Broadcasting a 1D array to a particular dimension of a varying nD array via .reshape(generator)

Question

I have a large matrix of the shape (2,2,2,...n) of nD dimensions, which often varies.

However I am also receiving incoming data which is always a 1D array of shape (2,).

Now I want to multiply my former matrix of nD dimensions with my 1D array via reshape... and I also have an 'index' of which dimensions I want to broadcast and modify in particular.

Thus I'm doing the following (within a loop):

matrix_nd *= array_1d.reshape(1 if i!=index else dimension for i, dimension in enumerate(matrix_nd.shape))

However this generator as input does not seem to be valid. Note that the dimension would always equal to 2 and only be placed once within our sequence.

For example, if we have a 5D matrix of shape (2,2,2,2,2) and an index of 3; we would want to reshape the 1D array to a (1,1,1,2,1).

Any ideas?

Thanks in advance.

EDIT:

So it turns out my entire approach is wrong: Getting the tuple that I was after still seems to broadcast the (2,) 1D array to all dimensions.

For example: I have numpy array test_nd.shape of (2,2,2) and which looks like this:

array([[[1, 1],
  [1, 1]],
 [[1, 1],
  [1, 1]]])

I then reshape a (2,) 1D array to be broadcasted to the 3rd dimensions only:

toBroadcast = numpy.asarray([0,0]).reshape(1,1,2)

Where toBroadcast has the form array([[[0, 0]]])

However... test_nd*toBroadcast returns the following result:

array([[[0, 0],
      [0, 0]],
     [[0, 0],
      [0, 0]]])

It seems to have been broadcasting to all the dimensions. Any ideas?

Answer 1

You can define a function like

def broadcast_axis(data, ndims, axis):
    newshape = [1] * ndims
    newshape[axis] = -1
    return data.reshape(*newshape)

and use it like

vector = broadcast_axis(vector, matrix.ndim, 3)