY_BYTE_ORDER __BYTE_ORDER
#define NPY_LITTLE_ENDIAN __LITTLE_ENDIAN
#define NPY_BIG_ENDIAN __BIG_ENDIAN
#endif
#endif
#ifndef NPY_BYTE_ORDER
/* Set endianness info using target CPU */
#include "npy_cpu.h"
#define NPY_LITTLE_ENDIAN 1234
#define NPY_BIG_ENDIAN 4321
#if defined(NPY_CPU_X86) \
|| defined(NPY_CPU_AMD64) \
|| defined(NPY_CPU_IA64) \
|| defined(NPY_CPU_ALPHA) \
|| defined(NPY_CPU_ARMEL) \
|| defined(NPY_CPU_ARMEL_AARCH32) \
|| defined(NPY_CPU_ARMEL_AARCH64) \
|| defined(NPY_CPU_SH_LE) \
|| defined(NPY_CPU_MIPSEL) \
|| defined(NPY_CPU_PPC64LE) \
|| defined(NPY_CPU_ARCEL) \
|| defined(NPY_CPU_RISCV64)
#define NPY_BYTE_ORDER NPY_LITTLE_ENDIAN
#elif defined(NPY_CPU_PPC) \
|| defined(NPY_CPU_SPARC) \
|| defined(NPY_CPU_S390) \
|| defined(NPY_CPU_HPPA) \
|| defined(NPY_CPU_PPC64) \
|| defined(NPY_CPU_ARMEB) \
|| defined(NPY_CPU_ARMEB_AARCH32) \
|| defined(NPY_CPU_ARMEB_AARCH64) \
|| defined(NPY_CPU_SH_BE) \
|| defined(NPY_CPU_MIPSEB) \
|| defined(NPY_CPU_OR1K) \
|| defined(NPY_CPU_M68K) \
|| defined(NPY_CPU_ARCEB)
#define NPY_BYTE_ORDER NPY_BIG_ENDIAN
#else
#error Unknown CPU: can not set endianness
#endif
#endif
#endif

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/* Signal handling:
This header file defines macros that allow your code to handle
interrupts received during processing. Interrupts that
could reasonably be handled:
SIGINT, SIGABRT, SIGALRM, SIGSEGV
****Warning***************
Do not allow code that creates temporary memory or increases reference
counts of Python objects to be interrupted unless you handle it
differently.
**************************
The mechanism for handling interrupts is conceptually simple:
- replace the signal handler with our own home-grown version
and store the old one.
- run the code to be interrupted -- if an interrupt occurs
the handler should basically just cause a return to the
calling function for finish work.
- restore the old signal handler
Of course, every code that allows interrupts must account for
returning via the interrupt and handle clean-up correctly. But,
even still, the simple paradigm is complicated by at least three
factors.
1) platform portability (i.e. Microsoft says not to use longjmp
to return from signal handling. They have a __try and __except
extension to C instead but what about mingw?).
2) how to handle threads: apparently whether signals are delivered to
every thread of the process or the "invoking" thread is platform
dependent. --- we don't handle threads for now.
3) do we need to worry about re-entrance. For now, assume the
code will not call-back into itself.
Ideas:
1) Start by implementing an approach that works on platforms that
can use setjmp and longjmp functionality and does nothing
on other platforms.
2) Ignore threads --- i.e. do not mix interrupt handling and threads
3) Add a default signal_handler function to the C-API but have the rest
use macros.
Simple Interface:
In your C-extension: around a block of code you want to be interruptible
with a SIGINT
NPY_SIGINT_ON
[code]
NPY_SIGINT_OFF
In order for this to work correctly, the
[code] block must not allocate any memory or alter the reference count of any
Python objects. In other words [code] must be interruptible so that continuation
after NPY_SIGINT_OFF will only be "missing some computations"
Interrupt handling does not work well with threads.
*/
/* Add signal handling macros
Make the global variable and signal handler part of the C-API
*/
#ifndef NPY_INTERRUPT_H
#define NPY_INTERRUPT_H
#ifndef NPY_NO_SIGNAL
#include <setjmp.h>
#include <signal.h>
#ifndef sigsetjmp
#define NPY_SIGSETJMP(arg1, arg2) setjmp(arg1)
#define NPY_SIGLONGJMP(arg1, arg2) longjmp(arg1, arg2)
#define NPY_SIGJMP_BUF jmp_buf
#else
#define NPY_SIGSETJMP(arg1, arg2) sigsetjmp(arg1, arg2)
#define NPY_SIGLONGJMP(arg1, arg2) siglongjmp(arg1, arg2)
#define NPY_SIGJMP_BUF sigjmp_buf
#endif
# define NPY_SIGINT_ON { \
PyOS_sighandler_t _npy_sig_save; \
_npy_sig_save = PyOS_setsig(SIGINT, _PyArray_SigintHandler); \
if (NPY_SIGSETJMP(*((NPY_SIGJMP_BUF *)_PyArray_GetSigintBuf()), \
1) == 0) { \
# define NPY_SIGINT_OFF } \
PyOS_setsig(SIGINT, _npy_sig_save); \
}
#else /* NPY_NO_SIGNAL */
#define NPY_SIGINT_ON
#define NPY_SIGINT_OFF
#endif /* HAVE_SIGSETJMP */
#endif /* NPY_INTERRUPT_H */

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#ifndef __NPY_MATH_C99_H_
#define __NPY_MATH_C99_H_
#ifdef __cplusplus
extern "C" {
#endif
#include <math.h>
#ifdef __SUNPRO_CC
#include <sunmath.h>
#endif
#ifdef HAVE_NPY_CONFIG_H
#include <npy_config.h>
#endif
#include <numpy/npy_common.h>
/* By adding static inline specifiers to npy_math function definitions when
appropriate, compiler is given the opportunity to optimize */
#if NPY_INLINE_MATH
#define NPY_INPLACE NPY_INLINE static
#else
#define NPY_INPLACE
#endif
/*
* NAN and INFINITY like macros (same behavior as glibc for NAN, same as C99
* for INFINITY)
*
* XXX: I should test whether INFINITY and NAN are available on the platform
*/
NPY_INLINE static float __npy_inff(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x7f800000UL};
return __bint.__f;
}
NPY_INLINE static float __npy_nanf(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x7fc00000UL};
return __bint.__f;
}
NPY_INLINE static float __npy_pzerof(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x00000000UL};
return __bint.__f;
}
NPY_INLINE static float __npy_nzerof(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x80000000UL};
return __bint.__f;
}
#define NPY_INFINITYF __npy_inff()
#define NPY_NANF __npy_nanf()
#define NPY_PZEROF __npy_pzerof()
#define NPY_NZEROF __npy_nzerof()
#define NPY_INFINITY ((npy_double)NPY_INFINITYF)
#define NPY_NAN ((npy_double)NPY_NANF)
#define NPY_PZERO ((npy_double)NPY_PZEROF)
#define NPY_NZERO ((npy_double)NPY_NZEROF)
#define NPY_INFINITYL ((npy_longdouble)NPY_INFINITYF)
#define NPY_NANL ((npy_longdouble)NPY_NANF)
#define NPY_PZEROL ((npy_longdouble)NPY_PZEROF)
#define NPY_NZEROL ((npy_longdouble)NPY_NZEROF)
/*
* Useful constants
*/
#define NPY_E 2.718281828459045235360287471352662498 /* e */
#define NPY_LOG2E 1.442695040888963407359924681001892137 /* log_2 e */
#define NPY_LOG10E 0.434294481903251827651128918916605082 /* log_10 e */
#define NPY_LOGE2 0.693147180559945309417232121458176568 /* log_e 2 */
#define NPY_LOGE10 2.302585092994045684017991454684364208 /* log_e 10 */
#define NPY_PI 3.141592653589793238462643383279502884 /* pi */
#define NPY_PI_2 1.570796326794896619231321691639751442 /* pi/2 */
#define NPY_PI_4 0.785398163397448309615660845819875721 /* pi/4 */
#define NPY_1_PI 0.318309886183790671537767526745028724 /* 1/pi */
#define NPY_2_PI 0.636619772367581343075535053490057448 /* 2/pi */
#define NPY_EULER 0.577215664901532860606512090082402431 /* Euler constant */
#define NPY_SQRT2 1.414213562373095048801688724209698079 /* sqrt(2) */
#define NPY_SQRT1_2 0.707106781186547524400844362104849039 /* 1/sqrt(2) */
#define NPY_Ef 2.718281828459045235360287471352662498F /* e */
#define NPY_LOG2Ef 1.442695040888963407359924681001892137F /* log_2 e */
#define NPY_LOG10Ef 0.434294481903251827651128918916605082F /* log_10 e */
#define NPY_LOGE2f 0.693147180559945309417232121458176568F /* log_e 2 */
#define NPY_LOGE10f 2.302585092994045684017991454684364208F /* log_e 10 */
#define NPY_PIf 3.141592653589793238462643383279502884F /* pi */
#define NPY_PI_2f 1.570796326794896619231321691639751442F /* pi/2 */
#define NPY_PI_4f 0.785398163397448309615660845819875721F /* pi/4 */
#define NPY_1_PIf 0.318309886183790671537767526745028724F /* 1/pi */
#define NPY_2_PIf 0.636619772367581343075535053490057448F /* 2/pi */
#define NPY_EULERf 0.577215664901532860606512090082402431F /* Euler constant */
#define NPY_SQRT2f 1.414213562373095048801688724209698079F /* sqrt(2) */
#define NPY_SQRT1_2f 0.707106781186547524400844362104849039F /* 1/sqrt(2) */
#define NPY_El 2.718281828459045235360287471352662498L /* e */
#define NPY_LOG2El 1.442695040888963407359924681001892137L /* log_2 e */
#define NPY_LOG10El 0.434294481903251827651128918916605082L /* log_10 e */
#define NPY_LOGE2l 0.693147180559945309417232121458176568L /* log_e 2 */
#define NPY_LOGE10l 2.302585092994045684017991454684364208L /* log_e 10 */
#define NPY_PIl 3.141592653589793238462643383279502884L /* pi */
#define NPY_PI_2l 1.570796326794896619231321691639751442L /* pi/2 */
#define NPY_PI_4l 0.785398163397448309615660845819875721L /* pi/4 */
#define NPY_1_PIl 0.318309886183790671537767526745028724L /* 1/pi */
#define NPY_2_PIl 0.636619772367581343075535053490057448L /* 2/pi */
#define NPY_EULERl 0.577215664901532860606512090082402431L /* Euler constant */
#define NPY_SQRT2l 1.414213562373095048801688724209698079L /* sqrt(2) */
#define NPY_SQRT1_2l 0.707106781186547524400844362104849039L /* 1/sqrt(2) */
/*
* Constants used in vector implementation of exp(x)
*/
#define NPY_RINT_CVT_MAGICf 0x1.800000p+23f
#define NPY_CODY_WAITE_LOGE_2_HIGHf -6.93145752e-1f
#define NPY_CODY_WAITE_LOGE_2_LOWf -1.42860677e-6f
#define NPY_COEFF_P0_EXPf 9.999999999980870924916e-01f
#define NPY_COEFF_P1_EXPf 7.257664613233124478488e-01f
#define NPY_COEFF_P2_EXPf 2.473615434895520810817e-01f
#define NPY_COEFF_P3_EXPf 5.114512081637298353406e-02f
#define NPY_COEFF_P4_EXPf 6.757896990527504603057e-03f
#define NPY_COEFF_P5_EXPf 5.082762527590693718096e-04f
#define NPY_COEFF_Q0_EXPf 1.000000000000000000000e+00f
#define NPY_COEFF_Q1_EXPf -2.742335390411667452936e-01f
#define NPY_COEFF_Q2_EXPf 2.159509375685829852307e-02f
/*
* Constants used in vector implementation of log(x)
*/
#define NPY_COEFF_P0_LOGf 0.000000000000000000000e+00f
#define NPY_COEFF_P1_LOGf 9.999999999999998702752e-01f
#define NPY_COEFF_P2_LOGf 2.112677543073053063722e+00f
#define NPY_COEFF_P3_LOGf 1.480000633576506585156e+00f
#define NPY_COEFF_P4_LOGf 3.808837741388407920751e-01f
#define NPY_COEFF_P5_LOGf 2.589979117907922693523e-02f
#define NPY_COEFF_Q0_LOGf 1.000000000000000000000e+00f
#define NPY_COEFF_Q1_LOGf 2.612677543073109236779e+00f
#define NPY_COEFF_Q2_LOGf 2.453006071784736363091e+00f
#define NPY_COEFF_Q3_LOGf 9.864942958519418960339e-01f
#define NPY_COEFF_Q4_LOGf 1.546476374983906719538e-01f
#define NPY_COEFF_Q5_LOGf 5.875095403124574342950e-03f
/*
* Constants used in vector implementation of sinf/cosf(x)
*/
#define NPY_TWO_O_PIf 0x1.45f306p-1f
#define NPY_CODY_WAITE_PI_O_2_HIGHf -0x1.921fb0p+00f
#define NPY_CODY_WAITE_PI_O_2_MEDf -0x1.5110b4p-22f
#define NPY_CODY_WAITE_PI_O_2_LOWf -0x1.846988p-48f
#define NPY_COEFF_INVF0_COSINEf 0x1.000000p+00f
#define NPY_COEFF_INVF2_COSINEf -0x1.000000p-01f
#define NPY_COEFF_INVF4_COSINEf 0x1.55553cp-05f
#define NPY_COEFF_INVF6_COSINEf -0x1.6c06dcp-10f
#define NPY_COEFF_INVF8_COSINEf 0x1.98e616p-16f
#define NPY_COEFF_INVF3_SINEf -0x1.555556p-03f
#define NPY_COEFF_INVF5_SINEf 0x1.11119ap-07f
#define NPY_COEFF_INVF7_SINEf -0x1.a06bbap-13f
#define NPY_COEFF_INVF9_SINEf 0x1.7d3bbcp-19f
/*
* Integer functions.
*/
NPY_INPLACE npy_uint npy_gcdu(npy_uint a, npy_uint b);
NPY_INPLACE npy_uint npy_lcmu(npy_uint a, npy_uint b);
NPY_INPLACE npy_ulong npy_gcdul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulong npy_lcmul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulonglong npy_gcdull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_ulonglong npy_lcmull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_int npy_gcd(npy_int a, npy_int b);
NPY_INPLACE npy_int npy_lcm(npy_int a, npy_int b);
NPY_INPLACE npy_long npy_gcdl(npy_long a, npy_long b);
NPY_INPLACE npy_long npy_lcml(npy_long a, npy_long b);
NPY_INPLACE npy_longlong npy_gcdll(npy_longlong a, npy_longlong b);
NPY_INPLACE npy_longlong npy_lcmll(npy_longlong a, npy_longlong b);
NPY_INPLACE npy_ubyte npy_rshiftuhh(npy_ubyte a, npy_ubyte b);
NPY_INPLACE npy_ubyte npy_lshiftuhh(npy_ubyte a, npy_ubyte b);
NPY_INPLACE npy_ushort npy_rshiftuh(npy_ushort a, npy_ushort b);
NPY_INPLACE npy_ushort npy_lshiftuh(npy_ushort a, npy_ushort b);
NPY_INPLACE npy_uint npy_rshiftu(npy_uint a, npy_uint b);
NPY_INPLACE npy_uint npy_lshiftu(npy_uint a, npy_uint b);
NPY_INPLACE npy_ulong npy_rshiftul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulong npy_lshiftul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulonglong npy_rshiftull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_ulonglong npy_lshiftull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_byte npy_rshifthh(npy_byte a, npy_byte b);
NPY_INPLACE npy_byte npy_lshifthh(npy_byte a, npy_byte b);
NPY_INPLACE npy_short npy_rshifth(npy_short a, npy_short b);
NPY_INPLACE npy_short npy_lshifth(npy_short a, npy_short b);
NPY_INPLACE npy_int npy_rshift(npy_int a, npy_int b);
NPY_INPLACE npy_int npy_lshift(npy_int a, npy_int b);
NPY_INPLACE npy_long npy_rshiftl(npy_long a, npy_long b);
NPY_INPLACE npy_long npy_lshiftl(npy_long a, npy_long b);
NPY_INPLACE npy_longlong npy_rshiftll(npy_longlong a, npy_longlong b);
NPY_INPLACE npy_longlong npy_lshiftll(npy_longlong a, npy_longlong b);
/*
* avx function has a common API for both sin & cos. This enum is used to
* distinguish between the two
*/
typedef enum {
npy_compute_sin,
npy_compute_cos
} NPY_TRIG_OP;
/*
* C99 double math funcs
*/
NPY_INPLACE double npy_sin(double x);
NPY_INPLACE double npy_cos(double x);
NPY_INPLACE double npy_tan(double x);
NPY_INPLACE double npy_sinh(double x);
NPY_INPLACE double npy_cosh(double x);
NPY_INPLACE double npy_tanh(double x);
NPY_INPLACE double npy_asin(double x);
NPY_INPLACE double npy_acos(double x);
NPY_INPLACE double npy_atan(double x);
NPY_INPLACE double npy_log(double x);
NPY_INPLACE double npy_log10(double x);
NPY_INPLACE double npy_exp(double x);
NPY_INPLACE double npy_sqrt(double x);
NPY_INPLACE double npy_cbrt(double x);
NPY_INPLACE double npy_fabs(double x);
NPY_INPLACE double npy_ceil(double x);
NPY_INPLACE double npy_fmod(double x, double y);
NPY_INPLACE double npy_floor(double x);
NPY_INPLACE double npy_expm1(double x);
NPY_INPLACE double npy_log1p(double x);
NPY_INPLACE double npy_hypot(double x, double y);
NPY_INPLACE double npy_acosh(double x);
NPY_INPLACE double npy_asinh(double xx);
NPY_INPLACE double npy_atanh(double x);
NPY_INPLACE double npy_rint(double x);
NPY_INPLACE double npy_trunc(double x);
NPY_INPLACE double npy_exp2(double x);
NPY_INPLACE double npy_log2(double x);
NPY_INPLACE double npy_atan2(double x, double y);
NPY_INPLACE double npy_pow(double x, double y);
NPY_INPLACE double npy_modf(double x, double* y);
NPY_INPLACE double npy_frexp(double x, int* y);
NPY_INPLACE double npy_ldexp(double n, int y);
NPY_INPLACE double npy_copysign(double x, double y);
double npy_nextafter(double x, double y);
double npy_spacing(double x);
/*
* IEEE 754 fpu handling. Those are guaranteed to be macros
*/
/* use builtins to avoid function calls in tight loops
* only available if npy_config.h is available (= numpys own build) */
#if HAVE___BUILTIN_ISNAN
#define npy_isnan(x) __builtin_isnan(x)
#else
#ifndef NPY_HAVE_DECL_ISNAN
#define npy_isnan(x) ((x) != (x))
#else
#if defined(_MSC_VER) && (_MSC_VER < 1900)
#define npy_isnan(x) _isnan((x))
#else
#define npy_isnan(x) isnan(x)
#endif
#endif
#endif
/* only available if npy_config.h is available (= numpys own build) */
#if HAVE___BUILTIN_ISFINITE
#define npy_isfinite(x) __builtin_isfinite(x)
#else
#ifndef NPY_HAVE_DECL_ISFINITE
#ifdef _MSC_VER
#define npy_isfinite(x) _finite((x))
#else
#define npy_isfinite(x) !npy_isnan((x) + (-x))
#endif
#else
#define npy_isfinite(x) isfinite((x))
#endif
#endif
/* only available if npy_config.h is available (= numpys own build) */
#if HAVE___BUILTIN_ISINF
#define npy_isinf(x) __builtin_isinf(x)
#else
#ifndef NPY_HAVE_DECL_ISINF
#define npy_isinf(x) (!npy_isfinite(x) && !npy_isnan(x))
#else
#if defined(_MSC_VER) && (_MSC_VER < 1900)
#define npy_isinf(x) (!_finite((x)) && !_isnan((x)))
#else
#define npy_isinf(x) isinf((x))
#endif
#endif
#endif
#ifndef NPY_HAVE_DECL_SIGNBIT
int _npy_signbit_f(float x);
int _npy_signbit_d(double x);
int _npy_signbit_ld(long double x);
#define npy_signbit(x) \
(sizeof (x) == sizeof (long double) ? _npy_signbit_ld (x) \
: sizeof (x) == sizeof (double) ? _npy_signbit_d (x) \
: _npy_signbit_f (x))
#else
#define npy_signbit(x) signbit((x))
#endif
/*
* float C99 math functions
*/
NPY_INPLACE float npy_sinf(float x);
NPY_INPLACE float npy_cosf(float x);
NPY_INPLACE float npy_tanf(float x);
NPY_INPLACE float npy_sinhf(float x);
NPY_INPLACE float npy_coshf(float x);
NPY_INPLACE float npy_tanhf(float x);
NPY_INPLACE float npy_fabsf(float x);
NPY_INPLACE float npy_floorf(float x);
NPY_INPLACE float npy_ceilf(float x);
NPY_INPLACE float npy_rintf(float x);
NPY_INPLACE float npy_truncf(float x);
NPY_INPLACE float npy_sqrtf(float x);
NPY_INPLACE float npy_cbrtf(float x);
NPY_INPLACE float npy_log10f(float x);
NPY_INPLACE float npy_logf(float x);
NPY_INPLACE float npy_expf(float x);
NPY_INPLACE float npy_expm1f(float x);
NPY_INPLACE float npy_asinf(float x);
NPY_INPLACE float npy_acosf(float x);
NPY_INPLACE float npy_atanf(float x);
NPY_INPLACE float npy_asinhf(float x);
NPY_INPLACE float npy_acoshf(float x);
NPY_INPLACE float npy_atanhf(float x);
NPY_INPLACE float npy_log1pf(float x);
NPY_INPLACE float npy_exp2f(float x);
NPY_INPLACE float npy_log2f(float x);
NPY_INPLACE float npy_atan2f(float x, float y);
NPY_INPLACE float npy_hypotf(float x, float y);
NPY_INPLACE float npy_powf(float x, float y);
NPY_INPLACE float npy_fmodf(float x, float y);
NPY_INPLACE float npy_modff(float x, float* y);
NPY_INPLACE float npy_frexpf(float x, int* y);
NPY_INPLACE float npy_ldexpf(float x, int y);
NPY_INPLACE float npy_copysignf(float x, float y);
float npy_nextafterf(float x, float y);
float npy_spacingf(float x);
/*
* long double C99 math functions
*/
NPY_INPLACE npy_longdouble npy_sinl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_cosl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_tanl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_sinhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_coshl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_tanhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_fabsl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_floorl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_ceill(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_rintl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_truncl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_sqrtl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_cbrtl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_log10l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_logl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_expl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_expm1l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_asinl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_acosl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_atanl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_asinhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_acoshl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_atanhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_log1pl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_exp2l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_log2l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_atan2l(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_hypotl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_powl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_fmodl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_modfl(npy_longdouble x, npy_longdouble* y);
NPY_INPLACE npy_longdouble npy_frexpl(npy_longdouble x, int* y);
NPY_INPLACE npy_longdouble npy_ldexpl(npy_longdouble x, int y);
NPY_INPLACE npy_longdouble npy_copysignl(npy_longdouble x, npy_longdouble y);
npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y);
npy_longdouble npy_spacingl(npy_longdouble x);
/*
* Non standard functions
*/
NPY_INPLACE double npy_deg2rad(double x);
NPY_INPLACE double npy_rad2deg(double x);
NPY_INPLACE double npy_logaddexp(double x, double y);
NPY_INPLACE double npy_logaddexp2(double x, double y);
NPY_INPLACE double npy_divmod(double x, double y, double *modulus);
NPY_INPLACE double npy_heaviside(double x, double h0);
NPY_INPLACE float npy_deg2radf(float x);
NPY_INPLACE float npy_rad2degf(float x);
NPY_INPLACE float npy_logaddexpf(float x, float y);
NPY_INPLACE float npy_logaddexp2f(float x, float y);
NPY_INPLACE float npy_divmodf(float x, float y, float *modulus);
NPY_INPLACE float npy_heavisidef(float x, float h0);
NPY_INPLACE npy_longdouble npy_deg2radl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_rad2degl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_logaddexpl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_logaddexp2l(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_divmodl(npy_longdouble x, npy_longdouble y,
npy_longdouble *modulus);
NPY_INPLACE npy_longdouble npy_heavisidel(npy_longdouble x, npy_longdouble h0);
#define npy_degrees npy_rad2deg
#define npy_degreesf npy_rad2degf
#define npy_degreesl npy_rad2degl
#define npy_radians npy_deg2rad
#define npy_radiansf npy_deg2radf
#define npy_radiansl npy_deg2radl
/*
* Complex declarations
*/
/*
* C99 specifies that complex numbers have the same representation as
* an array of two elements, where the first element is the real part
* and the second element is the imaginary part.
*/
#define __NPY_CPACK_IMP(x, y, type, ctype) \
union { \
ctype z; \
type a[2]; \
} z1;; \
\
z1.a[0] = (x); \
z1.a[1] = (y); \
\
return z1.z;
static NPY_INLINE npy_cdouble npy_cpack(double x, double y)
{
__NPY_CPACK_IMP(x, y, double, npy_cdouble);
}
static NPY_INLINE npy_cfloat npy_cpackf(float x, float y)
{
__NPY_CPACK_IMP(x, y, float, npy_cfloat);
}
static NPY_INLINE npy_clongdouble npy_cpackl(npy_longdouble x, npy_longdouble y)
{
__NPY_CPACK_IMP(x, y, npy_longdouble, npy_clongdouble);
}
#undef __NPY_CPACK_IMP
/*
* Same remark as above, but in the other direction: extract first/second
* member of complex number, assuming a C99-compatible representation
*
* Those are defineds as static inline, and such as a reasonable compiler would
* most likely compile this to one or two instructions (on CISC at least)
*/
#define __NPY_CEXTRACT_IMP(z, index, type, ctype) \
union { \
ctype z; \
type a[2]; \
} __z_repr; \
__z_repr.z = z; \
\
return __z_repr.a[index];
static NPY_INLINE double npy_creal(npy_cdouble z)
{
__NPY_CEXTRACT_IMP(z, 0, double, npy_cdouble);
}
static NPY_INLINE double npy_cimag(npy_cdouble z)
{
__NPY_CEXTRACT_IMP(z, 1, double, npy_cdouble);
}
static NPY_INLINE float npy_crealf(npy_cfloat z)
{
__NPY_CEXTRACT_IMP(z, 0, float, npy_cfloat);
}
static NPY_INLINE float npy_cimagf(npy_cfloat z)
{
__NPY_CEXTRACT_IMP(z, 1, float, npy_cfloat);
}
static NPY_INLINE npy_longdouble npy_creall(npy_clongdouble z)
{
__NPY_CEXTRACT_IMP(z, 0, npy_longdouble, npy_clongdouble);
}
static NPY_INLINE npy_longdouble npy_cimagl(npy_clongdouble z)
{
__NPY_CEXTRACT_IMP(z, 1, npy_longdouble, npy_clongdouble);
}
#undef __NPY_CEXTRACT_IMP
/*
* Double precision complex functions
*/
double npy_cabs(npy_cdouble z);
double npy_carg(npy_cdouble z);
npy_cdouble npy_cexp(npy_cdouble z);
npy_cdouble npy_clog(npy_cdouble z);
npy_cdouble npy_cpow(npy_cdouble x, npy_cdouble y);
npy_cdouble npy_csqrt(npy_cdouble z);
npy_cdouble npy_ccos(npy_cdouble z);
npy_cdouble npy_csin(npy_cdouble z);
npy_cdouble npy_ctan(npy_cdouble z);
npy_cdouble npy_ccosh(npy_cdouble z);
npy_cdouble npy_csinh(npy_cdouble z);
npy_cdouble npy_ctanh(npy_cdouble z);
npy_cdouble npy_cacos(npy_cdouble z);
npy_cdouble npy_casin(npy_cdouble z);
npy_cdouble npy_catan(npy_cdouble z);
npy_cdouble npy_cacosh(npy_cdouble z);
npy_cdouble npy_casinh(npy_cdouble z);
npy_cdouble npy_catanh(npy_cdouble z);
/*
* Single precision complex functions
*/
float npy_cabsf(npy_cfloat z);
float npy_cargf(npy_cfloat z);
npy_cfloat npy_cexpf(npy_cfloat z);
npy_cfloat npy_clogf(npy_cfloat z);
npy_cfloat npy_cpowf(npy_cfloat x, npy_cfloat y);
npy_cfloat npy_csqrtf(npy_cfloat z);
npy_cfloat npy_ccosf(npy_cfloat z);
npy_cfloat npy_csinf(npy_cfloat z);
npy_cfloat npy_ctanf(npy_cfloat z);
npy_cfloat npy_ccoshf(npy_cfloat z);
npy_cfloat npy_csinhf(npy_cfloat z);
npy_cfloat npy_ctanhf(npy_cfloat z);
npy_cfloat npy_cacosf(npy_cfloat z);
npy_cfloat npy_casinf(npy_cfloat z);
npy_cfloat npy_catanf(npy_cfloat z);
npy_cfloat npy_cacoshf(npy_cfloat z);
npy_cfloat npy_casinhf(npy_cfloat z);
npy_cfloat npy_catanhf(npy_cfloat z);
/*
* Extended precision complex functions
*/
npy_longdouble npy_cabsl(npy_clongdouble z);
npy_longdouble npy_cargl(npy_clongdouble z);
npy_clongdouble npy_cexpl(npy_clongdouble z);
npy_clongdouble npy_clogl(npy_clongdouble z);
npy_clongdouble npy_cpowl(npy_clongdouble x, npy_clongdouble y);
npy_clongdouble npy_csqrtl(npy_clongdouble z);
npy_clongdouble npy_ccosl(npy_clongdouble z);
npy_clongdouble npy_csinl(npy_clongdouble z);
npy_clongdouble npy_ctanl(npy_clongdouble z);
npy_clongdouble npy_ccoshl(npy_clongdouble z);
npy_clongdouble npy_csinhl(npy_clongdouble z);
npy_clongdouble npy_ctanhl(npy_clongdouble z);
npy_clongdouble npy_cacosl(npy_clongdouble z);
npy_clongdouble npy_casinl(npy_clongdouble z);
npy_clongdouble npy_catanl(npy_clongdouble z);
npy_clongdouble npy_cacoshl(npy_clongdouble z);
npy_clongdouble npy_casinhl(npy_clongdouble z);
npy_clongdouble npy_catanhl(npy_clongdouble z);
/*
* Functions that set the floating point error
* status word.
*/
/*
* platform-dependent code translates floating point
* status to an integer sum of these values
*/
#define NPY_FPE_DIVIDEBYZERO 1
#define NPY_FPE_OVERFLOW 2
#define NPY_FPE_UNDERFLOW 4
#define NPY_FPE_INVALID 8
int npy_clear_floatstatus_barrier(char*);
int npy_get_floatstatus_barrier(char*);
/*
* use caution with these - clang and gcc8.1 are known to reorder calls
* to this form of the function which can defeat the check. The _barrier
* form of the call is preferable, where the argument is
* (char*)&local_variable
*/
int npy_clear_floatstatus(void);
int npy_get_floatstatus(void);
void npy_set_floatstatus_divbyzero(void);
void npy_set_floatstatus_overflow(void);
void npy_set_floatstatus_underflow(void);
void npy_set_floatstatus_invalid(void);
#ifdef __cplusplus
}
#endif
#if NPY_INLINE_MATH
#include "npy_math_internal.h"
#endif
#endif

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/*
* This include file is provided for inclusion in Cython *.pyd files where
* one would like to define the NPY_NO_DEPRECATED_API macro. It can be
* included by
*
* cdef extern from "npy_no_deprecated_api.h": pass
*
*/
#ifndef NPY_NO_DEPRECATED_API
/* put this check here since there may be multiple includes in C extensions. */
#if defined(NDARRAYTYPES_H) || defined(_NPY_DEPRECATED_API_H) || \
defined(OLD_DEFINES_H)
#error "npy_no_deprecated_api.h" must be first among numpy includes.
#else
#define NPY_NO_DEPRECATED_API NPY_API_VERSION
#endif
#endif

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#ifndef _NPY_OS_H_
#define _NPY_OS_H_
#if defined(linux) || defined(__linux) || defined(__linux__)
#define NPY_OS_LINUX
#elif defined(__FreeBSD__) || defined(__NetBSD__) || \
defined(__OpenBSD__) || defined(__DragonFly__)
#define NPY_OS_BSD
#ifdef __FreeBSD__
#define NPY_OS_FREEBSD
#elif defined(__NetBSD__)
#define NPY_OS_NETBSD
#elif defined(__OpenBSD__)
#define NPY_OS_OPENBSD
#elif defined(__DragonFly__)
#define NPY_OS_DRAGONFLY
#endif
#elif defined(sun) || defined(__sun)
#define NPY_OS_SOLARIS
#elif defined(__CYGWIN__)
#define NPY_OS_CYGWIN
#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32)
#define NPY_OS_WIN32
#elif defined(__APPLE__)
#define NPY_OS_DARWIN
#else
#define NPY_OS_UNKNOWN
#endif
#endif

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#ifndef _NPY_NUMPYCONFIG_H_
#define _NPY_NUMPYCONFIG_H_
#include "_numpyconfig.h"
/*
* On Mac OS X, because there is only one configuration stage for all the archs
* in universal builds, any macro which depends on the arch needs to be
* hardcoded
*/
#ifdef __APPLE__
#undef NPY_SIZEOF_LONG
#undef NPY_SIZEOF_PY_INTPTR_T
#ifdef __LP64__
#define NPY_SIZEOF_LONG 8
#define NPY_SIZEOF_PY_INTPTR_T 8
#else
#define NPY_SIZEOF_LONG 4
#define NPY_SIZEOF_PY_INTPTR_T 4
#endif
#endif
/**
* To help with the NPY_NO_DEPRECATED_API macro, we include API version
* numbers for specific versions of NumPy. To exclude all API that was
* deprecated as of 1.7, add the following before #including any NumPy
* headers:
* #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
*/
#define NPY_1_7_API_VERSION 0x00000007
#define NPY_1_8_API_VERSION 0x00000008
#define NPY_1_9_API_VERSION 0x00000008
#define NPY_1_10_API_VERSION 0x00000008
#define NPY_1_11_API_VERSION 0x00000008
#define NPY_1_12_API_VERSION 0x00000008
#define NPY_1_13_API_VERSION 0x00000008
#define NPY_1_14_API_VERSION 0x00000008
#define NPY_1_15_API_VERSION 0x00000008
#define NPY_1_16_API_VERSION 0x00000008
#define NPY_1_17_API_VERSION 0x00000008
#define NPY_1_18_API_VERSION 0x00000008
#endif

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/* This header is deprecated as of NumPy 1.7 */
#ifndef OLD_DEFINES_H
#define OLD_DEFINES_H
#if defined(NPY_NO_DEPRECATED_API) && NPY_NO_DEPRECATED_API >= NPY_1_7_API_VERSION
#error The header "old_defines.h" is deprecated as of NumPy 1.7.
#endif
#define NDARRAY_VERSION NPY_VERSION
#define PyArray_MIN_BUFSIZE NPY_MIN_BUFSIZE
#define PyArray_MAX_BUFSIZE NPY_MAX_BUFSIZE
#define PyArray_BUFSIZE NPY_BUFSIZE
#define PyArray_PRIORITY NPY_PRIORITY
#define PyArray_SUBTYPE_PRIORITY NPY_PRIORITY
#define PyArray_NUM_FLOATTYPE NPY_NUM_FLOATTYPE
#define NPY_MAX PyArray_MAX
#define NPY_MIN PyArray_MIN
#define PyArray_TYPES NPY_TYPES
#define PyArray_BOOL NPY_BOOL
#define PyArray_BYTE NPY_BYTE
#define PyArray_UBYTE NPY_UBYTE
#define PyArray_SHORT NPY_SHORT
#define PyArray_USHORT NPY_USHORT
#define PyArray_INT NPY_INT
#define PyArray_UINT NPY_UINT
#define PyArray_LONG NPY_LONG
#define PyArray_ULONG NPY_ULONG
#define PyArray_LONGLONG NPY_LONGLONG
#define PyArray_ULONGLONG NPY_ULONGLONG
#define PyArray_HALF NPY_HALF
#define PyArray_FLOAT NPY_FLOAT
#define PyArray_DOUBLE NPY_DOUBLE
#define PyArray_LONGDOUBLE NPY_LONGDOUBLE
#define PyArray_CFLOAT NPY_CFLOAT
#define PyArray_CDOUBLE NPY_CDOUBLE
#define PyArray_CLONGDOUBLE NPY_CLONGDOUBLE
#define PyArray_OBJECT NPY_OBJECT
#define PyArray_STRING NPY_STRING
#define PyArray_UNICODE NPY_UNICODE
#define PyArray_VOID NPY_VOID
#define PyArray_DATETIME NPY_DATETIME
#define PyArray_TIMEDELTA NPY_TIMEDELTA
#define PyArray_NTYPES NPY_NTYPES
#define PyArray_NOTYPE NPY_NOTYPE
#define PyArray_CHAR NPY_CHAR
#define PyArray_USERDEF NPY_USERDEF
#define PyArray_NUMUSERTYPES NPY_NUMUSERTYPES
#define PyArray_INTP NPY_INTP
#define PyArray_UINTP NPY_UINTP
#define PyArray_INT8 NPY_INT8
#define PyArray_UINT8 NPY_UINT8
#define PyArray_INT16 NPY_INT16
#define PyArray_UINT16 NPY_UINT16
#define PyArray_INT32 NPY_INT32
#define PyArray_UINT32 NPY_UINT32
#ifdef NPY_INT64
#define PyArray_INT64 NPY_INT64
#define PyArray_UINT64 NPY_UINT64
#endif
#ifdef NPY_INT128
#define PyArray_INT128 NPY_INT128
#define PyArray_UINT128 NPY_UINT128
#endif
#ifdef NPY_FLOAT16
#define PyArray_FLOAT16 NPY_FLOAT16
#define PyArray_COMPLEX32 NPY_COMPLEX32
#endif
#ifdef NPY_FLOAT80
#define PyArray_FLOAT80 NPY_FLOAT80
#define PyArray_COMPLEX160 NPY_COMPLEX160
#endif
#ifdef NPY_FLOAT96
#define PyArray_FLOAT96 NPY_FLOAT96
#define PyArray_COMPLEX192 NPY_COMPLEX192
#endif
#ifdef NPY_FLOAT128
#define PyArray_FLOAT128 NPY_FLOAT128
#define PyArray_COMPLEX256 NPY_COMPLEX256
#endif
#define PyArray_FLOAT32 NPY_FLOAT32
#define PyArray_COMPLEX64 NPY_COMPLEX64
#define PyArray_FLOAT64 NPY_FLOAT64
#define PyArray_COMPLEX128 NPY_COMPLEX128
#define PyArray_TYPECHAR NPY_TYPECHAR
#define PyArray_BOOLLTR NPY_BOOLLTR
#define PyArray_BYTELTR NPY_BYTELTR
#define PyArray_UBYTELTR NPY_UBYTELTR
#define PyArray_SHORTLTR NPY_SHORTLTR
#define PyArray_USHORTLTR NPY_USHORTLTR
#define PyArray_INTLTR NPY_INTLTR
#define PyArray_UINTLTR NPY_UINTLTR
#define PyArray_LONGLTR NPY_LONGLTR
#define PyArray_ULONGLTR NPY_ULONGLTR
#define PyArray_LONGLONGLTR NPY_LONGLONGLTR
#define PyArray_ULONGLONGLTR NPY_ULONGLONGLTR
#define PyArray_HALFLTR NPY_HALFLTR
#define PyArray_FLOATLTR NPY_FLOATLTR
#define PyArray_DOUBLELTR NPY_DOUBLELTR
#define PyArray_LONGDOUBLELTR NPY_LONGDOUBLELTR
#define PyArray_CFLOATLTR NPY_CFLOATLTR
#define PyArray_CDOUBLELTR NPY_CDOUBLELTR
#define PyArray_CLONGDOUBLELTR NPY_CLONGDOUBLELTR
#define PyArray_OBJECTLTR NPY_OBJECTLTR
#define PyArray_STRINGLTR NPY_STRINGLTR
#define PyArray_STRINGLTR2 NPY_STRINGLTR2
#define PyArray_UNICODELTR NPY_UNICODELTR
#define PyArray_VOIDLTR NPY_VOIDLTR
#define PyArray_DATETIMELTR NPY_DATETIMELTR
#define PyArray_TIMEDELTALTR NPY_TIMEDELTALTR
#define PyArray_CHARLTR NPY_CHARLTR
#define PyArray_INTPLTR NPY_INTPLTR
#define PyArray_UINTPLTR NPY_UINTPLTR
#define PyArray_GENBOOLLTR NPY_GENBOOLLTR
#define PyArray_SIGNEDLTR NPY_SIGNEDLTR
#define PyArray_UNSIGNEDLTR NPY_UNSIGNEDLTR
#define PyArray_FLOATINGLTR NPY_FLOATINGLTR
#define PyArray_COMPLEXLTR NPY_COMPLEXLTR
#define PyArray_QUICKSORT NPY_QUICKSORT
#define PyArray_HEAPSORT NPY_HEAPSORT
#define PyArray_MERGESORT NPY_MERGESORT
#define PyArray_SORTKIND NPY_SORTKIND
#define PyArray_NSORTS NPY_NSORTS
#define PyArray_NOSCALAR NPY_NOSCALAR
#define PyArray_BOOL_SCALAR NPY_BOOL_SCALAR
#define PyArray_INTPOS_SCALAR NPY_INTPOS_SCALAR
#define PyArray_INTNEG_SCALAR NPY_INTNEG_SCALAR
#define PyArray_FLOAT_SCALAR NPY_FLOAT_SCALAR
#define PyArray_COMPLEX_SCALAR NPY_COMPLEX_SCALAR
#define PyArray_OBJECT_SCALAR NPY_OBJECT_SCALAR
#define PyArray_SCALARKIND NPY_SCALARKIND
#define PyArray_NSCALARKINDS NPY_NSCALARKINDS
#define PyArray_ANYORDER NPY_ANYORDER
#define PyArray_CORDER NPY_CORDER
#define PyArray_FORTRANORDER NPY_FORTRANORDER
#define PyArray_ORDER NPY_ORDER
#define PyDescr_ISBOOL PyDataType_ISBOOL
#define PyDescr_ISUNSIGNED PyDataType_ISUNSIGNED
#define PyDescr_ISSIGNED PyDataType_ISSIGNED
#define PyDescr_ISINTEGER PyDataType_ISINTEGER
#define PyDescr_ISFLOAT PyDataType_ISFLOAT
#define PyDescr_ISNUMBER PyDataType_ISNUMBER
#define PyDescr_ISSTRING PyDataType_ISSTRING
#define PyDescr_ISCOMPLEX PyDataType_ISCOMPLEX
#define PyDescr_ISPYTHON PyDataType_ISPYTHON
#define PyDescr_ISFLEXIBLE PyDataType_ISFLEXIBLE
#define PyDescr_ISUSERDEF PyDataType_ISUSERDEF
#define PyDescr_ISEXTENDED PyDataType_ISEXTENDED
#define PyDescr_ISOBJECT PyDataType_ISOBJECT
#define PyDescr_HASFIELDS PyDataType_HASFIELDS
#define PyArray_LITTLE NPY_LITTLE
#define PyArray_BIG NPY_BIG
#define PyArray_NATIVE NPY_NATIVE
#define PyArray_SWAP NPY_SWAP
#define PyArray_IGNORE NPY_IGNORE
#define PyArray_NATBYTE NPY_NATBYTE
#define PyArray_OPPBYTE NPY_OPPBYTE
#define PyArray_MAX_ELSIZE NPY_MAX_ELSIZE
#define PyArray_USE_PYMEM NPY_USE_PYMEM
#define PyArray_RemoveLargest PyArray_RemoveSmallest
#define PyArray_UCS4 npy_ucs4
#endif

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#include "arrayobject.h"
#ifndef PYPY_VERSION
#ifndef REFCOUNT
# define REFCOUNT NPY_REFCOUNT
# define MAX_ELSIZE 16
#endif
#endif
#define PyArray_UNSIGNED_TYPES
#define PyArray_SBYTE NPY_BYTE
#define PyArray_CopyArray PyArray_CopyInto
#define _PyArray_multiply_list PyArray_MultiplyIntList
#define PyArray_ISSPACESAVER(m) NPY_FALSE
#define PyScalarArray_Check PyArray_CheckScalar
#define CONTIGUOUS NPY_CONTIGUOUS
#define OWN_DIMENSIONS 0
#define OWN_STRIDES 0
#define OWN_DATA NPY_OWNDATA
#define SAVESPACE 0
#define SAVESPACEBIT 0
#undef import_array
#define import_array() { if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); } }

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#ifndef _RANDOM_BITGEN_H
#define _RANDOM_BITGEN_H
#pragma once
#include <stddef.h>
#include <stdbool.h>
#include <stdint.h>
/* Must match the declaration in numpy/random/<any>.pxd */
typedef struct bitgen {
void *state;
uint64_t (*next_uint64)(void *st);
uint32_t (*next_uint32)(void *st);
double (*next_double)(void *st);
uint64_t (*next_raw)(void *st);
} bitgen_t;
#endif

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#ifndef _RANDOMDGEN__DISTRIBUTIONS_H_
#define _RANDOMDGEN__DISTRIBUTIONS_H_
#include "Python.h"
#include "numpy/npy_common.h"
#include <stddef.h>
#include <stdbool.h>
#include <stdint.h>
#include "numpy/npy_math.h"
#include "numpy/random/bitgen.h"
/*
* RAND_INT_TYPE is used to share integer generators with RandomState which
* used long in place of int64_t. If changing a distribution that uses
* RAND_INT_TYPE, then the original unmodified copy must be retained for
* use in RandomState by copying to the legacy distributions source file.
*/
#ifdef NP_RANDOM_LEGACY
#define RAND_INT_TYPE long
#define RAND_INT_MAX LONG_MAX
#else
#define RAND_INT_TYPE int64_t
#define RAND_INT_MAX INT64_MAX
#endif
#ifdef _MSC_VER
#define DECLDIR __declspec(dllexport)
#else
#define DECLDIR extern
#endif
#ifndef MIN
#define MIN(x, y) (((x) < (y)) ? x : y)
#define MAX(x, y) (((x) > (y)) ? x : y)
#endif
#ifndef M_PI
#define M_PI 3.14159265358979323846264338328
#endif
typedef struct s_binomial_t {
int has_binomial; /* !=0: following parameters initialized for binomial */
double psave;
RAND_INT_TYPE nsave;
double r;
double q;
double fm;
RAND_INT_TYPE m;
double p1;
double xm;
double xl;
double xr;
double c;
double laml;
double lamr;
double p2;
double p3;
double p4;
} binomial_t;
DECLDIR float random_standard_uniform_f(bitgen_t *bitgen_state);
DECLDIR double random_standard_uniform(bitgen_t *bitgen_state);
DECLDIR void random_standard_uniform_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_uniform_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR int64_t random_positive_int64(bitgen_t *bitgen_state);
DECLDIR int32_t random_positive_int32(bitgen_t *bitgen_state);
DECLDIR int64_t random_positive_int(bitgen_t *bitgen_state);
DECLDIR uint64_t random_uint(bitgen_t *bitgen_state);
DECLDIR double random_standard_exponential(bitgen_t *bitgen_state);
DECLDIR float random_standard_exponential_f(bitgen_t *bitgen_state);
DECLDIR void random_standard_exponential_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_exponential_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR void random_standard_exponential_inv_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_exponential_inv_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR double random_standard_normal(bitgen_t *bitgen_state);
DECLDIR float random_standard_normal_f(bitgen_t *bitgen_state);
DECLDIR void random_standard_normal_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_normal_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR double random_standard_gamma(bitgen_t *bitgen_state, double shape);
DECLDIR float random_standard_gamma_f(bitgen_t *bitgen_state, float shape);
DECLDIR double random_normal(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_gamma(bitgen_t *bitgen_state, double shape, double scale);
DECLDIR float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale);
DECLDIR double random_exponential(bitgen_t *bitgen_state, double scale);
DECLDIR double random_uniform(bitgen_t *bitgen_state, double lower, double range);
DECLDIR double random_beta(bitgen_t *bitgen_state, double a, double b);
DECLDIR double random_chisquare(bitgen_t *bitgen_state, double df);
DECLDIR double random_f(bitgen_t *bitgen_state, double dfnum, double dfden);
DECLDIR double random_standard_cauchy(bitgen_t *bitgen_state);
DECLDIR double random_pareto(bitgen_t *bitgen_state, double a);
DECLDIR double random_weibull(bitgen_t *bitgen_state, double a);
DECLDIR double random_power(bitgen_t *bitgen_state, double a);
DECLDIR double random_laplace(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_gumbel(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_logistic(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma);
DECLDIR double random_rayleigh(bitgen_t *bitgen_state, double mode);
DECLDIR double random_standard_t(bitgen_t *bitgen_state, double df);
DECLDIR double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
double nonc);
DECLDIR double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
double dfden, double nonc);
DECLDIR double random_wald(bitgen_t *bitgen_state, double mean, double scale);
DECLDIR double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa);
DECLDIR double random_triangular(bitgen_t *bitgen_state, double left, double mode,
double right);
DECLDIR RAND_INT_TYPE random_poisson(bitgen_t *bitgen_state, double lam);
DECLDIR RAND_INT_TYPE random_negative_binomial(bitgen_t *bitgen_state, double n,
double p);
DECLDIR int64_t random_binomial(bitgen_t *bitgen_state, double p,
int64_t n, binomial_t *binomial);
DECLDIR RAND_INT_TYPE random_logseries(bitgen_t *bitgen_state, double p);
DECLDIR RAND_INT_TYPE random_geometric(bitgen_t *bitgen_state, double p);
DECLDIR RAND_INT_TYPE random_zipf(bitgen_t *bitgen_state, double a);
DECLDIR int64_t random_hypergeometric(bitgen_t *bitgen_state,
int64_t good, int64_t bad, int64_t sample);
DECLDIR uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max);
/* Generate random uint64 numbers in closed interval [off, off + rng]. */
DECLDIR uint64_t random_bounded_uint64(bitgen_t *bitgen_state, uint64_t off,
uint64_t rng, uint64_t mask,
bool use_masked);
/* Generate random uint32 numbers in closed interval [off, off + rng]. */
DECLDIR uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state,
uint32_t off, uint32_t rng,
uint32_t mask, bool use_masked,
int *bcnt, uint32_t *buf);
DECLDIR uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state,
uint16_t off, uint16_t rng,
uint16_t mask, bool use_masked,
int *bcnt, uint32_t *buf);
DECLDIR uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state, uint8_t off,
uint8_t rng, uint8_t mask,
bool use_masked, int *bcnt,
uint32_t *buf);
DECLDIR npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state, npy_bool off,
npy_bool rng, npy_bool mask,
bool use_masked, int *bcnt,
uint32_t *buf);
DECLDIR void random_bounded_uint64_fill(bitgen_t *bitgen_state, uint64_t off,
uint64_t rng, npy_intp cnt,
bool use_masked, uint64_t *out);
DECLDIR void random_bounded_uint32_fill(bitgen_t *bitgen_state, uint32_t off,
uint32_t rng, npy_intp cnt,
bool use_masked, uint32_t *out);
DECLDIR void random_bounded_uint16_fill(bitgen_t *bitgen_state, uint16_t off,
uint16_t rng, npy_intp cnt,
bool use_masked, uint16_t *out);
DECLDIR void random_bounded_uint8_fill(bitgen_t *bitgen_state, uint8_t off,
uint8_t rng, npy_intp cnt,
bool use_masked, uint8_t *out);
DECLDIR void random_bounded_bool_fill(bitgen_t *bitgen_state, npy_bool off,
npy_bool rng, npy_intp cnt,
bool use_masked, npy_bool *out);
DECLDIR void random_multinomial(bitgen_t *bitgen_state, RAND_INT_TYPE n, RAND_INT_TYPE *mnix,
double *pix, npy_intp d, binomial_t *binomial);
/* multivariate hypergeometric, "count" method */
DECLDIR int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
int64_t total,
size_t num_colors, int64_t *colors,
int64_t nsample,
size_t num_variates, int64_t *variates);
/* multivariate hypergeometric, "marginals" method */
DECLDIR void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
int64_t total,
size_t num_colors, int64_t *colors,
int64_t nsample,
size_t num_variates, int64_t *variates);
/* Common to legacy-distributions.c and distributions.c but not exported */
RAND_INT_TYPE random_binomial_btpe(bitgen_t *bitgen_state,
RAND_INT_TYPE n,
double p,
binomial_t *binomial);
RAND_INT_TYPE random_binomial_inversion(bitgen_t *bitgen_state,
RAND_INT_TYPE n,
double p,
binomial_t *binomial);
double random_loggam(double x);
static NPY_INLINE double next_double(bitgen_t *bitgen_state) {
return bitgen_state->next_double(bitgen_state->state);
}
#endif

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=================
NumPy Ufunc C-API
=================
::
PyObject *
PyUFunc_FromFuncAndData(PyUFuncGenericFunction *func, void
**data, char *types, int ntypes, int nin, int
nout, int identity, const char *name, const
char *doc, int unused)
::
int
PyUFunc_RegisterLoopForType(PyUFuncObject *ufunc, int
usertype, PyUFuncGenericFunction
function, const int *arg_types, void
*data)
::
int
PyUFunc_GenericFunction(PyUFuncObject *ufunc, PyObject *args, PyObject
*kwds, PyArrayObject **op)
This generic function is called with the ufunc object, the arguments to it,
and an array of (pointers to) PyArrayObjects which are NULL.
'op' is an array of at least NPY_MAXARGS PyArrayObject *.
::
void
PyUFunc_f_f_As_d_d(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_d_d(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_f_f(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_g_g(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_F_F_As_D_D(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_F_F(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_D_D(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_G_G(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_O_O(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_ff_f_As_dd_d(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_ff_f(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_dd_d(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_gg_g(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_FF_F_As_DD_D(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_DD_D(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_FF_F(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_GG_G(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_OO_O(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_O_O_method(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_OO_O_method(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_On_Om(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
int
PyUFunc_GetPyValues(char *name, int *bufsize, int *errmask, PyObject
**errobj)
On return, if errobj is populated with a non-NULL value, the caller
owns a new reference to errobj.
::
int
PyUFunc_checkfperr(int errmask, PyObject *errobj, int *first)
::
void
PyUFunc_clearfperr()
::
int
PyUFunc_getfperr(void )
::
int
PyUFunc_handlefperr(int errmask, PyObject *errobj, int retstatus, int
*first)
::
int
PyUFunc_ReplaceLoopBySignature(PyUFuncObject
*func, PyUFuncGenericFunction
newfunc, const int
*signature, PyUFuncGenericFunction
*oldfunc)
::
PyObject *
PyUFunc_FromFuncAndDataAndSignature(PyUFuncGenericFunction *func, void
**data, char *types, int
ntypes, int nin, int nout, int
identity, const char *name, const
char *doc, int unused, const char
*signature)
::
int
PyUFunc_SetUsesArraysAsData(void **data, size_t i)
::
void
PyUFunc_e_e(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_e_e_As_f_f(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_e_e_As_d_d(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_ee_e(char **args, npy_intp *dimensions, npy_intp *steps, void
*func)
::
void
PyUFunc_ee_e_As_ff_f(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
void
PyUFunc_ee_e_As_dd_d(char **args, npy_intp *dimensions, npy_intp
*steps, void *func)
::
int
PyUFunc_DefaultTypeResolver(PyUFuncObject *ufunc, NPY_CASTING
casting, PyArrayObject
**operands, PyObject
*type_tup, PyArray_Descr **out_dtypes)
This function applies the default type resolution rules
for the provided ufunc.
Returns 0 on success, -1 on error.
::
int
PyUFunc_ValidateCasting(PyUFuncObject *ufunc, NPY_CASTING
casting, PyArrayObject
**operands, PyArray_Descr **dtypes)
Validates that the input operands can be cast to
the input types, and the output types can be cast to
the output operands where provided.
Returns 0 on success, -1 (with exception raised) on validation failure.
::
int
PyUFunc_RegisterLoopForDescr(PyUFuncObject *ufunc, PyArray_Descr
*user_dtype, PyUFuncGenericFunction
function, PyArray_Descr
**arg_dtypes, void *data)
::
PyObject *
PyUFunc_FromFuncAndDataAndSignatureAndIdentity(PyUFuncGenericFunction
*func, void
**data, char
*types, int ntypes, int
nin, int nout, int
identity, const char
*name, const char
*doc, const int
unused, const char
*signature, PyObject
*identity_value)

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#ifndef Py_UFUNCOBJECT_H
#define Py_UFUNCOBJECT_H
#include <numpy/npy_math.h>
#include <numpy/npy_common.h>
#ifdef __cplusplus
extern "C" {
#endif
/*
* The legacy generic inner loop for a standard element-wise or
* generalized ufunc.
*/
typedef void (*PyUFuncGenericFunction)
(char **args,
npy_intp *dimensions,
npy_intp *strides,
void *innerloopdata);
/*
* The most generic one-dimensional inner loop for
* a masked standard element-wise ufunc. "Masked" here means that it skips
* doing calculations on any items for which the maskptr array has a true
* value.
*/
typedef void (PyUFunc_MaskedStridedInnerLoopFunc)(
char **dataptrs, npy_intp *strides,
char *maskptr, npy_intp mask_stride,
npy_intp count,
NpyAuxData *innerloopdata);
/* Forward declaration for the type resolver and loop selector typedefs */
struct _tagPyUFuncObject;
/*
* Given the operands for calling a ufunc, should determine the
* calculation input and output data types and return an inner loop function.
* This function should validate that the casting rule is being followed,
* and fail if it is not.
*
* For backwards compatibility, the regular type resolution function does not
* support auxiliary data with object semantics. The type resolution call
* which returns a masked generic function returns a standard NpyAuxData
* object, for which the NPY_AUXDATA_FREE and NPY_AUXDATA_CLONE macros
* work.
*
* ufunc: The ufunc object.
* casting: The 'casting' parameter provided to the ufunc.
* operands: An array of length (ufunc->nin + ufunc->nout),
* with the output parameters possibly NULL.
* type_tup: Either NULL, or the type_tup passed to the ufunc.
* out_dtypes: An array which should be populated with new
* references to (ufunc->nin + ufunc->nout) new
* dtypes, one for each input and output. These
* dtypes should all be in native-endian format.
*
* Should return 0 on success, -1 on failure (with exception set),
* or -2 if Py_NotImplemented should be returned.
*/
typedef int (PyUFunc_TypeResolutionFunc)(
struct _tagPyUFuncObject *ufunc,
NPY_CASTING casting,
PyArrayObject **operands,
PyObject *type_tup,
PyArray_Descr **out_dtypes);
/*
* Given an array of DTypes as returned by the PyUFunc_TypeResolutionFunc,
* and an array of fixed strides (the array will contain NPY_MAX_INTP for
* strides which are not necessarily fixed), returns an inner loop
* with associated auxiliary data.
*
* For backwards compatibility, there is a variant of the inner loop
* selection which returns an inner loop irrespective of the strides,
* and with a void* static auxiliary data instead of an NpyAuxData *
* dynamically allocatable auxiliary data.
*
* ufunc: The ufunc object.
* dtypes: An array which has been populated with dtypes,
* in most cases by the type resolution function
* for the same ufunc.
* fixed_strides: For each input/output, either the stride that
* will be used every time the function is called
* or NPY_MAX_INTP if the stride might change or
* is not known ahead of time. The loop selection
* function may use this stride to pick inner loops
* which are optimized for contiguous or 0-stride
* cases.
* out_innerloop: Should be populated with the correct ufunc inner
* loop for the given type.
* out_innerloopdata: Should be populated with the void* data to
* be passed into the out_innerloop function.
* out_needs_api: If the inner loop needs to use the Python API,
* should set the to 1, otherwise should leave
* this untouched.
*/
typedef int (PyUFunc_LegacyInnerLoopSelectionFunc)(
struct _tagPyUFuncObject *ufunc,
PyArray_Descr **dtypes,
PyUFuncGenericFunction *out_innerloop,
void **out_innerloopdata,
int *out_needs_api);
typedef int (PyUFunc_MaskedInnerLoopSelectionFunc)(
struct _tagPyUFuncObject *ufunc,
PyArray_Descr **dtypes,
PyArray_Descr *mask_dtype,
npy_intp *fixed_strides,
npy_intp fixed_mask_stride,
PyUFunc_MaskedStridedInnerLoopFunc **out_innerloop,
NpyAuxData **out_innerloopdata,
int *out_needs_api);
typedef struct _tagPyUFuncObject {
PyObject_HEAD
/*
* nin: Number of inputs
* nout: Number of outputs
* nargs: Always nin + nout (Why is it stored?)
*/
int nin, nout, nargs;
/*
* Identity for reduction, any of PyUFunc_One, PyUFunc_Zero
* PyUFunc_MinusOne, PyUFunc_None, PyUFunc_ReorderableNone,
* PyUFunc_IdentityValue.
*/
int identity;
/* Array of one-dimensional core loops */
PyUFuncGenericFunction *functions;
/* Array of funcdata that gets passed into the functions */
void **data;
/* The number of elements in 'functions' and 'data' */
int ntypes;
/* Used to be unused field 'check_return' */
int reserved1;
/* The name of the ufunc */
const char *name;
/* Array of type numbers, of size ('nargs' * 'ntypes') */
char *types;
/* Documentation string */
const char *doc;
void *ptr;
PyObject *obj;
PyObject *userloops;
/* generalized ufunc parameters */
/* 0 for scalar ufunc; 1 for generalized ufunc */
int core_enabled;
/* number of distinct dimension names in signature */
int core_num_dim_ix;
/*
* dimension indices of input/output argument k are stored in
* core_dim_ixs[core_offsets[k]..core_offsets[k]+core_num_dims[k]-1]
*/
/* numbers of core dimensions of each argument */
int *core_num_dims;
/*
* dimension indices in a flatted form; indices
* are in the range of [0,core_num_dim_ix)
*/
int *core_dim_ixs;
/*
* positions of 1st core dimensions of each
* argument in core_dim_ixs, equivalent to cumsum(core_num_dims)
*/
int *core_offsets;
/* signature string for printing purpose */
char *core_signature;
/*
* A function which resolves the types and fills an array
* with the dtypes for the inputs and outputs.
*/
PyUFunc_TypeResolutionFunc *type_resolver;
/*
* A function which returns an inner loop written for
* NumPy 1.6 and earlier ufuncs. This is for backwards
* compatibility, and may be NULL if inner_loop_selector
* is specified.
*/
PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector;
/*
* This was blocked off to be the "new" inner loop selector in 1.7,
* but this was never implemented. (This is also why the above
* selector is called the "legacy" selector.)
*/
void *reserved2;
/*
* A function which returns a masked inner loop for the ufunc.
*/
PyUFunc_MaskedInnerLoopSelectionFunc *masked_inner_loop_selector;
/*
* List of flags for each operand when ufunc is called by nditer object.
* These flags will be used in addition to the default flags for each
* operand set by nditer object.
*/
npy_uint32 *op_flags;
/*
* List of global flags used when ufunc is called by nditer object.
* These flags will be used in addition to the default global flags
* set by nditer object.
*/
npy_uint32 iter_flags;
/* New in NPY_API_VERSION 0x0000000D and above */
/*
* for each core_num_dim_ix distinct dimension names,
* the possible "frozen" size (-1 if not frozen).
*/
npy_intp *core_dim_sizes;
/*
* for each distinct core dimension, a set of UFUNC_CORE_DIM* flags
*/
npy_uint32 *core_dim_flags;
/* Identity for reduction, when identity == PyUFunc_IdentityValue */
PyObject *identity_value;
} PyUFuncObject;
#include "arrayobject.h"
/* Generalized ufunc; 0x0001 reserved for possible use as CORE_ENABLED */
/* the core dimension's size will be determined by the operands. */
#define UFUNC_CORE_DIM_SIZE_INFERRED 0x0002
/* the core dimension may be absent */
#define UFUNC_CORE_DIM_CAN_IGNORE 0x0004
/* flags inferred during execution */
#define UFUNC_CORE_DIM_MISSING 0x00040000
#define UFUNC_ERR_IGNORE 0
#define UFUNC_ERR_WARN 1
#define UFUNC_ERR_RAISE 2
#define UFUNC_ERR_CALL 3
#define UFUNC_ERR_PRINT 4
#define UFUNC_ERR_LOG 5
/* Python side integer mask */
#define UFUNC_MASK_DIVIDEBYZERO 0x07
#define UFUNC_MASK_OVERFLOW 0x3f
#define UFUNC_MASK_UNDERFLOW 0x1ff
#define UFUNC_MASK_INVALID 0xfff
#define UFUNC_SHIFT_DIVIDEBYZERO 0
#define UFUNC_SHIFT_OVERFLOW 3
#define UFUNC_SHIFT_UNDERFLOW 6
#define UFUNC_SHIFT_INVALID 9
#define UFUNC_OBJ_ISOBJECT 1
#define UFUNC_OBJ_NEEDS_API 2
/* Default user error mode */
#define UFUNC_ERR_DEFAULT \
(UFUNC_ERR_WARN << UFUNC_SHIFT_DIVIDEBYZERO) + \
(UFUNC_ERR_WARN << UFUNC_SHIFT_OVERFLOW) + \
(UFUNC_ERR_WARN << UFUNC_SHIFT_INVALID)
#if NPY_ALLOW_THREADS
#define NPY_LOOP_BEGIN_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) _save = PyEval_SaveThread();} while (0);
#define NPY_LOOP_END_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) PyEval_RestoreThread(_save);} while (0);
#else
#define NPY_LOOP_BEGIN_THREADS
#define NPY_LOOP_END_THREADS
#endif
/*
* UFunc has unit of 0, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_Zero 0
/*
* UFunc has unit of 1, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_One 1
/*
* UFunc has unit of -1, and the order of operations can be reordered
* This case allows reduction with multiple axes at once. Intended for
* bitwise_and reduction.
*/
#define PyUFunc_MinusOne 2
/*
* UFunc has no unit, and the order of operations cannot be reordered.
* This case does not allow reduction with multiple axes at once.
*/
#define PyUFunc_None -1
/*
* UFunc has no unit, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_ReorderableNone -2
/*
* UFunc unit is an identity_value, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_IdentityValue -3
#define UFUNC_REDUCE 0
#define UFUNC_ACCUMULATE 1
#define UFUNC_REDUCEAT 2
#define UFUNC_OUTER 3
typedef struct {
int nin;
int nout;
PyObject *callable;
} PyUFunc_PyFuncData;
/* A linked-list of function information for
user-defined 1-d loops.
*/
typedef struct _loop1d_info {
PyUFuncGenericFunction func;
void *data;
int *arg_types;
struct _loop1d_info *next;
int nargs;
PyArray_Descr **arg_dtypes;
} PyUFunc_Loop1d;
#include "__ufunc_api.h"
#define UFUNC_PYVALS_NAME "UFUNC_PYVALS"
/*
* THESE MACROS ARE DEPRECATED.
* Use npy_set_floatstatus_* in the npymath library.
*/
#define UFUNC_FPE_DIVIDEBYZERO NPY_FPE_DIVIDEBYZERO
#define UFUNC_FPE_OVERFLOW NPY_FPE_OVERFLOW
#define UFUNC_FPE_UNDERFLOW NPY_FPE_UNDERFLOW
#define UFUNC_FPE_INVALID NPY_FPE_INVALID
#define generate_divbyzero_error() npy_set_floatstatus_divbyzero()
#define generate_overflow_error() npy_set_floatstatus_overflow()
/* Make sure it gets defined if it isn't already */
#ifndef UFUNC_NOFPE
/* Clear the floating point exception default of Borland C++ */
#if defined(__BORLANDC__)
#define UFUNC_NOFPE _control87(MCW_EM, MCW_EM);
#else
#define UFUNC_NOFPE
#endif
#endif
#ifdef __cplusplus
}
#endif
#endif /* !Py_UFUNCOBJECT_H */

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#ifndef __NUMPY_UTILS_HEADER__
#define __NUMPY_UTILS_HEADER__
#ifndef __COMP_NPY_UNUSED
#if defined(__GNUC__)
#define __COMP_NPY_UNUSED __attribute__ ((__unused__))
# elif defined(__ICC)
#define __COMP_NPY_UNUSED __attribute__ ((__unused__))
# elif defined(__clang__)
#define __COMP_NPY_UNUSED __attribute__ ((unused))
#else
#define __COMP_NPY_UNUSED
#endif
#endif
/* Use this to tag a variable as not used. It will remove unused variable
* warning on support platforms (see __COM_NPY_UNUSED) and mangle the variable
* to avoid accidental use */
#define NPY_UNUSED(x) (__NPY_UNUSED_TAGGED ## x) __COMP_NPY_UNUSED
#endif

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[meta]
Name = mlib
Description = Math library used with this version of numpy
Version = 1.0
[default]
Libs=
Cflags=
[msvc]
Libs=
Cflags=

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[meta]
Name=npymath
Description=Portable, core math library implementing C99 standard
Version=0.1
[variables]
pkgname=numpy.core
prefix=${pkgdir}
libdir=${prefix}\lib
includedir=${prefix}\include
[default]
Libs=-L${libdir} -lnpymath
Cflags=-I${includedir}
Requires=mlib
[msvc]
Libs=/LIBPATH:${libdir} npymath.lib
Cflags=/INCLUDE:${includedir}
Requires=mlib

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"""
Machine arithmetics - determine the parameters of the
floating-point arithmetic system
Author: Pearu Peterson, September 2003
"""
from __future__ import division, absolute_import, print_function
__all__ = ['MachAr']
from numpy.core.fromnumeric import any
from numpy.core._ufunc_config import errstate
from numpy.core.overrides import set_module
# Need to speed this up...especially for longfloat
@set_module('numpy')
class MachAr(object):
"""
Diagnosing machine parameters.
Attributes
----------
ibeta : int
Radix in which numbers are represented.
it : int
Number of base-`ibeta` digits in the floating point mantissa M.
machep : int
Exponent of the smallest (most negative) power of `ibeta` that,
added to 1.0, gives something different from 1.0
eps : float
Floating-point number ``beta**machep`` (floating point precision)
negep : int
Exponent of the smallest power of `ibeta` that, subtracted
from 1.0, gives something different from 1.0.
epsneg : float
Floating-point number ``beta**negep``.
iexp : int
Number of bits in the exponent (including its sign and bias).
minexp : int
Smallest (most negative) power of `ibeta` consistent with there
being no leading zeros in the mantissa.
xmin : float
Floating point number ``beta**minexp`` (the smallest [in
magnitude] usable floating value).
maxexp : int
Smallest (positive) power of `ibeta` that causes overflow.
xmax : float
``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
usable floating value).
irnd : int
In ``range(6)``, information on what kind of rounding is done
in addition, and on how underflow is handled.
ngrd : int
Number of 'guard digits' used when truncating the product
of two mantissas to fit the representation.
epsilon : float
Same as `eps`.
tiny : float
Same as `xmin`.
huge : float
Same as `xmax`.
precision : float
``- int(-log10(eps))``
resolution : float
``- 10**(-precision)``
Parameters
----------
float_conv : function, optional
Function that converts an integer or integer array to a float
or float array. Default is `float`.
int_conv : function, optional
Function that converts a float or float array to an integer or
integer array. Default is `int`.
float_to_float : function, optional
Function that converts a float array to float. Default is `float`.
Note that this does not seem to do anything useful in the current
implementation.
float_to_str : function, optional
Function that converts a single float to a string. Default is
``lambda v:'%24.16e' %v``.
title : str, optional
Title that is printed in the string representation of `MachAr`.
See Also
--------
finfo : Machine limits for floating point types.
iinfo : Machine limits for integer types.
References
----------
.. [1] Press, Teukolsky, Vetterling and Flannery,
"Numerical Recipes in C++," 2nd ed,
Cambridge University Press, 2002, p. 31.
"""
def __init__(self, float_conv=float,int_conv=int,
float_to_float=float,
float_to_str=lambda v:'%24.16e' % v,
title='Python floating point number'):
"""
float_conv - convert integer to float (array)
int_conv - convert float (array) to integer
float_to_float - convert float array to float
float_to_str - convert array float to str
title - description of used floating point numbers
"""
# We ignore all errors here because we are purposely triggering
# underflow to detect the properties of the runninng arch.
with errstate(under='ignore'):
self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
max_iterN = 10000
msg = "Did not converge after %d tries with %s"
one = float_conv(1)
two = one + one
zero = one - one
# Do we really need to do this? Aren't they 2 and 2.0?
# Determine ibeta and beta
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
b = one
for _ in range(max_iterN):
b = b + b
temp = a + b
itemp = int_conv(temp-a)
if any(itemp != 0):
break
else:
raise RuntimeError(msg % (_, one.dtype))
ibeta = itemp
beta = float_conv(ibeta)
# Determine it and irnd
it = -1
b = one
for _ in range(max_iterN):
it = it + 1
b = b * beta
temp = b + one
temp1 = temp - b
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
betah = beta / two
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
temp = a + betah
irnd = 0
if any(temp-a != zero):
irnd = 1
tempa = a + beta
temp = tempa + betah
if irnd == 0 and any(temp-tempa != zero):
irnd = 2
# Determine negep and epsneg
negep = it + 3
betain = one / beta
a = one
for i in range(negep):
a = a * betain
b = a
for _ in range(max_iterN):
temp = one - a
if any(temp-one != zero):
break
a = a * beta
negep = negep - 1
# Prevent infinite loop on PPC with gcc 4.0:
if negep < 0:
raise RuntimeError("could not determine machine tolerance "
"for 'negep', locals() -> %s" % (locals()))
else:
raise RuntimeError(msg % (_, one.dtype))
negep = -negep
epsneg = a
# Determine machep and eps
machep = - it - 3
a = b
for _ in range(max_iterN):
temp = one + a
if any(temp-one != zero):
break
a = a * beta
machep = machep + 1
else:
raise RuntimeError(msg % (_, one.dtype))
eps = a
# Determine ngrd
ngrd = 0
temp = one + eps
if irnd == 0 and any(temp*one - one != zero):
ngrd = 1
# Determine iexp
i = 0
k = 1
z = betain
t = one + eps
nxres = 0
for _ in range(max_iterN):
y = z
z = y*y
a = z*one # Check here for underflow
temp = z*t
if any(a+a == zero) or any(abs(z) >= y):
break
temp1 = temp * betain
if any(temp1*beta == z):
break
i = i + 1
k = k + k
else:
raise RuntimeError(msg % (_, one.dtype))
if ibeta != 10:
iexp = i + 1
mx = k + k
else:
iexp = 2
iz = ibeta
while k >= iz:
iz = iz * ibeta
iexp = iexp + 1
mx = iz + iz - 1
# Determine minexp and xmin
for _ in range(max_iterN):
xmin = y
y = y * betain
a = y * one
temp = y * t
if any((a + a) != zero) and any(abs(y) < xmin):
k = k + 1
temp1 = temp * betain
if any(temp1*beta == y) and any(temp != y):
nxres = 3
xmin = y
break
else:
break
else:
raise RuntimeError(msg % (_, one.dtype))
minexp = -k
# Determine maxexp, xmax
if mx <= k + k - 3 and ibeta != 10:
mx = mx + mx
iexp = iexp + 1
maxexp = mx + minexp
irnd = irnd + nxres
if irnd >= 2:
maxexp = maxexp - 2
i = maxexp + minexp
if ibeta == 2 and not i:
maxexp = maxexp - 1
if i > 20:
maxexp = maxexp - 1
if any(a != y):
maxexp = maxexp - 2
xmax = one - epsneg
if any(xmax*one != xmax):
xmax = one - beta*epsneg
xmax = xmax / (xmin*beta*beta*beta)
i = maxexp + minexp + 3
for j in range(i):
if ibeta == 2:
xmax = xmax + xmax
else:
xmax = xmax * beta
self.ibeta = ibeta
self.it = it
self.negep = negep
self.epsneg = float_to_float(epsneg)
self._str_epsneg = float_to_str(epsneg)
self.machep = machep
self.eps = float_to_float(eps)
self._str_eps = float_to_str(eps)
self.ngrd = ngrd
self.iexp = iexp
self.minexp = minexp
self.xmin = float_to_float(xmin)
self._str_xmin = float_to_str(xmin)
self.maxexp = maxexp
self.xmax = float_to_float(xmax)
self._str_xmax = float_to_str(xmax)
self.irnd = irnd
self.title = title
# Commonly used parameters
self.epsilon = self.eps
self.tiny = self.xmin
self.huge = self.xmax
import math
self.precision = int(-math.log10(float_to_float(self.eps)))
ten = two + two + two + two + two
resolution = ten ** (-self.precision)
self.resolution = float_to_float(resolution)
self._str_resolution = float_to_str(resolution)
def __str__(self):
fmt = (
'Machine parameters for %(title)s\n'
'---------------------------------------------------------------------\n'
'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
'machep=%(machep)s eps=%(_str_eps)s (beta**machep == epsilon)\n'
'negep =%(negep)s epsneg=%(_str_epsneg)s (beta**epsneg)\n'
'minexp=%(minexp)s xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
'maxexp=%(maxexp)s xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
'---------------------------------------------------------------------\n'
)
return fmt % self.__dict__
if __name__ == '__main__':
print(MachAr())

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from __future__ import division, absolute_import, print_function
import numpy as np
from .numeric import uint8, ndarray, dtype
from numpy.compat import (
long, basestring, os_fspath, contextlib_nullcontext, is_pathlib_path
)
from numpy.core.overrides import set_module
__all__ = ['memmap']
dtypedescr = dtype
valid_filemodes = ["r", "c", "r+", "w+"]
writeable_filemodes = ["r+", "w+"]
mode_equivalents = {
"readonly":"r",
"copyonwrite":"c",
"readwrite":"r+",
"write":"w+"
}
@set_module('numpy')
class memmap(ndarray):
"""Create a memory-map to an array stored in a *binary* file on disk.
Memory-mapped files are used for accessing small segments of large files
on disk, without reading the entire file into memory. NumPy's
memmap's are array-like objects. This differs from Python's ``mmap``
module, which uses file-like objects.
This subclass of ndarray has some unpleasant interactions with
some operations, because it doesn't quite fit properly as a subclass.
An alternative to using this subclass is to create the ``mmap``
object yourself, then create an ndarray with ndarray.__new__ directly,
passing the object created in its 'buffer=' parameter.
This class may at some point be turned into a factory function
which returns a view into an mmap buffer.
Delete the memmap instance to close the memmap file.
Parameters
----------
filename : str, file-like object, or pathlib.Path instance
The file name or file object to be used as the array data buffer.
dtype : data-type, optional
The data-type used to interpret the file contents.
Default is `uint8`.
mode : {'r+', 'r', 'w+', 'c'}, optional
The file is opened in this mode:
+------+-------------------------------------------------------------+
| 'r' | Open existing file for reading only. |
+------+-------------------------------------------------------------+
| 'r+' | Open existing file for reading and writing. |
+------+-------------------------------------------------------------+
| 'w+' | Create or overwrite existing file for reading and writing. |
+------+-------------------------------------------------------------+
| 'c' | Copy-on-write: assignments affect data in memory, but |
| | changes are not saved to disk. The file on disk is |
| | read-only. |
+------+-------------------------------------------------------------+
Default is 'r+'.
offset : int, optional
In the file, array data starts at this offset. Since `offset` is
measured in bytes, it should normally be a multiple of the byte-size
of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of
file are valid; The file will be extended to accommodate the
additional data. By default, ``memmap`` will start at the beginning of
the file, even if ``filename`` is a file pointer ``fp`` and
``fp.tell() != 0``.
shape : tuple, optional
The desired shape of the array. If ``mode == 'r'`` and the number
of remaining bytes after `offset` is not a multiple of the byte-size
of `dtype`, you must specify `shape`. By default, the returned array
will be 1-D with the number of elements determined by file size
and data-type.
order : {'C', 'F'}, optional
Specify the order of the ndarray memory layout:
:term:`row-major`, C-style or :term:`column-major`,
Fortran-style. This only has an effect if the shape is
greater than 1-D. The default order is 'C'.
Attributes
----------
filename : str or pathlib.Path instance
Path to the mapped file.
offset : int
Offset position in the file.
mode : str
File mode.
Methods
-------
flush
Flush any changes in memory to file on disk.
When you delete a memmap object, flush is called first to write
changes to disk before removing the object.
See also
--------
lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
Notes
-----
The memmap object can be used anywhere an ndarray is accepted.
Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
``True``.
Memory-mapped files cannot be larger than 2GB on 32-bit systems.
When a memmap causes a file to be created or extended beyond its
current size in the filesystem, the contents of the new part are
unspecified. On systems with POSIX filesystem semantics, the extended
part will be filled with zero bytes.
Examples
--------
>>> data = np.arange(12, dtype='float32')
>>> data.resize((3,4))
This example uses a temporary file so that doctest doesn't write
files to your directory. You would use a 'normal' filename.
>>> from tempfile import mkdtemp
>>> import os.path as path
>>> filename = path.join(mkdtemp(), 'newfile.dat')
Create a memmap with dtype and shape that matches our data:
>>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
>>> fp
memmap([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]], dtype=float32)
Write data to memmap array:
>>> fp[:] = data[:]
>>> fp
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
>>> fp.filename == path.abspath(filename)
True
Deletion flushes memory changes to disk before removing the object:
>>> del fp
Load the memmap and verify data was stored:
>>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> newfp
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
Read-only memmap:
>>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> fpr.flags.writeable
False
Copy-on-write memmap:
>>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
>>> fpc.flags.writeable
True
It's possible to assign to copy-on-write array, but values are only
written into the memory copy of the array, and not written to disk:
>>> fpc
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
>>> fpc[0,:] = 0
>>> fpc
memmap([[ 0., 0., 0., 0.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
File on disk is unchanged:
>>> fpr
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
Offset into a memmap:
>>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
>>> fpo
memmap([ 4., 5., 6., 7., 8., 9., 10., 11.], dtype=float32)
"""
__array_priority__ = -100.0
def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
shape=None, order='C'):
# Import here to minimize 'import numpy' overhead
import mmap
import os.path
try:
mode = mode_equivalents[mode]
except KeyError:
if mode not in valid_filemodes:
raise ValueError("mode must be one of %s" %
(valid_filemodes + list(mode_equivalents.keys())))
if mode == 'w+' and shape is None:
raise ValueError("shape must be given")
if hasattr(filename, 'read'):
f_ctx = contextlib_nullcontext(filename)
else:
f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b')
with f_ctx as fid:
fid.seek(0, 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if bytes % _dbytes:
raise ValueError("Size of available data is not a "
"multiple of the data-type size.")
size = bytes // _dbytes
shape = (size,)
else:
if not isinstance(shape, tuple):
shape = (shape,)
size = np.intp(1) # avoid default choice of np.int_, which might overflow
for k in shape:
size *= k
bytes = long(offset + size*_dbytes)
if mode in ('w+', 'r+') and flen < bytes:
fid.seek(bytes - 1, 0)
fid.write(b'\0')
fid.flush()
if mode == 'c':
acc = mmap.ACCESS_COPY
elif mode == 'r':
acc = mmap.ACCESS_READ
else:
acc = mmap.ACCESS_WRITE
start = offset - offset % mmap.ALLOCATIONGRANULARITY
bytes -= start
array_offset = offset - start
mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
offset=array_offset, order=order)
self._mmap = mm
self.offset = offset
self.mode = mode
if is_pathlib_path(filename):
# special case - if we were constructed with a pathlib.path,
# then filename is a path object, not a string
self.filename = filename.resolve()
elif hasattr(fid, "name") and isinstance(fid.name, basestring):
# py3 returns int for TemporaryFile().name
self.filename = os.path.abspath(fid.name)
# same as memmap copies (e.g. memmap + 1)
else:
self.filename = None
return self
def __array_finalize__(self, obj):
if hasattr(obj, '_mmap') and np.may_share_memory(self, obj):
self._mmap = obj._mmap
self.filename = obj.filename
self.offset = obj.offset
self.mode = obj.mode
else:
self._mmap = None
self.filename = None
self.offset = None
self.mode = None
def flush(self):
"""
Write any changes in the array to the file on disk.
For further information, see `memmap`.
Parameters
----------
None
See Also
--------
memmap
"""
if self.base is not None and hasattr(self.base, 'flush'):
self.base.flush()
def __array_wrap__(self, arr, context=None):
arr = super(memmap, self).__array_wrap__(arr, context)
# Return a memmap if a memmap was given as the output of the
# ufunc. Leave the arr class unchanged if self is not a memmap
# to keep original memmap subclasses behavior
if self is arr or type(self) is not memmap:
return arr
# Return scalar instead of 0d memmap, e.g. for np.sum with
# axis=None
if arr.shape == ():
return arr[()]
# Return ndarray otherwise
return arr.view(np.ndarray)
def __getitem__(self, index):
res = super(memmap, self).__getitem__(index)
if type(res) is memmap and res._mmap is None:
return res.view(type=ndarray)
return res

