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
def gen_model(phase = 'lanina', cc = 0.95, bc = 0.80, quick = True):
from simpleNIPA import NIPAphase
from numpy import ravel
from smapFuncts import sstMap
lcrb_prcp = get_lcrb_prcp()
sst, slp, mei, phaseind = get_climate_data()
model = NIPAphase(lcrb_prcp, sst, mei, phaseind[phase])
model.bootcorr(corrconf = cc, bootconf = bc, quick = quick)
model.gridCheck(lim = 5, ntim = 3, debug = True)
model.crossvalpcr()
return model
def violin():
from data_load import get_lcrb_prcp, get_climate_data
prcp = get_lcrb_prcp()
_,_,_, phaseind = get_climate_data()
cmap = mpl.cm.get_cmap('viridis')
rgba = [cmap(0.25), cmap(0.7)]
data = [prcp[phaseind['lanina']], prcp, prcp[phaseind['elnino']]]
violin_parts = plt.violinplot(data, showmeans = True)
plt.ylim(0,650)
plt.xticks(range(1,4),['La Nina', 'All Years', 'El Nino'], fontsize = 18)
plt.title('MAMJ Precipitation in LCRB by Phase', fontsize = 22,
fontweight = 'bold')
for feature in ['cbars', 'cmins', 'cmeans', 'cmaxes']:
violin_parts[feature].set_color('black')
for pc, color in zip(violin_parts['bodies'], [cmap(0), cmap(0.75), cmap(0.4)]):
pc.set_facecolor(color)
plt.ylabel('Precipitation, mm', fontweight='bold')
plt.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/violin')
return
def timeseries():
###Make figure showing which years fall where
prcp = get_lcrb_prcp()
sst, slp, mei, phaseind = get_climate_data()
cmap = mpl.cm.get_cmap('seismic')
fig, ax = plt.subplots(1, figsize = (12,6))
ax.plot(prcp, color = 'k', linewidth = 2)
colors = [cmap(0.2), cmap(0.4), cmap(0.6), cmap(0.8)]
phases = ['lanina', 'neutneg', 'neutpos', 'elnino']
for phase, color in zip(phases, colors):
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.plot(prcp[phaseind[phase]], marker = 'o',
markersize = 10, color = color, linewidth = 0, label = title)
plt.legend(loc = 'best')
ax.set_ylim(0, 650)
ax.set_xlabel('Years', fontsize = 16, fontweight = 'bold')
ax.set_ylabel('Total MAMJ Precipitaton, mm', fontsize = 16, fontweight = 'bold')
fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/timeseries')
return
