# net = ScaleSpecificNetwork('%sair.mon.mean.levels.nc' % path_to_data, 'air',
# date(1949,1,1), date(2015,1,1), None, None, 0, dataset = "NCEP", sampling = 'monthly', anom = False)
# net = ScaleSpecificNetwork('%sERA/ERAconcat.t2m.mon.means.1958-2014.bin' % path_to_data, 't2m',
# date(1958,1,1), date(2015,1,1), None, None, None, 'monthly', anom = False, pickled = True)
# net_surrs = ScaleSpecificNetwork('%sERAconcat.t2m.mon.means.1958-2014.bin' % path_to_data, 't2m',
# date(1958,1,1), date(2015,1,1), None, None, None, 'monthly', anom = False, pickled = True)
# net = ScaleSpecificNetwork('%s20CR/20CR.t2m.mon.nc' % path_to_data, 't2m',
# date(1949,1,1), date(2011,1,1), None, None, dataset = "ERA", sampling = 'monthly', anom = False)
# net_surrs = ScaleSpecificNetwork('%sair.mon.mean.levels.nc' % path_to_data, 'air',
# date(1949,1,1), date(2015,1,1), None, None, 0, dataset = "NCEP", sampling = 'monthly', anom = False)
net = ScaleSpecificNetwork('%sECAD.tg.daily.nc' % path_to_data, 'tg', date(1950, 1, 1), date(2015,1,1), None,
None, None, dataset = 'ECA-reanalysis', anom = False)
net.get_monthly_data()
print net.data.shape
print net.get_date_from_ndx(0), net.get_date_from_ndx(-1)
# surr_field = SurrogateField()
# a = net.get_seasonality(detrend = True)
# surr_field.copy_field(net)
# net.return_seasonality(a[0], a[1], a[2])
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()
# print "computing phase conditioned on NINO3.4"
# to_do = [6, 8, 11, 15]
# for PERIOD in to_do:
# nino34_phase = load_nino34_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(1948,1,1), 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(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)
from src.data_class import DataField
from src.surrogates import get_single_FT_surrogate
from scipy.stats import pearsonr
import cPickle
# path_to_data = "/Users/nikola/work-ui/data/"
path_to_data = "/home/nikola/Work/phd/data/"
NUM_SURR = 1000
NUM_WORKERS = 20
net = ScaleSpecificNetwork('%sair.mon.mean.levels.nc' % path_to_data, 'air',
date(1948,1,1), date(2016,1,1), None, None, 0, dataset = "NCEP", sampling = 'monthly', anom = False)
pool = Pool(NUM_WORKERS)
net.wavelet(1, 'y', pool = pool, cut = 1)
net.get_continuous_phase(pool = pool)
net.get_phase_fluctuations(rewrite = True, pool = pool)
pool.close()
pool.join()
nao = DataField()
raw = np.loadtxt("%sNAO.station.monthly.1865-2016.txt" % (path_to_data))
raw = raw[:, 1:]
nao.data = raw.reshape(-1)
nao.create_time_array(date_from = date(1865, 1, 1), sampling = 'm')
nao.select_date(date(1949, 1, 1), date(2015, 1, 1))
nao.anomalise()
import h5py
import numpy as np
import pyclits as clt
from scale_network import ScaleSpecificNetwork
import matplotlib.pyplot as plt
from datetime import date
plt.style.use('ipython')
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)
t**s = ["NAO", "NINO3.4", "sunspot #", "PDO"]
with h5py.File("networks/phase_synch_eqq_bins=8_all_periods.h5") as hf:
to_do_periods = np.arange(2, 15.5, 0.5)
for period in to_do_periods:
plt.figure(figsize=(15, 7))
synch = hf['period_%.1fy' % (period)][:]
for i, tit in zip(range(synch.shape[0]), t**s):
plt.subplot(2, 2, i + 1)
vmax, vmin = np.nanmax(synch), np.nanmin(synch)
net.quick_render(field_to_plot=synch[i, ...],
tit=tit,
# import matplotlib.pyplot as plt
import src.wavelet_analysis as wvlt
import numpy as np
from src.surrogates import SurrogateField
NUM_SURR = 1000
# fname = "/Users/nikola/work-ui/data/NCEP/air.mon.mean.levels.nc"
fname = "/home/nikola/Work/phd/data/air.mon.mean.levels.nc"
net = ScaleSpecificNetwork(
fname,
"air",
date(1948, 1, 1),
