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]
# normalize pattern and find maxmium coefficient
norm_pattern = np.abs(pattern / pattern[i_max])
participants = df.participant
experiments = df.experiment
df = df.set_index("participant")
results_dir = "../results/ssd/"
os.makedirs(results_dir, exist_ok=True)
for participant, experiment in list(zip(participants, experiments)):
participant_id = "%s_%s" % (participant, experiment)
print(participant_id)
# get individual peaks
peaks, bin_width1 = helper.get_participant_peaks(participant,
experiment,
return_width=True)
# load raw-file
file_name = "../working/%s_raw.fif" % participant_id
raw = mne.io.read_raw_fif(file_name, preload=True)
raw.pick_types(ecog=True)
raw = helper.reject_channels(raw, participant, experiment)
for i_peak, peak in enumerate(peaks):
file_name = "%s/ssd_%s_peak_%.2f.npy" % (
results_dir,
participant_id,
peak,
)