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| author | Kasia <39829548+katleszek@users.noreply.github.com> | 2021-05-09 14:35:36 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-05-09 14:35:36 +0200 |
| commit | 7c61d16aa6a09172dc35662c0005fc2e289e403d (patch) | |
| tree | 7c68f9504341f5026985149fec7450b719e7e508 | |
| parent | 70dd918b8b531ce7be91bb9e6bb52fdc7c7199b9 (diff) | |
| parent | e3583316cab5e71f2b361c32a3eee748905f40c5 (diff) | |
| download | numpy-7c61d16aa6a09172dc35662c0005fc2e289e403d.tar.gz | |
Merge pull request #1 from numpy/main
update from main - fixing SVD
93 files changed, 5713 insertions, 1743 deletions
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 52d78a38d..cc4950590 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -5,10 +5,8 @@ on: branches: - main paths: - - './environment.yml' - tags: - - '*' - + - 'environment.yml' + jobs: build: name: Build base Docker image @@ -56,4 +54,4 @@ jobs: numpy/numpy-dev:${{ steps.getrefs.outputs.date }}-${{ steps.getrefs.outputs.branch}}-${{ steps.getrefs.outputs.sha8 }}, numpy/numpy-dev:latest - name: Image digest # Return details of the image build: sha and shell - run: echo ${{ steps.docker_build.outputs.digest }}
\ No newline at end of file + run: echo ${{ steps.docker_build.outputs.digest }} diff --git a/.gitignore b/.gitignore index 736597b6b..a2a1f2b68 100644 --- a/.gitignore +++ b/.gitignore @@ -187,6 +187,7 @@ numpy/random/legacy/*.c numpy/random/_mtrand/randint_helpers.pxi numpy/random/bounded_integers.pyx numpy/random/bounded_integers.pxd +numpy/random/lib/npyrandom.lib tools/swig/test/Array_wrap.cxx tools/swig/test/Farray_wrap.cxx tools/swig/test/Farray.py diff --git a/doc/changelog/1.17.0-changelog.rst b/doc/changelog/1.17.0-changelog.rst index 4177c848f..8179c180b 100644 --- a/doc/changelog/1.17.0-changelog.rst +++ b/doc/changelog/1.17.0-changelog.rst @@ -159,7 +159,7 @@ names contributed a patch for the first time. 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. @@ -174,6 +174,7 @@ A total of 531 pull requests were merged for this release. * `#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... diff --git a/doc/changelog/1.18.0-changelog.rst b/doc/changelog/1.18.0-changelog.rst index b86b3614a..266ff0807 100644 --- a/doc/changelog/1.18.0-changelog.rst +++ b/doc/changelog/1.18.0-changelog.rst @@ -123,20 +123,20 @@ names contributed a patch for the first time. 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 @@ -144,6 +144,7 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -162,6 +163,7 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -174,7 +176,7 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -184,7 +186,7 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -226,7 +228,7 @@ A total of 406 pull requests were merged for this release. * `#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. @@ -236,6 +238,7 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -249,36 +252,39 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -293,9 +299,9 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -344,7 +350,7 @@ A total of 406 pull requests were merged for this release. * `#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. @@ -383,7 +389,7 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -405,30 +411,31 @@ A total of 406 pull requests were merged for this release. * `#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 @@ -526,7 +533,7 @@ A total of 406 pull requests were merged for this release. * `#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) diff --git a/doc/changelog/1.19.0-changelog.rst b/doc/changelog/1.19.0-changelog.rst index bd743832a..bde002499 100644 --- a/doc/changelog/1.19.0-changelog.rst +++ b/doc/changelog/1.19.0-changelog.rst @@ -136,25 +136,28 @@ names contributed a patch for the first time. 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 @@ -165,6 +168,7 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -201,6 +205,7 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -215,6 +220,8 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -224,14 +231,14 @@ A total of 452 pull requests were merged for this release. * `#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. @@ -248,8 +255,9 @@ A total of 452 pull requests were merged for this release. * `#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_* @@ -261,7 +269,7 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -271,22 +279,25 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -303,11 +314,14 @@ A total of 452 pull requests were merged for this release. * `#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(). @@ -315,14 +329,15 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -330,21 +345,22 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -354,13 +370,15 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -369,6 +387,7 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -376,9 +395,10 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -386,16 +406,18 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -403,6 +425,7 @@ A total of 452 pull requests were merged for this release. * `#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... @@ -418,28 +441,30 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -447,8 +472,9 @@ A total of 452 pull requests were merged for this release. * `#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__ @@ -456,29 +482,34 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -496,6 +527,8 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -508,6 +541,7 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -517,10 +551,12 @@ A total of 452 pull requests were merged for this release. * `#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 @@ -571,7 +607,7 @@ A total of 452 pull requests were merged for this release. * `#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 diff --git a/doc/changelog/1.20.0-changelog.rst b/doc/changelog/1.20.0-changelog.rst index f0c2a2723..f06bd8a8d 100644 --- a/doc/changelog/1.20.0-changelog.rst +++ b/doc/changelog/1.20.0-changelog.rst @@ -193,41 +193,47 @@ names contributed a patch for the first time. 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 @@ -238,6 +244,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -254,14 +261,17 @@ A total of 684 pull requests were merged for this release. * `#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. @@ -301,6 +311,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -308,7 +319,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -340,6 +351,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -364,8 +376,9 @@ A total of 684 pull requests were merged for this release. * `#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. @@ -377,14 +390,15 @@ A total of 684 pull requests were merged for this 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 @@ -393,11 +407,12 @@ A total of 684 pull requests were merged for this release. * `#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() @@ -414,6 +429,8 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -467,7 +484,8 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -480,23 +498,24 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -505,18 +524,24 @@ A total of 684 pull requests were merged for this release. * `#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. @@ -530,7 +555,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -542,13 +567,13 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -558,22 +583,23 @@ A total of 684 pull requests were merged for this release. * `#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... @@ -583,7 +609,9 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -595,9 +623,9 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -616,23 +644,23 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -642,7 +670,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -654,6 +682,7 @@ A total of 684 pull requests were merged for this release. * `#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. @@ -661,12 +690,12 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -674,7 +703,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -685,7 +714,7 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -699,16 +728,17 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -726,15 +756,15 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -742,27 +772,27 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -773,8 +803,8 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -799,7 +829,9 @@ A total of 684 pull requests were merged for this release. * `#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 @@ -836,9 +868,9 @@ A total of 684 pull requests were merged for this release. * `#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. diff --git a/doc/neps/nep-0049.rst b/doc/neps/nep-0049.rst index 9adf0ff26..743dd2ad6 100644 --- a/doc/neps/nep-0049.rst +++ b/doc/neps/nep-0049.rst @@ -6,6 +6,7 @@ NEP 49 — Data allocation strategies :Status: Draft :Type: Standards Track :Created: 2021-04-18 +:Resolution: http://numpy-discussion.10968.n7.nabble.com/NEP-49-Data-allocation-strategies-tt49185.html Abstract @@ -101,6 +102,11 @@ time of its instantiation, and these will be used to reallocate or free the data memory of the instance. Internally NumPy may use ``memcpy`` or ``memset`` on the pointer to the data memory. +The name of the handler will be exposed on the python level via a +``numpy.core.multiarray.get_handler_name(arr)`` function. If called as +``numpy.core.multiarray.get_handler_name()`` it will return the name of the +global handler that will be used to allocate data for the next new `ndarrray`. + NumPy C-API functions ===================== @@ -185,7 +191,7 @@ the ``sz`` argument is correct. if (strncmp(real, "originally allocated", 20) != 0) { fprintf(stdout, "uh-oh, unmatched shift_free, " "no appropriate prefix\\n"); - /* Make gcc crash by calling free on the wrong address */ + /* Make the C runtime crash by calling free on the wrong address */ free((char *)p + 10); /* free(real); */ } @@ -194,7 +200,7 @@ the ``sz`` argument is correct. if (i != sz) { fprintf(stderr, "uh-oh, unmatched " "shift_free(ptr, %d) but allocated %d\\n", sz, i); - /* Make gcc crash by calling free on the wrong address */ + /* Make the C runtime crash by calling free on the wrong address */ /* free((char *)p + 10); */ free(real); } diff --git a/doc/release/upcoming_changes/18874.change.rst b/doc/release/upcoming_changes/18874.change.rst new file mode 100644 index 000000000..c86fed83e --- /dev/null +++ b/doc/release/upcoming_changes/18874.change.rst @@ -0,0 +1,11 @@ +Enable Accelerate Framework +---------------------------- +With the release of macOS 11.3, several different issues that +numpy was encountering when using Accelerate Framework's +implementation of BLAS and LAPACK should be resolved. This +change enables the Accelerate Framework as an option on macOS. +If additional issues are found, please file a bug report +against Accelerate using the developer feedback assistant +tool (https://developer.apple.com/bug-reporting/). We +intend to address issues promptly and plan to continue +supporting and updating our BLAS and LAPACK libraries. diff --git a/doc/release/upcoming_changes/18906.new_function.rst b/doc/release/upcoming_changes/18906.new_function.rst new file mode 100644 index 000000000..38444009d --- /dev/null +++ b/doc/release/upcoming_changes/18906.new_function.rst @@ -0,0 +1,17 @@ +.. currentmodule:: numpy.random + +Add `PCG64DXSM` `BitGenerator` +------------------------------ + +Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been +shown to have statistical weaknesses that were not apparent at the first +release in numpy 1.17. Most users will never observe this weakness and are +safe to continue to use `PCG64`. We have introduced a new `PCG64DXSM` +`BitGenerator` that will eventually become the new default `BitGenerator` +implementation used by `default_rng` in future releases. `PCG64DXSM` solves +the statistical weakness while preserving the performance and the features of +`PCG64`. + +See :ref:`upgrading-pcg64` for more details. + +.. currentmodule:: numpy diff --git a/doc/release/upcoming_changes/18934.improvement.rst b/doc/release/upcoming_changes/18934.improvement.rst new file mode 100644 index 000000000..582062f2f --- /dev/null +++ b/doc/release/upcoming_changes/18934.improvement.rst @@ -0,0 +1,5 @@ +Placeholder annotations have been improved +------------------------------------------ +All placeholder annotations, that were previously annotated as ``typing.Any``, +have been improved. Where appropiate they have been replaced with explicit +function definitions, classes or other miscellaneous objects. diff --git a/doc/source/dev/gitwash/development_setup.rst b/doc/source/dev/gitwash/development_setup.rst index badb73ca0..2be7125da 100644 --- a/doc/source/dev/gitwash/development_setup.rst +++ b/doc/source/dev/gitwash/development_setup.rst @@ -165,7 +165,7 @@ slightly. Instead of :: run :: - git clone git@github.com:numpy/numpy.git + git clone git@github.com:your-user-name/numpy.git and instead of showing an ``https`` URL, ``git remote -v`` will show :: diff --git a/doc/source/reference/random/bit_generators/index.rst b/doc/source/reference/random/bit_generators/index.rst index 6f8cf02ca..c5c349806 100644 --- a/doc/source/reference/random/bit_generators/index.rst +++ b/doc/source/reference/random/bit_generators/index.rst @@ -15,10 +15,13 @@ Supported BitGenerators The included BitGenerators are: -* PCG-64 - The default. A fast generator that supports many parallel streams - and can be advanced by an arbitrary amount. See the documentation for - :meth:`~.PCG64.advance`. PCG-64 has a period of :math:`2^{128}`. See the `PCG - author's page`_ for more details about this class of PRNG. +* PCG-64 - The default. A fast generator that can be advanced by an arbitrary + amount. See the documentation for :meth:`~.PCG64.advance`. PCG-64 has + a period of :math:`2^{128}`. See the `PCG author's page`_ for more details + about this class of PRNG. +* PCG-64 DXSM - An upgraded version of PCG-64 with better statistical + properties in parallel contexts. See :ref:`upgrading-pcg64` for more + information on these improvements. * MT19937 - The standard Python BitGenerator. Adds a `MT19937.jumped` function that returns a new generator with state as-if :math:`2^{128}` draws have been made. @@ -43,6 +46,7 @@ The included BitGenerators are: MT19937 <mt19937> PCG64 <pcg64> + PCG64DXSM <pcg64dxsm> Philox <philox> SFC64 <sfc64> diff --git a/doc/source/reference/random/bit_generators/pcg64dxsm.rst b/doc/source/reference/random/bit_generators/pcg64dxsm.rst new file mode 100644 index 000000000..e37efa5d3 --- /dev/null +++ b/doc/source/reference/random/bit_generators/pcg64dxsm.rst @@ -0,0 +1,32 @@ +Permuted Congruential Generator (64-bit, PCG64 DXSM) +---------------------------------------------------- + +.. currentmodule:: numpy.random + +.. autoclass:: PCG64DXSM + :members: __init__ + :exclude-members: __init__ + +State +===== + +.. autosummary:: + :toctree: generated/ + + ~PCG64DXSM.state + +Parallel generation +=================== +.. autosummary:: + :toctree: generated/ + + ~PCG64DXSM.advance + ~PCG64DXSM.jumped + +Extending +========= +.. autosummary:: + :toctree: generated/ + + ~PCG64DXSM.cffi + ~PCG64DXSM.ctypes diff --git a/doc/source/reference/random/index.rst b/doc/source/reference/random/index.rst index fc7743c64..96cd47017 100644 --- a/doc/source/reference/random/index.rst +++ b/doc/source/reference/random/index.rst @@ -222,6 +222,9 @@ one of three ways: * :ref:`independent-streams` * :ref:`parallel-jumped` +Users with a very large amount of parallelism will want to consult +:ref:`upgrading-pcg64`. + Concepts -------- .. toctree:: @@ -230,6 +233,7 @@ Concepts generator Legacy Generator (RandomState) <legacy> BitGenerators, SeedSequences <bit_generators/index> + Upgrading PCG64 with PCG64DXSM <upgrading-pcg64> Features -------- diff --git a/doc/source/reference/random/parallel.rst b/doc/source/reference/random/parallel.rst index 721584014..7f0207bde 100644 --- a/doc/source/reference/random/parallel.rst +++ b/doc/source/reference/random/parallel.rst @@ -88,10 +88,11 @@ territory ([2]_). estimate the naive upper bound on a napkin and take comfort knowing that the probability is actually lower. -.. [2] In this calculation, we can ignore the amount of numbers drawn from each - stream. Each of the PRNGs we provide has some extra protection built in +.. [2] In this calculation, we can mostly ignore the amount of numbers drawn from each + stream. See :ref:`upgrading-pcg64` for the technical details about + `PCG64`. The other PRNGs we provide have some extra protection built in that avoids overlaps if the `~SeedSequence` pools differ in the - slightest bit. `PCG64` has :math:`2^{127}` separate cycles + slightest bit. `PCG64DXSM` has :math:`2^{127}` separate cycles determined by the seed in addition to the position in the :math:`2^{128}` long period for each cycle, so one has to both get on or near the same cycle *and* seed a nearby position in the cycle. @@ -150,12 +151,14 @@ BitGenerator, the size of the jump and the bits in the default unsigned random are listed below. +-----------------+-------------------------+-------------------------+-------------------------+ -| BitGenerator | Period | Jump Size | Bits | +| BitGenerator | Period | Jump Size | Bits per Draw | +=================+=========================+=========================+=========================+ -| MT19937 | :math:`2^{19937}` | :math:`2^{128}` | 32 | +| MT19937 | :math:`2^{19937}-1` | :math:`2^{128}` | 32 | +-----------------+-------------------------+-------------------------+-------------------------+ | PCG64 | :math:`2^{128}` | :math:`~2^{127}` ([3]_) | 64 | +-----------------+-------------------------+-------------------------+-------------------------+ +| PCG64DXSM | :math:`2^{128}` | :math:`~2^{127}` ([3]_) | 64 | ++-----------------+-------------------------+-------------------------+-------------------------+ | Philox | :math:`2^{256}` | :math:`2^{128}` | 64 | +-----------------+-------------------------+-------------------------+-------------------------+ diff --git a/doc/source/reference/random/performance.py b/doc/source/reference/random/performance.py index 3267197f5..794142836 100644 --- a/doc/source/reference/random/performance.py +++ b/doc/source/reference/random/performance.py @@ -3,9 +3,9 @@ from timeit import repeat import pandas as pd import numpy as np -from numpy.random import MT19937, PCG64, Philox, SFC64 +from numpy.random import MT19937, PCG64, PCG64DXSM, Philox, SFC64 -PRNGS = [MT19937, PCG64, Philox, SFC64] +PRNGS = [MT19937, PCG64, PCG64DXSM, Philox, SFC64] funcs = {} integers = 'integers(0, 2**{bits},size=1000000, dtype="uint{bits}")' @@ -53,7 +53,7 @@ for key in npfuncs: col[key] = 1000 * min(t) table['RandomState'] = pd.Series(col) -columns = ['MT19937','PCG64','Philox','SFC64', 'RandomState'] +columns = ['MT19937', 'PCG64', 'PCG64DXSM', 'Philox', 'SFC64', 'RandomState'] table = pd.DataFrame(table) order = np.log(table).mean().sort_values().index table = table.T diff --git a/doc/source/reference/random/performance.rst b/doc/source/reference/random/performance.rst index 74dad4cc3..812c719f8 100644 --- a/doc/source/reference/random/performance.rst +++ b/doc/source/reference/random/performance.rst @@ -5,9 +5,12 @@ Performance Recommendation ************** -The recommended generator for general use is `PCG64`. It is -statistically high quality, full-featured, and fast on most platforms, but -somewhat slow when compiled for 32-bit processes. + +The recommended generator for general use is `PCG64` or its upgraded variant +`PCG64DXSM` for heavily-parallel use cases. They are statistically high quality, +full-featured, and fast on most platforms, but somewhat slow when compiled for +32-bit processes. See :ref:`upgrading-pcg64` for details on when heavy +parallelism would indicate using `PCG64DXSM`. `Philox` is fairly slow, but its statistical properties have very high quality, and it is easy to get assuredly-independent stream by using diff --git a/doc/source/reference/random/upgrading-pcg64.rst b/doc/source/reference/random/upgrading-pcg64.rst new file mode 100644 index 000000000..9e540ace9 --- /dev/null +++ b/doc/source/reference/random/upgrading-pcg64.rst @@ -0,0 +1,152 @@ +.. _upgrading-pcg64: + +.. currentmodule:: numpy.random + +Upgrading ``PCG64`` with ``PCG64DXSM`` +-------------------------------------- + +Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been +shown to have statistical weaknesses that were not apparent at the first +release in numpy 1.17. Most users will never observe this weakness and are +safe to continue to use `PCG64`. We have introduced a new `PCG64DXSM` +`BitGenerator` that will eventually become the new default `BitGenerator` +implementation used by `default_rng` in future releases. `PCG64DXSM` solves +the statistical weakness while preserving the performance and the features of +`PCG64`. + +Does this affect me? +==================== + +If you + + 1. only use a single `Generator` instance, + 2. only use `RandomState` or the functions in `numpy.random`, + 3. only use the `PCG64.jumped` method to generate parallel streams, + 4. explicitly use a `BitGenerator` other than `PCG64`, + +then this weakness does not affect you at all. Carry on. + +If you use moderate numbers of parallel streams created with `default_rng` or +`SeedSequence.spawn`, in the 1000s, then the chance of observing this weakness +is negligibly small. You can continue to use `PCG64` comfortably. + +If you use very large numbers of parallel streams, in the millions, and draw +large amounts of numbers from each, then the chance of observing this weakness +can become non-negligible, if still small. An example of such a use case would +be a very large distributed reinforcement learning problem with millions of +long Monte Carlo playouts each generating billions of random number draws. Such +use cases should consider using `PCG64DXSM` explicitly or another +modern `BitGenerator` like `SFC64` or `Philox`, but it is unlikely that any +old results you may have calculated are invalid. In any case, the weakness is +a kind of `Birthday Paradox <https://en.wikipedia.org/wiki/Birthday_problem>`_ +collision. That is, a single pair of parallel streams out of the millions, +considered together, might fail a stringent set of statistical tests of +randomness. The remaining millions of streams would all be perfectly fine, and +the effect of the bad pair in the whole calculation is very likely to be +swamped by the remaining streams in most applications. + +.. _upgrading-pcg64-details: + +Technical Details +================= + +Like many PRNG algorithms, `PCG64` is constructed from a transition function, +which advances a 128-bit state, and an output function, that mixes the 128-bit +state into a 64-bit integer to be output. One of the guiding design principles +of the PCG family of PRNGs is to balance the computational cost (and +pseudorandomness strength) between the transition function and the output +function. The transition function is a 128-bit linear congruential generator +(LCG), which consists of multiplying the 128-bit state with a fixed +multiplication constant and then adding a user-chosen increment, in 128-bit +modular arithmetic. LCGs are well-analyzed PRNGs with known weaknesses, though +128-bit LCGs are large enough to pass stringent statistical tests on their own, +with only the trivial output function. The output function of `PCG64` is +intended to patch up some of those known weaknesses by doing "just enough" +scrambling of the bits to assist in the statistical properties without adding +too much computational cost. + +One of these known weaknesses is that advancing the state of the LCG by steps +numbering a power of two (``bg.advance(2**N)``) will leave the lower ``N`` bits +identical to the state that was just left. For a single stream drawn from +sequentially, this is of little consequence. The remaining :math:`128-N` bits provide +plenty of pseudorandomness that will be mixed in for any practical ``N`` that can +be observed in a single stream, which is why one does not need to worry about +this if you only use a single stream in your application. Similarly, the +`PCG64.jumped` method uses a carefully chosen number of steps to avoid creating +these collisions. However, once you start creating "randomly-initialized" +parallel streams, either using OS entropy by calling `default_rng` repeatedly +or using `SeedSequence.spawn`, then we need to consider how many lower bits +need to "collide" in order to create a bad pair of streams, and then evaluate +the probability of creating such a collision. +`Empirically <https://github.com/numpy/numpy/issues/16313>`_, it has been +determined that if one shares the lower 58 bits of state and shares an +increment, then the pair of streams, when interleaved, will fail +`PractRand <http://pracrand.sourceforge.net/>`_ in +a reasonable amount of time, after drawing a few gigabytes of data. Following +the standard Birthday Paradox calculations for a collision of 58 bits, we can +see that we can create :math:`2^{29}`, or about half a billion, streams which is when +the probability of such a collision becomes high. Half a billion streams is +quite high, and the amount of data each stream needs to draw before the +statistical correlations become apparent to even the strict ``PractRand`` tests +is in the gigabytes. But this is on the horizon for very large applications +like distributed reinforcement learning. There are reasons to expect that even +in these applications a collision probably will not have a practical effect in +the total result, since the statistical problem is constrained to just the +colliding pair. + +Now, let us consider the case when the increment is not constrained to be the +same. Our implementation of `PCG64` seeds both the state and the increment; +that is, two calls to `default_rng` (almost certainly) have different states +and increments. Upon our first release, we believed that having the seeded +increment would provide a certain amount of extra protection, that one would +have to be "close" in both the state space and increment space in order to +observe correlations (``PractRand`` failures) in a pair of streams. If that were +true, then the "bottleneck" for collisions would be the 128-bit entropy pool +size inside of `SeedSequence` (and 128-bit collisions are in the +"preposterously unlikely" category). Unfortunately, this is not true. + +One of the known properties of an LCG is that different increments create +*distinct* streams, but with a known relationship. Each LCG has an orbit that +traverses all :math:`2^{128}` different 128-bit states. Two LCGs with different +increments are related in that one can "rotate" the orbit of the first LCG +(advance it by a number of steps that we can compute from the two increments) +such that then both LCGs will always then have the same state, up to an +additive constant and maybe an inversion of the bits. If you then iterate both +streams in lockstep, then the states will *always* remain related by that same +additive constant (and the inversion, if present). Recall that `PCG64` is +constructed from both a transition function (the LCG) and an output function. +It was expected that the scrambling effect of the output function would have +been strong enough to make the distinct streams practically independent (i.e. +"passing the ``PractRand`` tests") unless the two increments were +pathologically related to each other (e.g. 1 and 3). The output function XSL-RR +of the then-standard PCG algorithm that we implemented in `PCG64` turns out to +be too weak to cover up for the 58-bit collision of the underlying LCG that we +described above. For any given pair of increments, the size of the "colliding" +space of states is the same, so for this weakness, the extra distinctness +provided by the increments does not translate into extra protection from +statistical correlations that ``PractRand`` can detect. + +Fortunately, strengthening the output function is able to correct this weakness +and *does* turn the extra distinctness provided by differing increments into +additional protection from these low-bit collisions. To the `PCG author's +credit <https://github.com/numpy/numpy/issues/13635#issuecomment-506088698>`_, +she had developed a stronger output function in response to related discussions +during the long birth of the new `BitGenerator` system. We NumPy developers +chose to be "conservative" and use the XSL-RR variant that had undergone +a longer period of testing at that time. The DXSM output function adopts +a "xorshift-multiply" construction used in strong integer hashes that has much +better avalanche properties than the XSL-RR output function. While there are +"pathological" pairs of increments that induce "bad" additive constants that +relate the two streams, the vast majority of pairs induce "good" additive +constants that make the merely-distinct streams of LCG states into +practically-independent output streams. Indeed, now the claim we once made +about `PCG64` is actually true of `PCG64DXSM`: collisions are possible, but +both streams have to simultaneously be both "close" in the 128 bit state space +*and* "close" in the 127-bit increment space, so that would be less likely than +the negligible chance of colliding in the 128-bit internal `SeedSequence` pool. +The DXSM output function is more computationally intensive than XSL-RR, but +some optimizations in the LCG more than make up for the performance hit on most +machines, so `PCG64DXSM` is a good, safe upgrade. There are, of course, an +infinite number of stronger output functions that one could consider, but most +will have a greater computational cost, and the DXSM output function has now +received many CPU cycles of testing via ``PractRand`` at this time. diff --git a/environment.yml b/environment.yml index 19fe69a79..5e1d4df23 100644 --- a/environment.yml +++ b/environment.yml @@ -1,6 +1,8 @@ # To use: +# # $ conda env create -f environment.yml # `mamba` works too for this command # $ conda activate numpy-dev +# name: numpy-dev channels: - conda-forge diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 5c7a3c7e4..8824497f0 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -11,7 +11,6 @@ from numpy.typing import ( # Arrays ArrayLike, _ArrayND, - _ArrayOrScalar, _SupportsArray, _NestedSequence, _RecursiveSequence, @@ -1289,14 +1288,21 @@ class _ArrayOrScalarCommon: 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, @@ -1304,14 +1310,21 @@ class _ArrayOrScalarCommon: 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, @@ -1319,40 +1332,67 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... + @overload - def argmax(self, axis: None = ..., out: None = ...) -> intp: ... + def argmax( + self, + axis: None = ..., + out: None = ..., + ) -> intp: ... @overload def argmax( - self, axis: _ShapeLike = ..., out: None = ... - ) -> Union[ndarray, intp]: ... + 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 = ...) -> intp: ... + def argmin( + self, + axis: None = ..., + out: None = ..., + ) -> intp: ... @overload def argmin( - self, axis: _ShapeLike = ..., out: None = ... - ) -> Union[ndarray, intp]: ... + 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[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, @@ -1360,7 +1400,7 @@ class _ArrayOrScalarCommon: max: Optional[ArrayLike] = ..., out: None = ..., **kwargs: Any, - ) -> Union[number, ndarray]: ... + ) -> ndarray: ... @overload def clip( self, @@ -1368,7 +1408,7 @@ class _ArrayOrScalarCommon: max: ArrayLike = ..., out: None = ..., **kwargs: Any, - ) -> Union[number, ndarray]: ... + ) -> ndarray: ... @overload def clip( self, @@ -1385,6 +1425,7 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., **kwargs: Any, ) -> _NdArraySubClass: ... + @overload def compress( self, @@ -1399,8 +1440,11 @@ class _ArrayOrScalarCommon: axis: Optional[SupportsIndex] = ..., out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... + def conj(self: _ArraySelf) -> _ArraySelf: ... + def conjugate(self: _ArraySelf) -> _ArraySelf: ... + @overload def cumprod( self, @@ -1415,6 +1459,7 @@ class _ArrayOrScalarCommon: dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... + @overload def cumsum( self, @@ -1429,15 +1474,7 @@ class _ArrayOrScalarCommon: dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... - @overload - def max( - self, - axis: None = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + @overload def max( self, @@ -1446,7 +1483,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def max( self, @@ -1456,14 +1493,7 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., ) -> _NdArraySubClass: ... - @overload - def mean( - self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - ) -> number: ... + @overload def mean( self, @@ -1471,7 +1501,7 @@ class _ArrayOrScalarCommon: dtype: DTypeLike = ..., out: None = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def mean( self, @@ -1480,15 +1510,7 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... - @overload - def min( - self, - axis: None = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + @overload def min( self, @@ -1497,7 +1519,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def min( self, @@ -1507,17 +1529,12 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., 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_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + + def newbyteorder( + self: _ArraySelf, + __new_order: _ByteOrder = ..., + ) -> _ArraySelf: ... + @overload def prod( self, @@ -1527,7 +1544,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def prod( self, @@ -1538,14 +1555,14 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., 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, @@ -1553,26 +1570,26 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... + def repeat( - self, repeats: _ArrayLikeIntOrBool, axis: Optional[SupportsIndex] = ... + self, + repeats: _ArrayLikeIntOrBool, + axis: Optional[SupportsIndex] = ..., ) -> ndarray: ... + @overload def round( - self: _ArraySelf, decimals: SupportsIndex = ..., out: None = ... + self: _ArraySelf, + decimals: SupportsIndex = ..., + out: None = ..., ) -> _ArraySelf: ... @overload def round( - self, decimals: SupportsIndex = ..., out: _NdArraySubClass = ... - ) -> _NdArraySubClass: ... - @overload - def std( self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - ddof: int = ..., - keepdims: Literal[False] = ..., - ) -> number: ... + decimals: SupportsIndex = ..., + out: _NdArraySubClass = ..., + ) -> _NdArraySubClass: ... + @overload def std( self, @@ -1581,7 +1598,7 @@ class _ArrayOrScalarCommon: out: None = ..., ddof: int = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def std( self, @@ -1591,16 +1608,7 @@ class _ArrayOrScalarCommon: ddof: int = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... - @overload - def sum( - self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + @overload def sum( self, @@ -1610,7 +1618,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def sum( self, @@ -1621,6 +1629,7 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., ) -> _NdArraySubClass: ... + @overload def take( self, @@ -1628,7 +1637,7 @@ class _ArrayOrScalarCommon: axis: Optional[SupportsIndex] = ..., out: None = ..., mode: _ModeKind = ..., - ) -> generic: ... + ) -> Any: ... @overload def take( self, @@ -1645,15 +1654,7 @@ class _ArrayOrScalarCommon: 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, @@ -1662,7 +1663,7 @@ class _ArrayOrScalarCommon: out: None = ..., ddof: int = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def var( self, @@ -1772,7 +1773,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): axis2: SupportsIndex = ..., ) -> _ArraySelf: ... @overload - def dot(self, b: ArrayLike, out: None = ...) -> Union[number, ndarray]: ... + def dot(self, b: ArrayLike, out: None = ...) -> ndarray: ... @overload def dot(self, b: ArrayLike, out: _NdArraySubClass = ...) -> _NdArraySubClass: ... # `nonzero()` is deprecated for 0d arrays/generics @@ -1812,7 +1813,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): axis2: SupportsIndex = ..., dtype: DTypeLike = ..., out: None = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def trace( self, # >= 2D array @@ -1843,105 +1844,105 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __lt__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __lt__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... @overload def __le__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __le__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... @overload def __gt__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __gt__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... @overload def __ge__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __ge__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... # Unary ops @overload - def __abs__(self: _ArrayND[bool_]) -> _ArrayOrScalar[bool_]: ... + def __abs__(self: _ArrayND[bool_]) -> _ArrayND[bool_]: ... @overload - def __abs__(self: _ArrayND[complexfloating[_NBit1, _NBit1]]) -> _ArrayOrScalar[floating[_NBit1]]: ... + def __abs__(self: _ArrayND[complexfloating[_NBit1, _NBit1]]) -> _ArrayND[floating[_NBit1]]: ... @overload - def __abs__(self: _ArrayND[_NumberType]) -> _ArrayOrScalar[_NumberType]: ... + def __abs__(self: _ArrayND[_NumberType]) -> _ArrayND[_NumberType]: ... @overload - def __abs__(self: _ArrayND[timedelta64]) -> _ArrayOrScalar[timedelta64]: ... + def __abs__(self: _ArrayND[timedelta64]) -> _ArrayND[timedelta64]: ... @overload def __abs__(self: _ArrayND[object_]) -> Any: ... @overload - def __invert__(self: _ArrayND[bool_]) -> _ArrayOrScalar[bool_]: ... + def __invert__(self: _ArrayND[bool_]) -> _ArrayND[bool_]: ... @overload - def __invert__(self: _ArrayND[_IntType]) -> _ArrayOrScalar[_IntType]: ... + def __invert__(self: _ArrayND[_IntType]) -> _ArrayND[_IntType]: ... @overload def __invert__(self: _ArrayND[object_]) -> Any: ... @overload - def __pos__(self: _ArrayND[_NumberType]) -> _ArrayOrScalar[_NumberType]: ... + def __pos__(self: _ArrayND[_NumberType]) -> _ArrayND[_NumberType]: ... @overload - def __pos__(self: _ArrayND[timedelta64]) -> _ArrayOrScalar[timedelta64]: ... + def __pos__(self: _ArrayND[timedelta64]) -> _ArrayND[timedelta64]: ... @overload def __pos__(self: _ArrayND[object_]) -> Any: ... @overload - def __neg__(self: _ArrayND[_NumberType]) -> _ArrayOrScalar[_NumberType]: ... + def __neg__(self: _ArrayND[_NumberType]) -> _ArrayND[_NumberType]: ... @overload - def __neg__(self: _ArrayND[timedelta64]) -> _ArrayOrScalar[timedelta64]: ... + def __neg__(self: _ArrayND[timedelta64]) -> _ArrayND[timedelta64]: ... @overload def __neg__(self: _ArrayND[object_]) -> Any: ... @@ -1950,15 +1951,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __matmul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __matmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __matmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __matmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __matmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __matmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __matmul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1972,15 +1973,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmatmul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rmatmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __rmatmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __rmatmul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1994,15 +1995,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __mod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __mod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __mod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __mod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[timedelta64]: ... + def __mod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[timedelta64]: ... @overload def __mod__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2016,15 +2017,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[timedelta64]: ... + def __rmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[timedelta64]: ... @overload def __rmod__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2038,15 +2039,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __divmod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __divmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayOrScalar[int8]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayND[int8]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayOrScalar[unsignedinteger[Any]]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayND[unsignedinteger[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayOrScalar[signedinteger[Any]]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayND[signedinteger[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayOrScalar[floating[Any]]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayND[floating[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Union[Tuple[int64, timedelta64], Tuple[_ArrayND[int64], _ArrayND[timedelta64]]]: ... + def __divmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[_ArrayND[int64], _ArrayND[timedelta64]]: ... @overload def __divmod__( self: _ArrayND[Union[bool_, integer[Any], floating[Any], timedelta64]], @@ -2056,15 +2057,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rdivmod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rdivmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayOrScalar[int8]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayND[int8]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayOrScalar[unsignedinteger[Any]]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayND[unsignedinteger[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayOrScalar[signedinteger[Any]]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayND[signedinteger[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayOrScalar[floating[Any]]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayND[floating[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Union[Tuple[int64, timedelta64], Tuple[_ArrayND[int64], _ArrayND[timedelta64]]]: ... + def __rdivmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[_ArrayND[int64], _ArrayND[timedelta64]]: ... @overload def __rdivmod__( self: _ArrayND[Union[bool_, integer[Any], floating[Any], timedelta64]], @@ -2074,21 +2075,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __add__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __add__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __add__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __add__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayOrScalar[datetime64]: ... + def __add__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayND[datetime64]: ... @overload - def __add__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayOrScalar[datetime64]: ... + def __add__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayND[datetime64]: ... @overload def __add__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2102,21 +2103,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __radd__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __radd__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __radd__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __radd__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayOrScalar[datetime64]: ... + def __radd__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayND[datetime64]: ... @overload - def __radd__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayOrScalar[datetime64]: ... + def __radd__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayND[datetime64]: ... @overload def __radd__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2132,19 +2133,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __sub__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __sub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayOrScalar[datetime64]: ... + def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayND[datetime64]: ... @overload - def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[timedelta64]: ... + def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[timedelta64]: ... @overload def __sub__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2160,19 +2161,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rsub__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayOrScalar[datetime64]: ... # type: ignore[misc] + def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayND[datetime64]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rsub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[timedelta64]: ... @overload def __rsub__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2186,19 +2187,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __mul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __mul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __mul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __mul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload - def __mul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __mul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __mul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2212,19 +2213,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __rmul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload - def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __rmul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2238,21 +2239,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __floordiv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __floordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[int64]: ... + def __floordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[int64]: ... @overload def __floordiv__(self: _ArrayND[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __floordiv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __floordiv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload def __floordiv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2266,21 +2267,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rfloordiv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rfloordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[int64]: ... + def __rfloordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[int64]: ... @overload def __rfloordiv__(self: _ArrayND[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload - def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __rfloordiv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2294,15 +2295,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __pow__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __pow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __pow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __pow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __pow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __pow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __pow__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2316,15 +2317,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rpow__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rpow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rpow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rpow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rpow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __rpow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __rpow__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2338,17 +2339,17 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __truediv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayOrScalar[float64]: ... # type: ignore[misc] + def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayND[float64]: ... # type: ignore[misc] @overload - def __truediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __truediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[float64]: ... + def __truediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[float64]: ... @overload def __truediv__(self: _ArrayND[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __truediv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __truediv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload def __truediv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2362,17 +2363,17 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rtruediv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rtruediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayOrScalar[float64]: ... # type: ignore[misc] + def __rtruediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayND[float64]: ... # type: ignore[misc] @overload - def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[float64]: ... + def __rtruediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[float64]: ... @overload def __rtruediv__(self: _ArrayND[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload - def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __rtruediv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2386,11 +2387,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __lshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __lshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __lshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __lshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __lshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __lshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2404,11 +2405,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rlshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rlshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rlshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rlshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rlshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rlshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2422,11 +2423,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2440,11 +2441,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rrshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rrshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rrshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rrshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rrshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rrshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2458,11 +2459,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __and__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __and__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __and__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __and__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __and__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __and__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2476,11 +2477,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rand__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rand__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rand__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rand__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rand__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rand__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2494,11 +2495,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __xor__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __xor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __xor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __xor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __xor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __xor__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2512,11 +2513,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rxor__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rxor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rxor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rxor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rxor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rxor__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2530,11 +2531,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __or__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __or__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __or__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __or__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __or__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __or__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2548,11 +2549,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __ror__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __ror__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __ror__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __ror__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __ror__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __ror__(self: _ArrayND[object_], other: Any) -> Any: ... @overload diff --git a/numpy/core/einsumfunc.pyi b/numpy/core/einsumfunc.pyi index b33aff29f..2457e8719 100644 --- a/numpy/core/einsumfunc.pyi +++ b/numpy/core/einsumfunc.pyi @@ -13,7 +13,6 @@ from numpy import ( _OrderKACF, ) from numpy.typing import ( - _ArrayOrScalar, _ArrayLikeBool_co, _ArrayLikeUInt_co, _ArrayLikeInt_co, @@ -46,6 +45,11 @@ _CastingUnsafe = Literal["unsafe"] __all__: List[str] # TODO: Properly handle the `casting`-based combinatorics +# TODO: We need to evaluate the content `__subscripts` in order +# to identify whether or an array or scalar is returned. At a cursory +# glance this seems like something that can quite easilly be done with +# a mypy plugin. +# Something like `is_scalar = bool(__subscripts.partition("->")[-1])` @overload def einsum( __subscripts: str, @@ -55,7 +59,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[bool_]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -65,7 +69,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[unsignedinteger[Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -75,7 +79,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[signedinteger[Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -85,7 +89,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[floating[Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -95,7 +99,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -105,7 +109,7 @@ def einsum( out: None = ..., order: _OrderKACF = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[Any]: ... +) -> Any: ... @overload def einsum( __subscripts: str, diff --git a/numpy/core/fromnumeric.pyi b/numpy/core/fromnumeric.pyi index fc7f28a59..26a43c1a0 100644 --- a/numpy/core/fromnumeric.pyi +++ b/numpy/core/fromnumeric.pyi @@ -23,7 +23,6 @@ from numpy.typing import ( ArrayLike, _ShapeLike, _Shape, - _IntLike_co, _NumberLike_co, ) @@ -42,11 +41,7 @@ _ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, comp _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) @@ -55,67 +50,51 @@ _Number = TypeVar("_Number", bound=number) # 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: _IntLike_co, 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, @@ -123,54 +102,55 @@ def partition( 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]] = ..., -) -> intp: ... -@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] = ...) -> intp: ... +def argmax( + a: ArrayLike, + axis: None = ..., + out: Optional[ndarray] = ..., +) -> intp: ... @overload def argmax( - a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ... -) -> Union[ndarray, intp]: ... + a: ArrayLike, + axis: Optional[int] = ..., + out: Optional[ndarray] = ..., +) -> Any: ... + @overload -def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> intp: ... +def argmin( + a: ArrayLike, + axis: None = ..., + out: Optional[ndarray] = ..., +) -> intp: ... @overload def argmin( - a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ... -) -> Union[ndarray, intp]: ... + a: ArrayLike, + axis: Optional[int] = ..., + out: Optional[ndarray] = ..., +) -> Any: ... + @overload def searchsorted( a: ArrayLike, @@ -185,14 +165,30 @@ def searchsorted( 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 = ..., @@ -200,32 +196,21 @@ def trace( 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, @@ -233,7 +218,7 @@ def clip( a_max: Optional[ArrayLike], out: Optional[ndarray] = ..., **kwargs: Any, -) -> Union[number, ndarray]: ... +) -> Any: ... @overload def clip( a: ArrayLike, @@ -241,18 +226,8 @@ def clip( 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_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload +) -> Any: ... + def sum( a: ArrayLike, axis: _ShapeLike = ..., @@ -261,12 +236,13 @@ def sum( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... + @overload def all( a: ArrayLike, axis: None = ..., - out: Optional[ndarray] = ..., + out: None = ..., keepdims: Literal[False] = ..., ) -> bool_: ... @overload @@ -275,12 +251,13 @@ def all( 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 @@ -289,53 +266,22 @@ def any( 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_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload -def amax( - a: ArrayLike, - axis: None = ..., - out: Optional[ndarray] = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> number: ... -@overload +) -> Any: ... + def amax( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -343,26 +289,8 @@ def amax( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... -@overload -def amin( - a: _Number, - axis: Optional[_ShapeLike] = ..., - out: Optional[ndarray] = ..., - keepdims: bool = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload -def amin( - a: ArrayLike, - axis: None = ..., - out: Optional[ndarray] = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> number: ... -@overload +) -> Any: ... + def amin( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -370,7 +298,7 @@ def amin( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... # TODO: `np.prod()``: For object arrays `initial` does not necessarily # have to be a numerical scalar. @@ -379,27 +307,6 @@ def amin( # 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_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload -def prod( - a: ArrayLike, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> number: ... -@overload def prod( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -408,53 +315,33 @@ def prod( keepdims: bool = ..., initial: _NumberLike_co = ..., 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_co, 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] = ..., @@ -462,17 +349,8 @@ def std( 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] = ..., @@ -480,4 +358,4 @@ def var( out: Optional[ndarray] = ..., ddof: int = ..., keepdims: bool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... diff --git a/numpy/core/function_base.pyi b/numpy/core/function_base.pyi index d4543f281..b5d6ca6ab 100644 --- a/numpy/core/function_base.pyi +++ b/numpy/core/function_base.pyi @@ -1,7 +1,7 @@ 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_co if sys.version_info >= (3, 8): @@ -33,7 +33,8 @@ def linspace( retstep: Literal[True] = ..., dtype: DTypeLike = ..., axis: SupportsIndex = ..., -) -> Tuple[ndarray, inexact]: ... +) -> Tuple[ndarray, Any]: ... + def logspace( start: _ArrayLikeNumber, stop: _ArrayLikeNumber, @@ -43,6 +44,7 @@ def logspace( dtype: DTypeLike = ..., axis: SupportsIndex = ..., ) -> ndarray: ... + def geomspace( start: _ArrayLikeNumber, stop: _ArrayLikeNumber, diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi index d91cb31c2..6b9ef4b20 100644 --- a/numpy/core/numeric.pyi +++ b/numpy/core/numeric.pyi @@ -41,6 +41,7 @@ def zeros_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + def ones( shape: _ShapeLike, dtype: DTypeLike = ..., @@ -48,6 +49,7 @@ def ones( *, like: ArrayLike = ..., ) -> ndarray: ... + @overload def ones_like( a: _ArrayType, @@ -64,6 +66,7 @@ def ones_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + @overload def empty_like( a: _ArrayType, @@ -80,6 +83,7 @@ def empty_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + def full( shape: _ShapeLike, fill_value: Any, @@ -88,6 +92,7 @@ def full( *, like: ArrayLike = ..., ) -> ndarray: ... + @overload def full_like( a: _ArrayType, @@ -106,39 +111,73 @@ def full_like( 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, @@ -147,6 +186,7 @@ def cross( axisc: int = ..., axis: Optional[int] = ..., ) -> ndarray: ... + @overload def indices( dimensions: Sequence[int], @@ -159,6 +199,7 @@ def indices( dtype: DTypeLike = ..., sparse: Literal[True] = ..., ) -> Tuple[ndarray, ...]: ... + def fromfunction( function: Callable[..., _T], shape: Sequence[int], @@ -167,10 +208,20 @@ def fromfunction( 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, @@ -178,12 +229,15 @@ def allclose( 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: ... diff --git a/numpy/core/setup.py b/numpy/core/setup.py index df405bcaf..d1229ee8f 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -405,11 +405,6 @@ def configuration(parent_package='',top_path=None): from numpy.distutils.system_info import (get_info, blas_opt_info, lapack_opt_info) - # Accelerate is buggy, disallow it. See also numpy/linalg/setup.py - for opt_order in (blas_opt_info.blas_order, lapack_opt_info.lapack_order): - if 'accelerate' in opt_order: - opt_order.remove('accelerate') - config = Configuration('core', parent_package, top_path) local_dir = config.local_path codegen_dir = join(local_dir, 'code_generators') diff --git a/numpy/core/src/common/lowlevel_strided_loops.h b/numpy/core/src/common/lowlevel_strided_loops.h index 1255e51dd..3df054b40 100644 --- a/numpy/core/src/common/lowlevel_strided_loops.h +++ b/numpy/core/src/common/lowlevel_strided_loops.h @@ -647,25 +647,6 @@ npy_bswap8_unaligned(char * x) * // Create iterator, etc... * } * - * Here is example code for a pair of arrays: - * - * if (PyArray_TRIVIALLY_ITERABLE_PAIR(a1, a2)) { - * char *data1, *data2; - * npy_intp count, stride1, stride2; - * - * PyArray_PREPARE_TRIVIAL_PAIR_ITERATION(a1, a2, count, - * data1, data2, stride1, stride2); - * - * while (count--) { - * // Use the data1 and data2 pointers - * - * data1 += stride1; - * data2 += stride2; - * } - * } - * else { - * // Create iterator, etc... - * } */ /* @@ -776,16 +757,6 @@ PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(PyArrayObject *arr1, PyArrayObject *arr PyArray_STRIDE(arr, 0) : \ PyArray_ITEMSIZE(arr))); -#define PyArray_TRIVIALLY_ITERABLE_PAIR(arr1, arr2, arr1_read, arr2_read) ( \ - PyArray_TRIVIALLY_ITERABLE(arr1) && \ - (PyArray_NDIM(arr2) == 0 || \ - PyArray_EQUIVALENTLY_ITERABLE_BASE(arr1, arr2) || \ - (PyArray_NDIM(arr1) == 0 && \ - PyArray_TRIVIALLY_ITERABLE(arr2) \ - ) \ - ) && \ - PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(arr1, arr2, arr1_read, arr2_read) \ - ) #define PyArray_PREPARE_TRIVIAL_PAIR_ITERATION(arr1, arr2, \ count, \ data1, data2, \ @@ -799,45 +770,4 @@ PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(PyArrayObject *arr1, PyArrayObject *arr stride2 = PyArray_TRIVIAL_PAIR_ITERATION_STRIDE(size2, arr2); \ } -#define PyArray_TRIVIALLY_ITERABLE_TRIPLE(arr1, arr2, arr3, arr1_read, arr2_read, arr3_read) ( \ - PyArray_TRIVIALLY_ITERABLE(arr1) && \ - ((PyArray_NDIM(arr2) == 0 && \ - (PyArray_NDIM(arr3) == 0 || \ - PyArray_EQUIVALENTLY_ITERABLE_BASE(arr1, arr3) \ - ) \ - ) || \ - (PyArray_EQUIVALENTLY_ITERABLE_BASE(arr1, arr2) && \ - (PyArray_NDIM(arr3) == 0 || \ - PyArray_EQUIVALENTLY_ITERABLE_BASE(arr1, arr3) \ - ) \ - ) || \ - (PyArray_NDIM(arr1) == 0 && \ - PyArray_TRIVIALLY_ITERABLE(arr2) && \ - (PyArray_NDIM(arr3) == 0 || \ - PyArray_EQUIVALENTLY_ITERABLE_BASE(arr2, arr3) \ - ) \ - ) \ - ) && \ - PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(arr1, arr2, arr1_read, arr2_read) && \ - PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(arr1, arr3, arr1_read, arr3_read) && \ - PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(arr2, arr3, arr2_read, arr3_read) \ - ) - -#define PyArray_PREPARE_TRIVIAL_TRIPLE_ITERATION(arr1, arr2, arr3, \ - count, \ - data1, data2, data3, \ - stride1, stride2, stride3) { \ - npy_intp size1 = PyArray_SIZE(arr1); \ - npy_intp size2 = PyArray_SIZE(arr2); \ - npy_intp size3 = PyArray_SIZE(arr3); \ - count = ((size1 > size2) || size1 == 0) ? size1 : size2; \ - count = ((size3 > count) || size3 == 0) ? size3 : count; \ - data1 = PyArray_BYTES(arr1); \ - data2 = PyArray_BYTES(arr2); \ - data3 = PyArray_BYTES(arr3); \ - stride1 = PyArray_TRIVIAL_PAIR_ITERATION_STRIDE(size1, arr1); \ - stride2 = PyArray_TRIVIAL_PAIR_ITERATION_STRIDE(size2, arr2); \ - stride3 = PyArray_TRIVIAL_PAIR_ITERATION_STRIDE(size3, arr3); \ - } - #endif diff --git a/numpy/core/src/common/npy_pycompat.h b/numpy/core/src/common/npy_pycompat.h index aa0b5c122..9e94a9710 100644 --- a/numpy/core/src/common/npy_pycompat.h +++ b/numpy/core/src/common/npy_pycompat.h @@ -3,4 +3,20 @@ #include "numpy/npy_3kcompat.h" + +/* + * In Python 3.10a7 (or b1), python started using the identity for the hash + * when a value is NaN. See https://bugs.python.org/issue43475 + */ +#if PY_VERSION_HEX > 0x030a00a6 +#define Npy_HashDouble _Py_HashDouble +#else +static NPY_INLINE Py_hash_t +Npy_HashDouble(PyObject *NPY_UNUSED(identity), double val) +{ + return _Py_HashDouble(val); +} +#endif + + #endif /* _NPY_COMPAT_H_ */ diff --git a/numpy/core/src/multiarray/scalartypes.c.src b/numpy/core/src/multiarray/scalartypes.c.src index a001500b0..9930f7791 100644 --- a/numpy/core/src/multiarray/scalartypes.c.src +++ b/numpy/core/src/multiarray/scalartypes.c.src @@ -3172,7 +3172,7 @@ static npy_hash_t static npy_hash_t @lname@_arrtype_hash(PyObject *obj) { - return _Py_HashDouble((double) PyArrayScalar_VAL(obj, @name@)); + return Npy_HashDouble(obj, (double)PyArrayScalar_VAL(obj, @name@)); } /* borrowed from complex_hash */ @@ -3180,14 +3180,14 @@ static npy_hash_t c@lname@_arrtype_hash(PyObject *obj) { npy_hash_t hashreal, hashimag, combined; - hashreal = _Py_HashDouble((double) - PyArrayScalar_VAL(obj, C@name@).real); + hashreal = Npy_HashDouble( + obj, (double)PyArrayScalar_VAL(obj, C@name@).real); if (hashreal == -1) { return -1; } - hashimag = _Py_HashDouble((double) - PyArrayScalar_VAL(obj, C@name@).imag); + hashimag = Npy_HashDouble( + obj, (double)PyArrayScalar_VAL(obj, C@name@).imag); if (hashimag == -1) { return -1; } @@ -3202,7 +3202,8 @@ c@lname@_arrtype_hash(PyObject *obj) static npy_hash_t half_arrtype_hash(PyObject *obj) { - return _Py_HashDouble(npy_half_to_double(PyArrayScalar_VAL(obj, Half))); + return Npy_HashDouble( + obj, npy_half_to_double(PyArrayScalar_VAL(obj, Half))); } static npy_hash_t diff --git a/numpy/core/src/umath/loops_exponent_log.dispatch.c.src b/numpy/core/src/umath/loops_exponent_log.dispatch.c.src index 1dc24b226..41e0bf37b 100644 --- a/numpy/core/src/umath/loops_exponent_log.dispatch.c.src +++ b/numpy/core/src/umath/loops_exponent_log.dispatch.c.src @@ -465,6 +465,7 @@ simd_exp_FLOAT(npy_float * op, @mask@ xmax_mask, xmin_mask, nan_mask, inf_mask; @mask@ overflow_mask = @isa@_get_partial_load_mask_ps(0, num_lanes); + @mask@ underflow_mask = @isa@_get_partial_load_mask_ps(0, num_lanes); @mask@ load_mask = @isa@_get_full_load_mask_ps(); npy_intp num_remaining_elements = array_size; @@ -491,6 +492,7 @@ simd_exp_FLOAT(npy_float * op, inf_mask = _mm@vsize@_cmp_ps@vsub@(x, inf, _CMP_EQ_OQ); overflow_mask = @or_masks@(overflow_mask, @xor_masks@(xmax_mask, inf_mask)); + underflow_mask = @or_masks@(underflow_mask, xmin_mask); x = @isa@_set_masked_lanes_ps(x, zeros_f, @or_masks@( @or_masks@(nan_mask, xmin_mask), xmax_mask)); @@ -539,6 +541,10 @@ simd_exp_FLOAT(npy_float * op, if (@mask_to_int@(overflow_mask)) { npy_set_floatstatus_overflow(); } + + if (@mask_to_int@(underflow_mask)) { + npy_set_floatstatus_underflow(); + } } /* @@ -740,6 +746,7 @@ AVX512F_exp_DOUBLE(npy_double * op, __m512d mTable_tail_3 = _mm512_loadu_pd(&(EXP_Table_tail[8*3])); __mmask8 overflow_mask = avx512_get_partial_load_mask_pd(0, num_lanes); + __mmask8 underflow_mask = avx512_get_partial_load_mask_pd(0, num_lanes); __mmask8 load_mask = avx512_get_full_load_mask_pd(); __mmask8 xmin_mask, xmax_mask, inf_mask, nan_mask, nearzero_mask; @@ -769,6 +776,7 @@ AVX512F_exp_DOUBLE(npy_double * op, nearzero_mask = _mm512_kxor(nearzero_mask, nan_mask); overflow_mask = _mm512_kor(overflow_mask, _mm512_kxor(xmax_mask, inf_mask)); + underflow_mask = _mm512_kor(underflow_mask, xmin_mask); x = avx512_set_masked_lanes_pd(x, zeros_d, _mm512_kor(_mm512_kor(nan_mask, xmin_mask), _mm512_kor(xmax_mask, nearzero_mask))); @@ -828,6 +836,10 @@ AVX512F_exp_DOUBLE(npy_double * op, if (overflow_mask) { npy_set_floatstatus_overflow(); } + + if (underflow_mask) { + npy_set_floatstatus_underflow(); + } } /* * Vectorized implementation of log double using AVX512 diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index f17dd1e61..7dffb482f 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -1033,65 +1033,6 @@ check_for_trivial_loop(PyUFuncObject *ufunc, return 1; } -static void -trivial_two_operand_loop(PyArrayObject **op, - PyUFuncGenericFunction innerloop, - void *innerloopdata) -{ - char *data[2]; - npy_intp count[2], stride[2]; - int needs_api; - NPY_BEGIN_THREADS_DEF; - - needs_api = PyDataType_REFCHK(PyArray_DESCR(op[0])) || - PyDataType_REFCHK(PyArray_DESCR(op[1])); - - PyArray_PREPARE_TRIVIAL_PAIR_ITERATION(op[0], op[1], - count[0], - data[0], data[1], - stride[0], stride[1]); - count[1] = count[0]; - NPY_UF_DBG_PRINT1("two operand loop count %d\n", (int)count[0]); - - if (!needs_api) { - NPY_BEGIN_THREADS_THRESHOLDED(count[0]); - } - - innerloop(data, count, stride, innerloopdata); - - NPY_END_THREADS; -} - -static void -trivial_three_operand_loop(PyArrayObject **op, - PyUFuncGenericFunction innerloop, - void *innerloopdata) -{ - char *data[3]; - npy_intp count[3], stride[3]; - int needs_api; - NPY_BEGIN_THREADS_DEF; - - needs_api = PyDataType_REFCHK(PyArray_DESCR(op[0])) || - PyDataType_REFCHK(PyArray_DESCR(op[1])) || - PyDataType_REFCHK(PyArray_DESCR(op[2])); - - PyArray_PREPARE_TRIVIAL_TRIPLE_ITERATION(op[0], op[1], op[2], - count[0], - data[0], data[1], data[2], - stride[0], stride[1], stride[2]); - count[1] = count[0]; - count[2] = count[0]; - NPY_UF_DBG_PRINT1("three operand loop count %d\n", (int)count[0]); - - if (!needs_api) { - NPY_BEGIN_THREADS_THRESHOLDED(count[0]); - } - - innerloop(data, count, stride, innerloopdata); - - NPY_END_THREADS; -} /* * Calls the given __array_prepare__ function on the operand *op, @@ -1165,6 +1106,151 @@ prepare_ufunc_output(PyUFuncObject *ufunc, return 0; } + +/* + * Check whether a trivial loop is possible and call the innerloop if it is. + * A trivial loop is defined as one where a single strided inner-loop call + * is possible. + * + * This function only supports a single output (due to the overlap check). + * It always accepts 0-D arrays and will broadcast them. The function + * cannot broadcast any other array (as it requires a single stride). + * The function accepts all 1-D arrays, and N-D arrays that are either all + * C- or all F-contiguous. + * + * Returns -2 if a trivial loop is not possible, 0 on success and -1 on error. + */ +static NPY_INLINE int +try_trivial_single_output_loop(PyUFuncObject *ufunc, + PyArrayObject *op[], PyArray_Descr *dtypes[], + NPY_ORDER order, PyObject *arr_prep[], ufunc_full_args full_args, + PyUFuncGenericFunction innerloop, void *innerloopdata) +{ + int nin = ufunc->nin; + int nop = nin + 1; + assert(ufunc->nout == 1); + + /* The order of all N-D contiguous operands, can be fixed by `order` */ + int operation_order = 0; + if (order == NPY_CORDER) { + operation_order = NPY_ARRAY_C_CONTIGUOUS; + } + else if (order == NPY_FORTRANORDER) { + operation_order = NPY_ARRAY_F_CONTIGUOUS; + } + + int operation_ndim = 0; + npy_intp *operation_shape = NULL; + npy_intp fixed_strides[NPY_MAXARGS]; + + for (int iop = 0; iop < nop; iop++) { + if (op[iop] == NULL) { + /* The out argument may be NULL (and only that one); fill later */ + assert(iop == nin); + continue; + } + + int op_ndim = PyArray_NDIM(op[iop]); + + /* Special case 0-D since we can handle broadcasting using a 0-stride */ + if (op_ndim == 0) { + fixed_strides[iop] = 0; + continue; + } + + /* First non 0-D op: fix dimensions, shape (order is fixed later) */ + if (operation_ndim == 0) { + operation_ndim = op_ndim; + operation_shape = PyArray_SHAPE(op[iop]); + } + else if (op_ndim != operation_ndim) { + return -2; /* dimension mismatch (except 0-d ops) */ + } + else if (!PyArray_CompareLists( + operation_shape, PyArray_DIMS(op[iop]), op_ndim)) { + return -2; /* shape mismatch */ + } + + if (op_ndim == 1) { + fixed_strides[iop] = PyArray_STRIDES(op[iop])[0]; + } + else { + fixed_strides[iop] = PyArray_ITEMSIZE(op[iop]); /* contiguous */ + + /* This op must match the operation order (and be contiguous) */ + int op_order = (PyArray_FLAGS(op[iop]) & + (NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_F_CONTIGUOUS)); + if (op_order == 0) { + return -2; /* N-dimensional op must be contiguous */ + } + else if (operation_order == 0) { + operation_order = op_order; /* op fixes order */ + } + else if (operation_order != op_order) { + return -2; + } + } + } + + if (op[nin] == NULL) { + Py_INCREF(dtypes[nin]); + op[nin] = (PyArrayObject *) PyArray_NewFromDescr(&PyArray_Type, + dtypes[nin], operation_ndim, operation_shape, + NULL, NULL, operation_order==NPY_ARRAY_F_CONTIGUOUS, NULL); + if (op[nin] == NULL) { + return -1; + } + fixed_strides[nin] = dtypes[nin]->elsize; + } + else { + /* If any input overlaps with the output, we use the full path. */ + for (int iop = 0; iop < nin; iop++) { + if (!PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK( + op[iop], op[nin], + PyArray_TRIVIALLY_ITERABLE_OP_READ, + PyArray_TRIVIALLY_ITERABLE_OP_NOREAD)) { + return -2; + } + } + /* Check self-overlap (non 1-D are contiguous, perfect overlap is OK) */ + if (operation_ndim == 1 && + PyArray_STRIDES(op[nin])[0] < PyArray_ITEMSIZE(op[nin]) && + PyArray_STRIDES(op[nin])[0] != 0) { + return -2; + } + } + + /* Call the __prepare_array__ if necessary */ + if (prepare_ufunc_output(ufunc, &op[nin], + arr_prep[0], full_args, 0) < 0) { + return -1; + } + + /* + * We can use the trivial (single inner-loop call) optimization + * and `fixed_strides` holds the strides for that call. + */ + char *data[NPY_MAXARGS]; + npy_intp count = PyArray_MultiplyList(operation_shape, operation_ndim); + int needs_api = 0; + NPY_BEGIN_THREADS_DEF; + + for (int iop = 0; iop < nop; iop++) { + data[iop] = PyArray_BYTES(op[iop]); + needs_api |= PyDataType_REFCHK(dtypes[iop]); + } + + if (!needs_api) { + NPY_BEGIN_THREADS_THRESHOLDED(count); + } + + innerloop(data, &count, fixed_strides, innerloopdata); + + NPY_END_THREADS; + return 0; +} + + static int iterator_loop(PyUFuncObject *ufunc, PyArrayObject **op, @@ -1325,7 +1411,6 @@ execute_legacy_ufunc_loop(PyUFuncObject *ufunc, ufunc_full_args full_args, npy_uint32 *op_flags) { - npy_intp nin = ufunc->nin, nout = ufunc->nout; PyUFuncGenericFunction innerloop; void *innerloopdata; int needs_api = 0; @@ -1336,113 +1421,12 @@ execute_legacy_ufunc_loop(PyUFuncObject *ufunc, } /* First check for the trivial cases that don't need an iterator */ - if (trivial_loop_ok) { - if (nin == 1 && nout == 1) { - if (op[1] == NULL && - (order == NPY_ANYORDER || order == NPY_KEEPORDER) && - PyArray_TRIVIALLY_ITERABLE(op[0])) { - Py_INCREF(dtypes[1]); - op[1] = (PyArrayObject *)PyArray_NewFromDescr(&PyArray_Type, - dtypes[1], - PyArray_NDIM(op[0]), - PyArray_DIMS(op[0]), - NULL, NULL, - PyArray_ISFORTRAN(op[0]) ? - NPY_ARRAY_F_CONTIGUOUS : 0, - NULL); - if (op[1] == NULL) { - return -1; - } - - /* Call the __prepare_array__ if necessary */ - if (prepare_ufunc_output(ufunc, &op[1], - arr_prep[0], full_args, 0) < 0) { - return -1; - } - - NPY_UF_DBG_PRINT("trivial 1 input with allocated output\n"); - trivial_two_operand_loop(op, innerloop, innerloopdata); - - return 0; - } - else if (op[1] != NULL && - PyArray_NDIM(op[1]) >= PyArray_NDIM(op[0]) && - PyArray_TRIVIALLY_ITERABLE_PAIR(op[0], op[1], - PyArray_TRIVIALLY_ITERABLE_OP_READ, - PyArray_TRIVIALLY_ITERABLE_OP_NOREAD)) { - - /* Call the __prepare_array__ if necessary */ - if (prepare_ufunc_output(ufunc, &op[1], - arr_prep[0], full_args, 0) < 0) { - return -1; - } - - NPY_UF_DBG_PRINT("trivial 1 input\n"); - trivial_two_operand_loop(op, innerloop, innerloopdata); - - return 0; - } - } - else if (nin == 2 && nout == 1) { - if (op[2] == NULL && - (order == NPY_ANYORDER || order == NPY_KEEPORDER) && - PyArray_TRIVIALLY_ITERABLE_PAIR(op[0], op[1], - PyArray_TRIVIALLY_ITERABLE_OP_READ, - PyArray_TRIVIALLY_ITERABLE_OP_READ)) { - PyArrayObject *tmp; - /* - * Have to choose the input with more dimensions to clone, as - * one of them could be a scalar. - */ - if (PyArray_NDIM(op[0]) >= PyArray_NDIM(op[1])) { - tmp = op[0]; - } - else { - tmp = op[1]; - } - Py_INCREF(dtypes[2]); - op[2] = (PyArrayObject *)PyArray_NewFromDescr(&PyArray_Type, - dtypes[2], - PyArray_NDIM(tmp), - PyArray_DIMS(tmp), - NULL, NULL, - PyArray_ISFORTRAN(tmp) ? - NPY_ARRAY_F_CONTIGUOUS : 0, - NULL); - if (op[2] == NULL) { - return -1; - } - - /* Call the __prepare_array__ if necessary */ - if (prepare_ufunc_output(ufunc, &op[2], - arr_prep[0], full_args, 0) < 0) { - return -1; - } - - NPY_UF_DBG_PRINT("trivial 2 input with allocated output\n"); - trivial_three_operand_loop(op, innerloop, innerloopdata); - - return 0; - } - else if (op[2] != NULL && - PyArray_NDIM(op[2]) >= PyArray_NDIM(op[0]) && - PyArray_NDIM(op[2]) >= PyArray_NDIM(op[1]) && - PyArray_TRIVIALLY_ITERABLE_TRIPLE(op[0], op[1], op[2], - PyArray_TRIVIALLY_ITERABLE_OP_READ, - PyArray_TRIVIALLY_ITERABLE_OP_READ, - PyArray_TRIVIALLY_ITERABLE_OP_NOREAD)) { - - /* Call the __prepare_array__ if necessary */ - if (prepare_ufunc_output(ufunc, &op[2], - arr_prep[0], full_args, 0) < 0) { - return -1; - } - - NPY_UF_DBG_PRINT("trivial 2 input\n"); - trivial_three_operand_loop(op, innerloop, innerloopdata); - - return 0; - } + if (trivial_loop_ok && ufunc->nout == 1) { + int fast_path_result = try_trivial_single_output_loop(ufunc, + op, dtypes, order, arr_prep, full_args, + innerloop, innerloopdata); + if (fast_path_result != -2) { + return fast_path_result; } } @@ -1450,7 +1434,6 @@ execute_legacy_ufunc_loop(PyUFuncObject *ufunc, * If no trivial loop matched, an iterator is required to * resolve broadcasting, etc */ - NPY_UF_DBG_PRINT("iterator loop\n"); if (iterator_loop(ufunc, op, dtypes, order, buffersize, arr_prep, full_args, @@ -2457,7 +2440,7 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, /* Fill in any allocated outputs */ { PyArrayObject **operands = NpyIter_GetOperandArray(iter); - for (i = 0; i < nop; ++i) { + for (i = nin; i < nop; ++i) { if (op[i] == NULL) { op[i] = operands[i]; Py_INCREF(op[i]); @@ -2592,34 +2575,6 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, if (!needs_api && !NpyIter_IterationNeedsAPI(iter)) { NPY_END_THREADS; } - } else { - /** - * For each output operand, check if it has non-zero size, - * and assign the identity if it does. For example, a dot - * product of two zero-length arrays will be a scalar, - * which has size one. - */ - npy_bool reorderable; - PyObject *identity = _get_identity(ufunc, &reorderable); - if (identity == NULL) { - retval = -1; - goto fail; - } - - for (i = nin; i < nop; ++i) { - if (PyArray_SIZE(op[i]) != 0) { - if (identity == Py_None) { - PyErr_Format(PyExc_ValueError, - "ufunc %s ", - ufunc_name); - Py_DECREF(identity); - retval = -1; - goto fail; - } - PyArray_FillWithScalar(op[i], identity); - } - } - Py_DECREF(identity); } /* Check whether any errors occurred during the loop */ diff --git a/numpy/core/src/umath/ufunc_type_resolution.c b/numpy/core/src/umath/ufunc_type_resolution.c index 465deb87b..a3f97a8f3 100644 --- a/numpy/core/src/umath/ufunc_type_resolution.c +++ b/numpy/core/src/umath/ufunc_type_resolution.c @@ -540,7 +540,12 @@ PyUFunc_SimpleUniformOperationTypeResolver( out_dtypes[iop] = PyArray_DESCR(operands[iop]); Py_INCREF(out_dtypes[iop]); } - return raise_no_loop_found_error(ufunc, out_dtypes); + raise_no_loop_found_error(ufunc, out_dtypes); + for (iop = 0; iop < ufunc->nin; iop++) { + Py_DECREF(out_dtypes[iop]); + out_dtypes[iop] = NULL; + } + return -1; } out_dtypes[0] = PyArray_ResultType(ufunc->nin, operands, 0, NULL); } diff --git a/numpy/core/tests/test_mem_overlap.py b/numpy/core/tests/test_mem_overlap.py index 675613de4..24bdf477f 100644 --- a/numpy/core/tests/test_mem_overlap.py +++ b/numpy/core/tests/test_mem_overlap.py @@ -666,6 +666,11 @@ class TestUFunc: def test_unary_ufunc_call_fuzz(self): self.check_unary_fuzz(np.invert, None, np.int16) + @pytest.mark.slow + def test_unary_ufunc_call_complex_fuzz(self): + # Complex typically has a smaller alignment than itemsize + self.check_unary_fuzz(np.negative, None, np.complex128, count=500) + def test_binary_ufunc_accumulate_fuzz(self): def get_out_axis_size(a, b, axis): if axis is None: @@ -792,7 +797,7 @@ class TestUFunc: check(np.add, a, ind, a[25:75]) def test_unary_ufunc_1d_manual(self): - # Exercise branches in PyArray_EQUIVALENTLY_ITERABLE + # Exercise ufunc fast-paths (that avoid creation of an `np.nditer`) def check(a, b): a_orig = a.copy() diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py index 2dea5518c..b355c4618 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -6212,6 +6212,71 @@ class TestDot: assert_equal(np.dot(b, a), res) assert_equal(np.dot(b, b), res) + def test_accelerate_framework_sgemv_fix(self): + + def aligned_array(shape, align, dtype, order='C'): + d = dtype(0) + N = np.prod(shape) + tmp = np.zeros(N * d.nbytes + align, dtype=np.uint8) + address = tmp.