How to replace masked values from a numpy array with the average of the values immediately next to the masked value

Question

What is the most efficient numpy way to replace masked values in an array with the average of the closest unmasked values next to them?

eg:

a = np.array([2,6,4,8])
b = np.ma.masked_where(a>5,a)
print b

masked_array(data = [2 -- 4 --],
         mask = [False  True False  True],
   fill_value = 999999)

I want the masked values in b to be replaced with the average of values just next to them. Boundaries can repeat the closest unmasked value. So in this example, b will be the following:

b = [2,3,4,4]

The main reason for this question is to see whether this can be done efficiently without the use of an iterator.

Answer 1

you can use np.interp and np.where

import numpy as np

a = np.array([2,6,4,8])
mymask = a>5
b = np.ma.masked_where(mymask,a)

print b
# [2 -- 4 --]

c = np.interp(np.where(mymask)[0],np.where(~mymask)[0],b[np.where(~mymask)[0]])
b[np.where(mymask)[0]] = c

print b
# [2 3 4 4]