# print("Loading data ...")
# gf = GeoField()
# gf.load("data/pres.mon.mean.nc", 'pres')
# gf.transform_to_anomalies()
# gf.normalize_monthly_variance()
# gf.slice_date_range(date(1948, 1, 1), date(2012, 1, 1))
# gf.slice_spatial(None, [20, 89])
# gf.slice_months([12, 1, 2])
# construct a test system
S = np.zeros(shape = (20, 50), dtype = np.int32)
S[10:18, 25:45] = 1
S[0:3, 6:12] = 2
S[8:15, 2:12] = 3
v, Sr = constructVAR(S, [0.0, 0.4, 0.8, 0.7], [-0.5, 0.5], [0.0, 0.0])
# v, Sr = constructVAR(S, [0.0, 0.001, 0.01], [-0.1, 0.1], [0.00, 0.00], [0.01, 0.01])
ts = v.simulate(200)
gf = make_model_geofield(S, ts)
# initialize a parallel pool
pool = Pool(POOL_SIZE)
# replace field with surrogate field
sgf = SurrGeoFieldAR()
sgf.copy_field(gf)
sgf.prepare_surrogates(pool)
sgf.construct_surrogate_with_noise()
gf = sgf
gf.d = gf.surr_data().copy()
from spatial_model_generator import constructVAR, make_model_geofield
import numpy as np
import matplotlib.pyplot as plt
from geo_field import GeoField
from surr_geo_field_ar import SurrGeoFieldAR
import cPickle
# construct the testing model from a spec
S = np.zeros(shape=(20, 50), dtype=np.int32)
S[10:18, 25:45] = 1
S[0:3, 6:12] = 2
v, Sr = constructVAR(S, [0.0, 0.8, 0.8], [-0.1, 0.1], [0.0, 0.0])
#v, Sr = constructVAR2(S, [-0.2, 0.2], [0.0, 0.9, 0.9], 0.8)
#S = np.zeros(shape = (5, 10), dtype = np.int32)
#S[1:4, 0:2] = 1
#S[0:3, 6:9] = 2v, Sr = constructVAR(S, [0.0, 0.191, 0.120], [-0.1, 0.1], [0.00, 0.00], [0.01, 0.01])
ts = v.simulate(768)
gf = make_model_geofield(S, ts)
sgf = SurrGeoFieldAR()
sgf.copy_field(gf)
sgf.prepare_surrogates()
sgf.construct_surrogate_with_noise()
ts2 = sgf.surr_data()
plt.figure(figsize=(8, 8))
plt.imshow(S, interpolation='nearest')
plt.title('Structural matrix')
# compute RVDIM(1)
rvdims[i] = rlam[0] / np.sum(rlam**2)**0.5
return rvdims
if __name__ == "__main__":
print("Estimate PCA components script version 1.0")
S = np.zeros(shape=(20, 50), dtype=np.int32)
S[10:18, 25:45] = 1
S[0:3, 6:12] = 2
S[8:15, 2:12] = 3
v, Sr = constructVAR(S, [0.0, 0.6, 0.9, 0.7], [0.3, 0.5], [0.0, 0.0])
ts = v.simulate(200)
gf = make_model_geofield(S, ts)
# initialize a parallel pool
pool = Pool(POOL_SIZE)
# compute the eigenvalues/eigenvectos of the covariance matrix of
Ud, dlam, _ = pca_components_gf(gf.data())
drdims = np.zeros((NUM_EIGS, ))
for i in range(NUM_EIGS):
drdims[i] = dlam[i] / np.sum(dlam[i:]**2)**0.5
sd = SurrGeoFieldAR([0, 30], 'sbc')
sd.copy_field(gf)
sd.prepare_surrogates(pool)
# compute RVDIM(1)
rvdims[i] = rlam[0] / np.sum(rlam**2)**0.5
return rvdims
if __name__ == "__main__":
print("Estimate PCA components script version 1.0")
S = np.zeros(shape = (20, 50), dtype = np.int32)
S[10:18, 25:45] = 1
S[0:3, 6:12] = 2
S[8:15, 2:12] = 3
v, Sr = constructVAR(S, [0.0, 0.6, 0.9, 0.7], [0.3, 0.5], [0.0, 0.0])
ts = v.simulate(200)
gf = make_model_geofield(S, ts)
# initialize a parallel pool
pool = Pool(POOL_SIZE)
# compute the eigenvalues/eigenvectos of the covariance matrix of
Ud, dlam, _ = pca_components_gf(gf.data())
drdims = np.zeros((NUM_EIGS,))
for i in range(NUM_EIGS):
drdims[i] = dlam[i] / np.sum(dlam[i:]**2)**0.5
sd = SurrGeoFieldAR([0, 30], 'sbc')
sd.copy_field(gf)
sd.prepare_surrogates(pool)
from spatial_model_generator import constructVAR, make_model_geofield import numpy as np import matplotlib.pyplot as plt from geo_field import GeoField from surr_geo_field_ar import SurrGeoFieldAR import cPickle # construct the testing model from a spec S = np.zeros(shape = (20, 50), dtype = np.int32) S[10:18, 25:45] = 1 S[0:3, 6:12] = 2 v, Sr = constructVAR(S, [0.0, 0.8, 0.8], [-0.1, 0.1], [0.0, 0.0]) #v, Sr = constructVAR2(S, [-0.2, 0.2], [0.0, 0.9, 0.9], 0.8) #S = np.zeros(shape = (5, 10), dtype = np.int32) #S[1:4, 0:2] = 1 #S[0:3, 6:9] = 2v, Sr = constructVAR(S, [0.0, 0.191, 0.120], [-0.1, 0.1], [0.00, 0.00], [0.01, 0.01]) ts = v.simulate(768) gf = make_model_geofield(S, ts) sgf = SurrGeoFieldAR() sgf.copy_field(gf) sgf.prepare_surrogates() sgf.construct_surrogate_with_noise() ts2 = sgf.surr_data() plt.figure(figsize = (8, 8)) plt.imshow(S, interpolation = 'nearest')
# print("Loading data ...")
# gf = GeoField()
# gf.load("data/pres.mon.mean.nc", 'pres')
# gf.transform_to_anomalies()
# gf.normalize_monthly_variance()
# gf.slice_date_range(date(1948, 1, 1), date(2012, 1, 1))
# gf.slice_spatial(None, [20, 89])
# gf.slice_months([12, 1, 2])
# construct a test system
S = np.zeros(shape=(20, 50), dtype=np.int32)
S[10:18, 25:45] = 1
S[0:3, 6:12] = 2
S[8:15, 2:12] = 3
v, Sr = constructVAR(S, [0.0, 0.4, 0.8, 0.7], [-0.5, 0.5], [0.0, 0.0])
# v, Sr = constructVAR(S, [0.0, 0.001, 0.01], [-0.1, 0.1], [0.00, 0.00], [0.01, 0.01])
ts = v.simulate(200)
gf = make_model_geofield(S, ts)
# initialize a parallel pool
pool = Pool(POOL_SIZE)
# replace field with surrogate field
sgf = SurrGeoFieldAR()
sgf.copy_field(gf)
sgf.prepare_surrogates(pool)
sgf.construct_surrogate_with_noise()
gf = sgf
gf.d = gf.surr_data().copy()