def grid_interactive(event):
global pl
global ind
global fig2
ind = event.ind
ind = ind[0]
#find ind of closest lat/lon
#ind = modules.find_nearest_point_index(obs_lons,obs_lats,x_data,y_data)
try:
for i in range(len(pl)):
pl.pop(0).remove()
first_run = False
except:
first_run = True
pass
pl = m.plot([linear_lons[ind]], [linear_lats[ind]], 's', ms=20, alpha=0.6, color='yellow',zorder=20)
#get model spectra for site clicked
lat_n,lon_n = modules.obs_model_gridbox(lat_e,lon_e,linear_lats[ind],linear_lons[ind])
period = site_group.variables['period'][:,lat_n,lon_n]
spectrum = site_group.variables['amplitude'][:,lat_n,lon_n]
#period = periods[:,lat_n,lon_n]
#spectrum = amplitude[:,lat_n,lon_n]
fig.canvas.draw()
if first_run == False:
plt.close(fig2)
fig2, (ax1) = plt.subplots(1, 1, figsize =(24,12))
fig2.patch.set_facecolor('white')
ax1.plot(period, spectrum, color='black', markersize = 3)
ax1.set_xscale('log')
ax1.set_yscale('log')
#plt.legend(loc = 'lower right')
plt.tight_layout()
ax1.grid()
plt.show()
else:
fig2, (ax1) = plt.subplots(1, 1, figsize =(24,12))
fig2.patch.set_facecolor('white')
ax1.plot(period, spectrum, color='black', markersize = 3)
ax1.set_xscale('log')
ax1.set_yscale('log')
#plt.legend(loc = 'lower right')
plt.tight_layout()
ax1.grid()
plt.show()
obs_var = site_group.variables[species.lower()][:] obs_date = site_group.variables['date'][:] obs_time = site_group.variables['time'][:] obs_lat = site_group.latitude obs_lon = site_group.longitude obs_alt = site_group.altitude obs_group = site_group.process_group obs_var_mask = np.ma.masked_where(obs_var <= 0, obs_var) obs_var = obs_var[~np.isnan(obs_var_mask)] #---------------------------------------- #find model data gridbox to compare with obs. #get model gridbox for obs site lat_n, lon_n = modules.obs_model_gridbox(lat_e, lon_e, obs_lat, obs_lon) model_var = model_var[:, lat_n, lon_n] model_var = model_var * 1e9 model_var_mask = np.ma.masked_where(model_var <= 0, model_var) model_ave = np.ma.average(model_var_mask) model_var = model_var[~np.isnan(model_var_mask)] #-------------------------------------------- #get valid data and process time obs_time = np.array(modules.date_process(obs_date, obs_time, start_year)) model_time = np.array(modules.date_process(model_date, model_time, start_year)) model_test = model_var >= 0
def onpick(event):
global pl
global ind
global fig2
ind = event.ind
print 'ind = ',ind
ind = ind[0]
#x_data = event.xdata
#y_data = event.ydata
#find ind of closest lat/lon
#ind = modules.find_nearest_point_index(obs_lons,obs_lats,x_data,y_data)
try:
for i in range(len(pl)):
pl.pop(0).remove()
first_run = False
except:
first_run = True
pass
pl = m.plot([linear_lons[ind]], [linear_lats[ind]], 's', ms=20, alpha=0.6, color='yellow',zorder=20)
#get model timeseries for site clicked
lat_n,lon_n = modules.obs_model_gridbox(lat_e,lon_e,linear_lats[ind],linear_lons[ind])
model_var_pick = model_var[:,lat_n,lon_n]
model_var_pick = model_var_pick*1e9
model_var_mask = np.ma.masked_where(model_var_pick<=0,model_var_pick)
model_time_pd = pd.date_range(start = model_datetimes[0],end = model_datetimes[-1], freq = 'H')
model_var_pd = pd.Series(model_var_mask, index=model_time_pd)
#create sine wave from amp/phase
model_date_l = model_date.astype(int)
model_time_l = model_time.astype(int)
model_times = modules.date_process(model_date_l,model_time_l,start_year)
model_times = np.array(model_times)
pi2 = np.pi*2
ratio = 100./annual_amp[lat_n,lon_n]
ha_percent = ratio*annual_amp[lat_n,lon_n]
#convert phases to radians
calc = pi2/24.
calc = pi2/6.
ha_ph_r = ha_ph[lat_n,lon_n] * calc
calc = pi2/12.
annual_ph_r = annual_ph[lat_n,lon_n] * calc
ha_model_wave = ha_amp[lat_n,lon_n]*(np.cos((pi2*model_times/(365.25/2.))-(ha_ph_r)))
annual_model_wave = annual_amp[lat_n,lon_n]*(np.cos((pi2*model_times/(365.25))-(annual_ph_r)))
ha_primary = p_ha_ph[lat_n,lon_n]
ha_secondary = s_ha_ph[lat_n,lon_n]
ha_model_wave = ha_model_wave+ave[lat_n,lon_n]
annual_model_wave = annual_model_wave+ave[lat_n,lon_n]
model_ha_wave_pd = pd.Series(ha_model_wave, index=model_time_pd)
model_annual_wave_pd = pd.Series(annual_model_wave, index=model_time_pd)
fig.canvas.draw()
if first_run == False:
plt.close(fig2)
fig2, (axo) = plt.subplots(1,figsize=(24,12))
fig2.patch.set_facecolor('white')
axo.plot_date(model_time_pd.to_pydatetime(), model_var_pd, color='black', markersize = 3, label = 'Observations')
axo.plot_date(model_time_pd.to_pydatetime(), model_ha_wave_pd, color='green', markersize = 3, label = 'Ha Waveform',markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(), model_annual_wave_pd, color='red', markersize = 3, label = 'Annual Waveform',markeredgecolor='None')
#axo.set_title('Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s\nPrimary HA Phase = %s,Primary HA Regime = %s, HA Amp to Annual Amp Percent = %s' %(ref,country,continent,group,lat,lon,alt,complete,a_class,r_class,ha_primary,ha_regime,ha_percent))
plt.legend(loc = 'lower right')
plt.tight_layout()
axo.grid()
plt.show()
else:
fig2, (axo) = plt.subplots(1,figsize=(24,12))
fig2.patch.set_facecolor('white')
axo.plot_date(model_time_pd.to_pydatetime(), model_var_pd, color='black', markersize = 3, label = 'Observations')
axo.plot_date(model_time_pd.to_pydatetime(), model_ha_wave_pd, color='green', markersize = 3, label = 'Ha Waveform',markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(), model_annual_wave_pd, color='red', markersize = 3, label = 'Annual Waveform',markeredgecolor='None')
#axo.set_title('Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s\nPrimary HA Phase = %s,Primary HA Regime = %s, HA Amp to Annual Amp Percent = %s' %(ref,country,continent,group,lat,lon,alt,complete,a_class,r_class,ha_primary,ha_regime,ha_percent))
plt.legend(loc = 'lower right')
plt.tight_layout()
axo.grid()
plt.show()
#read in variables for site obs_var = site_group.variables[species.lower()][:] obs_date = site_group.variables['date'][:] obs_time = site_group.variables['time'][:] obs_lat = site_group.latitude obs_lon = site_group.longitude obs_alt = site_group.altitude obs_group = site_group.process_group obs_var_mask = np.ma.masked_where(obs_var<=0,obs_var) #---------------------------------------- #find model data gridbox to compare with obs. #get model gridbox for obs site lat_n,lon_n = modules.obs_model_gridbox(lat_e,lon_e,obs_lat,obs_lon) #print 'Obs Lat & Lon = ', obs_lat,obs_lon #print 'Lat Centre = ',lat_c #print 'Lon Centre = ',lon_c #print lat_n,lon_n #print lat_c[lat_n], lon_c[lon_n] #print model_var.shape model_var = model_var[:,lat_n,lon_n] model_var = model_var*1e9 model_var_mask = np.ma.masked_where(model_var<=0,model_var) #---------------------------------------- #process obs dates and obs times to datetimes, then process pandas objects
data_valid = False
print 'Persisent Data gap > 3 months'
break
if i > 365:
data_valid = False
print 'Data gap > 1 Year'
break
#calc gregorian time
obs_time = date2num(datetimes, units='hours since 0001-01-01 00:00:00', calendar='gregorian')
if data_valid == True:
#split datasets into model pressure bands by daily timestep
#find lat and lon indices of site
lat_i,lon_i = modules.obs_model_gridbox(model_lat_edges,model_lon_edges,np.float64(current_lat),np.float64(current_lon))
bad_inds = []
level_inds = []
for i in range(len(obs_time)):
obs_t = obs_time[i]
obs_press = all_press[i]
obs_var = all_var[i]
time_ind = np.searchsorted(model_time,obs_t)
if obs_t > model_time[-1]:
bad_inds.append(i)
continue
else:
time_model_press_edges = model_press_edges[time_ind,:,:,:]
def onpick(event):
global pl
global ind
global fig2
ind = event.ind
ind = ind[0]
#x_data = event.xdata
#y_data = event.ydata
#find ind of closest lat/lon
#ind = modules.find_nearest_point_index(obs_lons,obs_lats,x_data,y_data)
try:
for i in range(len(pl)):
pl.pop(0).remove()
first_run = False
except:
first_run = True
pass
pl = m.plot([X[ind]], [Y[ind]], 'o', ms=12, alpha=0.6, color='yellow',zorder=20)
#get model timeseries for site clicked
lat_n,lon_n = modules.obs_model_gridbox(lat_e,lon_e,obs_lats[ind],obs_lons[ind])
model_var_pick = model_var[:,lat_n,lon_n]
model_var_pick = model_var_pick*1e9
model_var_mask = np.ma.masked_where(model_var_pick<=0,model_var_pick)
if model_name == 'MACC':
model_time_pd = pd.date_range(start = model_datetimes[0],end = model_datetimes[-1], freq = 'H')
count = 0
valids = []
for i in range(len(model_time_pd)):
if count == 0:
valids.append(i)
count+=1
elif count == 2:
count = 0
else:
count+=1
model_time_pd = model_time_pd[valids]
model_var_pd = pd.Series(model_var_mask, index=model_time_pd)
else:
model_time_pd = pd.date_range(start = model_datetimes[0],end = model_datetimes[-1], freq = 'H')
model_var_pd = pd.Series(model_var_mask, index=model_time_pd)
#get obs timeseries for site clicked
ref = obs_refs[ind]
obs_ts_group = obs_root_grp.groups[ref]
obs_var = obs_ts_group.variables[species.lower()][:]
group = obs_ts_group.process_group
lat = obs_ts_group.latitude
lon = obs_ts_group.longitude
lon = obs_ts_group.longitude
alt = obs_ts_group.altitude
complete = obs_ts_group.completeness
a_class = obs_ts_group.anthrome_class
r_class = obs_ts_group.raw_class
continent = loc_dict[tags[ind]]
country = obs_ts_group.country
obs_var_mask = np.ma.masked_where(obs_var<=0,obs_var)
obs_time_pd = pd.date_range(start = obs_datetimes[0],end = obs_datetimes[-1], freq = 'H')
obs_var_pd = pd.Series(obs_var_mask, index=obs_time_pd)
#create sine wave from amp/phase
obs_date_l = obs_date.astype(int)
obs_time_l = obs_time.astype(int)
obs_times = modules.date_process(obs_date_l,obs_time_l,start_year)
obs_times = np.array(obs_times)
pi2 = np.pi*2
#convert phases to radians
calc = pi2/6.
