Scheduled monthly dependency update for May

[pyup-bot]

Update astropy from 5.1.1 to 5.2.2.

Changelog

5.2.1

==========================

Bug Fixes
---------

astropy.coordinates
^^^^^^^^^^^^^^^^^^^

- Fix to ITRS frame ``earth_location`` attribute to give the correct result for
a topocentric frame. [14180]

astropy.cosmology
^^^^^^^^^^^^^^^^^

- Bounds are no longer passed to the scipy minimizer for methods Brent and
Golden. The scipy minimizer never used the bounds but silently accepted them.
In scipy v1.11.0.dev0+ an error is raised, so we now pass None as the bounds
to the minimizer. Users should not be affected by this change. [14232]

astropy.io.fits
^^^^^^^^^^^^^^^

- Tables with multidimensional variable length array can now be properly read
and written. [13417]

astropy.units
^^^^^^^^^^^^^

- Modified the behavior of ``numpy.histogram()``,
``numpy.histogram_bin_edges()``, ``numpy.histogram2d()``, and
``numpy.histogramdd()`` so that the ``range`` argument must a compatible
instance of ``astropy.units.Quantity`` if the other arguments are instances of
``astropy.units.Quantity``. [14213]

astropy.visualization
^^^^^^^^^^^^^^^^^^^^^

- Improved the performance of drawing WCSAxes grids by skipping some unnecessary
computations. [14164]

- Fixed WCSAxes sometimes triggering a NumPy RuntimeWarning when determining the
coordinate range of the axes. [14211]

Other Changes and Additions
---------------------------

- Fix compatibility with Numpy 1.24. [14193]

5.2

========================

New Features
------------

astropy.coordinates
^^^^^^^^^^^^^^^^^^^

- Adds new topocentric ITRS frame and direct transforms to and from the observed
frames ``AltAz`` and ``HADec`` with the ability to add or remove refraction
corrections as required. Since these frames are all within the ITRS, there are
no corrections applied other than refraction in the transforms. This makes the
topocentric ITRS frame and these transforms convenient for observers of near
Earth objects where stellar aberration should be omitted. [13398]

- Allow comparing ``SkyCoord`` to frames with data. [13477]

astropy.cosmology
^^^^^^^^^^^^^^^^^

- Cosmology instance can be parsed from or converted to a HTML table using
the new HTML methods in Cosmology's ``to/from_format`` I/O. [13075]

- A new comparison function has been added -- ``cosmology_equal()`` -- that
mirrors its ``numpy`` counterpart but allows for the arguments to be converted
to a ``Cosmology`` and to compare flat cosmologies with their non-flat
equivalents. [13104]

- Cosmology equivalence for flat FLRW cosmologies has been generalized to apply
to all cosmologies using the FlatCosmology mixin. [13261]

- The cosmological redshift unit now has a physical type of ``"redshift"``. [13561]

astropy.io.ascii
^^^^^^^^^^^^^^^^

- Add ability to read and write a fixed width ASCII table that includes additional
header rows specifying any or all of the column dtype, unit, format, and
description. This is available in the ``fixed_width`` and
``fixed_width_two_line`` formats via the new ``header_rows`` keyword argument. [13734]

astropy.io.fits
^^^^^^^^^^^^^^^

- Added support to the ``io.fits`` API for reading and writing file paths of the
form ``~/file.fits`` or ``~<username>/file.fits``, referring to the home
directory of the current user or the specified user, respectively. [13131]

- Added support for opening remote and cloud-hosted FITS files using the
``fsspec`` package, which has been added as an optional dependency. [13238]

astropy.io.votable
^^^^^^^^^^^^^^^^^^

- Added support in ``io.votable`` for reading and writing file paths of the form
``~/file.xml`` or ``~<username>/file.xml``, referring to the home directory of
the current user or the specified user, respectively. [13149]

astropy.modeling
^^^^^^^^^^^^^^^^

- Add option to non-linear fitters which enables automatic
exclusion of non-finite values from the fit data. [13259]

astropy.nddata
^^^^^^^^^^^^^^

- Modified ``Cutout2D`` to allow objects of type ``astropy.io.fits.Section``
to be passed to the ``data`` parameter. [13238]

- Add a PSF image representation to ``astropy.nddata.NDData`` and ``astropy.nddata.CCDData``. [13743]

astropy.table
^^^^^^^^^^^^^

- An Astropy table can now be converted to a scalar NumPy object array. For NumPy
>= 1.20, a list of Astropy tables can be converted to an NumPy object array of
tables. [13469]

astropy.time
^^^^^^^^^^^^

- Added the ``astropy.time.Time.mean()`` method which also enables the ``numpy.mean()`` function to be used on instances of ``astropy.time.Time``. [13508]

