Konrad Hinsen wrote:
M = array(l) Mt = M.transpose()
just isn't that much worse than:
Mt = transpose(l)
No, but the automatic conversion enables me to write functions that accept any sequence type without even having to think about it.
I've used that to, but I also frequently use something like this: def function(A): A = array(A) ... Which is pretty simple to.
Moreover, it is almost essential in many situations to accept scalars in place of arrays, because scalars fulfill the role of rank-0 arrays.
Yes, this is critical. Isn't there a plan to make the scalar -- rank-0 array dicotomy a little cleaner in NumArray ?
I also agree that the point is not subclassing per se, it's polymorphism. It should be easy to write a class that acts like an array in all the ways that you need it to.
True, and that is a weak point of NumPy.
Is this getting any better with NumArray? -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov