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licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
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Each contributor grants you a non-exclusive, worldwide, royalty-free
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In the following three paragraphs, a "patent license" is any express
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If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
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then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
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consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
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receiving the covered work authorizing them to use, propagate, modify
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you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
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contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
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License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
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License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
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14. Revised Versions of this License.
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the GNU General Public License from time to time. Such new versions will
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Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
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Foundation. If the Program does not specify a version number of the
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If the Program specifies that a proxy can decide which future
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15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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Program, unless a warranty or assumption of liability accompanies a
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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):
os.environ.setdefault('PATH', '')
os.environ['PATH'] += os.pathsep + extra_dll_dir
blas_mkl_info={}
blis_info={}
openblas_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)]}
blas_opt_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)]}
lapack_mkl_info={}
openblas_lapack_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)]}
lapack_opt_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', '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 <http://www.scipy.org>`_.
We recommend exploring the docstrings using
`IPython <http://ipython.scipy.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__
from ._import_tools import PackageLoader
def pkgload(*packages, **options):
loader = PackageLoader(infunc=True)
return loader(*packages, **options)
from . import add_newdocs
__all__ = ['add_newdocs',
'ModuleDeprecationWarning',
'VisibleDeprecationWarning']
pkgload.__doc__ = PackageLoader.__call__.__doc__
# 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, _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
# Allow distributors to run custom init code
from . import _distributor_init
from . import core
from .core import *
from . import compat
from . import lib
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 *
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
__all__.extend(['__version__', 'pkgload', 'PackageLoader',
'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# Filter annoying Cython warnings that serve no good purpose.
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'
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases (e.g., the 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.")
raise RuntimeError(msg.format(__file__))
_sanity_check()
del _sanity_check

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""" Distributor init file
Distributors: you can add custom code here to support particular distributions
of numpy.
For example, this is a good place to put any checks for hardware requirements.
The numpy standard source distribution will not put code in this file, so you
can safely replace this file with your own version.
"""

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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.
"""
pass
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.
"""
pass
class _NoValue(object):
"""Special keyword value.
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.
"""
pass

