-Copyright (c) 2005-2021, NumPy Developers.
+Copyright (c) 2005-2022, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
Metadata-Version: 2.1
Name: numpy
-Version: 1.22.0
+Version: 1.22.1
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
This file contains a walkthrough of the NumPy 1.21.0 release on Linux, modified
-for building on azure and uploading to anaconda.org
-The commands can be copied into the command line, but be sure to
-replace 1.21.0 by the correct version.
-
+for building on azure and uploading to anaconda.org The commands can be copied
+into the command line, but be sure to replace 1.21.0 by the correct version.
This should be read together with the general directions in `releasing`.
+Facility Preparation
+====================
+
+Before beginning to make a release, use the ``*_requirements.txt`` files to
+ensure that you have the needed software. Most software can be installed with
+pip, but some will require apt-get, dnf, or whatever your system uses for
+software. Note that at this time the documentation cannot be built with Python
+3.10, for that use 3.8-3.9 instead. You will also need a GitHub personal access
+token (PAT) to push the documention. There are a few ways to streamline things.
+
+- Git can be set up to use a keyring to store your GitHub personal access token.
+ Search online for the details.
+- You can use the ``keyring`` app to store the PyPI password for twine. See the
+ online twine documentation for details.
+
+
Release Preparation
===================
$ paver write_release
-Reset the maintenance branch into a development state
------------------------------------------------------
+Reset the maintenance branch into a development state (skip for prereleases)
+----------------------------------------------------------------------------
Create release notes for next release and edit them to set the version. These
notes will be a skeleton and have little content::
- Hit the ``{Publish,Update} release`` button at the bottom.
-Upload documents to numpy.org
------------------------------
+Upload documents to numpy.org (skip for prereleases)
+----------------------------------------------------
+
+.. note:: You will need a GitHub personal access token to push the update.
This step is only needed for final releases and can be skipped for pre-releases
and most patch releases. ``make merge-doc`` clones the ``numpy/doc`` repo into
-``doc/build/merge`` and updates it with the new documentation::
-Note that if you have a `.local` numpy install, you should either remove it or
-install the current version for the docs to pick up the correct NumPy version.
+``doc/build/merge`` and updates it with the new documentation. If you already
+have a numpy installed, you need to locally install the new NumPy version so
+that document generation will use the correct NumPy. This is because ``make
+dist`` does not correctly set up the path. Note that Python 3.10 cannot be used
+for generating the docs as it has no ``easy_install``, use 3.9 or 3.8 instead::
$ pushd doc
$ make dist
$ make merge-doc
- $ popd
+ $ pushd build/merge
If the release series is a new one, you will need to add a new section to the
``doc/build/merge/index.html`` front page just after the "insert here" comment::
- $ gvim doc/build/merge/index.html +/'insert here'
+ $ gvim index.html +/'insert here'
Otherwise, only the ``zip`` and ``pdf`` links should be updated with the
new tag name::
- $ gvim doc/build/merge/index.html +/'tag v1.21'
+ $ gvim index.html +/'tag v1.21'
You can "test run" the new documentation in a browser to make sure the links
work::
- $ firefox doc/build/merge/index.html
+ $ firefox index.html # or google-chrome, etc.
-Update the stable link::
+Update the stable link and update::
$ ln -sfn 1.21 stable
+ $ ls -l # check the link
-Once everything seems satisfactory, commit and upload the changes::
+Once everything seems satisfactory, update, commit and upload the changes::
- $ pushd doc/build/merge
+ $ python3 update.py
$ git commit -a -m"Add documentation for v1.21.0"
$ git push
$ popd
+ $ popd
-Announce the release on scipy.org
----------------------------------
+Announce the release on numpy.org (skip for prereleases)
+--------------------------------------------------------
-This assumes that you have forked `<https://github.com/scipy/scipy.org>`_::
+This assumes that you have forked `<https://github.com/numpy/numpy.org>`_::
- $ cd ../scipy.org
+ $ cd ../numpy.org
$ git checkout master
$ git pull upstream master
- $ git checkout -b numpy-1.21.0
- $ gvim www/index.rst # edit the News section
- $ git commit -a
+ $ git checkout -b announce-numpy-1.21.0
+ $ gvim content/en/news.md
+
+- For all releases, go to the bottom of the page and add a one line link. Look
+ to the previous links for example.
+- For the ``*.0`` release in a cycle, add a new section at the top with a short
+ description of the new features and point the news link to it.
+
+commit and push::
+
+ $ git commit -a -m"announce the NumPy 1.21.0 release"
$ git push origin HEAD
-Now go to your fork and make a pull request for the branch.
+Go to your Github fork and make a pull request.
