old_contents: import sys
  import numpy as np
  import mne
  from my_settings import (tf_folder, epochs_folder)
  
  subject = sys.argv[1]
  
  # Load epochs from both conditions
  data_ctl_left = np.load(tf_folder + "%s_ctl_left-4-tfr.npy" % (subject))
  data_ent_left = np.load(tf_folder + "%s_ent_left-4-tfr.npy" % (subject))
  data_ctl_right = np.load(tf_folder + "%s_ctl_right-4-tfr.npy" % (subject))
  data_ent_right = np.load(tf_folder + "%s_ent_right-4-tfr.npy" % (subject))
  epochs = mne.read_epochs(
      epochs_folder + "%s_trial_start-epo.fif" % subject, preload=False)
  
  X = np.vstack([
      np.mean(data_ctl_left, axis=2), np.mean(data_ctl_right, axis=2),
      np.mean(data_ent_left, axis=2), np.mean(data_ent_right, axis=2)
  ])
  y = np.concatenate([
      np.zeros(len(data_ctl_left)), np.ones(len(data_ctl_right)),
      np.ones(len(data_ent_left)) * 2, np.ones(len(data_ent_right)) * 3
  ])
  
  # Create epochs to use for classification
  n_trial, n_chan, n_time = X.shape
  events = np.vstack((range(n_trial), np.zeros(n_trial, int), y.astype(int))).T
  
  info = epochs.info
  epochs_data = mne.EpochsArray(data=X, info=info, events=events, verbose=False)
  epochs_data.times = epochs.times[::4][:-1]
  
  event_id = {"ctl/left": 0, "ctl/right": 1, "ent/left": 2, "ent/right": 3}
  epochs_data.event_id = event_id
  
  epochs_data.save(tf_folder + "%s_power-epo.fif" % subject)
  
new_contents: import sys
  import numpy as np
  import mne
  from my_settings import (tf_folder, epochs_folder)
  
  subject = sys.argv[1]
  
  # Load epochs from both conditions
  data_ctl_left = np.load(tf_folder + "%s_ctl_left-4-tfr.npy" % (subject))
  data_ent_left = np.load(tf_folder + "%s_ent_left-4-tfr.npy" % (subject))
  data_ctl_right = np.load(tf_folder + "%s_ctl_right-4-tfr.npy" % (subject))
  data_ent_right = np.load(tf_folder + "%s_ent_right-4-tfr.npy" % (subject))
  epochs = mne.read_epochs(
      epochs_folder + "%s_trial_start-epo.fif" % subject, preload=False)
  
  X = np.vstack([
      np.mean(data_ctl_left, axis=2), np.mean(data_ctl_right, axis=2),
      np.mean(data_ent_left, axis=2), np.mean(data_ent_right, axis=2)
  ])
  y = np.concatenate([
      np.zeros(len(data_ctl_left)), np.ones(len(data_ctl_right)),
      np.ones(len(data_ent_left)) * 2, np.ones(len(data_ent_right)) * 3
  ])
  
  # Create epochs to use for classification
  n_trial, n_chan, n_time = X.shape
  events = np.vstack((range(n_trial), np.zeros(n_trial, int), y.astype(int))).T
  
  info = epochs.info
  info["sfreq"] = 250
  epochs_data = mne.EpochsArray(data=X, info=info, events=events, verbose=False)
  epochs_data.times = epochs.times[::4][:-1]
  
  event_id = {"ctl/left": 0, "ctl/right": 1, "ent/left": 2, "ent/right": 3}
  epochs_data.event_id = event_id
  
  epochs_data.save(tf_folder + "%s_power-epo.fif" % subject)
  
subject: add sfrew to info