CODEWITHSUNDEEP
pythonnumpy
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Reputation: 199

Python: Mapping between two arrays with an index array

I have a numpy array 

src = np.random.rand(320,240)

and another numpy array idx of size (2 x (320*240)). Each column of idx indexes an entry in a result array dst, e.g., idx[:,20] = [3,10] references row 3, column 10 in dst and the assumption is that 20 corresponds to the flattened index of src, i.e., idx establishes a mapping between the entries of src and dst. Assuming dst is initialized with all zeros, how can I copy the entries in src to their destination in dst without a loop?

Upvotes: 4

Views: 1218

Answers (2)

Paul Panzer
Paul Panzer

Reputation: 53029

Here is the canonical way of doing it:

>>> import numpy as np
>>> 
>>> src = np.random.rand(4, 3)
>>> src
array([[0.0309325 , 0.72261479, 0.98373595],
       [0.06357406, 0.44763809, 0.45116039],
       [0.63992938, 0.6445605 , 0.01267776],
       [0.76084312, 0.61888759, 0.2138713 ]])
>>> 
>>> idx = np.indices(src.shape).reshape(2, -1)
>>> np.random.shuffle(idx.T)
>>> idx
array([[3, 3, 0, 1, 0, 3, 1, 1, 2, 2, 2, 0],
       [1, 2, 2, 0, 1, 0, 1, 2, 2, 1, 0, 0]])
>>> 
>>> dst = np.empty_like(src)
>>> dst[tuple(idx)] = src.ravel()
>>> dst
array([[0.2138713 , 0.44763809, 0.98373595],
       [0.06357406, 0.63992938, 0.6445605 ],
       [0.61888759, 0.76084312, 0.01267776],
       [0.45116039, 0.0309325 , 0.72261479]])

If you can't be sure that idx is a proper shuffle it's a bit safer to use np.full with a fill value that does not appear in src instead of np.empty.

>>> dst = np.full_like(src, np.nan)
>>> dst[tuple(idx)] = src.ravel()
>>> 
>>> dst
array([[0.27020869, 0.71216066,        nan],
       [0.63812283, 0.69151451, 0.65843901],
       [       nan, 0.02406174, 0.47543061],
       [0.05650845,        nan,        nan]])

If you spot the fill value in dst, something is wrong with idx.

Upvotes: 3

P. Camilleri
P. Camilleri

Reputation: 13218

You can try:

dst[idx[0, :], idx[1, :]] = src.flat

In [33]: src = np.random.randn(2, 3)

In [34]: src
Out[34]: 
array([[ 0.68636938,  0.60275041,  1.26078727],
       [ 1.17937849, -1.0369404 ,  0.42847611]])

In [35]: dst = np.zeros_like(src)

In [37]: idx = np.array([[0, 1, 0, 1, 0, 0], [1, 2, 0, 1, 2, 0]])

In [38]: dst[idx[0, :], idx[1, :]] = src.flat

In [39]: dst
Out[39]: 
array([[ 0.42847611,  0.68636938, -1.0369404 ],
       [ 0.        ,  1.17937849,  0.60275041]])

dst[0, 1] is src[0, 0], etc.

Upvotes: 1

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