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### Py tut-handout

1. 1. Introduction Python Tutorial Numerics & Plotting Standard library An introduction to the Python programming language Prabhu Ramachandran Department of Aerospace Engineering IIT Bombay 16, July 2007 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Outline 1 Introduction Introduction to Python 2 Python Tutorial Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous 3 Numerics & Plotting NumPy Arrays Plotting: Matplotlib SciPy 4 Standard library Quick Tour Prabhu Ramachandran Introduction to Python
2. 2. Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Introduction Creator and BDFL: Guido van Rossum BDFL == Benevolent Dictator For Life Conceived in December 1989 The name “Python”: Monty Python’s Flying Circus Current stable version of Python is 2.5.x PSF license (like BSD: no strings attached) Highly cross platform Runs on the Nokia series 60! Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Resources Available as part of any sane GNU/Linux distribution Web: http://www.python.org Documentation: http://www.python.org/doc Free Tutorials: Ofﬁcial Python tutorial: http://docs.python.org/tut/tut.html Byte of Python: http://www.byteofpython.info/ Dive into Python: http://diveintopython.org/ Prabhu Ramachandran Introduction to Python
3. 3. Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Why Python? High level, interpreted, modular, OO Easy to learn Easy to read code Much faster development cycle Powerful interactive interpreter Rapid application development Powerful standard library Interfaces well to C++, C and FORTRAN libraries In short: there is little you can’t do with it Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python A quote I came across Python and its Numerical extension in 1998 . . . I quickly fell in love with Python programming which is a remarkable statement to make about a programming language. If I had not seen others with the same view, I might have seriously doubted my sanity. – Travis Oliphant (creator of NumPy) Prabhu Ramachandran Introduction to Python
4. 4. Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Why not ***lab? Open Source, Free Portable Python is a real programming language: large and small programs Can do much more than just array and math Wrap large C++ codes Build large code bases via SCons Interactive data analysis/plotting Parallel application Job scheduling on a custom cluster Miscellaneous scripts Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Why not Python? Can be slow for high-performance applications This can be fairly easily overcome by using C/C++/FORTRAN extensions Prabhu Ramachandran Introduction to Python
5. 5. Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Use cases NASA: Python Streamlines Space Shuttle Mission Design AstraZeneca Uses Python for Collaborative Drug Discovery ForecastWatch.com Uses Python To Help Meteorologists Industrial Light & Magic Runs on Python Zope: Commercial grade CMS RedHat: install scripts, sys-admin tools Numerous success stories: http://www.pythonology.com/success Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Introduction to Python Before we begin This is only an introduction Python is a full-ﬂedged programming language Please read the tutorial It is very well written and can be read in one afternoon Prabhu Ramachandran Introduction to Python
6. 6. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Preliminaries: The Interpreter Python is interpreted Interpreted vs. Compiled Interpreted languages allow for rapid testing/prototyping Dynamically typed Full introspection of code at runtime Did I say dynamic? Does not force OO or a particular programming paradigm down your throat! Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Preliminaries . . . Will follow the Ofﬁcial Python tutorial No lexical blocking Indentation speciﬁes scope Leads to easier to read code! Prabhu Ramachandran Introduction to Python
7. 7. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Using the interpreter Starting up: python or ipython Quitting: Control-D or Control-Z (on Win32) Can use it like a calculator Can execute one-liners via the -c option: python -c "print ’hello world’" Other options via python -h Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous IPython Recommended interpreter, IPython: http://ipython.scipy.org Better than the default Python shell Supports tab completion by default Easier object introspection Shell access! Command system to allow extending its own behavior Supports history (across sessions) and logging Can be embedded in your own Python code Support for macros A ﬂexible framework for your own custom interpreter Other miscellaneous conveniences We’ll get back to this later Prabhu Ramachandran Introduction to Python
8. 8. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Basic IPython features Startup: ipython [options] files ipython [-wthread|-gthread|-qthread]: Threading modes to support wxPython, pyGTK and Qt ipython -pylab: Support for matplotlib TAB completion: Type object_name.<TAB> to see list of options Also completes on ﬁle and directory names object? shows docstring/help for any Python object object?? presents more docs (and source if possible) Debugging with %pdb magic: pops up pdb on errors Access history (saved over earlier sessions also) Use <UpArrow>: move up history Use <Ctrl-r> string: search history backwards Use Esc >: get back to end of history %run [options] file[.py] lets you run Python code Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Basic concepts Dynamically typed Assignments need not specify a type a = 1 a = 1.1 a = " foo " a = SomeClass ( ) Comments: a = 1 # In−l i n e comments # Comment in a l i n e to i t s e l f . a = "# This i s not a comment ! " Prabhu Ramachandran Introduction to Python
