Course May 18, Advanced Computational Physics. Course Hartmut Ruhl, LMU, Munich. People involved. SP in Python: 3 basic points

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1 May 18, I/O

2 3 I/O 3 I/O

3 3 ASC, room A 238, phone , hartmut.ruhl@lmu.de Patrick Böhl, ASC, room A205, phone , patrick.boehl@physik.uni-muenchen.de. I/O

4 Scientific programming in Python: 3 basic points 3 There are 3 (simplified) things you should keep in mind when considering to use Python. They are: 1. Python supports lazy programmers. 2. Python performance is generally poor. 3. Python can be bloody dangerous. I/O

5 Scientific programming in Python: The cheapest possible program in C: #include <stdio. h> i n t main ( ) { p r i n t f ( Hello World \n ) ; return 0; } And the same thing written in Python: p r i n t Hello World See point 1. 3 I/O

6 Scientific programming in Python: Python supports the usual basic datatypes: int, float, bool and string. The int datatype in Python has dynamic size, there is no maximum integer number. Float is equivalent to double in C. Bool and string work as expected from C. Usage of datatypes in Python is dynamic and handled by compiler. There is no explicit declaration and the type of a variable may change at almost any point within the program. See point 3. If forced conversion between types is required, functions int() and float() are provided. bacon =3.0 # f l o a t eggs=3 # i n t e g e r spam= spam # s t r i n g p r i n t eggs /10 # = 0 p r i n t bacon /10 # = 0.3 p r i n t spam+1 # undefined, terminates program p r i n t spam * 3 # = spamspamspam p r i n t f l o a t (spam ) # t h i s w i l l crash print f l o a t ( eggs ) # 3.0 eggs=spam # eggs i s now s t r i n g c o n t a i n i n g spam 3 I/O

7 Scientific programming in Python: Strings and lists Arrays are called lists in Python. Unlike most other languages, Python supports different datatypes within a single list. See point 3. List indices use the C convention, running from 0 to size-1. Like C++ STL vectors, Python lists come with a bunch of useful functions (see example below). A Python string can be treated as a list of characters, if desired. It has some additional functionality though. bacon = [ ] #empy l i s t of size 0 spam=[ spam ] * 10 # l i s t of size 10, c o n t a i n i n g spam eggs=range (10) # l i s t of integers from 0 to 9 sausage =[42.0, True, because we can ] # mixed l i s t eggs. s o r t ( ) # what i t says sausage. s o r t ( ) # t h i s a c t u a l l y works len ( sausage ) # returns number of elements in l i s t sausage. append ( 7 ) #adds 7 to end of sausage del sausage [ 1 ] # removes element [ 1 ] from l i s t, decreasing length s= Hello World s. count ( l ) # = 3 s. f i n d ( l l ) # =2, f i r s t p o s i t i o n of l l i n s t r i n g s=s. replace ( World, Spam ) s=s. upper ( ) # i n case you r e a l l y need t h a t capslock p r i n t s 3 I/O

8 Scientific programming in Python: Control statements As usual, Python code is executed from top to bottom. Unlike C, code is not compiled as a whole before execution. If your code contains an invalid expression, everything before that point will still be executed. See point 3. Comments use #, obviously. No multi-line comments like /*stuff*/ provided. A main function is not required, if you really want you can hack one in though. There is no goto. Python uses indentation to separate code levels instead of C style brackets like {this}. Hitting the tab key is not an aesthetic choice here. The usual while and for loops are provided, as well as if-elif-else. While works exactly as expected from C, but for and if got some new tricks (see below). The command break can be used to exit any of these. x =[3,6,19,21,71,101] for i in x : # takes value for i from specified array x i f ( i * 2==42): p r i n t gotcha, found, i break for i in range ( 1 0 ) : # somewhat more t r a d i t i o n a l f o r loop p r i n t i #from 0 to 9 i f (21 in x ) : # t h i s makes searching s t u f f r a t h e r t r i v i a l p r i n t gotcha again at index, x. index (21) 3 I/O

9 Scientific programming in Python: Python functions are call by value when passing single objects as arguments, but call by reference when passing lists. See point 3. These functions can return any number of values of any type. Both amount and type of function returns may be dynamically determined by passed arguments. See point 3. Multiple values are returned as list, which means individual values can by accessed via func()[i]. See example below for syntax of function definition. def func ( a ) : i f ( a>0): return ( a ) else : return ( spam, 7. 3 ) p r i n t func ( 1 ) # 1 p r i n t func ( 1) # ( spam, ) p r i n t func ( 1 ) [ 0 ] # spam p r i n t func ( 1 ) # 1, see point 3. 3 I/O

