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Geodels / gospl

Global Scalable Paleo Landscape Evolution Model
https://gospl.readthedocs.io
GNU General Public License v3.0
56 stars 11 forks source link

PyUP - Dependency Update Scheduled daily dependency update on Wednesday #232

Closed pyup-bot closed 7 months ago

pyup-bot commented 11 months ago

Update numpy-indexed from 0.3.5 to 0.3.7.

The bot wasn't able to find a changelog for this release. Got an idea?

Links - PyPI: https://pypi.org/project/numpy-indexed - Repo: https://github.com/EelcoHoogendoorn/Numpy_arraysetops_EP

Update numpy from 1.19.5 to 1.25.2.

Changelog ### 1.25.2 ``` discovered after the 1.25.1 release. This is the last planned release in the 1.25.x series, the next release will be 1.26.0, which will use the meson build system and support Python 3.12. The Python versions supported by this release are 3.9-3.11. Contributors A total of 13 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Aaron Meurer - Andrew Nelson - Charles Harris - Kevin Sheppard - Matti Picus - Nathan Goldbaum - Peter Hawkins - Ralf Gommers - Randy Eckenrode + - Sam James + - Sebastian Berg - Tyler Reddy - dependabot\[bot\] Pull requests merged A total of 19 pull requests were merged for this release. - [24148](https://github.com/numpy/numpy/pull/24148): MAINT: prepare 1.25.x for further development - [24174](https://github.com/numpy/numpy/pull/24174): ENH: Improve clang-cl compliance - [24179](https://github.com/numpy/numpy/pull/24179): MAINT: Upgrade various build dependencies. - [24182](https://github.com/numpy/numpy/pull/24182): BLD: use `-ftrapping-math` with Clang on macOS - [24183](https://github.com/numpy/numpy/pull/24183): BUG: properly handle negative indexes in ufunc_at fast path - [24184](https://github.com/numpy/numpy/pull/24184): BUG: PyObject_IsTrue and PyObject_Not error handling in setflags - [24185](https://github.com/numpy/numpy/pull/24185): BUG: histogram small range robust - [24186](https://github.com/numpy/numpy/pull/24186): MAINT: Update meson.build files from main branch - [24234](https://github.com/numpy/numpy/pull/24234): MAINT: exclude min, max and round from `np.__all__` - [24241](https://github.com/numpy/numpy/pull/24241): MAINT: Dependabot updates - [24242](https://github.com/numpy/numpy/pull/24242): BUG: Fix the signature for np.array_api.take - [24243](https://github.com/numpy/numpy/pull/24243): BLD: update OpenBLAS to an intermeidate commit - [24244](https://github.com/numpy/numpy/pull/24244): BUG: Fix reference count leak in str(scalar). - [24245](https://github.com/numpy/numpy/pull/24245): BUG: fix invalid function pointer conversion error - [24255](https://github.com/numpy/numpy/pull/24255): BUG: Factor out slow `getenv` call used for memory policy warning - [24292](https://github.com/numpy/numpy/pull/24292): CI: correct URL in cirrus.star - [24293](https://github.com/numpy/numpy/pull/24293): BUG: Fix C types in scalartypes - [24294](https://github.com/numpy/numpy/pull/24294): BUG: do not modify the input to ufunc_at - [24295](https://github.com/numpy/numpy/pull/24295): BUG: Further fixes to indexing loop and added tests Checksums MD5 33518ccb4da8ee11f1dee4b9fef1e468 numpy-1.25.2-cp310-cp310-macosx_10_9_x86_64.whl b5cb0c3b33ef6d93ec2888f25b065636 numpy-1.25.2-cp310-cp310-macosx_11_0_arm64.whl ae027dd38bd73f09c07220b2f516f148 numpy-1.25.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 88cf69dc3c0d293492c4c7e75dccf3d8 numpy-1.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3e4e3ad02375ba71ae2cd05ccd97aba4 numpy-1.25.2-cp310-cp310-musllinux_1_1_x86_64.whl f52bb644682deb26c35ddec77198b65c numpy-1.25.2-cp310-cp310-win32.whl 4944cf36652be7560a6bcd0d5d56e8ea numpy-1.25.2-cp310-cp310-win_amd64.whl 5a56e639defebb7b871c8c5613960ca3 numpy-1.25.2-cp311-cp311-macosx_10_9_x86_64.whl 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1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55 numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901 numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760 numpy-1.25.2.tar.gz ``` ### 1.25.1 ``` discovered after the 1.25.0 release. The Python versions supported by this release are 3.9-3.11. Contributors A total of 10 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Andrew Nelson - Charles Harris - Developer-Ecosystem-Engineering - Hood Chatham - Nathan Goldbaum - Rohit Goswami - Sebastian