python find the intersection point of two numpy array

Question

I have two numpy array that describes a spatial curve, that are intersected on one point and I want to find the nearest value in both array for that intersection point, I have this code that works fine but its to slow for large amount of points.

from scipy import spatial def nearest(arr0, arr1): ptos = [] j = 0 for i in arr0: distance, index = spatial.KDTree(arr1).query(i) ptos.append([distance, index, j]) j += 1 ptos.sort() return (arr1[ptos[0][1]].tolist(), ptos[0][1], ptos[0][2])

the result will be (<point coordinates>,<position in arr1>,<position in arr0>)

@Alex the array are the coordinates x,y,z of a spatial line so nx3 the numbers of point are not fixed, but for now may work for a line in a x,y plane so nx2 may work too — efirvida
– efirvida, Commented Mar 15, 2016 at 20:30

Bi Rico · Accepted Answer · 2016-03-15 20:38:14Z

Your code is doing a lot of things you don't need. First you're rebuilding the KDtree on every loop and that's a waste. Also query takes an array of points, so no need to write your own loop. Ptos is an odd data structure, and you don't need it (and don't need to sort it). Try something like this.

from scipy import spatial def nearest(arr0, arr1): tree = spatial.KDTree(arr1) distance, arr1_index = tree.query(arr0) best_arr0 = distance.argmin() best_arr1 = arr1_index[best_arr0] two_closest_points = (arr0[best_arr0], arr1[best_arr1]) return two_closest_points, best_arr1, best_arr0

If that still isn't fast enough, you'll need to describe your problem in more detail and figure out if another search algorithm will work better for your problem.

thank you very much, the entire run processes go down from 3 min to 20 seconds, you saved me a lot of time

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