{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The easy way\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ipqa-research/ugropy/blob/main/docs/source/tutorial/easy_way.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "  import google.colab\n",
    "  IN_COLAB = True\n",
    "except:\n",
    "  IN_COLAB = False\n",
    "\n",
    "\n",
    "if IN_COLAB:\n",
    "  %pip install ugropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Groups class\n",
    "`ugropy` is relatively straightforward to use, but let's explore what it has to \n",
    "offer. Now, let's start with the easy methods...\n",
    "\n",
    "We'll utilize the Groups class to retrieve the subgroups of all the models \n",
    "supported by `ugropy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CH3': 2, 'CH2': 1, 'CH': 1, 'CH2=C': 1, 'CH=C': 1, 'CH2CO': 1}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ugropy import Groups\n",
    "\n",
    "carvone = Groups(\"carvone\")\n",
    "\n",
    "carvone.unifac.subgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that was easy... `ugropy` utilizes `PubChemPy` \n",
    "([link](https://github.com/mcs07/PubChemPy)) to access `PubChem` and \n",
    "retrieve the SMILES representation of the molecule. `ugropy` then employs the \n",
    "SMILES representation along with the `rdkit` \n",
    "([link](https://github.com/rdkit/rdkit)) library to identify the \n",
    "functional groups of the molecules.\n",
    "\n",
    "The complete signature of the Groups class is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ugropy import DefaultSolver\n",
    "\n",
    "carvone = Groups(\n",
    "    identifier=\"carvone\",\n",
    "    identifier_type=\"name\",\n",
    "    solver=DefaultSolver,\n",
    "    search_multiple_solutions=False,\n",
    "    normal_boiling_temperature=None\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The identifier_type argument (default: \"name\") can be set to \"name\", \"smiles\"\n",
    "or \"mol\".\n",
    "\n",
    "When \"name\" is set, `ugropy` will use the identifier argument to search in\n",
    "pubchem for the canonical SMILES of the molecule.\n",
    "\n",
    "When \"smiles\" is set, `ugropy` uses it directly, this also means that the \n",
    "library will not suffer the overhead of searching on pubchem. Try it yourself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CH3': 2, 'CH2': 1, 'CH': 1, 'CH2=C': 1, 'CH=C': 1, 'CH2CO': 1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carvone = Groups(\n",
    "    identifier=\"CC1=CCC(CC1=O)C(=C)C\",\n",
    "    identifier_type=\"smiles\",\n",
    ")\n",
    "\n",
    "carvone.unifac.subgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are familiar with the `rdkit` library, you'll know that there are\n",
    "numerous ways to define a molecule (e.g., SMILES, SMARTS, PDB file, InChIKey,\n",
    "etc.). `ugropy` supports the provision of a Mol object from the `rdkit`\n",
    "library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CH3': 2, 'CH2': 1, 'CH': 1, 'CH2=C': 1, 'CH=C': 1, 'CH2CO': 1}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "\n",
    "mol_obj = Chem.MolFromInchi(\"InChI=1S/C10H14O/c1-7(2)9-5-4-8(3)10(11)6-9/h4,9H,1,5-6H2,2-3H3\")\n",
    "\n",
    "carvone = Groups(\n",
    "    identifier=mol_obj,\n",
    "    identifier_type=\"mol\",\n",
    "    normal_boiling_temperature=None\n",
    ")\n",
    "\n",
    "carvone.unifac.subgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The current supported models are the classic liquid-vapor UNIFAC, Predictive\n",
    "Soave-Redlich-Kwong (PSRK), Joback and Abdulelah-Gani. You can access the \n",
    "functional groups this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CH3': 2, 'CH2': 1, 'CH': 1, 'CH2=C': 1, 'CH=C': 1, 'CH2CO': 1}\n",
      "{'CH3': 2, 'CH2': 1, 'CH': 1, 'CH2=C': 1, 'CH=C': 1, 'CH2CO': 1}\n",
      "{'-CH3': 2, '=CH2': 1, '=C<': 1, 'ring-CH2-': 2, 'ring>CH-': 1, 'ring=CH-': 1, 'ring=C<': 1, '>C=O (ring)': 1}\n",
      "{'CH3': 2, 'CH2=C': 1, 'CH2 (cyclic)': 2, 'CH (cyclic)': 1, 'CH=C (cyclic)': 1, 'CO (cyclic)': 1}\n"
     ]
    }
   ],
   "source": [
    "carvone = Groups(\"carvone\")\n",
    "\n",
    "print(carvone.unifac.subgroups)\n",
    "\n",
    "print(carvone.psrk.subgroups)\n",
    "\n",
    "print(carvone.joback.subgroups)\n",
    "\n",
    "print(carvone.agani.primary.subgroups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can obtain more information about the molecule from each model. For \n",
    "example, UNIFAC and PSRK are Excess Gibbs Models, soy you can obtain the \n",
    "estimation of the R and Q values of the molecule (molecule's reduced VdW volume\n",
    "and area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNIFAC R:  6.3751\n",
      "UNIFAC Q:  5.308\n",
      "PSRK R:  6.3751\n",
      "PSRK Q:  5.308\n"
     ]
    }
   ],
   "source": [
