From aeklant at gmail.com Sat Jul 4 01:46:33 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Sat, 4 Jul 2015 00:46:33 -0500 Subject: [SciPy-Dev] Deprecation of `stats.betai` and `stats.chisqprob` Message-ID: Hello all, In continuing with the statistics module cleanup `stats.betai` and `stats.chisqprob` were identified as candidates for deprecation. These functions are basically just aliases, delegating to `special.betainc` and `special.chdtrc` respectively. In accordance to gh-2741, the preferred way to use these distributions is via the corresponding `stats.distribution` function or by calling the `scipy.special` function directly. If there are any objections to deprecating these functions, please voice them here so we can ponder and make a decision. For more details refer to the following issues: - gh-2741 - gh-641 - gh-642 For details on the PR refer to: - gh-5017 Cheers, Abraham. -------------- next part -------------- An HTML attachment was scrubbed... URL: From ralf.gommers at gmail.com Sat Jul 4 04:57:32 2015 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Sat, 4 Jul 2015 10:57:32 +0200 Subject: [SciPy-Dev] 0.16.0rc1 plan Message-ID: Hi all, The last issues that were blocking for tagging 0.16.0rc1 have now been resolved (thanks go out to Eric Moore, Eric Quintero, Robert McGibbon and Christoph Gohlke). So I'd like to tag that RC asap. Only issue is that I can't build Windows binaries for the next two weeks, due to being on holiday (I'll be completely off-line for most of the next two weeks by the way). Unless anyone sees an issue with this, I therefore want to tag rc1 in ~12 hours and make that source-only for now. The MinGW Windows binaries were already completely OK for beta2, so missing them now is not critical I think. Cheers, Ralf -------------- next part -------------- An HTML attachment was scrubbed... URL: From pav at iki.fi Sat Jul 4 07:00:38 2015 From: pav at iki.fi (Pauli Virtanen) Date: Sat, 04 Jul 2015 14:00:38 +0300 Subject: [SciPy-Dev] 0.16.0rc1 plan In-Reply-To: References: Message-ID: 04.07.2015, 11:57, Ralf Gommers kirjoitti: [clip] > Unless anyone sees an issue with this, I therefore want to tag rc1 in ~12 > hours and make that source-only for now. The MinGW Windows binaries were > already completely OK for beta2, so missing them now is not critical I > think. Sounds good to me. We are in any case relying a lot on 3rd party Windows binaries. In case there are problems, rc2 is still possible. Pauli From ralf.gommers at gmail.com Sat Jul 4 17:40:07 2015 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Sat, 4 Jul 2015 23:40:07 +0200 Subject: [SciPy-Dev] ANN: Scipy 0.16.0 release candidate 1 Message-ID: Hi, I'm pleased to announce the availability of the first release candidate of Scipy 0.16.0. Please try it out and report any issues on the Github issue tracker or on the scipy-dev mailing list. This first RC is a source-only release. Sources and release notes can be found at https://github.com/scipy/scipy/releases/tag/v0.16.0rc1. Note that this is a bit of an experiment - it's the first time we use the Github Releases feature. Feedback welcome. Thanks to everyone who contributed to this release! Ralf ========================== SciPy 0.16.0 Release Notes ========================== .. note:: Scipy 0.16.0 is not released yet! .. contents:: SciPy 0.16.0 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 0.15.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 1.6.2 or greater. Highlights of this release include: - A Cython API for BLAS/LAPACK in `scipy.linalg` - A new benchmark suite. It's now straightforward to add new benchmarks, and they're routinely included with performance enhancement PRs. - Support for the second order sections (SOS) format in `scipy.signal`. New features ============ Benchmark suite --------------- The benchmark suite has switched to using `Airspeed Velocity `__ for benchmarking. You can run the suite locally via ``python runtests.py --bench``. For more details, see ``benchmarks/README.rst``. `scipy.linalg` improvements --------------------------- A full set of Cython wrappers for BLAS and LAPACK has been added in the modules `scipy.linalg.cython_blas` and `scipy.linalg.cython_lapack`. In Cython, these wrappers can now be cimported from their corresponding modules and used without linking directly against BLAS or LAPACK. The functions `scipy.linalg.qr_delete`, `scipy.linalg.qr_insert` and `scipy.linalg.qr_update` for updating QR decompositions were added. The function `scipy.linalg.solve_circulant` solves a linear system with a circulant coefficient matrix. The function `scipy.linalg.invpascal` computes the inverse of a Pascal matrix. The function `scipy.linalg.solve_toeplitz`, a Levinson-Durbin Toeplitz solver, was added. Added wrapper for potentially useful LAPACK function ``*lasd4``. It computes the square root of the i-th updated eigenvalue of a positive symmetric rank-one modification to a positive diagonal matrix. See its LAPACK documentation and unit tests for it to get more info. Added two extra wrappers for LAPACK least-square solvers. Namely, they are ``*gelsd`` and ``*gelsy``. Wrappers for the LAPACK ``*lange`` functions, which calculate various matrix norms, were added. Wrappers for ``*gtsv`` and ``*ptsv``, which solve ``A*X = B`` for tri-diagonal matrix ``A``, were added. `scipy.signal` improvements --------------------------- Support for second order sections (SOS) as a format for IIR filters was added. The new functions are: * `scipy.signal.sosfilt` * `scipy.signal.sosfilt_zi`, * `scipy.signal.sos2tf` * `scipy.signal.sos2zpk` * `scipy.signal.tf2sos` * `scipy.signal.zpk2sos`. Additionally, the filter design functions `iirdesign`, `iirfilter`, `butter`, `cheby1`, `cheby2`, `ellip`, and `bessel` can return the filter in the SOS format. The function `scipy.signal.place_poles`, which provides two methods to place poles for linear systems, was added. The option to use Gustafsson's method for choosing the initial conditions of the forward and backward passes was added to `scipy.signal.filtfilt`. New classes ``TransferFunction``, ``StateSpace`` and ``ZerosPolesGain`` were added. These classes are now returned when instantiating `scipy.signal.lti`. Conversion between those classes can be done explicitly now. An exponential (Poisson) window was added as `scipy.signal.exponential`, and a Tukey window was added as `scipy.signal.tukey`. The function for computing digital filter group delay was added as `scipy.signal.group_delay`. The functionality for spectral analysis and spectral density estimation has been significantly improved: `scipy.signal.welch` became ~8x faster and the functions `scipy.signal.spectrogram`, `scipy.signal.coherence` and `scipy.signal.csd` (cross-spectral density) were added. `scipy.signal.lsim` was rewritten - all known issues are fixed, so this function can now be used instead of ``lsim2``; ``lsim`` is orders of magnitude faster than ``lsim2`` in most cases. `scipy.sparse` improvements --------------------------- The function `scipy.sparse.norm`, which computes sparse matrix norms, was added. The function `scipy.sparse.random`, which allows to draw random variates from an arbitrary distribution, was added. `scipy.spatial` improvements ---------------------------- `scipy.spatial.cKDTree` has seen a major rewrite, which improved the performance of the ``query`` method significantly, added support for parallel queries, pickling, and options that affect the tree layout. See pull request 4374 for more details. The function `scipy.spatial.procrustes` for Procrustes analysis (statistical shape analysis) was added. `scipy.stats` improvements -------------------------- The Wishart distribution and its inverse have been added, as `scipy.stats.wishart` and `scipy.stats.invwishart`. The Exponentially Modified Normal distribution has been added as `scipy.stats.exponnorm`. The Generalized Normal distribution has been added as `scipy.stats.gennorm`. All distributions now contain a ``random_state`` property and allow specifying a specific ``numpy.random.RandomState`` random number generator when generating random variates. Many statistical tests and other `scipy.stats` functions that have multiple return values now return ``namedtuples``. See pull request 4709 for details. `scipy.optimize` improvements ----------------------------- A new derivative-free method DF-SANE has been added to the nonlinear equation system solving function `scipy.optimize.root`. Deprecated features =================== ``scipy.stats.pdf_fromgamma`` is deprecated. This function was undocumented, untested and rarely used. Statsmodels provides equivalent functionality with ``statsmodels.distributions.ExpandedNormal``. ``scipy.stats.fastsort`` is deprecated. This function is unnecessary, ``numpy.argsort`` can be used instead. ``scipy.stats.signaltonoise`` and ``scipy.stats.mstats.signaltonoise`` are deprecated. These functions did not belong in ``scipy.stats`` and are rarely used. See issue #609 for details. ``scipy.stats.histogram2`` is deprecated. This function is unnecessary, ``numpy.histogram2d`` can be used instead. Backwards incompatible changes ============================== The deprecated global optimizer ``scipy.optimize.anneal`` was