def lcrb_mam(phase = 'elnino'):
from pandas import Series
alldivDF, sst, mei, phaseind, slp = seasonal_setup('MAM', phase, n = 20)
index = alldivDF['Texas-06'].index
fp = '/Users/bz/Desktop/LCRB_MAMJ.tsv'
clim_data = Series(data = np.loadtxt(fp), index = index)
nipa = NIPAphase(clim_data, sst, mei, phaseind)
nipa.bootcorr(ntim = 1000, corrconf = 0.95, bootconf = 0.90)
sstMap(nipa); plt.show()
return nipa
def gen_sst_maps(base_fp = EV['HOME'] + '/Desktop/lcrb_sst/'):
from simpleNIPA import NIPAphase
from numpy import ravel
from smapFuncts import sstMap
lcrb_prcp = get_lcrb_prcp()
sst, slp, mei, phaseind = get_climate_data()
models = {}
for bootconf in [1.0]:
for corrconf in [.99]:
fig, axes = plt.subplots(nrows = 3, ncols = 2, figsize = (12, 12))
for phase, ax in zip(phaseind, ravel(axes)):
print 'Starting %s, %.2f, %.2f' % (phase, corrconf, bootconf)
model = NIPAphase(lcrb_prcp, sst, mei, phaseind[phase])
model.bootcorr(corrconf = corrconf, bootconf = bootconf, quick = True)
model.gridCheck(lim = 6)
model.crossvalpcr()
if not model.flags['noSST']:
fig, ax, m = sstMap(model, fig = fig, ax = ax)
ax.set_title('%s, %.2f, %i' % (phase, model.correlation, model.n_post_grid))
else:
print '%s has no significant SST'
models[phase] = model
fp = base_fp + '%i_%i_shift' % (int(corrconf * 100), int(bootconf*100))
fig.savefig(fp); plt.close(fig)
return models
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 division_sstmaps():
results = []
for phase, n_yrs in [('elnino', 30)]:
alldivDF, sst, mei, phaseind, slp = seasonal_setup(season = 'DJF', phase = phase, n = n_yrs)
div = 'Missouri-04'
nipa = NIPAphase(alldivDF[div], sst, mei, phaseind)
nipa.bootcorr(ntim = 1000, corrconf = 0.95, bootconf = 0.90)
nipa.gridCheck(lim = 6, debug = True)
nipa.crossvalpcr()
nipa.categorize()
nipa.categorize(hindcast = True)
fp = base_fp + '%s_%s_DJF_sst' % (div, phase)
fig, ax, m = sstMap(nipa)
fig.savefig(fp)
plt.close(fig)
results.append(nipa)
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 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
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
if __name__ == '__main__':
from climdiv_data import *
from simpleNIPA import *
from smapFuncts import sstMap
kwgroups = create_kwgroups(debug = True, climdiv_startyr = 1921, n_yrs = 90, \
climdiv_months = [9, 10, 11], n_mon_sst = 3, sst_lag = 3, n_mon_slp = 2, \
slp_lag = 2, n_mon_mei = 3, mei_lag = 3, filin = EV['PRCP'])
div = 'Mississippi-05'
alldivDF, sst, mei, phaseind, _, _ = get_data(kwgroups)
clim_data = alldivDF[div]
model = NIPAphase(clim_data, sst, mei, phaseind['lanina'])
model.bootcorr(corrconf = 0.9, bootconf = 0.8, quick = True)
model.gridCheck(lim = 5, debug = True)
print model.n_pre_grid, model.n_post_grid
model.crossvalpcr()
fig, ax, m = sstMap(model)
fig.suptitle(div + ', ' + str(model.correlation))
fig.savefig(EV['HOME'] + '/Desktop/CodePresentation/images/MS')
f.close()
return
alldivDF, sst, mei, phaseind, slp, clim_data = seasonal_setup()
x = pd.Series()
clim_data = alldivDF["Texas-06"] # + alldivDF['Texas-02'] )/2
# print '# yrs in phase %s is %i' % (phase, (phaseind[phase]).sum())
# clim_data = alldivDF['Texas-06']
for phase in ["lanina", "neutneg", "neutpos", "elnino"]:
nipa = NIPAphase(clim_data, sst, mei, phaseind[phase])
nipa.bootcorr(corrconf=0.95, bootconf=0.9, ntim=200)
nipa.crossvalpcr()
if not nipa.flags["noSST"]:
line = "%s,%.2f,%i\n" % (phase, nipa.correlation, nipa.n_pre_grid)
sstMap(nipa)
plt.show()
else:
line = "%s,%f,%f\n" % (phase, np.nan, np.nan)
print line
# x = x.sort_index()
# print np.corrcoef(x, clim_data)[0,1]
#
# def categorize(data, ncat = 3):
# from pandas import Series
# from numpy import sort
# x = sort(data)
# n = len(x)
# upper = x[((2 * n) / ncat) - 1]
# lower = x[(n / ncat) - 1]