9. 9. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Basic concepts No lexical scoping Scope determined by indentation for i in range ( 1 0 ) : print " inside loop : " , # Do something in the loop . print i , i ∗ i print " loop i s done ! " # This i s outside the loop ! Assignment to an object is by reference Essentially, names are bound to objects Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Basic types Basic objects: numbers (ﬂoat, int, long, complex), strings, tuples, lists, dictionaries, functions, classes, types etc. Types are of two kinds: mutable and immutable Immutable types: numbers, strings, None and tuples Immutables cannot be changed “in-place” Mutable types: lists, dictionaries, instances, etc. Mutable objects can be “changed” Prabhu Ramachandran Introduction to Python
10. 10. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous What is an object? A loose and informal but handy description An object is a particular instance of a general class of things Real world example Consider the class of cars made by Honda A Honda Accord on the road, is a particular instance of the general class of cars It is an object in the general sense of the term Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Objects in a programming language The object in the computer follows a similar idea An object has attributes (or state) and behavior Object contains or manages data (attributes) and has methods (behavior) Together this lets one create representation of “real” things on the computer Programmers then create objects and manipulate them through their methods to get things done In Python everything is essentially an object – you don’t have to worry about it Prabhu Ramachandran Introduction to Python
11. 11. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Numbers >>> a = 1 # I n t . >>> l = 1000000L # Long >>> e = 1.01325e5 # f l o a t >>> f = 3.14159 # f l o a t >>> c = 1+1 j # Complex ! >>> print f ∗c / a (3.14159+3.14159 j ) >>> print c . real , c . imag 1.0 1.0 >>> abs ( c ) 1.4142135623730951 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Boolean >>> t = True >>> f = not t False >>> f or t True >>> f and t False Prabhu Ramachandran Introduction to Python
12. 12. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Strings s = ’ t h i s i s a s t r i n g ’ s = ’ This one has " quotes " inside ! ’ s = "The reverse with ’ single−quotes ’ inside ! " l = "A long s t r i n g spanning several l i n e s one more l i n e yet another " t = " " "A t r i p l e quoted s t r i n g does not need to be escaped at the end and " can have nested quotes " and whatnot . " " " Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Strings >>> word = " hello " >>> 2∗word + " world " # Arithmetic on s t r i n g s h e l l o h e l l o world >>> print word [ 0 ] + word [ 2 ] + word[ −1] hlo >>> # Strings are " immutable " . . . word [ 0 ] = ’H ’ Traceback ( most recent c a l l l a s t ) : F i l e "<stdin >" , l i n e 1 , in ? TypeError : object doesnt support item assignment >>> len ( word ) # The length of the s t r i n g 5 >>> s = u ’ Unicode s t r i n g s ! ’ # unicode s t r i n g >>> # Raw s t r i n g s ( note the leading ’ r ’ ) . . . raw_s = r ’A LaTeX s t r i n g \$ alpha nu\$ ’ Prabhu Ramachandran Introduction to Python
13. 13. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous More on strings >>> a = ’ hello world ’ >>> a . s t a r t s w i t h ( ’ h e l l ’ ) True >>> a . endswith ( ’ ld ’ ) True >>> a . upper ( ) ’HELLO WORLD’ >>> a . upper ( ) . lower ( ) ’ hello world ’ >>> a . s p l i t ( ) [ ’ hello ’ , ’ world ’ ] >>> ’ ’ . j o i n ( [ ’a ’ , ’b ’ , ’ c ’ ] ) ’ abc ’ >>> x , y = 1 , 1.234 >>> ’ x i s %s , y i s %s ’%(x , y ) ’ x i s 1 , y i s 1.234 ’ >>> # could also do : ’ x i s %d , y i s %f ’%(x , y ) See http://docs.python.org/lib/typesseq-strings.html Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Lists Lists are mutable Items are indexed at 0 Last element is -1 Length: len(list) Prabhu Ramachandran Introduction to Python
14. 14. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous List: examples >>> a = [ ’spam ’ , ’ eggs ’ , 100 , 1234] >>> a [ 0 ] ’spam ’ >>> a [ 3 ] 1234 >>> a[ −2] 100 >>> a[1: −1] [ ’ eggs ’ , 100] >>> a [ : 2 ] + [ ’ bacon ’ , 2∗2] [ ’spam ’ , ’ eggs ’ , ’ bacon ’ , 4] >>> 2∗a [ : 3 ] + [ ’Boe ! ’ ] [ ’spam ’ , ’ eggs ’ , 100 , ’spam ’ , ’ eggs ’ , 100 , ’Boe ! ’ ] Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Lists are mutable >>> a = [ ’spam ’ , ’ eggs ’ , 100 , 1234] >>> a [ 2 ] = a [ 2 ] + 23 >>> a [ ’spam ’ , ’ eggs ’ , 123 , 1234] >>> a = [ ’spam ’ , ’ eggs ’ , 100 , 1234] >>> a [ 0 : 2 ] = [1 , 12] # Replace some items >>> a [1 , 12 , 123 , 1234] >>> a [ 0 : 2 ] = [ ] # Remove some items >>> a [123 , 1234] Prabhu Ramachandran Introduction to Python
