event, species, lat_e, lon_e, obs_datetimes, model_datetimes,
obs_ts_grp, model_ts_grp, obs_period_grp, model_period_grp, obs_refs,
tags, loc_dict, obs_d_waveform, obs_s_waveform, obs_all_waveform,
model_d_waveform, model_s_waveform, model_all_waveform, fig, all_m)
#get species from current directory
present_dir = os.getcwd()
obs_fname, species, start_year, end_year, vres, timeres = modules.get_obs_info(
present_dir)
model_fname, species, start_year, end_year = modules.get_model_info(
present_dir)
obs_refs, obs_raw_time, obs_ref_time, obs_datetime_time, obs_std_var, obs_lats, obs_lons, obs_alt, obs_gap_inds = modules.read_obs_all(
obs_fname, species, start_year, end_year)
model_raw_time, model_ref_time, model_datetime_time, model_std_var, lat_e, lon_e, lat_c, lon_c, grid_size, gridbox_count = modules.read_model_all(
model_fname, species, start_year, end_year)
#get obs lat_lon grid central points
obs_lats_centre, obs_lons_centre, model_indices = modules.grid_obs_centre_convergance(
lat_e, lon_e, obs_lats, obs_lons)
#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(np.copy(obs_refs))
#--------------------------------------------------------
#load in periodic lsp data
obs_period_grp = Dataset('../obs_%s_%s/obs_sig_periods.nc' % (vres, timeres))
model_period_grp = Dataset('model_sig_periods.nc')
obs_d_waveform = []
#get info of weather regimes through model fit.
grad1,grad2,grad3,bp1,bp2,bp_periods,bp_mag = modules.spectra_fit(periods,mag,ofac)
#get mean of values
mean_array = np.average(vals)
#correct all phases for start point
ph = modules.phase_start_point_correct_all(periods,ph,valid_times)
#convert phase to time(days)
ph = modules.convert_phase_units_actual_all(ph,periods)
return (x,periods,mag,ph,grad1,grad2,grad3,bp1,bp2,bp_mag)
model_raw_time,model_ref_time,model_datetime_time,model_std_var,lat_e,lon_e,lat_c,lon_c,grid_size,gridbox_count = modules.read_model_all(model_fname,species,start_year,end_year)
lat_i = 0
lon_i = 0
for siten in range(n_boxes):
linear_data.append(model_std_var[:,lat_i,lon_i])
lat_indices.append(lat_i)
lon_indices.append(lon_i)
if lon_i == (len(lon_c)-1):
lat_i+=1
lon_i=0
else:
lon_i+=1
year2100s = 2097
year2100e = 2100
#read in 2000 model period data
f2000 = '/work/home/db876/plotting_tools/model_files/%s_SURFACE_2000_2012_*_*_*_H_*.nc' % (
model)
#read in 2100 model period data
f2100 = '/work/home/db876/plotting_tools/model_files/%s_SURFACE_2095_2111_*_*_*_H_rcp85.nc' % (
model)
#read in 2100 model fixed emissions period data
f2100e = '/work/home/db876/plotting_tools/model_files/%s_SURFACE_2095_2111_*_*_*_H_rcp85em2000.nc' % (
model)
f2000raw_time, f2000ref_time, f2000datetime_time, f2000std_var, lat_e, lon_e, lat_c, lon_c, grid_size, gridbox_count = modules.read_model_all(
f2000, 'O3', year2000s, year2000e)
if model != 'GISSE2R':
f2100raw_time, f2100ref_time, f2100datetime_time, f2100std_var, lat_e, lon_e, lat_c, lon_c, grid_size, gridbox_count = modules.read_model_all(
f2100, 'O3', year2100s, year2100e)
if model != 'CMAM':
f2100eraw_time, f2100eref_time, f2100edatetime_time, f2100estd_var, lat_e, lon_e, lat_c, lon_c, grid_size, gridbox_count = modules.read_model_all(
f2100e, 'O3', year2100s, year2100e)
count = 0
for ax in axes.flat:
try:
area = areas[count]
except:
ax.axis('off')
continue
