<filename>pbdist/Binomial.py
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution

class Binomial(Distribution):    
    def __init__(self, prob=.5, size=20):
                
        self.n = size
        self.p = prob
        
        Distribution.__init__(self, self.calculate_mean(), self.calculate_stdev())
    

    def calculate_mean(self):
    
        self.mean = self.p * self.n
                
        return self.mean

    def calculate_stdev(self):

        self.stdev = math.sqrt(self.n * self.p * (1 - self.p))
        
        return self.stdev
   
    def replace_stats_with_data(self):
    
        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()
        
        return self.p, self.n
  
    def plot_bar(self):                
        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')
    
    def pdf(self, k):       
        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):  
        x = []
        y = []
        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()
        return x, y
        
    def __add__(self, other):        
        try:
            assert self.p == other.p, 'p values are not equal'
        except AssertionError as error:
            raise      
        result = Binomial()
        result.n = self.n + other.n
        result.p = self.p
        result.calculate_mean()
        result.calculate_stdev()
        
        return result
        
    def __repr__(self):
        return "mean {}, standard deviation {}, p {}, n {}".\
        format(self.mean, self.stdev, self.p, self.n)