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()
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' %
path_to_data = "/Users/nikola/work-ui/data"
# path_to_data = "/home/nikola/Work/phd/data"
# def _get_MI(args):
# i, j, ph1, ph2 = args
# # return i, j, MI.mutual_information(ph1, ph2, 'EQQ2', bins = 16)
# return i, j, MI.knn_mutual_information(ph1, ph2, k = 32, dualtree = True)
net = ScaleSpecificNetwork('%s/NCEP/air.mon.mean.levels.nc' % path_to_data,
'air',
date(1950, 1, 1),
date(2015, 1, 1),
None,
None,
0,
dataset="NCEP",
sampling='monthly',
anom=True)
# nao = DataField()
# raw = np.loadtxt("%s/NAO.monthly.1950-2016.txt" % (path_to_data))
# nao.data = raw[:, 2]
# nao.create_time_array(date_from = date(1950, 1, 1), sampling = 'm')
# nao.select_date(date(1950, 1, 1), date(2015, 1, 1))
# nao.anomalise()
# nao.wavelet(8, 'y', cut = 1)
# pool = Pool(5)
from scale_network import ScaleSpecificNetwork
from datetime import date
from pathos.multiprocessing import Pool
import numpy as np
from src.data_class import DataField
import csv
import matplotlib.pyplot as plt
import src.wavelet_analysis as wvlt
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"
## PHASE FLUCTUATIONS NETWORK correlation
print "Computing MI knn..."
# nino_s = clt.knn_mutual_information(ph, nino, k=128)
# sunspot_s = clt.knn_mutual_information(ph, sunspots, k=128)
# pdo_s = clt.knn_mutual_information(ph, pdo, k=128)
nao_s = clt.mutual_information(ph, nao, algorithm='EQQ2', bins=8)
nino_s = clt.mutual_information(ph, nino, algorithm='EQQ2', bins=8)
sunspot_s = clt.mutual_information(ph, sunspots, algorithm='EQQ2', bins=8)
pdo_s = clt.mutual_information(ph, pdo, algorithm='EQQ2', bins=8)
return (i, j, nao_s, nino_s, sunspot_s, pdo_s)
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]))
nino_s = clt.mutual_information(ph, nino, algorithm='EQQ2', bins=8)
sunspot_s = clt.mutual_information(ph, sunspots, algorithm='EQQ2', bins=8)
pdo_s = clt.mutual_information(ph, pdo, algorithm='EQQ2', bins=8)
return (i, j, nao_s, nino_s, sunspot_s, pdo_s)
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),
# date(1958,1,1), date(2015,1,1), None, None, None, 'monthly', anom = False, pickled = True)
# net_surrs = ScaleSpecificNetwork('%sERAconcat.t2m.mon.means.1958-2014.bin' % path_to_data, 't2m',
# date(1958,1,1), date(2015,1,1), None, None, None, 'monthly', anom = False, pickled = True)
# net = ScaleSpecificNetwork('%s20CR/20CR.t2m.mon.nc' % path_to_data, 't2m',
# date(1949,1,1), date(2011,1,1), None, None, dataset = "ERA", sampling = 'monthly', anom = False)
# net_surrs = ScaleSpecificNetwork('%sair.mon.mean.levels.nc' % path_to_data, 'air',
# date(1949,1,1), date(2015,1,1), None, None, 0, dataset = "NCEP", sampling = 'monthly', anom = False)
net = ScaleSpecificNetwork('%sECAD.tg.daily.nc' % path_to_data,
'tg',
date(1950, 1, 1),
date(2015, 1, 1),
None,
None,
None,
dataset='ECA-reanalysis',
anom=False)
net.get_monthly_data()
print net.data.shape
print net.get_date_from_ndx(0), net.get_date_from_ndx(-1)
# surr_field = SurrogateField()
# a = net.get_seasonality(detrend = True)
# surr_field.copy_field(net)
# net.return_seasonality(a[0], a[1], a[2])
def _hilbert_ssa(args):
varnorms = [True, False]
detrends = [True, False]
cos = [True, False]
for per in periods:
for var in varnorms:
for det in detrends:
for c in cos:
print(
"computing for %d period with %s for varnorm, %s for detrend and %s for cosweighting"
% (per, str(var), str(det), str(c)))
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)
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)
import numpy as np
