{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading ChemDraw files\n",
    "\n",
    "Molli features a robust Chemdraw parser, specifically designed to work with the `CDXML` format. This parser is capable of parsing point, axial, and planar chirality, along with full structure labels, substructure labels, atom labels, charges, and multiplicities. This notebook will explain its properties and how to use it.\n",
    "\n",
    "## Reading Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/blakeo2/new_molli/molli_dev/molli/molli/ftypes/cdxml.py:157: CDXMLSyntaxWarning: CDXML file /home/blakeo2/new_molli/molli_dev/molli/molli/files/parser_demo.cdxml contains redundant label 'naphthalene' Only the first occurrence will be kept.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "#This imports molli\n",
    "import molli as ml\n",
    "\n",
    "#This configures the visualization tool\n",
    "ml.visual.configure(bgcolor='white')\n",
    "\n",
    "# This just loads an example file that contains boundary cases\n",
    "cdxf = ml.CDXMLFile(ml.files.parser_demo_cdxml)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The file was designed to purposely show the warnings when duplicate structures are present!\n",
    "\n",
    "The `CDXMLFile` class can be thought of as a dictionary, with keys being the label, and the values being the `Molecule`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['benzene', 'naphthalene', 'stereo', 'decoy', 'isotopes', 'phenanthrene_ar', 'estradiol', 'taxadiene', 'substruct_stereo', 'high_complexity_1', 'r_binol', 'chiral_fragment', 'attached_acronym', 'Taniaphos', 'BrettPhos', 'toluene', 'stereo_groups', 'attachments'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will display the list of all named molecules in the file.\n",
    "cdxf.keys()"
   ]
  },
  {
   "attachments": {
    "Point_Chirality.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Point Chirality Example\n",
    "\n",
    "For the demonstration of how to work with these molecules, the following structure will be used to illustrate how point chirality is maintained. In addition, different functional groups are parsed into their expanded equivalent. Note: all structures should interpreted chemically, and all labels underneath the molecule should be BOLDED and NOT interpreted chemically!\n",
    "\n",
    "![Point_Chirality.PNG](attachment:Point_Chirality.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/3dmoljs_load.v0": "<script>\n            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n                \n                viewer_17295271057526786.render();\n            });\n            </script>",
      "text/html": [
       "<script>\n",
       "            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n",
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       "            });\n",
       "            </script>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "Molecule(name='high_complexity_1', formula='C47 F3 N3 O10')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The molecules can be accessed as the\n",
    "mol = cdxf[\"high_complexity_1\"]\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When chirality is detected, the parser rotates groups of atoms out of the 2D plane of the paper, much like a chemist's intuition would. The structure upon initial parsing does not have have hydrogens. These can be added via the method `add_implicit_hydrogens()`. This will not optimize the `Molecule` but gives a reasonable starting point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<script>\n",
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      "text/plain": [
       "Molecule(name='high_complexity_1', formula='C47 H56 F3 N3 O10')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adds implicit hydrogens to Molecule\n",
    "mol.add_implicit_hydrogens()\n",
    "mol"
   ]
  },
  {
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    "Taxadiene.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Label Example\n",
    "\n",
    "One of the most useful features of the CDXML parser is that unique labels can be maintained from the original `CDXML` file. For those that are unaware, it is possible to add your own labels by selecting an atom in the ChemDraw file, clicking the `'` key, then typing your unique label. `molli` maintains whatever label is used and assigns it to the atom once it is read in. This can be leveraged in downstream tasks if certain atoms are important, especially if lists of atoms get instantiated arbitrarily.\n",
