Scheduled monthly dependency update for January

[pyup-bot]

Update pandas from 1.5.1 to 2.1.4.

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Links

• PyPI: https://pypi.org/project/pandas
• Homepage: https://pandas.pydata.org

Update scipy from 1.9.3 to 1.11.4.

Changelog

1.11.4

compared to `1.11.3`.

Authors
=======
* Name (commits)
* Jake Bowhay (2)
* Ralf Gommers (4)
* Julien Jerphanion (2)
* Nikolay Mayorov (2)
* Melissa Weber Mendonça (1)
* Tirth Patel (1)
* Tyler Reddy (22)
* Dan Schult (3)
* Nicolas Vetsch (1) +

A total of 9 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.11.3

compared to `1.11.2`.



Authors
=======
* Name (commits)
* Jake Bowhay (2)
* CJ Carey (1)
* Colin Carroll (1) +
* Anirudh Dagar (2)
* drestebon (1) +
* Ralf Gommers (5)
* Matt Haberland (2)
* Julien Jerphanion (1)
* Uwe L. Korn (1) +
* Ellie Litwack (2)
* Andrew Nelson (5)
* Bharat Raghunathan (1)
* Tyler Reddy (37)
* Søren Fuglede Jørgensen (2)
* Hielke Walinga (1) +
* Warren Weckesser (1)
* Bernhard M. Wiedemann (1)

A total of 17 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.11.2

compared to `1.11.1`. Python `3.12` and musllinux wheels
are provided with this release.

Authors
=======
* Name (commits)
* Evgeni Burovski (2)
* CJ Carey (3)
* Dieter Werthmüller (1)
* elbarso (1) +
* Ralf Gommers (2)
* Matt Haberland (1)
* jokasimr (1) +
* Thilo Leitzbach (1) +
* LemonBoy (1) +
* Ellie Litwack (2) +
* Sturla Molden (1)
* Andrew Nelson (5)
* Tyler Reddy (39)
* Daniel Schmitz (6)
* Dan Schult (2)
* Albert Steppi (1)
* Matus Valo (1)
* Stefan van der Walt (1)

A total of 18 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.11.1

compared to `1.11.0`. In particular, a licensing issue
discovered after the release of `1.11.0` has been addressed.


Authors
=======

* Name (commits)
* h-vetinari (1)
* Robert Kern (1)
* Ilhan Polat (4)
* Tyler Reddy (8)

A total of 4 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.11.0

many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with ``python -Wd`` and check for ``DeprecationWarning`` s).
Our development attention will now shift to bug-fix releases on the
`1.11.x` branch, and on adding new features on the main branch.

This release requires Python `3.9+` and NumPy `1.21.6` or greater.

For running on PyPy, PyPy3 `6.0+` is required.


Highlights of this release
====================

- Several `scipy.sparse` array API improvements, including a new public base
class distinct from the older matrix class, proper 64-bit index support,
and numerous deprecations paving the way to a modern sparse array experience.
- Added three new statistical distributions, and wide-ranging performance and
precision improvements to several other statistical distributions.
- A new function was added for quasi-Monte Carlo integration, and linear
algebra functions ``det`` and ``lu`` now accept nD-arrays.
- An ``axes`` argument was added broadly to ``ndimage`` functions, facilitating
analysis of stacked image data.



New features
===========

`scipy.integrate` improvements
==============================
- Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration.
- For an even number of points, `scipy.integrate.simpson` now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.

`scipy.cluster` improvements
============================
- ``disjoint_set`` has a new method ``subset_size`` for providing the size
of a particular subset.


`scipy.constants` improvements
================================
- The ``quetta``, ``ronna``, ``ronto``, and ``quecto`` SI prefixes were added.


`scipy.linalg` improvements
===========================
- `scipy.linalg.det` is improved and now accepts nD-arrays.
- `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new
``p_indices`` switch the output permutation argument can be 1D ``(n,)``
permutation index instead of the full ``(n, n)`` array.


`scipy.ndimage` improvements
============================
- ``axes`` argument was added to ``rank_filter``, ``percentile_filter``,
``median_filter``, ``uniform_filter``, ``minimum_filter``,
``maximum_filter``, and ``gaussian_filter``, which can be useful for
processing stacks of image data.


`scipy.optimize` improvements
=============================
- `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS.
- `scipy.optimize.root_scalar` now uses Newton's method to be used without
providing ``fprime`` and the ``secant`` method to be used without a second
guess.
- `scipy.optimize.lsq_linear` now accepts ``bounds`` arguments of type
`scipy.optimize.Bounds`.
- `scipy.optimize.minimize` ``method='cobyla'`` now supports simple bound
constraints.
- Users can opt into a new callback interface for most methods of
`scipy.optimize.minimize`: If the provided callback callable accepts
a single keyword argument, ``intermediate_result``, `scipy.optimize.minimize`
now passes both the current solution and the optimal value of the objective
function to the callback as an instance of `scipy.optimize.OptimizeResult`.
It also allows the user to terminate optimization by raising a
``StopIteration`` exception from the callback function.
`scipy.optimize.minimize` will return normally, and the latest solution
information is provided in the result object.
- `scipy.optimize.curve_fit` now supports an optional ``nan_policy`` argument.
- `scipy.optimize.shgo` now has parallelization with the ``workers`` argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.


`scipy.signal` improvements
===========================
- ``istft`` has an improved warning message when the NOLA condition fails.

`scipy.sparse` improvements
===========================
- `scipy.sparse` array (not matrix) classes now return a sparse array instead
of a dense array when divided by a dense array.
- A new public base class `scipy.sparse.sparray` was introduced, allowing
`isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes,
while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse
matrix types.
- The behavior of `scipy.sparse.isspmatrix()` was updated to return True for
only the sparse matrix types. If you want to check for either sparse arrays
or sparse matrices, use `scipy.sparse.issparse()` instead. (Previously,
these had identical behavior.)
- Sparse arrays constructed with 64-bit indices will no longer automatically
downcast to 32-bit.
- A new `scipy.sparse.diags_array` function was added, which behaves like the
existing `scipy.sparse.diags` function except that it returns a sparse
array instead of a sparse matrix.
- ``argmin`` and ``argmax`` methods now return the correct result when no
implicit zeros are present.

`scipy.sparse.linalg` improvements
==================================
- dividing ``LinearOperator`` by a number now returns a
``_ScaledLinearOperator``
- ``LinearOperator`` now supports right multiplication by arrays
- ``lobpcg`` should be more efficient following removal of an extraneous
QR decomposition.


