100_years_of_solitude.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "polyglot_notebook": {
     "kernelName": "csharp"
    }
   },
   "outputs": [],
   "source": [
    "# download the book from the Internet Archive\n",
    "# !wget -O OneHundredYearsOfSolitude_201710.zip https://archive.org/compress/OneHundredYearsOfSolitude_201710\n",
    "# !unzip -p OneHundredYearsOfSolitude_201710.zip One_Hundred_Years_of_Solitude_djvu.txt>One_Hundred_Years_of_Solitude_djvu.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "input_file_path = \"One_Hundred_Years_of_Solitude_djvu.txt\"\n",
    "output_file_path = \"One_Hundred_Years_of_Solitude.txt\"\n",
    "\n",
    "with open(input_file_path, \"r\") as file:\n",
    "    lines = file.readlines()\n",
    "\n",
    "# Remove title pages, acknowledgements, and other front matter\n",
    "with open(output_file_path, \"w\") as file:\n",
    "    file.writelines(lines[186:])\n",
    "\n",
    "with open(output_file_path, \"r\", encoding=\"utf-8\") as file:\n",
    "    content = file.read()\n",
    "\n",
    "# Remove page numbers and the header that appears at the beginning of each page\n",
    "regex_page_header = r\"(\\n\\n\\n\\d+\\s)?\\n\\n\\n\\nGABRIEL GARCIA MARQUES x ONE HUNDRED YEARS OF SOLITUDE\\s\\n\\n\\n\"\n",
    "regex_line_breaks = r\"\\s\\n(?=[^\\n])\"\n",
    "\n",
    "content = re.sub(regex_page_header, \"\", content)\n",
    "content = re.sub(regex_line_breaks, \" \", content)\n",
    "\n",
    "# Save to new file\n",
    "with open(output_file_path, \"w\", encoding=\"utf-8\") as file:\n",
    "    file.write(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing what appear to be OCR errors\n",
    "with open(\"One_Hundred_Years_of_Solitude.txt\", \"r\") as file:\n",
    "    text = file.read()\n",
    "text = (\n",
    "    text.replace(\"Buendfa\", \"Buendia\")\n",
    "    .replace(\"Te6filo\", \"Teofilo\")\n",
    "    .replace(\"Pmdencio\", \"Prudencio\")\n",
    "    .replace(\"Magnlfico\", \"Magnifico\")\n",
    "    .replace(\"Aureiiano\", \"Aureliano\")\n",
    "    .replace(\"Buendla\", \"Buendia\")\n",
    "    .replace(\"Bmno\", \"Bruno\")\n",
    ")\n",
    "with open(\"One_Hundred_Years_of_Solitude_cleaned.txt\", \"w\") as file:\n",
    "    file.write(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !python -m spacy download en_core_web_sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/justn/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /Users/justn/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/justn/nltk_data...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "\n",
    "# Ensure NLTK resources are downloaded\n",
    "nltk.download(\"punkt\")\n",
    "nltk.download(\"stopwords\")\n",
    "nltk.download(\"wordnet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          name  count\n",
      "0            Aureliano Segundo    178\n",
      "1    Colonel Aureliano Buendia    176\n",
      "2         Jose Arcadio Buendia    154\n",
      "3                 Jose Arcadio    116\n",
      "4                       Rebeca     83\n",
      "..                         ...    ...\n",
      "98                Mary the Jew      1\n",
      "97                     Rodrigo      1\n",
      "96                 Both Gaston      1\n",
      "95                Monk Hermann      1\n",
      "153            Rafael Escalona      1\n",
      "\n",
      "[154 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Extracting named entities from the text\n",
    "\n",
    "import spacy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# Load spaCy model and process text\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "nlp.add_pipe(\"merge_noun_chunks\")\n",
    "nlp.add_pipe(\"merge_entities\")\n",
    "\n",
    "with open(\"One_Hundred_Years_of_Solitude_cleaned.txt\", \"r\", encoding=\"utf-8\") as file:\n",
    "    text = file.read()\n",
    "\n",
    "text = lemmatizer.lemmatize(text)\n",
    "\n",
    "doc = nlp(text)\n",
    "\n",
    "# Extract named entities that are persons\n",
    "person_entities = []\n",
    "for ent in doc.ents:\n",
    "    if ent.label_ == \"PERSON\" and ent.text[0].isupper():\n",
    "        person_entities.append(ent.text)\n",
    "\n",
    "# Converting list of names into a DataFrame with frequency counts\n",
    "entities_df = pd.DataFrame(person_entities, columns=[\"name\"])\n",
    "entity_counts_df = entities_df[\"name\"].value_counts().reset_index()\n",
    "entity_counts_df.columns = [\"name\", \"count\"]\n",
    "\n",
    "# Sorting values descending by count\n",
    "entity_counts_df.sort_values(by=\"count\", ascending=False, inplace=True)\n",
    "\n",
    "print(entity_counts_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>name</th>\n",
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       "      <td>Aureliano Segundo</td>\n",
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       "      <th>1</th>\n",
       "      <td>Colonel Aureliano Buendia</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jose Arcadio Buendia</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jose Arcadio</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rebeca</td>\n",
       "      <td>83</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Auerliano</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>General Jose Raquel Moncada</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Jose</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Argenida</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Magnifico Visbal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name  count\n",
       "0             Aureliano Segundo    178\n",
       "1     Colonel Aureliano Buendia    176\n",
       "2          Jose Arcadio Buendia    154\n",
       "3                  Jose Arcadio    116\n",
       "4                        Rebeca     83\n",
       "..                          ...    ...\n",
       "62                    Auerliano      2\n",
       "61  General Jose Raquel Moncada      2\n",
       "60                         Jose      2\n",
       "59                     Argenida      2\n",
       "64             Magnifico Visbal      2\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entity_counts_df[entity_counts_df[\"count\"] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spacy.tokens.token.Token"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(doc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "    from  to  weight\n",
       "0      0   5       2\n",
       "1      0  17       1\n",
       "2      0  24       1\n",
       "3      0  58       2\n",
       "4      1  17       1\n",
       "5      3   1       1\n",
       "6      3   6       4\n",
       "7      4   3       6\n",
       "8      4  11       4\n",
       "9      4  17       1\n",
       "10     6   3       1\n",
       "11     6   4       1\n",
       "12     6   9       1\n",
       "13     6  17       1\n",
       "14     7   0       1\n",
       "15     7  26       1\n",
       "16     7  28       1\n",
       "17     8   4       1\n",
       "18     9   5       1\n",
       "19     9   6       6\n",
       "20     9  28       1\n",
       "21    10   5       1\n",
       "22    11   0       1\n",
       "23    11   4       6\n",
       "24    11   8       1\n",
       "25    11  17       6\n",
       "26    11  28       2\n",
       "27    12   0       1\n",
       "28    13  10       1\n",
       "29    14   6       1\n",
       "30    14  11       1\n",
       "31    16   9       1\n",
       "32    17   2       1\n",
       "33    17   3       1\n",
       "34    17   8       2\n",
       "35    17  11       2\n",
       "36    17  57       1\n",
       "37    18  17       1\n",
       "38    20   0       1\n",
       "39    22   1       1\n",
       "40    22   6       1\n",
       "41    22  24       1\n",
       "42    23  72       2\n",
       "43    24   1       2\n",
       "44    28   0       1\n",
       "45    28   6       1\n",
       "46    44   2       1\n",
       "47    44   6       1\n",
       "48    58   0       1\n",
       "49    70   6       2\n",
       "50    72  23       1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "output_file_path = \"One_Hundred_Years_of_Solitude_cleaned.txt\"\n",
    "\n",
    "with open(output_file_path, \"r\", encoding=\"utf-8\") as file:\n",
    "    content = file.read()\n",
    "\n",
    "entities = entity_counts_df[entity_counts_df[\"count\"] > 1]\n",
    "characters = entities[\"name\"].tolist()\n",
    "\n",
    "\n",
    "# Initialize a dictionary for character names and ids\n",
    "character_dict = {\"name\": characters, \"id\": [i for i in range(len(characters))]}\n",
    "\n",
    "# Create the nodes DataFrame\n",
    "nodes = pd.DataFrame.from_dict(character_dict)\n",
    "\n",
    "# Initialize empty edges DataFrame\n",
    "edges = pd.DataFrame(columns=[\"from\", \"to\", \"weight\"])\n",
    "\n",
    "# Prepare stop words\n",
    "stop_words = nltk.corpus.stopwords.words(\"english\")\n",
    "\n",
    "# Tokenize the text content and remove stop words\n",
    "tokens = [word for word in nltk.word_tokenize(content) if word.isalnum() and word.lower() not in stop_words]\n",
    "nodes.to_csv(\"nodes.csv\", index=False)\n",
    "\n",
    "# Sliding window for co-occurrence\n",
    "window_size = 4\n",
    "edges = []\n",
    "edges_df = pd.DataFrame(columns=[\"from\", \"to\", \"weight\"])\n",
    "\n",
    "for index, row in nodes.iterrows():\n",
    "    for i, token in enumerate(doc):\n",
    "        word = str(token)\n",
    "        if word == row[\"name\"]:\n",
    "            for j in range(max(i - window_size // 2, 0), min(i + window_size // 2, len(doc))):\n",
    "                if str(doc[j]) in characters and str(doc[j]) != word:\n",
    "                    edges.extend([characters.index(str(doc[i])), characters.index(str(doc[j])), 1])\n",
    "\n",
    "edges_df = pd.DataFrame(np.array(edges).reshape(-1, 3), columns=[\"from\", \"to\", \"weight\"])\n",
    "edges_df = edges_df.groupby([\"from\", \"to\"]).size().reset_index(name=\"weight\")\n",
    "edges_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "from      int64\n",
       "to        int64\n",
       "weight    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in edges_df.columns:\n",
    "    edges_df[col] = pd.to_numeric(edges_df[col])\n",
    "\n",
    "edges_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_df.to_csv(\"edges.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>from</th>\n",
       "      <th>to</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    from  to  weight\n",
       "46    44   2       1\n",
       "47    44   6       1\n",
       "48    58   0       1\n",
       "49    70   6       2\n",
       "50    72  23       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  font-size: var(--jp-ui-font-size1);\n",
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       "}\n",
       "\n",
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       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
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       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "  \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "</div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aureliano Segundo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colonel Aureliano Buendia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jose Arcadio Buendia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jose Arcadio</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rebeca</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jose Arcadio Segundo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aureliano</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Meme</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pietro Crespi</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Amaranta Ursula</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  id\n",
       "0          Aureliano Segundo   0\n",
       "1  Colonel Aureliano Buendia   1\n",
       "2       Jose Arcadio Buendia   2\n",
       "3               Jose Arcadio   3\n",
       "4                     Rebeca   4\n",
       "5       Jose Arcadio Segundo   5\n",
       "6                  Aureliano   6\n",
       "7                       Meme   7\n",
       "8              Pietro Crespi   8\n",
       "9            Amaranta Ursula   9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import holoviews as hv\n",
    "\n",
    "hv.extension(\"bokeh\")\n",
    "\n",
    "hv_nodes = hv.Dataset(nodes, [\"index\"])\n",
    "hv_nodes.data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('angle', 29), ('text', 73), ('x', 29), ('y', 29)\n",
      "BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('angle', 29), ('text', 73), ('x', 29), ('y', 29)\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
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.pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"a039c090-fa96-467d-b656-cfd29a04fe60\"},\"html, body {background-color: #000000}\",{\"id\":\"617b3eab-09fd-41b3-8910-8682c83b1544\"},{\"id\":\"f6ca53eb-1ff1-40d6-96be-12d50bcda1fe\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
       "  var render_items = [{\"docid\":\"69c44e20-c4e6-4b36-bd2c-d905fdfa8871\",\"roots\":{\"2ee28363-7ab6-4ab5-8671-a228c999ae97\":\"b0870ba7-2c1b-49c1-ae80-b8ae9a042eb6\"},\"root_ids\":[\"2ee28363-7ab6-4ab5-8671-a228c999ae97\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  async function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t  for (const child of root_el.children) {\n",
       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Chord   [from,to]   (weight)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "2ee28363-7ab6-4ab5-8671-a228c999ae97"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from holoviews import opts, dim\n",
    "\n",
    "hv.output(size=250)\n",
    "\n",
    "chord = hv.Chord((edges_df, hv_nodes))\n",
    "chord.opts(\n",
    "    opts.Chord(\n",
    "        cmap=\"Category10\",\n",
    "        edge_cmap=\"Category10\",\n",
    "        edge_color=dim(\"from\").astype(str),\n",
    "        labels=\"name\",\n",
    "        node_color=dim(\"index\").astype(str),\n",
    "        bgcolor=\"black\",\n",
    "        label_text_color=\"white\",\n",
    "        edge_line_width=dim(\"weight\"),\n",
    "        edge_alpha=0.7,\n",
    "        label_text_alpha=0.9,\n",
    "        title_format=None,\n",
    "        hooks=[lambda p, _: p.state.update(border_fill_color=\"#000000\")],\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; 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       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"8170a497-d2fe-4b0e-9160-f01223929d01\":{\"version\":\"3.4.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"0e3f4f0b-df5b-4fcd-9aab-d49330c2a352\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"f707ea84-159c-404b-8ac9-1e9ac5d94ada\",\"attributes\":{\"plot_id\":\"0e3f4f0b-df5b-4fcd-9aab-d49330c2a352\",\"comm_id\":\"41011fb0c9b44545ad3a3fb0172ff8ac\",\"client_comm_id\":\"a95a6b7ce9b84fb1869f9a6642180814\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
       "  var render_items = [{\"docid\":\"8170a497-d2fe-4b0e-9160-f01223929d01\",\"roots\":{\"0e3f4f0b-df5b-4fcd-9aab-d49330c2a352\":\"e4c5e63e-41d7-44af-b63e-03fa566d96c3\"},\"root_ids\":[\"0e3f4f0b-df5b-4fcd-9aab-d49330c2a352\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
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       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  async function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t  for (const child of root_el.children) {\n",
       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "0e3f4f0b-df5b-4fcd-9aab-d49330c2a352"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('angle', 29), ('text', 73), ('x', 29), ('y', 29)\n",
      "BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('angle', 29), ('text', 73), ('x', 29), ('y', 29)\n"
     ]
    }
   ],
   "source": [
    "import holoviews as hv\n",
    "import panel as pn\n",
    "\n",
    "from bokeh.resources import INLINE, CDN\n",
    "\n",
    "pn.extension(raw_css=[\"html, body {background-color: #000000}\"])\n",
    "\n",
    "chord_pane = pn.panel(chord, name=\"Chord Diagram\")\n",
    "\n",
    "md_pane = pn.pane.Markdown(\n",
    "    \"\"\"\n",
    "    ## Chord Diagram of Character Co-occurrence in *[One Hundred Years of Solitude](https://www.goodreads.com/book/show/320.One_Hundred_Years_of_Solitude)*\n",
    "    \n",
    "    [View on GitHub](https://github.com/hrbn/100-years-of-solitude)\n",
    "    \"\"\",\n",
    "    styles={\"margin\": \"0.42em auto\", \"color\": \"#ffffff\"},\n",
    ")\n",
    "\n",
    "page = pn.Column(\n",
    "    md_pane,\n",
    "    chord_pane,\n",
    "    styles={\"margin\": \"0 auto\"},\n",
    ")\n",
    "\n",
    "page.save(\"pages/index.html\", resources=CDN)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py311",
   "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": "3.11.9"
  },
  "polyglot_notebook": {
   "kernelInfo": {
    "defaultKernelName": "csharp",
    "items": [
     {
      "aliases": [],
      "name": "csharp"
     }
    ]
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}