- Improve the performance of getting the string representation of a large ``Time``
or ``TimeDelta`` object. This is done via a new ``to_string()`` method that does
the time string format conversion only for the outputted values. Previously the
entire array was formatted in advance. [13555]

astropy.units
^^^^^^^^^^^^^

- It is now possible to use unit format names as string format specifiers for a
``Quantity``, e.g. ``f'{1e12*u.m/u.s:latex_inline}'`` now produces the string
``'$1 \\times 10^{12} \\; \\mathrm{m\\,s^{-1}}$'``. [13050]

- Ensure that the ``argmin`` and ``argmax`` methods of ``Quantity`` support the
``keepdims`` argument when numpy does (numpy version 1.22 and later). [13329]

- ``numpy.lib.recfunctions.merge_arrays()`` is registered with numpy overload for
``Quantity``. [13669]

- Added SI prefixes for quecto ("q", :math:`10^{-30}`), ronto ("r",
:math:`10^{-27}`), ronna ("R", :math:`10^{27}`), and quetta ("Q",
:math:`10^{30}`). [14046]

astropy.utils
^^^^^^^^^^^^^

- Added the ``use_fsspec``, ``fsspec_kwargs``, and ``close_files`` arguments
to ``utils.data.get_readable_fileobj``. [13238]

- Ensure that the ``argmin`` and ``argmax`` methods of ``Masked`` instances
support the ``keepdims`` argument when numpy does (numpy version 1.22 and
later). [13329]

astropy.visualization
^^^^^^^^^^^^^^^^^^^^^

- Add helper functions for WCSAxes instances to draw the instrument beam and a physical scale. [12102]

- Add a ``scatter_coord`` method to the ``wcsaxes`` functionality based on the
existing ``plot_coord`` method but that calls ``matplotlib.pyplot.scatter``. [13562]

- Added a ``sinh`` stretch option to ``simple_norm``. [13746]

- It is now possible to define "tickable" gridlines for the purpose of placing ticks or tick labels in the interior of WCSAxes plots. [13829]


API Changes
-----------

astropy.convolution
^^^^^^^^^^^^^^^^^^^

- Removed deprecated ``MexicanHat1DKernel`` and ``MexicanHat2DKernel``
classes. Please use ``RickerWavelet1DKernel`` and
``RickerWavelet2DKernel`` instead. [13300]

astropy.units
^^^^^^^^^^^^^

- Multiplying a ``LogQuantity`` like ``Magnitude`` with dimensionless physical
units by an array will no longer downcast to ``Quantity``. [12579]

- Quantity normally upcasts integer dtypes to floats, unless the dtype is
specifically provided.
Before this happened when ``dtype=None``; now the default has been changed to
``dtype=numpy.inexact`` and ``dtype=None`` has the same meaning as in `numpy`. [12941]

- In "in-place unit changes" of the form ``quantity <<= new_unit``, the result
will now share memory with the original only if the conversion could be done
through a simple multiplication with a scale factor. Hence, memory will not be
shared if the quantity has integer dtype or is structured, or when the
conversion is through an equivalency. [13638]

- When ``Quantity`` is constructed from a structured array and ``unit`` is
``None``, the default unit is now structured like the input data. [13676]

astropy.utils
^^^^^^^^^^^^^

- ``astropy.utils.misc.suppress`` has been removed, use ``contextlib.suppress``
instead. ``astropy.utils.namedtuple_asdict`` has been removed, instead use
method ``._asdict`` on a ``namedtuple``. ``override__dir__`` has been deprecated
and will be removed in a future version, see the docstring for the better
alternative. [13636]

- ``astropy.utils.misc.possible_filename`` has been removed. [13661]

astropy.visualization
^^^^^^^^^^^^^^^^^^^^^

- Rename number-of-samples keyword ``nsamples`` in ``ZScaleInterval`` to align
with the ``n_samples`` keyword used in all other ``Interval`` classes in
this module. [13810]


Bug Fixes
---------

astropy.convolution
^^^^^^^^^^^^^^^^^^^

- Fixed convolution Kernels to ensure the that returned kernels
are normalized to sum to one (e.g., ``Gaussian1DKernel``,
``Gaussian2DKernel``). Also fixed the Kernel ``truncation`` calculation. [13299]

- Fix import error with setuptools v65.6.0 by replacing
``numpy.ctypeslib.load_library`` with Cython to load the C convolution
extension. [14035]

astropy.coordinates
^^^^^^^^^^^^^^^^^^^

- ``BaseCoordinateFrame.get_frame_attr_names()`` had a misleading name,
because it actually provided a ``dict`` of attribute names and
their default values. It is now deprecated and replaced by ``BaseCoordinateFrame.get_frame_attr_defaults()``.
The fastest way to obtain the attribute names is ``BaseFrame.frame_attributes.keys()``. [13484]

- Fixed bug that caused ``earth_orientation.nutation_matrix()`` to error instead of returning output. [13572]

