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
        n (int) the total number of trials
                
    """   
    
    def __init__(self, prob=.5, size=20):
        self.p = prob
        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
        
        Args: 
            None
        
        Returns: 
            float: the p value
            float: the n value
    
        """        
        self.n = len(self.data)
        self.p = self.data.count(1)/self.n
        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(["0","1"] , [self.n * (1-self.p) , self.n*self.p])   
        plt.xlabel("data")
        plt.ylabel("counts")
        plt.show()     
    

    def __nCk(self,n,k):
        k = min(k,n-k)
        nchoosek=1
        for i in range(1,k+1):
            nchoosek*=(n-i+1)
            nchoosek /=i
        return nchoosek

    def pdf(self, k):
        """Probability density function calculator for the gaussian distribution.
        
        Args:
            k (float): point for calculating the probability density function
            
        
        Returns:
            float: probability density function output
        """
        
        
        return self.__nCk(self.n,k) * self.p**k * (1-self.p)**(self.n-k)
              

    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= list(range(0,self.n+1))
        y = [pdf(k) for k in x ]
        plt.bar(x,y)
        plt.show()
        plt.title('Distribution of Outcomes')
        plt.ylabel('Probability')
        plt.xlabel('Outcome')
        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
        
                
        return Binomial(self.p,self.n+other.n)
        
        
    def __repr__(self):
    
        """Function to output the characteristics of the Binomial instance
        
        Args:
            None
        
        Returns:
            string: characteristics of the Gaussian
        
        """
    
        return f"mean {self.mean}, standard deviation {self.stdev}, p {self.p}, n {self.n}"