obs_ha_phase_r = obs_ha_phase[ind] * calc
calc = pi2/12.
obs_annual_phase_r = obs_annual_phase[ind] * calc
ha_obs_wave = obs_ha_mag[ind]*(np.cos((pi2*obs_times/(365.25/2.))-(obs_ha_phase_r)))
annual_obs_wave = obs_annual_mag[ind]*(np.cos((pi2*obs_times/(365.25))-(obs_annual_phase_r)))
seasonal_obs_wave = (ha_obs_wave+annual_obs_wave)+obs_ave[ind]
obs_seasonal_wave_pd = pd.Series(seasonal_obs_wave, index=obs_time_pd)
#create sine wave from amp/phase
mod_date_l = model_date.astype(int)
mod_time_l = model_time.astype(int)
mod_times = modules.date_process(mod_date_l,mod_time_l,start_year)
mod_times = np.array(mod_times)
pi2 = np.pi*2
#convert phases to radians
calc = pi2/6.
model_ha_phase_r = model_ha_phase[ind] * calc
calc = pi2/12.
model_annual_phase_r = model_annual_phase[ind] * calc
ha_model_wave = model_ha_mag[ind]*(np.cos((pi2*mod_times/(365.25/2.))-(model_ha_phase_r)))
annual_model_wave = model_annual_mag[ind]*(np.cos((pi2*mod_times/(365.25))-(model_annual_phase_r)))
seasonal_model_wave = (ha_model_wave+annual_model_wave)+model_ave[ind]
model_seasonal_wave_pd = pd.Series(seasonal_model_wave, index=model_time_pd)
#get spectra data
site_group_obs = root_grp_obs_spec.groups[ref]
site_group_mod = root_grp_mod_spec.groups[ref]
obs_period = site_group_obs.variables['period'][:]
mod_period = site_group_mod.variables['period'][:]
obs_amp = site_group_obs.variables['amplitude'][:]
mod_amp = site_group_mod.variables['amplitude'][:]
fig.canvas.draw()
if first_run == False:
plt.close(fig2)
fig2, (axo,axo2) = plt.subplots(2,figsize=(24,12))
fig2.patch.set_facecolor('white')
#fig2 = plt.figure()
axo.plot_date(obs_time_pd.to_pydatetime(), obs_var_pd, color='black', markersize = 3, label = 'Observations')
axo.plot_date(model_time_pd.to_pydatetime(), model_var_pd, color='red',alpha=0.5, markersize = 3, label = '%s %s %s %s'%(model_name,version,grid_size,met),markeredgecolor='None')
axo.plot_date(obs_time_pd.to_pydatetime(), obs_seasonal_wave_pd, color='yellow', markersize = 3, label = 'Obs Seasonal Waveform',markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(), model_seasonal_wave_pd, color='green', markersize = 3, label = 'Model Seasonal Waveform',markeredgecolor='None')
axo2.loglog(obs_period,obs_amp,color='black',label='Obs')
axo2.loglog(mod_period,mod_amp,color='red',label = '%s %s %s %s'%(model_name,version,grid_size,met))
axo2.text(0.01, 0.95, 'Obs D Amp = %8.2f ppb'%(obs_daily_mag[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.92, 'Model D Amp = %8.2f ppb'%(model_daily_mag[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.85, 'Obs HA Amp = %8.2f ppb'%(obs_ha_mag[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.82, 'Model HA Amp = %8.2f ppb'%(model_ha_mag[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.75, 'Obs A Amp = %8.2f ppb'%(obs_annual_mag[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.72, 'Model A Amp = %8.2f ppb'%(model_annual_mag[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.55, 'Obs D Phase = %8.2f'%(obs_daily_phase[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.52, 'Model D Phase = %8.2f'%(model_daily_phase[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.45, 'Obs HA Phase = %8.2f'%(obs_ha_phase[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.42, 'Model HA Phase = %8.2f'%(model_ha_phase[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
obs_a_ph = obs_annual_phase[ind]
mod_a_ph = model_annual_phase[ind]
if obs_a_ph > 12:
obs_a_ph = obs_a_ph-12.
if mod_a_ph > 12:
mod_a_ph = mod_a_ph-12.
axo2.text(0.01, 0.35, 'Obs A Phase = %8.2f'%(obs_a_ph),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.32, 'Model A Phase = %8.2f'%(mod_a_ph),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.15, 'Obs Ave = %8.2f ppb'%(obs_ave[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.12, 'Model Ave = %8.2f ppb'%(model_ave[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.axvline(1.,ymin=0,ymax=1,color='blue',linestyle='--')
axo2.axvline(182.625,ymin=0,ymax=1,color='blue',linestyle='--')
axo2.axvline(365.25,ymin=0,ymax=1,color='blue',linestyle='--')
axo2.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
axo2.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.gca().xaxis.set_major_formatter(FuncFormatter(xformatter))
plt.gca().yaxis.set_major_formatter(FuncFormatter(yformatter))
axo.set_title('Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s, Grid Index = %s,%s' %(ref,country,continent,group,lat,lon,alt,complete,a_class,r_class,lat_n,lon_n))
plt.legend(loc = 'lower right')
plt.tight_layout()
axo.grid()
axo2.grid()
plt.show()
else:
#fig2 = plt.figure()
fig2, (axo,axo2) = plt.subplots(2,figsize=(24,12))
fig2.patch.set_facecolor('white')
axo.plot_date(obs_time_pd.to_pydatetime(), obs_var_pd, color='black', markersize = 3, label = 'Observations')
axo.plot_date(model_time_pd.to_pydatetime(), model_var_pd, color='red', markersize = 3,alpha=0.5, label = '%s %s %s %s'%(model_name,version,grid_size,met),markeredgecolor='None')
axo.plot_date(obs_time_pd.to_pydatetime(), obs_seasonal_wave_pd, color='yellow', markersize = 3, label = 'Obs Seasonal Waveform',markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(), model_seasonal_wave_pd, color='green', markersize = 3, label = 'Model Seasonal Waveform',markeredgecolor='None')
axo2.loglog(obs_period,obs_amp,color='black',label='Obs')
axo2.loglog(mod_period,mod_amp,color='red', label = '%s %s %s %s'%(model_name,version,grid_size,met))
axo2.text(0.01, 0.95, 'Obs D Amp = %8.2f ppb'%(obs_daily_mag[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.92, 'Model D Amp = %8.2f ppb'%(model_daily_mag[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.85, 'Obs HA Amp = %8.2f ppb'%(obs_ha_mag[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.82, 'Model HA Amp = %8.2f ppb'%(model_ha_mag[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.75, 'Obs A Amp = %8.2f ppb'%(obs_annual_mag[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.72, 'Model A Amp = %8.2f ppb'%(model_annual_mag[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.55, 'Obs D Phase = %8.2f'%(obs_daily_phase[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.52, 'Model D Phase = %8.2f'%(model_daily_phase[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.45, 'Obs HA Phase = %8.2f'%(obs_ha_phase[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.42, 'Model HA Phase = %8.2f'%(model_ha_phase[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
obs_a_ph = obs_annual_phase[ind]
mod_a_ph = model_annual_phase[ind]
if obs_a_ph > 12:
obs_a_ph = obs_a_ph-12.
if mod_a_ph > 12:
mod_a_ph = mod_a_ph-12.
axo2.text(0.01, 0.35, 'Obs A Phase = %8.2f'%(obs_a_ph),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.32, 'Model A Phase = %8.2f'%(mod_a_ph),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.text(0.01, 0.15, 'Obs Ave = %8.2f ppb'%(obs_ave[ind]),transform=axo2.transAxes,fontweight='bold')
axo2.text(0.01, 0.12, 'Model Ave = %8.2f ppb'%(model_ave[ind]),transform=axo2.transAxes,fontweight='bold',color='red')
axo2.axvline(1.,ymin=0,ymax=1,color='blue',linestyle='--')
axo2.axvline(182.625,ymin=0,ymax=1,color='blue',linestyle='--')
axo2.axvline(365.25,ymin=0,ymax=1,color='blue',linestyle='--')
axo2.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
axo2.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.gca().xaxis.set_major_formatter(FuncFormatter(xformatter))
plt.gca().yaxis.set_major_formatter(FuncFormatter(yformatter))
axo.set_title('Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s, Grid Index = %s,%s' %(ref,country,continent,group,lat,lon,alt,complete,a_class,r_class,lat_n,lon_n))
plt.legend(loc = 'lower right')
plt.tight_layout()
axo.grid()
axo2.grid()
plt.show()
data_valid = False
print 'Persisent Data gap > 3 months'
break
if i > 365:
data_valid = False
print 'Data gap > 1 Year'
break
#calc gregorian time
obs_time = date2num(datetimes, units='hours since 0001-01-01 00:00:00', calendar='gregorian')
if data_valid == True:
#split datasets into model pressure bands by daily timestep
#find lat and lon indices of site
lat_i,lon_i = modules.obs_model_gridbox(model_lat_edges,model_lon_edges,np.float64(current_lat),np.float64(current_lon))
bad_inds = []
level_inds = []
for i in range(len(obs_time)):
obs_t = obs_time[i]
obs_press = all_press[i]
obs_var = all_var[i]
time_ind = np.searchsorted(model_time,obs_t)
if obs_t > model_time[-1]:
bad_inds.append(i)
continue
else:
time_model_press_edges = model_press_edges[time_ind,:,:,:]
#get observational location tags
#EU = europe, AF = africa, NA = north america, SA = south america, ANT = antarctica, ARC = arctic, O = oceanic, OC = oceania, AS = asia
tags = modules.get_tags(obs_refs)
model_time_pd = pd.date_range(start=model_datetime_time[0],
end=model_datetime_time[-1],
freq='H')
key = [model_time_pd.month]
nox_by_site = []
nox_ave = []
#cut all lat/lon indices
for i in range(len(obs_lons)):
obs_lat = obs_lats[i]
obs_lon = obs_lons[i]
lat_i, lon_i = modules.obs_model_gridbox(lat_e, lon_e, obs_lat, obs_lon)
nox_model_var_pd = pd.Series(nox_model_std_var[:, lat_i, lon_i],
index=model_time_pd)
nox_ave.append(np.average(nox_model_std_var[:, lat_i, lon_i]))
group = nox_model_var_pd.groupby(key)
nox_monthly_mean = group.mean()
nox_by_site.append(nox_monthly_mean)
nox_by_site = np.array(nox_by_site)
nox_ave = np.array(nox_ave)
#--------------------------------------------------------
#load in periodic lsp data
root_grp = Dataset(model_f)
model_var = root_grp.variables[species.lower()][:]
model_date = root_grp.variables['date'][:]
model_time = root_grp.variables['time'][:]
lat_e = root_grp.variables['lat_edges'][:]
lon_e = root_grp.variables['lon_edges'][:]
lat_c = root_grp.variables['lat_centre'][:]
lon_c = root_grp.variables['lon_centre'][:]
grid_size = root_grp.variables['grid_size'][:]
grid_size = grid_size[0]
model_var_mask = np.ma.masked_where(model_var < 0, model_var)
gridbox_count = len(lat_c) * len(lon_c)
lat_n, lon_n = modules.obs_model_gridbox(lat_e, lon_e, 1., 114.)
