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

lena = scipy.misc.lena()

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

lena1 = lena.copy() 
xindices = get_indices(lena.shape[0])
yindices = get_indices(lena.shape[1])
lena1[xindices, yindices] = 0
plt.subplot(211)
plt.imshow(lena1)
lena2 = lena.copy() 
lena2[(lena > lena.max()/4) & (lena < 3 * lena.max()/4)] = 0
plt.subplot(212)
plt.imshow(lena2)
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:

   lena1 = lena.copy() 
   xindices = get_indices(lena.shape[0])
   yindices = get_indices(lena.shape[1])
   lena1[xindices, yindices] = 0
   plt.subplot(211)
   plt.imshow(lena1)

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:

   lena2[(lena > lena.max()/4) & (lena < 3 * lena.max()/4)] = 0

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