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signavio / sap-sam

Example source code for SAP Signavio Academic Models (SAP-SAM)
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Update dependency numpy to v2 #119

Open renovate[bot] opened 1 month ago

renovate[bot] commented 1 month ago

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (source, changelog) 1.26.4 -> 2.1.1 age adoption passing confidence

Release Notes

numpy/numpy (numpy) ### [`v2.1.1`](https://redirect.github.com/numpy/numpy/compare/v2.1.0...v2.1.1) [Compare Source](https://redirect.github.com/numpy/numpy/compare/v2.1.0...v2.1.1) ### [`v2.1.0`](https://redirect.github.com/numpy/numpy/compare/v2.0.1...v2.1.0) [Compare Source](https://redirect.github.com/numpy/numpy/compare/v2.0.2...v2.1.0) ### [`v2.0.2`](https://redirect.github.com/numpy/numpy/releases/tag/v2.0.2): NumPy 2.0.2 release (Aug 26, 2024) [Compare Source](https://redirect.github.com/numpy/numpy/compare/v2.0.1...v2.0.2) ##### NumPy 2.0.2 Release Notes NumPy 2.0.2 is a maintenance release that fixes bugs and regressions discovered after the 2.0.1 release. The Python versions supported by this release are 3.9-3.12. ##### Contributors A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bruno Oliveira + - Charles Harris - Chris Sidebottom - Christian Heimes + - Christopher Sidebottom - Mateusz Sokół - Matti Picus - Nathan Goldbaum - Pieter Eendebak - Raghuveer Devulapalli - Ralf Gommers - Sebastian Berg - Yair Chuchem + ##### Pull requests merged A total of 19 pull requests were merged for this release. - [#​27000](https://redirect.github.com/numpy/numpy/pull/27000): REL: Prepare for the NumPy 2.0.1 release \[wheel build] - [#​27001](https://redirect.github.com/numpy/numpy/pull/27001): MAINT: prepare 2.0.x for further development - [#​27021](https://redirect.github.com/numpy/numpy/pull/27021): BUG: cfuncs.py: fix crash when sys.stderr is not available - [#​27022](https://redirect.github.com/numpy/numpy/pull/27022): DOC: Fix migration note for `alltrue` and `sometrue` - [#​27061](https://redirect.github.com/numpy/numpy/pull/27061): BUG: use proper input and output descriptor in array_assign_subscript... - [#​27073](https://redirect.github.com/numpy/numpy/pull/27073): BUG: Mirror VQSORT_ENABLED logic in Quicksort - [#​27074](https://redirect.github.com/numpy/numpy/pull/27074): BUG: Bump Highway to latest master - [#​27077](https://redirect.github.com/numpy/numpy/pull/27077): BUG: Off by one in memory overlap check - [#​27122](https://redirect.github.com/numpy/numpy/pull/27122): BUG: Use the new `npyv_loadable_stride_` functions for ldexp and... - [#​27126](https://redirect.github.com/numpy/numpy/pull/27126): BUG: Bump Highway to latest - [#​27128](https://redirect.github.com/numpy/numpy/pull/27128): BUG: add missing error handling in public_dtype_api.c - [#​27129](https://redirect.github.com/numpy/numpy/pull/27129): BUG: fix another cast setup in array_assign_subscript - [#​27130](https://redirect.github.com/numpy/numpy/pull/27130): BUG: Fix building NumPy in FIPS mode - [#​27131](https://redirect.github.com/numpy/numpy/pull/27131): BLD: update vendored Meson for cross-compilation patches - [#​27146](https://redirect.github.com/numpy/numpy/pull/27146): MAINT: Scipy openblas 0.3.27.44.4 - [#​27151](https://redirect.github.com/numpy/numpy/pull/27151): BUG: Do not accidentally store dtype metadata in `np.save` - [#​27195](https://redirect.github.com/numpy/numpy/pull/27195): REV: Revert undef I and document it - [#​27213](https://redirect.github.com/numpy/numpy/pull/27213): BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds - [#​27279](https://redirect.github.com/numpy/numpy/pull/27279): BUG: Fix array_equal for numeric and non-numeric scalar types ##### Checksums ##### MD5 ae4bc199b56d20305984b7465d6fbdf1 numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl ecce0a682c2ccaaa14500b87ffb69f63 numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl a94f34bec8a62dab95ce9883a87a82a6 numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl a0a26dadf73264d31b7a6952b816d7c8 numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl 972f4366651a1a2ef00f630595104d15 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Source](https://redirect.github.com/numpy/numpy/compare/v1.26.4...v2.0.0) ### NumPy 2.0.0 Release Notes NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0, in addition to these release notes, include: - The [numpy-2-migration-guide](https://numpy.org/devdocs/numpy\_2\_0\_migration_guide.html) - The Numpy 2.0-specific advice in [for downstream package authors](https://numpy.org/devdocs/dev/depending_on_numpy.html) #### Highlights Highlights of this release include: - New features: - A new variable-length string dtype, `numpy.dtypes.StringDType` and a new `numpy.strings` namespace with performant ufuncs for string operations, - Support for `float32` and `longdouble` in all `numpy.fft` functions, - Support for the array API standard in the main `numpy` namespace. - Performance improvements: - Sorting functions `sort`, `argsort`, `partition`, `argpartition` have been accelerated through the use of the Intel x86-simd-sort and Google Highway libraries, and may see large (hardware-specific) speedups, - macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller, - `numpy.char` fixed-length