Homework 41 - Nagel Schreckenberg Model Implementation.ipynb

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      "name": "CS166PW41.ipynb",
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    {
      "metadata": {
        "id": "YokUDftI2wiI",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "#### CS166 Pre-class Work for Session 4.1:\n",
        "\n",
        "### Nagel–Schreckenberg Model Implementation\n",
        "\n",
        "_Yoav Rabinovich, February 2019_\n",
        "\n",
        "----------------------------"
      ]
    },
    {
      "metadata": {
        "id": "VsJ_vX-HKRmd",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "#### Figures:"
      ]
    },
    {
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        "id": "P1DXcK_O-m13",
        "colab_type": "code",
        "outputId": "8cfdc5e4-6ed1-42a2-d95d-bd7887465c37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "cell_type": "code",
      "source": [
        "# Figure 1\n",
        "sim = TrafficSimulation(100,0.03,5,0.5)\n",
        "sim.run(25,True)"
      ],
      "execution_count": 0,
      "outputs": [
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            "............4..................................1.1..................................5..........3....\n",
            "................4...............................1.2......................................5........3.\n",
            ".4..................5............................1..2.........................................4.....\n",
            ".....4...................4........................2...3...........................................5.\n",
            "...4.....5...................5......................3....4..........................................\n",
            ".......5......5...................4....................3.....5......................................\n",
            "............5......4..................5...................4.......5.................................\n",
            ".................4.....5...................5..................5........4............................\n",
            ".....................4......5...................4..................5.......5........................\n",
            ".........................5.......5..................5...................5.......5...................\n"
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          "name": "stdout"
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        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "# Figure 2\n",
        "sim2 = TrafficSimulation(100,0.1,5,0.5)\n",
        "sim2.run(50,True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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      "metadata": {
        "id": "Tnfc19qjKRvZ",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "#### Code:"
      ]
    },
    {
      "metadata": {
        "id": "1wi1_5q037U5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f63a8m152vr-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class TrafficSimulation:\n",
        "    \n",
        "    def __init__(self, length, density, max_v, slow_p):\n",
        "        self.length = length\n",
        "        self.density = density\n",
        "        self.max_v = max_v\n",
        "        self.slow_p = slow_p\n",
        "        self.state = self.initialize_state()\n",
        "        \n",
        "    def initialize_state(self): \n",
        "        state=np.array([-1]*self.length)\n",
        "        for index,cell in enumerate(state):\n",
        "            if np.random.rand() <= self.density:\n",
        "                state[index]=0\n",
        "                \n",
        "        return state\n",
        "    \n",
        "    def accelerate(self,cell):\n",
        "        if -1<cell<5:\n",
        "            cell +=1\n",
        "            \n",
        "        return cell\n",
        "    \n",
        "    def decelerate(self,index,cell):\n",
        "        if 0<cell:\n",
        "            distance = 0\n",
        "            horizon = np.concatenate([self.state[index+1:],self.state[0:index-1]])\n",
        "            for cell2 in horizon[0:5]:\n",
        "                if -1<cell2:\n",
        "                    break\n",
        "                else:\n",
        "                    distance+=1\n",
        "            if distance<cell:\n",
        "                cell=distance\n",
        "        return cell\n",
        "    \n",
        "    def randomize(self,cell):\n",
        "        if 0<cell:\n",
        "            x=np.random.rand()\n",
        "            if x <= self.slow_p:\n",
        "                cell -= 1\n",
        "                \n",
        "        return cell\n",
        "    \n",
        "    def move(self):\n",
        "        next_state = np.array([-1]*self.length)\n",
        "        for index,cell in enumerate(self.state):\n",
        "            if -1<cell:\n",
        "                next_state[(index+cell)%self.length]=cell\n",
        "        self.state = next_state\n",
        "        \n",
        "    def display(self):\n",
        "        print(''.join('.' if x == -1 else str(x) for x in self.state))\n",
        "\n",
        "    \n",
        "    def update(self,vis=False): \n",
        "        for index in range(len(self.state)):\n",
        "            self.state[index] = self.accelerate(self.state[index])\n",
        "            self.state[index] = self.decelerate(index,self.state[index])\n",
        "            self.state[index] = self.randomize(self.state[index])\n",
        "        if vis:\n",
        "            self.display()\n",
        "        self.move()\n",
        "        \n",
        "    def run(self,steps,vis=False):\n",
        "        self.display()\n",
        "        states = []\n",
        "        for i in range(steps):\n",
        "            states.append(self.update(vis))\n",
        "            \n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}