On Wed, Jun 22, 2011 at 4:57 PM, Darren Dale <[email protected]> wrote:
> On Wed, Jun 22, 2011 at 1:31 PM, Mark Wiebe <[email protected]> wrote: > > On Wed, Jun 22, 2011 at 7:34 AM, Lluís <[email protected]> wrote: > >> > >> Darren Dale writes: > >> > >> > On Tue, Jun 21, 2011 at 1:57 PM, Mark Wiebe <[email protected]> > wrote: > >> >> On Tue, Jun 21, 2011 at 12:36 PM, Charles R Harris > >> >> <[email protected]> wrote: > >> >>> How does the ufunc get called so it doesn't get caught in an endless > >> >>> loop? > >> > >> > [...] > >> > >> >> The function being called needs to ensure this, either by extracting > a > >> >> raw > >> >> ndarray from instances of its class, or adding a 'subok = False' > >> >> parameter > >> >> to the kwargs. > >> > >> > I didn't understand, could you please expand on that or show an > example? > >> > >> As I understood the initial description and examples, the ufunc overload > >> will keep being used as long as its arguments are of classes that > >> declare ufunc overrides (i.e., classes with the "_numpy_ufunc_" > >> attribute). > >> > >> Thus Mark's comment saying that you have to either transform the > >> arguments into raw ndarrays (either by creating new ones or passing a > >> view) or use the "subok = False" kwarg parameter to break a possible > >> overloading loop. > > > > The sequence of events is something like this: > > 1. You call np.sin(x) > > 2. The np.sin ufunc looks at x, sees the _numpy_ufunc_ attribute, and > calls > > x._numpy_ufunc_(np.sin, x) > > 3. _numpy_ufunc_ uses np.sin.name (which is "sin") to get the correct > my_sin > > function to call > > 4A. If my_sin called np.sin(x), we would go back to 1. and get an > infinite > > loop > > 4B. If x is a subclass of ndarray, my_sin can call np.sin(x, > subok=False), > > as this disables the subclass overloading mechanism. > > 4C. If x is not a subclass of ndarray, x needs to produce an ndarray, for > > instance it might have an x.arr property. Then it can call np.sin(x.arr) > > Ok, that seems straightforward and, for what its worth, it looks like > it would meet my needs. However, I wonder if the _numpy_func_ > mechanism is the best approach. This is a bit sketchy, but why not do > something like: > > class Proxy: > > def __init__(self, ufunc, *args): > self._ufunc = ufunc > self._args = args > > def __call__(self, func): > self._ufunc._registry[tuple(type(arg) for arg in self._args)] = func > return func > > > class UfuncObject: > > ... > > def __call__(self, *args, **kwargs): > func = self._registry.get(tuple(type(arg) for arg in args), None) > if func is None: > raise TypeError > return func(*args, **kwargs) > > def register(self, *args): > return Proxy(self, *args) > > > @np.sin.register(Quantity) > def sin(pq): > if pq.units != degrees: > pq = pq.rescale(degrees) > temp = np.sin(pq.view(np.ndarray)) > return Quantity(temp, copy=False) > > This way, classes don't have to implement special methods to support > ufunc registration, special attributes to claim primacy in ufunc > registration lookup, special versions of the functions for each numpy > ufunc, *and* the logic to determine whether the combination of > arguments is supported. By that I mean, if I call np.sum with a > quantity and a masked array, and Quantity wins the __array_priority__ > competition, then I also need to check that my special sum function(s) > know how to operate on that combination of inputs. With the decorator > approach, I just need to implement the special versions of the ufuncs, > and the decorators handle the logic of knowing what combinations of > arguments are supported. > > It might be worth considering using ABCs for registration and have > UfuncObject use isinstance to determine the appropriate special > function to call. > The thing I'm not sure about with this idea is how to do something general to modify all ufuncs for a particular class in one swoop. Having to individually override everything would be a hassle, I think. I also don't think this approach saves very much effort compared to the _numpy_ufunc_ call approach, checking the types and raising NotImplemented if they aren't what's wanted is pretty much the only difference, and that check could still be handled by a decorator like you're showing. -Mark > > Darren > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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