Announce to mailing lists
-------------------------
python-announce-list is BCC so that replies will not be sent to that list.
-Post-Release Tasks
-------------------
+Post-Release Tasks (skip for prereleases)
+-----------------------------------------
Checkout main and forward port the documentation changes::
--- /dev/null
+
+Contributors
+============
+
+A total of 14 people contributed to this release. People with a "+" by their
+names contributed a patch for the first time.
+
+* Arryan Singh
+* Bas van Beek
+* Charles Harris
+* Denis Laxalde
+* Isuru Fernando
+* Kevin Sheppard
+* Matthew Barber
+* Matti Picus
+* Melissa Weber Mendonça
+* Mukulika Pahari
+* Omid Rajaei +
+* Pearu Peterson
+* Ralf Gommers
+* Sebastian Berg
+
+Pull requests merged
+====================
+
+A total of 20 pull requests were merged for this release.
+
+* `#20702 <https://github.com/numpy/numpy/pull/20702>`__: MAINT, DOC: Post 1.22.0 release fixes.
+* `#20703 <https://github.com/numpy/numpy/pull/20703>`__: DOC, BUG: Use pngs instead of svgs.
+* `#20704 <https://github.com/numpy/numpy/pull/20704>`__: DOC:Fixed the link on user-guide landing page
+* `#20714 <https://github.com/numpy/numpy/pull/20714>`__: BUG: Restore vc141 support
+* `#20724 <https://github.com/numpy/numpy/pull/20724>`__: BUG: Fix array dimensions solver for multidimensional arguments...
+* `#20725 <https://github.com/numpy/numpy/pull/20725>`__: TYP: change type annotation for `__array_namespace__` to ModuleType
+* `#20726 <https://github.com/numpy/numpy/pull/20726>`__: TYP, MAINT: Allow `ndindex` to accept integer tuples
+* `#20757 <https://github.com/numpy/numpy/pull/20757>`__: BUG: Relax dtype identity check in reductions
+* `#20763 <https://github.com/numpy/numpy/pull/20763>`__: TYP: Allow time manipulation functions to accept `date` and `timedelta`...
+* `#20768 <https://github.com/numpy/numpy/pull/20768>`__: TYP: Relax the type of `ndarray.__array_finalize__`
+* `#20795 <https://github.com/numpy/numpy/pull/20795>`__: MAINT: Raise RuntimeError if setuptools version is too recent.
+* `#20796 <https://github.com/numpy/numpy/pull/20796>`__: BUG, DOC: Fixes SciPy docs build warnings
+* `#20797 <https://github.com/numpy/numpy/pull/20797>`__: DOC: fix OpenBLAS version in release note
+* `#20798 <https://github.com/numpy/numpy/pull/20798>`__: PERF: Optimize array check for bounded 0,1 values
+* `#20805 <https://github.com/numpy/numpy/pull/20805>`__: BUG: Fix that reduce-likes honor out always (and live in the...
+* `#20806 <https://github.com/numpy/numpy/pull/20806>`__: BUG: ``array_api.argsort(descending=True)`` respects relative...
+* `#20807 <https://github.com/numpy/numpy/pull/20807>`__: BUG: Allow integer inputs for pow-related functions in `array_api`
+* `#20814 <https://github.com/numpy/numpy/pull/20814>`__: DOC: Refer to NumPy, not pandas, in main page
+* `#20815 <https://github.com/numpy/numpy/pull/20815>`__: DOC: Update Copyright to 2022 [License]
+* `#20819 <https://github.com/numpy/numpy/pull/20819>`__: BUG: Return correctly shaped inverse indices in array_api set...
# General substitutions.
project = 'NumPy'
-copyright = '2008-2021, The NumPy community'
+copyright = '2008-2022, NumPy Developers'
# The default replacements for |version| and |release|, also used in various
# other places throughout the built documents.
^^^^^^^^^^
The user guide provides in-depth information on the
- key concepts of pandas with useful background information and explanation.
+ key concepts of NumPy with useful background information and explanation.
