使用可变长度索引向量在python中创建2D列表

Question

I am working on an image processing problem where I have code that looks like this (the code written below just illustrates the type of problem I want to solve):

for i in range(0,10):
  for j in range(0,10):
    number_length = round(random.random()*10)
    a = np.zeros(number_length)
    Z[i][j] = a

What I want to do is create some sort of 2D list or np.array (not really sure) where I essentially index a term for every pixel in an image, and have a vector/list of values for every individual pixel of which I can not anticipate its length, moreover, the length of each vector for every indexed pixel is different to each other. What is the best way to go about this?

In my MATLAB code the workaround is simple: I define a 2D cell and just assign any vector to any element in the 2D cell. Since cells do not complain about coherent length of every indexed vector, this is a good thing. What is the equivalent optimal solution to handle this in python?

Ideally the solution should not involve anticipating the maximum length of "a" for any pixel and to make all indexed vectors the same length (since this implies I have to do some sort of zero padding that will consume memory if the indexed vectors are high dimensional and these high dimensional vectors are sparse through out the image).

Answer 1

A NumPy array won't work because it requires fixed dimensions. You can use a 2d list (ie list of lists), where each element can be an array of arbitrary length. This is analogous to your setup in Matlab, using a 2d cell array of vectors.

Try this:

z = [[np.zeros(np.random.randint(10)+1) for j in range(10)] for i in range(10)]

This creates a 10x10 list, where z[i][j] is a NumPy array of zeros with random length (from 1 to 10).

Edit (nested loops requested in comment):

z = [[None for j in range(10)] for i in range(10)]

for i in range(len(z)):
    for j in range(len(z[i])):
        z[i][j] = np.zeros(np.random.randint(10)+1)