Jaime Fernández del Río | 27 May 19:57 2015
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[ndimage-gsoc2015] Weekly Summary 2015/05/25

Hi all,

Aman and I had our first weekly sync meeting yesterday. After each of these weekly calls, I have asked Aman to publish a short summary, just like this one, on progress to date and plans for the week. If you would like to participate on one of these weekly meetings, just let us know.

Design Choices
  1. Use the NumPy iterator (numpy.flatiter in Python or PyArrayIterObject in C) for all basic array iteration, rather than a custom implementation as ndimage currently does.
Plan for the week
  1. Learn to use the NumPy iterator in Cython properly.
  2. Send a first pull request against ndimage that:
    1. Provides supporting Cython code for basic array iteration.
    2. Translates an ndimage function that only requires basic iteration, to Cython. This will probably be find_objects from the measurements submodule.
  3. Document the progress in a blog post.
Deliverables from last week
  1. None yet.
Best,

Jaime

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Abraham Escalante | 26 May 23:46 2015
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`ss` and `square_of_sums` deprecation from `scipy.stats`

Hello all,

As part of StatisticsCleanup milestone `scipy.stats.ss` and `scipy.stats.square_of_sums` are to be deprecated. These are helper functions that were not meant to be exposed to users but we know for a fact that they are being used by some people, therefore we cannot just remove them from the API so we are going to put them through a normal deprecation period. 

If there are any objections to the deprecation of these functions, now would be a good time to speak up.

Cheers,
Abraham.
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Jaime Fernández del Río | 26 May 18:00 2015
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Re: Repository for GSoC project

Thanks everyone for the feedback. After some more thinking, I have asked Aman to start sending PRs directly against the current master of scipy.

To differentiate new from old implementations, we'll probably just prepend all file names with "ndi_" instead of the current "ni_". We will eventually start removing old functionality that has been replaced, but for quite sometime they will both coexist. This means that at some point in time, e.g. calling `ndimage.filters.uniform_filter1d` may use the new Cython code paths, while `ndimage.filters.minimum_filter1d` may still be using the old C code paths.

We will not merge anything, answering Juan's question, until we have tests that validate that functionality. That does not mean that the functionality will be available to end users just yet, e.g. we may merge an iterator before any top level function that uses it. But if it does not have tests, it does not get merged.

If anyone has a very strong opinion against this approach, please speak up now!

Jaime

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Ralf Gommers | 26 May 00:11 2015
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welcome Jaime and Anne to the Scipy core team

Hi all,

On behalf of the Scipy developers I'd like to welcome Jaime Fernandez del Rio and Anne Archibald as members of the Scipy core team. Jaime has been making improvements to mainly ndimage over the last year and will be mentoring Aman's GSoC project on ndimage. Anne used to have svn commit rights and made lots of improvements in scipy.spatial and scipy.interpolate in 2008/2009 - it's great to have her contributing again.

Cheers,
Ralf
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Jaime Fernández del Río | 25 May 21:37 2015
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Repository for GSoC project

Hi all,

This year I am mentoring Aman for one of the GSoC projects we have underway, "Rewriting ndimage in Cython." By its very nature it doesn't conform very well to the "many small pull requests" model: from the point of view of scipy, things are going to be broken up until almost the very last commit. I am not sure what the best way to set up a collaborative code development environment would be, and so am asking for the collective wisdom to help guide us.

Aman could simply create one ginormous pull request that will grow, and grow, and not be merged until everything was ready. I don't like this idea too much, as it is going to eventually be a confusing mess, and I think it would also make it difficult for others than Aman (that would mostly be me) to contribute code.

I think we could also use a branch, either on my fork of scipy or on Aman's, as the repository on which development would happen, and against which PRs would be created, and once completed send a single PR to the main scipy repo. This may work, but I don't like it much either.

What probably makes more sense is to create a new branch **in the main scipy repository**, and have PRs sent and merged against that branch, which would eventually be merged with master upon completion. NumPy seems to have a couple such experimental branches ('with_maskna' and 'enable_separate_by_default'), although there is none in SciPy that I see. This would also allow us to keep the project in a controlled environment, even if by the end of the summer not every single bit of ndimage has been ported.

If this third path is really the preferred way of doing things, I could probably set things up myself (Ralf gave me commit rights when I became a mentor for this project), but I'd like to hear what others think, before abusing my powers.

Thanks!

Jaime

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Pierre Haessig | 25 May 14:02 2015

clarity of linprog default bounds argument

Hi,

I'm looking at the new `linprog` routine that has just landed in Scipy
0.15. By coincidence, it just come on time for a new problem I have to
solve these days !

While studying the signature, I found that the default argument
bounds=None is a bit confusing. Indeed, if I got it right, bounds=None
actually implies non negative variables. Is that correct ? However,
bounds=None has a clear semantic meaning of "unbounded variables".

In order to make this default choice more obvious (note that I'm not
arguing for changing this default), wouldn't it be clearer to write
`bounds=(0, None)` in the signature ?
(maybe this was discussed already ? I didn't check Github...)

best,
Pierre
Ralf Gommers | 24 May 15:15 2015
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dropping OS X dmg installers

Hi,

When updating the build scripts and release docs for 0.16.x I realized that:
- the DMG installers haven't been built for 0.14.1 and 0.15.x
- we didn't officially decide this AFAIK, but no one complained
- the wheels we now have on pip for OS X are more general than the DMG installers; the latter only work with python.org Python.
- wheels are also easier to build [1] and test [2] (thanks Matthew!)

So shall we make it official that we won't build any more DMG installers for releases?

Ralf

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Amit Moscovich Eiger | 24 May 00:40 2015
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scipy.sparse.csgraph.laplacian enhancement proposal (random-walk normalized Laplacian)

Hi,
This is a proposal to add the functionality of computing the random-walk normalized Laplacian matrix to the scipy.sparse.csgraph.laplacian function.

Currently this function receives the optional boolean parameter 'normed'. If normed==False it computes the plain Laplacian matrix and if normed==True it computes the symmetric normalized Laplacian matrix. However the random-walk normalized Laplacian is sorely missing here (*) and may be the best choice in some contexts (**). I propose to add the random-walk normalized Laplacian functionality by changing the 'normed' boolean parameter to a 'normalization' parameter, which can be one of the following: None (default), 'symmetric' or 'randomwalk'.

The problem with this suggestion is that it breaks the interface of the laplacian() function, though it's easy to make a backwards-compatible interface (e.g. add the optional 'normalization' parameter in addition to the 'normed' boolean parameter).

Two questions:
1) I'm new here. Do I just implement the feature (and relevant unit test) and send a pull request?
2) Is it considered OK to break the interface in such cases, or should I make it backwards-compatible?

Thanks,
Amit Moscovich Eiger.

References:
(*) For the definition of the Laplacian, symmetric normalized Laplacian and the random-walk normalized Laplacian, see https://en.wikipedia.org/wiki/Laplacian_matrix
(**) See section 8.5 of "A Tutorial on Spectral Clustering" http://arxiv.org/abs/0711.0189,

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Amadou Coulibaly | 21 May 16:13 2015
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SDG

Dear all,

I'm new user of python. And I wanted to do the continuous wavelet analysis of my data. But first of all, when I tried to run the following command:

In[1]: import numpy as np

In[2]: from scipy.signal import SDG, Morlet, cwt

I got the following error messages:

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-43-e18eb7700f8b> in <module>()
----> 1 from scipy.signal import SDG, Morlet, cwt

ImportError: cannot import name SDG



​In[3]: ​from scipy.signal import Morlet, cwt
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-44-2cf611117e43> in <module>()
----> 1 from scipy.signal import Morlet, cwt

ImportError: cannot import name Morlet


​How can I solve this issues?

Best regards​

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Surya | 16 May 17:47 2015
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Testing and taking forward scipy central

Hello everyone,

Looks like I haven't posted anything in the list for a long time! I hope to be pretty much active as far as SciPy Central is concerned. 

There have been quite a lot of changes in the scipy central source
​ ​
in the past few years. 
​We have majorly developed a new front-end design, added some features like comments, feeds, and rewriting some pieces. 

Thanks to Sebastian, Pauli for their 
​help, and ​
contributions. 

​Brief information fo
r newbies in the community:

Repo: https://github.com/scipy/SciPyCentral
​  
​Development Environment: Python 2.7, Django 1.4.5; 
Front-end is based on Twitter Bootstrap 2.3.2, jQuery 1.9
Development setup is written in `README`​

Docs (initial version I started a year ago): 
https://github.com/scipy/SciPyCentral/wiki/Newbie

I've just started with manual testing to see if there are any critical bugs still present. My other agendas in coming days are to write documentation, identify bottlenecks - improve response times of pages.

So, it would be great to hear from anyone interested in contributing this project.

One can start looking into issue tracker in the repo and try to address them.

Otherwise, one can try to finish up the voting feature (to vote submissions) I started building (which couldn't be merged into master yet). It is built on the top of `django.contrib.comments` module.


Thanks
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Christopher McFarland | 14 May 22:14 2015
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Fwd: Adding Biweight Mid-Correlation to scipy.stats

Hi,

I have implemented a common "robust" correlation metric, the Biweight Mid-Correlation [1], for myself and wonder if this would be welcomed function for scipy.stats. This function in R is titled 'bicor' [2] and is especially popular in the biology community. 

If the addition is welcomed, I'd be happy to Unit Test/Document/properly stylize the function. I also wonder if a unified correlation function (with keyword argument method={'pearson', 'spearman', 'kendall', 'bicor'}) would be welcomed. All these functions have a similar purpose, inputs, and outputs, and are implemented in Pandas with simply one unified function. 

Chris

[1] "Introduction to Robust Estimation and Hypothesis Testing", Rand Wilcox, Academic Press, 1997. 

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