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
                
    """
    
    #       A binomial distribution is defined by two variables: 
    #           the probability of getting a positive outcome
    #           the number of trials
    
    #       If you know these two values, you can calculate the mean and the standard deviation
    #       
    #       For example, if you flip a fair coin 25 times, p = 0.5 and n = 25
    #       You can then calculate the mean and standard deviation with the following formula:
    #           mean = p * n
    #           standard deviation = sqrt(n * p * (1 - p))   

    def __init__(self, p = 0.5, n = 20):
        self.p = p * 1.0
        self.n = n * 1.0

        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.p * self.n * 1.0
        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)) * 1.0
        return self.stdev

    # TODO: write a replace_stats_with_data() method according to the specifications below. The read_data_file() from the Generaldistribution class can read in a data
    # file. Because the Binomaildistribution class inherits from the Generaldistribution class,
    # you don't need to re-write this method. However,  the method
    # doesn't update the mean or standard deviation of
    # a distribution. Hence you are going to write a method that calculates n, p, mean and
    # standard deviation from a data set and then updates the n, p, mean and stdev attributes.
    # Assume that the data is a list of zeros and ones like [0 1 0 1 1 0 1]. 
    #
    #       Write code that: 
    #           updates the n attribute of the binomial distribution
    #           updates the p value of the binomial distribution by calculating the
    #               number of positive trials divided by the total trials
    #           updates the mean attribute
    #           updates the standard deviation attribute
    #
    #       Hint: You can use the calculate_mean() and calculate_stdev() methods
    #           defined previously.
    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)
        count_positives = 0
        for data in self.data:
            if data == 1:
                count_positives += 1
        
        self.p = ( count_positives / self.n ) * 1.0
        
        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
        """
        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 = []
        
        # calculate the x values to visualize
        for i in range(self.n + 1):
            x.append(i)
            y.append(self.pdf(i))

        # make the plots
        plt.bar(x, y)
        plt.title('Distribution of Outcomes')
        plt.ylabel('Probability')
        plt.xlabel('Outcome')

        plt.show()
                

    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

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

        return result
                        
    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}, p {self.p}, n {self.n}"
