Update scipy to 1.9.1

[pyup-bot]

This PR updates scipy from 1.0.0 to 1.9.1.

Changelog

1.9.0

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

This release requires Python `3.8+` and NumPy `1.18.5` or greater.

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


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

- We have modernized our build system to use ``meson``, substantially reducing
our source build times
- Added `scipy.optimize.milp`, new function for mixed-integer linear
programming.
- Added `scipy.stats.fit` for fitting discrete and continuous distributions
to data.
- Tensor-product spline interpolation modes were added to
`scipy.interpolate.RegularGridInterpolator`.
- A new global optimizer (DIviding RECTangles algorithm)
`scipy.optimize.direct`



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


`scipy.interpolate` improvements
================================
- Speed up the ``RBFInterpolator`` evaluation with high dimensional
interpolants.
- Added new spline based interpolation methods for
`scipy.interpolate.RegularGridInterpolator` and its tutorial.
- `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn`
now accept descending ordered points.
- ``RegularGridInterpolator`` now handles length-1 grid axes.
- The ``BivariateSpline`` subclasses have a new method ``partial_derivative``
which constructs a new spline object representing a derivative of an
original spline. This mirrors the corresponding functionality for univariate
splines, ``splder`` and ``BSpline.derivative``, and can substantially speed
up repeated evaluation of derivatives.

`scipy.linalg` improvements
===========================
- `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved.
- Minimum required LAPACK version is bumped to ``3.7.1``.


`scipy.fft` improvements
========================
- Added ``uarray`` multimethods for `scipy.fft.fht` and `scipy.fft.ifht`
to allow provision of third party backend implementations such as those
recently added to CuPy.

`scipy.optimize` improvements
=============================
- A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm)
was added. For problems with inexpensive function evaluations, like the ones
in the SciPy benchmark suite, ``direct`` is competitive with the best other
solvers in SciPy (``dual_annealing`` and ``differential_evolution``) in terms
of execution time. See
`gh-14300 <https://github.com/scipy/scipy/pull/14300>`__ for more details.

- Add a ``full_output`` parameter to `scipy.optimize.curve_fit` to output
additional solution information.
- Add a ``integrality`` parameter to `scipy.optimize.differential_evolution`,
enabling integer constraints on parameters.
- Add a ``vectorized`` parameter to call a vectorized objective function only
once per iteration. This can improve minimization speed by reducing
interpreter overhead from the multiple objective function calls.
- The default method of `scipy.optimize.linprog` is now ``'highs'``.
- Added `scipy.optimize.milp`, new function for mixed-integer linear
programming.
- Added Newton-TFQMR method to ``newton_krylov``.
- Added support for the ``Bounds`` class in ``shgo`` and ``dual_annealing`` for
a more uniform API across `scipy.optimize`.
- Added the ``vectorized`` keyword to ``differential_evolution``.
- ``approx_fprime`` now works with vector-valued functions.

`scipy.signal` improvements
===========================
- The new window function `scipy.signal.windows.kaiser_bessel_derived` was
added to compute the Kaiser-Bessel derived window.
- Single-precision ``hilbert`` operations are now faster as a result of more
consistent ``dtype`` handling.

`scipy.sparse` improvements
===========================
- Add a ``copy`` parameter to `scipy.sparce.csgraph.laplacian`. Using inplace
computation with ``copy=False`` reduces the memory footprint.
- Add a ``dtype`` parameter to `scipy.sparce.csgraph.laplacian` for type casting.
- Add a ``symmetrized`` parameter to `scipy.sparce.csgraph.laplacian` to produce
symmetric Laplacian for directed graphs.
- Add a ``form`` parameter to `scipy.sparce.csgraph.laplacian` taking one of the
three values: ``array``, or ``function``, or ``lo`` determining the format of
the output Laplacian:
* ``array`` is a numpy array (backward compatible default);
* ``function`` is a pointer to a lambda-function evaluating the
Laplacian-vector or Laplacian-matrix product;
* ``lo`` results in the format of the ``LinearOperator``.

`scipy.sparse.linalg` improvements
==================================
- ``lobpcg`` performance improvements for small input cases.

`scipy.spatial` improvements
============================
- Add an ``order`` parameter to `scipy.spatial.transform.Rotation.from_quat` 
and `scipy.spatial.transform.Rotation.as_quat` to specify quaternion format.


`scipy.stats` improvements
==========================
- `scipy.stats.monte_carlo_test` performs one-sample Monte Carlo hypothesis
tests to assess whether a sample was drawn from a given distribution. Besides
reproducing the results of hypothesis tests like `scipy.stats.ks_1samp`,
`scipy.stats.normaltest`, and `scipy.stats.cramervonmises` without small sample
size limitations, it makes it possible to perform similar tests using arbitrary
statistics and distributions.

- Several `scipy.stats` functions support new ``axis`` (integer or tuple of
integers) and ``nan_policy`` ('raise', 'omit', or 'propagate'), and
``keepdims`` arguments.
These functions also support masked arrays as inputs, even if they do not have
a `scipy.stats.mstats` counterpart. Edge cases for multidimensional arrays,
such as when axis-slices have no unmasked elements or entire inputs are of
size zero, are handled consistently.

- Add a ``weight`` parameter to `scipy.stats.hmean`.

- Several improvements have been made to `scipy.stats.levy_stable`. Substantial
improvement has been made for numerical evaluation of the pdf and cdf,
resolving [12658](https://github.com/scipy/scipy/issues/12658) and
[14944](https://github.com/scipy/scipy/issues/14994). The improvement is
particularly dramatic for stability parameter ``alpha`` close to or equal to 1
and for ``alpha`` below but approaching its maximum value of 2. The alternative
fast Fourier transform based method for pdf calculation has also been updated
to use the approach of Wang and Zhang from their 2008 conference paper
*Simpson’s rule based FFT method to compute densities of stable distribution*,
making this method more competitive with the default method. In addition,
users now have the option to change the parametrization of the Levy Stable
distribution to Nolan's "S0" parametrization which is used internally by
SciPy's pdf and cdf implementations. The "S0"  parametrization is described in
Nolan's paper [*Numerical calculation of stable densities and distribution
functions*](https://doi.org/10.1080/15326349708807450) upon which SciPy's
implementation is based. "S0" has the advantage that ``delta`` and ``gamma``
are proper location and scale parameters. With ``delta`` and ``gamma`` fixed,
the location and scale of the resulting distribution remain unchanged as
``alpha`` and ``beta`` change. This is not the case for the default "S1"
parametrization. Finally, more options have been exposed to allow users to
trade off between runtime and accuracy for both the default and FFT methods of
pdf and cdf calculation. More information can be found in the documentation
here (to be linked).

- Added `scipy.stats.fit` for fitting discrete and continuous distributions to
data.

- The methods ``"pearson"`` and ``"tippet"`` from `scipy.stats.combine_pvalues`
have been fixed to return the correct p-values, resolving
[15373](https://github.com/scipy/scipy/issues/15373). In addition, the
documentation for `scipy.stats.combine_pvalues` has been expanded and improved.