- Ensure that ``angle.to_string()`` continues to work after pickling,
and that units passed on to ``to_string()`` or the ``Angle``
initializer can be composite units (like ``u.hour**1``), which might
result from preceding calculations. [13933]

astropy.io.fits
^^^^^^^^^^^^^^^

- ``report_diff_values()`` have now two new parameters ``rtol`` and ``atol`` to make the
report consistent with ``numpy.allclose`` results.
This fixes ``FITSDiff`` with multi-dimensional columns. [13465]

astropy.io.votable
^^^^^^^^^^^^^^^^^^

- Fixed two bugs in validator.validator.make_validation_report:
- ProgressBar iterator was not called correctly.
- make_validation_report now handles input string urls correctly. [14102]

astropy.timeseries
^^^^^^^^^^^^^^^^^^

- Fixed a performance regression in ``timeseries.aggregate_downsample``
introduced in Astropy 5.0 / 11266. [13069]

astropy.units
^^^^^^^^^^^^^

- Unit changes of the form ``quantity <<= new_unit`` will now work also if the
quantity is integer. The result will always be float. This means that the result
will not share memory with the original. [13638]

- Ensure dimensionless quantities can be added inplace to regular ndarray. [13913]

astropy.utils
^^^^^^^^^^^^^

- Fixed an incompatibility with latest Python 3.1x versions that kept
``astropy.utils.data.download_file`` from switching to TLS+FTP mode. [14092]

- ``np.quantile`` and ``np.percentile`` can now be used on ``Masked``
arrays and quantities also with ``keepdims=True``. [14113]

astropy.visualization
^^^^^^^^^^^^^^^^^^^^^

- Significantly improve performance of ``ManualInterval`` when both limits
are specified manually. [13898]


Other Changes and Additions
---------------------------

- The deprecated private ``astropy._erfa`` module has been removed. Use
``pyerfa``, which is a dependency of ``astropy`` and can be imported directly
using ``import erfa``. [13317]

- The minimum version required for numpy is now 1.20 and that for scipy 1.5. [13885]

- Updated the bundled CFITSIO library to 4.2.0. [14020]

Links

• PyPI: https://pypi.org/project/astropy
• Changelog: https://pyup.io/changelogs/astropy/
• Homepage: http://astropy.org

Update corner from 2.2.1 to 2.2.2.

Changelog

2.2.2

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

2.2.2rc1

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

Links

• PyPI: https://pypi.org/project/corner
• Changelog: https://pyup.io/changelogs/corner/

Update emcee from 3.1.3 to 3.1.4.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/emcee
• Changelog: https://pyup.io/changelogs/emcee/
• Docs: https://emcee.readthedocs.io

Update jsonschema from 4.16.0 to 4.17.3.

Changelog

4.17.3

=======

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

4.17.2

=======

* 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``).

4.17.1

=======

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

4.17.0

=======

* 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>`_.

4.16.1

=======

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

Links

• PyPI: https://pypi.org/project/jsonschema
• Changelog: https://pyup.io/changelogs/jsonschema/

Update matplotlib from 3.6.1 to 3.7.1.

Changelog

3.7.1

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

3.6.3

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`

3.6.2

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)

Links

• PyPI: https://pypi.org/project/matplotlib
• Changelog: https://pyup.io/changelogs/matplotlib/
• Homepage: https://matplotlib.org

Update numpy from 1.23.4 to 1.24.3.

Changelog

1.24.3

discovered after the 1.24.2 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Aleksei Nikiforov +
-   Alexander Heger
-   Bas van Beek
-   Bob Eldering
-   Brock Mendel
-   Charles Harris
-   Kyle Sunden
-   Peter Hawkins
-   Rohit Goswami
-   Sebastian Berg
-   Warren Weckesser
-   dependabot\[bot\]

Pull requests merged

A total of 17 pull requests were merged for this release.

-   [23206](https://github.com/numpy/numpy/pull/23206): BUG: fix for f2py string scalars (#23194)
-   [23207](https://github.com/numpy/numpy/pull/23207): BUG: datetime64/timedelta64 comparisons return NotImplemented
-   [23208](https://github.com/numpy/numpy/pull/23208): MAINT: Pin matplotlib to version 3.6.3 for refguide checks
-   [23221](https://github.com/numpy/numpy/pull/23221): DOC: Fix matplotlib error in documentation
-   [23226](https://github.com/numpy/numpy/pull/23226): CI: Ensure submodules are initialized in gitpod.
-   [23341](https://github.com/numpy/numpy/pull/23341): TYP: Replace duplicate reduce in ufunc type signature with reduceat.
-   [23342](https://github.com/numpy/numpy/pull/23342): TYP: Remove duplicate CLIP/WRAP/RAISE in `__init__.pyi`.
-   [23343](https://github.com/numpy/numpy/pull/23343): TYP: Mark `d` argument to fftfreq and rfftfreq as optional\...
-   [23344](https://github.com/numpy/numpy/pull/23344): TYP: Add type annotations for comparison operators to MaskedArray.
-   [23345](https://github.com/numpy/numpy/pull/23345): TYP: Remove some stray type-check-only imports of `msort`
-   [23370](https://github.com/numpy/numpy/pull/23370): BUG: Ensure like is only stripped for `like=` dispatched functions
-   [23543](https://github.com/numpy/numpy/pull/23543): BUG: fix loading and storing big arrays on s390x
-   [23544](https://github.com/numpy/numpy/pull/23544): MAINT: Bump larsoner/circleci-artifacts-redirector-action
-   [23634](https://github.com/numpy/numpy/pull/23634): BUG: Ignore invalid and overflow warnings in masked setitem
-   [23635](https://github.com/numpy/numpy/pull/23635): BUG: Fix masked array raveling when `order="A"` or `order="K"`
-   [23636](https://github.com/numpy/numpy/pull/23636): MAINT: Update conftest for newer hypothesis versions
-   [23637](https://github.com/numpy/numpy/pull/23637): BUG: Fix bug in parsing F77 style string arrays.

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 89e5e2e78407032290ae6acf6dcaea46  numpy-1.24.3.tar.gz

SHA256

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 d6cc757de514c00b24ae8cf5c876af2a7c3df189028d68c0cb4eaa9cd5afc2bf  numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl
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 35400e6a8d102fd07c71ed7dcadd9eb62ee9a6e84ec159bd48c28235bbb0f8e4  numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl
 ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155  numpy-1.24.3.tar.gz

1.24.2

discovered after the 1.24.1 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 14 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Bas van Beek
-   Charles Harris
-   Khem Raj +
-   Mark Harfouche
-   Matti Picus
-   Panagiotis Zestanakis +
-   Peter Hawkins
-   Pradipta Ghosh
-   Ross Barnowski
-   Sayed Adel
-   Sebastian Berg
-   Syam Gadde +
-   dmbelov +
-   pkubaj +

Pull requests merged

A total of 17 pull requests were merged for this release.

-   [22965](https://github.com/numpy/numpy/pull/22965): MAINT: Update python 3.11-dev to 3.11.
-   [22966](https://github.com/numpy/numpy/pull/22966): DOC: Remove dangling deprecation warning
-   [22967](https://github.com/numpy/numpy/pull/22967): ENH: Detect CPU features on FreeBSD/powerpc64\*
-   [22968](https://github.com/numpy/numpy/pull/22968): BUG: np.loadtxt cannot load text file with quoted fields separated\...
-   [22969](https://github.com/numpy/numpy/pull/22969): TST: Add fixture to avoid issue with randomizing test order.
-   [22970](https://github.com/numpy/numpy/pull/22970): BUG: Fix fill violating read-only flag. (#22959)
-   [22971](https://github.com/numpy/numpy/pull/22971): MAINT: Add additional information to missing scalar AttributeError
-   [22972](https://github.com/numpy/numpy/pull/22972): MAINT: Move export for scipy arm64 helper into main module
-   [22976](https://github.com/numpy/numpy/pull/22976): BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang
-   [22989](https://github.com/numpy/numpy/pull/22989): BUG: Ensure correct loop order in sin, cos, and arctan2
-   [23030](https://github.com/numpy/numpy/pull/23030): DOC: Add version added information for the strict parameter in\...
-   [23031](https://github.com/numpy/numpy/pull/23031): BUG: use `_Alignof` rather than `offsetof()` on most compilers
-   [23147](https://github.com/numpy/numpy/pull/23147): BUG: Fix for npyv\_\_trunc_s32_f32 (VXE)
-   [23148](https://github.com/numpy/numpy/pull/23148): BUG: Fix integer / float scalar promotion
-   [23149](https://github.com/numpy/numpy/pull/23149): BUG: Add missing \<type_traits> header.
-   [23150](https://github.com/numpy/numpy/pull/23150): TYP, MAINT: Add a missing explicit `Any` parameter to the `npt.ArrayLike`\...
-   [23161](https://github.com/numpy/numpy/pull/23161): BLD: remove redundant definition of npy_nextafter \[wheel build\]

Checksums

MD5

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SHA256

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 150947adbdfeceec4e5926d956a06865c1c690f2fd902efede4ca6fe2e657c3f  numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl
 003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22  numpy-1.24.2.tar.gz

1.24.1

discovered after the 1.24.0 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Ben Greiner +
-   Charles Harris
-   Clément Robert
-   Matteo Raso
-   Matti Picus
-   Melissa Weber Mendonça
-   Miles Cranmer
-   Ralf Gommers
-   Rohit Goswami
-   Sayed Adel
-   Sebastian Berg

Pull requests merged

A total of 18 pull requests were merged for this release.