removed. The following deprecated modules have been removed: ``scipy.lib.blas``, ``scipy.lib.lapack``, ``scipy.linalg.cblas``, ``scipy.linalg.fblas``, ``scipy.linalg.clapack``, ``scipy.linalg.flapack``. They had been deprecated since Scipy 0.12.0, the functionality should be accessed as `scipy.linalg.blas` and `scipy.linalg.lapack`. The deprecated function ``scipy.special.all_mat`` has been removed. The deprecated functions ``fprob``, ``ksprob``, ``zprob``, ``randwcdf`` and ``randwppf`` have been removed from `scipy.stats`. Other changes ============= The version numbering for development builds has been updated to comply with PEP 440. Building with ``python setup.py develop`` is now supported. -------------- next part -------------- An HTML attachment was scrubbed... URL: From giselle1701 at gmail.com Mon Jul 6 16:35:44 2015 From: giselle1701 at gmail.com (Giselle Sosa Jones) Date: Mon, 6 Jul 2015 16:05:44 -0430 Subject: [SciPy-Dev] SLSQP on Ubuntu Message-ID: Hello I'm running SLSQP algorithm from Scipy on Ubuntu 12.04 and Python 3.3. The algorithm doesn't converge unless the starting point is really close to the solution. I tried it on Windows and it works perfectly, it converges easily and doesn't take too much iterations. On Ubuntu I either get the message "Inequality constraints incompatible", and sometimes I even get the message "Optimization terminated succesfully", but the values are not correct. Has anyone experienced the same thing? I have no clue to what's going on. Also I tried it with Python 2.7 on Ubuntu and still doesn't work, but on Windows it does. Thank you very much, I'm desperate. GSJ -------------- next part -------------- An HTML attachment was scrubbed... URL: From charlesr.harris at gmail.com Mon Jul 6 16:45:02 2015 From: charlesr.harris at gmail.com (Charles R Harris) Date: Mon, 6 Jul 2015 16:45:02 -0400 Subject: [SciPy-Dev] SLSQP on Ubuntu In-Reply-To: References: Message-ID: On Mon, Jul 6, 2015 at 4:35 PM, Giselle Sosa Jones wrote: > Hello > > I'm running SLSQP algorithm from Scipy on Ubuntu 12.04 and Python 3.3. The > algorithm doesn't converge unless the starting point is really close to the > solution. I tried it on Windows and it works perfectly, it converges easily > and doesn't take too much iterations. On Ubuntu I either get the message > "Inequality constraints incompatible", and sometimes I even get the message > "Optimization terminated succesfully", but the values are not correct. Has > anyone experienced the same thing? I have no clue to what's going on. Also > I tried it with Python 2.7 on Ubuntu and still doesn't work, but on Windows > it does. > > Thank you very much, I'm desperate. > That is strange. Is the problem small enough that you can post it somewhere so we can try it out? Also, where did you get your windows scipy? There may be compiler/library differences. I assume 64 bit versions on both? Chuck -------------- next part -------------- An HTML attachment was scrubbed... URL: From giselle1701 at gmail.com Mon Jul 6 16:48:57 2015 From: giselle1701 at gmail.com (Giselle Sosa Jones) Date: Mon, 6 Jul 2015 16:18:57 -0430 Subject: [SciPy-Dev] SLSQP on Ubuntu In-Reply-To: References: Message-ID: It has a lot of code and functions, so it would be hard to post it. But in terms of amount of variables it's small (16 variables and 16 constraints). I got the Windows version from this website called Unofficial Windows Binaries for Python, and indeed I believe it's a compilation problem. Actually it's 32 bits, but it works fine on Windows, even with bigger problems. GSJ 2015-07-06 16:15 GMT-04:30 Charles R Harris : > > > On Mon, Jul 6, 2015 at 4:35 PM, Giselle Sosa Jones > wrote: > >> Hello >> >> I'm running SLSQP algorithm from Scipy on Ubuntu 12.04 and Python 3.3. >> The algorithm doesn't converge unless the starting point is really close to >> the solution. I tried it on Windows and it works perfectly, it converges >> easily and doesn't take too much iterations. On Ubuntu I either get the >> message "Inequality constraints incompatible", and sometimes I even get the >> message "Optimization terminated succesfully", but the values are not >> correct. Has anyone experienced the same thing? I have no clue to what's >> going on. Also I tried it with Python 2.7 on Ubuntu and still doesn't work, >> but on Windows it does. >> >> Thank you very much, I'm desperate. >> > > That is strange. Is the problem small enough that you can post it > somewhere so we can try it out? Also, where did you get your windows scipy? > There may be compiler/library differences. I assume 64 bit versions on both? > > Chuck > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-dev > > -- Lic. Giselle Sosa Jones Departamento de C?mputo Cient?fico y Estad?stica Universidad Sim?n Bol?var Caracas, Venezuela +58-212-9063398 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jenshnielsen at gmail.com Mon Jul 6 17:10:28 2015 From: jenshnielsen at gmail.com (Jens Nielsen) Date: Mon, 06 Jul 2015 21:10:28 +0000 Subject: [SciPy-Dev] SLSQP on Ubuntu In-Reply-To: References: Message-ID: Which versions of Scipy are you using (scipy.__version__)? The default version on Ubuntu 12.04 is probably fairly old and you might be better of with a newer one. Jens. man. 6. jul. 2015 kl. 15.49 skrev Giselle Sosa Jones : > It has a lot of code and functions, so it would be hard to post it. But in > terms of amount of variables it's small (16 variables and 16 constraints). > I got the Windows version from this website called Unofficial Windows > Binaries for Python, and indeed I believe it's a compilation problem. > Actually it's 32 bits, but it works fine on Windows, even with bigger > problems. > > GSJ > > 2015-07-06 16:15 GMT-04:30 Charles R Harris : > >> >> >> On Mon, Jul 6, 2015 at 4:35 PM, Giselle Sosa Jones > > wrote: >> >>> Hello >>> >>> I'm running SLSQP algorithm from Scipy on Ubuntu 12.04 and Python 3.3. >>> The algorithm doesn't converge unless the starting point is really close to >>> the solution. I tried it on Windows and it works perfectly, it converges >>> easily and doesn't take too much iterations. On Ubuntu I either get the >>> message "Inequality constraints incompatible", and sometimes I even get the >>> message "Optimization terminated succesfully", but the values are not >>> correct. Has anyone experienced the same thing? I have no clue to what's >>> going on. Also I tried it with Python 2.7 on Ubuntu and still doesn't work, >>> but on Windows it does. >>> >>> Thank you very much, I'm desperate. >>> >> >> That is strange. Is the problem small enough that you can post it >> somewhere so we can try it out? Also, where did you get your windows scipy? >> There may be compiler/library differences. I assume 64 bit versions on both? >> >> Chuck >> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at scipy.org >> http://mail.scipy.org/mailman/listinfo/scipy-dev >> >> > > > -- > Lic. Giselle Sosa Jones > Departamento de C?mputo Cient?fico y Estad?stica > Universidad Sim?n Bol?var > Caracas, Venezuela > +58-212-9063398 > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From giselle1701 at gmail.com Mon Jul 6 17:11:59 2015 From: giselle1701 at gmail.com (Giselle Sosa Jones) Date: Mon, 6 Jul 2015 16:41:59 -0430 Subject: [SciPy-Dev] SLSQP on Ubuntu In-Reply-To: References: Message-ID: No, I actually have 0.15 which is the newest one (except for 0.16 which is still beta). Also Numpy is 1.9 which is the newest one too. 2015-07-06 16:40 GMT-04:30 Jens Nielsen : > Which versions of Scipy are you using (scipy.__version__)? The default > version on Ubuntu 12.04 is probably fairly old and you might be better of > with a newer one. > > Jens. > > > > man. 6. jul. 2015 kl. 15.49 skrev Giselle Sosa Jones < > giselle1701 at gmail.com>: > >> It has a lot of code and functions, so it would be hard to post it. But >> in terms of amount of variables it's small (16 variables and 16 >> constraints). >> I got the Windows version from this website called Unofficial Windows >> Binaries for Python, and indeed I believe it's a compilation problem. >> Actually it's 32 bits, but it works fine on Windows, even with bigger >> problems. >> >> GSJ >> >> 2015-07-06 16:15 GMT-04:30 Charles R Harris : >> >>> >>> >>> On Mon, Jul 6, 2015 at 4:35 PM, Giselle Sosa Jones < >>> giselle1701 at gmail.com> wrote: >>> >>>> Hello >>>> >>>> I'm running SLSQP algorithm from Scipy on Ubuntu 12.04 and Python 3.3. >>>> The algorithm doesn't converge unless the starting point is really close to >>>> the solution. I tried it on Windows and it works perfectly, it converges >>>> easily and doesn't take too much iterations. On Ubuntu I either get the >>>> message "Inequality constraints incompatible", and sometimes I even get the >>>> message "Optimization terminated succesfully", but the values are not >>>> correct. Has anyone experienced the same thing? I have no clue to what's >>>> going on. Also I tried it with Python 2.7 on Ubuntu and still doesn't work, >>>> but on Windows it does. >>>> >>>> Thank you very much, I'm desperate. >>>> >>> >>> That is strange. Is the problem small enough that you can post it >>> somewhere so we can try it out? Also, where did you get your windows scipy? >>> There may be compiler/library differences. I assume 64 bit versions on both? >>> >>> Chuck >>> >>> _______________________________________________ >>> SciPy-Dev mailing list >>> SciPy-Dev at scipy.org >>> http://mail.scipy.org/mailman/listinfo/scipy-dev >>> >>> >> >> >> -- >> Lic. Giselle Sosa Jones >> Departamento de C?mputo Cient?fico y Estad?stica >> Universidad Sim?n Bol?var >> Caracas, Venezuela >> +58-212-9063398 >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at scipy.org >> http://mail.scipy.org/mailman/listinfo/scipy-dev >> > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-dev > > -- Lic. Giselle Sosa Jones Departamento de C?mputo Cient?fico y Estad?stica Universidad Sim?n Bol?var Caracas, Venezuela +58-212-9063398 -------------- next part -------------- An HTML attachment was scrubbed... URL: From aeklant at gmail.com Mon Jul 6 18:21:21 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Mon, 6 Jul 2015 17:21:21 -0500 Subject: [SciPy-Dev] On deprecating `stats.threshold` In-Reply-To: References: <5583C506.1050403@googlemail.com> Message-ID: Hello all, gh-4976 has been merged and thus `stats.threshold` deprecated. Thanks to all who contributed with the discussion/feedback. Cheers, Abraham. 