Geomodeller_to_MOOSE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Simple script to transform exported GeoModeller 3-D regular grid into MOOSE grid\n",
    "\n",
    "Note: we don't create a proper FE mesh here, but simply perform a \"mapping\" between a predefined mesh structure (in this case: a simple regular mesh, defined in MOOSE) and an exported GeoModeller model - with all limitations concerning refinement, problem size, etc. - however: a good way to get the first tests running!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# some basic imports:\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "# for 3-D visualisation:\n",
    "import ipyvolume.pylab as p3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Load exported GeoModeller file\n",
    "\n",
    "We use here simply the export functionality of GeoModeller (note: far better results can be achieved with a dedicated export using the API). The exported file has some header information and then the formations simply listed in an $x$-dominant way.\n",
    "\n",
    "We load this file here in a simple structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class GeoExport(object):\n",
    "    \n",
    "    def __init__(self, filename):\n",
    "        \"\"\"Load exported model\"\"\"\n",
    "        self.filename = filename\n",
    "        \n",
    "    def load_file(self):\n",
    "        \"\"\"Load file\"\"\"\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "voxet_file = r'/Users/flow/Documents/01_work/61_simulation_results/PerthBasin/notebooks/voxet_NPB.vox'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "vf = open(voxet_file, 'r').readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "vf_header = vf[:10]\n",
    "vf_entries = vf[10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# remove newlines and convert to numpy array\n",
    "vf_entries = np.array([entry.rstrip() for entry in vf_entries])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# determine all formation names in model:\n",
    "form_names = np.unique(vf_entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nx, ny, nz = int(vf_header[0].rstrip().split(\" \")[1]),\\\n",
    "int(vf_header[1].rstrip().split(\" \")[1]),\\\n",
    "int(vf_header[2].rstrip().split(\" \")[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 580, 28)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx, ny, nz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3248000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx * ny * nz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# create dictionary with formation names\n",
    "formation_dict = {}\n",
    "for i in range(len(form_names)):\n",
