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Merging PR_218 openai_rev package with new streamlit chat app

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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
covered work so as to satisfy simultaneously your obligations under this
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
combined work, and to convey the resulting work. The terms of this
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
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
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
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
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16. Limitation of Liability.
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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# This file is generated by numpy's setup.py
# It contains system_info results at the time of building this package.
__all__ = ["get_info","show"]
import os
import sys
extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
os.add_dll_directory(extra_dll_dir)
openblas64__info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)], 'runtime_library_dirs': ['/usr/local/lib']}
blas_ilp64_opt_info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)], 'runtime_library_dirs': ['/usr/local/lib']}
openblas64__lapack_info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)], 'runtime_library_dirs': ['/usr/local/lib']}
lapack_ilp64_opt_info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)], 'runtime_library_dirs': ['/usr/local/lib']}
def get_info(name):
g = globals()
return g.get(name, g.get(name + "_info", {}))
def show():
"""
Show libraries in the system on which NumPy was built.
Print information about various resources (libraries, library
directories, include directories, etc.) in the system on which
NumPy was built.
See Also
--------
get_include : Returns the directory containing NumPy C
header files.
Notes
-----
1. Classes specifying the information to be printed are defined
in the `numpy.distutils.system_info` module.
Information may include:
* ``language``: language used to write the libraries (mostly
C or f77)
* ``libraries``: names of libraries found in the system
* ``library_dirs``: directories containing the libraries
* ``include_dirs``: directories containing library header files
* ``src_dirs``: directories containing library source files
* ``define_macros``: preprocessor macros used by
``distutils.setup``
* ``baseline``: minimum CPU features required
* ``found``: dispatched features supported in the system
* ``not found``: dispatched features that are not supported
in the system
2. NumPy BLAS/LAPACK Installation Notes
Installing a numpy wheel (``pip install numpy`` or force it
via ``pip install numpy --only-binary :numpy: numpy``) includes
an OpenBLAS implementation of the BLAS and LAPACK linear algebra
APIs. In this case, ``library_dirs`` reports the original build
time configuration as compiled with gcc/gfortran; at run time
the OpenBLAS library is in
``site-packages/numpy.libs/`` (linux), or
``site-packages/numpy/.dylibs/`` (macOS), or
``site-packages/numpy/.libs/`` (windows).
Installing numpy from source
(``pip install numpy --no-binary numpy``) searches for BLAS and
LAPACK dynamic link libraries at build time as influenced by
environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
or the optional file ``~/.numpy-site.cfg``.
NumPy remembers those locations and expects to load the same
libraries at run-time.
In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
library) is in the default build-time search order after
'openblas'.
Examples
--------
>>> import numpy as np
>>> np.show_config()
blas_opt_info:
language = c
define_macros = [('HAVE_CBLAS', None)]
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
"""
from numpy.core._multiarray_umath import (
__cpu_features__, __cpu_baseline__, __cpu_dispatch__
)
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))
features_found, features_not_found = [], []
for feature in __cpu_dispatch__:
if __cpu_features__[feature]:
features_found.append(feature)
else:
features_not_found.append(feature)
print("Supported SIMD extensions in this NumPy install:")
print(" baseline = %s" % (','.join(__cpu_baseline__)))
print(" found = %s" % (','.join(features_found)))
print(" not found = %s" % (','.join(features_not_found)))

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"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <https://numpy.org>`_.
We recommend exploring the docstrings using
`IPython <https://ipython.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as ``np``::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
To search for documents containing a keyword, do::
>>> np.lookfor('keyword')
... # doctest: +SKIP
General-purpose documents like a glossary and help on the basic concepts
of numpy are available under the ``doc`` sub-module::
>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP
Available subpackages
---------------------
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
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.
Note: `numpy.dual` is deprecated. Use the functions from NumPy or Scipy
directly instead of importing them from `numpy.dual`.
matlib
Make everything matrices.
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython and import `numpy` usually under the alias ``np``: `import
numpy as np`. Then, directly past or use the ``%cpaste`` magic to paste
examples into the shell. To see which functions are available in `numpy`,
type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
import sys
import warnings
from ._globals import (
ModuleDeprecationWarning, VisibleDeprecationWarning,
_NoValue, _CopyMode
)
# 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 as e:
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 e
__all__ = ['ModuleDeprecationWarning',
'VisibleDeprecationWarning']
# mapping of {name: (value, deprecation_msg)}
__deprecated_attrs__ = {}
# Allow distributors to run custom init code
from . import _distributor_init
from . import core
from .core import *
from . import compat
from . import lib
# NOTE: to be revisited following future namespace cleanup.
# See gh-14454 and gh-15672 for discussion.
from .lib import *
from . import linalg
from . import fft
from . import polynomial
from . import random
from . import ctypeslib
from . import ma
from . import matrixlib as _mat
from .matrixlib import *
# Deprecations introduced in NumPy 1.20.0, 2020-06-06
import builtins as _builtins
_msg = (
"module 'numpy' has no attribute '{n}'.\n"
"`np.{n}` was a deprecated alias for the builtin `{n}`. "
"To avoid this error in existing code, use `{n}` by itself. "
"Doing this will not modify any behavior and is safe. {extended_msg}\n"
"The aliases was originally deprecated in NumPy 1.20; for more "
"details and guidance see the original release note at:\n"
" https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
_specific_msg = (
"If you specifically wanted the numpy scalar type, use `np.{}` here.")
_int_extended_msg = (
"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
"or `np.int32` to specify the precision. If you wish to review "
"your current use, check the release note link for "
"additional information.")
_type_info = [
("object", ""), # The NumPy scalar only exists by name.
("bool", _specific_msg.format("bool_")),
("float", _specific_msg.format("float64")),
("complex", _specific_msg.format("complex128")),
("str", _specific_msg.format("str_")),
("int", _int_extended_msg.format("int"))]
__former_attrs__ = {
n: _msg.format(n=n, extended_msg=extended_msg)
for n, extended_msg in _type_info
}
# Future warning introduced in NumPy 1.24.0, 2022-11-17
_msg = (
"`np.{n}` is a deprecated alias for `{an}`. (Deprecated NumPy 1.24)")
# Some of these are awkward (since `np.str` may be preferable in the long
# term), but overall the names ending in 0 seem undesireable
_type_info = [
("bool8", bool_, "np.bool_"),
("int0", intp, "np.intp"),
("uint0", uintp, "np.uintp"),
("str0", str_, "np.str_"),
("bytes0", bytes_, "np.bytes_"),
("void0", void, "np.void"),
("object0", object_,
"`np.object0` is a deprecated alias for `np.object_`. "
"`object` can be used instead. (Deprecated NumPy 1.24)")]
# Some of these could be defined right away, but most were aliases to
# the Python objects and only removed in NumPy 1.24. Defining them should
# probably wait for NumPy 1.26 or 2.0.
# When defined, these should possibly not be added to `__all__` to avoid
# import with `from numpy import *`.
__future_scalars__ = {"bool", "long", "ulong", "str", "bytes", "object"}
__deprecated_attrs__.update({
n: (alias, _msg.format(n=n, an=an)) for n, alias, an in _type_info})
del _msg, _type_info
from .core import round, abs, max, min
# now that numpy modules are imported, can initialize limits
core.getlimits._register_known_types()
__all__.extend(['__version__', 'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# Remove one of the two occurrences of `issubdtype`, which is exposed as
# both `numpy.core.issubdtype` and `numpy.lib.issubdtype`.
__all__.remove('issubdtype')
# These are exported by np.core, but are replaced by the builtins below
# remove them to ensure that we don't end up with `np.long == np.int_`,
# which would be a breaking change.
del long, unicode
__all__.remove('long')
__all__.remove('unicode')
# Remove things that are in the numpy.lib but not in the numpy namespace
# Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
# that prevents adding more things to the main namespace by accident.
# The list below will grow until the `from .lib import *` fixme above is
# taken care of
__all__.remove('Arrayterator')
del Arrayterator
# These names were removed in NumPy 1.20. For at least one release,
# attempts to access these names in the numpy namespace will trigger
# a warning, and calling the function will raise an exception.
_financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
'ppmt', 'pv', 'rate']
__expired_functions__ = {
name: (f'In accordance with NEP 32, the function {name} was removed '
'from NumPy version 1.20. A replacement for this function '
'is available in the numpy_financial library: '
'https://pypi.org/project/numpy-financial')
for name in _financial_names}
# Filter out Cython harmless warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
# oldnumeric and numarray were removed in 1.9. In case some packages import
# but do not use them, we define them here for backward compatibility.
oldnumeric = 'removed'
numarray = 'removed'
def __getattr__(attr):
# Warn for expired attributes, and return a dummy function
# that always raises an exception.
import warnings
try:
msg = __expired_functions__[attr]
except KeyError:
pass
else:
warnings.warn(msg, DeprecationWarning, stacklevel=2)
def _expired(*args, **kwds):
raise RuntimeError(msg)
return _expired
# Emit warnings for deprecated attributes
try:
val, msg = __deprecated_attrs__[attr]
except KeyError:
pass
else:
warnings.warn(msg, DeprecationWarning, stacklevel=2)
return val
if attr in __future_scalars__:
# And future warnings for those that will change, but also give
# the AttributeError
warnings.warn(
f"In the future `np.{attr}` will be defined as the "
"corresponding NumPy scalar.", FutureWarning, stacklevel=2)
if attr in __former_attrs__:
raise AttributeError(__former_attrs__[attr])
# Importing Tester requires importing all of UnitTest which is not a
# cheap import Since it is mainly used in test suits, we lazy import it
# here to save on the order of 10 ms of import time for most users
#
# The previous way Tester was imported also had a side effect of adding
# the full `numpy.testing` namespace
if attr == 'testing':
import numpy.testing as testing
return testing
elif attr == 'Tester':
from .testing import Tester
return Tester
raise AttributeError("module {!r} has no attribute "
"{!r}".format(__name__, attr))
def __dir__():
public_symbols = globals().keys() | {'Tester', 'testing'}
public_symbols -= {
"core", "matrixlib",
}
return list(public_symbols)
# Pytest testing
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases e.g. with wrong BLAS ABI that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - float32(2.0)) < 1e-5:
raise AssertionError()
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in, or by mixing "
"package managers (pip, conda, apt, ...). Search closed "
"numpy issues for similar problems.")
raise RuntimeError(msg.format(__file__)) from None
_sanity_check()
del _sanity_check
def _mac_os_check():
"""
Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
"""
try:
c = array([3., 2., 1.])
x = linspace(0, 2, 5)
y = polyval(c, x)
_ = polyfit(x, y, 2, cov=True)
except ValueError:
pass
if sys.platform == "darwin":
with warnings.catch_warnings(record=True) as w:
_mac_os_check()
# Throw runtime error, if the test failed Check for warning and error_message
error_message = ""
if len(w) > 0:
error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message))
msg = (
"Polyfit sanity test emitted a warning, most likely due "
"to using a buggy Accelerate backend."
"\nIf you compiled yourself, more information is available at:"
"\nhttps://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries"
"\nOtherwise report this to the vendor "
"that provided NumPy.\n{}\n".format(error_message))
raise RuntimeError(msg)
del _mac_os_check
# We usually use madvise hugepages support, but on some old kernels it
# is slow and thus better avoided.
# Specifically kernel version 4.6 had a bug fix which probably fixed this:
# https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
import os
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
if sys.platform == "linux" and use_hugepage is None:
# If there is an issue with parsing the kernel version,
# set use_hugepages to 0. Usage of LooseVersion will handle
# the kernel version parsing better, but avoided since it
# will increase the import time. See: #16679 for related discussion.
try:
use_hugepage = 1
kernel_version = os.uname().release.split(".")[:2]
kernel_version = tuple(int(v) for v in kernel_version)
if kernel_version < (4, 6):
use_hugepage = 0
except ValueError:
use_hugepages = 0
elif use_hugepage is None:
# This is not Linux, so it should not matter, just enable anyway
use_hugepage = 1
else:
use_hugepage = int(use_hugepage)
# Note that this will currently only make a difference on Linux
core.multiarray._set_madvise_hugepage(use_hugepage)
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
# We do this from python, since the C-module may not be reloaded and
# it is tidier organized.
core.multiarray._multiarray_umath._reload_guard()
core._set_promotion_state(os.environ.get("NPY_PROMOTION_STATE", "legacy"))
# Tell PyInstaller where to find hook-numpy.py
def _pyinstaller_hooks_dir():
from pathlib import Path
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
# Remove symbols imported for internal use
del os
# get the version using versioneer
from .version import __version__, git_revision as __git_version__
# Remove symbols imported for internal use
del sys, warnings

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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.
"""
import enum
__ALL__ = [
'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
'_NoValue', '_CopyMode'
]
# Disallow reloading this module so as to preserve the identities of the
# classes defined here.
if '_is_loaded' in globals():
raise RuntimeError('Reloading numpy._globals is not allowed')
_is_loaded = True
class ModuleDeprecationWarning(DeprecationWarning):
"""Module deprecation warning.
The nose tester turns ordinary Deprecation warnings into test failures.
That makes it hard to deprecate whole modules, because they get
imported by default. So this is a special Deprecation warning that the
nose tester will let pass without making tests fail.
"""
ModuleDeprecationWarning.__module__ = 'numpy'
class VisibleDeprecationWarning(UserWarning):
"""Visible deprecation warning.
By default, python will not show deprecation warnings, so this class
can be used when a very visible warning is helpful, for example because
the usage is most likely a user bug.
"""
VisibleDeprecationWarning.__module__ = 'numpy'
class _NoValueType:
"""Special keyword value.
The instance of this class may be used as the default value assigned to a
keyword if no other obvious default (e.g., `None`) is suitable,
Common reasons for using this keyword are:
- A new keyword is added to a function, and that function forwards its
inputs to another function or method which can be defined outside of
NumPy. For example, ``np.std(x)`` calls ``x.std``, so when a ``keepdims``
keyword was added that could only be forwarded if the user explicitly
specified ``keepdims``; downstream array libraries may not have added
the same keyword, so adding ``x.std(..., keepdims=keepdims)``
unconditionally could have broken previously working code.
- A keyword is being deprecated, and a deprecation warning must only be
emitted when the keyword is used.
"""
__instance = None
def __new__(cls):
# ensure that only one instance exists
if not cls.__instance:
cls.__instance = super().__new__(cls)
return cls.__instance
def __repr__(self):
return "<no value>"
_NoValue = _NoValueType()
class _CopyMode(enum.Enum):
"""
An enumeration for the copy modes supported
by numpy.copy() and numpy.array(). The following three modes are supported,
- ALWAYS: This means that a deep copy of the input
array will always be taken.
- IF_NEEDED: This means that a deep copy of the input
array will be taken only if necessary.
- NEVER: This means that the deep copy will never be taken.
If a copy cannot be avoided then a `ValueError` will be
raised.
Note that the buffer-protocol could in theory do copies. NumPy currently
assumes an object exporting the buffer protocol will never do this.
"""
ALWAYS = True
IF_NEEDED = False
NEVER = 2
def __bool__(self):
# For backwards compatibility
if self == _CopyMode.ALWAYS:
return True
if self == _CopyMode.IF_NEEDED:
return False
raise ValueError(f"{self} is neither True nor False.")
_CopyMode.__module__ = 'numpy'

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"""This hook should collect all binary files and any hidden modules that numpy
needs.
Our (some-what inadequate) docs for writing PyInstaller hooks are kept here:
https://pyinstaller.readthedocs.io/en/stable/hooks.html
"""
from PyInstaller.compat import is_conda, is_pure_conda
from PyInstaller.utils.hooks import collect_dynamic_libs, is_module_satisfies
# Collect all DLLs inside numpy's installation folder, dump them into built
# app's root.
binaries = collect_dynamic_libs("numpy", ".")
# If using Conda without any non-conda virtual environment manager:
if is_pure_conda:
# Assume running the NumPy from Conda-forge and collect it's DLLs from the
# communal Conda bin directory. DLLs from NumPy's dependencies must also be
# collected to capture MKL, OpenBlas, OpenMP, etc.
from PyInstaller.utils.hooks import conda_support
datas = conda_support.collect_dynamic_libs("numpy", dependencies=True)
# Submodules PyInstaller cannot detect (probably because they are only imported
# by extension modules, which PyInstaller cannot read).
hiddenimports = ['numpy.core._dtype_ctypes']
if is_conda:
hiddenimports.append("six")
# Remove testing and building code and packages that are referenced throughout
# NumPy but are not really dependencies.
excludedimports = [
"scipy",
"pytest",
"nose",
"f2py",
"setuptools",
"numpy.f2py",
"distutils",
"numpy.distutils",
]

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"""A crude *bit of everything* smoke test to verify PyInstaller compatibility.
PyInstaller typically goes wrong by forgetting to package modules, extension
modules or shared libraries. This script should aim to touch as many of those
as possible in an attempt to trip a ModuleNotFoundError or a DLL load failure
due to an uncollected resource. Missing resources are unlikely to lead to
arithmetic errors so there's generally no need to verify any calculation's
output - merely that it made it to the end OK. This script should not
explicitly import any of numpy's submodules as that gives PyInstaller undue
hints that those submodules exist and should be collected (accessing implicitly
loaded submodules is OK).
"""
import numpy as np
a = np.arange(1., 10.).reshape((3, 3)) % 5
np.linalg.det(a)
a @ a
a @ a.T
np.linalg.inv(a)
np.sin(np.exp(a))
np.linalg.svd(a)
np.linalg.eigh(a)
np.unique(np.random.randint(0, 10, 100))
np.sort(np.random.uniform(0, 10, 100))
np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
np.ma.masked_array(np.arange(10), np.random.rand(10) < .5).sum()
np.polynomial.Legendre([7, 8, 9]).roots()
print("I made it!")

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import subprocess
from pathlib import Path
import pytest
# PyInstaller has been very unproactive about replacing 'imp' with 'importlib'.
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
# It also leaks io.BytesIO()s.
@pytest.mark.filterwarnings('ignore::ResourceWarning')
@pytest.mark.parametrize("mode", ["--onedir", "--onefile"])
@pytest.mark.slow
def test_pyinstaller(mode, tmp_path):
"""Compile and run pyinstaller-smoke.py using PyInstaller."""
pyinstaller_cli = pytest.importorskip("PyInstaller.__main__").run
source = Path(__file__).with_name("pyinstaller-smoke.py").resolve()
args = [
# Place all generated files in ``tmp_path``.
'--workpath', str(tmp_path / "build"),
'--distpath', str(tmp_path / "dist"),
'--specpath', str(tmp_path),
mode,
str(source),
]
pyinstaller_cli(args)
if mode == "--onefile":
exe = tmp_path / "dist" / source.stem
else:
exe = tmp_path / "dist" / source.stem / source.stem
p = subprocess.run([str(exe)], check=True, stdout=subprocess.PIPE)
assert p.stdout.strip() == b"I made it!"

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

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from collections.abc import Iterable
from typing import Literal as L
__all__: list[str]
class PytestTester:
module_name: str
def __init__(self, module_name: str) -> None: ...
def __call__(
self,
label: L["fast", "full"] = ...,
verbose: int = ...,
extra_argv: None | Iterable[str] = ...,
doctests: L[False] = ...,
coverage: bool = ...,
durations: int = ...,
tests: None | Iterable[str] = ...,
) -> bool: ...

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"""Private counterpart of ``numpy.typing``."""
from __future__ import annotations
from numpy import ufunc
from numpy.core.overrides import set_module
from typing import TYPE_CHECKING, final
@final # Disallow the creation of arbitrary `NBitBase` subclasses
@set_module("numpy.typing")
class NBitBase:
"""
A type representing `numpy.number` precision during static type checking.
Used exclusively for the purpose static type checking, `NBitBase`
represents the base of a hierarchical set of subclasses.
Each subsequent subclass is herein used for representing a lower level
of precision, *e.g.* ``64Bit > 32Bit > 16Bit``.
.. versionadded:: 1.20
Examples
--------
Below is a typical usage example: `NBitBase` is herein used for annotating
a function that takes a float and integer of arbitrary precision
as arguments and returns a new float of whichever precision is largest
(*e.g.* ``np.float16 + np.int64 -> np.float64``).
.. code-block:: python
>>> from __future__ import annotations
>>> from typing import TypeVar, TYPE_CHECKING
>>> import numpy as np
>>> import numpy.typing as npt
>>> T1 = TypeVar("T1", bound=npt.NBitBase)
>>> T2 = TypeVar("T2", bound=npt.NBitBase)
>>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
... return a + b
>>> a = np.float16()
>>> b = np.int64()
>>> out = add(a, b)
>>> if TYPE_CHECKING:
... reveal_locals()
... # note: Revealed local types are:
... # note: a: numpy.floating[numpy.typing._16Bit*]
... # note: b: numpy.signedinteger[numpy.typing._64Bit*]
... # note: out: numpy.floating[numpy.typing._64Bit*]
"""
def __init_subclass__(cls) -> None:
allowed_names = {
"NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit",
"_64Bit", "_32Bit", "_16Bit", "_8Bit",
}
if cls.__name__ not in allowed_names:
raise TypeError('cannot inherit from final class "NBitBase"')
super().__init_subclass__()
# Silence errors about subclassing a `@final`-decorated class
class _256Bit(NBitBase): # type: ignore[misc]
pass
class _128Bit(_256Bit): # type: ignore[misc]
pass
class _96Bit(_128Bit): # type: ignore[misc]
pass
class _80Bit(_96Bit): # type: ignore[misc]
pass
class _64Bit(_80Bit): # type: ignore[misc]
pass
class _32Bit(_64Bit): # type: ignore[misc]
pass
class _16Bit(_32Bit): # type: ignore[misc]
pass
class _8Bit(_16Bit): # type: ignore[misc]
pass
from ._nested_sequence import (
_NestedSequence as _NestedSequence,
)
from ._nbit import (
_NBitByte as _NBitByte,
_NBitShort as _NBitShort,
_NBitIntC as _NBitIntC,
_NBitIntP as _NBitIntP,
_NBitInt as _NBitInt,
_NBitLongLong as _NBitLongLong,
_NBitHalf as _NBitHalf,
_NBitSingle as _NBitSingle,
_NBitDouble as _NBitDouble,
_NBitLongDouble as _NBitLongDouble,
)
from ._char_codes import (
_BoolCodes as _BoolCodes,
_UInt8Codes as _UInt8Codes,
_UInt16Codes as _UInt16Codes,
_UInt32Codes as _UInt32Codes,
_UInt64Codes as _UInt64Codes,
_Int8Codes as _Int8Codes,
_Int16Codes as _Int16Codes,
_Int32Codes as _Int32Codes,
_Int64Codes as _Int64Codes,
_Float16Codes as _Float16Codes,
_Float32Codes as _Float32Codes,
_Float64Codes as _Float64Codes,
_Complex64Codes as _Complex64Codes,
_Complex128Codes as _Complex128Codes,
_ByteCodes as _ByteCodes,
_ShortCodes as _ShortCodes,
_IntCCodes as _IntCCodes,
_IntPCodes as _IntPCodes,
_IntCodes as _IntCodes,
_LongLongCodes as _LongLongCodes,
_UByteCodes as _UByteCodes,
_UShortCodes as _UShortCodes,
_UIntCCodes as _UIntCCodes,
_UIntPCodes as _UIntPCodes,
_UIntCodes as _UIntCodes,
_ULongLongCodes as _ULongLongCodes,
_HalfCodes as _HalfCodes,
_SingleCodes as _SingleCodes,
_DoubleCodes as _DoubleCodes,
_LongDoubleCodes as _LongDoubleCodes,
_CSingleCodes as _CSingleCodes,
_CDoubleCodes as _CDoubleCodes,
_CLongDoubleCodes as _CLongDoubleCodes,
_DT64Codes as _DT64Codes,
_TD64Codes as _TD64Codes,
_StrCodes as _StrCodes,
_BytesCodes as _BytesCodes,
_VoidCodes as _VoidCodes,
_ObjectCodes as _ObjectCodes,
)
from ._scalars import (
_CharLike_co as _CharLike_co,
_BoolLike_co as _BoolLike_co,
_UIntLike_co as _UIntLike_co,
_IntLike_co as _IntLike_co,
_FloatLike_co as _FloatLike_co,
_ComplexLike_co as _ComplexLike_co,
_TD64Like_co as _TD64Like_co,
_NumberLike_co as _NumberLike_co,
_ScalarLike_co as _ScalarLike_co,
_VoidLike_co as _VoidLike_co,
)
from ._shape import (
_Shape as _Shape,
_ShapeLike as _ShapeLike,
)
from ._dtype_like import (
DTypeLike as DTypeLike,
_DTypeLike as _DTypeLike,
_SupportsDType as _SupportsDType,
_VoidDTypeLike as _VoidDTypeLike,
_DTypeLikeBool as _DTypeLikeBool,
_DTypeLikeUInt as _DTypeLikeUInt,
_DTypeLikeInt as _DTypeLikeInt,
_DTypeLikeFloat as _DTypeLikeFloat,
_DTypeLikeComplex as _DTypeLikeComplex,
_DTypeLikeTD64 as _DTypeLikeTD64,
_DTypeLikeDT64 as _DTypeLikeDT64,
_DTypeLikeObject as _DTypeLikeObject,
_DTypeLikeVoid as _DTypeLikeVoid,
_DTypeLikeStr as _DTypeLikeStr,
_DTypeLikeBytes as _DTypeLikeBytes,
_DTypeLikeComplex_co as _DTypeLikeComplex_co,
)
from ._array_like import (
ArrayLike as ArrayLike,
_ArrayLike as _ArrayLike,
_FiniteNestedSequence as _FiniteNestedSequence,
_SupportsArray as _SupportsArray,
_SupportsArrayFunc as _SupportsArrayFunc,
_ArrayLikeInt as _ArrayLikeInt,
_ArrayLikeBool_co as _ArrayLikeBool_co,
_ArrayLikeUInt_co as _ArrayLikeUInt_co,
_ArrayLikeInt_co as _ArrayLikeInt_co,
_ArrayLikeFloat_co as _ArrayLikeFloat_co,
_ArrayLikeComplex_co as _ArrayLikeComplex_co,
_ArrayLikeNumber_co as _ArrayLikeNumber_co,
_ArrayLikeTD64_co as _ArrayLikeTD64_co,
_ArrayLikeDT64_co as _ArrayLikeDT64_co,
_ArrayLikeObject_co as _ArrayLikeObject_co,
_ArrayLikeVoid_co as _ArrayLikeVoid_co,
_ArrayLikeStr_co as _ArrayLikeStr_co,
_ArrayLikeBytes_co as _ArrayLikeBytes_co,
_ArrayLikeUnknown as _ArrayLikeUnknown,
_UnknownType as _UnknownType,
)
from ._generic_alias import (
NDArray as NDArray,
_DType as _DType,
_GenericAlias as _GenericAlias,
)
if TYPE_CHECKING:
from ._ufunc import (
_UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1,
_UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1,
_UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2,
_UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2,
_GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1,
)
else:
# Declare the (type-check-only) ufunc subclasses as ufunc aliases during
# runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834)
_UFunc_Nin1_Nout1 = ufunc
_UFunc_Nin2_Nout1 = ufunc
_UFunc_Nin1_Nout2 = ufunc
_UFunc_Nin2_Nout2 = ufunc
_GUFunc_Nin2_Nout1 = ufunc

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"""A module for creating docstrings for sphinx ``data`` domains."""
import re
import textwrap
from ._generic_alias import NDArray
_docstrings_list = []
def add_newdoc(name: str, value: str, doc: str) -> None:
"""Append ``_docstrings_list`` with a docstring for `name`.
Parameters
----------
name : str
The name of the object.
value : str
A string-representation of the object.
doc : str
The docstring of the object.
"""
_docstrings_list.append((name, value, doc))
def _parse_docstrings() -> str:
"""Convert all docstrings in ``_docstrings_list`` into a single
sphinx-legible text block.
"""
type_list_ret = []
for name, value, doc in _docstrings_list:
s = textwrap.dedent(doc).replace("\n", "\n ")
# Replace sections by rubrics
lines = s.split("\n")
new_lines = []
indent = ""
for line in lines:
m = re.match(r'^(\s+)[-=]+\s*$', line)
if m and new_lines:
prev = textwrap.dedent(new_lines.pop())
if prev == "Examples":
indent = ""
new_lines.append(f'{m.group(1)}.. rubric:: {prev}')
else:
indent = 4 * " "
new_lines.append(f'{m.group(1)}.. admonition:: {prev}')
new_lines.append("")
else:
new_lines.append(f"{indent}{line}")
s = "\n".join(new_lines)
s_block = f""".. data:: {name}\n :value: {value}\n {s}"""
type_list_ret.append(s_block)
return "\n".join(type_list_ret)
add_newdoc('ArrayLike', 'typing.Union[...]',
"""
A `~typing.Union` representing objects that can be coerced
into an `~numpy.ndarray`.
Among others this includes the likes of:
* Scalars.
* (Nested) sequences.
* Objects implementing the `~class.__array__` protocol.
.. versionadded:: 1.20
See Also
--------
:term:`array_like`:
Any scalar or sequence that can be interpreted as an ndarray.
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> def as_array(a: npt.ArrayLike) -> np.ndarray:
... return np.array(a)
""")
add_newdoc('DTypeLike', 'typing.Union[...]',
"""
A `~typing.Union` representing objects that can be coerced
into a `~numpy.dtype`.
Among others this includes the likes of:
* :class:`type` objects.
* Character codes or the names of :class:`type` objects.
* Objects with the ``.dtype`` attribute.
.. versionadded:: 1.20
See Also
--------
:ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
A comprehensive overview of all objects that can be coerced
into data types.
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
... return np.dtype(d)
""")
add_newdoc('NDArray', repr(NDArray),
"""
A :term:`generic <generic type>` version of
`np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
Can be used during runtime for typing arrays with a given dtype
and unspecified shape.
.. versionadded:: 1.21
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> print(npt.NDArray)
numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
>>> print(npt.NDArray[np.float64])
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
>>> NDArrayInt = npt.NDArray[np.int_]
>>> a: NDArrayInt = np.arange(10)
>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
... return np.array(a)
""")
_docstrings = _parse_docstrings()

