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, )