


"""simple docstring"""







from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				cva.getAffineTransform(lowerCAmelCase_  ,						lowerCAmelCase_   )
					return cva.warpAffine(lowerCAmelCase_  ,						lowerCAmelCase_  ,						(rows, cols)   )


if __name__ == "__main__":
		# read original image
		a__				:							Any						=     cva.imread(
		    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
		)
		# turn image in gray scale value
		a__				:							Optional[Any]						=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
		# get image shape
		a__			,     a__				:							Union[str, Any]						=     gray_img.shape

		# set different points to rotate image
		a__				:							Optional[Any]						=     np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
		a__				:							List[str]						=     np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
		a__				:							Optional[Any]						=     np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
		a__				:							Tuple						=     np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)

		# add all rotated images in a list
		a__				:							Union[str, Any]						=     [
		    gray_img,
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		]

		# plot different image rotations
		a__				:							Any						=     plt.figure(1)
		a__				:							Optional[int]						=     ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
		for i, image in enumerate(images):
				plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
				plt.title(titles[i])
				plt.axis('''off''')
				plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
		plt.show()




"""simple docstring"""







from __future__ import annotations

import pandas as pd




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					# Copy the burst time into remaining_time[]
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i]

					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				9_9999_9999
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				False

					# Process until all processes are completed
					while complete != no_of_processes:
										for j in range(lowerCAmelCase_   ):
															if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																				if remaining_time[j] < minm:
																									__SCREAMING_SNAKE_CASE						   =				remaining_time[j]
																									__SCREAMING_SNAKE_CASE						   =				j
																									__SCREAMING_SNAKE_CASE						   =				True

										if not check:
															increment_time += 1
															continue
										remaining_time[short] -= 1

										__SCREAMING_SNAKE_CASE						   =				remaining_time[short]
										if minm == 0:
															__SCREAMING_SNAKE_CASE						   =				9_9999_9999

										if remaining_time[short] == 0:
															complete += 1
															__SCREAMING_SNAKE_CASE						   =				False

															# Find finish time of current process
															__SCREAMING_SNAKE_CASE						   =				increment_time + 1

															# Calculate waiting time
															__SCREAMING_SNAKE_CASE						   =				finish_time - arrival_time[short]
															__SCREAMING_SNAKE_CASE						   =				finar - burst_time[short]

															if waiting_time[short] < 0:
																				__SCREAMING_SNAKE_CASE						   =				0

        # Increment time
										increment_time += 1
					return waiting_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i] + waiting_time[i]
					return turn_around_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				total_waiting_time + waiting_time[i]
										__SCREAMING_SNAKE_CASE						   =				total_turn_around_time + turn_around_time[i]
					print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""   )
					print("Average turn around time ="  ,						total_turn_around_time / no_of_processes   )


if __name__ == "__main__":
		print('''Enter how many process you want to analyze''')
		a__				:							Optional[Any]						=     int(input())
		a__				:							Optional[int]						=     [0] * no_of_processes
		a__				:							int						=     [0] * no_of_processes
		a__				:							List[Any]						=     list(range(1, no_of_processes + 1))

		for i in range(no_of_processes):
				print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
				a__			,     a__				:							Tuple						=     map(int, input().split())

		a__				:							int						=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

		a__				:							Dict						=     burst_time
		a__				:							Any						=     no_of_processes
		a__				:							Optional[int]						=     waiting_time
		a__				:							Union[str, Any]						=     calculate_turnaroundtime(bt, n, wt)

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		a__				:							str						=     pd.DataFrame(
		    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
		    columns=[
		        '''Process''',
		        '''BurstTime''',
		        '''ArrivalTime''',
		        '''WaitingTime''',
		        '''TurnAroundTime''',
		    ],
		)

		# Printing the dataFrame
		pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
		print(fcfs)
