def categorize(data, ncat = 3, hindcast = False):
from pandas import Series
from numpy import sort
from data_load import get_lcrb_prcp
prcp = get_lcrb_prcp()
index = prcp.index
x = sort(data)
n = len(x)
upper = x[((2 * n) / ncat) - 1]
lower = x[(n / ncat) - 1]
cat_dat = Series(index = index)
for year in prcp.index.year:
if data[year] <= lower:
cat_dat[year] = 'B'
elif data[year] > upper:
cat_dat[year] = 'A'
else:
cat_dat[year] = 'N'
return cat_dat
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 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 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
elif data[year] > upper:
cat_dat[year] = 'A'
else:
cat_dat[year] = 'N'
return cat_dat
if __name__ == '__main__':
### I think to do PCA on all and then split just requires
### Using the None phase and then phasind.
from simpleNIPApca import *
from numpy import concatenate, corrcoef, array
phase = None
patterns, lats, lons, lams, pcs = combine_regions(phase)
prcp = get_lcrb_prcp()
sst, slp, mei, phaseind = get_climate_data()
pcs = concatenate((pcs['Indian'], pcs['Atlantic'], pcs['Pacific']))
# lams = concatenate((lams['Indian'], lams['Atlantic'], lams['Pacific']))
#
#
# phase = 'elnino'
#
# from numpy.random import randint
# idx = randint(0,89,90)
# for phase in phaseind:
# model = NIPAphase(prcp[idx], pcs[:,phaseind[phase]], mei, phaseind[phase])
# model.crossvalpcr()
# print '%s: %.2f' % (phase, model.correlation)
# print model.overall_index
# print lams[model.overall_index]
