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
model_time = np.array(model_time)
counter = 0
obs_smoothed = np.exp(obs_smoothed)
ax = fig.add_subplot(1, 1, 1)
#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
#loop through diff models and get respective obs. data
for i in range(len(mversion)):
if GAW_switch[counter] == 'n':
if mversion[counter] == 'v90102':
if res[counter] == '4x5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile(
"binary_logs/GEOS_v90102_4x5_CV_logs.npy",
"001") #001 represents single location
if met[counter] == 'MERRA':
model, names = modules.readfile(
"", "001") #001 represents single location
if res[counter] == '2x2.5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile(
"binary_logs/GEOS_v90103_2x25_CV_logs.npy",
"001") #001 represents single location
if met[counter] == 'MERRA':
model, names = modules.readfile(
"", "001") #001 represents single location
if mversion[counter] == 'v90103':
if res[counter] == '4x5':
label='%s Obs. %s Smoothed ' % (loc_label, actual_species_name))
#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
#loop through diff models and get respective obs. data
for i in range(len(mversion)):
if GAW_switch[counter] == 'n':
if mversion[counter] == 'v90102':
if res[counter] == '4x5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile(
"binary_logs/GEOS_v90102_4x5_CV_logs.npy",
"001") #001 represents single location
if met[counter] == 'MERRA':
model, names = modules.readfile(
"", "001") #001 represents single location
if res[counter] == '2x2.5':
if met[counter] == 'GEOS 5':
model, names = modules.readfile(
"binary_logs/GEOS_v90103_2x25_CV_logs.npy",
"001") #001 represents single location
if met[counter] == 'MERRA':
model, names = modules.readfile(
"", "001") #001 represents single location
if mversion[counter] == 'v90103':
if res[counter] == '4x5':
#Define sampling intervals
samp_spacing = 1. / 24.
counter = 0
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 plotting area & background to white
fig = plt.figure(figsize=(20, 12))
fig.patch.set_facecolor('white')
ax = plt.subplot(111)
model, names = modules.readfile("binary_logs/GEOS_v90103_4x5_CV_logs.npy",
"001")
# Processes the model date
date = model[:, 0]
time = model[:, 1]
model_time = modules.date_process(date, time)
k = names.index('O3')
model = model[:, k] * 1e9
#Define sampling frequency
samp_freq = 24
#FFT
samp_spacing = float(1. / 24.)
