{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(lecture04:homework)=\n",
    "# Homework 4\n",
    "\n",
    "## Instructions\n",
    "\n",
    "+ Type your name and email in the \"Student details\" section below.\n",
    "+ Develop the code and generate the figures you need to solve the problems using this notebook.\n",
    "+ For the answers that require a mathematical proof or derivation you can either:\n",
    "    \n",
    "    - Type the answer using the built-in latex capabilities. In this case, simply export the notebook as a pdf and upload it on gradescope; or\n",
    "    - You can print the notebook (after you are done with all the code), write your answers by hand, scan, turn your response to a single pdf, and upload on gradescope.\n",
    "\n",
    "+ The total homework points are 100. Please note that the problems are not weighed equally.\n",
    "\n",
    "```{note}\n",
    "+ This is due before the beginning of the next lecture.\n",
    "+ Please match all the pages corresponding to each of the questions when you submit on gradescope.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student details\n",
    "\n",
    "+ **First Name:**\n",
    "+ **Last Name:**\n",
    "+ **Email:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let me set you up with some nice code for plotting and downloading files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(rc={\"figure.dpi\":100, 'savefig.dpi':300})\n",
    "sns.set_context('notebook')\n",
    "sns.set_style(\"ticks\")\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('retina', 'svg')\n",
    "\n",
    "import requests\n",
    "import os\n",
    "\n",
    "def download(url, local_filename=None):\n",
    "    \"\"\"\n",
    "    Downloads the file in the ``url`` and saves it in the current working directory.\n",
    "    \"\"\"\n",
    "    data = requests.get(url)\n",
    "    if local_filename is None:\n",
    "        local_filename = os.path.basename(url)\n",
    "    with open(local_filename, 'wb') as fd:\n",
    "        fd.write(data.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1 - Visual analysis of a variable-speed compressor experiment\n",
    "\n",
    "In this problem we are going to need [this](https://raw.githubusercontent.com/PurdueMechanicalEngineering/me-297-intro-to-data-science/master/data/compressor_data.xlsx) dataset. The dataset was kindly provided to us by [Professor Davide Ziviani](https://scholar.google.com/citations?user=gPdAtg0AAAAJ&hl=en).\n",
    "As before, you can either put it on your Google drive or just download it with the code segment below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>T_e</th>\n",
       "      <th>DT_sh</th>\n",
       "      <th>T_c</th>\n",
       "      <th>DT_sc</th>\n",
       "      <th>T_amb</th>\n",
       "      <th>f</th>\n",
       "      <th>m_dot</th>\n",
       "      <th>m_dot.1</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Power</th>\n",
       "      <th>Current</th>\n",
       "      <th>COP</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-30</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>28.8</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1557</td>\n",
       "      <td>901</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-30</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.388889</td>\n",
       "      <td>1201</td>\n",
       "      <td>881</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-30</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>17.9</td>\n",
       "      <td>4.972222</td>\n",
       "      <td>892</td>\n",
       "      <td>858</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-25</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>46.4</td>\n",
       "      <td>12.888889</td>\n",
       "      <td>2509</td>\n",
       "      <td>1125</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-25</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>40.2</td>\n",
       "      <td>11.166667</td>\n",
       "      <td>2098</td>\n",
       "      <td>1122</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>245.2</td>\n",
       "      <td>68.111111</td>\n",
       "      <td>12057</td>\n",
       "      <td>2525</td>\n",
       "      <td>11.3</td>\n",
       "      <td>4.78</td>\n",
       "      <td>0.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>234.1</td>\n",
       "      <td>65.027778</td>\n",
       "      <td>10939</td>\n",
       "      <td>2740</td>\n",
       "      <td>12.3</td>\n",
       "      <td>3.99</td>\n",
       "      <td>0.719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>222.2</td>\n",
       "      <td>61.722222</td>\n",
       "      <td>9819</td>\n",
       "      <td>2929</td>\n",
       "      <td>13.1</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>209.3</td>\n",
       "      <td>58.138889</td>\n",
       "      <td>8697</td>\n",
       "      <td>3091</td>\n",
       "      <td>13.7</td>\n",
       "      <td>2.81</td>\n",
       "      <td>0.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>195.4</td>\n",
       "      <td>54.277778</td>\n",
       "      <td>7575</td>\n",
       "      <td>3223</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    T_e  DT_sh   T_c  DT_sc  T_amb   f  m_dot    m_dot.1  Capacity  Power  \\\n",
       "0   -30      11   25      8     35  60   28.8   8.000000      1557    901   \n",
       "1   -30      11   30      8     35  60   23.0   6.388889      1201    881   \n",
       "2   -30      11   35      8     35  60   17.9   4.972222       892    858   \n",
       "3   -25      11   25      8     35  60   46.4  12.888889      2509   1125   \n",
       "4   -25      11   30      8     35  60   40.2  11.166667      2098   1122   \n",
       "..  ...     ...  ...    ...    ...  ..    ...        ...       ...    ...   \n",
       "60   10      11   45      8     35  60  245.2  68.111111     12057   2525   \n",
       "61   10      11   50      8     35  60  234.1  65.027778     10939   2740   \n",
       "62   10      11   55      8     35  60  222.2  61.722222      9819   2929   \n",
       "63   10      11   60      8     35  60  209.3  58.138889      8697   3091   \n",
       "64   10      11   65      8     35  60  195.4  54.277778      7575   3223   \n",
       "\n",
       "    Current   COP  Efficiency  \n",
