sg.copy_field(g_for_avg)
sg.prepare_AR_surrogates()
year = 365.25
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
avg_bins_surr = np.zeros(
(NUM_SURR, 8,
2)) # num surr x bin no. x result no. (hot / cold extremes)
phase_bins = get_equidistant_bins()
for surr in range(NUM_SURR):
sg.construct_surrogates_with_residuals()
sg.add_seasonality(mean[:-1], var[:-1], trend[:-1]) # so SAT data
g_for_avg.data = sg.surr_data.copy()
g_for_avg.time = g_for_avg.time[:-1]
tg_temp = g_for_avg.copy_data()
g_for_avg.anomalise() # SATA data for phase
wave, _, _, _ = wvlt.continous_wavelet(g_for_avg.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
bins_surrogates = np.zeros((SU, NUM_SURR / NUM_FILES,
sg.data.shape[1], sg.data.shape[2], 8))
bins_surrogates_var = np.zeros_like(bins_surrogates)
for surr_completed in range(NUM_SURR / NUM_FILES):
# create surrogates field
if (SURR_TYPE == 'MF') or (SURR_TYPE == 'ALL'
and su_type == 0):
sg.construct_multifractal_surrogates(pool=pool)
sg.add_seasonality(0, var, trend)
elif (SURR_TYPE == 'FT') or (SURR_TYPE == 'ALL'
and su_type == 1):
sg.construct_fourier_surrogates_spatial(pool=pool)
sg.add_seasonality(0, var, trend)
elif (SURR_TYPE == 'AR') or (SURR_TYPE == 'ALL'
and su_type == 2):
sg.construct_surrogates_with_residuals(pool=pool)
sg.add_seasonality(0, var[:-1, ...], trend[:-1, ...])
# oscillatory modes
phase_surrs = np.zeros_like(sg.surr_data)
job_args = [(i, j, s0, sg.surr_data[:, i, j])
for i in range(sg.lats.shape[0])
for j in range(sg.lons.shape[0])]
job_result = map_func(_get_oscillatory_modes, job_args)
del job_args
# map results
for i, j, ph in job_result:
phase_surrs[:, i, j] = ph
del job_result
if AMPLITUDE:
# g_for_avg are SAT data
mean, var, trend = g_for_avg.get_seasonality(detrend = True)
sg.copy_field(g_for_avg)
sg.prepare_AR_surrogates()
year = 365.25
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
avg_bins_surr = np.zeros((NUM_SURR, 8, 2)) # num surr x bin no. x result no. (hot / cold extremes)
phase_bins = get_equidistant_bins()
for surr in range(NUM_SURR):
sg.construct_surrogates_with_residuals()
sg.add_seasonality(mean[:-1], var[:-1], trend[:-1]) # so SAT data
g_for_avg.data = sg.surr_data.copy()
g_for_avg.time = g_for_avg.time[:-1]
tg_temp = g_for_avg.copy_data()
g_for_avg.anomalise() # SATA data for phase
wave, _, _, _ = wvlt.continous_wavelet(g_for_avg.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
avg_ndx = g_for_avg.select_date(date(1844, 4, 14), date(1926,1,1))
phase = phase[0, avg_ndx]
tg_temp = tg_temp[avg_ndx]
sigma = np.std(tg_temp, axis = 0, ddof = 1)
for i in range(phase_bins.shape[0] - 1):
map_func = map
for su_type in range(SU):
for file_num in range(NUM_FILES):
bins_surrogates = np.zeros((SU, NUM_SURR/NUM_FILES, sg.data.shape[1], sg.data.shape[2], 8))
bins_surrogates_var = np.zeros_like(bins_surrogates)
for surr_completed in range(NUM_SURR/NUM_FILES):
# create surrogates field
if (SURR_TYPE == 'MF') or (SURR_TYPE == 'ALL' and su_type == 0):
sg.construct_multifractal_surrogates(pool = pool)
sg.add_seasonality(0, var, trend)
elif (SURR_TYPE == 'FT') or (SURR_TYPE == 'ALL' and su_type == 1):
sg.construct_fourier_surrogates_spatial(pool = pool)
sg.add_seasonality(0, var, trend)
elif (SURR_TYPE == 'AR') or (SURR_TYPE == 'ALL' and su_type == 2):
sg.construct_surrogates_with_residuals(pool = pool)
sg.add_seasonality(0, var[:-1, ...], trend[:-1, ...])
# oscillatory modes
phase_surrs = np.zeros_like(sg.surr_data)
job_args = [ (i, j, s0, sg.surr_data[:, i, j]) for i in range(sg.lats.shape[0]) for j in range(sg.lons.shape[0]) ]
job_result = map_func(_get_oscillatory_modes, job_args)
del job_args
# map results
for i, j, ph in job_result:
phase_surrs[:, i, j] = ph
del job_result
if AMPLITUDE:
# surrogates are now SATA, for amplitude we need SAT so plus mean, times 1 var, plus 0 trend
if SURR_TYPE == 'MF' or SURR_TYPE == 'FT':