# contains December-February averages of geopotential height at 500 hPa for # the European/Atlantic domain (80W-40E, 20-90N). ncin = cdms2.open('../../example_data/hgt_djf.nc', 'r') z_djf = ncin('z') ncin.close() # Compute anomalies by removing the time-mean. z_djf_mean = cdutil.averager(z_djf, axis='t') z_djf = z_djf - z_djf_mean z_djf.id = 'z' # 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(z_djf, weights='coslat') # Retrieve the leading EOF, expressed as the covariance between the leading PC # time series and the input SLP anomalies at each grid point. eof1 = solver.eofsAsCovariance(neofs=1) # Plot the leading EOF expressed as covariance in the European/Atlantic domain. m = Basemap(projection='ortho', lat_0=60., lon_0=-20.) lons, lats = eof1.getLongitude()[:], eof1.getLatitude()[:] x, y = m(*np.meshgrid(lons, lats)) m.contourf(x, y, eof1(squeeze=True), cmap=plt.cm.RdBu_r) m.drawcoastlines() m.drawparallels(np.arange(-80, 90, 20)) m.drawmeridians(np.arange(0, 360, 20)) plt.title('EOF1 expressed as covariance', fontsize=16) plt.show()