Interpolate A 3d Array In Python

I have a 3D NumPy array that looks like this: arr = np.empty((4,4,5)) arr[:] = np.nan arr[0] = 1 arr[3] = 4 arr >>> [[[ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.]

Solution 1:

A solution using apply_along_axis:

import numpy as np

def pad(data):
    good = np.isfinite(data)
    interpolated = np.interp(np.arange(data.shape[0]),
                             np.flatnonzero(good), 
                             data[good])
    return interpolated


arr = np.arange(6, dtype=float).reshape((3,2))
arr[1, 1] = np.nan
print(arr)

new = np.apply_along_axis(pad, 0, arr)
print(arr)
print(new)

output:

[[ 0.  1.]
 [ 2. nan]
 [ 4.  5.]][[ 0.  1.]
 [ 2. nan]
 [ 4.  5.]][[0. 1.]
 [2. 3.]
 [4. 5.]]

---

[edit] The first proposed solution:

With some modification of the code from this answer:

import numpy as np
from scipy import interpolate

A = np.empty((4,4,5))
A[:] = np.nan
A[0] = 1
A[3] = 4

indexes = np.arange(A.shape[0])
good = np.isfinite(A).all(axis=(1, 2)) 

f = interpolate.interp1d(indexes[good], A[good],
                         bounds_error=False,
                         axis=0)

B = f(indexes)
print(B)

gives:

[[[1. 1. 1. 1. 1.]
  [1. 1. 1. 1. 1.]
  [1. 1. 1. 1. 1.]
  [1. 1. 1. 1. 1.]][[2. 2. 2. 2. 2.]
  [2. 2. 2. 2. 2.]
  [2. 2. 2. 2. 2.]
  [2. 2. 2. 2. 2.]][[3. 3. 3. 3. 3.]
  [3. 3. 3. 3. 3.]
  [3. 3. 3. 3. 3.]
  [3. 3. 3. 3. 3.]][[4. 4. 4. 4. 4.]
  [4. 4. 4. 4. 4.]
  [4. 4. 4. 4. 4.]
  [4. 4. 4. 4. 4.]]]

It works well only if NaNs are all on same slice. The slice in which there is an isolated NaN will be ignored.

Solution 2:

From the comment provided by xdze2 and previous answer here, I came up with this:

import numpy as np

def pad(data):
    bad_indexes = np.isnan(data)
    good_indexes = np.logical_not(bad_indexes)
    good_data = data[good_indexes]
    interpolated = np.interp(bad_indexes.nonzero()[0], good_indexes.nonzero()[0], 
    good_data)
    data[bad_indexes] = interpolated
    returndata

arr = np.empty((4,4,5))
arr[:] = np.nan

arr[0] = 25
arr[3] = 32.5

# Apply the pad method to each 0 axis
new = np.apply_along_axis(pad, 0, arr)

The 'pad' method essentially applies the interpolation and the np.apply_along_axis method ensures that this is applied to the 3D array.