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 bugfixes, 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 bugfixes and optimizations. Moreover, our development attention will now shift to bugfix 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 3.23.4 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 twophase, densematrixbased 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 pvalues. `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
Paul, Wot, no AMD64? Colin W. On 11Jan15 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 bugfixes, 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 bugfixes and optimizations. Moreover, our development attention will now shift to bugfix 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 3.23.4 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 twophase, densematrixbased 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 pvalues.
`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 _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpydiscussion
Congrats all! On Sun, Jan 11, 2015 at 9:50 PM, cjw <cjw@ncf.ca> wrote:
Paul,
Wot, no AMD64?
Colin W. On 11Jan15 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 bugfixes, 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 bugfixes and optimizations. Moreover, our development attention will now shift to bugfix 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 3.23.4 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 twophase, densematrixbased 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 pvalues.
`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 _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpydiscussion
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 64bit compiler chain available at the moment with which we can build official binaries. You can get 64bit 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:
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 64bit compiler chain available at the moment with which we can build official binaries. You can get 64bit 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  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 lighthearted or not. Colin  just to add to Ralf's reply on the 64bit issue  here are a few links: * Stackoverflow answer with some references [2] * Numpy mailing list question about 64bit 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 mingww64 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/definetroll.html [2] http://stackoverflow.com/questions/11200137/installingnumpyon64bitwindow... [3] http://comments.gmane.org/gmane.comp.python.numeric.general/42118 [4] http://mail.scipy.org/pipermail/numpydiscussion/2013February/065339.html [5] https://github.com/numpy/numpy/wiki/NumericalsoftwareonWindows [6] https://github.com/numpy/numpy/wiki/Mingwstatictoolchain
participants (5)

Anthony Scopatz

cjw

Matthew Brett

Pauli Virtanen

Ralf Gommers