Reputation: 544
My example is close to that of the question Python: how to compute the distance between cells?, but what I want is to calculate the distance between 2 cells taking into account that the distance between adjacent cells is always 1 (including diagonals). Let me explain:
Suppose I have the following 2D grid:
20 21 22 23 24
15 16 17 18 19
10 11 12 13 14
5 6 7 8 9
0 1 2 3 4
What I want is a function that as an argument has 2 cell indices and return the distance between them, so that the distance between adjacent cells is 1 (diagonal cells are adjacent too).
I've tried with something like this:
import numpy as np
import numba as nb
@nb.njit # to speed up the code with numba
def distance(a,b,n=5):
x1 = a%n; x2 = b%n;
y1 = a//n; y2 = b//n
return np.floor(np.sqrt( (x1-x2)**2 + (y1-y2)**2 ))
However, this works for some cells but not for others, for example:
>>> distance(2,7)
1.0 # works!
>>> distance(3,11)
2.0 # works!
>>> distance(0,24)
5.0 # wrong! it should be 4.0
>>> distance(0, 23)
5.0 # wrong! it should be also 4.0
I mean, I think linear distances are well calculated but diagonal distances aren't all
Upvotes: 3
Views: 1948
Reputation: 4033
Try:
def distance(a, b, n=5):
x1, x2 = a%n, b%n
y1, y2 = a//n, b//n
dx = abs(x1 - x2)
dy = abs(y1 - y2)
return max([dx, dy])
>>> distance(2,7)
1
>>> distance(3,11)
2
>>> distance(0,24)
4
>>> distance(0, 23)
4
Explanation:
We can imagine that if we want to get from point a to point b in the shortest distance possible, we want to first exhaust as many diagonal moves as possible. Without diagonals, we would have had to make dx
+ dy
number of moves, however since we can move in a diagonal direction, we can effectively move in both directions in a single move. This means the smaller of the two (dx
and dy
) become irrelevant since if dy
= 10 and dx
= 5, we already need to move 10 in the vertical direction, so we can capture 5 moves in the horizontal direction by coverting 5 of those vertical moves into diagonal moves. Thus, the answer is max([dx, dy])
.
Upvotes: 3