old_contents: import sys
  import numpy as np
  import mne
  from mne.minimum_norm import read_inverse_operator, apply_inverse_epochs
  
  from my_settings import (mne_folder, epochs_folder, source_folder, conditions)
  
  subject = sys.argv[1]
  
  method = "dSPM"
  snr = 1.
  lambda2 = 1. / snr**2
  
  labels = mne.read_labels_from_annot(
      subject=subject, parc="PALS_B12_Brodmann", regexp="Brodmann")
  
  for condition in conditions:
      inv = read_inverse_operator(mne_folder + "%s_%s_ar-inv.fif" % (subject,
                                                                     condition))
      epochs = mne.read_epochs(epochs_folder + "%s_%s_ar-epo.fif" % (subject,
                                                                     condition))
      # epochs.resample(500)
  
      stcs = apply_inverse_epochs(
          epochs["press"], inv, lambda2, method=method, pick_ori=None)
      ts = [
          mne.extract_label_time_course(
              stc, labels, inv["src"], mode="mean_flip") for stc in stcs
      ]
  
      # for h, tc in enumerate(ts):
      #     for j, t in enumerate(tc):
      #         t *= np.sign(t[np.argmax(np.abs(t))])
      #         tc[j, :] = t
      #     ts[h] = tc
  
      ts = np.asarray(ts)
      # stc.save(source_folder + "%s_%s_ar_epo" % (subject, condition))
      np.save(source_folder + "ave_ts/%s_%s_ar_ts-epo.npy" %
              (subject, condition), ts)
      del epochs
      del stcs
      del ts
  
new_contents: import sys
  import numpy as np
  import mne
  from mne.minimum_norm import read_inverse_operator, apply_inverse_epochs
  
  from my_settings import (mne_folder, epochs_folder, source_folder, conditions)
  
  subject = sys.argv[1]
  
  method = "dSPM"
  snr = 1.
  lambda2 = 1. / snr**2
  
  # labels = mne.read_labels_from_annot(
  #     subject=subject, parc="PALS_B12_Brodmann", regexp="Brodmann")
  labels = mne.read_labels_from_annot(subject, parc="aparc")
  
  for condition in conditions:
      inv = read_inverse_operator(mne_folder + "%s_%s-inv.fif" % (subject,
                                                                  condition))
      epochs = mne.read_epochs(epochs_folder + "%s_%s-epo.fif" % (subject,
                                                                  condition))
  
      stcs = apply_inverse_epochs(
          epochs["press"], inv, lambda2, method=method, pick_ori=None)
      ts = [
          mne.extract_label_time_course(
              stc, labels, inv["src"], mode="mean_flip") for stc in stcs
      ]
  
      # for h, tc in enumerate(ts):
      #     for j, t in enumerate(tc):
      #         t *= np.sign(t[np.argmax(np.abs(t))])
      #         tc[j, :] = t
      #     ts[h] = tc
  
      ts = np.asarray(ts)
      # stc.save(source_folder + "%s_%s_ar_epo" % (subject, condition))
      np.save(source_folder + "ave_ts/%s_%s_ts_DKT-epo.npy" %
              (subject, condition), ts)
      del epochs
      del stcs
      del ts
  
subject: Change parcellation to DKT