koze[ke] = koze_ref[ke]
# salvo le serie temporali filtrate e non (_all)
for ke in ['labels', 'dist_centroid', 'pcs', 'dates']:
koze[ke] = koze_ref[ke][mask == 1]
# ricostruisco il pattern degli stati selezionati
patts = ctl.reconstruct_from_pcs(koze['pcs'], koze['model_eofs'])
cluspatt_ok = []
for reg in range(4):
okpo = koze['labels'] == reg
cluspatt_ok.append(np.mean(patts[okpo, ...], axis=0))
koze['cluspattern_area'] = np.stack(cluspatt_ok)
koze['freq_clus'] = ctl.calc_clus_freq(koze['labels'], 4)
koze['centroids'] = ctl.calc_effective_centroids(
koze['pcs'], koze['labels'], 4)
cd.plot_regimes(
koze['lat_area'],
koze['lon_area'],
koze['cluspattern_area'],
cart + 'regpatt_{}_{}_{}_v90.pdf'.format(season, area, tip),
clatlo=clatlo,
cbar_range=cbar_range,
draw_contour_lines=False,
cmappa='RdBu_r',
n_color_levels=21)
print(koze['lon_area'])
reg_events[(season, tip, area)] = koze
freqs[('hist', mem, 'tot50')] = ctl.calc_clus_freq(results_hist[mem]['labels'], numclus)
for reg in range(numclus):
alltimes = results_hist[mem]['resid_times'][reg]
residtimes[('hist', mem, 'mean', reg)] = np.mean(alltimes)
residtimes[('hist', mem, 'p90', reg)] = np.percentile(alltimes, 90)
patterns[('hist', mem, 'tot50')] = results_hist[mem]['eff_centroids']
dat1 = pd.Timestamp('09-01-1995').to_pydatetime()
dat2 = pd.Timestamp('04-01-2014').to_pydatetime()
alllabs_20, dats = ctl.sel_time_range(results_hist[mem]['labels'], results_hist[mem]['dates'], (dat1, dat2))
pcs, dats = ctl.sel_time_range(results_hist[mem]['pcs'], results_hist[mem]['dates'], (dat1, dat2))
alltimes_20, _, _ = ctl.calc_regime_residtimes(alllabs_20, dats)
effcen = ctl.calc_effective_centroids(pcs, alllabs_20, numclus)
for reg in range(numclus):
residtimes[('hist', mem, 'mean_last20', reg)] = np.mean(alltimes_20[reg])
residtimes[('hist', mem, 'p90_last20', reg)] = np.percentile(alltimes_20[reg], 90)
patterns[('hist', mem, 'last20')] = effcen
freqs[('hist', mem, 'last20')] = ctl.calc_clus_freq(alllabs_20, numclus)
for ssp in allssps:
for mem in okmods_ssp:
if mem not in results_ssp[ssp].keys(): continue
## Attach all members labels
dat1 = pd.Timestamp('09-01-2050').to_pydatetime()
dat2 = pd.Timestamp('04-01-2100').to_pydatetime()
labs, dats = ctl.sel_time_range(results_ssp[ssp][mem]['labels'], results_ssp[ssp][mem]['dates'], (dat1, dat2))