{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thermochemical Benchmark: closed shell reactions with core correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bak et al., [J. Chem. Phys. 112, 9229–9242 (2000)](https://doi.org/10.1063/1.481544)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T16:11:22.846308Z",
     "end_time": "2023-05-02T16:11:23.145554Z"
    }
   },
   "outputs": [],
   "source": [
    "import pymolpro\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T16:11:23.146634Z",
     "end_time": "2023-05-02T16:11:23.147738Z"
    }
   },
   "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_reactions'\n",
    "parallel = None  # how many jobs to run at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T16:11:23.149070Z",
     "end_time": "2023-05-02T16:11:23.150551Z"
    }
   },
   "outputs": [],
   "source": [
    "methods = ['HF', 'MP2', 'CCSD', 'CCSD(T)', 'CCSD(T)-F12A', 'CCSD(T)-F12B', 'CCSD(T)-F12C']\n",
    "bases = ['cc-pCVDZ', 'cc-pCVTZ', 'cc-pCVQZ', 'cc-pCV5Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-02T16:11:23.151605Z",
     "end_time": "2023-05-02T16:11:23.153117Z"
    }
   },
   "outputs": [],
   "source": [
    "db = pymolpro.database.load(\"Bak2000_reactions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCSD(T)-F12A cc-pCV5Z failed /Users/peterk/trees/pymolpro/docs/source/examples/Bak2000_reactions/ee88723c\n",
      "CCSD(T)-F12B cc-pCV5Z failed /Users/peterk/trees/pymolpro/docs/source/examples/Bak2000_reactions/7719cc98\n",
      "CCSD(T)-F12C cc-pCV5Z failed /Users/peterk/trees/pymolpro/docs/source/examples/Bak2000_reactions/8966d244\n"
     ]
    }
   ],
   "source": [
    "results = {}\n",
    "for method in methods:\n",
    "    results[method] = {}\n",
    "    for basis in bases:\n",
    "        results[method][basis] = pymolpro.database.run(db, method, basis + '-F12' if 'F12' in method else basis,\n",
    "                                                       location=project_name, backend=backend,\n",
    "                                                       preamble=\"core,small\", parallel=parallel)\n",
    "        if results[method][basis].failed: print(method, basis, 'failed', results[method][basis].project_directory)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T16:11:23.155646Z",
     "end_time": "2023-05-02T16:11:43.062661Z"
    }
   }
  },
  {
   "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": "2023-05-02T16:11:43.062976Z",
     "end_time": "2023-05-02T16:11:43.068922Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "                HF         MP2        CCSD     CCSD(T) CCSD(T)-F12A  \\\n       cc-pCV[45]Z cc-pCV[45]Z cc-pCV[45]Z cc-pCV[45]Z cc-pCVQZ-F12   \nMAD          29.06       15.42        9.93        1.19     1.12e+00   \nMAXD        113.66       51.83       17.75        3.49     2.27e+00   \nRMSD         40.69       21.57       11.04        1.43     1.27e+00   \nMSD           9.25      -11.94       -6.81       -0.83    -1.09e-03   \nSTDEVD       41.24       18.70        9.04        1.22     1.32e+00   \n\n       CCSD(T)-F12B CCSD(T)-F12C  \n       cc-pCVQZ-F12 cc-pCVQZ-F12  \nMAD            1.13         1.21  \nMAXD           2.11         3.00  \nRMSD           1.25         1.38  \nMSD            0.02        -0.40  \nSTDEVD         1.30         1.38  ",
      "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>HF</th>\n      <th>MP2</th>\n      <th>CCSD</th>\n      <th>CCSD(T)</th>\n      <th>CCSD(T)-F12A</th>\n      <th>CCSD(T)-F12B</th>\n      <th>CCSD(T)-F12C</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      <th>cc-pCVQZ-F12</th>\n      <th>cc-pCVQZ-F12</th>\n      <th>cc-pCVQZ-F12</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MAD</th>\n      <td>29.06</td>\n      <td>15.42</td>\n      <td>9.93</td>\n      <td>1.19</td>\n      <td>1.12e+00</td>\n      <td>1.13</td>\n      <td>1.21</td>\n    </tr>\n    <tr>\n      <th>MAXD</th>\n      <td>113.66</td>\n      <td>51.83</td>\n      <td>17.75</td>\n      <td>3.49</td>\n      <td>2.27e+00</td>\n      <td>2.11</td>\n      <td>3.00</td>\n    </tr>\n    <tr>\n      <th>RMSD</th>\n      <td>40.69</td>\n      <td>21.57</td>\n      <td>11.04</td>\n      <td>1.43</td>\n      <td>1.27e+00</td>\n      <td>1.25</td>\n      <td>1.38</td>\n    </tr>\n    <tr>\n      <th>MSD</th>\n      <td>9.25</td>\n      <td>-11.94</td>\n      <td>-6.81</td>\n      <td>-0.83</td>\n      <td>-1.09e-03</td>\n      <td>0.02</td>\n      <td>-0.40</td>\n    </tr>\n    <tr>\n      <th>STDEVD</th>\n      <td>41.24</td>\n      <td>18.70</td>\n      <td>9.04</td>\n      <td>1.22</td>\n      <td>1.32e+00</td>\n      <td>1.30</td>\n      <td>1.38</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.precision', 2)\n",
