[Numpy-discussion] ANN: SciPy 0.11.0 release candidate 2

Ralf Gommers ralf.gommers at gmail.com
Mon Aug 13 14:30:44 EDT 2012


I am pleased to announce the availability of the second release candidate
of SciPy 0.11.0. For this release many new features have been added, and
over 120 tickets and pull requests have been closed. Also noteworthy is
that the number of contributors for this release has risen to over 50. Some
of the highlights are:

  - A new module, sparse.csgraph, has been added which provides a number of
common sparse graph algorithms.
  - New unified interfaces to the existing optimization and root finding
functions have been added.

Sources and binaries can be found at
http://sourceforge.net/projects/scipy/files/scipy/0.11.0rc2/, release notes
are copied below.

For this release candidate all known issues (with the exception of one
Qhull issue on Debian, s390x platform) have been solved. In the meantime
also OS X 10.8 was released, this RC contains a few build fixes for that

If no more serious issues are reported, the final release will be in one


SciPy 0.11.0 Release Notes

.. note:: Scipy 0.11.0 is not released yet!

.. contents::

SciPy 0.11.0 is the culmination of 8 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and
better documentation.  Highlights of this release are:

  - A new module has been added which provides a number of common sparse
  - New unified interfaces to the existing optimization and root finding
    functions have been added.

All users are encouraged to upgrade to this release, as there are a large
number of bug-fixes and optimizations.  Our development attention will now
shift to bug-fix releases on the 0.11.x branch, and on adding new features
the master branch.

This release requires Python 2.4-2.7 or 3.1-3.2 and NumPy 1.5.1 or greater.

New features

Sparse Graph Submodule
The new submodule :mod:`scipy.sparse.csgraph` implements a number of
graph algorithms for graphs stored as sparse adjacency matrices.  Available
routines are:

   - :func:`connected_components` - determine connected components of a
   - :func:`laplacian` - compute the laplacian of a graph
   - :func:`shortest_path` - compute the shortest path between points on a
     positive graph
   - :func:`dijkstra` - use Dijkstra's algorithm for shortest path
   - :func:`floyd_warshall` - use the Floyd-Warshall algorithm for
     shortest path
   - :func:`breadth_first_order` - compute a breadth-first order of nodes
   - :func:`depth_first_order` - compute a depth-first order of nodes
   - :func:`breadth_first_tree` - construct the breadth-first tree from
     a given node
   - :func:`depth_first_tree` - construct a depth-first tree from a given
   - :func:`minimum_spanning_tree` - construct the minimum spanning
     tree of a graph

``scipy.optimize`` improvements

The optimize module has received a lot of attention this release.  In
to added tests, documentation improvements, bug fixes and code clean-up, the
following improvements were made:

- A unified interface to minimizers of univariate and multivariate
  functions has been added.
- A unified interface to root finding algorithms for multivariate functions
  has been added.
- The L-BFGS-B algorithm has been updated to version 3.0.

Unified interfaces to minimizers

Two new functions ``scipy.optimize.minimize`` and
``scipy.optimize.minimize_scalar`` were added to provide a common interface
to minimizers of multivariate and univariate functions respectively.
For multivariate functions, ``scipy.optimize.minimize`` provides an
interface to methods for unconstrained optimization (`fmin`, `fmin_powell`,
`fmin_cg`, `fmin_ncg`, `fmin_bfgs` and `anneal`) or constrained
optimization (`fmin_l_bfgs_b`, `fmin_tnc`, `fmin_cobyla` and `fmin_slsqp`).
For univariate functions, ``scipy.optimize.minimize_scalar`` provides an
interface to methods for unconstrained and bounded optimization (`brent`,
`golden`, `fminbound`).
This allows for easier comparing and switching between solvers.

Unified interface to root finding algorithms

The new function ``scipy.optimize.root`` provides a common interface to
root finding algorithms for multivariate functions, embeding `fsolve`,
`leastsq` and `nonlin` solvers.

``scipy.linalg`` improvements

New matrix equation solvers

Solvers for the Sylvester equation (``scipy.linalg.solve_sylvester``,
and continuous Lyapunov equations (``scipy.linalg.solve_lyapunov``,
``scipy.linalg.solve_discrete_lyapunov``) and discrete and continuous
Riccati equations (``scipy.linalg.solve_continuous_are``,
``scipy.linalg.solve_discrete_are``) have been added to ``scipy.linalg``.
These solvers are often used in the field of linear control theory.

QZ and QR Decomposition

It is now possible to calculate the QZ, or Generalized Schur, decomposition
using ``scipy.linalg.qz``. This function wraps the LAPACK routines sgges,
dgges, cgges, and zgges.

The function ``scipy.linalg.qr_multiply``, which allows efficient
of the matrix product of Q (from a QR decompostion) and a vector, has been

Pascal matrices

A function for creating Pascal matrices, ``scipy.linalg.pascal``, was added.

Sparse matrix construction and operations

Two new functions, ``scipy.sparse.diags`` and ``scipy.sparse.block_diag``,
added to easily construct diagonal and block-diagonal sparse matrices

``scipy.sparse.csc_matrix`` and ``csr_matrix`` now support the operations
``sin``, ``tan``, ``arcsin``, ``arctan``, ``sinh``, ``tanh``, ``arcsinh``,
``arctanh``, ``rint``, ``sign``, ``expm1``, ``log1p``, ``deg2rad``,
``floor``, ``ceil`` and ``trunc``.  Previously, these operations had to be
performed by operating on the matrices' ``data`` attribute.

LSMR iterative solver

LSMR, an iterative method for solving (sparse) linear and linear
least-squares systems, was added as ``scipy.sparse.linalg.lsmr``.

Discrete Sine Transform

Bindings for the discrete sine transform functions have been added to

``scipy.interpolate`` improvements

For interpolation in spherical coordinates, the three classes
``scipy.interpolate.LSQSphereBivariateSpline``, and
``scipy.interpolate.RectSphereBivariateSpline`` have been added.

Binned statistics (``scipy.stats``)

The stats module has gained functions to do binned statistics, which are a
generalization of histograms, in 1-D, 2-D and multiple dimensions:
``scipy.stats.binned_statistic``, ``scipy.stats.binned_statistic_2d`` and

Deprecated features

``scipy.sparse.cs_graph_components`` has been made a part of the sparse
submodule, and renamed to ``scipy.sparse.csgraph.connected_components``.
Calling the former routine will result in a deprecation warning.

``scipy.misc.radon`` has been deprecated.  A more full-featured radon
can be found in scikits-image.

``scipy.io.save_as_module`` has been deprecated.  A better way to save
Numpy arrays is the ``numpy.savez`` function.

The `xa` and `xb` parameters for all distributions in
``scipy.stats.distributions`` already weren't used; they have now been

Backwards incompatible changes

Removal of ``scipy.maxentropy``

The ``scipy.maxentropy`` module, which was deprecated in the 0.10.0 release,
has been removed.  Logistic regression in scikits.learn is a good and modern
alternative for this functionality.

Minor change in behavior of ``splev``

The spline evaluation function now behaves similarly to ``interp1d``
for size-1 arrays.  Previous behavior::

    >>> from scipy.interpolate import splev, splrep, interp1d
    >>> x = [1,2,3,4,5]
    >>> y = [4,5,6,7,8]
    >>> tck = splrep(x, y)
    >>> splev([1], tck)
    >>> splev(1, tck)

Corrected behavior::

    >>> splev([1], tck)
    array([ 4.])
    >>> splev(1, tck)

This affects also the ``UnivariateSpline`` classes.

Behavior of ``scipy.integrate.complex_ode``

The behavior of the ``y`` attribute of ``complex_ode`` is changed.
Previously, it expressed the complex-valued solution in the form::

    z = ode.y[::2] + 1j * ode.y[1::2]

Now, it is directly the complex-valued solution::

    z = ode.y

Minor change in behavior of T-tests

The T-tests ``scipy.stats.ttest_ind``, ``scipy.stats.ttest_rel`` and
``scipy.stats.ttest_1samp`` have been changed so that 0 / 0 now returns NaN
instead of 1.

Other changes

The SuperLU sources in ``scipy.sparse.linalg`` have been updated to version
from upstream.

The function ``scipy.signal.bode``, which calculates magnitude and phase
for a continuous-time system, has been added.

The two-sample T-test ``scipy.stats.ttest_ind`` gained an option to compare
samples with unequal variances, i.e. Welch's T-test.

``scipy.misc.logsumexp`` now takes an optional ``axis`` keyword argument.


This release contains work by the following people (contributed at least
one patch to this release, names in alphabetical order):

* Jeff Armstrong
* Chad Baker
* Brandon Beacher +
* behrisch +
* borishim +
* Matthew Brett
* Lars Buitinck
* Luis Pedro Coelho +
* Johann Cohen-Tanugi
* David Cournapeau
* dougal +
* Ali Ebrahim +
* endolith +
* Bjørn Forsman +
* Robert Gantner +
* Sebastian Gassner +
* Christoph Gohlke
* Ralf Gommers
* Yaroslav Halchenko
* Charles Harris
* Jonathan Helmus +
* Andreas Hilboll +
* Marc Honnorat +
* Jonathan Hunt +
* Maxim Ivanov +
* Thouis (Ray) Jones
* Christopher Kuster +
* Josh Lawrence +
* Denis Laxalde +
* Travis Oliphant
* Joonas Paalasmaa +
* Fabian Pedregosa
* Josef Perktold
* Gavin Price +
* Jim Radford +
* Andrew Schein +
* Skipper Seabold
* Jacob Silterra +
* Scott Sinclair
* Alexis Tabary +
* Martin Teichmann
* Matt Terry +
* Nicky van Foreest +
* Jacob Vanderplas
* Patrick Varilly +
* Pauli Virtanen
* Nils Wagner +
* Darryl Wally +
* Stefan van der Walt
* Liming Wang +
* David Warde-Farley +
* Warren Weckesser
* Sebastian Werk +
* Mike Wimmer +
* Tony S Yu +

A total of 55 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
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