# SciPy 0.12.0 Release Notes

Contents

- SciPy 0.12.0 Release Notes

  - New features

    - `scipy.spatial` improvements

      - cKDTree feature-complete

      - Voronoi diagrams and convex hulls

      - Delaunay improvements

    - Spectral estimators (`scipy.signal`)

    - `scipy.optimize` improvements

      - Callback functions in L-BFGS-B and TNC

      - Basin hopping global optimization (`scipy.optimize.basinhopping`)

    - `scipy.special` improvements

      - Revised complex error functions

      - Faster orthogonal polynomials

    - `scipy.sparse.linalg` features

    - Listing Matlab(R) file contents in `scipy.io`

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

    - Polynomial interpolation improvements (`scipy.interpolate`)

  - Deprecated features

    - scipy.lib.lapack

    - fblas and cblas

  - Backwards incompatible changes

    - Removal of `scipy.io.save_as_module`

    - axis argument added to `scipy.stats.scoreatpercentile`

  - Authors

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 `scipy.io`

A new function `whosmat` is available in `scipy.io` 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

### 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

### Removal of `scipy.io.save_as_module`

The function `scipy.io.save_as_module` was deprecated in Scipy 0.11.0, and is now removed.

Its private support modules `scipy.io.dumbdbm_patched` and `scipy.io.dumb_shelve` 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.)

## Authors

- 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.

SciPy 0.12.1 Release Notes SciPy 0.11.0 Release Notes