Metadata-Version: 1.2
Name: numpy
-Version: 1.20.2
+Version: 1.20.3
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
NumPy Docs
----------
-- https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt
+- https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt
SciPy.org wiki
================================
:ref:`NEP 29 <NEP29>` outlines which Python versions
are supported; For the first half of 2020, this will be Python >= 3.6. We test
-NumPy against all these versions every time we merge code to master. Binary
+NumPy against all these versions every time we merge code to main. Binary
installers may be available for a subset of these versions (see below).
OS X
release. It's best to discuss the timing on the mailing list first, in order
for people to get their commits in on time, get doc wiki edits merged, etc.
After a date is set, create a new maintenance/x.y.z branch, add new empty
-release notes for the next version in the master branch and update the Trac
+release notes for the next version in the main branch and update the Trac
Milestones.
gpg --send-keys <yourkeyid>
-Update the version of the master branch
----------------------------------------
+Update the version of the main branch
+-------------------------------------
Increment the release number in setup.py. Release candidates should have "rc1"
(or "rc2", "rcN") appended to the X.Y.Z format.
that needs modification is ``www/index.rst``. Search for ``News``.
+Update oldest-supported-numpy
+-----------------------------
+If this release is the first one to support a new Python version, or the first
+to provide wheels for a new platform or PyPy version, the version pinnings
+in https://github.com/scipy/oldest-supported-numpy should be updated.
+Either submit a PR with changes to ``setup.cfg`` there, or open an issue with
+info on needed changes.
+
+
Announce to the lists
---------------------
The release should be announced on the mailing lists of
done:
- Forward port changes in the release branch to release notes and release
- scripts, if any, to master branch.
+ scripts, if any, to main branch.
- Update the Milestones in Trac.
Pull requests merged
====================
-A total of 531 pull requests were merged for this release.
+A total of 532 pull requests were merged for this release.
* `#4808 <https://github.com/numpy/numpy/pull/4808>`__: ENH: Make the `mode` parameter of np.pad default to 'constant'
* `#8131 <https://github.com/numpy/numpy/pull/8131>`__: BUG: Fix help() formatting for deprecated functions.
* `#10855 <https://github.com/numpy/numpy/pull/10855>`__: ENH: Adding a count parameter to np.unpackbits
* `#11230 <https://github.com/numpy/numpy/pull/11230>`__: MAINT: More cleanup of einsum
* `#11233 <https://github.com/numpy/numpy/pull/11233>`__: BUG: ensure i0 does not change the shape.
+* `#11358 <https://github.com/numpy/numpy/pull/11358>`__: MAINT: Rewrite numpy.pad without concatenate
* `#11684 <https://github.com/numpy/numpy/pull/11684>`__: BUG: Raise when unravel_index, ravel_multi_index are given empty...
* `#11689 <https://github.com/numpy/numpy/pull/11689>`__: DOC: Add ref docs for C generic types.
* `#11721 <https://github.com/numpy/numpy/pull/11721>`__: BUG: Make `arr.ctypes.data` hold onto a reference to the underlying...
Pull requests merged
====================
-A total of 406 pull requests were merged for this release.
+A total of 413 pull requests were merged for this release.
* `#9301 <https://github.com/numpy/numpy/pull/9301>`__: DOC: added note to docstring of numpy.savez
* `#10151 <https://github.com/numpy/numpy/pull/10151>`__: BUG: Numpy scalar types sometimes have the same name
* `#12129 <https://github.com/numpy/numpy/pull/12129>`__: DOC: Improve axes shift description and example in np.tensordot
-* `#12205 <https://github.com/numpy/numpy/pull/12205>`__: MAINT: avoid relying on `np.generic.__name__` in `np.dtype.name`
+* `#12205 <https://github.com/numpy/numpy/pull/12205>`__: MAINT: avoid relying on ``np.generic.__name__`` in ``np.dtype.name``
* `#12284 <https://github.com/numpy/numpy/pull/12284>`__: ENH: supply our version of numpy.pxd, requires cython>=0.29
* `#12633 <https://github.com/numpy/numpy/pull/12633>`__: BUG: General fixes to f2py reference counts (dereferencing)
* `#12658 <https://github.com/numpy/numpy/pull/12658>`__: BUG: NaT now sorts to ends of arrays
* `#12828 <https://github.com/numpy/numpy/pull/12828>`__: DOC: Updates to nditer usage instructions
-* `#13003 <https://github.com/numpy/numpy/pull/13003>`__: BUG: Do not crash on recursive `.dtype` attribute lookup.
+* `#13003 <https://github.com/numpy/numpy/pull/13003>`__: BUG: Do not crash on recursive ``.dtype`` attribute lookup.
* `#13368 <https://github.com/numpy/numpy/pull/13368>`__: ENH: Use AVX for float32 implementation of np.sin & np.cos
* `#13605 <https://github.com/numpy/numpy/pull/13605>`__: DEP: Deprecate silent ignoring of bad data in fromfile/fromstring
-* `#13610 <https://github.com/numpy/numpy/pull/13610>`__: ENH: Always produce a consistent shape in the result of `argwhere`
+* `#13610 <https://github.com/numpy/numpy/pull/13610>`__: ENH: Always produce a consistent shape in the result of ``argwhere``
* `#13673 <https://github.com/numpy/numpy/pull/13673>`__: DOC: array(obj, dtype=dt) can downcast
* `#13698 <https://github.com/numpy/numpy/pull/13698>`__: DOC: Document ma.filled behavior with non-scalar fill_value
* `#13710 <https://github.com/numpy/numpy/pull/13710>`__: DOC: Add note to irfft-like functions about the default sizes
* `#13766 <https://github.com/numpy/numpy/pull/13766>`__: MAINT: Update NEP template.
* `#13794 <https://github.com/numpy/numpy/pull/13794>`__: ENH: random: Add the multivariate hypergeometric distribution.
* `#13799 <https://github.com/numpy/numpy/pull/13799>`__: DOC: Fix unrendered links
+* `#13802 <https://github.com/numpy/numpy/pull/13802>`__: BUG: Fixed maximum relative error reporting in assert_allclose
* `#13812 <https://github.com/numpy/numpy/pull/13812>`__: MAINT: Rewrite Floyd algorithm
* `#13825 <https://github.com/numpy/numpy/pull/13825>`__: DOC: Add missing macros to C-API documentation
* `#13829 <https://github.com/numpy/numpy/pull/13829>`__: ENH: Add axis argument to random.permutation and random.shuffle
* `#13892 <https://github.com/numpy/numpy/pull/13892>`__: DOC : Refactor Array API documentation -- Array Structure and...
* `#13895 <https://github.com/numpy/numpy/pull/13895>`__: DOC: Fix typo in "make_mask" documentation
* `#13896 <https://github.com/numpy/numpy/pull/13896>`__: MAINT: Delete unused _aliased_types.py
+* `#13899 <https://github.com/numpy/numpy/pull/13899>`__: MAINT: Change the type of error raised in set_printoptions
* `#13901 <https://github.com/numpy/numpy/pull/13901>`__: BLD: Remove Trusty dist in Travis CI build
* `#13907 <https://github.com/numpy/numpy/pull/13907>`__: BUG: Handle weird bytestrings in dtype()
* `#13908 <https://github.com/numpy/numpy/pull/13908>`__: ENH: use towncrier to build the release note
* `#13926 <https://github.com/numpy/numpy/pull/13926>`__: DOC: Remove explicit .next method calls with built-in next function...
* `#13928 <https://github.com/numpy/numpy/pull/13928>`__: DOC: Don't override MaskedArray.view documentation with the one...
* `#13930 <https://github.com/numpy/numpy/pull/13930>`__: BUG: Fix incorrect GIL release in array.nonzero
-* `#13935 <https://github.com/numpy/numpy/pull/13935>`__: MAINT: Warn if `_add_newdocs.py` is used to add docstrings to...
+* `#13935 <https://github.com/numpy/numpy/pull/13935>`__: MAINT: Warn if ``_add_newdocs.py`` is used to add docstrings to...
* `#13943 <https://github.com/numpy/numpy/pull/13943>`__: MAINT: Revert #13876, "MAINT,BUG,DOC: Fix errors in _add_newdocs"
* `#13944 <https://github.com/numpy/numpy/pull/13944>`__: MAINT,BUG,DOC: Fix errors in _add_newdocs
* `#13945 <https://github.com/numpy/numpy/pull/13945>`__: DOC, MAINT: emphasize random API changes, remove Generator.randint
* `#13950 <https://github.com/numpy/numpy/pull/13950>`__: Fixing failure on Python 2.7 on Windows 7
* `#13952 <https://github.com/numpy/numpy/pull/13952>`__: Fix a typo related to the range of indices
* `#13959 <https://github.com/numpy/numpy/pull/13959>`__: DOC: add space between words across lines
-* `#13964 <https://github.com/numpy/numpy/pull/13964>`__: BUG, DOC: add new recfunctions to `__all__`
+* `#13964 <https://github.com/numpy/numpy/pull/13964>`__: BUG, DOC: add new recfunctions to ``__all__``
* `#13967 <https://github.com/numpy/numpy/pull/13967>`__: DOC: Change (old) range() to np.arange()
* `#13968 <https://github.com/numpy/numpy/pull/13968>`__: DOC: improve np.sort docstring
* `#13970 <https://github.com/numpy/numpy/pull/13970>`__: DOC: spellcheck numpy/doc/broadcasting.py
* `#14076 <https://github.com/numpy/numpy/pull/14076>`__: TST: Add 3.8-dev to travisCI testing.
* `#14085 <https://github.com/numpy/numpy/pull/14085>`__: DOC: Add blank line above doctest for intersect1d
* `#14086 <https://github.com/numpy/numpy/pull/14086>`__: ENH: Propose standard policy for dropping support of old Python...
-* `#14089 <https://github.com/numpy/numpy/pull/14089>`__: DOC: Use `pip install .` where possible instead of calling setup.py
+* `#14089 <https://github.com/numpy/numpy/pull/14089>`__: DOC: Use ``pip install .`` where possible instead of calling setup.py
* `#14091 <https://github.com/numpy/numpy/pull/14091>`__: MAINT: adjustments to test_ufunc_noncontigous
* `#14092 <https://github.com/numpy/numpy/pull/14092>`__: MAINT: Improve NEP template
* `#14096 <https://github.com/numpy/numpy/pull/14096>`__: DOC: fix documentation of i and j for tri.
* `#14106 <https://github.com/numpy/numpy/pull/14106>`__: MAINT: remove duplicate variable assignments
* `#14108 <https://github.com/numpy/numpy/pull/14108>`__: BUG: initialize variable that is passed by pointer
* `#14110 <https://github.com/numpy/numpy/pull/14110>`__: DOC: fix typo in c-api/array.rst doc
+* `#14115 <https://github.com/numpy/numpy/pull/14115>`__: DOC: fix markup of news fragment readme
* `#14121 <https://github.com/numpy/numpy/pull/14121>`__: BUG: Add gcd/lcm definitions to npy_math.h
* `#14122 <https://github.com/numpy/numpy/pull/14122>`__: MAINT: Mark umath accuracy test xfail.
* `#14124 <https://github.com/numpy/numpy/pull/14124>`__: MAINT: Use equality instead of identity check with literal
* `#14153 <https://github.com/numpy/numpy/pull/14153>`__: TST: Allow fuss in testing strided/non-strided exp/log loops
* `#14170 <https://github.com/numpy/numpy/pull/14170>`__: NEP: Proposal for __duckarray__ protocol
* `#14171 <https://github.com/numpy/numpy/pull/14171>`__: BUG: Make advanced indexing result on read-only subclass writeable
+* `#14174 <https://github.com/numpy/numpy/pull/14174>`__: BUG: Check for existence of ``fromstr`` which used in ``fromstr_next_element``
* `#14178 <https://github.com/numpy/numpy/pull/14178>`__: TST: Clean up of test_pocketfft.py
* `#14181 <https://github.com/numpy/numpy/pull/14181>`__: DEP: Deprecate np.alen
+* `#14183 <https://github.com/numpy/numpy/pull/14183>`__: DOC: Fix misleading ``allclose`` docstring for ``equal_nan``
* `#14185 <https://github.com/numpy/numpy/pull/14185>`__: MAINT: Workaround for Intel compiler bug leading to failing test
-* `#14190 <https://github.com/numpy/numpy/pull/14190>`__: DOC: Fix hermitian argument docs in `svd`
+* `#14190 <https://github.com/numpy/numpy/pull/14190>`__: DOC: Fix hermitian argument docs in ``svd``
* `#14195 <https://github.com/numpy/numpy/pull/14195>`__: MAINT: Fix a docstring typo.
-* `#14196 <https://github.com/numpy/numpy/pull/14196>`__: DOC: Fix links in `/.github/CONTRIBUTING.md`.
+* `#14196 <https://github.com/numpy/numpy/pull/14196>`__: DOC: Fix links in ``/.github/CONTRIBUTING.md``.
* `#14197 <https://github.com/numpy/numpy/pull/14197>`__: ENH: Multivariate normal speedups
* `#14203 <https://github.com/numpy/numpy/pull/14203>`__: MAINT: Improve mismatch message of np.testing.assert_array_equal
* `#14204 <https://github.com/numpy/numpy/pull/14204>`__: DOC,MAINT: Move towncrier files and fixup categories
* `#14207 <https://github.com/numpy/numpy/pull/14207>`__: BUG: Fixed default BitGenerator name
* `#14209 <https://github.com/numpy/numpy/pull/14209>`__: BUG: Fix uint-overflow if padding with linear_ramp and negative...
* `#14216 <https://github.com/numpy/numpy/pull/14216>`__: ENH: Enable huge pages in all Linux builds
-* `#14217 <https://github.com/numpy/numpy/pull/14217>`__: BUG: Fix leak in the f2py-generated module init and `PyMem_Del`...
+* `#14217 <https://github.com/numpy/numpy/pull/14217>`__: BUG: Fix leak in the f2py-generated module init and ``PyMem_Del``...
* `#14219 <https://github.com/numpy/numpy/pull/14219>`__: DOC: new nan_to_num keywords are from 1.17 onwards
* `#14223 <https://github.com/numpy/numpy/pull/14223>`__: TST: Add tests for deprecated C functions (PyArray_As1D, PyArray_As1D)
-* `#14224 <https://github.com/numpy/numpy/pull/14224>`__: DOC: mention `take_along_axis` in `choose`
+* `#14224 <https://github.com/numpy/numpy/pull/14224>`__: DOC: mention ``take_along_axis`` in ``choose``
* `#14227 <https://github.com/numpy/numpy/pull/14227>`__: ENH: Parse complex number from string
* `#14231 <https://github.com/numpy/numpy/pull/14231>`__: DOC: update or remove outdated sourceforge links
* `#14234 <https://github.com/numpy/numpy/pull/14234>`__: MAINT: Better error message for norm
* `#14235 <https://github.com/numpy/numpy/pull/14235>`__: DOC: add backlinks to numpy.org
* `#14240 <https://github.com/numpy/numpy/pull/14240>`__: BUG: Don't fail when lexsorting some empty arrays.
-* `#14241 <https://github.com/numpy/numpy/pull/14241>`__: BUG: Fix segfault in `random.permutation(x)` when x is a string.
+* `#14241 <https://github.com/numpy/numpy/pull/14241>`__: BUG: Fix segfault in ``random.permutation(x)`` when x is a string.
* `#14245 <https://github.com/numpy/numpy/pull/14245>`__: Doc: fix a typo in NEP21
* `#14249 <https://github.com/numpy/numpy/pull/14249>`__: DOC: set status of NEP 28 (website redesign) to Accepted
* `#14250 <https://github.com/numpy/numpy/pull/14250>`__: BLD: MAINT: change default behavior of build flag appending.
* `#14252 <https://github.com/numpy/numpy/pull/14252>`__: BUG: Fixes StopIteration error from 'np.genfromtext' for empty...
-* `#14255 <https://github.com/numpy/numpy/pull/14255>`__: BUG: fix inconsistent axes ordering for axis in function `unique`
+* `#14255 <https://github.com/numpy/numpy/pull/14255>`__: BUG: fix inconsistent axes ordering for axis in function ``unique``
* `#14256 <https://github.com/numpy/numpy/pull/14256>`__: DEP: Deprecate load/dump functions in favour of pickle methods
* `#14257 <https://github.com/numpy/numpy/pull/14257>`__: MAINT: Update NEP-30
* `#14259 <https://github.com/numpy/numpy/pull/14259>`__: DEP: Deprecate arrayprint formatting functions
+* `#14263 <https://github.com/numpy/numpy/pull/14263>`__: DOC: Make Py3K docs C code snippets RST literal blocks
* `#14266 <https://github.com/numpy/numpy/pull/14266>`__: DOC: remove scipy.org from the breadcrumb formattiong
* `#14270 <https://github.com/numpy/numpy/pull/14270>`__: BUG: Fix formatting error in exception message
* `#14272 <https://github.com/numpy/numpy/pull/14272>`__: DOC: Address typos in dispatch docs
* `#14313 <https://github.com/numpy/numpy/pull/14313>`__: DOC: Clarify rules about broadcasting when empty arrays are involved.
* `#14321 <https://github.com/numpy/numpy/pull/14321>`__: TST, MAINT: bump to OpenBLAS 0.3.7 stable
* `#14325 <https://github.com/numpy/numpy/pull/14325>`__: DEP: numpy.testing.rand
-* `#14335 <https://github.com/numpy/numpy/pull/14335>`__: DEP: Deprecate class `SafeEval`
+* `#14335 <https://github.com/numpy/numpy/pull/14335>`__: DEP: Deprecate class ``SafeEval``
* `#14341 <https://github.com/numpy/numpy/pull/14341>`__: BUG: revert detecting and raising error on ragged arrays
-* `#14342 <https://github.com/numpy/numpy/pull/14342>`__: DOC: Improve documentation of `isscalar`.
+* `#14342 <https://github.com/numpy/numpy/pull/14342>`__: DOC: Improve documentation of ``isscalar``.
* `#14349 <https://github.com/numpy/numpy/pull/14349>`__: MAINT: Fix bloated mismatch error percentage in array comparisons.
* `#14351 <https://github.com/numpy/numpy/pull/14351>`__: DOC: Fix a minor typo in dispatch documentation.
* `#14352 <https://github.com/numpy/numpy/pull/14352>`__: MAINT: Remove redundant deprecation checks
* `#14475 <https://github.com/numpy/numpy/pull/14475>`__: DOC: add timedelta64 signature
* `#14477 <https://github.com/numpy/numpy/pull/14477>`__: MAINT: Extract raising of MemoryError to a helper function
* `#14483 <https://github.com/numpy/numpy/pull/14483>`__: BUG,MAINT: Some fixes and minor cleanup based on clang analysis
-* `#14484 <https://github.com/numpy/numpy/pull/14484>`__: MAINT: Add `NPY_UNUSED` and `const` qualified suggested by clang
+* `#14484 <https://github.com/numpy/numpy/pull/14484>`__: MAINT: Add ``NPY_UNUSED`` and ``const`` qualified suggested by clang
* `#14485 <https://github.com/numpy/numpy/pull/14485>`__: MAINT: Silence integer comparison build warnings in assert statements
* `#14486 <https://github.com/numpy/numpy/pull/14486>`__: MAINT: distutils: Add newline at the end of printed warnings.
* `#14490 <https://github.com/numpy/numpy/pull/14490>`__: BUG: random: Revert gh-14458 and refix gh-14557.
* `#14567 <https://github.com/numpy/numpy/pull/14567>`__: DEP: remove deprecated (and private) numpy.testing submodules.
* `#14568 <https://github.com/numpy/numpy/pull/14568>`__: BLD, DOC: fix gh-14518, add release note
* `#14570 <https://github.com/numpy/numpy/pull/14570>`__: BUG: importing build_src breaks setuptools monkeypatch for msvc14
-* `#14572 <https://github.com/numpy/numpy/pull/14572>`__: DOC: Note runtests.py `-- -s` method to use pytests `-s`
+* `#14572 <https://github.com/numpy/numpy/pull/14572>`__: DOC: Note runtests.py ``-- -s`` method to use pytests ``-s``
* `#14573 <https://github.com/numpy/numpy/pull/14573>`__: DOC: update submodule docstrings, remove info.py files
* `#14576 <https://github.com/numpy/numpy/pull/14576>`__: DOC: Document the NPY_SCALARKIND values as C variables.
* `#14582 <https://github.com/numpy/numpy/pull/14582>`__: MAINT: Bump pytest from 5.1.2 to 5.1.3
* `#14614 <https://github.com/numpy/numpy/pull/14614>`__: MAINT: Bump pytest from 5.1.3 to 5.2.0
* `#14615 <https://github.com/numpy/numpy/pull/14615>`__: MAINT: Add "MAINT" tag to dependabot commit msg
* `#14616 <https://github.com/numpy/numpy/pull/14616>`__: DOC: Updated sphinx directive formatting
-* `#14620 <https://github.com/numpy/numpy/pull/14620>`__: DEP: Finish deprecation of non-integer `num` in linspace
+* `#14620 <https://github.com/numpy/numpy/pull/14620>`__: DEP: Finish deprecation of non-integer ``num`` in linspace
* `#14621 <https://github.com/numpy/numpy/pull/14621>`__: DOC: s/OR/AND/ in np.logical_and docstring
* `#14623 <https://github.com/numpy/numpy/pull/14623>`__: DOC: misleading np.sinc() documentation
* `#14629 <https://github.com/numpy/numpy/pull/14629>`__: DOC: clarify residual in np.polyfit
* `#14630 <https://github.com/numpy/numpy/pull/14630>`__: BUILD: change to build_src --verbose-cfg, runtests.py --debug-info
* `#14631 <https://github.com/numpy/numpy/pull/14631>`__: BUG: always free clean_sep
-* `#14634 <https://github.com/numpy/numpy/pull/14634>`__: DOC: Create `class Extension` docstring and add it to documentation.
-* `#14636 <https://github.com/numpy/numpy/pull/14636>`__: DOC: add `printoptions` as a context manager to `set_printoptions`
+* `#14634 <https://github.com/numpy/numpy/pull/14634>`__: DOC: Create ``class Extension`` docstring and add it to documentation.
+* `#14636 <https://github.com/numpy/numpy/pull/14636>`__: DOC: add ``printoptions`` as a context manager to ``set_printoptions``
* `#14639 <https://github.com/numpy/numpy/pull/14639>`__: DOC: Fix typo in NEP 29
* `#14643 <https://github.com/numpy/numpy/pull/14643>`__: MAINT: Use scalar math power function directly
* `#14649 <https://github.com/numpy/numpy/pull/14649>`__: DOC: Add IPython to dependencies needed to build docs.
* `#14652 <https://github.com/numpy/numpy/pull/14652>`__: MAINT: Bump pytest-cov from 2.7.1 to 2.8.1
* `#14653 <https://github.com/numpy/numpy/pull/14653>`__: MAINT: Bump pytest from 5.2.0 to 5.2.1
* `#14654 <https://github.com/numpy/numpy/pull/14654>`__: MAINT: Bump pytz from 2019.2 to 2019.3
-* `#14656 <https://github.com/numpy/numpy/pull/14656>`__: MAINT: Use `extract_unit` throughout datetime
+* `#14656 <https://github.com/numpy/numpy/pull/14656>`__: MAINT: Use ``extract_unit`` throughout datetime
* `#14657 <https://github.com/numpy/numpy/pull/14657>`__: BUG: fix fromfile behavior when reading sub-array dtypes
* `#14662 <https://github.com/numpy/numpy/pull/14662>`__: BUG: random: Use correct length when axis is given to shuffle.
* `#14669 <https://github.com/numpy/numpy/pull/14669>`__: BUG: Do not rely on undefined behaviour to cast from float to...
* `#14674 <https://github.com/numpy/numpy/pull/14674>`__: NEP: add default-dtype-object-deprecation nep 34
* `#14681 <https://github.com/numpy/numpy/pull/14681>`__: MAINT: Remove unused boolean negative/subtract loops
-* `#14682 <https://github.com/numpy/numpy/pull/14682>`__: DEP: ufunc `out` argument must be a tuple for multiple outputs
-* `#14693 <https://github.com/numpy/numpy/pull/14693>`__: BUG: Fix `np.einsum` errors on Power9 Linux and z/Linux
+* `#14682 <https://github.com/numpy/numpy/pull/14682>`__: DEP: ufunc ``out`` argument must be a tuple for multiple outputs
+* `#14693 <https://github.com/numpy/numpy/pull/14693>`__: BUG: Fix ``np.einsum`` errors on Power9 Linux and z/Linux
* `#14696 <https://github.com/numpy/numpy/pull/14696>`__: DOC: Note release notes process changes on devdocs start page
* `#14699 <https://github.com/numpy/numpy/pull/14699>`__: Doc warnings
+* `#14703 <https://github.com/numpy/numpy/pull/14703>`__: TST: Adding CI stages, with one initial job to the Travis CI
* `#14705 <https://github.com/numpy/numpy/pull/14705>`__: DOC: Switch Markdown link to RST in NEP 29
* `#14709 <https://github.com/numpy/numpy/pull/14709>`__: TST: Divide Azure CI Pipelines into stages.
* `#14710 <https://github.com/numpy/numpy/pull/14710>`__: DEP: Finish the out kwarg deprecation for ufunc calls
* `#15058 <https://github.com/numpy/numpy/pull/15058>`__: API, DOC: change names to multivariate_hypergeometric, improve docs
* `#15059 <https://github.com/numpy/numpy/pull/15059>`__: REL: Prepare for NumPy 1.18.0 release.
* `#15109 <https://github.com/numpy/numpy/pull/15109>`__: TST: Check requires_memory immediately before the test
-* `#15111 <https://github.com/numpy/numpy/pull/15111>`__: ENH: Add support to sort timedelta64 `NaT` to end of the array
+* `#15111 <https://github.com/numpy/numpy/pull/15111>`__: ENH: Add support to sort timedelta64 ``NaT`` to end of the array
* `#15112 <https://github.com/numpy/numpy/pull/15112>`__: MAINT: follow-up cleanup for blas64 PR
* `#15113 <https://github.com/numpy/numpy/pull/15113>`__: ENH: f2py: add --f2cmap option for specifying the name of .f2py_f2cmap
* `#15114 <https://github.com/numpy/numpy/pull/15114>`__: ENH: add support for ILP64 OpenBLAS (without symbol suffix)
Pull requests merged
====================
-A total of 452 pull requests were merged for this release.
+A total of 488 pull requests were merged for this release.
* `#8255 <https://github.com/numpy/numpy/pull/8255>`__: ENH: add identity kwarg to frompyfunc
+* `#10600 <https://github.com/numpy/numpy/pull/10600>`__: DOC: Do not complain about contiguity when mutating ``ndarray.shape``
* `#12646 <https://github.com/numpy/numpy/pull/12646>`__: TST: check exception details in refguide_check.py
* `#13421 <https://github.com/numpy/numpy/pull/13421>`__: ENH: improve runtime detection of CPU features
* `#14326 <https://github.com/numpy/numpy/pull/14326>`__: TST: Add assert_array_equal test for big integer arrays.
