renovate[bot]
commented
3 weeks ago ⚠️ 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...
The current project's supported Python range (>=3.8,<4.0) is not compatible with some of the required packages Python requirement:
- numpy requires Python >=3.9, so it will not be satisfied for Python >=3.8,<3.9
- numpy requires Python >=3.9, so it will not be satisfied for Python >=3.8,<3.9
- numpy requires Python >=3.9, so it will not be satisfied for Python >=3.8,<3.9
- numpy requires Python >=3.10, so it will not be satisfied for Python >=3.8,<3.10
- numpy requires Python >=3.10, so it will not be satisfied for Python >=3.8,<3.10
- numpy requires Python >=3.10, so it will not be satisfied for Python >=3.8,<3.10
Because no versions of numpy match >2.0.0,<2.0.1 || >2.0.1,<2.0.2 || >2.0.2,<2.1.0 || >2.1.0,<2.1.1 || >2.1.1,<2.1.2 || >2.1.2,<3.0.0
and numpy (2.0.0) requires Python >=3.9, numpy is forbidden.
And because numpy (2.0.1) requires Python >=3.9
and numpy (2.0.2) requires Python >=3.9, numpy is forbidden.
And because numpy (2.1.0) requires Python >=3.10
and numpy (2.1.1) requires Python >=3.10, numpy is forbidden.
So, because numpy (2.1.2) requires Python >=3.10
and non-package-mode depends on numpy (^2.0.0), 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 ">=3.9,<4.0"
For numpy, a possible solution would be to set the `python` property to ">=3.9,<4.0"
For numpy, a possible solution would be to set the `python` property to ">=3.9,<4.0"
For numpy, a possible solution would be to set the `python` property to ">=3.10,<4.0"
For numpy, a possible solution would be to set the `python` property to ">=3.10,<4.0"
For numpy, a possible solution would be to set the `python` property to ">=3.10,<4.0"
https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
https://python-poetry.org/docs/dependency-specification/#using-environment-markers
This PR contains the following updates:
^1.0.0->^2.0.0Release Notes
numpy/numpy (numpy)
### [`v2.1.2`](https://redirect.github.com/numpy/numpy/compare/v2.1.1...v2.1.2) [Compare Source](https://redirect.github.com/numpy/numpy/compare/v2.1.1...v2.1.2) ### [`v2.1.1`](https://redirect.github.com/numpy/numpy/releases/tag/v2.1.1): 2.1.1 (Sep 3, 2024) [Compare Source](https://redirect.github.com/numpy/numpy/compare/v2.1.0...v2.1.1) ##### 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](https://redirect.github.com/numpy/numpy/pull/27236): REL: Prepare for the NumPy 2.1.0 release \[wheel build] - [#27252](https://redirect.github.com/numpy/numpy/pull/27252): MAINT: prepare 2.1.x for further development - [#27259](https://redirect.github.com/numpy/numpy/pull/27259): BUG: revert unintended change in the return value of set_printoptions - [#27266](https://redirect.github.com/numpy/numpy/pull/27266): BUG: fix reference counting bug in \__array_interface\_\_ implementation... - [#27267](https://redirect.github.com/numpy/numpy/pull/27267): TST: Add regression test for missing descr in array-interface - [#27276](https://redirect.github.com/numpy/numpy/pull/27276): BUG: Fix [#27256](https://redirect.github.com/numpy/numpy/issues/27256) and [#27257](https://redirect.github.com/numpy/numpy/issues/27257) - [#27278](https://redirect.github.com/numpy/numpy/pull/27278): BUG: Fix array_equal for numeric and non-numeric scalar types - [#27287](https://redirect.github.com/numpy/numpy/pull/27287): MAINT: Update maintenance/2.1.x after the 2.0.2 release - [#27303](https://redirect.github.com/numpy/numpy/pull/27303): BLD: cp311- macosx_arm64 wheels \[wheel build] - [#27304](https://redirect.github.com/numpy/numpy/pull/27304): BUG: f2py: better handle filtering of public/private subroutines ##### Checksums ##### MD5 3053a97400db800b7377749e691eb39e numpy-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl 84b752a2220dce7c96ff89eef4f4aec3 numpy-2.1.1-cp310-cp310-macosx_11_0_arm64.whl 47ed4f704a64261f07ca24ef2e674524 numpy-2.1.1-cp310-cp310-macosx_14_0_arm64.whl b8a45caa870aee980c298053cf064d28 numpy-2.1.1-cp310-cp310-macosx_14_0_x86_64.whl e097ad5eee572b791b4a25eedad6df4a 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7be6a07520b88214ea85d8ac8b7d6d8a1839b0b5cb87412ac9f49fa934eb15d5 numpy-2.1.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl 52ac2e48f5ad847cd43c4755520a2317f3380213493b9d8a4c5e37f3b87df504 numpy-2.1.