labok = oklabels == reg
cosa = np.mean(prec_anoms[labok, ...], axis=0)
compos['prec'].append(cosa)
fig = ctl.plot_map_contour(cosa,
coords_prec['lat'],
coords_prec['lon'],
title='Prec anom - regime {}'.format(reg),
plot_margins=(-120, 120, 20, 90),
cbar_range=(-5, 5),
cmap='RdBu')
allfigs.append(fig)
# Calculate and visualize composites. FILTER ON EVENT DURATION > 5 days
rsd_tim, rsd_dat, rsd_num = ctl.calc_regime_residtimes(
results_ref['labels'], dates=results_ref['dates'])
days_event, length_event = ctl.calc_days_event(results_ref['labels'], rsd_tim,
rsd_num)
okleneve, _ = ctl.sel_time_range(length_event,
results_ref['dates'],
dates_range=(dateprec[0], dateprec[-1]))
compos['temp_filt'] = []
compos['prec_filt'] = []
for reg in range(kwar['numclus']):
labok = (results_ref['labels'] == reg) & (length_event > 5)
cosa = np.mean(temp_anoms[labok, ...], axis=0)
compos['temp_filt'].append(cosa)
fig = ctl.plot_map_contour(
cosa,
for reg in range(numclus):
alltimes = np.concatenate([results_hist[mem]['resid_times'][reg] for mem in allmems])
residtimes[('hist', mod, 'mean', reg)] = np.mean(alltimes)
residtimes[('hist', mod, 'p90', reg)] = np.percentile(alltimes, 90)
num_event[('hist', mod, reg)] = freqs[(ssp, mod, 'tot50')][reg]/residtimes[(ssp, mod, 'mean', reg)]
alllabs_20 = []
alltimes_20 = []
for mem in allmems:
dat1 = pd.Timestamp('09-01-1995').to_pydatetime()
dat2 = pd.Timestamp('04-01-2014').to_pydatetime()
labs, dats = ctl.sel_time_range(results_hist[mem]['labels'], results_hist[mem]['dates'], (dat1, dat2))
alllabs_20.append(labs)
print(mem, len(labs))
restim, _, _ = ctl.calc_regime_residtimes(labs, dats)
alltimes_20.append(restim)
alllabs_20 = np.concatenate(alllabs_20)
alltimes_20 = [np.concatenate([cos[reg] for cos in alltimes_20]) for reg in range(numclus)]
for reg in range(numclus):
residtimes[('hist', mod, 'mean_last20', reg)] = np.mean(alltimes_20[reg])
residtimes[('hist', mod, 'p90_last20', reg)] = np.percentile(alltimes_20[reg], 90)
freqs[('hist', mod, 'last20')] = ctl.calc_clus_freq(alllabs_20, numclus)
for reg in range(numclus):
patterns_refEOF[('centroids', mod, reg)] = np.mean([res_hist_refEOF[mem]['centroids'][reg] for mem in allmems], axis = 0)
patterns_refEOF[('patcor', mod, reg)] = np.mean([res_hist_refEOF[mem]['patcor'][reg] for mem in allmems])
patterns_refEOF[('centdist', mod, reg)] = np.mean([ctl.distance(res_hist_refEOF[mem]['centroids'][reg], results_ref['centroids'][reg]) for mem in allmems])
varopt = ctl.calc_varopt_molt(pcs, centroids, labels)
autocorr = ctl.calc_autocorr_wlag(pcs, dates, out_lag1=True)
bootstraps['significance'].append(sig)
bootstraps['varopt'].append(varopt)
bootstraps['autocorr'].append(autocorr)
bootstraps['freq'].append(ctl.calc_clus_freq(labels, 4))
centdist = np.array([
ctl.distance(centroids[iclu], ref_cen[iclu])
for iclu in range(4)
])
bootstraps['dist_cen'].append(centdist)
bootstraps['centroids'].append(centroids)
resid_times = ctl.calc_regime_residtimes(labels, dates=dates)[0]
av_res = np.array([np.mean(resid_times[reg]) for reg in range(4)])
av_res_90 = np.array(
[np.percentile(resid_times[reg], 90) for reg in range(4)])
bootstraps['resid_times_av'].append(av_res)
bootstraps['resid_times_90'].append(av_res_90)
bootstraps['trans_matrix'].append(
ctl.calc_regime_transmatrix(1, labels, dates))
# relative entropy, RMS, patcor
relent_all = []
for reg in range(4):
okclu = labels == reg
okpc = pcs[okclu, :]
for comp in range(4):
seas10 = ctl.running_mean(seasfr[reg, :], yr10)
m, c, err_m, err_c = ctl.linear_regre_witherr(np.array(yr[~np.isnan(seas10)]), np.array(seas10[~np.isnan(seas10)]))
trend_ssp[(ssp, mem, 'trend', 'freq10', reg)] = m
trend_ssp[(ssp, mem, 'errtrend', 'freq10', reg)] = err_m
# devo fare ogni dieci anni e selezionare
restimem = dict()
for reg in range(4):
restimem[reg] = []
for ye in np.arange(2015, 2091, 5):
dat1 = pd.Timestamp('09-01-{}'.format(ye)).to_pydatetime()
dat2 = pd.Timestamp('04-01-{}'.format(ye+10)).to_pydatetime()
labs, dats = ctl.sel_time_range(results_ssp[ssp][mem]['labels'], results_ssp[ssp][mem]['dates'], (dat1, dat2))
resti, _, _ = ctl.calc_regime_residtimes(labs, dats)
for reg in range(4):
restimem[reg].append(np.mean(resti[reg]))
for reg in range(4):
m, c, err_m, err_c = ctl.linear_regre_witherr(np.arange(2015, 2091, 5), np.array(restimem[reg]))
residtime_ssp[(ssp, mem, 'trend', reg)] = m
residtime_ssp[(ssp, mem, 'errtrend', reg)] = err_m
for ssp in allssps:
for reg in range(4):
trend_ssp[(ssp, 'all', 'trend', 'seafreq', reg)] = np.array([trend_ssp[(ssp, mem, 'trend', 'seafreq', reg)] for mem in okmods_ssp])
trend_ssp[(ssp, 'all', 'errtrend', 'seafreq', reg)] = np.array([trend_ssp[(ssp, mem, 'errtrend', 'seafreq', reg)] for mem in okmods_ssp])
trend_ssp[(ssp, 'all', 'trend', 'freq10', reg)] = np.array([trend_ssp[(ssp, mem, 'trend', 'freq10', reg)] for mem in okmods_ssp])
trend_ssp[(ssp, 'all', 'errtrend', 'freq10', reg)] = np.array([trend_ssp[(ssp, mem, 'errtrend', 'freq10', reg)] for mem in okmods_ssp])
stoc_dists = np.concatenate([results[('lcs{}'.format(i), 'EAT', ran)]['dist_centroid'] for i in range(3)])
freq[('base', area, ran)] = ctl.calc_clus_freq(base_labels)
freq[('stoc', area, ran)] = ctl.calc_clus_freq(stoc_labels)
greylabs = base_dists > thres[area]
gigi = base_labels
gigi[greylabs] = np.max(base_labels)+1
freq[('base', area, ran, 'filt80')] = ctl.calc_clus_freq(gigi)
greylabs = stoc_dists > thres[area]
gigi = stoc_labels
gigi[greylabs] = np.max(stoc_labels)+1
freq[('stoc', area, ran, 'filt80')] = ctl.calc_clus_freq(gigi)
rs_base, dates_init_b = ctl.calc_regime_residtimes(base_labels, dates = dates_long)
resid_times[('base', area, ran)] = rs_base
rs_stoc, dates_init_s = ctl.calc_regime_residtimes(stoc_labels, dates = dates_long)
resid_times[('stoc', area, ran)] = rs_stoc
cartper = cart + 'resid_times/'
for area in ['EAT', 'PNA']:
binzzz = np.arange(0,36,5)
for clu, clunam in enumerate(patnames[area]):
ran = histran
pts = patnames_short[area][clu]
fig = plt.figure()
plt.title(clunam)
n, bins, patches = plt.hist(resid_times[('base', area, ran)][clu], bins = binzzz, alpha = 0.5, density = True, label = 'base')
n2, bins2, patches2 = plt.hist(resid_times[('stoc', area, ran)][clu], bins = binzzz, alpha = 0.5, density = True, label = 'stoc')
plt.legend()
import climtools_lib as ctl
import pandas as pd
import numpy as np
from numpy import linalg as LA
from matplotlib import pyplot as plt
import pickle
cart = '/home/fabiano/Research/lavori/WP2_deliverable_Oct2018/Results_WP2/regime_indices/'
indxfi = 'regime_indices_ECE_LR.txt'
labels = np.loadtxt(cart + indxfi)
var, level, lat, lon, dates, time_units, var_units, time_cal = ctl.read4Dncfield(
'/data-hobbes/fabiano/PRIMAVERA_Stream1_Z500remap/zg500_Aday_EC-Earth3_T255_regrid25_1979-2014.nc',
extract_level=50000.)
resid_times, dates_init = ctl.calc_regime_residtimes(labels, dates=dates)
resid_times_noskip, dt = ctl.calc_regime_residtimes(labels,
dates=dates,
skip_singleday_pause=False)
plt.ion()
patnames = ['NAO +', 'Blocking', 'NAO -', 'Atl. Ridge']
patnames_short = ['NP', 'BL', 'NN', 'AR']
binzzz = np.arange(0, 37, 2)
for clu, clunam in zip(list(range(4)), patnames):
pts = patnames_short[clu]
fig = plt.figure()
plt.title(clunam)
n, bins, patches = plt.hist(resid_times[clu],
bins=binzzz,
alpha=0.5,