Update scipy to 1.4.1

[pyup-bot]

This PR updates scipy from 1.1.0 to 1.4.1.

Changelog

1.4.1

compared to `1.4.0`. Importantly, it aims to fix a problem
where an older version of `pybind11` may cause a segmentation
fault when imported alongside incompatible libraries.

Authors
=======

* Ralf Gommers
* Tyler Reddy

1.4.0

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

This release requires Python 3.5+ and NumPy `>=1.13.3` (for Python 3.5, 3.6),
`>=1.14.5` (for Python 3.7), `>= 1.17.3` (for Python 3.8)

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

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

- a new submodule, `scipy.fft`, now supersedes `scipy.fftpack`; this
means support for ``long double`` transforms, faster multi-dimensional
transforms, improved algorithm time complexity, release of the global
intepreter lock, and control over threading behavior
- support for ``pydata/sparse`` arrays in `scipy.sparse.linalg`
- substantial improvement to the documentation and functionality of
several `scipy.special` functions, and some new additions
- the generalized inverse Gaussian distribution has been added to
`scipy.stats`
- an implementation of the Edmonds-Karp algorithm in
`scipy.sparse.csgraph.maximum_flow`
- `scipy.spatial.SphericalVoronoi` now supports n-dimensional input, 
has linear memory complexity, improved performance, and
supports single-hemisphere generators


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

Infrastructure
----------------
Documentation can now be built with ``runtests.py --doc``

A ``Dockerfile`` is now available in the ``scipy/scipy-dev`` repository to
facilitate getting started with SciPy development.

`scipy.constants` improvements
--------------------------------
`scipy.constants` has been updated with the CODATA 2018 constants.


`scipy.fft` added
-------------------
`scipy.fft` is a new submodule that supersedes the `scipy.fftpack` submodule. 
For the most part, this is a drop-in replacement for ``numpy.fft`` and 
`scipy.fftpack` alike. With some important differences, `scipy.fft`:
- uses NumPy's conventions for real transforms (``rfft``). This means the 
return value is a complex array, half the size of the full ``fft`` output.
This is different from the output of ``fftpack`` which returned a real array 
representing complex components packed together.
- the inverse real to real transforms (``idct`` and ``idst``) are normalized 
for ``norm=None`` in thesame way as ``ifft``. This means the identity 
``idct(dct(x)) == x`` is now ``True`` for all norm modes.
- does not include the convolutions or pseudo-differential operators
from ``fftpack``.

This submodule is based on the ``pypocketfft`` library, developed by the 
author of ``pocketfft`` which was recently adopted by NumPy as well.
``pypocketfft`` offers a number of advantages over fortran ``FFTPACK``:
- support for long double (``np.longfloat``) precision transforms.
- faster multi-dimensional transforms using vectorisation
- Bluestein’s algorithm removes the worst-case ``O(n^2)`` complexity of
``FFTPACK``
- the global interpreter lock (``GIL``) is released during transforms
- optional multithreading of multi-dimensional transforms via the ``workers``
argument

Note that `scipy.fftpack` has not been deprecated and will continue to be 
maintained but is now considered legacy. New code is recommended to use 
`scipy.fft` instead, where possible.

`scipy.fftpack` improvements
--------------------------------
`scipy.fftpack` now uses pypocketfft to perform its FFTs, offering the same
speed and accuracy benefits listed for scipy.fft above but without the
improved API.

`scipy.integrate` improvements
--------------------------------

The function `scipy.integrate.solve_ivp` now has an ``args`` argument.
This allows the user-defined functions passed to the function to have
additional parameters without having to create wrapper functions or
lambda expressions for them.

`scipy.integrate.solve_ivp` can now return a ``y_events`` attribute 
representing the solution of the ODE at event times

New ``OdeSolver`` is implemented --- ``DOP853``. This is a high-order explicit
Runge-Kutta method originally implemented in Fortran. Now we provide a pure 
Python implementation usable through ``solve_ivp`` with all its features.

`scipy.integrate.quad` provides better user feedback when break points are 
specified with a weighted integrand.

`scipy.integrate.quad_vec` is now available for general purpose integration
of vector-valued functions


`scipy.interpolate` improvements
----------------------------------
`scipy.interpolate.pade` now handles complex input data gracefully

`scipy.interpolate.Rbf` can now interpolate multi-dimensional functions

`scipy.io` improvements
-------------------------

`scipy.io.wavfile.read` can now read data from a `WAV` file that has a
malformed header, similar to other modern `WAV` file parsers

`scipy.io.FortranFile` now has an expanded set of available ``Exception``
classes for handling poorly-formatted files


`scipy.linalg` improvements
-----------------------------
The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for complex-valued matrices. Before this, the function only returned
correct values for real-valued matrices.

New boolean keyword argument ``check_finite`` for `scipy.linalg.norm`; whether 
to check that the input matrix contains only finite numbers. Disabling may 
give a performance gain, but may result in problems (crashes, non-termination)
if the inputs do contain infinities or NaNs.

