




"""simple docstring"""




from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""						)       ->	dict[str, float]:



       """simple docstring"""

       A__							=   url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
       A__							=   BeautifulSoup(requests.get(lowercase_						).text			,    '''html.parser'''						)
       A__							=   soup.find_all('''td'''			,    attrs='''titleColumn'''						)
       A__							=   soup.find_all('''td'''			,    class_='''ratingColumn imdbRating'''						)
       return {
           title.a.text: float(rating.strong.text						)
           for title, rating in zip(lowercase_			,    lowercase_						)
       }







def SCREAMING_SNAKE_CASE    (	lowercase_ = "IMDb_Top_250_Movies.csv"						)       ->	None:



       """simple docstring"""

       A__							=   get_imdb_top_aaa_movies()
       with open(lowercase_			,    '''w'''			,    newline=''''''						) as out_file:
              A__							=   csv.writer(lowercase_						)
              writer.writerow(['''Movie title''', '''IMDb rating''']						)
              for title, rating in movies.items():
                     writer.writerow([title, rating]						)


if __name__ == "__main__":
  write_movies()








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()


