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()