Log Message:
Updated py-scipy to 0.18.0.

Test failures reported upstream.

==========================
SciPy 0.18.0 Release Notes
==========================

.. contents::

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 <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.