ax.set_ylabel('EOF {}'.format(cou[1]))
ax.legend()
plt.suptitle('Regime {} - {}'.format(reg, nomecoso))
figs.append(fig)
fig.savefig(cart_out +
'regime{}_pdf_proj{}.pdf'.format(reg, filter_tag))
ctl.plot_pdfpages(cart_out + 'ERApre1988_vs_post{}.pdf'.format(filter_tag),
figs)
sys.exit()
pcs_ref_1, dates1 = ctl.sel_time_range(results_ref['pcs'],
results_ref['dates'],
ctl.range_years(1957, 1987))
pcs_ref_2, dates2 = ctl.sel_time_range(results_ref['pcs'],
results_ref['dates'],
ctl.range_years(1988, 2018))
lab_ref_1, dates1 = ctl.sel_time_range(nulabs_ref, results_ref['dates'],
ctl.range_years(1957, 1987))
lab_ref_2, dates2 = ctl.sel_time_range(nulabs_ref, results_ref['dates'],
ctl.range_years(1988, 2018))
for reg in range(4):
figs = []
print(reg)
extra1 = (lab_ref_1 != reg) & (nulabs_1 == reg)
extra2 = (lab_ref_2 != reg) & (nulabs_2 == reg)
print('Extra in series 1: {} of {}\n'.format(np.sum(extra1),
np.sum(lab_ref_1 == reg)))
import numpy as np
from numpy import linalg as LA
from matplotlib import pyplot as plt
from matplotlib import colors as mpl_colors
from matplotlib import cm
import pickle
import matplotlib.animation as animation
from matplotlib.animation import ImageMagickFileWriter
import cartopy.crs as ccrs
#cart = '/home/fabiano/Research/lavori/SPHINX_for_lisboa/'
cart = '/home/fedefab/Scrivania/Research/Post-doc/SPHINX/'
ref_period = ctl.range_years(1850, 1900)
filena = 'lcb0-1850-2100-tas_mon.nc'
var, lat, lon, dates, time_units, var_units = ctl.read3Dncfield(cart + filena)
dates_pdh = pd.to_datetime(dates)
# Global stuff
global_mean = ctl.global_mean(var, lat)
zonal_mean = ctl.zonal_mean(var)
climat_mon, dates_mon, climat_std = ctl.monthly_climatology(
var, dates, dates_range=ref_period)
climat_year = np.mean(climat_mon, axis=0)
yearly_anom, years = ctl.yearly_average(var, dates)
ctl.mkdir(cart_out)
ref_file = '/data-hobbes/fabiano/CMIP6/zg_day_EC-Earth3_ssp585_r4i1p1f1_gr_201501-210012_r25_rc.nc'
modnam = 'EC-Earth3P'
yearange = (2015, 2100)
cbar_range = [0., 3.]
cbar_range_notr = [-1, 1]
#############################################################################
var, coords, aux_info = ctl.read_iris_nc(ref_file, extract_level_hPa=500)
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates,
ctl.range_years(yearange[0], yearange[1]))
var_set, dates_set = ctl.seasonal_set(var, dates, 'DJF', seasonal_average=True)
years = np.array([da.year for da in dates_set])
############## PLOT GLOBAL TRENDS ######################
fig, ax = plt.subplots()
glob_mea = ctl.global_mean(var_set, lat)
g0 = glob_mea[0]
m, c = ctl.linear_regre(years, glob_mea)
ax.scatter(years, glob_mea - g0, label='Global', color='blue')
ax.plot(years, c + m * years - g0, color='blue')
var_area, lat_area, lon_area = ctl.sel_area(lat, lon, var_set, 'EAT')
eat_mea = ctl.global_mean(var_area, lat_area)
file_in = '/data-hobbes/fabiano/OBS/ERA/ERA40+Int_daily_1957-2018_zg500_remap25_meters.nc'
#var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa = 500)
var, coords, aux_info = ctl.readxDncfield(file_in, extract_level=500)
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
mean_field_all['ERA'], lowfrvar['ERA'], highfrvar['ERA'], stat_eddy_all[
'ERA'] = ctl.variability_lowhi(lat,
lon,
var,
dates,
season,
area=area,
dates_range=ctl.range_years(1957, 2014))
cart_in = '/data-hobbes/fabiano/PRIMAVERA/incoming/hist-1950/'
flist = cart_in + 'lista_filez_v7.dat'
