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SunPy : Python for solar physics

Steven Christe 1, , Matt Earnshaw 2 , Keith Hughitt 1 , Jack Ireland 1 , Florian Mayer 3 , Albert Shih 1 , Alex Young 1 1 NASA GSFC 2 Imperial College London 3 Vienna University of Technology. SunPy : Python for solar physics. Florian Mayer. Outline. What is Python?

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SunPy : Python for solar physics

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  1. Steven Christe1,, Matt Earnshaw2, Keith Hughitt1, Jack Ireland1, Florian Mayer3, Albert Shih1, Alex Young1 1 NASA GSFC2 Imperial College London 3Vienna University of Technology SunPy: Python for solar physics Florian Mayer

  2. Outline • What is Python? • Introduction to Python • Scientific Python • NumPy • Matplotlib • SciPy • Python in solar physics

  3. What is Python? • General-purpose • Object-oriented (disputed) • Cross-platform • Windows • Mac OS • Linux • Other Unices (FreeBSD, Solaris, etc.) • High-level

  4. Who uses Python? • Internet companies • Google • Rackspace • Games • Battlefield 2 • Civilization 4 • Graphics • Walt Disney • Science • NASA • ESRI

  5. Why Python? • Easy • Comprehensive standard library (“batteries included”) Quality does vary, though. • Good support for scientific tasks • Permissive open-source license On the downside: • Slower, but ways to speed up

  6. Python / IDL Python IDL Proprietary software License cost Small community Cumbersome plotting Solar software • Free open-source software • Without cost • General purpose • Good plotting • No solar software

  7. History of Python • Implementation started 1989 by Guido van Rossum (BDFL) • 2.0 appeared 2000 • Garbage collection • Unicode • 3.0 appeared 2008

  8. Python in science • Astronomy • Artificial intelligence & machine learning • Bayesian Statistics • Biology (including Neuroscience) • Dynamical systems • Economics and Econometrics • Electromagnetics • Electrical Engineering • Geosciences • Molecular modeling • Signal processing • Symbolic math, number theory

  9. Python in astronomy • pyFITS– read FITS files • pyRAF – run IRAF tasks • pywcs • pyephem– compute positions of objects in space  • spacepy (space sciences, just released) • Planned standard library AstroPy

  10. Zen of Python by Tim Peters • Beautiful is better than ugly. • Explicit is better than implicit. • Simple is better than complex. • Readabilitycounts. • There should be one – and preferably only one – obvious way to do it. • Although that way may not be obvious at first unless you're Dutch. • >>> import this

  11. Python tutorial Brief introduction into Python

  12. Python as a calculator • Infix notation operations • Python 2 defaults to floor division • More mathematical operations in math • Complex math in cmath

  13. Integers and Floats • Integers are arbitrary size. • Floats are platform doubles. • decimal module for arbitrary precision decimal numbers • fractions module for fractions

  14. Strings and Unicode Strings / bytes Unicode u"foo" Store unicode codepoints Useful for text Behave as expected for multibyte characters • "foo" • Store bytes • Useful for binary data

  15. Lists and Tuples • [1, 2, 3, 4] • Mutable • Multiple records • (1, u"foo") • Immutable • Different objects describing one record

  16. Control flow • if/elif/else • for-loop • break • continue • else • while-loop • pass

  17. Functions • Default arguments are evaluated once at compile time! • lambda alternative syntax for definition of trivial functions • Functions are objects, too!

  18. Dictionaries • Unordered key-value mappings • Approx. O(1) lookup and storage • Keys must be immutable (hashable)

  19. Sets • Unordered collection of unique objects • Approx. O(1) membership test • Members must be immutable (hashable)

  20. Object-orientation • Classes • Explicit self • Multiple inheritance • Also in IDL 8; no escaping it

  21. Exception handling • try / except / else • raise • Exceptions inherit from Exception

  22. Current versions Python 2.7 Python 3.2 Print function Bytes / String Float Division Absolute imports Views • Print statement • String / Unicode • Floor division • Relative imports • Lists Tons of other changes http://bit.ly/newpy3

  23. NumPy • Fundamental package for science in Python • Multidimensional fixed-size, homogenous arrays • Derived objects: e.g. matrices • More efficient • Less code

  24. NumPy: Create arrays • Python list • arange • linspace / logspace • ones / zeros / eye / diag • random

  25. NumPy: Vectorization • Absence of explicit looping • Conciseness – less bugs • Closer to mathematical notation • More pythonic. • Also possible for user functions

  26. NumPy: Broadcasting • Expansion of multidimensional arrays • Implicit element-by-element behavior

  27. NumPy: Broadcasting illustrated

  28. NumPy: Indexing

  29. arr[0,3:5]

  30. arr[4:,4:]

  31. arr[:,2]

  32. arr[2::2,::2]

  33. Other Options • Boolean area • Integer area

  34. NumPyData-types: Integers

  35. NumPyData-types: Unsigned

  36. NumPyData-types: Floating point

  37. NumPyData-types: Complex

  38. Optimize Python • NumPy: weave.blitz (fast NumPy expressions) • NumPy: weave.inline (inline C/C++) • f2py (interface Fortran) • Pyrex/Cython (python-like compiled language)

