Assignment2-Tasks graded by peers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa01f376",
   "metadata": {},
   "source": [
    "# Week 2: Tasks graded by peers.\n",
    "\n",
    "\n",
    "For this assignment, you must:\n",
    "\n",
    "1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.\n",
    "2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.\n",
    "3. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.\n",
    "4. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f3f2262",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f959631d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b3b85e",
   "metadata": {},
   "source": [
    "## Organize the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab377841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements = 17918\n"
     ]
    }
   ],
   "source": [
    "# Import data file\n",
    "df = pd.read_csv(\"SPE00120089.csv\")\n",
    "\n",
    "# Clean the input data for simplicity\n",
    "df = df.drop([\"STATION\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\", \"NAME\", \"PRCP\", \"PRCP_ATTRIBUTES\",\n",
    "             \"SNWD\", \"SNWD_ATTRIBUTES\", \"TMAX_ATTRIBUTES\", \"TMIN_ATTRIBUTES\", \"TAVG\",\n",
    "             \"TAVG_ATTRIBUTES\"], axis=1)\n",
    "\n",
    "print(\"Number of elements =\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "094f76cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the Date as year, month, and day\n",
    "year_list = []\n",
    "month_list = []\n",
    "day_list = []\n",
    "\n",
    "for item in df[\"DATE\"]:\n",
    "    date_split = item.split('-')\n",
    "    year_list.append(int(date_split[0]))\n",
    "    month_list.append(int(date_split[1]))\n",
    "    day_list.append(int(date_split[2]))\n",
    "    \n",
    "df[\"Year\"] = year_list\n",
    "df[\"Month\"] = month_list\n",
    "df[\"Day\"] = day_list\n",
    "\n",
    "df = df.drop([\"DATE\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36004399",
   "metadata": {},
   "source": [
    "### Remove leap days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87e0fbac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  17906\n"
     ]
    }
   ],
   "source": [
    "is_leap = df.loc[:, \"Month\"] == 2\n",
    "is_leap = is_leap.values.tolist()\n",
    "df_aux = df.loc[is_leap]\n",
    "df_aux.head()\n",
    "\n",
    "is_leap = df_aux.loc[:, \"Day\"] == 29\n",
    "is_leap = is_leap.values.tolist()\n",
    "df_aux = df_aux.loc[is_leap]\n",
    "\n",
    "leap_index = df_aux.query('Month == 2').index.tolist()\n",
    "\n",
    "df = df.drop(leap_index, axis=0)\n",
    "print(\"Number of elements = \", len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33940143",
   "metadata": {},
   "source": [
    "### Remove data out of range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77765b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  4015\n"
     ]
    }
   ],
   "source": [
    "# Remove data before 2005\n",
    "is_aux = df.loc[:, \"Year\"] >= 2005\n",
    "df = df.loc[is_aux]\n",
    "\n",
    "# Remove data after 2015\n",
    "is_aux = df.loc[:, \"Year\"] <= 2015\n",
    "df = df.loc[is_aux]\n",
    "\n",
    "print(\"Number of elements = \", len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b549f018",
   "metadata": {},
   "source": [
    "### Temperature calculation in the [2005, 2014] range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e4a958b",
   "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>TMAX</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>148.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TMAX  TMIN  Year  Month  Day\n",
       "0  149.0 -42.0  2005      1    1\n",
       "1  170.0 -33.0  2005      1    2\n",
       "2  148.0 -35.0  2005      1    3\n",
       "3  151.0 -21.0  2005      1    4\n",
       "4  148.0 -38.0  2005      1    5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all data before 2014\n",
    "is_aux = df.loc[:, \"Year\"] <= 2014\n",
    "df_range = df.loc[is_aux]\n",
    "df_range.reset_index(drop=True, inplace=True)\n",
    "df_range.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7148c1e5",
   "metadata": {},
   "source": [
    "#### Max. Temperature calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f0c85d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  3650\n"
     ]
    }
   ],
   "source": [
    "# Define a new dataframe to storage all plot data\n",
    "df_ymax = pd.DataFrame()\n",
    "df_ymax[\"TMAX\"] = None\n",
    "df_ymax[\"TMIN\"] = None\n",
    "df_ymax[\"Year\"] = None\n",
    "df_ymax[\"Month\"] = None\n",
    "df_ymax[\"Day\"] = None\n",
    "\n",
    "# Look for the max. temperatura per day in the year range 2005-2014\n",
    "for i in range(len(df_range)):\n",
    "    is_aux = df_range.loc[:, \"Month\"] == df_range[\"Month\"][i]\n",
    "    is_aux = is_aux.values.tolist()\n",
    "    df_aux = df_range.loc[is_aux]\n",
    "\n",
    "    is_aux = df_aux.loc[:, \"Day\"] == df_range[\"Day\"][i]\n",
    "    is_aux = is_aux.values.tolist()\n",
    "    df_aux = df_aux.loc[is_aux]\n",
    "\n",
    "    df_aux = df_aux.sort_values(by=\"TMAX\", ascending = False)\n",
    "    df_aux = df_aux.drop_duplicates(['Month'], keep='first')\n",
    "\n",
    "    df_ymax = pd.concat([df_ymax, df_aux])\n",
    "\n",