`scipy.spatial` improvements
============================
- Usage of new C++ backend for additional distance metrics, the majority of
which will see substantial performance improvements, though a few minor
regressions are known. These are focused on distances between boolean
arrays.


`scipy.special` improvements
============================
- The factorial functions ``factorial``, ``factorial2`` and ``factorialk``
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally, ``factorial2`` can now handle arrays with
``exact=True``, and ``factorialk`` can handle arrays.


`scipy.stats` improvements
==========================

New Features
------------
- `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices.
- `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple
experimental groups against the mean of a control group.
- `scipy.stats.ecdf` for computing the empirical CDF and complementary
CDF (survival function / SF) from uncensored or right-censored data. This
function is also useful for survival analysis / Kaplain-Meier estimation.
- `scipy.stats.logrank` to compare survival functions underlying samples.
- `scipy.stats.false_discovery_control` for adjusting p-values to control the
false discovery rate of multiple hypothesis tests using the
Benjamini-Hochberg or Benjamini-Yekutieli procedures.
- `scipy.stats.CensoredData` to represent censored data. It can be used as
input to the ``fit`` method of univariate distributions and to the new
``ecdf`` function.
- Filliben's goodness of fit test as ``method='Filliben'`` of
`scipy.stats.goodness_of_fit`.
- `scipy.stats.ttest_ind` has a new method, ``confidence_interval`` for
computing confidence intervals.
- `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and
`scipy.stats.BootstrapMethod` are new classes to configure resampling and/or
Monte Carlo versions of hypothesis tests. They can currently be used with
`scipy.stats.pearsonr`.

Statistical Distributions
-------------------------
- Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`.
This distribution is the most common analogue of the normal distribution
on the unit sphere.
- Added the relativistic Breit-Wigner distribution as
`scipy.stats.rel_breitwigner`.
It is used in high energy physics to model resonances.
- Added the Dirichlet multinomial distribution as
`scipy.stats.dirichlet_multinomial`.
- Improved the speed and precision of several univariate statistical
distributions.

- `scipy.stats.anglit` ``sf``
- `scipy.stats.beta` ``entropy``
- `scipy.stats.betaprime` ``cdf``, ``sf``, ``ppf``
- `scipy.stats.chi` ``entropy``
- `scipy.stats.chi2` ``entropy``
- `scipy.stats.dgamma` ``entropy``, ``cdf``, ``sf``, ``ppf``, and ``isf``
- `scipy.stats.dweibull` ``entropy``, ``sf``, and ``isf``
- `scipy.stats.exponweib` ``sf`` and ``isf``
- `scipy.stats.f` ``entropy``
- `scipy.stats.foldcauchy` ``sf``
- `scipy.stats.foldnorm` ``cdf`` and ``sf``
- `scipy.stats.gamma` ``entropy``
- `scipy.stats.genexpon` ``ppf``, ``isf``, ``rvs``
- `scipy.stats.gengamma` ``entropy``
- `scipy.stats.geom` ``entropy``
- `scipy.stats.genlogistic` ``entropy``, ``logcdf``, ``sf``, ``ppf``,
 and ``isf``
- `scipy.stats.genhyperbolic` ``cdf`` and ``sf``
- `scipy.stats.gibrat` ``sf`` and ``isf``
- `scipy.stats.gompertz` ``entropy``, ``sf``. and ``isf``
- `scipy.stats.halflogistic` ``sf``, and ``isf``
- `scipy.stats.halfcauchy` ``sf`` and ``isf``
- `scipy.stats.halfnorm` ``cdf``, ``sf``, and ``isf``
- `scipy.stats.invgamma` ``entropy``
- `scipy.stats.invgauss` ``entropy``
- `scipy.stats.johnsonsb` ``pdf``, ``cdf``, ``sf``, ``ppf``, and ``isf``
- `scipy.stats.johnsonsu` ``pdf``, ``sf``, ``isf``, and ``stats``
- `scipy.stats.lognorm` ``fit``
- `scipy.stats.loguniform` ``entropy``, ``logpdf``, ``pdf``, ``cdf``, ``ppf``,
 and ``stats``
- `scipy.stats.maxwell` ``sf`` and ``isf``
- `scipy.stats.nakagami` ``entropy``
- `scipy.stats.powerlaw` ``sf``
- `scipy.stats.powerlognorm` ``logpdf``, ``logsf``, ``sf``, and ``isf``
- `scipy.stats.powernorm` ``sf`` and ``isf``
- `scipy.stats.t` ``entropy``, ``logpdf``, and ``pdf``
- `scipy.stats.truncexpon` ``sf``, and ``isf``
- `scipy.stats.truncnorm` ``entropy``
- `scipy.stats.truncpareto` ``fit``
- `scipy.stats.vonmises` ``fit``

- `scipy.stats.multivariate_t` now has ``cdf`` and ``entropy`` methods.
- `scipy.stats.multivariate_normal`, `scipy.stats.matrix_normal`, and
`scipy.stats.invwishart` now have an ``entropy`` method.

Other Improvements
------------------
- `scipy.stats.monte_carlo_test` now supports multi-sample statistics.
- `scipy.stats.bootstrap` can now produce one-sided confidence intervals.
- `scipy.stats.rankdata` performance was improved for ``method=ordinal`` and
``method=dense``.
- `scipy.stats.moment` now supports non-central moment calculation.
- `scipy.stats.anderson` now supports the ``weibull_min`` distribution.
- `scipy.stats.sem` and `scipy.stats.iqr` now support ``axis``, ``nan_policy``,
and masked array input.


Deprecated features
=================

- Multi-Ellipsis sparse matrix indexing has been deprecated and will
be removed in SciPy 1.13.
- Several methods were deprecated for sparse arrays: ``asfptype``, ``getrow``,
``getcol``, ``get_shape``, ``getmaxprint``, ``set_shape``,
``getnnz``, and ``getformat``. Additionally, the ``.A`` and ``.H``
attributes were deprecated. Sparse matrix types are not affected.
- The `scipy.linalg` functions ``tri``, ``triu`` & ``tril`` are deprecated and
will be removed in SciPy 1.13. Users are recommended to use the NumPy
versions of these functions with identical names.
- The `scipy.signal` functions ``bspline``, ``quadratic`` & ``cubic`` are
deprecated and will be removed in SciPy 1.13. Users are recommended to use
`scipy.interpolate.BSpline` instead.
- The ``even`` keyword of `scipy.integrate.simpson` is deprecated and will be
removed in SciPy 1.13.0. Users should leave this as the default as this
gives improved accuracy compared to the other methods.
- Using ``exact=True`` when passing integers in a float array to ``factorial``
is deprecated and will be removed in SciPy 1.13.0.
- float128 and object dtypes are deprecated for `scipy.signal.medfilt` and
`scipy.signal.order_filter`
- The functions ``scipy.signal.{lsim2, impulse2, step2}`` had long been
deprecated in documentation only. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0.
- Importing window functions directly from `scipy.window` has been soft
deprecated since SciPy 1.1.0. They now raise a ``DeprecationWarning`` and
will be removed in SciPy 1.13.0. Users should instead import them from
`scipy.signal.window` or use the convenience function
`scipy.signal.get_window`.


