# Read SST anomalies using the cdms2 module from CDAT. The file contains
# November-March averages of SST anomaly in the central and northern Pacific.
ncin = cdms2.open("../../example_data/sst_ndjfm_anom.nc")
sst = ncin("sst")
ncin.close()
# 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.
m = Basemap(projection="cyl", llcrnrlon=120, llcrnrlat=-20, urcrnrlon=260, urcrnrlat=60)
lons, lats = eof1.getLongitude()[:], eof1.getLatitude()[:]
x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1, 1, 11)
m.contourf(x, y, eof1(squeeze=True), clevs, cmap=plt.cm.RdBu_r)
m.drawcoastlines()
m.drawparallels([-20, 0, 20, 40, 60])
m.drawmeridians([120, 140, 160, 180, 200, 220, 240, 260])
cb = plt.colorbar(orientation="horizontal")
cb.set_label("correlation coefficient", fontsize=12)
plt.title("EOF1 expressed as correlation", fontsize=16)
# Read SST anomalies using the cdms2 module from CDAT. The file contains
# November-March averages of SST anomaly in the central and northern Pacific.
ncin = cdms2.open('../../example_data/sst_ndjfm_anom.nc')
sst = ncin('sst')
ncin.close()
# 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.
m = Basemap(projection='cyl', llcrnrlon=120, llcrnrlat=-20,
urcrnrlon=260, urcrnrlat=60)
lons, lats = eof1.getLongitude()[:], eof1.getLatitude()[:]
x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1, 1, 11)
m.contourf(x, y, eof1(squeeze=True), clevs, cmap=plt.cm.RdBu_r)
m.drawcoastlines()
m.drawparallels([-20, 0, 20, 40, 60])
m.drawmeridians([120, 140, 160, 180, 200, 220, 240, 260])
cb = plt.colorbar(orientation='horizontal')
cb.set_label('correlation coefficient', fontsize=12)
plt.title('EOF1 expressed as correlation', fontsize=16)
