core/experiment-related/Udon_Experiment_Figures.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "phzsXMzTXbuC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import math\n",
        "import seaborn as sns\n",
        "from io import StringIO\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "from matplotlib import colors\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OBazCfhEmL3V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B420-PQ3XgGi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "udf_debug_overall_csv = \"\"\"\n",
        "name,UDF1,UDF2,UDF3,UDF4\n",
        "original,0.635,28.233,3.680,2.743\n",
        "debugger,113.051,57.810,7.561,4.321\n",
        "opt_1A,28.423,46.176,6.513,4.285\n",
        "opt_1B,0.631,30.291,6.274,4.257\n",
        "opt_2,18.036,28.687,6.208,4.332\n",
        "op1B_op2,1.452,28.547,3.727,4.375\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "I3roM7KiXgN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pdb_overhead_csv = \"\"\"\n",
        "num tuples\tUDF1 no debugger\tUDF1 with debugger\tUDF2 no debugger\tUDF2 with debugger\tUDF3 no debugger\tUDF3 with debugger\n",
        "10k\t9.21\t31.81\t215.49\t413.11\t8.94\t19.18\n",
        "20k\t17.76\t60.23\t428.09\t824.88\t18.36\t37.99\n",
        "30k\t25.86\t88.74\t633.44\t1200.14\t27.24\t56.09\n",
        "40k\t33.87\t117.2\t838.13\t1520.89\t36.38\t74.71\n",
        "50k\t41.81\t146.13\t1039.82\t1838.87\t45.61\t93.38\n",
        "60k\t50.05\t175.3\t1225.04\t2153.19\t54.73\t112.02\n",
        "70k\t58.28\t204.59\t1395.88\t2466.58\t63.70\t130.70\n",
        "80k\t66.31\t233.31\t1566.60\t2776.37\t71.54\t149.57\n",
        "90k\t74.4\t260.7\t1736.77\t3083.15\t80.64\t168.69\n",
        "100k\t82.38\t289.33\t1904.45\t3390.99\t89.97\t187.03\n",
        "110k\t90.49\t317.61\t2076.31\t3711.61\t99.09\t205.99\n",
        "120k\t98.45\t344.8\t2239.33\t4029.31\t108.42\t224.52\n",
        "130k\t106.43\t371.57\t2428.68\t4340.00\t117.89\t243.09\n",
        "140k\t114.5\t398.33\t2595.33\t4663.04\t127.22\t261.23\n",
        "150k\t122.44\t425.98\t2761.67\t4980.79\t136.69\t280.29\n",
        "160k\t130.63\t453.45\t2932.16\t5304.38\t145.75\t298.72\n",
        "170k\t138.82\t480.86\t3097.98\t5627.53\t154.76\t317.97\n",
        "180k\t146.83\t508.22\t3265.75\t5937.33\t164.10\t336.41\n",
        "190k\t154.72\t536.31\t3430.59\t6249.50\t173.20\t354.58\n",
        "200k\t162.64\t564.83\t3598.04\t6558.06\t182.43\t373.51\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "TBlSAd8ieCfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pdb_overhead = pd.read_csv(StringIO(pdb_overhead_csv), sep='\\t')"
      ],
      "metadata": {
        "id": "n2pXneRJeEz2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "overhead_x_axis = pdb_overhead[\"num tuples\"].apply(lambda x: int(x[:-1]) * 1000)\n",
        "\n",
        "plt.figure(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# sns.set_style(\"white\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF1 no debugger\"],  color='#9CB3D4', marker=\"o\", markersize=\"5\", label=\"UDF1 no debugger\", linewidth=\"2\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF1 with debugger\"], color='#414C87',  marker=\"o\", markersize=\"5\", label=\"UDF1 with debugger\", linewidth=\"2\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF3 no debugger\"], color='#DA9464',  marker=\"o\", markersize=\"5\", label=\"UDF3 no debugger\", linewidth=\"2\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF3 with debugger\"], color='#8C5D42',  marker=\"o\", markersize=\"5\", label=\"UDF3 with debugger\", linewidth=\"2\")\n",
        "\n",
        "# xlabels = ['{:,.2f}'.format(x) + 'K' for x in plt.get_xticks()/1000]\n",
        "\n",
        "plt.xticks(fontsize=fontsize)\n",
        "ax = plt.gca()\n",
        "ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{:,.0f}'.format(x/1000) + 'K'))\n",
        "\n",
        "plt.yticks(fontsize=fontsize)\n",
        "plt.legend(fontsize=20, ncol=2, loc='upper left', labelspacing=0.2, handletextpad=0.4, columnspacing=1)\n",
        "plt.ylabel(\"total execution time (s)\", fontsize=fontsize)\n",
        "plt.xlabel(\"number of tuples\", fontsize=fontsize)"
      ],
      "metadata": {
        "id": "ZeEcRBtheLxr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "7ce1c667-7413-4dcf-d466-be238f38d922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'number of tuples')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x640 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "overhead_x_axis = pdb_overhead[\"num tuples\"].apply(lambda x: int(x[:-1]) * 1000)\n",
        "\n",
        "plt.figure(figsize=(6, 6), dpi=80)\n",
        "\n",
        "# sns.set_style(\"white\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF1 no debugger\"],  color='#9CB3D4', marker=\"o\", markersize=\"5\", label=\"Without debugger\", linewidth=\"2\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF1 with debugger\"], color='#414C87',  marker=\"o\", markersize=\"5\", label=\"With debugger\", linewidth=\"2\")\n",
        "# xlabels = ['{:,.2f}'.format(x) + 'K' for x in plt.get_xticks()/1000]\n",
        "\n",
        "plt.xticks(fontsize=16)\n",
        "ax = plt.gca()\n",
        "ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{:,.0f}'.format(x/1000) + 'K'))\n",
        "\n",
        "plt.yticks(fontsize=fontsize)\n",
        "plt.legend(fontsize=20, ncol=1, loc='upper left', labelspacing=0.2, handletextpad=0.4, columnspacing=1)\n",
        "plt.ylabel(\"Total execution time (s)\", fontsize=fontsize)\n",
        "plt.xlabel(\"Number of tuples\", fontsize=fontsize)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "eA7quntSBMGm",
        "outputId": "cdee6cfe-0662-40e9-f145-98020f818e25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Number of tuples')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "overhead_x_axis = pdb_overhead[\"num tuples\"].apply(lambda x: int(x[:-1]) * 1000)\n",
        "\n",
        "plt.figure(figsize=(6, 6), dpi=80)\n",
        "\n",
        "# sns.set_style(\"white\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF2 no debugger\"],  color='#CD4432', marker=\"o\", markersize=\"5\", label=\"Without debugger\", linewidth=\"2\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF2 with debugger\"], color='#611E1E',  marker=\"o\", markersize=\"5\", label=\"With debugger\", linewidth=\"2\")\n",
        "# xlabels = ['{:,.2f}'.format(x) + 'K' for x in plt.get_xticks()/1000]\n",
        "\n",
        "plt.xticks(fontsize=16)\n",
        "ax = plt.gca()\n",
        "ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{:,.0f}'.format(x/1000) + 'K'))\n",
        "\n",
        "plt.yticks(fontsize=fontsize)\n",
        "plt.legend(fontsize=20, ncol=1, loc='upper left', labelspacing=0.2, handletextpad=0.4, columnspacing=1)\n",
        "plt.ylabel(\"Total execution time (s)\", fontsize=fontsize)\n",
        "plt.xlabel(\"Number of tuples\", fontsize=fontsize)"
      ],
      "metadata": {
        "id": "RgWWfPSZCLWy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "2b1ac09e-781e-4eb9-d749-328124940264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Number of tuples')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "overhead_x_axis = pdb_overhead[\"num tuples\"].apply(lambda x: int(x[:-1]) * 1000)\n",
        "\n",
        "plt.figure(figsize=(6, 6), dpi=80)\n",
        "\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF3 no debugger\"], color='#DA9464',  marker=\"o\", markersize=\"5\", label=\"Without debugger\", linewidth=\"2\")\n",
        "plt.plot(overhead_x_axis, pdb_overhead[\"UDF3 with debugger\"], color='#8C5D42',  marker=\"o\", markersize=\"5\", label=\"With debugger\", linewidth=\"2\")\n",
        "\n",
        "# xlabels = ['{:,.2f}'.format(x) + 'K' for x in plt.get_xticks()/1000]\n",
        "\n",
        "plt.xticks(fontsize=16)\n",
        "ax = plt.gca()\n",
        "ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{:,.0f}'.format(x/1000) + 'K'))\n",
        "\n",
        "plt.yticks(fontsize=fontsize)\n",
        "plt.legend(fontsize=20, ncol=1, loc='upper left', labelspacing=0.2, handletextpad=0.4, columnspacing=1)\n",
        "plt.ylabel(\"Total execution time (s)\", fontsize=fontsize)\n",
        "plt.xlabel(\"Number of tuples\", fontsize=fontsize)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "hAmNt7tSVuM-",
        "outputId": "378ff577-3e31-43be-8719-ebf50dfeebeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Number of tuples')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "udf_debug_compare_csv = \"\"\"\n",
        "name\tUDF1\tUDF2\tUDF3\n",
        "no debug\t162.64\t3598.04\t182.43\n",
        "debugger\t564.83\t6558.06\t373.51\n",
        "opt1\t198.08\t3749.97\t181.93\n",
        "opt2\t344.65\t6727.01\t352.02\n",
        "opt1+2\t173.89\t3926.89\t181\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "07MrM7w9ChDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "udf_debug_compare = pd.read_csv(StringIO(udf_debug_compare_csv), sep='\\t')\n",
        "udf_debug_compare = udf_debug_compare.set_index(\"name\")\n",
        "udf_debug_compare\n",
        "udf_debug_compare = udf_debug_compare.transpose()\n",
        "udf_debug_compare"
      ],
      "metadata": {
        "id": "hMro5LG6ChOc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "396204d5-6ebc-4926-f071-ce317d90a96b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name  no debug  debugger     opt1     opt2   opt1+2\n",
              "UDF1    162.64    564.83   198.08   344.65   173.89\n",
              "UDF2   3598.04   6558.06  3749.97  6727.01  3926.89\n",
              "UDF3    182.43    373.51   181.93   352.02   181.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64640059-3154-4790-a135-c556a5887546\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>name</th>\n",
              "      <th>no debug</th>\n",
              "      <th>debugger</th>\n",
              "      <th>opt1</th>\n",
              "      <th>opt2</th>\n",
              "      <th>opt1+2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>UDF1</th>\n",
              "      <td>162.64</td>\n",
              "      <td>564.83</td>\n",
              "      <td>198.08</td>\n",
              "      <td>344.65</td>\n",
              "      <td>173.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UDF2</th>\n",
              "      <td>3598.04</td>\n",
              "      <td>6558.06</td>\n",
              "      <td>3749.97</td>\n",
              "      <td>6727.01</td>\n",
              "      <td>3926.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UDF3</th>\n",
              "      <td>182.43</td>\n",
              "      <td>373.51</td>\n",
              "      <td>181.93</td>\n",
              "      <td>352.02</td>\n",
              "      <td>181.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64640059-3154-4790-a135-c556a5887546')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-64640059-3154-4790-a135-c556a5887546 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-64640059-3154-4790-a135-c556a5887546');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-55c740f7-a499-4e5a-bddb-c65cacab8dff\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55c740f7-a499-4e5a-bddb-c65cacab8dff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_26cd471e-dc76-4622-ae24-d357e2340fb4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('udf_debug_compare')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_26cd471e-dc76-4622-ae24-d357e2340fb4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('udf_debug_compare');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "udf_debug_compare",
