{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b629b764-2854-40ce-ab2a-1192d0825b7e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Scikit-Learn \n",
    "## Learning Objectives\n",
    "- Learn how to import and use Scikit-Learn in Python\n",
    "- Understand the basic concepts and purpose of Scikit-Learn in machine learning tasks\n",
    "- Implement linear regression, k-means clustering, and decision tree models using Scikit-Learn\n",
    "- Understand the applications and limitations of each model\n",
    "- Learn how to clean and preprocess data using pandas before feeding it into machine-learning models\n",
    "- Understand the importance of handling missing values and feature engineering\n",
    "- Learn how to evaluate the performance of different machine-learning models\n",
    "- Interpret the output of models to make informed decisions based on the data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fd7b159-8e6f-4894-b1a2-afd7c405ee08",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import the packages used before and read in the required data\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "air_pollution_data_2023_complete_dataset = pd.read_csv(\"data/LEED_air_pollution_monitoring_station_2023_complete_dataset.csv\", index_col=0)\n",
    "air_pollution_data_2023_complete_dataset = air_pollution_data_2023_complete_dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da886804-40e5-445f-a61d-1a80a84a9558",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scikit learn can be imported with the the following command\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601a8215-a645-4fe6-8334-1dc7434a589b",
   "metadata": {},
   "source": [
    "## What is Scikit-Learn?\n",
    "\n",
    "Scikit-Learn is a popular Python package that provides a set of algorithms and tools for machine learning that are both easy to use and effective. The package includes support for various tasks, including classification, regression, clustering, dimensionality reduction and model selection and normalization.\n",
    "\n",
    "In scikit-learn the models that are avaliable include a massive number of possible arguements, and so for the purpose of this course the default arguements have been used. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46f26ec1-c4a5-42f3-9f50-1ad40b0021f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "air_pollution_data_2023_complete_dataset[\"date\"] = pd.to_datetime(air_pollution_data_2023_complete_dataset[\"date\"], format=\"%d/%m/%Y %H:%M\")\n",
    "air_pollution_data_2023_complete_dataset[\"Hour\"] = air_pollution_data_2023_complete_dataset[\"date\"].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9950fa3b-8d1d-403d-960a-bf761f3a654a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>NO</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>site</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24664</th>\n",
       "      <td>2023-01-01 01:00:00</td>\n",
       "      <td>7.30306</td>\n",
       "      <td>76.61852</td>\n",
       "      <td>1.22702</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Leeds Centre</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24665</th>\n",
       "      <td>2023-01-01 02:00:00</td>\n",
       "      <td>4.31351</td>\n",
       "      <td>79.67418</td>\n",
       "      <td>0.82507</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Leeds Centre</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24666</th>\n",
       "      <td>2023-01-01 03:00:00</td>\n",
       "      <td>2.95539</td>\n",
       "      <td>81.50758</td>\n",
       "      <td>0.79333</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>Leeds Centre</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24667</th>\n",
       "      <td>2023-01-01 04:00:00</td>\n",
       "      <td>2.08340</td>\n",
       "      <td>81.45519</td>\n",
       "      <td>0.53947</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Leeds Centre</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24669</th>\n",
       "      <td>2023-01-01 06:00:00</td>\n",
       "      <td>2.95643</td>\n",
       "      <td>82.71238</td>\n",
       "      <td>0.57120</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Leeds Centre</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date      NO2        O3       NO  Wind Speed  \\\n",
       "24664 2023-01-01 01:00:00  7.30306  76.61852  1.22702         4.9   \n",
       "24665 2023-01-01 02:00:00  4.31351  79.67418  0.82507         7.0   \n",
       "24666 2023-01-01 03:00:00  2.95539  81.50758  0.79333         7.3   \n",
       "24667 2023-01-01 04:00:00  2.08340  81.45519  0.53947         7.2   \n",
       "24669 2023-01-01 06:00:00  2.95643  82.71238  0.57120         6.8   \n",
       "\n",
       "       Temperature          site    Year  Hour  \n",
       "24664          7.2  Leeds Centre  2023.0     1  \n",
       "24665          7.5  Leeds Centre  2023.0     2  \n",
       "24666          7.3  Leeds Centre  2023.0     3  \n",
       "24667          7.2  Leeds Centre  2023.0     4  \n",
       "24669          7.1  Leeds Centre  2023.0     6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(air_pollution_data_2023_complete_dataset.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f70a32-37f1-49a3-8f25-c213e4fd6102",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "Linear regression is a simple model that is able to predict a dependent variable based on one or more independent variables, with the assumption of a linear relationship between the two. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff0b337d-d984-40cc-82e9-89f7a45c7ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lb788/miniconda3/envs/CfRR/lib/python3.9/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept of the regression line: 25.91496846131205\n",
      "Slope of the regression line: -1.8904584011013008\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "# Create some simple data\n",
    "X = air_pollution_data_2023_complete_dataset[[\"Wind Speed\"]]\n",
    "y = air_pollution_data_2023_complete_dataset[[\"NO2\"]]\n",
    "\n",
    "# Train a linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Plotting the data points\n",
    "plt.scatter(X, y, color='blue', label='Observations', alpha=0.05)\n",
    "\n",
    "# Predicting the values to draw the regression line\n",
    "# We use minimum and maximum values of X to cover the whole range of data\n",
    "X_new = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)  # Making it a column vector\n",
    "y_predict = model.predict(X_new)\n",
    "\n",
    "# Plotting the regression line\n",
    "plt.plot(X_new, y_predict, color='red', linewidth=2, label='Regression Line')\n",
    "\n",
    "# Adding title and labels\n",
    "plt.title('Relationship Between Wind Speed and NO$_2$ Levels')\n",
    "plt.xlabel('Wind Speed m/s')\n",
    "plt.ylabel('NO$_2$ Concentration')\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Extracting and printing the intercept and slope\n",
    "intercept = model.intercept_\n",
    "slope = model.coef_\n",
    "\n",
    "print(\"Intercept of the regression line:\", intercept[0])  # Intercept is usually an array with a single element\n",
    "print(\"Slope of the regression line:\", slope[0][0])  # Slope is an array of arrays, each containing one element per feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c47368e9-c0c4-4ebf-a50a-96d64df0cae9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "               data-shuffleanswers=\"True\"\n",
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       "               data-numquestions=\"1000000\"\n",
       "               data-maxwidth=\"600\"\n",
       "               style=\"border-radius: 10px; text-align: left\"> <style>\n",
       "#UtSYwillTNTw {\n",
       "   --jq-multiple-choice-bg: #6f78ffff;\n",
       "   --jq-mc-button-bg: #fafafa;\n",
       "   --jq-mc-button-border: #e0e0e0e0;\n",
       "   --jq-mc-button-inset-shadow: #555555;\n",
       "   --jq-many-choice-bg: #f75c03ff;\n",
       "   --jq-numeric-bg: #392061ff;\n",
       "   --jq-numeric-input-bg: #c0c0c0;\n",
       "   --jq-numeric-input-label: #101010;\n",
       "   --jq-numeric-input-shadow: #999999;\n",
       "   --jq-incorrect-color: #c80202;\n",
       "   --jq-correct-color: #009113;\n",
       "   --jq-text-color: #fafafa;\n",
       "}\n",
       "\n",
       ".Quiz {\n",
       "    max-width: 600px;\n",
       "    margin-top: 15px;\n",
       "    margin-left: auto;\n",
       "    margin-right: auto;\n",
       "    margin-bottom: 15px;\n",
       "    padding-bottom: 4px;\n",
       "    padding-top: 4px;\n",
       "    line-height: 1.1;\n",
       "    font-size: 16pt;\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".QuizCode {\n",
       "    font-size: 14pt;\n",
       "    margin-top: 10px;\n",
       "    margin-left: 20px;\n",
       "    margin-right: 20px;\n",
       "}\n",
       "\n",
       ".QuizCode>pre {\n",
       "    padding: 4px;\n",
       "}\n",
       "\n",
       ".Answer {\n",
       "    margin: 10px 0;\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 1fr;\n",
       "    grid-gap: 10px;\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".Feedback {\n",
       "    font-size: 16pt;\n",
       "    text-align: center;\n",
       "    min-height: 2em;\n",
       "}\n",
       "\n",
       ".Input {\n",
       "    align: left;\n",
       "    font-size: 20pt;\n",
       "}\n",
       "\n",
       ".Input-text {\n",
       "    display: block;\n",
       "    margin: 10px;\n",
       "    color: inherit;\n",
       "    width: 140px;\n",
       "    background-color: var(--jq-numeric-input-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    padding: 5px;\n",
       "    padding-left: 10px;\n",
       "    font-family: inherit;\n",
       "    font-size: 20px;\n",
       "    font-weight: inherit;\n",
       "    line-height: 20pt;\n",
       "    border: none;\n",
       "    border-radius: 0.2rem;\n",
       "    transition: box-shadow 0.1s);\n",
       "}\n",
       "\n",
       ".Input-text:focus {\n",
       "    outline: none;\n",
       "    background-color: var(--jq-numeric-input-bg);\n",
       "    box-shadow: 0.6rem 0.8rem 1.4rem -0.5rem var(--jq-numeric-input-shadow);\n",
       "}\n",
       "\n",
       ".MCButton {\n",
       "    background: var(--jq-mc-button-bg);\n",
       "    border: 1px solid var(--jq-mc-button-border);\n",
       "    border-radius: inherit;\n",
       "    padding: 10px;\n",
       "    font-size: 16px;\n",
       "    cursor: pointer;\n",
       "    text-align: center;\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    justify-content: center;\n",
       "}\n",
       "\n",
       ".MCButton p {\n",
       "    color: inherit;\n",
       "}\n",
       "\n",
       ".MultipleChoiceQn {\n",
       "    padding: 10px;\n",
       "    background: var(--jq-multiple-choice-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".ManyChoiceQn {\n",
       "    padding: 10px;\n",
       "    background: var(--jq-many-choice-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".NumericQn {\n",
       "    padding: 10px;\n",
       "    background: var(--jq-numeric-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".NumericQn p {\n",
       "    color: inherit;\n",
       "}\n",
       "\n",
       ".InpLabel {\n",
       "    line-height: 34px;\n",
       "    float: left;\n",
       "    margin-right: 10px;\n",
       "    color: var(--jq-numeric-input-label);\n",
       "    font-size: 15pt;\n",
       "}\n",
       "\n",
       ".incorrect {\n",
       "    color: var(--jq-incorrect-color);\n",
       "}\n",
       "\n",
       ".correct {\n",
       "    color: var(--jq-correct-color);\n",
       "}\n",
       "\n",
       ".correctButton {\n",
       "    /*\n",
       "    background: var(--jq-correct-color);\n",
       "   */\n",
       "    animation: correct-anim 0.6s ease;\n",
       "    animation-fill-mode: forwards;\n",
       "    color: var(--jq-text-color);\n",
       "    box-shadow: inset 0px 0px 5px var(--jq-mc-button-inset-shadow);\n",
       "    outline: none;\n",
       "}\n",
       "\n",
       ".incorrectButton {\n",
       "    animation: incorrect-anim 0.8s ease;\n",
       "    animation-fill-mode: forwards;\n",
       "    color: var(--jq-text-color);\n",
       "    box-shadow: inset 0px 0px 5px var(--jq-mc-button-inset-shadow);\n",
       "    outline: none;\n",
       "}\n",
       "\n",
       "@keyframes incorrect-anim {\n",
       "    100% {\n",
       "        background-color: var(--jq-incorrect-color);\n",
       "    }\n",
       "}\n",
       "\n",
       "@keyframes correct-anim {\n",
       "    100% {\n",
       "        background-color: var(--jq-correct-color);\n",
       "    }\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var questionsUtSYwillTNTw=[\n",
       "    {\n",
       "        \"question\": \"Given the linear regression model above, between the wind speed and NO2 concentrations, based on the intercept and slope approximation, what would be the expected NO2 concentration when the wind speed is 15 km/h? Give the value to two decimal places.\",\n",
       "        \"type\": \"numeric\",\n",
       "        \"precision\":2,\n",
       "        \"answers\": [\n",
       "            {\n",
       "                \"type\": \"value\",\n",
       "                \"value\": 10.43,\n",
       "                \"correct\": true,\n",
       "                \"feedback\": \"Correct.\"\n",
       "            },\n",
       "            {\n",
       "                \"type\": \"default\",\n",
       "                \"feedback\": \"Thats not quite right, try again!\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "];\n",
       "    // Make a random ID\n",
       "function makeid(length) {\n",
       "    var result = [];\n",
       "    var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n",
       "    var charactersLength = characters.length;\n",
       "    for (var i = 0; i < length; i++) {\n",
       "        result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n",
       "    }\n",
       "    return result.join('');\n",
       "}\n",
       "\n",
       "// Choose a random subset of an array. Can also be used to shuffle the array\n",
       "function getRandomSubarray(arr, size) {\n",
       "    var shuffled = arr.slice(0), i = arr.length, temp, index;\n",
       "    while (i--) {\n",
       "        index = Math.floor((i + 1) * Math.random());\n",
       "        temp = shuffled[index];\n",
       "        shuffled[index] = shuffled[i];\n",
       "        shuffled[i] = temp;\n",
       "    }\n",
       "    return shuffled.slice(0, size);\n",
       "}\n",
       "\n",
       "function printResponses(responsesContainer) {\n",
       "    var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "    var stringResponses='<B>IMPORTANT!</B>To preserve this answer sequence for submission, when you have finalized your answers: <ol> <li> Copy the text in this cell below \"Answer String\"</li> <li> Double click on the cell directly below the Answer String, labeled \"Replace Me\"</li> <li> Select the whole \"Replace Me\" text</li> <li> Paste in your answer string and press shift-Enter.</li><li>Save the notebook using the save icon or File->Save Notebook menu item</li></ul><br><br><br><b>Answer String:</b><br> ';\n",
       "    console.log(responses);\n",
       "    responses.forEach((response, index) => {\n",
       "        if (response) {\n",
       "            console.log(index + ': ' + response);\n",
       "            stringResponses+= index + ': ' + response +\"<BR>\";\n",
       "        }\n",
       "    });\n",
       "    responsesContainer.innerHTML=stringResponses;\n",
       "}\n",
       "function check_mc() {\n",
       "    var id = this.id.split('-')[0];\n",
       "    //var response = this.id.split('-')[1];\n",
       "    //console.log(response);\n",
       "    //console.log(\"In check_mc(), id=\"+id);\n",
       "    //console.log(event.srcElement.id)           \n",
       "    //console.log(event.srcElement.dataset.correct)   \n",
       "    //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "    var label = event.srcElement;\n",
       "    //console.log(label, label.nodeName);\n",
       "    var depth = 0;\n",
       "    while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n",
       "        label = label.parentElement;\n",
       "        console.log(depth, label);\n",
       "        depth++;\n",
       "    }\n",
       "\n",
       "\n",
       "\n",
       "    var answers = label.parentElement.children;\n",
       "\n",
       "    //console.log(answers);\n",
       "\n",
       "\n",
       "    // Split behavior based on multiple choice vs many choice:\n",
       "    var fb = document.getElementById(\"fb\" + id);\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "    if (fb.dataset.numcorrect == 1) {\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            responses[qnum]= response;\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "        \n",
       "        for (var i = 0; i < answers.length; i++) {\n",
       "            var child = answers[i];\n",
       "            //console.log(child);\n",
       "            child.className = \"MCButton\";\n",
       "        }\n",
       "\n",
       "\n",
       "\n",
       "        if (label.dataset.correct == \"true\") {\n",
       "            // console.log(\"Correct action\");\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Correct!\";\n",
       "            }\n",
       "            label.classList.add(\"correctButton\");\n",
       "\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            //console.log(\"Error action\");\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "    }\n",
       "    else {\n",
       "        var reset = false;\n",
       "        var feedback;\n",
       "         if (label.dataset.correct == \"true\") {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Correct!\";\n",
       "            }\n",
       "            if (label.dataset.answered <= 0) {\n",
       "                if (fb.dataset.answeredcorrect < 0) {\n",
       "                    fb.dataset.answeredcorrect = 1;\n",
       "                    reset = true;\n",
       "                } else {\n",
       "                    fb.dataset.answeredcorrect++;\n",
       "                }\n",
       "                if (reset) {\n",
       "                    for (var i = 0; i < answers.length; i++) {\n",
       "                        var child = answers[i];\n",
       "                        child.className = \"MCButton\";\n",
       "                        child.dataset.answered = 0;\n",
       "                    }\n",
       "                }\n",
       "                label.classList.add(\"correctButton\");\n",
       "                label.dataset.answered = 1;\n",
       "                fb.className = \"Feedback\";\n",
       "                fb.classList.add(\"correct\");\n",
       "\n",
       "            }\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            if (fb.dataset.answeredcorrect > 0) {\n",
       "                fb.dataset.answeredcorrect = -1;\n",
       "                reset = true;\n",
       "            } else {\n",
       "                fb.dataset.answeredcorrect--;\n",
       "            }\n",
       "\n",
       "            if (reset) {\n",
       "                for (var i = 0; i < answers.length; i++) {\n",
       "                    var child = answers[i];\n",
       "                    child.className = \"MCButton\";\n",
       "                    child.dataset.answered = 0;\n",
       "                }\n",
       "            }\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            if (label.dataset.correct == \"true\") {\n",
       "                if (typeof(responses[qnum]) == \"object\"){\n",
       "                    if (!responses[qnum].includes(response))\n",
       "                        responses[qnum].push(response);\n",
       "                } else{\n",
       "                    responses[qnum]= [ response ];\n",
       "                }\n",
       "            } else {\n",
       "                responses[qnum]= response;\n",
       "            }\n",
       "            console.log(responses);\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End save responses stuff\n",
       "\n",
       "\n",
       "\n",
       "        var numcorrect = fb.dataset.numcorrect;\n",
       "        var answeredcorrect = fb.dataset.answeredcorrect;\n",
       "        if (answeredcorrect >= 0) {\n",
       "            fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n",
       "        } else {\n",
       "            fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n",
       "        }\n",
       "\n",
       "\n",
       "    }\n",
       "\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        var version = MathJax.version;\n",
       "        console.log('MathJax version', version);\n",
       "        if (version[0] == \"2\") {\n",
       "            MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "        } else if (version[0] == \"3\") {\n",
       "            MathJax.typeset([fb]);\n",
       "        }\n",
       "    } else {\n",
       "        console.log('MathJax not detected');\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n",
       "    var shuffled;\n",
       "    if (shuffle_answers == \"True\") {\n",
       "        //console.log(shuffle_answers+\" read as true\");\n",
       "        shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n",
       "    } else {\n",
       "        //console.log(shuffle_answers+\" read as false\");\n",
       "        shuffled = qa.answers;\n",
       "    }\n",
       "\n",
       "\n",
       "    var num_correct = 0;\n",
       "\n",
       "\n",
       "\n",
       "    shuffled.forEach((item, index, ans_array) => {\n",
       "        //console.log(answer);\n",
       "\n",
       "        // Make input element\n",
       "        var inp = document.createElement(\"input\");\n",
       "        inp.type = \"radio\";\n",
       "        inp.id = \"quizo\" + id + index;\n",
       "        inp.style = \"display:none;\";\n",
       "        aDiv.append(inp);\n",
       "\n",
       "        //Make label for input element\n",
       "        var lab = document.createElement(\"label\");\n",
       "        lab.className = \"MCButton\";\n",
       "        lab.id = id + '-' + index;\n",
       "        lab.onclick = check_mc;\n",
       "        var aSpan = document.createElement('span');\n",
       "        aSpan.classsName = \"\";\n",
       "        //qDiv.id=\"quizQn\"+id+index;\n",
       "        if (\"answer\" in item) {\n",
       "            aSpan.innerHTML = jaxify(item.answer);\n",
       "            //aSpan.innerHTML=item.answer;\n",
       "        }\n",
       "        lab.append(aSpan);\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeSpan;\n",
       "        if (\"code\" in item) {\n",
       "            codeSpan = document.createElement('span');\n",
       "            codeSpan.id = \"code\" + id + index;\n",
       "            codeSpan.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeSpan.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = item.code;\n",
       "            lab.append(codeSpan);\n",
       "            //console.log(codeSpan);\n",
       "        }\n",
       "\n",
       "        //lab.textContent=item.answer;\n",
       "\n",
       "        // Set the data attributes for the answer\n",
       "        lab.setAttribute('data-correct', item.correct);\n",
       "        if (item.correct) {\n",
       "            num_correct++;\n",
       "        }\n",
       "        if (\"feedback\" in item) {\n",
       "            lab.setAttribute('data-feedback', item.feedback);\n",
       "        }\n",
       "        lab.setAttribute('data-answered', 0);\n",
       "\n",
       "        aDiv.append(lab);\n",
       "\n",
       "    });\n",
       "\n",
       "    if (num_correct > 1) {\n",
       "        outerqDiv.className = \"ManyChoiceQn\";\n",
       "    } else {\n",
       "        outerqDiv.className = \"MultipleChoiceQn\";\n",
       "    }\n",
       "\n",
       "    return num_correct;\n",
       "\n",
       "}\n",
       "function check_numeric(ths, event) {\n",
       "\n",
       "    if (event.keyCode === 13) {\n",
       "        ths.blur();\n",
       "\n",
       "        var id = ths.id.split('-')[0];\n",
       "\n",
       "        var submission = ths.value;\n",
       "        if (submission.indexOf('/') != -1) {\n",
       "            var sub_parts = submission.split('/');\n",
       "            //console.log(sub_parts);\n",
       "            submission = sub_parts[0] / sub_parts[1];\n",
       "        }\n",
       "        //console.log(\"Reader entered\", submission);\n",
       "\n",
       "        if (\"precision\" in ths.dataset) {\n",
       "            var precision = ths.dataset.precision;\n",
       "            // console.log(\"1:\", submission)\n",
       "            submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n",
       "            // console.log(\"Rounded to \", submission, \" precision=\", precision  );\n",
       "        }\n",
       "\n",
       "\n",
       "        //console.log(\"In check_numeric(), id=\"+id);\n",
       "        //console.log(event.srcElement.id)           \n",
       "        //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "        var fb = document.getElementById(\"fb\" + id);\n",
       "        fb.style.display = \"none\";\n",
       "        fb.textContent = \"Incorrect -- try again.\";\n",
       "\n",
       "        var answers = JSON.parse(ths.dataset.answers);\n",
       "        //console.log(answers);\n",
       "\n",
       "        var defaultFB = \"\";\n",
       "        var correct;\n",
       "        var done = false;\n",
       "        answers.every(answer => {\n",
       "            //console.log(answer.type);\n",
       "\n",
       "            correct = false;\n",
       "            // if (answer.type==\"value\"){\n",
       "            if ('value' in answer) {\n",
       "                if (submission == answer.value) {\n",
       "                    if (\"feedback\" in answer) {\n",
       "                        fb.textContent = jaxify(answer.feedback);\n",
       "                    } else {\n",
       "                        fb.textContent = jaxify(\"Correct\");\n",
       "                    }\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "                // } else if (answer.type==\"range\") {\n",
       "            } else if ('range' in answer) {\n",
       "                //console.log(answer.range);\n",
       "                if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n",
       "                    fb.textContent = jaxify(answer.feedback);\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "            } else if (answer.type == \"default\") {\n",
       "                defaultFB = answer.feedback;\n",
       "            }\n",
       "            if (done) {\n",
       "                return false; // Break out of loop if this has been marked correct\n",
       "            } else {\n",
       "                return true; // Keep looking for case that includes this as a correct answer\n",
       "            }\n",
       "        });\n",
       "\n",
       "        if ((!done) && (defaultFB != \"\")) {\n",
       "            fb.innerHTML = jaxify(defaultFB);\n",
       "            //console.log(\"Default feedback\", defaultFB);\n",
       "        }\n",
       "\n",
       "        fb.style.display = \"block\";\n",
       "        if (correct) {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"correctButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "        } else {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            console.log(submission);\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            //console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            if (submission == ths.value){\n",
       "                responses[qnum]= submission;\n",
       "            } else {\n",
       "                responses[qnum]= ths.value + \"(\" + submission +\")\";\n",
       "            }\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "\n",
       "        if (typeof MathJax != 'undefined') {\n",
       "            var version = MathJax.version;\n",
       "            console.log('MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([fb]);\n",
       "            }\n",
       "        } else {\n",
       "            console.log('MathJax not detected');\n",
       "        }\n",
       "        return false;\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function isValid(el, charC) {\n",
       "    //console.log(\"Input char: \", charC);\n",
       "    if (charC == 46) {\n",
       "        if (el.value.indexOf('.') === -1) {\n",
       "            return true;\n",
       "        } else if (el.value.indexOf('/') != -1) {\n",
       "            var parts = el.value.split('/');\n",
       "            if (parts[1].indexOf('.') === -1) {\n",
       "                return true;\n",
       "            }\n",
       "        }\n",
       "        else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 47) {\n",
       "        if (el.value.indexOf('/') === -1) {\n",
       "            if ((el.value != \"\") && (el.value != \".\")) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 45) {\n",
       "        var edex = el.value.indexOf('e');\n",
       "        if (edex == -1) {\n",
       "            edex = el.value.indexOf('E');\n",
       "        }\n",
       "\n",
       "        if (el.value == \"\") {\n",
       "            return true;\n",
       "        } else if (edex == (el.value.length - 1)) { // If just after e or E\n",
       "            return true;\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 101) { // \"e\"\n",
       "        if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n",
       "            // Prev symbol must be digit or decimal point:\n",
       "            if (el.value.slice(-1).search(/\\d/) >= 0) {\n",
       "                return true;\n",
       "            } else if (el.value.slice(-1).search(/\\./) >= 0) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else {\n",
       "        if (charC > 31 && (charC < 48 || charC > 57))\n",
       "            return false;\n",
       "    }\n",
       "    return true;\n",
       "}\n",
       "\n",
       "function numeric_keypress(evnt) {\n",
       "    var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n",
       "\n",
       "    if (charC == 13) {\n",
       "        check_numeric(this, evnt);\n",
       "    } else {\n",
       "        return isValid(this, charC);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "function make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n",
       "\n",
       "\n",
       "\n",
       "    //console.log(answer);\n",
       "\n",
       "\n",
       "    outerqDiv.className = \"NumericQn\";\n",
       "    aDiv.style.display = 'block';\n",
       "\n",
       "    var lab = document.createElement(\"label\");\n",
       "    lab.className = \"InpLabel\";\n",
       "    lab.textContent = \"Type numeric answer here:\";\n",
       "    aDiv.append(lab);\n",
       "\n",
       "    var inp = document.createElement(\"input\");\n",
       "    inp.type = \"text\";\n",
       "    //inp.id=\"input-\"+id;\n",
       "    inp.id = id + \"-0\";\n",
       "    inp.className = \"Input-text\";\n",
       "    inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n",
       "    if (\"precision\" in qa) {\n",
       "        inp.setAttribute('data-precision', qa.precision);\n",
       "    }\n",
       "    aDiv.append(inp);\n",
       "    //console.log(inp);\n",
       "\n",
       "    //inp.addEventListener(\"keypress\", check_numeric);\n",
       "    //inp.addEventListener(\"keypress\", numeric_keypress);\n",
       "    /*\n",
       "    inp.addEventListener(\"keypress\", function(event) {\n",
       "        return numeric_keypress(this, event);\n",
       "    }\n",
       "                        );\n",
       "                        */\n",
       "    //inp.onkeypress=\"return numeric_keypress(this, event)\";\n",
       "    inp.onkeypress = numeric_keypress;\n",
       "    inp.onpaste = event => false;\n",
       "\n",
       "    inp.addEventListener(\"focus\", function (event) {\n",
       "        this.value = \"\";\n",
       "        return false;\n",
       "    }\n",
       "    );\n",
       "\n",
       "\n",
       "}\n",
       "function jaxify(string) {\n",
       "    var mystring = string;\n",
       "\n",
       "    var count = 0;\n",
       "    var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "\n",
       "    var count2 = 0;\n",
       "    var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "\n",
       "    //console.log(loc);\n",
       "\n",
       "    while ((loc >= 0) || (loc2 >= 0)) {\n",
       "\n",
       "        /* Have to replace all the double $$ first with current implementation */\n",
       "        if (loc2 >= 0) {\n",
       "            if (count2 % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n",
       "            }\n",
       "            count2++;\n",
       "        } else {\n",
       "            if (count % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n",
       "            }\n",
       "            count++;\n",
       "        }\n",
       "        loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "        loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "        //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n",
       "    }\n",
       "\n",
       "    //console.log(mystring);\n",
       "    return mystring;\n",
       "}\n",
       "\n",
       "\n",
       "function show_questions(json, mydiv) {\n",
       "    console.log('show_questions');\n",
       "    //var mydiv=document.getElementById(myid);\n",
       "    var shuffle_questions = mydiv.dataset.shufflequestions;\n",
       "    var num_questions = mydiv.dataset.numquestions;\n",
       "    var shuffle_answers = mydiv.dataset.shuffleanswers;\n",
       "    var max_width = mydiv.dataset.maxwidth;\n",
       "\n",
       "    if (num_questions > json.length) {\n",
       "        num_questions = json.length;\n",
       "    }\n",
       "\n",
       "    var questions;\n",
       "    if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n",
       "        //console.log(num_questions+\",\"+json.length);\n",
       "        questions = getRandomSubarray(json, num_questions);\n",
