example_battgenie.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import requests\n",
    "from rdflib import Graph, Namespace, RDF\n",
    "from ontopy import get_ontology\n",
    "\n",
    "# Loading from web\n",
    "battery = get_ontology('https://w3id.org/emmo/domain/battery/inferred').load()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data from https://raw.githubusercontent.com/BattGenie/battetl/main/tests/data/arbin_cycler_data/cccv_data_files/BG_Arbin_TestData_CCCV_Cell_2_Channel_26_Wb_1.CSV\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data from https://raw.githubusercontent.com/BattGenie/battetl/main/tests/data/maccor_cycler_data/simple_data/BG_Maccor_Testdata%20-%20079.txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_metadata(metadata_file):\n",
    "    with open(metadata_file, 'r') as f:\n",
    "        metadata = json.load(f)\n",
    "    return metadata\n",
    "\n",
    "def parse_metadata(metadata):\n",
    "    g = Graph()\n",
    "    g.parse(data=json.dumps(metadata), format='json-ld')\n",
    "    #print(f\"Number of triples in the graph after parsing metadata: {len(g)}\")\n",
    "    return g\n",
    "\n",
    "def query_datasets(graph):\n",
    "    query = \"\"\"\n",
    "    PREFIX dcat: <http://www.w3.org/ns/dcat#>\n",
    "    PREFIX csvw: <http://www.w3.org/ns/csvw#>\n",
    "    PREFIX dc: <http://purl.org/dc/terms/>\n",
    "\n",
    "    SELECT ?dataset ?csv_url ?table_schema_url ?title ?delimiter ?skipRows\n",
    "    WHERE {\n",
    "        ?dataset a dcat:Dataset ;\n",
    "                 csvw:url ?csv_url ;\n",
    "                 csvw:tableSchema ?table_schema_url ;\n",
    "                 dc:title ?title .\n",
    "        OPTIONAL {\n",
    "            ?dataset csvw:dialect ?dialect .\n",
    "            ?dialect csvw:delimiter ?delimiter .\n",
    "            ?dialect csvw:skipRows ?skipRows .\n",
    "        }\n",
    "    }\n",
    "    \"\"\"\n",
    "    return graph.query(query)\n",
    "\n",
    "def fetch_table_schema(url):\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        try:\n",
    "            return response.json()\n",
    "        except json.JSONDecodeError as e:\n",
    "            print(f\"Error decoding JSON from {url}: {e}\")\n",
    "    else:\n",
    "        print(f\"Error fetching table schema from {url}: HTTP {response.status_code}\")\n",
    "    return None\n",
    "\n",
    "def parse_table_schema(graph, table_schema):\n",
    "    graph.parse(data=json.dumps(table_schema), format='json-ld')\n",
    "    schema_query = \"\"\"\n",
    "    PREFIX csvw: <http://www.w3.org/ns/csvw#>\n",
    "    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
    "\n",
    "    SELECT ?column_name ?ontology_term\n",
    "    WHERE {\n",
    "        ?column csvw:propertyUrl ?property_url ;\n",
    "                csvw:name ?column_name .\n",
    "        ?property_url rdf:type ?ontology_term .\n",
    "    }\n",
    "    \"\"\"\n",
    "    return graph.query(schema_query)\n",
    "\n",
    "def plot_voltage_time_data(data, time_column, voltage_column, title):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(data[time_column], data[voltage_column])\n",
    "    plt.xlabel('Time / s')\n",
    "    plt.ylabel('Voltage / V')\n",
    "    plt.title(title)\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "def process_file(metadata_file):\n",
    "    metadata = load_metadata(metadata_file)\n",
    "    graph = parse_metadata(metadata)\n",
    "    results = query_datasets(graph)\n",
    "    #print(f\"Number of results: {len(results)}\")\n",
    "\n",
    "    for row in results:\n",
    "        dataset, csv_url, table_schema_url, title, delimiter, skip_rows = row\n",
    "        if csv_url and table_schema_url:\n",
    "            delimiter = delimiter if delimiter else ','  # Default to comma if no delimiter specified\n",
    "            skip_rows = int(skip_rows) if skip_rows else 0  # Default to 0 if no skipRows specifiedt\n",
    "            data = pd.read_csv(csv_url, delimiter=delimiter, skiprows=skip_rows)\n",
    "            print(f\"Loaded data from {csv_url}\")\n",
    "            #print(data.head())\n",
    "            \n",
    "            table_schema = fetch_table_schema(table_schema_url)\n",
    "            \n",
    "            if table_schema:\n",
    "                schema_results = parse_table_schema(graph, table_schema)\n",
    "                \n",
    "                column_mappings = {str(row.ontology_term): str(row.column_name) for row in schema_results}\n",
    "\n",
    "                time_column = column_mappings.get(battery.TestTime.iri)\n",
    "                voltage_column = column_mappings.get(battery.CellVoltage.iri)\n",
    "\n",
    "                if time_column and voltage_column:\n",
    "                    plot_voltage_time_data(data, time_column, voltage_column, title)\n",
    "\n",
    "# Run the workflow with a specified metadata file\n",
    "process_file(\"BG_Arbin_TestData_CCCV_Cell_2_Channel_26_Wb_1.metadata.json\")\n",
    "process_file(\"BG_Maccor_Testdata - 079.metadata.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import graphviz\n",
    "from IPython.display import display\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ExecutableNotFound",
