
'''simple docstring'''

from __future__ import annotations

from statistics import mean


def 	_UpperCAmelCase				(  _lowerCamelCase						:       list[int]		)    ->						list[int]:
  _lowerCAmelCase       :  List[Any]   				=					2
  _lowerCAmelCase       :  str   				=					list(_lowerCamelCase		)
  _lowerCAmelCase       :  int   				=					[0] * len(_lowerCamelCase		)
  _lowerCAmelCase       :  Optional[Any]   				=					0
  while True:
    _lowerCAmelCase       :  Union[str, Any]   				=					True
    for i, burst_time in enumerate(_lowerCamelCase		):
      if rem_burst_times[i] > 0:
        _lowerCAmelCase       :  Any   				=					False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          _lowerCAmelCase       :  Tuple   				=					t - burst_time
          _lowerCAmelCase       :  Dict   				=					0
    if done is True:
      return waiting_times


def 	_UpperCAmelCase				(  _lowerCamelCase						:       list[int]  ,      _lowerCamelCase						:       list[int]		)    ->						list[int]:
  return [burst + waiting for burst, waiting in zip(_lowerCamelCase  ,      _lowerCamelCase		)]


if __name__ == "__main__":
     UpperCamelCase_     			=       [3, 5, 7]
     UpperCamelCase_     			=       calculate_waiting_times(burst_times)
     UpperCamelCase_     			=       calculate_turn_around_times(burst_times, waiting_times)
     print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
     for i, burst_time in enumerate(burst_times):
          print(
              F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
              F'{turn_around_times[i]}'
          )
     print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
     print(F'Average turn around time = {mean(turn_around_times):.5f}')




'''simple docstring'''

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def 	_UpperCAmelCase				(  _lowerCamelCase						:       str  ,      _lowerCamelCase						:       List[Any]=3		)    ->						Union[str, Any]:
  _lowerCAmelCase       :  str   				=					int(mask / 2		)
  # copy image size
  _lowerCAmelCase       :  Dict   				=					zeros_like(_lowerCamelCase		)
  for i in range(_lowerCamelCase  ,      gray_img.shape[0] - bd		):
    for j in range(_lowerCamelCase  ,      gray_img.shape[1] - bd		):
      # get mask according with mask
      _lowerCAmelCase       :  Union[str, Any]   				=					ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
      # calculate mask median
      _lowerCAmelCase       :  str   				=					sort(_lowerCamelCase		)[inta(divide((multiply(_lowerCamelCase  ,      _lowerCamelCase		))  ,      2		) + 1		)]
      _lowerCAmelCase       :  Dict   				=					median
  return median_img


if __name__ == "__main__":
     # read original image
     UpperCamelCase_     			=       imread("""../image_data/lena.jpg""")
     # turn image in gray scale value
     UpperCamelCase_     			=       cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     UpperCamelCase_     			=       median_filter(gray, 3)
     UpperCamelCase_     			=       median_filter(gray, 5)

     # show result images
     imshow("""median filter with 3x3 mask""", medianaxa)
     imshow("""median filter with 5x5 mask""", medianaxa)
     waitKey(0)