- Unlike other reduction functions, ``stats.mode`` didn't consume the axis
being operated on and failed for negative axis inputs. Both the bugs have been
fixed. Note that ``stats.mode`` will now consume the input axis and return an
ndarray with the ``axis`` dimension removed.

- Replaced implementation of `scipy.stats.ncf` with the implementation from
Boost for improved reliability.

- Add a `bits` parameter to `scipy.stats.qmc.Sobol`. It allows to use from 0
to 64 bits to compute the sequence. Default is ``None`` which corresponds to
30 for backward compatibility. Using a higher value allow to sample more
points. Note: ``bits`` does not affect the output dtype.

- Add a `integers` method to `scipy.stats.qmc.QMCEngine`. It allows sampling
integers using any QMC sampler.

- Improved the fit speed and accuracy of ``stats.pareto``.

- Added ``qrvs`` method to ``NumericalInversePolynomial`` to match the
situation for ``NumericalInverseHermite``.

- Faster random variate generation for ``gennorm`` and ``nakagami``.

- ``lloyd_centroidal_voronoi_tessellation`` has been added to allow improved
sample distributions via iterative application of Voronoi diagrams and
centering operations

- Add `scipy.stats.qmc.PoissonDisk` to sample using the Poisson disk sampling
method. It guarantees that samples are separated from each other by a
given ``radius``.

- Add `scipy.stats.pmean` to calculate the weighted power mean also called
generalized mean.



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

- Due to collision with the shape parameter ``n`` of several distributions,
use of the distribution ``moment`` method with keyword argument ``n`` is
deprecated. Keyword ``n`` is replaced with keyword ``order``. 
- Similarly, use of the distribution ``interval`` method with keyword arguments
``alpha`` is deprecated. Keyword ``alpha`` is replaced with keyword
``confidence``.
- The ``'simplex'``, ``'revised simplex'``, and ``'interior-point'`` methods
of `scipy.optimize.linprog` are deprecated. Methods ``highs``, ``highs-ds``,
or ``highs-ipm`` should be used in new code.
- Support for non-numeric arrays has been deprecated from ``stats.mode``.
``pandas.DataFrame.mode`` can be used instead.
- The function `spatial.distance.kulsinski` has been deprecated in favor
of `spatial.distance.kulczynski1`.
- The ``maxiter`` keyword of the truncated Newton (TNC) algorithm has been
deprecated in favour of ``maxfun``.
- The ``vertices`` keyword of ``Delauney.qhull`` now raises a
DeprecationWarning, after having been deprecated in documentation only
for a long time.
- The ``extradoc`` keyword of ``rv_continuous``, ``rv_discrete`` and
``rv_sample`` now raises a DeprecationWarning, after having been deprecated in
documentation only for a long time.


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

- Object arrays in sparse matrices now raise an error.
- Inexact indices into sparse matrices now raise an error.
- Passing ``radius=None`` to `scipy.spatial.SphericalVoronoi` now raises an
error (not adding ``radius`` defaults to 1, as before).
- Several BSpline methods now raise an error if inputs have ``ndim > 1``.
- The ``_rvs`` method of statistical distributions now requires a ``size``
parameter.
- Passing a ``fillvalue`` that cannot be cast to the output type in
`scipy.signal.convolve2d` now raises an error.
- `scipy.spatial.distance` now enforces that the input vectors are
one-dimensional.
- Removed ``stats.itemfreq``.
- Removed ``stats.median_absolute_deviation``.
- Removed ``n_jobs`` keyword argument and use of ``k=None`` from
``kdtree.query``.
- Removed ``right`` keyword from ``interpolate.PPoly.extend``.
- Removed ``debug`` keyword from ``scipy.linalg.solve_*``.
- Removed class ``_ppform`` ``scipy.interpolate``.
- Removed BSR methods ``matvec`` and ``matmat``.
- Removed ``mlab`` truncation mode from ``cluster.dendrogram``.
- Removed ``cluster.vq.py_vq2``.
- Removed keyword arguments ``ftol`` and ``xtol`` from
``optimize.minimize(method='Nelder-Mead')``.
- Removed ``signal.windows.hanning``.
- Removed LAPACK ``gegv`` functions from ``linalg``; this raises the minimally
required LAPACK version to 3.7.1.
- Removed ``spatial.distance.matching``.
- Removed the alias ``scipy.random`` for ``numpy.random``.
- Removed docstring related functions from ``scipy.misc`` (``docformat``,
``inherit_docstring_from``, ``extend_notes_in_docstring``,
``replace_notes_in_docstring``, ``indentcount_lines``, ``filldoc``,
``unindent_dict``, ``unindent_string``).
- Removed ``linalg.pinv2``.


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

- Several `scipy.stats` functions now convert ``np.matrix`` to ``np.ndarray``s
before the calculation is performed. In this case, the output will be a scalar
or ``np.ndarray`` of appropriate shape rather than a 2D ``np.matrix``.
Similarly, while masked elements of masked arrays are still ignored, the
output will be a scalar or ``np.ndarray`` rather than a masked array with
``mask=False``.
- The default method of `scipy.optimize.linprog` is now ``'highs'``, not
``'interior-point'`` (which is now deprecated), so callback functions and some
options are no longer supported with the default method.
- For `scipy.stats.combine_pvalues`, the sign of the test statistic returned
for the method ``"pearson"`` has been flipped so that higher values of the
statistic now correspond to lower p-values, making the statistic more
consistent with those of the other methods and with the majority of the
literature.
- `scipy.linalg.expm` due to historical reasons was using the sparse
implementation and thus was accepting sparse arrays. Now it only works with
nDarrays. For sparse usage, `scipy.sparse.linalg.expm` needs to be used
explicitly.
- The definition of `scipy.stats.circvar` has reverted to the one that is
standard in the literature; note that this is not the same as the square of
`scipy.stats.circstd`.
- Remove inheritance to `QMCEngine` in `MultinomialQMC` and
`MultivariateNormalQMC`. It removes the methods `fast_forward` and `reset`.
- Init of `MultinomialQMC` now require the number of trials with `n_trials`.
Hence, `MultinomialQMC.random` output has now the correct shape ``(n, pvals)``.
- Several function-specific warnings (``F_onewayConstantInputWarning``,
``F_onewayBadInputSizesWarning``, ``PearsonRConstantInputWarning``, 
``PearsonRNearConstantInputWarning``, ``SpearmanRConstantInputWarning``, and
``BootstrapDegenerateDistributionWarning``) have been replaced with more
general warnings.



Other changes
============

- A draft developer CLI is available for SciPy, leveraging the ``doit``,
``click`` and ``rich-click`` tools. For more details, see
[gh-15959](https://github.com/scipy/scipy/pull/15959).

- The SciPy contributor guide has been reorganized and updated
(see [15947](https://github.com/scipy/scipy/pull/15947) for details).

