Assigning identical array indices at once in Python/Numpy
arrays, indices, numpy, python
Solution
Yes, it can be done, but it is a little tricky:
# 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
Problem
I want to find a fast way (without for loop) in Python to assign reoccuring indices of an array. 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 `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.