Bump numpy from 2.2.1 to 2.4.0

[dependabot]

Bumps numpy from 2.2.1 to 2.4.0.

Release notes

Sourced from numpy's releases.

2.4.0 (Dec 20, 2025)

NumPy 2.4.0 Release Notes

The NumPy 2.4.0 release continues the work to improve free threaded Python support, user dtypes implementation, and annotations. There are many expired deprecations and bug fixes as well.

This release supports Python versions 3.11-3.14

Highlights

Apart from annotations and same_value kwarg, the 2.4 highlights are mostly of interest to downstream developers. They should help in implementing new user dtypes.

• Many annotation improvements. In particular, runtime signature introspection.
• New casting kwarg 'same_value' for casting by value.
• New PyUFunc_AddLoopsFromSpec function that can be used to add user sort loops using the ArrayMethod API.
• New __numpy_dtype__ protocol.

Deprecations

Setting the strides attribute is deprecated

Setting the strides attribute is now deprecated since mutating an array is unsafe if an array is shared, especially by multiple threads. As an alternative, you can create a new view (no copy) via:

• np.lib.stride_tricks.strided_window_view if applicable,
• np.lib.stride_tricks.as_strided for the general case,
• or the np.ndarray constructor (buffer is the original array) for a light-weight version.

(gh-28925)

Positional out argument to np.maximum, np.minimum is deprecated

Passing the output array out positionally to numpy.maximum and numpy.minimum is deprecated. For example, np.maximum(a, b, c) will emit a deprecation warning, since c is treated as the output buffer rather than a third input.

Always pass the output with the keyword form, e.g. np.maximum(a, b, out=c). This makes intent clear and simplifies type annotations.

(gh-29052)

align= must be passed as boolean to np.dtype()

... (truncated)

Changelog

Sourced from numpy's changelog.

Update 2.4.0 milestones

Look at the issues/prs with 2.4.0 milestones and either push them off to a later version, or maybe remove the milestone. You may need to add a milestone.

Check the numpy-release repo

The things to check are the cibuildwheel version in .github/workflows/wheels.yml and the openblas versions in openblas_requirements.txt.

Make a release PR

Four documents usually need to be updated or created for the release PR:

• The changelog
• The release notes
• The .mailmap file
• The pyproject.toml file

These changes should be made in an ordinary PR against the maintenance branch. Other small, miscellaneous fixes may be part of this PR. The commit message might be something like::

REL: Prepare for the NumPy 2.4.0 release

Create 2.4.0-changelog.rst.
Update 2.4.0-notes.rst.
Update .mailmap.
Update pyproject.toml

Set the release version

Check the pyproject.toml file and set the release version and update the classifier if needed::

$ gvim pyproject.toml

Check the doc/source/release.rst file

make sure that the release notes have an entry in the release.rst file::

... (truncated)

Commits

• c5ab79c Merge pull request #30487 from charris/prepare-2.4.0.2
• 05e379a REL: Prepare for the NumPy 2.4.0 release (2)
• 485f1c4 REL: Prepare for the NumPy 2.4.0 release (1) (#30486)
• c683481 REL: Prepare for the NumPy 2.4.0 release (#30439)
• 11d9ed2 Merge pull request #30459 from charris/backport-30456
• 2a17ddb TYP: restore generic.__hash__ (#30456)
• d04a429 Merge pull request #30432 from charris/backport-30426
• f94a148 fix more data races in mtrand.pyx (#30426)
• 06a9d42 Merge pull request #30420 from charris/backport-30418
• 30819cd Merge pull request #30419 from charris/backport-30373
• Additional commits viewable in compare view

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[kamronald]

@lbluque and @qchempku2017, do you think we should keep updating the numpy version? Using numpy 2.4 will lose compatibility with Python 3.10, would this be an issue?

[qchempku2017]

@lbluque and @qchempku2017, do you think we should keep updating the numpy version? Using numpy 2.4 will lose compatibility with Python 3.10, would this be an issue?

I don't think this will kill, because not all users use GINAR. Switching to python >= 3.12 is already THE most rational choice in 2025.

[lbluque]

Hi @kamronald,

I agree with @qchempku2017. We should upgrade numpy and drop 3.10 support. We should then also consider updating to test python 3.14 or 3.13 if we cant support 3.14 yet.

[dependabot]

A newer version of numpy exists, but since this PR has been edited by someone other than Dependabot I haven't updated it. You'll get a PR for the updated version as normal once this PR is merged.