10 Scientific programming in Python: I/O Terminal output is accomplished by the print function. C style formating flags are available. User input is handled by input() and raw input(). The former tries to read the input as a number, the later treads all data as string. File handling for both formated and binary files is provided by open(). Optional arguments are rwab for read, write, append and binary. Default is r. As a nice bonus, Python can directly open network resources with urlopen(). import u r l l i b # required for urlopen below import os # required for f i l e removal p r i n t %4.2 f i s a %s number %(1.0e3 / 3. 0, long ) x=input ( some number : ) # t h i s w i l l crash when e n t e r i n g n i! y=raw input ( anything : ) # i n d e s t r u c t i b l e f i l e =open (. / s t u f f. dat, wa ) # create f i l e with w r i t e access f i l e. w r i t e ( y ) f i l e. close ( ) os. remove (. / s t u f f. dat ) # d e l e te f i l e, see p o i n t 3 f i l e = u r l l i b. urlopen ( h t t p : / / www. google. com ) content= f i l e. r e a d l i n e s ( ) # s t u f f s content of f i l e i n t o l i s t of s t r i n g s f i l e. close ( ) p r i n t content # the google html code, in t h i s case 3 I/O

11 Scientific programming in Python: are dynamic in Python as well. Objects inside a class do not have to be defined and can be created at any point. To avoid point 3, it is good practice to use the constructor function init () of the class to create all objects within. Unlike C structs, Python classes can contain functions without having to hack them in via pointers. The first argument to any class function is always self, which points to the object itself. This argument is mandatory even when not actually used inside the function. Output of python objects as formated strings is handled via the str () function supplied by the class. Do not expect favorable results if this function is not implemented. class somevector ( object ) : def i n i t ( s e l f ) : # t h a t s 2 underscores each s e l f. x =0.0 s e l f. y =0.0 s e l f. z =0.0 def s t r ( s e l f ) : return ( (%s %s %s ) %( s e l f. x, s e l f. y, s e l f. z ) ) a=somevector ( ) #what you intended b=somevector #b i s now copy of class d e f i n i t i o n a. x=3.0 #sets x in a to 3.0 b. x =3.0 # t h i n k about t h i s f o r a moment c=b ( ) #same as c=somevector ( ) p r i n t a # ( ) p r i n t b # <class m a i n. somevector > p r i n t c # ( ) 3 I/O

12 Scientific programming in Python: Additional Python packages for various functionalities can by used in your code with the import command. You have already seen some of those (urllib, os) in the examples. from packages are called with package.function(). Most of these are not part of the standard Python distribution. They are not hard to find though. math: Equivalent to cmath, math.h or whatever C math lib you use. numpy: High performance arrays, matrix manipulation, statistics, fourier transforms, polynomial roots, etc. Quite handy. scipy: If something you need is not in numpy, look here. pylab: Used for data visualization, can do graphs, histograms, etc. random: Random number generators 3 I/O

13 Scientific programming in Python: Example 1 The first example uses performance optimized numpy arrays to create a gauss curve and write data to both binary and formated files. import numpy import math var =1.0 x=numpy. arange ( 10,10,0.001) # fast array with fixed bounds and stepsize y = ( 1. 0 / ( var * math. s q r t ( 2. 0 * math. p i ) ) ) * numpy. exp ( 0.5 * x * x / var * * 2 ) #some curve # n o t i c e how we applied the exp f u n c t i o n to the whole array x at once x. t o f i l e ( gauss binary. dat ) # array to b inary f i l e y. t o f i l e ( g a u s s a s c i i. dat, sep= \n, format= %e ) # array to formated f i l e 3 I/O

14 Scientific programming in Python: Example 2 Here we read both datasets from the last example and plot one over the other. import numpy import pylab x=numpy. f r o m f i l e ( gauss binary. dat, dtype= f l o a t ) y=numpy. f r o m f i l e ( g a u s s a s c i i. dat, sep= \n ) pylab. p l o t ( x, y ) # p l o t data pylab. show ( ) # d i s p l a y p l o t 3 I/O

15 Scientific programming in Python: Example 3 This example creates a box shaped potential, Fourier transforms it and plots both graphs side by side. import pylab import numpy x=numpy. arange ( 10,10,0.001) #x array from 100 to 99, stepsize 0.01 y=numpy. z e r o s l i k e ( x ) # array l i k e x, but f i l l e d with 0.0 y [ ( x> 5.0) & ( x <5.0)] = 1.0 #above l i n e is equivalent to : # f o r i i n range ( y. s ize ) : # i f ( ( x [ i ]> 5.0) and ( x [ i ] <5.0) ) : # y [ i ]=1.0 pylab. subplot ( 2, 1, 1 ) # 2 columns, 1 row, 1 s t p l o t pylab. p l o t ( x, y ) pylab. ylim ( [ 0, 2 ] ) # y range of t h i s subplot f f t =numpy. f f t. f f t ( y ) # f o u r i e r transform of y f r e q =numpy. f f t. f f t f r e q ( len ( x ), d=0.001) # frequencies as array f o r x axis pylab. subplot ( 2, 1, 2 ) # 2 colums, 1 row, 2nd p l o t pylab. p l o t ( freq, numpy. abs ( f f t ) ) pylab. xlim ( [ 3,3]) # x range pylab. show ( ) 3 I/O

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