text: ### Made by Carlos Paredes for the course IMT1001 HOMEWORK PUC de Chile
  ### Licenced by MIT LICENSE 2019
  import string
  ################ this libraries will be used for data visualization ##############
  global Upperletters
  Upperletters = 'abcdefghijklmnopqrstuvwxyz'.upper()
  
  
  #################creating a form to delete non natural words###############################
  ascii_chars = string.printable
  global non_natural_caracters
  non_natural_caracters = []
  
  for letter in ascii_chars:
      if letter not in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ":
          non_natural_caracters.append(letter)
  ############################################################################################
  def non_natural_remover(line):
      non_natural = "".join(non_natural_caracters)
      newline = ""    
      for word in line:
          if word not in non_natural:
              newline+=word
      return newline
  #########################Creating the frequency analizer#####################################
  def frecuency_analisis(text):
      words = [k for k in Upperletters]
      globalcount = 0
      counters = [0 for i in range(len(words))]
      for line in text:
          line.replace(" ","").replace("\n","")
          trated_line = non_natural_remover(line)
          frecuency_analisis
          Distribution_Matrix = []
          line_to_analize = line.upper()
          for k in range(len(Upperletters)):
                  wordcount = line_to_analize.count(Upperletters[k])
                  counters[k]=counters[k]+wordcount
                  globalcount+=wordcount
      Distribution_Matrix.append(words)
      Distribution_Matrix.append(counters)
      return Distribution_Matrix, globalcount
  #############################################################################################
  
  
  
  