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from __future__ import division, absolute_import, print_function
import os
import sys
import warnings
__all__ = ['PackageLoader']
class PackageLoader(object):
def __init__(self, verbose=False, infunc=False):
""" Manages loading packages.
"""
if infunc:
_level = 2
else:
_level = 1
self.parent_frame = frame = sys._getframe(_level)
self.parent_name = eval('__name__', frame.f_globals, frame.f_locals)
parent_path = eval('__path__', frame.f_globals, frame.f_locals)
if isinstance(parent_path, str):
parent_path = [parent_path]
self.parent_path = parent_path
if '__all__' not in frame.f_locals:
exec('__all__ = []', frame.f_globals, frame.f_locals)
self.parent_export_names = eval('__all__', frame.f_globals, frame.f_locals)
self.info_modules = {}
self.imported_packages = []
self.verbose = None
def _get_info_files(self, package_dir, parent_path, parent_package=None):
""" Return list of (package name,info.py file) from parent_path subdirectories.
"""
from glob import glob
files = glob(os.path.join(parent_path, package_dir, 'info.py'))
for info_file in glob(os.path.join(parent_path, package_dir, 'info.pyc')):
if info_file[:-1] not in files:
files.append(info_file)
info_files = []
for info_file in files:
package_name = os.path.dirname(info_file[len(parent_path)+1:])\
.replace(os.sep, '.')
if parent_package:
package_name = parent_package + '.' + package_name
info_files.append((package_name, info_file))
info_files.extend(self._get_info_files('*',
os.path.dirname(info_file),
package_name))
return info_files
def _init_info_modules(self, packages=None):
"""Initialize info_modules = {<package_name>: <package info.py module>}.
"""
from numpy.compat import npy_load_module
info_files = []
info_modules = self.info_modules
if packages is None:
for path in self.parent_path:
info_files.extend(self._get_info_files('*', path))
else:
for package_name in packages:
package_dir = os.path.join(*package_name.split('.'))
for path in self.parent_path:
names_files = self._get_info_files(package_dir, path)
if names_files:
info_files.extend(names_files)
break
else:
try:
exec('import %s.info as info' % (package_name))
info_modules[package_name] = info
except ImportError as msg:
self.warn('No scipy-style subpackage %r found in %s. '\
'Ignoring: %s'\
% (package_name, ':'.join(self.parent_path), msg))
for package_name, info_file in info_files:
if package_name in info_modules:
continue
fullname = self.parent_name +'.'+ package_name
if info_file[-1]=='c':
filedescriptor = ('.pyc', 'rb', 2)
else:
filedescriptor = ('.py', 'U', 1)
try:
info_module = npy_load_module(fullname + '.info',
info_file,
filedescriptor)
except Exception as msg:
self.error(msg)
info_module = None
if info_module is None or getattr(info_module, 'ignore', False):
info_modules.pop(package_name, None)
else:
self._init_info_modules(getattr(info_module, 'depends', []))
info_modules[package_name] = info_module
return
def _get_sorted_names(self):
""" Return package names sorted in the order as they should be
imported due to dependence relations between packages.
"""
depend_dict = {}
for name, info_module in self.info_modules.items():
depend_dict[name] = getattr(info_module, 'depends', [])
package_names = []
for name in list(depend_dict.keys()):
if not depend_dict[name]:
package_names.append(name)
del depend_dict[name]
while depend_dict:
for name, lst in list(depend_dict.items()):
new_lst = [n for n in lst if n in depend_dict]
if not new_lst:
package_names.append(name)
del depend_dict[name]
else:
depend_dict[name] = new_lst
return package_names
def __call__(self,*packages, **options):
"""Load one or more packages into parent package top-level namespace.
This function is intended to shorten the need to import many
subpackages, say of scipy, constantly with statements such as
import scipy.linalg, scipy.fftpack, scipy.etc...
Instead, you can say:
import scipy
scipy.pkgload('linalg','fftpack',...)
or
scipy.pkgload()
to load all of them in one call.
If a name which doesn't exist in scipy's namespace is
given, a warning is shown.
Parameters
----------
*packages : arg-tuple
the names (one or more strings) of all the modules one
wishes to load into the top-level namespace.
verbose= : integer
verbosity level [default: -1].
verbose=-1 will suspend also warnings.
force= : bool
when True, force reloading loaded packages [default: False].
postpone= : bool
when True, don't load packages [default: False]
"""
# 2014-10-29, 1.10
warnings.warn('pkgload and PackageLoader are obsolete '
'and will be removed in a future version of numpy',
DeprecationWarning, stacklevel=2)
frame = self.parent_frame
self.info_modules = {}
if options.get('force', False):
self.imported_packages = []
