import math
import matplotlib.pyplot  as plt
from .Generaldistribution import Distribution

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, probability=0.5, size=20):
        self.p = probability
        self.n = size
        Distribution.__init__(self, self.calculate_mean(), self.calculate_stdev())

    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 = len([i for i in self.data if i == 1]) / len(self.data)

        self.mean = self.calculate_mean()
        self.stdev = self.calculate_stdev()

        return self.p, self.n
    
    def plot_bar(self):

        """Function to output a histogram of the instance variable data using 
        matplotlib pyplot library.
        
        Args:
            None
            
        Returns:
            None
        """
        plt.bar(x=['0','1'], height=[(1-self.p)*self.n, self.p*self.n])
        plt.title('Bar Chart of Data')
        plt.xlabel('outcome')
        plt.ylabel('count')
        plt.show()

    
    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
        """
    
        x = math.factorial(self.n) / (math.factorial(k) *  math.factorial(self.n - k))
        y = math.pow(self.p, k) * math.pow(1 - self.p, self.n - k) 
        pdf = x * y 
        return pdf       


    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))
       
        plt.bar(x=x, height=y)
        plt.title('Distribution of Outcomes')
        plt.ylabel('Probability')
        plt.xlabel('Outcome')
        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

        new_p = self.p
        new_n = self.n + other.n 

        sum = Binomial(new_p, new_n)

        return sum


    def __repr__(self):
        """Function to output the characteristics of the Binomial instance
        
        Args:
            None
        
        Returns:
            string: characteristics of the Binomial object
        
        """
        return f'mean = {self.mean}, standard deviation = {self.stdev}, probability = {self.p}, size = {self.n}'       