__array_interface__["data"][0] + for offset in range(align): + if (address + offset) % align == 0: + break + tmp = tmp[offset:offset+N*d.nbytes].view(dtype=dtype) + return tmp.reshape(shape, order=order) + + def as_aligned(arr, align, dtype, order='C'): + aligned = aligned_array(arr.shape, align, dtype, order) + aligned[:] = arr[:] + return aligned + + def assert_dot_close(A, X, desired): + assert_allclose(np.dot(A, X), desired, rtol=1e-5, atol=1e-7) + + m = aligned_array(100, 15, np.float32) + s = aligned_array((100, 100), 15, np.float32) + np.dot(s, m) # this will always segfault if the bug is present + + testdata = itertools.product((15, 32), (10000,), (200, 89), ('C', 'F')) + for align, m, n, a_order in testdata: + # Calculation in double precision + A_d = np.random.rand(m, n) + X_d = np.random.rand(n) + desired = np.dot(A_d, X_d) + # Calculation with aligned single precision + A_f = as_aligned(A_d, align, np.float32, order=a_order) + X_f = as_aligned(X_d, align, np.float32) + assert_dot_close(A_f, X_f, desired) + # Strided A rows + A_d_2 = A_d[::2] + desired = np.dot(A_d_2, X_d) + A_f_2 = A_f[::2] + assert_dot_close(A_f_2, X_f, desired) + # Strided A columns, strided X vector + A_d_22 = A_d_2[:, ::2] + X_d_2 = X_d[::2] + desired = np.dot(A_d_22, X_d_2) + A_f_22 = A_f_2[:, ::2] + X_f_2 = X_f[::2] + assert_dot_close(A_f_22, X_f_2, desired) + # Check the strides are as expected + if a_order == 'F': + assert_equal(A_f_22.strides, (8, 8 * m)) + else: + assert_equal(A_f_22.strides, (8 * n, 8)) + assert_equal(X_f_2.strides, (8,)) + # Strides in A rows + cols only + X_f_2c = as_aligned(X_f_2, align, np.float32) + assert_dot_close(A_f_22, X_f_2c, desired) + # Strides just in A cols + A_d_12 = A_d[:, ::2] + desired = np.dot(A_d_12, X_d_2) + A_f_12 = A_f[:, ::2] + assert_dot_close(A_f_12, X_f_2c, desired) + # Strides in A cols and X + assert_dot_close(A_f_12, X_f_2, desired) + + class MatmulCommon: """Common tests for '@' operator and numpy.matmul. @@ -6464,6 +6529,16 @@ class TestMatmul(MatmulCommon): c = c.astype(tgt.dtype) assert_array_equal(c, tgt) + def test_empty_out(self): + # Check that the output cannot be broadcast, so that it cannot be + # size zero when the outer dimensions (iterator size) has size zero. + arr = np.ones((0, 1, 1)) + out = np.ones((1, 1, 1)) + assert self.matmul(arr, arr).shape == (0, 1, 1) + + with pytest.raises(ValueError, match=r"non-broadcastable"): + self.matmul(arr, arr, out=out) + def test_out_contiguous(self): a = np.ones((5, 2), dtype=float) b = np.array([[1, 3], [5, 7]], dtype=float) diff --git a/numpy/core/tests/test_scalarmath.py b/numpy/core/tests/test_scalarmath.py index d91b4a391..09a734284 100644 --- a/numpy/core/tests/test_scalarmath.py +++ b/numpy/core/tests/test_scalarmath.py @@ -712,6 +712,40 @@ class TestBitShifts: assert_equal(res_arr, res_scl) +class TestHash: + @pytest.mark.parametrize("type_code", np.typecodes['AllInteger']) + def test_integer_hashes(self, type_code): + scalar = np.dtype(type_code).type + for i in range(128): + assert hash(i) == hash(scalar(i)) + + @pytest.mark.parametrize("type_code", np.typecodes['AllFloat']) + def test_float_and_complex_hashes(self, type_code): + scalar = np.dtype(type_code).type + for val in [np.pi, np.inf, 3, 6.]: + numpy_val = scalar(val) + # Cast back to Python, in case the NumPy scalar has less precision + if numpy_val.dtype.kind == 'c': + val = complex(numpy_val) + else: + val = float(numpy_val) + assert val == numpy_val + print(repr(numpy_val), repr(val)) + assert hash(val) == hash(numpy_val) + + if hash(float(np.nan)) != hash(float(np.nan)): + # If Python distinguises different NaNs we do so too (gh-18833) + assert hash(scalar(np.nan)) != hash(scalar(np.nan)) + + @pytest.mark.parametrize("type_code", np.typecodes['Complex']) + def test_complex_hashes(self, type_code): + # Test some complex valued hashes specifically: + scalar = np.dtype(type_code).type + for val in [np.pi+1j, np.inf-3j, 3j, 6.+1j]: + numpy_val = scalar(val) + assert hash(complex(numpy_val)) == hash(numpy_val) + + @contextlib.contextmanager def recursionlimit(n): o = sys.getrecursionlimit() diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py index c13865ce4..64ecb3780 100644 --- a/numpy/core/tests/test_ufunc.py +++ b/numpy/core/tests/test_ufunc.py @@ -175,7 +175,7 @@ class TestUfuncGenericLoops: ufunc(obj_arr) else: res_obj = ufunc(obj_arr) - assert_array_equal(res_num.astype("O"), res_obj) + assert_array_almost_equal(res_num.astype("O"), res_obj) def _pickleable_module_global(): diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index 75e1c99d3..572e736a0 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -919,6 +919,12 @@ class TestSpecialFloats: assert_raises(FloatingPointError, np.exp, np.float64(800.)) assert_raises(FloatingPointError, np.exp, np.float64(1E19)) + with np.errstate(under='raise'): + assert_raises(FloatingPointError, np.exp, np.float32(-1000.)) + assert_raises(FloatingPointError, np.exp, np.float32(-1E19)) + assert_raises(FloatingPointError, np.exp, np.float64(-1000.)) + assert_raises(FloatingPointError, np.exp, np.float64(-1E19)) + def test_log_values(self): with np.errstate(all='ignore'): x = [np.nan, np.nan, np.inf, np.nan, -np.inf, np.nan] diff --git a/numpy/distutils/ccompiler_opt.py b/numpy/distutils/ccompiler_opt.py index aea9835c7..316d3a338 100644 --- a/numpy/distutils/ccompiler_opt.py +++ b/numpy/distutils/ccompiler_opt.py @@ -747,12 +747,14 @@ class _Cache: self.cache_me = {} self.cache_private = set() self.cache_infile = False + self._cache_path = None if self.conf_nocache: self.dist_log("cache is disabled by `Config`") return - chash = self.cache_hash(*factors, *self.conf_cache_factors) + self._cache_hash = self.cache_hash(*factors, *self.conf_cache_factors) + self._cache_path = cache_path if cache_path: if os.path.exists(cache_path): self.dist_log("load cache from file ->", cache_path) @@ -765,7 +767,7 @@ class _Cache: elif not hasattr(cache_mod, "hash") or \ not hasattr(cache_mod, "data"): self.dist_log("invalid cache file", stderr=True) - elif chash == cache_mod.hash: + elif self._cache_hash == cache_mod.hash: self.dist_log("hit the file cache") for attr, val in cache_mod.data.items(): setattr(self, attr, val) @@ -773,10 +775,8 @@ class _Cache: else: self.dist_log("miss the file cache") - atexit.register(self._cache_write, cache_path, chash) - if not self.cache_infile: - other_cache = _share_cache.get(chash) + other_cache = _share_cache.get(self._cache_hash) if other_cache: self.dist_log("hit the memory cache") for attr, val in other_cache.__dict__.items(): @@ -785,32 +785,41 @@ class _Cache: continue setattr(self, attr, val) - _share_cache[chash] = self + _share_cache[self._cache_hash] = self + atexit.register(self.cache_flush) def __del__(self): - # TODO: remove the cache form share on del - pass + for h, o in _share_cache.items(): + if o == self: + _share_cache.pop(h) + break - def _cache_write(self, cache_path, cache_hash): + def cache_flush(self): + """ + Force update the cache. + """ + if not self._cache_path: + return # TODO: don't write if the cache doesn't change - self.dist_log("write cache to path ->", cache_path) - for attr in list(self.__dict__.keys()): + self.dist_log("write cache to path ->", self._cache_path) + cdict = self.__dict__.copy() + for attr in self.__dict__.keys(): if re.match(self._cache_ignore, attr): - self.__dict__.pop(attr) + cdict.pop(attr) - d = os.path.dirname(cache_path) + d = os.path.dirname(self._cache_path) if not os.path.exists(d): os.makedirs(d) - repr_dict = pprint.pformat(self.__dict__, compact=True) - with open(cache_path, "w") as f: + repr_dict = pprint.pformat(cdict, compact=True) + with open(self._cache_path, "w") as f: f.write(textwrap.dedent("""\ # AUTOGENERATED DON'T EDIT # Please make changes to the code generator \ (distutils/ccompiler_opt.py) hash = {} data = \\ - """).format(cache_hash)) + """).format(self._cache_hash)) f.write(repr_dict) def cache_hash(self, *factors): diff --git a/numpy/distutils/command/build_clib.py b/numpy/distutils/command/build_clib.py index e1f15d465..0e31a7dee 100644 --- a/numpy/distutils/command/build_clib.py +++ b/numpy/distutils/command/build_clib.py @@ -123,15 +123,20 @@ class build_clib(old_build_clib): opt_cache_path = os.path.abspath( os.path.join(self.build_temp, 'ccompiler_opt_cache_clib.py') ) + if hasattr(self, "compiler_opt"): + # By default `CCompilerOpt` update the cache at the exit of + # the process, which may lead to duplicate building + # (see build_extension()/force_rebuild) if run() called + # multiple times within the same os process/thread without + # giving the chance the previous instances of `CCompilerOpt` + # to update the cache. + self.compiler_opt.cache_flush() + self.compiler_opt = new_ccompiler_opt( compiler=self.compiler, dispatch_hpath=dispatch_hpath, cpu_baseline=self.cpu_baseline, cpu_dispatch=self.cpu_dispatch, cache_path=opt_cache_path ) - if not self.compiler_opt.is_cached(): - log.info("Detected changes on compiler optimizations, force rebuilding") - self.force = True - def report(copt): log.info("\n########### CLIB COMPILER OPTIMIZATION ###########") log.info(copt.report(full=True)) @@ -212,7 +217,12 @@ class build_clib(old_build_clib): lib_file = compiler.library_filename(lib_name, output_dir=self.build_clib) depends = sources + build_info.get('depends', []) - if not (self.force or newer_group(depends, lib_file, 'newer')): + + force_rebuild = self.force + if not self.disable_optimization and not self.compiler_opt.is_cached(): + log.debug("Detected changes on compiler optimizations") + force_rebuild = True + if not (force_rebuild or newer_group(depends, lib_file, 'newer')): log.debug("skipping '%s' library (up-to-date)", lib_name) return else: diff --git a/numpy/distutils/command/build_ext.py b/numpy/distutils/command/build_ext.py index ca29ad4c0..84ec8aa2c 100644 --- a/numpy/distutils/command/build_ext.py +++ b/numpy/distutils/command/build_ext.py @@ -151,15 +151,20 @@ class build_ext (old_build_ext): opt_cache_path = os.path.abspath( os.path.join(self.build_temp, 'ccompiler_opt_cache_ext.py') ) + if hasattr(self, "compiler_opt"): + # By default `CCompilerOpt` update the cache at the exit of + # the process, which may lead to duplicate building + # (see build_extension()/force_rebuild) if run() called + # multiple times within the same os process/thread without + # giving the chance the previous instances of `CCompilerOpt` + # to update the cache. + self.compiler_opt.cache_flush() + self.compiler_opt = new_ccompiler_opt( compiler=self.compiler, dispatch_hpath=dispatch_hpath, cpu_baseline=self.cpu_baseline, cpu_dispatch=self.cpu_dispatch, cache_path=opt_cache_path ) - if not self.compiler_opt.is_cached(): - log.info("Detected changes on compiler optimizations, force rebuilding") - self.force = True - def report(copt): log.info("\n########### EXT COMPILER OPTIMIZATION ###########") log.info(copt.report(full=True)) @@ -360,7 +365,11 @@ class build_ext (old_build_ext): self.get_ext_filename(fullname)) depends = sources + ext.depends - if not (self.force or newer_group(depends, ext_filename, 'newer')): + force_rebuild = self.force + if not self.disable_optimization and not self.compiler_opt.is_cached(): + log.debug("Detected changes on compiler optimizations") + force_rebuild = True + if not (force_rebuild or newer_group(depends, ext_filename, 'newer')): log.debug("skipping '%s' extension (up-to-date)", ext.name) return else: diff --git a/numpy/distutils/system_info.py b/numpy/distutils/system_info.py index 85a0c9321..82e864a5c 100644 --- a/numpy/distutils/system_info.py +++ b/numpy/distutils/system_info.py @@ -375,22 +375,6 @@ default_src_dirs = [_m for _m in default_src_dirs if os.path.isdir(_m)] so_ext = get_shared_lib_extension() -def is_symlink_to_accelerate(filename): - accelpath = '/System/Library/Frameworks/Accelerate.framework' - return (sys.platform == 'darwin' and os.path.islink(filename) and - os.path.realpath(filename).startswith(accelpath)) - - -_accel_msg = ( - 'Found {filename}, but that file is a symbolic link to the ' - 'MacOS Accelerate framework, which is not supported by NumPy. ' - 'You must configure the build to use a different optimized library, ' - 'or disable the use of optimized BLAS and LAPACK by setting the ' - 'environment variables NPY_BLAS_ORDER="" and NPY_LAPACK_ORDER="" ' - 'before building NumPy.' -) - - def get_standard_file(fname): """Returns a list of files named 'fname' from 1) System-wide directory (directory-location of this module) @@ -539,6 +523,7 @@ def get_info(name, notfound_action=0): 'blis': blis_info, # use blas_opt instead 'lapack_mkl': lapack_mkl_info, # use lapack_opt instead 'blas_mkl': blas_mkl_info, # use blas_opt instead + 'accelerate': accelerate_info, # use blas_opt instead 'openblas64_': openblas64__info, 'openblas64__lapack': openblas64__lapack_info, 'openblas_ilp64': openblas_ilp64_info, @@ -1029,9 +1014,6 @@ class system_info: for prefix in lib_prefixes: p = self.combine_paths(lib_dir, prefix + lib + ext) if p: - # p[0] is the full path to the binary library file. - if is_symlink_to_accelerate(p[0]): - raise RuntimeError(_accel_msg.format(filename=p[0])) break if p: assert len(p) == 1 @@ -1766,8 +1748,10 @@ def get_atlas_version(**config): class lapack_opt_info(system_info): notfounderror = LapackNotFoundError + # List of all known LAPACK libraries, in the default order - lapack_order = ['mkl', 'openblas', 'flame', 'atlas', 'lapack'] + lapack_order = ['mkl', 'openblas', 'flame', + 'accelerate', 'atlas', 'lapack'] order_env_var_name = 'NPY_LAPACK_ORDER' def _calc_info_mkl(self): @@ -1942,7 +1926,9 @@ class lapack64__opt_info(lapack_ilp64_opt_info): class blas_opt_info(system_info): notfounderror = BlasNotFoundError # List of all known BLAS libraries, in the default order - blas_order = ['mkl', 'blis', 'openblas', 'atlas', 'blas'] + + blas_order = ['mkl', 'blis', 'openblas', + 'accelerate', 'atlas', 'blas'] order_env_var_name = 'NPY_BLAS_ORDER' def _calc_info_mkl(self): diff --git a/numpy/f2py/cfuncs.py b/numpy/f2py/cfuncs.py index 974062f26..f403a66b5 100644 --- a/numpy/f2py/cfuncs.py +++ b/numpy/f2py/cfuncs.py @@ -545,7 +545,9 @@ cppmacros['OLDPYNUM'] = """\ """ 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) \\ diff --git a/numpy/lib/index_tricks.pyi b/numpy/lib/index_tricks.pyi index 3e5bc1adb..e486fe8f2 100644 --- a/numpy/lib/index_tricks.pyi +++ b/numpy/lib/index_tricks.pyi @@ -17,6 +17,7 @@ from numpy import ( ndindex as ndindex, ndarray, dtype, + integer, str_, bytes_, bool_, @@ -33,7 +34,6 @@ from numpy.typing import ( _NestedSequence, _RecursiveSequence, _ArrayND, - _ArrayOrScalar, _ArrayLikeInt, # DTypes @@ -57,18 +57,33 @@ _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: List[str] +@overload +def unravel_index( # type: ignore[misc] + indices: Union[int, integer[Any]], + shape: _ShapeLike, + order: _OrderCF = ... +) -> Tuple[intp, ...]: ... +@overload def unravel_index( indices: _ArrayLikeInt, shape: _ShapeLike, order: _OrderCF = ... -) -> Tuple[_ArrayOrScalar[intp], ...]: ... +) -> Tuple[_ArrayND[intp], ...]: ... +@overload +def ravel_multi_index( # type: ignore[misc] + multi_index: Sequence[Union[int, integer[Any]]], + dims: _ShapeLike, + mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., + order: _OrderCF = ... +) -> intp: ... +@overload def ravel_multi_index( - multi_index: ArrayLike, + multi_index: Sequence[_ArrayLikeInt], dims: _ShapeLike, mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., order: _OrderCF = ... -) -> _ArrayOrScalar[intp]: ... +) -> _ArrayND[intp]: ... @overload def ix_(*args: _NestedSequence[_SupportsDType[_DType]]) -> Tuple[ndarray[Any, _DType], ...]: ... diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py index 2a2982ab3..383fbda3c 100644 --- a/numpy/lib/type_check.py +++ b/numpy/lib/type_check.py @@ -262,6 +262,10 @@ def isreal(x): out : ndarray, bool Boolean array of same shape as `x`. + Notes + ----- + `isreal` may behave unexpectedly for string or object arrays (see examples) + See Also -------- iscomplex @@ -269,8 +273,28 @@ def isreal(x): Examples -------- - >>> np.isreal([1+1j, 1+0j, 4.5, 3, 2, 2j]) + >>> a = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j], dtype=complex) + >>> np.isreal(a) array([False, True, True, True, True, False]) + + The function does not work on string arrays. + + >>> a = np.array([2j, "a"], dtype="U") + >>> np.isreal(a) # Warns about non-elementwise comparison + False + + Returns True for all elements in input array of ``dtype=object`` even if + any of the elements is complex. + + >>> a = np.array([1, "2", 3+4j], dtype=object) + >>> np.isreal(a) + array([ True, True, True]) + + isreal should not be used with object arrays + + >>> a = np.array([1+2j, 2+1j], dtype=object) + >>> np.isreal(a) + array([ True, True]) """ return imag(x) == 0 diff --git a/numpy/lib/ufunclike.pyi b/numpy/lib/ufunclike.pyi index 3443fa7ae..e97383af8 100644 --- a/numpy/lib/ufunclike.pyi +++ b/numpy/lib/ufunclike.pyi @@ -1,10 +1,11 @@ from typing import Any, overload, TypeVar, List, Union -from numpy import floating, bool_, ndarray +from numpy import floating, bool_, object_, ndarray from numpy.typing import ( + _ArrayND, + _FloatLike_co, _ArrayLikeFloat_co, _ArrayLikeObject_co, - _ArrayOrScalar, ) _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) @@ -12,15 +13,20 @@ _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: List[str] @overload +def fix( # type: ignore[misc] + x: _FloatLike_co, + out: None = ..., +) -> floating[Any]: ... +@overload def fix( x: _ArrayLikeFloat_co, out: None = ..., -) -> _ArrayOrScalar[floating[Any]]: ... +) -> _ArrayND[floating[Any]]: ... @overload def fix( x: _ArrayLikeObject_co, out: None = ..., -) -> Any: ... +) -> _ArrayND[object_]: ... @overload def fix( x: Union[_ArrayLikeFloat_co, _ArrayLikeObject_co], @@ -28,10 +34,15 @@ def fix( ) -> _ArrayType: ... @overload +def isposinf( # type: ignore[misc] + x: _FloatLike_co, + out: None = ..., +) -> bool_: ... +@overload def isposinf( x: _ArrayLikeFloat_co, out: None = ..., -) -> _ArrayOrScalar[bool_]: ... +) -> _ArrayND[bool_]: ... @overload def isposinf( x: _ArrayLikeFloat_co, @@ -39,10 +50,15 @@ def isposinf( ) -> _ArrayType: ... @overload +def isneginf( # type: ignore[misc] + x: _FloatLike_co, + out: None = ..., +) -> bool_: ... +@overload def isneginf( x: _ArrayLikeFloat_co, out: None = ..., -) -> _ArrayOrScalar[bool_]: ... +) -> _ArrayND[bool_]: ... @overload def isneginf( x: _ArrayLikeFloat_co, diff --git a/numpy/lib/utils.pyi b/numpy/lib/utils.pyi index 5a1594149..0518655c6 100644 --- a/numpy/lib/utils.pyi +++ b/numpy/lib/utils.pyi @@ -1,4 +1,19 @@ -from typing import List +import sys +from ast import AST +from typing import ( + Any, + Callable, + List, + Mapping, + Optional, + overload, + Sequence, + Tuple, + TypeVar, + Union, +) + +from numpy import ndarray, generic from numpy.core.numerictypes import ( issubclass_ as issubclass_, @@ -6,14 +21,79 @@ from numpy.core.numerictypes import ( issubsctype as issubsctype, ) +if sys.version_info >= (3, 8): + from typing import Protocol +else: + from typing_extensions import Protocol + +_T_contra = TypeVar("_T_contra", contravariant=True) +_FuncType = TypeVar("_FuncType", bound=Callable[..., Any]) + +# A file-like object opened in `w` mode +class _SupportsWrite(Protocol[_T_contra]): + def write(self, __s: _T_contra) -> Any: ... + __all__: List[str] -def get_include(): ... -def deprecate(*args, **kwargs): ... -def deprecate_with_doc(msg): ... -def byte_bounds(a): ... -def who(vardict=...): ... -def info(object=..., maxwidth=..., output=..., toplevel=...): ... -def source(object, output=...): ... -def lookfor(what, module=..., import_modules=..., regenerate=..., output=...): ... -def safe_eval(source): ... +class _Deprecate: + old_name: Optional[str] + new_name: Optional[str] + message: Optional[str] + def __init__( + self, + old_name: Optional[str] = ..., + new_name: Optional[str] = ..., + message: Optional[str] = ..., + ) -> None: ... + # NOTE: `__call__` can in principle take arbitrary `*args` and `**kwargs`, + # even though they aren't used for anything + def __call__(self, func: _FuncType) -> _FuncType: ... + +def get_include() -> str: ... + +@overload +def deprecate( + *, + old_name: Optional[str] = ..., + new_name: Optional[str] = ..., + message: Optional[str] = ..., +) -> _Deprecate: ... +@overload +def deprecate( + __func: _FuncType, + old_name: Optional[str] = ..., + new_name: Optional[str] = ..., + message: Optional[str] = ..., +) -> _FuncType: ... + +def deprecate_with_doc(msg: Optional[str]) -> _Deprecate: ... + +# NOTE: In practice `byte_bounds` can (potentially) take any object +# implementing the `__array_interface__` protocol. The caveat is +# that certain keys, marked as optional in the spec, must be present for +# `byte_bounds`. This concerns `"strides"` and `"data"`. +def byte_bounds(a: Union[generic, ndarray[Any, Any]]) -> Tuple[int, int]: ... + +def who(vardict: Optional[Mapping[str, ndarray[Any, Any]]] = ...) -> None: ... + +def info( + object: object = ..., + maxwidth: int = ..., + output: Optional[_SupportsWrite[str]] = ..., + toplevel: str = ..., +) -> None: ... + +def source( + object: object, + output: Optional[_SupportsWrite[str]] = ..., +) -> None: ... + +def lookfor( + what: str, + module: Union[None, str, Sequence[str]] = ..., + import_modules: bool = ..., + regenerate: bool = ..., + output: Optional[_SupportsWrite[str]] =..., +) -> None: ... + +def safe_eval(source: Union[str, AST]) -> Any: ... diff --git a/numpy/linalg/setup.py b/numpy/linalg/setup.py index 5c9f2a4cb..e2944f38c 100644 --- a/numpy/linalg/setup.py +++ b/numpy/linalg/setup.py @@ -9,11 +9,6 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('tests') - # Accelerate is buggy, disallow it. See also numpy/core/setup.py - for opt_order in (blas_opt_info.blas_order, lapack_opt_info.lapack_order): - if 'accelerate' in opt_order: - opt_order.remove('accelerate') - # Configure lapack_lite src_dir = 'lapack_lite' diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 8a270f194..c6e8cdd03 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -684,7 +684,7 @@ class SVDHermitianCases(HermitianTestCase, HermitianGeneralizedTestCase): axes = list(range(mat.ndim)) axes[-1], axes[-2] = axes[-2], axes[-1] return np.conj(np.transpose(mat, axes=axes)) - + assert_almost_equal(np.matmul(u, hermitian(u)), np.broadcast_to(np.eye(u.shape[-1]), u.shape)) assert_almost_equal(np.matmul(vt, hermitian(vt)), np.broadcast_to(np.eye(vt.shape[-1]), vt.shape)) assert_equal(np.sort(s)[..., ::-1], s) @@ -766,6 +766,9 @@ class TestCond(CondCases): for A, p in itertools.product(As, p_neg): linalg.cond(A, p) + @pytest.mark.xfail(True, run=False, + reason="Platform/LAPACK-dependent failure, " + "see gh-18914") def test_nan(self): # nans should be passed through, not converted to infs ps = [None, 1, -1, 2, -2, 'fro'] @@ -981,7 +984,7 @@ class TestLstsq(LstsqCases): linalg.lstsq(A, y, rcond=None) -@pytest.mark.parametrize('dt', [np.dtype(c) for c in '?bBhHiIqQefdgFDGO']) +@pytest.mark.parametrize('dt', [np.dtype(c) for c in '?bBhHiIqQefdgFDGO']) class TestMatrixPower: rshft_0 = np.eye(4) @@ -1010,7 +1013,7 @@ class TestMatrixPower: mz = matrix_power(M, 0) assert_equal(mz, identity_like_generalized(M)) assert_equal(mz.dtype, M.dtype) - + for mat in self.rshft_all: tz(mat.astype(dt)) if dt != object: diff --git a/numpy/ma/__init__.pyi b/numpy/ma/__init__.pyi index 16e026272..a9a833e52 100644 --- a/numpy/ma/__init__.pyi +++ b/numpy/ma/__init__.pyi @@ -2,228 +2,231 @@ from typing import Any, List from numpy.ma import extras as extras -__all__: List[str] +from numpy.ma.core import ( + MAError as MAError, + MaskError as MaskError, + MaskType as MaskType, + MaskedArray as MaskedArray, + abs as abs, + absolute as absolute, + add as add, + all as all, + allclose as allclose, + allequal as allequal, + alltrue as alltrue, + amax as amax, + amin as amin, + angle as angle, + anom as anom, + anomalies as anomalies, + any as any, + append as append, + arange as arange, + arccos as arccos, + arccosh as arccosh, + arcsin as arcsin, + arcsinh as arcsinh, + arctan as arctan, + arctan2 as arctan2, + arctanh as arctanh, + argmax as argmax, + argmin as argmin, + argsort as argsort, + around as around, + array as array, + asanyarray as asanyarray, + asarray as asarray, + bitwise_and as bitwise_and, + bitwise_or as bitwise_or, + bitwise_xor as bitwise_xor, + bool_ as bool_, + ceil as ceil, + choose as choose, + clip as clip, + common_fill_value as common_fill_value, + compress as compress, + compressed as compressed, + concatenate as concatenate, + conjugate as conjugate, + convolve as convolve, + copy as copy, + correlate as correlate, + cos as cos, + cosh as cosh, + count as count, + cumprod as cumprod, + cumsum as cumsum, + default_fill_value as default_fill_value, + diag as diag, + diagonal as diagonal, + diff as diff, + divide as divide, + empty as empty, + empty_like as empty_like, + equal as equal, + exp as exp, + expand_dims as expand_dims, + fabs as fabs, + filled as filled, + fix_invalid as fix_invalid, + flatten_mask as flatten_mask, + flatten_structured_array as flatten_structured_array, + floor as floor, + floor_divide as floor_divide, + fmod as fmod, + frombuffer as frombuffer, + fromflex as fromflex, + fromfunction as fromfunction, + getdata as getdata, + getmask as getmask, + getmaskarray as getmaskarray, + greater as greater, + greater_equal as greater_equal, + harden_mask as harden_mask, + hypot as hypot, + identity as identity, + ids as ids, + indices as indices, + inner as inner, + innerproduct as innerproduct, + isMA as isMA, + isMaskedArray as isMaskedArray, + is_mask as is_mask, + is_masked as is_masked, + isarray as isarray, + left_shift as left_shift, + less as less, + less_equal as less_equal, + log as log, + log10 as log10, + log2 as log2, + logical_and as logical_and, + logical_not as logical_not, + logical_or as logical_or, + logical_xor as logical_xor, + make_mask as make_mask, + make_mask_descr as make_mask_descr, + make_mask_none as make_mask_none, + mask_or as mask_or, + masked as masked, + masked_array as masked_array, + masked_equal as masked_equal, + masked_greater as masked_greater, + masked_greater_equal as masked_greater_equal, + masked_inside as masked_inside, + masked_invalid as masked_invalid, + masked_less as masked_less, + masked_less_equal as masked_less_equal, + masked_not_equal as masked_not_equal, + masked_object as masked_object, + masked_outside as masked_outside, + masked_print_option as masked_print_option, + masked_singleton as masked_singleton, + masked_values as masked_values, + masked_where as masked_where, + max as max, + maximum as maximum, + maximum_fill_value as maximum_fill_value, + mean as mean, + min as min, + minimum as minimum, + minimum_fill_value as minimum_fill_value, + mod as mod, + multiply as multiply, + mvoid as mvoid, + ndim as ndim, + negative as negative, + nomask as nomask, + nonzero as nonzero, + not_equal as not_equal, + ones as ones, + outer as outer, + outerproduct as outerproduct, + power as power, + prod as prod, + product as product, + ptp as ptp, + put as put, + putmask as putmask, + ravel as ravel, + remainder as remainder, + repeat as repeat, + reshape as reshape, + resize as resize, + right_shift as right_shift, + round as round, + round_ as round_, + set_fill_value as set_fill_value, + shape as shape, + sin as sin, + sinh as sinh, + size as size, + soften_mask as