       "    } else {\n",
       "        questions = json;\n",
       "    }\n",
       "\n",
       "    //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n",
       "\n",
       "    // Iterate over questions\n",
       "    questions.forEach((qa, index, array) => {\n",
       "        //console.log(qa.question); \n",
       "\n",
       "        var id = makeid(8);\n",
       "        //console.log(id);\n",
       "\n",
       "\n",
       "        // Create Div to contain question and answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        //iDiv.id = 'quizWrap' + id + index;\n",
       "        iDiv.id = 'quizWrap' + id;\n",
       "        iDiv.className = 'Quiz';\n",
       "        iDiv.setAttribute('data-qnum', index);\n",
       "        iDiv.style.maxWidth  =max_width+\"px\";\n",
       "        mydiv.appendChild(iDiv);\n",
       "        // iDiv.innerHTML=qa.question;\n",
       "        \n",
       "        var outerqDiv = document.createElement('div');\n",
       "        outerqDiv.id = \"OuterquizQn\" + id + index;\n",
       "        // Create div to contain question part\n",
       "        var qDiv = document.createElement('div');\n",
       "        qDiv.id = \"quizQn\" + id + index;\n",
       "        \n",
       "        if (qa.question) {\n",
       "            iDiv.append(outerqDiv);\n",
       "\n",
       "            //qDiv.textContent=qa.question;\n",
       "            qDiv.innerHTML = jaxify(qa.question);\n",
       "            outerqDiv.append(qDiv);\n",
       "        }\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeDiv;\n",
       "        if (\"code\" in qa) {\n",
       "            codeDiv = document.createElement('div');\n",
       "            codeDiv.id = \"code\" + id + index;\n",
       "            codeDiv.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeDiv.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = qa.code;\n",
       "            outerqDiv.append(codeDiv);\n",
       "            //console.log(codeDiv);\n",
       "        }\n",
       "\n",
       "\n",
       "        // Create div to contain answer part\n",
       "        var aDiv = document.createElement('div');\n",
       "        aDiv.id = \"quizAns\" + id + index;\n",
       "        aDiv.className = 'Answer';\n",
       "        iDiv.append(aDiv);\n",
       "\n",
       "        //console.log(qa.type);\n",
       "\n",
       "        var num_correct;\n",
       "        if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n",
       "            num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n",
       "            if (\"answer_cols\" in qa) {\n",
       "                //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n",
       "                aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n",
       "            }\n",
       "        } else if (qa.type == \"numeric\") {\n",
       "            //console.log(\"numeric\");\n",
       "            make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n",
       "        }\n",
       "\n",
       "\n",
       "        //Make div for feedback\n",
       "        var fb = document.createElement(\"div\");\n",
       "        fb.id = \"fb\" + id;\n",
       "        //fb.style=\"font-size: 20px;text-align:center;\";\n",
       "        fb.className = \"Feedback\";\n",
       "        fb.setAttribute(\"data-answeredcorrect\", 0);\n",
       "        fb.setAttribute(\"data-numcorrect\", num_correct);\n",
       "        iDiv.append(fb);\n",
       "\n",
       "\n",
       "    });\n",
       "    var preserveResponses = mydiv.dataset.preserveresponses;\n",
       "    console.log(preserveResponses);\n",
       "    console.log(preserveResponses == \"true\");\n",
       "    if (preserveResponses == \"true\") {\n",
       "        console.log(preserveResponses);\n",
       "        // Create Div to contain record of answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        iDiv.id = 'responses' + mydiv.id;\n",
       "        iDiv.className = 'JCResponses';\n",
       "        // Create a place to store responses as an empty array\n",
       "        iDiv.setAttribute('data-responses', '[]');\n",
       "\n",
       "        // Dummy Text\n",
       "        iDiv.innerHTML=\"<b>Select your answers and then follow the directions that will appear here.</b>\"\n",
       "        //iDiv.className = 'Quiz';\n",
       "        mydiv.appendChild(iDiv);\n",
       "    }\n",
       "//console.log(\"At end of show_questions\");\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        console.log(\"MathJax version\", MathJax.version);\n",
       "        var version = MathJax.version;\n",
       "        setTimeout(function(){\n",
       "            var version = MathJax.version;\n",
       "            console.log('After sleep, MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            }\n",
       "        }, 500);\n",
       "if (typeof version == 'undefined') {\n",
       "        } else\n",
       "        {\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            } else {\n",
       "                console.log(\"MathJax not found\");\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "    return false;\n",
       "}\n",
       "/* This is to handle asynchrony issues in loading Jupyter notebooks\n",
       "           where the quiz has been previously run. The Javascript was generally\n",
       "           being run before the div was added to the DOM. I tried to do this\n",
       "           more elegantly using Mutation Observer, but I didn't get it to work.\n",
       "\n",
       "           Someone more knowledgeable could make this better ;-) */\n",
       "\n",
       "        function try_show() {\n",
       "          if(document.getElementById(\"UtSYwillTNTw\")) {\n",
       "            show_questions(questionsUtSYwillTNTw,  UtSYwillTNTw); \n",
       "          } else {\n",
       "             setTimeout(try_show, 200);\n",
       "          }\n",
       "        };\n",
       "    \n",
       "        {\n",
       "        // console.log(element);\n",
       "\n",
       "        //console.log(\"UtSYwillTNTw\");\n",
       "        // console.log(document.getElementById(\"UtSYwillTNTw\"));\n",
       "\n",
       "        try_show();\n",
       "        }\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jupyterquiz import display_quiz\n",
    "display_quiz(\"questions/scikitlearn_question_linear_regression_question.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b1be83-6562-4c23-8313-9677f0814b8e",
   "metadata": {},
   "source": [
    "## K-Means Clustering \n",
    "K-means clustering is a method for parritioning data into a specified number of clusters by minimizing the variance within each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47ce8b70-1587-4378-96b5-69a3717bd53e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "\n",
    "# Set the number of clusters\n",
    "k = 3  # Example number of clusters\n",
    "\n",
    "# Create KMeans model\n",
    "kmeans = KMeans(n_clusters=k, random_state=0)\n",
    "\n",
    "# Fit the model\n",
    "clusters = kmeans.fit_predict(air_pollution_data_2023_complete_dataset[['NO2', 'Temperature']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94af357d-5d27-4541-a442-ff2d29c5b24b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r7/wblx0jw96hz08nvjz9p3zsgr0000gp/T/ipykernel_32077/3890792038.py:5: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  colors = plt.cm.get_cmap('tab10', len(air_pollution_data_2023_complete_dataset['cluster'].unique()))  # 'tab10' supports up to 10 unique categories\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'clusters' has been added to your DataFrame\n",
    "air_pollution_data_2023_complete_dataset['cluster'] = clusters\n",
    "\n",
    "# Define a list of colors or use a colormap with discrete colors\n",
    "colors = plt.cm.get_cmap('tab10', len(air_pollution_data_2023_complete_dataset['cluster'].unique()))  # 'tab10' supports up to 10 unique categories\n",
    "\n",
    "# Plotting clusters categorically\n",
    "plt.figure(figsize=(10, 6))\n",
    "scatter = plt.scatter(air_pollution_data_2023_complete_dataset['NO2'], air_pollution_data_2023_complete_dataset['Wind Speed'], \n",
    "                      c=air_pollution_data_2023_complete_dataset['cluster'], cmap=colors, alpha=0.5)\n",
    "\n",
    "plt.title('Clusters of Air Pollution Data')\n",
    "plt.xlabel('NO2 Concentration')\n",
    "plt.ylabel('Wind Speed')  # Adjusted to match the y-axis label with your data\n",
    "\n",
    "# Create a legend for clusters\n",
    "# Create handles and labels for the legend\n",
    "handles, labels = scatter.legend_elements(prop='colors')\n",
    "plt.legend(handles, labels, title=\"Clusters\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "295aa375-393b-4d1f-9f3c-c221fa21e0b5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"NSVsSimyPwwt\" data-shufflequestions=\"False\"\n",
       "               data-shuffleanswers=\"True\"\n",
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       "               data-maxwidth=\"600\"\n",
       "               style=\"border-radius: 10px; text-align: left\"> <style>\n",
       "#NSVsSimyPwwt {\n",
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       ".ManyChoiceQn {\n",
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       "    background: var(--jq-many-choice-bg);\n",
       "    color: var(--jq-text-color);\n",
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       "    background: var(--jq-numeric-bg);\n",
       "    color: var(--jq-text-color);\n",
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       ".correct {\n",
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       ".correctButton {\n",
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       "    background: var(--jq-correct-color);\n",
       "   */\n",
       "    animation: correct-anim 0.6s ease;\n",
       "    animation-fill-mode: forwards;\n",
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       "\n",
       ".incorrectButton {\n",
       "    animation: incorrect-anim 0.8s ease;\n",
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       "\n",
       "@keyframes incorrect-anim {\n",
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       "        background-color: var(--jq-incorrect-color);\n",
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       "\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var questionsNSVsSimyPwwt=[\n",
       "    {\n",
       "        \"question\": \"Given the k-means clustering model shown above, what cluster would the data point Temperature: 6, NO2 Concentration: 30 be in? \",\n",
       "        \"type\": \"numeric\",\n",
       "        \"precision\":1,\n",
       "        \"answers\": [\n",
       "            {\n",
       "                \"type\": \"value\",\n",
       "                \"value\": 2,\n",
       "                \"correct\": true,\n",
       "                \"feedback\": \"Correct.\"\n",
       "            },\n",
       "            {\n",
       "                \"type\": \"default\",\n",
       "                \"feedback\": \"Thats not quite right, try again!\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "];\n",
       "    // Make a random ID\n",
       "function makeid(length) {\n",
       "    var result = [];\n",
       "    var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n",
       "    var charactersLength = characters.length;\n",
       "    for (var i = 0; i < length; i++) {\n",
       "        result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n",
       "    }\n",
       "    return result.join('');\n",
       "}\n",
       "\n",
       "// Choose a random subset of an array. Can also be used to shuffle the array\n",
       "function getRandomSubarray(arr, size) {\n",
       "    var shuffled = arr.slice(0), i = arr.length, temp, index;\n",
       "    while (i--) {\n",
       "        index = Math.floor((i + 1) * Math.random());\n",
       "        temp = shuffled[index];\n",
       "        shuffled[index] = shuffled[i];\n",
       "        shuffled[i] = temp;\n",
       "    }\n",
       "    return shuffled.slice(0, size);\n",
       "}\n",
       "\n",
       "function printResponses(responsesContainer) {\n",
       "    var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "    var stringResponses='<B>IMPORTANT!</B>To preserve this answer sequence for submission, when you have finalized your answers: <ol> <li> Copy the text in this cell below \"Answer String\"</li> <li> Double click on the cell directly below the Answer String, labeled \"Replace Me\"</li> <li> Select the whole \"Replace Me\" text</li> <li> Paste in your answer string and press shift-Enter.</li><li>Save the notebook using the save icon or File->Save Notebook menu item</li></ul><br><br><br><b>Answer String:</b><br> ';\n",
       "    console.log(responses);\n",
       "    responses.forEach((response, index) => {\n",
       "        if (response) {\n",
       "            console.log(index + ': ' + response);\n",
       "            stringResponses+= index + ': ' + response +\"<BR>\";\n",
       "        }\n",
       "    });\n",
       "    responsesContainer.innerHTML=stringResponses;\n",
       "}\n",
       "function check_mc() {\n",
       "    var id = this.id.split('-')[0];\n",
       "    //var response = this.id.split('-')[1];\n",
       "    //console.log(response);\n",
       "    //console.log(\"In check_mc(), id=\"+id);\n",
       "    //console.log(event.srcElement.id)           \n",
       "    //console.log(event.srcElement.dataset.correct)   \n",
       "    //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "    var label = event.srcElement;\n",
       "    //console.log(label, label.nodeName);\n",
       "    var depth = 0;\n",
       "    while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n",
       "        label = label.parentElement;\n",
       "        console.log(depth, label);\n",
       "        depth++;\n",
       "    }\n",
       "\n",
       "\n",
       "\n",
       "    var answers = label.parentElement.children;\n",
       "\n",
       "    //console.log(answers);\n",
       "\n",
       "\n",
       "    // Split behavior based on multiple choice vs many choice:\n",
       "    var fb = document.getElementById(\"fb\" + id);\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "    if (fb.dataset.numcorrect == 1) {\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            responses[qnum]= response;\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "        \n",
       "        for (var i = 0; i < answers.length; i++) {\n",
       "            var child = answers[i];\n",
       "            //console.log(child);\n",
       "            child.className = \"MCButton\";\n",
       "        }\n",
       "\n",
       "\n",
       "\n",
       "        if (label.dataset.correct == \"true\") {\n",
       "            // console.log(\"Correct action\");\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Correct!\";\n",
       "            }\n",
       "            label.classList.add(\"correctButton\");\n",
       "\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            //console.log(\"Error action\");\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "    }\n",
       "    else {\n",
       "        var reset = false;\n",
       "        var feedback;\n",
       "         if (label.dataset.correct == \"true\") {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Correct!\";\n",
       "            }\n",
       "            if (label.dataset.answered <= 0) {\n",
       "                if (fb.dataset.answeredcorrect < 0) {\n",
       "                    fb.dataset.answeredcorrect = 1;\n",
       "                    reset = true;\n",
       "                } else {\n",
       "                    fb.dataset.answeredcorrect++;\n",
       "                }\n",
       "                if (reset) {\n",
       "                    for (var i = 0; i < answers.length; i++) {\n",
       "                        var child = answers[i];\n",
       "                        child.className = \"MCButton\";\n",
       "                        child.dataset.answered = 0;\n",
       "                    }\n",
       "                }\n",
       "                label.classList.add(\"correctButton\");\n",
       "                label.dataset.answered = 1;\n",
       "                fb.className = \"Feedback\";\n",
       "                fb.classList.add(\"correct\");\n",
       "\n",
       "            }\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            if (fb.dataset.answeredcorrect > 0) {\n",
       "                fb.dataset.answeredcorrect = -1;\n",
       "                reset = true;\n",
       "            } else {\n",
       "                fb.dataset.answeredcorrect--;\n",
       "            }\n",
       "\n",
       "            if (reset) {\n",
       "                for (var i = 0; i < answers.length; i++) {\n",
       "                    var child = answers[i];\n",
       "                    child.className = \"MCButton\";\n",
       "                    child.dataset.answered = 0;\n",
       "                }\n",
       "            }\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            if (label.dataset.correct == \"true\") {\n",
