Iterating and Indexing in Nested Lists for Data Selection : #High_Performance_Python(Post_1)

Often in image processing, we encounter image data organized in nested lists. A notable example is the image data titled 'dtrain123.dat', available at the link 

http://www0.cs.ucl.ac.uk/staff/M.Herbster/SL/misc/

To effectively visualize such data, we use nested loops to traverse and manipulate these complex data structures. Let's start with a 'toy' example to illustrate the basic concept and its output. Following that, I'll provide the actual code needed to display the image data from 'dtrain123.dat'.

Code :

x = np.arange(10, 20)
y = np.array([0, 1, 2, 3, 0, 2, 1, 3, 0, 2])
print(x, y)

index = [np.argwhere(y == i).flatten() for i in range(4)]
print(index)

for i in range(4):
    if len(index[i]) >= 3: 
        img_index = index[i][2] 
        print(f"Category {i}, Index: {img_index}, 

           Value in x: {x[img_index]}")

The code creates an 'index' list that maps each category (0 to 3) to its occurrences in the array `y` using `np. argwhere(y == i).flatten()`. It then iterates over these categories, ensuring at least three instances exist to avoid indexing errors. For each qualifying category, it retrieves the index of the third occurrence from `index[i][2]` and uses this to access the related value in the `x` array.

This same logic is applied to the image data.

img_indices = [np.argwhere(y == i) for i in range(10)] 
plt.figure(figsize=(10, 3))
for i in range(10):
        plt.subplot(2, 5, i + 1)
        img_index = img_indices[i][2, 0]
        print(img_index)
        plt.imshow(x[img_index, :].reshape(16, 16))
        plt.axis('off')

The desired output is :