[Numpy-discussion] Changes to np.digitize since NumPy 1.9?

[Charles R Harris]

On Thu, Aug 13, 2015 at 12:09 AM, Jaime Fernández del Río <
jaime.frio at gmail.com> wrote:

> On Wed, Aug 12, 2015 at 2:03 PM, Nathan Goldbaum <nathan12343 at 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.

Chuck
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