- QUADPACK Fortran routines in `scipy.integrate`, which power
`scipy.integrate.quad`, have been marked as `recursive`. This should fix rare
issues in multivariate integration (`nquad` and friends) and obviate the need
for compiler-specific compile flags (`/recursive` for ifort etc). Please file
an issue if this change turns out problematic for you. This is also true for
``FITPACK`` routines in `scipy.interpolate`, which power ``splrep``,
``splev`` etc., and ``*UnivariateSpline`` and ``*BivariateSpline`` classes.

- the ``USE_PROPACK`` environment variable has been renamed to
``SCIPY_USE_PROPACK``; setting to a non-zero value will enable
the usage of the ``PROPACK`` library as before

Lazy access to subpackages
======================

Before this release, all subpackages of SciPy (`cluster`, `fft`, `ndimage`,
etc.) had to be explicitly imported. Now, these subpackages are lazily loaded
as soon as they are accessed, so that the following is possible (if desired
for interactive use, it's not actually recommended for code,
see :ref:`scipy-api`):
``import scipy as sp; sp.fft.dct([1, 2, 3])``. Advantages include: making it
easier to navigate SciPy in interactive terminals, reducing subpackage import
conflicts (which before required
``import networkx.linalg as nla; import scipy.linalg as sla``),
and avoiding repeatedly having to update imports during teaching &
experimentation. Also see
[the related community specification document](https://scientific-python.org/specs/spec-0001/).

SciPy switched to Meson as its build system
===========================================

This is the first release that ships with [Meson](https://mesonbuild.com) as
the build system. When installing with ``pip`` or ``pypa/build``, Meson will be
used (invoked via the ``meson-python`` build hook). This change brings
significant benefits - most importantly much faster build times, but also
better support for cross-compilation and cleaner build logs.

*Note*:

This release still ships with support for ``numpy.distutils``-based builds
as well. Those can be invoked through the ``setup.py`` command-line
interface (e.g., ``python setup.py install``). It is planned to remove
``numpy.distutils`` support before the 1.10.0 release.

When building from source, a number of things have changed compared to building
with ``numpy.distutils``:

- New build dependencies: ``meson``, ``ninja``, and ``pkg-config``.
``setuptools`` and ``wheel`` are no longer needed.
- BLAS and LAPACK libraries that are supported haven't changed, however the
discovery mechanism has: that is now using ``pkg-config`` instead of hardcoded
paths or a ``site.cfg`` file.
- The build defaults to using OpenBLAS. See :ref:`blas-lapack-selection` for
details.

The two CLIs that can be used to build wheels are ``pip`` and ``build``. In
addition, the SciPy repo contains a ``python dev.py`` CLI for any kind of
development task (see its ``--help`` for details). For a comparison between old
(``distutils``) and new (``meson``) build commands, see :ref:`meson-faq`.

For more information on the introduction of Meson support in SciPy, see
`gh-13615 <https://github.com/scipy/scipy/issues/13615>`__ and
`this blog post <https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson/>`__.



Authors
=======

* endolith (12)
* Caio Agiani (2) +
* Emmy Albert (1) +
* Joseph Albert (1)
* Tania Allard (3)
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* Kartik Anand (1) +
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A total of 153 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.

1.8.1

compared to `1.8.0`. Notably, usage of Pythran has been
restored for Windows builds/binaries.

Authors
=======

* Henry Schreiner
* Maximilian Nöthe
* Sebastian Berg (1)
* Sameer Deshmukh (1) +
* Niels Doucet (1) +
* DWesl (4)
* Isuru Fernando (1)
* Ralf Gommers (4)
* Matt Haberland (1)
* Andrew Nelson (1)
* Dimitri Papadopoulos Orfanos (1) +
* Tirth Patel (3)
* Tyler Reddy (46)
* Pamphile Roy (7)
* Niyas Sait (1) +
* H. Vetinari (2)
* Warren Weckesser (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.

1.8.0

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
1.8.x branch, and on adding new features on the master branch.

This release requires Python `3.8`+ and NumPy `1.17.3` or greater.

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


Highlights of this release
-------------------------

-  A sparse array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases.
- The sparse SVD library PROPACK is now vendored with SciPy, and an interface
is exposed via `scipy.sparse.svds` with ``solver='PROPACK'``.
- A new `scipy.stats.sampling` submodule that leverages the ``UNU.RAN`` C
library to sample from arbitrary univariate non-uniform continuous and
discrete distributions
- All namespaces that were private but happened to miss underscores in
their names have been deprecated.

New features
-------------

`scipy.fft` improvements
========================

Added an ``orthogonalize=None`` parameter to the real transforms in `scipy.fft`
which controls whether the modified definition of DCT/DST is used without
changing the overall scaling.

`scipy.fft` backend registration is now smoother, operating with a single
registration call and no longer requiring a context manager.

`scipy.integrate` improvements
==============================

`scipy.integrate.quad_vec` introduces a new optional keyword-only argument,
``args``. ``args`` takes in a tuple of extra arguments if any (default is
``args=()``), which is then internally used to pass into the callable function
(needing these extra arguments) which we wish to integrate.

`scipy.interpolate` improvements
================================

`scipy.interpolate.BSpline` has a new method, ``design_matrix``, which
constructs a design matrix of b-splines in the sparse CSR format.

A new method ``from_cubic`` in ``BSpline`` class allows to convert a
``CubicSpline`` object to ``BSpline`` object.

`scipy.linalg` improvements
===========================

`scipy.linalg` gained three new public array structure investigation functions.
`scipy.linalg.bandwidth` returns information about the bandedness of an array
and can be used to test for triangular structure discovery, while
`scipy.linalg.issymmetric` and `scipy.linalg.ishermitian` test the array for
exact and approximate symmetric/Hermitian structure.

`scipy.optimize` improvements
=============================

`scipy.optimize.check_grad` introduces two new optional keyword only arguments,
``direction`` and ``seed``. ``direction`` can take values, ``'all'`` (default),
in which case all the one hot direction vectors will be used for verifying
the input analytical gradient function and ``'random'``, in which case a
random direction vector will be used for the same purpose. ``seed``
(default is ``None``) can be used for reproducing the return value of
``check_grad`` function. It will be used only when ``direction='random'``.

The `scipy.optimize.minimize` ``TNC`` method has been rewritten to use Cython
bindings. This also fixes an issue with the callback altering the state of the
optimization.

Added optional parameters ``target_accept_rate`` and ``stepwise_factor`` for
adapative step size adjustment in ``basinhopping``.

The ``epsilon`` argument to ``approx_fprime`` is now optional so that it may
have a default value consistent with most other functions in `scipy.optimize`.

`scipy.signal` improvements
===========================

Add ``analog`` argument, default ``False``, to ``zpk2sos``, and add new pairing
option ``'minimal'`` to construct analog and minimal discrete SOS arrays.
``tf2sos`` uses zpk2sos; add ``analog`` argument here as well, and pass it on
to ``zpk2sos``.

