for PERIOD in periods:
for AVG in avg_to:
print("computing for %d year period and averaging up to %d" %
(PERIOD, 12 * AVG * PERIOD))
net = ScaleSpecificNetwork(
'/home/nikola/Work/phd/data/air.mon.mean.levels.nc',
'air',
date(1948, 1, 1),
date(2014, 1, 1),
None,
None,
0,
'monthly',
anom=True)
pool = Pool(WORKERS)
net.wavelet(PERIOD, get_amplitude=True, pool=pool)
print "wavelet on data done"
net.get_filtered_data(pool=pool)
print "filtered data acquired"
autocoherence = np.zeros(net.get_spatial_dims())
job_args = [(i, j, int(AVG * 12 * PERIOD), net.filtered_data[:, i,
j])
for i in range(net.lats.shape[0])
for j in range(net.lons.shape[0])]
job_result = pool.map(_get_autocoherence, job_args)
del job_args
pool.close()
for i, j, res in job_result:
autocoherence[i, j] = res
del job_result
print("computing phase conditioned on NAO")
nao_phase = load_NAOindex_wavelet_phase(date(1950, 1, 1), date(2014, 1, 1),
period, False)
net = ScaleSpecificNetwork(
'/home/nikola/Work/phd/data/air.mon.mean.levels.nc',
'air',
date(1950, 1, 1),
date(2014, 1, 1),
None,
None,
level=0,
dataset="NCEP",
sampling='monthly',
anom=False)
pool = Pool(WORKERS)
net.wavelet(period, period_unit="y", pool=pool, cut=2)
print("wavelet on data done")
pool.close()
pool.join()
net.get_adjacency_matrix_conditioned(nao_phase,
use_queue=True,
num_workers=WORKERS)
print("estimating adjacency matrix done")
net.save_net('networks/NCEP-SAT%dy-phase-adjmatCMIEQQcondNAOphase.bin' %
(period),
only_matrix=True)
print("computing phase conditioned on NINO")
nino_phase = load_nino34_wavelet_phase(date(1950, 1, 1), date(2014, 1, 1),
period, False)
net.get_adjacency_matrix_conditioned(nino_phase,
fname = '/home/nikola/Work/phd/data/air.mon.mean.sig995.nc'
# fname = "/Users/nikola/work-ui/data/air.mon.mean.sig995.nc"
## PHASE FLUCTUATIONS NETWORK L2 dist.
print "Computing L2 distance..."
net = ScaleSpecificNetwork(fname,
'air',
date(1950, 1, 1),
date(2016, 1, 1),
None,
None,
None,
'monthly',
anom=False)
pool = Pool(20)
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.phase_fluctuations -= np.nanmean(net.phase_fluctuations, axis=0)
net.get_adjacency_matrix(net.phase_fluctuations,
method="L2",
pool=None,
use_queue=True,
num_workers=20)
net.save_net('networks/NCEP-SATannual-phase-fluctuations-adjmatL2.bin',
only_matrix=True)
print "L2 done"
date(2016, 1, 1),
[-60, 0],
[40, 100],
level=0,
dataset="NCEP",
sampling="monthly",
anom=False,
)
surrs = SurrogateField()
a = net.get_seasonality(detrend=True)
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()
for method in METHODS:
for scale in SCALES:
print("Computing networks using %s method..." % (method))
# phase
if method in ['MIEQQ', 'MIGAU', 'MPC']:
# net = ScaleSpecificNetwork(fname, 'air', date(1948,1,1), date(2016,1,1), None, None, level = 0, dataset = "NCEP",
# sampling = 'monthly', anom = False)
net = ScaleSpecificNetwork(fname, 't2m', date(1958,1,1), date(2014,1,1), None, None, level=None, pickled=True,
sampling='monthly', anom=False)
pool = Pool(NUM_WORKERS)
# net.get_hilbert_phase_amp(period = 90, width = 12, pool = pool, cut = 1)
net.wavelet(scale, period_unit='m', cut=2, pool=pool)
pool.close()
pool.join()
net.get_adjacency_matrix(net.phase, method = method, pool = None, use_queue = True, num_workers = NUM_WORKERS)
net.save_net('networks/ERA-SATsurface-scale%dmonths-phase-adjmat%s.bin' % (scale, method), only_matrix = True)
# amplitude
if method in ['MIEQQ', 'MIGAU', 'CORR']:
# net = ScaleSpecificNetwork(fname, 'air', date(1948,1,1), date(2016,1,1), None, None, level = 0, dataset = "NCEP",
# sampling = 'monthly', anom = False)
net = ScaleSpecificNetwork(fname, 't2m', date(1958,1,1), date(2014,1,1), None, None, level=None, pickled=True,
sampling='monthly', anom=False)
pool = Pool(NUM_WORKERS)
# net.get_hilbert_phase_amp(period = 90, width = 12, pool = pool, cut = 1)
net.wavelet(scale, period_unit='m', cut=2, pool=pool)
pool.close()
import matplotlib.pyplot as plt
import numpy as np
WORKERS = 10
print "computing SAT wavelet coherence..."
to_do = [['WCOH', 4], ['WCOH', 6], ['WCOH', 8], ['WCOH', 11], ['WCOH', 15]]
for do in to_do:
METHOD = do[0]
PERIOD = do[1]
print("computing for %d period using %s method" % (PERIOD, METHOD))
net = ScaleSpecificNetwork('/home/nikola/Work/phd/data/air.mon.mean.levels.nc', 'air',
date(1948,1,1), date(2014,1,1), None, None, 0, 'monthly', anom = False)
pool = Pool(WORKERS)
net.wavelet(PERIOD, get_amplitude = False, save_wavelet = True, pool = pool)
print "wavelet on data done"
pool.close()
net.get_adjacency_matrix(net.wave, method = METHOD, pool = None, use_queue = True, num_workers = WORKERS)
print "estimating adjacency matrix done"
net.save_net('networks/NCEP-SATsurface-wave-adjmat%s-scale%dyears.bin' % (METHOD, PERIOD), only_matrix = True)
print "computing SATA wavelet coherence..."
to_do = [['WCOH', 4], ['WCOH', 6], ['WCOH', 8], ['WCOH', 11], ['WCOH', 15]]
for do in to_do:
METHOD = do[0]
PERIOD = do[1]
print("computing for %d period using %s method" % (PERIOD, METHOD))
net = ScaleSpecificNetwork('/home/nikola/Work/phd/data/air.mon.mean.levels.nc', 'air',
# del job_result
# with open("networks/NCEP-SATAsurface-autocoherence-phase-scale%dyears-avg-to-%.1f.bin" % (PERIOD, AVG), "wb") as f:
# cPickle.dump({'autocoherence' : autocoherence, 'lats' : net.lats, 'lons' : net.lons}, f, protocol = cPickle.HIGHEST_PROTOCOL)
if not PLOT:
## autocoherence filtered data - SATA
print "computing autocoherence for SATA filtered data"
for PERIOD in periods:
for AVG in avg_to:
print("computing for %d year period and averaging up to %d" % (PERIOD, 12*AVG*PERIOD))
net = ScaleSpecificNetwork('/home/nikola/Work/phd/data/air.mon.mean.levels.nc', 'air',
date(1948,1,1), date(2014,1,1), None, None, 0, 'monthly', anom = True)
pool = Pool(WORKERS)
net.wavelet(PERIOD, get_amplitude = True, pool = pool)
print "wavelet on data done"
net.get_filtered_data(pool = pool)
print "filtered data acquired"
autocoherence = np.zeros(net.get_spatial_dims())
job_args = [ (i, j, int(AVG*12*PERIOD), net.filtered_data[:, i, j]) for i in range(net.lats.shape[0]) for j in range(net.lons.shape[0]) ]
job_result = pool.map(_get_autocoherence, job_args)
del job_args
pool.close()
for i, j, res in job_result:
autocoherence[i, j] = res
del job_result
with open("networks/NCEP-SATAsurface-autocoherence-filtered-scale%dyears-avg-to-%.1f.bin" % (PERIOD, AVG), "wb") as f:
cPickle.dump({'autocoherence' : autocoherence, 'lats' : net.lats, 'lons' : net.lons}, f, protocol = cPickle.HIGHEST_PROTOCOL)
for do in to_do:
METHOD = do[0]
PERIOD = do[1]
print("computing for %d period using %s method" % (PERIOD, METHOD))
net = ScaleSpecificNetwork(
'/home/nikola/Work/phd/data/air.mon.mean.levels.nc',
'air',
date(1948, 1, 1),
date(2014, 1, 1),
None,
None,
0,
'monthly',
anom=False)
pool = Pool(WORKERS)
net.wavelet(PERIOD, get_amplitude=False, save_wavelet=True, pool=pool)
print "wavelet on data done"
pool.close()
net.get_adjacency_matrix(net.wave,
method=METHOD,
pool=None,
use_queue=True,
num_workers=WORKERS)
print "estimating adjacency matrix done"
net.save_net('networks/NCEP-SATsurface-wave-adjmat%s-scale%dyears.bin' %
(METHOD, PERIOD),
only_matrix=True)
print "computing SATA wavelet coherence..."
to_do = [['WCOH', 4], ['WCOH', 6], ['WCOH', 8], ['WCOH', 11], ['WCOH', 15]]
for do in to_do:
date(1948, 1, 1),
date(2016, 1, 1),
None,
None,
level=0,
dataset="NCEP",
sampling='monthly',
anom=False)
surrs = SurrogateField()
a = net.get_seasonality(detrend=True)
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=20,
pool=None,
use_queue=True)
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)
g.wavelet(period, period_unit="y", cut=2)
return g.phase.copy()
WORKERS = 20
to_do_periods = [4, 6, 8, 11, 15]
for period in to_do_periods:
print("computing phase conditioned on NAO")
nao_phase = load_NAOindex_wavelet_phase(date(1950,1,1), date(2014,1,1), period, False)
net = ScaleSpecificNetwork('/home/nikola/Work/phd/data/air.mon.mean.levels.nc',
'air', date(1950,1,1), date(2014,1,1), None, None,
level = 0, dataset="NCEP", sampling='monthly', anom=False)
pool = Pool(WORKERS)
net.wavelet(period, period_unit="y", pool=pool, cut=2)
print("wavelet on data done")
pool.close()
pool.join()
net.get_adjacency_matrix_conditioned(nao_phase, use_queue=True, num_workers=WORKERS)
print("estimating adjacency matrix done")
net.save_net('networks/NCEP-SAT%dy-phase-adjmatCMIEQQcondNAOphase.bin' % (period), only_matrix=True)
print("computing phase conditioned on NINO")
nino_phase = load_nino34_wavelet_phase(date(1950,1,1), date(2014,1,1), period, False)
net.get_adjacency_matrix_conditioned(nino_phase, use_queue=True, num_workers=WORKERS)
print("estimating adjacency matrix done")
net.save_net('networks/NCEP-SAT%dy-phase-adjmatCMIEQQcondNINOphase.bin' % (period), only_matrix=True)
print("computing phase conditioned on sunspots")
sunspot_phase = load_sunspot_number_phase(date(1950,1,1), date(2014,1,1), period, False)
None,
0,
"monthly",
anom=True,
)
if var:
net.get_seasonality(det)
if not var and det:
continue
if c:
net.data *= net.latitude_cos_weights()
pool = Pool(3)
net.wavelet(per, get_amplitude=True, pool=pool)
net.get_filtered_data(pool=pool)
pool.close()
fname = "filt-data/SATA-1000hPa-filtered%dperiod-%svarnorm-%sdetrend-%scosweighting.bin" % (
per,
"" if var else "NO",
"" if det else "NO",
"" if c else "NO",
)
with open(fname, "wb") as f:
cPickle.dump(
{"filt. data": net.filtered_data, "lats": net.lats, "lons": net.lons, "time": net.time},
f,
protocol=cPickle.HIGHEST_PROTOCOL,
_, nao_ph, sg_nao, a_nao = load_NAOindex_wavelet_phase(date(1950, 1, 1),
date(2014, 1, 1),
period,
anom=False)
_, nino_ph, sg_nino, a_nino = load_nino34_wavelet_phase(date(1950, 1, 1),
date(2014, 1, 1),
period,
anom=False)
_, sunspots_ph, sg_sunspots, a_sunspots = load_sunspot_number_phase(
date(1950, 1, 1), date(2014, 1, 1), period, anom=False)
_, pdo_ph, sg_pdo, a_pdo = load_pdo_phase(date(1950, 1, 1),
date(2014, 1, 1),
period,
anom=False)
pool = Pool(WORKERS)
net.wavelet(period, period_unit='y', cut=2, pool=pool)
args = [(net.phase[:, i, j], i, j, nao_ph, nino_ph, sunspots_ph, pdo_ph)
for i in range(net.lats.shape[0])
for j in range(net.lons.shape[0])]
result = pool.map(_compute_MI_synch, args)
synchs = np.zeros((4, net.lats.shape[0], net.lons.shape[0]))
synchs_surrs = np.zeros(
(NUM_SURRS, 4, net.lats.shape[0], net.lons.shape[0]))
for i, j, naos, ninos, suns, pdos in result:
synchs[0, i, j] = naos
synchs[1, i, j] = ninos
synchs[2, i, j] = suns
synchs[3, i, j] = pdos
for surr in range(NUM_SURRS):
