What's Changed
* Update package links in README by buhtz in https://github.com/borntyping/python-colorlog/pull/131
* Added [docs/CONTRIBUTING.md](https://github.com/borntyping/python-colorlog/blob/main/docs/CONTRIBUTING.md).
* Documented "bright" colours in the README.

New Contributors
* buhtz made their first contribution in https://github.com/borntyping/python-colorlog/pull/131

**Full Changelog**: https://github.com/borntyping/python-colorlog/compare/v6.8.0...v6.8.2

What's Changed
* Remove universal wheel, python 2 is unsupported by gopackgo90 in https://github.com/borntyping/python-colorlog/pull/126
* Fix running tests in environment with NO_COLOR=1 by mgorny in https://github.com/borntyping/python-colorlog/pull/130

New Contributors
* gopackgo90 made their first contribution in https://github.com/borntyping/python-colorlog/pull/126
* mgorny made their first contribution in https://github.com/borntyping/python-colorlog/pull/130

**Full Changelog**: https://github.com/borntyping/python-colorlog/compare/v6.7.0...v6.8.0

What's Changed
* add documentation about the levels argument by JohannesBuchner in https://github.com/dfm/corner.py/pull/229
* Updating baseline images for tests by dfm in https://github.com/dfm/corner.py/pull/239
* Allow specifying `fig` for 1D dataset by vandalt in https://github.com/dfm/corner.py/pull/243
* Fix 1d fig arg (close 110) by avivajpeyi in https://github.com/dfm/corner.py/pull/260
* Upgrade to GitHub-native Dependabot by dfm in https://github.com/dfm/corner.py/pull/265
* Switch to hatch for packaging by dfm in https://github.com/dfm/corner.py/pull/283

New Contributors
* JohannesBuchner made their first contribution in https://github.com/dfm/corner.py/pull/229
* vandalt made their first contribution in https://github.com/dfm/corner.py/pull/243
* avivajpeyi made their first contribution in https://github.com/dfm/corner.py/pull/260

**Full Changelog**: https://github.com/dfm/corner.py/compare/v2.2.2...v2.2.3

What's Changed
* Fixing infinite loop by dfm in https://github.com/dfm/corner.py/pull/154
* Added a reverse option to overplot_* by NeutralKaon in https://github.com/dfm/corner.py/pull/156
* Working to fix tests on CI by dfm in https://github.com/dfm/corner.py/pull/187
* download notebooks as .ipynb by Solosneros in https://github.com/dfm/corner.py/pull/188
* Add Returns block to main corner() docstring by adrn in https://github.com/dfm/corner.py/pull/190
* Proposed fix for title errorbars/quantiles bug  by jhmatthews in https://github.com/dfm/corner.py/pull/193
* Update corner hist2d to match axis background color by delinea in https://github.com/dfm/corner.py/pull/196
* Switching to using centralized packaging infrastructure by dfm in https://github.com/dfm/corner.py/pull/202
* Trying to silence font issues in docs by dfm in https://github.com/dfm/corner.py/pull/212
* Added option for log scaled axes. by castillohair in https://github.com/dfm/corner.py/pull/174
* Clarify support for using pandas column names as labels by zachjweiner in https://github.com/dfm/corner.py/pull/218
* Updated minimum python version and test outputs by dfm in https://github.com/dfm/corner.py/pull/221
* Fixing handling of range arugment when empty figure is provided by dfm in https://github.com/dfm/corner.py/pull/224
* Fixing outdated release workflow by dfm in https://github.com/dfm/corner.py/pull/227

New Contributors
* NeutralKaon made their first contribution in https://github.com/dfm/corner.py/pull/156
* pre-commit-ci made their first contribution in https://github.com/dfm/corner.py/pull/157
* Solosneros made their first contribution in https://github.com/dfm/corner.py/pull/188
* jhmatthews made their first contribution in https://github.com/dfm/corner.py/pull/193
* delinea made their first contribution in https://github.com/dfm/corner.py/pull/196
* castillohair made their first contribution in https://github.com/dfm/corner.py/pull/174
* zachjweiner made their first contribution in https://github.com/dfm/corner.py/pull/218

**Full Changelog**: https://github.com/dfm/corner.py/compare/v2.2.1...v2.2.2

What's Changed
* Fixed infinite loop by dfm in https://github.com/dfm/corner.py/pull/154
* Fixed tests on CI by dfm in https://github.com/dfm/corner.py/pull/187
* Fixed title errorbars/quantiles bug  by jhmatthews in https://github.com/dfm/corner.py/pull/193
* Added a reverse option to overplot_* by NeutralKaon in https://github.com/dfm/corner.py/pull/156
* Added option to download notebooks as .ipynb by Solosneros in https://github.com/dfm/corner.py/pull/188
* Added `Returns` block to main corner() docstring by adrn in https://github.com/dfm/corner.py/pull/190
* Updated corner hist2d to match axis background color by delinea in https://github.com/dfm/corner.py/pull/196
* Switched to using centralized packaging infrastructure by dfm in https://github.com/dfm/corner.py/pull/202

New Contributors
* NeutralKaon made their first contribution in https://github.com/dfm/corner.py/pull/156
* pre-commit-ci made their first contribution in https://github.com/dfm/corner.py/pull/157
* Solosneros made their first contribution in https://github.com/dfm/corner.py/pull/188
* jhmatthews made their first contribution in https://github.com/dfm/corner.py/pull/193
* delinea made their first contribution in https://github.com/dfm/corner.py/pull/196

**Full Changelog**: https://github.com/dfm/corner.py/compare/v2.2.1...v2.2.2rc1

---------------------------

Due to maintenance issues, 1.4.1 was not valid and we bumped the version to 1.4.2


- Fix a backward incompatible change in ``MemorizedFunc.call`` which needs to
return the metadata. Also make sure that ``NotMemorizedFunc.call`` return
an empty dict for metadata for consistency.
https://github.com/joblib/joblib/pull/1576

---------------------------

- Allow caching co-routines with `Memory.cache`.
https://github.com/joblib/joblib/pull/894

- Try to cast ``n_jobs`` to int in parallel and raise an error if
it fails. This means that ``n_jobs=2.3`` will now result in
``effective_n_jobs=2`` instead of failing.
https://github.com/joblib/joblib/pull/1539

- Ensure that errors in the task generator given to Parallel's call
are raised in the results consumming thread.
https://github.com/joblib/joblib/pull/1491

- Adjust codebase to NumPy 2.0 by changing ``np.NaN`` to ``np.nan``
and importing ``byte_bounds`` from ``np.lib.array_utils``.
https://github.com/joblib/joblib/pull/1501

- The parameter ``return_as`` in ``joblib.Parallel`` can now be set to
``generator_unordered``. In this case the results will be returned in the
order of task completion rather than the order of submission.
https://github.com/joblib/joblib/pull/1463

- dask backend now supports ``return_as=generator`` and
``return_as=generator_unordered``.
https://github.com/joblib/joblib/pull/1520

- Vendor cloudpickle 3.0.0 and end support for Python 3.7 which has
reached end of life.
https://github.com/joblib/joblib/pull/1487
https://github.com/joblib/joblib/pull/1515

---------------------------

- Fix a regression in ``joblib.Parallel`` introduced in 1.3.0 where
explicitly setting ``n_jobs=None`` was not interpreted as "unset".
https://github.com/joblib/joblib/pull/1475

- Fix a regression in ``joblib.Parallel`` introduced in 1.3.0 where
``joblib.Parallel`` logging methods exposed from inheritance to
``joblib.Logger`` didn't work because of missing logger
initialization.
https://github.com/joblib/joblib/pull/1494

- Various maintenance updates to the doc, the ci and the test.
https://github.com/joblib/joblib/pull/1480,
https://github.com/joblib/joblib/pull/1481,
https://github.com/joblib/joblib/pull/1476,
https://github.com/joblib/joblib/pull/1492

