# plt.imshow(np.corrcoef(ts, rowvar = 0), interpolation = 'nearest')
# plt.colorbar()
# plt.subplot(1,2,2)
# plt.imshow(S, interpolation = 'nearest')
# plt.colorbar()
# with open('data/test_gf.bin', 'r') as f:
# d = cPickle.load(f)
# initialize a parallel pool
pool = Pool(POOL_SIZE)
# compute the eigenvalues/eigenvectos of the covariance matrix of
d = gf.data()
if COSINE_REWEIGHTING:
d = d * gf.qea_latitude_weights()
Ud, dlam, _ = pca_components_gf(d)
Ud = Ud[:, :NUM_EIGS]
dlam = dlam[:NUM_EIGS]
sd = SurrGeoFieldAR([0, 30], 'sbc')
sd.copy_field(gf)
sd.prepare_surrogates(pool)
slam = np.zeros((NUM_SURR, NUM_EIGS))
maxU = np.zeros((NUM_SURR, NUM_EIGS))
# generate and compute eigenvalues for 20000 surrogates
t1 = datetime.now()
# construct the surrogates in parallel
