This kata is the second part of a series: Neighbourhood kata collection. You may want to do the first one before trying this version.

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Now we will implement the distance

The distance extends the reach of the neighbourhood - instead of returning only the closest neighbours, you'll be searching for all neighbours in a given maximum distance.

There are two popular types of cellular neighbourhoods:

• Moore neighborhood - the eight cells that surround the given cell
• Von Neumann neighborhood - the four cells that share a border with the given cell

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Task

Given a neighbourhood type ("moore" or "von_neumann"), a 2D matrix (a list of lists), a pair of coordinates and the distance, return the list of neighbours of the given cell.

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) should be applied like mat[m][n]

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

• Matrix is empty
• Coordinates are outside the matrix
• Distance is equal to 0

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Example:

\ n  0    1    2    3    4
m  --------------------------                     
0  | 0  | 1  | 2  | 3  | 4  |
1  | 5  | 6  | 7  | 8  | 9  |
2  | 10 | 11 | 12 | 13 | 14 |
3  | 15 | 16 | 17 | 18 | 19 |
4  | 20 | 21 | 22 | 23 | 24 |   
   --------------------------

get_neighborhood("moore", arr, (2,2), 1) == [6,7,8,11,13,16,17,18]
get_neighborhood("moore", arr, (2,2), 2) == [0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24]

get_neighborhood("von_neumann", arr, (2,2), 1) == [7,11,13,17]
get_neighborhood("von_neumann", arr, (2,2), 2) == [2,6,7,8,10,11,13,14,16,17,18,22]

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Translations are appreciated ^^

If you like chess take a look at Chess Aesthetics

If you like puzzles take a look at Rubik's cube