Pin scipy to latest version 1.1.0

[pyup-bot]

This PR pins scipy to the latest release 1.1.0.

Changelog

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.

1.0.0

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.  Moreover, our development attention
will now shift to bug-fix releases on the 1.0.x branch, and on adding
new features on the master branch.

Some of the highlights of this release are:

- Major build improvements.  Windows wheels are available on PyPI for the
first time, and continuous integration has been set up on Windows and OS X
in addition to Linux.
- A set of new ODE solvers and a unified interface to them
(`scipy.integrate.solve_ivp`).
- Two new trust region optimizers and a new linear programming method, with
improved performance compared to what `scipy.optimize` offered previously.
- Many new BLAS and LAPACK functions were wrapped.  The BLAS wrappers are now
complete.

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

This is also the last release to support LAPACK 3.1.x - 3.3.x.  Moving the
lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate
providing the LAPACK 3.2.1 API.  We have decided that it's time to either drop
Accelerate or, if there is enough interest, provide shims for functions added
in more recent LAPACK versions so it can still be used.


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

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

`scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a
linkage matrix to minimize distances between adjacent leaves, was added.


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

N-dimensional versions of the discrete sine and cosine transforms and their
inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``.


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

A set of new ODE solvers have been added to `scipy.integrate`.  The convenience
function `scipy.integrate.solve_ivp` allows uniform access to all solvers.
The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``)
can also be used directly.


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

The BLAS wrappers in `scipy.linalg.blas` have been completed.  Added functions
are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``,
``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``,
``*spr2``,

Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``,
``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``,
``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added.

The function `scipy.linalg.subspace_angles` has been added to compute the
subspace angles between two matrices.

The function `scipy.linalg.clarkson_woodruff_transform` has been added.
It finds low-rank matrix approximation via the Clarkson-Woodruff Transform.

The functions `scipy.linalg.eigh_tridiagonal` and
`scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and
eigenvectors of tridiagonal hermitian/symmetric matrices, were added.


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

Support for homogeneous coordinate transforms has been added to
`scipy.ndimage.affine_transform`.

The ``ndimage`` C code underwent a significant refactoring, and is now
a lot easier to understand and maintain.


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

The methods ``trust-region-exact`` and ``trust-krylov`` have been added to the
function `scipy.optimize.minimize`. These new trust-region methods solve the
subproblem with higher accuracy at the cost of more Hessian factorizations
(compared to dogleg) or more matrix vector products (compared to ncg) but
usually require less nonlinear iterations and are able to deal with indefinite
Hessians. They seem very competitive against the other Newton methods
implemented in scipy.

`scipy.optimize.linprog` gained an interior point method.  Its performance is
superior (both in accuracy and speed) to the older simplex method.


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

An argument ``fs`` (sampling frequency) was added to the following functions:
``firwin``, ``firwin2``, ``firls``, and ``remez``.  This makes these functions
consistent with many other functions in `scipy.signal` in which the sampling
frequency can be specified.

`scipy.signal.freqz` has been sped up significantly for FIR filters.


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

Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%.

The ``tocsr`` method of COO matrices is now several times faster.

The ``diagonal`` method of sparse matrices now takes a parameter, indicating
which diagonal to return.


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

A new iterative solver for large-scale nonsymmetric sparse linear systems,
`scipy.sparse.linalg.gcrotmk`, was added.  It implements ``GCROT(m,k)``, a
flexible variant of ``GCROT``.

`scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially
faster convergence.

SuperLU was updated to version 5.2.1.


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

Many distance metrics in `scipy.spatial.distance` gained support for weights.

The signatures of `scipy.spatial.distance.pdist` and
`scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to
support a wider range of metrics (e.g. string-based metrics that need extra
keywords).  Also, an optional ``out`` parameter was added to ``pdist`` and
``cdist`` allowing the user to specify where the resulting distance matrix is
to be stored


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

The methods ``cdf`` and ``logcdf`` were added to
`scipy.stats.multivariate_normal`, providing the cumulative distribution
function of the multivariate normal distribution.

New statistical distance functions were added, namely
`scipy.stats.wasserstein_distance` for the first Wasserstein distance and
`scipy.stats.energy_distance` for the energy distance.


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

The following functions in `scipy.misc` are deprecated: ``bytescale``,
``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
``imsave``, ``imshow`` and ``toimage``.  Most of those functions have unexpected
behavior (like rescaling and type casting image data without the user asking
for that).  Other functions simply have better alternatives.

``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule
are deprecated.  This was a never finished set of wrapper functions which is
not relevant anymore.

The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the
dtypes of the input arrays in the future and checked that it is a scalar or
an array with a single element.


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

The following deprecated functions have been removed from `scipy.stats`:
``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``,
``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and
``threshold``.

The following deprecated functions have been removed from `scipy.stats.mstats`:
``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``.

The deprecated ``a`` and ``reta`` keywords have been removed from
`scipy.stats.shapiro`.

The deprecated functions ``sparse.csgraph.cs_graph_components`` and
``sparse.linalg.symeig`` have been removed from `scipy.sparse`.

The following deprecated keywords have been removed in `scipy.sparse.linalg`:
``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``,
``cgs``, ``gmres``, ``qmr`` and ``minres``.

The deprecated functions ``expm2`` and ``expm3`` have been removed from
`scipy.linalg`.  The deprecated keyword ``q`` was removed from
`scipy.linalg.expm`.  And the deprecated submodule ``linalg.calc_lwork`` was
removed.

The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and
``K2F`` have been removed from `scipy.constants`.

The deprecated ``ppform`` class was removed from `scipy.interpolate`.

The deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`.

The default value for the ``zero_phase`` keyword of `scipy.signal.decimate`
has been changed to True.

The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the
method used for random initialization, so using a fixed random seed will
not necessarily produce the same results as in previous versions.

`scipy.special.gammaln` does not accept complex arguments anymore.

The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``,
``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use
the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and
``spherical_kn``. Be aware that the new functions have different
signatures.

The cross-class properties of `scipy.signal.lti` systems have been removed.
The following properties/setters have been removed:

Name - (accessing/setting has been removed) - (setting has been removed)

* StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``)
* TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``)
* ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - ()

``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``.  This
was a corner case for which it was unclear that the behavior was well-defined.

The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than
an ndarray when the length of alpha is 1.


