-----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-----
On Mon, Jan 12, 2015 at 4:50 AM, cjw <cjw@ncf.ca> wrote:
Paul,
Wot, no AMD64?
Colin, this is well known from previous scipy and numpy releases. It's due to not having a freely available 64-bit compiler chain available at the moment with which we can build official binaries. You can get 64-bit Windows installers of most scientific Python distributions (like Anaconda, Enthough Canopy, WinPython) and for just Scipy from the site of Christoph Gohlke. Your tone on this list is not appreciated by the way, it borders on trolling. If you have a serious question to which you really don't know the answer, please pose it in a less disrespectful way. Ralf
Hi, On Mon, Jan 12, 2015 at 7:13 AM, Ralf Gommers <ralf.gommers@gmail.com> wrote:
Ralf - honestly I think it's best not to use the term 'trolling' under any circumstances - it can be a heavy weapon [1], although it's perfectly reasonable to say to Colin that you would find it helpful if he used a different tone. In this case, I couldn't be sure whether Colin meant his email to be light-hearted or not. Colin - just to add to Ralf's reply on the 64-bit issue - here are a few links: * Stackoverflow answer with some references [2] * Numpy mailing list question about 64-bit installers in 2011 [3] * Another discussion I started in 2013 [4] * Commentary on problems for Numpy etc on Windows [5] * Our current best hope : Carl Kleffner's mingw-w64 build chain [6] If Carl K is listening here - Carl - what's the current best way to help? Cheers, Matthew [1] http://nipyworld.blogspot.co.uk/2012/06/define-troll.html [2] http://stackoverflow.com/questions/11200137/installing-numpy-on-64bit-window... [3] http://comments.gmane.org/gmane.comp.python.numeric.general/42118 [4] http://mail.scipy.org/pipermail/numpy-discussion/2013-February/065339.html [5] https://github.com/numpy/numpy/wiki/Numerical-software-on-Windows [6] https://github.com/numpy/numpy/wiki/Mingw-static-toolchain
participants (5)
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Anthony Scopatz -
cjw -
Matthew Brett -
Pauli Virtanen -
Ralf Gommers