Rounding a list of values to the nearest value from another list in python

Question

Suppose I have the following two arrays:

>>> a = np.random.normal(size=(5,))
>>> a
array([ 1.42185826,  1.85726088, -0.18968258,  0.55150255, -1.04356681])

>>> b = np.random.normal(size=(10,10))
>>> b
array([[ 0.64207828, -1.08930317,  0.22795289,  0.13990505, -0.9936441 ,
         1.07150754,  0.1701072 ,  0.83970818, -0.63938211, -0.76914925],
       [ 0.07776129, -0.37606964, -0.54082077,  0.33910246,  0.79950839,
         0.33353221,  0.00967273,  0.62224009, -0.2007335 , -0.3458876 ],
       [ 2.08751603, -0.52128218,  1.54390634,  0.96715102,  0.799938  ,
         0.03702108,  0.36095493, -0.13004965, -1.12163463,  0.32031951],
       [-2.34856521,  0.11583369, -0.0056261 ,  0.80155082,  0.33421475,
        -1.23644508, -1.49667424, -1.01799365, -0.58232326,  0.404464  ],
       [-0.6289335 ,  0.63654201, -1.28064055, -1.01977467,  0.86871352,
         0.84909353,  0.33036771,  0.2604609 , -0.21102014,  0.78748329],
       [ 1.44763687,  0.84205291,  0.76841512,  1.05214051,  2.11847126,
        -0.7389102 ,  0.74964783, -1.78074088, -0.57582084, -0.67956203],
       [-1.00599479, -0.93125754,  1.43709533,  1.39308038,  1.62793589,
        -0.2744919 , -0.52720952, -0.40644809,  0.14809867, -1.49267633],
       [-1.8240385 , -0.5416585 ,  1.10750423,  0.56598464,  0.73927224,
        -0.54362927,  0.84243497, -0.56753587,  0.70591902, -0.26271302],
       [-1.19179547, -1.38993415, -1.99469983, -1.09749452,  1.28697997,
        -0.74650318,  1.76384156,  0.33938808,  0.61647274, -0.42166111],
       [-0.14147554, -0.96192206,  0.14434349,  1.28437894, -0.38865447,
        -1.42540195,  0.93105528,  0.28993325, -1.16119916, -0.58244758]])

I have to find a way to round all values from b to the nearest value found in a.

Does anyone know of a good way to do this with python? I am at a total loss myself.

Answer 1

Here is something you can try

import numpy as np

def rounder(values):
    def f(x):
        idx = np.argmin(np.abs(values - x))
        return values[idx]
    return np.frompyfunc(f, 1, 1)

a = np.random.normal(size=(5,))
b = np.random.normal(size=(10,10))
rounded = rounder(a)(b)
print(rounded)

The rounder function takes the values which we want to round to. It creates a function which takes a scalar and returns the closest element from the values array. We then transform this function to a broadcast-able function using numpy.frompyfunc. This way you are not limited to using this on 2d arrays, numpy automatically does broadcasting for you without any loops.