y = model_var[:, lat_n, lon_n]
y = y * 1e9
#set up plot
fig = plt.figure(figsize=(23, 12.3))
fig.patch.set_facecolor('white')
ax = fig.add_subplot(1, 1, 1)
x = modules.date_process(model_date, model_time, 2005)
ofac = 4
model_periods, model_mag, model_ph, model_fr, model_fi, amp_corr = modules.take_lomb(
obs_lons = np.append(obs_lons,obs_site_group.longitude)
obs_alt = np.append(obs_alt,obs_site_group.altitude)
obs_date = obs_site_group.variables['date'][:]
obs_time = obs_site_group.variables['time'][:]
data = obs_site_group.variables[species.lower()][:]
test = data >= 0
tests.append(test)
obs_var.append(data[test])
for i in range(len(obs_refs)):
obs_refs[i] = obs_refs[i].lower()
#cut model var
model_var = []
for i in range(len(obs_refs)):
lat_n,lon_n = modules.obs_model_gridbox(lat_e,lon_e,obs_lats[i],obs_lons[i])
model_var.append(model_var_all[tests[i],lat_n,lon_n]*1e9)
#process obs dates and obs times to datetimes, then process pandas objects
year_val = []
month_val = []
day_val = []
hour_val = []
minute_val = []
#change obs times to datetimes
obs_date = obs_date.astype('str')
obs_time = obs_time.astype('str')
for date in obs_date:
year_val.append(int(date[0:4]))
def onpick(event):
global pl
global ind
global fig2
ind = event.ind
print 'ind = ', ind
ind = ind[0]
#x_data = event.xdata
#y_data = event.ydata
#find ind of closest lat/lon
#ind = modules.find_nearest_point_index(obs_lons,obs_lats,x_data,y_data)
try:
for i in range(len(pl)):
pl.pop(0).remove()
first_run = False
except:
first_run = True
pass
pl = m.plot([linear_lons[ind]], [linear_lats[ind]],
's',
ms=20,
alpha=0.6,
color='yellow',
zorder=20)
#get model timeseries for site clicked
lat_n, lon_n = modules.obs_model_gridbox(lat_e, lon_e, linear_lats[ind],
linear_lons[ind])
model_var_pick = model_var[:, lat_n, lon_n]
model_var_pick = model_var_pick * 1e9
model_var_mask = np.ma.masked_where(model_var_pick <= 0, model_var_pick)
model_time_pd = pd.date_range(start=model_datetimes[0],
end=model_datetimes[-1],
freq='H')
model_var_pd = pd.Series(model_var_mask, index=model_time_pd)
#create sine wave from amp/phase
model_date_l = model_date.astype(int)
model_time_l = model_time.astype(int)
model_times = modules.date_process(model_date_l, model_time_l, start_year)
model_times = np.array(model_times)
pi2 = np.pi * 2
ratio = 100. / annual_amp[lat_n, lon_n]
ha_percent = ratio * annual_amp[lat_n, lon_n]
#convert phases to radians
calc = pi2 / 24.
calc = pi2 / 6.
ha_ph_r = ha_ph[lat_n, lon_n] * calc
calc = pi2 / 12.
