Category Archives: Conferences

SciPy 2013 Conference Recap

Author: Eric Jones

Another year, another great conference.  Man, this thing grew a ton this year.  At final count, we had something like 340 participants which is way up from last year’s 200 or so attendees.  In fact, we had to close registration a couple of weeks before the program because that is all our venue could hold.  We’ll solve that next year.  Invite your friends.  We’d love to see 600 or even more.

Many thanks to the organizing team.  Andy Terrell and Jonathan Rocher did an amazing job as conference chairs this year both managing that growth and keeping the trains on time.  We expanded to 3 parallel sessions this year, which often made me want to be in 3 places at once.  Didn’t work.  Thankfully, the videos for all the talks and sessions are available online.  The video team really did a great job — thanks a ton.

I’ve wondered whether the size would change the feel of the conference, but I’m happy to report it still feels like an gathering of friends, new and old.  Aric Hagberg mentioned he thinks this is because it’s such a varied (motley?) crowd from disparate fields gathered to teach, learn, and share software tools and ideas.  This fosters a different atmosphere than some academic conferences where sparring about details of a talk is a common sport.  Hmh.  Re-watching the videos, I see Fernando Perez mentions this as well.

Thanks again to all who organized and all who attended.  I’m already looking forward to seeing you again next year.  Below are my personal musings on various topics at the conference:

  • The tutorials were, as usual, extremely well attended.  I spent the majority of my time there in the scikits learn track by Gael VaroquauxOlivier Grisel, and Jake VanderPlas.  Jeez, has this project gone far.  It is stunning to see the breath and quality of the algorithms that they have.  It’s obviously a hot topic these days; it is great to have such an important tool set at our disposal.
  • Fernando Perez gave a keynote this year about IPython.  We can safely say that 2013 is the year of the IPython notebook.  It was *everywhere*.  I’d guess 80+% of the talks and tutorials for the conference used it in their presentations.  Fernando went one step further, and his slide deck was actually live IPython notebooks.  Quite cool.  I do believe it’ll change the way people teach Python…  But, the most impressive thing is that Fernando still has and can execute the original 250 line script that was IPython 0.00001.  Scratch that.  The most impressive thing is to hear how Fernando has managed to build a community and a project that is now supported by a $1.1M grant from the Sloan foundation.  Well done sir.  The IPython project really does set the standard on so many levels.
  • Olivier Grisel, of scikits learn fame, gave a keynote on trends in machine learning.  It was really nice because he talked about the history of neural networks and the advances that have been made in “deep learning” in recent years.  I began grad school in NN research, and was embarrassed to realize how recent (1986) the back propagation learning algorithm was when I first coded it for research (1993).  It seemed old to me then — but I guess 7 years to a 23 year is, well, pretty old.  Over the years, I became a bit disenchanted with neural nets because they didn’t reveal the underlying physical process within the data.  I still have this bias, but Olivier’s discussion of the “deep learning” advances convinced me that I should get re-educated.  And, perhaps I’m getting more pragmatic as the gray hairs fill in (and the bald spot grows).  It does look like it’s effective for multiple problems in the detection and classification world.
  • William Schroeder, CEO of Kitware, gave a keynote on the importance of reproducible research which was one of the conference themes.  It was a privilege to have him because of the many ways Kitware illuminated the path for high quality scientific software in the open source world with VTK.  I’ve used it both in C++ and, of course, from Python for many, many years.  In his talk, Will talked about the existing scientific publication model doesn’t work so well anymore, and that, in fact, with the web and tools that are now available, direct publishing of results is the future together with publishing our data sets and code that generated them.  This actually dovetailed really well with Fernando’s talk, and I can’t help but think that we are on this track.
  • David Li has been working with the SymPy team, and his talk showed off the SymPy Live site that they have built to interactively try out symbolic calculations on the web.  I believe David is the 2nd high school student to present in the history of SciPy, yes? (Evan Patterson was the other that I remember)  Heh.  Aaand, what were you doing your senior year?  Both were composed, confident, and dang good — bodes well for our future.
  • There are always a few talks of the “what I have learned” flavor at Python.  This year, Brian Granger of IPython fame gave one about the dangers of features and the benefits of bugs.  Brian’s talks are almost always one of my favorites (sorta like I always make sure to see what crazy stuff David Beazley presents at PyCon).  Part of it is that he often talks about parallel computing for the masses which is dear to my heart, but it is also because he organizes his topics so well.
  • Nicholas Kridler also unexpectedly hooked me with another one of these talks.  I was walking out of conference hall after the keynote to go see what silly things the ever smiling Jake Vanderplas might be up to in his astronomy talk.  But derned if Nicholas didn’t start walking through how he approaches new machine learning problems in interesting ways.  My steps slowed, and I finally sat down, happy to know that I could watch Jake’s talk later.  Nicholas used his wits and scikits learn to win(!) the Kaggle whale detection competition earlier this year, and he gave us a great overview of how he did it.  Well worth a listen.
  • Both Brian and Nicholas’ talks started me thinking how much I like to see how experts approach problems.  The pros writing all the cool libraries often give talks on the features of their tools or the results of their research, but we rarely get a glimpse into their day-to-day process.  Sorta like pair programming with Martin Chilvers is a life changing experience (heh.  for better or worse… :-)), could we have a series of talks where we get to ride shotgun with a number of different people and see how they work?  How does Ondrej Certik work through a debugging session on SymPy development?  Does his shiny new cowboy hat from Allen Boots help or not?  When approaching a new simulation or analysis, how does Aric Hagberg use graph theory (and Networkx) to set the problem up?  When Serge Rey gets a new set of geospatial data, what are the common things he does to clean and organize the data for analysis with PySAL?  How does Wes McKinney think through API design trade-offs as he builds Pandas?  And, most importantly, how does Stefan Van Der Walt get the front of his hair to stand up like that? (comb or brush? hair dryer on low or high?)  Ok, maybe not Stefan, but you get the idea.  We always see a polished 25 minute presentation that sums up months or years of work that we all know had many false starts and painful points.  If we could learn about where people stubbed their toe and how to avoid it in our work, it would be pretty cool.  Just an idea, but I will run it by the committee for next year and see if there is any interest.
  • The sprints were nothing short of awesome.  Something like 130+ people were there on the first day sprinting on 10-20 different libraries including SymPy, NumPy, IPython, Matplotlib as well as more specific tools like scikits image and PySal.  Amazing to see.  Perhaps the bigger surprise was that at least half also stayed for Saturday’s sprints.  scikits learn had a team of about 10 people that worked two full days together (Friday and Saturday activity visible on the commit graph), and I think multiple other groups did as well.  While we’ve held sprints for a while, we had 2 top 3 times as many people as 2012, and this year’s can only be described as wildly successful.

