AI_ML_Vehicle_Routing_Problem.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akashlogics/AI-ML-Vehicle-Routing-Problem/blob/main/AI_ML_Vehicle_Routing_Problem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install deap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNl7bRLYY1pF",
        "outputId": "79e3480f-c704-4d50-9c31-013429292deb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting deap\n",
            "  Downloading deap-1.4.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from deap) (1.26.4)\n",
            "Downloading deap-1.4.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (135 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/135.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m133.1/135.3 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.3/135.3 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: deap\n",
            "Successfully installed deap-1.4.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5puGjqHgMnx9"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from deap import base, creator, tools, algorithms"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the number of locations (eg 10)\n",
        "num_locations = 10\n",
        "# Create a list of tuples representing location coordinates -\n",
        "locations = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(num_locations)]\n",
        "            # try to use a random number generator\n",
        "            # (x, y) could be between 0 and 100\n",
        "\n",
        "depot =(50, 50) # Define the coordinates for the depot\n",
        "\n",
        "num_vehicles = 3# Define the number of vehicles - (eg 3)\n",
        "\n",
        "# Display the problem setup\n",
        "print(f\"Number of locations: {num_locations}\")\n",
        "print(f\"Locations: {locations}\")\n",
        "print(f\"Depot: {depot}\")\n",
        "print(f\"Number of vehicles: {num_vehicles}\")\n",
        "\n",
        "# Visualize the problem\n",
        "plt.figure(figsize=(10, 10))\n",
        "x, y = zip(*locations)\n",
        "plt.scatter(x, y, c='blue', label='Locations')\n",
        "plt.scatter(depot[0], depot[1], c='red', s=200, marker='s', label='Depot')\n",
        "for i, loc in enumerate(locations):\n",
        "    plt.annotate(f\"L{i}\", (loc[0], loc[1]), xytext=(5, 5), textcoords='offset points')\n",
        "plt.title(\"Vehicle Routing Problem Setup\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KWk2KjnBP1Vw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "outputId": "9a47d655-1a13-4e0b-81a6-e237617e8a09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of locations: 10\n",
            "Locations: [(53, 51), (61, 75), (58, 34), (22, 45), (84, 11), (67, 55), (21, 93), (66, 62), (61, 98), (4, 25)]\n",
            "Depot: (50, 50)\n",
            "Number of vehicles: 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the fitness class (we want to minimize total distance)\n",
        "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,-1.0))\n",
        "\n",
        "# Define the Individual class\n",
        "# An individual will be a list of integers representing the order of visits\n",
        "creator.create(\"Individual\", list, fitness=creator.FitnessMin)"
      ],
      "metadata": {
        "id": "XOu0vfatT-hB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox = base.Toolbox()\n",
        "\n",
        "# Register the indices function to create a random permutation of locations\n",
        "toolbox.register(\"indices\", random.sample, range(num_locations), num_locations)\n",
        "\n",
        "# Register the individual creation operation\n",
        "toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.indices)\n",
        "\n",
        "# Register the population creation operation\n",
        "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n"
      ],
      "metadata": {
        "id": "whxmm82SUvlM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitness Function\n",
        "def evalVRP(individual):\n",
        "    total_distance = 0\n",
        "    distances = []  # Track distance traveled by each vehicle for balance calculation\n",
        "    # Split the list of locations among vehicles, ensuring each starts and ends at the depot\n",
        "    for i in range(num_vehicles):\n",
        "\n",
        "        vehicle_route = [depot] + [locations[individual[j]] for j in range(i, len(individual), num_vehicles)] + [depot]\n",
        "\n",
        "        # Calculate total distance traveled by this vehicle\n",
        "        vehicle_distance = sum(np.linalg.norm(np.array(vehicle_route[k+1]) - np.array(vehicle_route[k])) for k in range(len(vehicle_route)-1))\n",
        "\n",
        "        total_distance += vehicle_distance\n",
        "        distances.append(vehicle_distance)\n",
        "\n",
        "    balance_penalty = np.std(distances)  # Use standard deviation of distances as a penalty for imbalance among vehicles\n",
        "    return total_distance, balance_penalty\n",
        "\n",
        "toolbox.register(\"evaluate\", evalVRP)"
      ],
      "metadata": {
        "id": "-8OHLNUsypZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"evaluate\", evalVRP)"
      ],
      "metadata": {
        "id": "YZ_4r7NNeMks"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"mate\", tools.cxPartialyMatched)  # Register the crossover function suitable for permutation-based representation\n",
        "toolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb=0.05)  # Register the mutation function to shuffle indices with a 5% chance per index\n",
        "toolbox.register(\"select\", tools.selTournament, tournsize=3)  # Register the selection function using tournament selection"
      ],
      "metadata": {
        "id": "mxJ_lX-2ZwHg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_routes(individual, title=\"Routes\"):\n",
        "    plt.figure()\n",
        "    # Plot locations as blue dots and the depot as a red square\n",
        "    for (x, y) in locations:\n",
        "        plt.plot(x, y, 'bo')\n",
        "    plt.plot(depot[0], depot[1], 'rs')\n",
        "\n",
        "    # Draw routes for each vehicle\n",
        "    for i in range(num_vehicles):\n",
        "        vehicle_route = [depot] + [locations[individual[j]] for j in range(i, len(individual), num_vehicles)] + [depot]\n",
        "        plt.plot(*zip(*vehicle_route), '-', 'rs')\n",
        "\n",
        "    plt.title(title)\n",
        "    plt.xlabel('X Coordinate')\n",
        "    plt.ylabel('Y Coordinate')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "ozVLZAgRbppu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Running the Genetic Algorithm\n",
        "# Running the Genetic Algorithm\n",
        "def main():\n",
        "    random.seed(42)  # Seed for reproducibility\n",
        "    pop = toolbox.population(n=300)  # Generate initial population\n",
        "    hof = tools.HallOfFame(1)  # Hall of Fame to store the best individual\n",
        "\n",
        "    # Setup statistics to track\n",
        "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
        "    stats.register(\"avg\", np.mean)\n",
        "    stats.register(\"min\", np.min)\n",
        "\n",
        "    # Run the genetic algorithm\n",
