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. 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
