The new oceanspy.stations(lons, lats, **cutout_kwargs) will allow to "sample" model data returning the nearest neighbor to the prescribed (lon, lat) values. The output will be a new OceanDataset (od) with dimensions time, Z, stations. This will allow to "compare" with argo floats.
This is different from oceanspy.mooring_array(Xmoor, Ymoor, **cutout_kwargs) which, given an array of lons and lats, it extracts a zig-zaging array of model data in which all points are connected in index space (when in spherical geometry, the array follows a great circle path). The output of mooring_array is also more complex than that of would-beoceanspy.stations, since it extracts to "velocity" points for each "scalar" point.
Mikejmnez
commented
1 year ago
The new
oceanspy.stations(lons, lats, **cutout_kwargs)will allow to "sample" model data returning the nearest neighbor to the prescribed (lon, lat) values. The output will be a new OceanDataset (od) with dimensions time, Z, stations. This will allow to "compare" with argo floats.This is different from
oceanspy.mooring_array(Xmoor, Ymoor, **cutout_kwargs)which, given an array of lons and lats, it extracts a zig-zaging array of model data in which all points are connected in index space (when in spherical geometry, the array follows a great circle path). The output of mooring_array is also more complex than that of would-beoceanspy.stations, since it extracts to "velocity" points for each "scalar" point.