Backwards incompatible changes
============================
- The default for the ``legacy`` keyword of `scipy.special.comb` has changed
from ``True`` to ``False``, as announced since its introduction.


Expired Deprecations
==================
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:

- The ``n`` keyword has been removed from `scipy.stats.moment`.
- The ``alpha`` keyword has been removed from `scipy.stats.interval`.
- The misspelt ``gilbrat`` distribution has been removed (use
`scipy.stats.gibrat`).
- The deprecated spelling of the ``kulsinski`` distance metric has been
removed (use `scipy.spatial.distance.kulczynski1`).
- The ``vertices`` keyword of `scipy.spatial.Delauney.qhull` has been removed
(use simplices).
- The ``residual`` property of `scipy.sparse.csgraph.maximum_flow` has been
removed (use ``flow``).
- The ``extradoc`` keyword of `scipy.stats.rv_continuous`,
`scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed.
- The ``sym_pos`` keyword of `scipy.linalg.solve` has been removed.
- The `scipy.optimize.minimize` function now raises an error for ``x0`` with
``x0.ndim > 1``.
- In `scipy.stats.mode`, the default value of ``keepdims`` is now ``False``,
and support for non-numeric input has been removed.
- The function `scipy.signal.lsim` does not support non-uniform time steps
anymore.


Other changes
============
- Rewrote the source build docs and restructured the contributor guide.
- Improved support for cross-compiling with meson build system.
- MyST-NB notebook infrastructure has been added to our documentation.




Authors
=======

* h-vetinari (69)
* Oriol Abril-Pla (1) +
* Anton Akhmerov (13)
* Andrey Akinshin (1) +
* alice (1) +
* Oren Amsalem (1)
* Ross Barnowski (11)
* Christoph Baumgarten (2)
* Dawson Beatty (1) +
* Doron Behar (1) +
* Peter Bell (1)
* John Belmonte (1) +
* boeleman (1) +
* Jack Borchanian (1) +
* Matt Borland (3) +
* Jake Bowhay (40)
* Sienna Brent (1) +
* Matthew Brett (1)
* Evgeni Burovski (38)
* Matthias Bussonnier (2)
* Maria Cann (1) +
* Alfredo Carella (1) +
* CJ Carey (18)
* Hood Chatham (2)
* Anirudh Dagar (3)
* Alberto Defendi (1) +
* Pol del Aguila (1) +
* Hans Dembinski (1)
* Dennis (1) +
* Vinayak Dev (1) +
* Thomas Duvernay (1)
* DWesl (4)
* Stefan Endres (66)
* Evandro (1) +
* Tom Eversdijk (2) +
* Isuru Fernando (1)
* Franz Forstmayr (4)
* Joseph Fox-Rabinovitz (1)
* Stefano Frazzetto (1) +
* Neil Girdhar (1)
* Caden Gobat (1) +
* Ralf Gommers (146)
* GonVas (1) +
* Marco Gorelli (1)
* Brett Graham (2) +
* Matt Haberland (385)
* harshvardhan2707 (1) +
* Alex Herbert (1) +
* Guillaume Horel (1)
* Geert-Jan Huizing (1) +
* Jakob Jakobson (2)
* Julien Jerphanion (5)
* jyuv (2)
* Rajarshi Karmakar (1) +
* Ganesh Kathiresan (3) +
* Robert Kern (4)
* Andrew Knyazev (3)
* Sergey Koposov (1)
* Rishi Kulkarni (2) +
* Eric Larson (1)
* Zoufiné Lauer-Bare (2) +
* Antony Lee (3)
* Gregory R. Lee (8)
* Guillaume Lemaitre (1) +
* lilinjie (2) +
* Yannis Linardos (1) +
* Christian Lorentzen (5)
* Loïc Estève (1)
* Charlie Marsh (2) +
* Boris Martin (1) +
* Nicholas McKibben (10)
* Melissa Weber Mendonça (57)
* Michał Górny (1) +
* Jarrod Millman (2)
* Stefanie Molin (2) +
* Mark W. Mueller (1) +
* mustafacevik (1) +
* Takumasa N (1) +
* nboudrie (1)
* Andrew Nelson (111)
* Nico Schlömer (4)
* Lysandros Nikolaou (2) +
* Kyle Oman (1)
* OmarManzoor (2) +
* Simon Ott (1) +
* Geoffrey Oxberry (1) +
* Geoffrey M. Oxberry (2) +
* Sravya papaganti (1) +
* Tirth Patel (2)
* Ilhan Polat (32)
* Quentin Barthélemy (1)
* Matteo Raso (12) +
* Tyler Reddy (97)
* Lucas Roberts (1)
* Pamphile Roy (224)
* Jordan Rupprecht (1) +
* Atsushi Sakai (11)
* Omar Salman (7) +
* Leo Sandler (1) +
* Ujjwal Sarswat (3) +
* Saumya (1) +
* Daniel Schmitz (79)
* Henry Schreiner (2) +
* Dan Schult (3) +
* Eli Schwartz (6)
* Tomer Sery (2) +
* Scott Shambaugh (4) +
* Gagandeep Singh (1)
* Ethan Steinberg (6) +
* stepeos (2) +
* Albert Steppi (3)
* Strahinja Lukić (1)
* Kai Striega (4)
* suen-bit (1) +
* Tartopohm (2)
* Logan Thomas (2) +
* Jacopo Tissino (1) +
* Matus Valo (10) +
* Jacob Vanderplas (2)
* Christian Veenhuis (1) +
* Isaac Virshup (1)
* Stefan van der Walt (14)
* Warren Weckesser (63)
* windows-server-2003 (1)
* Levi John Wolf (3)
* Nobel Wong (1) +
* Benjamin Yeh (1) +
* Rory Yorke (1)
* Younes (2) +
* Zaikun ZHANG (1) +
* Alex Zverianskii (1) +

A total of 131 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.10.1

compared to `1.10.0`.