    "method_errors = \\\n",
    "    pymolpro.database.analyse([results[method]['cc-pCVQZ' if 'F12' in method else 'cc-pCV[45]Z'] for method in methods],\n",
    "                              db, 'kJ/mol')['reaction statistics']\n",
    "with open(project_name + '.method_errors.tex', 'w') as tf:\n",
    "    tf.write(\n",
    "        '\\\\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": "2023-05-02T16:11:43.068472Z",
     "end_time": "2023-05-02T16:11:43.329847Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "        CCSD(T)                                                               \n       cc-pCVDZ cc-pCVTZ cc-pCVQZ cc-pCV5Z cc-pCV[23]Z cc-pCV[34]Z cc-pCV[45]Z\nMAD       36.73    12.05     3.70     1.30        4.37        1.33        1.19\nMAXD      65.92    21.33     6.97     3.00       10.79        3.15        3.49\nRMSD      41.66    13.71     4.10     1.50        5.37        1.64        1.43\nMSD       33.39    11.31     3.02     0.39        2.70       -1.13       -0.83\nSTDEVD    25.92     8.07     2.88     1.51        4.83        1.23        1.22",
      "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\">CCSD(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>MAD</th>\n      <td>36.73</td>\n      <td>12.05</td>\n      <td>3.70</td>\n      <td>1.30</td>\n      <td>4.37</td>\n      <td>1.33</td>\n      <td>1.19</td>\n    </tr>\n    <tr>\n      <th>MAXD</th>\n      <td>65.92</td>\n      <td>21.33</td>\n      <td>6.97</td>\n      <td>3.00</td>\n      <td>10.79</td>\n      <td>3.15</td>\n      <td>3.49</td>\n    </tr>\n    <tr>\n      <th>RMSD</th>\n      <td>41.66</td>\n      <td>13.71</td>\n      <td>4.10</td>\n      <td>1.50</td>\n      <td>5.37</td>\n      <td>1.64</td>\n      <td>1.43</td>\n    </tr>\n    <tr>\n      <th>MSD</th>\n      <td>33.39</td>\n      <td>11.31</td>\n      <td>3.02</td>\n      <td>0.39</td>\n      <td>2.70</td>\n      <td>-1.13</td>\n      <td>-0.83</td>\n    </tr>\n    <tr>\n      <th>STDEVD</th>\n      <td>25.92</td>\n      <td>8.07</td>\n      <td>2.88</td>\n      <td>1.51</td>\n      <td>4.83</td>\n      <td>1.23</td>\n      <td>1.22</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 = \\\n",
    "    pymolpro.database.analyse([results['CCSD(T)'][basis] for basis in bases if basis in results['CCSD(T)']], db,\n",
    "                              'kJ/mol')['reaction statistics']\n",
    "with open(project_name + '.basis_errors.tex', 'w') as tf:\n",
    "    tf.write(\n",
    "        '\\\\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": "2023-05-02T16:11:43.335413Z",
     "end_time": "2023-05-02T16:11:43.341730Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x600 with 5 Axes>",
      "image/png": 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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",
    "    labels = []\n",
    "    for method in methods_pruned:\n",
    "        if bases_pruned[pane] in results[method] and results[method][bases_pruned[pane]].reaction_energies:\n",
    "            data.append(\n",
    "                pymolpro.database.analyse(results[method][bases_pruned[pane]],\n",
    "                                          db, 'kJ/mol')['reaction energy deviations'].to_numpy()[:, 0]\n",
    "            )\n",
    "            labels.append(method)\n",
    "    panes[pane].violinplot(data, showmeans=True, showextrema=True, vert=True, bw_method='silverman')\n",
    "    panes[pane].set_xticks(range(1, len(labels) + 1), labels=labels, 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\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-02T16:19:03.834871Z",
     "end_time": "2023-05-02T16:19:04.164298Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "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",
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