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

SciPy now has a formal governance structure.  It consists of a BDFL (Pauli
Virtanen) and a Steering Committee.  See `the governance document
<https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst>`_
for details.

It is now possible to build SciPy on Windows with MSVC + gfortran!  Continuous
integration has been set up for this build configuration on Appveyor, building
against OpenBLAS.

Continuous integration for OS X has been set up on TravisCI.

The SciPy test suite has been migrated from ``nose`` to ``pytest``.

``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to
add custom code that needs to run when importing SciPy (e.g. checks for
hardware, DLL search paths, etc.).

Support for PEP 518 (specifying build system requirements) was added - see
``pyproject.toml`` in the root of the SciPy repository.

In order to have consistent function names, the function
``scipy.linalg.solve_lyapunov`` is renamed to
`scipy.linalg.solve_continuous_lyapunov`.  The old name is kept for
backwards-compatibility.


Authors
=======

* arcady +
* xoviat +
* Anton Akhmerov
* Dominic Antonacci +
* Alessandro Pietro Bardelli
* Ved Basu +
* Michael James Bedford +
* Ray Bell +
* Juan M. Bello-Rivas +
* Sebastian Berg
* Felix Berkenkamp
* Jyotirmoy Bhattacharya +
* Matthew Brett
* Jonathan Bright
* Bruno Jiménez +
* Evgeni Burovski
* Patrick Callier
* Mark Campanelli +
* CJ Carey
* Adam Cox +
* Michael Danilov +
* David Haberthür +
* Andras Deak +
* Philip DeBoer
* Anne-Sylvie Deutsch
* Cathy Douglass +
* Dominic Else +
* Guo Fei +
* Roman Feldbauer +
* Yu Feng
* Jaime Fernandez del Rio
* Orestis Floros +
* David Freese +
* Adam Geitgey +
* James Gerity +
* Dezmond Goff +
* Christoph Gohlke
* Ralf Gommers
* Dirk Gorissen +
* Matt Haberland +
* David Hagen +
* Charles Harris
* Lam Yuen Hei +
* Jean Helie +
* Gaute Hope +
* Guillaume Horel +
* Franziska Horn +
* Yevhenii Hyzyla +
* Vladislav Iakovlev +
* Marvin Kastner +
* Mher Kazandjian
* Thomas Keck
* Adam Kurkiewicz +
* Ronan Lamy +
* J.L. Lanfranchi +
* Eric Larson
* Denis Laxalde
* Gregory R. Lee
* Felix Lenders +
* Evan Limanto
* Julian Lukwata +
* François Magimel
* Syrtis Major +
* Charles Masson +
* Nikolay Mayorov
* Tobias Megies
* Markus Meister +
* Roman Mirochnik +
* Jordi Montes +
* Nathan Musoke +
* Andrew Nelson
* M.J. Nichol
* Nico Schlömer +
* Juan Nunez-Iglesias
* Arno Onken +
* Dima Pasechnik +
* Ashwin Pathak +
* Stefan Peterson
* Ilhan Polat
* Andrey Portnoy +
* Ravi Kumar Prasad +
* Aman Pratik
* Eric Quintero
* Vedant Rathore +
* Tyler Reddy
* Joscha Reimer
* Philipp Rentzsch +
* Antonio Horta Ribeiro
* Ned Richards +
* Kevin Rose +
* Benoit Rostykus +
* Matt Ruffalo +
* Eli Sadoff +
* Pim Schellart
* Klaus Sembritzki +
* Nikolay Shebanov +
* Jonathan Tammo Siebert
* Scott Sievert
* Max Silbiger +
* Mandeep Singh +
* Michael Stewart +
* Jonathan Sutton +
* Deep Tavker +
* Martin Thoma
* James Tocknell +
* Aleksandar Trifunovic +
* Paul van Mulbregt +
* Jacob Vanderplas
* Aditya Vijaykumar
* Pauli Virtanen
* James Webber
* Warren Weckesser
* Eric Wieser +
* Josh Wilson
* Zhiqing Xiao +
* Evgeny Zhurko
* Nikolay Zinov +
* Zé Vinícius +

A total of 118 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.

1.0.0b1

*This is the beta release for SciPy 1.0.0*

0.19.1

- - gh-7211: BUG: convolve may yield inconsistent dtypes with method changed
- - gh-7216: BUG: integrate: fix refcounting bug in quad()
- - gh-7229: MAINT: special: Rewrite a test of wrightomega
- - gh-7261: FIX: Corrected the transformation matrix permutation
- - gh-7265: BUG: Fix broken axis handling in spectral functions
- - gh-7266: FIX 7262: ckdtree crashes in query_knn.
- - gh-7279: Upcast half- and single-precision floats to doubles in BSpline...
- - gh-7336: BUG: Fix signal.dfreqresp for StateSpace systems
- - gh-7419: Fix several issues in ``sparse.load_npz``, ``save_npz``
- - gh-7420: BUG: stats: allow integers as kappa4 shape parameters

0.19.0

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.  Moreover, our development attention
will now shift to bug-fix releases on the 0.19.x branch, and on adding
new features on the master branch.

This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater.

Highlights of this release include:

- - A unified foreign function interface layer, `scipy.LowLevelCallable`.
- - Cython API for scalar, typed versions of the universal functions from
the `scipy.special` module, via `cimport scipy.special.cython_special`.


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

Foreign function interface improvements
- ---------------------------------------

`scipy.LowLevelCallable` provides a new unified interface for wrapping
low-level compiled callback functions in the Python space. It supports
Cython imported "api" functions, ctypes function pointers, CFFI function
pointers, ``PyCapsules``, Numba jitted functions and more.
See `gh-6509 <https://github.com/scipy/scipy/pull/6509>`_ for details.


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

The function `scipy.linalg.solve` obtained two more keywords ``assume_a`` and
``transposed``. The underlying LAPACK routines are replaced with "expert"
versions and now can also be used to solve symmetric, hermitian and positive
definite coefficient matrices. Moreover, ill-conditioned matrices now cause
a warning to be emitted with the estimated condition number information. Old
``sym_pos`` keyword is kept for backwards compatibility reasons however it
is identical to using ``assume_a='pos'``. Moreover, the ``debug`` keyword,
which had no function but only printing the ``overwrite_<a, b>`` values, is
deprecated.

