SciPy 0.13.0 Release Notes¶
Contents
SciPy 0.13.0 is the culmination of 7 months of hard work. It contains many 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 0.13.x branch, and on adding new features on the master branch.
This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater. Highlights of this release are:
support for fancy indexing and boolean comparisons with sparse matrices
interpolative decompositions and matrix functions in the linalg module
two new trust-region solvers for unconstrained minimization
New features¶
scipy.integrate
improvements¶
N-dimensional numerical integration¶
A new function scipy.integrate.nquad
, which provides N-dimensional
integration functionality with a more flexible interface than dblquad
and
tplquad
, has been added.
dopri*
improvements¶
The intermediate results from the dopri
family of ODE solvers can now be
accessed by a solout callback function.
scipy.linalg
improvements¶
Interpolative decompositions¶
Scipy now includes a new module scipy.linalg.interpolative
containing routines for computing interpolative matrix decompositions
(ID). This feature is based on the ID software package by
P.G. Martinsson, V. Rokhlin, Y. Shkolnisky, and M. Tygert, previously
adapted for Python in the PymatrixId package by K.L. Ho.
Polar decomposition¶
A new function scipy.linalg.polar
, to compute the polar decomposition
of a matrix, was added.
BLAS level 3 functions¶
The BLAS functions symm
, syrk
, syr2k
, hemm
, herk
and
her2k
are now wrapped in scipy.linalg
.
Matrix functions¶
Several matrix function algorithms have been implemented or updated following
detailed descriptions in recent papers of Nick Higham and his co-authors.
These include the matrix square root (sqrtm
), the matrix logarithm
(logm
), the matrix exponential (expm
) and its Frechet derivative
(expm_frechet
), and fractional matrix powers (fractional_matrix_power
).
scipy.optimize
improvements¶
Trust-region unconstrained minimization algorithms¶
The minimize
function gained two trust-region solvers for unconstrained
minimization: dogleg
and trust-ncg
.
scipy.sparse
improvements¶
Boolean comparisons and sparse matrices¶
All sparse matrix types now support boolean data, and boolean operations. Two sparse matrices A and B can be compared in all the expected ways A < B, A >= B, A != B, producing similar results as dense Numpy arrays. Comparisons with dense matrices and scalars are also supported.
CSR and CSC fancy indexing¶
Compressed sparse row and column sparse matrix types now support fancy indexing with boolean matrices, slices, and lists. So where A is a (CSC or CSR) sparse matrix, you can do things like:
>>> A[A > 0.5] = 1 # since Boolean sparse matrices work
>>> A[:2, :3] = 2
>>> A[[1,2], 2] = 3
scipy.sparse.linalg
improvements¶
The new function onenormest
provides a lower bound of the 1-norm of a
linear operator and has been implemented according to Higham and Tisseur
(2000). This function is not only useful for sparse matrices, but can also be
used to estimate the norm of products or powers of dense matrices without
explicitly building the intermediate matrix.
The multiplicative action of the matrix exponential of a linear operator
(expm_multiply
) has been implemented following the description in Al-Mohy
and Higham (2011).
Abstract linear operators (scipy.sparse.linalg.LinearOperator
) can now be
multiplied, added to each other, and exponentiated, producing new linear
operators. This enables easier construction of composite linear operations.
scipy.spatial
improvements¶
The vertices of a ConvexHull can now be accessed via the vertices attribute, which gives proper orientation in 2-D.
scipy.signal
improvements¶
The cosine window function scipy.signal.cosine
was added.
scipy.special
improvements¶
New functions scipy.special.xlogy
and scipy.special.xlog1py
were added.
These functions can simplify and speed up code that has to calculate
x * log(y)
and give 0 when x == 0
.
scipy.io
improvements¶
Unformatted Fortran file reader¶
The new class scipy.io.FortranFile
facilitates reading unformatted
sequential files written by Fortran code.
scipy.io.wavfile
enhancements¶
scipy.io.wavfile.write
now accepts a file buffer. Previously it only
accepted a filename.
scipy.io.wavfile.read
and scipy.io.wavfile.write
can now handle floating
point WAV files.
scipy.interpolate
improvements¶
B-spline derivatives and antiderivatives¶
scipy.interpolate.splder
and scipy.interpolate.splantider
functions
for computing B-splines that represent derivatives and antiderivatives
of B-splines were added. These functions are also available in the
class-based FITPACK interface as UnivariateSpline.derivative
and
UnivariateSpline.antiderivative
.
scipy.stats
improvements¶
Distributions now allow using keyword parameters in addition to positional parameters in all methods.
The function scipy.stats.power_divergence
has been added for the
Cressie-Read power divergence statistic and goodness of fit test.
Included in this family of statistics is the “G-test”
(https://en.wikipedia.org/wiki/G-test).
scipy.stats.mood
now accepts multidimensional input.
An option was added to scipy.stats.wilcoxon
for continuity correction.
scipy.stats.chisquare
now has an axis argument.
scipy.stats.mstats.chisquare
now has axis and ddof arguments.
Deprecated features¶
expm2
and expm3
¶
The matrix exponential functions scipy.linalg.expm2 and scipy.linalg.expm3
are deprecated. All users should use the numerically more robust
scipy.linalg.expm
function instead.
scipy.stats
functions¶
scipy.stats.oneway is deprecated; scipy.stats.f_oneway
should be used
instead.
scipy.stats.glm is deprecated. scipy.stats.ttest_ind
is an equivalent
function; more full-featured general (and generalized) linear model
implementations can be found in statsmodels.
scipy.stats.cmedian is deprecated; numpy.median
should be used instead.
Backwards incompatible changes¶
LIL matrix assignment¶
Assigning values to LIL matrices with two index arrays now works similarly as assigning into ndarrays:
>>> x = lil_matrix((3, 3))
>>> x[[0,1,2],[0,1,2]]=[0,1,2]
>>> x.todense()
matrix([[ 0., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 2.]])
rather than giving the result:
>>> x.todense()
matrix([[ 0., 1., 2.],
[ 0., 1., 2.],
[ 0., 1., 2.]])
Users relying on the previous behavior will need to revisit their code.
The previous behavior is obtained by x[numpy.ix_([0,1,2],[0,1,2])] = ...
.
Deprecated radon
function removed¶
The misc.radon
function, which was deprecated in scipy 0.11.0, has been
removed. Users can find a more full-featured radon
function in
scikit-image.
Removed deprecated keywords xa
and xb
from stats.distributions
¶
The keywords xa
and xb
, which were deprecated since 0.11.0, have
been removed from the distributions in scipy.stats
.
Changes to MATLAB file readers / writers¶
The major change is that 1D arrays in numpy now become row vectors (shape 1, N)
when saved to a MATLAB 5 format file. Previously 1D arrays saved as column
vectors (N, 1). This is to harmonize the behavior of writing MATLAB 4 and 5
formats, and adapt to the defaults of numpy and MATLAB - for example
np.atleast_2d
returns 1D arrays as row vectors.
Trying to save arrays of greater than 2 dimensions in MATLAB 4 format now raises an error instead of silently reshaping the array as 2D.
scipy.io.loadmat('afile')
used to look for afile on the Python system path
(sys.path
); now loadmat
only looks in the current directory for a
relative path filename.
Other changes¶
Security fix: scipy.weave
previously used temporary directories in an
insecure manner under certain circumstances.
Cython is now required to build unreleased versions of scipy. The C files generated from Cython sources are not included in the git repo anymore. They are however still shipped in source releases.
The code base received a fairly large PEP8 cleanup. A tox pep8
command has been added; new code should pass this test command.
Scipy cannot be compiled with gfortran 4.1 anymore (at least on RH5), likely due to that compiler version not supporting entry constructs well.
Authors¶
This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order):
Jorge Cañardo Alastuey +
Tom Aldcroft +
Max Bolingbroke +
Joseph Jon Booker +
François Boulogne
Matthew Brett
Christian Brodbeck +
Per Brodtkorb +
Christian Brueffer +
Lars Buitinck
Evgeni Burovski +
Tim Cera
Lawrence Chan +
David Cournapeau
Dražen Lučanin +
Alexander J. Dunlap +
endolith
André Gaul +
Christoph Gohlke
Ralf Gommers
Alex Griffing +
Blake Griffith +
Charles Harris
Bob Helmbold +
Andreas Hilboll
Kat Huang +
Oleksandr (Sasha) Huziy +
Gert-Ludwig Ingold +
Thouis (Ray) Jones
Juan Luis Cano Rodríguez +
Robert Kern
Andreas Kloeckner +
Sytse Knypstra +
Gustav Larsson +
Denis Laxalde
Christopher Lee
Tim Leslie
Wendy Liu +
Clemens Novak +
Takuya Oshima +
Josef Perktold
Illia Polosukhin +
Przemek Porebski +
Steve Richardson +
Branden Rolston +
Skipper Seabold
Fazlul Shahriar
Leo Singer +
Rohit Sivaprasad +
Daniel B. Smith +
Julian Taylor
Louis Thibault +
Tomas Tomecek +
John Travers
Richard Tsai +
Jacob Vanderplas
Patrick Varilly
Pauli Virtanen
Stefan van der Walt
Warren Weckesser
Pedro Werneck +
Nils Werner +
Michael Wimmer +
Nathan Woods +
Tony S. Yu +
A total of 65 people contributed to this release. People with a “+” by their names contributed a patch for the first time.