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SunPy: Python for solar physics
Steven Christe1,, Matt Earnshaw2, Keith Hughitt1, Jack Ireland1, Florian Mayer3, Albert Shih1, Alex Young1
1 NASA GSFC2 Imperial College London3 Vienna University of Technology
Florian Mayer
Outline
What is Python? Introduction to Python Scientific Python
NumPy Matplotlib SciPy
Python in solar physics
What is Python?
General-purpose Object-oriented (disputed) Cross-platform
Windows Mac OS Linux Other Unices (FreeBSD, Solaris, etc.)
High-level
Who uses Python?
Internet companies Google Rackspace
Games Battlefield 2 Civilization 4
Graphics Walt Disney
Science NASA ESRI
Why Python?
Easy Comprehensive standard library
(“batteries included”)Quality does vary, though.
Good support for scientific tasksPermissive open-source license
On the downside: Slower, but ways to speed up
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
History of Python
Implementation started 1989 by Guido van Rossum (BDFL)
2.0 appeared 2000 Garbage collection Unicode
3.0 appeared 2008
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
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
Zen of Python by Tim Peters
Beautiful is better than ugly.Explicit is better than implicit.Simple is better than complex.Readability counts.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
Python tutorialBrief introduction into Python
Python as a calculator
Infix notation operations Python 2 defaults to floor division More mathematical operations in
math Complex math in cmath
Integers and Floats
Integers are arbitrary size. Floats are platform doubles. decimal module for arbitrary
precision decimal numbers fractions module for fractions
Strings and Unicode
STRINGS / BYTES
"foo" Store bytes Useful for binary data
UNICODE
u"foo" Store unicode
codepoints Useful for text Behave as expected
for multibyte characters
Lists and Tuples
[1, 2, 3, 4] Mutable Multiple records
(1, u"foo") Immutable Different objects
describing one record
Control flow
if/elif/else for-loop
break continue else
while-loop pass
Functions
Default arguments are evaluated once at compile time!
lambda alternative syntax for definition of trivial functions
Functions are objects, too!
Dictionaries
Unordered key-value mappings Approx. O(1) lookup and storage Keys must be immutable (hashable)
Sets
Unordered collection of unique objects
Approx. O(1) membership test Members must be immutable
(hashable)
Object-orientation
Classes Explicit self Multiple inheritance
Also in IDL 8; no escaping it
Exception handling
try / except / else raise Exceptions inherit from Exception
Current versions
PYTHON 2.7
Print statement String / Unicode Floor division Relative imports Lists
PYTHON 3.2
Print function Bytes / String Float Division Absolute imports Views
Tons of other changeshttp://bit.ly/newpy3
NumPy
Fundamental package for science in Python
Multidimensional fixed-size, homogenous arrays
Derived objects: e.g. matrices More efficient Less code
NumPy: Create arrays
Python list arange linspace / logspace ones / zeros / eye / diag random
NumPy: Vectorization
Absence of explicit looping Conciseness – less bugs Closer to mathematical notation More pythonic.
Also possible for user functions
NumPy: Broadcasting
Expansion of multidimensional arrays
Implicit element-by-element behavior
NumPy: Broadcasting illustrated
NumPy: Indexing
arr[0,3:5]
arr[4:,4:]
arr[:,2]
arr[2::2,::2]
Other Options
Boolean area Integer area
NumPy Data-types: IntegersType Remarks Character code
byte compatible: C char 'b'
short compatible: C short 'h'
intc compatible: C int 'i'
int_ compatible: Python int 'l'
longlong compatible: C long long 'q'
intp large enough to fit a pointer 'p'
int8 8 bits
int16 16 bits
int32 32 bits
int64 64 bits
NumPy Data-types: Unsigned
Type Remarks Character code
ubyte compatible: C u. char 'B'
ushort compatible: C u. short 'H'
uintc compatible: C unsigned int 'I'
uint compatible: Python int 'L'
ulonglong compatible: C long long 'Q'
uintp large enough to fit a pointer 'P'
uint8 8 bits
uint16 16 bits
uint32 32 bits
uint64 64 bits
NumPy Data-types: Floating point
Type Remarks Character code
half 'e'
single compatible: C float 'f'
double compatible: C double
float_ compatible: Python float 'd'
longfloat compatible: C long float 'g'
float16 16 bits
float32 32 bits
float64 64 bits
float96 96 bits, platform?
float128 128 bits, platform?
