{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test `alignparse.ccs.Summaries`\n",
    "Tests this class and makes sure it works with and without reports and `np` tags giving number of passes.\n",
    "This Jupyter notebook is designed to be run with `nbval` for the testing.\n",
    "\n",
    "First, import Python modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import contextlib\n",
    "import os\n",
    "import tempfile\n",
    "import warnings\n",
    "\n",
    "import Bio.SeqIO\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import alignparse.ccs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hide warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data frame giving the `ccs` report file and the CCS FASTQ file for each run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_names = ['recA_lib-1', 'recA_lib-2']\n",
    "ccs_dir = '../notebooks/input_files'\n",
    "\n",
    "ccs_df = pd.DataFrame(\n",
    "            {'name': run_names,\n",
    "             'report': [f\"{ccs_dir}/{name}_report.txt\" for name in run_names],\n",
    "             'fastq': [f\"{ccs_dir}/{name}_ccs.fastq\" for name in run_names]\n",
    "             })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an `alignparse.ccs.Summaries` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recA_lib-1\n",
      "recA_lib-2\n"
     ]
    }
   ],
   "source": [
    "summaries = alignparse.ccs.Summaries(ccs_df)\n",
    "\n",
    "for summary in summaries.summaries:\n",
    "    print(summary.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm ZMW stats exist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summaries.has_zmw_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get and plot the ZMW stats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>name</th>\n",
       "      <th>status</th>\n",
       "      <th>number</th>\n",
       "      <th>fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>recA_lib-1</td>\n",
       "      <td>Success -- CCS generated</td>\n",
       "      <td>139</td>\n",
       "      <td>0.837349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>recA_lib-1</td>\n",
       "      <td>Failed -- Lacking full passes</td>\n",
       "      <td>19</td>\n",
       "      <td>0.114458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>recA_lib-1</td>\n",
       "      <td>Failed -- Draft generation error</td>\n",
       "      <td>3</td>\n",
       "      <td>0.018072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>recA_lib-1</td>\n",
       "      <td>Failed -- Min coverage violation</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>recA_lib-1</td>\n",
       "      <td>Failed -- CCS below minimum RQ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>recA_lib-1</td>\n",
       "      <td>Failed -- Other reason</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>recA_lib-2</td>\n",
       "      <td>Success -- CCS generated</td>\n",
       "      <td>124</td>\n",
       "      <td>0.794872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>recA_lib-2</td>\n",
       "      <td>Failed -- Lacking full passes</td>\n",
       "      <td>22</td>\n",
       "      <td>0.141026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>recA_lib-2</td>\n",
       "      <td>Failed -- Draft generation error</td>\n",
       "      <td>4</td>\n",
       "      <td>0.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>recA_lib-2</td>\n",
       "      <td>Failed -- Min coverage violation</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>recA_lib-2</td>\n",
       "      <td>Failed -- CCS below minimum RQ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>recA_lib-2</td>\n",
       "      <td>Failed -- Other reason</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name                            status  number  fraction\n",
       "0   recA_lib-1          Success -- CCS generated     139  0.837349\n",
       "1   recA_lib-1     Failed -- Lacking full passes      19  0.114458\n",
       "2   recA_lib-1  Failed -- Draft generation error       3  0.018072\n",
       "3   recA_lib-1  Failed -- Min coverage violation       1  0.006024\n",
       "4   recA_lib-1    Failed -- CCS below minimum RQ       2  0.012048\n",
       "5   recA_lib-1            Failed -- Other reason       2  0.012048\n",
       "6   recA_lib-2          Success -- CCS generated     124  0.794872\n",
       "7   recA_lib-2     Failed -- Lacking full passes      22  0.141026\n",
       "8   recA_lib-2  Failed -- Draft generation error       4  0.025641\n",
       "9   recA_lib-2  Failed -- Min coverage violation       2  0.012821\n",
       "10  recA_lib-2    Failed -- CCS below minimum RQ       2  0.012821\n",
