sat_archive="/gss/gss_work/DRES_OGS_BiGe/Observations/TIME_RAW_DATA/ONLINE/SAT/MODIS/DAILY/CHECKED/"
DAILY_SAT_LIST=TS.get_daily_sat(forecasts_sublist,sat_archive)
# float aggregator already done by others
day=0
surf_layer=Layer(0,10)
for time,archived_file,satfile in DAILY_SAT_LIST:
avefile=INPUTDIR + os.path.basename(archived_file)[:-3]
day=day+1
outfile=OUTDIR + "misfit+%dh.nc" % (day*24)
print avefile
continue
Sat16 = Sat.convertinV4format( Sat.readfromfile(satfile) )
De = DataExtractor(TheMask,filename=avefile, varname='P_i')
Model = MapBuilder.get_layer_average(De, surf_layer)
Misfit = Sat16-Model
cloudsLand = (np.isnan(Sat16)) #| (Sat16 > 1.e19) | (Sat16<0)
modelLand = np.isnan(Model) #lands are nan
nodata = cloudsLand | modelLand
selection = ~nodata # & TheMask.mask_at_level(200.0)
Misfit[nodata] = np.NaN
netcdf3.write_2d_file(Misfit, 'chl_misfit', outfile, TheMask)
filelist=[]
for k in indexes:
t = TL.Timelist[k]
filename = INPUTDIR + "ave." + t.strftime("%Y%m%d-%H:%M:%S") + "." + var + ".nc"
filelist.append(filename)
# ----------------------------------------------------------
M3d = TimeAverager3D(filelist, weights, var, TheMask)
for layer in LAYERLIST:
print layer
De = DataExtractor(TheMask,rawdata=M3d)
integrated = MapBuilder.get_layer_average(De, layer)
clim = [M3d[TheMask.mask].min(), M3d[TheMask.mask].max()]
fig,ax = mapplot({'varname':var, 'clim':clim, 'layer':layer, 'data':integrated, 'date':req.string},fig=None,ax=None,mask=TheMask)
outfile = OUTPUTDIR + prefix + '.' + var + "." + layer.longname() + ".nc"
netcdf3.write_2d_file(integrated,var,outfile,TheMask)
outfile = OUTPUTDIR + var + "." + prefix + "." + layer.longname() + ".png"
fig.savefig(outfile)
pl.close(fig)
# Now, the whole year
import commons.timerequestors as requestors
MY_YEAR = TimeInterval('20140401','20150401',"%Y%m%d")
req = requestors.Generic_req(MY_YEAR)
indexes,weights = TL.select(req)