==========================
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, embeding `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 decompostion) 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.
Checksums
=========
103872eac380b62314f9f24628dac473 release/installers/scipy-0.11.0-py2.5-python.org-macosx10.3.dmg
56c9fa7f363d8e4e77b016e05441b78d release/installers/scipy-0.11.0-py2.6-python.org-macosx10.3.dmg
102f9d433654c9a1eeed1f359b7eaf9d release/installers/scipy-0.11.0-py2.7-python.org-macosx10.3.dmg
1b6c3868ea3192c41f0137b51cb58b81 release/installers/scipy-0.11.0-py2.7-python.org-macosx10.6.dmg
7d36857bda7de2d5846bb46e289f86aa release/installers/scipy-0.11.0-win32-superpack-python2.5.exe
ad1b6d4605e65d755cbdf204ac79a0f9 release/installers/scipy-0.11.0-win32-superpack-python2.6.exe
478b150094b195208b1efd1a0869606e release/installers/scipy-0.11.0-win32-superpack-python2.7.exe
3af083dfab94424cbc7d0811498ff044 release/installers/scipy-0.11.0-win32-superpack-python3.1.exe
00f79726835ee591e5ba7a1857baef96 release/installers/scipy-0.11.0-win32-superpack-python3.2.exe
842c81d35fd63579c41a8ca21a2419b9 release/installers/scipy-0.11.0.tar.gz
40b700ddde9ddab643b640fff7a9d753 release/installers/scipy-0.11.0.zip
Download Latest Version
scipy-0.16.1.tar.gz (12.2 MB)
