{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "938c4732-c253-4a21-8e20-711217cd5bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install saxonche\n",
    "#!pip install pathlib\n",
    "\n",
    "import os\n",
    "import pathlib\n",
    "from pathlib import Path\n",
    "import spacy\n",
    "import re as regex\n",
    "from saxonche import PySaxonProcessor\n",
    "from os import getcwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a60cc183-d3f2-4355-b05f-f691a8bd6763",
   "metadata": {},
   "outputs": [],
   "source": [
    "#nlp = spacy.cli.download(\"en_core_web_lg\")\n",
    "# ONLY NEED ABOVE LINE ONCE. REMEMBER: COMMENT OUT THE ABOVE LINE THE NEXT TIME YOU RUN THIS.\n",
    "nlp = spacy.load('en_core_web_lg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d0fd855-6842-479b-9c31-651d0598be75",
   "metadata": {},
   "outputs": [],
   "source": [
    "InputPath = 'JNscripts-xml'\n",
    "OutputPath = 'testOutput' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d4762df-d264-4c3d-a055-796e24de8447",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt   \n",
    "import seaborn as sns\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5660419-9381-4cf7-b675-c059cf8e56c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SaxonC-HE 12.4.2 from Saxonica\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"FRY,2684\"\n",
       "\"BENDER,2360\"\n",
       "\"LEELA,2117\"\n",
       "\"FARNSWORTH,990\"\n",
       "\"ZOIDBERG,581\"\n",
       "\"AMY,499\"\n",
       "\"HERMES,408\"\n",
       "\"ZAPP,336\"\n",
       "\"KIF,197\"\n",
       "\"CALCULON,114\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def xqueryOverFiles(InputPath):\n",
    "    # This time, let's try XQuery over a collection of files:\n",
    "    with PySaxonProcessor(license=False) as proc:\n",
    "        print(proc.version)\n",
    "        xq = proc.new_xquery_processor()\n",
    "        xq.set_query_base_uri(Path('.').absolute().as_uri() + '/')\n",
    "        xq.set_query_content('''\n",
    "let $futurama := collection('JNscripts-xml/?select=*.xml')\n",
    "let $speakers := $futurama//speak/@who => distinct-values() => sort()\n",
    "let $count := count($speakers)\n",
    "for $sp in $speakers\n",
    "    let $count := $futurama//speak[@who = $sp] => count()\n",
    "    where $count > 100\n",
    "    order by $count descending\n",
    "    return ($sp || ',' || $count)\n",
    " \n",
    "''')\n",
    "        r = xq.run_query_to_value()\n",
    "        return r\n",
    "                            \n",
    "\n",
    "xqueryOverFiles(InputPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2707385c-0e84-425b-b766-2bd14e1e5042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SaxonC-HE 12.4.2 from Saxonica\n",
      "['2684', '2360', '2117', '990', '581', '499', '408', '336', '197', '114']\n",
      "['FRY', 'BENDER', 'LEELA', 'FARNSWORTH', 'ZOIDBERG', 'AMY', 'HERMES', 'ZAPP', 'KIF', 'CALCULON']\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 859,
       "width": 937
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The cell above gives you a simple csv. \n",
    "# You could save the output as a .csv file (but you may not need to)\n",
    "# FIND OUT how to send comma-separated values to pandas dataframes\n",
    "# Look up / find out how to use pandas dataframes to read the values in your csv, and send them to matplotlip and seaborn\n",
    "\n",
    "results = xqueryOverFiles(InputPath)\n",
    "keys = []\n",
    "values = []\n",
    "for i in range(results.size):\n",
    "    keyAndValue = str(results[i]).split(\",\")\n",
    "    keys.append(keyAndValue[0])\n",
    "    values.append(keyAndValue[1])\n",
    "\n",
    "counts = values\n",
    "lems = keys\n",
    "print(counts)\n",
    "print(lems)\n",
    "# This is to help matplotlib to display plots inline in the Jupyter Notebook\n",
    "%matplotlib inline\n",
    "# Set figure size configuration\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# Create bar plot using seaborn\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.barplot(y=lems, x=counts,orient='h')\n",
    "# ebb: We will try the dataframe structure with key value columns using data here, like this:\n",
    "# sns.barplot(x='counts', y='lems', data=counts, palette=\"viridis\", orient='h')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('frequency')\n",
    "plt.ylabel('characters')\n",
    "plt.title('character dialouge')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0750620b-1b44-4963-b4bb-f26b46faa36a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}