Read_computational_data_json.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5716c85b",
   "metadata": {},
   "source": [
    "<h1><center>Computational data JSONS</center></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a45ab427",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gzip\n",
    "import ijson\n",
    "from pymatgen.io.lobster import Lobsterout\n",
    "from pymatgen.core import Structure\n",
    "from pymatgen.electronic_structure.plotter import DosPlotter\n",
    "from pymatgen.electronic_structure.cohp import Cohp\n",
    "from pymatgen.electronic_structure.cohp import CompleteCohp\n",
    "from pymatgen.electronic_structure.plotter import CohpPlotter\n",
    "from pymatgen.electronic_structure.dos import LobsterCompleteDos\n",
    "from atomate2.lobster.schemas import LobsterTaskDocument"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b28a68e",
   "metadata": {},
   "source": [
    "<h1><center>Top level keys to access data from Computational data JSON files </center></h1>\n",
    "<table>\n",
    "<thead>\n",
    "  <tr>\n",
    "    <th>Root Keys</th>\n",
    "    <th>Data type</th>\n",
    "    <th>Description</th>\n",
    "  </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "  <tr>\n",
    "    <td>structure</td>\n",
    "    <td>dict</td>\n",
    "    <td>Dict representation of the pymatgen structure object<br>used for the LOBSTER calculation</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>charges</td>\n",
    "    <td>dict</td>\n",
    "    <td>Atomic charges dict from LOBSTER based on <br>Mulliken and Loewdin charge analysis</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lobsterin</td>\n",
    "    <td>dict</td>\n",
    "    <td>LOBSTER calculation inputs</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lobsterout</td>\n",
    "    <td>dict</td>\n",
    "    <td>Information of LOBSTER calculation output</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lobsterpy_data</td>\n",
    "    <td>dict</td>\n",
    "    <td>Summarized bonding analysis data from Lobsterpy <br>(all bonds mode). It also includes Cohp objects <br>to plot the COHP curves from the automatic analysis</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lobsterpy_text</td>\n",
    "    <td>dict</td>\n",
    "    <td>LobsterPy automatic analysis summary text (all bonds mode)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>strongest_bonds_icohp</td>\n",
    "    <td>dict</td>\n",
    "    <td>Describes the strongest ICOHP bonds</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>strongest_bonds_icoop</td>\n",
    "    <td>dict</td>\n",
    "    <td>Describes the strongest ICOOP bonds</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>strongest_bonds_icobi</td>\n",
    "    <td>dict</td>\n",
    "    <td>Describes the strongest ICOBI bonds</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lobsterpy_data_cation_anion</td>\n",
    "    <td>dict</td>\n",
    "    <td>Summarized bonding analysis data from Lobsterpy <br>(cation-anion bonds mode). It also includes Cohp objects <br>to plot the COHP curves from the automatic analysis</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lobsterpy_text_cation_anion</td>\n",
    "    <td>dict</td>\n",
    "    <td>LobsterPy automatic analysis summary text <br>(cation-anion bonds mode)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>strongest_bonds_icohp_cation_anion</td>\n",
    "    <td>dict</td>\n",
    "    <td>Describes the strongest cation-anion ICOHP bonds</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>strongest_bonds_icoop_cation_anion</td>\n",
    "    <td>dict</td>\n",
    "    <td>Describes the strongest cation-anion ICOOP bonds</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>strongest_bonds_icobi_cation_anion</td>\n",
    "    <td>dict</td>\n",
    "    <td>Describes the strongest cation-anion ICOBI bonds</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>cohp_data</td>\n",
    "    <td>dict</td>\n",
    "    <td>Dict representation of pymatgen CompleteCohp object <br>including data to plot COHP curves</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>coop_data</td>\n",
