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fix(deps): update all dependencies #925

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@renovate renovate bot commented Feb 1, 2024

This PR contains the following updates:

Package Change Age Adoption Passing Confidence Type Update
numpy (source, changelog) ^1.22 -> ^2.1 age adoption passing confidence dependencies major
python ^3.9,<3.13 -> <3.14 age adoption passing confidence dependencies minor

Release Notes

numpy/numpy (numpy)

v2.1.2

Compare Source

v2.1.1: 2.1.1 (Sep 3, 2024)

Compare Source

NumPy 2.1.1 Release Notes

NumPy 2.1.1 is a maintenance release that fixes bugs and regressions
discovered after the 2.1.0 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

  • Andrew Nelson
  • Charles Harris
  • Mateusz Sokół
  • Maximilian Weigand +
  • Nathan Goldbaum
  • Pieter Eendebak
  • Sebastian Berg
Pull requests merged

A total of 10 pull requests were merged for this release.

  • #​27236: REL: Prepare for the NumPy 2.1.0 release [wheel build]
  • #​27252: MAINT: prepare 2.1.x for further development
  • #​27259: BUG: revert unintended change in the return value of set_printoptions
  • #​27266: BUG: fix reference counting bug in __array_interface__ implementation...
  • #​27267: TST: Add regression test for missing descr in array-interface
  • #​27276: BUG: Fix #​27256 and #​27257
  • #​27278: BUG: Fix array_equal for numeric and non-numeric scalar types
  • #​27287: MAINT: Update maintenance/2.1.x after the 2.0.2 release
  • #​27303: BLD: cp311- macosx_arm64 wheels [wheel build]
  • #​27304: BUG: f2py: better handle filtering of public/private subroutines
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v2.1.0

Compare Source

v2.0.2: NumPy 2.0.2 release (Aug 26, 2024)

Compare Source

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: REL: Prepare for the NumPy 2.0.1 release [wheel build]
  • #​27001: MAINT: prepare 2.0.x for further development
  • #​27021: BUG: cfuncs.py: fix crash when sys.stderr is not available
  • #​27022: DOC: Fix migration note for alltrue and sometrue
  • #​27061: BUG: use proper input and output descriptor in array_assign_subscript...
  • #​27073: BUG: Mirror VQSORT_ENABLED logic in Quicksort
  • #​27074: BUG: Bump Highway to latest master
  • #​27077: BUG: Off by one in memory overlap check
  • #​27122: BUG: Use the new npyv_loadable_stride_ functions for ldexp and...
  • #​27126: BUG: Bump Highway to latest
  • #​27128: BUG: add missing error handling in public_dtype_api.c
  • #​27129: BUG: fix another cast setup in array_assign_subscript
  • #​27130: BUG: Fix building NumPy in FIPS mode
  • #​27131: BLD: update vendored Meson for cross-compilation patches
  • #​27146: MAINT: Scipy openblas 0.3.27.44.4
  • #​27151: BUG: Do not accidentally store dtype metadata in np.save
  • #​27195: REV: Revert undef I and document it
  • #​27213: BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds
  • #​27279: BUG: Fix array_equal for numeric and non-numeric scalar types
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v2.0.1

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v2.0.0

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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:

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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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(<dtype>).itemsize
    instead.

    (gh-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)

  • Support for seven data type string aliases has been removed from
    np.dtype: int0, uint0, void0, object0, str0, bytes0
    and bool8.

    (gh-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)

__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)

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)

  • 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)

  • 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)

  • __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)

  • Arrays of 2-dimensional vectors for np.cross have been deprecated.
    Use arrays of 3-dimensional vectors instead.

    (gh-24818)

  • np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use
    np.dtype("S") alias instead.

    (gh-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)

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)

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)

Expired deprecations

  • The np.core.umath_tests submodule has been removed from the public
    API. (Deprecated in NumPy 1.15)

    (gh-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)

  • 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)

  • 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)

  • np.msort has been removed. For a replacement, np.sort(a, axis=0)
    should be used instead.

    (gh-24494)

  • np.dtype(("f8", 1) will now return a shape 1 subarray dtype rather
    than a non-subarray one.

    (gh-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)

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)

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)

  • 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 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
    are also an
    option.

    (gh-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)

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)

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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

  • 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)

  • 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
    script may be useful to convert them to the NPY_CONSTANT version.

    (gh-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)

  • NPY_INTPLTR has been removed to avoid confusion (see intp
    redefinition).

    (gh-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)

  • The NPY_MAX_ELSIZE macro has been removed, as it only ever
    reflected builtin numeric types and served no internal purpose.

    (gh-25149)

  • PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT
    instead.

    (gh-25156)

  • PyArrayFlags_Type and PyArray_NewFlagsObject as well as
    PyArrayFlagsObject are private now. There is no known use-case;
    use the Python API


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@renovate renovate bot requested a review from engnadeau as a code owner February 1, 2024 00:23
@renovate renovate bot force-pushed the renovate/all branch 2 times, most recently from fe89d89 to 673e43e Compare June 16, 2024 16:08
@renovate renovate bot changed the title chore(deps): update dependency black to v24 chore(deps): update all dependencies Jun 16, 2024
@renovate renovate bot changed the title chore(deps): update all dependencies fix(deps): update dependency numpy to v2 Aug 6, 2024
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renovate bot commented Aug 19, 2024

⚠️ Artifact update problem

Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.

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The artifact failure details are included below:

File name: poetry.lock
Updating dependencies
Resolving dependencies...

Creating virtualenv pybotics-2IsyJ_jn-py3.13 in /home/ubuntu/.cache/pypoetry/virtualenvs

The current project's supported Python range (<3.14) is not compatible with some of the required packages Python requirement:
  - numpy requires Python >=3.10, so it will not be satisfied for Python <3.10
  - numpy requires Python >=3.10, so it will not be satisfied for Python <3.10
  - numpy requires Python >=3.10, so it will not be satisfied for Python <3.10

Because no versions of numpy match >2.1,<2.1.1 || >2.1.1,<2.1.2 || >2.1.2,<3.0
 and numpy (2.1.0) requires Python >=3.10, numpy is forbidden.
And because numpy (2.1.1) requires Python >=3.10, numpy is forbidden.
So, because numpy (2.1.2) requires Python >=3.10
 and pybotics depends on numpy (^2.1), version solving failed.

  • Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
    
    For numpy, a possible solution would be to set the `python` property to ">=3.10,<3.14"
    For numpy, a possible solution would be to set the `python` property to ">=3.10,<3.14"
    For numpy, a possible solution would be to set the `python` property to ">=3.10,<3.14"

    https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
    https://python-poetry.org/docs/dependency-specification/#using-environment-markers

@renovate renovate bot changed the title fix(deps): update dependency numpy to v2 fix(deps): update all dependencies Oct 8, 2024
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