Metadata-Version: 2.1
Name: problib
Version: 0.2
Summary: Gaussian and Binomial Probability Distributions
Home-page: https://github.com/lisza/problib
Author: lisza
License: MIT
Description: # problib
        
        [![PyPI version](https://img.shields.io/pypi/v/problib.svg)](https://pypi.python.org/pypi/problib)
        ![GitHub license](https://img.shields.io/github/license/lisza/problib)
        
        Small Python library to calculate and plot probability density functions for Gaussian and Binomial distributions.
        
        Note that this is a test Python package, it works fine but has some issues and solves no new problem. You can totally give it a try though!
        
        
        ## Installation with pip
        ```
        pip install problib
        
        # test.pypi install
        pip install -i https://test.pypi.org/simple/ problib==0.2
        ```
        
        ## Use in Python
        #### Gaussian Distribution
        ```python
        from problib import Gaussian
        
        # Create new gaussian distribution with mu=25 and sigma=2
        gaussian = Gaussian(25, 2)
        gaussian   # mean 25, standard deviation 0.5
        
        # Return mean and standard deviation
        gaussian.mean    # 25.0
        gaussian.stdev   # 2.0
        
        # Compute probability density function at point x=25
        gaussian.pdf(25)    # 0.19947
        
        # Add two Gaussian distributions together
        gaussian_b =  Gaussian(80, 20)
        gaussian + gaussian_b    # 'mean 105, standard deviation 20.09975'
        
        # Read data from .txt file, recompute mean and stdev
        gaussian.read_data_file('numbers.txt')
        gaussian.calculate_mean()
        gaussian.calculate_stdev()
        
        # Plot data (requires loading data first)
        gaussian.plot_histogram()
        # Plot normalized histogram of data and probability density function (requires loading data first)
        gaussian.plot_histogram_pdf()
        ```
        ![gaussian_pdf_plot](https://github.com/lisza/problib/blob/master/gauss_histogram_pdf_plot.png)
        
        
        #### Binomial Distribution
        The Binomial distribution has the same methods as Gaussian but takes success probability and trial size as required inputs
        
        ```python
        from problib import Binomial
        
        # Create new Binomial distribution with p=0.4 and n=20
        binomial = Binomial(0.4, 20)
        
        binomial.p    # 0.4
        binomial.n    # 20
        binomial.mean    # 8.0
        binomial.stdev   # 2.19089
        
        # Compute probability density function for k=5
        binomial.pdf(5)    # 0.07465
        
        # Read data from .txt file, recalculate properties
        binomial.read_data_file('numbers_binomial.txt')
        binomial.replace_stats_with_data()
        
        # Plot data and probabilities (works with or without read data)
        binomial.plot_bar()
        binomial.plot_bar_pdf()
        ```
        ![binomial_pdf_plot](https://github.com/lisza/problib/blob/master/binomial_bar_pdf_plot.png)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
