numclus = 4
reg_names_area = dict()
reg_names_area['EAT'] = ['NAO+', 'SBL', 'AR', 'NAO-']
reg_names_area['PNA'] = ['PT', 'PNA+', 'PNA-', 'AR']
allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
area = 'EAT'
for area in ['EAT', 'PNA']:
pdfssp = dict()
cart_out = cart_out_orig + '{}_NDJFM/'.format(area)
ctl.mkdir(cart_out)
results_hist, results_ref = ctl.load_wrtool(file_hist.format(area))
res_hist_refEOF, _ = ctl.load_wrtool(file_hist_refEOF.format(area))
# Erasing incomplete runs
for ke in tuple(results_hist.keys()):
if len(results_hist[ke]['labels']) < 7000:
del results_hist[ke]
results_ssp = dict()
for ssp in allssps:
results_ssp[ssp], _ = ctl.load_wrtool(gen_file_ssp.format(ssp, area))
# Erasing incomplete runs
for ke in tuple(results_ssp[ssp].keys()):
if len(results_ssp[ssp][ke]['labels']) < 7000:
del results_ssp[ssp][ke]
#dtrtyp = 'light'
dtrtyp = 'histrebase'
cart_out_orig = '/home/fabiano/Research/lavori/CMIP6/Results_v6_eceens/'
ctl.mkdir(cart_out_orig)
cart_in = '/data-hobbes/fabiano/WR_CMIP6/'
#file_hist = cart_in + 'out_NEW_cmip6_hist_NDJFM_{}_4clus_4pcs_1964-2014_refCLUS_dtr_light.p'
file_hist = cart_in + 'out_eceens_hist_NDJFM_{}_4clus_4pcs_1964-2014_refCLUS_dtr.p'
file_hist_refEOF = cart_in + 'out_eceens_hist_NDJFM_{}_4clus_4pcs_1964-2014_refEOF_dtr.p'
gen_file_ssp = cart_in + 'out_eceens_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr_reb.p'
gen_file_ssp_noreb = cart_in + 'out_eceens_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr.p'
area = 'EAT'
ssp = 'ssp585'
reshist, resref = ctl.load_wrtool(file_hist.format(area))
#reshist_re, _ = ctl.load_wrtool(file_hist.format(area))
resssp, _ = ctl.load_wrtool(gen_file_ssp.format(ssp, area))
resssp_noreb, _ = ctl.load_wrtool(gen_file_ssp_noreb.format(ssp, area))
histbases = []
for mod in reshist.keys():
histbases.append(
np.mean(reshist[mod]['climate_mean'][:, 50:70, -8], axis=0))
lat = reshist[mod]['lat'][50:70]
lon = reshist[mod]['lon'][50:70]
sspbases = []
for mod in resssp_noreb.keys():
sspbases.append(
plt.rcParams['xtick.labelsize'] = 15
plt.rcParams['ytick.labelsize'] = 15
titlefont = 24
plt.rcParams['figure.titlesize'] = titlefont
plt.rcParams['axes.titlesize'] = 18
plt.rcParams['axes.labelsize'] = 18
#############################################################################
cart_out = '/home/fedef/Research/lavori/CMIP6/Clusters_of_regime_hist/'
ctl.mkdir(cart_out)
for area in ['EAT', 'PNA']:
filox = '/home/fedef/Research/lavori/CMIP6/cmip6_hist/out_cmip6_hist_NDJFM_{}_4clus_4pcs_1964-2014_refEOF_dtr.p'.format(
area)
coso, coso_ref = ctl.load_wrtool(filox)
lat = coso['BCC-CSM2-MR_r1i1p1f1']['lat']
lon = coso['BCC-CSM2-MR_r1i1p1f1']['lon']
for reg in range(4):
cluspatterns = [coso[mo]['cluspattern'][reg] for mo in coso.keys()]
centroids, labels, patts, repres, distances = cd.EnsClus_light(
cluspatterns, lat, numclus=4, numpcs=4, flag_perc=False, perc=None)
filename = cart_out + 'clu2_{}_{}.pdf'.format(area, reg)
ctl.plot_multimap_contour(patts,
lat,
lon,
filename,
clatlo = dict()
clatlo['EAT'] = (70., -20.)
clatlo['PNA'] = (70., -120.)
