def create_2D_input(cs_file, cosipy_file, static_file, start_date, end_date):
""" This function creates an input dataset from an offered csv file with input point data
Here you need to define how to interpolate the data.
Please note, there should be only one header line in the file.
Latest update:
Tobias Sauter 07.07.2019
"""
print('-------------------------------------------')
print('Create input \n')
print('Read input file %s' % (cs_file))
#-----------------------------------
# Read data
#-----------------------------------
date_parser = lambda x: dateutil.parser.parse(x, ignoretz=True)
df = pd.read_csv(cs_file,
delimiter=',', index_col=['TIMESTAMP'],
parse_dates=['TIMESTAMP'], na_values='NAN',date_parser=date_parser)
#-----------------------------------
# Select time slice
#-----------------------------------
if ((start_date != None) & (end_date !=None)):
df = df.loc[start_date:end_date]
#-----------------------------------
# Load static data
#-----------------------------------
print('Read static file %s \n' % (static_file))
ds = xr.open_dataset(static_file)
dso = xr.Dataset()
x, y = np.meshgrid(ds.west_east, ds.south_north)
dso.coords['time'] = (('time'), df.index.values)
dso.coords['lat'] = (('south_north','west_east'), y)
dso.coords['lon'] = (('south_north','west_east'), x)
#-----------------------------------
# Order variables
#-----------------------------------
df[T2_var] = df[T2_var].apply(pd.to_numeric, errors='coerce')
df[RH2_var] = df[RH2_var].apply(pd.to_numeric, errors='coerce')
df[U2_var] = df[U2_var].apply(pd.to_numeric, errors='coerce')
df[G_var] = df[G_var].apply(pd.to_numeric, errors='coerce')
df[PRES_var] = df[PRES_var].apply(pd.to_numeric, errors='coerce')
if (RRR_var in df):
df[RRR_var] = df[RRR_var].apply(pd.to_numeric, errors='coerce')
if (PRES_var not in df):
df[PRES_var] = 660.00
if (LWin_var not in df and N_var not in df):
print("ERROR no longwave incoming or cloud cover data")
sys.exit()
elif (LWin_var in df):
df[LWin_var] = df[LWin_var].apply(pd.to_numeric, errors='coerce')
elif (N_var in df):
df[N_var] = df[N_var].apply(pd.to_numeric, errors='coerce')
if (SNOWFALL_var in df):
df[SNOWFALL_var] = df[SNOWFALL_var].apply(pd.to_numeric, errors='coerce')
#-----------------------------------
# Get values from file
#-----------------------------------
T2 = df[T2_var] # Temperature
RH2 = df[RH2_var] # Relative humdity
U2 = df[U2_var] # Wind velocity
G = df[G_var] # Incoming shortwave radiation
PRES = df[PRES_var] # Pressure
#-----------------------------------
# Create numpy arrays for the 2D fields
#-----------------------------------
T_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
RH_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
U_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
G_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
P_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
LWin_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
N_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
if (RRR_var in df):
RRR = df[RRR_var] # Precipitation
RRR_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
if(SNOWFALL_var in df):
SNOWFALL = df[SNOWFALL_var] # Incoming longwave radiation
SNOWFALL_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
if(LWin_var in df):
LW = df[LWin_var] # Incoming longwave radiation
LW_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
if(N_var in df):
N = df[N_var] # Cloud cover fraction
N_interp = np.zeros([len(dso.time), len(ds.south_north), len(ds.west_east)])
#-----------------------------------
# Interpolate point data to grid
#-----------------------------------
print('Interpolate CR file to grid')
# Interpolate data (T, RH, RRR, U) to grid using lapse rates
for t in range(len(dso.time)):
T_interp[t,:,:] = (T2[t]) + (ds.HGT.values-stationAlt)*lapse_T
RH_interp[t,:,:] = RH2[t] + (ds.HGT.values-stationAlt)*lapse_RH
