mhar_times.ipynb

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  "metadata": {
    "colab": {
      "name": "mhar_times.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0V0bT0JMRgF2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('classic')\n",
        "%matplotlib inline\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YeQ0KTgTZRG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "d7e13fe3-9864-4978-ab56-b77531341fdd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/drive')"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /drive; to attempt to forcibly remount, call drive.mount(\"/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Tf2xauDTtOL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "path = '/drive/My Drive/mhar_test_times_p100/'\n",
        "files = [path + f for f in listdir(path)]"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6g02M_lWMJc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "cf7a4657-4500-4992-d68c-116ac7b83142"
      },
      "source": [
        "df = []\n",
        "\n",
        "for p_df in files:\n",
        "  df_ = pd.read_csv(p_df)\n",
        "  df_['file'] = p_df\n",
        "  df.append(df_)\n",
        "\n",
        "df = pd.concat(df, ignore_index=True)\n",
        "df.head()  "
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>time</th>\n",
              "      <th>thi</th>\n",
              "      <th>gpu</th>\n",
              "      <th>figure</th>\n",
              "      <th>samples_sec</th>\n",
              "      <th>file</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>63.91</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>78,232.81</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/733765323...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2500</td>\n",
              "      <td>1000</td>\n",
              "      <td>128.61</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>77,755.32</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/733765323...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5000</td>\n",
              "      <td>500</td>\n",
              "      <td>241.73</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>20,683.85</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/733765323...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>445.13</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>22,465.34</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/733765323...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>63.91</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>78,229.69</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/464637403...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      n     z  ...  samples_sec                                               file\n",
              "0  2500   500  ...    78,232.81  /drive/My Drive/mhar_test_times_p100/733765323...\n",
              "1  2500  1000  ...    77,755.32  /drive/My Drive/mhar_test_times_p100/733765323...\n",
              "2  5000   500  ...    20,683.85  /drive/My Drive/mhar_test_times_p100/733765323...\n",
              "3  5000  1000  ...    22,465.34  /drive/My Drive/mhar_test_times_p100/733765323...\n",
              "4  2500   500  ...    78,229.69  /drive/My Drive/mhar_test_times_p100/464637403...\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9542uOsXBFN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "outputId": "429bdf54-e6e0-4095-8017-dfe27ba82ff4"
      },
      "source": [
        "df['total_points'] = df['z']*df['thi']\n",
        "df['count'] = 1\n",
        "enough = df.groupby(['figure', 'z', 'total_points', 'n'], as_index=False)['count'].sum()\n",
        "del df['count']\n",
        "df = df.merge(enough,on=['figure', 'z', 'total_points', 'n'])\n",
        "df = df[df['count']>=10]\n",
        "df.head(3)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>time</th>\n",
              "      <th>thi</th>\n",
              "      <th>gpu</th>\n",
              "      <th>figure</th>\n",
              "      <th>samples_sec</th>\n",
              "      <th>file</th>\n",
              "      <th>total_points</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>63.91</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>78,232.81</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/733765323...</td>\n",
              "      <td>5000000</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>63.91</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>78,229.69</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/464637403...</td>\n",
              "      <td>5000000</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>63.91</td>\n",
              "      <td>10000</td>\n",
              "      <td>p100</td>\n",
              "      <td>simplex</td>\n",
              "      <td>78,232.32</td>\n",
              "      <td>/drive/My Drive/mhar_test_times_p100/397896345...</td>\n",
              "      <td>5000000</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "      n    z  ...  total_points  count\n",
              "0  2500  500  ...       5000000     11\n",
              "1  2500  500  ...       5000000     11\n",
              "2  2500  500  ...       5000000     11\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BE8EwsNH-ukE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df = df.sort_values(['figure', 'n', 'z','samples_sec'], ascending=False).groupby(['figure','n','z', 'thi', 'total_points'], as_index=False).head(10)\n",
        "# from shutil import copyfile\n",
        "# for f in df['file']:\n",
        "#   new_f = f.replace('/drive/My Drive/mhar_test_times_2/','')\n",
        "#   new_f = '/drive/My Drive/mhar_test_times_p100/' + new_f\n",
        "#   print(new_f)\n",
        "\n",
        "#   copyfile(f, new_f)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7coMadCdYCYR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "outputId": "bd46fcff-86a9-4eb8-a0bd-bd28b70dbbfe"
      },
      "source": [
        "df_times = df.copy()\n",
        "df_times.rename(columns={'samples_sec':'samples_per_sec_', 'time':'time_',}, \n",
        "                inplace=True)\n",
        "df_times = df_times.groupby(['figure', 'n','z', 'total_points'], as_index=False).agg(\n",
        "    {'samples_per_sec_':['mean', 'std'], 'time_':['mean', 'std']}\n",
        ")\n",
        "df_times.columns = [a[0] + a[1] for a in df_times.columns]\n",
        "df_times.head(1)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>figure</th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>total_points</th>\n",
              "      <th>samples_per_sec_mean</th>\n",
              "      <th>samples_per_sec_std</th>\n",
              "      <th>time_mean</th>\n",
              "      <th>time_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>30000</td>\n",
              "      <td>2,415.78</td>\n",
              "      <td>485.75</td>\n",
              "      <td>13.12</td>\n",
              "      <td>3.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      figure  n  z  ...  samples_per_sec_std  time_mean  time_std\n",
              "0  hypercube  3  1  ...               485.75      13.12      3.84\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6IPjJkCb6xy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "outputId": "db267f45-f01c-4f23-9286-c73149234c71"
      },
      "source": [
        "df_best_times = df_times.groupby(['figure', 'n'],\n",
        "                                 as_index=False)['samples_per_sec_mean'].max()\n",
        "\n",
        "\n",
        "df_best_times= df_times[df_times['samples_per_sec_mean'].isin(\n",
        "    df_best_times['samples_per_sec_mean'])].sort_values(['n'])\n",
        "df_show = df_best_times.copy()\n",
        "df_show = df_show.rename(columns={\n",
        "    'figure':'Figure',\n",
        "    'total_points': 'Points Sampled',\n",
        "}\n",
        ")\n",
        "\n",
        "df_show.head(1)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>Points Sampled</th>\n",
              "      <th>samples_per_sec_mean</th>\n",
              "      <th>samples_per_sec_std</th>\n",
              "      <th>time_mean</th>\n",
              "      <th>time_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,357,073.87</td>\n",
              "      <td>675,444.40</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Figure  n      z  ...  samples_per_sec_std  time_mean  time_std\n",
              "5  hypercube  3  10000  ...           675,444.40      11.84      0.32\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58jy-8Mtkg2X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "outputId": "c7425246-2cc1-40d1-e36f-450e2cb8eaa6"
      },
      "source": [
        "pd.set_option('float_format', '{:,.2f}'.format)\n",
        "df_show.columns = pd.MultiIndex.from_tuples([\n",
        "    ('', 'Figure'), ('', 'n'), ('','z'), ('', 'Points Sampled'),\n",
        "    ('Points Sampled Per Second', 'mean'), ('Points Sampled Per Second', 'std'),\n",
        "                           ('Running Time', 'mean'), ('Running Time', 'std')\n",
        "])\n",
        "df_show.replace( {'simplex':'Simplex', 'hypercube':'Hypercube'}, inplace=True)\n",
        "df_show"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\"></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>Points Sampled</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,357,073.87</td>\n",
              "      <td>675,444.40</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>19,795,014.21</td>\n",
              "      <td>2,628,558.29</td>\n",
              "      <td>15.38</td>\n",
              "      <td>1.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>22,878,783.33</td>\n",
              "      <td>1,258,481.83</td>\n",
              "      <td>13.15</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,206,089.93</td>\n",
              "      <td>376,068.96</td>\n",
              "      <td>22.73</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>15</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,269,548.32</td>\n",
              "      <td>302,854.48</td>\n",
              "      <td>12.36</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>15</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,344,794.68</td>\n",
              "      <td>655,021.48</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>25</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,338,761.06</td>\n",
              "      <td>168,300.75</td>\n",
              "      <td>12.33</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>25</td>\n",
              "      <td>5000</td>\n",
              "      <td>150000000</td>\n",
              "      <td>10,839,474.35</td>\n",
              "      <td>1,236,619.81</td>\n",
              "      <td>14.07</td>\n",
              "      <td>2.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>50</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,425,900.57</td>\n",
              "      <td>16,403.51</td>\n",
              "      <td>22.34</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>50</td>\n",
              "      <td>2500</td>\n",
              "      <td>75000000</td>\n",
              "      <td>5,151,516.81</td>\n",
              "      <td>612,241.73</td>\n",
              "      <td>14.83</td>\n",
              "      <td>2.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>100</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>7,255,837.08</td>\n",
              "      <td>135,616.62</td>\n",
              "      <td>12.41</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>100</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>4,363,525.70</td>\n",
              "      <td>10,619.65</td>\n",
              "      <td>27.50</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>250</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,656,449.22</td>\n",
              "      <td>4,440.59</td>\n",
              "      <td>45.17</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>250</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>1,219,419.53</td>\n",
              "      <td>8,630.27</td>\n",
              "      <td>73.81</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>300</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,315,606.64</td>\n",
              "      <td>1,199.00</td>\n",
              "      <td>51.82</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>500</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>621,554.24</td>\n",
              "      <td>782.70</td>\n",
              "      <td>193.06</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>500</td>\n",
              "      <td>1500</td>\n",
              "      <td>45000000</td>\n",
              "      <td>944,784.52</td>\n",
              "      <td>583.24</td>\n",
              "      <td>47.63</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>1000</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>248,513.69</td>\n",
              "      <td>182.97</td>\n",
              "      <td>482.87</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>1000</td>\n",
              "      <td>500</td>\n",
              "      <td>15000000</td>\n",
              "      <td>329,315.49</td>\n",
              "      <td>556.62</td>\n",
              "      <td>45.55</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>2500</td>\n",
              "      <td>1500</td>\n",
              "      <td>15000000</td>\n",
              "      <td>50,808.74</td>\n",
              "      <td>15.02</td>\n",
              "      <td>295.22</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>5000000</td>\n",
              "      <td>77,312.01</td>\n",
              "      <td>3,045.62</td>\n",
              "      <td>64.78</td>\n",
              "      <td>2.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "      <td>16,161.69</td>\n",
              "      <td>5.92</td>\n",
              "      <td>618.75</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "      <td>22,437.63</td>\n",
              "      <td>62.27</td>\n",
              "      <td>445.68</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             ... Points Sampled Per Second Running Time     \n",
              "        Figure     n      z  ...                       std         mean  std\n",
              "5    Hypercube     3  10000  ...                675,444.40        11.84 0.32\n",
              "61     Simplex     3  10000  ...              2,628,558.29        15.38 1.81\n",
              "67     Simplex     5  10000  ...              1,258,481.83        13.15 0.77\n",
              "11   Hypercube     5  10000  ...                376,068.96        22.73 0.66\n",
              "73     Simplex    15  10000  ...                302,854.48        12.36 0.16\n",
              "17   Hypercube    15  10000  ...                655,021.48        11.84 0.31\n",
              "79     Simplex    25  10000  ...                168,300.75        12.33 0.08\n",
              "22   Hypercube    25   5000  ...              1,236,619.81        14.07 2.28\n",
              "85     Simplex    50  10000  ...                 16,403.51        22.34 0.03\n",
              "27   Hypercube    50   2500  ...                612,241.73        14.83 2.54\n",
              "89     Simplex   100   3000  ...                135,616.62        12.41 0.23\n",
              "34   Hypercube   100   4000  ...                 10,619.65        27.50 0.07\n",
              "95     Simplex   250   4000  ...                  4,440.59        45.17 0.08\n",
              "38   Hypercube   250   3000  ...                  8,630.27        73.81 0.53\n",
              "100    Simplex   300   4000  ...                  1,199.00        51.82 0.03\n",
              "44   Hypercube   500   4000  ...                    782.70       193.06 0.24\n",
              "103    Simplex   500   1500  ...                    583.24        47.63 0.03\n",
              "49   Hypercube  1000   4000  ...                    182.97       482.87 0.36\n",
              "107    Simplex  1000    500  ...                    556.62        45.55 0.08\n",
              "52   Hypercube  2500   1500  ...                     15.02       295.22 0.09\n",
              "111    Simplex  2500    500  ...                  3,045.62        64.78 2.86\n",
              "54   Hypercube  5000   1000  ...                      5.92       618.75 0.23\n",
              "114    Simplex  5000   1000  ...                     62.27       445.68 1.25\n",
              "\n",
              "[23 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0etPAEm87wzA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "36aab756-7b5b-46d3-81b2-c409e2f1f63e"
      },
      "source": [
        "df_show.sort_values([('','Figure'), ('','n')]).to_latex(index=False)\n"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\\\begin{tabular}{lrrrrrrr}\\n\\\\toprule\\n           & \\\\multicolumn{2}{l}{Points Sampled Per Second} & \\\\multicolumn{2}{l}{Running Time} \\\\\\\\\\n    Figure &     n &      z & Points Sampled &                      mean &          std &         mean &  std \\\\\\\\\\n\\\\midrule\\n Hypercube &     3 &  10000 &      300000000 &             25,357,073.87 &   675,444.40 &        11.84 & 0.32 \\\\\\\\\\n Hypercube &     5 &  10000 &      300000000 &             13,206,089.93 &   376,068.96 &        22.73 & 0.66 \\\\\\\\\\n Hypercube &    15 &  10000 &      300000000 &             25,344,794.68 &   655,021.48 &        11.84 & 0.31 \\\\\\\\\\n Hypercube &    25 &   5000 &      150000000 &             10,839,474.35 & 1,236,619.81 &        14.07 & 2.28 \\\\\\\\\\n Hypercube &    50 &   2500 &       75000000 &              5,151,516.81 &   612,241.73 &        14.83 & 2.54 \\\\\\\\\\n Hypercube &   100 &   4000 &      120000000 &              4,363,525.70 &    10,619.65 &        27.50 & 0.07 \\\\\\\\\\n Hypercube &   250 &   3000 &       90000000 &              1,219,419.53 &     8,630.27 &        73.81 & 0.53 \\\\\\\\\\n Hypercube &   500 &   4000 &      120000000 &                621,554.24 &       782.70 &       193.06 & 0.24 \\\\\\\\\\n Hypercube &  1000 &   4000 &      120000000 &                248,513.69 &       182.97 &       482.87 & 0.36 \\\\\\\\\\n Hypercube &  2500 &   1500 &       15000000 &                 50,808.74 &        15.02 &       295.22 & 0.09 \\\\\\\\\\n Hypercube &  5000 &   1000 &       10000000 &                 16,161.69 &         5.92 &       618.75 & 0.23 \\\\\\\\\\n   Simplex &     3 &  10000 &      300000000 &             19,795,014.21 & 2,628,558.29 &        15.38 & 1.81 \\\\\\\\\\n   Simplex &     5 &  10000 &      300000000 &             22,878,783.33 & 1,258,481.83 &        13.15 & 0.77 \\\\\\\\\\n   Simplex &    15 &  10000 &      300000000 &             24,269,548.32 &   302,854.48 &        12.36 & 0.16 \\\\\\\\\\n   Simplex &    25 &  10000 &      300000000 &             24,338,761.06 &   168,300.75 &        12.33 & 0.08 \\\\\\\\\\n   Simplex &    50 &  10000 &      300000000 &             13,425,900.57 &    16,403.51 &        22.34 & 0.03 \\\\\\\\\\n   Simplex &   100 &   3000 &       90000000 &              7,255,837.08 &   135,616.62 &        12.41 & 0.23 \\\\\\\\\\n   Simplex &   250 &   4000 &      120000000 &              2,656,449.22 &     4,440.59 &        45.17 & 0.08 \\\\\\\\\\n   Simplex &   300 &   4000 &      120000000 &              2,315,606.64 &     1,199.00 &        51.82 & 0.03 \\\\\\\\\\n   Simplex &   500 &   1500 &       45000000 &                944,784.52 &       583.24 &        47.63 & 0.03 \\\\\\\\\\n   Simplex &  1000 &    500 &       15000000 &                329,315.49 &       556.62 &        45.55 & 0.08 \\\\\\\\\\n   Simplex &  2500 &    500 &        5000000 &                 77,312.01 &     3,045.62 &        64.78 & 2.86 \\\\\\\\\\n   Simplex &  5000 &   1000 &       10000000 &                 22,437.63 &        62.27 &       445.68 & 1.25 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3huIfwcFV-2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "outputId": "bd53506a-5ac0-4fd5-aee1-d743eefc7b89"
      },
      "source": [
        "df_show[('','mixing_time')] = df_show[('','n')]*df_show[('','n')]\n",
        "df_show[('','one_point')] = df_show[('','mixing_time')]/df_show[\n",
        "                                    ('Points Sampled Per Second','mean')]\n",
        "df_show"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\"></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "      <th colspan=\"2\" halign=\"left\"></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>Points Sampled</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>mixing_time</th>\n",
              "      <th>one_point</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,357,073.87</td>\n",
              "      <td>675,444.40</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.32</td>\n",
              "      <td>9</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>19,795,014.21</td>\n",
              "      <td>2,628,558.29</td>\n",
              "      <td>15.38</td>\n",
              "      <td>1.81</td>\n",
              "      <td>9</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>22,878,783.33</td>\n",
              "      <td>1,258,481.83</td>\n",
              "      <td>13.15</td>\n",
              "      <td>0.77</td>\n",
              "      <td>25</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,206,089.93</td>\n",
              "      <td>376,068.96</td>\n",
              "      <td>22.73</td>\n",
              "      <td>0.66</td>\n",
              "      <td>25</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>15</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,269,548.32</td>\n",
              "      <td>302,854.48</td>\n",
              "      <td>12.36</td>\n",
              "      <td>0.16</td>\n",
              "      <td>225</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>15</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,344,794.68</td>\n",
              "      <td>655,021.48</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.31</td>\n",
              "      <td>225</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>25</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,338,761.06</td>\n",
              "      <td>168,300.75</td>\n",
              "      <td>12.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>625</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>25</td>\n",
              "      <td>5000</td>\n",
              "      <td>150000000</td>\n",
              "      <td>10,839,474.35</td>\n",
              "      <td>1,236,619.81</td>\n",
              "      <td>14.07</td>\n",
              "      <td>2.28</td>\n",
              "      <td>625</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>50</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,425,900.57</td>\n",
              "      <td>16,403.51</td>\n",
              "      <td>22.34</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2500</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>50</td>\n",
              "      <td>2500</td>\n",
              "      <td>75000000</td>\n",
              "      <td>5,151,516.81</td>\n",
              "      <td>612,241.73</td>\n",
              "      <td>14.83</td>\n",
              "      <td>2.54</td>\n",
              "      <td>2500</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>100</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>7,255,837.08</td>\n",
              "      <td>135,616.62</td>\n",
              "      <td>12.41</td>\n",
              "      <td>0.23</td>\n",
              "      <td>10000</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>100</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>4,363,525.70</td>\n",
              "      <td>10,619.65</td>\n",
              "      <td>27.50</td>\n",
              "      <td>0.07</td>\n",
              "      <td>10000</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>250</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,656,449.22</td>\n",
              "      <td>4,440.59</td>\n",
              "      <td>45.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>62500</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>250</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>1,219,419.53</td>\n",
              "      <td>8,630.27</td>\n",
              "      <td>73.81</td>\n",
              "      <td>0.53</td>\n",
              "      <td>62500</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>300</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,315,606.64</td>\n",
              "      <td>1,199.00</td>\n",
              "      <td>51.82</td>\n",
              "      <td>0.03</td>\n",
              "      <td>90000</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>500</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>621,554.24</td>\n",
              "      <td>782.70</td>\n",
              "      <td>193.06</td>\n",
              "      <td>0.24</td>\n",
              "      <td>250000</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>500</td>\n",
              "      <td>1500</td>\n",
              "      <td>45000000</td>\n",
              "      <td>944,784.52</td>\n",
              "      <td>583.24</td>\n",
              "      <td>47.63</td>\n",
              "      <td>0.03</td>\n",
              "      <td>250000</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>1000</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>248,513.69</td>\n",
              "      <td>182.97</td>\n",
              "      <td>482.87</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1000000</td>\n",
              "      <td>4.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>1000</td>\n",
              "      <td>500</td>\n",
              "      <td>15000000</td>\n",
              "      <td>329,315.49</td>\n",
              "      <td>556.62</td>\n",
              "      <td>45.55</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1000000</td>\n",
              "      <td>3.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>2500</td>\n",
              "      <td>1500</td>\n",
              "      <td>15000000</td>\n",
              "      <td>50,808.74</td>\n",
              "      <td>15.02</td>\n",
              "      <td>295.22</td>\n",
              "      <td>0.09</td>\n",
              "      <td>6250000</td>\n",
              "      <td>123.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>5000000</td>\n",
              "      <td>77,312.01</td>\n",
              "      <td>3,045.62</td>\n",
              "      <td>64.78</td>\n",
              "      <td>2.86</td>\n",
              "      <td>6250000</td>\n",
              "      <td>80.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "      <td>16,161.69</td>\n",
              "      <td>5.92</td>\n",
              "      <td>618.75</td>\n",
              "      <td>0.23</td>\n",
              "      <td>25000000</td>\n",
              "      <td>1,546.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "      <td>22,437.63</td>\n",
              "      <td>62.27</td>\n",
              "      <td>445.68</td>\n",
              "      <td>1.25</td>\n",
              "      <td>25000000</td>\n",
              "      <td>1,114.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             ... Running Time                      \n",
              "        Figure     n      z  ...          std mixing_time one_point\n",
              "5    Hypercube     3  10000  ...         0.32           9      0.00\n",
              "61     Simplex     3  10000  ...         1.81           9      0.00\n",
              "67     Simplex     5  10000  ...         0.77          25      0.00\n",
              "11   Hypercube     5  10000  ...         0.66          25      0.00\n",
              "73     Simplex    15  10000  ...         0.16         225      0.00\n",
              "17   Hypercube    15  10000  ...         0.31         225      0.00\n",
              "79     Simplex    25  10000  ...         0.08         625      0.00\n",
              "22   Hypercube    25   5000  ...         2.28         625      0.00\n",
              "85     Simplex    50  10000  ...         0.03        2500      0.00\n",
              "27   Hypercube    50   2500  ...         2.54        2500      0.00\n",
              "89     Simplex   100   3000  ...         0.23       10000      0.00\n",
              "34   Hypercube   100   4000  ...         0.07       10000      0.00\n",
              "95     Simplex   250   4000  ...         0.08       62500      0.02\n",
              "38   Hypercube   250   3000  ...         0.53       62500      0.05\n",
              "100    Simplex   300   4000  ...         0.03       90000      0.04\n",
              "44   Hypercube   500   4000  ...         0.24      250000      0.40\n",
              "103    Simplex   500   1500  ...         0.03      250000      0.26\n",
              "49   Hypercube  1000   4000  ...         0.36     1000000      4.02\n",
              "107    Simplex  1000    500  ...         0.08     1000000      3.04\n",
              "52   Hypercube  2500   1500  ...         0.09     6250000    123.01\n",
              "111    Simplex  2500    500  ...         2.86     6250000     80.84\n",
              "54   Hypercube  5000   1000  ...         0.23    25000000  1,546.87\n",
              "114    Simplex  5000   1000  ...         1.25    25000000  1,114.20\n",
              "\n",
              "[23 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKvoGXdWuE2s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e713f55e-bb05-45b7-85c6-d7e9bc4e03e2"
      },
      "source": [
        "df_pad = df.copy()\n",
        "kws = dict(hue='z')\n",
        "df_pad = df_pad[df_pad['n'].isin([5,25, 50, 100, 500, 1000])]\n",
        "df_pad['samples_sec'] = (df_pad['samples_sec'])/1000\n",
        "df_pad.rename(inplace=True, columns={'samples_sec':'Avg. Samples Per Second'})\n",
        "g = sns.FacetGrid(df_pad, col=\"figure\",  row=\"n\",sharex=False, sharey=False)\n",
        "g.map(sns.boxplot,\"z\",'Avg. Samples Per Second')\n",
        "g.fig.set_size_inches(13,20)\n",
        "#g.savefig('/drive/My Drive/mhar_images/paddig_boxplot.png')"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:723: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x1440 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBCVlMIPvsMr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ad4652eb-6536-42e1-f6f4-87c4404c0280"
      },
      "source": [
        "# Save individuall images \n",
        "df_pad = df.copy()\n",
        "kws = dict(hue='z')\n",
        "df_pad = df_pad[df_pad['n'].isin([5,25, 50, 100, 500, 1000])]\n",
        "df_pad['samples_sec'] = (df_pad['samples_sec'])/1000\n",
        "df_pad.rename(inplace=True, columns={'samples_sec':'Avg. Samples Per Second'})\n",
        "\n",
        "df_pad[['figure', 'n']].drop_duplicates()\n",
        "\n",
        "for fig_ in df_pad['figure'].unique():\n",
        "  df_pad_ = df_pad[df_pad['figure']==fig_]\n",
        "  for n_ in df_pad_['n'].unique():\n",
        "    df_ = df_pad_[df_pad_['n']==n_]\n",
        "    plt.figure(figsize=(10,7)) # Clean Plot\n",
        "    g = sns.boxplot(data=df_, x='z', y='Avg. Samples Per Second')\n",
        "    g.set_title(fig_.capitalize() + '  |  n=' + str(n_))\n",
        "    fig = g.get_figure()\n",
        "    save_name = '/drive/My Drive/mhar_figures_pad/' + 'pad_times_'+ fig_+ '_' + str(n_) + '.png'\n",
        "    fig.savefig(save_name)\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyMsgcAAGBhlD0AAAALo+wBAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyMsgcAAGBhlD0AAAALo+wBAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyMsgcAAGBhlD0AAAALo+wBAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyswcpeWlqaEhMTde2112rv3r3e7QcPHtSECROUlJSkCRMm6NChQ0EdAwAAaEoarOyNGDFCK1asUOfOnWtsnz17tiZNmqTMzExNmjRJqampQR0DAABoShqs7MXHxysmJqbGtuLiYuXl5Sk5OVmSlJycrLy8PJWUlARlDAAAoKkJC+UPLygoUMeOHeVwOCRJDodDHTp0UEFBgQzDCPhYVFRUaH5RAACAEGGCBgAAgIWF9MxeTEyMCgsL5Xa75XA45Ha7VVRUpJiYGBmGEfCxqxUd3ToIvzUAAEDDCWnZi46OVlxcnDIyMpSSkqKMjAzFxcV5L7cGY+xqFBeXy+MxAvcLAwAABJjdbqv1BJXNMIwGaTPz58/Xpk2bdPLkSUVGRsrpdGrDhg3av3+/Zs6cqVOnTqlNmzZKS0tTz549JSkoY1eDsgcAAMzONGWvMaLsAQAAs6ur7DFBAwAAwMIoewAAABZG2QMAALAwyh4AAICFUfYAAAAs7Irr7Hk8Hp92YLfTFwEAAMzqimXve9/7nmw2W5072L17d0ADAQAAIHCuWPbeffdd79dZWVnKzMzU9OnTFRsbq/z8fP3hD3/QyJEjGyQkAAAA6senRZV//OMfa/Xq1WrTpo13W1lZmcaPH6+///3vQQ0YSiyqDAAAzC4giyqfPn1aFRUVNbZVVlbq9OnT/qUDAABAUF3xMu43jRs3Tvfdd5+mTJmiTp066fjx41q+fLnGjRsX7HwAAADwg0+XcT0ej9544w1t3LhRRUVFat++vUaPHq277rpLDoejIXKGBJdxAQCA2dV1GdenstdUUfYAAIDZ1VX2fLqMK0k5OTnavXu3zp49W2P7jBkz6p8OAAAAQeVT2Zs7d67efvttDRw4UC1atAh2JgAAAASIT2UvIyND6enpiomJCXYeAAAABJBPS69ERkbq3/7t34KdBQAAAAHm0wSNVatWKSsrS9OnT1e7du1qjHXt2jVo4UKNCRoAAMDsAjIb97rrrrv8m202Sz8bl7IHAADMjqVX/EDZAwAAZhewpVckKT8/X4WFherUqROTNQAAABoBn8peUVGRfvnLX2rHjh1yOp1yuVzq06ePnnvuOXXs2DHYGQEAAFBPPs3GnTNnjq677jp9/PHHysnJ0ccff6y4uDjNnj072PkAAADgB5/u2Rs4cKBycnLUrFkz77aqqiolJCRo69atQQ0YStyzBwAAzK6ue/Z8OrPXtm1b7d+/v8a2AwcOqE2bNv6lAwAAQFD5dM/eAw88oHvvvVd33HGHYmNjlZ+frzVr1vBcXAAAAJPzeemVf/zjH8rIyFBRUZE6dOig5ORkDRo0KNj5QorLuAAAwOxYZ88PlD0AAGB2Abln7+GHH1Zubm6Nbbm5uXr00Uf9SwcAAICg8qnsbdu2TTfccEONbX379rX0TFwAAAAr8KnshYeHq6Kiosa2s2fPKizsqh7AAQAAgAbmU9kbMmSIUlNTVV5eLkkqLy/X3LlzlZCQENRwAAAA8I9PZW/mzJkqLy9X//79NWjQIA0YMEDl5eWaNWtWsPMBAADAD1c1G/fEiRMqKChQTEyM2rdvH8xcpsBsXAAAYHYBmY0rSaWlpfrwww+1detWtW/fXoWFhTp+/HhAQgIAACA4fCp7H3/8sUaNGqX169frxRdflCQdPnxYc+bMCWY2AAAA+MmnsrdgwQItXrxYr7zyincGbp8+ffT5558HNRwAAAD841PZO3bsmPfRaDabTZLUrFkzud3u4CUDAACA33wqe9dcc42ys7NrbNuyZYt69eoVlFAAAAAIDJ9WRZ45c6amT5+u4cOHq7KyUqmpqXrvvfe89+8BAADAnHxeeqWwsFDr1q1Tfn6+YmJiNHbsWHXq1CnY+UKKpVcAAIDZ1bX0ylWts3dRWVmZ2rZt61ewxoCyBwAAzM6vdfbWrl1b4169Xbt2adiwYfrRj36kpKQkHThwIHBJAQAAEHC1lr1XXnmlxpMynn76aQ0ePFjr1q3T4MGD9cwzzwQ9IAAAAOqv1gkax48f9864LSgo0L59+/Tqq6/K6XTqV7/6lUaOHNkgIQEAAFA/tZ7ZczgcOn/+vCTp008/Vc+ePeV0OiVJLVq0UGVlZfATAgAAoN5qLXsDBgzQ888/rz179mj58uW66aabvGMHDhyocYkXAAAA5lNr2XvqqaeUl5enn/zkJ2rRooUefPBB71h6eroSEhICFmTz5s267bbblJKSorFjx2rTpk2SpIMHD2rChAlKSkrShAkTdOjQIe976jsGAADQVNRr6ZVAMwxDAwYM0IoVK9SrVy/t2bNHP/nJT/TJJ5/o3nvv1fjx45WSkqL09HStXr1ar732miTppz/9ab3GfMXSKwAAwOz8WnqlIdntdp0+fVqSdPr0aXXo0EGlpaXKy8tTcnKyJCk5OVl5eXkqKSlRcXFxvcYAAACaEp8elxZsNptNixcv1s9//nO1bNlSZ86c0csvv6yCggJ17NhRDodD0oUJIx06dFBBQYEMw6jXWFRUVMh+TwAAgIZmijN71dXVeumll/Tiiy9q8+bN+v3vf6/HHntMZ8+eDXU0AACARq3OM3tut1tvvvmmxo4dq/Dw8KCE2L17t4qKitSvXz9JUr9+/dSiRQtFRESosLBQbrdbDodDbrdbRUVFiomJkWEY9Rq7GrVd/wYAAGgM6ix7DodDv/3tb3XHHXcELUSnTp10/PhxHThwQD179tT+/ftVXFys7t27Ky4uThkZGUpJSVFGRobi4uK8l2LrO+YrJmgAAACzq2uChk+zcR9//HGNHj1aiYmJAQ33TevWrdMf/vAH2Ww2SdKjjz6qm2++Wfv379fMmTN16tQptWnTRmlpaerZs6ck1XvMV5Q9AABgdgEpe48++qjee+893XDDDerUqZO3kEmy9PNxKXsAAMDs6ip7Ps3G7dWrl/cZuQAAAGg8TLGosllxZg8AAJhdQM7sSdKHH36oDRs2qKSkRMuWLdPOnTtVXl6uQYMGBSQoAAAAAs+ndfaWL1+uOXPmqEePHtq2bZskqXnz5lqyZElQwwEA0FS5XKVasGCOXC5XqKOgkfOp7P35z3/Wn/70J02bNk12+4W39OzZUwcPHgxqOAAAmqr09NXau3eP0tNXhzoKGjmfyt6ZM2e8CxJfnIlbXV2tZs2aBS8ZAABNlMtVquzsLBmGoezsLM7uwS8+lb3+/fvr5ZdfrrHttdde08CBA4MSCgCApiw9fbUuzp80DA9n9+AXn2bjFhYW6qGHHpLL5VJhYaG6dOmiVq1a6aWXXlL79u0bImdIMBsXABAK06ffq8rKCu/r5s1b6KWXXg1dIJhaQGbjduzYUatXr9bOnTt17NgxxcTE6Prrr/fevwcAAAJn8OAh+uCDzaqurlZYWJgGD04IdSQ0YrW2tQMHDmjixIn64Q9/qJ/+9KeKjIzU6NGj1bdvX4oeAABBkpIy3nuPvM1mV0rK+BAnQmNWa2ObP3++unTpoueff14dOnTQwoULGyoXAABNltMZqYSE4bLZbEpIGC6n0xnqSGjEar2M+8UXX+iDDz5QRESE4uPjlZSU1FC5AABo0lJSxuvYsX9yVg9+q7XsnT9/XhEREZKkVq1aqaqqqkFCAQDQ1DmdkZo1a06oY8ACai17VVVVNZ6SUVlZeclTM2bMmBGcZAAAAPBbrWVvzJgxOn78uPf1rbfeWuM1AAAAzM2ndfaaKtbZAwAAZlfXOnusnwIAAGBhlD0AAAALo+wBAABYGGUPAADAwnwqex999JGOHj0qSSoqKtITTzyhJ598UidOnAhqOAAAAPjHp7L3m9/8Rg6HQ5KUlpam6upq2Ww2/dd//VdQwwEAAMA/ta6zd1FhYaFiY2NVXV2tnJwcvffee2rWrJkSEhKCnQ8AAAB+8KnstW7dWidPntS+fft0zTXXeB+dVl1dHex8AAAA8INPZe+ee+7RHXfcofPnz2vWrFmSpO3bt6tnz55BDQcAAAD/+PwEjYMHD8rhcKhbt27e11VVVbr22muDGjCUeIIGAAAwu4A9QaNLly4qKirSW2+9JUnq2LGjunbt6n9CAAAABI1Pl3G//PJLPfTQQwoPD1dhYaFuueUWbdu2TW+++aYWL14c7IwAAACoJ5/O7M2ZM0ePPvqoNm7cqLCwC/2wf//++uSTT4IaDgAAAP7xqex99dVXSklJkSTZbDZJUsuWLXXu3LngJQMAAIDffCp7nTt31q5du2ps+/zzz72TNQAAAGBOPt2zN2PGDE2fPl0TJ07U+fPn9dJLL2nVqlWaN29esPMBAADADz4vvZKXl6e//OUvys/PV6dOnXTXXXepd+/ewc4XUiy9AgAAzK6upVd8LntNEWUPAACYXV1l74qXcZcsWeLTD5gxY8bVpwIAAECDuGLZO378eEPmAAAEWE7O+8rOzgrIvsrKXJKktm2dfu8rIWG4hgwZ5vd+APjmimVv4cKFDZkDAGBiLlfgyh6AhuXzPXuHDh3S22+/raKiInXo0EGjR49Wjx49ghwvtLhnDwAuWLjwN5KkJ5+cHeIkAL4tIBM01q9fr9TUVA0bNkyxsbEqKChQVlaW5s6dqzFjxgQ0sJlQ9gCEwooVr+rIkcOhjlHD4cOHJEndu/cIaY5v69atu+6++95QxwBCqt4TNL5p8eLFevnll9W/f3/vttzcXP3617+2dNkDgFA4cuSwDny5W+0cPh2iG0SExyNJOvXVvhAn+dpJd3WoIwCNgk9HkjNnzqhv3741tvXp00dnz54NSigAaOraOcJ0uzMq1DFMbY2rJNQRgEbBp8el3XfffXruuee8z8KtrKzU888/r/vuuy+o4QAAAOAfn87svf766zp58qSWL1+uNm3a6NSpUzIMQ+3bt9fKlSu935eVlRWsnAAAAKgHn8reokWLgp0DAAAAQeBT2RswYECwcwAA/qWszKXi6mruSavDyepqGf9a7BnAlflU9qqrq5WRkaHdu3dfMilj3rx5QQkGAAAA//lU9h5//HHt3btXQ4cOVXR0dFCCnDt3TgsWLNA//vEPRUREqG/fvpo3b54OHjyomTNnyuVyyel0Ki0tzbuYc33HAMDM2rZ1ynbiBLNx67DGVaI2PNEDqJNPZS87O1tZWVlq3frKC/b5a9GiRYqIiFBmZqZsNptOnjwpSZo9e7YmTZqklJQUpaenKzU1Va+99ppfYwAAAE2FT0uvfOc731FZWVnQQpw5c0Zr167VjBkzZLPZJEnt2rVTcXGx8vLylJycLElKTk5WXl6eSkpK6j0GAADQlPg8G/fpp5/WjTfeqHbt2tUYu+222/wOcfToUTmdTr3wwgvaunWrWrVqpRkzZqh58+bq2LGjHA6HJMnhcKhDhw4qKCiQYRj1GouK4rIIAPM76TbXBI2z/3qCRku7T+cIGsRJd7XahDoE0Aj4VPbWrFmj3NxclZWVqXnz5t7tNpstIGXP7Xbr6NGj+t73vqcnnnhCn332mX72s59pyZIlfu8bABqbbt26hzrCJUr/9WzcTiZ6Nm4bmfOzAszGp7L32muvae3atbrmmmuCEiImJkZhYWHey659+vRRZGSkmjdvrsLCQrndbjkcDrndbhUVFSkmJkaGYdRr7GrU9lBhAAiWxx57JCD7effdd7Vp06aA7Mtuv3CLTbNmDr/3NXLkSI0YMcLv/QDwjU9lr127dlddlK5GVFSUBg4cqA8//FBDhgzRwYMHVVxcrB49eiguLk4ZGRlKSUlRRkaG4uLivJdi6zvmq+Licnk8RsB/XwBoCKdOVej8eXdA9tWmTVtJCsj+Tp2q0IkTp/3eD4AL7HZbrSeobIZh1NlmXn/9dX344Yd68MEHL1l6pWvXrv6n1IX79mbNmkjQ2XUAABXGSURBVCWXy6WwsDA99thjGjZsmPbv36+ZM2fq1KlTatOmjdLS0tSzZ09JqveYryh7AADA7AJS9q677rrLv9lm0+7du+ufzuQoewAAwOwCUvaaKsoeAAAwu7rKnnnm0AMAACDgfH427uuvv65t27aptLRU3zwZuGLFiqCFAwAAgH98OrO3cOFCvfHGG4qPj9cXX3yhkSNHqri4WD/60Y+CnQ8AAAB+8Knsbdq0SX/4wx80ZcoUORwOTZkyRUuXLtXWrVuDnQ8AAAB+8KnsVVZWetfZa968uSoqKnTNNdcoLy8vqOEAAADgH5/u2bvmmmu0c+dOXX/99erdu7d+97vfqXXr1urYsWOw8wEAAMAPPi298vnnn8vhcOj73/++Dh06pDlz5ujMmTN64oknFB8f3xA5Q4KlVwAAgNmxzp4fKHsAAMDs6ip7tV7GPXbsmBwOhzp16iRJqqio0LJly7R3717dcMMNuv/+++Vw+P9QbAAAAARHrRM0nnrqKe3cudP7eu7cudqwYYN69Oih1atXa8mSJUEPCAAAgPqr9TLuoEGD9O6776ply5Y6e/asBg0apBUrVqh3797av3+/pk2bpnfffbch8zYoLuMCAACz8+txaefPn1fLli0lSTt37lSrVq3Uu3dvSRdm6JaWlgYwKgAAAAKt1rLXpUsX78LJ7733ngYOHOgdKykpUYsWLYKbDgAAAH6pdYLGww8/rF/84hfq2rWrDhw4oOXLl3vH3n33Xf3gBz8IekAAAADUX51Lrxw5ckS7d+/W9773PXXt2tW7/ZNPPlGbNm303e9+N+ghQ4V79gAAgNmxzp4fKHsAAMDs/JqgAQAAgMaNsgcAAGBhlD0AAAALo+wBAABYmF9lb9q0aYHKAQAAgCDwq+z169cvUDkAAAAQBCy9UguWXgEAAGZX19IrtT5B46KjR49ednt4eLjat28vu51b/wAAAMzIp7L34x//WDabTZJkGIb3a0my2+1KTEzU7Nmz1a5du+CkBAAAQL34dBn3r3/9qz7++GM98sgj6tSpkwoKCvT73/9effv2Vf/+/fXss8+qWbNm+p//+Z+GyNxguIwLAADMLiCPSxs6dKjeeecdRUREeLdVVFQoKSlJH3zwgcrKyjRy5Eht3bo1MKlNgrIHAADMLiCPS/N4PPrnP/9ZY1t+fr48Ho8kqUWLFnK73X7EBAAAQDD4dM/elClTNGXKFI0fP16dOnXS8ePHtWbNGv30pz+VJH3wwQfq27dvUIMCAADg6vm89MoHH3ygjRs3qqioSO3bt9fo0aM1dOjQYOcLKS7jAgAAswvIPXslJSWKiooKaLDGgLIHAADMLiD37N1000168MEHtW7dOlVUVAQsHAAAAILL5zN7b7/9tjIyMrRnzx7ddNNNSk5O1tChQxUW5tNtf40SZ/YAAIDZBeQy7jcdO3ZMGzZs0Pr163XixAl99NFHfoc0K8oeAAAwu4Bcxv2m4uJinTx5UqWlpWrTpo1f4QAAABBcPl2D/eqrr5SRkaENGzaosrJSo0eP1osvvqjrr78+2PkAAADgB58u4/bv318jR45UcnKyBg4cKLv9wglBj8fj/dqKuIwLAADMLiD37FVVVSk8PNz7+ssvv9TatWu1fv165eTkBCapCVH2AACA2dVV9ny6jBseHq6SkhKtX79ea9eu1Z49exQfH6+nnnoqYEEBAAAQeLWWvfPnz+u9997Tm2++qZycHHXr1k233nqr8vPztXjxYkVHRzdUTgAAANRDrWXvxhtvlM1m0+23365HHnlE3//+9yVJK1eubJBwAAAA8E+tsyuuvfZanT59Wp999pl27typsrKyhsoFAACAAKhzgsaxY8e0du1apaenKz8/X0OGDNHHH3+st99+Wx07dmyonCHBBA0AAGB2AX2CRm5urtLT0/X222/L4XBo/Pjx+vWvfx2QoGZE2QMAAGYX8MelSdK5c+f0zjvvaO3atfrjH//oV0Azo+wBAACzC0rZayooewAAwOwC/mzcYHvhhRd07bXXau/evZKkHTt2aOzYsUpKStLUqVNVXFzs/d76jgEAADQVpip7X3zxhXbs2KHOnTtLuvA4tscff1ypqanKzMxUfHy8nn32Wb/GAAAAmhLTlL2qqirNnTtXc+bM8W7btWuXIiIiFB8fL0maOHGiNm7c6NcYAABAU2KasrdkyRKNHTtWXbp08W4rKChQbGys93VUVJQ8Ho9cLle9xwAAAJoSU5S9Tz/9VLt27dKkSZNCHQUAcBkuV6kWLJjDP5qBRqjWx6U1lG3btmn//v0aMWKEJOn48eO6//77NXnyZOXn53u/r6SkRHa7XU6nUzExMfUauxq1zWwBgKbkjTf+rL1792jTpnX6xS9+Eeo4AK6CKcretGnTNG3aNO/rxMRELVu2TN/5znf0l7/8Rbm5uYqPj9eqVas0atQoSVLv3r1VWVl51WNXg6VXAODCWb133nlHhmFo06Z3NHLk2Kv+xzOA4Klr6RVTlL0rsdvteuaZZzR79mydO3dOnTt31qJFi/waAwBcnfT01bq4JKtheJSevlpTptwf4lQAfMWiyrXgzB4ASNOn36vKygrv6+bNW+ill14NXSAANTS6RZUBAOYyePAQhYVduBAUFhamwYMTQpwIwNWg7AEAapWSMl42m02SZLPZlZIyPsSJAFwNyh4AoFZOZ6QSEobLZrMpIWE4kzOARsbUEzQAAOaQkjJex479k7N6QCPEBI1aMEEDAACYHRM0AAAAmjDKHgAAgIVR9gAAACyMsgcAAGBhlD0AAAALo+wBAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyMsgcAAGBhlD0AAAALo+wBAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyMsgcAAGBhlD0AAAALo+wBAABYGGUPAADAwih7AAAAFkbZAwAAsDDKHgAAgIVR9gAAACyMsgcAAGBhYaEOAKBpyMl5X9nZWQHZV1mZS5LUtq3T730lJAzXkCHD/N4PAJgVZQ9Ao+NyBa7sAYDV2QzDMEIdwqyKi8vl8fDxoGlbseJVHTlyONQxajh8+JAkqXv3HiHN8W3dunXX3XffG+oYAJoYu92m6OjWVxznzB6AWu3c+ZkKjufLFmaeW3yNf/0j7Mv9e0Kc5GtGtcd7eRkAzISyB6BOtjC7wpwRoY5hatWuc6GOAACXZZ5/qgMwJTPeF+eprJansjrUMS5hxs8KADizB6BW3bp1D8h+yspc3okV/jp3zi1JCqtu5ve+nE5nYEpadOA+KwAIJCZo1IIJGkDgsPQKAARHXRM0KHu1oOwBAACzq6vscc8eAACAhVH2AAAALIyyBwAAYGGUPQAAAAuj7AEAAFiYKcpeaWmpHnzwQSUlJWnMmDF6+OGHVVJSIknasWOHxo4dq6SkJE2dOlXFxcXe99V3DAAAoKkwRdmz2Wx64IEHlJmZqfXr16tr16569tln5fF49Pjjjys1NVWZmZmKj4/Xs88+K0n1HgMAAGhKTFH2nE6nBg4c6H3dt29f5efna9euXYqIiFB8fLwkaeLEidq4caMk1XsMAACgKTFF2fsmj8ejlStXKjExUQUFBYqNjfWORUVFyePxyOVy1XsMAACgKTFd2Zs3b55atmype+65J9RRAAAAGr2wUAf4prS0NB0+fFjLli2T3W5XTEyM8vPzveMlJSWy2+1yOp31HrsatT16BAAAoDEwTdl77rnntGvXLr388ssKDw+XJPXu3VuVlZXKzc1VfHy8Vq1apVGjRvk1djV4Ni4AADC7up6NazMMI+RtZt++fUpOTlaPHj3UvHlzSVKXLl20dOlSbd++XbNnz9a5c+fUuXNnLVq0SO3atZOkeo/5irIHAADMrlGUPbOi7AEAALOrq+yZboIGAAAAAoeyBwAAYGGUPQAAAAuj7AEAAFgYZQ8AAMDCKHsAAAAWRtkD0Oi4XKVasGAOz7sGAB9Q9gA0Ounpq7V37x6lp68OdRQAMD3KHoBGxeUqVXZ2lgzDUHZ2Fmf3AKAOlD0AjUp6+mpdfPCPYXg4uwcAdaDsAWhUtmzJUXV1tSSpurpaW7ZkhzgRAJgbZQ9AozJ48BCFhYVJksLCwjR4cEKIEwGAuVH2ADQqKSnjZbPZJEk2m10pKeNDnAgAzI2yB6BRcTojlZAwXDabTQkJw+V0OkMdCQBMLSzUAYCrsWLFqzpy5LDf+ykrc5lyFqfT6VTbtoEpL926ddfdd98bkH2ZTUrKeB079k/O6gGADyh7aFSOHDmsQ/t2q1PrZn7t53yVW54qT4BSBc551zlVnj3p936Ol58PQBrzcjojNWvWnFDHAIBGgbKHRqWszKVzbkMFJioz7n8tA+L4131k/jhd5dHpAJTQKrehsjLznbkEADQ8yh4alejodgG5/Op2V3uX7/CXx3Oh7Hns/pe9sLAwORz+/9+yuS58VgAA2IyLq5PiEsXF5d7/kMNacnLeV3Z2VkD2dfEMWiDutUtIGK4hQ4b5vR8AQNNht9sUHd36iuOUvVpQ9gAAgNnVVfZYegUAAMDCKHsAAAAWRtkDAACwMMoeAACAhVH2AAAALIyyBwAAYGGUPcBPLlepFiyYY8pn7QIAQNkD/JSevlp79+5RevrqUEcBAOASlD3ADy5XqbKzs2QYhrKzszi7BwAwHcoe4If09NW6+BAaw/Bwdg8AYDqUPcAPW7bkqLq6WpJUXV2tLVuyQ5wIAICaKHuAHwYPHqKwsDBJUlhYmAYPTghxIgAAaqLsAX5ISRkvm80mSbLZ7EpJGR/iRAAA1ETZA/zgdEYqIWG4bDabEhKGy+l0hjoSAAA1hIU6ANDYpaSM17Fj/+SsHgDAlGzGxamEuERxcbk8Hj4eAABgXna7TdHRra883oBZAAAA0MAoewAAABZG2QMAALAwyh4AAICFUfYAAAAsjLIHAABgYZQ9AAAAC6PsAQAAWBhlDwAAwMIoewAAABZG2QMAALAwS5e9gwcPasKECUpKStKECRN06NChUEcCAABoUJYue7Nnz9akSZOUmZmpSZMmKTU1NdSRgs7lKtWCBXPkcrlCHQUAAJiAZctecXGx8vLylJycLElKTk5WXl6eSkpKQpwsuNLTV2vv3j1KT18d6igAAMAELFv2CgoK1LFjRzkcDkmSw+FQhw4dVFBQEOJkweNylSo7O0uGYSg7O4uzewAAQGGhDmBmdrut1vHc3K16882/+f1zzp+vktvt9ns/khQZGen9+v/9v//ya18Oh0PNmoX7G0mSNG7cHYqPHxiQfQEAgK/V1VcsW/ZiYmJUWFgot9sth8Mht9utoqIixcTE+LyPyMhWtY4nJY1QUtIIf6MCAAAEjWUv40ZHRysuLk4ZGRmSpIyMDMXFxSkqKirEyQAAABqOzTAMI9QhgmX//v2aOXOmTp06pTZt2igtLU09e/YMdSwAAIAGY+myBwAA0NRZ9jIuAAAAKHsAAACWRtkDAACwMMoeAACAhVH2AAAALIyyBwAAYGGUPYtIS0tTYmKirr32Wu3duzfUcSwlMTFRo0aNUkpKilJSUpSdnS1J2rFjh8aOHaukpCRNnTpVxcXF3vfUNoZLXenv78GDBzVhwgQlJSVpwoQJOnTokN9juKC0tFQPPvigkpKSNGbMGD388MMqKSmRJF177bUaM2aM9+/8l19+6X3fe++9p1GjRunHP/6xHnvsMVVUVPg0hgsCfTzhWFNTQx5LGtVxxoAlbNu2zcjPzzduuukm48svvwx1HEu53GfqdruNm2++2di2bZthGIaxdOlSY+bMmXWO4fKu9Pd38uTJxtq1aw3DMIy1a9cakydP9nsMF5SWlhofffSR9/Vvf/tb48knnzQMwzB69epllJeXX/Ke8vJyY/DgwcbBgwcNwzCMWbNmGb/73e/qHMPXAnk84VhzqYY8ljSm4wxlz2Ioe4F3uc/0s88+M2699Vbv6+LiYqNv3751jqF23/ysT548afTr18+orq42DMMwqqurjX79+hnFxcX1HsOVbdy40ZgyZYphGFcue2+99ZYxbdo07+vPP//cuOWWW+ocw9cCeTzhWHNlwT6WNLbjTFiozywCjcF//ud/yjAM9evXT7/85S9VUFCg2NhY73hUVJQ8Ho9cLletY06nMxTxG6WCggJ17NhRDodDkuRwONShQwcVFBTIMIx6jfFs7MvzeDxauXKlEhMTvdsmT54st9utoUOH6pFHHlF4ePglf7djY2NVUFAgSbWOoaZAHU841vgmGMeSxnac4Z49oA4rVqzQunXrtHr1ahmGoblz54Y6EhBQ8+bNU8uWLXXPPfdIkrKysrRmzRqtWLFCX331lZYuXRrihNbB8QShQNkD6hATEyNJCg8P16RJk7R9+3bFxMQoPz/f+z0lJSWy2+1yOp21jsF3MTExKiwslNvtliS53W4VFRUpJiam3mO4VFpamg4fPqzFixfLbr/wn4SLn1Xr1q115513avv27d7t3/y7nZ+f7/3e2sbwtUAeTzjW+CYYx5LGdpyh7AG1OHv2rE6fPi1JMgxDb731luLi4tS7d29VVlYqNzdXkrRq1SqNGjVKkmodg++io6MVFxenjIwMSVJGRobi4uIUFRVV7zHU9Nxzz2nXrl1aunSpwsPDJUllZWWqrKyUJFVXVyszM1NxcXGSpISEBO3cudM763DVqlUaPXp0nWO4INDHE441vgnGsaSxHWdshmEYoQ4B/82fP1+bNm3SyZMnFRkZKafTqQ0bNoQ6VqN39OhRPfLII3K73fJ4PLrmmmv09NNPq0OHDtq+fbtmz56tc+fOqXPnzlq0aJHatWsnSbWO4VJX+vu7f/9+zZw5U6dOnVKbNm2Ulpamnj17SlK9x3DBvn37lJycrB49eqh58+aSpC5duuiBBx5QamqqbDabqqurdcMNN2jWrFlq1aqVJOnvf/+7Fi1aJI/Ho7i4OP32t79Vy5Yt6xxDcI4nHGtqashjSWM6zlD2AAAALIzLuAAAABZG2QMAALAwyh4AAICFUfYAAAAsjLIHAABgYZQ9AAAAC6PsAQAAWBhlDwAAwMIoewAAABYWFuoAANAUvPXWW3rqqae8r8+fP68bbrhBy5cvD2EqAE0Bj0sDgAZWXl6uO++8U1OmTNHEiRNDHQeAxXEZFwAakMfj0a9+9SsNGDCAogegQVD2AKABPf/88zpz5oyefvrpUEcB0ERwzx4ANJANGzZow4YN+tvf/qZmzZqFOg6AJoJ79gCgAeTl5Wnq1Kn605/+pLi4uFDHAdCEcGYPABrAu+++q1OnTmnSpEnebf369dMf//jHEKYC0BRwZg8AAMDCmKABAABgYZQ9AAAAC6PsAQAAWBhlDwAAwMIoewAAABZG2QMAALAwyh4AAICFUfYAAAAsjLIHAABgYf8fTEkbrkTCWlAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRlQS9NGyHEQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1215d78-628d-4496-913e-62672bc6cba8"
      },
      "source": [
        "dir(g)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getstate__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setstate__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_add_text',\n",
              " '_adjustable',\n",
              " '_agg_filter',\n",
              " '_alpha',\n",
              " '_anchor',\n",
              " '_animated',\n",
              " '_aspect',\n",
              " '_autoscaleXon',\n",
              " '_autoscaleYon',\n",
              " '_autotitlepos',\n",
              " '_axes',\n",
              " '_axes_class',\n",
              " '_axes_locator',\n",
              " '_axisbelow',\n",
              " '_clipon',\n",
              " '_clippath',\n",
              " '_contains',\n",
              " '_convert_dx',\n",
              " '_current_image',\n",
              " '_default_contains',\n",
              " '_facecolor',\n",
              " '_frameon',\n",
              " '_gci',\n",
              " '_gen_axes_patch',\n",
              " '_gen_axes_spines',\n",
              " '_get_axis_list',\n",
              " '_get_axis_map',\n",
              " '_get_clipping_extent_bbox',\n",
              " '_get_lines',\n",
              " '_get_patches_for_fill',\n",
              " '_get_view',\n",
              " '_gid',\n",
              " '_gridOn',\n",
              " '_in_layout',\n",
              " '_init_axis',\n",
              " '_label',\n",
              " '_layoutbox',\n",
              " '_left_title',\n",
              " '_make_twin_axes',\n",
              " '_mouseover',\n",
              " '_mouseover_set',\n",
              " '_navigate',\n",
              " '_navigate_mode',\n",
              " '_oid',\n",
              " '_on_units_changed',\n",
              " '_originalPosition',\n",
              " '_parse_scatter_color_args',\n",
              " '_path_effects',\n",
              " '_pcolorargs',\n",
              " '_picker',\n",
              " '_position',\n",
              " '_poslayoutbox',\n",
              " '_process_unit_info',\n",
              " '_prop_order',\n",
              " '_propobservers',\n",
              " '_quiver_units',\n",
              " '_rasterization_zorder',\n",
              " '_rasterized',\n",
              " '_remove_legend',\n",
              " '_remove_method',\n",
              " '_request_autoscale_view',\n",
              " '_right_title',\n",
              " '_sci',\n",
              " '_set_artist_props',\n",
              " '_set_gc_clip',\n",
              " '_set_lim_and_transforms',\n",
              " '_set_position',\n",
              " '_set_title_offset_trans',\n",
              " '_set_view',\n",
              " '_set_view_from_bbox',\n",
              " '_shared_x_axes',\n",
              " '_shared_y_axes',\n",
              " '_sharex',\n",
              " '_sharey',\n",
              " '_sketch',\n",
              " '_snap',\n",
              " '_stale',\n",
              " '_stale_viewlim_x',\n",
              " '_stale_viewlim_y',\n",
              " '_sticky_edges',\n",
              " '_subplotspec',\n",
              " '_tight',\n",
              " '_transform',\n",
              " '_transformSet',\n",
              " '_twinned_axes',\n",
              " '_unstale_viewLim',\n",
              " '_update_image_limits',\n",
              " '_update_line_limits',\n",
              " '_update_patch_limits',\n",
              " '_update_title_position',\n",
              " '_update_transScale',\n",
              " '_url',\n",
              " '_use_sticky_edges',\n",
              " '_validate_converted_limits',\n",
              " '_viewLim',\n",
              " '_visible',\n",
              " '_xaxis_transform',\n",
              " '_xcid',\n",
              " '_xmargin',\n",
              " '_yaxis_transform',\n",
              " '_ycid',\n",
              " '_ymargin',\n",
              " 'acorr',\n",
              " 'add_artist',\n",
              " 'add_callback',\n",
              " 'add_child_axes',\n",
              " 'add_collection',\n",
              " 'add_container',\n",
              " 'add_image',\n",
              " 'add_line',\n",
              " 'add_patch',\n",
              " 'add_table',\n",
              " 'aname',\n",
              " 'angle_spectrum',\n",
              " 'annotate',\n",
              " 'apply_aspect',\n",
              " 'arrow',\n",
              " 'artists',\n",
              " 'autoscale',\n",
              " 'autoscale_view',\n",
              " 'axes',\n",
              " 'axhline',\n",
              " 'axhspan',\n",
              " 'axis',\n",
              " 'axison',\n",
              " 'axvline',\n",
              " 'axvspan',\n",
              " 'bar',\n",
              " 'barbs',\n",
              " 'barh',\n",
              " 'bbox',\n",
              " 'boxplot',\n",
              " 'broken_barh',\n",
              " 'bxp',\n",
              " 'callbacks',\n",
              " 'can_pan',\n",
              " 'can_zoom',\n",
              " 'change_geometry',\n",
              " 'child_axes',\n",
              " 'cla',\n",
              " 'clabel',\n",
              " 'clear',\n",
              " 'clipbox',\n",
              " 'cohere',\n",
              " 'colNum',\n",
              " 'collections',\n",
              " 'containers',\n",
              " 'contains',\n",
              " 'contains_point',\n",
              " 'contour',\n",
              " 'contourf',\n",
              " 'convert_xunits',\n",
              " 'convert_yunits',\n",
              " 'csd',\n",
              " 'dataLim',\n",
              " 'drag_pan',\n",
              " 'draw',\n",
              " 'draw_artist',\n",
              " 'end_pan',\n",
              " 'errorbar',\n",
              " 'eventplot',\n",
              " 'eventson',\n",
              " 'figbox',\n",
              " 'figure',\n",
              " 'fill',\n",
              " 'fill_between',\n",
              " 'fill_betweenx',\n",
              " 'findobj',\n",
              " 'fmt_xdata',\n",
              " 'fmt_ydata',\n",
              " 'format_coord',\n",
              " 'format_cursor_data',\n",
              " 'format_xdata',\n",
              " 'format_ydata',\n",
              " 'get_adjustable',\n",
              " 'get_agg_filter',\n",
              " 'get_alpha',\n",
              " 'get_anchor',\n",
              " 'get_animated',\n",
              " 'get_aspect',\n",
              " 'get_autoscale_on',\n",
              " 'get_autoscalex_on',\n",
              " 'get_autoscaley_on',\n",
              " 'get_axes_locator',\n",
              " 'get_axisbelow',\n",
              " 'get_children',\n",
              " 'get_clip_box',\n",
              " 'get_clip_on',\n",
              " 'get_clip_path',\n",
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              " 'get_yaxis_text1_transform',\n",
              " 'get_yaxis_text2_transform',\n",
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              " 'get_ygridlines',\n",
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              " 'get_ylim',\n",
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              " 'hlines',\n",
              " 'ignore_existing_data_limits',\n",
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              " 'indicate_inset_zoom',\n",
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              " 'invert_xaxis',\n",
              " 'invert_yaxis',\n",
              " 'is_first_col',\n",
              " 'is_first_row',\n",
              " 'is_last_col',\n",
              " 'is_last_row',\n",
              " 'is_transform_set',\n",
              " 'label_outer',\n",
              " 'legend',\n",
              " 'legend_',\n",
              " 'lines',\n",
              " 'locator_params',\n",
              " 'loglog',\n",
              " 'magnitude_spectrum',\n",
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              " 'phase_spectrum',\n",
              " 'pick',\n",
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              " 'pie',\n",
              " 'plot',\n",
              " 'plot_date',\n",
              " 'properties',\n",
              " 'psd',\n",
              " 'quiver',\n",
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              " 'redraw_in_frame',\n",
              " 'relim',\n",
              " 'remove',\n",
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              " 'rowNum',\n",
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              " 'secondary_xaxis',\n",
              " 'secondary_yaxis',\n",
              " 'semilogx',\n",
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              " 'set',\n",
              " 'set_adjustable',\n",
              " 'set_agg_filter',\n",
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              " 'set_aspect',\n",
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              " 'set_prop_cycle',\n",
              " 'set_rasterization_zorder',\n",
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              " 'set_sketch_params',\n",
              " 'set_snap',\n",
              " 'set_subplotspec',\n",
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              " 'set_visible',\n",
              " 'set_xbound',\n",
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              " 'set_xticklabels',\n",
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              " 'set_ylabel',\n",
              " 'set_ylim',\n",
              " 'set_ymargin',\n",
              " 'set_yscale',\n",
              " 'set_yticklabels',\n",
              " 'set_yticks',\n",
              " 'set_zorder',\n",
              " 'specgram',\n",
              " 'spines',\n",
              " 'spy',\n",
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              " 'stale',\n",
              " 'stale_callback',\n",
              " 'start_pan',\n",
              " 'stem',\n",
              " 'step',\n",
              " 'sticky_edges',\n",
              " 'streamplot',\n",
              " 'table',\n",
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              " 'titleOffsetTrans',\n",
              " 'transAxes',\n",
              " 'transData',\n",
              " 'transLimits',\n",
              " 'transScale',\n",
              " 'tricontour',\n",
              " 'tricontourf',\n",
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              " 'triplot',\n",
              " 'twinx',\n",
              " 'twiny',\n",
              " 'update',\n",
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              " 'update_from',\n",
              " 'update_params',\n",
              " 'use_sticky_edges',\n",
              " 'viewLim',\n",
              " 'violin',\n",
              " 'violinplot',\n",
              " 'vlines',\n",
              " 'xaxis',\n",
              " 'xaxis_date',\n",
              " 'xaxis_inverted',\n",
              " 'xcorr',\n",
              " 'yaxis',\n",
              " 'yaxis_date',\n",
              " 'yaxis_inverted',\n",
              " 'zorder']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQ_eTAoxH_5n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "ffc172b8-08b3-4ae0-e238-00a5b00ee3af"
      },
      "source": [
        "\n",
        "df"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>n</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1308 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         n      z  ...  total_points  count\n",
              "0     2500    500  ...       5000000     11\n",
              "1     2500    500  ...       5000000     11\n",
              "2     2500    500  ...       5000000     11\n",
              "3     2500    500  ...       5000000     11\n",
              "4     2500    500  ...       5000000     11\n",
              "...    ...    ...  ...           ...    ...\n",
              "1372    50  10000  ...     300000000     10\n",
              "1373    50  10000  ...     300000000     10\n",
              "1374    50  10000  ...     300000000     10\n",
              "1375    50  10000  ...     300000000     10\n",
              "1376    50  10000  ...     300000000     10\n",
              "\n",
              "[1308 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zIc7NB_G96t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "8049c475-e46a-4116-fb2c-7f2c823ae9c5"
      },
      "source": [
        "path = '/drive/My Drive/tommi_times/'\n",
        "files = [path + f for f in listdir(path)]\n",
        "\n",
        "har = []\n",
        "for f in files:\n",
        "  har.append(pd.read_csv(f))\n",
        "\n",
        "har = pd.concat(har, ignore_index=True)\n",
        "har.columns = [c.lower() for c in har.columns]\n",
        "har['total_points'] = har['iter'] * har['samples']\n",
        "har['samples_sec'] = har['total_points'] / har['time']\n",
        "har.rename(inplace=True,\n",
        "           columns={\n",
        "               'dim':'n',\n",
        "               'type':'figure'\n",
        "           })\n",
        "har['figure'] = har['figure'].apply(lambda x: 'hypercube' if x==1 else 'simplex')\n",
        "har"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>30000</td>\n",
              "      <td>279,877.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>0.05</td>\n",
              "      <td>hypercube</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000</td>\n",
              "      <td>592,960.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>0.03</td>\n",
              "      <td>hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000</td>\n",
              "      <td>1,007,616.39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         time     figure    n  ...  thinning  total_points  samples_sec\n",
              "0   28,866.49    simplex  250  ...     30000         30000         1.04\n",
              "1      528.40    simplex  100  ...     30000         30000        56.77\n",
              "2       27.96    simplex   50  ...     30000         30000     1,072.88\n",
              "3        1.64    simplex   25  ...     30000         30000    18,266.74\n",
              "4        0.03    simplex    5  ...     30000         30000   993,385.18\n",
              "..        ...        ...  ...  ...       ...           ...          ...\n",
              "105      4.44  hypercube  500  ...     30000         30000     6,753.44\n",
              "106      0.75  hypercube  100  ...     30000         30000    40,022.43\n",
              "107      0.11  hypercube   50  ...     30000         30000   279,877.75\n",
              "108      0.05  hypercube   25  ...     30000         30000   592,960.20\n",
              "109      0.03  hypercube    5  ...     30000         30000 1,007,616.39\n",
              "\n",
              "[110 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KA9IM-lrJJgU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "4e972cef-0454-4563-cdbf-e365974278f9"
      },
      "source": [
        "har_times = har.copy()\n",
        "har_times.rename(columns={'samples_sec':'samples_per_sec_', 'time':'time_',}, \n",
        "                inplace=True)\n",
        "har_times = har_times.groupby(['figure', 'n', 'total_points'], as_index=False).agg(\n",
        "    {'samples_per_sec_':['mean', 'std'], 'time_':['mean', 'std']}\n",
        ")\n",
        "har_times.columns = [a[0] + a[1] for a in har_times.columns]\n",
        "\n",
        "\n",
        "har_best_times = har_times.groupby(['figure', 'n'],\n",
        "                                 as_index=False)['samples_per_sec_mean'].max()\n",
        "\n",
        "\n",
        "har_best_times= har_times[har_times['samples_per_sec_mean'].isin(\n",
        "    har_best_times['samples_per_sec_mean'])].sort_values(['n'])\n",
        "har_show = har_best_times.copy()\n",
        "har_show = har_show.rename(columns={\n",
        "    'figure':'Figure',\n",
        "    'total_points': 'Points Sampled',\n",
        "}\n",
        ")\n",
        "\n",
        "\n",
        "pd.set_option('float_format', '{:,.2f}'.format)\n",
        "har_show.columns = pd.MultiIndex.from_tuples([\n",
        "    ('', 'Figure'), ('', 'n'), ('', 'Points Sampled'),\n",
        "    ('Points Sampled Per Second', 'mean'), ('Points Sampled Per Second', 'std'),\n",
        "                           ('Running Time', 'mean'), ('Running Time', 'std')\n",
        "])\n",
        "har_show.replace( {'simplex':'Simplex', 'hypercube':'Hypercube'}, inplace=True)\n",
        "har_show.head(1)\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\"></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>Points Sampled</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>931,368.92</td>\n",
              "      <td>57,727.69</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                ... Points Sampled Per Second Running Time     \n",
              "      Figure  n Points Sampled  ...                       std         mean  std\n",
              "0  Hypercube  5          30000  ...                 57,727.69         0.03 0.00\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5J62hoEgTpUc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "outputId": "0584244e-cddc-475f-abe7-05954bc45171"
      },
      "source": [
        "mhar = df_show.copy()\n",
        "del mhar[('', 'mixing_time')]\n",
        "del mhar[('', 'one_point')]\n",
        "\n",
        "mhar_columns = [\n",
        "                   ('', 'Figure'),\n",
        "                   ('', 'n'),\n",
        "                   ('', 'z'),\n",
        "                   ('Tot. Samples', 'MHAR'),\n",
        "                   ('Points Sampled Per Second', 'MHAR mean'),\n",
        "                   ('Points Sampled Per Second', 'MHAR std'),\n",
        "                  ('Running Time', 'MHAR mean'),\n",
        "                   ('Running Time', 'MHAR std')\n",
        "]\n",
        "mhar.columns = pd.MultiIndex.from_tuples(mhar_columns)\n",
        "mhar"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\"></th>\n",
              "      <th>Tot. Samples</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>MHAR</th>\n",
              "      <th>MHAR mean</th>\n",
              "      <th>MHAR std</th>\n",
              "      <th>MHAR mean</th>\n",
              "      <th>MHAR std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,357,073.87</td>\n",
              "      <td>675,444.40</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>19,795,014.21</td>\n",
              "      <td>2,628,558.29</td>\n",
              "      <td>15.38</td>\n",
              "      <td>1.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>22,878,783.33</td>\n",
              "      <td>1,258,481.83</td>\n",
              "      <td>13.15</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,206,089.93</td>\n",
              "      <td>376,068.96</td>\n",
              "      <td>22.73</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>15</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,269,548.32</td>\n",
              "      <td>302,854.48</td>\n",
              "      <td>12.36</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>15</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,344,794.68</td>\n",
              "      <td>655,021.48</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>25</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,338,761.06</td>\n",
              "      <td>168,300.75</td>\n",
              "      <td>12.33</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>25</td>\n",
              "      <td>5000</td>\n",
              "      <td>150000000</td>\n",
              "      <td>10,839,474.35</td>\n",
              "      <td>1,236,619.81</td>\n",
              "      <td>14.07</td>\n",
              "      <td>2.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>50</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,425,900.57</td>\n",
              "      <td>16,403.51</td>\n",
              "      <td>22.34</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>50</td>\n",
              "      <td>2500</td>\n",
              "      <td>75000000</td>\n",
              "      <td>5,151,516.81</td>\n",
              "      <td>612,241.73</td>\n",
              "      <td>14.83</td>\n",
              "      <td>2.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>100</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>7,255,837.08</td>\n",
              "      <td>135,616.62</td>\n",
              "      <td>12.41</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>100</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>4,363,525.70</td>\n",
              "      <td>10,619.65</td>\n",
              "      <td>27.50</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>250</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,656,449.22</td>\n",
              "      <td>4,440.59</td>\n",
              "      <td>45.17</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>250</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>1,219,419.53</td>\n",
              "      <td>8,630.27</td>\n",
              "      <td>73.81</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>300</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,315,606.64</td>\n",
              "      <td>1,199.00</td>\n",
              "      <td>51.82</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>500</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>621,554.24</td>\n",
              "      <td>782.70</td>\n",
              "      <td>193.06</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>500</td>\n",
              "      <td>1500</td>\n",
              "      <td>45000000</td>\n",
              "      <td>944,784.52</td>\n",
              "      <td>583.24</td>\n",
              "      <td>47.63</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>1000</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>248,513.69</td>\n",
              "      <td>182.97</td>\n",
              "      <td>482.87</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>1000</td>\n",
              "      <td>500</td>\n",
              "      <td>15000000</td>\n",
              "      <td>329,315.49</td>\n",
              "      <td>556.62</td>\n",
              "      <td>45.55</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>2500</td>\n",
              "      <td>1500</td>\n",
              "      <td>15000000</td>\n",
              "      <td>50,808.74</td>\n",
              "      <td>15.02</td>\n",
              "      <td>295.22</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>2500</td>\n",
              "      <td>500</td>\n",
              "      <td>5000000</td>\n",
              "      <td>77,312.01</td>\n",
              "      <td>3,045.62</td>\n",
              "      <td>64.78</td>\n",
              "      <td>2.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "      <td>16,161.69</td>\n",
              "      <td>5.92</td>\n",
              "      <td>618.75</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "      <td>22,437.63</td>\n",
              "      <td>62.27</td>\n",
              "      <td>445.68</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             ... Points Sampled Per Second Running Time         \n",
              "        Figure     n      z  ...                  MHAR std    MHAR mean MHAR std\n",
              "5    Hypercube     3  10000  ...                675,444.40        11.84     0.32\n",
              "61     Simplex     3  10000  ...              2,628,558.29        15.38     1.81\n",
              "67     Simplex     5  10000  ...              1,258,481.83        13.15     0.77\n",
              "11   Hypercube     5  10000  ...                376,068.96        22.73     0.66\n",
              "73     Simplex    15  10000  ...                302,854.48        12.36     0.16\n",
              "17   Hypercube    15  10000  ...                655,021.48        11.84     0.31\n",
              "79     Simplex    25  10000  ...                168,300.75        12.33     0.08\n",
              "22   Hypercube    25   5000  ...              1,236,619.81        14.07     2.28\n",
              "85     Simplex    50  10000  ...                 16,403.51        22.34     0.03\n",
              "27   Hypercube    50   2500  ...                612,241.73        14.83     2.54\n",
              "89     Simplex   100   3000  ...                135,616.62        12.41     0.23\n",
              "34   Hypercube   100   4000  ...                 10,619.65        27.50     0.07\n",
              "95     Simplex   250   4000  ...                  4,440.59        45.17     0.08\n",
              "38   Hypercube   250   3000  ...                  8,630.27        73.81     0.53\n",
              "100    Simplex   300   4000  ...                  1,199.00        51.82     0.03\n",
              "44   Hypercube   500   4000  ...                    782.70       193.06     0.24\n",
              "103    Simplex   500   1500  ...                    583.24        47.63     0.03\n",
              "49   Hypercube  1000   4000  ...                    182.97       482.87     0.36\n",
              "107    Simplex  1000    500  ...                    556.62        45.55     0.08\n",
              "52   Hypercube  2500   1500  ...                     15.02       295.22     0.09\n",
              "111    Simplex  2500    500  ...                  3,045.62        64.78     2.86\n",
              "54   Hypercube  5000   1000  ...                      5.92       618.75     0.23\n",
              "114    Simplex  5000   1000  ...                     62.27       445.68     1.25\n",
              "\n",
              "[23 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZQ66EVaZ7X-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "2e828b92-f2ce-495d-9b3e-1d4b0b4d8fff"
      },
      "source": [
        "har = har_times.copy()\n",
        "\n",
        "har_columns = [\n",
        "                   ('', 'Figure'),\n",
        "                   ('', 'n'),\n",
        "                   ('Tot. Samples', 'HAR'),\n",
        "                   ('Points Sampled Per Second', 'HAR mean'),\n",
        "                   ('Points Sampled Per Second', 'HAR std'),\n",
        "                  ('Running Time', 'HAR mean'),\n",
        "                   ('Running Time', 'HAR std')\n",
        "]\n",
        "har.columns = pd.MultiIndex.from_tuples(har_columns)\n",
        "\n",
        "har\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\"></th>\n",
              "      <th>Tot. Samples</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>HAR</th>\n",
              "      <th>HAR mean</th>\n",
              "      <th>HAR std</th>\n",
              "      <th>HAR mean</th>\n",
              "      <th>HAR std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>931,368.92</td>\n",
              "      <td>57,727.69</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>25</td>\n",
              "      <td>30000</td>\n",
              "      <td>373,127.77</td>\n",
              "      <td>77,786.96</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>50</td>\n",
              "      <td>30000</td>\n",
              "      <td>235,742.22</td>\n",
              "      <td>20,636.30</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>100</td>\n",
              "      <td>30000</td>\n",
              "      <td>37,367.93</td>\n",
              "      <td>1,486.54</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>500</td>\n",
              "      <td>30000</td>\n",
              "      <td>6,528.56</td>\n",
              "      <td>157.76</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>1000</td>\n",
              "      <td>30000</td>\n",
              "      <td>1,000.79</td>\n",
              "      <td>18.15</td>\n",
              "      <td>29.99</td>\n",
              "      <td>0.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>simplex</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>988,580.92</td>\n",
              "      <td>126,254.73</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>simplex</td>\n",
              "      <td>25</td>\n",
              "      <td>30000</td>\n",
              "      <td>18,114.90</td>\n",
              "      <td>409.27</td>\n",
              "      <td>1.66</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>simplex</td>\n",
              "      <td>50</td>\n",
              "      <td>30000</td>\n",
              "      <td>1,062.94</td>\n",
              "      <td>17.33</td>\n",
              "      <td>28.23</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>simplex</td>\n",
              "      <td>100</td>\n",
              "      <td>30000</td>\n",
              "      <td>56.53</td>\n",
              "      <td>0.88</td>\n",
              "      <td>530.79</td>\n",
              "      <td>8.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>simplex</td>\n",
              "      <td>250</td>\n",
              "      <td>30000</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>28,811.76</td>\n",
              "      <td>55.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Tot. Samples  ... Points Sampled Per Second Running Time        \n",
              "       Figure     n          HAR  ...                   HAR std     HAR mean HAR std\n",
              "0   hypercube     5        30000  ...                 57,727.69         0.03    0.00\n",
              "1   hypercube    25        30000  ...                 77,786.96         0.08    0.01\n",
              "2   hypercube    50        30000  ...                 20,636.30         0.13    0.01\n",
              "3   hypercube   100        30000  ...                  1,486.54         0.80    0.03\n",
              "4   hypercube   500        30000  ...                    157.76         4.60    0.11\n",
              "5   hypercube  1000        30000  ...                     18.15        29.99    0.54\n",
              "6     simplex     5        30000  ...                126,254.73         0.03    0.00\n",
              "7     simplex    25        30000  ...                    409.27         1.66    0.04\n",
              "8     simplex    50        30000  ...                     17.33        28.23    0.48\n",
              "9     simplex   100        30000  ...                      0.88       530.79    8.63\n",
              "10    simplex   250        30000  ...                      0.00    28,811.76   55.20\n",
              "\n",
              "[11 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbeJz8HEdxhT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "c3234278-b0fc-4076-82c1-b2f0df9f4783"
      },
      "source": [
        "mhar.head(1)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\"></th>\n",
              "      <th>Tot. Samples</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>z</th>\n",
              "      <th>MHAR</th>\n",
              "      <th>MHAR mean</th>\n",
              "      <th>MHAR std</th>\n",
              "      <th>MHAR mean</th>\n",
              "      <th>MHAR std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,357,073.87</td>\n",
              "      <td>675,444.40</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        ... Points Sampled Per Second Running Time         \n",
              "      Figure  n      z  ...                  MHAR std    MHAR mean MHAR std\n",
              "5  Hypercube  3  10000  ...                675,444.40        11.84     0.32\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 131
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        "outputId": "2baac61e-7273-4857-af25-30624efc44f8"
      },
      "source": [
        "har.head(1)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\"></th>\n",
              "      <th>Tot. Samples</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Points Sampled Per Second</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Running Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Figure</th>\n",
              "      <th>n</th>\n",
              "      <th>HAR</th>\n",
              "      <th>HAR mean</th>\n",
              "      <th>HAR std</th>\n",
              "      <th>HAR mean</th>\n",
              "      <th>HAR std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>931,368.92</td>\n",
              "      <td>57,727.69</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Tot. Samples  ... Points Sampled Per Second Running Time        \n",
              "      Figure  n          HAR  ...                   HAR std     HAR mean HAR std\n",
              "0  hypercube  5        30000  ...                 57,727.69         0.03    0.00\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZP7b7pFder3w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        },
        "outputId": "04ac6633-9dc5-4461-dcc4-d1fa06068350"
      },
      "source": [
        "mhar.columns = [c[0] + '%' + c[1] for c in mhar.columns]\n",
        "har.columns = [c[0] + '%' + c[1] for c in har.columns]\n",
        "har.replace( {'simplex':'Simplex', 'hypercube':'Hypercube'}, inplace=True)\n",
        "\n",
        "har.head(1)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>%Figure</th>\n",
              "      <th>%n</th>\n",
              "      <th>Tot. Samples%HAR</th>\n",
              "      <th>Points Sampled Per Second%HAR mean</th>\n",
              "      <th>Points Sampled Per Second%HAR std</th>\n",
              "      <th>Running Time%HAR mean</th>\n",
              "      <th>Running Time%HAR std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>931,368.92</td>\n",
              "      <td>57,727.69</td>\n",
              "      <td>0.03</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     %Figure  %n  ...  Running Time%HAR mean  Running Time%HAR std\n",
              "0  Hypercube   5  ...                   0.03                  0.00\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 46
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "OWUnq2AJfYXB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        },
        "outputId": "352800fa-ded0-4a62-cd58-9aa7fcc68965"
      },
      "source": [
        "mhar.head(1)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>%Figure</th>\n",
              "      <th>%n</th>\n",
              "      <th>%z</th>\n",
              "      <th>Tot. Samples%MHAR</th>\n",
              "      <th>Points Sampled Per Second%MHAR mean</th>\n",
              "      <th>Points Sampled Per Second%MHAR std</th>\n",
              "      <th>Running Time%MHAR mean</th>\n",
              "      <th>Running Time%MHAR std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>3</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>25,357,073.87</td>\n",
              "      <td>675,444.40</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     %Figure  %n  ...  Running Time%MHAR mean  Running Time%MHAR std\n",
              "5  Hypercube   3  ...                   11.84                   0.32\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBHRRQVSfEHV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "dda609d9-635e-48c6-cde6-09e05caab1bc"
      },
      "source": [
        "speed_up = har.merge(mhar, on =['%Figure', '%n'])\n",
        "speed_up['%Speed Up']=speed_up['Points Sampled Per Second%MHAR mean'] /speed_up['Points Sampled Per Second%HAR mean']\n",
        "speed_up"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>%Figure</th>\n",
              "      <th>%n</th>\n",
              "      <th>Tot. Samples%HAR</th>\n",
              "      <th>Points Sampled Per Second%HAR mean</th>\n",
              "      <th>Points Sampled Per Second%HAR std</th>\n",
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              "      <th>Running Time%HAR std</th>\n",
              "      <th>%z</th>\n",
              "      <th>Tot. Samples%MHAR</th>\n",
              "      <th>Points Sampled Per Second%MHAR mean</th>\n",
              "      <th>Points Sampled Per Second%MHAR std</th>\n",
              "      <th>Running Time%MHAR mean</th>\n",
              "      <th>Running Time%MHAR std</th>\n",
              "      <th>%Speed Up</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>931,368.92</td>\n",
              "      <td>57,727.69</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,206,089.93</td>\n",
              "      <td>376,068.96</td>\n",
              "      <td>22.73</td>\n",
              "      <td>0.66</td>\n",
              "      <td>14.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>25</td>\n",
              "      <td>30000</td>\n",
              "      <td>373,127.77</td>\n",
              "      <td>77,786.96</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.01</td>\n",
              "      <td>5000</td>\n",
              "      <td>150000000</td>\n",
              "      <td>10,839,474.35</td>\n",
              "      <td>1,236,619.81</td>\n",
              "      <td>14.07</td>\n",
              "      <td>2.28</td>\n",
              "      <td>29.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>50</td>\n",
              "      <td>30000</td>\n",
              "      <td>235,742.22</td>\n",
              "      <td>20,636.30</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2500</td>\n",
              "      <td>75000000</td>\n",
              "      <td>5,151,516.81</td>\n",
              "      <td>612,241.73</td>\n",
              "      <td>14.83</td>\n",
              "      <td>2.54</td>\n",
              "      <td>21.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>100</td>\n",
              "      <td>30000</td>\n",
              "      <td>37,367.93</td>\n",