#allssps = 'ssp119 ssp126 ssp245 ssp370 ssp585'.split()
#allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
allssps = ['ssp585']
#oknam = ['EC-Earth3_r1i1p1f1', 'EC-Earth3_r4i1p1f1', 'MPI-ESM1-2-LR_r1i1p1f1']
ttests = dict()
for area in ['EAT', 'PNA']:
cart_out = cart_out_orig + '{}_NDJFM/'.format(area)
ctl.mkdir(cart_out)
results_hist, results_ref = ctl.load_wrtool(file_hist_tot.format(area))
histme = np.mean(
[results_hist[ke]['freq_clus'] for ke in results_hist.keys()], axis=0)
del results_hist
rescoso = dict()
for tip in tips:
rescoso[(tip,
'hist')], _ = ctl.load_wrtool(fil_hist.format(tip, tip, area))
rescoso[(tip, 'ssp585')], _ = ctl.load_wrtool(
fil_ssp.format(tip, tip, area))
# appiccico hist e ssp
#for cosone, histmem in zip([ece_ssp, ece_ssp_r4, mpi_ssp], ['EC-Earth3_r1i1p1f1', 'EC-Earth3_r4i1p1f1', 'MPI-ESM1-2-LR_r1i1p1f1']):
resdict = dict()
for tip in tips:
titlefont = 24
plt.rcParams['figure.titlesize'] = titlefont
plt.rcParams['axes.titlesize'] = 18
plt.rcParams['axes.labelsize'] = 18
#############################################################################
if os.uname()[1] == 'hobbes':
cart_in = '/home/fabiano/Research/lavori/WeatherRegimes/'
cart_out = '/home/fabiano/Research/lavori/prima_D45/'
elif os.uname()[1] == 'ff-clevo':
cart_out = '/home/fedefab/Scrivania/Research/Post-doc/lavori/prima_D45/'
fil_pres = 'prima_D45_pres/out_prima_D45_pres_DJF_EAT_4clus_4pcs_1979-2014_refEOF_dtr.p'
fil_fut = 'prima_D45_fut/out_prima_D45_fut_DJF_EAT_4clus_4pcs_2015-2050_refEOF_dtr.p'
results_pres, results_ref = ctl.load_wrtool(cart_in + fil_pres)
results_fut, _ = ctl.load_wrtool(cart_in + fil_fut)
cart_data = '/nas/PRIMAVERA/Stream1/'
filtas = 'highresSST-{}/{}/{}/day/{}/{}_day_{}_highresSST-{}_{}_*_r25_rc.nc'
#filtas.format(temp, mod, mem, varnam, varnam, mod, temp, mem)
# composites = dict()
# mod = 'ref'
# temp = 'present'
# file_ref = dict()
# file_ref['tas'] = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/ERAInt_daily_1979-2018_167_r25.nc'
# file_ref['pr'] = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/ERAInt_daily_1979-2018_228_pr_daysum_ok_r25.nc'
# for varnam in ['tas', 'pr']:
# var, coords, aux_info = ctl.read_iris_nc(file_ref[varnam])
# var_season, dates_season = ctl.sel_season(var, coords['dates'], 'DJF')
file_refit = cart_in + 'out_NEW_cmip6_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr_refit.p'
file_refit_2 = cart_in + 'out_NEW_cmip6_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr_refit_rebasetot.p'
fil_ece_ssp = cart_in + 'out_eceens_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr.p'
filref = '/home/fabiano/Research/lavori/WeatherRegimes/ERA_ref_r25_v4/out_ERA_NDJFM_{}_4clus_4pcs_1964-2014_dtr.p'
numclus = 4
reg_names_area = dict()
reg_names_area['EAT'] = ['NAO+', 'SBL', 'AR', 'NAO-']
reg_names_area['PNA'] = ['PT', 'PNA+', 'PNA-', 'AR']
area = 'EAT'
allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
for area in ['EAT', 'PNA']:
results_hist, results_ref = ctl.load_wrtool(file_hist.format(area))
results_ref = pickle.load(open(filref.format(area), 'rb'))
# del results_ref['var_glob']
# del results_ref['var_area']
# del results_ref['solver']
for mod in results_hist.keys():
#del results_hist[mod]['var_glob']
del results_hist[mod]['var_area']
del results_hist[mod]['solver']
restot = dict()
restot['models'] = results_hist
restot['reference'] = results_ref
for ssp in ['ssp585']:
print(ssp)
if type(inputs['ERA_ref_orig']) is list:
ref_cube = xr.load_dataset(inputs['ERA_ref_orig'][0])
elif '*' in inputs['ERA_ref_orig']:
filsref = glob.glob(inputs['ERA_ref_orig'])
ref_cube = xr.load_dataset(filsref[0])
