[Matplotlib-users] filled contours, and missing data

[Eric F. <ef...@ha...>]

John et al.,

I would like to phase in matplotlib to replace Matlab ASAP for plotting 
physical oceanographic observations, primarily current profile 
measurements.  I (and many other physical oceanographers) primarily use 
contourf to plot filled contours; I only rarely use line contours.  It 
looks to me like gcntr.c has the necessary functionality--the ability to 
output polygons enclosing regions between a pair of specified levels. 
Is someone already working on exposing that functionality in matplotlib, 
or is it planned?

It appears that gcntr.c also has the ability to handle missing data via 
setting elements of the reg array to zero, and that this could be 
exposed fairly easily in the contour method in axes.py by adding "reg" 
to the set of kwargs.  Correct?  If so, is this also planned?

The question of missing data handling in contour plotting brings up the 
more general issue of how to handle data gaps in plots.  For example, 
the ocean current profiles that I measure using a Doppler profiler 
extend to varying depths, and sometimes have holes in the middle where 
there are not enough acoustic scatterers to give a signal.  This sort of 
thing--data gaps--is universal in physical oceanography.  One of 
Matlab's major strengths is the way it handles them, using nan as a bad 
value flag.  Plotting a line with the plot command, the line is broken 
at each nan; so if there is a hole in the data, the plot shows exactly 
that.  The same for contouring: nans are automatically used as a mask.

Obviously, not everyone needs this kind of automatic handling of data 
gaps, but I think it would be very useful for many applications, so I 
hope it can be considered as a possible goal.  At the plotting level, 
collections may make it easier to implement than would have been the 
case in the early days of matplotlib.  At the array manipulation level, 
the implementation could involve either masked arrays or nans.  I would 
greatly prefer the Matlab-style nan approach, but I don't know whether 
this would work with Numeric.  Maybe in Numeric3?  Numarray appears 
better equipped, with its ieeespecial.py module.

Thanks for the enormous amount of beautiful work you have already done!

Eric