[Numpy-discussion] possible bug: __array_wrap__ is not called during arithmetic operations in some cases
Charles R Harris
charlesr.harris@gmail....
Sun Mar 8 15:54:46 CDT 2009
On Sun, Mar 8, 2009 at 2:48 PM, Charles R Harris
<charlesr.harris@gmail.com>wrote:
>
>
> On Sun, Mar 8, 2009 at 1:04 PM, Darren Dale <dsdale24@gmail.com> wrote:
>
>> On Sat, Mar 7, 2009 at 1:23 PM, Darren Dale <dsdale24@gmail.com> wrote:
>>
>>> On Sun, Feb 22, 2009 at 7:01 PM, Darren Dale <dsdale24@gmail.com> wrote:
>>>
>>>> On Sun, Feb 22, 2009 at 6:35 PM, Darren Dale <dsdale24@gmail.com>wrote:
>>>>
>>>>> On Sun, Feb 22, 2009 at 6:28 PM, Pierre GM <pgmdevlist@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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20090308/6693a5da/attachment.html
More information about the Numpy-discussion
mailing list