.. link-button:: user
:type: ref
-.. _reference:
-
.. module:: numpy
+.. _reference:
+
###############
NumPy Reference
###############
.. toctree::
:maxdepth: 3
+ 1.22.1 <release/1.22.1-notes>
1.22.0 <release/1.22.0-notes>
1.21.4 <release/1.21.4-notes>
1.21.3 <release/1.21.3-notes>
(`gh-19478 <https://github.com/numpy/numpy/pull/19478>`__)
-OpenBLAS v0.3.17
+OpenBLAS v0.3.18
----------------
-Update the OpenBLAS used in testing and in wheels to v0.3.17
+Update the OpenBLAS used in testing and in wheels to v0.3.18
-(`gh-19462 <https://github.com/numpy/numpy/pull/19462>`__)
+(`gh-20058 <https://github.com/numpy/numpy/pull/20058>`__)
--- /dev/null
+.. currentmodule:: numpy
+
+==========================
+NumPy 1.22.1 Release Notes
+==========================
+
+The NumPy 1.22.1 is maintenance release that fixes bugs discovered after the
+1.22.0 release. Notable fixes are:
+
+- Fix f2PY docstring problems (SciPy)
+- Fix reduction type problems (AstroPy)
+- Fix various typing bugs.
+
+The Python versions supported for this release are 3.8-3.10.
+
+
+Contributors
+============
+
+A total of 14 people contributed to this release. People with a "+" by their
+names contributed a patch for the first time.
+
+* Arryan Singh
+* Bas van Beek
+* Charles Harris
+* Denis Laxalde
+* Isuru Fernando
+* Kevin Sheppard
+* Matthew Barber
+* Matti Picus
+* Melissa Weber Mendonça
+* Mukulika Pahari
+* Omid Rajaei +
+* Pearu Peterson
+* Ralf Gommers
+* Sebastian Berg
+
+
+Pull requests merged
+====================
+
+A total of 20 pull requests were merged for this release.
+
+* `#20702 <https://github.com/numpy/numpy/pull/20702>`__: MAINT, DOC: Post 1.22.0 release fixes.
+* `#20703 <https://github.com/numpy/numpy/pull/20703>`__: DOC, BUG: Use pngs instead of svgs.
+* `#20704 <https://github.com/numpy/numpy/pull/20704>`__: DOC: Fixed the link on user-guide landing page
+* `#20714 <https://github.com/numpy/numpy/pull/20714>`__: BUG: Restore vc141 support
+* `#20724 <https://github.com/numpy/numpy/pull/20724>`__: BUG: Fix array dimensions solver for multidimensional arguments...
+* `#20725 <https://github.com/numpy/numpy/pull/20725>`__: TYP: change type annotation for ``__array_namespace__`` to ModuleType
+* `#20726 <https://github.com/numpy/numpy/pull/20726>`__: TYP, MAINT: Allow ``ndindex`` to accept integer tuples
+* `#20757 <https://github.com/numpy/numpy/pull/20757>`__: BUG: Relax dtype identity check in reductions
+* `#20763 <https://github.com/numpy/numpy/pull/20763>`__: TYP: Allow time manipulation functions to accept ``date`` and ``timedelta``...
+* `#20768 <https://github.com/numpy/numpy/pull/20768>`__: TYP: Relax the type of ``ndarray.__array_finalize__``
+* `#20795 <https://github.com/numpy/numpy/pull/20795>`__: MAINT: Raise RuntimeError if setuptools version is too recent.
+* `#20796 <https://github.com/numpy/numpy/pull/20796>`__: BUG, DOC: Fixes SciPy docs build warnings
+* `#20797 <https://github.com/numpy/numpy/pull/20797>`__: DOC: fix OpenBLAS version in release note
+* `#20798 <https://github.com/numpy/numpy/pull/20798>`__: PERF: Optimize array check for bounded 0,1 values
+* `#20805 <https://github.com/numpy/numpy/pull/20805>`__: BUG: Fix that reduce-likes honor out always (and live in the...
+* `#20806 <https://github.com/numpy/numpy/pull/20806>`__: BUG: ``array_api.argsort(descending=True)`` respects relative...
+* `#20807 <https://github.com/numpy/numpy/pull/20807>`__: BUG: Allow integer inputs for pow-related functions in ``array_api``
+* `#20814 <https://github.com/numpy/numpy/pull/20814>`__: DOC: Refer to NumPy, not pandas, in main page
+* `#20815 <https://github.com/numpy/numpy/pull/20815>`__: DOC: Update Copyright to 2022 [License]
+* `#20819 <https://github.com/numpy/numpy/pull/20819>`__: BUG: Return correctly shaped inverse indices in array_api set...
without actually making copies so that broadcasting operations are as
memory and computationally efficient as possible.
-.. figure:: broadcasting_1.svg
+.. figure:: broadcasting_1.png
:alt: A scalar is broadcast to match the shape of the 1-d array it
is being multiplied to.
:name: broadcasting.figure-1
In :ref:`broadcasting.figure-3`, an exception is raised because of the
incompatible shapes.