        "    algorithms.eaSimple(pop, toolbox, 0.7, 0.2, 300, stats=stats, halloffame=hof)\n",
        "\n",
        "    # Plot the best route found\n",
        "    plot_routes(hof[0], \"Optimal Route\")\n",
        "    return pop, stats, hof\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mpGz_x4bb86v",
        "outputId": "a5f39f59-8b80-430c-d20c-4ee437ebf347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gen\tnevals\tavg    \tmin    \n",
            "0  \t300   \t289.545\t3.92188\n",
            "1  \t220   \t280.803\t2.43537\n",
            "2  \t240   \t276.753\t2.41187\n",
            "3  \t229   \t272.076\t2.41187\n",
            "4  \t228   \t269.228\t2.41187\n",
            "5  \t218   \t268.561\t2.41187\n",
            "6  \t220   \t267.535\t2.4384 \n",
            "7  \t232   \t264.015\t2.16754\n",
            "8  \t223   \t262.894\t2.16754\n",
            "9  \t219   \t259.852\t2.16754\n",
            "10 \t251   \t262.997\t4.19071\n",
            "11 \t221   \t262.666\t5.62359\n",
            "12 \t221   \t253.484\t3.8443 \n",
            "13 \t212   \t247.714\t4.19071\n",
            "14 \t216   \t234.067\t4.19071\n",
            "15 \t222   \t215.851\t2.98009\n",
            "16 \t229   \t204.384\t4.66181\n",
            "17 \t230   \t203.379\t12.4371\n",
            "18 \t228   \t202.526\t4.66181\n",
            "19 \t244   \t200.872\t4.66181\n",
            "20 \t223   \t200.375\t12.7368\n",
            "21 \t222   \t203.141\t4.66181\n",
            "22 \t223   \t202.165\t12.1105\n",
            "23 \t218   \t201.23 \t2.29725\n",
            "24 \t240   \t200.94 \t4.57212\n",
            "25 \t227   \t201.403\t3.22757\n",
            "26 \t235   \t200.316\t4.57212\n",
            "27 \t235   \t202.376\t13.3437\n",
            "28 \t235   \t201.56 \t2.29725\n",
            "29 \t240   \t203.015\t15.7947\n",
            "30 \t235   \t200.87 \t13.3987\n",
            "31 \t232   \t200.907\t4.57212\n",
            "32 \t229   \t201.32 \t2.29725\n",
            "33 \t243   \t202.348\t13.3987\n",
            "34 \t214   \t201.831\t13.9488\n",
            "35 \t234   \t204.126\t13.3987\n",
            "36 \t228   \t201.412\t13.3437\n",
            "37 \t235   \t202.349\t13.3437\n",
            "38 \t233   \t200.866\t12.7368\n",
            "39 \t241   \t200.952\t2.29725\n",
            "40 \t206   \t200.422\t2.29725\n",
            "41 \t228   \t200.226\t13.3437\n",
            "42 \t230   \t201.392\t2.29725\n",
            "43 \t237   \t201.333\t12.7368\n",
            "44 \t229   \t202.338\t2.29725\n",
            "45 \t244   \t201.645\t2.29725\n",
            "46 \t220   \t201.524\t4.57212\n",
            "47 \t211   \t201.058\t12.7368\n",
            "48 \t251   \t202.251\t13.3437\n",
            "49 \t224   \t201.461\t8.43948\n",
            "50 \t206   \t201.472\t4.57212\n",
            "51 \t229   \t202.307\t2.29725\n",
            "52 \t224   \t201.874\t2.29725\n",
            "53 \t231   \t200.443\t13.3437\n",
            "54 \t230   \t201.463\t4.57212\n",
            "55 \t221   \t201.911\t2.29725\n",
            "56 \t238   \t202.459\t4.57212\n",
            "57 \t230   \t200.909\t14.8383\n",
            "58 \t242   \t201.46 \t1.22919\n",
            "59 \t220   \t201.768\t10.7343\n",
            "60 \t234   \t201.887\t2.29725\n",
            "61 \t214   \t203.774\t2.29725\n",
            "62 \t226   \t201.711\t13.3437\n",
            "63 \t215   \t202.117\t2.29725\n",
            "64 \t225   \t200.933\t15.7903\n",
            "65 \t220   \t201.748\t2.29725\n",
            "66 \t231   \t202.08 \t2.29725\n",
            "67 \t223   \t201.844\t4.57212\n",
