MODEL_DIR="/gpfs/work/OGS_prod/CalVal/Q_REP_MODEL_FORECAST/"
REF_DIR = "/gss/gss_work/DRES_OGS_BiGe/Observations/TIME_RAW_DATA/ONLINE/SAT/MODIS/WEEKLY/"
Timestart="20150701"
Time__end="20151001"
TI = TimeInterval(Timestart,Time__end,"%Y%m%d")
model_TL = TimeList.fromfilenames(TI, MODEL_DIR,"*.nc",prefix='',dateformat='%Y%m%d')
IonamesFile = '../postproc/IOnames_sat.xml'
IOname = IOnames.IOnames(IonamesFile)
ngib=52
for itime, time in enumerate(model_TL.Timelist[:1]):
satfile = REF_DIR + time.strftime(IOname.Input.dateformat) + IOname.Output.suffix + ".nc"
modfile = model_TL.filelist[itime]
ncIN = NC.netcdf_file(modfile,'r')
For = ncIN.variables['chl'].data[0,0,:,:].copy().astype(np.float64)
ncIN.close()
Sat16 = Sat.convertinV4format(Sat.readfromfile(satfile)).astype(np.float64)
Sat16 = Sat16[:,ngib:]
cloudsLand = np.isnan(Sat16)
modelLand = For > 1.0e+19
nodata = cloudsLand | modelLand
M = matchup.matchup(For[~nodata], Sat16[~nodata])
print M.bias()
print modeltime
CoupledList = sat_TL.couple_with([modeltime])
sattime = CoupledList[0][0]
satfile = REF_DIR + sattime.strftime(IOname.Input.dateformat) + IOname.Output.suffix + ".nc"
modfile = model_TL.filelist[itime]
De = DataExtractor(TheMask,filename=modfile, varname='P_i')
Model = MapBuilder.get_layer_average(De, surf_layer)
#ncIN = NC.netcdf_file(modfile,'r')
#Model = ncIN.variables['P_i'].data[0,0,:,:].copy()#.astype(np.float64)
#Model = ncIN.variables['lchlm'].data.copy()
#ncIN.close()
Sat16 = Sat.readfromfile(satfile,var='lchlm') #.astype(np.float64)
cloudsLand = (np.isnan(Sat16)) | (Sat16 > 1.e19)
modelLand = np.isnan(Model) #lands are nan
nodata = cloudsLand | modelLand
selection = ~nodata & mask200_2D
M = matchup.matchup(Model[selection], Sat16[selection])
for isub, sub in enumerate(OGS.P):
selection = SUB[sub.name] & (~nodata) & mask200_2D
M = matchup.matchup(Model[selection], Sat16[selection])
BGC_CLASS4_CHL_RMS_SURF_BASIN[itime,isub] = M.RMSE()
BGC_CLASS4_CHL_BIAS_SURF_BASIN[itime,isub] = M.bias()
BGC_CLASS4_CHL_EAN_RMS_SURF_BASIN = BGC_CLASS4_CHL_RMS_SURF_BASIN.mean(axis=0)
BGC_CLASS4_CHL_EAN_BIAS_SURF_BASIN = BGC_CLASS4_CHL_BIAS_SURF_BASIN.mean(axis=0)
De = DataExtractor(TheMask,filename=modfile, varname='P_i')
Model = MapBuilder.get_layer_average(De, surf_layer)
#ncIN = NC.netcdf_file(modfile,'r')
#Model = ncIN.variables['P_i'].data[0,0,:,:].copy()#.astype(np.float64)
#Model = ncIN.variables['lchlm'].data.copy()
#ncIN.close()
Sat16 = Sat.readfromfile(satfile,var='lchlm') #.astype(np.float64)
cloudsLand = (np.isnan(Sat16)) | (Sat16 > 1.e19) | (Sat16<0)
modelLand = np.isnan(Model) #lands are nan
nodata = cloudsLand | modelLand
selection = ~nodata & masksel_2D
M = matchup.matchup(Model[selection], Sat16[selection])
for isub, sub in enumerate(OGS.P):
selection = SUB[sub.name] & (~nodata) & masksel_2D
M = matchup.matchup(Model[selection], Sat16[selection])
BGC_CLASS4_CHL_RMS_SURF_BASIN[itime,isub] = M.RMSE()
BGC_CLASS4_CHL_BIAS_SURF_BASIN[itime,isub] = M.bias()
weight = TheMask.area[selection]
MODEL_MEAN[itime,isub] = weighted_mean( M.Model,weight)
SAT___MEAN[itime,isub] = weighted_mean( M.Ref, weight)
Mlog = matchup.matchup(np.log10(Model[selection]), np.log10(Sat16[selection])) #add matchup based on logarithm
BGC_CLASS4_CHL_RMS_SURF_BASIN_LOG[itime,isub] = Mlog.RMSE()
BGC_CLASS4_CHL_BIAS_SURF_BASIN_LOG[itime,isub] = Mlog.bias()
BGC_CLASS4_CHL_EAN_RMS_SURF_BASIN = BGC_CLASS4_CHL_RMS_SURF_BASIN.mean(axis=0)