       "0       4.4  1.73       0.467  \n",
       "1       4.0  1.36       0.425  \n",
       "2       3.7  1.04       0.382  \n",
       "3       5.3  2.23       0.548  \n",
       "4       5.1  1.87       0.519  \n",
       "..      ...   ...         ...  \n",
       "60     11.3  4.78       0.722  \n",
       "61     12.3  3.99       0.719  \n",
       "62     13.1  3.35       0.709  \n",
       "63     13.7  2.81       0.693  \n",
       "64     14.2  2.35       0.672  \n",
       "\n",
       "[65 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/PurdueMechanicalEngineering/me-297-intro-to-data-science/master/data/compressor_data.xlsx'\n",
    "download(url)\n",
    "\n",
    "import pandas as pd\n",
    "data = pd.read_excel('compressor_data.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data are part of a an experimental study of a variable speed reciprocating compressor.\n",
    "The experimentalists varied two temperatures $T_e$ and $T_c$ (both in degrees C) and they measured various other quantities.\n",
    "Our goal is to understand the experimental design and develop some understanding of the map between $T_e$ and $T_c$ and measured Capacity and Power (both in W).\n",
    "Answer the following questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the scatter plot of $T_e$ and $T_c$. This will reveal the experimental design picked by the experimentalists. Make sure you label the axes correctly. Hint: These are columns `T_e` and `T_c` of the data frame `data`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Is there a gap in the experimental design? If yes, why do you think they have a gap?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your explanation here.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the scatter plot between `T_e` and `Capacity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the scatter plot between `T_c` and `Capacity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the scatter plot between `T_e` and `Power`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the scatter plot between `T_c` and `Power`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ We are lucky that we only have two experimental control variables because can do a bit more thing with scatter. You can color each point in the scatter plot according to a scale that follows an output variable. Let me show you what I mean by doing the plot for the `Capacity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 372,
       "width": 560
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "fig, ax = plt.subplots()\n",
    "cs = ax.scatter(data['T_e'], data['T_c'], # So far a standard scatter plot\n",
    "                c=data['Capacity'], # This is telling matplotlib what the color\n",
    "                                 # of the points should be\n",
    "                cmap=cm.jet      # This is saying to use the jet colormap\n",
    "                                 # (blue = smallest values, red = highest values)\n",
    "               )\n",
    "plt.colorbar(cs, label='Capacity')   # This gives us a colorbar\n",
    "ax.set_xlabel('$T_e$')\n",
    "ax.set_ylabel('$T_c$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now repeat the same thing for the `Power`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2 - Visual analysis of an airfoil experiment\n",
    "\n",
    "In this problem, you are going to repeat what you did in Problem 1, but without my guidance!\n",
    "\n",
    "The dataset we are going to use is the [Airfoil Self-Noise Data Set](https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise#)\n",
    "From this reference, the descreption of the dataset is as follows:\n",
    "\n",
    "> The NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments.\n",
    "> \n",
    "> Attribute Information:\n",
    "> This problem has the following inputs:\n",
    "> 1. Frequency, in Hertzs.\n",
    "> 2. Angle of attack, in degrees.\n",
    "> 3. Chord length, in meters.\n",
    "> 4. Free-stream velocity, in meters per second.\n",
    "> 5. Suction side displacement thickness, in meters.\n",
    "\n",
    "> The only output is:\n",
    "> 6. Scaled sound pressure level, in decibels.\n",
    "\n",
    "Before we start, let's download and load the data.\n",
    "I am going to put them in a dataframe for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat'\n",
    "download(url)\n",
    "raw_data = np.loadtxt('airfoil_self_noise.dat')\n",
    "df = pd.DataFrame(raw_data, columns=['Frequency', 'Angle_of_attack', 'Chord_length',\n",
    "                                 'Velocity', 'Suction_thickness', 'Sound_pressure'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the histogtrams of all variables. Use as many code segments you need below to plot the histogram of each variable in a different plot. Make sure you label the axes correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here (as many blocks as you like)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Do the scatter plot between all input variables. This will give you an idea of the range of experimental conditions. Are there any holes in the experimental dataset, i.e., places where you have no data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here (as many blocks as you like)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your explanation here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+  Do the scatter plot between each input variable and the output. This will give you an idea of the relationship between each input and the output. Do you observe any obvious patterns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here (as many blocks as you like)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your explanation here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Now pick the two input variables you think are the most important and do the scatter plot between them using the output to color the points (see the last question of Problem 1). Feel free to repeat it with more than two pairs of inputs if you want. Briefly discuss your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here (as many blocks as you like)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your explanation here*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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