xba.append('{} - {}'.format(bi0, bi1))
okke = (bi0 <= invfr) & (invfr < bi1)
gig = np.sum(ps[okke])
barz.append(gig)
ax.bar(np.arange(len(barz))+shi, barz, color = col, width = 0.15)
ax.set_xticks(np.arange(len(barz)))
ax.set_xticklabels(xba, rotation = 30)
ax.set_title(ru)
if ax in axs[1, :]:
ax.set_xticks(np.arange(len(barz)))
ax.set_xticklabels(xba, rotation = 30)
ax.set_xlabel('period (yr)')
ctl.adjust_ax_scale(axs.flatten())
fig.savefig(cart_out + 'enso_spectra_bins_varying.pdf')
fig, ax = plt.subplots(figsize = (16,12))
allshi = [-0.3, -0.1, 0.1, 0.3]
for ru, col, shi in zip(allru, colors, allshi):
piuz = enso[ru]['tos'].groupby('time.year').mean()
data_all = piuz.values.flatten()
allcose = []
for ich, ye1 in zip(range(nchu), yesta):
data = data_all[ye1:ye1+nye]
ps = np.abs(np.fft.rfft(data, norm='forward'))**2
allfreqs[(ssp, area, season, tip, reg)] = []
for mod, col in zip(modoks, coloks):
frdiff = results_ssp585[mod]['freq_clus'][reg] - results_hist[mod][
'freq_clus'][reg]
ax.scatter(i, frdiff, marker='D', color=col, s=25)
allfreqs[(ssp, area, season, tip,
reg)].append(results_ssp585[mod]['freq_clus'][reg] -
results_hist[mod]['freq_clus'][reg])
# allfreqs[('hist', tip, reg)].append(results_hist[mod]['freq_clus'][reg])
i += 1
ax.axhline(0, color='gray', linewidth=0.5)
ax.set_xticks([])
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')
fig.savefig(cart_out + 'WR_freq_change_dtr_{}_{}_{}_{}.pdf'.format(*ke))
# Same but only for long-lasting (> 5 days) regimes
for mod in modoks:
results_ssp585[mod]['av_res'] = np.array([
np.mean(results_ssp585[mod]['resid_times'][reg])
for reg in range(4)
])
results_hist[mod]['av_res'] = np.array([
np.mean(results_hist[mod]['resid_times'][reg]) for reg in range(4)
])
axes = []
for llo1, llo2 in lon_sect:
i += 1
ax = plt.subplot(3, 3, i)
for num, col in zip(allnums, colors):
ax.plot(dtclmpd.dayofyear,
np.mean(clm_fullperiod[num][:, lla1:lla2, llo1:llo2],
axis=(1, 2)),
color=col)
if lla2 is None: lla2 = -1
if llo2 is None: llo2 = -1
ax.set_title('lat {:2.0f}/{:2.0f}, lon {:3.0f}/{:3.0f}'.format(
lat_area[lla1], lat_area[lla2], lon_area[llo1], lon_area[llo2]))
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.tight_layout()
fig = ctl.custom_legend(fig, colors, ['{}'.format(i) for i in allnums])
fig.savefig(cart_out + 'mean_climatology_zg.pdf')
figs = []
axaxes = [[], [], []]
colors = ctl.color_set(len(climate_mean_area))
for num in allnums:
cosone = climate_mean_dict[num]
ref = clm_fullperiod[num]
fig = plt.figure(figsize=(16, 12))
i = 0
okplostoc = np.mean(stoc_fut, axis=0)
ax.plot(lat, okplobase, label='base fut', linewidth=2.0)
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)
okjet = jetind[okin]
jetlat_comp[(mod, reg, tag)] = okjet
pdf = ctl.calc_pdf(okjet)
if vv in ['LR', 'OBS']:
axes[reg].plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
if vv in ['HR', 'OBS']:
axes_HR[reg].plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
pdf = ctl.calc_pdf(jetind)
if vv in ['LR', 'OBS']:
ax_all.plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
if vv in ['HR', 'OBS']:
ax_all_HR.plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
ctl.adjust_ax_scale(axes+axes_HR)
# ctl.custom_legend(fig, colors_wERA, model_names_all)
ctl.custom_legend(fig, colors_wERA2[0::2], model_coups + ['OBS'])
ctl.custom_legend(fig_HR, colors_wERA2[0::2], model_coups + ['OBS'])
#fig.suptitle('Jet Latitude and Weather Regimes (SR)')
fig.savefig(cart_out + 'jetlat_compos_LR{}.pdf'.format(tag))
#fig_HR.suptitle('Jet Latitude and Weather Regimes (HR)')
fig_HR.savefig(cart_out + 'jetlat_compos_HR{}.pdf'.format(tag))
# fig.savefig(cart_out + 'jetlat_compos_wECEstream2.pdf')
ctl.adjust_ax_scale([ax_all, ax_all_HR])
ctl.custom_legend(fig_all, colors_wERA2[0::2], model_coups + ['OBS'])
#ctl.custom_legend(fig_all, colors_wERA, model_names_all)
fig_all.savefig(cart_out + 'jetlat_allregs_LR{}.pdf'.format(tag))
ctl.custom_legend(fig_all_HR, colors_wERA2[0::2], model_coups + ['OBS'])
fig_all_HR.savefig(cart_out + 'jetlat_allregs_HR{}.pdf'.format(tag))
posthicks = [1.5, 4.5, 7.5, 10.5]
ax.boxplot(allfr, positions=np.array(pos))
ax.set_xticks(posthicks)
ax.set_xticklabels(reg_names)
ax.set_ylabel('WR frequency')
ax_all_ul[ise].boxplot(allfr, positions=np.array(pos))
ax_all_ul[ise].set_xticks(posthicks)
ax_all_ul[ise].set_xticklabels(reg_names)
ax_all_ul[ise].set_ylabel('WR frequency')
ax_all_ul[ise].set_title('season: {}'.format(seas))
fig.suptitle('Upper vs lower tercile of {} index'.format(indexname))
fig.savefig(cart_out +
'ERA_{}_vs_WRfreq_upperlowertercile_{}.pdf'.format(
indexname, seas))
ctl.adjust_ax_scale(ax_all + ax_all_ul)
fig_all.suptitle('Positive vs negative {} index'.format(indexname))
fig_all.savefig(cart_out_ind +
'ERA_{}_vs_WRfreq_posneg_allseas.pdf'.format(indexname))
fig_all_ul.suptitle('Upper vs lower tercile of {} index'.format(indexname))
fig_all_ul.savefig(
cart_out_ind +
'ERA_{}_vs_WRfreq_upperlowertercile_allseas.pdf'.format(indexname))
pickle.dump([ref_corrs, ref_freqs_posneg, ref_freqs_terc],
open(cart_out_all + 'ref_corrfreq_AMV_ENSO.p', 'wb'))
ctl.primavera_boxplot_on_ax(ax,
allpercs,
model_names_all,
colors_wERA,
color_main,
vers,
ens_names,
ens_colors,
wi=0.5,
plot_mean=plot_mean)
ax.set_title(reg)
ax.set_ylabel(allylabels[nam])
axes.append(ax)
ctl.adjust_ax_scale(axes)
#fig.suptitle(alltits[nam], fontsize = titlefont)
plt.subplots_adjust(top=0.9)
ctl.custom_legend(fig,
colors_wERA + ens_colors,
model_names_all + ens_names,
ncol=6)
fig.savefig(cart_out + '{}_bootstraps_v7_k{}.pdf'.format(nam, numclus))
allfigs.append(fig)
ax_pers_2 = []
fig_pers = plt.figure(figsize=(20, 12))
for i in range(numclus):
ax_pers_2.append(plt.subplot(2, 3, i + 1))
color='indianred')
ax.fill_between(np.arange(len(dat)),
dat,
np.zeros(len(dat)),
where=dat <= 0.,
interpolate=True,
color='steelblue')
ax.plot(np.arange(len(dat)), dat, color='grey', lw=0.1)
if ru != allru[-1]:
ax.set_xticks([])
else:
ax.set_xticks(np.arange(2100, 2601, 100))
ax.set_xticklabels(np.arange(0, 501, 100))
ctl.adjust_ax_scale(axs, sel_axis='y')
fig.savefig(cart_out + 'allru_redblue_{}.pdf'.format(ind))
# 2) plot variance: a) running mean all-in-one, b) boxplot (with dot for transient) (as function of eq. temperature? no)
fig, ax = plt.subplots(figsize=(12, 8))
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [
np.percentile(enso_abs50[ru], nu) for ru in allru
]
allpercs['mean'] = [np.mean(enso_abs50[ru]) for ru in allru]
allpercs['min'] = [np.min(enso_abs50[ru]) for ru in allru]
allpercs['max'] = [np.max(enso_abs50[ru]) for ru in allru]
tas10 = np.percentile(tasanom_pac[oknino], 10, axis=0)
axl.fill_between(paclons, tas10, tas90, color='indianred', alpha=0.3)
tasanom_pac[oknino].mean('year').plot(ax=axl,
linewidth=2,
color='indianred')
# tasanom_pac[oknina].plot(ax = axl, x = 'lon', hue = 'year', linewidth = 0.1, color = 'steelblue')
tas90 = np.percentile(tasanom_pac[oknina], 90, axis=0)
tas10 = np.percentile(tasanom_pac[oknina], 10, axis=0)
axl.fill_between(paclons, tas10, tas90, color='steelblue', alpha=0.3)
tasanom_pac[oknina].mean('year').plot(ax=axl,
linewidth=2,
color='steelblue')
axl.set_title(ru)
axl.grid()
ninodist = np.argmax(tasanom_pac[oknino].values, axis=1)
ninadist = np.argmin(tasanom_pac[oknina].values, axis=1)
axo.hist(paclons[ninodist], color=col, bins=np.arange(190, 271, 5))
axa.hist(paclons[ninadist], color=col, bins=np.arange(190, 271, 5))
ctl.adjust_ax_scale(axs_o.flatten())
ctl.adjust_ax_scale(axs_a.flatten())
ctl.adjust_ax_scale(axsli.flatten())
fig_o.savefig(cart_out + 'nino_lon_hist.pdf')
fig_a.savefig(cart_out + 'nina_lon_hist.pdf')
figli.savefig(cart_out + 'nino_lon_anom.pdf')
