for col, nam in zip(colors, resuu.keys()):
coso = resuu[nam][var].mean('lon').groupby("time.year").mean()
glomean = np.average(coso, weights = abs(np.cos(np.deg2rad(coso.lat))), axis = -1)
if var == 'evspsbl':
plt.plot(coso.year.data, np.abs(glomean), label = nam, color = col, linewidth = 2)
else:
plt.plot(coso.year.data, glomean, label = nam, color = col, linewidth = 2)
plt.grid()
plt.title(var)
#plt.legend()
ctl.custom_legend(fig, colors, resuu.keys(), ncol = 4, add_space_below = 0.03)
figs_global.append(fig)
ctl.plot_pdfpages(cart + 'global_timeseries_allmems.pdf', figs_global)
sys.exit()
#srf_net = ssr + str + sshf + slhf
surf_fluxs = ['rsds', 'rlds', 'rsus', 'rlus', 'hfss', 'hfls']
toa_fluxs = ['rlut', 'rsut', 'rsdt']
allvars = surf_fluxs + toa_fluxs + ['clt', 'clwvi', 'evspsbl']
fir_HR = '/home/paolo/work/data/REFORGE/EC-Earth3-TL799/rfrg-orog255-noparam/r2i1p1f1/mon/{}/{}_Amon_EC-Earth3-TL799_rfrg-orog255-noparam_r2i1p1f1_r144x73_*nc'
fir_LR = '/home/paolo/work/data/REFORGE/EC-Earth3/rfrg-ctrl-noparam/r1i1p1f1/mon/{}/{}_Amon_EC-Earth3_rfrg-ctrl-noparam_r1i1p1f1_r144x73_*nc'
fils_HR = np.concatenate([glob.glob(fir_HR.format(var, var)) for var in allvars])
fils_LR = np.concatenate([glob.glob(fir_LR.format(var, var)) for var in allvars])
flux_lr = xr.open_mfdataset(fils_LR, use_cftime = True)
flux_hr = xr.open_mfdataset(fils_HR, use_cftime = True)
ax.plot(lat, okplostoc, label='stoc fut', linewidth=2.0)
ax.plot(lat,
10 * (okplostoc - okplobase),
label='(diff s-b) x 10',
linewidth=1.0,
linestyle='--')
plt.xlabel('Latitude')
plt.ylabel('Heat flux (W)')
plt.legend()
plt.grid()
figures.append(fig)
axes.append(ax)
ctl.adjust_ax_scale(axes, sel_axis='both')
ctl.plot_pdfpages(figure_file, figures)
plt.close('all')
figure_file = cart_out + 'global_diff_net_fluxes.pdf'
figures = []
axes = []
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.title('Net flux at TOA')
for lett, tim in zip(['a', 'f'], ['_hist', '_fut']):
for cosone, ls in zip(['stoc', 'base'], [':', '--']):
coso = radclim[('global', cosone + tim, 'toa_balance')]
ax.plot(years[lett], coso, linewidth=0.5, color='grey', linestyle=ls)
rollcoso = ctl.running_mean(coso, wnd=10)
ax.plot(years[lett], rollcoso, label=cosone + tim, linewidth=2.0)
nam_ob, yea_ob = analogs[(opa, year, 'obs_eof')]
nam_ex, yea_ex = analogs[(opa, year, 'exp_eof')]
obsta = obs_stat[var].sel(member = opa, time = obs_stat['time.year'] == year).squeeze() - obsme[var]
mosta1 = mod_stat[var].sel(member = nam_ob, time = mod_stat['time.year'] == yea_ob).squeeze() - modme[var]
mosta2 = mod_stat[var].sel(member = nam_ex, time = mod_stat['time.year'] == yea_ex).squeeze() - modme[var]
# obsta = obs_states[opa][var].sel(time = obs_states[opa]['time.year'] == year).squeeze()
# mosta1 = mod_states[nam_ob][var].sel(time = mod_states[nam_ob]['time.year'] == yea_ob).squeeze()
# mosta2 = mod_states[nam_ex][var].sel(time = mod_states[nam_ex]['time.year'] == yea_ex).squeeze()
fig = ctl.plot_multimap_contour([obsta, mosta1, mosta2], fix_subplots_shape = (1,3), title = str(year), subtitles = ['obs', 'aobs: {} {}'.format(nam_ob, yea_ob), 'aexp: {} {}'.format(nam_ex, yea_ex)], figsize = (18,7), cbar_range = (-3, 3), cb_label = var + ' anomaly (K)')
figs.append(fig)
figs = np.concatenate(figs)
ctl.plot_pdfpages(cart_out + '{}_check_opa{}_analogs.pdf'.format(var, opa), figs)
sys.exit()
#####################################################################
# Selezione su spazio exp
solver_exp_dtr
pcs_ref = []
pcs_ref_dtr = []
for i in range(n_ref):
pcs_ref.append(solver.projectField(solver_exp.eofs(eofscaling=2)[i], neofs=n_ref, eofscaling=0, weighted=True))
# pcs_ref_dtr.append(solver_dtr.projectField(solver_exp_dtr.eofs(eofscaling=2)[i], neofs=n_ref, eofscaling=0, weighted=True))
ax.contour(xi,
