Update scipy to 1.14.1

[pyup-bot]

This PR updates scipy from 1.8.0 to 1.14.1.

Changelog

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

- All wavelet functions have been deprecated, as PyWavelets provides suitable
implementations; affected functions are: ``signal.{daub, qmf, cascade,
morlet, morlet2, ricker, cwt}``


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

- The ``centered`` keyword of `stats.qmc.LatinHypercube` has been removed.
Use ``scrambled=False`` instead of ``centered=True``.


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


Other changes
===========
- The arguments used to compile and link SciPy are now available via
``show_config``.



Authors
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A total of 161 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.

1.11.4

compared to `1.11.3`.

Authors
=======
* Name (commits)
* Jake Bowhay (2)
* Ralf Gommers (4)
* Julien Jerphanion (2)
* Nikolay Mayorov (2)
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* 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
=======
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* Anirudh Dagar (2)
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* Ellie Litwack (2)
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* 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.
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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.

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=======
* Name (commits)
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A total of 18 people contributed to this release.
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1.11.1

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


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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.qu