`scipy.linalg.solve_triangular` has improved performance for a C-ordered
triangular matrix

``LAPACK`` wrappers have been added for ``?geequ``, ``?geequb``, ``?syequb``,
and ``?heequb``

Some performance improvements may be observed due to an internal optimization
in operations involving LAPACK routines via ``_compute_lwork``. This is
particularly true for operations on small arrays.

Block ``QR`` wrappers are now available in `scipy.linalg.lapack`


`scipy.ndimage` improvements
------------------------------


`scipy.optimize` improvements
--------------------------------
It is now possible to use linear and non-linear constraints with 
`scipy.optimize.differential_evolution`.

`scipy.optimize.linear_sum_assignment` has been re-written in C++ to improve 
performance, and now allows input costs to be infinite.

A ``ScalarFunction.fun_and_grad`` method was added for convenient simultaneous
retrieval of a function and gradient evaluation

`scipy.optimize.minimize` ``BFGS`` method has improved performance by avoiding
duplicate evaluations in some cases

Better user feedback is provided when an objective function returns an array
instead of a scalar.


`scipy.signal` improvements
-----------------------------

Added a new function to calculate convolution using the overlap-add method,
named `scipy.signal.oaconvolve`. Like `scipy.signal.fftconvolve`, this
function supports specifying dimensions along which to do the convolution.

`scipy.signal.cwt` now supports complex wavelets.

The implementation of ``choose_conv_method`` has been updated to reflect the 
new FFT implementation. In addition, the performance has been significantly 
improved (with rather drastic improvements in edge cases).

The function ``upfirdn`` now has a ``mode`` keyword argument that can be used
to select the signal extension mode used at the signal boundaries. These modes
are also available for use in ``resample_poly`` via a newly added ``padtype``
argument.

`scipy.signal.sosfilt` now benefits from Cython code for improved performance

`scipy.signal.resample` should be more efficient by leveraging ``rfft`` when
possible

`scipy.sparse` improvements
-------------------------------
It is now possible to use the LOBPCG method in `scipy.sparse.linalg.svds`.

`scipy.sparse.linalg.LinearOperator` now supports the operation ``rmatmat`` 
for adjoint matrix-matrix multiplication, in addition to ``rmatvec``.

Multiple stability updates enable float32 support in the LOBPCG eigenvalue 
solver for symmetric and Hermitian eigenvalues problems in 
``scipy.sparse.linalg.lobpcg``.

A solver for the maximum flow problem has been added as
`scipy.sparse.csgraph.maximum_flow`.

`scipy.sparse.csgraph.maximum_bipartite_matching` now allows non-square inputs,
no longer requires a perfect matching to exist, and has improved performance.

`scipy.sparse.lil_matrix` conversions now perform better in some scenarios

Basic support is available for ``pydata/sparse`` arrays in
`scipy.sparse.linalg`

`scipy.sparse.linalg.spsolve_triangular` now supports the ``unit_diagonal``
argument to improve call signature similarity with its dense counterpart,
`scipy.linalg.solve_triangular`

``assertAlmostEqual`` may now be used with sparse matrices, which have added
support for ``__round__``

`scipy.spatial` improvements
------------------------------
The bundled Qhull library was upgraded to version 2019.1, fixing several
issues. Scipy-specific patches are no longer applied to it.

`scipy.spatial.SphericalVoronoi` now has linear memory complexity, improved
performance, and supports single-hemisphere generators. Support has also been
added for handling generators that lie on a great circle arc (geodesic input)
and for generators in n-dimensions.

`scipy.spatial.transform.Rotation` now includes functions for calculation of a
mean rotation, generation of the 3D rotation groups, and reduction of rotations
with rotational symmetries.

`scipy.spatial.transform.Slerp` is now callable with a scalar argument

`scipy.spatial.voronoi_plot_2d` now supports furthest site Voronoi diagrams

`scipy.spatial.Delaunay` and `scipy.spatial.Voronoi` now have attributes
for tracking whether they are furthest site diagrams

`scipy.special` improvements
------------------------------
The Voigt profile has been added as `scipy.special.voigt_profile`.

A real dispatch has been added for the Wright Omega function
(`scipy.special.wrightomega`).

The analytic continuation of the Riemann zeta function has been added. (The 
Riemann zeta function is the one-argument variant of `scipy.special.zeta`.)

The complete elliptic integral of the first kind (`scipy.special.ellipk`) is 
now available in `scipy.special.cython_special`.

The accuracy of `scipy.special.hyp1f1` for real arguments has been improved.

The documentation of many functions has been improved.

`scipy.stats` improvements
----------------------------
`scipy.stats.multiscale_graphcorr` added as an independence test that
operates on high dimensional and nonlinear data sets. It has higher statistical
power than other `scipy.stats` tests while being the only one that operates on
multivariate data.
The generalized inverse Gaussian distribution (`scipy.stats.geninvgauss`) has 
been added.