            "68 \t239   \t201.483\t2.29725\n",
            "69 \t222   \t201.11 \t2.29725\n",
            "70 \t216   \t201.128\t2.29725\n",
            "71 \t223   \t202.56 \t2.29725\n",
            "72 \t221   \t201.84 \t4.57212\n",
            "73 \t240   \t201.235\t2.29725\n",
            "74 \t251   \t202.264\t2.29725\n",
            "75 \t219   \t202.607\t12.7368\n",
            "76 \t227   \t201.597\t2.29725\n",
            "77 \t243   \t202.035\t14.8383\n",
            "78 \t235   \t202.523\t12.7368\n",
            "79 \t236   \t201.817\t15.7947\n",
            "80 \t230   \t202.333\t14.4961\n",
            "81 \t223   \t201.912\t13.3356\n",
            "82 \t236   \t201.783\t2.29725\n",
            "83 \t237   \t200.788\t2.29725\n",
            "84 \t236   \t202.322\t13.3437\n",
            "85 \t224   \t200.249\t2.29725\n",
            "86 \t228   \t201.828\t12.7368\n",
            "87 \t222   \t201.743\t14.4961\n",
            "88 \t230   \t200.858\t14.8383\n",
            "89 \t229   \t200.65 \t4.57212\n",
            "90 \t220   \t200.587\t13.3437\n",
            "91 \t220   \t201.671\t14.4961\n",
            "92 \t224   \t201.638\t13.3987\n",
            "93 \t236   \t200.172\t2.29725\n",
            "94 \t243   \t201.718\t4.57212\n",
            "95 \t237   \t201.771\t8.03103\n",
            "96 \t245   \t201.062\t4.57212\n",
            "97 \t247   \t200.783\t2.29725\n",
            "98 \t210   \t200.908\t2.21759\n",
            "99 \t220   \t200.191\t15.7947\n",
            "100\t211   \t201.62 \t2.29725\n",
            "101\t219   \t201.258\t4.57212\n",
            "102\t228   \t201.26 \t2.29725\n",
            "103\t209   \t201.595\t2.29725\n",
            "104\t224   \t201.76 \t12.7368\n",
            "105\t233   \t201.051\t4.57212\n",
            "106\t220   \t201.978\t2.29725\n",
            "107\t226   \t202.173\t4.57212\n",
            "108\t229   \t203.06 \t2.29725\n",
            "109\t230   \t201.178\t4.57212\n",
            "110\t213   \t201.441\t2.29725\n",
            "111\t218   \t203.144\t4.57212\n",
            "112\t226   \t199.595\t15.7947\n",
            "113\t254   \t201.901\t2.29725\n",
            "114\t239   \t200.771\t13.3437\n",
            "115\t242   \t201.929\t12.7368\n",
            "116\t231   \t202.047\t6.56212\n",
            "117\t230   \t201.57 \t2.29725\n",
            "118\t222   \t201.228\t12.7368\n",
            "119\t230   \t201.294\t2.29725\n",
            "120\t215   \t201.015\t4.7388 \n",
            "121\t223   \t201.049\t14.8383\n",
            "122\t234   \t200.818\t13.3437\n",
            "123\t231   \t200.879\t4.57212\n",
            "124\t210   \t201.157\t6.56212\n",
            "125\t235   \t201.896\t4.57212\n",
            "126\t225   \t201.295\t4.57212\n",
            "127\t230   \t202.183\t2.29725\n",
            "128\t223   \t201.281\t12.7368\n",
            "129\t218   \t200.888\t13.3437\n",
            "130\t224   \t201.704\t4.57212\n",
            "131\t227   \t201.823\t2.29725\n",
            "132\t231   \t201.617\t2.29725\n",
            "133\t224   \t201.579\t4.57212\n",
            "134\t223   \t201.106\t4.57212\n",
            "135\t243   \t201.921\t2.29725\n",
            "136\t227   \t201.943\t2.29725\n",
            "137\t234   \t201.071\t12.7368\n",
            "138\t221   \t201.482\t13.3437\n",
            "139\t230   \t201.869\t2.29725\n",
            "140\t233   \t200.387\t13.3437\n",
            "141\t203   \t202.554\t4.57212\n",
            "142\t238   \t200.782\t4.57212\n",
            "143\t215   \t202.683\t13.3987\n",
            "144\t248   \t201.916\t12.4018\n",
            "145\t215   \t200.077\t2.29725\n",
