def regparams():
from data_load import gen_models
models = gen_models(cc = 0.95, quick = True)
for phase in models:
print phase
print 'Param range is %.2f - %.2f' % (np.abs(models[phase].reg_stats['params']).max(), np.abs(models[phase].reg_stats['params']).min())
print 'Associate p-value range is %.5f - %.5f' % (models[phase].reg_stats['p_vals'].max(), models[phase].reg_stats['p_vals'].min())
print np.median(np.abs(models[phase].reg_stats['params']))
print np.median(models[phase].reg_stats['p_vals'])
return
def correlations():
from data_load import gen_models, combine_models
models = gen_models(cc = .95, quick = True)
c, h, e = combine_models(models)
fig, axes = plt.subplots(2, 2, figsize = (12,10), sharex = True, sharey = True)
phases = ['lanina', 'neutneg', 'neutpos', 'elnino']
cmap = mpl.cm.get_cmap('viridis')
rgba = [cmap(0.2), cmap(0.5), cmap(0.9)]
for phase, ax in zip(phases, axes.ravel()):
ax.scatter(models[phase].clim_data, models[phase].hindcast, s = 30, color = cmap(0.0))
ax.axis([0,550,0,550])
if phase == 'allyears': title = 'All Years'
if phase == 'lanina': title = 'La Nina'
if phase == 'elnino': title = 'El Nino'
if phase == 'neutpos': title = 'Neutral-Positive'
if phase == 'neutneg': title = 'Neutral-Negative'
ax.set_title(title, fontweight= 'bold')
ax.text(400,20, 'r = %.2f' % models[phase].correlation)
fig.text(0.5, 0.04, 'Observed MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20)
fig.text(0.06, 0.5, 'Hindcast MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20, rotation = 'vertical')
fig.suptitle('Hindcast Correlations by Phase', fontsize = 24, fontweight = 'bold')
#fig.tight_layout()
fig.savefig(EV['HOME']+'/Desktop/Feb20Response/images/phase_corr')
plt.close(fig)
fig, (ax1, ax2) = plt.subplots(1,2, figsize = (12,8), sharey = True)
fig = plt.figure(figsize = (12,6))
ax1 = fig.add_axes([.15, .2, .35, 0.6])
ax2 = fig.add_axes([0.55, 0.2, 0.35, 0.6])
ax1.scatter(c, h, s = 30, color = cmap(0.0))
ax1.axis([0, 550, 0, 550])
ax1.set_title('NIPA', fontweight = 'bold')
ax1.text(400,20, 'r = %.2f' % np.corrcoef(c, h)[0,1])
ax2.scatter(models['allyears'].clim_data, models['allyears'].hindcast, s= 30, color = cmap(0.0))
ax2.axis([0, 550, 0, 550])
ax2.set_yticks([])
ax2.set_title('All-years', fontweight = 'bold')
ax2.text(400, 20, 'r = %.2f' % models['allyears'].correlation)
fig.text(0.5, 0.04, 'Observed MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20)
fig.text(0.06, 0.5, 'Hindcast MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20, rotation = 'vertical')
fig.suptitle('NIPA vs One-Phase', fontsize = 24, fontweight = 'bold')
fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/aycorr')
plt.close(fig)
return
def sstmaps2():
from data_load import gen_models
from smapFuncts import sstMap, texDat
fig, axes = plt.subplots(2,2,figsize = (16,12))
models = gen_models(cc = 0.95, quick = True)
for phase, ax in zip(['lanina', 'neutneg', 'neutpos', 'elnino'], axes.ravel()):
print phase
fig, ax, m = sstMap(models[phase], cmap = mpl.cm.inferno, fig = fig, ax = ax)
fig, ax, m = texDat(fig = fig, ax = ax, m = m)
if phase == 'lanina': title = 'La Nina'
if phase == 'elnino': title = 'El Nino'
if phase == 'neutpos': title = 'Neutral-Positive'
if phase == 'neutneg': title = 'Neutral-Negative'
ax.set_title(title, fontsize = 18, fontweight = 'bold')
fig.savefig('/Users/bz/Desktop/Feb20Response/images/sst_corr')
plt.close(fig)
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
def timecompare():
from data_load import gen_models, combine_models
models = gen_models(cc = .95, quick = True)
c, h, e = combine_models(models)
cmap = mpl.cm.get_cmap('viridis')
rgba = [cmap(0.0), cmap(0.5), cmap(0.8)]
plt.plot(c, label = 'Observations',
linewidth = 3,
color = 'black')
plt.plot(h, label = 'Hindcast',
linewidth = 3,
color = rgba[1])
plt.plot(models['allyears'].hindcast,
label = 'One-Phase',
linewidth = 3,
color = rgba[2])
plt.legend()
plt.ylim(0,600)
plt.ylabel('Total MAMJ Precipitaton, mm', fontweight = 'bold')
plt.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/time_series')
return
def rpssexmp():
from data_load import gen_models, combine_models
from scipy.stats import gaussian_kde as gk
cmap = mpl.cm.get_cmap('viridis')
rgba = [cmap(0.8), cmap(0.2)]
models = gen_models()
c, h, e = combine_models(models)
x, x2 = 15, 20
fig, ax = plt.subplots(1, 1, figsize = (8,6))
ax.set_ylabel('Probability Density, $10^{-3}$')
ax.set_xlabel('Total MAMJ Precipitation, mm')
dNIPA = gk(e[:, x], bw_method = 0.5)
p_x = np.linspace(0,600,1000)
ax.plot(p_x, dNIPA.pdf(p_x)*1000, linewidth = 2, color = rgba[0], label = 'Bad')
ax.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color = rgba[0], alpha = 0.5)
dNIPA = gk(e[:, x2], bw_method = 0.5)
ax.plot(p_x, dNIPA.pdf(p_x)*1000, linewidth = 2, color = rgba[1], label = 'Worse')
ax.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color =rgba[1] , alpha = 0.5)
ax.vlines(100, 0,8, linewidth = 2, label = 'obs')
plt.legend()
fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/rpss_exmp')
return
def sstmaps():
from data_load import gen_models
from smapFuncts import sstMap, texDat
fig, ax = plt.subplots(1,1,figsize = (4,3))
models = gen_models(cc = 0.95, quick = True)
for phase in ['allyears']:#,'lanina', 'neutneg', 'neutpos', 'elnino']:
print phase
fig, ax = plt.subplots(1,1,figsize = (8,4))
fig, ax, m = sstMap(models[phase], cmap = mpl.cm.inferno, fig = fig, ax = ax)
fig, ax, m = texDat(fig = fig, ax = ax, m = m)
if phase == 'allyears':
title = 'All Years'
ax.add_patch(mpl.patches.Rectangle((0.30, 0.5),
0.2, 0.2,
fill = False))
if phase == 'lanina': title = 'La Nina'
if phase == 'elnino': title = 'El Nino'
if phase == 'neutpos': title = 'Neutral-Positive'
if phase == 'neutneg': title = 'Neutral-Negative'
ax.set_title(title, fontsize = 18, fontweight = 'bold')
fig.savefig('/Users/bz/Desktop/Feb20Response/images/aysst')
plt.close(fig)
return
from data_load import gen_models, combine_models, RPSS, HSS
import numpy as np
from matplotlib import pyplot as plt
models = gen_models(cc = 0.95, bc = 0.80, quick = True)
clim_data, hindcast, ensemble = combine_models(models)
for x in [10, 20, 30]:
print RPSS(clim_data, ensemble, x, x)
print HSS(clim_data, hindcast, x, x), '\n'
def terciles():
from data_load import gen_models, combine_models
from scipy.stats import gaussian_kde as gk
models = gen_models(cc = 0.95, quick = True)
c, h, e = combine_models(models)
cmap = mpl.cm.get_cmap('viridis')
rgba = [cmap(0.0), cmap(0.35), cmap(0.8)]
idx = np.argsort(c)
B = c[idx][24]
A = c[idx][66]
x = 20
dNIPA = gk(e[:,x], bw_method = 0.5)
dCLIM = gk(c, bw_method = 0.5)
bclim = round(dCLIM.integrate_box(0,B),2)* 100
aclim = round(dCLIM.integrate_box(A, 600),2)* 100
nclim = round(dCLIM.integrate_box(B,A),2)* 100
bnipa = round(dNIPA.integrate_box(0,B),2)* 100
anipa = round(dNIPA.integrate_box(A, 600),2)* 100
nnipa = round(dNIPA.integrate_box(B,A),2)* 100
p_x = np.linspace(0,600,1000)
below = p_x[p_x<B]
middle = p_x[(p_x>=B) & (p_x<=A)]
above = p_x[p_x>A]
fig, (ax1, ax2) = plt.subplots(1,2,figsize = (12,6), sharey = True)
ax1.plot(p_x, dCLIM.pdf(p_x)*1000, linewidth = 2, color = 'k')
ax1.fill_between(below, 0, dCLIM.pdf(below)*1000, color = rgba[2], alpha = 0.5)
ax1.fill_between(above, 0, dCLIM.pdf(above)*1000, color = rgba[0], alpha = 0.5)
ax1.fill_between(middle, 0, dCLIM.pdf(middle)*1000, color = rgba[1], alpha = 0.5)
line = '\n\nB: %.0f%%\nN: %.0f%%\nA: %.0f%%' % (bclim, nclim, aclim)
ax1.text(400,3,line)
ax1.set_title('Climatological PDF', fontsize = 22, fontweight = 'bold')
ax1.set_ylabel('Probability Density, $10^{-3}$', fontweight = 'bold')
ax2.plot(p_x, dCLIM.pdf(p_x)*1000, linewidth = 2,
color = 'grey', alpha = 0.7)
ax2.fill_between(p_x, dCLIM.pdf(p_x)*1000, label = 'Climatology', color = 'grey', alpha = 0.2)
ax2.plot(p_x, dNIPA.pdf(p_x)*1000, linewidth = 2, color = 'k')
ax2.fill_between(below, 0, dNIPA.pdf(below)*1000, color = rgba[2],
label = 'Below', alpha = 0.5)
ax2.fill_between(middle, 0, dNIPA.pdf(middle)*1000, color = rgba[1],
label = 'Normal', alpha = 0.5)
ax2.fill_between(above, 0, dNIPA.pdf(above)*1000, color = rgba[0],
label = 'Above', alpha = 0.5)
line = '\n\nB: %.0f%%\nN: %.0f%%\nA: %.0f%%' % (bnipa, nnipa, anipa)
ax2.text(400,3,line)
ax2.set_title('Forecast PDF', fontsize = 22, fontweight = 'bold')
h, l = ax2.get_legend_handles_labels()
fig.legend(h,l, loc = (0.395, 0.55))
fig.text(0.33, 0.015, 'Total MAMJ Precipitation, mm')
fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/tercile')
return