SciPy 0.9.0 Release Notes¶
Contents
SciPy 0.9.0 is the culmination of 6 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 0.9.x branch, and on adding new features on the development trunk.
This release requires Python 2.4 - 2.7 or 3.1 - and NumPy 1.5 or greater.
Please note that SciPy is still considered to have “Beta” status, as we work toward a SciPy 1.0.0 release. The 1.0.0 release will mark a major milestone in the development of SciPy, after which changing the package structure or API will be much more difficult. Whilst these pre-1.0 releases are considered to have “Beta” status, we are committed to making them as bug-free as possible.
However, until the 1.0 release, we are aggressively reviewing and refining the functionality, organization, and interface. This is being done in an effort to make the package as coherent, intuitive, and useful as possible. To achieve this, we need help from the community of users. Specifically, we need feedback regarding all aspects of the project - everything - from which algorithms we implement, to details about our function’s call signatures.
Python 3¶
Scipy 0.9.0 is the first SciPy release to support Python 3. The only module
that is not yet ported is scipy.weave
.
Scipy source code location to be changed¶
Soon after this release, Scipy will stop using SVN as the version control system, and move to Git. The development source code for Scipy can from then on be found at
New features¶
Delaunay tessellations (scipy.spatial
)¶
Scipy now includes routines for computing Delaunay tessellations in N
dimensions, powered by the Qhull computational geometry library. Such
calculations can now make use of the new scipy.spatial.Delaunay
interface.
N-dimensional interpolation (scipy.interpolate
)¶
Support for scattered data interpolation is now significantly
improved. This version includes a scipy.interpolate.griddata
function that can perform linear and nearest-neighbour interpolation
for N-dimensional scattered data, in addition to cubic spline
(C1-smooth) interpolation in 2D and 1D. An object-oriented interface
to each interpolator type is also available.
Nonlinear equation solvers (scipy.optimize
)¶
Scipy includes new routines for large-scale nonlinear equation solving
in scipy.optimize
. The following methods are implemented:
Newton-Krylov (
scipy.optimize.newton_krylov
)(Generalized) secant methods:
Limited-memory Broyden methods (
scipy.optimize.broyden1
,scipy.optimize.broyden2
)Anderson method (
scipy.optimize.anderson
)
Simple iterations (
scipy.optimize.diagbroyden
,scipy.optimize.excitingmixing
,scipy.optimize.linearmixing
)
The scipy.optimize.nonlin
module was completely rewritten, and
some of the functions were deprecated (see above).
New linear algebra routines (scipy.linalg
)¶
Scipy now contains routines for effectively solving triangular
equation systems (scipy.linalg.solve_triangular
).
Improved FIR filter design functions (scipy.signal
)¶
The function scipy.signal.firwin
was enhanced to allow the
design of highpass, bandpass, bandstop and multi-band FIR filters.
The function scipy.signal.firwin2
was added. This function
uses the window method to create a linear phase FIR filter with
an arbitrary frequency response.
The functions scipy.signal.kaiser_atten
and scipy.signal.kaiser_beta
were added.
Improved statistical tests (scipy.stats
)¶
A new function scipy.stats.fisher_exact
was added, that provides Fisher’s
exact test for 2x2 contingency tables.
The function scipy.stats.kendalltau
was rewritten to make it much faster
(O(n log(n)) vs O(n^2)).
Deprecated features¶
Obsolete nonlinear solvers (in scipy.optimize
)¶
The following nonlinear solvers from scipy.optimize
are
deprecated:
broyden_modified
(bad performance)broyden1_modified
(bad performance)broyden_generalized
(equivalent toanderson
)anderson2
(equivalent toanderson
)broyden3
(obsoleted by new limited-memory broyden methods)vackar
(renamed todiagbroyden
)
Removed features¶
The deprecated modules helpmod
, pexec
and ppimport
were removed
from scipy.misc
.
The output_type
keyword in many scipy.ndimage
interpolation functions
has been removed.
The econ
keyword in scipy.linalg.qr
has been removed. The same
functionality is still available by specifying mode='economic'
.
Old correlate/convolve behavior (in scipy.signal
)¶
The old behavior for scipy.signal.convolve
, scipy.signal.convolve2d
,
scipy.signal.correlate
and scipy.signal.correlate2d
was deprecated in
0.8.0 and has now been removed. Convolve and correlate used to swap their
arguments if the second argument has dimensions larger than the first one, and
the mode was relative to the input with the largest dimension. The current
behavior is to never swap the inputs, which is what most people expect, and is
how correlation is usually defined.
scipy.stats
¶
Many functions in scipy.stats
that are either available from numpy or have
been superseded, and have been deprecated since version 0.7, have been removed:
std, var, mean, median, cov, corrcoef, z, zs, stderr,
samplestd, samplevar, pdfapprox, pdf_moments and erfc. These changes
are mirrored in scipy.stats.mstats
.
scipy.sparse
¶
Several methods of the sparse matrix classes in scipy.sparse
which had
been deprecated since version 0.7 were removed: save, rowcol, getdata,
listprint, ensure_sorted_indices, matvec, matmat and rmatvec.
The functions spkron
, speye
, spidentity
, lil_eye
and
lil_diags
were removed from scipy.sparse
. The first three functions
are still available as scipy.sparse.kron
, scipy.sparse.eye
and
scipy.sparse.identity
.
The dims and nzmax keywords were removed from the sparse matrix constructor. The colind and rowind attributes were removed from CSR and CSC matrices respectively.
scipy.sparse.linalg.arpack.speigs
¶
A duplicated interface to the ARPACK library was removed.
Other changes¶
ARPACK interface changes¶
The interface to the ARPACK eigenvalue routines in
scipy.sparse.linalg
was changed for more robustness.
The eigenvalue and SVD routines now raise ArpackNoConvergence
if
the eigenvalue iteration fails to converge. If partially converged results
are desired, they can be accessed as follows:
import numpy as np
from scipy.sparse.linalg import eigs, ArpackNoConvergence
m = np.random.randn(30, 30)
try:
w, v = eigs(m, 6)
except ArpackNoConvergence, err:
partially_converged_w = err.eigenvalues
partially_converged_v = err.eigenvectors
Several bugs were also fixed.
The routines were moreover renamed as follows:
eigen –> eigs
eigen_symmetric –> eigsh
svd –> svds