annual_ph_r = annual_ph[lat_n, lon_n] * calc
ha_model_wave = ha_amp[lat_n, lon_n] * (np.cos((pi2 * model_times /
(365.25 / 2.)) -
(ha_ph_r)))
annual_model_wave = annual_amp[lat_n, lon_n] * (np.cos((pi2 * model_times /
(365.25)) -
(annual_ph_r)))
ha_primary = p_ha_ph[lat_n, lon_n]
ha_secondary = s_ha_ph[lat_n, lon_n]
ha_model_wave = ha_model_wave + ave[lat_n, lon_n]
annual_model_wave = annual_model_wave + ave[lat_n, lon_n]
model_ha_wave_pd = pd.Series(ha_model_wave, index=model_time_pd)
model_annual_wave_pd = pd.Series(annual_model_wave, index=model_time_pd)
fig.canvas.draw()
if first_run == False:
plt.close(fig2)
fig2, (axo) = plt.subplots(1, figsize=(24, 12))
fig2.patch.set_facecolor('white')
axo.plot_date(model_time_pd.to_pydatetime(),
model_var_pd,
color='black',
markersize=3,
label='Observations')
axo.plot_date(model_time_pd.to_pydatetime(),
model_ha_wave_pd,
color='green',
markersize=3,
label='Ha Waveform',
markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(),
model_annual_wave_pd,
color='red',
markersize=3,
label='Annual Waveform',
markeredgecolor='None')
#axo.set_title('Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s\nPrimary HA Phase = %s,Primary HA Regime = %s, HA Amp to Annual Amp Percent = %s' %(ref,country,continent,group,lat,lon,alt,complete,a_class,r_class,ha_primary,ha_regime,ha_percent))
plt.legend(loc='lower right')
plt.tight_layout()
axo.grid()
plt.show()
else:
fig2, (axo) = plt.subplots(1, figsize=(24, 12))
fig2.patch.set_facecolor('white')
axo.plot_date(model_time_pd.to_pydatetime(),
model_var_pd,
color='black',
markersize=3,
label='Observations')
axo.plot_date(model_time_pd.to_pydatetime(),
model_ha_wave_pd,
color='green',
markersize=3,
label='Ha Waveform',
markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(),
model_annual_wave_pd,
color='red',
markersize=3,
label='Annual Waveform',
markeredgecolor='None')
#axo.set_title('Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s\nPrimary HA Phase = %s,Primary HA Regime = %s, HA Amp to Annual Amp Percent = %s' %(ref,country,continent,group,lat,lon,alt,complete,a_class,r_class,ha_primary,ha_regime,ha_percent))
plt.legend(loc='lower right')
plt.tight_layout()
axo.grid()
plt.show()
def grid_interactive(event):
global pl
global ind
global fig2
ind = event.ind
ind = ind[0]
#find ind of closest lat/lon
#ind = modules.find_nearest_point_index(obs_lons,obs_lats,x_data,y_data)
try:
for i in range(len(pl)):
pl.pop(0).remove()
first_run = False
except:
first_run = True
pass
pl = m.plot([linear_lons[ind]], [linear_lats[ind]],
's',
ms=20,
alpha=0.6,
color='yellow',
zorder=20)
#get model spectra for site clicked
lat_n, lon_n = modules.obs_model_gridbox(lat_e, lon_e, linear_lats[ind],
linear_lons[ind])
period = site_group.variables['period'][:, lat_n, lon_n]
spectrum = site_group.variables['amplitude'][:, lat_n, lon_n]
#period = periods[:,lat_n,lon_n]
#spectrum = amplitude[:,lat_n,lon_n]
fig.canvas.draw()
if first_run == False:
plt.close(fig2)
fig2, (ax1) = plt.subplots(1, 1, figsize=(24, 12))
fig2.patch.set_facecolor('white')
ax1.plot(period, spectrum, color='black', markersize=3)
ax1.set_xscale('log')
ax1.set_yscale('log')
#plt.legend(loc = 'lower right')
plt.tight_layout()
ax1.grid()
plt.show()
else:
fig2, (ax1) = plt.subplots(1, 1, figsize=(24, 12))
fig2.patch.set_facecolor('white')
ax1.plot(period, spectrum, color='black', markersize=3)
ax1.set_xscale('log')
ax1.set_yscale('log')
#plt.legend(loc = 'lower right')
plt.tight_layout()
ax1.grid()
plt.show()
def onpick(event):
global pl
global ind
global fig2
ind = event.ind
ind = ind[0]
#x_data = event.xdata
#y_data = event.ydata
#find ind of closest lat/lon
#ind = modules.find_nearest_point_index(obs_lons,obs_lats,x_data,y_data)
try:
for i in range(len(pl)):
pl.pop(0).remove()
first_run = False
except:
first_run = True
pass
pl = m.plot([X[ind]], [Y[ind]],
'o',
ms=12,
alpha=0.6,
color='yellow',
zorder=20)
#get model timeseries for site clicked
lat_n, lon_n = modules.obs_model_gridbox(lat_e, lon_e, obs_lats[ind],
obs_lons[ind])
model_var_pick = model_var[:, lat_n, lon_n]
model_var_pick = model_var_pick * 1e9
model_var_mask = np.ma.masked_where(model_var_pick <= 0, model_var_pick)
if model_name == 'MACC':
model_time_pd = pd.date_range(start=model_datetimes[0],
end=model_datetimes[-1],
freq='H')
count = 0
valids = []
for i in range(len(model_time_pd)):
if count == 0:
valids.append(i)
count += 1
elif count == 2:
count = 0
else:
count += 1
model_time_pd = model_time_pd[valids]
model_var_pd = pd.Series(model_var_mask, index=model_time_pd)
else:
model_time_pd = pd.date_range(start=model_datetimes[0],
end=model_datetimes[-1],
freq='H')
model_var_pd = pd.Series(model_var_mask, index=model_time_pd)
#get obs timeseries for site clicked
ref = obs_refs[ind]
obs_ts_group = obs_root_grp.groups[ref]
obs_var = obs_ts_group.variables[species.lower()][:]
group = obs_ts_group.process_group
lat = obs_ts_group.latitude
lon = obs_ts_group.longitude
lon = obs_ts_group.longitude
alt = obs_ts_group.altitude
complete = obs_ts_group.completeness
a_class = obs_ts_group.anthrome_class
r_class = obs_ts_group.raw_class
continent = loc_dict[tags[ind]]
country = obs_ts_group.country
obs_var_mask = np.ma.masked_where(obs_var <= 0, obs_var)
obs_time_pd = pd.date_range(start=obs_datetimes[0],
end=obs_datetimes[-1],
freq='H')
obs_var_pd = pd.Series(obs_var_mask, index=obs_time_pd)
#create sine wave from amp/phase
obs_date_l = obs_date.astype(int)
obs_time_l = obs_time.astype(int)
obs_times = modules.date_process(obs_date_l, obs_time_l, start_year)
obs_times = np.array(obs_times)
pi2 = np.pi * 2
#convert phases to radians
calc = pi2 / 6.
