Make the matrix multiplication operator @ work for scalars in numpy

As ajcr suggested, you can work around this issue by forcing some minimal dimensionality on objects being multiplied. There are two reasonable options: atleast_1d and atleast_2d which have different results in regard to the type being returned by @: a scalar versus a 1-by-1 2D array.

x = 3y = 5z = np.atleast_1d(x) @ np.atleast_1d(y)   # returns 15 z = np.atleast_2d(x) @ np.atleast_2d(y)   # returns array([[15]])

However:

• Using atleast_2d will lead to an error if x and y are 1D-arrays that would otherwise be multiplied normally
• Using atleast_1d will result in the product that is either a scalar or a matrix, and you don't know which.
• Both of these are more verbose than np.dot(x, y) which would handle all of those cases.

Also, the atleast_1d version suffers from the same flaw that would also be shared by having scalar @ scalar = scalar: you don't know what can be done with the output. Will z.T or z.shape throw an error? These work for 1-by-1 matrices but not for scalars. In the setting of Python, one simply cannot ignore the distinction between scalars and 1-by-1 arrays without also giving up all the methods and properties that the latter have.