{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing genome wide CpG coverage of bismark alignments to v070 and v074 for _P.generosa_ juveniles (samples 205,206,214,215,220,221,226,and 227)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Variables to be set by user\n",
    "\n",
    "Re: two \"work_dir\" variables -\n",
    "Both are needed, as one is needed by Bash and the other by Python (respectively)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: work_dir=/Users/Shelly/Documents/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/compare_coverage/\n"
     ]
    }
   ],
   "source": [
    "%env work_dir = /Users/Shelly/Documents/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/compare_coverage/\n",
    "work_dir = \"/Users/Shelly/Documents/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/compare_coverage/\"\n",
    "output_plot = \"201900807_Pgenr_cov_comparison.png\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import os\n",
    "import numpy\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Make new working directory, download files and rename using wget ```--output-document``` argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Shelly/Documents/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/compare_coverage\n"
     ]
    }
   ],
   "source": [
    "cd $work_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loop through coverage files to calculate percent sequencing coverage for each Bismark subset\n",
    "\n",
    "** had to unzip my files first (gunzip *CpG_report.txt.gz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-205_S26_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-206_S27_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-214_S30_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-215_S31_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-220_S32_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-221_S33_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-226_S34_L004_cytosine_CpG_cov_report.CpG_report.txt', '/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-227_S35_L004_cytosine_CpG_cov_report.CpG_report.txt']\n",
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-205_S26_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Total coverage: 6879517'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'3x coverage: 1255993'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'5x coverage: 420589'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'10x coverage: 99839'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Mean coverage: 0.5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No coverage: 24544891'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent coverage: 21.9'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent 3x coverage: 4.0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent 5x coverage: 1.3'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent 10x coverage: 0.3'"
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       "                                        chrom  pos strand  meth  unmeth  \\\n",
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       "\n",
       "  C-context trinucleotide  coverage  \n",
       "0        CG           CGC         0  \n",
       "1        CG           CGC         0  \n",
       "2        CG           CGG         0  \n",
       "3        CG           CGG         0  \n",
       "4        CG           CGT         0  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-206_S27_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
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       "'Total coverage: 8851618'"
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       "'3x coverage: 2075782'"
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       "'5x coverage: 768131'"
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       "'10x coverage: 182693'"
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       "'No coverage: 22572790'"
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      "text/plain": [
       "'Percent coverage: 28.2'"
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     "metadata": {},
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      "text/plain": [
       "'Percent 3x coverage: 6.6'"
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      "text/plain": [
       "'Percent 5x coverage: 2.4'"
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       "3        CG           CGG         0  \n",
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     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-214_S30_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
     ]
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      "text/plain": [
       "'Total coverage: 11274157'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'3x coverage: 3256840'"
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      "text/plain": [
       "'5x coverage: 1283308'"
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      "text/plain": [
       "'10x coverage: 283539'"
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     "data": {
      "text/plain": [
       "'Mean coverage: 1.0'"
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     "data": {
      "text/plain": [
       "'No coverage: 20150251'"
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     "data": {
      "text/plain": [
       "'Percent coverage: 35.9'"
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     "metadata": {},
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     "data": {
      "text/plain": [
       "'Percent 3x coverage: 10.4'"
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    {
     "data": {
      "text/plain": [
       "'Percent 5x coverage: 4.1'"
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    {
     "data": {
      "text/plain": [
       "'Percent 10x coverage: 0.9'"
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       "                                        chrom  pos strand  meth  unmeth  \\\n",
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       "1  PGA_scaffold1__77_contigs__length_89643857    5      -     0       0   \n",
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       "3  PGA_scaffold1__77_contigs__length_89643857   19      -     0       0   \n",
       "4  PGA_scaffold1__77_contigs__length_89643857   54      +     0       1   \n",
       "\n",
       "  C-context trinucleotide  coverage  \n",
       "0        CG           CGC         0  \n",
       "1        CG           CGC         0  \n",
       "2        CG           CGG         0  \n",
       "3        CG           CGG         0  \n",
       "4        CG           CGT         1  "
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     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-215_S31_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
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       "'Total coverage: 8549789'"
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      "text/plain": [
       "'3x coverage: 2079399'"
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       "'5x coverage: 801290'"
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       "'10x coverage: 181652'"
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     "data": {
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       "'Mean coverage: 0.7'"
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     "metadata": {},
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     "data": {
      "text/plain": [
       "'No coverage: 22874619'"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Percent coverage: 27.2'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Percent 3x coverage: 6.6'"
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    {
     "data": {
      "text/plain": [
       "'Percent 5x coverage: 2.5'"
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     "data": {
      "text/plain": [
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     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-220_S32_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
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       "'Total coverage: 7168694'"
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     "data": {
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       "'3x coverage: 1300534'"
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       "'5x coverage: 421024'"
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       "'No coverage: 24255714'"
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    {
     "data": {
      "text/plain": [
       "'Percent coverage: 22.8'"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Percent 3x coverage: 4.1'"
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     "data": {
      "text/plain": [
       "'Percent 5x coverage: 1.3'"
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     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-221_S33_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
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       "'Total coverage: 7545672'"
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       "'3x coverage: 1420487'"
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       "'5x coverage: 467875'"
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       "'No coverage: 23878736'"
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    {
     "data": {
      "text/plain": [
       "'Percent coverage: 24.0'"
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     "data": {
      "text/plain": [
       "'Percent 3x coverage: 4.5'"
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     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-226_S34_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
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     "data": {
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       "'Percent coverage: 19.7'"
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>strand</th>\n",
       "      <th>meth</th>\n",
       "      <th>unmeth</th>\n",
       "      <th>C-context</th>\n",
       "      <th>trinucleotide</th>\n",
       "      <th>coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>4</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>5</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>18</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>19</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>54</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        chrom  pos strand  meth  unmeth  \\\n",
       "0  PGA_scaffold1__77_contigs__length_89643857    4      +     0       0   \n",
       "1  PGA_scaffold1__77_contigs__length_89643857    5      -     0       0   \n",
       "2  PGA_scaffold1__77_contigs__length_89643857   18      +     0       0   \n",
       "3  PGA_scaffold1__77_contigs__length_89643857   19      -     0       0   \n",
       "4  PGA_scaffold1__77_contigs__length_89643857   54      +     0       0   \n",
       "\n",
       "  C-context trinucleotide  coverage  \n",
       "0        CG           CGC         0  \n",
       "1        CG           CGC         0  \n",
       "2        CG           CGG         0  \n",
       "3        CG           CGG         0  \n",
       "4        CG           CGT         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------\n",
      "----------------------------------------------\n",
      "/Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/EPI-227_S35_L004_cytosine_CpG_cov_report.CpG_report.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Total coverage: 7059572'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'3x coverage: 1231454'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'5x coverage: 387772'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'10x coverage: 87477'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Mean coverage: 0.5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No coverage: 24364836'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent coverage: 22.5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent 3x coverage: 3.9'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent 5x coverage: 1.2'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Percent 10x coverage: 0.3'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>strand</th>\n",
       "      <th>meth</th>\n",
       "      <th>unmeth</th>\n",
       "      <th>C-context</th>\n",
       "      <th>trinucleotide</th>\n",
       "      <th>coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>4</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>5</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>18</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>19</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PGA_scaffold1__77_contigs__length_89643857</td>\n",
       "      <td>54</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CG</td>\n",
       "      <td>CGT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                        chrom  pos strand  meth  unmeth  \\\n",
       "0  PGA_scaffold1__77_contigs__length_89643857    4      +     0       0   \n",
       "1  PGA_scaffold1__77_contigs__length_89643857    5      -     0       0   \n",
       "2  PGA_scaffold1__77_contigs__length_89643857   18      +     0       0   \n",
       "3  PGA_scaffold1__77_contigs__length_89643857   19      -     0       0   \n",
       "4  PGA_scaffold1__77_contigs__length_89643857   54      +     0       0   \n",
       "\n",
       "  C-context trinucleotide  coverage  \n",
       "0        CG           CGC         0  \n",
       "1        CG           CGC         0  \n",
       "2        CG           CGG         0  \n",
       "3        CG           CGG         0  \n",
       "4        CG           CGT         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variable declaration\n",