obs_ha_phase_r = obs_ha_phase[ind] * calc
calc = pi2 / 12.
obs_annual_phase_r = obs_annual_phase[ind] * calc
ha_obs_wave = obs_ha_mag[ind] * (np.cos((pi2 * obs_times / (365.25 / 2.)) -
(obs_ha_phase_r)))
annual_obs_wave = obs_annual_mag[ind] * (np.cos((pi2 * obs_times /
(365.25)) -
(obs_annual_phase_r)))
seasonal_obs_wave = (ha_obs_wave + annual_obs_wave) + obs_ave[ind]
obs_seasonal_wave_pd = pd.Series(seasonal_obs_wave, index=obs_time_pd)
#create sine wave from amp/phase
mod_date_l = model_date.astype(int)
mod_time_l = model_time.astype(int)
mod_times = modules.date_process(mod_date_l, mod_time_l, start_year)
mod_times = np.array(mod_times)
pi2 = np.pi * 2
#convert phases to radians
calc = pi2 / 6.
model_ha_phase_r = model_ha_phase[ind] * calc
calc = pi2 / 12.
model_annual_phase_r = model_annual_phase[ind] * calc
ha_model_wave = model_ha_mag[ind] * (np.cos((pi2 * mod_times /
(365.25 / 2.)) -
(model_ha_phase_r)))
annual_model_wave = model_annual_mag[ind] * (np.cos(
(pi2 * mod_times / (365.25)) - (model_annual_phase_r)))
seasonal_model_wave = (ha_model_wave + annual_model_wave) + model_ave[ind]
model_seasonal_wave_pd = pd.Series(seasonal_model_wave,
index=model_time_pd)
#get spectra data
site_group_obs = root_grp_obs_spec.groups[ref]
site_group_mod = root_grp_mod_spec.groups[ref]
obs_period = site_group_obs.variables['period'][:]
mod_period = site_group_mod.variables['period'][:]
obs_amp = site_group_obs.variables['amplitude'][:]
mod_amp = site_group_mod.variables['amplitude'][:]
fig.canvas.draw()
if first_run == False:
plt.close(fig2)
fig2, (axo, axo2) = plt.subplots(2, figsize=(24, 12))
fig2.patch.set_facecolor('white')
#fig2 = plt.figure()
axo.plot_date(obs_time_pd.to_pydatetime(),
obs_var_pd,
color='black',
markersize=3,
label='Observations')
axo.plot_date(model_time_pd.to_pydatetime(),
model_var_pd,
color='red',
alpha=0.5,
markersize=3,
label='%s %s %s %s' %
(model_name, version, grid_size, met),
markeredgecolor='None')
axo.plot_date(obs_time_pd.to_pydatetime(),
obs_seasonal_wave_pd,
color='yellow',
markersize=3,
label='Obs Seasonal Waveform',
markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(),
model_seasonal_wave_pd,
color='green',
markersize=3,
label='Model Seasonal Waveform',
markeredgecolor='None')
axo2.loglog(obs_period, obs_amp, color='black', label='Obs')
axo2.loglog(mod_period,
mod_amp,
color='red',
label='%s %s %s %s' %
(model_name, version, grid_size, met))
axo2.text(0.01,
0.95,
'Obs D Amp = %8.2f ppb' % (obs_daily_mag[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.92,
'Model D Amp = %8.2f ppb' % (model_daily_mag[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.85,
'Obs HA Amp = %8.2f ppb' % (obs_ha_mag[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.82,
'Model HA Amp = %8.2f ppb' % (model_ha_mag[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.75,
'Obs A Amp = %8.2f ppb' % (obs_annual_mag[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.72,
'Model A Amp = %8.2f ppb' % (model_annual_mag[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.55,
'Obs D Phase = %8.2f' % (obs_daily_phase[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.52,
'Model D Phase = %8.2f' % (model_daily_phase[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.45,
'Obs HA Phase = %8.2f' % (obs_ha_phase[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.42,
'Model HA Phase = %8.2f' % (model_ha_phase[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
obs_a_ph = obs_annual_phase[ind]
mod_a_ph = model_annual_phase[ind]
if obs_a_ph > 12:
obs_a_ph = obs_a_ph - 12.
if mod_a_ph > 12:
mod_a_ph = mod_a_ph - 12.