-   [22820](https://github.com/numpy/numpy/pull/22820): BLD: add workaround in setup.py for newer setuptools
-   [22830](https://github.com/numpy/numpy/pull/22830): BLD: CIRRUS_TAG redux
-   [22831](https://github.com/numpy/numpy/pull/22831): DOC: fix a couple typos in 1.23 notes
-   [22832](https://github.com/numpy/numpy/pull/22832): BUG: Fix refcounting errors found using pytest-leaks
-   [22834](https://github.com/numpy/numpy/pull/22834): BUG, SIMD: Fix invalid value encountered in several ufuncs
-   [22837](https://github.com/numpy/numpy/pull/22837): TST: ignore more np.distutils.log imports
-   [22839](https://github.com/numpy/numpy/pull/22839): BUG: Do not use getdata() in np.ma.masked_invalid
-   [22847](https://github.com/numpy/numpy/pull/22847): BUG: Ensure correct behavior for rows ending in delimiter in\...
-   [22848](https://github.com/numpy/numpy/pull/22848): BUG, SIMD: Fix the bitmask of the boolean comparison
-   [22857](https://github.com/numpy/numpy/pull/22857): BLD: Help raspian arm + clang 13 about \_\_builtin_mul_overflow
-   [22858](https://github.com/numpy/numpy/pull/22858): API: Ensure a full mask is returned for masked_invalid
-   [22866](https://github.com/numpy/numpy/pull/22866): BUG: Polynomials now copy properly (#22669)
-   [22867](https://github.com/numpy/numpy/pull/22867): BUG, SIMD: Fix memory overlap in ufunc comparison loops
-   [22868](https://github.com/numpy/numpy/pull/22868): BUG: Fortify string casts against floating point warnings
-   [22875](https://github.com/numpy/numpy/pull/22875): TST: Ignore nan-warnings in randomized out tests
-   [22883](https://github.com/numpy/numpy/pull/22883): MAINT: restore npymath implementations needed for freebsd
-   [22884](https://github.com/numpy/numpy/pull/22884): BUG: Fix integer overflow in in1d for mixed integer dtypes #22877
-   [22887](https://github.com/numpy/numpy/pull/22887): BUG: Use whole file for encoding checks with `charset_normalizer`.

Checksums

MD5

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1.24

The NumPy 1.24.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There are also a large number of new and
expired deprecations due to changes in promotion and cleanups. This
might be called a deprecation release. Highlights are

-   Many new deprecations, check them out.
-   Many expired deprecations,
-   New F2PY features and fixes.
-   New \"dtype\" and \"casting\" keywords for stacking functions.

See below for the details,

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

The `numpy.fastCopyAndTranspose` function has been deprecated. Use the
corresponding copy and transpose methods directly:

 arr.T.copy()

The underlying C function `PyArray_CopyAndTranspose` has also been
deprecated from the NumPy C-API.

([gh-22313](https://github.com/numpy/numpy/pull/22313))

Conversion of out-of-bound Python integers

Attempting a conversion from a Python integer to a NumPy value will now
always check whether the result can be represented by NumPy. This means
the following examples will fail in the future and give a
`DeprecationWarning` now:

 np.uint8(-1)
 np.array([3000], dtype=np.int8)

Many of these did succeed before. Such code was mainly useful for
unsigned integers with negative values such as `np.uint8(-1)` giving
`np.iinfo(np.uint8).max`.

Note that conversion between NumPy integers is unaffected, so that
`np.array(-1).astype(np.uint8)` continues to work and use C integer
overflow logic.

([gh-22393](https://github.com/numpy/numpy/pull/22393))

Deprecate `msort`

The `numpy.msort` function is deprecated. Use `np.sort(a, axis=0)`
instead.

([gh-22456](https://github.com/numpy/numpy/pull/22456))

`np.str0` and similar are now deprecated

The scalar type aliases ending in a 0 bit size: `np.object0`, `np.str0`,
`np.bytes0`, `np.void0`, `np.int0`, `np.uint0` as well as `np.bool8` are
now deprecated and will eventually be removed.

([gh-22607](https://github.com/numpy/numpy/pull/22607))

Expired deprecations

-   The `normed` keyword argument has been removed from
 [np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and
 [np.histogramdd]{.title-ref}. Use `density` instead. If `normed` was
 passed by position, `density` is now used.

 ([gh-21645](https://github.com/numpy/numpy/pull/21645))

-   Ragged array creation will now always raise a `ValueError` unless
 `dtype=object` is passed. This includes very deeply nested
 sequences.

 ([gh-22004](https://github.com/numpy/numpy/pull/22004))

-   Support for Visual Studio 2015 and earlier has been removed.

-   Support for the Windows Interix POSIX interop layer has been
 removed.

 ([gh-22139](https://github.com/numpy/numpy/pull/22139))

-   Support for cygwin \< 3.3 has been removed.

 ([gh-22159](https://github.com/numpy/numpy/pull/22159))

-   The mini() method of `np.ma.MaskedArray` has been removed. Use
 either `np.ma.MaskedArray.min()` or `np.ma.minimum.reduce()`.

-   The single-argument form of `np.ma.minimum` and `np.ma.maximum` has
 been removed. Use `np.ma.minimum.reduce()` or
 `np.ma.maximum.reduce()` instead.