The function `scipy.linalg.matrix_balance` was added to perform the so-called
matrix balancing using the LAPACK xGEBAL routine family. This can be used to
approximately equate the row and column norms through diagonal similarity
transformations.

The functions `scipy.linalg.solve_continuous_are` and
`scipy.linalg.solve_discrete_are` have numerically more stable algorithms.
These functions can also solve generalized algebraic matrix Riccati equations.
Moreover, both gained a ``balanced`` keyword to turn balancing on and off.

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

`scipy.spatial.SphericalVoronoi.sort_vertices_of_regions` has been re-written in
Cython to improve performance.

`scipy.spatial.SphericalVoronoi` can handle > 200 k points (at least 10 million)
and has improved performance.

The function `scipy.spatial.distance.directed_hausdorff` was
added to calculate the directed Hausdorff distance.

``count_neighbors`` method of `scipy.spatial.cKDTree` gained an ability to
perform weighted pair counting via the new keywords ``weights`` and
``cumulative``. See `gh-5647 <https://github.com/scipy/scipy/pull/5647>`_ for
details.

`scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` now support
non-double custom metrics. 

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

The callback function C API supports PyCapsules in Python 2.7

Multidimensional filters now allow having different extrapolation modes for
different axes.

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

The `scipy.optimize.basinhopping` global minimizer obtained a new keyword,
`seed`, which can be used to seed the random number generator and obtain
repeatable minimizations.

The keyword `sigma` in `scipy.optimize.curve_fit` was overloaded to also accept
the covariance matrix of errors in the data.

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

The function `scipy.signal.correlate` and `scipy.signal.convolve` have a new
optional parameter `method`. The default value of `auto` estimates the fastest
of two computation methods, the direct approach and the Fourier transform
approach.

A new function has been added to choose the convolution/correlation method,
`scipy.signal.choose_conv_method` which may be appropriate if convolutions or
correlations are performed on many arrays of the same size.

New functions have been added to calculate complex short time fourier
transforms of an input signal, and to invert the transform to recover the
original signal: `scipy.signal.stft` and `scipy.signal.istft`. This
implementation also fixes the previously incorrect ouput of
`scipy.signal.spectrogram` when complex output data were requested.

The function `scipy.signal.sosfreqz` was added to compute the frequency
response from second-order sections.

The function `scipy.signal.unit_impulse` was added to conveniently
generate an impulse function.

The function `scipy.signal.iirnotch` was added to design second-order
IIR notch filters that can be used to remove a frequency component from
a signal. The dual function  `scipy.signal.iirpeak` was added to
compute the coefficients of a second-order IIR peak (resonant) filter.

The function `scipy.signal.minimum_phase` was added to convert linear-phase
FIR filters to minimum phase.

The functions `scipy.signal.upfirdn` and `scipy.signal.resample_poly` are now
substantially faster when operating on some n-dimensional arrays when n > 1.
The largest reduction in computation time is realized in cases where the size
of the array is small (<1k samples or so) along the axis to be filtered.

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

Fast Fourier transform routines now accept `np.float16` inputs and upcast
them to `np.float32`. Previously, they would raise an error.

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

Methods ``"centroid"`` and ``"median"`` of `scipy.cluster.hierarchy.linkage`
have been significantly sped up. Long-standing issues with using ``linkage`` on
large input data (over 16 GB) have been resolved.

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

The functions `scipy.sparse.save_npz` and `scipy.sparse.load_npz` were added,
providing simple serialization for some sparse formats.

The `prune` method of classes `bsr_matrix`, `csc_matrix`, and `csr_matrix`
was updated to reallocate backing arrays under certain conditions, reducing
memory usage.

The methods `argmin` and `argmax` were added to classes `coo_matrix`,
`csc_matrix`, `csr_matrix`, and `bsr_matrix`.

New function `scipy.sparse.csgraph.structural_rank` computes the structural
rank of a graph with a given sparsity pattern.

New function `scipy.sparse.linalg.spsolve_triangular` solves a sparse linear
system with a triangular left hand side matrix.


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

Scalar, typed versions of universal functions from `scipy.special` are available
in the Cython space via ``cimport`` from the new module
`scipy.special.cython_special`. These scalar functions can be expected to be
significantly faster then the universal functions for scalar arguments. See
the `scipy.special` tutorial for details.

Better control over special-function errors is offered by the
functions `scipy.special.geterr` and `scipy.special.seterr` and the
context manager `scipy.special.errstate`.

The names of orthogonal polynomial root functions have been changed to
be consistent with other functions relating to orthogonal
polynomials. For example, `scipy.special.j_roots` has been renamed
`scipy.special.roots_jacobi` for consistency with the related
functions `scipy.special.jacobi` and `scipy.special.eval_jacobi`. To
preserve back-compatibility the old names have been left as aliases.

Wright Omega function is implemented as `scipy.special.wrightomega`.


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

The function `scipy.stats.weightedtau` was added.  It provides a weighted
version of Kendall's tau.

New class `scipy.stats.multinomial` implements the multinomial distribution.

New class `scipy.stats.rv_histogram` constructs a continuous univariate
distribution with a piecewise linear CDF from a binned data sample.

New class `scipy.stats.argus` implements the Argus distribution.


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

New class `scipy.interpolate.BSpline` represents splines. ``BSpline`` objects
contain knots and coefficients and can evaluate the spline. The format is
consistent with FITPACK, so that one can do, for example::

 >>> t, c, k = splrep(x, y, s=0)
 >>> spl = BSpline(t, c, k)
 >>> np.allclose(spl(x), y)

``spl*`` functions, `scipy.interpolate.splev`, `scipy.interpolate.splint`,
`scipy.interpolate.splder` and `scipy.interpolate.splantider`, accept both
``BSpline`` objects and ``(t, c, k)`` tuples for backwards compatibility.

For multidimensional splines, ``c.ndim > 1``, ``BSpline`` objects are consistent
with piecewise polynomials, `scipy.interpolate.PPoly`. This means that
``BSpline`` objects are not immediately consistent with
`scipy.interpolate.splprep`, and one *cannot* do
``>>> BSpline(*splprep([x, y])[0])``. Consult the `scipy.interpolate` test suite
for examples of the precise equivalence.

In new code, prefer using ``scipy.interpolate.BSpline`` objects instead of
manipulating ``(t, c, k)`` tuples directly.

