> Travis, > > You have been getting mostly objections so far; I wouldn't characterize it that way, but yes 2 people have pushed back a bit, although one not directly speaking to the proposed behavior.
The issue is that [] notation does more than just "select from a container" for NumPy arrays. In particular, it is used to reshape an array to more dimensions: [..., newaxis] A common pattern is to reduce over a dimension and then re-shape the result so that it can be combined with the un-reduced object. Broadcasting makes this work if the dimension being reduced along is the first dimension. But, broadcasting is not enough if you want the reduction dimension to be arbitrary: Thus, y = add.reduce(x, axis=-1) produces an N-1 array if x is 2-d and a numpy scalar if x is 1-d. Suppose y needs to be subtracted from x. If x is 2-d, then >>> x - y[...,newaxis] is the needed code. But, if x is 1-d, then >>> x - y[..., newaxis] returns an error and a check must be done to handle the case separately. If y[..., newaxis] worked and produced a 1-d array when y was a numpy scalar, this could be avoided. -Travis O. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
