def test_PSD(epochs):
"""
Test PSD
"""
fmin = 10
fmax = 13
psd_tuple = analyses.pow(epochs.epo1,
fmin,
fmax,
n_fft=256,
n_per_seg=None,
epochs_average=True)
psd = psd_tuple.psd
freq_list = psd_tuple.freq_list
assert type(psd) == np.ndarray
assert psd.shape == (len(epochs.epo1.info['ch_names']), len(freq_list))
psd_tuple = analyses.pow(epochs.epo1,
fmin,
fmax,
n_fft=256,
n_per_seg=None,
epochs_average=False)
psd = psd_tuple.psd
assert psd.shape == (len(epochs.epo1), len(epochs.epo1.info['ch_names']),
len(freq_list))
def test_stats(epochs):
"""
Test stats
"""
# with PSD from Epochs with random values
random_r1 = utils.generate_random_epoch(epochs.epo1, mu=0, sigma=0.01)
random_r2 = utils.generate_random_epoch(epochs.epo2, mu=4, sigma=0.01)
fmin = 10
fmax = 13
psd_tuple = analyses.pow(random_r1,
fmin,
fmax,
n_fft=256,
n_per_seg=None,
epochs_average=False)
psd = psd_tuple.psd
statsCondTuple = stats.statsCond(psd, random_r1, 3000, 0.05, 0.05)
assert statsCondTuple.T_obs.shape[0] == len(epochs.epo1.info['ch_names'])
for i in range(0, len(statsCondTuple.p_values)):
assert statsCondTuple.p_values[i] <= statsCondTuple.adj_p[1][i]
assert statsCondTuple.T_obs_plot.shape[0] == len(
epochs.epo1.info['ch_names'])
psd_tuple2 = analyses.pow(random_r2,
fmin,
fmax,
n_fft=256,
n_per_seg=None,
epochs_average=False)
psd2 = psd_tuple2.psd
freq_list = psd_tuple2.freq_list
data = [psd, psd2]
con_matrixTuple = stats.con_matrix(random_r1, freq_list, draw=False)
statscondClusterTuple = stats.statscondCluster(
data,
freq_list,
scipy.sparse.bsr_matrix(con_matrixTuple.ch_con_freq),
tail=0,
n_permutations=3000,
alpha=0.05)
assert statscondClusterTuple.F_obs.shape[0] == len(
epochs.epo1.info['ch_names'])
for i in range(0, len(statscondClusterTuple.clusters)):
assert len(statscondClusterTuple.clusters[i]) < len(
epochs.epo1.info['ch_names'])
assert statscondClusterTuple.cluster_p_values.shape[0] == len(
statscondClusterTuple.clusters)
assert np.mean(statscondClusterTuple.cluster_p_values) != float(0)
def test_stats(epochs):
"""
Test stats
"""
fmin = 10
fmax = 13
psd_tuple = analyses.pow(epochs.epo1,
fmin,
fmax,
n_fft=256,
n_per_seg=None,
epochs_average=False)
psd = psd_tuple.psd
statsCondTuple = stats.statsCond(psd, epochs.epo1, 3000, 0.05, 0.05)
assert statsCondTuple.T_obs.shape[0] == len(epochs.epo1.info['ch_names'])
for i in range(0, len(statsCondTuple.p_values)):
assert statsCondTuple.p_values[i] <= statsCondTuple.adj_p[1][i]
assert statsCondTuple.T_obs_plot.shape[0] == len(
epochs.epo1.info['ch_names'])
psd_tuple2 = analyses.pow(epochs.epo2,
fmin,
fmax,
n_fft=256,
n_per_seg=None,
epochs_average=False)
psd2 = psd_tuple2.psd
freq_list = psd_tuple2.freq_list
data = [psd, psd2]
con_matrixTuple = stats.con_matrix(epochs.epo1, freq_list, draw=False)
statscondClusterTuple = stats.statscondCluster(
data,
freq_list,
scipy.sparse.bsr_matrix(con_matrixTuple.ch_con_freq),
tail=0,
n_permutations=3000,
alpha=0.05)
assert statscondClusterTuple.F_obs.shape[0] == len(
epochs.epo1.info['ch_names'])
for i in range(0, len(statscondClusterTuple.clusters)):
assert len(statscondClusterTuple.clusters[i]) < len(
epochs.epo1.info['ch_names'])
assert statscondClusterTuple.cluster_p_values.shape[0] == len(
statscondClusterTuple.clusters)
plt.close("all")
# Picking the preprocessed epochs for each participant
preproc_S1 = cleaned_epochs_AR[0]
preproc_S2 = cleaned_epochs_AR[1]
# Analysing data
# Welch Power Spectral Density
# Here for ex, the frequency-band-of-interest is restricted to Alpha_Low,
# frequencies for which power spectral density is actually computed
# are returned in freq_list,
# and PSD values are averaged across epochs
psd1 = analyses.pow(preproc_S1,
fmin=7.5,
fmax=11,
n_fft=1000,
n_per_seg=1000,
epochs_average=True)
psd2 = analyses.pow(preproc_S2,
fmin=7.5,
fmax=11,
n_fft=1000,
n_per_seg=1000,
epochs_average=True)
data_psd = np.array([psd1.psd, psd2.psd])
# Connectivity
# initializing data and storage
data_inter = np.array([preproc_S1, preproc_S2])
result_intra = []