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SciPy 0.12.0 Release Notes

Note

Scipy 0.12.0 is not released yet!

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.

New features

scipy.sparse.linalg features

  • In scipy.sparse.linalg.spsolve, the b argument can now be either a vector or a matrix.
  • scipy.sparse.linalg.inv was added. This uses spsolve to compute a sparse matrix inverse.
  • scipy.sparse.linalg.expm was added. This computes the exponential of a sparse matrix using a similar algorithm to the existing dense array implementation in scipy.linalg.expm.

cKDTRee improvements

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.

Voronoi diagrams and convex hulls

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.)

Delaunay improvements

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.

scipy.optimize improvements

A callback mechanism was added to L-BFGS-B and TNC minimization solvers.

Complex error functions in scipy.special

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 erfcx and erfi were also added, and the Dawson integral dawsn can now be evaluated for a complex argument.

Listing Matlab(R) file contents

A new function whosmat is available in scipy.io for inspecting contents of MAT files without reading them to memory.

Faster orthogonal polynomials

Evaluation of orthogonal polynomials (the eval_* routines) in now faster in scipy.special, and their out= argument functions properly.

Documented BLAS and LAPACK low-level interfaces

The modules scipy.linalg.blas and scipy.linalg.lapack can be used to access low-level BLAS and LAPACK functions.

Deprecated features

scipy.lib.lapack

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.

fblas and cblas

Accessing the modules scipy.linalg.fblas, cblas, flapack, clapack is deprecated. Instead, use the modules scipy.linalg.lapack and scipy.linalg.blas.

Backwards incompatible changes

Minor change in behavior of T-tests

Other changes

Authors

  • Anthony Scopatz (sparse linear algebra)
  • Jake Vanderplas (sparse linear algebra)
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