       "                if (typeof(responses[qnum]) == \"object\"){\n",
       "                    if (!responses[qnum].includes(response))\n",
       "                        responses[qnum].push(response);\n",
       "                } else{\n",
       "                    responses[qnum]= [ response ];\n",
       "                }\n",
       "            } else {\n",
       "                responses[qnum]= response;\n",
       "            }\n",
       "            console.log(responses);\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End save responses stuff\n",
       "\n",
       "\n",
       "\n",
       "        var numcorrect = fb.dataset.numcorrect;\n",
       "        var answeredcorrect = fb.dataset.answeredcorrect;\n",
       "        if (answeredcorrect >= 0) {\n",
       "            fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n",
       "        } else {\n",
       "            fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n",
       "        }\n",
       "\n",
       "\n",
       "    }\n",
       "\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        var version = MathJax.version;\n",
       "        console.log('MathJax version', version);\n",
       "        if (version[0] == \"2\") {\n",
       "            MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "        } else if (version[0] == \"3\") {\n",
       "            MathJax.typeset([fb]);\n",
       "        }\n",
       "    } else {\n",
       "        console.log('MathJax not detected');\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n",
       "    var shuffled;\n",
       "    if (shuffle_answers == \"True\") {\n",
       "        //console.log(shuffle_answers+\" read as true\");\n",
       "        shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n",
       "    } else {\n",
       "        //console.log(shuffle_answers+\" read as false\");\n",
       "        shuffled = qa.answers;\n",
       "    }\n",
       "\n",
       "\n",
       "    var num_correct = 0;\n",
       "\n",
       "\n",
       "\n",
       "    shuffled.forEach((item, index, ans_array) => {\n",
       "        //console.log(answer);\n",
       "\n",
       "        // Make input element\n",
       "        var inp = document.createElement(\"input\");\n",
       "        inp.type = \"radio\";\n",
       "        inp.id = \"quizo\" + id + index;\n",
       "        inp.style = \"display:none;\";\n",
       "        aDiv.append(inp);\n",
       "\n",
       "        //Make label for input element\n",
       "        var lab = document.createElement(\"label\");\n",
       "        lab.className = \"MCButton\";\n",
       "        lab.id = id + '-' + index;\n",
       "        lab.onclick = check_mc;\n",
       "        var aSpan = document.createElement('span');\n",
       "        aSpan.classsName = \"\";\n",
       "        //qDiv.id=\"quizQn\"+id+index;\n",
       "        if (\"answer\" in item) {\n",
       "            aSpan.innerHTML = jaxify(item.answer);\n",
       "            //aSpan.innerHTML=item.answer;\n",
       "        }\n",
       "        lab.append(aSpan);\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeSpan;\n",
       "        if (\"code\" in item) {\n",
       "            codeSpan = document.createElement('span');\n",
       "            codeSpan.id = \"code\" + id + index;\n",
       "            codeSpan.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeSpan.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = item.code;\n",
       "            lab.append(codeSpan);\n",
       "            //console.log(codeSpan);\n",
       "        }\n",
       "\n",
       "        //lab.textContent=item.answer;\n",
       "\n",
       "        // Set the data attributes for the answer\n",
       "        lab.setAttribute('data-correct', item.correct);\n",
       "        if (item.correct) {\n",
       "            num_correct++;\n",
       "        }\n",
       "        if (\"feedback\" in item) {\n",
       "            lab.setAttribute('data-feedback', item.feedback);\n",
       "        }\n",
       "        lab.setAttribute('data-answered', 0);\n",
       "\n",
       "        aDiv.append(lab);\n",
       "\n",
       "    });\n",
       "\n",
       "    if (num_correct > 1) {\n",
       "        outerqDiv.className = \"ManyChoiceQn\";\n",
       "    } else {\n",
       "        outerqDiv.className = \"MultipleChoiceQn\";\n",
       "    }\n",
       "\n",
       "    return num_correct;\n",
       "\n",
       "}\n",
       "function check_numeric(ths, event) {\n",
       "\n",
       "    if (event.keyCode === 13) {\n",
       "        ths.blur();\n",
       "\n",
       "        var id = ths.id.split('-')[0];\n",
       "\n",
       "        var submission = ths.value;\n",
       "        if (submission.indexOf('/') != -1) {\n",
       "            var sub_parts = submission.split('/');\n",
       "            //console.log(sub_parts);\n",
       "            submission = sub_parts[0] / sub_parts[1];\n",
       "        }\n",
       "        //console.log(\"Reader entered\", submission);\n",
       "\n",
       "        if (\"precision\" in ths.dataset) {\n",
       "            var precision = ths.dataset.precision;\n",
       "            // console.log(\"1:\", submission)\n",
       "            submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n",
       "            // console.log(\"Rounded to \", submission, \" precision=\", precision  );\n",
       "        }\n",
       "\n",
       "\n",
       "        //console.log(\"In check_numeric(), id=\"+id);\n",
       "        //console.log(event.srcElement.id)           \n",
       "        //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "        var fb = document.getElementById(\"fb\" + id);\n",
       "        fb.style.display = \"none\";\n",
       "        fb.textContent = \"Incorrect -- try again.\";\n",
       "\n",
       "        var answers = JSON.parse(ths.dataset.answers);\n",
       "        //console.log(answers);\n",
       "\n",
       "        var defaultFB = \"\";\n",
       "        var correct;\n",
       "        var done = false;\n",
       "        answers.every(answer => {\n",
       "            //console.log(answer.type);\n",
       "\n",
       "            correct = false;\n",
       "            // if (answer.type==\"value\"){\n",
       "            if ('value' in answer) {\n",
       "                if (submission == answer.value) {\n",
       "                    if (\"feedback\" in answer) {\n",
       "                        fb.textContent = jaxify(answer.feedback);\n",
       "                    } else {\n",
       "                        fb.textContent = jaxify(\"Correct\");\n",
       "                    }\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "                // } else if (answer.type==\"range\") {\n",
       "            } else if ('range' in answer) {\n",
       "                //console.log(answer.range);\n",
       "                if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n",
       "                    fb.textContent = jaxify(answer.feedback);\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "            } else if (answer.type == \"default\") {\n",
       "                defaultFB = answer.feedback;\n",
       "            }\n",
       "            if (done) {\n",
       "                return false; // Break out of loop if this has been marked correct\n",
       "            } else {\n",
       "                return true; // Keep looking for case that includes this as a correct answer\n",
       "            }\n",
       "        });\n",
       "\n",
       "        if ((!done) && (defaultFB != \"\")) {\n",
       "            fb.innerHTML = jaxify(defaultFB);\n",
       "            //console.log(\"Default feedback\", defaultFB);\n",
       "        }\n",
       "\n",
       "        fb.style.display = \"block\";\n",
       "        if (correct) {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"correctButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "        } else {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            console.log(submission);\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            //console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            if (submission == ths.value){\n",
       "                responses[qnum]= submission;\n",
       "            } else {\n",
       "                responses[qnum]= ths.value + \"(\" + submission +\")\";\n",
       "            }\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "\n",
       "        if (typeof MathJax != 'undefined') {\n",
       "            var version = MathJax.version;\n",
       "            console.log('MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([fb]);\n",
       "            }\n",
       "        } else {\n",
       "            console.log('MathJax not detected');\n",
       "        }\n",
       "        return false;\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function isValid(el, charC) {\n",
       "    //console.log(\"Input char: \", charC);\n",
       "    if (charC == 46) {\n",
       "        if (el.value.indexOf('.') === -1) {\n",
       "            return true;\n",
       "        } else if (el.value.indexOf('/') != -1) {\n",
       "            var parts = el.value.split('/');\n",
       "            if (parts[1].indexOf('.') === -1) {\n",
       "                return true;\n",
       "            }\n",
       "        }\n",
       "        else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 47) {\n",
       "        if (el.value.indexOf('/') === -1) {\n",
       "            if ((el.value != \"\") && (el.value != \".\")) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 45) {\n",
       "        var edex = el.value.indexOf('e');\n",
       "        if (edex == -1) {\n",
       "            edex = el.value.indexOf('E');\n",
       "        }\n",
       "\n",
       "        if (el.value == \"\") {\n",
       "            return true;\n",
       "        } else if (edex == (el.value.length - 1)) { // If just after e or E\n",
       "            return true;\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 101) { // \"e\"\n",
       "        if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n",
       "            // Prev symbol must be digit or decimal point:\n",
       "            if (el.value.slice(-1).search(/\\d/) >= 0) {\n",
       "                return true;\n",
       "            } else if (el.value.slice(-1).search(/\\./) >= 0) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else {\n",
       "        if (charC > 31 && (charC < 48 || charC > 57))\n",
       "            return false;\n",
       "    }\n",
       "    return true;\n",
       "}\n",
       "\n",
       "function numeric_keypress(evnt) {\n",
       "    var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n",
       "\n",
       "    if (charC == 13) {\n",
       "        check_numeric(this, evnt);\n",
       "    } else {\n",
       "        return isValid(this, charC);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "function make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n",
       "\n",
       "\n",
       "\n",
       "    //console.log(answer);\n",
       "\n",
       "\n",
       "    outerqDiv.className = \"NumericQn\";\n",
       "    aDiv.style.display = 'block';\n",
       "\n",
       "    var lab = document.createElement(\"label\");\n",
       "    lab.className = \"InpLabel\";\n",
       "    lab.textContent = \"Type numeric answer here:\";\n",
       "    aDiv.append(lab);\n",
       "\n",
       "    var inp = document.createElement(\"input\");\n",
       "    inp.type = \"text\";\n",
       "    //inp.id=\"input-\"+id;\n",
       "    inp.id = id + \"-0\";\n",
       "    inp.className = \"Input-text\";\n",
       "    inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n",
       "    if (\"precision\" in qa) {\n",
       "        inp.setAttribute('data-precision', qa.precision);\n",
       "    }\n",
       "    aDiv.append(inp);\n",
       "    //console.log(inp);\n",
       "\n",
       "    //inp.addEventListener(\"keypress\", check_numeric);\n",
       "    //inp.addEventListener(\"keypress\", numeric_keypress);\n",
       "    /*\n",
       "    inp.addEventListener(\"keypress\", function(event) {\n",
       "        return numeric_keypress(this, event);\n",
       "    }\n",
       "                        );\n",
       "                        */\n",
       "    //inp.onkeypress=\"return numeric_keypress(this, event)\";\n",
       "    inp.onkeypress = numeric_keypress;\n",
       "    inp.onpaste = event => false;\n",
       "\n",
       "    inp.addEventListener(\"focus\", function (event) {\n",
       "        this.value = \"\";\n",
       "        return false;\n",
       "    }\n",
       "    );\n",
       "\n",
       "\n",
       "}\n",
       "function jaxify(string) {\n",
       "    var mystring = string;\n",
       "\n",
       "    var count = 0;\n",
       "    var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "\n",
       "    var count2 = 0;\n",
       "    var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "\n",
       "    //console.log(loc);\n",
       "\n",
       "    while ((loc >= 0) || (loc2 >= 0)) {\n",
       "\n",
       "        /* Have to replace all the double $$ first with current implementation */\n",
       "        if (loc2 >= 0) {\n",
       "            if (count2 % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n",
       "            }\n",
       "            count2++;\n",
       "        } else {\n",
       "            if (count % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n",
       "            }\n",
       "            count++;\n",
       "        }\n",
       "        loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "        loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "        //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n",
       "    }\n",
       "\n",
       "    //console.log(mystring);\n",
       "    return mystring;\n",
       "}\n",
       "\n",
       "\n",
       "function show_questions(json, mydiv) {\n",
       "    console.log('show_questions');\n",
       "    //var mydiv=document.getElementById(myid);\n",
       "    var shuffle_questions = mydiv.dataset.shufflequestions;\n",
       "    var num_questions = mydiv.dataset.numquestions;\n",
       "    var shuffle_answers = mydiv.dataset.shuffleanswers;\n",
       "    var max_width = mydiv.dataset.maxwidth;\n",
       "\n",
       "    if (num_questions > json.length) {\n",
       "        num_questions = json.length;\n",
       "    }\n",
       "\n",
       "    var questions;\n",
       "    if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n",
       "        //console.log(num_questions+\",\"+json.length);\n",
       "        questions = getRandomSubarray(json, num_questions);\n",
       "    } else {\n",
       "        questions = json;\n",
       "    }\n",
       "\n",
       "    //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n",
       "\n",
       "    // Iterate over questions\n",
       "    questions.forEach((qa, index, array) => {\n",
       "        //console.log(qa.question); \n",
       "\n",
       "        var id = makeid(8);\n",
       "        //console.log(id);\n",
       "\n",
       "\n",
       "        // Create Div to contain question and answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        //iDiv.id = 'quizWrap' + id + index;\n",
       "        iDiv.id = 'quizWrap' + id;\n",
       "        iDiv.className = 'Quiz';\n",
       "        iDiv.setAttribute('data-qnum', index);\n",
       "        iDiv.style.maxWidth  =max_width+\"px\";\n",
       "        mydiv.appendChild(iDiv);\n",
       "        // iDiv.innerHTML=qa.question;\n",
       "        \n",
       "        var outerqDiv = document.createElement('div');\n",
       "        outerqDiv.id = \"OuterquizQn\" + id + index;\n",
       "        // Create div to contain question part\n",
       "        var qDiv = document.createElement('div');\n",
       "        qDiv.id = \"quizQn\" + id + index;\n",
       "        \n",
       "        if (qa.question) {\n",
       "            iDiv.append(outerqDiv);\n",
       "\n",
       "            //qDiv.textContent=qa.question;\n",
       "            qDiv.innerHTML = jaxify(qa.question);\n",
       "            outerqDiv.append(qDiv);\n",
       "        }\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeDiv;\n",
       "        if (\"code\" in qa) {\n",
       "            codeDiv = document.createElement('div');\n",
       "            codeDiv.id = \"code\" + id + index;\n",
       "            codeDiv.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeDiv.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = qa.code;\n",
       "            outerqDiv.append(codeDiv);\n",
       "            //console.log(codeDiv);\n",
       "        }\n",
       "\n",
       "\n",
       "        // Create div to contain answer part\n",
       "        var aDiv = document.createElement('div');\n",
       "        aDiv.id = \"quizAns\" + id + index;\n",
       "        aDiv.className = 'Answer';\n",
       "        iDiv.append(aDiv);\n",
       "\n",
       "        //console.log(qa.type);\n",
       "\n",
       "        var num_correct;\n",
       "        if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n",
