

from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     float):
									return 0.0







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       min([-20, np.min(fft_results[1 : samplerate // 2 - 1]    )]    )
						SCREAMING_SNAKE_CASE_: str  		=       max([20, np.max(fft_results[1 : samplerate // 2 - 1]    )]    )
						return lowest, highest







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       5_12
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [1] + [0] * (size - 1)
						SCREAMING_SNAKE_CASE_: str  		=       [filter_type.process(_UpperCAmelCase    ) for item in inputs]

						SCREAMING_SNAKE_CASE_: str  		=       [0] * (samplerate - size)  # zero-padding
						outputs += filler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       np.abs(np.fft.fft(_UpperCAmelCase    )    )
						SCREAMING_SNAKE_CASE_: int  		=       20 * np.logaa(_UpperCAmelCase    )

						# 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
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_bounds(_UpperCAmelCase				,	_UpperCAmelCase    )
						plt.ylim(max([-80, bounds[0]]    )				,	min([80, bounds[1]]    )    )
						plt.ylabel("Gain (dB)"    )

						plt.plot(_UpperCAmelCase    )
						plt.show()







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       5_12
						SCREAMING_SNAKE_CASE_: Tuple  		=       [1] + [0] * (size - 1)
						SCREAMING_SNAKE_CASE_: str  		=       [filter_type.process(_UpperCAmelCase    ) for item in inputs]

						SCREAMING_SNAKE_CASE_: Any  		=       [0] * (samplerate - size)  # zero-padding
						outputs += filler
						SCREAMING_SNAKE_CASE_: Tuple  		=       np.angle(np.fft.fft(_UpperCAmelCase    )    )

						# 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(_UpperCAmelCase				,	-2 * pi    )    )
						plt.show()









def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
						from doctest import testmod

						testmod()






