Python:Various

This page collects some notes/hints about the use of the Python programming language.

Super

In object-oriented programming, one must sometimes call upon the parent class or super class.

In python, a given object (self) can refer to its parent as:

#!/usr/bin/python
# -*- coding: utf-8 -*-

class Cube(Platonic):

    def __init__(self, args={}):
        super(Cube, self).__init__( args=args )

Also note that you can exploit * and ** to pass arguments along:

#!/usr/bin/python
class Foo(object):
    def __init__(self, value1, value2):
        # do something with the values
        print value1, value2

class MyFoo(Foo):
    def __init__(self, *args, **kwargs):
        # do something else, don't care about the args
        print 'myfoo'
        super(MyFoo, self).__init__(*args, **kwargs)

In Python 3+, you can simply use 'super()' instead of 'super(MyFoo)'.

Matrix

Multiply matrix/array/grid by vector

#!/usr/bin/python
import numpy as np

# 2D example
size = 11
extent = 1.0
#axis_x = np.linspace( -extent, +extent, size )
#axis_y = np.linspace( -extent, +extent, size )
X, Y = np.meshgrid( axis_x, axis_y ) # Example 2D arrays

v = np.asarray( [ np.linspace( 0, 1, size ) ] )

print X*v               # Multiplies across row (x-direction)
print X*v.transpose()   # Multiplies down columns (y-direction)


# 3D example
size = 3
extent = 1.0
X, Y, Z = np.mgrid[ -extent:+extent:size*1j , -extent:+extent:size*1j , -extent:+extent:size*1j ] # Example 3D arrays

# Example vectors we want to multiply with
u = np.linspace( 1, 2, size ).reshape(size,1,1) # Multiplies down layers (z-direction)
v = np.linspace( 1, 2, size ).reshape(1,size,1) # Multiplies down column (y-direction)
w = np.linspace( 1, 2, size ).reshape(1,1,size) # Multiplies across row (x-direction)


print X
print '--'
print u
print X*u
print '--'
print v
print X*v
print '--'
print w
print X*w    

Linear Regression

#!/usr/bin/python3
data = [ [0, 1], [10,11.1], [20,17.5] ]

import numpy as np
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(np.asarray(data)[:,0], np.asarray(data)[:,1])
print("m = {:.3f} ; b = {:.3f} [R^2 = {:.2f}]".format(slope, intercept, r_value))

See Also

• Python:Indexing