yr_sst = sst.aggregated_by('year', iris.analysis.MEAN)
clim_sst = yr_sst.collapsed('time', iris.analysis.MEAN)
# yr_sst_detrend = detrend(yr_sst)
# clim_sst_detrend = yr_sst_detrend.collapsed('time', iris.analysis.MEAN)
anom_sst = yr_sst - clim_sst
anom_sst_detrend = detrend(anom_sst)
# Create an EOF solver to do the EOF analysis. Square-root of cosine of
# latitude weights are applied before the computation of EOFs.
# sovler = Eof(anom_sst_detrend) #--> NO FUNCIONA PQ yr_sst_detrend SEMBLA QUE JA ESTÀ FETA LA ANOMALIA. BUENO CLAR PUTO,, SI LI TREUS LA REGRESSIÓ LINEAL ET CARREGUES TOT.
solver = Eof(anom_sst, weights='coslat')
# Retrieve the leading EOF, expressed as the correlation between the leading
# PC time series and the input SST anomalies at each grid point, and the
# leading PC time series itself.
n = 3
# eofs = solver.eofsAsCorrelation(neofs=n)
eofs = solver.eofsAsCorrelation(neofs=n)
pcs = solver.pcs(npcs=n)
variance_fractions = solver.varianceFraction(neigs=n)
print(variance_fractions.data)
for i in range(n):
plotter(eofs[i],pcs[:,i],variance_fractions.data[i]*100,i+1)
# plt.show()
from eofs.examples import example_data_path
# Read SST anomalies using the iris module. The file contains November-March
# averages of SST anomaly in the central and northern Pacific.
filename = example_data_path('sst_ndjfm_anom.nc')
sst = iris.load_cube(filename)
# Create an EOF solver to do the EOF analysis. Square-root of cosine of
# latitude weights are applied before the computation of EOFs.
solver = Eof(sst, weights='coslat')
# Retrieve the leading EOF, expressed as the correlation between the leading
# PC time series and the input SST anomalies at each grid point, and the
# leading PC time series itself.
eof1 = solver.eofsAsCorrelation(neofs=1)
pc1 = solver.pcs(npcs=1, pcscaling=1)
# Plot the leading EOF expressed as correlation in the Pacific domain.
clevs = np.linspace(-1, 1, 11)
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=190))
fill = iplt.contourf(eof1[0], clevs, cmap=plt.cm.RdBu_r)
ax.add_feature(cartopy.feature.LAND, facecolor='w', edgecolor='k')
cb = plt.colorbar(fill, orientation='horizontal')
cb.set_label('correlation coefficient', fontsize=12)
ax.set_title('EOF1 expressed as correlation', fontsize=16)
# Plot the leading PC time series.
plt.figure()
iplt.plot(pc1[:, 0], color='b', linewidth=2)
ax = plt.gca()
