Update scipy to 1.3.0

[pyup-bot]

This PR updates scipy from 1.0.0 to 1.3.0.

Changelog

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

1.2.1

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

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.

1.1.0

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

**Note: Scipy 1.1.0 is not released yet!**

SciPy 1.1.0 is the culmination of 7 months of hard work. It contains 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 1.1.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.

This release has improved but not necessarily 100% compatibility with the [PyPy](https://pypy.org/) Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.

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

scipy.integrate improvements

The argument `tfirst` has been added to the function scipy.integrate.odeint. This allows odeint to use the same user functions as scipy.integrate.solve\_ivp and scipy.integrate.ode without the need for wrapping them in a function that swaps the first two arguments.

Error messages from `quad()` are now clearer.

scipy.linalg improvements

The function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices.

Python wrappers for LAPACK `sygst`, `hegst` added in scipy.linalg.lapack.

Added scipy.linalg.null\_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf.

scipy.misc improvements

An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram.

scipy.ndimage improvements

The routines scipy.ndimage.binary\_opening, and scipy.ndimage.binary\_closing now support masks and different border values.

scipy.optimize improvements

The method `trust-constr` has been added to scipy.optimize.minimize. The method switches between two implementations depending on the problem definition. For equality constrained problems it is an implementation of a trust-region sequential quadratic programming solver and, when inequality constraints are imposed, it switches to a trust-region interior point method. Both methods are appropriate for large scale
problems. Quasi-Newton options BFGS and SR1 were implemented and can be used to approximate second order derivatives for this new method. Also, finite-differences can be used to approximate either first-order or
second-order derivatives.

Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were added to scipy.optimize.differential\_evolution as `randtobest1bin` and `randtobest1exp`, respectively. Note: These names were already in use but implemented a different mutation strategy. See [Backwards incompatible changes](backwards-incompatible-changes), below. The `init` keyword for the scipy.optimize.differential\_evolution function can now accept an array. This array allows the user to specify the
entire population.

Add an `adaptive` option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem.

Minor improvements in scipy.optimize.basinhopping.

scipy.signal improvements

Three new functions for peak finding in one-dimensional arrays were added. scipy.signal.find\_peaks searches for peaks (local maxima) based on simple value comparison of neighbouring samples and returns those
peaks whose properties match optionally specified conditions for their height, prominence, width, threshold and distance to each other. scipy.signal.peak\_prominences and scipy.signal.peak\_widths can directly calculate the prominences or widths of known peaks.

Added ZPK versions of frequency transformations: scipy.signal.bilinear\_zpk, scipy.signal.lp2bp\_zpk, scipy.signal.lp2bs\_zpk, scipy.signal.lp2hp\_zpk, scipy.signal.lp2lp\_zpk.

Added scipy.signal.windows.dpss, scipy.signal.windows.general\_cosine and scipy.signal.windows.general\_hamming.

scipy.sparse improvements

An in-place `resize` method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok\_matrix in previous releases.

scipy.special improvements

Added Owen's T function as scipy.special.owens\_t.

Accuracy improvements in `chndtr`, `digamma`, `gammaincinv`, `lambertw`, `zetac`.

scipy.stats improvements

The Moyal distribution has been added as scipy.stats.moyal.

Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss.

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

The iterative linear equation solvers in scipy.sparse.linalg had a sub-optimal way of how absolute tolerance is considered. The default behavior will be changed in a future Scipy release to a more standard and less surprising one. To silence deprecation warnings, set the `atol=` parameter explicitly.

scipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss.

The window functions in scipy.signal are now available in scipy.signal.windows. They will remain also available in the old location in the scipy.signal namespace in future Scipy versions. However, importing them from scipy.signal.windows is preferred, and new window functions will be added only there.

Indexing sparse matrices with floating-point numbers instead of integers is deprecated.

The function scipy.stats.itemfreq is deprecated.

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

Previously, scipy.linalg.orth used a singular value cutoff value appropriate for double precision numbers also for single-precision input. The cutoff value is now tunable, and the default has been changed to depend on the input data precision.

In previous versions of Scipy, the `randtobest1bin` and `randtobest1exp` mutation strategies in scipy.optimize.differential\_evolution were actually implemented using the Current-to-Best/1/bin and Current-to-Best/1/exp strategies, respectively. These strategies were renamed to `currenttobest1bin` and `currenttobest1exp` and the implementations of `randtobest1bin` and `randtobest1exp` strategies were corrected.

Functions in the ndimage module now always return their output array. Before this most functions only returned the output array if it had been allocated by the function, and would return `None` if it had been provided by the user.

Distance metrics in scipy.spatial.distance now require non-negative weights.

scipy.special.loggamma returns now real-valued result when the input is real-valued.

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

When building on Linux with GNU compilers, the `.so` Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter.

Authors
-------

-   Saurabh Agarwal +
-   Diogo Aguiam +
-   Joseph Albert +
-   Gerrit Ansmann +
-   Astrofysicus +
-   Jean-François B +
-   Vahan Babayan +
-   Alessandro Pietro Bardelli
-   Christoph Baumgarten +
-   Felix Berkenkamp
-   Lilian Besson +
-   Aditya Bharti +
-   Matthew Brett
-   Evgeni Burovski
-   CJ Carey
-   Martin Ø. Christensen +
-   Robert Cimrman
-   Vicky Close +
-   Peter Cock +
-   Philip DeBoer
-   Jaime Fernandez del Rio
-   Dieter Werthmüller +
-   Tom Donoghue +
-   Matt Dzugan +
-   Lars G +
-   Jacques Gaudin +
-   Andriy Gelman +
-   Sean Gillies +
-   Dezmond Goff
-   Christoph Gohlke
-   Ralf Gommers
-   Uri Goren +
-   Deepak Kumar Gouda +
-   Douglas Lessa Graciosa +
-   Matt Haberland
-   David Hagen
-   Charles Harris
-   Jordan Heemskerk +
-   Danny Hermes +
-   Stephan Hoyer +
-   Theodore Hu +
-   Jean-François B. +
-   Mads Jensen +
-   Jon Haitz Legarreta Gorroño +
-   Ben Jude +
-   Noel Kippers +
-   Julius Bier Kirkegaard +
-   Maria Knorps +
-   Mikkel Kristensen +
-   Eric Larson
-   Kasper Primdal Lauritzen +
-   Denis Laxalde
-   KangWon Lee +
-   Jan Lehky +
-   Jackie Leng +
-   P.L. Lim +
-   Nikolay Mayorov
-   Mihai Capotă +
-   Max Mikhaylov +
-   Mark Mikofski +
-   Jarrod Millman
-   Raden Muhammad +
-   Paul Nation
-   Andrew Nelson
-   Nico Schlömer
-   Joel Nothman
-   Kyle Oman +
-   Egor Panfilov +
-   Nick Papior
-   Anubhav Patel +
-   Oleksandr Pavlyk
-   Ilhan Polat
-   Robert Pollak +
-   Anant Prakash +
-   Aman Pratik
-   Sean Quinn +
-   Giftlin Rajaiah +
-   Tyler Reddy
-   Joscha Reimer
-   Antonio H Ribeiro +
-   Antonio Horta Ribeiro
-   Benjamin Rose +
-   Fabian Rost
-   Divakar Roy +
-   Scott Sievert
-   Leo Singer
-   Sourav Singh
-   Martino Sorbaro +
-   Eric Stansifer +
-   Martin Thoma
-   Phil Tooley +
-   Piotr Uchwat +
-   Paul van Mulbregt
-   Pauli Virtanen
-   Stefan van der Walt
-   Warren Weckesser
-   Florian Weimer +
-   Eric Wieser
-   Josh Wilson
-   Ted Ying +
-   Evgeny Zhurko
-   Zé Vinícius
-   awakenting +
-   endolith
-   FormerPhysicist +
-   gaulinmp +
-   hugovk
-   ksemb +
-   kshitij12345 +
-   luzpaz +
-   NKrvavica +
-   rafalalgo +
-   samyak0210 +
-   soluwalana +
-   sudheerachary +
-   Tokixix +
-   tttthomasssss +
-   vkk800 +
-   xoviat
-   ziejcow +

A total of 122 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.

1.0.1

SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0.
Probably the most important change is a fix for an incompatibility between
SciPy 1.0.0 and ``numpy.f2py`` in the NumPy master branch.

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

[pyup-bot]

Closing this in favor of #243