{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f53f9245",
   "metadata": {},
   "source": [
    "# Hello World"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d63ae3d",
   "metadata": {},
   "source": [
    "Install GenDifS: Until further notice, there is no installation via pip. Installation by hand is simple:\n",
    "\n",
    "* Download the module `gd06.py` into any directory, e.g. the directory `../py/`.\n",
    "* Include this directory in the Python import path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "baa35578",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../py/')\n",
    "from gd06 import GenDifS_Map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11dd6f0",
   "metadata": {},
   "source": [
    "We recommend to create mindmaps in a separate directory, e.g. `../mm/`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abf335ed",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GenDifS 0.61 (2023-05-20)\n",
      "cwd into input_dir: /home/dsci/a/l/LA_2023_ss/gendifs/mm\n",
      "reading /home/dsci/a/l/LA_2023_ss/gendifs/mm > hello_world.mm (9 nodes)\n"
     ]
    }
   ],
   "source": [
    "mindmap = \"hello_world\" # filename without .mm extension\n",
    "o = GenDifS_Map(f\"../mm/{mindmap}.mm\", verbose = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05783e43",
   "metadata": {},
   "source": [
    "If you plan to write back the current mindmap it might be wise to create a backup?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2171d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote  backup to ../mm/hello_world_backup_2023-05-20T08-21-29\n"
     ]
    }
   ],
   "source": [
    "backup_mindmap = True\n",
    "\n",
    "import time\n",
    "if backup_mindmap:\n",
    "    mindmap_backup_filename = f\"../mm/{mindmap}_backup_{time.strftime('%Y-%m-%dT%H-%M-%S')}\" \n",
    "    o.mindmap.write(mindmap_backup_filename, pretty_print=True)\n",
    "    print(f\"wrote  backup to {mindmap_backup_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08db3406",
   "metadata": {},
   "source": [
    "## Compile the mindmap into a ttl representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52e00147",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rdflib: 77 triples.\n"
     ]
    }
   ],
   "source": [
    "o.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddd7ac8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The compiled code is put into a pandas data frame\n",
    "# o.code_df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09207ea1",
   "metadata": {},
   "source": [
    "`o.compile()` generates all necessary class attributes, but does not perform inferencing. To get all instances which can be inferred use `o.owlrl()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92b7d849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "owlrl: 388 triples.\n"
     ]
    }
   ],
   "source": [
    "o.owlrl()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a836c1",
   "metadata": {},
   "source": [
    "## SPARQL\n",
    "\n",
    "Feel free to query the graph with SPARQL. Some useful default namespaces are contained in the attribute `o.sparql_namespaces`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c44066a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(rdflib.term.URIRef('http://example.net/namespace/cpt#milk'), rdflib.term.URIRef('http://example.net/namespace/cpt#topConcept'))\n",
      "(rdflib.term.URIRef('http://example.net/namespace/cpt#milk_of_a_cow'), rdflib.term.URIRef('http://example.net/namespace/cpt#milk'))\n",
      "(rdflib.term.URIRef('http://example.net/namespace/cpt#goat_milk'), rdflib.term.URIRef('http://example.net/namespace/cpt#milk'))\n"
     ]
    }
   ],
   "source": [
    "Q = o.sparql_namespaces + \"\"\"\n",
    "SELECT ?sub ?super\n",
    "WHERE { ?sub skos:broader ?super }\n",
    "\"\"\"\n",
    "\n",
    "qres = o.rdflib_graph.query(Q)\n",
    "\n",
    "for row in qres:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b0face",
   "metadata": {},
   "source": [
    "## Save the ontologies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d494a905",
   "metadata": {},
   "source": [
    "We recommend saving the generated taxonomy in Turtle format in a separate directory, e.g. `../ttl/`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b2616d",
   "metadata": {},
   "source": [
    "This is what the ttl code looks like, generated directly by GenDifS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b31071f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f\"../ttl/{mindmap}_mm.ttl\", \"w\") as file:\n",
    "    file.write(o.ttl_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3f76a6",
   "metadata": {},
   "source": [
    "Export of all triples through rdflib, *without* inferencing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d8ee67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(o.rdflib_graph.serialize(destination=f\"../ttl/{mindmap}_rdflib.ttl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4296ffa",
   "metadata": {},
   "source": [
    "Export of all triples through rdflib, *after* inferencing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9c614d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "388"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(o.owlrl_graph.serialize(destination=f\"../ttl/{mindmap}_owlrl.ttl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20895478",
   "metadata": {},
   "source": [
    "## Save back the mindmap\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f31abd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updated mindmap ../mm/hello_world.mm\n"
     ]
    }
   ],
   "source": [
    "update_mindmap = True\n",
    "\n",
    "if update_mindmap:\n",
    "    update_mindmap_filename = f\"../mm/{mindmap}.mm\"\n",
    "    o.mindmap.write(update_mindmap_filename,pretty_print=True)\n",
    "    print(f\"updated mindmap {update_mindmap_filename}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}