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from __future__ import annotations
# NOTE: Import `Sequence` from `typing` as we it is needed for a type-alias,
# not an annotation
import sys
from collections.abc import Collection, Callable
from typing import Any, Sequence, Protocol, Union, TypeVar, runtime_checkable
from numpy import (
ndarray,
dtype,
generic,
bool_,
unsignedinteger,
integer,
floating,
complexfloating,
number,
timedelta64,
datetime64,
object_,
void,
str_,
bytes_,
)
from ._nested_sequence import _NestedSequence
_T = TypeVar("_T")
_ScalarType = TypeVar("_ScalarType", bound=generic)
_DType = TypeVar("_DType", bound="dtype[Any]")
_DType_co = TypeVar("_DType_co", covariant=True, bound="dtype[Any]")
# The `_SupportsArray` protocol only cares about the default dtype
# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
# array.
# Concrete implementations of the protocol are responsible for adding
# any and all remaining overloads
@runtime_checkable
class _SupportsArray(Protocol[_DType_co]):
def __array__(self) -> ndarray[Any, _DType_co]: ...
@runtime_checkable
class _SupportsArrayFunc(Protocol):
"""A protocol class representing `~class.__array_function__`."""
def __array_function__(
self,
func: Callable[..., Any],
types: Collection[type[Any]],
args: tuple[Any, ...],
kwargs: dict[str, Any],
) -> object: ...
# TODO: Wait until mypy supports recursive objects in combination with typevars
_FiniteNestedSequence = Union[
_T,
Sequence[_T],
Sequence[Sequence[_T]],
Sequence[Sequence[Sequence[_T]]],
Sequence[Sequence[Sequence[Sequence[_T]]]],
]
# A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic`
_ArrayLike = Union[
_SupportsArray["dtype[_ScalarType]"],
_NestedSequence[_SupportsArray["dtype[_ScalarType]"]],
]
# A union representing array-like objects; consists of two typevars:
# One representing types that can be parametrized w.r.t. `np.dtype`
# and another one for the rest
_DualArrayLike = Union[
_SupportsArray[_DType],
_NestedSequence[_SupportsArray[_DType]],
_T,
_NestedSequence[_T],
]
# TODO: support buffer protocols once
#
# https://bugs.python.org/issue27501
#
# is resolved. See also the mypy issue:
#
# https://github.com/python/typing/issues/593
if sys.version_info[:2] < (3, 9):
ArrayLike = _DualArrayLike[
dtype,
Union[bool, int, float, complex, str, bytes],
]
else:
ArrayLike = _DualArrayLike[
dtype[Any],
Union[bool, int, float, complex, str, bytes],
]
# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
# given the casting rules `same_kind`
_ArrayLikeBool_co = _DualArrayLike[
"dtype[bool_]",
bool,
]
_ArrayLikeUInt_co = _DualArrayLike[
"dtype[Union[bool_, unsignedinteger[Any]]]",
bool,
]
_ArrayLikeInt_co = _DualArrayLike[
"dtype[Union[bool_, integer[Any]]]",
Union[bool, int],
]
_ArrayLikeFloat_co = _DualArrayLike[
"dtype[Union[bool_, integer[Any], floating[Any]]]",
Union[bool, int, float],
]
_ArrayLikeComplex_co = _DualArrayLike[
"dtype[Union[bool_, integer[Any], floating[Any], complexfloating[Any, Any]]]",
Union[bool, int, float, complex],
]
_ArrayLikeNumber_co = _DualArrayLike[
"dtype[Union[bool_, number[Any]]]",
Union[bool, int, float, complex],
]
_ArrayLikeTD64_co = _DualArrayLike[
"dtype[Union[bool_, integer[Any], timedelta64]]",
Union[bool, int],
]
_ArrayLikeDT64_co = Union[
_SupportsArray["dtype[datetime64]"],
_NestedSequence[_SupportsArray["dtype[datetime64]"]],
]
_ArrayLikeObject_co = Union[
_SupportsArray["dtype[object_]"],
_NestedSequence[_SupportsArray["dtype[object_]"]],
]
_ArrayLikeVoid_co = Union[
_SupportsArray["dtype[void]"],
_NestedSequence[_SupportsArray["dtype[void]"]],
]
_ArrayLikeStr_co = _DualArrayLike[
"dtype[str_]",
str,
]
_ArrayLikeBytes_co = _DualArrayLike[
"dtype[bytes_]",
bytes,
]
_ArrayLikeInt = _DualArrayLike[
"dtype[integer[Any]]",
int,
]
# Extra ArrayLike type so that pyright can deal with NDArray[Any]
# Used as the first overload, should only match NDArray[Any],
# not any actual types.
# https://github.com/numpy/numpy/pull/22193
class _UnknownType:
...
_ArrayLikeUnknown = _DualArrayLike[
"dtype[_UnknownType]",
_UnknownType,
]

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"""
A module with various ``typing.Protocol`` subclasses that implement
the ``__call__`` magic method.
See the `Mypy documentation`_ on protocols for more details.
.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols
"""
from __future__ import annotations
from typing import (
TypeVar,
overload,
Any,
NoReturn,
Protocol,
)
from numpy import (
ndarray,
dtype,
generic,
bool_,
timedelta64,
number,
integer,
unsignedinteger,
signedinteger,
int8,
int_,
floating,
float64,
complexfloating,
complex128,
)
from ._nbit import _NBitInt, _NBitDouble
from ._scalars import (
_BoolLike_co,
_IntLike_co,
_FloatLike_co,
_NumberLike_co,
)
from . import NBitBase
from ._generic_alias import NDArray
from ._nested_sequence import _NestedSequence
_T1 = TypeVar("_T1")
_T2 = TypeVar("_T2")
_T1_contra = TypeVar("_T1_contra", contravariant=True)
_T2_contra = TypeVar("_T2_contra", contravariant=True)
_2Tuple = tuple[_T1, _T1]
_NBit1 = TypeVar("_NBit1", bound=NBitBase)
_NBit2 = TypeVar("_NBit2", bound=NBitBase)
_IntType = TypeVar("_IntType", bound=integer)
_FloatType = TypeVar("_FloatType", bound=floating)
_NumberType = TypeVar("_NumberType", bound=number)
_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number)
_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic)
class _BoolOp(Protocol[_GenericType_co]):
@overload
def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: float, /) -> float64: ...
@overload
def __call__(self, other: complex, /) -> complex128: ...
@overload
def __call__(self, other: _NumberType, /) -> _NumberType: ...
class _BoolBitOp(Protocol[_GenericType_co]):
@overload
def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: _IntType, /) -> _IntType: ...
class _BoolSub(Protocol):
# Note that `other: bool_` is absent here
@overload
def __call__(self, other: bool, /) -> NoReturn: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: float, /) -> float64: ...
@overload
def __call__(self, other: complex, /) -> complex128: ...
@overload
def __call__(self, other: _NumberType, /) -> _NumberType: ...
class _BoolTrueDiv(Protocol):
@overload
def __call__(self, other: float | _IntLike_co, /) -> float64: ...
@overload
def __call__(self, other: complex, /) -> complex128: ...
@overload
def __call__(self, other: _NumberType, /) -> _NumberType: ...
class _BoolMod(Protocol):
@overload
def __call__(self, other: _BoolLike_co, /) -> int8: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: float, /) -> float64: ...
@overload
def __call__(self, other: _IntType, /) -> _IntType: ...
@overload
def __call__(self, other: _FloatType, /) -> _FloatType: ...
class _BoolDivMod(Protocol):
@overload
def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ...
@overload # platform dependent
def __call__(self, other: int, /) -> _2Tuple[int_]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ...
@overload
def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ...
class _TD64Div(Protocol[_NumberType_co]):
@overload
def __call__(self, other: timedelta64, /) -> _NumberType_co: ...
@overload
def __call__(self, other: _BoolLike_co, /) -> NoReturn: ...
@overload
def __call__(self, other: _FloatLike_co, /) -> timedelta64: ...
class _IntTrueDiv(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> floating[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 | _NBit2]: ...
class _UnsignedIntOp(Protocol[_NBit1]):
# NOTE: `uint64 + signedinteger -> float64`
@overload
def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
@overload
def __call__(
self, other: int | signedinteger[Any], /
) -> Any: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> unsignedinteger[_NBit1 | _NBit2]: ...
class _UnsignedIntBitOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[Any]: ...
@overload
def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> unsignedinteger[_NBit1 | _NBit2]: ...
class _UnsignedIntMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
@overload
def __call__(
self, other: int | signedinteger[Any], /
) -> Any: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> unsignedinteger[_NBit1 | _NBit2]: ...
class _UnsignedIntDivMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
@overload
def __call__(
self, other: int | signedinteger[Any], /
) -> _2Tuple[Any]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> _2Tuple[unsignedinteger[_NBit1 | _NBit2]]: ...
class _SignedIntOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> signedinteger[_NBit1 | _NBit2]: ...
class _SignedIntBitOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> signedinteger[_NBit1 | _NBit2]: ...
class _SignedIntMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> signedinteger[_NBit1 | _NBit2]: ...
class _SignedIntDivMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
@overload
def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 | _NBitInt]]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> _2Tuple[signedinteger[_NBit1 | _NBit2]]: ...
class _FloatOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> floating[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: integer[_NBit2] | floating[_NBit2], /
) -> floating[_NBit1 | _NBit2]: ...
class _FloatMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> floating[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: integer[_NBit2] | floating[_NBit2], /
) -> floating[_NBit1 | _NBit2]: ...
class _FloatDivMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ...
@overload
def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 | _NBitInt]]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(
self, other: integer[_NBit2] | floating[_NBit2], /
) -> _2Tuple[floating[_NBit1 | _NBit2]]: ...
class _ComplexOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ...
@overload
def __call__(self, other: int, /) -> complexfloating[_NBit1 | _NBitInt, _NBit1 | _NBitInt]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self,
other: (
integer[_NBit2]
| floating[_NBit2]
| complexfloating[_NBit2, _NBit2]
), /,
) -> complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]: ...
class _NumberOp(Protocol):
def __call__(self, other: _NumberLike_co, /) -> Any: ...
class _SupportsLT(Protocol):
def __lt__(self, other: Any, /) -> object: ...
class _SupportsGT(Protocol):
def __gt__(self, other: Any, /) -> object: ...
class _ComparisonOp(Protocol[_T1_contra, _T2_contra]):
@overload
def __call__(self, other: _T1_contra, /) -> bool_: ...
@overload
def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ...
@overload
def __call__(
self,
other: _SupportsLT | _SupportsGT | _NestedSequence[_SupportsLT | _SupportsGT],
/,
) -> Any: ...

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from typing import Literal
_BoolCodes = Literal["?", "=?", "<?", ">?", "bool", "bool_", "bool8"]
_UInt8Codes = Literal["uint8", "u1", "=u1", "<u1", ">u1"]
_UInt16Codes = Literal["uint16", "u2", "=u2", "<u2", ">u2"]
_UInt32Codes = Literal["uint32", "u4", "=u4", "<u4", ">u4"]
_UInt64Codes = Literal["uint64", "u8", "=u8", "<u8", ">u8"]
_Int8Codes = Literal["int8", "i1", "=i1", "<i1", ">i1"]
_Int16Codes = Literal["int16", "i2", "=i2", "<i2", ">i2"]
_Int32Codes = Literal["int32", "i4", "=i4", "<i4", ">i4"]
_Int64Codes = Literal["int64", "i8", "=i8", "<i8", ">i8"]
_Float16Codes = Literal["float16", "f2", "=f2", "<f2", ">f2"]
_Float32Codes = Literal["float32", "f4", "=f4", "<f4", ">f4"]
_Float64Codes = Literal["float64", "f8", "=f8", "<f8", ">f8"]
_Complex64Codes = Literal["complex64", "c8", "=c8", "<c8", ">c8"]
_Complex128Codes = Literal["complex128", "c16", "=c16", "<c16", ">c16"]
_ByteCodes = Literal["byte", "b", "=b", "<b", ">b"]
_ShortCodes = Literal["short", "h", "=h", "<h", ">h"]
_IntCCodes = Literal["intc", "i", "=i", "<i", ">i"]
_IntPCodes = Literal["intp", "int0", "p", "=p", "<p", ">p"]
_IntCodes = Literal["long", "int", "int_", "l", "=l", "<l", ">l"]
_LongLongCodes = Literal["longlong", "q", "=q", "<q", ">q"]
_UByteCodes = Literal["ubyte", "B", "=B", "<B", ">B"]
_UShortCodes = Literal["ushort", "H", "=H", "<H", ">H"]
_UIntCCodes = Literal["uintc", "I", "=I", "<I", ">I"]
_UIntPCodes = Literal["uintp", "uint0", "P", "=P", "<P", ">P"]
_UIntCodes = Literal["ulong", "uint", "L", "=L", "<L", ">L"]
_ULongLongCodes = Literal["ulonglong", "Q", "=Q", "<Q", ">Q"]
_HalfCodes = Literal["half", "e", "=e", "<e", ">e"]
_SingleCodes = Literal["single", "f", "=f", "<f", ">f"]
_DoubleCodes = Literal["double", "float", "float_", "d", "=d", "<d", ">d"]
_LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "<g", ">g"]
_CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "<F", ">F"]
_CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "<D", ">D"]
_CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "<G", ">G"]
_StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "<U", ">U"]
_BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "<S", ">S"]
_VoidCodes = Literal["void", "void0", "V", "=V", "<V", ">V"]
_ObjectCodes = Literal["object", "object_", "O", "=O", "<O", ">O"]
_DT64Codes = Literal[
"datetime64", "=datetime64", "<datetime64", ">datetime64",
"datetime64[Y]", "=datetime64[Y]", "<datetime64[Y]", ">datetime64[Y]",
"datetime64[M]", "=datetime64[M]", "<datetime64[M]", ">datetime64[M]",
"datetime64[W]", "=datetime64[W]", "<datetime64[W]", ">datetime64[W]",
"datetime64[D]", "=datetime64[D]", "<datetime64[D]", ">datetime64[D]",
"datetime64[h]", "=datetime64[h]", "<datetime64[h]", ">datetime64[h]",
"datetime64[m]", "=datetime64[m]", "<datetime64[m]", ">datetime64[m]",
"datetime64[s]", "=datetime64[s]", "<datetime64[s]", ">datetime64[s]",
"datetime64[ms]", "=datetime64[ms]", "<datetime64[ms]", ">datetime64[ms]",
"datetime64[us]", "=datetime64[us]", "<datetime64[us]", ">datetime64[us]",
"datetime64[ns]", "=datetime64[ns]", "<datetime64[ns]", ">datetime64[ns]",
"datetime64[ps]", "=datetime64[ps]", "<datetime64[ps]", ">datetime64[ps]",
"datetime64[fs]", "=datetime64[fs]", "<datetime64[fs]", ">datetime64[fs]",
"datetime64[as]", "=datetime64[as]", "<datetime64[as]", ">datetime64[as]",
"M", "=M", "<M", ">M",
"M8", "=M8", "<M8", ">M8",
"M8[Y]", "=M8[Y]", "<M8[Y]", ">M8[Y]",
"M8[M]", "=M8[M]", "<M8[M]", ">M8[M]",
"M8[W]", "=M8[W]", "<M8[W]", ">M8[W]",
"M8[D]", "=M8[D]", "<M8[D]", ">M8[D]",
"M8[h]", "=M8[h]", "<M8[h]", ">M8[h]",
"M8[m]", "=M8[m]", "<M8[m]", ">M8[m]",
"M8[s]", "=M8[s]", "<M8[s]", ">M8[s]",
"M8[ms]", "=M8[ms]", "<M8[ms]", ">M8[ms]",
"M8[us]", "=M8[us]", "<M8[us]", ">M8[us]",
"M8[ns]", "=M8[ns]", "<M8[ns]", ">M8[ns]",
"M8[ps]", "=M8[ps]", "<M8[ps]", ">M8[ps]",
"M8[fs]", "=M8[fs]", "<M8[fs]", ">M8[fs]",
"M8[as]", "=M8[as]", "<M8[as]", ">M8[as]",
]
_TD64Codes = Literal[
"timedelta64", "=timedelta64", "<timedelta64", ">timedelta64",
"timedelta64[Y]", "=timedelta64[Y]", "<timedelta64[Y]", ">timedelta64[Y]",
"timedelta64[M]", "=timedelta64[M]", "<timedelta64[M]", ">timedelta64[M]",
"timedelta64[W]", "=timedelta64[W]", "<timedelta64[W]", ">timedelta64[W]",
"timedelta64[D]", "=timedelta64[D]", "<timedelta64[D]", ">timedelta64[D]",
"timedelta64[h]", "=timedelta64[h]", "<timedelta64[h]", ">timedelta64[h]",
"timedelta64[m]", "=timedelta64[m]", "<timedelta64[m]", ">timedelta64[m]",
"timedelta64[s]", "=timedelta64[s]", "<timedelta64[s]", ">timedelta64[s]",
"timedelta64[ms]", "=timedelta64[ms]", "<timedelta64[ms]", ">timedelta64[ms]",
"timedelta64[us]", "=timedelta64[us]", "<timedelta64[us]", ">timedelta64[us]",
"timedelta64[ns]", "=timedelta64[ns]", "<timedelta64[ns]", ">timedelta64[ns]",
"timedelta64[ps]", "=timedelta64[ps]", "<timedelta64[ps]", ">timedelta64[ps]",
"timedelta64[fs]", "=timedelta64[fs]", "<timedelta64[fs]", ">timedelta64[fs]",
"timedelta64[as]", "=timedelta64[as]", "<timedelta64[as]", ">timedelta64[as]",
"m", "=m", "<m", ">m",
"m8", "=m8", "<m8", ">m8",
"m8[Y]", "=m8[Y]", "<m8[Y]", ">m8[Y]",
"m8[M]", "=m8[M]", "<m8[M]", ">m8[M]",
"m8[W]", "=m8[W]", "<m8[W]", ">m8[W]",
"m8[D]", "=m8[D]", "<m8[D]", ">m8[D]",
"m8[h]", "=m8[h]", "<m8[h]", ">m8[h]",
"m8[m]", "=m8[m]", "<m8[m]", ">m8[m]",
"m8[s]", "=m8[s]", "<m8[s]", ">m8[s]",
"m8[ms]", "=m8[ms]", "<m8[ms]", ">m8[ms]",
"m8[us]", "=m8[us]", "<m8[us]", ">m8[us]",
"m8[ns]", "=m8[ns]", "<m8[ns]", ">m8[ns]",
"m8[ps]", "=m8[ps]", "<m8[ps]", ">m8[ps]",
"m8[fs]", "=m8[fs]", "<m8[fs]", ">m8[fs]",
"m8[as]", "=m8[as]", "<m8[as]", ">m8[as]",
]

249
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from typing import (
Any,
List,
Sequence,
Tuple,
Union,
Type,
TypeVar,
Protocol,
TypedDict,
runtime_checkable,
)
import numpy as np
from ._shape import _ShapeLike
from ._generic_alias import _DType as DType
from ._char_codes import (
_BoolCodes,
_UInt8Codes,
_UInt16Codes,
_UInt32Codes,
_UInt64Codes,
_Int8Codes,
_Int16Codes,
_Int32Codes,
_Int64Codes,
_Float16Codes,
_Float32Codes,
_Float64Codes,
_Complex64Codes,
_Complex128Codes,
_ByteCodes,
_ShortCodes,
_IntCCodes,
_IntPCodes,
_IntCodes,
_LongLongCodes,
_UByteCodes,
_UShortCodes,
_UIntCCodes,
_UIntPCodes,
_UIntCodes,
_ULongLongCodes,
_HalfCodes,
_SingleCodes,
_DoubleCodes,
_LongDoubleCodes,
_CSingleCodes,
_CDoubleCodes,
_CLongDoubleCodes,
_DT64Codes,
_TD64Codes,
_StrCodes,
_BytesCodes,
_VoidCodes,
_ObjectCodes,
)
_SCT = TypeVar("_SCT", bound=np.generic)
_DType_co = TypeVar("_DType_co", covariant=True, bound=DType[Any])
_DTypeLikeNested = Any # TODO: wait for support for recursive types
# Mandatory keys
class _DTypeDictBase(TypedDict):
names: Sequence[str]
formats: Sequence[_DTypeLikeNested]
# Mandatory + optional keys
class _DTypeDict(_DTypeDictBase, total=False):
# Only `str` elements are usable as indexing aliases,
# but `titles` can in principle accept any object
offsets: Sequence[int]
titles: Sequence[Any]
itemsize: int
aligned: bool
# A protocol for anything with the dtype attribute
@runtime_checkable
class _SupportsDType(Protocol[_DType_co]):
@property
def dtype(self) -> _DType_co: ...
# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
_DTypeLike = Union[
"np.dtype[_SCT]",
Type[_SCT],
_SupportsDType["np.dtype[_SCT]"],
]
# Would create a dtype[np.void]
_VoidDTypeLike = Union[
# (flexible_dtype, itemsize)
Tuple[_DTypeLikeNested, int],
# (fixed_dtype, shape)
Tuple[_DTypeLikeNested, _ShapeLike],
# [(field_name, field_dtype, field_shape), ...]
#
# The type here is quite broad because NumPy accepts quite a wide
# range of inputs inside the list; see the tests for some
# examples.
List[Any],
# {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
# 'itemsize': ...}
_DTypeDict,
# (base_dtype, new_dtype)
Tuple[_DTypeLikeNested, _DTypeLikeNested],
]
# Anything that can be coerced into numpy.dtype.
# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
DTypeLike = Union[
DType[Any],
# default data type (float64)
None,
# array-scalar types and generic types
Type[Any], # NOTE: We're stuck with `Type[Any]` due to object dtypes
# anything with a dtype attribute
_SupportsDType[DType[Any]],
# character codes, type strings or comma-separated fields, e.g., 'float64'
str,
_VoidDTypeLike,
]
# NOTE: while it is possible to provide the dtype as a dict of
# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
# this syntax is officially discourged and
# therefore not included in the Union defining `DTypeLike`.
#
# See https://github.com/numpy/numpy/issues/16891 for more details.
# Aliases for commonly used dtype-like objects.
# Note that the precision of `np.number` subclasses is ignored herein.
_DTypeLikeBool = Union[
Type[bool],
Type[np.bool_],
DType[np.bool_],
_SupportsDType[DType[np.bool_]],
_BoolCodes,
]
_DTypeLikeUInt = Union[
Type[np.unsignedinteger],
DType[np.unsignedinteger],
_SupportsDType[DType[np.unsignedinteger]],
_UInt8Codes,
_UInt16Codes,
_UInt32Codes,
_UInt64Codes,
_UByteCodes,
_UShortCodes,
_UIntCCodes,
_UIntPCodes,
_UIntCodes,
_ULongLongCodes,
]
_DTypeLikeInt = Union[
Type[int],
Type[np.signedinteger],
DType[np.signedinteger],
_SupportsDType[DType[np.signedinteger]],
_Int8Codes,
_Int16Codes,
_Int32Codes,
_Int64Codes,
_ByteCodes,
_ShortCodes,
_IntCCodes,
_IntPCodes,
_IntCodes,
_LongLongCodes,
]
_DTypeLikeFloat = Union[
Type[float],
Type[np.floating],
DType[np.floating],
_SupportsDType[DType[np.floating]],
_Float16Codes,
_Float32Codes,
_Float64Codes,
_HalfCodes,
_SingleCodes,
_DoubleCodes,
_LongDoubleCodes,
]
_DTypeLikeComplex = Union[
Type[complex],
Type[np.complexfloating],
DType[np.complexfloating],
_SupportsDType[DType[np.complexfloating]],
_Complex64Codes,
_Complex128Codes,
_CSingleCodes,
_CDoubleCodes,
_CLongDoubleCodes,
]
_DTypeLikeDT64 = Union[
Type[np.timedelta64],
DType[np.timedelta64],
_SupportsDType[DType[np.timedelta64]],
_TD64Codes,
]
_DTypeLikeTD64 = Union[
Type[np.datetime64],
DType[np.datetime64],
_SupportsDType[DType[np.datetime64]],
_DT64Codes,
]
_DTypeLikeStr = Union[
Type[str],
Type[np.str_],
DType[np.str_],
_SupportsDType[DType[np.str_]],
_StrCodes,
]
_DTypeLikeBytes = Union[
Type[bytes],
Type[np.bytes_],
DType[np.bytes_],
_SupportsDType[DType[np.bytes_]],
_BytesCodes,
]
_DTypeLikeVoid = Union[
Type[np.void],
DType[np.void],
_SupportsDType[DType[np.void]],
_VoidCodes,
_VoidDTypeLike,
]
_DTypeLikeObject = Union[
type,
DType[np.object_],
_SupportsDType[DType[np.object_]],
_ObjectCodes,
]
_DTypeLikeComplex_co = Union[
_DTypeLikeBool,
_DTypeLikeUInt,
_DTypeLikeInt,
_DTypeLikeFloat,
_DTypeLikeComplex,
]

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"""A module with platform-specific extended precision
`numpy.number` subclasses.
The subclasses are defined here (instead of ``__init__.pyi``) such
that they can be imported conditionally via the numpy's mypy plugin.
"""
from typing import TYPE_CHECKING
import numpy as np
from . import (
_80Bit,
_96Bit,
_128Bit,
_256Bit,
)
if TYPE_CHECKING:
uint128 = np.unsignedinteger[_128Bit]
uint256 = np.unsignedinteger[_256Bit]
int128 = np.signedinteger[_128Bit]
int256 = np.signedinteger[_256Bit]
float80 = np.floating[_80Bit]
float96 = np.floating[_96Bit]
float128 = np.floating[_128Bit]
float256 = np.floating[_256Bit]
complex160 = np.complexfloating[_80Bit, _80Bit]
complex192 = np.complexfloating[_96Bit, _96Bit]
complex256 = np.complexfloating[_128Bit, _128Bit]
complex512 = np.complexfloating[_256Bit, _256Bit]
else:
uint128 = Any
uint256 = Any
int128 = Any
int256 = Any
float80 = Any
float96 = Any
float128 = Any
float256 = Any
complex160 = Any
complex192 = Any
complex256 = Any
complex512 = Any

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from __future__ import annotations
import sys
import types
from collections.abc import Generator, Iterable, Iterator
from typing import (
Any,
ClassVar,
NoReturn,
TypeVar,
TYPE_CHECKING,
)
import numpy as np
__all__ = ["_GenericAlias", "NDArray"]
_T = TypeVar("_T", bound="_GenericAlias")
def _to_str(obj: object) -> str:
"""Helper function for `_GenericAlias.__repr__`."""
if obj is Ellipsis:
return '...'
elif isinstance(obj, type) and not isinstance(obj, _GENERIC_ALIAS_TYPE):
if obj.__module__ == 'builtins':
return obj.__qualname__
else:
return f'{obj.__module__}.{obj.__qualname__}'
else:
return repr(obj)
def _parse_parameters(args: Iterable[Any]) -> Generator[TypeVar, None, None]:
"""Search for all typevars and typevar-containing objects in `args`.
Helper function for `_GenericAlias.__init__`.
"""
for i in args:
if hasattr(i, "__parameters__"):
yield from i.__parameters__
elif isinstance(i, TypeVar):
yield i
def _reconstruct_alias(alias: _T, parameters: Iterator[TypeVar]) -> _T:
"""Recursively replace all typevars with those from `parameters`.
Helper function for `_GenericAlias.__getitem__`.
"""
args = []
for i in alias.__args__:
if isinstance(i, TypeVar):
value: Any = next(parameters)
elif isinstance(i, _GenericAlias):
value = _reconstruct_alias(i, parameters)
elif hasattr(i, "__parameters__"):
prm_tup = tuple(next(parameters) for _ in i.__parameters__)
value = i[prm_tup]
else:
value = i
args.append(value)
cls = type(alias)
return cls(alias.__origin__, tuple(args), alias.__unpacked__)
class _GenericAlias:
"""A python-based backport of the `types.GenericAlias` class.
E.g. for ``t = list[int]``, ``t.__origin__`` is ``list`` and
``t.__args__`` is ``(int,)``.
See Also
--------
:pep:`585`
The PEP responsible for introducing `types.GenericAlias`.
"""
__slots__ = (
"__weakref__",
"_origin",
"_args",
"_parameters",
"_hash",
"_starred",
)
@property
def __origin__(self) -> type:
return super().__getattribute__("_origin")
@property
def __args__(self) -> tuple[object, ...]:
return super().__getattribute__("_args")
@property
def __parameters__(self) -> tuple[TypeVar, ...]:
"""Type variables in the ``GenericAlias``."""
return super().__getattribute__("_parameters")
@property
def __unpacked__(self) -> bool:
return super().__getattribute__("_starred")
@property
def __typing_unpacked_tuple_args__(self) -> tuple[object, ...] | None:
# NOTE: This should return `__args__` if `__origin__` is a tuple,
# which should never be the case with how `_GenericAlias` is used
# within numpy
return None
def __init__(
self,
origin: type,
args: object | tuple[object, ...],
starred: bool = False,
) -> None:
self._origin = origin
self._args = args if isinstance(args, tuple) else (args,)
self._parameters = tuple(_parse_parameters(self.__args__))
self._starred = starred
@property
def __call__(self) -> type[Any]:
return self.__origin__
def __reduce__(self: _T) -> tuple[
type[_T],
tuple[type[Any], tuple[object, ...], bool],
]:
cls = type(self)
return cls, (self.__origin__, self.__args__, self.__unpacked__)
def __mro_entries__(self, bases: Iterable[object]) -> tuple[type[Any]]:
return (self.__origin__,)
def __dir__(self) -> list[str]:
"""Implement ``dir(self)``."""
cls = type(self)
dir_origin = set(dir(self.__origin__))
return sorted(cls._ATTR_EXCEPTIONS | dir_origin)
def __hash__(self) -> int:
"""Return ``hash(self)``."""
# Attempt to use the cached hash
try:
return super().__getattribute__("_hash")
except AttributeError:
self._hash: int = (
hash(self.__origin__) ^
hash(self.__args__) ^
hash(self.__unpacked__)
)
return super().__getattribute__("_hash")
def __instancecheck__(self, obj: object) -> NoReturn:
"""Check if an `obj` is an instance."""
raise TypeError("isinstance() argument 2 cannot be a "
"parameterized generic")
def __subclasscheck__(self, cls: type) -> NoReturn:
"""Check if a `cls` is a subclass."""
raise TypeError("issubclass() argument 2 cannot be a "
"parameterized generic")
def __repr__(self) -> str:
"""Return ``repr(self)``."""
args = ", ".join(_to_str(i) for i in self.__args__)
origin = _to_str(self.__origin__)
prefix = "*" if self.__unpacked__ else ""
return f"{prefix}{origin}[{args}]"
def __getitem__(self: _T, key: object | tuple[object, ...]) -> _T:
"""Return ``self[key]``."""
key_tup = key if isinstance(key, tuple) else (key,)
if len(self.__parameters__) == 0:
raise TypeError(f"There are no type variables left in {self}")
elif len(key_tup) > len(self.__parameters__):
raise TypeError(f"Too many arguments for {self}")
elif len(key_tup) < len(self.__parameters__):
raise TypeError(f"Too few arguments for {self}")
key_iter = iter(key_tup)
return _reconstruct_alias(self, key_iter)
def __eq__(self, value: object) -> bool:
"""Return ``self == value``."""
if not isinstance(value, _GENERIC_ALIAS_TYPE):
return NotImplemented
return (
self.__origin__ == value.__origin__ and
self.__args__ == value.__args__ and
self.__unpacked__ == getattr(
value, "__unpacked__", self.__unpacked__
)
)
def __iter__(self: _T) -> Generator[_T, None, None]:
"""Return ``iter(self)``."""
cls = type(self)
yield cls(self.__origin__, self.__args__, True)
_ATTR_EXCEPTIONS: ClassVar[frozenset[str]] = frozenset({
"__origin__",
"__args__",
"__parameters__",
"__mro_entries__",
"__reduce__",
"__reduce_ex__",
"__copy__",
"__deepcopy__",
"__unpacked__",
"__typing_unpacked_tuple_args__",
"__class__",
})
def __getattribute__(self, name: str) -> Any:
"""Return ``getattr(self, name)``."""
# Pull the attribute from `__origin__` unless its
# name is in `_ATTR_EXCEPTIONS`
cls = type(self)
if name in cls._ATTR_EXCEPTIONS:
return super().__getattribute__(name)
return getattr(self.__origin__, name)
# See `_GenericAlias.__eq__`
if sys.version_info >= (3, 9):
_GENERIC_ALIAS_TYPE = (_GenericAlias, types.GenericAlias)
else:
_GENERIC_ALIAS_TYPE = (_GenericAlias,)
ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True)
if TYPE_CHECKING or sys.version_info >= (3, 9):
_DType = np.dtype[ScalarType]
NDArray = np.ndarray[Any, np.dtype[ScalarType]]
else:
_DType = _GenericAlias(np.dtype, (ScalarType,))
NDArray = _GenericAlias(np.ndarray, (Any, _DType))

