if NUM_SURR != 0:
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
WINDOW_LENGTH = 16384
WINDOW_SHIFT = 1 # years, delta in the sliding window analysis
MEANS = True # if True, compute conditional means, if False, compute conditional variance
WORKERS = 16
NUM_SURR = 100 # how many surrs will be used to evaluate
SURR_TYPE = 'AR' # MF, FT, AR
diff_ax = (0, 2) # means -> 0, 2, var -> 1, 8
mean_ax = (18, 22) # means -> -1, 1.5, var -> 9, 18
PLOT_PHASE = False
PHASE_ANALYSIS_YEAR = None # year of detailed analysis - phase and bins, or None
AMPLITUDE = True
## loading data
g = load_station_data('TG_STAID000027.txt', date(1834, 7, 28),
date(2014, 1, 1), ANOMALISE)
sg = SurrogateField()
if AMPLITUDE:
g_amp = load_station_data('TG_STAID000027.txt', date(1834, 7, 28),
date(2014, 1, 1), False)
sg_amp = SurrogateField()
print(
"[%s] Wavelet analysis in progress with %d year window shifted by %d year(s)..."
% (str(datetime.now()), WINDOW_LENGTH, WINDOW_SHIFT))
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
cond_means = np.zeros((8, ))
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,
plt.savefig(fname)
else:
plt.show()
cond_means_surr = np.zeros((NUM_SURR, BINS))
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)
WINDOW_LENGTH = 16384
WINDOW_SHIFT = 1 # years, delta in the sliding window analysis
MEANS = True # if True, compute conditional means, if False, compute conditional variance
WORKERS = 16
NUM_SURR = 100 # how many surrs will be used to evaluate
SURR_TYPE = 'AR' # MF, FT, AR
diff_ax = (0, 2) # means -> 0, 2, var -> 1, 8
mean_ax = (18, 22) # means -> -1, 1.5, var -> 9, 18
PLOT_PHASE = False
PHASE_ANALYSIS_YEAR = None # year of detailed analysis - phase and bins, or None
AMPLITUDE = True
## loading data
g = load_station_data('TG_STAID000027.txt', date(1834,7,28), date(2014,1,1), ANOMALISE)
sg = SurrogateField()
if AMPLITUDE:
g_amp = load_station_data('TG_STAID000027.txt', date(1834,7,28), date(2014, 1, 1), False)
sg_amp = SurrogateField()
print("[%s] Wavelet analysis in progress with %d year window shifted by %d year(s)..." % (str(datetime.now()), WINDOW_LENGTH, WINDOW_SHIFT))
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
cond_means = np.zeros((8,))
data_diff = cond_means[:, 1].max() - cond_means[:, 1].min()
if SURR:
cond_means_surr = np.zeros((NUM_SURR, BINS, 2))
amp_diff_surr = np.zeros((NUM_SURR, ))
surr_diff_surr = np.zeros((NUM_SURR, ))
amp_surr = np.zeros((NUM_SURR, ))
surr_completed = 0
jobQ = Queue()
resQ = Queue()
for i in range(NUM_SURR):
jobQ.put(1)
for i in range(WORKERS):
jobQ.put(None)
a = g_amp.get_seasonality(True)
sg = SurrogateField()
sg.copy_field(g_amp)
phase_bins = get_equidistant_bins(BINS)
workers = [
Process(target=_reconstruction_surrs, args=(sg, a, jobQ, resQ, idx))
for iota in range(WORKERS)
]
for w in workers:
w.start()
while surr_completed < NUM_SURR:
surr_means = resQ.get()
cond_means_surr[surr_completed, :, 0] = surr_means[0][:, 0]
cond_means_surr[surr_completed, :, 1] = surr_means[0][:, 1]
amp_diff_surr[surr_completed] = surr_means[1]
surr_diff_surr[surr_completed] = surr_means[2]
if SURR:
cond_means_surr = np.zeros((NUM_SURR, BINS, 2))
amp_diff_surr = np.zeros((NUM_SURR,))
surr_diff_surr = np.zeros((NUM_SURR,))
amp_surr = np.zeros((NUM_SURR,))
surr_completed = 0
jobQ = Queue()
resQ = Queue()
for i in range(NUM_SURR):
jobQ.put(1)
for i in range(WORKERS):
jobQ.put(None)
a = g_amp.get_seasonality(True)
sg = SurrogateField()
sg.copy_field(g_amp)
phase_bins = get_equidistant_bins(BINS)
workers = [Process(target = _reconstruction_surrs, args = (sg, a, jobQ, resQ, idx)) for iota in range(WORKERS)]
for w in workers:
w.start()
while surr_completed < NUM_SURR:
surr_means = resQ.get()
cond_means_surr[surr_completed, :, 0] = surr_means[0][:, 0]
cond_means_surr[surr_completed, :, 1] = surr_means[0][:, 1]
amp_diff_surr[surr_completed] = surr_means[1]
surr_diff_surr[surr_completed] = surr_means[2]
amp_surr[surr_completed] = surr_means[3]
phase_bins = get_equidistant_bins() # equidistant bins
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
if NUM_SURR != 0:
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()
created on May 6, 2014
@author: Nikola Jajcay
"""
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)
else:
plt.show()
cond_means_surr = np.zeros((NUM_SURR, BINS))
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)
phase_bins = get_equidistant_bins(BINS)
cond_means = np.zeros((BINS, 2)) # :,0 - SATA, :,1 - SATamp
for i in range(cond_means.shape[0]):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i+1]))
cond_means[i, 1] = np.mean(amplitude[ndx])
cond_means[i, 0] = np.mean(g_data.data[ndx])
cond_means_surr = np.zeros((NUM_SURR, BINS, 2))
mean, var, trend = g.get_seasonality(True)
mean2, var2, trend2 = g_amp.get_seasonality(True)
for su in range(NUM_SURR):
sg_amp = SurrogateField()
sg_amp.copy_field(g_amp)
sg = SurrogateField()
sg.copy_field(g)
# MF
sg_amp.construct_multifractal_surrogates()
sg_amp.add_seasonality(mean2, var2, trend2)
sg.construct_multifractal_surrogates()
sg.add_seasonality(mean, var, trend)
# AR
# sg_amp.prepare_AR_surrogates()
# sg_amp.construct_surrogates_with_residuals()
# sg_amp.add_seasonality(mean2[:-1], var2[:-1], trend2[:-1])
# sg.prepare_AR_surrogates()
created on May 6, 2014
@author: Nikola Jajcay
"""
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)
