numpy: minimal absolute value above threshold-CodePudding

From a multidimensional matrix I like to have the smallest absolute value above a tolerance value.

import numpy as np
np.random.seed(0)
matrix = np.random.randn(5,1,10)
tolerance = 0.1
np.amin(np.abs(matrix), axis=-1)
# array([[0.10321885],
#        [0.12167502],
#        [0.04575852],  # <- should not appear, as below tolerance
#        [0.15494743],
#        [0.21274028]])

Above code returns the absolute minimum over the last dimension. But I'd like to ignore small values (near 0) from determining the minimum. So in my example with tolerance = 0.1 the third row should contain the second smallest value.

With matrix[np.abs(matrix) >= tolerance] I can select values above tolerance but this flattens the array and therefore np.amin(...) cannot determine the minimum for the last dimension any more.

CodePudding user response：

You can replace the values smaller than 0.1 by for example 1, using np.where:

np.where(np.abs(matrix)< 0.1,1,np.abs(matrix))

Then apply np.amin on top :

np.amin(np.where(np.abs(matrix)< 0.1,1,np.abs(matrix)),axis=-1)

Result:

array([[0.10321885],
       [0.12167502],
       [0.18718385],
       [0.15494743],
       [0.21274028]])