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"""A module with the precisions of platform-specific `~numpy.number`s."""
from typing import Any
# To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin
_NBitByte = Any
_NBitShort = Any
_NBitIntC = Any
_NBitIntP = Any
_NBitInt = Any
_NBitLongLong = Any
_NBitHalf = Any
_NBitSingle = Any
_NBitDouble = Any
_NBitLongDouble = Any

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"""A module containing the `_NestedSequence` protocol."""
from __future__ import annotations
from typing import (
Any,
Iterator,
overload,
TypeVar,
Protocol,
runtime_checkable,
)
__all__ = ["_NestedSequence"]
_T_co = TypeVar("_T_co", covariant=True)
@runtime_checkable
class _NestedSequence(Protocol[_T_co]):
"""A protocol for representing nested sequences.
Warning
-------
`_NestedSequence` currently does not work in combination with typevars,
*e.g.* ``def func(a: _NestedSequnce[T]) -> T: ...``.
See Also
--------
collections.abc.Sequence
ABCs for read-only and mutable :term:`sequences`.
Examples
--------
.. code-block:: python
>>> from __future__ import annotations
>>> from typing import TYPE_CHECKING
>>> import numpy as np
>>> from numpy._typing import _NestedSequence
>>> def get_dtype(seq: _NestedSequence[float]) -> np.dtype[np.float64]:
... return np.asarray(seq).dtype
>>> a = get_dtype([1.0])
>>> b = get_dtype([[1.0]])
>>> c = get_dtype([[[1.0]]])
>>> d = get_dtype([[[[1.0]]]])
>>> if TYPE_CHECKING:
... reveal_locals()
... # note: Revealed local types are:
... # note: a: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
... # note: b: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
... # note: c: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
... # note: d: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
"""
def __len__(self, /) -> int:
"""Implement ``len(self)``."""
raise NotImplementedError
@overload
def __getitem__(self, index: int, /) -> _T_co | _NestedSequence[_T_co]: ...
@overload
def __getitem__(self, index: slice, /) -> _NestedSequence[_T_co]: ...
def __getitem__(self, index, /):
"""Implement ``self[x]``."""
raise NotImplementedError
def __contains__(self, x: object, /) -> bool:
"""Implement ``x in self``."""
raise NotImplementedError
def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
"""Implement ``iter(self)``."""
raise NotImplementedError
def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
"""Implement ``reversed(self)``."""
raise NotImplementedError
def count(self, value: Any, /) -> int:
"""Return the number of occurrences of `value`."""
raise NotImplementedError
def index(self, value: Any, /) -> int:
"""Return the first index of `value`."""
raise NotImplementedError

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from typing import Union, Tuple, Any
import numpy as np
# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and
# `np.bytes_` are already subclasses of their builtin counterpart
_CharLike_co = Union[str, bytes]
# The 6 `<X>Like_co` type-aliases below represent all scalars that can be
# coerced into `<X>` (with the casting rule `same_kind`)
_BoolLike_co = Union[bool, np.bool_]
_UIntLike_co = Union[_BoolLike_co, np.unsignedinteger]
_IntLike_co = Union[_BoolLike_co, int, np.integer]
_FloatLike_co = Union[_IntLike_co, float, np.floating]
_ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating]
_TD64Like_co = Union[_IntLike_co, np.timedelta64]
_NumberLike_co = Union[int, float, complex, np.number, np.bool_]
_ScalarLike_co = Union[
int,
float,
complex,
str,
bytes,
np.generic,
]
# `_VoidLike_co` is technically not a scalar, but it's close enough
_VoidLike_co = Union[Tuple[Any, ...], np.void]

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from typing import Sequence, Tuple, Union, SupportsIndex
_Shape = Tuple[int, ...]
# Anything that can be coerced to a shape tuple
_ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]

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"""A module with private type-check-only `numpy.ufunc` subclasses.
The signatures of the ufuncs are too varied to reasonably type
with a single class. So instead, `ufunc` has been expanded into
four private subclasses, one for each combination of
`~ufunc.nin` and `~ufunc.nout`.
"""
from typing import (
Any,
Generic,
overload,
TypeVar,
Literal,
SupportsIndex,
Protocol,
)
from numpy import ufunc, _CastingKind, _OrderKACF
from numpy.typing import NDArray
from ._shape import _ShapeLike
from ._scalars import _ScalarLike_co
from ._array_like import ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co
from ._dtype_like import DTypeLike
_T = TypeVar("_T")
_2Tuple = tuple[_T, _T]
_3Tuple = tuple[_T, _T, _T]
_4Tuple = tuple[_T, _T, _T, _T]
_NTypes = TypeVar("_NTypes", bound=int)
_IDType = TypeVar("_IDType", bound=Any)
_NameType = TypeVar("_NameType", bound=str)
class _SupportsArrayUFunc(Protocol):
def __array_ufunc__(
self,
ufunc: ufunc,
method: Literal["__call__", "reduce", "reduceat", "accumulate", "outer", "inner"],
*inputs: Any,
**kwargs: Any,
) -> Any: ...
# NOTE: In reality `extobj` should be a length of list 3 containing an
# int, an int, and a callable, but there's no way to properly express
# non-homogenous lists.
# Use `Any` over `Union` to avoid issues related to lists invariance.
# NOTE: `reduce`, `accumulate`, `reduceat` and `outer` raise a ValueError for
# ufuncs that don't accept two input arguments and return one output argument.
# In such cases the respective methods are simply typed as `None`.
# NOTE: Similarly, `at` won't be defined for ufuncs that return
# multiple outputs; in such cases `at` is typed as `None`
# NOTE: If 2 output types are returned then `out` must be a
# 2-tuple of arrays. Otherwise `None` or a plain array are also acceptable
class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc]
@property
def __name__(self) -> _NameType: ...
@property
def ntypes(self) -> _NTypes: ...
@property
def identity(self) -> _IDType: ...
@property
def nin(self) -> Literal[1]: ...
@property
def nout(self) -> Literal[1]: ...
@property
def nargs(self) -> Literal[2]: ...
@property
def signature(self) -> None: ...
@property
def reduce(self) -> None: ...
@property
def accumulate(self) -> None: ...
@property
def reduceat(self) -> None: ...
@property
def outer(self) -> None: ...
@overload
def __call__(
self,
__x1: _ScalarLike_co,
out: None = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _2Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> Any: ...
@overload
def __call__(
self,
__x1: ArrayLike,
out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _2Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> NDArray[Any]: ...
@overload
def __call__(
self,
__x1: _SupportsArrayUFunc,
out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _2Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> Any: ...
def at(
self,
a: _SupportsArrayUFunc,
indices: _ArrayLikeInt_co,
/,
) -> None: ...
class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc]
@property
def __name__(self) -> _NameType: ...
@property
def ntypes(self) -> _NTypes: ...
@property
def identity(self) -> _IDType: ...
@property
def nin(self) -> Literal[2]: ...
@property
def nout(self) -> Literal[1]: ...
@property
def nargs(self) -> Literal[3]: ...
@property
def signature(self) -> None: ...
@overload
def __call__(
self,
__x1: _ScalarLike_co,
__x2: _ScalarLike_co,
out: None = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> Any: ...
@overload
def __call__(
self,
__x1: ArrayLike,
__x2: ArrayLike,
out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> NDArray[Any]: ...
def at(
self,
a: NDArray[Any],
indices: _ArrayLikeInt_co,
b: ArrayLike,
/,
) -> None: ...
def reduce(
self,
array: ArrayLike,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: None | NDArray[Any] = ...,
keepdims: bool = ...,
initial: Any = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
def accumulate(
self,
array: ArrayLike,
axis: SupportsIndex = ...,
dtype: DTypeLike = ...,
out: None | NDArray[Any] = ...,
) -> NDArray[Any]: ...
def reduceat(
self,
array: ArrayLike,
indices: _ArrayLikeInt_co,
axis: SupportsIndex = ...,
dtype: DTypeLike = ...,
out: None | NDArray[Any] = ...,
) -> NDArray[Any]: ...
# Expand `**kwargs` into explicit keyword-only arguments
@overload
def outer(
self,
A: _ScalarLike_co,
B: _ScalarLike_co,
/, *,
out: None = ...,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> Any: ...
@overload
def outer( # type: ignore[misc]
self,
A: ArrayLike,
B: ArrayLike,
/, *,
out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> NDArray[Any]: ...
class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc]
@property
def __name__(self) -> _NameType: ...
@property
def ntypes(self) -> _NTypes: ...
@property
def identity(self) -> _IDType: ...
@property
def nin(self) -> Literal[1]: ...
@property
def nout(self) -> Literal[2]: ...
@property
def nargs(self) -> Literal[3]: ...
@property
def signature(self) -> None: ...
@property
def at(self) -> None: ...
@property
def reduce(self) -> None: ...
@property
def accumulate(self) -> None: ...
@property
def reduceat(self) -> None: ...
@property
def outer(self) -> None: ...
@overload
def __call__(
self,
__x1: _ScalarLike_co,
__out1: None = ...,
__out2: None = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> _2Tuple[Any]: ...
@overload
def __call__(
self,
__x1: ArrayLike,
__out1: None | NDArray[Any] = ...,
__out2: None | NDArray[Any] = ...,
*,
out: _2Tuple[NDArray[Any]] = ...,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> _2Tuple[NDArray[Any]]: ...
@overload
def __call__(
self,
__x1: _SupportsArrayUFunc,
__out1: None | NDArray[Any] = ...,
__out2: None | NDArray[Any] = ...,
*,
out: _2Tuple[NDArray[Any]] = ...,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> _2Tuple[Any]: ...
class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc]
@property
def __name__(self) -> _NameType: ...
@property
def ntypes(self) -> _NTypes: ...
@property
def identity(self) -> _IDType: ...
@property
def nin(self) -> Literal[2]: ...
@property
def nout(self) -> Literal[2]: ...
@property
def nargs(self) -> Literal[4]: ...
@property
def signature(self) -> None: ...
@property
def at(self) -> None: ...
@property
def reduce(self) -> None: ...
@property
def accumulate(self) -> None: ...
@property
def reduceat(self) -> None: ...
@property
def outer(self) -> None: ...
@overload
def __call__(
self,
__x1: _ScalarLike_co,
__x2: _ScalarLike_co,
__out1: None = ...,
__out2: None = ...,
*,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _4Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> _2Tuple[Any]: ...
@overload
def __call__(
self,
__x1: ArrayLike,
__x2: ArrayLike,
__out1: None | NDArray[Any] = ...,
__out2: None | NDArray[Any] = ...,
*,
out: _2Tuple[NDArray[Any]] = ...,
where: None | _ArrayLikeBool_co = ...,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _4Tuple[None | str] = ...,
extobj: list[Any] = ...,
) -> _2Tuple[NDArray[Any]]: ...
class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc]
@property
def __name__(self) -> _NameType: ...
@property
def ntypes(self) -> _NTypes: ...
@property
def identity(self) -> _IDType: ...
@property
def nin(self) -> Literal[2]: ...
@property
def nout(self) -> Literal[1]: ...
@property
def nargs(self) -> Literal[3]: ...
# NOTE: In practice the only gufunc in the main namespace is `matmul`,
# so we can use its signature here
@property
def signature(self) -> Literal["(n?,k),(k,m?)->(n?,m?)"]: ...
@property
def reduce(self) -> None: ...
@property
def accumulate(self) -> None: ...
@property
def reduceat(self) -> None: ...
@property
def outer(self) -> None: ...
@property
def at(self) -> None: ...
# Scalar for 1D array-likes; ndarray otherwise
@overload
def __call__(
self,
__x1: ArrayLike,
__x2: ArrayLike,
out: None = ...,
*,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
axes: list[_2Tuple[SupportsIndex]] = ...,
) -> Any: ...
@overload
def __call__(
self,
__x1: ArrayLike,
__x2: ArrayLike,
out: NDArray[Any] | tuple[NDArray[Any]],
*,
casting: _CastingKind = ...,
order: _OrderKACF = ...,
dtype: DTypeLike = ...,
subok: bool = ...,
signature: str | _3Tuple[None | str] = ...,
extobj: list[Any] = ...,
axes: list[_2Tuple[SupportsIndex]] = ...,
) -> NDArray[Any]: ...

10
venv/lib/python3.9/site-packages/numpy/_typing/setup.py Normal file
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def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('_typing', parent_package, top_path)
config.add_data_files('*.pyi')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)

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venv/lib/python3.9/site-packages/numpy/_version.py Normal file
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# This file was generated by 'versioneer.py' (0.26) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.
import json
version_json = '''
{
"date": "2023-04-22T13:47:13-0400",
"dirty": false,
"error": null,
"full-revisionid": "14bb214bca49b167abc375fa873466a811e62102",
"version": "1.24.3"
}
''' # END VERSION_JSON
def get_versions():
return json.loads(version_json)

377
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"""
A NumPy sub-namespace that conforms to the Python array API standard.
This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It
is still considered experimental, and will issue a warning when imported.
This is a proof-of-concept namespace that wraps the corresponding NumPy
functions to give a conforming implementation of the Python array API standard
(https://data-apis.github.io/array-api/latest/). The standard is currently in
an RFC phase and comments on it are both welcome and encouraged. Comments
should be made either at https://github.com/data-apis/array-api or at
https://github.com/data-apis/consortium-feedback/discussions.
NumPy already follows the proposed spec for the most part, so this module
serves mostly as a thin wrapper around it. However, NumPy also implements a
lot of behavior that is not included in the spec, so this serves as a
restricted subset of the API. Only those functions that are part of the spec
are included in this namespace, and all functions are given with the exact
signature given in the spec, including the use of position-only arguments, and
omitting any extra keyword arguments implemented by NumPy but not part of the
spec. The behavior of some functions is also modified from the NumPy behavior
to conform to the standard. Note that the underlying array object itself is
wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule
is implemented in pure Python with no C extensions.
The array API spec is designed as a "minimal API subset" and explicitly allows
libraries to include behaviors not specified by it. But users of this module
that intend to write portable code should be aware that only those behaviors
that are listed in the spec are guaranteed to be implemented across libraries.
Consequently, the NumPy implementation was chosen to be both conforming and
minimal, so that users can use this implementation of the array API namespace
and be sure that behaviors that it defines will be available in conforming
namespaces from other libraries.
A few notes about the current state of this submodule:
- There is a test suite that tests modules against the array API standard at
https://github.com/data-apis/array-api-tests. The test suite is still a work
in progress, but the existing tests pass on this module, with a few
exceptions:
- DLPack support (see https://github.com/data-apis/array-api/pull/106) is
not included here, as it requires a full implementation in NumPy proper
first.
The test suite is not yet complete, and even the tests that exist are not
guaranteed to give a comprehensive coverage of the spec. Therefore, when
reviewing and using this submodule, you should refer to the standard
documents themselves. There are some tests in numpy.array_api.tests, but
they primarily focus on things that are not tested by the official array API
test suite.
- There is a custom array object, numpy.array_api.Array, which is returned by
all functions in this module. All functions in the array API namespace
implicitly assume that they will only receive this object as input. The only
way to create instances of this object is to use one of the array creation
functions. It does not have a public constructor on the object itself. The
object is a small wrapper class around numpy.ndarray. The main purpose of it
is to restrict the namespace of the array object to only those dtypes and
only those methods that are required by the spec, as well as to limit/change
certain behavior that differs in the spec. In particular:
- The array API namespace does not have scalar objects, only 0-D arrays.
Operations on Array that would create a scalar in NumPy create a 0-D
array.
- Indexing: Only a subset of indices supported by NumPy are required by the
spec. The Array object restricts indexing to only allow those types of
indices that are required by the spec. See the docstring of the
numpy.array_api.Array._validate_indices helper function for more
information.
- Type promotion: Some type promotion rules are different in the spec. In
particular, the spec does not have any value-based casting. The spec also
does not require cross-kind casting, like integer -> floating-point. Only
those promotions that are explicitly required by the array API
specification are allowed in this module. See NEP 47 for more info.
- Functions do not automatically call asarray() on their input, and will not
work if the input type is not Array. The exception is array creation
functions, and Python operators on the Array object, which accept Python
scalars of the same type as the array dtype.
- All functions include type annotations, corresponding to those given in the
spec (see _typing.py for definitions of some custom types). These do not
currently fully pass mypy due to some limitations in mypy.
- Dtype objects are just the NumPy dtype objects, e.g., float64 =
np.dtype('float64'). The spec does not require any behavior on these dtype
objects other than that they be accessible by name and be comparable by
equality, but it was considered too much extra complexity to create custom
objects to represent dtypes.
- All places where the implementations in this submodule are known to deviate
from their corresponding functions in NumPy are marked with "# Note:"
comments.
Still TODO in this module are:
- DLPack support for numpy.ndarray is still in progress. See
https://github.com/numpy/numpy/pull/19083.
- The copy=False keyword argument to asarray() is not yet implemented. This
requires support in numpy.asarray() first.
- Some functions are not yet fully tested in the array API test suite, and may
require updates that are not yet known until the tests are written.
- The spec is still in an RFC phase and may still have minor updates, which
will need to be reflected here.
- Complex number support in array API spec is planned but not yet finalized,
as are the fft extension and certain linear algebra functions such as eig
that require complex dtypes.
"""
import warnings
warnings.warn(
"The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2
)
__array_api_version__ = "2021.12"
__all__ = ["__array_api_version__"]
from ._constants import e, inf, nan, pi
__all__ += ["e", "inf", "nan", "pi"]
from ._creation_functions import (
asarray,
arange,
empty,
empty_like,
eye,
from_dlpack,
full,
full_like,
linspace,
meshgrid,
ones,
ones_like,
tril,
triu,
zeros,
zeros_like,
)
__all__ += [
"asarray",
"arange",
"empty",
"empty_like",
"eye",
"from_dlpack",
"full",
"full_like",
"linspace",
"meshgrid",
"ones",
"ones_like",
"tril",
"triu",
"zeros",
"zeros_like",
]
from ._data_type_functions import (
astype,
broadcast_arrays,
broadcast_to,
can_cast,
finfo,
iinfo,
result_type,
)
__all__ += [
"astype",
"broadcast_arrays",
"broadcast_to",
"can_cast",
"finfo",
"iinfo",
"result_type",
]
from ._dtypes import (
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
float32,
float64,
bool,
)
__all__ += [
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float32",
"float64",
"bool",
]
from ._elementwise_functions import (
abs,
acos,
acosh,
add,
asin,
asinh,
atan,
atan2,
atanh,
bitwise_and,
bitwise_left_shift,
bitwise_invert,
bitwise_or,
bitwise_right_shift,
bitwise_xor,
ceil,
cos,
cosh,
divide,
equal,
exp,
expm1,
floor,
floor_divide,
greater,
greater_equal,
isfinite,
isinf,
isnan,
less,
less_equal,
log,
log1p,
log2,
log10,
logaddexp,
logical_and,
logical_not,
logical_or,
logical_xor,
multiply,
negative,
not_equal,
positive,
pow,
remainder,
round,
sign,
sin,
sinh,
square,
sqrt,
subtract,
tan,
tanh,
trunc,
)
__all__ += [
"abs",
"acos",
"acosh",
"add",
"asin",
"asinh",
"atan",
"atan2",
"atanh",
"bitwise_and",
"bitwise_left_shift",
"bitwise_invert",
"bitwise_or",
"bitwise_right_shift",
"bitwise_xor",
"ceil",
"cos",
"cosh",
"divide",
"equal",
"exp",
"expm1",
"floor",
"floor_divide",
"greater",
"greater_equal",
"isfinite",
"isinf",
"isnan",
"less",
"less_equal",
"log",
"log1p",
"log2",
"log10",
"logaddexp",
"logical_and",
"logical_not",
"logical_or",
"logical_xor",
"multiply",
"negative",
"not_equal",
"positive",
"pow",
"remainder",
"round",
"sign",
"sin",
"sinh",
"square",
"sqrt",
"subtract",
"tan",
"tanh",
"trunc",
]
# linalg is an extension in the array API spec, which is a sub-namespace. Only
# a subset of functions in it are imported into the top-level namespace.
from . import linalg
__all__ += ["linalg"]
from .linalg import matmul, tensordot, matrix_transpose, vecdot
__all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"]
from ._manipulation_functions import (
concat,
expand_dims,
flip,
permute_dims,
reshape,
roll,
squeeze,
stack,
)
__all__ += ["concat", "expand_dims", "flip", "permute_dims", "reshape", "roll", "squeeze", "stack"]
from ._searching_functions import argmax, argmin, nonzero, where
__all__ += ["argmax", "argmin", "nonzero", "where"]
from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values
__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"]
from ._sorting_functions import argsort, sort
__all__ += ["argsort", "sort"]
from ._statistical_functions import max, mean, min, prod, std, sum, var
__all__ += ["max", "mean", "min", "prod", "std", "sum", "var"]
from ._utility_functions import all, any
__all__ += ["all", "any"]

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venv/lib/python3.9/site-packages/numpy/array_api/_array_object.py Normal file
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venv/lib/python3.9/site-packages/numpy/array_api/_constants.py Normal file
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import numpy as np
e = np.e
inf = np.inf
nan = np.nan
pi = np.pi

351
venv/lib/python3.9/site-packages/numpy/array_api/_creation_functions.py Normal file
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from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
if TYPE_CHECKING:
from ._typing import (
Array,
Device,
Dtype,
NestedSequence,
SupportsBufferProtocol,
)
from collections.abc import Sequence
from ._dtypes import _all_dtypes
import numpy as np
def _check_valid_dtype(dtype):
# Note: Only spelling dtypes as the dtype objects is supported.
# We use this instead of "dtype in _all_dtypes" because the dtype objects
# define equality with the sorts of things we want to disallow.
for d in (None,) + _all_dtypes:
if dtype is d:
return
raise ValueError("dtype must be one of the supported dtypes")
def asarray(
obj: Union[
Array,
bool,
int,
float,
NestedSequence[bool | int | float],
SupportsBufferProtocol,
],
/,
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
copy: Optional[Union[bool, np._CopyMode]] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`.
See its docstring for more information.
"""
# _array_object imports in this file are inside the functions to avoid
# circular imports
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
if copy in (False, np._CopyMode.IF_NEEDED):
# Note: copy=False is not yet implemented in np.asarray
raise NotImplementedError("copy=False is not yet implemented")
if isinstance(obj, Array):
if dtype is not None and obj.dtype != dtype:
copy = True
if copy in (True, np._CopyMode.ALWAYS):
return Array._new(np.array(obj._array, copy=True, dtype=dtype))
return obj
if dtype is None and isinstance(obj, int) and (obj > 2 ** 64 or obj < -(2 ** 63)):
# Give a better error message in this case. NumPy would convert this
# to an object array. TODO: This won't handle large integers in lists.
raise OverflowError("Integer out of bounds for array dtypes")
res = np.asarray(obj, dtype=dtype)
return Array._new(res)
def arange(
start: Union[int, float],
/,
stop: Optional[Union[int, float]] = None,
step: Union[int, float] = 1,
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arange <numpy.arange>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype))
def empty(
shape: Union[int, Tuple[int, ...]],
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.empty(shape, dtype=dtype))
def empty_like(
x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.empty_like <numpy.empty_like>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.empty_like(x._array, dtype=dtype))
def eye(
n_rows: int,
n_cols: Optional[int] = None,
/,
*,
k: int = 0,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.eye <numpy.eye>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))
def from_dlpack(x: object, /) -> Array:
from ._array_object import Array
return Array._new(np.from_dlpack(x))
def full(
shape: Union[int, Tuple[int, ...]],
fill_value: Union[int, float],
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.full <numpy.full>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
if isinstance(fill_value, Array) and fill_value.ndim == 0:
fill_value = fill_value._array
res = np.full(shape, fill_value, dtype=dtype)
if res.dtype not in _all_dtypes:
# This will happen if the fill value is not something that NumPy
# coerces to one of the acceptable dtypes.
raise TypeError("Invalid input to full")
return Array._new(res)
def full_like(
x: Array,
/,
fill_value: Union[int, float],
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.full_like <numpy.full_like>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
res = np.full_like(x._array, fill_value, dtype=dtype)
if res.dtype not in _all_dtypes:
# This will happen if the fill value is not something that NumPy
# coerces to one of the acceptable dtypes.
raise TypeError("Invalid input to full_like")
return Array._new(res)
def linspace(
start: Union[int, float],
stop: Union[int, float],
/,
num: int,
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
endpoint: bool = True,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linspace <numpy.linspace>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))
def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]:
"""
Array API compatible wrapper for :py:func:`np.meshgrid <numpy.meshgrid>`.
See its docstring for more information.
"""
from ._array_object import Array
# Note: unlike np.meshgrid, only inputs with all the same dtype are
# allowed
if len({a.dtype for a in arrays}) > 1:
raise ValueError("meshgrid inputs must all have the same dtype")
return [
Array._new(array)
for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing)
]
def ones(
shape: Union[int, Tuple[int, ...]],
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.ones <numpy.ones>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.ones(shape, dtype=dtype))
def ones_like(
x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.ones_like <numpy.ones_like>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.ones_like(x._array, dtype=dtype))
def tril(x: Array, /, *, k: int = 0) -> Array:
"""
Array API compatible wrapper for :py:func:`np.tril <numpy.tril>`.
See its docstring for more information.
"""
from ._array_object import Array
if x.ndim < 2:
# Note: Unlike np.tril, x must be at least 2-D
raise ValueError("x must be at least 2-dimensional for tril")
return Array._new(np.tril(x._array, k=k))
def triu(x: Array, /, *, k: int = 0) -> Array:
"""
Array API compatible wrapper for :py:func:`np.triu <numpy.triu>`.
See its docstring for more information.
"""
from ._array_object import Array
if x.ndim < 2:
# Note: Unlike np.triu, x must be at least 2-D
raise ValueError("x must be at least 2-dimensional for triu")
return Array._new(np.triu(x._array, k=k))
def zeros(
shape: Union[int, Tuple[int, ...]],
*,
dtype: Optional[Dtype] = None,
device: Optional[Device] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.zeros <numpy.zeros>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.zeros(shape, dtype=dtype))
def zeros_like(
x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.zeros_like <numpy.zeros_like>`.
See its docstring for more information.
"""
from ._array_object import Array
_check_valid_dtype(dtype)
if device not in ["cpu", None]:
raise ValueError(f"Unsupported device {device!r}")
return Array._new(np.zeros_like(x._array, dtype=dtype))

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from __future__ import annotations
from ._array_object import Array
from ._dtypes import _all_dtypes, _result_type
from dataclasses import dataclass
from typing import TYPE_CHECKING, List, Tuple, Union
if TYPE_CHECKING:
from ._typing import Dtype
from collections.abc import Sequence
import numpy as np
# Note: astype is a function, not an array method as in NumPy.
def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array:
if not copy and dtype == x.dtype:
return x
return Array._new(x._array.astype(dtype=dtype, copy=copy))
def broadcast_arrays(*arrays: Array) -> List[Array]:
"""
Array API compatible wrapper for :py:func:`np.broadcast_arrays <numpy.broadcast_arrays>`.
See its docstring for more information.
"""
from ._array_object import Array
return [
Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays])
]
def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
"""
Array API compatible wrapper for :py:func:`np.broadcast_to <numpy.broadcast_to>`.
See its docstring for more information.
"""
from ._array_object import Array
return Array._new(np.broadcast_to(x._array, shape))
def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
"""
Array API compatible wrapper for :py:func:`np.can_cast <numpy.can_cast>`.
See its docstring for more information.
"""
if isinstance(from_, Array):
from_ = from_.dtype
elif from_ not in _all_dtypes:
raise TypeError(f"{from_=}, but should be an array_api array or dtype")
if to not in _all_dtypes:
raise TypeError(f"{to=}, but should be a dtype")
# Note: We avoid np.can_cast() as it has discrepancies with the array API,
# since NumPy allows cross-kind casting (e.g., NumPy allows bool -> int8).
# See https://github.com/numpy/numpy/issues/20870
try:
# We promote `from_` and `to` together. We then check if the promoted
# dtype is `to`, which indicates if `from_` can (up)cast to `to`.
dtype = _result_type(from_, to)
return to == dtype
except TypeError:
# _result_type() raises if the dtypes don't promote together
return False
# These are internal objects for the return types of finfo and iinfo, since
# the NumPy versions contain extra data that isn't part of the spec.
@dataclass
class finfo_object:
bits: int
# Note: The types of the float data here are float, whereas in NumPy they
# are scalars of the corresponding float dtype.
eps: float
max: float
min: float
smallest_normal: float
@dataclass
class iinfo_object:
bits: int
max: int
min: int
def finfo(type: Union[Dtype, Array], /) -> finfo_object:
"""
Array API compatible wrapper for :py:func:`np.finfo <numpy.finfo>`.
See its docstring for more information.
"""
fi = np.finfo(type)
# Note: The types of the float data here are float, whereas in NumPy they
# are scalars of the corresponding float dtype.
return finfo_object(
fi.bits,
float(fi.eps),
float(fi.max),
float(fi.min),
float(fi.smallest_normal),
)
def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
"""
Array API compatible wrapper for :py:func:`np.iinfo <numpy.iinfo>`.
See its docstring for more information.
"""
ii = np.iinfo(type)
return iinfo_object(ii.bits, ii.max, ii.min)
def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype:
"""
Array API compatible wrapper for :py:func:`np.result_type <numpy.result_type>`.
See its docstring for more information.
"""
# Note: we use a custom implementation that gives only the type promotions
# required by the spec rather than using np.result_type. NumPy implements
# too many extra type promotions like int64 + uint64 -> float64, and does
# value-based casting on scalar arrays.
A = []
for a in arrays_and_dtypes:
if isinstance(a, Array):
a = a.dtype
elif isinstance(a, np.ndarray) or a not in _all_dtypes:
raise TypeError("result_type() inputs must be array_api arrays or dtypes")
A.append(a)
if len(A) == 0:
raise ValueError("at least one array or dtype is required")
elif len(A) == 1:
return A[0]
else:
t = A[0]
for t2 in A[1:]:
t = _result_type(t, t2)
return t