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"""
numerictypes: Define the numeric type objects
This module is designed so "from numerictypes import \\*" is safe.
Exported symbols include:
Dictionary with all registered number types (including aliases):
typeDict
Type objects (not all will be available, depends on platform):
see variable sctypes for which ones you have
Bit-width names
int8 int16 int32 int64 int128
uint8 uint16 uint32 uint64 uint128
float16 float32 float64 float96 float128 float256
complex32 complex64 complex128 complex192 complex256 complex512
datetime64 timedelta64
c-based names
bool_
object_
void, str_, unicode_
byte, ubyte,
short, ushort
intc, uintc,
intp, uintp,
int_, uint,
longlong, ulonglong,
single, csingle,
float_, complex_,
longfloat, clongfloat,
As part of the type-hierarchy: xx -- is bit-width
generic
+-> bool_ (kind=b)
+-> number
| +-> integer
| | +-> signedinteger (intxx) (kind=i)
| | | byte
| | | short
| | | intc
| | | intp int0
| | | int_
| | | longlong
| | \\-> unsignedinteger (uintxx) (kind=u)
| | ubyte
| | ushort
| | uintc
| | uintp uint0
| | uint_
| | ulonglong
| +-> inexact
| +-> floating (floatxx) (kind=f)
| | half
| | single
| | float_ (double)
| | longfloat
| \\-> complexfloating (complexxx) (kind=c)
| csingle (singlecomplex)
| complex_ (cfloat, cdouble)
| clongfloat (longcomplex)
+-> flexible
| +-> character
| | str_ (string_, bytes_) (kind=S) [Python 2]
| | unicode_ (kind=U) [Python 2]
| |
| | bytes_ (string_) (kind=S) [Python 3]
| | str_ (unicode_) (kind=U) [Python 3]
| |
| \\-> void (kind=V)
\\-> object_ (not used much) (kind=O)
"""
from __future__ import division, absolute_import, print_function
import types as _types
import sys
import numbers
import warnings
from numpy.compat import bytes, long
from numpy.core.multiarray import (
typeinfo, ndarray, array, empty, dtype, datetime_data,
datetime_as_string, busday_offset, busday_count, is_busday,
busdaycalendar
)
from numpy.core.overrides import set_module
# we add more at the bottom
__all__ = ['sctypeDict', 'sctypeNA', 'typeDict', 'typeNA', 'sctypes',
'ScalarType', 'obj2sctype', 'cast', 'nbytes', 'sctype2char',
'maximum_sctype', 'issctype', 'typecodes', 'find_common_type',
'issubdtype', 'datetime_data', 'datetime_as_string',
'busday_offset', 'busday_count', 'is_busday', 'busdaycalendar',
]
# we don't need all these imports, but we need to keep them for compatibility
# for users using np.core.numerictypes.UPPER_TABLE
from ._string_helpers import (
english_lower, english_upper, english_capitalize, LOWER_TABLE, UPPER_TABLE
)
from ._type_aliases import (
sctypeDict,
sctypeNA,
allTypes,
bitname,
sctypes,
_concrete_types,
_concrete_typeinfo,
_bits_of,
)
from ._dtype import _kind_name
# we don't export these for import *, but we do want them accessible
# as numerictypes.bool, etc.
if sys.version_info[0] >= 3:
from builtins import bool, int, float, complex, object, str
unicode = str
else:
from __builtin__ import bool, int, float, complex, object, unicode, str
# We use this later
generic = allTypes['generic']
genericTypeRank = ['bool', 'int8', 'uint8', 'int16', 'uint16',
'int32', 'uint32', 'int64', 'uint64', 'int128',
'uint128', 'float16',
'float32', 'float64', 'float80', 'float96', 'float128',
'float256',
'complex32', 'complex64', 'complex128', 'complex160',
'complex192', 'complex256', 'complex512', 'object']
@set_module('numpy')
def maximum_sctype(t):
"""
Return the scalar type of highest precision of the same kind as the input.
Parameters
----------
t : dtype or dtype specifier
The input data type. This can be a `dtype` object or an object that
is convertible to a `dtype`.
Returns
-------
out : dtype
The highest precision data type of the same kind (`dtype.kind`) as `t`.
See Also
--------
obj2sctype, mintypecode, sctype2char
dtype
Examples
--------
>>> np.maximum_sctype(int)
<class 'numpy.int64'>
>>> np.maximum_sctype(np.uint8)
<class 'numpy.uint64'>
>>> np.maximum_sctype(complex)
<class 'numpy.complex256'> # may vary
>>> np.maximum_sctype(str)
<class 'numpy.str_'>
>>> np.maximum_sctype('i2')
<class 'numpy.int64'>
>>> np.maximum_sctype('f4')
<class 'numpy.float128'> # may vary
"""
g = obj2sctype(t)
if g is None:
return t
t = g
base = _kind_name(dtype(t))
if base in sctypes:
return sctypes[base][-1]
else:
return t
@set_module('numpy')
def issctype(rep):
"""
Determines whether the given object represents a scalar data-type.
Parameters
----------
rep : any
If `rep` is an instance of a scalar dtype, True is returned. If not,
False is returned.
Returns
-------
out : bool
Boolean result of check whether `rep` is a scalar dtype.
See Also
--------
issubsctype, issubdtype, obj2sctype, sctype2char
Examples
--------
>>> np.issctype(np.int32)
True
>>> np.issctype(list)
False
>>> np.issctype(1.1)
False
Strings are also a scalar type:
>>> np.issctype(np.dtype('str'))
True
"""
if not isinstance(rep, (type, dtype)):
return False
try:
res = obj2sctype(rep)
if res and res != object_:
return True
return False
except Exception:
return False
@set_module('numpy')
def obj2sctype(rep, default=None):
"""
Return the scalar dtype or NumPy equivalent of Python type of an object.
Parameters
----------
rep : any
The object of which the type is returned.
default : any, optional
If given, this is returned for objects whose types can not be
determined. If not given, None is returned for those objects.
Returns
-------
dtype : dtype or Python type
The data type of `rep`.
See Also
--------
sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
Examples
--------
>>> np.obj2sctype(np.int32)
<class 'numpy.int32'>
>>> np.obj2sctype(np.array([1., 2.]))
<class 'numpy.float64'>
>>> np.obj2sctype(np.array([1.j]))
<class 'numpy.complex128'>
>>> np.obj2sctype(dict)
<class 'numpy.object_'>
>>> np.obj2sctype('string')
>>> np.obj2sctype(1, default=list)
<class 'list'>
"""
# prevent abstract classes being upcast
if isinstance(rep, type) and issubclass(rep, generic):
return rep
# extract dtype from arrays
if isinstance(rep, ndarray):
return rep.dtype.type
# fall back on dtype to convert
try:
res = dtype(rep)
except Exception:
return default
else:
return res.type
@set_module('numpy')
def issubclass_(arg1, arg2):
"""
Determine if a class is a subclass of a second class.
`issubclass_` is equivalent to the Python built-in ``issubclass``,
except that it returns False instead of raising a TypeError if one
of the arguments is not a class.
Parameters
----------
arg1 : class
Input class. True is returned if `arg1` is a subclass of `arg2`.
arg2 : class or tuple of classes.
Input class. If a tuple of classes, True is returned if `arg1` is a
subclass of any of the tuple elements.
Returns
-------
out : bool
Whether `arg1` is a subclass of `arg2` or not.
See Also
--------
issubsctype, issubdtype, issctype
Examples
--------
>>> np.issubclass_(np.int32, int)
False # True on Python 2.7
>>> np.issubclass_(np.int32, float)
False
"""
try:
return issubclass(arg1, arg2)
except TypeError:
return False
@set_module('numpy')
def issubsctype(arg1, arg2):
"""
Determine if the first argument is a subclass of the second argument.
Parameters
----------
arg1, arg2 : dtype or dtype specifier
Data-types.
Returns
-------
out : bool
The result.
See Also
--------
issctype, issubdtype, obj2sctype
Examples
--------
>>> np.issubsctype('S8', str)
False
>>> np.issubsctype(np.array([1]), int)
True
>>> np.issubsctype(np.array([1]), float)
False
"""
return issubclass(obj2sctype(arg1), obj2sctype(arg2))
@set_module('numpy')
def issubdtype(arg1, arg2):
"""
Returns True if first argument is a typecode lower/equal in type hierarchy.
Parameters
----------
arg1, arg2 : dtype_like
dtype or string representing a typecode.
Returns
-------
out : bool
See Also
--------
issubsctype, issubclass_
numpy.core.numerictypes : Overview of numpy type hierarchy.
Examples
--------
>>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype(np.float64, np.float32)
False
"""
if not issubclass_(arg1, generic):
arg1 = dtype(arg1).type
if not issubclass_(arg2, generic):
arg2_orig = arg2
arg2 = dtype(arg2).type
if not isinstance(arg2_orig, dtype):
# weird deprecated behaviour, that tried to infer np.floating from
# float, and similar less obvious things, such as np.generic from
# basestring
mro = arg2.mro()
arg2 = mro[1] if len(mro) > 1 else mro[0]
def type_repr(x):
""" Helper to produce clear error messages """
if not isinstance(x, type):
return repr(x)
elif issubclass(x, generic):
return "np.{}".format(x.__name__)
else:
return x.__name__
# 1.14, 2017-08-01
warnings.warn(
"Conversion of the second argument of issubdtype from `{raw}` "
"to `{abstract}` is deprecated. In future, it will be treated "
"as `{concrete} == np.dtype({raw}).type`.".format(
raw=type_repr(arg2_orig),
abstract=type_repr(arg2),
concrete=type_repr(dtype(arg2_orig).type)
),
FutureWarning, stacklevel=2
)
return issubclass(arg1, arg2)
# This dictionary allows look up based on any alias for an array data-type
class _typedict(dict):
"""
Base object for a dictionary for look-up with any alias for an array dtype.
Instances of `_typedict` can not be used as dictionaries directly,
first they have to be populated.
"""
def __getitem__(self, obj):
return dict.__getitem__(self, obj2sctype(obj))
nbytes = _typedict()
_alignment = _typedict()
_maxvals = _typedict()
_minvals = _typedict()
def _construct_lookups():
for name, info in _concrete_typeinfo.items():
obj = info.type
nbytes[obj] = info.bits // 8
_alignment[obj] = info.alignment
if len(info) > 5:
_maxvals[obj] = info.max
_minvals[obj] = info.min
else:
_maxvals[obj] = None
_minvals[obj] = None
_construct_lookups()
@set_module('numpy')
def sctype2char(sctype):
"""
Return the string representation of a scalar dtype.
Parameters
----------
sctype : scalar dtype or object
If a scalar dtype, the corresponding string character is
returned. If an object, `sctype2char` tries to infer its scalar type
and then return the corresponding string character.
Returns
-------
typechar : str
The string character corresponding to the scalar type.
Raises
------
ValueError
If `sctype` is an object for which the type can not be inferred.
See Also
--------
obj2sctype, issctype, issubsctype, mintypecode
Examples
--------
>>> for sctype in [np.int32, np.double, np.complex_, np.string_, np.ndarray]:
... print(np.sctype2char(sctype))
l # may vary
d
D
S
O
>>> x = np.array([1., 2-1.j])
>>> np.sctype2char(x)
'D'
>>> np.sctype2char(list)
'O'
"""
sctype = obj2sctype(sctype)
if sctype is None:
raise ValueError("unrecognized type")
if sctype not in _concrete_types:
# for compatibility
raise KeyError(sctype)
return dtype(sctype).char
# Create dictionary of casting functions that wrap sequences
# indexed by type or type character
cast = _typedict()
for key in _concrete_types:
cast[key] = lambda x, k=key: array(x, copy=False).astype(k)
try:
ScalarType = [_types.IntType, _types.FloatType, _types.ComplexType,
_types.LongType, _types.BooleanType,
_types.StringType, _types.UnicodeType, _types.BufferType]
except AttributeError:
# Py3K
ScalarType = [int, float, complex, int, bool, bytes, str, memoryview]
ScalarType.extend(_concrete_types)
ScalarType = tuple(ScalarType)
# Now add the types we've determined to this module
for key in allTypes:
globals()[key] = allTypes[key]
__all__.append(key)
del key
typecodes = {'Character':'c',
'Integer':'bhilqp',
'UnsignedInteger':'BHILQP',
'Float':'efdg',
'Complex':'FDG',
'AllInteger':'bBhHiIlLqQpP',
'AllFloat':'efdgFDG',
'Datetime': 'Mm',
'All':'?bhilqpBHILQPefdgFDGSUVOMm'}
# backwards compatibility --- deprecated name
typeDict = sctypeDict
typeNA = sctypeNA
# b -> boolean
# u -> unsigned integer
# i -> signed integer
# f -> floating point
# c -> complex
# M -> datetime
# m -> timedelta
# S -> string
# U -> Unicode string
# V -> record
# O -> Python object
_kind_list = ['b', 'u', 'i', 'f', 'c', 'S', 'U', 'V', 'O', 'M', 'm']
__test_types = '?'+typecodes['AllInteger'][:-2]+typecodes['AllFloat']+'O'
__len_test_types = len(__test_types)
# Keep incrementing until a common type both can be coerced to
# is found. Otherwise, return None
def _find_common_coerce(a, b):
if a > b:
return a
try:
thisind = __test_types.index(a.char)
except ValueError:
return None
return _can_coerce_all([a, b], start=thisind)
# Find a data-type that all data-types in a list can be coerced to
def _can_coerce_all(dtypelist, start=0):
N = len(dtypelist)
if N == 0:
return None
if N == 1:
return dtypelist[0]
thisind = start
while thisind < __len_test_types:
newdtype = dtype(__test_types[thisind])
numcoerce = len([x for x in dtypelist if newdtype >= x])
if numcoerce == N:
return newdtype
thisind += 1
return None
def _register_types():
numbers.Integral.register(integer)
numbers.Complex.register(inexact)
numbers.Real.register(floating)
numbers.Number.register(number)
_register_types()
@set_module('numpy')
def find_common_type(array_types, scalar_types):
"""
Determine common type following standard coercion rules.
Parameters
----------
array_types : sequence
A list of dtypes or dtype convertible objects representing arrays.
scalar_types : sequence
A list of dtypes or dtype convertible objects representing scalars.
Returns
-------
datatype : dtype
The common data type, which is the maximum of `array_types` ignoring
`scalar_types`, unless the maximum of `scalar_types` is of a
different kind (`dtype.kind`). If the kind is not understood, then
None is returned.
See Also
--------
dtype, common_type, can_cast, mintypecode
Examples
--------
>>> np.find_common_type([], [np.int64, np.float32, complex])
dtype('complex128')
>>> np.find_common_type([np.int64, np.float32], [])
dtype('float64')
The standard casting rules ensure that a scalar cannot up-cast an
array unless the scalar is of a fundamentally different kind of data
(i.e. under a different hierarchy in the data type hierarchy) then
the array:
>>> np.find_common_type([np.float32], [np.int64, np.float64])
dtype('float32')
Complex is of a different type, so it up-casts the float in the
`array_types` argument:
>>> np.find_common_type([np.float32], [complex])
dtype('complex128')
Type specifier strings are convertible to dtypes and can therefore
be used instead of dtypes:
>>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
dtype('complex128')
"""
array_types = [dtype(x) for x in array_types]
scalar_types = [dtype(x) for x in scalar_types]
maxa = _can_coerce_all(array_types)
maxsc = _can_coerce_all(scalar_types)
if maxa is None:
return maxsc
if maxsc is None:
return maxa
try:
index_a = _kind_list.index(maxa.kind)
index_sc = _kind_list.index(maxsc.kind)
except ValueError:
return None
if index_sc > index_a:
return _find_common_coerce(maxsc, maxa)
else:
return maxa

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"""Implementation of __array_function__ overrides from NEP-18."""
import collections
import functools
import os
import textwrap
from numpy.core._multiarray_umath import (
add_docstring, implement_array_function, _get_implementing_args)
from numpy.compat._inspect import getargspec
ARRAY_FUNCTION_ENABLED = bool(
int(os.environ.get('NUMPY_EXPERIMENTAL_ARRAY_FUNCTION', 1)))
add_docstring(
implement_array_function,
"""
Implement a function with checks for __array_function__ overrides.
All arguments are required, and can only be passed by position.
Arguments
---------
implementation : function
Function that implements the operation on NumPy array without
overrides when called like ``implementation(*args, **kwargs)``.
public_api : function
Function exposed by NumPy's public API originally called like
``public_api(*args, **kwargs)`` on which arguments are now being
checked.
relevant_args : iterable
Iterable of arguments to check for __array_function__ methods.
args : tuple
Arbitrary positional arguments originally passed into ``public_api``.
kwargs : dict
Arbitrary keyword arguments originally passed into ``public_api``.
Returns
-------
Result from calling ``implementation()`` or an ``__array_function__``
method, as appropriate.
Raises
------
TypeError : if no implementation is found.
""")
# exposed for testing purposes; used internally by implement_array_function
add_docstring(
_get_implementing_args,
"""
Collect arguments on which to call __array_function__.
Parameters
----------
relevant_args : iterable of array-like
Iterable of possibly array-like arguments to check for
__array_function__ methods.
Returns
-------
Sequence of arguments with __array_function__ methods, in the order in
which they should be called.
""")
ArgSpec = collections.namedtuple('ArgSpec', 'args varargs keywords defaults')
def verify_matching_signatures(implementation, dispatcher):
"""Verify that a dispatcher function has the right signature."""
implementation_spec = ArgSpec(*getargspec(implementation))
dispatcher_spec = ArgSpec(*getargspec(dispatcher))
if (implementation_spec.args != dispatcher_spec.args or
implementation_spec.varargs != dispatcher_spec.varargs or
implementation_spec.keywords != dispatcher_spec.keywords or
(bool(implementation_spec.defaults) !=
bool(dispatcher_spec.defaults)) or
(implementation_spec.defaults is not None and
len(implementation_spec.defaults) !=
len(dispatcher_spec.defaults))):
raise RuntimeError('implementation and dispatcher for %s have '
'different function signatures' % implementation)
if implementation_spec.defaults is not None:
if dispatcher_spec.defaults != (None,) * len(dispatcher_spec.defaults):
raise RuntimeError('dispatcher functions can only use None for '
'default argument values')
def set_module(module):
"""Decorator for overriding __module__ on a function or class.
Example usage::
@set_module('numpy')
def example():
pass
assert example.__module__ == 'numpy'
"""
def decorator(func):
if module is not None:
func.__module__ = module
return func
return decorator
# Call textwrap.dedent here instead of in the function so as to avoid
# calling dedent multiple times on the same text
_wrapped_func_source = textwrap.dedent("""
@functools.wraps(implementation)
def {name}(*args, **kwargs):
relevant_args = dispatcher(*args, **kwargs)
return implement_array_function(
implementation, {name}, relevant_args, args, kwargs)
""")
def array_function_dispatch(dispatcher, module=None, verify=True,
docs_from_dispatcher=False):
"""Decorator for adding dispatch with the __array_function__ protocol.
See NEP-18 for example usage.
Parameters
----------
dispatcher : callable
Function that when called like ``dispatcher(*args, **kwargs)`` with
arguments from the NumPy function call returns an iterable of
array-like arguments to check for ``__array_function__``.
module : str, optional
__module__ attribute to set on new function, e.g., ``module='numpy'``.
By default, module is copied from the decorated function.
verify : bool, optional
If True, verify the that the signature of the dispatcher and decorated
function signatures match exactly: all required and optional arguments
should appear in order with the same names, but the default values for
all optional arguments should be ``None``. Only disable verification
if the dispatcher's signature needs to deviate for some particular
reason, e.g., because the function has a signature like
``func(*args, **kwargs)``.
docs_from_dispatcher : bool, optional
If True, copy docs from the dispatcher function onto the dispatched
function, rather than from the implementation. This is useful for
functions defined in C, which otherwise don't have docstrings.
Returns
-------
Function suitable for decorating the implementation of a NumPy function.
"""
if not ARRAY_FUNCTION_ENABLED:
def decorator(implementation):
if docs_from_dispatcher:
add_docstring(implementation, dispatcher.__doc__)
if module is not None:
implementation.__module__ = module
return implementation
return decorator
def decorator(implementation):
if verify:
verify_matching_signatures(implementation, dispatcher)
if docs_from_dispatcher:
add_docstring(implementation, dispatcher.__doc__)
# Equivalently, we could define this function directly instead of using
# exec. This version has the advantage of giving the helper function a
# more interpettable name. Otherwise, the original function does not
# show up at all in many cases, e.g., if it's written in C or if the
# dispatcher gets an invalid keyword argument.
source = _wrapped_func_source.format(name=implementation.__name__)
source_object = compile(
source, filename='<__array_function__ internals>', mode='exec')
scope = {
'implementation': implementation,
'dispatcher': dispatcher,
'functools': functools,
'implement_array_function': implement_array_function,
}
exec(source_object, scope)
public_api = scope[implementation.__name__]
if module is not None:
public_api.__module__ = module
public_api._implementation = implementation
return public_api
return decorator
def array_function_from_dispatcher(
implementation, module=None, verify=True, docs_from_dispatcher=True):
"""Like array_function_dispatcher, but with function arguments flipped."""
def decorator(dispatcher):
return array_function_dispatch(
dispatcher, module, verify=verify,
docs_from_dispatcher=docs_from_dispatcher)(implementation)
return decorator

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"""
Record Arrays
=============
Record arrays expose the fields of structured arrays as properties.
Most commonly, ndarrays contain elements of a single type, e.g. floats,
integers, bools etc. However, it is possible for elements to be combinations
of these using structured types, such as::
>>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', np.int64), ('y', np.float64)])
>>> a
array([(1, 2.), (1, 2.)], dtype=[('x', '<i8'), ('y', '<f8')])
Here, each element consists of two fields: x (and int), and y (a float).
This is known as a structured array. The different fields are analogous
to columns in a spread-sheet. The different fields can be accessed as
one would a dictionary::
>>> a['x']
array([1, 1])
>>> a['y']
array([2., 2.])
Record arrays allow us to access fields as properties::
>>> ar = np.rec.array(a)
>>> ar.x
array([1, 1])
>>> ar.y
array([2., 2.])
"""
from __future__ import division, absolute_import, print_function
import sys
import os
import warnings
from collections import Counter, OrderedDict
from . import numeric as sb
from . import numerictypes as nt
from numpy.compat import (
isfileobj, bytes, long, unicode, os_fspath, contextlib_nullcontext
)
from numpy.core.overrides import set_module
from .arrayprint import get_printoptions
# All of the functions allow formats to be a dtype
__all__ = ['record', 'recarray', 'format_parser']
ndarray = sb.ndarray
_byteorderconv = {'b':'>',
'l':'<',
'n':'=',
'B':'>',
'L':'<',
'N':'=',
'S':'s',
's':'s',
'>':'>',
'<':'<',
'=':'=',
'|':'|',
'I':'|',
'i':'|'}
# formats regular expression
# allows multidimension spec with a tuple syntax in front
# of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 '
# are equally allowed
numfmt = nt.typeDict
# taken from OrderedDict recipes in the Python documentation
# https://docs.python.org/3.3/library/collections.html#ordereddict-examples-and-recipes
class _OrderedCounter(Counter, OrderedDict):
"""Counter that remembers the order elements are first encountered"""
def __repr__(self):
return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))
def __reduce__(self):
return self.__class__, (OrderedDict(self),)
def find_duplicate(list):
"""Find duplication in a list, return a list of duplicated elements"""
return [
item
for item, counts in _OrderedCounter(list).items()
if counts > 1
]
@set_module('numpy')
class format_parser(object):
"""
Class to convert formats, names, titles description to a dtype.
After constructing the format_parser object, the dtype attribute is
the converted data-type:
``dtype = format_parser(formats, names, titles).dtype``
Attributes
----------
dtype : dtype
The converted data-type.
Parameters
----------
formats : str or list of str
The format description, either specified as a string with
comma-separated format descriptions in the form ``'f8, i4, a5'``, or
a list of format description strings in the form
``['f8', 'i4', 'a5']``.
names : str or list/tuple of str
The field names, either specified as a comma-separated string in the
form ``'col1, col2, col3'``, or as a list or tuple of strings in the
form ``['col1', 'col2', 'col3']``.
An empty list can be used, in that case default field names
('f0', 'f1', ...) are used.
titles : sequence
Sequence of title strings. An empty list can be used to leave titles
out.
aligned : bool, optional
If True, align the fields by padding as the C-compiler would.
Default is False.
byteorder : str, optional
If specified, all the fields will be changed to the
provided byte-order. Otherwise, the default byte-order is
used. For all available string specifiers, see `dtype.newbyteorder`.
See Also
--------
dtype, typename, sctype2char
Examples
--------
>>> np.format_parser(['<f8', '<i4', '<a5'], ['col1', 'col2', 'col3'],
... ['T1', 'T2', 'T3']).dtype
dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), 'S5')])
`names` and/or `titles` can be empty lists. If `titles` is an empty list,
titles will simply not appear. If `names` is empty, default field names
will be used.
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... []).dtype
dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '<S5')])
>>> np.format_parser(['<f8', '<i4', '<a5'], [], []).dtype
dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', 'S5')])
"""
def __init__(self, formats, names, titles, aligned=False, byteorder=None):
self._parseFormats(formats, aligned)
self._setfieldnames(names, titles)
self._createdescr(byteorder)
self.dtype = self._descr
def _parseFormats(self, formats, aligned=False):
""" Parse the field formats """
if formats is None:
raise ValueError("Need formats argument")
if isinstance(formats, list):
dtype = sb.dtype(
[('f{}'.format(i), format_) for i, format_ in enumerate(formats)],
aligned,
)
else:
dtype = sb.dtype(formats, aligned)
fields = dtype.fields
if fields is None:
dtype = sb.dtype([('f1', dtype)], aligned)
fields = dtype.fields
keys = dtype.names
self._f_formats = [fields[key][0] for key in keys]
self._offsets = [fields[key][1] for key in keys]
self._nfields = len(keys)
def _setfieldnames(self, names, titles):
"""convert input field names into a list and assign to the _names
attribute """
if (names):
if (type(names) in [list, tuple]):
pass
elif isinstance(names, (str, unicode)):
names = names.split(',')
else:
raise NameError("illegal input names %s" % repr(names))
self._names = [n.strip() for n in names[:self._nfields]]
else:
self._names = []
# if the names are not specified, they will be assigned as
# "f0, f1, f2,..."
# if not enough names are specified, they will be assigned as "f[n],
# f[n+1],..." etc. where n is the number of specified names..."
self._names += ['f%d' % i for i in range(len(self._names),
self._nfields)]
# check for redundant names
_dup = find_duplicate(self._names)
if _dup:
raise ValueError("Duplicate field names: %s" % _dup)
if (titles):
self._titles = [n.strip() for n in titles[:self._nfields]]
else:
self._titles = []
titles = []
if (self._nfields > len(titles)):
self._titles += [None] * (self._nfields - len(titles))
def _createdescr(self, byteorder):
descr = sb.dtype({'names':self._names,
'formats':self._f_formats,
'offsets':self._offsets,
'titles':self._titles})
if (byteorder is not None):
byteorder = _byteorderconv[byteorder[0]]
descr = descr.newbyteorder(byteorder)
self._descr = descr
class record(nt.void):
"""A data-type scalar that allows field access as attribute lookup.
"""
# manually set name and module so that this class's type shows up
# as numpy.record when printed
__name__ = 'record'
__module__ = 'numpy'
def __repr__(self):
if get_printoptions()['legacy'] == '1.13':
return self.__str__()
return super(record, self).__repr__()
def __str__(self):
if get_printoptions()['legacy'] == '1.13':
return str(self.item())
return super(record, self).__str__()
def __getattribute__(self, attr):
if attr in ['setfield', 'getfield', 'dtype']:
return nt.void.__getattribute__(self, attr)
try:
return nt.void.__getattribute__(self, attr)
except AttributeError:
pass
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
obj = self.getfield(*res[:2])
# if it has fields return a record,
# otherwise return the object
try:
dt = obj.dtype
except AttributeError:
#happens if field is Object type
return obj
if dt.names is not None:
return obj.view((self.__class__, obj.dtype))
return obj
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __setattr__(self, attr, val):
if attr in ['setfield', 'getfield', 'dtype']:
raise AttributeError("Cannot set '%s' attribute" % attr)
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
return self.setfield(val, *res[:2])
else:
if getattr(self, attr, None):
return nt.void.__setattr__(self, attr, val)
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __getitem__(self, indx):
obj = nt.void.__getitem__(self, indx)
# copy behavior of record.__getattribute__,
if isinstance(obj, nt.void) and obj.dtype.names is not None:
return obj.view((self.__class__, obj.dtype))
else:
# return a single element
return obj
def pprint(self):
"""Pretty-print all fields."""
# pretty-print all fields
names = self.dtype.names
maxlen = max(len(name) for name in names)
fmt = '%% %ds: %%s' % maxlen
rows = [fmt % (name, getattr(self, name)) for name in names]
return "\n".join(rows)
# The recarray is almost identical to a standard array (which supports
# named fields already) The biggest difference is that it can use
# attribute-lookup to find the fields and it is constructed using
# a record.
# If byteorder is given it forces a particular byteorder on all
# the fields (and any subfields)
class recarray(ndarray):
"""Construct an ndarray that allows field access using attributes.
Arrays may have a data-types containing fields, analogous
to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,
where each entry in the array is a pair of ``(int, float)``. Normally,
these attributes are accessed using dictionary lookups such as ``arr['x']``
and ``arr['y']``. Record arrays allow the fields to be accessed as members
of the array, using ``arr.x`` and ``arr.y``.
Parameters
----------
shape : tuple
Shape of output array.
dtype : data-type, optional
The desired data-type. By default, the data-type is determined
from `formats`, `names`, `titles`, `aligned` and `byteorder`.
formats : list of data-types, optional
A list containing the data-types for the different columns, e.g.
``['i4', 'f8', 'i4']``. `formats` does *not* support the new
convention of using types directly, i.e. ``(int, float, int)``.
Note that `formats` must be a list, not a tuple.
Given that `formats` is somewhat limited, we recommend specifying
`dtype` instead.
names : tuple of str, optional
The name of each column, e.g. ``('x', 'y', 'z')``.
buf : buffer, optional
By default, a new array is created of the given shape and data-type.
If `buf` is specified and is an object exposing the buffer interface,
the array will use the memory from the existing buffer. In this case,
the `offset` and `strides` keywords are available.
Other Parameters
----------------
titles : tuple of str, optional
Aliases for column names. For example, if `names` were
``('x', 'y', 'z')`` and `titles` is
``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then
``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.
byteorder : {'<', '>', '='}, optional
Byte-order for all fields.
aligned : bool, optional
Align the fields in memory as the C-compiler would.
strides : tuple of ints, optional
Buffer (`buf`) is interpreted according to these strides (strides
define how many bytes each array element, row, column, etc.
occupy in memory).
offset : int, optional
Start reading buffer (`buf`) from this offset onwards.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Returns
-------
rec : recarray
Empty array of the given shape and type.
See Also
--------
rec.fromrecords : Construct a record array from data.
record : fundamental data-type for `recarray`.
format_parser : determine a data-type from formats, names, titles.
Notes
-----
This constructor can be compared to ``empty``: it creates a new record
array but does not fill it with data. To create a record array from data,
use one of the following methods:
1. Create a standard ndarray and convert it to a record array,
using ``arr.view(np.recarray)``
2. Use the `buf` keyword.
3. Use `np.rec.fromrecords`.
Examples
--------
Create an array with two fields, ``x`` and ``y``:
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i8')])
>>> x
array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')])
>>> x['x']
array([1., 3.])
View the array as a record array:
>>> x = x.view(np.recarray)
>>> x.x
array([1., 3.])
>>> x.y
array([2, 4])
Create a new, empty record array:
>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
"""
# manually set name and module so that this class's type shows
# up as "numpy.recarray" when printed
__name__ = 'recarray'
__module__ = 'numpy'
def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None,
formats=None, names=None, titles=None,
byteorder=None, aligned=False, order='C'):
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
if buf is None:
self = ndarray.__new__(subtype, shape, (record, descr), order=order)
else:
self = ndarray.__new__(subtype, shape, (record, descr),
buffer=buf, offset=offset,
strides=strides, order=order)
return self
def __array_finalize__(self, obj):
if self.dtype.type is not record and self.dtype.names is not None:
# if self.dtype is not np.record, invoke __setattr__ which will
# convert it to a record if it is a void dtype.
self.dtype = self.dtype
def __getattribute__(self, attr):
# See if ndarray has this attr, and return it if so. (note that this
# means a field with the same name as an ndarray attr cannot be
# accessed by attribute).
try:
return object.__getattribute__(self, attr)
except AttributeError: # attr must be a fieldname
pass
# look for a field with this name
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("recarray has no attribute %s" % attr)
obj = self.getfield(*res)
# At this point obj will always be a recarray, since (see
# PyArray_GetField) the type of obj is inherited. Next, if obj.dtype is
# non-structured, convert it to an ndarray. Then if obj is structured
# with void type convert it to the same dtype.type (eg to preserve
# numpy.record type if present), since nested structured fields do not
# inherit type. Don't do this for non-void structures though.
if obj.dtype.names is not None:
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(ndarray)
# Save the dictionary.
# If the attr is a field name and not in the saved dictionary
# Undo any "setting" of the attribute and do a setfield
# Thus, you can't create attributes on-the-fly that are field names.
def __setattr__(self, attr, val):
# Automatically convert (void) structured types to records
# (but not non-void structures, subarrays, or non-structured voids)
if attr == 'dtype' and issubclass(val.type, nt.void) and val.names is not None:
val = sb.dtype((record, val))
newattr = attr not in self.__dict__
try:
ret = object.__setattr__(self, attr, val)
except Exception:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
raise
else:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except Exception:
return ret
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("record array has no attribute %s" % attr)
return self.setfield(val, *res)
def __getitem__(self, indx):
obj = super(recarray, self).__getitem__(indx)
# copy behavior of getattr, except that here
# we might also be returning a single element
if isinstance(obj, ndarray):
if obj.dtype.names is not None:
obj = obj.view(type(self))
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(type=ndarray)
else:
# return a single element
return obj
def __repr__(self):
repr_dtype = self.dtype
if (self.dtype.type is record
or (not issubclass(self.dtype.type, nt.void))):
# If this is a full record array (has numpy.record dtype),
# or if it has a scalar (non-void) dtype with no records,
# represent it using the rec.array function. Since rec.array
# converts dtype to a numpy.record for us, convert back
# to non-record before printing
if repr_dtype.type is record:
repr_dtype = sb.dtype((nt.void, repr_dtype))
prefix = "rec.array("
fmt = 'rec.array(%s,%sdtype=%s)'
else:
# otherwise represent it using np.array plus a view
# This should only happen if the user is playing
# strange games with dtypes.
prefix = "array("
fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
# get data/shape string. logic taken from numeric.array_repr
if self.size > 0 or self.shape == (0,):
lst = sb.array2string(
self, separator=', ', prefix=prefix, suffix=',')
else:
# show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(self.shape),)
lf = '\n'+' '*len(prefix)
if get_printoptions()['legacy'] == '1.13':
lf = ' ' + lf # trailing space
return fmt % (lst, lf, repr_dtype)
def field(self, attr, val=None):
if isinstance(attr, int):
names = ndarray.__getattribute__(self, 'dtype').names
attr = names[attr]
fielddict = ndarray.__getattribute__(self, 'dtype').fields
res = fielddict[attr][:2]
if val is None:
obj = self.getfield(*res)
if obj.dtype.names is not None:
return obj
return obj.view(ndarray)
else:
return self.setfield(val, *res)
def fromarrays(arrayList, dtype=None, shape=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
""" create a record array from a (flat) list of arrays
>>> x1=np.array([1,2,3,4])
>>> x2=np.array(['a','dd','xyz','12'])
>>> x3=np.array([1.1,2,3,4])
>>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
>>> print(r[1])
(2, 'dd', 2.0) # may vary
>>> x1[1]=34
>>> r.a
array([1, 2, 3, 4])
"""
arrayList = [sb.asarray(x) for x in arrayList]
if shape is None or shape == 0:
shape = arrayList[0].shape
if isinstance(shape, int):
shape = (shape,)
if formats is None and dtype is None:
# go through each object in the list to see if it is an ndarray
# and determine the formats.
formats = []
for obj in arrayList:
formats.append(obj.dtype)
if dtype is not None:
descr = sb.dtype(dtype)
_names = descr.names
else:
parsed = format_parser(formats, names, titles, aligned, byteorder)
_names = parsed._names
descr = parsed._descr
# Determine shape from data-type.
if len(descr) != len(arrayList):
raise ValueError("mismatch between the number of fields "
"and the number of arrays")
d0 = descr[0].shape
nn = len(d0)
if nn > 0:
shape = shape[:-nn]
for k, obj in enumerate(arrayList):
nn = descr[k].ndim
testshape = obj.shape[:obj.ndim - nn]
if testshape != shape:
raise ValueError("array-shape mismatch in array %d" % k)
_array = recarray(shape, descr)
# populate the record array (makes a copy)
for i in range(len(arrayList)):
_array[_names[i]] = arrayList[i]
return _array
def fromrecords(recList, dtype=None, shape=None, formats=None, names=None,
titles=None, aligned=False, byteorder=None):
""" create a recarray from a list of records in text form
The data in the same field can be heterogeneous, they will be promoted
to the highest data type. This method is intended for creating
smaller record arrays. If used to create large array without formats
defined
r=fromrecords([(2,3.,'abc')]*100000)
it can be slow.
If formats is None, then this will auto-detect formats. Use list of
tuples rather than list of lists for faster processing.
>>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
... names='col1,col2,col3')
>>> print(r[0])
(456, 'dbe', 1.2)
>>> r.col1
array([456, 2])
>>> r.col2
array(['dbe', 'de'], dtype='<U3')
>>> import pickle
>>> pickle.loads(pickle.dumps(r))
rec.array([(456, 'dbe', 1.2), ( 2, 'de', 1.3)],
dtype=[('col1', '<i8'), ('col2', '<U3'), ('col3', '<f8')])
"""
if formats is None and dtype is None: # slower
obj = sb.array(recList, dtype=object)
arrlist = [sb.array(obj[..., i].tolist()) for i in range(obj.shape[-1])]
return fromarrays(arrlist, formats=formats, shape=shape, names=names,
titles=titles, aligned=aligned, byteorder=byteorder)
if dtype is not None:
descr = sb.dtype((record, dtype))
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
try:
retval = sb.array(recList, dtype=descr)
except (TypeError, ValueError):
if (shape is None or shape == 0):
shape = len(recList)
if isinstance(shape, (int, long)):
shape = (shape,)
if len(shape) > 1:
raise ValueError("Can only deal with 1-d array.")
_array = recarray(shape, descr)
for k in range(_array.size):
_array[k] = tuple(recList[k])
# list of lists instead of list of tuples ?
# 2018-02-07, 1.14.1
warnings.warn(
"fromrecords expected a list of tuples, may have received a list "
"of lists instead. In the future that will raise an error",
FutureWarning, stacklevel=2)
return _array
else:
if shape is not None and retval.shape != shape:
retval.shape = shape
res = retval.view(recarray)
return res
def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
""" create a (read-only) record array from binary data contained in
a string"""
if dtype is None and formats is None:
raise TypeError("fromstring() needs a 'dtype' or 'formats' argument")
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
if (shape is None or shape == 0 or shape == -1):
shape = (len(datastring) - offset) // itemsize
_array = recarray(shape, descr, buf=datastring, offset=offset)
return _array
def get_remaining_size(fd):
try:
fn = fd.fileno()
except AttributeError:
return os.path.getsize(fd.name) - fd.tell()
st = os.fstat(fn)
size = st.st_size - fd.tell()
return size
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create an array from binary file data
If file is a string or a path-like object then that file is opened,
else it is assumed to be a file object. The file object must
support random access (i.e. it must have tell and seek methods).
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> _ = fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if dtype is None and formats is None:
raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")
if (shape is None or shape == 0):
shape = (-1,)
elif isinstance(shape, (int, long)):
shape = (shape,)
if isfileobj(fd):
# file already opened
ctx = contextlib_nullcontext(fd)
else:
# open file
ctx = open(os_fspath(fd), 'rb')
with ctx as fd:
if (offset > 0):
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod(dtype=nt.intp)
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size // -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod(dtype=nt.intp)
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise IOError("Didn't read as many bytes as expected")
return _array
def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None, copy=True):
"""Construct a record array from a wide-variety of objects.
"""
if ((isinstance(obj, (type(None), str)) or isfileobj(obj)) and
(formats is None) and (dtype is None)):
raise ValueError("Must define formats (or dtype) if object is "
"None, string, or an open file")
kwds = {}
if dtype is not None:
dtype = sb.dtype(dtype)
elif formats is not None:
dtype = format_parser(formats, names, titles,
aligned, byteorder)._descr
else:
kwds = {'formats': formats,
'names': names,
'titles': titles,
'aligned': aligned,
'byteorder': byteorder
}
if obj is None:
if shape is None:
raise ValueError("Must define a shape if obj is None")
return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)
elif isinstance(obj, bytes):
return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)
elif isinstance(obj, (list, tuple)):
if isinstance(obj[0], (tuple, list)):
return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
else:
return fromarrays(obj, dtype=dtype, shape=shape, **kwds)
elif isinstance(obj, recarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new
elif isfileobj(obj):
return fromfile(obj, dtype=dtype, shape=shape, offset=offset)
elif isinstance(obj, ndarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new.view(recarray)
else:
interface = getattr(obj, "__array_interface__", None)
if interface is None or not isinstance(interface, dict):
raise ValueError("Unknown input type")
obj = sb.array(obj)
if dtype is not None and (obj.dtype != dtype):
obj = obj.view(dtype)
return obj.view(recarray)

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from __future__ import division, print_function
import os
import sys
import pickle
import copy
import warnings
import platform
import textwrap
from os.path import join
from numpy.distutils import log
from distutils.dep_util import newer
from distutils.sysconfig import get_config_var
from numpy._build_utils.apple_accelerate import (
uses_accelerate_framework, get_sgemv_fix
)
from numpy.compat import npy_load_module
from setup_common import *
# Set to True to enable relaxed strides checking. This (mostly) means
# that `strides[dim]` is ignored if `shape[dim] == 1` when setting flags.
NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0")
# Put NPY_RELAXED_STRIDES_DEBUG=1 in the environment if you want numpy to use a
# bogus value for affected strides in order to help smoke out bad stride usage
# when relaxed stride checking is enabled.
NPY_RELAXED_STRIDES_DEBUG = (os.environ.get('NPY_RELAXED_STRIDES_DEBUG', "0") != "0")
NPY_RELAXED_STRIDES_DEBUG = NPY_RELAXED_STRIDES_DEBUG and NPY_RELAXED_STRIDES_CHECKING
# XXX: ugly, we use a class to avoid calling twice some expensive functions in
# config.h/numpyconfig.h. I don't see a better way because distutils force
# config.h generation inside an Extension class, and as such sharing
# configuration information between extensions is not easy.
# Using a pickled-based memoize does not work because config_cmd is an instance
# method, which cPickle does not like.
#
# Use pickle in all cases, as cPickle is gone in python3 and the difference
# in time is only in build. -- Charles Harris, 2013-03-30
class CallOnceOnly(object):
def __init__(self):
self._check_types = None
self._check_ieee_macros = None
self._check_complex = None
def check_types(self, *a, **kw):
if self._check_types is None:
out = check_types(*a, **kw)
self._check_types = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_types))
return out
def check_ieee_macros(self, *a, **kw):
if self._check_ieee_macros is None:
out = check_ieee_macros(*a, **kw)
self._check_ieee_macros = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_ieee_macros))
return out
def check_complex(self, *a, **kw):
if self._check_complex is None:
out = check_complex(*a, **kw)
self._check_complex = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_complex))
return out
def pythonlib_dir():
"""return path where libpython* is."""
if sys.platform == 'win32':
return os.path.join(sys.prefix, "libs")
else:
return get_config_var('LIBDIR')
def is_npy_no_signal():
"""Return True if the NPY_NO_SIGNAL symbol must be defined in configuration
header."""
return sys.platform == 'win32'
def is_npy_no_smp():
"""Return True if the NPY_NO_SMP symbol must be defined in public
header (when SMP support cannot be reliably enabled)."""
# Perhaps a fancier check is in order here.
# so that threads are only enabled if there
# are actually multiple CPUS? -- but
# threaded code can be nice even on a single
# CPU so that long-calculating code doesn't
# block.
return 'NPY_NOSMP' in os.environ
def win32_checks(deflist):
from numpy.distutils.misc_util import get_build_architecture
a = get_build_architecture()
# Distutils hack on AMD64 on windows
print('BUILD_ARCHITECTURE: %r, os.name=%r, sys.platform=%r' %
(a, os.name, sys.platform))
if a == 'AMD64':
deflist.append('DISTUTILS_USE_SDK')
# On win32, force long double format string to be 'g', not
# 'Lg', since the MS runtime does not support long double whose
# size is > sizeof(double)
if a == "Intel" or a == "AMD64":
deflist.append('FORCE_NO_LONG_DOUBLE_FORMATTING')
def check_math_capabilities(config, moredefs, mathlibs):
def check_func(func_name):
return config.check_func(func_name, libraries=mathlibs,
decl=True, call=True)
def check_funcs_once(funcs_name):
decl = dict([(f, True) for f in funcs_name])
st = config.check_funcs_once(funcs_name, libraries=mathlibs,
decl=decl, call=decl)
if st:
moredefs.extend([(fname2def(f), 1) for f in funcs_name])
return st
def check_funcs(funcs_name):
# Use check_funcs_once first, and if it does not work, test func per
# func. Return success only if all the functions are available
if not check_funcs_once(funcs_name):
# Global check failed, check func per func
for f in funcs_name:
if check_func(f):
moredefs.append((fname2def(f), 1))
return 0
else:
return 1
#use_msvc = config.check_decl("_MSC_VER")
if not check_funcs_once(MANDATORY_FUNCS):
raise SystemError("One of the required function to build numpy is not"
" available (the list is %s)." % str(MANDATORY_FUNCS))
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
for f in OPTIONAL_STDFUNCS_MAYBE:
if config.check_decl(fname2def(f),
headers=["Python.h", "math.h"]):
OPTIONAL_STDFUNCS.remove(f)
check_funcs(OPTIONAL_STDFUNCS)
for h in OPTIONAL_HEADERS:
if config.check_func("", decl=False, call=False, headers=[h]):
h = h.replace(".", "_").replace(os.path.sep, "_")
moredefs.append((fname2def(h), 1))
for tup in OPTIONAL_INTRINSICS:
headers = None
if len(tup) == 2:
f, args, m = tup[0], tup[1], fname2def(tup[0])
elif len(tup) == 3:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[0])
else:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[3])
if config.check_func(f, decl=False, call=True, call_args=args,
headers=headers):
moredefs.append((m, 1))
for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES:
if config.check_gcc_function_attribute(dec, fn):
moredefs.append((fname2def(fn), 1))
for dec, fn, code, header in OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS:
if config.check_gcc_function_attribute_with_intrinsics(dec, fn, code,
header):
moredefs.append((fname2def(fn), 1))
for fn in OPTIONAL_VARIABLE_ATTRIBUTES:
if config.check_gcc_variable_attribute(fn):
m = fn.replace("(", "_").replace(")", "_")
moredefs.append((fname2def(m), 1))
# C99 functions: float and long double versions
check_funcs(C99_FUNCS_SINGLE)
check_funcs(C99_FUNCS_EXTENDED)
def check_complex(config, mathlibs):
priv = []
pub = []
try:
if os.uname()[0] == "Interix":
warnings.warn("Disabling broken complex support. See #1365", stacklevel=2)
return priv, pub
except Exception:
# os.uname not available on all platforms. blanket except ugly but safe
pass
# Check for complex support
st = config.check_header('complex.h')
if st:
priv.append(('HAVE_COMPLEX_H', 1))
pub.append(('NPY_USE_C99_COMPLEX', 1))
for t in C99_COMPLEX_TYPES:
st = config.check_type(t, headers=["complex.h"])
if st:
pub.append(('NPY_HAVE_%s' % type2def(t), 1))
def check_prec(prec):
flist = [f + prec for f in C99_COMPLEX_FUNCS]
decl = dict([(f, True) for f in flist])
if not config.check_funcs_once(flist, call=decl, decl=decl,
libraries=mathlibs):
for f in flist:
if config.check_func(f, call=True, decl=True,
libraries=mathlibs):
priv.append((fname2def(f), 1))
else:
priv.extend([(fname2def(f), 1) for f in flist])
check_prec('')
check_prec('f')
check_prec('l')
return priv, pub
def check_ieee_macros(config):
priv = []
pub = []
macros = []
def _add_decl(f):
priv.append(fname2def("decl_%s" % f))
pub.append('NPY_%s' % fname2def("decl_%s" % f))
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
_macros = ["isnan", "isinf", "signbit", "isfinite"]
for f in _macros:
py_symbol = fname2def("decl_%s" % f)
already_declared = config.check_decl(py_symbol,
headers=["Python.h", "math.h"])
if already_declared:
if config.check_macro_true(py_symbol,
headers=["Python.h", "math.h"]):
pub.append('NPY_%s' % fname2def("decl_%s" % f))
else:
macros.append(f)
# Normally, isnan and isinf are macro (C99), but some platforms only have
# func, or both func and macro version. Check for macro only, and define
# replacement ones if not found.
# Note: including Python.h is necessary because it modifies some math.h
# definitions
for f in macros:
st = config.check_decl(f, headers=["Python.h", "math.h"])
if st:
_add_decl(f)
return priv, pub
def check_types(config_cmd, ext, build_dir):
private_defines = []
public_defines = []
# Expected size (in number of bytes) for each type. This is an
# optimization: those are only hints, and an exhaustive search for the size
# is done if the hints are wrong.
expected = {'short': [2], 'int': [4], 'long': [8, 4],
'float': [4], 'double': [8], 'long double': [16, 12, 8],
'Py_intptr_t': [8, 4], 'PY_LONG_LONG': [8], 'long long': [8],
'off_t': [8, 4]}
# Check we have the python header (-dev* packages on Linux)
result = config_cmd.check_header('Python.h')
if not result:
python = 'python'
if '__pypy__' in sys.builtin_module_names:
python = 'pypy'
raise SystemError(
"Cannot compile 'Python.h'. Perhaps you need to "
"install {0}-dev|{0}-devel.".format(python))
res = config_cmd.check_header("endian.h")
if res:
private_defines.append(('HAVE_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_ENDIAN_H', 1))
res = config_cmd.check_header("sys/endian.h")
if res:
private_defines.append(('HAVE_SYS_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_SYS_ENDIAN_H', 1))
# Check basic types sizes
for type in ('short', 'int', 'long'):
res = config_cmd.check_decl("SIZEOF_%s" % sym2def(type), headers=["Python.h"])
if res:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), "SIZEOF_%s" % sym2def(type)))
else:
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
for type in ('float', 'double', 'long double'):
already_declared = config_cmd.check_decl("SIZEOF_%s" % sym2def(type),
headers=["Python.h"])
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
if not already_declared and not type == 'long double':
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# Compute size of corresponding complex type: used to check that our
# definition is binary compatible with C99 complex type (check done at
# build time in npy_common.h)
complex_def = "struct {%s __x; %s __y;}" % (type, type)
res = config_cmd.check_type_size(complex_def,
expected=[2 * x for x in expected[type]])
if res >= 0:
public_defines.append(('NPY_SIZEOF_COMPLEX_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % complex_def)
for type in ('Py_intptr_t', 'off_t'):
res = config_cmd.check_type_size(type, headers=["Python.h"],
library_dirs=[pythonlib_dir()],
expected=expected[type])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# We check declaration AND type because that's how distutils does it.
if config_cmd.check_decl('PY_LONG_LONG', headers=['Python.h']):
res = config_cmd.check_type_size('PY_LONG_LONG', headers=['Python.h'],
library_dirs=[pythonlib_dir()],
expected=expected['PY_LONG_LONG'])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'PY_LONG_LONG')
res = config_cmd.check_type_size('long long',
expected=expected['long long'])
if res >= 0:
#private_defines.append(('SIZEOF_%s' % sym2def('long long'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('long long'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'long long')
if not config_cmd.check_decl('CHAR_BIT', headers=['Python.h']):
raise RuntimeError(
"Config wo CHAR_BIT is not supported"
", please contact the maintainers")
return private_defines, public_defines
def check_mathlib(config_cmd):
# Testing the C math library
mathlibs = []
mathlibs_choices = [[], ['m'], ['cpml']]
mathlib = os.environ.get('MATHLIB')
if mathlib:
mathlibs_choices.insert(0, mathlib.split(','))
for libs in mathlibs_choices:
if config_cmd.check_func("exp", libraries=libs, decl=True, call=True):
mathlibs = libs
break
else:
raise EnvironmentError("math library missing; rerun "
"setup.py after setting the "
"MATHLIB env variable")
return mathlibs
def visibility_define(config):
"""Return the define value to use for NPY_VISIBILITY_HIDDEN (may be empty
string)."""
hide = '__attribute__((visibility("hidden")))'
if config.check_gcc_function_attribute(hide, 'hideme'):
return hide
else:
return ''
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration, dot_join
from numpy.distutils.system_info import get_info, dict_append
config = Configuration('core', parent_package, top_path)
local_dir = config.local_path
codegen_dir = join(local_dir, 'code_generators')
if is_released(config):
warnings.simplefilter('error', MismatchCAPIWarning)
# Check whether we have a mismatch between the set C API VERSION and the
# actual C API VERSION
check_api_version(C_API_VERSION, codegen_dir)
generate_umath_py = join(codegen_dir, 'generate_umath.py')
n = dot_join(config.name, 'generate_umath')
generate_umath = npy_load_module('_'.join(n.split('.')),
generate_umath_py, ('.py', 'U', 1))
header_dir = 'include/numpy' # this is relative to config.path_in_package
cocache = CallOnceOnly()
def generate_config_h(ext, build_dir):
target = join(build_dir, header_dir, 'config.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
moredefs, ignored = cocache.check_types(config_cmd, ext, build_dir)
# Check math library and C99 math funcs availability
mathlibs = check_mathlib(config_cmd)
moredefs.append(('MATHLIB', ','.join(mathlibs)))
check_math_capabilities(config_cmd, moredefs, mathlibs)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[0])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[0])
# Signal check
if is_npy_no_signal():
moredefs.append('__NPY_PRIVATE_NO_SIGNAL')
# Windows checks
if sys.platform == 'win32' or os.name == 'nt':
win32_checks(moredefs)
# C99 restrict keyword
moredefs.append(('NPY_RESTRICT', config_cmd.check_restrict()))
# Inline check
inline = config_cmd.check_inline()
# Use relaxed stride checking
if NPY_RELAXED_STRIDES_CHECKING:
moredefs.append(('NPY_RELAXED_STRIDES_CHECKING', 1))
# Use bogus stride debug aid when relaxed strides are enabled
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
# Get long double representation
rep = check_long_double_representation(config_cmd)
moredefs.append(('HAVE_LDOUBLE_%s' % rep, 1))
if check_for_right_shift_internal_compiler_error(config_cmd):
moredefs.append('NPY_DO_NOT_OPTIMIZE_LONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_ULONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_LONGLONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_ULONGLONG_right_shift')
# Py3K check
if sys.version_info[0] >= 3:
moredefs.append(('NPY_PY3K', 1))
# Generate the config.h file from moredefs
with open(target, 'w') as target_f:
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# define inline to our keyword, or nothing
target_f.write('#ifndef __cplusplus\n')
if inline == 'inline':
target_f.write('/* #undef inline */\n')
else:
target_f.write('#define inline %s\n' % inline)
target_f.write('#endif\n')
# add the guard to make sure config.h is never included directly,
# but always through npy_config.h
target_f.write(textwrap.dedent("""
#ifndef _NPY_NPY_CONFIG_H_
#error config.h should never be included directly, include npy_config.h instead
#endif
"""))
log.info('File: %s' % target)
with open(target) as target_f:
log.info(target_f.read())
log.info('EOF')
else:
mathlibs = []
with open(target) as target_f:
for line in target_f:
s = '#define MATHLIB'
if line.startswith(s):
value = line[len(s):].strip()
if value:
mathlibs.extend(value.split(','))
# Ugly: this can be called within a library and not an extension,
# in which case there is no libraries attributes (and none is
# needed).
if hasattr(ext, 'libraries'):
ext.libraries.extend(mathlibs)
incl_dir = os.path.dirname(target)
if incl_dir not in config.numpy_include_dirs:
config.numpy_include_dirs.append(incl_dir)
return target
def generate_numpyconfig_h(ext, build_dir):
"""Depends on config.h: generate_config_h has to be called before !"""
# put common include directory in build_dir on search path
# allows using code generation in headers headers
config.add_include_dirs(join(build_dir, "src", "common"))
config.add_include_dirs(join(build_dir, "src", "npymath"))
target = join(build_dir, header_dir, '_numpyconfig.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
ignored, moredefs = cocache.check_types(config_cmd, ext, build_dir)
if is_npy_no_signal():
moredefs.append(('NPY_NO_SIGNAL', 1))
if is_npy_no_smp():
moredefs.append(('NPY_NO_SMP', 1))
else:
moredefs.append(('NPY_NO_SMP', 0))
mathlibs = check_mathlib(config_cmd)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[1])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[1])
if NPY_RELAXED_STRIDES_CHECKING:
moredefs.append(('NPY_RELAXED_STRIDES_CHECKING', 1))
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
# Check whether we can use inttypes (C99) formats
if config_cmd.check_decl('PRIdPTR', headers=['inttypes.h']):
moredefs.append(('NPY_USE_C99_FORMATS', 1))
# visibility check
hidden_visibility = visibility_define(config_cmd)
moredefs.append(('NPY_VISIBILITY_HIDDEN', hidden_visibility))
# Add the C API/ABI versions
moredefs.append(('NPY_ABI_VERSION', '0x%.8X' % C_ABI_VERSION))
moredefs.append(('NPY_API_VERSION', '0x%.8X' % C_API_VERSION))
# Add moredefs to header
with open(target, 'w') as target_f:
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# Define __STDC_FORMAT_MACROS
target_f.write(textwrap.dedent("""
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif
"""))
# Dump the numpyconfig.h header to stdout
log.info('File: %s' % target)
with open(target) as target_f:
log.info(target_f.read())
log.info('EOF')
config.add_data_files((header_dir, target))
return target
def generate_api_func(module_name):
def generate_api(ext, build_dir):
script = join(codegen_dir, module_name + '.py')
sys.path.insert(0, codegen_dir)
try:
m = __import__(module_name)
log.info('executing %s', script)
h_file, c_file, doc_file = m.generate_api(os.path.join(build_dir, header_dir))
finally:
del sys.path[0]
config.add_data_files((header_dir, h_file),
(header_dir, doc_file))
return (h_file,)
return generate_api
generate_numpy_api = generate_api_func('generate_numpy_api')
generate_ufunc_api = generate_api_func('generate_ufunc_api')
config.add_include_dirs(join(local_dir, "src", "common"))
config.add_include_dirs(join(local_dir, "src"))
config.add_include_dirs(join(local_dir))
config.add_data_dir('include/numpy')
config.add_include_dirs(join('src', 'npymath'))
config.add_include_dirs(join('src', 'multiarray'))
config.add_include_dirs(join('src', 'umath'))
config.add_include_dirs(join('src', 'npysort'))
config.add_define_macros([("NPY_INTERNAL_BUILD", "1")]) # this macro indicates that Numpy build is in process
config.add_define_macros([("HAVE_NPY_CONFIG_H", "1")])
if sys.platform[:3] == "aix":
config.add_define_macros([("_LARGE_FILES", None)])
else:
config.add_define_macros([("_FILE_OFFSET_BITS", "64")])
config.add_define_macros([('_LARGEFILE_SOURCE', '1')])
config.add_define_macros([('_LARGEFILE64_SOURCE', '1')])
config.numpy_include_dirs.extend(config.paths('include'))
deps = [join('src', 'npymath', '_signbit.c'),
join('include', 'numpy', '*object.h'),
join(codegen_dir, 'genapi.py'),
]
#######################################################################
# npymath library #
#######################################################################
subst_dict = dict([("sep", os.path.sep), ("pkgname", "numpy.core")])
def get_mathlib_info(*args):
# Another ugly hack: the mathlib info is known once build_src is run,
# but we cannot use add_installed_pkg_config here either, so we only
# update the substitution dictionary during npymath build
config_cmd = config.get_config_cmd()
# Check that the toolchain works, to fail early if it doesn't
# (avoid late errors with MATHLIB which are confusing if the
# compiler does not work).
st = config_cmd.try_link('int main(void) { return 0;}')
if not st:
# rerun the failing command in verbose mode
config_cmd.compiler.verbose = True
config_cmd.try_link('int main(void) { return 0;}')
raise RuntimeError("Broken toolchain: cannot link a simple C program")
mlibs = check_mathlib(config_cmd)
posix_mlib = ' '.join(['-l%s' % l for l in mlibs])
msvc_mlib = ' '.join(['%s.lib' % l for l in mlibs])
subst_dict["posix_mathlib"] = posix_mlib
subst_dict["msvc_mathlib"] = msvc_mlib
npymath_sources = [join('src', 'npymath', 'npy_math_internal.h.src'),
join('src', 'npymath', 'npy_math.c'),
join('src', 'npymath', 'ieee754.c.src'),
join('src', 'npymath', 'npy_math_complex.c.src'),
join('src', 'npymath', 'halffloat.c')
]
# Must be true for CRT compilers but not MinGW/cygwin. See gh-9977.
# Intel and Clang also don't seem happy with /GL
is_msvc = (platform.platform().startswith('Windows') and
platform.python_compiler().startswith('MS'))
config.add_installed_library('npymath',
sources=npymath_sources + [get_mathlib_info],
install_dir='lib',
build_info={
'include_dirs' : [], # empty list required for creating npy_math_internal.h
'extra_compiler_args' : (['/GL-'] if is_msvc else []),
})
config.add_npy_pkg_config("npymath.ini.in", "lib/npy-pkg-config",
subst_dict)
config.add_npy_pkg_config("mlib.ini.in", "lib/npy-pkg-config",
subst_dict)
#######################################################################
# npysort library #
#######################################################################
# This library is created for the build but it is not installed
npysort_sources = [join('src', 'common', 'npy_sort.h.src'),
join('src', 'npysort', 'quicksort.c.src'),
join('src', 'npysort', 'mergesort.c.src'),
join('src', 'npysort', 'timsort.c.src'),
join('src', 'npysort', 'heapsort.c.src'),
join('src', 'npysort', 'radixsort.c.src'),
join('src', 'common', 'npy_partition.h.src'),
join('src', 'npysort', 'selection.c.src'),
join('src', 'common', 'npy_binsearch.h.src'),
join('src', 'npysort', 'binsearch.c.src'),
]
config.add_library('npysort',
sources=npysort_sources,
include_dirs=[])
#######################################################################
# multiarray_tests module #
#######################################################################
config.add_extension('_multiarray_tests',
sources=[join('src', 'multiarray', '_multiarray_tests.c.src'),
join('src', 'common', 'mem_overlap.c')],
depends=[join('src', 'common', 'mem_overlap.h'),
join('src', 'common', 'npy_extint128.h')],
libraries=['npymath'])
#######################################################################
# _multiarray_umath module - common part #
#######################################################################
common_deps = [
join('src', 'common', 'array_assign.h'),
join('src', 'common', 'binop_override.h'),
join('src', 'common', 'cblasfuncs.h'),
join('src', 'common', 'lowlevel_strided_loops.h'),
join('src', 'common', 'mem_overlap.h'),
join('src', 'common', 'npy_cblas.h'),
join('src', 'common', 'npy_config.h'),
join('src', 'common', 'npy_ctypes.h'),
join('src', 'common', 'npy_extint128.h'),
join('src', 'common', 'npy_import.h'),
join('src', 'common', 'npy_longdouble.h'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'common', 'ucsnarrow.h'),
join('src', 'common', 'ufunc_override.h'),
join('src', 'common', 'umathmodule.h'),
join('src', 'common', 'numpyos.h'),
]
common_src = [
join('src', 'common', 'array_assign.c'),
join('src', 'common', 'mem_overlap.c'),
join('src', 'common', 'npy_longdouble.c'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'common', 'ucsnarrow.c'),
join('src', 'common', 'ufunc_override.c'),
join('src', 'common', 'numpyos.c'),
]
if os.environ.get('NPY_USE_BLAS_ILP64', "0") != "0":
blas_info = get_info('blas_ilp64_opt', 2)
else:
blas_info = get_info('blas_opt', 0)
have_blas = blas_info and ('HAVE_CBLAS', None) in blas_info.get('define_macros', [])
if have_blas:
extra_info = blas_info
# These files are also in MANIFEST.in so that they are always in
# the source distribution independently of HAVE_CBLAS.
common_src.extend([join('src', 'common', 'cblasfuncs.c'),
join('src', 'common', 'python_xerbla.c'),
])
if uses_accelerate_framework(blas_info):
common_src.extend(get_sgemv_fix())
else:
extra_info = {}
#######################################################################
# _multiarray_umath module - multiarray part #
#######################################################################
multiarray_deps = [
join('src', 'multiarray', 'arrayobject.h'),
join('src', 'multiarray', 'arraytypes.h'),
join('src', 'multiarray', 'arrayfunction_override.h'),
join('src', 'multiarray', 'npy_buffer.h'),
join('src', 'multiarray', 'calculation.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'convert_datatype.h'),
join('src', 'multiarray', 'convert.h'),
join('src', 'multiarray', 'conversion_utils.h'),
join('src', 'multiarray', 'ctors.h'),
join('src', 'multiarray', 'descriptor.h'),
join('src', 'multiarray', 'dragon4.h'),
join('src', 'multiarray', 'getset.h'),
join('src', 'multiarray', 'hashdescr.h'),
join('src', 'multiarray', 'iterators.h'),
join('src', 'multiarray', 'mapping.h'),
join('src', 'multiarray', 'methods.h'),
join('src', 'multiarray', 'multiarraymodule.h'),
join('src', 'multiarray', 'nditer_impl.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'multiarray', 'refcount.h'),
join('src', 'multiarray', 'scalartypes.h'),
join('src', 'multiarray', 'sequence.h'),
join('src', 'multiarray', 'shape.h'),
join('src', 'multiarray', 'strfuncs.h'),
join('src', 'multiarray', 'typeinfo.h'),
join('src', 'multiarray', 'usertypes.h'),
join('src', 'multiarray', 'vdot.h'),
join('include', 'numpy', 'arrayobject.h'),
join('include', 'numpy', '_neighborhood_iterator_imp.h'),
join('include', 'numpy', 'npy_endian.h'),
join('include', 'numpy', 'arrayscalars.h'),
join('include', 'numpy', 'noprefix.h'),
join('include', 'numpy', 'npy_interrupt.h'),
join('include', 'numpy', 'npy_3kcompat.h'),
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('include', 'numpy', 'npy_common.h'),
join('include', 'numpy', 'npy_os.h'),
join('include', 'numpy', 'utils.h'),
join('include', 'numpy', 'ndarrayobject.h'),
join('include', 'numpy', 'npy_cpu.h'),
join('include', 'numpy', 'numpyconfig.h'),
join('include', 'numpy', 'ndarraytypes.h'),
join('include', 'numpy', 'npy_1_7_deprecated_api.h'),
# add library sources as distuils does not consider libraries
# dependencies
] + npysort_sources + npymath_sources
multiarray_src = [
join('src', 'multiarray', 'alloc.c'),
join('src', 'multiarray', 'arrayobject.c'),
join('src', 'multiarray', 'arraytypes.c.src'),
join('src', 'multiarray', 'array_assign_scalar.c'),
join('src', 'multiarray', 'array_assign_array.c'),
join('src', 'multiarray', 'arrayfunction_override.c'),
join('src', 'multiarray', 'buffer.c'),
join('src', 'multiarray', 'calculation.c'),
join('src', 'multiarray', 'compiled_base.c'),
join('src', 'multiarray', 'common.c'),
join('src', 'multiarray', 'convert.c'),
join('src', 'multiarray', 'convert_datatype.c'),
join('src', 'multiarray', 'conversion_utils.c'),
join('src', 'multiarray', 'ctors.c'),
join('src', 'multiarray', 'datetime.c'),
join('src', 'multiarray', 'datetime_strings.c'),
join('src', 'multiarray', 'datetime_busday.c'),
join('src', 'multiarray', 'datetime_busdaycal.c'),
join('src', 'multiarray', 'descriptor.c'),
join('src', 'multiarray', 'dragon4.c'),
join('src', 'multiarray', 'dtype_transfer.c'),
join('src', 'multiarray', 'einsum.c.src'),
join('src', 'multiarray', 'flagsobject.c'),
join('src', 'multiarray', 'getset.c'),
join('src', 'multiarray', 'hashdescr.c'),
join('src', 'multiarray', 'item_selection.c'),
join('src', 'multiarray', 'iterators.c'),
join('src', 'multiarray', 'lowlevel_strided_loops.c.src'),
join('src', 'multiarray', 'mapping.c'),
join('src', 'multiarray', 'methods.c'),
join('src', 'multiarray', 'multiarraymodule.c'),
join('src', 'multiarray', 'nditer_templ.c.src'),
join('src', 'multiarray', 'nditer_api.c'),
join('src', 'multiarray', 'nditer_constr.c'),
join('src', 'multiarray', 'nditer_pywrap.c'),
join('src', 'multiarray', 'number.c'),
join('src', 'multiarray', 'refcount.c'),
join('src', 'multiarray', 'sequence.c'),
join('src', 'multiarray', 'shape.c'),
join('src', 'multiarray', 'scalarapi.c'),
join('src', 'multiarray', 'scalartypes.c.src'),
join('src', 'multiarray', 'strfuncs.c'),
join('src', 'multiarray', 'temp_elide.c'),
join('src', 'multiarray', 'typeinfo.c'),
join('src', 'multiarray', 'usertypes.c'),
join('src', 'multiarray', 'vdot.c'),
]
#######################################################################
# _multiarray_umath module - umath part #
#######################################################################
def generate_umath_c(ext, build_dir):
target = join(build_dir, header_dir, '__umath_generated.c')
dir = os.path.dirname(target)
if not os.path.exists(dir):
os.makedirs(dir)
script = generate_umath_py
if newer(script, target):
with open(target, 'w') as f:
f.write(generate_umath.make_code(generate_umath.defdict,
generate_umath.__file__))
return []
umath_src = [
join('src', 'umath', 'umathmodule.c'),
join('src', 'umath', 'reduction.c'),
join('src', 'umath', 'funcs.inc.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'loops.h.src'),
join('src', 'umath', 'loops.c.src'),
join('src', 'umath', 'matmul.h.src'),
join('src', 'umath', 'matmul.c.src'),
join('src', 'umath', 'clip.h.src'),
join('src', 'umath', 'clip.c.src'),
join('src', 'umath', 'ufunc_object.c'),
join('src', 'umath', 'extobj.c'),
join('src', 'umath', 'cpuid.c'),
join('src', 'umath', 'scalarmath.c.src'),
join('src', 'umath', 'ufunc_type_resolution.c'),
join('src', 'umath', 'override.c'),
]
umath_deps = [
generate_umath_py,
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'override.h'),
join(codegen_dir, 'generate_ufunc_api.py'),
]
config.add_extension('_multiarray_umath',
sources=multiarray_src + umath_src +
npymath_sources + common_src +
[generate_config_h,
generate_numpyconfig_h,
generate_numpy_api,
join(codegen_dir, 'generate_numpy_api.py'),
join('*.py'),
generate_umath_c,
generate_ufunc_api,
],
depends=deps + multiarray_deps + umath_deps +
common_deps,
libraries=['npymath', 'npysort'],
extra_info=extra_info)
#######################################################################
# umath_tests module #
#######################################################################
config.add_extension('_umath_tests',
sources=[join('src', 'umath', '_umath_tests.c.src')])
#######################################################################
# custom rational dtype module #
#######################################################################
config.add_extension('_rational_tests',
sources=[join('src', 'umath', '_rational_tests.c.src')])
#######################################################################
# struct_ufunc_test module #
#######################################################################
config.add_extension('_struct_ufunc_tests',
sources=[join('src', 'umath', '_struct_ufunc_tests.c.src')])
#######################################################################
# operand_flag_tests module #
#######################################################################
config.add_extension('_operand_flag_tests',
sources=[join('src', 'umath', '_operand_flag_tests.c.src')])
config.add_data_dir('tests')
config.add_data_dir('tests/data')
config.make_svn_version_py()
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)

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from __future__ import division, absolute_import, print_function
# Code common to build tools
import sys
import warnings
import copy
import binascii
import textwrap
from numpy.distutils.misc_util import mingw32
#-------------------
# Versioning support
#-------------------
# How to change C_API_VERSION ?
# - increase C_API_VERSION value
# - record the hash for the new C API with the cversions.py script
# and add the hash to cversions.txt
# The hash values are used to remind developers when the C API number was not
# updated - generates a MismatchCAPIWarning warning which is turned into an
# exception for released version.
# Binary compatibility version number. This number is increased whenever the
# C-API is changed such that binary compatibility is broken, i.e. whenever a
# recompile of extension modules is needed.
C_ABI_VERSION = 0x01000009
# Minor API version. This number is increased whenever a change is made to the
# C-API -- whether it breaks binary compatibility or not. Some changes, such
# as adding a function pointer to the end of the function table, can be made
# without breaking binary compatibility. In this case, only the C_API_VERSION
# (*not* C_ABI_VERSION) would be increased. Whenever binary compatibility is
# broken, both C_API_VERSION and C_ABI_VERSION should be increased.
#
# 0x00000008 - 1.7.x
# 0x00000009 - 1.8.x
# 0x00000009 - 1.9.x
# 0x0000000a - 1.10.x
# 0x0000000a - 1.11.x
# 0x0000000a - 1.12.x
# 0x0000000b - 1.13.x
# 0x0000000c - 1.14.x
# 0x0000000c - 1.15.x
# 0x0000000d - 1.16.x
C_API_VERSION = 0x0000000d
class MismatchCAPIWarning(Warning):
pass
def is_released(config):
"""Return True if a released version of numpy is detected."""
from distutils.version import LooseVersion
v = config.get_version('../version.py')
if v is None:
raise ValueError("Could not get version")
pv = LooseVersion(vstring=v).version
if len(pv) > 3:
return False
return True
def get_api_versions(apiversion, codegen_dir):
"""
Return current C API checksum and the recorded checksum.
Return current C API checksum and the recorded checksum for the given
version of the C API version.
"""
# Compute the hash of the current API as defined in the .txt files in
# code_generators
sys.path.insert(0, codegen_dir)
try:
m = __import__('genapi')
numpy_api = __import__('numpy_api')
curapi_hash = m.fullapi_hash(numpy_api.full_api)
apis_hash = m.get_versions_hash()
finally:
del sys.path[0]
return curapi_hash, apis_hash[apiversion]
def check_api_version(apiversion, codegen_dir):
"""Emits a MismatchCAPIWarning if the C API version needs updating."""
curapi_hash, api_hash = get_api_versions(apiversion, codegen_dir)
# If different hash, it means that the api .txt files in
# codegen_dir have been updated without the API version being
# updated. Any modification in those .txt files should be reflected
# in the api and eventually abi versions.
# To compute the checksum of the current API, use numpy/core/cversions.py
if not curapi_hash == api_hash:
msg = ("API mismatch detected, the C API version "
"numbers have to be updated. Current C api version is %d, "
"with checksum %s, but recorded checksum for C API version %d "
"in core/codegen_dir/cversions.txt is %s. If functions were "
"added in the C API, you have to update C_API_VERSION in %s."
)
warnings.warn(msg % (apiversion, curapi_hash, apiversion, api_hash,
__file__),
MismatchCAPIWarning, stacklevel=2)
# Mandatory functions: if not found, fail the build
MANDATORY_FUNCS = ["sin", "cos", "tan", "sinh", "cosh", "tanh", "fabs",
"floor", "ceil", "sqrt", "log10", "log", "exp", "asin",
"acos", "atan", "fmod", 'modf', 'frexp', 'ldexp']
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
OPTIONAL_STDFUNCS = ["expm1", "log1p", "acosh", "asinh", "atanh",
"rint", "trunc", "exp2", "log2", "hypot", "atan2", "pow",
"copysign", "nextafter", "ftello", "fseeko",
"strtoll", "strtoull", "cbrt", "strtold_l", "fallocate",
"backtrace", "madvise"]
OPTIONAL_HEADERS = [
# sse headers only enabled automatically on amd64/x32 builds
"xmmintrin.h", # SSE
"emmintrin.h", # SSE2
"immintrin.h", # AVX
"features.h", # for glibc version linux
"xlocale.h", # see GH#8367
"dlfcn.h", # dladdr
"sys/mman.h", #madvise
]
# optional gcc compiler builtins and their call arguments and optional a
# required header and definition name (HAVE_ prepended)
# call arguments are required as the compiler will do strict signature checking
OPTIONAL_INTRINSICS = [("__builtin_isnan", '5.'),
("__builtin_isinf", '5.'),
("__builtin_isfinite", '5.'),
("__builtin_bswap32", '5u'),
("__builtin_bswap64", '5u'),
("__builtin_expect", '5, 0'),
("__builtin_mul_overflow", '5, 5, (int*)5'),
# broken on OSX 10.11, make sure its not optimized away
("volatile int r = __builtin_cpu_supports", '"sse"',
"stdio.h", "__BUILTIN_CPU_SUPPORTS"),
("volatile int r = __builtin_cpu_supports", '"avx512f"',
"stdio.h", "__BUILTIN_CPU_SUPPORTS_AVX512F"),
# MMX only needed for icc, but some clangs don't have it
("_m_from_int64", '0', "emmintrin.h"),
("_mm_load_ps", '(float*)0', "xmmintrin.h"), # SSE
("_mm_prefetch", '(float*)0, _MM_HINT_NTA',
"xmmintrin.h"), # SSE
("_mm_load_pd", '(double*)0', "emmintrin.h"), # SSE2
("__builtin_prefetch", "(float*)0, 0, 3"),
# check that the linker can handle avx
("__asm__ volatile", '"vpand %xmm1, %xmm2, %xmm3"',
"stdio.h", "LINK_AVX"),
("__asm__ volatile", '"vpand %ymm1, %ymm2, %ymm3"',
"stdio.h", "LINK_AVX2"),
("__asm__ volatile", '"vpaddd %zmm1, %zmm2, %zmm3"',
"stdio.h", "LINK_AVX512F"),
("__asm__ volatile", '"xgetbv"', "stdio.h", "XGETBV"),
]
# function attributes
# tested via "int %s %s(void *);" % (attribute, name)
# function name will be converted to HAVE_<upper-case-name> preprocessor macro
OPTIONAL_FUNCTION_ATTRIBUTES = [('__attribute__((optimize("unroll-loops")))',
'attribute_optimize_unroll_loops'),
('__attribute__((optimize("O3")))',
'attribute_optimize_opt_3'),
('__attribute__((nonnull (1)))',
'attribute_nonnull'),
('__attribute__((target ("avx")))',
'attribute_target_avx'),
('__attribute__((target ("avx2")))',
'attribute_target_avx2'),
('__attribute__((target ("avx512f")))',
'attribute_target_avx512f'),
]
# function attributes with intrinsics
# To ensure your compiler can compile avx intrinsics with just the attributes
# gcc 4.8.4 support attributes but not with intrisics
# tested via "#include<%s> int %s %s(void *){code; return 0;};" % (header, attribute, name, code)
# function name will be converted to HAVE_<upper-case-name> preprocessor macro
OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS = [('__attribute__((target("avx2,fma")))',
'attribute_target_avx2_with_intrinsics',
'__m256 temp = _mm256_set1_ps(1.0); temp = \
_mm256_fmadd_ps(temp, temp, temp)',
'immintrin.h'),
('__attribute__((target("avx512f")))',
'attribute_target_avx512f_with_intrinsics',
'__m512 temp = _mm512_set1_ps(1.0)',
'immintrin.h'),
]
# variable attributes tested via "int %s a" % attribute
OPTIONAL_VARIABLE_ATTRIBUTES = ["__thread", "__declspec(thread)"]
# Subset of OPTIONAL_STDFUNCS which may already have HAVE_* defined by Python.h
OPTIONAL_STDFUNCS_MAYBE = [
"expm1", "log1p", "acosh", "atanh", "asinh", "hypot", "copysign",
"ftello", "fseeko"
]
# C99 functions: float and long double versions
C99_FUNCS = [
"sin", "cos", "tan", "sinh", "cosh", "tanh", "fabs", "floor", "ceil",
"rint", "trunc", "sqrt", "log10", "log", "log1p", "exp", "expm1",
"asin", "acos", "atan", "asinh", "acosh", "atanh", "hypot", "atan2",
"pow", "fmod", "modf", 'frexp', 'ldexp', "exp2", "log2", "copysign",
"nextafter", "cbrt"
]
C99_FUNCS_SINGLE = [f + 'f' for f in C99_FUNCS]
C99_FUNCS_EXTENDED = [f + 'l' for f in C99_FUNCS]
C99_COMPLEX_TYPES = [
'complex double', 'complex float', 'complex long double'
]
C99_COMPLEX_FUNCS = [
"cabs", "cacos", "cacosh", "carg", "casin", "casinh", "catan",
"catanh", "ccos", "ccosh", "cexp", "cimag", "clog", "conj", "cpow",
"cproj", "creal", "csin", "csinh", "csqrt", "ctan", "ctanh"
]
def fname2def(name):
return "HAVE_%s" % name.upper()
def sym2def(symbol):
define = symbol.replace(' ', '')
return define.upper()
def type2def(symbol):
define = symbol.replace(' ', '_')
return define.upper()
# Code to detect long double representation taken from MPFR m4 macro
def check_long_double_representation(cmd):
cmd._check_compiler()
body = LONG_DOUBLE_REPRESENTATION_SRC % {'type': 'long double'}
# Disable whole program optimization (the default on vs2015, with python 3.5+)
# which generates intermediary object files and prevents checking the
# float representation.
if sys.platform == "win32" and not mingw32():
try:
cmd.compiler.compile_options.remove("/GL")
except (AttributeError, ValueError):
pass
# Disable multi-file interprocedural optimization in the Intel compiler on Linux
# which generates intermediary object files and prevents checking the
# float representation.
elif (sys.platform != "win32"
and cmd.compiler.compiler_type.startswith('intel')
and '-ipo' in cmd.compiler.cc_exe):
newcompiler = cmd.compiler.cc_exe.replace(' -ipo', '')
cmd.compiler.set_executables(
compiler=newcompiler,
compiler_so=newcompiler,
compiler_cxx=newcompiler,
linker_exe=newcompiler,
linker_so=newcompiler + ' -shared'
)
# We need to use _compile because we need the object filename
src, obj = cmd._compile(body, None, None, 'c')
try:
ltype = long_double_representation(pyod(obj))
return ltype
except ValueError:
# try linking to support CC="gcc -flto" or icc -ipo
# struct needs to be volatile so it isn't optimized away
# additionally "clang -flto" requires the foo struct to be used
body = body.replace('struct', 'volatile struct')
body += "int main(void) { return foo.before[0]; }\n"
src, obj = cmd._compile(body, None, None, 'c')
cmd.temp_files.append("_configtest")
cmd.compiler.link_executable([obj], "_configtest")
ltype = long_double_representation(pyod("_configtest"))
return ltype
finally:
cmd._clean()
LONG_DOUBLE_REPRESENTATION_SRC = r"""
/* "before" is 16 bytes to ensure there's no padding between it and "x".
* We're not expecting any "long double" bigger than 16 bytes or with
* alignment requirements stricter than 16 bytes. */
typedef %(type)s test_type;
struct {
char before[16];
test_type x;
char after[8];
} foo = {
{ '\0', '\0', '\0', '\0', '\0', '\0', '\0', '\0',
'\001', '\043', '\105', '\147', '\211', '\253', '\315', '\357' },
-123456789.0,
{ '\376', '\334', '\272', '\230', '\166', '\124', '\062', '\020' }
};
"""
def pyod(filename):
"""Python implementation of the od UNIX utility (od -b, more exactly).
Parameters
----------
filename : str
name of the file to get the dump from.
Returns
-------
out : seq
list of lines of od output
Note
----
We only implement enough to get the necessary information for long double
representation, this is not intended as a compatible replacement for od.
"""
def _pyod2():
out = []
with open(filename, 'rb') as fid:
yo = [int(oct(int(binascii.b2a_hex(o), 16))) for o in fid.read()]
for i in range(0, len(yo), 16):
line = ['%07d' % int(oct(i))]
line.extend(['%03d' % c for c in yo[i:i+16]])
out.append(" ".join(line))
return out
def _pyod3():
out = []
with open(filename, 'rb') as fid:
yo2 = [oct(o)[2:] for o in fid.read()]
for i in range(0, len(yo2), 16):
line = ['%07d' % int(oct(i)[2:])]
line.extend(['%03d' % int(c) for c in yo2[i:i+16]])
out.append(" ".join(line))
return out
if sys.version_info[0] < 3:
return _pyod2()
else:
return _pyod3()
_BEFORE_SEQ = ['000', '000', '000', '000', '000', '000', '000', '000',
'001', '043', '105', '147', '211', '253', '315', '357']
_AFTER_SEQ = ['376', '334', '272', '230', '166', '124', '062', '020']
_IEEE_DOUBLE_BE = ['301', '235', '157', '064', '124', '000', '000', '000']
_IEEE_DOUBLE_LE = _IEEE_DOUBLE_BE[::-1]
_INTEL_EXTENDED_12B = ['000', '000', '000', '000', '240', '242', '171', '353',
'031', '300', '000', '000']
_INTEL_EXTENDED_16B = ['000', '000', '000', '000', '240', '242', '171', '353',
'031', '300', '000', '000', '000', '000', '000', '000']
_MOTOROLA_EXTENDED_12B = ['300', '031', '000', '000', '353', '171',
'242', '240', '000', '000', '000', '000']
_IEEE_QUAD_PREC_BE = ['300', '031', '326', '363', '105', '100', '000', '000',
'000', '000', '000', '000', '000', '000', '000', '000']
_IEEE_QUAD_PREC_LE = _IEEE_QUAD_PREC_BE[::-1]
_IBM_DOUBLE_DOUBLE_BE = (['301', '235', '157', '064', '124', '000', '000', '000'] +
['000'] * 8)
_IBM_DOUBLE_DOUBLE_LE = (['000', '000', '000', '124', '064', '157', '235', '301'] +
['000'] * 8)
def long_double_representation(lines):
"""Given a binary dump as given by GNU od -b, look for long double
representation."""
# Read contains a list of 32 items, each item is a byte (in octal
# representation, as a string). We 'slide' over the output until read is of
# the form before_seq + content + after_sequence, where content is the long double
# representation:
# - content is 12 bytes: 80 bits Intel representation
# - content is 16 bytes: 80 bits Intel representation (64 bits) or quad precision
# - content is 8 bytes: same as double (not implemented yet)
read = [''] * 32
saw = None
for line in lines:
# we skip the first word, as od -b output an index at the beginning of
# each line
for w in line.split()[1:]:
read.pop(0)
read.append(w)
# If the end of read is equal to the after_sequence, read contains
# the long double
if read[-8:] == _AFTER_SEQ:
saw = copy.copy(read)
# if the content was 12 bytes, we only have 32 - 8 - 12 = 12
# "before" bytes. In other words the first 4 "before" bytes went
# past the sliding window.
if read[:12] == _BEFORE_SEQ[4:]:
if read[12:-8] == _INTEL_EXTENDED_12B:
return 'INTEL_EXTENDED_12_BYTES_LE'
if read[12:-8] == _MOTOROLA_EXTENDED_12B:
return 'MOTOROLA_EXTENDED_12_BYTES_BE'
# if the content was 16 bytes, we are left with 32-8-16 = 16
# "before" bytes, so 8 went past the sliding window.
elif read[:8] == _BEFORE_SEQ[8:]:
if read[8:-8] == _INTEL_EXTENDED_16B:
return 'INTEL_EXTENDED_16_BYTES_LE'
elif read[8:-8] == _IEEE_QUAD_PREC_BE:
return 'IEEE_QUAD_BE'
elif read[8:-8] == _IEEE_QUAD_PREC_LE:
return 'IEEE_QUAD_LE'
elif read[8:-8] == _IBM_DOUBLE_DOUBLE_LE:
return 'IBM_DOUBLE_DOUBLE_LE'
elif read[8:-8] == _IBM_DOUBLE_DOUBLE_BE:
return 'IBM_DOUBLE_DOUBLE_BE'
# if the content was 8 bytes, left with 32-8-8 = 16 bytes
elif read[:16] == _BEFORE_SEQ:
if read[16:-8] == _IEEE_DOUBLE_LE:
return 'IEEE_DOUBLE_LE'
elif read[16:-8] == _IEEE_DOUBLE_BE:
return 'IEEE_DOUBLE_BE'
if saw is not None:
raise ValueError("Unrecognized format (%s)" % saw)
else:
# We never detected the after_sequence
raise ValueError("Could not lock sequences (%s)" % saw)
def check_for_right_shift_internal_compiler_error(cmd):
"""
On our arm CI, this fails with an internal compilation error
The failure looks like the following, and can be reproduced on ARM64 GCC 5.4:
<source>: In function 'right_shift':
<source>:4:20: internal compiler error: in expand_shift_1, at expmed.c:2349
ip1[i] = ip1[i] >> in2;
^
Please submit a full bug report,
with preprocessed source if appropriate.
See <http://gcc.gnu.org/bugs.html> for instructions.
Compiler returned: 1
This function returns True if this compiler bug is present, and we need to
turn off optimization for the function
"""
cmd._check_compiler()
has_optimize = cmd.try_compile(textwrap.dedent("""\
__attribute__((optimize("O3"))) void right_shift() {}
"""), None, None)
if not has_optimize:
return False
no_err = cmd.try_compile(textwrap.dedent("""\
typedef long the_type; /* fails also for unsigned and long long */
__attribute__((optimize("O3"))) void right_shift(the_type in2, the_type *ip1, int n) {
for (int i = 0; i < n; i++) {
if (in2 < (the_type)sizeof(the_type) * 8) {
ip1[i] = ip1[i] >> in2;
}
}
}
"""), None, None)
return not no_err

