src/playground.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# from math import cos, sin\n",
    "# import matplotlib.animation as animation\n",
    "\n",
    "random.seed(1)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [],
   "source": [
    "WORLD_SIZE = 250\n",
    "INITIAL_POPULATION = 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.world import World\n",
    "\n",
    "world = World(WORLD_SIZE, WORLD_SIZE)\n",
    "for _ in range(INITIAL_POPULATION):\n",
    "    location, _ = world.spawn(0)\n",
    "\n",
    "population_size = []\n",
    "avg_energy = []\n",
    "\n",
    "while len(world.locations) > 0:\n",
    "    world.step()\n",
    "\n",
    "    population_size.append(world.alive)\n",
    "\n",
    "    total_energy = 0\n",
    "    for being in world.locations.values():\n",
    "        total_energy += being.energy\n",
    "\n",
    "    avg_energy.append(total_energy)\n",
    "\n",
    "plt.title(f'Stats per epoch ({len(population_size)} epochs)')\n",
    "plt.plot(population_size, label='Population')\n",
    "plt.plot(avg_energy, label='Energy')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vision_pixel_size 18\n",
      "angles 225.0 315.0\n",
      "(1, 0.5) 27\n",
      "(-0.25, -0.5) 243\n",
      "  chunk: 1 0.5590169943749475\n",
      "(-0.1, -0.6) 261\n",
      "  chunk: 1 0.6082762530298219\n",
      "(-0.32, 0.2) 148\n",
      "\n",
      "Vision array (shows a 1 when there is a being within viewing distance in that direction)\n",
      "[0, 2, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Vision model\n",
    "\n",
    "vision_angle = 90\n",
    "vision_pixels = 5\n",
    "vision_distance = 1\n",
    "\n",
    "vision_pixel_size = vision_angle // vision_pixels\n",
    "\n",
    "print('vision_pixel_size', vision_pixel_size)\n",
    "\n",
    "# Test data\n",
    "main = (0, 0)\n",
    "direction = [0, -1]\n",
    "\n",
    "beings_x = [1, -0.25, -0.1, -0.32]\n",
    "beings_y = [0.5, -0.5, -0.6, 0.2]\n",
    "\n",
    "# Vision calculations\n",
    "\n",
    "direction_angle = math.degrees(math.atan2(direction[1], direction[0]))\n",
    "vision_line = (\n",
    "    main[0] + direction[0] * vision_distance,\n",
    "    main[1] + direction[1] * vision_distance,\n",
    ")\n",
    "\n",
    "# calculate angles between the main being and each of the others\n",
    "# the ones that are within +- 45 degrees + within the distance are \"visible\"\n",
    "# either this or we ray trace (calculate equations for the lines and see if any being falls within it)\n",
    "# and then use the distance for the color\n",
    "\n",
    "# the issue with the ray tracing is that as we get further, they'll be far from the lines\n",
    "# I think it'll be best to do the angle thing, and then map angles to pixels of vision\n",
    "\n",
    "min_angle = direction_angle - vision_angle / 2\n",
    "max_angle = direction_angle + vision_angle / 2\n",
    "\n",
    "min_angle %= 360\n",
    "max_angle %= 360\n",
    "\n",
    "# min_angle = min_angle - 360 if min_angle > 180 else min_angle\n",
    "# max_angle = max_angle - 360 if max_angle > 180 else max_angle\n",
    "\n",
    "print('angles', min_angle, max_angle)\n",
    "\n",
    "vision = [0] * vision_pixels\n",
    "\n",
    "for coords in zip(beings_x, beings_y):\n",
    "    # angle between two points:\n",
    "    # project each coord to the x/y axis\n",
    "    # PI = 180\n",
    "    # xx = x\n",
    "\n",
    "    y = coords[1] - main[1]\n",
    "    x = coords[0] - main[0]\n",
    "\n",
    "    angle = math.atan2(y, x)\n",
    "    angle_deg = math.degrees(angle)\n",
    "    angle_deg %= 360\n",
    "\n",
    "    print(coords, round(angle_deg))\n",
    "\n",
    "    if min_angle <= angle_deg <= max_angle:\n",
    "        dist = math.hypot(x, y)\n",
    "        if dist <= vision_distance:\n",
    "            # visible\n",
    "            vision_chunk = int((angle_deg - min_angle) // vision_pixel_size)\n",
    "            vision[vision_chunk] += 1\n",
    "\n",
    "            print('  chunk:', vision_chunk, dist)\n",
    "        else:\n",
    "            print('  Too far')\n",
    "\n",
    "print()\n",
    "print('Vision array (shows a 1 when there is a being within viewing distance in that direction)')\n",
    "print(vision)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "\n",
    "plt.title('Vision system test')\n",
    "plt.scatter(beings_x, beings_y)\n",
    "plt.plot([main[0]], [main[1]], marker='o', color='red')\n",
    "\n",
    "# Draw some axis to make it easier to \"debug\"\n",
    "plt.plot([main[0], -1], [main[1], -1], color='yellow')\n",
    "plt.plot([main[0], 1], [main[1], 1], color='yellow')\n",
    "plt.plot([main[0], 1], [main[1], -1], color='yellow')\n",
    "plt.plot([main[0], -1], [main[1], 1], color='yellow')\n",
    "\n",
    "for coord in zip(beings_x, beings_y):\n",
    "    plt.plot([main[0], coord[0]], [main[1], coord[1]], color='red')\n",
    "\n",
    "plt.plot([main[0], main[0] + direction[0] * vision_distance], [main[1], main[1] + direction[1] * vision_distance], color='green')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
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