g_working.data = g.data[start_idx:end_idx].copy()
g_working.time = g.time[start_idx:end_idx].copy()
if np.all(np.isnan(g_working.data) == False):
wave, _, _, _ = wavelet_analysis.continous_wavelet(
g_working.data,
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
start_cut = date(start_year + cnt * WINDOW_SHIFT, sm, sd)
idx = g_working.get_data_of_precise_length(WINDOW_LENGTH, start_cut,
None, True) # 16k or 13462
print 'data ', g.get_date_from_ndx(
start_idx), ' - ', g.get_date_from_ndx(end_idx)
print 'cut from ', start_cut, ' to ', g_working.get_date_from_ndx(-1)
last_mid_year = date.fromordinal(g_working.time[WINDOW_LENGTH /
2]).year
phase = phase[0, idx[0]:idx[1]]
phase_bins = get_equidistant_bins() # equidistant bins
for i in range(cond_means.shape[0]
): # get conditional means for current phase range
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i + 1]))
if MEANS:
cond_means[i] = np.mean(g_working.data[ndx])
else:
cond_means[i] = np.var(g_working.data[ndx], ddof=1)
difference_data.append(cond_means.max() -
period = PERIOD * year # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g_temp.data,
1,
False,
wvlt.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
idx = g_temp.get_data_of_precise_length(WINDOW_LENGTH,
date(sy, sm, sd), None,
True)
phase = phase[0, idx[0]:idx[1]]
tg_temp = tg_temp[idx[0]:idx[1]]
sigma = np.std(tg_temp, axis=0, ddof=1)
for i in range(phase_bins.shape[0] - 1):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i + 1]))
data_temp = g_temp.data[ndx].copy()
time_temp = g_temp.time[ndx].copy()
tg_sat_temp = tg_temp[ndx].copy()
# positive extremes
g_e = np.greater_equal(tg_sat_temp,
np.mean(tg_temp, axis=0) + 2 * sigma)
if AMPLITUDE:
wave, _, _, _ = wavelet_analysis.continous_wavelet(
g_working_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, :]
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_working_amp.data)[0]
amplitude = m * amplitude + c
start_cut = date(start_year + cnt * WINDOW_SHIFT, sm, sd)
idx = g_working.get_data_of_precise_length(WINDOW_LENGTH, start_cut, None, True)
print "data ", g.get_date_from_ndx(start_idx), " - ", g.get_date_from_ndx(end_idx)
print "cut from ", start_cut, " to ", g_working.get_date_from_ndx(-1)
# last_mid_year = date.fromordinal(g_working.time[WINDOW_LENGTH/2]).year
last_mid_year += 1
print last_mid_year
phase = phase[0, idx[0] : idx[1]]
if AMPLITUDE:
amplitude = amplitude[idx[0] : idx[1]]
if PLOT_PHASE and BEGIN:
phase_till = date(start_year + (cnt + 1) * WINDOW_SHIFT, sm, sd)
ndx = g_working.find_date_ndx(phase_till)
if ndx != None and cnt < 125:
phase_total.append(phase[:ndx])
else:
phase_total.append(phase)
g_temp.time = g.time.copy()
start = g_temp.find_date_ndx(date(sy - 4, sm, sd))
end = start + 16384 if WINDOW_LENGTH < 16000 else start + 32768
g_temp.data = g_temp.data[start : end]
g_temp.time = g_temp.time[start : end]
tg_temp = tg_temp[start : end]
k0 = 6. # wavenumber of Morlet wavelet used in analysis
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * year # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g_temp.data, 1, False, wvlt.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
idx = g_temp.get_data_of_precise_length(WINDOW_LENGTH, date(sy, sm, sd), None, True)
phase = phase[0, idx[0] : idx[1]]
tg_temp = tg_temp[idx[0] : idx[1]]
sigma = np.std(tg_temp, axis = 0, ddof = 1)
for i in range(phase_bins.shape[0] - 1):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i+1]))
data_temp = g_temp.data[ndx].copy()
time_temp = g_temp.time[ndx].copy()
tg_sat_temp = tg_temp[ndx].copy()
# positive extremes
g_e = np.greater_equal(tg_sat_temp, np.mean(tg_temp, axis = 0) + 2 * sigma)
result_temp_surr[surr, i, 0] = np.sum(g_e)
