In a large code base, I am using np.broadcast_to to broadcast arrays (just using simple examples here): In [1]: x = np.array([1,2,3]) In [2]: y = np.broadcast_to(x, (2,1,3)) In [3]: y.shape Out[3]: (2, 1, 3) Elsewhere in the code, I use third-party functions that can operate in a vectorized way on Numpy arrays but that are not ufuncs. These functions don't understand broadcasting, which means that calling such a function on arrays like y is inefficient. Solutions such as Numpy's vectorize aren't good either because while they understand broadcasting, they introduce a for loop over the array elements which is then very inefficient. Ideally, what I'd like to be able to do is to have a function, which we can call e.g. unbroadcast, that returns an array with a minimal shape that can be broadcasted back to the full size if needed. So e.g.: In [4]: z = unbroadcast(y) In [5]: z.shape Out[5]: (1, 1, 3) I can then run the third-party functions on z, then broadcast the result back to y.shape. Is there a way to implement unbroadcast that relies on Numpy's public API? If not, are there any hacks that would produce the desired result? Continue reading...
