SciPy 0.12.0 Release Notes

SciPy 0.12.0 is the culmination of 7 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.

Some of the highlights of this release are:

  • Completed QHull wrappers in scipy.spatial.
  • cKDTree now a drop-in replacement for KDTree.
  • A new global optimizer, basinhopping.
  • Support for Python 2 and Python 3 from the same code base (no more 2to3).

This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater. Support for Python 2.4 and 2.5 has been dropped as of this release.

New features

scipy.spatial improvements

cKDTree feature-complete

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.

Spectral estimators (scipy.signal)

The functions scipy.signal.periodogram and scipy.signal.welch were added, providing DFT-based spectral estimators.

scipy.optimize improvements

Callback functions in L-BFGS-B and TNC

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

Basin hopping global optimization (scipy.optimize.basinhopping)

A new global optimization algorithm. Basinhopping is designed to efficiently find the global minimum of a smooth function.

scipy.special improvements

Revised complex error functions

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.

Faster orthogonal polynomials

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

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.

Listing Matlab(R) file contents in

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

Documented BLAS and LAPACK low-level interfaces (scipy.linalg)

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

Polynomial interpolation improvements (scipy.interpolate)

The barycentric, Krogh, piecewise and pchip polynomial interpolators in scipy.interpolate accept now an axis argument.

Deprecated features


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

Removal of

The function was deprecated in Scipy 0.11.0, and is now removed.

Its private support modules and are also removed.

axis argument added to scipy.stats.scoreatpercentile

The function scipy.stats.scoreatpercentile has been given an axis argument. The default argument is axis=None, which means the calculation is done on the flattened array. Before this change, scoreatpercentile would act as if axis=0 had been given. Code using scoreatpercentile with a multidimensional array will need to add axis=0 to the function call to preserve the old behavior. (This API change was not noticed until long after the release of 0.12.0.)


  • Anton Akhmerov +
  • Alexander Eberspächer +
  • Anne Archibald
  • Jisk Attema +
  • K.-Michael Aye +
  • bemasc +
  • Sebastian Berg +
  • François Boulogne +
  • Matthew Brett
  • Lars Buitinck
  • Steven Byrnes +
  • Tim Cera +
  • Christian +
  • Keith Clawson +
  • David Cournapeau
  • Nathan Crock +
  • endolith
  • Bradley M. Froehle +
  • Matthew R Goodman
  • Christoph Gohlke
  • Ralf Gommers
  • Robert David Grant +
  • Yaroslav Halchenko
  • Charles Harris
  • Jonathan Helmus
  • Andreas Hilboll
  • Hugo +
  • Oleksandr Huziy
  • Jeroen Demeyer +
  • Johannes Schönberger +
  • Steven G. Johnson +
  • Chris Jordan-Squire
  • Jonathan Taylor +
  • Niklas Kroeger +
  • Jerome Kieffer +
  • kingson +
  • Josh Lawrence
  • Denis Laxalde
  • Alex Leach +
  • Tim Leslie
  • Richard Lindsley +
  • Lorenzo Luengo +
  • Stephen McQuay +
  • MinRK
  • Sturla Molden +
  • Eric Moore +
  • mszep +
  • Matt Newville +
  • Vlad Niculae
  • Travis Oliphant
  • David Parker +
  • Fabian Pedregosa
  • Josef Perktold
  • Zach Ploskey +
  • Alex Reinhart +
  • Gilles Rochefort +
  • Ciro Duran Santillli +
  • Jan Schlueter +
  • Jonathan Scholz +
  • Anthony Scopatz
  • Skipper Seabold
  • Fabrice Silva +
  • Scott Sinclair
  • Jacob Stevenson +
  • Sturla Molden +
  • Julian Taylor +
  • thorstenkranz +
  • John Travers +
  • True Price +
  • Nicky van Foreest
  • Jacob Vanderplas
  • Patrick Varilly
  • Daniel Velkov +
  • Pauli Virtanen
  • Stefan van der Walt
  • Warren Weckesser

A total of 75 people contributed to this release. People with a “+” by their names contributed a patch for the first time.