Hi Travis, On Mon, Oct 19, 2009 at 6:29 PM, Travis Oliphant <oliph...@enthought.com> wrote: > > On Oct 17, 2009, at 7:49 AM, Darren Dale wrote: [...] >> When calling numpy functions: >> >> 1) __input_prepare__ provides an opportunity to operate on the inputs >> to yield versions that are compatible with the operation (they should >> obviously not be modified in place) >> >> 2) the output array is established >> >> 3) __array_prepare__ is used to determine the class of the output >> array, as well as any metadata that needs to be established before the >> operation proceeds >> >> 4) the ufunc performs its operations >> >> 5) __array_wrap__ provides an opportunity to update the output array >> based on the results of the computation >> >> Comments, criticisms? If PEP 3124^ were already a part of the standard >> library, that could serve as the basis for generalizing numpy's >> functions. But I think the PEP will not be approved in its current >> form, and it is unclear when and if the author will revisit the >> proposal. The scheme I'm imagining might be sufficient for our >> purposes. > > This seems like it could work. So, basically ufuncs will take any > object as input and call it's __input__prepare__ method? This should > return a sub-class of an ndarray?
ufuncs would call __input_prepare__ on the input declaring the highest __array_priority__, just like ufuncs do with __array_wrap__, passing a tuple of inputs and the ufunc itself (provided for context). __input_prepare__ would return a tuple of inputs that the ufunc would use for computation, I'm not sure if these need to be arrays or not, I think I can give a better answer once I start the implementation (next few days I think). Darren _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
