def neutpos(ocn = 'Pac'):
phase = 'neutpos'
print fp+'/'+phase
if isfile(fp+'/'+phase):
f = open(fp+'/'+phase)
model = pickle.load(f)
else:
model = gen_model(phase = phase)
f = open(fp+'/'+phase, 'w')
pickle.dump(model, f)
_, _, _, phaseind = get_climate_data()
fig = plt.figure(figsize = (12,8)); axes = {}
years = model.clim_data.index.year
axes['eofmap'] = fig.add_axes([0.05,0.05,0.4,0.4])
axes['sstmap'] = fig.add_axes([0.05,0.55,0.4,0.4])
axes['sstpc'] = fig.add_axes([0.55,0.725,0.4,0.2])
axes['eofpc'] = fig.add_axes([0.55,0.1,0.4,0.2])
axes['scat'] = fig.add_axes([0.645, 0.35, 0.225, 0.3])
fig, axes['sstmap'], m = sstMap(model, fig = fig, cmap = cm.inferno, ax = axes['sstmap'])
if ocn == 'Pac':
region = ['Pacific']; eof = '1'; title = 'Pacific EOF-2'
switch = False
if ocn == 'Atl':
region = ['Atlantic']; eof = '3'; title = 'Atlantic EOF-4'
switch = False
final, lats, lons, pcs, prcp = combine_oceans(regions = region)
fig, axes['eofmap'], m = create_full_map_combined(final, lats, lons,
eof = eof,
fig = fig,
ax = axes['eofmap'],
switch = switch)
sstpc = model.crossvalpcr(xval = False)
sstpc = sstpc/abs(sstpc).max()
eofpc = pcs[0][phaseind[phase]]
eofpc = eofpc/abs(eofpc).max()
axes['sstpc'].plot(sstpc)
axes['eofpc'].plot(eofpc)
axes['scat'].scatter(sstpc, eofpc)
axes['sstmap'].set_title('NIPA Correlation Map')
axes['eofmap'].set_title(title)
idx = range(0, len(years),4)
axes['sstpc'].set_xticks(idx)
axes['sstpc'].set_xticklabels(years[idx])
axes['eofpc'].set_xticks(idx)
axes['eofpc'].set_xticklabels(years[idx])
fig.suptitle('Phase: Neutral Positive')
return fig, axes
def lanina():
phase = 'lanina'
print fp+'/'+phase
if isfile(fp+'/'+phase):
f = open(fp+'/'+phase)
model = pickle.load(f)
else:
model = gen_model(phase = phase)
f = open(fp+'/'+phase, 'w')
pickle.dump(model, f)
_, _, _, phaseind = get_climate_data()
fig = plt.figure(figsize = (12,8)); axes = {}
years = model.clim_data.index.year
axes['eofmap'] = fig.add_axes([0.05,0.05,0.4,0.4])
axes['sstmap'] = fig.add_axes([0.05,0.55,0.4,0.4])
axes['sstpc'] = fig.add_axes([0.55,0.725,0.4,0.2])
axes['eofpc'] = fig.add_axes([0.55,0.1,0.4,0.2])
axes['scat'] = fig.add_axes([0.645, 0.35, 0.225, 0.3])
fig, axes['sstmap'], m = sstMap(model, fig = fig, cmap = cm.inferno, ax = axes['sstmap'])
final, lats, lons, pcs, prcp = combine_oceans(regions = ['Pacific'])
fig, axes['eofmap'], m = create_full_map_combined(final, lats, lons,
eof = '0',
fig = fig,
ax = axes['eofmap'])
sstpc = model.crossvalpcr(xval = False)
sstpc = sstpc/abs(sstpc).max()
eofpc = pcs[0][phaseind[phase]]
eofpc = eofpc/abs(eofpc).max()
axes['sstpc'].plot(sstpc)
axes['eofpc'].plot(eofpc)
axes['scat'].scatter(sstpc, eofpc)
axes['scat'].axis([-1.1, 0, -1.1, 0])
axes['sstmap'].set_title('NIPA Correlation Map')
axes['eofmap'].set_title('Pacific EOF-1')
idx = range(0, len(years),2)
axes['sstpc'].set_xticks(idx)
axes['sstpc'].set_xticklabels(years[idx])
axes['eofpc'].set_xticks(idx)
axes['eofpc'].set_xticklabels(years[idx])
fig.suptitle('Phase: La Nina')
return fig, axes
