<commit_before># Diversity of immune strategies explained by adaptation to pathogen statistics

This repository contains the source code associated with the manuscript

<NAME>, <NAME> : [Diversity of immune strategies explained by adaptation to pathogen statistics](http://dx.doi.org/10.1073/pnas.1600663113), PNAS 2016

It allows reproduction of all numerical results reported in the manuscript.

## Quick-start: Follow these links to see the analysis code producing the figures

- [Figure 2](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/fig2/figure2.ipynb)
- [Figure S1](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIopt/figure-SIopt.ipynb)
- [Figure S2](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSInonfactorizing/figure-SInonfactorizing.ipynb)
- [Figure S3](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIaltphases/figure-SIaltphases.ipynb)
- [Figure S4](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIevol/figure-SIevol.ipynb)

## Installation requirements

The code uses Python 2.7+.

A number of standard scientific python packages are needed for the numerical simulations and visualizations. An easy way to install all of these is to install a Python distribution such as [Anaconda](https://www.continuum.io/downloads).

- [numpy](http://github.com/numpy/numpy/)
- [scipy](https://github.com/scipy/scipy)
- [pandas](http://github.com/pydata/pandas)
- [matplotlib](http://github.com/matplotlib/matplotlib)

Additionally the code also relies on these packages:

- [shapely](http://github.com/Toblerity/Shapely)
- [palettable](http://github.com/jiffyclub/palettable)
- [scipydirect](http://github.com/andim/scipydirect/)
- [noisyopt](http://github.com/andim/noisyopt)

And optionally for nicer progress output install:

- [pyprind](http://github.com/rasbt/pyprind)<commit_msg>added Zenodo badge<commit_after># Diversity of immune strategies explained by adaptation to pathogen statistics

This repository contains the source code associated with the manuscript

<NAME>, <NAME> : [Diversity of immune strategies explained by adaptation to pathogen statistics](http://dx.doi.org/10.1073/pnas.1600663113), PNAS 2016

It allows reproduction of all numerical results reported in the manuscript.

[![DOI](https://zenodo.org/badge/57219749.svg)](https://zenodo.org/badge/latestdoi/57219749)

## Quick-start: Follow these links to see the analysis code producing the figures

- [Figure 2](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/fig2/figure2.ipynb)
- [Figure S1](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIopt/figure-SIopt.ipynb)
- [Figure S2](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSInonfactorizing/figure-SInonfactorizing.ipynb)
- [Figure S3](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIaltphases/figure-SIaltphases.ipynb)
- [Figure S4](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIevol/figure-SIevol.ipynb)

## Installation requirements

The code uses Python 2.7+.

A number of standard scientific python packages are needed for the numerical simulations and visualizations. An easy way to install all of these is to install a Python distribution such as [Anaconda](https://www.continuum.io/downloads).

- [numpy](http://github.com/numpy/numpy/)
- [scipy](https://github.com/scipy/scipy)
- [pandas](http://github.com/pydata/pandas)
- [matplotlib](http://github.com/matplotlib/matplotlib)

Additionally the code also relies on these packages:

- [shapely](http://github.com/Toblerity/Shapely)
- [palettable](http://github.com/jiffyclub/palettable)
- [scipydirect](http://github.com/andim/scipydirect/)
- [noisyopt](http://github.com/andim/noisyopt)

And optionally for nicer progress output install:

- [pyprind](http://github.com/rasbt/pyprind)
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