On Mon, Feb 28, 2011 at 7:24 PM, Pierre GM <pgmdevl...@gmail.com> wrote: > > On Mar 1, 2011, at 1:05 AM, Bruce Southey wrote: > >> On Mon, Feb 28, 2011 at 4:52 PM, Wes McKinney <wesmck...@gmail.com> wrote: >>> I'm having some trouble with the zeros_like function via np.fix: >>> >>> def zeros_like(a): >>> if isinstance(a, ndarray): >>> res = ndarray.__new__(type(a), a.shape, a.dtype, order=a.flags.fnc) >>> res.fill(0) >>> return res >>> try: >>> wrap = a.__array_wrap__ >>> except AttributeError: >>> wrap = None >>> a = asarray(a) >>> res = zeros(a.shape, a.dtype) >>> if wrap: >>> res = wrap(res) >>> return res >>> >>> As you can see this is going to discard any metadata stored in a >>> subtype. I'm not sure whether this is a bug or a feature but wanted to >>> bring it up. >>> >>> Thanks, >>> Wes >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >> >> I guess this is ticket 929. >> http://projects.scipy.org/numpy/ticket/929 >> >> I was looking at it today but was not sure what is really desired >> here. I considered that this just meant shape and dtype but not sure >> about masked or record arrays behavior. So: >> What is the value of having the metadata? >> What is the meaning of 'like' here? > > Well, that depends on what you wanna do, of course. To handle metadata, I use > some kind of dictionary updated in the __array_finalize__. Check > numpy.ma.MaskedArray and its subclasses (like scikits.timeseries.TimeSeries) > for the details. > Now that you could store some extra data in the dtype (if I remmbr and > understand correctly), it might be worth considering a proper way to deal > with that. > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
The ticket is exactly related to the problem at hand-- having __array_finalize__ defined won't help you as it never gets called. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