-.. figure:: broadcasting_2.svg
+.. figure:: broadcasting_2.png
:alt: A 1-d array with shape (3) is strectched to match the 2-d array of
shape (4, 3) it is being added to, and the result is a 2-d array of shape
(4, 3).
broadcasting if number of 1-d array elements matches the number of 2-d
array columns.*
-.. figure:: broadcasting_3.svg
+.. figure:: broadcasting_3.png
:alt: A huge cross over the 2-d array of shape (4, 3) and the 1-d array
of shape (4) shows that they can not be broadcast due to mismatch
of shapes and thus produce no result.
[ 21., 22., 23.],
[ 31., 32., 33.]])
-.. figure:: broadcasting_4.svg
+.. figure:: broadcasting_4.png
:alt: A 2-d array of shape (4, 1) and a 1-d array of shape (3) are
stretched to match their shapes and produce a resultant array
of shape (4, 3).
Codes (2d array): 4 x 2
Diff (2d array): 4 x 2
-.. figure:: broadcasting_5.svg
+.. figure:: broadcasting_5.png
:alt: A height versus weight graph that shows data of a female
gymnast, marathon runner, basketball player, football
lineman and the athlete to be classified. Shortest distance
+# doxygen required, use apt-get or dnf
sphinx==4.2.0
numpydoc==1.1.0
+pydata-sphinx-theme==0.7.2
+sphinx-panels
ipython
scipy
matplotlib
pandas
-pydata-sphinx-theme
breathe
-sphinx-panels
kwargs: Mapping[str, Any],
) -> Any: ...
- @property
- def __array_finalize__(self) -> None: ...
+ __array_finalize__: Any
def __array_wrap__(
self,
def __iter__(self: _T) -> _T: ...
class ndindex:
+ @overload
+ def __init__(self, shape: tuple[SupportsIndex, ...], /) -> None: ...
+ @overload
def __init__(self, *shape: SupportsIndex) -> None: ...
def __iter__(self: _T) -> _T: ...
def __next__(self) -> _Shape: ...
def __new__(
cls,
weekmask: ArrayLike = ...,
- holidays: ArrayLike = ...,
+ holidays: ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
) -> busdaycalendar: ...
@property
def weekmask(self) -> NDArray[bool_]: ...
version_json = '''
{
- "date": "2021-12-30T14:37:35-0700",
+ "date": "2022-01-13T16:11:04-0700",
"dirty": false,
"error": null,
- "full-revisionid": "4adc87dff15a247e417d50f10cc4def8e1c17a03",
- "version": "1.22.0"
+ "full-revisionid": "7ce4118531b585b5d8f0380c6b896ae22d93bd96",
+ "version": "1.22.1"
}
''' # END VERSION_JSON
)
from typing import TYPE_CHECKING, Optional, Tuple, Union, Any
+import types
if TYPE_CHECKING:
from ._typing import Any, PyCapsule, Device, Dtype
def __array_namespace__(
self: Array, /, *, api_version: Optional[str] = None
- ) -> Any:
+ ) -> types.ModuleType:
if api_version is not None and not api_version.startswith("2021."):
raise ValueError(f"Unrecognized array API version: {api_version!r}")
return array_api
res = self._array.__pos__()
return self.__class__._new(res)
- # PEP 484 requires int to be a subtype of float, but __pow__ should not
- # accept int.
- def __pow__(self: Array, other: Union[float, Array], /) -> Array:
+ def __pow__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __pow__.
"""
from ._elementwise_functions import pow
- other = self._check_allowed_dtypes(other, "floating-point", "__pow__")
+ other = self._check_allowed_dtypes(other, "numeric", "__pow__")
if other is NotImplemented:
return other
# Note: NumPy's __pow__ does not follow type promotion rules for 0-d
res = self._array.__ror__(other._array)
return self.__class__._new(res)
- def __ipow__(self: Array, other: Union[float, Array], /) -> Array:
+ def __ipow__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __ipow__.
"""
- other = self._check_allowed_dtypes(other, "floating-point", "__ipow__")
+ other = self._check_allowed_dtypes(other, "numeric", "__ipow__")
if other is NotImplemented:
return other
self._array.__ipow__(other._array)
return self
- def __rpow__(self: Array, other: Union[float, Array], /) -> Array:
+ def __rpow__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __rpow__.
"""
from ._elementwise_functions import pow
- other = self._check_allowed_dtypes(other, "floating-point", "__rpow__")
+ other = self._check_allowed_dtypes(other, "numeric", "__rpow__")
if other is NotImplemented:
return other
# Note: NumPy's __pow__ does not follow the spec type promotion rules
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 pow")
+ 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)
See its docstring for more information.
"""
- res = np.unique(
+ values, indices, inverse_indices, counts = np.unique(
x._array,
return_counts=True,
return_index=True,
return_inverse=True,
)
-
- return UniqueAllResult(*[Array._new(i) for i in res])
+ # 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:
See its docstring for more information.
"""
- res = np.unique(
+ values, inverse_indices = np.unique(
x._array,
return_counts=False,
return_index=False,
return_inverse=True,
)
- return UniqueInverseResult(*[Array._new(i) for i in res])
+ # 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:
"""
# Note: this keyword argument is different, and the default is different.
kind = "stable" if stable else "quicksort"
- res = np.argsort(x._array, axis=axis, kind=kind)
- if descending:
- res = np.flip(res, axis=axis)
+ 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)
"__mul__": "numeric",
"__ne__": "all",
"__or__": "integer_or_boolean",
- "__pow__": "floating",
+ "__pow__": "numeric",
"__rshift__": "integer",
"__sub__": "numeric",
"__truediv__": "floating",
"negative": "numeric",
"not_equal": "all",
"positive": "numeric",
- "pow": "floating-point",
+ "pow": "numeric",
"remainder": "numeric",
"round": "numeric",
"sign": "numeric",
--- /dev/null
+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
--- /dev/null
+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))
NDArray,
ArrayLike,
_SupportsArray,
+ _NestedSequence,
_FiniteNestedSequence,
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
@overload
def busday_count( # type: ignore[misc]
- begindates: _ScalarLike_co,
- enddates: _ScalarLike_co,
+ begindates: _ScalarLike_co | dt.date,
+ enddates: _ScalarLike_co | dt.date,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> int_: ...
@overload
def busday_count( # type: ignore[misc]
- begindates: ArrayLike,
- enddates: ArrayLike,
+ begindates: ArrayLike | dt.date | _NestedSequence[dt.date],
+ enddates: ArrayLike | dt.date | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[int_]: ...
@overload
def busday_count(
- begindates: ArrayLike,
- enddates: ArrayLike,
+ begindates: ArrayLike | dt.date | _NestedSequence[dt.date],
+ enddates: ArrayLike | dt.date | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
# `roll="raise"` is (more or less?) equivalent to `casting="safe"`
@overload
def busday_offset( # type: ignore[misc]
- dates: datetime64,
- offsets: _TD64Like_co,
+ dates: datetime64 | dt.date,
+ offsets: _TD64Like_co | dt.timedelta,
roll: L["raise"] = ...,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> datetime64: ...
@overload
def busday_offset( # type: ignore[misc]
- dates: _ArrayLike[datetime64],
- offsets: _ArrayLikeTD64_co,
+ dates: _ArrayLike[datetime64] | dt.date | _NestedSequence[dt.date],
+ offsets: _ArrayLikeTD64_co | dt.timedelta | _NestedSequence[dt.timedelta],
roll: L["raise"] = ...,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[datetime64]: ...
@overload
def busday_offset( # type: ignore[misc]
- dates: _ArrayLike[datetime64],
- offsets: _ArrayLike[timedelta64],
+ dates: _ArrayLike[datetime64] | dt.date | _NestedSequence[dt.date],
+ offsets: _ArrayLikeTD64_co | dt.timedelta | _NestedSequence[dt.timedelta],
roll: L["raise"] = ...,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def busday_offset( # type: ignore[misc]
- dates: _ScalarLike_co,
- offsets: _ScalarLike_co,
+ dates: _ScalarLike_co | dt.date,
+ offsets: _ScalarLike_co | dt.timedelta,
roll: _RollKind,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> datetime64: ...
@overload
def busday_offset( # type: ignore[misc]
- dates: ArrayLike,
- offsets: ArrayLike,
+ dates: ArrayLike | dt.date | _NestedSequence[dt.date],
+ offsets: ArrayLike | dt.timedelta | _NestedSequence[dt.timedelta],
roll: _RollKind,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[datetime64]: ...
@overload
def busday_offset(
- dates: ArrayLike,
- offsets: ArrayLike,
+ dates: ArrayLike | dt.date | _NestedSequence[dt.date],
+ offsets: ArrayLike | dt.timedelta | _NestedSequence[dt.timedelta],
roll: _RollKind,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def is_busday( # type: ignore[misc]
- dates: _ScalarLike_co,
+ dates: _ScalarLike_co | dt.date,
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> bool_: ...
@overload
def is_busday( # type: ignore[misc]
- dates: ArrayLike,
+ dates: ArrayLike | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[bool_]: ...
@overload
def is_busday(
- dates: ArrayLike,
+ dates: ArrayLike | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
- holidays: None | ArrayLike = ...,
+ holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def datetime_as_string( # type: ignore[misc]
- arr: datetime64,
+ arr: datetime64 | dt.date,
unit: None | L["auto"] | _UnitKind = ...,
timezone: L["naive", "UTC", "local"] | dt.tzinfo = ...,
casting: _CastingKind = ...,
) -> str_: ...
