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:

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#!/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:

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#!/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

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#!/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

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#!/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