code: def plotBoostTrace(sp, inputVectors, columnIndex):
    
    numInputVector, inputSize = inputVectors.shape
    columnNumber = np.prod(sp.getColumnDimensions())
    boostFactorsTrace = np.zeros((columnNumber, numInputVector))
    activeDutyCycleTrace = np.zeros((columnNumber, numInputVector))
    minActiveDutyCycleTrace = np.zeros((columnNumber, numInputVector))
    for i in range(numInputVector):
      outputColumns = np.zeros(sp.getColumnDimensions(), dtype=uintType)
      inputVector = copy.deepcopy(inputVectors[i][:])
      sp.compute(inputVector, True, outputColumns)
  
      boostFactors = np.zeros((columnNumber, ), dtype=realDType)
      sp.getBoostFactors(boostFactors)
      boostFactorsTrace[:, i] = boostFactors
  
      activeDutyCycle = np.zeros((columnNumber, ), dtype=realDType)
      sp.getActiveDutyCycles(activeDutyCycle)
      activeDutyCycleTrace[:, i] = activeDutyCycle
  
      minActiveDutyCycle = np.zeros((columnNumber, ), dtype=realDType)
      sp.getMinActiveDutyCycles(minActiveDutyCycle)
      minActiveDutyCycleTrace[:, i] = minActiveDutyCycle
  
    plt.figure()
    plt.subplot(2, 1, 1)
    plt.plot(boostFactorsTrace[columnIndex, :])
    plt.ylabel('Boost Factor')
    plt.subplot(2, 1, 2)
    plt.plot(activeDutyCycleTrace[columnIndex, :])
    plt.plot(minActiveDutyCycleTrace[columnIndex, :])
    plt.xlabel(' Time ')
    plt.ylabel('Active Duty Cycle')
docs: Plot boostfactor for a selected column
  
    Note that learning is ON for SP here
  
    :param sp: sp instance
    :param inputVectors: input data
    :param columnIndex: index for the column of interest