fi = open(flist, 'r')
lista_files = [lin.rstrip() for lin in fi.readlines()]
fi.close()
#mean_RMSbias_all = dict()
for filo in lista_files:
metadata = ctl.cmip6_naming(filo)
mod_name = metadata['model'] + '_' + metadata['member']
#var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa = 500)
var, coords, aux_info = ctl.readxDncfield(cart_in + filo,
extract_level=500)
li.rstrip() for li in open(cart_mods + 'listamods', 'r').readlines() ] mean_field_all = dict() lowfrvar = dict() highfrvar = dict() stat_eddy_all = dict() file_in = '/data-hobbes/fabiano/OBS/ERA/ERA40+Int_daily_1957-2018_zg500_remap25_meters.nc' var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa=500) lat = coords['lat'] lon = coords['lon'] dates = coords['dates'] var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1979, 2014)) mean_field, _ = ctl.seasonal_climatology(var, dates, season) var_anoms = ctl.anomalies_daily_detrended(var, dates) # LOW FREQ VARIABILITY #var_low = ctl.running_mean(var_anoms, 5) var_low = ctl.lowpass_lanczos(var_anoms, 6) var_low_DJF, dates_DJF = ctl.sel_season(var_low, dates, season) lowfr_variab = np.std(var_low_DJF, axis=0) lowfr_variab_zonal = ctl.zonal_mean(lowfr_variab) # High freq var_high = var_anoms - var_low var_high_DJF, dates_DJF = ctl.sel_season(var_high, dates, season)
if varnam == 'tas':
ifile = cart + '1950-2160-tas-monthly_remap25_rechunk.nc'
var, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
ifile)
print(var.keys())
weights = abs(np.cos(np.deg2rad(lat)))
# var, coords, aux_info = ctl.read_iris_nc(ifile)
# lat = coords['lat']
# lon = coords['lon']
# dates = coords['dates']
for ens in ens_mem:
# zonal mean
for season in ['year', 'DJF', 'JJA']:
var_pre, dates_pre = ctl.sel_time_range(
var[ens], dates, ctl.range_years(2085, 2095))
var_pre_seas, _ = ctl.seasonal_climatology(var_pre, dates_pre,
season)
var_zon = ctl.zonal_mean(var_pre_seas)
var_zon_wcos = weights * var_zon
fields[(varnam, ens, 'pre', season, 'zonal')] = var_zon
fields[(varnam, ens, 'pre', season, 'zonal_wcos')] = var_zon_wcos
var_pre, dates_pre = ctl.sel_time_range(
var[ens], dates, ctl.range_years(2110, 2120))
var_pre_seas, _ = ctl.seasonal_climatology(var_pre, dates_pre,
season)
var_zon = ctl.zonal_mean(var_pre_seas)
okmods = [cos for cos in results_hist.keys() if cos in results_ssp.keys()]
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))
results_refEOF, results_ref = pickle.load(open(filogen, 'rb'))
results_refEOF['ERA'] = results_ref
##################################################################
cart_sst = '/data-woodstock/PRIMAVERA/stream1/merged/tos/'
filsst = 'tos-{}-1950-2014-{}-remap.nc'
#cart_sst = '/nas/PRIMAVERA/Stream1/hist-1950/{}/{}/tos/'
filera = '/nas/reference/ERA40+Int/sst_Amon_ERA40_195701-201812_1deg.nc'
sstera, datacoords, _ = ctl.readxDncfield(filera)
datesera = datacoords['dates']
lat = datacoords['lat']
lon = datacoords['lon']
sstera = sstera - 273.15
sstera_mean, sstera_sd = ctl.seasonal_climatology(sstera, datesera, 'DJF', dates_range = ctl.range_years(1957, 2014))
area_box = (-80, 10, 20, 80)
sstera_mean_area, latsel, lonsel = ctl.sel_area(lat, lon, sstera_mean, area_box)
okpoera = (sstera_mean_area < -100) | (sstera_mean_area > 500)
allcose = dict()
allrms = dict()
allpatcor = dict()
#for mod, mem in zip(model_names, ens_mems):
for mod in model_names:
print(mod)