yi,
zi.reshape(xi.shape),
cmap=cm.get_cmap('Reds'),
label='post')
ax.set_xlabel('EOF {}'.format(cou[0]))
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):
ax.bar(i, val, color=col, width=wi)
else:
ax.bar(i, val, color=col, width=wi, yerr=err, capsize=5)
i += 0.7
i += 0.3
ax.bar(i,
np.mean(allvalsdiff),
color='grey',
width=wi,
yerr=np.std(allvalsdiff),
capsize=5)
ax.set_xticks([])
ax.set_ylabel(tit + ' (difference: HR - LR)')
#ax.set_xlabel('Eff. atm. resolution (km)')
ax.axhline(0., color='grey', alpha=0.6)
ax.set_title(regnam[reg])
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.suptitle(tit)
plt.subplots_adjust(top=0.9)
fig = ctl.custom_legend(fig, colorscoup + ['grey'], model_coups + ['avg'])
fig.savefig(cart_out + cos + '_{}_1vs1.pdf'.format(vtag))
figures.append(fig)
ctl.plot_pdfpages(
cart_out + 'allfigs_primacoup_{}_wresolution.pdf'.format(vtag), figures)
cb_label=None,
cbar_range=(-50, 50),
plot_anomalies=True,
n_color_levels=21,
draw_contour_lines=False,
n_lines=5,
color_percentiles=(0, 100),
bounding_lat=30,
plot_margins='EAT',
add_rectangles=None,
draw_grid=True,
plot_type='filled_contour',
verbose=False,
lw_contour=0.5)
figs.append(fig)
ctl.plot_pdfpages(cart_out_orig + 'check_climate_mean_allmem.pdf', figs)
fig = plt.figure()
noreb = ctl.running_mean(resssp_noreb['EC-Earth3_r1i1p1f1']['pcs'][:, 0], 10)
reb = ctl.running_mean(resssp['EC-Earth3_r1i1p1f1']['pcs'][:, 0], 10)
plt.plot(noreb, label='rebase_ssp')
plt.plot(reb, label='rebase_hist')
plt.plot(reb - noreb, label='diff')
fig.savefig(cart_out_orig + 'check_first_eof_vs_rebase.pdf')
fig = plt.figure()
noreb = ctl.running_mean(resssp_noreb['EC-Earth3_r1i1p1f1']['pcs'][:300, 0],
10)
reb = ctl.running_mean(resssp['EC-Earth3_r1i1p1f1']['pcs'][:300, 0], 10)
plt.plot(noreb, label='rebase_ssp')
plt.plot(reb, label='rebase_hist')
bounding_lat=30,
cbar_range=[-50, 50],
draw_grid=True)
all_figs_stat.append(fig)
# for mod1, mod2 in zip(mod_LO, mod_HI):
# mod = '-'.join(mod2.split('-')[:-1])
# diff = abs(mean_field_all[mod2] - mean_field_all['era']) < abs(mean_field_all[mod1] -mean_field_all['era'])
# fig = ctl.plot_map_contour(diff, lat, lon, title = 'Mean field - better HR? - {}'.format(mod), visualization = projtype, bounding_lat = 30)
# all_figs_mf.append(fig)
#
# diff = abs(lowfrvar[mod2] - lowfrvar['era']) < abs(lowfrvar[mod1] -lowfrvar['era'])
# fig = ctl.plot_map_contour(diff, lat, lon, title = 'Low fr var - better HR? - {}'.format(mod), visualization = projtype, bounding_lat = 30)
# all_figs_lo.append(fig)
#
# diff = abs(highfrvar[mod2] - highfrvar['era']) < abs(highfrvar[mod1] -highfrvar['era'])
# fig = ctl.plot_map_contour(diff, lat, lon, title = 'High fr var - better HR? - {}'.format(mod), visualization = projtype, bounding_lat = 30)
# all_figs_hi.append(fig)
filename = cart_out + 'all_figs_mf.pdf'
ctl.plot_pdfpages(filename, all_figs_mf)
filename = cart_out + 'all_figs_lowvar.pdf'
ctl.plot_pdfpages(filename, all_figs_lo)
filename = cart_out + 'all_figs_highvar.pdf'
ctl.plot_pdfpages(filename, all_figs_hi)
filename = cart_out + 'all_figs_stateddy.pdf'
ctl.plot_pdfpages(filename, all_figs_stat)