``savgol_coeffs`` and ``savgol_filter`` now work for even window lengths.

Added the Chirp Z-transform and Zoom FFT available as `scipy.signal.CZT` and
`scipy.signal.ZoomFFT`.

`scipy.sparse` improvements
===========================

An array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases. Please refer to the `scipy.sparse`
docstring for more information.

``maximum_flow`` introduces optional keyword only argument, ``method``
which accepts either, ``'edmonds-karp'`` (Edmonds Karp algorithm) or
``'dinic'`` (Dinic's algorithm). Moreover, ``'dinic'`` is used as default
value for ``method`` which means that Dinic's algorithm is used for computing
maximum flow unless specified. See, the comparison between the supported
algorithms in
`this comment <https://github.com/scipy/scipy/pull/14358#issue-684212523>`_.

Parameters ``atol``, ``btol`` now default to 1e-6 in
`scipy.sparse.linalg.lsmr` to match with default values in
`scipy.sparse.linalg.lsqr`.

Add the Transpose-Free Quasi-Minimal Residual algorithm (TFQMR) for general
nonsingular non-Hermitian linear systems in `scipy.sparse.linalg.tfqmr`.

The sparse SVD library PROPACK is now vendored with SciPy, and an interface is
exposed via `scipy.sparse.svds` with ``solver='PROPACK'``. For some problems,
this may be faster and/or more accurate than the default, ARPACK.

``sparse.linalg`` iterative solvers now have a nonzero initial guess option,
which may be specified as ``x0 = 'Mb'``.

The ``trace`` method has been added for sparse matrices.

`scipy.spatial` improvements
============================

`scipy.spatial.transform.Rotation` now supports item assignment and has a new
``concatenate`` method.

Add `scipy.spatial.distance.kulczynski1` in favour of
`scipy.spatial.distance.kulsinski` which will be deprecated in the next
release.

`scipy.spatial.distance.minkowski` now also supports ``0<p<1``.

`scipy.special` improvements
============================

The new function `scipy.special.log_expit` computes the logarithm of the
logistic sigmoid function. The function is formulated to provide accurate
results for large positive and negative inputs, so it avoids the problems
that would occur in the naive implementation ``log(expit(x))``.

A suite of five new functions for elliptic integrals:
``scipy.special.ellipr{c,d,f,g,j}``. These are the
`Carlson symmetric elliptic integrals <https://dlmf.nist.gov/19.16>`_, which
have computational advantages over the classical Legendre integrals. Previous
versions included some elliptic integrals from the Cephes library
(``scipy.special.ellip{k,km1,kinc,e,einc}``) but was missing the integral of
third kind (Legendre's Pi), which can be evaluated using the new Carlson
functions. The new Carlson elliptic integral functions can be evaluated in the
complex plane, whereas the Cephes library's functions are only defined for
real inputs.

Several defects in `scipy.special.hyp2f1` have been corrected. Approximately
correct values are now returned for ``z`` near ``exp(+-i*pi/3)``, fixing
`8054 <https://github.com/scipy/scipy/issues/8054>`_. Evaluation for such ``z``
is now calculated through a series derived by
`López and Temme (2013) <https://arxiv.org/abs/1306.2046>`_ that converges in
these regions. In addition, degenerate cases with one or more of ``a``, ``b``,
and/or ``c`` a non-positive integer are now handled in a manner consistent with
`mpmath's hyp2f1 implementation <https://mpmath.org/doc/current/functions/hypergeometric.html>`_,
which fixes `7340 <https://github.com/scipy/scipy/issues/7340>`_. These fixes
were made as part of an effort to rewrite the Fortran 77 implementation of
hyp2f1 in Cython piece by piece. This rewriting is now roughly 50% complete.

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

`scipy.stats.qmc.LatinHypercube` introduces two new optional keyword-only
arguments, ``optimization`` and ``strength``. ``optimization`` is either
``None`` or ``random-cd``. In the latter, random permutations are performed to
improve the centered discrepancy. ``strength`` is either 1 or 2. 1 corresponds
to the classical LHS while 2 has better sub-projection properties. This
construction is referred to as an orthogonal array based LHS of strength 2.
In both cases, the output is still a LHS.

`scipy.stats.qmc.Halton` is faster as the underlying Van der Corput sequence
was ported to Cython.

The ``alternative`` parameter was added to the ``kendalltau`` and ``somersd``
functions to allow one-sided hypothesis testing. Similarly, the masked
versions of ``skewtest``, ``kurtosistest``, ``ttest_1samp``, ``ttest_ind``,
and ``ttest_rel`` now also have an ``alternative`` parameter.

Add `scipy.stats.gzscore` to calculate the geometrical z score.

Random variate generators to sample from arbitrary univariate non-uniform
continuous and discrete distributions have been added to the new
`scipy.stats.sampling` submodule. Implementations of a C library
`UNU.RAN <http://statmath.wu.ac.at/software/unuran/>`_ are used for
performance. The generators added are:

- TransformedDensityRejection
- DiscreteAliasUrn
- NumericalInversePolynomial
- DiscreteGuideTable
- SimpleRatioUniforms

The ``binned_statistic`` set of functions now have improved performance for
the ``std``, ``min``, ``max``, and ``median`` statistic calculations.

``somersd`` and ``_tau_b`` now have faster Pythran-based implementations.

Some general efficiency improvements to handling of ``nan`` values in
several ``stats`` functions.

Added the Tukey-Kramer test as `scipy.stats.tukey_hsd`.

Improved performance of `scipy.stats.argus` ``rvs`` method.

Added the parameter ``keepdims`` to `scipy.stats.variation` and prevent the
undesirable return of a masked array from the function in some cases.

``permutation_test`` performs an exact or randomized permutation test of a
given statistic on provided data.


Deprecated features
---------------------

Clear split between public and private API
==========================================

SciPy has always documented what its public API consisted of in
:ref:`its API reference docs <scipy-api>`,
however there never was a clear split between public and
private namespaces in the code base. In this release, all namespaces that were
private but happened to miss underscores in their names have been deprecated.
These include (as examples, there are many more):

- ``scipy.signal.spline``
- ``scipy.ndimage.filters``
- ``scipy.ndimage.fourier``
- ``scipy.ndimage.measurements``
- ``scipy.ndimage.morphology``
- ``scipy.ndimage.interpolation``
- ``scipy.sparse.linalg.solve``
- ``scipy.sparse.linalg.eigen``
- ``scipy.sparse.linalg.isolve``

All functions and other objects in these namespaces that were meant to be
public are accessible from their respective public namespace (e.g.
`scipy.signal`). The design principle is that any public object must be
accessible from a single namespace only; there are a few exceptions, mostly for
historical reasons (e.g., ``stats`` and ``stats.distributions`` overlap).
For other libraries aiming to provide a SciPy-compatible API, it is now
unambiguous what namespace structure to follow.  See
`gh-14360 <https://github.com/scipy/scipy/issues/14360>`_ for more details.