* `#14376 <https://github.com/numpy/numpy/pull/14376>`__: MAINT: Remove unnecessary 'from __future__ import ...' statements
* `#14530 <https://github.com/numpy/numpy/pull/14530>`__: MAINT: Fix typos and copy edit NEP-0030.
* `#14546 <https://github.com/numpy/numpy/pull/14546>`__: DOC: NumPy for absolute beginners tutorial
-* `#14715 <https://github.com/numpy/numpy/pull/14715>`__: NEP: Proposal for array creation dispatching with `__array_function__`
+* `#14715 <https://github.com/numpy/numpy/pull/14715>`__: NEP: Proposal for array creation dispatching with ``__array_function__``
* `#14867 <https://github.com/numpy/numpy/pull/14867>`__: ENH: Use AVX-512F for np.maximum and np.minimum
* `#14924 <https://github.com/numpy/numpy/pull/14924>`__: BUG: Fix numpy.random.dirichlet returns NaN for small 'alpha'...
-* `#14933 <https://github.com/numpy/numpy/pull/14933>`__: API: Use `ResultType` in `PyArray_ConvertToCommonType`
+* `#14933 <https://github.com/numpy/numpy/pull/14933>`__: API: Use ``ResultType`` in ``PyArray_ConvertToCommonType``
+* `#14940 <https://github.com/numpy/numpy/pull/14940>`__: BUG: pickle the content of a scalar containing objects, not the...
* `#14942 <https://github.com/numpy/numpy/pull/14942>`__: MAINT,API: ignore and NULL fasttake/fastputmask ArrFuncs slots
* `#14981 <https://github.com/numpy/numpy/pull/14981>`__: BUG: Make ``ediff1d`` kwarg casting consistent
* `#14988 <https://github.com/numpy/numpy/pull/14988>`__: DOC: linalg: Include information about scipy.linalg.
* `#14995 <https://github.com/numpy/numpy/pull/14995>`__: BUG: Use ``__array__`` during dimension discovery
* `#15011 <https://github.com/numpy/numpy/pull/15011>`__: MAINT: cleanup compat.py3k.py
* `#15022 <https://github.com/numpy/numpy/pull/15022>`__: ENH: f2py: improve error messages
+* `#15024 <https://github.com/numpy/numpy/pull/15024>`__: DOC: clarify documentation for transpose()
* `#15028 <https://github.com/numpy/numpy/pull/15028>`__: [DOC] LaTeX: fix preamble (closes #15026)
* `#15035 <https://github.com/numpy/numpy/pull/15035>`__: BUG: add endfunction, endsubroutine to valid fortran end words
* `#15040 <https://github.com/numpy/numpy/pull/15040>`__: TST: Add test for object method (and general unary) loops
* `#15052 <https://github.com/numpy/numpy/pull/15052>`__: MAINT: follow-up cleanup for blas64 PR
* `#15054 <https://github.com/numpy/numpy/pull/15054>`__: DOC: add docstrings to refguide-check
* `#15066 <https://github.com/numpy/numpy/pull/15066>`__: Revert "DEP: issue deprecation warning when creating ragged array...
+* `#15068 <https://github.com/numpy/numpy/pull/15068>`__: ENH: Add support to sort timedelta64 ``NaT`` to end of the array
* `#15069 <https://github.com/numpy/numpy/pull/15069>`__: ENH: add support for ILP64 OpenBLAS (without symbol suffix)
* `#15070 <https://github.com/numpy/numpy/pull/15070>`__: DOC: correct version for NaT sort
* `#15072 <https://github.com/numpy/numpy/pull/15072>`__: TST: Check requires_memory immediately before the test
* `#15187 <https://github.com/numpy/numpy/pull/15187>`__: MAINT: unskip test on win32
* `#15189 <https://github.com/numpy/numpy/pull/15189>`__: ENH: Add property-based tests using Hypothesis
* `#15194 <https://github.com/numpy/numpy/pull/15194>`__: BUG: test, fix for c++ compilation
+* `#15195 <https://github.com/numpy/numpy/pull/15195>`__: MAINT: refactoring in np.core.records
* `#15196 <https://github.com/numpy/numpy/pull/15196>`__: DOC: Adding instructions for building documentation to developer...
* `#15197 <https://github.com/numpy/numpy/pull/15197>`__: DOC: NEP 37: A dispatch protocol for NumPy-like modules
* `#15203 <https://github.com/numpy/numpy/pull/15203>`__: MAINT: Do not use private Python function in testing
* `#15227 <https://github.com/numpy/numpy/pull/15227>`__: DOC: typo in release.rst
* `#15228 <https://github.com/numpy/numpy/pull/15228>`__: NEP: universal SIMD NEP 38
* `#15229 <https://github.com/numpy/numpy/pull/15229>`__: MAINT: Remove unused int_asbuffer
+* `#15230 <https://github.com/numpy/numpy/pull/15230>`__: BUG: do not emit warnings for np.sign, np.equal when using nan
+* `#15231 <https://github.com/numpy/numpy/pull/15231>`__: MAINT: Remove Python2 specific C module setup [part2]
* `#15232 <https://github.com/numpy/numpy/pull/15232>`__: MAINT: Cleaning up PY_MAJOR_VERSION/PY_VERSION_HEX
* `#15233 <https://github.com/numpy/numpy/pull/15233>`__: MAINT: Clean up more PY_VERSION_HEX
* `#15236 <https://github.com/numpy/numpy/pull/15236>`__: MAINT: Remove implicit inheritance from object class
* `#15241 <https://github.com/numpy/numpy/pull/15241>`__: MAINT: Remove references to non-existent sys.exc_clear()
* `#15242 <https://github.com/numpy/numpy/pull/15242>`__: DOC: Update HOWTO_RELEASE.rst
* `#15248 <https://github.com/numpy/numpy/pull/15248>`__: MAINT: cleanup use of sys.exc_info
-* `#15249 <https://github.com/numpy/numpy/pull/15249>`__: MAINT: Eliminate some calls to `eval`
+* `#15249 <https://github.com/numpy/numpy/pull/15249>`__: MAINT: Eliminate some calls to ``eval``
* `#15251 <https://github.com/numpy/numpy/pull/15251>`__: MAINT: Improve const-correctness of shapes and strides
* `#15253 <https://github.com/numpy/numpy/pull/15253>`__: DOC: clarify the effect of None parameters passed to ndarray.view
* `#15254 <https://github.com/numpy/numpy/pull/15254>`__: MAINT: Improve const-correctness of string arguments
* `#15255 <https://github.com/numpy/numpy/pull/15255>`__: MAINT: Delete numpy.distutils.compat
* `#15256 <https://github.com/numpy/numpy/pull/15256>`__: MAINT: Implement keyword-only arguments as syntax
* `#15260 <https://github.com/numpy/numpy/pull/15260>`__: MAINT: Remove FIXME comments introduced in the previous commit
-* `#15261 <https://github.com/numpy/numpy/pull/15261>`__: MAINT: Work with unicode strings in `dtype('i8,i8')`
+* `#15261 <https://github.com/numpy/numpy/pull/15261>`__: MAINT: Work with unicode strings in ``dtype('i8,i8')``
* `#15262 <https://github.com/numpy/numpy/pull/15262>`__: BUG: Use PyDict_GetItemWithError() instead of PyDict_GetItem()
* `#15263 <https://github.com/numpy/numpy/pull/15263>`__: MAINT: Remove python2 array_{get,set}slice
* `#15264 <https://github.com/numpy/numpy/pull/15264>`__: DOC: Add some missing functions in the list of available ufuncs.
* `#15280 <https://github.com/numpy/numpy/pull/15280>`__: BENCH: Add basic benchmarks for take and putmask
* `#15281 <https://github.com/numpy/numpy/pull/15281>`__: MAINT: Cleanup most PY3K #ifdef guards
* `#15282 <https://github.com/numpy/numpy/pull/15282>`__: DOC: BLD: add empty release notes for 1.19.0 to fix doc build...
+* `#15283 <https://github.com/numpy/numpy/pull/15283>`__: MAINT: Cleanup more NPY_PY3K
* `#15284 <https://github.com/numpy/numpy/pull/15284>`__: MAINT: Use a simpler return convention for internal functions
-* `#15285 <https://github.com/numpy/numpy/pull/15285>`__: MAINT: Simplify np.int_ inheritance
+* `#15285 <https://github.com/numpy/numpy/pull/15285>`__: MAINT: Simplify ``np.int_`` inheritance
* `#15286 <https://github.com/numpy/numpy/pull/15286>`__: DOC" Update np.full docstring.
* `#15287 <https://github.com/numpy/numpy/pull/15287>`__: MAINT: Express PyArray_DescrAlignConverter in terms of _convert_from_any
* `#15288 <https://github.com/numpy/numpy/pull/15288>`__: MAINT: Push down declarations in _convert_from_*
* `#15304 <https://github.com/numpy/numpy/pull/15304>`__: MAINT: Remove NPY_PY3K constant
* `#15305 <https://github.com/numpy/numpy/pull/15305>`__: MAINT: Remove sys.version checks in tests
* `#15307 <https://github.com/numpy/numpy/pull/15307>`__: MAINT: cleanup sys.version dependant code
-* `#15310 <https://github.com/numpy/numpy/pull/15310>`__: MAINT: Ensure `_convert_from_*` functions set errors
+* `#15310 <https://github.com/numpy/numpy/pull/15310>`__: MAINT: Ensure ``_convert_from_*`` functions set errors
* `#15312 <https://github.com/numpy/numpy/pull/15312>`__: MAINT: Avoid escaping unicode in error messages
* `#15315 <https://github.com/numpy/numpy/pull/15315>`__: MAINT: Change file extension of ma README to rst.
* `#15319 <https://github.com/numpy/numpy/pull/15319>`__: BUG: fix NameError in clip nan propagation tests
* `#15329 <https://github.com/numpy/numpy/pull/15329>`__: TST: move _no_tracing to testing._private, remove testing.support
* `#15333 <https://github.com/numpy/numpy/pull/15333>`__: BUG: Add some missing C error handling
* `#15335 <https://github.com/numpy/numpy/pull/15335>`__: MAINT: Remove sys.version checks
-* `#15336 <https://github.com/numpy/numpy/pull/15336>`__: DEP: Deprecate `->f->fastclip` at registration time
+* `#15336 <https://github.com/numpy/numpy/pull/15336>`__: DEP: Deprecate ``->f->fastclip`` at registration time
* `#15338 <https://github.com/numpy/numpy/pull/15338>`__: DOC: document site.cfg.example
* `#15350 <https://github.com/numpy/numpy/pull/15350>`__: MAINT: Fix mistype in histogramdd docstring
* `#15351 <https://github.com/numpy/numpy/pull/15351>`__: DOC, BLD: reword release note, upgrade sphinx version
* `#15353 <https://github.com/numpy/numpy/pull/15353>`__: MAINT: Remove unnecessary calls to PyArray_DATA from binomial...
* `#15354 <https://github.com/numpy/numpy/pull/15354>`__: MAINT: Bump pytest from 5.3.2 to 5.3.3
+* `#15355 <https://github.com/numpy/numpy/pull/15355>`__: MAINT: Const qualify UFunc inner loops
* `#15358 <https://github.com/numpy/numpy/pull/15358>`__: MAINT: Remove six
* `#15361 <https://github.com/numpy/numpy/pull/15361>`__: MAINT: Revise imports from collections.abc module
* `#15362 <https://github.com/numpy/numpy/pull/15362>`__: MAINT: remove internal functions required to handle Python2/3...
* `#15364 <https://github.com/numpy/numpy/pull/15364>`__: MAINT: Remove other uses of six module
* `#15366 <https://github.com/numpy/numpy/pull/15366>`__: MAINT: resolve pyflake F403 'from module import *' used
+* `#15367 <https://github.com/numpy/numpy/pull/15367>`__: DOC: Fix Multithreaded Generation example docs
* `#15368 <https://github.com/numpy/numpy/pull/15368>`__: MAINT: Update tox for supported Python versions
* `#15369 <https://github.com/numpy/numpy/pull/15369>`__: MAINT: simd: Avoid signed comparison warning
-* `#15370 <https://github.com/numpy/numpy/pull/15370>`__: DOC: Updating Chararry Buffer datatypes #15360
+* `#15370 <https://github.com/numpy/numpy/pull/15370>`__: DOC: Updating Chararry Buffer datatypes
+* `#15373 <https://github.com/numpy/numpy/pull/15373>`__: MAINT: Remove sys.version checks
* `#15374 <https://github.com/numpy/numpy/pull/15374>`__: TST: Simplify unicode test
-* `#15375 <https://github.com/numpy/numpy/pull/15375>`__: MAINT: Use `with open` when possible
+* `#15375 <https://github.com/numpy/numpy/pull/15375>`__: MAINT: Use ``with open`` when possible
* `#15377 <https://github.com/numpy/numpy/pull/15377>`__: MAINT: Cleanup python2 references
* `#15379 <https://github.com/numpy/numpy/pull/15379>`__: MAINT: Python2 Cleanups
* `#15381 <https://github.com/numpy/numpy/pull/15381>`__: DEP: add PendingDeprecation to matlib.py funky namespace
* `#15407 <https://github.com/numpy/numpy/pull/15407>`__: MAINT: Replace basestring with str.
* `#15408 <https://github.com/numpy/numpy/pull/15408>`__: ENH: Use AVX-512F for complex number arithmetic, absolute, square...
* `#15414 <https://github.com/numpy/numpy/pull/15414>`__: MAINT: Remove Python2 workarounds
+* `#15415 <https://github.com/numpy/numpy/pull/15415>`__: MAINT: Revert f2py Python 2.6 workaround
* `#15417 <https://github.com/numpy/numpy/pull/15417>`__: MAINT: Cleanup references to python2
* `#15418 <https://github.com/numpy/numpy/pull/15418>`__: MAINT, DOC: Remove use of old Python __builtin__, now known as...
* `#15421 <https://github.com/numpy/numpy/pull/15421>`__: ENH: Make use of ExitStack in npyio.py
* `#15422 <https://github.com/numpy/numpy/pull/15422>`__: MAINT: Inline gentype_getreadbuf
* `#15423 <https://github.com/numpy/numpy/pull/15423>`__: MAINT: Use f-strings for clarity.
+* `#15425 <https://github.com/numpy/numpy/pull/15425>`__: MAINT: dir(numpy) returns duplicate "testing"
+* `#15426 <https://github.com/numpy/numpy/pull/15426>`__: MAINT: Use the PyArrayScalar_VAL macro where possible
* `#15427 <https://github.com/numpy/numpy/pull/15427>`__: DEP: Schedule unused C-API functions for removal/disabling
* `#15428 <https://github.com/numpy/numpy/pull/15428>`__: DOC: Improve ndarray.ctypes example
* `#15429 <https://github.com/numpy/numpy/pull/15429>`__: DOC: distutils: Add a docstring to show_config().
* `#15434 <https://github.com/numpy/numpy/pull/15434>`__: MAINT: Updated polynomial to use fstrings
* `#15435 <https://github.com/numpy/numpy/pull/15435>`__: DOC: Fix Incorrect document in Beginner Docs
* `#15436 <https://github.com/numpy/numpy/pull/15436>`__: MAINT: Update core.py with fstrings (issue #15420)
-* `#15439 <https://github.com/numpy/numpy/pull/15439>`__: DOC: fix docstrings so `python tools/refguide-check --rst <file>...
+* `#15439 <https://github.com/numpy/numpy/pull/15439>`__: DOC: fix docstrings so ``python tools/refguide-check --rst <file>``...
* `#15441 <https://github.com/numpy/numpy/pull/15441>`__: MAINT: Tidy macros in scalar_new
* `#15444 <https://github.com/numpy/numpy/pull/15444>`__: MAINT: use 'yield from <expr>' for simple cases
* `#15445 <https://github.com/numpy/numpy/pull/15445>`__: MAINT: Bump pytest from 5.3.3 to 5.3.4
* `#15446 <https://github.com/numpy/numpy/pull/15446>`__: BUG: Reject nonsense arguments to scalar constructors
* `#15449 <https://github.com/numpy/numpy/pull/15449>`__: DOC: Update refguide_check note on how to skip code
-* `#15451 <https://github.com/numpy/numpy/pull/15451>`__: MAINT: Simplify `np.object_.__new__`
+* `#15451 <https://github.com/numpy/numpy/pull/15451>`__: MAINT: Simplify ``np.object_.__new__``
* `#15452 <https://github.com/numpy/numpy/pull/15452>`__: STY,MAINT: avoid 'multiple imports on one line'
+* `#15463 <https://github.com/numpy/numpy/pull/15463>`__: ENH: expose ``bit_generator`` and random C-API to cython
* `#15464 <https://github.com/numpy/numpy/pull/15464>`__: MAINT: Cleanup duplicate line in refguide_check
* `#15465 <https://github.com/numpy/numpy/pull/15465>`__: MAINT: cleanup unused imports; avoid redefinition of imports
* `#15468 <https://github.com/numpy/numpy/pull/15468>`__: BUG: Fix for SVD not always sorted with hermitian=True
* `#15474 <https://github.com/numpy/numpy/pull/15474>`__: MAINT: Eliminate messy _WORK macro
* `#15476 <https://github.com/numpy/numpy/pull/15476>`__: update result of rng.random(3) to current rng output
* `#15480 <https://github.com/numpy/numpy/pull/15480>`__: DOC: Correct get_state doc
-* `#15482 <https://github.com/numpy/numpy/pull/15482>`__: MAINT: Use `.identifier = val` to fill type structs
+* `#15482 <https://github.com/numpy/numpy/pull/15482>`__: MAINT: Use ``.identifier = val`` to fill type structs
* `#15483 <https://github.com/numpy/numpy/pull/15483>`__: [DOC] Mention behaviour of np.squeeze with one element
* `#15484 <https://github.com/numpy/numpy/pull/15484>`__: ENH: fixing generic error messages to be more specific in multiarray/descriptor.c
* `#15487 <https://github.com/numpy/numpy/pull/15487>`__: BUG: Fixing result of np quantile edge case
* `#15491 <https://github.com/numpy/numpy/pull/15491>`__: TST: mark the top 3 slowest tests to save ~10 seconds
* `#15493 <https://github.com/numpy/numpy/pull/15493>`__: MAINT: Bump pytest from 5.3.4 to 5.3.5
* `#15500 <https://github.com/numpy/numpy/pull/15500>`__: MAINT: Use True/False instead of 1/0 in np.dtype.__reduce__
-* `#15503 <https://github.com/numpy/numpy/pull/15503>`__: MAINT: Do not allow `copyswap` and friends to fail silently
+* `#15503 <https://github.com/numpy/numpy/pull/15503>`__: MAINT: Do not allow ``copyswap`` and friends to fail silently
* `#15504 <https://github.com/numpy/numpy/pull/15504>`__: DOC: Remove duplicated code in true_divide docstring
* `#15505 <https://github.com/numpy/numpy/pull/15505>`__: NEP 40: Informational NEP about current DTypes
+* `#15506 <https://github.com/numpy/numpy/pull/15506>`__: NEP 41: First steps towards improved Datatype Support
* `#15510 <https://github.com/numpy/numpy/pull/15510>`__: DOC: Update unique docstring example
* `#15511 <https://github.com/numpy/numpy/pull/15511>`__: MAINT: Large overhead in some random functions
* `#15516 <https://github.com/numpy/numpy/pull/15516>`__: TST: Fix missing output in refguide-check
* `#15521 <https://github.com/numpy/numpy/pull/15521>`__: MAINT: Simplify arraydescr_richcompare
-* `#15522 <https://github.com/numpy/numpy/pull/15522>`__: MAINT: Fix internal misuses of `NPY_TITLE_KEY`
+* `#15522 <https://github.com/numpy/numpy/pull/15522>`__: MAINT: Fix internal misuses of ``NPY_TITLE_KEY``
* `#15524 <https://github.com/numpy/numpy/pull/15524>`__: DOC: Update instructions for building/archiving docs.
* `#15526 <https://github.com/numpy/numpy/pull/15526>`__: BUG: Fix inline assembly that detects cpu features on x86(32bit)
* `#15532 <https://github.com/numpy/numpy/pull/15532>`__: update doctests, small bugs and changes of repr
* `#15543 <https://github.com/numpy/numpy/pull/15543>`__: NEP: edit and move NEP 38 to accepted status
* `#15547 <https://github.com/numpy/numpy/pull/15547>`__: MAINT: Refresh Doxyfile and modernize numpyfilter.py
* `#15549 <https://github.com/numpy/numpy/pull/15549>`__: TST: Accuracy test float32 sin/cos/exp/log for AVX platforms
-* `#15550 <https://github.com/numpy/numpy/pull/15550>`__: DOC: Improve the `numpy.linalg.eig` docstring.
+* `#15550 <https://github.com/numpy/numpy/pull/15550>`__: DOC: Improve the ``numpy.linalg.eig`` docstring.
+* `#15553 <https://github.com/numpy/numpy/pull/15553>`__: BUG: Added missing error check in ``ndarray.__contains__``
* `#15554 <https://github.com/numpy/numpy/pull/15554>`__: NEP 44 - Restructuring the NumPy Documentation
* `#15556 <https://github.com/numpy/numpy/pull/15556>`__: TST: (Travis CI) Use full python3-dbg path for virtual env creation
* `#15560 <https://github.com/numpy/numpy/pull/15560>`__: BUG, DOC: restore missing import
* `#15566 <https://github.com/numpy/numpy/pull/15566>`__: DOC: Removing bad practices from quick start + some PEP8
* `#15574 <https://github.com/numpy/numpy/pull/15574>`__: TST: Do not create symbolic link named gfortran.
* `#15575 <https://github.com/numpy/numpy/pull/15575>`__: DOC: Document caveat in random.uniform
+* `#15577 <https://github.com/numpy/numpy/pull/15577>`__: TST: Test division by zero both with scalar and with array
* `#15579 <https://github.com/numpy/numpy/pull/15579>`__: DOC: numpy.clip is equivalent to minimum(..., maximum(...))
* `#15582 <https://github.com/numpy/numpy/pull/15582>`__: MAINT: Bump cython from 0.29.14 to 0.29.15
* `#15583 <https://github.com/numpy/numpy/pull/15583>`__: MAINT: Bump hypothesis from 5.3.0 to 5.5.4
* `#15600 <https://github.com/numpy/numpy/pull/15600>`__: TST: use manylinux2010 docker instead of ubuntu
* `#15610 <https://github.com/numpy/numpy/pull/15610>`__: TST: mask DeprecationWarning in xfailed test
* `#15612 <https://github.com/numpy/numpy/pull/15612>`__: BUG: Fix bug in AVX-512F np.maximum and np.minimum
+* `#15614 <https://github.com/numpy/numpy/pull/15614>`__: DOC: Reword docstring for assert_equal
* `#15615 <https://github.com/numpy/numpy/pull/15615>`__: BUG: Remove check requiring natural alignment of float/double...
* `#15616 <https://github.com/numpy/numpy/pull/15616>`__: DOC: Add missing imports, definitions and dummy file
* `#15619 <https://github.com/numpy/numpy/pull/15619>`__: DOC: Fix documentation for apply_along_axis
* `#15631 <https://github.com/numpy/numpy/pull/15631>`__: MAINT: Pull identical line out of conditional.
* `#15633 <https://github.com/numpy/numpy/pull/15633>`__: DOC: remove broken link in f2py tutorial
* `#15639 <https://github.com/numpy/numpy/pull/15639>`__: BLD: update openblas download to new location, use manylinux2010-base
+* `#15644 <https://github.com/numpy/numpy/pull/15644>`__: DOC: Update to clarify actual behavior real_if_(all elements)_close
* `#15648 <https://github.com/numpy/numpy/pull/15648>`__: MAINT: AVX512 implementation with intrinsic for float64 input...
* `#15653 <https://github.com/numpy/numpy/pull/15653>`__: BLD: update OpenBLAS to pre-0.3.9 version
-* `#15662 <https://github.com/numpy/numpy/pull/15662>`__: DOC: Refactor `np.polynomial` docs using `automodule`
+* `#15662 <https://github.com/numpy/numpy/pull/15662>`__: DOC: Refactor ``np.polynomial`` docs using ``automodule``
* `#15665 <https://github.com/numpy/numpy/pull/15665>`__: BUG: fix doctest exception messages
* `#15672 <https://github.com/numpy/numpy/pull/15672>`__: MAINT: Added comment pointing FIXME to relevant PR.
* `#15673 <https://github.com/numpy/numpy/pull/15673>`__: DOC: Make extension module wording more clear
* `#15680 <https://github.com/numpy/numpy/pull/15680>`__: DOC: Improve Benchmark README with environment setup and more...
* `#15682 <https://github.com/numpy/numpy/pull/15682>`__: MAINT: Bump hypothesis from 5.5.4 to 5.6.0
* `#15683 <https://github.com/numpy/numpy/pull/15683>`__: NEP: move NEP 44 to accepted status
+* `#15685 <https://github.com/numpy/numpy/pull/15685>`__: ENH: Add ``subok`` parameter to np.copy function (cf. #6509)
* `#15694 <https://github.com/numpy/numpy/pull/15694>`__: DOC: Fix indexing docs to pass refguide
* `#15695 <https://github.com/numpy/numpy/pull/15695>`__: MAINT: Test during import to detect bugs with Accelerate(MacOS)...
* `#15696 <https://github.com/numpy/numpy/pull/15696>`__: MAINT: Add a fast path to var for complex input
* `#15701 <https://github.com/numpy/numpy/pull/15701>`__: MAINT: Convert shebang from python to python3 (#15687)
* `#15702 <https://github.com/numpy/numpy/pull/15702>`__: MAINT: replace optparse with argparse for 'doc' and 'tools' scripts
* `#15703 <https://github.com/numpy/numpy/pull/15703>`__: DOC: Fix quickstart doc to pass refguide
+* `#15705 <https://github.com/numpy/numpy/pull/15705>`__: DOC: Change list to tuple in example description.
* `#15706 <https://github.com/numpy/numpy/pull/15706>`__: MAINT: Fixing typos in f2py comments and code.
* `#15710 <https://github.com/numpy/numpy/pull/15710>`__: DOC: fix SVD tutorial to pass refguide
* `#15714 <https://github.com/numpy/numpy/pull/15714>`__: MAINT: use list-based APIs to call subprocesses
-* `#15715 <https://github.com/numpy/numpy/pull/15715>`__: ENH: update numpy.linalg.multi_dot to accept an `out` argument
+* `#15715 <https://github.com/numpy/numpy/pull/15715>`__: ENH: update numpy.linalg.multi_dot to accept an ``out`` argument
* `#15716 <https://github.com/numpy/numpy/pull/15716>`__: TST: always use 'python -mpip' not 'pip'
* `#15717 <https://github.com/numpy/numpy/pull/15717>`__: DOC: update datetime reference to pass refguide
* `#15718 <https://github.com/numpy/numpy/pull/15718>`__: DOC: Fix coremath.rst to fix refguide_check
* `#15723 <https://github.com/numpy/numpy/pull/15723>`__: BUG: fix logic error when nm fails on 32-bit
* `#15724 <https://github.com/numpy/numpy/pull/15724>`__: TST: Remove nose from the test_requirements.txt file.
