codecov-commenter
commented
1 year ago Codecov Report
Base: 86.91% // Head: 86.95% // Increases project coverage by +0.04% :tada:
Coverage data is based on head (
f2a4f35) compared to base (9cdc955). Patch coverage: 100.00% of modified lines in pull request are covered.
Additional details and impacted files
```diff @@ Coverage Diff @@ ## main #109 +/- ## ========================================== + Coverage 86.91% 86.95% +0.04% ========================================== Files 38 41 +3 Lines 1146 1150 +4 ========================================== + Hits 996 1000 +4 Misses 150 150 ``` | [Impacted Files](https://codecov.io/gh/qnbhd/deployme/pull/109?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Konstantin+Templin) | Coverage Δ | | |---|---|---| | [deployme/\_\_init\_\_.py](https://codecov.io/gh/qnbhd/deployme/pull/109/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Konstantin+Templin#diff-ZGVwbG95bWUvX19pbml0X18ucHk=) | `100.00% <100.00%> (ø)` | | | [deployme/contrib/\_\_init\_\_.py](https://codecov.io/gh/qnbhd/deployme/pull/109/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Konstantin+Templin#diff-ZGVwbG95bWUvY29udHJpYi9fX2luaXRfXy5weQ==) | `100.00% <0.00%> (ø)` | | | [deployme/utils/\_\_init\_\_.py](https://codecov.io/gh/qnbhd/deployme/pull/109/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Konstantin+Templin#diff-ZGVwbG95bWUvdXRpbHMvX19pbml0X18ucHk=) | `100.00% <0.00%> (ø)` | | Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Konstantin+Templin). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Konstantin+Templin):umbrella: View full report at Codecov.
:loudspeaker: Do you have feedback about the report comment? Let us know in this issue.
This PR contains the following updates:
==1.23.5->==1.24.1Release Notes
numpy/numpy
### [`v1.24.1`](https://togithub.com/numpy/numpy/releases/tag/v1.24.1) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.24.0...v1.24.1) ### NumPy 1.24.1 Release Notes NumPy 1.24.1 is a maintenance release that fixes bugs and regressions discovered after the 1.24.0 release. The Python versions supported by this release are 3.8-3.11. #### Contributors A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andrew Nelson - Ben Greiner + - Charles Harris - Clément Robert - Matteo Raso - Matti Picus - Melissa Weber Mendonça - Miles Cranmer - Ralf Gommers - Rohit Goswami - Sayed Adel - Sebastian Berg #### Pull requests merged A total of 18 pull requests were merged for this release. - [#22820](https://togithub.com/numpy/numpy/pull/22820): BLD: add workaround in setup.py for newer setuptools - [#22830](https://togithub.com/numpy/numpy/pull/22830): BLD: CIRRUS_TAG redux - [#22831](https://togithub.com/numpy/numpy/pull/22831): DOC: fix a couple typos in 1.23 notes - [#22832](https://togithub.com/numpy/numpy/pull/22832): BUG: Fix refcounting errors found using pytest-leaks - [#22834](https://togithub.com/numpy/numpy/pull/22834): BUG, SIMD: Fix invalid value encountered in several ufuncs - [#22837](https://togithub.com/numpy/numpy/pull/22837): TST: ignore more np.distutils.log imports - [#22839](https://togithub.com/numpy/numpy/pull/22839): BUG: Do not use getdata() in np.ma.masked_invalid - [#22847](https://togithub.com/numpy/numpy/pull/22847): BUG: Ensure correct behavior for rows ending in delimiter in... - [#22848](https://togithub.com/numpy/numpy/pull/22848): BUG, SIMD: Fix the bitmask of the boolean comparison - [#22857](https://togithub.com/numpy/numpy/pull/22857): BLD: Help raspian arm + clang 13 about \__builtin_mul_overflow - [#22858](https://togithub.com/numpy/numpy/pull/22858): API: Ensure a full mask is returned for masked_invalid - [#22866](https://togithub.com/numpy/numpy/pull/22866): BUG: Polynomials now copy properly ([#22669](https://togithub.com/numpy/numpy/issues/22669)) - [#22867](https://togithub.com/numpy/numpy/pull/22867): BUG, SIMD: Fix memory overlap in ufunc comparison loops - [#22868](https://togithub.com/numpy/numpy/pull/22868): BUG: Fortify string casts against floating point warnings - [#22875](https://togithub.com/numpy/numpy/pull/22875): TST: Ignore nan-warnings in randomized out tests - [#22883](https://togithub.com/numpy/numpy/pull/22883): MAINT: restore npymath implementations needed for freebsd - [#22884](https://togithub.com/numpy/numpy/pull/22884): BUG: Fix integer overflow in in1d for mixed integer dtypes [#22877](https://togithub.com/numpy/numpy/issues/22877) - [#22887](https://togithub.com/numpy/numpy/pull/22887): BUG: Use whole file for encoding checks with `charset_normalizer`. #### Checksums ##### MD5 9e543db90493d6a00939bd54c2012085 numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl 4ebd7af622bf617b4876087e500d7586 numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl 0c0a3012b438bb455a6c2fadfb1be76a numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0bddb527345449df624d3cb9aa0e1b75 numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b246beb773689d97307f7b4c2970f061 numpy-1.24.1-cp310-cp310-win32.whl 1f3823999fce821a28dee10ac6fdd721 numpy-1.24.1-cp310-cp310-win_amd64.whl 8eedcacd6b096a568e4cb393d43b3ae5 numpy-1.24.1-cp311-cp311-macosx_10_9_x86_64.whl 50bddb05acd54b4396100a70522496dd numpy-1.24.1-cp311-cp311-macosx_11_0_arm64.whl 2a76bd9da8a78b44eb816bd70fa3aee3 numpy-1.24.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9e86658a414272f9749bde39344f9b76 numpy-1.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 915dfb89054e1631574a22a9b53a2b25 numpy-1.24.1-cp311-cp311-win32.whl ab7caa2c6c20e1fab977e1a94dede976 numpy-1.24.1-cp311-cp311-win_amd64.whl 8246de961f813f5aad89bca3d12f81e7 numpy-1.24.1-cp38-cp38-macosx_10_9_x86_64.whl 58366b1a559baa0547ce976e416ed76d numpy-1.24.1-cp38-cp38-macosx_11_0_arm64.whl a96f29bf106a64f82b9ba412635727d1 numpy-1.24.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4c32a43bdb85121614ab3e99929e33c7 numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 09b20949ed21683ad7c9cbdf9ebb2439 numpy-1.24.1-cp38-cp38-win32.whl 9e9f1577f874286a8bdff8dc5551eb9f numpy-1.24.1-cp38-cp38-win_amd64.whl 4383c1137f0287df67c364fbdba2bc72 numpy-1.24.1-cp39-cp39-macosx_10_9_x86_64.whl 987f22c49b2be084b5d72f88f347d31e numpy-1.24.1-cp39-cp39-macosx_11_0_arm64.whl 848ad020bba075ed8f19072c64dcd153 numpy-1.24.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 864b159e644848bc25f881907dbcf062 numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl db339ec0b2693cac2d7cf9ca75c334b1 numpy-1.24.1-cp39-cp39-win32.whl fec91d4c85066ad8a93816d71b627701 numpy-1.24.1-cp39-cp39-win_amd64.whl 619af9cd4f33b668822ae2350f446a15 numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 46f19b4b147f8836c2bd34262fabfffa numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e85b245c57a10891b3025579bf0cf298 numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl dd3aaeeada8e95cc2edf9a3a4aa8b5af numpy-1.24.1.tar.gz ##### SHA256 179a7ef0889ab769cc03573b6217f54c8bd8e16cef80aad369e1e8185f994cd7 numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl b09804ff570b907da323b3d762e74432fb07955701b17b08ff1b5ebaa8cfe6a9 numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl f1b739841821968798947d3afcefd386fa56da0caf97722a5de53e07c4ccedc7 numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0e3463e6ac25313462e04aea3fb8a0a30fb906d5d300f58b3bc2c23da6a15398 numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b31da69ed0c18be8b77bfce48d234e55d040793cebb25398e2a7d84199fbc7e2 numpy-1.24.1-cp310-cp310-win32.whl b07b40f5fb4fa034120a5796288f24c1fe0e0580bbfff99897ba6267af42def2 numpy-1.24.1-cp310-cp310-win_amd64.whl 7094891dcf79ccc6bc2a1f30428fa5edb1e6fb955411ffff3401fb4ea93780a8 numpy-1.24.1-cp311-cp311-macosx_10_9_x86_64.whl 28e418681372520c992805bb723e29d69d6b7aa411065f48216d8329d02ba032 numpy-1.24.1-cp311-cp311-macosx_11_0_arm64.whl e274f0f6c7efd0d577744f52032fdd24344f11c5ae668fe8d01aac0422611df1 numpy-1.24.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0044f7d944ee882400890f9ae955220d29b33d809a038923d88e4e01d652acd9 numpy-1.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 442feb5e5bada8408e8fcd43f3360b78683ff12a4444670a7d9e9824c1817d36 numpy-1.24.1-cp311-cp311-win32.whl de92efa737875329b052982e37bd4371d52cabf469f83e7b8be9bb7752d67e51 numpy-1.24.1-cp311-cp311-win_amd64.whl b162ac10ca38850510caf8ea33f89edcb7b0bb0dfa5592d59909419986b72407 numpy-1.24.1-cp38-cp38-macosx_10_9_x86_64.whl 26089487086f2648944f17adaa1a97ca6aee57f513ba5f1c0b7ebdabbe2b9954 numpy-1.24.1-cp38-cp38-macosx_11_0_arm64.whl caf65a396c0d1f9809596be2e444e3bd4190d86d5c1ce21f5fc4be60a3bc5b36 numpy-1.24.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b0677a52f5d896e84414761531947c7a330d1adc07c3a4372262f25d84af7bf7 numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl dae46bed2cb79a58d6496ff6d8da1e3b95ba09afeca2e277628171ca99b99db1 numpy-1.24.1-cp38-cp38-win32.whl 6ec0c021cd9fe732e5bab6401adea5a409214ca5592cd92a114f7067febcba0c numpy-1.24.1-cp38-cp38-win_amd64.whl 28bc9750ae1f75264ee0f10561709b1462d450a4808cd97c013046073ae64ab6 numpy-1.24.1-cp39-cp39-macosx_10_9_x86_64.whl 84e789a085aabef2f36c0515f45e459f02f570c4b4c4c108ac1179c34d475ed7 numpy-1.24.1-cp39-cp39-macosx_11_0_arm64.whl 8e669fbdcdd1e945691079c2cae335f3e3a56554e06bbd45d7609a6cf568c700 numpy-1.24.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ef85cf1f693c88c1fd229ccd1055570cb41cdf4875873b7728b6301f12cd05bf numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 87a118968fba001b248aac90e502c0b13606721b1343cdaddbc6e552e8dfb56f numpy-1.24.1-cp39-cp39-win32.whl ddc7ab52b322eb1e40521eb422c4e0a20716c271a306860979d450decbb51b8e numpy-1.24.1-cp39-cp39-win_amd64.whl ed5fb71d79e771ec930566fae9c02626b939e37271ec285e9efaf1b5d4370e7d numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl ad2925567f43643f51255220424c23d204024ed428afc5aad0f86f3ffc080086 numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cfa1161c6ac8f92dea03d625c2d0c05e084668f4a06568b77a25a89111621566 numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl 2386da9a471cc00a1f47845e27d916d5ec5346ae9696e01a8a34760858fe9dd2 numpy-1.24.1.tar.gz ### [`v1.24.0`](https://togithub.com/numpy/numpy/releases/tag/v1.24.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.23.5...v1.24.0) ##### NumPy 1.24 Release Notes The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are - Many new deprecations, check them out. - Many expired deprecations, - New F2PY features and fixes. - New "dtype" and "casting" keywords for stacking functions. See below for the details, This release supports Python versions 3.8-3.11. ##### Deprecations ##### Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose The `numpy.fastCopyAndTranspose` function has been deprecated. Use the corresponding copy and transpose methods directly: arr.T.copy() The underlying C function `PyArray_CopyAndTranspose` has also been deprecated from the NumPy C-API. ([gh-22313](https://togithub.com/numpy/numpy/pull/22313)) ##### Conversion of out-of-bound Python integers Attempting a conversion from a Python integer to a NumPy value will now always check whether the result can be represented by NumPy. This means the following examples will fail in the future and give a `DeprecationWarning` now: np.uint8(-1) np.array([3000], dtype=np.int8) Many of these did succeed before. Such code was mainly useful for unsigned integers with negative values such as `np.uint8(-1)` giving `np.iinfo(np.uint8).max`. Note that conversion between NumPy integers is unaffected, so that `np.array(-1).astype(np.uint8)` continues to work and use C integer overflow logic. For negative values, it will also work to view the array: `np.array(-1, dtype=np.int8).view(np.uint8)`. In some cases, using `np.iinfo(np.uint8).max` or `val % 2**8` may also work well. In rare cases input data may mix both negative values and very large unsigned values (i.e. `-1` and `2**63`). There it is unfortunately necessary to use `%` on the Python value or use signed or unsigned conversion depending on whether negative values are expected. ([gh-22385](https://togithub.com/numpy/numpy/pull/22385)) ##### Deprecate `msort` The `numpy.msort` function is deprecated. Use `np.sort(a, axis=0)` instead. ([gh-22456](https://togithub.com/numpy/numpy/pull/22456)) ##### `np.str0` and similar are now deprecated The scalar type aliases ending in a 0 bit size: `np.object0`, `np.str0`, `np.bytes0`, `np.void0`, `np.int0`, `np.uint0` as well as `np.bool8` are now deprecated and will eventually be removed. ([gh-22607](https://togithub.com/numpy/numpy/pull/22607)) ##### Expired deprecations - The `normed` keyword argument has been removed from \[np.histogram]{.title-ref}, \[np.histogram2d]{.title-ref}, and \[np.histogramdd]{.title-ref}. Use `density` instead. If `normed` was passed by position, `density` is now used. ([gh-21645](https://togithub.com/numpy/numpy/pull/21645)) - Ragged array creation will now always raise a `ValueError` unless `dtype=object` is passed. This includes very deeply nested sequences. ([gh-22004](https://togithub.com/numpy/numpy/pull/22004)) - Support for Visual Studio 2015 and earlier has been removed. - Support for the Windows Interix POSIX interop layer has been removed. ([gh-22139](https://togithub.com/numpy/numpy/pull/22139)) - Support for Cygwin < 3.3 has been removed. ([gh-22159](https://togithub.com/numpy/numpy/pull/22159)) - The mini() method of `np.ma.MaskedArray` has been removed. Use either `np.ma.MaskedArray.min()` or `np.ma.minimum.reduce()`. - The single-argument form of `np.ma.minimum` and `np.ma.maximum` has been removed. Use `np.ma.minimum.reduce()` or `np.ma.maximum.reduce()` instead. ([gh-22228](https://togithub.com/numpy/numpy/pull/22228)) - Passing dtype instances other than the canonical (mainly native byte-order) ones to `dtype=` or `signature=` in ufuncs will now raise a `TypeError`. We recommend passing the strings `"int8"` or scalar types `np.int8` since the byte-order, datetime/timedelta unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.) ([gh-22540](https://togithub.com/numpy/numpy/pull/22540)) - The `dtype=` argument to comparison ufuncs is now applied correctly. That means that only `bool` and `object` are valid values and `dtype=object` is enforced. ([gh-22541](https://togithub.com/numpy/numpy/pull/22541)) - The deprecation for the aliases `np.object`, `np.bool`, `np.float`, `np.complex`, `np.str`, and `np.int` is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future. ([gh-22607](https://togithub.com/numpy/numpy/pull/22607)) ##### Compatibility notes ##### `array.fill(scalar)` may behave slightly different `numpy.ndarray.fill` may in some cases behave slightly different now due to the fact that the logic is aligned with item assignment: arr = np.array([1]) # with any dtype/value arr.fill(scalar) ##### is now identical to: arr[0] = scalar Previously casting may have produced slightly different answers when using values that could not be represented in the target `dtype` or when the target had `object` dtype. ([gh-20924](https://togithub.com/numpy/numpy/pull/20924)) ##### Subarray to object cast now copies Casting a dtype that includes a subarray to an object will now ensure a copy of the subarray. Previously an unsafe view was returned: arr = np.ones(3, dtype=[("f", "i", 3)]) subarray_fields = arr.astype(object)[0] subarray = subarray_fields[0] # "f" field np.may_share_memory(subarray, arr) Is now always false. While previously it was true for the specific cast. ([gh-21925](https://togithub.com/numpy/numpy/pull/21925)) ##### Returned arrays respect uniqueness of dtype kwarg objects When the `dtype` keyword argument is used with :py`np.array()`{.interpreted-text role="func"} or :py`asarray()`{.interpreted-text role="func"}, the dtype of the returned array now always exactly matches the dtype provided by the caller. In some cases this change means that a *view* rather than the input array is returned. The following is an example for this on 64bit Linux where `long` and `longlong` are the same precision but different `dtypes`: >>> arr = np.array([1, 2, 3], dtype="long") >>> new_dtype = np.dtype("longlong") >>> new = np.asarray(arr, dtype=new_dtype) >>> new.dtype is new_dtype True >>> new is arr False Before the change, the `dtype` did not match because `new is arr` was `True`. ([gh-21995](https://togithub.com/numpy/numpy/pull/21995)) ##### DLPack export raises `BufferError` When an array buffer cannot be exported via DLPack a `BufferError` is now always raised where previously `TypeError` or `RuntimeError` was raised. This allows falling back to the buffer protocol or `__array_interface__` when DLPack was tried first. ([gh-22542](https://togithub.com/numpy/numpy/pull/22542)) ##### NumPy builds are no longer tested on GCC-6 Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available on Ubuntu 20.04, so builds using that compiler are no longer tested. We still test builds using GCC-7 and GCC-8. ([gh-22598](https://togithub.com/numpy/numpy/pull/22598)) ##### New Features ##### New attribute `symbol` added to polynomial classes The polynomial classes in the `numpy.polynomial` package have a new `symbol` attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing: >>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y") >>> print(P_y) 1.0 + 0.0·y¹ - 1.0·y² Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate: >>> P = np.polynomial.Polynomial([1, -1]) # default symbol is "x" >>> P_z = np.polynomial.Polynomial([1, 1], symbol="z") >>> P * P_z Traceback (most recent call last) ... ValueError: Polynomial symbols differ The symbol can be any valid Python identifier. The default is `symbol=x`, consistent with existing behavior. ([gh-16154](https://togithub.com/numpy/numpy/pull/16154)) ##### F2PY support for Fortran `character` strings F2PY now supports wrapping Fortran functions with: - character (e.g. `character x`) - character array (e.g. `character, dimension(n) :: x`) - character string (e.g. `character(len=10) x`) - and character string array (e.g. `character(len=10), dimension(n, m) :: x`) arguments, including passing Python unicode strings as Fortran character string arguments. ([gh-19388](https://togithub.com/numpy/numpy/pull/19388)) ##### New function `np.show_runtime` A new function `numpy.show_runtime` has been added to display the runtime information of the machine in addition to `numpy.show_config` which displays the build-related information. ([gh-21468](https://togithub.com/numpy/numpy/pull/21468)) ##### `strict` option for `testing.assert_array_equal` The `strict` option is now available for `testing.assert_array_equal`. Setting `strict=True` will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type. ([gh-21595](https://togithub.com/numpy/numpy/pull/21595)) ##### New parameter `equal_nan` added to `np.unique` `np.unique` was changed in 1.21 to treat all `NaN` values as equal and return a single `NaN`. Setting `equal_nan=False` will restore pre-1.21 behavior to treat `NaNs` as unique. Defaults to `True`. ([gh-21623](https://togithub.com/numpy/numpy/pull/21623)) ##### `casting` and `dtype` keyword arguments for `numpy.stack` The `casting` and `dtype` keyword arguments are now available for `numpy.stack`. To use them, write `np.stack(..., dtype=None, casting='same_kind')`. ##### `casting` and `dtype` keyword arguments for `numpy.vstack` The `casting` and `dtype` keyword arguments are now available for `numpy.vstack`. To use them, write `np.vstack(..., dtype=None, casting='same_kind')`. ##### `casting` and `dtype` keyword arguments for `numpy.hstack` The `casting` and `dtype` keyword arguments are now available for `numpy.hstack`. To use them, write `np.hstack(..., dtype=None, casting='same_kind')`. ([gh-21627](https://togithub.com/numpy/numpy/pull/21627)) ##### The bit generator underlying the singleton RandomState can be changed The singleton `RandomState` instance exposed in the `numpy.random` module is initialized at startup with the `MT19937` bit generator. The new function `set_bit_generator` allows the default bit generator to be replaced with a user-provided bit generator. This function has been introduced to provide a method allowing seamless integration of a high-quality, modern bit generator in new code with existing code that makes use of the singleton-provided random variate generating functions. The companion function `get_bit_generator` returns the current bit generator being used by the singleton `RandomState`. This is provided to simplify restoring the original source of randomness if required. The preferred method to generate reproducible random numbers is to use a modern bit generator in an instance of `Generator`. The function `default_rng` simplifies instantiation: >>> rg = np.random.default_rng(3728973198) >>> rg.random() The same bit generator can then be shared with the singleton instance so that calling functions in the `random` module will use the same bit generator: >>> orig_bit_gen = np.random.get_bit_generator() >>> np.random.set_bit_generator(rg.bit_generator) >>> np.random.normal() The swap is permanent (until reversed) and so any call to functions in the `random` module will use the new bit generator. The original can be restored if required for code to run correctly: >>> np.random.set_bit_generator(orig_bit_gen) ([gh-21976](https://togithub.com/numpy/numpy/pull/21976)) ##### `np.void` now has a `dtype` argument NumPy now allows constructing structured void scalars directly by passing the `dtype` argument to `np.void`. ([gh-22316](https://togithub.com/numpy/numpy/pull/22316)) ##### Improvements ##### F2PY Improvements - The generated extension modules don't use the deprecated NumPy-C API anymore - Improved `f2py` generated exception messages - Numerous bug and `flake8` warning fixes - various CPP macros that one can use within C-expressions of signature files are prefixed with `f2py_`. For example, one should use `f2py_len(x)` instead of `len(x)` - A new construct `character(f2py_len=...)` is introduced to support returning assumed length character strings (e.g. `character(len=*)`) from wrapper functions A hook to support rewriting `f2py` internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in `C` expressions. ([gh-19388](https://togithub.com/numpy/numpy/pull/19388)) ##### IBM zSystems Vector Extension Facility (SIMD) Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations: rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide. ([gh-20913](https://togithub.com/numpy/numpy/pull/20913)) ##### NumPy now gives floating point errors in casts In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like: np.array([2e300]).astype(np.float32) # overflow for float32 np.array([np.inf]).astype(np.int64) Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings. Users can modify the behavior of these warnings using `np.errstate`. Note that for float to int casts, the exact warnings that are given may be platform dependent. For example: arr = np.full(100, value=1000, dtype=np.float64) arr.astype(np.int8) May give a result equivalent to (the intermediate cast means no warning is given): arr.astype(np.int64).astype(np.int8) May return an undefined result, with a warning set: RuntimeWarning: invalid value encountered in cast The precise behavior is subject to the C99 standard and its implementation in both software and hardware. ([gh-21437](https://togithub.com/numpy/numpy/pull/21437)) ##### F2PY supports the value attribute The Fortran standard requires that variables declared with the `value` attribute must be passed by value instead of reference. F2PY now supports this use pattern correctly. So `integer, intent(in), value :: x` in Fortran codes will have correct wrappers generated. ([gh-21807](https://togithub.com/numpy/numpy/pull/21807)) ##### Added pickle support for third-party BitGenerators The pickle format for bit generators was extended to allow each bit generator to supply its own constructor when during pickling. Previous versions of NumPy only supported unpickling `Generator` instances created with one of the core set of bit generators supplied with NumPy. Attempting to unpickle a `Generator` that used a third-party bit generators would fail since the constructor used during the unpickling was only aware of the bit generators included in NumPy. ([gh-22014](https://togithub.com/numpy/numpy/pull/22014)) ##### arange() now explicitly fails with dtype=str Previously, the `np.arange(n, dtype=str)` function worked for `n=1` and `n=2`, but would raise a non-specific exception message for other values of `n`. Now, it raises a \[TypeError]{.title-ref} informing that `arange` does not support string dtypes: >>> np.arange(2, dtype=str) Traceback (most recent call last) ... TypeError: arange() not supported for inputs with DTypeConfiguration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
🔕 Ignore: Close this PR and you won't be reminded about this update again.
This PR has been generated by Mend Renovate. View repository job log here.