U_interp[t,:,:] = U2[t]
# Interpolate pressure using the barometric equation
SLP = PRES[t]/np.power((1-(0.0065*stationAlt)/(288.15)), 5.255)
P_interp[t,:,:] = SLP * np.power((1-(0.0065*ds.HGT.values)/(288.15)), 5.255)
if (RRR_var in df):
RRR_interp[t,:,:] = RRR[t] + (ds.HGT.values-stationAlt)*lapse_RRR
if (SNOWFALL_var in df):
SNOWFALL_interp[t, :, :] = SNOWFALL[t] + (ds.HGT.values-stationAlt)*lapse_SNOWFALL
if(LWin_var in df):
LW_interp[t,:,:] = LW[t]
if(N_var in df):
N_interp[t,:,:] = LW[t]
# Change aspect to south==0, east==negative, west==positive
ds['ASPECT'] = np.mod(ds['ASPECT']+180.0, 360.0)
mask = ds['ASPECT'].where(ds['ASPECT']<=180.0)
aspect = ds['ASPECT'].values
aspect[aspect<180] = aspect[aspect<180]*-1.0
aspect[aspect>=180] = 360.0 - aspect[aspect>=180]
ds['ASPECT'] = (('south_north','west_east'),aspect)
print(('Number of glacier cells: %i') % (np.count_nonzero(~np.isnan(ds['MASK'].values))))
# Auxiliary variables
mask = ds.MASK.values
slope = ds.SLOPE.values
aspect = ds.ASPECT.values
south_norths = ds.south_north.values
west_easts = ds.west_east.values
sw = G.values
#-----------------------------------
# Run radiation module
#-----------------------------------
if radiationModule:
print('Run the radiation module')
else:
print('No radiation module used')
for t in range(len(dso.time)):
doy = df.index[t].dayofyear
hour = df.index[t].hour
for i in range(len(ds.south_north)):
for j in range(len(ds.west_east)):
if (mask[i,j]==1):
if radiationModule:
G_interp[t,i,j] = np.maximum(0.0, correctRadiation(south_norths[i],west_easts[j], timezone_lon, doy, hour, slope[i,j], aspect[i,j], sw[t], zeni_thld))
else:
G_interp[t,i,j] = sw[t]
#-----------------------------------
# Check bounds for relative humidity
#-----------------------------------
RH_interp[RH_interp > 100.0] = 100.0
RH_interp[RH_interp < 0.0] = 0.1
#-----------------------------------
# Add variables to file
#-----------------------------------
add_variable_along_latlon(dso, ds.HGT.values, 'HGT', 'm', 'Elevation')
add_variable_along_latlon(dso, ds.ASPECT.values, 'ASPECT', 'degrees', 'Aspect of slope')
add_variable_along_latlon(dso, ds.SLOPE.values, 'SLOPE', 'degrees', 'Terrain slope')
add_variable_along_latlon(dso, ds.MASK.values, 'MASK', 'boolean', 'Glacier mask')
add_variable_along_timelatlon(dso, T_interp, 'T2', 'K', 'Temperature at 2 m')
add_variable_along_timelatlon(dso, RH_interp, 'RH2', '%', 'Relative humidity at 2 m')
add_variable_along_timelatlon(dso, U_interp, 'U2', 'm s\u207b\xb9', 'Wind velocity at 2 m')
add_variable_along_timelatlon(dso, G_interp, 'G', 'W m\u207b\xb2', 'Incoming shortwave radiation')
add_variable_along_timelatlon(dso, P_interp, 'PRES', 'hPa', 'Atmospheric Pressure')
if (RRR_var in df):
add_variable_along_timelatlon(dso, RRR_interp, 'RRR', 'mm', 'Total precipitation (liquid+solid)')
if(SNOWFALL_var in df):
add_variable_along_timelatlon(dso, SNOWFALL_interp, 'SNOWFALL', 'm', 'Snowfall')
if(LWin_var in df):
add_variable_along_timelatlon(dso, LW_interp, 'LWin', 'W m\u207b\xb2', 'Incoming longwave radiation')
if(N_var in df):
add_variable_along_timelatlon(dso, N_interp, 'N', '%', 'Cloud cover fraction')
#-----------------------------------
# Write file to disc
#-----------------------------------
dso.to_netcdf(cosipy_file)
print('Input file created \n')
print('-------------------------------------------')
#-----------------------------------
# Do some checks
#-----------------------------------
check(dso.T2,316.16,223.16)
check(dso.RH2,100.0,0.0)
check(dso.U2, 50.0, 0.0)
check(dso.RRR,20.0,0.0)
check(dso.G,1600.0,0.0)
check(dso.PRES,1080.0,200.0)
if(SNOWFALL_var in df):
check(ds.SNOWFALL,0.1,0.0)
def create_input(cs_file, cosipy_file, static_file, start_date, end_date):
""" This function creates an input dataset from the Hintereisferner CR3000 Logger Dataset
Here you need to define how to interpolate the data.
