{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework\n",
    "\n",
    "- Use this assignment to practice the chapter's concepts and coding skills in runnable Python code.\n",
    "- Sign in from the account menu before you start working on the assignment.\n",
    "- Write and run your code directly in the coding cells.\n",
    "- Multiple submissions are allowed. The highest score is kept.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**1. Figures and Axes**\n",
    "\n",
    "A marketing analyst builds a four-chart dashboard with `fig, axes = plt.subplots(2, 2)` and then calls `axes[0, 0].set_title(\"Q3 Marketing Dashboard\")`. A colleague says this puts the title above the whole dashboard.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 1 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q1\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q1\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# `axes[0, 0].set_title()` titles only the top-left chart. A title for the whole figure needs `fig.suptitle()`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**2. Object-Oriented Plotting**\n",
    "\n",
    "An analyst's report script builds six charts in one figure, and each chart will be adjusted later. The team lead recommends `plt.subplots()` and calling methods on each Axes object instead of repeated `plt.title()` and `plt.xlabel()` calls, because it makes clear which chart each command changes.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 2 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q2\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q2\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# The object-oriented style stores each Axes in a variable, so every command names the chart it changes. Stateful pyplot calls always act on the current axes, which is easy to lose track of in multi-chart figures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**3. Choosing a Plot Type**\n",
    "\n",
    "An operations manager wants to show how delivery times are spread out at three warehouses, including the median and any unusually slow deliveries. The analyst decides a line plot connecting each warehouse's delivery times in order is the best choice.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 3 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q3\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q3\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# Spread, median, and outliers describe a distribution. A boxplot (or histograms) shows them; a line plot suggests a trend over an ordered sequence.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**4. Shared Axes**\n",
    "\n",
    "Store A sells about 50 units a month and Store B about 500. An analyst puts their monthly sales in side-by-side bar charts created with `sharey=True`. Store A's bars will look much shorter than Store B's, which accurately reflects the difference in sales.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 4 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q4\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q4\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# With a shared y-axis, both charts use the same scale, so bar heights can be compared across charts. Without it, each chart would stretch to fit its own data and the stores would look similar.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**5. Saving Figures**\n",
    "\n",
    "A consultant saves a chart for a printed board report with `fig.savefig(\"sales.png\")`, and it looks blurry on paper. Saving it again with `fig.savefig(\"sales.png\", dpi=300)` makes a sharper image without changing the data in the chart.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 5 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q5\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch06-hw-q5\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# `dpi` controls the resolution of the saved file. A higher dpi gives more pixels per inch for print; the plotted data stays the same.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 6. Sales vs Target\n",
    "# The operations team wants monthly sales to stand out against the target.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Plot sales with linewidth=2.5, square markers (marker='s'),\n",
    "#      and the label \"Sales\".\n",
    "#   3. Plot target as a red dotted line (color='red', ls=':')\n",
    "#      with the label \"Target\".\n",
    "#   4. Set the y-label \"Units Sold\" and show the legend.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\n",
    "sales = [132, 128, 145, 151, 139, 160]\n",
    "target = [140, 140, 140, 140, 140, 140]\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\n",
    "sales = [132, 128, 145, 151, 139, 160]\n",
    "target = [140, 140, 140, 140, 140, 140]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(months, sales, linewidth=2.5, marker='s', label=\"Sales\")\n",
    "ax.plot(months, target, color='red', ls=':', label=\"Target\")\n",
    "ax.set_ylabel(\"Units Sold\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 7. Two Stores, One Histogram\n",
    "# A district manager wants to compare daily order counts at two stores.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Draw a histogram of store_a with bins=6, alpha=0.5, and the label \"Store A\".\n",
    "#   3. Draw a histogram of store_b on the same axes with bins=6, alpha=0.5,\n",
    "#      and the label \"Store B\".\n",
    "#   4. Set the x-label \"Daily Orders\" and show the legend.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "store_a = [42, 48, 51, 45, 39, 55, 47, 50, 44, 53, 46, 49]\n",
    "store_b = [58, 62, 55, 66, 60, 71, 57, 64, 59, 68, 61, 63]\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "store_a = [42, 48, 51, 45, 39, 55, 47, 50, 44, 53, 46, 49]\n",
    "store_b = [58, 62, 55, 66, 60, 71, 57, 64, 59, 68, 61, 63]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(store_a, bins=6, alpha=0.5, label=\"Store A\")\n",
    "ax.hist(store_b, bins=6, alpha=0.5, label=\"Store B\")\n",
    "ax.set_xlabel(\"Daily Orders\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 8. Year with a Q4 Inset\n",
    "# The CFO wants the full year on one chart with a close-up of Q4.\n",
    "#   1. Create a figure with fig = plt.figure(figsize=(6, 4)).\n",
    "#   2. Add a main axes with fig.add_axes([0.1, 0.1, 0.8, 0.8]), plot months vs revenue,\n",
    "#      and set the title \"Monthly Revenue\".\n",
    "#   3. Add an inset axes with fig.add_axes([0.2, 0.5, 0.3, 0.3]) and plot only\n",
    "#      the last three months (use slicing: months[-3:] and revenue[-3:]).\n",
    "#   4. Set the inset title \"Q4\".\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
    "          \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "revenue = [110, 104, 118, 121, 125, 119, 130, 128, 135, 150, 168, 190]\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
    "          \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "revenue = [110, 104, 118, 121, 125, 119, 130, 128, 135, 150, 168, 190]\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "main_ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "main_ax.plot(months, revenue)\n",
    "main_ax.set_title(\"Monthly Revenue\")\n",
    "\n",
    "inset_ax = fig.add_axes([0.2, 0.5, 0.3, 0.3])\n",
    "inset_ax.plot(months[-3:], revenue[-3:])\n",
    "inset_ax.set_title(\"Q4\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 9. Fair Regional Comparison\n",
    "# Three regional managers are comparing quarterly revenue. Separate y-axis\n",
    "# scales would make small regions look as big as large ones.\n",
    "#   1. Create one row of three axes that share the y-axis:\n",
    "#      fig, axes = plt.subplots(1, 3, figsize=(12, 3), sharey=True).\n",
    "#   2. Loop over the axes, region names, and revenue lists together with zip().\n",
    "#      On each axes, draw a bar chart of quarters vs that region's revenue\n",
    "#      and set the title to the region name.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "names = [\"North\", \"South\", \"West\"]\n",
    "revenue = [[150, 162, 158, 171], [120, 118, 131, 127], [60, 64, 59, 70]]\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "names = [\"North\", \"South\", \"West\"]\n",
    "revenue = [[150, 162, 158, 171], [120, 118, 131, 127], [60, 64, 59, 70]]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 3), sharey=True)\n",
    "for ax, name, values in zip(axes, names, revenue):\n",
    "    ax.bar(quarters, values)\n",
    "    ax.set_title(name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 10. Pandas Plot, Matplotlib Polish\n",
    "# pandas draws the chart quickly; Matplotlib finishes it for the report.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Draw the DataFrame on that axes with df.plot(ax=ax).\n",
    "#   3. Use the axes to set the title \"Sales by Channel\",\n",
    "#      the y-label \"Revenue ($K)\", and the y-axis range 0 to 200.\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\"Online\": [82, 90, 97, 105, 118, 126], \"In-Store\": [140, 136, 131, 129, 124, 120]},\n",
    "    index=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
    ")\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\"Online\": [82, 90, 97, 105, 118, 126], \"In-Store\": [140, 136, 131, 129, 124, 120]},\n",
    "    index=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "df.plot(ax=ax)\n",
    "ax.set_title(\"Sales by Channel\")\n",
    "ax.set_ylabel(\"Revenue ($K)\")\n",
    "ax.set_ylim(0, 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    ".ch06-hw-grader { max-width: 900px; margin-top: 1rem; }\n",
    ".ch06-hw-feedback-list { display: grid; gap: 0.45rem; margin: 0 0 1rem; }\n",
    ".ch06-hw-feedback-list[hidden] { display: none; }\n",