     "evalue": "failed to execute WindowsPath('dot'), make sure the Graphviz executables are on your systems' PATH",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\backend\\execute.py:78\u001b[0m, in \u001b[0;36mrun_check\u001b[1;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[0;32m     77\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 78\u001b[0m         proc \u001b[38;5;241m=\u001b[39m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\subprocess.py:548\u001b[0m, in \u001b[0;36mrun\u001b[1;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[0;32m    546\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstderr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m PIPE\n\u001b[1;32m--> 548\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpopenargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m process:\n\u001b[0;32m    549\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\subprocess.py:1026\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)\u001b[0m\n\u001b[0;32m   1023\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mTextIOWrapper(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr,\n\u001b[0;32m   1024\u001b[0m                     encoding\u001b[38;5;241m=\u001b[39mencoding, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m-> 1026\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreexec_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1027\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mpass_fds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1028\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1029\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mp2cread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp2cwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1030\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mc2pread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc2pwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1031\u001b[0m \u001b[43m                        \u001b[49m\u001b[43merrread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1032\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mrestore_signals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1033\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mgid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mumask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1034\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mstart_new_session\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess_group\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1035\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m   1036\u001b[0m     \u001b[38;5;66;03m# Cleanup if the child failed starting.\u001b[39;00m\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\subprocess.py:1538\u001b[0m, in \u001b[0;36mPopen._execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session, unused_process_group)\u001b[0m\n\u001b[0;32m   1537\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1538\u001b[0m     hp, ht, pid, tid \u001b[38;5;241m=\u001b[39m \u001b[43m_winapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCreateProcess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1539\u001b[0m \u001b[43m                             \u001b[49m\u001b[38;5;66;43;03m# no special security\u001b[39;49;00m\n\u001b[0;32m   1540\u001b[0m \u001b[43m                             \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1541\u001b[0m \u001b[43m                             \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1542\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1543\u001b[0m \u001b[43m                             \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1544\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1545\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1546\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m   1547\u001b[0m     \u001b[38;5;66;03m# Child is launched. Close the parent's copy of those pipe\u001b[39;00m\n\u001b[0;32m   1548\u001b[0m     \u001b[38;5;66;03m# handles that only the child should have open.  You need\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;66;03m# pipe will not close when the child process exits and the\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m     \u001b[38;5;66;03m# ReadFile will hang.\u001b[39;00m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mExecutableNotFound\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 60\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;66;03m# Render the graph to a file\u001b[39;00m\n\u001b[0;32m     59\u001b[0m output_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrdf_graph\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 60\u001b[0m \u001b[43mdot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;66;03m# Display the graph\u001b[39;00m\n\u001b[0;32m     63\u001b[0m display(dot)\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\rendering.py:122\u001b[0m, in \u001b[0;36mRender.render\u001b[1;34m(self, filename, directory, view, cleanup, format, renderer, formatter, neato_no_op, quiet, quiet_view, outfile, engine, raise_if_result_exists, overwrite_source)\u001b[0m\n\u001b[0;32m    118\u001b[0m filepath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave(filename, directory\u001b[38;5;241m=\u001b[39mdirectory, skip_existing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    120\u001b[0m args\u001b[38;5;241m.\u001b[39mappend(filepath)\n\u001b[1;32m--> 122\u001b[0m rendered \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_render\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cleanup:\n\u001b[0;32m    125\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdelete \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m, filepath)\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\backend\\rendering.py:326\u001b[0m, in \u001b[0;36mrender\u001b[1;34m(engine, format, filepath, renderer, formatter, neato_no_op, quiet, outfile, raise_if_result_exists, overwrite_filepath)\u001b[0m\n\u001b[0;32m    322\u001b[0m cmd \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m    324\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m filepath \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwork around pytype false alarm\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 326\u001b[0m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    327\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    328\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mquiet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    329\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mcapture_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m os\u001b[38;5;241m.\u001b[39mfspath(outfile)\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\backend\\execute.py:81\u001b[0m, in \u001b[0;36mrun_check\u001b[1;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m==\u001b[39m errno\u001b[38;5;241m.\u001b[39mENOENT:\n\u001b[1;32m---> 81\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ExecutableNotFound(cmd) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m     82\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m     84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m quiet \u001b[38;5;129;01mand\u001b[39;00m proc\u001b[38;5;241m.\u001b[39mstderr:\n",
      "\u001b[1;31mExecutableNotFound\u001b[0m: failed to execute WindowsPath('dot'), make sure the Graphviz executables are on your systems' PATH"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import graphviz\n",
    "from IPython.display import display\n",
    "\n",
    "# JSON-LD data\n",
    "jsonld_data = \"\"\"\n",
    "{\n",
    "    \"@context\": \"https://w3id.org/emmo/domain/battery/context\",\n",
    "    \"@graph\": [\n",
    "        {\n",
    "            \"@type\": \"Graphite\",\n",
    "            \"@id\": \"_graphite\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"SiliconOxide\",\n",
    "            \"@id\": \"_silicon_oxide\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"LithiumNickelManganeseCobaltOxide\",\n",
    "            \"@id\": \"_nmc\",\n",
    "            \"molecularFormula\": \"LiNi0.83Mn0.05Co0.12O2\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Electrolyte\",\n",
    "            \"@id\": \"_electroylte\",\n",
    "            \"hasSolvent\": {\n",
    "                \"@type\": \"MySolvent\",\n",
    "                \"@id\": \"_solvent\"\n",
    "            },\n",
    "            \"hasSolute\": {\n",
    "                \"@type\": \"MySolute\",\n",
    "                \"@id\": \"_solute\"\n",
    "            }\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Lithium\",\n",
    "            \"@id\": \"_lithium\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "# Parse JSON-LD data\n",
    "data = json.loads(jsonld_data)\n",
    "\n",
    "# Initialize graphviz Digraph\n",
    "dot = graphviz.Digraph(comment='JSON-LD Graph', format='png')\n",
    "\n",
    "# Add nodes and edges to the graph\n",
    "for entry in data[\"@graph\"]:\n",
    "    entry_id = entry.get(\"@id\", entry.get(\"@type\", \"\"))\n",
    "    dot.node(entry_id, label=entry.get(\"@type\", entry_id))\n",
    "    \n",
    "    for key, value in entry.items():\n",
    "        if isinstance(value, dict) and \"@id\" in value:\n",
    "            dot.edge(entry_id, value[\"@id\"], label=key)\n",
    "\n",
    "# Render the graph to a file\n",
    "output_path = \"rdf_graph\"\n",
    "dot.render(output_path)\n",
    "\n",
    "# Display the graph\n",
    "display(dot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ExecutableNotFound",
     "evalue": "failed to execute WindowsPath('dot'), make sure the Graphviz executables are on your systems' PATH",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\backend\\execute.py:78\u001b[0m, in \u001b[0;36mrun_check\u001b[1;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[0;32m     77\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 78\u001b[0m         proc \u001b[38;5;241m=\u001b[39m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\subprocess.py:548\u001b[0m, in \u001b[0;36mrun\u001b[1;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[0;32m    546\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstderr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m PIPE\n\u001b[1;32m--> 548\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpopenargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m process:\n\u001b[0;32m    549\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\subprocess.py:1026\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)\u001b[0m\n\u001b[0;32m   1023\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mTextIOWrapper(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr,\n\u001b[0;32m   1024\u001b[0m                     encoding\u001b[38;5;241m=\u001b[39mencoding, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m-> 1026\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreexec_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1027\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mpass_fds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1028\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1029\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mp2cread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp2cwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1030\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mc2pread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc2pwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1031\u001b[0m \u001b[43m                        \u001b[49m\u001b[43merrread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1032\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mrestore_signals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1033\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mgid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mumask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1034\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mstart_new_session\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess_group\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1035\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m   1036\u001b[0m     \u001b[38;5;66;03m# Cleanup if the child failed starting.\u001b[39;00m\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\subprocess.py:1538\u001b[0m, in \u001b[0;36mPopen._execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session, unused_process_group)\u001b[0m\n\u001b[0;32m   1537\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1538\u001b[0m     hp, ht, pid, tid \u001b[38;5;241m=\u001b[39m _winapi\u001b[38;5;241m.\u001b[39mCreateProcess(executable, args,\n\u001b[0;32m   1539\u001b[0m                              \u001b[38;5;66;03m# no special security\u001b[39;00m\n\u001b[0;32m   1540\u001b[0m                              \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1541\u001b[0m                              \u001b[38;5;28mint\u001b[39m(\u001b[38;5;129;01mnot\u001b[39;00m close_fds),\n\u001b[0;32m   1542\u001b[0m                              creationflags,\n\u001b[0;32m   1543\u001b[0m                              env,\n\u001b[0;32m   1544\u001b[0m                              cwd,\n\u001b[0;32m   1545\u001b[0m                              startupinfo)\n\u001b[0;32m   1546\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m   1547\u001b[0m     \u001b[38;5;66;03m# Child is launched. Close the parent's copy of those pipe\u001b[39;00m\n\u001b[0;32m   1548\u001b[0m     \u001b[38;5;66;03m# handles that only the child should have open.  You need\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;66;03m# pipe will not close when the child process exits and the\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m     \u001b[38;5;66;03m# ReadFile will hang.\u001b[39;00m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mExecutableNotFound\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 60\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;66;03m# Render the graph to a file\u001b[39;00m\n\u001b[0;32m     59\u001b[0m output_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrdf_graph\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 60\u001b[0m \u001b[43mdot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;66;03m# Display the graph\u001b[39;00m\n\u001b[0;32m     63\u001b[0m display(dot)\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\rendering.py:122\u001b[0m, in \u001b[0;36mRender.render\u001b[1;34m(self, filename, directory, view, cleanup, format, renderer, formatter, neato_no_op, quiet, quiet_view, outfile, engine, raise_if_result_exists, overwrite_source)\u001b[0m\n\u001b[0;32m    118\u001b[0m filepath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave(filename, directory\u001b[38;5;241m=\u001b[39mdirectory, skip_existing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    120\u001b[0m args\u001b[38;5;241m.\u001b[39mappend(filepath)\n\u001b[1;32m--> 122\u001b[0m rendered \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_render\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cleanup:\n\u001b[0;32m    125\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdelete \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m, filepath)\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\backend\\rendering.py:326\u001b[0m, in \u001b[0;36mrender\u001b[1;34m(engine, format, filepath, renderer, formatter, neato_no_op, quiet, outfile, raise_if_result_exists, overwrite_filepath)\u001b[0m\n\u001b[0;32m    322\u001b[0m cmd \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m    324\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m filepath \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwork around pytype false alarm\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 326\u001b[0m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    327\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    328\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mquiet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    329\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mcapture_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m os\u001b[38;5;241m.\u001b[39mfspath(outfile)\n",