---------------------------

- Fix compatibility with python 3.7 by vendor loky 3.4.1
which is compatible with this version.
https://github.com/joblib/joblib/pull/1472

---------------------------

- Ensure native byte order for memmap arrays in ``joblib.load``.
https://github.com/joblib/joblib/issues/1353

- Add ability to change default Parallel backend in tests by setting the
``JOBLIB_TESTS_DEFAULT_PARALLEL_BACKEND`` environment variable.
https://github.com/joblib/joblib/pull/1356

- Fix temporary folder creation in `joblib.Parallel` on Linux subsystems on Windows
which do have `/dev/shm` but don't have the `os.statvfs` function
https://github.com/joblib/joblib/issues/1353

- Drop runtime dependency on ``distutils``. ``distutils`` is going away
in Python 3.12 and is deprecated from Python 3.10 onwards. This import
was kept around to avoid breaking scikit-learn, however it's now been
long enough since scikit-learn deployed a fixed (version 1.1 was released
in May 2022) that it should be safe to remove this.
https://github.com/joblib/joblib/pull/1361

- A warning is raised when a pickling error occurs during caching operations.
In version 1.5, this warning will be turned into an error. For all other
errors, a new warning has been introduced: ``joblib.memory.CacheWarning``.
https://github.com/joblib/joblib/pull/1359

- Avoid (module, name) collisions when caching nested functions. This fix
changes the module name of nested functions, invalidating caches from
previous versions of Joblib.
https://github.com/joblib/joblib/pull/1374

- Add ``cache_validation_callback`` in :meth:`joblib.Memory.cache`, to allow
custom cache invalidation based on the metadata of the function call.
https://github.com/joblib/joblib/pull/1149

- Add a ``return_as`` parameter for ``Parallel``, that enables consuming
results asynchronously.
https://github.com/joblib/joblib/pull/1393,
https://github.com/joblib/joblib/pull/1458

- Improve the behavior of ``joblib`` for ``n_jobs=1``, with simplified
tracebacks and more efficient running time.
https://github.com/joblib/joblib/pull/1393

- Add the ``parallel_config`` context manager to allow for more fine-grained
control over the backend configuration. It should be used in place of the
``parallel_backend`` context manager. In particular, it has the advantage
of not requiring to set a specific backend in the context manager.
https://github.com/joblib/joblib/pull/1392,
https://github.com/joblib/joblib/pull/1457

- Add ``items_limit`` and ``age_limit`` in :meth:`joblib.Memory.reduce_size`
to make it easy to limit the number of items and remove items that have
not been accessed for a long time in the cache.
https://github.com/joblib/joblib/pull/1200

- Deprecate ``bytes_limit`` in ``Memory`` as this is not automatically enforced,
the limit can be directly passed to :meth:`joblib.Memory.reduce_size` which
needs to be called to actually enforce the limit.
https://github.com/joblib/joblib/pull/1447

- Vendor ``loky`` 3.4.0 which includes various fixes.
https://github.com/joblib/joblib/pull/1422

- Various updates to the documentation and to benchmarking tools.
https://github.com/joblib/joblib/pull/1343,
https://github.com/joblib/joblib/pull/1348,
https://github.com/joblib/joblib/pull/1411,
https://github.com/joblib/joblib/pull/1451,
https://github.com/joblib/joblib/pull/1427,
https://github.com/joblib/joblib/pull/1400

- Move project metadata to ``pyproject.toml``.
https://github.com/joblib/joblib/pull/1382,
https://github.com/joblib/joblib/pull/1433

- Add more tests to improve python ``nogil`` support.
https://github.com/joblib/joblib/pull/1394,
https://github.com/joblib/joblib/pull/1395

=======

* Do not reorder dictionaries (schemas, instances) that are printed as part of validation errors.
* Declare support for Py3.13

=======

* Improve ``best_match`` (and thereby error messages from ``jsonschema.validate``) in cases where there are multiple *sibling* errors from applying ``anyOf`` / ``allOf`` -- i.e. when multiple elements of a JSON array have errors, we now do prefer showing errors from earlier elements rather than simply showing an error for the full array (1250).
* (Micro-)optimize equality checks when comparing for JSON Schema equality by first checking for object identity, as ``==`` would.

=======

* Slightly speed up the ``contains`` keyword by removing some unnecessary validator (re-)creation.

=======

* Fix the behavior of ``enum`` in the presence of ``0`` or ``1`` to properly consider ``True`` and ``False`` unequal (1208).
* Special case the error message for ``{min,max}{Items,Length,Properties}`` when they're checking for emptiness rather than true length.

=======

* Properly consider items (and properties) to be evaluated by ``unevaluatedItems`` (resp. ``unevaluatedProperties``) when behind a ``$dynamicRef`` as specified by the 2020 and 2019 specifications.
* ``jsonschema.exceptions.ErrorTree.__setitem__`` is now deprecated.
More broadly, in general users of ``jsonschema`` should never be mutating objects owned by the library.

=======

* Fix the error message for additional items when used with heterogeneous arrays.
* Don't leak the ``additionalItems`` keyword into JSON Schema draft 2020-12, where it was replaced by ``items``.

=======

* Single label hostnames are now properly considered valid according to the ``hostname`` format.
This is the behavior specified by the relevant RFC (1123).
IDN hostname behavior was already correct.

=======

* Importing the ``Validator`` protocol directly from the package root is deprecated.
Import it from ``jsonschema.protocols.Validator`` instead.
* Automatic retrieval of remote references (which is still deprecated) now properly succeeds even if the retrieved resource does not declare which version of JSON Schema it uses.
Such resources are assumed to be 2020-12 schemas.
This more closely matches the pre-referencing library behavior.

=======

* Set a ``jsonschema`` specific user agent when automatically retrieving remote references (which is deprecated).

=======

* Declare support for Py3.12

=======

* Improve the hashability of wrapped referencing exceptions when they contain hashable data.

=======

* Properly preserve ``applicable_validators`` in extended validators.
Specifically, validators extending early drafts where siblings of ``$ref`` were ignored will properly ignore siblings in the extended validator.

=======

* Fix an additional regression with the deprecated ``jsonschema.RefResolver`` and pointer resolution.

=======

* Fix a regression with ``jsonschema.RefResolver`` based resolution when used in combination with a custom validation dialect (via ``jsonschema.validators.create``).

=======

This release majorly rehauls the way in which JSON Schema reference resolution is configured.
It does so in a way that *should* be backwards compatible, preserving old behavior whilst emitting deprecation warnings.

* ``jsonschema.RefResolver`` is now deprecated in favor of the new `referencing library <https://github.com/python-jsonschema/referencing/>`_.
``referencing`` will begin in beta, but already is more compliant than the existing ``$ref`` support.
This change is a culmination of a meaningful chunk of work to make ``$ref`` resolution more flexible and more correct.
Backwards compatibility *should* be preserved for existing code which uses ``RefResolver``, though doing so is again now deprecated, and all such use cases should be doable using the new APIs.
Please file issues on the ``referencing`` tracker if there is functionality missing from it, or here on the ``jsonschema`` issue tracker if you have issues with existing code not functioning the same, or with figuring out how to change it to use ``referencing``.
In particular, this referencing change includes a change concerning *automatic* retrieval of remote references (retrieving ``http://foo/bar`` automatically within a schema).
This behavior has always been a potential security risk and counter to the recommendations of the JSON Schema specifications; it has survived this long essentially only for backwards compatibility reasons, and now explicitly produces warnings.
The ``referencing`` library itself will *not* automatically retrieve references if you interact directly with it, so the deprecated behavior is only triggered if you fully rely on the default ``$ref`` resolution behavior and also include remote references in your schema, which will still be retrieved during the deprecation period (after which they will become an error).
* Support for Python 3.7 has been dropped, as it is nearing end-of-life.
This should not be a "visible" change in the sense that ``requires-python`` has been updated, so users using 3.7 should still receive ``v4.17.3`` when installing the library.
* On draft 2019-09, ``unevaluatedItems`` now properly does *not* consider items to be evaluated by an ``additionalItems`` schema if ``items`` is missing from the schema, as the specification says in this case that ``additionalItems`` must be completely ignored.
* Fix the ``date`` format checker on Python 3.11 (when format assertion behavior is enabled), where it was too liberal (1076).
* Speed up validation of ``unevaluatedProperties`` (1075).

Deprecations
------------

* ``jsonschema.RefResolver`` -- see above for details on the replacement
* ``jsonschema.RefResolutionError`` -- see above for details on the replacement
* relying on automatic resolution of remote references -- see above for details on the replacement
* importing ``jsonschema.ErrorTree`` -- instead import it via ``jsonschema.exceptions.ErrorTree``
* importing ``jsonschema.FormatError`` -- instead import it via ``jsonschema.exceptions.FormatError``

=======

* Fix instantiating validators with cached refs to boolean schemas
rather than objects (1018).

=======

* Empty strings are not valid relative JSON Pointers (aren't valid under the
RJP format).
* Durations without (trailing) units are not valid durations (aren't
valid under the duration format). This involves changing the dependency
used for validating durations (from ``isoduration`` to ``isodate``).

=======

* The error message when using ``unevaluatedProperties`` with a non-trivial
schema value (i.e. something other than ``false``) has been improved (996).

=======

* The ``check_schema`` method on ``jsonschema.protocols.Validator`` instances
now *enables* format validation by default when run. This can catch some
additional invalid schemas (e.g. containing invalid regular expressions)
where the issue is indeed uncovered by validating against the metaschema
with format validation enabled as an assertion.
* The ``jsonschema`` CLI (along with ``jsonschema.cli`` the module) are now
deprecated. Use ``check-jsonschema`` instead, which can be installed via
``pip install check-jsonschema`` and found
`here <https://github.com/python-jsonschema/check-jsonschema>`_.

=======

* Make ``ErrorTree`` have a more grammatically correct ``repr``.

This is the third bugfix release of the 3.9.x series.

This release contains several bug-fixes and adjustments:

- Fix `axline` with extremely small slopes
- Fix `axline` with non-linear axis scales
- Fix `minimumSizeHint` with Qt backend
- Fix config directory usage when it's behind a symlink
- Fix draggable legend when blitting is enabled
- Fix high CPU utilization in the `macosx` backend
- Fix multiple hatch `edgecolors` passed to `contourf`
- Improve compatibility with `pytest` 8.2.0

This is the second bugfix release of the 3.9.x series.

This release contains several bug-fixes and adjustments:

- Be more resilient to I/O failures when writing font cache
- Fix nondeterministic behavior with subplot spacing and constrained layout
- Fix sticky edge tolerance relative to data range
- Improve formatting of image values in cases of singular norms

Windows wheels now bundle the MSVC runtime DLL statically to avoid inconsistencies with other wheels and random crashes depending on import order.

This is the first bugfix release of the 3.9.x series.

This release contains several bug-fixes and adjustments:

- Add GitHub artifact attestations for sdist and wheels
- Re-add `matplotlib.cm.get_cmap`; note this function will still be removed at a later date
- Allow duplicate backend entry points
- Fix `Axes` autoscaling of thin bars at large locations
- Fix `Axes` autoscaling with `axhspan` / `axvspan`
- Fix `Axes3D` autoscaling of `Line3DCollection` / `Poly3DCollection`
- Fix `Axes3D` mouse interactivity with non-default roll angle
- Fix box aspect ratios in `Axes3D` with alternate vertical axis
- Fix case handling of backends specified as `module://...`
- Fix crash with TkAgg on Windows with `tk.window_focus: True`
- Fix interactive update of SubFigures
- Fix interactivity when using the IPython console
- Fix pickling of AxesWidgets and SubFigures
- Fix scaling on GTK3Cairo / GTK4Cairo backends
- Fix text wrapping within SubFigures
- Promote `mpltype` Sphinx role to a public extension; note this is only intended for development reasons

Highlights of this release include:

- Plotting and Annotation improvements
- Axes.inset_axes is no longer experimental
- Legend support for Boxplot
- Percent sign in pie labels auto-escaped with usetex=True
- hatch parameter for stackplot
- Add option to plot only one half of violin plot
- axhline and axhspan on polar axes
- Subplot titles can now be automatically aligned
- axisartist can now be used together with standard Formatters
- Toggle minorticks on Axis
- StrMethodFormatter now respects axes.unicode_minus
- Figure, Axes, and Legend Layout
- Subfigures now have controllable zorders
- Getters for xmargin, ymargin and zmargin
- Mathtext improvements
- mathtext documentation improvements
- mathtext spacing corrections
- Widget Improvements
- Check and Radio Button widgets support clearing
- 3D plotting improvements
- Setting 3D axis limits now set the limits exactly
- Other improvements
- New BackendRegistry for plotting backends
- Add widths, heights and angles setter to EllipseCollection
- image.interpolation_stage rcParam
- Arrow patch position is now modifiable
- NonUniformImage now has mouseover support

This is the second release candidate for the meso release 3.9.0.

This is the fourth micro release of the 3.8 series.
 
Highlights of the 3.8.4 release include:
 
- Enable building against numpy 2.0; released wheels are built against numpy 2
- macosx: Clean up single-shot timers correctly
- Add a draw during show for macos backend
- Fix color sequence data for Set2 and Set3
- gtk: Ensure pending draws are done before GTK draw
- Update "Created with" url in hand.svg
- Avoid modifying user input to Axes.bar
- fix quiver3d incorrect arrow colors

This is the third micro release of the 3.8 series.

Highlights of the 3.8.3 release include:

- Improvements to the MacOS backend
- Fix hanging on `plt.pause`
- Fix warnings about "Secure coding is not enabled for restorable state"
- Fix crash at exit for PGF backend

This is the second bugfix release of the 3.8 series.

Highlights of this release include:
- Fix a segfault in the MacOS backend when running on Python 3.12
- Fix Contour labeling manual positions selecting incorrect contours.
- Various documentation improvements

This is the first bugfix release of the 3.8.x series.


This release contains several bug fixes and adjustments:


- Bump setuptools required version because of setuptools_scm v8
- Update ``find_nearest_contour`` and revert contour deprecations
- ``allsegs`` and ``allkinds`` return individual segments
- Restore default behavior of hexbin mincnt with C provided
- Try/except import of Axes3D
- Ensure valid path mangling for ContourLabeler
- BLD: Remove development dependencies from sdists
- FIX 2-tuple of colors in to_rgba_array
- Fix issue with non-string labels and legend
- Fix issue with locale comma when not using math text
- Various type hinting improvements
- Various documentation improvements
- Improvements to the MacOS backend

This is the fifth bugfix release of the 3.7.x series.

This release contains two bug-fixes:

- Fix hanging on `plt.pause` on the MacOS backend
- Fix crash on exit when using the PGF backend on Windows

This is the fourth bugfix release of the 3.7.x series.

This release contains one bug-fix:

- Fix a segmentation fault when resizing on Python 3.12 and macOS 14

This is the third bugfix release of the 3.7.x series.

This release contains several bug-fixes and adjustments:

* Add Python 3.12 wheels
* Update the license for the bundled colorbrewer colormap data
* Fix Cairo backend when using cairocffi
* Fix axes_grid1 inset axes with `bbox_inches=tight`
* Fix bugs in `Path3DCollection` / `Poly3DCollection` constructors
* Fix setting array labelcolor on Tick
* Improve compatibility with latest NumPy
* Stop warning when calling `tight_layout` multiple times

This is the second bugfix release of the 3.7.x series.

