Hi, On Fri, Jul 1, 2011 at 5:17 PM, Benjamin Root <ben.r...@ou.edu> wrote: > > > On Fri, Jul 1, 2011 at 11:00 AM, Matthew Brett <matthew.br...@gmail.com> > wrote: >> >> > You can't switch between the two approaches without big changes in your >> > code. >> >> > >> Lluis provided a case, and it was obscure. That switch seems like a >> rare or non-existent use-case that should not guide the API. >> > > Just to respond to this specific issue. > > In matplotlib, there are often constructs like the following: > > plot_something(X, Y, V) > > From a module perspective, we have no clue about the nature of the input > data. We often have to do things like np.asanyarray, np.atleast_2d and such > to establish some base-level assumptions about the input data. Numpy > currently makes this fairly cheap by not performing a copy if it is not > needed. So far, so good. > > Next, some plotting functions needs to broadcast the arrays together (again, > numpy makes that fairly cheap). > > Then, we need to figure out the common elements to plot. With something > simple like plot(), this is straight-forward or-ing of any masks. Of > course, right now, this is not cheap because we can't assume that the array > supports masking semantics. This is where we either cast the arrays as > masked arrays, or perform our own masking semantics. But, essentially, a > point that was masked in X, may not be masked in Y and/or V, and we can not > change the original data (or else we would be a bad tool). > > For more complicated functions like pcolor() and contour(), the arrays needs > to know what the status of the neighboring points in itself, and for the > other arrays. Again, either we use numpy.ma to share a common mask across > the data arrays, or we implement our own semantics to deal with this. And > again, we can not change any of the original data. > > This is not an obscure case. This is existing code in matplotlib. I will > be evaluating the current missingdata branch later today to assess its > suitability for use in matplotlib.
I think I missed why your case needs NA and IGNORE to use the same API. Why can't you just use masks and IGNORE here? Best, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
