# 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)
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 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",
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' %
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,
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:]
# 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):