 ([gh-22228](https://github.com/numpy/numpy/pull/22228))

-   Passing dtype instances other than the canonical (mainly native
 byte-order) ones to `dtype=` or `signature=` in ufuncs will now
 raise a `TypeError`. We recommend passing the strings `"int8"` or
 scalar types `np.int8` since the byte-order, datetime/timedelta
 unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)

 ([gh-22540](https://github.com/numpy/numpy/pull/22540))

-   The `dtype=` argument to comparison ufuncs is now applied correctly.
 That means that only `bool` and `object` are valid values and
 `dtype=object` is enforced.

 ([gh-22541](https://github.com/numpy/numpy/pull/22541))

-   The deprecation for the aliases `np.object`, `np.bool`, `np.float`,
 `np.complex`, `np.str`, and `np.int` is expired (introduces NumPy
 1.20). Some of these will now give a FutureWarning in addition to
 raising an error since they will be mapped to the NumPy scalars in
 the future.

 ([gh-22607](https://github.com/numpy/numpy/pull/22607))

Compatibility notes

`array.fill(scalar)` may behave slightly different

`numpy.ndarray.fill` may in some cases behave slightly different now due
to the fact that the logic is aligned with item assignment:

 arr = np.array([1])   with any dtype/value
 arr.fill(scalar)
  is now identical to:
 arr[0] = scalar

Previously casting may have produced slightly different answers when
using values that could not be represented in the target `dtype` or when
the target had `object` dtype.

([gh-20924](https://github.com/numpy/numpy/pull/20924))

Subarray to object cast now copies

Casting a dtype that includes a subarray to an object will now ensure a
copy of the subarray. Previously an unsafe view was returned:

 arr = np.ones(3, dtype=[("f", "i", 3)])
 subarray_fields = arr.astype(object)[0]
 subarray = subarray_fields[0]   "f" field

 np.may_share_memory(subarray, arr)

Is now always false. While previously it was true for the specific cast.

([gh-21925](https://github.com/numpy/numpy/pull/21925))

Returned arrays respect uniqueness of dtype kwarg objects

When the `dtype` keyword argument is used with
:py`np.array()`{.interpreted-text role="func"} or
:py`asarray()`{.interpreted-text role="func"}, the dtype of the returned
array now always exactly matches the dtype provided by the caller.

In some cases this change means that a *view* rather than the input
array is returned. The following is an example for this on 64bit Linux
where `long` and `longlong` are the same precision but different
`dtypes`:

 >>> arr = np.array([1, 2, 3], dtype="long")
 >>> new_dtype = np.dtype("longlong")
 >>> new = np.asarray(arr, dtype=new_dtype)
 >>> new.dtype is new_dtype
 True
 >>> new is arr
 False

Before the change, the `dtype` did not match because `new is arr` was
`True`.

([gh-21995](https://github.com/numpy/numpy/pull/21995))

DLPack export raises `BufferError`

When an array buffer cannot be exported via DLPack a `BufferError` is
now always raised where previously `TypeError` or `RuntimeError` was
raised. This allows falling back to the buffer protocol or
`__array_interface__` when DLPack was tried first.

([gh-22542](https://github.com/numpy/numpy/pull/22542))

NumPy builds are no longer tested on GCC-6

Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available
on Ubuntu 20.04, so builds using that compiler are no longer tested. We
still test builds using GCC-7 and GCC-8.

([gh-22598](https://github.com/numpy/numpy/pull/22598))

New Features

New attribute `symbol` added to polynomial classes

The polynomial classes in the `numpy.polynomial` package have a new
`symbol` attribute which is used to represent the indeterminate of the
polynomial. This can be used to change the value of the variable when
printing:

 >>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
 >>> print(P_y)
 1.0 + 0.0·y¹ - 1.0·y²

Note that the polynomial classes only support 1D polynomials, so
operations that involve polynomials with different symbols are
disallowed when the result would be multivariate:

 >>> P = np.polynomial.Polynomial([1, -1])   default symbol is "x"
 >>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
 >>> P * P_z
 Traceback (most recent call last)
    ...
 ValueError: Polynomial symbols differ

The symbol can be any valid Python identifier. The default is
`symbol=x`, consistent with existing behavior.

([gh-16154](https://github.com/numpy/numpy/pull/16154))

F2PY support for Fortran `character` strings

F2PY now supports wrapping Fortran functions with:

-   character (e.g. `character x`)
-   character array (e.g. `character, dimension(n) :: x`)
-   character string (e.g. `character(len=10) x`)
-   and character string array (e.g.
 `character(len=10), dimension(n, m) :: x`)

arguments, including passing Python unicode strings as Fortran character
string arguments.

([gh-19388](https://github.com/numpy/numpy/pull/19388))

New function `np.show_runtime`

A new function `numpy.show_runtime` has been added to display the
runtime information of the machine in addition to `numpy.show_config`
which displays the build-related information.

