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()
