I advise you to do some of the previous katas from the: Neighbourhood collection.

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This is the next step. We are going multidimensional. You should build a function that return the neighbourhood of a cell within a matrix with the given distance and type. AND it should work for any dimension.

Input

• type "moore" or "von_neumann"
• matrix a N-dimensional matrix. N >= 0 (the matrix is always rectangular; could be a list or a tuple)
• coordinates of the cell. It is a N-length tuple. Order of the indices: The first index should be applied for the outer/first matrix layer. The last index for the most inner/last layer. coordinates = (m, n, k) should be apllied like mat[m][n][k]
• distance

Task

construct get_neighbourhood(n_type, matrix, coordinates, distance)

Ouput

The N dimension neighbours of the cell

Performance

Your code should be performant.

• There will be two Tests with a very highdimensional matrix
• There will be 30 Tests with 9 <= distance <= 12
• There will be 200 Tests with N == 4, Dimension size == 5
• There will be 30 Tests with N == 8, Dimension size == 3

Note: you should return an empty array if any of these conditions is true:

• Index is outside the matrix (python: negative indexes are outside the matrix too)
• Matrix is empty
• Distance equals 0

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Translations are appreciated.^^

If you like matrices chekout Array Hyperrectangularity

If you like puzzles chekout Rubik's cube