Update scipy to 1.15.1

[pyup-bot]

This PR updates scipy from 1.11.4 to 1.15.1.

Changelog

1.15.1

compared to `1.15.0`. Importantly, an issue with the
import of `scipy.optimize` breaking other packages
has been fixed.



Authors
=======
* Name (commits)
* Ralf Gommers (3)
* Rohit Goswami (1)
* Matt Haberland (2)
* Tyler Reddy (7)
* Daniel Schmitz (1)

A total of 5 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.

1.15

3.13.`` This allows SciPy functionality to execute in parallel with Python
threads
(see the ``threading`` stdlib module). This support was enabled by fixing a
significant number of thread-safety issues in both pure Python and
C/C++/Cython/Fortran extension modules. Wheels are provided on PyPI for this
release; NumPy ``>=2.1.3`` is required at runtime. Note that building for a
free-threaded interpreter requires a recent pre-release or nightly for Cython
``3.1.0``.

Support for free-threaded Python does not mean that SciPy is fully thread-safe.
Please see :ref:`scipy_thread_safety` for more details.

If you are interested in free-threaded Python, for example because you have a
multiprocessing-based workflow that you are interested in running with Python
threads, we encourage testing and experimentation. If you run into problems
that you suspect are because of SciPy, please open an issue, checking first if
the bug also occurs in the "regular" non-free-threaded CPython ``3.13`` build.
Many threading bugs can also occur in code that releases the GIL; disabling
the GIL only makes it easier to hit threading bugs.


Array API Standard Support
=====================
Experimental support for array libraries other than NumPy has been added to
existing sub-packages in recent versions of SciPy. Please consider testing
these features by setting an environment variable ``SCIPY_ARRAY_API=1`` and
providing PyTorch, JAX, ndonnx, or CuPy arrays as array arguments. Features
with support added for SciPy ``1.15.0`` include:

- All functions in `scipy.differentiate` (new sub-package)
- All functions in `scipy.optimize.elementwise` (new namespace)
- `scipy.optimize.rosen`, `scipy.optimize.rosen_der`, and
`scipy.optimize.rosen_hess`
- `scipy.special.logsumexp`
- `scipy.integrate.trapezoid`
- `scipy.integrate.tanhsinh` (newly public function)
- `scipy.integrate.cubature` (new function)
- `scipy.integrate.nsum` (new function)
- `scipy.special.chdtr`, `scipy.special.betainc`, and `scipy.special.betaincc`
- `scipy.stats.boxcox_llf`
- `scipy.stats.differential_entropy`
- `scipy.stats.zmap`, `scipy.stats.zscore`, and `scipy.stats.gzscore`
- `scipy.stats.tmean`, `scipy.stats.tvar`, `scipy.stats.tstd`,
`scipy.stats.tsem`, `scipy.stats.tmin`, and `scipy.stats.tmax`
- `scipy.stats.gmean`, `scipy.stats.hmean` and `scipy.stats.pmean`
- `scipy.stats.combine_pvalues`
- `scipy.stats.ttest_ind`, `scipy.stats.ttest_rel`
- `scipy.stats.directional_stats`
- `scipy.ndimage` functions will now delegate to ``cupyx.scipy.ndimage``,
and for other backends will transit via NumPy arrays on the host.



Deprecated features
================
- Functions `scipy.linalg.interpolative.rand` and
`scipy.linalg.interpolative.seed` have been deprecated and will be removed
in SciPy ``1.17.0``.
- Complex inputs to `scipy.spatial.distance.cosine` and
`scipy.spatial.distance.correlation` have been deprecated and will raise
an error in SciPy ``1.17.0``.
- `scipy.spatial.distance.kulczynski1` and
`scipy.spatial.distance.sokalmichener` were deprecated and will be removed
in SciPy ``1.17.0``.
- `scipy.stats.find_repeats` is deprecated as of SciPy ``1.15.0`` and will be
removed in SciPy ``1.17.0``. Please use
``numpy.unique``/``numpy.unique_counts`` instead.
- `scipy.linalg.kron` is deprecated in favour of ``numpy.kron``.
- Using object arrays and longdouble arrays in `scipy.signal`
convolution/correlation functions (`scipy.signal.correlate`,
`scipy.signal.convolve` and `scipy.signal.choose_conv_method`) and
filtering functions (`scipy.signal.lfilter`, `scipy.signal.sosfilt`) has
been deprecated as of SciPy ``1.15.0`` and will be removed in SciPy
``1.17.0``.
- `scipy.stats.linregress` has deprecated one-argument use; the two
variables must be specified as separate arguments.
- ``scipy.stats.trapz`` is deprecated in favor of `scipy.stats.trapezoid`.
- `scipy.special.lpn` is deprecated in favor of `scipy.special.legendre_p_all`.
- `scipy.special.lpmn` and `scipy.special.clpmn` are deprecated in favor of
`scipy.special.assoc_legendre_p_all`.
- The raveling of multi-dimensional input by `scipy.linalg.toeplitz` has
been deprecated. It will support batching in SciPy ``1.17.0``.
- The ``random_state`` and ``permutations`` arguments of
`scipy.stats.ttest_ind` are deprecated. Use ``method`` to perform a
permutation test, instead.


Expired Deprecations
================
- The wavelet functions in `scipy.signal` have been removed. This includes
``daub``, ``qmf``, ``cascade``, ``morlet``, ``morlet2``, ``ricker``,
and ``cwt``. Users should use ``pywavelets`` instead.
- ``scipy.signal.cmplx_sort`` has been removed.
- ``scipy.integrate.quadrature`` and ``scipy.integrate.romberg`` have been
removed in favour of `scipy.integrate.quad`.
- ``scipy.stats.rvs_ratio_uniforms`` has been removed in favor of
`scipy.stats.sampling.RatioUniforms`.
- `scipy.special.factorial` now raises an error for non-integer scalars when
``exact=True``.
- `scipy.integrate.cumulative_trapezoid` now raises an error for values of
``initial`` other than ``0`` and ``None``.
- Complex dtypes now raise an error in `scipy.interpolate.Akima1DInterpolator`
and `scipy.interpolate.PchipInterpolator`
- ``special.btdtr`` and ``special.btdtri`` have been removed.
- The default of the ``exact=`` kwarg in ``special.factorialk`` has changed
from ``True`` to ``False``.
- All functions in the ``scipy.misc`` submodule have been removed.


Backwards incompatible changes
==========================
- ``interpolate.BSpline.integrate`` output is now always a numpy array.
Previously, for 1D splines the output was a python float or a 0D array
depending on the value of the ``extrapolate`` argument.
- `scipy.stats.wilcoxon` now respects the ``method`` argument provided by the
user. Previously, even if ``method='exact'`` was specified, the function
would resort to ``method='approx'`` in some cases.


Other changes
============
- A separate accompanying type stubs package, ``scipy-stubs``, will be made
available with the ``1.15.0`` release. `Installation instructions are
available
<https://github.com/jorenham/scipy-stubs?tab=readme-ov-file#installation>`_.
- `scipy.stats.bootstrap` now emits a ``FutureWarning`` if the shapes of the
input arrays do not agree. Broadcast the arrays to the same batch shape
(i.e. for all dimensions except those specified by the ``axis`` argument)
to avoid the warning. Broadcasting will be performed automatically in the
future.
- SciPy endorsed `SPEC-7 <https://scientific-python.org/specs/spec-0007/>`_,
which proposes a ``rng`` argument to control pseudorandom number generation
(PRNG) in a standard way, replacing legacy arguments like ``seed`` and
``random_sate``. In many cases, use of ``rng`` will change the behavior of
the function unless the argument is already an instance of
``numpy.random.Generator``.

- Effective in SciPy ``1.15.0``:

 - The ``rng`` argument has been added to the following functions:
   `scipy.cluster.vq.kmeans`, `scipy.cluster.vq.kmeans2`,
   `scipy.interpolate.BarycentricInterpolator`,
   `scipy.interpolate.barycentric_interpolate`,
   `scipy.linalg.clarkson_woodruff_transform`,
   `scipy.optimize.basinhopping`,
   `scipy.optimize.differential_evolution`, `scipy.optimize.dual_annealing`,
   `scipy.optimize.check_grad`, `scipy.optimize.quadratic_assignment`,
   `scipy.sparse.random`, `scipy.sparse.random_array`, `scipy.sparse.rand`,
   `scipy.sparse.linalg.svds`, `scipy.spatial.transform.Rotation.random`,
   `scipy.spatial.distance.directed_hausdorff`,
   `scipy.stats.goodness_of_fit`, `scipy.stats.BootstrapMethod`,
   `scipy.stats.PermutationMethod`, `scipy.stats.bootstrap`,
   `scipy.stats.permutation_test`, `scipy.stats.dunnett`, all
   `scipy.stats.qmc` classes that consume random numbers, and
   `scipy.stats.sobol_indices`.
 - When passed by keyword, the ``rng`` argument will follow the SPEC 7
   standard behavior: the argument will be normalized with
   ``np.random.default_rng`` before being used.
 - When passed by position or legacy keyword, the behavior of the argument
   will remain unchanged (for now).

- It is planned that in ``1.17.0`` the legacy argument will start emitting
 warnings, and that in ``1.19.0`` the default behavior will change.
- In all cases, users can avoid future disruption by proactively passing
 an instance of ``np.random.Generator`` by keyword ``rng``. For details,
 see `SPEC-7 <https://scientific-python.org/specs/spec-0007/>`_.

- The SciPy build no longer adds ``-std=legacy`` for Fortran code,
except when using Gfortran. This avoids problems with the new Flang and
AMD Fortran compilers. It may make new build warnings appear for other
compilers - if so, please file an issue.


- ``scipy.signal.sosfreqz`` has been renamed to `scipy.signal.freqz_sos`.
New code should use the new name. The old name is maintained as an alias for
backwards compatibility.
- Testing thread-safety improvements related to Python ``3.13t`` have been
made in: `scipy.special`, `scipy.spatial`, `scipy.sparse`,
`scipy.interpolate`.



Authors (commits)
==============

* endolith (4)
* h-vetinari (61)
* a-drenaline (1) +
* Afleloup (1) +
* Ahmad Alkadri (1) +
* Luiz Eduardo Amaral (3) +
* Virgile Andreani (3)
* Isaac Alonso Asensio (2) +
* Matteo Bachetti (1) +
* Arash Badie-Modiri (1) +
* Arnaud Baguet (1) +
* Soutrik Bandyopadhyay (1) +
* Ankit Barik (1) +
* Christoph Baumgarten (1)
* Nickolai Belakovski (3)
* Krishan Bhasin (1) +
* Jake Bowhay (85)
* Michael Bratsch (2) +
* Matthew Brett (1)
* Keith Briggs (1) +
* Olly Britton (145) +
* Dietrich Brunn (11)
* Clemens Brunner (1)
* Evgeni Burovski (182)
* Matthias Bussonnier (7)
* CJ Carey (32)
* Cesar Carrasco (4) +
* Hood Chatham (1)
* Aadya Chinubhai (1)
* Alessandro Chitarrini (1) +
* Thibault de Coincy (1) +
* Lucas Colley (217)
* Martin Diehl (1) +
* Djip007 (1) +
* Kevin Doshi (2) +
* Michael Dunphy (2)
* Andy Everall (1) +
* Thomas J. Fan (2)
* fancidev (60)
* Sergey Fedorov (2) +
* Sahil Garje (1) +
* Gabriel Gerlero (2)
* Yotam Gingold (1) +
* Ralf Gommers (107)
* Rohit Goswami (62)
* Anil Gurses (1) +
* Oscar Gustafsson (1) +
* Matt Haberland (371)
* Matt Hall (1) +
* Joren Hammudoglu (3) +
* CY Han (1) +
* Daniel Isaac (4) +
* Maxim Ivanov (1)
* Jakob Jakobson (2)
* Janez Demšar (4) +
* Chris Jerdonek (2) +
* Adam Jones (4) +
* Aditi Juneja (1) +
* Nuri Jung (1) +
* Guus Kamphuis (1) +
* Aditya Karumanchi (2) +
* Robert Kern (5)
* Agriya Khetarpal (10)
* Andrew Knyazev (7)
* Gideon Genadi Kogan (1) +
* Damien LaRocque (1) +
* Eric Larson (10)
* Gregory R. Lee (4)
* Linfye (1) +
* Boyu Liu (1) +
* Drew Allan Loney (1) +
* Christian Lorentzen (1)
* Smit Lunagariya (1)
* Henry Lunn (1) +
* Marco Maggi (4)
* Lauren Main (1) +
* Martin Spišák (1) +
* Mateusz Sokół (4)
* Jan-Kristian Mathisen (1) +
* Nikolay Mayorov (2)
* Nicholas McKibben (1)
* Melissa Weber Mendonça (62)
* João Mendes (10)
* Gian Marco Messa (1) +
* Samuel Le Meur-Diebolt (1) +
* Michał Górny (2)
* Naoto Mizuno (2)
* Nicolas Mokus (2)
* musvaage (18) +
* Andrew Nelson (88)
* Jens Hedegaard Nielsen (1) +
* Roman Nigmatullin (8) +
* Nick ODell (37)
* Yagiz Olmez (4)
* Matti Picus (9)
* Diogo Pires (5) +
* Ilhan Polat (96)
* Zachary Potthoff (1) +
* Tom M. Ragonneau (2)
* Peter Ralph (1) +
* Stephan Rave (1) +
* Tyler Reddy (131)
* redha2404 (2) +
* Ritvik1sharma (1) +
* Érico Nogueira Rolim (1) +
* Heshy Roskes (1)
* Pamphile Roy (34)
* Mikhail Ryazanov (1) +
* Sina Saber (1) +
* Atsushi Sakai (1)
* Clemens Schmid (1) +
* Daniel Schmitz (15)
* Moritz Schreiber (1) +
* Dan Schult (88)
* Searchingdays (1) +
* Matias Senger (1) +
* Scott Shambaugh (1)
* Zhida Shang (1) +
* Sheila-nk (4)
* Romain Simon (2) +
* Gagandeep Singh (31)
* Albert Steppi (36)
* Kai Striega (1)
* Anushka Suyal (143) +
* Alex Szatmary (1)
* Svetlin Tassev (1) +
* Ewout ter Hoeven (1)
* Tibor Völcker (4) +
* Kanishk Tiwari (1) +
* Yusuke Toyama (1) +
* Edgar Andrés Margffoy Tuay (124)
* Adam Turner (2) +
* Nicole Vadot (1) +
* Andrew Valentine (1)
* Christian Veenhuis (2)
* vfdev (2) +
* Pauli Virtanen (2)
* Simon Waldherr (1) +
* Stefan van der Walt (2)
* Warren Weckesser (23)
* Anreas Weh (1)
* Benoît Wygas (2) +
* Pavadol Yamsiri (3) +
* ysard (1) +
* Xiao Yuan (2)
* Irwin Zaid (12)
* Gang Zhao (1)
* ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (10)

A total of 148 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.

1.15.0

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
1.15.x branch, and on adding new features on the main branch.

This release requires Python `3.10-3.13` and NumPy `1.23.5` or greater.


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

- Sparse arrays are now fully functional for 1-D and 2-D arrays. We recommend
that all new code use sparse arrays instead of sparse matrices and that
developers start to migrate their existing code from sparse matrix to sparse
array: [ `migration_to_sparray`](https://scipy.github.io/devdocs/reference/sparse.migration_to_sparray.html). Both ``sparse.linalg`` and ``sparse.csgraph``
work with either sparse matrix or sparse array and work internally with
sparse array.
- Sparse arrays now provide basic support for n-D arrays in the COO format
including ``add``, ``subtract``, ``reshape``, ``transpose``, ``matmul``,
``dot``, ``tensordot`` and others. More functionality is coming in future
releases.
- Preliminary support for free-threaded Python 3.13.
- New probability distribution features in `scipy.stats` can be used to improve
the speed and accuracy of existing continuous distributions and perform new
probability calculations.
- `scipy.differentiate` is a new top-level submodule for accurate
estimation of derivatives of black box functions.
- `scipy.optimize.elementwise` provides vectorized root-finding and
minimization of univariate functions, and it supports the array API
as do new ``integrate`` functions ``tanhsinh``, ``nsum``, and ``cubature``.
- `scipy.interpolate.AAA` adds the AAA algorithm for barycentric rational
approximation of real or complex functions.



