Working with ODYSSEA data¶
- ODYSSEA SST data can be considered from two feature perspectives :
Using ODYSSEA data as GridTimeSeries¶
We can ignore the fact that each grid at each time step is stored in an individual file, by using a storage class aggregating all files altogether. Only the file pattern is to be provided to this class.
>>> import data.gridtimeteries >>> import mapper.urlseries >>> >>> l4_pattern = '/home2/taveeg/data/operation/project/myocean/sst-tac/odyssea/v2/med/analysed_sst_002/%Y/%j/%Y%2m%2d-IFR-L4_GHRSST-SSTfnd-ODYSSEA-MED_002-v2.0-fv1.0.nc' >>> odysseaFiles = mapper.urlseries.URLSeries( urlpattern=l4_pattern ) >>> odysseaTimeSeries = data.gridtimeseries.GridTimeSeries() >>> odysseaTimeSeries.load( odysseaFiles )
Using ODYSSEA data as grid¶
If no temporal aspect is considered, and one wants to work on one or a few independant time steps, ODYSSEA data grids can be accessed directly.
>>> import datamodel.grid >>> import mapper.ncfile >>> ncf = mapper.ncfile.NCDataset( URL = "/home2/taveeg/data/operation/project/myocean/sst-tac/odyssea/v2/med/analysed_sst_002/2011/260/20110917-IFR-L4_GHRSST-SSTfnd-ODYSSEA-MED_002-v2.0-fv1.0.nc" ) >>> g = datamodel.grid.Grid() >>> g.load( ncf )
>>> g.display_map( 'analysed_sst', output='toto.png' )