old_contents: import numpy as np
  import bct
  from sklearn.externals import joblib
  from my_settings import (bands, source_folder)
  
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.cross_validation import (StratifiedKFold, cross_val_score,
                                        permutation_test_score)
  from sklearn.grid_search import GridSearchCV
  
  subjects = [
      "0008", "0009", "0010", "0012", "0014", "0015", "0016", "0017", "0018",
      "0019", "0020", "0021", "0022"
  ]
  
  cls_all = []
  pln_all = []
  
  scores_all = np.empty([4, 10])
  
  results_all = {}
  
  for subject in subjects:
      cls = np.load(source_folder + "graph_data/%s_classic_pow_pln.npy" %
                    subject).item()
  
      pln = np.load(source_folder + "graph_data/%s_plan_pow_pln.npy" %
                    subject).item()
  
      cls_all.append(cls)
      pln_all.append(pln)
  
  for k, band in enumerate(bands.keys()):
      data_cls = []
      for j in range(len(cls_all)):
          tmp = cls_all[j][band]
          data_cls.append(
              np.asarray([bct.strengths_und(g) for g in tmp]).mean(axis=0))
      data_pln = []
      for j in range(len(pln_all)):
          tmp = pln_all[j][band]
          data_pln.append(
              np.asarray([bct.strengths_und(g) for g in tmp]).mean(axis=0))
      data_cls = np.asarray(data_cls)
      data_pln = np.asarray(data_pln)
  
      X = np.vstack([data_cls, data_pln])
      y = np.concatenate([np.zeros(len(data_cls)), np.ones(len(data_pln))])
  
      cv = StratifiedKFold(y, n_folds=6, shuffle=True)
  
      model = joblib.load(source_folder +
                          "graph_data/sk_models/path-strength_ada_%s_pln.plk" %
                          band)
  
      score, perm_scores, pval = permutation_test_score(
          model, X, y, cv=cv, n_permutations=10000, n_jobs=1)
  
      result = {"score": score, "perm_scores": perm_scores, "pval": pval}
      results_all[band] = result
  
  np.save(source_folder + "graph_data/perm_test_path-strength_pln.npy",
          results_all)
  
new_contents: import numpy as np
  import bct
  from sklearn.externals import joblib
  from my_settings import (bands, source_folder)
  
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.cross_validation import (StratifiedKFold, cross_val_score,
                                        permutation_test_score)
  from sklearn.grid_search import GridSearchCV
  
  subjects = [
      "0008", "0009", "0010", "0012", "0014", "0015", "0016", "0017", "0018",
      "0019", "0020", "0021", "0022"
  ]
  
  cls_all = []
  pln_all = []
  
  scores_all = np.empty([4, 10])
  
  results_all = {}
  
  for subject in subjects:
      cls = np.load(source_folder + "graph_data/%s_classic_pow_pln.npy" %
                    subject).item()
  
      pln = np.load(source_folder + "graph_data/%s_plan_pow_pln.npy" %
                    subject).item()
  
      cls_all.append(cls)
      pln_all.append(pln)
  
  for k, band in enumerate(bands.keys()):
      data_cls = []
      for j in range(len(cls_all)):
          tmp = cls_all[j][band]
          data_cls.append(
              np.asarray([bct.strengths_und(g) for g in tmp]).mean(axis=0))
      data_pln = []
      for j in range(len(pln_all)):
          tmp = pln_all[j][band]
          data_pln.append(
              np.asarray([bct.strengths_und(g) for g in tmp]).mean(axis=0))
      data_cls = np.asarray(data_cls)
      data_pln = np.asarray(data_pln)
  
      X = np.vstack([data_cls, data_pln])
      y = np.concatenate([np.zeros(len(data_cls)), np.ones(len(data_pln))])
  
      cv = StratifiedKFold(y, n_folds=6, shuffle=True)
  
      model = joblib.load(source_folder +
                          "graph_data/sk_models/path-strength_ada_%s_pln.plk" %
                          band)
  
      score, perm_scores, pval = permutation_test_score(
          model, X, y, cv=cv, n_permutations=10000, n_jobs=1, verbose=2)
  
      result = {"score": score, "perm_scores": perm_scores, "pval": pval}
      results_all[band] = result
  
  np.save(source_folder + "graph_data/perm_test_path-strength_pln.npy",
          results_all)
  
subject: Test woth verbose level