    ".ch06-hw-feedback-row { border: 1px solid #d8dee4; border-radius: 6px; padding: 0.55rem 0.65rem; }\n",
    ".ch06-hw-feedback-row strong { margin-right: 0.35rem; }\n",
    ".ch06-hw-feedback-row.correct { border-color: #aceebb; background: #dafbe1; color: #116329; }\n",
    ".ch06-hw-feedback-row.incorrect { border-color: #ffcecb; background: #ffebe9; color: #cf222e; }\n",
    ".ch06-hw-actions { display: flex; gap: 0.75rem; align-items: center; margin: 1rem 0 0; flex-wrap: wrap; }\n",
    ".ch06-hw-actions button { border: 1px solid #176de8; border-radius: 6px; background: #176de8; color: white; padding: 0.55rem 0.9rem; cursor: pointer; font-weight: 600; }\n",
    ".ch06-hw-actions button.secondary { background: white; color: #176de8; }\n",
    ".ch06-hw-actions.is-locked { opacity: 0.55; }\n",
    ".ch06-hw-actions button:disabled { cursor: not-allowed; opacity: 0.65; }\n",
    ".ch06-hw-result { font-weight: 700; }\n",
    "</style>\n",
    "\n",
    "<div class='ch06-hw-grader' id='ch06-hw-grader'>\n",
    "  <div class='ch06-hw-feedback-list' aria-live='polite' hidden>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q1'><strong>Question 1</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q2'><strong>Question 2</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q3'><strong>Question 3</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q4'><strong>Question 4</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q5'><strong>Question 5</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q6'><strong>Question 6</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q7'><strong>Question 7</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q8'><strong>Question 8</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q9'><strong>Question 9</strong><span></span></div>\n",
    "    <div class='ch06-hw-feedback-row' data-question='q10'><strong>Question 10</strong><span></span></div>\n",
    "  </div>\n",
    "  <div class='ch06-hw-actions'>\n",
    "    <button type='button' id='ch06-hw-submit' disabled>Submit</button>\n",
    "    <button type='button' class='secondary' id='ch06-hw-reset'>Clear Results</button>\n",
    "    <span class='ch06-hw-result' id='ch06-hw-result' aria-live='polite'></span>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "<script>\n",
    "(function () {\n",
    "  var homeworkId = 'ch06-homework';\n",
    "\n",
    "  function getParams() {\n",
    "    var params = new URLSearchParams(window.location.search);\n",
    "    return {\n",
    "      canvas_course_id: params.get('canvas_course_id') || params.get('course_id') || '',\n",
    "      canvas_assignment_id: params.get('canvas_assignment_id') || params.get('assignment_id') || '',\n",
    "      canvas_user_id: params.get('canvas_user_id') || params.get('user_id') || ''\n",
    "    };\n",
    "  }\n",
    "\n",
    "  function loadSessionIdentity(identity, actions, submit, result) {\n",
    "    submit.disabled = true;\n",
    "    actions.classList.add('is-locked');\n",
    "    result.textContent = 'Sign in from the account menu to submit this assignment.';\n",
    "\n",
    "    return fetch('/api/v1/session.php', { credentials: 'same-origin' })\n",
    "      .then(function (response) { return response.ok ? response.json() : null; })\n",
    "      .then(function (payload) {\n",
    "        if (!payload || !payload.authenticated || !payload.identity) return identity;\n",
    "        identity.canvas_user_id = payload.identity.canvas_user_id || identity.canvas_user_id || '';\n",
    "        identity.student_identifier = payload.identity.student_identifier || '';\n",
    "        submit.disabled = false;\n",
    "        actions.classList.remove('is-locked');\n",
    "        result.textContent = payload.identity.display_name ? 'Signed in as ' + payload.identity.display_name + '.' : 'Signed in.';\n",
    "        return identity;\n",
    "      })\n",
    "      .catch(function () { return identity; });\n",
    "  }\n",
    "\n",
    "  function codeFromCell(cell) {\n",
    "    var cm = cell.querySelector('.CodeMirror');\n",
    "    if (cm && cm.CodeMirror && typeof cm.CodeMirror.getValue === 'function') return cm.CodeMirror.getValue();\n",
    "    var cmContent = cell.querySelector('.cm-content');\n",
    "    if (cmContent && cmContent.cmView && cmContent.cmView.view) return cmContent.cmView.view.state.doc.toString();\n",
    "    var textarea = cell.querySelector('textarea');\n",
    "    if (textarea) return textarea.value;\n",
    "    var pre = cell.querySelector('.cell_input pre');\n",
    "    return pre ? pre.textContent : '';\n",
    "  }\n",
    "\n",
    "  function collectCodeCells() {\n",
    "    var cells = Array.prototype.slice.call(document.querySelectorAll('div.cell.tag_thebe-interactive')).slice(0, 5);\n",
    "    var code = {};\n",
    "    cells.forEach(function (cell, index) {\n",
    "      code['q' + (index + 6)] = codeFromCell(cell);\n",
