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()