



'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A = 100  ,     ):



     """simple docstring"""



     _a     =  x_start
     _a     =  fnc(__A)
     _a     =  0.0

     for _ in range(__A):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _a     =  (x_end - x_start) / steps + xa
          _a     =  fnc(__A)
          area += abs(fxa + fxa) * (xa - xa) / 2

          # Increment step
          _a     =  xa
          _a     =  fxa
     return area


if __name__ == "__main__":




  def lowerCAmelCase  (__A):



       """simple docstring"""



       return x**3

  print("f(x) = x^3")
  print("The area between the curve, x = -10, x = 10 and the x axis is:")
  lowercase_			=  10
  while i <= 100_000:
    lowercase_			=  trapezoidal_area(f, -5, 5, i)
    print(F"""with {i} steps: {area}""")
    i *= 10








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



