run file

def runIoThroughNupic(inputData, model, gymName, plot):
  inputFile = open(inputData, "rb")
  csvReader = csv.reader(inputFile)

  # skip header row
  csvReader.next()

  shifter = InferenceShifter()
  print 'Got shifter'
  if plot:
    output = nupic_anomaly_output.NuPICPlotOutput(gymName)
  else:
    output = nupic_anomaly_output.NuPICFileOutput(gymName)


  counter = 0
  for row in csvReader:
    if counter%10 == 0:
      print counter
    counter += 1
    #timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT)

    x_response = float(row[0])*100
    y_response = float(row[1])*100 #row[1]

    '''
    result = model.run({
      #"timestamp": timestamp,
      #"x_Err": x_err,
      "x_Response": x_response,
      #"y_Err": y_err,
      "y_Response": y_response,
    })
    '''

    #xy = [int(x_response*1000),int(y_response*1000)]
    this_point = [x_response,y_response]

    vector = np.array(this_point).astype(int)

    try:
        last_point
    except:
        last_point = None

    radius = calculate_radius(last_point,this_point)
    #print 'Got radius: ',radius

    last_point = copy.deepcopy(this_point)

    modelInput = {
    "vector": (vector,radius)
    }

    result = model.run(modelInput)

    if counter % 100 == 0:
      print ("Read %i lines..." % counter)


    if plot:
      result = shifter.shift(result)

    #prediction = result.inferences["multiStepBestPredictions"][1]
    anomalyScore = result.inferences["anomalyScore"]
    output.write('_____', y_response, '_____', anomalyScore) #[timestamp]

  inputFile.close()
  output.close()