    "\n",
    "An example of this will be shown with the molecule below\n",
    "\n",
    "![Taxadiene.PNG](attachment:Taxadiene.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/3dmoljs_load.v0": "<div id=\"3dmolviewer_17295271057966053\"  style=\"position: relative; width: 100%; height: 500px;\">\n        <p id=\"3dmolwarning_17295271057966053\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  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       "        <p id=\"3dmolwarning_17295271057966053\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: <br>\n",
       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
       "        </div>\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<script>\n",
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    {
     "data": {
      "text/plain": [
       "Molecule(name='taxadiene', formula='C20 H32')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This accesses the molecule\n",
    "m = cdxf[\"taxadiene\"]\n",
    "\n",
    "#This adds implicit hydrogens\n",
    "m.add_implicit_hydrogens()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This illustrates that regardless of the `Molecule` instantiation, the labels are maintained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label='2', formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label='1', formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=C, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n",
      "Atom(element=H, isotope=None, label=None, formal_charge=0, formal_spin=0)\n"
     ]
    }
   ],
   "source": [
    "#This prints the full atom, including the labels\n",
    "for atom in m.atoms:\n",
    "    print(atom)\n"
   ]
  },
  {
   "attachments": {
    "AxialChirality.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Axial Chirality Example\n",
    "\n",
    "The `molli` CDXML parser also has the ability to parse axial chirality. It moves groups of atoms based on the boldness of certain bonds. This has been tested on a variety of structures, and upon force-field optimization, the structures never invert. Here is an example of an axial chiral structure that can be parsed.\n",
    "\n",
    "![AxialChirality.PNG](attachment:AxialChirality.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "<div id=\"3dmolviewer_17295271058185933\"  style=\"position: relative; width: 100%; height: 500px;\">\n",
       "        <p id=\"3dmolwarning_17295271058185933\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: <br>\n",
       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
       "    var savedexports, savedmodule;\n",
       "    if (typeof exports !== 'undefined') savedexports = exports;\n",
       "    else exports = {}\n",
       "    if (typeof module !== 'undefined') savedmodule = module;\n",
       "    else module = {}\n",
       "\n",
       "    var tag = document.createElement('script');\n",
       "    tag.src = uri;\n",
       "    tag.async = true;\n",
       "    tag.onload = () => {\n",
       "        exports = savedexports;\n",
       "        module = savedmodule;\n",
       "        resolve();\n",
       "    };\n",
       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
       "});\n",
       "};\n",
       "\n",
       "if(typeof $3Dmolpromise === 'undefined') {\n",
       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.0.4/3Dmol-min.js');\n",
       "}\n",
       "\n",
       "var viewer_17295271058185933 = null;\n",
       "var warn = document.getElementById(\"3dmolwarning_17295271058185933\");\n",
       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_17295271058185933 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17295271058185933\"),{backgroundColor:\"white\"});\n",
       "viewer_17295271058185933.zoomTo();\n",
