Contents

- SciPy 0.10.0 Release Notes
- New features
- Bento: new optional build system
- Generalized and shift-invert eigenvalue problems in
`scipy.sparse.linalg` - Discrete-Time Linear Systems (
`scipy.signal`) - Enhancements to
`scipy.signal` - Additional decomposition options (
`scipy.linalg`) - Additional special matrices (
`scipy.linalg`) - Enhancements to
`scipy.stats` - Basic support for Harwell-Boeing file format for sparse matrices

- Deprecated features
- Backwards-incompatible changes
- Other changes
- Authors

- New features

SciPy 0.10.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a limited number of deprecations and backwards-incompatible 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 master branch.

Release highlights:

- Support for Bento as optional build system.
- Support for generalized eigenvalue problems, and all shift-invert modes available in ARPACK.

This release requires Python 2.4-2.7 or 3.1- and NumPy 1.5 or greater.

Scipy can now be built with Bento. Bento has some nice features like parallel builds and partial rebuilds, that are not possible with the default build system (distutils). For usage instructions see BENTO_BUILD.txt in the scipy top-level directory.

Currently Scipy has three build systems, distutils, numscons and bento. Numscons is deprecated and is planned and will likely be removed in the next release.

The sparse eigenvalue problem solver functions
`scipy.sparse.eigs/eigh` now support generalized eigenvalue
problems, and all shift-invert modes available in ARPACK.

Support for simulating discrete-time linear systems, including
`scipy.signal.dlsim`, `scipy.signal.dimpulse`, and `scipy.signal.dstep`,
has been added to SciPy. Conversion of linear systems from continuous-time to
discrete-time representations is also present via the
`scipy.signal.cont2discrete` function.

A Lomb-Scargle periodogram can now be computed with the new function
`scipy.signal.lombscargle`.

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.

FIR filter design with `scipy.signal.firwin2` now has options to create
filters of type III (zero at zero and Nyquist frequencies) and IV (zero at zero
frequency).

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 in`stats.fisher_exact`. - The function
`stats.chi2_contingency`for computing the chi-square test of independence of factors in a contingency table has been added, along with the related utility functions`stats.contingency.margins`and`stats.contingency.expected_freq`.

Both read and write are support through a simple function-based API, as well as a more complete API to control number format. The functions may be found in scipy.sparse.io.

The following features are supported:

- Read and write sparse matrices in the CSC format
- Only real, symmetric, assembled matrix are supported (RUA format)

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`.

There are similar BLAS wrappers in `scipy.linalg` and `scipy.lib`. These
have now been consolidated as `scipy.linalg.blas`, and `scipy.lib.blas` is
deprecated.

The numscons build system is being replaced by Bento, and will be removed in one of the next scipy releases.

The deprecated name *invnorm* was removed from `scipy.stats.distributions`,
this distribution is available as *invgauss*.

The following deprecated nonlinear solvers from `scipy.optimize` have been
removed:

```
- ``broyden_modified`` (bad performance)
- ``broyden1_modified`` (bad performance)
- ``broyden_generalized`` (equivalent to ``anderson``)
- ``anderson2`` (equivalent to ``anderson``)
- ``broyden3`` (obsoleted by new limited-memory broyden methods)
- ``vackar`` (renamed to ``diagbroyden``)
```

`scipy.constants` has been updated with the CODATA 2010 constants.

`__all__` dicts have been added to all modules, which has cleaned up the
namespaces (particularly useful for interactive work).

An API section has been added to the documentation, giving recommended import guidelines and specifying which submodules are public and which aren’t.

This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order):

- Jeff Armstrong +
- Matthew Brett
- Lars Buitinck +
- David Cournapeau
- FI$H 2000 +
- Michael McNeil Forbes +
- Matty G +
- Christoph Gohlke
- Ralf Gommers
- Yaroslav Halchenko
- Charles Harris
- Thouis (Ray) Jones +
- Chris Jordan-Squire +
- Robert Kern
- Chris Lasher +
- Wes McKinney +
- Travis Oliphant
- Fabian Pedregosa
- Josef Perktold
- Thomas Robitaille +
- Pim Schellart +
- Anthony Scopatz +
- Skipper Seabold +
- Fazlul Shahriar +
- David Simcha +
- Scott Sinclair +
- Andrey Smirnov +
- Collin RM Stocks +
- Martin Teichmann +
- Jake Vanderplas +
- Gaël Varoquaux +
- Pauli Virtanen
- Stefan van der Walt
- Warren Weckesser
- Mark Wiebe +

A total of 35 people contributed to this release. People with a “+” by their names contributed a patch for the first time.