Mailman 3 December 2015 - SciPy-Dev

dense output option in integrate.ode
by Evgeni Burovski Feb. 20, 2017

Feb. 20, 2017

Hi David, Hi Jim, > I am new to SciPy-dev. Will the dense output option in scipy.integrate.ode > become available in 0.17.0? This is a feature already available in the > original FORTAN, but wasn't implemented in the wrapper. If the feature is sent as a pull request against the scipy master branch, the PR is reviewed by the maintainers of the integrate package and merged into master before the release split, then yes, it would be available in 0.17.0. So far I do not see any progress towards it. > I wrote these dense output extensions that you listed: > > https://github.com/jddmartin/scipy/tree/dense_output_from_dopri5_and_dop853 > https://github.com/jddmartin/dense_output_example_usage > > but I didn't issue any pull request to scipy. I sent this message to the > scipy developers list: > http://article.gmane.org/gmane.comp.python.scientific.devel/19635/ > explaining the changes. I was hoping for some feedback before issuing a > pull request. Ah, I see that the email likely fell through the cracks back in April. Sorry about that. You might want to ping that email thread once more or send a pull request on github (or both). <snip> Cheers, Evgeni

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win32 binaries and scipy 0.17.0 release
by Evgeni Burovski Feb. 14, 2016

Feb. 14, 2016

Hi, Here's a proposal to follow the numpy lead (https://mail.scipy.org/pipermail/numpy-discussion/2015-December/074422.html) and stop providing the official binaries for 32-bit windows. The reasons are basically the same as for numpy: these binaries contain BLAS/LAPACK which is necessarily lower quality than what's available from Christoph Gohlke/Canopy/Anaconda; they do not and cannot support python 3.5; the build toolchain is currently broken and to the best of my knowledge nobody knows how to fix it. With regards to the scipy 0.17.0 release, win32 problems are responsible for delaying the release for almost a month (OK, the original schedule was too optimistic, but still). The alternatives, as I see them, are: * stop providing the windows binaries (until a MinGW-64 based toolchain is ready, but this is separate) and make the release. * release 0.17.0 with only python 3.4 binaries available (this is the only python version I can build the binaries which do not segfault immediately :-)) * delay the release with no ETA and no clear path forward. Thoughts? Evgeni

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PR adding vectorized spherical Bessel functions
by Ted Pudlik Dec. 31, 2015

Dec. 31, 2015

Hello, I started a pull request <https://github.com/scipy/scipy/pull/5623> providing vectorized implementations of four spherical Bessel functions (jn, yn, in, kn) and their derivatives. This is my first large pull requests, so I would be grateful for any comments! Highlights: 1. Speedup of about an order of magnitude relative to `np.vectorize` (generally more, except for jn for real argument). 2. Recursion largely replaced with the relationship to cylindrical Bessel functions <http://dlmf.nist.gov/10.47.ii>, which resolves accuracy issues for large argument (see gh-1641 <https://github.com/scipy/scipy/issues/1641>, gh-2165 <https://github.com/scipy/scipy/issues/2165> for jn at large real argument, but similar problems existed for yn and kn at large complex argument). 3. More careful treatment of 0, infinity, and overflow. (E.g., sph_kn(125, 0.1) = inf, not 0; yn(5, 0) = -inf, not -10**300.) Best wishes, Ted

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dropping numpy 1.6.2 support
by Ralf Gommers Dec. 31, 2015

Dec. 31, 2015

Hi all, It's been a year since we dropped support for numpy 1.5.1 and decided to keep supporting numpy 1.6.2 for a while longer. There are regularly PRs that need to be adapted because they fail with numpy 1.6.2, latest example is https://github.com/scipy/scipy/pull/5641. So proposal: drop numpy 1.6.2, and make numpy 1.7.2 the lowest supported version. This gives us 4 supported numpy versions, which is enough for users. Other consideration: - Ubuntu 14.04 (latest LTS version) is on 1.8.1 [2] - Debian stable is on 1.8.2 [3] - Centos 7 is on 1.7.1 [4] So Centos is the only problematic one. We can either ignore Centos (they really should be using the latest bugfix version of a minor release) or we can check for >= 1.7.1 in scipy/__init__.py but test against 1.7.2 on TravisCI. For an impression of where we work around 1.6.2 things, try ``$ grin "'1.7.0'"`` Thoughts? Ralf [1] http://article.gmane.org/gmane.comp.python.scientific.devel/19283 [2] http://packages.ubuntu.com/search?keywords=numpy&searchon=names&suite=all&s… [3] https://packages.debian.org/search?keywords=numpy&searchon=names&suite=all&… [4] http://mirror.centos.org/centos/7/os/x86_64/Packages/

