I have two arrays of gridded data 'A' and 'B' of the same dimensions (1440, 400). I want to create a new array 'C' of the same dimension (1440, 400) where C contains the values from grid points of A that are only greater than B for the same grid point. If A < B, the values of A need to be replaced by NaN in C. Using the following code
C = A[A>B]
gives a one-dimensional array. In my case, it gives an array of shape (2362,).
You can use np.where to replace values where A > B is False with np.nan:
In [1]: import numpy as np
In [2]: A = np.arange(9).reshape(3, 3)
In [3]: B = np.full((3, 3), 4)
In [4]: np.where(A > B, A, np.nan)
Out[4]:
array([[nan, nan, nan],
[nan, nan, 5.],
[ 6., 7., 8.]])
I think np.where(A>B, A, np.nan) is what you're looking for. That will give you A when the condition (A>B) is True and B when it is False. Here's an example:
import numpy as np
A = np.ones((1440, 400))
B = np.zeros((1440, 400))
B[0, 0] = 3
C = np.where(A>B, A, np.nan)
This gives:
array([[nan, 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]])
EDIT: I misread how the final result should be so I updated this.