@overload
def datetime_as_string(
- arr: _ArrayLikeDT64_co,
+ arr: _ArrayLikeDT64_co | _NestedSequence[dt.date],
unit: None | L["auto"] | _UnitKind = ...,
timezone: L["naive", "UTC", "local"] | dt.tzinfo = ...,
casting: _CastingKind = ...,
* ====================================================
*/
#include "npy_math_private.h"
+#ifdef _MSC_VER
+# include <intrin.h> // for __popcnt
+#endif
/* Magic binary numbers used by bit_count
* For type T, the magic numbers are computed as follows:
* is NULL) so we pass `arr` instead in that case.
*/
PyArrayObject *ops[3] = {out ? out : arr, arr, out};
+
+ /*
+ * TODO: This is a dangerous hack, that works by relying on the GIL, it is
+ * terrible, terrifying, and trusts that nobody does crazy stuff
+ * in their type-resolvers.
+ * By mutating the `out` dimension, we ensure that reduce-likes
+ * live in a future without value-based promotion even when legacy
+ * promotion has to be used.
+ */
+ npy_bool evil_ndim_mutating_hack = NPY_FALSE;
+ if (out != NULL && PyArray_NDIM(out) == 0 && PyArray_NDIM(arr) != 0) {
+ evil_ndim_mutating_hack = NPY_TRUE;
+ ((PyArrayObject_fields *)out)->nd = 1;
+ }
+
/*
* TODO: If `out` is not provided, arguably `initial` could define
* the first DType (and maybe also the out one), that way
PyArrayMethodObject *ufuncimpl = promote_and_get_ufuncimpl(ufunc,
ops, signature, operation_DTypes, NPY_FALSE, NPY_TRUE, NPY_TRUE);
+ if (evil_ndim_mutating_hack) {
+ ((PyArrayObject_fields *)out)->nd = 0;
+ }
/* DTypes may currently get filled in fallbacks and XDECREF for error: */
Py_XDECREF(operation_DTypes[0]);
Py_XDECREF(operation_DTypes[1]);
* The first operand and output should be the same array, so they should
* be identical. The second argument can be different for reductions,
* but is checked to be identical for accumulate and reduceat.
+ * Ideally, the type-resolver ensures that all are identical, but we do
+ * not enforce this here strictly. Otherwise correct handling of
+ * byte-order changes (or metadata) requires a lot of care; see gh-20699.
*/
- if (out_descrs[0] != out_descrs[2] || (
- enforce_uniform_args && out_descrs[0] != out_descrs[1])) {
+ if (!PyArray_EquivTypes(out_descrs[0], out_descrs[2]) || (
+ enforce_uniform_args && !PyArray_EquivTypes(
+ out_descrs[0], out_descrs[1]))) {
PyErr_Format(PyExc_TypeError,
"the resolved dtypes are not compatible with %s.%s. "
"Resolved (%R, %R, %R)",
return NULL;
}
- /* The below code assumes that all descriptors are identical: */
- assert(descrs[0] == descrs[1] && descrs[0] == descrs[2]);
+ /*
+ * The below code assumes that all descriptors are interchangeable, we
+ * allow them to not be strictly identical (but they typically should be)
+ */
+ assert(PyArray_EquivTypes(descrs[0], descrs[1])
+ && PyArray_EquivTypes(descrs[0], descrs[2]));
if (PyDataType_REFCHK(descrs[2]) && descrs[2]->type_num != NPY_OBJECT) {
/* This can be removed, but the initial element copy needs fixing */
return NULL;
}
- /* The below code assumes that all descriptors are identical: */
- assert(descrs[0] == descrs[1] && descrs[0] == descrs[2]);
+ /*
+ * The below code assumes that all descriptors are interchangeable, we
+ * allow them to not be strictly identical (but they typically should be)
+ */
+ assert(PyArray_EquivTypes(descrs[0], descrs[1])
+ && PyArray_EquivTypes(descrs[0], descrs[2]));
if (PyDataType_REFCHK(descrs[2]) && descrs[2]->type_num != NPY_OBJECT) {
/* This can be removed, but the initial element copy needs fixing */
# It would be safe, but not equiv casting:
ufunc(a, c, out=out, casting="equiv")
+ def test_reducelike_byteorder_resolution(self):
+ # See gh-20699, byte-order changes need some extra care in the type
+ # resolution to make the following succeed:
+ arr_be = np.arange(10, dtype=">i8")
+ arr_le = np.arange(10, dtype="<i8")
+
+ assert np.add.reduce(arr_be) == np.add.reduce(arr_le)
+ assert_array_equal(
+ np.add.accumulate(arr_be), np.add.accumulate(arr_le))
+ assert_array_equal(
+ np.add.reduceat(arr_be, [1]), np.add.reduceat(arr_le, [1]))
+
def test_reducelike_out_promotes(self):
# Check that the out argument to reductions is considered for
# promotion. See also gh-20455.
if dsize.contains(s):
try:
a, b = dsize.linear_solve(s)
- solve_v = lambda s: (s - b) / a
+
+ def solve_v(s, a=a, b=b):
+ return (s - b) / a
+
all_symbols = set(a.symbols())
all_symbols.update(b.symbols())
except RuntimeError as msg:
language=language)
for a in self.data]
if language is Language.C:
- r = f'({cond} ? {expr1} : {expr2})'
+ r = f'({cond}?{expr1}:{expr2})'
elif language is Language.Python:
r = f'({expr1} if {cond} else {expr2})'
elif language is Language.Fortran:
end subroutine
""")
- linear_dimspecs = ['n', '2*n', '2:n', 'n/2', '5 - n/2', '3*n:20',
- 'n*(n+1):n*(n+5)']
- nonlinear_dimspecs = ['2*n:3*n*n+2*n']
+ linear_dimspecs = [
+ "n", "2*n", "2:n", "n/2", "5 - n/2", "3*n:20", "n*(n+1):n*(n+5)",
+ "2*n, n"
+ ]
+ nonlinear_dimspecs = ["2*n:3*n*n+2*n"]
all_dimspecs = linear_dimspecs + nonlinear_dimspecs
code = ''
for count, dimspec in enumerate(all_dimspecs):
+ lst = [(d.split(":")[0] if ":" in d else "1") for d in dimspec.split(',')]
code += code_template.format(
- count=count, dimspec=dimspec,
- first=dimspec.split(':')[0] if ':' in dimspec else '1')
+ count=count,
+ dimspec=dimspec,
+ first=", ".join(lst),
+ )
@pytest.mark.parametrize('dimspec', all_dimspecs)
def test_array_size(self, dimspec):
for n in [1, 2, 3, 4, 5]:
sz, a = get_arr_size(n)
- assert len(a) == sz
+ assert a.size == sz
@pytest.mark.parametrize('dimspec', all_dimspecs)
def test_inv_array_size(self, dimspec):
assert (x + (x - y) / (x + y) + n).tostring(
language=language) == '123 + x + (x - y) / (x + y)'
- assert as_ternary(x, y, z).tostring(language=language) == '(x ? y : z)'
- assert as_eq(x, y).tostring(language=language) == 'x == y'
- assert as_ne(x, y).tostring(language=language) == 'x != y'
- assert as_lt(x, y).tostring(language=language) == 'x < y'
- assert as_le(x, y).tostring(language=language) == 'x <= y'
- assert as_gt(x, y).tostring(language=language) == 'x > y'
- assert as_ge(x, y).tostring(language=language) == 'x >= y'
+ assert as_ternary(x, y, z).tostring(language=language) == "(x?y:z)"
+ assert as_eq(x, y).tostring(language=language) == "x == y"
+ assert as_ne(x, y).tostring(language=language) == "x != y"
+ assert as_lt(x, y).tostring(language=language) == "x < y"
+ assert as_le(x, y).tostring(language=language) == "x <= y"
+ assert as_gt(x, y).tostring(language=language) == "x > y"
+ assert as_ge(x, y).tostring(language=language) == "x >= y"
def test_operations(self):
x = as_symbol('x')
import sys
import numpy as np
cimport numpy as np
+cimport numpy.math as npmath
from libc.stdint cimport uintptr_t
out_array_data[i] = <float>random_func(state)
return out_array
+cdef int _check_array_cons_bounded_0_1(np.ndarray val, object name) except -1:
+ cdef double *val_data
+ cdef np.npy_intp i
+ cdef bint err = 0
+
+ if not np.PyArray_ISONESEGMENT(val) or np.PyArray_TYPE(val) != np.NPY_DOUBLE:
+ # slow path for non-contiguous arrays or any non-double dtypes
+ err = not np.all(np.greater_equal(val, 0)) or not np.all(np.less_equal(val, 1))
+ else:
+ val_data = <double *>np.PyArray_DATA(val)
+ for i in range(np.PyArray_SIZE(val)):
+ err = (not (val_data[i] >= 0)) or (not val_data[i] <= 1)
+ if err:
+ break
+ if err:
+ raise ValueError(f"{name} < 0, {name} > 1 or {name} contains NaNs")
+
+ return 0
cdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1:
if cons == CONS_NON_NEGATIVE:
elif np.any(np.less_equal(val, 0)):
