I have a binary numpy 2D array, say,
import numpy as np
arr = np.array([
# Col 0 Col 1 Col 2
[False, False, True], # Row 0
[True, False, False], # Row 1
[True, True, False], # Row 2
])
I want the row and column of each True
element in the matrix:
[(0, 2), (1, 0), (2, 0), (2, 1)]
I know I can do this through iteration:
links = []
nrows, ncols = arr.shape
for i in xrange(nrows):
for j in xrange(ncols):
if arr[i, j]:
links.append((i, j))
Is there a faster or more intuitive way?
You are looking for np.argwhere
-
np.argwhere(arr)
Sample run -
In [220]: arr
Out[220]:
array([[False, False, True],
[ True, False, False],
[ True, True, False]], dtype=bool)
In [221]: np.argwhere(arr)
Out[221]:
array([[0, 2],
[1, 0],
[2, 0],
[2, 1]])
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