def setup_class(cls): # Generate a scalar field and a corresponding EOF solution for the # standard numpy array interface. cls.sf, cls.eofs, cls.pcs = reference_solution('cdms2') cls.neofs = cls.eofs.shape[0] # Create an Eof for the scalar field. cls.eofobj = Eof(cls.sf)
def setup_class(cls): # Generate a scalar field and a corresponding EOF solution for the # cdms2 interface. try: cls.sf, cls.eofs, cls.pcs = reference_solution('cdms2') except ValueError: raise SkipTest('library component not available') # Create an Eof instance with the scalar field. cls.eofobj = Eof(cls.sf)
# Read geopotential height data using the cdms2 module from CDAT. The file # 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)
import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap import numpy as np from eof2 import Eof # 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()