string operations have been accelerated by implementing ufuncs that also support `numpy.dtypes.StringDType` in addition to the fixed-length string dtypes, - A new tracing and introspection API, `numpy.lib.introspect.opt_func_info`, to determine which hardware-specific kernels are available and will be dispatched to. - `numpy.save` now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays. - Python API improvements: - A clear split between public and private API, with a new module structure and each public function now available in a single place. - Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in `numpy.lib` by ~80%. - ` Canonical dtype names and a new `numpy.isdtype\` introspection function, - C API improvements: - A new public C API for creating custom dtypes, - Many outdated functions and macros removed, and private internals hidden to ease future extensibility, - New, easier to use, initialization functions: `PyArray_ImportNumPyAPI` and `PyUFunc_ImportUFuncAPI`. - Improved behavior: - Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations. - The default integer type on Windows is now `int64` rather than `int32`, matching the behavior on other platforms, - The maximum number of array dimensions is changed from 32 to 64 - Documentation: - The reference guide navigation was significantly improved, and there is now documentation on NumPy's module structure, - The building from source documentation was completely rewritten, Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future. The "no free lunch" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is: 1. Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide. Note that there is a `ruff` mode to auto-fix many things in Python code. 2. Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an `ImportError` with a message about binary incompatibility. It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details. **All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.** The Python versions supported by this release are 3.9-3.12. #### NumPy 2.0 Python API removals - `np.geterrobj`, `np.seterrobj` and the related ufunc keyword argument `extobj=` have been removed. The preferred replacement for all of these is using the context manager `with np.errstate():`. ([gh-23922](https://redirect.github.com/numpy/numpy/pull/23922)) - `np.cast` has been removed. The literal replacement for `np.cast[dtype](arg)` is `np.asarray(arg, dtype=dtype)`. - `np.source` has been removed. The preferred replacement is `inspect.getsource`. - `np.lookfor` has been removed. ([gh-24144](https://redirect.github.com/numpy/numpy/pull/24144)) - `numpy.who` has been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook. ([gh-24321](https://redirect.github.com/numpy/numpy/pull/24321)) - Warnings and exceptions present in `numpy.exceptions`, e.g, `numpy.exceptions.ComplexWarning`, `numpy.exceptions.VisibleDeprecationWarning`, are no longer exposed in the main namespace. - Multiple niche enums, expired members and functions have been removed from the main namespace, such as: `ERR_*`, `SHIFT_*`, `np.fastCopyAndTranspose`, `np.kernel_version`, `np.numarray`, `np.oldnumeric` and `np.set_numeric_ops`. ([gh-24316](https://redirect.github.com/numpy/numpy/pull/24316)) - Replaced `from ... import *` in the `numpy/__init__.py` with explicit imports. As a result, these main namespace members got removed: `np.FLOATING_POINT_SUPPORT`, `np.FPE_*`, `np.NINF`, `np.PINF`, `np.NZERO`, `np.PZERO`, `np.CLIP`, `np.WRAP`, `np.WRAP`, `np.RAISE`, `np.BUFSIZE`, `np.UFUNC_BUFSIZE_DEFAULT`, `np.UFUNC_PYVALS_NAME`, `np.ALLOW_THREADS`, `np.MAXDIMS`, `np.MAY_SHARE_EXACT`, `np.MAY_SHARE_BOUNDS`, `add_newdoc`, `np.add_docstring` and `np.add_newdoc_ufunc`. ([gh-24357](https://redirect.github.com/numpy/numpy/pull/24357)) - Alias `np.float_` has been removed. Use `np.float64` instead. - Alias `np.complex_` has been removed. Use `np.complex128` instead. - Alias `np.longfloat` has been removed. Use `np.longdouble` instead. - Alias `np.singlecomplex` has been removed. Use `np.complex64` instead. - Alias `np.cfloat` has been removed. Use `np.complex128` instead. - Alias `np.longcomplex` has been removed. Use `np.clongdouble` instead. - Alias `np.clongfloat` has been removed. Use `np.clongdouble` instead. - Alias `np.string_` has been removed. Use `np.bytes_` instead. - Alias `np.unicode_` has been removed. Use `np.str_` instead. - Alias `np.Inf` has been removed. Use `np.inf` instead. - Alias `np.Infinity` has been removed. Use `np.inf` instead. - Alias `np.NaN` has been removed. Use `np.nan` instead. - Alias `np.infty` has been removed. Use `np.inf` instead. - Alias `np.mat` has been removed. Use `np.asmatrix` instead. - `np.issubclass_` has been removed. Use the `issubclass` builtin instead. - `np.asfarray` has been removed. Use `np.asarray` with a proper dtype instead. - `np.set_string_function` has been removed. Use `np.set_printoptions` instead with a formatter for custom printing of NumPy objects. - `np.tracemalloc_domain` is now only available from `np.lib`. - `np.recfromcsv` and `recfromtxt` are now only available from `np.lib.npyio`. - `np.issctype`, `np.maximum_sctype`, `np.obj2sctype`, `np.sctype2char`, `np.sctypes`, `np.issubsctype` were all removed from the main namespace without replacement, as they where niche members. - Deprecated `np.deprecate` and `np.deprecate_with_doc` has been removed from the main namespace. Use `DeprecationWarning` instead. - Deprecated `np.safe_eval` has been removed from the main namespace. Use `ast.literal_eval` instead. ([gh-24376](https://redirect.github.com/numpy/numpy/pull/24376)) - `np.find_common_type` has been removed. Use `numpy.promote_types` or `numpy.result_type` instead. To achieve semantics for the `scalar_types` argument, use `numpy.result_type` and pass `0`, `0.0`, or `0j` as a Python scalar instead. - `np.round_` has been removed. Use `np.round` instead. - `np.nbytes` has been removed. Use `np.dtype().itemsize` instead. ([gh-24477](https://redirect.github.com/numpy/numpy/pull/24477)) - `np.compare_chararrays` has been removed from the main namespace. Use `np.char.compare_chararrays` instead. - The `charrarray` in the main namespace has been deprecated. It can be imported without a deprecation warning from `np.char.chararray` for now, but we are planning to fully deprecate and remove `chararray` in the future. - `np.format_parser` has been removed from the main namespace. Use `np.rec.format_parser` instead. ([gh-24587](https://redirect.github.com/numpy/numpy/pull/24587)) - Support for seven data type string aliases has been removed from `np.dtype`: `int0`, `uint0`, `void0`, `object0`, `str0`, `bytes0` and `bool8`. ([gh-24807](https://redirect.github.com/numpy/numpy/pull/24807)) - The experimental `numpy.array_api` submodule has been removed. Use the main `numpy` namespace for regular usage instead, or the separate `array-api-strict` package for the compliance testing use case for which `numpy.array_api` was mostly used. ([gh-25911](https://redirect.github.com/numpy/numpy/pull/25911)) ##### `__array_prepare__` is removed UFuncs called `__array_prepare__` before running computations for normal ufunc calls (not generalized ufuncs, reductions, etc.). The function was also called instead of `__array_wrap__` on the results of some linear algebra functions. It is now removed. If you use it, migrate to `__array_ufunc__` or rely on `__array_wrap__` which is called with a context in all cases, although only after the result array is filled. In those code paths, `__array_wrap__` will now be passed a base class, rather than a subclass array. ([gh-25105](https://redirect.github.com/numpy/numpy/pull/25105)) #### Deprecations - `np.compat` has been deprecated, as Python 2 is no longer supported. - `numpy.int8` and similar classes will no longer support conversion of out of bounds python integers to integer arrays. For example, conversion of 255 to int8 will not return -1. `numpy.iinfo(dtype)` can be used to check the machine limits for data types. For example, `np.iinfo(np.uint16)` returns min = 0 and max = 65535. `np.array(value).astype(dtype)` will give the desired result. - `np.safe_eval` has been deprecated. `ast.literal_eval` should be used instead. ([gh-23830](https://redirect.github.com/numpy/numpy/pull/23830)) - `np.recfromcsv`, `np.recfromtxt`, `np.disp`, `np.get_array_wrap`, `np.maximum_sctype`, `np.deprecate` and `np.deprecate_with_doc` have been deprecated. ([gh-24154](https://redirect.github.com/numpy/numpy/pull/24154)) - `np.trapz` has been deprecated. Use `np.trapezoid` or a `scipy.integrate` function instead. - `np.in1d` has been deprecated. Use `np.isin` instead. - Alias `np.row_stack` has been deprecated. Use `np.vstack` directly. ([gh-24445](https://redirect.github.com/numpy/numpy/pull/24445)) - `__array_wrap__` is now passed `arr, context, return_scalar` and support for implementations not accepting all three are deprecated. Its signature should be `__array_wrap__(self, arr, context=None, return_scalar=False)` ([gh-25409](https://redirect.github.com/numpy/numpy/pull/25409)) - Arrays of 2-dimensional vectors for `np.cross` have been deprecated. Use arrays of 3-dimensional vectors instead. ([gh-24818](https://redirect.github.com/numpy/numpy/pull/24818)) - `np.dtype("a")` alias for `np.dtype(np.bytes_)` was deprecated. Use `np.dtype("S")` alias instead. ([gh-24854](https://redirect.github.com/numpy/numpy/pull/24854)) - Use of keyword arguments `x` and `y` with functions `assert_array_equal` and `assert_array_almost_equal` has been deprecated. Pass the first two arguments as positional arguments instead. ([gh-24978](https://redirect.github.com/numpy/numpy/pull/24978)) ##### `numpy.fft` deprecations for n-D transforms with None values in arguments Using `fftn`, `ifftn`, `rfftn`, `irfftn`, `fft2`, `ifft2`, `rfft2` or `irfft2` with the `s` parameter set to a value that is not `None` and the `axes` parameter set to `None` has been deprecated, in line with the array API standard. To retain current behaviour, pass a sequence \[0, ..., k-1] to `axes` for an array of dimension k. Furthermore, passing an array to `s` which contains `None` values is deprecated as the parameter is documented to accept a sequence of integers in both the NumPy docs and the array API specification. To use the default behaviour of the corresponding 1-D transform, pass the value matching the default for its `n` parameter. To use the default behaviour for every axis, the `s` argument can be omitted. ([gh-25495](https://redirect.github.com/numpy/numpy/pull/25495)) ##### `np.linalg.lstsq` now defaults to a new `rcond` value `numpy.linalg.lstsq` now uses the new rcond value of the machine precision times `max(M, N)`. Previously, the machine precision was used but a FutureWarning was given to notify that this change will happen eventually. That old behavior can still be achieved by passing `rcond=-1`. ([gh-25721](https://redirect.github.com/numpy/numpy/pull/25721)) #### Expired deprecations - The `np.core.umath_tests` submodule has been removed from the public API. (Deprecated in NumPy 1.15) ([gh-23809](https://redirect.github.com/numpy/numpy/pull/23809)) - The `PyDataMem_SetEventHook` deprecation has expired and it is removed. Use `tracemalloc` and the `np.lib.tracemalloc_domain` domain. (Deprecated in NumPy 1.23) ([gh-23921](https://redirect.github.com/numpy/numpy/pull/23921)) - The deprecation of `set_numeric_ops` and the C functions `PyArray_SetNumericOps` and `PyArray_GetNumericOps` has been expired and the functions removed. (Deprecated in NumPy 1.16) ([gh-23998](https://redirect.github.com/numpy/numpy/pull/23998)) - The `fasttake`, `fastclip`, and `fastputmask` `ArrFuncs` deprecation is now finalized. - The deprecated function `fastCopyAndTranspose` and its C counterpart are now removed. - The deprecation of `PyArray_ScalarFromObject` is now finalized. ([gh-24312](https://redirect.github.com/numpy/numpy/pull/24312)) - `np.msort` has been removed. For a replacement, `np.sort(a, axis=0)` should be used instead. ([gh-24494](https://redirect.github.com/numpy/numpy/pull/24494)) - `np.dtype(("f8", 1)` will now return a shape 1 subarray dtype rather than a non-subarray one. ([gh-25761](https://redirect.github.com/numpy/numpy/pull/25761)) - Assigning to the `.data` attribute of an ndarray is disallowed and will raise. - `np.binary_repr(a, width)` will raise if width is too small. - Using `NPY_CHAR` in `PyArray_DescrFromType()` will raise, use `NPY_STRING` `NPY_UNICODE`, or `NPY_VSTRING` instead. ([gh-25794](https://redirect.github.com/numpy/numpy/pull/25794)) #### Compatibility notes ##### `loadtxt` and `genfromtxt` default encoding changed `loadtxt` and `genfromtxt` now both default to `encoding=None` which may mainly modify how `converters` work. These will now be passed `str` rather than `bytes`. Pass the encoding explicitly to always get the new or old behavior. For `genfromtxt` the change also means that returned values will now be unicode strings rather than bytes. ([gh-25158](https://redirect.github.com/numpy/numpy/pull/25158)) ##### `f2py` compatibility notes - `f2py` will no longer accept ambiguous `-m` and `.pyf` CLI combinations. When more than one `.pyf` file is passed, an error is raised. When both `-m` and a `.pyf` is passed, a warning is emitted and the `-m` provided name is ignored. ([gh-25181](https://redirect.github.com/numpy/numpy/pull/25181)) - The `f2py.compile()` helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thin `subprocess.run` wrapper. It was also one of the test bottlenecks. See [gh-25122](https://redirect.github.com/numpy/numpy/issues/25122) for the full rationale. It also used several `np.distutils` features which are too fragile to be ported to work with `meson`. - Users are urged to replace calls to `f2py.compile` with calls to `subprocess.run("python", "-m", "numpy.f2py",...` instead, and to use environment variables to interact with `meson`. [Native files](https://mesonbuild.com/Machine-files.html) are also an option. ([gh-25193](https://redirect.github.com/numpy/numpy/pull/25193)) ##### Minor changes in behavior of sorting functions Due to algorithmic changes and use of SIMD code, sorting functions with methods that aren't stable may return slightly different results in 2.0.0 compared to 1.26.x. This includes the default method of `numpy.argsort` and `numpy.argpartition`. ##### Removed ambiguity when broadcasting in `np.solve` The broadcasting rules for `np.solve(a, b)` were ambiguous when `b` had 1 fewer dimensions than `a`. This has been resolved in a backward-incompatible way and is now compliant with the Array API. The old behaviour can be reconstructed by using `np.solve(a, b[..., None])[..., 0]`. ([gh-25914](https://redirect.github.com/numpy/numpy/pull/25914)) ##### Modified representation for `Polynomial` The representation method for `numpy.polynomial.polynomial.Polynomial` was updated to include the domain in the representation. The plain text and latex representations are now consistent. For example the output of `str(np.polynomial.Polynomial([1, 1], domain=[.1, .2]))` used to be `1.0 + 1.0 x`, but now is `1.0 + 1.0 (-3.0000000000000004 + 20.0 x)`. ([gh-21760](https://redirect.github.com/numpy/numpy/pull/21760)) #### C API changes - The `PyArray_CGT`, `PyArray_CLT`, `PyArray_CGE`, `PyArray_CLE`, `PyArray_CEQ`, `PyArray_CNE` macros have been removed. - `PyArray_MIN` and `PyArray_MAX` have been moved from `ndarraytypes.h` to `npy_math.h`. ([gh-24258](https://redirect.github.com/numpy/numpy/pull/24258)) - A C API for working with `numpy.dtypes.StringDType` arrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries. - `NPY_NTYPES` has been renamed to `NPY_NTYPES_LEGACY` as it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes. ([gh-25347](https://redirect.github.com/numpy/numpy/pull/25347)) - The C-API now only exports the static inline function versions of the array accessors (previously this depended on using "deprecated API"). While we discourage it, the struct fields can still be used directly. ([gh-25789](https://redirect.github.com/numpy/numpy/pull/25789)) - NumPy now defines `PyArray_Pack` to set an individual memory address. Unlike `PyArray_SETITEM` this function is equivalent to setting an individual array item and does not require a NumPy array input. ([gh-25954](https://redirect.github.com/numpy/numpy/pull/25954)) - The `->f` slot has been removed from `PyArray_Descr`. If you use this slot, replace accessing it with `PyDataType_GetArrFuncs` (see its documentation and the `numpy-2-migration-guide`). In some cases using other functions like `PyArray_GETITEM` may be an alternatives. - `PyArray_GETITEM` and `PyArray_SETITEM` now require the import of the NumPy API table to be used and are no longer defined in `ndarraytypes.h`. ([gh-25812](https://redirect.github.com/numpy/numpy/pull/25812)) - Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from `numpy/ndarraytypes.h` and is only available after including `numpy/ndarrayobject.h` as it requires `import_array()`. This includes `PyDataType_FLAGCHK`, `PyDataType_REFCHK` and `NPY_BEGIN_THREADS_DESCR`. - The dtype flags on `PyArray_Descr` must now be accessed through the `PyDataType_FLAGS` inline function to be compatible with both 1.x and 2.x. This function is defined in `npy_2_compat.h` to allow backporting. Most or all users should use `PyDataType_FLAGCHK` which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use `PyDataType_FLAGCHK` instead. ([gh-25816](https://redirect.github.com/numpy/numpy/pull/25816)) ##### Datetime functionality exposed in the C API and Cython bindings The functions `NpyDatetime_ConvertDatetime64ToDatetimeStruct`, `NpyDatetime_ConvertDatetimeStructToDatetime64`, `NpyDatetime_ConvertPyDateTimeToDatetimeStruct`, `NpyDatetime_GetDatetimeISO8601StrLen`, `NpyDatetime_MakeISO8601Datetime`, and `NpyDatetime_ParseISO8601Datetime` have been added to the C API to facilitate converting between strings, Python datetimes, and NumPy datetimes in external libraries. ([gh-21199](https://redirect.github.com/numpy/numpy/pull/21199)) ##### Const correctness for the generalized ufunc C API The NumPy C API's functions for constructing generalized ufuncs (`PyUFunc_FromFuncAndData`, `PyUFunc_FromFuncAndDataAndSignature`, `PyUFunc_FromFuncAndDataAndSignatureAndIdentity`) take `types` and `data` arguments that are not modified by NumPy's internals. Like the `name` and `doc` arguments, third-party Python extension modules are likely to supply these arguments from static constants. The `types` and `data` arguments are now const-correct: they are declared as `const char *types` and `void *const *data`, respectively. C code should not be affected, but C++ code may be. ([gh-23847](https://redirect.github.com/numpy/numpy/pull/23847)) ##### Larger `NPY_MAXDIMS` and `NPY_MAXARGS`, `NPY_RAVEL_AXIS` introduced `NPY_MAXDIMS` is now 64, you may want to review its use. This is usually used in a stack allocation, where the increase should be safe. However, we do encourage generally to remove any use of `NPY_MAXDIMS` and `NPY_MAXARGS` to eventually allow removing the constraint completely. For the conversion helper and C-API functions mirroring Python ones such as `take`, `NPY_MAXDIMS` was used to mean `axis=None`. Such usage must be replaced with `NPY_RAVEL_AXIS`. See also `migration_maxdims`. ([gh-25149](https://redirect.github.com/numpy/numpy/pull/25149)) ##### `NPY_MAXARGS` not constant and `PyArrayMultiIterObject` size change Since `NPY_MAXARGS` was increased, it is now a runtime constant and not compile-time constant anymore. We expect almost no users to notice this. But if used for stack allocations it now must be replaced with a custom constant using `NPY_MAXARGS` as an additional runtime check. The `sizeof(PyArrayMultiIterObject)` no longer includes the full size of the object. We expect nobody to notice this change. It was necessary to avoid issues with Cython. ([gh-25271](https://redirect.github.com/numpy/numpy/pull/25271)) ##### Required changes for custom legacy user dtypes In order to improve our DTypes it is unfortunately necessary to break the ABI, which requires some changes for dtypes registered with `PyArray_RegisterDataType`. Please see the documentation of `PyArray_RegisterDataType` for how to adapt your code and achieve compatibility with both 1.x and 2.x. ([gh-25792](https://redirect.github.com/numpy/numpy/pull/25792)) ##### New Public DType API The C implementation of the NEP 42 DType API is now public. While the DType API has shipped in NumPy for a few versions, it was only usable in sessions with a special environment variable set. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal `import_array()` mechanism for importing the numpy C API. See `dtype-api` for more details about the API. As always with a new feature, please report any bugs you run into implementing or using a new DType. It is likely that downstream C code that works with dtypes will need to be updated to work correctly with new DTypes. ([gh-25754](https://redirect.github.com/numpy/numpy/pull/25754)) ##### New C-API import functions We have now added `PyArray_ImportNumPyAPI` and `PyUFunc_ImportUFuncAPI` as static inline functions to import the NumPy C-API tables. The new functions have two advantages over `import_array` and `import_ufunc`: - They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases). - The old mechanisms were macros rather than functions which included a `return` statement. The `PyArray_ImportNumPyAPI()` function is included in `npy_2_compat.h` for simpler backporting. ([gh-25866](https://redirect.github.com/numpy/numpy/pull/25866)) ##### Structured dtype information access through functions The dtype structures fields `c_metadata`, `names`, `fields`, and `subarray` must now be accessed through new functions following the same names, such as `PyDataType_NAMES`. Direct access of the fields is not valid as they do not exist for all `PyArray_Descr` instances. The `metadata` field is kept, but the macro version should also be preferred. ([gh-25802](https://redirect.github.com/numpy/numpy/pull/25802)) ##### Descriptor `elsize` and `alignment` access Unless compiling only with NumPy 2 support, the `elsize` and `aligment` fields must now be accessed via `PyDataType_ELSIZE`, `PyDataType_SET_ELSIZE`, and `PyDataType_ALIGNMENT`. In cases where the descriptor is attached to an array, we advise using `PyArray_ITEMSIZE` as it exists on all NumPy versions. Please see `migration_c_descr` for more information. ([gh-25943](https://redirect.github.com/numpy/numpy/pull/25943)) #### NumPy 2.0 C API removals - `npy_interrupt.h` and the corresponding macros like `NPY_SIGINT_ON` have been removed. We recommend querying `PyErr_CheckSignals()` or `PyOS_InterruptOccurred()` periodically (these do currently require holding the GIL though). - The `noprefix.h` header has been removed. Replace missing symbols with their prefixed counterparts (usually an added `NPY_` or `npy_`). ([gh-23919](https://redirect.github.com/numpy/numpy/pull/23919)) - `PyUFunc_GetPyVals`, `PyUFunc_handlefperr`, and `PyUFunc_checkfperr` have been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have made `with np.errstate()` fixes much more difficult). ([gh-23922](https://redirect.github.com/numpy/numpy/pull/23922)) - The `numpy/old_defines.h` which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form `PyArray_CONSTANT`. The [replace_old_macros.sed](https://redirect.github.com/numpy/numpy/blob/main/tools/replace_old_macros.sed) script may be useful to convert them to the `NPY_CONSTANT` version. ([gh-24011](https://redirect.github.com/numpy/numpy/pull/24011)) - The `legacy_inner_loop_selector` member of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member. ([gh-24271](https://redirect.github.com/numpy/numpy/pull/24271)) - `NPY_INTPLTR` has been removed to avoid confusion (see `intp` redefinition). ([gh-24888](https://redirect.github.com/numpy/numpy/pull/24888)) - The advanced indexing `MapIter` and related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed up `ufunc.at`, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together. ([gh-25138](https://redirect.github.com/numpy/numpy/pull/25138)) - The `NPY_MAX_ELSIZE` macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose. ([gh-25149](https://redirect.github.com/numpy/numpy/pull/25149)) - `PyArray_REFCNT` and `NPY_REFCOUNT` are removed. Use `Py_REFCNT` instead. ([gh-25156](https://redirect.github.com/numpy/numpy/pull/25156)) - `PyArrayFlags_Type` and `PyArray_NewFlagsObject` as well as `PyArrayFlagsObject` are private now. There is no known use-case; use the Python API if needed. - `PyArray_MoveInto`, `PyArray_CastTo`, `PyArray_CastAnyTo` are removed use `PyArray_CopyInto` and if absolutely needed `PyArray_CopyAnyInto` (the latter does a flat copy). - `PyArray_FillObjectArray` is removed, its only true use was for implementing `np.empty`. Create a new empty array or use `PyArray_FillWithScalar()` (decrefs existing objects). - `PyArray_CompareUCS4` and `PyArray_CompareString` are removed. Use the standard C string comparison functions. - `PyArray_ISPYTHON` is removed as it is misleading, has no known use-cases, and is easy to replace. - `PyArray_FieldNames` is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases. - `PyArray_TypestrConvert` is removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings. ([gh-25292](https://redirect.github.com/numpy/numpy/pull/25292)) - `PyDataType_GetDatetimeMetaData` is removed, it did not actually do anything since at least NumPy 1.7. ([gh-25802](https://redirect.github.com/numpy/numpy/pull/25802)) - `PyArray_GetCastFunc` is removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such as `PyArray_CastBuffer()` which could use old or new cast functions depending on the NumPy version. ([gh-25161](https://redirect.github.com/numpy/numpy/pull/25161)) #### New Features ##### `np.add` was extended to work with `unicode` and `bytes` dtypes. > ([gh-24858](https://redirect.github.com/numpy/numpy/pull/24858)) ##### A new `bitwise_count` function This new function counts the number of 1-bits in a number. `numpy.bitwise_count` works on all the numpy integer types and integer-like objects. ```python >>> a = np.array([2**i - 1 for i in range(16)]) >>> np.bitwise_count(a) array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint8) ``` ([gh-19355](https://redirect.github.com/numpy/numpy/pull/19355)) ##### macOS Accelerate support, including the ILP64 Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available. ([gh-24053](https://redirect.github.com/numpy/numpy/pull/24053)) Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS. ([gh-25255](https://redirect.github.com/numpy/numpy/pull/25255)) ##### Option to use weights for quantile and percentile functions A `weights` keyword is now available for `numpy.quantile`, `numpy.percentile`, `numpy.nanquantile` and `numpy.nanpercentile`. Only `method="inverted_cdf"` supports weights. ([gh-24254](https://redirect.github.com/numpy/numpy/pull/24254)) ##### Improved CPU optimization tracking A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions. A new function named `opt_func_info` has been added to the new namespace `numpy.lib.introspect`, offering this tracing capability. This function allows you to retrieve information about the enabled targets based on function names and data type signatures. ([gh-24420](https://redirect.github.com/numpy/numpy/pull/24420)) ##### A new Meson backend for `f2py` `f2py` in compile mode (i.e. `f2py -c`) now accepts the `--backend meson` option. This is the default option for Python >=3.12. For older Python versions, `f2py` will still default to `--backend distutils`. To support this in realistic use-cases, in compile mode `f2py` takes a `--dep` flag one or many times which maps to `dependency()` calls in the `meson` backend, and does nothing in the `distutils` backend. There are no changes for users of `f2py` only as a code generator, i.e. without `-c`. ([gh-24532](https://redirect.github.com/numpy/numpy/pull/24532)) ##### `bind(c)` support for `f2py` Both functions and subroutines can be annotated with `bind(c)`. `f2py` will handle both the correct type mapping, and preserve the unique label for other C interfaces. **Note:** `bind(c, name = 'routine_name_other_than_fortran_routine')` is not honored by the `f2py` bindings by design, since `bind(c)` with the `name` is meant to guarantee only the same name in C and Fortran, not in Python and Fortran. ([gh-24555](https://redirect.github.com/numpy/numpy/pull/24555)) ##### A new `strict` option for several testing functions The `strict` keyword is now available for `numpy.testing.assert_allclose`, `numpy.testing.assert_equal`, and `numpy.testing.assert_array_less`. Setting `strict=True` will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type. ([gh-24680](https://redirect.github.com/numpy/numpy/pull/24680), [gh-24770](https://redirect.github.com/numpy/numpy/pull/24770), [gh-24775](https://redirect.github.com/numpy/numpy/pull/24775)) ##### Add `np.core.umath.find` and `np.core.umath.rfind` UFuncs Add two `find` and `rfind` UFuncs that operate on unicode or byte strings and are used in `np.char`. They operate similar to `str.find` and `str.rfind`. ([gh-24868](https://redirect.github.com/numpy/numpy/pull/24868)) ##### `diagonal` and `trace` for `numpy.linalg` `numpy.linalg.diagonal` and `numpy.linalg.trace` have been added, which are array API standard-compatible variants of `numpy.diagonal` and `numpy.trace`. They differ in the default axis selection which define 2-D sub-arrays. ([gh-24887](https://redirect.github.com/numpy/numpy/pull/24887)) ##### New `long` and `ulong` dtypes `numpy.long` and `numpy.ulong` have been added as NumPy integers mapping to C's `long` and `unsigned long`. Prior to NumPy 1.24, `numpy.long` was an alias to Python's `int`. ([gh-24922](https://redirect.github.com/numpy/numpy/pull/24922)) ##### `svdvals` for `numpy.linalg` `numpy.linalg.svdvals` has been added. It computes singular values for (a stack of) matrices. Executing `np.svdvals(x)` is the same as calling `np.svd(x, compute_uv=False, hermitian=False)`. This function is compatible with the array API standard. ([gh-24940](https://redirect.github.com/numpy/numpy/pull/24940)) ##### A new `isdtype` function `numpy.isdtype` was added to provide a canonical way to classify NumPy's dtypes in compliance with the array API standard. ([gh-25054](https://redirect.github.com/numpy/numpy/pull/25054)) ##### A new `astype` function `numpy.astype` was added to provide an array API standard-compatible alternative to the `numpy.ndarray.astype` method. ([gh-25079](https://redirect.github.com/numpy/numpy/pull/25079)) ##### Array API compatible functions' aliases 13 aliases for existing functions were added to improve compatibility with the array API standard: - Trigonometry: `acos`, `acosh`, `asin`, `asinh`, `atan`, `atanh`, `atan2`. - Bitwise: `bitwise_left_shift`, `bitwise_invert`, `bitwise_right_shift`. - Misc: `concat`, `permute_dims`, `pow`. - In `numpy.linalg`: `tensordot`, `matmul`. ([gh-25086](https://redirect.github.com/numpy/numpy/pull/25086)) ##### New `unique_*` functions The `numpy.unique_all`, `numpy.unique_counts`, `numpy.unique_inverse`, and `numpy.unique_values` functions have been added. They provide functionality of `numpy.unique` with different sets of flags. They are array API standard-compatible, and because the number of arrays they return does not depend on the values of input arguments, they are easier to target for JIT compilation. ([gh-25088](https://redirect.github.com/numpy/numpy/pull/25088)) ##### Matrix transpose support for ndarrays NumPy now offers support for calculating the matrix transpose of an array (or stack of arrays). The matrix transpose is equivalent to swapping the last two axes of an array. Both `np.ndarray` and `np.ma.MaskedArray` now expose a `.mT` attribute, and there is a matching new `numpy.matrix_transpose` function. ([gh-23762](https://redirect.github.com/numpy/numpy/pull/23762)) ##### Array API compatible functions for `numpy.linalg` Six new functions and two aliases were added to improve compatibility with the Array API standard for \`numpy.linalg\`: - `numpy.linalg.matrix_norm` - Computes the matrix norm of a matrix (or a stack of matrices). - `numpy.linalg.vector_norm` - Computes the vector norm of a vector (or batch of vectors). - `numpy.vecdot` - Computes the (vector) dot product of two arrays. - `numpy.linalg.vecdot` - An alias for `numpy.vecdot`. - `numpy.linalg.matrix_transpose` - An alias for `numpy.matrix_transpose`. ([gh-25155](https://redirect.github.com/numpy/numpy/pull/25155)) - `numpy.linalg.outer` has been added. It computes the outer product of two vectors. It differs from `numpy.outer` by accepting one-dimensional arrays only. This function is compatible with the array API standard. ([gh-25101](https://redirect.github.com/numpy/numpy/pull/25101)) - `numpy.linalg.cross` has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from `numpy.cross` by accepting three-dimensional vectors only. This function is compatible with the array API standard. ([gh-25145](https://redirect.github.com/numpy/numpy/pull/25145)) ##### A `correction` argument for `var` and `std` A `correction` argument was added to `numpy.var` and `numpy.std`, which is an array API standard compatible alternative to `ddof`. As both arguments serve a similar purpose, only one of them can be provided at the same time. ([gh-25169](https://redirect.github.com/numpy/numpy/pull/25169)) ##### `ndarray.device` and `ndarray.to_device` An `ndarray.device` attribute and `ndarray.to_device` method were added to `numpy.ndarray` for array API standard compatibility. Additionally, `device` keyword-only arguments were added to: `numpy.asarray`, `numpy.arange`, `numpy.empty`, `numpy.empty_like`, `numpy.eye`, `numpy.full`, `numpy.full_like`, `numpy.linspace`, `numpy.ones`, `numpy.ones_like`, `numpy.zeros`, and `numpy.zeros_like`. For all these new arguments, only `device="cpu"` is supported. ([gh-25233](https://redirect.github.com/numpy/numpy/pull/25233)) ##### StringDType has been added to NumPy We have added a new variable-width UTF-8 encoded string data type, implementing a "NumPy array of Python strings", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See [NEP 55](https://numpy.org/neps/nep-0055-string_dtype.html) and the documentation of stringdtype for more details. ([gh-25347](https://redirect.github.com/numpy/numpy/pull/25347)) ##### New keywords for `cholesky` and `pinv` The `upper` and `rtol` keywords were added to `numpy.linalg.cholesky` and `numpy.linalg.pinv`, respectively, to improve array API standard compatibility. For `numpy.linalg.pinv`, if neither `rcond` nor `rtol` is specified, the `rcond`'s default is used. We plan to deprecate and remove `rcond` in the future. ([gh-25388](https://redirect.github.com/numpy/numpy/pull/25388)) ##### New keywords for `sort`, `argsort` and `linalg.matrix_rank` New keyword parameters were added to improve array API standard compatibility: - `rtol` was added to `numpy.linalg.matrix_rank`. - `stable` was added to `numpy.sort` and `numpy.argsort`. ([gh-25437](https://redirect.github.com/numpy/numpy/pull/25437)) ##### New `numpy.strings` namespace for string ufuncs NumPy now implements some string operations as ufuncs. The old `np.char` namespace is still available, and where possible the string manipulation functions in that namespace have been updated to use the new ufuncs, substantially improving their performance. Where possible, we suggest updating code to use functions in `np.strings` instead of `np.char`. In the future we may deprecate `np.char` in favor of `np.strings`. ([gh-25463](https://redirect.github.com/numpy/numpy/pull/25463)) ##### `numpy.fft` support for different precisions and in-place calculations The various FFT routines in `numpy.fft` now do their calculations natively in float, double, or long double precision, depending on the input precision, instead of always calculating in double precision. Hence, the calculation will now be less precise for single and more precise for long double precision. The data type of the output array will now be adjusted accordingly. Furthermore, all FFT routines have gained an `out` argument that can be used for in-place calculations. ([gh-25536](https://redirect.github.com/numpy/numpy/pull/25536)) ##### configtool and pkg-config support A new `numpy-config` CLI script is available that can be queried for the NumPy version and for compile flags needed to use the NumPy C API. This will allow build systems to better support the use of NumPy as a dependency. Also, a `numpy.pc` pkg-config file is now included with Numpy. In order to find its location for use with `PKG_CONFIG_PATH`, use `numpy-config --pkgconfigdir`. ([gh-25730](https://redirect.github.com/numpy/numpy/pull/25730)) ##### Array API standard support in the main namespace The main `numpy` namespace now supports the array API standard. See `array-api-standard-compatibility` for details. ([gh-25911](https://redirect.github.com/nu

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