def setup_module(module):
global tvfcg_plv_ts
global tvfcg_plv_fcgs
global tvfcg_pac_plv_fcgs
original_data = np.load("../examples/data/eeg_32chans_10secs.npy")
# TVFCGS with PLV
data = original_data[0:2, 0:1024]
fb = [1.0, 4.0]
fs = 128
estimator = PLV(fb, fs)
tvfcg_plv_fcgs = tvfcg(data, estimator, fb, fs)
# TVFCGS with PAC and PLV
data = original_data[..., 0:1024]
fb = [1.0, 4.0]
fs = 128
f_lo = fb
f_hi = [20.0, 30.0]
estimator = PLV(fb, fs)
pac = PAC(f_lo, f_hi, fs, estimator)
tvfcg_pac_plv_fcgs = tvfcg_cfc(data, pac, f_lo, f_hi, fs)
# TVFCGS with PLV (ts)
fb = [1.0, 4.0]
fs = 128.0
estimator = PLV(fb, fs)
u_phases = estimator.preprocess(data)
ts, avg = estimator.estimate(u_phases)
tvfcg_plv_ts = tvfcg_ts(ts, [1.0, 4.0], 128, avg_func=estimator.mean)
def try_TVFCG(data):
fb = [1.0, 4.0]
fs = 128
pairs = None
n_jobs = 1
cc = 2.0
step = 5
pli = PLI(fb, fs, pairs)
fcgs = tvfcg(data, pli, fb, fs, cc, step)
return fcgs
now = time.time()
ts, avg = plv(data, fb, fs)
print("Finished in", time.time() - now, "sec")
# TVFCGs from time seriess
now = time.time()
fcgs = tvfcg_ts(ts, [1.0, 4.0], 128)
print("Finished in", time.time() - now, "sec")
# TVFCGs
fb = [1.0, 4.0]
fs = 128.0
estimator = PLV(fb, fs)
fcgs = tvfcg(data, estimator, fb, fs)
# PAC
lo = [1.0, 4.0]
hi = [8.0, 13.0]
fs = 128.0
estimator = PLV(fb, fs)
now = time.time()
cfc_ts, cfc_avg = pac(data, lo, hi, fs, estimator)
print("Finished in", time.time() - now, "sec")
# TVFCGs + PAC
pac_estimator = PAC(lo, hi, fs, estimator)
now = time.time()