            "146\t236   \t200.12 \t7.4411 \n",
            "147\t222   \t201.351\t14.4961\n",
            "148\t231   \t201.398\t2.29725\n",
            "149\t235   \t203.368\t14.8383\n",
            "150\t214   \t201.733\t2.29725\n",
            "151\t232   \t201.828\t4.57212\n",
            "152\t240   \t200.433\t2.29725\n",
            "153\t223   \t201.978\t12.7368\n",
            "154\t245   \t200.741\t14.4961\n",
            "155\t237   \t202.356\t2.29725\n",
            "156\t209   \t200.684\t14.4961\n",
            "157\t239   \t201.301\t4.57212\n",
            "158\t237   \t200.687\t2.29725\n",
            "159\t221   \t200.471\t4.57212\n",
            "160\t234   \t205.057\t2.29725\n",
            "161\t244   \t202.136\t2.29725\n",
            "162\t234   \t200.693\t2.29725\n",
            "163\t220   \t204.041\t2.29725\n",
            "164\t210   \t201.578\t4.57212\n",
            "165\t227   \t200.797\t13.3437\n",
            "166\t202   \t203.573\t4.57212\n",
            "167\t228   \t203.01 \t4.57212\n",
            "168\t228   \t201.471\t4.57212\n",
            "169\t222   \t203.259\t2.29725\n",
            "170\t225   \t201.606\t6.23942\n",
            "171\t242   \t200.714\t2.29725\n",
            "172\t245   \t201.084\t13.3437\n",
            "173\t219   \t202.649\t13.3437\n",
            "174\t239   \t202.169\t2.29725\n",
            "175\t233   \t201.627\t4.57212\n",
            "176\t226   \t202.227\t2.29725\n",
            "177\t222   \t201.383\t2.29725\n",
            "178\t237   \t202.08 \t2.29725\n",
            "179\t208   \t201.712\t12.5304\n",
            "180\t218   \t202.249\t4.57212\n",
            "181\t227   \t201.26 \t2.29725\n",
            "182\t232   \t201.452\t2.29725\n",
            "183\t205   \t202.176\t4.57212\n",
            "184\t231   \t201.355\t2.29725\n",
            "185\t225   \t201.29 \t4.57212\n",
            "186\t235   \t201.689\t13.3437\n",
            "187\t235   \t201.274\t4.57212\n",
            "188\t236   \t203.179\t4.57212\n",
            "189\t243   \t201.264\t2.29725\n",
            "190\t225   \t200.227\t4.57212\n",
            "191\t224   \t201.65 \t2.29725\n",
            "192\t224   \t202.275\t2.29725\n",
            "193\t228   \t201.568\t12.7368\n",
            "194\t242   \t201.053\t13.3987\n",
            "195\t213   \t203.221\t9.3606 \n",
            "196\t221   \t200.385\t12.7368\n",
            "197\t224   \t201.563\t4.57212\n",
            "198\t228   \t201.629\t4.57212\n",
            "199\t226   \t201.996\t4.57212\n",
            "200\t233   \t202.326\t7.81206\n",
            "201\t222   \t201.238\t4.57212\n",
            "202\t226   \t202.539\t12.7368\n",
            "203\t231   \t202.408\t12.7368\n",
            "204\t227   \t200.832\t13.3437\n",
            "205\t224   \t201.387\t13.3987\n",
            "206\t227   \t202.264\t4.57212\n",
            "207\t226   \t201.62 \t2.29725\n",
            "208\t231   \t200.652\t13.3987\n",
            "209\t232   \t202.292\t2.29725\n",
            "210\t220   \t201.518\t4.57212\n",
            "211\t230   \t203.827\t4.57212\n",
            "212\t220   \t202.443\t14.4961\n",
            "213\t233   \t200.785\t13.3437\n",
            "214\t209   \t200.023\t4.57212\n",
            "215\t209   \t201.628\t2.29725\n",
            "216\t221   \t201.171\t14.4961\n",
            "217\t234   \t201.471\t4.57212\n",
            "218\t241   \t201.333\t2.29725\n",
            "219\t220   \t201.452\t12.7368\n",
            "220\t224   \t201.529\t4.57212\n",
            "221\t236   \t203.367\t4.57212\n",
            "222\t216   \t201.169\t2.29725\n",
            "223\t240   \t200.823\t13.3437\n",
            "224\t237   \t203.097\t2.29725\n",
            "225\t231   \t203.083\t2.29725\n",
            "226\t237   \t202.639\t12.7368\n",
            "227\t205   \t199.968\t15.7947\n",
            "228\t206   \t201.408\t2.29725\n",
            "229\t232   \t201.317\t4.56338\n",
            "230\t242   \t201.233\t2.29725\n",
            "231\t226   \t201.345\t2.29725\n",
            "232\t228   \t201.85 \t13.3437\n",
            "233\t222   \t200.803\t2.29725\n",
            "234\t233   \t202.024\t2.29725\n",
            "235\t218   \t201.242\t4.57212\n",
            "236\t229   \t202.826\t13.3437\n",
            "237\t224   \t201.089\t2.29725\n",
            "238\t225   \t202.696\t12.5304\n",
            "239\t224   \t201.572\t2.29725\n",
            "240\t221   \t202.246\t13.3437\n",
            "241\t222   \t201.149\t4.57212\n",
            "242\t231   \t202.625\t4.57212\n",
            "243\t225   \t201.673\t14.4961\n",
            "244\t228   \t201.543\t4.57212\n",
            "245\t237   \t201.921\t4.57212\n",
            "246\t214   \t200.063\t14.4961\n",
            "247\t226   \t201.025\t13.3987\n",
            "248\t233   \t200.369\t14.4961\n",
            "249\t231   \t200.918\t13.3437\n",
            "250\t220   \t201.318\t12.7368\n",
            "251\t213   \t201.673\t14.1367\n",
            "252\t234   \t202.116\t7.81206\n",
            "253\t221   \t199.963\t12.7368\n",
            "254\t237   \t203.629\t13.3987\n",
            "255\t240   \t202.254\t2.29725\n",
            "256\t219   \t200.494\t2.29725\n",
            "257\t253   \t201.227\t13.3437\n",
            "258\t226   \t201.239\t8.6928 \n",
            "259\t229   \t201.919\t14.4961\n",
            "260\t245   \t201.204\t15.7903\n",
            "261\t208   \t200.673\t4.57212\n",
            "262\t243   \t200.601\t2.29725\n",
            "263\t223   \t199.992\t12.7368\n",
            "264\t216   \t201.306\t2.29725\n",
            "265\t232   \t201.213\t12.7368\n",
            "266\t228   \t202.019\t2.29725\n",
            "267\t239   \t201.399\t4.57212\n",
            "268\t233   \t202.232\t12.7368\n",
            "269\t230   \t201.03 \t13.3437\n",
            "270\t223   \t201.124\t4.57212\n",
            "271\t219   \t201.624\t2.29725\n",
            "272\t224   \t201.522\t12.7368\n",
            "273\t224   \t202.382\t4.57212\n",
            "274\t212   \t200.107\t4.57212\n",
            "275\t218   \t200.689\t2.29725\n",
            "276\t224   \t201.854\t2.29725\n",
            "277\t249   \t202.348\t2.29725\n",
            "278\t230   \t202.972\t2.29725\n",
            "279\t241   \t200.286\t4.57212\n",
            "280\t226   \t202.753\t14.8383\n",
            "281\t216   \t200.723\t5.88906\n",
            "282\t230   \t200.415\t9.81586\n",
            "283\t220   \t202.008\t4.57212\n",
            "284\t236   \t200.656\t12.5304\n",
            "285\t230   \t201.197\t4.57212\n",
            "286\t225   \t201.079\t13.3437\n",
            "287\t224   \t200.96 \t2.29725\n",
            "288\t217   \t202.571\t12.1105\n",
            "289\t226   \t202.272\t11.6802\n",
            "290\t226   \t203.298\t5.79642\n",
            "291\t223   \t202.22 \t2.29725\n",
            "292\t240   \t201.392\t4.57212\n",
            "293\t219   \t201.647\t5.76159\n",
            "294\t220   \t202.447\t2.29725\n",
            "295\t211   \t200.879\t14.4961\n",
            "296\t225   \t200.323\t2.29725\n",
            "297\t209   \t202.202\t2.63429\n",
            "298\t216   \t201.355\t2.29725\n",
            "299\t238   \t202.282\t2.29725\n",
            "300\t228   \t202.919\t2.29725\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}