alltips = tuple(resdict.keys())
colorz = ctl.color_set(len(alltips))
runfreq = dict()
seasfreq = dict()
figs = []
for tip, col in zip(alltips, colorz):
okmods = resdict[tip].keys()
fig = plt.figure(figsize=(16, 12))
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
cosi = []
for mem in okmods:
seasfr, yr = ctl.calc_seasonal_clus_freq(
resdict[tip][mem]['labels'], resdict[tip][mem]['dates'],
numclus)
seasfreq[(tip, mem, reg)] = seasfr[reg, :]
seas1 = np.array(ctl.running_mean(seasfr[reg, :], 20))
runfreq[(tip, mem, reg)] = seas1
ax.plot(yr, seas1, label=tip, color=col)
ax.set_title(reg_names_area[area][reg])
#ax.legend()
fig.suptitle(tip)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'check_single_members_{}.pdf'.format(area),
figs)
fig = plt.figure(figsize=(16, 12))
for reg in range(4):
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, coloks, modoks, ncol=4)
fig.suptitle(
'Change of WR freq. in 2050-2100 wrt 1964-2014 - {}'.format(sea))
fig.savefig(
cart_out +
'WR_freq_change_dtr_{}_{}_{}_{}_subsea{}.pdf'.format(*ke, sea))
#### Prima posso guardare la cosa con le seasonal frequencies. O monthly? No seasonal.
## Devo. calcolare le seasonal freq. calcolare i percentili per hist e per ssp. Fare differenze di ogni percentile. Plottare. Ok vado.
for mod in modoks:
seasfr, yr = ctl.calc_seasonal_clus_freq(results_hist[mod]['labels'],
results_hist[mod]['dates'],
numclus)
results_hist[mod]['freq_clus_seasonal'] = seasfr
seasfr, yr = ctl.calc_seasonal_clus_freq(results_ssp585[mod]['labels'],
results_ssp585[mod]['dates'],
numclus)
results_ssp585[mod]['freq_clus_seasonal'] = seasfr
allpercs_regs = []
for reg in range(numclus):
allpercs = dict()
allpercs['mean'] = []
for pp in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(pp)] = []
for mod in modoks:
print(okmods)
print(len(okmods))
print('BAUUUUUUUUUUUUUUUUUUUUUUUUU')
## plots
runfreq = dict()
seasfreq = dict()
fig = plt.figure(figsize=(16, 12))
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
cosi = []
for mem in okmods:
labok, datok = ctl.sel_time_range(results_hist[mem]['labels'],
results_hist[mem]['dates'],
ctl.range_years(yr0, yr1))
seasfr, yr = ctl.calc_seasonal_clus_freq(labok, datok, numclus)
seasfreq[('hist_cmip5', mem, reg)] = seasfr[reg, :]
seas20 = np.array(ctl.running_mean(seasfr[reg, :], 20))
print(mem, len(seas20))
if len(seas20) == len(yr):
ax.plot(yr, seas20)
cosi.append(seas20)
else:
print(mem, len(seas20), 'too short')
coso = np.mean(cosi, axis=0)
runfreq[('hist_cmip5', reg)] = coso
ax.plot(yr, coso, color='black', linewidth=3)
ax.set_title(reg_names_area[area][reg])
fig.savefig(cart_out + 'models_run20_{}_hist.pdf'.format(area))
mod_ssp[ssp] = np.unique([cos.split('_')[0] for cos in results_ssp[ssp].keys()])
print(ssp, mod_ssp[ssp])
okmods_hist = list(results_hist.keys())
okmods_ssp = list(results_ssp[ssp].keys())
okmods = okmods_ssp
runfreq = dict()
seasfreq = dict()
fig = plt.figure(figsize = (16,12))
for reg in range(4):
ax = fig.add_subplot(2, 2, reg+1)
cosi = []
for mem in okmods_hist:
seasfr, yr = ctl.calc_seasonal_clus_freq(results_hist[mem]['labels'], results_hist[mem]['dates'], numclus)
seasfreq[('hist', mem, reg)] = seasfr[reg, :]
seas10 = np.array(ctl.running_mean(seasfr[reg, :], yr10))
ax.plot(yr, seas10)
cosi.append(seas10)
coso = np.mean(cosi, axis = 0)
runfreq[('hist', reg)] = coso
ax.plot(yr, coso, color = 'black', linewidth = 3)
ax.set_title(reg_names_area[area][reg])
fig.savefig(cart_out + 'models_freq10_{}_hist.pdf'.format(area, ssp))
trend_ssp = dict()
residtime_ssp = dict()
for ssp in allssps:
detrended_anom_for_clustering=True,
heavy_output=False)
resu2 = cd.WRtool_core(var_season,
lat,
lon,
dates_season,
area,
run_significance_calc=False,
ref_solver=ref_solver,
ref_patterns_area=ref_patterns_area,
detrended_eof_calculation=True,
detrended_anom_for_clustering=False,
heavy_output=False)
freq1 = ctl.calc_seasonal_clus_freq(resu1['labels'], resu1['dates'])
freq2 = ctl.calc_seasonal_clus_freq(resu2['labels'], resu2['dates'])
years = np.unique(pd.to_datetime(resu1['dates']).year)[:-1]
patnames = ['NAO +', 'Blocking', 'NAO -', 'Atl. Ridge']
fig = plt.figure()
plt.ylim(15., 35.)