       "11  recA_lib-2            Failed -- Other reason       2  0.012821"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summaries.zmw_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot these stats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 80x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = summaries.plot_zmw_stats()\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the same with a `Summaries` object with no reports defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "summaries_no_zmw = alignparse.ccs.Summaries(ccs_df, report_col=None, ncpus=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No ZMW stats defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summaries_no_zmw.has_zmw_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So trying to get stats raises an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot plot ZMW stats\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    summaries_no_zmw.plot_zmw_stats()\n",
    "except ValueError:\n",
    "    print('cannot plot ZMW stats')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now get information on CCS statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         name  passes\n",
      "0  recA_lib-1      28\n",
      "1  recA_lib-1      29\n",
      "2  recA_lib-1      22\n",
      "3  recA_lib-1      16\n",
      "4  recA_lib-1      20\n",
      "\n",
      "         name  length\n",
      "0  recA_lib-1    1325\n",
      "1  recA_lib-1    1340\n",
      "2  recA_lib-1    1339\n",
      "3  recA_lib-1     985\n",
      "4  recA_lib-1    1196\n",
      "\n",
      "         name  accuracy\n",
      "0  recA_lib-1  0.999998\n",
      "1  recA_lib-1  0.999976\n",
      "2  recA_lib-1  0.999686\n",
      "3  recA_lib-1  0.999986\n",
      "4  recA_lib-1  0.999592\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for stat in ['passes', 'length', 'accuracy']:\n",
    "    if summaries.has_stat(stat):\n",
    "        print(summaries.ccs_stats(stat).head(n=5))\n",
    "        print()\n",
    "    else:\n",
    "        print(f\"no {stat} stat\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot these stats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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adW9sOYVCAZVKZXnY/2dOG1Ls59G2pMpeUfuP/i01e8gudv9PymzN85H7eAPy5beH7JaSIrtdF9PAwEAkJyejrKxMf6mflZWFrl27Gm2bnJyMVatW6R/HxsZi8uTJovZX2dwvI0eONKsNb29vSbYx16RJk4yWbdq0SbL2yz3+fraULJ1zx1xPym7J76OyzNb8fsuzij3epvpnZf2gsvzW9ik5+wogb3+xJrtdFNPS0lKUlpairKwMZWVl0Ol0UCqVaN68OdRqNXbs2IGIiAikp6fj2rVr6NSpk1Eb0dHRBmMHKJVKUd/G0mg0oidWM8Wcfcr9LTEp21epVPDw8EBeXp4sk6RJddxNeTR7ZS8SscfrSZmtOf7lWaU43hXlsOSYm/Oc5O4rgHz9xdy+UhmzimlUVBQWLVqERo0aYf369QgPD0etWrUs2qEpW7duxZYtW/SP09LS0LNnT7zxxhuYPXs2VqxYgS1btqB27dqYNWsWvLy8jNrw9fU1+PaVVqsV9QsVBEGSDmBOG3J1NDnbL/8PT2pSHffKPKl9sft/UmZrnk/5z0pxvCv