       "            num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n",
       "            if (\"answer_cols\" in qa) {\n",
       "                //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n",
       "                aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n",
       "            }\n",
       "        } else if (qa.type == \"numeric\") {\n",
       "            //console.log(\"numeric\");\n",
       "            make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n",
       "        }\n",
       "\n",
       "\n",
       "        //Make div for feedback\n",
       "        var fb = document.createElement(\"div\");\n",
       "        fb.id = \"fb\" + id;\n",
       "        //fb.style=\"font-size: 20px;text-align:center;\";\n",
       "        fb.className = \"Feedback\";\n",
       "        fb.setAttribute(\"data-answeredcorrect\", 0);\n",
       "        fb.setAttribute(\"data-numcorrect\", num_correct);\n",
       "        iDiv.append(fb);\n",
       "\n",
       "\n",
       "    });\n",
       "    var preserveResponses = mydiv.dataset.preserveresponses;\n",
       "    console.log(preserveResponses);\n",
       "    console.log(preserveResponses == \"true\");\n",
       "    if (preserveResponses == \"true\") {\n",
       "        console.log(preserveResponses);\n",
       "        // Create Div to contain record of answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        iDiv.id = 'responses' + mydiv.id;\n",
       "        iDiv.className = 'JCResponses';\n",
       "        // Create a place to store responses as an empty array\n",
       "        iDiv.setAttribute('data-responses', '[]');\n",
       "\n",
       "        // Dummy Text\n",
       "        iDiv.innerHTML=\"<b>Select your answers and then follow the directions that will appear here.</b>\"\n",
       "        //iDiv.className = 'Quiz';\n",
       "        mydiv.appendChild(iDiv);\n",
       "    }\n",
       "//console.log(\"At end of show_questions\");\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        console.log(\"MathJax version\", MathJax.version);\n",
       "        var version = MathJax.version;\n",
       "        setTimeout(function(){\n",
       "            var version = MathJax.version;\n",
       "            console.log('After sleep, MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            }\n",
       "        }, 500);\n",
       "if (typeof version == 'undefined') {\n",
       "        } else\n",
       "        {\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            } else {\n",
       "                console.log(\"MathJax not found\");\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "    return false;\n",
       "}\n",
       "/* This is to handle asynchrony issues in loading Jupyter notebooks\n",
       "           where the quiz has been previously run. The Javascript was generally\n",
       "           being run before the div was added to the DOM. I tried to do this\n",
       "           more elegantly using Mutation Observer, but I didn't get it to work.\n",
       "\n",
       "           Someone more knowledgeable could make this better ;-) */\n",
       "\n",
       "        function try_show() {\n",
       "          if(document.getElementById(\"NSVsSimyPwwt\")) {\n",
       "            show_questions(questionsNSVsSimyPwwt,  NSVsSimyPwwt); \n",
       "          } else {\n",
       "             setTimeout(try_show, 200);\n",
       "          }\n",
       "        };\n",
       "    \n",
       "        {\n",
       "        // console.log(element);\n",
       "\n",
       "        //console.log(\"NSVsSimyPwwt\");\n",
       "        // console.log(document.getElementById(\"NSVsSimyPwwt\"));\n",
       "\n",
       "        try_show();\n",
       "        }\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_quiz(\"questions/sciktilearn_question_kmeans.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff85e826-ccea-439b-ac60-555059067ed9",
   "metadata": {},
   "source": [
    "## Decision Tree Models\n",
    "Decision Trees are a model framework that splits data into branches based on the some input data, in out case wind speed. The model resembles a tree structure with decision points and leaf nodes where each decision leads to further splits, eventually ending on a final prediction based on the input features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14617d91-10d1-4369-ae06-e33f6ac2c9de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 5000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Initialize the model\n",
    "decision_tree_regressor = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
    "\n",
    "# Train the model on the entire dataset\n",
    "decision_tree_regressor.fit(air_pollution_data_2023_complete_dataset[[\"Wind Speed\"]], air_pollution_data_2023_complete_dataset[\"NO2\"])\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "# Plot the decision tree\n",
    "plt.figure(figsize=(50,10))\n",
    "plot_tree(decision_tree_regressor, feature_names=['Wind Speed'], filled=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c93ee0cc-b30c-4cd6-b5eb-04a59f962e1a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "    {\n",
       "        \"question\": \"Given the decision tree model that has been created, if the NO2 air pollution concentration was predicted from a wind speed of 0.5 m/s, what estimation would be made?\",\n",
       "        \"type\": \"numeric\",\n",
       "        \"precision\":3,\n",
       "        \"answers\": [\n",
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       "                \"feedback\": \"Thats not quite right, try again!\"\n",
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       "    // Make a random ID\n",
       "function makeid(length) {\n",
       "    var result = [];\n",
       "    var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n",
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       "    while (i--) {\n",
       "        index = Math.floor((i + 1) * Math.random());\n",
       "        temp = shuffled[index];\n",
       "        shuffled[index] = shuffled[i];\n",
       "        shuffled[i] = temp;\n",
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       "    return shuffled.slice(0, size);\n",
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       "\n",
       "function printResponses(responsesContainer) {\n",
       "    var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "    var stringResponses='<B>IMPORTANT!</B>To preserve this answer sequence for submission, when you have finalized your answers: <ol> <li> Copy the text in this cell below \"Answer String\"</li> <li> Double click on the cell directly below the Answer String, labeled \"Replace Me\"</li> <li> Select the whole \"Replace Me\" text</li> <li> Paste in your answer string and press shift-Enter.</li><li>Save the notebook using the save icon or File->Save Notebook menu item</li></ul><br><br><br><b>Answer String:</b><br> ';\n",
       "    console.log(responses);\n",
       "    responses.forEach((response, index) => {\n",
       "        if (response) {\n",
       "            console.log(index + ': ' + response);\n",
       "            stringResponses+= index + ': ' + response +\"<BR>\";\n",
       "        }\n",
       "    });\n",
       "    responsesContainer.innerHTML=stringResponses;\n",
       "}\n",
       "function check_mc() {\n",
       "    var id = this.id.split('-')[0];\n",
       "    //var response = this.id.split('-')[1];\n",
       "    //console.log(response);\n",
       "    //console.log(\"In check_mc(), id=\"+id);\n",
       "    //console.log(event.srcElement.id)           \n",
       "    //console.log(event.srcElement.dataset.correct)   \n",
       "    //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "    var label = event.srcElement;\n",
       "    //console.log(label, label.nodeName);\n",
       "    var depth = 0;\n",
       "    while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n",
       "        label = label.parentElement;\n",
       "        console.log(depth, label);\n",
       "        depth++;\n",
       "    }\n",
       "\n",
       "\n",
       "\n",
       "    var answers = label.parentElement.children;\n",
       "\n",
       "    //console.log(answers);\n",
       "\n",
       "\n",
       "    // Split behavior based on multiple choice vs many choice:\n",
       "    var fb = document.getElementById(\"fb\" + id);\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "    if (fb.dataset.numcorrect == 1) {\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            responses[qnum]= response;\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "        \n",
       "        for (var i = 0; i < answers.length; i++) {\n",
       "            var child = answers[i];\n",
       "            //console.log(child);\n",
       "            child.className = \"MCButton\";\n",
       "        }\n",
       "\n",
       "\n",
       "\n",
       "        if (label.dataset.correct == \"true\") {\n",
       "            // console.log(\"Correct action\");\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Correct!\";\n",
       "            }\n",
       "            label.classList.add(\"correctButton\");\n",
       "\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            //console.log(\"Error action\");\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "    }\n",
       "    else {\n",
       "        var reset = false;\n",
       "        var feedback;\n",
       "         if (label.dataset.correct == \"true\") {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Correct!\";\n",
       "            }\n",
       "            if (label.dataset.answered <= 0) {\n",
       "                if (fb.dataset.answeredcorrect < 0) {\n",
       "                    fb.dataset.answeredcorrect = 1;\n",
       "                    reset = true;\n",
       "                } else {\n",
       "                    fb.dataset.answeredcorrect++;\n",
       "                }\n",
       "                if (reset) {\n",
       "                    for (var i = 0; i < answers.length; i++) {\n",
       "                        var child = answers[i];\n",
       "                        child.className = \"MCButton\";\n",
       "                        child.dataset.answered = 0;\n",
       "                    }\n",
       "                }\n",
       "                label.classList.add(\"correctButton\");\n",
       "                label.dataset.answered = 1;\n",
       "                fb.className = \"Feedback\";\n",
       "                fb.classList.add(\"correct\");\n",
       "\n",
       "            }\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            if (fb.dataset.answeredcorrect > 0) {\n",
       "                fb.dataset.answeredcorrect = -1;\n",
       "                reset = true;\n",
       "            } else {\n",
       "                fb.dataset.answeredcorrect--;\n",
       "            }\n",
       "\n",
       "            if (reset) {\n",
       "                for (var i = 0; i < answers.length; i++) {\n",
       "                    var child = answers[i];\n",
       "                    child.className = \"MCButton\";\n",
       "                    child.dataset.answered = 0;\n",
       "                }\n",
       "            }\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            if (label.dataset.correct == \"true\") {\n",
       "                if (typeof(responses[qnum]) == \"object\"){\n",
       "                    if (!responses[qnum].includes(response))\n",
       "                        responses[qnum].push(response);\n",
       "                } else{\n",
       "                    responses[qnum]= [ response ];\n",
       "                }\n",
       "            } else {\n",
       "                responses[qnum]= response;\n",
       "            }\n",
       "            console.log(responses);\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End save responses stuff\n",
       "\n",
       "\n",
       "\n",
       "        var numcorrect = fb.dataset.numcorrect;\n",
       "        var answeredcorrect = fb.dataset.answeredcorrect;\n",
       "        if (answeredcorrect >= 0) {\n",
       "            fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n",
       "        } else {\n",
       "            fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n",
       "        }\n",
       "\n",
       "\n",
       "    }\n",
       "\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        var version = MathJax.version;\n",
       "        console.log('MathJax version', version);\n",
       "        if (version[0] == \"2\") {\n",
       "            MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "        } else if (version[0] == \"3\") {\n",
       "            MathJax.typeset([fb]);\n",
       "        }\n",
       "    } else {\n",
       "        console.log('MathJax not detected');\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n",
       "    var shuffled;\n",
       "    if (shuffle_answers == \"True\") {\n",
       "        //console.log(shuffle_answers+\" read as true\");\n",
       "        shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n",
       "    } else {\n",
       "        //console.log(shuffle_answers+\" read as false\");\n",
       "        shuffled = qa.answers;\n",
       "    }\n",
       "\n",
       "\n",
       "    var num_correct = 0;\n",
       "\n",
       "\n",
       "\n",
       "    shuffled.forEach((item, index, ans_array) => {\n",
       "        //console.log(answer);\n",
       "\n",
       "        // Make input element\n",
       "        var inp = document.createElement(\"input\");\n",
       "        inp.type = \"radio\";\n",
       "        inp.id = \"quizo\" + id + index;\n",
       "        inp.style = \"display:none;\";\n",
       "        aDiv.append(inp);\n",
       "\n",
       "        //Make label for input element\n",
       "        var lab = document.createElement(\"label\");\n",
       "        lab.className = \"MCButton\";\n",
       "        lab.id = id + '-' + index;\n",
       "        lab.onclick = check_mc;\n",
       "        var aSpan = document.createElement('span');\n",
       "        aSpan.classsName = \"\";\n",
       "        //qDiv.id=\"quizQn\"+id+index;\n",
       "        if (\"answer\" in item) {\n",
       "            aSpan.innerHTML = jaxify(item.answer);\n",
       "            //aSpan.innerHTML=item.answer;\n",
       "        }\n",
       "        lab.append(aSpan);\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeSpan;\n",
       "        if (\"code\" in item) {\n",
       "            codeSpan = document.createElement('span');\n",
       "            codeSpan.id = \"code\" + id + index;\n",
       "            codeSpan.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeSpan.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = item.code;\n",
       "            lab.append(codeSpan);\n",
       "            //console.log(codeSpan);\n",
       "        }\n",
       "\n",
       "        //lab.textContent=item.answer;\n",
       "\n",
       "        // Set the data attributes for the answer\n",
       "        lab.setAttribute('data-correct', item.correct);\n",
       "        if (item.correct) {\n",
       "            num_correct++;\n",
       "        }\n",
       "        if (\"feedback\" in item) {\n",
       "            lab.setAttribute('data-feedback', item.feedback);\n",
       "        }\n",
       "        lab.setAttribute('data-answered', 0);\n",
       "\n",
       "        aDiv.append(lab);\n",
       "\n",
       "    });\n",
       "\n",
       "    if (num_correct > 1) {\n",
       "        outerqDiv.className = \"ManyChoiceQn\";\n",
       "    } else {\n",
       "        outerqDiv.className = \"MultipleChoiceQn\";\n",
       "    }\n",
       "\n",
       "    return num_correct;\n",
       "\n",
       "}\n",
       "function check_numeric(ths, event) {\n",
       "\n",
       "    if (event.keyCode === 13) {\n",
       "        ths.blur();\n",
       "\n",
       "        var id = ths.id.split('-')[0];\n",
       "\n",
       "        var submission = ths.value;\n",
       "        if (submission.indexOf('/') != -1) {\n",
       "            var sub_parts = submission.split('/');\n",
       "            //console.log(sub_parts);\n",
       "            submission = sub_parts[0] / sub_parts[1];\n",
       "        }\n",
       "        //console.log(\"Reader entered\", submission);\n",
       "\n",
       "        if (\"precision\" in ths.dataset) {\n",
       "            var precision = ths.dataset.precision;\n",
       "            // console.log(\"1:\", submission)\n",
       "            submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n",
       "            // console.log(\"Rounded to \", submission, \" precision=\", precision  );\n",
       "        }\n",
       "\n",
       "\n",
       "        //console.log(\"In check_numeric(), id=\"+id);\n",
       "        //console.log(event.srcElement.id)           \n",
       "        //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "        var fb = document.getElementById(\"fb\" + id);\n",
       "        fb.style.display = \"none\";\n",
       "        fb.textContent = \"Incorrect -- try again.\";\n",
       "\n",
