old_contents: import mne
  import sys
  
  from my_settings import (epochs_folder, conditions, mne_folder)
  import matplotlib
  matplotlib.use('Agg')
  import matplotlib.pyplot as plt
  
  reject = dict(
      grad=4000e-13,  # T / m (gradiometers)
      mag=4e-12)  # T (magnetometers)
  
  subject = sys.argv[1]
  
  for condition in conditions:
      epochs = mne.read_epochs(epochs_folder + "%s_%s_ar-epo.fif" % (subject,
                                                                     condition))
      epochs.drop_bad(reject)
  
      # Make noise cov
      cov = mne.compute_covariance(
          epochs,
          method=['empirical', 'shrunk'],
          tmin=None,
          tmax=-3.5,
          return_estimators=True,
          verbose=True)
  
      evoked = epochs.average()
      fig = evoked.plot_white(cov, show=False)
      fig.suptitle("subject: %s" % subject)
      fig.savefig(mne_folder + "plots_cov/sub_%s_%s.png" % (subject, condition))
  
new_contents: import mne
  import sys
  
  from my_settings import (epochs_folder, conditions, mne_folder)
  import matplotlib
  matplotlib.use('Agg')
  import matplotlib.pyplot as plt
  
  reject = dict(
      grad=4000e-13,  # T / m (gradiometers)
      mag=4e-12)  # T (magnetometers)
  
  subject = sys.argv[1]
  
  for condition in conditions[:2]:
      epochs = mne.read_epochs(epochs_folder + "%s_%s_ar-epo.fif" % (subject,
                                                                     condition))
      epochs.drop_bad(reject)
  
      # Make noise cov
      cov = mne.compute_covariance(
          epochs,
          method=['empirical', 'shrunk'],
          tmin=None,
          tmax=-3.5,
          return_estimators=True,
          verbose=True)
  
      evoked = epochs.average()
      fig = evoked.plot_white(cov, show=False)
      fig.suptitle("subject: %s" % subject)
      fig.savefig(mne_folder + "plots_cov/sub_%s_%s_ar-cov.png" % (subject,
                                                                   condition))
  
subject: save name