==========================
SciPy 0.11.0 Release Notes
==========================

.. 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 graph
    algorithms.
  - 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 on
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 efficient
graph algorithms for graphs stored as sparse adjacency matrices.  Available
routines are:

   - :func:`connected_components` - determine connected components of a graph
   - :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 node
   - :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 addition
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, embedding `fsolve`,
`leastsq` and `nonlin` solvers.


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

New matrix equation solvers
```````````````````````````

Solvers for the Sylvester equation (``scipy.linalg.solve_sylvester``, discrete
and continuous Lyapunov equations (``scipy.linalg.solve_lyapunov``,
``scipy.linalg.solve_discrete_lyapunov``) and discrete and continuous algebraic
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 computation
of the matrix product of Q (from a QR decomposition) and a vector, has been
added.

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``, were
added to easily construct diagonal and block-diagonal sparse matrices
respectively.

``scipy.sparse.csc_matrix`` and ``csr_matrix`` now support the operations
``sin``, ``tan``, ``arcsin``, ``arctan``, ``sinh``, ``tanh``, ``arcsinh``,
``arctanh``, ``rint``, ``sign``, ``expm1``, ``log1p``, ``deg2rad``, ``rad2deg``,
``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.fftpack``.


``scipy.interpolate`` improvements
----------------------------------

For interpolation in spherical coordinates, the three classes
``scipy.interpolate.SmoothSphereBivariateSpline``,
``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 
``scipy.stats.binned_statistic_dd``.


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

``scipy.sparse.cs_graph_components`` has been made a part of the sparse graph
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 transform
can be found in scikits-image.

``scipy.io.save_as_module`` has been deprecated.  A better way to save multiple
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
deprecated.


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)
    4.
    >>> splev(1, tck)
    4.

Corrected behavior::

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

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 4.3
from upstream.

The function ``scipy.signal.bode``, which calculates magnitude and phase data
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.


Authors
=======

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.