{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thermochemical benchmark: atomisation of closed shell molecules with core correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bak et al., [doi:10.1063/1.1357225](https://doi.org/10.1063/1.1357225) and [doi:10.1063/1.481544](https://doi.org/10.1063/1.481544)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:03.289048Z",
     "end_time": "2023-05-02T12:34:03.630476Z"
    }
   },
   "outputs": [],
   "source": [
    "import pymolpro\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:03.631561Z",
     "end_time": "2023-05-02T12:34:03.632981Z"
    }
   },
   "outputs": [],
   "source": [
    "backend = 'local'  # If preferred, change this to one of the backends in your ~/.sjef/molpro/backends.xml that is ssh-accessible\n",
    "project_name = 'Bak2000_atomisations'\n",
    "parallel = None  # how many jobs to run at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:03.634025Z",
     "end_time": "2023-05-02T12:34:03.635490Z"
    }
   },
   "outputs": [],
   "source": [
    "methods = {\"HF\": [\"hf\", \"uhf\"],\n",
    "\"MP2\": [\"mp2\", \"ump2\"],\n",
    "# \"MP3\": [\"mp3\", \"ump3\"],\n",
    "# \"MP4\": [\"mp4\", \"ump4\"],\n",
    "\"CCSD\": [\"ccsd\", \"uccsd\"], \"CCSD(T)\": [\"ccsd(t)\", \"uccsd(t)\"], }\n",
    "bases = ['cc-pCVDZ', 'cc-pCVTZ', 'cc-pCVQZ', 'cc-pCV5Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:03.636758Z",
     "end_time": "2023-05-02T12:34:03.638543Z"
    }
   },
   "outputs": [],
   "source": [
    "db = pymolpro.database.load(\"Bak2000_atomisations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "results = {}\n",
    "for method in methods:\n",
    "    results[method] = {}\n",
    "    for basis in bases:\n",
    "        results[method][basis] = pymolpro.database.run(db, methods[method], basis, location=project_name,\n",
    "                                                       backend=backend,\n",
    "                                                       preamble=\"core,small\", parallel=parallel)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:03.640653Z",
     "end_time": "2023-05-02T12:34:13.538617Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "for method in methods:\n",
    "    for result in pymolpro.database.basis_extrapolate(results[method].values(), results['HF'].values()):\n",
    "        results[method][result.basis] = result\n",
    "    for basis in results[method]:\n",
    "        if basis not in bases: bases.append(basis)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:13.537528Z",
     "end_time": "2023-05-02T12:34:13.544938Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "               UHF        UMP2       UCCSD    UCCSD(T)\n       cc-pCV[45]Z cc-pCV[45]Z cc-pCV[45]Z cc-pCV[45]Z\nMSD        -405.05       30.04      -28.48        0.08\nSTDEVD      143.84       32.64       16.43        1.12\nMAD         405.05       35.11       28.50        0.90\nMAXD        598.79      112.20       58.55        2.45\nRMSD        428.32       43.60       32.62        1.09",
      "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 tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>UHF</th>\n      <th>UMP2</th>\n      <th>UCCSD</th>\n      <th>UCCSD(T)</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>cc-pCV[45]Z</th>\n      <th>cc-pCV[45]Z</th>\n      <th>cc-pCV[45]Z</th>\n      <th>cc-pCV[45]Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MSD</th>\n      <td>-405.05</td>\n      <td>30.04</td>\n      <td>-28.48</td>\n      <td>0.08</td>\n    </tr>\n    <tr>\n      <th>STDEVD</th>\n      <td>143.84</td>\n      <td>32.64</td>\n      <td>16.43</td>\n      <td>1.12</td>\n    </tr>\n    <tr>\n      <th>MAD</th>\n      <td>405.05</td>\n      <td>35.11</td>\n      <td>28.50</td>\n      <td>0.90</td>\n    </tr>\n    <tr>\n      <th>MAXD</th>\n      <td>598.79</td>\n      <td>112.20</td>\n      <td>58.55</td>\n      <td>2.45</td>\n    </tr>\n    <tr>\n      <th>RMSD</th>\n      <td>428.32</td>\n      <td>43.60</td>\n      <td>32.62</td>\n      <td>1.09</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.precision', 2)\n",
    "method_errors=pymolpro.database.analyse([results[method]['cc-pCV[45]Z'] for method in methods], db, 'kj/mol')[\n",
    "    'reaction statistics']\n",
    "with open(project_name + '.method_errors.tex', 'w') as tf:\n",
    "    tf.write('\\\\ifx\\\\toprule\\\\undefined\\\\def\\\\toprule{\\\\hline\\\\hline}\\n\\\\def\\\\midrule{\\\\hline}\\n\\\\def\\\\bottomrule{\\\\hline\\\\hline}\\\\fi') # or \\usepackage{booktabs}\n",
