For what it's worth, also from my astropy perspective I think hat any index array should be a base ndarray! -- Marten
On Fri, Aug 14, 2015 at 7:11 AM, Jaime Fernández del Río < jaime.f...@gmail.com> wrote: > On Thu, Aug 13, 2015 at 9:57 AM, Jaime Fernández del Río < > jaime.f...@gmail.com> wrote: > >> On Thu, Aug 13, 2015 at 7:59 AM, Nathan Goldbaum <nathan12...@gmail.com> >> wrote: >> >>> >>> >>> On Thu, Aug 13, 2015 at 9:44 AM, Charles R Harris < >>> charlesr.har...@gmail.com> wrote: >>> >>>> >>>> >>>> On Thu, Aug 13, 2015 at 12:09 AM, Jaime Fernández del Río < >>>> jaime.f...@gmail.com> wrote: >>>> >>>>> On Wed, Aug 12, 2015 at 2:03 PM, Nathan Goldbaum < >>>>> nathan12...@gmail.com> wrote: >>>>> >>>>>> Hi all, >>>>>> >>>>>> I've been testing the package I spend most of my time on, yt, under >>>>>> numpy 1.10b1 since the announcement went out. >>>>>> >>>>>> I think I've narrowed down and fixed all of the test failures that >>>>>> cropped up except for one last issue. It seems that the behavior of >>>>>> np.digitize with respect to ndarray subclasses has changed since the >>>>>> NumPy >>>>>> 1.9 series. Consider the following test script: >>>>>> >>>>>> ```python >>>>>> import numpy as np >>>>>> >>>>>> >>>>>> class MyArray(np.ndarray): >>>>>> def __new__(cls, *args, **kwargs): >>>>>> return np.ndarray.__new__(cls, *args, **kwargs) >>>>>> >>>>>> data = np.arange(100) >>>>>> >>>>>> bins = np.arange(100) + 0.5 >>>>>> >>>>>> data = data.view(MyArray) >>>>>> >>>>>> bins = bins.view(MyArray) >>>>>> >>>>>> digits = np.digitize(data, bins) >>>>>> >>>>>> print type(digits) >>>>>> ``` >>>>>> >>>>>> Under NumPy 1.9.2, this prints "<type 'numpy.ndarray'>", but under >>>>>> the 1.10 beta, it prints "<class '__main__.MyArray'>" >>>>>> >>>>>> I'm curious why this change was made. Since digitize outputs index >>>>>> arrays, it doesn't make sense to me why it should return anything but a >>>>>> plain ndarray. I see in the release notes that digitize now uses >>>>>> searchsorted under the hood. Is this related? >>>>>> >>>>> >>>>> It is indeed searchsorted's fault, as it returns an object of the same >>>>> type as the needle (the items to search for): >>>>> >>>>> >>> import numpy as np >>>>> >>> class A(np.ndarray): pass >>>>> >>> class B(np.ndarray): pass >>>>> >>> np.arange(10).view(A).searchsorted(np.arange(5).view(B)) >>>>> B([0, 1, 2, 3, 4]) >>>>> >>>>> I am all for making index-returning functions always return a base >>>>> ndarray, and will be more than happy to send a PR fixing this if there is >>>>> some agreement. >>>>> >>>> >>>> I think that is the right thing to do. >>>> >>> >>> Awesome, I'd appreciate having a PR to fix this. Arguably the return >>> type *could* be the same type as the inputs, but given that it's a behavior >>> change I agree that it's best to add a patch so the output of serachsorted >>> is "sanitized" to be an ndarray before it's returned by digitize. >>> >> >> It is relatively simple to do, just replace Py_TYPE(ap2) with >> &PyArray_Type in this line: >> >> >> https://github.com/numpy/numpy/blob/maintenance/1.10.x/numpy/core/src/multiarray/item_selection.c#L1725 >> >> Then fix all the tests that are expecting searchsorted to return >> something else than a base ndarray. We already have modified nonzero to >> return base ndarray's in this release, see the release notes, so it will go >> with the same theme. >> >> For 1.11 I think we should try to extend this "if it returns an index, it >> will be a base ndarray" to all other functions that don't right now. Then >> sit back and watch AstroPy come down in flames... ;-))) >> >> Seriously, I think this makes a lot of sense, and should be documented as >> the way NumPy handles index arrays. >> >> Anyway, I will try to find time tonight to put this PR together, unless >> someone beats me to it, which I would be totally fine with. >> > > PR #6206 it is: https://github.com/numpy/numpy/pull/6206 > > Jaime > > -- > (\__/) > ( O.o) > ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes > de dominación mundial. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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