It is now possible to efficiently reuse `scipy.stats.binned_statistic_dd` 
with new values by providing the result of a previous call to the function.

`scipy.stats.hmean` now handles input with zeros more gracefully.

The beta-binomial distribution is now available in `scipy.stats.betabinom`.

`scipy.stats.zscore`, `scipy.stats.circmean`, `scipy.stats.circstd`, and
`scipy.stats.circvar` now support the ``nan_policy`` argument for enhanced
handling of ``NaN`` values

`scipy.stats.entropy` now accepts an ``axis`` argument

`scipy.stats.gaussian_kde.resample` now accepts a ``seed`` argument to empower
reproducibility

`scipy.stats.multiscale_graphcorr` has been added for calculation of the
multiscale graph correlation (MGC) test statistic

`scipy.stats.kendalltau` performance has improved, especially for large inputs,
due to improved cache usage

`scipy.stats.truncnorm` distribution has been rewritten to support much wider
tails


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

`scipy` deprecations
-----------------------
Support for NumPy functions exposed via the root SciPy namespace is deprecated
and will be removed in 2.0.0. For example, if you use ``scipy.rand`` or
``scipy.diag``, you should change your code to directly use
``numpy.random.default_rng`` or ``numpy.diag``, respectively.
They remain available in the currently continuing Scipy 1.x release series.

The exception to this rule is using ``scipy.fft`` as a function --
:mod:`scipy.fft` is now meant to be used only as a module, so the ability to
call ``scipy.fft(...)`` will be removed in SciPy 1.5.0.

In `scipy.spatial.Rotation` methods ``from_dcm``, ``as_dcm`` were renamed to 
``from_matrix``, ``as_matrix`` respectively. The old names will be removed in 
SciPy 1.6.0.

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

`scipy.special` changes
-----------------------------
The deprecated functions ``hyp2f0``, ``hyp1f2``, and ``hyp3f0`` have been
removed.

The deprecated function ``bessel_diff_formula`` has been removed.

The function ``i0`` is no longer registered with ``numpy.dual``, so that 
``numpy.dual.i0`` will unconditionally refer to the NumPy version regardless 
of whether `scipy.special` is imported.

The function ``expn`` has been changed to return ``nan`` outside of its 
domain of definition (``x, n < 0``) instead of ``inf``.

`scipy.sparse` changes
-----------------------------
Sparse matrix reshape now raises an error if shape is not two-dimensional, 
rather than guessing what was meant. The behavior is now the same as before 
SciPy 1.1.0.


`scipy.spatial` changes
--------------------------
The default behavior of the ``match_vectors`` method of 
`scipy.spatial.transform.Rotation` was changed for input vectors 
that are not normalized and not of equal lengths.
Previously, such vectors would be normalized within the method.  
Now, the calculated rotation takes the vector length into account, longer 
vectors will have a larger weight. For more details, see 
https://github.com/scipy/scipy/issues/10968.

`scipy.signal` changes
-------------------------
`scipy.signal.resample` behavior for length-1 signal inputs has been
fixed to output a constant (DC) value rather than an impulse, consistent with
the assumption of signal periodicity in the FFT method.

`scipy.signal.cwt` now performs complex conjugation and time-reversal of
wavelet data, which is a backwards-incompatible bugfix for
time-asymmetric wavelets.

`scipy.stats` changes
------------------------
`scipy.stats.loguniform` added with better documentation as (an alias for
``scipy.stats.reciprocal``). ``loguniform`` generates random variables
that are equally likely in the log space; e.g., ``1``, ``10`` and ``100``
are all equally likely if ``loguniform(10 ** 0, 10 ** 2).rvs()`` is used.


Other changes
=============
The ``LSODA`` method of `scipy.integrate.solve_ivp` now correctly detects stiff
problems.

`scipy.spatial.cKDTree` now accepts and correctly handles empty input data

`scipy.stats.binned_statistic_dd` now calculates the standard deviation 
statistic in a numerically stable way.

`scipy.stats.binned_statistic_dd` now throws an error if the input data 
contains either ``np.nan`` or ``np.inf``. Similarly, in `scipy.stats` now all 
continuous distributions' ``.fit()`` methods throw an error if the input data
contain any instance of either ``np.nan`` or ``np.inf``.


