Hauptmodell.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from src.FireFramework import * # importieren des Frameworks\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelldefinition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIZE = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dot(t1,t2):\n",
    "    return t1[0] * t2[0] + t1[1] * t2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Liste der pro Zelle zu speichernden Werte\n",
    "params = [\"fuel\", \"fire\", \"windx\", \"windy\", \"height\", \"burned\", \"inflammability\"]\n",
    "\n",
    "# Relativvektoren der zu betrachtenen Nachbarn (Kreis mit Radius #)\n",
    "radius = 1\n",
    "\n",
    "relative_neighbours = []\n",
    "for x in range(radius * 2 +1):\n",
    "    for y in range(radius*2 +1):\n",
    "        xcord = x - radius\n",
    "        ycord = y - radius\n",
    "        r = xcord**2 + ycord**2\n",
    "        \n",
    "        if r <= radius**2 and r != 0:\n",
    "            relative_neighbours.append((xcord,ycord))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transition(cellState, neighbourStates, returnState):\n",
    "    \n",
    "    #Parameter der Nachbarn:\n",
    "    slope = []\n",
    "    rel_wind = []\n",
    "    neighbour_fire = []\n",
    "    distance = []\n",
    "\n",
    "    #Vorbereitung der Werte der Nachbarn\n",
    "    for n in neighbourStates:\n",
    "        \n",
    "        slope_ = np.tanh(n.getParam(\"height\") - cellState.getParam(\"height\"))\n",
    "        fire_ = n.getParam(\"fire\")\n",
    "        distance_ = np.sqrt(dot(n.getRelativeVector(), n.getRelativeVector()))\n",
    "        wind = (n.getParam(\"windx\"), n.getParam(\"windy\"))\n",
    "        rel_wind_ = np.tanh(dot(wind, n.getRelativeVector()) / distance_)\n",
    "        \n",
    "        slope.append(slope_)\n",
    "        neighbour_fire.append(fire_)\n",
    "        distance.append(distance_)\n",
    "        rel_wind.append(rel_wind_)  \n",
    "\n",
    "    #Konvertieren zu numpy arrays\n",
    "    slope = np.array(slope)\n",
    "    rel_wind = np.array(rel_wind)\n",
    "    neighbour_fire = np.array(neighbour_fire)\n",
    "    distance = np.array(distance)\n",
    "\n",
    "    #Wahrscheinlichkeiten berechnen\n",
    "\n",
    "    slope_coeff = - np.sum(neighbour_fire * slope / distance)\n",
    "    wind_coeff = - np.sum(neighbour_fire * rel_wind / distance)\n",
    "    \n",
    "    p_burn = 1/8 * np.sum(neighbour_fire / distance) + 0.1 * slope_coeff + 0.1 * wind_coeff\n",
    "    p_burn *= cellState.getParam(\"inflammability\")\n",
    "    \n",
    "    p_stop = (1 - cellState.getParam(\"fuel\")) ** 8  #Erlöschen bei wenig Brennwert\n",
    "\n",
    "    \n",
    "    #Wahrscheinlichkeiten umsetzen\n",
    "    \n",
    "    \n",
    "    if p_burn >= np.random.uniform():\n",
    "        returnState.setParam(\"fire\", 1)\n",
    "    \n",
    "    if cellState.getParam(\"fire\") == 1:\n",
    "        if p_stop >= np.random.uniform():\n",
    "            returnState.setParam(\"fire\", 0)\n",
    "            returnState.setParam(\"burned\", 1)\n",
    "\n",
    "        \n",
    "    returnState.addToParam(\"fuel\", -0.05*cellState.getParam(\"fire\")) # Verringerung des Brennwerts\n",
    "    if returnState.getParam(\"fuel\") < 0:\n",
    "        returnState.setParam(\"fuel\", 0)\n",
    "    returnState.addToParam(\"inflammability\", -0.05*cellState.getParam(\"fire\"))\n",
    "    if returnState.getParam(\"inflammability\") < 0:\n",
    "        returnState.setParam(\"inflammability\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Startwerte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadImage(path, cropsection, size):\n",
    "    img_ = Image.open(path).convert('RGB').crop(cropsection).resize(size, Image.NEAREST) # Rezisemethode nicht ändern\n",
    "    img = np.array(img_)\n",
    "    img_.close()\n",
    "    return img\n",
    "\n",
    "\n",
    "def loadFuelData(path, cropsection, size):\n",
    "    \"\"\"\n",
    "    Erzeugt Fuelmap aus Kartenmaterial der ESA\n",
    "    path: Pfad zum Kartenmaterial\n",
    "    area: Bildausschnitt (links, oben, rechts, unten)\n",
    "    size: Auflösung der Fuelmap (x,y)\n",
    "    \n",
    "    return (fuelmap, inflammabilitymap) Karten mit entsprechenden Werten\n",
    "    \"\"\"\n",
    "\n",
    "    # Lädt Bild & konvertiert in Array\n",
    "    img = loadImage(path, cropsection, size)\n",
    "    plt.imshow(img) # Optional zur Überprüfung\n",
    "\n",
    "    # Farben des Kartenmaterials zu Brennwerten\n",
    "    # HIER VERSCHIEDENE BRENNWERTE EINSETZEN & AUSPROBIEREN\n",
    "    lookupfuel = {\n",
    "        #Tree Cover\n",
    "        (0,100,0): 0.8,\n",
    "        #Shrubland\n",
    "        (255,187,34): 0.1,\n",
    "        #Grassland\n",
    "        (255,255,76): 0.2,\n",
    "        #Cropland\n",
    "        (240,150,255): 0.2,\n",
    "        #Built-up\n",
    "        (250,0,0): 0.3,\n",
    "        #Bare/sparse vegetation\n",
    "        (180,180,180): 0.1,\n",
    "        #Snow and Ice\n",
    "        (240,240,240): 0,\n",
    "        #Permanent water bodies\n",
    "        (0,100,200): 0,\n",
    "        #Herbaceous wetland\n",
    "        (0,150,160): 0.1,\n",
    "        #Mangroves\n",
    "        (0,207,117): 0.1,\n",
    "        #Moss and lichen\n",
    "        (250,230,160): 0.1,\n",
    "    }\n",
    "\n",
    "    # Farben des Kartenmaterials zu Entzündbarkeiten\n",
    "    # HIER VERSCHIEDENE BRENNWERTE EINSETZEN & AUSPROBIEREN\n",
    "    lookupinflammability = {\n",
    "        #Tree Cover\n",
    "        (0,100,0): 0.7,\n",
    "        #Shrubland\n",
    "        (255,187,34): 0.95,\n",
    "        #Grassland\n",
    "        (255,255,76): 0.8,\n",
    "        #Cropland\n",
    "        (240,150,255): 0.6,\n",
    "        #Built-up\n",
    "        (250,0,0): 0.5,\n",
    "        #Bare/sparse vegetation\n",
    "        (180,180,180): 0.6,\n",
    "        #Snow and Ice\n",
    "        (240,240,240): 0,\n",
    "        #Permanent water bodies\n",
    "        (0,100,200): 0,\n",
    "        #Herbaceous wetland\n",
    "        (0,150,160): 0.6,\n",
    "        #Mangroves\n",
    "        (0,207,117): 0.7,\n",
    "        #Moss and lichen\n",
    "        (250,230,160): 0.8,\n",
    "    }\n",
    "\n",
    "    \n",
    "    fuelmap = np.zeros(size)\n",
    "    inflammabilitymap = np.zeros(size)\n",
    "    \n",
    "    # Konvertiert Karte zu Fuelmap durch Dictionary Lookup\n",
    "    for i in range(size[0]):\n",
    "        for j in range(size[1]):\n",
    "            fuelmap[i,j] = lookupfuel.get(tuple(img[i,j]), 0)\n",
    "            inflammabilitymap[i,j] = lookupinflammability.get(tuple(img[i,j]), 0)\n",
    "            \n",
    "        if(i % (size[0]/10) == 0): print(100 * i / size[0], \"%\") # Ladebalken\n",
    "\n",
    "    return (fuelmap, inflammabilitymap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 %\n",
      "10.0 %\n",
      "20.0 %\n",
      "30.0 %\n",
      "40.0 %\n",
      "50.0 %\n",
      "60.0 %\n",
      "70.0 %\n",
      "80.0 %\n",
      "90.0 %\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(fuelmap, inflammabilitymap) = loadFuelData(\"data/Oregon_Bitmap.bmp\", (0,0,4000,4000),(200,200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_field(row, col):\n",
    "\n",
    "    fuel = fuelmap[row, col]\n",
    "    inflammability = inflammabilitymap[row, col]\n",
    "    \n",
    "    fire = 0\n",
    "    if row == 100 and col == 50: fire = 1\n",
    "        \n",
    "    burned = 0\n",
    "\n",
    "    height = 0\n",
    "    windx = 0\n",
    "    windy = 0\n",
    "\n",
    "    return [fuel, fire, windx, windy, height, burned, inflammability] # Hier der Anfangswert-return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Modell erzeugen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = Grid(SIZE, SIZE, params, relative_neighbours, transition, initial_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawImage(field, index):\n",
    "    plt.imshow(np.stack([field[index,:,:,1],field[index,:,:,0],np.zeros(shape = (SIZE,SIZE))], axis = 2))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modell auswerten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n"
     ]
    }
   ],
   "source": [
    "# Läuft mehrere Iterationen durch und gibt das ganze Feld (mit zeitlicher Entwicklung) zurück.\n",
    "N = 100\n",
    "result = g.runModel(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save( \"result/Oregon0\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n",
      "61%\n",
      "71%\n",
      "81%\n",
      "91%\n",
      "1%\n",
      "11%\n",
      "21%\n",
      "31%\n",
      "41%\n",
      "51%\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    N = 100\n",
    "    result = g.runModel(N)\n",
    "    np.save( \"result/Oregon\"+str(i+1), result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}