Concatenation of numpy arrays of unknown dimension along arbitrary axis

Question

I have two arrays A and B of unknown dimensions that I want to concatenate along the Nth dimension. For example:

>>> A = rand(2,2)       # just for illustration, dimensions should be unknown
>>> B = rand(2,2)       # idem
>>> N = 5

>>> C = concatenate((A, B), axis=N)
numpy.core._internal.AxisError: axis 5 is out of bounds for array of dimension 2

>>> C = stack((A, B), axis=N)
numpy.core._internal.AxisError: axis 5 is out of bounds for array of dimension 3

A related question is asked here. Unfortunately, the solutions proposed do not work when the dimensions are unknown and we might have to add several new axis until getting a minimum dimension of N.

What I have done is to extend the shape with 1's up until the Nth dimension and then concatenate:

newshapeA = A.shape + (1,) * (N + 1 - A.ndim)
newshapeB = B.shape + (1,) * (N + 1 - B.ndim)
concatenate((A.reshape(newshapeA), B.reshape(newshapeB)), axis=N)

With this code I should be able to concatenate a (2,2,1,3) array with a (2,2) array along axis 3, for instance.

Are there better ways of achieving this?

ps: updated as suggested the first answer.

Answer 1

This should work:

def atleast_nd(x, n):
    return np.array(x, ndmin=n, subok=True, copy=False)

np.concatenate((atleast_nd(a, N+1), atleast_nd(b, N+1)), axis=N)