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1.2.0rc2

1.2.0rc1

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.

1.1.0rc1

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.

Page 13 of 17

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