       "        var answers = JSON.parse(ths.dataset.answers);\n",
       "        //console.log(answers);\n",
       "\n",
       "        var defaultFB = \"\";\n",
       "        var correct;\n",
       "        var done = false;\n",
       "        answers.every(answer => {\n",
       "            //console.log(answer.type);\n",
       "\n",
       "            correct = false;\n",
       "            // if (answer.type==\"value\"){\n",
       "            if ('value' in answer) {\n",
       "                if (submission == answer.value) {\n",
       "                    if (\"feedback\" in answer) {\n",
       "                        fb.textContent = jaxify(answer.feedback);\n",
       "                    } else {\n",
       "                        fb.textContent = jaxify(\"Correct\");\n",
       "                    }\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "                // } else if (answer.type==\"range\") {\n",
       "            } else if ('range' in answer) {\n",
       "                //console.log(answer.range);\n",
       "                if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n",
       "                    fb.textContent = jaxify(answer.feedback);\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "            } else if (answer.type == \"default\") {\n",
       "                defaultFB = answer.feedback;\n",
       "            }\n",
       "            if (done) {\n",
       "                return false; // Break out of loop if this has been marked correct\n",
       "            } else {\n",
       "                return true; // Keep looking for case that includes this as a correct answer\n",
       "            }\n",
       "        });\n",
       "\n",
       "        if ((!done) && (defaultFB != \"\")) {\n",
       "            fb.innerHTML = jaxify(defaultFB);\n",
       "            //console.log(\"Default feedback\", defaultFB);\n",
       "        }\n",
       "\n",
       "        fb.style.display = \"block\";\n",
       "        if (correct) {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"correctButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "        } else {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            console.log(submission);\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            //console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            if (submission == ths.value){\n",
       "                responses[qnum]= submission;\n",
       "            } else {\n",
       "                responses[qnum]= ths.value + \"(\" + submission +\")\";\n",
       "            }\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "\n",
       "        if (typeof MathJax != 'undefined') {\n",
       "            var version = MathJax.version;\n",
       "            console.log('MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([fb]);\n",
       "            }\n",
       "        } else {\n",
       "            console.log('MathJax not detected');\n",
       "        }\n",
       "        return false;\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function isValid(el, charC) {\n",
       "    //console.log(\"Input char: \", charC);\n",
       "    if (charC == 46) {\n",
       "        if (el.value.indexOf('.') === -1) {\n",
       "            return true;\n",
       "        } else if (el.value.indexOf('/') != -1) {\n",
       "            var parts = el.value.split('/');\n",
       "            if (parts[1].indexOf('.') === -1) {\n",
       "                return true;\n",
       "            }\n",
       "        }\n",
       "        else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 47) {\n",
       "        if (el.value.indexOf('/') === -1) {\n",
       "            if ((el.value != \"\") && (el.value != \".\")) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 45) {\n",
       "        var edex = el.value.indexOf('e');\n",
       "        if (edex == -1) {\n",
       "            edex = el.value.indexOf('E');\n",
       "        }\n",
       "\n",
       "        if (el.value == \"\") {\n",
       "            return true;\n",
       "        } else if (edex == (el.value.length - 1)) { // If just after e or E\n",
       "            return true;\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 101) { // \"e\"\n",
       "        if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n",
       "            // Prev symbol must be digit or decimal point:\n",
       "            if (el.value.slice(-1).search(/\\d/) >= 0) {\n",
       "                return true;\n",
       "            } else if (el.value.slice(-1).search(/\\./) >= 0) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else {\n",
       "        if (charC > 31 && (charC < 48 || charC > 57))\n",
       "            return false;\n",
       "    }\n",
       "    return true;\n",
       "}\n",
       "\n",
       "function numeric_keypress(evnt) {\n",
       "    var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n",
       "\n",
       "    if (charC == 13) {\n",
       "        check_numeric(this, evnt);\n",
       "    } else {\n",
       "        return isValid(this, charC);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "function make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n",
       "\n",
       "\n",
       "\n",
       "    //console.log(answer);\n",
       "\n",
       "\n",
       "    outerqDiv.className = \"NumericQn\";\n",
       "    aDiv.style.display = 'block';\n",
       "\n",
       "    var lab = document.createElement(\"label\");\n",
       "    lab.className = \"InpLabel\";\n",
       "    lab.textContent = \"Type numeric answer here:\";\n",
       "    aDiv.append(lab);\n",
       "\n",
       "    var inp = document.createElement(\"input\");\n",
       "    inp.type = \"text\";\n",
       "    //inp.id=\"input-\"+id;\n",
       "    inp.id = id + \"-0\";\n",
       "    inp.className = \"Input-text\";\n",
       "    inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n",
       "    if (\"precision\" in qa) {\n",
       "        inp.setAttribute('data-precision', qa.precision);\n",
       "    }\n",
       "    aDiv.append(inp);\n",
       "    //console.log(inp);\n",
       "\n",
       "    //inp.addEventListener(\"keypress\", check_numeric);\n",
       "    //inp.addEventListener(\"keypress\", numeric_keypress);\n",
       "    /*\n",
       "    inp.addEventListener(\"keypress\", function(event) {\n",
       "        return numeric_keypress(this, event);\n",
       "    }\n",
       "                        );\n",
       "                        */\n",
       "    //inp.onkeypress=\"return numeric_keypress(this, event)\";\n",
       "    inp.onkeypress = numeric_keypress;\n",
       "    inp.onpaste = event => false;\n",
       "\n",
       "    inp.addEventListener(\"focus\", function (event) {\n",
       "        this.value = \"\";\n",
       "        return false;\n",
       "    }\n",
       "    );\n",
       "\n",
       "\n",
       "}\n",
       "function jaxify(string) {\n",
       "    var mystring = string;\n",
       "\n",
       "    var count = 0;\n",
       "    var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "\n",
       "    var count2 = 0;\n",
       "    var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "\n",
       "    //console.log(loc);\n",
       "\n",
       "    while ((loc >= 0) || (loc2 >= 0)) {\n",
       "\n",
       "        /* Have to replace all the double $$ first with current implementation */\n",
       "        if (loc2 >= 0) {\n",
       "            if (count2 % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n",
       "            }\n",
       "            count2++;\n",
       "        } else {\n",
       "            if (count % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n",
       "            }\n",
       "            count++;\n",
       "        }\n",
       "        loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "        loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "        //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n",
       "    }\n",
       "\n",
       "    //console.log(mystring);\n",
       "    return mystring;\n",
       "}\n",
       "\n",
       "\n",
       "function show_questions(json, mydiv) {\n",
       "    console.log('show_questions');\n",
       "    //var mydiv=document.getElementById(myid);\n",
       "    var shuffle_questions = mydiv.dataset.shufflequestions;\n",
       "    var num_questions = mydiv.dataset.numquestions;\n",
       "    var shuffle_answers = mydiv.dataset.shuffleanswers;\n",
       "    var max_width = mydiv.dataset.maxwidth;\n",
       "\n",
       "    if (num_questions > json.length) {\n",
       "        num_questions = json.length;\n",
       "    }\n",
       "\n",
       "    var questions;\n",
       "    if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n",
       "        //console.log(num_questions+\",\"+json.length);\n",
       "        questions = getRandomSubarray(json, num_questions);\n",
       "    } else {\n",
       "        questions = json;\n",
       "    }\n",
       "\n",
       "    //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n",
       "\n",
       "    // Iterate over questions\n",
       "    questions.forEach((qa, index, array) => {\n",
       "        //console.log(qa.question); \n",
       "\n",
       "        var id = makeid(8);\n",
       "        //console.log(id);\n",
       "\n",
       "\n",
       "        // Create Div to contain question and answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        //iDiv.id = 'quizWrap' + id + index;\n",
       "        iDiv.id = 'quizWrap' + id;\n",
       "        iDiv.className = 'Quiz';\n",
       "        iDiv.setAttribute('data-qnum', index);\n",
       "        iDiv.style.maxWidth  =max_width+\"px\";\n",
       "        mydiv.appendChild(iDiv);\n",
       "        // iDiv.innerHTML=qa.question;\n",
       "        \n",
       "        var outerqDiv = document.createElement('div');\n",
       "        outerqDiv.id = \"OuterquizQn\" + id + index;\n",
       "        // Create div to contain question part\n",
       "        var qDiv = document.createElement('div');\n",
       "        qDiv.id = \"quizQn\" + id + index;\n",
       "        \n",
       "        if (qa.question) {\n",
       "            iDiv.append(outerqDiv);\n",
       "\n",
       "            //qDiv.textContent=qa.question;\n",
       "            qDiv.innerHTML = jaxify(qa.question);\n",
       "            outerqDiv.append(qDiv);\n",
       "        }\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeDiv;\n",
       "        if (\"code\" in qa) {\n",
       "            codeDiv = document.createElement('div');\n",
       "            codeDiv.id = \"code\" + id + index;\n",
       "            codeDiv.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeDiv.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = qa.code;\n",
       "            outerqDiv.append(codeDiv);\n",
       "            //console.log(codeDiv);\n",
       "        }\n",
       "\n",
       "\n",
       "        // Create div to contain answer part\n",
       "        var aDiv = document.createElement('div');\n",
       "        aDiv.id = \"quizAns\" + id + index;\n",
       "        aDiv.className = 'Answer';\n",
       "        iDiv.append(aDiv);\n",
       "\n",
       "        //console.log(qa.type);\n",
       "\n",
       "        var num_correct;\n",
       "        if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n",
       "            num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n",
       "            if (\"answer_cols\" in qa) {\n",
       "                //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n",
       "                aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n",
       "            }\n",
       "        } else if (qa.type == \"numeric\") {\n",
       "            //console.log(\"numeric\");\n",
       "            make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n",
       "        }\n",
       "\n",
       "\n",
       "        //Make div for feedback\n",
       "        var fb = document.createElement(\"div\");\n",
       "        fb.id = \"fb\" + id;\n",
       "        //fb.style=\"font-size: 20px;text-align:center;\";\n",
       "        fb.className = \"Feedback\";\n",
       "        fb.setAttribute(\"data-answeredcorrect\", 0);\n",
       "        fb.setAttribute(\"data-numcorrect\", num_correct);\n",
       "        iDiv.append(fb);\n",
       "\n",
       "\n",
       "    });\n",
       "    var preserveResponses = mydiv.dataset.preserveresponses;\n",
       "    console.log(preserveResponses);\n",
       "    console.log(preserveResponses == \"true\");\n",
       "    if (preserveResponses == \"true\") {\n",
       "        console.log(preserveResponses);\n",
       "        // Create Div to contain record of answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        iDiv.id = 'responses' + mydiv.id;\n",
       "        iDiv.className = 'JCResponses';\n",
       "        // Create a place to store responses as an empty array\n",
       "        iDiv.setAttribute('data-responses', '[]');\n",
       "\n",
       "        // Dummy Text\n",
       "        iDiv.innerHTML=\"<b>Select your answers and then follow the directions that will appear here.</b>\"\n",
       "        //iDiv.className = 'Quiz';\n",
       "        mydiv.appendChild(iDiv);\n",
       "    }\n",
       "//console.log(\"At end of show_questions\");\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        console.log(\"MathJax version\", MathJax.version);\n",
       "        var version = MathJax.version;\n",
       "        setTimeout(function(){\n",
       "            var version = MathJax.version;\n",
       "            console.log('After sleep, MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            }\n",
       "        }, 500);\n",
       "if (typeof version == 'undefined') {\n",
       "        } else\n",
       "        {\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            } else {\n",
       "                console.log(\"MathJax not found\");\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "    return false;\n",
       "}\n",
       "/* This is to handle asynchrony issues in loading Jupyter notebooks\n",
       "           where the quiz has been previously run. The Javascript was generally\n",
       "           being run before the div was added to the DOM. I tried to do this\n",
       "           more elegantly using Mutation Observer, but I didn't get it to work.\n",
       "\n",
       "           Someone more knowledgeable could make this better ;-) */\n",
       "\n",
       "        function try_show() {\n",
       "          if(document.getElementById(\"NRlvDVdQUDsv\")) {\n",
       "            show_questions(questionsNRlvDVdQUDsv,  NRlvDVdQUDsv); \n",
       "          } else {\n",
       "             setTimeout(try_show, 200);\n",
       "          }\n",
       "        };\n",
       "    \n",
       "        {\n",
       "        // console.log(element);\n",
       "\n",
       "        //console.log(\"NRlvDVdQUDsv\");\n",
       "        // console.log(document.getElementById(\"NRlvDVdQUDsv\"));\n",
       "\n",
       "        try_show();\n",
       "        }\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jupyterquiz import display_quiz\n",
    "display_quiz(\"questions/sciktilearn_question_decision_tree.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef15e32-bce8-4ff3-82d3-244e4efa7132",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Further Models\n",
    "This section of the course has only introducded a very small subset of models that are avaliable, alongside not diving into the different arguements that are avaliable. If you are interested in learning more about the models themselves, and key practical aspects of deploying and ensuring the validity of models you develop then please attend this couses next course \"Introduction to Machine Learning\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1693454a-790c-4820-a5d3-e2aacbce119d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"JHsHOqjqUfgx\" data-shufflequestions=\"False\"\n",
       "               data-shuffleanswers=\"True\"\n",
       "               data-preserveresponses=\"false\"\n",
       "               data-numquestions=\"1000000\"\n",
       "               data-maxwidth=\"600\"\n",
       "               style=\"border-radius: 10px; text-align: left\"> <style>\n",
       "#JHsHOqjqUfgx {\n",
       "   --jq-multiple-choice-bg: #6f78ffff;\n",
       "   --jq-mc-button-bg: #fafafa;\n",
       "   --jq-mc-button-border: #e0e0e0e0;\n",
       "   --jq-mc-button-inset-shadow: #555555;\n",
       "   --jq-many-choice-bg: #f75c03ff;\n",
       "   --jq-numeric-bg: #392061ff;\n",
       "   --jq-numeric-input-bg: #c0c0c0;\n",
       "   --jq-numeric-input-label: #101010;\n",
       "   --jq-numeric-input-shadow: #999999;\n",
       "   --jq-incorrect-color: #c80202;\n",
       "   --jq-correct-color: #009113;\n",
       "   --jq-text-color: #fafafa;\n",
       "}\n",
       "\n",
       ".Quiz {\n",
       "    max-width: 600px;\n",
       "    margin-top: 15px;\n",
       "    margin-left: auto;\n",
       "    margin-right: auto;\n",
       "    margin-bottom: 15px;\n",
       "    padding-bottom: 4px;\n",