6eUuOuZjt5eorgPz9xZq2zSqm33zzDd5++200atQIY8eOxQ8//CBpMR05cmSFl9FBQUFYsmSJZPsiIpKDWcW0fv36+Pbbb+Hv7w9BEHDr1i1kZ2dXuH1gYKBkAYmIHIFZxfSNN97A9OnTsWDBAigUCkRGRprcThAEKBQK2S/biIjsjVnFdOrUqRg4cCAyMjIwaNAgfPTRRwgJCZE7GxGRwzD70/xGjRqhUaNGiImJweDBgxEcHCxnLiIihyL61qi1a9fq/11YWIjc3Fx4eXmZvJGe7BvHshTP1DEjAiz8Ounu3bvRtm1buLu7IyAgAO7u7mjbti2+++47qfMRETkE0cX066+/RkREBNRqNRITE7Fp0yZ8/PHHcHFxwaBBg7Br1y45chIR2TXRl/kJCQkYMWIENm7caLB8ypQpGDVqFObNm4eIiAjJAhIROQLRZ6YZGRkYM2aMyXWjR49GRkaG1aGIiByN6GLq4+ODzMxMk+syMzPh4+NjdSgiIkcj+jJ/2LBheOedd+Dm5obBgwfDy8sL9+7dw7Zt2zB79my8/PLLcuQkIrJroovpggULcO3aNUyYMAHx8fFwdnZGcXExBEFAVFQUPvzwQzlyEhHZNdHF1MXFBcnJyThz5gyOHDmCnJwc+Pj4oHPnzmjevLkcGR2Kpfch8p5PIsdm8XimzZs3Z/EkIvp/dj0HFBGRo2AxJSKSgF1MWyIHtVoNFxcXs7d3cnKCu7u7jIn+x9z9WJrHmufx+M8qFAoAf00XIQiCxe1WRM7j/mj2ymg0GoPpxK1lzfMpzyrF8a4ohyXH3Jzt5e4rgHz9xdy+UplqW0x1Oh10Op3Z27u7uyM/P1/GRP9j7n4szWPN83j8Z1UqFdRqNQoKCmQZp1bO4/5o9somYZN6TiFrnk95VimOd0U5LDnm5mwvd18B5Osv5vaVyoj677ioqAiDBg3C4cOHLdoZEVF1JaqYurq64tChQxxJn4joMaIv8/v06YP9+/ejR48ecuR5KnBMTLIV9j35iC6mY8eORXx8PPLz89G/f3/4+/vr37wt16pVK8kCEhE5AtHFdMCAAQCAzz77DJ999plBIeWEekT0tBJdTP/zn//IkYOIyKGJLqbdunWTIwcRkUOz+D7T8+fP4+TJk7h+/TrGjRuHOnXq4NKlS/D396+ym9+JiOyF6GL64MEDxMXFYevWrVAqlSgrK0O/fv1Qp04dzJo1C8HBwVi0aJEcWYmI7Jbo79BNnz4dqampSElJQV5ensHXxvr374+9e/dKGpCIyBGIPjPdvn07Fi9ejD59+hh9ah8UFISrV69KlY0kZO79hY9vl5SUJEccqsCkSZMMHlszpq2U95RyvN0nE31mev/+fdStW9fkOqm/40xE5ChEF9MWLVogOTnZ5Lo9e/agTZs2VociInI0oi/z58yZg4iICDx48ABDhgyBQqHA8ePHsXnzZqxZswbfffedpAGXLl2Kw4cPw8npf1GTkpLg5+cn6X6IiKwhupiGh4djy5YtmDFjBr766isAwMSJExEQEICvvvoKYWFhkoeMiIhATEyM5O0SEUnFovtMBw8ejMGDB+PChQvQarXw8fFB48aNpc5GROQwrBocOiQkBCEhIVJlqdC+ffuwb98++Pr6YuDAgejdu7fs+yQiEsOiuRrOnTuHl156CY0aNYJGo0GjRo0watQonDt3Tup8GDhwIFauXIkNGzYgLi4O69atw7FjxyTfDxGRNUSfme7ZswdRUVEICAjAiy++CH9/f9y+fRtff/01WrVqhR07diA8PFyygA0bNtT/u0WLFggPD0daWho6duxosJ1Wq4VWq9U/ViqVoj6kUigUUKlU1geWkKV5pHwe5W3JdWzkPO7mZpd6/3Icf3v0eDa5+wogX3+RIrvoYjpjxgz069cPO3fuNJiEbPHixYiIiMCMGTMkLaaPUygUJifrSk5OxqpVq/SPY2NjMXnyZFFtWzr3i1y8vb2r9OdM8fDwMPhbDrGxsQaPN23aJGn7T8ou5fGqqL2RI0da1NbjN/ED0h8fS1V03OTsK4C8r1NrsosupllZWUhMTDSazVGpVGLSpEmIjIy0OIwpR48eRatWreDq6oqMjAzs2bMHEyZMMNouOjraYEQrpVKJnJwcs/ej0Wjs7ksHYvJL8XOm5OXlwcPDA3l5ebKMU2tqNkip8qtUKn32yl4kUh4vOdqr6vbN9XiOR4+3XGMay/U6NbevVEZ0MW3RogWysrJMrsvKykKzZs0sClKR3bt3IykpCWVlZfD19cWoUaPQtWtXo+18fX3h6+urf6zVakX9QgVBsLtBrS3NI+XzKG+rtLRUluNj6ipD6v08qb2q3p+9t2+uinLI1VcA+V+n1rQtupgmJSVh+PDhqFGjBl588UV4enri3r172LlzJz7++GNs3rzZ4jCmLFy4UNL2iIjkYFYxdXd3N5ieRKfTYdy4cRg3bhycnZ1RXFwMAHB2dkZYWBjy8vLkSUtEZKfMKqZvvvmm0aR5RET0P2YV03nz5skcg4jIsVl00z4RERmy6OukW7ZswbZt23D9+nUUFRUZrT99+rTVwWxJykF1qyMOFEwVMed+2uraV0QX03feeQcLFy5E69atERISYnc3uhMR2YLoYrpmzRrMnz8fs2fPliMPEZFDsug90/bt20udg4jIoYkupnFxcXbz3WAiInsh+jL/vffew5QpU9CpUyeEhYXBy8vLYL1CocDUqVMlC0hE5AhEF9PU1FR8+eWXyM/Pxw8//GC0nsWUiJ5Goi/zJ02ahDZt2uDMmTN4+PAhysrKDP7YyyAMRERVSfSZ6fXr17F8+XI0bdpUjjxVjveUyudpvR+VferpJPrMtHPnzsjMzJQjCxGRwxJ9Zvrhhx8iJiYGarUavXr1MvoACgB8fHwkCUdE5ChEF9O2bdsCAF555ZUKR5Li+6ZE9LSx6BtQjjAcn1qthouLi61jWMXd3b1Kf86U8mlFNBqNyVHxxe7z8e2cnIy7oFT5y/upqalRHqXRaIym4bFnUv5+rfF4DnPrgjX5nZycZHn+5vaVyogupo9PfmavdDoddDqdrWNYJT8/v0p/zpSCggKo1WoUFBRUesVh7j4f387UC0Oq/CqVSp+9sjEk7G3uryeR8vdrjcdzmDuzpzX53d3dZXn+5vaVyjjOf8dERHZM9JlpcHDwE0/nr1y5YnEgIiJHJLqYRkREGBXTnJwcHDp0CIIgICoqSrJwZBkp73M0NW+7NfuUMhvvY7UtS3OY+3uT8nlWRb8QXUyXLl1qcrlOp8OLL76I4OBgq0MRETkayd4zVavVmDx5MhYvXixVk0REDkPSD6C0Wq3dfNJIRFSVRF/m79ixw2iZTqfD+fPnsWLFCvTs2VOSYEREjkR0MR08eLDJ5c7OzoiKisLy5cutDkVE5GhEF9OsrCyjZa6urqhdu7ZDfDOKiEgOootpgwYN5MhBROTQRBdT4K+BTNLT0/Hbb7+hqKjIaP2YMWOsDiYXe7lHzxyOlFVK9vK87SXH06A6HGvRxfSnn35CVFQUrl+/bnLgC4VCYdfFlIhIDqKL6auvvgpPT098+eWXaNKkicWDAhARVSeii+m5c+ewbds2dOvWTY48Jt2/fx9JSUn46aef4ObmhsjISERERFTZ/omInkR0MQ0JCUFeXp4cWSr0r3/9C8XFxVi7di3u3LmDOXPmICAgAK1bt67SHEREFRH9DahPPvkECxYsQEZGhhx5jBQVFSEtLQ2jR49GjRo1EBQUhD59+uDAgQNVsn8iInOIPjOdPHkybt26hWbNmqFevXpGc0ApFAqcOnVKsoA3btyAIAgGt2QFBwfjhx9+kGwfRETWEl1MW7duXaU35xcVFaFGjRoGyzQaDQoLCw2WabVaaLVa/WOlUgk/P78qyUj249HR3sv//aQR4M0dIZ4cl7l9wJq+ILqYrlu3zuKdWcLV1dWocD548ABubm4Gy5KTk7Fq1Sr949jYWEyePNmovU2bNskTlOyWh4dHpeu9vb1NLmdfefo8qa9UxqKb9qtS/fr1AQDZ2dkIDAwE8NdXWsv/XS46OtrgDgOlUomcnByz96PRaGSbC0ilUsHDwwN5eXmyzNzK7KY9mr2yF4mYfgI49vEG5MtfXbJbWlDtvpi6urqiU6dO2LBhA6ZOnYq7d+9i//79mDJlisF2vr6+8PX11T/WarWifqGCIMg+RXVpaaks+2D2yj2pfbH7d+TjDcif39GzW8ruiykAxMfHY8WKFYiNjYWbmxuio6N5WxQR2RWHKKY1a9bEzJkzbR2DiKhCnOqZiEgCLKZERBJQCKaGfiJJabVaJCcnIzo62uBDMkfA7FXLETOXe9qz88y0Cmi1WqxatcrgSwWOgtmrliNmLve0Z2cxJSKSAIspEZEEVPPmzZtn6xBPAzc3N7Rp08ZonAFHwOxVyxEzl3uas/MDKCIiCfAyn4hIAiymREQSYDElIpKAQ3w339Hk5eXh6NGjyM7ORmFhIdzc3BAYGIjOnTtbNV5iVWD2quWImcsxuyGemUrs9OnTiI+Px6FDhwAAfn5+UCgUOHz4MOLj43HmzBkbJ6wYs1ctR8xcjtlNEEhSkyZNEo4cOWJy3ZEjR4SJEydWcSLzMXvVcsTM5ZjdGM9MJXb79m106NDB5Lr27dvjzp07VZzIfMxetRwxczlmN8ZiKrGgoCDs2rXL5LpvvvnGYJZVe8PsVcsRM5djdmO8aV9i165dw/vvvw+dTocGDRpAo9HgwYMHuHbtGtRqNWbPnm00f5W9qCj71atX4eLiwuwSY1+xDbmys5jKoLS0FGfOnMG1a9dQVFQEV1dXNGjQAM2bN7f7aYVLS0tx+vRpZGdn67PXrVsXrVu3dojsjx53Nzc3NGjQAM2aNbPb7OwrtiFHX2ExlUFmZiYyMjIQGBiIli1bGqzbvn07Bg8ebKNklomNjcWnn34KT09PW0ep0I8//ojg4GD4+/vjwYMHWLVqFU6ePAngr/fBxo8fbzQ9uD1gX6l6cvUVFlOJHTp0CCtXrsSzzz6