else:
ref_cube = xr.load_dataset(inputs['ERA_ref_orig'])
else:
ref_cube = None
if inputs['rebase_to_historical']:
if inputs['file_hist_rebase'] is not None:
print('Loading historical climate mean from {}\n'.format(inputs['file_hist_rebase']))
clim_rebase = dict()
dates_clim_rebase = dict()
results_hist, _ = ctl.load_wrtool(inputs['file_hist_rebase'])
if inputs['cmip6_naming']:
# Per consistenza uso sempre il primo member dello storico
okmodhist = np.unique([ke.split('_')[0] for ke in results_hist])
for mod in okmodhist:
okke = [ke for ke in results_hist if ke.split('_')[0] == mod]
if np.any(['r1i' in ke for ke in okke]):
ke = [kee for kee in okke if 'r1i' in kee][0]
else:
ke = okke[0]
clim_rebase[mod] = results_hist[ke]['climate_mean']
dates_clim_rebase[mod] = results_hist[ke]['climate_mean_dates']
else:
for ke in results_hist:
clim_rebase[ke] = results_hist[ke]['climate_mean']
dates_clim_rebase[ke] = results_hist[ke]['climate_mean_dates']
#
# cmean, dates_cm, _ = ctl.daily_climatology(zg_dtr, dates_seas, window = 20)
# climmeans.append(cmean)
#
# climate_mean[mod] = np.mean(climmeans, axis = 0)
# climate_std[mod] = np.std(climmeans, axis = 0)
#
# climate_mean_dates[mod] = dates_cm
# num_members[mod] = len(climmeans)
#
# pickle.dump([climate_mean, climate_mean_dates, climate_std, num_members], open(cart_out + 'climate_mean_hist_p2.p', 'wb'))
climate_mean, climate_mean_dates, climate_std, num_members = pickle.load(open(cart_out + 'climate_mean_hist_p2.p', 'rb'))
for area in ['EAT', 'PNA']:
res_old, _ = ctl.load_wrtool('/data-hobbes/fabiano/WR_CMIP6/out_NEW_cmip6_hist_NDJFM_{}_4clus_4pcs_1964-2014_refCLUS_dtr_light.p'.format(area))
figs = []
for mod in okmods_mo:
modmem = [ke for ke in res_old.keys() if mod in ke][0]
lat = res_old[modmem]['lat']
lon = res_old[modmem]['lon']
newcoso = np.mean(climate_mean[mod], axis = 0).squeeze()
oldcoso = np.mean(res_old[modmem]['climate_mean'], axis = 0)
fig = ctl.plot_map_contour(newcoso-oldcoso, lat, lon, filename = None, visualization = 'standard', central_lat_lon = None, cmap = 'RdBu_r', title = mod, xlabel = None, ylabel = None, cb_label = None, cbar_range = (-60, 60), plot_anomalies = True, n_color_levels = 21, draw_contour_lines = False, n_lines = 5, color_percentiles = (0,100), bounding_lat = 30, plot_margins = area, add_rectangles = None, draw_grid = True, plot_type = 'filled_contour', verbose = False, lw_contour = 0.5)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'map_ensrebase_diff_{}.pdf'.format(area), figs)
for area in ['EAT', 'PNA']:
clatlo['EAT'] = (70., -20.)
clatlo['PNA'] = (70., -120.)
#allssps = 'ssp119 ssp126 ssp245 ssp370 ssp585'.split()
allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
ttests = dict()
cosette = dict()
area = 'EAT'
for area in ['EAT', 'PNA']:
cart_out = cart_out_orig + '{}_NDJFM/'.format(area)
ctl.mkdir(cart_out)
reg_names = reg_names_area[area]
results_hist, results_ref = ctl.load_wrtool(file_hist.format(area))
trend_ssp, residtime_ssp = pickle.load(
open(
cart_v5.format(area) + 'trends_wrfreq_e_restime_{}.p'.format(area),
'rb'))
seasfreq, runfreq = pickle.load(
open(cart_v5.format(area) + 'seasfreqs_{}_v4.p'.format(area), 'rb'))
freqs, residtimes, patterns, num_event = pickle.load(
open(
cart_v5.format(area) + 'allresults_dicts_{}_v3.p'.format(area),
'rb'))
freqs_cmip5, trend_ssp_cmip5, residtimes_cmip5, num_event_cmip5 = pickle.load(
open(cart_cmip5.format(area) + 'freqs_cmip5_{}.p'.format(area), 'rb'))
seasfreq_cmip5, runfreq_cmip5 = pickle.load(
reg_names_area['PNA'] = ['PT', 'PNA+', 'PNA-', 'BR']
clatlo = dict()
clatlo['EAT'] = (70., -20.)
clatlo['PNA'] = (70., -120.)