Berg - Tim Paine + - dependabot\[bot\] - matoro + Pull requests merged A total of 14 pull requests were merged for this release. - [23968](https://github.com/numpy/numpy/pull/23968): MAINT: prepare 1.25.x for further development - [24036](https://github.com/numpy/numpy/pull/24036): BLD: Port long double identification to C for meson - [24037](https://github.com/numpy/numpy/pull/24037): BUG: Fix reduction `return NULL` to be `goto fail` - [24038](https://github.com/numpy/numpy/pull/24038): BUG: Avoid undefined behavior in array.astype() - [24039](https://github.com/numpy/numpy/pull/24039): BUG: Ensure `__array_ufunc__` works without any kwargs passed - [24117](https://github.com/numpy/numpy/pull/24117): MAINT: Pin urllib3 to avoid anaconda-client bug. - [24118](https://github.com/numpy/numpy/pull/24118): TST: Pin pydantic\<2 in Pyodide workflow - [24119](https://github.com/numpy/numpy/pull/24119): MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1 - [24120](https://github.com/numpy/numpy/pull/24120): MAINT: Bump actions/checkout from 3.5.2 to 3.5.3 - [24122](https://github.com/numpy/numpy/pull/24122): BUG: Multiply or Divides using SIMD without a full vector can\... - [24127](https://github.com/numpy/numpy/pull/24127): MAINT: testing for IS_MUSL closes #24074 - [24128](https://github.com/numpy/numpy/pull/24128): BUG: Only replace dtype temporarily if dimensions changed - [24129](https://github.com/numpy/numpy/pull/24129): MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0 - [24134](https://github.com/numpy/numpy/pull/24134): BUG: Fix private procedures in f2py modules Checksums MD5 d09d98643db31e892fad11b8c2b7af22 numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl d5b8d3b0424e2af41018f35a087c4500 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NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been work to prepare for the future NumPy 2.0.0 release, resulting in a large number of new and expired deprecation. Highlights are: - Support for MUSL, there are now MUSL wheels. - Support the Fujitsu C/C++ compiler. - Object arrays are now supported in einsum - Support for inplace matrix multiplication (`=`). We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is needed because distutils has been dropped by Python 3.12 and we will be switching to using meson for future builds. The next mainline release will be NumPy 2.0.0. We plan that the 2.0 series will still support downstream projects built against earlier versions of NumPy. The Python versions supported in this release are 3.9-3.11. Deprecations - `np.core.MachAr` is deprecated. It is private API. In names defined in `np.core` should generally be considered private. ([gh-22638](https://github.com/numpy/numpy/pull/22638)) - `np.finfo(None)` is deprecated. ([gh-23011](https://github.com/numpy/numpy/pull/23011)) - `np.round_` is deprecated. Use `np.round` instead. ([gh-23302](https://github.com/numpy/numpy/pull/23302)) - `np.product` is deprecated. Use `np.prod` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.cumproduct` is deprecated. Use `np.cumprod` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.sometrue` is deprecated. Use `np.any` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.alltrue` is deprecated. Use `np.all` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - Only ndim-0 arrays are treated as scalars. NumPy used to treat all arrays of size 1 (e.g., `np.array([3.14])`) as scalars. In the future, this will be limited to arrays of ndim 0 (e.g., `np.array(3.14)`). The following expressions will report a deprecation warning: python a = np.array([3.14]) float(a) better: a[0] to get the numpy.float or a.item() b = np.array([[3.14]]) c = numpy.random.rand(10) c[0] = b better: c[0] = b[0, 0] ([gh-10615](https://github.com/numpy/numpy/pull/10615)) - `numpy.find_common_type` is now deprecated and its use should be replaced with either `numpy.result_type` or `numpy.promote_types`. Most users leave the second `scalar_types` argument to `find_common_type` as `[]` in which case `np.result_type` and `np.promote_types` are both faster and more robust. When not using `scalar_types` the main difference is that the replacement intentionally converts non-native byte-order to native byte order. Further, `find_common_type` returns `object` dtype rather than failing promotion. This leads to differences when the inputs are not all numeric. Importantly, this