self.verbose = verbose = options.get('verbose', -1)
postpone = options.get('postpone', None)
self._init_info_modules(packages or None)
self.log('Imports to %r namespace\n----------------------------'\
% self.parent_name)
for package_name in self._get_sorted_names():
if package_name in self.imported_packages:
continue
info_module = self.info_modules[package_name]
global_symbols = getattr(info_module, 'global_symbols', [])
postpone_import = getattr(info_module, 'postpone_import', False)
if (postpone and not global_symbols) \
or (postpone_import and postpone is not None):
continue
old_object = frame.f_locals.get(package_name, None)
cmdstr = 'import '+package_name
if self._execcmd(cmdstr):
continue
self.imported_packages.append(package_name)
if verbose!=-1:
new_object = frame.f_locals.get(package_name)
if old_object is not None and old_object is not new_object:
self.warn('Overwriting %s=%s (was %s)' \
% (package_name, self._obj2repr(new_object),
self._obj2repr(old_object)))
if '.' not in package_name:
self.parent_export_names.append(package_name)
for symbol in global_symbols:
if symbol=='*':
symbols = eval('getattr(%s,"__all__",None)'\
% (package_name),
frame.f_globals, frame.f_locals)
if symbols is None:
symbols = eval('dir(%s)' % (package_name),
frame.f_globals, frame.f_locals)
symbols = [s for s in symbols if not s.startswith('_')]
else:
symbols = [symbol]
if verbose!=-1:
old_objects = {}
for s in symbols:
if s in frame.f_locals:
old_objects[s] = frame.f_locals[s]
cmdstr = 'from '+package_name+' import '+symbol
if self._execcmd(cmdstr):
continue
if verbose!=-1:
for s, old_object in old_objects.items():
new_object = frame.f_locals[s]
if new_object is not old_object:
self.warn('Overwriting %s=%s (was %s)' \
% (s, self._obj2repr(new_object),
self._obj2repr(old_object)))
if symbol=='*':
self.parent_export_names.extend(symbols)
else:
self.parent_export_names.append(symbol)
return
def _execcmd(self, cmdstr):
""" Execute command in parent_frame."""
frame = self.parent_frame
try:
exec (cmdstr, frame.f_globals, frame.f_locals)
except Exception as msg:
self.error('%s -> failed: %s' % (cmdstr, msg))
return True
else:
self.log('%s -> success' % (cmdstr))
return
def _obj2repr(self, obj):
""" Return repr(obj) with"""
module = getattr(obj, '__module__', None)
file = getattr(obj, '__file__', None)
if module is not None:
return repr(obj) + ' from ' + module
if file is not None:
return repr(obj) + ' from ' + file
return repr(obj)
def log(self, mess):
if self.verbose>1:
print(str(mess), file=sys.stderr)
def warn(self, mess):
if self.verbose>=0:
print(str(mess), file=sys.stderr)
def error(self, mess):
if self.verbose!=-1:
print(str(mess), file=sys.stderr)
def _get_doc_title(self, info_module):
""" Get the title from a package info.py file.
"""
title = getattr(info_module, '__doc_title__', None)
if title is not None:
return title
title = getattr(info_module, '__doc__', None)
if title is not None:
title = title.lstrip().split('\n', 1)[0]
return title
return '* Not Available *'
def _format_titles(self,titles,colsep='---'):
display_window_width = 70 # How to determine the correct value in runtime??
lengths = [len(name)-name.find('.')-1 for (name, title) in titles]+[0]
max_length = max(lengths)
lines = []
for (name, title) in titles:
name = name[name.find('.')+1:]
w = max_length - len(name)
words = title.split()
line = '%s%s %s' % (name, w*' ', colsep)
tab = len(line) * ' '
while words:
word = words.pop(0)
if len(line)+len(word)>display_window_width:
lines.append(line)
line = tab
line += ' ' + word
lines.append(line)
return '\n'.join(lines)
def get_pkgdocs(self):
""" Return documentation summary of subpackages.
"""
import sys
self.info_modules = {}
self._init_info_modules(None)
titles = []
symbols = []
for package_name, info_module in self.info_modules.items():
global_symbols = getattr(info_module, 'global_symbols', [])
fullname = self.parent_name +'.'+ package_name
note = ''
if fullname not in sys.modules:
note = ' [*]'
titles.append((fullname, self._get_doc_title(info_module) + note))
if global_symbols:
symbols.append((package_name, ', '.join(global_symbols)))
retstr = self._format_titles(titles) +\
'\n [*] - using a package requires explicit import (see pkgload)'
if symbols:
retstr += """\n\nGlobal symbols from subpackages"""\
"""\n-------------------------------\n""" +\
self._format_titles(symbols, '-->')
return retstr
class PackageLoaderDebug(PackageLoader):
def _execcmd(self, cmdstr):
""" Execute command in parent_frame."""
frame = self.parent_frame
print('Executing', repr(cmdstr), '...', end=' ')
sys.stdout.flush()
exec (cmdstr, frame.f_globals, frame.f_locals)
print('ok')
sys.stdout.flush()
return
if int(os.environ.get('NUMPY_IMPORT_DEBUG', '0')):
PackageLoader = PackageLoaderDebug

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

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

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"""
Python 3 compatibility tools.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path']
import sys
try:
from pathlib import Path
except ImportError:
Path = None
if sys.version_info[0] >= 3:
import io
long = int
integer_types = (int,)
basestring = str
unicode = str
bytes = bytes
def asunicode(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('latin1')
def asstr(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def isfileobj(f):
return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
def open_latin1(filename, mode='r'):
return open(filename, mode=mode, encoding='iso-8859-1')
def sixu(s):
return s
strchar = 'U'
else:
bytes = str
long = long
basestring = basestring
unicode = unicode
integer_types = (int, long)
asbytes = str
asstr = str
strchar = 'S'
def isfileobj(f):
return isinstance(f, file)
def asunicode(s):
if isinstance(s, unicode):
return s
return str(s).decode('ascii')
def open_latin1(filename, mode='r'):
return open(filename, mode=mode)
def sixu(s):
return unicode(s, 'unicode_escape')
def getexception():
return sys.exc_info()[1]
def asbytes_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asbytes_nested(y) for y in x]
else:
return asbytes(x)
def asunicode_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asunicode_nested(y) for y in x]
else:
return asunicode(x)
def is_pathlib_path(obj):
"""
Check whether obj is a pathlib.Path object.
"""
return Path is not None and isinstance(obj, Path)
if sys.version_info[0] >= 3 and sys.version_info[1] >= 4:
def npy_load_module(name, fn, info=None):
"""
Load a module.
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Only here for backward compatibility with Python 2.*.
Returns
-------
mod : module
"""
import importlib.machinery
return importlib.machinery.SourceFileLoader(name, fn).load_module()
else:
def npy_load_module(name, fn, info=None):
"""
Load a module.
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Information as returned by `imp.find_module`
(suffix, mode, type).
Returns
-------
mod : module
"""
import imp
import os
if info is None:
path = os.path.dirname(fn)
fo, fn, info = imp.find_module(name, [path])
else:
fo = open(fn, info[1])
try:
mod = imp.load_module(name, fo, fn, info)
finally:
fo.close()
return mod

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#!/usr/bin/env python
from __future__ import division, print_function
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('compat', parent_package, top_path)
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)

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"""
Pytest configuration and fixtures for the Numpy test suite.
"""
from __future__ import division, absolute_import, print_function
import warnings
import pytest
from numpy.core.multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
@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))

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from __future__ import division, absolute_import, print_function
from .info import __doc__
from numpy.version import version as __version__
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
import os
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:
msg = """
Importing the multiarray numpy extension module failed. Most
likely you are trying to import a failed build of numpy.
If you're working with a numpy git repo, try `git clean -xdf` (removes all
files not under version control). Otherwise reinstall numpy.
Original error was: %s
""" % (exc,)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
from . import _internal # for freeze programs
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
__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__
from numpy.testing import _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
# 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

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"""
A place for code to be called from core C-code.
Some things are more easily handled Python.
"""
from __future__ import division, absolute_import, print_function
import re
import sys
from numpy.compat import basestring
from .multiarray import dtype, array, ndarray
try:
import ctypes
except ImportError:
ctypes = None
from .numerictypes import object_
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> *[(]?[ ,0-9L]*[)]? *)'
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
def c_void_p(self, num):
return num
class _ctypes(object):
def __init__(self, array, ptr=None):
if ctypes:
self._ctypes = ctypes
else:
self._ctypes = _missing_ctypes()
self._arr = array
self._data = ptr
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
return self._ctypes.cast(self._data, obj)
def shape_as(self, obj):
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
def get_data(self):
return self._data
def get_shape(self):
return self.shape_as(_getintp_ctype())
def get_strides(self):
return self.strides_as(_getintp_ctype())
def get_as_parameter(self):
return self._ctypes.c_void_p(self._data)
data = property(get_data, None, doc="c-types data")
shape = property(get_shape, None, doc="c-types shape")
strides = property(get_strides, None, doc="c-types strides")
_as_parameter_ = property(get_as_parameter, None, doc="_as parameter_")
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):
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:
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())
def _dtype_from_pep3118(spec):
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__
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
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
# Exception used in shares_memory()
class TooHardError(RuntimeError):
pass
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)
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 _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
)