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import numpy as np
# Note: we use dtype objects instead of dtype classes. The spec does not
# require any behavior on dtypes other than equality.
int8 = np.dtype("int8")
int16 = np.dtype("int16")
int32 = np.dtype("int32")
int64 = np.dtype("int64")
uint8 = np.dtype("uint8")
uint16 = np.dtype("uint16")
uint32 = np.dtype("uint32")
uint64 = np.dtype("uint64")
float32 = np.dtype("float32")
float64 = np.dtype("float64")
# Note: This name is changed
bool = np.dtype("bool")
_all_dtypes = (
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
float32,
float64,
bool,
)
_boolean_dtypes = (bool,)
_floating_dtypes = (float32, float64)
_integer_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64)
_integer_or_boolean_dtypes = (
bool,
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
)
_numeric_dtypes = (
float32,
float64,
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
)
_dtype_categories = {
"all": _all_dtypes,
"numeric": _numeric_dtypes,
"integer": _integer_dtypes,
"integer or boolean": _integer_or_boolean_dtypes,
"boolean": _boolean_dtypes,
"floating-point": _floating_dtypes,
}
# Note: the spec defines a restricted type promotion table compared to NumPy.
# In particular, cross-kind promotions like integer + float or boolean +
# integer are not allowed, even for functions that accept both kinds.
# Additionally, NumPy promotes signed integer + uint64 to float64, but this
# promotion is not allowed here. To be clear, Python scalar int objects are
# allowed to promote to floating-point dtypes, but only in array operators
# (see Array._promote_scalar) method in _array_object.py.
_promotion_table = {
(int8, int8): int8,
(int8, int16): int16,
(int8, int32): int32,
(int8, int64): int64,
(int16, int8): int16,
(int16, int16): int16,
(int16, int32): int32,
(int16, int64): int64,
(int32, int8): int32,
(int32, int16): int32,
(int32, int32): int32,
(int32, int64): int64,
(int64, int8): int64,
(int64, int16): int64,
(int64, int32): int64,
(int64, int64): int64,
(uint8, uint8): uint8,
(uint8, uint16): uint16,
(uint8, uint32): uint32,
(uint8, uint64): uint64,
(uint16, uint8): uint16,
(uint16, uint16): uint16,
(uint16, uint32): uint32,
(uint16, uint64): uint64,
(uint32, uint8): uint32,
(uint32, uint16): uint32,
(uint32, uint32): uint32,
(uint32, uint64): uint64,
(uint64, uint8): uint64,
(uint64, uint16): uint64,
(uint64, uint32): uint64,
(uint64, uint64): uint64,
(int8, uint8): int16,
(int8, uint16): int32,
(int8, uint32): int64,
(int16, uint8): int16,
(int16, uint16): int32,
(int16, uint32): int64,
(int32, uint8): int32,
(int32, uint16): int32,
(int32, uint32): int64,
(int64, uint8): int64,
(int64, uint16): int64,
(int64, uint32): int64,
(uint8, int8): int16,
(uint16, int8): int32,
(uint32, int8): int64,
(uint8, int16): int16,
(uint16, int16): int32,
(uint32, int16): int64,
(uint8, int32): int32,
(uint16, int32): int32,
(uint32, int32): int64,
(uint8, int64): int64,
(uint16, int64): int64,
(uint32, int64): int64,
(float32, float32): float32,
(float32, float64): float64,
(float64, float32): float64,
(float64, float64): float64,
(bool, bool): bool,
}
def _result_type(type1, type2):
if (type1, type2) in _promotion_table:
return _promotion_table[type1, type2]
raise TypeError(f"{type1} and {type2} cannot be type promoted together")

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from __future__ import annotations
from ._dtypes import (
_boolean_dtypes,
_floating_dtypes,
_integer_dtypes,
_integer_or_boolean_dtypes,
_numeric_dtypes,
_result_type,
)
from ._array_object import Array
import numpy as np
def abs(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.abs <numpy.abs>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in abs")
return Array._new(np.abs(x._array))
# Note: the function name is different here
def acos(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arccos <numpy.arccos>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in acos")
return Array._new(np.arccos(x._array))
# Note: the function name is different here
def acosh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arccosh <numpy.arccosh>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in acosh")
return Array._new(np.arccosh(x._array))
def add(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.add <numpy.add>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in add")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.add(x1._array, x2._array))
# Note: the function name is different here
def asin(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arcsin <numpy.arcsin>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in asin")
return Array._new(np.arcsin(x._array))
# Note: the function name is different here
def asinh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arcsinh <numpy.arcsinh>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in asinh")
return Array._new(np.arcsinh(x._array))
# Note: the function name is different here
def atan(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arctan <numpy.arctan>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in atan")
return Array._new(np.arctan(x._array))
# Note: the function name is different here
def atan2(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arctan2 <numpy.arctan2>`.
See its docstring for more information.
"""
if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in atan2")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.arctan2(x1._array, x2._array))
# Note: the function name is different here
def atanh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arctanh <numpy.arctanh>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in atanh")
return Array._new(np.arctanh(x._array))
def bitwise_and(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.bitwise_and <numpy.bitwise_and>`.
See its docstring for more information.
"""
if (
x1.dtype not in _integer_or_boolean_dtypes
or x2.dtype not in _integer_or_boolean_dtypes
):
raise TypeError("Only integer or boolean dtypes are allowed in bitwise_and")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.bitwise_and(x1._array, x2._array))
# Note: the function name is different here
def bitwise_left_shift(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.left_shift <numpy.left_shift>`.
See its docstring for more information.
"""
if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
raise TypeError("Only integer dtypes are allowed in bitwise_left_shift")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
# Note: bitwise_left_shift is only defined for x2 nonnegative.
if np.any(x2._array < 0):
raise ValueError("bitwise_left_shift(x1, x2) is only defined for x2 >= 0")
return Array._new(np.left_shift(x1._array, x2._array))
# Note: the function name is different here
def bitwise_invert(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.invert <numpy.invert>`.
See its docstring for more information.
"""
if x.dtype not in _integer_or_boolean_dtypes:
raise TypeError("Only integer or boolean dtypes are allowed in bitwise_invert")
return Array._new(np.invert(x._array))
def bitwise_or(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.bitwise_or <numpy.bitwise_or>`.
See its docstring for more information.
"""
if (
x1.dtype not in _integer_or_boolean_dtypes
or x2.dtype not in _integer_or_boolean_dtypes
):
raise TypeError("Only integer or boolean dtypes are allowed in bitwise_or")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.bitwise_or(x1._array, x2._array))
# Note: the function name is different here
def bitwise_right_shift(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.right_shift <numpy.right_shift>`.
See its docstring for more information.
"""
if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
raise TypeError("Only integer dtypes are allowed in bitwise_right_shift")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
# Note: bitwise_right_shift is only defined for x2 nonnegative.
if np.any(x2._array < 0):
raise ValueError("bitwise_right_shift(x1, x2) is only defined for x2 >= 0")
return Array._new(np.right_shift(x1._array, x2._array))
def bitwise_xor(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.bitwise_xor <numpy.bitwise_xor>`.
See its docstring for more information.
"""
if (
x1.dtype not in _integer_or_boolean_dtypes
or x2.dtype not in _integer_or_boolean_dtypes
):
raise TypeError("Only integer or boolean dtypes are allowed in bitwise_xor")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.bitwise_xor(x1._array, x2._array))
def ceil(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.ceil <numpy.ceil>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in ceil")
if x.dtype in _integer_dtypes:
# Note: The return dtype of ceil is the same as the input
return x
return Array._new(np.ceil(x._array))
def cos(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.cos <numpy.cos>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in cos")
return Array._new(np.cos(x._array))
def cosh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.cosh <numpy.cosh>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in cosh")
return Array._new(np.cosh(x._array))
def divide(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.divide <numpy.divide>`.
See its docstring for more information.
"""
if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in divide")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.divide(x1._array, x2._array))
def equal(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.equal <numpy.equal>`.
See its docstring for more information.
"""
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.equal(x1._array, x2._array))
def exp(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.exp <numpy.exp>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in exp")
return Array._new(np.exp(x._array))
def expm1(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.expm1 <numpy.expm1>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in expm1")
return Array._new(np.expm1(x._array))
def floor(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.floor <numpy.floor>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in floor")
if x.dtype in _integer_dtypes:
# Note: The return dtype of floor is the same as the input
return x
return Array._new(np.floor(x._array))
def floor_divide(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.floor_divide <numpy.floor_divide>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in floor_divide")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.floor_divide(x1._array, x2._array))
def greater(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.greater <numpy.greater>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in greater")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.greater(x1._array, x2._array))
def greater_equal(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.greater_equal <numpy.greater_equal>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in greater_equal")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.greater_equal(x1._array, x2._array))
def isfinite(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.isfinite <numpy.isfinite>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in isfinite")
return Array._new(np.isfinite(x._array))
def isinf(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.isinf <numpy.isinf>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in isinf")
return Array._new(np.isinf(x._array))
def isnan(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.isnan <numpy.isnan>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in isnan")
return Array._new(np.isnan(x._array))
def less(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.less <numpy.less>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in less")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.less(x1._array, x2._array))
def less_equal(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.less_equal <numpy.less_equal>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in less_equal")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.less_equal(x1._array, x2._array))
def log(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.log <numpy.log>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in log")
return Array._new(np.log(x._array))
def log1p(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.log1p <numpy.log1p>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in log1p")
return Array._new(np.log1p(x._array))
def log2(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.log2 <numpy.log2>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in log2")
return Array._new(np.log2(x._array))
def log10(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.log10 <numpy.log10>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in log10")
return Array._new(np.log10(x._array))
def logaddexp(x1: Array, x2: Array) -> Array:
"""
Array API compatible wrapper for :py:func:`np.logaddexp <numpy.logaddexp>`.
See its docstring for more information.
"""
if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in logaddexp")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.logaddexp(x1._array, x2._array))
def logical_and(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.logical_and <numpy.logical_and>`.
See its docstring for more information.
"""
if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
raise TypeError("Only boolean dtypes are allowed in logical_and")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.logical_and(x1._array, x2._array))
def logical_not(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.logical_not <numpy.logical_not>`.
See its docstring for more information.
"""
if x.dtype not in _boolean_dtypes:
raise TypeError("Only boolean dtypes are allowed in logical_not")
return Array._new(np.logical_not(x._array))
def logical_or(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.logical_or <numpy.logical_or>`.
See its docstring for more information.
"""
if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
raise TypeError("Only boolean dtypes are allowed in logical_or")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.logical_or(x1._array, x2._array))
def logical_xor(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.logical_xor <numpy.logical_xor>`.
See its docstring for more information.
"""
if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
raise TypeError("Only boolean dtypes are allowed in logical_xor")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.logical_xor(x1._array, x2._array))
def multiply(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.multiply <numpy.multiply>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in multiply")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.multiply(x1._array, x2._array))
def negative(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.negative <numpy.negative>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in negative")
return Array._new(np.negative(x._array))
def not_equal(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.not_equal <numpy.not_equal>`.
See its docstring for more information.
"""
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.not_equal(x1._array, x2._array))
def positive(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.positive <numpy.positive>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in positive")
return Array._new(np.positive(x._array))
# Note: the function name is different here
def pow(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.power <numpy.power>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in pow")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.power(x1._array, x2._array))
def remainder(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.remainder <numpy.remainder>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in remainder")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.remainder(x1._array, x2._array))
def round(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.round <numpy.round>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in round")
return Array._new(np.round(x._array))
def sign(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.sign <numpy.sign>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in sign")
return Array._new(np.sign(x._array))
def sin(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.sin <numpy.sin>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in sin")
return Array._new(np.sin(x._array))
def sinh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.sinh <numpy.sinh>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in sinh")
return Array._new(np.sinh(x._array))
def square(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.square <numpy.square>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in square")
return Array._new(np.square(x._array))
def sqrt(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.sqrt <numpy.sqrt>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in sqrt")
return Array._new(np.sqrt(x._array))
def subtract(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.subtract <numpy.subtract>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in subtract")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.subtract(x1._array, x2._array))
def tan(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.tan <numpy.tan>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in tan")
return Array._new(np.tan(x._array))
def tanh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.tanh <numpy.tanh>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in tanh")
return Array._new(np.tanh(x._array))
def trunc(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.trunc <numpy.trunc>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in trunc")
if x.dtype in _integer_dtypes:
# Note: The return dtype of trunc is the same as the input
return x
return Array._new(np.trunc(x._array))

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from __future__ import annotations
from ._array_object import Array
from ._data_type_functions import result_type
from typing import List, Optional, Tuple, Union
import numpy as np
# Note: the function name is different here
def concat(
arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.concatenate <numpy.concatenate>`.
See its docstring for more information.
"""
# Note: Casting rules here are different from the np.concatenate default
# (no for scalars with axis=None, no cross-kind casting)
dtype = result_type(*arrays)
arrays = tuple(a._array for a in arrays)
return Array._new(np.concatenate(arrays, axis=axis, dtype=dtype))
def expand_dims(x: Array, /, *, axis: int) -> Array:
"""
Array API compatible wrapper for :py:func:`np.expand_dims <numpy.expand_dims>`.
See its docstring for more information.
"""
return Array._new(np.expand_dims(x._array, axis))
def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.flip <numpy.flip>`.
See its docstring for more information.
"""
return Array._new(np.flip(x._array, axis=axis))
# Note: The function name is different here (see also matrix_transpose).
# Unlike transpose(), the axes argument is required.
def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
"""
Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.
See its docstring for more information.
"""
return Array._new(np.transpose(x._array, axes))
# Note: the optional argument is called 'shape', not 'newshape'
def reshape(x: Array, /, shape: Tuple[int, ...]) -> Array:
"""
Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.
See its docstring for more information.
"""
return Array._new(np.reshape(x._array, shape))
def roll(
x: Array,
/,
shift: Union[int, Tuple[int, ...]],
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.roll <numpy.roll>`.
See its docstring for more information.
"""
return Array._new(np.roll(x._array, shift, axis=axis))
def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array:
"""
Array API compatible wrapper for :py:func:`np.squeeze <numpy.squeeze>`.
See its docstring for more information.
"""
return Array._new(np.squeeze(x._array, axis=axis))
def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array:
"""
Array API compatible wrapper for :py:func:`np.stack <numpy.stack>`.
See its docstring for more information.
"""
# Call result type here just to raise on disallowed type combinations
result_type(*arrays)
arrays = tuple(a._array for a in arrays)
return Array._new(np.stack(arrays, axis=axis))

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from __future__ import annotations
from ._array_object import Array
from ._dtypes import _result_type
from typing import Optional, Tuple
import numpy as np
def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
"""
Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
See its docstring for more information.
"""
return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
"""
Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
See its docstring for more information.
"""
return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
def nonzero(x: Array, /) -> Tuple[Array, ...]:
"""
Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
See its docstring for more information.
"""
return tuple(Array._new(i) for i in np.nonzero(x._array))
def where(condition: Array, x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
See its docstring for more information.
"""
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.where(condition._array, x1._array, x2._array))

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from __future__ import annotations
from ._array_object import Array
from typing import NamedTuple
import numpy as np
# Note: np.unique() is split into four functions in the array API:
# unique_all, unique_counts, unique_inverse, and unique_values (this is done
# to remove polymorphic return types).
# Note: The various unique() functions are supposed to return multiple NaNs.
# This does not match the NumPy behavior, however, this is currently left as a
# TODO in this implementation as this behavior may be reverted in np.unique().
# See https://github.com/numpy/numpy/issues/20326.
# Note: The functions here return a namedtuple (np.unique() returns a normal
# tuple).
class UniqueAllResult(NamedTuple):
values: Array
indices: Array
inverse_indices: Array
counts: Array
class UniqueCountsResult(NamedTuple):
values: Array
counts: Array
class UniqueInverseResult(NamedTuple):
values: Array
inverse_indices: Array
def unique_all(x: Array, /) -> UniqueAllResult:
"""
Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
See its docstring for more information.
"""
values, indices, inverse_indices, counts = np.unique(
x._array,
return_counts=True,
return_index=True,
return_inverse=True,
equal_nan=False,
)
# np.unique() flattens inverse indices, but they need to share x's shape
# See https://github.com/numpy/numpy/issues/20638
inverse_indices = inverse_indices.reshape(x.shape)
return UniqueAllResult(
Array._new(values),
Array._new(indices),
Array._new(inverse_indices),
Array._new(counts),
)
def unique_counts(x: Array, /) -> UniqueCountsResult:
res = np.unique(
x._array,
return_counts=True,
return_index=False,
return_inverse=False,
equal_nan=False,
)
return UniqueCountsResult(*[Array._new(i) for i in res])
def unique_inverse(x: Array, /) -> UniqueInverseResult:
"""
Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
See its docstring for more information.
"""
values, inverse_indices = np.unique(
x._array,
return_counts=False,
return_index=False,
return_inverse=True,
equal_nan=False,
)
# np.unique() flattens inverse indices, but they need to share x's shape
# See https://github.com/numpy/numpy/issues/20638
inverse_indices = inverse_indices.reshape(x.shape)
return UniqueInverseResult(Array._new(values), Array._new(inverse_indices))
def unique_values(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
See its docstring for more information.
"""
res = np.unique(
x._array,
return_counts=False,
return_index=False,
return_inverse=False,
equal_nan=False,
)
return Array._new(res)

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from __future__ import annotations
from ._array_object import Array
import numpy as np
# Note: the descending keyword argument is new in this function
def argsort(
x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.argsort <numpy.argsort>`.
See its docstring for more information.
"""
# Note: this keyword argument is different, and the default is different.
kind = "stable" if stable else "quicksort"
if not descending:
res = np.argsort(x._array, axis=axis, kind=kind)
else:
# As NumPy has no native descending sort, we imitate it here. Note that
# simply flipping the results of np.argsort(x._array, ...) would not
# respect the relative order like it would in native descending sorts.
res = np.flip(
np.argsort(np.flip(x._array, axis=axis), axis=axis, kind=kind),
axis=axis,
)
# Rely on flip()/argsort() to validate axis
normalised_axis = axis if axis >= 0 else x.ndim + axis
max_i = x.shape[normalised_axis] - 1
res = max_i - res
return Array._new(res)
# Note: the descending keyword argument is new in this function
def sort(
x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.sort <numpy.sort>`.
See its docstring for more information.
"""
# Note: this keyword argument is different, and the default is different.
kind = "stable" if stable else "quicksort"
res = np.sort(x._array, axis=axis, kind=kind)
if descending:
res = np.flip(res, axis=axis)
return Array._new(res)

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from __future__ import annotations
from ._dtypes import (
_floating_dtypes,
_numeric_dtypes,
)
from ._array_object import Array
from ._creation_functions import asarray
from ._dtypes import float32, float64
from typing import TYPE_CHECKING, Optional, Tuple, Union
if TYPE_CHECKING:
from ._typing import Dtype
import numpy as np
def max(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in max")
return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
def mean(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in mean")
return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims))
def min(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in min")
return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
def prod(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype: Optional[Dtype] = None,
keepdims: bool = False,
) -> Array:
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in prod")
# Note: sum() and prod() always upcast float32 to float64 for dtype=None
# We need to do so here before computing the product to avoid overflow
if dtype is None and x.dtype == float32:
dtype = float64
return Array._new(np.prod(x._array, dtype=dtype, axis=axis, keepdims=keepdims))
def std(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
correction: Union[int, float] = 0.0,
keepdims: bool = False,
) -> Array:
# Note: the keyword argument correction is different here
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in std")
return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims))
def sum(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype: Optional[Dtype] = None,
keepdims: bool = False,
) -> Array:
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in sum")
# Note: sum() and prod() always upcast integers to (u)int64 and float32 to
# float64 for dtype=None. `np.sum` does that too for integers, but not for
# float32, so we need to special-case it here
if dtype is None and x.dtype == float32:
dtype = float64
return Array._new(np.sum(x._array, axis=axis, dtype=dtype, keepdims=keepdims))
def var(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
correction: Union[int, float] = 0.0,
keepdims: bool = False,
) -> Array:
# Note: the keyword argument correction is different here
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in var")
return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))

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"""
This file defines the types for type annotations.
These names aren't part of the module namespace, but they are used in the
annotations in the function signatures. The functions in the module are only
valid for inputs that match the given type annotations.
"""
from __future__ import annotations
__all__ = [
"Array",
"Device",
"Dtype",
"SupportsDLPack",
"SupportsBufferProtocol",
"PyCapsule",
]
import sys
from typing import (
Any,
Literal,
Sequence,
Type,
Union,
TYPE_CHECKING,
TypeVar,
Protocol,
)
from ._array_object import Array
from numpy import (
dtype,
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
float32,
float64,
)
_T_co = TypeVar("_T_co", covariant=True)
class NestedSequence(Protocol[_T_co]):
def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ...
def __len__(self, /) -> int: ...
Device = Literal["cpu"]
if TYPE_CHECKING or sys.version_info >= (3, 9):
Dtype = dtype[Union[
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
float32,
float64,
]]
else:
Dtype = dtype
SupportsBufferProtocol = Any
PyCapsule = Any
class SupportsDLPack(Protocol):
def __dlpack__(self, /, *, stream: None = ...) -> PyCapsule: ...

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from __future__ import annotations
from ._array_object import Array
from typing import Optional, Tuple, Union
import numpy as np
def all(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.all <numpy.all>`.
See its docstring for more information.
"""
return Array._new(np.asarray(np.all(x._array, axis=axis, keepdims=keepdims)))
def any(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.any <numpy.any>`.
See its docstring for more information.
"""
return Array._new(np.asarray(np.any(x._array, axis=axis, keepdims=keepdims)))

446
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from __future__ import annotations
from ._dtypes import _floating_dtypes, _numeric_dtypes
from ._manipulation_functions import reshape
from ._array_object import Array
from ..core.numeric import normalize_axis_tuple
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._typing import Literal, Optional, Sequence, Tuple, Union
from typing import NamedTuple
import numpy.linalg
import numpy as np
class EighResult(NamedTuple):
eigenvalues: Array
eigenvectors: Array
class QRResult(NamedTuple):
Q: Array
R: Array
class SlogdetResult(NamedTuple):
sign: Array
logabsdet: Array
class SVDResult(NamedTuple):
U: Array
S: Array
Vh: Array
# Note: the inclusion of the upper keyword is different from
# np.linalg.cholesky, which does not have it.
def cholesky(x: Array, /, *, upper: bool = False) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.cholesky <numpy.linalg.cholesky>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.cholesky.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in cholesky')
L = np.linalg.cholesky(x._array)
if upper:
return Array._new(L).mT
return Array._new(L)
# Note: cross is the numpy top-level namespace, not np.linalg
def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
"""
Array API compatible wrapper for :py:func:`np.cross <numpy.cross>`.
See its docstring for more information.
"""
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in cross')
# Note: this is different from np.cross(), which broadcasts
if x1.shape != x2.shape:
raise ValueError('x1 and x2 must have the same shape')
if x1.ndim == 0:
raise ValueError('cross() requires arrays of dimension at least 1')
# Note: this is different from np.cross(), which allows dimension 2
if x1.shape[axis] != 3:
raise ValueError('cross() dimension must equal 3')
return Array._new(np.cross(x1._array, x2._array, axis=axis))
def det(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.det <numpy.linalg.det>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.det.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in det')
return Array._new(np.linalg.det(x._array))
# Note: diagonal is the numpy top-level namespace, not np.linalg
def diagonal(x: Array, /, *, offset: int = 0) -> Array:
"""
Array API compatible wrapper for :py:func:`np.diagonal <numpy.diagonal>`.
See its docstring for more information.
"""
# Note: diagonal always operates on the last two axes, whereas np.diagonal
# operates on the first two axes by default
return Array._new(np.diagonal(x._array, offset=offset, axis1=-2, axis2=-1))
def eigh(x: Array, /) -> EighResult:
"""
Array API compatible wrapper for :py:func:`np.linalg.eigh <numpy.linalg.eigh>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.eigh.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in eigh')
# Note: the return type here is a namedtuple, which is different from
# np.eigh, which only returns a tuple.
return EighResult(*map(Array._new, np.linalg.eigh(x._array)))
def eigvalsh(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.eigvalsh <numpy.linalg.eigvalsh>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.eigvalsh.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in eigvalsh')
return Array._new(np.linalg.eigvalsh(x._array))
def inv(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.inv <numpy.linalg.inv>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.inv.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in inv')
return Array._new(np.linalg.inv(x._array))
# Note: matmul is the numpy top-level namespace but not in np.linalg
def matmul(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
See its docstring for more information.
"""
# Note: the restriction to numeric dtypes only is different from
# np.matmul.
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in matmul')
return Array._new(np.matmul(x1._array, x2._array))
# Note: the name here is different from norm(). The array API norm is split
# into matrix_norm and vector_norm().
# The type for ord should be Optional[Union[int, float, Literal[np.inf,
# -np.inf, 'fro', 'nuc']]], but Literal does not support floating-point
# literals.
def matrix_norm(x: Array, /, *, keepdims: bool = False, ord: Optional[Union[int, float, Literal['fro', 'nuc']]] = 'fro') -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.norm.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in matrix_norm')
return Array._new(np.linalg.norm(x._array, axis=(-2, -1), keepdims=keepdims, ord=ord))
def matrix_power(x: Array, n: int, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.matrix_power <numpy.matrix_power>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.matrix_power.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed for the first argument of matrix_power')
# np.matrix_power already checks if n is an integer
return Array._new(np.linalg.matrix_power(x._array, n))
# Note: the keyword argument name rtol is different from np.linalg.matrix_rank
def matrix_rank(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.matrix_rank <numpy.matrix_rank>`.
See its docstring for more information.
"""
# Note: this is different from np.linalg.matrix_rank, which supports 1
# dimensional arrays.
if x.ndim < 2:
raise np.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
S = np.linalg.svd(x._array, compute_uv=False)
if rtol is None:
tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * np.finfo(S.dtype).eps
else:
if isinstance(rtol, Array):
rtol = rtol._array
# Note: this is different from np.linalg.matrix_rank, which does not multiply
# the tolerance by the largest singular value.
tol = S.max(axis=-1, keepdims=True)*np.asarray(rtol)[..., np.newaxis]
return Array._new(np.count_nonzero(S > tol, axis=-1))
# Note: this function is new in the array API spec. Unlike transpose, it only
# transposes the last two axes.
def matrix_transpose(x: Array, /) -> Array:
if x.ndim < 2:
raise ValueError("x must be at least 2-dimensional for matrix_transpose")
return Array._new(np.swapaxes(x._array, -1, -2))
# Note: outer is the numpy top-level namespace, not np.linalg
def outer(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.outer <numpy.outer>`.
See its docstring for more information.
"""
# Note: the restriction to numeric dtypes only is different from
# np.outer.
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in outer')
# Note: the restriction to only 1-dim arrays is different from np.outer
if x1.ndim != 1 or x2.ndim != 1:
raise ValueError('The input arrays to outer must be 1-dimensional')
return Array._new(np.outer(x1._array, x2._array))
# Note: the keyword argument name rtol is different from np.linalg.pinv
def pinv(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.pinv <numpy.linalg.pinv>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.pinv.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in pinv')
# Note: this is different from np.linalg.pinv, which does not multiply the
# default tolerance by max(M, N).
if rtol is None:
rtol = max(x.shape[-2:]) * np.finfo(x.dtype).eps
return Array._new(np.linalg.pinv(x._array, rcond=rtol))
def qr(x: Array, /, *, mode: Literal['reduced', 'complete'] = 'reduced') -> QRResult:
"""
Array API compatible wrapper for :py:func:`np.linalg.qr <numpy.linalg.qr>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.qr.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in qr')
# Note: the return type here is a namedtuple, which is different from
# np.linalg.qr, which only returns a tuple.
return QRResult(*map(Array._new, np.linalg.qr(x._array, mode=mode)))
def slogdet(x: Array, /) -> SlogdetResult:
"""
Array API compatible wrapper for :py:func:`np.linalg.slogdet <numpy.linalg.slogdet>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.slogdet.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in slogdet')
# Note: the return type here is a namedtuple, which is different from
# np.linalg.slogdet, which only returns a tuple.
return SlogdetResult(*map(Array._new, np.linalg.slogdet(x._array)))
# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
# of matrices. The np.linalg.solve behavior of allowing stacks of both
# matrices and vectors is ambiguous c.f.
# https://github.com/numpy/numpy/issues/15349 and
# https://github.com/data-apis/array-api/issues/285.
# To workaround this, the below is the code from np.linalg.solve except
# only calling solve1 in the exactly 1D case.
def _solve(a, b):
from ..linalg.linalg import (_makearray, _assert_stacked_2d,
_assert_stacked_square, _commonType,
isComplexType, get_linalg_error_extobj,
_raise_linalgerror_singular)
from ..linalg import _umath_linalg
a, _ = _makearray(a)
_assert_stacked_2d(a)
_assert_stacked_square(a)
b, wrap = _makearray(b)
t, result_t = _commonType(a, b)
# This part is different from np.linalg.solve
if b.ndim == 1:
gufunc = _umath_linalg.solve1
else:
gufunc = _umath_linalg.solve
# This does nothing currently but is left in because it will be relevant
# when complex dtype support is added to the spec in 2022.
signature = 'DD->D' if isComplexType(t) else 'dd->d'
extobj = get_linalg_error_extobj(_raise_linalgerror_singular)
r = gufunc(a, b, signature=signature, extobj=extobj)
return wrap(r.astype(result_t, copy=False))
def solve(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.solve <numpy.linalg.solve>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.solve.
if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in solve')
return Array._new(_solve(x1._array, x2._array))
def svd(x: Array, /, *, full_matrices: bool = True) -> SVDResult:
"""
Array API compatible wrapper for :py:func:`np.linalg.svd <numpy.linalg.svd>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.svd.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in svd')
# Note: the return type here is a namedtuple, which is different from
# np.svd, which only returns a tuple.
return SVDResult(*map(Array._new, np.linalg.svd(x._array, full_matrices=full_matrices)))
# Note: svdvals is not in NumPy (but it is in SciPy). It is equivalent to
# np.linalg.svd(compute_uv=False).
def svdvals(x: Array, /) -> Union[Array, Tuple[Array, ...]]:
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in svdvals')
return Array._new(np.linalg.svd(x._array, compute_uv=False))
# Note: tensordot is the numpy top-level namespace but not in np.linalg
# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
# Note: the restriction to numeric dtypes only is different from
# np.tensordot.
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in tensordot')
return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
# Note: trace is the numpy top-level namespace, not np.linalg
def trace(x: Array, /, *, offset: int = 0) -> Array:
"""
Array API compatible wrapper for :py:func:`np.trace <numpy.trace>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in trace')
# Note: trace always operates on the last two axes, whereas np.trace
# operates on the first two axes by default
return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1)))
# Note: vecdot is not in NumPy
def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in vecdot')
ndim = max(x1.ndim, x2.ndim)
x1_shape = (1,)*(ndim - x1.ndim) + tuple(x1.shape)
x2_shape = (1,)*(ndim - x2.ndim) + tuple(x2.shape)
if x1_shape[axis] != x2_shape[axis]:
raise ValueError("x1 and x2 must have the same size along the given axis")
x1_, x2_ = np.broadcast_arrays(x1._array, x2._array)
x1_ = np.moveaxis(x1_, axis, -1)
x2_ = np.moveaxis(x2_, axis, -1)
res = x1_[..., None, :] @ x2_[..., None]
return Array._new(res[..., 0, 0])
# Note: the name here is different from norm(). The array API norm is split
# into matrix_norm and vector_norm().
# The type for ord should be Optional[Union[int, float, Literal[np.inf,
# -np.inf]]] but Literal does not support floating-point literals.
def vector_norm(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ord: Optional[Union[int, float]] = 2) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
See its docstring for more information.
"""
# Note: the restriction to floating-point dtypes only is different from
# np.linalg.norm.
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in norm')
# np.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or
# when axis=None and the input is 2-D, so to force a vector norm, we make
# it so the input is 1-D (for axis=None), or reshape so that norm is done
# on a single dimension.
a = x._array
if axis is None:
# Note: np.linalg.norm() doesn't handle 0-D arrays
a = a.ravel()
_axis = 0
elif isinstance(axis, tuple):
# Note: The axis argument supports any number of axes, whereas
# np.linalg.norm() only supports a single axis for vector norm.
normalized_axis = normalize_axis_tuple(axis, x.ndim)
rest = tuple(i for i in range(a.ndim) if i not in normalized_axis)
newshape = axis + rest
a = np.transpose(a, newshape).reshape(
(np.prod([a.shape[i] for i in axis], dtype=int), *[a.shape[i] for i in rest]))
_axis = 0
else:
_axis = axis
res = Array._new(np.linalg.norm(a, axis=_axis, ord=ord))
if keepdims:
# We can't reuse np.linalg.norm(keepdims) because of the reshape hacks
# above to avoid matrix norm logic.
shape = list(x.shape)
_axis = normalize_axis_tuple(range(x.ndim) if axis is None else axis, x.ndim)
for i in _axis:
shape[i] = 1
res = reshape(res, tuple(shape))
return res
__all__ = ['cholesky', 'cross', 'det', 'diagonal', 'eigh', 'eigvalsh', 'inv', 'matmul', 'matrix_norm', 'matrix_power', 'matrix_rank', 'matrix_transpose', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'svdvals', 'tensordot', 'trace', 'vecdot', 'vector_norm']

