macula_greek_pandas.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ryderwishart/biblical-machine-learning/blob/main/structured-data-insights/macula_greek_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUY1ikEdAtFA"
      },
      "source": [
        "## Load MACULA data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If this notebook is being run in Colab (or anywhere on a hosted cloud environment), then you will need to clone the `macula-greek` repository before proceeding.\n",
        "\n",
        "Set the `local_or_cloud` value in the next cell to `local` if you are running this notebook locally."
      ],
      "metadata": {
        "id": "Q7SwEe8zyUz-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "local_or_cloud = 'cloud'\n",
        "\n",
        "import os\n",
        "os.getcwd()"
      ],
      "metadata": {
        "id": "U5N-CNGUyTnq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "04e3af73-31a5-4888-82db-75a31b42ac8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if local_or_cloud == 'cloud':\n",
        "  !git clone https://github.com/Clear-Bible/macula-greek.git"
      ],
      "metadata": {
        "id": "-xcZGRcWyrOh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b40b3e8-42ed-4bf4-a4f4-7f54ee9c2981"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'macula-greek'...\n",
            "remote: Enumerating objects: 4575, done.\u001b[K\n",
            "remote: Counting objects: 100% (415/415), done.\u001b[K\n",
            "remote: Compressing objects: 100% (86/86), done.\u001b[K\n",
            "remote: Total 4575 (delta 330), reused 409 (delta 329), pack-reused 4160\u001b[K\n",
            "Receiving objects: 100% (4575/4575), 565.87 MiB | 12.86 MiB/s, done.\n",
            "Resolving deltas: 100% (3352/3352), done.\n",
            "Updating files: 100% (133/133), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.listdir()"
      ],
      "metadata": {
        "id": "ntmeg8OWzfDU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1103cf83-8d84-47f0-8a98-d90476121359"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'macula-greek', 'sample_data']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnhQglp0wlfk"
      },
      "source": [
        "## Repository structure\n",
        "\n",
        "```bash\n",
        "macula-greek/\n",
        "- Nestle1904/\n",
        "  - nodes/ # contains the nodes trees\n",
        "  - lowfat/ # contains the lowfat trees\n",
        "  - TSV/ # contains the TSV files\n",
        "    - macula-greek.tsv\n",
        "- examples/\n",
        "  - macula.ipynb # this notebook\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33gdrOHfwlfl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1a06492-fa38-48c1-ba04-2562ef7b82f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (1.4.4)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.9/dist-packages (from pandas) (1.22.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas\n",
        "import pandas as pd\n",
        "# Load ../Nestle1904/TSV/macula-greek.tsv\n",
        "if local_or_cloud == 'local':\n",
        "  mg = pd.read_csv('../Nestle1904/TSV/macula-greek.tsv', sep='\\t', header=0)\n",
        "elif local_or_cloud == 'cloud':\n",
        "  mg = pd.read_csv('macula-greek/Nestle1904/TSV/macula-greek.tsv', sep='\\t', header=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YWblkKdHwlfm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ca421ef-fe72-456b-fea8-983a2e476f9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "xml:id\n",
            "ref\n",
            "role\n",
            "class\n",
            "type\n",
            "gloss\n",
            "text\n",
            "after\n",
            "lemma\n",
            "normalized\n",
            "strong\n",
            "morph\n",
            "person\n",
            "number\n",
            "gender\n",
            "case\n",
            "tense\n",
            "voice\n",
            "mood\n",
            "degree\n",
            "domain\n",
            "ln\n",
            "frame\n",
            "subjref\n",
            "referent\n"
          ]
        }
      ],
      "source": [
        "# Inspect the column headers to see what data is available for each word\n",
        "for column in mg.columns:\n",
        "    print(column)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxM7K5REwlfm"
      },
      "source": [
        "## Preprocess the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SmEtrWnQwlfn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "8142f9a4-d5ee-4482-d734-e6022114814d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         xml:id        ref role class    type               gloss      text  \\\n",
              "0  n40001001001  MAT 1:1!1  NaN  noun  common          [The] book    Βίβλος   \n",
              "1  n40001001002  MAT 1:1!2  NaN  noun  common  of [the] genealogy  γενέσεως   \n",
              "2  n40001001003  MAT 1:1!3  NaN  noun  proper            of Jesus     Ἰησοῦ   \n",
              "3  n40001001004  MAT 1:1!4  NaN  noun  proper              Christ   Χριστοῦ   \n",
              "4  n40001001005  MAT 1:1!5  NaN  noun  common                 son      υἱοῦ   \n",
              "\n",
              "  after    lemma normalized  ...         domain           ln frame subjref  \\\n",
              "0         βίβλος     Βίβλος  ...         033005        33.38   NaN     NaN   \n",
              "1        γένεσις   γενέσεως  ...  010002 033003  10.24 33.19   NaN     NaN   \n",
              "2         Ἰησοῦς      Ἰησοῦ  ...         093001      93.169a   NaN     NaN   \n",
              "3        Χριστός    Χριστοῦ  ...         093001       93.387   NaN     NaN   \n",
              "4           υἱός       υἱοῦ  ...         010002        10.30   NaN     NaN   \n",
              "\n",
              "  referent book chapter verse book_chapter book_chapter_verse  \n",
              "0      NaN  MAT       1     1        MAT 1            MAT 1:1  \n",
              "1      NaN  MAT       1     1        MAT 1            MAT 1:1  \n",
              "2      NaN  MAT       1     1        MAT 1            MAT 1:1  \n",
              "3      NaN  MAT       1     1        MAT 1            MAT 1:1  \n",
              "4      NaN  MAT       1     1        MAT 1            MAT 1:1  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a473c5e-d70a-4541-ba58-f0b1f704a49e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>xml:id</th>\n",
              "      <th>ref</th>\n",
              "      <th>role</th>\n",
              "      <th>class</th>\n",
              "      <th>type</th>\n",
              "      <th>gloss</th>\n",
              "      <th>text</th>\n",
              "      <th>after</th>\n",
              "      <th>lemma</th>\n",
              "      <th>normalized</th>\n",
              "      <th>...</th>\n",
              "      <th>domain</th>\n",
              "      <th>ln</th>\n",
              "      <th>frame</th>\n",
              "      <th>subjref</th>\n",
              "      <th>referent</th>\n",
              "      <th>book</th>\n",
              "      <th>chapter</th>\n",
              "      <th>verse</th>\n",
              "      <th>book_chapter</th>\n",
              "      <th>book_chapter_verse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>n40001001001</td>\n",
              "      <td>MAT 1:1!1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>noun</td>\n",
              "      <td>common</td>\n",
              "      <td>[The] book</td>\n",
              "      <td>Βίβλος</td>\n",
              "      <td></td>\n",
              "      <td>βίβλος</td>\n",
              "      <td>Βίβλος</td>\n",
              "      <td>...</td>\n",
              "      <td>033005</td>\n",
              "      <td>33.38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAT</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MAT 1</td>\n",
              "      <td>MAT 1:1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>n40001001002</td>\n",
              "      <td>MAT 1:1!2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>noun</td>\n",
              "      <td>common</td>\n",
              "      <td>of [the] genealogy</td>\n",
              "      <td>γενέσεως</td>\n",
              "      <td></td>\n",
              "      <td>γένεσις</td>\n",
              "      <td>γενέσεως</td>\n",
              "      <td>...</td>\n",
              "      <td>010002 033003</td>\n",
              "      <td>10.24 33.19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAT</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MAT 1</td>\n",
              "      <td>MAT 1:1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>n40001001003</td>\n",
              "      <td>MAT 1:1!3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>noun</td>\n",
              "      <td>proper</td>\n",
              "      <td>of Jesus</td>\n",
              "      <td>Ἰησοῦ</td>\n",
              "      <td></td>\n",
              "      <td>Ἰησοῦς</td>\n",
              "      <td>Ἰησοῦ</td>\n",
              "      <td>...</td>\n",
              "      <td>093001</td>\n",
              "      <td>93.169a</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAT</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MAT 1</td>\n",
              "      <td>MAT 1:1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>n40001001004</td>\n",
              "      <td>MAT 1:1!4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>noun</td>\n",
              "      <td>proper</td>\n",
              "      <td>Christ</td>\n",
              "      <td>Χριστοῦ</td>\n",
              "      <td></td>\n",
              "      <td>Χριστός</td>\n",
              "      <td>Χριστοῦ</td>\n",
              "      <td>...</td>\n",
              "      <td>093001</td>\n",
              "      <td>93.387</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAT</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MAT 1</td>\n",
              "      <td>MAT 1:1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>n40001001005</td>\n",
              "      <td>MAT 1:1!5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>noun</td>\n",
              "      <td>common</td>\n",
              "      <td>son</td>\n",
              "      <td>υἱοῦ</td>\n",
              "      <td></td>\n",
              "      <td>υἱός</td>\n",
              "      <td>υἱοῦ</td>\n",
              "      <td>...</td>\n",
              "      <td>010002</td>\n",
              "      <td>10.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAT</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MAT 1</td>\n",
              "      <td>MAT 1:1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a473c5e-d70a-4541-ba58-f0b1f704a49e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4a473c5e-d70a-4541-ba58-f0b1f704a49e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a473c5e-d70a-4541-ba58-f0b1f704a49e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Extract book, chapter, and verse into separate columns\n",
        "mg[['book', 'chapter', 'verse']] = mg['ref'].str.extract(r'(\\d?[A-Z]+)\\s(\\d+):(\\d+)')\n",
        "\n",
        "# Add columns for book + chapter, and book + chapter + verse for easier grouping\n",
        "mg['book_chapter'] = mg['book'] + ' ' + mg['chapter'].astype(str)\n",
        "mg['book_chapter_verse'] = mg['book_chapter'] + ':' + mg['verse'].astype(str)\n",
        "\n",
        "# Display the updated data frame\n",
        "mg.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iuj2e9mywlfn"
      },
      "source": [
        "Now that we have the book, chapter, and verse, let's group the data by book, and generate a list of unique Louw-Nida values for each book."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3vToNRJjwlfn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6423e2a6-bd4d-434d-b579-5deefd2e663d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word counts:\n",
            " book\n",
            "1CO    6820\n",
            "1JN    2136\n",
            "1PE    1676\n",
            "1TH    1473\n",
            "1TI    1588\n",
            "dtype: int64\n",
            "\n",
            "Unique Louw-Nida domains:\n",
            " book\n",
            "1CO    [93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...\n",
            "1JN    [92.27, 13.69, 67.131, 67.65, nan, 24.52, 24.1...\n",
            "1PE    [93.296, 53.74, 93.169a, 93.387, 30.93, 11.77,...\n",
            "1TH    [93.294a, nan, 93.340, 93.361, 92.24, 11.32, 9...\n",
            "1TI    [93.294a, 53.74, 93.387, 93.169a, nan, 33.326,...\n",
            "Name: ln, dtype: object\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "mg_word_count = mg.groupby('book').size()\n",
        "\n",
        "# Create an array of the unique Louw-Nida domain values for each book\n",
        "mg_unique_ln = mg.groupby('book')['ln'].unique()\n",
        "\n",
        "# Print some example results\n",
        "print('Word counts:\\n', mg_word_count[0:5])\n",
        "print('\\nUnique Louw-Nida domains:\\n', mg_unique_ln[0:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRVj-XuEwlfn"
      },
      "source": [
        "Let's combine these two data sets into a single data frame so we can plot the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vom8qxtzwlfo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "aa919821-027a-4f34-9f37-51d4415ab4a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      word_count                                                 ln  ln_count\n",
              "book                                                                         \n",
              "1CO         6820  [93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...      1308\n",
              "1JN         2136  [92.27, 13.69, 67.131, 67.65, nan, 24.52, 24.1...       327\n",
              "1PE         1676  [93.296, 53.74, 93.169a, 93.387, 30.93, 11.77,...       619\n",
              "1TH         1473  [93.294a, nan, 93.340, 93.361, 92.24, 11.32, 9...       468\n",
              "1TI         1588  [93.294a, 53.74, 93.387, 93.169a, nan, 33.326,...       537"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2aad194-fb28-4e13-ac79-471b8bff4fc7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word_count</th>\n",
              "      <th>ln</th>\n",
              "      <th>ln_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1CO</th>\n",
              "      <td>6820</td>\n",
              "      <td>[93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...</td>\n",
              "      <td>1308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1JN</th>\n",
              "      <td>2136</td>\n",
              "      <td>[92.27, 13.69, 67.131, 67.65, nan, 24.52, 24.1...</td>\n",
              "      <td>327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1PE</th>\n",
              "      <td>1676</td>\n",
              "      <td>[93.296, 53.74, 93.169a, 93.387, 30.93, 11.77,...</td>\n",
              "      <td>619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1TH</th>\n",
              "      <td>1473</td>\n",
              "      <td>[93.294a, nan, 93.340, 93.361, 92.24, 11.32, 9...</td>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1TI</th>\n",
              "      <td>1588</td>\n",
              "      <td>[93.294a, 53.74, 93.387, 93.169a, nan, 33.326,...</td>\n",
              "      <td>537</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2aad194-fb28-4e13-ac79-471b8bff4fc7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b2aad194-fb28-4e13-ac79-471b8bff4fc7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b2aad194-fb28-4e13-ac79-471b8bff4fc7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Turn both of the series into data frames\n",
        "words = mg_word_count.to_frame().rename(columns={0: 'word_count'})\n",
        "domains = mg_unique_ln.to_frame().rename(columns={0: 'ln'})\n",
        "\n",
        "# Merge the two data frames, and add a column for the number of unique ln values\n",
        "mg_books = pd.merge(words, domains, on='book')\n",
        "\n",
        "mg_books['ln_count'] = mg_books['ln'].apply(lambda x: len(x))\n",
        "\n",
        "mg_books.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C32KUJwBwlfo"
      },
      "outputs": [],
      "source": [
        "# group by verse\n",
        "# group by domain\n",
        "# find books with the most proper nouns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HOGRvfHYwlfo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "630dbb21-dc78-4376-eb2c-4bedebbb080d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (3.5.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (23.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (4.39.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (8.4.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.22.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install matplotlib \n",
        "from matplotlib import pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EP678hT_wlfo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "db250d6b-f952-47c6-e41e-74529edc1dbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot data\n",
        "ax = mg_books.sort_values('word_count').plot(kind='bar', legend=False)\n",
        "ax2 = ax.twinx() # create a second y-axis\n",
        "mg_books.sort_values('word_count')['ln_count'].plot(kind='line', color='C2', ax=ax2, legend=False)\n",
        "\n",
        "# format plot\n",
        "ax.set_ylabel('Word Count')\n",
        "ax2.set_ylabel('Unique ln values')\n",
        "ax.set_xlabel('Book')\n",
        "ax.set_title('Word Count and Unique ln values per Book')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JavgiBwewlfp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "7e028ee6-7e6f-408a-f3c8-596937833cfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rate of unique domains per word')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Find the books with the most unique ln values per word\n",
        "mg_books['ln_rate'] = mg_books['ln_count'] / mg_books['word_count']\n",
        "\n",
        "# Plot the ln_rate ('Semantic Density') for each book\n",
        "ax = mg_books.sort_values('ln_rate').plot(kind='bar', y='ln_rate', legend=False)\n",
        "ax.set_ylabel('Rate of unique domains')\n",
        "ax.set_xlabel('Book')\n",
        "ax.set_title('Rate of unique domains per word')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OQV95VG9wlfp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "10cb0794-ff06-4093-bf45-51f92d42712a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      word_count                                                 ln  ln_count  \\\n",
              "book                                                                            \n",
              "JHN        15643  [67.33, 67.65, 13.69, 92.24, 33.100, 89.93, 85...      1383   \n",
              "REV         9832  [28.38, 93.169a, 93.387, nan, 92.24, 12.1, 28....      1063   \n",
              "MAT        18299  [33.38, 10.24 33.19, 93.169a, 93.387, 10.30, 9...      2192   \n",
              "LUK        19456  [89.32, 59.1, 68.59, 62.3, 33.11, 90.24, 92.24...      2560   \n",
              "ACT        18393  [92.24, 91.6, 60.46, 33.51, 90.45, 90.24, 59.2...      2618   \n",
              "\n",
              "       ln_rate  \n",
              "book            \n",
              "JHN   0.088410  \n",
              "REV   0.108116  \n",
              "MAT   0.119788  \n",
              "LUK   0.131579  \n",
              "ACT   0.142337  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef362ead-0b7d-4365-af08-6623dca3181b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word_count</th>\n",
              "      <th>ln</th>\n",
              "      <th>ln_count</th>\n",
              "      <th>ln_rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>JHN</th>\n",
              "      <td>15643</td>\n",
              "      <td>[67.33, 67.65, 13.69, 92.24, 33.100, 89.93, 85...</td>\n",
              "      <td>1383</td>\n",
              "      <td>0.088410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>REV</th>\n",
              "      <td>9832</td>\n",
              "      <td>[28.38, 93.169a, 93.387, nan, 92.24, 12.1, 28....</td>\n",
              "      <td>1063</td>\n",
              "      <td>0.108116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAT</th>\n",
              "      <td>18299</td>\n",
              "      <td>[33.38, 10.24 33.19, 93.169a, 93.387, 10.30, 9...</td>\n",
              "      <td>2192</td>\n",
              "      <td>0.119788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LUK</th>\n",
              "      <td>19456</td>\n",
              "      <td>[89.32, 59.1, 68.59, 62.3, 33.11, 90.24, 92.24...</td>\n",
              "      <td>2560</td>\n",
              "      <td>0.131579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACT</th>\n",
              "      <td>18393</td>\n",
              "      <td>[92.24, 91.6, 60.46, 33.51, 90.45, 90.24, 59.2...</td>\n",
              "      <td>2618</td>\n",
              "      <td>0.142337</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef362ead-0b7d-4365-af08-6623dca3181b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef362ead-0b7d-4365-af08-6623dca3181b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ef362ead-0b7d-4365-af08-6623dca3181b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "mg_books.sort_values('ln_rate').head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-lOmE85wlfp"
      },
      "source": [
        "While that is interesting data, it is overly sensitive to the length of each book. Let's do the same breakdown, finding the rate of unique Louw-Nida domains per word, but let's group the data by book *and* chapter instead."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J9wP7vBtwlfq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "aba2ceee-fc8e-4a3b-a4f3-84996060bce6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              word_count                                                 ln  \\\n",
              "book_chapter                                                                  \n",
              "1CO 1                496  [93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...   \n",
              "1CO 10               463  [69.3, 25.1, 91.1, 92.7, 28.13 32.7, 11.23, 90...   \n",
              "1CO 11               531  [41.45, 92.1, 13.3, 64.14, 89.92 92.1, 93.387,...   \n",
              "1CO 12               470  [89.6, nan, 92.24, 12.21, 11.23, 69.3, 25.1, 9...   \n",
              "1CO 13               197  [89.67, 92.24, 33.2, 9.1, 33.70, 89.92, 12.28,...   \n",
              "\n",
              "              ln_count  \n",
              "book_chapter            \n",
              "1CO 1              192  \n",
              "1CO 10             204  \n",
              "1CO 11             204  \n",
              "1CO 12             173  \n",
              "1CO 13              93  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c6787736-c534-4275-a7d9-55547bcf3693\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word_count</th>\n",
              "      <th>ln</th>\n",
              "      <th>ln_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book_chapter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1CO 1</th>\n",
              "      <td>496</td>\n",
              "      <td>[93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 10</th>\n",
              "      <td>463</td>\n",
              "      <td>[69.3, 25.1, 91.1, 92.7, 28.13 32.7, 11.23, 90...</td>\n",
              "      <td>204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 11</th>\n",
              "      <td>531</td>\n",
              "      <td>[41.45, 92.1, 13.3, 64.14, 89.92 92.1, 93.387,...</td>\n",
              "      <td>204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 12</th>\n",
              "      <td>470</td>\n",
              "      <td>[89.6, nan, 92.24, 12.21, 11.23, 69.3, 25.1, 9...</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 13</th>\n",
              "      <td>197</td>\n",
              "      <td>[89.67, 92.24, 33.2, 9.1, 33.70, 89.92, 12.28,...</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6787736-c534-4275-a7d9-55547bcf3693')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6787736-c534-4275-a7d9-55547bcf3693 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c6787736-c534-4275-a7d9-55547bcf3693');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Group by book_chapter column\n",
        "mg_word_count = mg.groupby('book_chapter').size()\n",
        "\n",
        "# Create an array of the unique Louw-Nida domain values for each book\n",
        "mg_unique_ln = mg.groupby('book_chapter')['ln'].unique()\n",
        "\n",
        "# Turn both of the series into data frames\n",
        "words = mg_word_count.to_frame().rename(columns={0: 'word_count'})\n",
        "domains = mg_unique_ln.to_frame().rename(columns={0: 'ln'})\n",
        "\n",
        "# Merge the two data frames, and add a column for the number of unique ln values\n",
        "mg_chapters = pd.merge(words, domains, on='book_chapter')\n",
        "\n",
        "mg_chapters['ln_count'] = mg_chapters['ln'].apply(lambda x: len(x))\n",
        "\n",
        "mg_chapters.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2vNTbEFEwlfq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "b3108c08-d74b-4752-ee28-3d99ae8934a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rate of unique domains per word')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Find the chapters with the most unique ln values per word\n",
        "mg_chapters['ln_rate'] = mg_chapters['ln_count'] / mg_chapters['word_count']\n",
        "\n",
        "# Let's get the top and bottom 10 chapters based on ln_rate\n",
        "sorted_chs = mg_chapters.sort_values('ln_rate', ascending=False)\n",
        "top_chapters = sorted_chs.head(10)\n",
        "bottom_chapters = sorted_chs.tail(10)\n",
        "# Merge the top and bottom chapters into a single data frame\n",
        "top_bottom_chapters = pd.concat([top_chapters, bottom_chapters])\n",
        "\n",
        "# Plot the ln_rate ('Semantic Density') for each chapter\n",
        "ax = top_bottom_chapters.sort_values('ln_rate').plot(kind='bar', y='ln_rate', legend=False)\n",
        "ax.set_ylabel('Rate of unique domains')\n",
        "ax.set_xlabel('Chapters: Least semantically dense (left) to most (right)')\n",
        "ax.set_title('Rate of unique domains per word')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKOVodekwlfq"
      },
      "source": [
        "Looks like the age-old advice to \"start with John\" (or perhaps Johannine writings) is still good advice."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FmOcV62wlfq"
      },
      "source": [
        "Now let's imagine we have begun a new alignment task. We want to build up a tiny corpus of paired sentences for downstream NLP tasks, and we started with the chapters that had the most bang for our buck. Now we have several chapters complete. (e.g., Revelation 13, 1 John 4-5, John 6, 8)\n",
        "\n",
        "As we move forward with alignment, we could move on to the next chapters that give us the most bang for our buck, but we could also move on to the chapters that are most similar to the chapters we have already aligned, since this would allow us to leverage the work we have already done and get more examples for less work.\n",
        "\n",
        "One way to assess similarity between chapters would be to use the lemmas of the words in the chapter. One problem with this approach, however, is that it does not capture contextually sensitive information. For example, the word \"beast\" in the book of Revelation is very different from the word \"beast\" in Jesus's temptation in Mark 1:13. We would probably not consider those chapters to be particularly similar despite the presence of the same lemma.\n",
        "\n",
        "Let's compare the number of unique lemmas with the number of unique semantic domains in each chapter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mlo_avwkwlfq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "9fdabe3f-c2d3-4c6b-e78a-9fc789f0736f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              word_count                                                 ln  \\\n",
              "book_chapter                                                                  \n",
              "1CO 1                496  [93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...   \n",
              "1CO 10               463  [69.3, 25.1, 91.1, 92.7, 28.13 32.7, 11.23, 90...   \n",
              "1CO 11               531  [41.45, 92.1, 13.3, 64.14, 89.92 92.1, 93.387,...   \n",
              "1CO 12               470  [89.6, nan, 92.24, 12.21, 11.23, 69.3, 25.1, 9...   \n",
              "1CO 13               197  [89.67, 92.24, 33.2, 9.1, 33.70, 89.92, 12.28,...   \n",
              "\n",
              "              ln_count   ln_rate  \n",
              "book_chapter                      \n",
              "1CO 1              192  0.387097  \n",
              "1CO 10             204  0.440605  \n",
              "1CO 11             204  0.384181  \n",
              "1CO 12             173  0.368085  \n",
              "1CO 13              93  0.472081  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9bb8f09c-1fba-4886-a3e3-01b064202c5c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word_count</th>\n",
              "      <th>ln</th>\n",
              "      <th>ln_count</th>\n",
              "      <th>ln_rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book_chapter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1CO 1</th>\n",
              "      <td>496</td>\n",
              "      <td>[93.294a, 33.314, 53.74, 93.387, 93.169a, 89.7...</td>\n",
              "      <td>192</td>\n",
              "      <td>0.387097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 10</th>\n",
              "      <td>463</td>\n",
              "      <td>[69.3, 25.1, 91.1, 92.7, 28.13 32.7, 11.23, 90...</td>\n",
              "      <td>204</td>\n",
              "      <td>0.440605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 11</th>\n",
              "      <td>531</td>\n",
              "      <td>[41.45, 92.1, 13.3, 64.14, 89.92 92.1, 93.387,...</td>\n",
              "      <td>204</td>\n",
              "      <td>0.384181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 12</th>\n",
              "      <td>470</td>\n",
              "      <td>[89.6, nan, 92.24, 12.21, 11.23, 69.3, 25.1, 9...</td>\n",
              "      <td>173</td>\n",
              "      <td>0.368085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 13</th>\n",
              "      <td>197</td>\n",
              "      <td>[89.67, 92.24, 33.2, 9.1, 33.70, 89.92, 12.28,...</td>\n",
              "      <td>93</td>\n",
              "      <td>0.472081</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bb8f09c-1fba-4886-a3e3-01b064202c5c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9bb8f09c-1fba-4886-a3e3-01b064202c5c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9bb8f09c-1fba-4886-a3e3-01b064202c5c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "mg_chapters.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nFnx1ZmWwlfr"
      },
      "outputs": [],
      "source": [
        "# Find the number of unique lemmas in each chapter by adding a new column \n",
        "# based on the unique lemmas in the original mg data frame\n",
        "mg_chapters['lemma_count'] = mg.groupby('book_chapter')['lemma'].unique().apply(lambda x: len(x))\n",
        "mg_chapters['lemmas'] = mg.groupby('book_chapter')['lemma'].unique()\n",
        "mg_chapters['lemma_rate'] = mg_chapters['lemma_count'] / mg_chapters['word_count']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0GMhM6Wwlfr"
      },
      "source": [
        "Now let's check if unique lemmas correlate with unique semantic domains."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6wG-MtYZwlfr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "64b40d94-9344-41b0-ed49-de723680933b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation between Unique Lemma Rate and Unique Domain Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Check the correlation between lemma_rate and ln_rate, and plot the results\n",
        "ax = mg_chapters.plot(kind='scatter', x='lemma_rate', y='ln_rate')\n",
        "ax.set_xlabel('Unique Lemma Rate')\n",
        "ax.set_ylabel('Unique Domain Rate')\n",
        "ax.set_title('Correlation between Unique Lemma Rate and Unique Domain Rate')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WcJftBvwlfr"
      },
      "source": [
        "Unique lemmas and unique semantic domains are definitely correlated. But we can also see that there is a significant amount of variation here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4GpCQAi-wlfr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "4c3fa360-46ec-4e28-c941-0079ac00c048"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              word_count                                                 ln  \\\n",
              "book_chapter                                                                  \n",
