cls_all = [] pln_all = [] scores_all = np.empty([4, 10]) for subject in subjects: cls = np.load(source_folder + "graph_data/%s_classic_pow_post.npy" % subject).item() pln = np.load(source_folder + "graph_data/%s_plan_pow_post.npy" % subject).item() cls_all.append(cls) pln_all.append(pln) for k, band in enumerate(bands.keys()): data_cls = [] for j in range(len(cls_all)): tmp = cls_all[j][band] data_cls.append( np.asarray([bct.efficiency_wei(g) for g in tmp]).mean(axis=0)) data_pln = [] for j in range(len(pln_all)): tmp = pln_all[j][band] data_pln.append( np.asarray([bct.efficiency_wei(g) for g in tmp]).mean(axis=0)) data_cls = np.asarray(data_cls) data_pln = np.asarray(data_pln) X = np.vstack([data_cls, data_pln])
cls_all = [] pln_all = [] scores_all = np.empty([4, 10]) for subject in subjects: cls = np.load(source_folder + "graph_data/%s_classic_pow_pre.npy" % subject).item() pln = np.load(source_folder + "graph_data/%s_plan_pow_pre.npy" % subject).item() cls_all.append(cls) pln_all.append(pln) for k, band in enumerate(bands.keys()): data_cls = [] for j in range(len(cls_all)): tmp = cls_all[j][band] data_cls.append( np.asarray([bct.efficiency_wei(g) for g in tmp]).mean(axis=0)) data_pln = [] for j in range(len(pln_all)): tmp = pln_all[j][band] data_pln.append( np.asarray([bct.efficiency_wei(g) for g in tmp]).mean(axis=0)) data_cls = np.asarray(data_cls) data_pln = np.asarray(data_pln) X = np.vstack([data_cls, data_pln])
# -*- coding: utf-8 -*- """ Created on Mon Jun 13 12:50:46 2016 @author: mje """ import mne from scipy.signal import hilbert import numpy as np import sys from my_settings import (conditions, source_folder, bands) sfreq = 1000 subject = sys.argv[1] result = {} for condition in conditions: ts = np.load(source_folder + "ave_ts/%s_%s_ts-epo.npy" % (subject, condition)) for band in bands.keys(): data = mne.filter.filter_data(ts, sfreq, bands[band][0], bands[band][1]) ht_data = hilbert(data) result[band] = ht_data np.save(source_folder + "hilbert_data/%s_%s_ht-epo.npy" % (subject, condition), result)