from src.data_class import DataField
import csv
import matplotlib.pyplot as plt
import src.wavelet_analysis as wvlt
from src.surrogates import SurrogateField
NUM_SURRS = 1
# fname = '/home/nikola/Work/phd/data/air.mon.mean.sig995.nc'
fname = "/Users/nikola/work-ui/data/air.mon.mean.sig995.nc"
surrs = SurrogateField()
net = ScaleSpecificNetwork(fname, 'air', 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"
import h5py
import numpy as np
import pyclits as clt
from scale_network import ScaleSpecificNetwork
import matplotlib.pyplot as plt
from datetime import date
plt.style.use('ipython')
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)
t**s = ["NAO", "NINO3.4", "sunspot #", "PDO"]
with h5py.File("networks/phase_synch_eqq_bins=8_all_periods.h5") as hf:
to_do_periods = np.arange(2,15.5,0.5)
for period in to_do_periods:
plt.figure(figsize=(15,7))
synch = hf['period_%.1fy' % (period)][:]
for i, tit in zip(range(synch.shape[0]), t**s):
plt.subplot(2,2,i+1)
vmax, vmin = np.nanmax(synch), np.nanmin(synch)
net.quick_render(field_to_plot=synch[i, ...], tit=tit, symm=False, whole_world=True, subplot=True,
cmap='hot', vminmax=[vmin,0.8*vmax], extend = "max")
plt.suptitle("SYNCHRONIZATION SAT vs. X: %.1fyr phase [eqq 8]" % (period))
plt.savefig("networks/plots-synch/SATphase_synch%.1fyr_phase_eqq8.png" % (period), bbox_inches='tight')
from scale_network import ScaleSpecificNetwork
from datetime import date
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
methods = ['MIEQQ', 'MPC']
periods = [15, 11]
for METHOD in methods:
for PERIOD in periods:
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(3)
net.wavelet(PERIOD, get_amplitude = False, pool = pool)
pool.close()
print "wavelet on data done"
net.get_adjacency_matrix(net.phase, method = METHOD, pool = None, use_queue = True, num_workers = 3)
print "estimating adjacency matrix done"
net.save_net('networks/NCEP-SATAsurface-adjmat%s-scale%dyears.bin' % (METHOD, PERIOD), only_matrix = True)
WORKERS = 10
print "computing SAT amplitude data networks..."
to_do = [['MIEQQ', 4], ['MIEQQ', 6], ['MIEQQ', 8], ['MIEQQ', 11], ['MIEQQ', 15],
['MIGAU', 4], ['MIGAU', 6], ['MIGAU', 8], ['MIGAU', 11], ['MIGAU', 15]]
for do in to_do:
METHOD = do[0]
PERIOD = do[1]
print("computing for %d period using %s method" % (PERIOD, METHOD))
# pool.close()
# 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)
from src.data_class import DataField
from src.surrogates import get_single_FT_surrogate
from scipy.stats import pearsonr
import cPickle
# path_to_data = "/Users/nikola/work-ui/data/"
path_to_data = "/home/nikola/Work/phd/data/"
NUM_SURR = 1000
NUM_WORKERS = 20
net = ScaleSpecificNetwork('%sair.mon.mean.levels.nc' % path_to_data,
'air',
date(1948, 1, 1),
date(2015, 1, 1),
None,
None,
0,
dataset="NCEP",
sampling='monthly',
anom=False)
pool = Pool(NUM_WORKERS)
net.wavelet(1, 'y', pool=pool, cut=1)
net.get_continuous_phase(pool=pool)
net.get_phase_fluctuations(rewrite=True, pool=pool)
pool.close()
pool.join()
nao = DataField()
raw = np.loadtxt("%sWeMO.monthly.1821-2013.txt" % (path_to_data))
raw = raw[:, 1:]
# pool.close()
# for i, j, res in job_result:
# autocoherence[i, j] = res
# 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:
if anom:
g.anomalise()
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)
# pool.join()
# net.get_adjacency_matrix(net.phase, method = 'MPC', pool = None, use_queue = True, num_workers = NUM_WORKERS)
# net.save_net('networks/NCEP-SATsurface-7-8yrs-Hilb-phase-adjmat%s.bin' % ('MPC'), only_matrix = True)
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)
for per in periods:
for var in varnorms:
for det in detrends:
for c in cos:
print(
"computing for %d period with %s for varnorm, %s for detrend and %s for cosweighting"
% (per, str(var), str(det), str(c))
)
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,
)
if var:
net.get_seasonality(det)
if not var and det:
continue
if c:
net.data *= net.latitude_cos_weights()
pool = Pool(3)
import numpy as np
from src.data_class import DataField
import csv
import matplotlib.pyplot as plt
import src.wavelet_analysis as wvlt
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",
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' %
# 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)
from pathos.multiprocessing import Pool
# import matplotlib.pyplot as plt
import src.wavelet_analysis as wvlt
import numpy as np
from src.surrogates import SurrogateField
NUM_SURR = 1000
# fname = "/Users/nikola/work-ui/data/NCEP/air.mon.mean.levels.nc"
fname = "/home/nikola/Work/phd/data/air.mon.mean.levels.nc"
net = ScaleSpecificNetwork(fname,
'air',
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,
# import cPickle
# with open("networks/NCEPxERA-phase-diffs%d.bin" % (PERIOD), "wb") as f:
# 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)
import matplotlib.pyplot as plt
import src.wavelet_analysis as wvlt
from src.surrogates import SurrogateField
NUM_SURRS = 1
# fname = '/home/nikola/Work/phd/data/air.mon.mean.sig995.nc'
fname = "/Users/nikola/work-ui/data/air.mon.mean.sig995.nc"
surrs = SurrogateField()
net = ScaleSpecificNetwork(fname,
'air',
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()
import matplotlib.pyplot as plt
path_to_data = "/Users/nikola/work-ui/data"
# path_to_data = "/home/nikola/Work/phd/data"
# def _get_MI(args):
# i, j, ph1, ph2 = args
# # return i, j, MI.mutual_information(ph1, ph2, 'EQQ2', bins = 16)
# return i, j, MI.knn_mutual_information(ph1, ph2, k = 32, dualtree = True)
net = ScaleSpecificNetwork('%s/NCEP/air.mon.mean.levels.nc' % path_to_data, 'air',
date(1950,1,1), date(2015,1,1), None, None, 0, dataset = "NCEP", sampling = 'monthly', anom = True)
# nao = DataField()
# raw = np.loadtxt("%s/NAO.monthly.1950-2016.txt" % (path_to_data))
# nao.data = raw[:, 2]
# nao.create_time_array(date_from = date(1950, 1, 1), sampling = 'm')
# nao.select_date(date(1950, 1, 1), date(2015, 1, 1))
# nao.anomalise()
# nao.wavelet(8, 'y', cut = 1)
# pool = Pool(5)
# net.wavelet(8, 'y', pool = pool, cut = 1)
# print nao.phase.shape, net.phase.shape
from scale_network import ScaleSpecificNetwork
from datetime import date
import numpy as np
import cPickle
from src.surrogates import get_p_vals
net = ScaleSpecificNetwork('/Users/nikola/work-ui/data/NCEP/air.mon.mean.levels.nc', 'air',
date(1949,1,1), date(2015,1,1), None, None, 0, sampling = 'monthly', anom = False)
# net.quick_render(t = 0)
# net = ScaleSpecificNetwork('/Users/nikola/work-ui/data/ERA/ERAconcat.t2m.mon.means.1958-2014.bin', 't2m',
# date(1958,1,1), date(2015,1,1), None, None, None, 'monthly', anom = False, pickled = True)
# net = ScaleSpecificNetwork('/Users/nikola/work-ui/data/ECAD.tg.daily.nc', 'tg', date(1950, 1, 1), date(2015,1,1), None,
# None, None, dataset = 'ECA-reanalysis', anom = False)
# net.get_monthly_data()
print net.data.shape
print net.get_date_from_ndx(0), net.get_date_from_ndx(-1)
with open("../scale-nets/bins/WeMO-JFM_ann_means-NCEP-phase-fluc-1000FTsurrs-from-indices.bin", "rb") as f:
surr_res = cPickle.load(f)
# INDICES = ['TNA', 'SOI', 'SCAND', 'PNA', 'PDO', 'EA', 'AMO', 'NAO', 'NINO3.4', 'TPI', 'SAM']
P_VAL = 0.05
data_corrs = surr_res['data']
surr_corrs = surr_res['surrs']
