Congrats all! On Sun, Jan 11, 2015 at 9:50 PM, cjw <c...@ncf.ca> wrote:
> Paul, > > Wot, no AMD64? > > Colin W. > On 11-Jan-15 12:50 PM, Paul Virtanen wrote: > > -----BEGIN PGP SIGNED MESSAGE----- > > Hash: SHA1 > > > > Dear all, > > > > We are pleased to announce the Scipy 0.15.0 release. > > > > The 0.15.0 release contains bugfixes and new features, most important > > of which are mentioned in the excerpt from the release notes below. > > > > Source tarballs, binaries, and full release notes are available at > > https://sourceforge.net/projects/scipy/files/scipy/0.15.0/ > > > > Best regards, > > Pauli Virtanen > > > > > > ========================== > > SciPy 0.15.0 Release Notes > > ========================== > > > > 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. > > > > > > 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 `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. > > > > ``scipy.signal`` improvements > > - ----------------------------- > > > > The function `scipy.signal.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 > > - ----------------------------- > > > > 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`` 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 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. > > > > > > 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. > > > > ``scipy.stats.nanmean``, ``nanmedian`` and ``nanstd`` functions are > > deprecated > > in favor of their numpy equivalents. > > > > > > 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 > > > > iEYEARECAAYFAlSyt/cACgkQ6BQxb7O0pWA8SACfXmpUsJcXT5espj71OYpeaj5b > > JJwAoL10ud3q1f51A5Ij4lgqMeZGnHlj > > =ZmOl > > -----END PGP SIGNATURE----- > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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