NumPy replace values in matrix with np array-CodePudding

I have numpy array like:

x = np.array([
    ...
    [[0, 0, 0, 0],
     [0, 1, 1, 0],
     [0, 1, 1, 0],
     [0, 0, 0, 0]]
    ...
])

with shape (4800, 4, 4).

So i need to replace every 0 with [1, 1, 2] and every 1 with [5, 5, 9]

Result should be like this:

[[[1, 1, 2], [1, 1, 2], [1, 1, 2], [1, 1, 2]],
[[1, 1, 2], [5, 5, 9], [5, 5, 9], [1, 1, 2]],
[[1, 1, 2], [5, 5, 9], [5, 5, 9], [1, 1, 2]],
[[1, 1, 2], [1, 1, 2], [1, 1, 2], [1, 1, 2]]]

How do I do this?

CodePudding user response：

Take advantage of the fact that you have 0 and 1, define a mapper array and index it:

                  #    0          1
mapper = np.array([[1, 1, 2], [5, 5, 9]])

out = mapper[a]

output:

array([[[[1, 1, 2],
         [1, 1, 2],
         [1, 1, 2],
         [1, 1, 2]],

        [[1, 1, 2],
         [5, 5, 9],
         [5, 5, 9],
         [1, 1, 2]],

        [[1, 1, 2],
         [5, 5, 9],
         [5, 5, 9],
         [1, 1, 2]],

        [[1, 1, 2],
         [1, 1, 2],
         [1, 1, 2],
         [1, 1, 2]]]])

CodePudding user response：

You can create a new array and assign the values according to the value in the old one.

import numpy as np
x = np.array([
    [[0, 0, 0, 0],
     [0, 1, 1, 0],
     [0, 1, 1, 0],
     [0, 0, 0, 0]]
])

x_new = np.zeros((*(x.shape), 3))
x_new[x==0] = [1, 1, 2]
x_new[x==1] = [5, 5, 9]

print(x_new)

This results in the following output:

[[[[1. 1. 2.]
   [1. 1. 2.]
   [1. 1. 2.]
   [1. 1. 2.]]

  [[1. 1. 2.]
   [5. 5. 9.]
   [5. 5. 9.]
   [1. 1. 2.]]

  [[1. 1. 2.]
   [5. 5. 9.]
   [5. 5. 9.]
   [1. 1. 2.]]

  [[1. 1. 2.]
   [1. 1. 2.]
   [1. 1. 2.]
   [1. 1. 2.]]]]