[Numpy-discussion] alterNEP - was: missing data discussion round 2

[Benjamin Root]

On Fri, Jul 1, 2011 at 11:00 AM, Matthew Brett <matthew.brett at 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.

Ben Root
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