gauss-binom-test 0.2.4

Gaussian and Binomial distributions

Gaussian and Binomial Distributions

A basic package for calculating and visualizing Gaussian and Binomial Distributions. This is only a test.

How to import the classes using the interpret

$ python
>>> from gauss_binom_test import Gaussian, Binomial

Features

• Calculate the Gaussian and Binomial pdf of a data set
• Calculate the mean and standard deviation
• Calculate the sum of two pdfs
• Plot the histogram of data points and normalizdd histogram of the pdf

Example

Open the Python interpreter:

Create a standard normalmdistribution (zero mean and standard deviation equal to one)

>>> gaussian_normal = Gaussian()
>>> gaussian_normal
mean 0, standard deviation 1

Create a Gaussian distribution with mean=5 and stdv=2

>>> gaussian_one = Gaussian(5,2)
mean 5, standard deviation 2

Addition of two Gaussian distributions

>>> gaussian_sum = gaussian_normal + gaussian_one
>>> gaussian_sum.mean()
5
>>> sample stdev
>>> gaussian_sum.stdev()
2.2360679

Addition of three Gaussian distributions

>>> gaussian_sum = Gaussian(1,2) + Gaussian(2,3) + Gaussian(3,4)
>>> gaussian_sum
>>> mean 6, standard deviation 5.3851648

**Calculate the value of the Gaussian distribution function at a given point **

>>> gaussian_one = Gaussian(5,2)
>>> gaussian_one.pdf(6)
0.17603266338

Generate a Binomial distribution of 20 trials and 0.5 probability of an event occurring

>>> binom_one = Binomial(.5, 20)
>>> binom_one
mean 10.0, standard deviation 2.23606797749979, p 0.5, n 20

Calculate the probability of occurring 5 successes for a Binomial distribution of 20 trials and p=0.5

>>> Binomial(.5, 20).pdf(5)
0.0147857666015625

Adding two binomial distributions

>>> binom_sum = Binomial(.5, 20) + Binomial(.5, 10)
mean 15.0, standard deviation 2.7386127875258306, p 0.5, n 30