{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "747b245f",
   "metadata": {},
   "source": [
    "Build the Lego Knowledge Graph using the sources in `/data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90209948",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdflib import Graph, Namespace, XSD, OWL, RDF, RDFS, SKOS, URIRef, Literal\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe91fa67",
   "metadata": {},
   "source": [
    "Setup the requirements for building a knowledge graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e573135",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = Graph()\n",
    "thm = Namespace(\"https://thm.de/\")\n",
    "THM = Namespace(\"https://thm.de/ont/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56199d5",
   "metadata": {},
   "source": [
    "# Rebrickable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e1abb0",
   "metadata": {},
   "source": [
    "![Rebrickable](\\data\\rebrickable\\downloads_schema_v3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8a1fe84",
   "metadata": {},
   "outputs": [],
   "source": [
    "re_colors = pd.read_csv(\"data/rebrickable/colors.csv\")\n",
    "re_elements = pd.read_csv(\"data/rebrickable/elements.csv\")\n",
    "re_inventories = pd.read_csv(\"data/rebrickable/inventories.csv\")\n",
    "re_inventory_minifigs = pd.read_csv(\"data/rebrickable/inventory_minifigs.csv\")\n",
    "re_inventory_parts = pd.read_csv(\"data/rebrickable/inventory_parts.csv\")\n",
    "re_inventory_sets = pd.read_csv(\"data/rebrickable/inventory_sets.csv\")\n",
    "re_minifigs = pd.read_csv(\"data/rebrickable/minifigs.csv\")\n",
    "re_part_categories = pd.read_csv(\"data/rebrickable/part_categories.csv\")\n",
    "re_part_relationships = pd.read_csv(\"data/rebrickable/part_relationships.csv\")\n",
    "re_parts = pd.read_csv(\"data/rebrickable/parts.csv\")\n",
    "re_sets = pd.read_csv(\"data/rebrickable/sets.csv\")\n",
    "re_themes = pd.read_csv(\"data/rebrickable/themes.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3677416",
   "metadata": {},
   "source": [
    "Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae505704",
   "metadata": {},
   "outputs": [],
   "source": [
    "for color in re_colors.itertuples(index=False):\n",
    "    color_ref = thm[f\"colors/{color.id}\"]\n",
    "\n",
    "    g.add((color_ref, RDFS.label, Literal(color.name, lang=\"en\")))\n",
    "    g.add((color_ref, THM.color, Literal(color.rgb)))\n",
    "    g.add((color_ref, THM.is_transparent, Literal(color.is_trans, datatype=XSD.boolean)))\n",
    "    \n",
    "    if not pd.isna(color.y1):\n",
    "        # First appearance\n",
    "        g.add((color_ref, THM.first_year, Literal(datetime(year = int(color.y1), month=1, day=1))))\n",
    "    if not pd.isna(color.y2):\n",
    "        # Last appearance\n",
    "        g.add((color_ref, THM.last_year, Literal(datetime(year = int(color.y2), month=1, day=1))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27b2bc4",
   "metadata": {},
   "source": [
    "Part Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb9e17d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for part_category in re_part_categories.itertuples(index=False):\n",
    "    part_category_ref = thm[f\"part_category/{part_category.id}\"]\n",
    "\n",
    "    g.add((part_category_ref, RDFS.label, Literal(part_category_ref, lang=\"en\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea32849b",
   "metadata": {},
   "source": [
    "Parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8fdb080e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for part in re_parts.itertuples(index=False):\n",
    "    part_ref = thm[f\"part/{part.part_num}\"]\n",
    "\n",
    "    g.add((part_ref, RDFS.label, Literal(part.name, lang=\"en\")))\n",
    "    g.add((part_ref, THM.part_category, thm[f\"part_category/{part.part_cat_id}\"]))\n",
    "    g.add((part_ref, THM.part_material, Literal(part.part_material)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcaadd84",
   "metadata": {},
   "source": [
    "Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "579b1d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "for element in re_elements.itertuples(index=False):\n",
    "    part_ref = thm[f\"part/{element.part_num}\"]\n",
    "    color_ref = thm[f\"colors/{element.color_id}\"]\n",
    "\n",
    "    g.add((part_ref, THM.has_color, color_ref))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44dae336",
   "metadata": {},
   "source": [
    "Part Relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00db079a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for part_relationship in re_part_relationships.itertuples(index=False):\n",
    "    part_ref_parent = thm[f\"part/{part_relationship.parent_part_num}\"]\n",
    "    part_ref_child = thm[f\"part/{part_relationship.child_part_num}\"]\n",
    "\n",
    "    g.add((part_ref_parent, THM.has_child, part_ref_child))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dc64b8",
   "metadata": {},
   "source": [
    "Themes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a529aae",
   "metadata": {},
   "outputs": [],
   "source": [
    "for theme in re_themes.itertuples(index=False):\n",
    "    theme_ref = thm[f\"theme/{int(theme.id)}\"]\n",
    "\n",
    "    g.add((theme_ref, RDFS.label, Literal(theme.name, lang=\"en\")))\n",
    "\n",
    "    if not pd.isna(theme.parent_id):\n",
    "        g.add((theme_ref, THM.parent_theme, thm[f\"theme/{int(theme.parent_id)}\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f72c2e9",
   "metadata": {},
   "source": [
    "Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29b357ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "for lego_set in re_sets.itertuples(index=False):\n",
    "    set_ref = thm[f\"set/lego/{lego_set.set_num}\"]\n",
    "\n",
    "    g.add((set_ref, RDFS.label, Literal(lego_set.name, lang=\"en\")))\n",
    "    g.add((set_ref, THM.year, Literal(datetime(int(lego_set.year), 1, 1))))\n",
    "    g.add((set_ref, THM.theme, thm[f\"theme/{int(lego_set.theme_id)}\"]))\n",
    "    g.add((set_ref, THM.num_parts, Literal(int(lego_set.num_parts), datatype=XSD.integer)))\n",
    "    g.add((set_ref, THM.brand, Literal(\"Lego\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2616476",
   "metadata": {},
   "source": [
    "Minifigures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a67b3e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "for minifig in re_minifigs.itertuples(index=False):\n",
    "    minifig_ref = thm[f\"minifig/{minifig.fig_num}\"]\n",
    "\n",
    "    g.add((set_ref, RDFS.label, Literal(minifig.name, lang=\"en\")))\n",
    "    g.add((set_ref, THM.num_parts, Literal(int(minifig.num_parts), datatype=XSD.integer)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9baff1",
   "metadata": {},
   "source": [
    "Now the ugly part: Inventories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c97dc4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for inventory in re_inventories.itertuples(index=False):\n",
    "    inventory_ref = thm[f\"inventory/{inventory.id}\"]\n",
    "\n",
    "    g.add((inventory_ref, THM.set, thm[f\"sets/lego/{inventory.set_num}\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c962cf0",
   "metadata": {},
   "source": [
    "Inventories relate sets, minifigures and parts to each other, creating a kind of \"top level set\" \n",
    "(this takes a lot of time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc2ba03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for inventory_part in re_inventory_parts.itertuples(index=False):\n",
    "    inventory_part_ref = thm[f\"inventory_part/{inventory_part.inventory_id}/{inventory_part.part_num}\"]\n",
    "    \n",
    "    inventory_ref = thm[f\"inventory/{inventory_part.inventory_id}\"]\n",
    "    part_ref = thm[f\"part/{inventory_part.part_num}\"]\n",
    "\n",
    "    g.add((inventory_part_ref, RDFS.domain, inventory_ref))\n",
    "    g.add((inventory_part_ref, RDFS.range, part_ref))\n",
    "    g.add((inventory_part_ref, RDF.type, RDF.Property))\n",
    "    \n",
    "    g.add((inventory_part_ref, THM.quantity, Literal(int(inventory_part.quantity), datatype=XSD.integer)))\n",
