08_understanding_IFC_under_the_hood.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2209f650338bac01",
   "metadata": {},
   "source": [
    "# Understanding IFC \"under the hood\"\n",
    "\n",
    "In this notebook, you will be equipped with advanced visualization tools that may help you explore the underlying network of objects forming an IFC model. \n",
    "You may have already seen the graphViz utility in another notebook. \n",
    "In addition to those tools, we will now explore a `labeled property graph` representation of an IFC file. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afdbd05209a2a38",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "\n",
    "All you need is included in `IfcArrowsViz` file, which contains the IFCArrowsGraphGenerator class. \n",
    "We are going to use the arrows.app, which provides options to interactively draw graphs and export them in various representations (e.g., svg, png, json, cypher, or graphML). \n",
    "\n",
    "You can reach the app free of charge and without login here: https://arrows.app/ \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "from utils.IfcArrowsViz import IFCArrowsGraphGenerator\n",
    "\n",
    "visualizer = IFCArrowsGraphGenerator(model_path=\"data/cube_single.ifc\")\n",
    "json = visualizer.generate_arrows_visualization(ignore_null_values=True)\n",
    "\n",
    "print(json)\n"
   ]
  },
  {
   "attachments": {
    "arrows_import.png": {
     "image/png": 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TsjXu+9ZDseMdvxcrWrP7r431/3JLfKAySrivXf98U1z6Owtiw9m/G3/059+I+//yir5HISuejxeyAxpHnxFnlDYcg/bElv/zYHTujaipvyAuPAKfpX7qqaVjCbdufTp27z76puYHozFjRheXSsej3Teme5qwNJpYko/A5CNW3dOV+fasXkZXTVmWl94jZK9HPmLUW0rNVFGjU2jsqzSF3L5uSf4aq08kKU8hL8mmX4ugzeT7X56yrl56TKFWyUNkdNY5ByEbkeuO6T695j4WQZ4CLgvA0ghntp9Z/Jb+LnqcMJNFVL4Pl8dpa0rT+Ie8j30o/0dEaWp7/9Pn3T9PVdJr35b2sji0cl/5MYj3xZh8Gjpbsmnpsl5Ty2mpvOz8vStPX1cvxVQ29OHwozCTwrD2zI/G8PNu6HMZNvryqHlkW9QMPzVqT6jrsXTtOCH2PLkn9p7zW9E17teKB3zjjfjAf4tPNAyLp74+Kz763/8iVqV/YFbdflVM++MfxJ6TPhB/MOdgj8UaHsOzUbfOlbHwL/4uVm3s++zq4XnVPRhfn/d3cc8Pn+gRopkzr5gVHzjp+fjHL/5xfOOhztja+VB8Y8HsuHHhunghfil+6RfS/Tvvij++ZVU88MDfxqc/9Rf7Gc07Mf1bkXL3oS9Fc1O63yUTozE78ePqZfsd/dvxky2xPYbF6Q0/H40nPhbf+Po/xxPFdd1+Gt/80uJ4YO0DsfiqOfH36T+I6y//rUiZP2Tt2Hhv/MN3f7rP31U2svrT7/5N/O33n0v/CxsdF7//HXEkvl/nTW86Kf8cwuxbStavX59/jV1v2baOjo7842uOeq8+FRv/7d/i3w5q2RhP7ft29FA+yL/b2vjOmm1FaGSjetWBkQVHcTGXhci2WH9Xdpxc1XRlNmq1bU18p0clbIkteQUVkXZf98H/+XF2xeXXVnrONXdVnTyw9jux5gBnDudTyO3tPV9jpphCTldVpo4zpVi8r+foV3rx1av5FHohHyXt/dj7VXp/u0cs+/La+1h6jUvzYzPLLz3bz/b77out1a8l/RveM9ZK8Xow+3gg2YfB9zB6TOyvicuvtfvHrHT8aXWI7yOLxh77kY3mlpUCuHpaPltKj1+8dz1/+NKuHeyecSwa8K+5i5eejr2r/jhqdh3go2Z+/uNRc+57ipX+0S9fc5ed0fvZ6+NP/7Etnti5J5Xim2Lcez4Zn7vluvjP5ZG17Ozj5kURs1bE6rnj803ZhzXPaW2Klo7FMSOtP/Ptm+LD194dj+0aEadd8vvx1a++Jf6kYU601s+KFavnRnavXf++MK747Zb4f8+mxj73N2Lht/4otl2bPU59eujVkT10dvbxZ6+/NZav35qyY0S8adx74pOfuyWuy17MM9+Omy7/VCx7ZGfUvmlcTP/iR2Pb9fNi0ye7X1eu7UvxKzO+Huff8jcx950pDnd0xv23fyY++61sF1bH9OWTo7Q7pefM7fr3uO1DH4svb0h/hyPPjQ/84Y1Rd+c1sWpq6bGzs4+bF70YF77z5OhY+0jsjJFx7oz/GXd9oTnOGLHv+9HXe/ZGOdSvuat46aH42p33xmPpx6Jm5Olx7vi3x9vPODFefOJH8fDGR+OJF/emIBwZ45r/a3xkQjbmfOhez9fcZbG3/j/Wx87spJOamjjppBSKb86OGo149rlnY/sLpTOUT0+/Dc8959z88qEaFN99/NT3465vPBjPHMqA6Ii3xrs/fEW8q8+p/C3pl3sKv/uWpuAoNiWNl3ePvGTRmJ8BnGuMSZO2xvq4ovsja6pOIug5yFQ62aT7F/noGD3pfel+2Y2yEcg7Ks85etLlMWH90njyfb0fIykeP79pNvpYfSJG5cGzkaEDjAQVj5GdGNH7o3ZK+5edtFB1/Fqm+nkzo0dH44Qr4spLn0z7dV/2VqSY7H5f9rl/Jjt7tvtFVoyueh3586+fUNmvagfex+I9PO3yqtG97hN8yo+/Nj3+femNLr/S6ufe/z7WlZ47qh+7WnZW8n3p76/yqD1eW/Xrrvws9X6ubJSyeOy+34OePyPROCkmbV0fccUNcdG62+Ku6p/BpPdj9Hzvsl2bFO9L1/W1NzDgUdj1g79Mv3kfKNb6MABBmPHdx317/mtXxM9/7j9iwn/5vbj6Q5fGqU/fG/9w55fibzoujj9b8+W4rO8DJQ+oFIW9QvIo8bqjMNm19V/jW//729H+XOU77iqGvak+LvnwR+M9Z73+j3x5vd99nIXhT37yk9jyxJZspdhaSKE45vTTo77+nHSxpth4aAZFFAJ9Bmu2LTvMoXf0w8EY+Cj8l1sjtu7ncwwHKAgzonA/dj0Rq1s+FZ9d8sPS6OewkXHG+F+NTy34Qlze8PoC5liNwpI98fK2n0THjzui4ycvxolnvy3OPuft0TDmhMM+meb1RmHZ3r1789HBF7dnJymlmDvllDjl5FOitvbwjhoRhTBY9BpFTHqMgMIhGvgo3HBPRLb0NoBBmBGFHIzDj8KBc7hROFBEIcDQ1D8nmhxAzc+8L+KUs4u1FInDU6wNcBACAHBoBv5Ek7Kt7dH10tNRc1pjxIn7fg1XfzNSyMEwUnjojBQCDE0DPlJYkWIwHx08AkEIAMChOXJRCADAoCUKAQAQhQAAiEKOcSNHOiHpUAwfkX1nIwBDkSjkmPbmU17HV7gcq2oijj/uuGIFgKEm/0iaIfHl+L0Mto+keb1fKcbAe/XVV+O555+PnTtfLrYMDoPpI2myEcIsCIcNO9zvaQFgsKrZsWOHKDwCylEoDgGAwajmxRdfzItwqIXhYIrC6iAsLwAAg0lNR8fmoTdMOEht375dGAIAg1IehWPHnlusMlA2b34kzj77rKitrc0XUQgADCbOPj6CylP02Z9D8ThOAODoJQqPIDEIAAxWovAIEoQAwGAlCgEAEIUAAIhCAAASUQgAgCgEAEAUAgCQiEIAAEQhAACiEACARBQCANBfUdgW8yc3RMPMZcV6tdJ1fV41KL0cjz/2VOwq1gAAjgX9O1LYOucoir/9eHhxXPfR34yrlrQJQwDgmNGvUdjU1JS6cHLMbys2HLVejA0LrxOGAMAxo39HCqctjpamzlg0e34cqAvb5k+OhoaGYpkZpcHF0jTz5OqiXDYzXd8zMpfN3N80dX8ThgDAsaPfTzSZsXhFzIpF0byfcMuCsHnRedHS0REd2dISMWdyFpHjY+71TdG5cnklKJfd05r+f2esXF7ZEve01sesG2cU6wOtFIafW7W9WAcAGJr6PQoji7uFs6K+dU7PUb9cWyxf2RlNLYujknUzbkwRuTLy7ms8P+o7i8t5ADZFS0sKxY3t2YasEqO1fmpMH19aPRJqz5kSH37nScUaAMDQNABRmIyfG6uzmFs0u9fxhe2xsTM7H6U8dZwtzbGoszPy7hs/PabWFyODWQA2TYsZM6ZFU+s9+RRzNnJYP3V6ys4jo/b0D8QXFs6Nd72l2AAAMEQNTBRmZiyOFbMiFjXPjpXFpojGOL8+oqmlmDquWhbnQ4fjY/rU+nxkMAvApmnZxhkxrak17llWmjqeeoSGCfMg/KvPxy8LQgDgGDBwUZiMn7swZtV3RmdnsaGIvtYFPU9EaWvrXhs/fWo+9TyntSnyJkxmTGtK91kQm47E1PHJp0bd2F8VhADAMWVAozCLwLmrW6KpWMuMn7s6VkxdGc2V6eOGmD371u5p5nwKOf2ZTR2XtmRVGE1ZWR6JqeMxvx633/U5QQgAHFNqOjo2d40de26xykDZvPmRqKs7I4YPHx61tbVRU1OTLwAAg8EAjxQCAHA0EIUAAIhCAABEIQAAiSgEAEAUAgAgCgEASEQhAACiEAAAUQgAQCIKAQAQhQAAiEIAABJRCACAKAQAQBQCAJCIQgAARCEAAKIQAIBEFAIAIAoBABCFAAAkohAAAFEIAIAoBAAg6fcoXDazIRoaqpbJ86OtuO7o8HI8/thTsatYAwA4FvRfFLbNj8kpAudES3R0dFSWFVNXRnPaPnNZcbvB7uHFcd1HfzOuWtImDAGAY0Y/RWFbzJ+9KDqbUhAunlFsKxk/d3WsmFUfrXNmxtHShREvxoaF1wlDAOCY0T9R2LY8VnbWx6wbewZh2fi510dTtMY9RRVmU8yT57f1mGrO1rsti5nF9t7Xle67LOZP3t99+4swBACOHf0The0bozPOi8bxxfo+GuP8+ohN7d3x1rmoOe6ZVkwzr5gVsWh2lNouC8I5sWnWimIKekVMXdncY/q5c9GCiIXFfVua0nr5vv2tFIafW7W9WAcAGJr6/USTg9bUEpWZ5vHTY2p9Z6xcnspu2T3RWj8rFs4tF+b4mHt9U7SWhxkzTddH5eoZ0yJlYWxsL9b7We05U+LD7zypWAMAGJr6Jwobz4/62BRVA4G9tMfGzojz9juUOD4az4voTGXX1r4pGwrMT04pTw83zGnNhhmP+FnMtad/IL6wcG686y3FBgCAIap/orAY6Vt0a9+nkrTNXxCt0RTT+j7kMGmLrAXrz2+M8Vkd1s+KFcXZy5Vl9dyUjkdOHoR/9fn4ZUEIABwD+mn6eHzMXd0STa1zoqHXZ8+0zZ8czYs6o6llcfRowuqRv2W3xqLO+pg6PWVfNh3cuSh69mVbtB2pYcKTT426sb8qCAGAY0o/HlM4IxZ3dERLpDCsmvptXjk1H/Xr9Uk1ycqYXZke3hSzVqwujhPMHqclYk73YzQ0zI7Zy/sehex3Y349br/rc4IQADim1HR0bO4aO/bcYvXIyD5WJv+Q631LccjavPmRqKs7I4YPHx61tbVRU1OTLwAAg8Ebd/YxAACDxhsyUngsMlIIAAxmRgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAIB+jcJlMbOhIRoqy+SY31ZcdcS0xfzJ2XPPTK8GAICD1U9RmMXYnNg0a0V0dHSUlpbzYtGtRzjN2pbHys76qK9vjXtUIQDAQeufKCxibOr08cWGZMbi6Fg8o1g5Mpbduig6m66PhVPro3XB/JSqAAAcjP6JwvGNcV50HmBksDytWyyTS8HWNn9yH1O9pWnomcXGZTP3vV/flsU9rfUx68YZMX7u9dHUuTKW97hx368BAIB+mz6eETfOqo9onVNEV8/jCdvmz46VU7unllvOWxSz0w3yeIueU71t8xdEa1NLZIOMWRDO2TQrVhT3WzF1ZTSXa7G3ZfdEa/3UKA1WzohpTT0jNXsNi6L7sToWNka7KgQAyPXbiSbj564uhVsWh9moYXNDTC7KMLtu9dzuqeXG8+ujc2N7ulSKye6p3rZYvrIzmqZl087FyN/CuVG+Zx6Rrff0cRJJW8xf0Br1U6dXbjvjxllR3/u2nRsje9bc+Bkxo2q2GwDgWNZvUVhWHYedK5dXpmhLU8WlqdvmRZ3F1nT76VOjvjzVu+zWWNTZFHkTtrXHpiIuK1O+DXOiNW3dZ4QvP6YxNd+i5u7bNi9K9+4ehcxfV0vEnOL6crACADAAUVg2vvG84lJpGrh54/WladvKaGJh/Ny4vqkzVqYqXHZPa9TPujHy01Py4xTrY9aKYrq3sqyOqkHHXOkEk5Zetys9T48TTrKTX/LrWuK8FJD7m4kGADjW9E8ULpvZ68SN6unctmjfFMWUcOm6bIq42oxpTdG5cnYsaK0+g3nf4wIzbW29R/iyaebqx+/WcxRyWa9p5/o4vzH90TY/JlcdA5mf2FLZl54nvQAADFX9EoXLYlp+8khzeeq2oTk/saR0HOH4mHt9U7TOKV83O2JqU+mOZTNujFnRmX+cTPUo4IzFHdES5ZNXSsvs2ct7xN2ymdmUcjHl3Nv46TG1PoXl7PlxR/uCWFB5nOwzFReWnqt9Y/bMkR/iWARs5djDfAo7YpMzUgCAIa6mo2Nz19ix5xarDJTNmx+JurozYvjw4VFbWxs1NTX5AgAwGAzYMYUAABw9RCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAQERNR8fmrlEnjipWGSg7XtoRdXVnxPDhw6O2tjZqamryBQBgMMijcOzYc4tVBsrmzY+IQgBg0DJ9DACAKAQAQBQCAJCIQgAARCEAAKIQAIAk/0iaU099a7HKQHn66Wd8JA0AMGgZKQQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAAD6Lwo3zLs4zrx4Xmwo1geHu+PKK+8uLgMAsD9De6Tw7m/GquIiAAD7N6SjcMOPHi4uAQBwIAMWhXdfeWZcPO/umHfxmXHmmWnJp5bvjiuzy/lyccwrzzVvmBcXn3ll3J3/Wb4+rRdXZ/Lp6cp1Z0b1rPA+z5WuzLZNufPRiFXXVrYBANC3AR0pfPTOWyMWPx6PP/543D7uzphy5q3RuKq0vurqiDtnVh+DuCqunZndvLj9lLRehFwWhFPuHBe3F9c9nu788LU9w7D6uR5f8pH4yJLsOc6JmHJ7ZRsAAH0b2OnjKTfGzReULo5vTIF2zmXxoWL9grePSyXXHm2l1eScuHrxzVFcHR/54JTUid+Mu1M2/sO3Hk1ttyQqWXfBzXFjfnVVFVY9FwAAh2bwHlM4vjFl4sPxow1t0f5osa1KHpkP/2iQne0MAHB0GrxR2NYej8a4ePsF4yPrv97aslIc9/bKyCIAAK/fIIrCR6O9Mpe8IebduipiygfjIyn7PnTZObHq2qoTTzbMi9LVjhMEAOgPgygKz4mHby2fXTwl7hx3e+XkkAtufiBWXf1wXFuceXzmlDtj3O2Px2udO3LBhy6Lc8pnHw+6D9YGABg8ajo6Nnedeupbi9U3SPZRNFO+FZetemDInizy9NPPRF3dGTF8+PCora2NmpqafAEAGAwG7zGFAAAcMYNjpPAYYKQQABjMjBQCACAKAQAQhQAAJKIQAABRCACAKAQAIBGFAACIQgAARCEAAEn+jSZjx55brDJQNm9+xDeaAACDlpFCAABEIQAAohAAgEQUAgAgCgEAEIUAACT5R9KMOnFUscpA2fHSDh9JAwAMWj6n8AjxOYUAwGBm+hgAAFEIAIAoBAAgEYUAAIhCAABEIQAAiSgEAEAUAgAgCgEASEQhAACiEAAAUQgAQCIKAQAQhQAAiEIAABJRCACAKAQAQBQCAJCIQgAARCEAAP0WhW0xf3JDTJ7fVqxXWxYzGxpi5rJi9YBKj3NwtwUAoL8YKQQAQBQCAPAGRWHb/MnR0NBQLDNjf7PF+e1mLut5e3PLAAD97ohHYRZ4zYvOi5aOjujIlpaIOZPnR19HI+Za58TsWFi6bbpxU1rXhQAA/atfo7BzUXPVCGB5mROtxfXZiSTLV3ZGU8vimFFsiRk3xqxYGcv3V4X1s2Lh3PHFyoyY1pQ68R5VCADQn/o1CutnrShG9KqXlkgdV2iPjZ3Z4F91NDbHos7O2Nhe3OQ1NJ5fH7Gpff8jiwAAHLIjPH3cGFnTNbX0DseOWFwZOjyw9qwqz2uM8tghAACH7whH4fiYPrU+Whf0PIawre0A436dG6MyiNg2Pxa0pqicdpAFCQDAQTniJ5qMn7s6VkxdGc1Vxx3Onn1r9Pm515n6TbGgfNvmRXFey8GPKgIAcHBqOjo2d40de26xOrjkZyqvnBorVs896qeLN29+JOrqzojhw4dHbW1t1NTU5AsAwGBwxEcKAQAYfAb1SOFQYqQQABjMjBQCACAKAQAQhQAAJKIQAABRCACAKAQAIBGFR5CPoAEABqvaUaNGxgsvvFCsMhCy9zd7nwEABqsUhaPimWe2CcMBkr2v2fubvc/lD6z2wdUAwGBT8/LLL3e9+uqrsWPHjrTsLDbTX7IRwiwIjzvuuBg2bJhvMwEABqWaFIRde/fujWzp6uqqLBye6hHBLATLS/V2AIDBomb37t2pAbsqUZgRhYevHH3lKKyOwfJ1AACDRc2ePXvyAsxCUAz2v+oIFIQAwGBVs3fv3koJisL+Vx2BghAAGKxqUgiKwgEkBAGAo0GPKAQA4NjkG00AABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABAIgoBABCFAACIQgAAElEIAIAoBABAFAIAkIhCAABEIQAAohAAgEQUAgAgCgEAEIUAACSiEAAAUQgAgCgEACARhQAAiEIAAEQhAACJKAQAQBQCACAKAQBIRCEAAKIQAABRCABARPx/wtM3FA/haC0AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "6737ce42ccc9ef30",
   "metadata": {},
   "source": [
    "## Explore and build visualization\n",
    "\n",
    "Using the JSON produced in the cell above, you can now move into the arrows app. \n",
    "Please select \"import\" in the upper left corner and paste the json. \n",
    "\n",
    "![arrows_import.png](img\\arrows_import.png)\n",
    "\n",
    "Now, you can proceed and arrange the nodes and relationships according to your needs. \n",
    "There are several export options available. \n",
    "\n",
    "The following example illustrates the Cube-single IFC model. \n",
    "\n",
    "![CubeSingle_Graph3.png](img\\CubeSingle_Graph3.png) \n",
    "\n",
    "\n",
    "The corresponding (curated) JSON file is located in the img folder: `img/CubeSingle_Graph3.json`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae19a6ac6a090cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}