How to Access Multidimensional Arrays with Fewer Dimensions?

Accessing Multidimensional Arrays with Fewer Dimensions

Consider an n-dimensional array, such as a, and an (n-1)-dimensional array, idx. To access a using idx along a given dimension, we can employ advanced indexing.

For a 3-dimensional array a, we can calculate the maximum values along the first dimension using idx as follows:

<code class="python">m, n = a.shape[1:]
I, J = np.ogrid[:m, :n]
a_max_values = a[idx, I, J]</code>

This approach can be generalized for arrays with any number of dimensions:

<code class="python">def argmax_to_max(arr, argmax, 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)]</code>

To index an n-dimensional array with an (n-1)-dimensional array, we can create a grid of indices for all axes:

<code class="python">def all_idx(idx, axis):
    grid = np.ogrid[tuple(map(slice, idx.shape))]
    grid.insert(axis, idx)
    return tuple(grid)</code>

Using this grid, we can index into the input arrays:

<code class="python">a_max_values = a[all_idx(idx, axis=axis)]
b_max_values = b[all_idx(idx, axis=axis)]</code>

This approach provides an elegant solution for accessing multidimensional arrays with fewer dimensions.

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