Indexing NumPy arrays with Booleans

Boolean indexing is indexing based on a Boolean array and falls in the family of fancy indexing. Since Boolean indexing is a kind of fancy indexing, the way it works is essentially the same.

The following is the code for this segment (refer to boolean_indexing.py in this book's code bundle):

import scipy.misc 
import matplotlib.pyplot as plt 
import numpy as np 
 
face = scipy.misc.face() 
xmax = face.shape[0] 
ymax = face.shape[1] 
face=face[:min(xmax,ymax),:min(xmax,ymax)] 
 
def get_indices(size): 
   arr = np.arange(size) 
   return arr % 4 == 0 
 
face1 = face.copy()  
xindices = get_indices(face.shape[0]) 
yindices = get_indices(face.shape[1]) 
face1[xindices, yindices] = 0 
plt.subplot(211) 
plt.imshow(face1) 
face2 = face.copy()  
face2[(face > face.max()/4) & (face < 3 * face.max()/4)] = 0 
plt.subplot(212) 
plt.imshow(face2) 
plt.show() 

The preceding code implies that indexing occurs with the aid of a special iterator object.

The following steps will give you a brief explanation of the preceding code:

1. Image with dots on the diagonal.

   This is in some manner similar to the Fancy indexing section. This time we choose modulo 4 points on the diagonal of the picture:

             def get_indices(size):
              arr = np.arange(size)
              return arr % 4 == 0

   Then, we just use this selection and plot the points:

             face1 = face.copy()  
             xindices = get_indices(face.shape[0]) 
             yindices = get_indices(face.shape[1]) 
             face1[xindices, yindices] = 0 
             plt.subplot(211) 
             plt.imshow(face1) 
   

2. Set to 0 based on value.

   Select array values between one quarter and three quarters of the maximum value and set them to 0:

             face2[(face > face.max()/4) & (face < 3 * face.max()/4)] =  0 
   

   The diagram with the two new pictures is presented as follows: