cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.607120Z",
     "start_time": "2023-05-25T18:07:11.579069Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import requests\n",
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import silhouette_score\n",
    "from nltk.corpus import stopwords\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "cv = CountVectorizer()\n",
    "# nltk.download(\"stopwords\")\n",
    "# nltk.download('punkt')\n",
    "stop_words = set(stopwords.words(\"english\"))\n",
    "\n",
    "stop_words.add('also')\n",
    "stop_words.add('understand')\n",
    "stop_words.add('end')\n",
    "stop_words.add('youll')\n",
    "stop_words.add('well')\n",
    "stop_words.add('include')\n",
    "stop_words.add('time')\n",
    "stop_words.add('goal')\n",
    "stop_words.add('path')\n",
    "stop_words.add('total')\n",
    "stop_words.add('go')\n",
    "stop_words.add('students')\n",
    "stop_words.add('use')\n",
    "stop_words.add('using')\n",
    "stop_words.add('new')\n",
    "stop_words.add('job')\n",
    "stop_words.add('term')\n",
    "stop_words.add('find')\n",
    "stop_words.add('example')\n",
    "stop_words.add('non')\n",
    "stop_words.add('finally')\n",
    "stop_words.add('times')\n",
    "stop_words.add('learn')\n",
    "stop_words.add('two')\n",
    "stop_words.add('field')\n",
    "stop_words.add('tools')\n",
    "stop_words.add('often')\n",
    "stop_words.add('use')\n",
    "stop_words.add('tip')\n",
    "stop_words.add('next')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.622393Z",
     "start_time": "2023-05-25T18:07:11.582768Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = 'http://localhost:4000/graphql'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Data collection\n",
    "Get all the modules in the DB and convert it to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.624507Z",
     "start_time": "2023-05-25T18:07:11.585331Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# moduleData = requests.post(url, json={'query': '{ module(input:{}){id, name, prefix, hours, objectives, description, keywords} }'}).json()\n",
    "\n",
    "# load module data from json file\n",
    "import json\n",
    "with open('input/603_gpt.json') as json_file:\n",
    "    moduleData = json.load(json_file)\n",
    "\n",
    "# MODULE_COUNT = len(moduleData.get('data').get('module'))\n",
    "MODULE_COUNT = len(moduleData)\n",
    "CLUSTER_COUNT = np.sqrt(MODULE_COUNT)\n",
    "# CLUSTER_COUNT = 37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.637158Z",
     "start_time": "2023-05-25T18:07:11.589763Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>prefix</th>\n",
       "      <th>number</th>\n",
       "      <th>objectives</th>\n",
       "      <th>hours</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644827afc3faa4c9ea96a800</td>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>DS</td>\n",
       "      <td>72</td>\n",
       "      <td>[Linear Regression, Correlation, Regression An...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>This module is designed to introduce students ...</td>\n",
       "      <td>[Linear regression, Statistical technique, Mod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64496079d5fb014e40d5ea39</td>\n",
       "      <td>Statistical Analysis</td>\n",
       "      <td>DS</td>\n",
       "      <td>9</td>\n",
       "      <td>[Statistical Analysis, Descriptive Statistics,...</td>\n",
       "      <td>1.50</td>\n",
       "      <td>This module is designed to teach students the ...</td>\n",
       "      <td>[Statistical Analysis, Data Collection, Data O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644960add5fb014e40d5ea3a</td>\n",
       "      <td>Logarithms</td>\n",
       "      <td>DS</td>\n",
       "      <td>121</td>\n",
       "      <td>[Logarithms, Exponential Functions, Logarithmi...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>This module is designed to introduce students ...</td>\n",
       "      <td>[Logarithms, Exponential equations, Problem-so...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>644960bcd5fb014e40d5ea3b</td>\n",
       "      <td>Arithmetics</td>\n",
       "      <td>DS</td>\n",
       "      <td>2</td>\n",
       "      <td>[Arithmetic, Operations, Addition, Subtraction...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>This module is designed to teach students the ...</td>\n",
       "      <td>[Arithmetic, Whole Number Operations, Fraction...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64496111d5fb014e40d5ea3c</td>\n",
       "      <td>Euclidean distance</td>\n",
       "      <td>DS</td>\n",
       "      <td>887</td>\n",
       "      <td>[Euclidean Distance, Distance Formula, Pythago...</td>\n",
       "      <td>2.25</td>\n",
       "      <td>In this module, we'll be learning about the Eu...</td>\n",
       "      <td>[Euclidean Distance, Mathematics, Computer Sci...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id                  name prefix  number  \\\n",
       "0  644827afc3faa4c9ea96a800     Linear Regression     DS      72   \n",
       "1  64496079d5fb014e40d5ea39  Statistical Analysis     DS       9   \n",
       "2  644960add5fb014e40d5ea3a            Logarithms     DS     121   \n",
       "3  644960bcd5fb014e40d5ea3b           Arithmetics     DS       2   \n",
       "4  64496111d5fb014e40d5ea3c    Euclidean distance     DS     887   \n",
       "\n",
       "                                          objectives  hours  \\\n",
       "0  [Linear Regression, Correlation, Regression An...   0.75   \n",
       "1  [Statistical Analysis, Descriptive Statistics,...   1.50   \n",
       "2  [Logarithms, Exponential Functions, Logarithmi...   2.00   \n",
       "3  [Arithmetic, Operations, Addition, Subtraction...   0.50   \n",
       "4  [Euclidean Distance, Distance Formula, Pythago...   2.25   \n",
       "\n",
       "                                         description  \\\n",
       "0  This module is designed to introduce students ...   \n",
       "1  This module is designed to teach students the ...   \n",
       "2  This module is designed to introduce students ...   \n",
       "3  This module is designed to teach students the ...   \n",
       "4  In this module, we'll be learning about the Eu...   \n",
       "\n",
       "                                            keywords  \n",
       "0  [Linear regression, Statistical technique, Mod...  \n",
       "1  [Statistical Analysis, Data Collection, Data O...  \n",
       "2  [Logarithms, Exponential equations, Problem-so...  \n",
       "3  [Arithmetic, Whole Number Operations, Fraction...  \n",
       "4  [Euclidean Distance, Mathematics, Computer Sci...  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# moduleDF = pd.DataFrame(moduleData.get('data').get('module'))\n",
    "moduleDF = pd.DataFrame(moduleData)\n",
    "\n",
    "moduleDF.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Verifying the sparsity of the dataset\n",
    "\n",
    "Since we have a lot of modules without a description, the results will be skewed towards the modules with a description or just overall more text content. In future works, it might be optimal to use a generative model to generate a description for the modules without one using the context clues from other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.672689Z",
     "start_time": "2023-05-25T18:07:11.607269Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>prefix</th>\n",
       "      <th>number</th>\n",
       "      <th>objectives</th>\n",
       "      <th>hours</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, name, prefix, number, objectives, hours, description, keywords]\n",
       "Index: []"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moduleDF[moduleDF['description'] == '']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Data cleaning and Feature engineering\n",
    "\n",
    "Merge all the text content into a single column for each module. This is an essential step to be able to perform the text analysis and text vectorization on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.672850Z",
     "start_time": "2023-05-25T18:07:11.607532Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def scalar_to_str(df):\n",
    "    lst_o = list(df['objectives'])\n",
    "    lst_k = list(df['keywords'])\n",
    "\n",
    "    string_obj_feature = ''\n",
    "    string_key_feature = ''\n",
    "\n",
    "    for obj in lst_o:\n",
    "        string_obj_feature += obj + ', '\n",
    "\n",
    "    for key in lst_k:\n",
    "        string_key_feature += key + ', '\n",
    "\n",
    "    df['objectives'] = string_obj_feature\n",
    "    df['keywords'] = string_key_feature\n",
    "    return df\n",
    "\n",
    "moduleDF = moduleDF.apply(scalar_to_str, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.672934Z",
     "start_time": "2023-05-25T18:07:11.607616Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>true_cluster</th>\n",
       "      <th>hours</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644827afc3faa4c9ea96a800</td>\n",
       "      <td>DS</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Linear Regression, Linear Regression, Correlat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64496079d5fb014e40d5ea39</td>\n",
       "      <td>DS</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Statistical Analysis, Statistical Analysis, De...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644960add5fb014e40d5ea3a</td>\n",
       "      <td>DS</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Logarithms, Logarithms, Exponential Functions,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>644960bcd5fb014e40d5ea3b</td>\n",
       "      <td>DS</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Arithmetics, Arithmetic, Operations, Addition,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64496111d5fb014e40d5ea3c</td>\n",
       "      <td>DS</td>\n",
       "      <td>2.25</td>\n",
       "      <td>Euclidean distance, Euclidean Distance, Distan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id true_cluster  hours  \\\n",
       "0  644827afc3faa4c9ea96a800           DS   0.75   \n",
       "1  64496079d5fb014e40d5ea39           DS   1.50   \n",
       "2  644960add5fb014e40d5ea3a           DS   2.00   \n",
       "3  644960bcd5fb014e40d5ea3b           DS   0.50   \n",
       "4  64496111d5fb014e40d5ea3c           DS   2.25   \n",
       "\n",
       "                                             feature  \n",
       "0  Linear Regression, Linear Regression, Correlat...  \n",
       "1  Statistical Analysis, Statistical Analysis, De...  \n",
       "2  Logarithms, Logarithms, Exponential Functions,...  \n",
       "3  Arithmetics, Arithmetic, Operations, Addition,...  \n",
       "4  Euclidean distance, Euclidean Distance, Distan...  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moduleDF['feature'] = moduleDF['name'] + ', ' + moduleDF['objectives'] + ', ' + moduleDF['description'] + ', ' + moduleDF['keywords'] + ', ' + moduleDF['prefix']\n",
    "\n",
    "moduleDF.drop(['name', 'objectives', 'description', 'keywords', 'number'], axis=1, inplace=True)\n",
    "\n",
    "moduleDF.rename(columns={'prefix': 'true_cluster'}, inplace=True)\n",
    "\n",
    "moduleDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.672984Z",
     "start_time": "2023-05-25T18:07:11.613003Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def _text_processing(col: pd.DataFrame()):\n",
    "    \"\"\"\n",
    "    This function takes in a column of text and performs the following:\n",
    "    1. Converts all text to lowercase\n",
    "    2. Removes all non-alphabetic characters\n",
    "    3. Removes stop words\n",
    "    5. returns the column\n",
    "    \"\"\"\n",
    "    column = col.str.lower()\n",
    "    column = column.str.replace(\"[^a-z ]\", \"\")\n",
    "\n",
    "    word_tokens = column.str.split()\n",
    "    keys = word_tokens.apply(\n",
    "        lambda x: [item for item in x if item not in stop_words]\n",
    "    )\n",
    "    for j in range(len(keys)):\n",
    "        keys[j] = \" \".join(keys[j])\n",
    "        column = keys\n",
    "    return column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.673066Z",
     "start_time": "2023-05-25T18:07:11.618572Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tp/4mcknzvj6zj5kcx78g7f5nmw0000gp/T/ipykernel_16930/630142521.py:10: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  column = column.str.replace(\"[^a-z ]\", \"\")\n"
     ]
    }
   ],
   "source": [
    "moduleDF['cleaned_feature'] = _text_processing(moduleDF['feature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.673411Z",
     "start_time": "2023-05-25T18:07:11.621505Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_tokens(text):\n",
    "    ps = PorterStemmer()\n",
    "    payload = \"\"\n",
    "\n",
    "    tokens = word_tokenize(text)\n",
    "\n",
    "    tokens = [t for t in tokens if not t in stop_words]\n",
    "\n",
    "    for t in range(len(tokens)):\n",
    "        tokens[t] = ps.stem(tokens[t])\n",
    "\n",
    "    for token in tokens:\n",
    "        payload += token + ' '\n",
    "\n",
    "    return payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.776471Z",
     "start_time": "2023-05-25T18:07:11.629376Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>true_cluster</th>\n",
       "      <th>hours</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644827afc3faa4c9ea96a800</td>\n",
       "      <td>DS</td>\n",
       "      <td>0.750</td>\n",
       "      <td>linear regress linear regress correl regress a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64496079d5fb014e40d5ea39</td>\n",
       "      <td>DS</td>\n",
       "      <td>1.500</td>\n",
       "      <td>statist analysi statist analysi descript stati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644960add5fb014e40d5ea3a</td>\n",
       "      <td>DS</td>\n",
       "      <td>2.000</td>\n",
       "      <td>logarithm logarithm exponenti function logarit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>644960bcd5fb014e40d5ea3b</td>\n",
       "      <td>DS</td>\n",
       "      <td>0.500</td>\n",
       "      <td>arithmet arithmet oper addit subtract multipl ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64496111d5fb014e40d5ea3c</td>\n",
       "      <td>DS</td>\n",
       "      <td>2.250</td>\n",
       "      <td>euclidean distanc euclidean distanc distanc fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64511f49f9ce74323575fffe</td>\n",
       "      <td>CSLP</td>\n",
       "      <td>0.300</td>\n",
       "      <td>introduct linear program model comput implemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64511f4bf9ce74323575ffff</td>\n",
       "      <td>CSLP</td>\n",
       "      <td>0.300</td>\n",
       "      <td>comput solut lp formul linear program comput s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>646270b939769687f2a6d858</td>\n",
       "      <td>LP-ME</td>\n",
       "      <td>0.300</td>\n",
       "      <td>linear program model linear program model line...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>646273ce39769687f2a6d860</td>\n",
       "      <td>TP</td>\n",
       "      <td>0.700</td>\n",
       "      <td>transport transport transport introduct transp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>646273e539769687f2a6d861</td>\n",
       "      <td>TP</td>\n",
       "      <td>0.730</td>\n",
       "      <td>transship transship transship introduct transs...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6462741d39769687f2a6d862</td>\n",
       "      <td>TP</td>\n",
       "      <td>0.525</td>\n",
       "      <td>assign problem transship transport shipment tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6462745c39769687f2a6d863</td>\n",
       "      <td>TP</td>\n",
       "      <td>0.265</td>\n",
