code: def runExperimentS(dirName):
    
    # Results are put into a pkl file which can be used to generate the plots.
    # dirName is the absolute path where the pkl file will be placed.
    resultsFilename = os.path.join(dirName, "superimposed_training.pkl")
    results = runExperiment(
      {
        "numSequences": 50,
        "numObjects": 50,
        "seqLength": 10,
        "numFeatures": 100,
        "trialNum": 8,
        "numLocations": 100,
        "numRepetitions": 30,
        "sampleSizeProximalL2": 15,
        "minThresholdProximalL2": 10,
        "figure": "S",
        "stripStats": False,
      }
    )
  
    # Pickle results for plotting and possible later debugging
    with open(resultsFilename, "wb") as f:
      cPickle.dump(results, f)
  
    # Debugging
    with open(resultsFilename, "rb") as f:
      r = cPickle.load(f)
  
      r.pop("objects")
      r.pop("sequences")
      stat = r.pop("statistics")
      pprint.pprint(r)
      sObject = 0
      sSequence = 0
      for i in range(0, 50):
        sObject += sum(stat[i]['L4 PredictedActive C0'])
      for i in range(50, 100):
        sSequence += sum(stat[i]['L4 PredictedActive C0'])
      print sObject, sSequence
docs: This runs an experiment where the network is trained on stream containing a
    mixture of temporal and sensorimotor sequences.