species_list = ['O3']
#Get variables for location chosen
obsfile, loc_label, model_index = modules.location_check(location)
for species in species_list:
units, obs_data_name, unit_cut, species_type, actual_species_name, obs_switch, model_cut_switch, ofac = modules.obs_variable_finder(
species)
#set GAW_switch on or off. 'y' = multiple location GAW sim, 'n' = 1 location output
GAW_switch = 'y'
# Read in the model output
if GAW_switch == 'y':
model, names = modules.readfile_GAW(
"binary_logs/GEOS_v90103_2x2.5_GAW_O3_logs.npy",
model_index) #model index represents gaw location
else:
model, names = modules.readfile(
"binary_logs/GEOS_v90103_4x5_CV_logs.npy",
"001") #001 represents single location
# Processes the model date
date = model[:, 0]
time = model[:, 1]
model_time = modules.date_process(date, time)
#Define sampling intervals
samp_spacing = 1. / 24.
#Convert model time array into numpy array
if met[counter] == 'MERRA':
model, names = modules.readfile(
"", "001") #001 represents single location
if res[counter] == '0.5x0.666 Nested Europe':
if met[counter] == 'GEOS 5':
model, names = modules.readfile(
"", "001") #001 represents single location
if met[counter] == 'MERRA':
model, names = modules.readfile(
"", "001") #001 represents single location
if GAW_switch[counter] == 'y':
if mversion[counter] == 'v90102':
if res[counter] == '4x5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile_GAW(
"", model_index) #model index represents gaw location
if met[counter] == 'MERRA':
model, names = modules.readfile_GAW(
"", model_index) #model index represents gaw location
if res[counter] == '2x2.5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile_GAW(
"", model_index) #model index represents gaw location
if met[counter] == 'MERRA':
model, names = modules.readfile_GAW(
"", model_index) #model index represents gaw location
if mversion[counter] == 'v90103':
if res[counter] == '4x5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile_GAW(
#Met.
#GEOS 5
#MERRA
met = 'GEOS 5'
#Get variables for location chosen
obsfile, loc_label, model_index = modules.location_check(location)
#set GAW_switch on or off. 'y' = multiple location GAW sim, 'n' = 1 location output
GAW_switch = 'y'
# Read in the model output
if GAW_switch == 'y':
model, names = modules.readfile_GAW(
"binary_logs/convectoff_gaw_logs_O3.npy",
model_index) #model index represents gaw location
else:
model, names = modules.readfile(
"binary_logs/GEOS_v90103_4x5_MERRA_Mace_Head_logs.npy",
"001") #001 represents single location
# Processes the model date
date = model[:, 0]
time = model[:, 1]
model_time = modules.date_process(date, time)
#Define sampling intervals
samp_spacing = 1. / 24.
#Convert model time array into numpy array
#Calculate Nyquist frequency, Si and Si x 2 for normalisation checks.
#nyquist_freq_lomb_model = frequencies[-1]
#Si_lomb_model = np.mean(fy)*nyquist_freq_lomb_model
#print nyquist_freq_lomb_model, Si_lomb_model, Si_lomb_model*2
model_list = [
'binary_logs/GEOS_v90103_4x5_GAW_O3_logs.npy',
'binary_logs/convectoff_gaw_logs_O3.npy',
'binary_logs/drydepoff_gaw_logs_O3.npy',
'binary_logs/emissionsoff_gaw_logs_O3.npy',
'binary_logs/transportoff_gaw_logs_O3.npy'
]
#loop through diff models and get respective obs. data
for i in model_list:
model, names = modules.readfile_GAW(i, model_index)
# Processes the model date
date = model[:, 0]
time = model[:, 1]
model_time = modules.date_process(date, time)
print counter
print res[counter]
print met[counter]
#print date
#print time
#Define sampling intervals
samp_spacing = 1. / 24.