([gh-21468](https://github.com/numpy/numpy/pull/21468))

`strict` option for `testing.assert_array_equal`

The `strict` option is now available for `testing.assert_array_equal`.
Setting `strict=True` will disable the broadcasting behaviour for
scalars and ensure that input arrays have the same data type.

([gh-21595](https://github.com/numpy/numpy/pull/21595))

New parameter `equal_nan` added to `np.unique`

`np.unique` was changed in 1.21 to treat all `NaN` values as equal and
return a single `NaN`. Setting `equal_nan=False` will restore pre-1.21
behavior to treat `NaNs` as unique. Defaults to `True`.

([gh-21623](https://github.com/numpy/numpy/pull/21623))

`casting` and `dtype` keyword arguments for `numpy.stack`

The `casting` and `dtype` keyword arguments are now available for
`numpy.stack`. To use them, write
`np.stack(..., dtype=None, casting='same_kind')`.

`casting` and `dtype` keyword arguments for `numpy.vstack`

The `casting` and `dtype` keyword arguments are now available for
`numpy.vstack`. To use them, write
`np.vstack(..., dtype=None, casting='same_kind')`.

`casting` and `dtype` keyword arguments for `numpy.hstack`

The `casting` and `dtype` keyword arguments are now available for
`numpy.hstack`. To use them, write
`np.hstack(..., dtype=None, casting='same_kind')`.

([gh-21627](https://github.com/numpy/numpy/pull/21627))

The bit generator underlying the singleton RandomState can be changed

The singleton `RandomState` instance exposed in the `numpy.random`
module is initialized at startup with the `MT19937` bit generator. The
new function `set_bit_generator` allows the default bit generator to be
replaced with a user-provided bit generator. This function has been
introduced to provide a method allowing seamless integration of a
high-quality, modern bit generator in new code with existing code that
makes use of the singleton-provided random variate generating functions.
The companion function `get_bit_generator` returns the current bit
generator being used by the singleton `RandomState`. This is provided to
simplify restoring the original source of randomness if required.

The preferred method to generate reproducible random numbers is to use a
modern bit generator in an instance of `Generator`. The function
`default_rng` simplifies instantiation:

 >>> rg = np.random.default_rng(3728973198)
 >>> rg.random()

The same bit generator can then be shared with the singleton instance so
that calling functions in the `random` module will use the same bit
generator:

 >>> orig_bit_gen = np.random.get_bit_generator()
 >>> np.random.set_bit_generator(rg.bit_generator)
 >>> np.random.normal()

The swap is permanent (until reversed) and so any call to functions in
the `random` module will use the new bit generator. The original can be
restored if required for code to run correctly:

 >>> np.random.set_bit_generator(orig_bit_gen)

([gh-21976](https://github.com/numpy/numpy/pull/21976))

`np.void` now has a `dtype` argument

NumPy now allows constructing structured void scalars directly by
passing the `dtype` argument to `np.void`.

([gh-22316](https://github.com/numpy/numpy/pull/22316))

Improvements

F2PY Improvements

-   The generated extension modules don\'t use the deprecated NumPy-C
 API anymore
-   Improved `f2py` generated exception messages
-   Numerous bug and `flake8` warning fixes
-   various CPP macros that one can use within C-expressions of
 signature files are prefixed with `f2py_`. For example, one should
 use `f2py_len(x)` instead of `len(x)`
-   A new construct `character(f2py_len=...)` is introduced to support
 returning assumed length character strings (e.g. `character(len=*)`)
 from wrapper functions

A hook to support rewriting `f2py` internal data structures after
reading all its input files is introduced. This is required, for
instance, for BC of SciPy support where character arguments are treated
as character strings arguments in `C` expressions.

([gh-19388](https://github.com/numpy/numpy/pull/19388))

IBM zSystems Vector Extension Facility (SIMD)

Added support for SIMD extensions of zSystem (z13, z14, z15), through
the universal intrinsics interface. This support leads to performance
improvements for all SIMD kernels implemented using the universal
intrinsics, including the following operations: rint, floor, trunc,
ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal,
not_equal, greater, greater_equal, less, less_equal, maximum, minimum,
fmax, fmin, argmax, argmin, add, subtract, multiply, divide.

([gh-20913](https://github.com/numpy/numpy/pull/20913))

NumPy now gives floating point errors in casts

In most cases, NumPy previously did not give floating point warnings or
errors when these happened during casts. For examples, casts like:

 np.array([2e300]).astype(np.float32)   overflow for float32
 np.array([np.inf]).astype(np.int64)

Should now generally give floating point warnings. These warnings should
warn that floating point overflow occurred. For errors when converting
floating point values to integers users should expect invalid value
warnings.

Users can modify the behavior of these warnings using `np.errstate`.

Note that for float to int casts, the exact warnings that are given may
be platform dependent. For example:

 arr = np.full(100, value=1000, dtype=np.float64)
 arr.astype(np.int8)

May give a result equivalent to (the intermediate cast means no warning
is given):

 arr.astype(np.int64).astype(np.int8)

May return an undefined result, with a warning set:

 RuntimeWarning: invalid value encountered in cast

The precise behavior is subject to the C99 standard and its
implementation in both software and hardware.

