Assign zeros to minimum values in numpy 3d array

August 3, 2022

I have a numpy array of shape (100, 100, 20) (in python 3)

I want to find for each ‘pixel’ the 15 channels with minimum values, and make them zeros (meaning: make the array sparse, keep only the 5 highest values).

Example:

input: array = [[1,2,3], [7,6,9], [12,71,3]], num_channles_to_zero = 2

output: [[0,0,3], [0,0,9], [0,71,0]]

How can I do it?

what I have for now:

array = numpy.random.rand(100, 100, 20) 
inds = numpy.argsort(array, axis=-1) # also shape (100, 100, 20)

I want to do something like

array[..., inds[..., :15]] = 0

but it doesn’t give me what I want

>Solution :

np.argsort outputs indices suitable for the [...]_along_axis functions of numpy. This includes np.put_along_axis:

import numpy as np

array = np.random.rand(100, 100, 20) 
print(array[0,0])
#[0.44116124 0.94656705 0.20833932 0.29239585 0.33001399 0.82396784
# 0.35841905 0.20670957 0.41473762 0.01568006 0.1435386  0.75231818
# 0.5532527  0.69366173 0.17247832 0.28939985 0.95098187 0.63648877
# 0.90629116 0.35841627]

inds = np.argsort(array, axis=-1)
np.put_along_axis(array, inds[..., :15], 0, axis=-1)
print(array[0,0])
#[0.         0.94656705 0.         0.         0.         0.82396784
# 0.         0.         0.         0.         0.         0.75231818
# 0.         0.         0.         0.         0.95098187 0.
# 0.90629116 0.        ]