match_visualize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Match Terms and Visualize\n",
    "\n",
    "This notebook uses the results from the `parse_resources.ipynb` notebook.\n",
    "The parse resources step pulls data from ArchivesSpace and creates a\n",
    "dataframe that was output to a CSV file. \n",
    "This notebook starts from the CSV file, but it could\n",
    "relatively easily be changed to take the previous dataframe\n",
    "as an input. \n",
    "\n",
    "## Setup\n",
    "\n",
    "If continuing or adapting this code, you have likely already imported these libraries.\n",
    "We will use `pandas` for data processing, `re` for text processing and parsing,\n",
    "and a few others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import plotly.express as px\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pd.set_option('display.max_colwidth', -1)**: controls the maximum width of each column in a pandas dataframe. By setting it to -1, pandas will display the full contents of each column, without any truncation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pd.set_option('display.max_rows', None)**: Sets the maximum number of rows displayed when a pandas DataFrame or Series is printed to be unlimited. When this option is set to an integer (as in the commented line # **pd.set_option('display.max_rows', 10)**), only the specified number of rows would be displayed. This is useful for controlling the output length, especially when working with large DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the following functions and code is based on\n",
    "work by Ella Li, who created an initial version of this project\n",
    "that parsed EAD data from XML files. \n",
    "The process here is similar but\n",
    "continues to use the data pulled from the ArchivesSpace API,\n",
    "which exports data in JSON rather than XML. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Provide Terms\n",
    "\n",
    "Terms of interest are supplied in a plain text file, with\n",
    "each term of interest on its own line. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the txt file term list\n",
    "term_list_file = 'terms-nativeAmerican.txt'\n",
    "# term_list_file = 'terms-philippines.txt'\n",
    "\n",
    "with open(term_list_file, 'r') as f:\n",
    "    terms = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read term list from terms-nativeAmerican.txt and recorded 52 terms of interest.\n"
     ]
    }
   ],
   "source": [
    "print(f'Read term list from {term_list_file} and recorded {len(terms)} terms of interest.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match Terms\n",
    "\n",
    "The next block defines the `match_terms` function, which analyses the EAD data\n",
    "and identify occurences, tally occurences, and record their source tags.\n",
    "First, pull the CSV `results.csv` data into a dataframe.\n",
    "Then, the function searches for each term in the specified columns of a given row in your DataFrame. It performs an exact match, searching for the whole term, not part of it (due to the use of the `\\b` word boundary in the regular expression). The search is case-insensitive due to the `re.IGNORECASE` flag.\n",
    "\n",
    "If a term is found in a paragraph of the column, a dictionary is created with details about the match, including the term, the number of matches in the paragraph, the paragraph text itself, and other related information. These dictionaries are appended to the results list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# match terms\n",
    "\n",
    "def match_terms(row, terms, columns):\n",
    "    results = []\n",
    "    for term in terms:\n",
    "        for col in columns:\n",
    "            if not isinstance(row[col], float):\n",
    "                # split the column into paragraphs\n",
    "                # wonky try/except to work through integers, if not converted to strings\n",
    "                try:\n",
    "                    paragraphs = row[col].split('\\n')\n",
    "                except:\n",
    "                    paragraphs = str(row[col]).split('\\n')\n",
    "                # loop through each paragraph\n",
    "                for paragraph in paragraphs:\n",
    "                    # check if the term is in the current paragraph\n",
    "                    if re.search(r'\\b' + re.escape(term) + r'\\b', paragraph, re.IGNORECASE):\n",
    "                        results.append({\n",
    "                            'ead_id': row['ead_id'],\n",
    "#                            'source_filename': row['source_filename'],\n",
    "                            'resource_id': row['resource_id'],\n",
    "                            'titleproper': row.get('titleproper', None), \n",
    "                            'Term': term, \n",
    "                            'Matched_Times': len(re.findall(r'\\b' + re.escape(term) + r'\\b', paragraph, re.IGNORECASE)),\n",
    "                            'Matched_From': col, \n",
    "                            'Matched_Paragraph': paragraph\n",
    "                        })\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, using the match term function, tally and record the results.\n",
    "These outputs will be the basis of additional reports and visualizations,\n",
    "including the term in context report, charts, and other visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_and_visualize(df, name):\n",
    "    # Match results\n",
    "    results_df = pd.DataFrame([result for index, row in df.iterrows() for result in match_terms(row, terms, df.columns)])\n",
    "    \n",
    "    # Show matched results\n",
    "    print('Matched results for', name)\n",
    "    display(results_df) # control dislay\n",
    "\n",
    "    return results_df  # Return the DataFrame for later use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the above functions, create a dataframe (or reuse the data from the `parse_resources.ipynb` scripts), then initialize the functions. \n",
    "The following code block demonstrates how to do this with `results.csv`\n",
    "as an input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "eads_df = pd.read_csv('results-nativeAmerican.csv', encoding='utf-8')\n",
    "# eads_df = pd.read_csv('results-philippines.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resource_id</th>\n",
       "      <th>ead_id</th>\n",
       "      <th>titleproper</th>\n",
       "      <th>abstract</th>\n",
       "      <th>language</th>\n",
       "      <th>scopecontent</th>\n",
       "      <th>bioghist</th>\n",
       "      <th>subject_ids</th>\n",
       "      <th>subjects</th>\n",
       "      <th>subjects_source</th>\n",
       "      <th>...</th>\n",
       "      <th>geognames_source</th>\n",
       "      <th>persname_ids</th>\n",
       "      <th>persnames</th>\n",
       "      <th>persnames_source</th>\n",
       "      <th>corpname_ids</th>\n",
       "      <th>corpnames</th>\n",
       "      <th>corpnames_source</th>\n",
       "      <th>famname_ids</th>\n",
       "      <th>famnames</th>\n",
       "      <th>famnames_source</th>\n",
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       "      <th>0</th>\n",
       "      <td>229</td>\n",
       "      <td>umich-bhl-92745</td>\n",
       "      <td>Ed Beach photographs, 1931-1948</td>\n",
       "      <td>Ed Beach was an amateur photographer whose pho...</td>\n",
       "      <td>The finding aid is written in English</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298</td>\n",
       "      <td>Beach family.</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8482</td>\n",
       "      <td>No EAD ID</td>\n",
       "      <td>the Francis S. Belton drawing collection.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The finding aid is written in English</td>\n",
       "      <td>Drawing of Mackinac Island from Round Island.</td>\n",
       "      <td>Officer in the United States Army.</td>\n",
       "      <td>6130; 3941</td>\n",
       "      <td>Indians of North America; Ships</td>\n",
       "      <td>lcsh; lctgm</td>\n",
       "      <td>...</td>\n",
       "      <td>lcsh; lcsh</td>\n",
       "      <td>14333</td>\n",
       "      <td>Belton, Francis S.</td>\n",
       "      <td>local</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4460</td>\n",
       "      <td>No EAD ID</td>\n",
       "      <td>the Bentley Historical Library Realia Collection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The finding aid is written in English</td>\n",
       "      <td>A collection of artifacts and three-dimensiona...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>lcsh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8371; 6527</td>\n",
       "      <td>Bentley Historical Library.; University of Mic...</td>\n",
       "      <td>local; local</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>256</td>\n",
       "      <td>umich-bhl-93291</td>\n",
       "      <td>Fred E. Benz motion picture collection, 1929-1950</td>\n",
       "      <td>Amateur photographer; sixty-two reels of film ...</td>\n",
       "      <td>The finding aid is written in English</td>\n",
       "      <td>When the University of Michigan Media Resource...</td>\n",
       "      <td>In 1911, Fred Benz, an Ann Arbor, Michigan nat...</td>\n",
       "      <td>3773; 3840; 3841; 3582; 6328; 3842; 6329; 3843...</td>\n",
       "      <td>Animals.; Bullfighting.; Cemeteries.; Children...</td>\n",
       "      <td>lctgm; lctgm; lctgm; lctgm; lcsh; lctgm; lcsh;...</td>\n",
       "      <td>...</td>\n",
       "      <td>lcsh; lcsh; lcsh; lcsh; lcsh; lcsh; lcsh; lcsh...</td>\n",
       "      <td>6041</td>\n",
       "      <td>Benz, Fred E., -1963</td>\n",
       "      <td>lcnaf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8480</td>\n",
       "      <td>No EAD ID</td>\n",
       "      <td>the Black Hawk visual material collection.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The finding aid is written in English</td>\n",
       "      <td>Photoprint of portrait painting.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6806</td>\n",
       "      <td>Indians of North America.</td>\n",
       "      <td>lcsh</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14702</td>\n",
       "      <td>Black Hawk, Sauk chief, 1767-1838</td>\n",
       "      <td>lcnaf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   resource_id           ead_id  \\\n",
       "0          229  umich-bhl-92745   \n",
       "1         8482        No EAD ID   \n",
       "2         4460        No EAD ID   \n",
       "3          256  umich-bhl-93291   \n",
       "4         8480        No EAD ID   \n",
       "\n",
       "                                         titleproper  \\\n",
       "0                    Ed Beach photographs, 1931-1948   \n",
       "1          the Francis S. Belton drawing collection.   \n",
       "2   the Bentley Historical Library Realia Collection   \n",
       "3  Fred E. Benz motion picture collection, 1929-1950   \n",
       "4         the Black Hawk visual material collection.   \n",
       "\n",
       "                                            abstract  \\\n",
       "0  Ed Beach was an amateur photographer whose pho...   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3  Amateur photographer; sixty-two reels of film ...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                language  \\\n",
       "0  The finding aid is written in English   \n",
       "1  The finding aid is written in English   \n",
       "2  The finding aid is written in English   \n",
       "3  The finding aid is written in English   \n",
       "4  The finding aid is written in English   \n",
       "\n",
       "                                        scopecontent  \\\n",
       "0  The Ed Beach collection consists of photograph...   \n",
       "1      Drawing of Mackinac Island from Round Island.   \n",
       "2  A collection of artifacts and three-dimensiona...   \n",
       "3  When the University of Michigan Media Resource...   \n",
       "4                   Photoprint of portrait painting.   \n",
       "\n",
       "                                            bioghist  \\\n",
       "0  William E. Beach, known to all as Ed Beach, wa...   \n",
       "1                 Officer in the United States Army.   \n",
       "2                                                NaN   \n",
       "3  In 1911, Fred Benz, an Ann Arbor, Michigan nat...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         subject_ids  \\\n",
       "0  3784; 3785; 3663; 3786; 3787; 3788; 3789; 3790...   \n",
       "1                                         6130; 3941   \n",
       "2                                                NaN   \n",
       "3  3773; 3840; 3841; 3582; 6328; 3842; 6329; 3843...   \n",
       "4                                               6806   \n",
       "\n",
       "                                            subjects  \\\n",
       "0  Airports; Animals; Automobiles; Automobile ser...   \n",
       "1                    Indians of North America; Ships   \n",
       "2                                                NaN   \n",
       "3  Animals.; Bullfighting.; Cemeteries.; Children...   \n",
       "4                          Indians of North America.   \n",
       "\n",
       "                                     subjects_source  ...  \\\n",
       "0  lctgm; lctgm; lctgm; lctgm; lctgm; lctgm; lctg...  ...   \n",
       "1                                        lcsh; lctgm  ...   \n",
       "2                                                NaN  ...   \n",
       "3  lctgm; lctgm; lctgm; lctgm; lcsh; lctgm; lcsh;...  ...   \n",
       "4                                               lcsh  ...   \n",
       "\n",
       "                                    geognames_source persname_ids  \\\n",
       "0  lcsh; lcsh; lcsh; lcsh; lcsh; lcsh; lcsh; lcsh...   8552; 4615   \n",
       "1                                         lcsh; lcsh        14333   \n",
       "2                                               lcsh          NaN   \n",
       "3  lcsh; lcsh; lcsh; lcsh; lcsh; lcsh; lcsh; lcsh...         6041   \n",
       "4                                                NaN        14702   \n",
       "\n",
       "                                           persnames persnames_source  \\\n",
       "0  Beach, William Edward.; Winans, Edwin B., 1826...     lcnaf; lcnaf   \n",
       "1                                 Belton, Francis S.            local   \n",
       "2                                                NaN              NaN   \n",
       "3                               Benz, Fred E., -1963            lcnaf   \n",
       "4                  Black Hawk, Sauk chief, 1767-1838            lcnaf   \n",
       "\n",
       "  corpname_ids                                          corpnames  \\\n",
       "0          NaN                                                NaN   \n",
       "1          NaN                                                NaN   \n",
       "2   8371; 6527  Bentley Historical Library.; University of Mic...   \n",
       "3          NaN                                                NaN   \n",
       "4          NaN                                                NaN   \n",
       "\n",
       "  corpnames_source famname_ids       famnames famnames_source  \n",
       "0              NaN         298  Beach family.           lcnaf  \n",
       "1              NaN         NaN            NaN             NaN  \n",
       "2     local; local         NaN            NaN             NaN  \n",
       "3              NaN         NaN            NaN             NaN  \n",
       "4              NaN         NaN            NaN             NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eads_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['resource_id', 'ead_id', 'titleproper', 'abstract', 'language',\n",
       "       'scopecontent', 'bioghist', 'subject_ids', 'subjects',\n",
       "       'subjects_source', 'genreform_ids', 'genreforms', 'genreforms_source',\n",
       "       'geogname_ids', 'geognames', 'geognames_source', 'persname_ids',\n",
       "       'persnames', 'persnames_source', 'corpname_ids', 'corpnames',\n",
       "       'corpnames_source', 'famname_ids', 'famnames', 'famnames_source'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eads_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename eadid to ead_id\n",
    "eads_df = eads_df.rename(columns={'eadid':'ead_id'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tally and Record\n",
    "\n",
    "Use the `match_and_visualize()` function to count the matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched results for nativeAmerican\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ead_id</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>titleproper</th>\n",
       "      <th>Term</th>\n",
       "      <th>Matched_Times</th>\n",
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       "      <td>umich-bhl-92745</td>\n",
       "      <td>229</td>\n",
       "      <td>Ed Beach photographs, 1931-1948</td>\n",
       "      <td>Dwellings</td>\n",
       "      <td>1</td>\n",
       "      <td>subjects</td>\n",
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       "      <th>1</th>\n",
       "      <td>umich-bhl-92745</td>\n",
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       "      <td>Ed Beach photographs, 1931-1948</td>\n",
       "      <td>Indian</td>\n",
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       "      <td>scopecontent</td>\n",
       "      <td>Beach created the photograph albums around br...</td>\n",
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       "      <th>2</th>\n",
       "      <td>umich-bhl-92745</td>\n",
       "      <td>229</td>\n",
       "      <td>Ed Beach photographs, 1931-1948</td>\n",
       "      <td>Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>subjects</td>\n",
       "      <td>Airports; Animals; Automobiles; Automobile ser...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>umich-bhl-92745</td>\n",
       "      <td>229</td>\n",
       "      <td>Ed Beach photographs, 1931-1948</td>\n",
       "      <td>Copper</td>\n",
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       "      <td>Acme (Mich.); Ada (Mich.); Adrian (Mich.); Alb...</td>\n",
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       "      <th>4</th>\n",
       "      <td>No EAD ID</td>\n",
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       "      <td>the Francis S. Belton drawing collection.</td>\n",
       "      <td>Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>subjects</td>\n",
       "      <td>Indians of North America; Ships</td>\n",
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       "      <th>...</th>\n",
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       "      <th>774</th>\n",
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       "      <td>Mounds</td>\n",
       "      <td>1</td>\n",
       "      <td>bioghist</td>\n",
       "      <td>The Scientific Club succeeded an earlier orga...</td>\n",
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       "      <th>775</th>\n",
       "      <td>umich-bhl-87257</td>\n",
       "      <td>2634</td>\n",
       "      <td>Scientific Club (University of Michigan) Recor...</td>\n",
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       "      <td>1</td>\n",
       "      <td>bioghist</td>\n",
       "      <td>The Scientific Club succeeded an earlier orga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>umich-bhl-87290</td>\n",
       "      <td>2710</td>\n",
       "      <td>Vice President for Student Life (University of...</td>\n",
       "      <td>Native</td>\n",
       "      <td>4</td>\n",
       "      <td>bioghist</td>\n",
       "      <td>In February 2000, the Students of Color Coali...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>umich-bhl-2014119</td>\n",
       "      <td>2843</td>\n",
       "      <td>William Edward Wise visual materials collectio...</td>\n",
       "      <td>Indian</td>\n",
       "      <td>2</td>\n",
       "      <td>corpnames</td>\n",
       "      <td>First Unitarian Church (Ann Arbor, Mich.); Wes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>umich-bhl-2014119</td>\n",
       "      <td>2843</td>\n",
       "      <td>William Edward Wise visual materials collectio...</td>\n",
       "      <td>Savage</td>\n",
       "      <td>1</td>\n",
       "      <td>persnames</td>\n",
       "      <td>Wise, William Edward.; Sunderland, Jabez Thoma...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>779 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ead_id  resource_id  \\\n",
       "0      umich-bhl-92745          229   \n",
       "1      umich-bhl-92745          229   \n",
       "2      umich-bhl-92745          229   \n",
       "3      umich-bhl-92745          229   \n",
       "4            No EAD ID         8482   \n",
       "..                 ...          ...   \n",
       "774    umich-bhl-87257         2634   \n",
       "775    umich-bhl-87257         2634   \n",
       "776    umich-bhl-87290         2710   \n",
       "777  umich-bhl-2014119         2843   \n",
       "778  umich-bhl-2014119         2843   \n",
       "\n",
       "                                           titleproper       Term  \\\n",
       "0                      Ed Beach photographs, 1931-1948  Dwellings   \n",
       "1                      Ed Beach photographs, 1931-1948     Indian   \n",
       "2                      Ed Beach photographs, 1931-1948    Indians   \n",
       "3                      Ed Beach photographs, 1931-1948     Copper   \n",
       "4            the Francis S. Belton drawing collection.    Indians   \n",
       "..                                                 ...        ...   \n",
       "774  Scientific Club (University of Michigan) Recor...     Mounds   \n",
       "775  Scientific Club (University of Michigan) Recor...     Indian   \n",
       "776  Vice President for Student Life (University of...     Native   \n",
       "777  William Edward Wise visual materials collectio...     Indian   \n",
       "778  William Edward Wise visual materials collectio...     Savage   \n",
       "\n",
       "     Matched_Times  Matched_From  \\\n",
       "0                1      subjects   \n",
       "1                1  scopecontent   \n",
       "2                1      subjects   \n",
       "3                1     geognames   \n",
       "4                1      subjects   \n",
       "..             ...           ...   \n",
       "774              1      bioghist   \n",
       "775              1      bioghist   \n",
       "776              4      bioghist   \n",
       "777              2     corpnames   \n",
       "778              1     persnames   \n",
       "\n",
       "                                     Matched_Paragraph  \n",
       "0    Airports; Animals; Automobiles; Automobile ser...  \n",
       "1     Beach created the photograph albums around br...  \n",
       "2    Airports; Animals; Automobiles; Automobile ser...  \n",
       "3    Acme (Mich.); Ada (Mich.); Adrian (Mich.); Alb...  \n",
       "4                      Indians of North America; Ships  \n",
       "..                                                 ...  \n",
       "774   The Scientific Club succeeded an earlier orga...  \n",
       "775   The Scientific Club succeeded an earlier orga...  \n",
       "776   In February 2000, the Students of Color Coali...  \n",
       "777  First Unitarian Church (Ann Arbor, Mich.); Wes...  \n",
       "778  Wise, William Edward.; Sunderland, Jabez Thoma...  \n",
       "\n",
       "[779 rows x 7 columns]"
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      "text/plain": [
       "                ead_id  resource_id  \\\n",
       "0      umich-bhl-92745          229   \n",
       "1      umich-bhl-92745          229   \n",
       "2      umich-bhl-92745          229   \n",
       "3      umich-bhl-92745          229   \n",
       "4            No EAD ID         8482   \n",
       "..                 ...          ...   \n",
       "774    umich-bhl-87257         2634   \n",
       "775    umich-bhl-87257         2634   \n",
       "776    umich-bhl-87290         2710   \n",
       "777  umich-bhl-2014119         2843   \n",
       "778  umich-bhl-2014119         2843   \n",
       "\n",
       "                                           titleproper       Term  \\\n",
       "0                      Ed Beach photographs, 1931-1948  Dwellings   \n",
       "1                      Ed Beach photographs, 1931-1948     Indian   \n",
       "2                      Ed Beach photographs, 1931-1948    Indians   \n",
       "3                      Ed Beach photographs, 1931-1948     Copper   \n",
       "4            the Francis S. Belton drawing collection.    Indians   \n",
       "..                                                 ...        ...   \n",
       "774  Scientific Club (University of Michigan) Recor...     Mounds   \n",
       "775  Scientific Club (University of Michigan) Recor...     Indian   \n",
       "776  Vice President for Student Life (University of...     Native   \n",
       "777  William Edward Wise visual materials collectio...     Indian   \n",
       "778  William Edward Wise visual materials collectio...     Savage   \n",
       "\n",
       "     Matched_Times  Matched_From  \\\n",
       "0                1      subjects   \n",
       "1                1  scopecontent   \n",
       "2                1      subjects   \n",
       "3                1     geognames   \n",
       "4                1      subjects   \n",
       "..             ...           ...   \n",
       "774              1      bioghist   \n",
       "775              1      bioghist   \n",
       "776              4      bioghist   \n",
       "777              2     corpnames   \n",
       "778              1     persnames   \n",
       "\n",
       "                                     Matched_Paragraph  \n",
       "0    Airports; Animals; Automobiles; Automobile ser...  \n",
       "1     Beach created the photograph albums around br...  \n",
       "2    Airports; Animals; Automobiles; Automobile ser...  \n",
       "3    Acme (Mich.); Ada (Mich.); Adrian (Mich.); Alb...  \n",
       "4                      Indians of North America; Ships  \n",
       "..                                                 ...  \n",
       "774   The Scientific Club succeeded an earlier orga...  \n",
       "775   The Scientific Club succeeded an earlier orga...  \n",
       "776   In February 2000, the Students of Color Coali...  \n",
       "777  First Unitarian Church (Ann Arbor, Mich.); Wes...  \n",
       "778  Wise, William Edward.; Sunderland, Jabez Thoma...  \n",
       "\n",
       "[779 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_and_visualize(eads_df, 'nativeAmerican')\n",
    "# match_and_visualize(eads_df, 'philippines')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `calculate_term_frequency()` function uses the matched term list\n",
    "and regroups according to the matched terms and tallies the frequncy of the terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_term_frequency(df, df_name):\n",
    "    term_frequency = df.groupby('Term')['Matched_Times'].sum().reset_index()\n",
    "    term_frequency.rename(columns={'Matched_Times': 'Total_Frequency'}, inplace=True)\n",
    "    term_frequency['DataFrame'] = df_name\n",
    "\n",
    "    # Sort in descending order\n",
    "    term_frequency.sort_values(by='Total_Frequency', ascending=False, inplace=True)\n",
    "    \n",
    "    # Show frequency table\n",
    "    print('Term frequency for', df_name)\n",
    "    display(term_frequency)\n",
    "\n",
    "    return term_frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match from multiple sources\n",
    "\n",
    "In our originating use case at the University of Michigan,\n",
    "we were interested to see how the terms were used across different\n",
    "collections. We were, specifically, working with three on-campus repositories:\n",
    "the Bentley Historical Library, Clements Library, and the Special Collections Research Center (part of the University Library).\n",
    "\n",
    "If you are working with multiple groups of finding aids, \n",
    "you might adapt the following code, which concatenates multiple\n",
    "lists (may be provided as dataframes or CSVs) and tallies across them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# for each dataframe representing matched terms\\nfile_list = [(df1_Bentley, 'Bentley'), (df2_Clements, 'Clements'), (df3_SCRC, 'SCRC')]\\nmatched_results = {name: match_and_visualize(df, name) for df, name in file_list}\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# for each dataframe representing matched terms\n",
    "file_list = [(df1_Bentley, 'Bentley'), (df2_Clements, 'Clements'), (df3_SCRC, 'SCRC')]\n",
    "matched_results = {name: match_and_visualize(df, name) for df, name in file_list}'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize matches\n",
    "\n",
    "This should probably be split to its own file, but here for dev purposes\n",
    "\n",
    "### Visualization values\n",
    "\n",
    "Use the below to set global values, such as the names of repositories\n",
    "under analysis, or fonts and colors for charts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched results for Bentley\n"
     ]
    },
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>umich-bhl-87257</td>\n",
       "      <td>2634</td>\n",
       "      <td>Scientific Club (University of Michigan) Recor...</td>\n",
       "      <td>Mounds</td>\n",
       "      <td>1</td>\n",
       "      <td>bioghist</td>\n",
       "      <td>The Scientific Club succeeded an earlier orga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>umich-bhl-87257</td>\n",
       "      <td>2634</td>\n",
       "      <td>Scientific Club (University of Michigan) Recor...</td>\n",
       "      <td>Indian</td>\n",
       "      <td>1</td>\n",
       "      <td>bioghist</td>\n",
       "      <td>The Scientific Club succeeded an earlier orga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>umich-bhl-87290</td>\n",
       "      <td>2710</td>\n",
       "      <td>Vice President for Student Life (University of...</td>\n",
       "      <td>Native</td>\n",
       "      <td>4</td>\n",
       "      <td>bioghist</td>\n",
       "      <td>In February 2000, the Students of Color Coali...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>umich-bhl-2014119</td>\n",
       "      <td>2843</td>\n",
       "      <td>William Edward Wise visual materials collectio...</td>\n",
       "      <td>Indian</td>\n",
       "      <td>2</td>\n",
       "      <td>corpnames</td>\n",
       "      <td>First Unitarian Church (Ann Arbor, Mich.); Wes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>umich-bhl-2014119</td>\n",
       "      <td>2843</td>\n",
       "      <td>William Edward Wise visual materials collectio...</td>\n",
       "      <td>Savage</td>\n",
       "      <td>1</td>\n",
       "      <td>persnames</td>\n",
       "      <td>Wise, William Edward.; Sunderland, Jabez Thoma...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>779 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ead_id  resource_id  \\\n",
       "0      umich-bhl-92745          229   \n",
       "1      umich-bhl-92745          229   \n",
       "2      umich-bhl-92745          229   \n",
       "3      umich-bhl-92745          229   \n",
       "4            No EAD ID         8482   \n",
       "..                 ...          ...   \n",
       "774    umich-bhl-87257         2634   \n",
       "775    umich-bhl-87257         2634   \n",
       "776    umich-bhl-87290         2710   \n",
       "777  umich-bhl-2014119         2843   \n",
       "778  umich-bhl-2014119         2843   \n",
       "\n",
       "                                           titleproper       Term  \\\n",
       "0                      Ed Beach photographs, 1931-1948  Dwellings   \n",
       "1                      Ed Beach photographs, 1931-1948     Indian   \n",
       "2                      Ed Beach photographs, 1931-1948    Indians   \n",
       "3                      Ed Beach photographs, 1931-1948     Copper   \n",
       "4            the Francis S. Belton drawing collection.    Indians   \n",
       "..                                                 ...        ...   \n",
       "774  Scientific Club (University of Michigan) Recor...     Mounds   \n",
       "775  Scientific Club (University of Michigan) Recor...     Indian   \n",
       "776  Vice President for Student Life (University of...     Native   \n",
       "777  William Edward Wise visual materials collectio...     Indian   \n",
       "778  William Edward Wise visual materials collectio...     Savage   \n",
       "\n",
       "     Matched_Times  Matched_From  \\\n",
       "0                1      subjects   \n",
       "1                1  scopecontent   \n",
       "2                1      subjects   \n",
       "3                1     geognames   \n",
       "4                1      subjects   \n",
       "..             ...           ...   \n",
       "774              1      bioghist   \n",
       "775              1      bioghist   \n",
       "776              4      bioghist   \n",
       "777              2     corpnames   \n",
       "778              1     persnames   \n",
       "\n",
       "                                     Matched_Paragraph  \n",
       "0    Airports; Animals; Automobiles; Automobile ser...  \n",
       "1     Beach created the photograph albums around br...  \n",
       "2    Airports; Animals; Automobiles; Automobile ser...  \n",
       "3    Acme (Mich.); Ada (Mich.); Adrian (Mich.); Alb...  \n",
       "4                      Indians of North America; Ships  \n",
       "..                                                 ...  \n",
       "774   The Scientific Club succeeded an earlier orga...  \n",
       "775   The Scientific Club succeeded an earlier orga...  \n",
       "776   In February 2000, the Students of Color Coali...  \n",
       "777  First Unitarian Church (Ann Arbor, Mich.); Wes...  \n",
       "778  Wise, William Edward.; Sunderland, Jabez Thoma...  \n",
       "\n",
       "[779 rows x 7 columns]"
      ]
     },
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   ],
   "source": [
    "# set names and colors here\n",
    "repo_list = ['Bentley']\n",
    "#repo_list = ['Bentley','Clements','SCRC']\n",
    "global_font_info = {'font_family':'Georgia'}\n",
    "colors = {'Bentley': '#CFC096', 'Clements': '#A5A508', 'SCRC': '#FFCB05'}\n",
    "project_name = 'Native American'\n",
    "# project_name = 'Philippines)\n",
    "\n",
    "# create a manifest to select which result lists you want to visualize\n",
    "match_groups = [(eads_df, 'Bentley')]\n",
    "matched_results = {name: match_and_visualize(df, name) for df, name in match_groups}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize group matches\n",
    "\n",
    "In the use case at U Michigan, the groups represented finding aids\n",
    "created by different repositories on campus. This can be useful for\n",
    "working with subgroups of finding aids, or in comparing usage between\n",
    "different organizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term frequency for Bentley\n"
     ]
    },
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       "      <th>17</th>\n",
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       "      <th>3</th>\n",
       "      <td>Captive</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anishnaabe</td>\n",
       "      <td>1</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Potawatomie</td>\n",
       "      <td>1</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Savage</td>\n",
       "      <td>1</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Term  Total_Frequency DataFrame\n",
       "17      Indians              292   Bentley\n",
       "15       Indian              197   Bentley\n",
       "22       Native              154   Bentley\n",
       "4         Chief               41   Bentley\n",
       "6      Chippewa               40   Bentley\n",
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       "2       Burials                2   Bentley\n",
       "3       Captive                1   Bentley\n",
       "0    Anishnaabe                1   Bentley\n",
       "28  Potawatomie                1   Bentley\n",
       "34       Savage                1   Bentley\n",
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    "# bar charts for individual groups\n",
    "\n",
    "def visualize_individual_repository_terms_bar(list_of_dataframes):\n",
    "    '''\n",
    "    This function takes a list of strings (list_of_dataframes), which identify the names of dataframes (corresponding to an archival repository),\n",
    "    then generates a bar chart to show how many times a list of requested terms appears in the corresponding finding aid documents.\n",
    "    Requires the calculate_term_frequency function.\n",
    "    Processes data using pandas and generates charts using plotly (px) & plotly express (pio).\n",
    "    '''\n",
    "    for i in range(len(list_of_dataframes)):\n",
    "        # calculate term frequency\n",
    "        term_frequency = calculate_term_frequency(matched_results[repo_list[i]], repo_list[i])\n",
    "        term_frequency.head()\n",
    "\n",
    "        # Visualization in Multiple Charts\n",
    "        fig = px.bar(term_frequency, x='Term', y='Total_Frequency', text='Total_Frequency', \n",
    "                        color='DataFrame', color_discrete_map=colors, text_auto=True,\n",
    "                        labels={'Total_Frequency':'Term Occurence Count','DataFrame':'Repository'})\n",
    "        fig.update_traces(textposition='outside', insidetextanchor='middle',\n",
    "                          textfont=dict(family='Arial', size=8))\n",
    "        fig.update_layout(title_text=f'Term Frequency for { repo_list[i] } ({ project_name })', \n",
    "                          xaxis_title_standoff=10, \n",
    "                          showlegend=False, font_family=global_font_info['font_family'])\n",
    "        fig.update_xaxes(tickangle=45, tickfont=dict(family='Arial', color='black', size=10))\n",
    "        fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "        fig.show()\n",
    "        # pio.write_image(fig, f'term_frequency_byrepo_{ repo_list[i] }.png', width=700)\n",
    "\n",
    "visualize_individual_repository_terms_bar(repo_list)"
   ]
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    "The above shows a basic bar chart, ordered from most frequent\n",
    "to least frequent term. If looking at multiple groupings, the above\n",
    "function will create multiple bar charts. \n",
    "\n",
    "The following sections demonstrate additional visualizations, for a single grouping\n",
    "of finding aid data. For the case of multiple groups (or, in our case repositories)\n",
    "see the additional visualization notebooks.\n",
    "\n",
    "## Visualization: occurence in a horizontal bar chart"
   ]
  },
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    "# bar charts for individual repos - horizontal\n",
    "\n",
    "def visualize_individual_repository_terms_bar_horiz(list_of_dataframes):\n",
    "    '''\n",
    "    This function takes a list of strings (list_of_dataframes), which identify the names of dataframes (corresponding to an archival repository),\n",
    "    then generates a horizontal bar chart to show how many times a list of requested terms appears in the corresponding finding aid documents.\n",
    "    Requires the calculate_term_frequency function.\n",
    "    Processes data using pandas and generates charts using plotly (px) & plotly express (pio).\n",
    "    '''\n",
    "    for i in range(len(list_of_dataframes)):\n",
    "        # calculate term frequency\n",
    "        term_frequency = calculate_term_frequency(matched_results[repo_list[i]], repo_list[i]).sort_values('Total_Frequency', ascending=True)\n",
    "        term_frequency.head()\n",
    "\n",
    "        # Visualization in Charts Horizontallly\n",
    "        fig = px.bar(term_frequency, y='Term', x='Total_Frequency', text='Total_Frequency',\n",
    "                        color='DataFrame', color_discrete_map=colors, text_auto=True,\n",
    "                        labels={'Total_Frequency':'Term Occurence Count','DataFrame':'Repository'})\n",
    "        fig.update_traces(textposition='outside', insidetextanchor='middle', \n",
    "                          textfont=dict(family='Arial',size=8))\n",
    "        fig.update_layout(title_text=f'Term Frequency for { repo_list[i] } ({project_name})',\n",
    "                          xaxis_title_standoff=10, height=600, \n",
    "                          showlegend=False, font_family=global_font_info['font_family'])\n",
    "        fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "        fig.show()\n",
    "        # pio.write_image(fig, f'term_frequency_byrepo_{ repo_list[i] }_horizbar.png', scale=4)\n",
    "\n",
    "visualize_individual_repository_terms_bar_horiz(repo_list)"
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    "The horizontal bar chart seems more well-suited to displaying occurences\n",
    "of the terms, since the terms are easier to read in this orientation.\n",
    "\n",
    "## Visualization: Occurence by EAD tag/element\n",
    "\n",
    "This section of the script helps identify the EAD (Encoded Archival Description) elements with the highest occurrences of the specified harmful terms.\n",
    "\n",
    "The function `calculate_element_frequency(df, df_name)` is used to calculate the sum of matched terms for each subsection in a DataFrame. The results are sorted in descending order and returned as a DataFrame with columns `Subsection`, `harmful_terms_frequency`, and `Source`.\n",
    "\n",
    "This function is applied to each DataFrame in matched_results, and the results are concatenated into a single DataFrame, `all_element_frequencies`.\n",
    "\n",
    "Finally, a grouped bar chart is created to visualize the frequency of terms across different subsections and sources.\n",
    "\n",
    "In the chart, the x-axis represents the subsections, the y-axis shows the frequency of harmful terms, and different colors distinguish between sources. The `barmode='group'` setting places the bars side by side for easier comparison between sources."
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      "Element frequencies across all file pools:\n"
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       "     Subsection  harmful_terms_frequency   Source\n",
       "0      bioghist                      347  Bentley\n",
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         "text": "Term Occurancy Frequency (Stacked Totals) for Bentley (Native American)"
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   "source": [
    "def calculate_element_frequency(df, df_name):\n",
    "    element_counts = df.groupby('Matched_From')['Matched_Times'].sum()\n",
    "    element_counts_sorted = element_counts.sort_values(ascending=False)\n",
    "    df_element_counts = pd.DataFrame(list(element_counts_sorted.items()), columns=['Subsection', 'harmful_terms_frequency'])\n",
    "    df_element_counts['Source'] = df_name  # Indicate the source dataframe\n",
    "    return df_element_counts\n",
    "\n",
    "# Use the function for each dataframes\n",
    "element_frequencies_list = [calculate_element_frequency(df, name) for name, df in matched_results.items()]\n",
    "\n",
    "# Concatenate all element frequencies\n",
    "all_element_frequencies = pd.concat(element_frequencies_list)\n",
    "\n",
    "# Show the DataFrame\n",
    "print(\"Element frequencies across all file pools:\")\n",
    "display(all_element_frequencies)\n",
    "\n",
    "# Set colors\n",
    "colors = {'Bentley': '#CFC096', 'Clements': '#A5A508', 'SCRC': '#FFCB05'}\n",
    "\n",
    "# Visualization\n",
    "fig = px.bar(all_element_frequencies, x='Subsection', y='harmful_terms_frequency', color='Source', \n",
    "             text='harmful_terms_frequency', barmode='group',\n",
    "             labels={'Subsection':'EAD Tag','Source':'Repository'},\n",
    "             color_discrete_map=colors)\n",
    "fig.update_traces(textposition='outside', textfont=dict(family='Arial',size=8))\n",
    "fig.update_layout(title_text=f'Term Occurancy Frequency for { repo_list[0] } ({project_name})', yaxis_title=\"Term Frequency\", height=600,\n",
    "                  font_family=global_font_info['font_family'])\n",
    "fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "fig.show()\n",
    "# pio.write_image(fig, 'element_frequency_across_dfs.png', scale=5)\n",
    "\n",
    "# Visualization Variation: Stacked bars\n",
    "fig = px.bar(all_element_frequencies, x='Subsection', y='harmful_terms_frequency', color='Source', \n",
    "             text='harmful_terms_frequency', barmode='stack',\n",
    "             labels={'Subsection':'EAD Tag','Source':'Repository'},\n",
    "             color_discrete_map=colors)\n",
    "fig.update_traces(textposition='inside', insidetextanchor='middle', textfont=dict(family='Arial',size=8))\n",
    "fig.update_layout(title_text=f'Term Occurancy Frequency (Stacked Totals) for { repo_list[0] } ({project_name})', yaxis_title=\"Term Frequency\", height=600,\n",
    "                  font_family=global_font_info['font_family'])\n",
    "fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "fig.show()\n",
    "# pio.write_image(fig, 'element_frequency_across_dfs_stacked.png', scale=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Term Frequencies Across Subsections\n",
    "\n",
    "This section calculates the term frequencies in different subsections of each data source and visualizes the results using treemap and sunburst diagrams.\n",
    "\n",
    "The `calculate_subsection_term_frequency(df, df_name)` function is used to calculate the sum of matched terms for each term in each subsection of a DataFrame. The results are returned as a DataFrame with columns `Subsection`, `Term`, `Term_Frequency`, and `Source`. This function is applied to each DataFrame in matched_results, and the results are concatenated into a single DataFrame, `all_subsection_term_frequencies`.\n",
    "\n",
    "Treemap diagrams are then created to visualize the frequencies of terms in each subsection for every data source. The size of each section in the treemap corresponds to the term frequency in that section. Two types of treemaps are generated: one with uniform colors and one with a color scale indicating term frequency. The color scale ranges from yellow ('Yl') for lower frequencies to red ('Rd') for higher frequencies.\n",
    "\n",
    "A sunburst diagram is created with a similar color scale. In this diagram, the data sources are represented in the inner circle, subsections in the middle ring, and terms in the outer ring. This hierarchical view allows for easy comparison of term frequencies across different sources and subsections. The resulting visualizations are displayed but not saved. If you wish to save them, you can use fig.write_image('filename.png') as in the previous sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_subsection_term_frequency(df, df_name):\n",
    "    subsection_term_frequency = df.groupby(['Matched_From', 'Term'])['Matched_Times'].sum().reset_index()\n",
    "    subsection_term_frequency.rename(columns={'Matched_From': 'Subsection', 'Matched_Times': 'Term_Frequency'}, inplace=True)\n",
    "    subsection_term_frequency['Source'] = df_name\n",
    "    return subsection_term_frequency\n",
    "\n",
    "# Use the function for each pool\n",
    "subsection_term_frequencies_list = [calculate_subsection_term_frequency(df, name) for name, df in matched_results.items()]\n",
    "\n",
    "# Concatenate all subsection term frequencies\n",
    "all_subsection_term_frequencies = pd.concat(subsection_term_frequencies_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treemap visualization\n",
    "\n",
    "The tree map visualization, initially used in digital context to\n",
    "represent the proportional size of files and directories on a hard disk,\n",
    "provides a useful way to show and explore hierarchy. \n",
    "\n",
    "The primary grouping in this treemap is the holding repository. In the case of U-Michigan,\n",
    "we were interested in how occurence patterns might differ between repositories. But in other cases, the hierarchy might be used to separate finding aid groups by\n",
    "topic, time period, media, or any other grouping that might be of interest.\n",
    "\n",
    "The secondary groping in this tree map is the EAD tag. It would, instead, be\n",
    "possible to group by term (and then tag), or in other orders. For our case,\n",
    "it was most interesting to point out the \"biggest\" (that is, most prevalent)\n",
    "tags that included terms of interest. Finally, individual terms are grouped in their own box. \n",
    "\n",
    "The first tree map uses individual colors for each top-level parent tag. The second tree map uses a gradient so that color intensity, in addition to box size helps to indicate term prevalence (more intense and larger is a greater occurence while lighter and smaller indicates less occurence). "
   ]
  },
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            "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": {
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           },
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
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           "ticks": "",
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           "zerolinewidth": 2
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         }
        },
        "width": 1500
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Treemap\n",
    "color_map = {'Bentley' : 'blue', 'Clements': 'red', 'SCRC': 'green'}\n",
    "fig = px.treemap(all_subsection_term_frequencies, path=['Source', 'Subsection', 'Term'], values='Term_Frequency')\n",
    "fig.update_layout(width=1500, height=800)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A color gradient indicates higher term prevalence in darker, or more intense, colors,\n",
    "while lighter colors indicate lower occurence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
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   "source": [
    "# Add a dummy color column\n",
    "all_subsection_term_frequencies['color'] = all_subsection_term_frequencies['Term_Frequency']\n",
    "\n",
    "# Treemap - color scale\n",
    "fig = px.treemap(all_subsection_term_frequencies, path=['Source', 'Subsection', 'Term'], values='Term_Frequency',\n",
    "                 color='color', color_continuous_scale='ylgnbu')\n",
    "fig.update_layout(width=1500, height=800)\n",
    "fig.show()"
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   "metadata": {},
   "source": [
    "## Sunburst visualization\n",
    "\n",
    "The sunburst visualization is similarly useful for visualizing components in a hierarchy.\n",
    "In this case, the added benefit of the subsections of each layer of the circle\n",
    "give a sense of proportion of the whole group. So, for example, in this case, one may note that nearly half of the potentially problematic terms occur in the `bioghist`\n",
    "note. \n",
    "\n",
    "In the second example, the EAD tag is used as the central component, or level of\n",
    "first division for the hierarchy. Given that this visualization presumes\n",
    "a knowledge of the EAD tags and terminology, we provisionally described\n",
    "this as an \"archivist's view\".\n",
    "\n",
    "The third example places the potentially problematic terms at the center.\n",
    "In this example, we suggested this might be a \"community view\" since it focuses\n",
    "on the potentially problematic terms, which may be useful starting points\n",
    "for discussion with community members who are interested in how certain terms\n",
    "are used by the archives, how frequently they occur, and potentially in which\n",
    "finding aids they may be most prevalent."
   ]
  },
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            "gridwidth": 2,
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            "showbackground": true,
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     },
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   "source": [
    "# Sunburst - color scale\n",
    "fig = px.sunburst(all_subsection_term_frequencies, path=['Source', 'Subsection', 'Term'], values='Term_Frequency', \n",
    "                  color='Term_Frequency', color_continuous_scale='ylgnbu')\n",
    "fig.update_layout(width=1300, height=800, title='Sunburst with holding repository as central component')\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
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            "gridcolor": "white",
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            "ticks": ""
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           "bgcolor": "#E5ECF6",
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            "linecolor": "white",
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          },
          "title": {
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           "ticks": "",
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        },
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     "output_type": "display_data"
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   ],
   "source": [
    "# Sunburst - color scale\n",
    "fig = px.sunburst(all_subsection_term_frequencies, path=['Subsection','Term','Source'], values='Term_Frequency', \n",
    "                  color='Term_Frequency', color_continuous_scale='blues')\n",
    "fig.update_layout(width=1300, height=800, title='Sunburst divided by EAD tag, term, then repository (archivists view?)')\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
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          "title": {
           "x": 0.05
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        },
        "title": {
         "text": "Sunburst divided by term, then repository, then EAD tag (\"community\" view?)"
        },
        "width": 1300
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Sunburst - color scale\n",
    "fig = px.sunburst(all_subsection_term_frequencies, path=['Term','Source','Subsection'], values='Term_Frequency', \n",
    "                  color='Term_Frequency', color_continuous_scale='blues')\n",
    "fig.update_layout(width=1300, height=800, title='Sunburst divided by term, then repository, then EAD tag (\"community\" view?)')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization: Source Frequencies\n",
    "\n",
    "This section of the script is designed to create bar plots displaying the frequencies of source controlled vocabularies in the EAD `controlaccess` section.\n",
    "\n",
    "The `source_columns` list includes the following column names:\n",
    "\n",
    "* 'subjects_source'\n",
    "* 'genreforms_source'\n",
    "* 'geognames_source'\n",
    "* 'persnames_source'\n",
    "* 'corpnames_source'\n",
    "* 'famnames_source'\n",
    "\n",
    "You can modify this list according to the source columns present in your specific DataFrames.\n",
    "\n",
    "Note: In the case where a DataFrame doesn't have the specified column, an empty series is created to prevent errors. The sources in the column are separated using `str.split('; ').explode()`, which handles multiple sources in the same cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: this code originally assumed three repositories - the conversion was hacky, no longer works with multiple sets?\n",
    "\n",
    "def plot_source_frequencies(df, df_name, column):\n",
    "    if column in df.columns:\n",
    "        df_counts = df[column].fillna('').str.split('; ').explode().value_counts()\n",
    "    else:\n",
    "        df_counts = pd.Series(dtype='int')\n",
    "        \n",
    "    # Create a new DataFrame to store these counts\n",
    "    counts_df = pd.DataFrame({\n",
    "        df_name: df_counts,\n",
    "        #df2_name: df2_counts,\n",
    "        #df3_name: df3_counts\n",
    "    }).fillna(0)\n",
    "    print(counts_df.columns)\n",
    "\n",
    "    fig = px.bar(counts_df, barmode='group',\n",
    "                 labels={'value':'Term Occurence in Controlled Element'}, \n",
    "                 color_discrete_map=colors)\n",
    "    fig.show()\n",
    "\n",
    "    # Create a color palette\n",
    "    colors = {'df1_Bentley': '#2F65A7', 'df2_Clements': '#9A3324', 'df3_SCRC': '#A5A508'}\n",
    "\n",
    "    # Plot with rotated x-axis labels\n",
    "    counts_df.plot(kind='bar', figsize=(6, 4), rot=0, color=colors)\n",
    "    tag_label = column.split('_')[0].capitalize()\n",
    "    plt.title(f'Source Frequencies for {tag_label}')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.xlabel(f'{tag_label} Source')\n",
    "    plt.legend(labels={'Bentley':'df1_Bentley','Clements':'df2_Clements','SCRC':'df3_SCRC'})\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create a dataframe with the counts that can be reshaped for visualization\n",
    "def control_term_frequency(df, df_name, column):\n",
    "    if column in df.columns:\n",
    "        df_counts = df[column].fillna('').str.split('; ').explode().value_counts()\n",
    "    else:\n",
    "        df_counts = pd.Series(dtype='int')\n",
    "        \n",
    "    # Create a new DataFrame to store these counts\n",
    "    counts_df = pd.DataFrame({\n",
    "        df_name: df_counts,\n",
    "        #df2_name: df2_counts,\n",
    "        #df3_name: df3_counts\n",
    "    }).fillna(0)\n",
    "\n",
    "    return counts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the `control_term_frequency()` function with one EAD controlled tag example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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            "standoff": 15
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           "zerolinewidth": 2
          },
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           "automargin": true,
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        },
        "title": {
         "text": "Term Count per Controlled Subject Tags (Native American)"
        },
        "width": 600,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         "tickangle": 0,
         "tickfont": {
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          "family": "Arial",
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         "title": {
          "text": "Term Subject Authority Source"
         }
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         "anchor": "x",
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          "family": "Arial",
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          "text": "Term Count"
         }
        }
       }
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "source_columns = ['subjects_source', 'genreforms_source', 'geognames_source', 'persnames_source', 'corpnames_source', 'famnames_source']\n",
    "\n",
    "# calculate subject terms\n",
    "controlterm_counts_df = control_term_frequency(eads_df, 'Bentley', source_columns[0])#'subjects_source')\n",
    "\n",
    "controlterm = source_columns[0]\n",
    "controlterm_label = controlterm.split('_')[0].capitalize()[:-1]\n",
    "\n",
    "# Visualize in a bar chart\n",
    "fig = px.bar(controlterm_counts_df, barmode='group',\n",
    "             labels={'variable':'Repository'},\n",
    "             color_discrete_map=colors,\n",
    "             text='value')\n",
    "fig.update_layout(title_text=f'Term Count per Controlled {controlterm_label} Tags' + ' (' + project_name + ')',\n",
    "                  yaxis_title='Term Count', xaxis_title=f'Term {controlterm_label} Authority Source', width=600,\n",
    "                  font_family=global_font_info['font_family'])\n",
    "fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "fig.update_traces(textposition='outside', textfont=dict(family='Arial',size=8))\n",
    "fig.show()\n",
    "\n",
    "# pio.write_image(fig, f'term_frequency_by_{controlterm_label}.png', scale=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above function may be used for any individual controlled EAD tag source.\n",
    "Since we knew there were multiple controlled tags, however, the following\n",
    "code blocks visualize the authorities consulted for all of the controlled\n",
    "tags. In cases where no authority was given, the columns are blank,\n",
    "indicating no authority source was given, indicating likely usage of a local file\n",
    "or one-off occurence of names or terms.\n",
    "\n",
    "The multiple controlled tag function is `visualize_controlterms()`. This function takes\n",
    "a pandas dataframe (`df`), a group name (`df_name`) that can indicate the source repository\n",
    "or a subgroup of finding aid data, the `source_list` which must be a list and\n",
    "can list one or more source EAD tags, and the `savePNG` argument. \n",
    "The last argument can be used to choose whether or not the function should\n",
    "create and save a standalone graphic file for the visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to visualize a source_column\n",
    "def visualize_controlterms(df, df_name, source_list, savePNG=False):\n",
    "    '''This function requires the controlterm_frequency() function to produce the dataframe with controlled term counts.\n",
    "    source_list should be a list with column names that designate the controlled vocabularies you want to count and visualize.\n",
    "    '''\n",
    "    for i in range(len(source_list)):\n",
    "        controlterm_counts_df = control_term_frequency(df, df_name, source_list[i])\n",
    "\n",
    "        controlterm = source_list[i]\n",
    "        controlterm_label = controlterm.split('_')[0].capitalize()[:-1]\n",
    "        print(f'Generating visualization for {controlterm_label}')\n",
    "\n",
    "        # Visualize in a bar chart\n",
    "        fig = px.bar(controlterm_counts_df, barmode='group',\n",
    "                    labels={'variable':'Repository'},\n",
    "                    color_discrete_map=colors,\n",
    "                    text='value')\n",
    "        fig.update_layout(title_text=f'Term Count per Controlled {controlterm_label} Tags (' + project_name + ')',\n",
    "                        xaxis_title=f'Authority Sources for {controlterm_label}', \n",
    "                        yaxis=dict(\n",
    "                            title_text='Term Count',\n",
    "                            titlefont=dict(\n",
    "                                size=12\n",
    "                            ),\n",
    "                            tickfont=dict(\n",
    "                                family='Arial',\n",
    "                                size=10\n",
    "                            ),\n",
    "                            showticklabels=False\n",
    "                        ),\n",
    "                        width=600,\n",
    "                        font_family=global_font_info['font_family'])\n",
    "        fig.update_yaxes(title_standoff=1)\n",
    "        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "        fig.update_traces(textposition='outside', textfont=dict(family='Arial',size=8))\n",
    "        fig.show()\n",
    "        # if savePNG:\n",
    "            # pio.write_image(fig, f'term_frequency_by_{controlterm_label}.png', scale=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subjects_source <class 'str'>\n",
      "genreforms_source <class 'str'>\n",
      "geognames_source <class 'str'>\n",
      "persnames_source <class 'str'>\n",
      "corpnames_source <class 'str'>\n",
      "famnames_source <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "source_columns = ['subjects_source', 'genreforms_source', 'geognames_source', 'persnames_source', 'corpnames_source', 'famnames_source']\n",
    "for i in range(len(source_columns)):\n",
    "    print(source_columns[i], type(source_columns[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating visualization for Subject\n"
     ]
    },
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            "gridcolor": "white",
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            "ticks": ""
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            "gridcolor": "white",
            "linecolor": "white",
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           },
           "bgcolor": "#E5ECF6",
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          "title": {
           "x": 0.05
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           "ticks": "",
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           "zerolinewidth": 2
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          "yaxis": {
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           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Term Count per Controlled Famname Tags (Native American)"
        },
        "width": 600,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "tickangle": 0,
         "tickfont": {
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          "size": 10
         },
         "title": {
          "text": "Authority Sources for Famname"
         }
        },
        "yaxis": {
         "anchor": "x",
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         "showticklabels": false,
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     },
     "metadata": {},
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   ],
   "source": [
    "source_columns = ['subjects_source', 'genreforms_source', 'geognames_source', 'persnames_source', 'corpnames_source', 'famnames_source']\n",
    "\n",
    "#for i in range(len(source_columns)):\n",
    "#    visualize_controlterms(df=eads_df, df_name='Bentley', source=str(source_columns[i]))\n",
    "\n",
    "visualize_controlterms(eads_df, 'Bentley', source_columns, savePNG=True)"
   ]
  }
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