also happens for e.g. timedelta/datetime for which NumPy promotion rules are currently sometimes surprising. When the `scalar_types` argument is not `[]` things are more complicated. In most cases, using `np.result_type` and passing the Python values `0`, `0.0`, or `0j` has the same result as using `int`, `float`, or `complex` in `scalar_types`. When `scalar_types` is constructed, `np.result_type` is the correct replacement and it may be passed scalar values like `np.float32(0.0)`. Passing values other than 0, may lead to value-inspecting behavior (which `np.find_common_type` never used and NEP 50 may change in the future). The main possible change in behavior in this case, is when the array types are signed integers and scalar types are unsigned. If you are unsure about how to replace a use of `scalar_types` or when non-numeric dtypes are likely, please do not hesitate to open a NumPy issue to ask for help. ([gh-22539](https://github.com/numpy/numpy/pull/22539)) Expired deprecations - `np.core.machar` and `np.finfo.machar` have been removed. ([gh-22638](https://github.com/numpy/numpy/pull/22638)) - `+arr` will now raise an error when the dtype is not numeric (and positive is undefined). ([gh-22998](https://github.com/numpy/numpy/pull/22998)) - A sequence must now be passed into the stacking family of functions (`stack`, `vstack`, `hstack`, `dstack` and `column_stack`). ([gh-23019](https://github.com/numpy/numpy/pull/23019)) - `np.clip` now defaults to same-kind casting. Falling back to unsafe casting was deprecated in NumPy 1.17. ([gh-23403](https://github.com/numpy/numpy/pull/23403)) - `np.clip` will now propagate `np.nan` values passed as `min` or `max`. Previously, a scalar NaN was usually ignored. This was deprecated in NumPy 1.17. ([gh-23403](https://github.com/numpy/numpy/pull/23403)) - The `np.dual` submodule has been removed. ([gh-23480](https://github.com/numpy/numpy/pull/23480)) - NumPy now always ignores sequence behavior for an array-like (defining one of the array protocols). (Deprecation started NumPy 1.20) ([gh-23660](https://github.com/numpy/numpy/pull/23660)) - The niche `FutureWarning` when casting to a subarray dtype in `astype` or the array creation functions such as `asarray` is now finalized. The behavior is now always the same as if the subarray dtype was wrapped into a single field (which was the workaround, previously). (FutureWarning since NumPy 1.20) ([gh-23666](https://github.com/numpy/numpy/pull/23666)) - `==` and `!=` warnings have been finalized. The `==` and `!=` operators on arrays now always: - raise errors that occur during comparisons such as when the arrays have incompatible shapes (`np.array([1, 2]) == np.array([1, 2, 3])`). - return an array of all `True` or all `False` when values are fundamentally not comparable (e.g. have different dtypes). An example is `np.array(["a"]) == np.array([1])`. This mimics the Python behavior of returning `False` and `True` when comparing incompatible types like `"a" == 1` and `"a" != 1`. For a long time these gave `DeprecationWarning` or `FutureWarning`. ([gh-22707](https://github.com/numpy/numpy/pull/22707)) - Nose support has been removed. NumPy switched to using pytest in 2018 and nose has been unmaintained for many years. We have kept NumPy\'s nose support to avoid breaking downstream projects who might have been using it and not yet switched to pytest or some other testing framework. With the arrival of Python 3.12, unpatched nose will raise an error. It is time to move on. *Decorators removed*: - raises - slow - setastest - skipif - knownfailif - deprecated - parametrize - \_needs_refcount These are not to be confused with pytest versions with similar names, e.g., pytest.mark.slow, pytest.mark.skipif, pytest.mark.parametrize. *Functions removed*: - Tester - import_nose - run_module_suite ([gh-23041](https://github.com/numpy/numpy/pull/23041)) - The `numpy.testing.utils` shim has been removed. Importing from the `numpy.testing.utils` shim has been deprecated since 2019, the shim has now been removed. All imports should be made directly from `numpy.testing`. ([gh-23060](https://github.com/numpy/numpy/pull/23060)) - The environment variable to disable dispatching has been removed. Support for the `NUMPY_EXPERIMENTAL_ARRAY_FUNCTION` environment variable has been removed. This variable disabled dispatching with `__array_function__`. ([gh-23376](https://github.com/numpy/numpy/pull/23376)) - Support for `y=` as an alias of `out=` has been removed. The `fix`, `isposinf` and `isneginf` functions allowed using `y=` as a (deprecated) alias for `out=`. This is no longer supported. ([gh-23376](https://github.com/numpy/numpy/pull/23376)) Compatibility notes - The `busday_count` method now correctly handles cases where the `begindates` is later in time than the `enddates`. Previously, the `enddates` was included, even though the documentation states it is always excluded. ([gh-23229](https://github.com/numpy/numpy/pull/23229)) - When comparing datetimes and timedelta using `np.equal` or `np.not_equal` numpy previously allowed the comparison with `casting="unsafe"`. This operation now fails. Forcing the output dtype using the `dtype` kwarg can make the operation succeed, but we do not recommend it. ([gh-22707](https://github.com/numpy/numpy/pull/22707)) - When loading data from a file handle using `np.load`, if the handle is at the end of file, as can happen when reading multiple arrays by calling `np.load` repeatedly, numpy previously raised `ValueError` if `allow_pickle=False`, and `OSError` if `allow_pickle=True`. Now it raises `EOFError` instead, in both cases. ([gh-23105](https://github.com/numpy/numpy/pull/23105)) `np.pad` with `mode=wrap` pads with strict multiples of original data Code based on earlier version of `pad` that uses `mode="wrap"` will return different results when the padding size is larger than initial array. `np.pad` with `mode=wrap` now always fills the space with strict multiples of original data even if the padding size is larger than the initial array. ([gh-22575](https://github.com/numpy/numpy/pull/22575)) Cython `long_t` and `ulong_t` removed `long_t` and `ulong_t` were aliases for `longlong_t` and `ulonglong_t` and confusing (a remainder from of Python 2). This change may lead to the errors: 'long_t' is not a type identifier 'ulong_t' is not a type identifier We recommend use of bit-sized types such as `cnp.int64_t` or the use of `cnp.intp_t` which is 32 bits on 32 bit systems and 64 bits on 64 bit systems (this is most compatible with indexing). If C `long` is desired, use plain `long` or `npy_long`. `cnp.int_t` is also `long` (NumPy\'s default integer). However, `long` is 32 bit on 64 bit windows and we may wish to adjust this even in NumPy. (Please do not hesitate to contact NumPy developers if you are curious about this.) ([gh-22637](https://github.com/numpy/numpy/pull/22637)) Changed error message and type for bad `axes` argument to `ufunc` The error message and type when a wrong `axes` value is passed to `ufunc(..., axes=[...])` has changed. The message is now more indicative of the problem, and if the value is mismatched an `AxisError` will be raised. A `TypeError` will still be raised for invalidinput types. ([gh-22675](https://github.com/numpy/numpy/pull/22675)) Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where` If the `where` keyword argument of a `numpy.ufunc`{.interpreted-text role="class"} is a subclass of `numpy.ndarray`{.interpreted-text role="class"} or is a duck type that defines `numpy.class.__array_ufunc__`{.interpreted-text role="func"} it can override the behavior of the ufunc using the same mechanism as the input and output arguments. Note that for this to work properly, the `where.__array_ufunc__` implementation will have to unwrap the `where` argument to pass it into the default implementation of the `ufunc` or, for `numpy.ndarray`{.interpreted-text role="class"} subclasses before using `super().__array_ufunc__`. ([gh-23240](https://github.com/numpy/numpy/pull/23240)) Compiling against the NumPy C API is now backwards compatible by default NumPy now defaults to exposing a backwards compatible subset of the C-API. This makes the use of `oldest-supported-numpy` unnecessary. Libraries can override the default minimal version to be compatible with using: define NPY_TARGET_VERSION NPY_1_22_API_VERSION before including NumPy or by passing the equivalent `-D` option to the compiler. The NumPy 1.25 default is `NPY_1_19_API_VERSION`. Because the ``` ### 1.24.4 ``` discovered after the 1.24.3 release. It is the last planned release in the 1.24.x cycle. The Python versions supported by this release are 3.8-3.11. Contributors A total of 4 people contributed to this release. 