              "summary": "{\n  \"name\": \"udf_debug_compare\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"no debug\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1977.7409872629935,\n        \"min\": 162.64,\n        \"max\": 3598.04,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          162.64,\n          3598.04,\n          182.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"debugger\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3516.723566944095,\n        \"min\": 373.51,\n        \"max\": 6558.06,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          564.83,\n          6558.06,\n          373.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opt1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2055.3626134334545,\n        \"min\": 181.93,\n        \"max\": 3749.97,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          198.08,\n          3749.97,\n          181.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opt2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3682.7315719585104,\n        \"min\": 344.65,\n        \"max\": 6727.01,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          344.65,\n          6727.01,\n          352.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opt1+2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2164.7459991186033,\n        \"min\": 173.89,\n        \"max\": 3926.89,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          173.89,\n          3926.89,\n          181.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "udf_debug_compare = udf_debug_compare.div(udf_debug_compare.iloc[:,0],axis=0).mul(100).add(0.5).round()\n",
        "udf_debug_compare"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "l9VoxlHKP195",
        "outputId": "4290a236-4676-4443-d250-8486fad71117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name  no debug  debugger   opt1   opt2  opt1+2\n",
              "UDF1     100.0     348.0  122.0  212.0   107.0\n",
              "UDF2     100.0     183.0  105.0  187.0   110.0\n",
              "UDF3     100.0     205.0  100.0  193.0   100.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8be8a588-11f2-4e8d-ac9d-8ef67331a954\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>name</th>\n",
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              "      <td>348.0</td>\n",
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              "    <tr>\n",
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              "      <td>100.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>187.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>UDF3</th>\n",
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              "      <td>205.0</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8be8a588-11f2-4e8d-ac9d-8ef67331a954');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_d0940e63-5943-440b-84d2-e09e263642d7\">\n",
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              "        display: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('udf_debug_compare')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "udf_debug_compare",
              "summary": "{\n  \"name\": \"udf_debug_compare\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"no debug\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 100.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"debugger\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.58980596771785,\n        \"min\": 183.0,\n        \"max\": 348.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opt1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.532562594670797,\n        \"min\": 100.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          122.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opt2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.051181300301263,\n        \"min\": 187.0,\n        \"max\": 212.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opt1+2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.131601439446884,\n        \"min\": 100.0,\n        \"max\": 110.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          107.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from IPython.display import set_matplotlib_formats\n",
        "\n",
        "# Set the output format to SVG\n",
        "set_matplotlib_formats('svg')\n",
        "\n",
        "# Set the figure size and DPI\n",
        "fig, ax = plt.subplots(figsize=(10, 4.5), dpi=300)\n",
        "\n",
        "breakpoint_checks = [283.8, 325.0, 110.2, 387.6, 156.5]\n",
        "condition_evaluations = [168.2, 58.8, 94.6, 196.8,240.2]\n",
        "udf_code = [184.2, 595.6, 178.8, 321.4, 102.4]\n",
        "\n",
        "names = ['UDF1', 'UDF2', 'UDF3', 'UDF4', 'UDF5']\n",
        "no_debug = [184.2, 595.6, 178.8, 321.4, 102.4]\n",
        "opt0 = [638.2, 982.5, 385.7, 906.61, 530.5]\n",
        "opt1 = [384.61, 673.52, 284.96, 462.74, 383.56]\n",
        "opt2 = [363.52, 1012.77, 295.13, 723.24,278.99]\n",
        "opt3 = [202.74, 634.12, 180.41, 379.81,146.48]\n",
        "\n",
        "# Set the position of the bars on the x-axis\n",
        "r = np.arange(len(names))\n",
        "\n",
        "# Set the width of the bars\n",
        "bar_width = 0.1\n",
        "\n",
        "# Define the color map\n",
        "color_map = ['#ffffff', '#A9A9A9', '#FFD479', '#FF9300', '#66CCFF']\n",
        "\n",
        "# Plot the bars and store the artists\n",
        "p1 = plt.bar(r, no_debug, color=color_map[0], width=bar_width, edgecolor='black')\n",
        "p2 = plt.bar(r + bar_width, opt0, color=color_map[1], width=bar_width, edgecolor='black')\n",
        "p3 = plt.bar(r + bar_width*2, opt1, color=color_map[2], width=bar_width, edgecolor='black')\n",
        "p4 = plt.bar(r + bar_width*3, opt2, color=color_map[3], width=bar_width, edgecolor='black')\n",
        "p5 = plt.bar(r + bar_width*4, opt3, color=color_map[4], width=bar_width, edgecolor='black')\n",
        "\n",
        "# Add xticks on the middle of the group bars\n",
        "\n",
        "plt.ylabel('Total Execution Time (seconds)', fontsize=16)\n",
        "ax.set_ylim([0, 1400])\n",
        "plt.yticks(fontsize = 14)\n",
        "plt.xticks([r + bar_width*2 for r in range(len(names))], names, fontsize=14)\n",
        "\n",
        "# Define the legend explicitly\n",
        "plt.legend((p1, p2, p3, p4, p5), ('No Debugger', 'Pdb', 'Enable opt1', 'Enable opt2', 'Enable opt1 + opt2'),ncol=3, fontsize=14)\n",
        "\n",
        "# Show the chart\n",
        "from google.colab import files\n",
        "plt.savefig(\"compare-optimizations.png\")\n",
        "files.download(\"compare-optimizations.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "5cSsKHVoQQkc",
        "outputId": "e029e0f2-515e-4ff5-eb63-ab3f5a4dc53a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-42f773fc5ee5>:6: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
            "  set_matplotlib_formats('svg')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.Javascript object>"
            ],
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              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_6e9dd74b-0b54-4a41-8535-5a0fa1029418\", \"compare-optimizations.png\", 118643)"
            ]
          },
          "metadata": {}
        },
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "cost_functions = ['CF1', 'CF2', 'CF3']\n",
        "num_states = [1048576, 1048576, 192]\n",
        "search_time = [93924, 101822, 147]\n",
        "\n",
        "# Define the width of the bars\n",
        "bar_width = 0.35\n",
        "\n",
        "# Set the positions for the bars\n",
        "r1 = np.arange(len(cost_functions))\n",
        "r2 = [x + bar_width for x in r1]\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "plt.bar(r1, num_states, color='lightgray', width=bar_width, edgecolor='black', linewidth=1, label='Number of States (log scale)')\n",
        "plt.bar(r2, search_time, color='darkgray', width=bar_width, edgecolor='black', linewidth=1, label='Search Time')\n",
        "\n",
        "plt.xlabel('Cost Functions')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Number of States and Search Time for Cost Functions')\n",
        "plt.xticks([r + bar_width / 2 for r in range(len(cost_functions))], cost_functions)\n",
        "plt.yscale('log')  # Setting y-axis to log scale\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "65A9T6lwChkm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "5205e4c3-af84-4785-b21b-a61761b4d337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fF1RZHsbhDZo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "x = [200, 400, 600, 800, 1000, 1200, 1400, 1600]\n",
        "y1 = [25.6, 51.2, 76.8, 102.4, 128, 153.6, 179.2, 204.8]\n",
        "y2 = [17.6, 35.2, 52.8, 70.4, 88, 105.6, 123.2, 140.8]\n",
        "\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define grayscale color scheme\n",
        "colors = ['#333333', '#666666']\n",
        "\n",
        "# Create the lines with data points as filled circles/triangles and rename the lines\n",
        "p1, = ax.plot(x, y1, 'o-', markersize=10, color=colors[0], label='With YDB', markerfacecolor='white')\n",
        "p2, = ax.plot(x, y2, 's-', markersize=10, color=colors[1], label='Without debugger', markerfacecolor='white')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_xlabel('Number of tuples (K)', fontsize=20)\n",
        "ax.set_ylabel('Total execution time (s)', fontsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Set x and y axis labels font size\n",
        "ax.tick_params(axis='both', labelsize=20)\n",
        "\n",
        "# Set y-axis limit to 220\n",
        "ax.set_ylim([0, 220])\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "J2mQTmMTJoaR",
        "outputId": "e5b4a3c2-ef68-445c-ddc6-638db9b1152b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the data\n",
        "without_debugger = [157.26, 321.19, 493.04, 654.17, 819.28, 985.04, 1150.21, 1316.61]\n",
        "with_ydb = [173.49, 353.22, 533.05, 712.89, 893.26, 1073.95, 1254.72, 1434.92]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(without_debugger))\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#333333']\n",
        "\n",
        "# Create the stacked area chart\n",
        "p1 = ax.fill_between(x, without_debugger, color=colors[0], alpha=0.8)\n",
        "p2 = ax.fill_between(x, without_debugger, with_ydb, color=colors[1], alpha=0.8)\n",
        "\n",
        "# Add data points\n",
        "markersize = 10\n",
        "ax.plot(x, without_debugger, marker='o', markersize=markersize, linestyle='None', color=colors[0], markeredgecolor='black')\n",
        "ax.plot(x, with_ydb, marker='s', markersize=markersize, linestyle='None', color=colors[1], markeredgecolor='black')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('Number of Tuples', fontsize=20)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(['200K', '400K', '600K', '800K', '1M', '1.2M', '1.4M', '1.6M'], fontsize=20)\n",
        "ax.legend((p1, p2), ('Without Debugger', 'With YDB'), loc='upper left', fontsize=20)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "x3P8UyNcbS2x",
        "outputId": "ab7ee5c0-7c73-4d38-b3aa-aaa0568f5d9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the data\n",
        "without_debugger = [157.26, 321.19, 493.04, 654.17, 819.28, 985.04, 1150.21, 1316.61]\n",
        "with_ydb = [173.49, 353.22, 533.05, 712.89, 893.26, 1073.95, 1254.72, 1434.92]\n",
        "\n",
        "# Calculate percentage difference\n",
        "diff_percentage = [(y - x) / x * 100 for x, y in zip(without_debugger, with_ydb)]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax1 = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(without_debugger))\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#333333', '#ff7f0e']\n",
        "\n",
        "# Create the stacked area chart\n",
        "p1 = ax1.fill_between(x, without_debugger, color=colors[0], alpha=0.8)\n",
        "p2 = ax1.fill_between(x, without_debugger, with_ydb, color=colors[1], alpha=0.8)\n",
        "\n",
        "# Add data points\n",
        "markersize = 10\n",
        "ax1.plot(x, without_debugger, marker='s', markersize=markersize, linestyle='None', color=colors[0], markeredgecolor='black')\n",
        "ax1.plot(x, with_ydb, marker='^', markersize=markersize, linestyle='None', color=colors[1], markeredgecolor='black')\n",
        "\n",
        "# Add labels, title, and legend for the left y-axis\n",
        "ax1.set_ylabel('Total Execution Time (seconds)', fontsize=20)\n",
        "ax1.set_yticklabels(['0', '200', '400', '600', '800', '1000', '1200', '1400'], fontsize = 20)\n",
        "ax1.set_xlabel('Number of Tuples', fontsize=20)\n",
        "ax1.set_xticks(x)\n",
        "ax1.set_xticklabels(['200K', '400K', '600K', '800K', '1M', '1.2M', '1.4M', '1.6M'], fontsize=20)\n",
        "ax1.legend((p1, p2, p3), ('Without Debugger', 'With YDB', 'Overhead Ratio'), loc='upper left', fontsize=20)\n",
        "\n",
        "# Add percentage difference line and legend for the right y-axis\n",
        "ax2 = ax1.twinx()\n",
        "p3, = ax2.plot(x, diff_percentage,linewidth=2, marker='o', markersize=markersize, linestyle='--', color=colors[2])\n",
        "ax2.set_ylabel('% Difference', fontsize=20)\n",
        "ax2.set_ylim([5, max(diff_percentage) + 5])\n",
        "ax2.tick_params(axis='y', labelsize=20)\n",
        "# ax2.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "zWNapOOT5aY_",
        "outputId": "393955d3-13a5-4d80-e335-876073a171ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-4217cdfab0ee>:31: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax1.set_yticklabels(['0', '200', '400', '600', '800', '1000', '1200', '1400'], fontsize = 20)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from IPython.display import set_matplotlib_formats\n",
        "set_matplotlib_formats('svg')\n",
        "# Define the data\n",
        "without_debugger = [157.26, 321.19, 493.04, 654.17, 819.28, 985.04, 1150.21, 1316.61]\n",
        "with_ydb = [173.49, 353.22, 533.05, 712.89, 893.26, 1073.95, 1254.72, 1434.92]\n",
        "diff_percentage = [(y - x) / x * 100 for x, y in zip(without_debugger, with_ydb)]\n",
        "print(diff_percentage)\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(10, 4.5), dpi=300)\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(without_debugger))\n",
        "width = 0.5\n",
        "\n",
        "# Define the colors\n",
        "# colors = ['#DCEEF3', '#005CAB', '#FFC325']\n",
        "colors = ['#999999', '#66CCFF', '#ff7f0e']\n",
        "\n",
        "# Create the stacked bar chart\n",
        "bottom = np.zeros(len(without_debugger))\n",
        "p1 = ax.bar(x, without_debugger, width, color='white', edgecolor='black', linewidth=1.5, alpha=0.8)\n",
        "p2 = ax.bar(x, np.array(with_ydb) - np.array(without_debugger), width, bottom=without_debugger, color=colors[1], edgecolor='black', linewidth=1.5,alpha=0.8)\n",
        "\n",
        "# Add data points\n",
        "markersize = 10\n",
        "ax.plot(x, without_debugger, markersize=markersize, linestyle='None', color=colors[0], markeredgecolor='black')\n",
        "ax.plot(x, with_ydb, markersize=markersize, linestyle='None', color=colors[1], markeredgecolor='black')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=16)\n",
        "ax.set_yticklabels(['0', '', '400', '','800', '', '1200', '', '1600'], fontsize=14)\n",
        "ax.set_xlabel('Number of Tuples', fontsize=16)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(['200K', '400K', '600K', '800K', '1M', '1.2M', '1.4M', '1.6M'], fontsize=14)\n",
        "\n",
        "ax.set_ylim([0, 1600])\n",
        "# Add percentage difference line and legend\n",
        "ax2 = ax.twinx()\n",
        "p3 = ax2.plot(x, diff_percentage, linewidth=2, marker='o', markersize=markersize, linestyle='--', color=colors[2])\n",
        "ax2.set_ylabel('Overhead Ratio (%)', fontsize=16)\n",
        "ax2.set_ylim([0, 20])\n",
        "ax2.set_yticklabels(['0.0', '', '5.0', '', '10.0', '', '15.0', '', '20.0'], fontsize=14)\n",
        "ax.legend((p1[0], p2[0], p3[0]), ('UDF Code', 'Udon', 'Overhead Ratio'), loc='upper left', fontsize=14, ncol=3)\n",
        "# plt.show()\n",
        "\n",
        "from google.colab import files\n",
        "plt.savefig(\"scale-up.png\")\n",
        "files.download(\"scale-up.png\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "1CRL_D3jpMjW",
        "outputId": "1ae88114-fb40-4aa8-e20a-3c0623a945ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-9ebe9ab59b2f>:4: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
            "  set_matplotlib_formats('svg')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10.320488363220157, 9.97229054453751, 8.11496024663312, 8.976259993579655, 9.029879894541551, 9.026029399821335, 9.086166873875204, 8.985956357615404]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-9ebe9ab59b2f>:33: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_yticklabels(['0', '', '400', '','800', '', '1200', '', '1600'], fontsize=14)\n",
            "<ipython-input-21-9ebe9ab59b2f>:44: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax2.set_yticklabels(['0.0', '', '5.0', '', '10.0', '', '15.0', '', '20.0'], fontsize=14)\n"
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              "<IPython.core.display.Javascript object>"
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              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
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              "      a.click();\n",
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nhgzeDpVDIES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from IPython.display import set_matplotlib_formats\n",
        "set_matplotlib_formats('svg')\n",
        "# Define the data\n",
        "workers = [1, 2, 3, 4, 5, 6, 7, 8]\n",
        "with_ydb =          [712.89, 726.88, 732.76, 738.64, 759.52, 761.40, 763.28, 765.16]\n",
        "without_debugger =  [654.17, 672.60, 686.43, 692.26, 715.09, 717.92, 719.25, 721.58]\n",
        "diff = [(with_ydb[i]-without_debugger[i])/without_debugger[i]*100 for i in range(len(with_ydb))]\n",
        "print(diff)\n",
        "# Set up the plot\n",
        "fig, ax1 = plt.subplots(figsize=(10, 4.5), dpi=300)\n",
        "ax2 = ax1.twinx()\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(with_ydb))\n",
        "width = 0.4\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#66CCFF', '#ff7f0e']\n",
        "\n",
        "# Create the bar chart\n",
        "p1 = ax1.bar(x, without_debugger, width, color='white', edgecolor='black', linewidth=1.5, alpha=0.8)\n",
        "p2 = ax1.bar(x, np.array(with_ydb) - np.array(without_debugger), width, bottom=without_debugger, color=colors[1], edgecolor='black', linewidth=1.5,alpha=0.8)\n",
        "\n",
        "\n",
        "# Create the line chart\n",
        "p3, = ax2.plot(x, diff, color=colors[2], linewidth=2, marker='o', markersize=10)\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax1.set_ylabel('Total Execution Time (seconds)', fontsize=16)\n",
