Python Programming for Computational Physics: Conditionals, Loops, and I/O, Exercises of Computational Physics

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Python Programming

Computational Physics

Python Programming

Outline

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Useful Programming Tools

– Conditional Execution

– Loops

– Input/Output

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Python Scripts, Modules, and Packages

if...elif...else statement example

enter a value to test

X = input('Enter Value of X: ')

now we do the test with if statement

if X < 0: print 'X is less than 0!' elif X == 0: print 'X is zero!' elif X == 1: print 'X is one!' else: print 'X = ',X,' is not a special case'

another example of if...else

if X < 0 and X > -2: print 'X < 0 but X > -2 !' else: print 'X is not between -2 and 0.'

enter a value to test

X = input('Enter Value of X: ')

now we do the test with if statement

if X < 0: print 'X is less than 0!' elif X == 0: print 'X is zero!' elif X == 1: print 'X is one!' else: print 'X = ',X,' is not a special case'

another example of if...else

if X < 0 and X > -2: print 'X < 0 but X > -2 !' else: print 'X is not between -2 and 0.' If X is less than 0 we do this Otherwise, if X is equal To 0 we do this. == means “is equal to” Otherwise, if X is equal To 1 we do this. Otherwise, if X doesn't Match any of above, Condition can be any Then we do this.

Logical Statement

Loops

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Loops are a special type

of conditional execution.

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Loops iterate over a

sequence of items.

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In python, the items can

be any items in a list.

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We will often iterate

through the indices that

point to items in NumPy

arrays.

Item from sequence Execute Statement(s) on Item Ready for More Fun For iterating_var in sequence: statements No More Items In the Sequence Next Item

for loop example

enter an array for example

t = np.linspace(0.,1.,11)

use for look to iterate through array

for x in t: print x

loop on INDEX to the t array

for i in range(len(t)): print 'index = ',i, ' Value = ',t[i]

enter an array for example

t = np.linspace(0.,1.,11)

use for look to iterate through array

for x in t: print x

loop on INDEX to the t array

for i in range(len(t)): print 'index = ',i, ' Value = ',t[i] In this example, we consider each possible index in the t array. len(t) gives the number of elements. In this case len(t) = 11. range(t) makes a list starting at 0 that has len(t) elements. In this case: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Output is: index = 0 Value = 0. index = 1 Value = 0. index = 2 Value = 0. index = 3 Value = 0. index = 4 Value = 0. index = 5 Value = 0. index = 6 Value = 0. index = 7 Value = 0. index = 8 Value = 0. index = 9 Value = 0. index = 10 Value = 1.

while loops

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While loops continue to

execute the statements

in the loop as long as a

condition is True.

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Note that if statements

do not change the

condition, the loop will

continue forever.

Test Condition Execute Statement(s) Ready for More Fun while condition: statements False True

Input and Output

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Input

– We have already seen “input” in action.

X = input('Set the value of X: ')

– NumPy provides a simple way to read in a 2D

array of values: np.loadtxt(' filename ')

A = np.loadtxt('mydata.dat')

mydata.dat is a text fle with a 2D array

arranged in rows and columns. A will

be a NumPy array with the data

arranged in rows and columns.

Input and Output

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Input (continued)

– np.loadtxt can also read a csv text fle, such

as those made by Excel.

– In a csv fle, individual values are separated

by a “delimiter” ... often a , semicolon

(;) or comma (,)

A = np.loadtxt('mydata.csv', delimiter=';')

– Some rules about loadtxt:

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2D only

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All data of same type (as in NumPy)

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Number of items in each row must be the

same.

Input and Output

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NumPy loadtxt() and savetxt() are very

useful for quickly loading and saving

simple array data for our programs.

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There is an equivalent load() and save()

that deal with NumPy arrays in binary

form.

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Sometimes we need to read and write

data according to some more specifc

format. Maybe we want to mix types....

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We can do this by reading and writing

from fles.

File I/O

SEE: docs.python.org/2/tutorial/inputoutput.html

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Steps:

– Open the fle with open() method

– Read or Write to the fle with read() or write()

method

– Close the fle with close() method

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Open the fle “f.dat”

– For writing

F = open('f.dat','w')

– For reading

F = open('f.dat','r')

Read Example

open f.dat for reading

F = open('f.dat', 'r')

read all the lines

Z = F.readlines()

use print to show contents

for x in Z: print eval(x) F.close()

open f.dat for reading

F = open('f.dat', 'r')

read all the lines

Z = F.readlines()

use print to show contents

for x in Z: print eval(x) F.close() f.dat: (^) Open the file Read all the lines into an array named Z Print each element of Z after evaluating it Close the file Output:

6

Writing Data to File

F.write(s)

Writes the string s to open fle F.

Note that if you wish to write a number,

you must convert it to a string.

Let a be a float and b be an int:

Old Way: F.write( '%5.3f %4d '% (a,b) )

New Way:

F.write('{0:5.3f} {1:4d}'.format(a,b) )

5.3f => write float in 5 spaces with 3 digits after

the decimal point.

4d => write int in 4 spaces

Scripts, Modules, Packages

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We write “programs” in python using text

fles. We may distinguish:

– Scripts : a fle with a set of python

statements that we wish to run. It's the

same as typing them into ipython.

– Modules : a fle that defnes functions,

classes, and/or other variables that we want

to use with other pieces of python code.

– Packages : a package is a set of Modules

which are related and maintained together in

the same directory.

Why?

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We use Modules to try to stay organized.

Functions and classes are separate from

the scripts that call them.

– They can be used by MANY diffferent scripts

– We don't risk changing them as we edit new

scripts.

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Packages keep related Modules together.

– Keep individual modules from getting too big

to be easily maintained.

– Easy to gather the whole group together for

others to use.