SciPy 0.10.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a limited number of deprecations and backwards-incompatible 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 0.10.x branch, and on adding new features on the development master branch.
Support for Bento as optional build system.
Support for generalized eigenvalue problems, and all shift-invert modes available in ARPACK.
This release requires Python 2.4-2.7 or 3.1- and NumPy 1.5 or greater.
Scipy can now be built with Bento. Bento has some nice features like parallel builds and partial rebuilds, that are not possible with the default build system (distutils). For usage instructions see BENTO_BUILD.txt in the scipy top-level directory.
Currently Scipy has three build systems, distutils, numscons and bento. Numscons is deprecated and is planned and will likely be removed in the next release.
The sparse eigenvalue problem solver functions
scipy.sparse.eigs/eigh now support generalized eigenvalue
problems, and all shift-invert modes available in ARPACK.
Support for simulating discrete-time linear systems, including
has been added to SciPy. Conversion of linear systems from continuous-time to
discrete-time representations is also present via the
A Lomb-Scargle periodogram can now be computed with the new function
The forward-backward filter function
scipy.signal.filtfilt can now
filter the data in a given axis of an n-dimensional numpy array.
(Previously it only handled a 1-dimensional array.) Options have been
added to allow more control over how the data is extended before filtering.
FIR filter design with
scipy.signal.firwin2 now has options to create
filters of type III (zero at zero and Nyquist frequencies) and IV (zero at zero
A sort keyword has been added to the Schur decomposition routine
scipy.linalg.schur) to allow the sorting of eigenvalues in
the resultant Schur form.
invhilbert were added to
The one-sided form of Fisher’s exact test is now also implemented in
stats.chi2_contingencyfor computing the chi-square test of independence of factors in a contingency table has been added, along with the related utility functions
logit(p) = log(p/(1-p))
expit(x) = 1/(1+exp(-x)) have been implemented as
Both read and write are support through a simple function-based API, as well as a more complete API to control number format. The functions may be found in scipy.sparse.io.
The following features are supported:
Read and write sparse matrices in the CSC format
Only real, symmetric, assembled matrix are supported (RUA format)
The maxentropy module is unmaintained, rarely used and has not been functioning
well for several releases. Therefore it has been deprecated for this release,
and will be removed for scipy 0.11. Logistic regression in scikits.learn is a
good alternative for this functionality. The
function has been moved to
There are similar BLAS wrappers in
have now been consolidated as
The numscons build system is being replaced by Bento, and will be removed in one of the next scipy releases.
The deprecated name invnorm was removed from
this distribution is available as invgauss.
The following deprecated nonlinear solvers from
scipy.optimize have been
- ``broyden_modified`` (bad performance) - ``broyden1_modified`` (bad performance) - ``broyden_generalized`` (equivalent to ``anderson``) - ``anderson2`` (equivalent to ``anderson``) - ``broyden3`` (obsoleted by new limited-memory broyden methods) - ``vackar`` (renamed to ``diagbroyden``)
scipy.constants has been updated with the CODATA 2010 constants.
__all__ dicts have been added to all modules, which has cleaned up the
namespaces (particularly useful for interactive work).
An API section has been added to the documentation, giving recommended import guidelines and specifying which submodules are public and which aren’t.