I have very sparse matrices, so I want to extract the smallest rectangular region of a matrix that has non-zero values. I know that numpy.nonzero(a) gives you the indices of the elements that are non-zero, but how can I use this to extract a submatrix that contains the elements of the matrix at those indices.
To give an example, this is what I am aiming for:
>>> test
array([[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 0]])
>>> np.nonzero(test)
(array([1, 1, 1, 1, 2, 2]), array([1, 2, 3, 4, 2, 3]))
>>> submatrix(test)
array([[1, 1, 1, 1],
[0, 1, 1, 0]])
Does anyone know a simple way to do this in numpy? Thanks.
It seems like you're looking to find the smallest region of your matrix that contains all the nonzero elements. If that's true, here's a method:
import numpy as np
def submatrix(arr):
x, y = np.nonzero(arr)
# Using the smallest and largest x and y indices of nonzero elements,
# we can find the desired rectangular bounds.
# And don't forget to add 1 to the top bound to avoid the fencepost problem.
return arr[x.min():x.max()+1, y.min():y.max()+1]
test = np.array([[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 0]])
print submatrix(test)
# Result:
# [[1 1 1 1]
# [0 1 1 0]]
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With