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6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835 numpy-1.24.4-cp39-cp39-win32.whl befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8 numpy-1.24.4-cp39-cp39-win_amd64.whl 31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2 numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl 80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463 numpy-1.24.4.tar.gz ``` ### 1.24.3 ``` discovered after the 1.24.2 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. - Aleksei Nikiforov + - Alexander Heger - Bas van Beek - Bob Eldering - Brock Mendel - Charles Harris - Kyle Sunden - Peter Hawkins - Rohit Goswami - Sebastian Berg - Warren Weckesser - dependabot\[bot\] Pull requests merged A total of 17 pull requests were merged for this release. - [23206](https://github.com/numpy/numpy/pull/23206): BUG: fix for f2py string scalars (#23194) - [23207](https://github.com/numpy/numpy/pull/23207): BUG: datetime64/timedelta64 comparisons return NotImplemented - [23208](https://github.com/numpy/numpy/pull/23208): MAINT: Pin matplotlib to version 3.6.3 for refguide checks - [23221](https://github.com/numpy/numpy/pull/23221): DOC: Fix matplotlib error in documentation - [23226](https://github.com/numpy/numpy/pull/23226): CI: Ensure submodules are initialized in gitpod. - [23341](https://github.com/numpy/numpy/pull/23341): TYP: Replace duplicate reduce in ufunc type signature with reduceat. - [23342](https://github.com/numpy/numpy/pull/23342): TYP: Remove duplicate CLIP/WRAP/RAISE in `__init__.pyi`. - [23343](https://github.com/numpy/numpy/pull/23343): TYP: Mark `d` argument to fftfreq and rfftfreq as optional\... - [23344](https://github.com/numpy/numpy/pull/23344): TYP: Add type annotations for comparison operators to MaskedArray. - [23345](https://github.com/numpy/numpy/pull/23345): TYP: Remove some stray type-check-only imports of `msort` - [23370](https://github.com/numpy/numpy/pull/23370): BUG: Ensure like is only stripped for `like=` dispatched functions - [23543](https://github.com/numpy/numpy/pull/23543): BUG: fix loading and storing big arrays on s390x - [23544](https://github.com/numpy/numpy/pull/23544): MAINT: Bump larsoner/circleci-artifacts-redirector-action - [23634](https://github.com/numpy/numpy/pull/23634): BUG: Ignore invalid and overflow warnings in masked setitem - [23635](https://github.com/numpy/numpy/pull/23635): BUG: Fix masked array raveling when `order="A"` or `order="K"` - [23636](https://github.com/numpy/numpy/pull/23636): MAINT: Update conftest for newer hypothesis versions - [23637](https://github.com/numpy/numpy/pull/23637): BUG: Fix bug in parsing F77 style string arrays. 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210461d87fb02a84ef243cac5e814aad2b7f4be953b32cb53327bb49fd77fbb4 numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 784c6da1a07818491b0ffd63c6bbe5a33deaa0e25a20e1b3ea20cf0e43f8046c numpy-1.24.3-cp39-cp39-win32.whl d5036197ecae68d7f491fcdb4df90082b0d4960ca6599ba2659957aafced7c17 numpy-1.24.3-cp39-cp39-win_amd64.whl 352ee00c7f8387b44d19f4cada524586f07379c0d49270f87233983bc5087ca0 numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 1a7d6acc2e7524c9955e5c903160aa4ea083736fde7e91276b0e5d98e6332812 numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 35400e6a8d102fd07c71ed7dcadd9eb62ee9a6e84ec159bd48c28235bbb0f8e4 numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155 numpy-1.24.3.tar.gz ``` ### 1.24.2 ``` discovered after the 1.24.1 release. The Python versions supported by this release are 3.8-3.11. Contributors A total of 14 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Khem Raj + - Mark Harfouche - Matti Picus - Panagiotis Zestanakis + - Peter Hawkins - Pradipta Ghosh - Ross Barnowski - Sayed Adel - Sebastian Berg - Syam Gadde + - dmbelov + - pkubaj + Pull requests merged A total of 17 pull requests were merged for this release. - [22965](https://github.com/numpy/numpy/pull/22965): MAINT: Update python 3.11-dev to 3.11. - [22966](https://github.com/numpy/numpy/pull/22966): DOC: Remove dangling deprecation warning - [22967](https://github.com/numpy/numpy/pull/22967): ENH: Detect CPU features on FreeBSD/powerpc64\* - [22968](https://github.com/numpy/numpy/pull/22968): BUG: np.loadtxt cannot load text file with quoted fields separated\... - [22969](https://github.com/numpy/numpy/pull/22969): TST: Add fixture to avoid issue with randomizing test order. - [22970](https://github.com/numpy/numpy/pull/22970): BUG: Fix fill violating read-only flag. (#22959) - [22971](https://github.com/numpy/numpy/pull/22971): MAINT: Add additional information to missing scalar AttributeError - [22972](https://github.com/numpy/numpy/pull/22972): MAINT: Move export for scipy arm64 helper into main module - [22976](https://github.com/numpy/numpy/pull/22976): BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang - [22989](https://github.com/numpy/numpy/pull/22989): BUG: Ensure correct loop order in sin, cos, and arctan2 - [23030](https://github.com/numpy/numpy/pull/23030): DOC: Add version added information for the strict parameter in\... - [23031](https://github.com/numpy/numpy/pull/23031): BUG: use `_Alignof` rather than `offsetof()` on most compilers - [23147](https://github.com/numpy/numpy/pull/23147): BUG: Fix for npyv\_\_trunc_s32_f32 (VXE) - [23148](https://github.com/numpy/numpy/pull/23148): BUG: Fix integer / float scalar promotion - [23149](https://github.com/numpy/numpy/pull/23149): BUG: Add missing \<type_traits> header. - 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003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22 numpy-1.24.2.tar.gz ``` ### 1.24.1 ``` 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://github.com/numpy/numpy/pull/22820): BLD: add workaround in setup.py for newer setuptools - [22830](https://github.com/numpy/numpy/pull/22830): BLD: CIRRUS_TAG redux - [22831](https://github.com/numpy/numpy/pull/22831): DOC: fix a couple typos in 1.23 notes - [22832](https://github.com/numpy/numpy/pull/22832): BUG: Fix refcounting errors found using pytest-leaks - [22834](https://github.com/numpy/numpy/pull/22834): BUG, SIMD: Fix invalid value encountered in several ufuncs - [22837](https://github.com/numpy/numpy/pull/22837): TST: ignore more np.distutils.log imports - [22839](https://github.com/numpy/numpy/pull/22839): BUG: Do not use getdata() in np.ma.masked_invalid - [22847](https://github.com/numpy/numpy/pull/22847): BUG: Ensure correct behavior for rows ending in delimiter in\... - [22848](https://github.com/numpy/numpy/pull/22848): BUG, SIMD: Fix the bitmask of the boolean comparison - [22857](https://github.com/numpy/numpy/pull/22857): BLD: Help raspian arm + clang 13 about \_\_builtin_mul_overflow - [22858](https://github.com/numpy/numpy/pull/22858): API: Ensure a full mask is returned for masked_invalid - [22866](https://github.com/numpy/numpy/pull/22866): BUG: Polynomials now copy properly (#22669) - [22867](https://github.com/numpy/numpy/pull/22867): BUG, SIMD: Fix memory overlap in ufunc comparison loops - [22868](https://github.com/numpy/numpy/pull/22868): BUG: Fortify string casts against floating point warnings - [22875](https://github.com/numpy/numpy/pull/22875): TST: Ignore nan-warnings in randomized out tests - [22883](https://github.com/numpy/numpy/pull/22883): MAINT: restore npymath implementations needed for freebsd - [22884](https://github.com/numpy/numpy/pull/22884): BUG: Fix integer overflow in in1d for mixed integer dtypes #22877 - [22887](https://github.com/numpy/numpy/pull/22887): BUG: Use whole file for encoding checks with `charset_normalizer`. 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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 ``` ### 1.24 ``` 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, 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://github.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. ([gh-22393](https://github.com/numpy/numpy/pull/22393)) Deprecate `msort` The `numpy.msort` function is deprecated. Use `np.sort(a, axis=0)` instead. ([gh-22456](https://github.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://github.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://github.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://github.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://github.com/numpy/numpy/pull/22139)) - Support for cygwin \< 3.3 has been removed. ([gh-22159](https://github.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://github.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://github.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://github.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://github.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://github.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 th