    "df_ymax = df_ymax.drop([\"TMIN\", \"Year\"], axis = 1)\n",
    "print(\"Number of elements = \", len(df_ymax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a8b47a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  365\n"
     ]
    }
   ],
   "source": [
    "# Remove all duplicated data\n",
    "df_ymax = df_ymax.drop_duplicates()\n",
    "df_ymax.reset_index(drop=True, inplace=True)\n",
    "print(\"Number of elements = \", len(df_ymax))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287a476d",
   "metadata": {},
   "source": [
    "#### Min. Temperature calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b43e1fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  3650\n"
     ]
    }
   ],
   "source": [
    "# Define a new dataframe to storage all plot data\n",
    "df_ymin = pd.DataFrame()\n",
    "df_ymin[\"TMAX\"] = None\n",
    "df_ymin[\"TMIN\"] = None\n",
    "df_ymin[\"Year\"] = None\n",
    "df_ymin[\"Month\"] = None\n",
    "df_ymin[\"Day\"] = None\n",
    "\n",
    "# Look for the min. temperatura per day in the year range 2005-2014\n",
    "for i in range(len(df_range)):\n",
    "    is_aux = df_range.loc[:, \"Month\"] == df_range[\"Month\"][i]\n",
    "    is_aux = is_aux.values.tolist()\n",
    "    df_aux = df_range.loc[is_aux]\n",
    "\n",
    "    is_aux = df_aux.loc[:, \"Day\"] == df_range[\"Day\"][i]\n",
    "    is_aux = is_aux.values.tolist()\n",
    "    df_aux = df_aux.loc[is_aux]\n",
    "\n",
    "    df_aux = df_aux.sort_values(by=\"TMIN\", ascending = True)\n",
    "    df_aux = df_aux.drop_duplicates(['Month'], keep='first')\n",
    "\n",
    "    df_ymin = pd.concat([df_ymin, df_aux])\n",
    "\n",
    "df_ymin = df_ymin.drop([\"TMAX\", \"Year\"], axis = 1)\n",
    "print(\"Number of elements = \", len(df_ymin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a73fec25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  365\n"
     ]
    }
   ],
   "source": [
    "# Remove all duplicated data\n",
    "df_ymin = df_ymin.drop_duplicates()\n",
    "df_ymin.reset_index(drop=True, inplace=True)\n",
    "print(\"Number of elements = \", len(df_ymin))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd714482",
   "metadata": {},
   "source": [
    "### Break temperature 2015 calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8c16ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements =  365\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>175.0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>169.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TMAX  TMIN  Year  Month  Day\n",
       "0  148.0 -70.0  2015      1    1\n",
       "1  157.0 -55.0  2015      1    2\n",
       "2  175.0 -52.0  2015      1    3\n",
       "3  179.0 -53.0  2015      1    4\n",
       "4  169.0 -58.0  2015      1    5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all data before 2014\n",
    "is_aux = df.loc[:, \"Year\"] > 2014\n",
    "df_break = df.loc[is_aux]\n",
    "df_break.reset_index(drop=True, inplace=True)\n",
    "print(\"Number of elements = \", len(df_break))\n",
    "df_break.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e33b5702",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look for the days that breaks the max. temperature record\n",
    "break_max = []\n",
    "for i in range(len(df_break)):\n",
    "    if df_break[\"TMAX\"][i] > df_ymax[\"TMAX\"][i]:\n",
    "        break_max.append([i, df_break[\"TMAX\"][i]])\n",
    "        \n",
    "break_max = np.array(break_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "919469af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look for the days that breaks the min. temperature record\n",
    "break_min = []\n",
    "for i in range(len(df_break)):\n",
    "    if df_break[\"TMIN\"][i] < df_ymin[\"TMIN\"][i]:\n",
    "        break_min.append([i, df_break[\"TMIN\"][i]])\n",
    "        \n",
    "break_min = np.array(break_min)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5724376",
   "metadata": {},
   "source": [
    "# Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b40d8a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 365.0, -20.0, 45.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_values = range(1, 366)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x_values, df_ymax[\"TMAX\"]/10, \"r-\", linewidth=0.3, label=\"Max. Temperature\")\n",
    "plt.plot(x_values, df_ymin[\"TMIN\"]/10, \"b-\", linewidth=0.3, label=\"Min. Temperature\")\n",
    "plt.gca().fill_between(x_values, df_ymax[\"TMAX\"]/10, df_ymin[\"TMIN\"]/10, facecolor='blue', alpha=0.1)\n",
    "plt.scatter(break_max[:, 0], break_max[:, 1]/10, color=\"r\", s=4, label=\"2015 break max.\")\n",
    "plt.scatter(break_min[:, 0], break_min[:, 1]/10, color=\"b\", s=4, label=\"2015 break min.\")\n",
    "\n",
    "plt.ylabel(\"Temperature ($^\\circ C$)\")\n",
    "plt.title('Temperature range between 2005-2014 in southern Spain', size=14)\n",
    "plt.legend(loc=4, fontsize=8)\n",
    "\n",
    "tick = [0,31,59,90,120,151,181,212,243,273,304,334]\n",
    "plt.xticks(tick, ['Jan','Feb', 'Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "ax = plt.gca() \n",
    "ax.axis([0,365,-20,45])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04d39903",
   "metadata": {},
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}