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def configuration(parent_package="", top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration("array_api", parent_package, top_path)
config.add_subpackage("tests")
return config
if __name__ == "__main__":
from numpy.distutils.core import setup
setup(configuration=configuration)

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"""
Tests for the array API namespace.
Note, full compliance with the array API can be tested with the official array API test
suite https://github.com/data-apis/array-api-tests. This test suite primarily
focuses on those things that are not tested by the official test suite.
"""

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import operator
from numpy.testing import assert_raises
import numpy as np
import pytest
from .. import ones, asarray, reshape, result_type, all, equal
from .._array_object import Array
from .._dtypes import (
_all_dtypes,
_boolean_dtypes,
_floating_dtypes,
_integer_dtypes,
_integer_or_boolean_dtypes,
_numeric_dtypes,
int8,
int16,
int32,
int64,
uint64,
bool as bool_,
)
def test_validate_index():
# The indexing tests in the official array API test suite test that the
# array object correctly handles the subset of indices that are required
# by the spec. But the NumPy array API implementation specifically
# disallows any index not required by the spec, via Array._validate_index.
# This test focuses on testing that non-valid indices are correctly
# rejected. See
# https://data-apis.org/array-api/latest/API_specification/indexing.html
# and the docstring of Array._validate_index for the exact indexing
# behavior that should be allowed. This does not test indices that are
# already invalid in NumPy itself because Array will generally just pass
# such indices directly to the underlying np.ndarray.
a = ones((3, 4))
# Out of bounds slices are not allowed
assert_raises(IndexError, lambda: a[:4])
assert_raises(IndexError, lambda: a[:-4])
assert_raises(IndexError, lambda: a[:3:-1])
assert_raises(IndexError, lambda: a[:-5:-1])
assert_raises(IndexError, lambda: a[4:])
assert_raises(IndexError, lambda: a[-4:])
assert_raises(IndexError, lambda: a[4::-1])
assert_raises(IndexError, lambda: a[-4::-1])
assert_raises(IndexError, lambda: a[...,:5])
assert_raises(IndexError, lambda: a[...,:-5])
assert_raises(IndexError, lambda: a[...,:5:-1])
assert_raises(IndexError, lambda: a[...,:-6:-1])
assert_raises(IndexError, lambda: a[...,5:])
assert_raises(IndexError, lambda: a[...,-5:])
assert_raises(IndexError, lambda: a[...,5::-1])
assert_raises(IndexError, lambda: a[...,-5::-1])
# Boolean indices cannot be part of a larger tuple index
assert_raises(IndexError, lambda: a[a[:,0]==1,0])
assert_raises(IndexError, lambda: a[a[:,0]==1,...])
assert_raises(IndexError, lambda: a[..., a[0]==1])
assert_raises(IndexError, lambda: a[[True, True, True]])
assert_raises(IndexError, lambda: a[(True, True, True),])
# Integer array indices are not allowed (except for 0-D)
idx = asarray([[0, 1]])
assert_raises(IndexError, lambda: a[idx])
assert_raises(IndexError, lambda: a[idx,])
assert_raises(IndexError, lambda: a[[0, 1]])
assert_raises(IndexError, lambda: a[(0, 1), (0, 1)])
assert_raises(IndexError, lambda: a[[0, 1]])
assert_raises(IndexError, lambda: a[np.array([[0, 1]])])
# Multiaxis indices must contain exactly as many indices as dimensions
assert_raises(IndexError, lambda: a[()])
assert_raises(IndexError, lambda: a[0,])
assert_raises(IndexError, lambda: a[0])
assert_raises(IndexError, lambda: a[:])
def test_operators():
# For every operator, we test that it works for the required type
# combinations and raises TypeError otherwise
binary_op_dtypes = {
"__add__": "numeric",
"__and__": "integer_or_boolean",
"__eq__": "all",
"__floordiv__": "numeric",
"__ge__": "numeric",
"__gt__": "numeric",
"__le__": "numeric",
"__lshift__": "integer",
"__lt__": "numeric",
"__mod__": "numeric",
"__mul__": "numeric",
"__ne__": "all",
"__or__": "integer_or_boolean",
"__pow__": "numeric",
"__rshift__": "integer",
"__sub__": "numeric",
"__truediv__": "floating",
"__xor__": "integer_or_boolean",
}
# Recompute each time because of in-place ops
def _array_vals():
for d in _integer_dtypes:
yield asarray(1, dtype=d)
for d in _boolean_dtypes:
yield asarray(False, dtype=d)
for d in _floating_dtypes:
yield asarray(1.0, dtype=d)
for op, dtypes in binary_op_dtypes.items():
ops = [op]
if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]:
rop = "__r" + op[2:]
iop = "__i" + op[2:]
ops += [rop, iop]
for s in [1, 1.0, False]:
for _op in ops:
for a in _array_vals():
# Test array op scalar. From the spec, the following combinations
# are supported:
# - Python bool for a bool array dtype,
# - a Python int within the bounds of the given dtype for integer array dtypes,
# - a Python int or float for floating-point array dtypes
# We do not do bounds checking for int scalars, but rather use the default
# NumPy behavior for casting in that case.
if ((dtypes == "all"
or dtypes == "numeric" and a.dtype in _numeric_dtypes
or dtypes == "integer" and a.dtype in _integer_dtypes
or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
or dtypes == "boolean" and a.dtype in _boolean_dtypes
or dtypes == "floating" and a.dtype in _floating_dtypes
)
# bool is a subtype of int, which is why we avoid
# isinstance here.
and (a.dtype in _boolean_dtypes and type(s) == bool
or a.dtype in _integer_dtypes and type(s) == int
or a.dtype in _floating_dtypes and type(s) in [float, int]
)):
# Only test for no error
getattr(a, _op)(s)
else:
assert_raises(TypeError, lambda: getattr(a, _op)(s))
# Test array op array.
for _op in ops:
for x in _array_vals():
for y in _array_vals():
# See the promotion table in NEP 47 or the array
# API spec page on type promotion. Mixed kind
# promotion is not defined.
if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes
or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes
or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
):
assert_raises(TypeError, lambda: getattr(x, _op)(y))
# Ensure in-place operators only promote to the same dtype as the left operand.
elif (
_op.startswith("__i")
and result_type(x.dtype, y.dtype) != x.dtype
):
assert_raises(TypeError, lambda: getattr(x, _op)(y))
# Ensure only those dtypes that are required for every operator are allowed.
elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _numeric_dtypes
or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes
):
getattr(x, _op)(y)
else:
assert_raises(TypeError, lambda: getattr(x, _op)(y))
unary_op_dtypes = {
"__abs__": "numeric",
"__invert__": "integer_or_boolean",
"__neg__": "numeric",
"__pos__": "numeric",
}
for op, dtypes in unary_op_dtypes.items():
for a in _array_vals():
if (
dtypes == "numeric"
and a.dtype in _numeric_dtypes
or dtypes == "integer_or_boolean"
and a.dtype in _integer_or_boolean_dtypes
):
# Only test for no error
getattr(a, op)()
else:
assert_raises(TypeError, lambda: getattr(a, op)())
# Finally, matmul() must be tested separately, because it works a bit
# different from the other operations.
def _matmul_array_vals():
for a in _array_vals():
yield a
for d in _all_dtypes:
yield ones((3, 4), dtype=d)
yield ones((4, 2), dtype=d)
yield ones((4, 4), dtype=d)
# Scalars always error
for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]:
for s in [1, 1.0, False]:
for a in _matmul_array_vals():
if (type(s) in [float, int] and a.dtype in _floating_dtypes
or type(s) == int and a.dtype in _integer_dtypes):
# Type promotion is valid, but @ is not allowed on 0-D
# inputs, so the error is a ValueError
assert_raises(ValueError, lambda: getattr(a, _op)(s))
else:
assert_raises(TypeError, lambda: getattr(a, _op)(s))
for x in _matmul_array_vals():
for y in _matmul_array_vals():
if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
or x.dtype in _boolean_dtypes
or y.dtype in _boolean_dtypes
):
assert_raises(TypeError, lambda: x.__matmul__(y))
assert_raises(TypeError, lambda: y.__rmatmul__(x))
assert_raises(TypeError, lambda: x.__imatmul__(y))
elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]:
assert_raises(ValueError, lambda: x.__matmul__(y))
assert_raises(ValueError, lambda: y.__rmatmul__(x))
if result_type(x.dtype, y.dtype) != x.dtype:
assert_raises(TypeError, lambda: x.__imatmul__(y))
else:
assert_raises(ValueError, lambda: x.__imatmul__(y))
else:
x.__matmul__(y)
y.__rmatmul__(x)
if result_type(x.dtype, y.dtype) != x.dtype:
assert_raises(TypeError, lambda: x.__imatmul__(y))
elif y.shape[0] != y.shape[1]:
# This one fails because x @ y has a different shape from x
assert_raises(ValueError, lambda: x.__imatmul__(y))
else:
x.__imatmul__(y)
def test_python_scalar_construtors():
b = asarray(False)
i = asarray(0)
f = asarray(0.0)
assert bool(b) == False
assert int(i) == 0
assert float(f) == 0.0
assert operator.index(i) == 0
# bool/int/float should only be allowed on 0-D arrays.
assert_raises(TypeError, lambda: bool(asarray([False])))
assert_raises(TypeError, lambda: int(asarray([0])))
assert_raises(TypeError, lambda: float(asarray([0.0])))
assert_raises(TypeError, lambda: operator.index(asarray([0])))
# bool/int/float should only be allowed on arrays of the corresponding
# dtype
assert_raises(ValueError, lambda: bool(i))
assert_raises(ValueError, lambda: bool(f))
assert_raises(ValueError, lambda: int(b))
assert_raises(ValueError, lambda: int(f))
assert_raises(ValueError, lambda: float(b))
assert_raises(ValueError, lambda: float(i))
assert_raises(TypeError, lambda: operator.index(b))
assert_raises(TypeError, lambda: operator.index(f))
def test_device_property():
a = ones((3, 4))
assert a.device == 'cpu'
assert all(equal(a.to_device('cpu'), a))
assert_raises(ValueError, lambda: a.to_device('gpu'))
assert all(equal(asarray(a, device='cpu'), a))
assert_raises(ValueError, lambda: asarray(a, device='gpu'))
def test_array_properties():
a = ones((1, 2, 3))
b = ones((2, 3))
assert_raises(ValueError, lambda: a.T)
assert isinstance(b.T, Array)
assert b.T.shape == (3, 2)
assert isinstance(a.mT, Array)
assert a.mT.shape == (1, 3, 2)
assert isinstance(b.mT, Array)
assert b.mT.shape == (3, 2)
def test___array__():
a = ones((2, 3), dtype=int16)
assert np.asarray(a) is a._array
b = np.asarray(a, dtype=np.float64)
assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
assert b.dtype == np.float64
def test_allow_newaxis():
a = ones(5)
indexed_a = a[None, :]
assert indexed_a.shape == (1, 5)
def test_disallow_flat_indexing_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, 0, 0]
def test_disallow_mask_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, asarray(True)]
@pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)])
@pytest.mark.parametrize("index", ["string", False, True])
def test_error_on_invalid_index(shape, index):
a = ones(shape)
with pytest.raises(IndexError):
a[index]
def test_mask_0d_array_without_errors():
a = ones(())
a[asarray(True)]
@pytest.mark.parametrize(
"i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])]
)
def test_error_on_invalid_index_with_ellipsis(i):
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[..., i]
with pytest.raises(IndexError):
a[i, ...]
def test_array_keys_use_private_array():
"""
Indexing operations convert array keys before indexing the internal array
Fails when array_api array keys are not converted into NumPy-proper arrays
in __getitem__(). This is achieved by passing array_api arrays with 0-sized
dimensions, which NumPy-proper treats erroneously - not sure why!
TODO: Find and use appropriate __setitem__() case.
"""
a = ones((0, 0), dtype=bool_)
assert a[a].shape == (0,)
a = ones((0,), dtype=bool_)
key = ones((0, 0), dtype=bool_)
with pytest.raises(IndexError):
a[key]

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from numpy.testing import assert_raises
import numpy as np
from .. import all
from .._creation_functions import (
asarray,
arange,
empty,
empty_like,
eye,
full,
full_like,
linspace,
meshgrid,
ones,
ones_like,
zeros,
zeros_like,
)
from .._dtypes import float32, float64
from .._array_object import Array
def test_asarray_errors():
# Test various protections against incorrect usage
assert_raises(TypeError, lambda: Array([1]))
assert_raises(TypeError, lambda: asarray(["a"]))
assert_raises(ValueError, lambda: asarray([1.0], dtype=np.float16))
assert_raises(OverflowError, lambda: asarray(2**100))
# Preferably this would be OverflowError
# assert_raises(OverflowError, lambda: asarray([2**100]))
assert_raises(TypeError, lambda: asarray([2**100]))
asarray([1], device="cpu") # Doesn't error
assert_raises(ValueError, lambda: asarray([1], device="gpu"))
assert_raises(ValueError, lambda: asarray([1], dtype=int))
assert_raises(ValueError, lambda: asarray([1], dtype="i"))
def test_asarray_copy():
a = asarray([1])
b = asarray(a, copy=True)
a[0] = 0
assert all(b[0] == 1)
assert all(a[0] == 0)
a = asarray([1])
b = asarray(a, copy=np._CopyMode.ALWAYS)
a[0] = 0
assert all(b[0] == 1)
assert all(a[0] == 0)
a = asarray([1])
b = asarray(a, copy=np._CopyMode.NEVER)
a[0] = 0
assert all(b[0] == 0)
assert_raises(NotImplementedError, lambda: asarray(a, copy=False))
assert_raises(NotImplementedError,
lambda: asarray(a, copy=np._CopyMode.IF_NEEDED))
def test_arange_errors():
arange(1, device="cpu") # Doesn't error
assert_raises(ValueError, lambda: arange(1, device="gpu"))
assert_raises(ValueError, lambda: arange(1, dtype=int))
assert_raises(ValueError, lambda: arange(1, dtype="i"))
def test_empty_errors():
empty((1,), device="cpu") # Doesn't error
assert_raises(ValueError, lambda: empty((1,), device="gpu"))
assert_raises(ValueError, lambda: empty((1,), dtype=int))
assert_raises(ValueError, lambda: empty((1,), dtype="i"))
def test_empty_like_errors():
empty_like(asarray(1), device="cpu") # Doesn't error
assert_raises(ValueError, lambda: empty_like(asarray(1), device="gpu"))
assert_raises(ValueError, lambda: empty_like(asarray(1), dtype=int))
assert_raises(ValueError, lambda: empty_like(asarray(1), dtype="i"))
def test_eye_errors():
eye(1, device="cpu") # Doesn't error
assert_raises(ValueError, lambda: eye(1, device="gpu"))
assert_raises(ValueError, lambda: eye(1, dtype=int))
assert_raises(ValueError, lambda: eye(1, dtype="i"))
def test_full_errors():
full((1,), 0, device="cpu") # Doesn't error
assert_raises(ValueError, lambda: full((1,), 0, device="gpu"))
assert_raises(ValueError, lambda: full((1,), 0, dtype=int))
assert_raises(ValueError, lambda: full((1,), 0, dtype="i"))
def test_full_like_errors():
full_like(asarray(1), 0, device="cpu") # Doesn't error
assert_raises(ValueError, lambda: full_like(asarray(1), 0, device="gpu"))
assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype=int))
assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype="i"))
def test_linspace_errors():
linspace(0, 1, 10, device="cpu") # Doesn't error
assert_raises(ValueError, lambda: linspace(0, 1, 10, device="gpu"))
assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype=float))
assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype="f"))
def test_ones_errors():
ones((1,), device="cpu") # Doesn't error
assert_raises(ValueError, lambda: ones((1,), device="gpu"))
assert_raises(ValueError, lambda: ones((1,), dtype=int))
assert_raises(ValueError, lambda: ones((1,), dtype="i"))
def test_ones_like_errors():
ones_like(asarray(1), device="cpu") # Doesn't error
assert_raises(ValueError, lambda: ones_like(asarray(1), device="gpu"))
assert_raises(ValueError, lambda: ones_like(asarray(1), dtype=int))
assert_raises(ValueError, lambda: ones_like(asarray(1), dtype="i"))
def test_zeros_errors():
zeros((1,), device="cpu") # Doesn't error
assert_raises(ValueError, lambda: zeros((1,), device="gpu"))
assert_raises(ValueError, lambda: zeros((1,), dtype=int))
assert_raises(ValueError, lambda: zeros((1,), dtype="i"))
def test_zeros_like_errors():
zeros_like(asarray(1), device="cpu") # Doesn't error
assert_raises(ValueError, lambda: zeros_like(asarray(1), device="gpu"))
assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype=int))
assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype="i"))
def test_meshgrid_dtype_errors():
# Doesn't raise
meshgrid()
meshgrid(asarray([1.], dtype=float32))
meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float32))
assert_raises(ValueError, lambda: meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float64)))

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import pytest
from numpy import array_api as xp
@pytest.mark.parametrize(
"from_, to, expected",
[
(xp.int8, xp.int16, True),
(xp.int16, xp.int8, False),
(xp.bool, xp.int8, False),
(xp.asarray(0, dtype=xp.uint8), xp.int8, False),
],
)
def test_can_cast(from_, to, expected):
"""
can_cast() returns correct result
"""
assert xp.can_cast(from_, to) == expected

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from inspect import getfullargspec
from numpy.testing import assert_raises
from .. import asarray, _elementwise_functions
from .._elementwise_functions import bitwise_left_shift, bitwise_right_shift
from .._dtypes import (
_dtype_categories,
_boolean_dtypes,
_floating_dtypes,
_integer_dtypes,
)
def nargs(func):
return len(getfullargspec(func).args)
def test_function_types():
# Test that every function accepts only the required input types. We only
# test the negative cases here (error). The positive cases are tested in
# the array API test suite.
elementwise_function_input_types = {
"abs": "numeric",
"acos": "floating-point",
"acosh": "floating-point",
"add": "numeric",
"asin": "floating-point",
"asinh": "floating-point",
"atan": "floating-point",
"atan2": "floating-point",
"atanh": "floating-point",
"bitwise_and": "integer or boolean",
"bitwise_invert": "integer or boolean",
"bitwise_left_shift": "integer",
"bitwise_or": "integer or boolean",
"bitwise_right_shift": "integer",
"bitwise_xor": "integer or boolean",
"ceil": "numeric",
"cos": "floating-point",
"cosh": "floating-point",
"divide": "floating-point",
"equal": "all",
"exp": "floating-point",
"expm1": "floating-point",
"floor": "numeric",
"floor_divide": "numeric",
"greater": "numeric",
"greater_equal": "numeric",
"isfinite": "numeric",
"isinf": "numeric",
"isnan": "numeric",
"less": "numeric",
"less_equal": "numeric",
"log": "floating-point",
"logaddexp": "floating-point",
"log10": "floating-point",
"log1p": "floating-point",
"log2": "floating-point",
"logical_and": "boolean",
"logical_not": "boolean",
"logical_or": "boolean",
"logical_xor": "boolean",
"multiply": "numeric",
"negative": "numeric",
"not_equal": "all",
"positive": "numeric",
"pow": "numeric",
"remainder": "numeric",
"round": "numeric",
"sign": "numeric",
"sin": "floating-point",
"sinh": "floating-point",
"sqrt": "floating-point",
"square": "numeric",
"subtract": "numeric",
"tan": "floating-point",
"tanh": "floating-point",
"trunc": "numeric",
}
def _array_vals():
for d in _integer_dtypes:
yield asarray(1, dtype=d)
for d in _boolean_dtypes:
yield asarray(False, dtype=d)
for d in _floating_dtypes:
yield asarray(1.0, dtype=d)
for x in _array_vals():
for func_name, types in elementwise_function_input_types.items():
dtypes = _dtype_categories[types]
func = getattr(_elementwise_functions, func_name)
if nargs(func) == 2:
for y in _array_vals():
if x.dtype not in dtypes or y.dtype not in dtypes:
assert_raises(TypeError, lambda: func(x, y))
else:
if x.dtype not in dtypes:
assert_raises(TypeError, lambda: func(x))
def test_bitwise_shift_error():
# bitwise shift functions should raise when the second argument is negative
assert_raises(
ValueError, lambda: bitwise_left_shift(asarray([1, 1]), asarray([1, -1]))
)
assert_raises(
ValueError, lambda: bitwise_right_shift(asarray([1, 1]), asarray([1, -1]))
)

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import pytest
from hypothesis import given
from hypothesis.extra.array_api import make_strategies_namespace
from numpy import array_api as xp
xps = make_strategies_namespace(xp)
@pytest.mark.parametrize("func", [xp.unique_all, xp.unique_inverse])
@given(xps.arrays(dtype=xps.scalar_dtypes(), shape=xps.array_shapes()))
def test_inverse_indices_shape(func, x):
"""
Inverse indices share shape of input array
See https://github.com/numpy/numpy/issues/20638
"""
out = func(x)
assert out.inverse_indices.shape == x.shape

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import pytest
from numpy import array_api as xp
@pytest.mark.parametrize(
"obj, axis, expected",
[
([0, 0], -1, [0, 1]),
([0, 1, 0], -1, [1, 0, 2]),
([[0, 1], [1, 1]], 0, [[1, 0], [0, 1]]),
([[0, 1], [1, 1]], 1, [[1, 0], [0, 1]]),
],
)
def test_stable_desc_argsort(obj, axis, expected):
"""
Indices respect relative order of a descending stable-sort
See https://github.com/numpy/numpy/issues/20778
"""
x = xp.asarray(obj)
out = xp.argsort(x, axis=axis, stable=True, descending=True)
assert xp.all(out == xp.asarray(expected))

27
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from typing import Callable
import pytest
from numpy import array_api as xp
def p(func: Callable, *args, **kwargs):
f_sig = ", ".join(
[str(a) for a in args] + [f"{k}={v}" for k, v in kwargs.items()]
)
id_ = f"{func.__name__}({f_sig})"
return pytest.param(func, args, kwargs, id=id_)
@pytest.mark.parametrize(
"func, args, kwargs",
[
p(xp.can_cast, 42, xp.int8),
p(xp.can_cast, xp.int8, 42),
p(xp.result_type, 42),
],
)
def test_raises_on_invalid_types(func, args, kwargs):
"""Function raises TypeError when passed invalidly-typed inputs"""
with pytest.raises(TypeError):
func(*args, **kwargs)

18
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"""
Compatibility module.
This module contains duplicated code from Python itself or 3rd party
extensions, which may be included for the following reasons:
* compatibility
* we may only need a small subset of the copied library/module
"""
from . import _inspect
from . import py3k
from ._inspect import getargspec, formatargspec
from .py3k import *
__all__ = []
__all__.extend(_inspect.__all__)
__all__.extend(py3k.__all__)

