Note
Scipy 0.10.0 is not released yet!
SciPy 0.10.0 is the culmination of XXX 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 0.10.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. For example, in addition to fixing numerous bugs in this release, we have also doubled the number of unit tests since the last release.
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.
The sparse eigenvalue problem solver functions scipy.sparse.eigs/eigh now support generalized eigenvalue problems, and all shift-invert modes available in ARPACK.
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.
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.
The functions hilbert and invhilbert were added to scipy.linalg.
The one-sided form of Fisher’s exact test is now also implemented.
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 scipy.maxentropy.logsumexp function has been moved to scipy.misc.
The deprecated name invnorm was removed from scipy.stats.distributions, this distribution is available as invgauss.