This release contains several bug-fixes and adjustments:

* Avoid modifying input masks in `pcolor`/`pcolormesh`
* Fix 3D set_aspect error cases
* Fix IPython's `%pylab` mode detection
* Fix `Figure.get_constrained_layout_pads()`
* Fix `Legend.set_draggable()` with `update="bbox"`
* Fix `TransformedBbox.{,full_}contains`
* Fix clipping of `bar_label` text
* Fix colorbar size when saving with explicit `bbox_inches`
* Fix errors when an input is entirely NaN
* Fix leaks of exception tracebacks and `LayoutGrid` objects
* Fix non-interpolated imshow in PDF export
* Fix palettized image optimization in PDF export
* Fix pgf tests with TeXLive 2022
* Fix removal of `Axes` that contain widgets that are grabbing the mouse
* Fix removal of draggable artists
* Fix subslice optimization for long, fully nan lines
* Fix tight layout if Figure has an existing layout manager
* Fix window extent of AnnotationBbox before first draw
* Fix wspace and hspace in subfigures
* Improve Qt compatibility
* Improve compatibility with Python 3.12
* Prevent under the hood downcasting of values in `xcorr`
* Remove some NumPy function overrides from `pylab`
* Remove warning with symlog scales on mouseover

This is the first bugfix release of the 3.7.x series.

This release contains several bug-fixes and adjustments:

* Ensure Qhull license is included in binary wheels
* Fix application of rcParams on Axes labels
* Fix compatibility with Pandas datetime unit converter
* Fix compatibility with latest GTK4
* Fix import of styles with relative path
* Fix Lasso unresponsiveness when clicking and immediately releasing
* Fix pickling of draggable legends
* Fix RangeSlider.set_val when new value is outside existing value
* Fix size of Tk spacers when changing display DPI
* Fix wrapped text in constrained layout
* Improve compatibility with third-party backends
* Improve error if animation save path does not exist

This is the third bugfix release of the 3.6.x series.

This release contains several bug-fixes and adjustments:

* Fix Artist removal from `axes_grid1` Axes classes
* Fix `inset_locator` in subfigures
* Fix `scatter` on masked arrays with units
* Fix colorbar ticks with log norm contours
* Fix deprecation warnings in GTK4 backend
* Fix using relative paths in `HTMLWriter`
* Improve failure message from rcParams string validation for tuple inputs
* Improve performance of QtAgg backends
* No longer modify `pil_kwargs` argument to `imsave` and `savefig`

This is the second bugfix release of the 3.6.x series.

This release contains several bug-fixes and adjustments:

* Avoid mutating dictionaries passed to `subplots`
* Fix `bbox_inches='tight'` on a figure with constrained layout enabled
* Fix auto-scaling of `ax.hist` density with `histtype='step'`
* Fix compatibility with PySide6 6.4
* Fix evaluating colormaps on non-NumPy arrays
* Fix key reporting in pick events
* Fix thread check on PyPy 3.8
* Handle input to `ax.bar` that is all NaN
* Make rubber band more visible on Tk and Wx backends
* Restore (and warn on) seaborn styles in `style.library`
* Restore `get_renderer` function in deprecated `tight_layout`
* nb/webagg: Fix resize handle on WebKit browsers (e.g., Safari)

discovered after the 2.1.2 release. This release also adds support
for free threaded Python 3.13 on Windows.

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

Improvements

-   Fixed a number of issues around promotion for string ufuncs with
 StringDType arguments. Mixing StringDType and the fixed-width DTypes
 using the string ufuncs should now generate much more uniform
 results.

 ([gh-27636](https://github.com/numpy/numpy/pull/27636))

Changes

-   `numpy.fix` now won\'t perform casting to a floating
 data-type for integer and boolean data-type input arrays.

 ([gh-26766](https://github.com/numpy/numpy/pull/26766))

Contributors

A total of 15 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Abhishek Kumar +
-   Austin +
-   Benjamin A. Beasley +
-   Charles Harris
-   Christian Lorentzen
-   Marcel Telka +
-   Matti Picus
-   Michael Davidsaver +
-   Nathan Goldbaum
-   Peter Hawkins
-   Raghuveer Devulapalli
-   Ralf Gommers
-   Sebastian Berg
-   dependabot\[bot\]
-   kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

-   [27512](https://github.com/numpy/numpy/pull/27512): MAINT: prepare 2.1.x for further development
-   [27537](https://github.com/numpy/numpy/pull/27537): MAINT: Bump actions/cache from 4.0.2 to 4.1.1
-   [27538](https://github.com/numpy/numpy/pull/27538): MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
-   [27539](https://github.com/numpy/numpy/pull/27539): MAINT: MSVC does not support #warning directive
-   [27543](https://github.com/numpy/numpy/pull/27543): BUG: Fix user dtype can-cast with python scalar during promotion
-   [27561](https://github.com/numpy/numpy/pull/27561): DEV: bump `python` to 3.12 in environment.yml
-   [27562](https://github.com/numpy/numpy/pull/27562): BLD: update vendored Meson to 1.5.2
-   [27563](https://github.com/numpy/numpy/pull/27563): BUG: weighted quantile for some zero weights (#27549)
-   [27565](https://github.com/numpy/numpy/pull/27565): MAINT: Use miniforge for macos conda test.
-   [27566](https://github.com/numpy/numpy/pull/27566): BUILD: satisfy gcc-13 pendantic errors
-   [27569](https://github.com/numpy/numpy/pull/27569): BUG: handle possible error for PyTraceMallocTrack
-   [27570](https://github.com/numpy/numpy/pull/27570): BLD: start building Windows free-threaded wheels \[wheel build\]
-   [27571](https://github.com/numpy/numpy/pull/27571): BUILD: vendor tempita from Cython
-   [27574](https://github.com/numpy/numpy/pull/27574): BUG: Fix warning \"differs in levels of indirection\" in npy_atomic.h\...
-   [27592](https://github.com/numpy/numpy/pull/27592): MAINT: Update Highway to latest
-   [27593](https://github.com/numpy/numpy/pull/27593): BUG: Adjust numpy.i for SWIG 4.3 compatibility
-   [27616](https://github.com/numpy/numpy/pull/27616): BUG: Fix Linux QEMU CI workflow
-   [27668](https://github.com/numpy/numpy/pull/27668): BLD: Do not set \_\_STDC_VERSION\_\_ to zero during build
-   [27669](https://github.com/numpy/numpy/pull/27669): ENH: fix wasm32 runtime type error in numpy.\_core
-   [27672](https://github.com/numpy/numpy/pull/27672): BUG: Fix a reference count leak in npy_find_descr_for_scalar.
-   [27673](https://github.com/numpy/numpy/pull/27673): BUG: fixes for StringDType/unicode promoters