2015-06-29 18:36 GMT-05:00 Abraham Escalante : > Hello all, > > As per the reasons and discussion held within this thread we will be > moving forward with the deprecation `stats.threshold` unless we can find a > compelling argument against it. > > So please, if you have an opinion to share on this matter, feel free to > respond here. Otherwise we will merge gh-4976 > with the deprecation in the > next three or four days, most likely. > > Kind regards, > Abraham. > > 2015-06-25 17:51 GMT-05:00 Abraham Escalante : > >> I think we should move forward with the deprecation since `np.clip` >> pretty much covers this and as Ralf points out, the function doesn't seem >> to fit in `scipy.stats`. >> >> It would make more sense for `np.clip` to be enhanced with the option to >> use the same value to substitute anything below and above the limits, >> although that would be outside the scope of this project. It may be a nice >> addition. >> >> Regards, >> Abraham. >> >> 2015-06-21 9:52 GMT-05:00 Ralf Gommers : >> >>> >>> >>> On Fri, Jun 19, 2015 at 9:30 AM, Julian Taylor < >>> jtaylor.debian at googlemail.com> wrote: >>> >>>> On 18.06.2015 14:27, josef.pktd at gmail.com wrote: >>>> > >>>> > >>>> > On Thu, Jun 18, 2015 at 6:16 AM, Julian Taylor >>>> > >>> >> >>>> > wrote: >>>> > >>>> > On Wed, Jun 17, 2015 at 10:44 PM, Abraham Escalante >>>> > > wrote: >>>> > > Hello all, >>>> > > >>>> > > As part of the ongoing scipy.stats improvements we are >>>> pondering the >>>> > > deprecation of `stats.threshold` (and its masked array >>>> counterpart: >>>> > > `mstats.threshold`) for the following reasons. >>>> > > >>>> > > The functionality it provides is nearly identical to `np.clip`. >>>> > > Its usage does not seem to be common (Ralf made a search with >>>> searchcode; it >>>> > > is not used in scipy as a helper function either). >>>> > >>>> > I don't think those are sufficient reasons for deprecation. >>>> > It does fullfil a purpose as its not exactly the same np.clip, the >>>> > implementation is simple and maintainable and its documented well. >>>> > There has to be something bad or dangerous about the function to >>>> > warrant issuing warnings on usage. >>>> >>> >>> Those are not the only possible reasons for deprecation. In this case >>> it's a function that doesn't really fit in scipy.stats and seems to have >>> become a public function only by accident. The goal here, like for multiple >>> other recent deprecations, is to make scipy.stats a coherent package of >>> statistics functions that are well documented and tested. In this case >>> docs/tests are OK but the function simply doesn't belong in Scipy. >>> >>> >>> >>>> > I pretty much share the view of David, It has interesting use cases >>>> but >>>> > it's not worth it. >>>> >>>> I don't see the cost in keeping it, but the cost of removing it is >>>> unknown. Just because we can't find any users does not mean they don't >>>> exist. >>> >>> >>> You could make that argument about any deprecation. >>> >>> While the Scipy deprecation policy is similar to Numpy, this kind of >>> case is the main difference in my opinion. There's a reason Scipy still has >>> an 0.xx version number. >>> >>> Ralf >>> >>> >>> >>> >>> _______________________________________________ >>> SciPy-Dev mailing list >>> SciPy-Dev at scipy.org >>> http://mail.scipy.org/mailman/listinfo/scipy-dev >>> >>> >> > -------------- next part -------------- An HTML attachment was scrubbed... URL: From aeklant at gmail.com Wed Jul 15 16:31:59 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Wed, 15 Jul 2015 15:31:59 -0500 Subject: [SciPy-Dev] Weekly Summary 2015/07/15 Message-ID: Hello all, Here is last week's progress and this week's plan for `scipy.stats` improvements *Week 6 topics:* - `ppcc_max` PR has been merged. - Deprecation of `betai` and `chisqprob` has been merged. - 'alternative' and 'exact' keywords for `ansari`. - nan checks PR initial review. *Week 7 topics:* - `moment` documentation proof reading. - nan checks PR improvements based on feedback. - new batch of PRs for StatisticsCleanup milestone. There's a couple of PRs that are on hold (`ansari` and `variation`) until next week. Meanwhile, I will advance on nan checks and `moment` and also do a new batch of PRs for other functions like `bayes_mvs`, `kstat` and `kstatvar`. For your reference: - Open Pull Requests - Open StatisticsCleanup issues Regards, Abraham. -------------- next part -------------- An HTML attachment was scrubbed... URL: From aeklant at gmail.com Wed Jul 15 16:37:22 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Wed, 15 Jul 2015 15:37:22 -0500 Subject: [SciPy-Dev] Deprecation of `stats.betai` and `stats.chisqprob` In-Reply-To: References: Message-ID: Update: since there were no objections regarding the deprecation of `stats.betai` and `stats.chisqprob`, we have moved forward with it. The functions will go through a normal deprecation cycle, so we expect them to be removed somewhere around version 0.19 For further details please refer to: - gh-2741 - gh-641 - gh-642 - gh-5017 (PR) Cheers, Abraham. 2015-07-04 0:46 GMT-05:00 Abraham Escalante : > Hello all, > > In continuing with the statistics module cleanup `stats.betai` and > `stats.chisqprob` were identified as candidates for deprecation. These > functions are basically just aliases, delegating to `special.betainc` and > `special.chdtrc` respectively. > > In accordance to gh-2741, the preferred way to use these distributions is > via the corresponding `stats.distribution` function or by calling the > `scipy.special` function directly. > > If there are any objections to deprecating these functions, please voice > them here so we can ponder and make a decision. > > For more details refer to the following issues: > > - gh-2741 > - gh-641 > - gh-642 > > For details on the PR refer to: > > - gh-5017 > > > Cheers, > Abraham. > -------------- next part -------------- An HTML attachment was scrubbed... URL: From aeklant at gmail.com Tue Jul 21 01:19:35 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Tue, 21 Jul 2015 00:19:35 -0500 Subject: [SciPy-Dev] Weekly Summary 2015/07/21 Message-ID: Hello all, Here is last week's progress and this week's plan for `scipy.stats` improvements *Week 7 topics:* - nan checks PR moving forward. Potential roadblock for 'omit' keyword. - `moment` should be about ready for merging. - A couple new PRs opened (kstats and bayes_mvs). *Week 8 topics:* - Figuring out how best to deal with nan checks roadblock. - Add NIST test cases for `f_oneway` and `linregress`. - Start working on `mstats` being consistent with `stats`. I also hope to close a few PRs that have been open for a while so I can start working on new ones. For your reference: - Open Pull Requests - Open StatisticsCleanup issues Regards, Abraham. -------------- next part -------------- An HTML attachment was scrubbed... URL: From phillip.m.feldman at gmail.com Wed Jul 22 00:25:31 2015 From: phillip.m.feldman at gmail.com (Phillip Feldman) Date: Tue, 21 Jul 2015 21:25:31 -0700 Subject: [SciPy-Dev] concern about using `scipy.interpolate.LSQSphereBivariateSpline` Message-ID: I have a concern about using `scipy.interpolate.LSQSphereBivariateSpline`. The underlying Fortran function (http://www.netlib.org/dierckx/sphere.f) accepts a smoothing parameter `s`, but it looks as though the wrapper function `scipy.interpolate.LSQSphereBivariateSpline` does not accept this parameter. So, I wonder what value of `s` gets passed to the Fortran. Phillip -------------- next part -------------- An HTML attachment was scrubbed... URL: From ralf.gommers at gmail.com Wed Jul 22 14:39:49 2015 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Wed, 22 Jul 2015 20:39:49 +0200 Subject: [SciPy-Dev] finalizing 0.16.0 Message-ID: Hi all, There seem to not have been any issues with 0.16.0rc1, so I'm planning to tag the final release tomorrow. If I missed anything that still needs addressing, please let me know. Cheers, Ralf -------------- next part -------------- An HTML attachment was scrubbed... URL: From nevion at gmail.com Thu Jul 23 18:14:12 2015 From: nevion at gmail.com (Jason Newton) Date: Thu, 23 Jul 2015 15:14:12 -0700 Subject: [SciPy-Dev] constructing record dtypes from the numpy c-api Message-ID: Hi folks, Cross-posting from the numpy ML; I can't get my account past the moderator to get my email out to the list. Also accidentally sent one out to the scipy-user list.. I've been a longtime subscriber to scipy-dev though - so I hope this one gets through in a timely manner. I was trying to figure out how to make a dtype for a c-struct on the c-side and storing that in some boost python libraries I'm making. Imagine the following c-struct, greatly simplified of course from the real ones I need to expose: struct datum{ double position[3]; float velocity[3]; int32_t d0; uint64_t time_of_receipt; }; How would you make the dtype/PyArray_Descr for this? I have as a point of reference compound types in HDF for similar struct descriptions (h5py makes these nearly 1:1 and converts back and forth to dtypes and hdf5 types, it uses Cython to accomplish this) - but I don't want to bring in hdf for this task - I'm not sure how well the offsets would go over in that translation to h5py too. Proper/best solutions would make use of offsetof as we insert entries to the dtype/PyArray_Descr. The language I'm working in is C++11. The end goal is probably going to be to create a helper infrastructure to allow this to be made automatically-ish provided implementation of a [static] visitor pattern in C++. The idea is to make numpy compatible c++ POD types rather than use Boost.Python wrapped proxies for every object which will cut down on some complicated and time consuming code (both of computer and developer) when ndarrays are what's called for. Thanks for any advisement! -Jason -------------- next part -------------- An HTML attachment was scrubbed... URL: From lists at hilboll.de Thu Jul 23 20:20:31 2015 From: lists at hilboll.de (Andreas Hilboll) Date: Fri, 24 Jul 2015 02:20:31 +0200 Subject: [SciPy-Dev] concern about using `scipy.interpolate.LSQSphereBivariateSpline` In-Reply-To: References: Message-ID: <55B184CF.4090704@hilboll.de> Hi Phillip, LSQSphereBivariateSpline sets s=0.0: https://github.com/scipy/scipy/blob/master/scipy/interpolate/src/fitpack.pyf#L584 If you want smoothing, you can use SmoothSphereBivariateSpline, which is also a wrapper for sphere.f, where you can set s to your liking. Best, Andreas. On 22.07.2015 06:25, Phillip Feldman wrote: > I have a concern about using > `scipy.interpolate.LSQSphereBivariateSpline`. The underlying Fortran > function (http://www.netlib.org/dierckx/sphere.f) accepts a smoothing > parameter `s`, but it looks as though the wrapper function > `scipy.interpolate.LSQSphereBivariateSpline` does not accept this > parameter. So, I wonder what value of `s` gets passed to the Fortran. > > Phillip > > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-dev > -- -- Andreas. From ralf.gommers at gmail.com Fri Jul 24 15:39:54 2015 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Fri, 24 Jul 2015 21:39:54 +0200 Subject: [SciPy-Dev] ANN: Scipy 0.16.0 release Message-ID: Hi all, On behalf of the Scipy development team I'm pleased to announce the availability of Scipy 0.16.0. This release contains some exciting new features (see release notes below) and more than half a years' worth of maintenance work. 93 people contributed to this release. This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.6.2 or greater. Sources, binaries and release notes can be found at https://github.com/scipy/scipy/releases/tag/v0.16.0 Enjoy, Ralf -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 ========================== SciPy 0.16.0 Release Notes ========================== SciPy 0.16.0 is the culmination of 7 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 0.16.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 1.6.2 or greater. Highlights of this release include: - - A Cython API for BLAS/LAPACK in `scipy.linalg` - - A new benchmark suite. It's now straightforward to add new benchmarks, and they're routinely included with performance enhancement PRs. - - Support for the second order sections (SOS) format in `scipy.signal`. New features ============ Benchmark suite - --------------- The benchmark suite has switched to using `Airspeed Velocity `__ for benchmarking. You can run the suite locally via ``python runtests.py --bench``. For more details, see ``benchmarks/README.rst``. `scipy.linalg` improvements - --------------------------- A full set of Cython wrappers for BLAS and LAPACK has been added in the modules `scipy.linalg.cython_blas` and `scipy.linalg.cython_lapack`. In Cython, these wrappers can now be cimported from their corresponding modules and used without linking directly against BLAS or LAPACK. The functions `scipy.linalg.qr_delete`, `scipy.linalg.qr_insert` and `scipy.linalg.qr_update` for updating QR decompositions were added. The function `scipy.linalg.solve_circulant` solves a linear system with a circulant coefficient matrix. The function `scipy.linalg.invpascal` computes the inverse of a Pascal matrix. The function `scipy.linalg.solve_toeplitz`, a Levinson-Durbin Toeplitz solver, was added. Added wrapper for potentially useful LAPACK function ``*lasd4``. It computes the square root of the i-th updated eigenvalue of a positive symmetric rank-one modification to a positive diagonal matrix. See its LAPACK documentation and unit tests for it to get more info. Added two extra wrappers for LAPACK least-square solvers. Namely, they are ``*gelsd`` and ``*gelsy``. Wrappers for the LAPACK ``*lange`` functions, which calculate various matrix norms, were added. Wrappers for ``*gtsv`` and ``*ptsv``, which solve ``A*X = B`` for tri-diagonal matrix ``A``, were added. `scipy.signal` improvements - --------------------------- Support for second order sections (SOS) as a format for IIR filters was added. The new functions are: * `scipy.signal.sosfilt` * `scipy.signal.sosfilt_zi`, * `scipy.signal.sos2tf` * `scipy.signal.sos2zpk` * `scipy.signal.tf2sos` * `scipy.signal.zpk2sos`. Additionally, the filter design functions `iirdesign`, `iirfilter`, `butter`, `cheby1`, `cheby2`, `ellip`, and `bessel` can return the filter in the SOS format. The function `scipy.signal.place_poles`, which provides two methods to place poles for linear systems, was added. The option to use Gustafsson's method for choosing the initial conditions of the forward and backward passes was added to `scipy.signal.filtfilt`. New classes ``TransferFunction``, ``StateSpace`` and ``ZerosPolesGain`` were added. These classes are now returned when instantiating `scipy.signal.lti`. Conversion between those classes can be done explicitly now. An exponential (Poisson) window was added as `scipy.signal.exponential`, and a Tukey window was added as `scipy.signal.tukey`. The function for computing digital filter group delay was added as `scipy.signal.group_delay`. The functionality for spectral analysis and spectral density estimation has been significantly improved: `scipy.signal.welch` became ~8x faster and the functions `scipy.signal.spectrogram`, `scipy.signal.coherence` and `scipy.signal.csd` (cross-spectral density) were added. `scipy.signal.lsim` was rewritten - all known issues are fixed, so this function can now be used instead of ``lsim2``; ``lsim`` is orders of magnitude faster than ``lsim2`` in most cases. `scipy.sparse` improvements - --------------------------- The function `scipy.sparse.norm`, which computes sparse matrix norms, was added. The function `scipy.sparse.random`, which allows to draw random variates from an arbitrary distribution, was added. `scipy.spatial` improvements - ---------------------------- `scipy.spatial.cKDTree` has seen a major rewrite, which improved the performance of the ``query`` method significantly, added support for parallel queries, pickling, and options that affect the tree layout. See pull request 4374 for more details. The function `scipy.spatial.procrustes` for Procrustes analysis (statistical shape analysis) was added. `scipy.stats` improvements - -------------------------- The Wishart distribution and its inverse have been added, as `scipy.stats.wishart` and `scipy.stats.invwishart`. The Exponentially Modified Normal distribution has been added as `scipy.stats.exponnorm`. The Generalized Normal distribution has been added as `scipy.stats.gennorm`. All distributions now contain a ``random_state`` property and allow specifying a specific ``numpy.random.RandomState`` random number generator when generating random variates. Many statistical tests and other `scipy.stats` functions that have multiple return values now return ``namedtuples``. See pull request 4709 for details. `scipy.optimize` improvements - ----------------------------- A new derivative-free method DF-SANE has been added to the nonlinear equation system solving function `scipy.optimize.root`. Deprecated features =================== ``scipy.stats.pdf_fromgamma`` is deprecated. This function was undocumented, untested and rarely used. Statsmodels provides equivalent functionality with ``statsmodels.distributions.ExpandedNormal``. ``scipy.stats.fastsort`` is deprecated. This function is unnecessary, ``numpy.argsort`` can be used instead. ``scipy.stats.signaltonoise`` and ``scipy.stats.mstats.signaltonoise`` are deprecated. These functions did not belong in ``scipy.stats`` and are rarely used. See issue #609 for details. ``scipy.stats.histogram2`` is deprecated. This function is unnecessary, ``numpy.histogram2d`` can be used instead. Backwards incompatible changes ============================== The deprecated global optimizer ``scipy.optimize.anneal`` was removed. The following deprecated modules have been removed: ``scipy.lib.blas``, ``scipy.lib.lapack``, ``scipy.linalg.cblas``, ``scipy.linalg.fblas``, ``scipy.linalg.clapack``, ``scipy.linalg.flapack``. They had been deprecated since Scipy 0.12.0, the functionality should be accessed as `scipy.linalg.blas` and `scipy.linalg.lapack`. The deprecated function ``scipy.special.all_mat`` has been removed. The deprecated functions ``fprob``, ``ksprob``, ``zprob``, ``randwcdf`` and ``randwppf`` have been removed from `scipy.stats`. Other changes ============= The version numbering for development builds has been updated to comply with PEP 440. Building with ``python setup.py develop`` is now supported. Authors ======= * @axiru + * @endolith * Elliott Sales de Andrade + * Anne Archibald * Yoshiki V?zquez Baeza + * Sylvain Bellemare * Felix Berkenkamp + * Raoul Bourquin + * Matthew Brett * Per Brodtkorb * Christian Brueffer * Lars Buitinck * Evgeni Burovski * Steven Byrnes * CJ Carey * George Castillo + * Alex Conley + * Liam Damewood + * Rupak Das + * Abraham Escalante + * Matthias Feurer + * Eric Firing + * Clark Fitzgerald * Chad Fulton * Andr? Gaul * Andreea Georgescu + * Christoph Gohlke * Andrey Golovizin + * Ralf Gommers * J.J. Green + * Alex Griffing * Alexander Grigorievskiy + * Hans Moritz Gunther + * Jonas Hahnfeld + * Charles Harris * Ian Henriksen * Andreas Hilboll * ?smund Hjulstad + * Jan Schl?ter + * Janko Slavi? + * Daniel Jensen + * Johannes Ball? + * Terry Jones + * Amato Kasahara + * Eric Larson * Denis Laxalde * Antony Lee * Gregory R. Lee * Perry Lee + * Lo?c Est?ve * Martin Manns + * Eric Martin + * Mat?j Koci?n + * Andreas Mayer + * Nikolay Mayorov + * Robert McGibbon + * Sturla Molden * Nicola Montecchio + * Eric Moore * Jamie Morton + * Nikolas Moya + * Maniteja Nandana + * Andrew Nelson * Joel Nothman * Aldrian Obaja * Regina Ongowarsito + * Paul Ortyl + * Pedro L?pez-Adeva Fern?ndez-Layos + * Stefan Peterson + * Irvin Probst + * Eric Quintero + * John David Reaver + * Juha Remes + * Thomas Robitaille * Clancy Rowley + * Tobias Schmidt + * Skipper Seabold * Aman Singh + * Eric Soroos * Valentine Svensson + * Julian Taylor * Aman Thakral + * Helmut Toplitzer + * Fukumu Tsutsumi + * Anastasiia Tsyplia + * Jacob Vanderplas * Pauli Virtanen * Matteo Visconti + * Warren Weckesser * Florian Wilhelm + * Nathan Woods * Haochen Wu + * Daan Wynen + A total of 93 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. Issues closed for 0.16.0 - ------------------------ - - `#1063 `__: Implement a whishart distribution (Trac #536) - - `#1885 `__: Rbf: floating point warnings - possible bug (Trac #1360) - - `#2020 `__: Rbf default epsilon too large (Trac #1495) - - `#2325 `__: extending distributions, hypergeom, to degenerate cases (Trac... - - `#3502 `__: [ENH] linalg.hessenberg should use ORGHR for calc_q=True - - `#3603 `__: Passing array as window into signal.resample() fails - - `#3675 `__: Intermittent failures for signal.slepian on Windows - - `#3742 `__: Pchipinterpolator inconvenient as ppoly - - `#3786 `__: add procrustes? - - `#3798 `__: scipy.io.savemat fails for empty dicts - - `#3975 `__: Use RandomState in scipy.stats - - `#4022 `__: savemat incorrectly saves logical arrays - - `#4028 `__: scipy.stats.geom.logpmf(1,1) returns nan. The correct value is... - - `#4030 `__: simplify scipy.stats.betaprime.cdf - - `#4031 `__: improve accuracy of scipy.stats.gompertz distribution for small... - - `#4033 `__: improve accuracy of scipy.stats.lomax distribution for small... - - `#4034 `__: improve accuracy of scipy.stats.rayleigh distribution for large... - - `#4035 `__: improve accuracy of scipy.stats.truncexpon distribution for small... - - `#4081 `__: Error when reading matlab file: buffer is too small for requested... - - `#4100 `__: Why does qr(a, lwork=0) not fail? - - `#4134 `__: scipy.stats: rv_frozen has no expect() method - - `#4204 `__: Please add docstring to scipy.optimize.RootResults - - `#4206 `__: Wrap LAPACK tridiagonal solve routine `gtsv` - - `#4208 `__: Empty sparse matrices written to MAT file cannot be read by MATLAB - - `#4217 `__: use a TravisCI configuration with numpy built with NPY_RELAXED_STRIDES_CHECKING=1 - - `#4282 `__: integrate.odeint raises an exception when full_output=1 and the... - - `#4301 `__: scipy and numpy version names do not follow pep 440 - - `#4355 `__: PPoly.antiderivative() produces incorrect output - - `#4391 `__: spsolve becomes extremely slow with large b matrix - - `#4393 `__: Documentation glitsch in sparse.linalg.spilu - - `#4408 `__: Vector-valued constraints in minimize() et al - - `#4412 `__: Documentation of scipy.signal.cwt error - - `#4428 `__: dok.__setitem__ problem with negative indices - - `#4434 `__: Incomplete documentation for sparse.linalg.spsolve - - `#4438 `__: linprog() documentation example wrong - - `#4445 `__: Typo in scipy.special.expit doc - - `#4467 `__: Documentation Error in scipy.optimize options for TNC - - `#4492 `__: solve_toeplitz benchmark is bitrotting already - - `#4506 `__: lobpcg/sparse performance regression Jun 2014? - - `#4520 `__: g77_abi_wrappers needed on Linux for MKL as well - - `#4521 `__: Broken check in uses_mkl for newer versions of the library - - `#4523 `__: rbf with gaussian kernel seems to produce more noise than original... - - `#4526 `__: error in site documentation for poisson.pmf() method - - `#4527 `__: KDTree example doesn't work in Python 3 - - `#4550 `__: `scipy.stats.mode` - UnboundLocalError on empty sequence - - `#4554 `__: filter out convergence warnings in optimization tests - - `#4565 `__: odeint messages - - `#4569 `__: remez: "ValueError: Failure to converge after 25 iterations.... - - `#4582 `__: DOC: optimize: _minimize_scalar_brent does not have a disp option - - `#4585 `__: DOC: Erroneous latex-related characters in tutorial. - - `#4590 `__: sparse.linalg.svds should throw an exception if which not in... - - `#4594 `__: scipy.optimize.linprog IndexError when a callback is providen - - `#4596 `__: scipy.linalg.block_diag misbehavior with empty array inputs (v0.13.3) - - `#4599 `__: scipy.integrate.nquad should call _OptFunc when called with only... - - `#4612 `__: Crash in signal.lfilter on nd input with wrong shaped zi - - `#4613 `__: scipy.io.readsav error on reading sav file - - `#4673 `__: scipy.interpolate.RectBivariateSpline construction locks PyQt... - - `#4681 `__: Broadcasting in signal.lfilter still not quite right. - - `#4705 `__: kmeans k_or_guess parameter error if guess is not square array - - `#4719 `__: Build failure on 14.04.2 - - `#4724 `__: GenGamma _munp function fails due to overflow - - `#4726 `__: FAIL: test_cobyla.test_vector_constraints - - `#4734 `__: Failing tests in stats with numpy master. - - `#4736 `__: qr_update bug or incompatibility with numpy 1.10? - - `#4746 `__: linprog returns solution violating equality constraint - - `#4757 `__: optimize.leastsq docstring mismatch - - `#4774 `__: Update contributor list for v0.16 - - `#4779 `__: circmean and others do not appear in the documentation - - `#4788 `__: problems with scipy sparse linalg isolve iterative.py when complex - - `#4791 `__: BUG: scipy.spatial: incremental Voronoi doesn't increase size... Pull requests for 0.16.0 - ------------------------ - - `#3116 `__: sparse: enhancements for DIA format - - `#3157 `__: ENH: linalg: add the function 'solve_circulant' for solving a... - - `#3442 `__: ENH: signal: Add Gustafsson's method as an option for the filtfilt... - - `#3679 `__: WIP: fix sporadic slepian failures - - `#3680 `__: Some cleanups in stats - - `#3717 `__: ENH: Add second-order sections filtering - - `#3741 `__: Dltisys changes - - `#3956 `__: add note to scipy.signal.resample about prime sample numbers - - `#3980 `__: Add check_finite flag to UnivariateSpline - - `#3996 `__: MAINT: stricter linalg argument checking - - `#4001 `__: BUG: numerical precision in dirichlet - - `#4012 `__: ENH: linalg: Add a function to compute the inverse of a Pascal... - - `#4021 `__: ENH: Cython api for lapack and blas - - `#4089 `__: Fixes for various PEP8 issues. - - `#4116 `__: MAINT: fitpack: trim down compiler warnings (unused labels, variables) - - `#4129 `__: ENH: stats: add a random_state property to distributions - - `#4135 `__: ENH: Add Wishart and inverse Wishart distributions - - `#4195 `__: improve the interpolate docs - - `#4200 `__: ENH: Add t-test from descriptive stats function. - - `#4202 `__: Dendrogram threshold color - - `#4205 `__: BLD: fix a number of Bento build warnings. - - `#4211 `__: add an ufunc for the inverse Box-Cox transfrom - - `#4212 `__: MRG:fix for gh-4208 - - `#4213 `__: ENH: specific warning if matlab file is empty - - `#4215 `__: Issue #4209: splprep documentation