  39. Matplotlib • 2D plotting library • Some 3D support • Publication-quality figures • “Make easy things easy and hard things possible” • Configurable using matplotlibrc

  40. Matplotlib: Simple Plot importnumpyasnp frommatplotlibimportpyplotasplt t=np.linspace(0,2,200) s=np.sin(2*pi*t) plt.plot(t,s,linewidth=1.0) plt.xlabel('time (s)') plt.ylabel('voltage (mV)') plt.title('About as simple as it gets, folks') plt.grid(True) plt.show()

  41. Matplotlib: Subplots importnumpyasnp frommatplotlibimportpyplotasplt deff(t): s1=np.cos(2*pi*t) e1=np.exp(-t) returnnp.multiply(s1,e1) t1=np.arange(0.0,5.0,0.1) t2=np.arange(0.0,5.0,0.02) t3=np.arange(0.0,2.0,0.01) plt.subplot(211) l=plot(t1,f(t1),'bo',t2,f(t2),'k--', markerfacecolor='green') plt.grid(True) plt.title('A tale of 2 subplots') plt.ylabel('Damped oscillation') plt.subplot(212) plt.plot(t3,np.cos(2*pi*t3),'r.') plt.grid(True) plt.xlabel('time (s)') plt.ylabel('Undamped') plt.show()

  42. Matplotlib: Paths importnumpyasnp importmatplotlib.pathasmpath importmatplotlib.patchesasmpatches importmatplotlib.pyplotasplt Path=mpath.Path fig=plt.figure() ax=fig.add_subplot(111) pathdata=[ (Path.MOVETO,(1.58,-2.57)), (Path.CURVE4,(0.35,-1.1)), (Path.CURVE4,(-1.75,2.0)), (Path.CURVE4,(0.375,2.0)), (Path.LINETO,(0.85,1.15)), (Path.CURVE4,(2.2,3.2)), (Path.CURVE4,(3,0.05)), (Path.CURVE4,(2.0,-0.5)), (Path.CLOSEPOLY,(1.58,-2.57)), ] codes,verts=zip(*pathdata) path=mpath.Path(verts,codes) patch=mpatches.PathPatch(path,facecolor='red',edgecolor='yellow',alpha=0.5) ax.add_patch(patch) x,y=zip(*path.vertices) line,=ax.plot(x,y,'go-') ax.grid() ax.set_xlim(-3,4) ax.set_ylim(-3,4) ax.set_title('spline paths') plt.show()

  43. Matplotlib: mplot3d frommpl_toolkits.mplot3dimportAxes3D frommatplotlibimportcm frommatplotlib.tickerimport(LinearLocator,FixedLocator,FormatStrFormatter) importmatplotlib.pyplotasplt importnumpyasnp fig=plt.figure() ax=fig.gca(projection='3d') X=np.arange(-5,5,0.25) Y=np.arange(-5,5,0.25) X,Y=np.meshgrid(X,Y) R=np.sqrt(X**2+Y**2) Z=np.sin(R) surf=ax.plot_surface(X,Y,Z,rstride=1,cstride=1, cmap=cm.jet, linewidth=0,antialiased=False) ax.set_zlim3d(-1.01,1.01) ax.w_zaxis.set_major_locator(LinearLocator(10)) ax.w_zaxis.set_major_formatter(FormatStrFormatter('%.03f')) fig.colorbar(surf,shrink=0.5,aspect=5) plt.show()

  44. Matplotlib: Ellipses importnumpyasnp frommatplotlibimportpyplotasplt frommatplotlib.patchesimportEllipse NUM=250 ells=[ Ellipse(xy=rand(2)*10,width=np.rand(), height=np.rand(),angle=np.rand()*360) foriinxrange(NUM)] fig=plt.figure() ax=fig.add_subplot(111,aspect='equal') foreinells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(rand()) e.set_facecolor(rand(3)) ax.set_xlim(0,10) ax.set_ylim(0,10) plt.show()

  45. MatplotlibExample: EEG viewer

  46. Matplotlib: Gallery

  47. SciPy • Statistics • Optimization • Numerical integration • Linear algebra • Fourier transforms • Signal processing • Image processing • ODE solvers • Special functions • And more.

  48. SciPy Example: Problem • Three phases • Glass sample – light grey • Bubbles – black • Sand grains – dark grey • Determine • Fraction of the sample covered by these • Typical size of sand grains or bubbles

  49. SciPy Example: Solution • Open image and examine it • Crop away panel at bottom • Examine histogram • Apply median filter • Determine thresholds • Display colored image • Use mathematical morphology to clean the different phases • Attribute labels to all bubbles and sand grains • Remove from the sand mask grains that are smaller than 10 pixels • Compute the mean size of bubbles.

  50. SunPy • Spatially aware maps • Read FITS files • RHESSI • SDO/AIA • EIT • TRACE • LASCO • standard color tables and hist equalization • basic image coalignment • VSO • HEK

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