lat=D.variables['lat'][:] lon=D.variables['lon'][:] Y= lat.size X= lon.size vars= ['PRECT'] data_filt={} data_map_filt={} self_corr= {} self_corr_mask= {} for vv in vars: #== trop mean data tmp= data[vv] tmp= tmp.reshape(tmp.size) tmp= panta(tmp) tmp_smooth= running_mean(tmp,6) tmp_f = tmp - tmp_smooth; data_filt[vv]= tmp_f #== map data tmp= data_map[vv] tmp= tmp.reshape(tmp.shape[0]*tmp.shape[1], tmp.shape[2], tmp.shape[3]) tmp= panta(tmp) tmp_smooth= running_mean(tmp,6) tmp_map_f= tmp - tmp_smooth; data_map_filt[vv]= tmp_map_f #== correlation Y,X= tmp_map_f.shape[1:3] cor= np.zeros((Y,X)) cor_mask= np.zeros((Y,X)) for yy in range(Y): for xx in range(X): tmp_map_f_loc= tmp_map_f[:, yy, xx] prec_mask= (tmp_map_f_loc > 10e-1)
with open(data_file, 'rb') as f: data_map = pickle.load(f) data_file = data_path_P with open(data_file, 'rb') as f: pEOF = pickle.load(f) plat = pEOF['lat'] plon = pEOF['lon'] data_file = '/data/cloud/Goldtimes5/data/GCM_SPCAM/CPL64/CRM_region_mean/PRECT.pk' with open(data_file, 'rb') as f: CRMP = pickle.load(f) CRMP = CRMP.reshape((CRMP.size)) CRMP = panta(CRMP) CRMP_f = running_mean(CRMP, 6) CRMP = CRMP_f #Slow CRMP = CRMP * 0.2583 # CRM region area / total tropical area vars = [ 'RCE', 'PRECT', 'Rad_cool', 'SHFLX', 'FLNS', 'FSNS', 'FLNT', 'FSNTOA', 'FSDTOA', 'FSUTOA' ] data_filt = {} data_map_filt = {} self_corr = {} for vv in vars: #== trop mean data tmp = data[vv] tmp = tmp.reshape(tmp.size)