    "    });\n",
    "    return code;\n",
    "  }\n",
    "\n",
    "  function collectAnswers() {\n",
    "    var answers = {};\n",
    "    ['q1', 'q2', 'q3', 'q4', 'q5'].forEach(function (question) {\n",
    "      var checked = document.querySelector('input[name=\"ch06-hw-' + question + '\"]:checked');\n",
    "      answers[question] = checked ? checked.value : '';\n",
    "    });\n",
    "    return answers;\n",
    "  }\n",
    "\n",
    "  function applyFeedback(grader, feedback) {\n",
    "    var list = grader.querySelector('.ch06-hw-feedback-list');\n",
    "    list.hidden = false;\n",
    "    ['q1', 'q2', 'q3', 'q4', 'q5', 'q6', 'q7', 'q8', 'q9', 'q10'].forEach(function (question) {\n",
    "      var row = grader.querySelector('[data-question=' + question + ']');\n",
    "      var message = row.querySelector('span');\n",
    "      var item = feedback[question];\n",
    "      row.className = 'ch06-hw-feedback-row';\n",
    "      if (!item) return;\n",
    "      message.textContent = item.message + ' (' + item.score + ' / ' + item.max_score + ')';\n",
    "      row.classList.add(item.correct ? 'correct' : 'incorrect');\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function clearFeedback(grader) {\n",
    "    var list = grader.querySelector('.ch06-hw-feedback-list');\n",
    "    list.hidden = true;\n",
    "    grader.querySelectorAll('.ch06-hw-feedback-row').forEach(function (row) {\n",
    "      row.className = 'ch06-hw-feedback-row';\n",
    "      var message = row.querySelector('span');\n",
    "      if (message) message.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function saveAttempt(payload) {\n",
    "    return fetch('/api/v1/homework-attempts.php', {\n",
    "      method: 'POST',\n",
    "      headers: { 'Content-Type': 'application/json' },\n",
    "      credentials: 'same-origin',\n",
    "      body: JSON.stringify(payload)\n",
    "    }).then(function (response) {\n",
    "      return response.text().then(function (text) {\n",
    "        var payload = text ? JSON.parse(text) : null;\n",
    "        if (!payload || !response.ok || !payload.ok) {\n",
    "          throw new Error((payload && (payload.message || payload.error)) || 'Submission API is unavailable.');\n",
    "        }\n",
    "        return payload;\n",
    "      });\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function initHomeworkGrader() {\n",
    "    var grader = document.getElementById('ch06-hw-grader');\n",
    "    if (!grader || grader.dataset.ready === 'true') return;\n",
    "    grader.dataset.ready = 'true';\n",
    "\n",
    "    var identity = getParams();\n",
    "    var actions = grader.querySelector('.ch06-hw-actions');\n",
    "    var result = document.getElementById('ch06-hw-result');\n",
    "    var submit = document.getElementById('ch06-hw-submit');\n",
    "    var reset = document.getElementById('ch06-hw-reset');\n",
    "\n",
    "    loadSessionIdentity(identity, actions, submit, result);\n",
    "\n",
    "    submit.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      submit.disabled = true;\n",
    "      result.textContent = 'Submitting homework.';\n",
    "\n",
    "      saveAttempt({\n",
    "        homework_id: homeworkId,\n",
    "        answers: collectAnswers(),\n",
    "        code: collectCodeCells(),\n",
    "        canvas_course_id: identity.canvas_course_id,\n",
    "        canvas_assignment_id: identity.canvas_assignment_id,\n",
    "        canvas_user_id: identity.canvas_user_id\n",
    "      })\n",
    "        .then(function (payload) {\n",
    "          var best = (payload.best_score === null || typeof payload.best_score === 'undefined') ? payload.score : payload.best_score;\n",
    "          var attempt = (payload.attempt_count === null || typeof payload.attempt_count === 'undefined') ? '' : ' Attempt ' + payload.attempt_count + '.';\n",
    "          applyFeedback(grader, payload.feedback || {});\n",
    "          result.textContent = 'Submission saved.' + attempt + ' Score: ' + payload.score + ' / ' + payload.max_score + '. Best score: ' + best + ' / ' + payload.max_score + '. Canvas sync: ' + payload.canvas_sync_status + '.';\n",
    "        })\n",
    "        .catch(function (error) {\n",
    "          result.textContent = 'Could not save this attempt: ' + error.message;\n",
    "        })\n",
    "        .finally(function () {\n",
    "          submit.disabled = false;\n",
    "        });\n",
    "    });\n",
    "\n",
    "    reset.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      result.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  if (document.readyState === 'loading') {\n",
    "    document.addEventListener('DOMContentLoaded', initHomeworkGrader);\n",
    "  } else {\n",
    "    initHomeworkGrader();\n",
    "  }\n",
    "})();\n",
    "</script>\n"
   ]
  }
 ],
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