{
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   "cell_type": "code",
   "execution_count": null,
   "id": "2385237c-6c24-4da2-b82d-733de99c4661",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from scipy.ndimage import convolve\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e90c13ef-ecb2-4cc0-b7c8-6521dd73e481",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(0,2,size=(50,100))\n",
    "fig = px.imshow(x)\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0a6cb6a-21fa-404c-8f92-419a0dbfae59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step(x):\n",
    "    kernel = np.array([[1,1,1], [1,0,1], [1,1,1]])\n",
    "    neighbours = convolve(x, kernel)\n",
    "    diff = np.zeros_like(neighbours)\n",
    "    diff[(neighbours < 2) & (x == 1)] = -1\n",
    "    diff[(neighbours > 3) & (x == 1)] = -1\n",
    "    diff[(neighbours == 3) & (x == 0)] = +1\n",
    "    return x + diff\n",
    "\n",
    "l = []\n",
    "for _ in range(10):\n",
    "    l += [x]\n",
    "    x = step(x)\n",
    "    \n",
    "fig = px.imshow(np.array(l), animation_frame=0)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33ed64c0-18f8-4c4d-9cb7-5eb0791e4d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The list l contains a new np.array for each time-step of the game of life\n",
    "# Calculate, how the entropy changes over time and visualize the output\n",
    "# We recommend to use a plotly line-chart for visualization https://plotly.com/python/line-charts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b575990-70e6-4418-a9e9-e581d3e99faa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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