Checksums

MD5

 3f2f22827dd321ae86b5ab4fa888d0db  numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl
 13da2761d1abe71731a2806537369115  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl
 5aef4a78b69cd90d0f6fff8f88817991  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl
 12da7f09cd5707634878f85845c9de10  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whl
 5b999693362815b56855533469aea0ca  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 8c49f457127bfb4f167c91583e5167af  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 f31c0e80b18afc0c04cada401cbe0358  numpy-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl
 2c0709812e27bcaf74d75ac8ed45614b  numpy-2.1.3-cp310-cp310-musllinux_1_2_aarch64.whl
 a65b28800e78942b9e60e03e96cfd0c0  numpy-2.1.3-cp310-cp310-win32.whl
 d8358545732fe4ee1ecf407b06567d81  numpy-2.1.3-cp310-cp310-win_amd64.whl
 34942f9a1391532e2c3168043c0021d5  numpy-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl
 0d69ec06e303b5112788db68a8fdde1b  numpy-2.1.3-cp311-cp311-macosx_11_0_arm64.whl
 da1988c8d3a9db5947a2bd51290b8b95  numpy-2.1.3-cp311-cp311-macosx_14_0_arm64.whl
 b5eba73c2abaf5a81535f4b1034fe8d2  numpy-2.1.3-cp311-cp311-macosx_14_0_x86_64.whl
 63cc090209718aa1d0f0fbd3fd03bc0b  numpy-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 55f14ca7b55554d4a043369ae5f1837f  numpy-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 4e58e0645d81ff84c0fb75311d2a97d6  numpy-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl
 30235088a5f86d1f343bfec458f6292d  numpy-2.1.3-cp311-cp311-musllinux_1_2_aarch64.whl
 c80a03952b2f4950f1eb9d1656413fec  numpy-2.1.3-cp311-cp311-win32.whl
 d8c1a5a441b89591af8f09dfa0b2d4d5  numpy-2.1.3-cp311-cp311-win_amd64.whl
 2cebcea71e71e8b09a25179b240ee240  numpy-2.1.3-cp312-cp312-macosx_10_13_x86_64.whl
 faf5df4bd35ca362795cda193da49591  numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl
 573f195910fc3b3e9ac5379816280f89  numpy-2.1.3-cp312-cp312-macosx_14_0_arm64.whl
 900548b2acb82ed0e306943fb68de802  numpy-2.1.3-cp312-cp312-macosx_14_0_x86_64.whl
 81cded28bb87c4987b1d975fe768c3a1  numpy-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2b83cb346bca97475fa5e39e704c45f1  numpy-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 06d8593cb7a2aae157e028c3d4cb3c96  numpy-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl
 eea8b148a6a2fee37b87291043e00bda  numpy-2.1.3-cp312-cp312-musllinux_1_2_aarch64.whl
 d407b7c48457789914f28004f41d6ea2  numpy-2.1.3-cp312-cp312-win32.whl
 117574ee1a645e63a6d69e20c8673665  numpy-2.1.3-cp312-cp312-win_amd64.whl
 0c9ffd1f1f1e96186f30a578b85da653  numpy-2.1.3-cp313-cp313-macosx_10_13_x86_64.whl
 cd430b2caf09d21680616aef5d4a439d  numpy-2.1.3-cp313-cp313-macosx_11_0_arm64.whl
 b431935148221b79bda9490b1d069e3c  numpy-2.1.3-cp313-cp313-macosx_14_0_arm64.whl
 b3ff577c78097b187bd58f20b6e88642  numpy-2.1.3-cp313-cp313-macosx_14_0_x86_64.whl
 8186f86f8d94a5505e6dcebe6c056ab7  numpy-2.1.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2c5b2381a4a4e3d9865ccb346d44a7ed  numpy-2.1.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 85786d12388d60b904c02eb12df55b37  numpy-2.1.3-cp313-cp313-musllinux_1_1_x86_64.whl
 da68282c0418a22730643906e5dd58a1  numpy-2.1.3-cp313-cp313-musllinux_1_2_aarch64.whl
 fe47e181a70d3e865e5d6a27e5fa71cd  numpy-2.1.3-cp313-cp313-win32.whl
 8b7f290784c95cf620e0ac1af5470f1d  numpy-2.1.3-cp313-cp313-win_amd64.whl
 4f0c3f8c81cb6bd43a9f1f7bef7db82d  numpy-2.1.3-cp313-cp313t-macosx_10_13_x86_64.whl
 133905fd003c9504fc5bb9ce71e4103b  numpy-2.1.3-cp313-cp313t-macosx_11_0_arm64.whl
 12fe4f265dbda251309f109cbcd46f07  numpy-2.1.3-cp313-cp313t-macosx_14_0_arm64.whl
 b60e418506b969e6df2c0d600bf3c6d4  numpy-2.1.3-cp313-cp313t-macosx_14_0_x86_64.whl
 c2b7160b748f4c1c483a7954e5024250  numpy-2.1.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 8097ddb45c8c821085c19d940bcbe6de  numpy-2.1.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 209f55dc1ed6da23a5ea3e11ca962308  numpy-2.1.3-cp313-cp313t-musllinux_1_1_x86_64.whl
 06a1792849b601c7bdd38e39bc5cb5f1  numpy-2.1.3-cp313-cp313t-musllinux_1_2_aarch64.whl
 86630bf207e8cbe6933232cb2a47a6c0  numpy-2.1.3-cp313-cp313t-win32.whl
 6af9109b82c0acdcf8b0e81dc0e4c517  numpy-2.1.3-cp313-cp313t-win_amd64.whl
 c7e821e086346afc0078acb237f30431  numpy-2.1.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
 5b938b2da78b1c84044df8cdb2e8e63a  numpy-2.1.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
 ef251f3b6aa022b1c2fac14889d6d9d3  numpy-2.1.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 356c7bb6067ae0dccc4a54efc1879e74  numpy-2.1.3-pp310-pypy310_pp73-win_amd64.whl
 11096358375945114577a0c82b2c6038  numpy-2.1.3.tar.gz