        "ax1.set_yticklabels(['0','200', '400', '600', '800', '1000'], fontsize= 14)\n",
        "ax1.set_xlabel('Number of Worker Nodes', fontsize=16)\n",
        "ax1.set_xticks(x)\n",
        "ax1.set_xticklabels(['1', '2', '3', '4', '5', '6', '7', '8'], fontsize=14)\n",
        "# ax1.legend(['Without Debugger', 'With YDB'], fontsize=20)\n",
        "ax1.set_ylim([0, 1000])\n",
        "ax1.xaxis.set_tick_params(labelsize=14)\n",
        "ax1.yaxis.set_tick_params(labelsize=14)\n",
        "ax2.set_ylabel('Overhead Ratio (%)', fontsize=16)\n",
        "ax2.set_ylim([0.0, 16.0])\n",
        "ax2.set_yticklabels(['0.0', '', '4.0', '', '8.0', '', '12.0', '', '16.0'], fontsize=14)\n",
        "ax2.yaxis.set_tick_params(labelsize=14)\n",
        "# ax2.legend(['% Difference'], fontsize=20)\n",
        "ax1.legend((p1, p2, p3), ('UDF Code', 'Udon', 'Overhead Ratio'), loc='upper left', fontsize=14, ncol=3)\n",
        "\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "from google.colab import files\n",
        "plt.savefig(\"scale-out.png\")\n",
        "files.download(\"scale-out.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "UWk1azT1DwPX",
        "outputId": "0d4b5b25-49f5-45b1-b1d8-c78758383992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-e0ae20f342dc>:4: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
            "  set_matplotlib_formats('svg')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[8.976259993579655, 8.070175438596488, 6.749413632854048, 6.699794874758039, 6.213203932372142, 6.056385112547362, 6.1216545012165415, 6.039524377061438]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-e0ae20f342dc>:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax1.set_yticklabels(['0','200', '400', '600', '800', '1000'], fontsize= 14)\n",
            "<ipython-input-22-e0ae20f342dc>:42: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax2.set_yticklabels(['0.0', '', '4.0', '', '8.0', '', '12.0', '', '16.0'], fontsize=14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
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              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from IPython.display import set_matplotlib_formats\n",
        "\n",
        "set_matplotlib_formats('svg')\n",
        "\n",
        "# Define the data\n",
        "no_transfer = [55.82, 55.82, 55.82]\n",
        "passive = [96.28, 103.19, 161.06]\n",
        "active = [57.42, 57.92, 61.54]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(8, 3.5), dpi=300)\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(no_transfer))\n",
        "width = 0.15\n",
        "\n",
        "# Define the colors\n",
        "colors = ['white', '#FFCC66', '#FF9900']\n",
        "\n",
        "# Define the patterns\n",
        "patterns = ['', '', '']\n",
        "\n",
        "# Create the bar chart\n",
        "p1 = ax.bar(x, no_transfer, width, color=colors[0], edgecolor='black', hatch=patterns[0], alpha=0.8, label='No Transfer')\n",
        "p2 = ax.bar(x + width, passive, width, color=colors[1], edgecolor='black', hatch=patterns[1], alpha=0.8, label='Passive')\n",
        "p3 = ax.bar(x + width*2, active, width, color=colors[2], edgecolor='black', hatch=patterns[2], alpha=0.8, label='Active')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (s)', fontsize=16)\n",
        "ax.set_xlabel('State Size', fontsize=16)\n",
        "ax.yaxis.set_tick_params(labelsize=14)\n",
        "ax.set_xticks(x + width)\n",
        "ax.set_xticklabels(['0.1 KB', '1 KB', '10 KB'], fontsize=14)\n",
        "ax.set_ylim([0, 220])\n",
        "ax.legend(fontsize=14,loc='upper left', ncol = 3)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "from google.colab import files\n",
        "plt.savefig(\"passive-vs-active.png\")\n",
        "files.download(\"passive-vs-active.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "0_2aNSqsVnjQ",
        "outputId": "2c6e7747-dce8-4a7b-9d02-b61982743762"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-ce228fd847e1>:5: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
            "  set_matplotlib_formats('svg')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_dfc02d29-fca9-43ba-9471-9e956b970a4a\", \"passive-vs-active.png\", 83935)"
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the data\n",
        "ratios = ['100%', '50%', '10%', '1%']\n",
        "size_0_1KB = [96.28, 71.34, 59.76, 58.02]\n",
        "size_1KB = [130.19, 114.09, 64.53, 58.46]\n",
        "size_10KB = [161.06, 135.85, 65.21, 59.54]\n",
        "size_100KB = [695.11, 424.28, 117.37, 68.15]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(ratios))\n",
        "width = 0.2\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#666666', '#333333', '#000000']\n",
        "\n",
        "# Create the grouped bar chart\n",
        "p1 = ax.bar(x - 1.5*width, size_0_1KB, width, color=colors[0], alpha=0.8, label='0.1 KB')\n",
        "p2 = ax.bar(x - 0.5*width, size_1KB, width, color=colors[1], alpha=0.8, label='1 KB')\n",
        "p3 = ax.bar(x + 0.5*width, size_10KB, width, color=colors[2], alpha=0.8, label='10 KB')\n",
        "p4 = ax.bar(x + 1.5*width, size_100KB, width, color=colors[3], alpha=0.8, label='100 KB')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('Ratio of Requests', fontsize=20)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(ratios, fontsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "9CmPQ2YAaUnD",
        "outputId": "dd6ff64b-726a-4ce4-b618-9a58c3d87ab2"
      },
      "execution_count": null,
      "outputs": [
        {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "ratios = ['100%', '50%', '10%', '1%']\n",
        "sizes = ['0.1KB', '1KB', '10KB', '100KB']\n",
        "values = [\n",
        "    [96.28, 130.19, 161.06, 695.11],\n",
        "    [71.34, 114.09, 135.85, 424.28],\n",
        "    [59.76, 64.53, 65.21, 117.37],\n",
        "    [58.02, 58.46, 59.54, 68.15]\n",
        "]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#333333', 'gray', 'black']\n",
        "\n",
        "# Create the line chart for each ratio\n",
        "for i in range(len(ratios)):\n",