# ax = plt.subplot(1, 3, ind)
# allsigs = [np.mean(np.array([cos[ireg, ii] for cos in bootstri[mod][nam]])/normpers[mod]) for mod in model_names_all]
# allerrs = [np.std(np.array([cos[ireg, ii] for cos in bootstri[mod][nam]])/normpers[mod]) for mod in model_names_all]
# allp90 = [(np.percentile(np.array([cos[ireg, ii] for cos in bootstri[mod][nam]])/normpers[mod], 10), np.percentile(np.array([cos[ireg, ii] for cos in bootstri[mod][nam]])/normpers[mod], 90)) for mod in model_names_all]
# plt.scatter(list(range(nmods)), allsigs, c = colors, s = 20)
# for imod, sig, err, errp9, col in zip(range(nmods), allsigs, allerrs, allp90, colors):
# #plt.errorbar(imod, sig, yerr = err, ecolor = col, linestyle = 'None', elinewidth = 2, capsize = 10)
# plt.errorbar([imod], [sig], yerr = [[sig - errp9[0]], [errp9[1]-sig]], ecolor = col, linestyle = 'None', elinewidth = 2, capsize = 5)
# ax.set_title('trans {} -> {}'.format(regnam[ireg], regnam[ii]))
# axes_diff.append(ax)
#
# ax.axhline(allsigs[-1], color = 'grey', alpha = 0.6)
# ax.axhline(allp90[-1][0], color = 'grey', alpha = 0.6, ls = '--')
# ax.axhline(allp90[-1][1], color = 'grey', alpha = 0.6, ls = '--')
#
# fig.suptitle(alltits[nam], fontsize = titlefont)
# plt.subplots_adjust(top = 0.9)
#
# ctl.custom_legend(fig, colors, model_names_all)
# figs.append(fig)
# allfigs.append(fig)
#
# ctl.adjust_ax_scale(axes_diff)
#
# for fig, ireg in zip(figs, range(numclus)):
# fig.savefig(cart_out + 'transonly_reg{}_bootstraps_v7.pdf'.format(ireg))
ctl.plot_pdfpages(cart_out + 'all_bootplots_k{}.pdf'.format(numclus),
allfigs,
save_single_figs=False)
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']:
res = dict()
for mod in okmods_mo:
res[mod+'_ensmean'] = dict()
#climmean_area, _, _ = ctl.sel_area(lat, lon, climate_mean[(area, mod)], area)
res[mod+'_ensmean']['climate_mean'] = climate_mean[mod].squeeze() #climmean_area
res[mod+'_ensmean']['climate_mean_dates'] = climate_mean_dates[mod]
pickle.dump([res, dict()], open(cart_out + 'dict_climate_mean_hist_{}.p'.format(area), 'wb'))
print('------------------------\n')
for mod in okmods_mo:
gigi = all_mems[mod]
# for mod, col in zip(all_res.keys(), colors):
# pats = [coso['patcor'][i] for coso in all_res[mod]]
# rmss = [coso['RMS'][i]/nsqr for coso in all_res[mod]]
# ax.scatter(pats, rmss, color = col, s = 5)
ax.set_xlim(limitz[var][0], 1.0)
ax.set_ylim(0., limitz[var][1])
ax.tick_params(labelsize=14)
plt.gca().invert_xaxis()
ax.set_xlabel('Pattern correlation', fontsize = 18)
ax.set_ylabel('RMS ({})'.format(varunits[var]), fontsize = 18)
axes.append(ax)
ctl.adjust_ax_scale(axes)
plt.tight_layout()
plt.subplots_adjust(top = 0.9)
plt.suptitle('Regime composites of {}'.format(var), fontsize = 28)
fig.savefig(cart_out + 'ellipse_plot_{}.pdf'.format(var))
allfigs_compare.append(fig)
for var in ['temp', 'prec']:
for i in range(4):
for cos in ['base', 'stoc']:
fig = ctl.plot_triple_sidebyside(area_compos[(var, cos)][i], area_compos[(var, 'ERA')][i], lat_area, lon_area, plot_margins = plotmarg, title = '{} - {} vs ERA - {}'.format(var, cos, pattnames[i]), stitle_1 = cos, stitle_2 = 'ERA', cb_label = varunits[var], cbar_range = (-4, 4))
allfigs_compare.append(fig)
fig = ctl.plot_triple_sidebyside(area_compos[(var, 'stoc')][i], area_compos[(var, 'base')][i], lat_area, lon_area, plot_margins = plotmarg, title = '{} - stoc vs base - {}'.format(var, pattnames[i]), stitle_1 = 'stoc', stitle_2 = 'base', cb_label = varunits[var], cbar_range = (-4, 4))
allfigs_compare.append(fig)
ctl.plot_pdfpages(cart_out + 'compos_SPHINX_vs_ERA.pdf', allfigs_compare)
# gregory
try:
#ax_greg.plot(glomeans[(ru, 'tas')][1]-pimean['tas'], glomeans[(ru, 'net_toa')][1], label = ru, color = col)
ctl.gregplot_on_ax(ax_greg, glomeans[(ru, 'tas')][1]-pimean['tas'], glomeans[(ru, 'net_toa')][1], color = col, label = ru, calc_ERF = False, calc_ECS = False)
except Exception as exc:
print(ru, exc)
pass
ax_greg.legend()
ax_greg.grid()
for ax in axs_glob:
ax.legend()
ax.grid()
ctl.plot_pdfpages(cart_out + 'bottino_glomeans.pdf', figs_glob, True, )
ax_greg.set_xlabel('Global mean tas (K)')
ax_greg.set_ylabel('Global net incoming TOA flux (W/m2)')
fig_greg.savefig(cart_out + 'bottino_gregory.pdf')
ax_greg.set_xlim((-0.5, 0.5))
ax_greg.set_ylim((-0.5, 0.5))
fig_greg.savefig(cart_out + 'bottino_gregory_pizoom.pdf')
sys.exit()
# var_map_200 = 'clt pr tas rlut uas'.split() # plot last 200 mean map, stddev, low/high var wrt pi
#
# allcopls = ['seamean', 'seastd', 'seap10', 'seap90']
patcor = np.array([results[ex]['av_res'][reg] for ex in exps_all])
xs = np.arange(len(patcor))
ax.scatter(xs, patcor, c=colors_all, s=100)
ax.set_title(patnames[reg])
ax.set_xticks([])
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.suptitle('Av. persistence')
ctl.custom_legend(fig, colors_all, exps_all)
fig.savefig(cart_out + 'persistence.pdf')
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'all_metrics.pdf', figs)
all_figures = []
for ex in exps_all:
patt = results[ex]['cluspattern_area']
if ex != 'ERA': patt = patt / gg
patt_ref = results_ref['cluspattern_area']
lat_ref = results[ex]['lat_area']
lon_ref = results[ex]['lon_area']
figs = ctl.plot_multimap_contour(patt,
lat_ref,
lon_ref,
None,
visualization='nearside',
central_lat_lon=(70, -20),
cmap='RdBu_r',
ax = fig.add_subplot(2, 2, reg + 1)
cosi = []
for tip, col in zip(alltips, colorz):
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(mem)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'check_models_refit_{}.pdf'.format(area),
figs)
okmods = [mod for mod in okmods if 'EC-Earth3' not in mod]
okmods.append('EC-Earth3_r1i1p1f1')
fig = plt.figure(figsize=(16, 12))
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
for tip, col in zip(alltips, colorz):
cosi = [runfreq[(tip, mem, reg)] for mem in okmods]
coso = np.mean(cosi, axis=0)
runfreq[(tip, reg)] = coso
coserr = np.std(cosi, axis=0)
ax.fill_between(yr,
coso - coserr,
coso + coserr,
lw_contour=0.5)
figs.append(fig)
gigi = sspcoso - histcoso
gogo, _, _ = ctl.sel_area(lat, lon, gigi, area)
diff = ref_solver.projectField(gogo,
neofs=4,
eofscaling=0,
weighted=True)
stri = 4 * '{:7.2f}' + '\n'
cosi = [ctl.cosine(diff, cen) for cen in results_ref['centroids']]
print(stri.format(*cosi))
ctl.plot_pdfpages(cart_out_orig + 'map_rebase_diff_{}.pdf'.format(area),
figs)
bau = results_hist[mod]['var_dtr']
bauda = np.arange(1965, 2015)
gigi = results_ssp[mod]['var_dtr']
gigida = np.arange(2015, 2100)
fig = plt.figure()
plt.plot(bauda, bau)
plt.plot(bauda,
np.polyval(results_hist[mod]['coeffs_dtr'], bauda),
linestyle='--')
plt.plot(gigida, gigi)
plt.plot(gigida,
np.polyval(results_ssp[mod]['coeffs_dtr'], gigida),
linestyle='--')
fig.savefig(cart_out_orig + 'vardtr_check.pdf')
fig = plt.figure(figsize = (16, 12))
ind = 0
axes = []
for reg in range(4):
ind += 1
ax = plt.subplot(2, 2, ind)
allvals = np.array([all_mod_stats[(nam, mod, reg)] for mod in model_names])
#ax.bar(np.arange(len(model_names)), allvals, color = colors)
#ax.scatter(np.arange(len(model_names)), allvals, color = 'grey', marker = 'x', s = 200)
for res, val, col, mark in zip(resolution, allvals, colors, sym_all):
ax.scatter(res, val, color = col, marker = mark, s = 100, linewidth = 3)
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 50)], color = 'grey', alpha = 0.6)
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 10)], color = 'grey', alpha = 0.6, linestyle = '--')
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 90)], color = 'grey', alpha = 0.6, linestyle = '--')
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 1)], color = 'grey', alpha = 0.6, linestyle = ':')
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 99)], color = 'grey', alpha = 0.6, linestyle = ':')
ax.set_title('Reg {}'.format(reg))
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.suptitle(tit)
plt.subplots_adjust(top = 0.9)
fig = ctl.custom_legend(fig, colors, model_names)