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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.numeric import asanyarray
from numpy.core import numerictypes as nt
# 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):
return umr_maximum(a, axis, None, out, keepdims)
def _amin(a, axis=None, out=None, keepdims=False):
return umr_minimum(a, axis, None, out, keepdims)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_sum(a, axis, dtype, out, keepdims)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_prod(a, axis, dtype, out, keepdims)
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
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.complexfloating):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
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

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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 warnings
import operator
from . import numeric as _nx
from .numeric import (result_type, NaN, shares_memory, MAY_SHARE_BOUNDS,
TooHardError,asanyarray)
__all__ = ['logspace', 'linspace', 'geomspace']
def _index_deprecate(i, stacklevel=2):
try:
i = operator.index(i)
except TypeError:
msg = ("object of type {} cannot be safely interpreted as "
"an integer.".format(type(i)))
i = int(i)
stacklevel += 1
warnings.warn(msg, DeprecationWarning, stacklevel=stacklevel)
return i
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None):
"""
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.
Parameters
----------
start : scalar
The starting value of the sequence.
stop : scalar
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
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).
logspace : Samples uniformly distributed in log space.
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()
"""
# 2016-02-25, 1.12
num = _index_deprecate(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
y = _nx.arange(0, num, dtype=dt)
delta = stop - start
# 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 num > 1:
step = delta / div
if 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:
# 0 and 1 item long sequences 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 retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None):
"""
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).
Parameters
----------
start : float
``base ** start`` is the starting value of the sequence.
stop : float
``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.
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)
if dtype is None:
return _nx.power(base, y)
return _nx.power(base, y).astype(dtype)
def geomspace(start, stop, num=50, endpoint=True, dtype=None):
"""
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.
Parameters
----------
start : scalar
The starting value of the sequence.
stop : scalar
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.
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([-1.00000000+0.j , -0.70710678+0.70710678j,
0.00000000+1.j , 0.70710678+0.70710678j,
1.00000000+0.j ])
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')
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
>>> plt.axis([0.5, 2000, 0, 3])
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()
"""
if start == 0 or stop == 0:
raise ValueError('Geometric sequence cannot include zero')
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
else:
# complex to dtype('complex128'), for instance
dtype = _nx.dtype(dtype)
# Avoid negligible real or imaginary parts in output by rotating to
# positive real, calculating, then undoing rotation
out_sign = 1
if start.real == stop.real == 0:
start, stop = start.imag, stop.imag
out_sign = 1j * out_sign
if _nx.sign(start) == _nx.sign(stop) == -1:
start, stop = -start, -stop
out_sign = -out_sign
# Promote both arguments to the same dtype in case, for instance, one is
# complex and another is negative and log would produce NaN otherwise
start = start + (stop - stop)
stop = stop + (start - start)
if _nx.issubdtype(dtype, _nx.complexfloating):
start = start + 0j
stop = stop + 0j
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)
return result.astype(dtype)

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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");
PyObject *c_api = NULL;
if (numpy == NULL) {
PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import");
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)
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 . 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
_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'))}
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))
self._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))
# Properties below to delay need for float_to_str, and thus avoid circular
# imports during early numpy module loading.
# See: https://github.com/numpy/numpy/pull/8983#discussion_r115838683
@property
def _str_eps(self):
return self._float_to_str(self.eps)
@property
def _str_epsneg(self):
return self._float_to_str(self.epsneg)
@property
def _str_xmin(self):
return self._float_to_str(self.xmin)
@property
def _str_xmax(self):
return self._float_to_str(self.xmax)
@property
def _str_resolution(self):
return self._float_to_str(self.resolution)
# 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))
# 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)))
# 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)
# 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)
# 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)
# 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)))
# 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 = {
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf' : _float64_ma,
b'\xcd\xcc\xcc\xbd' : _float32_ma,
b'f\xae' : _float16_ma,
# float80, first 10 bytes containing actual storage
b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf' : _float80_ma,
# double double; low, high order (e.g. PPC 64)
b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf' :
_float_dd_ma,
# double double; high, low order (e.g. PPC 64 le)
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<' :
_float_dd_ma,
# IEEE 754 128-bit binary float
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf' :
_float128_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'])
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)
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.")
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
min = property(min)
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
max = property(max)
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, 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, 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 *);
#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, 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, 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])
static NPY_INLINE int
_import_umath(void)
{
PyObject *numpy = PyImport_ImportModule("numpy.core.umath");
PyObject *c_api = NULL;
if (numpy == NULL) {
PyErr_SetString(PyExc_ImportError, "numpy.core.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;
}