([gh-21437](https://github.com/numpy/numpy/pull/21437))

F2PY supports the value attribute

The Fortran standard requires that variables declared with the `value`
attribute must be passed by value instead of reference. F2PY now
supports this use pattern correctly. So
`integer, intent(in), value :: x` in Fortran codes will have correct
wrappers generated.

([gh-21807](https://github.com/numpy/numpy/pull/21807))

Added pickle support for third-party BitGenerators

The pickle format for bit generators was extended to allow each bit
generator to supply its own constructor when during pickling. Previous
versions of NumPy only supported unpickling `Generator` instances
created with one of the core set of bit generators supplied with NumPy.
Attempting to unpickle a `Generator` that used a third-party bit
generators would fail since the constructor used during the unpickling
was only aware of the bit generators included in NumPy.

([gh-22014](https://github.com/numpy/numpy/pull/22014))

arange() now explicitly fails with dtype=str

Previously, the `np.arange(n, dtype=str)` function worked for `n=1` and
`n=2`, but would raise a non-specific exception message for other values
of `n`. Now, it raises a [TypeError]{.title-ref} informing that `arange`
does not support string dtypes:

 >>> np.arange(2, dtype=str)
 Traceback (most recent call last)
    ...
 TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.

([gh-22055](https://github.com/numpy/numpy/pull/22055))

`numpy.typing` protocols are now runtime checkable

The protocols used in `numpy.typing.ArrayLike` and
`numpy.typing.DTypeLike` are now properly marked as runtime checkable,
making them easier to use for runtime type checkers.

([gh-22357](https://github.com/numpy/numpy/pull/22357))

Performance improvements and changes

Faster version of `np.isin` and `np.in1d` for integer arrays

`np.in1d` (used by `np.isin`) can now switch to a faster algorithm (up
to \>10x faster) when it is passed two integer arrays. This is often
automatically used, but you can use `kind="sort"` or `kind="table"` to
force the old or new method, respectively.

([gh-12065](https://github.com/numpy/numpy/pull/12065))

Faster comparison operators

The comparison functions (`numpy.equal`, `numpy.not_equal`,
`numpy.less`, `numpy.less_equal`, `numpy.greater` and
`numpy.greater_equal`) are now much faster as they are now vectorized
with universal intrinsics. For a CPU with SIMD extension AVX512BW, the
performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and
boolean data types, respectively (with N=50000).

([gh-21483](https://github.com/numpy/numpy/pull/21483))

Changes

Better reporting of integer division overflow

Integer division overflow of scalars and arrays used to provide a
`RuntimeWarning` and the return value was undefined leading to crashes
at rare occasions:

 >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
 <stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
 array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)

Integer division overflow now returns the input dtype\'s minimum value
and raise the following `RuntimeWarning`:

 >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
 <stdin>:1: RuntimeWarning: overflow encountered in floor_divide
 array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
        -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
       dtype=int32)

([gh-21506](https://github.com/numpy/numpy/pull/21506))

`masked_invalid` now modifies the mask in-place

When used with `copy=False`, `numpy.ma.masked_invalid` now modifies the
input masked array in-place. This makes it behave identically to
`masked_where` and better matches the documentation.

([gh-22046](https://github.com/numpy/numpy/pull/22046))

`nditer`/`NpyIter` allows all allocating all operands

The NumPy iterator available through `np.nditer` in Python and as
`NpyIter` in C now supports allocating all arrays. The iterator shape
defaults to `()` in this case. The operands dtype must be provided,
since a \"common dtype\" cannot be inferred from the other inputs.

([gh-22457](https://github.com/numpy/numpy/pull/22457))

Checksums

MD5

 1f08c901040ebe1324d16cfc71fe3cd2  numpy-1.24.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
 d35a59a1ccf1542d690860ad85fbb0f0  numpy-1.24.0rc1-cp310-cp310-macosx_11_0_arm64.whl
 c7db37964986d7b9756fd1aa077b7e72  numpy-1.24.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 72c2dad61fc86c4d87e23d0de975e0b6  numpy-1.24.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 3c769f1089253266d7a522144696bde3  numpy-1.24.0rc1-cp310-cp310-win32.whl
 96226a2045063b9caff40fe2a2098e72  numpy-1.24.0rc1-cp310-cp310-win_amd64.whl
 b20897446f52e7fcde80e12c7cc1dc1e  numpy-1.24.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
 9cafe21759e90c705533d1f3201d35aa  numpy-1.24.0rc1-cp311-cp311-macosx_11_0_arm64.whl
 0e8621d07dae7ffaba6cfe83f7288042  numpy-1.24.0rc1-cp311-cp311-manylinux_2_17_aarch

[pyup-bot]

Closing this in favor of #63