  • While I was there, I spent most of my time checking in on the PySide sprint where John Erhsman of Wingware got a new release ready for the 4.8 series of Qt (bless him), and Robin Dunn, Corran Webster, Stefan Landgovt, and John Wiggins investigated paths forward toward 5.x compatibility.  No one was too excited about Shiboken, but the alternatives are also not a walk in the park.  I think the feeling is, long term, we’ll need to bite the bullet and go a different direction than Shiboken.

Avoiding “Excel Hell!” using a Python-based Toolchain

Update (Feb 6, 2014):  Enthought is now the exclusive distributor of PyXLL, a solution that helps users avoid “Excel Hell” by making it easy to develop add-ins for Excel in Python. Learn more here.

Didrik Pinte gave an informative, provocatively-titled presentation at the second, in-person New York Quantitative Python User’s Group (NY QPUG) meeting earlier this month.

There are a lot of examples in the press of Excel workflow mess-ups and spreadsheet errors contributing to some eye-popping mishaps in the finance world (e.g. JP Morgan’s spreadsheet issues may have led to the 2012 massive loss at “the London Whale”). Most of these can be traced to similar fundamental issues:

  • Data referencing/traceability

  • Numerical errors

  • Error-prone manual operations (cut & paste, …)

  • Tracing IO’s in libraries/API’s

  • Missing version control

  • Toolchain that doesn’t meet the needs of researchers, analysts, IT, etc.

Python, the coding language and its tool ecosystem, can provide a nice solution to these challenges, and many organizations are already turning to Python-based workflows in response. And with integration tools like PyXLL (to execute Python functions within Excel) and others, organizations can adopt Python-based workflows incrementally and start improving their current “Excel Hell” situation quickly.