    "    g.add((inventory_part_ref, THM.is_spare, Literal(inventory_part.is_spare, datatype=XSD.boolean)))\n",
    "    g.add((inventory_part_ref, THM.color, thm[f\"color/{inventory_part.color_id}\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8715a1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "for inventory_set in re_inventory_sets.itertuples(index=False):\n",
    "    inventory_set_ref = thm[f\"inventory_set/{inventory_set.inventory_id}/{inventory_set.set_num}\"]\n",
    "\n",
    "    inventory_ref = thm[f\"inventory/{inventory_set.inventory_id}\"]\n",
    "    set_ref = thm[f\"set/lego/{inventory_set.set_num}\"]\n",
    "\n",
    "    g.add((inventory_set_ref, RDFS.domain, inventory_ref))\n",
    "    g.add((inventory_set_ref, RDFS.range, set_ref))\n",
    "    g.add((inventory_set_ref, RDF.type, RDF.Property))\n",
    "\n",
    "    g.add((inventory_set_ref, THM.quantity, Literal(int(inventory_set.quantity), datatype=XSD.integer)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "08c2c580",
   "metadata": {},
   "outputs": [],
   "source": [
    "for inventory_minifig in re_inventory_minifigs.itertuples(index=False):\n",
    "    inventory_minifig_ref = thm[f\"inventory_minifig/{inventory_minifig.inventory_id}/{inventory_minifig.fig_num}\"]\n",
    "\n",
    "    inventory_ref = thm[f\"inventory/{inventory_minifig.inventory_id}\"]\n",
    "    minifig_ref = thm[f\"minifig/lego/{inventory_minifig.fig_num}\"]\n",
    "\n",
    "    g.add((inventory_minifig_ref, RDFS.domain, inventory_ref))\n",
    "    g.add((inventory_minifig_ref, RDFS.range, minifig_ref))\n",
    "    g.add((inventory_minifig_ref, RDF.type, RDF.Property))\n",
    "\n",
    "    g.add((inventory_minifig_ref, THM.quantity, Literal(int(inventory_minifig.quantity), datatype=XSD.integer)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbab237",
   "metadata": {},
   "source": [
    "# Brickset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fb5374",
   "metadata": {},
   "source": [
    "add for prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1e0ac437",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_sets = pd.read_csv(\"./data/brickset/sets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd944ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for bs_set in bs_sets.itertuples(index=False):\n",
    "    num = f\"{str(bs_set.Number).strip()}-{str(bs_set.Variant)}\" #Error for Set 853357\n",
    "    set_ref = thm[f\"set/lego/{num}\"]\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfab0c73",
   "metadata": {},
   "source": [
    "Serialize the graph in turtle format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abd6894",
   "metadata": {},
   "source": [
    "```\n",
    "                             ___-------___\n",
    "                         _-~~             ~~-_\n",
    "                      _-~                    /~-_\n",
    "   /^\\__/^\\         /~  \\                   /    \\\n",
    " /|  O|| O|        /      \\_______________/        \\\n",
    "| |___||__|      /       /                \\          \\\n",
    "|          \\    /      /                    \\          \\\n",
    "|   (_______) /______/                        \\_________ \\\n",
    "|         / /         \\                      /            \\\n",
    " \\         \\^\\\\         \\                  /               \\     /\n",
    "   \\         ||           \\______________/      _-_       //\\__//\n",
    "     \\       ||------_-~~-_ ------------- \\ --/~   ~\\    || __/\n",
    "       ~-----||====/~     |==================|       |/~~~~~\n",
    "        (_(__/  ./     /                    \\_\\      \\.\n",
    "               (_(___/                         \\_____)_)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a30bff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "g.bind(\"thmont\", THM)\n",
    "\n",
    "#g.serialize(\"lego_graph_rebrickable.ttl\", format=\"turtle\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv (3.14.4)",
   "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": "3.14.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}