fig.savefig(cart_out + '_'.join(nam.split())+'_models_primacoup_{}_wresolution.pdf'.format(tag))
allfigs.append(fig)
ctl.plot_pdfpages(cart_out + 'allstats_models_primacoup_{}.pdf'.format(tag), allfigs, save_single_figs = False)
seas20 = np.array(
ctl.running_mean(
np.concatenate([
seasfreq[('hist', mem, reg)],
seasfreq[(ssp, mem, reg)]
]), 20))
ax.plot(yr, seas20, color=col, linewidth=2)
ax.set_title(reg_names_area[area][reg])
ax.axvline(2015, color='lightslategray', linewidth=0.2)
ctl.custom_legend(fig, colsim[1:], allsims[1:], ncol=4)
fig.suptitle(mem)
allmemfig.append(fig)
ctl.plot_pdfpages(cart_out + 'allmods_freq20_{}.pdf'.format(area),
allmemfig,
save_single_figs=False)
plt.close('all')
fig = plt.figure(figsize=(16, 12))
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
for col, ssp in zip(colsim[1:], allssps):
coso = runfreq[(ssp, 'lanc20', reg)]
coserr = runfreq[(ssp, 'lanc20err', reg)]
ax.fill_between(yr,
coso - coserr,
coso + coserr,
color=col,
alpha=0.15)
ax.plot(yr, coso, label=ssp, color=col, linewidth=2)
for per in periods:
for cos in ['base', 'stoc', 'diff']:
trenddict[('toa_net', per, cos)] = -(trenddict[('rsus', per, cos)] +
trenddict[('rlut', per, cos)])
for varnam in allvars + ['toa_net']:
figs = []
for per in periods:
for cos in ['base', 'stoc', 'diff']:
if varnam == 'tas':
cbar_range = (-2., 2.)
elif 'cc' in varnam:
cbar_range = (-0.09, 0.09)
else:
cbar_range = (-20., 20.)
if cos == 'diff' and cbar_range is not None:
cbar_range = (cbar_range[0] / 4., cbar_range[1] / 4.)
fig = ctl.plot_map_contour(trenddict[(varnam, per, cos)],
lat,
lon,
visualization='Robinson',
central_lat_lon=(0., 180.),
title='{} - {} - {}'.format(
varnam, per, cos),
cbar_range=cbar_range)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'trend_patterns_v2_{}.pdf'.format(varnam),
figs)
std_trm = np.std(all_trm, axis=0)
ax = fig.add_subplot(121)
gigi = ax.imshow(mean_trm, norm=LogNorm(vmin=0.01, vmax=1.0))
ax.xaxis.tick_top()
ax.set_xticks(np.arange(numclus), minor=False)
ax.set_xticklabels(pattnames, size='small')
ax.set_yticks(np.arange(numclus), minor=False)
ax.set_yticklabels(pattnames, size='small')
cb = plt.colorbar(gigi, orientation='horizontal')
ax = fig.add_subplot(122)
gigi = ax.imshow(std_trm / mean_trm, vmin=0., vmax=0.5)
#ax.set_title('Ratio std dev/ mean')
cb = plt.colorbar(gigi, orientation='horizontal')
ax.xaxis.tick_top()
ax.set_xticks(np.arange(numclus), minor=False)
ax.set_xticklabels(pattnames, size='small')
ax.set_yticks(np.arange(numclus), minor=False)
ax.set_yticklabels(pattnames, size='small')
fig.savefig(
cart_out +
'Transmatrix_{}_{}yr_{}.pdf'.format(mod, n_choice, n_bootstrap))
all_figures.append(fig)
ctl.plot_pdfpages(
cart_out + 'SPHINX_variability_{}yr_{}.pdf'.format(n_choice, n_bootstrap),
all_figures)
era_lon,
title=fluxlongnames[flun] +
' - {} {} {}'.format(exp, year, seas))
figures_era.append(fig)
total = np.sum([era_fluxes_maps[(seas, flun)] for flun in fluxnames],
axis=0)
fig = ctl.plot_map_contour(total,
era_lat,
era_lon,
title='Total meridional flux - {} {} {}'.format(
exp, year, seas))
figures_era.append(fig)
file_era = cart_out + 'ERA_reference_fluxes_1988.pdf'
ctl.plot_pdfpages(file_era, figures_era)
ally = [1975, 2000, 2025, 2050, 2075, 2090, 2100]
# exp = 'lcs0'
freq = '6hrs'
# year = 1988
# Ora per gli exps
#cart_in = '/data-hobbes/fabiano/SPHINX/heat_flux/lcs0_1988_6hrs/'
cart_in = '/data-hobbes/fabiano/SPHINX/heat_flux/data_6hrs/'
allfluxes = dict()
# for exp in ['lcb0', 'lcs0']:
# for year in [1975, 2000, 2025, 2050, 2075, 2090, 2100]:
for exp in ['las1']:
for year in [1988]:
pressurefile = cart_in + '{}_day_{}_ps.nc'.format(exp, year)
koze = res_all[ke]
if 'ua' in ke:
cbar_range = (-15, 15.)