Other deprecations
--------------------

``NumericalInverseHermite`` has been deprecated from `scipy.stats` and moved
to the `scipy.stats.sampling` submodule. It now uses the C implementation of
the UNU.RAN library so the result of methods like ``ppf`` may vary slightly.
Parameter ``tol`` has been deprecated and renamed to ``u_resolution``. The
parameter ``max_intervals`` has also been deprecated and will be removed in a
future release of SciPy.


Backwards incompatible changes
----------------------------------

- SciPy has raised the minimum compiler versions to GCC 6.3 on linux and
VS2019 on windows. In particular, this means that SciPy may now use C99 and
C++14 features. For more details see
`here <https://docs.scipy.org/doc/scipy/reference/dev/toolchain.html>`_.
- The result for empty bins for `scipy.stats.binned_statistic` with the builtin
``'std'`` metric is now ``nan``, for consistency with ``np.std``.
- The function `scipy.spatial.distance.wminkowski` has been removed. To achieve
the same results as before, please use the ``minkowski`` distance function
with the (optional) ``w=`` keyword-argument for the given weight.

Other changes
---------------

Some Fortran 77 code was modernized to be compatible with NAG's nagfor Fortran
compiler (see, e.g., `PR 13229 <https://github.com/scipy/scipy/pull/13229>`_).

``threadpoolctl`` may now be used by our test suite to substantially improve
the efficiency of parallel test suite runs.

Authors
---------

* endolith
* adamadanandy +
* akeemlh +
* Anton Akhmerov
* Marvin Albert +
* alegresor +
* Andrew Annex +
* Pantelis Antonoudiou +
* Ross Barnowski +
* Christoph Baumgarten
* Stephen Becker +
* Nickolai Belakovski
* Peter Bell
* berberto +
* Georgii Bocharov +
* Evgeni Burovski
* Matthias Bussonnier
* CJ Carey
* Justin Charlong +
* Dennis Collaris +
* David Cottrell +
* cruyffturn +
* da-woods +
* Anirudh Dagar
* Tiger Du +
* Thomas Duvernay
* Dani El-Ayyass +
* Castedo Ellerman +
* Donnie Erb +
* Andreas Esders-Kopecky +
* Livio F +
* Isuru Fernando
* Evelyn Fitzgerald +
* Sara Fridovich-Keil +
* Mark E Fuller +
* Ralf Gommers
* Kevin Richard Green +
* guiweber +
* Nitish Gupta +
* h-vetinari
* Matt Haberland
* J. Hariharan +
* Charles Harris
* Trever Hines
* Ian Hunt-Isaak +
* ich +
* Itrimel +
* Jan-Hendrik Müller +
* Jebby993 +
* Evan W Jones +
* Nathaniel Jones +
* Jeffrey Kelling +
* Malik Idrees Hasan Khan +
* Sergey B Kirpichev
* Kadatatlu Kishore +
* Andrew Knyazev
* Ravin Kumar +
* Peter Mahler Larsen
* Eric Larson
* Antony Lee
* Gregory R. Lee
* Tim Leslie
* lezcano +
* Xingyu Liu
* Christian Lorentzen
* Lorenzo +
* Smit Lunagariya +
* Lv101Magikarp +
* Yair M +
* Cong Ma
* Lorenzo Maffioli +
* majiang +
* Brian McFee +
* Nicholas McKibben
* John Speed Meyers +
* millivolt9 +
* Jarrod Millman
* Harsh Mishra +
* Boaz Mohar +
* naelsondouglas +
* Andrew Nelson
* Nico Schlömer
* Thomas Nowotny +
* nullptr +
* Teddy Ort +
* Nick Papior
* ParticularMiner +
* Dima Pasechnik
* Tirth Patel
* Matti Picus
* Ilhan Polat
* Adrian Price-Whelan +
* Quentin Barthélemy +
* Sundar R +
* Judah Rand +
* Tyler Reddy
* Renal-Of-Loon +
* Frederic Renner +
* Pamphile Roy
* Bharath Saiguhan +
* Atsushi Sakai
* Eric Schanet +
* Sebastian Wallkötter
* serge-sans-paille
* Reshama Shaikh +
* Namami Shanker
* Walter Simson +
* Gagandeep Singh +
* Leo C. Stein +
* Albert Steppi
* Kai Striega
* Diana Sukhoverkhova
* Søren Fuglede Jørgensen
* Mike Taves
* Ben Thompson +
* Bas van Beek
* Jacob Vanderplas
* Dhruv Vats +
* H. Vetinari +
* Thomas Viehmann +
* Pauli Virtanen
* Vlad +
* Arthur Volant
* Samuel Wallan
* Stefan van der Walt
* Warren Weckesser
* Josh Wilson
* Haoyin Xu +
* Rory Yorke
* Egor Zemlyanoy
* Gang Zhao +
* 赵丰 (Zhao Feng) +

A total of 132 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.

1.7.3

for MacOS arm64 with Python `3.8`, `3.9`, and `3.10`. The MacOS arm64 wheels
are only available for MacOS version `12.0` and greater, as explained
in [Issue 14688](https://github.com/scipy/scipy/issues/14688).

Authors
=======

* Anirudh Dagar
* Ralf Gommers
* Tyler Reddy
* Pamphile Roy
* Olivier Grisel
* Isuru Fernando

A total of 6 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.

1.7.2

compared to `1.7.1`. Notably, the release includes wheels
for Python `3.10`, and wheels are now built with a newer
version of OpenBLAS, `0.3.17`. Python `3.10` wheels are provided
for MacOS x86_64 (thin, not universal2 or arm64 at this time),
and Windows/Linux 64-bit. Many wheels are now built with newer
versions of manylinux, which may require newer versions of pip.

Authors
=======

* Peter Bell
* da-woods +
* Isuru Fernando
* Ralf Gommers
* Matt Haberland
* Nicholas McKibben
* Ilhan Polat
* Judah Rand +
* Tyler Reddy
* Pamphile Roy
* Charles Harris
* Matti Picus
* Hugo van Kemenade
* Jacob Vanderplas

A total of 14 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.

1.7.1

compared to `1.7.0`.

Authors
=======

* Peter Bell
* Evgeni Burovski
* Justin Charlong +
* Ralf Gommers
* Matti Picus
* Tyler Reddy
* Pamphile Roy
* Sebastian Wallkötter
* Arthur Volant

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.

1.7.0

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
1.7.x branch, and on adding new features on the master branch.

This release requires Python `3.7+` and NumPy `1.16.5` or greater.

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


Highlights of this release


-  A new submodule for quasi-Monte Carlo, `scipy.stats.qmc`, was added
-  The documentation design was updated to use the same PyData-Sphinx theme as
other NumFOCUS packages like NumPy.
-  We now vendor and leverage the Boost C++ library to enable numerous
improvements for long-standing weaknesses in `scipy.stats`
-  `scipy.stats` has six new distributions, eight new (or overhauled)
hypothesis tests, a new function for bootstrapping, a class that enables
fast random variate sampling and percentile point function evaluation, 
and many other enhancements.
-  ``cdist`` and ``pdist`` distance calculations are faster for several metrics,
especially weighted cases, thanks to a rewrite to a new C++ backend framework
-  A new class for radial basis function interpolation, `RBFInterpolator`, was
added to address issues with the `Rbf` class.

*We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of the improvements to*
`scipy.stats`.


New features


`scipy.cluster` improvements

An optional argument, ``seed``, has been added to ``kmeans`` and ``kmeans2`` to
set the random generator and random state.

`scipy.interpolate` improvements

Improved input validation and error messages for ``fitpack.bispev`` and
``fitpack.parder`` for scenarios that previously caused substantial confusion
for users.

The class `RBFInterpolator` was added to supersede the `Rbf` class. The new
class has usage that more closely follows other interpolator classes, corrects
sign errors that caused unexpected smoothing behavior, includes polynomial
terms in the interpolant (which are necessary for some RBF choices), and
supports interpolation using only the k-nearest neighbors for memory
efficiency.

`scipy.linalg` improvements

An LAPACK wrapper was added for access to the ``tgexc`` subroutine.

`scipy.ndimage` improvements

`scipy.ndimage.affine_transform` is now able to infer the ``output_shape`` from
the ``out`` array.

`scipy.optimize` improvements

The optional parameter ``bounds`` was added to
``_minimize_neldermead`` to support bounds constraints
for the Nelder-Mead solver.

``trustregion`` methods ``trust-krylov``, ``dogleg`` and ``trust-ncg`` can now
estimate ``hess`` by finite difference using one of
``["2-point", "3-point", "cs"]``.

``halton`` was added as a ``sampling_method`` in `scipy.optimize.shgo`.
``sobol`` was fixed and is now using `scipy.stats.qmc.Sobol`.

``halton`` and ``sobol`` were added as ``init`` methods in
`scipy.optimize.differential_evolution.`

``differential_evolution`` now accepts an ``x0`` parameter to provide an
initial guess for the minimization.

``least_squares`` has a modest performance improvement when SciPy is built
with Pythran transpiler enabled.

When ``linprog`` is used with ``method`` ``'highs'``, ``'highs-ipm'``, or
``'highs-ds'``, the result object now reports the marginals (AKA shadow
prices, dual values) and residuals associated with each constraint.

`scipy.signal` improvements

``get_window`` supports ``general_cosine`` and ``general_hamming`` window
functions.

`scipy.signal.medfilt2d` now releases the GIL where appropriate to enable
performance gains via multithreaded calculations.

`scipy.sparse` improvements

Addition of ``dia_matrix`` sparse matrices is now faster.


`scipy.spatial` improvements

``distance.cdist`` and ``distance.pdist`` performance has greatly improved for
certain weighted metrics. Namely: ``minkowski``, ``euclidean``, ``chebyshev``,
``canberra``, and ``cityblock``.

Modest performance improvements for many of the unweighted ``cdist`` and
``pdist`` metrics noted above.

The parameter ``seed`` was added to `scipy.spatial.vq.kmeans` and
`scipy.spatial.vq.kmeans2`.

The parameters ``axis`` and ``keepdims`` where added to
`scipy.spatial.distance.jensenshannon`.

The ``rotation`` methods ``from_rotvec`` and ``as_rotvec`` now accept a
``degrees`` argument to specify usage of degrees instead of radians.

`scipy.special` improvements

Wright's generalized Bessel function for positive arguments was added as
`scipy.special.wright_bessel.`

An implementation of the inverse of the Log CDF of the Normal Distribution is
now available via `scipy.special.ndtri_exp`.

`scipy.stats` improvements

Hypothesis Tests

The Mann-Whitney-Wilcoxon test, ``mannwhitneyu``, has been rewritten. It now
supports n-dimensional input, an exact test method when there are no ties,
and improved documentation. Please see "Other changes" for adjustments to
default behavior.

The new function `scipy.stats.binomtest` replaces `scipy.stats.binom_test`. The
new function returns an object that calculates a confidence intervals of the
proportion parameter. Also, performance was improved from O(n) to O(log(n)) by
using binary search.

The two-sample version of the Cramer-von Mises test is implemented in
`scipy.stats.cramervonmises_2samp`.

The Alexander-Govern test is implemented in the new function
`scipy.stats.alexandergovern`.

The new functions `scipy.stats.barnard_exact` and  `scipy.stats. boschloo_exact`
respectively perform Barnard's exact test and Boschloo's exact test
for 2x2 contingency tables.

The new function `scipy.stats.page_trend_test` performs Page's test for ordered
alternatives.

The new function `scipy.stats.somersd` performs Somers' D test for ordinal
association between two variables.

An option, ``permutations``, has been added in `scipy.stats.ttest_ind` to
perform permutation t-tests. A ``trim`` option was also added to perform
a trimmed (Yuen's) t-test.

The ``alternative`` parameter was added to the ``skewtest``, ``kurtosistest``,
``ranksums``, ``mood``, ``ansari``, ``linregress``, and ``spearmanr`` functions
to allow one-sided hypothesis testing.

Sample statistics

The new function `scipy.stats.differential_entropy` estimates the differential
entropy of a continuous distribution from a sample.

The ``boxcox`` and ``boxcox_normmax`` now allow the user to control the
optimizer used to minimize the negative log-likelihood function.

A new function `scipy.stats.contingency.relative_risk` calculates the
relative risk, or risk ratio, of a 2x2 contingency table. The object
returned has a method to compute the confidence interval of the relative risk.

Performance improvements in the ``skew`` and ``kurtosis`` functions achieved
by removal of repeated/redundant calculations.

Substantial performance improvements in `scipy.stats.mstats.hdquantiles_sd`.

The new function `scipy.stats.contingency.association` computes several
measures of association for a contingency table: Pearsons contingency
coefficient, Cramer's V, and Tschuprow's T.

The parameter ``nan_policy`` was added to `scipy.stats.zmap` to provide options
for handling the occurrence of ``nan`` in the input data.

The parameter ``ddof`` was added to `scipy.stats.variation` and
`scipy.stats.mstats.variation`.

The parameter ``weights`` was added to `scipy.stats.gmean`.

Statistical Distributions

We now vendor and leverage the Boost C++ library to address a number of
previously reported issues in ``stats``. Notably, ``beta``, ``binom``,
``nbinom`` now have Boost backends, and it is straightforward to leverage
the backend for additional functions.

The skew Cauchy probability distribution has been implemented as
`scipy.stats.skewcauchy`.

The Zipfian probability distribution has been implemented as
`scipy.stats.zipfian`.

The new distributions ``nchypergeom_fisher`` and ``nchypergeom_wallenius``
implement the Fisher and Wallenius versions of the noncentral hypergeometric
distribution, respectively.

The generalized hyperbolic distribution was added in
`scipy.stats.genhyperbolic`.

The studentized range distribution was added in `scipy.stats.studentized_range`.

`scipy.stats.argus` now has improved handling for small parameter values.

Better argument handling/preparation has resulted in performance improvements
for many distributions.

The ``cosine`` distribution has added ufuncs for ``ppf``, ``cdf``, ``sf``, and
``isf`` methods including numerical precision improvements at the edges of the
support of the distribution.

An option to fit the distribution to data by the method of moments has been
added to the ``fit`` method of the univariate continuous distributions.

Other

`scipy.stats.bootstrap` has been added to allow estimation of the confidence
interval and standard error of a statistic.

The new function `scipy.stats.contingency.crosstab` computes a contingency
table (i.e. a table of counts of unique entries) for the given data.

`scipy.stats.NumericalInverseHermite` enables fast random variate sampling
and percentile point function evaluation of an arbitrary univariate statistical
distribution.

New `scipy.stats.qmc` module

This new module provides Quasi-Monte Carlo (QMC) generators and associated
helper functions.

It provides a generic class `scipy.stats.qmc.QMCEngine` which defines a QMC
engine/sampler. An engine is state aware: it can be continued, advanced and
reset. 3 base samplers are available:

-  `scipy.stats.qmc.Sobol` the well known Sobol low discrepancy sequence.
Several warnings have been added to guide the user into properly using this
sampler. The sequence is scrambled by default.
-  `scipy.stats.qmc.Halton`: Halton low discrepancy sequence. The sequence is
scrambled by default.
-  `scipy.stats.qmc.LatinHypercube`: plain LHS design.

And 2 special samplers are available:

-  `scipy.stats.qmc.MultinomialQMC`: sampling from a multinomial distribution
using any of the base `scipy.stats.qmc.QMCEngine`.
-  `scipy.stats.qmc.MultivariateNormalQMC`: sampling from a multivariate Normal
using any of the base `scipy.stats.qmc.QMCEngine`.

The module also provide the following helpers:

-  `scipy.stats.qmc.discrepancy`: assess the quality of a set of points in terms
of space coverage.
-  `scipy.stats.qmc.update_discrepancy`: can be used in an optimization loop to
construct a good set of points.
-  `scipy.stats.qmc.scale`: easily scale a set of points from (to) the unit
interval to (from) a given range.



Deprecated features


`scipy.linalg` deprecations

-  `scipy.linalg.pinv2` is deprecated and its functionality is completely
subsumed into `scipy.linalg.pinv`
-  Both ``rcond``, ``cond`` keywords of `scipy.linalg.pinv` and
`scipy.linalg.pinvh` were not working and now are deprecated. They are now
replaced with functioning ``atol`` and ``rtol`` keywords with clear usage.

`scipy.spatial` deprecations

-  `scipy.spatial.distance` metrics expect 1d input vectors but will call
``np.squeeze`` on their inputs to accept any extra length-1 dimensions. That
behaviour is now deprecated.


Backwards incompatible changes

Other changes

We now accept and leverage performance improvements from the ahead-of-time
Python-to-C++ transpiler, Pythran, which can be optionally disabled (via
``export SCIPY_USE_PYTHRAN=0``) but is enabled by default at build time.

There are two changes to the default behavior of `scipy.stats.mannwhitenyu`:

-  For years, use of the default ``alternative=None`` was deprecated; explicit
``alternative`` specification was required. Use of the new default value of
``alternative``, "two-sided", is now permitted.
-  Previously, all p-values were based on an asymptotic approximation. Now, for
small samples without ties, the p-values returned are exact by default.

Support has been added for PEP 621 (project metadata in ``pyproject.toml``)

We now support a Gitpod environment to reduce the barrier to entry for SciPy
development; for more details see `quickstart-gitpod`.



Authors

* endolith
* Jelle Aalbers +
* Adam +
* Tania Allard +
* Sven Baars +
* Max Balandat +
* baumgarc +
* Christoph Baumgarten
* Peter Bell
* Lilian Besson
* Robinson Besson +
* Max Bolingbroke
* Blair Bonnett +
* Jordão Bragantini
* Harm Buisman +
* Evgeni Burovski
* Matthias Bussonnier
* Dominic C
* CJ Carey
* Ramón Casero +
* Chachay +
* charlotte12l +
* Benjamin Curtice Corbett +
* Falcon Dai +
* Ian Dall +
* Terry Davis
* droussea2001 +
* DWesl +
* dwight200 +
* Thomas J. Fan +
* Joseph Fox-Rabinovitz
* Max Frei +
* Laura Gutierrez Funderburk +
* gbonomib +
* Matthias Geier +
* Pradipta Ghosh +
* Ralf Gommers
* Evan H +
* h-vetinari
* Matt Haberland
* Anselm Hahn +
* Alex Henrie
* Piet Hessenius +
* Trever Hines +
* Elisha Hollander +
* Stephan Hoyer
* Tom Hu +
* Kei Ishikawa +
* Julien Jerphanion
* Robert Kern
* Shashank KS +
* Peter Mahler Larsen
* Eric Larson
* Cheng H. Lee +
* Gregory R. Lee
* Jean-Benoist Leger +
* lgfunderburk +
* liam-o-marsh +
* Xingyu Liu +
* Alex Loftus +
* Christian Lorentzen +
* Cong Ma
* Marc +
* MarkPundurs +
* Markus Löning +
* Liam Marsh +
* Nicholas McKibben
* melissawm +
* Jamie Morton
* Andrew Nelson
* Nikola Forró
* Tor Nordam +
* Olivier Gauthé +
* Rohit Pandey +
* Avanindra Kumar Pandeya +
* Tirth Patel
* paugier +
* Alex H. Wagner, PhD +
* Jeff Plourde +
* Ilhan Polat
* pranavrajpal +
* Vladyslav Rachek
* Bharat Raghunathan
* Recursing +
* Tyler Reddy
* Lucas Roberts
* Gregor Robinson +
* Pamphile Roy +
* Atsushi Sakai
* Benjamin Santos
* Martin K. Scherer +
* Thomas Schmelzer +
* Daniel Scott +
* Sebastian Wallkötter +
* serge-sans-paille +
* Namami Shanker +
* Masashi Shibata +
* Alexandre de Siqueira +
* Albert Steppi +
* Adam J. Stewart +
* Kai Striega
* Diana Sukhoverkhova
* Søren Fuglede Jørgensen
* Mike Taves
* Dan Temkin +
* Nicolas Tessore +
* tsubota20 +
* Robert Uhl
* christos val +
* Bas van Beek +
* Ashutosh Varma +
* Jose Vazquez +
* Sebastiano Vigna
* Aditya Vijaykumar
* VNMabus
* Arthur Volant +
* Samuel Wallan
* Stefan van der Walt
* Warren Weckesser
* Anreas Weh
* Josh Wilson
* Rory Yorke
* Egor Zemlyanoy
* Marc Zoeller +
* zoj613 +
* 秋纫 +

A total of 126 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.

1.6.3

compared to `1.6.2`.