Authors
=======

* endolith
* Abhinav +
* Anne Archibald
* ashwinpathak20nov1996 +
* Danilo Augusto +
* Nelson Auner +
* aypiggott +
* Christoph Baumgarten
* Peter Bell
* Sebastian Berg
* Arman Bilge +
* Benedikt Boecking +
* Christoph Boeddeker +
* Daniel Bunting
* Evgeni Burovski
* Angeline Burrell +
* Angeline G. Burrell +
* CJ Carey
* Carlos Ramos Carreño +
* Mak Sze Chun +
* Malayaja Chutani +
* Christian Clauss +
* Jonathan Conroy +
* Stephen P Cook +
* Dylan Cutler +
* Anirudh Dagar +
* Aidan Dang +
* dankleeman +
* Brandon David +
* Tyler Dawson +
* Dieter Werthmüller
* Joe Driscoll +
* Jakub Dyczek +
* Dávid Bodnár
* Fletcher Easton +
* Stefan Endres
* etienne +
* Johann Faouzi
* Yu Feng
* Isuru Fernando +
* Matthew H Flamm
* Martin Gauch +
* Gabriel Gerlero +
* Ralf Gommers
* Chris Gorgolewski +
* Domen Gorjup +
* Edouard Goudenhoofdt +
* Jan Gwinner +
* Maja Gwozdz +
* Matt Haberland
* hadshirt +
* Pierre Haessig +
* David Hagen
* Charles Harris
* Gina Helfrich +
* Alex Henrie +
* Francisco J. Hernandez Heras +
* Andreas Hilboll
* Lindsey Hiltner
* Thomas Hisch
* Min ho Kim +
* Gert-Ludwig Ingold
* jakobjakobson13 +
* Todd Jennings
* He Jia
* Muhammad Firmansyah Kasim +
* Andrew Knyazev +
* Holger Kohr +
* Mateusz Konieczny +
* Krzysztof Pióro +
* Philipp Lang +
* Peter Mahler Larsen +
* Eric Larson
* Antony Lee
* Gregory R. Lee
* Chelsea Liu +
* Jesse Livezey
* Peter Lysakovski +
* Jason Manley +
* Michael Marien +
* Nikolay Mayorov
* G. D. McBain +
* Sam McCormack +
* Melissa Weber Mendonça +
* Kevin Michel +
* mikeWShef +
* Sturla Molden
* Eric Moore
* Peyton Murray +
* Andrew Nelson
* Clement Ng +
* Juan Nunez-Iglesias
* Renee Otten +
* Kellie Ottoboni +
* Ayappan P
* Sambit Panda +
* Tapasweni Pathak +
* Oleksandr Pavlyk
* Fabian Pedregosa
* Petar Mlinarić
* Matti Picus
* Marcel Plch +
* Christoph Pohl +
* Ilhan Polat
* Siddhesh Poyarekar +
* Ioannis Prapas +
* James Alan Preiss +
* Yisheng Qiu +
* Eric Quintero
* Bharat Raghunathan +
* Tyler Reddy
* Joscha Reimer
* Antonio Horta Ribeiro
* Lucas Roberts
* rtshort +
* Josua Sassen
* Kevin Sheppard
* Scott Sievert
* Leo Singer
* Kai Striega
* Søren Fuglede Jørgensen
* tborisow +
* Étienne Tremblay +
* tuxcell +
* Miguel de Val-Borro
* Andrew Valentine +
* Hugo van Kemenade
* Paul van Mulbregt
* Sebastiano Vigna
* Pauli Virtanen
* Dany Vohl +
* Ben Walsh +
* Huize Wang +
* Warren Weckesser
* Anreas Weh +
* Joseph Weston +
* Adrian Wijaya +
* Timothy Willard +
* Josh Wilson
* Kentaro Yamamoto +
* Dave Zbarsky +

A total of 141 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.

1.3.3

compared to `1.3.2`. In particular, a test suite issue
involving multiprocessing was fixed for Windows and
Python `3.8` on macOS. 

Wheels were also updated to place `msvcp140.dll` at the 
appropriate location, which was previously causing issues.

Authors
=======

Ilhan Polat
Tyler Reddy
Ralf Gommers

1.3.2

SciPy `1.3.2` is a bug-fix and maintenance release that adds support for Python `3.8`.

Authors
=====

* CJ Carey
* Dany Vohl
* Martin Gauch +
* Ralf Gommers
* Matt Haberland
* Eric Larson
* Nikolay Mayorov
* Sam McCormack +
* Andrew Nelson
* Tyler Reddy
* Pauli Virtanen
* Huize Wang +
* Warren Weckesser
* Joseph Weston +

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.

1.3.1

SciPy `1.3.1` is a bug-fix release with no new features compared to `1.3.0`.

Authors
=======

* Matt Haberland
* Geordie McBain
* Yu Feng
* Evgeni Burovski
* Sturla Molden
* Tapasweni Pathak
* Eric Larson
* Peter Bell
* Carlos Ramos Carreño +
* Ralf Gommers
* David Hagen
* Antony Lee
* Ayappan P
* Tyler Reddy
* Pauli Virtanen

A total of 15 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.

1.3.0

many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been some 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
1.3.x branch, and on adding new features on the master branch.

This release requires Python 3.5+ and NumPy 1.13.3 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

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

- Three new ``stats`` functions, a rewrite of ``pearsonr``, and an exact
computation of the Kolmogorov-Smirnov two-sample test
- A new Cython API for bounded scalar-function root-finders in `scipy.optimize`
- Substantial ``CSR`` and ``CSC`` sparse matrix indexing performance
improvements
- Added support for interpolation of rotations with continuous angular
rate and acceleration in ``RotationSpline``


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

`scipy.interpolate` improvements
--------------------------------

A new class ``CubicHermiteSpline`` is introduced. It is a piecewise-cubic 
interpolator which matches observed values and first derivatives. Existing 
cubic interpolators ``CubicSpline``, ``PchipInterpolator`` and 
``Akima1DInterpolator`` were made subclasses of ``CubicHermiteSpline``.

`scipy.io` improvements
-----------------------

For the Attribute-Relation File Format (ARFF) `scipy.io.arff.loadarff` 
now supports relational attributes.

`scipy.io.mmread` can now parse Matrix Market format files with empty lines.

`scipy.linalg` improvements
---------------------------

Added wrappers for ``?syconv`` routines, which convert a symmetric matrix 
given by a triangular matrix factorization into two matrices and vice versa.

`scipy.linalg.clarkson_woodruff_transform` now uses an algorithm that leverages
sparsity. This may provide a 60-90 percent speedup for dense input matrices.
Truly sparse input matrices should also benefit from the improved sketch
algorithm, which now correctly runs in ``O(nnz(A))`` time.

Added new functions to calculate symmetric Fiedler matrices and
Fiedler companion matrices, named `scipy.linalg.fiedler` and 
`scipy.linalg.fiedler_companion`, respectively. These may be used
for root finding.

`scipy.ndimage` improvements
----------------------------

Gaussian filter performances may improve by an order of magnitude in
some cases, thanks to removal of a dependence on ``np.polynomial``. This
may impact `scipy.ndimage.gaussian_filter` for example.

`scipy.optimize` improvements
-----------------------------

The `scipy.optimize.brute` minimizer obtained a new keyword ``workers``, which
can be used to parallelize computation.

A Cython API for bounded scalar-function root-finders in `scipy.optimize`
is available in a new module `scipy.optimize.cython_optimize` via ``cimport``.
This API may be used with ``nogil`` and ``prange`` to loop 
over an array of function arguments to solve for an array of roots more 
quickly than with pure Python.

``'interior-point'`` is now the default method for ``linprog``, and 
``'interior-point'`` now uses SuiteSparse for sparse problems when the 
required scikits  (scikit-umfpack and scikit-sparse) are available. 
On benchmark problems (gh-10026), execution time reductions by factors of 2-3 
were typical. Also, a new ``method='revised simplex'`` has been added. 
It is not as fast or robust as ``method='interior-point'``, but it is a faster,
more robust, and equally accurate substitute for the legacy 
``method='simplex'``.

``differential_evolution`` can now use a ``Bounds`` class to specify the
bounds for the optimizing argument of a function.

`scipy.optimize.dual_annealing` performance improvements related to
vectorisation of some internal code.

`scipy.signal` improvements
---------------------------

Two additional methods of discretization are now supported by 
`scipy.signal.cont2discrete`: ``impulse`` and ``foh``.

`scipy.signal.firls` now uses faster solvers

`scipy.signal.detrend` now has a lower physical memory footprint in some
cases, which may be leveraged using the new ``overwrite_data`` keyword argument

`scipy.signal.firwin` ``pass_zero`` argument now accepts new string arguments
that allow specification of the desired filter type: ``'bandpass'``,
``'lowpass'``, ``'highpass'``, and ``'bandstop'``

`scipy.signal.sosfilt` may have improved performance due to lower retention
of the global interpreter lock (GIL) in algorithm

`scipy.sparse` improvements
---------------------------

A new keyword was added to ``csgraph.dijsktra`` that 
allows users to query the shortest path to ANY of the passed in indices,
as opposed to the shortest path to EVERY passed index.

`scipy.sparse.linalg.lsmr` performance has been improved by roughly 10 percent
on large problems

Improved performance and reduced physical memory footprint of the algorithm
used by `scipy.sparse.linalg.lobpcg`

``CSR`` and ``CSC`` sparse matrix fancy indexing performance has been
improved substantially

`scipy.spatial` improvements
----------------------------

`scipy.spatial.ConvexHull` now has a ``good`` attribute that can be used 
alongsize the ``QGn`` Qhull options to determine which external facets of a 
convex hull are visible from an external query point.

`scipy.spatial.cKDTree.query_ball_point` has been modernized to use some newer 
Cython features, including GIL handling and exception translation. An issue 
with ``return_sorted=True`` and scalar queries was fixed, and a new mode named 
``return_length`` was added. ``return_length`` only computes the length of the 
returned indices list instead of allocating the array every time.

`scipy.spatial.transform.RotationSpline` has been added to enable interpolation
of rotations with continuous angular rates and acceleration

`scipy.stats` improvements
--------------------------

Added a new function to compute the Epps-Singleton test statistic,
`scipy.stats.epps_singleton_2samp`, which can be applied to continuous and
discrete distributions.

New functions `scipy.stats.median_absolute_deviation` and `scipy.stats.gstd`
(geometric standard deviation) were added. The `scipy.stats.combine_pvalues` 
method now supports ``pearson``,  ``tippett`` and ``mudholkar_george`` pvalue 
combination methods.

The `scipy.stats.ortho_group` and `scipy.stats.special_ortho_group` 
``rvs(dim)`` functions' algorithms were updated from a ``O(dim^4)`` 
implementation to a ``O(dim^3)`` which gives large speed improvements 
for ``dim>100``.

A rewrite of `scipy.stats.pearsonr` to use a more robust algorithm,
provide meaningful exceptions and warnings on potentially pathological input,
and fix at least five separate reported issues in the original implementation.

Improved the precision of ``hypergeom.logcdf`` and ``hypergeom.logsf``.

Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing
the previously approximate computation for the two-sided test `stats.ks_2samp`.
Also added a one-sided, two-sample KS test, and a keyword ``alternative`` to 
`stats.ks_2samp`.

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

`scipy.interpolate` changes
---------------------------

Functions from ``scipy.interpolate`` (``spleval``, ``spline``, ``splmake``,
and ``spltopp``) and functions from ``scipy.misc`` (``bytescale``,
``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
``imsave``, ``imshow``, ``toimage``) have been removed. The former set has 
been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
Similarly, aliases from ``scipy.misc`` (``comb``, ``factorial``,
``factorial2``, ``factorialk``, ``logsumexp``, ``pade``, ``info``, ``source``,
``who``) which have been deprecated since v1.0.0 are removed. 
`SciPy documentation for

1.2.2

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

SciPy `1.2.2` is a bug-fix release with no new features compared to `1.2.1`.
Importantly, the SciPy 1.2.2 wheels are built with OpenBLAS `0.3.7.dev` to
alleviate issues with SkylakeX AVX512 kernels.

Authors
=======

* CJ Carey
* Tyler Dawson +
* Ralf Gommers
* Kai Striega
* Andrew Nelson
* Tyler Reddy
* Kevin Sheppard +

A total of 7 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.

1.2.1

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

SciPy `1.2.1` is a bug-fix release with no new features compared to `1.2.0`.
Most importantly, it solves the issue that `1.2.0` cannot be installed
from source on Python `2.7` because of non-ASCII character issues.

It is also notable that SciPy `1.2.1` wheels were built with OpenBLAS

1.2.0

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

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

**Note**: This will be the last SciPy release to support Python 2.7.
       Consequently, the 1.2.x series will be a long term support (LTS)
       release; we will backport bug fixes until 1 Jan 2020.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

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

- 1-D root finding improvements with a new solver, ``toms748``, and a new
unified interface, ``root_scalar``
- New ``dual_annealing`` optimization method that combines stochastic and
local deterministic searching
- A new optimization algorithm, ``shgo`` (simplicial homology
global optimization) for derivative free optimization problems
- A new category of quaternion-based transformations are available in
`scipy.spatial.transform`

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

`scipy.ndimage` improvements
--------------------------------

Proper spline coefficient calculations have been added for the ``mirror``,
``wrap``, and ``reflect`` modes of `scipy.ndimage.rotate`

`scipy.fftpack` improvements
--------------------------------

DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in
`scipy.fftpack`.

`scipy.interpolate` improvements
--------------------------------

`scipy.interpolate.pade` now accepts a new argument for the order of the
numerator

`scipy.cluster` improvements
----------------------------

`scipy.cluster.vq.kmeans2` gained a new initialization method, kmeans++.

`scipy.special` improvements
----------------------------

The function ``softmax`` was added to `scipy.special`.

`scipy.optimize` improvements
-----------------------------

The one-dimensional nonlinear solvers have been given a unified interface
`scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface
for multi-dimensional solvers. ``scipy.optimize.root_scalar(f, bracket=[a ,b],
method="brenth")`` is equivalent to ``scipy.optimize.brenth(f, a ,b)``.  If no
``method`` is specified, an appropriate one will be selected based upon the
bracket and the number of derivatives available.

The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within
an enclosing interval has been added as `scipy.optimize.toms748`. This provides
guaranteed convergence to a root with convergence rate per function evaluation
of approximately 1.65 (for sufficiently well-behaved functions.)

``differential_evolution`` now has the ``updating`` and ``workers`` keywords.
The first chooses between continuous updating of the best solution vector (the
default), or once per generation. Continuous updating can lead to faster
convergence. The ``workers`` keyword accepts an ``int`` or map-like callable,
and parallelises the solver (having the side effect of updating once per
generation). Supplying an ``int`` evaluates the trial solutions in N parallel
parts. Supplying a map-like callable allows other parallelisation approaches
(such as ``mpi4py``, or ``joblib``) to be used.

``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose
global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing
processes to accelerate the convergence towards the global minimum of an
objective mathematical function. The first annealing process controls the
stochastic Markov chain searching and the second annealing process controls the
deterministic minimization. So, dual annealing is a hybrid method that takes
advantage of stochastic and local deterministic searching in an efficient way.

``shgo`` (simplicial homology global optimization) is a similar algorithm
appropriate for solving black box and derivative free optimization (DFO)
problems. The algorithm generally converges to the global solution in finite
time. The convergence holds for non-linear inequality and
equality constraints. In addition to returning a global minimum, the
algorithm also returns any other global and local minima found after every
iteration. This makes it useful for exploring the solutions in a domain.

`scipy.optimize.newton` can now accept a scalar or an array

``MINPACK`` usage is now thread-safe, such that ``MINPACK`` + callbacks may
be used on multiple threads.

`scipy.signal` improvements
---------------------------

Digital filter design functions now include a parameter to specify the sampling
rate. Previously, digital filters could only be specified using normalized
frequency, but different functions used different scales (e.g. 0 to 1 for
``butter`` vs 0 to π for ``freqz``), leading to errors and confusion.  With
the ``fs`` parameter, ordinary frequencies can now be entered directly into
functions, with the normalization handled internally.

``find_peaks`` and related functions no longer raise an exception if the
properties of a peak have unexpected values (e.g. a prominence of 0). A
``PeakPropertyWarning`` is given instead.

The new keyword argument ``plateau_size`` was added to ``find_peaks``.
``plateau_size`` may be used to select peaks based on the length of the
flat top of a peak.

``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation
of a median average PSD, using ``average='mean'`` keyword

`scipy.sparse` improvements
---------------------------

The `scipy.sparse.bsr_matrix.tocsr` method is now implemented directly instead
of converting via COO format, and the `scipy.sparse.bsr_matrix.tocsc` method
is now also routed via CSR conversion instead of COO. The efficiency of both
conversions is now improved.

The issue where SuperLU or UMFPACK solvers crashed on matrices with
non-canonical format in `scipy.sparse.linalg` was fixed. The solver wrapper
canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK
solver.

The ``largest`` option of `scipy.sparse.linalg.lobpcg()` was fixed to have
a correct (and expected) behavior. The order of the eigenvalues was made
consistent with the ARPACK solver (``eigs()``), i.e. ascending for the
smallest eigenvalues, and descending for the largest eigenvalues.

The `scipy.sparse.random` function is now faster and also supports integer and
complex values by passing the appropriate value to the ``dtype`` argument.

`scipy.spatial` improvements
----------------------------

The function `scipy.spatial.distance.jaccard` was modified to return 0 instead
of ``np.nan`` when two all-zero vectors are compared.

Support for the Jensen Shannon distance, the square-root of the divergence, has
been added under `scipy.spatial.distance.jensenshannon`

An optional keyword was added to the function
`scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned
indices. Not sorting the indices can speed up calls.

A new category of quaternion-based transformations are available in
`scipy.spatial.transform`, including spherical linear interpolation of
rotations (``Slerp``), conversions to and from quaternions, Euler angles,
and general rotation and inversion capabilities
(`spatial.transform.Rotation`), and uniform random sampling of 3D
rotations (`spatial.transform.Rotation.random`).

`scipy.stats` improvements
--------------------------

The Yeo-Johnson power transformation is now supported (``yeojohnson``,
``yeojohnson_llf``, ``yeojohnson_normmax``, ``yeojohnson_normplot``). Unlike
the Box-Cox transformation, the Yeo-Johnson transformation can accept negative
values.

Added a general method to sample random variates based on the density only, in
the new function ``rvs_ratio_uniforms``.

The Yule-Simon distribution (``yulesimon``) was added -- this is a new
discrete probability distribution.

``stats`` and ``mstats`` now have access to a new regression method,
``siegelslopes``, a robust linear regression algorithm

`scipy.stats.gaussian_kde` now has the ability to deal with weighted samples,
and should have a modest improvement in performance

Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported
for `scipy.stats.levy_stable`.

The Brunner-Munzel test is now available as ``brunnermunzel`` in ``stats``
and ``mstats``

`scipy.linalg` improvements
--------------------------

`scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular
Full Packed storage (RFP) for upper triangular, lower triangular, symmetric,
or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition
routines are now available as well.

Deprecated features
===================
The functions ``hyp2f0``, ``hyp1f2`` and ``hyp3f0`` in ``scipy.special`` have
been deprecated.


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

LAPACK version 3.4.0 or later is now required. Building with
Apple Accelerate is no longer supported.

The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for all angles. Before this, the function only returned
correct values for those angles which were greater than pi/4.

Support for the Bento build system has been removed. Bento has not been
maintained for several years, and did not have good Python 3 or wheel support,
hence it was time to remove it.

The required signature of `scipy.optimize.lingprog` ``method=simplex``
callback function has changed. Before iteration begins, the simplex solver
first converts the problem into a standard form that does not, in general,
have the same variables or constraints
as the problem defined by the user. Previously, the simplex solver would pass a
user-specified callback function several separate arguments, such as the
current solution vector ``xk``, corresponding to this standard form problem.
Unfortunately, the relationship between the standard form problem and the
user-defined problem was not documented, limiting the utility of the
information passed to the callback function.

In addition to numerous bug fix changes, the simplex solver now passes a
user-specified callback function a single ``OptimizeResult`` object containing
information that corresponds directly to the user-defined problem. In future
releases, this ``OptimizeResult`` object may be expanded to include additional
information, such as variables corresponding to the standard-form problem and
information concerning the relationship between the standard-form and
user-defined problems.

The implementation of `scipy.sparse.random` has changed, and this affects the
numerical values returned for both ``sparse.random`` and ``sparse.rand`` for
some matrix shapes and a given seed.

`scipy.optimize.newton` will no longer use Halley's method in cases where it
negatively impacts convergence

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


Authors
=======

* endolith
* luzpaz
* Hameer Abbasi +
* akahard2dj +
* Anton Akhmerov
* Joseph Albert
* alexthomas93 +
* ashish +
* atpage +
* Blair Azzopardi +
* Yoshiki Vázquez Baeza
* Bence Bagi +
* Christoph Baumgarten
* Lucas Bellomo +
* BH4 +
* Aditya Bharti
* Max Bolingbroke
* François Boulogne
* Ward Bradt +
* Matthew Brett
* Evgeni Burovski
* Rafał Byczek +
* Alfredo Canziani +
* CJ Carey
* Lucía Cheung +
* Poom Chiarawongse +
* Jeanne Choo +
* Robert Cimrman
* Graham Clenaghan +
* cynthia-rempel +
* Johannes Damp +
* Jaime Fernandez del Rio
* Dowon +
* emmi474 +
* Stefan Endres +
* Thomas Etherington +
* Alex Fikl +
* fo40225 +
* Joseph Fox-Rabinovitz
* Lars G
* Abhinav Gautam +
* Stiaan Gerber +
* C.A.M. Gerlach +
* Ralf Gommers
* Todd Goodall
* Lars Grueter +
* Sylvain Gubian +
* Matt Haberland
* David Hagen
* Will Handley +
* Charles Harris
* Ian Henriksen
* Thomas Hisch +
* Theodore Hu
* Michael Hudson-Doyle +
* Nicolas Hug +
* jakirkham +
* Jakob Jakobson +
* James +
* Jan Schlüter
* jeanpauphilet +
* josephmernst +
* Kai +
* Kai-Striega +
* kalash04 +
* Toshiki Kataoka +
* Konrad0 +
* Tom Krauss +
* Johannes Kulick
* Lars Grüter +
* Eric Larson
* Denis Laxalde
* Will Lee +
* Katrin Leinweber +
* Yin Li +
* P. L. Lim +
* Jesse Livezey +
* Duncan Macleod +
* MatthewFlamm +
* Nikolay Mayorov
* Mike McClurg +
* Christian Meyer +
* Mark Mikofski
* Naoto Mizuno +
* mohmmadd +
* Nathan Musoke
* Anju Geetha Nair +
* Andrew Nelson
* Ayappan P +
* Nick Papior
* Haesun Park +
* Ronny Pfannschmidt +
* pijyoi +
* Ilhan Polat
* Anthony Polloreno +
* Ted Pudlik
* puenka
* Eric Quintero
* Pradeep Reddy Raamana +
* Vyas Ramasubramani +
* Ramon Viñas +
* Tyler Reddy
* Joscha Reimer
* Antonio H Ribeiro
* richardjgowers +
* Rob +
* robbystk +
* Lucas Roberts +
* rohan +
* Joaquin Derrac Rus +
* Josua Sassen +
* Bruce Sharpe +
* Max Shinn +
* Scott Sievert
* Sourav Singh
* Strahinja Lukić +
* Kai Striega +
* Shinya SUZUKI +
* Mike Toews +
* Piotr Uchwat
* Miguel de Val-Borro +
* Nicky van Foreest
* Paul van Mulbregt
* Gael Varoquaux
* Pauli Virtanen
* Stefan van der Walt
* Warren Weckesser
* Joshua Wharton +
* Bernhard M. Wiedemann +
* Eric Wieser
* Josh Wilson
* Tony Xiang +
* Roman Yurchak +
* Roy Zywina +

A total of 137 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Links

• PyPI: https://pypi.org/project/scipy
• Changelog: https://pyup.io/changelogs/scipy/
• Repo: https://github.com/scipy/scipy/releases
• Homepage: https://www.scipy.org