Authors
=======
* Name (commits)
* alice (1) +
* Matt Borland (2) +
* Evgeni Burovski (2)
* CJ Carey (1)
* Ralf Gommers (9)
* Brett Graham (1) +
* Matt Haberland (5)
* Alex Herbert (1) +
* Ganesh Kathiresan (2) +
* Rishi Kulkarni (1) +
* Loïc Estève (1)
* Michał Górny (1) +
* Jarrod Millman (1)
* Andrew Nelson (4)
* Tyler Reddy (50)
* Pamphile Roy (2)
* Eli Schwartz (2)
* Tomer Sery (1) +
* Kai Striega (1)
* Jacopo Tissino (1) +
* windows-server-2003 (1)

A total of 21 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.10.0

many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with ``python -Wd`` and check for ``DeprecationWarning`` s).
Our development attention will now shift to bug-fix releases on the
1.10.x branch, and on adding new features on the main branch.

This release requires Python `3.8+` and NumPy `1.19.5` or greater.

For running on PyPy, PyPy3 `6.0+` is required.



Highlights of this release
====================

- A new dedicated datasets submodule (`scipy.datasets`) has been added, and is
now preferred over usage of `scipy.misc` for dataset retrieval.
- A new `scipy.interpolate.make_smoothing_spline` function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.
- `scipy.stats` has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.


New features
===========

`scipy.datasets` introduction
========================
- A new dedicated ``datasets`` submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from `scipy.misc` have been added to `scipy.datasets`
(and deprecated in `scipy.misc`).
- The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/)
(a new optional dependency for SciPy), a Python package to simplify fetching
data files. This move will, in a subsequent release, facilitate SciPy
to trim down the sdist/wheel sizes, by decoupling the data files and
moving them out of the SciPy repository, hosting them externally and
downloading them when requested. After downloading the datasets once,
the files are cached to avoid network dependence and repeated usage.
- Added datasets from ``scipy.misc``: `scipy.datasets.face`,
`scipy.datasets.ascent`, `scipy.datasets.electrocardiogram`
- Added download and caching functionality:

- `scipy.datasets.download_all`: a function to download all the `scipy.datasets`
 associated files at once.
- `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset
 files from the file system.
- ``scipy/datasets/_download_all.py`` can be run as a standalone script for
 packaging purposes to avoid any external dependency at build or test time.
 This can be used by SciPy packagers (e.g., for Linux distros) which may
 have to adhere to rules that forbid downloading sources from external
 repositories at package build time.

`scipy.integrate` improvements
==============================
- Added `scipy.integrate.qmc_quad`, which performs quadrature using Quasi-Monte
Carlo points.
- Added parameter ``complex_func`` to `scipy.integrate.quad`, which can be set
``True`` to integrate a complex integrand.


`scipy.interpolate` improvements
================================
- `scipy.interpolate.interpn` now supports tensor-product interpolation methods
(``slinear``, ``cubic``, ``quintic`` and ``pchip``)
- Tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and
``pchip``) in `scipy.interpolate.interpn` and
`scipy.interpolate.RegularGridInterpolator` now allow values with trailing
dimensions.
- `scipy.interpolate.RegularGridInterpolator` has a new fast path for
``method="linear"`` with 2D data, and ``RegularGridInterpolator`` is now
easier to subclass
- `scipy.interpolate.interp1d` now can take a single value for non-spline
methods.
- A new ``extrapolate`` argument is available to `scipy.interpolate.BSpline.design_matrix`,
allowing extrapolation based on the first and last intervals.
- A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an
implementation of the generalized cross-validation spline smoothing
algorithm. The ``lam=None`` (default) mode of this function is a clean-room
reimplementation of the classic ``gcvspl.f`` Fortran algorithm for
constructing GCV splines.
- A new ``method="pchip"`` mode was aded to
`scipy.interpolate.RegularGridInterpolator`. This mode constructs an
interpolator using tensor products of C1-continuous monotone splines
(essentially, a `scipy.interpolate.PchipInterpolator` instance per
dimension).



`scipy.sparse.linalg` improvements
==================================
- The spectral 2-norm is now available in `scipy.sparse.linalg.norm`.
- The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius
norm) has been improved.
- LAPACK wrappers were added for ``trexc`` and ``trsen``.
- The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding
the following improvements:

- a simple tunable restart potentially increases the attainable
 accuracy for edge cases,
- internal postprocessing runs one final exact Rayleigh-Ritz method
 giving more accurate and orthonormal eigenvectors,
- output the computed iterate with the smallest max norm of the residual
 and drop the history of subsequent iterations,
- remove the check for ``LinearOperator`` format input and thus allow
 a simple function handle of a callable object as an input,
- better handling of common user errors with input data, rather
 than letting the algorithm fail.


`scipy.linalg` improvements
===========================
- `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted
to square arrays.


`scipy.ndimage` improvements
============================
- The new `scipy.ndimage.value_indices` function provides a time-efficient method to
search for the locations of individual values with an array of image data.
- A new ``radius`` argument is supported by `scipy.ndimage.gaussian_filter1d` and
`scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter.


`scipy.optimize` improvements
=============================
- `scipy.optimize.brute` now coerces non-iterable/single-value ``args`` into a
tuple.
- `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept
`scipy.optimize.Bounds` for bounds constraints.
- Added a tutorial for `scipy.optimize.milp`.
- Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects.
- Additional options (``parallel``, ``threads``, ``mip_rel_gap``) can now
be passed to `scipy.optimize.linprog` with ``method='highs'``.


`scipy.signal` improvements
===========================
- The new window function `scipy.signal.windows.lanczos` was added to compute a
Lanczos window, also known as a sinc window.


`scipy.sparse.csgraph` improvements
===================================
- the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and
star graphs in particular see a marked performance improvement


`scipy.special` improvements
============================
- The new function `scipy.special.powm1`, a ufunc with signature
``powm1(x, y)``, computes ``x**y - 1``. The function avoids the loss of
precision that can result when ``y`` is close to 0 or when ``x`` is close to
1.
- `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under
the hood.


`scipy.stats` improvements
==========================
- Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for
use with any univariate distribution, any combination of known and unknown
parameters, and several choices of test statistic (Kolmogorov-Smirnov,
Cramer-von Mises, and Anderson-Darling).
- Improved `scipy.stats.bootstrap`: Default method ``'BCa'`` now supports
multi-sample statistics. Also, the bootstrap distribution is returned in the
result object, and the result object can be passed into the function as
parameter ``bootstrap_result`` to add additional resamples or change the
confidence interval level and type.
- Added maximum spacing estimation to `scipy.stats.fit`.
- Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`.
- Added new sample statistics.

- Added `scipy.stats.contingency.odds_ratio` to compute both the conditional
 and unconditional odds ratios and corresponding confidence intervals for
 2x2 contingency tables.
- Added `scipy.stats.directional_stats` to compute sample statistics of
 n-dimensional directional data.
- Added `scipy.stats.expectile`, which generalizes the expected value in the
 same way as quantiles are a generalization of the median.

- Added new statistical distributions.

- Added `scipy.stats.uniform_direction`, a multivariate distribution to
 sample uniformly from the surface of a hypersphere.
- Added `scipy.stats.random_table`, a multivariate distribution to sample
 uniformly from m x n contingency tables with provided marginals.
- Added `scipy.stats.truncpareto`, the truncated Pareto distribution.

- Improved the ``fit`` method of several distributions.

- `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical
 solution when ``method='mm'``, which also serves a starting guess to
 improve the performance of ``method='mle'``.
- `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum
 likelihood estimates have been extended to the cases in which location or
 scale are fixed by the user.
- Analytical maximum likelihood estimates have been added for
 `scipy.stats.powerlaw`.

- Improved random variate sampling of several distributions.

- Drawing multiple samples from `scipy.stats.matrix_normal`,
 `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and
 `scipy.stats.unitary_group` is faster.
- The ``rvs`` method of `scipy.stats.vonmises` now wraps to the interval
 ``[-np.pi, np.pi]``.
- Improved the reliability of `scipy.stats.loggamma` ``rvs`` method for small
 values of the shape parameter.

- Improved the speed and/or accuracy of functions of several statistical
distributions.

- Added `scipy.stats.Covariance` for better speed, accuracy, and user control
 in multivariate normal calculations.
- `scipy.stats.skewnorm` methods ``cdf``, ``sf``, ``ppf``, and ``isf``
 methods now use the implementations from Boost, improving speed while
 maintaining accuracy. The calculation of higher-order moments is also
 faster and more accurate.
- `scipy.stats.invgauss` methods ``ppf`` and ``isf`` methods now use the
 implementations from Boost, improving speed and accuracy.
- `scipy.stats.invweibull` methods ``sf`` and ``isf`` are more accurate for
 small probability masses.
- `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations
 from Boost, improving speed and accuracy.
- Implemented the ``logpdf`` method of `scipy.stats.vonmises` for reliability
 in extreme tails.
- Implemented the ``isf`` method of `scipy.stats.levy` for speed and
 accuracy.
- Improved the robustness of `scipy.stats.studentized_range` for large ``df``
 by adding an infinite degree-of-freedom approximation.
- Added a parameter ``lower_limit`` to `scipy.stats.multivariate_normal`,
 allowing the user to change the integration limit from -inf to a desired
 value.
- Improved the robustness of ``entropy`` of `scipy.stats.vonmises` for large
 concentration values.

- Enhanced `scipy.stats.gaussian_kde`.

- Added `scipy.stats.gaussian_kde.marginal`, which returns the desired
 marginal distribution of the original kernel density estimate distribution.
- The ``cdf`` method of `scipy.stats.gaussian_kde` now accepts a
 ``lower_limit`` parameter for integrating the PDF over a rectangular region.
- Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython,
 improving speed.
- The global interpreter lock is released by the ``pdf`` method of
 `scipy.stats.gaussian_kde` for improved multithreading performance.
- Replaced explicit matrix inversion with Cholesky decomposition for speed
 and accuracy.

- Enhanced the result objects returned by many `scipy.stats` functions

- Added a ``confidence_interval`` method to the result object returned by
 `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`.
- The `scipy.stats` functions ``combine_pvalues``, ``fisher_exact``,
 ``chi2_contingency``, ``median_test`` and ``mood`` now return
 bunch objects rather than plain tuples, allowing attributes to be
 accessed by name.
- Attributes of the result objects returned by ``multiscale_graphcorr``,
 ``anderson_ksamp``, ``binomtest``, ``crosstab``, ``pointbiserialr``,
 ``spearmanr``, ``kendalltau``, and ``weightedtau`` have been renamed to
 ``statistic`` and ``pvalue`` for consistency throughout `scipy.stats`.
 Old attribute names are still allowed for backward compatibility.
- `scipy.stats.anderson` now returns the parameters of the fitted
 distribution in a `scipy.stats._result_classes.FitResult` object.
- The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts
 a ``plot_type`` parameter; the options are ``'hist'`` (histogram, default),
 ``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF
 plot).
- Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the
 location (argmax) at which the statistic is calculated and the variant
 of the statistic used.

- Improved the performance of several `scipy.stats` functions.

- Improved the performance of `scipy.stats.cramervonmises_2samp` and
 `scipy.stats.ks_2samp` with ``method='exact'``.
- Improved the performance of `scipy.stats.siegelslopes`.
- Improved the performance of `scipy.stats.mstats.hdquantile_sd`.
- Improved the performance of `scipy.stats.binned_statistic_dd` for several
 NumPy statistics, and binned statistics methods now support complex data.

- Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`.
It replaces ``centered``, which is now deprecated.
- Added a parameter ``optimization`` to all `scipy.stats.qmc.QMCEngine`
subclasses to improve characteristics of the quasi-random variates.
- Added tie correction to `scipy.stats.mood`.
- Added tutorials for resampling methods in `scipy.stats`.
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
`scipy.stats.monte_carlo_test` now automatically detect whether the provided
``statistic`` is vectorized, so passing the ``vectorized`` argument
explicitly is no longer required to take advantage of vectorized statistics.
- Improved the speed of `scipy.stats.permutation_test` for permutation types
``'samples'`` and ``'pairings'``.
- Added ``axis``, ``nan_policy``, and masked array support to
`scipy.stats.jarque_bera`.
- Added the ``nan_policy`` optional argument to `scipy.stats.rankdata`.



Deprecated features
=================
- `scipy.misc` module and all the methods in ``misc`` are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize the `scipy.datasets` module instead for the dataset methods.
- `scipy.stats.qmc.LatinHypercube` parameter ``centered`` has been deprecated.
It is replaced by the ``scramble`` argument for more consistency with other
QMC engines.
- `scipy.interpolate.interp2d` class has been deprecated.  The docstring of the
deprecated routine lists recommended replacements.


Expired Deprecations
==================
- There is an ongoing effort to follow through on long-standing deprecations.
- The following previously deprecated features are affected:

- Removed ``cond`` & ``rcond`` kwargs in ``linalg.pinv``
- Removed wrappers ``scipy.linalg.blas.{clapack, flapack}``
- Removed ``scipy.stats.NumericalInverseHermite`` and removed ``tol`` & ``max_intervals`` kwargs from ``scipy.stats.sampling.NumericalInverseHermite``
- Removed ``local_search_options`` kwarg frrom ``scipy.optimize.dual_annealing``.



Other changes
============
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
`scipy.stats.monte_carlo_test` now automatically detect whether the provided
``statistic`` is vectorized by looking for an ``axis`` parameter in the
signature of ``statistic``. If an ``axis`` parameter is present in
``statistic`` but should not be relied on for vectorized calls, users must
pass option ``vectorized==False`` explicitly.
- `scipy.stats.multivariate_normal` will now raise a ``ValueError`` when the
covariance matrix is not positive semidefinite, regardless of which method
is called.




Authors
=======

* Name (commits)
* h-vetinari (10)
* Jelle Aalbers (1)
* Alan-Hung (1) +
* Tania Allard (7)
* Oren Amsalem (1) +
* Sven Baars (10)
* Balthasar (1) +
* Ross Barnowski (1)
* Christoph Baumgarten (2)
* Peter Bell (2)
* Sebastian Berg (1)
* Aaron Berk (1) +
* boatwrong (1) +
* Jake Bowhay (50)
* Matthew Brett (4)
* Evgeni Burovski (93)
* Matthias Bussonnier (6)
* Dominic C (2)
* Mingbo Cai (1) +
* James Campbell (2) +
* CJ Carey (4)
* cesaregarza (1) +
* charlie0389 (1) +
* Hood Chatham (5)
* Andrew Chin (1) +
* Daniel Ching (1) +
* Leo Chow (1) +
* chris (3) +
* John Clow (1) +
* cm7S (1) +
* cmgodwin (1) +
* Christopher Cowden (2) +
* Henry Cuzco (2) +
* Anirudh Dagar (10)
* Hans Dembinski (2) +
* Jaiden di Lanzo (24) +
* Felipe Dias (1) +
* Dieter Werthmüller (1)
* Giuseppe Dilillo (1) +
* dpoerio (1) +
* drpeteb (1) +
* Christopher Dupuis (1) +
* Jordan Edmunds (1) +
* Pieter Eendebak (1) +
* Jérome Eertmans (1) +
* Fabian Egli (2) +
* Sebastian Ehlert (2) +
* Kian Eliasi (1) +
* Tomohiro Endo (1) +
* Stefan Endres (1)
* Zeb Engberg (4) +
* Jonas Eschle (1) +
* Thomas J. Fan (9)
* fiveseven (1) +
* Neil Flood (1) +
* Franz Forstmayr (1)
* Sara Fridovich-Keil (1)
* David Gilbertson (1) +
* Ralf Gommers (251)
* Marco Gorelli (2) +
* Matt Haberland (381)
* Andrew Hawryluk (2) +
* Christoph Hohnerlein (2) +
* Loïc Houpert (2) +
* Shamus Husheer (1) +
* ideasrule (1) +
* imoiwm (1) +
* Lakshaya Inani (1) +
* Joseph T. Iosue (1)
* iwbc-mzk (1) +
* Nathan Jacobi (3) +
* Julien Jerphanion (5)
* He Jia (1)
* jmkuebler (1) +
* Johannes Müller (1) +
* Vedant Jolly (1) +
* Juan Luis Cano Rodríguez (2)
* Justin (1) +
* jvavrek (1) +
* jyuv (2)
* Kai Mühlbauer (1) +
* Nikita Karetnikov (3) +
* Reinert Huseby Karlsen (1) +
* kaspar (2) +
* Toshiki Kataoka (1)
* Robert Kern (3)
* Joshua Klein (1) +
* Andrew Knyazev (7)
* Jozsef Kutas (16) +
* Eric Larson (4)
* Lechnio (1) +
* Antony Lee (2)
* Aditya Limaye (1) +
* Xingyu Liu (2)
* Christian Lorentzen (4)
* Loïc Estève (2)
* Thibaut Lunet (2) +
* Peter Lysakovski (1)
* marianasalamoni (2) +
* mariprudencio (1) +
* Paige Martin (1) +
* Arno Marty (1) +
* matthewborish (3) +
* Damon McDougall (1)
* Nicholas McKibben (22)
* McLP (1) +
* mdmahendri (1) +
* Melissa Weber Mendonça (9)
* Jarrod Millman (1)
* Naoto Mizuno (2)
* Shashaank N (1)
* Pablo S Naharro (1) +
* nboudrie (1) +
* Andrew Nelson (52)
* Nico Schlömer (1)
* NiMlr (1) +
* o-alexandre-felipe (1) +
* Maureen Ononiwu (1) +
* Dimitri Papadopoulos (2) +
* partev (1) +
* Tirth Patel (10)
* Paulius Šarka (1) +
* Josef Perktold (1)
* Giacomo Petrillo (3) +
* Matti Picus (1)
* Rafael Pinto (1) +
* PKNaveen (1) +
* Ilhan Polat (6)
* Akshita Prasanth (2) +
* Sean Quinn (1)
* Tyler Reddy (117)
* Martin Reinecke (1)
* Ned Richards (1)
* Marie Roald (1) +
* Sam Rosen (4) +
* Pamphile Roy (103)
* sabonerune (2) +
* Atsushi Sakai (94)
* Daniel Schmitz (27)
* Anna Scholtz (1) +
* Eli Schwartz (11)
* serge-sans-paille (2)
* JEEVANSHI SHARMA (1) +
* ehsan shirvanian (2) +
* siddhantwahal (2)
* Mathieu Dutour Sikiric (1) +
* Sourav Singh (1)
* Alexander Soare (1) +
* Bjørge Solli (2) +
* Scott Staniewicz (1)
* Albert Steppi (3)
* Thomas Stoeger (1) +
* Kai Striega (4)
* Tartopohm (1) +
* Mamoru TASAKA (2) +
* Ewout ter Hoeven (5)
* TianyiQ (1) +
* Tiger (1) +
* Will Tirone (1)
* Edgar Andrés Margffoy Tuay (1) +
* Dmitry Ulyumdzhiev (1) +
* Hari Vamsi (1) +
* VitalyChait (1) +
* Rik Voorhaar (1) +
* Samuel Wallan (4)
* Stefan van der Walt (2)
* Warren Weckesser (145)
* wei2222 (1) +
* windows-server-2003 (3) +
* Marek Wojciechowski (2) +
* Niels Wouda (1) +
* WRKampi (1) +
* Yeonjoo Yoo (1) +
* Rory Yorke (1)
* Xiao Yuan (2) +
* Meekail Zain (2) +
* Fabio Zanini (1) +
* Steffen Zeile (1) +
* Egor Zemlyanoy (19)
* Gavin Zhang (3) +

