elif jj == 'BARPR':
df2.loc[:,
'PRSFC'] = df2.loc[:,
'PRSFC'] * 0.01 #convert model Pascals-->millibars
elif jj == 'PRECIP':
df2.loc[:,
'PRECIP'] = df2.loc[:,
'PRECIP'] * 0.1 #convert obs mm-->cm
elif jj == 'TEMP':
df2.loc[:,
'TEMP2'] = df2.loc[:,
'TEMP2'] - 273.16 #convert model K-->C
elif jj == 'RHUM':
#convert model mixing ratio to relative humidity
df2.loc[:, 'Q2'] = get_relhum(df2.loc[:, 'TEMP2'],
df2.loc[:, 'PRSFC'],
df2.loc[:, 'Q2'])
df2.rename(index=str, columns={"Q2": "RH_mod"}, inplace=True)
elif jj == 'CO':
df2.loc[:,
'CO'] = df2.loc[:,
'CO'] * 1000.0 #convert obs ppm-->ppb
else:
df2 = df2
#Calculates average statistics over entire file time
if reg is True and subset_giorgi is False:
stats = open(
finput[0].replace('.hdf', '_') + startdatename + '_' +
enddatename + '_reg_stats_domain.txt', 'a')
elif reg is True and subset_giorgi is True:
stats = open(
df2.loc[:,
'BARPR'] = df2.loc[:,
'BARPR'] / 0.01 #convert obs millibars-->Pascals ***Conform to model units for overlay ***
elif jj == 'PRECIP':
df2.loc[:,
'PRECIP'] = df2.loc[:,
'PRECIP'] * 0.1 #convert obs mm-->cm
elif jj == 'TEMP':
#df2.loc[:,'TEMP2'] = df2.loc[:,'TEMP2']-273.16 #convert model K-->C
df2.loc[:,
'TEMP'] = df2.loc[:,
'TEMP'] + 273.16 #convert obs C-->K ***Conform to model units for overlay ***
elif jj == 'RHUM':
#convert model mixing ratio to relative humidity
df2.loc[:, 'Q2'] = get_relhum(
df2.loc[:, 'TEMP2'], df2.loc[:, 'PRSFC'], df2.loc[:, 'Q2']
) # *** Currently not supported for spatial overlay ***
#df2.rename(index=str,columns={"Q2": "RH_mod"},inplace=True)
elif jj == 'CO':
df2.loc[:,
'CO'] = df2.loc[:, 'CO'] * 1000.0 #convert obs ppm-->ppb
else:
df2 = df2
#subset for period, or use output frequency
if startdate != None and enddate != None:
mask = (df2['time'] >= startdate) & (df2['time'] <= enddate)
dfnew = df2.loc[mask]
import datetime
startdatename_obj = datetime.datetime.strptime(
startdate, '%Y-%m-%d %H:%M:%S')
enddatename_obj = datetime.datetime.strptime(