906
Restaurant/Marta/venv/Lib/site-packages/numpy/core/shape_base.py Normal file
View File

@ -0,0 +1,906 @@
from __future__ import division, absolute_import, print_function
__all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'block', 'hstack',
'stack', 'vstack']
import functools
import operator
import warnings
from . import numeric as _nx
from . import overrides
from ._asarray import array, asanyarray
from .multiarray import normalize_axis_index
from . import fromnumeric as _from_nx
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
def _atleast_1d_dispatcher(*arys):
return arys
@array_function_dispatch(_atleast_1d_dispatcher)
def atleast_1d(*arys):
"""
Convert inputs to arrays with at least one dimension.
Scalar inputs are converted to 1-dimensional arrays, whilst
higher-dimensional inputs are preserved.
Parameters
----------
arys1, arys2, ... : array_like
One or more input arrays.
Returns
-------
ret : ndarray
An array, or list of arrays, each with ``a.ndim >= 1``.
Copies are made only if necessary.
See Also
--------
atleast_2d, atleast_3d
Examples
--------
>>> np.atleast_1d(1.0)
array([1.])
>>> x = np.arange(9.0).reshape(3,3)
>>> np.atleast_1d(x)
array([[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]])
>>> np.atleast_1d(x) is x
True
>>> np.atleast_1d(1, [3, 4])
[array([1]), array([3, 4])]
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1)
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def _atleast_2d_dispatcher(*arys):
return arys
@array_function_dispatch(_atleast_2d_dispatcher)
def atleast_2d(*arys):
"""
View inputs as arrays with at least two dimensions.
Parameters
----------
arys1, arys2, ... : array_like
One or more array-like sequences. Non-array inputs are converted
to arrays. Arrays that already have two or more dimensions are
preserved.
Returns
-------
res, res2, ... : ndarray
An array, or list of arrays, each with ``a.ndim >= 2``.
Copies are avoided where possible, and views with two or more
dimensions are returned.
See Also
--------
atleast_1d, atleast_3d
Examples
--------
>>> np.atleast_2d(3.0)
array([[3.]])
>>> x = np.arange(3.0)
>>> np.atleast_2d(x)
array([[0., 1., 2.]])
>>> np.atleast_2d(x).base is x
True
>>> np.atleast_2d(1, [1, 2], [[1, 2]])
[array([[1]]), array([[1, 2]]), array([[1, 2]])]
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1, 1)
elif ary.ndim == 1:
result = ary[_nx.newaxis, :]
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def _atleast_3d_dispatcher(*arys):
return arys
@array_function_dispatch(_atleast_3d_dispatcher)
def atleast_3d(*arys):
"""
View inputs as arrays with at least three dimensions.
Parameters
----------
arys1, arys2, ... : array_like
One or more array-like sequences. Non-array inputs are converted to
arrays. Arrays that already have three or more dimensions are
preserved.
Returns
-------
res1, res2, ... : ndarray
An array, or list of arrays, each with ``a.ndim >= 3``. Copies are
avoided where possible, and views with three or more dimensions are
returned. For example, a 1-D array of shape ``(N,)`` becomes a view
of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a
view of shape ``(M, N, 1)``.
See Also
--------
atleast_1d, atleast_2d
Examples
--------
>>> np.atleast_3d(3.0)
array([[[3.]]])
>>> x = np.arange(3.0)
>>> np.atleast_3d(x).shape
(1, 3, 1)
>>> x = np.arange(12.0).reshape(4,3)
>>> np.atleast_3d(x).shape
(4, 3, 1)
>>> np.atleast_3d(x).base is x.base # x is a reshape, so not base itself
True
>>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):
... print(arr, arr.shape) # doctest: +SKIP
...
[[[1]
[2]]] (1, 2, 1)
[[[1]
[2]]] (1, 2, 1)
[[[1 2]]] (1, 1, 2)
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1, 1, 1)
elif ary.ndim == 1:
result = ary[_nx.newaxis, :, _nx.newaxis]
elif ary.ndim == 2:
result = ary[:, :, _nx.newaxis]
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def _arrays_for_stack_dispatcher(arrays, stacklevel=4):
if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
warnings.warn('arrays to stack must be passed as a "sequence" type '
'such as list or tuple. Support for non-sequence '
'iterables such as generators is deprecated as of '
'NumPy 1.16 and will raise an error in the future.',
FutureWarning, stacklevel=stacklevel)
return ()
return arrays
def _vhstack_dispatcher(tup):
return _arrays_for_stack_dispatcher(tup)
@array_function_dispatch(_vhstack_dispatcher)
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
This is equivalent to concatenation along the first axis after 1-D arrays
of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
`vsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of ndarrays
The arrays must have the same shape along all but the first axis.
1-D arrays must have the same length.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 2-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
concatenate : Join a sequence of arrays along an existing axis.
vsplit : Split array into a list of multiple sub-arrays vertically.
block : Assemble arrays from blocks.
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(tup, stacklevel=2)
arrs = atleast_2d(*tup)
if not isinstance(arrs, list):
arrs = [arrs]
return _nx.concatenate(arrs, 0)
@array_function_dispatch(_vhstack_dispatcher)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D
arrays where it concatenates along the first axis. Rebuilds arrays divided
by `hsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of ndarrays
The arrays must have the same shape along all but the second axis,
except 1-D arrays which can be any length.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays along an existing axis.
hsplit : Split array along second axis.
block : Assemble arrays from blocks.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(tup, stacklevel=2)
arrs = atleast_1d(*tup)
if not isinstance(arrs, list):
arrs = [arrs]
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs and arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
else:
return _nx.concatenate(arrs, 1)
def _stack_dispatcher(arrays, axis=None, out=None):
arrays = _arrays_for_stack_dispatcher(arrays, stacklevel=6)
if out is not None:
# optimize for the typical case where only arrays is provided
arrays = list(arrays)
arrays.append(out)
return arrays
@array_function_dispatch(_stack_dispatcher)
def stack(arrays, axis=0, out=None):
"""
Join a sequence of arrays along a new axis.
The ``axis`` parameter specifies the index of the new axis in the
dimensions of the result. For example, if ``axis=0`` it will be the first
dimension and if ``axis=-1`` it will be the last dimension.
.. versionadded:: 1.10.0
Parameters
----------
arrays : sequence of array_like
Each array must have the same shape.
axis : int, optional
The axis in the result array along which the input arrays are stacked.
out : ndarray, optional
If provided, the destination to place the result. The shape must be
correct, matching that of what stack would have returned if no
out argument were specified.
Returns
-------
stacked : ndarray
The stacked array has one more dimension than the input arrays.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
split : Split array into a list of multiple sub-arrays of equal size.
block : Assemble arrays from blocks.
Examples
--------
>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.stack((a, b))
array([[1, 2, 3],
[2, 3, 4]])
>>> np.stack((a, b), axis=-1)
array([[1, 2],
[2, 3],
[3, 4]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(arrays, stacklevel=2)
arrays = [asanyarray(arr) for arr in arrays]
if not arrays:
raise ValueError('need at least one array to stack')
shapes = {arr.shape for arr in arrays}
if len(shapes) != 1:
raise ValueError('all input arrays must have the same shape')
result_ndim = arrays[0].ndim + 1
axis = normalize_axis_index(axis, result_ndim)
sl = (slice(None),) * axis + (_nx.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return _nx.concatenate(expanded_arrays, axis=axis, out=out)
# Internal functions to eliminate the overhead of repeated dispatch in one of
# the two possible paths inside np.block.
# Use getattr to protect against __array_function__ being disabled.
_size = getattr(_from_nx.size, '__wrapped__', _from_nx.size)
_ndim = getattr(_from_nx.ndim, '__wrapped__', _from_nx.ndim)
_concatenate = getattr(_from_nx.concatenate, '__wrapped__', _from_nx.concatenate)
def _block_format_index(index):
"""
Convert a list of indices ``[0, 1, 2]`` into ``"arrays[0][1][2]"``.
"""
idx_str = ''.join('[{}]'.format(i) for i in index if i is not None)
return 'arrays' + idx_str
def _block_check_depths_match(arrays, parent_index=[]):
"""
Recursive function checking that the depths of nested lists in `arrays`
all match. Mismatch raises a ValueError as described in the block
docstring below.
The entire index (rather than just the depth) needs to be calculated
for each innermost list, in case an error needs to be raised, so that
the index of the offending list can be printed as part of the error.
Parameters
----------
arrays : nested list of arrays
The arrays to check
parent_index : list of int
The full index of `arrays` within the nested lists passed to
`_block_check_depths_match` at the top of the recursion.
Returns
-------
first_index : list of int
The full index of an element from the bottom of the nesting in
`arrays`. If any element at the bottom is an empty list, this will
refer to it, and the last index along the empty axis will be None.
max_arr_ndim : int
The maximum of the ndims of the arrays nested in `arrays`.
final_size: int
The number of elements in the final array. This is used the motivate
the choice of algorithm used using benchmarking wisdom.
"""
if type(arrays) is tuple:
# not strictly necessary, but saves us from:
# - more than one way to do things - no point treating tuples like
# lists
# - horribly confusing behaviour that results when tuples are
# treated like ndarray
raise TypeError(
'{} is a tuple. '
'Only lists can be used to arrange blocks, and np.block does '
'not allow implicit conversion from tuple to ndarray.'.format(
_block_format_index(parent_index)
)
)
elif type(arrays) is list and len(arrays) > 0:
idxs_ndims = (_block_check_depths_match(arr, parent_index + [i])
for i, arr in enumerate(arrays))
first_index, max_arr_ndim, final_size = next(idxs_ndims)
for index, ndim, size in idxs_ndims:
final_size += size
if ndim > max_arr_ndim:
max_arr_ndim = ndim
if len(index) != len(first_index):
raise ValueError(
"List depths are mismatched. First element was at depth "
"{}, but there is an element at depth {} ({})".format(
len(first_index),
len(index),
_block_format_index(index)
)
)
# propagate our flag that indicates an empty list at the bottom
if index[-1] is None:
first_index = index
return first_index, max_arr_ndim, final_size
elif type(arrays) is list and len(arrays) == 0:
# We've 'bottomed out' on an empty list
return parent_index + [None], 0, 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
size = _size(arrays)
return parent_index, _ndim(arrays), size
def _atleast_nd(a, ndim):
# Ensures `a` has at least `ndim` dimensions by prepending
# ones to `a.shape` as necessary
return array(a, ndmin=ndim, copy=False, subok=True)
def _accumulate(values):
# Helper function because Python 2.7 doesn't have
# itertools.accumulate
value = 0
accumulated = []
for v in values:
value += v
accumulated.append(value)
return accumulated
def _concatenate_shapes(shapes, axis):
"""Given array shapes, return the resulting shape and slices prefixes.
These help in nested concatation.
Returns
-------
shape: tuple of int
This tuple satisfies:
```
shape, _ = _concatenate_shapes([arr.shape for shape in arrs], axis)
shape == concatenate(arrs, axis).shape
```
slice_prefixes: tuple of (slice(start, end), )
For a list of arrays being concatenated, this returns the slice
in the larger array at axis that needs to be sliced into.
For example, the following holds:
```
ret = concatenate([a, b, c], axis)
_, (sl_a, sl_b, sl_c) = concatenate_slices([a, b, c], axis)
ret[(slice(None),) * axis + sl_a] == a
ret[(slice(None),) * axis + sl_b] == b
ret[(slice(None),) * axis + sl_c] == c
```
These are called slice prefixes since they are used in the recursive
blocking algorithm to compute the left-most slices during the
recursion. Therefore, they must be prepended to rest of the slice
that was computed deeper in the recursion.
These are returned as tuples to ensure that they can quickly be added
to existing slice tuple without creating a new tuple every time.
"""
# Cache a result that will be reused.
shape_at_axis = [shape[axis] for shape in shapes]
# Take a shape, any shape
first_shape = shapes[0]
first_shape_pre = first_shape[:axis]
first_shape_post = first_shape[axis+1:]
if any(shape[:axis] != first_shape_pre or
shape[axis+1:] != first_shape_post for shape in shapes):
raise ValueError(
'Mismatched array shapes in block along axis {}.'.format(axis))
shape = (first_shape_pre + (sum(shape_at_axis),) + first_shape[axis+1:])
offsets_at_axis = _accumulate(shape_at_axis)
slice_prefixes = [(slice(start, end),)
for start, end in zip([0] + offsets_at_axis,
offsets_at_axis)]
return shape, slice_prefixes
def _block_info_recursion(arrays, max_depth, result_ndim, depth=0):
"""
Returns the shape of the final array, along with a list
of slices and a list of arrays that can be used for assignment inside the
new array
Parameters
----------
arrays : nested list of arrays
The arrays to check
max_depth : list of int
The number of nested lists
result_ndim: int
The number of dimensions in thefinal array.
Returns
-------
shape : tuple of int
The shape that the final array will take on.
slices: list of tuple of slices
The slices into the full array required for assignment. These are
required to be prepended with ``(Ellipsis, )`` to obtain to correct
final index.
arrays: list of ndarray
The data to assign to each slice of the full array
"""
if depth < max_depth:
shapes, slices, arrays = zip(
*[_block_info_recursion(arr, max_depth, result_ndim, depth+1)
for arr in arrays])
axis = result_ndim - max_depth + depth
shape, slice_prefixes = _concatenate_shapes(shapes, axis)
# Prepend the slice prefix and flatten the slices
slices = [slice_prefix + the_slice
for slice_prefix, inner_slices in zip(slice_prefixes, slices)
for the_slice in inner_slices]
# Flatten the array list
arrays = functools.reduce(operator.add, arrays)
return shape, slices, arrays
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
# Return the slice and the array inside a list to be consistent with
# the recursive case.
arr = _atleast_nd(arrays, result_ndim)
return arr.shape, [()], [arr]
def _block(arrays, max_depth, result_ndim, depth=0):
"""
Internal implementation of block based on repeated concatenation.
`arrays` is the argument passed to
block. `max_depth` is the depth of nested lists within `arrays` and
`result_ndim` is the greatest of the dimensions of the arrays in
`arrays` and the depth of the lists in `arrays` (see block docstring
for details).
"""
if depth < max_depth:
arrs = [_block(arr, max_depth, result_ndim, depth+1)
for arr in arrays]
return _concatenate(arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
return _atleast_nd(arrays, result_ndim)
def _block_dispatcher(arrays):
# Use type(...) is list to match the behavior of np.block(), which special
# cases list specifically rather than allowing for generic iterables or
# tuple. Also, we know that list.__array_function__ will never exist.
if type(arrays) is list:
for subarrays in arrays:
for subarray in _block_dispatcher(subarrays):
yield subarray
else:
yield arrays
@array_function_dispatch(_block_dispatcher)
def block(arrays):
"""
Assemble an nd-array from nested lists of blocks.
Blocks in the innermost lists are concatenated (see `concatenate`) along
the last dimension (-1), then these are concatenated along the
second-last dimension (-2), and so on until the outermost list is reached.
Blocks can be of any dimension, but will not be broadcasted using the normal
rules. Instead, leading axes of size 1 are inserted, to make ``block.ndim``
the same for all blocks. This is primarily useful for working with scalars,
and means that code like ``np.block([v, 1])`` is valid, where
``v.ndim == 1``.
When the nested list is two levels deep, this allows block matrices to be
constructed from their components.
.. versionadded:: 1.13.0
Parameters
----------
arrays : nested list of array_like or scalars (but not tuples)
If passed a single ndarray or scalar (a nested list of depth 0), this
is returned unmodified (and not copied).
Elements shapes must match along the appropriate axes (without
broadcasting), but leading 1s will be prepended to the shape as
necessary to make the dimensions match.
Returns
-------
block_array : ndarray
The array assembled from the given blocks.
The dimensionality of the output is equal to the greatest of:
* the dimensionality of all the inputs
* the depth to which the input list is nested
Raises
------
ValueError
* If list depths are mismatched - for instance, ``[[a, b], c]`` is
illegal, and should be spelt ``[[a, b], [c]]``
* If lists are empty - for instance, ``[[a, b], []]``
See Also
--------
concatenate : Join a sequence of arrays together.
stack : Stack arrays in sequence along a new dimension.
hstack : Stack arrays in sequence horizontally (column wise).
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
vsplit : Split array into a list of multiple sub-arrays vertically.
Notes
-----
When called with only scalars, ``np.block`` is equivalent to an ndarray
call. So ``np.block([[1, 2], [3, 4]])`` is equivalent to
``np.array([[1, 2], [3, 4]])``.
This function does not enforce that the blocks lie on a fixed grid.
``np.block([[a, b], [c, d]])`` is not restricted to arrays of the form::
AAAbb
AAAbb
cccDD
But is also allowed to produce, for some ``a, b, c, d``::
AAAbb
AAAbb
cDDDD
Since concatenation happens along the last axis first, `block` is _not_
capable of producing the following directly::
AAAbb
cccbb
cccDD
Matlab's "square bracket stacking", ``[A, B, ...; p, q, ...]``, is
equivalent to ``np.block([[A, B, ...], [p, q, ...]])``.
Examples
--------
The most common use of this function is to build a block matrix
>>> A = np.eye(2) * 2
>>> B = np.eye(3) * 3
>>> np.block([
... [A, np.zeros((2, 3))],
... [np.ones((3, 2)), B ]
... ])
array([[2., 0., 0., 0., 0.],
[0., 2., 0., 0., 0.],
[1., 1., 3., 0., 0.],
[1., 1., 0., 3., 0.],
[1., 1., 0., 0., 3.]])
With a list of depth 1, `block` can be used as `hstack`
>>> np.block([1, 2, 3]) # hstack([1, 2, 3])
array([1, 2, 3])
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.block([a, b, 10]) # hstack([a, b, 10])
array([ 1, 2, 3, 2, 3, 4, 10])
>>> A = np.ones((2, 2), int)
>>> B = 2 * A
>>> np.block([A, B]) # hstack([A, B])
array([[1, 1, 2, 2],
[1, 1, 2, 2]])
With a list of depth 2, `block` can be used in place of `vstack`:
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.block([[a], [b]]) # vstack([a, b])
array([[1, 2, 3],
[2, 3, 4]])
>>> A = np.ones((2, 2), int)
>>> B = 2 * A
>>> np.block([[A], [B]]) # vstack([A, B])
array([[1, 1],
[1, 1],
[2, 2],
[2, 2]])
It can also be used in places of `atleast_1d` and `atleast_2d`
>>> a = np.array(0)
>>> b = np.array([1])
>>> np.block([a]) # atleast_1d(a)
array([0])
>>> np.block([b]) # atleast_1d(b)
array([1])
>>> np.block([[a]]) # atleast_2d(a)
array([[0]])
>>> np.block([[b]]) # atleast_2d(b)
array([[1]])
"""
arrays, list_ndim, result_ndim, final_size = _block_setup(arrays)
# It was found through benchmarking that making an array of final size
# around 256x256 was faster by straight concatenation on a
# i7-7700HQ processor and dual channel ram 2400MHz.
# It didn't seem to matter heavily on the dtype used.
#
# A 2D array using repeated concatenation requires 2 copies of the array.
#
# The fastest algorithm will depend on the ratio of CPU power to memory
# speed.
# One can monitor the results of the benchmark
# https://pv.github.io/numpy-bench/#bench_shape_base.Block2D.time_block2d
# to tune this parameter until a C version of the `_block_info_recursion`
# algorithm is implemented which would likely be faster than the python
# version.
if list_ndim * final_size > (2 * 512 * 512):
return _block_slicing(arrays, list_ndim, result_ndim)
else:
return _block_concatenate(arrays, list_ndim, result_ndim)
# These helper functions are mostly used for testing.
# They allow us to write tests that directly call `_block_slicing`
# or `_block_concatenate` without blocking large arrays to force the wisdom
# to trigger the desired path.
def _block_setup(arrays):
"""
Returns
(`arrays`, list_ndim, result_ndim, final_size)
"""
bottom_index, arr_ndim, final_size = _block_check_depths_match(arrays)
list_ndim = len(bottom_index)
if bottom_index and bottom_index[-1] is None:
raise ValueError(
'List at {} cannot be empty'.format(
_block_format_index(bottom_index)
)
)
result_ndim = max(arr_ndim, list_ndim)
return arrays, list_ndim, result_ndim, final_size
def _block_slicing(arrays, list_ndim, result_ndim):
shape, slices, arrays = _block_info_recursion(
arrays, list_ndim, result_ndim)
dtype = _nx.result_type(*[arr.dtype for arr in arrays])
# Test preferring F only in the case that all input arrays are F
F_order = all(arr.flags['F_CONTIGUOUS'] for arr in arrays)
C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays)
order = 'F' if F_order and not C_order else 'C'
result = _nx.empty(shape=shape, dtype=dtype, order=order)
# Note: In a c implementation, the function
# PyArray_CreateMultiSortedStridePerm could be used for more advanced
# guessing of the desired order.
for the_slice, arr in zip(slices, arrays):
result[(Ellipsis,) + the_slice] = arr
return result
def _block_concatenate(arrays, list_ndim, result_ndim):
result = _block(arrays, list_ndim, result_ndim)
if list_ndim == 0:
# Catch an edge case where _block returns a view because
# `arrays` is a single numpy array and not a list of numpy arrays.
# This might copy scalars or lists twice, but this isn't a likely
# usecase for those interested in performance
result = result.copy()
return result

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"""Provide class for testing in French locale
"""
from __future__ import division, absolute_import, print_function
import sys
import locale
import pytest
__ALL__ = ['CommaDecimalPointLocale']
def find_comma_decimal_point_locale():
"""See if platform has a decimal point as comma locale.
Find a locale that uses a comma instead of a period as the
decimal point.
Returns
-------
old_locale: str
Locale when the function was called.
new_locale: {str, None)
First French locale found, None if none found.
"""
if sys.platform == 'win32':
locales = ['FRENCH']
else:
locales = ['fr_FR', 'fr_FR.UTF-8', 'fi_FI', 'fi_FI.UTF-8']
old_locale = locale.getlocale(locale.LC_NUMERIC)
new_locale = None
try:
for loc in locales:
try:
locale.setlocale(locale.LC_NUMERIC, loc)
new_locale = loc
break
except locale.Error:
pass
finally:
locale.setlocale(locale.LC_NUMERIC, locale=old_locale)
return old_locale, new_locale
class CommaDecimalPointLocale(object):
"""Sets LC_NUMERIC to a locale with comma as decimal point.
Classes derived from this class have setup and teardown methods that run
tests with locale.LC_NUMERIC set to a locale where commas (',') are used as
the decimal point instead of periods ('.'). On exit the locale is restored
to the initial locale. It also serves as context manager with the same
effect. If no such locale is available, the test is skipped.
.. versionadded:: 1.15.0
"""
(cur_locale, tst_locale) = find_comma_decimal_point_locale()
def setup(self):
if self.tst_locale is None:
pytest.skip("No French locale available")
locale.setlocale(locale.LC_NUMERIC, locale=self.tst_locale)
def teardown(self):
locale.setlocale(locale.LC_NUMERIC, locale=self.cur_locale)
def __enter__(self):
if self.tst_locale is None:
pytest.skip("No French locale available")
locale.setlocale(locale.LC_NUMERIC, locale=self.tst_locale)
def __exit__(self, type, value, traceback):
locale.setlocale(locale.LC_NUMERIC, locale=self.cur_locale)

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Steps to validate transcendental functions:
1) Add a file 'umath-validation-set-<ufuncname>', where ufuncname is name of
the function in NumPy you want to validate
2) The file should contain 4 columns: dtype,input,expected output,ulperror
a. dtype: one of np.float16, np.float32, np.float64
b. input: floating point input to ufunc in hex. Example: 0x414570a4
represents 12.340000152587890625
c. expected output: floating point output for the corresponding input in hex.
This should be computed using a high(er) precision library and then rounded to
same format as the input.
d. ulperror: expected maximum ulp error of the function. This
should be same across all rows of the same dtype. Otherwise, the function is
tested for the maximum ulp error among all entries of that dtype.
3) Add file umath-validation-set-<ufuncname> to the test file test_umath_accuracy.py
which will then validate your ufunc.

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dtype,input,output,ulperrortol
## +ve denormals ##
np.float32,0x004b4716,0x3f800000,2
np.float32,0x007b2490,0x3f800000,2
np.float32,0x007c99fa,0x3f800000,2
np.float32,0x00734a0c,0x3f800000,2
np.float32,0x0070de24,0x3f800000,2
np.float32,0x007fffff,0x3f800000,2
np.float32,0x00000001,0x3f800000,2
## -ve denormals ##
np.float32,0x80495d65,0x3f800000,2
np.float32,0x806894f6,0x3f800000,2
np.float32,0x80555a76,0x3f800000,2
np.float32,0x804e1fb8,0x3f800000,2
np.float32,0x80687de9,0x3f800000,2
np.float32,0x807fffff,0x3f800000,2
np.float32,0x80000001,0x3f800000,2
## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##
np.float32,0x00000000,0x3f800000,2
np.float32,0x80000000,0x3f800000,2
np.float32,0x00800000,0x3f800000,2
np.float32,0x7f7fffff,0x3f5a5f96,2
np.float32,0x80800000,0x3f800000,2
np.float32,0xff7fffff,0x3f5a5f96,2
## 1.00f + 0x00000001 ##
np.float32,0x3f800000,0x3f0a5140,2
np.float32,0x3f800001,0x3f0a513f,2
np.float32,0x3f800002,0x3f0a513d,2
np.float32,0xc090a8b0,0xbe4332ce,2
np.float32,0x41ce3184,0x3f4d1de1,2
np.float32,0xc1d85848,0xbeaa8980,2
np.float32,0x402b8820,0xbf653aa3,2
np.float32,0x42b4e454,0xbf4a338b,2
np.float32,0x42a67a60,0x3c58202e,2
np.float32,0x41d92388,0xbed987c7,2
np.float32,0x422dd66c,0x3f5dcab3,2
np.float32,0xc28f5be6,0xbf5688d8,2
np.float32,0x41ab2674,0xbf53aa3b,2
np.float32,0xd0102756,0x3f45d12d,2
np.float32,0xcf99405e,0xbe9cf281,2
np.float32,0xcfd83a12,0x3eaae4ca,2
np.float32,0x4fb54db0,0xbf7b2894,2
np.float32,0xcfcca29d,0x3f752e4e,2
np.float32,0xceec2ac0,0xbf745303,2
np.float32,0xcfdca97f,0x3ef554a7,2
np.float32,0xcfe92b0a,0x3f4618f2,2
np.float32,0x5014b0eb,0x3ee933e6,2
np.float32,0xcfa7ee96,0xbeedeeb2,2
np.float32,0x754c09a0,0xbef298de,2
np.float32,0x77a731fb,0x3f24599f,2
np.float32,0x76de2494,0x3f79576c,2
np.float32,0xf74920dc,0xbf4d196e,2
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np.float32,0x423c7edd,0xbf800000,2
np.float32,0xc23c7edd,0xbf800000,2
np.float32,0x42bc7edd,0x3f800000,2
np.float32,0xc2bc7edd,0x3f800000,2
np.float32,0x433c7edd,0x3f800000,2
np.float32,0xc33c7edd,0x3f800000,2
np.float32,0x423fa31d,0xbf3504d7,2
np.float32,0xc23fa31d,0xbf3504d7,2
np.float32,0x42bfa31d,0xb69d7828,2
np.float32,0xc2bfa31d,0xb69d7828,2
np.float32,0x433fa31d,0xbf800000,2
np.float32,0xc33fa31d,0xbf800000,2
np.float32,0x4242c75c,0x354bbe8a,2
np.float32,0xc242c75c,0x354bbe8a,2
np.float32,0x42c2c75c,0xbf800000,2
np.float32,0xc2c2c75c,0xbf800000,2
np.float32,0x4342c75c,0x3f800000,2
np.float32,0xc342c75c,0x3f800000,2
np.float32,0x4245eb9c,0x3f350517,2
np.float32,0xc245eb9c,0x3f350517,2
np.float32,0x42c5eb9c,0x36c8671d,2
np.float32,0xc2c5eb9c,0x36c8671d,2
np.float32,0x4345eb9c,0xbf800000,2
np.float32,0xc345eb9c,0xbf800000,2
np.float32,0x42490fdb,0x3f800000,2
np.float32,0xc2490fdb,0x3f800000,2
np.float32,0x42c90fdb,0x3f800000,2
np.float32,0xc2c90fdb,0x3f800000,2
np.float32,0x43490fdb,0x3f800000,2
np.float32,0xc3490fdb,0x3f800000,2
np.float32,0x424c341a,0x3f3504f5,2
np.float32,0xc24c341a,0x3f3504f5,2
np.float32,0x42cc341a,0x34ca9ee6,2
np.float32,0xc2cc341a,0x34ca9ee6,2
np.float32,0x434c341a,0xbf800000,2
np.float32,0xc34c341a,0xbf800000,2
np.float32,0x424f585a,0xb608cd8c,2
np.float32,0xc24f585a,0xb608cd8c,2
np.float32,0x42cf585a,0xbf800000,2
np.float32,0xc2cf585a,0xbf800000,2
np.float32,0x434f585a,0x3f800000,2
np.float32,0xc34f585a,0x3f800000,2
np.float32,0x42527c99,0xbf3504f9,2
np.float32,0xc2527c99,0xbf3504f9,2
np.float32,0x42d27c99,0x35722833,2
np.float32,0xc2d27c99,0x35722833,2
np.float32,0x43527c99,0xbf800000,2
np.float32,0xc3527c99,0xbf800000,2
np.float32,0x4255a0d9,0xbf800000,2
np.float32,0xc255a0d9,0xbf800000,2
np.float32,0x42d5a0d9,0x3f800000,2
np.float32,0xc2d5a0d9,0x3f800000,2
np.float32,0x4355a0d9,0x3f800000,2
np.float32,0xc355a0d9,0x3f800000,2
np.float32,0x4258c518,0xbf3504e6,2
np.float32,0xc258c518,0xbf3504e6,2
np.float32,0x42d8c518,0xb61267f6,2
np.float32,0xc2d8c518,0xb61267f6,2
np.float32,0x4358c518,0xbf800000,2
np.float32,0xc358c518,0xbf800000,2
np.float32,0x425be958,0x365eab75,2
np.float32,0xc25be958,0x365eab75,2
np.float32,0x42dbe958,0xbf800000,2
np.float32,0xc2dbe958,0xbf800000,2
np.float32,0x435be958,0x3f800000,2
np.float32,0xc35be958,0x3f800000,2
np.float32,0x425f0d97,0x3f350508,2
np.float32,0xc25f0d97,0x3f350508,2
np.float32,0x42df0d97,0x366845e0,2
np.float32,0xc2df0d97,0x366845e0,2
np.float32,0x435f0d97,0xbf800000,2
np.float32,0xc35f0d97,0xbf800000,2
np.float32,0x426231d6,0x3f800000,2
np.float32,0xc26231d6,0x3f800000,2
np.float32,0x42e231d6,0x3f800000,2
np.float32,0xc2e231d6,0x3f800000,2
np.float32,0x436231d6,0x3f800000,2
np.float32,0xc36231d6,0x3f800000,2
np.float32,0x42655616,0x3f3504d7,2
np.float32,0xc2655616,0x3f3504d7,2
np.float32,0x42e55616,0xb69f11e5,2
np.float32,0xc2e55616,0xb69f11e5,2
np.float32,0x43655616,0xbf800000,2
np.float32,0xc3655616,0xbf800000,2
np.float32,0x42687a55,0xb552257b,2
np.float32,0xc2687a55,0xb552257b,2
np.float32,0x42e87a55,0xbf800000,2
np.float32,0xc2e87a55,0xbf800000,2
np.float32,0x43687a55,0x3f800000,2
np.float32,0xc3687a55,0x3f800000,2
np.float32,0x426b9e95,0xbf350517,2
np.float32,0xc26b9e95,0xbf350517,2
np.float32,0x42eb9e95,0x36ca00d9,2
np.float32,0xc2eb9e95,0x36ca00d9,2
np.float32,0x436b9e95,0xbf800000,2
np.float32,0xc36b9e95,0xbf800000,2
np.float32,0x426ec2d4,0xbf800000,2
np.float32,0xc26ec2d4,0xbf800000,2
np.float32,0x42eec2d4,0x3f800000,2
np.float32,0xc2eec2d4,0x3f800000,2
np.float32,0x436ec2d4,0x3f800000,2
np.float32,0xc36ec2d4,0x3f800000,2
np.float32,0x4271e713,0xbf3504f5,2
np.float32,0xc271e713,0xbf3504f5,2
np.float32,0x42f1e713,0x34b10321,2
np.float32,0xc2f1e713,0x34b10321,2
np.float32,0x4371e713,0xbf800000,2
np.float32,0xc371e713,0xbf800000,2
np.float32,0x42750b53,0x360a6748,2
np.float32,0xc2750b53,0x360a6748,2
np.float32,0x42f50b53,0xbf800000,2
np.float32,0xc2f50b53,0xbf800000,2
np.float32,0x43750b53,0x3f800000,2
np.float32,0xc3750b53,0x3f800000,2
np.float32,0x42782f92,0x3f3504f9,2
np.float32,0xc2782f92,0x3f3504f9,2
np.float32,0x42f82f92,0x357ef616,2
np.float32,0xc2f82f92,0x357ef616,2
np.float32,0x43782f92,0xbf800000,2
np.float32,0xc3782f92,0xbf800000,2
np.float32,0x427b53d2,0x3f800000,2
np.float32,0xc27b53d2,0x3f800000,2
np.float32,0x42fb53d2,0x3f800000,2
np.float32,0xc2fb53d2,0x3f800000,2
np.float32,0x437b53d2,0x3f800000,2
np.float32,0xc37b53d2,0x3f800000,2
np.float32,0x427e7811,0x3f3504e6,2
np.float32,0xc27e7811,0x3f3504e6,2
np.float32,0x42fe7811,0xb6159b6f,2
np.float32,0xc2fe7811,0xb6159b6f,2
np.float32,0x437e7811,0xbf800000,2
np.float32,0xc37e7811,0xbf800000,2
np.float32,0x4280ce28,0x34fdd672,2
np.float32,0xc280ce28,0x34fdd672,2
np.float32,0x4300ce28,0xbf800000,2
np.float32,0xc300ce28,0xbf800000,2
np.float32,0x4380ce28,0x3f800000,2
np.float32,0xc380ce28,0x3f800000,2
np.float32,0x42826048,0xbf350508,2
np.float32,0xc2826048,0xbf350508,2
np.float32,0x43026048,0x366b7958,2
np.float32,0xc3026048,0x366b7958,2
np.float32,0x43826048,0xbf800000,2
np.float32,0xc3826048,0xbf800000,2
np.float32,0x4283f268,0xbf800000,2
np.float32,0xc283f268,0xbf800000,2
np.float32,0x4303f268,0x3f800000,2
np.float32,0xc303f268,0x3f800000,2
np.float32,0x4383f268,0x3f800000,2
np.float32,0xc383f268,0x3f800000,2
np.float32,0x42858487,0xbf350504,2
np.float32,0xc2858487,0xbf350504,2
np.float32,0x43058487,0x363ea8be,2
np.float32,0xc3058487,0x363ea8be,2
np.float32,0x43858487,0xbf800000,2
np.float32,0xc3858487,0xbf800000,2
np.float32,0x428716a7,0x35588c6d,2
np.float32,0xc28716a7,0x35588c6d,2
np.float32,0x430716a7,0xbf800000,2
np.float32,0xc30716a7,0xbf800000,2
np.float32,0x438716a7,0x3f800000,2
np.float32,0xc38716a7,0x3f800000,2
np.float32,0x4288a8c7,0x3f350517,2
np.float32,0xc288a8c7,0x3f350517,2
np.float32,0x4308a8c7,0x36cb9a96,2
np.float32,0xc308a8c7,0x36cb9a96,2
np.float32,0x4388a8c7,0xbf800000,2
np.float32,0xc388a8c7,0xbf800000,2
np.float32,0x428a3ae7,0x3f800000,2
np.float32,0xc28a3ae7,0x3f800000,2
np.float32,0x430a3ae7,0x3f800000,2
np.float32,0xc30a3ae7,0x3f800000,2
np.float32,0x438a3ae7,0x3f800000,2
np.float32,0xc38a3ae7,0x3f800000,2
np.float32,0x428bcd06,0x3f3504f5,2
np.float32,0xc28bcd06,0x3f3504f5,2
np.float32,0x430bcd06,0x3497675b,2
np.float32,0xc30bcd06,0x3497675b,2
np.float32,0x438bcd06,0xbf800000,2
np.float32,0xc38bcd06,0xbf800000,2
np.float32,0x428d5f26,0xb60c0105,2
np.float32,0xc28d5f26,0xb60c0105,2
np.float32,0x430d5f26,0xbf800000,2
np.float32,0xc30d5f26,0xbf800000,2
np.float32,0x438d5f26,0x3f800000,2
np.float32,0xc38d5f26,0x3f800000,2
np.float32,0x428ef146,0xbf350526,2
np.float32,0xc28ef146,0xbf350526,2
np.float32,0x430ef146,0x3710bc40,2
np.float32,0xc30ef146,0x3710bc40,2
np.float32,0x438ef146,0xbf800000,2
np.float32,0xc38ef146,0xbf800000,2
np.float32,0x42908365,0xbf800000,2
np.float32,0xc2908365,0xbf800000,2
np.float32,0x43108365,0x3f800000,2
np.float32,0xc3108365,0x3f800000,2
np.float32,0x43908365,0x3f800000,2
np.float32,0xc3908365,0x3f800000,2
np.float32,0x42921585,0xbf3504e6,2
np.float32,0xc2921585,0xbf3504e6,2
np.float32,0x43121585,0xb618cee8,2
np.float32,0xc3121585,0xb618cee8,2
np.float32,0x43921585,0xbf800000,2
np.float32,0xc3921585,0xbf800000,2
np.float32,0x4293a7a5,0x3661deee,2
np.float32,0xc293a7a5,0x3661deee,2
np.float32,0x4313a7a5,0xbf800000,2
np.float32,0xc313a7a5,0xbf800000,2
np.float32,0x4393a7a5,0x3f800000,2
np.float32,0xc393a7a5,0x3f800000,2
np.float32,0x429539c5,0x3f350536,2
np.float32,0xc29539c5,0x3f350536,2
np.float32,0x431539c5,0x373bab34,2
np.float32,0xc31539c5,0x373bab34,2
np.float32,0x439539c5,0xbf800000,2
np.float32,0xc39539c5,0xbf800000,2
np.float32,0x4296cbe4,0x3f800000,2
np.float32,0xc296cbe4,0x3f800000,2
np.float32,0x4316cbe4,0x3f800000,2
np.float32,0xc316cbe4,0x3f800000,2
np.float32,0x4396cbe4,0x3f800000,2
np.float32,0xc396cbe4,0x3f800000,2
np.float32,0x42985e04,0x3f3504d7,2
np.float32,0xc2985e04,0x3f3504d7,2
np.float32,0x43185e04,0xb6a2455d,2
np.float32,0xc3185e04,0xb6a2455d,2
np.float32,0x43985e04,0xbf800000,2
np.float32,0xc3985e04,0xbf800000,2
np.float32,0x4299f024,0xb69bde6c,2
np.float32,0xc299f024,0xb69bde6c,2
np.float32,0x4319f024,0xbf800000,2
np.float32,0xc319f024,0xbf800000,2
np.float32,0x4399f024,0x3f800000,2
np.float32,0xc399f024,0x3f800000,2
np.float32,0x429b8243,0xbf3504ea,2
np.float32,0xc29b8243,0xbf3504ea,2
np.float32,0x431b8243,0xb5cb2eb8,2
np.float32,0xc31b8243,0xb5cb2eb8,2
np.float32,0x439b8243,0xbf800000,2
np.float32,0xc39b8243,0xbf800000,2
np.float32,0x435b2047,0x3f3504c1,2
np.float32,0x42a038a2,0xb5e4ca7e,2
np.float32,0x432038a2,0xbf800000,2
np.float32,0x4345eb9b,0xbf800000,2
np.float32,0x42c5eb9b,0xb5de638c,2
np.float32,0x42eb9e94,0xb5d7fc9b,2
np.float32,0x4350ea79,0x3631dadb,2
np.float32,0x42dbe957,0xbf800000,2
np.float32,0x425be957,0xb505522a,2
np.float32,0x435be957,0x3f800000,2
np.float32,0x487fe5ab,0xba140185,2
np.float32,0x497fe5ab,0x3f7fffd5,2
np.float32,0x49ffe5ab,0x3f7fff55,2
np.float32,0x49ffeb37,0x3b9382f5,2
np.float32,0x497ff0c3,0x3b13049f,2
np.float32,0x49fff0c3,0xbf7fff57,2
np.float32,0x49fff64f,0xbb928618,2
np.float32,0x497ffbdb,0xbf7fffd6,2
np.float32,0x49fffbdb,0x3f7fff59,2
np.float32,0x48fffbdb,0xba9207c6,2
np.float32,0x4e736e56,0xbf800000,2
np.float32,0x4d4da377,0xbf800000,2
np.float32,0x4ece58c3,0xbf800000,2
np.float32,0x4ee0db9c,0xbf800000,2
np.float32,0x4dee7002,0x3f800000,2
np.float32,0x4ee86afc,0x38857a23,2
np.float32,0x4dca4f3f,0xbf800000,2
np.float32,0x4ecb48af,0xb95d1e10,2
np.float32,0x4e51e33f,0xbf800000,2
np.float32,0x4ef5f421,0xbf800000,2
np.float32,0x46027eb2,0x3e7d94c9,2
np.float32,0x4477baed,0xbe7f1824,2
np.float32,0x454b8024,0x3e7f5268,2
np.float32,0x455d2c09,0x3e7f40cb,2
np.float32,0x4768d3de,0xba14b4af,2
np.float32,0x46c1e7cd,0x3e7fb102,2
np.float32,0x44a52949,0xbe7dc9d5,2
np.float32,0x4454633a,0x3e7dbc7d,2
np.float32,0x4689810b,0x3e7eb02b,2
np.float32,0x473473cd,0xbe7eef6f,2
np.float32,0x44a5193f,0x3e7e1b1f,2
np.float32,0x46004b36,0x3e7dac59,2
np.float32,0x467f604b,0x3d7ffd3a,2
np.float32,0x45ea1805,0x3dffd2e0,2
np.float32,0x457b6af3,0x3dff7831,2
np.float32,0x44996159,0xbe7d85f4,2
np.float32,0x47883553,0xbb80584e,2
np.float32,0x44e19f0c,0xbdffcfe6,2
np.float32,0x472b3bf6,0xbe7f7a82,2
np.float32,0x4600bb4e,0x3a135e33,2
np.float32,0x449f4556,0x3e7e42e5,2
np.float32,0x474e9420,0x3dff77b2,2
np.float32,0x45cbdb23,0x3dff7240,2
np.float32,0x44222747,0x3dffb039,2
np.float32,0x4772e419,0xbdff74b8,2