updated to reflect dimensional... - - `#4219 `__: DOC: silence several Sphinx warnings when building the docs - - `#4223 `__: MAINT: remove two redundant lines of code - - `#4226 `__: try forcing the numpy rebuild with relaxed strides - - `#4228 `__: BLD: some updates to Bento config files and docs. Closes gh-3978. - - `#4232 `__: wrong references in the docs - - `#4242 `__: DOC: change example sample spacing - - `#4245 `__: Arff fixes - - `#4246 `__: MAINT: C fixes - - `#4247 `__: MAINT: remove some unused code - - `#4249 `__: Add routines for updating QR decompositions - - `#4250 `__: MAINT: Some pyflakes-driven cleanup in linalg and sparse - - `#4252 `__: MAINT trim away >10 kLOC of generated C code - - `#4253 `__: TST: stop shadowing ellip* tests vs boost data - - `#4254 `__: MAINT: special: use NPY_PI, not M_PI - - `#4255 `__: DOC: INSTALL: use Py3-compatible print syntax, and don't mention... - - `#4256 `__: ENH: spatial: reimplement cdist_cosine using np.dot - - `#4258 `__: BUG: io.arff #4429 #2088 - - `#4261 `__: MAINT: signal: PEP8 and related style clean up. - - `#4262 `__: BUG: newton_krylov() was ignoring norm_tol argument, closes #4259 - - `#4263 `__: MAINT: clean up test noise and optimize tests for docstrings... - - `#4266 `__: MAINT: io: Give an informative error when attempting to read... - - `#4268 `__: MAINT: fftpack benchmark integer division vs true division - - `#4269 `__: MAINT: avoid shadowing the eigvals function - - `#4272 `__: BUG: sparse: Fix bench_sparse.py - - `#4276 `__: DOC: remove confusing parts of the documentation related to writing... - - `#4281 `__: Sparse matrix multiplication: only convert array if needed (with... - - `#4284 `__: BUG: integrate: odeint crashed when the integration time was... - - `#4286 `__: MRG: fix matlab output type of logical array - - `#4287 `__: DEP: deprecate stats.pdf_fromgamma. Closes gh-699. - - `#4291 `__: DOC: linalg: fix layout in cholesky_banded docstring - - `#4292 `__: BUG: allow empty dict as proxy for empty struct - - `#4293 `__: MAINT: != -> not_equal in hamming distance implementation - - `#4295 `__: Pole placement - - `#4296 `__: MAINT: some cleanups in tests of several modules - - `#4302 `__: ENH: Solve toeplitz linear systems - - `#4306 `__: Add benchmark for conjugate gradient solver. - - `#4307 `__: BLD: PEP 440 - - `#4310 `__: BUG: make stats.geom.logpmf(1,1) return 0.0 instead of nan - - `#4311 `__: TST: restore a test that uses slogdet now that we have dropped... - - `#4313 `__: Some minor fixes for stats.wishart addition. - - `#4315 `__: MAINT: drop numpy 1.5 compatibility code in sparse matrix tests - - `#4318 `__: ENH: Add random_state to multivariate distributions - - `#4319 `__: MAINT: fix hamming distance regression for exotic arrays, with... - - `#4320 `__: TST: a few changes like self.assertTrue(x == y, message) -> assert_equal(x,... - - `#4321 `__: TST: more changes like self.assertTrue(x == y, message) -> assert_equal(x,... - - `#4322 `__: TST: in test_signaltools, changes like self.assertTrue(x == y,... - - `#4323 `__: MAINT: clean up benchmarks so they can all be run as single files. - - `#4324 `__: Add more detailed committer guidelines, update MAINTAINERS.txt - - `#4326 `__: TST: use numpy.testing in test_hierarchy.py - - `#4329 `__: MAINT: stats: rename check_random_state test function - - `#4330 `__: Update distance tests - - `#4333 `__: MAINT: import comb, factorial from scipy.special, not scipy.misc - - `#4338 `__: TST: more conversions from nose to numpy.testing - - `#4339 `__: MAINT: remove the deprecated all_mat function from special_matrices.py - - `#4340 `__: add several features to frozen distributions - - `#4344 `__: BUG: Fix/test invalid lwork param in qr - - `#4345 `__: Fix test noise visible with Python 3.x - - `#4347 `__: Remove deprecated blas/lapack imports, rename lib to _lib - - `#4349 `__: DOC: add a nontrivial example to stats.binned_statistic. - - `#4350 `__: MAINT: remove optimize.anneal for 0.16.0 (was deprecated in 0.14.0). - - `#4351 `__: MAINT: fix usage of deprecated Numpy C API in optimize... - - `#4352 `__: MAINT: fix a number of special test failures - - `#4353 `__: implement cdf for betaprime distribution - - `#4357 `__: BUG: piecewise polynomial antiderivative - - `#4358 `__: BUG: integrate: fix handling of banded Jacobians in odeint, plus... - - `#4359 `__: MAINT: remove a code path taken for Python version < 2.5 - - `#4360 `__: MAINT: stats.mstats: Remove some unused variables (thanks, pyflakes). - - `#4362 `__: Removed erroneous reference to smoothing parameter #4072 - - `#4363 `__: MAINT: interpolate: clean up in fitpack.py - - `#4364 `__: MAINT: lib: don't export "partial" from decorator - - `#4365 `__: svdvals now returns a length-0 sequence of singular values given... - - `#4367 `__: DOC: slightly improve TeX rendering of wishart/invwishart docstring - - `#4373 `__: ENH: wrap gtsv and ptsv for solve_banded and solveh_banded. - - `#4374 `__: ENH: Enhancements to spatial.cKDTree - - `#4376 `__: BF: fix reading off-spec matlab logical sparse - - `#4377 `__: MAINT: integrate: Clean up some Fortran test code. - - `#4378 `__: MAINT: fix usage of deprecated Numpy C API in signal - - `#4380 `__: MAINT: scipy.optimize, removing further anneal references - - `#4381 `__: ENH: Make DCT and DST accept int and complex types like fft - - `#4392 `__: ENH: optimize: add DF-SANE nonlinear derivative-free solver - - `#4394 `__: Make reordering algorithms 64-bit clean - - `#4396 `__: BUG: bundle cblas.h in Accelerate ABI wrappers to enable compilation... - - `#4398 `__: FIX pdist bug where wminkowski's w.dtype != double - - `#4402 `__: BUG: fix stat.hypergeom argcheck - - `#4404 `__: MAINT: Fill in the full symmetric squareform in the C loop - - `#4405 `__: BUG: avoid X += X.T (refs #4401) - - `#4407 `__: improved accuracy of gompertz distribution for small x - - `#4414 `__: DOC:fix error in scipy.signal.cwt documentation. - - `#4415 `__: ENH: Improve accuracy of lomax for small x. - - `#4416 `__: DOC: correct a parameter name in docstring of SuperLU.solve.... - - `#4419 `__: Restore scipy.linalg.calc_lwork also in master - - `#4420 `__: fix a performance issue with a sparse solver - - `#4423 `__: ENH: improve rayleigh accuracy for large x. - - `#4424 `__: BUG: optimize.minimize: fix overflow issue with integer x0 input. - - `#4425 `__: ENH: Improve accuracy of truncexpon for small x - - `#4426 `__: ENH: improve rayleigh accuracy for large x. - - `#4427 `__: MAINT: optimize: cleanup of TNC code - - `#4429 `__: BLD: fix build failure with numpy 1.7.x and 1.8.x. - - `#4430 `__: BUG: fix a sparse.dok_matrix set/get copy-paste bug - - `#4433 `__: Update _minimize.py - - `#4435 `__: ENH: release GIL around batch distance computations - - `#4436 `__: Fixed incomplete documentation for spsolve - - `#4439 `__: MAINT: integrate: Some clean up in the tests. - - `#4440 `__: Fast permutation t-test - - `#4442 `__: DOC: optimize: fix wrong result in docstring - - `#4447 `__: DOC: signal: Some additional documentation to go along with the... - - `#4448 `__: DOC: tweak the docstring of lapack.linalg module - - `#4449 `__: fix a typo in the expit docstring - - `#4451 `__: ENH: vectorize distance loops with gcc - - `#4456 `__: MAINT: don't fail large data tests on MemoryError - - `#4461 `__: CI: use travis_retry to deal with network timeouts - - `#4462 `__: DOC: rationalize minimize() et al. documentation - - `#4470 `__: MAINT: sparse: inherit dok_matrix.toarray from spmatrix - - `#4473 `__: BUG: signal: Fix validation of the zi shape in sosfilt. - - `#4475 `__: BLD: setup.py: update min numpy version and support "setup.py... - - `#4481 `__: ENH: add a new linalg special matrix: the Helmert matrix - - `#4485 `__: MRG: some changes to allow reading bad mat files - - `#4490 `__: [ENH] linalg.hessenberg: use orghr - rebase - - `#4491 `__: ENH: linalg: Adding wrapper for potentially useful LAPACK function... - - `#4493 `__: BENCH: the solve_toeplitz benchmark used outdated syntax and... - - `#4494 `__: MAINT: stats: remove duplicated code - - `#4496 `__: References added for watershed_ift algorithm - - `#4499 `__: DOC: reshuffle stats distributions documentation - - `#4501 `__: Replace benchmark suite with airspeed velocity - - `#4502 `__: SLSQP should strictly satisfy bound constraints - - `#4503 `__: DOC: forward port 0.15.x release notes and update author name... - - `#4504 `__: ENH: option to avoid computing possibly unused svd matrix - - `#4505 `__: Rebase of PR 3303 (sparse matrix norms) - - `#4507 `__: MAINT: fix lobpcg performance regression - - `#4509 `__: DOC: sparse: replace dead link - - `#4511 `__: Fixed differential evolution bug - - `#4512 `__: Change to fully PEP440 compliant dev version numbers (always... - - `#4525 `__: made tiny style corrections (pep8) - - `#4533 `__: Add exponentially modified gaussian distribution (scipy.stats.expongauss) - - `#4534 `__: MAINT: benchmarks: make benchmark suite importable on all scipy... - - `#4535 `__: BUG: Changed zip() to list(zip()) so that it could work in Python... - - `#4536 `__: Follow up to pr 4348 (exponential window) - - `#4540 `__: ENH: spatial: Add procrustes analysis - - `#4541 `__: Bench fixes - - `#4542 `__: TST: NumpyVersion dev -> dev0 - - `#4543 `__: BUG: Overflow in savgol_coeffs - - `#4544 `__: pep8 fixes for stats - - `#4546 `__: MAINT: use reduction axis arguments in one-norm estimation - - `#4549 `__: ENH : Added group_delay to scipy.signal - - `#4553 `__: ENH: Significantly faster moment function - - `#4556 `__: DOC: document the changes of the sparse.linalg.svds (optional... - - `#4559 `__: DOC: stats: describe loc and scale parameters in the docstring... - - `#4563 `__: ENH: rewrite of stats.ppcc_plot - - `#4564 `__: Be more (or less) forgiving when user passes +-inf instead of... - - `#4566 `__: DEP: remove a bunch of deprecated function from scipy.stats,... - - `#4570 `__: MNT: Suppress LineSearchWarning's in scipy.optimize tests - - `#4572 `__: ENH: Extract inverse hessian information from L-BFGS-B - - `#4576 `__: ENH: Split signal.lti into subclasses, part of #2912 - - `#4578 `__: MNT: Reconcile docstrings and function signatures - - `#4581 `__: Fix build with Intel MKL on Linux - - `#4583 `__: DOC: optimize: remove references to unused disp kwarg - - `#4584 `__: ENH: scipy.signal - Tukey window - - `#4587 `__: Hermite asymptotic - - `#4593 `__: DOC - add example to RegularGridInterpolator - - `#4595 `__: DOC: Fix erroneous latex characters in tutorial/optimize. - - `#4600 `__: Add return codes to optimize.tnc docs - - `#4603 `__: ENH: Wrap LAPACK ``*lange`` functions for matrix norms - - `#4604 `__: scipy.stats: generalized normal distribution - - `#4609 `__: MAINT: interpolate: fix a few inconsistencies between docstrings... - - `#4610 `__: MAINT: make runtest.py --bench-compare use asv continuous and... - - `#4611 `__: DOC: stats: explain rice scaling; add a note to the tutorial... - - `#4614 `__: BUG: lfilter, the size of zi was not checked correctly for nd... - - `#4617 `__: MAINT: integrate: Clean the C code behind odeint. - - `#4618 `__: FIX: Raise error when window length != data length - - `#4619 `__: Issue #4550: `scipy.stats.mode` - UnboundLocalError on empty... - - `#4620 `__: Fixed a problem (#4590) with svds accepting wrong eigenvalue... - - `#4621 `__: Speed up special.ai_zeros/bi_zeros by 10x - - `#4623 `__: MAINT: some tweaks to spatial.procrustes (private file, html... - - `#4628 `__: Speed up signal.lfilter and add a convolution path for FIR filters - - `#4629 `__: Bug: integrate.nquad; resolve issue #4599 - - `#4631 `__: MAINT: integrate: Remove unused variables in a Fortran test function. - - `#4633 `__: MAINT: Fix convergence message for remez - - `#4635 `__: PEP8: indentation (so that pep8 bot does not complain) - - `#4637 `__: MAINT: generalize a sign function to do the right thing for complex... - - `#4639 `__: Amended typo in apple_sgemv_fix.c - - `#4642 `__: MAINT: use lapack for scipy.linalg.norm - - `#4643 `__: RBF default epsilon too large 2020 - - `#4646 `__: Added atleast_1d around poly in invres and invresz - - `#4647 `__: fix doc pdf build - - `#4648 `__: BUG: Fixes #4408: Vector-valued constraints in minimize() et... - - `#4649 `__: Vonmisesfix - - `#4650 `__: Signal example clean up in Tukey and place_poles - - `#4652 `__: DOC: Fix the error in convolve for same mode - - `#4653 `__: improve erf performance - - `#4655 `__: DEP: deprecate scipy.stats.histogram2 in favour of np.histogram2d - - `#4656 `__: DEP: deprecate scipy.stats.signaltonoise - - `#4660 `__: Avoid extra copy for sparse compressed [:, seq] and [seq, :]... - - `#4661 `__: Clean, rebase of #4478, adding ?gelsy and ?gelsd wrappers - - `#4662 `__: MAINT: Correct odeint messages - - `#4664 `__: Update _monotone.py - - `#4672 `__: fix behavior of scipy.linalg.block_diag for empty input - - `#4675 `__: Fix lsim - - `#4676 `__: Added missing colon to :math: directive in docstring. - - `#4679 `__: ENH: sparse randn - - `#4682 `__: ENH: scipy.signal - Addition of CSD, coherence; Enhancement of... - - `#4684 `__: BUG: various errors in weight calculations in orthogonal.py - - `#4685 `__: BUG: Fixes #4594: optimize.linprog IndexError when a callback... - - `#4686 `__: MAINT: cluster: Clean up duplicated exception raising code. - - `#4688 `__: Improve is_distance_dm exception message - - `#4692 `__: MAINT: stats: Simplify the calculation in tukeylambda._ppf - - `#4693 `__: ENH: added functionality to handle scalars in `stats._chk_asarray` - - `#4694 `__: Vectorization of Anderson-Darling computations. - - `#4696 `__: Fix singleton expansion in lfilter. - - `#4698 `__: MAINT: quiet warnings from cephes. - - `#4701 `__: add Bpoly.antiderivatives / integrals - - `#4703 `__: Add citation of published paper - - `#4706 `__: MAINT: special: avoid out-of-bounds access in specfun - - `#4707 `__: MAINT: fix issues with np.matrix as input to functions related... - - `#4709 `__: ENH: `scipy.stats` now returns namedtuples. - - `#4710 `__: scipy.io.idl: make reader more robust to missing variables in... - - `#4711 `__: Fix crash for unknown chunks at the end of file - - `#4712 `__: Reduce onenormest memory usage - - `#4713 `__: MAINT: interpolate: no need to pass dtype around if it can be... - - `#4714 `__: BENCH: Add benchmarks for stats module - - `#4715 `__: MAINT: polish signal.place_poles and signal/test_ltisys.py - - `#4716 `__: DEP: deprecate mstats.signaltonoise ... - - `#4717 `__: MAINT: basinhopping: fix error in tests, silence /0 warning,... - - `#4718 `__: ENH: stats: can specify f-shapes to fix in fitting by name - - `#4721 `__: Document that imresize converts the input to a PIL image - - `#4722 `__: MAINT: PyArray_BASE is not an lvalue unless the deprecated API... - - `#4725 `__: Fix gengamma _nump failure - - `#4728 `__: DOC: add poch to the list of scipy special function descriptions - - `#4735 `__: MAINT: stats: avoid (a spurious) division-by-zero in skew - - `#4738 `__: TST: silence runtime warnings for some corner cases in `stats`... - - `#4739 `__: BLD: try to build numpy instead of using the one on TravisCI - - `#4740 `__: DOC: Update some docstrings with 'versionadded'. - - `#4742 `__: BLD: make sure that relaxed strides checking is in effect on... - - `#4750 `__: DOC: special: TeX typesetting of rel_entr, kl_div and pseudo_huber - - `#4751 `__: BENCH: add sparse null slice benchmark - - `#4753 `__: BUG: Fixed compilation with recent Cython versions. - - `#4756 `__: BUG: Fixes #4733: optimize.brute finish option is not compatible... - - `#4758 `__: DOC: optimize.leastsq default maxfev clarification - - `#4759 `__: improved stats mle fit - - `#4760 `__: MAINT: count bfgs updates more carefully - - `#4762 `__: BUGS: Fixes #4746 and #4594: linprog returns solution violating... - - `#4763 `__: fix small linprog bugs - - `#4766 `__: BENCH: add signal.lsim benchmark - - `#4768 `__: fix python syntax errors in docstring examples - - `#4769 `__: Fixes #4726: test_cobyla.test_vector_constraints - - `#4770 `__: Mark FITPACK functions as thread safe. - - `#4771 `__: edited scipy/stats/stats.py to fix doctest for fisher_exact - - `#4773 `__: DOC: update 0.16.0 release notes. - - `#4775 `__: DOC: linalg: add funm_psd as a docstring example - - `#4778 `__: Use a dictionary for function name synonyms - - `#4780 `__: Include apparently-forgotten functions in docs - - `#4783 `__: Added many missing special functions to docs - - `#4784 `__: add an axis attribute to PPoly and friends - - `#4785 `__: Brief note about origin of Lena image - - `#4786 `__: DOC: reformat the Methods section of the KDE docstring - - `#4787 `__: Add rice cdf and ppf. - - `#4792 `__: CI: add a kludge for detecting test failures which try to disguise... - - `#4795 `__: Make refguide_check smarter about false positives - - `#4797 `__: BUG/TST: numpoints not updated for incremental Voronoi - - `#4799 `__: BUG: spatial: Fix a couple edge cases for the Mahalanobis metric... - - `#4801 `__: BUG: Fix TypeError in scipy.optimize._trust-region.py when disp=True. - - `#4803 `__: Issues with relaxed strides in QR updating routines - - `#4806 `__: MAINT: use an informed initial guess for cauchy fit - - `#4810 `__: PEP8ify codata.py - - `#4812 `__: BUG: Relaxed strides cleanup in decomp_update.pyx.in - - `#4820 `__: BLD: update Bento build for sgemv fix and install cython blas/lapack... - - `#4823 `__: ENH: scipy.signal - Addition of spectrogram function - - `#4827 `__: DOC: add csd and coherence to __init__.py - - `#4833 `__: BLD: fix issue in linalg ``*lange`` wrappers for g77 builds. - - `#4841 `__: TST: fix test failures in scipy.special with mingw32 due to test... - - `#4842 `__: DOC: update site.cfg.example. Mostly taken over from Numpy - - `#4845 `__: BUG: signal: Make spectrogram's return values order match the... - - `#4849 `__: DOC:Fix error in ode docstring example - - `#4856 `__: BUG: fix typo causing memleak Checksums ========= MD5 ~~~ 1c6faa58d12c7b642e64a44b57c311c3 scipy-0.16.0-win32-superpack-python2.7.exe 18b9b53af7216ab897495cc77068285b scipy-0.16.0-win32-superpack-python3.3.exe 9cef8bc882e21854791ec80140258bc9 scipy-0.16.0-win32-superpack-python3.4.exe eb95dda0f36cc3096673993a350cde77 scipy-0.16.0.tar.gz fe1425745ab68d9d4ccaf59e854f1c28 scipy-0.16.0.tar.xz 1764bd452a72698b968ad13e51e28053 scipy-0.16.0.zip SHA256 ~~~~~~ b752366fafa3fddb96352d7b3259f7b3e58fae9f45d5a98eb91bd80005d75dfc scipy-0.16.0-win32-superpack-python2.7.exe 369777d27da760d498a9312696235ab9e90359d9f4e02347669cbf56a42312a8 scipy-0.16.0-win32-superpack-python3.3.exe bcd480ce8e8289942e57e7868d07e9a35982bc30a79150006ad085ce4c06803e scipy-0.16.0-win32-superpack-python3.4.exe 92592f40097098f3fdbe7f5855d535b29bb16719c2bb59c728bce5e7a28790e0 scipy-0.16.0.tar.gz e27f5cfa985cb1253e15aaeddc3dcd512d6853b05e84454f7f43b53b35514071 scipy-0.16.0.tar.xz c9758971df994d238a4d0ff1d47ba5b02f1cb402d6e1925c921a452bc430a3d5 scipy-0.16.0.zip -----BEGIN PGP SIGNATURE----- Version: GnuPG v1 iQEcBAEBAgAGBQJVspIaAAoJEO2+o3i/Gl69n3cIAJdoEGIq/8MTsywYL6k5zsqA aBK1Q9aB4qJcCLwM6ULKErxhY9lROzgljSvl22dCaD7YYYgD4Q03+BaXjIrHenbc +sX5CzBPoz+BFjh7tTnfU5a6pVhqjQbW17A0TF0j6jah29pFnM2Xdf3zgHc+3f/B U6JC698wDKROGlvKqWcwKcs2+EPBuu92gNa/rRCmMdnt9dIqVM8+otRNMgPVCZ+R SgfneSGjZ4vXuBK3zWgcP0+r8Ek0DkUuFhEAK3W8NhEFCqd1kHkdvN+RIl6pGfHZ OAHbzds6+VHgvQ3a4g2efJY3CD0LvtOgeS3R3NdmT3gCxkJtZpHAsczFhwKIWHM= =QZFz -----END PGP SIGNATURE----- -------------- next part -------------- An HTML attachment was scrubbed... URL: From sinha.piyush0609 at gmail.com Mon Jul 27 17:37:24 2015 From: sinha.piyush0609 at gmail.com (Piyush Sinha) Date: Tue, 28 Jul 2015 03:07:24 +0530 Subject: [SciPy-Dev] some guidelines about diving into open source Message-ID: Hi, I'm a student developer interested in getting some open source experience and I was wondering if anyone could suggest some bugs that are suitable for a beginner in this project?" -------------- next part -------------- An HTML attachment was scrubbed... URL: From pmhobson at gmail.com Mon Jul 27 20:17:51 2015 From: pmhobson at gmail.com (Paul Hobson) Date: Mon, 27 Jul 2015 17:17:51 -0700 Subject: [SciPy-Dev] some guidelines about diving into open source In-Reply-To: References: Message-ID: On Mon, Jul 27, 2015 at 2:37 PM, Piyush Sinha wrote: > Hi, I'm a student developer interested in getting some open source > experience and I was wondering if anyone could suggest some bugs that are > suitable for a beginner in this project?" > Most Github libraries you tags to organize their issues. Often there's a tag that indicates that the problem is minor and perhaps a good place for new devs to look. In scipy's case, that tag is "easy-fix": https://github.com/scipy/scipy/issues?q=is%3Aopen+is%3Aissue+label%3Aeasy-fix -------------- next part -------------- An HTML attachment was scrubbed... URL: From aeklant at gmail.com Mon Jul 27 22:07:28 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Mon, 27 Jul 2015 21:07:28 -0500 Subject: [SciPy-Dev] some guidelines about diving into open source In-Reply-To: References: Message-ID: Hi, As someone who was recently in your shoes (three or four months ago), here are my pointers and suggestions: 1. Before anything, you should get your development environment up and running. Fork scipy and clone it into a virtual environment (I use virtualenvwrapper ) and make sure you are able to run the regression tests (i.e. `runtests.py`). You can find some information on setting up your environment here . I would pay special attention to `python runtests.py -v` and `python runtests.py --ipython`. 2. Pick a topic where you feel you are most qualified to contribute (e.g. if you have some good statistics background, `scipy.stats` might be the best place to start). Getting acquainted with the code is also a good source of confidence. 3. Do search for some of the issues labeled 'easy-fix' but give special consideration to the issues that have an ongoing discussion (even if they are not labeled as 'easy-fix'). It is usually easier to understand what needs to be done once the problem has been discussed. This also ensures that there are some people interested in the issue. Those people are valuable; you can learn from them. 4. When you have found a problem that you understand and feel like you can contribute, just work on it, do a Pull Request (PR), and ask for feedback (via a comment in the discussion). The community is quite welcoming and they will let you know their point of view and guide you in the right direction. Here are some other things you might find useful: - Development workflow (I review the part about writing commit messages almost every time I do a PR) - My first PR (You can see the back and forth of feedback and improvements until it was ready to merge) If you find yourself stuck at some point, don't be afraid to ask questions, just make sure you have tried a few things so you can have a conversation about the problem you are facing. Most people (maybe everyone) here contributes voluntarily, which means they have a day job and so they appreciate when they can see someone who has taken the time to do his research, is able to ask thoughtful questions and give examples of what he is trying to convey. I hope this helps you get started. Welcome to the community. Cheers, Abraham. 2015-07-27 19:17 GMT-05:00 Paul Hobson : > On Mon, Jul 27, 2015 at 2:37 PM, Piyush Sinha > wrote: > >> Hi, I'm a student developer interested in getting some open source >> experience and I was wondering if anyone could suggest some bugs that are >> suitable for a beginner in this project?" >> > > > Most Github libraries you tags to organize their issues. Often there's a > tag that indicates that the problem is minor and perhaps a good place for > new devs to look. > > In scipy's case, that tag is "easy-fix": > > https://github.com/scipy/scipy/issues?q=is%3Aopen+is%3Aissue+label%3Aeasy-fix > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-dev > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From matthew.brett at gmail.com Tue Jul 28 02:05:37 2015 From: matthew.brett at gmail.com (Matthew Brett) Date: Tue, 28 Jul 2015 07:05:37 +0100 Subject: [SciPy-Dev] some guidelines about diving into open source In-Reply-To: References: Message-ID: Hi, On Tue, Jul 28, 2015 at 3:07 AM, Abraham Escalante wrote: > Hi, > > As someone who was recently in your shoes (three or four months ago), here > are my pointers and suggestions: > > Before anything, you should get your development environment up and running. > Fork scipy and clone it into a virtual environment (I use virtualenvwrapper) > and make sure you are able to run the regression tests (i.e. `runtests.py`). > You can find some information on setting up your environment here. I would > pay special attention to `python runtests.py -v` and `python runtests.py > --ipython`. > Pick a topic where you feel you are most qualified to contribute (e.g. if > you have some good statistics background, `scipy.stats` might be the best > place to start). Getting acquainted with the code is also a good source of > confidence. > Do search for some of the issues labeled 'easy-fix' but give special > consideration to the issues that have an ongoing discussion (even if they > are not labeled as 'easy-fix'). It is usually easier to understand what > needs to be done once the problem has been discussed. This also ensures that > there are some people interested in the issue. Those people are valuable; > you can learn from them. > When you have found a problem that you understand and feel like you can > contribute, just work on it, do a Pull Request (PR), and ask for feedback > (via a comment in the discussion). The community is quite welcoming and they > will let you know their point of view and guide you in the right direction. > > Here are some other things you might find useful: > > Development workflow (I review the part about writing commit messages almost > every time I do a PR) > My first PR (You can see the back and forth of feedback and improvements > until it was ready to merge) > > If you find yourself stuck at some point, don't be afraid to ask questions, > just make sure you have tried a few things so you can have a conversation > about the problem you are facing. Most people (maybe everyone) here > contributes voluntarily, which means they have a day job and so they > appreciate when they can see someone who has taken the time to do his > research, is able to ask thoughtful questions and give examples of what he > is trying to convey. > > I hope this helps you get started. Welcome to the community. Wow - what an excellent summary! Thanks very much, Matthew From aeklant at gmail.com Tue Jul 28 21:42:13 2015 From: aeklant at gmail.com (Abraham Escalante) Date: Tue, 28 Jul 2015 20:42:13 -0500 Subject: [SciPy-Dev] Weekly Summary 2015/07/28 Message-ID: Hello all, Here is last last week's progress and this week's plan for `scipy.stats` improvements. *Week 8 topics:* - Work on nan policy; the roadblock is being dealt with but there are still some details to be addressed. - NIST test cases for `f_oneway` have been added; some tests fail. - `moment` has been merged. *Week 9 topics:* - More work on nan policy. - `mstats` consistency with `stats` is being (in part) indirectly addressed with nan policy so it will be deferred until that issue is resolved. - Work on last details to merge `kstat`, `kstatvar` and `bayes_mvs`. If we manage to close a few PRs this week I will put some work into some of the pending functions (like shapiro and kruskal). For your reference: - Open Pull Requests - Open StatisticsCleanup issues Regards, Abraham. -------------- next part -------------- An HTML attachment was scrubbed... URL: