[Numpy-discussion] ANN: Scipy 0.15.0 beta 1 release

Pauli Virtanen pav at iki.fi
Sun Nov 23 18:13:03 EST 2014

Hash: SHA1

Dear all,

We have finally finished preparing the Scipy 0.15.0 beta 1 release.
Please try it and report any issues on the scipy-dev mailing list,
and/or on Github.

If no surprises turn up, the final release is planned on Dec 20 in
three weeks.

Source tarballs and full release notes are available at
Binary installers should also be up soon.

Best regards,
Pauli Virtanen

- --------------------------------------------

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 3.2-3.3 and NumPy 1.5.1 or

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

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

The function ``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
- - -----------------------------

Add function ``orthogonal_procrustes`` for solving the procrustes
linear algebra problem.

``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 distribution as multivariate distribution.

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,
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
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
and therefore will be removed from the public API in a following release.

Backwards incompatible changes

- - -------------

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

- - ---------------

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

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