


'''simple docstring'''



from statistics import mean

import numpy as np







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[Any]       				=       0
			# Number of processes finished
			UpperCAmelCase__					:   List[Any]       				=       0
			# Displays the finished process.
			# If it is 0, the performance is completed if it is 1, before the performance.
			UpperCAmelCase__					:   Any       				=       [0] * no_of_process
			# List to include calculation results
			UpperCAmelCase__					:   Dict       				=       [0] * no_of_process

			# Sort by arrival time.
			UpperCAmelCase__					:   List[Any]       				=       [burst_time[i] for i in np.argsort(UpperCamelCase__   )]
			UpperCAmelCase__					:   Optional[Any]       				=       [process_name[i] for i in np.argsort(UpperCamelCase__   )]
			arrival_time.sort()

			while no_of_process > finished_process_count:
						UpperCAmelCase__					:   Union[str, Any]       				=       0
						while finished_process[i] == 1:
									i += 1
						if current_time < arrival_time[i]:
									UpperCAmelCase__					:   Optional[Any]       				=       arrival_time[i]

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

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

			return turn_around_time







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       [0] * no_of_process
			for i in range(0	, UpperCamelCase__   ):
						UpperCAmelCase__					:   List[Any]       				=       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'''



import colorsys

from PIL import Image  # type: ignore







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       x
			UpperCAmelCase__					:   Optional[int]       				=       y
			for step in range(UpperCamelCase__   ):  # noqa: B007
						UpperCAmelCase__					:   List[str]       				=       a * a - b * b + x
						UpperCAmelCase__					:   Optional[int]       				=       2 * a * b + y
						UpperCAmelCase__					:   int       				=       a_new

						# divergence happens for all complex number with an absolute value
						# greater than 4
						if a * a + b * b > 4:
									break
			return step / (max_step - 1)







def       _UpperCamelCase (						UpperCamelCase__   ):
			if distance == 1:
						return (0, 0, 0)
			else:
						return (2_5_5, 2_5_5, 2_5_5)







def       _UpperCamelCase (						UpperCamelCase__   ):
			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(UpperCamelCase__	, 1	, 1   )   )







def       _UpperCamelCase (						UpperCamelCase__ = 8_0_0	, UpperCamelCase__ = 6_0_0	, UpperCamelCase__ = -0.6	, UpperCamelCase__ = 0	, UpperCamelCase__ = 3.2	, UpperCamelCase__ = 5_0	, UpperCamelCase__ = True	, ):
			UpperCAmelCase__					:   str       				=       Image.new("""RGB"""	, (image_width, image_height)   )
			UpperCAmelCase__					:   Optional[int]       				=       img.load()

			# loop through the image-coordinates
			for image_x in range(UpperCamelCase__   ):
						for image_y in range(UpperCamelCase__   ):
									# determine the figure-coordinates based on the image-coordinates
									UpperCAmelCase__					:   Union[str, Any]       				=       figure_width / image_width * image_height
									UpperCAmelCase__					:   Union[str, Any]       				=       figure_center_x + (image_x / image_width - 0.5) * figure_width
									UpperCAmelCase__					:   Tuple       				=       figure_center_y + (image_y / image_height - 0.5) * figure_height

									UpperCAmelCase__					:   List[str]       				=       get_distance(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												UpperCAmelCase__					:   Any       				=       get_color_coded_rgb(UpperCamelCase__   )
									else:
												UpperCAmelCase__					:   Tuple       				=       get_black_and_white_rgb(UpperCamelCase__   )

			return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__A		=get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()