LixcvIjQ0FDNmzICLiwsAYNiwYdi6dauNU5r21ltvmVx+6dIlBAUFwcnJCYsWLariVOaJi4tDYmIiPDw8sGzZMuTm5mLIkCEQBAE7duyAp6cnXnvtNVvHNMC+Yhty9RXetC+x7du3Y86cOWjSpAmKioqwdOlSzJ07F/PmzYOrq6ut41Xqxo0baNCgAcLCwgyWX79+HT169LDrkYAene/8559/RlJSkj5vUFAQXnnlFVvGM4l9xTbk6iv8NF9id+/eRZMmTQAArq6uePvttxEQEIA5c+agoKDAxukq9/nnn8Pf3x8HDhxAw4YNERYWhrCwMKjVanTp0sXohWNP/P39kZGRAeCv415UVKRf9/DhQ9nnubcE+4ptyNVXOJ6pxPbt24cOHTpAo9EAABQKBdq1a4fLly9j+/btyM3NxdChQ22c0jQXFxd06NABderUwfLly3Hr1i00btwYe/bsQb9+/ez6bMnLywsrVqyAm5sbAgICsHHjRjg7OyMrKwurV69G2+Nhi5sAAAWUSURBVLZt0bp1a1vHNMC+Yhty9RW+ZyqxFStWwMfHByNHjjRat3LlSqSkpFR4j5s9KSkpwfbt25Gamorc3Fx88cUX8PLysnWsSp06dQqbN2/GhQsX9GcXvr6+6N27N4YMGWJ3nzCzr9iOHH2FxVRixcXFKCsr03+I8Li7d+/Cz8+vilNZ7ubNm/j111/RtWtXODs72zqOWcrKypCbmwsXFxf9WZ89Yl+xPSn7CospEZEE+AEUEZEEWEyJiCTAYkpEJAEWUyIiCbCYEhFJgMWUiEgCLKZERBJgMSWbio2NRbNmzZCSkoJmzZrB1dUVrVu3xo8//qjfZv369ejcuTN8fHzg7e2N7t274/jx4wbt/Pbbbxg6dCj8/f3h6uqK4OBgTJ061ez1AHD+/HlERETA09MTGo0G4eHhuHz5ssE2a9asQdOmTeHm5oZatWqhc+fOOHHihAxHhhwNR40im7t58yYmTpyIefPmwdvbGwsXLkTfvn1x8eJF1K5dG1evXsWYMWPQsGFD6HQ6bN68GV27dsXp06cREhICABgzZgx+//13LFu2DP7+/sjOztaPUWnO+itXrqBjx45o1qwZ1q1bB6VSiQ8++ABhYWHIzMyEi4sLDh8+jPHjx2P69Ono378/Hjx4gOPHjyM3N7fKjxnZIYum4SOSSExMjABAOHjwoH5Zbm6u4O7uLsycOdNo+9LSUqG4uFgIDQ0VZs2apV+u0WiEZcuWVbifJ60fM2aM8MwzzwiFhYX6ZXfu3BFq1qwpJCUlCYIgCIsXLxZ8fHxEPT96evAyn2zO09MTPXv2NHjcq1cvpKenA/jr8jsyMhL+/v5QqVRwdnZGZmYmLly4oP+ZVq1aYcmSJfj8889x6dIlo308af3+/fsxaNAgODk5oaSkBCUlJfD29kbLli31l/GtWrXCn3/+idjYWBw4cAAPHjyQ+lCQI7N1NaenW0xMjNCoUSOj5a+88orQuHFjIS8vTwgICBBatmwpbNy4UThy5Ihw4sQJ4bnnnhPCw8P12//+++/C2LFjBW9vbwGAEBoaKiQnJ5u93snJSQBg8k9YWJh+uw0bNght2rQRFAqF4ObmJowePVr4448/ZDo65EhYTMmmYmJiBE9PT6PlkZGRQo8ePYR9+/YJAIRffvnFYH1QUJBBMS1XWloqHD9+XBg0aJDg5OQkXL582az1tWvXFsaOHSucOHHC6E9GRobRfu7evSusXr1acHd3F8aNG2fNIaBqgpf5ZHP37t1DamqqwePvv/8e7du3R2FhIQBArVbr1x87dgxXr1412ZZSqUTbtm3x/vvvo6SkxOiSvqL1vXr1wtmzZ9GyZUu0adPG4E9oaKjRfnx9fTF+/Hj07t0b58+ft/YQUDXAT/PJ5nx8fDB+/HgkJCTAy8sLCxcuhCAIeOONNwAANWvWxKRJkzBz5kzcuHEDc+fORf369fU/f+/ePfTt2xejR49GaGgodDodli9fDi8vL7Rq1eqJ6wEgISEBbdu2Rd++fTFhwgT4+/vj1q1bOHToELp06YIRI0Zg7ty5+OOPP9C9e3fUrl0bZ86cwd69ezFt2jSbHDeyM7Y+NaanW0xMjNC0aVNh9+7dwrPPPiuo1WqhZcuWQlpamn6blJQUoWnTpoKrq6vQokUL4bvvvhO6deumv8wvKioS4uLihNDQUMHNzU3w8fER+vTpIxw/ftys9eUuXLggDB06VKhVq5bg4uIiBAUFCWPGjBHOnj0rCIIgfPvtt0JYWJjg5+cnuLi4CA0bNhTmzp0rFBcXV9HRInvGwaHJpmJjY3Hy5EmcPXvW1lGIrML3TImIJMBiSkQkAV7mExFJgGemREQSYDElIpIAiykRkQRYTImIJMBiSkQkARZTIiIJsJgSEUmAxZSISAIspkREEvg/pj9aORhzdrQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for stat in ['length', 'passes', 'accuracy']:\n",
