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(
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)
## autocoherence filtered data - SAT
print "computing autocoherence for SAT filtered data"
for PERIOD in periods:
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()
jfm_index = nao.select_months([1,2,3], apply_to_data = False)
jfm_nao = nao.data[jfm_index]
_, _, y = nao.extract_day_month_year()
y = y[jfm_index]
ann_nao = []
for year in np.unique(y):
ann_nao.append(np.mean(jfm_nao[np.where(year == y)[0]]))
ann_nao = np.array(ann_nao)
ann_phase_fluc = np.zeros([ann_nao.shape[0]] + list(net.get_spatial_dims()))
for lat in range(net.lats.shape[0]):
for lon in range(net.lons.shape[0]):
jfm_data = net.phase[jfm_index, lat, lon]
for i, year in zip(range(np.unique(y).shape[0]), np.unique(y)):
ann_phase_fluc[i, lat, lon] = np.mean(jfm_data[np.where(year == y)[0]])
corrs = np.zeros_like(net.data[0, ...])
for lat in range(net.lats.shape[0]):
for lon in range(net.lons.shape[0]):
corrs[lat, lon] = pearsonr(ann_nao, ann_phase_fluc[:, lat, lon])[0]
def _corrs_surrs_ind(args):
nao_surr = nao.copy()
nao_surr.data = get_single_FT_surrogate(nao.data)
nao.data = raw.reshape(-1)
nao.create_time_array(date_from=date(1821, 1, 1), sampling='m')
nao.select_date(date(1949, 1, 1), date(2014, 1, 1))
nao.anomalise()
jfm_index = nao.select_months([1, 2, 3], apply_to_data=False)
jfm_nao = nao.data[jfm_index]
_, _, y = nao.extract_day_month_year()
y = y[jfm_index]
ann_nao = []
for year in np.unique(y):
ann_nao.append(np.mean(jfm_nao[np.where(year == y)[0]]))
ann_nao = np.array(ann_nao)
ann_phase_fluc = np.zeros([ann_nao.shape[0]] + list(net.get_spatial_dims()))
for lat in range(net.lats.shape[0]):
for lon in range(net.lons.shape[0]):
jfm_data = net.phase[jfm_index, lat, lon]
for i, year in zip(range(np.unique(y).shape[0]), np.unique(y)):
ann_phase_fluc[i, lat,
lon] = np.mean(jfm_data[np.where(year == y)[0]])
corrs = np.zeros_like(net.data[0, ...])
for lat in range(net.lats.shape[0]):
for lon in range(net.lons.shape[0]):
corrs[lat, lon] = pearsonr(ann_nao, ann_phase_fluc[:, lat, lon])[0]
def _corrs_surrs_ind(args):
nao_surr = nao.copy()