#allssps = 'ssp119 ssp126 ssp245 ssp370 ssp585'.split()
#allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
allssps = ['ssp585']
ttests = dict()
for area in ['EAT', 'PNA']:
cart_out = cart_out_orig + '{}_NDJFM/'.format(area)
ctl.mkdir(cart_out)
results_hist, results_ref = ctl.load_wrtool(file_hist.format(area))
# Erasing incomplete runs
for ke in tuple(results_hist.keys()):
if len(results_hist[ke]['labels']) < 7000:
del results_hist[ke]
results_ssp, _ = ctl.load_wrtool(file_ssp.format(ssp, area))
results_ssp_rebase, _ = ctl.load_wrtool(file_ssp_rebase.format(ssp, area))
results_refit, _ = ctl.load_wrtool(file_refit.format(ssp, area))
results_refit2, _ = ctl.load_wrtool(file_refit2.format(ssp, area))
# Adding the ece ensemble
ece_ssp, _ = ctl.load_wrtool(fil_ece_ssp.format(ssp, area))
ece_ssp_rebase, _ = ctl.load_wrtool(fil_ece_ssp_rebase.format(ssp, area))
#######################################
cart_in = '/home/fabiano/Research/lavori/prima_regimes_KS/'
plot_sig = True
plot_mean = True
for numclus in [3, 4, 5, 6]:
cart_out = cart_in + 'plots_k{}/'.format(numclus)
if not os.path.exists(cart_out): os.mkdir(cart_out)
#filon = open(cart_in + 'res_bootstrap_v7_KJ_k{}.p'.format(numclus), 'rb')
filon_ref = open(cart_in + 'res_bootstrap_v7_KJ_ref_k{}.p'.format(numclus),
'rb')
filogen = cart_in + 'out_prima_coup_v7_DJF_EAT_4clus_4pcs_1957-2014_refEOF_FILTEREDKJ_k{}.p'.format(
numclus)
results, results_ref = ctl.load_wrtool(filogen)
popke = [
'HadGEM3-GC31-LL_r1i1p2f1', 'EC-Earth3P_r1i1p1f1',
'EC-Earth3P-HR_r1i1p1f1'
]
for ke in popke:
if ke in results:
results.pop(ke)
allresmembers = list(results.keys())
all_mods = np.array([ke.split('_')[0] for ke in results.keys()] + ['ERA'])
all_mems = np.array([ke.split('_')[1] for ke in results.keys()] + ['0'])
del results
gen_file_ssp = cart_in + 'out_NEW_cmip6_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr.p'
file_rebase = cart_in + 'out_NEW_cmip6_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr_histrebase.p'
file_light = cart_in + 'out_NEW_cmip6_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr_light.p'
filref = '/home/fabiano/Research/lavori/WeatherRegimes/ERA_ref_r25_v4/out_ERA_NDJFM_{}_4clus_4pcs_1964-2014_dtr.p'
numclus = 4
reg_names_area = dict()
reg_names_area['EAT'] = ['NAO+', 'SBL', 'AR', 'NAO-']
reg_names_area['PNA'] = ['PT', 'PNA+', 'PNA-', 'AR']
area = 'EAT'
allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
for area in ['EAT', 'PNA']:
print(area)
results_hist, results_ref = ctl.load_wrtool(file_hist.format(area))
results_ref = pickle.load(open(filref.format(area), 'rb'))
del results_ref['var_glob']
del results_ref['var_area']
ref_solver = results_ref['solver']
for mod in results_hist.keys():
if mod == 'MPI-ESM1-2-LR_r1i1p1f1':
continue
del results_hist[mod]['var_glob']
del results_hist[mod]['var_area']
del results_hist[mod]['solver']
results_ssp, _ = ctl.load_wrtool(gen_file_ssp.format('ssp585', area))
for mod in results_ssp.keys():
if mod == 'MPI-ESM1-2-LR_r1i1p1f1':
colormip = dict()
colormip[('cmip5', 'EAT')] = ctl.color_set(7)[4]
colormip[('cmip6', 'EAT')] = ctl.color_set(7)[0]
