Mailman 3 March 2015 - SciPy-Dev

Re: [SciPy-Dev] Regarding taking up project ideas and GSoC 2015
by Maniteja Nandana 25 Mar '15

25 Mar '15

Hello everyone, I am writing this mail to enquire about implementing numerical differentiation package in scipy. There have been discussions before (Issue #2035 <https://github.com/scipy/scipy/issues/2035>) and some PRs (PR #2835 <https://github.com/scipy/scipy/pull/2835>) to include tools to compute derivatives in scipy. According to the comments made in them, as far as I can understand, I see that there are some ways to do derivatives on the computer with varying generality and accuracy ( by Alex Griffing <https://github.com/scipy/scipy/issues/2035#issuecomment-23628615> ) : 1. 1st order derivatives of special functions 2. derivatives of univariate functions 3. symbolic differentiation 4. numerical derivatives - finite differences 5. automatic or algorithmic differentiation Clearly, as suggested in the thread, the 1st option is already done in functions like *jv* and *jvp* in *scipy.special. * I think everyone agreed that symbolic derivatives is out of scope of scipy. Though I would like to hear more about the univariate functions. Coming to finite differences, the modules described there, *statsmodels *and *numdifftools, *they vary in aspects of speed and accuracy, in terms of approaches followed as mentioned in Joseph Perktold comment <https://github.com/scipy/scipy/pull/2835#issuecomment-52372036> - *Statsmodels *used complex step derivatives, which are for first order derivatives and have only truncation error, no roundoff error since there is no subtraction. - *Numdifftools *uses adaptive step-size to calculate finite differences, but will suffer from dilemma to choose small step-size to reduce truncation error but at the same time avoid subtractive cancellation at too small values I have read the papers used by both the implementations: *Statsmodels *Statistical applications of the complex-step method of numerical differentiation, Ridout, M.S. <https://drive.google.com/file/d/0BwUeCS0FJLRucXdCc1JOTEY0cGc/view?usp=shari…> *Numdifftools *The pdf attached in the github repository DERIVEST.pdf <https://drive.google.com/file/d/0BwUeCS0FJLRuYW1MNlp2enJCaHM/view?usp=shari…> Just pointing out in this platform, I think there is an error in equation 13 in DERIVEST, It should be f'-0() = 2f'-delta/2() - f'-delta(), instead of f'-0() = 2f'-delta() - f'-delta/2() as also correctly mentioned in the matlab code that followed the equation As much as my understanding from the discussions goes, the statsmodels implementation uses elegant broadcasting. Though I get the idea seeing the code, I would really appreciate some examples that clearly explain this. Also the complex-step method is only for first order derivatives and that function is analytic, so that Cauchy-Riemann equations are valid. So, is it possible to differentiate any function with this ? Also as I was discussing with Christoph Deil, the API implementation issue of whether to use classes, as in numdifftools or as functions, as in statsmodels came to the fore. Though I am not an expert in it, I would love to hear some suggestions on it. Though at this point AD seems ahead of time, it is powerful in forward and reverse methods, moreover complex-step is somewhat similar to it. The packages *ad *and *algopy *use AD. Also, there were concerns with interfacing these methods with C/ Fortran functions. It would also be great if there could be suggestions regarding whether to implement these methods. At the same time, it would be really helpful if any new methods or packages to be looked into could be suggested. Waiting in anticipation for your feedback and response. Happy to learn :) Thanks for reading along my lengthy mail. Please do correct if I did some mistake. I have attached the documents I have related to these issues, most importantly *The Complex-Step Derivative Approximation by **JOAQUIM R. R. A. MARTINS* *Numerical differentiation <https://drive.google.com/folderview?id=0BwUeCS0FJLRufnJaVko3MGpJX0Nud3R0dHg…>* Cheers, Maniteja. _______________________________________________ SciPy-Dev mailing list SciPy-Dev(a)scipy.org http://mail.scipy.org/mailman/listinfo/scipy-dev

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GSOC 2015 projects
by Ian Henriksen 24 Mar '15

24 Mar '15

Hi all, I'm putting together an application for GSOC 2015, and, although the deadline is approaching fast, I would still appreciate your feedback on a few of my ideas. I am a masters student studying mathematics at Brigham Young University. Thus far my primary contribution to SciPy has been the Cython API for BLAS and LAPACK (see https://github.com/scipy/scipy/pull/4021). My research is in Isogeometric Analysis, i.e. finite element analysis on spline curves. My open source work and research at BYU have given me a great deal of experience with Python and Cython, but I am also familiar with C, C++, and Fortran. As such, I have been reflecting on projects that would be best suited to my skill set, as well as most beneficial to SciPy. I'm curious to know which of the following projects would be of greatest interest to the SciPy community: 1. Wrapping the PROPACK library for sparse linear algebra and using it for sparse SVD computation in scipy.sparse. There has been some initial work on f2py wrappers for PROPACK at https://github.com/jakevdp/pypropack, though it appears the wrappers are still incomplete. 2. Implementing an improved library for spline operations in SciPy. I am very familiar with the different refinement techniques used in CAD (knot insertion, degree elevation, etc.) and could implement a library that would be able to perform them all. My ideal here would be to write a C++ or Fortran library to do this and then wrap it via Cython. The emphasis would be primarily on writing code for refinement and evaluation that is both fast and general. I could include code for spline subdivision methods as well. 3. Adding support for Cython to both f2py and numpy.distutils. The goal here would be to allow f2py to generate cython-compatible wrappers from existing pyf files. I would also modify numpy.distutils so it could compile Cython files. 4. Wrap ffts (https://github.com/anthonix/ffts) and use it as an alternative to FITPACK in scipy.fft for use cases where it is faster. Which of these projects would be most appreciated? I certainly want to be able to make a valid and, more importantly, useful contribution. Thanks! - Ian Henriksen

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Speeding up scipy.special.erf()?
by Will Adler 21 Mar '15

21 Mar '15

I hope this is the right place to post this. A user on StackOverflow told me to <http://stackoverflow.com/questions/29170588/will-cython-speed-up-erf-calcul…> report this. I am trying to transition from MATLAB to Python. The majority of my computational time is spent calling erf on millions or billions of vectors. Unfortunately, it seems that scipy.special.erf() takes about 3 times as long as MATLAB’s erf(). Is there anything that can be done to speed up SciPy’s erf()? Check for yourself if you wish: MATLAB r=rand(1,1e7) tic;erf(r);toc % repeat this line a few times Python import numpy as np import scipy.special as sps r=np.random.rand(1e7) %timeit sps.erf(r) Thanks! Will Adler PhD Candidate Ma Lab <http://www.cns.nyu.edu/malab/> Center for Neural Science New York University

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Sparse compressed major axis slicing with sequence is slow
by Eric Martin 21 Mar '15

21 Mar '15

Hi, I filed https://github.com/scipy/scipy/issues/4573 a few weeks ago and am still waiting for some contact from someone involved with Scipy development that this work is wanted. I recommend reading the issue, but the summary is that slicing a compressed sparse matrix along the major axis with a sequence is quite slow. My method offers about a 100x speedup when selecting only a small number of rows/columns, and causes a bit of a slowdown if selecting many rows (but perhaps this slowdown could be limited with more development time). I also observed that the compressed sparse matrix initialization takes a large amount of time validating input data. I'd really appreciate some feedback on things like (1) is it OK if the code takes 2 different paths depending on input size (based on speculation of which would be faster)? (2) can I add code paths for compressed matrix initialization that skip input data sanity checks? before I take the time to make a PR. Thanks a ton, Eric Martin

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Tensors
by Robert Lucente - Pipeline 19 Mar '15

19 Mar '15

You guys are probably aware of the following article but just in case Let's build open source tensor libraries for data science - Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries by Ben Lorica http://radar.oreilly.com/2015/03/lets-build-open-source-tensor-libraries-for -data-science.html Maybe this type of thing is inappropriate for this mailing list?

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GSoC' 15 Ideas: define general integrators; parallelize integration; higher-order ODEs
by Max Mertens 19 Mar '15

19 Mar '15

Hello everyone, I am a bit late to write to this mailing list, therefore sorry for that. My name is Max Mertens; I am a Electrical Engineering student at the University of Ulm, Germany. I have done a few projects in C(++) on physical simulations of multibody dynamics with collision detection, and molecular dynamics with interatomic potentials. I got interested in SciPy as it facilitates physical simulations in an easy way, but have not worked with SciPy so far. I would like to contribute to SciPy as part of the GSoC, and am willing to contribute by providing an "easy-fix" to one of the open issues. Is there already a possibility to perform multibody/molecular dynamics in SciPy or are there contributions needed in this scope? The scipy.integrate module with ode and odeint provides several methods to integrate differential equations. For this I have the following ideas: * add a interface/class/method to define general integration methods: For now you can specify various integrators like "vode" and "dopri5" as a string. The new code would allow to enter a Butcher tableau to define implicit/explicit (embedded) Runge-Kutta methods, which would cover "dopri5" and "dopri853" (see [0]); and possibly other general integrator descriptions as needed. * add distributed integration: Linear multistep integrators like Runge-Kutta with multiple differential equations can be parallelized to speed up calculations. This would distribute integrations to multiple threads; and/or, if needed for complicated equations like in multibody dynamics, distribute to multiple physical machines (I have developed a few ideas on how to accomplish this). * provide methods to integrate higher-order differential equations: is this needed, or are users of the library expected to express these as multiple first-order differential equations? Could this step be automated? Do you think this would be a useful contribution to SciPy? Do you have any suggestions? Thank you for your feedback. Max Mertens [0] https://en.wikipedia.org/wiki/List_of_Runge%E2%80%93Kutta_methods

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SciPy 2015 Call for Propsals Open - tutorial & talk submissions due April 1st
by Courtenay Godshall (Enthought) 18 Mar '15

18 Mar '15

**SciPy 2015 Conference (Scientific Computing with Python) Call for Proposals: Submit Your Tutorial and Talk Ideas by April 1, 2015 at <http://www.scipy2015.scipy.org> http://scipy2015.scipy.org.** SciPy 2015, the fourteenth annual Scientific Computing with Python conference, will be held July 6-12, 2015 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference brings together over 500 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of two days of tutorials by followed by three days of presentations, and concludes with two days of developer sprints. More info available on the conference website at <http://www.scipy2015.scipy.org> http://scipy2015.scipy.org; you can also sign up on the website for mailing list updates or follow @scipyconf on Twitter. We hope you'll join us - early bird registration is open until May 15, 2015 at http://scipy2015.scipy.org We encourage you to submit tutorial or talk proposals in the categories below; please also share with others who you'd like to see participate! Submit via the conference website @ <http://www.scipy2015.scipy.org> http://scipy2015.scipy.org. *SCIPY TUTORIAL SESSION PROPOSALS - DEADLINE EXTENDED TO WED APRIL 1, 2015* The SciPy experience kicks off with two days of tutorials. These sessions provide extremely affordable access to expert training, and consistently receive fantastic feedback from participants. We're looking for submissions on topics from introductory to advanced - we'll have attendees across the gamut looking to learn. Whether you are a major contributor to a scientific Python library or an expert-level user, this is a great opportunity to share your knowledge and stipends are available. Submit Your Tutorial Proposal on the SciPy 2015 website: <http://scipy2015.scipy.org> http://scipy2015.scipy.org *SCIPY TALK AND POSTER SUBMISSIONS - DUE April 1, 2015* SciPy 2015 will include 3 major topic tracks and 7 mini-symposia tracks. Submit Your Talk Proposal on the SciPy 2015 website: <http://scipy2015.scipy.org> http://scipy2015.scipy.org Major topic tracks include: - Scientific Computing in Python (General track) - Python in Data Science - Quantitative and Computational Social Sciences Mini-symposia will include the applications of Python in: - Astronomy and astrophysics - Computational life and medical sciences - Engineering - Geographic information systems (GIS) - Geophysics - Oceanography and meteorology - Visualization, vision and imaging If you have any questions or comments, feel free to contact us at: scipy-organizers(a)scipy.org.

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GSOC: scipy.stats improvement
by Vidit Bhargava 16 Mar '15

16 Mar '15

Hello Everyone, I know I am late, Sorry for that. My name is Vidit Bhargava. I am a Computer Science and Engineering student at National Institute of Technology, Karnataka, India. I am proficient in Python and C++. I am interested in the stats improvement project. Any suggestions on how I should go about it?

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GSoC 2015
by Сергей Чепарухин 16 Mar '15

16 Mar '15

Hi, I'm new for Open Source community and I would like to contribute. I want to participate in GSoC 2015, can someone get me started? -- Best regards, Cheparukhin Sergey. *cheparukhin.s(a)gmail.com <cheparukhin.s(a)gmail.com>*

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wiki.scipy.org is down
by Warren Weckesser 16 Mar '15

16 Mar '15

It appears that wiki.scipy.org is down. Warren

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