Assigning the same array indices immediately in Python / Numpy

Question

I want to find a quick way (without a loop) in Python to assign reoccuring array indices. This is the desired result using a for loop:

import numpy as np a=np.arange(9, dtype=np.float64).reshape((3,3)) # The array indices: [2,3,4] are identical. Px = np.uint64(np.array([0,1,1,1,2])) Py = np.uint64(np.array([0,0,0,0,0])) # The array to be added at the array indices (may also contain random numbers). x = np.array([.1,.1,.1,.1,.1]) for m in np.arange(len(x)): a[Px[m]][Py[m]] += x print a %[[ 0.1 1. 2.] %[ 3.3 4. 5.] %[ 6.1 7. 8.]] 

When I try to add x to a at the indices of Px,Py , I obviously do not get the same result (3.3 vs 3.1):

 a[Px,Py] += x print a %[[ 0.1 1. 2.] %[ 3.1 4. 5.] %[ 6.1 7. 8.]] 

Is there a way to do this with numpy? Thanks.

Answer 1

Yes, it can be done, but it is a bit complicated:

 # convert yourmulti-dim indices to flat indices flat_idx = np.ravel_multi_index((Px, Py), dims=a.shape) # extract the unique indices and their position unique_idx, idx_idx = np.unique(flat_idx, return_inverse=True) # Aggregate the repeated indices deltas = np.bincount(idx_idx, weights=x) # Sum them to your array a.flat[unique_idx] += deltas