



'''simple docstring'''

from statistics import mean

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			# Number of processes finished
			snake_case:    Tuple      							=0
			# Displays the finished process.
			# If it is 0, the performance is completed if it is 1, before the performance.
			snake_case:    Optional[Any]      							=[0] * no_of_process
			# List to include calculation results
			snake_case:    List[Any]      							=[0] * no_of_process

			# Sort by arrival time.
			snake_case:    Union[str, Any]      							=[burst_time[i] for i in np.argsort(__UpperCAmelCase					)]
			snake_case:    Optional[int]      							=[process_name[i] for i in np.argsort(__UpperCAmelCase					)]
			arrival_time.sort()

			while no_of_process > finished_process_count:
						snake_case:    List[Any]      							=0
						while finished_process[i] == 1:
									i += 1
						if current_time < arrival_time[i]:
									snake_case:    Tuple      							=arrival_time[i]

						snake_case:    List[Any]      							=0
						# Index showing the location of the process being performed
						snake_case:    int      							=0
						# Saves the current response ratio.
						snake_case:    Tuple      							=0
						for i in range(0				,							__UpperCAmelCase					):
									if finished_process[i] == 0 and arrival_time[i] <= current_time:
												snake_case:    List[Any]      							=(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
												    i
												]
									if response_ratio < temp:
												snake_case:    str      							=temp
												snake_case:    Optional[Any]      							=i

        # Calculate the turn around time
						snake_case:    Any      							=current_time + burst_time[loc] - arrival_time[loc]
						current_time += burst_time[loc]
						# Indicates that the process has been performed.
						snake_case:    int      							=1
						# Increase finished_process_count by 1
						finished_process_count += 1

			return turn_around_time







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    int      							=[0] * no_of_process
			for i in range(0				,							__UpperCAmelCase					):
						snake_case:    int      							=turn_around_time[i] - burst_time[i]
			return waiting_time


if __name__ == "__main__":
	a							   =				5
	a							   =				['A', 'B', 'C', 'D', 'E']
	a							   =				[1, 2, 3, 4, 5]
	a							   =				[1, 2, 3, 4, 5]

	a							   =				calculate_turn_around_time(
	    process_name, arrival_time, burst_time, no_of_process
	)
	a							   =				calculate_waiting_time(
	    process_name, turn_around_time, burst_time, no_of_process
	)

	print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
	for i in range(0, no_of_process):
		print(
		    F"""{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"""
		    F"""{turn_around_time[i]}\t\t\t{waiting_time[i]}"""
		)

	print(F"""average waiting time : {mean(waiting_time):.5f}""")
	print(F"""average turn around time : {mean(turn_around_time):.5f}""")









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