      "File \u001b[1;32mc:\\Users\\John\\Desktop\\import\\Pybop\\Lib\\site-packages\\graphviz\\backend\\execute.py:81\u001b[0m, in \u001b[0;36mrun_check\u001b[1;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m==\u001b[39m errno\u001b[38;5;241m.\u001b[39mENOENT:\n\u001b[1;32m---> 81\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ExecutableNotFound(cmd) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m     82\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m     84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m quiet \u001b[38;5;129;01mand\u001b[39;00m proc\u001b[38;5;241m.\u001b[39mstderr:\n",
      "\u001b[1;31mExecutableNotFound\u001b[0m: failed to execute WindowsPath('dot'), make sure the Graphviz executables are on your systems' PATH"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import graphviz\n",
    "from IPython.display import display\n",
    "\n",
    "# JSON-LD data\n",
    "jsonld_data = \"\"\"\n",
    "{\n",
    "    \"@context\": \"https://w3id.org/emmo/domain/battery/context\",\n",
    "    \"@graph\": [\n",
    "        {\n",
    "            \"@type\": \"Graphite\",\n",
    "            \"@id\": \"_graphite\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"SiliconOxide\",\n",
    "            \"@id\": \"_silicon_oxide\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"LithiumNickelManganeseCobaltOxide\",\n",
    "            \"@id\": \"_nmc\",\n",
    "            \"molecularFormula\": \"LiNi0.83Mn0.05Co0.12O2\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Electrolyte\",\n",
    "            \"@id\": \"_electroylte\",\n",
    "            \"hasSolvent\": {\n",
    "                \"@type\": \"MySolvent\",\n",
    "                \"@id\": \"_solvent\"\n",
    "            },\n",
    "            \"hasSolute\": {\n",
    "                \"@type\": \"MySolute\",\n",
    "                \"@id\": \"_solute\"\n",
    "            }\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Lithium\",\n",
    "            \"@id\": \"_lithium\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "# Parse JSON-LD data\n",
    "data = json.loads(jsonld_data)\n",
    "\n",
    "# Initialize graphviz Digraph\n",
    "dot = graphviz.Digraph(comment='JSON-LD Graph', format='png')\n",
    "\n",
    "# Add nodes and edges to the graph\n",
    "for entry in data[\"@graph\"]:\n",
    "    entry_id = entry.get(\"@id\", entry.get(\"@type\", \"\"))\n",
    "    dot.node(entry_id, label=entry.get(\"@type\", entry_id))\n",
    "    \n",
    "    for key, value in entry.items():\n",
    "        if isinstance(value, dict) and \"@id\" in value:\n",
    "            dot.edge(entry_id, value[\"@id\"], label=key)\n",
    "\n",
    "# Render the graph to a file\n",
    "output_path = \"rdf_graph\"\n",
    "dot.render(output_path)\n",
    "\n",
    "# Display the graph\n",
    "display(dot)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'C:\\\\Users\\\\John\\\\Desktop\\\\import'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Set the path to the Graphviz executables (adjust the path if needed)\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menviron\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mJohn\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDesktop\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mimport\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpathsep \u001b[38;5;241m+\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mProgram Files\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mGraphviz\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mbin\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# JSON-LD data\u001b[39;00m\n\u001b[0;32m     10\u001b[0m jsonld_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;124m{\u001b[39m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;124m    \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m@context\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://w3id.org/emmo/domain/battery/context\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[38;5;124m}\u001b[39m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n",
      "File \u001b[1;32m<frozen os>:679\u001b[0m, in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'C:\\\\Users\\\\John\\\\Desktop\\\\import'"
     ]
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "import json\n",
    "import graphviz\n",
    "from IPython.display import display\n",
    "\n",
    "# Set the path to the Graphviz executables (adjust the path if needed)\n",
    "os.environ[r\"C:\\Users\\John\\Desktop\\import\"] += os.pathsep + r'C:\\Program Files\\Graphviz\\bin'\n",
    "\n",
    "# JSON-LD data\n",
    "jsonld_data = \"\"\"\n",
    "{\n",
    "    \"@context\": \"https://w3id.org/emmo/domain/battery/context\",\n",
    "    \"@graph\": [\n",
    "        {\n",
    "            \"@type\": \"Graphite\",\n",
    "            \"@id\": \"_graphite\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"SiliconOxide\",\n",
    "            \"@id\": \"_silicon_oxide\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"LithiumNickelManganeseCobaltOxide\",\n",
    "            \"@id\": \"_nmc\",\n",
    "            \"molecularFormula\": \"LiNi0.83Mn0.05Co0.12O2\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Electrolyte\",\n",
    "            \"@id\": \"_electroylte\",\n",
    "            \"hasSolvent\": {\n",
    "                \"@type\": \"MySolvent\",\n",
    "                \"@id\": \"_solvent\"\n",
    "            },\n",
    "            \"hasSolute\": {\n",
    "                \"@type\": \"MySolute\",\n",
    "                \"@id\": \"_solute\"\n",
    "            }\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Lithium\",\n",
    "            \"@id\": \"_lithium\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "# Parse JSON-LD data\n",
    "data = json.loads(jsonld_data)\n",
    "\n",
    "# Initialize graphviz Digraph\n",
    "dot = graphviz.Digraph(comment='JSON-LD Graph', format='png')\n",
    "\n",
    "# Add nodes and edges to the graph\n",
    "for entry in data[\"@graph\"]:\n",
    "    entry_id = entry.get(\"@id\", entry.get(\"@type\", \"\"))\n",
    "    dot.node(entry_id, label=entry.get(\"@type\", entry_id))\n",
    "    \n",
    "    for key, value in entry.items():\n",
    "        if isinstance(value, dict) and \"@id\" in value:\n",
    "            dot.edge(entry_id, value[\"@id\"], label=key)\n",
    "\n",
    "# Render the graph to a file\n",
    "output_path = \"rdf_graph\"\n",
    "dot.render(output_path)\n",
    "\n",
    "# Display the graph\n",
    "display(dot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PATH environment variable:\n",
      "c:\\Users\\John\\Desktop\\import\\Pybop\\Scripts;C:\\Users\\John\\Desktop\\import\\Pybop\\Scripts;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Program Files\\Polyspace\\R2020b\\runtime\\win64;C:\\Program Files\\Polyspace\\R2020b\\bin;C:\\Program Files\\Polyspace\\R2020b\\polyspace\\bin;C:\\Program Files (x86)\\Bitvise SSH Client;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\John\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\Scripts;C:\\Users\\John\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\John\\AppData\\Local\\GitHubDesktop\\bin\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Print the PATH environment variable\n",
    "print(\"PATH environment variable:\")\n",
    "print(os.environ[\"PATH\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import graphviz\n",
    "from IPython.display import display\n",
    "\n",
    "# Print the PATH environment variable\n",
    "print(\"PATH environment variable before modification:\")\n",
    "print(os.environ[\"PATH\"])\n",
    "\n",
    "# Set the path to the Graphviz executables (adjust the path if needed)\n",
    "os.environ[\"PATH\"] += os.pathsep + r'C:\\Program Files\\Graphviz\\bin'\n",
    "\n",
    "# Print the PATH environment variable after modification\n",
    "print(\"PATH environment variable after modification:\")\n",
    "print(os.environ[\"PATH\"])\n",
    "\n",
    "# JSON-LD data\n",
    "jsonld_data = \"\"\"\n",
    "{\n",
    "    \"@context\": \"https://w3id.org/emmo/domain/battery/context\",\n",
    "    \"@graph\": [\n",
    "        {\n",
    "            \"@type\": \"Graphite\",\n",
    "            \"@id\": \"_graphite\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"SiliconOxide\",\n",
    "            \"@id\": \"_silicon_oxide\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"LithiumNickelManganeseCobaltOxide\",\n",
    "            \"@id\": \"_nmc\",\n",
    "            \"molecularFormula\": \"LiNi0.83Mn0.05Co0.12O2\"\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Electrolyte\",\n",
    "            \"@id\": \"_electroylte\",\n",
    "            \"hasSolvent\": {\n",
    "                \"@type\": \"MySolvent\",\n",
    "                \"@id\": \"_solvent\"\n",
    "            },\n",
    "            \"hasSolute\": {\n",
    "                \"@type\": \"MySolute\",\n",
    "                \"@id\": \"_solute\"\n",
    "            }\n",
    "        },\n",
    "        {\n",
    "            \"@type\": \"Lithium\",\n",
    "            \"@id\": \"_lithium\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "# Parse JSON-LD data\n",
    "data = json.loads(jsonld_data)\n",
    "\n",
    "# Initialize graphviz Digraph\n",
    "dot = graphviz.Digraph(comment='JSON-LD Graph', format='png')\n",
    "\n",
    "# Add nodes and edges to the graph\n",
    "for entry in data[\"@graph\"]:\n",
    "    entry_id = entry.get(\"@id\", entry.get(\"@type\", \"\"))\n",
    "    dot.node(entry_id, label=entry.get(\"@type\", entry_id))\n",
    "    \n",
    "    for key, value in entry.items():\n",
    "        if isinstance(value, dict) and \"@id\" in value:\n",
    "            dot.edge(entry_id, value[\"@id\"], label=key)\n",
    "\n",
    "# Render the graph to a file\n",
    "output_path = \"rdf_graph\"\n",
    "dot.render(output_path)\n",
    "\n",
    "# Display the graph\n",
    "display(dot)\n"
   ]
  }
 ],
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