    "bismark_subset_list = []\n",
    "mean_seq_coverage = []\n",
    "percent_seq_coverage = []\n",
    "percent_3x_seq_coverage = []\n",
    "percent_5x_seq_coverage = []\n",
    "percent_10x_seq_coverage = []\n",
    "\n",
    "# Create list of coverage files in current directory\n",
    "\n",
    "cov_files = !find /Volumes/web/metacarcinus/Pgenerosa/analyses/20190806_v074/*.CpG_report.txt\n",
    "print(cov_files)\n",
    "\n",
    "# Loop through coverage files\n",
    "for file in cov_files:\n",
    "    print(\"----------------------------------------------\")\n",
    "    print(\"----------------------------------------------\")\n",
    "    print (file)\n",
    "    subset_name = file[:-4] # Remove file suffix (.txt)\n",
    "    bismark_subset_list.append(subset_name)\n",
    "    #\n",
    "    #\n",
    "    # Create dataframe and add column names (taken from Bismark documentation)\n",
    "    dataframe = pandas.read_csv(\n",
    "        file,\n",
    "        sep='\\t',\n",
    "        header=None,\n",
    "        names=[\"chrom\", \"pos\", \"strand\", \"meth\", \"unmeth\", \"C-context\", \"trinucleotide\"])\n",
    "    \n",
    "    dataframe['coverage'] = dataframe['meth'] + dataframe['unmeth'] # Sum of methylated and unmethylated coverage for each position.\n",
    "    \n",
    "    total_CpG = len(dataframe) # Count of all CpGs in genome.\n",
    "    \n",
    "    \n",
    "    coverage = sum(dataframe['coverage']>0) # Count of all CpG positions with sequence coverage\n",
    "    coverage_3x = sum(dataframe['coverage']>=3)\n",
    "    coverage_5x = sum(dataframe['coverage']>=5)\n",
    "    coverage_10x = sum(dataframe['coverage']>=10)\n",
    "    mean_coverage = round(dataframe[\"coverage\"].mean(), 1)\n",
    "    \n",
    "    display(\"Total coverage: \" + str(coverage))\n",
    "    display(\"3x coverage: \" + str(coverage_3x))\n",
    "    display(\"5x coverage: \" + str(coverage_5x))\n",
    "    display(\"10x coverage: \" + str(coverage_10x))\n",
    "    display(\"Mean coverage: \" + str(mean_coverage))\n",
    "    \n",
    "    no_coverage = sum(dataframe['coverage']==0) # Count of all CpG posiitions with no sequence coverage\n",
    "    percent_coverage = round((coverage / total_CpG * 100.0), 1) # Rounds to 1 decimal\n",
    "    percent_3x_coverage = round((coverage_3x / total_CpG * 100.0), 1) # Rounds to 1 decimal\n",
    "    percent_5x_coverage = round((coverage_5x / total_CpG * 100.0), 1) # Rounds to 1 decimal\n",
    "    percent_10x_coverage = round((coverage_10x / total_CpG * 100.0), 1) # Rounds to 1 decimal\n",
    "    \n",
    "    mean_seq_coverage.append(mean_coverage)\n",
    "    percent_seq_coverage.append(percent_coverage)\n",
    "    percent_3x_seq_coverage.append(percent_3x_coverage)\n",
    "    percent_5x_seq_coverage.append(percent_5x_coverage)\n",
    "    percent_10x_seq_coverage.append(percent_10x_coverage)\n",
    "    \n",
    "    display(\"No coverage: \" + str(no_coverage))\n",
    "    display(\"Percent coverage: \" + str(percent_coverage))\n",
    "    display(\"Percent 3x coverage: \" + str(percent_3x_coverage))\n",
    "    display(\"Percent 5x coverage: \" + str(percent_5x_coverage))\n",
    "    display(\"Percent 10x coverage: \" + str(percent_10x_coverage))\n",
    "    display(dataframe.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bismark Sample</th>\n",
       "      <th>Mean Coverage</th>\n",
       "      <th>Percent 10x Coverage</th>\n",
       "      <th>Percent 3x Coverage</th>\n",
       "      <th>Percent 5x Coverage</th>\n",
       "      <th>Percent Coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>205</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>214</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>27.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>220</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>221</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>226</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>227</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bismark Sample  Mean Coverage  Percent 10x Coverage  Percent 3x Coverage  \\\n",
       "0            205            0.5                   0.3                  4.0   \n",
       "1            206            0.7                   0.6                  6.6   \n",
       "2            214            1.0                   0.9                 10.4   \n",
       "3            215            0.7                   0.6                  6.6   \n",
       "4            220            0.5                   0.3                  4.1   \n",
       "5            221            0.5                   0.3                  4.5   \n",
       "6            226            0.4                   0.2                  3.3   \n",
       "7            227            0.5                   0.3                  3.9   \n",
       "\n",
       "   Percent 5x Coverage  Percent Coverage  \n",
       "0                  1.3              21.9  \n",
       "1                  2.4              28.2  \n",
       "2                  4.1              35.9  \n",
       "3                  2.5              27.2  \n",
       "4                  1.3              22.8  \n",
       "5                  1.5              24.0  \n",
       "6                  1.1              19.7  \n",
       "7                  1.2              22.5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage_dataframe = pandas.DataFrame(\n",
    "    {\n",
    "        'Bismark Sample': ['205','206','214','215', '220', '221', '226', '227'],\n",
    "        'Mean Coverage': mean_seq_coverage,\n",
    "        'Percent Coverage': percent_seq_coverage,\n",
    "        'Percent 3x Coverage': percent_3x_seq_coverage,\n",
    "        'Percent 5x Coverage': percent_5x_seq_coverage,\n",
    "        'Percent 10x Coverage': percent_10x_seq_coverage,\n",
    "        \n",
    "    })\n",
    "\n",
    "coverage_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create line plot overlayed on bar chart, showing percent sequencing coverage for each Bismark subset option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort data\n",
    "coverage_dataframe = coverage_dataframe.sort_values(by=['Percent Coverage'])\n",
    "\n",
    "\n",
    "exclude = ['Mean Coverage']\n",
    "coverage_dataframe = coverage_dataframe.loc[:, coverage_dataframe.columns.difference(exclude)] # Don't plot Mean Coverage\n",
    "ax = coverage_dataframe.plot(x='Bismark Sample',marker='o') # Line plot with marker points.\n",
    "coverage_dataframe.plot(kind='bar', x='Bismark Sample', ax=ax) # Overlays bar chart using axes defined in line plot\n",
    "\n",
    "# Save figure to file.\n",
    "# \"bbox_inches\" argument needed to prevent x asis lables from getting cut off.\n",
    "plt.savefig(output_plot, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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