On Sun, Mar 8, 2009 at 2:48 PM, Charles R Harris <charlesr.har...@gmail.com>wrote:
> > > On Sun, Mar 8, 2009 at 1:04 PM, Darren Dale <dsdal...@gmail.com> wrote: > >> On Sat, Mar 7, 2009 at 1:23 PM, Darren Dale <dsdal...@gmail.com> wrote: >> >>> On Sun, Feb 22, 2009 at 7:01 PM, Darren Dale <dsdal...@gmail.com> wrote: >>> >>>> On Sun, Feb 22, 2009 at 6:35 PM, Darren Dale <dsdal...@gmail.com>wrote: >>>> >>>>> On Sun, Feb 22, 2009 at 6:28 PM, Pierre GM <pgmdevl...@gmail.com>wrote: >>>>> >>>>>> >>>>>> On Feb 22, 2009, at 6:21 PM, Eric Firing wrote: >>>>>> >>>>>> > Darren Dale wrote: >>>>>> >> Does anyone know why __array_wrap__ is not called for subclasses >>>>>> >> during >>>>>> >> arithmetic operations where an iterable like a list or tuple >>>>>> >> appears to >>>>>> >> the right of the subclass? When I do "mine*[1,2,3]", array_wrap is >>>>>> >> not >>>>>> >> called and I get an ndarray instead of a MyArray. "[1,2,3]*mine" is >>>>>> >> fine, as is "mine*array([1,2,3])". I see the same issue with >>>>>> >> division, >>>>>> > >>>>>> > The masked array subclass does not show this behavior: >>>>>> >>>>>> Because MaskedArray.__mul__ and others are redefined. >>>>>> >>>>>> Darren, you can fix your problem by redefining MyArray.__mul__ as: >>>>>> >>>>>> def __mul__(self, other): >>>>>> return np.ndarray.__mul__(self, np.asanyarray(other)) >>>>>> >>>>>> forcing the second term to be a ndarray (or a subclass of). You can do >>>>>> the same thing for the other functions (__add__, __radd__, ...) >>>>> >>>>> >>>>> Thanks for the suggestion. I know this can be done, but ufuncs like >>>>> np.multiply(mine,[1,2,3]) will still not work. Plus, if I reimplement >>>>> these >>>>> methods, I take some small performance hit. I've been putting a lot of >>>>> work >>>>> in lately to get quantities to work with numpy's stock ufuncs. >>>>> >>>> >>>> I should point out: >>>> >>>> import numpy as np >>>> >>>> a=np.array([1,2,3,4]) >>>> b=np.ma.masked_where(a>2,a) >>>> np.multiply([1,2,3,4],b) # yields a masked array >>>> np.multiply(b,[1,2,3,4]) # yields an ndarray >>>> >>>> >>> I'm not familiar with the numpy codebase, could anyone help me figure out >>> where I should look to try to fix this bug? I've got a nice set of >>> generators that work with nosetools to test all combinations of numerical >>> dtypes, including combinations of scalars, arrays, and iterables of each >>> type. In my quantities package, just testing multiplication yields 1031 >>> failures, all of which appear to be caused by this bug (#1026 on trak) or >>> bug #826. >> >> >> >> I finally managed to track done the source of this problem. >> _find_array_wrap steps through the inputs, asking each of them for their >> __array_wrap__ and binding it to wrap. If more than one input defines >> __array_wrap__, you enter a block that selects one based on array priority, >> and binds it back to wrap. The problem was when the first input defines >> array_wrap but the second one does not. In that case, _find_array_wrap never >> bothered to rebind the desired wraps[0] to wrap, so wrap remains Null or >> None, and wrap is what is returned to the calling function. >> >> I've tested numpy with this patch applied, and didn't see any regressions. >> Would someone please consider committing it? >> >> Thanks, >> Darren >> >> $ svn diff numpy/core/src/umath_ufunc_object.inc >> Index: numpy/core/src/umath_ufunc_object.inc >> =================================================================== >> --- numpy/core/src/umath_ufunc_object.inc (revision 6569) >> +++ numpy/core/src/umath_ufunc_object.inc (working copy) >> @@ -3173,8 +3173,10 @@ >> PyErr_Clear(); >> } >> } >> + if (np >= 1) { >> + wrap = wraps[0]; >> + } >> if (np >= 2) { >> - wrap = wraps[0]; >> maxpriority = PyArray_GetPriority(with_wrap[0], >> PyArray_SUBTYPE_PRIORITY); >> for (i = 1; i < np; ++i) { >> > > Applied in r6573. Thanks. > Oh, and can you provide a test for this fix? Chuck
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