I would like to apply a mask all around the edges of an array, as example in a 3x3 array :
0 0 0
0 1 0
0 0 0
In stack i found this command, but i can t apply a second condition to get my specific array...
import numpy as np
np.logical_and.outer(np.arange(3) >= 2, np.arange(3) >= 2)
I get that :
0 0 0
0 0 0
0 0 1
You could always construct a mask to deselect the first and last rows/columns like this:
>>> mask = np.ones((3, 3), dtype=bool)
>>> mask
array([[True, True, True],
[True, True, True],
[True, True, True]], dtype=bool)
>>> mask[0], mask[-1], mask[:,0], mask[:,-1] = False, False, False, False
>>> mask
array([[ False, False, False],
[ False, True, False],
[ False, False, False]], dtype=bool)
You can construct the 2-d mask directly from a 1-d mask:
In [6]: np.logical_and.outer([0,1,0],[0,1,0])
Out[6]:
array([[False, False, False],
[False, True, False],
[False, False, False]], dtype=bool)
EDIT: For the generic case you can do something like:
In [11]: np.logical_and.outer([0]+[1]*3+[0],[0]+[1]*5+[0]).astype(int)
Out[11]:
array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]])
