def calc_n34(self):
#print('got to function')
vtsub, nyrs = basic_functions.time_subset(self.var, self.time0,
self.tim1, self.tim2)
# Compute monthly climatology
sst_clim = np.zeros(
[12, len(self.dimdict['lat']),
len(self.dimdict['lon'])])
for i in range(12):
j = np.arange(i, nyrs * 12, 12)
sst_clim[i, :, :] = np.mean(vtsub[j, :, :], axis=0)
# Compute anomaly
sst_anom = np.zeros([vtsub.shape[0], vtsub.shape[1], vtsub.shape[2]])
for i in range(vtsub.shape[0]):
j = i % 12
print(j)
sst_anom[i, :, :] = vtsub[i, :, :] - sst_clim[j, :, :]
# Area mean
illat = np.where(self.dimdict['lat'] >= -5)[0][0]
iulat = np.where(self.dimdict['lat'] >= 5)[0][0] + 1
illon = np.where(self.dimdict['lon'] >= -170)[0][0]
iulon = np.where(self.dimdict['lon'] >= -120)[0][0] + 1
sst_anom_nino34 = np.mean(sst_anom[:, illat:iulat, illon:iulon],
axis=(1, 2)) # should weight latitude
# 5-month running mean
smooth_length = 5
x = (smooth_length - 1) / 2
nino34 = np.zeros([sst_anom_nino34.shape[0] - smooth_length + 1])
#time = range(nyrs*12)
t = []
for i in range(sst_anom_nino34.shape[0] - smooth_length + 1):
nino34[i] = np.mean(sst_anom_nino34[i:i + smooth_length])
t.append(x + i)
#years.append(self.tim1+x+i)
fig, ax = plt.subplots()
plt.plot(t, nino34, color='k')
ax.grid()
plt.axhline(y=0, color='k')
plt.fill_between(t, y1=0, y2=nino34, where=nino34 > 0, color='r')
plt.fill_between(t, y1=0, y2=nino34, where=nino34 < 0, color='b')
plt.savefig('nino34_numpy.png')
'''
def climatology_lon_lat(self, time_mean):
# seasonal mean then average over climatological time period
clim_var = np.zeros(
[len(self.dimdict['lat']),
len(self.dimdict['lon'])])
for ilat in range(len(self.dimdict['lat'])):
for ilon in range(len(self.dimdict['lon'])):
var_tsub, nsubyrs = basic_functions.time_subset(
self.var[:, ilat, ilon], self.time0, self.tim1, self.tim2)
var_seas = basic_functions.seasonal_mean(
var_tsub, nsubyrs, time_mean)
var_clim_seas = np.mean(var_seas, axis=0)
clim_var[ilat, ilon] = var_clim_seas
return clim_var
def climatology_shgt_slat(self, selhgt, sellat, time_mean):
shgt = np.where(self.dimdict['hgt'] >= selhgt)[0][0]
slat = np.where(self.dimdict['lat'] >= sellat)[0][0]
# select variable at height and latitude
var = self.var[:, shgt, slat]
# climatological monthly mean
var_tsub, nsubyrs = basic_functions.time_subset(
var, self.time0, self.tim1, self.tim2)
var_seas = basic_functions.seasonal_mean(var_tsub, nsubyrs, time_mean)
var_clim_seas = var_seas.mean()
print var_clim_seas.shape
return var_clim_seas
def climatology_polar_hgt_mon(self):
# polar cap mean (70-90N)
var_amean = np.zeros(
[len(self.dimdict['tim']),
len(self.dimdict['hgt'])])
for itim in range(len(self.dimdict['tim'])):
for ihgt in range(len(self.dimdict['hgt'])):
var_amean[itim, ihgt] = basic_functions.latitude_mean(
self.var[itim, ihgt, :], self.dimdict['lat'], 70, 90)
# climatological monthly mean
clim_var = np.zeros([12, len(self.dimdict['hgt'])])
for ihgt in range(len(self.dimdict['hgt'])):
var_tsub, nsubyrs = basic_functions.time_subset(
var_amean[:, ihgt], self.time0, self.tim1, self.tim2)
var_seas = basic_functions.seasonal_mean(var_tsub, nsubyrs,
'clim_monthly')
clim_var[:, ihgt] = var_seas
return clim_var