       "    padding-top: 4px;\n",
       "    line-height: 1.1;\n",
       "    font-size: 16pt;\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".QuizCode {\n",
       "    font-size: 14pt;\n",
       "    margin-top: 10px;\n",
       "    margin-left: 20px;\n",
       "    margin-right: 20px;\n",
       "}\n",
       "\n",
       ".QuizCode>pre {\n",
       "    padding: 4px;\n",
       "}\n",
       "\n",
       ".Answer {\n",
       "    margin: 10px 0;\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 1fr;\n",
       "    grid-gap: 10px;\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".Feedback {\n",
       "    font-size: 16pt;\n",
       "    text-align: center;\n",
       "    min-height: 2em;\n",
       "}\n",
       "\n",
       ".Input {\n",
       "    align: left;\n",
       "    font-size: 20pt;\n",
       "}\n",
       "\n",
       ".Input-text {\n",
       "    display: block;\n",
       "    margin: 10px;\n",
       "    color: inherit;\n",
       "    width: 140px;\n",
       "    background-color: var(--jq-numeric-input-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    padding: 5px;\n",
       "    padding-left: 10px;\n",
       "    font-family: inherit;\n",
       "    font-size: 20px;\n",
       "    font-weight: inherit;\n",
       "    line-height: 20pt;\n",
       "    border: none;\n",
       "    border-radius: 0.2rem;\n",
       "    transition: box-shadow 0.1s);\n",
       "}\n",
       "\n",
       ".Input-text:focus {\n",
       "    outline: none;\n",
       "    background-color: var(--jq-numeric-input-bg);\n",
       "    box-shadow: 0.6rem 0.8rem 1.4rem -0.5rem var(--jq-numeric-input-shadow);\n",
       "}\n",
       "\n",
       ".MCButton {\n",
       "    background: var(--jq-mc-button-bg);\n",
       "    border: 1px solid var(--jq-mc-button-border);\n",
       "    border-radius: inherit;\n",
       "    padding: 10px;\n",
       "    font-size: 16px;\n",
       "    cursor: pointer;\n",
       "    text-align: center;\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    justify-content: center;\n",
       "}\n",
       "\n",
       ".MCButton p {\n",
       "    color: inherit;\n",
       "}\n",
       "\n",
       ".MultipleChoiceQn {\n",
       "    padding: 10px;\n",
       "    background: var(--jq-multiple-choice-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".ManyChoiceQn {\n",
       "    padding: 10px;\n",
       "    background: var(--jq-many-choice-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".NumericQn {\n",
       "    padding: 10px;\n",
       "    background: var(--jq-numeric-bg);\n",
       "    color: var(--jq-text-color);\n",
       "    border-radius: inherit;\n",
       "}\n",
       "\n",
       ".NumericQn p {\n",
       "    color: inherit;\n",
       "}\n",
       "\n",
       ".InpLabel {\n",
       "    line-height: 34px;\n",
       "    float: left;\n",
       "    margin-right: 10px;\n",
       "    color: var(--jq-numeric-input-label);\n",
       "    font-size: 15pt;\n",
       "}\n",
       "\n",
       ".incorrect {\n",
       "    color: var(--jq-incorrect-color);\n",
       "}\n",
       "\n",
       ".correct {\n",
       "    color: var(--jq-correct-color);\n",
       "}\n",
       "\n",
       ".correctButton {\n",
       "    /*\n",
       "    background: var(--jq-correct-color);\n",
       "   */\n",
       "    animation: correct-anim 0.6s ease;\n",
       "    animation-fill-mode: forwards;\n",
       "    color: var(--jq-text-color);\n",
       "    box-shadow: inset 0px 0px 5px var(--jq-mc-button-inset-shadow);\n",
       "    outline: none;\n",
       "}\n",
       "\n",
       ".incorrectButton {\n",
       "    animation: incorrect-anim 0.8s ease;\n",
       "    animation-fill-mode: forwards;\n",
       "    color: var(--jq-text-color);\n",
       "    box-shadow: inset 0px 0px 5px var(--jq-mc-button-inset-shadow);\n",
       "    outline: none;\n",
       "}\n",
       "\n",
       "@keyframes incorrect-anim {\n",
       "    100% {\n",
       "        background-color: var(--jq-incorrect-color);\n",
       "    }\n",
       "}\n",
       "\n",
       "@keyframes correct-anim {\n",
       "    100% {\n",
       "        background-color: var(--jq-correct-color);\n",
       "    }\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var questionsJHsHOqjqUfgx=[\n",
       "    {\n",
       "        \"question\": \"Which of the following tasks can be performed using Scikit-Learn?\",\n",
       "        \"type\": \"many_choice\",\n",
       "        \"answers\": [\n",
       "            {\n",
       "                \"answer\": \"Classification\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"Regression\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"Clustering\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"All other choices\",\n",
       "                \"correct\": true,\n",
       "                \"feedback\": \"Correct\"\n",
       "            }\n",
       "        ]\n",
       "    },\n",
       "    {\n",
       "        \"question\": \"In a linear regression model, which function is used to fit the model to the data?\",\n",
       "        \"type\": \"many_choice\",\n",
       "        \"answers\": [\n",
       "            {\n",
       "                \"answer\": \"model.predict()\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"model.transform()\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"model.fit()\",\n",
       "                \"correct\": true,\n",
       "                \"feedback\": \"Correct\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"model.evaluate()\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            }\n",
       "        ]\n",
       "    },\n",
       "    {\n",
       "        \"question\": \"In the context of decision trees, what does a leaf node represent?\",\n",
       "        \"type\": \"many_choice\",\n",
       "        \"answers\": [\n",
       "            {\n",
       "                \"answer\": \"The start of a decision process\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"A split in the data\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"The outcome or final decision\",\n",
       "                \"correct\": true,\n",
       "                \"feedback\": \"Correct\"\n",
       "            },\n",
       "            {\n",
       "                \"answer\": \"An intermediate step in the decision-making process\",\n",
       "                \"correct\": false,\n",
       "                \"feedback\": \"Incorrect\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "];\n",
       "    // Make a random ID\n",
       "function makeid(length) {\n",
       "    var result = [];\n",
       "    var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n",
       "    var charactersLength = characters.length;\n",
       "    for (var i = 0; i < length; i++) {\n",
       "        result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n",
       "    }\n",
       "    return result.join('');\n",
       "}\n",
       "\n",
       "// Choose a random subset of an array. Can also be used to shuffle the array\n",
       "function getRandomSubarray(arr, size) {\n",
       "    var shuffled = arr.slice(0), i = arr.length, temp, index;\n",
       "    while (i--) {\n",
       "        index = Math.floor((i + 1) * Math.random());\n",
       "        temp = shuffled[index];\n",
       "        shuffled[index] = shuffled[i];\n",
       "        shuffled[i] = temp;\n",
       "    }\n",
       "    return shuffled.slice(0, size);\n",
       "}\n",
       "\n",
       "function printResponses(responsesContainer) {\n",
       "    var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "    var stringResponses='<B>IMPORTANT!</B>To preserve this answer sequence for submission, when you have finalized your answers: <ol> <li> Copy the text in this cell below \"Answer String\"</li> <li> Double click on the cell directly below the Answer String, labeled \"Replace Me\"</li> <li> Select the whole \"Replace Me\" text</li> <li> Paste in your answer string and press shift-Enter.</li><li>Save the notebook using the save icon or File->Save Notebook menu item</li></ul><br><br><br><b>Answer String:</b><br> ';\n",
       "    console.log(responses);\n",
       "    responses.forEach((response, index) => {\n",
       "        if (response) {\n",
       "            console.log(index + ': ' + response);\n",
       "            stringResponses+= index + ': ' + response +\"<BR>\";\n",
       "        }\n",
       "    });\n",
       "    responsesContainer.innerHTML=stringResponses;\n",
       "}\n",
       "function check_mc() {\n",
       "    var id = this.id.split('-')[0];\n",
       "    //var response = this.id.split('-')[1];\n",
       "    //console.log(response);\n",
       "    //console.log(\"In check_mc(), id=\"+id);\n",
       "    //console.log(event.srcElement.id)           \n",
       "    //console.log(event.srcElement.dataset.correct)   \n",
       "    //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "    var label = event.srcElement;\n",
       "    //console.log(label, label.nodeName);\n",
       "    var depth = 0;\n",
       "    while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n",
       "        label = label.parentElement;\n",
       "        console.log(depth, label);\n",
       "        depth++;\n",
       "    }\n",
       "\n",
       "\n",
       "\n",
       "    var answers = label.parentElement.children;\n",
       "\n",
       "    //console.log(answers);\n",
       "\n",
       "\n",
       "    // Split behavior based on multiple choice vs many choice:\n",
       "    var fb = document.getElementById(\"fb\" + id);\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "    if (fb.dataset.numcorrect == 1) {\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            responses[qnum]= response;\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "        \n",
       "        for (var i = 0; i < answers.length; i++) {\n",
       "            var child = answers[i];\n",
       "            //console.log(child);\n",
       "            child.className = \"MCButton\";\n",
       "        }\n",
       "\n",
       "\n",
       "\n",
       "        if (label.dataset.correct == \"true\") {\n",
       "            // console.log(\"Correct action\");\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Correct!\";\n",
       "            }\n",
       "            label.classList.add(\"correctButton\");\n",
       "\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                fb.textContent = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                fb.textContent = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            //console.log(\"Error action\");\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "    }\n",
       "    else {\n",
       "        var reset = false;\n",
       "        var feedback;\n",
       "         if (label.dataset.correct == \"true\") {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Correct!\";\n",
       "            }\n",
       "            if (label.dataset.answered <= 0) {\n",
       "                if (fb.dataset.answeredcorrect < 0) {\n",
       "                    fb.dataset.answeredcorrect = 1;\n",
       "                    reset = true;\n",
       "                } else {\n",
       "                    fb.dataset.answeredcorrect++;\n",
       "                }\n",
       "                if (reset) {\n",
       "                    for (var i = 0; i < answers.length; i++) {\n",
       "                        var child = answers[i];\n",
       "                        child.className = \"MCButton\";\n",
       "                        child.dataset.answered = 0;\n",
       "                    }\n",
       "                }\n",
       "                label.classList.add(\"correctButton\");\n",
       "                label.dataset.answered = 1;\n",
       "                fb.className = \"Feedback\";\n",
       "                fb.classList.add(\"correct\");\n",
       "\n",
       "            }\n",
       "        } else {\n",
       "            if (\"feedback\" in label.dataset) {\n",
       "                feedback = jaxify(label.dataset.feedback);\n",
       "            } else {\n",
       "                feedback = \"Incorrect -- try again.\";\n",
       "            }\n",
       "            if (fb.dataset.answeredcorrect > 0) {\n",
       "                fb.dataset.answeredcorrect = -1;\n",
       "                reset = true;\n",
       "            } else {\n",
       "                fb.dataset.answeredcorrect--;\n",
       "            }\n",
       "\n",
       "            if (reset) {\n",
       "                for (var i = 0; i < answers.length; i++) {\n",
       "                    var child = answers[i];\n",
       "                    child.className = \"MCButton\";\n",
       "                    child.dataset.answered = 0;\n",
       "                }\n",
       "            }\n",
       "            label.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
       "            var response = label.firstChild.innerText;\n",
       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            if (label.dataset.correct == \"true\") {\n",
       "                if (typeof(responses[qnum]) == \"object\"){\n",
       "                    if (!responses[qnum].includes(response))\n",
       "                        responses[qnum].push(response);\n",
       "                } else{\n",
       "                    responses[qnum]= [ response ];\n",
       "                }\n",
       "            } else {\n",
       "                responses[qnum]= response;\n",
       "            }\n",
       "            console.log(responses);\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End save responses stuff\n",
       "\n",
       "\n",
       "\n",
       "        var numcorrect = fb.dataset.numcorrect;\n",
       "        var answeredcorrect = fb.dataset.answeredcorrect;\n",
       "        if (answeredcorrect >= 0) {\n",
       "            fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n",
       "        } else {\n",
       "            fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n",
       "        }\n",
       "\n",
       "\n",
       "    }\n",
       "\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        var version = MathJax.version;\n",
       "        console.log('MathJax version', version);\n",
       "        if (version[0] == \"2\") {\n",
       "            MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "        } else if (version[0] == \"3\") {\n",
       "            MathJax.typeset([fb]);\n",
       "        }\n",
       "    } else {\n",
       "        console.log('MathJax not detected');\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n",
       "    var shuffled;\n",
       "    if (shuffle_answers == \"True\") {\n",
       "        //console.log(shuffle_answers+\" read as true\");\n",
       "        shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n",
       "    } else {\n",
       "        //console.log(shuffle_answers+\" read as false\");\n",
       "        shuffled = qa.answers;\n",
       "    }\n",
       "\n",
       "\n",
       "    var num_correct = 0;\n",
       "\n",
       "\n",
       "\n",
       "    shuffled.forEach((item, index, ans_array) => {\n",
       "        //console.log(answer);\n",
       "\n",
       "        // Make input element\n",
       "        var inp = document.createElement(\"input\");\n",
       "        inp.type = \"radio\";\n",
       "        inp.id = \"quizo\" + id + index;\n",
       "        inp.style = \"display:none;\";\n",
       "        aDiv.append(inp);\n",
       "\n",
       "        //Make label for input element\n",
       "        var lab = document.createElement(\"label\");\n",
       "        lab.className = \"MCButton\";\n",
       "        lab.id = id + '-' + index;\n",
       "        lab.onclick = check_mc;\n",
       "        var aSpan = document.createElement('span');\n",
       "        aSpan.classsName = \"\";\n",
       "        //qDiv.id=\"quizQn\"+id+index;\n",
       "        if (\"answer\" in item) {\n",
       "            aSpan.innerHTML = jaxify(item.answer);\n",
       "            //aSpan.innerHTML=item.answer;\n",
       "        }\n",
       "        lab.append(aSpan);\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeSpan;\n",
       "        if (\"code\" in item) {\n",
       "            codeSpan = document.createElement('span');\n",
       "            codeSpan.id = \"code\" + id + index;\n",
       "            codeSpan.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeSpan.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = item.code;\n",
       "            lab.append(codeSpan);\n",
       "            //console.log(codeSpan);\n",
       "        }\n",
       "\n",
       "        //lab.textContent=item.answer;\n",
       "\n",
       "        // Set the data attributes for the answer\n",
       "        lab.setAttribute('data-correct', item.correct);\n",
       "        if (item.correct) {\n",
       "            num_correct++;\n",
       "        }\n",
       "        if (\"feedback\" in item) {\n",
       "            lab.setAttribute('data-feedback', item.feedback);\n",
       "        }\n",
       "        lab.setAttribute('data-answered', 0);\n",
       "\n",
       "        aDiv.append(lab);\n",
       "\n",
       "    });\n",
       "\n",
       "    if (num_correct > 1) {\n",
       "        outerqDiv.className = \"ManyChoiceQn\";\n",
       "    } else {\n",
       "        outerqDiv.className = \"MultipleChoiceQn\";\n",
       "    }\n",
       "\n",
       "    return num_correct;\n",
       "\n",
       "}\n",
       "function check_numeric(ths, event) {\n",
       "\n",
       "    if (event.keyCode === 13) {\n",
       "        ths.blur();\n",
       "\n",
       "        var id = ths.id.split('-')[0];\n",
       "\n",
       "        var submission = ths.value;\n",
       "        if (submission.indexOf('/') != -1) {\n",
       "            var sub_parts = submission.split('/');\n",
       "            //console.log(sub_parts);\n",
       "            submission = sub_parts[0] / sub_parts[1];\n",
       "        }\n",
       "        //console.log(\"Reader entered\", submission);\n",
       "\n",
       "        if (\"precision\" in ths.dataset) {\n",
       "            var precision = ths.dataset.precision;\n",
       "            // console.log(\"1:\", submission)\n",
       "            submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n",
       "            // console.log(\"Rounded to \", submission, \" precision=\", precision  );\n",
       "        }\n",
       "\n",
       "\n",
       "        //console.log(\"In check_numeric(), id=\"+id);\n",
       "        //console.log(event.srcElement.id)           \n",
       "        //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "        var fb = document.getElementById(\"fb\" + id);\n",
       "        fb.style.display = \"none\";\n",
       "        fb.textContent = \"Incorrect -- try again.\";\n",
       "\n",
       "        var answers = JSON.parse(ths.dataset.answers);\n",
       "        //console.log(answers);\n",
       "\n",
       "        var defaultFB = \"\";\n",
       "        var correct;\n",
       "        var done = false;\n",
       "        answers.every(answer => {\n",
       "            //console.log(answer.type);\n",
       "\n",
       "            correct = false;\n",
       "            // if (answer.type==\"value\"){\n",
       "            if ('value' in answer) {\n",
       "                if (submission == answer.value) {\n",
       "                    if (\"feedback\" in answer) {\n",
       "                        fb.textContent = jaxify(answer.feedback);\n",
       "                    } else {\n",
       "                        fb.textContent = jaxify(\"Correct\");\n",
       "                    }\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "                // } else if (answer.type==\"range\") {\n",
       "            } else if ('range' in answer) {\n",
       "                //console.log(answer.range);\n",
       "                if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n",
       "                    fb.textContent = jaxify(answer.feedback);\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "            } else if (answer.type == \"default\") {\n",
       "                defaultFB = answer.feedback;\n",
       "            }\n",
       "            if (done) {\n",
       "                return false; // Break out of loop if this has been marked correct\n",
       "            } else {\n",
       "                return true; // Keep looking for case that includes this as a correct answer\n",
       "            }\n",
       "        });\n",
       "\n",
       "        if ((!done) && (defaultFB != \"\")) {\n",
       "            fb.innerHTML = jaxify(defaultFB);\n",
       "            //console.log(\"Default feedback\", defaultFB);\n",
       "        }\n",
       "\n",
       "        fb.style.display = \"block\";\n",
       "        if (correct) {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"correctButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "        } else {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            console.log(submission);\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            //console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            if (submission == ths.value){\n",
       "                responses[qnum]= submission;\n",
       "            } else {\n",
       "                responses[qnum]= ths.value + \"(\" + submission +\")\";\n",
       "            }\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "\n",
       "        if (typeof MathJax != 'undefined') {\n",
       "            var version = MathJax.version;\n",
       "            console.log('MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([fb]);\n",
       "            }\n",
       "        } else {\n",
       "            console.log('MathJax not detected');\n",
       "        }\n",
       "        return false;\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function isValid(el, charC) {\n",
       "    //console.log(\"Input char: \", charC);\n",
       "    if (charC == 46) {\n",
       "        if (el.value.indexOf('.') === -1) {\n",
       "            return true;\n",
       "        } else if (el.value.indexOf('/') != -1) {\n",
       "            var parts = el.value.split('/');\n",
       "            if (parts[1].indexOf('.') === -1) {\n",
       "                return true;\n",
       "            }\n",
       "        }\n",
       "        else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 47) {\n",
       "        if (el.value.indexOf('/') === -1) {\n",
       "            if ((el.value != \"\") && (el.value != \".\")) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 45) {\n",
       "        var edex = el.value.indexOf('e');\n",
       "        if (edex == -1) {\n",
       "            edex = el.value.indexOf('E');\n",
       "        }\n",
       "\n",
       "        if (el.value == \"\") {\n",
       "            return true;\n",
       "        } else if (edex == (el.value.length - 1)) { // If just after e or E\n",
       "            return true;\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 101) { // \"e\"\n",
       "        if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n",
       "            // Prev symbol must be digit or decimal point:\n",
       "            if (el.value.slice(-1).search(/\\d/) >= 0) {\n",
       "                return true;\n",
       "            } else if (el.value.slice(-1).search(/\\./) >= 0) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else {\n",
       "        if (charC > 31 && (charC < 48 || charC > 57))\n",
       "            return false;\n",
       "    }\n",
       "    return true;\n",
       "}\n",
       "\n",
       "function numeric_keypress(evnt) {\n",
       "    var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n",
       "\n",
       "    if (charC == 13) {\n",
       "        check_numeric(this, evnt);\n",
       "    } else {\n",
       "        return isValid(this, charC);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "function make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n",
       "\n",
       "\n",
       "\n",
       "    //console.log(answer);\n",
       "\n",
       "\n",
       "    outerqDiv.className = \"NumericQn\";\n",
       "    aDiv.style.display = 'block';\n",
       "\n",
       "    var lab = document.createElement(\"label\");\n",
       "    lab.className = \"InpLabel\";\n",
       "    lab.textContent = \"Type numeric answer here:\";\n",
       "    aDiv.append(lab);\n",
       "\n",
       "    var inp = document.createElement(\"input\");\n",
       "    inp.type = \"text\";\n",
       "    //inp.id=\"input-\"+id;\n",
       "    inp.id = id + \"-0\";\n",
       "    inp.className = \"Input-text\";\n",
       "    inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n",
       "    if (\"precision\" in qa) {\n",
       "        inp.setAttribute('data-precision', qa.precision);\n",
       "    }\n",
       "    aDiv.append(inp);\n",
       "    //console.log(inp);\n",
       "\n",
       "    //inp.addEventListener(\"keypress\", check_numeric);\n",
       "    //inp.addEventListener(\"keypress\", numeric_keypress);\n",
       "    /*\n",
       "    inp.addEventListener(\"keypress\", function(event) {\n",
       "        return numeric_keypress(this, event);\n",
       "    }\n",
       "                        );\n",
       "                        */\n",
       "    //inp.onkeypress=\"return numeric_keypress(this, event)\";\n",
       "    inp.onkeypress = numeric_keypress;\n",
       "    inp.onpaste = event => false;\n",
       "\n",
       "    inp.addEventListener(\"focus\", function (event) {\n",
       "        this.value = \"\";\n",
       "        return false;\n",
       "    }\n",
       "    );\n",
       "\n",
       "\n",
       "}\n",
       "function jaxify(string) {\n",
       "    var mystring = string;\n",
       "\n",
       "    var count = 0;\n",
       "    var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "\n",
       "    var count2 = 0;\n",
       "    var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "\n",
       "    //console.log(loc);\n",
       "\n",
       "    while ((loc >= 0) || (loc2 >= 0)) {\n",
       "\n",
       "        /* Have to replace all the double $$ first with current implementation */\n",
       "        if (loc2 >= 0) {\n",
       "            if (count2 % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n",
       "            }\n",
       "            count2++;\n",
       "        } else {\n",
       "            if (count % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n",
       "            }\n",
       "            count++;\n",
       "        }\n",
       "        loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "        loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "        //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n",
       "    }\n",
       "\n",
       "    //console.log(mystring);\n",
       "    return mystring;\n",
       "}\n",
       "\n",
       "\n",
       "function show_questions(json, mydiv) {\n",
       "    console.log('show_questions');\n",
       "    //var mydiv=document.getElementById(myid);\n",
       "    var shuffle_questions = mydiv.dataset.shufflequestions;\n",
       "    var num_questions = mydiv.dataset.numquestions;\n",
       "    var shuffle_answers = mydiv.dataset.shuffleanswers;\n",
       "    var max_width = mydiv.dataset.maxwidth;\n",
       "\n",
       "    if (num_questions > json.length) {\n",
       "        num_questions = json.length;\n",
       "    }\n",
       "\n",
       "    var questions;\n",
       "    if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n",
       "        //console.log(num_questions+\",\"+json.length);\n",
       "        questions = getRandomSubarray(json, num_questions);\n",
       "    } else {\n",
       "        questions = json;\n",
       "    }\n",
       "\n",
       "    //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n",
       "\n",
       "    // Iterate over questions\n",
       "    questions.forEach((qa, index, array) => {\n",
       "        //console.log(qa.question); \n",
       "\n",
       "        var id = makeid(8);\n",
       "        //console.log(id);\n",
       "\n",
       "\n",
       "        // Create Div to contain question and answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        //iDiv.id = 'quizWrap' + id + index;\n",
       "        iDiv.id = 'quizWrap' + id;\n",
       "        iDiv.className = 'Quiz';\n",
       "        iDiv.setAttribute('data-qnum', index);\n",
       "        iDiv.style.maxWidth  =max_width+\"px\";\n",
       "        mydiv.appendChild(iDiv);\n",
       "        // iDiv.innerHTML=qa.question;\n",
       "        \n",
       "        var outerqDiv = document.createElement('div');\n",
       "        outerqDiv.id = \"OuterquizQn\" + id + index;\n",
       "        // Create div to contain question part\n",
       "        var qDiv = document.createElement('div');\n",
       "        qDiv.id = \"quizQn\" + id + index;\n",
       "        \n",
       "        if (qa.question) {\n",
       "            iDiv.append(outerqDiv);\n",
       "\n",
       "            //qDiv.textContent=qa.question;\n",
       "            qDiv.innerHTML = jaxify(qa.question);\n",
       "            outerqDiv.append(qDiv);\n",
       "        }\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeDiv;\n",
       "        if (\"code\" in qa) {\n",
       "            codeDiv = document.createElement('div');\n",
       "            codeDiv.id = \"code\" + id + index;\n",
       "            codeDiv.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeDiv.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = qa.code;\n",
       "            outerqDiv.append(codeDiv);\n",
       "            //console.log(codeDiv);\n",
       "        }\n",
       "\n",
       "\n",
       "        // Create div to contain answer part\n",
       "        var aDiv = document.createElement('div');\n",
       "        aDiv.id = \"quizAns\" + id + index;\n",
       "        aDiv.className = 'Answer';\n",
       "        iDiv.append(aDiv);\n",
       "\n",
       "        //console.log(qa.type);\n",
       "\n",
       "        var num_correct;\n",
       "        if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n",
       "            num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n",
       "            if (\"answer_cols\" in qa) {\n",
       "                //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n",
       "                aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n",
       "            }\n",
       "        } else if (qa.type == \"numeric\") {\n",
       "            //console.log(\"numeric\");\n",
       "            make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n",
       "        }\n",
       "\n",
       "\n",
       "        //Make div for feedback\n",
       "        var fb = document.createElement(\"div\");\n",
       "        fb.id = \"fb\" + id;\n",
       "        //fb.style=\"font-size: 20px;text-align:center;\";\n",
       "        fb.className = \"Feedback\";\n",
       "        fb.setAttribute(\"data-answeredcorrect\", 0);\n",
       "        fb.setAttribute(\"data-numcorrect\", num_correct);\n",
       "        iDiv.append(fb);\n",
       "\n",
       "\n",
       "    });\n",
       "    var preserveResponses = mydiv.dataset.preserveresponses;\n",
       "    console.log(preserveResponses);\n",
       "    console.log(preserveResponses == \"true\");\n",
       "    if (preserveResponses == \"true\") {\n",
       "        console.log(preserveResponses);\n",
       "        // Create Div to contain record of answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        iDiv.id = 'responses' + mydiv.id;\n",
       "        iDiv.className = 'JCResponses';\n",
       "        // Create a place to store responses as an empty array\n",
       "        iDiv.setAttribute('data-responses', '[]');\n",
       "\n",
       "        // Dummy Text\n",
       "        iDiv.innerHTML=\"<b>Select your answers and then follow the directions that will appear here.</b>\"\n",
       "        //iDiv.className = 'Quiz';\n",
       "        mydiv.appendChild(iDiv);\n",
       "    }\n",
       "//console.log(\"At end of show_questions\");\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        console.log(\"MathJax version\", MathJax.version);\n",
       "        var version = MathJax.version;\n",
       "        setTimeout(function(){\n",
       "            var version = MathJax.version;\n",
       "            console.log('After sleep, MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            }\n",
       "        }, 500);\n",
       "if (typeof version == 'undefined') {\n",
       "        } else\n",
       "        {\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            } else {\n",
       "                console.log(\"MathJax not found\");\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "    return false;\n",
       "}\n",
       "/* This is to handle asynchrony issues in loading Jupyter notebooks\n",
       "           where the quiz has been previously run. The Javascript was generally\n",
       "           being run before the div was added to the DOM. I tried to do this\n",
       "           more elegantly using Mutation Observer, but I didn't get it to work.\n",
       "\n",
       "           Someone more knowledgeable could make this better ;-) */\n",
       "\n",
       "        function try_show() {\n",
       "          if(document.getElementById(\"JHsHOqjqUfgx\")) {\n",
       "            show_questions(questionsJHsHOqjqUfgx,  JHsHOqjqUfgx); \n",
       "          } else {\n",
       "             setTimeout(try_show, 200);\n",
       "          }\n",
       "        };\n",
       "    \n",
       "        {\n",
       "        // console.log(element);\n",
       "\n",
       "        //console.log(\"JHsHOqjqUfgx\");\n",
       "        // console.log(document.getElementById(\"JHsHOqjqUfgx\"));\n",
       "\n",
       "        try_show();\n",
       "        }\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jupyterquiz import display_quiz\n",
    "display_quiz(\"questions/summary_scikitlearn.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66165bb0-4fff-4b9c-91c6-57e8c5b64ba7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}