       "\tviewer_17295271058185933.addModel(\"@<TRIPOS>MOLECULE\\nr_binol\\n22 25 0 0 0\\nSMALL\\nNO_CHARGES\\n\\n@<TRIPOS>ATOM\\n1 None -2.9524 3.0000 0.0000 C\\n2 None -2.9524 1.5000 0.0000 C\\n3 None -1.6538 0.7500 0.0000 C\\n4 None -0.3538 1.5000 0.0000 C\\n5 None -0.3538 3.0000 0.0000 C\\n6 None -1.6538 3.7500 0.0000 C\\n7 None 0.9448 0.7500 1.0000 C\\n8 None 2.2434 1.5000 2.0000 C\\n9 None 2.2434 3.0000 2.0000 C\\n10 None 0.9448 3.7500 1.0000 C\\n11 None 0.9448 -0.7500 1.0000 C\\n12 None -0.3538 -1.5000 1.0000 C\\n13 None -0.3538 -3.0000 1.0000 C\\n14 None 0.9448 -3.7500 1.0000 C\\n15 None 2.2434 -3.0000 1.0000 C\\n16 None 2.2434 -1.5000 1.0000 C\\n17 None -1.6538 -0.7500 2.0000 C\\n18 None -2.9524 -1.5000 3.0000 C\\n19 None -2.9524 -3.0000 3.0000 C\\n20 None -1.6538 -3.7500 2.0000 C\\n21 None 3.5434 0.7500 2.0000 O\\n22 None 3.5434 -0.7500 1.0000 O\\n@<TRIPOS>BOND\\n1 1 2 2\\n2 2 3 1\\n3 3 4 2\\n4 4 5 1\\n5 5 6 2\\n6 6 1 1\\n7 4 7 1\\n8 7 8 2\\n9 8 9 1\\n10 9 10 2\\n11 10 5 1\\n12 7 11 1\\n13 11 12 2\\n14 12 13 1\\n15 13 14 2\\n16 14 15 1\\n17 15 16 2\\n18 16 11 1\\n19 12 17 1\\n20 17 18 2\\n21 18 19 1\\n22 19 20 2\\n23 20 13 1\\n24 8 21 1\\n25 16 22 1\\n\\n\",\"mol2\");\n",
       "\tviewer_17295271058185933.setStyle({\"stick\": {\"radius\": 0.1}, \"sphere\": {\"scale\": 0.15}});\n",
       "\tviewer_17295271058185933.setHoverable({},true,\"\\n        function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n                atom.label = viewer.addLabel(atom.elem + '(' + atom.index + ')', {position: atom, backgroundColor: 'silver', fontColor:'black'});\\n            }\\n        }\\n        \",\"\\n        function(atom,viewer) { \\n            if(atom.label) {\\n                viewer.removeLabel(atom.label);\\n                delete atom.label;\\n            }\\n        }\\n        \");\n",
       "\tviewer_17295271058185933.zoomTo();\n",
       "\tviewer_17295271058185933.setBackgroundColor(\"white\");\n",
       "viewer_17295271058185933.render();\n",
       "});\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/3dmoljs_load.v0": "<script>\n            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n                \n                viewer_17295271058185933.render();\n            });\n            </script>",
      "text/html": [
       "<script>\n",
       "            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n",
       "                \n",
       "                viewer_17295271058185933.render();\n",
       "            });\n",
       "            </script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Molecule(name='r_binol', formula='C20 O2')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This accesses the molecule\n",
    "m = cdxf['r_binol']\n",
    "m"
   ]
  },
  {
   "attachments": {
    "Taniaphos.PNG": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Planar Chirality Example\n",
    "\n",
    "`molli` has the ability to parse multi-attachment points and actually maintain a significant amount of information about bonding information that is often difficult to access with the `CDXML` format. The first example illustrates maintaining both planar AND point chirality at the same time.\n",
    "\n",
    "![Taniaphos.PNG](attachment:Taniaphos.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "<div id=\"3dmolviewer_1729527105844459\"  style=\"position: relative; width: 100%; height: 500px;\">\n",
       "        <p id=\"3dmolwarning_1729527105844459\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: <br>\n",
       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
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       "    };\n",
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       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.0.4/3Dmol-min.js');\n",
       "}\n",
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       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_1729527105844459 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1729527105844459\"),{backgroundColor:\"white\"});\n",
       "viewer_1729527105844459.zoomTo();\n",