              "JHN 6               1238  [67.48, 92.29, 15.37, 92.24, 93.169a, 83.43, 1...   \n",
              "JHN 17               498  [92.29, 33.70, 93.169a, 89.87, 24.34, 92.24, 8...   \n",
              "JHN 8               1087  [93.169a, 89.124, 15.34, 84.16, 92.24, 1.46, 3...   \n",
              "1JN 4                449  [25.45, nan, 59.23, 12.18 12.33, 31.35, 89.125...   \n",
              "JHN 9                691  [91.1, 15.15, 24.1, 9.24, 24.38, 67.131, 23.46...   \n",
              "\n",
              "              ln_count   ln_rate  lemma_count  \\\n",
              "book_chapter                                    \n",
              "JHN 6              296  0.239095          240   \n",
              "JHN 17             139  0.279116          101   \n",
              "JHN 8              262  0.241030          222   \n",
              "1JN 4              108  0.240535           94   \n",
              "JHN 9              186  0.269175          147   \n",
              "\n",
              "                                                         lemmas  lemma_rate  \n",
              "book_chapter                                                                 \n",
              "JHN 6         [μετά, οὗτος, ἀπέρχομαι, ὁ, Ἰησοῦς, πέραν, θάλ...    0.193861  \n",
              "JHN 17        [οὗτος, λαλέω, Ἰησοῦς, καί, ἐπαίρω, ὁ, ὀφθαλμό...    0.202811  \n",
              "JHN 8         [Ἰησοῦς, δέ, πορεύομαι, εἰς, ὁ, ὄρος, ἐλαία, ὄ...    0.204232  \n",
              "1JN 4         [ἀγαπητός, μή, πᾶς, πνεῦμα, πιστεύω, ἀλλά, δοκ...    0.209354  \n",
              "JHN 9         [καί, παράγω, ὁράω, ἄνθρωπος, τυφλός, ἐκ, γενε...    0.212735  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d889619-770f-4789-8e72-5c1dd718fdfa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word_count</th>\n",
              "      <th>ln</th>\n",
              "      <th>ln_count</th>\n",
              "      <th>ln_rate</th>\n",
              "      <th>lemma_count</th>\n",
              "      <th>lemmas</th>\n",
              "      <th>lemma_rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book_chapter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>JHN 6</th>\n",
              "      <td>1238</td>\n",
              "      <td>[67.48, 92.29, 15.37, 92.24, 93.169a, 83.43, 1...</td>\n",
              "      <td>296</td>\n",
              "      <td>0.239095</td>\n",
              "      <td>240</td>\n",
              "      <td>[μετά, οὗτος, ἀπέρχομαι, ὁ, Ἰησοῦς, πέραν, θάλ...</td>\n",
              "      <td>0.193861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JHN 17</th>\n",
              "      <td>498</td>\n",
              "      <td>[92.29, 33.70, 93.169a, 89.87, 24.34, 92.24, 8...</td>\n",
              "      <td>139</td>\n",
              "      <td>0.279116</td>\n",
              "      <td>101</td>\n",
              "      <td>[οὗτος, λαλέω, Ἰησοῦς, καί, ἐπαίρω, ὁ, ὀφθαλμό...</td>\n",
              "      <td>0.202811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JHN 8</th>\n",
              "      <td>1087</td>\n",
              "      <td>[93.169a, 89.124, 15.34, 84.16, 92.24, 1.46, 3...</td>\n",
              "      <td>262</td>\n",
              "      <td>0.241030</td>\n",
              "      <td>222</td>\n",
              "      <td>[Ἰησοῦς, δέ, πορεύομαι, εἰς, ὁ, ὄρος, ἐλαία, ὄ...</td>\n",
              "      <td>0.204232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1JN 4</th>\n",
              "      <td>449</td>\n",
              "      <td>[25.45, nan, 59.23, 12.18 12.33, 31.35, 89.125...</td>\n",
              "      <td>108</td>\n",
              "      <td>0.240535</td>\n",
              "      <td>94</td>\n",
              "      <td>[ἀγαπητός, μή, πᾶς, πνεῦμα, πιστεύω, ἀλλά, δοκ...</td>\n",
              "      <td>0.209354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JHN 9</th>\n",
              "      <td>691</td>\n",
              "      <td>[91.1, 15.15, 24.1, 9.24, 24.38, 67.131, 23.46...</td>\n",
              "      <td>186</td>\n",
              "      <td>0.269175</td>\n",
              "      <td>147</td>\n",
              "      <td>[καί, παράγω, ὁράω, ἄνθρωπος, τυφλός, ἐκ, γενε...</td>\n",
              "      <td>0.212735</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d889619-770f-4789-8e72-5c1dd718fdfa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d889619-770f-4789-8e72-5c1dd718fdfa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d889619-770f-4789-8e72-5c1dd718fdfa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# What are the bottom 5 chapters with the lowest lemma_rate?\n",
        "mg_chapters.sort_values('lemma_rate', ascending=True).head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b77P9KQKwlfr"
      },
      "source": [
        "Now let's one-hot encode the semantic domains by chapter, and then do a simple cosine similarity between the chapters based on semantic domains. This next block can take around 30 seconds on Colab to run. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHD-e1e8wlfr"
      },
      "outputs": [],
      "source": [
        "# Get all the unique domains in the MACULA Greek data set\n",
        "all_unique_domains = mg['ln'].unique()\n",
        "\n",
        "# Create a one-hot encoding for each chapter's domains\n",
        "mg_chapters['one_hot_domains'] = mg_chapters['ln'].apply(lambda x: [1 if domain in x else 0 for domain in all_unique_domains])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2ZW7fNVwlfs"
      },
      "source": [
        "## Cluster the chapters based on semantic domain similarity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bEDhoO47wlfs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2914e0dc-704f-40ef-d84a-967e8186fede"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.post1.tar.gz (3.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: sklearn\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2955 sha256=d8cb4f1a97d6679ad77373a66bf44758dec27c69067262d7c0571c9ae4e6a8bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/e0/3d/9d0c2020c44a519b9f02ab4fa6d2a4a996c98d79ab2f569fa1\n",
            "Successfully built sklearn\n",
            "Installing collected packages: sklearn\n",
            "Successfully installed sklearn-0.0.post1\n"
          ]
        }
      ],
      "source": [
        "# Identify every chapter's similarity to every other chapter by comparing\n",
        "# the one-hot encodings of their domains using the cosine similarity metric\n",
        "\n",
        "!pip install sklearn\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XmQUn46Uwlfs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "ffd766d0-6f95-49ce-96f7-aefa4ecfe0fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.013565948792607994 1.0000000000000036\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "book_chapter       1CO 1      1CO 10      1CO 11      1CO 12      1CO 13  \\\n",
              "book_chapter                                                               \n",
              "1CO 1         100.000000   28.485122   27.455454   26.030841   12.390308   \n",
              "1CO 10         28.485122  100.000000   29.586648   25.750995   14.944468   \n",
              "1CO 11         27.455454   29.586648  100.000000   26.836045   15.686274   \n",
              "1CO 12         26.030841   25.750995   26.836045  100.000000   16.354249   \n",
              "1CO 13         12.390308   14.944468   15.686274   16.354249  100.000000   \n",
              "\n",
              "book_chapter     1CO 14     1CO 15     1CO 16      1CO 2      1CO 3  ...  \\\n",
              "book_chapter                                                         ...   \n",
              "1CO 1         22.045578  27.037540  24.061111  29.953573  30.521803  ...   \n",
              "1CO 10        27.968889  24.031820  21.054837  24.389119  25.399683  ...   \n",
              "1CO 11        28.442182  31.352502  26.101606  27.692244  27.184679  ...   \n",
              "1CO 12        28.961251  26.226612  23.601655  26.614777  26.419843  ...   \n",
              "1CO 13        21.825270  16.535538  16.116947  21.195025  20.720563  ...   \n",
              "\n",
              "book_chapter      ROM 3      ROM 4      ROM 5      ROM 6      ROM 7  \\\n",
              "book_chapter                                                          \n",
              "1CO 1         23.299472  22.794547  22.991142  21.436854  23.075601   \n",
              "1CO 10        25.160617  24.537134  21.669102  25.054920  27.085340   \n",
              "1CO 11        23.079372  23.920172  25.214387  24.440265  25.306553   \n",
              "1CO 12        25.191887  22.754031  24.301715  21.328323  25.034866   \n",
              "1CO 13        16.401200  16.037436  15.301353  16.885373  17.120675   \n",
              "\n",
              "book_chapter      ROM 8      ROM 9      TIT 1      TIT 2      TIT 3  \n",
              "book_chapter                                                         \n",
              "1CO 1         24.167412  21.210532  14.795619  11.100846  15.161979  \n",
              "1CO 10        22.448025  23.795705  11.986584  10.054159  11.580941  \n",
              "1CO 11        26.259750  23.795705  14.891331  12.071114  10.963980  \n",
              "1CO 12        22.435501  24.450914  15.034252  13.232679  17.391968  \n",
              "1CO 13        16.318732  14.550059   7.254383   9.610296  11.455150  \n",
              "\n",
              "[5 rows x 260 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e938ca7-0165-41ec-9f69-652f4f97ad7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>book_chapter</th>\n",
              "      <th>1CO 1</th>\n",
              "      <th>1CO 10</th>\n",
              "      <th>1CO 11</th>\n",
              "      <th>1CO 12</th>\n",
              "      <th>1CO 13</th>\n",
              "      <th>1CO 14</th>\n",
              "      <th>1CO 15</th>\n",
              "      <th>1CO 16</th>\n",
              "      <th>1CO 2</th>\n",
              "      <th>1CO 3</th>\n",
              "      <th>...</th>\n",
              "      <th>ROM 3</th>\n",
              "      <th>ROM 4</th>\n",
              "      <th>ROM 5</th>\n",
              "      <th>ROM 6</th>\n",
              "      <th>ROM 7</th>\n",
              "      <th>ROM 8</th>\n",
              "      <th>ROM 9</th>\n",
              "      <th>TIT 1</th>\n",
              "      <th>TIT 2</th>\n",
              "      <th>TIT 3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book_chapter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1CO 1</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>28.485122</td>\n",
              "      <td>27.455454</td>\n",
              "      <td>26.030841</td>\n",
              "      <td>12.390308</td>\n",
              "      <td>22.045578</td>\n",
              "      <td>27.037540</td>\n",
              "      <td>24.061111</td>\n",
              "      <td>29.953573</td>\n",
              "      <td>30.521803</td>\n",
              "      <td>...</td>\n",
              "      <td>23.299472</td>\n",
              "      <td>22.794547</td>\n",
              "      <td>22.991142</td>\n",
              "      <td>21.436854</td>\n",
              "      <td>23.075601</td>\n",
              "      <td>24.167412</td>\n",
              "      <td>21.210532</td>\n",
              "      <td>14.795619</td>\n",
              "      <td>11.100846</td>\n",
              "      <td>15.161979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 10</th>\n",
              "      <td>28.485122</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>29.586648</td>\n",
              "      <td>25.750995</td>\n",
              "      <td>14.944468</td>\n",
              "      <td>27.968889</td>\n",
              "      <td>24.031820</td>\n",
              "      <td>21.054837</td>\n",
              "      <td>24.389119</td>\n",
              "      <td>25.399683</td>\n",
              "      <td>...</td>\n",
              "      <td>25.160617</td>\n",
              "      <td>24.537134</td>\n",
              "      <td>21.669102</td>\n",
              "      <td>25.054920</td>\n",
              "      <td>27.085340</td>\n",
              "      <td>22.448025</td>\n",
              "      <td>23.795705</td>\n",
              "      <td>11.986584</td>\n",
              "      <td>10.054159</td>\n",
              "      <td>11.580941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 11</th>\n",
              "      <td>27.455454</td>\n",
              "      <td>29.586648</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>26.836045</td>\n",
              "      <td>15.686274</td>\n",
              "      <td>28.442182</td>\n",
              "      <td>31.352502</td>\n",
              "      <td>26.101606</td>\n",
              "      <td>27.692244</td>\n",
              "      <td>27.184679</td>\n",
              "      <td>...</td>\n",
              "      <td>23.079372</td>\n",
              "      <td>23.920172</td>\n",
              "      <td>25.214387</td>\n",
              "      <td>24.440265</td>\n",
              "      <td>25.306553</td>\n",
              "      <td>26.259750</td>\n",
              "      <td>23.795705</td>\n",
              "      <td>14.891331</td>\n",
              "      <td>12.071114</td>\n",
              "      <td>10.963980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 12</th>\n",
              "      <td>26.030841</td>\n",
              "      <td>25.750995</td>\n",
              "      <td>26.836045</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>16.354249</td>\n",
              "      <td>28.961251</td>\n",
              "      <td>26.226612</td>\n",
              "      <td>23.601655</td>\n",
              "      <td>26.614777</td>\n",
              "      <td>26.419843</td>\n",
              "      <td>...</td>\n",
              "      <td>25.191887</td>\n",
              "      <td>22.754031</td>\n",
              "      <td>24.301715</td>\n",
              "      <td>21.328323</td>\n",
              "      <td>25.034866</td>\n",
              "      <td>22.435501</td>\n",
              "      <td>24.450914</td>\n",
              "      <td>15.034252</td>\n",
              "      <td>13.232679</td>\n",
              "      <td>17.391968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1CO 13</th>\n",
              "      <td>12.390308</td>\n",
              "      <td>14.944468</td>\n",
              "      <td>15.686274</td>\n",
              "      <td>16.354249</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>21.825270</td>\n",
              "      <td>16.535538</td>\n",
              "      <td>16.116947</td>\n",
              "      <td>21.195025</td>\n",
              "      <td>20.720563</td>\n",
              "      <td>...</td>\n",
              "      <td>16.401200</td>\n",
              "      <td>16.037436</td>\n",
              "      <td>15.301353</td>\n",
              "      <td>16.885373</td>\n",
              "      <td>17.120675</td>\n",
              "      <td>16.318732</td>\n",
              "      <td>14.550059</td>\n",
              "      <td>7.254383</td>\n",
              "      <td>9.610296</td>\n",
              "      <td>11.455150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 260 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e938ca7-0165-41ec-9f69-652f4f97ad7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2e938ca7-0165-41ec-9f69-652f4f97ad7b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2e938ca7-0165-41ec-9f69-652f4f97ad7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Create a new data frame with the cosine similarity values\n",
        "chapter_sims = pd.DataFrame(cosine_similarity(mg_chapters['one_hot_domains'].tolist()), index=mg_chapters.index, columns=mg_chapters.index)\n",
        "\n",
        "# Let's also normalize these similarities on a 0–100 scale:\n",
        "\n",
        "# Find the minimum and maximum values\n",
        "min_val = chapter_sims.min().min() # the minimum value of all the minimum column values\n",
        "max_val = chapter_sims.max().max() # same for maximum\n",
        "print(min_val, max_val)\n",
        "\n",
        "# Normalize the data frame\n",
        "chapter_sims_norm = chapter_sims.applymap(lambda x: ((x - min_val) / (max_val - min_val)) * 100)\n",
        "\n",
        "chapter_sims_norm.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's return to our imagined scenario, where we have Revelation 13, 1 John 4-5, and John 6 and 8 aligned, and we want to move on to the next-most-similar chapters."
      ],
      "metadata": {
        "id": "MkrA9Gho7cVO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly\n",
        "!pip install networkx\n",
        "import plotly.graph_objects as go\n",
        "import networkx as nx"
      ],
      "metadata": {
        "id": "aN6Pg5MPBtqy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "761bcf26-748a-4319-ef05-999b1ac970f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.9/dist-packages (5.5.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from plotly) (8.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from plotly) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's set the threshold for what counts as \"similar\" chapters in semantic domains\n",
        "threshold = 0 # percentage value\n",
        "\n",
        "# Create a new data frame subset based on your selected book\n",
        "\n",
        "# Instantiate a graph\n",
        "graph = nx.Graph()"
      ],
      "metadata": {
        "id": "rsWAGodVDfuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Populate graph nodes\n",
        "\n",
        "# Let's decide which chapter we want to query\n",
        "ch_query = 'REV 13'\n",
        "\n",
        "# Add nodes for each book in the query\n",
        "for ch in chapter_sims_norm.index:\n",
        "    if ch in ch_query:\n",
        "        graph.add_node(ch)\n",
        "\n",
        "# ### Populate graph edges\n",
        "\n",
        "n = 3  # number of top-n values to keep\n",
        "\n",
        "# Create a dictionary to store the top-n values for each node\n",
        "top_n = {ch: [] for ch in chapter_sims_norm.index}\n",
        "\n",
        "# Loop through the matrix and keep track of the top-n values for each node\n",
        "for i, ch1 in enumerate(chapter_sims_norm.index):\n",
        "    for j, ch2 in enumerate(chapter_sims_norm.columns):\n",
        "        if not i == j:\n",
        "            similarity = chapter_sims_norm.loc[ch1, ch2]\n",
        "            if similarity >= threshold:\n",
        "                top_n[ch1].append((similarity, ch2))\n",
        "                top_n[ch2].append((similarity, ch1))\n",
        "\n",
        "# Sort the top-n values for each node and keep only the top-n values\n",
        "for ch in top_n:\n",
        "    top_n[ch] = sorted(top_n[ch], reverse=True)[:n]\n",
        "\n",
        "# Add edges for the top-n values for each node\n",
        "for ch in top_n:\n",
        "    for similarity, ch2 in top_n[ch]:\n",
        "        graph.add_edge(ch, ch2, weight=similarity)\n"
      ],
      "metadata": {
        "id": "_YqnIvA1F-Lz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for ch, sim_list in top_n.items():\n",
        "    print(sorted(sim_list, key=lambda x: x[1]))\n",
        "    break\n",
        "    top_n[ch] = sim_list[:n]"
      ],
      "metadata": {
        "id": "40kFHkyTnpfA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fcc1cb6-d315-4ef6-9500-7e0fa42cf632"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(30.521803423673443, '1CO 3'), (30.521803423673443, '1CO 3'), (30.38733767338671, '2CO 1')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw edges on graph\n",
        "positions = nx.spring_layout(graph)\n",
        "\n",
        "edge_x = []\n",
        "edge_y = []\n",
        "for edge in graph.edges():\n",
        "    x0, y0 = positions[edge[0]]\n",
        "    x1, y1 = positions[edge[1]]\n",
        "    edge_x.append(x0)\n",
        "    edge_x.append(x1)\n",
        "    edge_x.append(None)\n",
        "    edge_y.append(y0)\n",
        "    edge_y.append(y1)\n",
        "    edge_y.append(None)\n",
        "\n",
        "edge_trace = go.Scatter(\n",
        "    x=edge_x, y=edge_y,\n",
        "    line=dict(width=0.5, color='#888'),\n",
        "    hoverinfo='none',\n",
        "    mode='lines')\n",
        "\n",
        "# Draw nodes on graph\n",
        "\n",
        "node_x = []\n",
        "node_y = []\n",
        "for node in graph.nodes():\n",
        "    x, y = positions[node]\n",
        "    node_x.append(x)\n",
        "    node_y.append(y)\n",
        "\n",
        "node_trace = go.Scatter(\n",
        "    x=node_x, y=node_y,\n",
        "    mode='markers+text',\n",
        "    hoverinfo='text',\n",
        "    marker=dict(\n",
        "        showscale=True,\n",
        "        # colorscale options\n",
        "        #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |\n",
        "        #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |\n",
        "        #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |\n",
        "        colorscale='YlGnBu',\n",
        "        reversescale=True,\n",
        "        color=[],\n",
        "        size=20,\n",
        "        colorbar=dict(\n",
        "            thickness=15,\n",
        "            title='Node Connections',\n",
        "            xanchor='left',\n",
        "            titleside='right'\n",
        "        ),\n",
        "        line_width=2))"
      ],
      "metadata": {
        "id": "tO8pqPU5E536"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Color nodes\n",
        "\n",
        "node_adjacencies = []\n",
        "node_text = []\n",
        "for node, adjacencies in enumerate(graph.adjacency()):\n",
        "    node_adjacencies.append(len(adjacencies[1]))\n",
        "    node_text.append(adjacencies[0] + ': # of connections: '+str(len(adjacencies[1])))\n",
        "\n",
        "node_trace.marker.color = node_adjacencies\n",
        "node_trace.text = node_text"
      ],
      "metadata": {
        "id": "eoqlevwEIgsz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the graph\n",
        "\n",
        "fig = go.Figure(data=[edge_trace, node_trace],\n",
        "             layout=go.Layout(\n",
        "                title='<br>Network graph made with Python',\n",
        "                titlefont_size=16,\n",
        "                showlegend=False,\n",
        "                hovermode='closest',\n",
        "                margin=dict(b=20,l=5,r=5,t=40),\n",
        "                annotations=[ dict(\n",
        "                    text=\"Python code: <a href='https://plotly.com/ipython-notebooks/network-graphs/'> https://plotly.com/ipython-notebooks/network-graphs/</a>\",\n",
        "                    showarrow=False,\n",
        "                    xref=\"paper\", yref=\"paper\",\n",
        "                    x=0.005, y=-0.002 ) ],\n",
        "                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n",
        "                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n",
        "                )\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "zTBGT7vsIuAX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "6de2739a-9c0e-4bc5-af85-f4eeb2aab9bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"e3e08f62-b980-44ec-9a0a-cee60888231e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e3e08f62-b980-44ec-9a0a-cee60888231e\")) {                    Plotly.newPlot(                        \"e3e08f62-b980-44ec-9a0a-cee60888231e\",                        [{\"hoverinfo\":\"none\",\"line\":{\"color\":\"#888\",\"width\":0.5},\"mode\":\"lines\",\"x\":[0.9552999802323117,0.9687466482896196,null,0.9552999802323117,0.9019020025281921,null,0.9552999802323117,0.9999999999999999,null,0.9552999802323117,0.9897316438689584,null,0.9157723778263235,0.8774440364769983,null,0.9157723778263235,0.8772425926199103,null,0.1362827305267113,0.08843201384345266,null,0.1362827305267113,0.11657913399672568,null,0.08843201384345266,0.04646281556179068,null,0.08843201384345266,0.09086121049295609,null,0.08843201384345266,0.055916745448746134,null,0.08843201384345266,0.098293990608412,null,0.11657913399672568,0.08558660118454715,null,0.11657913399672568,0.14405952587408888,null,0.11657913399672568,-0.01545479724369979,null,0.11657913399672568,0.25115457327171004,null,0.11657913399672568,0.1437616817767355,null,0.11657913399672568,0.17728485977279207,null,0.11657913399672568,0.12873710791691628,null,0.11657913399672568,0.21479260882833923,null,0.11657913399672568,0.1382792557050979,null,0.11657913399672568,0.03744848231977054,null,0.11657913399672568,0.05214494723784596,null,0.11657913399672568,-0.06614385195519047,null,0.11657913399672568,0.08230114187805664,null,0.11657913399672568,0.17245653998737137,null,0.11657913399672568,0.10547843381540101,null,0.11657913399672568,0.21494076647196722,null,0.11657913399672568,0.15766904852565972,null,0.08405154737440483,0.09086121049295609,null,0.08405154737440483,0.036521934922920815,null,0.09086121049295609,0.15854281022239686,null,0.09086121049295609,0.015056261434569061,null,0.036521934922920815,0.10151200763657674,null,0.036521934922920815,0.10596668827851721,null,0.036521934922920815,-0.03918437248433018,null,0.036521934922920815,0.016416541423770546,null,0.15700203933746726,0.10151200763657674,null,0.15700203933746726,0.1437616817767355,null,0.1437616817767355,0.14405952587408888,null,0.1437616817767355,0.24249786461605227,null,0.1437616817767355,0.12873710791691628,null,0.1437616817767355,0.21479260882833923,null,0.1437616817767355,0.1681476128214417,null,0.1437616817767355,0.20227613698054844,null,0.1437616817767355,0.28179952635148503,null,0.1437616817767355,0.07204811645444513,null,0.1437616817767355,0.010941292598778686,null,0.1437616817767355,0.16195735289797145,null,0.1437616817767355,0.07118812986484449,null,0.1437616817767355,0.17245653998737137,null,0.03919967837906931,-0.031159923214225452,null,0.03919967837906931,-0.024960570512592824,null,0.03919967837906931,0.16229607947025895,null,-0.031159923214225452,-0.03330657748978564,null,-0.031159923214225452,-0.00756256189953802,null,-0.031159923214225452,-0.10059796534263028,null,-0.031159923214225452,-0.008338563108177276,null,-0.031159923214225452,-0.005173496920277382,null,-0.031159923214225452,-0.04788051929924048,null,-0.031159923214225452,-0.07032819863473151,null,-0.024960570512592824,0.04646281556179068,null,-0.024960570512592824,0.03051645494810054,null,-0.024960570512592824,-0.12409862104416185,null,-0.024960570512592824,-0.10807652955097384,null,-0.024960570512592824,0.05214494723784596,null,-0.024960570512592824,-0.03330657748978564,null,-0.024960570512592824,-0.03199530674634295,null,-0.024960570512592824,0.016416541423770546,null,-0.024960570512592824,-0.00948382045736814,null,-0.024960570512592824,-0.0025999085136414303,null,-0.024960570512592824,-0.04788051929924048,null,-0.024960570512592824,-0.11269947208860413,null,0.15854281022239686,0.12873710791691628,null,0.12873710791691628,0.005088121675163547,null,0.12873710791691628,0.2230563641954201,null,0.12873710791691628,0.1983111752609072,null,0.08558660118454715,0.04646281556179068,null,0.04646281556179068,0.03051645494810054,null,0.14405952587408888,0.15766904852565972,null,0.29550405105573213,0.34972586672700856,null,0.29550405105573213,0.21479260882833923,null,0.34972586672700856,0.30311070347417596,null,0.21479260882833923,0.2664820253751229,null,0.21479260882833923,0.2547984592527346,null,0.21479260882833923,0.2927189840298951,null,0.21479260882833923,0.23982324994979562,null,0.21479260882833923,0.17728485977279207,null,0.30311070347417596,0.2927189840298951,null,0.30311070347417596,0.21494076647196722,null,0.005088121675163547,-0.11690668753625967,null,-0.11690668753625967,-0.10807652955097384,null,-0.11690668753625967,-0.20504263506687528,null,-0.11690668753625967,-0.1476074104300688,null,0.055916745448746134,0.015056261434569061,null,0.015056261434569061,-0.06995219615067527,null,0.09943059551277142,0.10429715424756447,null,0.09943059551277142,0.09540048970293202,null,0.10429715424756447,0.09540048970293202,null,0.10429715424756447,0.11124151114737141,null,0.10429715424756447,0.12122913840024231,null,0.10429715424756447,0.11996471876359412,null,0.09540048970293202,0.11124151114737141,null,0.11124151114737141,0.12122913840024231,null,0.11124151114737141,0.11996471876359412,null,-0.01545479724369979,-0.13971339962658247,null,-0.01545479724369979,-0.061245293517503,null,-0.13971339962658247,-0.20953797536789293,null,-0.13971339962658247,-0.17830522022597906,null,-0.16451615590653815,-0.19364043622910368,null,-0.16451615590653815,-0.11269947208860413,null,-0.16451615590653815,-0.15026821247719294,null,-0.19364043622910368,-0.2552303690963533,null,-0.19364043622910368,-0.10325047033394677,null,-0.11269947208860413,-0.14343593162301038,null,-0.15026821247719294,-0.06995219615067527,null,-0.06995219615067527,-0.11523200476392337,null,-0.06995219615067527,-0.04915617422031615,null,-0.06995219615067527,-0.09098880990819883,null,-0.061245293517503,-0.10883021228553322,null,-0.10883021228553322,-0.17830522022597906,null,-0.10883021228553322,-0.04915617422031615,null,0.32458678172171196,0.34778363793176753,null,0.32458678172171196,0.2230563641954201,null,0.34778363793176753,0.28179952635148503,null,0.34778363793176753,0.3446010770094448,null,0.2230563641954201,0.16229607947025895,null,0.2230563641954201,0.21572944936034746,null,0.2230563641954201,0.3041890520717568,null,0.25115457327171004,0.2958858316243668,null,0.25115457327171004,0.24139143496891624,null,0.25115457327171004,0.35383295197368664,null,0.25115457327171004,0.28179952635148503,null,0.2958858316243668,0.24249786461605227,null,0.2958858316243668,0.3041890520717568,null,0.24139143496891624,0.16229607947025895,null,0.21572944936034746,0.16195735289797145,null,0.28179952635148503,0.3948449136360835,null,0.3446010770094448,0.41235588183383404,null,0.3446010770094448,0.39464773467609904,null,0.3446010770094448,0.2266193464455241,null,0.41235588183383404,0.39464773467609904,null,0.41235588183383404,0.4416228558869236,null,0.4698202380737744,0.3948449136360835,null,0.4698202380737744,0.4416228558869236,null,0.3948449136360835,0.4416228558869236,null,0.2266193464455241,0.098293990608412,null,0.098293990608412,0.016416541423770546,null,0.098293990608412,0.08700903798677691,null,0.098293990608412,0.04133891614368571,null,0.17728485977279207,0.23982324994979562,null,0.17728485977279207,0.20227613698054844,null,0.17728485977279207,0.10596668827851721,null,0.17728485977279207,0.21494076647196722,null,0.1983111752609072,0.21494076647196722,null,0.2664820253751229,0.2547984592527346,null,0.1382792557050979,0.1681476128214417,null,0.03744848231977054,-0.04915617422031615,null,-0.04915617422031615,-0.09098880990819883,null,0.11996471876359412,0.14301340214197944,null,0.35383295197368664,0.4329364387144856,null,0.4329364387144856,0.4913380023721494,null,0.4329364387144856,0.48752940585278487,null,0.4913380023721494,0.48752940585278487,null,0.16195735289797145,0.14301340214197944,null,0.16195735289797145,0.11727948210599927,null,-0.12409862104416185,-0.19116976932142196,null,-0.19116976932142196,-0.1476074104300688,null,-0.1476074104300688,-0.10059796534263028,null,-0.36995288105576685,-0.4732301001546511,null,-0.36995288105576685,-0.2595616044030423,null,-0.4732301001546511,-0.5273211800608226,null,-0.4732301001546511,-0.47255790415160287,null,-0.4732301001546511,-0.503348941965345,null,-0.4732301001546511,-0.38246410282582144,null,-0.4732301001546511,-0.5846402383896447,null,-0.2595616044030423,-0.11936881258240646,null,-0.2595616044030423,-0.36772032987561826,null,-0.5273211800608226,-0.47255790415160287,null,-0.5273211800608226,-0.6097234688369254,null,-0.5273211800608226,-0.584279458592185,null,-0.5273211800608226,-0.4395967292874797,null,-0.5273211800608226,-0.5874096045185813,null,-0.47255790415160287,-0.503348941965345,null,-0.47255790415160287,-0.3894814301910162,null,-0.47255790415160287,-0.45199425959403966,null,-0.47255790415160287,-0.5058189948340637,null,-0.3894814301910162,-0.45199425959403966,null,-0.3894814301910162,-0.44280310260858674,null,-0.3894814301910162,-0.2749791996828912,null,-0.45199425959403966,-0.44280310260858674,null,-0.45199425959403966,-0.5058189948340637,null,-0.6097234688369254,-0.5846402383896447,null,-0.6097234688369254,-0.6896729331269446,null,-0.5846402383896447,-0.6562984490030181,null,-0.5846402383896447,-0.6438054157770697,null,-0.5846402383896447,-0.6686901191638355,null,-0.5846402383896447,-0.584279458592185,null,-0.7628284586718217,-0.6896729331269446,null,-0.7628284586718217,-0.7902416025033387,null,-0.6896729331269446,-0.6562984490030181,null,-0.7902416025033387,-0.7410947933510762,null,-0.7902416025033387,-0.7627670198119602,null,-0.6562984490030181,-0.6438054157770697,null,-0.38246410282582144,-0.343370286921526,null,-0.38246410282582144,-0.312903483514654,null,-0.343370286921526,-0.22630936760624226,null,-0.343370286921526,-0.4395967292874797,null,-0.6522564652300094,-0.5874096045185813,null,-0.6522564652300094,-0.7064668184286323,null,-0.7064668184286323,-0.7627670198119602,null,-0.7410947933510762,-0.7627670198119602,null,-0.7410947933510762,-0.6686901191638355,null,-0.22630936760624226,-0.10053704970413707,null,-0.10053704970413707,-0.17069203777532974,null,-0.10053704970413707,-0.04813009450192303,null,-0.10053704970413707,-0.05678601993290698,null,-0.10053704970413707,-0.027622303475060214,null,-0.10053704970413707,-0.021992456604187294,null,-0.10053704970413707,-0.11549655869766236,null,-0.2749791996828912,-0.18311957915908092,null,-0.2749791996828912,-0.33428811941750863,null,-0.2749791996828912,-0.27980984541817655,null,-0.2749791996828912,-0.2656962605186722,null,-0.18311957915908092,-0.15657870783661268,null,-0.18311957915908092,-0.1561758624155468,null,-0.18311957915908092,-0.10913215710589227,null,-0.20953797536789293,-0.17830522022597906,null,-0.20953797536789293,-0.16700929992612243,null,-0.20953797536789293,-0.13485044966656792,null,-0.20953797536789293,-0.312903483514654,null,-0.16700929992612243,-0.09098880990819883,null,-0.09098880990819883,-0.03918437248433018,null,-0.036079635509313084,-0.13485044966656792,null,-0.036079635509313084,0.07204811645444513,null,0.07204811645444513,0.11727948210599927,null,0.07204811645444513,0.10547843381540101,null,0.010941292598778686,-0.10325047033394677,null,-0.10325047033394677,-0.11523200476392337,null,-0.10325047033394677,-0.08461527994328112,null,-0.11523200476392337,-0.10910602694455512,null,-0.11523200476392337,-0.14343593162301038,null,-0.08461527994328112,-0.04788051929924048,null,-0.04788051929924048,-0.03367817436785675,null,-0.04788051929924048,-0.008338563108177276,null,-0.04788051929924048,-0.07032819863473151,null,-0.20504263506687528,-0.2613408648289707,null,-0.2613408648289707,-0.2552303690963533,null,-0.32073406682235833,-0.38648987603733675,null,-0.32073406682235833,-0.24157916332922472,null,-0.38648987603733675,-0.42885016741964493,null,-0.38648987603733675,-0.44587583888897614,null,-0.38648987603733675,-0.33161636516287424,null,-0.24157916332922472,-0.2359926756504242,null,-0.24157916332922472,-0.12617245341876002,null,-0.24157916332922472,-0.29137260806083043,null,-0.24157916332922472,-0.2739201107305846,null,-0.2327032990263254,-0.31566133899371634,null,-0.2327032990263254,-0.06614385195519047,null,-0.2327032990263254,-0.27980984541817655,null,-0.2327032990263254,-0.33161636516287424,null,-0.2327032990263254,-0.2656962605186722,null,-0.31566133899371634,-0.38911656094940905,null,-0.33428811941750863,-0.388383664866293,null,-0.388383664866293,-0.42885016741964493,null,-0.388383664866293,-0.38911656094940905,null,-0.42885016741964493,-0.44587583888897614,null,0.016416541423770546,0.04133891614368571,null,0.016416541423770546,-0.0025999085136414303,null,0.016416541423770546,-0.00948382045736814,null,0.016416541423770546,0.07118812986484449,null,-0.10910602694455512,-0.03918437248433018,null,0.08700903798677691,0.10596668827851721,null,-0.10953494457505011,-0.10059796534263028,null,-0.10953494457505011,-0.07032819863473151,null,-0.03330657748978564,-0.035790186182617444,null,-0.034639870844783975,0.009568704389344847,null,-0.034639870844783975,-0.005173496920277382,null,-0.034639870844783975,-0.035790186182617444,null,-0.034639870844783975,-0.03367817436785675,null,-0.034639870844783975,-0.07379293146820669,null,0.009568704389344847,-0.005173496920277382,null,-0.005173496920277382,0.026701091321770117,null,-0.03199530674634295,-0.0025999085136414303,null,-0.5128387442306164,-0.4376569905055443,null,-0.5128387442306164,-0.5827651775803566,null,-0.4376569905055443,-0.40416425497901093,null,-0.4376569905055443,-0.36772032987561826,null,-0.5827651775803566,-0.6195336549027654,null,-0.5827651775803566,-0.6121198792205849,null,-0.6195336549027654,-0.6350532601135453,null,-0.6195336549027654,-0.6121198792205849,null,-0.6121198792205849,-0.6350532601135453,null,-0.00756256189953802,-0.03367817436785675,null,-0.035790186182617444,-0.017657584284185967,null,-0.035790186182617444,-0.03759813326525296,null,0.026701091321770117,-0.008338563108177276,null,-0.15657870783661268,-0.14481707208165262,null,-0.15657870783661268,-0.165805070991341,null,-0.14481707208165262,-0.18979722670740465,null,-0.14481707208165262,-0.11936881258240646,null,-0.18979722670740465,-0.22579637465455496,null,-0.11936881258240646,-0.0888755110850219,null,-0.11936881258240646,-0.08687290737941705,null,-0.11936881258240646,-0.10427390424191392,null,-0.11936881258240646,-0.07159213372108957,null,-0.11936881258240646,-0.06285709010184719,null,-0.08842777050800435,-0.10425046039208771,null,-0.08842777050800435,-0.078451131552667,null,-0.10425046039208771,-0.12310270788241509,null,-0.10425046039208771,-0.12444780050433346,null,-0.078451131552667,-0.07379293146820669,null,-0.2359926756504242,-0.17344760492518468,null,-0.2359926756504242,-0.28600283041455377,null,-0.17344760492518468,-0.07379293146820669,null,0.08757895851482117,0.038943579006180326,null,0.08757895851482117,0.06776095593734784,null,0.038943579006180326,0.06776095593734784,null,0.038943579006180326,-0.021992456604187294,null,0.038943579006180326,0.06913076752645556,null,0.038943579006180326,-0.027622303475060214,null,0.06776095593734784,0.06828958289153332,null,0.06776095593734784,0.06913076752645556,null,-0.17069203777532974,-0.22579637465455496,null,-0.22579637465455496,-0.30709074727781144,null,-0.04813009450192303,-0.018807750215435872,null,-0.018807750215435872,-0.12617245341876002,null,-0.018807750215435872,-0.07379293146820669,null,-0.018807750215435872,0.0915132383073119,null,0.06828958289153332,0.020544974980620075,null,0.020544974980620075,-0.01563296064924459,null,0.020544974980620075,-0.0016567939810350693,null,-0.01563296064924459,-0.0016567939810350693,null,-0.01563296064924459,-0.05678601993290698,null,-0.1561758624155468,-0.15466079868328123,null,-0.1561758624155468,-0.11354210330586192,null,-0.021992456604187294,-0.027622303475060214,null,-0.29137260806083043,-0.2739201107305846,null,-0.2739201107305846,-0.28600283041455377,null,-0.40416425497901093,-0.36772032987561826,null,-0.16263896627384483,-0.15121293169187147,null,-0.16263896627384483,-0.11354210330586192,null,-0.16263896627384483,-0.165805070991341,null,-0.15121293169187147,-0.11354210330586192,null,-0.11354210330586192,-0.15466079868328123,null,-0.11354210330586192,-0.025885273301385906,null,-0.35199669228264374,-0.34861957157495566,null,-0.35199669228264374,-0.30709074727781144,null,-0.34861957157495566,-0.30709074727781144,null,-0.12310270788241509,-0.12444780050433346,null,-0.12310270788241509,-0.11549655869766236,null,0.1472000825180689,0.07544011143994238,null,0.1472000825180689,0.19469699083959677,null,0.1472000825180689,0.16176645171036833,null,0.07544011143994238,-0.025885273301385906,null,0.19469699083959677,0.16176645171036833,null,-0.0888755110850219,-0.05441803414478577,null,-0.0888755110850219,-0.08759696003720249,null,-0.0888755110850219,-0.10427390424191392,null,-0.0888755110850219,-0.08175167085432135,null,-0.08687290737941705,-0.07159213372108957,null,0.16176645171036833,0.0915132383073119,null,-0.05441803414478577,-0.06285709010184719,null,-0.08759696003720249,-0.08175167085432135,null,-0.10913215710589227,-0.025885273301385906,null,-0.017657584284185967,-0.03759813326525296,null,0.07118812986484449,0.08230114187805664,null,0.9687466482896196,0.9415910018695208,null,0.9019020025281921,0.9138225100381723,null,0.9019020025281921,0.8533076788080826,null,0.820691168093154,0.8093131349532543,null,0.820691168093154,0.8147608364678747,null,0.8093131349532543,0.8533076788080826,null,0.8093131349532543,0.8147608364678747,null,0.8093131349532543,0.7894655211187835,null,0.8147608364678747,0.8722282938405187,null,0.8147608364678747,0.8239429869740492,null,0.8147608364678747,0.7894655211187835,null,0.8533076788080826,0.9045942651064433,null,0.8533076788080826,0.8772425926199103,null,0.8533076788080826,0.8793490621737636,null,0.8533076788080826,0.8774440364769983,null,0.8533076788080826,0.8239429869740492,null,0.8533076788080826,0.89700582754049,null,0.9045942651064433,0.9314560402543443,null,0.9314560402543443,0.8772425926199103,null,0.8774440364769983,0.9025075431639317,null,0.8772425926199103,0.8722282938405187,null,0.8772425926199103,0.9025075431639317,null,0.8722282938405187,0.9415910018695208,null,0.8793490621737636,0.9138225100381723,null,0.9138225100381723,0.89700582754049,null,0.9999999999999999,0.9897316438689584,null],\"y\":[-0.37240492946532905,-0.3374293987939815,null,-0.37240492946532905,-0.3321485172574282,null,-0.37240492946532905,-0.3983847630219399,null,-0.37240492946532905,-0.4156200668093289,null,-0.19775257174585548,-0.2063358111729551,null,-0.19775257174585548,-0.22682629473750976,null,0.19217163889604522,0.15075583527966213,null,0.19217163889604522,0.2454886627763174,null,0.15075583527966213,0.08566567921692263,null,0.15075583527966213,0.1830964742681806,null,0.15075583527966213,0.19401895442915182,null,0.15075583527966213,0.23041742666531898,null,0.2454886627763174,0.1550169829954827,null,0.2454886627763174,0.29503112351354943,null,0.2454886627763174,0.3516145699150102,null,0.2454886627763174,0.13154649239383534,null,0.2454886627763174,0.2654036719786655,null,0.2454886627763174,0.29007712253287554,null,0.2454886627763174,0.17506689132945674,null,0.2454886627763174,0.30543403943722586,null,0.2454886627763174,0.3186419315988115,null,0.2454886627763174,0.32013704421228933,null,0.2454886627763174,0.14108455802716335,null,0.2454886627763174,0.21563241045367157,null,0.2454886627763174,0.2653030092467741,null,0.2454886627763174,0.2504609035075665,null,0.2454886627763174,0.3298363992762925,null,0.2454886627763174,0.26215663829991787,null,0.2454886627763174,0.30744507720316483,null,0.2181013486456451,0.1830964742681806,null,0.2181013486456451,0.2483228368145617,null,0.1830964742681806,0.14424970007537646,null,0.1830964742681806,0.2392034271076642,null,0.2483228368145617,0.271260060467474,null,0.2483228368145617,0.29598111932934784,null,0.2483228368145617,0.31312525443376327,null,0.2483228368145617,0.1377875159793443,null,0.2846247910773329,0.271260060467474,null,0.2846247910773329,0.2654036719786655,null,0.2654036719786655,0.29503112351354943,null,0.2654036719786655,0.2081621616018118,null,0.2654036719786655,0.17506689132945674,null,0.2654036719786655,0.30543403943722586,null,0.2654036719786655,0.33864524890790293,null,0.2654036719786655,0.2991909876884884,null,0.2654036719786655,0.2411534275196166,null,0.2654036719786655,0.36193468815947716,null,0.2654036719786655,0.21566804491061756,null,0.2654036719786655,0.3607657934915629,null,0.2654036719786655,0.2134996906353854,null,0.2654036719786655,0.2504609035075665,null,-0.030368016512711055,-0.11983746333406903,null,-0.030368016512711055,0.03626196322979892,null,-0.030368016512711055,0.0366056490882638,null,-0.11983746333406903,-0.08571366148529053,null,-0.11983746333406903,-0.16158369535996817,null,-0.11983746333406903,-0.11363140836135341,null,-0.11983746333406903,-0.12933072726070455,null,-0.11983746333406903,-0.19842470252249572,null,-0.11983746333406903,-0.0702983948515315,null,-0.11983746333406903,-0.12378694802268922,null,0.03626196322979892,0.08566567921692263,null,0.03626196322979892,0.03684804866049509,null,0.03626196322979892,-0.007682993946868869,null,0.03626196322979892,0.025113190781107096,null,0.03626196322979892,0.14108455802716335,null,0.03626196322979892,-0.08571366148529053,null,0.03626196322979892,0.04261646806613268,null,0.03626196322979892,0.1377875159793443,null,0.03626196322979892,0.0860057201114828,null,0.03626196322979892,0.07350777654618794,null,0.03626196322979892,-0.0702983948515315,null,0.03626196322979892,0.14298047086348784,null,0.14424970007537646,0.17506689132945674,null,0.17506689132945674,0.11166443763779435,null,0.17506689132945674,0.13819890704551469,null,0.17506689132945674,0.20263586684673746,null,0.1550169829954827,0.08566567921692263,null,0.08566567921692263,0.03684804866049509,null,0.29503112351354943,0.30744507720316483,null,0.34851680904682253,0.3529438119542119,null,0.34851680904682253,0.30543403943722586,null,0.3529438119542119,0.30706425822851685,null,0.30543403943722586,0.3632425442321436,null,0.30543403943722586,0.37662676296045994,null,0.30543403943722586,0.3328627332186449,null,0.30543403943722586,0.3305203472323494,null,0.30543403943722586,0.29007712253287554,null,0.30706425822851685,0.3328627332186449,null,0.30706425822851685,0.26215663829991787,null,0.11166443763779435,0.06365781540764814,null,0.06365781540764814,0.025113190781107096,null,0.06365781540764814,0.10073898266664141,null,0.06365781540764814,-0.03739883140009328,null,0.19401895442915182,0.2392034271076642,null,0.2392034271076642,0.30055361993148366,null,0.7653507456655524,0.7046352399474886,null,0.7653507456655524,0.7338604404036875,null,0.7046352399474886,0.7338604404036875,null,0.7046352399474886,0.6917712524150075,null,0.7046352399474886,0.7375601774308901,null,0.7046352399474886,0.6219018978059887,null,0.7338604404036875,0.6917712524150075,null,0.6917712524150075,0.7375601774308901,null,0.6917712524150075,0.6219018978059887,null,0.3516145699150102,0.389571647866414,null,0.3516145699150102,0.4399550767201524,null,0.389571647866414,0.3618863295267559,null,0.389571647866414,0.4203679715114439,null,0.23108495875707386,0.20279460222162488,null,0.23108495875707386,0.14298047086348784,null,0.23108495875707386,0.3082183797623673,null,0.20279460222162488,0.20645165438202456,null,0.20279460222162488,0.169993850352805,null,0.14298047086348784,0.20701348015454596,null,0.3082183797623673,0.30055361993148366,null,0.30055361993148366,0.2504817006580823,null,0.30055361993148366,0.36198181909010485,null,0.30055361993148366,0.3539759783324054,null,0.4399550767201524,0.4212983778982788,null,0.4212983778982788,0.4203679715114439,null,0.4212983778982788,0.36198181909010485,null,0.18590656428104443,0.2757720911099285,null,0.18590656428104443,0.13819890704551469,null,0.2757720911099285,0.2411534275196166,null,0.2757720911099285,0.3766648961343273,null,0.13819890704551469,0.0366056490882638,null,0.13819890704551469,0.24735077082932477,null,0.13819890704551469,0.11950898580112584,null,0.13154649239383534,0.1543753346157171,null,0.13154649239383534,0.054752682912606494,null,0.13154649239383534,-0.014857067497180139,null,0.13154649239383534,0.2411534275196166,null,0.1543753346157171,0.2081621616018118,null,0.1543753346157171,0.11950898580112584,null,0.054752682912606494,0.0366056490882638,null,0.24735077082932477,0.3607657934915629,null,0.2411534275196166,0.31505135206092616,null,0.3766648961343273,0.42520879053337635,null,0.3766648961343273,0.4483247146873722,null,0.3766648961343273,0.3221746426727045,null,0.42520879053337635,0.4483247146873722,null,0.42520879053337635,0.38443169116237164,null,0.36037711884171436,0.31505135206092616,null,0.36037711884171436,0.38443169116237164,null,0.31505135206092616,0.38443169116237164,null,0.3221746426727045,0.23041742666531898,null,0.23041742666531898,0.1377875159793443,null,0.23041742666531898,0.3243067640455657,null,0.23041742666531898,0.19206031109554633,null,0.29007712253287554,0.3305203472323494,null,0.29007712253287554,0.2991909876884884,null,0.29007712253287554,0.29598111932934784,null,0.29007712253287554,0.26215663829991787,null,0.20263586684673746,0.26215663829991787,null,0.3632425442321436,0.37662676296045994,null,0.3186419315988115,0.33864524890790293,null,0.32013704421228933,0.36198181909010485,null,0.36198181909010485,0.3539759783324054,null,0.6219018978059887,0.5003839520670941,null,-0.014857067497180139,-0.12443081088360805,null,-0.12443081088360805,-0.19428771751509255,null,-0.12443081088360805,-0.1751118087341746,null,-0.19428771751509255,-0.1751118087341746,null,0.3607657934915629,0.5003839520670941,null,0.3607657934915629,0.408109777502545,null,-0.007682993946868869,-0.03725334796971406,null,-0.03725334796971406,-0.03739883140009328,null,-0.03739883140009328,-0.11363140836135341,null,-0.005873460859353229,-0.03920725461121444,null,-0.005873460859353229,0.027453988336491368,null,-0.03920725461121444,-0.09135608055886298,null,-0.03920725461121444,-0.08921568200355183,null,-0.03920725461121444,-0.05493224572130224,null,-0.03920725461121444,0.009371385112070151,null,-0.03920725461121444,-0.05720981324274344,null,0.027453988336491368,0.013989398574171455,null,0.027453988336491368,0.06936843455429295,null,-0.09135608055886298,-0.08921568200355183,null,-0.09135608055886298,-0.08522008845803193,null,-0.09135608055886298,-0.08867614744685523,null,-0.09135608055886298,-0.10521813183536624,null,-0.09135608055886298,-0.13624362425295938,null,-0.08921568200355183,-0.05493224572130224,null,-0.08921568200355183,-0.10311929524942626,null,-0.08921568200355183,-0.12104500517361247,null,-0.08921568200355183,-0.13255737729097572,null,-0.10311929524942626,-0.12104500517361247,null,-0.10311929524942626,-0.14504163472104564,null,-0.10311929524942626,-0.07679217867681357,null,-0.12104500517361247,-0.14504163472104564,null,-0.12104500517361247,-0.13255737729097572,null,-0.08522008845803193,-0.05720981324274344,null,-0.08522008845803193,-0.0913900622305909,null,-0.05720981324274344,-0.062478564046614816,null,-0.05720981324274344,-0.040947299621655595,null,-0.05720981324274344,-0.09121607128788516,null,-0.05720981324274344,-0.08867614744685523,null,-0.11177937613392876,-0.0913900622305909,null,-0.11177937613392876,-0.14326324060206508,null,-0.0913900622305909,-0.062478564046614816,null,-0.14326324060206508,-0.1314827821483172,null,-0.14326324060206508,-0.17091549820691795,null,-0.062478564046614816,-0.040947299621655595,null,0.009371385112070151,-0.11348188076753256,null,0.009371385112070151,0.19964088956349696,null,-0.11348188076753256,-0.2322213467952002,null,-0.11348188076753256,-0.10521813183536624,null,-0.17156141351637322,-0.13624362425295938,null,-0.17156141351637322,-0.187211842429619,null,-0.187211842429619,-0.17091549820691795,null,-0.1314827821483172,-0.17091549820691795,null,-0.1314827821483172,-0.09121607128788516,null,-0.2322213467952002,-0.35292483650127376,null,-0.35292483650127376,-0.3162744324463791,null,-0.35292483650127376,-0.3691916700399427,null,-0.35292483650127376,-0.4383935453070841,null,-0.35292483650127376,-0.3826601033148906,null,-0.35292483650127376,-0.39008793280443504,null,-0.35292483650127376,-0.4712391971075695,null,-0.07679217867681357,-0.2414869429811536,null,-0.07679217867681357,0.0420687819793774,null,-0.07679217867681357,0.05454727126026576,null,-0.07679217867681357,0.04684022327834231,null,-0.2414869429811536,-0.21879583024800317,null,-0.2414869429811536,-0.354563051271868,null,-0.2414869429811536,-0.34975711119667247,null,0.3618863295267559,0.4203679715114439,null,0.3618863295267559,0.38611930136567774,null,0.3618863295267559,0.42252117064244427,null,0.3618863295267559,0.19964088956349696,null,0.38611930136567774,0.3539759783324054,null,0.3539759783324054,0.31312525443376327,null,0.41920139009169416,0.42252117064244427,null,0.41920139009169416,0.36193468815947716,null,0.36193468815947716,0.408109777502545,null,0.36193468815947716,0.3298363992762925,null,0.21566804491061756,0.169993850352805,null,0.169993850352805,0.2504817006580823,null,0.169993850352805,0.046298283075200536,null,0.2504817006580823,0.3210170344956606,null,0.2504817006580823,0.20701348015454596,null,0.046298283075200536,-0.0702983948515315,null,-0.0702983948515315,-0.17015232928217444,null,-0.0702983948515315,-0.12933072726070455,null,-0.0702983948515315,-0.12378694802268922,null,0.10073898266664141,0.15150164321386023,null,0.15150164321386023,0.20645165438202456,null,-0.059309807949645646,0.1128430894564842,null,-0.059309807949645646,-0.24180296252474442,null,0.1128430894564842,0.18352172290457647,null,0.1128430894564842,0.17755442106912744,null,0.1128430894564842,0.1726741121951856,null,-0.24180296252474442,-0.3191779293147338,null,-0.24180296252474442,-0.3037039171532386,null,-0.24180296252474442,-0.2707465657900469,null,-0.24180296252474442,-0.28072985305676573,null,0.1652135527723422,0.23284861834382736,null,0.1652135527723422,0.21563241045367157,null,0.1652135527723422,0.05454727126026576,null,0.1652135527723422,0.1726741121951856,null,0.1652135527723422,0.04684022327834231,null,0.23284861834382736,0.2430606819993742,null,0.0420687819793774,0.1666196689465796,null,0.1666196689465796,0.18352172290457647,null,0.1666196689465796,0.2430606819993742,null,0.18352172290457647,0.17755442106912744,null,0.1377875159793443,0.19206031109554633,null,0.1377875159793443,0.07350777654618794,null,0.1377875159793443,0.0860057201114828,null,0.1377875159793443,0.2134996906353854,null,0.3210170344956606,0.31312525443376327,null,0.3243067640455657,0.29598111932934784,null,-0.14980911090206858,-0.11363140836135341,null,-0.14980911090206858,-0.12378694802268922,null,-0.08571366148529053,-0.2031396428008066,null,-0.24867846705319904,-0.2480612724540569,null,-0.24867846705319904,-0.19842470252249572,null,-0.24867846705319904,-0.2031396428008066,null,-0.24867846705319904,-0.17015232928217444,null,-0.24867846705319904,-0.3652673119457543,null,-0.2480612724540569,-0.19842470252249572,null,-0.19842470252249572,-0.18141973896785582,null,0.04261646806613268,0.07350777654618794,null,0.12752417526391777,0.09596598456977655,null,0.12752417526391777,0.1562395031855529,null,0.09596598456977655,0.08230624180813509,null,0.09596598456977655,0.06936843455429295,null,0.1562395031855529,0.1619339074015076,null,0.1562395031855529,0.17737191098393437,null,0.1619339074015076,0.18029750840950184,null,0.1619339074015076,0.17737191098393437,null,0.17737191098393437,0.18029750840950184,null,-0.16158369535996817,-0.17015232928217444,null,-0.2031396428008066,-0.26074754051428856,null,-0.2031396428008066,-0.26283540565248553,null,-0.18141973896785582,-0.12933072726070455,null,-0.21879583024800317,-0.1138249568080285,null,-0.21879583024800317,-0.3290209806050995,null,-0.1138249568080285,-0.19309297479094437,null,-0.1138249568080285,0.013989398574171455,null,-0.19309297479094437,-0.26695681691941986,null,0.013989398574171455,0.09825456749487667,null,0.013989398574171455,-0.016011643997270016,null,0.013989398574171455,0.06932685080214793,null,0.013989398574171455,-0.009755489983887169,null,0.013989398574171455,0.05131293399620939,null,-0.5511808844811344,-0.595737355025823,null,-0.5511808844811344,-0.47171022462219114,null,-0.595737355025823,-0.5639728244127816,null,-0.595737355025823,-0.6215193746338189,null,-0.47171022462219114,-0.3652673119457543,null,-0.3191779293147338,-0.3604044706645263,null,-0.3191779293147338,-0.3323781239801865,null,-0.3604044706645263,-0.3652673119457543,null,-0.4662556388281956,-0.42216923115335353,null,-0.4662556388281956,-0.48453662264928055,null,-0.42216923115335353,-0.48453662264928055,null,-0.42216923115335353,-0.39008793280443504,null,-0.42216923115335353,-0.4557874808906719,null,-0.42216923115335353,-0.3826601033148906,null,-0.48453662264928055,-0.5521194603477158,null,-0.48453662264928055,-0.4557874808906719,null,-0.3162744324463791,-0.26695681691941986,null,-0.26695681691941986,-0.3097609475979624,null,-0.3691916700399427,-0.36336879435120817,null,-0.36336879435120817,-0.3037039171532386,null,-0.36336879435120817,-0.3652673119457543,null,-0.36336879435120817,-0.4222661087486999,null,-0.5521194603477158,-0.5388454882808523,null,-0.5388454882808523,-0.5044773680339041,null,-0.5388454882808523,-0.5544296521807888,null,-0.5044773680339041,-0.5544296521807888,null,-0.5044773680339041,-0.4383935453070841,null,-0.354563051271868,-0.4175467714161522,null,-0.354563051271868,-0.4273217904057072,null,-0.39008793280443504,-0.3826601033148906,null,-0.2707465657900469,-0.28072985305676573,null,-0.28072985305676573,-0.3323781239801865,null,0.08230624180813509,0.06936843455429295,null,-0.427557818357066,-0.46852751187381964,null,-0.427557818357066,-0.4273217904057072,null,-0.427557818357066,-0.3290209806050995,null,-0.46852751187381964,-0.4273217904057072,null,-0.4273217904057072,-0.4175467714161522,null,-0.4273217904057072,-0.4403005485227828,null,-0.3512456445820385,-0.3323889182263431,null,-0.3512456445820385,-0.3097609475979624,null,-0.3323889182263431,-0.3097609475979624,null,-0.5639728244127816,-0.6215193746338189,null,-0.5639728244127816,-0.4712391971075695,null,-0.5172504500745676,-0.4950586446677678,null,-0.5172504500745676,-0.5243632789018423,null,-0.5172504500745676,-0.48480001450796517,null,-0.4950586446677678,-0.4403005485227828,null,-0.5243632789018423,-0.48480001450796517,null,0.09825456749487667,0.08739204106296261,null,0.09825456749487667,0.14967602342127242,null,0.09825456749487667,0.06932685080214793,null,0.09825456749487667,0.1635276234475943,null,-0.016011643997270016,-0.009755489983887169,null,-0.48480001450796517,-0.4222661087486999,null,0.08739204106296261,0.05131293399620939,null,0.14967602342127242,0.1635276234475943,null,-0.34975711119667247,-0.4403005485227828,null,-0.26074754051428856,-0.26283540565248553,null,0.2134996906353854,0.2653030092467741,null,-0.3374293987939815,-0.30312345066441543,null,-0.3321485172574282,-0.31991745649674624,null,-0.3321485172574282,-0.2695408079517971,null,-0.3421740373459021,-0.30919791525780227,null,-0.3421740373459021,-0.29678359597451404,null,-0.30919791525780227,-0.2695408079517971,null,-0.30919791525780227,-0.29678359597451404,null,-0.30919791525780227,-0.33159618749323755,null,-0.29678359597451404,-0.2761333921677379,null,-0.29678359597451404,-0.2689383885532357,null,-0.29678359597451404,-0.33159618749323755,null,-0.2695408079517971,-0.2479823009665101,null,-0.2695408079517971,-0.22682629473750976,null,-0.2695408079517971,-0.30975998686046147,null,-0.2695408079517971,-0.2063358111729551,null,-0.2695408079517971,-0.2689383885532357,null,-0.2695408079517971,-0.2925476591575128,null,-0.2479823009665101,-0.2277503307314646,null,-0.2277503307314646,-0.22682629473750976,null,-0.2063358111729551,-0.1797625041116096,null,-0.22682629473750976,-0.2761333921677379,null,-0.22682629473750976,-0.1797625041116096,null,-0.2761333921677379,-0.30312345066441543,null,-0.30975998686046147,-0.31991745649674624,null,-0.31991745649674624,-0.2925476591575128,null,-0.3983847630219399,-0.4156200668093289,null],\"type\":\"scatter\"},{\"hoverinfo\":\"text\",\"marker\":{\"color\":[4,2,2,5,18,2,4,5,2,2,14,3,8,13,2,6,2,4,3,2,2,2,8,3,2,4,2,3,2,5,3,4,2,3,3,3,3,3,2,5,2,3,2,3,5,5,3,2,3,2,2,4,4,3,2,2,3,3,2,5,6,2,4,2,2,2,2,2,4,2,2,2,3,2,3,2,2,2,4,2,2,3,2,2,2,6,3,6,6,2,4,4,2,3,6,2,3,3,3,3,3,2,2,2,2,3,3,2,2,2,7,2,2,5,4,5,3,2,4,2,2,4,2,2,2,4,4,2,6,2,2,2,4,5,5,2,2,2,3,2,3,2,2,2,2,7,2,3,2,3,2,2,2,3,3,3,5,2,4,2,3,2,2,3,3,3,3,2,3,4,2,3,3,3,2,7,2,3,2,3,2,2,5,4,2,3,2,4,2,3,2,3,2,2,3,3,3,2,3,2,3,2,3,2,5,2,2,2,3,3,2,3,2,2,5,2,2,2,3,2,2,2,2,2,2,4,2,3,2,2,2,2,2,3,2,2,3,2,4,5,8,2,2,3,5,3,2,3,2,2,2,2,2,2,2,2,2,2,2,2],\"colorbar\":{\"thickness\":15,\"title\":{\"side\":\"right\",\"text\":\"Node Connections\"},\"xanchor\":\"left\"},\"colorscale\":[[0.0,\"rgb(255,255,217)\"],[0.125,\"rgb(237,248,177)\"],[0.25,\"rgb(199,233,180)\"],[0.375,\"rgb(127,205,187)\"],[0.5,\"rgb(65,182,196)\"],[0.625,\"rgb(29,145,192)\"],[0.75,\"rgb(34,94,168)\"],[0.875,\"rgb(37,52,148)\"],[1.0,\"rgb(8,29,88)\"]],\"line\":{\"width\":2},\"reversescale\":true,\"showscale\":true,\"size\":20},\"mode\":\"markers+text\",\"text\":[\"REV 1: # of connections: 4\",\"REV 13: # of connections: 2\",\"1CO 1: # of connections: 2\",\"1CO 3: # of connections: 5\",\"2CO 1: # of connections: 18\",\"1CO 10: # of connections: 2\",\"1CO 8: # of connections: 4\",\"ROM 14: # of connections: 5\",\"1CO 11: # of connections: 2\",\"1CO 15: # of connections: 2\",\"PHP 1: # of connections: 14\",\"1CO 12: # of connections: 3\",\"JHN 7: # of connections: 8\",\"JHN 14: # of connections: 13\",\"1CO 13: # of connections: 2\",\"2CO 13: # of connections: 6\",\"1CO 14: # of connections: 2\",\"1CO 2: # of connections: 4\",\"1CO 16: # of connections: 3\",\"1CO 4: # of connections: 2\",\"1CO 5: # of connections: 2\",\"1CO 6: # of connections: 2\",\"2CO 2: # of connections: 8\",\"EPH 5: # of connections: 3\",\"1CO 7: # of connections: 2\",\"GAL 6: # of connections: 4\",\"1CO 9: # of connections: 2\",\"PHP 3: # of connections: 3\",\"1JN 1: # of connections: 2\",\"1JN 2: # of connections: 5\",\"1JN 3: # of connections: 3\",\"1JN 4: # of connections: 4\",\"1JN 5: # of connections: 2\",\"1PE 1: # of connections: 3\",\"EPH 1: # of connections: 3\",\"1PE 2: # of connections: 3\",\"ROM 3: # of connections: 3\",\"ROM 9: # of connections: 3\",\"1PE 3: # of connections: 2\",\"ROM 8: # of connections: 5\",\"1PE 4: # of connections: 2\",\"EPH 2: # of connections: 3\",\"1PE 5: # of connections: 2\",\"1TI 1: # of connections: 3\",\"1TH 5: # of connections: 5\",\"1TH 1: # of connections: 5\",\"2TH 1: # of connections: 3\",\"1TH 2: # of connections: 2\",\"1TH 4: # of connections: 3\",\"1TH 3: # of connections: 2\",\"2TH 3: # of connections: 2\",\"2TI 1: # of connections: 4\",\"1TI 4: # of connections: 4\",\"1TI 2: # of connections: 3\",\"1TI 5: # of connections: 2\",\"1TI 3: # of connections: 2\",\"TIT 1: # of connections: 3\",\"TIT 2: # of connections: 3\",\"1TI 6: # of connections: 2\",\"JAS 1: # of connections: 5\",\"2CO 8: # of connections: 6\",\"2CO 10: # of connections: 2\",\"ROM 15: # of connections: 4\",\"2CO 11: # of connections: 2\",\"2CO 12: # of connections: 2\",\"2CO 3: # of connections: 2\",\"2CO 4: # of connections: 2\",\"2CO 5: # of connections: 2\",\"ROM 5: # of connections: 4\",\"2CO 6: # of connections: 2\",\"2CO 7: # of connections: 2\",\"2CO 9: # of connections: 2\",\"2JN 1: # of connections: 3\",\"2PE 1: # of connections: 2\",\"2PE 3: # of connections: 3\",\"2PE 2: # of connections: 2\",\"JUD 1: # of connections: 2\",\"2TH 2: # of connections: 2\",\"PHM 1: # of connections: 4\",\"2TI 2: # of connections: 2\",\"2TI 3: # of connections: 2\",\"TIT 3: # of connections: 3\",\"2TI 4: # of connections: 2\",\"3JN 1: # of connections: 2\",\"ACT 1: # of connections: 2\",\"ACT 8: # of connections: 6\",\"LUK 24: # of connections: 3\",\"ACT 10: # of connections: 6\",\"ACT 11: # of connections: 6\",\"ACT 12: # of connections: 2\",\"ACT 13: # of connections: 4\",\"ACT 15: # of connections: 4\",\"ACT 14: # of connections: 2\",\"ACT 16: # of connections: 3\",\"ACT 21: # of connections: 6\",\"ACT 17: # of connections: 2\",\"ACT 19: # of connections: 3\",\"ACT 25: # of connections: 3\",\"ACT 18: # of connections: 3\",\"ACT 2: # of connections: 3\",\"ACT 4: # of connections: 3\",\"ACT 20: # of connections: 2\",\"ACT 22: # of connections: 2\",\"ACT 9: # of connections: 2\",\"ACT 26: # of connections: 2\",\"ACT 23: # of connections: 3\",\"ACT 24: # of connections: 3\",\"ACT 27: # of connections: 2\",\"ACT 28: # of connections: 2\",\"ACT 3: # of connections: 2\",\"MAT 22: # of connections: 7\",\"ACT 5: # of connections: 2\",\"ACT 6: # of connections: 2\",\"ACT 7: # of connections: 5\",\"LUK 2: # of connections: 4\",\"COL 1: # of connections: 5\",\"EPH 3: # of connections: 3\",\"COL 2: # of connections: 2\",\"ROM 6: # of connections: 4\",\"COL 3: # of connections: 2\",\"EPH 4: # of connections: 2\",\"EPH 6: # of connections: 4\",\"COL 4: # of connections: 2\",\"GAL 1: # of connections: 2\",\"GAL 2: # of connections: 2\",\"GAL 3: # of connections: 4\",\"ROM 4: # of connections: 4\",\"GAL 4: # of connections: 2\",\"JHN 8: # of connections: 6\",\"GAL 5: # of connections: 2\",\"ROM 13: # of connections: 2\",\"HEB 1: # of connections: 2\",\"HEB 3: # of connections: 4\",\"MAT 24: # of connections: 5\",\"HEB 10: # of connections: 5\",\"HEB 9: # of connections: 2\",\"HEB 13: # of connections: 2\",\"HEB 11: # of connections: 2\",\"HEB 5: # of connections: 3\",\"HEB 12: # of connections: 2\",\"HEB 2: # of connections: 3\",\"HEB 4: # of connections: 2\",\"HEB 7: # of connections: 2\",\"HEB 6: # of connections: 2\",\"HEB 8: # of connections: 2\",\"JHN 16: # of connections: 7\",\"JAS 2: # of connections: 2\",\"ROM 7: # of connections: 3\",\"JAS 3: # of connections: 2\",\"ROM 12: # of connections: 3\",\"JAS 4: # of connections: 2\",\"JAS 5: # of connections: 2\",\"JHN 1: # of connections: 2\",\"JHN 3: # of connections: 3\",\"JHN 9: # of connections: 3\",\"JHN 10: # of connections: 3\",\"JHN 11: # of connections: 5\",\"JHN 12: # of connections: 2\",\"JHN 6: # of connections: 4\",\"JHN 13: # of connections: 2\",\"JHN 15: # of connections: 3\",\"JHN 17: # of connections: 2\",\"JHN 18: # of connections: 2\",\"LUK 22: # of connections: 3\",\"JHN 19: # of connections: 3\",\"MRK 15: # of connections: 3\",\"MAT 27: # of connections: 3\",\"JHN 2: # of connections: 2\",\"JHN 4: # of connections: 3\",\"JHN 20: # of connections: 4\",\"JHN 21: # of connections: 2\",\"JHN 5: # of connections: 3\",\"LUK 1: # of connections: 3\",\"LUK 10: # of connections: 3\",\"MAT 11: # of connections: 2\",\"LUK 9: # of connections: 7\",\"LUK 11: # of connections: 2\",\"MAT 12: # of connections: 3\",\"MAT 23: # of connections: 2\",\"LUK 12: # of connections: 3\",\"MAT 6: # of connections: 2\",\"LUK 13: # of connections: 2\",\"MAT 21: # of connections: 5\",\"LUK 19: # of connections: 4\",\"LUK 14: # of connections: 2\",\"LUK 7: # of connections: 3\",\"LUK 15: # of connections: 2\",\"MAT 18: # of connections: 4\",\"LUK 16: # of connections: 2\",\"LUK 18: # of connections: 3\",\"LUK 17: # of connections: 2\",\"MRK 10: # of connections: 3\",\"MAT 19: # of connections: 2\",\"MRK 11: # of connections: 2\",\"LUK 4: # of connections: 3\",\"LUK 20: # of connections: 3\",\"MRK 12: # of connections: 3\",\"LUK 21: # of connections: 2\",\"MRK 13: # of connections: 3\",\"MRK 14: # of connections: 2\",\"MAT 26: # of connections: 3\",\"LUK 23: # of connections: 2\",\"LUK 3: # of connections: 3\",\"MAT 3: # of connections: 2\",\"MRK 1: # of connections: 5\",\"MAT 4: # of connections: 2\",\"LUK 5: # of connections: 2\",\"MRK 2: # of connections: 2\",\"MAT 9: # of connections: 3\",\"LUK 6: # of connections: 3\",\"MRK 3: # of connections: 2\",\"LUK 8: # of connections: 3\",\"MRK 5: # of connections: 2\",\"MRK 4: # of connections: 2\",\"MRK 8: # of connections: 5\",\"MRK 9: # of connections: 2\",\"MAT 1: # of connections: 2\",\"MAT 10: # of connections: 2\",\"MAT 13: # of connections: 3\",\"MAT 14: # of connections: 2\",\"MRK 6: # of connections: 2\",\"MAT 15: # of connections: 2\",\"MRK 7: # of connections: 2\",\"MAT 16: # of connections: 2\",\"MAT 17: # of connections: 2\",\"MAT 5: # of connections: 4\",\"MAT 2: # of connections: 2\",\"MAT 8: # of connections: 3\",\"MAT 20: # of connections: 2\",\"MAT 25: # of connections: 2\",\"MAT 28: # of connections: 2\",\"MRK 16: # of connections: 2\",\"MAT 7: # of connections: 2\",\"PHP 2: # of connections: 3\",\"PHP 4: # of connections: 2\",\"REV 22: # of connections: 2\",\"REV 4: # of connections: 3\",\"REV 10: # of connections: 2\",\"REV 11: # of connections: 4\",\"REV 8: # of connections: 5\",\"REV 19: # of connections: 8\",\"REV 12: # of connections: 2\",\"REV 20: # of connections: 2\",\"REV 17: # of connections: 3\",\"REV 14: # of connections: 5\",\"REV 16: # of connections: 3\",\"REV 15: # of connections: 2\",\"REV 5: # of connections: 3\",\"REV 18: # of connections: 2\",\"REV 2: # of connections: 2\",\"REV 3: # of connections: 2\",\"REV 21: # of connections: 2\",\"REV 7: # of connections: 2\",\"REV 6: # of connections: 2\",\"REV 9: # of connections: 2\",\"ROM 1: # of connections: 2\",\"ROM 10: # of connections: 2\",\"ROM 11: # of connections: 2\",\"ROM 2: # of connections: 2\",\"ROM 16: # of connections: 2\"],\"x\":[0.9552999802323117,0.9157723778263235,0.1362827305267113,0.08843201384345266,0.11657913399672568,0.08405154737440483,0.09086121049295609,0.036521934922920815,0.15700203933746726,0.10151200763657674,0.1437616817767355,0.03919967837906931,-0.031159923214225452,-0.024960570512592824,0.15854281022239686,0.12873710791691628,0.08558660118454715,0.04646281556179068,0.14405952587408888,0.03051645494810054,0.29550405105573213,0.34972586672700856,0.21479260882833923,0.30311070347417596,0.005088121675163547,-0.11690668753625967,0.055916745448746134,0.015056261434569061,0.09943059551277142,0.10429715424756447,0.09540048970293202,0.11124151114737141,0.12122913840024231,-0.01545479724369979,-0.13971339962658247,-0.16451615590653815,-0.19364043622910368,-0.11269947208860413,-0.15026821247719294,-0.06995219615067527,-0.061245293517503,-0.10883021228553322,0.32458678172171196,0.34778363793176753,0.2230563641954201,0.25115457327171004,0.2958858316243668,0.24139143496891624,0.16229607947025895,0.24249786461605227,0.21572944936034746,0.28179952635148503,0.3446010770094448,0.41235588183383404,0.39464773467609904,0.4698202380737744,0.3948449136360835,0.4416228558869236,0.2266193464455241,0.098293990608412,0.17728485977279207,0.1983111752609072,0.21494076647196722,0.2664820253751229,0.2547984592527346,0.1382792557050979,0.1681476128214417,0.03744848231977054,-0.04915617422031615,0.2927189840298951,0.23982324994979562,0.20227613698054844,0.11996471876359412,0.35383295197368664,0.4329364387144856,0.4913380023721494,0.48752940585278487,0.3041890520717568,0.16195735289797145,-0.12409862104416185,-0.19116976932142196,-0.1476074104300688,-0.10807652955097384,0.14301340214197944,-0.36995288105576685,-0.4732301001546511,-0.2595616044030423,-0.5273211800608226,-0.47255790415160287,-0.503348941965345,-0.3894814301910162,-0.45199425959403966,-0.44280310260858674,-0.6097234688369254,-0.5846402383896447,-0.7628284586718217,-0.6896729331269446,-0.7902416025033387,-0.6562984490030181,-0.38246410282582144,-0.343370286921526,-0.6438054157770697,-0.6522564652300094,-0.5874096045185813,-0.7064668184286323,-0.7410947933510762,-0.7627670198119602,-0.6686901191638355,-0.584279458592185,-0.22630936760624226,-0.10053704970413707,-0.4395967292874797,-0.5058189948340637,-0.2749791996828912,-0.18311957915908092,-0.20953797536789293,-0.17830522022597906,-0.16700929992612243,-0.09098880990819883,-0.036079635509313084,-0.13485044966656792,0.07204811645444513,0.11727948210599927,0.05214494723784596,0.010941292598778686,-0.10325047033394677,-0.11523200476392337,-0.08461527994328112,-0.04788051929924048,-0.20504263506687528,-0.2613408648289707,-0.32073406682235833,-0.38648987603733675,-0.24157916332922472,-0.2327032990263254,-0.31566133899371634,-0.06614385195519047,-0.33428811941750863,-0.388383664866293,-0.27980984541817655,-0.42885016741964493,-0.44587583888897614,-0.38911656094940905,-0.33161636516287424,-0.2656962605186722,0.016416541423770546,-0.10910602694455512,-0.03918437248433018,0.08700903798677691,0.10596668827851721,0.04133891614368571,-0.312903483514654,-0.10953494457505011,-0.10059796534263028,-0.07032819863473151,-0.03330657748978564,-0.034639870844783975,0.009568704389344847,-0.005173496920277382,-0.03199530674634295,-0.0025999085136414303,-0.00948382045736814,-0.5128387442306164,-0.4376569905055443,-0.5827651775803566,-0.6195336549027654,-0.6121198792205849,-0.00756256189953802,-0.03367817436785675,-0.035790186182617444,0.026701091321770117,-0.008338563108177276,-0.15657870783661268,-0.14481707208165262,-0.18979722670740465,-0.11936881258240646,-0.08842777050800435,-0.10425046039208771,-0.078451131552667,-0.2359926756504242,-0.17344760492518468,0.08757895851482117,0.038943579006180326,0.06776095593734784,-0.17069203777532974,-0.22579637465455496,-0.04813009450192303,-0.018807750215435872,0.06828958289153332,0.020544974980620075,-0.12617245341876002,-0.01563296064924459,-0.0016567939810350693,0.06913076752645556,-0.1561758624155468,-0.021992456604187294,-0.027622303475060214,-0.29137260806083043,-0.2739201107305846,-0.40416425497901093,-0.36772032987561826,-0.6350532601135453,-0.16263896627384483,-0.15121293169187147,-0.11354210330586192,-0.15466079868328123,-0.35199669228264374,-0.34861957157495566,-0.30709074727781144,-0.12310270788241509,-0.12444780050433346,0.1472000825180689,0.07544011143994238,0.19469699083959677,-0.0888755110850219,-0.08687290737941705,-0.165805070991341,-0.28600283041455377,0.16176645171036833,-0.05441803414478577,-0.06285709010184719,-0.08759696003720249,-0.08175167085432135,-0.10427390424191392,-0.07159213372108957,-0.07379293146820669,-0.10913215710589227,-0.025885273301385906,-0.05678601993290698,0.0915132383073119,-0.017657584284185967,-0.03759813326525296,-0.11549655869766236,0.07118812986484449,0.08230114187805664,0.9687466482896196,0.9019020025281921,0.820691168093154,0.8093131349532543,0.8147608364678747,0.8533076788080826,0.9045942651064433,0.9314560402543443,0.8774440364769983,0.8772425926199103,0.8722282938405187,0.8793490621737636,0.9138225100381723,0.9025075431639317,0.9999999999999999,0.9897316438689584,0.9415910018695208,0.89700582754049,0.8239429869740492,0.7894655211187835,0.17245653998737137,0.10547843381540101,-0.14343593162301038,-0.2552303690963533,0.15766904852565972],\"y\":[-0.37240492946532905,-0.19775257174585548,0.19217163889604522,0.15075583527966213,0.2454886627763174,0.2181013486456451,0.1830964742681806,0.2483228368145617,0.2846247910773329,0.271260060467474,0.2654036719786655,-0.030368016512711055,-0.11983746333406903,0.03626196322979892,0.14424970007537646,0.17506689132945674,0.1550169829954827,0.08566567921692263,0.29503112351354943,0.03684804866049509,0.34851680904682253,0.3529438119542119,0.30543403943722586,0.30706425822851685,0.11166443763779435,0.06365781540764814,0.19401895442915182,0.2392034271076642,0.7653507456655524,0.7046352399474886,0.7338604404036875,0.6917712524150075,0.7375601774308901,0.3516145699150102,0.389571647866414,0.23108495875707386,0.20279460222162488,0.14298047086348784,0.3082183797623673,0.30055361993148366,0.4399550767201524,0.4212983778982788,0.18590656428104443,0.2757720911099285,0.13819890704551469,0.13154649239383534,0.1543753346157171,0.054752682912606494,0.0366056490882638,0.2081621616018118,0.24735077082932477,0.2411534275196166,0.3766648961343273,0.42520879053337635,0.4483247146873722,0.36037711884171436,0.31505135206092616,0.38443169116237164,0.3221746426727045,0.23041742666531898,0.29007712253287554,0.20263586684673746,0.26215663829991787,0.3632425442321436,0.37662676296045994,0.3186419315988115,0.33864524890790293,0.32013704421228933,0.36198181909010485,0.3328627332186449,0.3305203472323494,0.2991909876884884,0.6219018978059887,-0.014857067497180139,-0.12443081088360805,-0.19428771751509255,-0.1751118087341746,0.11950898580112584,0.3607657934915629,-0.007682993946868869,-0.03725334796971406,-0.03739883140009328,0.025113190781107096,0.5003839520670941,-0.005873460859353229,-0.03920725461121444,0.027453988336491368,-0.09135608055886298,-0.08921568200355183,-0.05493224572130224,-0.10311929524942626,-0.12104500517361247,-0.14504163472104564,-0.08522008845803193,-0.05720981324274344,-0.11177937613392876,-0.0913900622305909,-0.14326324060206508,-0.062478564046614816,0.009371385112070151,-0.11348188076753256,-0.040947299621655595,-0.17156141351637322,-0.13624362425295938,-0.187211842429619,-0.1314827821483172,-0.17091549820691795,-0.09121607128788516,-0.08867614744685523,-0.2322213467952002,-0.35292483650127376,-0.10521813183536624,-0.13255737729097572,-0.07679217867681357,-0.2414869429811536,0.3618863295267559,0.4203679715114439,0.38611930136567774,0.3539759783324054,0.41920139009169416,0.42252117064244427,0.36193468815947716,0.408109777502545,0.14108455802716335,0.21566804491061756,0.169993850352805,0.2504817006580823,0.046298283075200536,-0.0702983948515315,0.10073898266664141,0.15150164321386023,-0.059309807949645646,0.1128430894564842,-0.24180296252474442,0.1652135527723422,0.23284861834382736,0.21563241045367157,0.0420687819793774,0.1666196689465796,0.05454727126026576,0.18352172290457647,0.17755442106912744,0.2430606819993742,0.1726741121951856,0.04684022327834231,0.1377875159793443,0.3210170344956606,0.31312525443376327,0.3243067640455657,0.29598111932934784,0.19206031109554633,0.19964088956349696,-0.14980911090206858,-0.11363140836135341,-0.12378694802268922,-0.08571366148529053,-0.24867846705319904,-0.2480612724540569,-0.19842470252249572,0.04261646806613268,0.07350777654618794,0.0860057201114828,0.12752417526391777,0.09596598456977655,0.1562395031855529,0.1619339074015076,0.17737191098393437,-0.16158369535996817,-0.17015232928217444,-0.2031396428008066,-0.18141973896785582,-0.12933072726070455,-0.21879583024800317,-0.1138249568080285,-0.19309297479094437,0.013989398574171455,-0.5511808844811344,-0.595737355025823,-0.47171022462219114,-0.3191779293147338,-0.3604044706645263,-0.4662556388281956,-0.42216923115335353,-0.48453662264928055,-0.3162744324463791,-0.26695681691941986,-0.3691916700399427,-0.36336879435120817,-0.5521194603477158,-0.5388454882808523,-0.3037039171532386,-0.5044773680339041,-0.5544296521807888,-0.4557874808906719,-0.354563051271868,-0.39008793280443504,-0.3826601033148906,-0.2707465657900469,-0.28072985305676573,0.08230624180813509,0.06936843455429295,0.18029750840950184,-0.427557818357066,-0.46852751187381964,-0.4273217904057072,-0.4175467714161522,-0.3512456445820385,-0.3323889182263431,-0.3097609475979624,-0.5639728244127816,-0.6215193746338189,-0.5172504500745676,-0.4950586446677678,-0.5243632789018423,0.09825456749487667,-0.016011643997270016,-0.3290209806050995,-0.3323781239801865,-0.48480001450796517,0.08739204106296261,0.05131293399620939,0.14967602342127242,0.1635276234475943,0.06932685080214793,-0.009755489983887169,-0.3652673119457543,-0.34975711119667247,-0.4403005485227828,-0.4383935453070841,-0.4222661087486999,-0.26074754051428856,-0.26283540565248553,-0.4712391971075695,0.2134996906353854,0.2653030092467741,-0.3374293987939815,-0.3321485172574282,-0.3421740373459021,-0.30919791525780227,-0.29678359597451404,-0.2695408079517971,-0.2479823009665101,-0.2277503307314646,-0.2063358111729551,-0.22682629473750976,-0.2761333921677379,-0.30975998686046147,-0.31991745649674624,-0.1797625041116096,-0.3983847630219399,-0.4156200668093289,-0.30312345066441543,-0.2925476591575128,-0.2689383885532357,-0.33159618749323755,0.2504609035075665,0.3298363992762925,0.20701348015454596,0.20645165438202456,0.30744507720316483],\"type\":\"scatter\"}],                        {\"annotations\":[{\"showarrow\":false,\"text\":\"Python code: <a href='https://plotly.com/ipython-notebooks/network-graphs/'> https://plotly.com/ipython-notebooks/network-graphs/</a>\",\"x\":0.005,\"xref\":\"paper\",\"y\":-0.002,\"yref\":\"paper\"}],\"hovermode\":\"closest\",\"margin\":{\"b\":20,\"l\":5,\"r\":5,\"t\":40},\"showlegend\":false,\"title\":{\"font\":{\"size\":16},\"text\":\"<br>Network graph made with Python\"},\"xaxis\":{\"showgrid\":false,\"showticklabels\":false,\"zeroline\":false},\"yaxis\":{\"showgrid\":false,\"showticklabels\":false,\"zeroline\":false},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('e3e08f62-b980-44ec-9a0a-cee60888231e');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pos = nx.spring_layout(graph)\n",
        "# nx.draw_networkx_nodes(graph, pos)\n",
        "# nx.draw_networkx_edges(graph, pos)\n",
        "# nx.draw_networkx_labels(graph, pos)"
      ],
      "metadata": {
        "id": "WX4TAS7-C515"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del graph"
      ],
      "metadata": {
        "id": "0UoKLEKWFD8w"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "261c81d9770836645c1875bc212c956f9f8ac154131031456fd81a36e7fefac3"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}