sg_nao.construct_fourier_surrogates(algorithm='FT')
sg_nao.add_seasonality(a_nao[0], a_nao[1], a_nao[2])
WORKERS = 5
NUM_SURRS = 100
to_do_periods = np.arange(2,15.5,0.5)
net = ScaleSpecificNetwork('/Users/nikola/work-ui/data/NCEP/air.mon.mean.levels.nc',
'air', date(1950,1,1), date(2014,1,1), None, None,
level = 0, dataset="NCEP", sampling='monthly', anom=False)
synchronization = {}
for period in to_do_periods:
print("running for %.1f period..." % (period))
_, nao_ph, sg_nao, a_nao = load_NAOindex_wavelet_phase(date(1950,1,1), date(2014,1,1), period, anom=False)
_, nino_ph, sg_nino, a_nino = load_nino34_wavelet_phase(date(1950,1,1), date(2014,1,1), period, anom=False)
_, sunspots_ph, sg_sunspots, a_sunspots = load_sunspot_number_phase(date(1950,1,1), date(2014,1,1), period, anom=False)
_, pdo_ph, sg_pdo, a_pdo = load_pdo_phase(date(1950,1,1), date(2014,1,1), period, anom=False)
pool = Pool(WORKERS)
net.wavelet(period, period_unit='y', cut=2, pool=pool)
args = [(net.phase[:, i, j], i, j, nao_ph, nino_ph, sunspots_ph, pdo_ph) for i in range(net.lats.shape[0]) for j in range(net.lons.shape[0])]
result = pool.map(_compute_MI_synch, args)
synchs = np.zeros((4, net.lats.shape[0], net.lons.shape[0]))
synchs_surrs = np.zeros((NUM_SURRS, 4, net.lats.shape[0], net.lons.shape[0]))
for i, j, naos, ninos, suns, pdos in result:
synchs[0, i, j] = naos
synchs[1, i, j] = ninos
synchs[2, i, j] = suns
synchs[3, i, j] = pdos
for surr in range(NUM_SURRS):
sg_nao.construct_fourier_surrogates(algorithm='FT')
sg_nao.add_seasonality(a_nao[0], a_nao[1], a_nao[2])
sg_nao.wavelet(period, period_unit="y", cut=2)
sg_nino.construct_fourier_surrogates(algorithm='FT')
sg_nino.add_seasonality(a_nino[0], a_nino[1], a_nino[2])
None,
None,
0,
'monthly',
anom=True)
if var:
net.get_seasonality(det)
if not var and det:
continue
if c:
net.data *= net.latitude_cos_weights()
pool = Pool(3)
net.wavelet(per, get_amplitude=True, pool=pool)
net.get_filtered_data(pool=pool)
pool.close()
fname = (
"filt-data/SATA-1000hPa-filtered%dperiod-%svarnorm-%sdetrend-%scosweighting.bin"
% (per, '' if var else 'NO', '' if det else 'NO',
'' if c else 'NO'))
with open(fname, 'wb') as f:
cPickle.dump(
{
'filt. data': net.filtered_data,
'lats': net.lats,
'lons': net.lons,
'time': net.time
# cPickle.dump({'mean_phase_diff' : np.mean(phase_diffs, axis = 0).flatten(),
# 'std_phase_diff' : np.std(phase_diffs, axis = 0, ddof = 1).flatten(),
# 'var_phase_diff' : np.var(phase_diffs, axis = 0, ddof = 1).flatten()}, f, protocol = cPickle.HIGHEST_PROTOCOL)
to_do = [['MIGAU', 8], ['MIGAU', 6]]
for do in to_do:
METHOD = do[0]
PERIOD = do[1]
print("computing for %d period using %s method" % (PERIOD, METHOD))
net = ScaleSpecificNetwork('/home/nikola/Work/phd/data/air.mon.mean.levels.nc', 'air',
date(1958,1,1), date(2014,1,1), None, None, 0, 'monthly', anom = True)
pool = Pool(WORKERS)
net.wavelet(PERIOD, get_amplitude = False, pool = pool)
print "wavelet on data done"
pool.close()
net.get_adjacency_matrix(net.phase, method = METHOD, pool = None, use_queue = True, num_workers = WORKERS)
print "estimating adjacency matrix done"
net.save_net('networks/NCEP-SATAsurface-phase-span-as-ERA-adjmat%s-scale%dyears.bin' % (METHOD, PERIOD), only_matrix = True)
# print phase_diffs.shape
# net.get_adjacency_matrix(phase_diffs, method = METHOD, pool = None, use_queue = True, num_workers = WORKERS)
# net.save_net('networks/NCEP-ERA-phase-diff-adjmat%s-scale%dyears.bin' % (METHOD, PERIOD), only_matrix = True)
# net.get_adjacency_matrix(net.phase, method = METHOD, pool = None, use_queue = True, num_workers = WORKERS)
# print "estimating adjacency matrix done"
# net.save_net('networks/ERA-SATAsurface-phase-adjmat%s-scale%dyears.bin' % (METHOD, PERIOD), only_matrix = True)
# net.get_adjacency_matrix_conditioned(nino34_phase, use_queue = True, num_workers = WORKERS)
# print "estimating adjacency matrix done"
# net.save_net('networks/NCEP-SAT%dy-phase-adjmatCMIEQQcondNINOphase.bin' % (PERIOD), only_matrix = True)
print "computing phase conditioned on NAO"
to_do = [4, 6, 11, 15]
for PERIOD in to_do:
nao_phase = load_NAOindex_wavelet_phase(date(1948, 1, 1), date(2014, 1, 1),
True)
net = ScaleSpecificNetwork(
'/home/nikola/Work/phd/data/air.mon.mean.levels.nc',
'air',
date(1950, 1, 1),
date(2014, 1, 1),
None,
None,
0,
'monthly',
anom=False)
pool = Pool(WORKERS)
net.wavelet(PERIOD, get_amplitude=False, pool=pool)
print "wavelet on data done"
pool.close()
net.get_adjacency_matrix_conditioned(nao_phase,
use_queue=True,
num_workers=WORKERS)
print "estimating adjacency matrix done"
net.save_net('networks/NCEP-SAT%dy-phase-adjmatCMIEQQcondNAOphase.bin' %
(PERIOD),
only_matrix=True)
def _hilbert_ssa(args):
i, j, data = args
if not np.any(np.isnan(data)):
ssa = ssa_class(data, M = 12)
_, _, _, rc = ssa.run_ssa()
real_part = rc[:, 0] + rc[:, 1]
imag_part = np.imag(hilbert(real_part))
phase_hilb_rc = np.arctan2(imag_part[12:-12], real_part[12:-12])
else:
phase_hilb_rc = np.nan
return i, j, phase_hilb_rc
pool = Pool(NUM_WORKERS)
net.wavelet(1, 'y', pool = pool, cut = 1)
# Hilbert on RC SSA fluctuations
# args = [ (i, j, net.data[:, i, j]) for i in range(net.lats.shape[0]) for j in range(net.lons.shape[0]) ]
# results = pool.map(_hilbert_ssa, args)
# for i, j, res in results:
# net.phase[:, i, j] = res
net.get_continuous_phase(pool = pool)
net.get_phase_fluctuations(rewrite = True, pool = pool)
pool.close()
pool.join()
# index_correlations = {}
# index_datas = {}
SCALES = np.arange(24, 186, 6) # 2 - 15yrs, 0.5yr step, in months
METHODS = ['MIEQQ', 'MIKNN']
for method in METHODS:
for scale in SCALES:
print("Computing networks for %d month scale using %s method..." % (scale, method))
# phase
net = ScaleSpecificNetwork(fname, 'air', date(1948,1,1), date(2016,1,1), None, None, level = 0, dataset = "NCEP",
sampling = 'monthly', anom = False)
pool = Pool(NUM_WORKERS)
net.wavelet(scale, 'm', pool = pool, cut = 1)
pool.close()
pool.join()
net.get_adjacency_matrix(net.phase, method = method, pool = None, use_queue = True, num_workers = NUM_WORKERS)
net.save_net('networks/NCEP-SATsurface-scale%dmonths-phase-adjmat%s.bin' % (scale, method), only_matrix = True)
# amplitude
net = ScaleSpecificNetwork(fname, 'air', date(1948,1,1), date(2016,1,1), None, None, level = 0, dataset = "NCEP",
sampling = 'monthly', anom = False)
pool = Pool(NUM_WORKERS)
net.wavelet(scale, 'm', pool = pool, cut = 1)
pool.close()
pool.join()
net.get_adjacency_matrix(net.amplitude, method = method, pool = None, use_queue = True, num_workers = NUM_WORKERS)
net.save_net('networks/NCEP-SATsurface-scale%dmonths-amplitude-adjmat%s.bin' % (scale, method), only_matrix = True)
