On Thu, Jan 17, 2013 at 2:10 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> > > On Thu, Jan 17, 2013 at 5:04 PM, Eric Firing <efir...@hawaii.edu> wrote: > >> On 2013/01/17 4:13 AM, Pierre Haessig wrote: >> > Hi, >> > >> > Le 14/01/2013 20:05, Benjamin Root a écrit : >> >> I do like the way you are thinking in terms of the broadcasting >> >> semantics, but I wonder if that is a bit awkward. What I mean is, if >> >> one were to use broadcasting semantics for creating an array, wouldn't >> >> one have just simply used broadcasting anyway? The point of >> >> broadcasting is to _avoid_ the creation of unneeded arrays. But maybe >> >> I can be convinced with some examples. >> > >> > I feel that one of the point of the discussion is : although a new (or >> > not so new...) function to create a filled array would be more elegant >> > than the existing pair of functions "np.zeros" and "np.ones", there are >> > maybe not so many usecases for filled arrays *other than zeros values*. >> > >> > I can remember having initialized a non-zero array *some months ago*. >> > For the anecdote it was a vector of discretized vehicule speed values >> > which I wanted to be initialized with a predefined mean speed value >> > prior to some optimization. In that usecase, I really didn't care about >> > the performance of this initialization step. >> > >> > So my overall feeling after this thread is >> > - *yes* a single dedicated fill/init/someverb function would give a >> > slightly better API, >> > - but *no* it's not important because np.empty and np.zeros covers 95 >> > % usecases ! >> >> I agree with your summary and conclusion. >> >> Eric >> >> > Can we at least have a np.nans() and np.infs() functions? This should > cover an additional 4% of use-cases. > > Ben Root > > P.S. - I know they aren't verbs... > Would it be too weird or clumsy to extend the empty and empty_like functions to do the filling? np.empty((10, 10), fill=np.nan) np.empty_like(my_arr, fill=np.nan) -Mark > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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