NumPy Data-types: Complex
Type Remarks Character code
csingle 'F'
complex_ compatible: Python complex 'D'
clongfloat 'G'
complex64 two 32-bit floats
complex128 two 64-bit floats
complex192 two 96-bit floats, platform?
complex256 two 128-bit floats, platform?
Optimize Python
NumPy: weave.blitz (fast NumPy expressions)
NumPy: weave.inline (inline C/C++) f2py (interface Fortran) Pyrex/Cython (python-like compiled
language)
Matplotlib
2D plotting library Some 3D support Publication-quality
figures “Make easy things
easy and hard things possible”
Configurable using matplotlibrc
Matplotlib: Simple Plot
import numpy as npfrom matplotlib import pyplot as plt
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()
Matplotlib: Subplots
import numpy as npfrom matplotlib import pyplot as plt
def f(t): s1 = np.cos(2*pi*t) e1 = np.exp(-t) return np.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()
Matplotlib: Paths
import numpy as npimport matplotlib.path as mpathimport matplotlib.patches as mpatchesimport matplotlib.pyplot as plt
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()
Matplotlib: mplot3d
from mpl_toolkits.mplot3d import Axes3Dfrom matplotlib import cmfrom matplotlib.ticker import (LinearLocator, FixedLocator, FormatStrFormatter)import matplotlib.pyplot as pltimport numpy as np 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()
Matplotlib: Ellipses
import numpy as npfrom matplotlib import pyplot as pltfrom matplotlib.patches import Ellipse
NUM = 250
ells = [ Ellipse(xy=rand(2)*10, width=np.rand(), height=np.rand(), angle=np.rand()*360) for i in xrange(NUM)]
fig = plt.figure()ax = fig.add_subplot(111, aspect='equal')for e in ells: 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()
Matplotlib Example: EEG viewer
Matplotlib: Gallery
SciPy
Statistics Optimization Numerical integration Linear algebra Fourier transforms Signal processing Image processing ODE solvers Special functions And more.
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
SciPy Example: Solution
1. Open image and examine it2. Crop away panel at bottom
Examine histogram3. Apply median filter4. Determine thresholds5. Display colored image6. Use mathematical morphology to clean the
different phases7. Attribute labels to all bubbles and sand grains
Remove from the sand mask grains that are smaller than 10 pixels
8. Compute the mean size of bubbles.
SunPy
Spatially aware maps Read FITS files
RHESSI SDO/AIA EIT TRACE LASCO
standard color tables and hist equalization basic image coalignment VSO HEK
SunPy: Maps
Spatially aware array NumPy array Based on SolarSoft Map.
MapCube
SunPy: VSO
Two APIs Legacy API (tries to mimic IDL
vso_search) New API based on boolean operations
SunPy: HEK HER
Create VSO queries from HER responses
WIP: Plot HER events over images
SunPy: Plotman
SunPy: Get involved!
Use it! File feature requests Express opinion on the mailing list /
in IRC File bug reports Contribute documentation Contribute code
SunPy: Reaching us
Website: http://sunpy.org Mailing list: http://bit.ly/sunpy-forum IRC: #sunpy on irc.freenode.net Git code repository: https://
github.com/sunpy/sunpy
Resources
SciPy: http://scipy.org Astronomical modules: http://
bit.ly/astropy Science modules: http://
bit.ly/sciencepy NumPy/IDL: http://hvrd.me/numpy-idl Python for interactive data analysis:
http://bit.ly/pydatatut SciPy lecture notes: http://
bit.ly/scipylec This talk: http://graz-talk.bitsrc.org SunPy doc: http://sunpy.org/doc/
Thanks
Steven Christe1,
Matt Earnshaw2
Keith Hughitt1
Jack Ireland1
Florian Mayer3
Albert Shih1
Alex Young1
1 NASA GSFC2 Imperial College London3 Vienna University of Technology
Thanks to