15. 15. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous List methods >>> a = [ ’spam ’ , ’ eggs ’ , 100 , 1234] >>> len ( a ) 4 >>> a . reverse ( ) >>> a [1234 , 100 , ’ eggs ’ , ’spam ’ ] >>> a . append ( [ ’ x ’ , 1 ] ) # L i s t s can contain l i s t s . >>> a [1234 , 100 , ’ eggs ’ , ’spam ’ , [ ’ x ’ , 1 ] ] >>> a . extend ( [ 1 , 2 ] ) # Extend the l i s t . >>> a [1234 , 100 , ’ eggs ’ , ’spam ’ , [ ’ x ’ , 1] , 1 , 2] Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Tuples >>> t = (0 , 1 , 2) >>> print t [ 0 ] , t [ 1 ] , t [ 2 ] , t [ −1] , t [ −2] , t [ −3] 0 1 2 2 1 0 >>> # Tuples are immutable ! . . . t [ 0 ] = 1 Traceback ( most recent c a l l l a s t ) : F i l e "<stdin >" , l i n e 1 , in ? TypeError : object doesn ’ t support item assignment Prabhu Ramachandran Introduction to Python
16. 16. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Dictionaries Associative arrays/mappings Indexed by “keys” (keys must be immutable) dict[key] = value keys() returns all keys of the dict values() returns the values of the dict has_key(key) returns if key is in the dict Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Dictionaries: example >>> t e l = { ’ jack ’ : 4098, ’ sape ’ : 4139} >>> t e l [ ’ guido ’ ] = 4127 >>> t e l { ’ sape ’ : 4139, ’ guido ’ : 4127, ’ jack ’ : 4098} >>> t e l [ ’ jack ’ ] 4098 >>> del t e l [ ’ sape ’ ] >>> t e l [ ’ i r v ’ ] = 4127 >>> t e l { ’ guido ’ : 4127, ’ i r v ’ : 4127, ’ jack ’ : 4098} >>> t e l . keys ( ) [ ’ guido ’ , ’ i r v ’ , ’ jack ’ ] >>> t e l . has_key ( ’ guido ’ ) True Prabhu Ramachandran Introduction to Python
17. 17. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Control ﬂow Control ﬂow is primarily achieved using the following: if/elif/else for while break, continue, else may be used to further control pass can be used when syntactically necessary but nothing needs to be done Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous If example x = i n t ( raw_input ( " Please enter an integer : " ) ) # raw_input asks the user f o r input . # i n t ( ) typecasts the r e s u l t i n g s t r i n g i n t o an i n t . i f x < 0: x = 0 print ’ Negative changed to zero ’ e l i f x == 0: print ’ Zero ’ e l i f x == 1: print ’ Single ’ else : print ’ More ’ Prabhu Ramachandran Introduction to Python
18. 18. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous If example >>> a = [ ’ cat ’ , ’ window ’ , ’ defenestrate ’ ] >>> i f ’ cat ’ in a : . . . print "meaw" . . . meaw >>> pets = { ’ cat ’ : 1 , ’ dog ’ :2 , ’ croc ’ : 10} >>> i f ’ croc ’ in pets : . . . print pets [ ’ croc ’ ] . . . 10 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous for example >>> a = [ ’ cat ’ , ’ window ’ , ’ defenestrate ’ ] >>> for x in a : . . . print x , len ( x ) . . . cat 3 window 6 defenestrate 12 >>> knights = { ’ gallahad ’ : ’ the pure ’ , . . . ’ robin ’ : ’ the brave ’ } >>> for k , v in knights . i t e r i t e m s ( ) : . . . print k , v . . . gallahad the pure robin the brave Prabhu Ramachandran Introduction to Python
19. 19. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous for and range example >>> for i in range ( 5 ) : . . . print i , i ∗ i . . . 0 0 1 1 2 4 3 9 4 16 >>> a = [ ’a ’ , ’b ’ , ’ c ’ ] >>> for i , x in enumerate ( a ) : . . . print i , x . . . 0 ’a ’ 1 ’b ’ 2 ’ c ’ Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous while example >>> # Fibonacci series : . . . # the sum of two elements defines the next . . . a , b = 0 , 1 >>> while b < 10: . . . print b . . . a , b = b , a+b . . . 1 1 2 3 5 8 Prabhu Ramachandran Introduction to Python
20. 20. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Functions Support default and keyword arguments Scope of variables in the function is local Mutable items are passed by reference First line after deﬁnition may be a documentation string (recommended!) Function deﬁnition and execution deﬁnes a name bound to the function You can assign a variable to a function! Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Functions: examples >>> def f i b ( n ) : # write Fibonacci series up to n . . . " " " P r i n t a Fibonacci series up to n . " " " . . . a , b = 0 , 1 . . . while b < n : . . . print b , . . . a , b = b , a+b . . . >>> # Now c a l l the function we j u s t defined : . . . f i b (2000) 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 >>> f = f i b # Assign a variable to a function >>> f (10) 1 1 2 3 5 8 Prabhu Ramachandran Introduction to Python
21. 21. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Functions: default arguments def ask_ok ( prompt , r e t r i e s =4 , complaint= ’Yes or no ! ’ ) : while True : ok = raw_input ( prompt ) i f ok in ( ’ y ’ , ’ ye ’ , ’ yes ’ ) : return True i f ok in ( ’n ’ , ’ no ’ , ’ nop ’ , ’ nope ’ ) : return False r e t r i e s = r e t r i e s − 1 i f r e t r i e s < 0: raise IOError , ’ bad user ’ print complaint Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Functions: keyword arguments def parrot ( voltage , state= ’a s t i f f ’ , action= ’voom ’ , type= ’ Norwegian Blue ’ ) : print "−− This parrot wouldn ’ t " , action , print " i f you put " , voltage , " Volts through i t . " print "−− Lovely plumage , the " , type print "−− I t ’ s " , state , " ! " parrot (1000) parrot ( action = ’VOOOOOM’ , voltage = 1000000) parrot ( ’a thousand ’ , state = ’ pushing up the daisies ’ ) parrot ( ’a m i l l i o n ’ , ’ bereft of l i f e ’ , ’ jump ’ ) Prabhu Ramachandran Introduction to Python