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

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"""Utility to compare pep440 compatible version strings.
The LooseVersion and StrictVersion classes that distutils provides don't
work; they don't recognize anything like alpha/beta/rc/dev versions.
"""
# Copyright (c) Donald Stufft and individual contributors.
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import collections
import itertools
import re
__all__ = [
"parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN",
]
# BEGIN packaging/_structures.py
class Infinity:
def __repr__(self):
return "Infinity"
def __hash__(self):
return hash(repr(self))
def __lt__(self, other):
return False
def __le__(self, other):
return False
def __eq__(self, other):
return isinstance(other, self.__class__)
def __ne__(self, other):
return not isinstance(other, self.__class__)
def __gt__(self, other):
return True
def __ge__(self, other):
return True
def __neg__(self):
return NegativeInfinity
Infinity = Infinity()
class NegativeInfinity:
def __repr__(self):
return "-Infinity"
def __hash__(self):
return hash(repr(self))
def __lt__(self, other):
return True
def __le__(self, other):
return True
def __eq__(self, other):
return isinstance(other, self.__class__)
def __ne__(self, other):
return not isinstance(other, self.__class__)
def __gt__(self, other):
return False
def __ge__(self, other):
return False
def __neg__(self):
return Infinity
# BEGIN packaging/version.py
NegativeInfinity = NegativeInfinity()
_Version = collections.namedtuple(
"_Version",
["epoch", "release", "dev", "pre", "post", "local"],
)
def parse(version):
"""
Parse the given version string and return either a :class:`Version` object
or a :class:`LegacyVersion` object depending on if the given version is
a valid PEP 440 version or a legacy version.
"""
try:
return Version(version)
except InvalidVersion:
return LegacyVersion(version)
class InvalidVersion(ValueError):
"""
An invalid version was found, users should refer to PEP 440.
"""
class _BaseVersion:
def __hash__(self):
return hash(self._key)
def __lt__(self, other):
return self._compare(other, lambda s, o: s < o)
def __le__(self, other):
return self._compare(other, lambda s, o: s <= o)
def __eq__(self, other):
return self._compare(other, lambda s, o: s == o)
def __ge__(self, other):
return self._compare(other, lambda s, o: s >= o)
def __gt__(self, other):
return self._compare(other, lambda s, o: s > o)
def __ne__(self, other):
return self._compare(other, lambda s, o: s != o)
def _compare(self, other, method):
if not isinstance(other, _BaseVersion):
return NotImplemented
return method(self._key, other._key)
class LegacyVersion(_BaseVersion):
def __init__(self, version):
self._version = str(version)
self._key = _legacy_cmpkey(self._version)
def __str__(self):
return self._version
def __repr__(self):
return "<LegacyVersion({0})>".format(repr(str(self)))
@property
def public(self):
return self._version
@property
def base_version(self):
return self._version
@property
def local(self):
return None
@property
def is_prerelease(self):
return False
@property
def is_postrelease(self):
return False
_legacy_version_component_re = re.compile(
r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE,
)
_legacy_version_replacement_map = {
"pre": "c", "preview": "c", "-": "final-", "rc": "c", "dev": "@",
}
def _parse_version_parts(s):
for part in _legacy_version_component_re.split(s):
part = _legacy_version_replacement_map.get(part, part)
if not part or part == ".":
continue
if part[:1] in "0123456789":
# pad for numeric comparison
yield part.zfill(8)
else:
yield "*" + part
# ensure that alpha/beta/candidate are before final
yield "*final"
def _legacy_cmpkey(version):
# We hardcode an epoch of -1 here. A PEP 440 version can only have an epoch
# greater than or equal to 0. This will effectively put the LegacyVersion,
# which uses the defacto standard originally implemented by setuptools,
# as before all PEP 440 versions.
epoch = -1
# This scheme is taken from pkg_resources.parse_version setuptools prior to
# its adoption of the packaging library.
parts = []
for part in _parse_version_parts(version.lower()):
if part.startswith("*"):
# remove "-" before a prerelease tag
if part < "*final":
while parts and parts[-1] == "*final-":
parts.pop()
# remove trailing zeros from each series of numeric parts
while parts and parts[-1] == "00000000":
parts.pop()
parts.append(part)
parts = tuple(parts)
return epoch, parts
# Deliberately not anchored to the start and end of the string, to make it
# easier for 3rd party code to reuse
VERSION_PATTERN = r"""
v?
(?:
(?:(?P<epoch>[0-9]+)!)? # epoch
(?P<release>[0-9]+(?:\.[0-9]+)*) # release segment
(?P<pre> # pre-release
[-_\.]?
(?P<pre_l>(a|b|c|rc|alpha|beta|pre|preview))
[-_\.]?
(?P<pre_n>[0-9]+)?
)?
(?P<post> # post release
(?:-(?P<post_n1>[0-9]+))
|
(?:
[-_\.]?
(?P<post_l>post|rev|r)
[-_\.]?
(?P<post_n2>[0-9]+)?
)
)?
(?P<dev> # dev release
[-_\.]?
(?P<dev_l>dev)
[-_\.]?
(?P<dev_n>[0-9]+)?
)?
)
(?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))? # local version
"""
class Version(_BaseVersion):
_regex = re.compile(
r"^\s*" + VERSION_PATTERN + r"\s*$",
re.VERBOSE | re.IGNORECASE,
)
def __init__(self, version):
# Validate the version and parse it into pieces
match = self._regex.search(version)
if not match:
raise InvalidVersion("Invalid version: '{0}'".format(version))
# Store the parsed out pieces of the version
self._version = _Version(
epoch=int(match.group("epoch")) if match.group("epoch") else 0,
release=tuple(int(i) for i in match.group("release").split(".")),
pre=_parse_letter_version(
match.group("pre_l"),
match.group("pre_n"),
),
post=_parse_letter_version(
match.group("post_l"),
match.group("post_n1") or match.group("post_n2"),
),
dev=_parse_letter_version(
match.group("dev_l"),
match.group("dev_n"),
),
local=_parse_local_version(match.group("local")),
)
# Generate a key which will be used for sorting
self._key = _cmpkey(
self._version.epoch,
self._version.release,
self._version.pre,
self._version.post,
self._version.dev,
self._version.local,
)
def __repr__(self):
return "<Version({0})>".format(repr(str(self)))
def __str__(self):
parts = []
# Epoch
if self._version.epoch != 0:
parts.append("{0}!".format(self._version.epoch))
# Release segment
parts.append(".".join(str(x) for x in self._version.release))
# Pre-release
if self._version.pre is not None:
parts.append("".join(str(x) for x in self._version.pre))
# Post-release
if self._version.post is not None:
parts.append(".post{0}".format(self._version.post[1]))
# Development release
if self._version.dev is not None:
parts.append(".dev{0}".format(self._version.dev[1]))
# Local version segment
if self._version.local is not None:
parts.append(
"+{0}".format(".".join(str(x) for x in self._version.local))
)
return "".join(parts)
@property
def public(self):
return str(self).split("+", 1)[0]
@property
def base_version(self):
parts = []
# Epoch
if self._version.epoch != 0:
parts.append("{0}!".format(self._version.epoch))
# Release segment
parts.append(".".join(str(x) for x in self._version.release))
return "".join(parts)
@property
def local(self):
version_string = str(self)
if "+" in version_string:
return version_string.split("+", 1)[1]
@property
def is_prerelease(self):
return bool(self._version.dev or self._version.pre)
@property
def is_postrelease(self):
return bool(self._version.post)
def _parse_letter_version(letter, number):
if letter:
# We assume there is an implicit 0 in a pre-release if there is
# no numeral associated with it.
if number is None:
number = 0
# We normalize any letters to their lower-case form
letter = letter.lower()
# We consider some words to be alternate spellings of other words and
# in those cases we want to normalize the spellings to our preferred
# spelling.
if letter == "alpha":
letter = "a"
elif letter == "beta":
letter = "b"
elif letter in ["c", "pre", "preview"]:
letter = "rc"
elif letter in ["rev", "r"]:
letter = "post"
return letter, int(number)
if not letter and number:
# We assume that if we are given a number but not given a letter,
# then this is using the implicit post release syntax (e.g., 1.0-1)
letter = "post"
return letter, int(number)
_local_version_seperators = re.compile(r"[\._-]")
def _parse_local_version(local):
"""
Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
"""
if local is not None:
return tuple(
part.lower() if not part.isdigit() else int(part)
for part in _local_version_seperators.split(local)
)
def _cmpkey(epoch, release, pre, post, dev, local):
# When we compare a release version, we want to compare it with all of the
# trailing zeros removed. So we'll use a reverse the list, drop all the now
# leading zeros until we come to something non-zero, then take the rest,
# re-reverse it back into the correct order, and make it a tuple and use
# that for our sorting key.
release = tuple(
reversed(list(
itertools.dropwhile(
lambda x: x == 0,
reversed(release),
)
))
)
# We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
# We'll do this by abusing the pre-segment, but we _only_ want to do this
# if there is no pre- or a post-segment. If we have one of those, then
# the normal sorting rules will handle this case correctly.
if pre is None and post is None and dev is not None:
pre = -Infinity
# Versions without a pre-release (except as noted above) should sort after
# those with one.
elif pre is None:
pre = Infinity
# Versions without a post-segment should sort before those with one.
if post is None:
post = -Infinity
# Versions without a development segment should sort after those with one.
if dev is None:
dev = Infinity
if local is None:
# Versions without a local segment should sort before those with one.
local = -Infinity
else:
# Versions with a local segment need that segment parsed to implement
# the sorting rules in PEP440.
# - Alphanumeric segments sort before numeric segments
# - Alphanumeric segments sort lexicographically
# - Numeric segments sort numerically
# - Shorter versions sort before longer versions when the prefixes
# match exactly
local = tuple(
(i, "") if isinstance(i, int) else (-Infinity, i)
for i in local
)
return epoch, release, pre, post, dev, local

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"""
Python 3.X compatibility tools.
While this file was originally intended for Python 2 -> 3 transition,
it is now used to create a compatibility layer between different
minor versions of Python 3.
While the active version of numpy may not support a given version of python, we
allow downstream libraries to continue to use these shims for forward
compatibility with numpy while they transition their code to newer versions of
Python.
"""
__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path',
'pickle', 'contextlib_nullcontext', 'os_fspath', 'os_PathLike']
import sys
import os
from pathlib import Path
import io
try:
import pickle5 as pickle
except ImportError:
import pickle
long = int
integer_types = (int,)
basestring = str
unicode = str
bytes = bytes
def asunicode(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('latin1')
def asstr(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def isfileobj(f):
return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
def open_latin1(filename, mode='r'):
return open(filename, mode=mode, encoding='iso-8859-1')
def sixu(s):
return s
strchar = 'U'
def getexception():
return sys.exc_info()[1]
def asbytes_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asbytes_nested(y) for y in x]
else:
return asbytes(x)
def asunicode_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asunicode_nested(y) for y in x]
else:
return asunicode(x)
def is_pathlib_path(obj):
"""
Check whether obj is a `pathlib.Path` object.
Prefer using ``isinstance(obj, os.PathLike)`` instead of this function.
"""
return isinstance(obj, Path)
# from Python 3.7
class contextlib_nullcontext:
"""Context manager that does no additional processing.
Used as a stand-in for a normal context manager, when a particular
block of code is only sometimes used with a normal context manager:
cm = optional_cm if condition else nullcontext()
with cm:
# Perform operation, using optional_cm if condition is True
.. note::
Prefer using `contextlib.nullcontext` instead of this context manager.
"""
def __init__(self, enter_result=None):
self.enter_result = enter_result
def __enter__(self):
return self.enter_result
def __exit__(self, *excinfo):
pass
def npy_load_module(name, fn, info=None):
"""
Load a module. Uses ``load_module`` which will be deprecated in python
3.12. An alternative that uses ``exec_module`` is in
numpy.distutils.misc_util.exec_mod_from_location
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Only here for backward compatibility with Python 2.*.
Returns
-------
mod : module
"""
# Explicitly lazy import this to avoid paying the cost
# of importing importlib at startup
from importlib.machinery import SourceFileLoader
return SourceFileLoader(name, fn).load_module()
os_fspath = os.fspath
os_PathLike = os.PathLike

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

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venv/lib/python3.9/site-packages/numpy/compat/tests/test_compat.py Normal file
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from os.path import join
from numpy.compat import isfileobj
from numpy.testing import assert_
from numpy.testing import tempdir
def test_isfileobj():
with tempdir(prefix="numpy_test_compat_") as folder:
filename = join(folder, 'a.bin')
with open(filename, 'wb') as f:
assert_(isfileobj(f))
with open(filename, 'ab') as f:
assert_(isfileobj(f))
with open(filename, 'rb') as f:
assert_(isfileobj(f))

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"""
Pytest configuration and fixtures for the Numpy test suite.
"""
import os
import tempfile
import hypothesis
import pytest
import numpy
from numpy.core._multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
# Use a known and persistent tmpdir for hypothesis' caches, which
# can be automatically cleared by the OS or user.
hypothesis.configuration.set_hypothesis_home_dir(
os.path.join(tempfile.gettempdir(), ".hypothesis")
)
# We register two custom profiles for Numpy - for details see
# https://hypothesis.readthedocs.io/en/latest/settings.html
# The first is designed for our own CI runs; the latter also
# forces determinism and is designed for use via np.test()
hypothesis.settings.register_profile(
name="numpy-profile", deadline=None, print_blob=True,
)
hypothesis.settings.register_profile(
name="np.test() profile",
deadline=None, print_blob=True, database=None, derandomize=True,
suppress_health_check=list(hypothesis.HealthCheck),
)
# Note that the default profile is chosen based on the presence
# of pytest.ini, but can be overridden by passing the
# --hypothesis-profile=NAME argument to pytest.
_pytest_ini = os.path.join(os.path.dirname(__file__), "..", "pytest.ini")
hypothesis.settings.load_profile(
"numpy-profile" if os.path.isfile(_pytest_ini) else "np.test() profile"
)
def pytest_configure(config):
config.addinivalue_line("markers",
"valgrind_error: Tests that are known to error under valgrind.")
config.addinivalue_line("markers",
"leaks_references: Tests that are known to leak references.")
config.addinivalue_line("markers",
"slow: Tests that are very slow.")
config.addinivalue_line("markers",
"slow_pypy: Tests that are very slow on pypy.")
def pytest_addoption(parser):
parser.addoption("--available-memory", action="store", default=None,
help=("Set amount of memory available for running the "
"test suite. This can result to tests requiring "
"especially large amounts of memory to be skipped. "
"Equivalent to setting environment variable "
"NPY_AVAILABLE_MEM. Default: determined"
"automatically."))
def pytest_sessionstart(session):
available_mem = session.config.getoption('available_memory')
if available_mem is not None:
os.environ['NPY_AVAILABLE_MEM'] = available_mem
#FIXME when yield tests are gone.
@pytest.hookimpl()
def pytest_itemcollected(item):
"""
Check FPU precision mode was not changed during test collection.
The clumsy way we do it here is mainly necessary because numpy
still uses yield tests, which can execute code at test collection
time.
"""
global _old_fpu_mode
mode = get_fpu_mode()
if _old_fpu_mode is None:
_old_fpu_mode = mode
elif mode != _old_fpu_mode:
_collect_results[item] = (_old_fpu_mode, mode)
_old_fpu_mode = mode
@pytest.fixture(scope="function", autouse=True)
def check_fpu_mode(request):
"""
Check FPU precision mode was not changed during the test.
"""
old_mode = get_fpu_mode()
yield
new_mode = get_fpu_mode()
if old_mode != new_mode:
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" during the test".format(old_mode, new_mode))
collect_result = _collect_results.get(request.node)
if collect_result is not None:
old_mode, new_mode = collect_result
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" when collecting the test".format(old_mode,
new_mode))
@pytest.fixture(autouse=True)
def add_np(doctest_namespace):
doctest_namespace['np'] = numpy
@pytest.fixture(autouse=True)
def env_setup(monkeypatch):
monkeypatch.setenv('PYTHONHASHSEED', '0')
@pytest.fixture(params=[True, False])
def weak_promotion(request):
"""
Fixture to ensure "legacy" promotion state or change it to use the new
weak promotion (plus warning). `old_promotion` should be used as a
parameter in the function.
"""
state = numpy._get_promotion_state()
if request.param:
numpy._set_promotion_state("weak_and_warn")
else:
numpy._set_promotion_state("legacy")
yield request.param
numpy._set_promotion_state(state)

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"""
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Please note that this module is private. All functions and objects
are available in the main ``numpy`` namespace - use that instead.
"""
from numpy.version import version as __version__
import os
import warnings
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
import sys
msg = """
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python%d.%d from "%s"
* The NumPy version is: "%s"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: %s
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
__version__, exc)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
hasattr(umath, '_multiarray_umath')):
import sys
path = sys.modules['numpy'].__path__
msg = ("Something is wrong with the numpy installation. "
"While importing we detected an older version of "
"numpy in {}. One method of fixing this is to repeatedly uninstall "
"numpy until none is found, then reinstall this version.")
raise ImportError(msg.format(path))
from . import numerictypes as nt
multiarray.set_typeDict(nt.sctypeDict)
from . import numeric
from .numeric import *
from . import fromnumeric
from .fromnumeric import *
from . import defchararray as char
from . import records
from . import records as rec
from .records import record, recarray, format_parser
# Note: module name memmap is overwritten by a class with same name
from .memmap import *
from .defchararray import chararray
from . import function_base
from .function_base import *
from . import _machar
from ._machar import *
from . import getlimits
from .getlimits import *
from . import shape_base
from .shape_base import *
from . import einsumfunc
from .einsumfunc import *
del nt
from .fromnumeric import amax as max, amin as min, round_ as round
from .numeric import absolute as abs
# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
from . import _add_newdocs
from . import _add_newdocs_scalars
# add these for module-freeze analysis (like PyInstaller)
from . import _dtype_ctypes
from . import _internal
from . import _dtype
from . import _methods
__all__ = ['char', 'rec', 'memmap']
__all__ += numeric.__all__
__all__ += ['record', 'recarray', 'format_parser']
__all__ += ['chararray']
__all__ += function_base.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
# but old pickles saved before 1.20 will be using it, and there is no reason
# to break loading them.
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):
# Report the `__name__`. pickle will try to find the module. Note that
# pickle supports for this `__name__` to be a `__qualname__`. It may
# make sense to add a `__qualname__` to ufuncs, to allow this more
# explicitly (Numba has ufuncs as attributes).
# See also: https://github.com/dask/distributed/issues/3450
return func.__name__
def _DType_reconstruct(scalar_type):
# This is a work-around to pickle type(np.dtype(np.float64)), etc.
# and it should eventually be replaced with a better solution, e.g. when
# DTypes become HeapTypes.
return type(dtype(scalar_type))
def _DType_reduce(DType):
# To pickle a DType without having to add top-level names, pickle the
# scalar type for now (and assume that reconstruction will be possible).
if DType is dtype:
return "dtype" # must pickle `np.dtype` as a singleton.
scalar_type = DType.type # pickle the scalar type for reconstruction
return _DType_reconstruct, (scalar_type,)
def __getattr__(name):
# Deprecated 2021-10-20, NumPy 1.22
if name == "machar":
warnings.warn(
"The `np.core.machar` module is deprecated (NumPy 1.22)",
DeprecationWarning, stacklevel=2,
)
return _machar
raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
import copyreg
copyreg.pickle(ufunc, _ufunc_reduce)
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
# Unclutter namespace (must keep _*_reconstruct for unpickling)
del copyreg
del _ufunc_reduce
del _DType_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

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# NOTE: The `np.core` namespace is deliberately kept empty due to it
# being private (despite the lack of leading underscore)

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"""
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
import sys
import os
from numpy.core import dtype
from numpy.core import numerictypes as _numerictypes
from numpy.core.function_base import add_newdoc
##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################
def numeric_type_aliases(aliases):
def type_aliases_gen():
for alias, doc in aliases:
try:
alias_type = getattr(_numerictypes, alias)
except AttributeError:
# The set of aliases that actually exist varies between platforms
pass
else:
yield (alias_type, alias, doc)
return list(type_aliases_gen())
possible_aliases = numeric_type_aliases([
('int8', '8-bit signed integer (``-128`` to ``127``)'),
('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
('float96', '96-bit extended-precision floating-point number type'),
('float128', '128-bit extended-precision floating-point number type'),
('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
])
def _get_platform_and_machine():
try:
system, _, _, _, machine = os.uname()
except AttributeError:
system = sys.platform
if system == 'win32':
machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
or os.environ.get('PROCESSOR_ARCHITECTURE', '')
else:
machine = 'unknown'
return system, machine
_system, _machine = _get_platform_and_machine()
_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = "" if obj == o.__name__ else \
f":Canonical name: `numpy.{obj}`\n "
if fixed_aliases:
alias_doc = ''.join(f":Alias: `numpy.{alias}`\n "
for alias in fixed_aliases)
else:
alias_doc = ''
alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n "
for (alias_type, alias, doc) in possible_aliases if alias_type is o)
docstring = f"""
{doc.strip()}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
"""
add_newdoc('numpy.core.numerictypes', obj, docstring)
add_newdoc_for_scalar_type('bool_', ['bool8'],
"""
Boolean type (True or False), stored as a byte.
.. warning::
The :class:`bool_` type is not a subclass of the :class:`int_` type
(the :class:`bool_` is not even a number type). This is different
than Python's default implementation of :class:`bool` as a
sub-class of :class:`int`.
""")
add_newdoc_for_scalar_type('byte', [],
"""
Signed integer type, compatible with C ``char``.
""")
add_newdoc_for_scalar_type('short', [],
"""
Signed integer type, compatible with C ``short``.
""")
add_newdoc_for_scalar_type('intc', [],
"""
Signed integer type, compatible with C ``int``.
""")
add_newdoc_for_scalar_type('int_', [],
"""
Signed integer type, compatible with Python `int` and C ``long``.
""")
add_newdoc_for_scalar_type('longlong', [],
"""
Signed integer type, compatible with C ``long long``.
""")
add_newdoc_for_scalar_type('ubyte', [],
"""
Unsigned integer type, compatible with C ``unsigned char``.
""")
add_newdoc_for_scalar_type('ushort', [],
"""
Unsigned integer type, compatible with C ``unsigned short``.
""")
add_newdoc_for_scalar_type('uintc', [],
"""
Unsigned integer type, compatible with C ``unsigned int``.
""")
add_newdoc_for_scalar_type('uint', [],
"""
Unsigned integer type, compatible with C ``unsigned long``.
""")
add_newdoc_for_scalar_type('ulonglong', [],
"""
Signed integer type, compatible with C ``unsigned long long``.
""")
add_newdoc_for_scalar_type('half', [],
"""
Half-precision floating-point number type.
""")
add_newdoc_for_scalar_type('single', [],
"""
Single-precision floating-point number type, compatible with C ``float``.
""")
add_newdoc_for_scalar_type('double', ['float_'],
"""
Double-precision floating-point number type, compatible with Python `float`
and C ``double``.
""")
add_newdoc_for_scalar_type('longdouble', ['longfloat'],
"""
Extended-precision floating-point number type, compatible with C
``long double`` but not necessarily with IEEE 754 quadruple-precision.
""")
add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
"""
Complex number type composed of two single-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
"""
Complex number type composed of two double-precision floating-point
numbers, compatible with Python `complex`.
""")
add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
"""
Complex number type composed of two extended-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('object_', [],
"""
Any Python object.
""")
add_newdoc_for_scalar_type('str_', ['unicode_'],
r"""
A unicode string.
When used in arrays, this type strips trailing null codepoints.
Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
contents as UCS4:
>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
""")
add_newdoc_for_scalar_type('bytes_', ['string_'],
r"""
A byte string.
When used in arrays, this type strips trailing null bytes.
""")
add_newdoc_for_scalar_type('void', [],
r"""
np.void(length_or_data, /, dtype=None)
Create a new structured or unstructured void scalar.
Parameters
----------
length_or_data : int, array-like, bytes-like, object
One of multiple meanings (see notes). The length or
bytes data of an unstructured void. Or alternatively,
the data to be stored in the new scalar when `dtype`
is provided.
This can be an array-like, in which case an array may
be returned.
dtype : dtype, optional
If provided the dtype of the new scalar. This dtype must
be "void" dtype (i.e. a structured or unstructured void,
see also :ref:`defining-structured-types`).
..versionadded:: 1.24
Notes
-----
For historical reasons and because void scalars can represent both
arbitrary byte data and structured dtypes, the void constructor
has three calling conventions:
1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
``\0`` bytes. The 5 can be a Python or NumPy integer.
2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
The dtype itemsize will match the byte string length, here ``"V10"``.
3. When a ``dtype=`` is passed the call is rougly the same as an
array creation. However, a void scalar rather than array is returned.
Please see the examples which show all three different conventions.
Examples
--------
>>> np.void(5)
void(b'\x00\x00\x00\x00\x00')
>>> np.void(b'abcd')
void(b'\x61\x62\x63\x64')
>>> np.void((5, 3.2, "eggs"), dtype="i,d,S5")
(5, 3.2, b'eggs') # looks like a tuple, but is `np.void`
>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
(3, 3) # looks like a tuple, but is `np.void`
""")
add_newdoc_for_scalar_type('datetime64', [],
"""
If created from a 64-bit integer, it represents an offset from
``1970-01-01T00:00:00``.
If created from string, the string can be in ISO 8601 date
or datetime format.
>>> np.datetime64(10, 'Y')
numpy.datetime64('1980')
>>> np.datetime64('1980', 'Y')
numpy.datetime64('1980')
>>> np.datetime64(10, 'D')
numpy.datetime64('1970-01-11')
See :ref:`arrays.datetime` for more information.
""")
add_newdoc_for_scalar_type('timedelta64', [],
"""
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
add_newdoc('numpy.core.numerictypes', "integer", ('is_integer',
"""
integer.is_integer() -> bool
Return ``True`` if the number is finite with integral value.
.. versionadded:: 1.22
Examples
--------
>>> np.int64(-2).is_integer()
True
>>> np.uint32(5).is_integer()
True
"""))
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
"""
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise `OverflowError` on infinities and a `ValueError` on NaNs.
>>> np.{ftype}(10.0).as_integer_ratio()
(10, 1)
>>> np.{ftype}(0.0).as_integer_ratio()
(0, 1)
>>> np.{ftype}(-.25).as_integer_ratio()
(-1, 4)
""".format(ftype=float_name)))
add_newdoc('numpy.core.numerictypes', float_name, ('is_integer',
f"""
{float_name}.is_integer() -> bool
Return ``True`` if the floating point number is finite with integral
value, and ``False`` otherwise.
.. versionadded:: 1.22
Examples
--------
>>> np.{float_name}(-2.0).is_integer()
True
>>> np.{float_name}(3.2).is_integer()
False
"""))
for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
# Add negative examples for signed cases by checking typecode
add_newdoc('numpy.core.numerictypes', int_name, ('bit_count',
f"""
{int_name}.bit_count() -> int
Computes the number of 1-bits in the absolute value of the input.
Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
Examples
--------
>>> np.{int_name}(127).bit_count()
7""" +
(f"""
>>> np.{int_name}(-127).bit_count()
7
""" if dtype(int_name).char.islower() else "")))

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"""
Functions in the ``as*array`` family that promote array-likes into arrays.
`require` fits this category despite its name not matching this pattern.
"""
from .overrides import (
array_function_dispatch,
set_array_function_like_doc,
set_module,
)
from .multiarray import array, asanyarray
__all__ = ["require"]
POSSIBLE_FLAGS = {
'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'
}
def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
return (like,)
@set_array_function_like_doc
@set_module('numpy')
def require(a, dtype=None, requirements=None, *, like=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or sequence of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
"""
if like is not None:
return _require_with_like(
a,
dtype=dtype,
requirements=requirements,
like=like,
)
if not requirements:
return asanyarray(a, dtype=dtype)
requirements = {POSSIBLE_FLAGS[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
return arr.copy(order)
return arr
_require_with_like = array_function_dispatch(
_require_dispatcher, use_like=True
)(require)

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from collections.abc import Iterable
from typing import TypeVar, Union, overload, Literal
from numpy import ndarray
from numpy._typing import DTypeLike, _SupportsArrayFunc
_ArrayType = TypeVar("_ArrayType", bound=ndarray)
_Requirements = Literal[
"C", "C_CONTIGUOUS", "CONTIGUOUS",
"F", "F_CONTIGUOUS", "FORTRAN",
"A", "ALIGNED",
"W", "WRITEABLE",
"O", "OWNDATA"
]
_E = Literal["E", "ENSUREARRAY"]
_RequirementsWithE = Union[_Requirements, _E]
@overload
def require(
a: _ArrayType,
dtype: None = ...,
requirements: None | _Requirements | Iterable[_Requirements] = ...,
*,
like: _SupportsArrayFunc = ...
) -> _ArrayType: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: _E | Iterable[_RequirementsWithE] = ...,
*,
like: _SupportsArrayFunc = ...
) -> ndarray: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: None | _Requirements | Iterable[_Requirements] = ...,
*,
like: _SupportsArrayFunc = ...
) -> ndarray: ...

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"""
A place for code to be called from the implementation of np.dtype
String handling is much easier to do correctly in python.
"""
import numpy as np
_kind_to_stem = {
'u': 'uint',
'i': 'int',
'c': 'complex',
'f': 'float',
'b': 'bool',
'V': 'void',
'O': 'object',
'M': 'datetime',
'm': 'timedelta',
'S': 'bytes',
'U': 'str',
}
def _kind_name(dtype):
try:
return _kind_to_stem[dtype.kind]
except KeyError as e:
raise RuntimeError(
"internal dtype error, unknown kind {!r}"
.format(dtype.kind)
) from None
def __str__(dtype):
if dtype.fields is not None:
return _struct_str(dtype, include_align=True)
elif dtype.subdtype:
return _subarray_str(dtype)
elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
return dtype.str
else:
return dtype.name
def __repr__(dtype):
arg_str = _construction_repr(dtype, include_align=False)
if dtype.isalignedstruct:
arg_str = arg_str + ", align=True"
return "dtype({})".format(arg_str)
def _unpack_field(dtype, offset, title=None):
"""
Helper function to normalize the items in dtype.fields.
Call as:
dtype, offset, title = _unpack_field(*dtype.fields[name])
"""
return dtype, offset, title
def _isunsized(dtype):
# PyDataType_ISUNSIZED
return dtype.itemsize == 0
def _construction_repr(dtype, include_align=False, short=False):
"""
Creates a string repr of the dtype, excluding the 'dtype()' part
surrounding the object. This object may be a string, a list, or
a dict depending on the nature of the dtype. This
is the object passed as the first parameter to the dtype
constructor, and if no additional constructor parameters are
given, will reproduce the exact memory layout.
Parameters
----------
short : bool
If true, this creates a shorter repr using 'kind' and 'itemsize', instead
of the longer type name.
include_align : bool
If true, this includes the 'align=True' parameter
inside the struct dtype construction dict when needed. Use this flag
if you want a proper repr string without the 'dtype()' part around it.
If false, this does not preserve the
'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
struct arrays like the regular repr does, because the 'align'
flag is not part of first dtype constructor parameter. This
mode is intended for a full 'repr', where the 'align=True' is
provided as the second parameter.
"""
if dtype.fields is not None:
return _struct_str(dtype, include_align=include_align)
elif dtype.subdtype:
return _subarray_str(dtype)
else:
return _scalar_str(dtype, short=short)
def _scalar_str(dtype, short):
byteorder = _byte_order_str(dtype)
if dtype.type == np.bool_:
if short:
return "'?'"
else:
return "'bool'"
elif dtype.type == np.object_:
# The object reference may be different sizes on different
# platforms, so it should never include the itemsize here.
return "'O'"
elif dtype.type == np.string_:
if _isunsized(dtype):
return "'S'"
else:
return "'S%d'" % dtype.itemsize
elif dtype.type == np.unicode_:
if _isunsized(dtype):
return "'%sU'" % byteorder
else:
return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
# unlike the other types, subclasses of void are preserved - but
# historically the repr does not actually reveal the subclass
elif issubclass(dtype.type, np.void):
if _isunsized(dtype):
return "'V'"
else:
return "'V%d'" % dtype.itemsize
elif dtype.type == np.datetime64:
return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif dtype.type == np.timedelta64:
return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif np.issubdtype(dtype, np.number):
# Short repr with endianness, like '<f8'
if short or dtype.byteorder not in ('=', '|'):
return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
# Longer repr, like 'float64'
else:
return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
elif dtype.isbuiltin == 2:
return dtype.type.__name__
else:
raise RuntimeError(
"Internal error: NumPy dtype unrecognized type number")
def _byte_order_str(dtype):
""" Normalize byteorder to '<' or '>' """
# hack to obtain the native and swapped byte order characters
swapped = np.dtype(int).newbyteorder('S')
native = swapped.newbyteorder('S')
byteorder = dtype.byteorder
if byteorder == '=':
return native.byteorder
if byteorder == 'S':
# TODO: this path can never be reached
return swapped.byteorder
elif byteorder == '|':
return ''
else:
return byteorder
def _datetime_metadata_str(dtype):
# TODO: this duplicates the C metastr_to_unicode functionality
unit, count = np.datetime_data(dtype)
if unit == 'generic':
return ''
elif count == 1:
return '[{}]'.format(unit)
else:
return '[{}{}]'.format(count, unit)
def _struct_dict_str(dtype, includealignedflag):
# unpack the fields dictionary into ls
names = dtype.names
fld_dtypes = []
offsets = []
titles = []
for name in names:
fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
fld_dtypes.append(fld_dtype)
offsets.append(offset)
titles.append(title)
# Build up a string to make the dictionary
if np.core.arrayprint._get_legacy_print_mode() <= 121:
colon = ":"
fieldsep = ","
else:
colon = ": "
fieldsep = ", "
# First, the names
ret = "{'names'%s[" % colon
ret += fieldsep.join(repr(name) for name in names)
# Second, the formats
ret += "], 'formats'%s[" % colon
ret += fieldsep.join(
_construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
# Third, the offsets
ret += "], 'offsets'%s[" % colon
ret += fieldsep.join("%d" % offset for offset in offsets)
# Fourth, the titles
if any(title is not None for title in titles):
ret += "], 'titles'%s[" % colon
ret += fieldsep.join(repr(title) for title in titles)
# Fifth, the itemsize
ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize)
if (includealignedflag and dtype.isalignedstruct):
# Finally, the aligned flag
ret += ", 'aligned'%sTrue}" % colon
else:
ret += "}"
return ret
def _aligned_offset(offset, alignment):
# round up offset:
return - (-offset // alignment) * alignment
def _is_packed(dtype):
"""
Checks whether the structured data type in 'dtype'
has a simple layout, where all the fields are in order,
and follow each other with no alignment padding.
When this returns true, the dtype can be reconstructed
from a list of the field names and dtypes with no additional
dtype parameters.
Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
"""
align = dtype.isalignedstruct
max_alignment = 1
total_offset = 0
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
if align:
total_offset = _aligned_offset(total_offset, fld_dtype.alignment)
max_alignment = max(max_alignment, fld_dtype.alignment)
if fld_offset != total_offset:
return False
total_offset += fld_dtype.itemsize
if align:
total_offset = _aligned_offset(total_offset, max_alignment)
if total_offset != dtype.itemsize:
return False
return True
def _struct_list_str(dtype):
items = []
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
item = "("
if title is not None:
item += "({!r}, {!r}), ".format(title, name)
else:
item += "{!r}, ".format(name)
# Special case subarray handling here
if fld_dtype.subdtype is not None:
base, shape = fld_dtype.subdtype
item += "{}, {}".format(
_construction_repr(base, short=True),
shape
)
else:
item += _construction_repr(fld_dtype, short=True)
item += ")"
items.append(item)
return "[" + ", ".join(items) + "]"
def _struct_str(dtype, include_align):
# The list str representation can't include the 'align=' flag,
# so if it is requested and the struct has the aligned flag set,
# we must use the dict str instead.
if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
sub = _struct_list_str(dtype)
else:
sub = _struct_dict_str(dtype, include_align)
# If the data type isn't the default, void, show it
if dtype.type != np.void:
return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
else:
return sub
def _subarray_str(dtype):
base, shape = dtype.subdtype
return "({}, {})".format(
_construction_repr(base, short=True),
shape
)
def _name_includes_bit_suffix(dtype):
if dtype.type == np.object_:
# pointer size varies by system, best to omit it
return False
elif dtype.type == np.bool_:
# implied
return False
elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
# unspecified
return False
else:
return True
def _name_get(dtype):
# provides dtype.name.__get__, documented as returning a "bit name"
if dtype.isbuiltin == 2:
# user dtypes don't promise to do anything special
return dtype.type.__name__
if issubclass(dtype.type, np.void):
# historically, void subclasses preserve their name, eg `record64`
name = dtype.type.__name__
else:
name = _kind_name(dtype)
# append bit counts
if _name_includes_bit_suffix(dtype):
name += "{}".format(dtype.itemsize * 8)
# append metadata to datetimes
if dtype.type in (np.datetime64, np.timedelta64):
name += _datetime_metadata_str(dtype)
return name

