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Lecture 2

Lecture 2. More python. Good books: “Learning Python”, Lutz & Ascher, O’Reilly (the ‘rat book’); “Python in a Nutshell”, Martelli, O’Reilly (the ‘snake book’). FITS files. OO.

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Lecture 2

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  1. Lecture 2 • More python. • Good books: • “Learning Python”, Lutz & Ascher, O’Reilly (the ‘rat book’); • “Python in a Nutshell”, Martelli, O’Reilly (the ‘snake book’). • FITS files.

  2. OO • OO programming is the business of constructing objects. This is done via the class statement. You probably won’t need to use this. However, objects themselves are inescapable in python – if you don’t make them, someone else will. • In fact, almost everything in python is an object.

  3. OO • Objects have two extra features: • Attributes, which have names separated from the object name by a dot, eg fred.shape is an attribute named shape which belongs to object type of which fred is an example (eg a numpy array object). • Methods: these are attributes which are functions. Like any function they can accept arguments, and must still be written with empty brackets even if there are no arguments. Eg the append() method of lists and the copy() method of dictionaries.

  4. Python variables {‘foo’: , ‘pi’:3.14, ‘him’:’fred’…} ( , 3.14, ‘fred’,…) [ , 3.14, ‘fred’,…] • Ways to arrange several objects: • A single one is called a scalar. • A list: elements numbered*, data types can be different. • A tuple: similar to a list but written with round brackets rather than square. • A dictionary: elements accessed by a key, data types can be different; curly brackets. • These groupings are themselves objects. *Python numbering starts from zero.

  5. Python scalars • Numbers • Integers • Floating-point • Complex • Logical • Strings • Special • Objects (includes everything) -43, 0, 1, 219 -2.9, 4.76e6 0.2 + 5.3j True, False ‘Fred’, ‘impossible’, ‘23’, ‘True’ None

  6. Python operations • They are mostly pretty standard. • Arithmetic binary operators: • Note that they can have different effect on strings. • Note that is short for • Logical operators: • Comparison operators: +, -, *, / a += 1 # etc a = a + 1 # etc and, or, not >, >=, <, <=, ==

  7. Python traps • The most common is really a unix trap. Suppose you write your nice script dothings.py. You’ve been careful with your grammar, and have as your first line • You try to run dothings.py on the command line, but get: • To fix this you have to do • This makes dothings.py ‘executable’. #!/usr/bin/python bash: ./dothings.py: Permission denied chmod +x dothings.py

  8. Python traps • For some types of python object, several names can point to a single data area. Changing one of these then changes all the others. This is usually not what you want to happen. • Since it is a common thing to want to create a new variable name with the value of an old one, we have to be careful of this behaviour in python, and find work-arounds for variable types where it is a problem.

  9. Python traps • Compare the behavior of scalars with lists. We can spawn a new variable name from a scalar, which then has its own memory area: • This should print ‘4’. But now try: • You’ll get ‘[999,2,3]’. Cf mutability… >>>a=4 >>>b=a >>>a=3 >>>print b >>>aa=[1,2,3] >>>bb=aa >>>aa[0]=999 >>>print bb

  10. Trouble with ‘change in place’

  11. More python traps: • The range statement: • Note last element is not 4, but 3. • List slices: • Last element is not 2, but 1. • CHECK THESE THINGS before you accept the results of your code! >>>mylist = range(4) >>>print mylist [0, 1, 2, 3] >>>print mylist[:2] [0, 1]

  12. More python traps: • Truncation during division. Try: • This returns, not 1.333, but 1. This is usually not what you want. • Better to get into the habit of ALWAYS dividing by a floating-point. Eg: • yields 1.3333. If you WANT the truncation, use // not /. >>>a=4 >>>b=3 >>>print a/b >>>a=4 >>>b=3 >>>print a/float(b)

  13. Python control structures if <test1>: # do some stuff elif <test2>: # you can have 0 or more of these # do some other stuff else: # you can have 0 or 1 of these # third lot of stuff • There is NO ‘goto’ statement in python. This is a feature. • Anything after a # is a comment. while <test>: # do loop stuff break # optional – dumps out and avoids the ‘else’. continue # like ‘goto while’. else: # optional - processing after normal loop exit. # stuff to do after normal loop exit for <item> in <list>: # etc

  14. Some neato modules • math • Gives you access to constants like pi, + math operations such as sqrt(), cos(), atan2() etc. • os - particularly os.path • Lets you check if files exist, lets you delete files etc import os if os.path.exists(‘myfile’): # do something. Maybe do os.remove(‘myfile’)

  15. One more neato module • sys • sys.argv is a list of command-line arguments. • Beware though, they are all passed as strings. • Suppose you put the following statements in your python script bigdogs.py: • If you then run it on the command line, with an additional argument 0: • Because bb is a string ‘0’ and cc is an integer 0. #!/usr/bin/python import sys bb = sys.argv[1] cc = int(bb) print bb>0, cc>0 $ bigdogs.py 0 True False