22. 22. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Modules Deﬁne variables, functions and classes in a ﬁle with a .py extension This ﬁle becomes a module! Modules are searched in the following: Current directory Standard: /usr/lib/python2.3/site-packages/ etc. Directories speciﬁed in PYTHONPATH sys.path: current path settings (from the sys module) The import keyword “loads” a module One can also use: from module import name1, name2, name2 where name1 etc. are names in the module, “module” from module import * — imports everything from module, use only in interactive mode Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Modules: example # −−− foo . py −−− some_var = 1 def f i b ( n ) : # write Fibonacci series up to n " " " P r i n t a Fibonacci series up to n . " " " a , b = 0 , 1 while b < n : print b , a , b = b , a+b # EOF >>> import foo >>> foo . f i b (10) 1 1 2 3 5 8 >>> foo . some_var 1 Prabhu Ramachandran Introduction to Python
23. 23. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Namespaces A mapping from names to objects Modules introduce a namespace So do classes The running script’s namespace is __main__ A modules namespace is identiﬁed by its name The standard functions (like len) are in the __builtin__ namespace Namespaces help organize different names and their bindings to different objects Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Exceptions Python’s way of notifying you of errors Several standard exceptions: SyntaxError, IOError etc. Users can also raise errors Users can create their own exceptions Exceptions can be “caught” via try/except blocks Prabhu Ramachandran Introduction to Python
24. 24. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Exception: examples >>> 10 ∗ ( 1 / 0 ) Traceback ( most recent c a l l l a s t ) : F i l e "<stdin >" , l i n e 1 , in ? ZeroDivisionError : integer d i v i s i o n or modulo by zero >>> 4 + spam∗3 Traceback ( most recent c a l l l a s t ) : F i l e "<stdin >" , l i n e 1 , in ? NameError : name ’spam ’ is not defined >>> ’2 ’ + 2 Traceback ( most recent c a l l l a s t ) : F i l e "<stdin >" , l i n e 1 , in ? TypeError : cannot concatenate ’ s t r ’ and ’ i n t ’ objects Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Exception: examples >>> while True : . . . try : . . . x = i n t ( raw_input ( " Enter a number : " ) ) . . . break . . . except ValueError : . . . print " I n v a l i d number , t r y again . . . " . . . >>> # To raise exceptions . . . raise ValueError , " your error message" Traceback ( most recent c a l l l a s t ) : F i l e "<stdin >" , l i n e 2 , in ? ValueError : your error message Prabhu Ramachandran Introduction to Python
25. 25. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes: the big picture Lets you create new data types Class is a template for an object belonging to that class Note: in Python a class is also an object Instantiating a class creates an instance (an object) An instance encapsulates the state (data) and behavior (methods) Allows you to deﬁne an inheritance hierarchy “A Honda car is a car.” “A car is an automobile.” “A Python is a reptile.” Programmers need to think OO Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes: what’s the big deal? Lets you create objects that mimic a real problem being simulated Makes problem solving more natural and elegant Easier to create code Allows for code-reuse Polymorphism Prabhu Ramachandran Introduction to Python
26. 26. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Class deﬁnition and instantiation Class deﬁnitions when executed create class objects Instantiating the class object creates an instance of the class class Foo ( object ) : pass # class object created . # Create an instance of Foo . f = Foo ( ) # Can assign an a t t r i b u t e to the instance f . a = 100 print f . a 100 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes . . . All attributes are accessed via the object.attribute syntax Both class and instance attributes are supported Methods represent the behavior of an object: crudely think of them as functions “belonging” to the object All methods in Python are “virtual” Inheritance through subclassing Multiple inheritance is supported No special public and private attributes: only good conventions object.public(): public object._private() & object.__priv(): non-public Prabhu Ramachandran Introduction to Python
27. 27. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes: examples class MyClass ( object ) : " " " Example class ( t h i s i s the class docstring ) . " " " i = 12345 # A class a t t r i b u t e def f ( s e l f ) : " " " This i s the method docstring " " " return ’ hello world ’ >>> a = MyClass ( ) # creates an instance >>> a . f ( ) ’ hello world ’ >>> # a . f ( ) i s equivalent to MyClass . f ( a ) . . . # This also explains why f has a ’ s e l f ’ argument . . . . MyClass . f ( a ) ’ hello world ’ Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes (continued) self is conventionally the ﬁrst argument for a method In previous example, a.f is a method object When a.f is called, it is passed the instance a as the ﬁrst argument If a method called __init__ exists, it is called when the object is created If a method called __del__ exists, it is called before the object is garbage collected Instance attributes are set by simply “setting” them in self Other special methods (by convention) like __add__ let you deﬁne numeric types: http://docs.python.org/ref/specialnames.html http://docs.python.org/ref/numeric-types.html Prabhu Ramachandran Introduction to Python