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@ -0,0 +1,135 @@
dtype,input,output,ulperrortol
## +ve denormals ##
np.float32,0x004b4716,0x3f800000,3
np.float32,0x007b2490,0x3f800000,3
np.float32,0x007c99fa,0x3f800000,3
np.float32,0x00734a0c,0x3f800000,3
np.float32,0x0070de24,0x3f800000,3
np.float32,0x00495d65,0x3f800000,3
np.float32,0x006894f6,0x3f800000,3
np.float32,0x00555a76,0x3f800000,3
np.float32,0x004e1fb8,0x3f800000,3
np.float32,0x00687de9,0x3f800000,3
## -ve denormals ##
np.float32,0x805b59af,0x3f800000,3
np.float32,0x807ed8ed,0x3f800000,3
np.float32,0x807142ad,0x3f800000,3
np.float32,0x80772002,0x3f800000,3
np.float32,0x8062abcb,0x3f800000,3
np.float32,0x8045e31c,0x3f800000,3
np.float32,0x805f01c2,0x3f800000,3
np.float32,0x80506432,0x3f800000,3
np.float32,0x8060089d,0x3f800000,3
np.float32,0x8071292f,0x3f800000,3
## floats that output a denormal ##
np.float32,0xc2cf3fc1,0x00000001,3
np.float32,0xc2c79726,0x00000021,3
np.float32,0xc2cb295d,0x00000005,3
np.float32,0xc2b49e6b,0x00068c4c,3
np.float32,0xc2ca8116,0x00000008,3
np.float32,0xc2c23f82,0x000001d7,3
np.float32,0xc2cb69c0,0x00000005,3
np.float32,0xc2cc1f4d,0x00000003,3
np.float32,0xc2ae094e,0x00affc4c,3
np.float32,0xc2c86c44,0x00000015,3
## random floats between -87.0f and 88.0f ##
np.float32,0x4030d7e0,0x417d9a05,3
np.float32,0x426f60e8,0x6aa1be2c,3
np.float32,0x41a1b220,0x4e0efc11,3
np.float32,0xc20cc722,0x26159da7,3
np.float32,0x41c492bc,0x512ec79d,3
np.float32,0x40980210,0x42e73a0e,3
np.float32,0xbf1f7b80,0x3f094de3,3
np.float32,0x42a678a4,0x7b87a383,3
np.float32,0xc20f3cfd,0x25a1c304,3
np.float32,0x423ff34c,0x6216467f,3
np.float32,0x00000000,0x3f800000,3
## floats that cause an overflow ##
np.float32,0x7f06d8c1,0x7f800000,3
np.float32,0x7f451912,0x7f800000,3
np.float32,0x7ecceac3,0x7f800000,3
np.float32,0x7f643b45,0x7f800000,3
np.float32,0x7e910ea0,0x7f800000,3
np.float32,0x7eb4756b,0x7f800000,3
np.float32,0x7f4ec708,0x7f800000,3
np.float32,0x7f6b4551,0x7f800000,3
np.float32,0x7d8edbda,0x7f800000,3
np.float32,0x7f730718,0x7f800000,3
np.float32,0x42b17217,0x7f7fff84,3
np.float32,0x42b17218,0x7f800000,3
np.float32,0x42b17219,0x7f800000,3
np.float32,0xfef2b0bc,0x00000000,3
np.float32,0xff69f83e,0x00000000,3
np.float32,0xff4ecb12,0x00000000,3
np.float32,0xfeac6d86,0x00000000,3
np.float32,0xfde0cdb8,0x00000000,3
np.float32,0xff26aef4,0x00000000,3
np.float32,0xff6f9277,0x00000000,3
np.float32,0xff7adfc4,0x00000000,3
np.float32,0xff0ad40e,0x00000000,3
np.float32,0xff6fd8f3,0x00000000,3
np.float32,0xc2cff1b4,0x00000001,3
np.float32,0xc2cff1b5,0x00000000,3
np.float32,0xc2cff1b6,0x00000000,3
np.float32,0x7f800000,0x7f800000,3
np.float32,0xff800000,0x00000000,3
np.float32,0x4292f27c,0x7480000a,3
np.float32,0x42a920be,0x7c7fff94,3
np.float32,0x41c214c9,0x50ffffd9,3
np.float32,0x41abe686,0x4effffd9,3
np.float32,0x4287db5a,0x707fffd3,3
np.float32,0x41902cbb,0x4c800078,3
np.float32,0x42609466,0x67ffffeb,3
np.float32,0x41a65af5,0x4e7fffd1,3
np.float32,0x417f13ff,0x4affffc9,3
np.float32,0x426d0e6c,0x6a3504f2,3
np.float32,0x41bc8934,0x507fff51,3
np.float32,0x42a7bdde,0x7c0000d6,3
np.float32,0x4120cf66,0x46b504f6,3
np.float32,0x4244da8f,0x62ffff1a,3
np.float32,0x41a0cf69,0x4e000034,3
np.float32,0x41cd2bec,0x52000005,3
np.float32,0x42893e41,0x7100009e,3
np.float32,0x41b437e1,0x4fb50502,3
np.float32,0x41d8430f,0x5300001d,3
np.float32,0x4244da92,0x62ffffda,3
np.float32,0x41a0cf63,0x4dffffa9,3
np.float32,0x3eb17218,0x3fb504f3,3
np.float32,0x428729e8,0x703504dc,3
np.float32,0x41a0cf67,0x4e000014,3
np.float32,0x4252b77d,0x65800011,3
np.float32,0x41902cb9,0x4c800058,3
np.float32,0x42a0cf67,0x79800052,3
np.float32,0x4152b77b,0x48ffffe9,3
np.float32,0x41265af3,0x46ffffc8,3
np.float32,0x42187e0b,0x5affff9a,3
np.float32,0xc0d2b77c,0x3ab504f6,3
np.float32,0xc283b2ac,0x10000072,3
np.float32,0xc1cff1b4,0x2cb504f5,3
np.float32,0xc05dce9e,0x3d000000,3
np.float32,0xc28ec9d2,0x0bfffea5,3
np.float32,0xc23c893a,0x1d7fffde,3
np.float32,0xc2a920c0,0x027fff6c,3
np.float32,0xc1f9886f,0x2900002b,3
np.float32,0xc2c42920,0x000000b5,3
np.float32,0xc2893e41,0x0dfffec5,3
np.float32,0xc2c4da93,0x00000080,3
np.float32,0xc17f1401,0x3400000c,3
np.float32,0xc1902cb6,0x327fffaf,3
np.float32,0xc27c4e3b,0x11ffffc5,3
np.float32,0xc268e5c5,0x157ffe9d,3
np.float32,0xc2b4e953,0x0005a826,3
np.float32,0xc287db5a,0x0e800016,3
np.float32,0xc207db5a,0x2700000b,3
np.float32,0xc2b2d4fe,0x000ffff1,3
np.float32,0xc268e5c0,0x157fffdd,3
np.float32,0xc22920bd,0x2100003b,3
np.float32,0xc2902caf,0x0b80011e,3
np.float32,0xc1902cba,0x327fff2f,3
np.float32,0xc2ca6625,0x00000008,3
np.float32,0xc280ece8,0x10fffeb5,3
np.float32,0xc2918f94,0x0b0000ea,3
np.float32,0xc29b43d5,0x077ffffc,3
np.float32,0xc1e61ff7,0x2ab504f5,3
np.float32,0xc2867878,0x0effff15,3
np.float32,0xc2a2324a,0x04fffff4,3

118
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@ -0,0 +1,118 @@
dtype,input,output,ulperrortol
## +ve denormals ##
np.float32,0x004b4716,0xc2afbc1b,4
np.float32,0x007b2490,0xc2aec01e,4
np.float32,0x007c99fa,0xc2aeba17,4
np.float32,0x00734a0c,0xc2aee1dc,4
np.float32,0x0070de24,0xc2aeecba,4
np.float32,0x007fffff,0xc2aeac50,4
np.float32,0x00000001,0xc2ce8ed0,4
## -ve denormals ##
np.float32,0x80495d65,0xffc00000,4
np.float32,0x806894f6,0xffc00000,4
np.float32,0x80555a76,0xffc00000,4
np.float32,0x804e1fb8,0xffc00000,4
np.float32,0x80687de9,0xffc00000,4
np.float32,0x807fffff,0xffc00000,4
np.float32,0x80000001,0xffc00000,4
## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##
np.float32,0x00000000,0xff800000,4
np.float32,0x80000000,0xff800000,4
np.float32,0x7f7fffff,0x42b17218,4
np.float32,0x80800000,0xffc00000,4
np.float32,0xff7fffff,0xffc00000,4
## 1.00f + 0x00000001 ##
np.float32,0x3f800000,0x00000000,4
np.float32,0x3f800001,0x33ffffff,4
np.float32,0x3f800002,0x347ffffe,4
np.float32,0x3f7fffff,0xb3800000,4
np.float32,0x3f7ffffe,0xb4000000,4
np.float32,0x3f7ffffd,0xb4400001,4
np.float32,0x402df853,0x3f7ffffe,4
np.float32,0x402df854,0x3f7fffff,4
np.float32,0x402df855,0x3f800000,4
np.float32,0x402df856,0x3f800001,4
np.float32,0x3ebc5ab0,0xbf800001,4
np.float32,0x3ebc5ab1,0xbf800000,4
np.float32,0x3ebc5ab2,0xbf800000,4
np.float32,0x3ebc5ab3,0xbf7ffffe,4
np.float32,0x423ef575,0x407768ab,4
np.float32,0x427b8c61,0x408485dd,4
np.float32,0x4211e9ee,0x406630b0,4
np.float32,0x424d5c41,0x407c0fed,4
np.float32,0x42be722a,0x4091cc91,4
np.float32,0x42b73d30,0x4090908b,4
np.float32,0x427e48e2,0x4084de7f,4
np.float32,0x428f759b,0x4088bba3,4
np.float32,0x41629069,0x4029a0cc,4
np.float32,0x4272c99d,0x40836379,4
np.float32,0x4d1b7458,0x4197463d,4
np.float32,0x4f10c594,0x41ace2b2,4
np.float32,0x4ea397c2,0x41a85171,4
np.float32,0x4fefa9d1,0x41b6769c,4
np.float32,0x4ebac6ab,0x41a960dc,4
np.float32,0x4f6efb42,0x41b0e535,4
np.float32,0x4e9ab8e7,0x41a7df44,4
np.float32,0x4e81b5d1,0x41a67625,4
np.float32,0x5014d9f2,0x41b832bd,4
np.float32,0x4f02175c,0x41ac07b8,4
np.float32,0x7f034f89,0x42b01c47,4
np.float32,0x7f56d00e,0x42b11849,4
np.float32,0x7f1cd5f6,0x42b0773a,4
np.float32,0x7e979174,0x42af02d7,4
np.float32,0x7f23369f,0x42b08ba2,4
np.float32,0x7f0637ae,0x42b0277d,4
np.float32,0x7efcb6e8,0x42b00897,4
np.float32,0x7f7907c8,0x42b163f6,4
np.float32,0x7e95c4c2,0x42aefcba,4
np.float32,0x7f4577b2,0x42b0ed2d,4
np.float32,0x3f49c92e,0xbe73ae84,4
np.float32,0x3f4a23d1,0xbe71e2f8,4
np.float32,0x3f4abb67,0xbe6ee430,4
np.float32,0x3f48169a,0xbe7c5532,4
np.float32,0x3f47f5fa,0xbe7cfc37,4
np.float32,0x3f488309,0xbe7a2ad8,4
np.float32,0x3f479df4,0xbe7ebf5f,4
np.float32,0x3f47cfff,0xbe7dbec9,4
np.float32,0x3f496704,0xbe75a125,4
np.float32,0x3f478ee8,0xbe7f0c92,4
np.float32,0x3f4a763b,0xbe7041ce,4
np.float32,0x3f47a108,0xbe7eaf94,4
np.float32,0x3f48136c,0xbe7c6578,4
np.float32,0x3f481c17,0xbe7c391c,4
np.float32,0x3f47cd28,0xbe7dcd56,4
np.float32,0x3f478be8,0xbe7f1bf7,4
np.float32,0x3f4c1f8e,0xbe67e367,4
np.float32,0x3f489b0c,0xbe79b03f,4
np.float32,0x3f4934cf,0xbe76a08a,4
np.float32,0x3f4954df,0xbe75fd6a,4
np.float32,0x3f47a3f5,0xbe7ea093,4
np.float32,0x3f4ba4fc,0xbe6a4b02,4
np.float32,0x3f47a0e1,0xbe7eb05c,4
np.float32,0x3f48c30a,0xbe78e42f,4
np.float32,0x3f48cab8,0xbe78bd05,4
np.float32,0x3f4b0569,0xbe6d6ea4,4
np.float32,0x3f47de32,0xbe7d7607,4
np.float32,0x3f477328,0xbe7f9b00,4
np.float32,0x3f496dab,0xbe757f52,4
np.float32,0x3f47662c,0xbe7fddac,4
np.float32,0x3f48ddd8,0xbe785b80,4
np.float32,0x3f481866,0xbe7c4bff,4
np.float32,0x3f48b119,0xbe793fb6,4
np.float32,0x3f48c7e8,0xbe78cb5c,4
np.float32,0x3f4985f6,0xbe7503da,4
np.float32,0x3f483fdf,0xbe7b8212,4
np.float32,0x3f4b1c76,0xbe6cfa67,4
np.float32,0x3f480b2e,0xbe7c8fa8,4
np.float32,0x3f48745f,0xbe7a75bf,4
np.float32,0x3f485bda,0xbe7af308,4
np.float32,0x3f47a660,0xbe7e942c,4
np.float32,0x3f47d4d5,0xbe7da600,4
np.float32,0x3f4b0a26,0xbe6d56be,4
np.float32,0x3f4a4883,0xbe712924,4
np.float32,0x3f4769e7,0xbe7fca84,4
np.float32,0x3f499702,0xbe74ad3f,4
np.float32,0x3f494ab1,0xbe763131,4
np.float32,0x3f476b69,0xbe7fc2c6,4
np.float32,0x3f4884e8,0xbe7a214a,4
np.float32,0x3f486945,0xbe7aae76,4

707
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@ -0,0 +1,707 @@
dtype,input,output,ulperrortol
## +ve denormals ##
np.float32,0x004b4716,0x004b4716,2
np.float32,0x007b2490,0x007b2490,2
np.float32,0x007c99fa,0x007c99fa,2
np.float32,0x00734a0c,0x00734a0c,2
np.float32,0x0070de24,0x0070de24,2
np.float32,0x007fffff,0x007fffff,2
np.float32,0x00000001,0x00000001,2
## -ve denormals ##
np.float32,0x80495d65,0x80495d65,2
np.float32,0x806894f6,0x806894f6,2
np.float32,0x80555a76,0x80555a76,2
np.float32,0x804e1fb8,0x804e1fb8,2
np.float32,0x80687de9,0x80687de9,2
np.float32,0x807fffff,0x807fffff,2
np.float32,0x80000001,0x80000001,2
## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##
np.float32,0x00000000,0x00000000,2
np.float32,0x80000000,0x80000000,2
np.float32,0x00800000,0x00800000,2
np.float32,0x7f7fffff,0xbf0599b3,2
np.float32,0x80800000,0x80800000,2
np.float32,0xff7fffff,0x3f0599b3,2
## 1.00f ##
np.float32,0x3f800000,0x3f576aa4,2
np.float32,0x3f800001,0x3f576aa6,2
np.float32,0x3f800002,0x3f576aa7,2
np.float32,0xc090a8b0,0x3f7b4e48,2
np.float32,0x41ce3184,0x3f192d43,2
np.float32,0xc1d85848,0xbf7161cb,2
np.float32,0x402b8820,0x3ee3f29f,2
np.float32,0x42b4e454,0x3f1d0151,2
np.float32,0x42a67a60,0x3f7ffa4c,2
np.float32,0x41d92388,0x3f67beef,2
np.float32,0x422dd66c,0xbeffb0c1,2
np.float32,0xc28f5be6,0xbf0bae79,2
np.float32,0x41ab2674,0x3f0ffe2b,2
np.float32,0xd0102756,0x3f227e8a,2
np.float32,0xcf99405e,0x3f73ad00,2
np.float32,0xcfd83a12,0xbf7151a7,2
np.float32,0x4fb54db0,0xbe46354b,2
np.float32,0xcfcca29d,0xbe9345e6,2
np.float32,0xceec2ac0,0x3e98dc89,2
np.float32,0xcfdca97f,0xbf60b2b4,2
np.float32,0xcfe92b0a,0xbf222705,2
np.float32,0x5014b0eb,0x3f63e75c,2
np.float32,0xcfa7ee96,0x3f62ada4,2
np.float32,0x754c09a0,0xbf617056,2
np.float32,0x77a731fb,0x3f44472b,2
np.float32,0x76de2494,0xbe680739,2
np.float32,0xf74920dc,0xbf193338,2
np.float32,0x7707a312,0xbf6f51b1,2
np.float32,0x75bf9790,0xbd0f1a47,2
np.float32,0xf4ca7c40,0xbf7d45e7,2
np.float32,0x77e91899,0x3f767181,2
np.float32,0xf74c9820,0xbd685b75,2
np.float32,0x7785ca29,0x3f78ee61,2
np.float32,0x3f490fdb,0x3f3504f3,2
np.float32,0xbf490fdb,0xbf3504f3,2
np.float32,0x3fc90fdb,0x3f800000,2
np.float32,0xbfc90fdb,0xbf800000,2
np.float32,0x40490fdb,0xb3bbbd2e,2
np.float32,0xc0490fdb,0x33bbbd2e,2
np.float32,0x3fc90fdb,0x3f800000,2
np.float32,0xbfc90fdb,0xbf800000,2
np.float32,0x40490fdb,0xb3bbbd2e,2
np.float32,0xc0490fdb,0x33bbbd2e,2
np.float32,0x40c90fdb,0x343bbd2e,2
np.float32,0xc0c90fdb,0xb43bbd2e,2
np.float32,0x4016cbe4,0x3f3504f3,2
np.float32,0xc016cbe4,0xbf3504f3,2
np.float32,0x4096cbe4,0xbf800000,2
np.float32,0xc096cbe4,0x3f800000,2
np.float32,0x4116cbe4,0xb2ccde2e,2
np.float32,0xc116cbe4,0x32ccde2e,2
np.float32,0x40490fdb,0xb3bbbd2e,2
np.float32,0xc0490fdb,0x33bbbd2e,2
np.float32,0x40c90fdb,0x343bbd2e,2
np.float32,0xc0c90fdb,0xb43bbd2e,2
np.float32,0x41490fdb,0x34bbbd2e,2
np.float32,0xc1490fdb,0xb4bbbd2e,2
np.float32,0x407b53d2,0xbf3504f5,2
np.float32,0xc07b53d2,0x3f3504f5,2
np.float32,0x40fb53d2,0x3f800000,2
np.float32,0xc0fb53d2,0xbf800000,2
np.float32,0x417b53d2,0xb535563d,2
np.float32,0xc17b53d2,0x3535563d,2
np.float32,0x4096cbe4,0xbf800000,2
np.float32,0xc096cbe4,0x3f800000,2
np.float32,0x4116cbe4,0xb2ccde2e,2
np.float32,0xc116cbe4,0x32ccde2e,2
np.float32,0x4196cbe4,0x334cde2e,2
np.float32,0xc196cbe4,0xb34cde2e,2
np.float32,0x40afede0,0xbf3504ef,2
np.float32,0xc0afede0,0x3f3504ef,2
np.float32,0x412fede0,0xbf800000,2
np.float32,0xc12fede0,0x3f800000,2
np.float32,0x41afede0,0xb5b222c4,2
np.float32,0xc1afede0,0x35b222c4,2
np.float32,0x40c90fdb,0x343bbd2e,2
np.float32,0xc0c90fdb,0xb43bbd2e,2
np.float32,0x41490fdb,0x34bbbd2e,2
np.float32,0xc1490fdb,0xb4bbbd2e,2
np.float32,0x41c90fdb,0x353bbd2e,2
np.float32,0xc1c90fdb,0xb53bbd2e,2
np.float32,0x40e231d6,0x3f3504f3,2
np.float32,0xc0e231d6,0xbf3504f3,2
np.float32,0x416231d6,0x3f800000,2
np.float32,0xc16231d6,0xbf800000,2
np.float32,0x41e231d6,0xb399a6a2,2
np.float32,0xc1e231d6,0x3399a6a2,2
np.float32,0x40fb53d2,0x3f800000,2
np.float32,0xc0fb53d2,0xbf800000,2
np.float32,0x417b53d2,0xb535563d,2
np.float32,0xc17b53d2,0x3535563d,2
np.float32,0x41fb53d2,0x35b5563d,2
np.float32,0xc1fb53d2,0xb5b5563d,2
np.float32,0x410a3ae7,0x3f3504eb,2
np.float32,0xc10a3ae7,0xbf3504eb,2
np.float32,0x418a3ae7,0xbf800000,2
np.float32,0xc18a3ae7,0x3f800000,2
np.float32,0x420a3ae7,0xb6308908,2
np.float32,0xc20a3ae7,0x36308908,2
np.float32,0x4116cbe4,0xb2ccde2e,2
np.float32,0xc116cbe4,0x32ccde2e,2
np.float32,0x4196cbe4,0x334cde2e,2
np.float32,0xc196cbe4,0xb34cde2e,2
np.float32,0x4216cbe4,0x33ccde2e,2
np.float32,0xc216cbe4,0xb3ccde2e,2
np.float32,0x41235ce2,0xbf3504f7,2
np.float32,0xc1235ce2,0x3f3504f7,2
np.float32,0x41a35ce2,0x3f800000,2
np.float32,0xc1a35ce2,0xbf800000,2
np.float32,0x42235ce2,0xb5b889b6,2
np.float32,0xc2235ce2,0x35b889b6,2
np.float32,0x412fede0,0xbf800000,2
np.float32,0xc12fede0,0x3f800000,2
np.float32,0x41afede0,0xb5b222c4,2
np.float32,0xc1afede0,0x35b222c4,2
np.float32,0x422fede0,0x363222c4,2
np.float32,0xc22fede0,0xb63222c4,2
np.float32,0x413c7edd,0xbf3504f3,2
np.float32,0xc13c7edd,0x3f3504f3,2
np.float32,0x41bc7edd,0xbf800000,2
np.float32,0xc1bc7edd,0x3f800000,2
np.float32,0x423c7edd,0xb4000add,2
np.float32,0xc23c7edd,0x34000add,2
np.float32,0x41490fdb,0x34bbbd2e,2
np.float32,0xc1490fdb,0xb4bbbd2e,2
np.float32,0x41c90fdb,0x353bbd2e,2
np.float32,0xc1c90fdb,0xb53bbd2e,2
np.float32,0x42490fdb,0x35bbbd2e,2
np.float32,0xc2490fdb,0xb5bbbd2e,2
np.float32,0x4155a0d9,0x3f3504fb,2
np.float32,0xc155a0d9,0xbf3504fb,2
np.float32,0x41d5a0d9,0x3f800000,2
np.float32,0xc1d5a0d9,0xbf800000,2
np.float32,0x4255a0d9,0xb633bc81,2
np.float32,0xc255a0d9,0x3633bc81,2
np.float32,0x416231d6,0x3f800000,2
np.float32,0xc16231d6,0xbf800000,2
np.float32,0x41e231d6,0xb399a6a2,2
np.float32,0xc1e231d6,0x3399a6a2,2
np.float32,0x426231d6,0x3419a6a2,2
np.float32,0xc26231d6,0xb419a6a2,2
np.float32,0x416ec2d4,0x3f3504ef,2
np.float32,0xc16ec2d4,0xbf3504ef,2
np.float32,0x41eec2d4,0xbf800000,2
np.float32,0xc1eec2d4,0x3f800000,2
np.float32,0x426ec2d4,0xb5bef0a7,2
np.float32,0xc26ec2d4,0x35bef0a7,2
np.float32,0x417b53d2,0xb535563d,2
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np.float32,0xc3782f92,0x35fef616,2
np.float32,0x427b53d2,0x3635563d,2
np.float32,0xc27b53d2,0xb635563d,2
np.float32,0x42fb53d2,0x36b5563d,2
np.float32,0xc2fb53d2,0xb6b5563d,2
np.float32,0x437b53d2,0x3735563d,2
np.float32,0xc37b53d2,0xb735563d,2
np.float32,0x427e7811,0x3f350500,2
np.float32,0xc27e7811,0xbf350500,2
np.float32,0x42fe7811,0x3f800000,2
np.float32,0xc2fe7811,0xbf800000,2
np.float32,0x437e7811,0xb6959b6f,2
np.float32,0xc37e7811,0x36959b6f,2
np.float32,0x4280ce28,0x3f800000,2
np.float32,0xc280ce28,0xbf800000,2
np.float32,0x4300ce28,0x357dd672,2
np.float32,0xc300ce28,0xb57dd672,2
np.float32,0x4380ce28,0xb5fdd672,2
np.float32,0xc380ce28,0x35fdd672,2
np.float32,0x42826048,0x3f3504de,2
np.float32,0xc2826048,0xbf3504de,2
np.float32,0x43026048,0xbf800000,2
np.float32,0xc3026048,0x3f800000,2
np.float32,0x43826048,0xb6eb7958,2
np.float32,0xc3826048,0x36eb7958,2
np.float32,0x4283f268,0xb6859a13,2
np.float32,0xc283f268,0x36859a13,2
np.float32,0x4303f268,0x37059a13,2
np.float32,0xc303f268,0xb7059a13,2
np.float32,0x4383f268,0x37859a13,2
np.float32,0xc383f268,0xb7859a13,2
np.float32,0x42858487,0xbf3504e2,2
np.float32,0xc2858487,0x3f3504e2,2
np.float32,0x43058487,0x3f800000,2
np.float32,0xc3058487,0xbf800000,2
np.float32,0x43858487,0x36bea8be,2
np.float32,0xc3858487,0xb6bea8be,2
np.float32,0x428716a7,0xbf800000,2
np.float32,0xc28716a7,0x3f800000,2
np.float32,0x430716a7,0xb5d88c6d,2
np.float32,0xc30716a7,0x35d88c6d,2
np.float32,0x438716a7,0x36588c6d,2
np.float32,0xc38716a7,0xb6588c6d,2
np.float32,0x4288a8c7,0xbf3504cf,2
np.float32,0xc288a8c7,0x3f3504cf,2
np.float32,0x4308a8c7,0xbf800000,2
np.float32,0xc308a8c7,0x3f800000,2
np.float32,0x4388a8c7,0xb74b9a96,2
np.float32,0xc388a8c7,0x374b9a96,2
np.float32,0x428a3ae7,0x36b08908,2
np.float32,0xc28a3ae7,0xb6b08908,2
np.float32,0x430a3ae7,0x37308908,2
np.float32,0xc30a3ae7,0xb7308908,2
np.float32,0x438a3ae7,0x37b08908,2
np.float32,0xc38a3ae7,0xb7b08908,2
np.float32,0x428bcd06,0x3f3504f2,2
np.float32,0xc28bcd06,0xbf3504f2,2
np.float32,0x430bcd06,0x3f800000,2
np.float32,0xc30bcd06,0xbf800000,2
np.float32,0x438bcd06,0x3517675b,2
np.float32,0xc38bcd06,0xb517675b,2
np.float32,0x428d5f26,0x3f800000,2
np.float32,0xc28d5f26,0xbf800000,2
np.float32,0x430d5f26,0xb68c0105,2
np.float32,0xc30d5f26,0x368c0105,2
np.float32,0x438d5f26,0x370c0105,2
np.float32,0xc38d5f26,0xb70c0105,2
np.float32,0x428ef146,0x3f3504c0,2
np.float32,0xc28ef146,0xbf3504c0,2
np.float32,0x430ef146,0xbf800000,2
np.float32,0xc30ef146,0x3f800000,2
np.float32,0x438ef146,0xb790bc40,2
np.float32,0xc38ef146,0x3790bc40,2
np.float32,0x42908365,0x3592200d,2
np.float32,0xc2908365,0xb592200d,2
np.float32,0x43108365,0xb612200d,2
np.float32,0xc3108365,0x3612200d,2
np.float32,0x43908365,0xb692200d,2
np.float32,0xc3908365,0x3692200d,2
np.float32,0x42921585,0xbf350501,2
np.float32,0xc2921585,0x3f350501,2
np.float32,0x43121585,0x3f800000,2
np.float32,0xc3121585,0xbf800000,2
np.float32,0x43921585,0xb698cee8,2
np.float32,0xc3921585,0x3698cee8,2
np.float32,0x4293a7a5,0xbf800000,2
np.float32,0xc293a7a5,0x3f800000,2
np.float32,0x4313a7a5,0xb6e1deee,2
np.float32,0xc313a7a5,0x36e1deee,2
np.float32,0x4393a7a5,0x3761deee,2
np.float32,0xc393a7a5,0xb761deee,2
np.float32,0x429539c5,0xbf3504b1,2
np.float32,0xc29539c5,0x3f3504b1,2
np.float32,0x431539c5,0xbf800000,2
np.float32,0xc31539c5,0x3f800000,2
np.float32,0x439539c5,0xb7bbab34,2
np.float32,0xc39539c5,0x37bbab34,2
np.float32,0x4296cbe4,0x344cde2e,2
np.float32,0xc296cbe4,0xb44cde2e,2
np.float32,0x4316cbe4,0x34ccde2e,2
np.float32,0xc316cbe4,0xb4ccde2e,2
np.float32,0x4396cbe4,0x354cde2e,2
np.float32,0xc396cbe4,0xb54cde2e,2
np.float32,0x42985e04,0x3f350510,2
np.float32,0xc2985e04,0xbf350510,2
np.float32,0x43185e04,0x3f800000,2
np.float32,0xc3185e04,0xbf800000,2
np.float32,0x43985e04,0xb722455d,2
np.float32,0xc3985e04,0x3722455d,2
np.float32,0x4299f024,0x3f800000,2
np.float32,0xc299f024,0xbf800000,2
np.float32,0x4319f024,0xb71bde6c,2
np.float32,0xc319f024,0x371bde6c,2
np.float32,0x4399f024,0x379bde6c,2
np.float32,0xc399f024,0xb79bde6c,2
np.float32,0x429b8243,0x3f3504fc,2
np.float32,0xc29b8243,0xbf3504fc,2
np.float32,0x431b8243,0xbf800000,2
np.float32,0xc31b8243,0x3f800000,2
np.float32,0x439b8243,0x364b2eb8,2
np.float32,0xc39b8243,0xb64b2eb8,2
np.float32,0x435b2047,0xbf350525,2
np.float32,0x42a038a2,0xbf800000,2
np.float32,0x432038a2,0x3664ca7e,2
np.float32,0x4345eb9b,0x365e638c,2
np.float32,0x42c5eb9b,0xbf800000,2
np.float32,0x42eb9e94,0xbf800000,2
np.float32,0x4350ea79,0x3f800000,2
np.float32,0x42dbe957,0x3585522a,2
np.float32,0x425be957,0xbf800000,2
np.float32,0x435be957,0xb605522a,2
np.float32,0x487fe5ab,0xbf7ffffd,2
np.float32,0x497fe5ab,0xbb14017d,2
np.float32,0x49ffe5ab,0xbb940164,2
np.float32,0x49ffeb37,0x3f7fff56,2
np.float32,0x497ff0c3,0x3f7fffd6,2
np.float32,0x49fff0c3,0x3b930487,2
np.float32,0x49fff64f,0xbf7fff58,2
np.float32,0x497ffbdb,0x3b1207c0,2
np.float32,0x49fffbdb,0xbb9207a9,2
np.float32,0x48fffbdb,0xbf7ffff6,2
np.float32,0x4e736e56,0x397fa7f2,2
np.float32,0x4d4da377,0xb57c64bc,2
np.float32,0x4ece58c3,0xb80846c8,2
np.float32,0x4ee0db9c,0x394c4786,2
np.float32,0x4dee7002,0x381bce96,2
np.float32,0x4ee86afc,0x3f800000,2
np.float32,0x4dca4f3f,0xb8e25111,2
np.float32,0x4ecb48af,0xbf800000,2
np.float32,0x4e51e33f,0xb8a4fa6f,2
np.float32,0x4ef5f421,0x387ca7df,2
np.float32,0x476362a2,0xbd7ff911,2
np.float32,0x464c99a4,0x3e7f4d41,2
np.float32,0x4471f73d,0x3e7fe1b0,2
np.float32,0x445a6752,0x3e7ef367,2
np.float32,0x474fa400,0x3e7f9fcd,2
np.float32,0x47c9e70e,0xbb4bba09,2
np.float32,0x45c1e72f,0xbe7fc7af,2
np.float32,0x4558c91d,0x3e7e9f31,2
np.float32,0x43784f94,0xbdff6654,2
np.float32,0x466e8500,0xbe7ea0a3,2
np.float32,0x468e1c25,0x3e7e22fb,2
np.float32,0x47d28adc,0xbe7d5e6b,2
np.float32,0x44ea6cfc,0x3dff70c3,2
np.float32,0x4605126c,0x3e7f89ef,2
np.float32,0x4788b3c6,0xbb87d853,2
np.float32,0x4531b042,0x3dffd163,2
np.float32,0x47e46c29,0xbe7def2b,2
np.float32,0x47c10e07,0xbdff63d4,2
np.float32,0x43f1f71d,0x3dfff387,2
np.float32,0x47c3e38c,0x3e7f0b2f,2
np.float32,0x462c3fa5,0xbd7fe13d,2
np.float32,0x441c5354,0xbdff76b4,2
np.float32,0x44908b69,0x3e7dcf0d,2
np.float32,0x478813ad,0xbe7e9d80,2
np.float32,0x441c4351,0x3dff937b,2

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"""
Tests of the ._exceptions module. Primarily for exercising the __str__ methods.
"""
import numpy as np
_ArrayMemoryError = np.core._exceptions._ArrayMemoryError
class TestArrayMemoryError:
def test_str(self):
e = _ArrayMemoryError((1023,), np.dtype(np.uint8))
str(e) # not crashing is enough
# testing these properties is easier than testing the full string repr
def test__size_to_string(self):
""" Test e._size_to_string """
f = _ArrayMemoryError._size_to_string
Ki = 1024
assert f(0) == '0 bytes'
assert f(1) == '1 bytes'
assert f(1023) == '1023 bytes'
assert f(Ki) == '1.00 KiB'
assert f(Ki+1) == '1.00 KiB'
assert f(10*Ki) == '10.0 KiB'
assert f(int(999.4*Ki)) == '999. KiB'
assert f(int(1023.4*Ki)) == '1023. KiB'
assert f(int(1023.5*Ki)) == '1.00 MiB'
assert f(Ki*Ki) == '1.00 MiB'
# 1023.9999 Mib should round to 1 GiB
assert f(int(Ki*Ki*Ki*0.9999)) == '1.00 GiB'
assert f(Ki*Ki*Ki*Ki*Ki*Ki) == '1.00 EiB'
# larger than sys.maxsize, adding larger prefices isn't going to help
# anyway.
assert f(Ki*Ki*Ki*Ki*Ki*Ki*123456) == '123456. EiB'
def test__total_size(self):
""" Test e._total_size """
e = _ArrayMemoryError((1,), np.dtype(np.uint8))
assert e._total_size == 1
e = _ArrayMemoryError((2, 4), np.dtype((np.uint64, 16)))
assert e._total_size == 1024

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from __future__ import division, absolute_import, print_function
from numpy.testing import assert_
import numbers
import numpy as np
from numpy.core.numerictypes import sctypes
class TestABC(object):
def test_abstract(self):
assert_(issubclass(np.number, numbers.Number))
assert_(issubclass(np.inexact, numbers.Complex))
assert_(issubclass(np.complexfloating, numbers.Complex))
assert_(issubclass(np.floating, numbers.Real))
assert_(issubclass(np.integer, numbers.Integral))
assert_(issubclass(np.signedinteger, numbers.Integral))
assert_(issubclass(np.unsignedinteger, numbers.Integral))
def test_floats(self):
for t in sctypes['float']:
assert_(isinstance(t(), numbers.Real),
"{0} is not instance of Real".format(t.__name__))
assert_(issubclass(t, numbers.Real),
"{0} is not subclass of Real".format(t.__name__))
assert_(not isinstance(t(), numbers.Rational),
"{0} is instance of Rational".format(t.__name__))
assert_(not issubclass(t, numbers.Rational),
"{0} is subclass of Rational".format(t.__name__))
def test_complex(self):
for t in sctypes['complex']:
assert_(isinstance(t(), numbers.Complex),
"{0} is not instance of Complex".format(t.__name__))
assert_(issubclass(t, numbers.Complex),
"{0} is not subclass of Complex".format(t.__name__))
assert_(not isinstance(t(), numbers.Real),
"{0} is instance of Real".format(t.__name__))
assert_(not issubclass(t, numbers.Real),
"{0} is subclass of Real".format(t.__name__))
def test_int(self):
for t in sctypes['int']:
assert_(isinstance(t(), numbers.Integral),
"{0} is not instance of Integral".format(t.__name__))
assert_(issubclass(t, numbers.Integral),
"{0} is not subclass of Integral".format(t.__name__))
def test_uint(self):
for t in sctypes['uint']:
assert_(isinstance(t(), numbers.Integral),
"{0} is not instance of Integral".format(t.__name__))
assert_(issubclass(t, numbers.Integral),
"{0} is not subclass of Integral".format(t.__name__))

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from __future__ import division, absolute_import, print_function
import sys
import numpy as np
import pytest
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_raises, assert_warns,
HAS_REFCOUNT
)
# Switch between new behaviour when NPY_RELAXED_STRIDES_CHECKING is set.
NPY_RELAXED_STRIDES_CHECKING = np.ones((10, 1), order='C').flags.f_contiguous
def test_array_array():
tobj = type(object)
ones11 = np.ones((1, 1), np.float64)
tndarray = type(ones11)
# Test is_ndarray
assert_equal(np.array(ones11, dtype=np.float64), ones11)
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(tndarray)
np.array(ones11)
assert_equal(old_refcount, sys.getrefcount(tndarray))
# test None
assert_equal(np.array(None, dtype=np.float64),
np.array(np.nan, dtype=np.float64))
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(tobj)
np.array(None, dtype=np.float64)
assert_equal(old_refcount, sys.getrefcount(tobj))
# test scalar
assert_equal(np.array(1.0, dtype=np.float64),
np.ones((), dtype=np.float64))
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(np.float64)
np.array(np.array(1.0, dtype=np.float64), dtype=np.float64)
assert_equal(old_refcount, sys.getrefcount(np.float64))
# test string
S2 = np.dtype((str, 2))
S3 = np.dtype((str, 3))
S5 = np.dtype((str, 5))
assert_equal(np.array("1.0", dtype=np.float64),
np.ones((), dtype=np.float64))
assert_equal(np.array("1.0").dtype, S3)
assert_equal(np.array("1.0", dtype=str).dtype, S3)
assert_equal(np.array("1.0", dtype=S2), np.array("1."))
assert_equal(np.array("1", dtype=S5), np.ones((), dtype=S5))
# test unicode
_unicode = globals().get("unicode")
if _unicode:
U2 = np.dtype((_unicode, 2))
U3 = np.dtype((_unicode, 3))
U5 = np.dtype((_unicode, 5))
assert_equal(np.array(_unicode("1.0"), dtype=np.float64),
np.ones((), dtype=np.float64))
assert_equal(np.array(_unicode("1.0")).dtype, U3)
assert_equal(np.array(_unicode("1.0"), dtype=_unicode).dtype, U3)
assert_equal(np.array(_unicode("1.0"), dtype=U2),
np.array(_unicode("1.")))
assert_equal(np.array(_unicode("1"), dtype=U5),
np.ones((), dtype=U5))
builtins = getattr(__builtins__, '__dict__', __builtins__)
assert_(hasattr(builtins, 'get'))
# test buffer
_buffer = builtins.get("buffer")
if _buffer and sys.version_info[:3] >= (2, 7, 5):
# This test fails for earlier versions of Python.
# Evidently a bug got fixed in 2.7.5.
dat = np.array(_buffer('1.0'), dtype=np.float64)
assert_equal(dat, [49.0, 46.0, 48.0])
assert_(dat.dtype.type is np.float64)
dat = np.array(_buffer(b'1.0'))
assert_equal(dat, [49, 46, 48])
assert_(dat.dtype.type is np.uint8)
# test memoryview, new version of buffer
_memoryview = builtins.get("memoryview")
if _memoryview:
dat = np.array(_memoryview(b'1.0'), dtype=np.float64)
assert_equal(dat, [49.0, 46.0, 48.0])
assert_(dat.dtype.type is np.float64)
dat = np.array(_memoryview(b'1.0'))
assert_equal(dat, [49, 46, 48])
assert_(dat.dtype.type is np.uint8)
# test array interface
a = np.array(100.0, dtype=np.float64)
o = type("o", (object,),
dict(__array_interface__=a.__array_interface__))
assert_equal(np.array(o, dtype=np.float64), a)
# test array_struct interface
a = np.array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('f0', int), ('f1', float), ('f2', str)])
o = type("o", (object,),
dict(__array_struct__=a.__array_struct__))
## wasn't what I expected... is np.array(o) supposed to equal a ?
## instead we get a array([...], dtype=">V18")
assert_equal(bytes(np.array(o).data), bytes(a.data))
# test array
o = type("o", (object,),
dict(__array__=lambda *x: np.array(100.0, dtype=np.float64)))()
assert_equal(np.array(o, dtype=np.float64), np.array(100.0, np.float64))
# test recursion
nested = 1.5
for i in range(np.MAXDIMS):
nested = [nested]
# no error
np.array(nested)
# Exceeds recursion limit
assert_raises(ValueError, np.array, [nested], dtype=np.float64)
# Try with lists...
assert_equal(np.array([None] * 10, dtype=np.float64),
np.full((10,), np.nan, dtype=np.float64))
assert_equal(np.array([[None]] * 10, dtype=np.float64),
np.full((10, 1), np.nan, dtype=np.float64))
assert_equal(np.array([[None] * 10], dtype=np.float64),
np.full((1, 10), np.nan, dtype=np.float64))
assert_equal(np.array([[None] * 10] * 10, dtype=np.float64),
np.full((10, 10), np.nan, dtype=np.float64))
assert_equal(np.array([1.0] * 10, dtype=np.float64),
np.ones((10,), dtype=np.float64))
assert_equal(np.array([[1.0]] * 10, dtype=np.float64),
np.ones((10, 1), dtype=np.float64))
assert_equal(np.array([[1.0] * 10], dtype=np.float64),
np.ones((1, 10), dtype=np.float64))
assert_equal(np.array([[1.0] * 10] * 10, dtype=np.float64),
np.ones((10, 10), dtype=np.float64))
# Try with tuples
assert_equal(np.array((None,) * 10, dtype=np.float64),
np.full((10,), np.nan, dtype=np.float64))
assert_equal(np.array([(None,)] * 10, dtype=np.float64),
np.full((10, 1), np.nan, dtype=np.float64))
assert_equal(np.array([(None,) * 10], dtype=np.float64),
np.full((1, 10), np.nan, dtype=np.float64))
assert_equal(np.array([(None,) * 10] * 10, dtype=np.float64),
np.full((10, 10), np.nan, dtype=np.float64))
assert_equal(np.array((1.0,) * 10, dtype=np.float64),
np.ones((10,), dtype=np.float64))
assert_equal(np.array([(1.0,)] * 10, dtype=np.float64),
np.ones((10, 1), dtype=np.float64))
assert_equal(np.array([(1.0,) * 10], dtype=np.float64),
np.ones((1, 10), dtype=np.float64))
assert_equal(np.array([(1.0,) * 10] * 10, dtype=np.float64),
np.ones((10, 10), dtype=np.float64))
def test_fastCopyAndTranspose():
# 0D array
a = np.array(2)
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert_(b.flags.owndata)
# 1D array
a = np.array([3, 2, 7, 0])
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert_(b.flags.owndata)
# 2D array
a = np.arange(6).reshape(2, 3)
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert_(b.flags.owndata)
def test_array_astype():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Default behavior: allows unsafe casts, keeps memory layout,
# always copies.
b = a.astype('i4')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('i4'))
assert_equal(a.strides, b.strides)
b = a.T.astype('i4')
assert_equal(a.T, b)
assert_equal(b.dtype, np.dtype('i4'))
assert_equal(a.T.strides, b.strides)
b = a.astype('f4')
assert_equal(a, b)
assert_(not (a is b))
# copy=False parameter can sometimes skip a copy
b = a.astype('f4', copy=False)
assert_(a is b)
# order parameter allows overriding of the memory layout,
# forcing a copy if the layout is wrong
b = a.astype('f4', order='F', copy=False)
assert_equal(a, b)
assert_(not (a is b))
assert_(b.flags.f_contiguous)
b = a.astype('f4', order='C', copy=False)
assert_equal(a, b)
assert_(a is b)
assert_(b.flags.c_contiguous)
# casting parameter allows catching bad casts
b = a.astype('c8', casting='safe')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('c8'))
assert_raises(TypeError, a.astype, 'i4', casting='safe')
# subok=False passes through a non-subclassed array
b = a.astype('f4', subok=0, copy=False)
assert_(a is b)
class MyNDArray(np.ndarray):
pass
a = np.array([[0, 1, 2], [3, 4, 5]], dtype='f4').view(MyNDArray)
# subok=True passes through a subclass
b = a.astype('f4', subok=True, copy=False)
assert_(a is b)
# subok=True is default, and creates a subtype on a cast
b = a.astype('i4', copy=False)
assert_equal(a, b)
assert_equal(type(b), MyNDArray)
# subok=False never returns a subclass
b = a.astype('f4', subok=False, copy=False)
assert_equal(a, b)
assert_(not (a is b))
assert_(type(b) is not MyNDArray)
# Make sure converting from string object to fixed length string
# does not truncate.
a = np.array([b'a'*100], dtype='O')
b = a.astype('S')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('S100'))
a = np.array([u'a'*100], dtype='O')
b = a.astype('U')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('U100'))
# Same test as above but for strings shorter than 64 characters
a = np.array([b'a'*10], dtype='O')
b = a.astype('S')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('S10'))
a = np.array([u'a'*10], dtype='O')
b = a.astype('U')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('U10'))
a = np.array(123456789012345678901234567890, dtype='O').astype('S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array(123456789012345678901234567890, dtype='O').astype('U')
assert_array_equal(a, np.array(u'1234567890' * 3, dtype='U30'))
a = np.array([123456789012345678901234567890], dtype='O').astype('S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array([123456789012345678901234567890], dtype='O').astype('U')
assert_array_equal(a, np.array(u'1234567890' * 3, dtype='U30'))
a = np.array(123456789012345678901234567890, dtype='S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array(123456789012345678901234567890, dtype='U')
assert_array_equal(a, np.array(u'1234567890' * 3, dtype='U30'))
a = np.array(u'a\u0140', dtype='U')
b = np.ndarray(buffer=a, dtype='uint32', shape=2)
assert_(b.size == 2)
a = np.array([1000], dtype='i4')
assert_raises(TypeError, a.astype, 'S1', casting='safe')
a = np.array(1000, dtype='i4')
assert_raises(TypeError, a.astype, 'U1', casting='safe')
@pytest.mark.parametrize("t",
np.sctypes['uint'] + np.sctypes['int'] + np.sctypes['float']
)
def test_array_astype_warning(t):
# test ComplexWarning when casting from complex to float or int
a = np.array(10, dtype=np.complex_)
assert_warns(np.ComplexWarning, a.astype, t)
def test_copyto_fromscalar():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Simple copy
np.copyto(a, 1.5)
assert_equal(a, 1.5)
np.copyto(a.T, 2.5)
assert_equal(a, 2.5)
# Where-masked copy
mask = np.array([[0, 1, 0], [0, 0, 1]], dtype='?')
np.copyto(a, 3.5, where=mask)
assert_equal(a, [[2.5, 3.5, 2.5], [2.5, 2.5, 3.5]])
mask = np.array([[0, 1], [1, 1], [1, 0]], dtype='?')
np.copyto(a.T, 4.5, where=mask)
assert_equal(a, [[2.5, 4.5, 4.5], [4.5, 4.5, 3.5]])
def test_copyto():
a = np.arange(6, dtype='i4').reshape(2, 3)
# Simple copy
np.copyto(a, [[3, 1, 5], [6, 2, 1]])
assert_equal(a, [[3, 1, 5], [6, 2, 1]])
# Overlapping copy should work
np.copyto(a[:, :2], a[::-1, 1::-1])
assert_equal(a, [[2, 6, 5], [1, 3, 1]])
# Defaults to 'same_kind' casting
assert_raises(TypeError, np.copyto, a, 1.5)
# Force a copy with 'unsafe' casting, truncating 1.5 to 1
np.copyto(a, 1.5, casting='unsafe')
assert_equal(a, 1)
# Copying with a mask
np.copyto(a, 3, where=[True, False, True])
assert_equal(a, [[3, 1, 3], [3, 1, 3]])
# Casting rule still applies with a mask
assert_raises(TypeError, np.copyto, a, 3.5, where=[True, False, True])
# Lists of integer 0's and 1's is ok too
np.copyto(a, 4.0, casting='unsafe', where=[[0, 1, 1], [1, 0, 0]])
assert_equal(a, [[3, 4, 4], [4, 1, 3]])
# Overlapping copy with mask should work
np.copyto(a[:, :2], a[::-1, 1::-1], where=[[0, 1], [1, 1]])
assert_equal(a, [[3, 4, 4], [4, 3, 3]])
# 'dst' must be an array
assert_raises(TypeError, np.copyto, [1, 2, 3], [2, 3, 4])
def test_copyto_permut():
# test explicit overflow case
pad = 500
l = [True] * pad + [True, True, True, True]
r = np.zeros(len(l)-pad)
d = np.ones(len(l)-pad)
mask = np.array(l)[pad:]
np.copyto(r, d, where=mask[::-1])
# test all permutation of possible masks, 9 should be sufficient for
# current 4 byte unrolled code
power = 9
d = np.ones(power)
for i in range(2**power):
r = np.zeros(power)
l = [(i & x) != 0 for x in range(power)]
mask = np.array(l)
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=mask[::-1])
assert_array_equal(r == 1, l[::-1])
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::2])
assert_array_equal(r[::2] == 1, l[::2])
assert_equal(r[::2].sum(), sum(l[::2]))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::-2])
assert_array_equal(r[::2] == 1, l[::-2])
assert_equal(r[::2].sum(), sum(l[::-2]))
for c in [0xFF, 0x7F, 0x02, 0x10]:
r = np.zeros(power)
mask = np.array(l)
imask = np.array(l).view(np.uint8)
imask[mask != 0] = c
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=True)
assert_equal(r.sum(), r.size)
r = np.ones(power)
d = np.zeros(power)
np.copyto(r, d, where=False)
assert_equal(r.sum(), r.size)
def test_copy_order():
a = np.arange(24).reshape(2, 1, 3, 4)
b = a.copy(order='F')
c = np.arange(24).reshape(2, 1, 4, 3).swapaxes(2, 3)
def check_copy_result(x, y, ccontig, fcontig, strides=False):
assert_(not (x is y))
assert_equal(x, y)
assert_equal(res.flags.c_contiguous, ccontig)
assert_equal(res.flags.f_contiguous, fcontig)
# This check is impossible only because
# NPY_RELAXED_STRIDES_CHECKING changes the strides actively
if not NPY_RELAXED_STRIDES_CHECKING:
if strides:
assert_equal(x.strides, y.strides)
else:
assert_(x.strides != y.strides)
# Validate the initial state of a, b, and c
assert_(a.flags.c_contiguous)
assert_(not a.flags.f_contiguous)
assert_(not b.flags.c_contiguous)
assert_(b.flags.f_contiguous)
assert_(not c.flags.c_contiguous)
assert_(not c.flags.f_contiguous)
# Copy with order='C'
res = a.copy(order='C')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = b.copy(order='C')
check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)
res = c.copy(order='C')
check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)
res = np.copy(a, order='C')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = np.copy(b, order='C')
check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)
res = np.copy(c, order='C')
check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)
# Copy with order='F'
res = a.copy(order='F')
check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)
res = b.copy(order='F')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = c.copy(order='F')
check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)
res = np.copy(a, order='F')
check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)
res = np.copy(b, order='F')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = np.copy(c, order='F')
check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)
# Copy with order='K'
res = a.copy(order='K')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = b.copy(order='K')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = c.copy(order='K')
check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)
res = np.copy(a, order='K')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = np.copy(b, order='K')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = np.copy(c, order='K')
check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)
def test_contiguous_flags():
a = np.ones((4, 4, 1))[::2,:,:]
if NPY_RELAXED_STRIDES_CHECKING:
a.strides = a.strides[:2] + (-123,)
b = np.ones((2, 2, 1, 2, 2)).swapaxes(3, 4)
def check_contig(a, ccontig, fcontig):
assert_(a.flags.c_contiguous == ccontig)
assert_(a.flags.f_contiguous == fcontig)
# Check if new arrays are correct:
check_contig(a, False, False)
check_contig(b, False, False)
if NPY_RELAXED_STRIDES_CHECKING:
check_contig(np.empty((2, 2, 0, 2, 2)), True, True)
check_contig(np.array([[[1], [2]]], order='F'), True, True)
else:
check_contig(np.empty((2, 2, 0, 2, 2)), True, False)
check_contig(np.array([[[1], [2]]], order='F'), False, True)
check_contig(np.empty((2, 2)), True, False)
check_contig(np.empty((2, 2), order='F'), False, True)
# Check that np.array creates correct contiguous flags:
check_contig(np.array(a, copy=False), False, False)
check_contig(np.array(a, copy=False, order='C'), True, False)
check_contig(np.array(a, ndmin=4, copy=False, order='F'), False, True)
if NPY_RELAXED_STRIDES_CHECKING:
# Check slicing update of flags and :
check_contig(a[0], True, True)
check_contig(a[None, ::4, ..., None], True, True)
check_contig(b[0, 0, ...], False, True)
check_contig(b[:,:, 0:0,:,:], True, True)
else:
# Check slicing update of flags:
check_contig(a[0], True, False)
# Would be nice if this was C-Contiguous:
check_contig(a[None, 0, ..., None], False, False)
check_contig(b[0, 0, 0, ...], False, True)
# Test ravel and squeeze.
check_contig(a.ravel(), True, True)
check_contig(np.ones((1, 3, 1)).squeeze(), True, True)
def test_broadcast_arrays():
# Test user defined dtypes
a = np.array([(1, 2, 3)], dtype='u4,u4,u4')
b = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4')
result = np.broadcast_arrays(a, b)
assert_equal(result[0], np.array([(1, 2, 3), (1, 2, 3), (1, 2, 3)], dtype='u4,u4,u4'))
assert_equal(result[1], np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4'))

