

'''simple docstring'''



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__snake_case			:     Union[str, Any]										=      200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__snake_case			:     Dict										=      50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__snake_case			:     Optional[int]										=      0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		str	)	->							tuple[str, float]:






						"""simple docstring"""





						A__					:       Optional[Any]     =len([g for position, g in enumerate(__snake_case	) if g == main_target[position]]	)
						return (item, float(__snake_case	))
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		str	)	->							tuple[str, str]:






						"""simple docstring"""





						A__					:       Dict     =random.randint(0,		len(__snake_case	) - 1	)
						A__					:       Tuple     =parent_a[:random_slice] + parent_a[random_slice:]
						A__					:       List[Any]     =parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		list[str]	)	->							str:






						"""simple docstring"""





						A__					:       int     =list(__snake_case	)
						if random.uniform(0,		1	) < MUTATION_PROBABILITY:
												A__					:       Any     =random.choice(__snake_case	)
						return "".join(__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		tuple[str, float],		__snake_case					:		list[tuple[str, float]],		__snake_case					:		list[str],		)	->							list[str]:






						"""simple docstring"""





						A__					:       Tuple     =[]
						# Generate more children proportionally to the fitness score.
						A__					:       Tuple     =int(parent_a[1] * 100	) + 1
						A__					:       Tuple     =10 if child_n >= 10 else child_n
						for _ in range(__snake_case	):
												A__					:       Tuple     =population_score[random.randint(0,		__snake_case	)][0]

												A__					:       Dict     =crossover(parent_a[0],		__snake_case	)
												# Append new string to the population list.
												pop.append(mutate(__snake_case,		__snake_case	)	)
												pop.append(mutate(__snake_case,		__snake_case	)	)
						return pop
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		list[str],		__snake_case					:		bool = True	)	->							tuple[int, int, str]:






						"""simple docstring"""





						if N_POPULATION < N_SELECTED:
												A__					:       Any     =f"{N_POPULATION} must be bigger than {N_SELECTED}"
												raise ValueError(__snake_case	)
						# Verify that the target contains no genes besides the ones inside genes variable.
						A__					:       Optional[int]     =sorted({c for c in target if c not in genes}	)
						if not_in_genes_list:
												A__					:       str     =f"{not_in_genes_list} is not in genes list, evolution cannot converge"
												raise ValueError(__snake_case	)

						# Generate random starting population.
						A__					:       Tuple     =[]
						for _ in range(__snake_case	):
												population.append("""""".join([random.choice(__snake_case	) for i in range(len(__snake_case	)	)]	)	)

						# Just some logs to know what the algorithms is doing.
						A__					:       Any     =0, 0

						# This loop will end when we find a perfect match for our target.
						while True:
												generation += 1
												total_population += len(__snake_case	)

												# Random population created. Now it's time to evaluate.

												# Adding a bit of concurrency can make everything faster,
												#
												# import concurrent.futures
												# population_score: list[tuple[str, float]] = []
												# with concurrent.futures.ThreadPoolExecutor(
												#                                   max_workers=NUM_WORKERS) as executor:
												#     futures = {executor.submit(evaluate, item) for item in population}
												#     concurrent.futures.wait(futures)
												#     population_score = [item.result() for item in futures]
												#
												# but with a simple algorithm like this, it will probably be slower.
												# We just need to call evaluate for every item inside the population.
												A__					:       Optional[int]     =[evaluate(__snake_case,		__snake_case	) for item in population]

												# Check if there is a matching evolution.
												A__					:       Optional[Any]     =sorted(__snake_case,		key=lambda __snake_case	: x[1],		reverse=__snake_case	)
												if population_score[0][0] == target:
																		return (generation, total_population, population_score[0][0])

												# Print the best result every 10 generation.
												# Just to know that the algorithm is working.
												if debug and generation % 10 == 0:
																		print(
																		    f"\nGeneration: {generation}"
																		    f"\nTotal Population:{total_population}"
																		    f"\nBest score: {population_score[0][1]}"
																		    f"\nBest string: {population_score[0][0]}"	)

												# Flush the old population, keeping some of the best evolutions.
												# Keeping this avoid regression of evolution.
												A__					:       Union[str, Any]     =population[: int(N_POPULATION / 3	)]
												population.clear()
												population.extend(__snake_case	)
												# Normalize population score to be between 0 and 1.
												A__					:       List[str]     =[
												    (item, score / len(__snake_case	)) for item, score in population_score
												]

												# This is selection
												for i in range(__snake_case	):
																		population.extend(select(population_score[int(__snake_case	)],		__snake_case,		__snake_case	)	)
																		# Check if the population has already reached the maximum value and if so,
																		# break the cycle.  If this check is disabled, the algorithm will take
																		# forever to compute large strings, but will also calculate small strings in
																		# a far fewer generations.
																		if len(__snake_case	) > N_POPULATION:
																								break


if __name__ == "__main__":
		__snake_case			:     Optional[Any]										=      (
		    'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
		)
		__snake_case			:     Optional[Any]										=      list(
		    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
		    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
		)
		__snake_case			:     Tuple										=      basic(target_str, genes_list)
		print(
		    F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
		)




'''simple docstring'''



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						float			)      ->		float:





									'''simple docstring'''
									return 0.0


def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		int	)	->							tuple[int | float, int | float]:






						"""simple docstring"""





						A__					:       Tuple     =min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
						A__					:       str     =max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
						return lowest, highest
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       Any     =512
						A__					:       int     =[1] + [0] * (size - 1)
						A__					:       int     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       List[Any]     =np.abs(np.fft.fft(__snake_case	)	)
						A__					:       int     =20 * np.logaa(__snake_case	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						# Display within reasonable bounds
						A__					:       Union[str, Any]     =get_bounds(__snake_case,		__snake_case	)
						plt.ylim(max([-80, bounds[0]]	),		min([80, bounds[1]]	)	)
						plt.ylabel("""Gain (dB)"""	)

						plt.plot(__snake_case	)
						plt.show()
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       List[Any]     =512
						A__					:       List[Any]     =[1] + [0] * (size - 1)
						A__					:       Dict     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       str     =np.angle(np.fft.fft(__snake_case	)	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						plt.ylim(-2 * pi,		2 * pi	)
						plt.ylabel("""Phase shift (Radians)"""	)
						plt.plot(np.unwrap(__snake_case,		-2 * pi	)	)
						plt.show()