32
projecten1/lib/python3.6/site-packages/numpy/core/include/numpy/_numpyconfig.h Normal file
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#define NPY_HAVE_ENDIAN_H 1
#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 16
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32
#define NPY_SIZEOF_PY_INTPTR_T 8
#define NPY_SIZEOF_OFF_T 8
#define NPY_SIZEOF_PY_LONG_LONG 8
#define NPY_SIZEOF_LONGLONG 8
#define NPY_NO_SMP 0
#define NPY_HAVE_DECL_ISNAN
#define NPY_HAVE_DECL_ISINF
#define NPY_HAVE_DECL_ISFINITE
#define NPY_HAVE_DECL_SIGNBIT
#define NPY_USE_C99_COMPLEX 1
#define NPY_HAVE_COMPLEX_DOUBLE 1
#define NPY_HAVE_COMPLEX_FLOAT 1
#define NPY_HAVE_COMPLEX_LONG_DOUBLE 1
#define NPY_RELAXED_STRIDES_CHECKING 1
#define NPY_USE_C99_FORMATS 1
#define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
#define NPY_ABI_VERSION 0x01000009
#define NPY_API_VERSION 0x0000000C
#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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291
projecten1/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h Normal file
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/*
* DON'T INCLUDE THIS DIRECTLY.
*/
#ifndef NPY_NDARRAYOBJECT_H
#define NPY_NDARRAYOBJECT_H
#ifdef __cplusplus
#define CONFUSE_EMACS {
#define CONFUSE_EMACS2 }
extern "C" CONFUSE_EMACS
#undef CONFUSE_EMACS
#undef CONFUSE_EMACS2
/* ... otherwise a semi-smart identer (like emacs) tries to indent
everything when you're typing */
#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*)(op))->ob_type==&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_GET_SIZE(value) != 3) {
return 0;
}
title = PyTuple_GET_ITEM(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
#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 by " \
"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION")
#elif defined(__GNUC__)
#warning "Using deprecated NumPy API, disable it by " \
"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
#endif
/* TODO: How to do this warning message for other compilers? */
/*
* 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
/*
* 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)
{
PyObject *newobj;
newobj = PyUnicode_Concat(*left, right);
Py_DECREF(*left);
Py_DECREF(right);
*left = newobj;
}
static NPY_INLINE void
PyUnicode_Concat2(PyObject **left, PyObject *right)
{
PyObject *newobj;
newobj = PyUnicode_Concat(*left, right);
Py_DECREF(*left);
*left = newobj;
}
/*
* 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");
}
/* 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;
}
/*
* 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
*/
#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(__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
*/
#define NPY_CPU_PPC
#elif defined(__ppc64le__)
#define NPY_CPU_PPC64LE
#elif defined(__ppc64__)
#define NPY_CPU_PPC64
#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(__ARMEL__)
#define NPY_CPU_ARMEL
#elif defined(__arm__) && defined(__ARMEB__)
#define NPY_CPU_ARMEB
#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(__aarch64__)
#define NPY_CPU_AARCH64
#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
#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_AARCH64) \
|| defined(NPY_CPU_SH_LE) \
|| defined(NPY_CPU_MIPSEL) \
|| defined(NPY_CPU_PPC64LE) \
|| defined(NPY_CPU_ARCEL)
#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_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 interruptable
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) */
/*
* 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
*/
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
#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

25
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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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=================
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, 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, 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)