  16. Python ins and outs • We’re going to mostly read our data from FITS files, using a module called pyfits. • http://www.stsci.edu/resources/software_hardware/pyfits/ • We’ll crunch up the data using modules called numpy and scipy. • http://numpy.scipy.org/ • http://www.scipy.org/ • For graphical output we’ll use module pylab. • http://matplotlib.sourceforge.net/index.html

  17. Manuals and example code • You won’t need to download manuals for pyfits, numpy, scipy or pylab. You can get them from my home page: • http://www.ast.uct.ac.za/~ims/teaching/teaching.html • Some of these manuals are huge – I recommend you neither print or read them in entirety, but rather: • Read the bits you need using acroread. • Look at the example code I’ll provide you from time to time. There are already a few available from my page.

  18. Another few cm of the python. • Function returns. Note: • This is a tuple. It is better to be explicit... >>> def some_func(arg1,arg2): ... a = arg1 + arg2 ... b = arg1 – arg2 ... return a, b ... >>> c = some_func(42, 2) >>> print c (44, 40) >>> def some_func(arg1,arg2): ... a = arg1 + arg2 ... b = arg1 – arg2 ... return (a, b)

  19. Another few cm of the python. • Also in the function call: • is better than • You can also force it to return a list: >>> (summ, diff) = some_func(42, 2) >>> c = some_func(42, 2) >>> def anuther_func(arg1,arg2): ... a = arg1 + arg2 ... b = arg1 – arg2 ... return [a, b] ... >>> c = anuther_func(20, 19) >>> print c [39, 1]

  20. Another few cm of the python. • The for statement - most of you know this way: • But you can also do this: • Saves a bit of typing. >>> mylist = [‘peas’,’beans’,’dead dog’] >>> for i in range(len(mylist)): ... item = mylist[i] ... print item ... peas beans dead dog >>> mylist = [‘peas’,’beans’,’dead dog’] >>> for item in mylist: ... print item ... peas beans dead dog

  21. FITS = Flexible Image Transport System Header-Data Units Primary HDU Extension 1 Extension 2 Extension 3 etc… This will be the default data format for this course. See: http://fits.gsfc.nasa.gov/fits_standard.html “Cards” 2880-byte blocks 1 80 1 Header 2 3 4 Header … 36 KEYWORD = VALUE / [UNIT] COMMENT etc… Unused cards of last header block filled with spaces (ASCII 32) Binary table: Data Data or ‘Image’ Data Data etc… Unused bytes of last data block filled with spaces (ASCII 32)

  22. WCS keywords of FITS files • WCS stands for World Coordinate System. • http://fits.gsfc.nasa.gov/fits_wcs.html • What they’re for: pixellated data – ie samples of some quantity on a regular grid. • WCS keywords define the mapping between the pixel index and a world coordinate system. • Eg: a 2d image of the sky. We want to know which sky direction the (j,k)th pixel corresponds to.

  23. Eg, projection onto a tangent plane. WCS must encode the relation between θ and the pixel number. Pixel grid on tangent plane θ

  24. WCS example continued w- wref The general formula in this case is (p - pref) * scale = tan(w – wref). p is the pixel coordinate and w the world coordinate. w might eg be right ascension or declination. p- pref • WCS must describe 4 things: • pref • wref • scale • the nature of the functional relation. • Perhaps also world units. Note: (1) pref can be real-valued; (2) By convention, p at the centre of the 1st pixel = 1.0.

  25. WCS keywords for array extensions • In what follows, n is an integer, corresponding to one of the dimensions of the array. • CRVALn – wref. • CRPIXn – pref. • CDELTn – scale. • CTYPEn – an 8-character string encoding the function type (eg ‘TAN---RA’). There is an agreed list of these. • CUNITn – string encoding the unit of w (eg ‘deg’). Also an agreed list. • In addition, rotated coordinate systems can be defined via either addingPCi_j keywords to the above scheme, or replacing CDELTn by CDi_j keywords. But I don’t want to get too deeply into this. • Analogous (starting with T) WCS keywords are also defined for table columns.

  26. FITS interfaces and software: • CFITSIO • Aging, user-unfriendly interface to FITS. Most more modern interfaces are built on top of it though. • FTOOLS (from HEASARC web site) • Very useful set of tools for doing everything you can think of to or with FITS-format data. • But (Feb 2010: were) too many tasks bundled into the one large, not-very-portable package. • DS9 • A very useful, compact, portable FITS image viewer.

  27. A pyfits trap: • On the nassp computers, pyfits does not return numpy arrays, it returns numarray arrays. So if you do eg • you will get an error. A workaround is: • And similar for reading files – convert them using numpy.array(). >>>import numpy as nu >>>import pyfits as pf >>>fred = nu.zeros([3,3],nu.float) >>>hdu = pf.PrimaryHDU(fred) >>>import numarray as old_nu >>> hdu = pf.PrimaryHDU(old_nu.array(fred))

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