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 scipy.sparse.csgraph implements a number of efficient
graph algorithms for graphs stored as sparse adjacency matrices. Available
routines are:
connected_components- determine connected components of a graph
laplacian- compute the laplacian of a graph
shortest_path- compute the shortest path between points on a positive graph
dijkstra- use Dijkstra’s algorithm for shortest path
floyd_warshall- use the Floyd-Warshall algorithm for shortest path
breadth_first_order- compute a breadth-first order of nodes
depth_first_order- compute a depth-first order of nodes
breadth_first_tree- construct the breadth-first tree from a given node
depth_first_tree- construct a depth-first tree from a given node
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.