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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, either PyUFunc_One or PyUFunc_Zero */
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
*/
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;
} PyUFuncObject;
#include "arrayobject.h"
#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
#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"
#define UFUNC_CHECK_ERROR(arg) \
do {if ((((arg)->obj & UFUNC_OBJ_NEEDS_API) && PyErr_Occurred()) || \
((arg)->errormask && \
PyUFunc_checkfperr((arg)->errormask, \
(arg)->errobj, \
&(arg)->first))) \
goto fail;} while (0)
/* keep in sync with ieee754.c.src */
#if defined(sun) || defined(__BSD__) || defined(__OpenBSD__) || \
(defined(__FreeBSD__) && (__FreeBSD_version < 502114)) || \
defined(__NetBSD__) || \
defined(__GLIBC__) || defined(__APPLE__) || \
defined(__CYGWIN__) || defined(__MINGW32__) || \
(defined(__FreeBSD__) && (__FreeBSD_version >= 502114)) || \
defined(_AIX) || \
defined(_MSC_VER) || \
defined(__osf__) && defined(__alpha)
#else
#define NO_FLOATING_POINT_SUPPORT
#endif
/*
* 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 UFUNC_CHECK_STATUS(ret) \
{ \
ret = npy_clear_floatstatus(); \
}
#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__))
#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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"""Defines a multi-dimensional array and useful procedures for Numerical computation.
Functions
- array - NumPy Array construction
- zeros - Return an array of all zeros
- empty - Return an uninitialized array
- shape - Return shape of sequence or array
- rank - Return number of dimensions
- size - Return number of elements in entire array or a
certain dimension
- fromstring - Construct array from (byte) string
- take - Select sub-arrays using sequence of indices
- put - Set sub-arrays using sequence of 1-D indices
- putmask - Set portion of arrays using a mask
- reshape - Return array with new shape
- repeat - Repeat elements of array
- choose - Construct new array from indexed array tuple
- correlate - Correlate two 1-d arrays
- searchsorted - Search for element in 1-d array
- sum - Total sum over a specified dimension
- average - Average, possibly weighted, over axis or array.
- cumsum - Cumulative sum over a specified dimension
- product - Total product over a specified dimension
- cumproduct - Cumulative product over a specified dimension
- alltrue - Logical and over an entire axis
- sometrue - Logical or over an entire axis
- allclose - Tests if sequences are essentially equal
More Functions:
- arange - Return regularly spaced array
- asarray - Guarantee NumPy array
- convolve - Convolve two 1-d arrays
- swapaxes - Exchange axes
- concatenate - Join arrays together
- transpose - Permute axes
- sort - Sort elements of array
- argsort - Indices of sorted array
- argmax - Index of largest value
- argmin - Index of smallest value
- inner - Innerproduct of two arrays
- dot - Dot product (matrix multiplication)
- outer - Outerproduct of two arrays
- resize - Return array with arbitrary new shape
- indices - Tuple of indices
- fromfunction - Construct array from universal function
- diagonal - Return diagonal array
- trace - Trace of array
- dump - Dump array to file object (pickle)
- dumps - Return pickled string representing data
- load - Return array stored in file object
- loads - Return array from pickled string
- ravel - Return array as 1-D
- nonzero - Indices of nonzero elements for 1-D array
- shape - Shape of array
- where - Construct array from binary result
- compress - Elements of array where condition is true
- clip - Clip array between two values
- ones - Array of all ones
- identity - 2-D identity array (matrix)
(Universal) Math Functions
add logical_or exp
subtract logical_xor log
multiply logical_not log10
divide maximum sin
divide_safe minimum sinh
conjugate bitwise_and sqrt
power bitwise_or tan
absolute bitwise_xor tanh
negative invert ceil
greater left_shift fabs
greater_equal right_shift floor
less arccos arctan2
less_equal arcsin fmod
equal arctan hypot
not_equal cos around
logical_and cosh sign
arccosh arcsinh arctanh
"""
from __future__ import division, absolute_import, print_function
depends = ['testing']
global_symbols = ['*']

