Package: lb-dsnd-distributions
lb-dsnd-distributions packageThis package provides the Gaussian distribution and Binomial distribution classes.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.Methods:calculate_mean() - Function to calculate the mean of the data set.
  
  calculate_stdev() - Function to calculate the standard deviation of the data set.
  
  plot_histogram() -  Function to output a histogram of the instance variable data 
  using matplotlib pyplot library.
  
  read_data_file(filename) -  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. 
  
  pdf(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
          
  plot_histogram_pdf(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
          
  __add__(other) - Function to add together two Gaussian distributions
      Args:
          other (Gaussian): Gaussian instance
      Returns:
          Gaussian: Gaussian distribution
      
  __repr__() - Function to output the characteristics of the Gaussian instanceBinomial - 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) number of trialsMethods:calculate_mean() - Function to calculate the mean from p and n
  
  calculate_stdev() - Function to calculate the standard deviation from p and n.
  
  read_data_file(filename) -  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.
  
  replace_stats_with_data() - Function to calculate p and n from the data set
      Args: 
          None
      Returns: 
          float: the p value
          float: the n value
      
  plot_bar() - Function to output a histogram of the instance variable data using 
  matplotlib pyplot library.
  
  pdf(k) - Probability density function calculator for the gaussian distribution.
      Args:
          x (float): point for calculating the probability density function
      Returns:
          float: probability density function output
      
  plot_bar_pdf() - 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 
      
  __add__(other) -  Function to add together two Binomial distributions with equal p
      Args:
          other (Binomial): Binomial instance            
      Returns:
          Binomial: Binomial distribution   
      
  __repr__() - Function to output the characteristics of the Binomial instance.FilesGeneraldistribution.py - contains Distribution class, its attributes and methods being inherited by Gaussian and Binomial class.Gaussiandistribution.py - contains Gaussian class, its attributes and methods stated above in lb-dsnd-distributions package summary.Binomialdistribution.py - contains Binomial class, its attributes and methods stated above in lb-dsnd-distributions package summary.InstallationThe code should run with no issues using Python versions 3.*.No extra besides the built-in libraries from Anaconda needed to run this project
Math
matplotlib