        "    ax.plot(sizes, values[i], marker='o', markersize=10, color=colors[i], label=ratios[i])\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('State Size', fontsize=20)\n",
        "ax.set_xticklabels(sizes, fontsize=16)\n",
        "ax.set_yticklabels([0, 200, 400, 600, 800], fontsize=16)\n",
        "ax.legend(fontsize=16)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "kX_vZLbFnF9x",
        "outputId": "6e1d2ae8-d7d5-4dfc-8ecf-870044a3423b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-bffc5ede938b>:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(sizes, fontsize=16)\n",
            "<ipython-input-25-bffc5ede938b>:27: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_yticklabels([0, 200, 400, 600, 800], fontsize=16)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "ratios = [0.01, 0.1, 0.5, 1]\n",
        "sizes = [0.1, 1, 10, 100]\n",
        "passive = [[58.02, 58.46, 59.54, 68.15],\n",
        "           [59.76, 64.53, 65.21, 117.37],\n",
        "           [71.34, 114.09, 135.85, 424.28],\n",
        "           [96.28, 130.19, 161.06, 695.11]]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#333333', 'gray', 'black']\n",
        "\n",
        "# Create the line chart\n",
        "for i in range(len(sizes)):\n",
        "    ax.plot(ratios, passive[i], marker='o', markersize=10, color=colors[i], linewidth=2,\n",
        "            label='State size: {} KB'.format(sizes[i]))\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('Ratio of Requests', fontsize=20)\n",
        "ax.set_xscale('log')\n",
        "ax.set_xticks(ratios)\n",
        "ax.set_xticklabels(['0.01', '0.1', '0.5', '1'], fontsize=20)\n",
        "ax.yaxis.set_tick_params(labelsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Add grid lines\n",
        "ax.grid(True, which='major', axis='both', linestyle='--', color='#cccccc')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "-XbPvheMnVFC",
        "outputId": "7a0132b0-4c6c-467b-d70c-78528b0a01cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the data\n",
        "ratios = [0.01, 0.1, 0.5, 1]\n",
        "state_sizes = [0.1, 1, 10]\n",
        "passive = [[58.02, 58.46, 59.54],\n",
        "           [59.76, 64.53, 65.21],\n",
        "           [71.34, 114.09, 135.85],\n",
        "           [96.28, 130.19, 161.06]]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#333333', '#666666', '#bbbbbb']\n",
        "\n",
        "# Create the line chart\n",
        "for i in range(len(ratios)):\n",
        "    ax.plot(state_sizes, passive[i], marker='o', markersize=10, color=colors[i], label=str(int(ratios[i]*100))+'%')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('State Size (KB)', fontsize=20)\n",
        "# ax.set_xscale('log')\n",
        "ax.set_xticks(state_sizes)\n",
        "ax.set_xticklabels(state_sizes, fontsize=20)\n",
        "ax.set_yticks(np.arange(0, 800, 100))\n",
        "ax.set_yticklabels(np.arange(0, 800, 100), fontsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "cljRLp9bo0rd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "ratios = [0.01, 0.1, 0.5, 0.75, 1]\n",
        "sizes = [0.1, 1, 10, 100]\n",
        "passive = [[58.02, 58.46, 59.54, 68.15],\n",
        "           [59.76, 64.53, 65.21, 117.37],\n",
        "           [71.34, 114.09, 135.85, 424.28],\n",
        "           [81.88, 122.32, 147.37, 528.92],\n",
        "           [96.28, 130.19, 161.06, 695.11]]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#999999', '#333333', 'gray', 'black', 'orange']\n",
        "\n",
        "# Create the line chart\n",
        "for i in range(len(sizes)):\n",
        "    ax.plot(ratios, [r[i] for r in passive], marker='o', markersize=10, color=colors[i], linewidth=2,\n",
        "            label='State size: {} KB'.format(sizes[i]))\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('Ratio of Requests', fontsize=20)\n",
        "ax.set_xticks(ratios)\n",
        "ax.set_xticklabels(['0.01', '0.1', '0.5', '0.75', '1'], fontsize=20)\n",
        "ax.yaxis.set_tick_params(labelsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Add grid lines\n",
        "ax.grid(True, which='major', axis='both', linestyle='--', color='#cccccc')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "tsWoezj39UmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from IPython.display import set_matplotlib_formats\n",
        "\n",
        "set_matplotlib_formats('svg')\n",
        "\n",
        "# Define the data\n",
        "ratios = [0.01, 0.1, 0.5, 0.75, 1]\n",
        "sizes = [0.1, 1, 10]\n",
        "passive = [[58.02, 58.46, 59.54],\n",
        "           [59.76, 64.53, 65.21],\n",
        "           [71.34, 114.09, 135.85],\n",
        "           [81.88, 122.32, 147.37],\n",
        "           [96.28, 130.19, 161.06]]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(10, 4.5), dpi=300)\n",
        "\n",
        "# Define the marker shapes\n",
        "markers = ['o', 's', '^', '*', 'x']\n",
        "linestyles = ['-', '--', '-.']\n",
        "\n",
        "# plt.plot(x, y1, label='Line 1', marker='o', linestyle='-')\n",
        "# plt.plot(x, y2, label='Line 2', marker='s', linestyle='--')\n",
        "# plt.plot(x, y3, label='Line 3', marker='^', linestyle='-.')\n",
        "# plt.plot(x, y4, label='Line 4', marker='d', linestyle=':')\n",
        "\n",
        "# Create the line chart\n",
        "for i in range(len(sizes)):\n",
        "    ax.plot(ratios, [r[i] for r in passive], marker=markers[i], linestyle = linestyles[i], markersize=12, color='black', linewidth=2,\n",
        "            label='State size: {} KB'.format(sizes[i]))\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=16)\n",
        "ax.set_xlabel('Ratio of Requests', fontsize=16)\n",
        "ax.set_xticks(ratios)\n",
        "# ax.set_ylim([0,160])\n",
        "ax.set_xticklabels(['0.01', '0.1', '0.5', '0.75', '1'], fontsize=14)\n",
        "ax.yaxis.set_tick_params(labelsize=14)\n",
        "# ax.legend(fontsize=14)\n",
        "\n",
        "\n",
        "\n",
        "# Collect the legend handles and labels\n",
        "handles, labels = ax.get_legend_handles_labels()\n",
