[Numpy-discussion] Numpy BoF at SciPy 2014 - quick report

[Robert Lupton the Good]

Having just re-read the PEP I'm concerned that this proposal leaves at least one major (?) trap for naive users, namely
	x = np.array([1, 10])
	print X.T at x
which will print 101, not [[1, 10], [10, 100]]

Yes, I know why this is happening but it's still a problem -- the user said, "I'm thinking matrices" when they wrote @ but the x.T had done the "wrong" thing before the @ kicked in.  And yes, a savvy user would have written x = np.ones([[1, 10]]) (but then np.dot(x, x.T) isn't a scalar).

This is the way things are at present, but with the new @ syntax coming in I think we should consider fixing it.

I can think of three possibilities:
	1. Leave this as a trap for the unwary, and a reason for people to stick to np.matrix (np.matrix([1, 10]) behaves "correctly")
	2. Make x.T a syntax error for 1-D arrays.  It's a no-op and IMHO a trap. 
	3. Make x.T promote the shape == (2,) array to (1, 2) and return a (2, 1) array.  This may be too magic, but it's my preferred solution.

						R

> Implementation of @ (matrix multiplication)
>  - will be in 3.5 ~ 18months
>  - no work started yet -- have to make sure we do it.
>  - @@ was not added.
>  - The PEP for numpy is well-defined. Not much thinking to be done. (Good for a sprint)
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