sm = date.fromordinal(g.time[0]).month
sd = date.fromordinal(g.time[0]).day
start_idx = 0
end_idx = to_wavelet
_, _, idx = g.get_data_of_precise_length(WINDOW_LENGTH,
date.fromordinal(g.time[4 * y]), None,
False)
first_mid_year = date.fromordinal(g.time[idx[0] + WINDOW_LENGTH / 2]).year
while end_idx < g.data.shape[0]:
# data
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
if USE_SURR:
result_temp_surr = np.zeros((NUM_SURR, 8, 2))
for surr in range(NUM_SURR):
sg.construct_surrogates_with_residuals()
sg.add_seasonality(mean[:-1], var[:-1], trend[:-1]) # so SAT data
g.data = sg.surr_data.copy()
tg_sat = g.copy_data()
g.time = g.time[:-1]
g.anomalise()
g_temp = DataField()
tg_temp = tg_sat.copy()
sy = int(MIDDLE_YEAR - (WINDOW_LENGTH / year) / 2)
g_temp.data = g.data.copy()
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,
start_idx = 0
end_idx = to_wavelet
_, _, idx = g.get_data_of_precise_length(WINDOW_LENGTH, date.fromordinal(g.time[4 * y]), None, False)
first_mid_year = date.fromordinal(g.time[idx[0] + WINDOW_LENGTH / 2]).year
last_mid_year = first_mid_year
if PLOT_PHASE:
phase_total = []
if PLOT_PHASE and not BEGIN:
last_day = g.get_date_from_ndx(4 * y)
while end_idx < g.data.shape[0]:
# data
g_working.data = g.data[start_idx:end_idx].copy()
g_working.time = g.time[start_idx:end_idx].copy()
if AMPLITUDE:
g_working_amp.data = g_amp.data[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
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))
scaling = []
hw = []
cw = []
if JUST_SCALING:
scaling_min = []
scaling_max = []
for i in range(phase_bins.shape[0] - 1):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i + 1]))
data_temp = g.data[ndx].copy() #g.data[ndx].copy()
time_temp = g.time[ndx].copy()
max_temp = g_max.data[ndx].copy()
min_temp = g_min.data[ndx].copy()
tg_sat_temp = tg_sat[ndx].copy()
if not JUST_SCALING:
g_temp.time = time_temp.copy()
_, m, _ = g_temp.extract_day_month_year()
# positive extremes - 2sigma
g_e = np.greater_equal(data_temp, np.mean(g.data) + 2 * sigma_max)
result[i, 0] = np.sum((m[g_e] == 12) | (m[g_e] <= 2)) # DJF
result[i, 1] = np.sum((m[g_e] > 2) & (m[g_e] <= 5)) # MAM
result[i, 2] = np.sum((m[g_e] > 5) & (m[g_e] <= 8)) # JJA
result[i, 3] = np.sum((m[g_e] > 8) & (m[g_e] <= 11)) # SON
# positive extremes - 3sigma
g_e = np.greater_equal(data_temp, np.mean(g.data) + 3 * sigma_max)
result[i, 4] = np.sum((m[g_e] == 12) | (m[g_e] <= 2)) # DJF
result[i, 5] = np.sum((m[g_e] > 2) & (m[g_e] <= 5)) # MAM
result[i, 6] = np.sum((m[g_e] > 5) & (m[g_e] <= 8)) # JJA
result[i, 7] = np.sum((m[g_e] > 8) & (m[g_e] <= 11)) # SON
# negative extremes - 2sigma
l_e = np.less_equal(data_temp, np.mean(g.data) - 2 * sigma_min)
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()
g.time = sg.time.copy()
_, var_surr_FT, _ = g.get_seasonality(True)
delta = timedelta(days = 1)
d = date(1895,1,1)
if USE_SURR:
result_temp_surr = np.zeros((NUM_SURR, 8,2))
for surr in range(NUM_SURR):
sg.construct_surrogates_with_residuals()
sg.add_seasonality(mean[:-1], var[:-1], trend[:-1]) # so SAT data
g.data = sg.surr_data.copy()
tg_sat = g.copy_data()
g.time = g.time[:-1]
g.anomalise()
g_temp = DataField()
tg_temp = tg_sat.copy()
sy = int(MIDDLE_YEAR - (WINDOW_LENGTH/year)/2)
g_temp.data = g.data.copy()
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)
scaling = []
hw = []
cw = []
if JUST_SCALING:
scaling_min = []
scaling_max = []
for i in range(phase_bins.shape[0] - 1):
ndx = (phase >= phase_bins[i]) & (phase <= phase_bins[i + 1])
data_temp = g.data[ndx].copy() # g.data[ndx].copy()
time_temp = g.time[ndx].copy()
max_temp = g_max.data[ndx].copy()
min_temp = g_min.data[ndx].copy()
tg_sat_temp = tg_sat[ndx].copy()
if not JUST_SCALING:
g_temp.time = time_temp.copy()
_, m, _ = g_temp.extract_day_month_year()
# positive extremes - 2sigma
g_e = np.greater_equal(data_temp, np.mean(g.data) + 2 * sigma_max)
result[i, 0] = np.sum((m[g_e] == 12) | (m[g_e] <= 2)) # DJF
result[i, 1] = np.sum((m[g_e] > 2) & (m[g_e] <= 5)) # MAM
result[i, 2] = np.sum((m[g_e] > 5) & (m[g_e] <= 8)) # JJA
result[i, 3] = np.sum((m[g_e] > 8) & (m[g_e] <= 11)) # SON
# positive extremes - 3sigma
g_e = np.greater_equal(data_temp, np.mean(g.data) + 3 * sigma_max)
result[i, 4] = np.sum((m[g_e] == 12) | (m[g_e] <= 2)) # DJF
result[i, 5] = np.sum((m[g_e] > 2) & (m[g_e] <= 5)) # MAM
result[i, 6] = np.sum((m[g_e] > 5) & (m[g_e] <= 8)) # JJA
result[i, 7] = np.sum((m[g_e] > 8) & (m[g_e] <= 11)) # SON
# negative extremes - 2sigma
l_e = np.less_equal(data_temp, np.mean(g.data) - 2 * sigma_min)
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()
g.time = sg.time.copy()
_, var_surr_FT, _ = g.get_seasonality(True)
delta = timedelta(days=1)
d = date(1895, 1, 1)
