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    "info": {
        "author": "Zach Sailer",
        "author_email": "zachsailer@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
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        "description": "\n# GPSeer\n*An opinionated approach to inferring missing data in sparsely measured genotype-phenotype maps.*\n\n## Basic Usage\n\nThe simplest use-case is to call `gpseer` on an input `.csv` file containing genotype-phenotype data.\n\nFor example:\n```bash\ngpseer -i phenotypes.csv    \\\n      --wildtype \"00000\"    \\\n      --threshold 1         \\\n      --spline_order 2      \\\n      --epistasis_order 2\n```\nOutput:\n```bash\n[GPSeer] Running GPSeer on phenotypes.csv. Look for a predictions.csv file with your results.\n\n[GPSeer] + Reading data...\n[GPSeer] \u2514\u2500\u2500 Done reading data.\n\n[GPSeer] + Fitting data...\n[GPSeer] \u2514\u2500\u2500 Done fitting data.\n\n[GPSeer] + Predicting missing data...\n[GPSeer] \u2514\u2500\u2500 Done predicting...\n\n[GPSeer] GPSeer finished!\n```\nwhich returns a set of phenotype predictions using a 2nd-order spline .\n\n## Command-line options\nTo see all configuration items, call `gpseer --help`:\n```\nA tool for predicting phenotypes in a sparsely sampled genotype-phenotype maps.\n\nOptions\n-------\n\n-i <Unicode> (GPSeer.infile)\n    Default: 'test'\n    Input file.\n-o <Unicode> (GPSeer.outfile)\n    Default: 'predictions.csv'\n    Output file\n--model_definition=<Instance> (GPSeer.model_definition)\n    Default: None\n    An epistasis model definition written in Python.\n--wildtype=<Unicode> (GPSeer.wildtype)\n    Default: ''\n    The wildtype sequence\n--threshold=<Float> (GPSeer.threshold)\n    Default: 0.0\n    Experimental detection threshold, used by classifer.\n--spline_order=<Int> (GPSeer.spline_order)\n    Default: 0.0\n    Order of spline..\n--spline_smoothness=<Int> (GPSeer.spline_smoothness)\n    Default: 10\n    Smoothness of spline.\n--epistasis_order=<Int> (GPSeer.epistasis_order)\n    Default: 1\n    Order of epistasis in the model.\n--nreplicates=<Int> (GPSeer.nreplicates)\n    Default: None\n    Number of replicates for calculating uncertainty.\n--model_file=<Unicode> (GPSeer.model_file)\n    Default: ''\n    File containing epistasis model definition.\n```\n\n## Advanced epistasis models\n\nMore advanced models are possible by writing a short models file:\n```python\n# model.py\nfrom epistasis.models import (\n      EpistasisPipeline,\n      EpistasisLogisticRegression,\n      EpistasisSpline,\n      EpistasisLinearRegression\n)\n\nc.GPSeer.model_definition = EpistasisPipeline([\n      EpistasisLogisticRegression(threshold=5),\n      EpistasisSpline(k=3),\n      EpistasisLinearRegression(order=3)\n])\n```\nthen call the `gpseer` command.\n```\ngpseer -i phenotypes.csv --model_file=model.py\n```\n\n\n## Install\n\nClone this repository and install with pip:\n\n```\npip install gpseer\n```\n\n## Dependencies\n\n1. gpmap : Python API for analyzing genotype phenotype maps.\n2. epistasis : Python API for extracting high-order epistasis in genotype-phenotype maps\n3. traitlets: static typing and configurable objects in Python\n\n",
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