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Map image with a sparse array


  I have the following problem. I have an array of geographically
colocated data (0.5 by 0.5 degree grid) that is the result of averaging
all 253 swaths of one cycle of Topex data into this grid. Topex data has
a small (3 or maybe 10km) swath, so the majority of the grid location
(65%) are 'bad' in the sense that those grid elements contain no
averaged data.

  I want to display this by mapping it using map_set/map_image. The old
method simply 'tv'd the image to the screen and then finessed applying
the continents/grid lines to the image. A bit of a boondogle, and not
very upgradeable.

  The problem is that there seems to be no way to tell map_image (and
map_patch too) that certain data (the 'bad' data value) should be
excluded from whatever
averaging/bilinear-interpolation/nearest-neighbor-chosing method is used
and the 'mapped' image has places that are clearly corrupted by the
presence of the bad data. The problem is ameliorated by use nearest
neighbor rather than bilinear interpolation (i.e. bilinear=0) and I am
setting compress=0, so that the inverse transformation is done on each
pixel. Also, I've started out with a window set to the size of the input
data array and with map_set,position=[0.,0,1,1] so that the mapping
coordinate system occupies the entire window. These remedies I hit upon
thinking that they would minimize the damage, and they have done that,
but when I compare my results with the older, more 'pristine' but vastly
less portible, upgradeable, maintainable method, there are big

  The 'missing' keyword just sets elements outside the range input via
the 'min' and 'max' keywords and those  outside of the mapping
coordinates to the bad value, it doesn't allow one to exclude data from
the averaging/interpolation/chosing method.

  Will I have to hack map_image? Or go back to the old way?


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