              "      <td>1,486.54</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.03</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>4,363,525.70</td>\n",
              "      <td>10,619.65</td>\n",
              "      <td>27.50</td>\n",
              "      <td>0.07</td>\n",
              "      <td>116.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>500</td>\n",
              "      <td>30000</td>\n",
              "      <td>6,528.56</td>\n",
              "      <td>157.76</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.11</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>621,554.24</td>\n",
              "      <td>782.70</td>\n",
              "      <td>193.06</td>\n",
              "      <td>0.24</td>\n",
              "      <td>95.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hypercube</td>\n",
              "      <td>1000</td>\n",
              "      <td>30000</td>\n",
              "      <td>1,000.79</td>\n",
              "      <td>18.15</td>\n",
              "      <td>29.99</td>\n",
              "      <td>0.54</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>248,513.69</td>\n",
              "      <td>182.97</td>\n",
              "      <td>482.87</td>\n",
              "      <td>0.36</td>\n",
              "      <td>248.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
              "      <td>988,580.92</td>\n",
              "      <td>126,254.73</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>22,878,783.33</td>\n",
              "      <td>1,258,481.83</td>\n",
              "      <td>13.15</td>\n",
              "      <td>0.77</td>\n",
              "      <td>23.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>25</td>\n",
              "      <td>30000</td>\n",
              "      <td>18,114.90</td>\n",
              "      <td>409.27</td>\n",
              "      <td>1.66</td>\n",
              "      <td>0.04</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>24,338,761.06</td>\n",
              "      <td>168,300.75</td>\n",
              "      <td>12.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1,343.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>50</td>\n",
              "      <td>30000</td>\n",
              "      <td>1,062.94</td>\n",
              "      <td>17.33</td>\n",
              "      <td>28.23</td>\n",
              "      <td>0.48</td>\n",
              "      <td>10000</td>\n",
              "      <td>300000000</td>\n",
              "      <td>13,425,900.57</td>\n",
              "      <td>16,403.51</td>\n",
              "      <td>22.34</td>\n",
              "      <td>0.03</td>\n",
              "      <td>12,630.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>100</td>\n",
              "      <td>30000</td>\n",
              "      <td>56.53</td>\n",
              "      <td>0.88</td>\n",
              "      <td>530.79</td>\n",
              "      <td>8.63</td>\n",
              "      <td>3000</td>\n",
              "      <td>90000000</td>\n",
              "      <td>7,255,837.08</td>\n",
              "      <td>135,616.62</td>\n",
              "      <td>12.41</td>\n",
              "      <td>0.23</td>\n",
              "      <td>128,348.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Simplex</td>\n",
              "      <td>250</td>\n",
              "      <td>30000</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>28,811.76</td>\n",
              "      <td>55.20</td>\n",
              "      <td>4000</td>\n",
              "      <td>120000000</td>\n",
              "      <td>2,656,449.22</td>\n",
              "      <td>4,440.59</td>\n",
              "      <td>45.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2,551,224.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      %Figure    %n  ...  Running Time%MHAR std    %Speed Up\n",
              "0   Hypercube     5  ...                   0.66        14.18\n",
              "1   Hypercube    25  ...                   2.28        29.05\n",
              "2   Hypercube    50  ...                   2.54        21.85\n",
              "3   Hypercube   100  ...                   0.07       116.77\n",
              "4   Hypercube   500  ...                   0.24        95.21\n",
              "5   Hypercube  1000  ...                   0.36       248.32\n",
              "6     Simplex     5  ...                   0.77        23.14\n",
              "7     Simplex    25  ...                   0.08     1,343.58\n",
              "8     Simplex    50  ...                   0.03    12,630.89\n",
              "9     Simplex   100  ...                   0.23   128,348.67\n",
              "10    Simplex   250  ...                   0.08 2,551,224.17\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0ymusy_jE0g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "outputId": "541d4371-c0bb-4c9b-a2fa-083c878bdd13"
      },
      "source": [
        "speed_up.columns"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['%Figure', '%n', 'Tot. Samples%HAR',\n",
              "       'Points Sampled Per Second%HAR mean',\n",
              "       'Points Sampled Per Second%HAR std', 'Running Time%HAR mean',\n",
              "       'Running Time%HAR std', '%z', 'Tot. Samples%MHAR',\n",
              "       'Points Sampled Per Second%MHAR mean',\n",
              "       'Points Sampled Per Second%MHAR std', 'Running Time%MHAR mean',\n",
              "       'Running Time%MHAR std', '%Speed Up'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94MY8Eqai2Wa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "648cde1f-456f-47b2-8d83-4f1499823219"
      },
      "source": [
        "\n",
        "speed_up_cols=[('','Figure'), ('','n'), \n",
        "               ('Tot. Samples','HAR'),\n",
        "       ('Points Sampled Per Second','HAR mean'),\n",
        "       ('Points Sampled Per Second','HAR std'), \n",
        "      ('Running Time', 'HAR mean'),\n",
        "       ('Running Time','HAR std'),\n",
        "        ('','z'), \n",
        "        ('Tot. Samples','MHAR'),\n",
        "       ('Points Sampled Per Second','MHAR mean'),\n",
        "       ('Points Sampled Per Second','MHAR std'), \n",
        "        ('Running Time', 'MHAR mean'),\n",
        "       ('Running Time','MHAR std'), ('','Speed Up')]\n",
        "speed_up.columns = pd.MultiIndex.from_tuples(speed_up_cols)\n",
        "speed_up[[('','Figure'), ('','n'), ('','z'), ('', 'Speed Up'),\n",
        "          ('Tot. Samples','MHAR'), ('Tot. Samples','HAR'),\n",
        "        ('Points Sampled Per Second','MHAR mean'),\n",
        "       ('Points Sampled Per Second','HAR mean'), \n",
        "      ('Points Sampled Per Second','MHAR std'),\n",
        "       ('Points Sampled Per Second','HAR std'),\n",
        "      ('Running Time', 'MHAR mean'),\n",
        "       ('Running Time','HAR mean'),\n",
        "             ('Running Time', 'MHAR std'),\n",
        "       ('Running Time','HAR std'),\n",
        "\n",
        "]].to_latex(index=False)\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\\\begin{tabular}{lrrrrrrrrrrrrr}\\n\\\\toprule\\n           & \\\\multicolumn{2}{l}{Tot. Samples} & \\\\multicolumn{4}{l}{Points Sampled Per Second} & \\\\multicolumn{4}{l}{Running Time} \\\\\\\\\\n    Figure &     n &      z &     Speed Up &         MHAR &    HAR &                 MHAR mean &   HAR mean &     MHAR std &    HAR std &    MHAR mean &  HAR mean & MHAR std & HAR std \\\\\\\\\\n\\\\midrule\\n Hypercube &     5 &  10000 &        14.18 &    300000000 &  30000 &             13,206,089.93 & 931,368.92 &   376,068.96 &  57,727.69 &        22.73 &      0.03 &     0.66 &    0.00 \\\\\\\\\\n Hypercube &    25 &   5000 &        29.05 &    150000000 &  30000 &             10,839,474.35 & 373,127.77 & 1,236,619.81 &  77,786.96 &        14.07 &      0.08 &     2.28 &    0.01 \\\\\\\\\\n Hypercube &    50 &   2500 &        21.85 &     75000000 &  30000 &              5,151,516.81 & 235,742.22 &   612,241.73 &  20,636.30 &        14.83 &      0.13 &     2.54 &    0.01 \\\\\\\\\\n Hypercube &   100 &   4000 &       116.77 &    120000000 &  30000 &              4,363,525.70 &  37,367.93 &    10,619.65 &   1,486.54 &        27.50 &      0.80 &     0.07 &    0.03 \\\\\\\\\\n Hypercube &   500 &   4000 &        95.21 &    120000000 &  30000 &                621,554.24 &   6,528.56 &       782.70 &     157.76 &       193.06 &      4.60 &     0.24 &    0.11 \\\\\\\\\\n Hypercube &  1000 &   4000 &       248.32 &    120000000 &  30000 &                248,513.69 &   1,000.79 &       182.97 &      18.15 &       482.87 &     29.99 &     0.36 &    0.54 \\\\\\\\\\n   Simplex &     5 &  10000 &        23.14 &    300000000 &  30000 &             22,878,783.33 & 988,580.92 & 1,258,481.83 & 126,254.73 &        13.15 &      0.03 &     0.77 &    0.00 \\\\\\\\\\n   Simplex &    25 &  10000 &     1,343.58 &    300000000 &  30000 &             24,338,761.06 &  18,114.90 &   168,300.75 &     409.27 &        12.33 &      1.66 &     0.08 &    0.04 \\\\\\\\\\n   Simplex &    50 &  10000 &    12,630.89 &    300000000 &  30000 &             13,425,900.57 &   1,062.94 &    16,403.51 &      17.33 &        22.34 &     28.23 &     0.03 &    0.48 \\\\\\\\\\n   Simplex &   100 &   3000 &   128,348.67 &     90000000 &  30000 &              7,255,837.08 &      56.53 &   135,616.62 &       0.88 &        12.41 &    530.79 &     0.23 &    8.63 \\\\\\\\\\n   Simplex &   250 &   4000 & 2,551,224.17 &    120000000 &  30000 &              2,656,449.22 &       1.04 &     4,440.59 &       0.00 &        45.17 & 28,811.76 &     0.08 &   55.20 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZKBUbDSg9gO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "aa8a0df5-1b73-4dfa-bbc0-eae7323acc16"
      },
      "source": [
        "\n",
        "\n",
        "speed_up[[('','Figure'), ('','n'), ('','z'), ('', 'Speed Up'),\n",
        "        ('Points Sampled Per Second','MHAR mean'),\n",
        "       ('Points Sampled Per Second','HAR mean'), \n",
        "      ('Points Sampled Per Second','MHAR std'),\n",
        "       ('Points Sampled Per Second','HAR std')\n",
        "\n",
        "]].to_latex(index=False)\n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\\\begin{tabular}{lrrrrrrr}\\n\\\\toprule\\n           & \\\\multicolumn{4}{l}{Points Sampled Per Second} \\\\\\\\\\n    Figure &     n &      z &     Speed Up &                 MHAR mean &   HAR mean &     MHAR std &    HAR std \\\\\\\\\\n\\\\midrule\\n Hypercube &     5 &  10000 &        14.18 &             13,206,089.93 & 931,368.92 &   376,068.96 &  57,727.69 \\\\\\\\\\n Hypercube &    25 &   5000 &        29.05 &             10,839,474.35 & 373,127.77 & 1,236,619.81 &  77,786.96 \\\\\\\\\\n Hypercube &    50 &   2500 &        21.85 &              5,151,516.81 & 235,742.22 &   612,241.73 &  20,636.30 \\\\\\\\\\n Hypercube &   100 &   4000 &       116.77 &              4,363,525.70 &  37,367.93 &    10,619.65 &   1,486.54 \\\\\\\\\\n Hypercube &   500 &   4000 &        95.21 &                621,554.24 &   6,528.56 &       782.70 &     157.76 \\\\\\\\\\n Hypercube &  1000 &   4000 &       248.32 &                248,513.69 &   1,000.79 &       182.97 &      18.15 \\\\\\\\\\n   Simplex &     5 &  10000 &        23.14 &             22,878,783.33 & 988,580.92 & 1,258,481.83 & 126,254.73 \\\\\\\\\\n   Simplex &    25 &  10000 &     1,343.58 &             24,338,761.06 &  18,114.90 &   168,300.75 &     409.27 \\\\\\\\\\n   Simplex &    50 &  10000 &    12,630.89 &             13,425,900.57 &   1,062.94 &    16,403.51 &      17.33 \\\\\\\\\\n   Simplex &   100 &   3000 &   128,348.67 &              7,255,837.08 &      56.53 &   135,616.62 &       0.88 \\\\\\\\\\n   Simplex &   250 &   4000 & 2,551,224.17 &              2,656,449.22 &       1.04 &     4,440.59 &       0.00 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    }
  ]
}