filmod_part = 'tos-{}-1950-2014-'.format(mod)
if 'AWI' in mod:
filmod_part = 'tos-{}-1950-2010-'.format(mod)
zg_noglob_zonme = ctl.zonal_mean(zg_noglob)
zg_se = zg_noglob - zg_noglob_zonme[..., np.newaxis]
se_trend, se_errtrend, _, _ = ctl.local_lineartrend_climate(
lat, lon, zg_se, dates, None)
try:
zghist, coords, _ = ctl.readxDncfield(cartmon_hist +
filhist.format(mod))
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
zgmean, zgstd = ctl.seasonal_climatology(zghist,
dates,
season,
dates_range=ctl.range_years(
1964, 2014))
cose[('hist mean', mod)] = zgmean
cose[('hist std', mod)] = zgstd
except:
print('passsssss ' + mod)
pass
cose[('trend', mod)] = trend
cose[('errtrend', mod)] = errtrend
cose[('se_trend', mod)] = se_trend
cose[('se_errtrend', mod)] = se_errtrend
cose[('zontrend', mod)] = zontrend
cose[('zonerrtrend', mod)] = zonerrtrend
pickle.dump(cose, open(cart_out_orig + 'zgtrends_ssp585.p', 'wb'))
def dopdf(var, xi, bnd_width):
pdf = ctl.calc_pdf(var, bnd_width=bnd_width)
pdfok = pdf(xi)
pdfok /= np.sum(pdfok)
return pdfok
for ru, col in zip(allru, colors):
jli = resdict[(ru, 'jli')]
jspeed = resdict[(ru, 'jspeed')]
if ru == 'ho03':
jli, _ = ctl.sel_time_range(jli,
resdict[(ru, 'dates')],
ctl.range_years(1860, 1880),
ignore_HHMM=False)
jspeed, _ = ctl.sel_time_range(jspeed,
resdict[(ru, 'dates')],
ctl.range_years(1860, 1880),
ignore_HHMM=False)
elif ru == 'pi':
jliserie = ctl.bootstrap(jli,
resdict[(ru, 'dates')],
None,
apply_func=dopdf,
func_args=[latsel, 0.22],
n_choice=20,
n_bootstrap=1000)
jspedserie = ctl.bootstrap(jspeed,
resdict[(ru, 'dates')],
modok = 'AWI-CM-1-0-LR'
else:
modok = 'AWI-CM-1-0-HR'
amv_ref, dates = read_file_index(
file_mod.format(modok, mem, sedat))
else:
sedat = '1950-2014'
y2 = 2014
amv_ref, dates = read_file_index(
file_mod.format(mod, mem, sedat))
except FileNotFoundError:
print('NOT FOUND!!!')
continue
amv_ref, dates = ctl.sel_time_range(amv_ref, dates,
ctl.range_years(1957, y2 - 1))
# Yearly amv index
amv_ref_yr, yrdates = ctl.yearly_average(amv_ref, dates)
amv_ref_yr = np.squeeze(amv_ref_yr)
if len(amv_ref_yr) < y2 - 1957:
for ye in range(1957, y2):
if ye not in [da.year for da in yrdates]:
print('Missing {}'.format(ye))
continue
# amv_ref_djf, dates_djf = ctl.sel_season(amv_ref, dates, seas)
# amv_ref_djf = np.squeeze(amv_ref_djf)
amv_nyr = np.array(ctl.running_mean(amv_ref_yr, n_yr))
amvc = amv_nyr[~np.isnan(amv_nyr)]
fig_all = plt.figure(figsize=(16, 12))
ax_all = []
# leggi da file e salva in dict
fil = cart_in + '{}_{}_1950-2100.nc'.format(exp, varna)
print(fil)
var, lat, lon, dates, time_units, var_units = ctl.read3Dncfield(fil)
yearsok = np.unique(pd.to_datetime(dates).year)
for y in yearsall:
if y not in yearsok:
print('{} {} - {} missing\n'.format(exp, varna, y))
varfac = var * fac
all_fluxes_yrl[(exp,
varna)], yrl_dates = ctl.yearly_average(varfac, dates)
monthly_clim_hist[(exp,
varna)], mon_dates, _ = ctl.monthly_climatology(
varfac,
dates,
dates_range=ctl.range_years(1950, 2025))
monthly_clim_fut[(exp, varna)], mon_dates, _ = ctl.monthly_climatology(
varfac, dates, dates_range=ctl.range_years(2026, 2100))
all_fluxes_yrl[(exp, 'all')] = np.sum(np.stack(
[all_fluxes_yrl[cos] for cos in all_fluxes_yrl.keys() if exp in cos]),
axis=0)
varnames.append('all')
fluxnames.append('Total')
map_means = dict()
zonal_ints = dict()
zonal_factor = 2 * np.pi * Rearth * np.cos(np.deg2rad(lat))
