[Numpy-discussion] Re: The idiom for supporting matrices and arrays in a function
Travis Oliphant
oliphant at ee.byu.edu
Wed Mar 1 17:10:38 EST 2006
Robert Kern wrote:
>Bill Baxter wrote:
>
>
>>The NumPy for Matlab Users's wiki is currently pleading to have someone
>>fill in "/*the idiom for supporting both matrices and arrays in a
>>function".
>>* /Does anyone know what this is?
>>
>>
>
>The subclassing functionality is rather new, so I don't know if the proper
>idioms have really been discovered. I would suggest converting to arrays using
>asarray() right at the beginning of the function. If you want to spit out
>matrices/what-have-you out again, then you will need to do some more work. I
>would suggest looking at the procedure that ufuncs do with __array_finalize__
>and __array_priority__ and creating a pure Python reference implementation that
>others could use. It's possible that you would be able to turn it into a decorator.
>
>
This is kind of emerging as the right strategy (although it's the
__array_wrap__ and __array_priority__ methods that are actually relevant
here. But, it is all rather new so we can forgive Robert this time ;-) )
The __array_priority__ is important if you've got "competing" wrappers
(i.e. a 2-input, 1-output function) and don't know which __array_wrap__
to use for the output.
In matlab you have one basic type of object. In NumPy you have a
basic ndarray object that can be sub-classed which gives rise to all
kinds of possibilities. I think we are still figuring out what the best
thing to do is.
Ufuncs convert everything to base-class arrays (what asarray does) and
then use the __array_wrap__ of the input with the highest
__array_priority__ to wrap all of the outputs (except that now output
arrays passed in use their own __array_wrap__).
So, I suppose following their example is a reasonable approach.
Look in numpy/linlag/linlag.py for examples of using the
asarray/__array_wrap__ strategy...
The other approach is to let sub-classes through the array conversion
(asanyarray), but this approach could rise to later errors if the
sub-class redefines an operation you didn't expect it to, so is probably
not safe unless all of your operations are functions that can handle any
array subclass.
And yes, I think a decorator could be written that would manage this.
I'll leave that for the motivated...
-Travis
More information about the NumPy-Discussion
mailing list