* `#15733 <https://github.com/numpy/numpy/pull/15733>`__: DOC: Allow NEPs to link to python, numpy, scipy, and matplotlib...
+* `#15735 <https://github.com/numpy/numpy/pull/15735>`__: DOC: LICENSE 2019 -> 2020
* `#15736 <https://github.com/numpy/numpy/pull/15736>`__: BUG: Guarantee array is in valid state after memory error occurs...
* `#15738 <https://github.com/numpy/numpy/pull/15738>`__: MAINT: Remove non-native byte order from _var check.
* `#15740 <https://github.com/numpy/numpy/pull/15740>`__: MAINT: Add better error handling in linalg.norm for vectors and...
* `#15769 <https://github.com/numpy/numpy/pull/15769>`__: ENH: Allow toggling madvise hugepage and fix default
* `#15771 <https://github.com/numpy/numpy/pull/15771>`__: DOC: Fix runtests example in developer docs
* `#15773 <https://github.com/numpy/numpy/pull/15773>`__: DEP: Make issubdtype consistent for types and dtypes
-* `#15774 <https://github.com/numpy/numpy/pull/15774>`__: MAINT: remove useless `global` statements
+* `#15774 <https://github.com/numpy/numpy/pull/15774>`__: MAINT: remove useless ``global`` statements
* `#15778 <https://github.com/numpy/numpy/pull/15778>`__: BLD: Add requirements.txt file for building docs
* `#15781 <https://github.com/numpy/numpy/pull/15781>`__: BUG: don't add 'public' or 'private' if the other one exists
-* `#15784 <https://github.com/numpy/numpy/pull/15784>`__: ENH: Use TypeError in `np.array` for python consistency
+* `#15784 <https://github.com/numpy/numpy/pull/15784>`__: ENH: Use TypeError in ``np.array`` for python consistency
* `#15794 <https://github.com/numpy/numpy/pull/15794>`__: BUG: Add basic __format__ for masked element to fix incorrect...
* `#15797 <https://github.com/numpy/numpy/pull/15797>`__: TST: Add unit test for out=None of np.einsum
* `#15799 <https://github.com/numpy/numpy/pull/15799>`__: MAINT: Cleanups to np.insert and np.delete
* `#15800 <https://github.com/numpy/numpy/pull/15800>`__: BUG: Add error-checking versions of strided casts.
-* `#15802 <https://github.com/numpy/numpy/pull/15802>`__: DEP: Make `np.insert` and `np.delete` on 0d arrays with an axis...
-* `#15803 <https://github.com/numpy/numpy/pull/15803>`__: DOC: correct possible list lengths for `extobj` in ufunc calls
+* `#15802 <https://github.com/numpy/numpy/pull/15802>`__: DEP: Make ``np.insert`` and ``np.delete`` on 0d arrays with an axis...
+* `#15803 <https://github.com/numpy/numpy/pull/15803>`__: DOC: correct possible list lengths for ``extobj`` in ufunc calls
* `#15804 <https://github.com/numpy/numpy/pull/15804>`__: DEP: Make np.delete on out-of-bounds indices an error
-* `#15805 <https://github.com/numpy/numpy/pull/15805>`__: DEP: Forbid passing non-integral index arrays to `insert` and...
+* `#15805 <https://github.com/numpy/numpy/pull/15805>`__: DEP: Forbid passing non-integral index arrays to ``insert`` and...
* `#15806 <https://github.com/numpy/numpy/pull/15806>`__: TST: Parametrize sort test
* `#15809 <https://github.com/numpy/numpy/pull/15809>`__: TST: switch PyPy job with CPython
* `#15812 <https://github.com/numpy/numpy/pull/15812>`__: TST: Remove code that is not supposed to warn out of warning...
* `#15815 <https://github.com/numpy/numpy/pull/15815>`__: DEP: Do not cast boolean indices to integers in np.delete
* `#15816 <https://github.com/numpy/numpy/pull/15816>`__: MAINT: simplify code that assumes str/unicode and int/long are...
+* `#15827 <https://github.com/numpy/numpy/pull/15827>`__: BUG: Break on all errors when performing strided casts.
* `#15830 <https://github.com/numpy/numpy/pull/15830>`__: MAINT: pathlib and hashlib are in stdlib in Python 3.5+
-* `#15832 <https://github.com/numpy/numpy/pull/15832>`__: ENH: improved error message `IndexError: too many indices for...
+* `#15832 <https://github.com/numpy/numpy/pull/15832>`__: ENH: improved error message ``IndexError: too many indices for``...
+* `#15834 <https://github.com/numpy/numpy/pull/15834>`__: NEP: Add paragraph to NEP 41 about no array-object use and fix...
* `#15836 <https://github.com/numpy/numpy/pull/15836>`__: BUG: Fix IndexError for illegal axis in np.mean
* `#15839 <https://github.com/numpy/numpy/pull/15839>`__: DOC: Minor fix to _hist_bin_fd documentation
-* `#15840 <https://github.com/numpy/numpy/pull/15840>`__: BUG,DEP: Make `scalar.__round__()` behave like pythons round
+* `#15840 <https://github.com/numpy/numpy/pull/15840>`__: BUG,DEP: Make ``scalar.__round__()`` behave like pythons round
* `#15843 <https://github.com/numpy/numpy/pull/15843>`__: DOC: First steps towards docs restructuring (NEP 44)
* `#15848 <https://github.com/numpy/numpy/pull/15848>`__: DOC, TST: enable refguide_check in circleci
* `#15850 <https://github.com/numpy/numpy/pull/15850>`__: DOC: fix typo in C-API reference
* `#15866 <https://github.com/numpy/numpy/pull/15866>`__: MAINT: Bump cython from 0.29.15 to 0.29.16
* `#15867 <https://github.com/numpy/numpy/pull/15867>`__: DEP: Deprecate ndarray.tostring()
* `#15868 <https://github.com/numpy/numpy/pull/15868>`__: TST: use draft OpenBLAS build
+* `#15870 <https://github.com/numpy/numpy/pull/15870>`__: ENH: Add keepdims argument to count_nonzero
* `#15872 <https://github.com/numpy/numpy/pull/15872>`__: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
-* `#15876 <https://github.com/numpy/numpy/pull/15876>`__: BUG: Check that `pvals` is 1D in `_generator.multinomial`.
+* `#15876 <https://github.com/numpy/numpy/pull/15876>`__: BUG: Check that ``pvals`` is 1D in ``_generator.multinomial``.
* `#15877 <https://github.com/numpy/numpy/pull/15877>`__: DOC: Add missing signature from nditer docstring
* `#15881 <https://github.com/numpy/numpy/pull/15881>`__: BUG: Fix empty_like to respect shape=()
* `#15882 <https://github.com/numpy/numpy/pull/15882>`__: BUG: Do not ignore empty tuple of strides in ndarray.__new__
* `#15884 <https://github.com/numpy/numpy/pull/15884>`__: BUG: Setting a 0d array's strides to themselves should be legal
* `#15885 <https://github.com/numpy/numpy/pull/15885>`__: BUG: Respect itershape=() in nditer
* `#15887 <https://github.com/numpy/numpy/pull/15887>`__: MAINT: Clean-up 'next = __next__' used for Python 2 compatibility
+* `#15891 <https://github.com/numpy/numpy/pull/15891>`__: DOC: Clarify docs on mixed advanced indexing and slicing
* `#15893 <https://github.com/numpy/numpy/pull/15893>`__: TST: Run test_large_zip in a child process
* `#15894 <https://github.com/numpy/numpy/pull/15894>`__: DOC: Add missing doc of numpy.ma.apply_over_axes in API list.
* `#15899 <https://github.com/numpy/numpy/pull/15899>`__: DOC: Improve record module documentation
* `#15901 <https://github.com/numpy/numpy/pull/15901>`__: DOC: Fixed order of items and link to mailing list in dev docs...
* `#15903 <https://github.com/numpy/numpy/pull/15903>`__: BLD: report clang version on macOS
-* `#15904 <https://github.com/numpy/numpy/pull/15904>`__: MAINT: records: Remove private `format_parser._descr` attribute
+* `#15904 <https://github.com/numpy/numpy/pull/15904>`__: MAINT: records: Remove private ``format_parser._descr`` attribute
+* `#15907 <https://github.com/numpy/numpy/pull/15907>`__: DOC: Update documentation w.r.t. NPY_RELAXED_STRIDES_CHECKING
* `#15914 <https://github.com/numpy/numpy/pull/15914>`__: BUG: random: Disallow p=0 in negative_binomial
+* `#15920 <https://github.com/numpy/numpy/pull/15920>`__: DOC: Improve docstring for numpy.linalg.lstsq
* `#15921 <https://github.com/numpy/numpy/pull/15921>`__: ENH: Use sysconfig instead of probing Makefile
* `#15928 <https://github.com/numpy/numpy/pull/15928>`__: DOC: Update np.copy docstring to include ragged case
* `#15931 <https://github.com/numpy/numpy/pull/15931>`__: DOC: Correct private function name to PyArray_AdaptFlexibleDType
-* `#15936 <https://github.com/numpy/numpy/pull/15936>`__: MAINT: Fix capitalization in error message in `mtrand.pyx`
+* `#15936 <https://github.com/numpy/numpy/pull/15936>`__: MAINT: Fix capitalization in error message in ``mtrand.pyx``
+* `#15938 <https://github.com/numpy/numpy/pull/15938>`__: BUG: Add _LARGE_FILES to def_macros[] when platform is AIX.
* `#15939 <https://github.com/numpy/numpy/pull/15939>`__: DOC: Update np.rollaxis docstring
* `#15949 <https://github.com/numpy/numpy/pull/15949>`__: BUG: fix AttributeError on accessing object in nested MaskedArray.
-* `#15951 <https://github.com/numpy/numpy/pull/15951>`__: BUG: Alpha parameter must be 1D in `generator.dirichlet`
+* `#15951 <https://github.com/numpy/numpy/pull/15951>`__: BUG: Alpha parameter must be 1D in ``generator.dirichlet``
* `#15953 <https://github.com/numpy/numpy/pull/15953>`__: NEP: minor maintenance, update filename and fix a cross-reference
* `#15964 <https://github.com/numpy/numpy/pull/15964>`__: MAINT: Bump hypothesis from 5.8.0 to 5.8.3
* `#15967 <https://github.com/numpy/numpy/pull/15967>`__: TST: Add slow_pypy support
* `#15968 <https://github.com/numpy/numpy/pull/15968>`__: DOC: Added note to angle function docstring about angle(0) being...
* `#15982 <https://github.com/numpy/numpy/pull/15982>`__: MAINT/BUG: Cleanup and minor fixes to conform_reduce_result
-* `#15985 <https://github.com/numpy/numpy/pull/15985>`__: BUG: Avoid duplication in stack trace of `linspace(a, b, num=1.5)`
-* `#15988 <https://github.com/numpy/numpy/pull/15988>`__: BUG: Fix inf and NaN-warnings in half float `nextafter`
+* `#15985 <https://github.com/numpy/numpy/pull/15985>`__: BUG: Avoid duplication in stack trace of ``linspace(a, b, num=1.5)``
+* `#15988 <https://github.com/numpy/numpy/pull/15988>`__: BUG: Fix inf and NaN-warnings in half float ``nextafter``
* `#15989 <https://github.com/numpy/numpy/pull/15989>`__: MAINT: Remove 0d check for PyArray_ISONESEGMENT
* `#15990 <https://github.com/numpy/numpy/pull/15990>`__: DEV: Pass additional runtests.py args to ASV
+* `#15991 <https://github.com/numpy/numpy/pull/15991>`__: BUG: max/min of a masked array dtype fix
* `#15993 <https://github.com/numpy/numpy/pull/15993>`__: DOC: Fix method documentation of function sort in MaskedArray
* `#16000 <https://github.com/numpy/numpy/pull/16000>`__: NEP: Improve Value Based Casting paragraph in NEP 40
* `#16001 <https://github.com/numpy/numpy/pull/16001>`__: DOC: add note on flatten ordering in matlab page
* `#16038 <https://github.com/numpy/numpy/pull/16038>`__: MAINT,TST: Move _repr_latex tests to test_printing.
* `#16041 <https://github.com/numpy/numpy/pull/16041>`__: BUG: missing 'f' prefix for fstring
* `#16042 <https://github.com/numpy/numpy/pull/16042>`__: ENH: Fix exception causes in build_ext.py
+* `#16043 <https://github.com/numpy/numpy/pull/16043>`__: DOC: Add converters example to the loadtxt docstring
+* `#16051 <https://github.com/numpy/numpy/pull/16051>`__: DOC: Add missing bracket
* `#16053 <https://github.com/numpy/numpy/pull/16053>`__: DOC: Small typo fixes to NEP 40.
* `#16054 <https://github.com/numpy/numpy/pull/16054>`__: DOC, BLD: update release howto and walkthrough for ananconda.org...
* `#16061 <https://github.com/numpy/numpy/pull/16061>`__: ENH: Chained exceptions in linalg.py and polyutils.py
* `#16077 <https://github.com/numpy/numpy/pull/16077>`__: BLD: fix path to libgfortran on macOS
* `#16078 <https://github.com/numpy/numpy/pull/16078>`__: DOC: Add axis to random module "new or different" docs
* `#16079 <https://github.com/numpy/numpy/pull/16079>`__: DOC,BLD: Limit timeit iterations in random docs.
+* `#16080 <https://github.com/numpy/numpy/pull/16080>`__: BUG: numpy.einsum indexing arrays now accept numpy int type
* `#16081 <https://github.com/numpy/numpy/pull/16081>`__: DOC: add note on type casting to numpy.left_shift().
* `#16083 <https://github.com/numpy/numpy/pull/16083>`__: DOC: improve development debugging doc
* `#16084 <https://github.com/numpy/numpy/pull/16084>`__: DOC: tweak neps/scope.rst
* `#16097 <https://github.com/numpy/numpy/pull/16097>`__: MAINT, DOC: Improve grammar on a comment in the quickstart
* `#16100 <https://github.com/numpy/numpy/pull/16100>`__: NEP 41: Accept NEP 41 and add DType<->scalar duplication paragraph
* `#16101 <https://github.com/numpy/numpy/pull/16101>`__: BLD: put openblas library in local directory on windows
+* `#16102 <https://github.com/numpy/numpy/pull/16102>`__: ENH: correct identity for logaddexp2 ufunc: -inf
* `#16113 <https://github.com/numpy/numpy/pull/16113>`__: MAINT: Fix random.PCG64 signature
* `#16119 <https://github.com/numpy/numpy/pull/16119>`__: DOC: Move misplaced news fragment for gh-13421
* `#16122 <https://github.com/numpy/numpy/pull/16122>`__: DOC: Fix links for NEP 40 in NEP 41
* `#16125 <https://github.com/numpy/numpy/pull/16125>`__: BUG: lib: Fix a problem with vectorize with default parameters.
+* `#16128 <https://github.com/numpy/numpy/pull/16128>`__: ENH: Add equal_nan keyword argument to array_equal
* `#16129 <https://github.com/numpy/numpy/pull/16129>`__: ENH: Better error message when ``bins`` has float value in ``histogramdd``.
* `#16133 <https://github.com/numpy/numpy/pull/16133>`__: MAINT: Unify casting error creation (outside the iterator)
* `#16141 <https://github.com/numpy/numpy/pull/16141>`__: BENCH: Default to building HEAD instead of master
* `#16344 <https://github.com/numpy/numpy/pull/16344>`__: BUG: Allow attaching documentation twice in add_docstring
* `#16355 <https://github.com/numpy/numpy/pull/16355>`__: MAINT: Remove f-strings in setup.py. (gh-16346)
* `#16356 <https://github.com/numpy/numpy/pull/16356>`__: BUG: Indentation for docstrings
-* `#16358 <https://github.com/numpy/numpy/pull/16358>`__: BUG: Fix dtype leak in `PyArray_FromAny` error path
+* `#16358 <https://github.com/numpy/numpy/pull/16358>`__: BUG: Fix dtype leak in ``PyArray_FromAny`` error path
* `#16383 <https://github.com/numpy/numpy/pull/16383>`__: ENH: Optimize Cpu feature detect in X86, fix for GCC on macOS...
* `#16398 <https://github.com/numpy/numpy/pull/16398>`__: MAINT: core: Use a raw string for the fromstring docstring.
* `#16399 <https://github.com/numpy/numpy/pull/16399>`__: MAINT: Make ctypes optional on Windows
Pull requests merged
====================
-A total of 684 pull requests were merged for this release.
+A total of 716 pull requests were merged for this release.
* `#13516 <https://github.com/numpy/numpy/pull/13516>`__: ENH: enable multi-platform SIMD compiler optimizations
* `#14779 <https://github.com/numpy/numpy/pull/14779>`__: NEP 36 (fair play)
* `#14882 <https://github.com/numpy/numpy/pull/14882>`__: DEP: Deprecate aliases of builtin types in python 3.7+
-* `#15037 <https://github.com/numpy/numpy/pull/15037>`__: BUG: `np.resize` negative shape and subclasses edge case fixes
-* `#15121 <https://github.com/numpy/numpy/pull/15121>`__: ENH: random: Add the method `permuted` to Generator.
+* `#15037 <https://github.com/numpy/numpy/pull/15037>`__: BUG: ``np.resize`` negative shape and subclasses edge case fixes
+* `#15121 <https://github.com/numpy/numpy/pull/15121>`__: ENH: random: Add the method ``permuted`` to Generator.
* `#15162 <https://github.com/numpy/numpy/pull/15162>`__: BUG,MAINT: Fix issues with non-reduce broadcasting axes
* `#15471 <https://github.com/numpy/numpy/pull/15471>`__: BUG: Ensure PyArray_FromScalar always returns the requested dtype
* `#15507 <https://github.com/numpy/numpy/pull/15507>`__: NEP 42: Technical decisions for new DTypes
* `#15508 <https://github.com/numpy/numpy/pull/15508>`__: API: Create Preliminary DTypeMeta class and np.dtype subclasses
+* `#15551 <https://github.com/numpy/numpy/pull/15551>`__: DOC: Simd optimization documentation
* `#15604 <https://github.com/numpy/numpy/pull/15604>`__: MAINT: Avoid exception in NpzFile destructor if constructor raises...
-* `#15666 <https://github.com/numpy/numpy/pull/15666>`__: ENH: Improved `__str__` for polynomials
+* `#15666 <https://github.com/numpy/numpy/pull/15666>`__: ENH: Improved ``__str__`` for polynomials
* `#15759 <https://github.com/numpy/numpy/pull/15759>`__: BUILD: Remove Accelerate support
* `#15791 <https://github.com/numpy/numpy/pull/15791>`__: [DOC] Added tutorial about the numpy.ma module.
* `#15852 <https://github.com/numpy/numpy/pull/15852>`__: ENH: Add where argument to np.mean
* `#15886 <https://github.com/numpy/numpy/pull/15886>`__: DEP: Deprecate passing shape=None to mean shape=()
* `#15900 <https://github.com/numpy/numpy/pull/15900>`__: DEP: Ensure indexing errors will be raised even on empty results
* `#15997 <https://github.com/numpy/numpy/pull/15997>`__: ENH: improve printing of arrays with multi-line reprs
+* `#16056 <https://github.com/numpy/numpy/pull/16056>`__: DEP: Deprecate inexact matches for mode, searchside
* `#16130 <https://github.com/numpy/numpy/pull/16130>`__: DOC: Correct documentation of ``__array__`` when used as output...
* `#16134 <https://github.com/numpy/numpy/pull/16134>`__: ENH: Implement concatenate dtype and casting keyword arguments
-* `#16156 <https://github.com/numpy/numpy/pull/16156>`__: DEP: Deprecate `numpy.dual`.
+* `#16156 <https://github.com/numpy/numpy/pull/16156>`__: DEP: Deprecate ``numpy.dual``.
* `#16161 <https://github.com/numpy/numpy/pull/16161>`__: BUG: Potential fix for divmod(1.0, 0.0) to raise divbyzero and...
* `#16167 <https://github.com/numpy/numpy/pull/16167>`__: DOC: Increase guidance and detail of np.polynomial docstring
* `#16174 <https://github.com/numpy/numpy/pull/16174>`__: DOC: Add transition note to all lib/poly functions
* `#16200 <https://github.com/numpy/numpy/pull/16200>`__: ENH: Rewrite of array-coercion to support new dtypes
* `#16205 <https://github.com/numpy/numpy/pull/16205>`__: ENH: Add ``full_output`` argument to ``f2py.compile``.
+* `#16207 <https://github.com/numpy/numpy/pull/16207>`__: DOC: Add PyArray_ContiguousFromObject C docs
* `#16232 <https://github.com/numpy/numpy/pull/16232>`__: DEP: Deprecate ufunc.outer with matrix inputs
+* `#16237 <https://github.com/numpy/numpy/pull/16237>`__: MAINT: precompute ``log(2.0 * M_PI)`` in ``random_loggam``
* `#16238 <https://github.com/numpy/numpy/pull/16238>`__: MAINT: Unify cached (C-level static) imports
* `#16239 <https://github.com/numpy/numpy/pull/16239>`__: BUG,DOC: Allow attach docs twice but error if wrong
* `#16242 <https://github.com/numpy/numpy/pull/16242>`__: BUG: Fix default fallback in genfromtxt
* `#16247 <https://github.com/numpy/numpy/pull/16247>`__: ENH:Umath Replace raw SIMD of unary float point(32-64) with NPYV...
* `#16248 <https://github.com/numpy/numpy/pull/16248>`__: MRG, ENH: added edge keyword argument to digitize
+* `#16253 <https://github.com/numpy/numpy/pull/16253>`__: DOC: Clarify tiny/xmin in finfo and machar
+* `#16254 <https://github.com/numpy/numpy/pull/16254>`__: MAINT: Chain exceptions in generate_umath.py
* `#16257 <https://github.com/numpy/numpy/pull/16257>`__: DOC: Update the f2py section of the "Using Python as Glue" page.
-* `#16260 <https://github.com/numpy/numpy/pull/16260>`__: DOC: Improve `rec.array` function documentation (#15853)
+* `#16260 <https://github.com/numpy/numpy/pull/16260>`__: DOC: Improve ``rec.array`` function documentation
* `#16266 <https://github.com/numpy/numpy/pull/16266>`__: ENH: include dt64/td64 isinstance checks in ``__init__.pxd``
* `#16267 <https://github.com/numpy/numpy/pull/16267>`__: DOC: Clarifications for np.std
* `#16273 <https://github.com/numpy/numpy/pull/16273>`__: BUG: Order percentile monotonically
* `#16283 <https://github.com/numpy/numpy/pull/16283>`__: DOC: Add a note about performance of isclose compared to math.isclose
* `#16284 <https://github.com/numpy/numpy/pull/16284>`__: MAINT: Clean up the implementation of quantile
* `#16285 <https://github.com/numpy/numpy/pull/16285>`__: MAINT: Bump hypothesis from 5.12.0 to 5.14.0
+* `#16288 <https://github.com/numpy/numpy/pull/16288>`__: BLD: Avoid "visibility attribute not supported" warning
* `#16291 <https://github.com/numpy/numpy/pull/16291>`__: DOC: Improve "tobytes" docstring.
* `#16292 <https://github.com/numpy/numpy/pull/16292>`__: BUG: Fix tools/download-wheels.py.
* `#16295 <https://github.com/numpy/numpy/pull/16295>`__: BUG: Require Python >= 3.6 in setup.py
* `#16318 <https://github.com/numpy/numpy/pull/16318>`__: MAINT: Stop Using PyEval_Call* and simplify some uses
* `#16321 <https://github.com/numpy/numpy/pull/16321>`__: ENH: Improve the ARM cpu feature detection by parsing /proc/cpuinfo
* `#16323 <https://github.com/numpy/numpy/pull/16323>`__: DOC: Reconstruct Testing Guideline.
+* `#16327 <https://github.com/numpy/numpy/pull/16327>`__: BUG: Don't segfault on bad __len__ when assigning.
* `#16329 <https://github.com/numpy/numpy/pull/16329>`__: MAINT: Cleanup 'tools/download-wheels.py'
* `#16332 <https://github.com/numpy/numpy/pull/16332>`__: DOC: link np.interp to SciPy's interpolation functions (closes...
* `#16333 <https://github.com/numpy/numpy/pull/16333>`__: DOC: Fix spelling typo - homogenous to homogeneous. (#16324)
* `#16334 <https://github.com/numpy/numpy/pull/16334>`__: ENH: Use AVX-512 for np.isnan, np.infinite, np.isinf and np.signbit
* `#16336 <https://github.com/numpy/numpy/pull/16336>`__: BUG: Fix refcounting in add_newdoc
* `#16337 <https://github.com/numpy/numpy/pull/16337>`__: CI: Create a link for the circleCI artifact
-* `#16348 <https://github.com/numpy/numpy/pull/16348>`__: BUG: Fix dtype leak in `PyArray_FromAny` error path
+* `#16346 <https://github.com/numpy/numpy/pull/16346>`__: MAINT: Remove f-strings in setup.py.
+* `#16348 <https://github.com/numpy/numpy/pull/16348>`__: BUG: Fix dtype leak in ``PyArray_FromAny`` error path
* `#16349 <https://github.com/numpy/numpy/pull/16349>`__: BUG: Indentation for docstrings
+* `#16350 <https://github.com/numpy/numpy/pull/16350>`__: BUG: Set readonly flag in array interface
* `#16351 <https://github.com/numpy/numpy/pull/16351>`__: BUG: Fix small leaks in error path and ``empty_like`` with shape
* `#16362 <https://github.com/numpy/numpy/pull/16362>`__: MAINT: Streamline download-wheels.
* `#16365 <https://github.com/numpy/numpy/pull/16365>`__: DOC: Fix an obvious mistake in a message printed in doc/Makefile.
* `#16447 <https://github.com/numpy/numpy/pull/16447>`__: DOC: add a "make show" command to doc/Makefile
* `#16450 <https://github.com/numpy/numpy/pull/16450>`__: DOC: Add a NEP link to all neps.
* `#16452 <https://github.com/numpy/numpy/pull/16452>`__: DOC,ENH: extend error message when Accelerate is detected
+* `#16454 <https://github.com/numpy/numpy/pull/16454>`__: TST: Add tests for PyArray_IntpConverter
* `#16463 <https://github.com/numpy/numpy/pull/16463>`__: DOC: Improve assert_warns docstring with example
* `#16464 <https://github.com/numpy/numpy/pull/16464>`__: MAINT: Bump hypothesis from 5.15.1 to 5.16.0
* `#16465 <https://github.com/numpy/numpy/pull/16465>`__: DOC: Fix development_workflow links
* `#16471 <https://github.com/numpy/numpy/pull/16471>`__: BLD: install mingw32 v7.3.0 for win32
* `#16472 <https://github.com/numpy/numpy/pull/16472>`__: DOC: Fixes for 18 broken links
* `#16474 <https://github.com/numpy/numpy/pull/16474>`__: MAINT: use zip instead of range in piecewise
-* `#16476 <https://github.com/numpy/numpy/pull/16476>`__: ENH: add `norm=forward,backward` to numpy.fft functions
+* `#16476 <https://github.com/numpy/numpy/pull/16476>`__: ENH: add ``norm=forward,backward`` to numpy.fft functions
* `#16482 <https://github.com/numpy/numpy/pull/16482>`__: SIMD: Optimize the performace of np.packbits in ARM-based machine.
* `#16485 <https://github.com/numpy/numpy/pull/16485>`__: BUG: Fix result when a gufunc output broadcasts the inputs.
* `#16500 <https://github.com/numpy/numpy/pull/16500>`__: DOC: Point Contributing page to new NEP 45
* `#16574 <https://github.com/numpy/numpy/pull/16574>`__: MAINT: fix name of first parameter to dtype constructor in type...
* `#16581 <https://github.com/numpy/numpy/pull/16581>`__: DOC: Added an example for np.transpose(4d_array)
* `#16583 <https://github.com/numpy/numpy/pull/16583>`__: MAINT: changed np.generic arguments to positional-only
+* `#16589 <https://github.com/numpy/numpy/pull/16589>`__: MAINT: Remove nickname from polynomial classes.
* `#16590 <https://github.com/numpy/numpy/pull/16590>`__: DOC: Clarify dtype default for logspace and geomspace
* `#16591 <https://github.com/numpy/numpy/pull/16591>`__: DOC: Disallow complex args in arange
* `#16592 <https://github.com/numpy/numpy/pull/16592>`__: BUG: Raise TypeError for float->timedelta promotion
* `#16633 <https://github.com/numpy/numpy/pull/16633>`__: MAINT: lib: Some code clean up in loadtxt
* `#16635 <https://github.com/numpy/numpy/pull/16635>`__: BENCH: remove obsolete goal_time param
* `#16639 <https://github.com/numpy/numpy/pull/16639>`__: BUG: Fix uint->timedelta promotion to raise TypeError
-* `#16642 <https://github.com/numpy/numpy/pull/16642>`__: MAINT: Replace `PyUString_GET_SIZE` with `PyUnicode_GetLength`.
+* `#16642 <https://github.com/numpy/numpy/pull/16642>`__: MAINT: Replace ``PyUString_GET_SIZE`` with ``PyUnicode_GetLength``.
* `#16643 <https://github.com/numpy/numpy/pull/16643>`__: REL: Fix outdated docs link
+* `#16644 <https://github.com/numpy/numpy/pull/16644>`__: MAINT: Improve performance of np.full
* `#16646 <https://github.com/numpy/numpy/pull/16646>`__: TST: add a static typing test for memoryviews as ArrayLikes
* `#16647 <https://github.com/numpy/numpy/pull/16647>`__: ENH: Added annotations to 8 functions from np.core.fromnumeric
* `#16648 <https://github.com/numpy/numpy/pull/16648>`__: REL: Update master after 1.19.0 release.
* `#16664 <https://github.com/numpy/numpy/pull/16664>`__: DOC: Add lib.format.open_memmap to autosummary.
* `#16666 <https://github.com/numpy/numpy/pull/16666>`__: BUG: Fix bug in AVX complex absolute while processing array of...
* `#16669 <https://github.com/numpy/numpy/pull/16669>`__: MAINT: remove blacklist/whitelist terms
+* `#16671 <https://github.com/numpy/numpy/pull/16671>`__: DOC: Simplify and update git setup page
* `#16674 <https://github.com/numpy/numpy/pull/16674>`__: TST: Add extra debugging information to CPU features detection
* `#16675 <https://github.com/numpy/numpy/pull/16675>`__: ENH: Add support for file like objects to np.core.records.fromfile
* `#16683 <https://github.com/numpy/numpy/pull/16683>`__: DOC: updated gcc minimum recommend version to build from source
-* `#16684 <https://github.com/numpy/numpy/pull/16684>`__: MAINT: Allow `None` to be passed to certain `generic` subclasses
+* `#16684 <https://github.com/numpy/numpy/pull/16684>`__: MAINT: Allow None to be passed to certain generic subclasses
* `#16690 <https://github.com/numpy/numpy/pull/16690>`__: DOC: fixed docstring for descr_to_dtype
-* `#16691 <https://github.com/numpy/numpy/pull/16691>`__: DOC: Remove "matrix" from `triu` docstring.
+* `#16691 <https://github.com/numpy/numpy/pull/16691>`__: DOC: Remove "matrix" from ``triu`` docstring.
* `#16696 <https://github.com/numpy/numpy/pull/16696>`__: MAINT: add py.typed sentinel to package manifest
-* `#16699 <https://github.com/numpy/numpy/pull/16699>`__: MAINT: Fixup quantile tests to not use `np.float`
+* `#16699 <https://github.com/numpy/numpy/pull/16699>`__: MAINT: Fixup quantile tests to not use ``np.float``
* `#16702 <https://github.com/numpy/numpy/pull/16702>`__: BLD: Add CPU entry for Emscripten / WebAssembly
* `#16704 <https://github.com/numpy/numpy/pull/16704>`__: TST: Disable Python 3.9-dev testing.
* `#16706 <https://github.com/numpy/numpy/pull/16706>`__: DOC: Add instruction about stable symlink
* `#16710 <https://github.com/numpy/numpy/pull/16710>`__: ENH, BLD: Add RPATH support for AIX
* `#16718 <https://github.com/numpy/numpy/pull/16718>`__: DOC: fix typo
* `#16720 <https://github.com/numpy/numpy/pull/16720>`__: BUG: Fix PyArray_SearchSorted signature.
+* `#16723 <https://github.com/numpy/numpy/pull/16723>`__: NEP: Initial draft for NEP 43 for extensible ufuncs
* `#16729 <https://github.com/numpy/numpy/pull/16729>`__: ENH: Add annotations to the last 8 functions in numpy.core.fromnumeric
* `#16730 <https://github.com/numpy/numpy/pull/16730>`__: ENH: Use f90 compiler specified in f2py command line args for...
* `#16731 <https://github.com/numpy/numpy/pull/16731>`__: DOC: reword random c-api introduction, cython is documented in...
* `#16735 <https://github.com/numpy/numpy/pull/16735>`__: DOC: Tweak a sentence about broadcasting.
-* `#16736 <https://github.com/numpy/numpy/pull/16736>`__: DOC: Prepend `ma.` to references in ``numpy.ma``
+* `#16736 <https://github.com/numpy/numpy/pull/16736>`__: DOC: Prepend ``ma.`` to references in ``numpy.ma``
* `#16738 <https://github.com/numpy/numpy/pull/16738>`__: DOC: Remove redundant word
* `#16742 <https://github.com/numpy/numpy/pull/16742>`__: DOC: add unique() to See Also of repeat()
* `#16743 <https://github.com/numpy/numpy/pull/16743>`__: DOC: add example to unique() and make connection to repeat()
* `#16770 <https://github.com/numpy/numpy/pull/16770>`__: MAINT: Remove unneeded call to PyUnicode_READY
* `#16771 <https://github.com/numpy/numpy/pull/16771>`__: MAINT: Fix deprecated functions in scalarapi.c
* `#16775 <https://github.com/numpy/numpy/pull/16775>`__: DOC: switch to logo with text
+* `#16777 <https://github.com/numpy/numpy/pull/16777>`__: BUG: Added missing return after raising error in methods.c
+* `#16778 <https://github.com/numpy/numpy/pull/16778>`__: NEP: Update NEP 42 to note the issue of circular references
* `#16782 <https://github.com/numpy/numpy/pull/16782>`__: ENH, TST: Bring the NumPy C SIMD vectorization interface "NPYV"...
* `#16786 <https://github.com/numpy/numpy/pull/16786>`__: BENCH: Add basic benchmarks for scalar indexing and assignment
* `#16789 <https://github.com/numpy/numpy/pull/16789>`__: BUG: fix decode error when building and get rid of warn
* `#16886 <https://github.com/numpy/numpy/pull/16886>`__: DOC: Fix types including curly braces
* `#16887 <https://github.com/numpy/numpy/pull/16887>`__: DOC: Remove the links for ``True`` and ``False``
* `#16888 <https://github.com/numpy/numpy/pull/16888>`__: ENH: Integrate the new CPU dispatcher with umath generator
-* `#16894 <https://github.com/numpy/numpy/pull/16894>`__: DOC: Fix wrong markups in `arrays.dtypes`
+* `#16890 <https://github.com/numpy/numpy/pull/16890>`__: TST, BUG: Re-raise MemoryError exception in test_large_zip's...
+* `#16894 <https://github.com/numpy/numpy/pull/16894>`__: DOC: Fix wrong markups in ``arrays.dtypes``
* `#16896 <https://github.com/numpy/numpy/pull/16896>`__: DOC: Remove links for C codes
* `#16897 <https://github.com/numpy/numpy/pull/16897>`__: DOC: Fix the declarations of C fuctions
* `#16899 <https://github.com/numpy/numpy/pull/16899>`__: MNT: also use Py_SET_REFCNT instead of Py_REFCNT
* `#16919 <https://github.com/numpy/numpy/pull/16919>`__: DOC: Add ufunc docstring to generated docs.
* `#16925 <https://github.com/numpy/numpy/pull/16925>`__: REL: Update master after 1.19.1 release.
* `#16931 <https://github.com/numpy/numpy/pull/16931>`__: Revert "Merge pull request #16248 from alexrockhill/edge"
+* `#16935 <https://github.com/numpy/numpy/pull/16935>`__: ENH: implement NEP-35's ``like=`` argument
* `#16936 <https://github.com/numpy/numpy/pull/16936>`__: BUG: Fix memory leak of buffer-info cache due to relaxed strides
* `#16938 <https://github.com/numpy/numpy/pull/16938>`__: ENH,API: Store exported buffer info on the array
* `#16940 <https://github.com/numpy/numpy/pull/16940>`__: BLD: update OpenBLAS build
* `#16941 <https://github.com/numpy/numpy/pull/16941>`__: BUG: Allow array-like types to be coerced as object array elements
* `#16943 <https://github.com/numpy/numpy/pull/16943>`__: DEP: Deprecate size-one ragged array coercion
* `#16944 <https://github.com/numpy/numpy/pull/16944>`__: Change the name of the folder "icons" to "logo".
-* `#16949 <https://github.com/numpy/numpy/pull/16949>`__: ENH: enable colors for `runtests.py --ipython`
+* `#16949 <https://github.com/numpy/numpy/pull/16949>`__: ENH: enable colors for ``runtests.py --ipython``
* `#16950 <https://github.com/numpy/numpy/pull/16950>`__: DOC: Clarify input to irfft/irfft2/irfftn
* `#16952 <https://github.com/numpy/numpy/pull/16952>`__: MAINT: Bump hypothesis from 5.20.2 to 5.23.2
* `#16953 <https://github.com/numpy/numpy/pull/16953>`__: update numpy/lib/arraypad.py with appropriate chain exception
* `#16957 <https://github.com/numpy/numpy/pull/16957>`__: MAINT: Use arm64 instead of aarch64 on travisCI.
* `#16962 <https://github.com/numpy/numpy/pull/16962>`__: MAINT: Chain exception in ``distutils/fcompiler/environment.py``.
-* `#16966 <https://github.com/numpy/numpy/pull/16966>`__: MAINT: Added the `order` parameter to `np.array()`
+* `#16966 <https://github.com/numpy/numpy/pull/16966>`__: MAINT: Added the ``order`` parameter to ``np.array()``
* `#16969 <https://github.com/numpy/numpy/pull/16969>`__: ENH: Add Neon SIMD implementations for add, sub, mul, and div
* `#16973 <https://github.com/numpy/numpy/pull/16973>`__: DOC: Fixed typo in lib/recfunctions.py
* `#16974 <https://github.com/numpy/numpy/pull/16974>`__: TST: Add pypy win32 CI testing.
-* `#16982 <https://github.com/numpy/numpy/pull/16982>`__: ENH: Increase the use of `Literal` types
+* `#16982 <https://github.com/numpy/numpy/pull/16982>`__: ENH: Increase the use of ``Literal`` types
* `#16986 <https://github.com/numpy/numpy/pull/16986>`__: ENH: Add NumPy declarations to be used by Cython 3.0+
* `#16988 <https://github.com/numpy/numpy/pull/16988>`__: DOC: Add the new NumPy logo to Sphinx pages
* `#16991 <https://github.com/numpy/numpy/pull/16991>`__: MAINT: Bump hypothesis from 5.23.2 to 5.23.9
* `#16996 <https://github.com/numpy/numpy/pull/16996>`__: DOC: Revise glossary page
* `#17002 <https://github.com/numpy/numpy/pull/17002>`__: DOC: clip() allows arguments.
* `#17009 <https://github.com/numpy/numpy/pull/17009>`__: NEP: Updated NEP-35 with keyword-only instruction
+* `#17010 <https://github.com/numpy/numpy/pull/17010>`__: BUG: Raise correct errors in boolean indexing fast path
* `#17013 <https://github.com/numpy/numpy/pull/17013>`__: MAINT: Simplify scalar power
* `#17014 <https://github.com/numpy/numpy/pull/17014>`__: MAINT: Improve error handling in umathmodule setup
+* `#17022 <https://github.com/numpy/numpy/pull/17022>`__: DOC: Fix non-matching pronoun.
* `#17028 <https://github.com/numpy/numpy/pull/17028>`__: DOC: Disclaimer for FFT library
* `#17029 <https://github.com/numpy/numpy/pull/17029>`__: MAINT: Add error return to all casting functionality and NpyIter
* `#17033 <https://github.com/numpy/numpy/pull/17033>`__: BUG: fix a compile and a test warning
-* `#17036 <https://github.com/numpy/numpy/pull/17036>`__: DOC: Clarify that `np.char` comparison functions always return...
+* `#17036 <https://github.com/numpy/numpy/pull/17036>`__: DOC: Clarify that ``np.char`` comparison functions always return...
* `#17039 <https://github.com/numpy/numpy/pull/17039>`__: DOC: Use a less ambiguous example for array_split
* `#17041 <https://github.com/numpy/numpy/pull/17041>`__: MAINT: Bump hypothesis from 5.23.9 to 5.23.12
* `#17048 <https://github.com/numpy/numpy/pull/17048>`__: STY: core._internal style fixups
* `#17050 <https://github.com/numpy/numpy/pull/17050>`__: MAINT: Remove _EXTRAFLAGS variable
+* `#17050 <https://github.com/numpy/numpy/pull/17051>`__: MAINT: change ``for line in open()`` to ``with open() as f``
+* `#17052 <https://github.com/numpy/numpy/pull/17052>`__: MAINT: Delete obsolete conversion to list
* `#17053 <https://github.com/numpy/numpy/pull/17053>`__: BUG: fix typo in polydiv that prevented promotion to poly1d
+* `#17055 <https://github.com/numpy/numpy/pull/17055>`__: MAINT: Replace lambda function by list comprehension
* `#17058 <https://github.com/numpy/numpy/pull/17058>`__: MAINT: Revert boolean casting back to elementwise comparisons...
+* `#17059 <https://github.com/numpy/numpy/pull/17059>`__: BUG: fix pickling of arrays larger than 2GiB
* `#17062 <https://github.com/numpy/numpy/pull/17062>`__: API, BUG: Raise error on complex input to i0
* `#17063 <https://github.com/numpy/numpy/pull/17063>`__: MAINT: Remove obsolete conversion to set
* `#17067 <https://github.com/numpy/numpy/pull/17067>`__: DEP: lib: Remove the deprecated financial functions.
* `#17109 <https://github.com/numpy/numpy/pull/17109>`__: MAINT: Split einsum into multiple files
* `#17112 <https://github.com/numpy/numpy/pull/17112>`__: BUG: Handle errors from the PyCapsule API
* `#17115 <https://github.com/numpy/numpy/pull/17115>`__: DOC: Fix spacing in vectorize doc
-* `#17116 <https://github.com/numpy/numpy/pull/17116>`__: API: Remove `np.ctypeslib.ctypes_load_library`
+* `#17116 <https://github.com/numpy/numpy/pull/17116>`__: API: Remove ``np.ctypeslib.ctypes_load_library``
* `#17119 <https://github.com/numpy/numpy/pull/17119>`__: DOC: make spacing consistent in NEP 41 bullet points
* `#17121 <https://github.com/numpy/numpy/pull/17121>`__: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
* `#17123 <https://github.com/numpy/numpy/pull/17123>`__: ENH: allow running mypy through runtests.py
* `#17141 <https://github.com/numpy/numpy/pull/17141>`__: MAINT: Make arrayprint str and repr the ndarray defaults.
* `#17142 <https://github.com/numpy/numpy/pull/17142>`__: DOC: NEP-42: Fix a few typos.
* `#17143 <https://github.com/numpy/numpy/pull/17143>`__: MAINT: Change handling of the expired financial functions.
-* `#17144 <https://github.com/numpy/numpy/pull/17144>`__: ENH: Add annotations to 3 functions in `np.core.function_base`
+* `#17144 <https://github.com/numpy/numpy/pull/17144>`__: ENH: Add annotations to 3 functions in ``np.core.function_base``
* `#17145 <https://github.com/numpy/numpy/pull/17145>`__: MAINT, BUG: Replace uses of PyString_AsString.
* `#17146 <https://github.com/numpy/numpy/pull/17146>`__: MAINT: ``Replace PyUString_*`` by ``PyUnicode_*`` equivalents.
* `#17149 <https://github.com/numpy/numpy/pull/17149>`__: MAINT: Replace PyInt macros with their PyLong replacement
* `#17150 <https://github.com/numpy/numpy/pull/17150>`__: ENH: Add support for the abstract scalars to cython code
* `#17151 <https://github.com/numpy/numpy/pull/17151>`__: BUG: Fix incorrect cython definition of npy_cfloat
-* `#17152 <https://github.com/numpy/numpy/pull/17152>`__: MAINT: Clean up some Npy_ vs Py_ macro usage
+* `#17152 <https://github.com/numpy/numpy/pull/17152>`__: MAINT: Clean up some ``Npy_`` vs ``Py_`` macro usage
* `#17154 <https://github.com/numpy/numpy/pull/17154>`__: DOC: Remove references to PyCObject
* `#17159 <https://github.com/numpy/numpy/pull/17159>`__: DOC: Update numpy4matlab
* `#17160 <https://github.com/numpy/numpy/pull/17160>`__: Clean up some more bytes vs unicode handling
* `#17167 <https://github.com/numpy/numpy/pull/17167>`__: BLD: Merge the npysort library into multiarray
* `#17168 <https://github.com/numpy/numpy/pull/17168>`__: TST: Add tests mapping out the rules for metadata in promotion
* `#17171 <https://github.com/numpy/numpy/pull/17171>`__: BUG: revert trim_zeros changes from gh-16911
-* `#17172 <https://github.com/numpy/numpy/pull/17172>`__: ENH: Make `np.complexfloating` generic w.r.t. `np.floating`
+* `#17172 <https://github.com/numpy/numpy/pull/17172>`__: ENH: Make ``np.complexfloating`` generic w.r.t. ``np.floating``
* `#17176 <https://github.com/numpy/numpy/pull/17176>`__: MAINT/ENH: datetime: remove calls to PyUnicode_AsASCIIString,...
-* `#17180 <https://github.com/numpy/numpy/pull/17180>`__: ENH: Added missing methods to `np.flatiter`
+* `#17180 <https://github.com/numpy/numpy/pull/17180>`__: ENH: Added missing methods to ``np.flatiter``
* `#17181 <https://github.com/numpy/numpy/pull/17181>`__: DOC: Correct error in description of ndarray.base
-* `#17182 <https://github.com/numpy/numpy/pull/17182>`__: DOC: Document `dtype.metadata`
+* `#17182 <https://github.com/numpy/numpy/pull/17182>`__: DOC: Document ``dtype.metadata``
* `#17186 <https://github.com/numpy/numpy/pull/17186>`__: MAINT: Use utf8 strings in more of datetime
-* `#17188 <https://github.com/numpy/numpy/pull/17188>`__: MAINT: Add placeholder stubs for `ndarray` and `generic`
+* `#17188 <https://github.com/numpy/numpy/pull/17188>`__: MAINT: Add placeholder stubs for ``ndarray`` and ``generic``
* `#17191 <https://github.com/numpy/numpy/pull/17191>`__: MAINT: Bump hypothesis from 5.26.0 to 5.30.0
* `#17193 <https://github.com/numpy/numpy/pull/17193>`__: MAINT: Remove some callers of functions in numpy.compat
* `#17195 <https://github.com/numpy/numpy/pull/17195>`__: ENH: Make the window functions exactly symmetric
* `#17197 <https://github.com/numpy/numpy/pull/17197>`__: MAINT: Improve error handling in npy_cpu_init
-* `#17199 <https://github.com/numpy/numpy/pull/17199>`__: DOC: Fix the documented signatures of four `ufunc` methods
-* `#17201 <https://github.com/numpy/numpy/pull/17201>`__: MAINT: Make the `NPY_CPU_DISPATCH_CALL` macros expressions not...
+* `#17199 <https://github.com/numpy/numpy/pull/17199>`__: DOC: Fix the documented signatures of four ``ufunc`` methods
+* `#17201 <https://github.com/numpy/numpy/pull/17201>`__: MAINT: Make the ``NPY_CPU_DISPATCH_CALL`` macros expressions not...
* `#17204 <https://github.com/numpy/numpy/pull/17204>`__: DOC: Fixed headings for tutorials so they appear at new theme...
* `#17210 <https://github.com/numpy/numpy/pull/17210>`__: DOC: Canonical_urls
-* `#17214 <https://github.com/numpy/numpy/pull/17214>`__: MAINT: Fix various issues with the `np.generic` annotations
+* `#17214 <https://github.com/numpy/numpy/pull/17214>`__: MAINT: Fix various issues with the ``np.generic`` annotations
+* `#17215 <https://github.com/numpy/numpy/pull/17215>`__: DOC: Use official MATLAB spelling in numpy-for-matlab-users.rst
* `#17219 <https://github.com/numpy/numpy/pull/17219>`__: BLD: enabled negation of library choices in NPY_*_ORDER
* `#17220 <https://github.com/numpy/numpy/pull/17220>`__: BUG, DOC: comment out metadata added via javascript
* `#17222 <https://github.com/numpy/numpy/pull/17222>`__: MAINT, DOC: move informational files from numpy.doc.*.py to their...
* `#17233 <https://github.com/numpy/numpy/pull/17233>`__: DEP: Deprecated ndindex.ndincr
* `#17235 <https://github.com/numpy/numpy/pull/17235>`__: MAINT: Remove old PY_VERSION_HEX and sys.version_info code
* `#17237 <https://github.com/numpy/numpy/pull/17237>`__: BUG: Avoid using ``np.random`` in typing tests.
+* `#17238 <https://github.com/numpy/numpy/pull/17238>`__: DOC: Use SPDX license expressions with correct license
* `#17239 <https://github.com/numpy/numpy/pull/17239>`__: DOC: Fix link quick-start in old random API functions
+* `#17240 <https://github.com/numpy/numpy/pull/17240>`__: MAINT: added exception chaining in shape_base.py
* `#17241 <https://github.com/numpy/numpy/pull/17241>`__: MAINT: ``__array_interface__`` data address cannot be bytes
* `#17242 <https://github.com/numpy/numpy/pull/17242>`__: MAINT: Run slow CI jobs earlier so builds finishes sooner
* `#17247 <https://github.com/numpy/numpy/pull/17247>`__: ENH: Add tool to help speed up Travis CI
* `#17260 <https://github.com/numpy/numpy/pull/17260>`__: MAINT: Bump pydata-sphinx-theme from 0.3.2 to 0.4.0
* `#17263 <https://github.com/numpy/numpy/pull/17263>`__: DOC: add new glossary terms
* `#17264 <https://github.com/numpy/numpy/pull/17264>`__: DOC: remove some glosssary terms
-* `#17267 <https://github.com/numpy/numpy/pull/17267>`__: TST: Fix the path to `mypy.ini` in `runtests.py`
+* `#17267 <https://github.com/numpy/numpy/pull/17267>`__: TST: Fix the path to ``mypy.ini`` in ``runtests.py``
* `#17268 <https://github.com/numpy/numpy/pull/17268>`__: BUG: sysconfig attributes/distutils issue
-* `#17273 <https://github.com/numpy/numpy/pull/17273>`__: ENH: Annotate the arithmetic operations of `ndarray` and `generic`
+* `#17273 <https://github.com/numpy/numpy/pull/17273>`__: ENH: Annotate the arithmetic operations of ``ndarray`` and ``generic``
* `#17278 <https://github.com/numpy/numpy/pull/17278>`__: MAINT: Merge together index page content into a single file
* `#17279 <https://github.com/numpy/numpy/pull/17279>`__: DOC: Fix a typo in shape_base.
* `#17284 <https://github.com/numpy/numpy/pull/17284>`__: ENH: Pass optimizations arguments to asv build
* `#17304 <https://github.com/numpy/numpy/pull/17304>`__: BUILD: pin pygments to 2.6.1, 2.7.0 breaks custom NumPyC lexer
* `#17307 <https://github.com/numpy/numpy/pull/17307>`__: MAINT: Bump hypothesis from 5.33.0 to 5.35.1
* `#17308 <https://github.com/numpy/numpy/pull/17308>`__: MAINT: Bump pytest from 6.0.1 to 6.0.2
-* `#17309 <https://github.com/numpy/numpy/pull/17309>`__: MAINT: Move the `fromnumeric` annotations to their own stub file
+* `#17309 <https://github.com/numpy/numpy/pull/17309>`__: MAINT: Move the ``fromnumeric`` annotations to their own stub file
* `#17312 <https://github.com/numpy/numpy/pull/17312>`__: MAINT: Syntax-highlight .src files on github
* `#17313 <https://github.com/numpy/numpy/pull/17313>`__: MAINT: Mark vendored/generated files in .gitattributes
* `#17315 <https://github.com/numpy/numpy/pull/17315>`__: MAINT: Cleanup f2py/cfuncs.py
* `#17319 <https://github.com/numpy/numpy/pull/17319>`__: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
* `#17320 <https://github.com/numpy/numpy/pull/17320>`__: BUG: allow registration of hard-coded structured dtypes
* `#17326 <https://github.com/numpy/numpy/pull/17326>`__: ENH: Add annotations for five array construction functions
-* `#17329 <https://github.com/numpy/numpy/pull/17329>`__: DOC: Fix incorrect `.. deprecated::` syntax that led to this...
