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+{
+    "python.linting.flake8Enabled": true,
+    "python.linting.enabled": true,
+    "python.linting.flake8Args": ["--ignore=E501, F401"]
+}

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+theme: jekyll-leap-year-theme

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+## probdists
+
+### Overview  
+
+`probdists` is a Python package to model probability distributions.  
+Supports Bernoulli, Binomial, Exponential, Gamma, Gaussian, Triangular, and Uniform Distributions with support for Poisson, Bates, and Irwin-Hall on the way!  
+
+For usage, see [here](#usage).
+
+### Installation 
+
+Installing using pip:
+```
+>>> pip install probdists
+```
+
+Installing using virtual-environment (recommended):
+```
+>>> conda create -n myEnv python=3.6 anaconda
+>>> conda activate myEnv
+>>> pip install probdists
+```
+
+You can find the project on PyPi [here](https://pypi.org/project/probdists/).
+
+### Usage  
+
+Here, we shall demonstrate a general workflow by taking a particular class, say, Gaussian. 
+
+After installation, we can likewise interact with the package:  
+
+```
+>>> from probdists import Gaussian
+
+>>> gaussian = Gaussian()
+>>> gaussian.read_data_file('demo_gaussian_data')
+# for your own file, replace 'demo_gaussian_data' with 'my_text_file.txt'
+
+# to access data
+>>> print(gaussian.data)
+[1, 3, 99, 100, 120, 32, 330, 23, 76, 44, 31]
+
+# to calculate mean
+>>> print(gaussian.calculate_mean())
+78.09
+
+# to calculate standard deviation
+>>> print(gaussian.calculate_stdev())
+92.87
+
+# to calculate pdf
+>>> print(gaussian.calculate_pdf(25, 5))
+0.00365
+
+# to calculate cdf
+>>> print(gaussian.cdf(25, 5))
+0.28378
+
+# to add two individual distributions
+>>> gaussian_one = Gaussian(25, 3)
+>>> gaussian_two = Gaussian(30, 4)
+>>> gaussian_sum = gaussian_one + gaussian_two
+>>> print(gaussian_sum.mean)
+55
+>>> print(gaussian_sum.stdev)
+5.0
+
+# plot histogram of data
+>>> gaussian.plot_histogram()
+
+# plot normalized histogram of data and plot of pdf along same range
+>>> gaussian.plot_histogram_pdf(n_spaces = 50)
+```
+
+## Documentation   
+
+### Distribution  
+
+The Distribution class is a base class that all other concrete classes inherit from. It provides common fields such as `mean`, `stdev`, `pdf`, and `cdf` and implements `read_data_file()` function.  
+
+> `read_data_file(self, file_name, separator='\\n', header=None)` 
+Function to read in data from a txt file, csv file
+and excel formats (xls, xlsx, xlsm, xlsb, odf, ods and odt)
+
+The txt file should have one number (float) per line or
+numbers should be separator seperated.
+
+No need for separator argument with csv file, it will
+by default be ',' so csv files should have , seperated
+numbers
+
+For excel file formats. There should only be one column
+containing numbers, and if 0th row is header then header
+argument should be 0. The numbers are taken from next row
+mentioned in header parameter.
+
+The numbers are stored in the data attribute.
+
+**Args**:   
+    file_name (string): name of a file to read from   
+    separator (character): custom separator to use if required   
+    header (int or by default None): to specify if excel file   
+    contains header.    
+**Returns**:   
+    None    
+
+You should instantiate a child of Distribution class and not this class itself.  
+
+### Bernoulli Distribution   
+
+Bernoulli distribution class for calculating and
+    visualizing a Bernoulli 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) to be extracted from the data file   
+    p (float) representing the probability of an event occurring (1). Default 0.5   
+
+> ` __init__(self, prob=0.5)`   
+Constructor function for Bernoulli class. 
+
+**Args**:   
+    p (float): representing the probability of an event occurring (1). Default 0.5
+> `calculate_mean(self, round_to=2)`  
+Method to calculate the mean of a Bernoulli distribution
+
+**Args**:   
+    round_to (int): Round the mean value. Defaults to 2.      
+**Returns**:    
+    float: mean of the data set      
+
+> `calculate_stdev(self, round_to=2)`  
+Function to calculate the standard deviation from p.
+
+**Args**:   
+    round_to (int): Round the mean value. Defaults to 2.     
+**Returns**:   
+    float: standard deviation of the data set.     
+
+> `replace_stats_with_data(self)` 
+Method to calculate p from the data set
+
+**Args**:   
+    None     
+**Returns**:   
+    float: the p value      
+
+> `plot_bar(self)`  
+Method to plot a histogram of the instance variable data using matplotlib pyplot library.
+
+**Args**:   
+    None   
+**Returns**:  
+    None   
+
+> `calculate_pdf(self, k, round_to=2)`
+Method to calculate pdf for the bernoulli distribution.
+
+**Args**:   
+    k (float): point for calculating the probability density function. Range of k: {0, 1}
+    round_to (int): Round the mean value. [Default value: 2 floating point]   
+**Returns**:   
+    float: probability density function output   
+
+> `calculate_cdf(self, k, round_to=2)` 
+Method to calculate cdf for the bernoulli distribution.
+
+**Args**:   
+    k (float): point for calculating the cumulative distribution function   
+    round_to (int): Round the mean value. [Default value: 2 floating point]   
+**Returns**:   
+    float: cumulative distribution function output   
+
+> `plot_bar_pdf(self)`
+Method to plot the pdf of the bernoulli distribution
+
+**Args**:   
+    None   
+**Returns**:   
+    list: x values for the pdf plot   
+    list: y values for the pdf plot   
+
+The Bernoulli class overrides `__add__()` and `__repr__()` functions to allow you to
+add two objects and get characteristic about it, respectively.  
+
+### 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) to be extracted from the data file   
+    p (float) representing the probability of an event occurring   
+    n (int) number of trials   
+
+> `__init__(self, prob=.5, size=20)`  
+Constructor function for Binomial Distribution.   
+
+> `calculate_mean(self, round_to=2)`  
+Function to calculate the mean from p and n
+
+**Args**:   
+    round_to (int): Round the mean value. [Default value: 2 floating point]   
+**Returns**:   
+    float: mean of the data set    
+
+> `calculate_stdev(self, round_to=2)`   
+Function to calculate the standard deviation from p and n.
+
+**Args**:   
+    round_to (int): Round the mean value. [Default value: 2 floating point]    
+**Returns**:    
+    float: standard deviation of the data set    
+
+> `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   
+
+> `plot_bar(self)`  
+Function to output a histogram of the instance variable data using
+        matplotlib pyplot library.
+
+**Args**:   
+    None   
+**Returns**:   
+    None   
+
+> `calculate_pdf(self, k, round_to=2)`   
+Probability density function calculator for the binomial distribution.
+
+**Args**:   
+    k (float): point for calculating the probability density function    
+    round_to (int): Round the mean value. [Default value: 2 floating point]   
+**Returns**:   
+    float: probability density function output   
+
+> `calculate_cdf(self, k, round_to=2)`   
+Cumulative distribution function calculator for the binomial distribution.
+
+**Args**:
+    k (float): point for calculating the cumulative distribution function   
+    round_to (int): Round the mean value. [Default value: 2 floating point]   
+**Returns**:    
+    float: cumulative distribution function output   
+
+> `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   
+
+The Binomial class overrides `__add__()` and `__repr__()` functions to allow you to
+add two objects and get characteristic about it, respectively.  
+
+<!-- TODO:
+### Gamma Distribution
+### Gaussian Distribution
+### Triangular Distribution
+### Uniform Distribution -->
+
+## Code Repository    
+
+Please find the code repository for `probdists` [here](https://github.com/hot9cups/probdists/).   
+
+## About   
+
+<!-- TODO: Add demo plots in ## usage -->
+<!-- TODO: Link to documentation page when page is online -->
+
+## License  
+
+`probdists` is distributed under [MIT License](LICENCE.txt).