"""
print('-------------------------------------------')
print('Create input \n')
print('Read input file %s' % (cs_file))
# Read data
date_parser = lambda x: dateutil.parser.parse(x, ignoretz=True)
df = pd.read_csv(cs_file,
delimiter=',',
index_col=['TIMESTAMP'],
parse_dates=['TIMESTAMP'],
na_values='NAN',
date_parser=date_parser)
df[T2_var] = df[T2_var].apply(pd.to_numeric, errors='coerce')
df[RH2_var] = df[RH2_var].apply(pd.to_numeric, errors='coerce')
df[U2_var] = df[U2_var].apply(pd.to_numeric, errors='coerce')
df[G_var] = df[G_var].apply(pd.to_numeric, errors='coerce')
df[PRES_var] = df[PRES_var].apply(pd.to_numeric, errors='coerce')
df[RRR_var] = df[RRR_var].apply(pd.to_numeric, errors='coerce')
df[N_var] = df[N_var].apply(pd.to_numeric, errors='coerce')
# Select time slice
df = df.loc[start_date:end_date]
if (SNOWFALL_var in df):
# Make hourly data
df = df.resample('H').agg({
T2_var: 'mean',
RH2_var: 'mean',
U2_var: 'mean',
RRR_var: 'sum',
G_var: 'mean',
PRES_var: 'mean',
N_var: 'mean',
SNOWFALL_var: 'sum'
})
else:
df = df.resample('H').agg({
T2_var: 'mean',
RH2_var: 'mean',
U2_var: 'mean',
RRR_var: 'sum',
G_var: 'mean',
PRES_var: 'mean',
N_var: 'mean'
})
# Load static data
print('Read static file %s \n' % (static_file))
ds = xr.open_dataset(static_file)
ds.coords['time'] = df.index.values
# Variable names in csv file
T2 = df[T2_var] # Temperature
RH2 = df[RH2_var] # Relative humdity
U2 = df[U2_var] # Wind velocity
G = df[G_var] # Incoming shortwave radiation
PRES = df[PRES_var] # Pressure
RRR = df[RRR_var] # Precipitation
N = df[N_var] # Cloud cover fraction
# Create data arrays for the 2D fields
T_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
RH_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
RRR_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
U_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
G_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
P_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
N_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
if (SNOWFALL_var in df):
SNOWFALL = df[SNOWFALL_var] # Incoming longwave radiation
SNOWFALL_interp = np.zeros([len(ds.time), len(ds.lat), len(ds.lon)])
print('Interpolate CR file to grid')
# Interpolate data (T, RH, RRR, U) to grid using lapse rates
for t in range(len(ds.time)):
T_interp[t, :, :] = (T2[t]) + (ds.HGT.values - stationAlt) * lapse_T
RH_interp[t, :, :] = RH2[t] + (ds.HGT.values - stationAlt) * lapse_RH
RRR_interp[t, :, :] = RRR[t]
U_interp[t, :, :] = U2[t]
N_interp[t, :, :] = N[t]
# Interpolate pressure using the barometric equation
SLP = PRES[t] / np.power((1 - (0.0065 * stationAlt) / (288.15)), 5.255)
P_interp[t, :, :] = SLP * np.power(
(1 - (0.0065 * ds.HGT.values) / (288.15)), 5.255)
if (SNOWFALL_var in df):
SNOWFALL_interp[t, :, :] = SNOWFALL[t]
print("T_inter after", np.min(T_interp))
# Change aspect to south==0, east==negative, west==positive
ds['ASPECT'] = np.mod(ds['ASPECT'] + 180.0, 360.0)
mask = ds['ASPECT'].where(ds['ASPECT'] <= 180.0)
aspect = ds['ASPECT'].values
aspect[aspect < 180] = aspect[aspect < 180] * -1.0
aspect[aspect >= 180] = 360.0 - aspect[aspect >= 180]
ds['ASPECT'] = (('lat', 'lon'), aspect)
print(np.count_nonzero(~np.isnan(ds['MASK'].values)))
# Auxiliary variables
mask = ds.MASK.values
slope = ds.SLOPE.values
aspect = ds.ASPECT.values
lats = ds.lat.values
lons = ds.lon.values
sw = G.values
if radiationModule:
print('Run the radiation module')
else:
print('No radiation module used')
for t in range(len(ds.time)):
doy = df.index[t].dayofyear
hour = df.index[t].hour
for i in range(len(ds.lat)):
for j in range(len(ds.lon)):
if (mask[i, j] == 1):
if radiationModule:
G_interp[t, i, j] = np.maximum(
0.0,
correctRadiation(lats[i], lons[j], timezone_lon,
doy, hour, slope[i, j],
aspect[i, j], sw[t], zeni_thld))
else:
G_interp[t, i, j] = sw[t]
# Check temperature, rh2, pressure,
RH_interp[RH_interp > 100.0] = 100.0
RH_interp[RH_interp < 0.0] = 0.1
# Add arrays to dataset and write file
add_variable_2D(ds, T_interp, 'T2', 'K', 'Temperature at 2 m')
add_variable_2D(ds, RH_interp, 'RH2', '%', 'Relative humidity at 2 m')
add_variable_2D(ds, RRR_interp, 'RRR', 'mm',
'Total precipitation (liquid+solid)')
add_variable_2D(ds, U_interp, 'U2', 'm s\u207b\xb9',
'Wind velocity at 2 m')
add_variable_2D(ds, G_interp, 'G', 'W m\u207b\xb2',
'Incoming shortwave radiation')
add_variable_2D(ds, P_interp, 'PRES', 'hPa', 'Atmospheric Pressure')
add_variable_2D(ds, N_interp, 'N', '%', 'Cloud cover fraction')
if (SNOWFALL_var in df):
add_variable_2D(ds, SNOWFALL_interp, 'SNOWFALL', 'm', 'Snowfall')
ds.to_netcdf(cosipy_file)
print('Input file created \n')
print('-------------------------------------------')
print(ds)
check(ds.T2, 316.16, 223.16)
check(ds.RH2, 100.0, 0.0)
check(ds.U2, 50.0, 0.0)
check(ds.RRR, 20.0, 0.0)
check(ds.G, 1600.0, 0.0)
check(ds.PRES, 1080.0, 200.0)
check(ds.N, 1.0, 0.0)
if (SNOWFALL_var in df):
check(ds.SNOWFALL, 0.1, 0.0)