# plot SRMSE skills
ax = plt.figure(figsize=(6.5,3.5)).gca()
for j in range(n_decades-1):
plt.plot(lead_times, srmse_dec[:,j], c=decade_color[j], label=f"Deep Ens. ({decade_name[j]})")
plt.plot(lead_times, srmse, label="Deep Ens. (1962-2017)", c='k', lw=2)
plt.ylim(0,1.5)
plt.xlim(0.,lead_times[-1])
plt.xlabel('Lead Time [Months]')
plt.ylabel('SRMSE')
plt.grid()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.tight_layout()
# =============================================================================
# Seasonal correlation skills
# =============================================================================
# evaluate the model in different seasons
r_seas, p_seas = evaluation_seasonal_correlation(f'{model_name}', variable_name='prediction')
plot_seasonal_skill(lead_times, r_seas, vmin=0, vmax=1)
plt.contour(np.arange(1,13),lead_times, p_seas, levels=[0.01, 0.05, 0.1], linestyles=['solid', 'dashed', 'dotted'], colors='k')
plt.title('Correlation skill')
plt.tight_layout()
srsme_seas = evaluation_seasonal_srmse(f'{model_name}', variable_name='prediction')
plot_seasonal_skill(lead_times, srsme_seas, vmin=0, vmax=1.5, cmap=plt.cm.inferno_r, extend='max')
plt.title('SSRMSE skill')
plt.tight_layout()
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.tight_layout()
#%% =============================================================================
# Seasonal correlation skills
# =============================================================================
# evaluate the model in different seasons
background = "all"
#background = "el-nino-like"
r_seas, p_seas = evaluation_seasonal_correlation(f'{model_name}_prob',
background=background,
variable_name=f'median')
plot_seasonal_skill(lead_times, r_seas, vmin=0, vmax=1)
plt.contour(np.arange(1, 13),
lead_times,
p_seas,
levels=[0.01, 0.05, 0.1],
linestyles=['solid', 'dashed', 'dotted'],
colors='k')
plt.title('Correlation skill')
plt.tight_layout()
#%%
pca_dec = reader.read_statistic('pca',
variable='dec_sst',
dataset='ERSSTv5',
processed='anom')['pca1']
lead = 1
lw=2)
plt.ylim(0., 2)
plt.xlim(0, lead_time_arr[-1])
plt.xlabel('Lead Time [Months]')
plt.ylabel('SSRMSE')
#plt.title('Normalized RMSE')
plt.grid()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.tight_layout()
if period == "" and save:
plt.savefig(join(plotdir, f'ed_all_season_rmse.pdf'))
#%% contour skill plots ONI
plot_seasonal_skill(lead_time_arr, seas_corr.T, vmin=0, vmax=1)
plt.contour(np.arange(1, 13),
lead_time_arr,
seas_p.T,
levels=[0.01, 0.05, 0.1],
linestyles=['solid', 'dashed', 'dotted'],
colors='k')
plt.title('Correlation skill')
plt.tight_layout()
if save:
plt.savefig(join(plotdir, f'ed_seasonal_corr{period}.pdf'))
plot_seasonal_skill(lead_time_arr,
seas_rmse.T,
vmin=0,
vmax=1.2,