SciPy 0.12.0 Release Notes
Scipy 0.12.0 is not released yet!
- New features
- Deprecated features
- Backwards incompatible changes
- Other changes
SciPy 0.12.0 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 0.12.x branch, and on adding new features on the master branch.
This release requires Python 2.4-2.7 or 3.1- and NumPy 1.X.X or greater.
bargument can now be either a vector or a matrix.
scipy.sparse.linalg.invwas added. This uses
spsolveto compute a sparse matrix inverse.
scipy.sparse.linalg.expmwas added. This computes the exponential of a sparse matrix using a similar algorithm to the existing dense array implementation in
scipy.spatial: Cython version of KDTree, cKDTree, is now feature-complete. Most operations (construction, query, query_ball_point, query_pairs, count_neighbors and sparse_distance_matrix) are between 200 and 1000 times faster in cKDTree than in KDTree. With very minor caveats, cKDTree has exactly the same interface as KDTree, and can be used as a drop-in replacement.
scipy.spatial now contains functionality for computing Voronoi diagrams and convex hulls using the Qhull library. (Delaunay triangulation was available since Scipy 0.9.0.)
It's now possible to pass in custom Qhull options in Delaunay triangulation. Coplanar points are now also recorded, if present. Incremental construction of Delaunay triangulations is now also possible.
A callback mechanism was added to L-BFGS-B and TNC minimization solvers.
The computation of special functions related to the error function now uses a
new Faddeeva library from MIT which
increases their numerical precision. The scaled and imaginary error functions
erfi were also added, and the Dawson integral
dawsn can now be
evaluated for a complex argument.
A new function
whosmat is available in
scipy.io for inspecting contents
of MAT files without reading them to memory.
Evaluation of orthogonal polynomials (the
eval_* routines) in now
scipy.special, and their
out= argument functions
The modules scipy.linalg.blas and scipy.linalg.lapack can be used to access low-level BLAS and LAPACK functions.
The module scipy.lib.lapack is deprecated. You can use scipy.linalg.lapack instead. The module scipy.lib.blas was deprecated earlier in Scipy 0.10.0.
Accessing the modules scipy.linalg.fblas, cblas, flapack, clapack is deprecated. Instead, use the modules scipy.linalg.lapack and scipy.linalg.blas.
Minor change in behavior of T-tests
- Anthony Scopatz (sparse linear algebra)
- Jake Vanderplas (sparse linear algebra)