New function `scipy.interpolate.make_interp_spline` constructs an interpolating
spline given data points and boundary conditions.

New function `scipy.interpolate.make_lsq_spline` constructs a least-squares
spline approximation given data points.

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

Now `scipy.integrate.fixed_quad` supports vector-valued functions.


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

`scipy.interpolate.splmake`, `scipy.interpolate.spleval` and
`scipy.interpolate.spline` are deprecated. The format used by `splmake/spleval`
was inconsistent with `splrep/splev` which was confusing to users.

`scipy.special.errprint` is deprecated. Improved functionality is
available in `scipy.special.seterr`.

calling `scipy.spatial.distance.pdist` or `scipy.spatial.distance.cdist` with
arguments not needed by the chosen metric is deprecated. Also, metrics
`"old_cosine"` and `"old_cos"` are deprecated.


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

The deprecated ``scipy.weave`` submodule was removed.

`scipy.spatial.distance.squareform` now returns arrays of the same dtype as
the input, instead of always float64.

`scipy.special.errprint` now returns a boolean.

The function `scipy.signal.find_peaks_cwt` now returns an array instead of
a list.

`scipy.stats.kendalltau` now computes the correct p-value in case the
input contains ties. The p-value is also identical to that computed by
`scipy.stats.mstats.kendalltau` and by R. If the input does not
contain ties there is no change w.r.t. the previous implementation.

The function `scipy.linalg.block_diag` will not ignore zero-sized matrices anymore.
Instead it will insert rows or columns of zeros of the appropriate size.
See gh-4908 for more details.


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

SciPy wheels will now report their dependency on ``numpy`` on all platforms.
This change was made because Numpy wheels are available, and because the pip
upgrade behavior is finally changing for the better (use
``--upgrade-strategy=only-if-needed`` for ``pip >= 8.2``; that behavior will
become the default in the next major version of ``pip``).

Numerical values returned by `scipy.interpolate.interp1d` with ``kind="cubic"``
and ``"quadratic"`` may change relative to previous scipy versions. If your
code depended on specific numeric values (i.e., on implementation
details of the interpolators), you may want to double-check your results.


Authors
=======

* endolith
* Max Argus +
* Hervé Audren
* Alessandro Pietro Bardelli +
* Michael Benfield +
* Felix Berkenkamp
* Matthew Brett
* Per Brodtkorb
* Evgeni Burovski
* Pierre de Buyl
* CJ Carey
* Brandon Carter +
* Tim Cera
* Klesk Chonkin
* Christian Häggström +
* Luca Citi
* Peadar Coyle +
* Daniel da Silva +
* Greg Dooper +
* John Draper +
* drlvk +
* David Ellis +
* Yu Feng
* Baptiste Fontaine +
* Jed Frey +
* Siddhartha Gandhi +
* Wim Glenn +
* Akash Goel +
* Christoph Gohlke
* Ralf Gommers
* Alexander Goncearenco +
* Richard Gowers +
* Alex Griffing
* Radoslaw Guzinski +
* Charles Harris
* Callum Jacob Hays +
* Ian Henriksen
* Randy Heydon +
* Lindsey Hiltner +
* Gerrit Holl +
* Hiroki IKEDA +
* jfinkels +
* Mher Kazandjian +
* Thomas Keck +
* keuj6 +
* Kornel Kielczewski +
* Sergey B Kirpichev +
* Vasily Kokorev +
* Eric Larson
* Denis Laxalde
* Gregory R. Lee
* Josh Lefler +
* Julien Lhermitte +
* Evan Limanto +
* Jin-Guo Liu +
* Nikolay Mayorov
* Geordie McBain +
* Josue Melka +
* Matthieu Melot
* michaelvmartin15 +
* Surhud More +
* Brett M. Morris +
* Chris Mutel +
* Paul Nation
* Andrew Nelson
* David Nicholson +
* Aaron Nielsen +
* Joel Nothman
* nrnrk +
* Juan Nunez-Iglesias
* Mikhail Pak +
* Gavin Parnaby +
* Thomas Pingel +
* Ilhan Polat +
* Aman Pratik +
* Sebastian Pucilowski
* Ted Pudlik
* puenka +
* Eric Quintero
* Tyler Reddy
* Joscha Reimer
* Antonio Horta Ribeiro +
* Edward Richards +
* Roman Ring +
* Rafael Rossi +
* Colm Ryan +
* Sami Salonen +
* Alvaro Sanchez-Gonzalez +
* Johannes Schmitz
* Kari Schoonbee
* Yurii Shevchuk +
* Jonathan Siebert +
* Jonathan Tammo Siebert +
* Scott Sievert +
* Sourav Singh
* Byron Smith +
* Srikiran +
* Samuel St-Jean +
* Yoni Teitelbaum +
* Bhavika Tekwani
* Martin Thoma
* timbalam +
* Svend Vanderveken +
* Sebastiano Vigna +
* Aditya Vijaykumar +
* Santi Villalba +
* Ze Vinicius
* Pauli Virtanen
* Matteo Visconti
* Yusuke Watanabe +
* Warren Weckesser
* Phillip Weinberg +
* Nils Werner
* Jakub Wilk
* Josh Wilson
* wirew0rm +
* David Wolever +
* Nathan Woods
* ybeltukov +
* G Young
* Evgeny Zhurko +

A total of 121 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.

0.19.0rc2

This is the second release candidate for scipy 0.19.0. See https://github.com/scipy/scipy/blob/maintenance/0.19.x/doc/release/0.19.0-notes.rst for the release notes.

The main difference to rc1 is several Windows-specific issues that were fixed (Thanks Christoph!)

Please note that this is a source-only release. OS X and manylinux1 wheels will be produced for the final release.

If no issues are reported for this release, it will become the final 0.19.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).

0.19.0rc1

This is the first release candidate for scipy 0.19.0. See https://github.com/scipy/scipy/blob/maintenance/0.19.x/doc/release/0.19.0-notes.rst for the release notes.

Please note that this is a source-only release.

If no issues are reported for this release, it will become the final 0.19.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).

0.18.1

---
- \- `6405 <https://github.com/scipy/scipy/pull/6405>`__: BUG: sparse: fix elementwise divide for CSR/CSC
- \- `6431 <https://github.com/scipy/scipy/pull/6431>`__: BUG: result for insufficient neighbours from cKDTree is wrong.
- \- `6432 <https://github.com/scipy/scipy/pull/6432>`__: BUG Issue 6421: scipy.linalg.solve_banded overwrites input 'b'...
- \- `6455 <https://github.com/scipy/scipy/pull/6455>`__: DOC: add links to release notes
- \- `6462 <https://github.com/scipy/scipy/pull/6462>`__: BUG: interpolate: fix .roots method of PchipInterpolator
- \- `6492 <https://github.com/scipy/scipy/pull/6492>`__: BUG: Fix regression in dblquad: 6458
- \- `6543 <https://github.com/scipy/scipy/pull/6543>`__: fix the regression in circmean
- \- `6545 <https://github.com/scipy/scipy/pull/6545>`__: Revert gh-5938, restore ks_2samp
- \- `6557 <https://github.com/scipy/scipy/pull/6557>`__: Backports for 0.18.1

0.18.01

This is the same tag as v0.18.0, but re-issued to obtain a DOI.
The content and release notes can be found here: https://github.com/scipy/scipy/releases/tag/v0.18.0

0.18.0

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.  Moreover, our development attention
will now shift to bug-fix releases on the 0.19.x branch, and on adding
new features on the master branch.

This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater.

Highlights of this release include:
- \- A new ODE solver for two-point boundary value problems,
`scipy.optimize.solve_bvp`.
- \- A new class, `CubicSpline`, for cubic spline interpolation of data.
- \- N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`.
- \- Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`.
- \- Support for discrete-time linear systems, `scipy.signal.dlti`.

New features

`scipy.integrate` improvements

---

A solver of two-point boundary value problems for ODE systems has been
implemented in `scipy.integrate.solve_bvp`. The solver allows for non-separated
boundary conditions, unknown parameters and certain singular terms. It finds
a C1 continious solution using a fourth-order collocation algorithm.

`scipy.interpolate` improvements

---

Cubic spline interpolation is now available via `scipy.interpolate.CubicSpline`.
This class represents a piecewise cubic polynomial passing through given points
and C2 continuous. It is represented in the standard polynomial basis on each
segment.

A representation of n-dimensional tensor product piecewise polynomials is
available as the `scipy.interpolate.NdPPoly` class.

Univariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be
evaluated on periodic domains. Use `extrapolate="periodic"` keyword
argument for this.

`scipy.fftpack` improvements

---

`scipy.fftpack.next_fast_len` function computes the next "regular" number for
FFTPACK. Padding the input to this length can give significant performance
increase for `scipy.fftpack.fft`.

`scipy.signal` improvements

---

Resampling using polyphase filtering has been implemented in the function
`scipy.signal.resample_poly`. This method upsamples a signal, applies a
zero-phase low-pass FIR filter, and downsamples using `scipy.signal.upfirdn`
(which is also new in 0.18.0).  This method can be faster than FFT-based
filtering provided by `scipy.signal.resample` for some signals.

`scipy.signal.firls`, which constructs FIR filters using least-squares error
minimization, was added.

`scipy.signal.sosfiltfilt`, which does forward-backward filtering like
`scipy.signal.filtfilt` but for second-order sections, was added.

Discrete-time linear systems



`scipy.signal.dlti` provides an implementation of discrete-time linear systems.
Accordingly, the `StateSpace`, `TransferFunction` and `ZerosPolesGain` classes
have learned a the new keyword, `dt`, which can be used to create discrete-time
instances of the corresponding system representation.


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

The functions `sum`, `max`, `mean`, `min`, `transpose`, and `reshape` in
`scipy.sparse` have had their signatures augmented with additional arguments
and functionality so as to improve compatibility with analogously defined
functions in `numpy`.

Sparse matrices now have a `count_nonzero` method, which counts the number of
nonzero elements in the matrix. Unlike `getnnz()` and ``nnz`` propety,
which return the number of stored entries (the length of the data attribute),
this method counts the actual number of non-zero entries in data.


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

The implementation of Nelder-Mead minimization,
`scipy.minimize(..., method="Nelder-Mead")`, obtained a new keyword,
`initial_simplex`, which can be used to specify the initial simplex for the
optimization process.

Initial step size selection in CG and BFGS minimizers has been improved. We
expect that this change will improve numeric stability of optimization in some
cases. See pull request gh-5536 for details.

Handling of infinite bounds in SLSQP optimization has been improved. We expect
that this change will improve numeric stability of optimization in the some
cases. See pull request gh-6024 for details.

A large suite of global optimization benchmarks has been added to 
``scipy/benchmarks/go_benchmark_functions``. See pull request gh-4191 for details.

Nelder-Mead and Powell minimization will now only set defaults for
maximum iterations or function evaluations if neither limit is set by
the caller. In some cases with a slow converging function and only 1
limit set, the minimization may continue for longer than with previous
versions and so is more likely to reach convergence. See issue gh-5966.

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

Trapezoidal distribution has been implemented as `scipy.stats.trapz`.
Skew normal distribution has been implemented as `scipy.stats.skewnorm`.
Burr type XII distribution has been implemented as `scipy.stats.burr12`.
Three- and four-parameter kappa distributions have been implemented as
`scipy.stats.kappa3` and `scipy.stats.kappa4`, respectively.

New `scipy.stats.iqr` function computes the interquartile region of a
distribution.

Random matrices


`scipy.stats.special_ortho_group` and `scipy.stats.ortho_group` provide
generators of random matrices in the SO(N) and O(N) groups, respectively. They
generate matrices in the Haar distribution, the only uniform distribution on
these group manifolds.

`scipy.stats.random_correlation` provides a generator for random
correlation matrices, given specified eigenvalues.

`scipy.linalg` improvements

---

`scipy.linalg.svd` gained a new keyword argument, `lapack_driver`. Available
drivers are `gesdd` (default) and `gesvd`.

`scipy.linalg.lapack.ilaver` returns the version of the LAPACK library SciPy
links to.

`scipy.spatial` improvements

---

Boolean distances, `scipy.spatial.pdist`, have been sped up. Improvements vary
by the function and the input size. In many cases, one can expect a speed-up
of x2--x10. 

New class `scipy.spatial.SphericalVoronoi` constructs Voronoi diagrams on the
surface of a sphere. See pull request gh-5232 for details.