28. 28. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes: examples class Bag( MyClass ) : # Shows how to derive classes def __init__ ( s e l f ) : # called on object creation . s e l f . data = [ ] # an instance a t t r i b u t e def add ( self , x ) : s e l f . data . append ( x ) def addtwice ( self , x ) : s e l f . add ( x ) s e l f . add ( x ) >>> a = Bag ( ) >>> a . f ( ) # I n h e ri t e d method ’ hello world ’ >>> a . add ( 1 ) ; a . addtwice (2) >>> a . data [1 , 2 , 2] Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Derived classes Call the parent’s __init__ if needed If you don’t need a new constructor, no need to deﬁne it in subclass Can also use the super built-in function class AnotherBag (Bag ) : def __init__ ( s e l f ) : # Must c a l l parent ’ s __init__ e x p l i c i t l y Bag . __init__ ( s e l f ) # A l t e r n a t i v e l y use t h i s : super ( AnotherBag , s e l f ) . __init__ ( ) # Now setup any more data . s e l f . more_data = [ ] Prabhu Ramachandran Introduction to Python
29. 29. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Classes: polymorphism class Drawable ( object ) : def draw ( s e l f ) : # Just a s p e c i f i c a t i o n . pass class Square ( Drawable ) : def draw ( s e l f ) : # draw a square . class Circle ( Drawable ) : def draw ( s e l f ) : # draw a c i r c l e . class A r t i s t ( Drawable ) : def draw ( s e l f ) : for obj in s e l f . drawables : obj . draw ( ) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Stand-alone scripts Consider a ﬁle f.py: # ! / usr / bin / env python " " " Module l e v e l documentation . " " " # F i r s t l i n e t e l l s the s h e l l that i t should use Python # to i n t e r p r e t the code in the f i l e . def f ( ) : print " f " # Check i f we are running standalone or as module . # When imported , __name__ w i l l not be ’ __main__ ’ i f __name__ == ’ __main__ ’ : # This i s not executed when f . py i s imported . f ( ) Prabhu Ramachandran Introduction to Python
30. 30. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous More IPython features Input and output caching: In: a list of all entered input Out: a dict of all output _, __, __ are the last three results as is _N %hist [-n] macro shows previous history, -n suppresses line number information Log the session using %logstart, %logon and %logoff %run [options] file[.py] – running Python code %run -d [-b<N>]: debug script with pdb N is the line number to break at (defaults to 1) %run -t: time the script %run -p: Proﬁle the script %prun runs a statement/expression under the proﬁler %macro [options] macro_name n1-n2 n3-n4 n6 save speciﬁed lines to a macro with name macro_name Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous More IPython features . . . %edit [options] [args]: edit lines of code or ﬁle speciﬁed in editor (conﬁgure editor via \$EDITOR) %cd changes directory, see also %pushd, %popd, %dhist Shell access !command runs a shell command and returns its output files = %sx ls or files = !ls sets files to all result of the ls command %sx is quiet !ls \$files passes the files variable to the shell command %alias alias_name cmd creates an alias for a system command %colors lets you change the color scheme to NoColor, Linux, LightBG Prabhu Ramachandran Introduction to Python
31. 31. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous More IPython features . . . Use ; at the end of a statement to suppress printing output %who, %whos: print information on variables %save [options] filename n1-n2 n3-n4: save lines to a ﬁle %time statement: Time execution of a Python statement or expression %timeit [-n<N> -r<R> [-t|-c]] statement: time execution using Python’s timeit module Can deﬁne and use proﬁles to setup IPython differently: math, scipy, numeric, pysh etc. %magic: Show help on all magics Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous File handling >>> # Reading f i l e s : . . . f = open ( ’ / path / to / file_name ’ ) >>> data = f . read ( ) # Read e n t i r e f i l e . >>> l i n e = f . readline ( ) # Read one l i n e . >>> # Read e n t i r e f i l e appending each l i n e i n t o a l i s t . . . l i n e s = f . readlines ( ) >>> f . close ( ) # close the f i l e . >>> # Writing f i l e s : . . . f = open ( ’ / path / to / file_name ’ , ’w ’ ) >>> f . write ( ’ hello world n ’ ) tell(): returns int of current position seek(pos): moves current position to speciﬁed byte Call close() when done using a ﬁle Prabhu Ramachandran Introduction to Python
32. 32. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Math math module provides basic math routines for ﬂoats cmath module provides math routies for complex numbers random: provides pseudo-random number generators for various distributions These are always available and part of the standard library More serious math is provided by the NumPy/SciPy modules – these are not standard and need to be installed separately Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Timing and proﬁling Timing code: use the time module Read up on time.time() and time.clock() timeit: is a better way of doing timing IPython has handy time and timeit macros (type timeit? for help) IPython lets you debug and proﬁle code via the run macro (type run? on the prompt to learn more) Prabhu Ramachandran Introduction to Python
