Science and Python: a interactively biased retrospective of a (mostly) successful decade

Slides for my PyCon Canada keynote, that cover similar material to other recent talks but with a slightly different emphasis.

Video: http://www.youtube.com/watch?v=F4rFuIb1Ie4

1. Me and computers? SciComp Examples IPython Trouble Science and Python: a interactively biased retrospective of a (mostly) successful decade Fernando Pérez http://fperez.org, @fperez_org Fernando.Perez@berkeley.edu PyCon Canada Toronto Nov 11, 2012
2. Me and computers? SciComp Examples IPython Trouble Outline 1 Me and computers? 2 Scientific Computing 3 Two examples 4 IPython: Interactive Python 5 Trouble in paradise FP (UC Berkeley) Python for science 11/11/12 2 / 46
3. Me and computers? SciComp Examples IPython Trouble Outline 1 Me and computers? 2 Scientific Computing 3 Two examples 4 IPython: Interactive Python 5 Trouble in paradise FP (UC Berkeley) Python for science 11/11/12 3 / 46
4. Physics undergrad, plot fractals in TurboPascal Program on paper, use mom’s office PC on weekends. Debug on paper. Think a lot away from the screen.
5. VGA graphics! Binary compiled in 1991, screenshot taken May 2011!
6. Me and computers? SciComp Examples IPython Trouble Clueless about the internet (which I unplugged) Image credit: Carlos Latuff FP (UC Berkeley) Python for science 11/11/12 6 / 46
7. Me and computers? SciComp Examples IPython Trouble A learning moment... Image credit: crazytales562 on Flickr FP (UC Berkeley) Python for science 11/11/12 7 / 46
8. Me and computers? SciComp Examples IPython Trouble Outline 1 Me and computers? 2 Scientific Computing 3 Two examples 4 IPython: Interactive Python 5 Trouble in paradise FP (UC Berkeley) Python for science 11/11/12 8 / 46
9. Computing is not the ’third branch’ of science... It is now the backbone of theory and experiment!
10. Me and computers? SciComp Examples IPython Trouble Scientific computing The computer as a microscope Exploratory: Problem’s definition evolves as we understand it. No ‘requirements’ to build an application against. Mathematica, Maple, Matlab, IDL, etc. All have an interactive environment. Applications Languages FP (UC Berkeley) Python for science 11/11/12 10 / 46
11. Beyond (Floating Point) Number Crunching Hardware floating point Arbitrary precision integers Rationals Interval arithmetic Symbolic manipulation FORTRAN Extended precision floating point Text processing Databases Graphical user interfaces Web interfaces Hardware control Multi-language integration Data formats: HDF5, XML, ...
12. Scientific Python: a Rich Ecosystem IPython NetworkX
13. Me and computers? SciComp Examples IPython Trouble EEG analysis for epilepsy High quality plotting: matplotlib JPL Outline 1 Me and computers? 2 Scientific Computing 3 Two examples 4 IPython: Interactive Python 5 Trouble in paradise FP (UC Berkeley) Python for science 11/11/12 13 / 46
14. Data analysis for epilepsy surgery Isolating the origin of drug-resistant epileptic seizures which require surgery. John Hunter, Department of Pediatric Neurology, University of Chicago.
15. Electrode location in 3D, combined with MRI data
16. Correlation analysis of seizure data
17. Matplotlib: 2d plotting Matplotlib 1.2.0 is official, with Python 3 support!!!
18. Matplotlib: 3d plotting
19. JPL: Mars mission trajectory design and nav data Ted Drain and Lynn Craig, Jet Propulsion Laboratory (NASA/Caltech) From: Name Elided <nameelided@jpl.nasa.gov> Date: Oct 2, 2007 7:15 PM Subject: Fwd: matplotlib bug numbers To: John Hunter <jdh2358@gmail.com> One of my lead developers mentioned that they had sent a bug to you about the annotations feature of MatPlotLib. Would you be able to let me know what the timeline is to resolve that bug? The reason is that the feature is needed for the Phoenix project and their arrival at Mars will be in March sometime, but they are doing their testing in the coming few months. This annotation feature is used on reports that present the analysis of the trajectory to the navigation team and it shows up on our schedule. It would really help me to know approximately when it could be resolved. B-plane plots are used to show the trajectory of a spacecraft with respect to the target body (specifically perpendicular to the incoming asymptote of the spacecraft trajectory) and we plot them with the y-axis inverted. The plot is used heavily in flight operations so it is important to our customers. In addition, we have what is called a thundering heard plot where many different trajectory solutions (determined from different measurement sources) are plotted together. The annotations are import there so we can see which plot corresponds to each source of data. I hope it helps to know how your code will be used in spacecraft navigation. Thanks for all your efforts.
