pool.join()
# index_correlations = {}
# index_datas = {}
# # SURROGATES
# for index, ndx_type, start_date, end_year in zip(INDICES, DATE_TYPE, START_DATES, END_YEARS):
# load index
# print index
# if index != 'NINO3.4':
index_data = DataField()
raw = np.loadtxt("%sNAO.station.monthly.1865-2016.txt" % (path_to_data))
raw = raw[:, 1:]
index_data.data = raw.reshape(-1)
index_data.create_time_array(date_from = date(1865, 1, 1), sampling = 'm')
index_data.select_date(date(1951, 1, 1), date(2014, 1, 1))
index_data.anomalise()
index_correlations = get_corrs(net, index_data)
# with open("20CRtemp-phase-fluct-corr-with-%sindex-1950-2014.bin" % index, "wb") as f:
# cPickle.dump({('%scorrs' % index) : index_correlations[index].reshape(np.prod(index_correlations[index].shape))}, f)
# # plotting
# tit = ("ECA&D annual phase SSA RC fluctuations x %s correlations" % index)
# fname = ("../scale-nets/ECAD-SAT-annual-phase-fluc-SSA-RC-%scorrs.png" % index)
# net.quick_render(field_to_plot = index_correlations[index], tit = tit, symm = True, whole_world = False, fname = fname)
# def _corrs_surrs(args):
# index_correlations_surrs = {}
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:]
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]):
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()
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]):
pool.join()
# index_correlations = {}
# index_datas = {}
# # SURROGATES
# for index, ndx_type, start_date, end_year in zip(INDICES, DATE_TYPE, START_DATES, END_YEARS):
# load index
# print index
# if index != 'NINO3.4':
index_data = DataField()
raw = np.loadtxt("%sNAO.station.monthly.1865-2016.txt" % (path_to_data))
raw = raw[:, 1:]
index_data.data = raw.reshape(-1)
index_data.create_time_array(date_from=date(1865, 1, 1), sampling='m')
index_data.select_date(date(1951, 1, 1), date(2014, 1, 1))
index_data.anomalise()
index_correlations = get_corrs(net, index_data)
# with open("20CRtemp-phase-fluct-corr-with-%sindex-1950-2014.bin" % index, "wb") as f:
# cPickle.dump({('%scorrs' % index) : index_correlations[index].reshape(np.prod(index_correlations[index].shape))}, f)
# # plotting
# tit = ("ECA&D annual phase SSA RC fluctuations x %s correlations" % index)
# fname = ("../scale-nets/ECAD-SAT-annual-phase-fluc-SSA-RC-%scorrs.png" % index)
# net.quick_render(field_to_plot = index_correlations[index], tit = tit, symm = True, whole_world = False, fname = fname)
# def _corrs_surrs(args):
# index_correlations_surrs = {}
# surr_field.construct_fourier_surrogates()
