# TODO: import necessary libraries
from matplotlib import pyplot as plt
import math
from .Generaldistribution import Distribution

# TODO: make a Binomial class that inherits from the Distribution class. Use the specifications below.


class Binomial(Distribution):
    """ Binomial distribution class for calculating and
    visualizing a Binomial distribution.

    Attributes:
        mean (float) representing the mean value of the distribution
        stdev (float) representing the standard deviation of the distribution
        data_list (list of floats) a list of floats to be extracted from the data file
        p (float) representing the probability of an event occurring

    """

    def __init__(self, prob=0.5, size=20):
        Distribution.__init__(self, self.calculate_mean, self.calculate_stdev)

        self.p = prob
        self.n = size

    def calculate_mean(self):
        """
        Function to calculate the mean from p and n

        Args:
            None

        Returns:
            float: mean of the data set
        """
        self.mean = self.n * self.p
        return self.mean

    def calculate_stdev(self):
        """
        Function to calculate the standard deviation from p and n.

        Args:
            None

        Returns:
            float: standard deviation of the data set
        """
        self.stdev = math.sqrt(self.n * self.p * (1 - self.p))
        return self.stdev

    def replace_stats_with_data(self):
        """
        Function to calculate p and n from the data set. The function updates the p and n variables of the object.

        Args:
            None

        Returns:
            float: the p value
            float: the n value

        """
        self.n = len(self.data)
        self.p = 1.0 * sum(self.data) / len(self.data)
        self.mean = self.calculate_mean()
        self.stdev = self.calculate_stdev()

    def plot_bar(self):
        """
        Function to output a histogram of the instance variable data using
        matplotlib pyplot library.

        Args:
            None

        Returns:
            None
        """
        plt.hist(self.data)
        plt.title('Bar chart of data')
        plt.xlabel('outcome')
        plt.ylabel('count')

    def pdf(self, k):
        """
        Probability density function calculator for the binomial distribution.

            Args:
                k (float): point for calculating the probability density function


            Returns:
                float: probability density function output
        """
        a = math.factorial(self.n) / (math.factorial(k)
                                      * math.factorial(self.n - k))
        b = (self.p ** k) * ((1 - self.p) ** (self.n - k))

        return a*b

    def plot_bar_pdf(self):
        """
        Function to plot the pdf of the binomial distribution

        Args:
            None

        Returns:
            list: x values for the pdf plot
            list: y values for the pdf plot
        """
        x = []
        y = []

        for i in range(self.n + 1):
            x.append(i)
            y.append(self.pdf(i))

        # calculate the x values to visualize
        plt.bar(x, y)
        plt.title('Distribution of Outcomes')
        plt.xlabel('Outcome')
        plt.ylabel('Probability')

        plt.show()
        return x, y

    def __add__(self, other):
        """Function to add together two Binomial distributions with equal p

            Args:
                other (Binomial): Binomial instance

            Returns:
                Binomial: Binomial distribution

            """

        try:
            assert self.p == other.p, 'p values are not equal'
        except AssertionError as error:
            raise error

        result = Binomial()
        result.n = self.n + other.n
        result.p = self.p + other.p
        result.calculate_mean()
        result.calculate_stdev()

        return result

    def __repr__(self):
        """Function to output the characteristics of the Binomial instance

            Args:
                None

            Returns:
                string: characteristics of the Binomial object
        """

        return "mean {}, standard deviation {}, p {}, n {}".format(self.mean, self.stdev, self.p, self.n)