20. JPL: Mars mission data visualization Expected communication power levels between an orbiting spacecraft and a lander as it goes through the atmosphere:
21. August 23, 2011 The astronomy ‘event of a generation’ Josh Bloom, UC Berkeley Astronomy @profjsb
22. Monday Tuesday Wednesday Supernova PTF11kyl: Event of a Generation found on Tuesday Most nearby Type Ia supernova in > 25 years Soon visible with binoculars http://bit.ly/ptf11kly
23. Me and computers? SciComp Examples IPython Trouble Outline 1 Me and computers? 2 Scientific Computing 3 Two examples 4 IPython: Interactive Python 5 Trouble in paradise FP (UC Berkeley) Python for science 11/11/12 22 / 46
24. Why IPython? (something other than “I’d rather not finish my dissertation”)
25. Why IPython? (something other than “I’d rather not finish my dissertation”)
26. Me and computers? SciComp Examples IPython Trouble The Lifecycle of a Scientific Idea (schematically) 1 Individual exploratory work 2 Collaborative development 3 Production work (HPC, cloud, parallel) 4 Publication (with reproducible results!) 5 Education 6 Goto 1. The Problem with most tools Barriers and discontinuities in workflow in between all the steps FP (UC Berkeley) Python for science 11/11/12 24 / 46
27. Me and computers? SciComp Examples IPython Trouble The Lifecycle of a Scientific Idea (schematically) 1 Individual exploratory work 2 Collaborative development 3 Production work (HPC, cloud, parallel) 4 Publication (with reproducible results!) 5 Education 6 Goto 1. The Problem with most tools Barriers and discontinuities in workflow in between all the steps FP (UC Berkeley) Python for science 11/11/12 24 / 46
28. IPython’s goal: Fluid transitions in all these steps
29. Demo
30. Me and computers? SciComp Examples IPython Trouble Pillar #1: An architecture for interactive computing FP (UC Berkeley) Python for science 11/11/12 27 / 46
31. Me and computers? SciComp Examples IPython Trouble Pillar #2: the Notebook Format JSON but version control-friendly Easy for machine processing, fixable by hand if need be. Lots of hooks for metadata Not Python-specific (R and Ruby notebooks exist, Julia planned) Produce Markdown, reST, L A TEX, HTML, etc... An open format for sharing, publishing and archiving executable computational work FP (UC Berkeley) Python for science 11/11/12 28 / 46
32. Documented protocols and formats: a growing ecosystem around IPython
33. Microsoft Visual Studio 2010 integrated console Dino Viehland and Shahrokh Mortazavi (Microsoft); http://pytools.codeplex.com
34. A vim client to control an IPython kernel/console Paul Ivanov (Berkeley), https://github.com/ivanov/vim-ipython
35. An Emacs Notebook Client! Takafumi Arakaki: http://tkf.github.com/emacs-ipython-notebook.
36. Notebooks on Windows Azure Cloud Shahrokh Mortazavi (Microsoft), B.G., F.P.: http://bit.ly/JQeojD.
37. Star Cluster: IPython parallel+Notebook on Amazon EC2 Justin Riley (MIT): http://web.mit.edu/star/cluster
38. One-click single notebook on Amazon EC2 Carl Smith (UK): https://notebookcloud.appspot.com.
39. Other projects using IPython Scientific EPD: Enthought Python Distribution. Sage: open source mathematics. PyRAF: Space Telescope Science Institute CASA: Nat. Radio Astronomy Observatory Ganga: CERN PyMAD: neutron spectrom., Laue Langevin Sardana: European Synchrotron Radiation ASCEND: eng. modeling (Carnegie Mellon). JModelica: dynamical systems. DASH: Denver Aerosol Sources and Health. Trilinos: Sandia National Lab. DoD: baseline configuration. Mayavi: 3d visualization, Enthought. NiPype: computational pipelines, MIT. PyIMSL Studio, by Visual Numerics. ... Web/Other Visual Studio 2010: MS. Django. Turbo Gears. Pylons web framework Zope and Plone CMS. Axon Shell, BBC Kamaelia. Schevo database. Pitz: distributed task/bug tracking. iVR (interactive Virtual Reality). Movable Python (portable Python environment). ...
