surr_mom = []
mean, var, trend = g.get_seasonality(True)
if AMPLITUDE:
mean2, var2, trend2 = g_amp.get_seasonality(True)
su = 0
tot = 0
while su < NUM_SURR:
if AMPLITUDE:
sg_amp = SurrogateField()
sg_amp.copy_field(g_amp)
sg = SurrogateField()
sg.copy_field(g)
if SURR_TYPE == 'MF':
if AMPLITUDE:
sg_amp.construct_multifractal_surrogates()
sg_amp.add_seasonality(mean2, var2, trend2)
sg.construct_multifractal_surrogates()
sg.add_seasonality(mean, var, trend)
elif SURR_TYPE == 'FT':
if AMPLITUDE:
sg_amp.construct_fourier_surrogates_spatial()
sg_amp.add_seasonality(mean2, var2, trend2)
sg.construct_fourier_surrogates_spatial()
sg.add_seasonality(mean, var, trend)
elif SURR_TYPE == 'AR':
if AMPLITUDE:
sg_amp.prepare_AR_surrogates()
sg_amp.construct_surrogates_with_residuals()
sg_amp.add_seasonality(mean2, var2, trend2)
sg.prepare_AR_surrogates()
import numpy as np
from surrogates.surrogates import SurrogateField
import calendar
ts = OscillatoryTimeSeries('TG_STAID000027.txt', date(1834,7,28), date(2014,1,1), False)
sg = SurrogateField()
g = DataField()
daily_var = np.zeros((365,3))
mean, var_data, trend = ts.g.get_seasonality(True)
sg.copy_field(ts.g)
#MF
sg.construct_multifractal_surrogates()
sg.add_seasonality(mean, var_data, trend)
g.data = sg.surr_data.copy()
g.time = sg.time.copy()
_, var_surr_MF, _ = g.get_seasonality(True)
#FT
sg.construct_fourier_surrogates_spatial()
sg.add_seasonality(mean, var_data, trend)
g.data = sg.surr_data.copy()
g.time = sg.time.copy()
_, var_surr_FT, _ = g.get_seasonality(True)
