# 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, sg_amp.surr_data)[0]
amplitude = m * amplitude + c
if AA:
if AMPLITUDE:
sg_amp.amplitude_adjust_surrogates(mean2, var2, trend2)
else:
sg.amplitude_adjust_surrogates(mean, var, trend)
_, _, idx = g.get_data_of_precise_length('16k', start_cut, None, False)
phase = phase[0, idx[0] : idx[1]]
if AMPLITUDE:
amplitude = amplitude[idx[0] : idx[1]]
else:
sg.surr_data = sg.surr_data[idx[0] : idx[1]]
# subselect season
if EVAL_SEASON:
phase = phase[ndx_season]
if AMPLITUDE:
amplitude = amplitude[ndx_season]
else:
sg.surr_data = sg.surr_data[ndx_season]
temp_means = np.zeros((8,))
for i in range(cond_means.shape[0]):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i+1]))
if AMPLITUDE:
temp_means[i] = func(amplitude[ndx])
else:
temp_means[i] = func(sg.surr_data[ndx])
# 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, sg_amp.surr_data)[0]
amplitude = m * amplitude + c
if AA:
if AMPLITUDE:
sg_amp.amplitude_adjust_surrogates(mean2, var2, trend2)
else:
sg.amplitude_adjust_surrogates(mean, var, trend)
_, _, idx = g.get_data_of_precise_length('16k', start_cut, None, False)
phase = phase[0, idx[0]:idx[1]]
if AMPLITUDE:
amplitude = amplitude[idx[0]:idx[1]]
else:
sg.surr_data = sg.surr_data[idx[0]:idx[1]]
# subselect season
if EVAL_SEASON:
phase = phase[ndx_season]
if AMPLITUDE:
amplitude = amplitude[ndx_season]
else:
sg.surr_data = sg.surr_data[ndx_season]
temp_means = np.zeros((8, ))
for i in range(cond_means.shape[0]):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i + 1]))
if AMPLITUDE:
temp_means[i] = func(amplitude[ndx])
else:
temp_means[i] = func(sg.surr_data[ndx])
wave, _, _, _ = wavelet_analysis.continous_wavelet(sg_amp.surr_data, 1, True, 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, sg_amp.surr_data)[0]
amplitude = m * amplitude + c
_, _, idx = g.get_data_of_precise_length('16k', start_cut, None, False)
phase = phase[0, idx[0] : idx[1]]
amplitude = amplitude[idx[0] : idx[1]]
sg.surr_data = sg.surr_data[idx[0] : idx[1]]
for i in range(cond_means_surr.shape[1]):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i+1]))
cond_means_surr[su, i, 1] = np.mean(amplitude[ndx])
cond_means_surr[su, i, 0] = np.mean(sg.surr_data[ndx])
if (su+1) % 10 == 0:
print("%d. surrogate done..." % (su + 1))
fig = plt.figure(figsize = (10,8), frameon = False)
gs = gridspec.GridSpec(1, 2)
gs.update(left = 0.12, right = 0.95, top = 0.95, bottom = 0.1, wspace = 0.4, hspace = 0.4)
ax = plt.Subplot(fig, gs[0, 0])
