def nmmehist():
from nmme import gen_ensembles
df = gen_ensembles()
prcp = get_lcrb_prcp()
fig, (ax1, ax2) = plt.subplots(1,2, figsize = (12,4))
ax1.hist(df[1998], label = 'NMME Ensembles', color = 'gray')
ax1.vlines(prcp['1998'], 0, 17, label = 'Observed', linewidth = 4)
ax1.set_title('1998')
ax1.set_xlabel('Prcp')
ax2.hist(df[2004], label = 'NMME Ensembles', color = 'gray')
ax2.vlines(prcp['2004'], 0, 12, label = 'Observed', linewidth = 4)
ax2.set_title('2004')
ax2.set_xlabel('Prcp')
plt.legend()
fig.suptitle('NMME Ensemble Histograms', fontsize = 18, fontweight = 'bold')
fig.savefig(EV['HOME'] + '/Desktop/CodePresentation/images/nmmehist')
return
def nmmecompare():
from nmme import gen_ensembles
from data_load import gen_models, combine_models, RPSS, HSS
from scipy.stats import gaussian_kde as gk
models = gen_models(cc = 0.95, quick = True)
clim_data, hindcast, ensemble = combine_models(models)
df = gen_ensembles()
cmap = mpl.cm.get_cmap('viridis')
rgba = [cmap(0.25), cmap(0.7)]
idx = np.arange(1921,2011)>=1982
x = 16
dNIPA = gk(ensemble[:,idx][x], bw_method = 0.5)
dNMME = gk(df.values[x], bw_method = 0.5)
p_x = np.linspace(0, 600, 1000)
fig, (ax1, ax2) = plt.subplots(1,2,sharey = True, figsize = (14,10))
ax1.plot(p_x, dNMME.pdf(p_x)*1000, color = rgba[0], label = 'NMME', linewidth = 2)
ax1.plot(p_x, dNIPA.pdf(p_x)*1000, color = rgba[1], label = 'NIPA', linewidth = 2)
ax1.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color = rgba[1], alpha = 0.5)
ax1.fill_between(p_x, 0, dNMME.pdf(p_x)*1000, color = rgba[0],alpha = 0.5)
ax1.vlines(clim_data[idx][x], 0, 6, linewidth = 4, color = 'k')
ax1.set_title(str(clim_data.index.year[idx][x]),fontsize = 18, fontweight='bold')
ax1.set_ylabel('Probability Density, $10^{-3}$', fontweight = 'bold')
h, l = ax1.get_legend_handles_labels()
ax1.legend(h, l)
x = 25
dNIPA = gk(ensemble[:,idx][x], bw_method = 0.5)
dNMME = gk(df.values[x], bw_method = 0.5)
p_x = np.linspace(0, 600, 1000)
ax2.plot(p_x, dNMME.pdf(p_x)*1000, color = rgba[0], label = 'NMME', linewidth = 2)
ax2.plot(p_x, dNIPA.pdf(p_x)*1000, color = rgba[1], label = 'NIPA', linewidth = 2)
ax2.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color = rgba[1], alpha = 0.5)
ax2.fill_between(p_x, 0, dNMME.pdf(p_x)*1000, color = rgba[0],alpha = 0.5)
ax2.vlines(clim_data[idx][x], 0, 6, linewidth = 4, color = 'k')
ax2.set_title(str(clim_data.index.year[idx][x]), fontsize = 18, fontweight='bold')
fig.text(0.33, 0.015, 'Total MAMJ Precipitation, mm')
fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/nmmecompare')
plt.close(fig)
return