SHA256

 c894b4305373b9c5576d7a12b473702afdf48ce5369c074ba304cc5ad8730dff  numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl
 b47fbb433d3260adcd51eb54f92a2ffbc90a4595f8970ee00e064c644ac788f5  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl
 825656d0743699c529c5943554d223c021ff0494ff1442152ce887ef4f7561a1  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl
 6a4825252fcc430a182ac4dee5a505053d262c807f8a924603d411f6718b88fd  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whl
 e711e02f49e176a01d0349d82cb5f05ba4db7d5e7e0defd026328e5cfb3226d3  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 78574ac2d1a4a02421f25da9559850d59457bac82f2b8d7a44fe83a64f770098  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 c7662f0e3673fe4e832fe07b65c50342ea27d989f92c80355658c7f888fcc83c  numpy-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl
 fa2d1337dc61c8dc417fbccf20f6d1e139896a30721b7f1e832b2bb6ef4eb6c4  numpy-2.1.3-cp310-cp310-musllinux_1_2_aarch64.whl
 72dcc4a35a8515d83e76b58fdf8113a5c969ccd505c8a946759b24e3182d1f23  numpy-2.1.3-cp310-cp310-win32.whl
 ecc76a9ba2911d8d37ac01de72834d8849e55473457558e12995f4cd53e778e0  numpy-2.1.3-cp310-cp310-win_amd64.whl
 4d1167c53b93f1f5d8a139a742b3c6f4d429b54e74e6b57d0eff40045187b15d  numpy-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl
 c80e4a09b3d95b4e1cac08643f1152fa71a0a821a2d4277334c88d54b2219a41  numpy-2.1.3-cp311-cp311-macosx_11_0_arm64.whl
 576a1c1d25e9e02ed7fa5477f30a127fe56debd53b8d2c89d5578f9857d03ca9  numpy-2.1.3-cp311-cp311-macosx_14_0_arm64.whl
 973faafebaae4c0aaa1a1ca1ce02434554d67e628b8d805e61f874b84e136b09  numpy-2.1.3-cp311-cp311-macosx_14_0_x86_64.whl
 762479be47a4863e261a840e8e01608d124ee1361e48b96916f38b119cfda04a  numpy-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 bc6f24b3d1ecc1eebfbf5d6051faa49af40b03be1aaa781ebdadcbc090b4539b  numpy-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 17ee83a1f4fef3c94d16dc1802b998668b5419362c8a4f4e8a491de1b41cc3ee  numpy-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl
 15cb89f39fa6d0bdfb600ea24b250e5f1a3df23f901f51c8debaa6a5d122b2f0  numpy-2.1.3-cp311-cp311-musllinux_1_2_aarch64.whl
 d9beb777a78c331580705326d2367488d5bc473b49a9bc3036c154832520aca9  numpy-2.1.3-cp311-cp311-win32.whl
 d89dd2b6da69c4fff5e39c28a382199ddedc3a5be5390115608345dec660b9e2  numpy-2.1.3-cp311-cp311-win_amd64.whl
 f55ba01150f52b1027829b50d70ef1dafd9821ea82905b63936668403c3b471e  numpy-2.1.3-cp312-cp312-macosx_10_13_x86_64.whl
 13138eadd4f4da03074851a698ffa7e405f41a0845a6b1ad135b81596e4e9958  numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl
 a6b46587b14b888e95e4a24d7b13ae91fa22386c199ee7b418f449032b2fa3b8  numpy-2.1.3-cp312-cp312-macosx_14_0_arm64.whl
 0fa14563cc46422e99daef53d725d0c326e99e468a9320a240affffe87852564  numpy-2.1.3-cp312-cp312-macosx_14_0_x86_64.whl
 8637dcd2caa676e475503d1f8fdb327bc495554e10838019651b76d17b98e512  numpy-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2312b2aa89e1f43ecea6da6ea9a810d06aae08321609d8dc0d0eda6d946a541b  numpy-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 a38c19106902bb19351b83802531fea19dee18e5b37b36454f27f11ff956f7fc  numpy-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl
 02135ade8b8a84011cbb67dc44e07c58f28575cf9ecf8ab304e51c05528c19f0  numpy-2.1.3-cp312-cp312-musllinux_1_2_aarch64.whl
 e6988e90fcf617da2b5c78902fe8e668361b43b4fe26dbf2d7b0f8034d4cafb9  numpy-2.1.3-cp312-cp312-win32.whl
 0d30c543f02e84e92c4b1f415b7c6b5326cbe45ee7882b6b77db7195fb971e3a  numpy-2.1.3-cp312-cp312-win_amd64.whl
 96fe52fcdb9345b7cd82ecd34547fca4321f7656d500eca497eb7ea5a926692f  numpy-2.1.3-cp313-cp313-macosx_10_13_x86_64.whl
 f653490b33e9c3a4c1c01d41bc2aef08f9475af51146e4a7710c450cf9761598  numpy-2.1.3-cp313-cp313-macosx_11_0_arm64.whl
 dc258a761a16daa791081d026f0ed4399b582712e6fc887a95af09df10c5ca57  numpy-2.1.3-cp313-cp313-macosx_14_0_arm64.whl
 016d0f6f5e77b0f0d45d77387ffa4bb89816b57c835580c3ce8e099ef830befe  numpy-2.1.3-cp313-cp313-macosx_14_0_x86_64.whl
 c181ba05ce8299c7aa3125c27b9c2167bca4a4445b7ce73d5febc411ca692e43  numpy-2.1.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 5641516794ca9e5f8a4d17bb45446998c6554704d888f86df9b200e66bdcce56  numpy-2.1.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 ea4dedd6e394a9c180b33c2c872b92f7ce0f8e7ad93e9585312b0c5a04777a4a  numpy-2.1.3-cp313-cp313-musllinux_1_1_x86_64.whl
 b0df3635b9c8ef48bd3be5f862cf71b0a4716fa0e702155c45067c6b711ddcef  numpy-2.1.3-cp313-cp313-musllinux_1_2_aarch64.whl
 50ca6aba6e163363f132b5c101ba078b8cbd3fa92c7865fd7d4d62d9779ac29f  numpy-2.1.3-cp313-cp313-win32.whl
 747641635d3d44bcb380d950679462fae44f54b131be347d5ec2bce47d3df9ed  numpy-2.1.3-cp313-cp313-win_amd64.whl
 996bb9399059c5b82f76b53ff8bb686069c05acc94656bb259b1d63d04a9506f  numpy-2.1.3-cp313-cp313t-macosx_10_13_x86_64.whl
 45966d859916ad02b779706bb43b954281db43e185015df6eb3323120188f9e4  numpy-2.1.3-cp313-cp313t-macosx_11_0_arm64.whl
 baed7e8d7481bfe0874b566850cb0b85243e982388b7b23348c6db2ee2b2ae8e  numpy-2.1.3-cp313-cp313t-macosx_14_0_arm64.whl
 a9f7f672a3388133335589cfca93ed468509cb7b93ba3105fce780d04a6576a0  numpy-2.1.3-cp313-cp313t-macosx_14_0_x86_64.whl
 d7aac50327da5d208db2eec22eb11e491e3fe13d22653dce51b0f4109101b408  numpy-2.1.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 4394bc0dbd074b7f9b52024832d16e019decebf86caf909d94f6b3f77a8ee3b6  numpy-2.1.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 50d18c4358a0a8a53f12a8ba9d772ab2d460321e6a93d6064fc22443d189853f  numpy-2.1.3-cp313-cp313t-musllinux_1_1_x86_64.whl
 14e253bd43fc6b37af4921b10f6add6925878a42a0c5fe83daee390bca80bc17  numpy-2.1.3-cp313-cp313t-musllinux_1_2_aarch64.whl
 08788d27a5fd867a663f6fc753fd7c3ad7e92747efc73c53bca2f19f8bc06f48  numpy-2.1.3-cp313-cp313t-win32.whl
 2564fbdf2b99b3f815f2107c1bbc93e2de8ee655a69c261363a1172a79a257d4  numpy-2.1.3-cp313-cp313t-win_amd64.whl
 4f2015dfe437dfebbfce7c85c7b53d81ba49e71ba7eadbf1df40c915af75979f  numpy-2.1.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
 3522b0dfe983a575e6a9ab3a4a4dfe156c3e428468ff08ce582b9bb6bd1d71d4  numpy-2.1.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
 c006b607a865b07cd981ccb218a04fc86b600411d83d6fc261357f1c0966755d  numpy-2.1.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e14e26956e6f1696070788252dcdff11b4aca4c3e8bd166e0df1bb8f315a67cb  numpy-2.1.3-pp310-pypy310_pp73-win_amd64.whl
 aa08e04e08aaf974d4458def539dece0d28146d866a39da5639596f4921fd761  numpy-2.1.3.tar.gz

discovered after the 2.1.1 release.

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

Contributors

A total of 11 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Charles Harris
-   Chris Sidebottom
-   Ishan Koradia +
-   João Eiras +
-   Katie Rust +
-   Marten van Kerkwijk
-   Matti Picus
-   Nathan Goldbaum
-   Peter Hawkins
-   Pieter Eendebak
-   Slava Gorloff +

Pull requests merged

A total of 14 pull requests were merged for this release.

-   [27333](https://github.com/numpy/numpy/pull/27333): MAINT: prepare 2.1.x for further development
-   [27400](https://github.com/numpy/numpy/pull/27400): BUG: apply critical sections around populating the dispatch cache
-   [27406](https://github.com/numpy/numpy/pull/27406): BUG: Stub out get_build_msvc_version if distutils.msvccompiler\...
-   [27416](https://github.com/numpy/numpy/pull/27416): BUILD: fix missing include for std::ptrdiff_t for C++23 language\...
-   [27433](https://github.com/numpy/numpy/pull/27433): BLD: pin setuptools to avoid breaking numpy.distutils
-   [27437](https://github.com/numpy/numpy/pull/27437): BUG: Allow unsigned shift argument for np.roll
-   [27439](https://github.com/numpy/numpy/pull/27439): BUG: Disable SVE VQSort
-   [27471](https://github.com/numpy/numpy/pull/27471): BUG: rfftn axis bug
-   [27479](https://github.com/numpy/numpy/pull/27479): BUG: Fix extra decref of PyArray_UInt8DType.
-   [27480](https://github.com/numpy/numpy/pull/27480): CI: use PyPI not scientific-python-nightly-wheels for CI doc\...
-   [27481](https://github.com/numpy/numpy/pull/27481): MAINT: Check for SVE support on demand
-   [27484](https://github.com/numpy/numpy/pull/27484): BUG: initialize the promotion state to be weak
-   [27501](https://github.com/numpy/numpy/pull/27501): MAINT: Bump pypa/cibuildwheel from 2.20.0 to 2.21.2
-   [27506](https://github.com/numpy/numpy/pull/27506): BUG: avoid segfault on bad arguments in ndarray.\_\_array_function\_\_

