2021-05-09T11:16 PM
(This post was last modified: 2021-05-09T11:17 PM by Sylvain Herlédan.)
Hi Pete,
I think the developer guide and other manuals available on https://ftp.odl.bzh/odl/syntool/ are the only documentation we published about Syntool.
In most cases there are three product-specific steps that you have to look out for when writing a new Syntool reader:
The syntool_converter package contains several readers for data defined on a regular lat/lon grid and stored as NetCDF files: you can have a look at the syntool_converter/smos/smos_l4_sss_ifr.py file in the converter source code for a basic example. If it does not match your use case, could you provide more information about the data you want to add so I can point you to a more relevant example?
Cheers,
Sylvain
I think the developer guide and other manuals available on https://ftp.odl.bzh/odl/syntool/ are the only documentation we published about Syntool.
In most cases there are three product-specific steps that you have to look out for when writing a new Syntool reader:
- choosing the mask to apply on the data variables (requires data expertise to make sensible choices)
- defining geolocation so that the resulting GeoTIFF can be warped by GDAL with sufficient precision during the ingestion step. For L3 and L4 products it is usually a matter of defining the geotransform (a 2x3 transformation matrix, flattened in a 6-items list in Syntool readers) and the coordinates system in which the grid is regular (lat/lon or y/x with a well-known projection) whereas for L1 and L2 data you have to use GCPs ("Ground Control Points" that associate the indices of a subset of cells in the data matrix with their geographical coordinates) because the data grid uses the instrument geometry and not a well-known projection.
- choosing the min and max values to preserve data dynamics as much as possible when packing floating point geophysical values into 8 bits GeoTIFF bands (i.e. only 255 possible values because value '255' is reserved for transparency) .
The syntool_converter package contains several readers for data defined on a regular lat/lon grid and stored as NetCDF files: you can have a look at the syntool_converter/smos/smos_l4_sss_ifr.py file in the converter source code for a basic example. If it does not match your use case, could you provide more information about the data you want to add so I can point you to a more relevant example?
Cheers,
Sylvain