Giselle Sosa Jones | 6 Jul 22:35 2015
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SLSQP on Ubuntu

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
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Ralf Gommers | 4 Jul 10:57 2015
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0.16.0rc1 plan

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

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Abraham Escalante | 4 Jul 07:46 2015
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Deprecation of `stats.betai` and `stats.chisqprob`

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:
For details on the PR refer to:
Cheers,
Abraham.
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Abraham Escalante | 1 Jul 03:45 2015
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Weekly Summary 2015/06/30

Hello,

Here is last week's progress and this week's plan for `scipy.stats` improvements.

Week 5 topics:
  • nan checks discussion.
  • Deprecation of `threshold` discussion.
  • `ppcc_max`
  • `fligner` and `bartlett`
  • `cumfreq` and `relfreq`
Mid-term evaluation week topics:
  • Mid term evaluation: submitted.
  • nan checks initial PR.
  • 'alternative' keyword for `ansari` or `ks_2samp`.
  • Deprecation of `threshold` scheduled for this week.

This week's main goal is to have a good idea of implementation details on the nan checks and 'alternative' keyword addition. Also to begin discussion on several other stats functions issues to have as many as possible to work on for the next two weeks or so that Ralf will be on break.

For your reference:
Regards,
Abraham.
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Pavel Ponomarev | 26 Jun 17:02 2015
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ENH: API: DEP: Added parallelization for differential evolution

Hello

Here are the files which enable parallelization for DE using MPI or joblib. The parallelization is realized in the same way as in emcee. Please check


BR, Pavel Ponomarev
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Phillip Feldman | 24 Jun 20:41 2015
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scipy.optimize.fmin_cg

The optimization algorithm of scipy.optimize.fmin_cg appears to be quite powerful, but the interface is rather limited.  In particular, it would be great if one could specify the `xtol` and `ftol` convergence criteria that most of the other SciPy optimizers accept.

Phillip
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Abraham Escalante | 23 Jun 21:09 2015
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Weekly Summary 2015/06/23

Hello all,

This week we had a lot of merged PRs and closed issues for `scipy.stats` so the project is seeing some progress and I'm feeling good with the decision to change the approach. 

Here is a summary of the most important things:

Week 4 topics:
  • Trimmed statistics functions PR has been merged.
  • nan checks: An agreement seems close.
  • Deprecation of (M)ANOVA `f_value*` functions.
  • Deprecation of `threshold` is still in discussion.
  • 'alternative' keyword addition to `binom_test` and `mannwhitneu`.
Week 5 topics:
  • nan checks discussion.
  • Deprecation of `threshold` discussion.
  • Resume some previously started tasks:
    • ppcc_max.
    • 'alternative' keyword addition.
  • New batch of PRs:
    • `fligner`, `bartlett`, `ansari`, `shapiro`, `variation`, `moment`, `kruskal` and `kendalltau` are the likeliest prospects. I will start with as many as possible.

As usual, for your convenience here are some links of interest so that you can contribute and/or follow the progress of the project.

Regards,
Abraham.
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Joseph C. Slater | 22 Jun 13:59 2015
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Is SciPy-User down?

I apologize for using the wrong list- but why I'm not using the correct list is self-explanatory. I don't
know how to contact the maintainer of scipy-user. I sent to the list Friday, and twice since. gmane
(http://news.gmane.org/gmane.comp.python.scientific.user) doesn't show any activity, and I
haven't received any emails from the list since last week.

Thank you,
Joe
Nicolas Petitclerc | 18 Jun 17:06 2015
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Signal Smooth

Hi,
Coming from IDL, I've always wondered why there was no 'smooth' function in SciPy, seems like a standard/common operation. But I found an implementation in the Cookbook: http://wiki.scipy.org/Cookbook/SignalSmooth that does what I would expect such a function to do.

But I found an indexing bug and a few usability and coding style improvement to the Cookbook. My version of the function is in attachment.

Any reason why this function is not part of Scipy?

Thanks,
Nicolas 
Attachment (smooth.py): text/x-python-script, 2909 bytes
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Abraham Escalante | 18 Jun 06:10 2015
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Weekly Summary 2015/06/17

Hello all,

I am a little late for this week's weekly report, I know. This past week I realised I needed to change the course of action because the bulk of the project is comprised of issues and the regular PR can usually take two weeks even if they go smoothly; and a lot longer if not so smooth.

Long story short, I decided to start working on as many PRs as possible to keep the waiting time between feedback cycles to a minimum. Otherwise, the project's completion could be jeopardised.

Since there will be many open PRs at a time and I expect to be working on several issues through several weeks, I will try to mention here only the most relevant ones and I encourage you to check the current status of:

Week 3 topics:
  • Trimmed statistics functions.
  • nan checks: How to deal with nan values in scipy.stats?
  • Deprecation of `find_repeats`.
  • 'alternative' keyword addition to `binom_test` and `mannwhitneu`.
This week's topics:
  • Resolution of nan handling discussion.
  • Deprecation of (M)ANOVA `f_value*` functions.
  • Deprecation of `threshold`.
Again, this is not a comprehensive list. For more information regarding any of the topics feel free to browse through the open issues or PRs. You can also contact me and I will be more than happy to let you know the details of whichever of those topics you might find interesting (or at least point you in the direction of someone who knows what he's talking about).

Regards,
Abraham.
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Abraham Escalante | 17 Jun 22:44 2015
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On deprecating `stats.threshold`

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).
Of course, before we deprecate anything, we would like to know if anyone in the community is a regular user of this function and/or if you guys may have a use case where it may be preferable to use `stats.threshold` over `np.clip`.

Please reply if you have any objections to this deprecation.

You can find the corresponding PR here: gh-4976

Regards,
Abraham.


PS. For reference, both `np.clip` and `stats.threshold` replace the values outside a threshold from an array_like input. The difference is that `stats.threshold` replaces all values below the minimum or above the maximum with the same new value whereas `np.clip` uses the minimum to replace those below and the maximum for those above.

Example:

>>> a = np.arange(10)

>>> a

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


>>> np.clip(a, 3, 7)

array([3, 3, 3, 3, 4, 5, 6, 7, 7, 7])


>>> stats.threshold(a, 3, 7, -1)

array([-1, -1, -1,  3,  4,  5,  6,  7, -1, -1])

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