Index N Dimensional Array with (N-1) D Array
What Is the Most Elegant Way to Access an N Dimensional Array with an (N-1) Dimensional Array Along a Given Dimension as in the Dummy Example a = Np. Random...
What is the most elegant way to access an n dimensional array with an (n-1) dimensional array along a given dimension as in the dummy example
a = np.random.random_sample((3,4,4))
b = np.random.random_sample((3,4,4))
idx = np.argmax(a, axis=0)
How can I access now with idx a to get the maxima in a as if I had used a.max(axis=0)? or how to retrieve the values specified by idx in b?
I thought about using np.meshgrid but I think it is an overkill. Note that the dimension axis can be any usefull axis (0,1,2) and is not known in advance. Is there an elegant way to do this?
1 Answer
Make use of advanced-indexing -
m,n = a.shape[1:]
I,J = np.ogrid[:m,:n]
a_max_values = a[idx, I, J]
b_max_values = b[idx, I, J]
For the general case:
def argmax_to_max(arr, argmax, axis):
"""argmax_to_max(arr, arr.argmax(axis), axis) == arr.max(axis)"""
new_shape = list(arr.shape)
del new_shape[axis]
grid = np.ogrid[tuple(map(slice, new_shape))]
grid.insert(axis, argmax)
return arr[tuple(grid)]
Quite a bit more awkward than such a natural operation should be, unfortunately.
For indexing a n dim array with a (n-1) dim array, we could simplify it a bit to give us the grid of indices for all axes, like so -
def all_idx(idx, axis):
grid = np.ogrid[tuple(map(slice, idx.shape))]
grid.insert(axis, idx)
return tuple(grid)
Hence, use it to index into input arrays -
axis = 0
a_max_values = a[all_idx(idx, axis=axis)]
b_max_values = b[all_idx(idx, axis=axis)]
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