    "    formation_dict[form_names[i]] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Basement': 0,\n",
       " 'Cattamarra_Coal_Measures': 1,\n",
       " 'DefaultCover': 2,\n",
       " 'Eneabba_Fm': 3,\n",
       " 'Kockatea_Shale': 4,\n",
       " 'Late_Permian': 5,\n",
       " 'Lesueur_Ss': 6,\n",
       " 'Neocomian_Unc': 7,\n",
       " 'Topo_and_bathy': 8,\n",
       " 'Woodada_Fm': 9,\n",
       " 'Yarragadee_Fm': 10,\n",
       " 'Yigarn': 11,\n",
       " 'out': 12}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formation_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# read file and transform string arrays into appropriate integer values\n",
    "vox_int = [int(formation_dict[entry]) for entry in vf_entries]\n",
    "vox_int = np.array(vox_int, dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# reshape into 3-D array\n",
    "vox_3d = vox_int.reshape(nz, ny, nx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# swap axes to get x,y,z array\n",
    "vox_3d = np.swapaxes(vox_3d, 0, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Some quick checks in 2-D slices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x134912d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.imshow(vox_3d[:,-1,:].transpose(), origin='bottom')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Z')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAABRCAYAAAAU5vr2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11231aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.imshow(vox_3d[0,:,:].transpose(), origin='bottom')\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('Z')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIsAAAEKCAYAAAAigqY2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEQRJREFUeJztnWusHdV1gL/VG2NTDAQXgnwD1aWpi2qj5IKQbUzauhjH\nTooKP1orrhrlB7VlFQT0zUuqKuEofQhFVZMgOzhFamrqKi8rJbg3BqtqTQJ26zgYh+IEA8HUbkxx\nkqY11+7qjzNjzz2ec86ex57ZM7M+6eieM2fOzJb8ee01e+9ZI6qKYbjwE3U3wGgOJovhjMliOGOy\nGM6YLIYzJovhjMliOGOyGM6YLIYz76i7AUU4T2brHC4o/bg/994fl37Mutm7/+T3VfWyIsdotCxz\nuIAlsqL04+7Ysa/0Y9bN2PxDrxQ9hnVDhjMmi+GMyWI4Y7KksGp8su4mBInJYjhjshjOmCyGMybL\nACxvOReTxXDGZDGcMVkMZ0wWwxmTxXDGZBmCXRHNxGQxnDFZDGdMFsMZk8VwxmQZgSW5ZzFZDGdM\nFsMZk8UB64p6mCyGMyaL4YzJYjjjVRYROSwi3xKRfSKyJ9o2T0SmROSl6O8lif3vE5FDIvKiiKzy\n2TYjO1VEll9W1UlVvT76fC+wU1UXADujz4jIQuDDwCJgNfApERmroH1OWJJbTzd0K/BY9P4x4LbE\n9sdV9aSqvgwcAhbX0D5jAL5lUeBrIrJXRNZH2y5X1Tei9/8BXB69fzfwWuK334u2zUBE1ovIHhHZ\nM81JX+02UvBdReH9qvq6iLwLmBKRbye/VFUVkUxVm1V1E7AJ4CKZZxWfK8RrZFHV16O/x4Av0utW\njorIfIDo77Fo99eBKxM/vyLaZgSCN1lE5AIRuTB+D3wAeB7YDnw02u2jwJej99uBD4vIbBG5ClgA\nPOurfXnoepLrsxu6HPiiiMTn+VtVfVJEngO2icjtwCvAGgBVPSAi24AXgFPAHap62mP7jIx4k0VV\nvwu8L2X7cSC1XJOqbgQ2+mqTUQwbwc1Il7sik