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)
# tvfcg_plv_fcgs = np.real(tvfcg_plv_fcgs)
# np.save("data/test_tvfcgs_plv.npy", tvfcg_plv_fcgs)
# 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)
def test_pac_multiple_channels():
data = np.load("../examples/data/eeg_32chans_10secs.npy")
fs = 128
fb_lo = [1.0, 4.0]
fb_hi = [
20.0,
30.0,
]
estimator = PLV(fb_lo, fs)
ts, avg = pac(data, fb_lo, fb_hi, 128, estimator)
ts = np.nan_to_num(ts)
avg = np.nan_to_num(avg)
expected_ts = np.load("data/test_pac_plv_ts.npy")
expected_ts = np.nan_to_num(ts)
np.testing.assert_array_equal(ts, expected_ts)
avg = np.float32(avg)
expected_avg = np.load("data/test_pac_plv_avg.npy")
expected_avg = np.nan_to_num(expected_avg)
expected_avg = np.float32(expected_avg)
np.testing.assert_allclose(avg, expected_avg, rtol=1e-10, atol=0.0)
def try_TVFCG_PAC(data):
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)
fcgs = tvfcg_cfc(data, pac, f_lo, f_hi, fs)
return fcgs
def test_pac_one_channel():
data = np.load("../examples/data/eeg_32chans_10secs.npy")
data = data[0:1, 0:128]
fs = 128
f_lo = [1.0, 4.0]
f_hi = [20.0, 30.0]
estimator = PLV(f_lo, fs)
ts, avg = pac(data, f_lo, f_hi, fs, estimator)
avg = np.squeeze(np.real(avg))
expected = 0.468296707219
nose.tools.assert_almost_equal(avg, expected)
def try_PAC(data):
fb = [1.0, 4.0]
fs = 128
pairs = None
n_jobs = 1
plv = PLV(fb, fs, pairs)
f_lo = [1.0, 4.0]
f_hi = [20.0, 30.0]
pac = PAC(f_lo, f_hi, fs, plv)
phases, phases_lohi = pac.preprocess(data)
cfc, avg = pac.estimate(phases, phases_lohi)
return cfc, avg
def test_pac_multiple_channels():
data = np.load("../examples/data/eeg_32chans_10secs.npy")
fs = 128
fb_lo = [1.0, 4.0]
fb_hi = [
20.0,
30.0,
]
estimator = PLV(fb_lo, fs)
ts, avg = pac(data, fb_lo, fb_hi, 128, estimator)
ts = np.real(ts)
expected_ts = np.load("data/test_pac_plv_ts.npy")
expected_ts = np.real(ts)
np.testing.assert_array_equal(ts, expected_ts)
expected_avg = np.load("data/test_pac_plv_avg.npy")
np.testing.assert_array_equal(avg, expected_avg)
fs = 128.0
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)
import numpy as np
np.set_printoptions(precision=3, linewidth=256)
from dyfunconn.fc import pac, PAC, PLV
if __name__ == "__main__":
data = np.load(
"/home/makism/Github/dyfunconn/examples/data/eeg_32chans_10secs.npy")
#
# Common configuration
#
fb = [1.0, 4.0]
fs = 128
pairs = None
estimator = PLV(fb, fs, pairs)
f_lo = [1.0, 4.0]
f_hi = [20.0, 30.0]
#
# Functional
#
cfc, avg = pac(data, f_lo, f_hi, fs, estimator, pairs=None)
print(avg)
#
# Object-oriented
#
pac = PAC(f_lo, f_hi, fs, estimator)