       "\tviewer_1729527105844459.addModel(\"@<TRIPOS>MOLECULE\\nTaniaphos\\n47 61 0 0 0\\nSMALL\\nNO_CHARGES\\n\\n@<TRIPOS>ATOM\\n1 None -6.3861 0.2778 -0.7500 C\\n2 None -6.9319 -0.0042 0.0000 C\\n3 None -5.5097 0.0208 2.2500 C\\n4 None -4.0986 0.0000 0.0000 C\\n5 None -4.6361 0.2653 -0.7500 C\\n6 None -6.8611 -3.0458 0.0000 C\\n7 None -6.3333 -3.6931 1.7500 C\\n8 None -4.5292 -3.7042 1.7500 C\\n9 None -3.9472 -3.0653 0.0000 C\\n10 None -5.3806 -3.1611 -0.7500 C\\n11 None -5.4403 -2.1417 0.7500 Fe\\n12 None -2.7986 -0.7500 0.0000 C\\n13 None 0.0000 0.0000 0.0000 C\\n14 None 0.7500 1.2986 0.0000 C\\n15 None 0.0000 2.5972 0.0000 C\\n16 None -1.5000 2.5972 0.0000 C\\n17 None -2.2500 1.2986 0.0000 C\\n18 None -1.5000 0.0000 0.0000 C\\n19 None -3.0254 1.1955 -0.7500 P\\n20 None -1.8176 0.5987 -1.3971 C\\n21 None -1.9040 -0.7456 -2.0443 C\\n22 None -0.6962 -1.3436 -2.6920 C\\n23 None 0.5982 -0.5949 -2.6914 C\\n24 None 0.6846 0.7494 -2.0443 C\\n25 None -0.5233 1.3462 -1.3971 C\\n26 None -2.9390 2.5398 -0.1029 C\\n27 None -1.6447 3.2885 -0.1023 C\\n28 None -1.5583 4.6328 0.5449 C\\n29 None -2.7662 5.2296 1.1920 C\\n30 None -4.0604 4.4821 1.1920 C\\n31 None -4.1468 3.1378 0.5449 C\\n32 None -2.7986 -1.4800 1.2644 N\\n33 None -2.1507 -2.8255 1.3504 C\\n34 None -3.4458 -0.8835 2.4732 C\\n35 None 0.9295 -1.6111 0.0000 P\\n36 None 0.3314 -2.8190 -0.6476 C\\n37 None -1.0128 -2.7327 -1.2949 C\\n38 None -1.6095 -3.9406 -1.9418 C\\n39 None -0.8621 -5.2350 -1.9415 C\\n40 None 0.4821 -5.3213 -1.2942 C\\n41 None 1.0801 -4.1134 -0.6466 C\\n42 None 2.2737 -1.6975 0.6473 C\\n43 None 3.0211 -2.9918 0.6477 C\\n44 None 4.3653 -3.0781 1.2950 C\\n45 None 4.9634 -1.8702 1.9425 C\\n46 None 4.2159 -0.5759 1.9422 C\\n47 None 2.8717 -0.4896 1.2949 C\\n@<TRIPOS>BOND\\n1 11 1 un\\n2 11 5 un\\n3 11 2 un\\n4 11 4 un\\n5 11 3 un\\n6 1 2 ar\\n7 2 3 ar\\n8 3 4 ar\\n9 4 5 ar\\n10 1 5 ar\\n11 6 7 ar\\n12 7 8 ar\\n13 8 9 ar\\n14 9 10 ar\\n15 6 10 ar\\n16 11 10 un\\n17 11 7 un\\n18 11 9 un\\n19 11 8 un\\n20 11 6 un\\n21 4 12 1\\n22 12 18 1\\n23 13 18 1\\n24 14 13 2\\n25 15 14 1\\n26 16 15 2\\n27 17 16 1\\n28 18 17 2\\n29 30 31 2\\n30 29 30 1\\n31 28 29 2\\n32 27 28 1\\n33 26 27 2\\n34 26 31 1\\n35 24 25 2\\n36 23 24 1\\n37 22 23 2\\n38 21 22 1\\n39 20 21 2\\n40 20 25 1\\n41 19 20 1\\n42 19 26 1\\n43 5 19 1\\n44 32 33 1\\n45 32 34 1\\n46 12 32 1\\n47 46 47 2\\n48 45 46 1\\n49 44 45 2\\n50 43 44 1\\n51 42 43 2\\n52 42 47 1\\n53 40 41 2\\n54 39 40 1\\n55 38 39 2\\n56 37 38 1\\n57 36 37 2\\n58 36 41 1\\n59 35 36 1\\n60 35 42 1\\n61 13 35 1\\n\\n\",\"mol2\");\n",
       "\tviewer_1729527105844459.setStyle({\"stick\": {\"radius\": 0.1}, \"sphere\": {\"scale\": 0.15}});\n",
       "\tviewer_1729527105844459.setHoverable({},true,\"\\n        function(atom,viewer,event,container) {\\n            if(!atom.label) {\\n                atom.label = viewer.addLabel(atom.elem + '(' + atom.index + ')', {position: atom, backgroundColor: 'silver', fontColor:'black'});\\n            }\\n        }\\n        \",\"\\n        function(atom,viewer) { \\n            if(atom.label) {\\n                viewer.removeLabel(atom.label);\\n                delete atom.label;\\n            }\\n        }\\n        \");\n",
       "\tviewer_1729527105844459.zoomTo();\n",
       "\tviewer_1729527105844459.setBackgroundColor(\"white\");\n",
       "viewer_1729527105844459.render();\n",
       "});\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/3dmoljs_load.v0": "<script>\n            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n                \n                viewer_1729527105844459.render();\n            });\n            </script>",
      "text/html": [
       "<script>\n",
       "            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n",
       "                \n",
       "                viewer_1729527105844459.render();\n",
       "            });\n",
       "            </script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Molecule(name='Taniaphos', formula='C43 Fe1 N1 P2')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This accesses the structure\n",
    "m = cdxf['Taniaphos']\n",
    "m"
   ]
  },
  {
   "attachments": {
    "BrettPhos.PNG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an additional structure showing the ability to parse pi-allyl complexes and even non-traditional bonds via a Pd-allyl BrettPhos structure:\n",
    "\n",
    "![BrettPhos.PNG](attachment:BrettPhos.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
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       "<script>\n",
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      "text/plain": [
       "Molecule(name='BrettPhos', formula='C44 O2 P1 Pd1')"
      ]
     },
     "execution_count": 9,
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   "source": [