-* `#17330 <https://github.com/numpy/numpy/pull/17330>`__: DOC: improve `issubdtype` and scalar type docs
-* `#17331 <https://github.com/numpy/numpy/pull/17331>`__: DOC: Remove the tables of scalar types, and use `..autoclass`...
+* `#17329 <https://github.com/numpy/numpy/pull/17329>`__: DOC: Fix incorrect ``.. deprecated::`` syntax that led to this...
+* `#17330 <https://github.com/numpy/numpy/pull/17330>`__: DOC: improve ``issubdtype`` and scalar type docs
+* `#17331 <https://github.com/numpy/numpy/pull/17331>`__: DOC: Remove the tables of scalar types, and use ``..autoclass``...
* `#17332 <https://github.com/numpy/numpy/pull/17332>`__: DOC, BLD: update lexer highlighting and make numpydocs a regular...
* `#17334 <https://github.com/numpy/numpy/pull/17334>`__: MAINT: Chaining exceptions in npyio.py
* `#17337 <https://github.com/numpy/numpy/pull/17337>`__: NEP: Regenerate table in NEP 29 (add numpy 1.18 and 1.19 to list)
* `#17338 <https://github.com/numpy/numpy/pull/17338>`__: DOC: Fix syntax errors in docstrings for versionchanged, versionadded
* `#17340 <https://github.com/numpy/numpy/pull/17340>`__: SIMD: Add partial/non-contig load and store intrinsics for 32/64-bit
* `#17344 <https://github.com/numpy/numpy/pull/17344>`__: ENH, BLD: Support for the NVIDIA HPC SDK nvfortran compiler
-* `#17346 <https://github.com/numpy/numpy/pull/17346>`__: BLD,BUG: Fix a macOS build failure when `NPY_BLAS_ORDER=""`
+* `#17346 <https://github.com/numpy/numpy/pull/17346>`__: BLD,BUG: Fix a macOS build failure when ``NPY_BLAS_ORDER=""``
* `#17350 <https://github.com/numpy/numpy/pull/17350>`__: DEV: Add PR prefix labeler and numpy prefix mapping
* `#17352 <https://github.com/numpy/numpy/pull/17352>`__: DOC: Guide to writing how-tos
* `#17353 <https://github.com/numpy/numpy/pull/17353>`__: DOC: How-to guide for I/O
* `#17364 <https://github.com/numpy/numpy/pull/17364>`__: MAINT: Finish replacing PyInt_Check
* `#17369 <https://github.com/numpy/numpy/pull/17369>`__: DOC: distutils: Remove an obsolete paragraph.
* `#17370 <https://github.com/numpy/numpy/pull/17370>`__: NEP: Edit nep-0042 for more clarity
-* `#17372 <https://github.com/numpy/numpy/pull/17372>`__: ENH: Add annotations for remaining `ndarray` / `generic` non-magic...
+* `#17372 <https://github.com/numpy/numpy/pull/17372>`__: ENH: Add annotations for remaining ``ndarray`` / ``generic`` non-magic...
* `#17373 <https://github.com/numpy/numpy/pull/17373>`__: BUG: Fixes module data docstrings.
* `#17375 <https://github.com/numpy/numpy/pull/17375>`__: DOC: Fix default_rng docstring
* `#17377 <https://github.com/numpy/numpy/pull/17377>`__: BUG: ensure _UFuncNoLoopError can be pickled
* `#17391 <https://github.com/numpy/numpy/pull/17391>`__: DOC: Replace "About NumPy" with "Document conventions"
* `#17392 <https://github.com/numpy/numpy/pull/17392>`__: DOC: Update info on doc style rules
* `#17393 <https://github.com/numpy/numpy/pull/17393>`__: BUG: Fix default void, datetime, and timedelta in array coercion
+* `#17394 <https://github.com/numpy/numpy/pull/17394>`__: ENH: Implement sliding window
* `#17396 <https://github.com/numpy/numpy/pull/17396>`__: MAINT: Replace append_metastr_to_string function.
* `#17399 <https://github.com/numpy/numpy/pull/17399>`__: BLD: Fixed ARGOUTVIEWM memory deallocation. Closes #17398.
* `#17400 <https://github.com/numpy/numpy/pull/17400>`__: DOC: rm incorrect alias from recarray user article.
* `#17402 <https://github.com/numpy/numpy/pull/17402>`__: DOC: Add arraysetops to an autosummary
* `#17404 <https://github.com/numpy/numpy/pull/17404>`__: MAINT: Replace PyUString_ConcatAndDel in nditer_constr.c.
* `#17405 <https://github.com/numpy/numpy/pull/17405>`__: MAINT: Replace PyUString_ConcatAndDel in mapping.c.
-* `#17406 <https://github.com/numpy/numpy/pull/17406>`__: ENH: Replace the module-level `__getattr__` with explicit type...
+* `#17406 <https://github.com/numpy/numpy/pull/17406>`__: ENH: Replace the module-level ``__getattr__`` with explicit type...
* `#17407 <https://github.com/numpy/numpy/pull/17407>`__: DOC: in PR template, set expectations for PR review timeline
* `#17409 <https://github.com/numpy/numpy/pull/17409>`__: MAINT: Cleanup remaining PyUString_ConcatAndDel use.
* `#17410 <https://github.com/numpy/numpy/pull/17410>`__: API: Special case how numpy scalars are coerced to signed integer
* `#17411 <https://github.com/numpy/numpy/pull/17411>`__: TST: Mark the typing tests as slow
-* `#17412 <https://github.com/numpy/numpy/pull/17412>`__: DOC: Fix a parameter type in the `putmask` docs
+* `#17412 <https://github.com/numpy/numpy/pull/17412>`__: DOC: Fix a parameter type in the ``putmask`` docs
* `#17418 <https://github.com/numpy/numpy/pull/17418>`__: DOC: adding operational form documentation for array ops
* `#17419 <https://github.com/numpy/numpy/pull/17419>`__: DEP: Deprecate coercion to subarray dtypes
* `#17421 <https://github.com/numpy/numpy/pull/17421>`__: BUG: Fix memory leak in array-coercion error paths
* `#17423 <https://github.com/numpy/numpy/pull/17423>`__: DOC: Remove bogus reference to _a_
* `#17424 <https://github.com/numpy/numpy/pull/17424>`__: DOC: Fix formatting issues in description of .c.src files
* `#17427 <https://github.com/numpy/numpy/pull/17427>`__: NEP: nep-0029 typo correction
-* `#17429 <https://github.com/numpy/numpy/pull/17429>`__: MAINT: Move aliases for common scalar unions to `numpy.typing`
+* `#17429 <https://github.com/numpy/numpy/pull/17429>`__: MAINT: Move aliases for common scalar unions to ``numpy.typing``
* `#17430 <https://github.com/numpy/numpy/pull/17430>`__: BUG: Fix memoryleaks related to NEP 37 function overrides
* `#17431 <https://github.com/numpy/numpy/pull/17431>`__: DOC: Fix the links for ``Ellipsis``
* `#17432 <https://github.com/numpy/numpy/pull/17432>`__: DOC: add references to einops and opt_einsum
* `#17440 <https://github.com/numpy/numpy/pull/17440>`__: DOC: Cleaner template for PRs
* `#17442 <https://github.com/numpy/numpy/pull/17442>`__: MAINT: fix exception chaining in format.py
* `#17443 <https://github.com/numpy/numpy/pull/17443>`__: ENH: Warn on unsupported Python 3.10+
-* `#17444 <https://github.com/numpy/numpy/pull/17444>`__: ENH: Add `Typing :: Typed` to the PyPi classifier
+* `#17444 <https://github.com/numpy/numpy/pull/17444>`__: ENH: Add ``Typing :: Typed`` to the PyPi classifier
* `#17445 <https://github.com/numpy/numpy/pull/17445>`__: DOC: Fix the references for macros
* `#17447 <https://github.com/numpy/numpy/pull/17447>`__: NEP: update NEP 42 with discussion of type hinting applications
* `#17448 <https://github.com/numpy/numpy/pull/17448>`__: DOC: Remove CoC pages from Sphinx
* `#17468 <https://github.com/numpy/numpy/pull/17468>`__: DOC: add some missing scalar aliases
* `#17472 <https://github.com/numpy/numpy/pull/17472>`__: TST: Fix doctest for full_like
* `#17473 <https://github.com/numpy/numpy/pull/17473>`__: MAINT: py3k: remove os.fspath and os.PathLike backports
-* `#17474 <https://github.com/numpy/numpy/pull/17474>`__: MAINT: Move the `np.core.numeric` annotations to their own stub...
-* `#17479 <https://github.com/numpy/numpy/pull/17479>`__: ENH: type np.unicode_ as np.str_
+* `#17474 <https://github.com/numpy/numpy/pull/17474>`__: MAINT: Move the ``np.core.numeric`` annotations to their own stub...
+* `#17479 <https://github.com/numpy/numpy/pull/17479>`__: ENH: type ``np.unicode_`` as ``np.str_``
* `#17481 <https://github.com/numpy/numpy/pull/17481>`__: DOC: Fix the entries for members of structures
-* `#17483 <https://github.com/numpy/numpy/pull/17483>`__: DOC: Fix the references for `random.*`
+* `#17483 <https://github.com/numpy/numpy/pull/17483>`__: DOC: Fix the references for ``random.*``
* `#17485 <https://github.com/numpy/numpy/pull/17485>`__: BLD: circleCI- merge before build, add -n to sphinx
* `#17487 <https://github.com/numpy/numpy/pull/17487>`__: MAINT: Remove duplicate placeholder annotations
+* `#17493 <https://github.com/numpy/numpy/pull/17493>`__: DOC: New round of NEP 42 edits
* `#17497 <https://github.com/numpy/numpy/pull/17497>`__: DOC: Use consistent lowercase on docs landing page
* `#17498 <https://github.com/numpy/numpy/pull/17498>`__: MAINT: fix incompatible type comparison in numpy.lib.utils.info
* `#17501 <https://github.com/numpy/numpy/pull/17501>`__: BUG: Fix failures in master related to userdtype registeration
-* `#17502 <https://github.com/numpy/numpy/pull/17502>`__: BUG: remove `sys` from the type stubs
+* `#17502 <https://github.com/numpy/numpy/pull/17502>`__: BUG: remove ``sys`` from the type stubs
* `#17503 <https://github.com/numpy/numpy/pull/17503>`__: DOC: Fix empty 'C style guide' page
* `#17504 <https://github.com/numpy/numpy/pull/17504>`__: DOC: Rename 'Quickstart tutorial'
* `#17508 <https://github.com/numpy/numpy/pull/17508>`__: ENH: Added the Final feature for all constants
* `#17537 <https://github.com/numpy/numpy/pull/17537>`__: MAINT: Bump hypothesis from 5.37.0 to 5.37.1
* `#17538 <https://github.com/numpy/numpy/pull/17538>`__: MAINT: Bump pydata-sphinx-theme from 0.4.0 to 0.4.1
* `#17539 <https://github.com/numpy/numpy/pull/17539>`__: MAINT: Bump mypy from 0.782 to 0.790
-* `#17540 <https://github.com/numpy/numpy/pull/17540>`__: ENH: Make `np.number` generic with respect to its precision
+* `#17540 <https://github.com/numpy/numpy/pull/17540>`__: ENH: Make ``np.number`` generic with respect to its precision
* `#17541 <https://github.com/numpy/numpy/pull/17541>`__: CI: fix conditional for PR merge command
-* `#17546 <https://github.com/numpy/numpy/pull/17546>`__: MAINT: explicit disabling `CCompilerOpt` in F2PY
+* `#17546 <https://github.com/numpy/numpy/pull/17546>`__: MAINT: explicit disabling ``CCompilerOpt`` in F2PY
* `#17548 <https://github.com/numpy/numpy/pull/17548>`__: BUG: Cygwin Workaround for #14787 on affected platforms
* `#17549 <https://github.com/numpy/numpy/pull/17549>`__: DOC: Fix the entries of C functions
* `#17555 <https://github.com/numpy/numpy/pull/17555>`__: DOC: Fix wrong blockquotes
* `#17558 <https://github.com/numpy/numpy/pull/17558>`__: DOC: MAINT: Add NEP 43 links to NEP 42
* `#17559 <https://github.com/numpy/numpy/pull/17559>`__: DOC: Remove directives for some constants
-* `#17564 <https://github.com/numpy/numpy/pull/17564>`__: MAINT: Update the annotations in `np.core.numeric`
+* `#17564 <https://github.com/numpy/numpy/pull/17564>`__: MAINT: Update the annotations in ``np.core.numeric``
* `#17570 <https://github.com/numpy/numpy/pull/17570>`__: DOC: Add the entry for ``NPY_FEATURE_VERSION``
* `#17571 <https://github.com/numpy/numpy/pull/17571>`__: DOC: Fix typos
* `#17572 <https://github.com/numpy/numpy/pull/17572>`__: ENH: Add annotations for three new constants
* `#17577 <https://github.com/numpy/numpy/pull/17577>`__: BUG: Respect dtype of all-zero argument to poly1d
* `#17578 <https://github.com/numpy/numpy/pull/17578>`__: NEP36: include additional feedback
* `#17580 <https://github.com/numpy/numpy/pull/17580>`__: MAINT: Cleanup swig for Python 3.
-* `#17581 <https://github.com/numpy/numpy/pull/17581>`__: MAINT: Move the `np.core.numerictypes` annotations to their own...
+* `#17581 <https://github.com/numpy/numpy/pull/17581>`__: MAINT: Move the ``np.core.numerictypes`` annotations to their own...
* `#17583 <https://github.com/numpy/numpy/pull/17583>`__: MAINT: Bump hypothesis from 5.37.1 to 5.37.3
-* `#17584 <https://github.com/numpy/numpy/pull/17584>`__: ENH: Add annotations for `np.core._type_aliases`
+* `#17584 <https://github.com/numpy/numpy/pull/17584>`__: ENH: Add annotations for ``np.core._type_aliases``
* `#17594 <https://github.com/numpy/numpy/pull/17594>`__: DOC: Typo in lexsort docstring
* `#17596 <https://github.com/numpy/numpy/pull/17596>`__: DEP,BUG: Coercion/cast of array to a subarray dtype will be fixed
* `#17597 <https://github.com/numpy/numpy/pull/17597>`__: TST: Clean up the errors of the typing tests
* `#17598 <https://github.com/numpy/numpy/pull/17598>`__: BUG: Fixed file handle leak in array_tofile.
-* `#17601 <https://github.com/numpy/numpy/pull/17601>`__: TST: Fix a broken `np.core.numeric` test
+* `#17601 <https://github.com/numpy/numpy/pull/17601>`__: TST: Fix a broken ``np.core.numeric`` test
* `#17603 <https://github.com/numpy/numpy/pull/17603>`__: MAINT: Mark dead code as intentional for clang.
* `#17607 <https://github.com/numpy/numpy/pull/17607>`__: DOC: removed old references to submodule licenses
* `#17608 <https://github.com/numpy/numpy/pull/17608>`__: DOC: Fix typos (general documentation)
* `#17610 <https://github.com/numpy/numpy/pull/17610>`__: Fully qualify license trove classifier
* `#17611 <https://github.com/numpy/numpy/pull/17611>`__: BUG: mac dylib treated as part of extra objects by f2py
-* `#17613 <https://github.com/numpy/numpy/pull/17613>`__: ENH: Add annotations for 9 `ndarray`/`generic` magic methods
+* `#17613 <https://github.com/numpy/numpy/pull/17613>`__: ENH: Add annotations for 9 ``ndarray``/``generic`` magic methods
* `#17614 <https://github.com/numpy/numpy/pull/17614>`__: DOC: Fix the document for arrays interface
* `#17618 <https://github.com/numpy/numpy/pull/17618>`__: MAINT: Conversion of some strings to f-strings
* `#17619 <https://github.com/numpy/numpy/pull/17619>`__: DOC: Fix some references
* `#17621 <https://github.com/numpy/numpy/pull/17621>`__: TST: Valid docstring for config_py function show()
* `#17622 <https://github.com/numpy/numpy/pull/17622>`__: MAINT: Conversion of some strings to fstrings, part II
* `#17623 <https://github.com/numpy/numpy/pull/17623>`__: MAINT: Conversion of some strings to fstrings, part III
-* `#17624 <https://github.com/numpy/numpy/pull/17624>`__: DOC: Tidy up references to str_ / bytes_
+* `#17624 <https://github.com/numpy/numpy/pull/17624>`__: DOC: Tidy up references to ``str_`` / ``bytes_``
* `#17625 <https://github.com/numpy/numpy/pull/17625>`__: MAINT: Conversion of some strings to fstrings, part iv
* `#17627 <https://github.com/numpy/numpy/pull/17627>`__: DOC: Fix the references for ``__array_*__``
* `#17628 <https://github.com/numpy/numpy/pull/17628>`__: DOC: Add entries for macros
* `#17639 <https://github.com/numpy/numpy/pull/17639>`__: MAINT: Bump hypothesis from 5.37.3 to 5.38.0
* `#17641 <https://github.com/numpy/numpy/pull/17641>`__: MAINT, BLD: update to OpenBLAS v0.3.12
* `#17642 <https://github.com/numpy/numpy/pull/17642>`__: DOC: Fix reference to atleast_1d
-* `#17643 <https://github.com/numpy/numpy/pull/17643>`__: ENH: Add annotations for `np.core._ufunc_config`
-* `#17644 <https://github.com/numpy/numpy/pull/17644>`__: ENH: Add annotations for `np.core.shape_base`
+* `#17643 <https://github.com/numpy/numpy/pull/17643>`__: ENH: Add annotations for ``np.core._ufunc_config``
+* `#17644 <https://github.com/numpy/numpy/pull/17644>`__: ENH: Add annotations for ``np.core.shape_base``
* `#17645 <https://github.com/numpy/numpy/pull/17645>`__: BUG: fix np.timedelta64('nat').__format__ throwing an exception
* `#17654 <https://github.com/numpy/numpy/pull/17654>`__: BUG: f2py incorrectly translates dimension declarations.
* `#17655 <https://github.com/numpy/numpy/pull/17655>`__: BLD: Fix installing Numpy on z/OS
* `#17700 <https://github.com/numpy/numpy/pull/17700>`__: Fix small typos.
* `#17701 <https://github.com/numpy/numpy/pull/17701>`__: BUG: Fixed an issue where ``.pyi`` files were ignored by numpy...
* `#17703 <https://github.com/numpy/numpy/pull/17703>`__: Fix Doc Typos & Added Example
+* `#17706 <https://github.com/numpy/numpy/pull/17706>`__: BUG: Raise promotion error if a DType was provided in array coercion
* `#17708 <https://github.com/numpy/numpy/pull/17708>`__: Improve the einsum bench by adding new bench cases and variable...
+* `#17711 <https://github.com/numpy/numpy/pull/17711>`__: ENH: adds type hints to numpy.version
* `#17715 <https://github.com/numpy/numpy/pull/17715>`__: REV: Revert gh-17654 - f2py incorrectly translates dimension...
* `#17717 <https://github.com/numpy/numpy/pull/17717>`__: MAINT: Add more files to ``.gitgnore``
* `#17720 <https://github.com/numpy/numpy/pull/17720>`__: API: Do not import sliding_window_view to main namespace
* `#17830 <https://github.com/numpy/numpy/pull/17830>`__: TST: Add back durations flag for DEBUG builds.
* `#17832 <https://github.com/numpy/numpy/pull/17832>`__: BUG: Fix subarray dtype used with too large count in fromfile
* `#17833 <https://github.com/numpy/numpy/pull/17833>`__: BUG: Fix pickling of scalars with NPY_LISTPICKLE
-* `#17838 <https://github.com/numpy/numpy/pull/17838>`__: DOC: Update the `numpy.typing` documentation
+* `#17838 <https://github.com/numpy/numpy/pull/17838>`__: DOC: Update the ``numpy.typing`` documentation
* `#17841 <https://github.com/numpy/numpy/pull/17841>`__: DOC: Fixing boilerplate code example
-* `#17844 <https://github.com/numpy/numpy/pull/17844>`__: MAINT: Add ``__all__`` to `numpy.typing`
+* `#17844 <https://github.com/numpy/numpy/pull/17844>`__: MAINT: Add ``__all__`` to ``numpy.typing``
* `#17848 <https://github.com/numpy/numpy/pull/17848>`__: DOC: Add release note for gh-16161.
* `#17855 <https://github.com/numpy/numpy/pull/17855>`__: BUG: Fix incorrect C function prototypes/declarations.
* `#17857 <https://github.com/numpy/numpy/pull/17857>`__: MAINT: Prepare for the NumPy 1.20.x branch.
--- /dev/null
+
+Contributors
+============
+
+A total of 7 people contributed to this release. People with a "+" by their
+names contributed a patch for the first time.
+
+* Anne Archibald
+* Bas van Beek
+* Charles Harris
+* Dong Keun Oh +
+* Kamil Choudhury +
+* Sayed Adel
+* Sebastian Berg
+
+Pull requests merged
+====================
+
+A total of 15 pull requests were merged for this release.
+
+* `#18763 <https://github.com/numpy/numpy/pull/18763>`__: BUG: Correct ``datetime64`` missing type overload for ``datetime.date``...
+* `#18764 <https://github.com/numpy/numpy/pull/18764>`__: MAINT: Remove ``__all__`` in favor of explicit re-exports
+* `#18768 <https://github.com/numpy/numpy/pull/18768>`__: BLD: Strip extra newline when dumping gfortran version on MacOS
+* `#18769 <https://github.com/numpy/numpy/pull/18769>`__: BUG: fix segfault in object/longdouble operations
+* `#18794 <https://github.com/numpy/numpy/pull/18794>`__: MAINT: Use towncrier build explicitly
+* `#18887 <https://github.com/numpy/numpy/pull/18887>`__: MAINT: Relax certain integer-type constraints
+* `#18915 <https://github.com/numpy/numpy/pull/18915>`__: MAINT: Remove unsafe unions and ABCs from return-annotations
+* `#18921 <https://github.com/numpy/numpy/pull/18921>`__: MAINT: Allow more recursion depth for scalar tests.
+* `#18922 <https://github.com/numpy/numpy/pull/18922>`__: BUG: Initialize the full nditer buffer in case of error
+* `#18923 <https://github.com/numpy/numpy/pull/18923>`__: BLD: remove unnecessary flag ``-faltivec`` on macOS
+* `#18924 <https://github.com/numpy/numpy/pull/18924>`__: MAINT, CI: treats _SIMD module build warnings as errors through...
+* `#18925 <https://github.com/numpy/numpy/pull/18925>`__: BUG: for MINGW, threads.h existence test requires GLIBC > 2.12
+* `#18941 <https://github.com/numpy/numpy/pull/18941>`__: BUG: Make changelog recognize gh- as a PR number prefix.
+* `#18948 <https://github.com/numpy/numpy/pull/18948>`__: REL, DOC: Prepare for the NumPy 1.20.3 release.
+* `#18953 <https://github.com/numpy/numpy/pull/18953>`__: BUG: Fix failing mypy test in 1.20.x.
If you are unsure what pull request type to use, don't hesitate to ask in your
PR.
-You can install ``towncrier`` and run ``towncrier --draft --version 1.18``
+You can install ``towncrier`` and run ``towncrier build --draft --version 1.18``
if you want to get a preview of how your change will look in the final release
notes.
.. toctree::
:maxdepth: 3
+ 1.20.3 <release/1.20.3-notes>
1.20.2 <release/1.20.2-notes>
1.20.1 <release/1.20.1-notes>
1.20.0 <release/1.20.0-notes>
NumPy 1.20.2 Release Notes
==========================
-NumPy 1,20.2 is a bugfix release containing several fixes merged to the main
+NumPy 1.20.2 is a bugfix release containing several fixes merged to the main
branch after the NumPy 1.20.1 release.
-
Contributors
============
--- /dev/null
+.. currentmodule:: numpy
+
+==========================
+NumPy 1.20.3 Release Notes
+==========================
+
+NumPy 1.20.3 is a bugfix release containing several fixes merged to the main
+branch after the NumPy 1.20.2 release.
+
+Contributors
+============
+
+A total of 7 people contributed to this release. People with a "+" by their
+names contributed a patch for the first time.
+
+* Anne Archibald
+* Bas van Beek
+* Charles Harris
+* Dong Keun Oh +
+* Kamil Choudhury +
+* Sayed Adel
+* Sebastian Berg
+
+Pull requests merged
+====================
+
+A total of 15 pull requests were merged for this release.
+
+* `#18763 <https://github.com/numpy/numpy/pull/18763>`__: BUG: Correct ``datetime64`` missing type overload for ``datetime.date``...
+* `#18764 <https://github.com/numpy/numpy/pull/18764>`__: MAINT: Remove ``__all__`` in favor of explicit re-exports
+* `#18768 <https://github.com/numpy/numpy/pull/18768>`__: BLD: Strip extra newline when dumping gfortran version on MacOS
+* `#18769 <https://github.com/numpy/numpy/pull/18769>`__: BUG: fix segfault in object/longdouble operations
+* `#18794 <https://github.com/numpy/numpy/pull/18794>`__: MAINT: Use towncrier build explicitly
+* `#18887 <https://github.com/numpy/numpy/pull/18887>`__: MAINT: Relax certain integer-type constraints
+* `#18915 <https://github.com/numpy/numpy/pull/18915>`__: MAINT: Remove unsafe unions and ABCs from return-annotations
+* `#18921 <https://github.com/numpy/numpy/pull/18921>`__: MAINT: Allow more recursion depth for scalar tests.
+* `#18922 <https://github.com/numpy/numpy/pull/18922>`__: BUG: Initialize the full nditer buffer in case of error
+* `#18923 <https://github.com/numpy/numpy/pull/18923>`__: BLD: remove unnecessary flag ``-faltivec`` on macOS
+* `#18924 <https://github.com/numpy/numpy/pull/18924>`__: MAINT, CI: treats _SIMD module build warnings as errors through...
+* `#18925 <https://github.com/numpy/numpy/pull/18925>`__: BUG: for MINGW, threads.h existence test requires GLIBC > 2.12
+* `#18941 <https://github.com/numpy/numpy/pull/18941>`__: BUG: Make changelog recognize gh- as a PR number prefix.
+* `#18948 <https://github.com/numpy/numpy/pull/18948>`__: REL, DOC: Prepare for the NumPy 1.20.3 release.
+* `#18953 <https://github.com/numpy/numpy/pull/18953>`__: BUG: Fix failing mypy test in 1.20.x.
