Given an array A of shape m x n x n
(i.e., a stack of square matrices),
I want an n x n array that gives the
minimum depth to a nonzero element.
E.g., the 0,0 element of the result is
np.flatnonzero(A[:,0,0])[0]
Can this be vectorized?
(Assuming a nonzero element exists is ok,
but dealing nicely
Hi Alan,
You can abuse np.argmax to calculate the first nonzero element in a
vectorized manner:
import numpy as np
A = (2 * np.random.rand(100, 50, 50)).astype(int)
Compare:
np.argmax(A != 0, axis=0)
np.array([[np.flatnonzero(A[:,i,j])[0] for j in range(50)] for i in
range(50)])
You'll also
I agree; argmax would the best option here; though I would hardly call it
abuse. It seems perfectly readable and idiomatic to me. Though the !=
comparison requires an extra pass over the array, that's the kind of
tradeoff you make in using numpy.
On Thu, Apr 17, 2014 at 7:45 PM, Stephan Hoyer