    "#This accesses the structure\n",
    "m = cdxf['BrettPhos']\n",
    "m"
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`molli` can understand not only the charges of certain structures, but also the multiplicity of varying structures. This results in a very robust parsing algorithm, where even upon adding implicit hydrogens, it does not add too many. A simple example can be shown utilizing N-Oxide compounds\n",
    "\n",
    "## Charge Example \n",
    "\n",
    "![Charge.PNG](attachment:Charge.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The formal charge on Nitrogen = 1\n",
      "The formal charge on Oxygen = -1\n",
      "The charge of the Molecule = 0\n"
     ]
    },
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    },
    {
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       "<script>\n",
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       "            </script>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Molecule(name='a3', formula='C6 H11 N1 O1')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This reads the cdxml file\n",
    "cdxf2 = ml.CDXMLFile(ml.files.charges_mult_cdxml)\n",
    "\n",
    "#This accesses the structure\n",
    "m = cdxf2['a3']\n",
    "\n",
    "#This finds the nitrogen\n",
    "nitrogen = m.get_atom(ml.Element.N)\n",
    "\n",
    "#This finds the oxygen\n",
    "oxygen = m.get_atom(ml.Element.O)\n",
    "\n",
    "print(f'The formal charge on Nitrogen = {nitrogen.formal_charge}')\n",
    "print(f'The formal charge on Oxygen = {oxygen.formal_charge}')\n",
    "print(f'The charge of the Molecule = {m.charge}')\n",
    "\n",
    "\n",
    "#This adds the implicit hydrogens\n",
    "m.add_implicit_hydrogens()\n",
    "m"
   ]
  },
  {
   "attachments": {
    "chargemult.PNG": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charge and Multiplicity Example \n",
    "\n",
    "Since `molli` can parse the electrons drawn on an atom, it can perceive if hydrogens are necessary upon addition of implicit hydrogens\n",
    "\n",
    "![chargemult.PNG](attachment:chargemult.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The formal charge on Nitrogen = 1, The formal spin on Nitrogen = 1\n",
      "The charge of the Molecule = 1, The multiplicity of the Molecule = 2 \n"
     ]
    },
    {
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       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/html": [
       "<script>\n",
       "            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n",
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       "            </script>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Molecule(name='a6', formula='C8 H19 N1')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This accesses the structure\n",
    "m = cdxf2['a6']\n",
    "\n",
    "#This finds the nitrogen\n",
    "nitrogen = m.get_atom(ml.Element.N)\n",
    "print(f'The formal charge on Nitrogen = {nitrogen.formal_charge}, The formal spin on Nitrogen = {nitrogen.formal_spin}')\n",
    "print(f'The charge of the Molecule = {m.charge}, The multiplicity of the Molecule = {m.mult} ')\n",
    "\n",
    "#This adds the implicit hydrogens\n",
    "m.add_implicit_hydrogens()\n",
    "m"
   ]
  },
  {
   "attachments": {
    "Mult.PNG": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiplicity Example\n",
    "\n",
    "![Mult.PNG](attachment:Mult.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The formal charge on Carbon = 0, The formal spin on Carbon = 2\n",
      "The charge of the Molecule = 0, The multiplicity of the Molecule = 3 \n"
     ]
    },
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      "text/plain": [
       "Molecule(name='a8', formula='C1 Cl2')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This accesses the structure\n",
    "m = cdxf2['a8']\n",
    "\n",
    "#This finds the nitrogen\n",
    "carbon = m.get_atom(ml.Element.C)\n",
    "print(f'The formal charge on Carbon = {carbon.formal_charge}, The formal spin on Carbon = {carbon.formal_spin}')\n",
    "print(f'The charge of the Molecule = {m.charge}, The multiplicity of the Molecule = {m.mult} ')\n",
    "\n",
    "#This adds the implicit hydrogens\n",
    "m.add_implicit_hydrogens()\n",
    "m"
   ]
  }
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