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
CodePudding user response:
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. ]
CodePudding user response:
As it mentioned in the numpy documentation
From each row, a specific element should be selected. The row index is just [0, 1, 2] and the column index specifies the element to choose for the corresponding row, here [0, 1, 0]. Using both together the task can be solved using advanced indexing:
>>>x = np.array([[1, 2], [3, 4], [5, 6]]) >>>x[[0, 1, 2], [0, 1, 0]] array([1, 4, 5])
So, for your example:
a = np.array([[1,2,3], [7,6,9], [12,71,3]])
amax = a.argmax(axis=-1)
a[np.arange(a.shape[0]), amax] = 0
a
array([[ 1, 2, 0],
[ 7, 6, 0],
[12, 0, 3]])