Extract 2d ndarray from arbitrarily dimensional ndarray using index arrays

Issue

This Content is from Stack Overflow. Question asked by deemel

I want to extract parts of an numpy ndarray based on arrays of index positions for some of the dimensions. Let me show this on an example

Example data

dummy = np.random.rand(5,2,100)
X = np.array([[0,1],[4,1],[2,0]])

dummy is the original ndarray with dimensionality 5x2x100. This dimensionality is arbitrary, it could as well be 5x2x4x100.
X is a matrix of index values, here X[:,0] are the indices of the first dimension of dummy, X[:,1] those of the second dimension. The number of columns in X is always the number of dimensions in dummy minus 1.

Example output

I want to extract an ndarray of the following form for this example

[
  dummy[0,1,:],
  dummy[4,1,:],
  dummy[2,0,:]
]

Complications

If the number of dimensions in dummy were fixed, this could just be done by dummy[X[:,0],X[:,1],:] . Sadly the dimensionality can be different, e.g. dummy could be a 5x2x4x6x100 ndarray and X correspondingly would then be 3×4 . My attempts at dealing with it have not yielded the desired result.

  • dummy[X,:] yields a 3x2x2x100 ndarray for this example same as dummy[X]
  • Iteratively reducing dummy by doing something like dummy = dummy[X[:,i],:] with i an iterator over the number of columns of X also does not reduce the ndarray in the example past 3x2x100

I have a feeling that this should be pretty simple with numpy indexing, but I guess my search for a solution was missing the right terms for this.
Does anyone have a solution to this?



Solution

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