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

`scipy.differentiate` introduction
=========================
The new `scipy.differentiate` sub-package contains functions for accurate
estimation of derivatives of black box functions.

* Use `scipy.differentiate.derivative` for first-order derivatives of
scalar-in, scalar-out functions.
* Use `scipy.differentiate.jacobian` for first-order partial derivatives of
vector-in, vector-out functions.
* Use `scipy.differentiate.hessian` for second-order partial derivatives of
vector-in, scalar-out functions.

All functions use high-order finite difference rules with adaptive (real)
step size. To facilitate batch computation, these functions are vectorized
and support several Array API compatible array libraries in addition to NumPy
(see "Array API Standard Support" below).

`scipy.integrate` improvements
========================
- The ``QUADPACK`` Fortran77 package has been ported to C.
- `scipy.integrate.lebedev_rule` computes abscissae and weights for
integration over the surface of a sphere.
- `scipy.integrate.nsum` evaluates finite and infinite series and their
logarithms.
- `scipy.integrate.tanhsinh` is now exposed for public use, allowing
evaluation of a convergent integral using tanh-sinh quadrature.
- The new `scipy.integrate.cubature` function supports multidimensional
integration, and has support for approximating integrals with
one or more sets of infinite limits.


`scipy.interpolate` improvements
===========================
- `scipy.interpolate.AAA` adds the AAA algorithm for barycentric rational
approximation of real or complex functions.
- `scipy.interpolate.FloaterHormannInterpolator` adds barycentric rational
interpolation.
- New functions `scipy.interpolate.make_splrep` and
`scipy.interpolate.make_splprep` implement construction of smoothing splines.
The algorithmic content is equivalent to FITPACK (``splrep`` and ``splprep``
functions, and ``*UnivariateSpline`` classes) and the user API is consistent
with ``make_interp_spline``: these functions receive data arrays and return
a `scipy.interpolate.BSpline` instance.
- New generator function `scipy.interpolate.generate_knots` implements the
FITPACK strategy for selecting knots of a smoothing spline given the
smoothness parameter, ``s``. The function exposes the internal logic of knot
selection that ``splrep`` and ``*UnivariateSpline`` was using.


`scipy.linalg` improvements
======================
- `scipy.linalg.interpolative` Fortran77 code has been ported to Cython.
- `scipy.linalg.solve` supports several new values for the ``assume_a``
argument, enabling faster computation for diagonal, tri-diagonal, banded, and
triangular matrices. Also, when ``assume_a`` is left unspecified, the
function now automatically detects and exploits diagonal, tri-diagonal,
and triangular structures.
- `scipy.linalg` matrix creation functions (`scipy.linalg.circulant`,
`scipy.linalg.companion`, `scipy.linalg.convolution_matrix`,
`scipy.linalg.fiedler`, `scipy.linalg.fiedler_companion`, and
`scipy.linalg.leslie`) now support batch
matrix creation.
- `scipy.linalg.funm` is faster.
- `scipy.linalg.orthogonal_procrustes` now supports complex input.
- Wrappers for the following LAPACK routines have been added in
`scipy.linalg.lapack`: ``?lantr``, ``?sytrs``, ``?hetrs``, ``?trcon``,
and ``?gtcon``.
- `scipy.linalg.expm` was rewritten in C.
- `scipy.linalg.null_space` now accepts the new arguments ``overwrite_a``,
``check_finite``, and ``lapack_driver``.
- ``id_dist`` Fortran code was rewritten in Cython.


`scipy.ndimage` improvements
========================
- Several additional filtering functions now support an ``axes`` argument
that specifies which axes of the input filtering is to be performed on.
These include ``correlate``, ``convolve``, ``generic_laplace``, ``laplace``,
``gaussian_laplace``, ``derivative2``, ``generic_gradient_magnitude``,
``gaussian_gradient_magnitude`` and ``generic_filter``.
- The binary and grayscale morphology functions now support an ``axes``
argument that specifies which axes of the input filtering is to be performed
on.
- `scipy.ndimage.rank_filter` time complexity has improved from ``n`` to
``log(n)``.



`scipy.optimize` improvements
========================
- The vendored HiGHS library has been upgraded from ``1.4.0`` to ``1.8.0``,
bringing accuracy and performance improvements to solvers.
- The ``MINPACK`` Fortran77 package has been ported to C.
- The ``L-BFGS-B`` Fortran77 package has been ported to C.
- The new `scipy.optimize.elementwise` namespace includes functions
``bracket_root``, ``find_root``, ``bracket_minimum``, and ``find_minimum``
for root-finding and minimization of univariate functions. To facilitate
batch computation, these functions are vectorized and support several
Array API compatible array libraries in addition to NumPy (see
"Array API Standard Support" below). Compared to existing functions (e.g.
`scipy.optimize.root_scalar` and `scipy.optimize.minimize_scalar`),
these functions can offer speedups of over 100x when used with NumPy arrays,
and even greater gains are possible with other Array API Standard compatible
array libraries (e.g. CuPy).
- `scipy.optimize.differential_evolution` now supports more general use of
``workers``, such as passing a map-like callable.
- `scipy.optimize.nnls` was rewritten in Cython.
- ``HessianUpdateStrategy`` now supports ``__matmul__``.


`scipy.signal` improvements
======================
- Add functionality of complex-valued waveforms to ``signal.chirp()``.
- `scipy.signal.lombscargle` has two new arguments, ``weights`` and
``floating_mean``, enabling sample weighting and removal of an unknown
y-offset independently for each frequency. Additionally, the ``normalize``
argument includes a new option to return the complex representation of the
amplitude and phase.
- New function `scipy.signal.envelope` for computation of the envelope of a
real or complex valued signal.


`scipy.sparse` improvements
=======================
- A :ref:`migration guide<migration_to_sparray>` is now available for
moving from sparse.matrix to sparse.array in your code/library.
- Sparse arrays now support indexing for 1-D and 2-D arrays. So, sparse
arrays are now fully functional for 1-D and 2D.
- n-D sparse arrays in COO format can now be constructed, reshaped and used
for basic arithmetic.
- New functions ``sparse.linalg.is_sptriangular`` and
``sparse.linalg.spbandwidth`` mimic the existing dense tools
``linalg.is_triangular`` and ``linalg.bandwidth``.
- ``sparse.linalg`` and ``sparse.csgraph`` now work with sparse arrays. Be
careful that your index arrays are 32-bit. We are working on 64bit support.
- The vendored ``ARPACK`` library has been upgraded to version ``3.9.1``.
- COO, CSR, CSC and LIL formats now support the ``axis`` argument for
``count_nonzero``.
- Sparse arrays and matrices may now raise errors when initialized with
incompatible data types, such as ``float16``.
- ``min``, ``max``, ``argmin``, and ``argmax`` now support computation
over nonzero elements only via the new ``explicit`` argument.
- New functions ``get_index_dtype`` and ``safely_cast_index_arrays`` are
available to facilitate index array casting in ``sparse``.


`scipy.spatial` improvements
======================
- ``Rotation.concatenate`` now accepts a bare ``Rotation`` object, and will
return a copy of it.