        "handles = handles[::-1]\n",
        "labels = labels[::-1]\n",
        "\n",
        "# Create a new legend with the reversed order\n",
        "ax.legend(handles, labels, fontsize = 14)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "from google.colab import files\n",
        "plt.savefig(\"passive-different-ratio.png\")\n",
        "files.download(\"passive-different-ratio.png\")\n"
      ],
      "metadata": {
        "id": "X5WXK5u8Bxd4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "passive = [96.28, 103.19, 161.06, 695.11]\n",
        "active = [57.42, 57.92, 61.54, 75.69]\n",
        "sizes = [0.1, 1, 10, 100]\n",
        "ratios = ['0.1KB', '1KB', '10KB', '100KB']\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the markers\n",
        "markers = ['o', 's', '^', 'd']\n",
        "\n",
        "# Create the line chart for passive method\n",
        "ax.plot(ratios, passive, marker=markers[0], markersize=10, color='black', linewidth=2,\n",
        "        label='Passive')\n",
        "\n",
        "# Create the line chart for active method\n",
        "ax.plot(ratios, active, marker=markers[1], markersize=10, color='black', linewidth=2,\n",
        "        label='Active')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('State Size', fontsize=20)\n",
        "ax.set_xticks(ratios)\n",
        "ax.set_xticklabels(sizes, fontsize=20)\n",
        "ax.yaxis.set_tick_params(labelsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Add grid lines\n",
        "ax.grid(True, which='major', axis='both', linestyle='--', color='#cccccc')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "uNVu4SbMDYKI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the data\n",
        "state_sizes = [0.1, 1, 10, 100]\n",
        "passive = [96.28, 103.19, 161.06, 695.11]\n",
        "active = [57.42, 57.92, 61.54, 75.69]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8), dpi=80)\n",
        "\n",
        "# Define the x locations and width of the bars\n",
        "x = np.arange(len(state_sizes))\n",
        "width = 0.4\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#aaaaaa', '#cccccc']\n",
        "\n",
        "# Create the bar chart for passive data\n",
        "p1 = ax.bar(x - width/2, passive, width, color=colors[0], alpha=0.8, label='Passive')\n",
        "\n",
        "# Create the bar chart for active data\n",
        "p2 = ax.bar(x + width/2, active, width, color=colors[1], alpha=0.8, label='Active')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=20)\n",
        "ax.set_xlabel('State Size (KB)', fontsize=20)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(state_sizes, fontsize=20)\n",
        "ax.yaxis.set_tick_params(labelsize=20)\n",
        "ax.legend(fontsize=20)\n",
        "\n",
        "# Add grid lines\n",
        "ax.grid(True, which='major', axis='y', linestyle='--', color='#cccccc')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "_wn7YrFdD2r8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from IPython.display import set_matplotlib_formats\n",
        "set_matplotlib_formats('svg')\n",
        "\n",
        "# Define the data\n",
        "x = [0, 1, 2, 4, 5, 8, 10]\n",
        "y1 = [55.82, 56.23, 56.89, 57.25, 57.40, 58.34, 58.78]\n",
        "y2 = [56.42, 56.45, 57.17, 57.31, 57.21, 58.50, 58.65]\n",
        "y3 = [56.46, 56.51, 57.41, 58.50, 59.17, 63.43, 65.21]\n",
        "y4 = [57.12, 57.99, 62.11, 67.43, 69.28, 76.95, 82.49]\n",
        "\n",
        "# Set up the plot\n",
        "fig, ax = plt.subplots(figsize=(10, 4.5), dpi=300)\n",
        "markers = ['o', 's', '^', '*', 'x']\n",
        "linestyles = ['-', '--', '-.']\n",
        "\n",
        "# Create the line chart\n",
        "plt.plot(x, y1, marker='*', markersize=10, linestyle='-', color='black', label='No transfer')\n",
        "plt.plot(x, y2, marker='o', markersize=10, linestyle='--', color='black', label='0.1 KB')\n",
        "plt.plot(x, y3, marker='s', markersize=10, linestyle='-.', color='black', label='1 KB')\n",
        "plt.plot(x, y4, marker='^', markersize=10, linestyle=':', color='black', label='10 KB')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_ylabel('Total Execution Time (seconds)', fontsize=16)\n",
        "ax.set_xlabel('Number of Dummy Operators', fontsize=16)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(x, fontsize=14)\n",
        "ax.yaxis.set_tick_params(labelsize=14)\n",
        "ax.set_ylim([50, 90])\n",
        "ax.legend(fontsize=14)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "from google.colab import files\n",
        "plt.savefig(\"number-of-dummy-operators.png\")\n",
        "files.download(\"number-of-dummy-operators.png\")\n"
      ],
      "metadata": {
        "id": "SMumLr_amORf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Data\n",
        "L1 = [184.24, 595.66, 178.82, 321.43, 102.40]\n",
        "L2 = [235.12, 626.73, 193.58, 382.01, 151.82]\n",
        "L3 = [638.99, 979.75, 383.68, 905.18, 500.10]\n",
        "L4 = [689.87, 1010.82, 398.44, 965.76, 549.52]\n",
        "\n",
        "# X-axis labels\n",
        "labels = ['Data1', 'Data2', 'Data3', 'Data4', 'Data5']\n",
        "\n",
        "# Width of each bar\n",
        "width = 0.2\n",
        "\n",
        "# Create the figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plot Bar1\n",
        "x = np.arange(len(labels))\n",
        "ax.bar(x, L1, width, label='L1')\n",
        "\n",
        "# Plot Bar2\n",
        "ax.bar(x, [L3[i] - L1[i] for i in range(len(L1))], width, bottom=L1, label='L3 - L1')\n",
        "\n",
        "# Plot Bar3\n",
        "ax.bar(x + width, L2, width, label='L2', alpha=0.5)\n",
        "\n",
        "# Plot Bar4\n",
        "ax.bar(x + width, [L4[i] - L2[i] for i in range(len(L1))], width, bottom=L2, label='L4 - L2', alpha=0.5)\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_xticks(x + width / 2)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.set_xlabel('Index')\n",
        "ax.set_ylabel('Values')\n",
        "ax.set_title('Bar Chart with L1, L3 - L1, L2, L4 - L2')\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "NpOK638RhFuf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}