33. 33. Introduction Python Tutorial Numerics & Plotting Standard library Preliminaries Data types Control ﬂow, functions Modules, exceptions, classes Miscellaneous Odds and ends dir([object]) function: attributes of given object type(object): returns type information str(), repr(): convert object to string representation isinstance, issubclass assert statements let you do debugging assertions in code csv module: reading and writing CSV ﬁles pickle: lets you save and load Python objects (serialization) os.path: common path manipulations Check out the Python Library reference: http://docs.python.org/lib/lib.html Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy The numpy module Manipulating large Python lists for scientiﬁc computing is slow Most complex computations can be reduced to a few standard operations The numpy module provides: An efﬁcient and powerful array type for various common data types Abstracts out the most commonly used standard operations on arrays Numeric was the ﬁrst, then came numarray. numpy is the latest and is the future This course uses numpy and only covers the absolute basics Prabhu Ramachandran Introduction to Python
34. 34. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Basic concepts numpy arrays are of a ﬁxed size (arr.size) and have the same type (arr.dtype) numpy arrays may have arbitrary dimensionality The shape of an array is the extent (length) of the array along each dimension The rank(arr) of an array is the “dimensionality” of the array The arr.itemsize is the number of bytes (8-bits) used for each element of the array Note: The shape and rank may change as long as the size of the array is ﬁxed Note: len(arr) != arr.size in general Note: By default array operations are performed elementwise Indices start from 0 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Examples of numpy # Simple array math example >>> from numpy import ∗ >>> a = array ( [ 1 , 2 , 3 , 4 ] ) >>> b = array ( [ 2 , 3 , 4 , 5 ] ) >>> a + b # Element wise addition ! array ( [ 3 , 5 , 7 , 9 ] ) >>> print pi , e # Pi and e are defined . 3.14159265359 2.71828182846 # Create array from 0 to 10 >>> x = arange (0.0 , 10.0 , 0.05) >>> x ∗= 2∗ pi /10 # m u l t i p l y array by scalar value array ( [ 0 . , 0 . 0 3 1 4 , . . . , 6 . 2 5 2 ] ) # apply functions to array . >>> y = sin ( x ) Prabhu Ramachandran Introduction to Python
35. 35. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy More examples of numpy # Size , shape , rank , type etc . >>> x = array ( [ 1 . , 2 , 3 , 4 ] ) >>> size ( x ) 4 >>> x . dtype # or x . dtype . char ’d ’ >>> x . shape (4 ,) >>> print rank ( x ) , x . itemsize 1 8 >>> x . t o l i s t ( ) [ 1. 0 , 2.0 , 3.0 , 4.0] # Array indexing >>> x [ 0 ] = 10 >>> print x [ 0 ] , x[ −1] 10.0 4.0 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Multi-dimensional arrays >>> a = array ( [ [ 0 , 1 , 2 , 3] , . . . [10 ,11 ,12 ,13]]) >>> a . shape # ( rows , columns ) (2 , 4) # Accessing and s e t t i n g values >>> a [1 , 3 ] 13 >>> a [1 , 3 ] = −1 >>> a [ 1 ] # The second row array ([10 ,11 ,12 , −1]) # Flatten / ravel arrays to 1D arrays >>> a . f l a t # or ravel ( a ) array ([0 ,1 ,2 ,3 ,10 ,11 ,12 , −1]) # Note : f l a t references o r i g i n a l memory Prabhu Ramachandran Introduction to Python
36. 36. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Slicing arrays >>> a = array ( [ [ 1 , 2 , 3 ] , [4 ,5 ,6] , [ 7 , 8 , 9 ] ] ) >>> a [ 0 , 1 : 3 ] array ( [ 2 , 3 ] ) >>> a [ 1 : , 1 : ] array ( [ [ 5 , 6] , [8 , 9 ] ] ) >>> a [ : , 2 ] array ( [ 3 , 6 , 9 ] ) # S t r i d i n g . . . >>> a [ 0 : : 2 , 0 : : 2 ] array ( [ [ 1 , 3] , [7 , 9 ] ] ) # A l l these s l i c e s are references to the same memory ! Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Array creation functions array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0) arange(start, stop=None, step=1, dtype=None) linspace(start, stop, num=50, endpoint=True, retstep=False) ones(shape, dtype=None, order=’C’) zeros((d1,...,dn),dtype=float,order=’C’) identity(n) empty((d1,...,dn),dtype=float,order=’C’) ones_like(x), zeros_like(x), empty_like(x) Prabhu Ramachandran Introduction to Python
37. 37. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Array math Basic elementwise math (given two arrays a, b): a + b → add(a, b) a - b, → subtract(a, b) a * b, → multiply(a, b) a / b, → divide(a, b) a % b, → remainder(a, b) a ** b, → power(a, b) Inplace operators: a += b, or add(a, b, a) etc. Logical operations: equal (==), not_equal (!=), less (<), greater (>) etc. Trig and other functions: sin(x), arcsin(x), sinh(x), exp(x), sqrt(x) etc. sum(x, axis=0), product(x, axis=0): sum and product of array elements dot(a, b) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Advanced Only scratched the surface of numpy Ufunc methods: reduce, accumulate, outer, reduceat Typecasting More functions: take, choose, where, compress, concatenate Array broadcasting and None Prabhu Ramachandran Introduction to Python
38. 38. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy About matplotlib Easy to use, scriptable, “Matlab-like” 2D plotting Publication quality ﬁgures and interactive capabilities Plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc. Also does polar plots, maps, contours Support for simple TEX markup Multiple output backends (images, EPS, SVG, wx, Agg, Tk, GTK) Cross-platform: Linux, Win32, Mac OS X Good idea to use via IPython: ipython -pylab From scripts use: import pylab Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy More information More information here: http://matplotlib.sf.net http://matplotlib.sf.net/tutorial.html http://matplotlib.sf.net/screenshots.html Prabhu Ramachandran Introduction to Python
