{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# mqapRNA in rna-tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rna_tools.rna_tools_lib as rtl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = rtl.RNAStructure('../rna_tools/input/mq/5e3hBC.pdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RNAStructure /Users/magnus/work/src/rna-tools/rna_tools/input/mq/5e3hBC.pdb"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-16410.4, 83589.0, -0.196323, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.mq(method=\"RASP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r.mq(method=\"Dfire\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76172626"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.mq(method=\"rna3dcnn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r.mq(method=\"qrna\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.mq(method=\"clashscore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.mq(method=\"analyzegeometry\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnas = rtl.load_rnas('../rna_tools/input/mq/*.pdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5e3hBC.pdb [-16410.4, 83589.0, -0.196323, 0.0, 0.0, 0.0]\n",
      "S_000001_000.pdb [-15191.9, 460077.0, -0.0330204, 0.0, 0.0, 0.0]\n",
      "1a9nR.pdb [-9869.61, 66447.0, -0.148534, 0.0, 0.0, 0.0]\n",
      "3b58ABC.pdb [-31456.9, 344202.0, -0.0913908, 0.0, 0.0, 0.0]\n",
      "1a9n.pdb [-9869.61, 66447.0, -0.148534, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "for r in rnas:\n",
    "    print(r.name, r.mq(method=\"RASP\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5e3hBC.pdb -19451.089802\n",
      "S_000001_000.pdb -52615.344214\n",
      "1a9nR.pdb -12480.188898\n",
      "3b58ABC.pdb -53013.816472\n",
      "1a9n.pdb -12480.188898\n"
     ]
    }
   ],
   "source": [
    "rnas_names = []\n",
    "rnas_scores = []\n",
    "for r in rnas:\n",
    "    print(r.name, r.mq(method=\"Dfire\"))\n",
    "    rnas_names.append(r.name)\n",
    "    rnas_scores.append(r.mq(method=\"Dfire\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3b58ABC.pdb</td>\n",
       "      <td>-53013.816472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S_000001_000.pdb</td>\n",
       "      <td>-52615.344214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e3hBC.pdb</td>\n",
       "      <td>-19451.089802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1a9nR.pdb</td>\n",
       "      <td>-12480.188898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1a9n.pdb</td>\n",
       "      <td>-12480.188898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name         Score\n",
       "3       3b58ABC.pdb -53013.816472\n",
       "1  S_000001_000.pdb -52615.344214\n",
       "0        5e3hBC.pdb -19451.089802\n",
       "2         1a9nR.pdb -12480.188898\n",
       "4          1a9n.pdb -12480.188898"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(list(zip(  rnas_names, rnas_scores)), columns=['Name', 'Score'])\n",
    "df = df.sort_values('Score')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8e19ca990>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Name', y='Score', rot=45, title=\"mq for pdb files\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "66px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
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   "widenNotebook": false
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}
