Question

I have a one-dimensional grid that is defined as a list of sorted floating point values. The points are not equidistant, but it is guaranteed that there is no any pair of clashing points (distance==0).

I need to find the most efficient way to snap any given value to the closest grid point. The most clever way I could think of was as follows (np is numpy and myGrid is a numpy array)

absDiff = np.abs(myGrid - myValue)
ix = np.argmax(absDiff)
snappedValue = myGrid[ix]

The problem is that this approach is too slow and I need a more efficient one.

Answer 1

import bisect
def snap(myGrid, myValue):
    ix = bisect.bisect_right(myGrid, myValue)
    if ix == 0:
        return myGrid[0]
    elif ix == len(myGrid):
        return myGrid[-1]
    else:
        return min(myGrid[ix - 1], myGrid[ix], key=lambda gridValue: abs(gridValue - myValue))