    "print(\"UNIFAC R: \", carvone.unifac.r)\n",
    "print(\"UNIFAC Q: \", carvone.unifac.q)\n",
    "\n",
    "print(\"PSRK R: \", carvone.psrk.r)\n",
    "print(\"PSRK Q: \", carvone.psrk.q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the Joback model, you can obtain the estimation of different properties.\n",
    "We will discuss the Properties estimators later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42452945182153057 dimensionless\n",
      "516.47 kelvin\n",
      "742.5207962108279 kelvin\n",
      "28.596757127741714 bar\n",
      "503.5 centimeter ** 3 / mole\n",
      "0.09232883692318564 bar\n"
     ]
    }
   ],
   "source": [
    "print(carvone.joback.acentric_factor)\n",
    "print(carvone.joback.normal_boiling_point)\n",
    "print(carvone.joback.critical_temperature)\n",
    "print(carvone.joback.critical_pressure)\n",
    "print(carvone.joback.critical_volume)\n",
    "print(carvone.joback.vapor_pressure(430))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal_boiling_temperature parameter is provided, it is used in\n",
    "the Joback properties calculations instead of the Joback-estimated normal\n",
    "boiling temperature (refer to the Joback tutorial)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally the search_multiple_solutions parameter is used to determine if the \n",
    "solver should return multiple solutions or not. If set to True, the solver will\n",
    "return multiple solutions if they exist. If set to False, the solver will \n",
    "return only one solution. The default value is False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<ugropy.core.frag_classes.gibbs_model.gibbs_result.GibbsFragmentationResult at 0x7f49f2b12e90>,\n",
       " <ugropy.core.frag_classes.gibbs_model.gibbs_result.GibbsFragmentationResult at 0x7f49f2b13b80>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of multiple solutions\n",
    "\n",
    "molecule = Groups(\"CCCC1=CC=C(CC(=O)OC)C=C1\", \"smiles\", search_multiple_solutions=True)\n",
    "\n",
    "molecule.unifac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see we obtained a list of GibbsFragmentationResult objects. The\n",
    "result always will be a list when the search_multiple_solutions parameter is\n",
    "set to True independently of the number of solutions found.\n",
    "\n",
    "We can check both solutions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CH3': 2, 'CH2': 1, 'ACH': 4, 'ACCH2': 2, 'COO': 1}\n",
      "{'CH3': 2, 'CH2': 1, 'ACH': 4, 'AC': 1, 'ACCH2': 1, 'CH2COO': 1}\n"
     ]
    }
   ],
   "source": [
    "print(molecule.unifac[0].subgroups)\n",
    "print(molecule.unifac[1].subgroups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple solution searching is intended to get all the solution for a given\n",
    "model and try different representations of the molecule to obtain properties.\n",
    "For example, different UNIFAC representations could lead to different\n",
    "liquid-liquid or liquid-vapor equilibrium predictions.\n",
    "\n",
    "\n",
    "The full documentation of the `Groups` class may be accessed in the API\n",
    "documentation. Or you can do..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mInit signature:\u001b[0m\n",
      "\u001b[0mGroups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0midentifier\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0midentifier_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msolver\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mugropy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0milp_solvers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0milp_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mILPSolver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m<\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0;34m'ugropy.core.ilp_solvers.default_solver.DefaultSolver'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msearch_multiple_solutions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mnormal_boiling_temperature\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m     \n",
      "Groups class.\n",
      "\n",
      "Stores the solved FragmentationModels subgroups of a molecule. This class\n",
      "was implemented on an early version of the `ugropy` library. Is not really\n",
      "meant to be used, instead is recommended to use directly the corresponding\n",
      "FragmentationModel independently since it provides more flexibility and\n",
      "control over the results. The class is kept since in most cases is very\n",
      "comfortable to have all the results in a single object.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "identifier : Union[str, Chem.rdchem.Mol]\n",
      "        Identifier of the molecule. You can use either the name of the\n",
      "        molecule, the SMILEs of the molecule or a rdkit Mol object.\n",