soften_mask, + sometrue as sometrue, + sort as sort, + sqrt as sqrt, + squeeze as squeeze, + std as std, + subtract as subtract, + sum as sum, + swapaxes as swapaxes, + take as take, + tan as tan, + tanh as tanh, + trace as trace, + transpose as transpose, + true_divide as true_divide, + var as var, + where as where, + zeros as zeros, +) + +from numpy.ma.extras import ( + apply_along_axis as apply_along_axis, + apply_over_axes as apply_over_axes, + atleast_1d as atleast_1d, + atleast_2d as atleast_2d, + atleast_3d as atleast_3d, + average as average, + clump_masked as clump_masked, + clump_unmasked as clump_unmasked, + column_stack as column_stack, + compress_cols as compress_cols, + compress_nd as compress_nd, + compress_rowcols as compress_rowcols, + compress_rows as compress_rows, + count_masked as count_masked, + corrcoef as corrcoef, + cov as cov, + diagflat as diagflat, + dot as dot, + dstack as dstack, + ediff1d as ediff1d, + flatnotmasked_contiguous as flatnotmasked_contiguous, + flatnotmasked_edges as flatnotmasked_edges, + hsplit as hsplit, + hstack as hstack, + isin as isin, + in1d as in1d, + intersect1d as intersect1d, + mask_cols as mask_cols, + mask_rowcols as mask_rowcols, + mask_rows as mask_rows, + masked_all as masked_all, + masked_all_like as masked_all_like, + median as median, + mr_ as mr_, + notmasked_contiguous as notmasked_contiguous, + notmasked_edges as notmasked_edges, + polyfit as polyfit, + row_stack as row_stack, + setdiff1d as setdiff1d, + setxor1d as setxor1d, + stack as stack, + unique as unique, + union1d as union1d, + vander as vander, + vstack as vstack, +) -core: Any -extras: Any -MAError: Any -MaskError: Any -MaskType: Any -MaskedArray: Any -abs: Any -absolute: Any -add: Any -all: Any -allclose: Any -allequal: Any -alltrue: Any -amax: Any -amin: Any -angle: Any -anom: Any -anomalies: Any -any: Any -append: Any -arange: Any -arccos: Any -arccosh: Any -arcsin: Any -arcsinh: Any -arctan: Any -arctan2: Any -arctanh: Any -argmax: Any -argmin: Any -argsort: Any -around: Any -array: Any -asanyarray: Any -asarray: Any -bitwise_and: Any -bitwise_or: Any -bitwise_xor: Any -bool_: Any -ceil: Any -choose: Any -clip: Any -common_fill_value: Any -compress: Any -compressed: Any -concatenate: Any -conjugate: Any -convolve: Any -copy: Any -correlate: Any -cos: Any -cosh: Any -count: Any -cumprod: Any -cumsum: Any -default_fill_value: Any -diag: Any -diagonal: Any -diff: Any -divide: Any -empty: Any -empty_like: Any -equal: Any -exp: Any -expand_dims: Any -fabs: Any -filled: Any -fix_invalid: Any -flatten_mask: Any -flatten_structured_array: Any -floor: Any -floor_divide: Any -fmod: Any -frombuffer: Any -fromflex: Any -fromfunction: Any -getdata: Any -getmask: Any -getmaskarray: Any -greater: Any -greater_equal: Any -harden_mask: Any -hypot: Any -identity: Any -ids: Any -indices: Any -inner: Any -innerproduct: Any -isMA: Any -isMaskedArray: Any -is_mask: Any -is_masked: Any -isarray: Any -left_shift: Any -less: Any -less_equal: Any -log: Any -log10: Any -log2: Any -logical_and: Any -logical_not: Any -logical_or: Any -logical_xor: Any -make_mask: Any -make_mask_descr: Any -make_mask_none: Any -mask_or: Any -masked: Any -masked_array: Any -masked_equal: Any -masked_greater: Any -masked_greater_equal: Any -masked_inside: Any -masked_invalid: Any -masked_less: Any -masked_less_equal: Any -masked_not_equal: Any -masked_object: Any -masked_outside: Any -masked_print_option: Any -masked_singleton: Any -masked_values: Any -masked_where: Any -max: Any -maximum: Any -maximum_fill_value: Any -mean: Any -min: Any -minimum: Any -minimum_fill_value: Any -mod: Any -multiply: Any -mvoid: Any -ndim: Any -negative: Any -nomask: Any -nonzero: Any -not_equal: Any -ones: Any -outer: Any -outerproduct: Any -power: Any -prod: Any -product: Any -ptp: Any -put: Any -putmask: Any -ravel: Any -remainder: Any -repeat: Any -reshape: Any -resize: Any -right_shift: Any -round: Any -round_: Any -set_fill_value: Any -shape: Any -sin: Any -sinh: Any -size: Any -soften_mask: Any -sometrue: Any -sort: Any -sqrt: Any -squeeze: Any -std: Any -subtract: Any -sum: Any -swapaxes: Any -take: Any -tan: Any -tanh: Any -trace: Any -transpose: Any -true_divide: Any -var: Any -where: Any -zeros: Any -apply_along_axis: Any -apply_over_axes: Any -atleast_1d: Any -atleast_2d: Any -atleast_3d: Any -average: Any -clump_masked: Any -clump_unmasked: Any -column_stack: Any -compress_cols: Any -compress_nd: Any -compress_rowcols: Any -compress_rows: Any -count_masked: Any -corrcoef: Any -cov: Any -diagflat: Any -dot: Any -dstack: Any -ediff1d: Any -flatnotmasked_contiguous: Any -flatnotmasked_edges: Any -hsplit: Any -hstack: Any -isin: Any -in1d: Any -intersect1d: Any -mask_cols: Any -mask_rowcols: Any -mask_rows: Any -masked_all: Any -masked_all_like: Any -median: Any -mr_: Any -notmasked_contiguous: Any -notmasked_edges: Any -polyfit: Any -row_stack: Any -setdiff1d: Any -setxor1d: Any -stack: Any -unique: Any -union1d: Any -vander: Any -vstack: Any +__all__: List[str] diff --git a/numpy/ma/core.pyi b/numpy/ma/core.pyi new file mode 100644 index 000000000..e7e3f1f36 --- /dev/null +++ b/numpy/ma/core.pyi @@ -0,0 +1,468 @@ +from typing import Any, List, TypeVar, Callable +from numpy import ndarray, dtype, float64 + +from numpy import ( + amax as amax, + amin as amin, + bool_ as bool_, + expand_dims as expand_dims, + diff as diff, + clip as clip, + indices as indices, + ones_like as ones_like, + squeeze as squeeze, + zeros_like as zeros_like, +) + +from numpy.lib.function_base import ( + angle as angle, +) + +# TODO: Set the `bound` to something more suitable once we +# have proper shape support +_ShapeType = TypeVar("_ShapeType", bound=Any) +_DType_co = TypeVar("_DType_co", bound=dtype[Any], covariant=True) + +__all__: List[str] + +MaskType = bool_ +nomask: bool_ + +class MaskedArrayFutureWarning(FutureWarning): ... +class MAError(Exception): ... +class MaskError(MAError): ... + +def default_fill_value(obj): ... +def minimum_fill_value(obj): ... +def maximum_fill_value(obj): ... +def set_fill_value(a, fill_value): ... +def common_fill_value(a, b): ... +def filled(a, fill_value=...): ... +def getdata(a, subok=...): ... +get_data = getdata + +def fix_invalid(a, mask=..., copy=..., fill_value=...): ... + +class _MaskedUFunc: + f: Any + __doc__: Any + __name__: Any + def __init__(self, ufunc): ... + +class _MaskedUnaryOperation(_MaskedUFunc): + fill: Any + domain: Any + def __init__(self, mufunc, fill=..., domain=...): ... + def __call__(self, a, *args, **kwargs): ... + +class _MaskedBinaryOperation(_MaskedUFunc): + fillx: Any + filly: Any + def __init__(self, mbfunc, fillx=..., filly=...): ... + def __call__(self, a, b, *args, **kwargs): ... + def reduce(self, target, axis=..., dtype=...): ... + def outer(self, a, b): ... + def accumulate(self, target, axis=...): ... + +class _DomainedBinaryOperation(_MaskedUFunc): + domain: Any + fillx: Any + filly: Any + def __init__(self, dbfunc, domain, fillx=..., filly=...): ... + def __call__(self, a, b, *args, **kwargs): ... + +exp: _MaskedUnaryOperation +conjugate: _MaskedUnaryOperation +sin: _MaskedUnaryOperation +cos: _MaskedUnaryOperation +arctan: _MaskedUnaryOperation +arcsinh: _MaskedUnaryOperation +sinh: _MaskedUnaryOperation +cosh: _MaskedUnaryOperation +tanh: _MaskedUnaryOperation +abs: _MaskedUnaryOperation +absolute: _MaskedUnaryOperation +fabs: _MaskedUnaryOperation +negative: _MaskedUnaryOperation +floor: _MaskedUnaryOperation +ceil: _MaskedUnaryOperation +around: _MaskedUnaryOperation +logical_not: _MaskedUnaryOperation +sqrt: _MaskedUnaryOperation +log: _MaskedUnaryOperation +log2: _MaskedUnaryOperation +log10: _MaskedUnaryOperation +tan: _MaskedUnaryOperation +arcsin: _MaskedUnaryOperation +arccos: _MaskedUnaryOperation +arccosh: _MaskedUnaryOperation +arctanh: _MaskedUnaryOperation + +add: _MaskedBinaryOperation +subtract: _MaskedBinaryOperation +multiply: _MaskedBinaryOperation +arctan2: _MaskedBinaryOperation +equal: _MaskedBinaryOperation +not_equal: _MaskedBinaryOperation +less_equal: _MaskedBinaryOperation +greater_equal: _MaskedBinaryOperation +less: _MaskedBinaryOperation +greater: _MaskedBinaryOperation +logical_and: _MaskedBinaryOperation +alltrue: _MaskedBinaryOperation +logical_or: _MaskedBinaryOperation +sometrue: Callable[..., Any] +logical_xor: _MaskedBinaryOperation +bitwise_and: _MaskedBinaryOperation +bitwise_or: _MaskedBinaryOperation +bitwise_xor: _MaskedBinaryOperation +hypot: _MaskedBinaryOperation +divide: _MaskedBinaryOperation +true_divide: _MaskedBinaryOperation +floor_divide: _MaskedBinaryOperation +remainder: _MaskedBinaryOperation +fmod: _MaskedBinaryOperation +mod: _MaskedBinaryOperation + +def make_mask_descr(ndtype): ... +def getmask(a): ... +get_mask = getmask + +def getmaskarray(arr): ... +def is_mask(m): ... +def make_mask(m, copy=..., shrink=..., dtype=...): ... +def make_mask_none(newshape, dtype=...): ... +def mask_or(m1, m2, copy=..., shrink=...): ... +def flatten_mask(mask): ... +def masked_where(condition, a, copy=...): ... +def masked_greater(x, value, copy=...): ... +def masked_greater_equal(x, value, copy=...): ... +def masked_less(x, value, copy=...): ... +def masked_less_equal(x, value, copy=...): ... +def masked_not_equal(x, value, copy=...): ... +def masked_equal(x, value, copy=...): ... +def masked_inside(x, v1, v2, copy=...): ... +def masked_outside(x, v1, v2, copy=...): ... +def masked_object(x, value, copy=..., shrink=...): ... +def masked_values(x, value, rtol=..., atol=..., copy=..., shrink=...): ... +def masked_invalid(a, copy=...): ... + +class _MaskedPrintOption: + def __init__(self, display): ... + def display(self): ... + def set_display(self, s): ... + def enabled(self): ... + def enable(self, shrink=...): ... + +masked_print_option: _MaskedPrintOption + +def flatten_structured_array(a): ... + +class MaskedIterator: + ma: Any + dataiter: Any + maskiter: Any + def __init__(self, ma): ... + def __iter__(self): ... + def __getitem__(self, indx): ... + def __setitem__(self, index, value): ... + def __next__(self): ... + +class MaskedArray(ndarray[_ShapeType, _DType_co]): + __array_priority__: Any + def __new__(cls, data=..., mask=..., dtype=..., copy=..., subok=..., ndmin=..., fill_value=..., keep_mask=..., hard_mask=..., shrink=..., order=...): ... + def __array_finalize__(self, obj): ... + def __array_wrap__(self, obj, context=...): ... + def view(self, dtype=..., type=..., fill_value=...): ... + def __getitem__(self, indx): ... + def __setitem__(self, indx, value): ... + @property + def dtype(self): ... + @dtype.setter + def dtype(self, dtype): ... + @property + def shape(self): ... + @shape.setter + def shape(self, shape): ... + def __setmask__(self, mask, copy=...): ... + @property + def mask(self): ... + @mask.setter + def mask(self, value): ... + @property + def recordmask(self): ... + @recordmask.setter + def recordmask(self, mask): ... + def harden_mask(self): ... + def soften_mask(self): ... + @property + def hardmask(self): ... + def unshare_mask(self): ... + @property + def sharedmask(self): ... + def shrink_mask(self): ... + @property + def baseclass(self): ... + data: Any + @property + def flat(self): ... + @flat.setter + def flat(self, value): ... + @property + def fill_value(self): ... + @fill_value.setter + def fill_value(self, value=...): ... + get_fill_value: Any + set_fill_value: Any + def filled(self, fill_value=...): ... + def compressed(self): ... + def compress(self, condition, axis=..., out=...): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def __add__(self, other): ... + def __radd__(self, other): ... + def __sub__(self, other): ... + def __rsub__(self, other): ... + def __mul__(self, other): ... + def __rmul__(self, other): ... + def __div__(self, other): ... + def __truediv__(self, other): ... + def __rtruediv__(self, other): ... + def __floordiv__(self, other): ... + def __rfloordiv__(self, other): ... + def __pow__(self, other): ... + def __rpow__(self, other): ... + def __iadd__(self, other): ... + def __isub__(self, other): ... + def __imul__(self, other): ... + def __idiv__(self, other): ... + def __ifloordiv__(self, other): ... + def __itruediv__(self, other): ... + def __ipow__(self, other): ... + def __float__(self): ... + def __int__(self): ... + @property # type: ignore[misc] + def imag(self): ... + get_imag: Any + @property # type: ignore[misc] + def real(self): ... + get_real: Any + def count(self, axis=..., keepdims=...): ... + def ravel(self, order=...): ... + def reshape(self, *s, **kwargs): ... + def resize(self, newshape, refcheck=..., order=...): ... + def put(self, indices, values, mode=...): ... + def ids(self): ... + def iscontiguous(self): ... + def all(self, axis=..., out=..., keepdims=...): ... + def any(self, axis=..., out=..., keepdims=...): ... + def nonzero(self): ... + def trace(self, offset=..., axis1=..., axis2=..., dtype=..., out=...): ... + def dot(self, b, out=..., strict=...): ... + def sum(self, axis=..., dtype=..., out=..., keepdims=...): ... + def cumsum(self, axis=..., dtype=..., out=...): ... + def prod(self, axis=..., dtype=..., out=..., keepdims=...): ... + product: Any + def cumprod(self, axis=..., dtype=..., out=...): ... + def mean(self, axis=..., dtype=..., out=..., keepdims=...): ... + def anom(self, axis=..., dtype=...): ... + def var(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... + def std(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... + def round(self, decimals=..., out=...): ... + def argsort(self, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... + def argmin(self, axis=..., fill_value=..., out=...): ... + def argmax(self, axis=..., fill_value=..., out=...): ... + def sort(self, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... + def min(self, axis=..., out=..., fill_value=..., keepdims=...): ... + # NOTE: deprecated + # def mini(self, axis=...): ... + # def tostring(self, fill_value=..., order=...): ... + def max(self, axis=..., out=..., fill_value=..., keepdims=...): ... + def ptp(self, axis=..., out=..., fill_value=..., keepdims=...): ... + def partition(self, *args, **kwargs): ... + def argpartition(self, *args, **kwargs): ... + def take(self, indices, axis=..., out=..., mode=...): ... + copy: Any + diagonal: Any + flatten: Any + repeat: Any + squeeze: Any + swapaxes: Any + T: Any + transpose: Any + def tolist(self, fill_value=...): ... + def tobytes(self, fill_value=..., order=...): ... + def tofile(self, fid, sep=..., format=...): ... + def toflex(self): ... + torecords: Any + def __reduce__(self): ... + def __deepcopy__(self, memo=...): ... + +class mvoid(MaskedArray[_ShapeType, _DType_co]): + def __new__( + self, + data, + mask=..., + dtype=..., + fill_value=..., + hardmask=..., + copy=..., + subok=..., + ): ... + def __getitem__(self, indx): ... + def __setitem__(self, indx, value): ... + def __iter__(self): ... + def __len__(self): ... + def filled(self, fill_value=...): ... + def tolist(self): ... + +def isMaskedArray(x): ... +isarray = isMaskedArray +isMA = isMaskedArray + +# 0D float64 array +class MaskedConstant(MaskedArray[Any, dtype[float64]]): + def __new__(cls): ... + __class__: Any + def __array_finalize__(self, obj): ... + def __array_prepare__(self, obj, context=...): ... + def __array_wrap__(self, obj, context=...): ... + def __format__(self, format_spec): ... + def __reduce__(self): ... + def __iop__(self, other): ... + __iadd__: Any + __isub__: Any + __imul__: Any + __ifloordiv__: Any + __itruediv__: Any + __ipow__: Any + def copy(self, *args, **kwargs): ... + def __copy__(self): ... + def __deepcopy__(self, memo): ... + def __setattr__(self, attr, value): ... + +masked: MaskedConstant +masked_singleton: MaskedConstant +masked_array = MaskedArray + +def array( + data, + dtype=..., + copy=..., + order=..., + mask=..., + fill_value=..., + keep_mask=..., + hard_mask=..., + shrink=..., + subok=..., + ndmin=..., +): ... +def is_masked(x): ... + +class _extrema_operation(_MaskedUFunc): + compare: Any + fill_value_func: Any + def __init__(self, ufunc, compare, fill_value): ... + # NOTE: in practice `b` has a default value, but users should + # explicitly provide a value here as the default is deprecated + def __call__(self, a, b): ... + def reduce(self, target, axis=...): ... + def outer(self, a, b): ... + +def min(obj, axis=..., out=..., fill_value=..., keepdims=...): ... +def max(obj, axis=..., out=..., fill_value=..., keepdims=...): ... +def ptp(obj, axis=..., out=..., fill_value=..., keepdims=...): ... + +class _frommethod: + __name__: Any + __doc__: Any + reversed: Any + def __init__(self, methodname, reversed=...): ... + def getdoc(self): ... + def __call__(self, a, *args, **params): ... + +all: _frommethod +anomalies: _frommethod +anom: _frommethod +any: _frommethod +compress: _frommethod +cumprod: _frommethod +cumsum: _frommethod +copy: _frommethod +diagonal: _frommethod +harden_mask: _frommethod +ids: _frommethod +mean: _frommethod +nonzero: _frommethod +prod: _frommethod +product: _frommethod +ravel: _frommethod +repeat: _frommethod +soften_mask: _frommethod +std: _frommethod +sum: _frommethod +swapaxes: _frommethod +trace: _frommethod +var: _frommethod +count: _frommethod +argmin: _frommethod +argmax: _frommethod + +minimum: _extrema_operation +maximum: _extrema_operation + +def take(a, indices, axis=..., out=..., mode=...): ... +def power(a, b, third=...): ... +def argsort(a, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... +def sort(a, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... +def compressed(x): ... +def concatenate(arrays, axis=...): ... +def diag(v, k=...): ... +def left_shift(a, n): ... +def right_shift(a, n): ... +def put(a, indices, values, mode=...): ... +def putmask(a, mask, values): ... +def transpose(a, axes=...): ... +def reshape(a, new_shape, order=...): ... +def resize(x, new_shape): ... +def ndim(obj): ... +def shape(obj): ... +def size(obj, axis=...): ... +def where(condition, x=..., y=...): ... +def choose(indices, choices, out=..., mode=...): ... +def round_(a, decimals=..., out=...): ... +round = round_ + +def inner(a, b): ... +innerproduct = inner + +def outer(a, b): ... +outerproduct = outer + +def correlate(a, v, mode=..., propagate_mask=...): ... +def convolve(a, v, mode=..., propagate_mask=...): ... +def allequal(a, b, fill_value=...): ... +def allclose(a, b, masked_equal=..., rtol=..., atol=...): ... +def asarray(a, dtype=..., order=...): ... +def asanyarray(a, dtype=...): ... +def fromflex(fxarray): ... + +class _convert2ma: + __doc__: Any + def __init__(self, funcname, params=...): ... + def getdoc(self): ... + def __call__(self, *args, **params): ... + +arange: _convert2ma +empty: _convert2ma +empty_like: _convert2ma +frombuffer: _convert2ma +fromfunction: _convert2ma +identity: _convert2ma +ones: _convert2ma +zeros: _convert2ma + +def append(a, b, axis=...): ... +def dot(a, b, strict=..., out=...): ... +def mask_rowcols(a, axis=...): ... diff --git a/numpy/ma/extras.pyi b/numpy/ma/extras.pyi new file mode 100644 index 000000000..e58e43bad --- /dev/null +++ b/numpy/ma/extras.pyi @@ -0,0 +1,84 @@ +from typing import Any, List +from numpy.lib.index_tricks import AxisConcatenator + +from numpy.ma.core import ( + dot as dot, + mask_rowcols as mask_rowcols, +) + +__all__: List[str] + +def count_masked(arr, axis=...): ... +def masked_all(shape, dtype = ...): ... +def masked_all_like(arr): ... + +class _fromnxfunction: + __name__: Any + __doc__: Any + def __init__(self, funcname): ... + def getdoc(self): ... + def __call__(self, *args, **params): ... + +class _fromnxfunction_single(_fromnxfunction): + def __call__(self, x, *args, **params): ... + +class _fromnxfunction_seq(_fromnxfunction): + def __call__(self, x, *args, **params): ... + +class _fromnxfunction_allargs(_fromnxfunction): + def __call__(self, *args, **params): ... + +atleast_1d: _fromnxfunction_allargs +atleast_2d: _fromnxfunction_allargs +atleast_3d: _fromnxfunction_allargs + +vstack: _fromnxfunction_seq +row_stack: _fromnxfunction_seq +hstack: _fromnxfunction_seq +column_stack: _fromnxfunction_seq +dstack: _fromnxfunction_seq +stack: _fromnxfunction_seq + +hsplit: _fromnxfunction_single +diagflat: _fromnxfunction_single + +def apply_along_axis(func1d, axis, arr, *args, **kwargs): ... +def apply_over_axes(func, a, axes): ... +def average(a, axis=..., weights=..., returned=...): ... +def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ... +def compress_nd(x, axis=...): ... +def compress_rowcols(x, axis=...): ... +def compress_rows(a): ... +def compress_cols(a): ... +def mask_rows(a, axis = ...): ... +def mask_cols(a, axis = ...): ... +def ediff1d(arr, to_end=..., to_begin=...): ... +def unique(ar1, return_index=..., return_inverse=...): ... +def intersect1d(ar1, ar2, assume_unique=...): ... +def setxor1d(ar1, ar2, assume_unique=...): ... +def in1d(ar1, ar2, assume_unique=..., invert=...): ... +def isin(element, test_elements, assume_unique=..., invert=...): ... +def union1d(ar1, ar2): ... +def setdiff1d(ar1, ar2, assume_unique=...): ... +def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ... +def corrcoef(x, y=..., rowvar=..., bias = ..., allow_masked=..., ddof = ...): ... + +class MAxisConcatenator(AxisConcatenator): + concatenate: Any + @classmethod + def makemat(cls, arr): ... + def __getitem__(self, key): ... + +class mr_class(MAxisConcatenator): + def __init__(self): ... + +mr_: mr_class + +def flatnotmasked_edges(a): ... +def notmasked_edges(a, axis=...): ... +def flatnotmasked_contiguous(a): ... +def notmasked_contiguous(a, axis=...): ... +def clump_unmasked(a): ... +def clump_masked(a): ... +def vander(x, n=...): ... +def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ... diff --git a/numpy/ma/mrecords.pyi b/numpy/ma/mrecords.pyi new file mode 100644 index 000000000..92d5afb89 --- /dev/null +++ b/numpy/ma/mrecords.pyi @@ -0,0 +1,88 @@ +from typing import List, Any, TypeVar + +from numpy import dtype +from numpy.ma import MaskedArray + +__all__: List[str] + +# TODO: Set the `bound` to something more suitable once we +# have proper shape support +_ShapeType = TypeVar("_ShapeType", bound=Any) +_DType_co = TypeVar("_DType_co", bound=dtype[Any], covariant=True) + +class MaskedRecords(MaskedArray[_ShapeType, _DType_co]): + def __new__( + cls, + shape, + dtype=..., + buf=..., + offset=..., + strides=..., + formats=..., + names=..., + titles=..., + byteorder=..., + aligned=..., + mask=..., + hard_mask=..., + fill_value=..., + keep_mask=..., + copy=..., + **options, + ): ... + _mask: Any + _fill_value: Any + @property + def _data(self): ... + @property + def _fieldmask(self): ... + def __array_finalize__(self, obj): ... + def __len__(self): ... + def __getattribute__(self, attr): ... + def __setattr__(self, attr, val): ... + def __getitem__(self, indx): ... + def __setitem__(self, indx, value): ... + def view(self, dtype=..., type=...): ... + def harden_mask(self): ... + def soften_mask(self): ... + def copy(self): ... + def tolist(self, fill_value=...): ... + def __reduce__(self): ... + +mrecarray = MaskedRecords + +def fromarrays( + arraylist, + dtype=..., + shape=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., + fill_value=..., +): ... + +def fromrecords( + reclist, + dtype=..., + shape=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., + fill_value=..., + mask=..., +): ... + +def fromtextfile( + fname, + delimitor=..., + commentchar=..., + missingchar=..., + varnames=..., + vartypes=..., +): ... + +def addfield(mrecord, newfield, newfieldname=...): ... diff --git a/numpy/polynomial/__init__.pyi b/numpy/polynomial/__init__.pyi index 6a7406041..bebedb3a6 100644 --- a/numpy/polynomial/__init__.pyi +++ b/numpy/polynomial/__init__.pyi @@ -1,4 +1,4 @@ -from typing import Any, List +from typing import List from numpy.polynomial import ( chebyshev as chebyshev, @@ -8,13 +8,13 @@ from numpy.polynomial import ( legendre as legendre, polynomial as polynomial, ) +from numpy.polynomial.chebyshev import Chebyshev as Chebyshev +from numpy.polynomial.hermite import Hermite as Hermite +from numpy.polynomial.hermite_e import HermiteE as HermiteE +from numpy.polynomial.laguerre import Laguerre as Laguerre +from numpy.polynomial.legendre import Legendre as Legendre +from numpy.polynomial.polynomial import Polynomial as Polynomial __all__: List[str] -Polynomial: Any -Chebyshev: Any -Legendre: Any -Hermite: Any -HermiteE: Any -Laguerre: Any -set_default_printstyle: Any +def set_default_printstyle(style): ... diff --git a/numpy/polynomial/_polybase.pyi b/numpy/polynomial/_polybase.pyi new file mode 100644 index 000000000..c41601469 --- /dev/null +++ b/numpy/polynomial/_polybase.pyi @@ -0,0 +1,69 @@ +import abc +from typing import Any, List, ClassVar + +__all__: List[str] + +class ABCPolyBase(abc.ABC): + __hash__: ClassVar[None] # type: ignore[assignment] + __array_ufunc__: ClassVar[None] + maxpower: ClassVar[int] + coef: Any + @property + @abc.abstractmethod + def domain(self): ... + @property + @abc.abstractmethod + def window(self): ... + @property + @abc.abstractmethod + def basis_name(self): ... + def has_samecoef(self, other): ... + def has_samedomain(self, other): ... + def has_samewindow(self, other): ... + def has_sametype(self, other): ... + def __init__(self, coef, domain=..., window=...): ... + def __format__(self, fmt_str): ... + def __call__(self, arg): ... + def __iter__(self): ... + def __len__(self): ... + def __neg__(self): ... + def __pos__(self): ... + def __add__(self, other): ... + def __sub__(self, other): ... + def __mul__(self, other): ... + def __truediv__(self, other): ... + def __floordiv__(self, other): ... + def __mod__(self, other): ... + def __divmod__(self, other): ... + def __pow__(self, other): ... + def __radd__(self, other): ... + def __rsub__(self, other): ... + def __rmul__(self, other): ... + def __rdiv__(self, other): ... + def __rtruediv__(self, other): ... + def __rfloordiv__(self, other): ... + def __rmod__(self, other): ... + def __rdivmod__(self, other): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def copy(self): ... + def degree(self): ... + def cutdeg(self, deg): ... + def trim(self, tol=...): ... + def truncate(self, size): ... + def convert(self, domain=..., kind=..., window=...): ... + def mapparms(self): ... + def integ(self, m=..., k = ..., lbnd=...): ... + def deriv(self, m=...): ... + def roots(self): ... + def linspace(self, n=..., domain=...): ... + @classmethod + def fit(cls, x, y, deg, domain=..., rcond=..., full=..., w=..., window=...): ... + @classmethod + def fromroots(cls, roots, domain = ..., window=...): ... + @classmethod + def identity(cls, domain=..., window=...): ... + @classmethod + def basis(cls, deg, domain=..., window=...): ... + @classmethod + def cast(cls, series, domain=..., window=...): ... diff --git a/numpy/polynomial/chebyshev.pyi b/numpy/polynomial/chebyshev.pyi new file mode 100644 index 000000000..841c0859b --- /dev/null +++ b/numpy/polynomial/chebyshev.pyi @@ -0,0 +1,51 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: List[str] + +chebtrim = trimcoef + +def poly2cheb(pol): ... +def cheb2poly(c): ... + +chebdomain: ndarray[Any, dtype[int_]] +chebzero: ndarray[Any, dtype[int_]] +chebone: ndarray[Any, dtype[int_]] +chebx: ndarray[Any, dtype[int_]] + +def chebline(off, scl): ... +def chebfromroots(roots): ... +def chebadd(c1, c2): ... +def chebsub(c1, c2): ... +def chebmulx(c): ... +def chebmul(c1, c2): ... +def chebdiv(c1, c2): ... +def chebpow(c, pow, maxpower=...): ... +def chebder(c, m=..., scl=..., axis=...): ... +def chebint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def chebval(x, c, tensor=...): ... +def chebval2d(x, y, c): ... +def chebgrid2d(x, y, c): ... +def chebval3d(x, y, z, c): ... +def chebgrid3d(x, y, z, c): ... +def chebvander(x, deg): ... +def chebvander2d(x, y, deg): ... +def chebvander3d(x, y, z, deg): ... +def chebfit(x, y, deg, rcond=..., full=..., w=...): ... +def chebcompanion(c): ... +def chebroots(c): ... +def chebinterpolate(func, deg, args = ...): ... +def chebgauss(deg): ... +def chebweight(x): ... +def chebpts1(npts): ... +def chebpts2(npts): ... + +class Chebyshev(ABCPolyBase): + @classmethod + def interpolate(cls, func, deg, domain=..., args = ...): ... + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/hermite.pyi b/numpy/polynomial/hermite.pyi new file mode 100644 index 000000000..8364a5b0f --- /dev/null +++ b/numpy/polynomial/hermite.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_, float_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +hermtrim = trimcoef + +def poly2herm(pol): ... +def herm2poly(c): ... + +hermdomain: ndarray[Any, dtype[int_]] +hermzero: ndarray[Any, dtype[int_]] +hermone: ndarray[Any, dtype[int_]] +hermx: ndarray[Any, dtype[float_]] + +def hermline(off, scl): ... +def hermfromroots(roots): ... +def hermadd(c1, c2): ... +def hermsub(c1, c2): ... +def hermmulx(c): ... +def hermmul(c1, c2): ... +def hermdiv(c1, c2): ... +def hermpow(c, pow, maxpower=...): ... +def hermder(c, m=..., scl=..., axis=...): ... +def hermint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def hermval(x, c, tensor=...): ... +def hermval2d(x, y, c): ... +def hermgrid2d(x, y, c): ... +def hermval3d(x, y, z, c): ... +def hermgrid3d(x, y, z, c): ... +def hermvander(x, deg): ... +def hermvander2d(x, y, deg): ... +def hermvander3d(x, y, z, deg): ... +def hermfit(x, y, deg, rcond=..., full=..., w=...): ... +def hermcompanion(c): ... +def hermroots(c): ... +def hermgauss(deg): ... +def hermweight(x): ... + +class Hermite(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/hermite_e.pyi b/numpy/polynomial/hermite_e.pyi new file mode 100644 index 000000000..c029bfda7 --- /dev/null +++ b/numpy/polynomial/hermite_e.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +hermetrim = trimcoef + +def poly2herme(pol): ... +def herme2poly(c): ... + +hermedomain: ndarray[Any, dtype[int_]] +hermezero: ndarray[Any, dtype[int_]] +hermeone: ndarray[Any, dtype[int_]] +hermex: ndarray[Any, dtype[int_]] + +def hermeline(off, scl): ... +def hermefromroots(roots): ... +def hermeadd(c1, c2): ... +def hermesub(c1, c2): ... +def hermemulx(c): ... +def hermemul(c1, c2): ... +def hermediv(c1, c2): ... +def hermepow(c, pow, maxpower=...): ... +def hermeder(c, m=..., scl=..., axis=...): ... +def hermeint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def hermeval(x, c, tensor=...): ... +def hermeval2d(x, y, c): ... +def hermegrid2d(x, y, c): ... +def hermeval3d(x, y, z, c): ... +def hermegrid3d(x, y, z, c): ... +def hermevander(x, deg): ... +def hermevander2d(x, y, deg): ... +def hermevander3d(x, y, z, deg): ... +def hermefit(x, y, deg, rcond=..., full=..., w=...): ... +def hermecompanion(c): ... +def hermeroots(c): ... +def hermegauss(deg): ... +def hermeweight(x): ... + +class HermiteE(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/laguerre.pyi b/numpy/polynomial/laguerre.pyi new file mode 100644 index 000000000..2b9ab34e0 --- /dev/null +++ b/numpy/polynomial/laguerre.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +lagtrim = trimcoef + +def poly2lag(pol): ... +def lag2poly(c): ... + +lagdomain: ndarray[Any, dtype[int_]] +lagzero: ndarray[Any, dtype[int_]] +lagone: ndarray[Any, dtype[int_]] +lagx: ndarray[Any, dtype[int_]] + +def lagline(off, scl): ... +def lagfromroots(roots): ... +def lagadd(c1, c2): ... +def lagsub(c1, c2): ... +def lagmulx(c): ... +def lagmul(c1, c2): ... +def lagdiv(c1, c2): ... +def lagpow(c, pow, maxpower=...): ... +def lagder(c, m=..., scl=..., axis=...): ... +def lagint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def lagval(x, c, tensor=...): ... +def lagval2d(x, y, c): ... +def laggrid2d(x, y, c): ... +def lagval3d(x, y, z, c): ... +def laggrid3d(x, y, z, c): ... +def lagvander(x, deg): ... +def lagvander2d(x, y, deg): ... +def lagvander3d(x, y, z, deg): ... +def lagfit(x, y, deg, rcond=..., full=..., w=...): ... +def lagcompanion(c): ... +def lagroots(c): ... +def laggauss(deg): ... +def lagweight(x): ... + +class Laguerre(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/legendre.pyi b/numpy/polynomial/legendre.pyi new file mode 100644 index 000000000..86aef1793 --- /dev/null +++ b/numpy/polynomial/legendre.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +legtrim = trimcoef + +def poly2leg(pol): ... +def leg2poly(c): ... + +legdomain: ndarray[Any, dtype[int_]] +legzero: ndarray[Any, dtype[int_]] +legone: ndarray[Any, dtype[int_]] +legx: ndarray[Any, dtype[int_]] + +def legline(off, scl): ... +def legfromroots(roots): ... +def legadd(c1, c2): ... +def legsub(c1, c2): ... +def legmulx(c): ... +def legmul(c1, c2): ... +def legdiv(c1, c2): ... +def legpow(c, pow, maxpower=...): ... +def legder(c, m=..., scl=..., axis=...): ... +def legint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def legval(x, c, tensor=...): ... +def legval2d(x, y, c): ... +def leggrid2d(x, y, c): ... +def legval3d(x, y, z, c): ... +def leggrid3d(x, y, z, c): ... +def legvander(x, deg): ... +def legvander2d(x, y, deg): ... +def legvander3d(x, y, z, deg): ... +def legfit(x, y, deg, rcond=..., full=..., w=...): ... +def legcompanion(c): ... +def legroots(c): ... +def leggauss(deg): ... +def legweight(x): ... + +class Legendre(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/polynomial.pyi b/numpy/polynomial/polynomial.pyi new file mode 100644 index 000000000..f779300a9 --- /dev/null +++ b/numpy/polynomial/polynomial.pyi @@ -0,0 +1,41 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +polytrim = trimcoef + +polydomain: ndarray[Any, dtype[int_]] +polyzero: ndarray[Any, dtype[int_]] +polyone: ndarray[Any, dtype[int_]] +polyx: ndarray[Any, dtype[int_]] + +def polyline(off, scl): ... +def polyfromroots(roots): ... +def polyadd(c1, c2): ... +def polysub(c1, c2): ... +def polymulx(c): ... +def polymul(c1, c2): ... +def polydiv(c1, c2): ... +def polypow(c, pow, maxpower=...): ... +def polyder(c, m=..., scl=..., axis=...): ... +def polyint(c, m=..., k=..., lbnd=..., scl=..., axis=...): ... +def polyval(x, c, tensor=...): ... +def polyvalfromroots(x, r, tensor=...): ... +def polyval2d(x, y, c): ... +def polygrid2d(x, y, c): ... +def polyval3d(x, y, z, c): ... +def polygrid3d(x, y, z, c): ... +def polyvander(x, deg): ... +def polyvander2d(x, y, deg): ... +def polyvander3d(x, y, z, deg): ... +def polyfit(x, y, deg, rcond=..., full=..., w=...): ... +def polyroots(c): ... + +class Polynomial(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/polyutils.pyi b/numpy/polynomial/polyutils.pyi new file mode 100644 index 000000000..0befa740e --- /dev/null +++ b/numpy/polynomial/polyutils.pyi @@ -0,0 +1,17 @@ +from typing import List + +__all__: List[str] + +class RankWarning(UserWarning): ... +class PolyError(Exception): ... +class PolyDomainError(PolyError): ... + +# NOTE: Deprecated +# class PolyBase: ... + +def trimseq(seq): ... +def as_series(alist, trim=...): ... +def trimcoef(c, tol=...): ... +def getdomain(x): ... +def mapparms(old, new): ... +def mapdomain(x, old, new): ... diff --git a/numpy/random/__init__.py b/numpy/random/__init__.py index 7efa5c07f..2e8f99fe3 100644 --- a/numpy/random/__init__.py +++ b/numpy/random/__init__.py @@ -17,6 +17,7 @@ BitGenerator Streams that work with Generator --------------------------------------------- --- MT19937 PCG64 +PCG64DXSM Philox SFC64 ============================================= === @@ -183,13 +184,14 @@ from . import _bounded_integers from ._generator import Generator, default_rng from .bit_generator import SeedSequence, BitGenerator from ._mt19937 import MT19937 -from ._pcg64 import PCG64 +from ._pcg64 import PCG64, PCG64DXSM from ._philox import Philox from ._sfc64 import SFC64 from .mtrand import * __all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937', - 'Philox', 'PCG64', 'SFC64', 'default_rng', 'BitGenerator'] + 'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng', + 'BitGenerator'] def __RandomState_ctor(): diff --git a/numpy/random/__init__.pyi b/numpy/random/__init__.pyi index 1187b7d8e..48b620c48 100644 --- a/numpy/random/__init__.pyi +++ b/numpy/random/__init__.pyi @@ -3,7 +3,10 @@ from typing import List from numpy.random._generator import Generator as Generator from numpy.random._generator import default_rng as default_rng from numpy.random._mt19937 import MT19937 as MT19937 -from numpy.random._pcg64 import PCG64 as PCG64 +from numpy.random._pcg64 import ( + PCG64 as PCG64, + PCG64DXSM as PCG64DXSM, +) from numpy.random._philox import Philox as Philox from numpy.random._sfc64 import SFC64 as SFC64 from numpy.random.bit_generator import BitGenerator as BitGenerator diff --git a/numpy/random/_pcg64.pyi b/numpy/random/_pcg64.pyi index a4f6e0760..25e2fdde6 100644 --- a/numpy/random/_pcg64.pyi +++ b/numpy/random/_pcg64.pyi @@ -32,3 +32,17 @@ class PCG64(BitGenerator): value: _PCG64State, ) -> None: ... def advance(self, delta: int) -> PCG64: ... + +class PCG64DXSM(BitGenerator): + def __init__(self, seed: Union[None, _ArrayLikeInt_co, SeedSequence] = ...) -> None: ... + def jumped(self, jumps: int = ...) -> PCG64DXSM: ... + @property + def state( + self, + ) -> _PCG64State: ... + @state.setter + def state( + self, + value: _PCG64State, + ) -> None: ... + def advance(self, delta: int) -> PCG64DXSM: ... diff --git a/numpy/random/_pcg64.pyx b/numpy/random/_pcg64.pyx index 605aae4bc..8a00dc265 100644 --- a/numpy/random/_pcg64.pyx +++ b/numpy/random/_pcg64.pyx @@ -26,6 +26,10 @@ cdef extern from "src/pcg64/pcg64.h": void pcg64_get_state(pcg64_state *state, uint64_t *state_arr, int *has_uint32, uint32_t *uinteger) void pcg64_set_state(pcg64_state *state, uint64_t *state_arr, int has_uint32, uint32_t uinteger) + uint64_t pcg64_cm_next64(pcg64_state *state) nogil + uint32_t pcg64_cm_next32(pcg64_state *state) nogil + void pcg64_cm_advance(pcg64_state *state, uint64_t *step) + cdef uint64_t pcg64_uint64(void* st) nogil: return pcg64_next64(<pcg64_state *>st) @@ -35,6 +39,14 @@ cdef uint32_t pcg64_uint32(void *st) nogil: cdef double pcg64_double(void* st) nogil: return uint64_to_double(pcg64_next64(<pcg64_state *>st)) +cdef uint64_t pcg64_cm_uint64(void* st) nogil: + return pcg64_cm_next64(<pcg64_state *>st) + +cdef uint32_t pcg64_cm_uint32(void *st) nogil: + return pcg64_cm_next32(<pcg64_state *> st) + +cdef double pcg64_cm_double(void* st) nogil: + return uint64_to_double(pcg64_cm_next64(<pcg64_state *>st)) cdef class PCG64(BitGenerator): """ @@ -268,3 +280,239 @@ cdef class PCG64(BitGenerator): pcg64_advance(&self.rng_state, <uint64_t *>np.PyArray_DATA(d)) self._reset_state_variables() return self + + +cdef class PCG64DXSM(BitGenerator): + """ + PCG64DXSM(seed=None) + + BitGenerator for the PCG-64 DXSM pseudo-random number generator. + + Parameters + ---------- + seed : {None, int, array_like[ints], SeedSequence}, optional + A seed to initialize the `BitGenerator`. If None, then fresh, + unpredictable entropy will be pulled from the OS. If an ``int`` or + ``array_like[ints]`` is passed, then it will be passed to + `SeedSequence` to derive the initial `BitGenerator` state. One may also + pass in a `SeedSequence` instance. + + Notes + ----- + PCG-64 DXSM is a 128-bit implementation of O'Neill's permutation congruential + generator ([1]_, [2]_). PCG-64 DXSM has a period of :math:`2^{128}` and supports + advancing an arbitrary number of steps as well as :math:`2^{127}` streams. + The specific member of the PCG family that we use is PCG CM DXSM 128/64. It + differs from ``PCG64`` in that it uses the stronger DXSM output function, + a 64-bit "cheap multiplier" in the LCG, and outputs from the state before + advancing it rather than advance-then-output. + + ``PCG64DXSM`` provides a capsule containing function pointers that produce + doubles, and unsigned 32 and 64- bit integers. These are not + directly consumable in Python and must be consumed by a ``Generator`` + or similar object that supports low-level access. + + Supports the method :meth:`advance` to advance the RNG an arbitrary number of + steps. The state of the PCG-64 DXSM RNG is represented by 2 128-bit unsigned + integers. + + **State and Seeding** + + The ``PCG64DXSM`` state vector consists of 2 unsigned 128-bit values, + which are represented externally as Python ints. One is the state of the + PRNG, which is advanced by a linear congruential generator (LCG). The + second is a fixed odd increment used in the LCG. + + The input seed is processed by `SeedSequence` to generate both values. The + increment is not independently settable. + + **Parallel Features** + + The preferred way to use a BitGenerator in parallel applications is to use + the `SeedSequence.spawn` method to obtain entropy values, and to use these + to generate new BitGenerators: + + >>> from numpy.random import Generator, PCG64DXSM, SeedSequence + >>> sg = SeedSequence(1234) + >>> rg = [Generator(PCG64DXSM(s)) for s in sg.spawn(10)] + + **Compatibility Guarantee** + + ``PCG64DXSM`` makes a guarantee that a fixed seed and will always produce + the same random integer stream. + + References + ---------- + .. [1] `"PCG, A Family of Better Random Number Generators" + <http://www.pcg-random.org/>`_ + .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient + Statistically Good Algorithms for Random Number Generation" + <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_ + """ + cdef pcg64_state rng_state + cdef pcg64_random_t pcg64_random_state + + def __init__(self, seed=None): + BitGenerator.__init__(self, seed) + self.rng_state.pcg_state = &self.pcg64_random_state + + self._bitgen.state = <void *>&self.rng_state + self._bitgen.next_uint64 = &pcg64_cm_uint64 + self._bitgen.next_uint32 = &pcg64_cm_uint32 + self._bitgen.next_double = &pcg64_cm_double + self._bitgen.next_raw = &pcg64_cm_uint64 + # Seed the _bitgen + val = self._seed_seq.generate_state(4, np.uint64) + pcg64_set_seed(&self.rng_state, + <uint64_t *>np.PyArray_DATA(val), + (<uint64_t *>np.PyArray_DATA(val) + 2)) + self._reset_state_variables() + + cdef _reset_state_variables(self): + self.rng_state.has_uint32 = 0 + self.rng_state.uinteger = 0 + + cdef jump_inplace(self, jumps): + """ + Jump state in-place + Not part of public API + + Parameters + ---------- + jumps : integer, positive + Number of times to jump the state of the rng. + + Notes + ----- + The step size is phi-1 when multiplied by 2**128 where phi is the + golden ratio. + """ + step = 0x9e3779b97f4a7c15f39cc0605cedc835 + self.advance(step * int(jumps)) + + def jumped(self, jumps=1): + """ + jumped(jumps=1) + + Returns a new bit generator with the state jumped. + + Jumps the state as-if jumps * 210306068529402873165736369884012333109 + random numbers have been generated. + + Parameters + ---------- + jumps : integer, positive + Number of times to jump the state of the bit generator returned + + Returns + ------- + bit_generator : PCG64DXSM + New instance of generator jumped iter times + + Notes + ----- + The step size is phi-1 when multiplied by 2**128 where phi is the + golden ratio. + """ + cdef PCG64DXSM bit_generator + + bit_generator = self.__class__() + bit_generator.state = self.state + bit_generator.jump_inplace(jumps) + + return bit_generator + + @property + def state(self): + """ + Get or set the PRNG state + + Returns + ------- + state : dict + Dictionary containing the information required to describe the + state of the PRNG + """ + cdef np.ndarray state_vec + cdef int has_uint32 + cdef uint32_t uinteger + + # state_vec is state.high, state.low, inc.high, inc.low + state_vec = <np.ndarray>np.empty(4, dtype=np.uint64) + pcg64_get_state(&self.rng_state, + <uint64_t *>np.PyArray_DATA(state_vec), + &has_uint32, &uinteger) + state = int(state_vec[0]) * 2**64 + int(state_vec[1]) + inc = int(state_vec[2]) * 2**64 + int(state_vec[3]) + return {'bit_generator': self.__class__.__name__, + 'state': {'state': state, 'inc': inc}, + 'has_uint32': has_uint32, + 'uinteger': uinteger} + + @state.setter + def state(self, value): + cdef np.ndarray state_vec + cdef int has_uint32 + cdef uint32_t uinteger + if not isinstance(value, dict): + raise TypeError('state must be a dict') + bitgen = value.get('bit_generator', '') + if bitgen != self.__class__.__name__: + raise ValueError('state must be for a {0} ' + 'RNG'.format(self.__class__.__name__)) + state_vec = <np.ndarray>np.empty(4, dtype=np.uint64) + state_vec[0] = value['state']['state'] // 2 ** 64 + state_vec[1] = value['state']['state'] % 2 ** 64 + state_vec[2] = value['state']['inc'] // 2 ** 64 + state_vec[3] = value['state']['inc'] % 2 ** 64 + has_uint32 = value['has_uint32'] + uinteger = value['uinteger'] + pcg64_set_state(&self.rng_state, + <uint64_t *>np.PyArray_DATA(state_vec), + has_uint32, uinteger) + + def advance(self, delta): + """ + advance(delta) + + Advance the underlying RNG as-if delta draws have occurred. + + Parameters + ---------- + delta : integer, positive + Number of draws to advance the RNG. Must be less than the + size state variable in the underlying RNG. + + Returns + ------- + self : PCG64 + RNG advanced delta steps + + Notes + ----- + Advancing a RNG updates the underlying RNG state as-if a given + number of calls to the underlying RNG have been made. In general + there is not a one-to-one relationship between the number output + random values from a particular distribution and the number of + draws from the core RNG. This occurs for two reasons: + + * The random values are simulated using a rejection-based method + and so, on average, more than one value from the underlying + RNG is required to generate an single draw. + * The number of bits required to generate a simulated value + differs from the number of bits generated by the underlying + RNG. For example, two 16-bit integer values can be simulated + from a single draw of a 32-bit RNG. + + Advancing the RNG state resets any pre-computed random numbers. + This is required to ensure exact reproducibility. + """ + delta = wrap_int(delta, 128) + + cdef np.ndarray d = np.empty(2, dtype=np.uint64) + d[0] = delta // 2**64 + d[1] = delta % 2**64 + pcg64_cm_advance(&self.rng_state, <uint64_t *>np.PyArray_DATA(d)) + self._reset_state_variables() + return self + diff --git a/numpy/random/_pickle.py b/numpy/random/_pickle.py index 71b01d6cd..a32f64f4a 100644 --- a/numpy/random/_pickle.py +++ b/numpy/random/_pickle.py @@ -1,6 +1,6 @@ from .mtrand import RandomState from ._philox import Philox -from ._pcg64 import PCG64 +from ._pcg64 import PCG64, PCG64DXSM from ._sfc64 import SFC64 from ._generator import Generator @@ -8,6 +8,7 @@ from ._mt19937 import MT19937 BitGenerators = {'MT19937': MT19937, 'PCG64': PCG64, + 'PCG64DXSM': PCG64DXSM, 'Philox': Philox, 'SFC64': SFC64, } diff --git a/numpy/random/src/pcg64/pcg64.c b/numpy/random/src/pcg64/pcg64.c index b15973aef..c623c809b 100644 --- a/numpy/random/src/pcg64/pcg64.c +++ b/numpy/random/src/pcg64/pcg64.c @@ -61,6 +61,10 @@ pcg_setseq_128_xsl_rr_64_boundedrand_r(pcg_state_setseq_128 *rng, uint64_t bound); extern inline void pcg_setseq_128_advance_r(pcg_state_setseq_128 *rng, pcg128_t delta); +extern inline uint64_t pcg_cm_random_r(pcg_state_setseq_128 *rng); +extern inline void pcg_cm_step_r(pcg_state_setseq_128 *rng); +extern inline uint64_t pcg_output_cm_128_64(pcg128_t state); +extern inline void pcg_cm_srandom_r(pcg_state_setseq_128 *rng, pcg128_t initstate, pcg128_t initseq); /* Multi-step advance functions (jump-ahead, jump-back) * @@ -117,6 +121,9 @@ pcg128_t pcg_advance_lcg_128(pcg128_t state, pcg128_t delta, pcg128_t cur_mult, extern inline uint64_t pcg64_next64(pcg64_state *state); extern inline uint32_t pcg64_next32(pcg64_state *state); +extern inline uint64_t pcg64_cm_next64(pcg64_state *state); +extern inline uint32_t pcg64_cm_next32(pcg64_state *state); + extern void pcg64_advance(pcg64_state *state, uint64_t *step) { pcg128_t delta; #ifndef PCG_EMULATED_128BIT_MATH @@ -128,6 +135,17 @@ extern void pcg64_advance(pcg64_state *state, uint64_t *step) { pcg64_advance_r(state->pcg_state, delta); } +extern void pcg64_cm_advance(pcg64_state *state, uint64_t *step) { + pcg128_t delta; +#ifndef PCG_EMULATED_128BIT_MATH + delta = (((pcg128_t)step[0]) << 64) | step[1]; +#else + delta.high = step[0]; + delta.low = step[1]; +#endif + pcg_cm_advance_r(state->pcg_state, delta); +} + extern void pcg64_set_seed(pcg64_state *state, uint64_t *seed, uint64_t *inc) { pcg128_t s, i; #ifndef PCG_EMULATED_128BIT_MATH diff --git a/numpy/random/src/pcg64/pcg64.h b/numpy/random/src/pcg64/pcg64.h index 31899cbc1..6691a18fc 100644 --- a/numpy/random/src/pcg64/pcg64.h +++ b/numpy/random/src/pcg64/pcg64.h @@ -104,6 +104,9 @@ typedef struct { , PCG_128BIT_CONSTANT(0x0000000000000001ULL, 0xda3e39cb94b95bdbULL) \ } +#define PCG_CHEAP_MULTIPLIER_128 (0xda942042e4dd58b5ULL) + + static inline uint64_t pcg_rotr_64(uint64_t value, unsigned int rot) { #ifdef _WIN32 return _rotr64(value, rot); @@ -198,6 +201,62 @@ pcg_setseq_128_xsl_rr_64_random_r(pcg_state_setseq_128 *rng) { #endif } +static inline pcg128_t pcg128_mult_64(pcg128_t a, uint64_t b) { + uint64_t h1; + pcg128_t result; + + h1 = a.high * b; + _pcg_mult64(a.low, b, &(result.high), &(result.low)); + result.high += h1; + return result; +} + +static inline void pcg_cm_step_r(pcg_state_setseq_128 *rng) { +#if defined _WIN32 && _MSC_VER >= 1900 && _M_AMD64 + uint64_t h1; + pcg128_t product; + + /* Manually inline the multiplication and addition using intrinsics */ + h1 = rng->state.high * PCG_CHEAP_MULTIPLIER_128; + product.low = + _umul128(rng->state.low, PCG_CHEAP_MULTIPLIER_128, &(product.high)); + product.high += h1; + _addcarry_u64(_addcarry_u64(0, product.low, rng->inc.low, &(rng->state.low)), + product.high, rng->inc.high, &(rng->state.high)); +#else + rng->state = pcg128_add(pcg128_mult_64(rng->state, PCG_CHEAP_MULTIPLIER_128), + rng->inc); +#endif +} + +static inline uint64_t pcg_output_cm_128_64(pcg128_t state) { + uint64_t hi = state.high; + uint64_t lo = state.low; + + lo |= 1; + hi ^= hi >> 32; + hi *= 0xda942042e4dd58b5ULL; + hi ^= hi >> 48; + hi *= lo; + return hi; +} + +static inline void pcg_cm_srandom_r(pcg_state_setseq_128 *rng, pcg128_t initstate, pcg128_t initseq) { + rng->state = PCG_128BIT_CONSTANT(0ULL, 0ULL); + rng->inc.high = initseq.high << 1u; + rng->inc.high |= initseq.low >> 63u; + rng->inc.low = (initseq.low << 1u) | 1u; + pcg_cm_step_r(rng); + rng->state = pcg128_add(rng->state, initstate); + pcg_cm_step_r(rng); +} + +static inline uint64_t pcg_cm_random_r(pcg_state_setseq_128* rng) +{ + uint64_t ret = pcg_output_cm_128_64(rng->state); + pcg_cm_step_r(rng); + return ret; +} #else /* PCG_EMULATED_128BIT_MATH */ static inline void pcg_setseq_128_step_r(pcg_state_setseq_128 *rng) { @@ -209,6 +268,37 @@ static inline uint64_t pcg_output_xsl_rr_128_64(pcg128_t state) { state >> 122u); } +static inline void pcg_cm_step_r(pcg_state_setseq_128 *rng) { + rng-> state = rng->state * PCG_CHEAP_MULTIPLIER_128 + rng->inc; +} + +static inline uint64_t pcg_output_cm_128_64(pcg128_t state) { + uint64_t hi = state >> 64; + uint64_t lo = state; + + lo |= 1; + hi ^= hi >> 32; + hi *= 0xda942042e4dd58b5ULL; + hi ^= hi >> 48; + hi *= lo; + return hi; +} + +static inline void pcg_cm_srandom_r(pcg_state_setseq_128 *rng, pcg128_t initstate, pcg128_t initseq) { + rng->state = 0U; + rng->inc = (initseq << 1u) | 1u; + pcg_cm_step_r(rng); + rng->state += initstate; + pcg_cm_step_r(rng); +} + +static inline uint64_t pcg_cm_random_r(pcg_state_setseq_128* rng) +{ + uint64_t ret = pcg_output_cm_128_64(rng->state); + pcg_cm_step_r(rng); + return ret; +} + static inline uint64_t pcg_setseq_128_xsl_rr_64_random_r(pcg_state_setseq_128* rng) { @@ -248,6 +338,12 @@ static inline void pcg_setseq_128_advance_r(pcg_state_setseq_128 *rng, PCG_DEFAULT_MULTIPLIER_128, rng->inc); } +static inline void pcg_cm_advance_r(pcg_state_setseq_128 *rng, pcg128_t delta) { + rng->state = pcg_advance_lcg_128(rng->state, delta, + PCG_128BIT_CONSTANT(0, PCG_CHEAP_MULTIPLIER_128), + rng->inc); +} + typedef pcg_state_setseq_128 pcg64_random_t; #define pcg64_random_r pcg_setseq_128_xsl_rr_64_random_r #define pcg64_boundedrand_r pcg_setseq_128_xsl_rr_64_boundedrand_r @@ -281,7 +377,24 @@ static inline uint32_t pcg64_next32(pcg64_state *state) { return (uint32_t)(next & 0xffffffff); } +static inline uint64_t pcg64_cm_next64(pcg64_state *state) { + return pcg_cm_random_r(state->pcg_state); +} + +static inline uint32_t pcg64_cm_next32(pcg64_state *state) { + uint64_t next; + if (state->has_uint32) { + state->has_uint32 = 0; + return state->uinteger; + } + next = pcg_cm_random_r(state->pcg_state); + state->has_uint32 = 1; + state->uinteger = (uint32_t)(next >> 32); + return (uint32_t)(next & 0xffffffff); +} + void pcg64_advance(pcg64_state *state, uint64_t *step); +void pcg64_cm_advance(pcg64_state *state, uint64_t *step); void pcg64_set_seed(pcg64_state *state, uint64_t *seed, uint64_t *inc); diff --git a/numpy/random/tests/data/pcg64dxsm-testset-1.csv b/numpy/random/tests/data/pcg64dxsm-testset-1.csv new file mode 100644 index 000000000..39cef057f --- /dev/null +++ b/numpy/random/tests/data/pcg64dxsm-testset-1.csv @@ -0,0 +1,1001 @@ +seed, 0xdeadbeaf +0, 0xdf1ddcf1e22521fe +1, 0xc71b2f9c706cf151 +2, 0x6922a8cc24ad96b2 +3, 0x82738c549beccc30 +4, 0x5e8415cdb1f17580 +5, 0x64c54ad0c09cb43 +6, 0x361a17a607dce278 +7, 0x4346f6afb7acad68 +8, 0x6e9f14d4f6398d6b +9, 0xf818d4343f8ed822 +10, 0x6327647daf508ed6 +11, 0xe1d1dbe5496a262a +12, 0xfc081e619076b2e0 +13, 0x37126563a956ab1 +14, 0x8bb46e155db16b9 +15, 0x56449f006c9f3fb4 +16, 0x34a9273550941803 +17, 0x5b4df62660f99462 +18, 0xb8665cad532e3018 +19, 0x72fc3e5f7f84216a +20, 0x71d3c47f6fd59939 +21, 0xfd4218afa1de463b +22, 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0x1a4a320fb5e8b098 +980, 0x8819cccd8b4ab32f +981, 0x42daa88531fd0bfd +982, 0xcf732226409be17c +983, 0xfddcdb25ccbf378c +984, 0x9b15b603bf589fc1 +985, 0x2436066b95d366fe +986, 0x8d42eff2e9cbda90 +987, 0x694b2fc8a4e8303c +988, 0x8e207f98aaea3ccd +989, 0x4730d7a620f822d9 +990, 0x468dc9ca30fe2fd4 +991, 0x74b36d8a1c0f031b +992, 0x3c1aac1c488c1a94 +993, 0x19d0101042444585 +994, 0x8ec50c56d0c8adf4 +995, 0x721ec629e4d66394 +996, 0x3ca5ad93abeac4a4 +997, 0xaaebc76e71592623 +998, 0x969cc319e3ed6058 +999, 0xc0a277e3b2bfc3de diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py index d602b36b4..29054b70b 100644 --- a/numpy/random/tests/test_direct.py +++ b/numpy/random/tests/test_direct.py @@ -8,8 +8,8 @@ from numpy.testing import (assert_equal, assert_allclose, assert_array_equal, import pytest from numpy.random import ( - Generator, MT19937, PCG64, Philox, RandomState, SeedSequence, SFC64, - default_rng + Generator, MT19937, PCG64, PCG64DXSM, Philox, RandomState, SeedSequence, + SFC64, default_rng ) from numpy.random._common import interface @@ -359,6 +359,34 @@ class TestPCG64(Base): assert val_big == val_pos +class TestPCG64DXSM(Base): + @classmethod + def setup_class(cls): + cls.bit_generator = PCG64DXSM + cls.bits = 64 + cls.dtype = np.uint64 + cls.data1 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-1.csv')) + cls.data2 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-2.csv')) + cls.seed_error_type = (ValueError, TypeError) + cls.invalid_init_types = [(3.2,), ([None],), (1, None)] + cls.invalid_init_values = [(-1,)] + + def test_advance_symmetry(self): + rs = Generator(self.bit_generator(*self.data1['seed'])) + state = rs.bit_generator.state + step = -0x9e3779b97f4a7c150000000000000000 + rs.bit_generator.advance(step) + val_neg = rs.integers(10) + rs.bit_generator.state = state + rs.bit_generator.advance(2**128 + step) + val_pos = rs.integers(10) + rs.bit_generator.state = state + rs.bit_generator.advance(10 * 2**128 + step) + val_big = rs.integers(10) + assert val_neg == val_pos + assert val_big == val_pos + + class TestMT19937(Base): @classmethod def setup_class(cls): diff --git a/numpy/random/tests/test_smoke.py b/numpy/random/tests/test_smoke.py index 909bfaa8d..9becc434d 100644 --- a/numpy/random/tests/test_smoke.py +++ b/numpy/random/tests/test_smoke.py @@ -4,7 +4,7 @@ from functools import partial import numpy as np import pytest from numpy.testing import assert_equal, assert_, assert_array_equal -from numpy.random import (Generator, MT19937, PCG64, Philox, SFC64) +from numpy.random import (Generator, MT19937, PCG64, PCG64DXSM, Philox, SFC64) @pytest.fixture(scope='module', params=(np.bool_, np.int8, np.int16, np.int32, np.int64, @@ -774,6 +774,18 @@ class TestPCG64(RNG): cls._extra_setup() +class TestPCG64DXSM(RNG): + @classmethod + def setup_class(cls): + cls.bit_generator = PCG64DXSM + cls.advance = 2**63 + 2**31 + 2**15 + 1 + cls.seed = [12345] + cls.rg = Generator(cls.bit_generator(*cls.seed)) + cls.initial_state = cls.rg.bit_generator.state + cls.seed_vector_bits = 64 + cls._extra_setup() + + class TestDefaultRNG(RNG): @classmethod def setup_class(cls): diff --git a/numpy/testing/__init__.pyi b/numpy/testing/__init__.pyi index efb6bcd91..395626f6b 100644 --- a/numpy/testing/__init__.pyi +++ b/numpy/testing/__init__.pyi @@ -7,12 +7,18 @@ if sys.version_info >= (3, 8): else: from typing_extensions import Final +from unittest import ( + TestCase as TestCase, +) + from unittest.case import ( SkipTest as SkipTest, ) __all__: List[str] +def run_module_suite(file_to_run=..., argv=...): ... + class KnownFailureException(Exception): ... class IgnoreException(Exception): ... diff --git a/numpy/typing/__init__.py b/numpy/typing/__init__.py index d71ec0719..a2d4c60fb 100644 --- a/numpy/typing/__init__.py +++ b/numpy/typing/__init__.py @@ -140,6 +140,20 @@ Timedelta64 The `~numpy.timedelta64` class is not considered a subclass of `~numpy.signedinteger`, the former only inheriting from `~numpy.generic` while static type checking. +0D arrays +~~~~~~~~~ + +During runtime numpy aggressively casts any passed 0D arrays into their +corresponding `~numpy.generic` instance. Until the introduction of shape +typing (see :pep:`646`) it is unfortunately not possible to make the +necessary distinction between 0D and >0D arrays. While thus not strictly +correct, all operations are that can potentially perform a 0D-array -> scalar +cast are currently annotated as exclusively returning an `ndarray`. + +If it is known in advance that an operation _will_ perform a +0D-array -> scalar cast, then one can consider manually remedying the +situation with either `typing.cast` or a ``# type: ignore`` comment. + API --- @@ -326,7 +340,6 @@ from ._array_like import ( _RecursiveSequence, _SupportsArray, _ArrayND, - _ArrayOrScalar, _ArrayLikeInt, _ArrayLikeBool_co, _ArrayLikeUInt_co, diff --git a/numpy/typing/_array_like.py b/numpy/typing/_array_like.py index eb8d3f4e1..582d3db9a 100644 --- a/numpy/typing/_array_like.py +++ b/numpy/typing/_array_like.py @@ -132,7 +132,5 @@ _ArrayLikeInt = _ArrayLike[ if TYPE_CHECKING: _ArrayND = ndarray[Any, dtype[_ScalarType]] - _ArrayOrScalar = Union[_ScalarType, _ArrayND[_ScalarType]] else: _ArrayND = Any - _ArrayOrScalar = Any diff --git a/numpy/typing/_callable.py b/numpy/typing/_callable.py index 23cabd47a..30c210b62 100644 --- a/numpy/typing/_callable.py +++ b/numpy/typing/_callable.py @@ -47,7 +47,7 @@ from ._scalars import ( _NumberLike_co, ) from . import NBitBase -from ._array_like import ArrayLike, _ArrayOrScalar +from ._array_like import ArrayLike, _ArrayND if sys.version_info >= (3, 8): from typing import Protocol @@ -168,7 +168,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__( self, __other: Union[int, signedinteger[Any]] - ) -> Union[signedinteger[Any], float64]: ... + ) -> Any: ... @overload def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... @overload @@ -198,7 +198,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__( self, __other: Union[int, signedinteger[Any]] - ) -> Union[signedinteger[Any], float64]: ... + ) -> Any: ... @overload def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... @overload @@ -212,7 +212,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__( self, __other: Union[int, signedinteger[Any]] - ) -> Union[_2Tuple[signedinteger[Any]], _2Tuple[float64]]: ... + ) -> _2Tuple[Any]: ... @overload def __call__(self, __other: float) -> _2Tuple[floating[Union[_NBit1, _NBitDouble]]]: ... @overload @@ -336,7 +336,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__(self, __other: _T1) -> bool_: ... @overload - def __call__(self, __other: _T2) -> _ArrayOrScalar[bool_]: ... + def __call__(self, __other: _T2) -> _ArrayND[bool_]: ... else: _BoolOp = Any diff --git a/numpy/typing/tests/data/fail/fromnumeric.py b/numpy/typing/tests/data/fail/fromnumeric.py index c9156895d..d8f7a5d69 100644 --- a/numpy/typing/tests/data/fail/fromnumeric.py +++ b/numpy/typing/tests/data/fail/fromnumeric.py @@ -7,17 +7,17 @@ A.setflags(write=False) 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 @@ -38,14 +38,14 @@ np.partition( 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 @@ -93,62 +93,62 @@ np.compress( 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 diff --git a/numpy/typing/tests/data/fail/index_tricks.py b/numpy/typing/tests/data/fail/index_tricks.py index cbc43fd54..c508bf3ae 100644 --- a/numpy/typing/tests/data/fail/index_tricks.py +++ b/numpy/typing/tests/data/fail/index_tricks.py @@ -5,7 +5,7 @@ AR_LIKE_i: List[int] AR_LIKE_f: List[float] np.unravel_index(AR_LIKE_f, (1, 2, 3)) # E: incompatible type -np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob") # E: incompatible type +np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob") # E: No overload variant np.mgrid[1] # E: Invalid index type np.mgrid[...] # E: Invalid index type np.ogrid[1] # E: Invalid index type diff --git a/numpy/typing/tests/data/fail/lib_utils.py b/numpy/typing/tests/data/fail/lib_utils.py new file mode 100644 index 000000000..e16c926aa --- /dev/null +++ b/numpy/typing/tests/data/fail/lib_utils.py @@ -0,0 +1,13 @@ +import numpy as np + +np.deprecate(1) # E: No overload variant + +np.deprecate_with_doc(1) # E: incompatible type + +np.byte_bounds(1) # E: incompatible type + +np.who(1) # E: incompatible type + +np.lookfor(None) # E: incompatible type + +np.safe_eval(None) # E: incompatible type diff --git a/numpy/typing/tests/data/pass/lib_utils.py b/numpy/typing/tests/data/pass/lib_utils.py new file mode 100644 index 000000000..c602923d9 --- /dev/null +++ b/numpy/typing/tests/data/pass/lib_utils.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +from io import StringIO +from typing import Any + +import numpy as np + +FILE = StringIO() +AR: np.ndarray[Any, np.dtype[np.float64]] = np.arange(10).astype(np.float64) + +def func(a: int) -> bool: ... + +np.deprecate(func) +np.deprecate() + +np.deprecate_with_doc("test") +np.deprecate_with_doc(None) + +np.byte_bounds(AR) +np.byte_bounds(np.float64()) + +np.info(1, output=FILE) + +np.source(np.interp, output=FILE) + +np.lookfor("binary representation", output=FILE) diff --git a/numpy/typing/tests/data/reveal/arithmetic.py b/numpy/typing/tests/data/reveal/arithmetic.py index 53a3a846d..f5d185206 100644 --- a/numpy/typing/tests/data/reveal/arithmetic.py +++ b/numpy/typing/tests/data/reveal/arithmetic.py @@ -45,104 +45,104 @@ AR_LIKE_O: List[np.object_] # Array subtraction -reveal_type(AR_b - AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_b - AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_b - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_b - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_b - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_b - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_b - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_b - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_u - AR_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i - AR_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f - AR_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m - AR_b) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_b) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_u - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_b) # E: Any -reveal_type(AR_u - AR_LIKE_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u - AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u - AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_u - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_u - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_u - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_u - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_u - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_u - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_u - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_u - AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i - AR_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f - AR_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m - AR_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_u) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_b - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_u) # E: Any -reveal_type(AR_i - AR_LIKE_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i - AR_LIKE_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i - AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_i - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_i - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_i - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_i - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_i - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_i - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_u - AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_i - AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f - AR_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m - AR_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_i) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_b - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_i) # E: Any -reveal_type(AR_f - AR_LIKE_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_f - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_f - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_u - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_i - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_f - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_u - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_i - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_f - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O - AR_f) # E: Any -reveal_type(AR_c - AR_LIKE_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_c - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_c - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_u - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_i - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_f - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_c - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_u - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_i - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_f - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_c - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O - AR_c) # E: Any -reveal_type(AR_m - AR_LIKE_b) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m - AR_LIKE_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m - AR_LIKE_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_m - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_m - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_u - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_i - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_m - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_m) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_b - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_u - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_i - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_m - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_m) # E: Any -reveal_type(AR_M - AR_LIKE_b) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_u) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_i) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_m) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_M) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_M - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_M - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_M - AR_M) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_M - AR_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O - AR_M) # E: Any reveal_type(AR_O - AR_LIKE_b) # E: Any @@ -165,86 +165,86 @@ reveal_type(AR_LIKE_O - AR_O) # E: Any # Array floor division -reveal_type(AR_b // AR_LIKE_b) # E: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] -reveal_type(AR_b // AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_b // AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_b // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_b // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_b // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(AR_b // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_b // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_b) # E: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] -reveal_type(AR_LIKE_u // AR_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i // AR_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f // AR_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b // AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(AR_LIKE_u // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O // AR_b) # E: Any -reveal_type(AR_u // AR_LIKE_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u // AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u // AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_u // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_u // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_u // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_u // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_u // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_u // AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i // AR_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f // AR_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m // AR_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_b // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O // AR_u) # E: Any -reveal_type(AR_i // AR_LIKE_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i // AR_LIKE_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i // AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_i // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_i // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_i // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_i // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_u // AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_i // AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f // AR_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m // AR_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_b // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O // AR_i) # E: Any -reveal_type(AR_f // AR_LIKE_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_f // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_f // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_u // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_i // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_f // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m // AR_f) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_b // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_u // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_i // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_f // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O // AR_f) # E: Any -reveal_type(AR_c // AR_LIKE_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_c // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_c // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_u // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_i // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_f // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_c // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_u // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_i // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_f // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_c // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O // AR_c) # E: Any -reveal_type(AR_m // AR_LIKE_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m // AR_LIKE_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m // AR_LIKE_f) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m // AR_LIKE_m) # E: Union[{int64}, numpy.ndarray[Any, numpy.dtype[{int64}]]] +reveal_type(AR_m // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m // AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] reveal_type(AR_m // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_m // AR_m) # E: Union[{int64}, numpy.ndarray[Any, numpy.dtype[{int64}]]] +reveal_type(AR_LIKE_m // AR_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] reveal_type(AR_LIKE_O // AR_m) # E: Any reveal_type(AR_O // AR_LIKE_b) # E: Any @@ -472,9 +472,9 @@ reveal_type(AR_f + f4) # E: Any # Int reveal_type(i8 + i8) # E: {int64} -reveal_type(i8 + u8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i8 + u8) # E: Any reveal_type(i8 + i4) # E: {int64} -reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i8 + u4) # E: Any reveal_type(i8 + b_) # E: {int64} reveal_type(i8 + b) # E: {int64} reveal_type(i8 + c) # E: {complex128} @@ -483,19 +483,19 @@ reveal_type(i8 + i) # E: {int64} reveal_type(i8 + AR_f) # E: Any reveal_type(u8 + u8) # E: {uint64} -reveal_type(u8 + i4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u8 + i4) # E: Any reveal_type(u8 + u4) # E: {uint64} reveal_type(u8 + b_) # E: {uint64} reveal_type(u8 + b) # E: {uint64} reveal_type(u8 + c) # E: {complex128} reveal_type(u8 + f) # E: {float64} -reveal_type(u8 + i) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u8 + i) # E: Any reveal_type(u8 + AR_f) # E: Any reveal_type(i8 + i8) # E: {int64} -reveal_type(u8 + i8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u8 + i8) # E: Any reveal_type(i4 + i8) # E: {int64} -reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u4 + i8) # E: Any reveal_type(b_ + i8) # E: {int64} reveal_type(b + i8) # E: {int64} reveal_type(c + i8) # E: {complex128} @@ -504,13 +504,13 @@ reveal_type(i + i8) # E: {int64} reveal_type(AR_f + i8) # E: Any reveal_type(u8 + u8) # E: {uint64} -reveal_type(i4 + u8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i4 + u8) # E: Any reveal_type(u4 + u8) # E: {uint64} reveal_type(b_ + u8) # E: {uint64} reveal_type(b + u8) # E: {uint64} reveal_type(c + u8) # E: {complex128} reveal_type(f + u8) # E: {float64} -reveal_type(i + u8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i + u8) # E: Any reveal_type(AR_f + u8) # E: Any reveal_type(i4 + i8) # E: {int64} @@ -520,11 +520,11 @@ reveal_type(i4 + b_) # E: {int32} reveal_type(i4 + b) # E: {int32} reveal_type(i4 + AR_f) # E: Any -reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], {float64}] -reveal_type(u4 + i4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u4 + i8) # E: Any +reveal_type(u4 + i4) # E: Any reveal_type(u4 + u8) # E: {uint64} reveal_type(u4 + u4) # E: {uint32} -reveal_type(u4 + i) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u4 + i) # E: Any reveal_type(u4 + b_) # E: {uint32} reveal_type(u4 + b) # E: {uint32} reveal_type(u4 + AR_f) # E: Any @@ -536,11 +536,11 @@ reveal_type(b_ + i4) # E: {int32} reveal_type(b + i4) # E: {int32} reveal_type(AR_f + i4) # E: Any -reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], {float64}] -reveal_type(i4 + u4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i8 + u4) # E: Any +reveal_type(i4 + u4) # E: Any reveal_type(u8 + u4) # E: {uint64} reveal_type(u4 + u4) # E: {uint32} reveal_type(b_ + u4) # E: {uint32} reveal_type(b + u4) # E: {uint32} -reveal_type(i + u4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i + u4) # E: Any reveal_type(AR_f + u4) # E: Any diff --git a/numpy/typing/tests/data/reveal/array_constructors.py b/numpy/typing/tests/data/reveal/array_constructors.py index 04d5cd229..2e803a365 100644 --- a/numpy/typing/tests/data/reveal/array_constructors.py +++ b/numpy/typing/tests/data/reveal/array_constructors.py @@ -41,7 +41,7 @@ reveal_type(np.require(B, requirements="A")) # E: SubClass reveal_type(np.require(C)) # E: numpy.ndarray[Any, Any] reveal_type(np.linspace(0, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray[Any, Any], numpy.inexact[Any]] +reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray[Any, Any], Any] reveal_type(np.logspace(0, 10)) # E: numpy.ndarray[Any, Any] reveal_type(np.geomspace(1, 10)) # E: numpy.ndarray[Any, Any] diff --git a/numpy/typing/tests/data/reveal/comparisons.py b/numpy/typing/tests/data/reveal/comparisons.py index 5053a9e82..16f21cc39 100644 --- a/numpy/typing/tests/data/reveal/comparisons.py +++ b/numpy/typing/tests/data/reveal/comparisons.py @@ -34,12 +34,12 @@ reveal_type(td > i) # E: numpy.bool_ reveal_type(td > i4) # E: numpy.bool_ reveal_type(td > i8) # E: numpy.bool_ -reveal_type(td > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(td > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(AR > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(AR > td) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > td) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(td > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(td > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # boolean @@ -56,8 +56,8 @@ reveal_type(b_ > f4) # E: numpy.bool_ reveal_type(b_ > c) # E: numpy.bool_ reveal_type(b_ > c16) # E: numpy.bool_ reveal_type(b_ > c8) # E: numpy.bool_ -reveal_type(b_ > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(b_ > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(b_ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(b_ > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # Complex @@ -72,8 +72,8 @@ reveal_type(c16 > b) # E: numpy.bool_ reveal_type(c16 > c) # E: numpy.bool_ reveal_type(c16 > f) # E: numpy.bool_ reveal_type(c16 > i) # E: numpy.bool_ -reveal_type(c16 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(c16 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(c16 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(c16 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(c16 > c16) # E: numpy.bool_ reveal_type(f8 > c16) # E: numpy.bool_ @@ -86,8 +86,8 @@ reveal_type(b > c16) # E: numpy.bool_ reveal_type(c > c16) # E: numpy.bool_ reveal_type(f > c16) # E: numpy.bool_ reveal_type(i > c16) # E: numpy.bool_ -reveal_type(AR > c16) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > c16) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > c16) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > c16) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(c8 > c16) # E: numpy.bool_ reveal_type(c8 > f8) # E: numpy.bool_ @@ -100,8 +100,8 @@ reveal_type(c8 > b) # E: numpy.bool_ reveal_type(c8 > c) # E: numpy.bool_ reveal_type(c8 > f) # E: numpy.bool_ reveal_type(c8 > i) # E: numpy.bool_ -reveal_type(c8 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(c8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(c8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(c8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(c16 > c8) # E: numpy.bool_ reveal_type(f8 > c8) # E: numpy.bool_ @@ -114,8 +114,8 @@ reveal_type(b > c8) # E: numpy.bool_ reveal_type(c > c8) # E: numpy.bool_ reveal_type(f > c8) # E: numpy.bool_ reveal_type(i > c8) # E: numpy.bool_ -reveal_type(AR > c8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > c8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # Float @@ -128,8 +128,8 @@ reveal_type(f8 > b) # E: numpy.bool_ reveal_type(f8 > c) # E: numpy.bool_ reveal_type(f8 > f) # E: numpy.bool_ reveal_type(f8 > i) # E: numpy.bool_ -reveal_type(f8 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(f8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(f8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(f8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(f8 > f8) # E: numpy.bool_ reveal_type(i8 > f8) # E: numpy.bool_ @@ -140,8 +140,8 @@ reveal_type(b > f8) # E: numpy.bool_ reveal_type(c > f8) # E: numpy.bool_ reveal_type(f > f8) # E: numpy.bool_ reveal_type(i > f8) # E: numpy.bool_ -reveal_type(AR > f8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > f8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(f4 > f8) # E: numpy.bool_ reveal_type(f4 > i8) # E: numpy.bool_ @@ -152,8 +152,8 @@ reveal_type(f4 > b) # E: numpy.bool_ reveal_type(f4 > c) # E: numpy.bool_ reveal_type(f4 > f) # E: numpy.bool_ reveal_type(f4 > i) # E: numpy.bool_ -reveal_type(f4 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(f4 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(f4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(f4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(f8 > f4) # E: numpy.bool_ reveal_type(i8 > f4) # E: numpy.bool_ @@ -164,8 +164,8 @@ reveal_type(b > f4) # E: numpy.bool_ reveal_type(c > f4) # E: numpy.bool_ reveal_type(f > f4) # E: numpy.bool_ reveal_type(i > f4) # E: numpy.bool_ -reveal_type(AR > f4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > f4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # Int @@ -178,8 +178,8 @@ reveal_type(i8 > b) # E: numpy.bool_ reveal_type(i8 > c) # E: numpy.bool_ reveal_type(i8 > f) # E: numpy.bool_ reveal_type(i8 > i) # E: numpy.bool_ -reveal_type(i8 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(i8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(i8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(i8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(u8 > u8) # E: numpy.bool_ reveal_type(u8 > i4) # E: numpy.bool_ @@ -189,8 +189,8 @@ reveal_type(u8 > b) # E: numpy.bool_ reveal_type(u8 > c) # E: numpy.bool_ reveal_type(u8 > f) # E: numpy.bool_ reveal_type(u8 > i) # E: numpy.bool_ -reveal_type(u8 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(u8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(u8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(u8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(i8 > i8) # E: numpy.bool_ reveal_type(u8 > i8) # E: numpy.bool_ @@ -201,8 +201,8 @@ reveal_type(b > i8) # E: numpy.bool_ reveal_type(c > i8) # E: numpy.bool_ reveal_type(f > i8) # E: numpy.bool_ reveal_type(i > i8) # E: numpy.bool_ -reveal_type(AR > i8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > i8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(u8 > u8) # E: numpy.bool_ reveal_type(i4 > u8) # E: numpy.bool_ @@ -212,16 +212,16 @@ reveal_type(b > u8) # E: numpy.bool_ reveal_type(c > u8) # E: numpy.bool_ reveal_type(f > u8) # E: numpy.bool_ reveal_type(i > u8) # E: numpy.bool_ -reveal_type(AR > u8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > u8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > u8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > u8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(i4 > i8) # E: numpy.bool_ reveal_type(i4 > i4) # E: numpy.bool_ reveal_type(i4 > i) # E: numpy.bool_ reveal_type(i4 > b_) # E: numpy.bool_ reveal_type(i4 > b) # E: numpy.bool_ -reveal_type(i4 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(i4 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(i4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(i4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(u4 > i8) # E: numpy.bool_ reveal_type(u4 > i4) # E: numpy.bool_ @@ -230,16 +230,16 @@ reveal_type(u4 > u4) # E: numpy.bool_ reveal_type(u4 > i) # E: numpy.bool_ reveal_type(u4 > b_) # E: numpy.bool_ reveal_type(u4 > b) # E: numpy.bool_ -reveal_type(u4 > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(u4 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(u4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(u4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(i8 > i4) # E: numpy.bool_ reveal_type(i4 > i4) # E: numpy.bool_ reveal_type(i > i4) # E: numpy.bool_ reveal_type(b_ > i4) # E: numpy.bool_ reveal_type(b > i4) # E: numpy.bool_ -reveal_type(AR > i4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > i4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > i4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > i4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(i8 > u4) # E: numpy.bool_ reveal_type(i4 > u4) # E: numpy.bool_ @@ -248,5 +248,5 @@ reveal_type(u4 > u4) # E: numpy.bool_ reveal_type(b_ > u4) # E: numpy.bool_ reveal_type(b > u4) # E: numpy.bool_ reveal_type(i > u4) # E: numpy.bool_ -reveal_type(AR > u4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > u4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] diff --git a/numpy/typing/tests/data/reveal/einsumfunc.py b/numpy/typing/tests/data/reveal/einsumfunc.py index 18c192b0b..f1a90428d 100644 --- a/numpy/typing/tests/data/reveal/einsumfunc.py +++ b/numpy/typing/tests/data/reveal/einsumfunc.py @@ -10,17 +10,17 @@ AR_LIKE_U: List[str] OUT_f: np.ndarray[Any, np.dtype[np.float64]] -reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)) # E: Any +reveal_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)) # E: Any reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}] reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}] -reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")) # E: Any reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe")) # E: Any reveal_type(np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b)) # E: Tuple[builtins.list[Any], builtins.str] diff --git a/numpy/typing/tests/data/reveal/fromnumeric.py b/numpy/typing/tests/data/reveal/fromnumeric.py index 2b58f019f..bbcfbb85a 100644 --- a/numpy/typing/tests/data/reveal/fromnumeric.py +++ b/numpy/typing/tests/data/reveal/fromnumeric.py @@ -12,27 +12,13 @@ b = np.float32(1.0) 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: {float32} -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[Any, Any]] - A, [0] - ) -) -reveal_type( - np.take( # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray[Any, Any]] - 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[Any, Any] reveal_type(np.reshape(b, 1)) # E: numpy.ndarray[Any, Any] @@ -40,8 +26,8 @@ reveal_type(np.reshape(c, 1)) # E: numpy.ndarray[Any, Any] reveal_type(np.reshape(A, 1)) # E: numpy.ndarray[Any, Any] reveal_type(np.reshape(B, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.choose(a, [True, True])) # E: numpy.bool_ -reveal_type(np.choose(A, [True, True])) # E: numpy.ndarray[Any, Any] +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[Any, Any] reveal_type(np.repeat(b, 1)) # E: numpy.ndarray[Any, Any] @@ -66,11 +52,11 @@ reveal_type(np.partition(c, 0, axis=None)) # E: numpy.ndarray[Any, Any] reveal_type(np.partition(A, 0)) # E: numpy.ndarray[Any, Any] reveal_type(np.partition(B, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argpartition(a, 0)) # E: {intp} -reveal_type(np.argpartition(b, 0)) # E: {intp} -reveal_type(np.argpartition(c, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argpartition(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argpartition(B, 0)) # E: numpy.ndarray[Any, Any] +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[Any, Any] reveal_type(np.sort(B, 0)) # E: numpy.ndarray[Any, Any] @@ -80,13 +66,13 @@ reveal_type(np.argsort(B, 0)) # E: numpy.ndarray[Any, Any] reveal_type(np.argmax(A)) # E: {intp} reveal_type(np.argmax(B)) # E: {intp} -reveal_type(np.argmax(A, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] -reveal_type(np.argmax(B, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(np.argmax(A, axis=0)) # E: Any +reveal_type(np.argmax(B, axis=0)) # E: Any reveal_type(np.argmin(A)) # E: {intp} reveal_type(np.argmin(B)) # E: {intp} -reveal_type(np.argmin(A, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] -reveal_type(np.argmin(B, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +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: {intp} reveal_type(np.searchsorted(B[0], 0)) # E: {intp} @@ -108,8 +94,8 @@ reveal_type(np.squeeze(B)) # E: numpy.ndarray[Any, Any] reveal_type(np.diagonal(A)) # E: numpy.ndarray[Any, Any] reveal_type(np.diagonal(B)) # E: numpy.ndarray[Any, Any] -reveal_type(np.trace(A)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.trace(B)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(np.trace(A)) # E: Any +reveal_type(np.trace(B)) # E: Any reveal_type(np.ravel(a)) # E: numpy.ndarray[Any, Any] reveal_type(np.ravel(b)) # E: numpy.ndarray[Any, Any] @@ -135,39 +121,39 @@ reveal_type(np.compress([True], c)) # E: numpy.ndarray[Any, Any] reveal_type(np.compress([True], A)) # E: numpy.ndarray[Any, Any] reveal_type(np.compress([True], B)) # E: numpy.ndarray[Any, Any] -reveal_type(np.clip(a, 0, 1.0)) # E: numpy.number[Any] -reveal_type(np.clip(b, -1, 1)) # E: {float32} -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[Any, Any]] -reveal_type(np.clip(B, 0, 1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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: {float32} -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[Any, Any]] -reveal_type(np.sum(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(np.all(B, axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.all(A, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.all(B, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(np.any(B, axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.any(A, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.any(B, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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[Any, Any] reveal_type(np.cumsum(b)) # E: numpy.ndarray[Any, Any] @@ -175,47 +161,47 @@ reveal_type(np.cumsum(c)) # E: numpy.ndarray[Any, Any] reveal_type(np.cumsum(A)) # E: numpy.ndarray[Any, Any] reveal_type(np.cumsum(B)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ptp(a)) # E: numpy.number[Any] -reveal_type(np.ptp(b)) # E: {float32} -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[Any, Any]] -reveal_type(np.ptp(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.ptp(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.ptp(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] - -reveal_type(np.amax(a)) # E: numpy.number[Any] -reveal_type(np.amax(b)) # E: {float32} -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[Any, Any]] -reveal_type(np.amax(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amax(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amax(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] - -reveal_type(np.amin(a)) # E: numpy.number[Any] -reveal_type(np.amin(b)) # E: {float32} -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[Any, Any]] -reveal_type(np.amin(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amin(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amin(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] - -reveal_type(np.prod(a)) # E: numpy.number[Any] -reveal_type(np.prod(b)) # E: {float32} -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[Any, Any]] -reveal_type(np.prod(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.prod(B, out=d)) # E: numpy.ndarray[Any, Any] +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[Any, Any] reveal_type(np.cumprod(b)) # E: numpy.ndarray[Any, Any] @@ -235,44 +221,44 @@ reveal_type(np.size(c)) # E: int 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: {float32} -reveal_type(np.around(c)) # E: numpy.number[Any] -reveal_type(np.around(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.around(B)) # E: numpy.ndarray[Any, Any] - -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[Any, Any]] -reveal_type(np.mean(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.mean(B, out=d)) # E: numpy.ndarray[Any, Any] - -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[Any, Any]] -reveal_type(np.std(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.std(B, out=d)) # E: numpy.ndarray[Any, Any] - -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[Any, Any]] -reveal_type(np.var(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.var(B, out=d)) # E: numpy.ndarray[Any, Any] +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 diff --git a/numpy/typing/tests/data/reveal/index_tricks.py b/numpy/typing/tests/data/reveal/index_tricks.py index ec2013025..863d60220 100644 --- a/numpy/typing/tests/data/reveal/index_tricks.py +++ b/numpy/typing/tests/data/reveal/index_tricks.py @@ -27,15 +27,16 @@ reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple reveal_type(iter(np.ndindex(1, 2, 3))) # E: Iterator[builtins.tuple[builtins.int]] reveal_type(next(np.ndindex(1, 2, 3))) # E: builtins.tuple[builtins.int] -reveal_type(np.unravel_index([22, 41, 37], (7, 6))) # E: tuple[Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]]] -reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]]] -reveal_type(np.unravel_index(1621, (6, 7, 8, 9))) # E: tuple[Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]]] - -reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6))) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F")) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip")) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap"))) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.unravel_index([22, 41, 37], (7, 6))) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.unravel_index(1621, (6, 7, 8, 9))) # E: tuple[{intp}] + +reveal_type(np.ravel_multi_index([[1]], (7, 6))) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6))) # E: {intp} +reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F")) # E: {intp} +reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip")) # E: {intp} +reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap"))) # E: {intp} +reveal_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))) # E: {intp} reveal_type(np.mgrid[1:1:2]) # E: numpy.ndarray[Any, numpy.dtype[Any]] reveal_type(np.mgrid[1:1:2, None:10]) # E: numpy.ndarray[Any, numpy.dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/lib_utils.py b/numpy/typing/tests/data/reveal/lib_utils.py new file mode 100644 index 000000000..d82012707 --- /dev/null +++ b/numpy/typing/tests/data/reveal/lib_utils.py @@ -0,0 +1,30 @@ +from io import StringIO +from typing import Any, Dict + +import numpy as np + +AR: np.ndarray[Any, np.dtype[np.float64]] +AR_DICT: Dict[str, np.ndarray[Any, np.dtype[np.float64]]] +FILE: StringIO + +def func(a: int) -> bool: ... + +reveal_type(np.deprecate(func)) # E: def (a: builtins.int) -> builtins.bool +reveal_type(np.deprecate()) # E: _Deprecate + +reveal_type(np.deprecate_with_doc("test")) # E: _Deprecate +reveal_type(np.deprecate_with_doc(None)) # E: _Deprecate + +reveal_type(np.byte_bounds(AR)) # E: Tuple[builtins.int, builtins.int] +reveal_type(np.byte_bounds(np.float64())) # E: Tuple[builtins.int, builtins.int] + +reveal_type(np.who(None)) # E: None +reveal_type(np.who(AR_DICT)) # E: None + +reveal_type(np.info(1, output=FILE)) # E: None + +reveal_type(np.source(np.interp, output=FILE)) # E: None + +reveal_type(np.lookfor("binary representation", output=FILE)) # E: None + +reveal_type(np.safe_eval("1 + 1")) # E: Any diff --git a/numpy/typing/tests/data/reveal/mod.py b/numpy/typing/tests/data/reveal/mod.py index 4a913f11a..bf45b8c58 100644 --- a/numpy/typing/tests/data/reveal/mod.py +++ b/numpy/typing/tests/data/reveal/mod.py @@ -26,8 +26,8 @@ reveal_type(AR_m % td) # E: Any reveal_type(td % AR_m) # E: Any reveal_type(divmod(td, td)) # E: Tuple[{int64}, numpy.timedelta64] -reveal_type(divmod(AR_m, td)) # E: Union[Tuple[numpy.signedinteger[numpy.typing._64Bit], numpy.timedelta64], Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]]] -reveal_type(divmod(td, AR_m)) # E: Union[Tuple[numpy.signedinteger[numpy.typing._64Bit], numpy.timedelta64], Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]]] +reveal_type(divmod(AR_m, td)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(divmod(td, AR_m)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] # Bool @@ -38,7 +38,7 @@ reveal_type(b_ % b_) # E: {int8} reveal_type(b_ % i8) # E: {int64} reveal_type(b_ % u8) # E: {uint64} reveal_type(b_ % f8) # E: {float64} -reveal_type(b_ % AR_b) # E: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(b_ % AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] reveal_type(divmod(b_, b)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(b_, i)) # E: Tuple[{int_}, {int_}] @@ -47,7 +47,7 @@ reveal_type(divmod(b_, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(b_, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(b_, u8)) # E: Tuple[{uint64}, {uint64}] reveal_type(divmod(b_, f8)) # E: Tuple[{float64}, {float64}] -reveal_type(divmod(b_, AR_b)) # E: Tuple[Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]], Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]]] +reveal_type(divmod(b_, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.dtype[{int8}]]] reveal_type(b % b_) # E: {int8} reveal_type(i % b_) # E: {int_} @@ -56,7 +56,7 @@ reveal_type(b_ % b_) # E: {int8} reveal_type(i8 % b_) # E: {int64} reveal_type(u8 % b_) # E: {uint64} reveal_type(f8 % b_) # E: {float64} -reveal_type(AR_b % b_) # E: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(AR_b % b_) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] reveal_type(divmod(b, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(i, b_)) # E: Tuple[{int_}, {int_}] @@ -65,7 +65,7 @@ reveal_type(divmod(b_, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(i8, b_)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(u8, b_)) # E: Tuple[{uint64}, {uint64}] reveal_type(divmod(f8, b_)) # E: Tuple[{float64}, {float64}] -reveal_type(divmod(AR_b, b_)) # E: Tuple[Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]], Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]]] +reveal_type(divmod(AR_b, b_)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.dtype[{int8}]]] # int @@ -78,7 +78,7 @@ reveal_type(i4 % i8) # E: {int64} reveal_type(i4 % f8) # E: {float64} reveal_type(i4 % i4) # E: {int32} reveal_type(i4 % f4) # E: {float32} -reveal_type(i8 % AR_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(i8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] reveal_type(divmod(i8, b)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i8, i)) # E: Tuple[{int64}, {int64}] @@ -89,7 +89,7 @@ reveal_type(divmod(i8, i4)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i8, f4)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i4, i4)) # E: Tuple[{int32}, {int32}] reveal_type(divmod(i4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(i8, AR_b)) # E: Tuple[Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]], Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]]] +reveal_type(divmod(i8, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] reveal_type(b % i8) # E: {int64} reveal_type(i % i8) # E: {int64} @@ -100,7 +100,7 @@ reveal_type(i8 % i4) # E: {int64} reveal_type(f8 % i4) # E: {float64} reveal_type(i4 % i4) # E: {int32} reveal_type(f4 % i4) # E: {float32} -reveal_type(AR_b % i8) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(AR_b % i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] reveal_type(divmod(b, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i, i8)) # E: Tuple[{int64}, {int64}] @@ -111,7 +111,7 @@ reveal_type(divmod(i4, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(f4, i8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i4, i4)) # E: Tuple[{int32}, {int32}] reveal_type(divmod(f4, i4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(AR_b, i8)) # E: Tuple[Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]], Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]]] +reveal_type(divmod(AR_b, i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] # float @@ -120,7 +120,7 @@ reveal_type(f8 % i) # E: {float64} reveal_type(f8 % f) # E: {float64} reveal_type(i8 % f4) # E: {float64} reveal_type(f4 % f4) # E: {float32} -reveal_type(f8 % AR_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(f8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] reveal_type(divmod(f8, b)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, i)) # E: Tuple[{float64}, {float64}] @@ -128,7 +128,7 @@ reveal_type(divmod(f8, f)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f4)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(f8, AR_b)) # E: Tuple[Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]], Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]]] +reveal_type(divmod(f8, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] reveal_type(b % f8) # E: {float64} reveal_type(i % f8) # E: {float64} @@ -136,7 +136,7 @@ reveal_type(f % f8) # E: {float64} reveal_type(f8 % f8) # E: {float64} reveal_type(f8 % f8) # E: {float64} reveal_type(f4 % f4) # E: {float32} -reveal_type(AR_b % f8) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(AR_b % f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] reveal_type(divmod(b, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i, f8)) # E: Tuple[{float64}, {float64}] @@ -144,4 +144,4 @@ reveal_type(divmod(f, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(AR_b, f8)) # E: Tuple[Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]], Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]]] +reveal_type(divmod(AR_b, f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] diff --git a/numpy/typing/tests/data/reveal/ndarray_misc.py b/numpy/typing/tests/data/reveal/ndarray_misc.py index 0f7345358..e0f44bcbc 100644 --- a/numpy/typing/tests/data/reveal/ndarray_misc.py +++ b/numpy/typing/tests/data/reveal/ndarray_misc.py @@ -16,24 +16,24 @@ B: SubClass 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[Any, Any]] -reveal_type(A.all(keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(A.any(keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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: {intp} reveal_type(A.argmax()) # E: {intp} -reveal_type(A.argmax(axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(A.argmax(axis=0)) # E: Any reveal_type(A.argmax(out=B)) # E: SubClass reveal_type(f8.argmin()) # E: {intp} reveal_type(A.argmin()) # E: {intp} -reveal_type(A.argmin(axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(A.argmin(axis=0)) # E: Any reveal_type(A.argmin(out=B)) # E: SubClass reveal_type(f8.argsort()) # E: numpy.ndarray[Any, Any] @@ -43,9 +43,9 @@ reveal_type(f8.astype(np.int64).choose([()])) # E: numpy.ndarray[Any, Any] reveal_type(A.choose([0])) # E: numpy.ndarray[Any, Any] reveal_type(A.choose([0], out=B)) # E: SubClass -reveal_type(f8.clip(1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.clip(1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.clip(None, 1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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 @@ -69,38 +69,38 @@ reveal_type(f8.cumsum()) # E: numpy.ndarray[Any, Any] reveal_type(A.cumsum()) # E: numpy.ndarray[Any, Any] 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[Any, Any]] -reveal_type(A.max(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(A.mean(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(A.min(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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: {float64} reveal_type(A.newbyteorder()) # E: numpy.ndarray[Any, Any] 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[Any, Any]] -reveal_type(A.prod(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(A.ptp(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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: {float64} @@ -111,40 +111,40 @@ reveal_type(f8.repeat(1)) # E: numpy.ndarray[Any, Any] reveal_type(A.repeat(1)) # E: numpy.ndarray[Any, Any] reveal_type(B.repeat(1)) # E: numpy.ndarray[Any, Any] -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[Any, Any]] -reveal_type(A.std(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any]] -reveal_type(A.sum(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any] 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[Any, Any]] -reveal_type(A.var(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +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[Any, Any] reveal_type(A.diagonal()) # E: numpy.ndarray[Any, Any] -reveal_type(A.dot(1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(A.dot(1)) # E: Any reveal_type(A.dot(1, out=B)) # E: SubClass reveal_type(A.nonzero()) # E: tuple[numpy.ndarray[Any, Any]] reveal_type(A.searchsorted([1])) # E: numpy.ndarray[Any, Any] -reveal_type(A.trace()) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(A.trace()) # E: Any reveal_type(A.trace(out=B)) # E: SubClass diff --git a/numpy/typing/tests/data/reveal/numeric.py b/numpy/typing/tests/data/reveal/numeric.py index 78e5c1d61..ec6e47ca0 100644 --- a/numpy/typing/tests/data/reveal/numeric.py +++ b/numpy/typing/tests/data/reveal/numeric.py @@ -20,8 +20,8 @@ C: 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[Any, Any]] -reveal_type(np.count_nonzero(A, axis=0)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, Any]] +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 @@ -76,9 +76,9 @@ reveal_type(np.allclose(i8, 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[Any, Any]] -reveal_type(np.isclose(B, A)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.isclose(A, A)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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 diff --git a/numpy/typing/tests/data/reveal/ufunclike.py b/numpy/typing/tests/data/reveal/ufunclike.py index 35e45a824..8b3aea7ce 100644 --- a/numpy/typing/tests/data/reveal/ufunclike.py +++ b/numpy/typing/tests/data/reveal/ufunclike.py @@ -9,21 +9,21 @@ AR_LIKE_O: List[np.object_] AR_U: np.ndarray[Any, np.dtype[np.str_]] -reveal_type(np.fix(AR_LIKE_b)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.fix(AR_LIKE_u)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.fix(AR_LIKE_i)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.fix(AR_LIKE_f)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(np.fix(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] reveal_type(np.fix(AR_LIKE_O)) # E: Any reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.isposinf(AR_LIKE_b)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isposinf(AR_LIKE_u)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isposinf(AR_LIKE_i)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isposinf(AR_LIKE_f)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(np.isposinf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isposinf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isposinf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isposinf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.isposinf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.isneginf(AR_LIKE_b)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isneginf(AR_LIKE_u)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isneginf(AR_LIKE_i)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isneginf(AR_LIKE_f)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(np.isneginf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isneginf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isneginf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isneginf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.isneginf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] diff --git a/tools/changelog.py b/tools/changelog.py index 920f5b87f..9da330500 100755 --- a/tools/changelog.py +++ b/tools/changelog.py @@ -66,10 +66,14 @@ def get_authors(revision_range): 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() @@ -92,8 +96,8 @@ def get_pull_requests(repo, revision_range): # 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() diff --git a/tools/lint_diff.ini b/tools/lint_diff.ini index ba091468e..3b66d3c3e 100644 --- a/tools/lint_diff.ini +++ b/tools/lint_diff.ini @@ -1,5 +1,4 @@ [pycodestyle] max_line_length = 79 statistics = True -ignore = E121,E122,E123,E125,E126,E127,E128,E226,E251,E265,E266,E302,E402,E712,E721,E731,E741,W291,W293,W391,W503,W504 -exclude = numpy/__config__.py +ignore = E121,E122,E123,E125,E126,E127,E128,E226,E251,E265,E266,E302,E402,E704,E712,E721,E731,E741,W291,W293,W391,W503,W504 diff --git a/tools/linter.py b/tools/linter.py index b1bab2b70..fd229dbef 100644 --- a/tools/linter.py +++ b/tools/linter.py @@ -9,6 +9,14 @@ CONFIG = os.path.join( 'lint_diff.ini', ) +# NOTE: The `diff` and `exclude` options of pycodestyle seem to be +# incompatible, so instead just exclude the necessary files when +# computing the diff itself. +EXCLUDE = ( + "numpy/typing/tests/data/", + "numpy/__config__.py", +) + class DiffLinter: def __init__(self, branch): @@ -29,11 +37,15 @@ class DiffLinter: print(f"Branch with name `{self.branch}` does not exist") sys.exit(1) + exclude = [f':(exclude){i}' for i in EXCLUDE] if uncommitted: - diff = self.repo.git.diff(self.head, '--unified=0', '***.py') + diff = self.repo.git.diff( + self.head, '--unified=0', '***.py', *exclude + ) else: - diff = self.repo.git.diff(commit, self.head, - '--unified=0', '***.py') + diff = self.repo.git.diff( + commit, self.head, '--unified=0', '***.py', *exclude + ) return diff def run_pycodestyle(self, diff): |