# Ensures that the stubs are picked up
from numpy import (
- char,
- ctypeslib,
- emath,
- fft,
- lib,
- linalg,
- ma,
- matrixlib,
- polynomial,
- random,
- rec,
- testing,
- version,
+ char as char,
+ ctypeslib as ctypeslib,
+ emath as emath,
+ fft as fft,
+ lib as lib,
+ linalg as linalg,
+ ma as ma,
+ matrixlib as matrixlib,
+ polynomial as polynomial,
+ random as random,
+ rec as rec,
+ testing as testing,
+ version as version,
)
from numpy.core.function_base import (
- linspace,
- logspace,
- geomspace,
+ linspace as linspace,
+ logspace as logspace,
+ geomspace as geomspace,
)
from numpy.core.fromnumeric import (
- take,
- reshape,
- choose,
- repeat,
- put,
- swapaxes,
- transpose,
- partition,
- argpartition,
- sort,
- argsort,
- argmax,
- argmin,
- searchsorted,
- resize,
- squeeze,
- diagonal,
- trace,
- ravel,
- nonzero,
- shape,
- compress,
- clip,
- sum,
- all,
- any,
- cumsum,
- ptp,
- amax,
- amin,
- prod,
- cumprod,
- ndim,
- size,
- around,
- mean,
- std,
- var,
+ take as take,
+ reshape as reshape,
+ choose as choose,
+ repeat as repeat,
+ put as put,
+ swapaxes as swapaxes,
+ transpose as transpose,
+ partition as partition,
+ argpartition as argpartition,
+ sort as sort,
+ argsort as argsort,
+ argmax as argmax,
+ argmin as argmin,
+ searchsorted as searchsorted,
+ resize as resize,
+ squeeze as squeeze,
+ diagonal as diagonal,
+ trace as trace,
+ ravel as ravel,
+ nonzero as nonzero,
+ shape as shape,
+ compress as compress,
+ clip as clip,
+ sum as sum,
+ all as all,
+ any as any,
+ cumsum as cumsum,
+ ptp as ptp,
+ amax as amax,
+ amin as amin,
+ prod as prod,
+ cumprod as cumprod,
+ ndim as ndim,
+ size as size,
+ around as around,
+ mean as mean,
+ std as std,
+ var as var,
)
from numpy.core._asarray import (
vstack as vstack,
)
-# Add an object to `__all__` if their stubs are defined in an external file;
-# their stubs will not be recognized otherwise.
-# NOTE: This is redundant for objects defined within this file.
-__all__ = [
- "linspace",
- "logspace",
- "geomspace",
- "take",
- "reshape",
- "choose",
- "repeat",
- "put",
- "swapaxes",
- "transpose",
- "partition",
- "argpartition",
- "sort",
- "argsort",
- "argmax",
- "argmin",
- "searchsorted",
- "resize",
- "squeeze",
- "diagonal",
- "trace",
- "ravel",
- "nonzero",
- "shape",
- "compress",
- "clip",
- "sum",
- "all",
- "any",
- "cumsum",
- "ptp",
- "amax",
- "amin",
- "prod",
- "cumprod",
- "ndim",
- "size",
- "around",
- "mean",
- "std",
- "var",
-]
-
+__all__: List[str]
__path__: List[str]
__version__: str
def ravel(self, order: _OrderKACF = ...) -> ndarray: ...
@overload
def reshape(
- self, __shape: Sequence[int], *, order: _OrderACF = ...
+ self, __shape: _ShapeLike, *, order: _OrderACF = ...
) -> ndarray: ...
@overload
def reshape(
- self, *shape: int, order: _OrderACF = ...
+ self, *shape: SupportsIndex, order: _OrderACF = ...
) -> ndarray: ...
def tobytes(self, order: _OrderKACF = ...) -> bytes: ...
# NOTE: `tostring()` is deprecated and therefore excluded
def __array_wrap__(array, context=...): ...
def __setstate__(self, __state): ...
# a `bool_` is returned when `keepdims=True` and `self` is a 0d array
+
@overload
def all(
- self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ...
+ self,
+ axis: None = ...,
+ out: None = ...,
+ keepdims: Literal[False] = ...,
) -> bool_: ...
@overload
def all(
- self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ...
- ) -> Union[bool_, ndarray]: ...
+ self,
+ axis: Optional[_ShapeLike] = ...,
+ out: None = ...,
+ keepdims: bool = ...,
+ ) -> Any: ...
@overload
def all(
self,
out: _NdArraySubClass = ...,
keepdims: bool = ...,
) -> _NdArraySubClass: ...
+
@overload
def any(
- self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ...
+ self,
+ axis: None = ...,
+ out: None = ...,
+ keepdims: Literal[False] = ...,
) -> bool_: ...
@overload
def any(
- self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ...
- ) -> Union[bool_, ndarray]: ...
+ self,
+ axis: Optional[_ShapeLike] = ...,
+ out: None = ...,
+ keepdims: bool = ...,
+ ) -> Any: ...
@overload
def any(
self,
out: _NdArraySubClass = ...,
keepdims: bool = ...,
) -> _NdArraySubClass: ...
+
@overload
- def argmax(self, axis: None = ..., out: None = ...) -> signedinteger: ...
+ def argmax(
+ self,
+ axis: None = ...,
+ out: None = ...,
+ ) -> signedinteger[Any]: ...
@overload
def argmax(
- self, axis: _ShapeLike = ..., out: None = ...
- ) -> Union[signedinteger, ndarray]: ...
+ self,
+ axis: _ShapeLike = ...,
+ out: None = ...,
+ ) -> Any: ...
@overload
def argmax(
- self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ...
+ self,
+ axis: Optional[_ShapeLike] = ...,
+ out: _NdArraySubClass = ...,
) -> _NdArraySubClass: ...
+
@overload
- def argmin(self, axis: None = ..., out: None = ...) -> signedinteger: ...
+ def argmin(
+ self,
+ axis: None = ...,
+ out: None = ...,
+ ) -> signedinteger[Any]: ...
@overload
def argmin(
- self, axis: _ShapeLike = ..., out: None = ...
- ) -> Union[signedinteger, ndarray]: ...
+ self,
+ axis: _ShapeLike = ...,
+ out: None = ...,
+ ) -> Any: ...
@overload
def argmin(
- self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ...
+ self,
+ axis: Optional[_ShapeLike] = ...,
+ out: _NdArraySubClass = ...,
) -> _NdArraySubClass: ...
+
def argsort(
self,
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
+
@overload
def choose(
- self, choices: ArrayLike, out: None = ..., mode: _ModeKind = ...,
+ self,
+ choices: ArrayLike,
+ out: None = ...,
+ mode: _ModeKind = ...,
) -> ndarray: ...
@overload
def choose(
- self, choices: ArrayLike, out: _NdArraySubClass = ..., mode: _ModeKind = ...,
+ self,
+ choices: ArrayLike,
+ out: _NdArraySubClass = ...,
+ mode: _ModeKind = ...,
) -> _NdArraySubClass: ...
+
@overload
def clip(
self,
max: Optional[ArrayLike] = ...,
out: None = ...,
**kwargs: Any,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def clip(
self,
max: ArrayLike = ...,
out: None = ...,
**kwargs: Any,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def clip(
self,
out: _NdArraySubClass = ...,
**kwargs: Any,
) -> _NdArraySubClass: ...
+
@overload
def compress(
- self, a: ArrayLike, axis: Optional[int] = ..., out: None = ...,
+ self,
+ a: ArrayLike,
+ axis: Optional[SupportsIndex] = ...,
+ out: None = ...,
) -> ndarray: ...
@overload
def compress(
- self, a: ArrayLike, axis: Optional[int] = ..., out: _NdArraySubClass = ...,
+ self,
+ a: ArrayLike,
+ axis: Optional[SupportsIndex] = ...,
+ out: _NdArraySubClass = ...,
) -> _NdArraySubClass: ...
+
def conj(self: _ArraySelf) -> _ArraySelf: ...
+
def conjugate(self: _ArraySelf) -> _ArraySelf: ...
+
@overload
def cumprod(
- self, axis: Optional[int] = ..., dtype: DTypeLike = ..., out: None = ...,
+ self,
+ axis: Optional[SupportsIndex] = ...,
+ dtype: DTypeLike = ...,
+ out: None = ...,
) -> ndarray: ...
@overload
def cumprod(
self,
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
dtype: DTypeLike = ...,
out: _NdArraySubClass = ...,
) -> _NdArraySubClass: ...
+
@overload
def cumsum(
- self, axis: Optional[int] = ..., dtype: DTypeLike = ..., out: None = ...,
+ self,
+ axis: Optional[SupportsIndex] = ...,
+ dtype: DTypeLike = ...,
+ out: None = ...,
) -> ndarray: ...
@overload
def cumsum(
self,
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
dtype: DTypeLike = ...,
out: _NdArraySubClass = ...,
) -> _NdArraySubClass: ...
- @overload
- def max(
- self,
- axis: None = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
- ) -> number: ...
+
@overload
def max(
self,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def max(
self,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
) -> _NdArraySubClass: ...
- @overload
- def mean(
- self,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
- ) -> number: ...
+
@overload
def mean(
self,
dtype: DTypeLike = ...,
out: None = ...,
keepdims: bool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def mean(
self,
out: _NdArraySubClass = ...,
keepdims: bool = ...,
) -> _NdArraySubClass: ...
- @overload
- def min(
- self,
- axis: None = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
- ) -> number: ...
+
@overload
def min(
self,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def min(
self,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
) -> _NdArraySubClass: ...
- def newbyteorder(self: _ArraySelf, __new_order: _ByteOrder = ...) -> _ArraySelf: ...
- @overload
- def prod(
- self,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
- ) -> number: ...
+
+ def newbyteorder(
+ self: _ArraySelf,
+ __new_order: _ByteOrder = ...,
+ ) -> _ArraySelf: ...
+
@overload
def prod(
self,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def prod(
self,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
) -> _NdArraySubClass: ...
+
@overload
def ptp(
- self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ...,
- ) -> number: ...
- @overload
- def ptp(
- self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ...,
- ) -> Union[number, ndarray]: ...
+ self,
+ axis: Optional[_ShapeLike] = ...,
+ out: None = ...,
+ keepdims: bool = ...,
+ ) -> Any: ...
@overload
def ptp(
self,
out: _NdArraySubClass = ...,
keepdims: bool = ...,
) -> _NdArraySubClass: ...
+
def repeat(
- self, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
+ self,
+ repeats: _ArrayLikeIntOrBool,
+ axis: Optional[SupportsIndex] = ...,
) -> ndarray: ...
- @overload
- def round(self: _ArraySelf, decimals: int = ..., out: None = ...) -> _ArraySelf: ...
+
@overload
def round(
- self, decimals: int = ..., out: _NdArraySubClass = ...
- ) -> _NdArraySubClass: ...
+ self: _ArraySelf,
+ decimals: SupportsIndex = ...,
+ out: None = ...,
+ ) -> _ArraySelf: ...
@overload
- def std(
+ def round(
self,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- ddof: int = ...,
- keepdims: Literal[False] = ...,
- ) -> number: ...
+ decimals: SupportsIndex = ...,
+ out: _NdArraySubClass = ...,
+ ) -> _NdArraySubClass: ...
+
@overload
def std(
self,
out: None = ...,
ddof: int = ...,
keepdims: bool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def std(
self,
ddof: int = ...,
keepdims: bool = ...,
) -> _NdArraySubClass: ...
- @overload
- def sum(
- self,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
- ) -> number: ...
+
@overload
def sum(
self,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def sum(
self,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
) -> _NdArraySubClass: ...
+
@overload
def take(
self,
indices: Union[_IntLike, _BoolLike],
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
out: None = ...,
mode: _ModeKind = ...,
- ) -> generic: ...
+ ) -> Any: ...
@overload
def take(
self,
indices: _ArrayLikeIntOrBool,
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
out: None = ...,
mode: _ModeKind = ...,
) -> ndarray: ...
def take(
self,
indices: _ArrayLikeIntOrBool,
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
out: _NdArraySubClass = ...,
mode: _ModeKind = ...,
) -> _NdArraySubClass: ...
- @overload
- def var(
- self,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- ddof: int = ...,
- keepdims: Literal[False] = ...,
- ) -> number: ...
+
@overload
def var(
self,
out: None = ...,
ddof: int = ...,
keepdims: bool = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def var(
self,
def imag(self, value: ArrayLike) -> None: ...
def __new__(
cls: Type[_ArraySelf],
- shape: Sequence[int],
+ shape: _ShapeLike,
dtype: DTypeLike = ...,
buffer: _BufferType = ...,
offset: int = ...,
@property
def flat(self: _ArraySelf) -> flatiter[_ArraySelf]: ...
@overload
- def item(self, *args: int) -> Any: ...
+ def item(self, *args: SupportsIndex) -> Any: ...
@overload
- def item(self, __args: Tuple[int, ...]) -> Any: ...
+ def item(self, __args: Tuple[SupportsIndex, ...]) -> Any: ...
@overload
def itemset(self, __value: Any) -> None: ...
@overload
def itemset(self, __item: _ShapeLike, __value: Any) -> None: ...
@overload
- def resize(self, __new_shape: Sequence[int], *, refcheck: bool = ...) -> None: ...
+ def resize(self, __new_shape: _ShapeLike, *, refcheck: bool = ...) -> None: ...
@overload
- def resize(self, *new_shape: int, refcheck: bool = ...) -> None: ...
+ def resize(self, *new_shape: SupportsIndex, refcheck: bool = ...) -> None: ...
def setflags(
self, write: bool = ..., align: bool = ..., uic: bool = ...
) -> None: ...
def squeeze(
- self: _ArraySelf, axis: Union[int, Tuple[int, ...]] = ...
+ self: _ArraySelf, axis: Union[SupportsIndex, Tuple[SupportsIndex, ...]] = ...
) -> _ArraySelf: ...
- def swapaxes(self: _ArraySelf, axis1: int, axis2: int) -> _ArraySelf: ...
+ def swapaxes(self: _ArraySelf, axis1: SupportsIndex, axis2: SupportsIndex) -> _ArraySelf: ...
@overload
- def transpose(self: _ArraySelf, __axes: Sequence[int]) -> _ArraySelf: ...
+ def transpose(self: _ArraySelf, __axes: _ShapeLike) -> _ArraySelf: ...
@overload
- def transpose(self: _ArraySelf, *axes: int) -> _ArraySelf: ...
+ def transpose(self: _ArraySelf, *axes: SupportsIndex) -> _ArraySelf: ...
def argpartition(
self,
kth: _ArrayLikeIntOrBool,
- axis: Optional[int] = ...,
+ axis: Optional[SupportsIndex] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
def diagonal(
- self: _ArraySelf, offset: int = ..., axis1: int = ..., axis2: int = ...
+ self: _ArraySelf,
+ offset: SupportsIndex = ...,
+ axis1: SupportsIndex = ...,
+ axis2: SupportsIndex = ...,
) -> _ArraySelf: ...
@overload
- def dot(self, b: ArrayLike, out: None = ...) -> Union[number, ndarray]: ...
+ def dot(self, b: ArrayLike, out: None = ...) -> Any: ...
@overload
def dot(self, b: ArrayLike, out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
# `nonzero()` is deprecated for 0d arrays/generics
def partition(
self,
kth: _ArrayLikeIntOrBool,
- axis: int = ...,
+ axis: SupportsIndex = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> None: ...
sorter: Optional[_ArrayLikeIntOrBool] = ..., # 1D int array
) -> ndarray: ...
def setfield(
- self, val: ArrayLike, dtype: DTypeLike, offset: int = ...
+ self, val: ArrayLike, dtype: DTypeLike, offset: SupportsIndex = ...
) -> None: ...
def sort(
self,
- axis: int = ...,
+ axis: SupportsIndex = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> None: ...
@overload
def trace(
self, # >= 2D array
- offset: int = ...,
- axis1: int = ...,
- axis2: int = ...,
+ offset: SupportsIndex = ...,
+ axis1: SupportsIndex = ...,
+ axis2: SupportsIndex = ...,
dtype: DTypeLike = ...,
out: None = ...,
- ) -> Union[number, ndarray]: ...
+ ) -> Any: ...
@overload
def trace(
self, # >= 2D array
- offset: int = ...,
- axis1: int = ...,
- axis2: int = ...,
+ offset: SupportsIndex = ...,
+ axis1: SupportsIndex = ...,
+ axis2: SupportsIndex = ...,
dtype: DTypeLike = ...,
out: _NdArraySubClass = ...,
) -> _NdArraySubClass: ...
@property
def imag(self: _ArraySelf) -> _ArraySelf: ...
+# The `datetime64` constructors requires an object with the three attributes below,
+# and thus supports datetime duck typing
+class _DatetimeScalar(Protocol):
+ @property
+ def day(self) -> int: ...
+ @property
+ def month(self) -> int: ...
+ @property
+ def year(self) -> int: ...
+
class datetime64(generic):
@overload
def __init__(
self,
- __value: Union[None, datetime64, _CharLike, dt.datetime] = ...,
+ __value: Union[None, datetime64, _CharLike, _DatetimeScalar] = ...,
__format: Union[_CharLike, Tuple[_CharLike, _IntLike]] = ...,
) -> None: ...
@overload
from numpy import (
ndarray,
- number,
- integer,
+ signedinteger,
bool_,
generic,
_OrderKACF,
ArrayLike,
_ShapeLike,
_Shape,
- _IntLike,
- _BoolLike,
_NumberLike,
)
_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
# Integers and booleans can generally be used interchangeably
-_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_])
_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
-_ScalarGenericDT = TypeVar(
- "_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic]
-)
-
-_Number = TypeVar("_Number", bound=number)
# The signature of take() follows a common theme with its overloads:
# 1. A generic comes in; the same generic comes out
# 2. A scalar comes in; a generic comes out
# 3. An array-like object comes in; some keyword ensures that a generic comes out
# 4. An array-like object comes in; an ndarray or generic comes out
-@overload
-def take(
- a: _ScalarGenericDT,
- indices: int,
- axis: Optional[int] = ...,
- out: Optional[ndarray] = ...,
- mode: _ModeKind = ...,
-) -> _ScalarGenericDT: ...
-@overload
-def take(
- a: _Scalar,
- indices: int,
- axis: Optional[int] = ...,
- out: Optional[ndarray] = ...,
- mode: _ModeKind = ...,
-) -> _ScalarNumpy: ...
-@overload
-def take(
- a: ArrayLike,
- indices: int,
- axis: Optional[int] = ...,
- out: Optional[ndarray] = ...,
- mode: _ModeKind = ...,
-) -> _ScalarNumpy: ...
-@overload
def take(
a: ArrayLike,
indices: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _ModeKind = ...,
-) -> Union[_ScalarNumpy, ndarray]: ...
-def reshape(a: ArrayLike, newshape: _ShapeLike, order: _OrderACF = ...) -> ndarray: ...
-@overload
-def choose(
- a: _ScalarIntOrBool,
- choices: ArrayLike,
- out: Optional[ndarray] = ...,
- mode: _ModeKind = ...,
-) -> _ScalarIntOrBool: ...
-@overload
-def choose(
- a: Union[_IntLike, _BoolLike], choices: ArrayLike, out: Optional[ndarray] = ..., mode: _ModeKind = ...
-) -> Union[integer, bool_]: ...
-@overload
+) -> Any: ...
+
+def reshape(
+ a: ArrayLike,
+ newshape: _ShapeLike,
+ order: _OrderACF = ...,
+) -> ndarray: ...
+
def choose(
a: _ArrayLikeIntOrBool,
choices: ArrayLike,
out: Optional[ndarray] = ...,
mode: _ModeKind = ...,
-) -> ndarray: ...
+) -> Any: ...
+
def repeat(
- a: ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
+ a: ArrayLike,
+ repeats: _ArrayLikeIntOrBool,
+ axis: Optional[int] = ...,
) -> ndarray: ...
+
def put(
- a: ndarray, ind: _ArrayLikeIntOrBool, v: ArrayLike, mode: _ModeKind = ...
+ a: ndarray,
+ ind: _ArrayLikeIntOrBool,
+ v: ArrayLike,
+ mode: _ModeKind = ...,
) -> None: ...
-def swapaxes(a: ArrayLike, axis1: int, axis2: int) -> ndarray: ...
+
+def swapaxes(
+ a: ArrayLike,
+ axis1: int,
+ axis2: int,
+) -> ndarray: ...
+
def transpose(
- a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ...
+ a: ArrayLike,
+ axes: Union[None, Sequence[int], ndarray] = ...
) -> ndarray: ...
+
def partition(
a: ArrayLike,
kth: _ArrayLikeIntOrBool,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
-@overload
-def argpartition(
- a: generic,
- kth: _ArrayLikeIntOrBool,
- axis: Optional[int] = ...,
- kind: _PartitionKind = ...,
- order: Union[None, str, Sequence[str]] = ...,
-) -> integer: ...
-@overload
-def argpartition(
- a: _ScalarBuiltin,
- kth: _ArrayLikeIntOrBool,
- axis: Optional[int] = ...,
- kind: _PartitionKind = ...,
- order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-@overload
+
def argpartition(
a: ArrayLike,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
+) -> Any: ...
+
def sort(
a: ArrayLike,
axis: Optional[int] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
+
def argsort(
a: ArrayLike,
axis: Optional[int] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
+
@overload
-def argmax(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
+def argmax(
+ a: ArrayLike,
+ axis: None = ...,
+ out: Optional[ndarray] = ...,
+) -> signedinteger[Any]: ...
@overload
def argmax(
- a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[integer, ndarray]: ...
+ a: ArrayLike,
+ axis: Optional[int] = ...,
+ out: Optional[ndarray] = ...,
+) -> Any: ...
+
@overload
-def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
+def argmin(
+ a: ArrayLike,
+ axis: None = ...,
+ out: Optional[ndarray] = ...,
+) -> signedinteger[Any]: ...
@overload
def argmin(
- a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[integer, ndarray]: ...
+ a: ArrayLike,
+ axis: Optional[int] = ...,
+ out: Optional[ndarray] = ...,
+) -> Any: ...
+
@overload
def searchsorted(
a: ArrayLike,
v: _Scalar,
side: _SortSide = ...,
sorter: Optional[_ArrayLikeIntOrBool] = ..., # 1D int array
-) -> integer: ...
+) -> signedinteger[Any]: ...
@overload
def searchsorted(
a: ArrayLike,
side: _SortSide = ...,
sorter: Optional[_ArrayLikeIntOrBool] = ..., # 1D int array
) -> ndarray: ...
-def resize(a: ArrayLike, new_shape: _ShapeLike) -> ndarray: ...
+
+def resize(
+ a: ArrayLike,
+ new_shape: _ShapeLike,
+) -> ndarray: ...
+
@overload
-def squeeze(a: _ScalarGeneric, axis: Optional[_ShapeLike] = ...) -> _ScalarGeneric: ...
+def squeeze(
+ a: _ScalarGeneric,
+ axis: Optional[_ShapeLike] = ...,
+) -> _ScalarGeneric: ...
@overload
-def squeeze(a: ArrayLike, axis: Optional[_ShapeLike] = ...) -> ndarray: ...
+def squeeze(
+ a: ArrayLike,
+ axis: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+
def diagonal(
- a: ArrayLike, offset: int = ..., axis1: int = ..., axis2: int = ... # >= 2D array
+ a: ArrayLike,
+ offset: int = ...,
+ axis1: int = ...,
+ axis2: int = ..., # >= 2D array
) -> ndarray: ...
+
def trace(
a: ArrayLike, # >= 2D array
offset: int = ...,
axis2: int = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
+
def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ...
+
def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
+
def shape(a: ArrayLike) -> _Shape: ...
+
def compress(
condition: ArrayLike, # 1D bool array
a: ArrayLike,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
-@overload
-def clip(
- a: _Number,
- a_min: ArrayLike,
- a_max: Optional[ArrayLike],
- out: Optional[ndarray] = ...,
- **kwargs: Any,
-) -> _Number: ...
-@overload
-def clip(
- a: _Number,
- a_min: None,
- a_max: ArrayLike,
- out: Optional[ndarray] = ...,
- **kwargs: Any,
-) -> _Number: ...
+
@overload
def clip(
a: ArrayLike,
a_max: Optional[ArrayLike],
out: Optional[ndarray] = ...,
**kwargs: Any,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
@overload
def clip(
a: ArrayLike,
a_max: ArrayLike,
out: Optional[ndarray] = ...,
**kwargs: Any,
-) -> Union[number, ndarray]: ...
-@overload
-def sum(
- a: _Number,
- axis: Optional[_ShapeLike] = ...,
- dtype: DTypeLike = ...,
- out: Optional[ndarray] = ...,
- keepdims: bool = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
+) -> Any: ...
+
def sum(
a: ArrayLike,
axis: _ShapeLike = ...,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
+
@overload
def all(
a: ArrayLike,
axis: None = ...,
- out: Optional[ndarray] = ...,
+ out: None = ...,
keepdims: Literal[False] = ...,
) -> bool_: ...
@overload
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
-) -> Union[bool_, ndarray]: ...
+) -> Any: ...
+
@overload
def any(
a: ArrayLike,
axis: None = ...,
- out: Optional[ndarray] = ...,
+ out: None = ...,
keepdims: Literal[False] = ...,
) -> bool_: ...
@overload
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
-) -> Union[bool_, ndarray]: ...
+) -> Any: ...
+
def cumsum(
a: ArrayLike,
axis: Optional[int] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
-@overload
-def ptp(
- a: _Number,
- axis: Optional[_ShapeLike] = ...,
- out: Optional[ndarray] = ...,
- keepdims: bool = ...,
-) -> _Number: ...
-@overload
-def ptp(
- a: ArrayLike,
- axis: None = ...,
- out: Optional[ndarray] = ...,
- keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+
def ptp(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amax(
- a: _Number,
- axis: Optional[_ShapeLike] = ...,
- out: Optional[ndarray] = ...,
- keepdims: bool = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amax(
- a: ArrayLike,
- axis: None = ...,
- out: Optional[ndarray] = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
def amax(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amin(
- a: _Number,
- axis: Optional[_ShapeLike] = ...,
- out: Optional[ndarray] = ...,
- keepdims: bool = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amin(
- a: ArrayLike,
- axis: None = ...,
- out: Optional[ndarray] = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
def amin(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
# TODO: `np.prod()``: For object arrays `initial` does not necessarily
# have to be a numerical scalar.
# Note that the same situation holds for all wrappers around
# `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
-@overload
-def prod(
- a: _Number,
- axis: Optional[_ShapeLike] = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- keepdims: bool = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def prod(
- a: ArrayLike,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
- initial: _NumberLike = ...,
- where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
def prod(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
keepdims: bool = ...,
initial: _NumberLike = ...,
where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
+
def cumprod(
a: ArrayLike,
axis: Optional[int] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
+
def ndim(a: ArrayLike) -> int: ...
+
def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ...
-@overload
-def around(
- a: _Number, decimals: int = ..., out: Optional[ndarray] = ...
-) -> _Number: ...
-@overload
-def around(
- a: _NumberLike, decimals: int = ..., out: Optional[ndarray] = ...
-) -> number: ...
-@overload
+
def around(
- a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ...
-) -> ndarray: ...
-@overload
-def mean(
a: ArrayLike,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+ decimals: int = ...,
+ out: Optional[ndarray] = ...,
+) -> Any: ...
