OK, After some more research, I found the rasterize.py autotest file
which was a huge help and I have code working that burns the tracks into
an image. But its not exactly what I hoping for so I'll give the
gdal.Grid() a try.
Thanks,
-Steve
On 6/14/2019 1:58 PM, Stephen Woodbridge wrote:
Hi all,
My goal is to take satellite track data and create a gtiff file using
Python.
The satellite data is in a NetCDF file, which I can read in Python and
has variables lat, lon, ssha. There are a continue stream of the
NetCDF data over time so I plan to just keep loading them as they
become available.
My thought is to take adjacent track points as a LineStringZ and burn
them into the image overwriting any existing data. This will keep the
gtiff up to date with the most current data for any given area.
Maybe there is a better way to do this? suggestions welcome.
I was looking at gdal.RasterizeOptions() and gdal.Rasterize() and my
thought was that I could create feature with geom like (lon, lat,
ssha) and then rasterize that into the image using the option
"useZ=True" but this seems to imply that I need to have an ogr source
for the vector data. It would be much more convenient to be able to
just pass features from python to the Rasterize() function.
I see callback and callback_data in the options can these be used for
that? How?
Rasterize(destNameOrDestDS, srcDS, **kwargs)
I can open the gtiff file and then pass the handle of that to
destNameOrDestDS.
Can a create an in memory srcDS that I've loaded with the track
segments. The NetCDF files have about 5-6000 track points in them so
an eqivalent number of LineStringZ features.
Is there an example of something similar you can link me to.
Thanks,
-Steve
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