j1=0,
k0=k0) # perform wavelet
amplitude = np.sqrt(
np.power(np.real(wave), 2) + np.power(np.imag(wave), 2))
amplitude = amplitude[0, :]
phase_amp = np.arctan2(np.imag(wave), np.real(wave))
phase_amp = phase_amp[0, :]
# fitting oscillatory phase / amplitude to actual SAT
reconstruction = amplitude * np.cos(phase_amp)
fit_x = np.vstack([reconstruction, np.ones(reconstruction.shape[0])]).T
m, c = np.linalg.lstsq(fit_x, g_amp.data)[0]
amplitude = m * amplitude + c
mean, var, trend = g.get_seasonality(True)
sg.copy_field(g)
g.return_seasonality(mean, var, trend)
if AMPLITUDE:
mean2, var2, trend2 = g_amp.get_seasonality(True)
sg_amp.copy_field(g_amp)
g_amp.return_seasonality(mean2, var2, trend2)
main_cut_ndx = g.select_date(date(1838, 7, 28), date(2010, 1, 1))
y1 = 1838
phase = phase[0, main_cut_ndx]
if AMPLITUDE:
amplitude = amplitude[main_cut_ndx]
difference_data = []
meanvar_data = []
cond_means[iota] = np.mean(g.data[ndx])
else:
cond_means[iota] = np.var(g.data[ndx], ddof=1)
difference[i, j] = cond_means.max() - cond_means.min(
) # append difference to list
mean_var[i, j] = np.mean(cond_means)
print(
"[%s] Wavelet analysis done. Now computing wavelet for MF surrogates in parallel..."
% str(datetime.now()))
surrogates_difference = np.zeros([num_surr] + list(difference.shape))
surrogates_mean_var = np.zeros_like(surrogates_difference)
surr_completed = 0
sg = SurrogateField()
sg.copy_field(g)
mean, var, trend = g.get_seasonality(DETREND=True)
def _cond_difference_surrogates(sg, jobq, resq):
while jobq.get() is not None:
difference = np.zeros((sg.lats.shape[0], sg.lons.shape[0]))
mean_var = np.zeros_like(difference)
sg.construct_multifractal_surrogates()
sg.add_seasonality(mean, var, trend)
for i in range(sg.lats.shape[0]):
for j in range(sg.lons.shape[0]):
wave, _, _, _ = wavelet_analysis.continous_wavelet(
sg.surr_data[:, i, j],
1,
False,
surr_completed = 0
diffs = np.zeros((NUM_SURR, ))
mean_vars = np.zeros_like(diffs)
g_surrs.data = g.data[start_idx:end_idx].copy()
g_surrs.time = g.time[start_idx:end_idx].copy()
if np.all(np.isnan(g_surrs.data) == False):
# construct the job queue
jobQ = Queue()
resQ = Queue()
for i in range(NUM_SURR):
jobQ.put(1)
for i in range(WORKERS):
jobQ.put(None)
a = g_surrs.get_seasonality(DETREND=True)
sg = SurrogateField()
sg.copy_field(g_surrs)
if SURR_TYPE == 'AR':
sg.prepare_AR_surrogates()
workers = [
Process(target=_cond_difference_surrogates,
args=(sg, g_surrs, a, start_cut, jobQ, resQ))
for iota in range(WORKERS)
]
for w in workers:
w.start()
while surr_completed < NUM_SURR:
# get result
diff, meanVar = resQ.get()
diffs[surr_completed] = diff
mean_vars[surr_completed] = meanVar
surr_completed += 1
if AMPLITUDE:
s0_amp = (1 * y) / fourier_factor
wave, _, _, _ = wavelet_analysis.continous_wavelet(g_amp.data, 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0_amp, j1 = 0, k0 = k0) # perform wavelet
amplitude = np.sqrt(np.power(np.real(wave),2) + np.power(np.imag(wave),2))
amplitude = amplitude[0, :]
phase_amp = np.arctan2(np.imag(wave), np.real(wave))
phase_amp = phase_amp[0, :]
# fitting oscillatory phase / amplitude to actual SAT
reconstruction = amplitude * np.cos(phase_amp)
fit_x = np.vstack([reconstruction, np.ones(reconstruction.shape[0])]).T
m, c = np.linalg.lstsq(fit_x, g_amp.data)[0]
amplitude = m * amplitude + c
mean, var, trend = g.get_seasonality(True)
sg.copy_field(g)
g.return_seasonality(mean, var, trend)
if AMPLITUDE:
mean2, var2, trend2 = g_amp.get_seasonality(True)
sg_amp.copy_field(g_amp)
g_amp.return_seasonality(mean2, var2, trend2)
main_cut_ndx = g.select_date(date(1838,7,28), date(2010,1,1))
y1 = 1838
phase = phase[0, main_cut_ndx]
if AMPLITUDE:
amplitude = amplitude[main_cut_ndx]
difference_data = []
meanvar_data = []
ndx = ((phase[0,:] >= phase_bins[iota]) & (phase[0,:] <= phase_bins[iota+1]))
if MEANS:
cond_means[iota] = np.mean(g.data[ndx])
else:
cond_means[iota] = np.var(g.data[ndx], ddof = 1)
difference[i, j] = cond_means.max() - cond_means.min() # append difference to list
mean_var[i, j] = np.mean(cond_means)
print("[%s] Wavelet analysis done. Now computing wavelet for MF surrogates in parallel..." % str(datetime.now()))
surrogates_difference = np.zeros([num_surr] + list(difference.shape))
surrogates_mean_var = np.zeros_like(surrogates_difference)
surr_completed = 0
sg = SurrogateField()
sg.copy_field(g)
mean, var, trend = g.get_seasonality(DETREND = True)
def _cond_difference_surrogates(sg, jobq, resq):
while jobq.get() is not None:
difference = np.zeros((sg.lats.shape[0], sg.lons.shape[0]))
mean_var = np.zeros_like(difference)
sg.construct_multifractal_surrogates()
sg.add_seasonality(mean, var, trend)
for i in range(sg.lats.shape[0]):
for j in range(sg.lons.shape[0]):
wave, _, _, _ = wavelet_analysis.continous_wavelet(sg.surr_data[:, i, j], 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave)) # get phases from oscillatory modes
for iota in range(cond_means.shape[0]): # get conditional means for current phase range
#phase_bins = get_equiquantal_bins(phase_temp) # equiquantal bins
phase_bins = get_equidistant_bins() # equidistant bins
surr_completed = 0
diffs = np.zeros((NUM_SURR,))
mean_vars = np.zeros_like(diffs)
g_surrs.data = g.data[start_idx : end_idx].copy()
g_surrs.time = g.time[start_idx : end_idx].copy()
if np.all(np.isnan(g_surrs.data) == False):
# construct the job queue
jobQ = Queue()
resQ = Queue()
for i in range(NUM_SURR):
jobQ.put(1)
for i in range(WORKERS):
jobQ.put(None)
a = g_surrs.get_seasonality(DETREND = True)
sg = SurrogateField()
sg.copy_field(g_surrs)
if SURR_TYPE == 'AR':
sg.prepare_AR_surrogates()
workers = [Process(target = _cond_difference_surrogates, args = (sg, g_surrs, a, start_cut, jobQ, resQ)) for iota in range(WORKERS)]
for w in workers:
w.start()
while surr_completed < NUM_SURR:
# get result
diff, meanVar = resQ.get()
diffs[surr_completed] = diff
mean_vars[surr_completed] = meanVar
surr_completed += 1
for w in workers:
w.join()
difference_surr.append(np.mean(diffs))
from src.data_class import DataField
from datetime import date, timedelta
import matplotlib.pyplot as plt
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()
from src.oscillatory_time_series import OscillatoryTimeSeries
from src.data_class import DataField
from datetime import date, timedelta
import matplotlib.pyplot as plt
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()