`scipy.special` improvements
========================
- The factorial functions ``special.{factorial,factorial2,factorialk}`` now
offer an extension to the complex domain by passing the kwarg
``extend='complex'``. This is opt-in because it changes the values for
negative inputs (which by default return 0), as well as for some integers
(in the case of ``factorial2`` and ``factorialk``; for more details,
check the respective docstrings).
- `scipy.special.zeta` now defines the Riemann zeta function on the complex
plane.
- `scipy.special.softplus` computes the softplus function
- The spherical Bessel functions (`scipy.special.spherical_jn`,
`scipy.special.spherical_yn`, `scipy.special.spherical_in`, and
`scipy.special.spherical_kn`) now support negative arguments with real dtype.
- `scipy.special.logsumexp` now preserves precision when one element of the
sum has magnitude much bigger than the rest.
- The accuracy of several functions has been improved:

- `scipy.special.ncfdtr` and `scipy.special.nctdtr` have been improved
 throughout the domain.
- `scipy.special.hyperu` is improved for the case of ``b=1``, small ``x``,
 and small ``a``.
- `scipy.special.logit` is improved near the argument ``p=0.5``.
- `scipy.special.rel_entr` is improved when ``x/y`` overflows, underflows,
 or is close to ``1``.

- `scipy.special.gdtrib` may now be used in a CuPy ``ElementwiseKernel`` on
GPUs.
- `scipy.special.ndtr` is now more efficient.

`scipy.stats` improvements
=====================
- A new probability distribution infrastructure has been added for the
implementation of univariate, continuous distributions with speed,
accuracy, and memory advantages:

- `scipy.stats.Normal` represents the normal distribution with the new
 interface. In typical cases, its methods are faster and more accurate than
 those of `scipy.stats.norm`.
- Use `scipy.stats.make_distribution` to treat an existing continuous
 distribution (e.g. `scipy.stats.norm`) with the new infrastructure.
 This can improve the speed and accuracy of existing distributions,
 especially for methods not overridden with custom formulas in the
 implementation.

- `scipy.stats.Mixture` has been added to represent mixture distributions.
- Instances of `scipy.stats.Normal` and the classes returned by
`scipy.stats.make_distribution` are supported by several new mathematical
transformations.

- `scipy.stats.truncate` for truncation of the support.
- `scipy.stats.order_statistic` for the order statistics of a given number
 of IID random variables.
- `scipy.stats.abs`, `scipy.stats.exp`, and `scipy.stats.log`. For example,
 ``scipy.stats.abs(Normal())`` is distributed according to the folded normal
 and ``scipy.stats.exp(Normal())`` is lognormally distributed.

- The new `scipy.stats.lmoment` calculates sample l-moments and l-moment
ratios. Notably, these sample estimators are unbiased.
- `scipy.stats.chatterjeexi` computes the Xi correlation coefficient, which
can detect nonlinear dependence. The function also performs a hypothesis
test of independence between samples.
- `scipy.stats.wilcoxon` has improved method resolution logic for the default
``method='auto'``. Other values of ``method`` provided by the user are now
respected in all cases, and the method argument ``approx`` has been
renamed to ``asymptotic`` for consistency with similar functions. (Use of
``approx`` is still allowed for backward compatibility.)
- There are several new probability distributions:

- `scipy.stats.dpareto_lognorm` represents the double Pareto lognormal
 distribution.
- `scipy.stats.landau` represents the Landau distribution.
- `scipy.stats.normal_inverse_gamma` represents the normal-inverse-gamma
 distribution.
- `scipy.stats.poisson_binom` represents the Poisson binomial distribution.

- Batch calculation with `scipy.stats.alexandergovern` and
`scipy.stats.combine_pvalues` is faster.
- `scipy.stats.chisquare` added an argument ``sum_check``. By default, the
function raises an error when the sum of expected and obseved frequencies
are not equal; setting ``sum_check=False`` disables this check to
facilitate hypothesis tests other than Pearson's chi-squared test.
- The accuracy of several distribution methods has been improved, including:

- `scipy.stats.nct` method ``pdf``
- `scipy.stats.crystalball` method ``sf``
- `scipy.stats.geom` method ``rvs``
- `scipy.stats.cauchy` methods ``logpdf``, ``pdf``, ``ppf`` and ``isf``
- The ``logcdf`` and/or ``logsf`` methods of distributions that do not
 override the generic implementation of these methods, including
 `scipy.stats.beta`, `scipy.stats.betaprime`, `scipy.stats.cauchy`,
 `scipy.stats.chi`, `scipy.stats.chi2`, `scipy.stats.exponweib`,
 `scipy.stats.gamma`, `scipy.stats.gompertz`, `scipy.stats.halflogistic`,
 `scipy.stats.hypsecant`, `scipy.stats.invgamma`, `scipy.stats.laplace`,
 `scipy.stats.levy`, `scipy.stats.loggamma`, `scipy.stats.maxwell`,
 `scipy.stats.nakagami`, and `scipy.stats.t`.

- `scipy.stats.qmc.PoissonDisk` now accepts lower and upper bounds
parameters ``l_bounds`` and ``u_bounds``.
- `scipy.stats.fisher_exact` now supports two-dimensional tables with shapes
other than ``(2, 2)``.

Preliminary Support for Free-Threaded CPython 3.13
========================================

1.14.1

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

SciPy `1.14.1` adds support for Python `3.13`, including binary
wheels on PyPI. Apart from that, it is a bug-fix release with
no new features compared to `1.14.0`.



Authors
=======
* Name (commits)
* h-vetinari (1)
* Evgeni Burovski (1)
* CJ Carey (2)
* Lucas Colley (3)
* Ralf Gommers (3)
* Melissa Weber Mendonça (1)
* Andrew Nelson (3)
* Nick ODell (1)
* Tyler Reddy (36)
* Daniel Schmitz (1)
* Dan Schult (4)
* Albert Steppi (2)
* Ewout ter Hoeven (1)
* Tibor Völcker (2) +
* Adam Turner (1) +
* Warren Weckesser (2)
* ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (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.

1.14.0

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
1.14.x branch, and on adding new features on the main branch.

This release requires Python `3.10+` and NumPy `1.23.5` or greater.

For running on PyPy, PyPy3 6.0+ is required.


Highlights of this release
===================
- SciPy now supports the new Accelerate library introduced in macOS 13.3, and
has wheels built against Accelerate for macOS >=14 resulting in significant
performance improvements for many linear algebra operations.
- A new method, ``cobyqa``, has been added to `scipy.optimize.minimize` - this
is an interface for COBYQA (Constrained Optimization BY Quadratic
Approximations), a derivative-free optimization solver, designed to
supersede COBYLA, developed by the Department of Applied Mathematics, The
Hong Kong Polytechnic University.
- `scipy.sparse.linalg.spsolve_triangular` is now more than an order of
magnitude faster in many cases.

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

`scipy.fft` improvements
========================
- A new function, `scipy.fft.prev_fast_len`, has been added. This function
finds the largest composite of FFT radices that is less than the target
length. It is useful for discarding a minimal number of samples before FFT.

`scipy.io` improvements
=======================
- ``wavfile`` now supports reading and writing of ``wav`` files in the RF64
format, allowing files greater than 4 GB in size to be handled.

`scipy.constants` improvements
==============================
- Experimental support for the array API standard has been added.


`scipy.interpolate` improvements
================================
- `scipy.interpolate.Akima1DInterpolator` now supports extrapolation via the
``extrapolate`` argument.

`scipy.optimize` improvements
=============================
- `scipy.optimize.HessianUpdateStrategy` now also accepts square arrays for
``init_scale``.
- A new method, ``cobyqa``, has been added to `scipy.optimize.minimize` - this
is an interface for COBYQA (Constrained Optimization BY Quadratic
Approximations), a derivative-free optimization solver, designed to
supersede COBYLA, developed by the Department of Applied Mathematics, The
Hong Kong Polytechnic University.
- There are some performance improvements in
`scipy.optimize.differential_evolution`.
- `scipy.optimize.approx_fprime` now has linear space complexity.


`scipy.signal` improvements
===========================
- `scipy.signal.minimum_phase` has a new argument ``half``, allowing the
provision of a filter of the same length as the linear-phase FIR filter
coefficients and with the same magnitude spectrum.


`scipy.sparse` improvements
===========================
- A special case has been added to handle multiplying a ``dia_array`` by a
scalar, which avoids a potentially costly conversion to CSR format.
- `scipy.sparse.csgraph.yen` has been added, allowing usage of Yen's K-Shortest
Paths algorithm on a directed on undirected graph.
- Addition between DIA-format sparse arrays and matrices is now faster.
- `scipy.sparse.linalg.spsolve_triangular` is now more than an order of
magnitude faster in many cases.


`scipy.spatial` improvements
============================
- ``Rotation`` supports an alternative "scalar-first" convention of quaternion
component ordering. It is available via the keyword argument ``scalar_first``
of ``from_quat`` and ``as_quat`` methods.
- Some minor performance improvements for inverting of ``Rotation`` objects.

`scipy.special` improvements
============================
- Added `scipy.special.log_wright_bessel`, for calculation of the logarithm of
Wright's Bessel function.
- The relative error in `scipy.special.hyp2f1` calculations has improved
substantially.
- Improved behavior of ``boxcox``, ``inv_boxcox``, ``boxcox1p``, and
``inv_boxcox1p`` by preventing premature overflow.


`scipy.stats` improvements
==========================
- A new function `scipy.stats.power` can be used for simulating the power
of a hypothesis test with respect to a specified alternative.
- The Irwin-Hall (AKA Uniform Sum) distribution has been added as
`scipy.stats.irwinhall`.
- Exact p-value calculations of `scipy.stats.mannwhitneyu` are much faster
and use less memory.
- `scipy.stats.pearsonr` now accepts n-D arrays and computes the statistic
along a specified ``axis``.
- `scipy.stats.kstat`, `scipy.stats.kstatvar`, and `scipy.stats.bartlett`
are faster at performing calculations along an axis of a large n-D array.



Array API Standard Support
=====================
*Experimental* support for array libraries other than NumPy has been added to
existing sub-packages in recent versions of SciPy. Please consider testing
these features by setting an environment variable ``SCIPY_ARRAY_API=1`` and
providing PyTorch, JAX, or CuPy arrays as array arguments.

As of 1.14.0, there is support for

- `scipy.cluster`
- `scipy.fft`
- `scipy.constants`
- `scipy.special`: (select functions)

- `scipy.special.log_ndtr`
- `scipy.special.ndtr`
- `scipy.special.ndtri`
- `scipy.special.erf`
- `scipy.special.erfc`
- `scipy.special.i0`
- `scipy.special.i0e`
- `scipy.special.i1`
- `scipy.special.i1e`
- `scipy.special.gammaln`
- `scipy.special.gammainc`
- `scipy.special.gammaincc`
- `scipy.special.logit`
- `scipy.special.expit`
- `scipy.special.entr`
- `scipy.special.rel_entr`
- `scipy.special.xlogy`
- `scipy.special.chdtrc`

- `scipy.stats`: (select functions)

- `scipy.stats.moment`
- `scipy.stats.skew`
- `scipy.stats.kurtosis`
- `scipy.stats.kstat`
- `scipy.stats.kstatvar`
- `scipy.stats.circmean`
- `scipy.stats.circvar`
- `scipy.stats.circstd`
- `scipy.stats.entropy`
- `scipy.stats.variation`
- `scipy.stats.sem`
- `scipy.stats.ttest_1samp`
- `scipy.stats.pearsonr`
- `scipy.stats.chisquare`
- `scipy.stats.skewtest`
- `scipy.stats.kurtosistest`
- `scipy.stats.normaltest`
- `scipy.stats.jarque_bera`
- `scipy.stats.bartlett`
- `scipy.stats.power_divergence`
- `scipy.stats.monte_carlo_test`


Deprecated features
===============
- `scipy.stats.gstd`, `scipy.stats.chisquare`, and
`scipy.stats.power_divergence` have deprecated support for masked array
input.
- `scipy.stats.linregress` has deprecated support for specifying both samples
in one argument; ``x`` and ``y`` are to be provided as separate arguments.
- The ``conjtransp`` method for `scipy.sparse.dok_array` and
`scipy.sparse.dok_matrix` has been deprecated and will be removed in SciPy
1.16.0.
- The option ``quadrature="trapz"`` in `scipy.integrate.quad_vec` has been
deprecated in favour of ``quadrature="trapezoid"`` and will be removed in
SciPy 1.16.0.
- `scipy.special.comb` has deprecated support for use of ``exact=True`` in
conjunction with non-integral ``N`` and/or ``k``.


Backwards incompatible changes
=========================
- Many `scipy.stats` functions now produce a standardized warning message when
an input sample is too small (e.g. zero size). Previously, these functions
may have raised an error, emitted one or more less informative warnings, or
emitted no warnings. In most cases, returned results are unchanged; in almost
all cases the correct result is ``NaN``.

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

- Several previously deprecated methods for sparse arrays were removed:
``asfptype``, ``getrow``, ``getcol``, ``get_shape``, ``getmaxprint``,
``set_shape``, ``getnnz``, and ``getformat``. Additionally, the ``.A`` and
``.H`` attributes were removed.
- ``scipy.integrate.{simps,trapz,cumtrapz}`` have been removed in favour of
``simpson``, ``trapezoid``, and ``cumulative_trapezoid``.
- The ``tol`` argument of ``scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk,
mres,lgmres,minres,qmr,tfqmr}`` has been removed in favour of ``rtol``.
Furthermore, the default value of ``atol`` for these functions has changed
to ``0.0``.
- The ``restrt`` argument of `scipy.sparse.linalg.gmres` has been removed in
favour of ``restart``.
- The ``initial_lexsort`` argument of `scipy.stats.kendalltau` has been
removed.
- The ``cond`` and ``rcond`` arguments of `scipy.linalg.pinv` have been
removed.
- The ``even`` argument of `scipy.integrate.simpson` has been removed.
- The ``turbo`` and ``eigvals`` arguments from ``scipy.linalg.{eigh,eigvalsh}``
have been removed.
- The ``legacy`` argument of `scipy.special.comb` has been removed.
- The ``hz``/``nyq`` argument of ``signal.{firls, firwin, firwin2, remez}`` has
been removed.
- Objects that weren't part of the public interface but were accessible through
deprecated submodules have been removed.
- ``float128``, ``float96``, and object arrays now raise an error in
`scipy.signal.medfilt` and `scipy.signal.order_filter`.
- ``scipy.interpolate.interp2d`` has been replaced by an empty stub (to be
removed completely in the future).
- Coinciding with changes to function signatures (e.g. removal of a deprecated
keyword), we had deprecated positional use of keyword arguments for the
affected functions, which will now raise an error. Affected functions are:

- ``sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres,
 qmr, tfqmr}``
- ``stats.kendalltau``
- ``linalg.pinv``
- ``integrate.simpson``
- ``linalg.{eigh,eigvalsh}``
- ``special.comb``
- ``signal.{firls, firwin, firwin2, remez}``



Other changes
===========
- SciPy now uses C17 as the C standard to build with, instead of C99. The C++
standard remains C++17.
- macOS Accelerate, which got a major upgrade in macOS 13.3, is now supported.
This results in significant performance improvements for linear algebra
operations, as well as smaller binary wheels.
- Cross-compilation should be smoother and QEMU or similar is no longer needed
to run the cross interpreter.
- Experimental array API support for the JAX backend has been added to several
parts of SciPy.



Authors
======
* Name (commits)
* h-vetinari (30)
* Steven Adams (1) +
* Max Aehle (1) +
* Ataf Fazledin Ahamed (2) +
* Trinh Quoc Anh (1) +
* Miguel A. Batalla (7) +
* Tim Beyer (1) +
* Andrea Blengino (1) +
* boatwrong (1)
* Jake Bowhay (47)
* Dietrich Brunn (2)
* Evgeni Burovski (174)
* Tim Butters (7) +
* CJ Carey (5)
* Sean Cheah (46)
* Lucas Colley (72)
* Giuseppe "Peppe" Dilillo (1) +
* DWesl (2)
* Pieter Eendebak (5)
* Kenji S Emerson (1) +
* Jonas Eschle (1)
* fancidev (2)
* Anthony Frazier (1) +
* Ilan Gold (1) +
* Ralf Gommers (122)
* Rohit Goswami (28)
* Ben Greiner (1) +
* Lorenzo Gualniera (1) +
* Matt Haberland (250)
* Shawn Hsu (1) +
* Budjen Jovan (3) +
* Jozsef Kutas (1)
* Eric Larson (3)
* Gregory R. Lee (4)
* Philip Loche (1) +
* Christian Lorentzen (5)
* Sijo Valayakkad Manikandan (2) +
* marinelay (2) +
* Nikolay Mayorov (1)
* Nicholas McKibben (2)
* Melissa Weber Mendonça (6)
* João Mendes (1) +
* Tomiță Militaru (2) +
* Andrew Nelson (32)
* Lysandros Nikolaou (1)
* Nick ODell (5) +
* Jacob Ogle (1) +
* Pearu Peterson (1)
* Matti Picus (4)
* Ilhan Polat (8)
* pwcnorthrop (3) +
* Bharat Raghunathan (1)
* Tom M. Ragonneau (2) +
* Tyler Reddy (47)
* Pamphile Roy (17)
* Atsushi Sakai (9)
* Daniel Schmitz (5)
* Julien Schueller (2) +
* Dan Schult (12)
* Tomer Sery (7)
* Scott Shambaugh (4)
* Tuhin Sharma (1) +
* Sheila-nk (4)
* Skylake (1) +
* Albert Steppi (214)
* Kai Striega (6)
* Zhibing Sun (2) +
* Nimish Telang (1) +
* toofooboo (1) +
* tpl2go (1) +
* Edgar Andrés Margffoy Tuay (44)
* Valerix (1) +
* Christian Veenhuis (1)
* void (2) +
* Warren Weckesser (3)
* Xuefeng Xu (1)
* Rory Yorke (1)
* Xiao Yuan (1)
* Irwin Zaid (35)
* Elmar Zander (1) +
* ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (2) +

A total of 81 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.

1.13.1

compared to `1.13.0`. The version of OpenBLAS shipped with
the PyPI binaries has been increased to `0.3.27`.


Authors
=======
* Name (commits)
* h-vetinari (1)
* Jake Bowhay (2)
* Evgeni Burovski (6)
* Sean Cheah (2)
* Lucas Colley (2)
* DWesl (2)
* Ralf Gommers (7)
* Ben Greiner (1) +
* Matt Haberland (2)
* Gregory R. Lee (1)
* Philip Loche (1) +
* Sijo Valayakkad Manikandan (1) +
* Matti Picus (1)
* Tyler Reddy (62)
* Atsushi Sakai (1)
* Daniel Schmitz (2)
* Dan Schult (3)
* Scott Shambaugh (2)
* Edgar Andrés Margffoy Tuay (1)

A total of 19 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.

1.13.0

out-of-band release aims to support NumPy ``2.0.0``, and is backwards
compatible to NumPy ``1.22.4``. The version of OpenBLAS used to build
the PyPI wheels has been increased to ``0.3.26``.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.


Highlights of this release
===================
- Support for NumPy ``2.0.0``.
- Interactive examples have been added to the documentation, allowing users
to run the examples locally on embedded Jupyterlite notebooks in their
browser.
- Preliminary 1D array support for the COO and DOK sparse formats.
- Several `scipy.stats` functions have gained support for additional
``axis``, ``nan_policy``, and ``keepdims`` arguments. `scipy.stats` also
has several performance and accuracy improvements.


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

`scipy.integrate` improvements
==============================
- The ``terminal`` attribute of `scipy.integrate.solve_ivp` ``events``
callables now additionally accepts integer values to specify a number
of occurrences required for termination, rather than the previous restriction
of only accepting a ``bool`` value to terminate on the first registered
event.


`scipy.io` improvements
=======================
- `scipy.io.wavfile.write` has improved ``dtype`` input validation.


`scipy.interpolate` improvements
================================
- The Modified Akima Interpolation has been added to
``interpolate.Akima1DInterpolator``, available via the new ``method``
argument.
- ``RegularGridInterpolator`` gained the functionality to compute derivatives
in place. For instance, ``RegularGridInterolator((x, y), values,
method="cubic")(xi, nu=(1, 1))`` evaluates the mixed second derivative,
:math:`\partial^2 / \partial x \partial y` at ``xi``.
- Performance characteristics of tensor-product spline methods of
``RegularGridInterpolator`` have been changed: evaluations should be
significantly faster, while construction might be slower. If you experience
issues with construction times, you may need to experiment with optional
keyword arguments ``solver`` and ``solver_args``. Previous behavior (fast
construction, slow evaluations) can be obtained via `"*_legacy"` methods:
``method="cubic_legacy"`` is exactly equivalent to ``method="cubic"`` in
previous releases. See ``gh-19633`` for details.


`scipy.signal` improvements
===========================
- Many filter design functions now have improved input validation for the
sampling frequency (``fs``).


`scipy.sparse` improvements
===========================
- ``coo_array`` now supports 1D shapes, and has additional 1D support for
``min``, ``max``, ``argmin``, and ``argmax``. The DOK format now has
preliminary 1D support as well, though only supports simple integer indices
at the time of writing.
- Experimental support has been added for ``pydata/sparse`` array inputs to
`scipy.sparse.csgraph`.
- ``dok_array`` and ``dok_matrix`` now have proper implementations of
``fromkeys``.
- ``csr`` and ``csc`` formats now have improved ``setdiag`` performance.


`scipy.spatial` improvements
============================
- ``voronoi_plot_2d`` now draws Voronoi edges to infinity more clearly
when the aspect ratio is skewed.


`scipy.special` improvements
============================
- All Fortran code, namely, ``AMOS``, ``specfun``, and ``cdflib`` libraries
that the majority of special functions depend on, is ported to Cython/C.
- The function ``factorialk`` now also supports faster, approximate
calculation using ``exact=False``.


`scipy.stats` improvements
==========================
- `scipy.stats.rankdata` and `scipy.stats.wilcoxon` have been vectorized,
improving their performance and the performance of hypothesis tests that
depend on them.
- ``stats.mannwhitneyu`` should now be faster due to a vectorized statistic
calculation, improved caching, improved exploitation of symmetry, and a
memory reduction. ``PermutationMethod`` support was also added.
- `scipy.stats.mood` now has ``nan_policy`` and ``keepdims`` support.
- `scipy.stats.brunnermunzel` now has ``axis`` and ``keepdims`` support.
- `scipy.stats.friedmanchisquare`, `scipy.stats.shapiro`,
`scipy.stats.normaltest`, `scipy.stats.skewtest`,
`scipy.stats.kurtosistest`, `scipy.stats.f_oneway`,
`scipy.stats.alexandergovern`, `scipy.stats.combine_pvalues`, and
`scipy.stats.kstest` have gained ``axis``, ``nan_policy`` and
``keepdims`` support.
- `scipy.stats.boxcox_normmax` has gained a ``ymax`` parameter to allow user
specification of the maximum value of the transformed data.
- `scipy.stats.vonmises` ``pdf`` method has been extended to support
``kappa=0``. The ``fit`` method is also more performant due to the use of
non-trivial bounds to solve for ``kappa``.
- High order ``moment`` calculations for `scipy.stats.powerlaw` are now more
accurate.
- The ``fit`` methods of  `scipy.stats.gamma` (with ``method='mm'``) and
`scipy.stats.loglaplace` are faster and more reliable.
- `scipy.stats.goodness_of_fit` now supports the use of a custom ``statistic``
provided by the user.
- `scipy.stats.wilcoxon` now supports ``PermutationMethod``, enabling
calculation of accurate p-values in the presence of ties and zeros.
- `scipy.stats.monte_carlo_test` now has improved robustness in the face of
numerical noise.
- `scipy.stats.wasserstein_distance_nd` was introduced to compute the
Wasserstein-1 distance between two N-D discrete distributions.



Deprecated features
=================
- Complex dtypes in ``PchipInterpolator`` and ``Akima1DInterpolator`` have
been deprecated and will raise an error in SciPy 1.15.0. If you are trying
to use the real components of the passed array, use ``np.real`` on ``y``.




Backwards incompatible changes
=========================


Other changes
===========
- The second argument of `scipy.stats.moment` has been renamed to ``order``
while maintaining backward compatibility.




Authors
======

* Name (commits)
* h-vetinari (50)
* acceptacross (1) +
* Petteri Aimonen (1) +
* Francis Allanah (2) +
* Jonas Kock am Brink (1) +
* anupriyakkumari (12) +
* Aman Atman (2) +
* Aaditya Bansal (1) +
* Christoph Baumgarten (2)
* Sebastian Berg (4)
* Nicolas Bloyet (2) +
* Matt Borland (1)
* Jonas Bosse (1) +
* Jake Bowhay (25)
* Matthew Brett (1)
* Dietrich Brunn (7)
* Evgeni Burovski (48)
* Matthias Bussonnier (4)
* Cale (1) +
* CJ Carey (4)
* Thomas A Caswell (1)
* Sean Cheah (44) +
* Lucas Colley (97)
* com3dian (1)
* Gianluca Detommaso (1) +
* Thomas Duvernay (1)
* DWesl (2)
* f380cedric (1) +
* fancidev (13) +
* Daniel Garcia (1) +
* Lukas Geiger (3)
* Ralf Gommers (139)
* Matt Haberland (79)
* Tessa van der Heiden (2) +
* inky (3) +
* Jannes Münchmeyer (2) +
* Aditya Vidyadhar Kamath (2) +
* Agriya Khetarpal (1) +
* Andrew Landau (1) +
* Eric Larson (7)
* Zhen-Qi Liu (1) +
* Adam Lugowski (4)
* m-maggi (6) +
* Chethin Manage (1) +
* Ben Mares (1)
* Chris Markiewicz (1) +
* Mateusz Sokół (3)
* Daniel McCloy (1) +
* Melissa Weber Mendonça (6)
* Josue Melka (1)
* Michał Górny (4)
* Juan Montesinos (1) +
* Juan F. Montesinos (1) +
* Takumasa Nakamura (1)
* Andrew Nelson (26)
* Praveer Nidamaluri (1)
* Yagiz Olmez (5) +
* Dimitri Papadopoulos Orfanos (1)
* Drew Parsons (1) +
* Tirth Patel (7)
* Matti Picus (3)
* Rambaud Pierrick (1) +
* Ilhan Polat (30)
* Quentin Barthélemy (1)
* Tyler Reddy (81)
* Pamphile Roy (10)
* Atsushi Sakai (4)
* Daniel Schmitz (10)
* Dan Schult (16)
* Eli Schwartz (4)
* Stefanie Senger (1) +
* Scott Shambaugh (2)
* Kevin Sheppard (2)
* sidsrinivasan (4) +
* Samuel St-Jean (1)
* Albert Steppi (30)
* Adam J. Stewart (4)
* Kai Striega (3)
* Ruikang Sun (1) +
* Mike Taves (1)
* Nicolas Tessore (3)
* Benedict T Thekkel (1) +
* Will Tirone (4)
* Jacob Vanderplas (2)
* Christian Veenhuis (1)
* Isaac Virshup (2)
* Ben Wallace (1) +
* Xuefeng Xu (3)
* Xiao Yuan (5)
* Irwin Zaid (6)
* Mathias Zechmeister (1) +

A total of 91 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.

1.12.0

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
`1.12.x` branch, and on adding new features on the main branch.

This release requires Python `3.9+` and NumPy `1.22.4` or greater.

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


Highlights of this release
==================
- Experimental support for the array API standard has been added to part of
`scipy.special`, and to all of `scipy.fft` and `scipy.cluster`. There are
likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
tensors, and other array API compatible libraries is appreciated. Use the
``SCIPY_ARRAY_API`` environment variable for testing.
- A new class, ``ShortTimeFFT``, provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
- Several new constructors have been added for sparse arrays, and many operations
now additionally support sparse arrays, further facilitating the migration
from sparse matrices.
- A large portion of the `scipy.stats` API now has improved support for handling
``NaN`` values, masked arrays, and more fine-grained shape-handling. The
accuracy and performance of a number of ``stats`` methods have been improved,
and a number of new statistical tests and distributions have been added.


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

`scipy.cluster` improvements
======================
- Experimental support added for the array API standard; PyTorch tensors,
CuPy arrays and array API compatible array libraries are now accepted
(GPU support is limited to functions with pure Python implementations).
CPU arrays which can be converted to and from NumPy are supported
module-wide and returned arrays will match the input type.
This behaviour is enabled by setting the ``SCIPY_ARRAY_API`` environment
variable before importing ``scipy``. This experimental support is still
under development and likely to contain bugs - testing is very welcome.


`scipy.fft` improvements
===================
- Experimental support added for the array API standard; functions which are
part of the ``fft`` array API standard extension module, as well as the 
Fast Hankel Transforms and the basic FFTs which are not in the extension
module, now accept PyTorch tensors, CuPy arrays and array API compatible
array libraries. CPU arrays which can be converted to and from NumPy arrays
are supported module-wide and returned arrays will match the input type.
This behaviour is enabled by setting the ``SCIPY_ARRAY_API`` environment
variable before importing ``scipy``. This experimental support is still under
development and likely to contain bugs - testing is very welcome.

`scipy.integrate` improvements
========================
- Added `scipy.integrate.cumulative_simpson` for cumulative quadrature
from sampled data using Simpson's 1/3 rule.

`scipy.interpolate` improvements
=========================
- New class ``NdBSpline`` represents tensor-product splines in N dimensions.
This class only knows how to evaluate a tensor product given coefficients
and knot vectors. This way it generalizes ``BSpline`` for 1D data to N-D, and
parallels ``NdPPoly`` (which represents N-D tensor product polynomials).
Evaluations exploit the localized nature of b-splines.
- ``NearestNDInterpolator.__call__`` accepts ``**query_options``, which are
passed through to the ``KDTree.query`` call to find nearest neighbors. This
allows, for instance, to limit the neighbor search distance and parallelize
the query using the ``workers`` keyword.
- ``BarycentricInterpolator`` now allows computing the derivatives.
- It is now possible to change interpolation values in an existing
``CloughTocher2DInterpolator`` instance, while also saving the barycentric
coordinates of interpolation points.

`scipy.linalg` improvements
=====================
- Access to new low-level LAPACK functions is provided via ``dtgsyl`` and
``stgsyl``.

`scipy.ndimage` improvements
=======================


`scipy.optimize` improvements
=======================
- `scipy.optimize.nnls` is rewritten in Python and now implements the so-called
fnnls or fast nnls.
- The result object of `scipy.optimize.root` and `scipy.optimize.root_scalar`
now reports the method used.
- The ``callback`` method of `scipy.optimize.differential_evolution` can now be
passed more detailed information via the ``intermediate_results`` keyword
parameter. Also, the evolution ``strategy`` now accepts a callable for
additional customization. The performance of ``differential_evolution`` has
also been improved.
- ``minimize`` method ``Newton-CG`` has been made slightly more efficient.
- ``minimize`` method ``BFGS`` now accepts an initial estimate for the inverse
of the Hessian, which allows for more efficient workflows in some
circumstances. The new parameter is ``hess_inv0``.
- ``minimize`` methods ``CG``, ``Newton-CG``, and ``BFGS`` now accept parameters
``c1`` and ``c2``, allowing specification of the Armijo and curvature rule
parameters, respectively.
- ``curve_fit`` performance has improved due to more efficient memoization
of the callable function.
- ``isotonic_regression`` has been added to allow nonparametric isotonic
regression.

`scipy.signal` improvements
=====================
- ``freqz``, ``freqz_zpk``, and ``group_delay`` are now more accurate
when ``fs`` has a default value.
- The new class ``ShortTimeFFT`` provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
dual windows and provides more fine-grained control of the parametrization especially
in regard to scaling and phase-shift. Functionality was implemented to ease
working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
providing algorithmic details. The functions ``stft``, ``istft`` and ``spectrogram``
have been marked as legacy.

`scipy.sparse` improvements
======================
- ``sparse.linalg`` iterative solvers ``sparse.linalg.cg``,
``sparse.linalg.cgs``, ``sparse.linalg.bicg``, ``sparse.linalg.bicgstab``,
``sparse.linalg.gmres``, and ``sparse.linalg.qmr`` are rewritten in Python.
- Updated vendored SuperLU version to ``6.0.1``, along with a few additional
fixes.
- Sparse arrays have gained additional constructors: ``eye_array``,
``random_array``, ``block_array``, and ``identity``. ``kron`` and ``kronsum``
have been adjusted to additionally support operation on sparse arrays.
- Sparse matrices now support a transpose with ``axes=(1, 0)``, to mirror
the ``.T``  method.
- ``LaplacianNd`` now allows selection of the largest subset of eigenvalues,
and additionally now supports retrieval of the corresponding eigenvectors.
The performance of ``LaplacianNd`` has also been improved.
- The performance of ``dok_matrix`` and ``dok_array`` has been improved,
and their inheritance behavior should be more robust.
- ``hstack``, ``vstack``, and ``block_diag`` now work with sparse arrays, and
preserve the input sparse type.
- A new function, `scipy.sparse.linalg.matrix_power`, has been added, allowing
for exponentiation of sparse arrays.


`scipy.spatial` improvements
======================
- Two new methods were implemented for ``spatial.transform.Rotation``:
``__pow__`` to raise a rotation to integer or fractional power and
``approx_equal`` to check if two rotations are approximately equal.
- The method ``Rotation.align_vectors`` was extended to solve a constrained
alignment problem where two vectors are required to be aligned precisely.
Also when given a single pair of vectors, the algorithm now returns the
rotation with minimal magnitude, which can be considered as a minor
backward incompatible change.
- A new representation for ``spatial.transform.Rotation`` called Davenport
angles is available through ``from_davenport`` and ``as_davenport`` methods.
- Performance improvements have been added to ``distance.hamming`` and
``distance.correlation``.
- Improved performance of ``SphericalVoronoi`` ``sort_vertices_of_regions``
and two dimensional area calculations.

`scipy.special` improvements
======================
- Added `scipy.special.stirling2` for computation of Stirling numbers of the
second kind. Both exact calculation and an asymptotic approximation
(the default) are supported via ``exact=True`` and ``exact=False`` (the
default) respectively.
-  Added `scipy.special.betaincc` for computation of the complementary incomplete Beta function and `scipy.special.betainccinv` for computation of its inverse.
- Improved precision of `scipy.special.betainc` and `scipy.special.betaincinv`
- Experimental support added for alternative backends: functions
`scipy.special.log_ndtr`, `scipy.special.ndtr`, `scipy.special.ndtri`, 
`scipy.special.erf`, `scipy.special.erfc`, `scipy.special.i0`, 
`scipy.special.i0e`, `scipy.special.i1`, `scipy.special.i1e`, 
`scipy.special.gammaln`, `scipy.special.gammainc`, `scipy.special.gammaincc`,
`scipy.special.logit`, and `scipy.special.expit` now accept PyTorch tensors
and CuPy arrays. These features are still under development and likely to 
contain bugs, so they are disabled by default; enable them by setting a 
``SCIPY_ARRAY_API``  environment variable to ``1`` before importing ``scipy``. 
Testing is appreciated!


`scipy.stats` improvements
=====================
- Added `scipy.stats.quantile_test`, a nonparametric test of whether a
hypothesized value is the quantile associated with a specified probability.
The ``confidence_interval`` method of the result object gives a confidence
interval of the quantile.
- `scipy.stats.wasserstein_distance` now computes the Wasserstein distance
in the multidimensional case.
- `scipy.stats.sampling.FastGeneratorInversion` provides a convenient
interface to fast random sampling via numerical inversion of distribution
CDFs.
- `scipy.stats.geometric_discrepancy` adds geometric/topological discrepancy
metrics for random samples.
- `scipy.stats.multivariate_normal` now has a ``fit`` method for fitting
distribution parameters to data via maximum likelihood estimation.
- `scipy.stats.bws_test` performs the Baumgartner-Weiss-Schindler test of
whether two-samples were drawn from the same distribution.
- `scipy.stats.jf_skew_t` implements the Jones and Faddy skew-t distribution.
- `scipy.stats.anderson_ksamp` now supports a permutation version of the test
using the ``method`` parameter.
- The ``fit`` methods of `scipy.stats.halfcauchy`, `scipy.stats.halflogistic`, and
`scipy.stats.halfnorm` are faster and more accurate.
- `scipy.stats.beta` ``entropy`` accuracy has been improved for extreme values of
distribution parameters.
- The accuracy of ``sf`` and/or ``isf`` methods have been improved for
several distributions: `scipy.stats.burr`, `scipy.stats.hypsecant`,
`scipy.stats.kappa3`, `scipy.stats.loglaplace`, `scipy.stats.lognorm`,
`scipy.stats.lomax`, `scipy.stats.pearson3`, `scipy.stats.rdist`, and
`scipy.stats.pareto`.
- The following functions now support parameters ``axis``, ``nan_policy``, and ``keep_dims``: `scipy.stats.entropy`, `scipy.stats.differential_entropy`, `scipy.stats.variation`, `scipy.stats.ansari`, `scipy.stats.bartlett`, `scipy.stats.levene`, `scipy.stats.fligner`, `scipy.stats.cirmean, `scipy.stats.circvar`, `scipy.stats.circstd`, `scipy.stats.tmean`, `scipy.stats.tvar`, `scipy.stats.tstd`, `scipy.stats.tmin`, `scipy.stats.tmax`, and `scipy.stats.tsem`.
- The ``logpdf`` and ``fit`` methods of `scipy.stats.skewnorm` have been improved.
- The beta negative binomial distribution is implemented as `scipy.stats.betanbinom`.
- The speed of `scipy.stats.invwishart` ``rvs`` and ``logpdf`` have been improved.
- A source of intermediate overflow in `scipy.stats.boxcox_normmax` with ``method='mle'`` has been eliminated, and the returned value of ``lmbda`` is constrained such that the transformed data will not overflow.
- `scipy.stats.nakagami` ``stats`` is more accurate and reliable.
- A source of intermediate overflow in `scipy.norminvgauss.pdf` has been eliminated.
- Added support for masked arrays to ``stats.circmean``, ``stats.circvar``,
``stats.circstd``, and ``stats.entropy``.
- ``dirichlet`` has gained a new covariance (``cov``) method.
- Improved accuracy of ``multivariate_t`` entropy with large degrees of
freedom.
- ``loggamma`` has an improved ``entropy`` method.



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

- Error messages have been made clearer for objects that don't exist in the
public namespace and warnings sharpened for private attributes that are not
supposed to be imported at all.
- `scipy.signal.cmplx_sort` has been deprecated and will be removed in
SciPy 1.14. A replacement you can use is provided in the deprecation message.
- Values the the argument ``initial`` of `scipy.integrate.cumulative_trapezoid`
other than ``0`` and ``None`` are now deprecated.
- `scipy.stats.rvs_ratio_uniforms` is deprecated in favour of
`scipy.stats.sampling.RatioUniforms`
- `scipy.integrate.quadrature` and `scipy.integrate.romberg` have been
deprecated due to accuracy issues and interface shortcomings. They will
be removed in SciPy 1.14. Please use `scipy.integrate.quad` instead.
- Coinciding with upcoming changes to function signatures (e.g. removal of a
deprecated keyword), we are deprecating positional use of keyword arguments
for the affected functions, which will raise an error starting with
SciPy 1.14. In some cases, this has delayed the originally announced
removal date, to give time to respond to the second part of the deprecation.
Affected functions are: 

- ``linalg.{eigh, eigvalsh, pinv}``