    "    <td>dict</td>\n",
    "    <td>Dict representation of pymatgen CompleteCohp object <br>including data to plot COOP curves</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>cobi_data</td>\n",
    "    <td>dict</td>\n",
    "    <td>Dict representation of pymatgen CompleteCohp object <br>including data to plot COBI curves</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>dos</td>\n",
    "    <td>dict</td>\n",
    "    <td>Dict representation of pymatgen LobsterCompleteDos object <br>including the DOSCAR.lobster data</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>lso_dos</td>\n",
    "    <td>dict</td>\n",
    "    <td>Dict representation of pymatgen LobsterCompleteDos object <br>including the DOSCAR.LSO.lobster data</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>madelung_energies</td>\n",
    "    <td>dict</td>\n",
    "    <td>Consists of the Madelung energies of the structure derived from <br>the Mulliken and Löwdin charges</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>site_potentials</td>\n",
    "    <td>dict</td>\n",
    "    <td>Site potentials dict based on Mulliken and Loewdin charges</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>gross_populations</td>\n",
    "    <td>dict</td>\n",
    "    <td>Gross populations dict based on Mulliken and Loewdin charges <br>with each site as a key and the gross population as a value.</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>band_overlaps</td>\n",
    "    <td>dict</td>\n",
    "    <td>Band overlaps data for each k-point from bandOverlaps.lobster <br>file if it exists</td>\n",
    "  </tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a19b44c",
   "metadata": {},
   "source": [
    "### Function below can reads one Root key data from the json at a time. Due to file large sizes it is recommended approach to avoid running of out memory issues "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfe2ba54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_lobstertask_doc_json(filename: str,pymatgen_objs: bool=False, query='structure'):\n",
    "    \"\"\"\n",
    "    This function loads the lobster task json.gz files and returns a dictionary corresponding to query.\n",
    "    \n",
    "    Args:\n",
    "        filename (str) : name of file\n",
    "        pymatgen_objs (bool) : if True will convert structure,coop,cobi,cohp and dos to pymatgen objects\n",
    "        query (str) : Filters the json file based on this key and returns data corresponding to it.  \n",
    "        Possible query corresponds to root keys column of the table above.\n",
    "    Returns:\n",
    "        Returns a dictionary with lobster task json data corresponding to query.\n",
    "    \"\"\"\n",
    "    lobster_data={}\n",
    "    with gzip.open(filename, 'rb') as f:\n",
    "        objects = ijson.items(f,'item',use_float=True)\n",
    "        #filtered_objects = (obj for obj in objects if list(obj.keys())[0]==query)\n",
    "        for obj in objects:\n",
    "            if query in obj:\n",
    "                for k, v in obj.items():\n",
    "                    if pymatgen_objs:\n",
    "                        if k=='structure':\n",
    "                            lobster_data[k]=Structure.from_dict(v)\n",
    "                        elif k=='lobsterpy_data' or k=='lobsterpy_data_cation_anion':\n",
    "                            lobster_data[k]=v\n",
    "                            if lobster_data[k]:\n",
    "                                for plotlabel, cohp in lobster_data[k][\"cohp_plot_data\"].items():\n",
    "                                    lobster_data[k][\"cohp_plot_data\"][plotlabel]= Cohp.from_dict(cohp)\n",
    "                        elif k=='cohp_data' or k=='cobi_data' or k=='coop_data':\n",
    "                            lobster_data[k]=CompleteCohp.from_dict(v)\n",
    "                        elif k=='lso_dos' or k=='dos':\n",
    "                            lobster_data[k]=LobsterCompleteDos.from_dict(v)\n",
    "                        else:\n",
    "                            lobster_data[k]=v\n",
    "                    else:\n",
    "                        lobster_data[k]=v\n",
    "                break\n",
    "                    \n",
    "    return lobster_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1601ecca",
   "metadata": {},
   "source": [
    "### Section below provides some example snippets of how to access some data from the computational data json based on keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9020e907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Structure Summary\n",
       "Lattice\n",
       "    abc : 4.666904755831213 4.666904755831213 4.666904755831213\n",
       " angles : 59.99999999999999 59.99999999999999 59.99999999999999\n",
       " volume : 71.874\n",
       "      A : 0.0 3.3 3.3\n",
       "      B : 3.3 0.0 3.3\n",
       "      C : 3.3 3.3 0.0\n",
       "    pbc : True True True\n",
       "PeriodicSite: Ca (0.0000, 0.0000, 0.0000) [0.0000, 0.0000, 0.0000]\n",
       "PeriodicSite: Se (1.6500, 1.6500, 1.6500) [0.2500, 0.2500, 0.2500]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get structure\n",
    "lobtask= read_lobstertask_doc_json(filename='Example_data/Computational_data_jsons/mp-1008223.json.gz', \n",
    "                                   pymatgen_objs=True, query='structure')\n",
    "lobtask['structure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3beab5cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The compound CaSe has 2 symmetry-independent atoms(s) with relevant bonds: Ca1, Se2. Ca1 has a tetrahedral (CN=4) coordination environment. It has 4 Ca-Se (mean ICOHP: -0.83 eV, 10.922 percent antibonding interaction below EFermi) bonds. Se2 has a tetrahedral (CN=4) coordination environment. It has 4 Se-Ca (mean ICOHP: -0.83 eV, 10.922 percent antibonding interaction below EFermi) bonds.'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get lobsterpy summary analysis summary text\n",
    "lobtask= read_lobstertask_doc_json(filename='Example_data/Computational_data_jsons/mp-1008223.json.gz', \n",
    "                                   pymatgen_objs=True, query='lobsterpy_text')\n",
    "lobtask['lobsterpy_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2fede6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get lobster cohpcar pymatgen object and plot cohp by label\n",
    "lobtask= read_lobstertask_doc_json(filename='Example_data/Computational_data_jsons/mp-1008223.json.gz', \n",
    "                                   pymatgen_objs=True, query='cohp_data')\n",
    "cohppplot = CohpPlotter()\n",
    "cohppplot.add_cohp(label='17',cohp=lobtask['cohp_data'].get_cohp_by_label('17')) #plot cohp by bond label\n",
    "plot = cohppplot.get_plot(integrated=False)\n",
    "plot.ylim([-12, 2])\n",
    "plot.xlim([-1, 2])\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcc6bb8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get lobster dos pymatgen object and plot projected dos\n",
    "lobtask= read_lobstertask_doc_json(filename='Example_data/Computational_data_jsons/mp-1008223.json.gz', \n",
    "                                   pymatgen_objs=True, query='lso_dos')\n",
    "plotter = DosPlotter()\n",
    "plotter.add_dos_dict(lobtask['lso_dos'].get_spd_dos())\n",
    "plotter.get_plot(xlim=(-15, 0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98185842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': {'element': 'Ca', 'Mulliken GP': {'3s': 2.0, '4s': 0.49, '3p_y': 2.0, '3p_z': 2.0, '3p_x': 2.0, 'total': 8.49}, 'Loewdin GP': {'3s': 1.99, '4s': 0.58, '3p_y': 2.0, '3p_z': 2.0, '3p_x': 2.0, 'total': 8.57}}, '1': {'element': 'Se', 'Mulliken GP': {'4s': 1.92, '4p_y': 1.86, '4p_z': 1.86, '4p_x': 1.86, 'total': 7.51}, 'Loewdin GP': {'4s': 1.85, '4p_y': 1.86, '4p_z': 1.86, '4p_x': 1.86, 'total': 7.43}}}\n"
     ]
    }
   ],
   "source": [
    "#get lobster gross populations\n",
    "lobtask= read_lobstertask_doc_json(filename='Example_data/Computational_data_jsons/mp-1008223.json.gz', \n",
    "                                   pymatgen_objs=True, query='gross_populations')\n",
    "print(lobtask['gross_populations'])"
   ]
  }
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