I have an array A with shape (3,3). I want to identify all non-zero distinct elements and their indices. I present the expected output.
import numpy as np
A=np.array([[10,2,0],[2,20,1.3],[0,1.3,30]])
The expected output is
Distinct=[10,2,20,1.3,30]
Indices=[[(0,0)],[(0,1),(1,0)],[(1,1)],[(1,2),(2,1)],[(2,2)]]
Not the prettiest option perhaps, but this does the job.
import numpy as np
A=np.array([[10,2,0],[2,20,1.3],[0,1.3,30]])
Distinct=sorted(set(list(np.reshape(A,A.shape[0]*A.shape[1]))))
Distinct = [x for x in Distinct if x!=0]
Indices = [[] for x in Distinct]
for i,x in enumerate(Distinct):
for j in range(A.shape[0]):
for k in range(A.shape[1]):
if x==A[j,k]:
Indices[i].append((j,k))
Or a numpy solution, taking inspiration from Kilian's solution:
import numpy as np
A=np.array([[10,2,0],[2,20,1.3],[0,1.3,30]])
Distinct = np.unique(A[A!=0])
Indices = [np.argwhere(A==x) for x in Distinct]
Here you go!
import numpy as np
A = np.array([[10,2,0], [2,20,1.3], [0,1.3,30]])
indices = np.argwhere(A!=0)
distinct = np.unique(A[A!=0])
print(indices)
print(distinct)
[[0 0] [0 1] [1 0] [1 1] [1 2] [2 1] [2 2]] [ 1.3 2. 10. 20. 30. ] which doesn't match the expected output.