I am trying to do some matrix calculations with numpy and some sparse matrices. For that I want to ignore the zeros in my matrix and just access some values, but I also need to overwrite them.
import numpy as np
a=np.random.rand(5,5)
#does not change a:
a[[1,2],...][...,[1,2]]=np.array([[0,0],[0,0]])
#just changes (1,1) and (2,2)
a[[1,2],[1,2]]=np.array([0,0])
I would like to overwrite [1,1],[1,2],[2,1],[2,2] with zeros.
I think you need something like this:
import numpy as np
a = np.arange(25).reshape((5, 5))
i, j = np.ix_([1, 2], [1, 2])
a[i, j] = np.zeros((2, 2))
print(a)
# [[ 0 1 2 3 4]
# [ 5 0 0 8 9]
# [10 0 0 13 14]
# [15 16 17 18 19]
# [20 21 22 23 24]]
One general way for any given list of indices could be to simply loop over your index pairs where you want to assign 0
import numpy as np
a=np.random.rand(5,5)
indices = [[1,1],[1,2],[2,1],[2,2] ]
for i in indices:
a[tuple(i)] = 0
print (a)
array([[0.16014178, 0.68771817, 0.97822325, 0.30983165, 0.60145224],
[0.10440995, 0. , 0. , 0.09527387, 0.38472278],
[0.93199524, 0. , 0. , 0.11230965, 0.81220929],
[0.91941358, 0.96513491, 0.07891327, 0.43564498, 0.43580541],
[0.94158242, 0.78145344, 0.73241028, 0.35964791, 0.62645245]])