       "      <td>shortest shortest shortest short shortest welc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6462747b39769687f2a6d864</td>\n",
       "      <td>TP</td>\n",
       "      <td>0.351</td>\n",
       "      <td>maxim flow problem maxim flow problem graph th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6462749a39769687f2a6d865</td>\n",
       "      <td>TP</td>\n",
       "      <td>0.750</td>\n",
       "      <td>travel salesman problem travel ai algorithm pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>646274c539769687f2a6d866</td>\n",
       "      <td>NFM</td>\n",
       "      <td>0.225</td>\n",
       "      <td>nonlinear profit analysisconstrain optim nonli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>646274e939769687f2a6d867</td>\n",
       "      <td>NFM</td>\n",
       "      <td>0.320</td>\n",
       "      <td>facil locat problem facil locat problem facil ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6462750d39769687f2a6d868</td>\n",
       "      <td>NFM</td>\n",
       "      <td>0.645</td>\n",
       "      <td>mix integ nonlinear program mix integ nonlinea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6462752e39769687f2a6d869</td>\n",
       "      <td>NFM</td>\n",
       "      <td>0.743</td>\n",
       "      <td>solv unconstrain nonlinear equationsnumer unco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6462756339769687f2a6d86a</td>\n",
       "      <td>MCD</td>\n",
       "      <td>0.220</td>\n",
       "      <td>program formul graphic interpret program formu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6462758c39769687f2a6d86b</td>\n",
       "      <td>MCD</td>\n",
       "      <td>0.450</td>\n",
       "      <td>program comput solut program comput solut comp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          id true_cluster  hours  \\\n",
       "0   644827afc3faa4c9ea96a800           DS  0.750   \n",
       "1   64496079d5fb014e40d5ea39           DS  1.500   \n",
       "2   644960add5fb014e40d5ea3a           DS  2.000   \n",
       "3   644960bcd5fb014e40d5ea3b           DS  0.500   \n",
       "4   64496111d5fb014e40d5ea3c           DS  2.250   \n",
       "5   64511f49f9ce74323575fffe         CSLP  0.300   \n",
       "6   64511f4bf9ce74323575ffff         CSLP  0.300   \n",
       "7   646270b939769687f2a6d858        LP-ME  0.300   \n",
       "8   646273ce39769687f2a6d860           TP  0.700   \n",
       "9   646273e539769687f2a6d861           TP  0.730   \n",
       "10  6462741d39769687f2a6d862           TP  0.525   \n",
       "11  6462745c39769687f2a6d863           TP  0.265   \n",
       "12  6462747b39769687f2a6d864           TP  0.351   \n",
       "13  6462749a39769687f2a6d865           TP  0.750   \n",
       "14  646274c539769687f2a6d866          NFM  0.225   \n",
       "15  646274e939769687f2a6d867          NFM  0.320   \n",
       "16  6462750d39769687f2a6d868          NFM  0.645   \n",
       "17  6462752e39769687f2a6d869          NFM  0.743   \n",
       "18  6462756339769687f2a6d86a          MCD  0.220   \n",
       "19  6462758c39769687f2a6d86b          MCD  0.450   \n",
       "\n",
       "                                               tokens  \n",
       "0   linear regress linear regress correl regress a...  \n",
       "1   statist analysi statist analysi descript stati...  \n",
       "2   logarithm logarithm exponenti function logarit...  \n",
       "3   arithmet arithmet oper addit subtract multipl ...  \n",
       "4   euclidean distanc euclidean distanc distanc fo...  \n",
       "5   introduct linear program model comput implemen...  \n",
       "6   comput solut lp formul linear program comput s...  \n",
       "7   linear program model linear program model line...  \n",
       "8   transport transport transport introduct transp...  \n",
       "9   transship transship transship introduct transs...  \n",
       "10  assign problem transship transport shipment tr...  \n",
       "11  shortest shortest shortest short shortest welc...  \n",
       "12  maxim flow problem maxim flow problem graph th...  \n",
       "13  travel salesman problem travel ai algorithm pr...  \n",
       "14  nonlinear profit analysisconstrain optim nonli...  \n",
       "15  facil locat problem facil locat problem facil ...  \n",
       "16  mix integ nonlinear program mix integ nonlinea...  \n",
       "17  solv unconstrain nonlinear equationsnumer unco...  \n",
       "18  program formul graphic interpret program formu...  \n",
       "19  program comput solut program comput solut comp...  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moduleDF['tokens'] = moduleDF['cleaned_feature'].apply(lambda x: get_tokens(x))\n",
    "\n",
    "moduleDF.drop(['feature', 'cleaned_feature'], axis=1, inplace=True)\n",
    "\n",
    "moduleDF.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "\n",
    "def generate_word_cloud(text):\n",
    "    wordcloud = WordCloud(max_font_size=50, max_words=100, background_color=\"white\").generate(text)\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "generate_word_cloud(moduleDF['tokens'].to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:11.776753Z",
     "start_time": "2023-05-25T18:07:11.721376Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>true_cluster</th>\n",
       "      <th>hours</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644827afc3faa4c9ea96a800</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750</td>\n",
       "      <td>linear regress linear regress correl regress a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64496079d5fb014e40d5ea39</td>\n",
       "      <td>4</td>\n",
       "      <td>1.500</td>\n",
       "      <td>statist analysi statist analysi descript stati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644960add5fb014e40d5ea3a</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000</td>\n",
       "      <td>logarithm logarithm exponenti function logarit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>644960bcd5fb014e40d5ea3b</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>arithmet arithmet oper addit subtract multipl ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64496111d5fb014e40d5ea3c</td>\n",
       "      <td>4</td>\n",
       "      <td>2.250</td>\n",
       "      <td>euclidean distanc euclidean distanc distanc fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64511f49f9ce74323575fffe</td>\n",
       "      <td>2</td>\n",
       "      <td>0.300</td>\n",
       "      <td>introduct linear program model comput implemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64511f4bf9ce74323575ffff</td>\n",
       "      <td>2</td>\n",
       "      <td>0.300</td>\n",
       "      <td>comput solut lp formul linear program comput s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>646270b939769687f2a6d858</td>\n",
       "      <td>11</td>\n",
       "      <td>0.300</td>\n",
       "      <td>linear program model linear program model line...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>646273ce39769687f2a6d860</td>\n",
       "      <td>20</td>\n",
       "      <td>0.700</td>\n",
       "      <td>transport transport transport introduct transp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>646273e539769687f2a6d861</td>\n",
       "      <td>20</td>\n",
       "      <td>0.730</td>\n",
       "      <td>transship transship transship introduct transs...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6462741d39769687f2a6d862</td>\n",
       "      <td>20</td>\n",
       "      <td>0.525</td>\n",
       "      <td>assign problem transship transport shipment tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6462745c39769687f2a6d863</td>\n",
       "      <td>20</td>\n",
       "      <td>0.265</td>\n",
       "      <td>shortest shortest shortest short shortest welc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6462747b39769687f2a6d864</td>\n",
       "      <td>20</td>\n",
       "      <td>0.351</td>\n",
       "      <td>maxim flow problem maxim flow problem graph th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6462749a39769687f2a6d865</td>\n",
       "      <td>20</td>\n",
       "      <td>0.750</td>\n",
       "      <td>travel salesman problem travel ai algorithm pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>646274c539769687f2a6d866</td>\n",
       "      <td>16</td>\n",
       "      <td>0.225</td>\n",
       "      <td>nonlinear profit analysisconstrain optim nonli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>646274e939769687f2a6d867</td>\n",
       "      <td>16</td>\n",
       "      <td>0.320</td>\n",
       "      <td>facil locat problem facil locat problem facil ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6462750d39769687f2a6d868</td>\n",
       "      <td>16</td>\n",
       "      <td>0.645</td>\n",
       "      <td>mix integ nonlinear program mix integ nonlinea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6462752e39769687f2a6d869</td>\n",
       "      <td>16</td>\n",
       "      <td>0.743</td>\n",
       "      <td>solv unconstrain nonlinear equationsnumer unco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6462756339769687f2a6d86a</td>\n",
       "      <td>15</td>\n",
       "      <td>0.220</td>\n",
       "      <td>program formul graphic interpret program formu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6462758c39769687f2a6d86b</td>\n",
       "      <td>15</td>\n",
       "      <td>0.450</td>\n",
       "      <td>program comput solut program comput solut comp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          id  true_cluster  hours  \\\n",
       "0   644827afc3faa4c9ea96a800             4  0.750   \n",
       "1   64496079d5fb014e40d5ea39             4  1.500   \n",
       "2   644960add5fb014e40d5ea3a             4  2.000   \n",
       "3   644960bcd5fb014e40d5ea3b             4  0.500   \n",
       "4   64496111d5fb014e40d5ea3c             4  2.250   \n",
       "5   64511f49f9ce74323575fffe             2  0.300   \n",
       "6   64511f4bf9ce74323575ffff             2  0.300   \n",
       "7   646270b939769687f2a6d858            11  0.300   \n",
       "8   646273ce39769687f2a6d860            20  0.700   \n",
       "9   646273e539769687f2a6d861            20  0.730   \n",
       "10  6462741d39769687f2a6d862            20  0.525   \n",
       "11  6462745c39769687f2a6d863            20  0.265   \n",
       "12  6462747b39769687f2a6d864            20  0.351   \n",
       "13  6462749a39769687f2a6d865            20  0.750   \n",
       "14  646274c539769687f2a6d866            16  0.225   \n",
       "15  646274e939769687f2a6d867            16  0.320   \n",
       "16  6462750d39769687f2a6d868            16  0.645   \n",
       "17  6462752e39769687f2a6d869            16  0.743   \n",
       "18  6462756339769687f2a6d86a            15  0.220   \n",
       "19  6462758c39769687f2a6d86b            15  0.450   \n",
       "\n",
       "                                               tokens  \n",
       "0   linear regress linear regress correl regress a...  \n",
       "1   statist analysi statist analysi descript stati...  \n",
       "2   logarithm logarithm exponenti function logarit...  \n",
       "3   arithmet arithmet oper addit subtract multipl ...  \n",
       "4   euclidean distanc euclidean distanc distanc fo...  \n",
       "5   introduct linear program model comput implemen...  \n",
       "6   comput solut lp formul linear program comput s...  \n",
       "7   linear program model linear program model line...  \n",
       "8   transport transport transport introduct transp...  \n",
       "9   transship transship transship introduct transs...  \n",
       "10  assign problem transship transport shipment tr...  \n",
       "11  shortest shortest shortest short shortest welc...  \n",
       "12  maxim flow problem maxim flow problem graph th...  \n",
       "13  travel salesman problem travel ai algorithm pr...  \n",
       "14  nonlinear profit analysisconstrain optim nonli...  \n",
       "15  facil locat problem facil locat problem facil ...  \n",
       "16  mix integ nonlinear program mix integ nonlinea...  \n",
       "17  solv unconstrain nonlinear equationsnumer unco...  \n",
       "18  program formul graphic interpret program formu...  \n",
       "19  program comput solut program comput solut comp...  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "moduleDF['true_cluster'] = le.fit_transform(moduleDF['true_cluster'])\n",
    "\n",
    "moduleDF.head(20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:12.148951Z",
     "start_time": "2023-05-25T18:07:11.729943Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>true_cluster</th>\n",
       "      <th>hours</th>\n",
       "      <th>tokens</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644827afc3faa4c9ea96a800</td>\n",
       "      <td>4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>linear regress linear regress correl regress a...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64496079d5fb014e40d5ea39</td>\n",
       "      <td>4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>statist analysi statist analysi descript stati...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644960add5fb014e40d5ea3a</td>\n",
       "      <td>4</td>\n",
       "      <td>2.00</td>\n",
       "      <td>logarithm logarithm exponenti function logarit...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>644960bcd5fb014e40d5ea3b</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>arithmet arithmet oper addit subtract multipl ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64496111d5fb014e40d5ea3c</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>euclidean distanc euclidean distanc distanc fo...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  true_cluster  hours  \\\n",
       "0  644827afc3faa4c9ea96a800             4   0.75   \n",
       "1  64496079d5fb014e40d5ea39             4   1.50   \n",
       "2  644960add5fb014e40d5ea3a             4   2.00   \n",
       "3  644960bcd5fb014e40d5ea3b             4   0.50   \n",
       "4  64496111d5fb014e40d5ea3c             4   2.25   \n",
       "\n",
       "                                              tokens  cluster  \n",
       "0  linear regress linear regress correl regress a...        2  \n",
       "1  statist analysi statist analysi descript stati...        2  \n",
       "2  logarithm logarithm exponenti function logarit...        2  \n",
       "3  arithmet arithmet oper addit subtract multipl ...        2  \n",
       "4  euclidean distanc euclidean distanc distanc fo...        4  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "\n",
    "moduleDF['cluster'] = 0\n",
    "\n",
    "X = vectorizer.fit_transform(moduleDF['tokens'])\n",
    "Y = moduleDF['cluster']\n",
    "\n",
    "kmeans = KMeans(n_clusters=int(CLUSTER_COUNT), random_state=42, n_init=100, max_iter=1000).fit(X)\n",
    "\n",
    "moduleDF['cluster'] = kmeans.predict(X)\n",
    "\n",
    "moduleDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.029694Z",
     "start_time": "2023-05-25T18:07:12.149140Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=int(CLUSTER_COUNT) // 2, random_state=42)\n",
    "\n",
    "reduced_features = pca.fit_transform(X.toarray())\n",
    "\n",
    "x0 = reduced_features[:, 0]\n",
    "x1 = reduced_features[:, 1]\n",
    "\n",
    "moduleDF['x0'] = x0\n",
    "moduleDF['x1'] = x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.097387Z",
     "start_time": "2023-05-25T18:07:14.032582Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0\n",
      "predict,process,transit,probabl,state,system,ergod,absorb,chain,markov\n",
      "\n",
      "Cluster 1\n",
      "sensit,solv,solut,program,model,method,linear,problem,simplex,lp\n",
      "\n",
      "Cluster 2\n",
      "statist,regress,problem,network,sensit,facil,locat,flow,analysi,model\n",
      "\n",
      "Cluster 3\n",
      "simplex,solv,interpret,solut,model,object,graphic,problem,linear,program\n",
      "\n",
      "Cluster 4\n",
      "alloc,program,distanc,project,algorithm,medic,assign,problem,shortest,dynam\n",
      "\n",
      "Cluster 5\n",
      "solv,mix,equat,make,profit,decis,numer,optim,program,nonlinear\n",
      "\n",
      "Cluster 6\n",
      "algorithm,solv,comput,techniqu,select,set,solut,problem,program,integ\n",
      "\n",
      "Cluster 7\n",
      "concept,strategi,work,advantag,conclus,reallif,economi,global,busi,transship\n",
      "\n",
      "Cluster 8\n",
      "finit,multipl,custom,design,perform,servic,server,queue,system,queu\n"
     ]
    },
    {
     "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>Cluster 0</th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "      <th>Cluster 5</th>\n",
       "      <th>Cluster 6</th>\n",
       "      <th>Cluster 7</th>\n",
       "      <th>Cluster 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>predict</td>\n",
       "      <td>sensit</td>\n",
       "      <td>statist</td>\n",
       "      <td>simplex</td>\n",
       "      <td>alloc</td>\n",
       "      <td>solv</td>\n",
       "      <td>algorithm</td>\n",
       "      <td>concept</td>\n",
       "      <td>finit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>process</td>\n",
       "      <td>solv</td>\n",
       "      <td>regress</td>\n",
       "      <td>solv</td>\n",
       "      <td>program</td>\n",
       "      <td>mix</td>\n",
       "      <td>solv</td>\n",
       "      <td>strategi</td>\n",
       "      <td>multipl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>transit</td>\n",
       "      <td>solut</td>\n",
       "      <td>problem</td>\n",
       "      <td>interpret</td>\n",
       "      <td>distanc</td>\n",
       "      <td>equat</td>\n",
       "      <td>comput</td>\n",
       "      <td>work</td>\n",
       "      <td>custom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>probabl</td>\n",
       "      <td>program</td>\n",
       "      <td>network</td>\n",
       "      <td>solut</td>\n",
       "      <td>project</td>\n",
       "      <td>make</td>\n",
       "      <td>techniqu</td>\n",
       "      <td>advantag</td>\n",
       "      <td>design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>state</td>\n",
       "      <td>model</td>\n",
       "      <td>sensit</td>\n",
       "      <td>model</td>\n",
       "      <td>algorithm</td>\n",
       "      <td>profit</td>\n",
       "      <td>select</td>\n",
       "      <td>conclus</td>\n",
       "      <td>perform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>system</td>\n",
       "      <td>method</td>\n",
       "      <td>facil</td>\n",
       "      <td>object</td>\n",
       "      <td>medic</td>\n",
       "      <td>decis</td>\n",
       "      <td>set</td>\n",
       "      <td>reallif</td>\n",
       "      <td>servic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ergod</td>\n",
       "      <td>linear</td>\n",
       "      <td>locat</td>\n",
       "      <td>graphic</td>\n",
       "      <td>assign</td>\n",
       "      <td>numer</td>\n",
       "      <td>solut</td>\n",
       "      <td>economi</td>\n",
       "      <td>server</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>absorb</td>\n",
       "      <td>problem</td>\n",
       "      <td>flow</td>\n",
       "      <td>problem</td>\n",
       "      <td>problem</td>\n",
       "      <td>optim</td>\n",
       "      <td>problem</td>\n",
       "      <td>global</td>\n",
       "      <td>queue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chain</td>\n",
       "      <td>simplex</td>\n",
       "      <td>analysi</td>\n",
       "      <td>linear</td>\n",
       "      <td>shortest</td>\n",
       "      <td>program</td>\n",
       "      <td>program</td>\n",
       "      <td>busi</td>\n",
       "      <td>system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>markov</td>\n",
       "      <td>lp</td>\n",
       "      <td>model</td>\n",
       "      <td>program</td>\n",
       "      <td>dynam</td>\n",
       "      <td>nonlinear</td>\n",
       "      <td>integ</td>\n",
       "      <td>transship</td>\n",
       "      <td>queu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cluster 0 Cluster 1 Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  \\\n",
       "0   predict    sensit   statist    simplex      alloc       solv  algorithm   \n",
       "1   process      solv   regress       solv    program        mix       solv   \n",
       "2   transit     solut   problem  interpret    distanc      equat     comput   \n",
       "3   probabl   program   network      solut    project       make   techniqu   \n",
       "4     state     model    sensit      model  algorithm     profit     select   \n",
       "5    system    method     facil     object      medic      decis        set   \n",
       "6     ergod    linear     locat    graphic     assign      numer      solut   \n",
       "7    absorb   problem      flow    problem    problem      optim    problem   \n",
       "8     chain   simplex   analysi     linear   shortest    program    program   \n",
       "9    markov        lp     model    program      dynam  nonlinear      integ   \n",
       "\n",
       "   Cluster 7 Cluster 8  \n",
       "0    concept     finit  \n",
       "1   strategi   multipl  \n",
       "2       work    custom  \n",
       "3   advantag    design  \n",
       "4    conclus   perform  \n",
       "5    reallif    servic  \n",
       "6    economi    server  \n",
       "7     global     queue  \n",
       "8       busi    system  \n",
       "9  transship      queu  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top_keywords(n_terms, data = moduleDF['cluster']):\n",
    "    \"\"\"This function returns the keywords for each centroid of the KMeans\"\"\"\n",
    "    df = pd.DataFrame(X.todense()).groupby(data).mean() # groups the TF-IDF vector by cluster\n",
    "    terms = vectorizer.get_feature_names_out() # access tf-idf terms\n",
    "    for k,r in df.iterrows():\n",
    "        print('\\nCluster {}'.format(k))\n",
    "        print(','.join([terms[t] for t in np.argsort(r)[-n_terms:]])) # for each row of the dataframe, find the n terms that have the highest tf idf score\n",
    "\n",
    "    termsDF = pd.DataFrame()\n",
    "    for k,r in df.iterrows():\n",
    "        termsDF['Cluster {}'.format(k)] = [terms[t] for t in np.argsort(r)[-n_terms:]]\n",
    "    return termsDF\n",
    "\n",
    "\n",
    "get_top_keywords(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.098215Z",
     "start_time": "2023-05-25T18:07:14.060135Z"
    },
    "collapsed": false
   },
   "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>id</th>\n",
       "      <th>true_cluster</th>\n",
       "      <th>hours</th>\n",
       "      <th>tokens</th>\n",
       "      <th>cluster</th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644827afc3faa4c9ea96a800</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750</td>\n",
       "      <td>linear regress linear regress correl regress a...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.057503</td>\n",
       "      <td>-0.154762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64496079d5fb014e40d5ea39</td>\n",
       "      <td>4</td>\n",
       "      <td>1.500</td>\n",
       "      <td>statist analysi statist analysi descript stati...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.139221</td>\n",
       "      <td>-0.239507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644960add5fb014e40d5ea3a</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000</td>\n",
       "      <td>logarithm logarithm exponenti function logarit...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.096230</td>\n",
       "      <td>-0.161873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>644960bcd5fb014e40d5ea3b</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>arithmet arithmet oper addit subtract multipl ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.089890</td>\n",
       "      <td>-0.167647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64496111d5fb014e40d5ea3c</td>\n",
       "      <td>4</td>\n",
       "      <td>2.250</td>\n",
       "      <td>euclidean distanc euclidean distanc distanc fo...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.120797</td>\n",
       "      <td>-0.177824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64511f49f9ce74323575fffe</td>\n",
       "      <td>2</td>\n",
       "      <td>0.300</td>\n",
       "      <td>introduct linear program model comput implemen...</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.041930</td>\n",
       "      <td>-0.155656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64511f4bf9ce74323575ffff</td>\n",
       "      <td>2</td>\n",
       "      <td>0.300</td>\n",
       "      <td>comput solut lp formul linear program comput s...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.196230</td>\n",
       "      <td>-0.040827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>646270b939769687f2a6d858</td>\n",
       "      <td>11</td>\n",
       "      <td>0.300</td>\n",
       "      <td>linear program model linear program model line...</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.350917</td>\n",
       "      <td>0.232248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>646273ce39769687f2a6d860</td>\n",
       "      <td>20</td>\n",
       "      <td>0.700</td>\n",
       "      <td>transport transport transport introduct transp...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.096971</td>\n",
       "      <td>-0.172967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>646273e539769687f2a6d861</td>\n",
       "      <td>20</td>\n",
       "      <td>0.730</td>\n",
       "      <td>transship transship transship introduct transs...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.121585</td>\n",
       "      <td>-0.213435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6462741d39769687f2a6d862</td>\n",
       "      <td>20</td>\n",
       "      <td>0.525</td>\n",
       "      <td>assign problem transship transport shipment tr...</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.011557</td>\n",
       "      <td>-0.100737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6462745c39769687f2a6d863</td>\n",
       "      <td>20</td>\n",
       "      <td>0.265</td>\n",
       "      <td>shortest shortest shortest short shortest welc...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.067306</td>\n",
       "      <td>-0.158048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6462747b39769687f2a6d864</td>\n",
       "      <td>20</td>\n",
       "      <td>0.351</td>\n",
       "      <td>maxim flow problem maxim flow problem graph th...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.048321</td>\n",
       "      <td>-0.134010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6462749a39769687f2a6d865</td>\n",
       "      <td>20</td>\n",
       "      <td>0.750</td>\n",
       "      <td>travel salesman problem travel ai algorithm pr...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.016672</td>\n",
       "      <td>-0.103366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>646274c539769687f2a6d866</td>\n",
       "      <td>16</td>\n",
       "      <td>0.225</td>\n",
       "      <td>nonlinear profit analysisconstrain optim nonli...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.002616</td>\n",
       "      <td>-0.177139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>646274e939769687f2a6d867</td>\n",
       "      <td>16</td>\n",
       "      <td>0.320</td>\n",
       "      <td>facil locat problem facil locat problem facil ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.072184</td>\n",
       "      <td>-0.175418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6462750d39769687f2a6d868</td>\n",
       "      <td>16</td>\n",
       "      <td>0.645</td>\n",
       "      <td>mix integ nonlinear program mix integ nonlinea...</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.217988</td>\n",
       "      <td>0.158948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6462752e39769687f2a6d869</td>\n",
       "      <td>16</td>\n",
       "      <td>0.743</td>\n",
       "      <td>solv unconstrain nonlinear equationsnumer unco...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.002492</td>\n",
       "      <td>-0.150493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6462756339769687f2a6d86a</td>\n",
       "      <td>15</td>\n",
       "      <td>0.220</td>\n",
       "      <td>program formul graphic interpret program formu...</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.240612</td>\n",
       "      <td>0.071585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6462758c39769687f2a6d86b</td>\n",
       "      <td>15</td>\n",
       "      <td>0.450</td>\n",
       "      <td>program comput solut program comput solut comp...</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.230863</td>\n",
       "      <td>0.166341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          id  true_cluster  hours  \\\n",
       "0   644827afc3faa4c9ea96a800             4  0.750   \n",
       "1   64496079d5fb014e40d5ea39             4  1.500   \n",
       "2   644960add5fb014e40d5ea3a             4  2.000   \n",
       "3   644960bcd5fb014e40d5ea3b             4  0.500   \n",
       "4   64496111d5fb014e40d5ea3c             4  2.250   \n",
       "5   64511f49f9ce74323575fffe             2  0.300   \n",
       "6   64511f4bf9ce74323575ffff             2  0.300   \n",
       "7   646270b939769687f2a6d858            11  0.300   \n",
       "8   646273ce39769687f2a6d860            20  0.700   \n",
       "9   646273e539769687f2a6d861            20  0.730   \n",
       "10  6462741d39769687f2a6d862            20  0.525   \n",
       "11  6462745c39769687f2a6d863            20  0.265   \n",
       "12  6462747b39769687f2a6d864            20  0.351   \n",
       "13  6462749a39769687f2a6d865            20  0.750   \n",
       "14  646274c539769687f2a6d866            16  0.225   \n",
       "15  646274e939769687f2a6d867            16  0.320   \n",
       "16  6462750d39769687f2a6d868            16  0.645   \n",
       "17  6462752e39769687f2a6d869            16  0.743   \n",
       "18  6462756339769687f2a6d86a            15  0.220   \n",
       "19  6462758c39769687f2a6d86b            15  0.450   \n",
       "\n",
       "                                               tokens  cluster        x0  \\\n",
       "0   linear regress linear regress correl regress a...        2  0.057503   \n",
       "1   statist analysi statist analysi descript stati...        2  0.139221   \n",
       "2   logarithm logarithm exponenti function logarit...        2  0.096230   \n",
       "3   arithmet arithmet oper addit subtract multipl ...        2  0.089890   \n",
       "4   euclidean distanc euclidean distanc distanc fo...        4  0.120797   \n",
       "5   introduct linear program model comput implemen...        2 -0.041930   \n",
       "6   comput solut lp formul linear program comput s...        1 -0.196230   \n",
       "7   linear program model linear program model line...        3 -0.350917   \n",
       "8   transport transport transport introduct transp...        2  0.096971   \n",
       "9   transship transship transship introduct transs...        7  0.121585   \n",
       "10  assign problem transship transport shipment tr...        4 -0.011557   \n",
       "11  shortest shortest shortest short shortest welc...        4  0.067306   \n",
       "12  maxim flow problem maxim flow problem graph th...        2  0.048321   \n",
       "13  travel salesman problem travel ai algorithm pr...        4  0.016672   \n",
       "14  nonlinear profit analysisconstrain optim nonli...        5  0.002616   \n",
       "15  facil locat problem facil locat problem facil ...        2  0.072184   \n",
       "16  mix integ nonlinear program mix integ nonlinea...        5 -0.217988   \n",
       "17  solv unconstrain nonlinear equationsnumer unco...        5  0.002492   \n",
       "18  program formul graphic interpret program formu...        3 -0.240612   \n",
       "19  program comput solut program comput solut comp...        6 -0.230863   \n",
       "\n",
       "          x1  \n",
       "0  -0.154762  \n",
       "1  -0.239507  \n",
       "2  -0.161873  \n",
       "3  -0.167647  \n",
       "4  -0.177824  \n",
       "5  -0.155656  \n",
       "6  -0.040827  \n",
       "7   0.232248  \n",
       "8  -0.172967  \n",
       "9  -0.213435  \n",
       "10 -0.100737  \n",
       "11 -0.158048  \n",
       "12 -0.134010  \n",
       "13 -0.103366  \n",
       "14 -0.177139  \n",
       "15 -0.175418  \n",
       "16  0.158948  \n",
       "17 -0.150493  \n",
       "18  0.071585  \n",
       "19  0.166341  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moduleDF.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.371458Z",
     "start_time": "2023-05-25T18:07:14.089817Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'TF-IDF + Ground Truth clustering'}, xlabel='X0', ylabel='x1'>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 14))\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='X0', ylabel='X1')\n",
    "    ax.label_outer()\n",
    "    ax.set_aspect(1)\n",
    "\n",
    "axs[0].set_title('TF-IDF + KMeans clustering')\n",
    "axs[1].set_title('TF-IDF + Ground Truth clustering')\n",
    "\n",
    "sns.scatterplot(data=moduleDF, x='x0', y='x1', hue='cluster', palette=\"tab10\", ax=axs[0])\n",
    "sns.scatterplot(data=moduleDF, x='x0', y='x1', hue='true_cluster', palette=\"tab20\", ax=axs[1], legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.375838Z",
     "start_time": "2023-05-25T18:07:14.371896Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "2    19.844402\n",
       "4    11.538752\n",
       "0    10.812385\n",
       "3    10.307585\n",
       "5     9.414316\n",
       "1     9.005588\n",
       "6     8.451804\n",
       "8     4.783075\n",
       "7     1.444769\n",
       "Name: hours, dtype: float64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum the number of hours for each cluster\n",
    "moduleDF.groupby('cluster')['hours'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.531030Z",
     "start_time": "2023-05-25T18:07:14.380323Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'ENMA 603 - Total Hours and Module count per Cluster'}, xlabel='Cluster', ylabel='Hours & Count'>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABl4AAAJrCAYAAACBT+4aAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABnsElEQVR4nOzdeZxWZd0/8M+wDfvgwqrIpomaguG+LwioDy5ZKmqIW7nm7pOVAmZhmmuY2qK4pmku9aCokEtumQsmqYQGosniBggoEHP//vDH5MQio2ccRt7v1+u84lznOtf5nnOfmer+zHVOWalUKgUAAAAAAIDPrUFdFwAAAAAAAPBlIXgBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAgC+JUaNGpaysLFOmTKnrUr70ysrKMmzYsLouY7l22WWX7LLLLp9p3yFDhqRr166F1kPdmTJlSsrKyjJq1Ki6LgUAYLUheAEAqEeWfLG+vOWpp56q6ruk7eKLL17uOM8880xV27Bhw1JWVpYGDRrkjTfeWGqfOXPmpFmzZikrK8uJJ564zPpefvnllJWVpWnTppk1a1aNz2/s2LHZbbfdUlFRkVatWqVPnz657bbblur3hz/8IV/72tfStGnTrLfeehk6dGj+/e9/V+vz6KOPZp999knnzp3TtGnTdOjQIQMGDMjjjz9e47qWZ8kXmiuzfFoYMn/+/AwbNiwPP/xwYfUtz5LP+p133lnm9q5du+Z//ud/ar2OL7tP3h/nn3/+MvsceuihKSsrS8uWLb/g6ljipZdeyrBhw+plYPnwww/n61//ejp06JAmTZqkXbt2GThwYO68884vrIb6fP0AAGpLo7ouAACAmjvvvPPSrVu3pdrXX3/9pdouuuiiHHfccWnevPlKjV1eXp7f/va3Oeuss6q1r8wXeTfddFM6dOiQ999/P3fccUeOPvrolTpmklx33XU56qijsscee+QnP/lJGjZsmIkTJy4VAt13333Zb7/9sssuu+TnP/95XnzxxZx//vmZOXNmrrrqqqp+//jHP9KgQYMce+yxVTXddNNN2WmnnTJ69OgMGDBgpWtbnrZt2+bGG2+s1nbxxRfnzTffzKWXXrpU3xWZP39+hg8fniSfeaYCq6amTZvmt7/9bX74wx9Wa583b17uueeeNG3atI4qI/k4OBg+fHh22WWXejXTZ+jQoTnvvPOywQYb5Dvf+U66dOmSd999N/fee28OOOCA3HzzzTnkkENqvY76ev0AAGqT4AUAoB7ac889s8UWW3xqv969e2f8+PG5+uqrc9ppp63U2Hvttdcyg5dbbrkle++9d37/+98vc79SqZRbbrklhxxySCZPnpybb755pYOXKVOm5IQTTshJJ52Uyy+/fIV9zzjjjGy22WZ54IEH0qjRx/9ztnXr1vnJT36Sk08+OT179kySHH300Usd//jjj0/37t1z2WWXFRK8tGjRIocddli1tltvvTXvv//+Uu18unnz5qVFixZ1XUbh9tprr9x555154YUX0qtXr6r2e+65JwsXLsyAAQPypz/9qQ4rZFVTKpXy0UcfpVmzZsvcfscdd+S8887LN77xjdxyyy1p3Lhx1bYzzzwz999/fxYtWvRFlVsrvqy/DwCA1YNHjQEAfIltv/322W233XLhhRfmww8/XKl9DjnkkIwfPz6vvPJKVdv06dPzpz/9aYV/Pf34449nypQpOfjgg3PwwQfn0UcfzZtvvrlSx7z66quzePHinHfeeUmSuXPnplQqLdXvpZdeyksvvZRvf/vbVaFL8nGgUiqVcscdd6zwOM2bN0/btm0/02PQPo+ZM2fmqKOOSvv27dO0adP06tUr119/fdX2KVOmVM2IGT58eNXjqZa8Q+Rvf/tbhgwZku7du1c9Nu3II4/Mu++++4Wdw7x583L66aenc+fOKS8vz4Ybbpif/exn1T6nFb1L4r/fibLkcWcvvfRSDjnkkKyxxhrZYYcdknx8vx1xxBFZd911U15eno4dO2bffff91EcZrex1WnLsV199NUOGDEmbNm1SUVGRI444IvPnz6/Wd8GCBTn11FPTtm3btGrVKvvss89K39dLbLvttunWrVtuueWWau0333xzBgwYkDXXXHOZ+/3iF7/IJptskvLy8nTq1CknnHDCMu/dX/7yl+nRo0eaNWuWrbbaKn/+85+X6rO89/88/PDDKSsr+9RH3FVWVuayyy7LJptskqZNm6Z9+/b5zne+k/fff3+F+y3xyiuv5MADD0zbtm3TrFmzbLjhhvnBD35Qrc/zzz+fPffcM61bt07Lli2z++67V3t8YvKfz25lzm/JI/Mee+yxbLXVVmnatGm6d++eG264odp+3/zmN5Mku+66a9XP3oqux5AhQ9KyZcv885//TP/+/dOiRYt06tQp55133lK/t1b2ui2p9f77788WW2yRZs2a5ZprrlluDeecc07WXHPNXHvttdVClyX69++/wscFLu8dQMt6v8+tt96aPn36pFWrVmndunU23XTTqoB8Za7ffffdlx133DEtWrRIq1atsvfee+fvf//7Usdt2bJlXnvttey1115p1apVDj300OXWDwCwqhO8AADUQ7Nnz84777xTbVnel/DDhg3LjBkzqj2Ga0V22mmnrLvuutW+JL7tttvSsmXL7L333svd7+abb06PHj2y5ZZbZuDAgWnevHl++9vfrtQxx44dm549e+bee+/Nuuuum1atWmWttdbKOeeck8rKyqp+zz//fJIsNdunU6dOWXfddau2f9KcOXPyzjvv5JVXXsn3v//9TJgwIbvvvvtK1VWEDz/8MLvssktuvPHGHHroobnoootSUVGRIUOGVH152bZt26rPZ//998+NN96YG2+8MV//+teTJA8++GD++c9/5ogjjsjPf/7zHHzwwbn11luz1157LTOgWlnvvffeUvfRO++8U+2aJx//9f0+++yTSy+9NAMGDMgll1ySDTfcMGeeeeZKz6Ranm9+85uZP39+fvKTn+SYY45JkhxwwAG56667csQRR+QXv/hFvvvd7+aDDz7I1KlTVzhWTa/TgQcemA8++CAjRozIgQcemFGjRlU97m2Jo48+Opdddln69euXCy64II0bN17hz8HyDBo0KLfeemtVHe+8804eeOCB5YaZw4YNywknnJBOnTrl4osvzgEHHJBrrrkm/fr1qzaT4Te/+U2+853vpEOHDrnwwguz/fbbZ5999lnme5o+j+985zs588wzs/322+fyyy/PEUcckZtvvjn9+/f/1JkVf/vb37L11lvnT3/6U4455phcfvnl2W+//fLHP/6xqs/f//737LjjjnnhhRdy1lln5ZxzzsnkyZOzyy675C9/+ctnrvvVV1/NN77xjeyxxx65+OKLs8Yaa2TIkCFVX/zvtNNO+e53v5sk+f73v1/1s7fRRhutcNzFixdnwIABad++fS688ML06dMnQ4cOzdChQ6v1q8l1mzhxYgYNGpQ99tgjl19+eXr37r3MY0+aNCmvvPJK9ttvv7Rq1eozXpmV8+CDD2bQoEFZY4018tOf/jQXXHBBdtlll6p3ZX3a9bvxxhuz9957p2XLlvnpT3+ac845Jy+99FJ22GGHpULAf//73+nfv3/atWuXn/3sZznggANq9dwAAGpVCQCAeuO6664rJVnmUl5eXq1vktIJJ5xQKpVKpV133bXUoUOH0vz586uN89e//rWq/9ChQ0tJSm+//XbpjDPOKK2//vpV27bccsvSEUccsdS4SyxcuLC01lprlX7wgx9UtR1yyCGlXr16rdR5tW7durTGGmuUysvLS+ecc07pjjvuKB1yyCGlJKXvfe97Vf0uuuiiUpLS1KlTlxpjyy23LG2zzTZLtffv37/qGjVp0qT0ne98p/Thhx+uVF2fxd57713q0qVL1fpll11WSlK66aabqtoWLlxY2nbbbUstW7YszZkzp1QqlUpvv/12KUlp6NChS4255HP7pN/+9relJKVHH320qm3J5zp58uQV1rjks17Rsvfee1f1v/vuu0tJSueff361cb7xjW+UysrKSq+++mqpVCqVJk+eXEpSuu6665Y65n+f25IaBg0aVK3f+++/X0pSuuiii1Z4DsuystdpybGPPPLIan3333//0lprrVW1Pn78+FKS0vHHH1+t35J7c1mf1SctuR4XXXRRacKECaUkpT//+c+lUqlUuvLKK0stW7YszZs3r3T44YeXWrRoUbXfzJkzS02aNCn169evtHjx4qr2kSNHlpKUrr322lKp9PF91K5du1Lv3r1LCxYsqOr3y1/+spSktPPOO1e1Le/eeOihh0pJSg899FBV2+GHH17tHv7zn/9cSlK6+eabq+07ZsyYZbb/t5122qnUqlWr0uuvv16tvbKysurf++23X6lJkyal1157rartrbfeKrVq1aq00047VbUt+ez+27LOr0uXLkt99jNnziyVl5eXTj/99Kq222+/falrsCKHH354KUnppJNOqnYue++9d6lJkyalt99+u1Qq1ey6Lal1zJgxn3r8e+65p5SkdOmll65Uvcv6udx5552r3R+fPLdPfvYnn3xyqXXr1qV///vfyx1/edfvgw8+KLVp06Z0zDHHVGufPn16qaKiolr7kmv6yd/3AAD1mRkvAAD10JVXXpkHH3yw2nLfffctt/+wYcMyffr0XH311Ss1/iGHHJJXX301f/3rX6v+c0WPGbvvvvvy7rvvZtCgQVVtgwYNygsvvLDUI2WWZe7cuXn//fczfPjwnHfeeVUvhh4wYEAuv/zyfPDBB0lS9bi08vLypcZo2rTpMh+ndsEFF+SBBx7Ib37zm2yzzTZZuHBh/v3vf39qTUW5995706FDh2rXpnHjxvnud7+buXPn5pFHHvnUMT75noePPvoo77zzTrbZZpskyXPPPfeZa/v973+/1H304IMPpn379kudQ8OGDav+sn2J008/PaVSaYX33qc59thjq603a9YsTZo0ycMPP7zSj7H65L5LrMx1+u9j77jjjnn33XczZ86cJB+fd5KlzvuUU06pUV1Jsskmm2SzzTarmgV2yy23ZN99903z5s2X6jt27NgsXLgwp5xySho0+M//ZTvmmGPSunXrjB49OknyzDPPZObMmTn22GPTpEmTqn5DhgxJRUVFjWtcnttvvz0VFRXZY489qs2M6tOnT1q2bJmHHnpoufu+/fbbefTRR3PkkUdmvfXWq7ZtySPDFi9enAceeCD77bdfunfvXrW9Y8eOOeSQQ/LYY49VfSY1tfHGG2fHHXesWm/btm023HDD/POf//xM433SiSeeWPXvsrKynHjiiVm4cGHGjh2bpObXrVu3bunfv/+nHnfJtajt2S5J0qZNm8ybNy8PPvhgjfd98MEHM2vWrAwaNKja+Tds2DBbb731Mu+b4447roiyAQDqXKNP7wIAwKpmq622WupxWyuy0047Zdddd82FF1641JfNy7L55punZ8+eueWWW9KmTZt06NAhu+2223L733TTTenWrVvKy8vz6quvJkl69OiR5s2b5+abb85PfvKTFR6vWbNmmTdvXrVwIvk4vBkzZkyef/757LTTTlVfrC9YsGCpMZb3IupPPq7nsMMOy9e+9rUMGTJkhe+DmTt3bubOnVu13rBhw6p3sNTU66+/ng022KDaF+hJqh7F8/rrr3/qGO+9916GDx+eW2+9NTNnzqy2bfbs2Z+pruTj+2Lttddeqr1p06bV1l9//fV06tRpqS96a3IOy9OtW7dq6+Xl5fnpT3+a008/Pe3bt88222yT//mf/8ngwYPToUOHFY5V0+v030HAGmuskSR5//3307p167z++utp0KBBevToUa3fhhtuuNLn90mHHHJILr744px66ql54okn8v3vf3+Z/ZZcz/8+TpMmTdK9e/eq7Uv+c4MNNqjWr3HjxtUCjM9r0qRJmT17dtq1a7fM7f99rT9pScDx1a9+dbl93n777cyfP3+Z13WjjTZKZWVl3njjjWyyySY1rHzpzzj5+HOuaaj33xo0aLDUNf7KV76SJFWP0Krpdfvvn4Xlad26dZJUBdK16fjjj8/vfve77LnnnllnnXXSr1+/HHjggRkwYMCn7jtp0qQkWe5/dyw5jyUaNWqUdddd9/MXDQCwChC8AACsJoYOHZpddtkl11xzTdq0afOp/Q855JBcddVVadWqVQ466KClgoMl5syZkz/+8Y/56KOPlvoCOPn4L/t//OMfL/OF2Et06tQpkyZNWmqmxZIvLJd8SdqxY8ckybRp09K5c+dqfadNm5atttpqhefUpEmT7LPPPrngggvy4YcfLjOoSZKf/exn1d710aVLl099sXttOvDAA/PEE0/kzDPPTO/evdOyZctUVlZmwIABS72PpS4t7zNevHjxcvdZ1mdwyimnZODAgbn77rtz//3355xzzsmIESPypz/9KZtvvvlyx6rpdWrYsOEyxyl9jvfmrMigQYNy9tln55hjjslaa62Vfv361cpxluWzfDZLVFZWpl27drn55puXuf2zhpKfRU3P44v+jD+pptdteb+P/lvPnj2TJC+++OJnrq2srGyZ1+C/r2O7du0yfvz43H///bnvvvty33335brrrsvgwYNz/fXXr/AYS37mbrzxxmWGpo0aVf86ory8fLn/PQMAUN8IXgAAVhM777xzdtlll/z0pz/Nueee+6n9DznkkJx77rmZNm1abrzxxuX2u/POO/PRRx/lqquuWmr2xMSJE/PDH/4wjz/+eHbYYYfljtGnT59MmjQp//rXv6r9Fflbb72V5D9fUC6ZvfLMM89UC1neeuutvPnmm/n2t7/9qef14YcfplQq5YMPPljuF52DBw+uVu/KfiG6LF26dMnf/va3VFZWVvtS8ZVXXqnaniz/C+X3338/48aNy/Dhw6t9bkv+mvyL0KVLl4wdOzYffPBBtVkv/30OS2aMzJo1q9r+n2VGTI8ePXL66afn9NNPz6RJk9K7d+9cfPHFuemmm5bZvzauU5cuXVJZWZnXXnut2myMiRMnfqbx1ltvvWy//fZ5+OGHc9xxxy31xfMnj7vkOJ/8eVi4cGEmT56cvn37Vus3adKkarMKFi1alMmTJ6dXr15VbZ/ns+nRo0fGjh2b7bffvsY/C0vqnzBhwnL7tG3bNs2bN1/mdX3llVfSoEGDqqD1k+fxyQD588y6WlEovDyVlZX55z//WTXLJUn+8Y9/JEm6du2a5PNdtxX5yle+kg033DD33HNPLr/88rRs2bLGY6yxxhrLfNzasq5jkyZNMnDgwAwcODCVlZU5/vjjc8011+Scc87J+uuvv9zrt2SmWLt27aruWQCA1YU/JwEAWI0sedfLL3/5y0/t26NHj1x22WUZMWLECmeS3HTTTenevXuOPfbYfOMb36i2nHHGGWnZsuVy/+J7iYMOOihJ8pvf/KaqrbKyMtddd13WXHPN9OnTJ8nH78no2bNnfvnLX1b7y+yrrroqZWVl+cY3vlHVtqzHH82aNSu///3v07lz5+U+/if5+Mvivn37Vi3bb7/9Cutfkb322ivTp0/PbbfdVtX273//Oz//+c/TsmXL7LzzzklS9a6P//5ifMlf7P/3X6dfdtlln7mmmtprr72yePHijBw5slr7pZdemrKysuy5555JPn500Nprr51HH320Wr9f/OIXK32s+fPn56OPPqrW1qNHj7Rq1WqZj5hbojau05LzuuKKKwob8/zzz8/QoUNz0kknLbdP375906RJk1xxxRXVzuc3v/lNZs+enb333jtJssUWW6Rt27a5+uqrs3Dhwqp+o0aNWuo+WvIl+Cc/m8WLF6/U74IDDzwwixcvzo9+9KOltv373/9e6lif1LZt2+y000659tprM3Xq1Grblpxbw4YN069fv9xzzz3VZpbNmDEjt9xyS3bYYYeqx1It6zzmzZv3qbMvVqRFixZJlv7Z+zSf/HkolUoZOXJkGjdunN133z3J57tun2b48OF59913c/TRRy/znVUPPPBA/u///m+5+/fo0SOvvPJK3n777aq2F154IY8//ni1fu+++2619QYNGmSzzTZL8p9HPi7v+vXv3z+tW7fOT37ykyxatGipGj55bACALxszXgAA6qH77ruvarbBJ2233XYrfLfDzjvvnJ133nmlXuieJCeffPIKt7/11lt56KGHlnr5+BLl5eXp379/br/99lxxxRVp3LjxMvvtu+++2X333TNixIi888476dWrV+6+++489thjueaaa1JeXl7V96KLLso+++yTfv365eCDD86ECRMycuTIHH300VXvHEk+/tJ83XXXzdZbb5127dpl6tSpue666/LWW29VC0Fq27e//e1cc801GTJkSJ599tl07do1d9xxRx5//PFcdtllVTNImjVrlo033ji33XZbvvKVr2TNNdfMV7/61Xz1q1/NTjvtlAsvvDCLFi3KOuuskwceeCCTJ0/+ws5h4MCB2XXXXfODH/wgU6ZMSa9evfLAAw/knnvuySmnnFLtHShHH310Lrjgghx99NHZYost8uijj1bNBFgZ//jHP7L77rvnwAMPzMYbb5xGjRrlrrvuyowZM3LwwQcvd7/WrVsXfp169+6dQYMG5Re/+EVmz56d7bbbLuPGjat6j9FnseRncEXatm2bs88+O8OHD8+AAQOyzz77ZOLEifnFL36RLbfcMocddliSj9/lcv755+c73/lOdttttxx00EGZPHlyrrvuuqV+D2yyySbZZpttcvbZZ+e9997LmmuumVtvvXWZX9ovq+bvfOc7GTFiRMaPH59+/fqlcePGmTRpUm6//fZcfvnl1ULP/3bFFVdkhx12yNe+9rV8+9vfTrdu3TJlypSMHj0648ePT/JxIPXggw9mhx12yPHHH59GjRrlmmuuyYIFC3LhhRdWjdWvX7+st956Oeqoo3LmmWemYcOGufbaa9O2bdulgp2V1bt37zRs2DA//elPM3v27JSXl2e33XZbYTjbtGnTjBkzJocffni23nrr3HfffRk9enS+//3vV83Q+7zXbUUOOuigvPjii/nxj3+c559/PoMGDUqXLl3y7rvvZsyYMRk3blxuueWW5e5/5JFH5pJLLkn//v1z1FFHZebMmbn66quzySabZM6cOVX9jj766Lz33nvZbbfdsu666+b111/Pz3/+8/Tu3bvq9+2Krt9VV12Vb33rW/na176Wgw8+uOpzGj16dLbffvulwlwAgC+NEgAA9cZ1111XSrLc5brrrqvqm6R0wgknLDXGQw89VNX/r3/9a1X70KFDS0lKb7/99gpr+OS4F198cSlJady4ccvtP2rUqFKS0j333LPCcT/44IPSySefXOrQoUOpSZMmpU033bR00003LbPvXXfdVerdu3epvLy8tO6665Z++MMflhYuXFitz8iRI0s77LBDae211y41atSo1LZt29LAgQNLjz766Arr+Lz23nvvUpcuXaq1zZgxo3TEEUeU1l577apz++RntcQTTzxR6tOnT6lJkyalJKWhQ4eWSqVS6c033yztv//+pTZt2pQqKipK3/zmN0tvvfVWtT6l0n/uj8mTJ6+wxk/7rLt06VLae++9q7V98MEHpVNPPbXUqVOnUuPGjUsbbLBB6aKLLipVVlZW6zd//vzSUUcdVaqoqCi1atWqdOCBB5Zmzpy5VK3Lq+Gdd94pnXDCCaWePXuWWrRoUaqoqChtvfXWpd/97ncrPKeaXKflHXtZ1+/DDz8sffe73y2ttdZapRYtWpQGDhxYeuONN5Yac1kmT55cSlK66KKLVtjv8MMPL7Vo0WKp9pEjR5Z69uxZaty4cal9+/al4447rvT+++8v1e8Xv/hFqVu3bqXy8vLSFltsUXr00UdLO++8c2nnnXeu1u+1114r9e3bt1ReXl5q37596fvf/37pwQcfLCUpPfTQQ9Xq+e97uFQqlX75y1+W+vTpU2rWrFmpVatWpU033bR01llnld56660Vnl+pVCpNmDCh6rNp2rRpacMNNyydc8451fo899xzpf79+5datmxZat68eWnXXXctPfHEE0uN9eyzz5a23nrrUpMmTUrrrbde6ZJLLlnmZ7es+7hUKi3z2vzqV78qde/evdSwYcOlrsd/W/J5vfbaa6V+/fqVmjdvXmrfvn1p6NChpcWLFy/Vf2Wu2/Jq/TTjxo0r7bvvvqV27dpV+z33yd+3S+7D//6dc9NNN5W6d+9eatKkSal3796l+++/f6nP/o477ij169ev1K5du6rr/Z3vfKc0bdq0amOt6Po99NBDpf79+5cqKipKTZs2LfXo0aM0ZMiQ0jPPPLPUNQUA+LIoK5W+gLcKAgAAwJfAkCFDcscdd2Tu3Ll1XQoAAKso73gBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAgnjHCwAAAAAAQEHMeAEAAAAAACiI4AUAAAAAAKAgjeq6gFVRZWVl3nrrrbRq1SplZWV1XQ4AAAAAAFCHSqVSPvjgg3Tq1CkNGqx4TovgZRneeuutdO7cua7LAAAAAAAAViFvvPFG1l133RX2EbwsQ6tWrZJ8fAFbt25dx9UAAAAAAAB1ac6cOencuXNVfrAigpdlWPJ4sdatWwteAAAAAACAJFmp15Os+EFkAAAAAAAArDTBCwAAAAAAQEEELwAAAAAAAAXxjhcAAAAAAKhHFi9enEWLFtV1GV8qjRs3TsOGDQsZS/ACAAAAAAD1QKlUyvTp0zNr1qy6LuVLqU2bNunQoUPKyso+1ziCFwAAAAAAqAeWhC7t2rVL8+bNP3dAwMdKpVLmz5+fmTNnJkk6duz4ucYTvAAAAAAAwCpu8eLFVaHLWmutVdflfOk0a9YsSTJz5sy0a9fucz12rEFRRQEAAAAAALVjyTtdmjdvXseVfHktubaf9/05ghcAAAAAAKgnPF6s9hR1bQUvAAAAAAAABRG8AAAAAAAAtWaXXXbJKaecUtdlfGEa1XUBAAAAAADAZ9f1e6O/sGNNuWDvL+xY9ZUZLwAAAAAAwJfKwoUL6+zYghcAAAAAAKBWVVZW5qyzzsqaa66ZDh06ZNiwYVXbpk6dmn333TctW7ZM69atc+CBB2bGjBlV24cMGZL99tuv2ninnHJKdtlll6r1XXbZJSeeeGJOOeWUrL322unfv39KpVKGDRuW9dZbL+Xl5enUqVO++93v1vKZ1nHwMmLEiGy55ZZp1apV2rVrl/322y8TJ06s1uejjz7KCSeckLXWWistW7bMAQccUO2CL0upVMq5556bjh07plmzZunbt28mTZpUm6cCAAAAAAAsx/XXX58WLVrkL3/5Sy688MKcd955efDBB1NZWZl999037733Xh555JE8+OCD+ec//5mDDjroMx2jSZMmefzxx3P11Vfn97//fS699NJcc801mTRpUu6+++5suummtXB21dXpO14eeeSRnHDCCdlyyy3z73//O9///vfTr1+/vPTSS2nRokWS5NRTT83o0aNz++23p6KiIieeeGK+/vWv5/HHH1/uuBdeeGGuuOKKXH/99enWrVvOOeec9O/fPy+99FKaNm36RZ0eAAAAAACQZLPNNsvQoUOTJBtssEFGjhyZcePGJUlefPHFTJ48OZ07d06S3HDDDdlkk03y17/+NVtuueVKH2ODDTbIhRdeWLU+evTodOjQIX379k3jxo2z3nrrZauttirwrJatTme8jBkzJkOGDMkmm2ySXr16ZdSoUZk6dWqeffbZJMns2bPzm9/8Jpdcckl222239OnTJ9ddd12eeOKJPPXUU8scs1Qq5bLLLssPf/jD7Lvvvtlss81yww035K233srdd9/9BZ4dAAAAAACQfBy8fFLHjh0zc+bMvPzyy+ncuXNV6JIkG2+8cdq0aZOXX365Rsfo06dPtfVvfvOb+fDDD9O9e/ccc8wxueuuu/Lvf//7s5/ESlql3vEye/bsJMmaa66ZJHn22WezaNGi9O3bt6pPz549s9566+XJJ59c5hiTJ0/O9OnTq+1TUVGRrbfeern7LFiwIHPmzKm2AAAAAAAAxWjcuHG19bKyslRWVq7Uvg0aNEipVKrWtmjRoqX6LXmS1hKdO3fOxIkT84tf/CLNmjXL8ccfn5122mmZ+xZplQleKisrc8opp2T77bfPV7/61STJ9OnT06RJk7Rp06Za3/bt22f69OnLHGdJe/v27Vd6nxEjRqSioqJq+WSyBgAAAAAA1I6NNtoob7zxRt54442qtpdeeimzZs3KxhtvnCRp27Ztpk2bVm2/8ePHr9T4zZo1y8CBA3PFFVfk4YcfzpNPPpkXX3yxsPqXZZUJXk444YRMmDAht9566xd+7LPPPjuzZ8+uWj75AQMAAAAAALWjb9++2XTTTXPooYfmueeey9NPP53Bgwdn5513zhZbbJEk2W233fLMM8/khhtuyKRJkzJ06NBMmDDhU8ceNWpUfvOb32TChAn55z//mZtuuinNmjVLly5davWcGtXq6CvpxBNPzP/93//l0UcfzbrrrlvV3qFDhyxcuDCzZs2qNutlxowZ6dChwzLHWtI+Y8aMdOzYsdo+vXv3XuY+5eXlKS8v//wnAgAAAAAAX7ApF+xd1yV8ZmVlZbnnnnty0kknZaeddkqDBg0yYMCA/PznP6/q079//5xzzjk566yz8tFHH+XII4/M4MGDP3XmSps2bXLBBRfktNNOy+LFi7Ppppvmj3/8Y9Zaa63aPafSfz8Y7QtUKpVy0kkn5a677srDDz+cDTbYoNr22bNnp23btvntb3+bAw44IEkyceLE9OzZM08++WS22WabZY7ZqVOnnHHGGTn99NOTJHPmzEm7du0yatSoHHzwwZ9a15w5c1JRUZHZs2endevWBZwpAAAAAAB8dh999FEmT56cbt26pWnTpnVdzpfSiq5xTXKDOn3U2AknnJCbbropt9xyS1q1apXp06dn+vTp+fDDD5MkFRUVOeqoo3LaaafloYceyrPPPpsjjjgi2267bbXQpWfPnrnrrruSfJyOnXLKKTn//PPzhz/8IS+++GIGDx6cTp06Zb/99quL0wQAAAAAAFYTdfqosauuuipJsssuu1Rrv+666zJkyJAkyaWXXpoGDRrkgAMOyIIFC9K/f//84he/qNZ/4sSJmT17dtX6WWedlXnz5uXb3/52Zs2alR122CFjxoyRAgJQY12/N7quS1hp9XlaMQAAAMCXRZ0+amxV5VFjACwheAEAAABWBR41Vvu+FI8aAwAAAAAA+DIRvAAAAAAAABRE8AIAAAAAAFAQwQsAAAAAAEBBBC8AAAAAAAAFEbwAAAAAAAAURPACAAAAAABQkEZ1XQAAAAAAAPA5DKv4Ao81+4s7VkGmTJmSbt265fnnn0/v3r1r/XhmvAAAAAAAABRE8AIAAAAAANSaysrKXHjhhVl//fVTXl6e9dZbLz/+8Y+TJC+++GJ22223NGvWLGuttVa+/e1vZ+7cuVX77rLLLjnllFOqjbfffvtlyJAhVetdu3bNT37ykxx55JFp1apV1ltvvfzyl7+s2t6tW7ckyeabb56ysrLssssutXauieAFAAAAAACoRWeffXYuuOCCnHPOOXnppZdyyy23pH379pk3b1769++fNdZYI3/9619z++23Z+zYsTnxxBNrfIyLL744W2yxRZ5//vkcf/zxOe644zJx4sQkydNPP50kGTt2bKZNm5Y777yz0PP7b97xAgAAAAAA1IoPPvggl19+eUaOHJnDDz88SdKjR4/ssMMO+dWvfpWPPvooN9xwQ1q0aJEkGTlyZAYOHJif/vSnad++/UofZ6+99srxxx+fJPnf//3fXHrppXnooYey4YYbpm3btkmStdZaKx06dCj4DJdmxgsAAAAAAFArXn755SxYsCC77777Mrf16tWrKnRJku233z6VlZVVs1VW1mabbVb177KysnTo0CEzZ8787IV/DoIXAAAAAACgVjRr1uxz7d+gQYOUSqVqbYsWLVqqX+PGjautl5WVpbKy8nMd+7MSvAAAAAAAALVigw02SLNmzTJu3Liltm200UZ54YUXMm/evKq2xx9/PA0aNMiGG26YJGnbtm2mTZtWtX3x4sWZMGFCjWpo0qRJ1b5fBMELAAAAAABQK5o2bZr//d//zVlnnZUbbrghr732Wp566qn85je/yaGHHpqmTZvm8MMPz4QJE/LQQw/lpJNOyre+9a2q97vstttuGT16dEaPHp1XXnklxx13XGbNmlWjGtq1a5dmzZplzJgxmTFjRmbPnl0LZ/ofjWp1dAAAAAAAoHYNq90g4fM655xz0qhRo5x77rl566230rFjxxx77LFp3rx57r///px88snZcsst07x58xxwwAG55JJLqvY98sgj88ILL2Tw4MFp1KhRTj311Oy66641On6jRo1yxRVX5Lzzzsu5556bHXfcMQ8//HDBZ/kfZaX/fjgamTNnTioqKjJ79uy0bt26rssBoA51/d7oui5hpU25YO+6LgEAAACoJR999FEmT56cbt26pWnTpnVdzpfSiq5xTXIDjxoDAAAAAAAoiEeNAcCXxbCKuq5g5a3iU6ABAAAAPiszXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAOqJUqlU1yV8aRV1bQUvAAAAAACwimvcuHGSZP78+XVcyZfXkmu75Fp/Vo2KKAYAAAAAAKg9DRs2TJs2bTJz5swkSfPmzVNWVlbHVX05lEqlzJ8/PzNnzkybNm3SsGHDzzWe4AUAAAAAAOqBDh06JElV+EKx2rRpU3WNPw/BCwAAAAAA1ANlZWXp2LFj2rVrl0WLFtV1OV8qjRs3/twzXZYQvAAAAAAAQD3SsGHDwkICitegrgsAAAAAAAD4shC8AAAAAAAAFETwAgAAAAAAUBDBCwAAAAAAQEEELwAAAAAAAAURvAAAAAAAABRE8AIAAAAAAFAQwQsAAAAAAEBBBC8AAAAAAAAFEbwAAAAAAAAURPACAAAAAABQEMELAAAAAABAQQQvAAAAAAAABRG8AAAAAAAAFETwAgAAAAAAUBDBCwAAAAAAQEEELwAAAAAAAAURvAAAAAAAABRE8AIAAAAAAFAQwQsAAAAAAEBBBC8AAAAAAAAFEbwAAAAAAAAURPACAAAAAABQEMELAAAAAABAQQQvAAAAAAAABRG8AAAAAAAAFKROg5dHH300AwcOTKdOnVJWVpa777672vaysrJlLhdddNFyxxw2bNhS/Xv27FnLZwIAAAAAAFDHwcu8efPSq1evXHnllcvcPm3atGrLtddem7KyshxwwAErHHeTTTaptt9jjz1WG+UDAAAAAABU06guD77nnntmzz33XO72Dh06VFu/5557suuuu6Z79+4rHLdRo0ZL7QsAAAAAAFDb6s07XmbMmJHRo0fnqKOO+tS+kyZNSqdOndK9e/cceuihmTp16gr7L1iwIHPmzKm2AAAAAAAA1FS9CV6uv/76tGrVKl//+tdX2G/rrbfOqFGjMmbMmFx11VWZPHlydtxxx3zwwQfL3WfEiBGpqKioWjp37lx0+QAAAAAAwGqg3gQv1157bQ499NA0bdp0hf323HPPfPOb38xmm22W/v375957782sWbPyu9/9brn7nH322Zk9e3bV8sYbbxRdPgAAAAAAsBqo03e8rKw///nPmThxYm677bYa79umTZt85StfyauvvrrcPuXl5SkvL/88JQIAAAAAANSPGS+/+c1v0qdPn/Tq1avG+86dOzevvfZaOnbsWAuVAQAAAAAA/EedBi9z587N+PHjM378+CTJ5MmTM378+EydOrWqz5w5c3L77bfn6KOPXuYYu+++e0aOHFm1fsYZZ+SRRx7JlClT8sQTT2T//fdPw4YNM2jQoFo9FwAAAAAAgDp91NgzzzyTXXfdtWr9tNNOS5IcfvjhGTVqVJLk1ltvTalUWm5w8tprr+Wdd96pWn/zzTczaNCgvPvuu2nbtm122GGHPPXUU2nbtm3tnQgAAAAAAECSslKpVKrrIlY1c+bMSUVFRWbPnp3WrVvXdTkA1KGu3xtd1yWstClND6nrElbesNl1XQEAAADASqtJblAv3vECAAAAAABQHwheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAApSp8HLo48+moEDB6ZTp04pKyvL3XffXW37kCFDUlZWVm0ZMGDAp4575ZVXpmvXrmnatGm23nrrPP3007V0BgAAAAAAAP9Rp8HLvHnz0qtXr1x55ZXL7TNgwIBMmzatavntb3+7wjFvu+22nHbaaRk6dGiee+659OrVK/3798/MmTOLLh8AAAAAAKCaRnV58D333DN77rnnCvuUl5enQ4cOKz3mJZdckmOOOSZHHHFEkuTqq6/O6NGjc+211+Z73/ve56oXAAAAAABgRVb5d7w8/PDDadeuXTbccMMcd9xxeffdd5fbd+HChXn22WfTt2/fqrYGDRqkb9++efLJJ5e734IFCzJnzpxqCwAAAAAAQE2t0sHLgAEDcsMNN2TcuHH56U9/mkceeSR77rlnFi9evMz+77zzThYvXpz27dtXa2/fvn2mT5++3OOMGDEiFRUVVUvnzp0LPQ8AAAAAAGD1UKePGvs0Bx98cNW/N91002y22Wbp0aNHHn744ey+++6FHefss8/OaaedVrU+Z84c4QsAAAAAAFBjq/SMl//WvXv3rL322nn11VeXuX3ttddOw4YNM2PGjGrtM2bMWOF7YsrLy9O6detqCwAAAAAAQE3Vq+DlzTffzLvvvpuOHTsuc3uTJk3Sp0+fjBs3rqqtsrIy48aNy7bbbvtFlQkAAAAAAKym6jR4mTt3bsaPH5/x48cnSSZPnpzx48dn6tSpmTt3bs4888w89dRTmTJlSsaNG5d9990366+/fvr37181xu67756RI0dWrZ922mn51a9+leuvvz4vv/xyjjvuuMybNy9HHHHEF316AAAAAADAaqZO3/HyzDPPZNddd61aX/KelcMPPzxXXXVV/va3v+X666/PrFmz0qlTp/Tr1y8/+tGPUl5eXrXPa6+9lnfeeadq/aCDDsrbb7+dc889N9OnT0/v3r0zZsyYtG/f/os7MQAAAAAAYLVUViqVSnVdxKpmzpw5qaioyOzZs73vBWA11/V7o+u6hJU2pekhdV3Cyhs2u64rAAAAAFhpNckN6tU7XgAAAAAAAFZlghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIHUavDz66KMZOHBgOnXqlLKystx9991V2xYtWpT//d//zaabbpoWLVqkU6dOGTx4cN56660Vjjls2LCUlZVVW3r27FnLZwIAAAAAAFDHwcu8efPSq1evXHnllUttmz9/fp577rmcc845ee6553LnnXdm4sSJ2WeffT513E022STTpk2rWh577LHaKB8AAAAAAKCaRnV58D333DN77rnnMrdVVFTkwQcfrNY2cuTIbLXVVpk6dWrWW2+95Y7bqFGjdOjQodBaAQAAAAAAPk29esfL7NmzU1ZWljZt2qyw36RJk9KpU6d07949hx56aKZOnbrC/gsWLMicOXOqLQAAAAAAADVVb4KXjz76KP/7v/+bQYMGpXXr1svtt/XWW2fUqFEZM2ZMrrrqqkyePDk77rhjPvjgg+XuM2LEiFRUVFQtnTt3ro1TAAAAAAAAvuTqRfCyaNGiHHjggSmVSrnqqqtW2HfPPffMN7/5zWy22Wbp379/7r333syaNSu/+93vlrvP2WefndmzZ1ctb7zxRtGnAAAAAAAArAbq9B0vK2NJ6PL666/nT3/60wpnuyxLmzZt8pWvfCWvvvrqcvuUl5envLz885YKAAAAAACs5lbpGS9LQpdJkyZl7NixWWuttWo8xty5c/Paa6+lY8eOtVAhAAAAAADAf9Rp8DJ37tyMHz8+48ePT5JMnjw548ePz9SpU7No0aJ84xvfyDPPPJObb745ixcvzvTp0zN9+vQsXLiwaozdd989I0eOrFo/44wz8sgjj2TKlCl54oknsv/++6dhw4YZNGjQF316AAAAAADAaqbGwUv37t3z7rvvLtU+a9asdO/evUZjPfPMM9l8882z+eabJ0lOO+20bL755jn33HPzr3/9K3/4wx/y5ptvpnfv3unYsWPV8sQTT1SN8dprr+Wdd96pWn/zzTczaNCgbLjhhjnwwAOz1lpr5amnnkrbtm1reqoAAAAAAAA1UuN3vEyZMiWLFy9eqn3BggX517/+VaOxdtlll5RKpeVuX9G2T9bzSbfeemuNagAAAAAAACjKSgcvf/jDH6r+ff/996eioqJqffHixRk3bly6du1aaHEAAAAAAAD1yUoHL/vtt1+SpKysLIcffni1bY0bN07Xrl1z8cUXF1ocAAAAAABAfbLSwUtlZWWSpFu3bvnrX/+atddeu9aKAgAAAAAAqI9q/I6XyZMn10YdAAAAAAAA9V6Ng5ckGTduXMaNG5eZM2dWzYRZ4tprry2kMAAAAAAAgPqmxsHL8OHDc95552WLLbZIx44dU1ZWVht1AQAAAAAA1Ds1Dl6uvvrqjBo1Kt/61rdqox4AAAAAAIB6q0FNd1i4cGG222672qgFAAAAAACgXqtx8HL00UfnlltuqY1aAAAAAAAA6rUaP2rso48+yi9/+cuMHTs2m222WRo3blxt+yWXXFJYcQAAAAAAAPVJjYOXv/3tb+ndu3eSZMKECdW2lZWVFVIUAAAAAABAfVTj4OWhhx6qjToAAAAAAADqvRq/4wUAAAAAAIBlq/GMl1133XWFjxT705/+9LkKAgAAAAAAqK9qHLwseb/LEosWLcr48eMzYcKEHH744UXVBQAAAAAAUO/UOHi59NJLl9k+bNiwzJ0793MXBAAAAAAAUF8V9o6Xww47LNdee21RwwEAAAAAANQ7hQUvTz75ZJo2bVrUcAAAAAAAAPVOjR819vWvf73aeqlUyrRp0/LMM8/knHPOKawwAADgy6vr90bXdQkrbcoFe9d1CQAAQD1S4+CloqKi2nqDBg2y4YYb5rzzzku/fv0KKwwAAAAAAKC+qXHwct1119VGHQAAAAAAAPVejYOXJZ599tm8/PLLSZJNNtkkm2++eWFFAQAAAAAA1Ec1Dl5mzpyZgw8+OA8//HDatGmTJJk1a1Z23XXX3HrrrWnbtm3RNQIAAAAAANQLDWq6w0knnZQPPvggf//73/Pee+/lvffey4QJEzJnzpx897vfrY0aAQAAAAAA6oUaz3gZM2ZMxo4dm4022qiqbeONN86VV16Zfv36FVocAAAAAABAfVLjGS+VlZVp3LjxUu2NGzdOZWVlIUUBAAAAAADURzUOXnbbbbecfPLJeeutt6ra/vWvf+XUU0/N7rvvXmhxAAAAAAAA9UmNg5eRI0dmzpw56dq1a3r06JEePXqkW7dumTNnTn7+85/XRo0AAAAAAAD1Qo3f8dK5c+c899xzGTt2bF555ZUkyUYbbZS+ffsWXhwAAAAAAEB9UuPgJUnKysqyxx57ZI899ii6HgAAAAAAgHprpR819qc//Skbb7xx5syZs9S22bNnZ5NNNsmf//znQosDAAAAAACoT1Y6eLnssstyzDHHpHXr1kttq6ioyHe+851ccsklhRYHAAAAAABQn6x08PLCCy9kwIABy93er1+/PPvss4UUBQAAAAAAUB+tdPAyY8aMNG7ceLnbGzVqlLfffruQogAAAAAAAOqjlQ5e1llnnUyYMGG52//2t7+lY8eOhRQFAAAAAABQH6108LLXXnvlnHPOyUcffbTUtg8//DBDhw7N//zP/xRaHAAAAAAAQH3SaGU7/vCHP8ydd96Zr3zlKznxxBOz4YYbJkleeeWVXHnllVm8eHF+8IMf1FqhAAAAAAAAq7qVDl7at2+fJ554Iscdd1zOPvvslEqlJElZWVn69++fK6+8Mu3bt6+1Qlm+rt8bXdclrLQpF+xd1yUAAAAAAECtWengJUm6dOmSe++9N++//35effXVlEqlbLDBBlljjTVqqz4AAAAAAIB6o0bByxJrrLFGttxyy6JrAQAAAAAAqNca1HUBAAAAAAAAXxaCFwAAAAAAgIIIXgAAAAAAAApSaPCyYMGCIocDAAAAAACoVz5z8LLPPvvkZz/7WaZNm5Ykefvtt7PrrrsWVhgAAAAAAEB985mDl65du2b06NHp0aNHTj311GyzzTb58MMPi6wNAAAAAACgXmn0WXe84oorkiS33nprDjnkkLRs2TLPPPNMYYUBAAAAAADUNys94+XEE0/Mr3/962ptr732Wk499dQceeSR6dOnT37+858XXiAAAAAAAEB9sdLByz333JOtttqqan3atGnZY489cvDBB+fXv/51fvCDH+TGG2+slSIBAAAAAADqg5UOXt599920bNkySfL++++nf//++da3vpVLL700SdK9e/f861//qp0qAQAAAAAA6oGVfsdLz549c/755+eQQw7JWWedlX333TfDhw+v2v7444+nS5cutVIkAACfruv3Rtd1CSttygV713UJAAAAUCtWesbLT37yk9x222054IAD0qNHj9x66625//77M2PGjNx+++05/fTTc/jhh9dmrQAAAAAAAKu0lZ7xMmDAgLz33ntJkvLy8gwfPjz77bdfFi5cmFKplIMPPjhnnHFGrRUKAAAAAACwqlvp4CX5OHBZYujQoTn55JMzceLErLPOOll33XULLw4AAAAAAKA+WelHjS1LmzZtsvXWW3/m0OXRRx/NwIED06lTp5SVleXuu++utr1UKuXcc89Nx44d06xZs/Tt2zeTJk361HGvvPLKdO3aNU2bNs3WW2+dp59++jPVBwAAAAAAUBOfK3j5vObNm5devXrlyiuvXOb2Cy+8MFdccUWuvvrq/OUvf0mLFi3Sv3//fPTRR8sd87bbbstpp52WoUOH5rnnnkuvXr3Sv3//zJw5s7ZOAwAAAAAAIEkdBy977rlnzj///Oy///5LbSuVSrnsssvywx/+MPvuu28222yz3HDDDXnrrbeWmhnzSZdcckmOOeaYHHHEEdl4441z9dVXp3nz5rn22mtr8UwAAAAAAADqOHhZkcmTJ2f69Onp27dvVVtFRUW23nrrPPnkk8vcZ+HChXn22Wer7dOgQYP07dt3ufskyYIFCzJnzpxqCwAAAAAAQE01qusClmf69OlJkvbt21drb9++fdW2//bOO+9k8eLFy9znlVdeWe6xRowYkeHDh3/OigEAgC+lYRV1XcHKGza7riugrrlfAQDqXI1nvIwZMyaPPfZY1fqVV16Z3r1755BDDsn7779faHFflLPPPjuzZ8+uWt544426LgkAAAAAAKiHahy8nHnmmVWP4nrxxRdz+umnZ6+99srkyZNz2mmnFVZYhw4dkiQzZsyo1j5jxoyqbf9t7bXXTsOGDWu0T5KUl5endevW1RYAAAAAAICaqnHwMnny5Gy88cZJkt///vf5n//5n/zkJz/JlVdemfvuu6+wwrp165YOHTpk3LhxVW1z5szJX/7yl2y77bbL3KdJkybp06dPtX0qKyszbty45e4DAAAAAABQlBoHL02aNMn8+fOTJGPHjk2/fv2SJGuuuWaNX0o/d+7cjB8/PuPHj0/ycagzfvz4TJ06NWVlZTnllFNy/vnn5w9/+ENefPHFDB48OJ06dcp+++1XNcbuu++ekSNHVq2fdtpp+dWvfpXrr78+L7/8co477rjMmzcvRxxxRE1PFQAAAAAAoEYa1XSH7bffPqeddlq23377PP3007ntttuSJP/4xz+y7rrr1misZ555JrvuumvV+pJHlR1++OEZNWpUzjrrrMybNy/f/va3M2vWrOywww4ZM2ZMmjZtWrXPa6+9lnfeeadq/aCDDsrbb7+dc889N9OnT0/v3r0zZsyYtG/fvqanCgAAAAAAUCM1Dl6uvPLKnHDCCbnjjjty1VVXZZ111kmS3HfffRkwYECNxtpll11SKpWWu72srCznnXdezjvvvOX2mTJlylJtJ554Yk488cQa1QIAAAAAAPB51Sh4+fe//52HH344v/rVr5Z6Wf2ll15aaGEAAAAAAAD1TY3e8dKoUaMce+yxWbBgQW3VAwAAAAAAUG/VKHhJkq222irPP/98bdQCAAAAAABQr9X4HS/HH398Tj/99Lz55pvp06dPWrRoUW37ZpttVlhxAAAAAAAA9UmNg5eDDz44SfLd7363qq2srCylUillZWVZvHhxcdUBAAAAAADUIzUOXiZPnlwbdQAAAAAAANR7NQ5eunTpUht1AAAAAAAA1Hs1Dl5uuOGGFW4fPHjwZy4GAAAAAACgPqtx8HLyySdXW1+0aFHmz5+fJk2apHnz5oIXAAAAvlS6fm90XZew0qY0resKAABoUNMd3n///WrL3LlzM3HixOywww757W9/Wxs1AgAAAAAA1As1Dl6WZYMNNsgFF1yw1GwYAAAAAACA1UkhwUuSNGrUKG+99VZRwwEAAAAAANQ7NX7Hyx/+8Idq66VSKdOmTcvIkSOz/fbbF1YYAAAAAABAfVPj4GW//fartl5WVpa2bdtmt912y8UXX1xUXQAAAAAAAPVOjYOXysrK2qgDAAAAAACg3vtc73gplUoplUpF1QIAAAAAAFCvfabg5YYbbsimm26aZs2apVmzZtlss81y4403Fl0bAAAAAABAvVLjR41dcsklOeecc3LiiSdm++23T5I89thjOfbYY/POO+/k1FNPLbxIAAAAAACA+qDGwcvPf/7zXHXVVRk8eHBV2z777JNNNtkkw4YNE7wAAAAAAACrrRoHL9OmTct22223VPt2222XadOmFVIUX2LDKuq6gpU3bHZdV/Cl1PV7o+u6hJU2pekhdV3CynO/AvWN/00AAADAl1SN3/Gy/vrr53e/+91S7bfddls22GCDQooCAAAAAACoj2o842X48OE56KCD8uijj1a94+Xxxx/PuHHjlhnIAAAAAAAArC5qPOPlgAMOyF/+8pesvfbaufvuu3P33Xdn7bXXztNPP53999+/NmoEAAAAAACoF2o84yVJ+vTpk5tuuqnoWgAAAAAAAOq1lQ5e5syZs1L9Wrdu/ZmLAQAAAAAAqM9WOnhp06ZNysrKlru9VCqlrKwsixcvLqQwAAAAAACA+malg5eHHnqo6t+lUil77bVXfv3rX2edddaplcIAAAAAAADqm5UOXnbeeedq6w0bNsw222yT7t27F14UAAAAAABAfdSgrgsAAAAAAAD4shC8AAAAAAAAFORzBS9lZWVF1QEAAAAAAFDvrfQ7Xr7+9a9XW//oo49y7LHHpkWLFtXa77zzzmIqAwAAAAAAqGdWOnipqKiotn7YYYcVXgwAAAAAAEB9ttLBy3XXXVebdQAAAADwOXX93ui6LmGlTblg77ouAQBqxed6xwsAAAAAAAD/IXgBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIKs8sFL165dU1ZWttRywgknLLP/qFGjlurbtGnTL7hqAAAAAABgddSorgv4NH/961+zePHiqvUJEyZkjz32yDe/+c3l7tO6detMnDixar2srKxWawQAAAAAAEjqQfDStm3bausXXHBBevTokZ133nm5+5SVlaVDhw61XRoAAAAAAEA1q/yjxj5p4cKFuemmm3LkkUeucBbL3Llz06VLl3Tu3Dn77rtv/v73v69w3AULFmTOnDnVFgAAAAAAgJqqV8HL3XffnVmzZmXIkCHL7bPhhhvm2muvzT333JObbroplZWV2W677fLmm28ud58RI0akoqKiauncuXMtVA8AAAAAAHzZ1avg5Te/+U323HPPdOrUabl9tt122wwePDi9e/fOzjvvnDvvvDNt27bNNddcs9x9zj777MyePbtqeeONN2qjfAAAAAAA4EtulX/HyxKvv/56xo4dmzvvvLNG+zVu3Dibb755Xn311eX2KS8vT3l5+ectEQAAAAAAWM3Vmxkv1113Xdq1a5e99967RvstXrw4L774Yjp27FhLlQEAAAAAAHysXgQvlZWVue6663L44YenUaPqk3QGDx6cs88+u2r9vPPOywMPPJB//vOfee6553LYYYfl9ddfz9FHH/1Flw0AAAAAAKxm6sWjxsaOHZupU6fmyCOPXGrb1KlT06DBf/Kj999/P8ccc0ymT5+eNdZYI3369MkTTzyRjTfe+IssGQAAAAAAWA3Vi+ClX79+KZVKy9z28MMPV1u/9NJLc+mll34BVQEAAAAAAFRXLx41BgAAAAAAUB8IXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAK0qiuCwAAAABgNTSsoq4rqJlhs+u6AgDqCTNeAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACtKorgsAAAAAAAAKNKyiritYecNm13UFhTPjBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoyCodvAwbNixlZWXVlp49e65wn9tvvz09e/ZM06ZNs+mmm+bee+/9gqoFAAAAAABWd6t08JIkm2yySaZNm1a1PPbYY8vt+8QTT2TQoEE56qij8vzzz2e//fbLfvvtlwkTJnyBFQMAAAAAAKurVT54adSoUTp06FC1rL322svte/nll2fAgAE588wzs9FGG+VHP/pRvva1r2XkyJFfYMUAAAAAAMDqapUPXiZNmpROnTqle/fuOfTQQzN16tTl9n3yySfTt2/fam39+/fPk08+WdtlAgAAAAAApFFdF7AiW2+9dUaNGpUNN9ww06ZNy/Dhw7PjjjtmwoQJadWq1VL9p0+fnvbt21dra9++faZPn77C4yxYsCALFiyoWp8zZ04xJwAAAAAAAKxWVungZc8996z692abbZatt946Xbp0ye9+97scddRRhR1nxIgRGT58eGHjAQAAAAAAq6dV/lFjn9SmTZt85StfyauvvrrM7R06dMiMGTOqtc2YMSMdOnRY4bhnn312Zs+eXbW88cYbhdUMAAAAAACsPupV8DJ37ty89tpr6dix4zK3b7vtthk3bly1tgcffDDbbrvtCsctLy9P69atqy0AAAAAAAA1tUoHL2eccUYeeeSRTJkyJU888UT233//NGzYMIMGDUqSDB48OGeffXZV/5NPPjljxozJxRdfnFdeeSXDhg3LM888kxNPPLGuTgEAAAAAAFiNrNLveHnzzTczaNCgvPvuu2nbtm122GGHPPXUU2nbtm2SZOrUqWnQ4D/Z0XbbbZdbbrklP/zhD/P9738/G2ywQe6+++589atfratTAAAAAAAAViOrdPBy6623rnD7ww8/vFTbN7/5zXzzm9+spYoAAAAAAFgddf3e6LouYaVNaVrXFazeVulHjQEAAAAAANQnghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAgqzSwcuIESOy5ZZbplWrVmnXrl3222+/TJw4cYX7jBo1KmVlZdWWpk2bfkEVAwAAAAAAq7NVOnh55JFHcsIJJ+Spp57Kgw8+mEWLFqVfv36ZN2/eCvdr3bp1pk2bVrW8/vrrX1DFAAAAAADA6qxRXRewImPGjKm2PmrUqLRr1y7PPvtsdtppp+XuV1ZWlg4dOtR2eQAAAAAAANWs0jNe/tvs2bOTJGuuueYK+82dOzddunRJ586ds+++++bvf//7CvsvWLAgc+bMqbYAAAAAAADUVL0JXiorK3PKKadk++23z1e/+tXl9ttwww1z7bXX5p577slNN92UysrKbLfddnnzzTeXu8+IESNSUVFRtXTu3Lk2TgEAAAAAAPiSqzfBywknnJAJEybk1ltvXWG/bbfdNoMHD07v3r2z8847584770zbtm1zzTXXLHefs88+O7Nnz65a3njjjaLLBwAAAAAAVgOr9DteljjxxBPzf//3f3n00Uez7rrr1mjfxo0bZ/PNN8+rr7663D7l5eUpLy//vGUCAAAAAACruVV6xkupVMqJJ56Yu+66K3/605/SrVu3Go+xePHivPjii+nYsWMtVAgAAAAAAPAfq/SMlxNOOCG33HJL7rnnnrRq1SrTp09PklRUVKRZs2ZJksGDB2edddbJiBEjkiTnnXdettlmm6y//vqZNWtWLrroorz++us5+uij6+w8AAAAAACA1cMqHbxcddVVSZJddtmlWvt1112XIUOGJEmmTp2aBg3+M3Hn/fffzzHHHJPp06dnjTXWSJ8+ffLEE09k4403/qLKBgAAAAAAVlOrdPBSKpU+tc/DDz9cbf3SSy/NpZdeWksVAQAAAAAALN8q/Y4XAAAAAACA+kTwAgAAAAAAUBDBCwAAAAAAQEEELwAAAAAAAAURvAAAAAAAABRE8AIAAAAAAFCQRnVdAAAAAADAKm9YRV1XsPKGza7rCmC1ZsYLAAAAAABAQQQvAAAAAAAABRG8AAAAAAAAFETwAgAAAAAAUBDBCwAAAAAAQEEELwAAAAAAAAURvAAAAAAAABRE8AIAAAAAAFAQwQsAAAAAAEBBBC8AAAAAAAAFEbwAAAAAAAAURPACAAAAAABQEMELAAAAAABAQRrVdQEAAAAAwOqp6/dG13UJK21K07quAKgvzHgBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAACiI4AUAAAAAAKAgghcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAAAAAACgIIIXAAAAAACAggheAAAAAAAACiJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAAAAAAoieAEAAAAAAChIvQherrzyynTt2jVNmzbN1ltvnaeffnqF/W+//fb07NkzTZs2zaabbpp77733C6oUAAAAAABYna3ywcttt92W0047LUOHDs1zzz2XXr16pX///pk5c+Yy+z/xxBMZNGhQjjrqqDz//PPZb7/9st9++2XChAlfcOUAAAAAAMDqZpUPXi655JIcc8wxOeKII7Lxxhvn6quvTvPmzXPttdcus//ll1+eAQMG5Mwzz8xGG22UH/3oR/na176WkSNHfsGVAwAAAAAAq5tGdV3AiixcuDDPPvtszj777Kq2Bg0apG/fvnnyySeXuc+TTz6Z0047rVpb//79c/fddy/3OAsWLMiCBQuq1mfPnp0kmTNnzueo/otTuWB+XZew0uaUleq6hJVXTz7/+sb9Wkvcr7XGPVtL3LO1wv1aS9yvtcL9Wkvcr7XC/VpL3K+1wv1ai9yztcI9W0vcr7XC/VpL6sn9uiQvKJU+/dqu0sHLO++8k8WLF6d9+/bV2tu3b59XXnllmftMnz59mf2nT5++3OOMGDEiw4cPX6q9c+fOn6FqVqSirguoiQvqVbXUgnp1B7hfiXuW+qVe3QHu19VevboD3K+rvXp1B7hfV3v17g5wz6726tUd4H5d7dWrO6Ce3a8ffPBBKipWXPMqHbx8Uc4+++xqs2QqKyvz3nvvZa211kpZWVkdVvblMmfOnHTu3DlvvPFGWrduXdflwAq5X6lv3LPUJ+5X6hP3K/WJ+5X6xP1KfeOepT5xv9aOUqmUDz74IJ06dfrUvqt08LL22munYcOGmTFjRrX2GTNmpEOHDsvcp0OHDjXqnyTl5eUpLy+v1tamTZvPVjSfqnXr1n7gqTfcr9Q37lnqE/cr9Yn7lfrE/Up94n6lvnHPUp+4X4v3aTNdlmhQy3V8Lk2aNEmfPn0ybty4qrbKysqMGzcu22677TL32Xbbbav1T5IHH3xwuf0BAAAAAACKskrPeEmS0047LYcffni22GKLbLXVVrnssssyb968HHHEEUmSwYMHZ5111smIESOSJCeffHJ23nnnXHzxxdl7771z66235plnnskvf/nLujwNAAAAAABgNbDKBy8HHXRQ3n777Zx77rmZPn16evfunTFjxqR9+/ZJkqlTp6ZBg/9M3Nluu+1yyy235Ic//GG+//3vZ4MNNsjdd9+dr371q3V1Cvx/5eXlGTp06FKPdYNVkfuV+sY9S33ifqU+cb9Sn7hfqU/cr9Q37lnqE/dr3SsrlUqlui4CAAAAAADgy2CVfscLAAAAAABAfSJ4AQAAAAAAKIjgBQAAAAAAoCCCFwAAAAAAgIIIXgAAAFagVCrVdQkAAEA90qiuC+DL65133sm1116bJ598MtOnT0+SdOjQIdttt12GDBmStm3b1nGFAADw6crLy/PCCy9ko402qutSAACAeqCs5M+3qAV//etf079//zRv3jx9+/ZN+/btkyQzZszIuHHjMn/+/Nx///3ZYost6rhSWDlvvPFGhg4dmmuvvbauS4EkyYcffphnn302a665ZjbeeONq2z766KP87ne/y+DBg+uoOqju5ZdfzlNPPZVtt902PXv2zCuvvJLLL788CxYsyGGHHZbddtutrkuEJMlpp522zPbLL788hx12WNZaa60kySWXXPJFlgUrZd68efnd736XV199NR07dsygQYOq7lmoa88991zWWGONdOvWLUly44035uqrr87UqVPTpUuXnHjiiTn44IPruEr4j5NOOikHHnhgdtxxx7ouBVbKyJEj8/TTT2evvfbKwQcfnBtvvDEjRoxIZWVlvv71r+e8885Lo0bmYHyRBC/Uim222Sa9evXK1VdfnbKysmrbSqVSjj322Pztb3/Lk08+WUcVQs288MIL+drXvpbFixfXdSmQf/zjH+nXr1+mTp2asrKy7LDDDrn11lvTsWPHJB+H3J06dXK/skoYM2ZM9t1337Rs2TLz58/PXXfdlcGDB6dXr16prKzMI488kgceeED4wiqhQYMG6dWrV9q0aVOt/ZFHHskWW2yRFi1apKysLH/605/qpkD4hI033jiPPfZY1lxzzbzxxhvZaaed8v777+crX/lKXnvttTRq1ChPPfVU1RfdUJd69eqViy++OH379s2vf/3rfPe7380xxxyTjTbaKBMnTsyvf/3rXH755TnyyCPrulRI8vH/JigrK0uPHj1y1FFH5fDDD0+HDh3quixYpvPPPz8XXnhh+vXrl8cffzynnHJKLrroopx66qlp0KBBLr300hx33HEZPnx4XZe6WhG8UCuaNWuW559/Pj179lzm9ldeeSWbb755Pvzwwy+4Mli2P/zhDyvc/s9//jOnn366L7JZJey///5ZtGhRRo0alVmzZuWUU07JSy+9lIcffjjrrbee4IVVynbbbZfddtst559/fm699dYcf/zxOe644/LjH/84SXL22Wfn2WefzQMPPFDHlUJywQUX5Je//GV+/etfVwsDGzdunBdeeGGpGYZQlxo0aJDp06enXbt2OeywwzJ58uTce++9qaioyNy5c7P//vunbdu2ueWWW+q6VEjz5s3z8ssvp0uXLvna176W4447Lsccc0zV9ltuuSU//vGP8/e//70Oq4T/aNCgQR588MH88Y9/zM0335zZs2dnzz33zDHHHJO99torDRp4bTarjvXXXz8XXnhhvv71r+eFF15Inz59cv311+fQQw9Nktx1110566yzMmnSpDqudPUieKFWdOvWLcOHD1/uY25uuOGGnHvuuZkyZcoXWxgsx5K/ZlnRr8SysjJfZLNKaN++fcaOHZtNN900ycczCY8//vjce++9eeihh9KiRQvBC6uMioqKPPvss1l//fVTWVmZ8vLyPP3009l8882TJBMmTEjfvn2r3gcHde2vf/1rDjvssAwcODAjRoxI48aNBS+skj4ZvPTo0SNXX3119thjj6rtTzzxRA4++OBMnTq1DquEj6299tq5//7706dPn7Rv3z4PPPBAevXqVbX9tddey6abbpr58+fXYZXwH5/8Hbto0aLcddddufbaazN27Ni0b98+Q4YMyRFHHJH111+/rkuFNG/ePK+88krWW2+9JEmTJk3y/PPPZ5NNNkmSvP7669l4440zb968uixztSOepVacccYZ+fa3v52TTz45f/jDH/KXv/wlf/nLX/KHP/whJ598co499ticddZZdV0mVOnYsWPuvPPOVFZWLnN57rnn6rpEqPLhhx9WezZrWVlZrrrqqgwcODA777xz/vGPf9RhdbC0JY8dbdCgQZo2bZqKioqqba1atcrs2bPrqjRYypZbbplnn302b7/9drbYYotMmDBhqUfnwqpiyb350UcfVT1ydIl11lknb7/9dl2UBUvZc889c9VVVyVJdt5559xxxx3Vtv/ud7/zBTarrMaNG+fAAw/MmDFj8s9//jPHHHNMbr755my44YZ1XRokSTp06JCXXnopSTJp0qQsXry4aj1J/v73v6ddu3Z1Vd5qyxt1qBUnnHBC1l577Vx66aX5xS9+UfVX1w0bNkyfPn0yatSoHHjggXVcJfxHnz598uyzz2bfffdd5vZPmw0DX6SePXvmmWeeyUYbbVStfeTIkUmSffbZpy7KgmXq2rVrJk2alB49eiRJnnzyyaq/xEqSqVOnLvVlIdS1li1b5vrrr8+tt96avn37mkHIKmv33XdPo0aNMmfOnEycODFf/epXq7a9/vrrWWutteqwOviPn/70p9l+++2z8847Z4sttsjFF1+chx9+uOodL0899VTuuuuuui4TPtV6662XYcOGZejQoRk7dmxdlwNJkkMPPTSDBw/Ovvvum3HjxuWss87KGWeckXfffTdlZWX58Y9/nG984xt1XeZqR/BCrTnooINy0EEHZdGiRXnnnXeSfDy9uHHjxnVcGSztzDPPXOGUy/XXXz8PPfTQF1gRLN/++++f3/72t/nWt7611LaRI0emsrIyV199dR1UBks77rjjqn1p/ckvBZPkvvvuq/YuDViVHHzwwdlhhx3y7LPPpkuXLnVdDlQzdOjQaustW7astv7HP/4xO+644xdZEixXp06d8vzzz+eCCy7IH//4x5RKpTz99NN54403sv322+fxxx/PFltsUddlQpUuXbqkYcOGy91eVlZW7fGOUJeGDx+eZs2a5cknn8wxxxyT733ve+nVq1fOOuuszJ8/PwMHDsyPfvSjui5zteMdLwAAAAAAAAXxjhcAAAAAAICCCF4AAAAAAAAKIngBAAAAAAAoiOAFAABYLZSVleXuu++u6zIAAIAvOcELAADwpTB9+vScdNJJ6d69e8rLy9O5c+cMHDgw48aNK/xYDz/8cMrKyjJr1qzCxwYAAOq3RnVdAAAAwOc1ZcqUbL/99mnTpk0uuuiibLrpplm0aFHuv//+nHDCCXnllVfqusRlKpVKWbx4cRo18n/NAADgy8KMFwAAoN47/vjjU1ZWlqeffjoHHHBAvvKVr2STTTbJaaedlqeeemqp/suasTJ+/PiUlZVlypQpSZLXX389AwcOzBprrJEWLVpkk002yb333pspU6Zk1113TZKsscYaKSsry5AhQ5IklZWVGTFiRLp165ZmzZqlV69eueOOO5Y67n333Zc+ffqkvLw8jz32WK1dFwAA4Ivnz6oAAIB67b333suYMWPy4x//OC1atFhqe5s2bT7TuCeccEIWLlyYRx99NC1atMhLL72Uli1bpnPnzvn973+fAw44IBMnTkzr1q3TrFmzJMmIESNy00035eqrr84GG2yQRx99NIcddljatm2bnXfeuWrs733ve/nZz36W7t27Z4011vhM9QEAAKsmwQsAAFCvvfrqqymVSunZs2eh406dOjUHHHBANt100yRJ9+7dq7atueaaSZJ27dpVBTsLFizIT37yk4wdOzbbbrtt1T6PPfZYrrnmmmrBy3nnnZc99tij0HoBAIBVg+AFAACo10qlUq2M+93vfjfHHXdcHnjggfTt2zcHHHBANttss+X2f/XVVzN//vylApWFCxdm8803r9a2xRZb1ErNAABA3RO8AAAA9doGG2yQsrKyvPLKKyu9T4MGH7/u8pOhzaJFi6r1Ofroo9O/f/+MHj06DzzwQEaMGJGLL744J5100jLHnDt3bpJk9OjRWWeddaptKy8vr7a+rEeiAQAAXw4N6roAAACAz2PNNddM//79c+WVV2bevHlLbZ81a9ZSbW3btk2STJs2rapt/PjxS/Xr3Llzjj322Nx55505/fTT86tf/SpJ0qRJkyTJ4sWLq/puvPHGKS8vz9SpU7P++utXWzp37vx5ThEAAKhHBC8AAEC9d+WVV2bx4sXZaqut8vvf/z6TJk3Kyy+/nCuuuKLqfSuftCQMGTZsWCZNmpTRo0fn4osvrtbnlFNOyf3335/Jkyfnueeey0MPPZSNNtooSdKlS5eUlZXl//7v//L2229n7ty5adWqVc4444yceuqpuf766/Paa6/lueeey89//vNcf/31X8h1AP5fe3eMmkAUhWH0TzFrmMJKC2FgcA8WgnvRzmaaKURB3IAuwRVYzhas3cyQdIGQ9kkSck59X3Pbj8sDAPh5wgsAAPDnzWazPB6PLJfL7Ha7tG2b1WqVYRhyvV6/zVdVldvtlufzmcVikfP5nOPx+GVmHMdsNps0TZP1ep35fJ7L5ZIkmUwm2e/36boudV1nu90mSQ6HQ/q+z+l0+nx3v98znU5fvwQAAOBXeHt/1U+UAAAAAAAA/4yLFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEKEFwAAAAAAgEI+AMAtk+HyNW3NAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [\n",
    "        moduleDF.groupby('cluster')['hours'].sum().rename('hours'),\n",
    "        moduleDF.groupby('cluster')['id'].count().rename('count')\n",
    "    ],\n",
    "    axis=1\n",
    ").plot(kind='bar', figsize=(20, 7), title='ENMA 603 - Total Hours and Module count per Cluster', xlabel='Cluster', ylabel='Hours & Count', legend=['Hours', 'Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:07:14.638529Z",
     "start_time": "2023-05-25T18:07:14.507531Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'hours'}>]], dtype=object)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "moduleDF.hist(figsize=(10, 4), bins=8, column='hours')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:21:01.016854Z",
     "start_time": "2023-05-25T18:21:01.008575Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11160445339425283"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "silhouette_score(X, moduleDF['cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:28:56.195746Z",
     "start_time": "2023-05-25T18:28:56.184400Z"
    },
    "collapsed": false
   },
   "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>name</th>\n",
       "      <th>prefix</th>\n",
       "      <th>features</th>\n",
       "      <th>hours</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Course Introduction, History, and Tools for Co...</td>\n",
       "      <td>INT</td>\n",
       "      <td>Welcome to the Quality System Controls course!...</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Variation, Normal Distribution, Sampling, Popu...</td>\n",
       "      <td>MATH</td>\n",
       "      <td>In this module, we will explore the fundamenta...</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control Charts for Variables</td>\n",
       "      <td>CC</td>\n",
       "      <td>In this module, we will dive into the variable...</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Start, Interpret X and R Control Charts, Trial...</td>\n",
       "      <td>CC</td>\n",
       "      <td>In this module, we will begin our journey into...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rational Subgroups, Process Capability Assessment</td>\n",
       "      <td>MATH</td>\n",
       "      <td>In this module, we will explore the importance...</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name prefix  \\\n",
       "0  Course Introduction, History, and Tools for Co...    INT   \n",
       "1  Variation, Normal Distribution, Sampling, Popu...   MATH   \n",
       "2                       Control Charts for Variables     CC   \n",
       "3  Start, Interpret X and R Control Charts, Trial...     CC   \n",
       "4  Rational Subgroups, Process Capability Assessment   MATH   \n",
       "\n",
       "                                            features  hours  cluster  \n",
       "0  Welcome to the Quality System Controls course!...   0.32        1  \n",
       "1  In this module, we will explore the fundamenta...   2.08        2  \n",
       "2  In this module, we will dive into the variable...   1.87        3  \n",
       "3  In this module, we will begin our journey into...   0.98        3  \n",
       "4  In this module, we will explore the importance...   1.48        2  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new dataframe with test data and evaluate the model\n",
    "testDF = pd.read_csv('input/614_num.tsv', sep='\\t')\n",
    "\n",
    "testDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:28:57.042072Z",
     "start_time": "2023-05-25T18:28:57.038131Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tp/4mcknzvj6zj5kcx78g7f5nmw0000gp/T/ipykernel_16930/630142521.py:10: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  column = column.str.replace(\"[^a-z ]\", \"\")\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>prefix</th>\n",
       "      <th>hours</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Course Introduction, History, and Tools for Co...</td>\n",
       "      <td>INT</td>\n",
       "      <td>0.32</td>\n",
       "      <td>welcom qualiti system control cours throughout...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Variation, Normal Distribution, Sampling, Popu...</td>\n",
       "      <td>MATH</td>\n",
       "      <td>2.08</td>\n",
       "      <td>modul explor fundament concept variat normal d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control Charts for Variables</td>\n",
       "      <td>CC</td>\n",
       "      <td>1.87</td>\n",
       "      <td>modul dive variabl control chart control limit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Start, Interpret X and R Control Charts, Trial...</td>\n",
       "      <td>CC</td>\n",
       "      <td>0.98</td>\n",
       "      <td>modul begin journey world control chart focus ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rational Subgroups, Process Capability Assessment</td>\n",
       "      <td>MATH</td>\n",
       "      <td>1.48</td>\n",
       "      <td>modul explor import ration subgroup control ch...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name prefix  hours  \\\n",
       "0  Course Introduction, History, and Tools for Co...    INT   0.32   \n",
       "1  Variation, Normal Distribution, Sampling, Popu...   MATH   2.08   \n",
       "2                       Control Charts for Variables     CC   1.87   \n",
       "3  Start, Interpret X and R Control Charts, Trial...     CC   0.98   \n",
       "4  Rational Subgroups, Process Capability Assessment   MATH   1.48   \n",
       "\n",
       "                                              tokens  \n",
       "0  welcom qualiti system control cours throughout...  \n",
       "1  modul explor fundament concept variat normal d...  \n",
       "2  modul dive variabl control chart control limit...  \n",
       "3  modul begin journey world control chart focus ...  \n",
       "4  modul explor import ration subgroup control ch...  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testDF['cleaned_feature'] = _text_processing(testDF['features'])\n",
    "\n",
    "testDF['tokens'] = testDF['cleaned_feature'].apply(lambda x: get_tokens(x))\n",
    "\n",
    "testDF.drop(['features', 'cleaned_feature', 'cluster'], axis=1, inplace=True)\n",
    "\n",
    "testDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster CC\n",
      "whole,math,oper,fraction,decim,percentag,number,exponenti,arithmet,logarithm\n",
      "\n",
      "Cluster CTR\n",
      "post,graphic,simplex,uncertainti,model,analysi,sensit,method,lp,comput\n",
      "\n",
      "Cluster DA\n",
      "mathemat,solv,main,begin,maxim,model,minim,problem,program,linear\n",
      "\n",
      "Cluster INT\n",
      "techniqu,variabl,scienc,build,relationship,model,predict,statist,linear,regress\n",
      "\n",
      "Cluster MATH\n",
      "test,theorem,vector,space,scienc,data,analysi,euclidean,statist,distanc\n",
      "\n",
      "Cluster Q\n",
      "environment,form,train,boat,buse,car,reallif,economi,transport,transship\n"
     ]
    },
    {
     "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>Cluster CC</th>\n",
       "      <th>Cluster CTR</th>\n",
       "      <th>Cluster DA</th>\n",
       "      <th>Cluster INT</th>\n",
       "      <th>Cluster MATH</th>\n",
       "      <th>Cluster Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>whole</td>\n",
       "      <td>post</td>\n",
       "      <td>mathemat</td>\n",
       "      <td>techniqu</td>\n",
       "      <td>test</td>\n",
       "      <td>environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>math</td>\n",
       "      <td>graphic</td>\n",
       "      <td>solv</td>\n",
       "      <td>variabl</td>\n",
       "      <td>theorem</td>\n",
       "      <td>form</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oper</td>\n",
       "      <td>simplex</td>\n",
       "      <td>main</td>\n",
       "      <td>scienc</td>\n",
       "      <td>vector</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fraction</td>\n",
       "      <td>uncertainti</td>\n",
       "      <td>begin</td>\n",
       "      <td>build</td>\n",
       "      <td>space</td>\n",
       "      <td>boat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>decim</td>\n",
       "      <td>model</td>\n",
       "      <td>maxim</td>\n",
       "      <td>relationship</td>\n",
       "      <td>scienc</td>\n",
       "      <td>buse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>percentag</td>\n",
       "      <td>analysi</td>\n",
       "      <td>model</td>\n",
       "      <td>model</td>\n",
       "      <td>data</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>number</td>\n",
       "      <td>sensit</td>\n",
       "      <td>minim</td>\n",
       "      <td>predict</td>\n",
       "      <td>analysi</td>\n",
       "      <td>reallif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>exponenti</td>\n",
       "      <td>method</td>\n",
       "      <td>problem</td>\n",
       "      <td>statist</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>economi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arithmet</td>\n",
       "      <td>lp</td>\n",
       "      <td>program</td>\n",
       "      <td>linear</td>\n",
       "      <td>statist</td>\n",
       "      <td>transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>logarithm</td>\n",
       "      <td>comput</td>\n",
       "      <td>linear</td>\n",
       "      <td>regress</td>\n",
       "      <td>distanc</td>\n",
       "      <td>transship</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cluster CC  Cluster CTR Cluster DA   Cluster INT Cluster MATH    Cluster Q\n",
       "0      whole         post   mathemat      techniqu         test  environment\n",
       "1       math      graphic       solv       variabl      theorem         form\n",
       "2       oper      simplex       main        scienc       vector        train\n",
       "3   fraction  uncertainti      begin         build        space         boat\n",
       "4      decim        model      maxim  relationship       scienc         buse\n",
       "5  percentag      analysi      model         model         data          car\n",
       "6     number       sensit      minim       predict      analysi      reallif\n",
       "7  exponenti       method    problem       statist    euclidean      economi\n",
       "8   arithmet           lp    program        linear      statist    transport\n",
       "9  logarithm       comput     linear       regress      distanc    transship"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_top_keywords(10, testDF['prefix'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_word_cloud(testDF['tokens'].to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:29:00.767655Z",
     "start_time": "2023-05-25T18:29:00.749489Z"
    },
    "collapsed": false
   },
   "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>name</th>\n",
       "      <th>prefix</th>\n",
       "      <th>hours</th>\n",
       "      <th>tokens</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Course Introduction, History, and Tools for Co...</td>\n",
       "      <td>INT</td>\n",
       "      <td>0.32</td>\n",
       "      <td>welcom qualiti system control cours throughout...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Variation, Normal Distribution, Sampling, Popu...</td>\n",
       "      <td>MATH</td>\n",
       "      <td>2.08</td>\n",
       "      <td>modul explor fundament concept variat normal d...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control Charts for Variables</td>\n",
       "      <td>CC</td>\n",
       "      <td>1.87</td>\n",
       "      <td>modul dive variabl control chart control limit...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Start, Interpret X and R Control Charts, Trial...</td>\n",
       "      <td>CC</td>\n",
       "      <td>0.98</td>\n",
       "      <td>modul begin journey world control chart focus ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rational Subgroups, Process Capability Assessment</td>\n",
       "      <td>MATH</td>\n",
       "      <td>1.48</td>\n",
       "      <td>modul explor import ration subgroup control ch...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Statistics for Control Charts for Attributes</td>\n",
       "      <td>CTR</td>\n",
       "      <td>0.35</td>\n",
       "      <td>chapter shift focu control chart attribut use ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Control Charts for Fraction Defective</td>\n",
       "      <td>CTR</td>\n",
       "      <td>0.62</td>\n",
       "      <td>chapter delv control chart specif design monit...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Design and Analysis of Experiments</td>\n",
       "      <td>DA</td>\n",
       "      <td>1.97</td>\n",
       "      <td>chapter embark journey realm experiment design...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Quality Standards</td>\n",
       "      <td>Q</td>\n",
       "      <td>0.35</td>\n",
       "      <td>chapter delv world qualiti standard signific e...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Quality Function Deployment</td>\n",
       "      <td>Q</td>\n",
       "      <td>1.04</td>\n",
       "      <td>chapter dive qualiti function deploy qfd power...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name prefix  hours  \\\n",
       "0  Course Introduction, History, and Tools for Co...    INT   0.32   \n",
       "1  Variation, Normal Distribution, Sampling, Popu...   MATH   2.08   \n",
       "2                       Control Charts for Variables     CC   1.87   \n",
       "3  Start, Interpret X and R Control Charts, Trial...     CC   0.98   \n",
       "4  Rational Subgroups, Process Capability Assessment   MATH   1.48   \n",
       "5       Statistics for Control Charts for Attributes    CTR   0.35   \n",
       "6              Control Charts for Fraction Defective    CTR   0.62   \n",
       "7                 Design and Analysis of Experiments     DA   1.97   \n",
       "8                                  Quality Standards      Q   0.35   \n",
       "9                        Quality Function Deployment      Q   1.04   \n",
       "\n",
       "                                              tokens  cluster  \n",
       "0  welcom qualiti system control cours throughout...        2  \n",
       "1  modul explor fundament concept variat normal d...        2  \n",
       "2  modul dive variabl control chart control limit...        2  \n",
       "3  modul begin journey world control chart focus ...        2  \n",
       "4  modul explor import ration subgroup control ch...        2  \n",
       "5  chapter shift focu control chart attribut use ...        2  \n",
       "6  chapter delv control chart specif design monit...        2  \n",
       "7  chapter embark journey realm experiment design...        2  \n",
       "8  chapter delv world qualiti standard signific e...        2  \n",
       "9  chapter dive qualiti function deploy qfd power...        2  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = vectorizer.transform(testDF['tokens'])\n",
    "\n",
    "testDF['cluster'] = kmeans.predict(X_test)\n",
    "\n",
    "testDF.head(20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Exporting to JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:08:02.439593Z",
     "start_time": "2023-05-25T18:08:02.438184Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"collections\": [\n",
      "    {\n",
      "      \"id\": \"6ab6cf0735004d0f889d66652ccf4883\",\n",
      "      \"number\": 0,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"64627a0e39769687f2a6d872\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"64627a2739769687f2a6d873\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"64627a3b39769687f2a6d874\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"64627c5239769687f2a6d875\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f68019c4d441f6ec38\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f78019c4d441f6ec39\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f78019c4d441f6ec3a\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f78019c4d441f6ec3b\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f78019c4d441f6ec3c\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"c95ae2a0ddd24b4883c5bfad9cb5e795\",\n",
      "      \"number\": 1,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"64511f4bf9ce74323575ffff\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec0f\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c38019c4d441f6ec12\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f18019c4d441f6ec13\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f18019c4d441f6ec14\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f18019c4d441f6ec15\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec18\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec1a\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec24\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"c6b4169f14334b7f82925da8775fa4e7\",\n",
      "      \"number\": 2,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"644827afc3faa4c9ea96a800\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"64496079d5fb014e40d5ea39\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"644960add5fb014e40d5ea3a\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"644960bcd5fb014e40d5ea3b\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"64511f49f9ce74323575fffe\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646273ce39769687f2a6d860\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462747b39769687f2a6d864\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646274e939769687f2a6d867\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec0a\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec0b\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec0c\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec0d\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec0e\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec10\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec19\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec1b\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec1c\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec27\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec29\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"13b5322241bc4c19b295750433255639\",\n",
      "      \"number\": 3,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"646270b939769687f2a6d858\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462756339769687f2a6d86a\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506c28019c4d441f6ec11\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f18019c4d441f6ec16\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec17\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec2c\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec2d\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec2e\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f58019c4d441f6ec30\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"b87216ecb2ce4036af2c6878188da7cb\",\n",
      "      \"number\": 4,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"64496111d5fb014e40d5ea3c\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462741d39769687f2a6d862\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462745c39769687f2a6d863\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462749a39769687f2a6d865\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646275ce39769687f2a6d86d\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646275e739769687f2a6d86e\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec1d\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec26\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f58019c4d441f6ec31\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f58019c4d441f6ec32\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"5488f2846d3d4d0ea62ffaaaf0b0b1d7\",\n",
      "      \"number\": 5,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"646274c539769687f2a6d866\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462750d39769687f2a6d868\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462752e39769687f2a6d869\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646275a839769687f2a6d86c\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec28\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec2a\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f48019c4d441f6ec2b\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f58019c4d441f6ec2f\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f58019c4d441f6ec33\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f68019c4d441f6ec37\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"2665370e11a241e9a8f08e7aad46928b\",\n",
      "      \"number\": 6,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"6462758c39769687f2a6d86b\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f28019c4d441f6ec1e\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec1f\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec20\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec21\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec22\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec23\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"309ebc42d7af4dab98e27a051ab68643\",\n",
      "      \"number\": 7,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"646273e539769687f2a6d861\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f38019c4d441f6ec25\"\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"1cf2fc074b2246068504800822e97bb8\",\n",
      "      \"number\": 8,\n",
      "      \"modules\": [\n",
      "        {\n",
      "          \"id\": \"6462763e39769687f2a6d86f\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462765939769687f2a6d870\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"6462768539769687f2a6d871\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f68019c4d441f6ec34\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f68019c4d441f6ec35\"\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"646506f68019c4d441f6ec36\"\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import uuid\n",
    "\n",
    "# create a json list of objects from the moduleDF where each module is grouped by the cluster number.\n",
    "# The result should be a list of collection objects with a modules key that is representative of the cluster's modules\n",
    "\n",
    "# create a list of clusters\n",
    "clusters = moduleDF['cluster'].unique().tolist()\n",
    "\n",
    "# create a list of modules\n",
    "modules = moduleDF['id'].unique().tolist()\n",
    "\n",
    "jsonList = []\n",
    "\n",
    "for cluster in clusters:\n",
    "    jsonList.append({\n",
    "        'id': uuid.uuid4().hex,\n",
    "        'number': cluster,\n",
    "        'modules': [\n",
    "             # create a list of objects for each module in the cluster with a single key of id\n",
    "            {\n",
    "                'id': module\n",
    "            } for module in modules if moduleDF.loc[moduleDF['id'] == module, 'cluster'].iloc[0] == cluster\n",
    "        ]\n",
    "    })\n",
    "\n",
    "payload = {\n",
    "    \"collections\": sorted(jsonList, key=lambda x: x['number'])\n",
    "}\n",
    "\n",
    "json_payload = json.dumps(payload, indent=2)\n",
    "\n",
    "print(json_payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:08:13.693817Z",
     "start_time": "2023-05-25T18:08:13.688393Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save json payload as json file\n",
    "\n",
    "with open('enma603.json', 'w') as outfile:\n",
    "    json.dump(payload, outfile, indent=2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "TODO:\n",
    "1. Separate the clusters into groups of sections based on the similarity of the modules within the cluster. More similar clusters should be grouped together\n",
    "2. Map the created sections to existing sections for the given course from the DB\n",
    "3. Generate a learning path for the single course, it's sections, it's collections, and it's modules\n",
    "4. Generate learning outcomes for the course, based on the sections that are mapped to the course\n",
    "5. Aggregate the number of hours for the entire path and add the value to the hours satisfies property in the final path json object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:11:13.686948Z",
     "start_time": "2023-05-25T18:11:13.633414Z"
    },
    "collapsed": false
   },
   "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>2</th>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>7</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.262580</td>\n",
       "      <td>0.417880</td>\n",
       "      <td>0.435551</td>\n",
       "      <td>0.070842</td>\n",
       "      <td>0.274199</td>\n",
       "      <td>0.272186</td>\n",
       "      <td>0.166550</td>\n",
       "      <td>0.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.262580</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.225219</td>\n",
       "      <td>0.312652</td>\n",
       "      <td>0.072436</td>\n",
       "      <td>0.187374</td>\n",
       "      <td>0.288460</td>\n",
       "      <td>0.064380</td>\n",
       "      <td>0.056984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.417880</td>\n",
       "      <td>0.225219</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.582497</td>\n",
       "      <td>0.042266</td>\n",
       "      <td>0.246996</td>\n",
       "      <td>0.310326</td>\n",
       "      <td>0.064578</td>\n",
       "      <td>0.069159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.435551</td>\n",
       "      <td>0.312652</td>\n",
       "      <td>0.582497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059885</td>\n",
       "      <td>0.379217</td>\n",
       "      <td>0.576806</td>\n",
       "      <td>0.072655</td>\n",
       "      <td>0.068441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.070842</td>\n",
       "      <td>0.072436</td>\n",
       "      <td>0.042266</td>\n",
       "      <td>0.059885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046001</td>\n",
       "      <td>0.051854</td>\n",
       "      <td>0.034702</td>\n",
       "      <td>0.025795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.274199</td>\n",
       "      <td>0.187374</td>\n",
       "      <td>0.246996</td>\n",
       "      <td>0.379217</td>\n",
       "      <td>0.046001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325355</td>\n",
       "      <td>0.062507</td>\n",
       "      <td>0.062940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.272186</td>\n",
       "      <td>0.288460</td>\n",
       "      <td>0.310326</td>\n",
       "      <td>0.576806</td>\n",
       "      <td>0.051854</td>\n",
       "      <td>0.325355</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053846</td>\n",
       "      <td>0.049148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.166550</td>\n",
       "      <td>0.064380</td>\n",
       "      <td>0.064578</td>\n",
       "      <td>0.072655</td>\n",
       "      <td>0.034702</td>\n",
       "      <td>0.062507</td>\n",
       "      <td>0.053846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.123453</td>\n",
       "      <td>0.056984</td>\n",
       "      <td>0.069159</td>\n",
       "      <td>0.068441</td>\n",
       "      <td>0.025795</td>\n",
       "      <td>0.062940</td>\n",
       "      <td>0.049148</td>\n",
       "      <td>0.107547</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2         4         1         3         7         5         6  \\\n",
       "2  1.000000  0.262580  0.417880  0.435551  0.070842  0.274199  0.272186   \n",
       "4  0.262580  1.000000  0.225219  0.312652  0.072436  0.187374  0.288460   \n",
       "1  0.417880  0.225219  1.000000  0.582497  0.042266  0.246996  0.310326   \n",
       "3  0.435551  0.312652  0.582497  1.000000  0.059885  0.379217  0.576806   \n",
       "7  0.070842  0.072436  0.042266  0.059885  1.000000  0.046001  0.051854   \n",
       "5  0.274199  0.187374  0.246996  0.379217  0.046001  1.000000  0.325355   \n",
       "6  0.272186  0.288460  0.310326  0.576806  0.051854  0.325355  1.000000   \n",
       "8  0.166550  0.064380  0.064578  0.072655  0.034702  0.062507  0.053846   \n",
       "0  0.123453  0.056984  0.069159  0.068441  0.025795  0.062940  0.049148   \n",
       "\n",
       "          8         0  \n",
       "2  0.166550  0.123453  \n",
       "4  0.064380  0.056984  \n",
       "1  0.064578  0.069159  \n",
       "3  0.072655  0.068441  \n",
       "7  0.034702  0.025795  \n",
       "5  0.062507  0.062940  \n",
       "6  0.053846  0.049148  \n",
       "8  1.000000  0.107547  \n",
       "0  0.107547  1.000000  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the similarity between each collection\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "cluster_similarity_df = pd.DataFrame(columns=clusters, index=clusters)\n",
    "\n",
    "for cluster in clusters:\n",
    "    for cluster2 in clusters:\n",
    "        cluster_similarity_df.loc[cluster, cluster2] = cosine_similarity(\n",
    "            np.asarray(X[moduleDF.loc[moduleDF['cluster'] == cluster].index.tolist()].mean(axis=0)),\n",
    "            np.asarray(X[moduleDF.loc[moduleDF['cluster'] == cluster2].index.tolist()].mean(axis=0))\n",
    "        )[0][0]\n",
    "\n",
    "cluster_similarity_df = cluster_similarity_df.astype(float)\n",
    "\n",
    "cluster_similarity_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:11:23.679997Z",
     "start_time": "2023-05-25T18:11:23.504882Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the similarity between clusters\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "sns.heatmap(cluster_similarity_df, annot=True, ax=ax)\n",
    "\n",
    "ax.set_title('ENMA 603 - Cluster Similarity')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "From the heatmap we can see that\n",
    "- clusters 3, 6 and 1 are the most similar to each other\n",
    "- clusters 2, 4 and 5 are somewhat similar to each other\n",
    "- clusters 0, 7 and 8 are the least similar to every other cluster, so they should be seperated into their own sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:42:45.627484Z",
     "start_time": "2023-05-25T18:42:45.617254Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 1, 3, 6, 4, 5], [4, 3, 2, 1, 6, 5], [7], [8], [0]]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a list of sections based on the similarity of the clusters\n",
    "# each cluster should only be in a single section\n",
    "# the sections should be a list of clusters\n",
    "\n",
    "sections = []\n",
    "\n",
    "for cluster in clusters:\n",
    "    if cluster not in [item for sublist in sections for item in sublist]:\n",
    "        sections.append([cluster])\n",
    "\n",
    "    for cluster2 in clusters:\n",
    "        if cluster != cluster2 and cluster_similarity_df.loc[cluster, cluster2] > 0.3:\n",
    "            for section in sections:\n",
    "                if cluster in section and cluster2 not in section:\n",
    "                    section.append(cluster2)\n",
    "                    break\n",
    "                else:\n",
    "                    if cluster not in [item for sublist in sections for item in sublist]:\n",
    "                        sections.append([cluster])\n",
    "                    if cluster2 not in [item for sublist in sections for item in sublist]:\n",
    "                        sections.append([cluster2])\n",
    "\n",
    "sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:40:16.103018Z",
     "start_time": "2023-05-25T18:40:16.064574Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"id\": \"bab976bce16e4d339f00bf94fd92fac9\",\n",
      "  \"sections\": [\n",
      "    {\n",
      "      \"id\": \"ca5c7428c4164d43a0536a60d17f6212\",\n",
      "      \"number\": 0,\n",
      "      \"collections\": [\n",
      "        {\n",
      "          \"id\": \"21d5b06a471f4a77919fff5d166376c2\",\n",
      "          \"cluster\": 2,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"644827afc3faa4c9ea96a800\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64496079d5fb014e40d5ea39\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"644960add5fb014e40d5ea3a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"644960bcd5fb014e40d5ea3b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64511f49f9ce74323575fffe\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646273ce39769687f2a6d860\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462747b39769687f2a6d864\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646274e939769687f2a6d867\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec10\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec19\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec27\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec29\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"08447348fab74c78b948f5d17ee564a6\",\n",
      "          \"cluster\": 1,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"64511f4bf9ce74323575ffff\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c38019c4d441f6ec12\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec13\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec14\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec15\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec18\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec24\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"5f2c69862755497db1910b7455d66bab\",\n",
      "          \"cluster\": 3,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"646270b939769687f2a6d858\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462756339769687f2a6d86a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec11\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec16\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec17\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec30\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"61714924c21f4d888748597f7bb39ae7\",\n",
      "          \"cluster\": 6,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"6462758c39769687f2a6d86b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec1f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec20\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec21\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec22\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec23\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"a2621578949a49cfb64258a5685250d5\",\n",
      "          \"cluster\": 4,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"64496111d5fb014e40d5ea3c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462741d39769687f2a6d862\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462745c39769687f2a6d863\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462749a39769687f2a6d865\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646275ce39769687f2a6d86d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646275e739769687f2a6d86e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec26\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec31\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec32\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"49f9c86d08e349dfbc7e917b50885396\",\n",
      "          \"cluster\": 5,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"646274c539769687f2a6d866\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462750d39769687f2a6d868\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462752e39769687f2a6d869\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646275a839769687f2a6d86c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec28\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec2f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec33\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f68019c4d441f6ec37\"\n",
      "            }\n",
      "          ]\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"48df09aa4ccf4d4aa816eb30aa7d42e5\",\n",
      "      \"number\": 1,\n",
      "      \"collections\": [\n",
      "        {\n",
      "          \"id\": \"5112f22d6ddd42bf93288d859d31bb3e\",\n",
      "          \"cluster\": 4,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"64496111d5fb014e40d5ea3c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462741d39769687f2a6d862\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462745c39769687f2a6d863\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462749a39769687f2a6d865\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646275ce39769687f2a6d86d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646275e739769687f2a6d86e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec26\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec31\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec32\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"bdd9ad11775f4c7d9cd6aa765a1f05f5\",\n",
      "          \"cluster\": 3,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"646270b939769687f2a6d858\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462756339769687f2a6d86a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec11\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec16\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec17\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec30\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"05a79e45fb634fb1b23724907af10fcc\",\n",
      "          \"cluster\": 2,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"644827afc3faa4c9ea96a800\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64496079d5fb014e40d5ea39\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"644960add5fb014e40d5ea3a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"644960bcd5fb014e40d5ea3b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64511f49f9ce74323575fffe\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646273ce39769687f2a6d860\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462747b39769687f2a6d864\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646274e939769687f2a6d867\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0d\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec10\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec19\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec27\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec29\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"e774040e0faa40d2ace8f2da8884be03\",\n",
      "          \"cluster\": 1,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"64511f4bf9ce74323575ffff\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c28019c4d441f6ec0f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506c38019c4d441f6ec12\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec13\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec14\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f18019c4d441f6ec15\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec18\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec24\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"70d2628cd9164cae884c7eba6c7589e2\",\n",
      "          \"cluster\": 6,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"6462758c39769687f2a6d86b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f28019c4d441f6ec1e\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec1f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec20\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec21\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec22\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec23\"\n",
      "            }\n",
      "          ]\n",
      "        },\n",
      "        {\n",
      "          \"id\": \"1c79687f0d01497583f056d88d84c633\",\n",
      "          \"cluster\": 5,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"646274c539769687f2a6d866\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462750d39769687f2a6d868\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462752e39769687f2a6d869\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646275a839769687f2a6d86c\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec28\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f48019c4d441f6ec2b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec2f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f58019c4d441f6ec33\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f68019c4d441f6ec37\"\n",
      "            }\n",
      "          ]\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"ed479211f69146f285e3dd6ce4e21e98\",\n",
      "      \"number\": 2,\n",
      "      \"collections\": [\n",
      "        {\n",
      "          \"id\": \"26c3ee28002b4062a75d2e642c0e8f4b\",\n",
      "          \"cluster\": 7,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"646273e539769687f2a6d861\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f38019c4d441f6ec25\"\n",
      "            }\n",
      "          ]\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"559c198783514ac28a3e7e5603471651\",\n",
      "      \"number\": 3,\n",
      "      \"collections\": [\n",
      "        {\n",
      "          \"id\": \"a5554570f4be4c93a704ea21f48c4653\",\n",
      "          \"cluster\": 8,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"6462763e39769687f2a6d86f\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462765939769687f2a6d870\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"6462768539769687f2a6d871\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f68019c4d441f6ec34\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f68019c4d441f6ec35\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f68019c4d441f6ec36\"\n",
      "            }\n",
      "          ]\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"5116c95a8b2c415788eb4505c8bcd7cf\",\n",
      "      \"number\": 4,\n",
      "      \"collections\": [\n",
      "        {\n",
      "          \"id\": \"fdbfb459d46f43fa93c8a86e02c5d65c\",\n",
      "          \"cluster\": 0,\n",
      "          \"modules\": [\n",
      "            {\n",
      "              \"id\": \"64627a0e39769687f2a6d872\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64627a2739769687f2a6d873\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64627a3b39769687f2a6d874\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"64627c5239769687f2a6d875\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f68019c4d441f6ec38\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f78019c4d441f6ec39\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f78019c4d441f6ec3a\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f78019c4d441f6ec3b\"\n",
      "            },\n",
      "            {\n",
      "              \"id\": \"646506f78019c4d441f6ec3c\"\n",
      "            }\n",
      "          ]\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# convert the sections list to a json object with its list of clusters and each clusters list of modules\n",
    "# TODO: filter out duplicate clusters from the sections list before converting to json\n",
    "\n",
    "jsonList = []\n",
    "\n",
    "for section in sections:\n",
    "    jsonList.append({\n",
    "        'id': uuid.uuid4().hex,\n",
    "        'number': sections.index(section),\n",
    "        'collections': [\n",
    "            {\n",
    "                'id': uuid.uuid4().hex,\n",
    "                'cluster': cluster,\n",
    "                'modules': [\n",
    "                    {\n",
    "                        'id': module\n",
    "                    } for module in modules if moduleDF.loc[moduleDF['id'] == module, 'cluster'].iloc[0] == cluster\n",
    "                ]\n",
    "            } for cluster in section\n",
    "        ]\n",
    "    })\n",
    "\n",
    "payload = {\n",
    "    'id': uuid.uuid4().hex,\n",
    "    \"sections\": sorted(jsonList, key=lambda x: x['number'])\n",
    "}\n",
    "\n",
    "json_payload = json.dumps(payload, indent=2)\n",
    "\n",
    "print(json_payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-25T18:40:40.254210Z",
     "start_time": "2023-05-25T18:40:40.230653Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save json payload as json file\n",
    "\n",
    "with open('enma603_sections.json', 'w') as outfile:\n",
    "    json.dump(payload, outfile, indent=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}