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
surrs.copy_field(net)
net.return_seasonality(a[0], a[1], a[2])
pool = Pool(20)
net.wavelet(8, "y", cut=1, pool=pool)
net.get_adjacency_matrix(net.phase, method="MIEQQ", num_workers=0, pool=pool, use_queue=False)
pool.close()
pool.join()
data_adj_matrix = net.adjacency_matrix.copy()
surrs_adj_matrices = []
for i in range(NUM_SURR):
print("surr %d/%d computing..." % (i + 1, NUM_SURR))
pool = Pool(20)
surrs.construct_fourier_surrogates(pool=pool)
surrs.add_seasonality(a[0], a[1], a[2])
net.data = surrs.get_surr()
net.wavelet(8, "y", cut=1, pool=pool)
net.get_adjacency_matrix(net.phase, method="MIEQQ", num_workers=0, pool=pool, use_queue=False)
pool.close()
pool.join()
surrs_adj_matrices.append(net.adjacency_matrix)
import cPickle
with open("8yr-phase-scale-net-surrs-test.bin", "wb") as f:
cPickle.dump({"data": data_adj_matrix, "surrs": surrs_adj_matrices}, f, protocol=cPickle.HIGHEST_PROTOCOL)
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):
ndx = ((phase >= phase_bins[i]) & (phase <= phase_bins[i+1]))
date(1950, 1, 1),
date(2016, 1, 1),
None,
None,
None,
'monthly',
anom=False)
a = net.get_seasonality(detrend=True)
surrs.copy_field(net)
# surrs.construct_fourier_surrogates()
# surrs.add_seasonality(a[0], a[1], a[2])
for num in range(NUM_SURRS):
pool = Pool(20)
surrs.construct_fourier_surrogates(pool=pool)
surrs.add_seasonality(a[0], a[1], a[2])
net.data = surrs.get_surr()
net.wavelet(1, 'y', pool=pool, cut=1)
net.get_continuous_phase(pool=pool)
print "wavelet done"
net.get_phase_fluctuations(rewrite=True, pool=pool)
print "fluctuations done"
pool.close()
pool.join()
net.get_adjacency_matrix(net.phase_fluctuations,
method="MIEQQ",
pool=None,
use_queue=True,
num_workers=20)
net.save_net(
map_func = pool.map
else:
pool = None
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)
if WORKERS != 0:
pool = Pool(WORKERS)
map_func = pool.map
else:
pool = None
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