A total of 180 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Links

• PyPI: https://pypi.org/project/scipy
• Changelog: https://data.safetycli.com/changelogs/scipy/
• Homepage: https://scipy.org/

Update numpy from 1.23.4 to 1.26.2.

Changelog

1.26.1

discovered after the 1.26.0 release. In addition, it adds new
functionality for detecting BLAS and LAPACK when building from source.
Highlights are:

-   Improved detection of BLAS and LAPACK libraries for meson builds
-   Pickle compatibility with the upcoming NumPy 2.0.

The 1.26.release series is the last planned minor release series before
NumPy 2.0. The Python versions supported by this release are 3.9-3.12.

Build system changes

Improved BLAS/LAPACK detection and control

Auto-detection for a number of BLAS and LAPACK is now implemented for
Meson. By default, the build system will try to detect MKL, Accelerate
(on macOS \>=13.3), OpenBLAS, FlexiBLAS, BLIS and reference BLAS/LAPACK.
Support for MKL was significantly improved, and support for FlexiBLAS
was added.

New command-line flags are available to further control the selection of
the BLAS and LAPACK libraries to build against.

To select a specific library, use the config-settings interface via
`pip` or `pypa/build`. E.g., to select `libblas`/`liblapack`, use:

 $ pip install numpy -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack
 $  OR
 $ python -m build . -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack

This works not only for the libraries named above, but for any library
that Meson is able to detect with the given name through `pkg-config` or
CMake.

Besides `-Dblas` and `-Dlapack`, a number of other new flags are
available to control BLAS/LAPACK selection and behavior:

-   `-Dblas-order` and `-Dlapack-order`: a list of library names to
 search for in order, overriding the default search order.
-   `-Duse-ilp64`: if set to `true`, use ILP64 (64-bit integer) BLAS and
 LAPACK. Note that with this release, ILP64 support has been extended
 to include MKL and FlexiBLAS. OpenBLAS and Accelerate were supported
 in previous releases.
-   `-Dallow-noblas`: if set to `true`, allow NumPy to build with its
 internal (very slow) fallback routines instead of linking against an
 external BLAS/LAPACK library. *The default for this flag may be
 changed to \`\`true\`\` in a future 1.26.x release, however for
 1.26.1 we\'d prefer to keep it as \`\`false\`\` because if failures
 to detect an installed library are happening, we\'d like a bug
 report for that, so we can quickly assess whether the new
 auto-detection machinery needs further improvements.*
-   `-Dmkl-threading`: to select the threading layer for MKL. There are
 four options: `seq`, `iomp`, `gomp` and `tbb`. The default is
 `auto`, which selects from those four as appropriate given the
 version of MKL selected.
-   `-Dblas-symbol-suffix`: manually select the symbol suffix to use for
 the library - should only be needed for linking against libraries
 built in a non-standard way.

New features

`numpy._core` submodule stubs

`numpy._core` submodule stubs were added to provide compatibility with
pickled arrays created using NumPy 2.0 when running Numpy 1.26.

Contributors

A total of 13 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Anton Prosekin +
-   Charles Harris
-   Chongyun Lee +
-   Ivan A. Melnikov +
-   Jake Lishman +
-   Mahder Gebremedhin +
-   Mateusz Sokół
-   Matti Picus
-   Munira Alduraibi +
-   Ralf Gommers
-   Rohit Goswami
-   Sayed Adel

Pull requests merged

A total of 20 pull requests were merged for this release.

-   [24742](https://github.com/numpy/numpy/pull/24742): MAINT: Update cibuildwheel version
-   [24748](https://github.com/numpy/numpy/pull/24748): MAINT: fix version string in wheels built with setup.py
-   [24771](https://github.com/numpy/numpy/pull/24771): BLD, BUG: Fix build failure for host flags e.g. `-march=native`\...
-   [24773](https://github.com/numpy/numpy/pull/24773): DOC: Updated the f2py docs to remove a note on -fimplicit-none
-   [24776](https://github.com/numpy/numpy/pull/24776): BUG: Fix SIMD f32 trunc test on s390x when baseline is none
-   [24785](https://github.com/numpy/numpy/pull/24785): BLD: add libquadmath to licences and other tweaks (#24753)
-   [24786](https://github.com/numpy/numpy/pull/24786): MAINT: Activate `use-compute-credits` for Cirrus.
-   [24803](https://github.com/numpy/numpy/pull/24803): BLD: updated vendored-meson/meson for mips64 fix
-   [24804](https://github.com/numpy/numpy/pull/24804): MAINT: fix licence path win
-   [24813](https://github.com/numpy/numpy/pull/24813): BUG: Fix order of Windows OS detection macros.
-   [24831](https://github.com/numpy/numpy/pull/24831): BUG, SIMD: use scalar cmul on bad Apple clang x86_64 (#24828)
-   [24840](https://github.com/numpy/numpy/pull/24840): BUG: Fix DATA statements for f2py
-   [24870](https://github.com/numpy/numpy/pull/24870): API: Add `NumpyUnpickler` for backporting
-   [24872](https://github.com/numpy/numpy/pull/24872): MAINT: Xfail test failing on PyPy.
-   [24879](https://github.com/numpy/numpy/pull/24879): BLD: fix math func feature checks, fix FreeBSD build, add CI\...
-   [24899](https://github.com/numpy/numpy/pull/24899): ENH: meson: implement BLAS/LAPACK auto-detection and many CI\...
-   [24902](https://github.com/numpy/numpy/pull/24902): DOC: add a 1.26.1 release notes section for BLAS/LAPACK build\...
-   [24906](https://github.com/numpy/numpy/pull/24906): MAINT: Backport `numpy._core` stubs. Remove `NumpyUnpickler`
-   [24911](https://github.com/numpy/numpy/pull/24911): MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2
-   [24912](https://github.com/numpy/numpy/pull/24912): BUG: loongarch doesn\'t use REAL(10)

