{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gathergrams - display gather results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pylab\n",
    "import numpy\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>intersect_bp</th>\n",
       "      <th>f_orig_query</th>\n",
       "      <th>f_match</th>\n",
       "      <th>f_unique_to_query</th>\n",
       "      <th>f_unique_weighted</th>\n",
       "      <th>average_abund</th>\n",
       "      <th>median_abund</th>\n",
       "      <th>std_abund</th>\n",
       "      <th>name</th>\n",
       "      <th>filename</th>\n",
       "      <th>md5</th>\n",
       "      <th>f_match_orig</th>\n",
       "      <th>unique_intersect_bp</th>\n",
       "      <th>gather_result_rank</th>\n",
       "      <th>remaining_bp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9323000</td>\n",
       "      <td>0.024885</td>\n",
       "      <td>0.977254</td>\n",
       "      <td>0.024885</td>\n",
       "      <td>0.008649</td>\n",
       "      <td>6.831814</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.847358</td>\n",
       "      <td>GCA_000013645</td>\n",
       "      <td>all-gather-genomes.sbt.zip</td>\n",
       "      <td>ee9cb05018009af43429e83e7d502a0a</td>\n",
       "      <td>0.977254</td>\n",
       "      <td>9323000</td>\n",
       "      <td>0</td>\n",
       "      <td>365320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7288000</td>\n",
       "      <td>0.019453</td>\n",
       "      <td>0.999040</td>\n",
       "      <td>0.019453</td>\n",
       "      <td>0.023517</td>\n",
       "      <td>23.763035</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.637611</td>\n",
       "      <td>GCA_000009705</td>\n",
       "      <td>all-gather-genomes.sbt.zip</td>\n",
       "      <td>264cfdad44548ad96c4a24b6a514a877</td>\n",
       "      <td>0.999040</td>\n",
       "      <td>7288000</td>\n",
       "      <td>1</td>\n",
       "      <td>358032000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7039000</td>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.999858</td>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.089562</td>\n",
       "      <td>93.700242</td>\n",
       "      <td>92.0</td>\n",
       "      <td>27.877040</td>\n",
       "      <td>GCA_000196115</td>\n",
       "      <td>all-gather-genomes.sbt.zip</td>\n",
       "      <td>7141c3fbf206734950d13cadba0bf46c</td>\n",
       "      <td>0.999858</td>\n",
       "      <td>7039000</td>\n",
       "      <td>2</td>\n",
       "      <td>350993000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6604000</td>\n",
       "      <td>0.017627</td>\n",
       "      <td>0.999697</td>\n",
       "      <td>0.017627</td>\n",
       "      <td>0.014621</td>\n",
       "      <td>16.304058</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.636986</td>\n",
       "      <td>GCA_000018565</td>\n",
       "      <td>all-gather-genomes.sbt.zip</td>\n",
       "      <td>5165e0f27ae96c8a78da00c2d30a8e36</td>\n",
       "      <td>0.999697</td>\n",
       "      <td>6604000</td>\n",
       "      <td>3</td>\n",
       "      <td>344389000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   intersect_bp  f_orig_query   f_match  f_unique_to_query  f_unique_weighted  \\\n",
       "0       9323000      0.024885  0.977254           0.024885           0.008649   \n",
       "1       7288000      0.019453  0.999040           0.019453           0.023517   \n",
       "2       7039000      0.018789  0.999858           0.018789           0.089562   \n",
       "3       6604000      0.017627  0.999697           0.017627           0.014621   \n",
       "\n",
       "   average_abund  median_abund  std_abund           name  \\\n",
       "0       6.831814           6.0   5.847358  GCA_000013645   \n",
       "1      23.763035          18.0  29.637611  GCA_000009705   \n",
       "2      93.700242          92.0  27.877040  GCA_000196115   \n",
       "3      16.304058          16.0   6.636986  GCA_000018565   \n",
       "\n",
       "                     filename                               md5  f_match_orig  \\\n",
       "0  all-gather-genomes.sbt.zip  ee9cb05018009af43429e83e7d502a0a      0.977254   \n",
       "1  all-gather-genomes.sbt.zip  264cfdad44548ad96c4a24b6a514a877      0.999040   \n",
       "2  all-gather-genomes.sbt.zip  7141c3fbf206734950d13cadba0bf46c      0.999858   \n",
       "3  all-gather-genomes.sbt.zip  5165e0f27ae96c8a78da00c2d30a8e36      0.999697   \n",
       "\n",
       "   unique_intersect_bp  gather_result_rank  remaining_bp  \n",
       "0              9323000                   0     365320000  \n",
       "1              7288000                   1     358032000  \n",
       "2              7039000                   2     350993000  \n",
       "3              6604000                   3     344389000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_id = 'SRR606249'\n",
    "df = pd.read_csv(f'outputs/{sample_id}.gather.csv')\n",
    "\n",
    "def fix_name(x):\n",
    "    return \"_\".join(x.split('_')[:2]).split('.')[0]\n",
    "\n",
    "df['name'] = df['name'].apply(fix_name)\n",
    "df[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 1: gathergram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.figure(num=None, figsize=(8, 6))\n",
    "\n",
    "pylab.plot(df.index, df.intersect_bp / 1e6, 'x', label='all hashes classified to this genome')\n",
    "pylab.plot(df.index, df.unique_intersect_bp / 1e6, 'o', label='hashes specific to this genome')\n",
    "#pylab.plot(df.index, df.remaining_bp / 1e6, '-', label='unclassified bp remaining')\n",
    "\n",
    "pylab.xlabel('genome gather rank')\n",
    "pylab.ylabel('num hashes (millions)')\n",
    "pylab.legend(loc='upper right')\n",
    "pylab.title('hu-s1 gather x genbank')\n",
    "\n",
    "pylab.savefig(f'/tmp/gathergram-{sample_id}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (myenv)",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}