{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "%pylab inline"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import numpy as np\n",

    "import pandas as pd\n",

    "from IPython.display import display\n",

    "\n",

    "def gradient_func(val):\n",

    "    return '<span style=\"background: linear-gradient(90deg, #d65f5f {0}%, transparent 0%)\">{0:.3f}</span>'.format(val)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "summary = pd.read_table('cutadapt.txt', sep='\\t', index_col=0)\n",

    "summary_reads = summary.loc[:, ['total_reads', 'reads_with_adapters', 'reads_too_short', 'reads_kept']]\n",

    "display(summary_reads.style.set_caption('Reads summary'))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "percent_reads = 100.0*summary_reads.div(summary_reads['total_reads'], axis=0)\n",

    "percent_reads.drop(columns=['total_reads'], inplace=True)\n",

    "display(percent_reads.style.format(gradient_func).set_caption('Reads percentage'))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "summary_bp = summary.loc[:, ['total_bp', 'bp_quality_trimmed', 'bp_kept']]\n",

    "display(summary_bp.style.set_caption('Base pairs summary'))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "percent_bp = 100.0*summary_bp.div(summary_bp['total_bp'], axis=0)\n",

    "percent_bp.drop(columns=['total_bp'], inplace=True)\n",

    "display(percent_bp.style.format(gradient_func).set_caption('Base pairs percentage'))"

   ]

  }

 ],

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  "toc": {

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   "title_cell": "Table of Contents",

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