




def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	float:



       """simple docstring"""

       def get_matched_characters(lowercase_			,    lowercase_						) -> str:
              A__							=   []
              A__							=   min(len(_stra						)			,    len(_stra						)						) // 2
              for i, l in enumerate(_stra						):
                     A__							=   int(max(0			,    i - limit						)						)
                     A__							=   int(min(i + limit + 1			,    len(_stra						)						)						)
                     if l in _stra[left:right]:
                            matched.append(lowercase_						)
                            A__							=   f"""{_stra[0:_stra.index(lowercase_						)]} {_stra[_stra.index(lowercase_						) + 1:]}"""

              return "".join(lowercase_						)

       # matching characters
       A__							=   get_matched_characters(lowercase_			,    lowercase_						)
       A__							=   get_matched_characters(lowercase_			,    lowercase_						)
       A__							=   len(lowercase_						)

       # transposition
       A__							=   (
           len([(ca, ca) for ca, ca in zip(lowercase_			,    lowercase_						) if ca != ca]						) // 2
       )

       if not match_count:
              A__							=   0.0
       else:
              A__							=   (
                  1
                  / 3
                  * (
                      match_count / len(lowercase_						)
                      + match_count / len(lowercase_						)
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       A__							=   0
       for ca, ca in zip(stra[:4]			,    stra[:4]						):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

       A__							=   min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
       A__							=   max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
       return lowest, highest







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

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

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.abs(np.fft.fft(lowercase_						)						)
       A__							=   20 * np.logaa(lowercase_						)

       # 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__							=   get_bounds(lowercase_			,    lowercase_						)
       plt.ylim(max([-80, bounds[0]]						)			,    min([80, bounds[1]]						)						)
       plt.ylabel('''Gain (dB)'''						)

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

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

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.angle(np.fft.fft(lowercase_						)						)

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


