experiments = df_sub.experiment
df_sub = df_sub.set_index("participant")
nr_seconds = 3
fmax = 55
fmin = 2
for i_sub, (participant, exp) in enumerate(zip(participants, experiments)):
print(participant, exp)
dfs = []
participant_id = "%s_%s" % (participant, exp)
raw_file = "../working/%s_raw.fif" % participant_id
raw = mne.io.read_raw_fif(raw_file)
raw.load_data()
raw.pick_types(ecog=True)
raw = helper.reject_channels(raw, participant, exp)
psd, freq = mne.time_frequency.psd_welch(
raw,
fmin=fmin,
fmax=fmax,
n_fft=nr_seconds * int(raw.info["sfreq"]),
)
# calculate spectral parametrization for each channel
plot_folder = "../results/psd_param/%s/" % participant
os.makedirs(plot_folder, exist_ok=True)
# always plot 9 electrodes together in a figure for saving some space
fig, ax = plt.subplots(3, 3)
counter = 0
participants = df.participant
experiments = df.experiment
df = df.set_index("participant")
nr_patterns = 1
df2 = pd.DataFrame()
for i_s, (participant, exp) in enumerate(zip(participants, experiments)):
# load electrode positions
participant_id = "%s_%s" % (participant, exp)
raw = mne.io.read_raw_fif("../working/%s_raw.fif" % participant_id)
raw.crop(0, 1)
raw.load_data()
raw.pick_types(ecog=True)
raw = helper.reject_channels(raw, participant, exp)
# load patterns for all peak frequencies
peaks = helper.get_participant_peaks(participant, exp)
for i_p, peak in enumerate(peaks):
patterns, filters = helper.load_ssd(participant_id, peak)
for idx in range(nr_patterns):
pattern = patterns[:, idx]
xyz = np.array([ich["loc"][:3] for ich in raw.info["chs"]])
distance = squareform(pdist(xyz, "cityblock"))
# find maximum value & select distance to maximum
i_max = np.argmax(np.abs(pattern))
dist_to_max = distance[i_max]