code: def runExperimentSP(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_128mcs.pkl")
  
    # We run a bunch of trials with these combinations
    numTrials = 10
    featureRange = [1000]
    objectRange = [50]
  
    # Comment this out if you  are re-running analysis on already saved results.
    runExperimentPool(
      numSequences=objectRange,
      numObjects=objectRange,
      numFeatures=featureRange,
      numLocations=[100],
      nTrials=numTrials,
      numWorkers=cpu_count() - 1,
      resultsName=resultsFilename,
      figure="S",
      numRepetitions=30,
      sampleSizeProximalL2=[15],
      minThresholdProximalL2=[10],
      synPermProximalDecL2=[0.001],
      # basalPredictedSegmentDecrement=[0.0, 0.001, 0.002, 0.003, 0.004, 0.005],
      basalPredictedSegmentDecrement=[0.0, 0.001, 0.002, 0.003, 0.004, 0.005, 0.01, 0.02, 0.04, 0.08, 0.12],
      inputSize=[128],
    )
  
    print "Done with experiments"
docs: This runs a pool of experiments where the network is trained on stream
    containing a mixture of temporal and sensorimotor sequences.