+
def mean(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def std(
- a: ArrayLike,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- ddof: int = ...,
- keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
def std(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
ddof: int = ...,
keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def var(
- a: ArrayLike,
- axis: None = ...,
- dtype: DTypeLike = ...,
- out: None = ...,
- ddof: int = ...,
- keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
def var(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
ddof: int = ...,
keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
import sys
from typing import overload, Tuple, Union, Sequence, Any
-from numpy import ndarray, inexact
+from numpy import ndarray
from numpy.typing import ArrayLike, DTypeLike, _SupportsArray, _NumberLike
if sys.version_info >= (3, 8):
retstep: Literal[True] = ...,
dtype: DTypeLike = ...,
axis: SupportsIndex = ...,
-) -> Tuple[ndarray, inexact]: ...
+) -> Tuple[ndarray, Any]: ...
+
def logspace(
start: _ArrayLikeNumber,
stop: _ArrayLikeNumber,
dtype: DTypeLike = ...,
axis: SupportsIndex = ...,
) -> ndarray: ...
+
def geomspace(
start: _ArrayLikeNumber,
stop: _ArrayLikeNumber,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
+
def ones(
shape: _ShapeLike,
dtype: DTypeLike = ...,
*,
like: ArrayLike = ...,
) -> ndarray: ...
+
@overload
def ones_like(
a: _ArrayType,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
+
@overload
def empty_like(
a: _ArrayType,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
+
def full(
shape: _ShapeLike,
fill_value: Any,
*,
like: ArrayLike = ...,
) -> ndarray: ...
+
@overload
def full_like(
a: _ArrayType,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
+
@overload
def count_nonzero(
- a: ArrayLike, axis: None = ..., *, keepdims: Literal[False] = ...
+ a: ArrayLike,
+ axis: None = ...,
+ *,
+ keepdims: Literal[False] = ...,
) -> int: ...
@overload
def count_nonzero(
- a: ArrayLike, axis: _ShapeLike = ..., *, keepdims: bool = ...
-) -> Union[signedinteger[Any], ndarray]: ... # TODO: np.intp
+ a: ArrayLike,
+ axis: _ShapeLike = ...,
+ *,
+ keepdims: bool = ...,
+) -> Any: ... # TODO: np.intp or ndarray[np.intp]
+
def isfortran(a: Union[ndarray, generic]) -> bool: ...
+
def argwhere(a: ArrayLike) -> ndarray: ...
+
def flatnonzero(a: ArrayLike) -> ndarray: ...
-def correlate(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ...
-def convolve(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ...
+
+def correlate(
+ a: ArrayLike,
+ v: ArrayLike,
+ mode: _CorrelateMode = ...,
+) -> ndarray: ...
+
+def convolve(
+ a: ArrayLike,
+ v: ArrayLike,
+ mode: _CorrelateMode = ...,
+) -> ndarray: ...
+
@overload
-def outer(a: ArrayLike, b: ArrayLike, out: None = ...) -> ndarray: ...
+def outer(
+ a: ArrayLike,
+ b: ArrayLike,
+ out: None = ...,
+) -> ndarray: ...
@overload
-def outer(a: ArrayLike, b: ArrayLike, out: _ArrayType = ...) -> _ArrayType: ...
+def outer(
+ a: ArrayLike,
+ b: ArrayLike,
+ out: _ArrayType = ...,
+) -> _ArrayType: ...
+
def tensordot(
a: ArrayLike,
b: ArrayLike,
axes: Union[int, Tuple[_ShapeLike, _ShapeLike]] = ...,
) -> ndarray: ...
+
def roll(
a: ArrayLike,
shift: _ShapeLike,
axis: Optional[_ShapeLike] = ...,
) -> ndarray: ...
+
def rollaxis(a: ndarray, axis: int, start: int = ...) -> ndarray: ...
+
def moveaxis(
a: ndarray,
source: _ShapeLike,
destination: _ShapeLike,
) -> ndarray: ...
+
def cross(
a: ArrayLike,
b: ArrayLike,
axisc: int = ...,
axis: Optional[int] = ...,
) -> ndarray: ...
+
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike = ...,
sparse: Literal[True] = ...,
) -> Tuple[ndarray, ...]: ...
+
def fromfunction(
function: Callable[..., _T],
shape: Sequence[int],
like: ArrayLike = ...,
**kwargs: Any,
) -> _T: ...
+
def isscalar(element: Any) -> bool: ...
+
def binary_repr(num: int, width: Optional[int] = ...) -> str: ...
+
def base_repr(number: int, base: int = ..., padding: int = ...) -> str: ...
-def identity(n: int, dtype: DTypeLike = ..., *, like: ArrayLike = ...) -> ndarray: ...
+
+def identity(
+ n: int,
+ dtype: DTypeLike = ...,
+ *,
+ like: ArrayLike = ...,
+) -> ndarray: ...
+
def allclose(
a: ArrayLike,
b: ArrayLike,
atol: float = ...,
equal_nan: bool = ...,
) -> bool: ...
+
def isclose(
a: ArrayLike,
b: ArrayLike,
rtol: float = ...,
atol: float = ...,
equal_nan: bool = ...,
-) -> Union[bool_, ndarray]: ...
+) -> Any: ...
+
def array_equal(a1: ArrayLike, a2: ArrayLike) -> bool: ...
+
def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...
-/*@targets $werror #simd_test*/
+/*@targets #simd_test*/
#include "_simd.h"
#include "_simd_inc.h"
}
goto fail;
}
+ if (PyDataType_FLAGCHK(op_dtype[iop], NPY_NEEDS_INIT)) {
+ memset(buffer, '\0', itemsize*buffersize);
+ }
buffers[iop] = buffer;
}
}
skip_transfer = 1;
}
- /* If the data type requires zero-inititialization */
- if (PyDataType_FLAGCHK(dtypes[iop], NPY_NEEDS_INIT)) {
- NPY_IT_DBG_PRINT("Iterator: Buffer requires init, "
- "memsetting to 0\n");
- memset(ptrs[iop], 0, dtypes[iop]->elsize*op_transfersize);
- /* Can't skip the transfer in this case */
- skip_transfer = 0;
- }
-
- if (!skip_transfer) {
+ /*
+ * Copy data to the buffers if necessary.
+ *
+ * We always copy if the operand has references. In that case
+ * a "write" function must be in use that either copies or clears
+ * the buffer.
+ * This write from buffer call does not check for skip-transfer
+ * so we have to assume the buffer is cleared. For dtypes that
+ * do not have references, we can assume that the write function
+ * will leave the source (buffer) unmodified.
+ */
+ if (!skip_transfer || PyDataType_REFCHK(dtypes[iop])) {
NPY_IT_DBG_PRINT2("Iterator: Copying operand %d to "
"buffer (%d items)\n",
(int)iop, (int)op_transfersize);
}
}
}
- else if (ptrs[iop] == buffers[iop]) {
- /* If the data type requires zero-inititialization */
- if (PyDataType_FLAGCHK(dtypes[iop], NPY_NEEDS_INIT)) {
- NPY_IT_DBG_PRINT1("Iterator: Write-only buffer for "
- "operand %d requires init, "
- "memsetting to 0\n", (int)iop);
- memset(ptrs[iop], 0, dtypes[iop]->elsize*transfersize);
- }
- }
-
}
/*
if (buffers[iop] == NULL) {
out_of_memory = 1;
}
+ if (PyDataType_FLAGCHK(dtypes[iop], NPY_NEEDS_INIT)) {
+ memset(buffers[iop], '\0', itemsize*buffersize);
+ }
}
}
{
int ret;
ret = _@name@_convert_to_ctype(a, arg1);
+ if (ret == -2) {
+ ret = -3;
+ }
if (ret < 0) {
return ret;
}
/**begin repeat
*
- * #name = cfloat, cdouble, clongdouble#
+ * #name = cfloat, cdouble#
*
*/
/**end repeat**/
+/**begin repeat
+ *
+ * #oper = divmod, remainder#
+ *
+ */
+
+/*
+Complex numbers do not support remainder operations. Unfortunately,
+the type inference for long doubles is complicated, and if a remainder
+operation is not defined - if the relevant field is left NULL - then
+operations between long doubles and objects lead to an infinite recursion
+instead of a TypeError. This should ensure that once everything gets
+converted to complex long doubles you correctly get a reasonably
+informative TypeError. This fixes the last part of bug gh-18548.
+*/
+
+static PyObject *
+clongdouble_@oper@(PyObject *a, PyObject *b)
+{
+ npy_clongdouble arg1, arg2;
+
+ BINOP_GIVE_UP_IF_NEEDED(a, b, nb_@oper@, clongdouble_@oper@);
+
+ switch(_clongdouble_convert2_to_ctypes(a, &arg1, b, &arg2)) {
+ case 0:
+ break;
+ case -1:
+ /* one of them can't be cast safely must be mixed-types*/
+ return PyArray_Type.tp_as_number->nb_@oper@(a,b);
+ case -2:
+ /* use default handling */
+ if (PyErr_Occurred()) {
+ return NULL;
+ }
+ return PyGenericArrType_Type.tp_as_number->nb_@oper@(a,b);
+ case -3:
+ /*
+ * special case for longdouble and clongdouble
+ * because they have a recursive getitem in their dtype
+ */
+ Py_INCREF(Py_NotImplemented);
+ return Py_NotImplemented;
+ }
+
+ /*
+ * here we do the actual calculation with arg1 and arg2
+ * as a function call.
+ */
+ PyErr_SetString(PyExc_TypeError, "complex long doubles do not support remainder");
+ return NULL;
+}
+
+/**end repeat**/
+
/**begin repeat
*
* #name = half, float, double, longdouble, cfloat, cdouble, clongdouble#
assert_raises(TypeError, np.logical_or.reduce,
np.array([T(), T()], dtype='O'))
+def test_object_iter_cleanup_reduce():
+ # Similar as above, but a complex reduction case that was previously
+ # missed (see gh-18810).
+ # The following array is special in that it cannot be flattened:
+ arr = np.array([[None, 1], [-1, -1], [None, 2], [-1, -1]])[::2]
+ with pytest.raises(TypeError):
+ np.sum(arr)
+
+@pytest.mark.parametrize("arr", [
+ np.ones((8000, 4, 2), dtype=object)[:, ::2, :],
+ np.ones((8000, 4, 2), dtype=object, order="F")[:, ::2, :],
+ np.ones((8000, 4, 2), dtype=object)[:, ::2, :].copy("F")])
+def test_object_iter_cleanup_large_reduce(arr):
+ # More complicated calls are possible for large arrays:
+ out = np.ones(8000, dtype=np.intp)
+ # force casting with `dtype=object`
+ res = np.sum(arr, axis=(1, 2), dtype=object, out=out)
+ assert_array_equal(res, np.full(8000, 4, dtype=object))
+
def test_iter_too_large():
# The total size of the iterator must not exceed the maximum intp due
# to broadcasting. Dividing by 1024 will keep it small enough to
+import contextlib
import sys
import warnings
import itertools
import operator
import platform
import pytest
+from hypothesis import given, settings, Verbosity, assume
+from hypothesis.strategies import sampled_from
import numpy as np
from numpy.testing import (
shift_arr = np.array([shift]*32, dtype=dt)
res_arr = op(val_arr, shift_arr)
assert_equal(res_arr, res_scl)
+
+
+@contextlib.contextmanager
+def recursionlimit(n):
+ o = sys.getrecursionlimit()
+ try:
+ sys.setrecursionlimit(n)
+ yield
+ finally:
+ sys.setrecursionlimit(o)
+
+
+objecty_things = [object(), None]
+reasonable_operators_for_scalars = [
+ operator.lt, operator.le, operator.eq, operator.ne, operator.ge,
+ operator.gt, operator.add, operator.floordiv, operator.mod,
+ operator.mul, operator.matmul, operator.pow, operator.sub,
+ operator.truediv,
+]
+
+
+@given(sampled_from(objecty_things),
+ sampled_from(reasonable_operators_for_scalars),
+ sampled_from(types))
+@settings(verbosity=Verbosity.verbose)
+def test_operator_object_left(o, op, type_):
+ try:
+ with recursionlimit(200):
+ op(o, type_(1))
+ except TypeError:
+ pass
+
+
+@given(sampled_from(objecty_things),
+ sampled_from(reasonable_operators_for_scalars),
+ sampled_from(types))
+def test_operator_object_right(o, op, type_):
+ try:
+ with recursionlimit(200):
+ op(type_(1), o)
+ except TypeError:
+ pass
+
+
+@given(sampled_from(reasonable_operators_for_scalars),
+ sampled_from(types),
+ sampled_from(types))
+def test_operator_scalars(op, type1, type2):
+ try:
+ op(type1(1), type2(1))
+ except TypeError:
+ pass
+
+
+@pytest.mark.parametrize("op", reasonable_operators_for_scalars)
+def test_longdouble_inf_loop(op):
+ try:
+ op(np.longdouble(3), None)
+ except TypeError:
+ pass
+ try:
+ op(None, np.longdouble(3))
+ except TypeError:
+ pass
+
+
+@pytest.mark.parametrize("op", reasonable_operators_for_scalars)
+def test_clongdouble_inf_loop(op):
+ if op in {operator.mod} and False:
+ pytest.xfail("The modulo operator is known to be broken")
+ try:
+ op(np.clongdouble(3), None)
+ except TypeError:
+ pass
+ try:
+ op(None, np.longdouble(3))
+ except TypeError:
+ pass
"""Handle the different versions of GNU fortran compilers"""
# Strip warning(s) that may be emitted by gfortran
while version_string.startswith('gfortran: warning'):
- version_string = version_string[version_string.find('\n') + 1:]
+ version_string =\
+ version_string[version_string.find('\n') + 1:].strip()
# Gfortran versions from after 2010 will output a simple string
# (usually "x.y", "x.y.z" or "x.y.z-q") for ``-dumpversion``; older
'accelerate' in libraries):
if intel:
args.extend(['-msse3'])
- else:
- args.extend(['-faltivec'])
args.extend([
'-I/System/Library/Frameworks/vecLib.framework/Headers'])
link_args.extend(['-Wl,-framework', '-Wl,Accelerate'])
'veclib' in libraries):
if intel:
args.extend(['-msse3'])
- else:
- args.extend(['-faltivec'])
args.extend([
'-I/System/Library/Frameworks/vecLib.framework/Headers'])
link_args.extend(['-Wl,-framework', '-Wl,vecLib'])
"""
cppmacros["F2PY_THREAD_LOCAL_DECL"] = """\
#ifndef F2PY_THREAD_LOCAL_DECL
-#if defined(_MSC_VER)
+#if defined(_MSC_VER) \\
+ || defined(_WIN32) || defined(_WIN64) \\
+ || defined(__MINGW32__) || defined(__MINGW64__)
#define F2PY_THREAD_LOCAL_DECL __declspec(thread)
#elif defined(__STDC_VERSION__) \\
&& (__STDC_VERSION__ >= 201112L) \\
@overload
def __call__(
self, __other: Union[int, signedinteger[Any]]
- ) -> Union[signedinteger[Any], float64]: ...
+ ) -> Any: ...
@overload
def __call__(self, __other: float) -> float64: ...
@overload
@overload
def __call__(
self, __other: Union[int, signedinteger[Any]]
- ) -> Union[signedinteger[Any], float64]: ...
+ ) -> Any: ...
@overload
def __call__(self, __other: float) -> float64: ...
@overload
@overload
def __call__(
self, __other: Union[int, signedinteger[Any]]
- ) -> Union[_2Tuple[signedinteger[Any]], _2Tuple[float64]]: ...
+ ) -> _2Tuple[Any]: ...
@overload
def __call__(self, __other: float) -> _2Tuple[float64]: ...
@overload
-from typing import Sequence, Tuple, Union
+import sys
+from typing import Sequence, Tuple, Union, TYPE_CHECKING
+
+if TYPE_CHECKING:
+ if sys.version_info >= (3, 8):
+ from typing import SupportsIndex
+ else:
+ from typing_extensions import Protocol
+ class SupportsIndex(Protocol):
+ def __index__(self) -> int: ...
_Shape = Tuple[int, ...]
# Anything that can be coerced to a shape tuple
-_ShapeLike = Union[int, Sequence[int]]
+_ShapeLike = Union["SupportsIndex", Sequence["SupportsIndex"]]
np.require(a, requirements="TEST") # E: incompatible type
np.zeros("test") # E: incompatible type
-np.zeros() # E: Too few arguments
+np.zeros() # E: Missing positional argument
np.ones("test") # E: incompatible type
-np.ones() # E: Too few arguments
+np.ones() # E: Missing positional argument
np.array(0, float, True) # E: Too many positional
a = np.bool_(True)
-np.take(a, None) # E: No overload variant of "take" matches argument type
-np.take(a, axis=1.0) # E: No overload variant of "take" matches argument type
-np.take(A, out=1) # E: No overload variant of "take" matches argument type
-np.take(A, mode="bob") # E: No overload variant of "take" matches argument type
+np.take(a, None) # E: incompatible type
+np.take(a, axis=1.0) # E: incompatible type
+np.take(A, out=1) # E: incompatible type
+np.take(A, mode="bob") # E: incompatible type
np.reshape(a, None) # E: Argument 2 to "reshape" has incompatible type
np.reshape(A, 1, order="bob") # E: Argument "order" to "reshape" has incompatible type
-np.choose(a, None) # E: No overload variant of "choose" matches argument type
-np.choose(a, out=1.0) # E: No overload variant of "choose" matches argument type
-np.choose(A, mode="bob") # E: No overload variant of "choose" matches argument type
+np.choose(a, None) # E: incompatible type
+np.choose(a, out=1.0) # E: incompatible type
+np.choose(A, mode="bob") # E: incompatible type
np.repeat(a, None) # E: Argument 2 to "repeat" has incompatible type
np.repeat(A, 1, axis=1.0) # E: Argument "axis" to "repeat" has incompatible type
A, 0, order=range(5) # E: Argument "order" to "partition" has incompatible type
)
-np.argpartition( # E: No overload variant of "argpartition" matches argument type
- a, None
+np.argpartition(
+ a, None # E: incompatible type
)
-np.argpartition( # E: No overload variant of "argpartition" matches argument type
- a, 0, axis="bob"
+np.argpartition(
+ a, 0, axis="bob" # E: incompatible type
)
-np.argpartition( # E: No overload variant of "argpartition" matches argument type
- A, 0, kind="bob"
+np.argpartition(
+ A, 0, kind="bob" # E: incompatible type
)
np.argpartition(
A, 0, order=range(5) # E: Argument "order" to "argpartition" has incompatible type
np.clip(a, 1, 2, out=1) # E: No overload variant of "clip" matches argument type
np.clip(1, None, None) # E: No overload variant of "clip" matches argument type
-np.sum(a, axis=1.0) # E: No overload variant of "sum" matches argument type
-np.sum(a, keepdims=1.0) # E: No overload variant of "sum" matches argument type
-np.sum(a, initial=[1]) # E: No overload variant of "sum" matches argument type
+np.sum(a, axis=1.0) # E: incompatible type
+np.sum(a, keepdims=1.0) # E: incompatible type
+np.sum(a, initial=[1]) # E: incompatible type
-np.all(a, axis=1.0) # E: No overload variant of "all" matches argument type
-np.all(a, keepdims=1.0) # E: No overload variant of "all" matches argument type
-np.all(a, out=1.0) # E: No overload variant of "all" matches argument type
+np.all(a, axis=1.0) # E: No overload variant
+np.all(a, keepdims=1.0) # E: No overload variant
+np.all(a, out=1.0) # E: No overload variant
-np.any(a, axis=1.0) # E: No overload variant of "any" matches argument type
-np.any(a, keepdims=1.0) # E: No overload variant of "any" matches argument type
-np.any(a, out=1.0) # E: No overload variant of "any" matches argument type
+np.any(a, axis=1.0) # E: No overload variant
+np.any(a, keepdims=1.0) # E: No overload variant
+np.any(a, out=1.0) # E: No overload variant
-np.cumsum(a, axis=1.0) # E: Argument "axis" to "cumsum" has incompatible type
-np.cumsum(a, dtype=1.0) # E: Argument "dtype" to "cumsum" has incompatible type
-np.cumsum(a, out=1.0) # E: Argument "out" to "cumsum" has incompatible type
+np.cumsum(a, axis=1.0) # E: incompatible type
+np.cumsum(a, dtype=1.0) # E: incompatible type
+np.cumsum(a, out=1.0) # E: incompatible type
-np.ptp(a, axis=1.0) # E: No overload variant of "ptp" matches argument type
-np.ptp(a, keepdims=1.0) # E: No overload variant of "ptp" matches argument type
-np.ptp(a, out=1.0) # E: No overload variant of "ptp" matches argument type
+np.ptp(a, axis=1.0) # E: incompatible type
+np.ptp(a, keepdims=1.0) # E: incompatible type
+np.ptp(a, out=1.0) # E: incompatible type
-np.amax(a, axis=1.0) # E: No overload variant of "amax" matches argument type
-np.amax(a, keepdims=1.0) # E: No overload variant of "amax" matches argument type
-np.amax(a, out=1.0) # E: No overload variant of "amax" matches argument type
-np.amax(a, initial=[1.0]) # E: No overload variant of "amax" matches argument type
+np.amax(a, axis=1.0) # E: incompatible type
+np.amax(a, keepdims=1.0) # E: incompatible type
+np.amax(a, out=1.0) # E: incompatible type
+np.amax(a, initial=[1.0]) # E: incompatible type
np.amax(a, where=[1.0]) # E: List item 0 has incompatible type
-np.amin(a, axis=1.0) # E: No overload variant of "amin" matches argument type
-np.amin(a, keepdims=1.0) # E: No overload variant of "amin" matches argument type
-np.amin(a, out=1.0) # E: No overload variant of "amin" matches argument type
-np.amin(a, initial=[1.0]) # E: No overload variant of "amin" matches argument type
+np.amin(a, axis=1.0) # E: incompatible type
+np.amin(a, keepdims=1.0) # E: incompatible type
+np.amin(a, out=1.0) # E: incompatible type
+np.amin(a, initial=[1.0]) # E: incompatible type
np.amin(a, where=[1.0]) # E: List item 0 has incompatible type
-np.prod(a, axis=1.0) # E: No overload variant of "prod" matches argument type
-np.prod(a, out=False) # E: No overload variant of "prod" matches argument type
-np.prod(a, keepdims=1.0) # E: No overload variant of "prod" matches argument type
-np.prod(a, initial=int) # E: No overload variant of "prod" matches argument type
-np.prod(a, where=1.0) # E: No overload variant of "prod" matches argument type
+np.prod(a, axis=1.0) # E: incompatible type
+np.prod(a, out=False) # E: incompatible type
+np.prod(a, keepdims=1.0) # E: incompatible type
+np.prod(a, initial=int) # E: incompatible type
+np.prod(a, where=1.0) # E: incompatible type
np.cumprod(a, axis=1.0) # E: Argument "axis" to "cumprod" has incompatible type
np.cumprod(a, out=False) # E: Argument "out" to "cumprod" has incompatible type
np.size(a, axis=1.0) # E: Argument "axis" to "size" has incompatible type
-np.around(a, decimals=1.0) # E: No overload variant of "around" matches argument type
-np.around(a, out=type) # E: No overload variant of "around" matches argument type
+np.around(a, decimals=1.0) # E: incompatible type
+np.around(a, out=type) # E: incompatible type
-np.mean(a, axis=1.0) # E: No overload variant of "mean" matches argument type
-np.mean(a, out=False) # E: No overload variant of "mean" matches argument type
-np.mean(a, keepdims=1.0) # E: No overload variant of "mean" matches argument type
+np.mean(a, axis=1.0) # E: incompatible type
+np.mean(a, out=False) # E: incompatible type
+np.mean(a, keepdims=1.0) # E: incompatible type
-np.std(a, axis=1.0) # E: No overload variant of "std" matches argument type
-np.std(a, out=False) # E: No overload variant of "std" matches argument type
-np.std(a, ddof='test') # E: No overload variant of "std" matches argument type
-np.std(a, keepdims=1.0) # E: No overload variant of "std" matches argument type
+np.std(a, axis=1.0) # E: incompatible type
+np.std(a, out=False) # E: incompatible type
+np.std(a, ddof='test') # E: incompatible type
+np.std(a, keepdims=1.0) # E: incompatible type
-np.var(a, axis=1.0) # E: No overload variant of "var" matches argument type
-np.var(a, out=False) # E: No overload variant of "var" matches argument type
-np.var(a, ddof='test') # E: No overload variant of "var" matches argument type
-np.var(a, keepdims=1.0) # E: No overload variant of "var" matches argument type
+np.var(a, axis=1.0) # E: incompatible type
+np.var(a, out=False) # E: incompatible type
+np.var(a, ddof='test') # E: incompatible type
+np.var(a, keepdims=1.0) # E: incompatible type
+import sys
from typing import List, Any
import numpy as np
+
class Index:
def __index__(self) -> int:
return 0
-class SubClass(np.ndarray): ...
