[Python] Magic methods

Thinking how to add tow Guessin object together? It is similar idea to Functional programming, Data type.

import math
import matplotlib.pyplot as plt

class Gaussian():
    """ Gaussian distribution class for calculating and 
    visualizing a Gaussian 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 extracted from the data file
            
    """
    def __init__(self, mu = 0, sigma = 1):
        
        self.mean = mu
        self.stdev = sigma
        self.data = []

    
    def calculate_mean(self):
    
        """Function to calculate the mean of the data set.
        
        Args: 
            None
        
        Returns: 
            float: mean of the data set
    
        """
                    
        avg = 1.0 * sum(self.data) / len(self.data)
        
        self.mean = avg
        
        return self.mean



    def calculate_stdev(self, sample=True):

        """Function to calculate the standard deviation of the data set.
        
        Args: 
            sample (bool): whether the data represents a sample or population
        
        Returns: 
            float: standard deviation of the data set
    
        """

        if sample:
            n = len(self.data) - 1
        else:
            n = len(self.data)
    
        mean = self.mean
    
        sigma = 0
    
        for d in self.data:
            sigma += (d - mean) ** 2
        
        sigma = math.sqrt(sigma / n)
    
        self.stdev = sigma
        
        return self.stdev
        

    def read_data_file(self, file_name, sample=True):
    
        """Function to read in data from a txt file. The txt file should have
        one number (float) per line. The numbers are stored in the data attribute. 
        After reading in the file, the mean and standard deviation are calculated
                
        Args:
            file_name (string): name of a file to read from
        
        Returns:
            None
        
        """
            
        with open(file_name) as file:
            data_list = []
            line = file.readline()
            while line:
                data_list.append(int(line))
                line = file.readline()
        file.close()
    
        self.data = data_list
        self.mean = self.calculate_mean()
        self.stdev = self.calculate_stdev(sample)
        
        
    def plot_histogram(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('Histogram of Data')
        plt.xlabel('data')
        plt.ylabel('count')
        
        
        
    def pdf(self, x):
        """Probability density function calculator for the gaussian distribution.
        
        Args:
            x (float): point for calculating the probability density function
            
        
        Returns:
            float: probability density function output
        """
        
        return (1.0 / (self.stdev * math.sqrt(2*math.pi))) * math.exp(-0.5*((x - self.mean) / self.stdev) ** 2)
        

    def plot_histogram_pdf(self, n_spaces = 50):

        """Function to plot the normalized histogram of the data and a plot of the 
        probability density function along the same range
        
        Args:
            n_spaces (int): number of data points 
        
        Returns:
            list: x values for the pdf plot
            list: y values for the pdf plot
            
        """
        
        mu = self.mean
        sigma = self.stdev

        min_range = min(self.data)
        max_range = max(self.data)
        
         # calculates the interval between x values
        interval = 1.0 * (max_range - min_range) / n_spaces

        x = []
        y = []
        
        # calculate the x values to visualize
        for i in range(n_spaces):
            tmp = min_range + interval*i
            x.append(tmp)
            y.append(self.pdf(tmp))

        # make the plots
        fig, axes = plt.subplots(2,sharex=True)
        fig.subplots_adjust(hspace=.5)
        axes[0].hist(self.data, density=True)
        axes[0].set_title('Normed Histogram of Data')
        axes[0].set_ylabel('Density')

        axes[1].plot(x, y)
        axes[1].set_title('Normal Distribution for \n Sample Mean and Sample Standard Deviation')
        axes[0].set_ylabel('Density')
        plt.show()

        return x, y
        
    def __add__(self, other):
        
        """Function to add together two Gaussian distributions
        
        Args:
            other (Gaussian): Gaussian instance
            
        Returns:
            Gaussian: Gaussian distribution
            
        """
        
        result = Gaussian()
        result.mean = self.mean + other.mean
        result.stdev = math.sqrt(self.stdev ** 2 + other.stdev ** 2)
        
        return result
        
        
    def __repr__(self):
    
        """Function to output the characteristics of the Gaussian instance
        
        Args:
            None
        
        Returns:
            string: characteristics of the Gaussian
        
        """
        
        return "mean {}, standard deviation {}".format(self.mean, self.stdev)

 

In the class, __in__, __add__, __repr__ are all magic methods.

 

import unittest

class TestGaussianClass(unittest.TestCase):
    def setUp(self):
        self.gaussian = Gaussian(25, 2)

    def test_initialization(self): 
        self.assertEqual(self.gaussian.mean, 25, 'incorrect mean')
        self.assertEqual(self.gaussian.stdev, 2, 'incorrect standard deviation')

    def test_pdf(self):
        self.assertEqual(round(self.gaussian.pdf(25), 5), 0.19947,\
         'pdf function does not give expected result') 

    def test_meancalculation(self):
        self.gaussian.read_data_file('numbers.txt', True)
        self.assertEqual(self.gaussian.calculate_mean(),\
         sum(self.gaussian.data) / float(len(self.gaussian.data)), 'calculated mean not as expected')

    def test_stdevcalculation(self):
        self.gaussian.read_data_file('numbers.txt', True)
        self.assertEqual(round(self.gaussian.stdev, 2), 92.87, 'sample standard deviation incorrect')
        self.gaussian.read_data_file('numbers.txt', False)
        self.assertEqual(round(self.gaussian.stdev, 2), 88.55, 'population standard deviation incorrect')

    def test_add(self):
        gaussian_one = Gaussian(25, 3)
        gaussian_two = Gaussian(30, 4)
        gaussian_sum = gaussian_one + gaussian_two
        
        self.assertEqual(gaussian_sum.mean, 55)
        self.assertEqual(gaussian_sum.stdev, 5)

    def test_repr(self):
        gaussian_one = Gaussian(25, 3)
        
        self.assertEqual(str(gaussian_one), "mean 25, standard deviation 3")
        
tests = TestGaussianClass()

tests_loaded = unittest.TestLoader().loadTestsFromModule(tests)

unittest.TextTestRunner().run(tests_loaded)