from scipy import spatial
x, y, z = np.mgrid[0:5, 2:8, 2:3]
data = zip(x.ravel(), y.ravel(), z.ravel())
tree = spatial.KDTree(data)
print 'ball', [data[i] for i in tree.query_ball_point(array([1,2,2]), 1)]
distance, index = tree.query(np.array([[2, 2, 2.2]]))
print 'query', distance, index, data[index[0]]
pts = np.array([[2, 2, 2.2]])

tree.query(pts)

import heapq
def euclideanDistance(x,y):
    return math.sqrt(sum([(a-b)**2 for (a,b) in zip(x,y)]))
closestPoints = heapq.nsmallest(1, enumerate(pts), key=lambda y: euclideanDistance(x, y[1]))

print closestPoints