Software Open Access

Pauli Virtanen; Ralf Gommers; Travis Oliphant; David Cournapeau; Evgeni Burovski; Pearu Peterson; alexbrc; Warren Weckesser; wnbell; mattknox_ca; endolith; Stefan van der Walt; Matthew Brett; Jarrod Millman; Lars; Denis Laxalde; eric-jones; Robert Kern; Eric Moore; Tim Leslie; argriffing; Andrew Nelson; Jake Vanderplas; Blake Griffith; CJ Carey; Abraham Escalante; Josh Wilson; Rob Falck; Nikolay Mayorov; Eric Larson; Charles Harris; Sturla Molden

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

SciPy 0.18.0 Release Notes

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

SciPy 0.18.0 is the culmination of 6 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. 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 `__

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.

Name | Size | |
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scipy-v0.18.0-1.zip md5:34310962922207c9105ff4f96b400092 |
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