[SciPy-User] ANN: Scipy 0.15.0 release

[Pauli Virtanen]

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Dear all,

We are pleased to announce the Scipy 0.15.0 release.

The 0.15.0 release contains bugfixes and new features, most important
of which are mentioned in the excerpt from the release notes below.

Source tarballs, binaries, and full release notes are available at
https://sourceforge.net/projects/scipy/files/scipy/0.15.0/

Best regards,
Pauli Virtanen


==========================
SciPy 0.15.0 Release Notes
==========================

SciPy 0.15.0 is the culmination of 6 months of hard work. It contains
several 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 0.16.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or
greater.


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

Linear Programming Interface
- ----------------------------

The new function `scipy.optimize.linprog` provides a generic
linear programming similar to the way `scipy.optimize.minimize`
provides a generic interface to nonlinear programming optimizers.
Currently the only method supported is *simplex* which provides
a two-phase, dense-matrix-based simplex algorithm. Callbacks
functions are supported, allowing the user to monitor the progress
of the algorithm.

Differential evolution, a global optimizer
- ------------------------------------------

A new `scipy.optimize.differential_evolution` function has been added
to the
``optimize`` module.  Differential Evolution is an algorithm used for
finding
the global minimum of multivariate functions. It is stochastic in
nature (does
not use gradient methods), and can search large areas of candidate
space, but
often requires larger numbers of function evaluations than conventional
gradient based techniques.

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

The function `scipy.signal.max_len_seq` was added, which computes a
Maximum
Length Sequence (MLS) signal.

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

It is now possible to use `scipy.integrate` routines to integrate
multivariate ctypes functions, thus avoiding callbacks to Python and
providing better performance.

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

The function `scipy.linalg.orthogonal_procrustes` for solving the
procrustes
linear algebra problem was added.

BLAS level 2 functions ``her``, ``syr``, ``her2`` and ``syr2`` are now
wrapped
in ``scipy.linalg``.

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

`scipy.sparse.linalg.svds` can now take a ``LinearOperator`` as its
main input.

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

Values of ellipsoidal harmonic (i.e. Lame) functions and associated
normalization constants can be now computed using ``ellip_harm``,
``ellip_harm_2``, and ``ellip_normal``.

New convenience functions ``entr``, ``rel_entr`` ``kl_div``,
``huber``, and ``pseudo_huber`` were added.

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

Routines ``reverse_cuthill_mckee`` and ``maximum_bipartite_matching``
for computing reorderings of sparse graphs were added.

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

Added a Dirichlet multivariate distribution, `scipy.stats.dirichlet`.

The new function `scipy.stats.median_test` computes Mood's median test.

The new function `scipy.stats.combine_pvalues` implements Fisher's
and Stouffer's methods for combining p-values.

`scipy.stats.describe` returns a namedtuple rather than a tuple, allowing
users to access results by index or by name.


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

The `scipy.weave` module is deprecated.  It was the only module never
ported
to Python 3.x, and is not recommended to be used for new code - use Cython
instead.  In order to support existing code, ``scipy.weave`` has been
packaged
separately: https://github.com/scipy/weave.  It is a pure Python
package, and
can easily be installed with ``pip install weave``.

`scipy.special.bessel_diff_formula` is deprecated.  It is a private
function,
and therefore will be removed from the public API in a following release.

``scipy.stats.nanmean``, ``nanmedian`` and ``nanstd`` functions are
deprecated
in favor of their numpy equivalents.


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

scipy.ndimage
- -------------

The functions `scipy.ndimage.minimum_positions`,
`scipy.ndimage.maximum_positions`` and `scipy.ndimage.extrema` return
positions as ints instead of floats.

scipy.integrate
- ---------------

The format of banded Jacobians in `scipy.integrate.ode` solvers is
changed. Note that the previous documentation of this feature was
erroneous.
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