axo2.text(0.01,
0.35,
'Obs A Phase = %8.2f' % (obs_a_ph),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.32,
'Model A Phase = %8.2f' % (mod_a_ph),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.15,
'Obs Ave = %8.2f ppb' % (obs_ave[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.12,
'Model Ave = %8.2f ppb' % (model_ave[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.axvline(1., ymin=0, ymax=1, color='blue', linestyle='--')
axo2.axvline(182.625, ymin=0, ymax=1, color='blue', linestyle='--')
axo2.axvline(365.25, ymin=0, ymax=1, color='blue', linestyle='--')
axo2.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
axo2.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.gca().xaxis.set_major_formatter(FuncFormatter(xformatter))
plt.gca().yaxis.set_major_formatter(FuncFormatter(yformatter))
axo.set_title(
'Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s, Grid Index = %s,%s'
% (ref, country, continent, group, lat, lon, alt, complete,
a_class, r_class, lat_n, lon_n))
plt.legend(loc='lower right')
plt.tight_layout()
axo.grid()
axo2.grid()
plt.show()
else:
#fig2 = plt.figure()
fig2, (axo, axo2) = plt.subplots(2, figsize=(24, 12))
fig2.patch.set_facecolor('white')
axo.plot_date(obs_time_pd.to_pydatetime(),
obs_var_pd,
color='black',
markersize=3,
label='Observations')
axo.plot_date(model_time_pd.to_pydatetime(),
model_var_pd,
color='red',
markersize=3,
alpha=0.5,
label='%s %s %s %s' %
(model_name, version, grid_size, met),
markeredgecolor='None')
axo.plot_date(obs_time_pd.to_pydatetime(),
obs_seasonal_wave_pd,
color='yellow',
markersize=3,
label='Obs Seasonal Waveform',
markeredgecolor='None')
axo.plot_date(model_time_pd.to_pydatetime(),
model_seasonal_wave_pd,
color='green',
markersize=3,
label='Model Seasonal Waveform',
markeredgecolor='None')
axo2.loglog(obs_period, obs_amp, color='black', label='Obs')
axo2.loglog(mod_period,
mod_amp,
color='red',
label='%s %s %s %s' %
(model_name, version, grid_size, met))
axo2.text(0.01,
0.95,
'Obs D Amp = %8.2f ppb' % (obs_daily_mag[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.92,
'Model D Amp = %8.2f ppb' % (model_daily_mag[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.85,
'Obs HA Amp = %8.2f ppb' % (obs_ha_mag[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.82,
'Model HA Amp = %8.2f ppb' % (model_ha_mag[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.75,
'Obs A Amp = %8.2f ppb' % (obs_annual_mag[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.72,
'Model A Amp = %8.2f ppb' % (model_annual_mag[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.55,
'Obs D Phase = %8.2f' % (obs_daily_phase[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.52,
'Model D Phase = %8.2f' % (model_daily_phase[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.45,
'Obs HA Phase = %8.2f' % (obs_ha_phase[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.42,
'Model HA Phase = %8.2f' % (model_ha_phase[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
obs_a_ph = obs_annual_phase[ind]
mod_a_ph = model_annual_phase[ind]
if obs_a_ph > 12:
obs_a_ph = obs_a_ph - 12.
if mod_a_ph > 12:
mod_a_ph = mod_a_ph - 12.
axo2.text(0.01,
0.35,
'Obs A Phase = %8.2f' % (obs_a_ph),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.32,
'Model A Phase = %8.2f' % (mod_a_ph),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.text(0.01,
0.15,
'Obs Ave = %8.2f ppb' % (obs_ave[ind]),
transform=axo2.transAxes,
fontweight='bold')
axo2.text(0.01,
0.12,
'Model Ave = %8.2f ppb' % (model_ave[ind]),
transform=axo2.transAxes,
fontweight='bold',
color='red')
axo2.axvline(1., ymin=0, ymax=1, color='blue', linestyle='--')
axo2.axvline(182.625, ymin=0, ymax=1, color='blue', linestyle='--')
axo2.axvline(365.25, ymin=0, ymax=1, color='blue', linestyle='--')
axo2.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
axo2.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.gca().xaxis.set_major_formatter(FuncFormatter(xformatter))
plt.gca().yaxis.set_major_formatter(FuncFormatter(yformatter))
axo.set_title(
'Site = %s, Country = %s, Continent = %s, Process Group = %s, Lat = %s, Lon = %s, Alt = %sm,\n Data Completeness = %s%%, Anthrome Class = %s, Raw Class = %s, Grid Index = %s,%s'
% (ref, country, continent, group, lat, lon, alt, complete,
a_class, r_class, lat_n, lon_n))
plt.legend(loc='lower right')
plt.tight_layout()
axo.grid()
axo2.grid()
plt.show()
species_dict = {'O3':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_O3.npy','CO':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_CO.npy','NO':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_NO.npy','NO2':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_NO2.npy', \
'PAN':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_PAN.npy','C2H6':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_C2H6.npy','C3H8':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_C3H8.npy','ISOP':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_ISOP.npy',}
#species = raw_input('Choose species\n%s\n'%(' '.join(i for i in species_dict)))
for species in species_dict:
model_f = species_dict[species]
#get model grid dims. for sim. type
lat_c, lat_e, lon_c, lon_e = modules.model_grids(model_version)
gridbox_count = len(lat_c) * len(lon_c)
#get model gridbox for obs site
gridbox_n = modules.obs_model_gridbox(lat_e, lon_e, -20.2, 57.5)
model_data = np.load(model_f)
model_time = np.arange(0, 2191, 1. / 24)
#get model grid dims. for sim. type
lat_c, lat_e, lon_c, lon_e = modules.model_grids(model_version)
gridbox_count = len(lat_c) * len(lon_c)
model_var = model_data[gridbox_n::gridbox_count]
model_var = model_var * 1e9
#set plotting area & background to white
fig = plt.figure(figsize=(20, 12))
fig.patch.set_facecolor('white')
ax = fig.add_subplot(1, 1, 1)