      "identifier_type : str, optional\n",
      "    Identifier type of the molecule. Use \"name\" if you are providing\n",
      "    the molecules' name, \"smiles\" if you are providing the SMILES\n",
      "    or \"mol\" if you are providing a rdkir mol object, by default \"name\"\n",
      "solver : ILPSolver, optional\n",
      "    ILP solver class, by default DefaultSolver\n",
      "search_multiple_solutions : bool, optional\n",
      "    Weather search for multiple solutions or not, by default False\n",
      "    If False the return will be a FragmentationResult object, if True\n",
      "    the return will be a list of FragmentationResult objects.\n",
      "normal_boiling_temperature : float, optional\n",
      "    If provided, will be used to estimate critical temperature, acentric\n",
      "    factor, and vapor pressure instead of the estimated normal boiling\n",
      "    point in the Joback group contribution model, by default None.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "identifier : str\n",
      "    Identifier of a molecule. Example: hexane or CCCCCC.\n",
      "identifier_type : str, optional\n",
      "    Use 'name' to search a molecule by name or 'smiles' to provide the\n",
      "    molecule SMILES representation, by default \"name\".\n",
      "mol_object : rdkit.Chem.rdchem.Mol\n",
      "    RDKit Mol object.\n",
      "molecular_weight : float\n",
      "    Molecule's molecular weight from rdkit.Chem.Descriptors.MolWt [g/mol].\n",
      "unifac : Union[GibbsFragmentationResult, List[GibbsFragmentationResult]]\n",
      "    Classic LV-UNIFAC subgroups.\n",
      "psrk : Union[GibbsFragmentationResult, List[GibbsFragmentationResult]]\n",
      "    Predictive Soave-Redlich-Kwong subgroups.\n",
      "joback : Union[JobackFragmentationResult, List[JobackFragmentationResult]]\n",
      "    JobackFragmentationResult object that contains the Joback subgroups and\n",
      "    the estimated properties of the molecule.\n",
      "agani : Union[AGaniFragmentationResult, List[AGaniFragmentationResult]]\n",
      "    AGaniFragmentationResult object that contains the Abdulelah-Gani\n",
      "    subgroups and the estimated properties of the molecule.\n",
      "\u001b[0;31mFile:\u001b[0m           ~/code/ugropy/ugropy/groups.py\n",
      "\u001b[0;31mType:\u001b[0m           type\n",
      "\u001b[0;31mSubclasses:\u001b[0m     "
     ]
    }
   ],
   "source": [
    "?Groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, you can visualize your fragmentations results. Let's see the multiple\n",
    "solutions obtained before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<path class=\"bond-10 atom-10 atom-11\" d=\"M 651.5,90.5 L 672.9,110.0\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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       "<path class=\"bond-11 atom-6 atom-12\" d=\"M 443.2,74.8 L 427.4,147.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-12 atom-12 atom-13\" d=\"M 427.4,147.2 L 356.8,169.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-12 atom-12 atom-13\" d=\"M 417.9,138.6 L 359.5,157.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-13 atom-13 atom-3\" d=\"M 356.8,169.8 L 301.9,119.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 172.8,93.6 L 176.4,92.5 L 179.1,95.0\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n",
       "<path d=\"M 228.5,139.9 L 231.2,142.4 L 234.8,141.3\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n",
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       "<path d=\"M 510.4,53.3 L 513.9,52.2 L 516.6,54.7\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n",
       "<path d=\"M 566.0,99.6 L 568.8,102.1 L 570.2,101.6\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n",
       "<path d=\"M 428.2,143.6 L 427.4,147.2 L 423.9,148.4\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n",
       "<path d=\"M 360.3,168.7 L 356.8,169.8 L 354.0,167.3\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n",
       "<path class=\"atom-9\" d=\"M 543.3 174.6 Q 543.3 169.6, 545.8 166.8 Q 548.3 164.0, 553.0 164.0 Q 557.6 164.0, 560.1 166.8 Q 562.6 169.6, 562.6 174.6 Q 562.6 179.7, 560.1 182.7 Q 557.6 185.5, 553.0 185.5 Q 548.3 185.5, 545.8 182.7 Q 543.3 179.8, 543.3 174.6 M 553.0 183.2 Q 556.2 183.2, 557.9 181.0 Q 559.6 178.9, 559.6 174.6 Q 559.6 170.5, 557.9 168.4 Q 556.2 166.3, 553.0 166.3 Q 549.8 166.3, 548.0 168.4 Q 546.3 170.5, 546.3 174.6 Q 546.3 178.9, 548.0 181.0 Q 549.8 183.2, 553.0 183.2 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-10\" d=\"M 629.8 79.6 Q 629.8 74.6, 632.3 71.7 Q 634.8 68.9, 639.4 68.9 Q 644.1 68.9, 646.6 71.7 Q 649.1 74.6, 649.1 79.6 Q 649.1 84.7, 646.5 87.6 Q 644.0 90.5, 639.4 90.5 Q 634.8 90.5, 632.3 87.6 Q 629.8 84.7, 629.8 79.6 M 639.4 88.1 Q 642.6 88.1, 644.4 86.0 Q 646.1 83.8, 646.1 79.6 Q 646.1 75.5, 644.4 73.4 Q 642.6 71.3, 639.4 71.3 Q 636.2 71.3, 634.5 73.4 Q 632.8 75.4, 632.8 79.6 Q 632.8 83.8, 634.5 86.0 Q 636.2 88.1, 639.4 88.1 \" fill=\"#FF0000\"/>\n",
       "<rect x=\"1\" y=\"5\" width=\"18.0\" height=\"18.0\" fill=\"rgb(31, 119, 179)\"/><text x=\"19.200000000000003\" y=\"20\" font-family=\"Helvetica\" font-size=\"12\" fill=\"black\">CH3: 2</text><rect x=\"1\" y=\"30\" width=\"18.0\" height=\"18.0\" fill=\"rgb(255, 127, 13)\"/><text x=\"19.200000000000003\" y=\"45\" font-family=\"Helvetica\" font-size=\"12\" fill=\"black\">CH2: 1</text><rect x=\"1\" y=\"55\" width=\"18.0\" height=\"18.0\" fill=\"rgb(44, 159, 44)\"/><text x=\"19.200000000000003\" y=\"70\" font-family=\"Helvetica\" font-size=\"12\" fill=\"black\">ACH: 4</text><rect x=\"1\" y=\"80\" width=\"18.0\" height=\"18.0\" fill=\"rgb(214, 39, 40)\"/><text x=\"19.200000000000003\" y=\"95\" font-family=\"Helvetica\" font-size=\"12\" fill=\"black\">ACCH2: 2</text><rect x=\"1\" y=\"105\" width=\"18.0\" height=\"18.0\" fill=\"rgb(148, 103, 188)\"/><text x=\"19.200000000000003\" y=\"120\" font-family=\"Helvetica\" font-size=\"12\" fill=\"black\">COO: 1</text><text x=\"400.0\" y=\"40\" font-family=\"Helvetica\" font-size=\"12\" font-weight=\"bold\" fill=\"black\" text-anchor=\"middle\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecule.unifac[0].draw(width=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecule.unifac[1].draw(width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also draw the carvone solutions obtained before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "carvone.unifac.draw(width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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    "carvone.psrk.draw(width=600)"
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   "execution_count": 21,
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carvone.agani.primary.draw(width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save the figure by doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"figure.svg\", \"w\") as f:\n",
    "    f.write(carvone.unifac.get_solution_svg(width=600))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the full documentation of the draw funcion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mcarvone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munifac\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mwidth\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mheight\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtitle_font_size\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mlegend_font_size\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mfont\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Helvetica'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Create a IPython SVG object of the fragmentation result.\n",
      "\n",
      "This function is meant to be used in Jupyter notebooks to directly\n",
      "obtain the visual SVG figure on the notebook. It requires the IPython\n",
      "library to be installed.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "title : str, optional\n",
      "    Graph title, by default \"\"\n",
      "width : int, optional\n",
      "    Graph width, by default 400\n",
      "height : int, optional\n",
      "    Graph height, by default 200\n",
      "title_font_size : int, optional\n",
      "    Font size of graph's title, by default 12\n",
      "legend_font_size : int, optional\n",
      "    Legend font size, by default 12\n",
      "font : str, optional\n",
      "    Text font, by default \"Helvetica\"\n",
      "\n",
      "Returns\n",
      "-------\n",
      "IPython.display.SVG\n",
      "    SVG object of the fragmentation result.\n",
      "\n",
      "Raises\n",
      "------\n",
      "ImportError\n",
      "    IPython is not installed.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/code/ugropy/ugropy/core/frag_classes/base/fragmentation_result.py\n",
      "\u001b[0;31mType:\u001b[0m      method"
     ]
    }
   ],
   "source": [
    "?carvone.unifac.draw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, lets draw the `ugropy` logo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Groups(\"CCCC1=C(COC(C)(C)COC(=O)OCC)C=C(CC2=CC=CC=C2)C=C1\", \"smiles\")\n",
    "\n",
    "mol.unifac.draw(\n",
    "    title=\"ugropy\",\n",
    "    width=900,\n",
    "    height=450,\n",
    "    title_font_size=50,\n",
    "    legend_font_size=14\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WARNING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the UNIFAC, and PSRK groups the aldehyde group name is changed to HCO\n",
    "according to the discussion:\n",
    "https://github.com/ClapeyronThermo/Clapeyron.jl/issues/225\n",
    "\n",
    "This is more consistent with the ether groups and formate group."
   ]
  }
 ],
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