On Mon, Jun 20, 2011 at 12:32 PM, Mark Wiebe <[email protected]> wrote:
> NumPy has a mechanism built in to allow subclasses to adjust or override
> aspects of the ufunc behavior. While this goal is important, this mechanism
> only allows for very limited customization, making for instance the masked
> arrays unable to work with the native ufuncs in a full and proper way. I
> would like to deprecate the current mechanism, in particular
> __array_prepare__ and __array_wrap__, and introduce a new method I will
> describe below. If you've ever used these mechanisms, please review this
> design to see if it meets your needs.
>
>
>
The current approach is at a dead end, so something better needs to be done.
> Any class type which would like to override its behavior in ufuncs would
> define a method called _numpy_ufunc_, and optionally an attribute
> __array_priority__ as can already be done. The class which wins the priority
> battle gets its _numpy_ufunc_ function called as follows:
>
> return arr._numpy_ufunc_(current_ufunc, *args, **kwargs)
>
>
> To support this overloading, the ufunc would get a new support method,
> result_type, and there would be a new global function, broadcast_empty_like.
>
> The function ufunc.empty_like behaves like the global np.result_type, but
> produces the output type or a tuple of output types specific to the ufunc,
> which may follow a different convention than regular arithmetic type
> promotion. This allows for a class to create an output array of the correct
> type to pass to the ufunc if it needs to be different than the default.
>
> The function broadcast_empty_like is just like empty_like, but takes a list
> or tuple of arrays which are to be broadcast together for producing the
> output, instead of just one.
>
>
How does the ufunc get called so it doesn't get caught in an endless loop? I
like the proposed method if it can also be used for classes that don't
subclass ndarray. Masked array, for instance, should probably not subclass
ndarray.
> Thanks,
> Mark
>
>
> A simple class which overrides the ufuncs might look as follows:
>
> def sin(ufunc, *args, **kwargs):
> # Convert degrees to radians
> args[0] = np.deg2rad(args[0])
> # Return a regular array, since the result is not in degrees
> return ufunc(*args, **kwargs)
>
> class MyDegreesClass:
> """Array-like object with a degrees unit"""
>
> def __init__(arr):
> self.arr = arr
>
> def _numpy_ufunc_(ufunc, *args, **kwargs):
> override = globals().get(ufunc.name)
> if override:
> return override(ufunc, *args, **kwargs)
> else:
> raise TypeError, 'ufunc %s incompatible with MyDegreesClass' %
> ufunc.name
>
>
>
> A more complex example will be something like this:
>
> def my_general_ufunc(ufunc, *args, **kwargs):
> # Extract the 'out' argument. This only supports ufuncs with
> # one output, currently.
> out = kwargs.get('out')
> if len(args) > ufunc.nin:
> if out is None:
> out = args[ufunc.nin]
> else:
> raise ValueError, "'out' given as both a position and keyword
> argument"
>
> # Just want the inputs from here on
> args = args[:ufunc.nin]
>
> # Strip out MyArrayClass, but allow operations with regular ndarrays
> raw_in = []
> for a in args:
> if isinstance(a, MyArrayClass):
> raw_in.append(a.arr)
> else:
> raw_in.append(a)
>
> # Allocate the output array
> if not out is None:
> if isinstance(out, MyArrayClass):
> raise TypeError, "'out' must have type MyArrayClass"
> else:
> # Create the output array, obeying the 'order' parameter,
> # but disallowing subclasses
> out = np.broadcast_empty_like([args,
> order=kwargs.get('order'),
> dtype=ufunc.result_type(args),
> subok=False)
>
> # Override the output argument
> kwargs['out'] = out.arr
>
> # Call the ufunc
> ufunc(*args, **kwargs)
>
> # Return the output
> return out
>
> class MyArrayClass:
> def __init__(arr):
> self.arr = arr
>
> def _numpy_ufunc_(ufunc, *args, **kwargs):
> override = globals().get(ufunc.name)
> if override:
> return override(ufunc, *args, **kwargs)
> else:
> return my_general_ufunc(ufunc, *args, **kwargs)
>
>
Chuck
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