python: Appending 2D and 3D array to make new 3D (bigger 3rd dimension)

Question

I have two different arrays. A = [3124, 5] (representing 3124 models with 5 reference parameters) B = [3124, 19, 12288] (representing 3124 models, 19 time steps per model, 12288 temperature field data points per time step)

I want to add the same 5 values from A (parameter) array to the beginning of the temperature field array B for each time step, so that I end up with a new array AB = [3124, 19, 12293].

I have tried to use dstack AB = np.dstack((A, B)).shape

but I got the error message ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 5 and the array at index 1 has size 19

Can anyone please help me?

Answer 1

With more modest shapes (your B is too big for my machine):

In [4]: A = np.ones((3,4)); B = 2*np.ones((3,5,133))

We can expand A to match with:

In [5]: A[:,None,:].shape
Out[5]: (3, 1, 4)
In [6]: A[:,None,:].repeat(5,1).shape
Out[6]: (3, 5, 4)

Now the arrays match on axis 0 and 1, all except the last joining one:

In [7]: AB=np.concatenate((A[:,None,:].repeat(5,1),B),axis=2)
In [8]: AB.shape
Out[8]: (3, 5, 137)

That corrects the problem raised in your error message:

ValueError: all the input array dimensions for the concatenation 
axis must match exactly, but along dimension 1, the array at 
index 0 has size 5 and the array at index 1 has size 19

Answer 2

Something like this will work:

import numpy

A = numpy.asarray([3124, 5])
B = numpy.asarray([3124, 19, 12288])

C = numpy.copy(B)

C[2] += A[1]

print(list(C))

output: [3124, 19, 12293]

However, you don't make it clear what your overarching objective is. The solution seems rather more direct than what you may want...