raise ValueError(name + " <= 0")
elif cons == CONS_BOUNDED_0_1:
- if not np.all(np.greater_equal(val, 0)) or \
- not np.all(np.less_equal(val, 1)):
- raise ValueError("{0} < 0, {0} > 1 or {0} contains NaNs".format(name))
+ return _check_array_cons_bounded_0_1(val, name)
elif cons == CONS_BOUNDED_GT_0_1:
if not np.all(np.greater(val, 0)) or not np.all(np.less_equal(val, 1)):
raise ValueError("{0} <= 0, {0} > 1 or {0} contains NaNs".format(name))
AR_LIKE_i: List[int]
AR_LIKE_f: List[float]
+np.ndindex([1, 2, 3]) # E: No overload variant
np.unravel_index(AR_LIKE_f, (1, 2, 3)) # E: incompatible type
np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob") # E: No overload variant
np.mgrid[1] # E: Invalid index type
reveal_type(iter(np.ndenumerate(AR_LIKE_f))) # E: Iterator[Tuple[builtins.tuple[builtins.int], {double}]]
reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple[builtins.int], str_]]
+reveal_type(np.ndindex(1, 2, 3)) # E: numpy.ndindex
+reveal_type(np.ndindex((1, 2, 3))) # E: numpy.ndindex
reveal_type(iter(np.ndindex(1, 2, 3))) # E: Iterator[builtins.tuple[builtins.int]]
reveal_type(next(np.ndindex(1, 2, 3))) # E: builtins.tuple[builtins.int]
+import datetime as dt
from typing import Any, List, TypeVar
from pathlib import Path
nditer_obj: np.nditer
+date_scalar: dt.date
+date_seq: list[dt.date]
+timedelta_seq: list[dt.timedelta]
+
def func(a: int) -> bool: ...
reveal_type(next(b_f8)) # E: tuple[Any]
reveal_type(np.busday_count("2011-01", "2011-02")) # E: {int_}
reveal_type(np.busday_count(["2011-01"], "2011-02")) # E: ndarray[Any, dtype[{int_}]]
+reveal_type(np.busday_count(["2011-01"], date_scalar)) # E: ndarray[Any, dtype[{int_}]]
reveal_type(np.busday_offset(M, m)) # E: datetime64
+reveal_type(np.busday_offset(date_scalar, m)) # E: datetime64
reveal_type(np.busday_offset(M, 5)) # E: datetime64
reveal_type(np.busday_offset(AR_M, m)) # E: ndarray[Any, dtype[datetime64]]
+reveal_type(np.busday_offset(M, timedelta_seq)) # E: ndarray[Any, dtype[datetime64]]
reveal_type(np.busday_offset("2011-01", "2011-02", roll="forward")) # E: datetime64
reveal_type(np.busday_offset(["2011-01"], "2011-02", roll="forward")) # E: ndarray[Any, dtype[datetime64]]
reveal_type(np.is_busday("2012")) # E: bool_
+reveal_type(np.is_busday(date_scalar)) # E: bool_
reveal_type(np.is_busday(["2012"])) # E: ndarray[Any, dtype[bool_]]
reveal_type(np.datetime_as_string(M)) # E: str_
reveal_type(np.datetime_as_string(AR_M)) # E: ndarray[Any, dtype[str_]]
+reveal_type(np.busdaycalendar(holidays=date_seq)) # E: busdaycalendar
+reveal_type(np.busdaycalendar(holidays=[M])) # E: busdaycalendar
+
reveal_type(np.compare_chararrays("a", "b", "!=", rstrip=False)) # E: ndarray[Any, dtype[bool_]]
reveal_type(np.compare_chararrays(b"a", b"a", "==", True)) # E: ndarray[Any, dtype[bool_]]
#-----------------------------------
# Path to the release notes
-RELEASE_NOTES = 'doc/source/release/1.22.0-notes.rst'
+RELEASE_NOTES = 'doc/source/release/1.22.1-notes.rst'
#-------------------------------------------------------
# so that it is in sys.modules
import numpy.distutils.command.sdist
import setuptools
+if int(setuptools.__version__.split('.')[0]) >= 60:
+ raise RuntimeError(
+ "Setuptools version is '{}', version < '60.0.0' is required. "
+ "See pyproject.toml".format(setuptools.__version__))
# Initialize cmdclass from versioneer
from numpy.distutils.core import numpy_cmdclass
projects / platform / upstream / python3-numpy.git / commitdiff