plt.title('detr.EOFs + detrended anomalies')
for clu, clunam in enumerate(patnames):
smut = ctl.running_mean(freq1[:, clu], wnd=30)
plt.plot(years, smut, label=clunam)
fig = plt.figure()
plt.ylim(20., 30.)
plt.title('detr.EOFs + non-detrended anomalies')
for clu, clunam in enumerate(patnames):
smut = ctl.running_mean(freq2[:, clu], wnd=30)
dates_pdh = pd.to_datetime(dates)
pi = (dates_pdh.month == 12) | (dates_pdh.month == 1) | (dates_pdh.month == 2)
pi2 = (pi) & (dates_pdh > dates_pdh[0]+timed) & (dates_pdh < dates_pdh[-1]-timed)
datespi2 = dates[pi2]
labs_fullp = []
dist_fullp = []
freqs_yr_all = dict()
for ens in ensmem:
labs_fullp.append(results_fullp[(ens, 'EAT', (1850,2100))]['labels'])
dist_fullp.append(results_fullp[(ens, 'EAT', (1850,2100))]['dist_centroid'])
if ens == 'lcb1':
dates_ok = datespi2[90:]
else:
dates_ok = datespi2
freqs_yr_all[ens] = ctl.calc_seasonal_clus_freq(labs_fullp[-1], dates_ok)
cartfr = cart+'freq_fullp/'
if not os.path.exists(cartfr):
os.mkdir(cartfr)
plt.close('all')
yearsall = np.arange(1851, 2101)
for ens in ensmem:
fig = plt.figure()
if ens == 'lcb1':
years = yearsall[1:]
else:
years = yearsall
timeseries = dict()
for ke in resu.keys():
timeseries[ke] = xr.DataArray(resu[ke]['freq_clus_seasonal'],
dims=('reg', 'time'),
coords={
'reg': [0, 1, 2, 3],
'time':
resu[ke]['freq_clus_seasonal_years']
})
# resu2, resu_ref2 = ctl.load_wrtool(cart_in + 'out_tipes_nnetau_proj_NDJFM_EAT_4clus_4pcs_allyrs_{}.p'.format(tip))
# timeseries['eta'] = xr.DataArray(resu2['eta']['freq_clus_seasonal'], dims = ('reg', 'time'), coords = {'reg': [0, 1, 2, 3], 'time': resu2['eta']['freq_clus_seasonal_years']})
if 'freq_clus_seasonal' not in resu_ref:
resu_ref['freq_clus_seasonal'], resu_ref[
'freq_clus_seasonal_years'] = ctl.calc_seasonal_clus_freq(
resu_ref['labels'], resu_ref['dates'], 4)
#gigi_obs = xr.DataArray(resu_ref['freq_clus_seasonal'], dims = ('reg', 'time'), coords = {'reg': [0, 1, 2, 3], 'time': resu_ref['freq_clus_seasonal_years']})
fig = plt.figure(figsize=(16, 12))
axes = []
for num, patt in enumerate(patnames[seas]):
ax = plt.subplot(2, 2, num + 1)
for ke, col in zip(keall, colorz):
timeseries[ke].sel(reg=num).plot(ax=ax, color=col, linewidth=0.2)
rupi = ctl.running_mean(timeseries[ke].sel(reg=num).values, 10)
ax.plot(timeseries[ke].time,
rupi,
color=col,
linewidth=3,