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    "info": {
        "author": "Zachary R. Sailer",
        "author_email": "zachsailer@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "\n# Epistasis\n\n[![Join the chat at https://gitter.im/harmslab/epistasis](https://badges.gitter.im/harmslab/epistasis.svg)](https://gitter.im/harmslab/epistasis?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)\n[![Binder](http://mybinder.org/badge.svg)](https://beta.mybinder.org/v2/gh/harmslab/epistasis-notebooks/master)\n[![Documentation Status](https://readthedocs.org/projects/epistasis/badge/?version=latest)](http://epistasis.readthedocs.io/?badge=latest)\n[![Build Status](https://travis-ci.org/harmslab/epistasis.svg?branch=master)](https://travis-ci.org/harmslab/epistasis)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1215853.svg)](https://doi.org/10.5281/zenodo.1215853)\n\n*Python API for estimating statistical, high-order epistasis in genotype-phenotype maps.*\n\nAll models follow a *Scikit-learn* interface and thus seamlessly plug in to the PyData ecosystem. For more information about the type of models included in this package,\nread our [docs](http://epistasis.readthedocs.io/?badge=latest). You can also read more about the theory behind these models in our [paper](https://doi.org/10.1534/genetics.116.195214).\n\nFinally, if you'd like to test out this package without any installing, try these Jupyter notebooks [here](https://mybinder.org/v2/gh/harmslab/epistasis-notebooks/master) (thank you [Binder](https://mybinder.org/)!).\n\n## Examples\n\nThe Epistasis package works best in combinations with GPMap, an API for managing\ngenotype-phenotype map data. Construct a GenotypePhenotypeMap object and pass it\ndirectly to an epistasis model.\n\n\n```python\n# Import a model and the plotting module\nfrom gpmap import GenotypePhenotypeMap\nfrom epistasis.models import EpistasisLinearRegression\nfrom epistasis.pyplot import plot_coefs\n\n# Genotype-phenotype map data.\nwildtype = \"AAA\"\ngenotypes = [\"ATT\", \"AAT\", \"ATA\", \"TAA\", \"ATT\", \"TAT\", \"TTA\", \"TTT\"]\nphenotypes = [0.1, 0.2, 0.4, 0.3, 0.3, 0.6, 0.8, 1.0]\n\n# Create genotype-phenotype map object.\ngpm = GenotypePhenotypeMap(wildtype=wildtype,\n                           genotypes=genotypes,\n                           phenotypes=phenotypes)\n\n# Initialize an epistasis model.\nmodel = EpistasisLinearRegression(order=3)\n\n# Add the genotype phenotype map.\nmodel.add_gpm(gpm)\n\n# Fit model to given genotype-phenotype map.\nmodel.fit()\n\n# Plot coefficients (powered by matplotlib).\nplot_coefs(model, figsize=(3,5))\n```\n\n<img src=\"docs/img/coef_example.png\" width=\"200\">\n\nMore examples can be found in these [binder notebooks](https://mybinder.org/v2/gh/harmslab/epistasis-notebooks/master).\n\n## Installation\n\nEpistasis works in Python 3+ (we do not guarantee it will work in Python 2.)\n\nTo install the most recent release on PyPi:\n```\npip install epistasis\n```\n\nTo install from source, clone this repo and run:\n```\npip install -e .\n```\n\n## Documentation\n\nDocumentation and API reference can be viewed [here](http://epistasis.readthedocs.io/).\n\n## Dependencies\n\n* [gpmap](https://github.com/harmslab/gpmap): Module for constructing powerful genotype-phenotype map python data-structures.\n* [Scikit-learn](http://scikit-learn.org/stable/): Simple to use machine-learning algorithms\n* [Numpy](http://www.numpy.org/): Python's array manipulation packaged\n* [Scipy](http://www.scipy.org/): Efficient scientific array manipulations and fitting.\n* [lmfit](https://lmfit.github.io/lmfit-py/): Non-linear least-squares minimization and curve fitting in Python.\n\n### Optional dependencies\n\n* [matplotlib](): Python plotting API.\n* [ipython](): interactive python kernel.\n* [jupyter notebook](): interactive notebook application for running python kernels interactively.   \n* [ipywidgets](): interactive widgets in python.\n\n## Development\n\nWe welcome pull requests! If you find a bug, we'd love to have you fix it. If\nthere is a feature you'd like to add, feel free to submit a\npull request with a description of the addition. We also ask that you write the\nappropriate unit-tests for the new feature and add documentation to our Sphinx docs.\n\nTo run the tests on this package, make sure you have `pytest` installed and run from the base directory:\n\n```\npytest\n```\n\n## Citing\nIf you use this API for research, please cite this [paper](https://doi.org/10.1534/genetics.116.195214).\n\nYou can also cite the software directly:\n\n```\n@misc{zachary_sailer_2017_252927,\n  author       = {Zachary Sailer and Mike Harms},\n  title        = {harmslab/epistasis: Genetics paper release},\n  month        = jan,\n  year         = 2017,\n  doi          = {10.5281/zenodo.1215853},\n  url          = {https://doi.org/10.5281/zenodo.1215853}\n}\n```\n\n\n",
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