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# -*- coding: utf-8 -*-
from __future__ import division, absolute_import, print_function
import sys
import gc
import pytest
import numpy as np
from numpy.testing import (
assert_, assert_equal, assert_raises, assert_warns, HAS_REFCOUNT,
assert_raises_regex,
)
import textwrap
class TestArrayRepr(object):
def test_nan_inf(self):
x = np.array([np.nan, np.inf])
assert_equal(repr(x), 'array([nan, inf])')
def test_subclass(self):
class sub(np.ndarray): pass
# one dimensional
x1d = np.array([1, 2]).view(sub)
assert_equal(repr(x1d), 'sub([1, 2])')
# two dimensional
x2d = np.array([[1, 2], [3, 4]]).view(sub)
assert_equal(repr(x2d),
'sub([[1, 2],\n'
' [3, 4]])')
# two dimensional with flexible dtype
xstruct = np.ones((2,2), dtype=[('a', '<i4')]).view(sub)
assert_equal(repr(xstruct),
"sub([[(1,), (1,)],\n"
" [(1,), (1,)]], dtype=[('a', '<i4')])"
)
@pytest.mark.xfail(reason="See gh-10544")
def test_object_subclass(self):
class sub(np.ndarray):
def __new__(cls, inp):
obj = np.asarray(inp).view(cls)
return obj
def __getitem__(self, ind):
ret = super(sub, self).__getitem__(ind)
return sub(ret)
# test that object + subclass is OK:
x = sub([None, None])
assert_equal(repr(x), 'sub([None, None], dtype=object)')
assert_equal(str(x), '[None None]')
x = sub([None, sub([None, None])])
assert_equal(repr(x),
'sub([None, sub([None, None], dtype=object)], dtype=object)')
assert_equal(str(x), '[None sub([None, None], dtype=object)]')
def test_0d_object_subclass(self):
# make sure that subclasses which return 0ds instead
# of scalars don't cause infinite recursion in str
class sub(np.ndarray):
def __new__(cls, inp):
obj = np.asarray(inp).view(cls)
return obj
def __getitem__(self, ind):
ret = super(sub, self).__getitem__(ind)
return sub(ret)
x = sub(1)
assert_equal(repr(x), 'sub(1)')
assert_equal(str(x), '1')
x = sub([1, 1])
assert_equal(repr(x), 'sub([1, 1])')
assert_equal(str(x), '[1 1]')
# check it works properly with object arrays too
x = sub(None)
assert_equal(repr(x), 'sub(None, dtype=object)')
assert_equal(str(x), 'None')
# plus recursive object arrays (even depth > 1)
y = sub(None)
x[()] = y
y[()] = x
assert_equal(repr(x),
'sub(sub(sub(..., dtype=object), dtype=object), dtype=object)')
assert_equal(str(x), '...')
x[()] = 0 # resolve circular references for garbage collector
# nested 0d-subclass-object
x = sub(None)
x[()] = sub(None)
assert_equal(repr(x), 'sub(sub(None, dtype=object), dtype=object)')
assert_equal(str(x), 'None')
# gh-10663
class DuckCounter(np.ndarray):
def __getitem__(self, item):
result = super(DuckCounter, self).__getitem__(item)
if not isinstance(result, DuckCounter):
result = result[...].view(DuckCounter)
return result
def to_string(self):
return {0: 'zero', 1: 'one', 2: 'two'}.get(self.item(), 'many')
def __str__(self):
if self.shape == ():
return self.to_string()
else:
fmt = {'all': lambda x: x.to_string()}
return np.array2string(self, formatter=fmt)
dc = np.arange(5).view(DuckCounter)
assert_equal(str(dc), "[zero one two many many]")
assert_equal(str(dc[0]), "zero")
def test_self_containing(self):
arr0d = np.array(None)
arr0d[()] = arr0d
assert_equal(repr(arr0d),
'array(array(..., dtype=object), dtype=object)')
arr0d[()] = 0 # resolve recursion for garbage collector
arr1d = np.array([None, None])
arr1d[1] = arr1d
assert_equal(repr(arr1d),
'array([None, array(..., dtype=object)], dtype=object)')
arr1d[1] = 0 # resolve recursion for garbage collector
first = np.array(None)
second = np.array(None)
first[()] = second
second[()] = first
assert_equal(repr(first),
'array(array(array(..., dtype=object), dtype=object), dtype=object)')
first[()] = 0 # resolve circular references for garbage collector
def test_containing_list(self):
# printing square brackets directly would be ambiguuous
arr1d = np.array([None, None])
arr1d[0] = [1, 2]
arr1d[1] = [3]
assert_equal(repr(arr1d),
'array([list([1, 2]), list([3])], dtype=object)')
def test_void_scalar_recursion(self):
# gh-9345
repr(np.void(b'test')) # RecursionError ?
def test_fieldless_structured(self):
# gh-10366
no_fields = np.dtype([])
arr_no_fields = np.empty(4, dtype=no_fields)
assert_equal(repr(arr_no_fields), 'array([(), (), (), ()], dtype=[])')
class TestComplexArray(object):
def test_str(self):
rvals = [0, 1, -1, np.inf, -np.inf, np.nan]
cvals = [complex(rp, ip) for rp in rvals for ip in rvals]
dtypes = [np.complex64, np.cdouble, np.clongdouble]
actual = [str(np.array([c], dt)) for c in cvals for dt in dtypes]
wanted = [
'[0.+0.j]', '[0.+0.j]', '[0.+0.j]',
'[0.+1.j]', '[0.+1.j]', '[0.+1.j]',
'[0.-1.j]', '[0.-1.j]', '[0.-1.j]',
'[0.+infj]', '[0.+infj]', '[0.+infj]',
'[0.-infj]', '[0.-infj]', '[0.-infj]',
'[0.+nanj]', '[0.+nanj]', '[0.+nanj]',
'[1.+0.j]', '[1.+0.j]', '[1.+0.j]',
'[1.+1.j]', '[1.+1.j]', '[1.+1.j]',
'[1.-1.j]', '[1.-1.j]', '[1.-1.j]',
'[1.+infj]', '[1.+infj]', '[1.+infj]',
'[1.-infj]', '[1.-infj]', '[1.-infj]',
'[1.+nanj]', '[1.+nanj]', '[1.+nanj]',
'[-1.+0.j]', '[-1.+0.j]', '[-1.+0.j]',
'[-1.+1.j]', '[-1.+1.j]', '[-1.+1.j]',
'[-1.-1.j]', '[-1.-1.j]', '[-1.-1.j]',
'[-1.+infj]', '[-1.+infj]', '[-1.+infj]',
'[-1.-infj]', '[-1.-infj]', '[-1.-infj]',
'[-1.+nanj]', '[-1.+nanj]', '[-1.+nanj]',
'[inf+0.j]', '[inf+0.j]', '[inf+0.j]',
'[inf+1.j]', '[inf+1.j]', '[inf+1.j]',
'[inf-1.j]', '[inf-1.j]', '[inf-1.j]',
'[inf+infj]', '[inf+infj]', '[inf+infj]',
'[inf-infj]', '[inf-infj]', '[inf-infj]',
'[inf+nanj]', '[inf+nanj]', '[inf+nanj]',
'[-inf+0.j]', '[-inf+0.j]', '[-inf+0.j]',
'[-inf+1.j]', '[-inf+1.j]', '[-inf+1.j]',
'[-inf-1.j]', '[-inf-1.j]', '[-inf-1.j]',
'[-inf+infj]', '[-inf+infj]', '[-inf+infj]',
'[-inf-infj]', '[-inf-infj]', '[-inf-infj]',
'[-inf+nanj]', '[-inf+nanj]', '[-inf+nanj]',
'[nan+0.j]', '[nan+0.j]', '[nan+0.j]',
'[nan+1.j]', '[nan+1.j]', '[nan+1.j]',
'[nan-1.j]', '[nan-1.j]', '[nan-1.j]',
'[nan+infj]', '[nan+infj]', '[nan+infj]',
'[nan-infj]', '[nan-infj]', '[nan-infj]',
'[nan+nanj]', '[nan+nanj]', '[nan+nanj]']
for res, val in zip(actual, wanted):
assert_equal(res, val)
class TestArray2String(object):
def test_basic(self):
"""Basic test of array2string."""
a = np.arange(3)
assert_(np.array2string(a) == '[0 1 2]')
assert_(np.array2string(a, max_line_width=4, legacy='1.13') == '[0 1\n 2]')
assert_(np.array2string(a, max_line_width=4) == '[0\n 1\n 2]')
def test_unexpected_kwarg(self):
# ensure than an appropriate TypeError
# is raised when array2string receives
# an unexpected kwarg
with assert_raises_regex(TypeError, 'nonsense'):
np.array2string(np.array([1, 2, 3]),
nonsense=None)
def test_format_function(self):
"""Test custom format function for each element in array."""
def _format_function(x):
if np.abs(x) < 1:
return '.'
elif np.abs(x) < 2:
return 'o'
else:
return 'O'
x = np.arange(3)
if sys.version_info[0] >= 3:
x_hex = "[0x0 0x1 0x2]"
x_oct = "[0o0 0o1 0o2]"
else:
x_hex = "[0x0L 0x1L 0x2L]"
x_oct = "[0L 01L 02L]"
assert_(np.array2string(x, formatter={'all':_format_function}) ==
"[. o O]")
assert_(np.array2string(x, formatter={'int_kind':_format_function}) ==
"[. o O]")
assert_(np.array2string(x, formatter={'all':lambda x: "%.4f" % x}) ==
"[0.0000 1.0000 2.0000]")
assert_equal(np.array2string(x, formatter={'int':lambda x: hex(x)}),
x_hex)
assert_equal(np.array2string(x, formatter={'int':lambda x: oct(x)}),
x_oct)
x = np.arange(3.)
assert_(np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x}) ==
"[0.00 1.00 2.00]")
assert_(np.array2string(x, formatter={'float':lambda x: "%.2f" % x}) ==
"[0.00 1.00 2.00]")
s = np.array(['abc', 'def'])
assert_(np.array2string(s, formatter={'numpystr':lambda s: s*2}) ==
'[abcabc defdef]')
def test_structure_format(self):
dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt)
assert_equal(np.array2string(x),
"[('Sarah', [8., 7.]) ('John', [6., 7.])]")
np.set_printoptions(legacy='1.13')
try:
# for issue #5692
A = np.zeros(shape=10, dtype=[("A", "M8[s]")])
A[5:].fill(np.datetime64('NaT'))
assert_equal(
np.array2string(A),
textwrap.dedent("""\
[('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',)
('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',) ('NaT',) ('NaT',)
('NaT',) ('NaT',) ('NaT',)]""")
)
finally:
np.set_printoptions(legacy=False)
# same again, but with non-legacy behavior
assert_equal(
np.array2string(A),
textwrap.dedent("""\
[('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',)
('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',)
('1970-01-01T00:00:00',) ( 'NaT',)
( 'NaT',) ( 'NaT',)
( 'NaT',) ( 'NaT',)]""")
)
# and again, with timedeltas
A = np.full(10, 123456, dtype=[("A", "m8[s]")])
A[5:].fill(np.datetime64('NaT'))
assert_equal(
np.array2string(A),
textwrap.dedent("""\
[(123456,) (123456,) (123456,) (123456,) (123456,) ( 'NaT',) ( 'NaT',)
( 'NaT',) ( 'NaT',) ( 'NaT',)]""")
)
# See #8160
struct_int = np.array([([1, -1],), ([123, 1],)], dtype=[('B', 'i4', 2)])
assert_equal(np.array2string(struct_int),
"[([ 1, -1],) ([123, 1],)]")
struct_2dint = np.array([([[0, 1], [2, 3]],), ([[12, 0], [0, 0]],)],
dtype=[('B', 'i4', (2, 2))])
assert_equal(np.array2string(struct_2dint),
"[([[ 0, 1], [ 2, 3]],) ([[12, 0], [ 0, 0]],)]")
# See #8172
array_scalar = np.array(
(1., 2.1234567890123456789, 3.), dtype=('f8,f8,f8'))
assert_equal(np.array2string(array_scalar), "(1., 2.12345679, 3.)")
def test_unstructured_void_repr(self):
a = np.array([27, 91, 50, 75, 7, 65, 10, 8,
27, 91, 51, 49,109, 82,101,100], dtype='u1').view('V8')
assert_equal(repr(a[0]), r"void(b'\x1B\x5B\x32\x4B\x07\x41\x0A\x08')")
assert_equal(str(a[0]), r"b'\x1B\x5B\x32\x4B\x07\x41\x0A\x08'")
assert_equal(repr(a),
r"array([b'\x1B\x5B\x32\x4B\x07\x41\x0A\x08'," "\n"
r" b'\x1B\x5B\x33\x31\x6D\x52\x65\x64'], dtype='|V8')")
assert_equal(eval(repr(a), vars(np)), a)
assert_equal(eval(repr(a[0]), vars(np)), a[0])
def test_edgeitems_kwarg(self):
# previously the global print options would be taken over the kwarg
arr = np.zeros(3, int)
assert_equal(
np.array2string(arr, edgeitems=1, threshold=0),
"[0 ... 0]"
)
def test_summarize_1d(self):
A = np.arange(1001)
strA = '[ 0 1 2 ... 998 999 1000]'
assert_equal(str(A), strA)
reprA = 'array([ 0, 1, 2, ..., 998, 999, 1000])'
assert_equal(repr(A), reprA)
def test_summarize_2d(self):
A = np.arange(1002).reshape(2, 501)
strA = '[[ 0 1 2 ... 498 499 500]\n' \
' [ 501 502 503 ... 999 1000 1001]]'
assert_equal(str(A), strA)
reprA = 'array([[ 0, 1, 2, ..., 498, 499, 500],\n' \
' [ 501, 502, 503, ..., 999, 1000, 1001]])'
assert_equal(repr(A), reprA)
def test_linewidth(self):
a = np.full(6, 1)
def make_str(a, width, **kw):
return np.array2string(a, separator="", max_line_width=width, **kw)
assert_equal(make_str(a, 8, legacy='1.13'), '[111111]')
assert_equal(make_str(a, 7, legacy='1.13'), '[111111]')
assert_equal(make_str(a, 5, legacy='1.13'), '[1111\n'
' 11]')
assert_equal(make_str(a, 8), '[111111]')
assert_equal(make_str(a, 7), '[11111\n'
' 1]')
assert_equal(make_str(a, 5), '[111\n'
' 111]')
b = a[None,None,:]
assert_equal(make_str(b, 12, legacy='1.13'), '[[[111111]]]')
assert_equal(make_str(b, 9, legacy='1.13'), '[[[111111]]]')
assert_equal(make_str(b, 8, legacy='1.13'), '[[[11111\n'
' 1]]]')
assert_equal(make_str(b, 12), '[[[111111]]]')
assert_equal(make_str(b, 9), '[[[111\n'
' 111]]]')
assert_equal(make_str(b, 8), '[[[11\n'
' 11\n'
' 11]]]')
def test_wide_element(self):
a = np.array(['xxxxx'])
assert_equal(
np.array2string(a, max_line_width=5),
"['xxxxx']"
)
assert_equal(
np.array2string(a, max_line_width=5, legacy='1.13'),
"[ 'xxxxx']"
)
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
def test_refcount(self):
# make sure we do not hold references to the array due to a recursive
# closure (gh-10620)
gc.disable()
a = np.arange(2)
r1 = sys.getrefcount(a)
np.array2string(a)
np.array2string(a)
r2 = sys.getrefcount(a)
gc.collect()
gc.enable()
assert_(r1 == r2)
class TestPrintOptions(object):
"""Test getting and setting global print options."""
def setup(self):
self.oldopts = np.get_printoptions()
def teardown(self):
np.set_printoptions(**self.oldopts)
def test_basic(self):
x = np.array([1.5, 0, 1.234567890])
assert_equal(repr(x), "array([1.5 , 0. , 1.23456789])")
np.set_printoptions(precision=4)
assert_equal(repr(x), "array([1.5 , 0. , 1.2346])")
def test_precision_zero(self):
np.set_printoptions(precision=0)
for values, string in (
([0.], "0."), ([.3], "0."), ([-.3], "-0."), ([.7], "1."),
([1.5], "2."), ([-1.5], "-2."), ([-15.34], "-15."),
([100.], "100."), ([.2, -1, 122.51], " 0., -1., 123."),
([0], "0"), ([-12], "-12"), ([complex(.3, -.7)], "0.-1.j")):
x = np.array(values)
assert_equal(repr(x), "array([%s])" % string)
def test_formatter(self):
x = np.arange(3)
np.set_printoptions(formatter={'all':lambda x: str(x-1)})
assert_equal(repr(x), "array([-1, 0, 1])")
def test_formatter_reset(self):
x = np.arange(3)
np.set_printoptions(formatter={'all':lambda x: str(x-1)})
assert_equal(repr(x), "array([-1, 0, 1])")
np.set_printoptions(formatter={'int':None})
assert_equal(repr(x), "array([0, 1, 2])")
np.set_printoptions(formatter={'all':lambda x: str(x-1)})
assert_equal(repr(x), "array([-1, 0, 1])")
np.set_printoptions(formatter={'all':None})
assert_equal(repr(x), "array([0, 1, 2])")
np.set_printoptions(formatter={'int':lambda x: str(x-1)})
assert_equal(repr(x), "array([-1, 0, 1])")
np.set_printoptions(formatter={'int_kind':None})
assert_equal(repr(x), "array([0, 1, 2])")
x = np.arange(3.)
np.set_printoptions(formatter={'float':lambda x: str(x-1)})
assert_equal(repr(x), "array([-1.0, 0.0, 1.0])")
np.set_printoptions(formatter={'float_kind':None})
assert_equal(repr(x), "array([0., 1., 2.])")
def test_0d_arrays(self):
unicode = type(u'')
assert_equal(unicode(np.array(u'café', '<U4')), u'café')
if sys.version_info[0] >= 3:
assert_equal(repr(np.array('café', '<U4')),
"array('café', dtype='<U4')")
else:
assert_equal(repr(np.array(u'café', '<U4')),
"array(u'caf\\xe9', dtype='<U4')")
assert_equal(str(np.array('test', np.str_)), 'test')
a = np.zeros(1, dtype=[('a', '<i4', (3,))])
assert_equal(str(a[0]), '([0, 0, 0],)')
assert_equal(repr(np.datetime64('2005-02-25')[...]),
"array('2005-02-25', dtype='datetime64[D]')")
assert_equal(repr(np.timedelta64('10', 'Y')[...]),
"array(10, dtype='timedelta64[Y]')")
# repr of 0d arrays is affected by printoptions
x = np.array(1)
np.set_printoptions(formatter={'all':lambda x: "test"})
assert_equal(repr(x), "array(test)")
# str is unaffected
assert_equal(str(x), "1")
# check `style` arg raises
assert_warns(DeprecationWarning, np.array2string,
np.array(1.), style=repr)
# but not in legacy mode
np.array2string(np.array(1.), style=repr, legacy='1.13')
# gh-10934 style was broken in legacy mode, check it works
np.array2string(np.array(1.), legacy='1.13')
def test_float_spacing(self):
x = np.array([1., 2., 3.])
y = np.array([1., 2., -10.])
z = np.array([100., 2., -1.])
w = np.array([-100., 2., 1.])
assert_equal(repr(x), 'array([1., 2., 3.])')
assert_equal(repr(y), 'array([ 1., 2., -10.])')
assert_equal(repr(np.array(y[0])), 'array(1.)')
assert_equal(repr(np.array(y[-1])), 'array(-10.)')
assert_equal(repr(z), 'array([100., 2., -1.])')
assert_equal(repr(w), 'array([-100., 2., 1.])')
assert_equal(repr(np.array([np.nan, np.inf])), 'array([nan, inf])')
assert_equal(repr(np.array([np.nan, -np.inf])), 'array([ nan, -inf])')
x = np.array([np.inf, 100000, 1.1234])
y = np.array([np.inf, 100000, -1.1234])
z = np.array([np.inf, 1.1234, -1e120])
np.set_printoptions(precision=2)
assert_equal(repr(x), 'array([ inf, 1.00e+05, 1.12e+00])')
assert_equal(repr(y), 'array([ inf, 1.00e+05, -1.12e+00])')
assert_equal(repr(z), 'array([ inf, 1.12e+000, -1.00e+120])')
def test_bool_spacing(self):
assert_equal(repr(np.array([True, True])),
'array([ True, True])')
assert_equal(repr(np.array([True, False])),
'array([ True, False])')
assert_equal(repr(np.array([True])),
'array([ True])')
assert_equal(repr(np.array(True)),
'array(True)')
assert_equal(repr(np.array(False)),
'array(False)')
def test_sign_spacing(self):
a = np.arange(4.)
b = np.array([1.234e9])
c = np.array([1.0 + 1.0j, 1.123456789 + 1.123456789j], dtype='c16')
assert_equal(repr(a), 'array([0., 1., 2., 3.])')
assert_equal(repr(np.array(1.)), 'array(1.)')
assert_equal(repr(b), 'array([1.234e+09])')
assert_equal(repr(np.array([0.])), 'array([0.])')
assert_equal(repr(c),
"array([1. +1.j , 1.12345679+1.12345679j])")
assert_equal(repr(np.array([0., -0.])), 'array([ 0., -0.])')
np.set_printoptions(sign=' ')
assert_equal(repr(a), 'array([ 0., 1., 2., 3.])')
assert_equal(repr(np.array(1.)), 'array( 1.)')
assert_equal(repr(b), 'array([ 1.234e+09])')
assert_equal(repr(c),
"array([ 1. +1.j , 1.12345679+1.12345679j])")
assert_equal(repr(np.array([0., -0.])), 'array([ 0., -0.])')
np.set_printoptions(sign='+')
assert_equal(repr(a), 'array([+0., +1., +2., +3.])')
assert_equal(repr(np.array(1.)), 'array(+1.)')
assert_equal(repr(b), 'array([+1.234e+09])')
assert_equal(repr(c),
"array([+1. +1.j , +1.12345679+1.12345679j])")
np.set_printoptions(legacy='1.13')
assert_equal(repr(a), 'array([ 0., 1., 2., 3.])')
assert_equal(repr(b), 'array([ 1.23400000e+09])')
assert_equal(repr(-b), 'array([ -1.23400000e+09])')
assert_equal(repr(np.array(1.)), 'array(1.0)')
assert_equal(repr(np.array([0.])), 'array([ 0.])')
assert_equal(repr(c),
"array([ 1.00000000+1.j , 1.12345679+1.12345679j])")
# gh-10383
assert_equal(str(np.array([-1., 10])), "[ -1. 10.]")
assert_raises(TypeError, np.set_printoptions, wrongarg=True)
def test_float_overflow_nowarn(self):
# make sure internal computations in FloatingFormat don't
# warn about overflow
repr(np.array([1e4, 0.1], dtype='f2'))
def test_sign_spacing_structured(self):
a = np.ones(2, dtype='<f,<f')
assert_equal(repr(a),
"array([(1., 1.), (1., 1.)], dtype=[('f0', '<f4'), ('f1', '<f4')])")
assert_equal(repr(a[0]), "(1., 1.)")
def test_floatmode(self):
x = np.array([0.6104, 0.922, 0.457, 0.0906, 0.3733, 0.007244,
0.5933, 0.947, 0.2383, 0.4226], dtype=np.float16)
y = np.array([0.2918820979355541, 0.5064172631089138,
0.2848750619642916, 0.4342965294660567,
0.7326538397312751, 0.3459503329096204,
0.0862072768214508, 0.39112753029631175],
dtype=np.float64)
z = np.arange(6, dtype=np.float16)/10
c = np.array([1.0 + 1.0j, 1.123456789 + 1.123456789j], dtype='c16')
# also make sure 1e23 is right (is between two fp numbers)
w = np.array(['1e{}'.format(i) for i in range(25)], dtype=np.float64)
# note: we construct w from the strings `1eXX` instead of doing
# `10.**arange(24)` because it turns out the two are not equivalent in
# python. On some architectures `1e23 != 10.**23`.
wp = np.array([1.234e1, 1e2, 1e123])
# unique mode
np.set_printoptions(floatmode='unique')
assert_equal(repr(x),
"array([0.6104 , 0.922 , 0.457 , 0.0906 , 0.3733 , 0.007244,\n"
" 0.5933 , 0.947 , 0.2383 , 0.4226 ], dtype=float16)")
assert_equal(repr(y),
"array([0.2918820979355541 , 0.5064172631089138 , 0.2848750619642916 ,\n"
" 0.4342965294660567 , 0.7326538397312751 , 0.3459503329096204 ,\n"
" 0.0862072768214508 , 0.39112753029631175])")
assert_equal(repr(z),
"array([0. , 0.1, 0.2, 0.3, 0.4, 0.5], dtype=float16)")
assert_equal(repr(w),
"array([1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07,\n"
" 1.e+08, 1.e+09, 1.e+10, 1.e+11, 1.e+12, 1.e+13, 1.e+14, 1.e+15,\n"
" 1.e+16, 1.e+17, 1.e+18, 1.e+19, 1.e+20, 1.e+21, 1.e+22, 1.e+23,\n"
" 1.e+24])")
assert_equal(repr(wp), "array([1.234e+001, 1.000e+002, 1.000e+123])")
assert_equal(repr(c),
"array([1. +1.j , 1.123456789+1.123456789j])")
# maxprec mode, precision=8
np.set_printoptions(floatmode='maxprec', precision=8)
assert_equal(repr(x),
"array([0.6104 , 0.922 , 0.457 , 0.0906 , 0.3733 , 0.007244,\n"
" 0.5933 , 0.947 , 0.2383 , 0.4226 ], dtype=float16)")
assert_equal(repr(y),
"array([0.2918821 , 0.50641726, 0.28487506, 0.43429653, 0.73265384,\n"
" 0.34595033, 0.08620728, 0.39112753])")
assert_equal(repr(z),
"array([0. , 0.1, 0.2, 0.3, 0.4, 0.5], dtype=float16)")
assert_equal(repr(w[::5]),
"array([1.e+00, 1.e+05, 1.e+10, 1.e+15, 1.e+20])")
assert_equal(repr(wp), "array([1.234e+001, 1.000e+002, 1.000e+123])")
assert_equal(repr(c),
"array([1. +1.j , 1.12345679+1.12345679j])")
# fixed mode, precision=4
np.set_printoptions(floatmode='fixed', precision=4)
assert_equal(repr(x),
"array([0.6104, 0.9219, 0.4570, 0.0906, 0.3733, 0.0072, 0.5933, 0.9468,\n"
" 0.2383, 0.4226], dtype=float16)")
assert_equal(repr(y),
"array([0.2919, 0.5064, 0.2849, 0.4343, 0.7327, 0.3460, 0.0862, 0.3911])")
assert_equal(repr(z),
"array([0.0000, 0.1000, 0.2000, 0.3000, 0.3999, 0.5000], dtype=float16)")
assert_equal(repr(w[::5]),
"array([1.0000e+00, 1.0000e+05, 1.0000e+10, 1.0000e+15, 1.0000e+20])")
assert_equal(repr(wp), "array([1.2340e+001, 1.0000e+002, 1.0000e+123])")
assert_equal(repr(np.zeros(3)), "array([0.0000, 0.0000, 0.0000])")
assert_equal(repr(c),
"array([1.0000+1.0000j, 1.1235+1.1235j])")
# for larger precision, representation error becomes more apparent:
np.set_printoptions(floatmode='fixed', precision=8)
assert_equal(repr(z),
"array([0.00000000, 0.09997559, 0.19995117, 0.30004883, 0.39990234,\n"
" 0.50000000], dtype=float16)")
# maxprec_equal mode, precision=8
np.set_printoptions(floatmode='maxprec_equal', precision=8)
assert_equal(repr(x),
"array([0.610352, 0.921875, 0.457031, 0.090576, 0.373291, 0.007244,\n"
" 0.593262, 0.946777, 0.238281, 0.422607], dtype=float16)")
assert_equal(repr(y),
"array([0.29188210, 0.50641726, 0.28487506, 0.43429653, 0.73265384,\n"
" 0.34595033, 0.08620728, 0.39112753])")
assert_equal(repr(z),
"array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5], dtype=float16)")
assert_equal(repr(w[::5]),
"array([1.e+00, 1.e+05, 1.e+10, 1.e+15, 1.e+20])")
assert_equal(repr(wp), "array([1.234e+001, 1.000e+002, 1.000e+123])")
assert_equal(repr(c),
"array([1.00000000+1.00000000j, 1.12345679+1.12345679j])")
def test_legacy_mode_scalars(self):
# in legacy mode, str of floats get truncated, and complex scalars
# use * for non-finite imaginary part
np.set_printoptions(legacy='1.13')
assert_equal(str(np.float64(1.123456789123456789)), '1.12345678912')
assert_equal(str(np.complex128(complex(1, np.nan))), '(1+nan*j)')
np.set_printoptions(legacy=False)
assert_equal(str(np.float64(1.123456789123456789)),
'1.1234567891234568')
assert_equal(str(np.complex128(complex(1, np.nan))), '(1+nanj)')
def test_legacy_stray_comma(self):
np.set_printoptions(legacy='1.13')
assert_equal(str(np.arange(10000)), '[ 0 1 2 ..., 9997 9998 9999]')
np.set_printoptions(legacy=False)
assert_equal(str(np.arange(10000)), '[ 0 1 2 ... 9997 9998 9999]')
def test_dtype_linewidth_wrapping(self):
np.set_printoptions(linewidth=75)
assert_equal(repr(np.arange(10,20., dtype='f4')),
"array([10., 11., 12., 13., 14., 15., 16., 17., 18., 19.], dtype=float32)")
assert_equal(repr(np.arange(10,23., dtype='f4')), textwrap.dedent("""\
array([10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.],
dtype=float32)"""))
styp = '<U4' if sys.version_info[0] >= 3 else '|S4'
assert_equal(repr(np.ones(3, dtype=styp)),
"array(['1', '1', '1'], dtype='{}')".format(styp))
assert_equal(repr(np.ones(12, dtype=styp)), textwrap.dedent("""\
array(['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1'],
dtype='{}')""".format(styp)))
def test_linewidth_repr(self):
a = np.full(7, fill_value=2)
np.set_printoptions(linewidth=17)
assert_equal(
repr(a),
textwrap.dedent("""\
array([2, 2, 2,
2, 2, 2,
2])""")
)
np.set_printoptions(linewidth=17, legacy='1.13')
assert_equal(
repr(a),
textwrap.dedent("""\
array([2, 2, 2,
2, 2, 2, 2])""")
)
a = np.full(8, fill_value=2)
np.set_printoptions(linewidth=18, legacy=False)
assert_equal(
repr(a),
textwrap.dedent("""\
array([2, 2, 2,
2, 2, 2,
2, 2])""")
)
np.set_printoptions(linewidth=18, legacy='1.13')
assert_equal(
repr(a),
textwrap.dedent("""\
array([2, 2, 2, 2,
2, 2, 2, 2])""")
)
def test_linewidth_str(self):
a = np.full(18, fill_value=2)
np.set_printoptions(linewidth=18)
assert_equal(
str(a),
textwrap.dedent("""\
[2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2
2 2]""")
)
np.set_printoptions(linewidth=18, legacy='1.13')
assert_equal(
str(a),
textwrap.dedent("""\
[2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2]""")
)
def test_edgeitems(self):
np.set_printoptions(edgeitems=1, threshold=1)
a = np.arange(27).reshape((3, 3, 3))
assert_equal(
repr(a),
textwrap.dedent("""\
array([[[ 0, ..., 2],
...,
[ 6, ..., 8]],
...,
[[18, ..., 20],
...,
[24, ..., 26]]])""")
)
b = np.zeros((3, 3, 1, 1))
assert_equal(
repr(b),
textwrap.dedent("""\
array([[[[0.]],
...,
[[0.]]],
...,
[[[0.]],
...,
[[0.]]]])""")
)
# 1.13 had extra trailing spaces, and was missing newlines
np.set_printoptions(legacy='1.13')
assert_equal(
repr(a),
textwrap.dedent("""\
array([[[ 0, ..., 2],
...,
[ 6, ..., 8]],
...,
[[18, ..., 20],
...,
[24, ..., 26]]])""")
)
assert_equal(
repr(b),
textwrap.dedent("""\
array([[[[ 0.]],
...,
[[ 0.]]],
...,
[[[ 0.]],
...,
[[ 0.]]]])""")
)
def test_bad_args(self):
assert_raises(ValueError, np.set_printoptions, threshold=float('nan'))
assert_raises(TypeError, np.set_printoptions, threshold='1')
assert_raises(TypeError, np.set_printoptions, threshold=b'1')
def test_unicode_object_array():
import sys
if sys.version_info[0] >= 3:
expected = "array(['é'], dtype=object)"
else:
expected = "array([u'\\xe9'], dtype=object)"
x = np.array([u'\xe9'], dtype=object)
assert_equal(repr(x), expected)
class TestContextManager(object):
def test_ctx_mgr(self):
# test that context manager actuall works
with np.printoptions(precision=2):
s = str(np.array([2.0]) / 3)
assert_equal(s, '[0.67]')
def test_ctx_mgr_restores(self):
# test that print options are actually restrored
opts = np.get_printoptions()
with np.printoptions(precision=opts['precision'] - 1,
linewidth=opts['linewidth'] - 4):
pass
assert_equal(np.get_printoptions(), opts)
def test_ctx_mgr_exceptions(self):
# test that print options are restored even if an exception is raised
opts = np.get_printoptions()
try:
with np.printoptions(precision=2, linewidth=11):
raise ValueError
except ValueError:
pass
assert_equal(np.get_printoptions(), opts)
def test_ctx_mgr_as_smth(self):
opts = {"precision": 2}
with np.printoptions(**opts) as ctx:
saved_opts = ctx.copy()
assert_equal({k: saved_opts[k] for k in opts}, opts)