8VwxmQxnDFZDGdMFsMZkyUHXU1yTRbDGZPFcMZk\nMZwxWXLSxbzFZDGcMVkMZ0yWAnStKzJZDGdMFsMZk8VwxmQpSJfyFpPFcMZkMZwxWQxnTJYS6Ere\nYrIYzpgsJdGF6GKyGM6YLIYzJkuJtL0rMlkMZ0wWwxnvsojImIj8m4h8Jfrc6gKEbe6KqogsdwMH\nE59rLUB4+KEbOPzQDTM+l01bhfFd2vQK4FeAzyQ211aAME2SiQefKfMUrcZ3ZPkE8IfA/yW2FSpA\nWCaxMHG0KTPKtDG6eCvmIyK3AMdUda+ILE/bJ08Bwqjq5XqAOfyk8+8GidC/vf+zRZ6z+IwsNwK/\nKiKHgceBm0TkbyhYgFBVN6nq9ap6/SxmOzWkSMTwkdM0FW+yqOp9qnqFqk7QS1yfUtXfpIEFCPMK\n07auqI5xlo8DK0XkJeDm6DOqegCICxA+SUUFCF27GROmIllUdZeq3hK9P66qK1R1garerKpvJvbb\nqKrvUdWrVfWrZZ1/lBCWl7jRmRHciQefOUeKrJJ0PX/xXY49OOqIIqvGJ9lxZF/l5y2bzkQWF9Ki\nj3EWkyWBSzfT5a7IZOnDlwxtuCoyWQxnOilL2jyQ5Sqj6aQsMXm7nK4O0HVaFuh2wpqVzssCJowr\notrcxwxeJPN0iaQ+uigTeWQpkuPUMUA3Nv/Q3sTjknPR+sjiIoIlt260NrKkSeIiRZYok1cyiywN\nILmMsk6aelXUKVmMYnROljKjS5FjNDG6dE4WIz8mi+FMp2WxRDcbnZbFyEbnZckz+3z+MRn4+6w0\nKbq0VhZfo7KxKElhukJrZfFBvyBlCdOU6GKyFKRLEabVt4JMPPhMaYuwuyTFICyyODBKlKN3LSt8\njiZ0RSaL4YzJMgLrfs7iTRYRmSMiz4rIN0XkgIj8SbS9lQUIy+iKQsdnZDkJ3KSq7wMmgdUispSK\nCxDaKrjy8FnMR1X1R9HHWdFLqbEAYeiEnuT6rlY5JiL76JUCm1LVbxBQAcJRZM1X2t4VeZVFVU+r\n6iS9+nCLReSavu+VXrRxRkTWi8geEdkzzUmn31hXVA4DZRGRJ0RkooyTqOpbwNP0cpHKCxBWSZuj\ny7DI8lngH0XkARGZlfXAInKZiLwzen8+sBL4Ng0sQFglIectA2VR1b8HrgMuAvaIyO+LyO/GL4dj\nzweeFpH9wHP0cpavUFMBwiq7orZGl1FzQ28D/w3MBi5kZqXsoajqfuDalO3HgdSbfVR1I7DR9Rw+\nscG4cxkoi4isBh6m1z1cp6o/rqxVLeDoXcu4/C93192MUhkWWR4Afj3qHjqFRZV0Bsqiqr9QZUOa\nxNp1U+ds27p55Tnb2hZdWnuv8yCKrF3pl+SR3csB2LBs15ltadLkEabs+6HLuNe51YufyiQWJRYk\nSXLbhmi/NGmaTueWKBS5hE4TJW2fR3YvnxGF2nIp3TlZ8rB23ZSTKEmy7t8EOilLlugySpTxnWOM\n70xfSdG26NJJWWCwMOcfE9aum+LEoumhovRLkiZNke4oxGH/zsoCg4WJBRkmyiDShGkLnZYFsie8\nw0RJ7pPc78Si6TPvm9wd2aXzAMZ3jnFkxWknOUYdA84Kc/GBzBP4wdD5yDIMV1He+eyRM6+0Y/RH\nGdfoElreYpGlAGlyJLe9tXj8zPuZ4jlP3gdF52VJDv9nmUBMEyXLPm+tWcrcbV93Pl8IdL4byjOi\n6yJKG+m8LIOIE1NX7n/qS9z/1Jec92+icJ3vhrIy6B/5Yzfdlrp9EG8tHmfu4VfLaFJlWGSpkR+t\nWTpyn5CuiEyWBP/zrrNrewZdNievcLqGycLMJDdtFZwPmpizmCweyJrsNgWTJSKOLlu+fHOh4yQl\nGSVM06KLyZLAx41obYowJksOuprkmiwpnPef5Q4/FY0uoVw+mywlM2hwrg1Jr8kyhGFD/mV1Re+Y\n+OlSjlMFJssAYlGOrDg945VkkDDDhv6bHF18Vqu8UkSeFpEXomqVd0fbG12t0nWCMetcURPwGVlO\nAb+nqguBpcAdUUXKSqtV5uHty04N/T4pTNbuKE0il1tqQ0hyvc06R0UG34je/1BEDtIrKHgrsDza\n7TFgF/BHJKpVAi+LSFytsraCcMl7mGPi1fpF1+cm+dnPvM6hh24IvvZdJUsUotp01wKjqlUml47V\nVq1yw7JdPLJ7eer9yhty3J3oShxhQpXGe4IrInOBzwP3qOoPkt9VVa0yK1s3r0yNKvF3vjn80A1n\nXiHhuw7uLHqifE5VvxBtDrZaZfIfJ5QqCCEJ4/NqSIBHgYOq+nDiK6tWmcBlsC4UYXzmLDcCHwG+\nFVXZBrifXnXKbSJyO/AKsAZ61SpFJK5WeYqSq1VWTdnjKT1hyi3wkxWftfv/WVVFVd+rqpPR6wlV\nPa6qK1R1garerKpvJn6zUVXfo6pXq+pXfbWtKIPymSJs+Y1Pcv4xmfEKDRvBDZjQhDFZclBl8htS\nlDFZasZ1WiAEYUyWnGxYtmvoPFGWuaGmzCOZLCkkbwkpQlYJPnnPXw39fvLjv12kOYUxWTzTL4wt\nUeg4XVmTa7IkqGoCr6nRxWQZQNa8pazoMipvqROTJSdpyxTyLLNsEibLCKq697kJmCwFyVr0p8mY\nLH0kk9xBecuwaOP7yqjOtbgmS0a63C2ZLA7EgqQ9c6iMRduul9J1i2qypJDsiuISHMMeTpXEtYxG\nLEiTxlxMlgEkhYkfOFX2qv5Bogwaa6n7oREmSwHKum/IhWQXVFeSa7I4kFaCo0pR4GxUcalw6QuT\nZQjJriiWY9iTy3yX/YrPW1flBZMlA3mjSdEkNtkF1VmHzmQJlBAnFE2WkvD5Pz6UuyNNFsMZk8WB\nS/cXW5Obd4lCaF2RyTKEUO4xBj93QWbFZKmINiyAMlkGEEcV1y7IZWlCEWFCSHK9VVEQkS3ALcAx\nVb0m2jYP+DtgAjgMrFHV/4q+uw+4HTgN3KWqO3y1bRRVdj9pArmMy6wan2THkWqrKviMLH9Nr5Bg\nkuCLDyZFGVWIMA9JOQZFmlC7LJ8lN/4JeLNv8630ig4S/b0tsf1xVT2pqi8DcfHB2ojvLc6ybNLH\nKrmQroiqzlmGFR98LbFfLcUH+7sf31cgTVrLAjUmuHmKD0I1BQjjqBInlWnVtevkVE0P4qxalkLF\nB8FfAcKykto238patSzBFx8cVAfF1/qVLF1R3dHN56XzVnqVtC8Vke8Bf0xHig+2FZ9XQ2tVdb6q\nzlLVK1T10TYUH6yKtMvnDct21drN2QhuDeQdR9m6eSVzt3199I6eMFkYnNxWdZ9OUy6hTRbDGZMl\nYEIb9jdZHPF92TqoK7rjE3d6PW8WTJaayBs1jt61rOSWuFPJw6m6RJ6F267i9JcAqXqZgkWWIfQ/\npKrsruhjN92WS5S6lnuaLAyvUplFGF+3g5RVxLkoJosDwx6DVxd1RBeTxZGkMFVP6IXwkAcwWTIx\natF0GfM2Lo/Bi6k6upgsGfGV8PZL0i9MCNHFZIlwLcWejC5pa1zKnBUOTRiTxQNZhckykVinMCZL\nDkY9mCorWUdz63ropsmSg1GJbpUFd6p8YJXJUoA6o0sdmCwJsjxv6OIDs4DyhQlZGpOlAL5GdUMV\nxmadC7B180pYND10n+SVUZ3FA8vAIovhjMmSwMddiXE0aXpUAZOlFEYluW0QBUwWIwMmS0TeLsjH\nJXSomCwlkHYJXWRCMdSbzuzSmbNRJe9cy9bNK7kYOLKidxldpOJCqKKARZZSufjArBmTjD5nnwFO\nLJrmxIhxnjIJThYRWS0iL4rIIRG51/f5fKw2yzMrnUWUteumOLFoeujjbHwgvWpdYRBVqPx3YCW9\nunLPAWtV9YW0/S+SebpEVuQ+X78oZU75x/+YMPjSeZQg8d2IcfRIipE85j/s3j6yPWPzD+1V1etH\n7jiE0HKWxcAhVf0ugIg8Tq+SZaosIbNh2S6270wX2TWKxMK9e/vRocf4l/+FG+f47yRCkyWtauUS\nHyeq60atLN3N+M6xMxEkhMQ3tG7o14DVqvpb0eePAEtU9c7EPuuB9dHHq4HjwPerbusILiW8Nl2t\nqhcWOUBokWVk1UpV3QRsij+LyJ6ifXHZhNqmoscI7WroOWCBiFwlIufRK9E+OnszKiGoyKKqp0Tk\nTmAHMAZsUdUDNTfLiAhKFgBVfQJ4IsNPNo3epXJa2aagElwjbELLWYyAaawsVU8L9J17i4gcE5Hn\nE9vmiciUiLwU/b0k8d19UTtfFJFVntp0pYg8LSIviMgBEbm79HapauNe9JLf7wA/A5wHfBNYWOH5\nfxG4Dng+se3PgHuj9/cCfxq9Xxi1bzZwVdTuMQ9tmg9cF72/kN60ycIy29XUyHJmWkBV3wbiaYFK\n0AAfvKWqb6jqv0bvfwgcpDciXlq7mipLEA+z6iOYB2+JyARwLfCNMtvVVFmCRjXfg7fKQETmAp8H\n7lHVH5TZrqbK4vwwqwop/OCtoojILHqifE5Vv1B2u5oqS4jTArU+eEtEBHgUOKiqD3tpV91XNgWy\n/w/Ry/i/AzxQ8bm3Am8A0/T6+tuBn6L3+OGXgK8B8xL7PxC180Xgg57a9H56Xcx+YF/0+lCZ7bIR\nXMOZpnZDRg2YLIYzJovhjMliOGOyGM6YLCURzfq+LCLzos+XRJ8n6m1ZeZgsJaGqrwGfpvegc6K/\nm1T1cG2NKhkbZymRaLh9L7AFWAdMqmp1NyN7Jrg1uE1GVadF5A+AJ4EPtEkUsG7IBx+kNxVwTd0N\nKRuTpUREZJLeTf1Lgd+JZ3vbgslSEtGs76fprSN5Ffhz4C/qbVW5mCzlsQ54VVWnos+fAn5eRH6p\nxjaVil0NGc5YZDGcMVkMZ0wWwxmTxXDGZDGcMVkMZ0wWwxmTxXDm/wHc34+I9boL2AAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d8ac208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.imshow(vox_3d[:,:,0].transpose(), origin='bottom')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## And as a 3-D view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flow/anaconda3/lib/python3.6/site-packages/ipyvolume/serialize.py:27: RuntimeWarning: invalid value encountered in true_divide\n",
      "  gradient = gradient / np.sqrt(gradient[0]**2 + gradient[1]**2 + gradient[2]**2)\n",
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d599955af944b939f21221a549842c6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipyvolume\n",
    "ipyvolume.quickvolshow(vox_3d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# extract submodel for faster vis-test\n",
    "nx_sub = nz\n",
    "ny_sub = 80\n",
    "nz_sub = nz\n",
    "\n",
    "x = np.arange(nx_sub)\n",
    "y = np.arange(ny_sub)\n",
    "z = np.arange(nz_sub)\n",
    "X,Y,Z = np.meshgrid(y,x,z)\n",
    "\n",
    "# extract subvolume\n",
    "vox_sub = vox_3d[:nx_sub, :ny_sub, :nz_sub]\n",
    "vox_sub = np.swapaxes(vox_sub, 0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flow/anaconda3/lib/python3.6/site-packages/ipyvolume/serialize.py:27: RuntimeWarning: invalid value encountered in true_divide\n",
      "  gradient = gradient / np.sqrt(gradient[0]**2 + gradient[1]**2 + gradient[2]**2)\n",
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab03ab6aa55e44bda8c39d6390f9cc96"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipyvolume.quickvolshow(vox_sub)\n",
    "# vox_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  9, 10, 12])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(vox_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# create colour array:\n",
    "nc = np.max(vox_sub)\n",
    "vox_c = [plt.cm.viridis(i/nc)[:3] for i in vox_sub.ravel()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(62720,)\n",
      "(62720,)\n"
     ]
    }
   ],
   "source": [
    "print(X.ravel().shape)\n",
    "print(vox_sub.ravel().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flow/anaconda3/lib/python3.6/site-packages/traitlets/traitlets.py:565: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  silent = bool(old_value == new_value)\n",
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3787bc1347b4abb8409e775871b2470"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p3.figure()\n",
    "p3.scatter(X.ravel(), Y.ravel(), Z.ravel(), color=vox_c, marker='box', size=3.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab76cb52240f49859d5c9d1c95431229"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p3.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export model\n",
    "\n",
    "Simply store as np array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.save(\"PB_sub.txt\", vox_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 80, 28)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vox_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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