EpiPy

A Python package for epidemiology. Epipy is a Python package for epidemiology. It contains tools for analyzing and visualizing epidemiology data. Epipy can currently produce:

• stratified summary statistics
• case tree and checkerboard plots
• epicurves
• analysis of case attribute (e.g. sex) by generation
• 2x2 tables with odds ratio and relative risk
• summary of cluster basic reproduction numbers

Installation

The github version of epipy is substantially further along that the pip version. I suggest installing from this repo when possible.

Install using pip:

pip install epipy

Or clone the repository and install using setup.py:

git clone https://github.com/cmrivers/epipy.git
cd ./epipy
pip install -r requirements.txt
python setup.py install

EpiPy is in development. Please feel free to contribute. Contact me at caitlin.rivers@gmail.com or @cmyeaton with any questions.

Learning Python

New to Python? I teach a self-paced course on Python for epidemiologists over at episkills.teachable.com.

Contributing/Development

If you want to contribute in this great project. First fork this repo in github.

Clone your forked repo in your terminal using the appropriate command:

git clone https://github.com/your-git-user-name/epipy.git
cd ./epipy

Add this repo as upstream remote:

git remote add upstream git@github.com:cmrivers/epipy.git

We use gitflow. Follow this instructions to install.

git branch master origin/master
git flow init -d
git flow feature start <your feature>

For install the tools for TDD use:

pip install -r requirements.txt
pip install -r requirements-tdd.txt

To run the test suit use:

cd ./epipy
py.test test

Then, do work and commit your changes. After finish your feature with coverage of test, please pull any change that ocurred from the upstream repo. You can use:

git pull upstream master

If git fast-forward error is issue then use:

git rebase upstream/master

Resolve the merge conflicts that couid exist using:

git mergetool
git rebase --continue

After everything is ok then:

git flow feature publish <your feature>

When done, open a pull request to your feature branch.

Documentation

The docs live at: cmrivers.github.io/epipy