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