    "    try:\n",
    "        p = summaries.plot_ccs_stats(stat)\n",
    "        _ = p.draw()\n",
    "    except ValueError:\n",
    "        print(f\"Cannot plot {stat}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the same for a `Summaries` defined using FASTQ files without the `np` tag giving the number of passes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with contextlib.ExitStack() as stack:\n",
    "    tempfiles = [stack.enter_context(tempfile.NamedTemporaryFile('wt', suffix='.fastq'))\n",
    "                 for _ in range(len(ccs_df))]\n",
    "    ccs_df = ccs_df.assign(fastq_nopasstag=[f.name for f in tempfiles])\n",
    "    for fout, tup in zip(tempfiles, ccs_df.itertuples()):\n",
    "        seqs = []\n",
    "        for iseq, seq in enumerate(Bio.SeqIO.parse(tup.fastq, 'fastq')):\n",
    "            if iseq == 0:  # drop np tag from just first\n",
    "                seq.description = seq.description.split()[0]\n",
    "            seqs.append(seq)\n",
    "        Bio.SeqIO.write(seqs, fout, 'fastq')\n",
    "        fout.flush()\n",
    "    summaries_nopasstag = alignparse.ccs.Summaries(ccs_df, fastq_col='fastq_nopasstag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now print and plot information without `np` tag:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no passes stat\n",
      "\n",
      "         name  length\n",
      "0  recA_lib-1    1325\n",
      "1  recA_lib-1    1340\n",
      "2  recA_lib-1    1339\n",
      "3  recA_lib-1     985\n",
      "4  recA_lib-1    1196\n",
      "\n",
      "         name  accuracy\n",
      "0  recA_lib-1  0.999998\n",
      "1  recA_lib-1  0.999976\n",
      "2  recA_lib-1  0.999686\n",
      "3  recA_lib-1  0.999986\n",
      "4  recA_lib-1  0.999592\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for stat in ['passes', 'length', 'accuracy']:\n",
    "    if summaries_nopasstag.has_stat(stat):\n",
    "        print(summaries_nopasstag.ccs_stats(stat).head(n=5))\n",
    "        print()\n",
    "    else:\n",
    "        print(f\"no {stat} stat\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot plot passes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for stat in ['length', 'passes', 'accuracy']:\n",
    "    try:\n",
    "        p = summaries_nopasstag.plot_ccs_stats(stat)\n",
    "        _ = p.draw()\n",
    "    except ValueError:\n",
    "        print(f\"Cannot plot {stat}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}