jupyterlab-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyterlab testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import analysis_utils\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Code Name</th>\n",
       "      <th>Subtask #</th>\n",
       "      <th>Correct path</th>\n",
       "      <th>Time User Starts ST</th>\n",
       "      <th>First Action: initial steps user takes  \n",
       "(C if correct path)</th>\n",
       "      <th>First Success (Y or N)</th>\n",
       "      <th>Second Success (Y or N)</th>\n",
       "      <th>Eventual Success (# or N)</th>\n",
       "      <th>Notes: where they got confused, interesting things users said</th>\n",
       "      <th>Time on task</th>\n",
       "      <th>Start time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>1</td>\n",
       "      <td>Create a new Python 3 Notebook</td>\n",
       "      <td>2:12</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>User did this action after making a folder.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>2</td>\n",
       "      <td>Rename the Untitled Notebook</td>\n",
       "      <td>3:00</td>\n",
       "      <td>Double clicked on filename in tab</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>3</td>\n",
       "      <td>Create a new Folder</td>\n",
       "      <td>2:37</td>\n",
       "      <td>Right click on file browser</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>User did this action before making a notebook ...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>4</td>\n",
       "      <td>Rename the Untitled Folder</td>\n",
       "      <td>2:40</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right click to open up context menu for folder.</td>\n",
       "      <td>18.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>5</td>\n",
       "      <td>Move notebook into the folder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>User moved into the folder first.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>6</td>\n",
       "      <td>Save the notebook</td>\n",
       "      <td>3:34</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keyboard shortcut to save the file.</td>\n",
       "      <td>39.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>7</td>\n",
       "      <td>Close MyDataNotebook.ipynb</td>\n",
       "      <td>4:17</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Was unsure whether or not the notebook meant c...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>8</td>\n",
       "      <td>Navigate back to home folder</td>\n",
       "      <td>4:20</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>9</td>\n",
       "      <td>Delete CollectionMethods.ipynb</td>\n",
       "      <td>4:32</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CrimpleBackAntiqueCake</td>\n",
       "      <td>10</td>\n",
       "      <td>Create a new Python 2 notebook</td>\n",
       "      <td>4:50</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Code Name  Subtask #                    Correct path  \\\n",
       "0  CrimpleBackAntiqueCake          1  Create a new Python 3 Notebook   \n",
       "1  CrimpleBackAntiqueCake          2    Rename the Untitled Notebook   \n",