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl 50a95ca3560a6058d6ea91d4629a83a897ee27c00630aed9d933dff191f170cd numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 99f4a9ee60eed1385a86e82288971a51e71df052ed0b2900ed30bc840c0f2e39 numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl d0cf7d55b1051387807405b3898efafa862997b4cba8aa5dbe657be794afeafd numpy-2.1.1.tar.gz ### [`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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NumPy 2.0.1 is the last planned release in the 2.0.x series, 2.1.0rc1 should be out shortly. The Python versions supported by this release are 3.9-3.12. ***NOTE:*** Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage. #### Improvements ##### `np.quantile` with method `closest_observation` chooses nearest even order statistic This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations. ([gh-26656](https://redirect.github.com/numpy/numpy/pull/26656)) #### Contributors A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - [@vahidmech](https://redirect.github.com/vahidmech) + - Alex Herbert + - Charles Harris - Giovanni Del Monte + - Leo Singer - Lysandros Nikolaou - Matti Picus - Nathan Goldbaum - Patrick J. Roddy + - Raghuveer Devulapalli - Ralf Gommers - Rostan Tabet + - Sebastian Berg - Tyler Reddy - Yannik Wicke + #### Pull requests merged A total of 24 pull requests were merged for this release. - [#26711](https://redirect.github.com/numpy/numpy/pull/26711): MAINT: prepare 2.0.x for further development - [#26792](https://redirect.github.com/numpy/numpy/pull/26792): TYP: fix incorrect import in `ma/extras.pyi` stub - [#26793](https://redirect.github.com/numpy/numpy/pull/26793): DOC: Mention '1.25' legacy printing mode in `set_printoptions` - [#26794](https://redirect.github.com/numpy/numpy/pull/26794): DOC: Remove mention of NaN and NAN aliases from constants - [#26821](https://redirect.github.com/numpy/numpy/pull/26821): BLD: Fix x86-simd-sort build failure on openBSD - [#26822](https://redirect.github.com/numpy/numpy/pull/26822): BUG: Ensure output order follows input in numpy.fft - [#26823](https://redirect.github.com/numpy/numpy/pull/26823): TYP: fix missing sys import in numeric.pyi - [#26832](https://redirect.github.com/numpy/numpy/pull/26832): DOC: remove hack to override \_add_newdocs_scalars - [#26835](https://redirect.github.com/numpy/numpy/pull/26835): BUG: avoid side-effect of 'include complex.h' - [#26836](https://redirect.github.com/numpy/numpy/pull/26836): BUG: fix max_rows and chunked string/datetime reading in `loadtxt` - [#26837](https://redirect.github.com/numpy/numpy/pull/26837): BUG: fix PyArray_ImportNumPyAPI under -Werror=strict-prototypes - [#26856](https://redirect.github.com/numpy/numpy/pull/26856): DOC: Update some documentation - [#26868](https://redirect.github.com/numpy/numpy/pull/26868): BUG: fancy indexing copy - [#26869](https://redirect.github.com/numpy/numpy/pull/26869): BUG: Mismatched allocation domains in `PyArray_FillWithScalar` - [#26870](https://redirect.github.com/numpy/numpy/pull/26870): BUG: Handle --f77flags and --f90flags for meson \[wheel build] - [#26887](https://redirect.github.com/numpy/numpy/pull/26887): BUG: Fix new DTypes and new string promotion when signature is... - [#26888](https://redirect.github.com/numpy/numpy/pull/26888): BUG: remove numpy.f2py from excludedimports - [#26959](https://redirect.github.com/numpy/numpy/pull/26959): BUG: Quantile closest_observation to round to nearest even order - [#26960](https://redirect.github.com/numpy/numpy/pull/26960): BUG: Fix off-by-one error in amount of characters in strip - [#26961](https://redirect.github.com/numpy/numpy/pull/26961): API: Partially revert unique with return_inverse - [#26962](https://redirect.github.com/numpy/numpy/pull/26962): BUG,MAINT: Fix utf-8 character stripping memory access - [#26963](https://redirect.github.com/numpy/numpy/pull/26963): BUG: Fix out-of-bound minimum offset for in1d table method - [#26971](https://redirect.github.com/numpy/numpy/pull/26971): BUG: fix f2py tests to work with v2 API - [#26995](https://redirect.github.com/numpy/numpy/pull/26995): BUG: Add object cast to avoid warning with limited 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numpy-2.0.1-pp39-pypy39_pp73-win_amd64.whl 485b87235796410c3519a699cfe1faab097e509e90ebb05dcd098db2ae87e7b3 numpy-2.0.1.tar.gz ### [`v2.0.0`](https://redirect.github.com/numpy/numpy/releases/tag/v2.0.0) [Compare 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(Configuration
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