`scipy.cluster` improvements

---

A new clustering algorithm, the nearest neighbor chain algorithm, has been
implemented for `scipy.cluster.hierarchy.linkage`. As a result, one can expect
a significant algorithmic improvement (:math:`O(N^2)` instead of :math:`O(N^3)`)
for several linkage methods.

`scipy.special` improvements

---

The new function `scipy.special.loggamma` computes the principal branch of the
logarithm of the Gamma function. For real input, `loggamma` is compatible
with `scipy.special.gammaln`. For complex input, it has more consistent
behavior in the complex plane and should be preferred over `gammaln`.

Vectorized forms of spherical Bessel functions have been implemented as 
`scipy.special.spherical_jn`, `scipy.special.spherical_kn`,
`scipy.special.spherical_in` and `scipy.special.spherical_yn`.
They are recommended for use over `sph_*` functions, which are now deprecated.

Several special functions have been extended to the complex domain and/or
have seen domain/stability improvements. This includes `spence`, `digamma`,
`log1p` and several others.

Deprecated features

The cross-class properties of `lti` systems have been deprecated. The
following properties/setters will raise a `DeprecationWarning`:

Name - (accessing/setting raises warning) - (setting raises warning)
- StateSpace - (`num`, `den`, `gain`) - (`zeros`, `poles`)
- TransferFunction (`A`, `B`, `C`, `D`, `gain`) - (`zeros`, `poles`)
- ZerosPolesGain (`A`, `B`, `C`, `D`, `num`, `den`) - ()

Spherical Bessel functions, `sph_in`, `sph_jn`, `sph_kn`, `sph_yn`,
`sph_jnyn` and `sph_inkn` have been deprecated in favor of
`scipy.special.spherical_jn` and `spherical_kn`, `spherical_yn`,
`spherical_in`.

The following functions in `scipy.constants` are deprecated: `C2K`, `K2C`,
`C2F`, `F2C`, `F2K` and `K2F`.  They are superceded by a new function
`scipy.constants.convert_temperature` that can perform all those conversions
plus to/from the Rankine temperature scale.

Backwards incompatible changes

`scipy.optimize`

---

The convergence criterion for `optimize.bisect`,
`optimize.brentq`, `optimize.brenth`, and `optimize.ridder` now
works the same as `numpy.allclose`.

`scipy.ndimage`

---

The offset in `ndimage.iterpolation.affine_transform`
is now consistently added after the matrix is applied,
independent of if the matrix is specified using a one-dimensional
or a two-dimensional array.

`scipy.stats`

---

`stats.ks_2samp` used to return nonsensical values if the input was
not real or contained nans.  It now raises an exception for such inputs.

Several deprecated methods of `scipy.stats` distributions have been removed:
`est_loc_scale`, `vecfunc`, `veccdf` and `vec_generic_moment`.

Deprecated functions `nanmean`, `nanstd` and `nanmedian` have been removed
from `scipy.stats`. These functions were deprecated in scipy 0.15.0 in favor
of their `numpy` equivalents.

A bug in the `rvs()` method of the distributions in `scipy.stats` has
been fixed.  When arguments to `rvs()` were given that were shaped for
broadcasting, in many cases the returned random samples were not random.
A simple example of the problem is `stats.norm.rvs(loc=np.zeros(10))`.
Because of the bug, that call would return 10 identical values.  The bug
only affected code that relied on the broadcasting of the shape, location
and scale parameters.

The `rvs()` method also accepted some arguments that it should not have.
There is a potential for backwards incompatibility in cases where `rvs()`
accepted arguments that are not, in fact, compatible with broadcasting.
An example is


stats.gamma.rvs([2, 5, 10, 15], size=(2,2))


The shape of the first argument is not compatible with the requested size,
but the function still returned an array with shape (2, 2).  In scipy 0.18,
that call generates a `ValueError`.

`scipy.io`

---

`scipy.io.netcdf` masking now gives precedence to the `_FillValue` attribute
over the `missing_value` attribute, if both are given. Also, data are only
treated as missing if they match one of these attributes exactly: values that
differ by roundoff from `_FillValue` or `missing_value` are no longer
treated as missing values.

`scipy.interpolate`

---

`scipy.interpolate.PiecewisePolynomial` class has been removed. It has been
deprecated in scipy 0.14.0, and `scipy.interpolate.BPoly.from_derivatives` serves
as a drop-in replacement.

Other changes

Scipy now uses `setuptools` for its builds instead of plain distutils.  This
fixes usage of `install_requires='scipy'` in the `setup.py` files of
projects that depend on Scipy (see Numpy issue gh-6551 for details).  It
potentially affects the way that build/install methods for Scipy itself behave
though.  Please report any unexpected behavior on the Scipy issue tracker.

PR `6240 <https://github.com/scipy/scipy/pull/6240>`__
changes the interpretation of the `maxfun` option in `L-BFGS-B` based routines
in the `scipy.optimize` module.
An `L-BFGS-B` search consists of multiple iterations,
with each iteration consisting of one or more function evaluations.
Whereas the old search strategy terminated immediately upon reaching `maxfun`
function evaluations, the new strategy allows the current iteration
to finish despite reaching `maxfun`.

The bundled copy of Qhull in the `scipy.spatial` subpackage has been upgraded to
version 2015.2.

The bundled copy of ARPACK in the `scipy.sparse.linalg` subpackage has been
upgraded to arpack-ng 3.3.0.

The bundled copy of SuperLU in the `scipy.sparse` subpackage has been upgraded
to version 5.1.1.

Authors
- endolith
- yanxun827 +
- kleskjr +
- MYheavyGo +
- solarjoe +
- Gregory Allen +
- Gilles Aouizerate +
- Tom Augspurger +
- Henrik Bengtsson +
- Felix Berkenkamp
- Per Brodtkorb
- Lars Buitinck
- Daniel Bunting +
- Evgeni Burovski
- CJ Carey
- Tim Cera
- Grey Christoforo +
- Robert Cimrman
- Philip DeBoer +
- Yves Delley +
- Dávid Bodnár +
- Ion Elberdin +
- Gabriele Farina +
- Yu Feng
- Andrew Fowlie +
- Joseph Fox-Rabinovitz
- Simon Gibbons +
- Neil Girdhar +
- Kolja Glogowski +
- Christoph Gohlke
- Ralf Gommers
- Todd Goodall +
- Johnnie Gray +
- Alex Griffing
- Olivier Grisel
- Thomas Haslwanter +
- Michael Hirsch +
- Derek Homeier
- Golnaz Irannejad +
- Marek Jacob +
- InSuk Joung +
- Tetsuo Koyama +
- Eugene Krokhalev +
- Eric Larson
- Denis Laxalde
- Antony Lee
- Jerry Li +
- Henry Lin +
- Nelson Liu +
- Loïc Estève
- Lei Ma +
- Osvaldo Martin +
- Stefano Martina +
- Nikolay Mayorov
- Matthieu Melot +
- Sturla Molden
- Eric Moore
- Alistair Muldal +
- Maniteja Nandana
- Tavi Nathanson +
- Andrew Nelson
- Joel Nothman
- Behzad Nouri
- Nikolai Nowaczyk +
- Juan Nunez-Iglesias +
- Ted Pudlik
- Eric Quintero
- Yoav Ram
- Jonas Rauber +
- Tyler Reddy +
- Juha Remes
- Garrett Reynolds +
- Ariel Rokem +
- Fabian Rost +
- Bill Sacks +
- Jona Sassenhagen +
- Kari Schoonbee +
- Marcello Seri +
- Sourav Singh +
- Martin Spacek +
- Søren Fuglede Jørgensen +
- Bhavika Tekwani +
- Martin Thoma +
- Sam Tygier +
- Meet Udeshi +
- Utkarsh Upadhyay
- Bram Vandekerckhove +
- Sebastián Vanrell +
- Ze Vinicius +
- Pauli Virtanen
- Stefan van der Walt
- Warren Weckesser
- Jakub Wilk +
- Josh Wilson
- Phillip J. Wolfram +
- Nathan Woods
- Haochen Wu
- G Young +

A total of 99 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.

0.18.0rc2

This is the second rc for scipy 0.18.0.
Please test it --- both the release itself on your machines and your code against this release --- and report breakage (hopefully, there isn't one) on Github issue tracker or scipy-dev mailing list.

If no issues are reported for this rc, it will graduate into the final release.

0.18.0rc1

This is the first release candidate for scipy 0.18.0. See https://github.com/scipy/scipy/blob/maintenance/0.18.x/doc/release/0.18.0-notes.rst for the release notes.

Please note that this is a source-only release. 

If no issues are reported for this release, it will become the final 0.18.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).

0.17.1

SciPy 0.17.1 is a bug-fix release with no new features compared to 0.17.0.

0.17.0

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.  Moreover, our development attention
will now shift to bug-fix releases on the 0.17.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater.

Release highlights:


- New functions for linear and nonlinear least squares optimization with
constraints: `scipy.optimize.lsq_linear` and
`scipy.optimize.least_squares`
- Support for fitting with bounds in `scipy.optimize.curve_fit`.
- Significant improvements to `scipy.stats`, providing many functions with
better handing of inputs which have NaNs or are empty, improved
documentation, and consistent behavior between `scipy.stats` and
`scipy.stats.mstats`.
- Significant performance improvements and new functionality in
`scipy.spatial.cKDTree`.


New features

`scipy.cluster` improvements

---

A new function `scipy.cluster.hierarchy.cut_tree`, which determines a cut tree
from a linkage matrix, was added.

`scipy.io` improvements

---

`scipy.io.mmwrite` gained support for symmetric sparse matrices.

`scipy.io.netcdf` gained support for masking and scaling data based on data
attributes.

`scipy.optimize` improvements

---

Linear assignment problem solver



`scipy.optimize.linear_sum_assignment` is a new function for solving the
linear sum assignment problem.  It uses the Hungarian algorithm (Kuhn-Munkres).

Least squares optimization


A new function for _nonlinear_ least squares optimization with constraints was
added: `scipy.optimize.least_squares`.  It provides several methods:
Levenberg-Marquardt for unconstrained problems, and two trust-region methods
for constrained ones.  Furthermore it provides different loss functions.
New trust-region methods also handle sparse Jacobians.

A new function for _linear_ least squares optimization with constraints was
added: `scipy.optimize.lsq_linear`.  It provides a trust-region method as well
as an implementation of the Bounded-Variable Least-Squares (BVLS) algorithm.

`scipy.optimize.curve_fit` now supports fitting with bounds.

`scipy.signal` improvements

---

A `mode` keyword was added to `scipy.signal.spectrogram`, to let it return
other spectrograms than power spectral density.

`scipy.stats` improvements

---

Many functions in `scipy.stats` have gained a `nan_policy` keyword, which
allows specifying how to treat input with NaNs in them: propagate the NaNs,
raise an error, or omit the NaNs.

Many functions in `scipy.stats` have been improved to correctly handle input
arrays that are empty or contain infs/nans.

A number of functions with the same name in `scipy.stats` and
`scipy.stats.mstats` were changed to have matching signature and behavior.
See `gh-5474 <https://github.com/scipy/scipy/issues/5474>`__ for details.

`scipy.stats.binom_test` and `scipy.stats.mannwhitneyu` gained a keyword
`alternative`, which allows specifying the hypothesis to test for.
Eventually all hypothesis testing functions will get this keyword.

For methods of many continuous distributions, complex input is now accepted.

Matrix normal distribution has been implemented as `scipy.stats.matrix_normal`.

`scipy.sparse` improvements

---

The `axis` keyword was added to sparse norms, `scipy.sparse.linalg.norm`.

`scipy.spatial` improvements

---

`scipy.spatial.cKDTree` was partly rewritten for improved performance and
several new features were added to it:
- \- the `query_ball_point` method became significantly faster
- \- `query` and `query_ball_point` gained an `n_jobs` keyword for parallel
execution
- \- build and query methods now release the GIL
- \- full pickling support
- \- support for periodic spaces
- \- the `sparse_distance_matrix` method can now return and sparse matrix type

`scipy.interpolate` improvements

---

Out-of-bounds behavior of `scipy.interpolate.interp1d` has been improved.
Use a two-element tuple for the `fill_value` argument to specify separate
fill values for input below and above the interpolation range.
Linear and nearest interpolation kinds of `scipy.interpolate.interp1d` support
extrapolation via the `fill_value="extrapolate"` keyword.

`fill_value` can also be set to an array-like (or a two-element tuple of
array-likes for separate below and above values) so long as it broadcasts
properly to the non-interpolated dimensions of an array. This was implicitly
supported by previous versions of scipy, but support has now been formalized
and gets compatibility-checked before use. For example, a set of `y` values
to interpolate with shape `(2, 3, 5)` interpolated along the last axis (2)
could accept a `fill_value` array with shape `()` (singleton), `(1,)`,
`(2, 1)`, `(1, 3)`, `(3,)`, or `(2, 3)`; or it can be a 2-element tuple
to specify separate below and above bounds, where each of the two tuple
elements obeys proper broadcasting rules.

`scipy.linalg` improvements

---

The default algorithm for `scipy.linalg.leastsq` has been changed to use
LAPACK's function `*gelsd`. Users wanting to get the previous behavior
can use a new keyword `lapack_driver="gelss"` (allowed values are 
"gelss", "gelsd" and "gelsy").

`scipy.sparse` matrices and linear operators now support the matmul (``)
operator when available (Python 3.5+). See
[PEP 465](http://legacy.python.org/dev/peps/pep-0465/)

A new function `scipy.linalg.ordqz`, for QZ decomposition with reordering, has
been added.

Deprecated features

`scipy.stats.histogram` is deprecated in favor of `np.histogram`, which is
faster and provides the same functionality.

`scipy.stats.threshold` and `scipy.mstats.threshold` are deprecated
in favor of `np.clip`. See issue 617 for details.

`scipy.stats.ss` is deprecated. This is a support function, not meant to 
be exposed to the user. Also, the name is unclear. See issue 663 for details.

`scipy.stats.square_of_sums` is deprecated. This too is a support function 
not meant to be exposed to the user. See issues 665 and 663 for details.

`scipy.stats.f_value`, `scipy.stats.f_value_multivariate`,
`scipy.stats.f_value_wilks_lambda`, and `scipy.mstats.f_value_wilks_lambda` 
are deprecated. These are related to ANOVA, for which `scipy.stats` provides 
quite limited functionality and these functions are not very useful standalone.
See issues 660 and 650 for details.

`scipy.stats.chisqprob` is deprecated. This is an alias. `stats.chi2.sf` 
should be used instead. 

`scipy.stats.betai` is deprecated. This is an alias for `special.betainc` 
which should be used instead.

Backwards incompatible changes

The functions `stats.trim1` and `stats.trimboth` now make sure the 
elements trimmed are the lowest and/or highest, depending on the case.
Slicing without at least partial sorting was previously done, but didn't
make sense for unsorted input.

When `variable_names` is set to an empty list, `scipy.io.loadmat` now
correctly returns no values instead of all the contents of the MAT file.

Element-wise multiplication of sparse matrices now returns a sparse result
in all cases. Previously, multiplying a sparse matrix with a dense matrix or
array would return a dense matrix.

The function `misc.lena` has been removed due to license incompatibility.

The constructor for `sparse.coo_matrix` no longer accepts `(None, (m,n))`
to construct an all-zero matrix of shape `(m,n)`. This functionality was
deprecated since at least 2007 and was already broken in the previous SciPy
release. Use `coo_matrix((m,n))` instead.

The Cython wrappers in `linalg.cython_lapack` for the LAPACK routines
`*gegs`, `*gegv`, `*gelsx`, `*geqpf`, `*ggsvd`, `*ggsvp`,
`*lahrd`, `*latzm`, `*tzrqf` have been removed since these routines
are not present in the new LAPACK 3.6.0 release. With the exception of
the routines `*ggsvd` and `*ggsvp`, these were all deprecated in favor
of routines that are currently present in our Cython LAPACK wrappers.

Because the LAPACK `*gegv` routines were removed in LAPACK 3.6.0. The
corresponding Python wrappers in `scipy.linalg.lapack` are now
deprecated and will be removed in a future release. The source files for
these routines have been temporarily included as a part of `scipy.linalg`
so that SciPy can be built against LAPACK versions that do not provide
these deprecated routines.

Other changes

Html and pdf documentation of development versions of Scipy is now
automatically rebuilt after every merged pull request.

`scipy.constants` is updated to the CODATA 2014 recommended values.

Usage of `scipy.fftpack` functions within Scipy has been changed in such a
way that `PyFFTW <http://hgomersall.github.io/pyFFTW/>`__ can easily replace
`scipy.fftpack` functions (with improved performance).  See
`gh-5295 <https://github.com/scipy/scipy/pull/5295>`__ for details.

The `imread` functions in `scipy.misc` and `scipy.ndimage` were unified, for
which a `mode` argument was added to `scipy.misc.imread`.  Also, bugs for
1-bit and indexed RGB image formats were fixed.

`runtests.py`, the development script to build and test Scipy, now allows
building in parallel with `--parallel`.

Authors
- cel4 +
- chemelnucfin +
- endolith
- mamrehn +
- tosh1ki +
- Joshua L. Adelman +
- Anne Archibald
- Hervé Audren +
- Vincent Barrielle +
- Bruno Beltran +
- Sumit Binnani +
- Joseph Jon Booker
- Olga Botvinnik +
- Michael Boyle +
- Matthew Brett
- Zaz Brown +
- Lars Buitinck
- Pete Bunch +
- Evgeni Burovski
- CJ Carey
- Ien Cheng +
- Cody +
- Jaime Fernandez del Rio
- Ales Erjavec +
- Abraham Escalante
- Yves-Rémi Van Eycke +
- Yu Feng +
- Eric Firing
- Francis T. O'Donovan +
- André Gaul
- Christoph Gohlke
- Ralf Gommers
- Alex Griffing
- Alexander Grigorievskiy
- Charles Harris
- Jörn Hees +
- Ian Henriksen
- Derek Homeier +
- David Menéndez Hurtado
- Gert-Ludwig Ingold
- Aakash Jain +
- Rohit Jamuar +
- Jan Schlüter
- Johannes Ballé
- Luke Zoltan Kelley +
- Jason King +
- Andreas Kopecky +
- Eric Larson
- Denis Laxalde
- Antony Lee
- Gregory R. Lee
- Josh Levy-Kramer +
- Sam Lewis +
- François Magimel +
- Martín Gaitán 

[tnir]

Closed in favor of #250