The Unexpected Effectiveness of Python in Science

PyCon 2017 opening keynote; see the video here: https://www.youtube.com/watch?v=ZyjCqQEUa8o

1. The Unexpected Effectiveness of Python in Science Jake VanderPlas @jakevdp PyCon 2017
2. PyCon’s Mosaic
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5. Code: Books: $ whoami jakevdp Blog: http://jakevdp.github.io
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8. Charles Barsotti, New Yorker $ whoami jakevdp
9. Edwin Hubble at the 48" Schmidt Telescope, Palomar Observatory, 1949. (credit: PNAS) Astronomy Then . . .
10. Astronomy Now . . . Source: http://spacetelescope.org/ Source: http://sdss.org/ Hubble Space Telescope Sloan Digital Sky Survey
11. Source: http://spacetelescope.org Hubble’s “Ultra Deep Field”
12. Source: http://spacetelescope.org Hubble’s “Ultra Deep Field”
13. Source: http://sdss.org SDSS Galaxy Catalog
14. Source: http://sdss.org SDSS Galaxy Catalog
15. Astronomy in the 21st Century . . . Kepler (2009) JWST (2018) LSST (2020)
16. Artist’s Impression: Wikipedia TRAPPIST-1 Exoplanetary System
17. K2 Data: Ethan Kruse Kepler Telescope: NASA TRAPPIST-1 Exoplanetary System Kepler (K2) Observations
19. Source: NASA James Webb Space Telescope (JWST)
20. James Webb Space Telescope (JWST) Source: NASA/JWST
21. James Webb Space Telescope (JWST) Source: NASA/JWST
23. Large Synoptic Survey Telescope (credit: LSST Corp) Large Synoptic Survey Telescope
24. 8.4-meter Primary Mirror
25. 3 Gigapixel Camera
26. 3 Gigapixel Camera
27. 3 Gigapixel Camera
28. 3 Gigapixel Camera = ~1500 HD TVs
29. - Survey mode: 2 exposures every ~30 seconds - Images the full southern sky every three nights for a decade - 15-30 TB/night! - Final 10-year catalog: 100s of Petabytes
30. What will we do with all this data? (Left as a 616-page exercise for the reader) https://www.lsst.org/scientists/scibook
32. Thanks to Juan Nunez-Iglesias, Thomas P. Robitaille, and Chris Beaumont. Mentions of Software in Astronomy Publications: Compiled from NASA ADS (code).
33. The Unexpected Effectiveness of Python in Science
34. But Why Python? Python is a “teaching language” . . . created to “bridge the gap between the shell and C” “never intended. . . to be the primary language for programmers.” Guido Van Rossum The Making of Python
35. “I thought we'd write small Python programs, maybe 10 lines, maybe 50, maybe 500 lines — that would be a big one” Guido Van Rossum The Making of Python
36. Why is Python such an effective tool in science?
37. Why is Python such an effective tool in science? 1. Interoperability with Other Languages
38. “If I have seen further, it is by standing on the shoulders of giants.” - Isaac Newton
39. “If I have seen further, it is by importing from the code of giants.” - Definitely Not Isaac Newton
40. “Scientists... work with a wide variety of systems ranging from simulation codes, data analysis packages, databases, visualization tools, and home-grown software-each of which presents the user with a different set of interfaces and file formats. As a result, a scientist may spend a considerable amount of time simply trying to get all of these components to work together in some manner...” - David Beazley Pythonista Extraordinaire Scientific Computing with Python (ACM vol. 216, 2000) Science Before Python . . .
41. Science Before Python . . . “I had a hodge-podge of work processes. I would have Perl scripts that called C++ numerical routines that would dump data files, and I would load them up into MatLab to plot them. After a while I got tired of the MatLab dependency. . . so I started loading them up in GnuPlot.” -John Hunter creator of Matplotlib SciPy 2012 Keynote
42. Science Before Python . . . “My advisor had a heavily customized awk/sed/bash workflow to manage job submissions and postprocessing of C codes for supercomputing runs… So I used her scripts to run my jobs, and on top of that had added my own layer of Perl, plus a hefty amount of Gnuplot, IDL and Mathematica.” - Fernando Perez creator of IPython via email
43. Python is Glue.
44. Python glues together this hodge-podge of scientific tools. High-level syntax wraps low-level C/Fortran libraries, which is (mostly) where the computation happens. Python is Glue.
45. Why is Python such an effective tool in science? 1. Interoperability with Other Languages 2. “Batteries Included” + Third-Party Modules
46. Python has built-in libraries for nearly everything . . . . . . and there are third-party libraries for everything else.
47. The Genesis of Scientific Python “Prior to Python, I used Perl (for a year) and then Matlab and shell scripts & Fortran & C/C++ libraries. When I discovered Python, I really liked the language... But, it was very nascent and lacked a lot of libraries. I felt like I could add value to the world by connecting low-level libraries to high-level usage in Python.” - Travis Oliphant creator of NumPy & SciPy via email
48. Python’s Scientific Stack
49. Python’s Scientific Stack
50. Bokeh Python’s Scientific Stack
51. Bokeh Python’s Scientific Stack
52. Python’s Scientific Ecosystem (and many, many more) Bokeh
53. Why is Python such an effective tool in science? 1. Interoperability with Other Languages 2. “Batteries Included” + Third-Party Modules 3. Simplicity & Dynamic Nature
54. https://xkcd.com/353/
55. Python Enters Science: Python in Astronomy 2015 “Python is a language that is very powerful for developers, but is also accessible to Astronomers. Getting those two classes of people using the same tools, I think, provides a huge benefit that’s not always noticed or mentioned.” - Perry Greenfield Space Telescope Science Institute PyAstro 2015
56. Often-overlooked fact . . . For day-to-day scientific data exploration, speed of development is primary, and speed of execution is often secondary. Background Source
57. Why don’t you use C instead of Python? It’s so much faster!
58. Why don’t you commute by airplane instead of by car? It’s so much faster! Why don’t you use C instead of Python? It’s so much faster!
59. Ada Marie did what scientists do: She asked a small question, and then she asked two. And each of those led her to three questions more, And some of those questions resulted in four. From Ada Twist, Scientist by Andrea Beaty & David Roberts Scientific Coding is Nonlinear and Exploratory
60. Jupyter notebooks embody this kind of quick, nonlinear exploration:
61. Why is Python such an effective tool in science? 1. Interoperability with Other Languages 2. “Batteries Included” + Third-Party Modules 3. Simplicity & Dynamic Nature 4. Open ethos well-fit to science
69. “An article about computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.” –Buckheit and Donoho (1995)
70. LIGO Gravitational Wave Event (GW150914) Source: LIGO
71. LIGO Gravitational Wave Event (GW150914) Source: LIGO
72. LIGO Gravitational Wave Event (GW150914) Source: LIGO
73. My Projects: Same Open Philosophy
74. My Projects: Same Open Philosophy Entire content available on GitHub as Jupyter Notebooks
75. Python World Influencing Science . . . Scientists are increasingly hosting research code on Github & similar services to aid in reproducibility.
76. Traditional Astronomy Software Python & Open Source Possessive/non-sharing Cooperative/sharing Fragmented & Overlapping efforts Build on common projects Top-down planning Bottom-up/Loose organization Committee-oriented design Design by “doers” Endless analysis & argument Action-oriented & experimentation Unwilling to discard old tech Good at replacing old tech No leader to resolve conflicts BDFL resolves conflicts Adapted From Perry Greenfield’s PyData Keynote Python World Influencing Science . . . Python’s software practices increasingly adopted by academia
77. Why is Python such an effective tool in science? 1. Interoperability with Other Languages 2. “Batteries Included” + Third-Party Modules 3. Simplicity & Dynamic Nature 4. Open ethos well-fit to science
78. PyCon’s Mosaic
79. Email: jakevdp@uw.edu Twitter: @jakevdp Github: jakevdp Web: http://vanderplas.com/ Blog: http://jakevdp.github.io/ Thank You!