On Wednesday, February 15, 2012, Benjamin Root <ben.r...@ou.edu> wrote: > > > On Wednesday, February 15, 2012, David Gowers (kampu) <00a...@gmail.com> wrote: >> Hi all, >> >> This email is about the difference, given a recarray 'arr', >> between >> >> >> A) >> >> arr.foo.x[0] >> >> and B) >> >> arr.foo[0].x >> >> >> >> Specifically, form A returns the 0-th x value, whereas form B raises >> AttributeError: >> >> >> Some code demonstrating this: >> >>>>> arr = np.zeros((4,), dtype = [('foo',[('x','H'),('y','H')])]) >>>>> a2 = arr.view (np.recarray) >>>>> a2.foo >> rec.array([(0, 0), (0, 0), (0, 0), (0, 0)], >> dtype=[('x', '<u2'), ('y', '<u2')]) >> >>>>> a2.foo.x >> array([0, 0, 0, 0], dtype=uint16) >> >>>>> a2.foo.x[0] >> 0 >> >>>>> a2.foo[0] >> (0, 0) >>>>> a2.foo[0].x >> Traceback (most recent call last): >> File "<stdin>", line 1, in <module> >> AttributeError: 'numpy.void' object has no attribute 'x' >> >> (similarly, ``a2[0].foo`` raises an identical AttributeError) >> >> >> This is obstructive, particularly since ``a2.foo[0].x`` is the more >> logical grouping than ``a2.foo.x[0]`` -- we want the x field of item 0 >> in foo, not the 0th x-value in foo. >> >> I see this issue has come up previously... >> http://mail.scipy.org/pipermail/numpy-discussion/2008-August/036429.html >> >> The solution proposed by Travis in that email: >> >> ('arr.view(dtype=(np.record, b.dtype), type=np.recarray)') >> >> is ineffective with current versions of NumPy; the result is exactly >> the same as if you had not done it at all. >> I've tried various other methods including subclassing recarray and >> overriding __getitem__ and __getattribute__, with no success. >> >> My question is, is there a way to resolve this so that ``a2.foo[0].x`` >> does actually do what you'd expect it to? >> >> Thanks, >> David >> > > Rather than recarrays, I just use structured arrays like so: > > A = np.array([(0, 0), (0, 0), (0, 0), (0, 0)], > dtype=[('x', '<u2'), ('y', '<u2')]) > > I can then do: > > A['x'][0] > > Or > > A[0]['x'] > > This allows me to slice and access the data any way I want. I have even been able to use this dictionary idiom to format strings and such. > > Does that help? > Ben Root
Sorry, didn't see that you have nested dtypes. Is there a particular reason why you need record arrays over structured arrays? Ben Root
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion