print gf.d.shape
print gf.lons[0], gf.lons[-1]
print gf.lats[0], gf.lats[-1]
print gf.d.shape[1] * gf.d.shape[2]
# <codecell>
if USE_SURROGATE_MODEL:
pool = Pool(POOL_SIZE)
sgf = SurrGeoFieldAR([0, MAX_AR_ORDER], 'sbc')
print("Running preparation of surrogates ...")
sgf.copy_field(gf)
sgf.prepare_surrogates(pool)
sgf.construct_surrogate_with_noise()
sgf.d = sgf.sd # hack to replace original data with surrogate
print("Max AR order is %d ..." % sgf.max_ord)
gf = sgf
print("Replaced field with surrogate field.")
pool.close()
del pool
print("Analyzing data ...")
d = gf.data()
if COSINE_REWEIGHTING:
d *= gf.qea_latitude_weights()
Ud, sd, Vtd = pca_components_gf(d)
Ud = Ud[:, :NUM_COMPONENTS]
if not ROTATE_NORMALIZED:
Ud *= sd[np.newaxis, :NUM_COMPONENTS]
print gf.d.shape
print gf.lons[0], gf.lons[-1]
print gf.lats[0], gf.lats[-1]
print gf.d.shape[1] * gf.d.shape[2]
# <codecell>
if USE_SURROGATE_MODEL:
pool = Pool(POOL_SIZE)
sgf = SurrGeoFieldAR([0, MAX_AR_ORDER], 'sbc')
print("Running preparation of surrogates ...")
sgf.copy_field(gf)
sgf.prepare_surrogates(pool)
sgf.construct_surrogate_with_noise()
sgf.d = sgf.sd # hack to replace original data with surrogate
print("Max AR order is %d ..." % sgf.max_ord)
gf = sgf
print("Replaced field with surrogate field.")
pool.close()
del pool
print("Analyzing data ...")
d = gf.data()
if COSINE_REWEIGHTING:
d *= gf.qea_latitude_weights()
Ud, sd, Vtd = pca_components_gf(d)
Ud = Ud[:, :NUM_COMPONENTS]
if not ROTATE_NORMALIZED:
Ud *= sd[np.newaxis, :NUM_COMPONENTS]
# load up the monthly SLP geo-field
print("[%s] Loading geo field..." % (str(datetime.now())))
gf = load_monthly_data_general("data/hgt.mon.mean.nc", "hgt",
date(1948, 1, 1), date(2012, 1, 1),
None, None, None, 5)
# load up the monthly SLP geo-field
if USE_MUVAR:
print("[%s] Constructing F2 surrogate ..." % (str(datetime.now())))
sgf = SurrGeoFieldAR()
sgf.copy_field(gf)
sgf.construct_fourier2_surrogates()
sgf.d = sgf.sd.copy()
# slide in fourier2 surrogate
orig_gf = gf
gf = sgf
# load up the monthly SLP geo-field
print("[%s] Done loading, data hase shape %s." % (str(datetime.now()), gf.d.shape))
# <markdowncell>
# **Surrogate construction**
#
# The next cell constructs surrogates in three ways. At this point I'm verifying the previous results obtained
# using residuals by feeding in normally distributed noise according to the model covariance matrix (noise variance in case of AR(k) models).
os.chdir('/home/martin/Projects/ndw-climate/')
# load up the monthly SLP geo-field
print("[%s] Loading geo field..." % (str(datetime.now())))
gf = load_monthly_data_general("data/hgt.mon.mean.nc", "hgt", date(1948, 1, 1),
date(2012, 1, 1), None, None, None, 5)
# load up the monthly SLP geo-field
if USE_MUVAR:
print("[%s] Constructing F2 surrogate ..." % (str(datetime.now())))
sgf = SurrGeoFieldAR()
sgf.copy_field(gf)
sgf.construct_fourier2_surrogates()
sgf.d = sgf.sd.copy()
# slide in fourier2 surrogate
orig_gf = gf
gf = sgf
# load up the monthly SLP geo-field
print("[%s] Done loading, data hase shape %s." %
(str(datetime.now()), gf.d.shape))
# <markdowncell>
# **Surrogate construction**
#
# The next cell constructs surrogates in three ways. At this point I'm verifying the previous results obtained
# using residuals by feeding in normally distributed noise according to the model covariance matrix (noise variance in case of AR(k) models).
