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)
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()
m.drawparallels([-20, 0, 20, 40, 60])
# 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)
# 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)
def genEofVars(infiles, outfile, eobjf=True, latitude=(-30, 30, 'cob'),
NEOF=4, season='all', year=None, **kwarg):
"""
"""
eofobj_endname = kwarg.get('eofobj_endname', None)
for name, varName, infile in infiles:
if season == 'all':
f = cdms2.open(infile)
if year:
# add the time axis year statements here in future.
data = f(varName, time=year, latitude=latitude)
else:
data = f(varName, latitude=latitude)
# end of if year:
elif season == 'sum':
data = timeobj.getSummerData(varName, infile, latitude=latitude,
year=year)
elif season == 'win':
data = timeobj.getWinterData(varName, infile, latitude=latitude,
year=year, cyclic=True)
else:
raise ValueError("arg 'season' must be either 'all/sum/win' only")
# end of if season == 'all':
data = data(squeeze=1)
cdutil.setSlabTimeBoundsDaily(data)
print "Multiplying coslat with data of %s for %s" % (name, season)
lat = data.getLatitude()
coslat = _coslat_weights(lat)
for l in range(len(lat)):
data[:, l] *= coslat[l]
# end of for l in range(len(lat)):
print "Doing EOF of %s for %s" % (name, season)
eofobj = Eof(data, weights=None)
if eobjf:
# generate the eofobj binary file name and its path
path = os.path.dirname(outfile)
eofobj_filename = ['eofobj', 'level1', name, season]
eofobj_filename = '_'.join(eofobj_filename)
if eofobj_endname: eofobj_filename += '_' + eofobj_endname
eofobj_filename += '.pkl'
# end of if not eofobj_filename:
eofobj_fpath = os.path.join(path, eofobj_filename)
# store the eofobj into binary file using pickle module
objf = open(eofobj_fpath, 'wb')
pickle.dump(eofobj, objf, 2)
comment = ''
pickle.dump(comment, objf, 2)
objf.close()
print "Saved the eofobj in", eofobj_fpath
# end of if eobjf:
pcts = eofobj.pcs(pcscaling=0, npcs=2)
eof_data = eofobj.eofs(neofs=NEOF, eofscaling=2)
per_exp = eofobj.varianceFraction(neigs=NEOF) * 100
pcts.id = '_'.join(['pcs', name, season])
pcts.comment = ''
per_exp.id = '_'.join(['per_exp', name, season])
per_exp.comment = ''
eof_data.id = '_'.join(['eof', name, season])
eof_data.comment = ''
out = cdms2.open(outfile, 'a')
out.write(pcts)
out.write(per_exp)
out.write(eof_data)
out.close()
# make memory free
del data, pcts, per_exp, eof_data, lat
# for name, varName, infile in infiles:
print "Saved the eof_vars_*.nc file in", outfile
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()
