Re: Numpy-discussion digest, Vol 1 #693 - 3 msgs
1. Add a type parameter to sum which defaults to widest type.
2. Add a type parameter to reductions (and fix output type handling). Default is same-type as it is now. No major changes to C-code.
3. Add a WidestType(array) function:
Bool --> Bool Int8,Int16,Int32,Int64 --> Int64 UInt8, UInt16,UInt32,UInt64 --> UInt64 (Int64 on win32) Float32, Float64 --> Float64 Complex32, Complex64 --> Complex64
This sounds like a good solution.
The only thing this really leaves out, is a higher performance implementation of sum/mean which Peter referred to a few times.
Is this really the case? It depends on how you plan to implement the output conversion. If you will do this by allocating a temporary converted copy of the complete input before the calculations then this potentially requires a lot of temporary storage. But it is certainly possible to come up with an implementation that avoids this. Have you given this some thought?
Peter, if you want to write a specialized module, we'd be happy to put it in the add-ons package.
I hope that the reduce methods can be made sufficiently efficient so that this will not be necessary. Cheers, Peter
participants (2)
-
Perry Greenfield
-
Peter Verveer