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dropping Python 2.6, 3.2 and 3.3 support (was: dropping numpy 1.6.2 support)
by Ralf Gommers Dec. 31, 2015

Dec. 31, 2015

On Thu, Dec 31, 2015 at 7:18 AM, Sturla Molden <sturla.molden(a)gmail.com> wrote: > On 30/12/15 22:01, Evgeni Burovski wrote: > > While we're here, I guess it makes sense to think about dropping Python >> 2.6, 3.2 and maybe even 3.3. >> > > I am +1 for this, at least for 2.6 and 3.2. +1 for this, those versions (including 3.3) are pretty much just a waste of developer effort and CI resources by now. Changing the subject line - this is a separate decision I think with possibly other interested users than the numpy 1.6.2 decision. Ralf P.S. here's the thread in which numpy decided to drop support after numpy 1.11: http://article.gmane.org/gmane.comp.python.numeric.general/61987

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Updating special function docs from AMOS source files
by Ted Pudlik Dec. 24, 2015

Dec. 24, 2015

Hello, I noticed that the SciPy reference documentation for some special functions is minimal (e.g., scipy.special.jv <scipy.special.jvhttp://docs.scipy.org/doc/scipy/reference/generated/scipy.s…>), although the AMOS source files which these functions wrap contain extensive comments on the algorithms used and their limitations, including references (e.g., zbesj.f <zbesj.fhttps://github.com/scipy/scipy/blob/master/scipy/special/amos/zbesj.f>). Would it be appropriate to update the reference documentation using the information in the source files? (Are there any copyright issues?) Best, Ted

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ANN: first release candidate for scipy 0.17.0
by Evgeni Burovski Dec. 24, 2015

Dec. 24, 2015

Hi, I'm pleased to announce the availability of the first release candidate for Scipy 0.17.0. Please try this rc and report any issues on Github tracker or scipy-dev mailing list. Source tarballs and full release notes are available from Github Releases: https://github.com/scipy/scipy/releases/tag/v0.17.0rc1 Please note that this is a source-only release. We do not provide win32 installers for this release. See the email thread starting from <https://mail.scipy.org/pipermail/scipy-dev/2015-December/021103.html> for the rationale and discussion. The updated release schedule is as follows: 10 Jan 2016: rc2 (if needed) 17 Jan 2016: final release. Thanks to everyone who contributed to this release! Evgeni Below is a part of the release notes. ========================== SciPy 0.17.0 Release Notes ========================== .. note:: Scipy 0.17.0 is not released yet! .. contents:: SciPy 0.17.0 is the culmination of 6 months of hard work. It contains many 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 0.17.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater. Release highlights: - New functions for linear and nonlinear least squares optimization with constraints: `scipy.optimize.lsq_linear` and `scipy.optimize.least_squares` - Support for fitting with bounds in `scipy.optimize.curve_fit`. - Significant improvements to `scipy.stats`, providing many functions with better handing of inputs which have NaNs or are empty, improved documentation, and consistent behavior between `scipy.stats` and `scipy.stats.mstats`. - Significant performance improvements and new functionality in `scipy.spatial.cKDTree`. New features ============ `scipy.cluster` improvements - ---------------------------- A new function `scipy.cluster.hierarchy.cut_tree`, which determines a cut tree from a linkage matrix, was added. `scipy.io` improvements - ----------------------- `scipy.io.mmwrite` gained support for symmetric sparse matrices. `scipy.io.netcdf` gained support for masking and scaling data based on data attributes. `scipy.optimize` improvements - ----------------------------- Linear assignment problem solver ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `scipy.optimize.linear_sum_assignment` is a new function for solving the linear sum assignment problem. It uses the Hungarian algorithm (Kuhn-Munkres). Least squares optimization ~~~~~~~~~~~~~~~~~~~~~~~~~~ A new function for *nonlinear* least squares optimization with constraints was added: `scipy.optimize.least_squares`. It provides several methods: Levenberg-Marquardt for unconstrained problems, and two trust-region methods for constrained ones. Furthermore it provides different loss functions. New trust-region methods also handle sparse Jacobians. A new function for *linear* least squares optimization with constraints was added: `scipy.optimize.lsq_linear`. It provides a trust-region method as well as an implementation of the Bounded-Variable Least-Squares (BVLS) algorithm. `scipy.optimize.curve_fit` now supports fitting with bounds. `scipy.signal` improvements - -------------------------- A ``mode`` keyword was added to `scipy.signal.spectrogram`, to let it return other spectrograms than power spectral density. `scipy.stats` improvements - -------------------------- Many functions in `scipy.stats` have gained a ``nan_policy`` keyword, which allows specifying how to treat input with NaNs in them: propagate the NaNs, raise an error, or omit the NaNs. Many functions in `scipy.stats` have been improved to correctly handle input arrays that are empty or contain infs/nans. A number of functions with the same name in `scipy.stats` and `scipy.stats.mstats` were changed to have matching signature and behavior. See `gh-5474 <https://github.com/scipy/scipy/issues/5474>`__ for details. `scipy.stats.binom_test` and `scipy.stats.mannwhitneyu` gained a keyword ``alternative``, which allows specifying the hypothesis to test for. Eventually all hypothesis testing functions will get this keyword. For methods of many continuous distributions, complex input is now accepted. Matrix normal distribution has been implemented as `scipy.stats.matrix_normal`. `scipy.sparse` improvements - -------------------------- The `axis` keyword was added to sparse norms, `scipy.sparse.linalg.norm`. `scipy.spatial` improvements - ---------------------------- `scipy.spatial.cKDTree` was partly rewritten for improved performance and several new features were added to it: - - the ``query_ball_point`` method became significantly faster - - ``query`` and ``query_ball_point`` gained an ``n_jobs`` keyword for parallel execution - - build and query methods now release the GIL - - full pickling support - - support for periodic spaces - - the ``sparse_distance_matrix`` method can now return and sparse matrix type `scipy.interpolate` improvements - -------------------------------- Out-of-bounds behavior of `scipy.interpolate.interp1d` has been improved. Use a two-element tuple for the ``fill_value`` argument to specify separate fill values for input above and below the interpolation range. Linear and nearest interpolation kinds of `scipy.interpolate.interp1d` support extrapolation via the ``fill_value="extrapolate"`` keyword. `scipy.linalg` improvements - --------------------------- The default algorithm for `scipy.linalg.leastsq` has been changed to use LAPACK's function ``*gelsd``. Users wanting to get the previous behavior can use a new keyword ``lapack_driver="gelss"`` (allowed values are "gelss", "gelsd" and "gelsy"). ``scipy.sparse`` matrices and linear operators now support the matmul (``@``) operator when available (Python 3.5+). See [PEP 465](http://legacy.python.org/dev/peps/pep-0465/) A new function `scipy.linalg.ordqz`, for QZ decomposition with reordering, has been added. Deprecated features =================== ``scipy.stats.histogram`` is deprecated in favor of ``np.histogram``, which is faster and provides the same functionality. ``scipy.stats.threshold`` and ``scipy.mstats.threshold`` are deprecated in favor of ``np.clip``. See issue #617 for details. ``scipy.stats.ss`` is deprecated. This is a support function, not meant to be exposed to the user. Also, the name is unclear. See issue #663 for details. ``scipy.stats.square_of_sums`` is deprecated. This too is a support function not meant to be exposed to the user. See issues #665 and #663 for details. ``scipy.stats.f_value``, ``scipy.stats.f_value_multivariate``, ``scipy.stats.f_value_wilks_lambda``, and ``scipy.mstats.f_value_wilks_lambda`` are deprecated. These are related to ANOVA, for which ``scipy.stats`` provides quite limited functionality and these functions are not very useful standalone. See issues #660 and #650 for details. ``scipy.stats.chisqprob`` is deprecated. This is an alias. ``stats.chi2.sf`` should be used instead. ``scipy.stats.betai`` is deprecated. This is an alias for ``special.betainc`` which should be used instead. Backwards incompatible changes ============================== The functions ``stats.trim1`` and ``stats.trimboth`` now make sure the elements trimmed are the lowest and/or highest, depending on the case. Slicing without at least partial sorting was previously done, but didn't make sense for unsorted input. When ``variable_names`` is set to an empty list, ``scipy.io.loadmat`` now correctly returns no values instead of all the contents of the MAT file. Element-wise multiplication of sparse matrices now returns a sparse result in all cases. Previously, multiplying a sparse matrix with a dense matrix or array would return a dense matrix. The function ``misc.lena`` has been removed due to license incompatibility. The constructor for ``sparse.coo_matrix`` no longer accepts ``(None, (m,n))`` to construct an all-zero matrix of shape ``(m,n)``. This functionality was deprecated since at least 2007 and was already broken in the previous SciPy release. Use ``coo_matrix((m,n))`` instead. The Cython wrappers in ``linalg.cython_lapack`` for the LAPACK routines ``*gegs``, ``*gegv``, ``*gelsx``, ``*geqpf``, ``*ggsvd``, ``*ggsvp``, ``*lahrd``, ``*latzm``, ``*tzrqf`` have been removed since these routines are not present in the new LAPACK 3.6.0 release. With the exception of the routines ``*ggsvd`` and ``*ggsvp``, these were all deprecated in favor of routines that are currently present in our Cython LAPACK wrappers. Because the LAPACK ``*gegv`` routines were removed in LAPACK 3.6.0. The corresponding Python wrappers in ``scipy.linalg.lapack`` are now deprecated and will be removed in a future release. The source files for these routines have been temporarily included as a part of ``scipy.linalg`` so that SciPy can be built against LAPACK versions that do not provide these deprecated routines. Other changes ============= Html and pdf documentation of development versions of Scipy is now automatically rebuilt after every merged pull request. `scipy.constants` is updated to the CODATA 2014 recommended values. Usage of `scipy.fftpack` functions within Scipy has been changed in such a way that `PyFFTW <http://hgomersall.github.io/pyFFTW/>`__ can easily replace `scipy.fftpack` functions (with improved performance). See `gh-5295 <https://github.com/scipy/scipy/pull/5295>`__ for details. The ``imread`` functions in `scipy.misc` and `scipy.ndimage` were unified, for which a ``mode`` argument was added to `scipy.misc.imread`. Also, bugs for 1-bit and indexed RGB image formats were fixed. ``runtests.py``, the development script to build and test Scipy, now allows building in parallel with ``--parallel``. Authors ======= * @cel4 + * @chemelnucfin + * @endolith * @mamrehn + * @tosh1ki + * Joshua L. Adelman + * Anne Archibald * Hervé Audren + * Vincent Barrielle + * Bruno Beltran + * Sumit Binnani + * Joseph Jon Booker * Olga Botvinnik + * Michael Boyle + * Matthew Brett * Zaz Brown + * Lars Buitinck * Pete Bunch + * Evgeni Burovski * CJ Carey * Ien Cheng + * Cody + * Jaime Fernandez del Rio * Ales Erjavec + * Abraham Escalante * Yves-Rémi Van Eycke + * Yu Feng + * Eric Firing * Francis T. O'Donovan + * André Gaul * Christoph Gohlke * Ralf Gommers * Alex Griffing * Alexander Grigorievskiy * Charles Harris * Jörn Hees + * Ian Henriksen * David Menéndez Hurtado * Gert-Ludwig Ingold * Aakash Jain + * Rohit Jamuar + * Jan Schlüter * Johannes Ballé * Luke Zoltan Kelley + * Jason King + * Andreas Kopecky + * Eric Larson * Denis Laxalde * Antony Lee * Gregory R. Lee * Josh Levy-Kramer + * Sam Lewis + * François Magimel + * Martín Gaitán + * Sam Mason + * Andreas Mayer * Nikolay Mayorov * Damon McDougall + * Robert McGibbon * Sturla Molden * Will Monroe + * Eric Moore * Maniteja Nandana * Vikram Natarajan + * Andrew Nelson * Marti Nito + * Behzad Nouri + * Daisuke Oyama + * Giorgio Patrini + * Fabian Paul + * Christoph Paulik + * Mad Physicist + * Irvin Probst * Sebastian Pucilowski + * Ted Pudlik + * Eric Quintero * Yoav Ram + * Joscha Reimer + * Juha Remes * Frederik Rietdijk + * Rémy Léone + * Christian Sachs + * Skipper Seabold * Sebastian Skoupý + * Alex Seewald + * Andreas Sorge + * Bernardo Sulzbach + * Julian Taylor * Louis Tiao + * Utkarsh Upadhyay + * Jacob Vanderplas * Gael Varoquaux + * Pauli Virtanen * Fredrik Wallner + * Stefan van der Walt * James Webber + * Warren Weckesser * Raphael Wettinger + * Josh Wilson + * Nat Wilson + * Peter Yin + A total of 101 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.

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win32 binaries and scipy 0.17.0 release
by Robert Lucente Dec. 23, 2015

Dec. 23, 2015

>Intel compilers If we approach Intel about getting their compilers for free for SciPy, they might agree to it?

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scipy 0.17.x branched
by Evgeni Burovski Dec. 22, 2015

Dec. 22, 2015

Hi, maintenance/0.17.x has been branched and master is now for 0.18-dev. I plan to tag and cut the source-only release candidate tomorrow. (Let's keep the discussion of the windows binaries in the other email thread.) Evgeni

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Enquiry
by Shakir Khan Dec. 21, 2015

Dec. 21, 2015

Hello I would like to ask the following thing in my post: "Hi, I am looking for Fast Gauss Transform algorithm in python. There are a number of matlab (mex) function available but I am in need of pure python code. Has anyone any idea? Thanks in advance." Regards, Md Shakir Khan DMT Student | EIT Digital Master SchoolT: +31 6 53113203E: M.S.Khan(a)student.tudelft.nl | mskha(a)kth.se

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