    "    tf.write(method_errors.style.format(precision=2).to_latex(hrules=True,multicol_align='c',caption='Method errors'))\n",
    "method_errors"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T12:35:01.784504Z",
     "end_time": "2023-05-02T12:35:01.803738Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "       UCCSD(T)                                                               \n       cc-pCVDZ cc-pCVTZ cc-pCVQZ cc-pCV5Z cc-pCV[23]Z cc-pCV[34]Z cc-pCV[45]Z\nMSD     -103.07   -34.00   -13.46    -6.61      -14.66       -0.23        0.08\nSTDEVD    37.19    13.58     5.63     3.08        8.39        2.15        1.12\nMAD      103.07    34.00    13.46     6.61       14.74        1.68        0.90\nMAXD     155.71    51.58    20.15    10.71       29.53        4.01        2.45\nRMSD     109.18    36.45    14.52     7.25       16.76        2.10        1.09",
      "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 tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"7\" halign=\"left\">UCCSD(T)</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>cc-pCVDZ</th>\n      <th>cc-pCVTZ</th>\n      <th>cc-pCVQZ</th>\n      <th>cc-pCV5Z</th>\n      <th>cc-pCV[23]Z</th>\n      <th>cc-pCV[34]Z</th>\n      <th>cc-pCV[45]Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MSD</th>\n      <td>-103.07</td>\n      <td>-34.00</td>\n      <td>-13.46</td>\n      <td>-6.61</td>\n      <td>-14.66</td>\n      <td>-0.23</td>\n      <td>0.08</td>\n    </tr>\n    <tr>\n      <th>STDEVD</th>\n      <td>37.19</td>\n      <td>13.58</td>\n      <td>5.63</td>\n      <td>3.08</td>\n      <td>8.39</td>\n      <td>2.15</td>\n      <td>1.12</td>\n    </tr>\n    <tr>\n      <th>MAD</th>\n      <td>103.07</td>\n      <td>34.00</td>\n      <td>13.46</td>\n      <td>6.61</td>\n      <td>14.74</td>\n      <td>1.68</td>\n      <td>0.90</td>\n    </tr>\n    <tr>\n      <th>MAXD</th>\n      <td>155.71</td>\n      <td>51.58</td>\n      <td>20.15</td>\n      <td>10.71</td>\n      <td>29.53</td>\n      <td>4.01</td>\n      <td>2.45</td>\n    </tr>\n    <tr>\n      <th>RMSD</th>\n      <td>109.18</td>\n      <td>36.45</td>\n      <td>14.52</td>\n      <td>7.25</td>\n      <td>16.76</td>\n      <td>2.10</td>\n      <td>1.09</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.precision', 2)\n",
    "basis_errors=pymolpro.database.analyse([results['CCSD(T)'][basis] for basis in bases], db, 'kj/mol')[\n",
    "    'reaction statistics']\n",
    "with open(project_name + '.basis_errors.tex', 'w') as tf:\n",
    "    tf.write('\\\\ifx\\\\toprule\\\\undefined\\\\def\\\\toprule{\\\\hline\\\\hline}\\n\\\\def\\\\midrule{\\\\hline}\\n\\\\def\\\\bottomrule{\\\\hline\\\\hline}\\\\fi') # or \\usepackage{booktabs}\n",
    "    tf.write(basis_errors.style.format(precision=2).to_latex(hrules=True,multicol_align='c',caption='Basis errors'))\n",
    "basis_errors"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:13.804384Z",
     "end_time": "2023-05-02T12:34:13.812432Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x600 with 5 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "methods_pruned = [method for method in methods if method != 'HF']\n",
    "bases_pruned = ['cc-pCVTZ', 'cc-pCVQZ', 'cc-pCV5Z', 'cc-pCV[34]Z', 'cc-pCV[45]Z']\n",
    "fig, panes = plt.subplots(nrows=1, ncols=len(bases_pruned), sharey=True, figsize=(18, 6))\n",
    "\n",
    "for pane in range(len(bases_pruned)):\n",
    "    data = []\n",
    "    for method in methods_pruned:\n",
    "        data.append(\n",
    "            pymolpro.database.analyse(results[method][bases_pruned[pane]],\n",
    "                                      db,'kJ/mol')['reaction energy deviations'].to_numpy()[:, 0]\n",
    "        )\n",
    "    panes[pane].violinplot(data, showmeans=True, showextrema=True, vert=True, bw_method='silverman')\n",
    "    panes[pane].set_xticks(range(1, len(methods_pruned) + 1), labels=methods_pruned, rotation=-90)\n",
    "    panes[pane].set_title(bases_pruned[pane])\n",
    "panes[0].set_ylabel('Error / kJ/mol')\n",
    "plt.savefig(project_name + \".violin.pdf\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T12:34:13.814899Z",
     "end_time": "2023-05-02T12:34:14.194458Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "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": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
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    }
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 },
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 "nbformat_minor": 4
}