On Sun, Mar 8, 2009 at 6:04 PM, Charles R Harris <charlesr.har...@gmail.com>wrote:
> > > On Sun, Mar 8, 2009 at 3:27 PM, Darren Dale <dsdal...@gmail.com> wrote: > >> On Sun, Mar 8, 2009 at 5:02 PM, Darren Dale <dsdal...@gmail.com> wrote: >> >>> On Sun, Mar 8, 2009 at 4:54 PM, Charles R Harris < >>> charlesr.har...@gmail.com> wrote: >>> >>>> >>>> >>>> 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? >>>> >>> >>> Yes, I'll send a patch for a test as soon as its ready. 6573 closes two >>> tickets, 1026 and 1022. Did you see the patch I sent for issue #826? It is >>> also posted at the bug report. >> >> >> >> Index: numpy/core/tests/test_umath.py >> =================================================================== >> --- numpy/core/tests/test_umath.py (revision 6573) >> +++ numpy/core/tests/test_umath.py (working copy) >> @@ -240,6 +240,19 @@ >> assert_equal(args[1], a) >> self.failUnlessEqual(i, 0) >> >> + def test_wrap_with_iterable(self): >> + # test fix for bug #1026: >> + class with_wrap(np.ndarray): >> + __array_priority = 10 >> + def __new__(cls): >> + return np.asarray(1).view(cls).copy() >> + def __array_wrap__(self, arr, context): >> + return arr.view(type(self)) >> + a = with_wrap() >> + x = ncu.multiply(a, (1, 2, 3)) >> + self.failUnless(isinstance(x, with_wrap)) >> + assert_array_equal(x, np.array((1, 2, 3))) >> + >> def test_old_wrap(self): >> class with_wrap(object): >> def __array__(self): >> > > Thanks. This was applied in r6575. > Chuck, I'm sorry, there was a typo in that test. It should have said __array_priority__, not __array_priority. It didnt influence the test result, which failed without the patch and passed with it, but I think it should still be fixed. Darren
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