text: import numpy as np
  import statistics as stat
  
  treshold = 3
  
  def removeFalseData(input_data):
      while(1):
          try:
              input_data.remove(False)
          except ValueError:
              break
      return(input_data)
  
  def removeNegatives(input_data):
      processed_data = []
      for val in input_data:
          if val > 0:
              processed_data.append(val)
      return processed_data
  
  def removeOutliers(input_data):
      #dataset = removeNegatives(input_data)
      processed_data = []
      med = stat.median(input_data)
      avg = np.mean(input_data)
      stdev = np.std(input_data)
      for val in input_data:
          score = abs(val-med)
          if score < 500:
              processed_data.append(val)
      #    z_score = abs(((val) - avg)/stdev)
      #    if z_score < treshold:
      #        processed_data.append(val)
      return processed_data
  
  def filterData(input_data):
      #print(input_data)
      dataset = removeFalseData(input_data)
      dataset = removeOutliers(dataset)
      #print('filtered')
      #print(dataset)
      return np.mean(dataset)
  