- SciPy 0.15.0 Release Notes
- New features
- Deprecated features
- Backwards incompatible changes
SciPy 0.15.0 is the culmination of 6 months of hard work. It contains several 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 0.16.x branch, and on adding new features on the master branch.
This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater.
The new function scipy.optimize.linprog provides a generic linear programming similar to the way scipy.optimize.minimize provides a generic interface to nonlinear programming optimizers. Currently the only method supported is simplex which provides a two-phase, dense-matrix-based simplex algorithm. Callbacks functions are supported, allowing the user to monitor the progress of the algorithm.
A new scipy.optimize.differential_evolution function has been added to the optimize module. Differential Evolution is an algorithm used for finding the global minimum of multivariate functions. It is stochastic in nature (does not use gradient methods), and can search large areas of candidate space, but often requires larger numbers of function evaluations than conventional gradient based techniques.
The function scipy.signal.max_len_seq was added, which computes a Maximum Length Sequence (MLS) signal.
It is now possible to use scipy.integrate routines to integrate multivariate ctypes functions, thus avoiding callbacks to Python and providing better performance.
The function scipy.linalg.orthogonal_procrustes for solving the procrustes linear algebra problem was added.
BLAS level 2 functions her, syr, her2 and syr2 are now wrapped in scipy.linalg.
scipy.sparse.linalg.svds can now take a LinearOperator as its main input.
Values of ellipsoidal harmonic (i.e. Lame) functions and associated normalization constants can be now computed using ellip_harm, ellip_harm_2, and ellip_normal.
New convenience functions entr, rel_entr kl_div, huber, and pseudo_huber were added.
Routines reverse_cuthill_mckee and maximum_bipartite_matching for computing reorderings of sparse graphs were added.
Added a Dirichlet multivariate distribution, scipy.stats.dirichlet.
The new function scipy.stats.median_test computes Mood’s median test.
The new function scipy.stats.combine_pvalues implements Fisher’s and Stouffer’s methods for combining p-values.
scipy.stats.describe returns a namedtuple rather than a tuple, allowing users to access results by index or by name.
The scipy.weave module is deprecated. It was the only module never ported to Python 3.x, and is not recommended to be used for new code - use Cython instead. In order to support existing code, scipy.weave has been packaged separately: https://github.com/scipy/weave. It is a pure Python package, and can easily be installed with pip install weave.
scipy.special.bessel_diff_formula is deprecated. It is a private function, and therefore will be removed from the public API in a following release.
scipy.stats.nanmean, nanmedian and nanstd functions are deprecated in favor of their numpy equivalents.
The functions scipy.ndimage.minimum_positions, scipy.ndimage.maximum_positions` and scipy.ndimage.extrema return positions as ints instead of floats.