-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Dear all,
We have finally finished preparing the Scipy 0.15.0 beta 1 release. Please try it and report any issues on the scipy-dev mailing list, and/or on Github. If no surprises turn up, the final release is planned on Dec 20 in three weeks. Source tarballs and full release notes are available at https://sourceforge.net/projects/scipy/files/SciPy/0.15.0b1/ Binary installers should also be up soon. Best regards, Pauli Virtanen - -------------------------------------------- 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 3.2-3.3 and NumPy 1.5.1 or greater. New features ============ Linear Programming Interface - - ---------------------------- 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. Differential_evolution, a global optimizer - - ------------------------------------------ A new ``differential_evolution`` function is available in the ``scipy.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. ``scipy.signal`` improvements - - ----------------------------- The function ``max_len_seq`` was added, which computes a Maximum Length Sequence (MLS) signal. ``scipy.integrate`` improvements - - -------------------------------- It is now possible to use ``scipy.integrate`` routines to integrate multivariate ctypes functions, thus avoiding callbacks to Python and providing better performance. ``scipy.linalg`` improvements - - ----------------------------- Add function ``orthogonal_procrustes`` for solving the procrustes linear algebra problem. ``scipy.sparse`` improvements - - ----------------------------- ``scipy.sparse.linalg.svds`` can now take a ``LinearOperator`` as its main input. ``scipy.special`` improvements - - ------------------------------ 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. ``scipy.sparse.csgraph`` improvements - - ------------------------------------- Routines ``reverse_cuthill_mckee`` and ``maximum_bipartite_matching`` for computing reorderings of sparse graphs were added. ``scipy.stats`` improvements - - ---------------------------- Added a Dirichlet distribution as multivariate distribution. 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. Deprecated features =================== 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. Backwards incompatible changes ============================== scipy.ndimage - - ------------- The functions ``scipy.ndimage.minimum_positions``, ``scipy.ndimage.maximum_positions`` and ``scipy.ndimage.extrema`` return positions as ints instead of floats. scipy.integrate - - --------------- The format of banded Jacobians in ``scipy.integrate.ode`` solvers is changed. Note that the previous documentation of this feature was erroneous. -----BEGIN PGP SIGNATURE----- Version: GnuPG v1 iEYEARECAAYFAlRyaf8ACgkQ6BQxb7O0pWC7XQCeNtdJD4ZNDXvFeNFs7N3KjQn6 8QkAoK3pFmhMrTwCrgkusl+fRNMboN2r =WSpM -----END PGP SIGNATURE----- _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion