




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
    # Create universe of discourse in Python using linspace ()
    a													=			np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

    # Create two fuzzy sets by defining any membership function
    # (trapmf(), gbellmf(), gaussmf(), etc).
    a													=			[0, 25, 50]
    a													=			[25, 50, 75]
    a													=			fuzz.membership.trimf(X, abca)
    a													=			fuzz.membership.trimf(X, abca)

    # Compute the different operations using inbuilt functions.
    a													=			np.ones(75)
    a													=			np.zeros((75,))
    # 1. Union = max(µA(x), µB(x))
    a													=			fuzz.fuzzy_or(X, young, X, middle_aged)[1]
    # 2. Intersection = min(µA(x), µB(x))
    a													=			fuzz.fuzzy_and(X, young, X, middle_aged)[1]
    # 3. Complement (A) = (1- min(µA(x))
    a													=			fuzz.fuzzy_not(young)
    # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
    a													=			fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
    # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
    a													=			young + middle_aged - (young * middle_aged)
    # 6. Algebraic Product = (µA(x) * µB(x))
    a													=			young * middle_aged
    # 7. Bounded Sum = min[1,(µA(x), µB(x))]
    a													=			fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
    # 8. Bounded difference = min[0,(µA(x), µB(x))]
    a													=			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, 10)
    plt.plot(X, bdd_difference)
    plt.title("bdd_difference")
    plt.grid(True)

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





import csv

import tweepy

# Twitter API credentials
a													=			""
a													=			""
a													=			""
a													=			""




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				None:
						# authorize twitter, initialize tweepy
						_UpperCAmelCase      =			tweepy.OAuthHandler(snake_case			,     snake_case       )
						auth.set_access_token(snake_case			,     snake_case       )
						_UpperCAmelCase      =			tweepy.API(snake_case       )

						# initialize a list to hold all the tweepy Tweets
						_UpperCAmelCase      =			[]

						# make initial request for most recent tweets (200 is the maximum allowed count)
						_UpperCAmelCase      =			api.user_timeline(screen_name=snake_case			,     count=2_0_0       )

						# save most recent tweets
						alltweets.extend(snake_case       )

						# save the id of the oldest tweet less one
						_UpperCAmelCase      =			alltweets[-1].id - 1

						# keep grabbing tweets until there are no tweets left to grab
						while len(snake_case       ) > 0:
												print(f"getting tweets before {oldest}"       )

												# all subsequent requests use the max_id param to prevent duplicates
												_UpperCAmelCase      =			api.user_timeline(
												    screen_name=snake_case			,     count=2_0_0			,     max_id=snake_case       )

												# save most recent tweets
												alltweets.extend(snake_case       )

												# update the id of the oldest tweet less one
												_UpperCAmelCase      =			alltweets[-1].id - 1

												print(f"...{len(snake_case       )} tweets downloaded so far"       )

						# transform the tweepy tweets into a 2D array that will populate the csv
						_UpperCAmelCase      =			[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

						# write the csv
						with open(f"new_{screen_name}_tweets.csv"			,     """w"""       ) as f:
												_UpperCAmelCase      =			csv.writer(snake_case       )
												writer.writerow(["""id""", """created_at""", """text"""]       )
												writer.writerows(snake_case       )


if __name__ == "__main__":
				# pass in the username of the account you want to download
				get_all_tweets("FirePing32")