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)
