Simone Giannecchini a écrit :
> How would you classify data like GTOPO30 and GTOPO60 which are
> elevation data, 16 bits unsigned where the No Data values is -9999?
Packed. And I would add that if "no data" values is -9999, then it can't be
unsigned 16 bits. It is probably signed 16 bits.
The "sample to geophysics" transform replaces -9999 by NaN, and keep the other
values unchanged if they are already in meters. This is just a particular case
where scale=1 and offset=0.
> Or DTED (Military Elevation Data) which are as well 16 bits unsigned,
> or SRTM () which are signed integers elevations that can range from
> -32767 to 32767 meters, encompassing the range of
> elevation to be found on the Earth. Nodata are flagged with the value -32768.
Same as above.
> This data cannot be visualized directly, but they would not even be
> strictly "geophysics", moreover there is no specific colormap to apply
> and also there is a Nodata value which is an integer and not NaN.
Yes the can be visualized directly. In theory IndexColorModel supports only
unsigned shorts, but you can map signed shorts using the "complement by 2"
integer arithmetic. You may not have noticed this but I already do that for
NetCDF data (IFREMER's Coriolis data: signed 16 bits from -23000 to 20000 with
"no data" mapped to 32767).
If we happen to have non-geophysics data that can't be rendered directly (e.g.
32 bits integers), the distinction between PACKED and RENDERED enumeration exist
exactly for that purpose.
And yes there is an integer value for "no data" which is not NaN, and this is
exactly the "sample to geophysics" transform's job to replace this integer by
Float.NaN.
As for having no specific colormap, we just apply a grayscale by default -
useful at least for debugging. The user applies its own colormap at a later
stage.
Martin
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