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

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"""
Various richly-typed exceptions, that also help us deal with string formatting
in python where it's easier.
By putting the formatting in `__str__`, we also avoid paying the cost for
users who silence the exceptions.
"""
from numpy.core.overrides import set_module
def _unpack_tuple(tup):
if len(tup) == 1:
return tup[0]
else:
return tup
def _display_as_base(cls):
"""
A decorator that makes an exception class look like its base.
We use this to hide subclasses that are implementation details - the user
should catch the base type, which is what the traceback will show them.
Classes decorated with this decorator are subject to removal without a
deprecation warning.
"""
assert issubclass(cls, Exception)
cls.__name__ = cls.__base__.__name__
return cls
class UFuncTypeError(TypeError):
""" Base class for all ufunc exceptions """
def __init__(self, ufunc):
self.ufunc = ufunc
@_display_as_base
class _UFuncBinaryResolutionError(UFuncTypeError):
""" Thrown when a binary resolution fails """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
assert len(self.dtypes) == 2
def __str__(self):
return (
"ufunc {!r} cannot use operands with types {!r} and {!r}"
).format(
self.ufunc.__name__, *self.dtypes
)
@_display_as_base
class _UFuncNoLoopError(UFuncTypeError):
""" Thrown when a ufunc loop cannot be found """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
def __str__(self):
return (
"ufunc {!r} did not contain a loop with signature matching types "
"{!r} -> {!r}"
).format(
self.ufunc.__name__,
_unpack_tuple(self.dtypes[:self.ufunc.nin]),
_unpack_tuple(self.dtypes[self.ufunc.nin:])
)
@_display_as_base
class _UFuncCastingError(UFuncTypeError):
def __init__(self, ufunc, casting, from_, to):
super().__init__(ufunc)
self.casting = casting
self.from_ = from_
self.to = to
@_display_as_base
class _UFuncInputCastingError(_UFuncCastingError):
""" Thrown when a ufunc input cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.in_i = i
def __str__(self):
# only show the number if more than one input exists
i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
return (
"Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
@_display_as_base
class _UFuncOutputCastingError(_UFuncCastingError):
""" Thrown when a ufunc output cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.out_i = i
def __str__(self):
# only show the number if more than one output exists
i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
return (
"Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
# Exception used in shares_memory()
@set_module('numpy')
class TooHardError(RuntimeError):
"""max_work was exceeded.
This is raised whenever the maximum number of candidate solutions
to consider specified by the ``max_work`` parameter is exceeded.
Assigning a finite number to max_work may have caused the operation
to fail.
"""
pass
@set_module('numpy')
class AxisError(ValueError, IndexError):
"""Axis supplied was invalid.
This is raised whenever an ``axis`` parameter is specified that is larger
than the number of array dimensions.
For compatibility with code written against older numpy versions, which
raised a mixture of `ValueError` and `IndexError` for this situation, this
exception subclasses both to ensure that ``except ValueError`` and
``except IndexError`` statements continue to catch `AxisError`.
.. versionadded:: 1.13
Parameters
----------
axis : int or str
The out of bounds axis or a custom exception message.
If an axis is provided, then `ndim` should be specified as well.
ndim : int, optional
The number of array dimensions.
msg_prefix : str, optional
A prefix for the exception message.
Attributes
----------
axis : int, optional
The out of bounds axis or ``None`` if a custom exception
message was provided. This should be the axis as passed by
the user, before any normalization to resolve negative indices.
.. versionadded:: 1.22
ndim : int, optional
The number of array dimensions or ``None`` if a custom exception
message was provided.
.. versionadded:: 1.22
Examples
--------
>>> array_1d = np.arange(10)
>>> np.cumsum(array_1d, axis=1)
Traceback (most recent call last):
...
numpy.AxisError: axis 1 is out of bounds for array of dimension 1
Negative axes are preserved:
>>> np.cumsum(array_1d, axis=-2)
Traceback (most recent call last):
...
numpy.AxisError: axis -2 is out of bounds for array of dimension 1
The class constructor generally takes the axis and arrays'
dimensionality as arguments:
>>> print(np.AxisError(2, 1, msg_prefix='error'))
error: axis 2 is out of bounds for array of dimension 1
Alternatively, a custom exception message can be passed:
>>> print(np.AxisError('Custom error message'))
Custom error message
"""
__slots__ = ("axis", "ndim", "_msg")
def __init__(self, axis, ndim=None, msg_prefix=None):
if ndim is msg_prefix is None:
# single-argument form: directly set the error message
self._msg = axis
self.axis = None
self.ndim = None
else:
self._msg = msg_prefix
self.axis = axis
self.ndim = ndim
def __str__(self):
axis = self.axis
ndim = self.ndim
if axis is ndim is None:
return self._msg
else:
msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
if self._msg is not None:
msg = f"{self._msg}: {msg}"
return msg
@_display_as_base
class _ArrayMemoryError(MemoryError):
""" Thrown when an array cannot be allocated"""
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
@property
def _total_size(self):
num_bytes = self.dtype.itemsize
for dim in self.shape:
num_bytes *= dim
return num_bytes
@staticmethod
def _size_to_string(num_bytes):
""" Convert a number of bytes into a binary size string """
# https://en.wikipedia.org/wiki/Binary_prefix
LOG2_STEP = 10
STEP = 1024
units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
unit_val = 1 << (unit_i * LOG2_STEP)
n_units = num_bytes / unit_val
del unit_val
# ensure we pick a unit that is correct after rounding
if round(n_units) == STEP:
unit_i += 1
n_units /= STEP
# deal with sizes so large that we don't have units for them
if unit_i >= len(units):
new_unit_i = len(units) - 1
n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
unit_i = new_unit_i
unit_name = units[unit_i]
# format with a sensible number of digits
if unit_i == 0:
# no decimal point on bytes
return '{:.0f} {}'.format(n_units, unit_name)
elif round(n_units) < 1000:
# 3 significant figures, if none are dropped to the left of the .
return '{:#.3g} {}'.format(n_units, unit_name)
else:
# just give all the digits otherwise
return '{:#.0f} {}'.format(n_units, unit_name)
def __str__(self):
size_str = self._size_to_string(self._total_size)
return (
"Unable to allocate {} for an array with shape {} and data type {}"
.format(size_str, self.shape, self.dtype)
)

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"""
A place for internal code
Some things are more easily handled Python.
"""
import ast
import re
import sys
import warnings
from .multiarray import dtype, array, ndarray, promote_types
try:
import ctypes
except ImportError:
ctypes = None
IS_PYPY = sys.implementation.name == 'pypy'
if sys.byteorder == 'little':
_nbo = '<'
else:
_nbo = '>'
def _makenames_list(adict, align):
allfields = []
for fname, obj in adict.items():
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(('', f'|V{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(('', f'|V{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(r'(?P<order1>[<>|=]?)'
r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
r'(?P<order2>[<>|=]?)'
r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(r'\s*,\s*')
space_re = re.compile(r'\s+$')
# astr is a string (perhaps comma separated)
_convorder = {'=': _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(
f'format number {len(result)+1} of "{astr}" is not recognized'
) from None
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 == '':
order = order1
elif order1 == '':
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 ('|', '=', _nbo):
order = ''
dtype = order + dtype
if (repeats == ''):
newitem = dtype
else:
newitem = (dtype, ast.literal_eval(repeats))
result.append(newitem)
return result
class dummy_ctype:
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:
def cast(self, num, obj):
return num.value
class c_void_p:
def __init__(self, ptr):
self.value = ptr
class _ctypes:
def __init__(self, array, ptr=None):
self._arr = array
if ctypes:
self._ctypes = ctypes
self._data = self._ctypes.c_void_p(ptr)
else:
# fake a pointer-like object that holds onto the reference
self._ctypes = _missing_ctypes()
self._data = self._ctypes.c_void_p(ptr)
self._data._objects = array
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
"""
Return the data pointer cast to a particular c-types object.
For example, calling ``self._as_parameter_`` is equivalent to
``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
pointer to a ctypes array of floating-point data:
``self.data_as(ctypes.POINTER(ctypes.c_double))``.
The returned pointer will keep a reference to the array.
"""
# _ctypes.cast function causes a circular reference of self._data in
# self._data._objects. Attributes of self._data cannot be released
# until gc.collect is called. Make a copy of the pointer first then let
# it hold the array reference. This is a workaround to circumvent the
# CPython bug https://bugs.python.org/issue12836
ptr = self._ctypes.cast(self._data, obj)
ptr._arr = self._arr
return ptr
def shape_as(self, obj):
"""
Return the shape tuple as an array of some other c-types
type. For example: ``self.shape_as(ctypes.c_short)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
"""
Return the strides tuple as an array of some other
c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
@property
def data(self):
"""
A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byte-order. The memory area may not even be writeable. The array
flags and data-type of this array should be respected when passing this
attribute to arbitrary C-code to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as ``self._array_interface_['data'][0]``.
Note that unlike ``data_as``, a reference will not be kept to the array:
code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
pointer to a deallocated array, and should be spelt
``(a + b).ctypes.data_as(ctypes.c_void_p)``
"""
return self._data.value
@property
def shape(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to ``dtype('p')`` on this
platform (see `~numpy.ctypeslib.c_intp`). This base-type could be
`ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on
the platform. The ctypes array contains the shape of
the underlying array.
"""
return self.shape_as(_getintp_ctype())
@property
def strides(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes array
contains the strides information from the underlying array. This strides
information is important for showing how many bytes must be jumped to
get to the next element in the array.
"""
return self.strides_as(_getintp_ctype())
@property
def _as_parameter_(self):
"""
Overrides the ctypes semi-magic method
Enables `c_func(some_array.ctypes)`
"""
return self.data_as(ctypes.c_void_p)
# Numpy 1.21.0, 2021-05-18
def get_data(self):
"""Deprecated getter for the `_ctypes.data` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_data" is deprecated. Use "data" instead',
DeprecationWarning, stacklevel=2)
return self.data
def get_shape(self):
"""Deprecated getter for the `_ctypes.shape` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_shape" is deprecated. Use "shape" instead',
DeprecationWarning, stacklevel=2)
return self.shape
def get_strides(self):
"""Deprecated getter for the `_ctypes.strides` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_strides" is deprecated. Use "strides" instead',
DeprecationWarning, stacklevel=2)
return self.strides
def get_as_parameter(self):
"""Deprecated getter for the `_ctypes._as_parameter_` property.
.. deprecated:: 1.21
"""
warnings.warn(
'"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
DeprecationWarning, stacklevel=2,
)
return self._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(f"duplicate field name: {name}") from None
else:
raise ValueError(f"unknown field name: {name}") from None
seen.add(name)
return tuple(list(order) + nameslist)
raise ValueError(f"unsupported order value: {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 _promote_fields(dt1, dt2):
""" Perform type promotion for two structured dtypes.
Parameters
----------
dt1 : structured dtype
First dtype.
dt2 : structured dtype
Second dtype.
Returns
-------
out : dtype
The promoted dtype
Notes
-----
If one of the inputs is aligned, the result will be. The titles of
both descriptors must match (point to the same field).
"""
# Both must be structured and have the same names in the same order
if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
raise TypeError("invalid type promotion")
# if both are identical, we can (maybe!) just return the same dtype.
identical = dt1 is dt2
new_fields = []
for name in dt1.names:
field1 = dt1.fields[name]
field2 = dt2.fields[name]
new_descr = promote_types(field1[0], field2[0])
identical = identical and new_descr is field1[0]
# Check that the titles match (if given):
if field1[2:] != field2[2:]:
raise TypeError("invalid type promotion")
if len(field1) == 2:
new_fields.append((name, new_descr))
else:
new_fields.append(((field1[2], name), new_descr))
res = dtype(new_fields, align=dt1.isalignedstruct or dt2.isalignedstruct)
# Might as well preserve identity (and metadata) if the dtype is identical
# and the itemsize, offsets are also unmodified. This could probably be
# sped up, but also probably just be removed entirely.
if identical and res.itemsize == dt1.itemsize:
for name in dt1.names:
if dt1.fields[name][1] != res.fields[name][1]:
return res # the dtype changed.
return dt1
return res
def _getfield_is_safe(oldtype, newtype, offset):
""" Checks safety of getfield for object arrays.
As in _view_is_safe, we need to check that memory containing objects is not
reinterpreted as a non-object datatype and vice versa.
Parameters
----------
oldtype : data-type
Data type of the original ndarray.
newtype : data-type
Data type of the field being accessed by ndarray.getfield
offset : int
Offset of the field being accessed by ndarray.getfield
Raises
------
TypeError
If the field access is invalid
"""
if newtype.hasobject or oldtype.hasobject:
if offset == 0 and newtype == oldtype:
return
if oldtype.names is not None:
for name in oldtype.names:
if (oldtype.fields[name][1] == offset and
oldtype.fields[name][0] == newtype):
return
raise TypeError("Cannot get/set field of an object array")
return
def _view_is_safe(oldtype, newtype):
""" Checks safety of a view involving object arrays, for example when
doing::
np.zeros(10, dtype=oldtype).view(newtype)
Parameters
----------
oldtype : data-type
Data type of original ndarray
newtype : data-type
Data type of the view
Raises
------
TypeError
If the new type is incompatible with the old type.
"""
# if the types are equivalent, there is no problem.
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
if oldtype == newtype:
return
if newtype.hasobject or oldtype.hasobject:
raise TypeError("Cannot change data-type for object array.")
return
# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'h',
'H': 'H',
'i': 'i',
'I': 'I',
'l': 'l',
'L': 'L',
'q': 'q',
'Q': 'Q',
'e': 'e',
'f': 'f',
'd': 'd',
'g': 'g',
'Zf': 'F',
'Zd': 'D',
'Zg': 'G',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
_pep3118_standard_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'i2',
'H': 'u2',
'i': 'i4',
'I': 'u4',
'l': 'i4',
'L': 'u4',
'q': 'i8',
'Q': 'u8',
'e': 'f2',
'f': 'f',
'd': 'd',
'Zf': 'F',
'Zd': 'D',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
_pep3118_unsupported_map = {
'u': 'UCS-2 strings',
'&': 'pointers',
't': 'bitfields',
'X': 'function pointers',
}
class _Stream:
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)
def _dtype_from_pep3118(spec):
stream = _Stream(spec)
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
return dtype
def __dtype_from_pep3118(stream, is_subdtype):
field_spec = dict(
names=[],
formats=[],
offsets=[],
itemsize=0
)
offset = 0
common_alignment = 1
is_padding = False
# Parse spec
while stream:
value = None
# End of structure, bail out to upper level
if stream.consume('}'):
break
# Sub-arrays (1)
shape = None
if stream.consume('('):
shape = stream.consume_until(')')
shape = tuple(map(int, shape.split(',')))
# Byte order
if stream.next in ('@', '=', '<', '>', '^', '!'):
byteorder = stream.advance(1)
if byteorder == '!':
byteorder = '>'
stream.byteorder = byteorder
# Byte order characters also control native vs. standard type sizes
if stream.byteorder in ('@', '^'):
type_map = _pep3118_native_map
type_map_chars = _pep3118_native_typechars
else:
type_map = _pep3118_standard_map
type_map_chars = _pep3118_standard_typechars
# Item sizes
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
if itemsize_str:
itemsize = int(itemsize_str)
else:
itemsize = 1
# Data types
is_padding = False
if stream.consume('T{'):
value, align = __dtype_from_pep3118(
stream, is_subdtype=True)
elif stream.next in type_map_chars:
if stream.next == 'Z':
typechar = stream.advance(2)
else:
typechar = stream.advance(1)
is_padding = (typechar == 'x')
dtypechar = type_map[typechar]
if dtypechar in 'USV':
dtypechar += '%d' % itemsize
itemsize = 1
numpy_byteorder = {'@': '=', '^': '='}.get(
stream.byteorder, stream.byteorder)
value = dtype(numpy_byteorder + dtypechar)
align = value.alignment
elif stream.next in _pep3118_unsupported_map:
desc = _pep3118_unsupported_map[stream.next]
raise NotImplementedError(
"Unrepresentable PEP 3118 data type {!r} ({})"
.format(stream.next, desc))
else:
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
#
# Native alignment may require padding
#
# Here we assume that the presence of a '@' character implicitly implies
# that the start of the array is *already* aligned.
#
extra_offset = 0
if stream.byteorder == '@':
start_padding = (-offset) % align
intra_padding = (-value.itemsize) % align
offset += start_padding
if intra_padding != 0:
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
# Inject internal padding to the end of the sub-item
value = _add_trailing_padding(value, intra_padding)
else:
# We can postpone the injection of internal padding,
# as the item appears at most once
extra_offset += intra_padding
# Update common alignment
common_alignment = _lcm(align, common_alignment)
# Convert itemsize to sub-array
if itemsize != 1:
value = dtype((value, (itemsize,)))
# Sub-arrays (2)
if shape is not None:
value = dtype((value, shape))
# Field name
if stream.consume(':'):
name = stream.consume_until(':')
else:
name = None
if not (is_padding and name is None):
if name is not None and name in field_spec['names']:
raise RuntimeError(f"Duplicate field name '{name}' in PEP3118 format")
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'f{j}'
if name not in names:
break
j = j + 1
names[i] = name
def _add_trailing_padding(value, padding):
"""Inject the specified number of padding bytes at the end of a dtype"""
if value.fields is None:
field_spec = dict(
names=['f0'],
formats=[value],
offsets=[0],
itemsize=value.itemsize
)
else:
fields = value.fields
names = value.names
field_spec = dict(
names=names,
formats=[fields[name][0] for name in names],
offsets=[fields[name][1] for name in names],
itemsize=value.itemsize
)
field_spec['itemsize'] += padding
return dtype(field_spec)
def _prod(a):
p = 1
for x in a:
p *= x
return p
def _gcd(a, b):
"""Calculate the greatest common divisor of a and b"""
while b:
a, b = b, a % b
return a
def _lcm(a, b):
return a // _gcd(a, b) * b
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
""" Format the error message for when __array_ufunc__ gives up. """
args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
['{}={!r}'.format(k, v)
for k, v in kwargs.items()])
args = inputs + kwargs.get('out', ())
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
return ('operand type(s) all returned NotImplemented from '
'__array_ufunc__({!r}, {!r}, {}): {}'
.format(ufunc, method, args_string, types_string))
def array_function_errmsg_formatter(public_api, types):
""" Format the error message for when __array_ufunc__ gives up. """
func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
return ("no implementation found for '{}' on types that implement "
'__array_function__: {}'.format(func_name, list(types)))
def _ufunc_doc_signature_formatter(ufunc):
"""
Builds a signature string which resembles PEP 457
This is used to construct the first line of the docstring
"""
# input arguments are simple
if ufunc.nin == 1:
in_args = 'x'
else:
in_args = ', '.join(f'x{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"
)
# NOTE: gufuncs may or may not support the `axis` parameter
if ufunc.signature is None:
kwargs = f", where=True{kwargs}[, signature, extobj]"
else:
kwargs += "[, signature, extobj, axes, axis]"
# join all the parts together
return '{name}({in_args}{out_args}, *{kwargs})'.format(
name=ufunc.__name__,
in_args=in_args,
out_args=out_args,
kwargs=kwargs
)
def npy_ctypes_check(cls):
# determine if a class comes from ctypes, in order to work around
# a bug in the buffer protocol for those objects, bpo-10746
try:
# ctypes class are new-style, so have an __mro__. This probably fails
# for ctypes classes with multiple inheritance.
if IS_PYPY:
# (..., _ctypes.basics._CData, Bufferable, object)
ctype_base = cls.__mro__[-3]
else:
# # (..., _ctypes._CData, object)
ctype_base = cls.__mro__[-2]
# right now, they're part of the _ctypes module
return '_ctypes' in ctype_base.__module__
except Exception:
return False

30
venv/lib/python3.9/site-packages/numpy/core/_internal.pyi Normal file
View File

@@ -0,0 +1,30 @@
from typing import Any, TypeVar, overload, Generic
import ctypes as ct
from numpy import ndarray
from numpy.ctypeslib import c_intp
_CastT = TypeVar("_CastT", bound=ct._CanCastTo) # Copied from `ctypes.cast`
_CT = TypeVar("_CT", bound=ct._CData)
_PT = TypeVar("_PT", bound=None | int)
# TODO: Let the likes of `shape_as` and `strides_as` return `None`
# for 0D arrays once we've got shape-support
class _ctypes(Generic[_PT]):
@overload
def __new__(cls, array: ndarray[Any, Any], ptr: None = ...) -> _ctypes[None]: ...
@overload
def __new__(cls, array: ndarray[Any, Any], ptr: _PT) -> _ctypes[_PT]: ...
@property
def data(self) -> _PT: ...
@property
def shape(self) -> ct.Array[c_intp]: ...
@property
def strides(self) -> ct.Array[c_intp]: ...
@property
def _as_parameter_(self) -> ct.c_void_p: ...
def data_as(self, obj: type[_CastT]) -> _CastT: ...
def shape_as(self, obj: type[_CT]) -> ct.Array[_CT]: ...
def strides_as(self, obj: type[_CT]) -> ct.Array[_CT]: ...

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"""
Machine arithmetic - determine the parameters of the
floating-point arithmetic system
Author: Pearu Peterson, September 2003
"""
__all__ = ['MachAr']
from numpy.core.fromnumeric import any
from numpy.core._ufunc_config import errstate
from numpy.core.overrides import set_module
# Need to speed this up...especially for longfloat
# Deprecated 2021-10-20, NumPy 1.22
@set_module('numpy')
class MachAr:
"""
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] positive floating point number with full precision).
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
An alias for `smallest_normal`, kept for backwards compatibility.
huge : float
Same as `xmax`.
precision : float
``- int(-log10(eps))``
resolution : float
``- 10**(-precision)``
smallest_normal : float
The smallest positive floating point number with 1 as leading bit in
the mantissa following IEEE-754. Same as `xmin`.
smallest_subnormal : float
The smallest positive floating point number with 0 as leading bit in
the mantissa following IEEE-754.
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
smallest_subnormal = abs(xmin / beta ** (it))
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
self.smallest_normal = self.xmin
self._str_smallest_normal = float_to_str(self.xmin)
self.smallest_subnormal = float_to_float(smallest_subnormal)
self._str_smallest_subnormal = float_to_str(smallest_subnormal)
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'
'smallest_normal=%(smallest_normal)s '
'smallest_subnormal=%(smallest_subnormal)s\n'
'---------------------------------------------------------------------\n'
)
return fmt % self.__dict__
if __name__ == '__main__':
print(MachAr())

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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
"""
import warnings
from contextlib import nullcontext
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core.multiarray import asanyarray
from numpy.core import numerictypes as nt
from numpy.core import _exceptions
from numpy.core._ufunc_config import _no_nep50_warning
from numpy._globals import _NoValue
from numpy.compat import pickle, os_fspath
# 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
# Complex types to -> (2,)float view for fast-path computation in _var()
_complex_to_float = {
nt.dtype(nt.csingle) : nt.dtype(nt.single),
nt.dtype(nt.cdouble) : nt.dtype(nt.double),
}
# Special case for windows: ensure double takes precedence
if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
_complex_to_float.update({
nt.dtype(nt.clongdouble) : nt.dtype(nt.longdouble),
})
# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
# small reductions
def _amax(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_maximum(a, axis, None, out, keepdims, initial, where)
def _amin(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_minimum(a, axis, None, out, keepdims, initial, where)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_prod(a, axis, dtype, out, keepdims, initial, where)
def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_any(a, axis, dtype, out, keepdims)
return umr_any(a, axis, dtype, out, keepdims, where=where)
def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_all(a, axis, dtype, out, keepdims)
return umr_all(a, axis, dtype, out, keepdims, where=where)
def _count_reduce_items(arr, axis, keepdims=False, where=True):
# fast-path for the default case
if where is True:
# no boolean mask given, calculate items according to axis
if axis is None:
axis = tuple(range(arr.ndim))
elif not isinstance(axis, tuple):
axis = (axis,)
items = 1
for ax in axis:
items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
items = nt.intp(items)
else:
# TODO: Optimize case when `where` is broadcast along a non-reduction
# axis and full sum is more excessive than needed.
# guarded to protect circular imports
from numpy.lib.stride_tricks import broadcast_to
# count True values in (potentially broadcasted) boolean mask
items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
keepdims)
return items
# Numpy 1.17.0, 2019-02-24
# Various clip behavior deprecations, marked with _clip_dep as a prefix.
def _clip_dep_is_scalar_nan(a):
# guarded to protect circular imports
from numpy.core.fromnumeric import ndim
if ndim(a) != 0:
return False
try:
return um.isnan(a)
except TypeError:
return False
def _clip_dep_is_byte_swapped(a):
if isinstance(a, mu.ndarray):
return not a.dtype.isnative
return False
def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
# normal path
if casting is not None:
return ufunc(*args, out=out, casting=casting, **kwargs)
# try to deal with broken casting rules
try:
return ufunc(*args, out=out, **kwargs)
except _exceptions._UFuncOutputCastingError as e:
# Numpy 1.17.0, 2019-02-24
warnings.warn(
"Converting the output of clip from {!r} to {!r} is deprecated. "
"Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
"correct the type of the variables.".format(e.from_, e.to),
DeprecationWarning,
stacklevel=2
)
return ufunc(*args, out=out, casting="unsafe", **kwargs)
def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
if min is None and max is None:
raise ValueError("One of max or min must be given")
# Numpy 1.17.0, 2019-02-24
# This deprecation probably incurs a substantial slowdown for small arrays,
# it will be good to get rid of it.
if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
using_deprecated_nan = False
if _clip_dep_is_scalar_nan(min):
min = -float('inf')
using_deprecated_nan = True
if _clip_dep_is_scalar_nan(max):
max = float('inf')
using_deprecated_nan = True
if using_deprecated_nan:
warnings.warn(
"Passing `np.nan` to mean no clipping in np.clip has always "
"been unreliable, and is now deprecated. "
"In future, this will always return nan, like it already does "
"when min or max are arrays that contain nan. "
"To skip a bound, pass either None or an np.inf of an "
"appropriate sign.",
DeprecationWarning,
stacklevel=2
)
if min is None:
return _clip_dep_invoke_with_casting(
um.minimum, a, max, out=out, casting=casting, **kwargs)
elif max is None:
return _clip_dep_invoke_with_casting(
um.maximum, a, min, out=out, casting=casting, **kwargs)
else:
return _clip_dep_invoke_with_casting(
um.clip, a, min, max, out=out, casting=casting, **kwargs)
def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
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, where=where)
if isinstance(ret, mu.ndarray):
with _no_nep50_warning():
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, *,
where=True):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
# Make this warning show up on top.
if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
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, where=where)
# The shape of rcount has to match arrmean to not change the shape of out
# in broadcasting. Otherwise, it cannot be stored back to arrmean.
if rcount.ndim == 0:
# fast-path for default case when where is True
div = rcount
else:
# matching rcount to arrmean when where is specified as array
div = rcount.reshape(arrmean.shape)
if isinstance(arrmean, mu.ndarray):
with _no_nep50_warning():
arrmean = um.true_divide(arrmean, div, out=arrmean,
casting='unsafe', subok=False)
elif hasattr(arrmean, "dtype"):
arrmean = arrmean.dtype.type(arrmean / rcount)
else:
arrmean = arrmean / rcount
# Compute sum of squared deviations from mean
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
x = um.multiply(x, x, out=x)
# Fast-paths for built-in complex types
elif x.dtype in _complex_to_float:
xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
um.multiply(xv, xv, out=xv)
x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
# Most general case; includes handling object arrays containing imaginary
# numbers and complex types with non-native byteorder
else:
x = um.multiply(x, um.conjugate(x), out=x).real
ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
# Compute degrees of freedom and make sure it is not negative.
rcount = um.maximum(rcount - ddof, 0)
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
with _no_nep50_warning():
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, *,
where=True):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims, where=where)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
def _ptp(a, axis=None, out=None, keepdims=False):
return um.subtract(
umr_maximum(a, axis, None, out, keepdims),
umr_minimum(a, axis, None, None, keepdims),
out
)
def _dump(self, file, protocol=2):
if hasattr(file, 'write'):
ctx = nullcontext(file)
else:
ctx = open(os_fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def _dumps(self, protocol=2):
return pickle.dumps(self, protocol=protocol)

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

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

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from typing import TypedDict
from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating
class _SCTypes(TypedDict):
int: list[type[signedinteger]]
uint: list[type[unsignedinteger]]
float: list[type[floating]]
complex: list[type[complexfloating]]
others: list[type]
sctypeDict: dict[int | str, type[generic]]
sctypes: _SCTypes

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"""
Functions for changing global ufunc configuration
This provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`
"""
import collections.abc
import contextlib
import contextvars
from .overrides import set_module
from .umath import (
UFUNC_BUFSIZE_DEFAULT,
ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,
)
from . import umath
__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate", '_no_nep50_warning'
]
_errdict = {"ignore": ERR_IGNORE,
"warn": ERR_WARN,
"raise": ERR_RAISE,
"call": ERR_CALL,
"print": ERR_PRINT,
"log": ERR_LOG}
_errdict_rev = {value: key for key, value in _errdict.items()}
@set_module('numpy')
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
Set how floating-point errors are handled.
Note that operations on integer scalar types (such as `int16`) are
handled like floating point, and are affected by these settings.
Parameters
----------
all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Set treatment for all types of floating-point errors at once:
- ignore: Take no action when the exception occurs.
- warn: Print a `RuntimeWarning` (via the Python `warnings` module).
- raise: Raise a `FloatingPointError`.
- call: Call a function specified using the `seterrcall` function.
- print: Print a warning directly to ``stdout``.
- log: Record error in a Log object specified by `seterrcall`.
The default is not to change the current behavior.
divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for division by zero.
over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point overflow.
under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point underflow.
invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for invalid floating-point operation.
Returns
-------
old_settings : dict
Dictionary containing the old settings.
See also
--------
seterrcall : Set a callback function for the 'call' mode.
geterr, geterrcall, errstate
Notes
-----
The floating-point exceptions are defined in the IEEE 754 standard [1]_:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
- Underflow: result so close to zero that some precision
was lost.
- Invalid operation: result is not an expressible number, typically
indicates that a NaN was produced.
.. [1] https://en.wikipedia.org/wiki/IEEE_754
Examples
--------
>>> old_settings = np.seterr(all='ignore') #seterr to known value
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.seterr(**old_settings) # reset to default
{'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in scalar multiply
>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
>>> np.int16(32000) * np.int16(3)
30464
"""
pyvals = umath.geterrobj()
old = geterr()
if divide is None:
divide = all or old['divide']
if over is None:
over = all or old['over']
if under is None:
under = all or old['under']
if invalid is None:
invalid = all or old['invalid']
maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
(_errdict[over] << SHIFT_OVERFLOW) +
(_errdict[under] << SHIFT_UNDERFLOW) +
(_errdict[invalid] << SHIFT_INVALID))
pyvals[1] = maskvalue
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterr():
"""
Get the current way of handling floating-point errors.
Returns
-------
res : dict
A dictionary with keys "divide", "over", "under", and "invalid",
whose values are from the strings "ignore", "print", "log", "warn",
"raise", and "call". The keys represent possible floating-point
exceptions, and the values define how these exceptions are handled.
See Also
--------
geterrcall, seterr, seterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterr()
{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> np.geterr()
{'divide': 'warn', 'over': 'raise', 'under': 'warn', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
"""
maskvalue = umath.geterrobj()[1]
mask = 7
res = {}
val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
res['divide'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_OVERFLOW) & mask
res['over'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_UNDERFLOW) & mask
res['under'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_INVALID) & mask
res['invalid'] = _errdict_rev[val]
return res
@set_module('numpy')
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
Parameters
----------
size : int
Size of buffer.
"""
if size > 10e6:
raise ValueError("Buffer size, %s, is too big." % size)
if size < 5:
raise ValueError("Buffer size, %s, is too small." % size)
if size % 16 != 0:
raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
pyvals = umath.geterrobj()
old = getbufsize()
pyvals[0] = size
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
"""
return umath.geterrobj()[0]
@set_module('numpy')
def seterrcall(func):
"""
Set the floating-point error callback function or log object.
There are two ways to capture floating-point error messages. The first
is to set the error-handler to 'call', using `seterr`. Then, set
the function to call using this function.
The second is to set the error-handler to 'log', using `seterr`.
Floating-point errors then trigger a call to the 'write' method of
the provided object.
Parameters
----------
func : callable f(err, flag) or object with write method
Function to call upon floating-point errors ('call'-mode) or
object whose 'write' method is used to log such message ('log'-mode).
The call function takes two arguments. The first is a string describing
the type of error (such as "divide by zero", "overflow", "underflow",
or "invalid value"), and the second is the status flag. The flag is a
byte, whose four least-significant bits indicate the type of error, one
of "divide", "over", "under", "invalid"::
[0 0 0 0 divide over under invalid]
In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
If an object is provided, its write method should take one argument,
a string.
Returns
-------
h : callable, log instance or None
The old error handler.
See Also
--------
seterr, geterr, geterrcall
Examples
--------
Callback upon error:
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<function err_handler at 0x...>
>>> np.seterr(**save_err)
{'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}
Log error message:
>>> class Log:
... def write(self, msg):
... print("LOG: %s" % msg)
...
>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')
>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in divide
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<numpy.core.numeric.Log object at 0x...>
>>> np.seterr(**save_err)
{'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}
"""
if func is not None and not isinstance(func, collections.abc.Callable):
if (not hasattr(func, 'write') or
not isinstance(func.write, collections.abc.Callable)):
raise ValueError("Only callable can be used as callback")
pyvals = umath.geterrobj()
old = geterrcall()
pyvals[2] = func
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
Returns
-------
errobj : callable, log instance or None
The current error handler. If no handler was set through `seterrcall`,
``None`` is returned.
See Also
--------
seterrcall, seterr, geterr
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrcall() # we did not yet set a handler, returns None
>>> oldsettings = np.seterr(all='call')
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
>>> oldhandler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True
"""
return umath.geterrobj()[2]
class _unspecified:
pass
_Unspecified = _unspecified()
@set_module('numpy')
class errstate(contextlib.ContextDecorator):
"""
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
.. versionchanged:: 1.17.0
`errstate` is also usable as a function decorator, saving
a level of indentation if an entire function is wrapped.
See :py:class:`contextlib.ContextDecorator` for more information.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([nan, inf, inf])
>>> with np.errstate(divide='warn'):
... np.arange(3) / 0.
array([nan, inf, inf])
>>> np.sqrt(-1)
nan
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> np.geterr()
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
"""
def __init__(self, *, call=_Unspecified, **kwargs):
self.call = call
self.kwargs = kwargs
def __enter__(self):
self.oldstate = seterr(**self.kwargs)
if self.call is not _Unspecified:
self.oldcall = seterrcall(self.call)
def __exit__(self, *exc_info):
seterr(**self.oldstate)
if self.call is not _Unspecified:
seterrcall(self.oldcall)
def _setdef():
defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]
umath.seterrobj(defval)
# set the default values
_setdef()
NO_NEP50_WARNING = contextvars.ContextVar("_no_nep50_warning", default=False)
@set_module('numpy')
@contextlib.contextmanager
def _no_nep50_warning():
"""
Context manager to disable NEP 50 warnings. This context manager is
only relevant if the NEP 50 warnings are enabled globally (which is not
thread/context safe).
This warning context manager itself is fully safe, however.
"""
token = NO_NEP50_WARNING.set(True)
try:
yield
finally:
NO_NEP50_WARNING.reset(token)

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from collections.abc import Callable
from typing import Any, Literal, TypedDict
from numpy import _SupportsWrite
_ErrKind = Literal["ignore", "warn", "raise", "call", "print", "log"]
_ErrFunc = Callable[[str, int], Any]
class _ErrDict(TypedDict):
divide: _ErrKind
over: _ErrKind
under: _ErrKind
invalid: _ErrKind
class _ErrDictOptional(TypedDict, total=False):
all: None | _ErrKind
divide: None | _ErrKind
over: None | _ErrKind
under: None | _ErrKind
invalid: None | _ErrKind
def seterr(
all: None | _ErrKind = ...,
divide: None | _ErrKind = ...,
over: None | _ErrKind = ...,
under: None | _ErrKind = ...,
invalid: None | _ErrKind = ...,
) -> _ErrDict: ...
def geterr() -> _ErrDict: ...
def setbufsize(size: int) -> int: ...
def getbufsize() -> int: ...
def seterrcall(
func: None | _ErrFunc | _SupportsWrite[str]
) -> None | _ErrFunc | _SupportsWrite[str]: ...
def geterrcall() -> None | _ErrFunc | _SupportsWrite[str]: ...
# See `numpy/__init__.pyi` for the `errstate` class and `no_nep5_warnings`

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from types import TracebackType
from collections.abc import Callable
from typing import Any, Literal, TypedDict, SupportsIndex
# Using a private class is by no means ideal, but it is simply a consequence
# of a `contextlib.context` returning an instance of aforementioned class
from contextlib import _GeneratorContextManager
from numpy import (
ndarray,
generic,
bool_,
integer,
timedelta64,
datetime64,
floating,
complexfloating,
void,
str_,
bytes_,
longdouble,
clongdouble,
)
from numpy._typing import ArrayLike, _CharLike_co, _FloatLike_co
_FloatMode = Literal["fixed", "unique", "maxprec", "maxprec_equal"]
class _FormatDict(TypedDict, total=False):
bool: Callable[[bool_], str]
int: Callable[[integer[Any]], str]
timedelta: Callable[[timedelta64], str]
datetime: Callable[[datetime64], str]
float: Callable[[floating[Any]], str]
longfloat: Callable[[longdouble], str]
complexfloat: Callable[[complexfloating[Any, Any]], str]
longcomplexfloat: Callable[[clongdouble], str]
void: Callable[[void], str]
numpystr: Callable[[_CharLike_co], str]
object: Callable[[object], str]
all: Callable[[object], str]
int_kind: Callable[[integer[Any]], str]
float_kind: Callable[[floating[Any]], str]
complex_kind: Callable[[complexfloating[Any, Any]], str]
str_kind: Callable[[_CharLike_co], str]
class _FormatOptions(TypedDict):
precision: int
threshold: int
edgeitems: int
linewidth: int
suppress: bool
nanstr: str
infstr: str
formatter: None | _FormatDict
sign: Literal["-", "+", " "]
floatmode: _FloatMode
legacy: Literal[False, "1.13", "1.21"]
def set_printoptions(
precision: None | SupportsIndex = ...,
threshold: None | int = ...,
edgeitems: None | int = ...,
linewidth: None | int = ...,
suppress: None | bool = ...,
nanstr: None | str = ...,
infstr: None | str = ...,
formatter: None | _FormatDict = ...,
sign: Literal[None, "-", "+", " "] = ...,
floatmode: None | _FloatMode = ...,
*,
legacy: Literal[None, False, "1.13", "1.21"] = ...
) -> None: ...
def get_printoptions() -> _FormatOptions: ...
def array2string(
a: ndarray[Any, Any],
max_line_width: None | int = ...,
precision: None | SupportsIndex = ...,
suppress_small: None | bool = ...,
separator: str = ...,
prefix: str = ...,
# NOTE: With the `style` argument being deprecated,
# all arguments between `formatter` and `suffix` are de facto
# keyworld-only arguments
*,
formatter: None | _FormatDict = ...,
threshold: None | int = ...,
edgeitems: None | int = ...,
sign: Literal[None, "-", "+", " "] = ...,
floatmode: None | _FloatMode = ...,
suffix: str = ...,
legacy: Literal[None, False, "1.13", "1.21"] = ...,
) -> str: ...
def format_float_scientific(
x: _FloatLike_co,
precision: None | int = ...,
unique: bool = ...,
trim: Literal["k", ".", "0", "-"] = ...,
sign: bool = ...,
pad_left: None | int = ...,
exp_digits: None | int = ...,
min_digits: None | int = ...,
) -> str: ...
def format_float_positional(
x: _FloatLike_co,
precision: None | int = ...,
unique: bool = ...,
fractional: bool = ...,
trim: Literal["k", ".", "0", "-"] = ...,
sign: bool = ...,
pad_left: None | int = ...,
pad_right: None | int = ...,
min_digits: None | int = ...,
) -> str: ...
def array_repr(
arr: ndarray[Any, Any],
max_line_width: None | int = ...,
precision: None | SupportsIndex = ...,
suppress_small: None | bool = ...,
) -> str: ...
def array_str(
a: ndarray[Any, Any],
max_line_width: None | int = ...,
precision: None | SupportsIndex = ...,
suppress_small: None | bool = ...,
) -> str: ...
def set_string_function(
f: None | Callable[[ndarray[Any, Any]], str], repr: bool = ...
) -> None: ...
def printoptions(
precision: None | SupportsIndex = ...,
threshold: None | int = ...,
edgeitems: None | int = ...,
linewidth: None | int = ...,
suppress: None | bool = ...,
nanstr: None | str = ...,
infstr: None | str = ...,
formatter: None | _FormatDict = ...,
sign: Literal[None, "-", "+", " "] = ...,
floatmode: None | _FloatMode = ...,
*,
legacy: Literal[None, False, "1.13", "1.21"] = ...
) -> _GeneratorContextManager[_FormatOptions]: ...

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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 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 typing import (
Literal as L,
overload,
TypeVar,
Any,
)
from numpy import (
chararray as chararray,
dtype,
str_,
bytes_,
int_,
bool_,
object_,
_OrderKACF,
)
from numpy._typing import (
NDArray,
_ArrayLikeStr_co as U_co,
_ArrayLikeBytes_co as S_co,
_ArrayLikeInt_co as i_co,
_ArrayLikeBool_co as b_co,
)
from numpy.core.multiarray import compare_chararrays as compare_chararrays
_SCT = TypeVar("_SCT", str_, bytes_)
_CharArray = chararray[Any, dtype[_SCT]]
__all__: list[str]
# Comparison
@overload
def equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def not_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def not_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def greater_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def greater_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def less_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def less_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def greater(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def greater(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def less(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def less(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
# String operations
@overload
def add(x1: U_co, x2: U_co) -> NDArray[str_]: ...
@overload
def add(x1: S_co, x2: S_co) -> NDArray[bytes_]: ...
@overload
def multiply(a: U_co, i: i_co) -> NDArray[str_]: ...
@overload
def multiply(a: S_co, i: i_co) -> NDArray[bytes_]: ...
@overload
def mod(a: U_co, value: Any) -> NDArray[str_]: ...
@overload
def mod(a: S_co, value: Any) -> NDArray[bytes_]: ...
@overload
def capitalize(a: U_co) -> NDArray[str_]: ...
@overload
def capitalize(a: S_co) -> NDArray[bytes_]: ...
@overload
def center(a: U_co, width: i_co, fillchar: U_co = ...) -> NDArray[str_]: ...
@overload
def center(a: S_co, width: i_co, fillchar: S_co = ...) -> NDArray[bytes_]: ...
def decode(
a: S_co,
encoding: None | str = ...,
errors: None | str = ...,
) -> NDArray[str_]: ...
def encode(
a: U_co,
encoding: None | str = ...,
errors: None | str = ...,
) -> NDArray[bytes_]: ...
@overload
def expandtabs(a: U_co, tabsize: i_co = ...) -> NDArray[str_]: ...
@overload
def expandtabs(a: S_co, tabsize: i_co = ...) -> NDArray[bytes_]: ...
@overload
def join(sep: U_co, seq: U_co) -> NDArray[str_]: ...
@overload
def join(sep: S_co, seq: S_co) -> NDArray[bytes_]: ...
@overload
def ljust(a: U_co, width: i_co, fillchar: U_co = ...) -> NDArray[str_]: ...
@overload
def ljust(a: S_co, width: i_co, fillchar: S_co = ...) -> NDArray[bytes_]: ...
@overload
def lower(a: U_co) -> NDArray[str_]: ...
@overload
def lower(a: S_co) -> NDArray[bytes_]: ...
@overload
def lstrip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ...
@overload
def lstrip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ...
@overload
def partition(a: U_co, sep: U_co) -> NDArray[str_]: ...
@overload
def partition(a: S_co, sep: S_co) -> NDArray[bytes_]: ...
@overload
def replace(
a: U_co,
old: U_co,
new: U_co,
count: None | i_co = ...,
) -> NDArray[str_]: ...
@overload
def replace(
a: S_co,
old: S_co,
new: S_co,
count: None | i_co = ...,
) -> NDArray[bytes_]: ...
@overload
def rjust(
a: U_co,
width: i_co,
fillchar: U_co = ...,
) -> NDArray[str_]: ...
@overload
def rjust(
a: S_co,
width: i_co,
fillchar: S_co = ...,
) -> NDArray[bytes_]: ...
@overload
def rpartition(a: U_co, sep: U_co) -> NDArray[str_]: ...
@overload
def rpartition(a: S_co, sep: S_co) -> NDArray[bytes_]: ...
@overload
def rsplit(
a: U_co,
sep: None | U_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def rsplit(
a: S_co,
sep: None | S_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def rstrip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ...
@overload
def rstrip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ...
@overload
def split(
a: U_co,
sep: None | U_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def split(
a: S_co,
sep: None | S_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def splitlines(a: U_co, keepends: None | b_co = ...) -> NDArray[object_]: ...
@overload
def splitlines(a: S_co, keepends: None | b_co = ...) -> NDArray[object_]: ...
@overload
def strip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ...
@overload
def strip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ...
@overload
def swapcase(a: U_co) -> NDArray[str_]: ...
@overload
def swapcase(a: S_co) -> NDArray[bytes_]: ...
@overload
def title(a: U_co) -> NDArray[str_]: ...
@overload
def title(a: S_co) -> NDArray[bytes_]: ...
@overload
def translate(
a: U_co,
table: U_co,
deletechars: None | U_co = ...,
) -> NDArray[str_]: ...
@overload
def translate(
a: S_co,
table: S_co,
deletechars: None | S_co = ...,
) -> NDArray[bytes_]: ...
@overload
def upper(a: U_co) -> NDArray[str_]: ...
@overload
def upper(a: S_co) -> NDArray[bytes_]: ...
@overload
def zfill(a: U_co, width: i_co) -> NDArray[str_]: ...
@overload
def zfill(a: S_co, width: i_co) -> NDArray[bytes_]: ...
# String information
@overload
def count(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def count(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def endswith(
a: U_co,
suffix: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
@overload
def endswith(
a: S_co,
suffix: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
@overload
def find(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def find(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def index(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def index(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
def isalpha(a: U_co | S_co) -> NDArray[bool_]: ...
def isalnum(a: U_co | S_co) -> NDArray[bool_]: ...
def isdecimal(a: U_co | S_co) -> NDArray[bool_]: ...
def isdigit(a: U_co | S_co) -> NDArray[bool_]: ...
def islower(a: U_co | S_co) -> NDArray[bool_]: ...
def isnumeric(a: U_co | S_co) -> NDArray[bool_]: ...
def isspace(a: U_co | S_co) -> NDArray[bool_]: ...
def istitle(a: U_co | S_co) -> NDArray[bool_]: ...
def isupper(a: U_co | S_co) -> NDArray[bool_]: ...
@overload
def rfind(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def rfind(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def rindex(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def rindex(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def startswith(
a: U_co,
prefix: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
@overload
def startswith(
a: S_co,
prefix: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
def str_len(A: U_co | S_co) -> NDArray[int_]: ...
# Overload 1 and 2: str- or bytes-based array-likes
# overload 3: arbitrary object with unicode=False (-> bytes_)
# overload 4: arbitrary object with unicode=True (-> str_)
@overload
def array(
obj: U_co,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
@overload
def array(
obj: S_co,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def array(
obj: object,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def array(
obj: object,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[True] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
@overload
def asarray(
obj: U_co,
itemsize: None | int = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
@overload
def asarray(
obj: S_co,
itemsize: None | int = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def asarray(
obj: object,
itemsize: None | int = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def asarray(
obj: object,
itemsize: None | int = ...,
unicode: L[True] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...

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from collections.abc import Sequence
from typing import TypeVar, Any, overload, Union, Literal
from numpy import (
ndarray,
dtype,
bool_,
unsignedinteger,
signedinteger,
floating,
complexfloating,
number,
_OrderKACF,
)
from numpy._typing import (
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
_ArrayLikeInt_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
_DTypeLikeBool,
_DTypeLikeUInt,
_DTypeLikeInt,
_DTypeLikeFloat,
_DTypeLikeComplex,
_DTypeLikeComplex_co,
)
_ArrayType = TypeVar(
"_ArrayType",
bound=ndarray[Any, dtype[Union[bool_, number[Any]]]],
)
_OptimizeKind = None | bool | Literal["greedy", "optimal"] | Sequence[Any]
_CastingSafe = Literal["no", "equiv", "safe", "same_kind"]
_CastingUnsafe = Literal["unsafe"]
__all__: list[str]
# TODO: Properly handle the `casting`-based combinatorics
# TODO: We need to evaluate the content `__subscripts` in order
# to identify whether or an array or scalar is returned. At a cursory
# glance this seems like something that can quite easily be done with
# a mypy plugin.
# Something like `is_scalar = bool(__subscripts.partition("->")[-1])`
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeBool_co,
out: None = ...,
dtype: None | _DTypeLikeBool = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeUInt_co,
out: None = ...,
dtype: None | _DTypeLikeUInt = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeInt_co,
out: None = ...,
dtype: None | _DTypeLikeInt = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeFloat_co,
out: None = ...,
dtype: None | _DTypeLikeFloat = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
out: None = ...,
dtype: None | _DTypeLikeComplex = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
casting: _CastingUnsafe,
dtype: None | _DTypeLikeComplex_co = ...,
out: None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
out: _ArrayType,
dtype: None | _DTypeLikeComplex_co = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayType: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
out: _ArrayType,
casting: _CastingUnsafe,
dtype: None | _DTypeLikeComplex_co = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayType: ...
# NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
# It is therefore excluded from the signatures below.
# NOTE: In practice the list consists of a `str` (first element)
# and a variable number of integer tuples.
def einsum_path(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
optimize: _OptimizeKind = ...,
) -> tuple[list[Any], str]: ...

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import functools
import warnings
import operator
import types
from . import numeric as _nx
from .numeric import result_type, NaN, asanyarray, ndim
from numpy.core.multiarray import add_docstring
from numpy.core import overrides
__all__ = ['logspace', 'linspace', 'geomspace']
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None,
dtype=None, axis=None):
return (start, stop)
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
.. versionchanged:: 1.20.0
Values are rounded towards ``-inf`` instead of ``0`` when an
integer ``dtype`` is specified. The old behavior can
still be obtained with ``np.linspace(start, stop, num).astype(int)``
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred dtype will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points specified as
logarithms.
:ref:`how-to-partition`
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()
"""
num = operator.index(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
integer_dtype = False
else:
integer_dtype = _nx.issubdtype(dtype, _nx.integer)
delta = stop - start
y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
# In-place multiplication y *= delta/div is faster, but prevents the multiplicant
# from overriding what class is produced, and thus prevents, e.g. use of Quantities,
# see gh-7142. Hence, we multiply in place only for standard scalar types.
if div > 0:
_mult_inplace = _nx.isscalar(delta)
step = delta / div
any_step_zero = (
step == 0 if _mult_inplace else _nx.asanyarray(step == 0).any())
if any_step_zero:
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
else:
if _mult_inplace:
y *= step
else:
y = y * step
else:
# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
# have an undefined step
step = NaN
# Multiply with delta to allow possible override of output class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if integer_dtype:
_nx.floor(y, out=y)
if retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
dtype=None, axis=None):
return (start, stop)
@array_function_dispatch(_logspace_dispatcher)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
axis=0):
"""
Return numbers spaced evenly on a log scale.
In linear space, the sequence starts at ``base ** start``
(`base` to the power of `start`) and ends with ``base ** stop``
(see `endpoint` below).
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
``base ** start`` is the starting value of the sequence.
stop : array_like
``base ** stop`` is the final value of the sequence, unless `endpoint`
is False. In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
base : array_like, 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, the data type
is inferred from `start` and `stop`. The inferred type will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
arange : Similar to linspace, with the step size specified instead of the
number of samples. Note that, when used with a float endpoint, the
endpoint may or may not be included.
linspace : Similar to logspace, but with the samples uniformly distributed
in linear space, instead of log space.
geomspace : Similar to logspace, but with endpoints specified directly.
:ref:`how-to-partition`
Notes
-----
Logspace is equivalent to the code
>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP
Examples
--------
>>> np.logspace(2.0, 3.0, num=4)
array([ 100. , 215.443469 , 464.15888336, 1000. ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100. , 177.827941 , 316.22776602, 562.34132519])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4. , 5.0396842 , 6.34960421, 8. ])
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> x1 = np.logspace(0.1, 1, N, endpoint=True)
>>> x2 = np.logspace(0.1, 1, N, endpoint=False)
>>> y = np.zeros(N)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
if dtype is None:
return _nx.power(base, y)
return _nx.power(base, y).astype(dtype, copy=False)
def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None,
axis=None):
return (start, stop)
@array_function_dispatch(_geomspace_dispatcher)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
"""
Return numbers spaced evenly on a log scale (a geometric progression).
This is similar to `logspace`, but with endpoints specified directly.
Each output sample is a constant multiple of the previous.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The final value of the sequence, unless `endpoint` is False.
In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
dtype : dtype
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred dtype will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
logspace : Similar to geomspace, but with endpoints specified using log
and base.
linspace : Similar to geomspace, but with arithmetic instead of geometric
progression.
arange : Similar to linspace, with the step size specified instead of the
number of samples.
:ref:`how-to-partition`
Notes
-----
If the inputs or dtype are complex, the output will follow a logarithmic
spiral in the complex plane. (There are an infinite number of spirals
passing through two points; the output will follow the shortest such path.)
Examples
--------
>>> np.geomspace(1, 1000, num=4)
array([ 1., 10., 100., 1000.])
>>> np.geomspace(1, 1000, num=3, endpoint=False)
array([ 1., 10., 100.])
>>> np.geomspace(1, 1000, num=4, endpoint=False)
array([ 1. , 5.62341325, 31.6227766 , 177.827941 ])
>>> np.geomspace(1, 256, num=9)
array([ 1., 2., 4., 8., 16., 32., 64., 128., 256.])
Note that the above may not produce exact integers:
>>> np.geomspace(1, 256, num=9, dtype=int)
array([ 1, 2, 4, 7, 16, 32, 63, 127, 256])
>>> np.around(np.geomspace(1, 256, num=9)).astype(int)
array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])
Negative, decreasing, and complex inputs are allowed:
>>> np.geomspace(1000, 1, num=4)
array([1000., 100., 10., 1.])
>>> np.geomspace(-1000, -1, num=4)
array([-1000., -100., -10., -1.])
>>> np.geomspace(1j, 1000j, num=4) # Straight line
array([0. +1.j, 0. +10.j, 0. +100.j, 0.+1000.j])
>>> np.geomspace(-1+0j, 1+0j, num=5) # Circle
array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
6.12323400e-17+1.00000000e+00j, 7.07106781e-01+7.07106781e-01j,
1.00000000e+00+0.00000000e+00j])
Graphical illustration of `endpoint` parameter:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> y = np.zeros(N)
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.axis([0.5, 2000, 0, 3])
[0.5, 2000, 0, 3]
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()
"""
start = asanyarray(start)
stop = asanyarray(stop)
if _nx.any(start == 0) or _nx.any(stop == 0):
raise ValueError('Geometric sequence cannot include zero')
dt = result_type(start, stop, float(num), _nx.zeros((), dtype))
if dtype is None:
dtype = dt
else:
# complex to dtype('complex128'), for instance
dtype = _nx.dtype(dtype)
# Promote both arguments to the same dtype in case, for instance, one is
# complex and another is negative and log would produce NaN otherwise.
# Copy since we may change things in-place further down.
start = start.astype(dt, copy=True)
stop = stop.astype(dt, copy=True)
out_sign = _nx.ones(_nx.broadcast(start, stop).shape, dt)
# Avoid negligible real or imaginary parts in output by rotating to
# positive real, calculating, then undoing rotation
if _nx.issubdtype(dt, _nx.complexfloating):
all_imag = (start.real == 0.) & (stop.real == 0.)
if _nx.any(all_imag):
start[all_imag] = start[all_imag].imag
stop[all_imag] = stop[all_imag].imag
out_sign[all_imag] = 1j
both_negative = (_nx.sign(start) == -1) & (_nx.sign(stop) == -1)
if _nx.any(both_negative):
_nx.negative(start, out=start, where=both_negative)
_nx.negative(stop, out=stop, where=both_negative)
_nx.negative(out_sign, out=out_sign, where=both_negative)
log_start = _nx.log10(start)
log_stop = _nx.log10(stop)
result = logspace(log_start, log_stop, num=num,
endpoint=endpoint, base=10.0, dtype=dtype)
# Make sure the endpoints match the start and stop arguments. This is
# necessary because np.exp(np.log(x)) is not necessarily equal to x.
if num > 0:
result[0] = start
if num > 1 and endpoint:
result[-1] = stop
result = out_sign * result
if axis != 0:
result = _nx.moveaxis(result, 0, axis)
return result.astype(dtype, copy=False)
def _needs_add_docstring(obj):
"""
Returns true if the only way to set the docstring of `obj` from python is
via add_docstring.
This function errs on the side of being overly conservative.
"""
Py_TPFLAGS_HEAPTYPE = 1 << 9
if isinstance(obj, (types.FunctionType, types.MethodType, property)):
return False
if isinstance(obj, type) and obj.__flags__ & Py_TPFLAGS_HEAPTYPE:
return False
return True
def _add_docstring(obj, doc, warn_on_python):
if warn_on_python and not _needs_add_docstring(obj):
warnings.warn(
"add_newdoc was used on a pure-python object {}. "
"Prefer to attach it directly to the source."
.format(obj),
UserWarning,
stacklevel=3)
try:
add_docstring(obj, doc)
except Exception:
pass
def add_newdoc(place, obj, doc, warn_on_python=True):
"""
Add documentation to an existing object, typically one defined in C
The purpose is to allow easier editing of the docstrings without requiring
a re-compile. This exists primarily for internal use within numpy itself.
Parameters
----------
place : str
The absolute name of the module to import from
obj : str
The name of the object to add documentation to, typically a class or
function name
doc : {str, Tuple[str, str], List[Tuple[str, str]]}
If a string, the documentation to apply to `obj`
If a tuple, then the first element is interpreted as an attribute of
`obj` and the second as the docstring to apply - ``(method, docstring)``
If a list, then each element of the list should be a tuple of length
two - ``[(method1, docstring1), (method2, docstring2), ...]``
warn_on_python : bool
If True, the default, emit `UserWarning` if this is used to attach
documentation to a pure-python object.
Notes
-----
This routine never raises an error if the docstring can't be written, but
will raise an error if the object being documented does not exist.
This routine cannot modify read-only docstrings, as appear
in new-style classes or built-in functions. Because this
routine never raises an error the caller must check manually
that the docstrings were changed.
Since this function grabs the ``char *`` from a c-level str object and puts
it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
C-API best-practices, by:
- modifying a `PyTypeObject` after calling `PyType_Ready`
- calling `Py_INCREF` on the str and losing the reference, so the str
will never be released
If possible it should be avoided.
"""
new = getattr(__import__(place, globals(), {}, [obj]), obj)
if isinstance(doc, str):
_add_docstring(new, doc.strip(), warn_on_python)
elif isinstance(doc, tuple):
attr, docstring = doc
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
elif isinstance(doc, list):
for attr, docstring in doc:
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)

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