Checksums

MD5

 bda38de1a047dd9fdddae16c0d9fb358  numpy-1.26.1-cp310-cp310-macosx_10_9_x86_64.whl
 196d2e39047da64ab28e177760c95461  numpy-1.26.1-cp310-cp310-macosx_11_0_arm64.whl
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1.26.0

The NumPy 1.26.0 release is a continuation of the 1.25.x release cycle
with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have
chosen to use the Meson build system instead, and this is the first
NumPy release supporting it. This is also the first release that
supports Cython 3.0 in addition to retaining 0.29.X compatibility.
Supporting those two upgrades was a large project, over 100 files have
been touched in this release. The changelog doesn\'t capture the full
extent of the work, special thanks to Ralf Gommers, Sayed Adel, Stéfan
van der Walt, and Matti Picus who did much of the work in the main
development branch.

The highlights of this release are:

-   Python 3.12.0 support.
-   Cython 3.0.0 compatibility.
-   Use of the Meson build system
-   Updated SIMD support

The Python versions supported in this release are 3.9-3.12.

Build system changes

In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel
can be done with standard tools like `pip` and `pypa/build`. The
following are supported:

-   Regular installs: `pip install numpy` or (in a cloned repo)
 `pip install .`
-   Building a wheel: `python -m build` (preferred), or `pip wheel .`
-   Editable installs: `pip install -e . --no-build-isolation`
-   Development builds through the custom CLI implemented with
 [spin](https://github.com/scientific-python/spin): `spin build`.

All the regular `pip` and `pypa/build` flags (e.g.,
`--no-build-isolation`) should work as expected.

NumPy-specific build customization

Many of the NumPy-specific ways of customizing builds have changed. The
`NPY_*` environment variables which control BLAS/LAPACK, SIMD,
threading, and other such options are no longer supported, nor is a
`site.cfg` file to select BLAS and LAPACK. Instead, there are
command-line flags that can be passed to the build via `pip`/`build`\'s
config-settings interface. These flags are all listed in the
`meson_options.txt` file in the root of the repo. Detailed documented
will be available before the final 1.26.0 release; for now please see
[the SciPy \"building from source\"docs](http://scipy.github.io/devdocs/building/index.html) since most
build customization works in an almost identical way in SciPy as it does
in NumPy.

Build dependencies

While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the `[build-system]`
section of `pyproject.toml` for details.

Troubleshooting

This build system change is quite large. In case of unexpected issues,
it is still possible to use a `setup.py`-based build as a temporary
workaround (on Python 3.9-3.11, not 3.12), by copying
`pyproject.toml.setuppy` to `pyproject.toml`. However, please open an
issue with details on the NumPy issue tracker. We aim to phase out
`setup.py` builds as soon as possible, and therefore would like to see
all potential blockers surfaced early on in the 1.26.0 release cycle.

Contributors

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

-   Bas van Beek
-   Charles Harris
-   Matti Picus
-   Melissa Weber Mendonça
-   Ralf Gommers
-   Sayed Adel
-   Sebastian Berg
-   Stefan van der Walt
-   Tyler Reddy
-   Warren Weckesser

Pull requests merged

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

-   [24305](https://github.com/numpy/numpy/pull/24305): MAINT: Prepare 1.26.x branch for development
-   [24308](https://github.com/numpy/numpy/pull/24308): MAINT: Massive update of files from main for numpy 1.26
-   [24322](https://github.com/numpy/numpy/pull/24322): CI: fix wheel builds on the 1.26.x branch
-   [24326](https://github.com/numpy/numpy/pull/24326): BLD: update openblas to newer version
-   [24327](https://github.com/numpy/numpy/pull/24327): TYP: Trim down the `_NestedSequence.__getitem__` signature
-   [24328](https://github.com/numpy/numpy/pull/24328): BUG: fix choose refcount leak
-   [24337](https://github.com/numpy/numpy/pull/24337): TST: fix running the test suite in builds without BLAS/LAPACK
-   [24338](https://github.com/numpy/numpy/pull/24338): BUG: random: Fix generation of nan by dirichlet.
-   [24340](https://github.com/numpy/numpy/pull/24340): MAINT: Dependabot updates from main
-   [24342](https://github.com/numpy/numpy/pull/24342): MAINT: Add back NPY_RUN_MYPY_IN_TESTSUITE=1
-   [24353](https://github.com/numpy/numpy/pull/24353): MAINT: Update `extbuild.py` from main.
-   [24356](https://github.com/numpy/numpy/pull/24356): TST: fix distutils tests for deprecations in recent setuptools\...
-   [24375](https://github.com/numpy/numpy/pull/24375): MAINT: Update cibuildwheel to version 2.15.0
-   [24381](https://github.com/numpy/numpy/pull/24381): MAINT: Fix codespaces setup.sh script
-   [24403](https://github.com/numpy/numpy/pull/24403): ENH: Vendor meson for multi-target build support
-   [24404](https://github.com/numpy/numpy/pull/24404): BLD: vendor meson-python to make the Windows builds with SIMD\...
-   [24405](https://github.com/numpy/numpy/pull/24405): BLD, SIMD: The meson CPU dispatcher implementation
-   [24406](https://github.com/numpy/numpy/pull/24406): MAINT: Remove versioneer

Checksums

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 3c6c5804671fa1697e3d0cbc608a65c55794fb6682f4e04e9f6d65d0ddfc47c7  numpy-1.26.0b1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 3aa806da215e9c10ba89e9037a69c7a56367e059615679ef1a5cf937eedfbf61  numpy-1.26.0b1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 b66135c02ee55f9113dce3c8c5130b5feaead8767cd2c7ad36547a3d5e264230  numpy-1.26.0b1-cp312-cp312-musllinux_1_1_x86_64.whl
 87f2799f475e9e7aee69254dfe357975b163d409550d4641a0bca4cb4f64b725  numpy-1.26.0b1-cp312-cp312-win_amd64.whl
 2b258f67ca4a8245c74470da66a87684ddb3f06dde98760efc7ca792a44ee254  numpy-1.26.0b1-cp39-cp39-macosx_10_9_x86_64.whl
 a31d9109ffed9fc5566e73346a076fffbc7db00e62

[pyup-bot]

Closing this in favor of #65