colormip[('cmip5', 'PNA')] = ctl.color_set(7)[3]
colormip[('cmip6', 'PNA')] = ctl.color_set(7)[6]
area = 'EAT'
plocos = dict()
res_short = dict()
for area in ['EAT', 'PNA']:
print(area)
cart_out = cart_out_orig + '{}_NDJFM/'.format(area)
ctl.mkdir(cart_out)
results_hist, results_ref = ctl.load_wrtool(
file_hist.format(area, ye1, ye2))
results_hist_refEOF, _ = ctl.load_wrtool(
file_hist_refEOF.format(area, ye1, ye2))
results_hist_cmip5, _ = ctl.load_wrtool(
file_hist_cmip5.format(area, ye1, ye2))
results_hist_refEOF_cmip5, _ = ctl.load_wrtool(
file_hist_refEOF_cmip5.format(area, ye1, ye2))
resdict = dict()
resdict['cmip6'] = results_hist
resdict['cmip5'] = results_hist_cmip5
resdict['cmip6_refEOF'] = results_hist_refEOF
resdict['cmip5_refEOF'] = results_hist_refEOF_cmip5
var_ratio = dict()
freqbias = dict()
#cart = '/home/fabiano/Research/lavori/WeatherRegimes/ERA5/'
cart = '/home/fedef/Research/lavori/valembo_era5/'
#fil = 'out_ERA5_{}_{}_4clus_55perc_allyrs.p'
fil = 'ERA5_v90/out_ERA5_v90_{}_{}_4clus_90perc_allyrs.p'
filmask = cart + 'mask_1D_{}_{}.p'
reg_events = dict()
regime_ref = dict()
figs_scatter = []
figs_clouds = []
for area in ['EAT', 'PNA', 'NML']:
for season in ['DJF', 'JJA']:
resu, resu_ref = ctl.load_wrtool(
cart + fil.format(season, area)) # carico i regimi del wrtool
gigi = resu['ERA5']
# conto i giorni
if season == 'DJF':
nye = 33 # from 1979 to 2012
lensea = 90
add = 9 + 31
skip = 59
elif season == 'JJA':
nye = 34
lensea = 92
add = 0
skip = 0
for ke in ['labels', 'dist_centroid', 'pcs', 'dates']:
cart_in = '/home/fedef/Research/lavori/tipes/'
patnames = dict()
patnames['DJFM'] = ['NAO+', 'SBL', 'NAO-', 'AR']
patnames['JJAS'] = ['SBL', 'NAO+', 'NAO-/AL', 'AR']
#for tip in ['refCLUS', 'refEOF', 'refCLUS_dtr_reb']:
tip = 'refCLUS_dtr_reb'
keall = ['pi', 'ho03', 'c3r5'] #, 'eta']
colorz = ['steelblue', 'indianred', 'forestgreen'] #, 'orange']
# read output
for seas in ['JJAS', 'DJFM']:
resu, resu_ref = ctl.load_wrtool(
cart_in +
'out_tipes_hosing_projpi_{}_EAT_4clus_4pcs_allyrs_{}.p'.format(
seas, tip))
# calculate frequency in running windows
# 30-yr windows, distribution of seasonal frequency
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))
'piControl', 'stabilization-ssp585-2025', 'stabilization-ssp585-2050',
'stabilization-ssp585-2100'
]
colorz = ['black', 'forestgreen', 'orange', 'violet']
patnames = dict()
patnames['DJFM'] = ['NAO+', 'SBL', 'NAO-', 'AR']
patnames['JJAS'] = ['SBL', 'NAO+', 'NAO-/AL', 'AR']
tip = 'refCLUS_dtr_reb'
# read output
for seas in ['JJAS', 'DJFM']:
resu, resu_ref = ctl.load_wrtool(
cart_in +
'out_bottino_{}_EAT_4clus_4pcs_allyrs_{}.p'.format(seas, tip))
# calculate frequency in running windows
# 30-yr windows, distribution of seasonal frequency
timeseries = dict()
for ke in resu.keys():
if 'freq_clus_seasonal' not in resu[ke]:
resu[ke]['freq_clus_seasonal'], resu[ke][
'freq_clus_seasonal_years'] = ctl.calc_seasonal_clus_freq(
resu[ke]['labels'], resu[ke]['dates'], 4)
timeseries[ke] = xr.DataArray(resu[ke]['freq_clus_seasonal'],
dims=('reg', 'time'),
coords={
'reg': [0, 1, 2, 3],
'time':