       "2  CrimpleBackAntiqueCake          3             Create a new Folder   \n",
       "3  CrimpleBackAntiqueCake          4      Rename the Untitled Folder   \n",
       "4  CrimpleBackAntiqueCake          5   Move notebook into the folder   \n",
       "5  CrimpleBackAntiqueCake          6               Save the notebook   \n",
       "6  CrimpleBackAntiqueCake          7      Close MyDataNotebook.ipynb   \n",
       "7  CrimpleBackAntiqueCake          8    Navigate back to home folder   \n",
       "8  CrimpleBackAntiqueCake          9  Delete CollectionMethods.ipynb   \n",
       "9  CrimpleBackAntiqueCake         10  Create a new Python 2 notebook   \n",
       "\n",
       "  Time User Starts ST  \\\n",
       "0                2:12   \n",
       "1                3:00   \n",
       "2                2:37   \n",
       "3                2:40   \n",
       "4                 NaN   \n",
       "5                3:34   \n",
       "6                4:17   \n",
       "7                4:20   \n",
       "8                4:32   \n",
       "9                4:50   \n",
       "\n",
       "  First Action: initial steps user takes  \\n(C if correct path)  \\\n",
       "0                                                  C              \n",
       "1                  Double clicked on filename in tab              \n",
       "2                        Right click on file browser              \n",
       "3                                                  C              \n",
       "4                 User moved into the folder first.               \n",
       "5                                                  C              \n",
       "6                                                  C              \n",
       "7                                                  C              \n",
       "8                                                  C              \n",
       "9                                                  C              \n",
       "\n",
       "  First Success (Y or N)  Second Success (Y or N)  Eventual Success (# or N)  \\\n",
       "0                       Y                      NaN                       NaN   \n",
       "1                       N                        Y                       NaN   \n",
       "2                       N                        Y                       NaN   \n",
       "3                       Y                      NaN                       NaN   \n",
       "4                     NaN                      NaN                       NaN   \n",
       "5                       Y                      NaN                       NaN   \n",
       "6                       Y                      NaN                       NaN   \n",
       "7                       Y                      NaN                       NaN   \n",
       "8                       Y                      NaN                       NaN   \n",
       "9                       Y                      NaN                       NaN   \n",
       "\n",
       "  Notes: where they got confused, interesting things users said  Time on task  \\\n",
       "0       User did this action after making a folder.                       0.0   \n",
       "1                                                NaN                     34.0   \n",
       "2  User did this action before making a notebook ...                      3.0   \n",
       "3   Right click to open up context menu for folder.                      18.0   \n",
       "4                                                NaN                      NaN   \n",
       "5               Keyboard shortcut to save the file.                      39.0   \n",
       "6  Was unsure whether or not the notebook meant c...                      3.0   \n",
       "7                                                NaN                     12.0   \n",
       "8                                                NaN                      3.0   \n",
       "9                                                NaN                     12.0   \n",
       "\n",
       "   Start time  \n",
       "0       132.0  \n",
       "1       180.0  \n",
       "2       157.0  \n",
       "3       160.0  \n",
       "4         NaN  \n",
       "5       214.0  \n",
       "6       257.0  \n",
       "7       260.0  \n",
       "8       272.0  \n",
       "9       290.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data.\n",
    "data = analysis_utils.load_data()\n",
    "\n",
    "# Task mapping    \n",
    "task_mapping = {\n",
    "    34: [70, 71],\n",
    "    35: [72, 73, 74],\n",
    "    36: [75, 76, 77],\n",
    "    37: [78, 79]\n",
    "}\n",
    "\n",
    "data.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset dataframes\n",
    "task = 34\n",
    "\n",
    "def success_plot(task, ax):\n",
    "    \n",
    "    filters = (\n",
    "        (data[\"Subtask #\"].isin(task_mapping[task]))\n",
    "    )\n",
    "    df = data[filters]\n",
    "    \n",
    "    x = [\n",
    "        len(df[df[\"Eventual Success (# or N)\"].isin(range(1,10))]),\n",
    "        len(df[df[\"Second Success (Y or N) \"] == \"Y\"]),\n",
    "        len(df[df[\"First Success (Y or N) \"] == \"Y\"]),\n",
    "    ]\n",
    "    x = np.array(x)\n",
    "    x = x / x.sum()\n",
    "    \n",
    "    y = np.array([0, 1, 2])\n",
    "    ax.barh(y, x, height=0.5, align='center')\n",
    "\n",
    "    ax.set_yticks([0, 1, 2])\n",
    "    ax.set_yticklabels([\"Eventual success\", \"Second success\", \"First success\"])\n",
    "\n",
    "    ax.set_title(f\"Task #{task}\")\n",
    "    ax.set_xlim([0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Fraction of Tests')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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j4vPdND6zqhxyZoWQdDIwBtgE2AqYGhHXS/oYcHNuA7gzIia3OvZY0qxsaES80KJ9KjAc2JkUYlsCfyfphogYI+knEbFM0pZAP2BJPvQzQIOk+Xk810bE9evkws3a4XtyZgWQ1AcYTQqpQcBI4N/y5lOARRExmLTcOVBSfYtjTwAuBBpbBhxARFwAHE+ale0JfBv4ekSMyduXSToLWEwKwFn50GX59QGkkJwoadjav3Kz9jnkzAoQEW8Bw4BhkqYA5wEfyZtnAyMk3Q2cDJwTEW/nbfsC/w5cHREvt3H6PYAn8+tBLV6v6vsq0izxLmBmbpscEVMi4i8R8Ufgeir37sy6jUPOrACStieFzyeAB4GLgDqAiHgE2BG4AfgH4FFJe+ZDlwCfBaZI2q7KeacCVwIjJT0FfBG4VtINkgatOk9ErARuBAbn486U9IkWp6ojze7MupVDzqwMewGvAdOAe0mzul4Aki4HJkXET4AzgAA+mY97PiLmANcCMyTVtTxpXq58Efg0cCjwXETslpcr9wRuyA+jAIwC5ubXBwJn5/7/F2kpdeZavmaz1XLImZVhNtBECrCFQH9gqaSdSDOxvSX9Bng87/PDVsdfSlpynNCyMd+72ywi/hvYH3igxeYZwD3A45KeBnYCxuZtpwI75j4fAr4VEXMx62b+FgIzMyuWZ3JmZlYsh5yZmRXLIWdmZsVyyJmZWbH8Z71qYPnyFc1Ll75b62H0CH37boFr4Tq05FpUuBYVDQ31davf68M8k6uB3r03qvUQegzXInEdKlyLCtdizTnkzMysWA45MzMrlkPOzMyK5QdPamDY2bNWv5OZWQ9006QhtR5Cp3gmZ2ZmxXLImZlZsRxyZmZWLIecmZkVq1sfPJG0A/A08ESL5rnAncDwiLi0A+fYCvhcRNyyTgZpZmbFqMXTlc9FRGOV9qc6ePzuwHDAIWdmZu3qER8hkNQIjIuIEZIWA4tI3278ADARWAa8CIwCLgD2kDQ2Iq5vcY6pwBDSEuytEXGVpPvzeRdJGgf0j4jJki4EjiRd/7UR8d022r4CHA80A7dFxHRJR1cZ077AFbltKTAyIt5eR+UyM7MOqkXIDczhs8rIVtu3BQZHxBJJPwSujIjbJI0C+gBTScF1favjRgEHAq8CJ7bVuaRBwFBgH2BT4LI22j4JHAvsTwq5X0r6BXBclTEdCdwBfJM0y+wLOOTMrDgNDfW1HkKn9IjlSkkDWrz9U0Qsya8nAOdJGk+a2f20nfOOAC4D+gOzq2xf9ResBSyIiBXAu8CZkkZUafsSsD0wJx/XF9i5jTFNI80w5wCvAPPbrYCZ2Xqqqak2v793NVx74tOVK1u8HgtMjoiDSCF1VN7+N+OWtClwDGmWNQQ4UdL2wPvANnm3wfnnImCwpF6SNpZ0H/CHKm0BPAscnEN5BvBMG2MaCcyIiIPzMWPXVjHMzKzremLItbQAuE/SXNIM7S7gd8Buks5atVNEfAD8mfTwylzgXuAlYDpwdV5m3Cjv+xRwD/BrYB7wHxExv0rbf5FmZvMkPQYMIM3Sqo3pUeD7kv6TFLI3r7OKmJlZh9U1NzfXegwbnGFnz3LRzWy9VKu/XekvTTUzM2vFIWdmZsVyyJmZWbEccmZmViw/eFIbzbX6rElP09BQX7PP3fQkrkOFa1HhWlT4wRMzM7NWHHJmZlYsh5yZmRXLIWdmZsVyyJmZWbEccmZmViyHnJmZFcshZ2ZmxXLImZlZsRxyZmZWLIecmZkVyyFnZmbFcsiZmVmxHHJmZlYsh5yZmRXLIWdmZsXqXesBbIiGnT2rJv3eNGlITfo1M6sVz+TMzKxYDjkzMyuWQ87MzIrlkDMzs2J16cETSZOAQ4CVQDNwfkQ8vjYH1k7ftwHXRcT93dGfmZmtvzo9k5M0EBgOHBoRhwETgZvW9sDMzMzWVFdmcm8A2wGjJd0TEU9J2htA0m7AdKAOWAKMBt7ObXsDmwCXRMQsSVcA++dz3hIR35I0A/gA2AHYBjgxIp6QdBowBngN6Nd6QJKOJoXtMuBFYBRwMfB6RFwnaRfS7K9R0uHAJfnQJ4FxwOertB0ATAVWAL8DTgF2BGbkfpbnfv4C3E76hWFjYFxEPNPpqpqZ2VrX6ZlcRPyJNJPbD3hY0iLg8Lz5e8BpEdEI/Bw4FzgC2Doi9gY+B+yVg2ZH4NOkoDs+ByTA4oj4LPBtYKykLYEz875HkIKyteOAKyNif+BeoE+1sUvqDXwH+EJE7AW8DHyiStu2+VqOjoiDgFeAE4FDgcdJS7VTgb6k8H4TGAqc0VbfZmbW/To9k5O0M/BWRIzO7z8F/FzSr4BdgWskQZrVPA8IeBggIl4HLpR0DvBgRDQDyyQ9AgzMXTyZf/6RFKS7AM9GxAe5vwVVhjUBOE/SeGAh8NNW2+vyz62BpRHxRh7PpZL6V2nrR5pJzszXsjkpPKeSZoz3kILtfGA2MACYRZrhTelgKbtdQ0N9rYdQVU8dV3dzHSpciwrXYs10Zblyd2C8pGER8T4pyN4kLesFMCoiXpK0HykolgHHAORZ2UzSLO3LwJWSNgY+A3yfNBtqbtXf74GBkjYnLQ0OAn7Qap+xwOSIeEPSd4GjgPdz/wCD8883gI9K2ioi/ixpej5XtbaXgSMi4k1Jw4F3SDPJByPia5KOIwXe/wVei4jDJO0LTAMO7kJd17mmprdrPYQPaWio75Hj6m6uQ4VrUeFaVHQ17DsdchFxh6RdgfmS3iEteZ6Tw2A8cLOkjfLuJwEvAIdImpf7+1pEzJbUKOlh0vLjzHzvrVp/TZIuBh4CmoD/qTKsBcB9kpaQ7gHeRVo2nCnpQNISIxGxUtKpwN2SVpBmjY8C1drOzG29gLdI99/qgR9IWk56svSrwGLgdklnkYL+0s7W1MzM1o265ubWEydb14adPasmRe+Jf7vSv6kmrkOFa1HhWlQ0NNTXrX6vD/OHwc3MrFgOOTMzK5ZDzszMiuWQMzOzYvlLU2vgZ1cc4ZvJZmbdwDM5MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFgOOTMzK5ZDzszMiuWQMzOzYjnkzMysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFgOOTMzK5ZDzszMitW7vY2SGoGZwHMtmpsi4pg17VjSdsAeEfGzTh43GXg9Iq5b0zGYmVnZ2g25bG5EjFgHfQ8BdgE6FXJmZmYd1ZGQ+xBJWwMPAgMjolnS1cAvgd8C04E6YAkwGhgETAT+AuwI3A58A5gEbCHpIWACMC4iFkkaB/SPiMmSLgM+BdQDCyPiy22MZz/gCmAZsBQYCfwzsEtETJK0GbAoInaQtA/wrTzGV/K+u1dp27nKtWySx98L2BgYB7xAmu1uCWwOnBsR93elrmZmtnZ1JOSGSLq/xfu7I+JySU8DB0iaDzQCZwLzgNER8Zykk4BzgfuA7UlBsinwakRMlfQNUgjdKWlC604l9QGWRsShknoBz0r6eBtjPBK4A/gmMBzo2871XA+MiIiFkk4Fdm2j7Zoq1/IQ8CZwPDAQ6APsBPQHDgH6Af/YTt9/1dBQ35HdNgiuReI6VLgWFa7FmlmT5crvAf9C+g/+zohYLmlX4BpJkGY6z+d9n4mI5cBySe+tpr+6/PM9oJ+kW4F3gI/kc1YzDbgAmEOaic1v45wAfx8RCwEi4hoASdXaql3LbGAAMIs0a5wSEc/mmeyteb/pq7k+AJqa3u7IbsVraKh3LXAdWnItKlyLiq6G/Zo8XTmHtBQ5GrgxtwUwKiIaSTOfu3N7c5XjV7bo/31gm/x6cP45FNg2Io4DzictBdZR3UhgRkQcDDwLjG3jnACvShoAIGmipKPaaKt2LY3AaxFxGDAFmCZpN6A+Ir5ACv1vtzFGMzPrZl1ZrgQYGhHvSfoRcEhE/Da3jwdulrRRfn8S8LE2zvsMcIGkJ0izn6sl/ZE0EwNYAFwk6RHgA+D37ZzrUeD7kt4h3fsbS7o3N17SPOBx4K287ynATZJWAq8BVwEvV2l7qcq1LAFul3QWsAK4lHRP7hJJo3LfF7cxRjMz62Z1zc3VJlm2jjV7CSLxckziOlS4FhWuRUVDQ31bK3nt8ofBzcysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2L5w+BmZlYsz+TMzKxYDjkzMyuWQ87MzIrlkDMzs2J15Kt2zKwHkDQdODC/HQj8gfTlwgD7RsTqvpC49fnGAIdHxJEd2Pci4NWIuFHSA8AJpK+jmgqsOv4R4LT8NVwbAZcAw4AtgJ9FxL92Znxma4NDzmw9ERFnrHot6UVgZEQ81k3dHwScImlzoF9ELJb0JdIXCe8BLAd+DJwOXA6cDewH7JuPf1DSFyPiR900XjPAIWdWDEknA2OATYCtgKkRcb2kjwE35zaAOyNicqtjjyXNyoZGxAst2qcCw4GdSSG2JfB3km6IiDGSfhIRyyRtCfQjfbEwwCjg9Ih4P5/nKNKXH5t1K9+TMyuApD7AaFJIDQJGAv+WN58CLIqIwaTlzoGS6lscewJwIdDYMuAAIuIC4HhgbkTsCXwb+HpEjMnbl0k6C1hMCsBZ+dB/BP63pDmSngbGUglAs27jkDMrQES8Rbr/NUzSFOA84CN582xghKS7gZOBcyJi1ddN7wv8O3B1RLzcxun3AJ7Mrwe1eL2q76tIs8S7gJmS6kirRPsAnwcOAA4GTl3T6zTrLIecWQEkbU8Kn08ADwIXAXUAEfEIsCNwA/APwKOS9syHLgE+C0yRtF2V804FrgRGSnoK+CJwraQbJA1adZ6IWAncCAyOiGbgNeCWiPggIt4EfkTl/pxZt3HImZVhL1KwTAPuJc3qegFIuhyYFBE/Ac4AAvhkPu75iJgDXAvMyLOwv8rLlS8CnwYOBZ6LiN3ycuWewA35YRRI9+Hm5tc/Ak6Q1EvSJsAXgEfX+lWbrYZDzqwMs4EmUoAtBPoDSyXtRJqJ7S3pN8DjeZ8ftjr+UtKS44SWjfne3WYR8d/A/sADLTbPAO4BHs/33XYi3XuDtFz6Z+A3+d8i4Dtr40LNOsN/oNnMzIrlmZyZmRXLIWdmZsVyyJmZWbEccmZmViyHnJmZFct/u7IGli9f0bx06bu1HkaP0LfvFrgWrkNLrkWFa1HR0FBft/q9PswzuRro3XujWg+hx3AtEtehwrWocC3WnEPOzMyK5ZAzM7Ni+Z5cDQw7e9bqdzIz66FumjSk1kPoMM/kzMysWA45MzMrlkPOzMyK5ZAzM7NideuDJ5J2AJ4GnmjRPBe4ExgeEZd24BxbAZ+LiFvWySDNzKwYtXi68rmIaKzS/lQHj98dGA445MzMrF094iMEkhqBcRExQtJi0rcILyR9C/FEYBnwIjAKuADYQ9LYiLi+xTmmAkNIS7C3RsRVku7P510kaRzQPyImS7oQOJJ0/ddGxHfbaPsKcDzQDNwWEdMlHV1lTPsCV+S2pcDIiHh7HZXLzMw6qBb35AZKur/Fv4+32r4tcHxEnAUcB1wZEfsD9wJ9gKnA3JYBl40iBdKBwHttdS5pEDAU2Af4TB5PtbZPAscC++d/R0pSG2M6ErgDOAi4CejbhbqYmdla1iOWKyUNaPH2TxGxJL+eAJwnaTxpZvfTds47ArgM6A/MrrJ91R/3FLAgIlYA7wJnShpRpe1LwPbAnHxcX2DnNsY0jTTDnAO8AsxvtwJmZuuxhob6Wg+hw3rEcmUrK1u8HgtMjog3JH0XOAr4A61moJI2BY4hzbLqgGcl3Qa8D2xDWv4cTAqgRcB4Sb2AjYCfAxdWaftX4FlgaEQ0S/oq8EwbY6oHZkTEv0o6L+/ztbVcFzOzHqGpqfvvxnQ1WHv6RwgWAPdJmkuaod0F/A7YTdJZq3aKiA+AP5MeXplLWkZ8CZgOXC3pF6TwIiKeAu4Bfg3MA/4jIuZXafsv0sxsnqTHgAGkkKw2pkeB70v6T9J9wZvXWUXMzKzD6pqbm2s9hg3OsLNnuehmtt6qxd+u9PfJmZmZteKQMzOzYjnkzMysWA45MzMrlh/5c7WzAAAbsklEQVQ8qY3mWjyC2xM1NNTX5HHknsZ1qHAtKlyLCj94YmZm1opDzszMiuWQMzOzYjnkzMysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFgOOTMzK5ZDzszMiuWQMzOzYjnkzMysWL1rPYAN0bCzZ3V7nzdNGtLtfZqZ1ZpncmZmViyHnJmZFcshZ2ZmxXLImZlZsRxyZmZWrC49XSlpEnAIsBJoBs6PiMfX5sDa6fs24LqIuL87+jMzs/VXp2dykgYCw4FDI+IwYCJw09oemJmZ2ZrqykzuDWA7YLSkeyLiKUl7A0jaDZgO1AFLgNHA27ltb2AT4JKImCXpCmD/fM5bIuJbkmYAHwA7ANsAJ0bEE5JOA8YArwH9Wg9I0tGksF0GvAiMAi4GXo+I6yTtQpr9NUo6HLgkH/okMA74fJW2A4CpwArgd8ApwI7AjNzP8tzPX4DbSb8wbAyMi4hnOl1VMzNb6zodchHxJ0nDgdOBSyS9C1wA/Bj4HjA6Ip6TdBJwLvAYsHVE7C2pP3C6pBWkwPh0HsM8SXNzF4sj4hRJJwNjJU0EzgR2Iy2PVlsWPQ64MiJukzQK6FNt7JJ6A98B9o6INyRdDHyiStu2+Vr2z21fB04khfTjwARSCPYFtgfeBI4HBrbVd601NNTXeght6slj606uQ4VrUeFarJlOh5yknYG3ImJ0fv8p4OeSfgXsClwjCdKs5nlAwMMAEfE6cKGkc4AHI6IZWCbpEVJAQJpJAfwR2A/YBXg2Ij7I/S2oMqwJwHmSxgMLgZ+22l6Xf24NLI2IN/J4Ls3B27qtH2kmOTNfy+bAvaSZ3UTgHlKwnQ/MBgYAs0gzvCkdLGW3amp6u9ZDqKqhob7Hjq07uQ4VrkWFa1HR1bDvytOVuwPXStosv3+e9B/+CiCAURHRSJrF3U0Knb0AJG0p6Re5bf/ctjHwGeCFfL7mVv39HhgoaXNJGwGDqoxpLDA5Ig4iBdpRwPukoAIYnH++AXxU0la57+mkpdfWbTsALwNH5GuZCvwKOIIUzv8E/JAUeI3Aa/n+5BRg2uoKaGZm3aMry5V3SNoVmC/pHVJQnhMRb+aZ1M05jABOIoXXIZLm5f6+FhGzJTVKepi0BDgz33ur1l9TXkJ8CGgC/qfKsBYA90laQroHeBdp2XCmpAPJS5wRsVLSqcDdecn0SeBRoFrbmbmtF/AW6f5bPfADSctJS6dfBRYDt0s6ixT0l3a2pmZmtm7UNTe3njjZujbs7FndXvSe+geavRyTuA4VrkWFa1HR0FBft/q9PswfBjczs2I55MzMrFgOOTMzK5ZDzszMiuVvBq+Bn11xhG8mm5l1A8/kzMysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFgOOTMzK5ZDzszMiuWQMzOzYjnkzMysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFi929soqRGYCTzXorkpIo5Z044lbQfsERE/6+Rxk4HXI+K6NR2DmZmVrd2Qy+ZGxIh10PcQYBegUyFnZmbWUR0JuQ+RtDXwIDAwIpolXQ38EvgtMB2oA5YAo4FBwETgL8COwO3AN4BJwBaSHgImAOMiYpGkcUD/iJgs6TLgU0A9sDAivtzGePYDrgCWAUuBkcA/A7tExCRJmwGLImIHSfsA38pjfCXvu3uVtp2rXMsmefy9gI2BccALpNnulsDmwLkRcX9X6mpmZmtXR0JuiKT7W7y/OyIul/Q0cICk+UAjcCYwDxgdEc9JOgk4F7gP2J4UJJsCr0bEVEnfIIXQnZImtO5UUh9gaUQcKqkX8Kykj7cxxiOBO4BvAsOBvu1cz/XAiIhYKOlUYNc22q6pci0PAW8CxwMDgT7ATkB/4BCgH/CP7fT9Vw0N9R3ZbYPgWiSuQ4VrUeFarJk1Wa78HvAvpP/g74yI5ZJ2Ba6RBGmm83ze95mIWA4sl/Teavqryz/fA/pJuhV4B/hIPmc104ALgDmkmdj8Ns4J8PcRsRAgIq4BkFStrdq1zAYGALNIs8YpEfFsnsnemvebvprrA6Cp6e2O7Fa8hoZ61wLXoSXXosK1qOhq2K/J05VzSEuRo4Ebc1sAoyKikTTzuTu3N1c5fmWL/t8HtsmvB+efQ4FtI+I44HzSUmAd1Y0EZkTEwcCzwNg2zgnwqqQBAJImSjqqjbZq19IIvBYRhwFTgGmSdgPqI+ILpND/dhtjNDOzbtaV5UqAoRHxnqQfAYdExG9z+3jgZkkb5fcnAR9r47zPABdIeoI0+7la0h9JMzGABcBFkh4BPgB+3865HgW+L+kd0r2/saR7c+MlzQMeB97K+54C3CRpJfAacBXwcpW2l6pcyxLgdklnASuAS0n35C6RNCr3fXEbYzQzs25W19xcbZJl61izlyASL8ckrkOFa1HhWlQ0NNS3tZLXLn8Y3MzMiuWQMzOzYjnkzMysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWPwxuZmbF8kzOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrVke+TM7MeQNJ04MD8diDwB+C9/H7fiHiv6oFtn28McHhEHNmBfS8CXo2IGyU9AJxA+s7FqcCq4x8BTsvfNTkf2LTFKXYBromICZ0Zo9macsiZrSci4oxVryW9CIyMiMe6qfuDgFMkbQ70i4jFkr4ENAJ7AMuBHwOnA5dHxD4txno06QuGJ3fTWM3+yiFnVghJJwNjgE2ArYCpEXG9pI8BN+c2gDsjYnKrY48lzcqGRsQLLdqnAsOBnUkhtiXwd5JuiIgxkn4SEcskbQn0A5a0Ou/WwNXA5yPirbV+0War4XtyZgWQ1AcYTQqpQcBI4N/y5lOARRExmLTcOVBSfYtjTwAuBBpbBhxARFwAHA/MjYg9gW8DX4+IMXn7MklnAYtJATir1dDOA2ZFxJNr9YLNOsghZ1aAPEsaBgyTNIUULh/Jm2cDIyTdDZwMnBMRb+dt+wL/DlwdES+3cfo9gFUhNajF61V9X0WaJd4FzFzVLmkL0szysjW7OrOuc8iZFUDS9qTw+QTwIHARUAcQEY8AOwI3AP8APCppz3zoEuCzwBRJ21U571TgSmCkpKeALwLXSrpB0qBV54mIlcCNwOAWh38BWBARi9f29Zp1lEPOrAx7Aa8B04B7SbO6XgCSLgcmRcRPgDOAAD6Zj3s+IuYA1wIzJNW1PGlernwR+DRwKPBcROyWlyv3BG7ID6MAjALmtjj8IGDOWr5Os05xyJmVYTbQRAqwhUB/YKmknUgzsb0l/QZ4PO/zw1bHX0pacvybR/zzvbvNIuK/gf2BB1psngHcAzwu6WlgJ2Bsi+0DSAFpVjP+FgIzMyuWZ3JmZlYsh5yZmRXLIWdmZsVyyJmZWbH8Z71qYPnyFc1Ll75b62H0CH37boFr4Tq05FpUuBYVDQ31davf68M8k6uB3r03qvUQegzXInEdKlyLCtdizTnkzMysWA45MzMrlkPOzMyK5QdPamDY2a2/jcTMrPvdNGlIrYewznkmZ2ZmxXLImZlZsRxyZmZWLIecmZkVq1sfPJG0A/A08ESL5rnAncDwiLi0A+fYCvhcRNyyTgZpZmbFqMXTlc9FRGOV9qc6ePzuwHDAIWdmZu3qER8hkNQIjIuIEZIWA4tI3278ADARWEb6huFRwAXAHpLGRsT1Lc4xFRhCWoK9NSKuknR/Pu8iSeOA/hExWdKFwJGk6782Ir7bRttXgOOBZuC2iJgu6egqY9oXuCK3LQVGRsTb66hcZmbWQbUIuYE5fFYZ2Wr7tsDgiFgi6YfAlRFxm6RRQB9gKim4rm913CjgQOBV4MS2Opc0CBgK7ANsClzWRtsngWOB/Ukh90tJvwCOqzKmI4E7gG+SZpl9AYecmfVoDQ31tR7COtcjlislDWjx9k8RsSS/ngCcJ2k8aWb303bOOwK4DOgPzK6yfdVfsBawICJWAO8CZ0oaUaXtS8D2wJx8XF9g5zbGNI00w5wDvALMb7cCZmY9QFPT+vO7eFcDuSc+XbmyxeuxwOSIOIgUUkfl7X8zbkmbAseQZllDgBMlbQ+8D2yTdxucfy4CBkvqJWljSfcBf6jSFsCzwME5lGcAz7QxppHAjIg4OB8zdm0Vw8zMuq4nhlxLC4D7JM0lzdDuAn4H7CbprFU7RcQHwJ9JD6/MBe4FXgKmA1fnZcaN8r5PAfcAvwbmAf8REfOrtP0XaWY2T9JjwADSLK3amB4Fvi/pP0khe/M6q4iZmXVYXXNzc63HsMEZdvYsF93Mam59+tuV/tJUMzOzVhxyZmZWLIecmZkVyyFnZmbF8oMntdG8Pn0+ZV1qaKhfrz6rs664DhWuRYVrUeEHT8zMzFpxyJmZWbEccmZmViyHnJmZFcshZ2ZmxXLImZlZsRxyZmZWLIecmZkVyyFnZmbFcsiZmVmxHHJmZlYsh5yZmRXLIWdmZsVyyJmZWbEccmZmViyHnJmZFat3rQewIRp29qxaD6HDbpo0pNZDMDPrMs/kzMysWA45MzMrlkPOzMyK5ZAzM7NidenBE0mTgEOAlUAzcH5EPL42B9ZO37cB10XE/d3Rn5mZrb86PZOTNBAYDhwaEYcBE4Gb1vbAzMzM1lRXZnJvANsBoyXdExFPSdobQNJuwHSgDlgCjAbezm17A5sAl0TELElXAPvnc94SEd+SNAP4ANgB2AY4MSKekHQaMAZ4DejXekCSjiaF7TLgRWAUcDHwekRcJ2kX0uyvUdLhwCX50CeBccDnq7QdAEwFVgC/A04BdgRm5H6W537+AtxO+oVhY2BcRDzT6aqamdla1+mZXET8iTST2w94WNIi4PC8+XvAaRHRCPwcOBc4Atg6IvYGPgfslYNmR+DTpKA7PgckwOKI+CzwbWCspC2BM/O+R5CCsrXjgCsjYn/gXqBPtbFL6g18B/hCROwFvAx8okrbtvlajo6Ig4BXgBOBQ4HHSUu1U4G+pPB+ExgKnNFW32Zm1v06PZOTtDPwVkSMzu8/Bfxc0q+AXYFrJEGa1TwPCHgYICJeBy6UdA7wYEQ0A8skPQIMzF08mX/+kRSkuwDPRsQHub8FVYY1AThP0nhgIfDTVtvr8s+tgaUR8UYez6WS+ldp60eaSc7M17I5KTynkmaM95CC7XxgNjAAmEWa4U3pYCnXCw0N9UX0sT5wHSpciwrXYs10Zblyd2C8pGER8T4pyN4kLesFMCoiXpK0HykolgHHAORZ2UzSLO3LwJWSNgY+A3yfNBtqbtXf74GBkjYnLQ0OAn7Qap+xwOSIeEPSd4GjgPdz/wCD8883gI9K2ioi/ixpej5XtbaXgSMi4k1Jw4F3SDPJByPia5KOIwXe/wVei4jDJO0LTAMO7kJde6SmprfX6fkbGurXeR/rA9ehwrWocC0quhr2nQ65iLhD0q7AfEnvkJY8z8lhMB64WdJGefeTgBeAQyTNy/19LSJmS2qU9DBp+XFmvvdWrb8mSRcDDwFNwP9UGdYC4D5JS0j3AO8iLRvOlHQgaYmRiFgp6VTgbkkrSLPGR4FqbWfmtl7AW6T7b/XADyQtJz1Z+lVgMXC7pLNIQX9pZ2tqZmbrRl1zc+uJk61rw86etd4UfV3/7Ur/ppq4DhWuRYVrUdHQUF+3+r0+zB8GNzOzYjnkzMysWA45MzMrlkPOzMyK5S9NrYGfXXGEbyabmXUDz+TMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFgOOTMzK5ZDzszMiuWQMzOzYjnkzMysWA45MzMrlkPOzMyK5ZAzM7NiOeTMzKxYDjkzMyuWQ87MzIrlkDMzs2I55MzMrFgOOTMzK5ZDzszMiuWQMzOzYjnkzMysWA45MzMrVu/2NkpqBGYCz7VoboqIY9a0Y0nbAXtExM86edxk4PWIuG5Nx2BmZmVrN+SyuRExYh30PQTYBehUyJmZmXVUR0LuQyRtDTwIDIyIZklXA78EfgtMB+qAJcBoYBAwEfgLsCNwO/ANYBKwhaSHgAnAuIhYJGkc0D8iJku6DPgUUA8sjIgvtzGe/YArgGXAUmAk8M/ALhExSdJmwKKI2EHSPsC38hhfyfvuXqVt5yrXskkefy9gY2Ac8AJptrslsDlwbkTc35W6mpnZ2tWRkBsi6f4W7++OiMslPQ0cIGk+0AicCcwDRkfEc5JOAs4F7gO2JwXJpsCrETFV0jdIIXSnpAmtO5XUB1gaEYdK6gU8K+njbYzxSOAO4JvAcKBvO9dzPTAiIhZKOhXYtY22a6pcy0PAm8DxwECgD7AT0B84BOgH/GM7ff9VQ0N9R3bbILgWietQ4VpUuBZrZk2WK78H/AvpP/g7I2K5pF2BayRBmuk8n/d9JiKWA8slvbea/uryz/eAfpJuBd4BPpLPWc004AJgDmkmNr+NcwL8fUQsBIiIawAkVWurdi2zgQHALNKscUpEPJtnsrfm/aav5voAaGp6uyO7Fa+hod61wHVoybWocC0quhr2a/J05RzSUuRo4MbcFsCoiGgkzXzuzu3NVY5f2aL/94Ft8uvB+edQYNuIOA44n7QUWEd1I4EZEXEw8Cwwto1zArwqaQCApImSjmqjrdq1NAKvRcRhwBRgmqTdgPqI+AIp9L/dxhjNzKybdWW5EmBoRLwn6UfA/2/vzmPlKuswjn9Li61ABbFEqRHBCE9QltrUlrIIIlbKoqAQa5VNKItEBIQKEkARMCgg1oCiWAraqKipoAhoUgxLKS1lCW4PqUQBWWUVBUoX/3hP6Xi9M729cM/Uc59PcpM7Z8458zu/zJzfvO+Z87572F5cLT8GuFLS0Orx4cDoNvu9FzhN0p2U1s/Fkh6ktMQAFgCnS5oPvATc32FfC4ErJD1PufZ3JOXa3DGSbgEWAc9V6x4FzJS0HHgEuAh4qJdlD/RyLE8CP5F0PLAMOItyTe5MSQdXr31GmxgjIqJmQ1as6K2RFQNsRboginTHFMnDKsnFKsnFKptsMrJdT15HuRk8IiIaK0UuIiIaK0UuIiIaK0UuIiIaK0UuIiIaK0UuIiIaK0UuIiIaK0UuIiIaKzeDR0REY6UlFxERjZUiFxERjZUiFxERjZUiFxERjZUiFxERjZUiFxERjdWXSVOjHyStA1wCbE+Z9PWIlsllkTSNMoHrUuBs27/qSqA16EMuTgCmVA9/bfvL9UdZj9XlomWda4GrbX+n/ijr0Yf3xWTgzOrhncCxtht5z1MfcnES8AlgOXCu7TldCbRGkiYA59nercfyfSmTUy8FZtr+Xqf9pCU3cPYDRtieCJwCXLDyCUlvAY4DdgI+BHxV0vCuRFmPTrl4B/BJYEdgIjBJ0nZdibIebXPR4mxg41qj6o5O74uRwNeBfWzvAPwVGNWNIGvSKRcbUc4XE4FJwEVdibBGkqYDlwEjeixfF/gGJQ+7AkdW59O2UuQGzs7A9QC25wPjWp4bD9xq+yXbzwKLgSaf2Dvl4kFgT9vLbC8H1gVerD/E2nTKBZIOoHxbv67+0GrXKRc7AvcCF0i6GXjM9hP1h1ibTrn4F/A3YP3qb3nt0dXvL8BHe1m+NbDY9tO2lwC3ALt02lGK3MB5A/Bsy+Nlkoa1ee6fwIZ1BdYFbXNh+2Xb/5A0RNL5wF227+tKlPVomwtJ2wBTKV0xg0Gnz8go4P3AF4DJwPGStqo5vjp1ygWUL4N/pHTbzqgzsG6w/XPg5V6eWuNzZ4rcwHkOGNnyeB3bS9s8NxJ4pq7AuqBTLpA0AphdrfOZmmOrW6dcHAy8FZgLHAqcKGnPesOrVadcPAkstP2o7eeBm4AxdQdYo065mAxsCmwBbAbsJ2l8zfGtLdb43JkiN3BuBfYCkLQDpetlpQXALpJGSNqQ0gT/ff0h1qZtLiQNAa4G7rF9lO1l3QmxNm1zYXu67QnVhfZZwIW2r+9GkDXp9BlZBGwjaVTVotmB0pJpqk65eBp4AXjJ9ouUk/pGtUe4dvgTsKWkjSW9DngfcFunDfLryoEzB/igpHnAEOAwSSdS+pOvkTQDuJnyReO06s3bVG1zAQylXEAeXv2aDuBU2x3fuP/HOr4vuhta7Vb3GTkVuKFa9yrbTf4iuLpc7AHMl7Scch3qt12MtXaSpgIb2P5ulZcbKOfOmbb/3mnbzEIQERGNle7KiIhorBS5iIhorBS5iIhorBS5iIhorBS5iIhorNxCEFETSZsD9/G/93vta/vBV7HfDYFZtveXNBq4zPZe/Y90ta83E9iNcuvLj6plpwEHVqtsD9xT/f9T2+es4f7PAa61Pe+1iTgGsxS5iHo9bPu1HrnjjcB7AGw/THVT8QA6lDKY8JKVC6pCdg6ApBWv8hh3ZXCM3Rk1SJGLWAtImgW8CXgnMJ0y+vrngdcDw4FP254naQxwKbAe8BRlBocZwGhJc4ATgN/Z3lzSm4HvU4aCWgp80fb1kr5EGT5sS+DtlJbff7W2qqlfLgI+AKwAfmD7PEnXUG5WXiBpku3H+3Bsw4DzKaNTDKteb4akzYAfVseyDPgs8G7K8F2XS/oIsDdwUBX/fNtNH/YtXmO5JhdRr9GS7m75O7nluSdtb02ZS+5oyjQz2wNfA06t1pkNfMX2tsCPgc9RpmF52Pb+PV7rW8Bc29sBBwAzq8IHZdaLScAE4JRqOpdWRwNvq9YbD3xM0t62Pwxge0xfClzLvpbYHgu8FzhQ0kRgGjDH9jjgLGAn25cDdwOHUUbEOQkYSxmVf/3VTasS0VNachH16tRdeTuA7eWS9gf2lSTK9a9lkkYBm66cYNf2t+GVa3292Z1SSLB9v6TbKUUN4Maqu/FxSU9RRnJ/pse2s6qxRP8taTalVXdtP455D8o4lJOqxyOBbSlDU/1M0rhqv5e0bmR7iaQ7gIWU8U3Ps/1oP14/BrG05CLWHi8ASNqAMoj3FpTR92dQughfpnQdUq03opp0tp2en+8hrPpi2zpW6orqub5uu6aGAidWrb8xlEJ7pe2bgHdRit1U4Be9bLsPcGy1j99I2rmfMcQglSIXsfbZilJ4zgVupEweObSaYPehlhbRQZRuvqX0XoDmAofDKzOw78RqRmzvse0hkoZKWo9y7e/G/h0Oc4FpkoZVM37fBoyTdCEwxfYsSpfr2Gr9pcAwSZsCf6DMUHF6tZ9t+xlDDFIpchFrn3so16X+TDnJP0H5gQjAp4AzJN0NfBw4GXgMeEBSzyJ0HLC7pHspraQjbD/SxxguBR6qYrkL+KXtOf08nouBB6p93QFcavsW4JvAlOpYrgIOqda/DriM8oOZmcAiSYso56sr+hlDDFKZhSAiIhorLbmIiGisFLmIiGisFLmIiGisFLmIiGisFLmIiGisFLmIiGisFLmIiGisFLmIiGis/wBQtno50IYPKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=4, figsize=(6, 8), sharex=True)\n",
    "\n",
    "success_plot(34, axes[0])\n",
    "success_plot(35, axes[1])\n",
    "success_plot(36, axes[2])\n",
    "success_plot(37, axes[3])\n",
    "axes[3].set_xlabel(\"Fraction of Tests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tot_plot(task, ax):\n",
    "    filters = (\n",
    "        (data[\"Subtask #\"].isin(task_mapping[task])) &\n",
    "        (data[\"Time on task\"] < 100)\n",
    "    )\n",
    "    df = data[filters]\n",
    "    \n",
    "    sns.distplot(df[\"Time on task\"], ax=ax, rug=True, hist=False, kde_kws={\"shade\": True})\n",
    "    \n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_title(\"Kernel density estimation\")\n",
    "    ax.set_xlim(0, 100)\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_title(f\"Task #{task}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=4, figsize=(6, 12), sharex=True)\n",
    "\n",
    "tot_plot(34, axes[0])\n",
    "tot_plot(35, axes[1])\n",
    "tot_plot(36, axes[2])\n",
    "tot_plot(37, axes[3])\n",
    "axes[3].set_xlabel(\"Time on Task (s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "omnipotent (Python 3.6)",
   "language": "python",
   "name": "omnipotent"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}