For the details check out the video of Didrik’s NY QPUG presentation.  He demonstrates a an example solution using PyXLL and Enthought Canopy.

[vimeo 67327735 http://vimeo.com/67327735]

And grab the PDF of his slides here.

QPUG_20130514_ExcelHell_Slides

It would be great to hear your stories about “Excel Hell”. Let us know below.

–Brett Murphy

DataGotham…Complete!

Well, DataGotham is over. The conference featured a wide cross section of the data community in NYC. Talks spanned topics from “urban science” to “finding racism on FourSquare” to “creating an API for spaces.” Don’t worry, the videos will be online soon so you can investigate yourself. The organizers did a great job putting a conference of this size together on relatively short notice. Bravo NYC data crunchers!

One thing I somehow missed was a network graph created by the organizers to illustrate the tools used by attendees. I am happy to see python leading the way! The thickness of the edge indicates the number of people using both tools. It seems there are a lot of people trying to make Python and R “two great tastes that go great together.” I’m curious as to why more Python users aren’t using numpy and scipy. Food for thought…

Got tools?

SIGGRAPH 2012: Mobile, OpenGL 4.3 and Python

I recently had the opportunity to attend SIGGRAPH in LA. For those of you who don’t know, SIGGRAPH is an annual conference for Graphics and Visualization that does a great job of attracting people from both the scientific and artistic halves of the visualization community. For broader coverage, you can read some of the usual blog coverage here. I was particularly interested, however, in the OpenGL developments.

Increased focus on performance/watt for mobile applications

SIGGRAPH 2012 was important for many reasons, but particularly for those of us that use OpenGL (this year was the 20th anniversary of the OpenGL API). OpenGL 4.3 and OpenGL ES 3.0 were announced and there were many interesting sessions on the new OpenGL release (more about this later) and graphics on mobile devices.

The rapid ascent of mobile and its dominance as the primary computer that people interact with on a daily basis has opened up an interesting challenge for people designing games and visualization — the difference in the power envelope between mobile devices and their desktop brethren. The power envelope of high-end desktop devices is ~300 watt while the power envelope of mobile gpu’s is < 1 watt. This power disparity implies a massive gulf in performance across the full spectrum of devices, assuming similar architectures are in use.

Multiple speakers stressed that power consumption should be a first class design metric when designing graphics algorithms along with the traditional metric of performance. Currently, the tools for profiling and measuring power consumption of algorithms are almost non-existent and nowhere near the sophistication of tools for measuring performance. Nevertheless, data transfers over a bus were recognized as an expensive power activity and a place where power savings can be realized.

To this end, OpenGL (finally) announced new texture compression formats that are royalty free, work on both OpenGL and OpenGL ES and are guaranteed on all compliant OpenGL implementations. This is great for developers since we can finally assume that this functionality will be available across all devices. More information about the new texture compression formats lives here.

Using OpenGL 4.3 from Python: Rabbit of Caerbannog

The OpenGL ARB committee started an effort to modernise OpenGL around OpenGL 3.x and large parts of the old fixed pipeline functionality was deprecated. These changes were great from a driver implementors point of view and should allow developers to write code that runs faster on modern GPU’s. However the deprecations have obsoleted much of the OpenGL tutorials that exist on the internet. I have listed two examples here which do not use deprecated functionality and can be used as a starting point to write modern OpenGL graphics examples in Python.

So, how do you use the brand spanking new 4.3 api’s from Python? I’ve written a code sample that uses modern opengl (no deprecated functionality used) to draw a triangle from Python.

Opengl display output showing a colored triangle

Triangle drawn using modern opengl and Python

For the code please refer to https://gist.github.com/3494203

For fun, here’s a screenshot of Stanford bunny drawn using modern OpenGL –

Stanford bunny drawn using modern OpenGL and python

Stanford bunny drawn using modern OpenGL and python

For the code please refer to  https://gist.github.com/3494560

Notes:

  1. Right now only Nvidia has Opengl 4.3 drivers available
  2. OSX only supports Opengl versions upto 3.2 as of today
  3. You will require a trunk release of PyOpenGL to create a OpenGL 4.3 context
  4. Code for the examples can be obtained from https://github.com/enthought/glfwpy.git
  5. I have conservatively marked the OpenGL version as 3.2 here since many readers will not have 4.3 working on their machines. To enable OpenGL 4.3 change the OpenWindowHint call for OPENGL_VERSION_MAJOR to 4 and the OpenWindowHint call for OPENGL_VERSION_MINOR to 3.

EuroScipy 2012

EuroScipy 2012 starts tomorrow! Four days of exciting tutorials and talks. The conference is hosted in Brussels at ULB (which you probably know if you went to FOSDEM).

The first two days are dedicated to a great set of tutorials. The introductory track should please any new data analyst starting with Python:

  • array manipulation with NumPy
  • plotting with Matplotlib
  • introduction to scientific computing with Scipy.

In the advanced track, HPC and parallel computing are the main focus but tutorials also offer:

  • advanced numpy and scipy
  • time series data analysis with Pandas
  • visualisation
  • packaging and scientific software development insights.

 

Last but not least, the European Enthought team will offer:

  • a tutorial on Enaml, a new library that makes GUI programming fun
  • a tutorial on how to write robust scientific code with testing
  • a tutorial about Bento, a pythonic packaging system for Python software

Plan for an exciting weekend as well with various talks covering finance to geophysics to biology. Don’t forget to come for the keynote sessions with David Beazley on Saturday and Eric Jones, Enthought’s CEO, on Sunday!

 

See you in Brussels!

TGIF: SciPy 2012 Recap Video

As we wait for the SciPy talk videos to make their way onto the web, we’d like to share a short film recapping SciPy 2012.

The latest iteration of the SciPy conference was another great example of the scientific python community coming together to share “the latest and greatest.” Most organizations want to change the world in some way or another. At Enthought, we attempt to do this by building tools that help our customers – in both academia and industry – concentrate on solving their actual problems rather than wrestling with technology. We believe Python’s ability to operate smoothly in different contexts (e.g., desktop, web, array-based and distributed computing, etc.) makes it a highly productive and pragmatic tool with which to build solutions.

The SciPy community is changing the world by continually pushing technical computing forward in a pragmatic way. One just has to look at the content and tools presented at SciPy historically to know that this community has been been up to its neck in “data science” for some time. One could also argue, however, that SciPy is one of the best kept secrets in technical computing. As the recent focus on MapReduce solutions illustrates, the world is in the grips of “big computation.” It will only get tougher in the foreseeable future. At the same time, “big data” is a relative term. “Big” for a bioinformatician is different than for a macro hedge fund analyst, and these differences can often be measured in orders of magnitude. And when it comes to solutions, rarely does one size fit all.

In contrast, SciPy addresses a broad array of problems. SciPy 2012 offered High Performance Computing and Visualization tracks, with tutorials on machine learning, plotting, parallel computing, and time series analysis. Sometimes all these topics could be found in a single talk (see VisIt). The community also demonstrated some open-mindedness by inviting Jeff Bezanson, one of the authors of Julia, to share his experience building a language specifically designed for technical computing. It turns out there is a fair amount of overlap between what the SciPy community and the Julia team are planning. With LLVM IR increasingly being viewed as a common target, there is real excitement about what the future holds for language development and interaction.

This is all to say that SciPy has a lot to offer the world. Stay tuned for a bigger and better SciPy next year!

Mayavi – Talk at Fifth Elephant

The lead developer and creator of the 3D visualization package Mayavi, Dr. Prabhu Ramachandran, will provide a brief overview of Mayavi followed by his experience throughout the development of the package at the Fifth Elephant conference to be held in Bangalore, India.

Here is a brief video preview of the Mayavi user interface and Prabhu’s talk at Fifth Elephant.

Scipy 2012

No Mas

Scipy 2012 is wrapping up today tomorrow as bands of sprinters come together to hack away on their projects of interest. Many thanks to the sponsors and volunteers that made this year’s Scipy another success. The Scipy conference has always been highly technical. Although “data science” and “big data” have become buzzwords recently, Scipy has been exploring these themes for many years. Projects featuring machine learning, high performance computing, and visualization were in full attendance at this year’s Scipy. Stay tuned for links to talk videos (care of Next Day Video)!