





import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      SCREAMING_SNAKE_CASE_						       =	np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

      # Create two fuzzy sets by defining any membership function
      # (trapmf(), gbellmf(), gaussmf(), etc).
      SCREAMING_SNAKE_CASE_						       =	[0, 2_5, 5_0]
      SCREAMING_SNAKE_CASE_						       =	[2_5, 5_0, 7_5]
      SCREAMING_SNAKE_CASE_						       =	fuzz.membership.trimf(X, abca)
      SCREAMING_SNAKE_CASE_						       =	fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      SCREAMING_SNAKE_CASE_						       =	np.ones(7_5)
      SCREAMING_SNAKE_CASE_						       =	np.zeros((7_5,))
      # 1. Union = max(µA(x), µB(x))
      SCREAMING_SNAKE_CASE_						       =	fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      SCREAMING_SNAKE_CASE_						       =	fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      SCREAMING_SNAKE_CASE_						       =	fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      SCREAMING_SNAKE_CASE_						       =	fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      SCREAMING_SNAKE_CASE_						       =	young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      SCREAMING_SNAKE_CASE_						       =	young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      SCREAMING_SNAKE_CASE_						       =	fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      SCREAMING_SNAKE_CASE_						       =	fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

      # max-min composition
      # max-product composition

      # Plot each set A, set B and each operation result using plot() and subplot().
      from matplotlib import pyplot as plt

      plt.figure()

      plt.subplot(4, 3, 1)
      plt.plot(X, young)
      plt.title("""Young""")
      plt.grid(True)

      plt.subplot(4, 3, 2)
      plt.plot(X, middle_aged)
      plt.title("""Middle aged""")
      plt.grid(True)

      plt.subplot(4, 3, 3)
      plt.plot(X, union)
      plt.title("""union""")
      plt.grid(True)

      plt.subplot(4, 3, 4)
      plt.plot(X, intersection)
      plt.title("""intersection""")
      plt.grid(True)

      plt.subplot(4, 3, 5)
      plt.plot(X, complement_a)
      plt.title("""complement_a""")
      plt.grid(True)

      plt.subplot(4, 3, 6)
      plt.plot(X, difference)
      plt.title("""difference a/b""")
      plt.grid(True)

      plt.subplot(4, 3, 7)
      plt.plot(X, alg_sum)
      plt.title("""alg_sum""")
      plt.grid(True)

      plt.subplot(4, 3, 8)
      plt.plot(X, alg_product)
      plt.title("""alg_product""")
      plt.grid(True)

      plt.subplot(4, 3, 9)
      plt.plot(X, bdd_sum)
      plt.title("""bdd_sum""")
      plt.grid(True)

      plt.subplot(4, 3, 1_0)
      plt.plot(X, bdd_difference)
      plt.title("""bdd_difference""")
      plt.grid(True)

      plt.subplots_adjust(hspace=0.5)
      plt.show()









from PIL import Image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