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from __future__ import division, absolute_import, print_function
import sys
import numpy as np
from numpy.core.multiarray import _vec_string
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_raises,
assert_raises_regex, suppress_warnings,
)
kw_unicode_true = {'unicode': True} # make 2to3 work properly
kw_unicode_false = {'unicode': False}
class TestBasic(object):
def test_from_object_array(self):
A = np.array([['abc', 2],
['long ', '0123456789']], dtype='O')
B = np.char.array(A)
assert_equal(B.dtype.itemsize, 10)
assert_array_equal(B, [[b'abc', b'2'],
[b'long', b'0123456789']])
def test_from_object_array_unicode(self):
A = np.array([['abc', u'Sigma \u03a3'],
['long ', '0123456789']], dtype='O')
assert_raises(ValueError, np.char.array, (A,))
B = np.char.array(A, **kw_unicode_true)
assert_equal(B.dtype.itemsize, 10 * np.array('a', 'U').dtype.itemsize)
assert_array_equal(B, [['abc', u'Sigma \u03a3'],
['long', '0123456789']])
def test_from_string_array(self):
A = np.array([[b'abc', b'foo'],
[b'long ', b'0123456789']])
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_from_unicode_array(self):
A = np.array([['abc', u'Sigma \u03a3'],
['long ', '0123456789']])
assert_equal(A.dtype.type, np.unicode_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B = np.char.array(A, **kw_unicode_true)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
def fail():
np.char.array(A, **kw_unicode_false)
assert_raises(UnicodeEncodeError, fail)
def test_unicode_upconvert(self):
A = np.char.array(['abc'])
B = np.char.array([u'\u03a3'])
assert_(issubclass((A + B).dtype.type, np.unicode_))
def test_from_string(self):
A = np.char.array(b'abc')
assert_equal(len(A), 1)
assert_equal(len(A[0]), 3)
assert_(issubclass(A.dtype.type, np.string_))
def test_from_unicode(self):
A = np.char.array(u'\u03a3')
assert_equal(len(A), 1)
assert_equal(len(A[0]), 1)
assert_equal(A.itemsize, 4)
assert_(issubclass(A.dtype.type, np.unicode_))
class TestVecString(object):
def test_non_existent_method(self):
def fail():
_vec_string('a', np.string_, 'bogus')
assert_raises(AttributeError, fail)
def test_non_string_array(self):
def fail():
_vec_string(1, np.string_, 'strip')
assert_raises(TypeError, fail)
def test_invalid_args_tuple(self):
def fail():
_vec_string(['a'], np.string_, 'strip', 1)
assert_raises(TypeError, fail)
def test_invalid_type_descr(self):
def fail():
_vec_string(['a'], 'BOGUS', 'strip')
assert_raises(TypeError, fail)
def test_invalid_function_args(self):
def fail():
_vec_string(['a'], np.string_, 'strip', (1,))
assert_raises(TypeError, fail)
def test_invalid_result_type(self):
def fail():
_vec_string(['a'], np.integer, 'strip')
assert_raises(TypeError, fail)
def test_broadcast_error(self):
def fail():
_vec_string([['abc', 'def']], np.integer, 'find', (['a', 'd', 'j'],))
assert_raises(ValueError, fail)
class TestWhitespace(object):
def setup(self):
self.A = np.array([['abc ', '123 '],
['789 ', 'xyz ']]).view(np.chararray)
self.B = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
def test1(self):
assert_(np.all(self.A == self.B))
assert_(np.all(self.A >= self.B))
assert_(np.all(self.A <= self.B))
assert_(not np.any(self.A > self.B))
assert_(not np.any(self.A < self.B))
assert_(not np.any(self.A != self.B))
class TestChar(object):
def setup(self):
self.A = np.array('abc1', dtype='c').view(np.chararray)
def test_it(self):
assert_equal(self.A.shape, (4,))
assert_equal(self.A.upper()[:2].tobytes(), b'AB')
class TestComparisons(object):
def setup(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '123 '],
['051', 'tuv']]).view(np.chararray)
def test_not_equal(self):
assert_array_equal((self.A != self.B), [[True, False], [True, True]])
def test_equal(self):
assert_array_equal((self.A == self.B), [[False, True], [False, False]])
def test_greater_equal(self):
assert_array_equal((self.A >= self.B), [[False, True], [True, True]])
def test_less_equal(self):
assert_array_equal((self.A <= self.B), [[True, True], [False, False]])
def test_greater(self):
assert_array_equal((self.A > self.B), [[False, False], [True, True]])
def test_less(self):
assert_array_equal((self.A < self.B), [[True, False], [False, False]])
class TestComparisonsMixed1(TestComparisons):
"""Ticket #1276"""
def setup(self):
TestComparisons.setup(self)
self.B = np.array([['efg', '123 '],
['051', 'tuv']], np.unicode_).view(np.chararray)
class TestComparisonsMixed2(TestComparisons):
"""Ticket #1276"""
def setup(self):
TestComparisons.setup(self)
self.A = np.array([['abc', '123'],
['789', 'xyz']], np.unicode_).view(np.chararray)
class TestInformation(object):
def setup(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]).view(np.chararray)
self.B = np.array([[u' \u03a3 ', u''],
[u'12345', u'MixedCase'],
[u'123 \t 345 \0 ', u'UPPER']]).view(np.chararray)
def test_len(self):
assert_(issubclass(np.char.str_len(self.A).dtype.type, np.integer))
assert_array_equal(np.char.str_len(self.A), [[5, 0], [5, 9], [12, 5]])
assert_array_equal(np.char.str_len(self.B), [[3, 0], [5, 9], [12, 5]])
def test_count(self):
assert_(issubclass(self.A.count('').dtype.type, np.integer))
assert_array_equal(self.A.count('a'), [[1, 0], [0, 1], [0, 0]])
assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])
# Python doesn't seem to like counting NULL characters
# assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])
assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])
# assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])
def test_endswith(self):
assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))
assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])
assert_array_equal(self.A.endswith('3', 0, 3), [[0, 0], [1, 0], [1, 0]])
def fail():
self.A.endswith('3', 'fdjk')
assert_raises(TypeError, fail)
def test_find(self):
assert_(issubclass(self.A.find('a').dtype.type, np.integer))
assert_array_equal(self.A.find('a'), [[1, -1], [-1, 6], [-1, -1]])
assert_array_equal(self.A.find('3'), [[-1, -1], [2, -1], [2, -1]])
assert_array_equal(self.A.find('a', 0, 2), [[1, -1], [-1, -1], [-1, -1]])
assert_array_equal(self.A.find(['1', 'P']), [[-1, -1], [0, -1], [0, 1]])
def test_index(self):
def fail():
self.A.index('a')
assert_raises(ValueError, fail)
assert_(np.char.index('abcba', 'b') == 1)
assert_(issubclass(np.char.index('abcba', 'b').dtype.type, np.integer))
def test_isalnum(self):
assert_(issubclass(self.A.isalnum().dtype.type, np.bool_))
assert_array_equal(self.A.isalnum(), [[False, False], [True, True], [False, True]])
def test_isalpha(self):
assert_(issubclass(self.A.isalpha().dtype.type, np.bool_))
assert_array_equal(self.A.isalpha(), [[False, False], [False, True], [False, True]])
def test_isdigit(self):
assert_(issubclass(self.A.isdigit().dtype.type, np.bool_))
assert_array_equal(self.A.isdigit(), [[False, False], [True, False], [False, False]])
def test_islower(self):
assert_(issubclass(self.A.islower().dtype.type, np.bool_))
assert_array_equal(self.A.islower(), [[True, False], [False, False], [False, False]])
def test_isspace(self):
assert_(issubclass(self.A.isspace().dtype.type, np.bool_))
assert_array_equal(self.A.isspace(), [[False, False], [False, False], [False, False]])
def test_istitle(self):
assert_(issubclass(self.A.istitle().dtype.type, np.bool_))
assert_array_equal(self.A.istitle(), [[False, False], [False, False], [False, False]])
def test_isupper(self):
assert_(issubclass(self.A.isupper().dtype.type, np.bool_))
assert_array_equal(self.A.isupper(), [[False, False], [False, False], [False, True]])
def test_rfind(self):
assert_(issubclass(self.A.rfind('a').dtype.type, np.integer))
assert_array_equal(self.A.rfind('a'), [[1, -1], [-1, 6], [-1, -1]])
assert_array_equal(self.A.rfind('3'), [[-1, -1], [2, -1], [6, -1]])
assert_array_equal(self.A.rfind('a', 0, 2), [[1, -1], [-1, -1], [-1, -1]])
assert_array_equal(self.A.rfind(['1', 'P']), [[-1, -1], [0, -1], [0, 2]])
def test_rindex(self):
def fail():
self.A.rindex('a')
assert_raises(ValueError, fail)
assert_(np.char.rindex('abcba', 'b') == 3)
assert_(issubclass(np.char.rindex('abcba', 'b').dtype.type, np.integer))
def test_startswith(self):
assert_(issubclass(self.A.startswith('').dtype.type, np.bool_))
assert_array_equal(self.A.startswith(' '), [[1, 0], [0, 0], [0, 0]])
assert_array_equal(self.A.startswith('1', 0, 3), [[0, 0], [1, 0], [1, 0]])
def fail():
self.A.startswith('3', 'fdjk')
assert_raises(TypeError, fail)
class TestMethods(object):
def setup(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']],
dtype='S').view(np.chararray)
self.B = np.array([[u' \u03a3 ', u''],
[u'12345', u'MixedCase'],
[u'123 \t 345 \0 ', u'UPPER']]).view(np.chararray)
def test_capitalize(self):
tgt = [[b' abc ', b''],
[b'12345', b'Mixedcase'],
[b'123 \t 345 \0 ', b'Upper']]
assert_(issubclass(self.A.capitalize().dtype.type, np.string_))
assert_array_equal(self.A.capitalize(), tgt)
tgt = [[u' \u03c3 ', ''],
['12345', 'Mixedcase'],
['123 \t 345 \0 ', 'Upper']]
assert_(issubclass(self.B.capitalize().dtype.type, np.unicode_))
assert_array_equal(self.B.capitalize(), tgt)
def test_center(self):
assert_(issubclass(self.A.center(10).dtype.type, np.string_))
C = self.A.center([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.center(20, b'#')
assert_(np.all(C.startswith(b'#')))
assert_(np.all(C.endswith(b'#')))
C = np.char.center(b'FOO', [[10, 20], [15, 8]])
tgt = [[b' FOO ', b' FOO '],
[b' FOO ', b' FOO ']]
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_decode(self):
if sys.version_info[0] >= 3:
A = np.char.array([b'\\u03a3'])
assert_(A.decode('unicode-escape')[0] == '\u03a3')
else:
with suppress_warnings() as sup:
if sys.py3kwarning:
sup.filter(DeprecationWarning, "'hex_codec'")
A = np.char.array(['736563726574206d657373616765'])
assert_(A.decode('hex_codec')[0] == 'secret message')
def test_encode(self):
B = self.B.encode('unicode_escape')
assert_(B[0][0] == str(' \\u03a3 ').encode('latin1'))
def test_expandtabs(self):
T = self.A.expandtabs()
assert_(T[2, 0] == b'123 345 \0')
def test_join(self):
if sys.version_info[0] >= 3:
# NOTE: list(b'123') == [49, 50, 51]
# so that b','.join(b'123') results to an error on Py3
A0 = self.A.decode('ascii')
else:
A0 = self.A
A = np.char.join([',', '#'], A0)
if sys.version_info[0] >= 3:
assert_(issubclass(A.dtype.type, np.unicode_))
else:
assert_(issubclass(A.dtype.type, np.string_))
tgt = np.array([[' ,a,b,c, ', ''],
['1,2,3,4,5', 'M#i#x#e#d#C#a#s#e'],
['1,2,3, ,\t, ,3,4,5, ,\x00, ', 'U#P#P#E#R']])
assert_array_equal(np.char.join([',', '#'], A0), tgt)
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, b'#')
assert_array_equal(C.startswith(b'#'), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(b'#')))
C = np.char.ljust(b'FOO', [[10, 20], [15, 8]])
tgt = [[b'FOO ', b'FOO '],
[b'FOO ', b'FOO ']]
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lower(self):
tgt = [[b' abc ', b''],
[b'12345', b'mixedcase'],
[b'123 \t 345 \0 ', b'upper']]
assert_(issubclass(self.A.lower().dtype.type, np.string_))
assert_array_equal(self.A.lower(), tgt)
tgt = [[u' \u03c3 ', u''],
[u'12345', u'mixedcase'],
[u'123 \t 345 \0 ', u'upper']]
assert_(issubclass(self.B.lower().dtype.type, np.unicode_))
assert_array_equal(self.B.lower(), tgt)
def test_lstrip(self):
tgt = [[b'abc ', b''],
[b'12345', b'MixedCase'],
[b'123 \t 345 \0 ', b'UPPER']]
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = [[b' abc', b''],
[b'2345', b'ixedCase'],
[b'23 \t 345 \x00', b'UPPER']]
assert_array_equal(self.A.lstrip([b'1', b'M']), tgt)
tgt = [[u'\u03a3 ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_partition(self):
P = self.A.partition([b'3', b'M'])
tgt = [[(b' abc ', b'', b''), (b'', b'', b'')],
[(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],
[(b'12', b'3', b' \t 345 \0 '), (b'UPPER', b'', b'')]]
assert_(issubclass(P.dtype.type, np.string_))
assert_array_equal(P, tgt)
def test_replace(self):
R = self.A.replace([b'3', b'a'],
[b'##########', b'@'])
tgt = [[b' abc ', b''],
[b'12##########45', b'MixedC@se'],
[b'12########## \t ##########45 \x00', b'UPPER']]
assert_(issubclass(R.dtype.type, np.string_))
assert_array_equal(R, tgt)
if sys.version_info[0] < 3:
# NOTE: b'abc'.replace(b'a', 'b') is not allowed on Py3
R = self.A.replace(b'a', u'\u03a3')
tgt = [[u' \u03a3bc ', ''],
['12345', u'MixedC\u03a3se'],
['123 \t 345 \x00', 'UPPER']]
assert_(issubclass(R.dtype.type, np.unicode_))
assert_array_equal(R, tgt)
def test_rjust(self):
assert_(issubclass(self.A.rjust(10).dtype.type, np.string_))
C = self.A.rjust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.rjust(20, b'#')
assert_(np.all(C.startswith(b'#')))
assert_array_equal(C.endswith(b'#'),
[[False, True], [False, False], [False, False]])
C = np.char.rjust(b'FOO', [[10, 20], [15, 8]])
tgt = [[b' FOO', b' FOO'],
[b' FOO', b' FOO']]
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_rpartition(self):
P = self.A.rpartition([b'3', b'M'])
tgt = [[(b'', b'', b' abc '), (b'', b'', b'')],
[(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],
[(b'123 \t ', b'3', b'45 \0 '), (b'', b'', b'UPPER')]]
assert_(issubclass(P.dtype.type, np.string_))
assert_array_equal(P, tgt)
def test_rsplit(self):
A = self.A.rsplit(b'3')
tgt = [[[b' abc '], [b'']],
[[b'12', b'45'], [b'MixedCase']],
[[b'12', b' \t ', b'45 \x00 '], [b'UPPER']]]
assert_(issubclass(A.dtype.type, np.object_))
assert_equal(A.tolist(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = [[b' abc', b''],
[b'12345', b'MixedCase'],
[b'123 \t 345', b'UPPER']]
assert_array_equal(self.A.rstrip(), tgt)
tgt = [[b' abc ', b''],
[b'1234', b'MixedCase'],
[b'123 \t 345 \x00', b'UPP']
]
assert_array_equal(self.A.rstrip([b'5', b'ER']), tgt)
tgt = [[u' \u03a3', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = [[b'abc', b''],
[b'12345', b'MixedCase'],
[b'123 \t 345', b'UPPER']]
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = [[b' abc ', b''],
[b'234', b'ixedCas'],
[b'23 \t 345 \x00', b'UPP']]
assert_array_equal(self.A.strip([b'15', b'EReM']), tgt)
tgt = [[u'\u03a3', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def test_split(self):
A = self.A.split(b'3')
tgt = [
[[b' abc '], [b'']],
[[b'12', b'45'], [b'MixedCase']],
[[b'12', b' \t ', b'45 \x00 '], [b'UPPER']]]
assert_(issubclass(A.dtype.type, np.object_))
assert_equal(A.tolist(), tgt)
def test_splitlines(self):
A = np.char.array(['abc\nfds\nwer']).splitlines()
assert_(issubclass(A.dtype.type, np.object_))
assert_(A.shape == (1,))
assert_(len(A[0]) == 3)
def test_swapcase(self):
tgt = [[b' ABC ', b''],
[b'12345', b'mIXEDcASE'],
[b'123 \t 345 \0 ', b'upper']]
assert_(issubclass(self.A.swapcase().dtype.type, np.string_))
assert_array_equal(self.A.swapcase(), tgt)
tgt = [[u' \u03c3 ', u''],
[u'12345', u'mIXEDcASE'],
[u'123 \t 345 \0 ', u'upper']]
assert_(issubclass(self.B.swapcase().dtype.type, np.unicode_))
assert_array_equal(self.B.swapcase(), tgt)
def test_title(self):
tgt = [[b' Abc ', b''],
[b'12345', b'Mixedcase'],
[b'123 \t 345 \0 ', b'Upper']]
assert_(issubclass(self.A.title().dtype.type, np.string_))
assert_array_equal(self.A.title(), tgt)
tgt = [[u' \u03a3 ', u''],
[u'12345', u'Mixedcase'],
[u'123 \t 345 \0 ', u'Upper']]
assert_(issubclass(self.B.title().dtype.type, np.unicode_))
assert_array_equal(self.B.title(), tgt)
def test_upper(self):
tgt = [[b' ABC ', b''],
[b'12345', b'MIXEDCASE'],
[b'123 \t 345 \0 ', b'UPPER']]
assert_(issubclass(self.A.upper().dtype.type, np.string_))
assert_array_equal(self.A.upper(), tgt)
tgt = [[u' \u03a3 ', u''],
[u'12345', u'MIXEDCASE'],
[u'123 \t 345 \0 ', u'UPPER']]
assert_(issubclass(self.B.upper().dtype.type, np.unicode_))
assert_array_equal(self.B.upper(), tgt)
def test_isnumeric(self):
def fail():
self.A.isnumeric()
assert_raises(TypeError, fail)
assert_(issubclass(self.B.isnumeric().dtype.type, np.bool_))
assert_array_equal(self.B.isnumeric(), [
[False, False], [True, False], [False, False]])
def test_isdecimal(self):
def fail():
self.A.isdecimal()
assert_raises(TypeError, fail)
assert_(issubclass(self.B.isdecimal().dtype.type, np.bool_))
assert_array_equal(self.B.isdecimal(), [
[False, False], [True, False], [False, False]])
class TestOperations(object):
def setup(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '456'],
['051', 'tuv']]).view(np.chararray)
def test_add(self):
AB = np.array([['abcefg', '123456'],
['789051', 'xyztuv']]).view(np.chararray)
assert_array_equal(AB, (self.A + self.B))
assert_(len((self.A + self.B)[0][0]) == 6)
def test_radd(self):
QA = np.array([['qabc', 'q123'],
['q789', 'qxyz']]).view(np.chararray)
assert_array_equal(QA, ('q' + self.A))
def test_mul(self):
A = self.A
for r in (2, 3, 5, 7, 197):
Ar = np.array([[A[0, 0]*r, A[0, 1]*r],
[A[1, 0]*r, A[1, 1]*r]]).view(np.chararray)
assert_array_equal(Ar, (self.A * r))
for ob in [object(), 'qrs']:
with assert_raises_regex(ValueError,
'Can only multiply by integers'):
A*ob
def test_rmul(self):
A = self.A
for r in (2, 3, 5, 7, 197):
Ar = np.array([[A[0, 0]*r, A[0, 1]*r],
[A[1, 0]*r, A[1, 1]*r]]).view(np.chararray)
assert_array_equal(Ar, (r * self.A))
for ob in [object(), 'qrs']:
with assert_raises_regex(ValueError,
'Can only multiply by integers'):
ob * A
def test_mod(self):
"""Ticket #856"""
F = np.array([['%d', '%f'], ['%s', '%r']]).view(np.chararray)
C = np.array([[3, 7], [19, 1]])
FC = np.array([['3', '7.000000'],
['19', '1']]).view(np.chararray)
assert_array_equal(FC, F % C)
A = np.array([['%.3f', '%d'], ['%s', '%r']]).view(np.chararray)
A1 = np.array([['1.000', '1'], ['1', '1']]).view(np.chararray)
assert_array_equal(A1, (A % 1))
A2 = np.array([['1.000', '2'], ['3', '4']]).view(np.chararray)
assert_array_equal(A2, (A % [[1, 2], [3, 4]]))
def test_rmod(self):
assert_(("%s" % self.A) == str(self.A))
assert_(("%r" % self.A) == repr(self.A))
for ob in [42, object()]:
with assert_raises_regex(
TypeError, "unsupported operand type.* and 'chararray'"):
ob % self.A
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == b'abc')
def test_empty_indexing():
"""Regression test for ticket 1948."""
# Check that indexing a chararray with an empty list/array returns an
# empty chararray instead of a chararray with a single empty string in it.
s = np.chararray((4,))
assert_(s[[]].size == 0)

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"""
Tests related to deprecation warnings. Also a convenient place
to document how deprecations should eventually be turned into errors.
"""
from __future__ import division, absolute_import, print_function
import datetime
import sys
import operator
import warnings
import pytest
import shutil
import tempfile
import numpy as np
from numpy.testing import (
assert_raises, assert_warns, assert_, assert_array_equal
)
from numpy.core._multiarray_tests import fromstring_null_term_c_api
try:
import pytz
_has_pytz = True
except ImportError:
_has_pytz = False
class _DeprecationTestCase(object):
# Just as warning: warnings uses re.match, so the start of this message
# must match.
message = ''
warning_cls = DeprecationWarning
def setup(self):
self.warn_ctx = warnings.catch_warnings(record=True)
self.log = self.warn_ctx.__enter__()
# Do *not* ignore other DeprecationWarnings. Ignoring warnings
# can give very confusing results because of
# https://bugs.python.org/issue4180 and it is probably simplest to
# try to keep the tests cleanly giving only the right warning type.
# (While checking them set to "error" those are ignored anyway)
# We still have them show up, because otherwise they would be raised
warnings.filterwarnings("always", category=self.warning_cls)
warnings.filterwarnings("always", message=self.message,
category=self.warning_cls)
def teardown(self):
self.warn_ctx.__exit__()
def assert_deprecated(self, function, num=1, ignore_others=False,
function_fails=False,
exceptions=np._NoValue,
args=(), kwargs={}):
"""Test if DeprecationWarnings are given and raised.
This first checks if the function when called gives `num`
DeprecationWarnings, after that it tries to raise these
DeprecationWarnings and compares them with `exceptions`.
The exceptions can be different for cases where this code path
is simply not anticipated and the exception is replaced.
Parameters
----------
function : callable
The function to test
num : int
Number of DeprecationWarnings to expect. This should normally be 1.
ignore_others : bool
Whether warnings of the wrong type should be ignored (note that
the message is not checked)
function_fails : bool
If the function would normally fail, setting this will check for
warnings inside a try/except block.
exceptions : Exception or tuple of Exceptions
Exception to expect when turning the warnings into an error.
The default checks for DeprecationWarnings. If exceptions is
empty the function is expected to run successfully.
args : tuple
Arguments for `function`
kwargs : dict
Keyword arguments for `function`
"""
# reset the log
self.log[:] = []
if exceptions is np._NoValue:
exceptions = (self.warning_cls,)
try:
function(*args, **kwargs)
except (Exception if function_fails else tuple()):
pass
# just in case, clear the registry
num_found = 0
for warning in self.log:
if warning.category is self.warning_cls:
num_found += 1
elif not ignore_others:
raise AssertionError(
"expected %s but got: %s" %
(self.warning_cls.__name__, warning.category))
if num is not None and num_found != num:
msg = "%i warnings found but %i expected." % (len(self.log), num)
lst = [str(w) for w in self.log]
raise AssertionError("\n".join([msg] + lst))
with warnings.catch_warnings():
warnings.filterwarnings("error", message=self.message,
category=self.warning_cls)
try:
function(*args, **kwargs)
if exceptions != tuple():
raise AssertionError(
"No error raised during function call")
except exceptions:
if exceptions == tuple():
raise AssertionError(
"Error raised during function call")
def assert_not_deprecated(self, function, args=(), kwargs={}):
"""Test that warnings are not raised.
This is just a shorthand for:
self.assert_deprecated(function, num=0, ignore_others=True,
exceptions=tuple(), args=args, kwargs=kwargs)
"""
self.assert_deprecated(function, num=0, ignore_others=True,
exceptions=tuple(), args=args, kwargs=kwargs)
class _VisibleDeprecationTestCase(_DeprecationTestCase):
warning_cls = np.VisibleDeprecationWarning
class TestNonTupleNDIndexDeprecation(object):
def test_basic(self):
a = np.zeros((5, 5))
with warnings.catch_warnings():
warnings.filterwarnings('always')
assert_warns(FutureWarning, a.__getitem__, [[0, 1], [0, 1]])
assert_warns(FutureWarning, a.__getitem__, [slice(None)])
warnings.filterwarnings('error')
assert_raises(FutureWarning, a.__getitem__, [[0, 1], [0, 1]])
assert_raises(FutureWarning, a.__getitem__, [slice(None)])
# a a[[0, 1]] always was advanced indexing, so no error/warning
a[[0, 1]]
class TestComparisonDeprecations(_DeprecationTestCase):
"""This tests the deprecation, for non-element-wise comparison logic.
This used to mean that when an error occurred during element-wise comparison
(i.e. broadcasting) NotImplemented was returned, but also in the comparison
itself, False was given instead of the error.
Also test FutureWarning for the None comparison.
"""
message = "elementwise.* comparison failed; .*"
def test_normal_types(self):
for op in (operator.eq, operator.ne):
# Broadcasting errors:
self.assert_deprecated(op, args=(np.zeros(3), []))
a = np.zeros(3, dtype='i,i')
# (warning is issued a couple of times here)
self.assert_deprecated(op, args=(a, a[:-1]), num=None)
# ragged array comparison returns True/False
a = np.array([1, np.array([1,2,3])], dtype=object)
b = np.array([1, np.array([1,2,3])], dtype=object)
self.assert_deprecated(op, args=(a, b), num=None)
def test_string(self):
# For two string arrays, strings always raised the broadcasting error:
a = np.array(['a', 'b'])
b = np.array(['a', 'b', 'c'])
assert_raises(ValueError, lambda x, y: x == y, a, b)
# The empty list is not cast to string, and this used to pass due
# to dtype mismatch; now (2018-06-21) it correctly leads to a
# FutureWarning.
assert_warns(FutureWarning, lambda: a == [])
def test_void_dtype_equality_failures(self):
class NotArray(object):
def __array__(self):
raise TypeError
# Needed so Python 3 does not raise DeprecationWarning twice.
def __ne__(self, other):
return NotImplemented
self.assert_deprecated(lambda: np.arange(2) == NotArray())
self.assert_deprecated(lambda: np.arange(2) != NotArray())
struct1 = np.zeros(2, dtype="i4,i4")
struct2 = np.zeros(2, dtype="i4,i4,i4")
assert_warns(FutureWarning, lambda: struct1 == 1)
assert_warns(FutureWarning, lambda: struct1 == struct2)
assert_warns(FutureWarning, lambda: struct1 != 1)
assert_warns(FutureWarning, lambda: struct1 != struct2)
def test_array_richcompare_legacy_weirdness(self):
# It doesn't really work to use assert_deprecated here, b/c part of
# the point of assert_deprecated is to check that when warnings are
# set to "error" mode then the error is propagated -- which is good!
# But here we are testing a bunch of code that is deprecated *because*
# it has the habit of swallowing up errors and converting them into
# different warnings. So assert_warns will have to be sufficient.
assert_warns(FutureWarning, lambda: np.arange(2) == "a")
assert_warns(FutureWarning, lambda: np.arange(2) != "a")
# No warning for scalar comparisons
with warnings.catch_warnings():
warnings.filterwarnings("error")
assert_(not (np.array(0) == "a"))
assert_(np.array(0) != "a")
assert_(not (np.int16(0) == "a"))
assert_(np.int16(0) != "a")
for arg1 in [np.asarray(0), np.int16(0)]:
struct = np.zeros(2, dtype="i4,i4")
for arg2 in [struct, "a"]:
for f in [operator.lt, operator.le, operator.gt, operator.ge]:
if sys.version_info[0] >= 3:
# py3
with warnings.catch_warnings() as l:
warnings.filterwarnings("always")
assert_raises(TypeError, f, arg1, arg2)
assert_(not l)
else:
# py2
assert_warns(DeprecationWarning, f, arg1, arg2)
class TestDatetime64Timezone(_DeprecationTestCase):
"""Parsing of datetime64 with timezones deprecated in 1.11.0, because
datetime64 is now timezone naive rather than UTC only.
It will be quite a while before we can remove this, because, at the very
least, a lot of existing code uses the 'Z' modifier to avoid conversion
from local time to UTC, even if otherwise it handles time in a timezone
naive fashion.
"""
def test_string(self):
self.assert_deprecated(np.datetime64, args=('2000-01-01T00+01',))
self.assert_deprecated(np.datetime64, args=('2000-01-01T00Z',))
@pytest.mark.skipif(not _has_pytz,
reason="The pytz module is not available.")
def test_datetime(self):
tz = pytz.timezone('US/Eastern')
dt = datetime.datetime(2000, 1, 1, 0, 0, tzinfo=tz)
self.assert_deprecated(np.datetime64, args=(dt,))
class TestNonCContiguousViewDeprecation(_DeprecationTestCase):
"""View of non-C-contiguous arrays deprecated in 1.11.0.
The deprecation will not be raised for arrays that are both C and F
contiguous, as C contiguous is dominant. There are more such arrays
with relaxed stride checking than without so the deprecation is not
as visible with relaxed stride checking in force.
"""
def test_fortran_contiguous(self):
self.assert_deprecated(np.ones((2,2)).T.view, args=(complex,))
self.assert_deprecated(np.ones((2,2)).T.view, args=(np.int8,))
class TestArrayDataAttributeAssignmentDeprecation(_DeprecationTestCase):
"""Assigning the 'data' attribute of an ndarray is unsafe as pointed
out in gh-7093. Eventually, such assignment should NOT be allowed, but
in the interests of maintaining backwards compatibility, only a Deprecation-
Warning will be raised instead for the time being to give developers time to
refactor relevant code.
"""
def test_data_attr_assignment(self):
a = np.arange(10)
b = np.linspace(0, 1, 10)
self.message = ("Assigning the 'data' attribute is an "
"inherently unsafe operation and will "
"be removed in the future.")
self.assert_deprecated(a.__setattr__, args=('data', b.data))
class TestBinaryReprInsufficientWidthParameterForRepresentation(_DeprecationTestCase):
"""
If a 'width' parameter is passed into ``binary_repr`` that is insufficient to
represent the number in base 2 (positive) or 2's complement (negative) form,
the function used to silently ignore the parameter and return a representation
using the minimal number of bits needed for the form in question. Such behavior
is now considered unsafe from a user perspective and will raise an error in the future.
"""
def test_insufficient_width_positive(self):
args = (10,)
kwargs = {'width': 2}
self.message = ("Insufficient bit width provided. This behavior "
"will raise an error in the future.")
self.assert_deprecated(np.binary_repr, args=args, kwargs=kwargs)
def test_insufficient_width_negative(self):
args = (-5,)
kwargs = {'width': 2}
self.message = ("Insufficient bit width provided. This behavior "
"will raise an error in the future.")
self.assert_deprecated(np.binary_repr, args=args, kwargs=kwargs)
class TestNumericStyleTypecodes(_DeprecationTestCase):
"""
Deprecate the old numeric-style dtypes, which are especially
confusing for complex types, e.g. Complex32 -> complex64. When the
deprecation cycle is complete, the check for the strings should be
removed from PyArray_DescrConverter in descriptor.c, and the
deprecated keys should not be added as capitalized aliases in
_add_aliases in numerictypes.py.
"""
def test_all_dtypes(self):
deprecated_types = [
'Bool', 'Complex32', 'Complex64', 'Float16', 'Float32', 'Float64',
'Int8', 'Int16', 'Int32', 'Int64', 'Object0', 'Timedelta64',
'UInt8', 'UInt16', 'UInt32', 'UInt64', 'Void0'
]
if sys.version_info[0] < 3:
deprecated_types.extend(['Unicode0', 'String0'])
for dt in deprecated_types:
self.assert_deprecated(np.dtype, exceptions=(TypeError,),
args=(dt,))
class TestTestDeprecated(object):
def test_assert_deprecated(self):
test_case_instance = _DeprecationTestCase()
test_case_instance.setup()
assert_raises(AssertionError,
test_case_instance.assert_deprecated,
lambda: None)
def foo():
warnings.warn("foo", category=DeprecationWarning, stacklevel=2)
test_case_instance.assert_deprecated(foo)
test_case_instance.teardown()
class TestClassicIntDivision(_DeprecationTestCase):
"""
See #7949. Deprecate the numeric-style dtypes with -3 flag in python 2
if used for division
List of data types: https://docs.scipy.org/doc/numpy/user/basics.types.html
"""
def test_int_dtypes(self):
#scramble types and do some mix and match testing
deprecated_types = [
'bool_', 'int_', 'intc', 'uint8', 'int8', 'uint64', 'int32', 'uint16',
'intp', 'int64', 'uint32', 'int16'
]
if sys.version_info[0] < 3 and sys.py3kwarning:
import operator as op
dt2 = 'bool_'
for dt1 in deprecated_types:
a = np.array([1,2,3], dtype=dt1)
b = np.array([1,2,3], dtype=dt2)
self.assert_deprecated(op.div, args=(a,b))
dt2 = dt1
class TestNonNumericConjugate(_DeprecationTestCase):
"""
Deprecate no-op behavior of ndarray.conjugate on non-numeric dtypes,
which conflicts with the error behavior of np.conjugate.
"""
def test_conjugate(self):
for a in np.array(5), np.array(5j):
self.assert_not_deprecated(a.conjugate)
for a in (np.array('s'), np.array('2016', 'M'),
np.array((1, 2), [('a', int), ('b', int)])):
self.assert_deprecated(a.conjugate)
class TestNPY_CHAR(_DeprecationTestCase):
# 2017-05-03, 1.13.0
def test_npy_char_deprecation(self):
from numpy.core._multiarray_tests import npy_char_deprecation
self.assert_deprecated(npy_char_deprecation)
assert_(npy_char_deprecation() == 'S1')
class TestPyArray_AS1D(_DeprecationTestCase):
def test_npy_pyarrayas1d_deprecation(self):
from numpy.core._multiarray_tests import npy_pyarrayas1d_deprecation
assert_raises(NotImplementedError, npy_pyarrayas1d_deprecation)
class TestPyArray_AS2D(_DeprecationTestCase):
def test_npy_pyarrayas2d_deprecation(self):
from numpy.core._multiarray_tests import npy_pyarrayas2d_deprecation
assert_raises(NotImplementedError, npy_pyarrayas2d_deprecation)
class Test_UPDATEIFCOPY(_DeprecationTestCase):
"""
v1.14 deprecates creating an array with the UPDATEIFCOPY flag, use
WRITEBACKIFCOPY instead
"""
def test_npy_updateifcopy_deprecation(self):
from numpy.core._multiarray_tests import npy_updateifcopy_deprecation
arr = np.arange(9).reshape(3, 3)
v = arr.T
self.assert_deprecated(npy_updateifcopy_deprecation, args=(v,))
class TestDatetimeEvent(_DeprecationTestCase):
# 2017-08-11, 1.14.0
def test_3_tuple(self):
for cls in (np.datetime64, np.timedelta64):
# two valid uses - (unit, num) and (unit, num, den, None)
self.assert_not_deprecated(cls, args=(1, ('ms', 2)))
self.assert_not_deprecated(cls, args=(1, ('ms', 2, 1, None)))
# trying to use the event argument, removed in 1.7.0, is deprecated
# it used to be a uint8
self.assert_deprecated(cls, args=(1, ('ms', 2, 'event')))
self.assert_deprecated(cls, args=(1, ('ms', 2, 63)))
self.assert_deprecated(cls, args=(1, ('ms', 2, 1, 'event')))
self.assert_deprecated(cls, args=(1, ('ms', 2, 1, 63)))
class TestTruthTestingEmptyArrays(_DeprecationTestCase):
# 2017-09-25, 1.14.0
message = '.*truth value of an empty array is ambiguous.*'
def test_1d(self):
self.assert_deprecated(bool, args=(np.array([]),))
def test_2d(self):
self.assert_deprecated(bool, args=(np.zeros((1, 0)),))
self.assert_deprecated(bool, args=(np.zeros((0, 1)),))
self.assert_deprecated(bool, args=(np.zeros((0, 0)),))
class TestBincount(_DeprecationTestCase):
# 2017-06-01, 1.14.0
def test_bincount_minlength(self):
self.assert_deprecated(lambda: np.bincount([1, 2, 3], minlength=None))
class TestAlen(_DeprecationTestCase):
# 2019-08-02, 1.18.0
def test_alen(self):
self.assert_deprecated(lambda: np.alen(np.array([1, 2, 3])))
class TestGeneratorSum(_DeprecationTestCase):
# 2018-02-25, 1.15.0
def test_generator_sum(self):
self.assert_deprecated(np.sum, args=((i for i in range(5)),))
class TestSctypeNA(_VisibleDeprecationTestCase):
# 2018-06-24, 1.16
def test_sctypeNA(self):
self.assert_deprecated(lambda: np.sctypeNA['?'])
self.assert_deprecated(lambda: np.typeNA['?'])
self.assert_deprecated(lambda: np.typeNA.get('?'))
class TestPositiveOnNonNumerical(_DeprecationTestCase):
# 2018-06-28, 1.16.0
def test_positive_on_non_number(self):
self.assert_deprecated(operator.pos, args=(np.array('foo'),))
class TestFromstring(_DeprecationTestCase):
# 2017-10-19, 1.14
def test_fromstring(self):
self.assert_deprecated(np.fromstring, args=('\x00'*80,))
class TestFromStringAndFileInvalidData(_DeprecationTestCase):
# 2019-06-08, 1.17.0
# Tests should be moved to real tests when deprecation is done.
message = "string or file could not be read to its end"
@pytest.mark.parametrize("invalid_str", [",invalid_data", "invalid_sep"])
def test_deprecate_unparsable_data_file(self, invalid_str):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
with tempfile.TemporaryFile(mode="w") as f:
x.tofile(f, sep=',', format='%.2f')
f.write(invalid_str)
f.seek(0)
self.assert_deprecated(lambda: np.fromfile(f, sep=","))
f.seek(0)
self.assert_deprecated(lambda: np.fromfile(f, sep=",", count=5))
# Should not raise:
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
f.seek(0)
res = np.fromfile(f, sep=",", count=4)
assert_array_equal(res, x)
@pytest.mark.parametrize("invalid_str", [",invalid_data", "invalid_sep"])
def test_deprecate_unparsable_string(self, invalid_str):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
x_str = "1.51,2,3.51,4{}".format(invalid_str)
self.assert_deprecated(lambda: np.fromstring(x_str, sep=","))
self.assert_deprecated(lambda: np.fromstring(x_str, sep=",", count=5))
# The C-level API can use not fixed size, but 0 terminated strings,
# so test that as well:
bytestr = x_str.encode("ascii")
self.assert_deprecated(lambda: fromstring_null_term_c_api(bytestr))
with assert_warns(DeprecationWarning):
# this is slightly strange, in that fromstring leaves data
# potentially uninitialized (would be good to error when all is
# read, but count is larger then actual data maybe).
res = np.fromstring(x_str, sep=",", count=5)
assert_array_equal(res[:-1], x)
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
# Should not raise:
res = np.fromstring(x_str, sep=",", count=4)
assert_array_equal(res, x)
class Test_GetSet_NumericOps(_DeprecationTestCase):
# 2018-09-20, 1.16.0
def test_get_numeric_ops(self):
from numpy.core._multiarray_tests import getset_numericops
self.assert_deprecated(getset_numericops, num=2)
# empty kwargs prevents any state actually changing which would break
# other tests.
self.assert_deprecated(np.set_numeric_ops, kwargs={})
assert_raises(ValueError, np.set_numeric_ops, add='abc')
class TestShape1Fields(_DeprecationTestCase):
warning_cls = FutureWarning
# 2019-05-20, 1.17.0
def test_shape_1_fields(self):
self.assert_deprecated(np.dtype, args=([('a', int, 1)],))
class TestNonZero(_DeprecationTestCase):
# 2019-05-26, 1.17.0
def test_zerod(self):
self.assert_deprecated(lambda: np.nonzero(np.array(0)))
self.assert_deprecated(lambda: np.nonzero(np.array(1)))

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from __future__ import division, absolute_import, print_function
import platform
import pytest
import numpy as np
from numpy.testing import assert_, assert_raises
class TestErrstate(object):
@pytest.mark.skipif(platform.machine() == "armv5tel", reason="See gh-413.")
def test_invalid(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(invalid='ignore'):
np.sqrt(a)
# While this should fail!
with assert_raises(FloatingPointError):
np.sqrt(a)
def test_divide(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(divide='ignore'):
a // 0
# While this should fail!
with assert_raises(FloatingPointError):
a // 0
def test_errcall(self):
def foo(*args):
print(args)
olderrcall = np.geterrcall()
with np.errstate(call=foo):
assert_(np.geterrcall() is foo, 'call is not foo')
with np.errstate(call=None):
assert_(np.geterrcall() is None, 'call is not None')
assert_(np.geterrcall() is olderrcall, 'call is not olderrcall')
def test_errstate_decorator(self):
@np.errstate(all='ignore')
def foo():
a = -np.arange(3)
a // 0
foo()

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from __future__ import division, absolute_import, print_function
import itertools
import contextlib
import operator
import pytest
import numpy as np
import numpy.core._multiarray_tests as mt
from numpy.testing import assert_raises, assert_equal
INT64_MAX = np.iinfo(np.int64).max
INT64_MIN = np.iinfo(np.int64).min
INT64_MID = 2**32
# int128 is not two's complement, the sign bit is separate
INT128_MAX = 2**128 - 1
INT128_MIN = -INT128_MAX
INT128_MID = 2**64
INT64_VALUES = (
[INT64_MIN + j for j in range(20)] +
[INT64_MAX - j for j in range(20)] +
[INT64_MID + j for j in range(-20, 20)] +
[2*INT64_MID + j for j in range(-20, 20)] +
[INT64_MID//2 + j for j in range(-20, 20)] +
list(range(-70, 70))
)
INT128_VALUES = (
[INT128_MIN + j for j in range(20)] +
[INT128_MAX - j for j in range(20)] +
[INT128_MID + j for j in range(-20, 20)] +
[2*INT128_MID + j for j in range(-20, 20)] +
[INT128_MID//2 + j for j in range(-20, 20)] +
list(range(-70, 70)) +
[False] # negative zero
)
INT64_POS_VALUES = [x for x in INT64_VALUES if x > 0]
@contextlib.contextmanager
def exc_iter(*args):
"""
Iterate over Cartesian product of *args, and if an exception is raised,
add information of the current iterate.
"""
value = [None]
def iterate():
for v in itertools.product(*args):
value[0] = v
yield v
try:
yield iterate()
except Exception:
import traceback
msg = "At: %r\n%s" % (repr(value[0]),
traceback.format_exc())
raise AssertionError(msg)
def test_safe_binop():
# Test checked arithmetic routines
ops = [
(operator.add, 1),
(operator.sub, 2),
(operator.mul, 3)
]
with exc_iter(ops, INT64_VALUES, INT64_VALUES) as it:
for xop, a, b in it:
pyop, op = xop
c = pyop(a, b)
if not (INT64_MIN <= c <= INT64_MAX):
assert_raises(OverflowError, mt.extint_safe_binop, a, b, op)
else:
d = mt.extint_safe_binop(a, b, op)
if c != d:
# assert_equal is slow
assert_equal(d, c)
def test_to_128():
with exc_iter(INT64_VALUES) as it:
for a, in it:
b = mt.extint_to_128(a)
if a != b:
assert_equal(b, a)
def test_to_64():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if not (INT64_MIN <= a <= INT64_MAX):
assert_raises(OverflowError, mt.extint_to_64, a)
else:
b = mt.extint_to_64(a)
if a != b:
assert_equal(b, a)
def test_mul_64_64():
with exc_iter(INT64_VALUES, INT64_VALUES) as it:
for a, b in it:
c = a * b
d = mt.extint_mul_64_64(a, b)
if c != d:
assert_equal(d, c)
def test_add_128():
with exc_iter(INT128_VALUES, INT128_VALUES) as it:
for a, b in it:
c = a + b
if not (INT128_MIN <= c <= INT128_MAX):
assert_raises(OverflowError, mt.extint_add_128, a, b)
else:
d = mt.extint_add_128(a, b)
if c != d:
assert_equal(d, c)
def test_sub_128():
with exc_iter(INT128_VALUES, INT128_VALUES) as it:
for a, b in it:
c = a - b
if not (INT128_MIN <= c <= INT128_MAX):
assert_raises(OverflowError, mt.extint_sub_128, a, b)
else:
d = mt.extint_sub_128(a, b)
if c != d:
assert_equal(d, c)
def test_neg_128():
with exc_iter(INT128_VALUES) as it:
for a, in it:
b = -a
c = mt.extint_neg_128(a)
if b != c:
assert_equal(c, b)
def test_shl_128():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if a < 0:
b = -(((-a) << 1) & (2**128-1))
else:
b = (a << 1) & (2**128-1)
c = mt.extint_shl_128(a)
if b != c:
assert_equal(c, b)
def test_shr_128():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if a < 0:
b = -((-a) >> 1)
else:
b = a >> 1
c = mt.extint_shr_128(a)
if b != c:
assert_equal(c, b)
def test_gt_128():
with exc_iter(INT128_VALUES, INT128_VALUES) as it:
for a, b in it:
c = a > b
d = mt.extint_gt_128(a, b)
if c != d:
assert_equal(d, c)
@pytest.mark.slow
def test_divmod_128_64():
with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:
for a, b in it:
if a >= 0:
c, cr = divmod(a, b)
else:
c, cr = divmod(-a, b)
c = -c
cr = -cr
d, dr = mt.extint_divmod_128_64(a, b)
if c != d or d != dr or b*d + dr != a:
assert_equal(d, c)
assert_equal(dr, cr)
assert_equal(b*d + dr, a)
def test_floordiv_128_64():
with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:
for a, b in it:
c = a // b
d = mt.extint_floordiv_128_64(a, b)
if c != d:
assert_equal(d, c)
def test_ceildiv_128_64():
with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:
for a, b in it:
c = (a + b - 1) // b
d = mt.extint_ceildiv_128_64(a, b)
if c != d:
assert_equal(d, c)

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