+
+class SubClass(np.ndarray):
+ pass
+
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass:
+ return B
+
i8 = np.int64(1)
B_stack = np.array([[1], [1]]).view(SubClass)
C = [1]
-def func(i: int, j: int, **kwargs: Any) -> SubClass:
- return B
+if sys.version_info >= (3, 8):
+ np.ndarray(Index())
+ np.ndarray([Index()])
np.array(1, dtype=float)
np.array(1, copy=False)
np.datetime64(np.datetime64())
np.datetime64(dt.datetime(2000, 5, 3))
np.datetime64(None)
+np.datetime64(dt.date(2000, 5, 3))
np.datetime64(None, "D")
np.timedelta64()
# Int
reveal_type(i8 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(i8 + u8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i8 + u8) # E: Any
reveal_type(i8 + i4) # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i8 + u4) # E: Any
reveal_type(i8 + b_) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i8 + b) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i8 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i8 + AR) # E: Any
reveal_type(u8 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
-reveal_type(u8 + i4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u8 + i4) # E: Any
reveal_type(u8 + u4) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + b_) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + b) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(u8 + f) # E: numpy.floating[numpy.typing._64Bit]
-reveal_type(u8 + i) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u8 + i) # E: Any
reveal_type(u8 + AR) # E: Any
reveal_type(i8 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(u8 + i8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u8 + i8) # E: Any
reveal_type(i4 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u4 + i8) # E: Any
reveal_type(b_ + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(b + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(c + i8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(AR + i8) # E: Any
reveal_type(u8 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
-reveal_type(i4 + u8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i4 + u8) # E: Any
reveal_type(u4 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(b_ + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(b + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(c + u8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + u8) # E: numpy.floating[numpy.typing._64Bit]
-reveal_type(i + u8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i + u8) # E: Any
reveal_type(AR + u8) # E: Any
reveal_type(i4 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i4 + b) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(i4 + AR) # E: Any
-reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
-reveal_type(u4 + i4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u4 + i8) # E: Any
+reveal_type(u4 + i4) # E: Any
reveal_type(u4 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u4 + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
-reveal_type(u4 + i) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u4 + i) # E: Any
reveal_type(u4 + b_) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(u4 + b) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(u4 + AR) # E: Any
reveal_type(b + i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(AR + i4) # E: Any
-reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
-reveal_type(i4 + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i8 + u4) # E: Any
+reveal_type(i4 + u4) # E: Any
reveal_type(u8 + u4) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u4 + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(b_ + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(b + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
-reveal_type(i + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i + u4) # E: Any
reveal_type(AR + u4) # E: Any
reveal_type(np.require(C)) # E: ndarray
reveal_type(np.linspace(0, 10)) # E: numpy.ndarray
-reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray, numpy.inexact[Any]]
+reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray, Any]
reveal_type(np.logspace(0, 10)) # E: numpy.ndarray
reveal_type(np.geomspace(1, 10)) # E: numpy.ndarray
c = 1.0
d = np.array(1.0, dtype=np.float32) # writeable
-reveal_type(np.take(a, 0)) # E: numpy.bool_
-reveal_type(np.take(b, 0)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(
- np.take(c, 0) # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
-)
-reveal_type(
- np.take(A, 0) # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
-)
-reveal_type(
- np.take(B, 0) # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
-)
-reveal_type(
- np.take( # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray]
- A, [0]
- )
-)
-reveal_type(
- np.take( # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray]
- B, [0]
- )
-)
+reveal_type(np.take(a, 0)) # E: Any
+reveal_type(np.take(b, 0)) # E: Any
+reveal_type(np.take(c, 0)) # E: Any
+reveal_type(np.take(A, 0)) # E: Any
+reveal_type(np.take(B, 0)) # E: Any
+reveal_type(np.take(A, [0])) # E: Any
+reveal_type(np.take(B, [0])) # E: Any
reveal_type(np.reshape(a, 1)) # E: numpy.ndarray
reveal_type(np.reshape(b, 1)) # E: numpy.ndarray
reveal_type(np.reshape(A, 1)) # E: numpy.ndarray
reveal_type(np.reshape(B, 1)) # E: numpy.ndarray
-reveal_type(np.choose(a, [True, True])) # E: numpy.bool_
-reveal_type(np.choose(A, [True, True])) # E: numpy.ndarray
+reveal_type(np.choose(a, [True, True])) # E: Any
+reveal_type(np.choose(A, [True, True])) # E: Any
reveal_type(np.repeat(a, 1)) # E: numpy.ndarray
reveal_type(np.repeat(b, 1)) # E: numpy.ndarray
reveal_type(np.partition(A, 0)) # E: numpy.ndarray
reveal_type(np.partition(B, 0)) # E: numpy.ndarray
-reveal_type(np.argpartition(a, 0)) # E: numpy.integer[Any]
-reveal_type(np.argpartition(b, 0)) # E: numpy.integer[Any]
-reveal_type(np.argpartition(c, 0)) # E: numpy.ndarray
-reveal_type(np.argpartition(A, 0)) # E: numpy.ndarray
-reveal_type(np.argpartition(B, 0)) # E: numpy.ndarray
+reveal_type(np.argpartition(a, 0)) # E: Any
+reveal_type(np.argpartition(b, 0)) # E: Any
+reveal_type(np.argpartition(c, 0)) # E: Any
+reveal_type(np.argpartition(A, 0)) # E: Any
+reveal_type(np.argpartition(B, 0)) # E: Any
reveal_type(np.sort(A, 0)) # E: numpy.ndarray
reveal_type(np.sort(B, 0)) # E: numpy.ndarray
reveal_type(np.argsort(A, 0)) # E: numpy.ndarray
reveal_type(np.argsort(B, 0)) # E: numpy.ndarray
-reveal_type(np.argmax(A)) # E: numpy.integer[Any]
-reveal_type(np.argmax(B)) # E: numpy.integer[Any]
-reveal_type(np.argmax(A, axis=0)) # E: Union[numpy.integer[Any], numpy.ndarray]
-reveal_type(np.argmax(B, axis=0)) # E: Union[numpy.integer[Any], numpy.ndarray]
+reveal_type(np.argmax(A)) # E: numpy.signedinteger[Any]
+reveal_type(np.argmax(B)) # E: numpy.signedinteger[Any]
+reveal_type(np.argmax(A, axis=0)) # E: Any
+reveal_type(np.argmax(B, axis=0)) # E: Any
-reveal_type(np.argmin(A)) # E: numpy.integer[Any]
-reveal_type(np.argmin(B)) # E: numpy.integer[Any]
-reveal_type(np.argmin(A, axis=0)) # E: Union[numpy.integer[Any], numpy.ndarray]
-reveal_type(np.argmin(B, axis=0)) # E: Union[numpy.integer[Any], numpy.ndarray]
+reveal_type(np.argmin(A)) # E: numpy.signedinteger[Any]
+reveal_type(np.argmin(B)) # E: numpy.signedinteger[Any]
+reveal_type(np.argmin(A, axis=0)) # E: Any
+reveal_type(np.argmin(B, axis=0)) # E: Any
-reveal_type(np.searchsorted(A[0], 0)) # E: numpy.integer[Any]
-reveal_type(np.searchsorted(B[0], 0)) # E: numpy.integer[Any]
+reveal_type(np.searchsorted(A[0], 0)) # E: numpy.signedinteger[Any]
+reveal_type(np.searchsorted(B[0], 0)) # E: numpy.signedinteger[Any]
reveal_type(np.searchsorted(A[0], [0])) # E: numpy.ndarray
reveal_type(np.searchsorted(B[0], [0])) # E: numpy.ndarray
reveal_type(np.diagonal(A)) # E: numpy.ndarray
reveal_type(np.diagonal(B)) # E: numpy.ndarray
-reveal_type(np.trace(A)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.trace(B)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(np.trace(A)) # E: Any
+reveal_type(np.trace(B)) # E: Any
reveal_type(np.ravel(a)) # E: numpy.ndarray
reveal_type(np.ravel(b)) # E: numpy.ndarray
reveal_type(np.compress([True], A)) # E: numpy.ndarray
reveal_type(np.compress([True], B)) # E: numpy.ndarray
-reveal_type(np.clip(a, 0, 1.0)) # E: numpy.number[Any]
-reveal_type(np.clip(b, -1, 1)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.clip(c, 0, 1)) # E: numpy.number[Any]
-reveal_type(np.clip(A, 0, 1)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.clip(B, 0, 1)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(np.clip(a, 0, 1.0)) # E: Any
+reveal_type(np.clip(b, -1, 1)) # E: Any
+reveal_type(np.clip(c, 0, 1)) # E: Any
+reveal_type(np.clip(A, 0, 1)) # E: Any
+reveal_type(np.clip(B, 0, 1)) # E: Any
-reveal_type(np.sum(a)) # E: numpy.number[Any]
-reveal_type(np.sum(b)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.sum(c)) # E: numpy.number[Any]
-reveal_type(np.sum(A)) # E: numpy.number[Any]
-reveal_type(np.sum(B)) # E: numpy.number[Any]
-reveal_type(np.sum(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.sum(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(np.sum(a)) # E: Any
+reveal_type(np.sum(b)) # E: Any
+reveal_type(np.sum(c)) # E: Any
+reveal_type(np.sum(A)) # E: Any
+reveal_type(np.sum(B)) # E: Any
+reveal_type(np.sum(A, axis=0)) # E: Any
+reveal_type(np.sum(B, axis=0)) # E: Any
reveal_type(np.all(a)) # E: numpy.bool_
reveal_type(np.all(b)) # E: numpy.bool_
reveal_type(np.all(c)) # E: numpy.bool_
reveal_type(np.all(A)) # E: numpy.bool_
reveal_type(np.all(B)) # E: numpy.bool_
-reveal_type(np.all(A, axis=0)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.all(B, axis=0)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.all(A, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.all(B, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(np.all(A, axis=0)) # E: Any
+reveal_type(np.all(B, axis=0)) # E: Any
+reveal_type(np.all(A, keepdims=True)) # E: Any
+reveal_type(np.all(B, keepdims=True)) # E: Any
reveal_type(np.any(a)) # E: numpy.bool_
reveal_type(np.any(b)) # E: numpy.bool_
reveal_type(np.any(c)) # E: numpy.bool_
reveal_type(np.any(A)) # E: numpy.bool_
reveal_type(np.any(B)) # E: numpy.bool_
-reveal_type(np.any(A, axis=0)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.any(B, axis=0)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.any(A, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.any(B, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(np.any(A, axis=0)) # E: Any
+reveal_type(np.any(B, axis=0)) # E: Any
+reveal_type(np.any(A, keepdims=True)) # E: Any
+reveal_type(np.any(B, keepdims=True)) # E: Any
reveal_type(np.cumsum(a)) # E: numpy.ndarray
reveal_type(np.cumsum(b)) # E: numpy.ndarray
reveal_type(np.cumsum(A)) # E: numpy.ndarray
reveal_type(np.cumsum(B)) # E: numpy.ndarray
-reveal_type(np.ptp(a)) # E: numpy.number[Any]
-reveal_type(np.ptp(b)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.ptp(c)) # E: numpy.number[Any]
-reveal_type(np.ptp(A)) # E: numpy.number[Any]
-reveal_type(np.ptp(B)) # E: numpy.number[Any]
-reveal_type(np.ptp(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.ptp(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.ptp(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.ptp(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-
-reveal_type(np.amax(a)) # E: numpy.number[Any]
-reveal_type(np.amax(b)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.amax(c)) # E: numpy.number[Any]
-reveal_type(np.amax(A)) # E: numpy.number[Any]
-reveal_type(np.amax(B)) # E: numpy.number[Any]
-reveal_type(np.amax(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amax(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amax(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amax(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-
-reveal_type(np.amin(a)) # E: numpy.number[Any]
-reveal_type(np.amin(b)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.amin(c)) # E: numpy.number[Any]
-reveal_type(np.amin(A)) # E: numpy.number[Any]
-reveal_type(np.amin(B)) # E: numpy.number[Any]
-reveal_type(np.amin(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amin(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amin(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amin(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-
-reveal_type(np.prod(a)) # E: numpy.number[Any]
-reveal_type(np.prod(b)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.prod(c)) # E: numpy.number[Any]
-reveal_type(np.prod(A)) # E: numpy.number[Any]
-reveal_type(np.prod(B)) # E: numpy.number[Any]
-reveal_type(np.prod(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(b, out=d)) # E: numpy.ndarray
-reveal_type(np.prod(B, out=d)) # E: numpy.ndarray
+reveal_type(np.ptp(a)) # E: Any
+reveal_type(np.ptp(b)) # E: Any
+reveal_type(np.ptp(c)) # E: Any
+reveal_type(np.ptp(A)) # E: Any
+reveal_type(np.ptp(B)) # E: Any
+reveal_type(np.ptp(A, axis=0)) # E: Any
+reveal_type(np.ptp(B, axis=0)) # E: Any
+reveal_type(np.ptp(A, keepdims=True)) # E: Any
+reveal_type(np.ptp(B, keepdims=True)) # E: Any
+
+reveal_type(np.amax(a)) # E: Any
+reveal_type(np.amax(b)) # E: Any
+reveal_type(np.amax(c)) # E: Any
+reveal_type(np.amax(A)) # E: Any
+reveal_type(np.amax(B)) # E: Any
+reveal_type(np.amax(A, axis=0)) # E: Any
+reveal_type(np.amax(B, axis=0)) # E: Any
+reveal_type(np.amax(A, keepdims=True)) # E: Any
+reveal_type(np.amax(B, keepdims=True)) # E: Any
+
+reveal_type(np.amin(a)) # E: Any
+reveal_type(np.amin(b)) # E: Any
+reveal_type(np.amin(c)) # E: Any
+reveal_type(np.amin(A)) # E: Any
+reveal_type(np.amin(B)) # E: Any
+reveal_type(np.amin(A, axis=0)) # E: Any
+reveal_type(np.amin(B, axis=0)) # E: Any
+reveal_type(np.amin(A, keepdims=True)) # E: Any
+reveal_type(np.amin(B, keepdims=True)) # E: Any
+
+reveal_type(np.prod(a)) # E: Any
+reveal_type(np.prod(b)) # E: Any
+reveal_type(np.prod(c)) # E: Any
+reveal_type(np.prod(A)) # E: Any
+reveal_type(np.prod(B)) # E: Any
+reveal_type(np.prod(A, axis=0)) # E: Any
+reveal_type(np.prod(B, axis=0)) # E: Any
+reveal_type(np.prod(A, keepdims=True)) # E: Any
+reveal_type(np.prod(B, keepdims=True)) # E: Any
+reveal_type(np.prod(b, out=d)) # E: Any
+reveal_type(np.prod(B, out=d)) # E: Any
reveal_type(np.cumprod(a)) # E: numpy.ndarray
reveal_type(np.cumprod(b)) # E: numpy.ndarray
reveal_type(np.size(A)) # E: int
reveal_type(np.size(B)) # E: int
-reveal_type(np.around(a)) # E: numpy.number[Any]
-reveal_type(np.around(b)) # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.around(c)) # E: numpy.number[Any]
-reveal_type(np.around(A)) # E: numpy.ndarray
-reveal_type(np.around(B)) # E: numpy.ndarray
-
-reveal_type(np.mean(a)) # E: numpy.number[Any]
-reveal_type(np.mean(b)) # E: numpy.number[Any]
-reveal_type(np.mean(c)) # E: numpy.number[Any]
-reveal_type(np.mean(A)) # E: numpy.number[Any]
-reveal_type(np.mean(B)) # E: numpy.number[Any]
-reveal_type(np.mean(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(b, out=d)) # E: numpy.ndarray
-reveal_type(np.mean(B, out=d)) # E: numpy.ndarray
-
-reveal_type(np.std(a)) # E: numpy.number[Any]
-reveal_type(np.std(b)) # E: numpy.number[Any]
-reveal_type(np.std(c)) # E: numpy.number[Any]
-reveal_type(np.std(A)) # E: numpy.number[Any]
-reveal_type(np.std(B)) # E: numpy.number[Any]
-reveal_type(np.std(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(b, out=d)) # E: numpy.ndarray
-reveal_type(np.std(B, out=d)) # E: numpy.ndarray
-
-reveal_type(np.var(a)) # E: numpy.number[Any]
-reveal_type(np.var(b)) # E: numpy.number[Any]
-reveal_type(np.var(c)) # E: numpy.number[Any]
-reveal_type(np.var(A)) # E: numpy.number[Any]
-reveal_type(np.var(B)) # E: numpy.number[Any]
-reveal_type(np.var(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(b, out=d)) # E: numpy.ndarray
-reveal_type(np.var(B, out=d)) # E: numpy.ndarray
+reveal_type(np.around(a)) # E: Any
+reveal_type(np.around(b)) # E: Any
+reveal_type(np.around(c)) # E: Any
+reveal_type(np.around(A)) # E: Any
+reveal_type(np.around(B)) # E: Any
+
+reveal_type(np.mean(a)) # E: Any
+reveal_type(np.mean(b)) # E: Any
+reveal_type(np.mean(c)) # E: Any
+reveal_type(np.mean(A)) # E: Any
+reveal_type(np.mean(B)) # E: Any
+reveal_type(np.mean(A, axis=0)) # E: Any
+reveal_type(np.mean(B, axis=0)) # E: Any
+reveal_type(np.mean(A, keepdims=True)) # E: Any
+reveal_type(np.mean(B, keepdims=True)) # E: Any
+reveal_type(np.mean(b, out=d)) # E: Any
+reveal_type(np.mean(B, out=d)) # E: Any
+
+reveal_type(np.std(a)) # E: Any
+reveal_type(np.std(b)) # E: Any
+reveal_type(np.std(c)) # E: Any
+reveal_type(np.std(A)) # E: Any
+reveal_type(np.std(B)) # E: Any
+reveal_type(np.std(A, axis=0)) # E: Any
+reveal_type(np.std(B, axis=0)) # E: Any
+reveal_type(np.std(A, keepdims=True)) # E: Any
+reveal_type(np.std(B, keepdims=True)) # E: Any
+reveal_type(np.std(b, out=d)) # E: Any
+reveal_type(np.std(B, out=d)) # E: Any
+
+reveal_type(np.var(a)) # E: Any
+reveal_type(np.var(b)) # E: Any
+reveal_type(np.var(c)) # E: Any
+reveal_type(np.var(A)) # E: Any
+reveal_type(np.var(B)) # E: Any
+reveal_type(np.var(A, axis=0)) # E: Any
+reveal_type(np.var(B, axis=0)) # E: Any
+reveal_type(np.var(A, keepdims=True)) # E: Any
+reveal_type(np.var(B, keepdims=True)) # E: Any
+reveal_type(np.var(b, out=d)) # E: Any
+reveal_type(np.var(B, out=d)) # E: Any
reveal_type(f8.all()) # E: numpy.bool_
reveal_type(A.all()) # E: numpy.bool_
-reveal_type(A.all(axis=0)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(A.all(keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(A.all(axis=0)) # E: Any
+reveal_type(A.all(keepdims=True)) # E: Any
reveal_type(A.all(out=B)) # E: SubClass
reveal_type(f8.any()) # E: numpy.bool_
reveal_type(A.any()) # E: numpy.bool_
-reveal_type(A.any(axis=0)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(A.any(keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(A.any(axis=0)) # E: Any
+reveal_type(A.any(keepdims=True)) # E: Any
reveal_type(A.any(out=B)) # E: SubClass
reveal_type(f8.argmax()) # E: numpy.signedinteger[Any]
reveal_type(A.argmax()) # E: numpy.signedinteger[Any]
-reveal_type(A.argmax(axis=0)) # E: Union[numpy.signedinteger[Any], numpy.ndarray]
+reveal_type(A.argmax(axis=0)) # E: Any
reveal_type(A.argmax(out=B)) # E: SubClass
reveal_type(f8.argmin()) # E: numpy.signedinteger[Any]
reveal_type(A.argmin()) # E: numpy.signedinteger[Any]
-reveal_type(A.argmin(axis=0)) # E: Union[numpy.signedinteger[Any], numpy.ndarray]
+reveal_type(A.argmin(axis=0)) # E: Any
reveal_type(A.argmin(out=B)) # E: SubClass
reveal_type(f8.argsort()) # E: numpy.ndarray
reveal_type(A.choose([0])) # E: numpy.ndarray
reveal_type(A.choose([0], out=B)) # E: SubClass
-reveal_type(f8.clip(1)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.clip(1)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.clip(None, 1)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.clip(1)) # E: Any
+reveal_type(A.clip(1)) # E: Any
+reveal_type(A.clip(None, 1)) # E: Any
reveal_type(A.clip(1, out=B)) # E: SubClass
reveal_type(A.clip(None, 1, out=B)) # E: SubClass
reveal_type(A.cumsum()) # E: numpy.ndarray
reveal_type(A.cumsum(out=B)) # E: SubClass
-reveal_type(f8.max()) # E: numpy.number[Any]
-reveal_type(A.max()) # E: numpy.number[Any]
-reveal_type(A.max(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.max(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.max()) # E: Any
+reveal_type(A.max()) # E: Any
+reveal_type(A.max(axis=0)) # E: Any
+reveal_type(A.max(keepdims=True)) # E: Any
reveal_type(A.max(out=B)) # E: SubClass
-reveal_type(f8.mean()) # E: numpy.number[Any]
-reveal_type(A.mean()) # E: numpy.number[Any]
-reveal_type(A.mean(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.mean(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.mean()) # E: Any
+reveal_type(A.mean()) # E: Any
+reveal_type(A.mean(axis=0)) # E: Any
+reveal_type(A.mean(keepdims=True)) # E: Any
reveal_type(A.mean(out=B)) # E: SubClass
-reveal_type(f8.min()) # E: numpy.number[Any]
-reveal_type(A.min()) # E: numpy.number[Any]
-reveal_type(A.min(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.min(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.min()) # E: Any
+reveal_type(A.min()) # E: Any
+reveal_type(A.min(axis=0)) # E: Any
+reveal_type(A.min(keepdims=True)) # E: Any
reveal_type(A.min(out=B)) # E: SubClass
reveal_type(f8.newbyteorder()) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(A.newbyteorder()) # E: numpy.ndarray
reveal_type(B.newbyteorder('|')) # E: SubClass
-reveal_type(f8.prod()) # E: numpy.number[Any]
-reveal_type(A.prod()) # E: numpy.number[Any]
-reveal_type(A.prod(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.prod(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.prod()) # E: Any
+reveal_type(A.prod()) # E: Any
+reveal_type(A.prod(axis=0)) # E: Any
+reveal_type(A.prod(keepdims=True)) # E: Any
reveal_type(A.prod(out=B)) # E: SubClass
-reveal_type(f8.ptp()) # E: numpy.number[Any]
-reveal_type(A.ptp()) # E: numpy.number[Any]
-reveal_type(A.ptp(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.ptp(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.ptp()) # E: Any
+reveal_type(A.ptp()) # E: Any
+reveal_type(A.ptp(axis=0)) # E: Any
+reveal_type(A.ptp(keepdims=True)) # E: Any
reveal_type(A.ptp(out=B)) # E: SubClass
reveal_type(f8.round()) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(A.repeat(1)) # E: numpy.ndarray
reveal_type(B.repeat(1)) # E: numpy.ndarray
-reveal_type(f8.std()) # E: numpy.number[Any]
-reveal_type(A.std()) # E: numpy.number[Any]
-reveal_type(A.std(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.std(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.std()) # E: Any
+reveal_type(A.std()) # E: Any
+reveal_type(A.std(axis=0)) # E: Any
+reveal_type(A.std(keepdims=True)) # E: Any
reveal_type(A.std(out=B)) # E: SubClass
-reveal_type(f8.sum()) # E: numpy.number[Any]
-reveal_type(A.sum()) # E: numpy.number[Any]
-reveal_type(A.sum(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.sum(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.sum()) # E: Any
+reveal_type(A.sum()) # E: Any
+reveal_type(A.sum(axis=0)) # E: Any
+reveal_type(A.sum(keepdims=True)) # E: Any
reveal_type(A.sum(out=B)) # E: SubClass
-reveal_type(f8.take(0)) # E: numpy.generic
-reveal_type(A.take(0)) # E: numpy.generic
+reveal_type(f8.take(0)) # E: Any
+reveal_type(A.take(0)) # E: Any
reveal_type(A.take([0])) # E: numpy.ndarray
reveal_type(A.take(0, out=B)) # E: SubClass
reveal_type(A.take([0], out=B)) # E: SubClass
-reveal_type(f8.var()) # E: numpy.number[Any]
-reveal_type(A.var()) # E: numpy.number[Any]
-reveal_type(A.var(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.var(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.var()) # E: Any
+reveal_type(A.var()) # E: Any
+reveal_type(A.var(axis=0)) # E: Any
+reveal_type(A.var(keepdims=True)) # E: Any
reveal_type(A.var(out=B)) # E: SubClass
reveal_type(A.argpartition([0])) # E: numpy.ndarray
reveal_type(A.diagonal()) # E: numpy.ndarray
-reveal_type(A.dot(1)) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(A.dot(1)) # E: Any
reveal_type(A.dot(1, out=B)) # E: SubClass
reveal_type(A.nonzero()) # E: tuple[numpy.ndarray]
reveal_type(A.searchsorted([1])) # E: numpy.ndarray
-reveal_type(A.trace()) # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(A.trace()) # E: Any
reveal_type(A.trace(out=B)) # E: SubClass
reveal_type(np.count_nonzero(i8)) # E: int
reveal_type(np.count_nonzero(A)) # E: int
reveal_type(np.count_nonzero(B)) # E: int
-reveal_type(np.count_nonzero(A, keepdims=True)) # E: Union[numpy.signedinteger[Any], numpy.ndarray]
-reveal_type(np.count_nonzero(A, axis=0)) # E: Union[numpy.signedinteger[Any], numpy.ndarray]
+reveal_type(np.count_nonzero(A, keepdims=True)) # E: Any
+reveal_type(np.count_nonzero(A, axis=0)) # E: Any
reveal_type(np.isfortran(i8)) # E: bool
reveal_type(np.isfortran(A)) # E: bool
reveal_type(np.allclose(B, A)) # E: bool
reveal_type(np.allclose(A, A)) # E: bool
-reveal_type(np.isclose(i8, A)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.isclose(B, A)) # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.isclose(A, A)) # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(np.isclose(i8, A)) # E: Any
+reveal_type(np.isclose(B, A)) # E: Any
+reveal_type(np.isclose(A, A)) # E: Any
reveal_type(np.array_equal(i8, A)) # E: bool
reveal_type(np.array_equal(B, A)) # E: bool
# THIS FILE IS GENERATED FROM NUMPY SETUP.PY
#
# To compare versions robustly, use `numpy.lib.NumpyVersion`
-short_version: str = '1.20.2'
-version: str = '1.20.2'
-full_version: str = '1.20.2'
-git_revision: str = 'b19ad5bfa396a4600a52a598a30a65d4e993f831'
+short_version: str = '1.20.3'
+version: str = '1.20.3'
+full_version: str = '1.20.3'
+git_revision: str = '27b98cbe0dd9d2969e9c227e7a2070aa56f41d6d'
release: bool = True
if not release:
# building and notes
Paver
-towncrier
+git+https://github.com/hawkowl/towncrier.git@master
MAJOR = 1
MINOR = 20
-MICRO = 2
+MICRO = 3
ISRELEASED = True
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
cffi
# For testing types. Notes on the restrictions:
# - Mypy relies on C API features not present in PyPy
-# - Mypy doesn't currently work on Python 3.9
-mypy==0.790; platform_python_implementation != "PyPy"
-typing_extensions
+# - There is no point in installing typing_extensions without mypy
+mypy==0.812; platform_python_implementation != "PyPy"
+typing_extensions==3.10.0.0; platform_python_implementation != "PyPy"
pre = set(re.findall(pat, this_repo.git.shortlog('-s', lst_release),
re.M))
- # Homu is the author of auto merges, clean him out.
+ # Ignore the bot Homu.
cur.discard('Homu')
pre.discard('Homu')
+ # Ignore the bot dependabot-preview
+ cur.discard('dependabot-preview')
+ pre.discard('dependabot-preview')
+
# Append '+' to new authors.
authors = [s + u' +' for s in cur - pre] + [s for s in cur & pre]
authors.sort()
# From fast forward squash-merges
commits = this_repo.git.log(
'--oneline', '--no-merges', '--first-parent', revision_range)
- issues = re.findall(u'^.*\\(\\#(\\d+)\\)$', commits, re.M)
- prnums.extend(int(s) for s in issues)
+ issues = re.findall(u'^.*\\((\\#|gh-|gh-\\#)(\\d+)\\)$', commits, re.M)
+ prnums.extend(int(s[1]) for s in issues)
# get PR data from github repo
prnums.sort()
if fragments:
print("The following files were not found by towncrier:")
- print(" " + " \n".join(fragments))
+ print(" " + "\n ".join(fragments))
sys.exit(1)
projects / platform / upstream / python3-numpy.git / commitdiff