Daπid | 2 Sep 10:36 2015
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Missing releases at Scipy.org news

The releases of Scipy 0.16 and Numpy 1.9.2 are missing from the front page:
That has led people to believe 0.16 was not out, and thus they couldn't depend on it.

If updating it is difficult, can we put a big fat button to Pypi, that will always hold the latest release?



/David.
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Ariel Rokem | 27 Aug 02:11 2015
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RBF epsilon (changes 0.15 => 0.16)

Hi everyone, 

In our work on dipy, we've run into a small issue moving from 0.15 to 0.16: 


The gist of this is that we are using RBF to interpolate a signal measured on the unit sphere. To do this, we have designed a custom 'norm' function based on the angle between measurements (rather than the default Euclidean). Code is here: 


This seemed to work fine with 0.15, but the changes to the default epsilon introduced here: https://github.com/scipy/scipy/pull/4643 seem to be throwing us off. 

I've taken a look at the discussion on that PR and the issue that led to this work, and it does make sense, but I wanted to get some feedback to see how we should proceed. Setting a larger default epsilon in our code (effectively undoing this change) would be one way to go, but I want to make sure that it wouldn't be an obvious blunder, and that there isn't some issue on scipy that we should be thinking about.

Thanks! 

Ariel
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MANUEL HUMBERTO MUNOZ ARIAS | 26 Aug 16:14 2015
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scipy.sparse.linalg.eigsh routine crashing for negative values of the parameter.

Good Day scipy-dev members,

Recently I have been working with the diagonalization routines of scipy, I am doing an study if the eigenvalues of a certain matrix, in particular I have interest in the differences between these eigenvalues. In order to do that I am doing the diagonalization process of each of the values of a parameter in a linespace, the diagonalization it has been carried out with the scipy.sparse.linalg.eigsh routine for few eigenvalues, however it seems to crash for negative values of the parameter. Why is this happening? In order to make my point more clear I have attached a plot of the difference between the first two eigenvalues.

Thanks in advance,

Manuel.
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Fernando Perez | 26 Aug 07:24 2015
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Python extensions for Python 3.5 - useful info...

Just an FYI for the upcoming Python release, a very detailed post from Steve Dower, the Microsoft developer who is now in charge of the Windows releases for Python, on how the build process will change in 3.5 regarding extensions:


Cheers,

f
--
Fernando Perez ( <at> fperez_org; http://fperez.org)
fperez.net-at-gmail: mailing lists only (I ignore this when swamped!)
fernando.perez-at-berkeley: contact me here for any direct mail
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Ralf Gommers | 18 Aug 23:26 2015
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offline for ~6 weeks

Hi all,

Just a quick note to say that I'll be offline for pretty much the whole period 25 Aug - 9 Oct.

Regarding a possible 0.16.1 release: I don't see a reason to cut a release right now, but if a quick release is required for whatever reason then please go ahead without me.

Cheers,
Ralf

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Surya | 17 Aug 18:45 2015
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Need help on scipy central header + logo

​Hello, 

I'm finalizing on scipy central logo now. Thus, looking forward for any suggestions/opinions.

I found the theme described in https://github.com/tonysyu/SciPy-Central-Logo interesting, and used it partially. 

The below are the two variants (I'd prefer the latter - for the reason that the green part isn't visible anyway).

Thanks
Surya




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Graham Clenaghan | 15 Aug 23:56 2015
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Fit method for discrete distributions in scipy.stats?

Hi all,

Recently I needed to fit a Zipf distribution to some data for an application and noticed that while scipy has a fit method for the continuous distributions, it has none for any discrete distributions.

Is there any reason for this, or has it just not been done yet? There aren't that many discrete distributions and I'd be happy to try to put together a PR to add this, but I'd just like to make sure that there is no specific reason it's not already there before I start writing any code.

One possible issue I can think of is that some distributions have discrete shape parameters (i.e. binomial and the number of trials): an MLE estimate would probably return a non-integer (the distributions accept non-integers just fine).

Thoughts?

Thanks,
Graham Clenaghan
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Ralf Gommers | 15 Aug 10:43 2015
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removing the Lena image

Hi all,

The Lena image that we ship is a standard image in the image processing literature, however it's also controversial (see https://en.wikipedia.org/wiki/Lenna). That was the reason for:
- adding 2 new images in scipy 0.12.0 (https://github.com/scipy/scipy/pull/351)
- recommending to not using Lena in the documentation in scipy 0.16.0 (https://github.com/scipy/scipy/pull/4785)

Now it looks like Debian was decided that the licensing of this image is NOK and that it needs to be removed from all of Debian. Which hasn't affected Scipy yet but has affected scikit-image (it's "marked for auto-removal"):
https://groups.google.com/forum/#!topic/scikit-image/q36bajdvWew
https://groups.google.com/forum/#!topic/scikit-image/8d15Egg7qAY

I know opinions on the image itself and on Debian's action will vary, but I'd like to be pragmatic here and prevent issues with Scipy packaging in Debian. Therefore I propose to do the same as what scikit-image will do: remove Lena for the next release and raise an error from the scipy.misc.lena() function.

Ralf

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Marcel Oliver | 14 Aug 18:45 2015
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solve_banded is very slow as a tridiagonal solver

Hello,

I noticed that using solve_banded to solve a tridiagonal linear system
is at least an order of magnitude slower than it should be based on a
straightforward operation count of the Thomas algorithm.

In the python source code, solve_banded is pretty much a
straightforward call into gbsv from lapack, but I did not dare to
follow up on what is going on in there.  Maybe it's doing pivoting, or
there is some inefficiency due to being coded for more general
band-diagonal problems.

In any event, in a lot of textbook applications of tridiagonal
solvers, pivoting is not necessary (see, e.g., Higham, Accuracy and
Stability of Numerical Algorithms, SIAM 1996, Section 9.5) and the
Thomas algorithm does not vectorize efficiently.

So it would be very good if either solve_banded could be fixed if it
does something stupid, or otherwise if a plain Thomas algorithm
tridiagonal solver could be added as this would cover a lot of
run-off-the-mill problems while solve_banded could handle the rest.

In any event, just to demonstrate that the performance gap is really
there, I coded up a Thomas algorithm in cython and here is the output
for a tridiagonal system of size 10^6:

CPU time Thomas algorithm in python: 3.47022
CPU time solve_banded: 0.428928
CPU time Thomas algorithm in cython: 0.032048

Speedup python/cython: 108.281952072
Speedup solve_banded/cython: 13.3839241138

The code is attached, but it's quickly put together and I am not
claiming that it's well.  In fact, it's the first time I try cython
which was easy enough, except that  <at> cython.boundscheck(False) makes
the code run slower, not faster...

Regards,
Marcel

Attachment (tridiag.pyx): application/octet-stream, 1969 bytes
Attachment (tridiag_test.py): application/octet-stream, 2143 bytes
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Abraham Escalante | 13 Aug 07:11 2015
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Weekly Summary 2015/08/12

Hello all,


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

Week 10 topics:
  • NaN policy ready to be merged. 
  • `f_oneway` NIST test cases ready to be merged.
Week 11 topics:
  • `mstats` consistency with `stats`: WIP.
  • `linregress`: same treatment as `f_oneway`.
  • `shapiro`: WIP
  • `kendalltau`: standby
  • `pointbiserialr`: standby
For your reference:
Regards,
Abraham.
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Charles R Harris | 11 Aug 23:23 2015
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ANN: Numpy 1.10.0b1 release

Hi All,

give this release a whirl and report any problems either on the numpy-discussion list or by opening an issue on github.
I'm pleased to announce the first beta release of Numpy 1.10.0. There is over a year's worth of enhancements and bug fixes in the 1.10.0 release, so please give this release a whirl and report any problems either on the numpy-discussion list or by opening an issue on github. Tarballs, installers, and release notes may be found in the usual place at Sourceforge.

Chuck

 
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Gmane