40. (Incomplete) Cast of Characters Brian Granger - Physics, Cal State San Luis Obispo Min Ragan-Kelley - Nuclear Engineering, UC Berkeley Matthias Bussonnier - Physics, Institut Curie, Paris Jonathan March- Enthought Thomas Kluyver - Biology, U. Sheffield Jörgen Stenarson - Elect. Engineering, Sweden. Paul Ivanov - Neuroscience, UC Berkeley. Robert Kern - Enthought Evan Patterson - Physics, Caltech/Enthought Brad Froehle - Mathematics, UC Berkeley Stefan van der Walt - UC Berkeley John Hunter - TradeLink Securities, Chicago. Prabhu Ramachandran - Aerospace Engineering, IIT Bombay. Satra Ghosh- MIT Neuroscience Gaël Varoquaux - Neurospin (Orsay, France) Ville Vainio - CS, Tampere University of Technology, Finland Barry Wark - Neuroscience, U. Washington. Ondrej Certik - Physics, U Nevada Reno Darren Dale - Cornell Justin Riley - MIT Mark Voorhies - UC San Francisco Nicholas Rougier - INRIA Nancy Grand Est Thomas Spura - Fedora project Many more! (~150 commit authors)
41. Support Thank you! Enthought, Austin, TX: Lots! Microsoft: WinHPC support, Visual Studio integration, Azure (thanks to Shahrokh Mortazavi). DoD/DRC Inc: funding through Sept. 2012 (thanks to Jose Unpingco and Chris Keees). NIH: via NiPy grant NSF: via Sage compmath grant Google: summer of code 2005, 2010. Tech-X Corp., Boulder, CO: Parallel/notebook (previous versions)
42. Me and computers? SciComp Examples IPython Trouble Outline 1 Me and computers? 2 Scientific Computing 3 Two examples 4 IPython: Interactive Python 5 Trouble in paradise FP (UC Berkeley) Python for science 11/11/12 39 / 46
43. Me and computers? SciComp Examples IPython Trouble Too few are lifting too many 1 2 3 4 5 6 7 8 9 10 Individual Committer 0.0 0.2 0.4 0.6 0.8 1.0 Commit rate Normalized commit rates since Jan-2010 cython ipython matplotlib mayavi numpy scipy sympy FP (UC Berkeley) Python for science 11/11/12 40 / 46
44. Challenges, weaknesses? Speed MATTERS! See JuliaLang.org numpy.f2py: Fortran Cython Numba: an LLVM JIT for typed code/arrays. Very exciting development. Packaging The neverending nightmare.
45. Challenges, weaknesses? Speed MATTERS! See JuliaLang.org numpy.f2py: Fortran Cython Numba: an LLVM JIT for typed code/arrays. Very exciting development. Packaging The neverending nightmare.
46. Distutils, you say? Image credit: http://goodsky.homestead.com/files/Labyrinth800X600.jpg
47. OK, we’re brave, we go in... Image credit: http://xkcd.com/246
48. setuptool & friends It’s called easy_install, so it should be easy... Image credit: http://pixdaus.com/single.php?id=47779
49. Me and computers? SciComp Examples IPython Trouble The language lured me in... But I stayed for the community! Real friendships and incredible people A culture of generous and mutual cross-project collaboration But we have a ton of work to do! The tools we need must be built by scientists. Lots of space for truly innovative thinking, and Python is an expressive tool for the exercise. FP (UC Berkeley) Python for science 11/11/12 45 / 46
50. John D. Hunter, 1968-2012: matplotlib.org Memorial fund: numfocus.org/johnhunter