Authors
======

* Peter Bell
* Ralf Gommers
* Matt Haberland
* Peter Mahler Larsen
* Tirth Patel
* Tyler Reddy
* Pamphile ROY +
* Xingyu Liu +

A total of 8 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.

1.6.2

compared to `1.6.1`. This is also the first SciPy release
to place upper bounds on some dependencies to improve
the long-term repeatability of source builds.

Authors
=======

* Pradipta Ghosh +
* Tyler Reddy
* Ralf Gommers
* Martin K. Scherer +
* Robert Uhl
* Warren Weckesser

A total of 6 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.

1.6.1

compared to `1.6.0`.

Please note that for SciPy wheels to correctly install with pip on
macOS 11, pip `>= 20.3.3` is needed.

Authors
=======

* Peter Bell
* Evgeni Burovski
* CJ Carey
* Ralf Gommers
* Peter Mahler Larsen
* Cheng H. Lee +
* Cong Ma
* Nicholas McKibben
* Nikola Forró
* Tyler Reddy
* Warren Weckesser

A total of 11 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.

1.6.0

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
1.6.x branch, and on adding new features on the master branch.

This release requires Python `3.7`+ and NumPy `1.16.5` or greater.

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

Highlights of this release
----------------------------

-  `scipy.ndimage` improvements: Fixes and ehancements to boundary extension 
modes for interpolation functions. Support for complex-valued inputs in many
filtering and interpolation functions. New ``grid_mode`` option for 
`scipy.ndimage.zoom` to enable results consistent with scikit-image's 
``rescale``.
-  `scipy.optimize.linprog` has fast, new methods for large, sparse problems 
from the ``HiGHS`` library.
- `scipy.stats` improvements including new distributions, a new test, and
enhancements to existing distributions and tests


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

`scipy.special` improvements
-----------------------------
`scipy.special` now has improved support for 64-bit ``LAPACK`` backend

`scipy.odr` improvements
-------------------------
`scipy.odr` now has support for 64-bit integer ``BLAS``

`scipy.odr.ODR` has gained an optional ``overwrite`` argument so that existing
files may be overwritten.

`scipy.integrate` improvements
-------------------------------
Some renames of functions with poor names were done, with the old names 
retained without being in the reference guide for backwards compatibility 
reasons:
-  ``integrate.simps`` was renamed to ``integrate.simpson``
-  ``integrate.trapz`` was renamed to ``integrate.trapezoid``
-  ``integrate.cumtrapz`` was renamed to ``integrate.cumulative_trapezoid``

`scipy.cluster` improvements
-------------------------------
`scipy.cluster.hierarchy.DisjointSet` has been added for incremental 
connectivity queries.

`scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color
information in `leaves_color_list`.

`scipy.interpolate` improvements
---------------------------------
`scipy.interpolate.interp1d` has a new method ``nearest-up``, similar to the 
existing method ``nearest`` but rounds half-integers up instead of down.

`scipy.io` improvements
------------------------
Support has been added for reading arbitrary bit depth integer PCM WAV files 
from 1- to 32-bit, including the commonly-requested 24-bit depth.

`scipy.linalg` improvements
----------------------------
The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the 
product of a Toeplitz matrix with another matrix.

`scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements
thanks to additional Cython code.

Python ``LAPACK`` wrappers have been added for ``pptrf``, ``pptrs``, ``ppsv``,
``pptri``, and ``ppcon``.

`scipy.linalg.norm` and the ``svd`` family of functions will now use 64-bit
integer backends when available.

`scipy.ndimage` improvements
-----------------------------
`scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts 
now accept both complex-valued images and/or complex-valued filter kernels. All 
convolution-based filters also now accept complex-valued inputs 
(e.g. ``gaussian_filter``, ``uniform_filter``, etc.).

Multiple fixes and enhancements to boundary handling were introduced to 
`scipy.ndimage` interpolation functions (i.e. ``affine_transform``,
``geometric_transform``, ``map_coordinates``, ``rotate``, ``shift``, ``zoom``).

A new boundary mode, ``grid-wrap`` was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast 
to the existing ``wrap`` mode which uses a period that is one sample smaller 
than the original signal extent along each dimension.

A long-standing bug in the ``reflect`` boundary condition has been fixed and 
the mode ``grid-mirror`` was introduced as a synonym for ``reflect``.

A new boundary mode, ``grid-constant`` is now available. This is similar to 
the existing ndimage ``constant`` mode, but interpolation will still performed 
at coordinate values outside of the original image extent. This 
``grid-constant`` mode is consistent with OpenCV's ``BORDER_CONSTANT`` mode 
and scikit-image's ``constant`` mode.

Spline pre-filtering (used internally by ``ndimage`` interpolation functions 
when ``order >= 2``), now supports all boundary modes rather than always 
defaulting to mirror boundary conditions. The standalone functions 
``spline_filter`` and ``spline_filter1d`` have analytical boundary conditions 
that match modes ``mirror``, ``grid-wrap`` and ``reflect``.

`scipy.ndimage` interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and 
imaginary components.

The ``ndimage`` tutorials 
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been 
updated with new figures to better clarify the exact behavior of all of the 
interpolation boundary modes.

`scipy.ndimage.zoom` now has a ``grid_mode`` option that changes the coordinate 
of the center of the first pixel along an axis from 0 to 0.5. This allows 
resizing in a manner that is consistent with the behavior of scikit-image's 
``resize`` and ``rescale`` functions (and OpenCV's ``cv2.resize``).

`scipy.optimize` improvements
------------------------------
`scipy.optimize.linprog` has fast, new methods for large, sparse problems from 
the ``HiGHS`` C++ library. ``method='highs-ds'`` uses a high performance dual 
revised simplex implementation (HSOL), ``method='highs-ipm'`` uses an 
interior-point method with crossover, and ``method='highs'`` chooses between 
the two automatically. These methods are typically much faster and often exceed 
the accuracy of other ``linprog`` methods, so we recommend explicitly 
specifying one of these three method values when using ``linprog``.

`scipy.optimize.quadratic_assignment` has been added for approximate solution 
of the quadratic assignment problem.

`scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead
for small cost matrix sizes

`scipy.optimize.least_squares` has improved performance when the user provides
the jacobian as a sparse jacobian already in ``csr_matrix`` format

`scipy.optimize.linprog` now has an ``rr_method`` argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.

`scipy.signal` improvements
----------------------------
`scipy.signal.gammatone` has been added to design FIR or IIR filters that 
model the human auditory system.

`scipy.signal.iircomb` has been added to design IIR peaking/notching comb 
filters that can boost/attenuate a frequency from a signal.

`scipy.signal.sosfilt` performance has been improved to avoid some previously-
observed slowdowns

`scipy.signal.windows.taylor` has been added--the Taylor window function is
commonly used in radar digital signal processing

`scipy.signal.gauss_spline` now supports ``list`` type input for consistency
with other related SciPy functions

`scipy.signal.correlation_lags` has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.

`scipy.sparse` improvements
----------------------------
A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
`scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this
provides func