39. 39. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Basic plotting with matplotlib >>> x = arange (0 , 2∗pi , 0.05) >>> p l o t ( x , sin ( x ) ) # Same as p l o t ( x , sin ( x ) , ’b− ’) >>> p l o t ( x , sin ( x ) , ’ ro ’ ) >>> axis ([0 ,2∗ pi , −1 ,1]) >>> xlabel ( r ’\$ chi\$ ’ , color= ’g ’ ) >>> ylabel ( r ’ sin ( \$ chi\$ ) ’ , color= ’ r ’ ) >>> t i t l e ( ’A simple f i g u r e ’ , fo nts iz e =20) >>> savefig ( ’ / tmp / t e s t . eps ’ ) # M ul ti pl e plots in one f i g u r e >>> t = arange (0.0 , 5.2 , 0.2) # red dashes , blue squares and green t r i a n g l e s >>> p l o t ( t , t , ’ r−−’ , t , t ∗∗2 , ’ bs ’ , t , t ∗∗3 , ’g^ ’ ) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Basic plotting . . . # Set properties of objects : >>> p l o t ( x , sin ( x ) , l i n e w i d t h =2.0 , color= ’ r ’ ) >>> l , = p l o t ( x , sin ( x ) ) >>> setp ( l , l i n e w i d t h =2.0 , color= ’ r ’ ) >>> l . set_linewidth ( 2 . 0 ) ; l . set_color ( ’ r ’ ) >>> draw ( ) # Redraws current f i g u r e . >>> setp ( l ) # Prints available properties >>> close ( ) # Closes the f i g u r e . # M ul ti pl e figure s : >>> f i g u r e ( 1 ) ; p l o t ( x , sin ( x ) ) >>> f i g u r e ( 2 ) ; p l o t ( x , tanh ( x ) ) >>> f i g u r e ( 1 ) ; t i t l e ( ’ Easy as 1 ,2 ,3 ’ ) Prabhu Ramachandran Introduction to Python
40. 40. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Basic plotting . . . >>> f i g u r e (1) >>> subplot (211) # Same as subplot (2 , 1 , 1) >>> p l o t ( x , cos (5∗x )∗ exp(−x ) ) >>> subplot (2 , 1 , 2) >>> p l o t ( x , cos (5∗x ) , ’ r−−’ , label = ’ cosine ’ ) >>> p l o t ( x , sin (5∗x ) , ’g−−’ , label = ’ sine ’ ) >>> legend ( ) # Or legend ( [ ’ cosine ’ , ’ sine ’ ] ) >>> t e x t (1 ,0 , ’ (1 ,0) ’ ) >>> axes = gca ( ) # Current axis >>> f i g = gcf ( ) # Current f i g u r e Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy X-Y plot Example code t1 = arange (0.0 , 5.0 , 0.1) t2 = arange (0.0 , 5.0 , 0.02) t3 = arange (0.0 , 2.0 , 0.01) subplot (211) p l o t ( t1 , cos(2∗ pi∗t1 )∗exp(−t1 ) , ’ bo ’ , t2 , cos(2∗ pi∗t2 )∗exp(−t2 ) , ’ k ’ ) grid ( True ) t i t l e ( ’A t a l e of 2 subplots ’ ) ylabel ( ’Damped ’ ) subplot (212) p l o t ( t3 , cos(2∗ pi∗t3 ) , ’ r−−’ ) grid ( True ) xlabel ( ’ time ( s ) ’ ) ylabel ( ’Undamped ’ ) Prabhu Ramachandran Introduction to Python
41. 41. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Errorbar Example code t = arange (0.1 , 4 , 0.1) s = exp(−t ) e = 0.1∗abs ( randn ( len ( s ) ) ) f = 0.1∗abs ( randn ( len ( s ) ) ) g = 2∗e h = 2∗f errorbar ( t , s , [ e , g ] , f , fmt= ’o ’ ) xlabel ( ’ Distance (m) ’ ) ylabel ( ’ Height (m) ’ ) t i t l e ( ’Mean and standard error ’ ’ as a function of distance ’ ) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Semi-log and log-log plots Example code dt = 0.01 t = arange ( dt , 20.0 , dt ) subplot (311) semilogy ( t , exp(−t / 5 . 0 ) ) ylabel ( ’ semilogy ’ ) grid ( True ) subplot (312) semilogx ( t , sin (2∗ pi∗t ) ) ylabel ( ’ semilogx ’ ) grid ( True ) # minor grid on too gca ( ) . xaxis . grid ( True , which= ’ minor ’ ) subplot (313) loglog ( t , 20∗exp(−t / 1 0 . 0 ) , basex=4) grid ( True ) ylabel ( ’ loglog base 4 on x ’ ) Prabhu Ramachandran Introduction to Python
42. 42. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Histogram Example code mu, sigma = 100 , 15 x = mu + sigma∗randn (10000) # the histogram of the data n , bins , patches = h i s t ( x , 100 , normed=1) # add a ’ best f i t ’ l i n e y = normpdf ( bins , mu, sigma ) l = p l o t ( bins , y , ’ r−−’ , l i n e w i d t h =2) xlim (40 , 160) xlabel ( ’ Smarts ’ ) ylabel ( ’P ’ ) t i t l e ( r ’\$ rm{ IQ : } / mu=100 ,/ sigma=15\$ ’ ) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Bar charts Example code N = 5 menMeans = (20 , 35 , 30 , 35 , 27) menStd = ( 2 , 3 , 4 , 1 , 2) # the x locations f o r the groups ind = arange (N) # the width of the bars width = 0.35 p1 = bar ( ind , menMeans, width , color= ’ r ’ , yerr=menStd ) womenMeans = (25 , 32 , 34 , 20 , 25) womenStd = ( 3 , 5 , 2 , 3 , 3) p2 = bar ( ind+width , womenMeans, width , color= ’ y ’ , yerr=womenStd) ylabel ( ’ Scores ’ ) t i t l e ( ’ Scores by group and gender ’ ) x t i c k s ( ind+width , ( ’G1 ’ , ’G2 ’ , ’G3 ’ , ’G4 ’ , ’G5 ’ ) ) xlim(−width , len ( ind ) ) y t i c k s ( arange (0 ,41 ,10)) legend ( ( p1 [ 0 ] , p2 [ 0 ] ) , ( ’Men ’ , ’Women ’ ) , shadow=True ) Prabhu Ramachandran Introduction to Python
43. 43. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Pie charts Example code # make a square f i g u r e and axes f i g u r e (1 , f i g s i z e =(8 ,8)) ax = axes ( [ 0 . 1 , 0.1 , 0.8 , 0 . 8 ] ) labels = ’ Frogs ’ , ’Hogs ’ , ’Dogs ’ , ’ Logs ’ fracs = [15 ,30 ,45 , 10] explode =(0 , 0.05 , 0 , 0) pie ( fracs , explode=explode , labels=labels , autopct= ’ %1.1 f%%’ , shadow=True ) t i t l e ( ’ Raining Hogs and Dogs ’ , bbox={ ’ facecolor ’ : ’ 0.8 ’ , ’ pad ’ : 5 } ) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Scatter plots Example code N = 30 x = 0.9∗rand (N) y = 0.9∗rand (N) # 0 to 10 point radiuses area = pi ∗(10 ∗ rand (N))∗∗2 volume = 400 + rand (N)∗450 scat ter ( x , y , s=area , marker= ’o ’ , c=volume , alpha =0.75) xlabel ( r ’\$ Delta_i\$ ’ , size= ’ x−large ’ ) ylabel ( r ’\$ Delta_ { i +1}\$ ’ , size= ’ x−large ’ ) t i t l e ( r ’ Volume and percent change ’ ) grid ( True ) colorbar ( ) savefig ( ’ scatt er ’ ) Prabhu Ramachandran Introduction to Python
44. 44. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Polar Example code f i g u r e ( f i g s i z e =(8 ,8)) ax = axes ( [ 0 . 1 , 0.1 , 0.8 , 0 . 8 ] , polar=True , axisbg= ’#d5de9c ’ ) r = arange (0 ,1 ,0.001) theta = 2∗2∗pi∗r polar ( theta , r , color= ’#ee8d18 ’ , lw =3) # the radius of the grid labels setp ( ax . thetagridlabels , y=1.075) t i t l e ( r "\$ theta =4 pi r " , fo nts iz e =20) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Contours Example code x = arange( −3.0 , 3.0 , 0.025) y = arange( −2.0 , 2.0 , 0.025) X, Y = meshgrid ( x , y ) Z1 = bivariate_normal (X, Y, 1.0 , 1.0 , 0.0 , 0.0) Z2 = bivariate_normal (X, Y, 1.5 , 0.5 , 1 , 1) # difference of Gaussians Z = 10.0 ∗ (Z2 − Z1) im = imshow (Z , i n t e r p o l a t i o n = ’ b i l i n e a r ’ , o r i g i n = ’ lower ’ , cmap=cm. gray , extent =(−3,3,−2,2)) leve ls = arange( −1.2 , 1.6 , 0.2) # label every second l e v e l clabel (CS, levels [ 1 : : 2 ] , i n l i n e =1 , fmt= ’ %1.1 f ’ , f o n ts iz e =14) CS = contour (Z , levels , o r i g i n = ’ lower ’ , linewidths =2 , extent =(−3,3,−2,2)) # make a colorbar f o r the contour l i n e s CB = colorbar (CS, shrink =0.8 , extend= ’ both ’ ) t i t l e ( ’ Lines with colorbar ’ ) hot ( ) ; f l a g ( ) Prabhu Ramachandran Introduction to Python
45. 45. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Velocity vectors Example code X,Y = meshgrid ( arange (0 ,2∗ pi , . 2 ) , arange (0 ,2∗ pi , . 2 ) ) U = cos (X) V = sin (Y) Q = quiver (X [ : : 3 , : : 3 ] , Y [ : : 3 , : : 3 ] , U[ : : 3 , : : 3 ] , V [ : : 3 , : : 3 ] , color= ’ r ’ , units = ’ x ’ , linewidths =(2 ,) , edgecolors =( ’ k ’ ) , headaxislength=5 ) qk = quiverkey (Q, 0.5 , 0.03 , 1 , ’1 m/ s ’ , f o n t p r o p e r t i e s = { ’ weight ’ : ’ bold ’ } ) axis ([ −1 , 7 , −1, 7 ] ) t i t l e ( ’ t r i a n g u l a r head ; scale ’ ’ with x view ; black edges ’ ) Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Maps For details see http://matplotlib.sourceforge.net/screenshots/plotmap.py Prabhu Ramachandran Introduction to Python
46. 46. Introduction Python Tutorial Numerics & Plotting Standard library NumPy Arrays Plotting: Matplotlib SciPy Using SciPy SciPy is Open Source software for mathematics, science, and engineering import scipy Built on NumPy Provides modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, genetic algorithms, ODE solvers, special functions, and more Used widely by scientists world over Details are beyond the scope of this tutorial Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Standard library Very powerful “Batteries included” Example standard modules taken from the tutorial Operating system interface: os System, Command line arguments: sys Regular expressions: re Math: math, random Internet access: urllib2, smtplib Data compression: zlib, gzip, bz2, zipfile, and tarfile Unit testing: doctest and unittest And a whole lot more! Check out the Python Library reference: http://docs.python.org/lib/lib.html Prabhu Ramachandran Introduction to Python
47. 47. Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Stdlib: examples >>> import os >>> os . system ( ’ date ’ ) F r i Jun 10 22:13:09 IST 2005 0 >>> os . getcwd ( ) ’ /home/ prabhu ’ >>> os . chdir ( ’ / tmp ’ ) >>> import os >>> d i r ( os ) <returns a l i s t of a l l module functions > >>> help ( os ) <extensive manual page from module ’ s docstrings > Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Stdlib: examples >>> import sys >>> # P r i n t the l i s t of command l i n e args to Python . . . print sys . argv [ ’ ’ ] >>> import re # Regular expressions >>> re . f i n d a l l ( r ’ bf [ a−z ]∗ ’ , . . . ’ which foot or hand f e l l f a s t e s t ’ ) [ ’ foot ’ , ’ f e l l ’ , ’ f a s t e s t ’ ] >>> re . sub ( r ’ ( b [ a−z ] + ) 1 ’ , r ’ 1 ’ , . . . ’ cat in the the hat ’ ) ’ cat in the hat ’ Prabhu Ramachandran Introduction to Python
48. 48. Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Stdlib: examples >>> import math >>> math . cos ( math . pi / 4.0) 0.70710678118654757 >>> math . log (1024 , 2) 10.0 >>> import random >>> random . choice ( [ ’ apple ’ , ’ pear ’ , ’ banana ’ ] ) ’ pear ’ Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Stdlib: examples >>> import u r l l i b 2 >>> f = u r l l i b 2 . urlopen ( ’ http : / /www. python . org / ’ ) >>> print f . read (100) <!DOCTYPE html PUBLIC " −//W3C/ / DTD HTML 4.01 T r a n s i t i o n a l / <?xml−stylesheet href=" . / css / ht2html Prabhu Ramachandran Introduction to Python
49. 49. Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Stdlib: examples >>> import z l i b >>> s = ’ witch which has which witches w r i s t watch ’ >>> len ( s ) 41 >>> t = z l i b . compress ( s ) >>> len ( t ) 37 >>> z l i b . decompress ( t ) ’ witch which has which witches w r i s t watch ’ >>> z l i b . crc32 ( t ) −1438085031 Prabhu Ramachandran Introduction to Python Introduction Python Tutorial Numerics & Plotting Standard library Quick Tour Summary Introduced Python Basic syntax Basic types and data structures Control ﬂow Functions Modules Exceptions Classes Standard library Prabhu Ramachandran Introduction to Python