cb_label = 'u (m/s)'
elif ke == 'zg_full':
cbar_range = (-250, 250.)
cb_label = 'zg (m)'
else:
cbar_range = (-160, 160.)
cb_label = 'zg (m)'
fix_subplots_shape = (2, 2)
if len(koze['cluspattern']) == 5: fix_subplots_shape = (2, 3)
patts = koze['cluspattern']
if ke == 'zg_full':
patts = patts - np.mean(koze['cluspattern_area'])
fig = cd.plot_regimes(koze['lat'],
koze['lon'],
patts,
None,
cbar_range=cbar_range,
cb_label=cb_label,
reg_freq=koze['freq_clus'],
plot_type='pcolormesh')
fig[0].suptitle(ke)
figs.append(fig[0])
#figs = np.concatenate(figs)
ctl.plot_pdfpages(cart_out + 'test_regimes_pattern.pdf', figs)
cbar_range=cbar_range,
n_color_levels=11,
n_lines=11,
draw_contour_lines=True,
draw_grid=True,
cb_label='Trend (m/year)',
title=tit)
allfigs.append(fig)
trend_yea.append(m)
intrc_yea.append(c)
trend_yea = np.stack(trend_yea)
intrc_yea = np.stack(intrc_yea)
ctl.plot_pdfpages(
cart_out + '{}_trend_{}-{}_NDJFM_alongseason.pdf'.format(
modnam, yearange[0], yearange[1]), allfigs)
##############################################################################
sys.exit()
nya = 30 # number of years for running mean
climate_mean_dict = dict()
for num in [1, 5, 10, 15, 20]:
climate_mean, dates_climate_mean = ctl.trend_daily_climat(var,
dates,
window_days=num,
window_years=nya)
pdfok = pdf(vsel)
pdfok /= np.sum(pdfok)
jlimin = np.percentile(jspedserie, 10, axis = 0)
jlimax = np.percentile(jspedserie, 90, axis = 0)
ax2.fill_between(vsel, jlimin, jlimax, color = col, alpha = 0.2)
ax2.plot(vsel, pdfok, label = ru, color = col, linewidth = 3)
if axlu is not None:
axlu.grid()
axlu.set_xlabel('Latitude')
axlu.set_title(area)
axlu.legend()
ax1.grid()
ax2.grid()
ax1.set_xlabel('Latitude')
ax2.set_xlabel('u wind (m/s)')
ax1.set_title('Jet latitude index')
ax2.set_title('Jet speed')
ax1.legend()
ax2.legend()
fig.suptitle('{} - {}'.format(area, season))
#fig.savefig(cart_out + 'jlinspeed_bottino_{}{}.pdf'.format(area, tip))
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'jlinspeed_bottinoall{}.pdf'.format(tip), figs)
figN.savefig(cart_out + 'jlibott_allareas_N{}.pdf'.format(tip))
figS.savefig(cart_out + 'jlibott_allareas_S{}.pdf'.format(tip))
cosa = np.mean(temp_anoms[labok, ...], axis=0)
compos['temp_filt'].append(cosa)
fig = ctl.plot_map_contour(
cosa,
coords_temp['lat'],
coords_temp['lon'],
title='Temp anom - regime {} - filt 5 days'.format(reg),
plot_margins=(-120, 120, 20, 90),
cbar_range=(-5, 5))
allfigs.append(fig)
labok = (oklabels == reg) & (okleneve > 5)
labok = oklabels == reg
cosa = np.mean(prec_anoms[labok, ...], axis=0)
compos['prec_filt'].append(cosa)
fig = ctl.plot_map_contour(
cosa,
coords_prec['lat'],
coords_prec['lon'],
title='Prec anom - regime {} - filt 5 days'.format(reg),
plot_margins=(-120, 120, 20, 90),
cbar_range=(-5, 5),
cmap='RdBu')
allfigs.append(fig)
ctl.plot_pdfpages(cart_out + 'temprec_composites_wERA.pdf', allfigs)
pickle.dump(compos, open(cart_out + 'out_composites_ERA_precERA.p', 'wb'))
fig = plt.figure()
plt.title('{} - base vs stoc wdiff - {}'.format(varnam, season))
for i, cos in enumerate(['base', 'stoc']):
mea = fields[(varnam, cos + '_mean', 'diff', season, 'zonal_wcos')]
std = fields[(varnam, cos + '_std', 'diff', season, 'zonal_wcos')]
plt.fill_between(lat,
mea - std,
mea + std,
color=colors[i],
alpha=0.2)
plt.plot(lat,
mea,
label='w. diff post-pre - {}'.format(cos),
color=colors[i])
plt.axhline(0., color='grey')
plt.legend()
figures.append(fig)
figures_dict[(varnam, season, 'wdiff')] = fig
filefig = cart_out + 'figures_SPHINX_reloaded_{}.pdf'.format(season)
ctl.plot_pdfpages(filefig, figures, save_single_figs=True)
# for season in ['year', 'DJF', 'JJA']:
# figures = []
# for varnam in varlist + ['surf_rad_bal', 'surf_tot_bal']:
# fig = plt.figure()
# plt.title('{} - {}'.format(varnam, season))
# for cos, col in zip(['base', 'stoc'], colors):
# plt.plot(lat, fields[(varnam, cos + '_mean', 'pre', season, 'zonal')], label = cos+' (pre)', color = col, linestyle = ':')
# plt.plot(lat, fields[(varnam, cos + '_mean', 'post', season, 'zonal')], label = cos+' (post)', color = col, linestyle = '-')
figures = []
for bandnam, band in zip(names, lat_bands):
axes = []
for coso in ['base', 'stoc']:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('{} - {}'.format(coso, bandnam))
for th in temp_horiz:
ax.plot(np.arange(12),
band_clim[(coso, 'tas', th, band)],
label=th)
ax.legend()
figures.append(fig)
axes.append(ax)
ctl.adjust_ax_scale(axes, sel_axis='both')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('Diff stoc-base - {}'.format(bandnam))
for th in temp_horiz:
ax.plot(np.arange(12),
band_clim[('stoc', 'tas', th, band)] -
band_clim[('base', 'tas', th, band)],
label=th)
ax.legend()
ax.grid()
figures.append(fig)
ctl.plot_pdfpages(cart_out + 'seas_cycle_all.pdf', figures)
# plt.yscale('log')
# plt.ylim(1.e5, 1.e3)
hrko1 = np.mean(hrko_diff[:10, :], axis=0)
hrko2 = np.mean(hrko_diff[10:20, :], axis=0)
hrko3 = np.mean(hrko_diff[20:, :], axis=0)
lrko1 = np.mean(lrko_diff[:10, :], axis=0)
lrko2 = np.mean(lrko_diff[10:20, :], axis=0)
lrko3 = np.mean(lrko_diff[20:, :], axis=0)
colok = [cols[0], cols[15], cols[-1]]
fig = plt.figure()
plt.plot(hrko1, levs, linestyle='-', color=colok[0], label='days 1-10')
plt.plot(hrko2, levs, linestyle='-', color=colok[1], label='days 10-20')
plt.plot(hrko3, levs, linestyle='-', color=colok[2], label='days 20-30')
plt.plot(lrko1, levs, linestyle='--', color=colok[0])
plt.plot(lrko2, levs, linestyle='--', color=colok[1])
plt.plot(lrko3, levs, linestyle='--', color=colok[2])
plt.yscale('log')
plt.ylim(1.e5, 1.e3)
plt.grid()
plt.legend()
plt.xlabel('daily tendency')
plt.title(var)
plt.savefig(cart + '{}_profs_1mo_799-cntrl.pdf'.format(var))
figs_prof.append(fig)
# ctl.plot_pdfpages(cart + 'day_facet_1m.pdf', figs_facet+figs_facet_3d)
# ctl.plot_pdfpages(cart + 'day_ovmol_2m.pdf', figs_ovmol)
ctl.plot_pdfpages(cart + 'day_prof_1m.pdf', figs_prof)
plt.close('all')
#plt.plot(era_lat, mpefzon3, label = 'PE - daily more lev')
#plt.plot(lat, mpefzon, label = 'PE - sphinx daily')
plt.plot(era_lat, era_mpef_Febzon, label = 'PE era ref', linestyle = ':')
plt.legend()
figures.append(fig)
fig = plt.figure()
plt.title('ERA calc - February 1988 - LH diffs')
#plt.plot(era_lat, mlhfzon1, label = 'LH - daily')
plt.plot(era_lat, mlhfzon2, label = 'LH - 6hrs')
plt.plot(era_lat, mlhfzon2p0, label = 'LH - 6hrs - int_p0')
plt.plot(era_lat, mlhfzon2p0_recalc, label = 'LH - hfc recalc')
#plt.plot(era_lat, mlhfzon3, label = 'LH - daily more lev')
#plt.plot(lat, mlhfzon, label = 'LH - sphinx daily')
plt.plot(era_lat, era_mlhf_Febzon, label = 'LH era ref', linestyle = ':')
plt.legend()
figures.append(fig)
# fig = ctl.plot_double_sidebyside(era_mshf_Feb, mshfint, [era_lat, lat], [era_lon, lon], title = 'era_ref vs sphinx', use_different_grids = True)
# figures.append(fig)
# fig = ctl.plot_double_sidebyside(era_mshf_Feb, mshfint2, era_lat, era_lon, title = 'era_ref vs era 6 hrs')
# figures.append(fig)
# fig = ctl.plot_double_sidebyside(mshfint2, mshfint2p0, era_lat, era_lon, title = 'era 6 hrs vs era 6 hrs int p0')
# figures.append(fig)
ctl.plot_pdfpages(figure_file2, figures)
figures_cross = np.array(figures_cross)
figures_cross = figures_cross[[0,4,8,1,5,9,2,6,10,3,7,11]]
ctl.plot_pdfpages(figure_file_cross, figures_cross)
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 = fig.add_subplot(2, 2, reg + 1)
for tip, col in zip(alltips, colorz):
okmods = resdict[tip].keys()
cosi = [runfreq[(tip, mem, reg)] for mem in okmods]
coso = np.mean(cosi, axis=0)
runfreq[(tip, reg)] = coso
coserr = np.std(cosi, axis=0) / np.sqrt(len(okmods) - 1)
ax.fill_between(yr,
coso - coserr,
coso + coserr,
color=col,
alpha=0.3)
#fig.savefig(cart + 'scatter_{}_{}.pdf'.format(season, area))
figs_scatter.append(fig)
# #### regime clouds
# clouds = dict()
# clouds['reference'] = koze_ref
# clouds['pos'] = coso_pos
# clouds['neg'] = coso_neg
#
# xlims = (np.percentile(clouds['reference']['pcs'], 1), np.percentile(clouds['reference']['pcs'], 99))
#
# fig = ctl.plot_multimodel_regime_pdfs(clouds, model_names = ['reference', 'pos', 'neg'], eof_proj = [(0,1), (2,3)], reference = 'reference', check_for_eofs = False, colors = ['black', 'forestgreen', 'indianred'], eof_axis_lim = xlims)
# fig.suptitle(area + ' - ' + season)
# figs_clouds.append(fig)
ctl.plot_pdfpages(cart + 'scatter_regimes_v90.pdf', figs_scatter)
ctl.plot_pdfpages(cart + 'clouds_regimes_v90.pdf', figs_clouds)
pickle.dump(reg_events, open(cart + 'regimes_masked_v90.p', 'wb'))
pickle.dump([ttests, deltadist_all], open(cart + 'delta_pcs_v90.p', 'wb'))
ofidpc.close()
ofidpc = open(cart + 'rel_delta_pcs_latex.txt', 'w')
from matplotlib import colors as mcolors
colo = '#d73027 #f46d43 #fdae61 #fee090 #ffffff #e0f3f8 #abd9e9 #74add1 #4575b4'
colo = colo.split()
colo = colo[::-1]
add_vector_field=[
ug[levs[i + 1]] - ug[lev], vg[levs[i + 1]] - vg[lev]
],
title='vertical wind shear - lev {} hPa'.format(lev),
plot_anomalies=False,
plot_margins=area,
quiver_scale=quiver_scale,
vec_every=vec_every,
plot_type='pcolormesh',
figsize=figsize)
figs.append(fig)
fig = ctl.plot_map_contour(ta_[i],
lat,
lon,
add_vector_field=[
ug[levs[i + 1]] - ug[lev] - ut_lev[lev],
vg[levs[i + 1]] - vg[lev] - vt_lev[lev]
],
title='residual - lev {} hPa'.format(lev),
plot_anomalies=False,
plot_margins=area,
quiver_scale=quiver_scale,
vec_every=vec_every,
plot_type='pcolormesh',
figsize=figsize)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'geostrophy_exe.pdf', figs)
plt.close('all')
