I have a matrix, e.g., generated as follows
x = np.random.randint(10,size=(20,20))
How to visualize the matrix with respect to the distribution of a given value, i.e., 6 In other words, how to show the matrix as an image, where the pixels with corresponding matrix entries being equivalent to 6 will be shown as white, while other pixels will be shown as black.
The simplest way to display the distribution of a given value through a black and white image is using a boolean array like x == 6. If you wish to improve visualization by replacing black and white with custom colors, NumPy's where will come in handy:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(10, size=(20, 20))
value = 6
foreground = [255, 0, 0] # red
background = [0, 0, 255] # blue
bw = x == value
rgb = np.where(bw[:, :, None], foreground, background)
fig, ax = plt.subplots(1, 2)
ax[0].imshow(bw, cmap='gray')
ax[0].set_title('Black & white')
ax[1].imshow(rgb)
ax[1].set_title('RGB')
plt.show(fig)
I think you want this:
from PIL import Image
import numpy as np
# Initialise data
x = np.random.randint(10,size=(20,20),dtype=np.uint8)
# Make all pixels with value 6 into white and all else black
x[x==6] = 255
x[x!=255] = 0
# Make PIL Image from Numpy array
pi = Image.fromarray(x)
# Display image
pi.show()
# Save PIL Image
pi.save('result.png')