Checksums

MD5

 4aae28b7919b126485c1aaccee37a6ba  numpy-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl
 172614423a82ef73d8752ad8a59cbafc  numpy-2.1.2-cp310-cp310-macosx_11_0_arm64.whl
 5ee5e7a8a892cbe96ee228ca5fe7546b  numpy-2.1.2-cp310-cp310-macosx_14_0_arm64.whl
 9ce6f9222dfabd32e66b883f1fe015aa  numpy-2.1.2-cp310-cp310-macosx_14_0_x86_64.whl
 291da8bfeb7c9a3491ec35ecb2596335  numpy-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 9317d9b049f09c0193f074a6458cf79b  numpy-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 1f2c121533715d8b099d6498e4498f81  numpy-2.1.2-cp310-cp310-musllinux_1_1_x86_64.whl
 2834df46e2cb2e81cbe4fd1ce9b96b4b  numpy-2.1.2-cp310-cp310-musllinux_1_2_aarch64.whl
 cbc3ae2c176324fe2a9c04ec0aff181f  numpy-2.1.2-cp310-cp310-win32.whl
 e4d74f9d188dc3fe7a65adf8c01e98cc  numpy-2.1.2-cp310-cp310-win_amd64.whl
 cbcece9c21ed1daf60f3729a37b32266  numpy-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl
 0e62474993ff6faca9c467f68cc16ceb  numpy-2.1.2-cp311-cp311-macosx_11_0_arm64.whl
 8747e85e09b2000a0af5a8226740dc92  numpy-2.1.2-cp311-cp311-macosx_14_0_arm64.whl
 34e7f3591ce81926518a36c92038a056  numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl
 0ec3e617161b42d643aaa4b8d3e477f5  numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 e2a6a419b4672bfb4f3f6a98c0e575bb  numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 8c14b4d03fc8672e43eddd3ede89be09  numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whl
 dc183e12b24317bf210fb093da598d29  numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl
 4918f2c32ca3be20c7c5d8551e649757  numpy-2.1.2-cp311-cp311-win32.whl
 a8991919b6fae3c7a77c260f60a5e2e2  numpy-2.1.2-cp311-cp311-win_amd64.whl
 879f307d16f9222c49508be5ea6491fc  numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl
 fe9dfac7bee0cff178737e1706aee61a  numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl
 1f0c671db3294f4df8bffedc41a2e37f  numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl
 d131c4bd6ba29b05a5b7fa74e87a0506  numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl
 8f9cca33590be334d44cc026a3716966  numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 3692a9290dd430e56e1b15387c25b7af  numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 3549439284dbb1a05785b535c3de60d9  numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whl
 b9934410f20505e5c4b70974cd8fdc26  numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl
 96759e3380e4893b9b88d5d498d856b2  numpy-2.1.2-cp312-cp312-win32.whl
 f94c7405ed72a136e374ab82400fefdc  numpy-2.1.2-cp312-cp312-win_amd64.whl
 2ea775cb4da02f39edf3089af60bddd5  numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl
 354d0970154dd002573f4291e0e9de76  numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl
 bbfee75640b337e12f894d0b54727d66  numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl
 a443fff50571df87f687ad55c9060d25  numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl
 9f8cd7de5b5aa5ad8ba52608a4b0a3b8  numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 c198fe3deaa77fb94d15284b4e26b875  numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 0a59171c983fc2d8ea599bdf382c3d6a  numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl
 5ba974cd59fb8c9fc94787c754a5f636  numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl
 93d5c642606fe8abeff0e6db31ebe88f  numpy-2.1.2-cp313-cp313-win32.whl
 f6455bb4311ddde071a5ea2e14016003  numpy-2.1.2-cp313-cp313-win_amd64.whl
 d2a21857c924d4b1b3c8ae8a9e9b9bb4  numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl
 cd6afcbd05835255750a2fba6012c565  numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl
 d2fab663ea84f1cfe13dfc00dae74fb6  numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl
 9477b923000d63617324c487a4ce0e28  numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl
 84b621a2c9a8c077bc9c471abd2b3933  numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 b1c341c7192d03e8f0f5e7c4b9b6f894  numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 b59750ea55cf274854f64109bf67a112  numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl
 33f4d63f81ad85c1ea873197f2189d89  numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl
 f26a9ac42953c84c94f8203b2dbc61c0  numpy-2.1.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
 e7cf2857582d507dfa3e8644dd3562a6  numpy-2.1.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
 9e3d44cb302c629c00fde8f25809b04d  numpy-2.1.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 3f97ee2d9962cf9d84624f725bdd2a8f  numpy-2.1.2-pp310-pypy310_pp73-win_amd64.whl
 3d92e07d34f60dbac6b82a0982a98757  numpy-2.1.2.tar.gz

SHA256

 30d53720b726ec36a7f88dc873f0eec8447fbc93d93a8f079dfac2629598d6ee  numpy-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl
 e8d3ca0a72dd8846eb6f7dfe8f19088060fcb76931ed592d29128e0219652884  numpy-2.1.2-cp310-cp310-macosx_11_0_arm64.whl
 fc44e3c68ff00fd991b59092a54350e6e4911152682b4782f68070985aa9e648  numpy-2.1.2-cp310-cp310-macosx_14_0_arm64.whl
 7c1c60328bd964b53f8b835df69ae8198659e2b9302ff9ebb7de4e5a5994db3d  numpy-2.1.2-cp310-cp310-macosx_14_0_x86_64.whl
 6cdb606a7478f9ad91c6283e238544451e3a95f30fb5467fbf715964341a8a86  numpy-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 d666cb72687559689e9906197e3bec7b736764df6a2e58ee265e360663e9baf7  numpy-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 c6eef7a2dbd0abfb0d9eaf78b73017dbfd0b54051102ff4e6a7b2980d5ac1a03  numpy-2.1.2-cp310-cp310-musllinux_1_1_x86_64.whl
 12edb90831ff481f7ef5f6bc6431a9d74dc0e5ff401559a71e5e4611d4f2d466  numpy-2.1.2-cp310-cp310-musllinux_1_2_aarch64.whl
 a65acfdb9c6ebb8368490dbafe83c03c7e277b37e6857f0caeadbbc56e12f4fb  numpy-2.1.2-cp310-cp310-win32.whl
 860ec6e63e2c5c2ee5e9121808145c7bf86c96cca9ad396c0bd3e0f2798ccbe2  numpy-2.1.2-cp310-cp310-win_amd64.whl
 b42a1a511c81cc78cbc4539675713bbcf9d9c3913386243ceff0e9429ca892fe  numpy-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl
 faa88bc527d0f097abdc2c663cddf37c05a1c2f113716601555249805cf573f1  numpy-2.1.2-cp311-cp311-macosx_11_0_arm64.whl
 c82af4b2ddd2ee72d1fc0c6695048d457e00b3582ccde72d8a1c991b808bb20f  numpy-2.1.2-cp311-cp311-macosx_14_0_arm64.whl
 13602b3174432a35b16c4cfb5de9a12d229727c3dd47a6ce35111f2ebdf66ff4  numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl
 1ebec5fd716c5a5b3d8dfcc439be82a8407b7b24b230d0ad28a81b61c2f4659a  numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 e2b49c3c0804e8ecb05d59af8386ec2f74877f7ca8fd9c1e00be2672e4d399b1  numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 2cbba4b30bf31ddbe97f1c7205ef976909a93a66bb1583e983adbd155ba72ac2  numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whl
 8e00ea6fc82e8a804433d3e9cedaa1051a1422cb6e443011590c14d2dea59146  numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl
 5006b13a06e0b38d561fab5ccc37581f23c9511879be7693bd33c7cd15ca227c  numpy-2.1.2-cp311-cp311-win32.whl
 f1eb068ead09f4994dec71c24b2844f1e4e4e013b9629f812f292f04bd1510d9  numpy-2.1.2-cp311-cp311-win_amd64.whl
 d7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b  numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl
 b1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db  numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl
 f751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1  numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl
 bd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426  numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl
 1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0  numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 6d95f286b8244b3649b477ac066c6906fbb2905f8ac19b170e2175d3d799f4df  numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 ab4754d432e3ac42d33a269c8567413bdb541689b02d93788af4131018cbf366  numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whl
 e585c8ae871fd38ac50598f4763d73ec5497b0de9a0ab4ef5b69f01c6a046142  numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl
 9c6c754df29ce6a89ed23afb25550d1c2d5fdb9901d9c67a16e0b16eaf7e2550  numpy-2.1.2-cp312-cp312-win32.whl
 456e3b11cb79ac9946c822a56346ec80275eaf2950314b249b512896c0d2505e  numpy-2.1.2-cp312-cp312-win_amd64.whl
 a84498e0d0a1174f2b3ed769b67b656aa5460c92c9554039e11f20a05650f00d  numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl
 4d6ec0d4222e8ffdab1744da2560f07856421b367928026fb540e1945f2eeeaf  numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl
 259ec80d54999cc34cd1eb8ded513cb053c3bf4829152a2e00de2371bd406f5e  numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl
 675c741d4739af2dc20cd6c6a5c4b7355c728167845e3c6b0e824e4e5d36a6c3  numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl
 05b2d4e667895cc55e3ff2b56077e4c8a5604361fc21a042845ea3ad67465aa8  numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 43cca367bf94a14aca50b89e9bc2061683116cfe864e56740e083392f533ce7a  numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 76322dcdb16fccf2ac56f99048af32259dcc488d9b7e25b51e5eca5147a3fb98  numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl
 32e16a03138cabe0cb28e1007ee82264296ac0983714094380b408097a418cfe  numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl
 242b39d00e4944431a3cd2db2f5377e15b5785920421993770cddb89992c3f3a  numpy-2.1.2-cp313-cp313-win32.whl
 f2ded8d9b6f68cc26f8425eda5d3877b47343e68ca23d0d0846f4d312ecaa445  numpy-2.1.2-cp313-cp313-win_amd64.whl
 2ffef621c14ebb0188a8633348504a35c13680d6da93ab5cb86f4e54b7e922b5  numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl
 ad369ed238b1959dfbade9018a740fb9392c5ac4f9b5173f420bd4f37ba1f7a0  numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl
 d82075752f40c0ddf57e6e02673a17f6cb0f8eb3f587f63ca1eaab5594da5b17  numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl
 1600068c262af1ca9580a527d43dc9d959b0b1d8e56f8a05d830eea39b7c8af6  numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl
 a26ae94658d3ba3781d5e103ac07a876b3e9b29db53f68ed7df432fd033358a8  numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 13311c2db4c5f7609b462bc0f43d3c465424d25c626d95040f073e30f7570e35  numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 2abbf905a0b568706391ec6fa15161fad0fb5d8b68d73c461b3c1bab6064dd62  numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl
 ef444c57d664d35cac4e18c298c47d7b504c66b17c2ea91312e979fcfbdfb08a  numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl
 bdd407c40483463898b84490770199d5714dcc9dd9b792f6c6caccc523c00952  numpy-2.1.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
 da65fb46d4cbb75cb417cddf6ba5e7582eb7bb0b47db4b99c9fe5787ce5d91f5  numpy-2.1.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
 1c193d0b0238638e6fc5f10f1b074a6993cb13b0b431f64079a509d63d3aa8b7  numpy-2.1.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 a7d80b2e904faa63068ead63107189164ca443b42dd1930299e0d1cb041cec2e  numpy-2.1.2-pp310-pypy310_pp73-win_amd64.whl
 13532a088217fa624c99b843eeb54640de23b3414b14aa66d023805eb731066c  numpy-2.1.2.tar.gz

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://github.com/numpy/numpy/pull/27236): REL: Prepare for the NumPy 2.1.0 release \[wheel build\]
-   [27252](https://github.com/numpy/numpy/pull/27252): MAINT: prepare 2.1.x for further development
-   [27259](https://github.com/numpy/numpy/pull/27259): BUG: revert unintended change in the return value of set_printoptions
-   [27266](https://github.com/numpy/numpy/pull/27266): BUG: fix reference counting bug in \_\_array_interface\_\_ implementation...
-   [27267](https://github.com/numpy/numpy/pull/27267): TST: Add regression test for missing descr in array-interface
-   [27276](https://github.com/numpy/numpy/pull/27276): BUG: Fix #27256 and 27257
-   [27278](https://github.com/numpy/numpy/pull/27278): BUG: Fix array_equal for numeric and non-numeric scalar types
-   [27287](https://github.com/numpy/numpy/pull/27287): MAINT: Update maintenance/2.1.x after the 2.0.2 release
-   [27303](https://github.com/numpy/numpy/pull/27303): BLD: cp311- macosx_arm64 wheels \[wheel build\]
-   [27304](https://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  numpy-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 ae502c99315884cda7f0236a07c035c4  numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 841a859d975c55090c0b60b72aab93a3  numpy-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl
 d51be2b17f5b87aac64ab80fdfafc85e  numpy-2.1.1-cp310-cp310-musllinux_1_2_aarch64.whl
 1f8249bd725397c6233fe6a0e8ad18b1  numpy-2.1.1-cp310-cp310-win32.whl
 d38d6f06589c1ec104a6a31ff6035781  numpy-2.1.1-cp310-cp310-win_amd64.whl
 6a18fe3029aae00986975250313bf16f  numpy-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl
 5b0b3aa01fbd0b5a8b0f354bb878351e  numpy-2.1.1-cp311-cp311-macosx_11_0_arm64.whl
 1c492dad399abe7b97274b4c6c12ae53  numpy-2.1.1-cp311-cp311-macosx_14_0_arm64.whl
 4d55d91e71b62eb5fa6561c606524f60  numpy-2.1.1-cp311-cp311-macosx_14_0_x86_64.whl
 88e99ecd063c178f25bc08d20792a9bf  numpy-2.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 f3c8b0e4fb059b9219e8ec86d9fda861  numpy-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 df632b5fed7eb78d39e7194d2475c19b  numpy-2.1.1-cp311-cp311-musllinux_1_1_x86_64.whl
 65499daccdb178d26e322d9f359cf146  numpy-2.1.1-cp311-cp311-musllinux_1_2_aarch64.whl
 eb97327fd7aa6027e2409d0dcca1129a  numpy-2.1.1-cp311-cp311-win32.whl
 9e4b05b38cbff22c2bdfead528b9d2bc  numpy-2.1.1-cp311-cp311-win_amd64.whl
 6b8a359bb865b5c624fd9ffc848393e1  numpy-2.1.1-cp312-cp312-macosx_10_9_x86_64.whl
 eaf8dce312efa2b0f17ad46612fb1681  numpy-2.1.1-cp312-cp312-macosx_11_0_arm64.whl
 c861ff048b336284fe7c0791b1a6b0b4  numpy-2.1.1-cp312-cp312-macosx_14_0_arm64.whl
 7e1befccfe729dc5d6c450a5fb6b801c  numpy-2.1.1-cp312-cp312-macosx_14_0_x86_64.whl
 ea0a401ef653a167221987a10cbef260  numpy-2.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 97326ac792d26f2e536a519c82f2d6bc  numpy-2.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 fdd2a82232c03d11bbc7cec0a8e01ab0  numpy-2.1.1-cp312-cp312-musllinux_1_1_x86_64.whl
 0d6716e9a7b2c0d6e5ace9c01b9bca01  numpy-2.1.1-cp312-cp312-musllinux_1_2_aarch64.whl
 ba589ed2a79c88187c3b8574ae72a1c7  numpy-2.1.1-cp312-cp312-win32.whl
 806ca7c1e2a2013b786edbb619f6da47  numpy-2.1.1-cp312-cp312-win_amd64.whl
 647665353e5af5884df4e51610990c22  numpy-2.1.1-cp313-cp313-macosx_10_13_x86_64.whl
 bfd3b3c5c4616ef99d917bd94d39114a  numpy-2.1.1-cp313-