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[meta]
Name = mlib
Description = Math library used with this version of numpy
Version = 1.0
[default]
Libs=-lm
Cflags=
[msvc]
Libs=m.lib
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.numeric import errstate
# Need to speed this up...especially for longfloat
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, is_pathlib_path
__all__ = ['memmap']
dtypedescr = dtype
valid_filemodes = ["r", "c", "r+", "w+"]
writeable_filemodes = ["r+", "w+"]
mode_equivalents = {
"readonly":"r",
"copyonwrite":"c",
"readwrite":"r+",
"write":"w+"
}
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.
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 hasattr(filename, 'read'):
fid = filename
own_file = False
elif is_pathlib_path(filename):
fid = filename.open((mode == 'c' and 'r' or mode)+'b')
own_file = True
else:
fid = open(filename, (mode == 'c' and 'r' or mode)+'b')
own_file = True
if (mode == 'w+') and shape is None:
raise ValueError("shape must be given")
fid.seek(0, 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if (bytes % _dbytes):
fid.close()
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 = 1
for k in shape:
size *= k
bytes = long(offset + size*_dbytes)
if mode == 'w+' or (mode == '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 isinstance(filename, basestring):
self.filename = os.path.abspath(filename)
elif is_pathlib_path(filename):
self.filename = filename.resolve()
# py3 returns int for TemporaryFile().name
elif (hasattr(filename, "name") and
isinstance(filename.name, basestring)):
self.filename = os.path.abspath(filename.name)
# same as memmap copies (e.g. memmap + 1)
else:
self.filename = None
if own_file:
fid.close()
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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"""
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', int), ('y', float)])
>>> a
array([(1, 2.0), (1, 2.0)],
dtype=[('x', '<i4'), ('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 . import numeric as sb
from . import numerictypes as nt
from numpy.compat import isfileobj, bytes, long
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
def find_duplicate(list):
"""Find duplication in a list, return a list of duplicated elements"""
dup = []
for i in range(len(list)):
if (list[i] in list[i + 1:]):
if (list[i] not in dup):
dup.append(list[i])
return dup
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=0):
""" Parse the field formats """
if formats is None:
raise ValueError("Need formats argument")
if isinstance(formats, list):
if len(formats) < 2:
formats.append('')
formats = ','.join(formats)
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):
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.fields:
return obj.view((self.__class__, obj.dtype.fields))
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.fields:
return obj.view((self.__class__, obj.dtype.fields))
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)
rows = []
fmt = '%% %ds: %%s' % maxlen
for name in names:
rows.append(fmt % (name, getattr(self, name)))
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', float), ('y', int)])
>>> x
array([(1.0, 2), (3.0, 4)],
dtype=[('x', '<f8'), ('y', '<i4')])
>>> 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.fields:
# 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.fields:
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.fields:
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:
exctype, value = sys.exc_info()[:2]
raise exctype(value)
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.fields:
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.fields:
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)
>>> 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:
if not isinstance(obj, ndarray):
raise ValueError("item in the array list must be an ndarray.")
formats.append(obj.dtype.str)
formats = ','.join(formats)
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='|S3')
>>> import pickle
>>> print(pickle.loads(pickle.dumps(r)))
[(456, 'dbe', 1.2) (2, 'de', 1.3)]
"""
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
# deprecated back-compat block for numpy 1.14, to be removed in a later
# release. This converts list-of-list input to list-of-tuples in some
# cases, as done in numpy <= 1.13. In the future we will require tuples.
if (isinstance(recList, list) and len(recList) > 0
and isinstance(recList[0], list) and len(recList[0]) > 0
and not isinstance(recList[0][0], (list, tuple))):
try:
memoryview(recList[0][0])
except:
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:
pass
retval = sb.array(recList, dtype=descr)
if shape is not None and retval.shape != shape:
retval.shape = shape
return retval.view(recarray)
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 ValueError("Must have dtype= or formats=")
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 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 (shape is None or shape == 0):
shape = (-1,)
elif isinstance(shape, (int, long)):
shape = (shape,)
name = 0
if isinstance(fd, str):
name = 1
fd = open(fd, 'rb')
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()
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size / -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod()
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")
if name:
fd.close()
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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