{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Way number eight of looking at the correlation coefficient\n",
    "\n",
    "This is a notebook to accompany the blog post [\"Way number eight of looking at the correlation coefficient\"](http://composition.al/blog/2019/01/31/way-number-eight-of-looking-at-the-correlation-coefficient/). Read the post for additional context!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from datascience import *\n",
    "from datetime import *\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recap from last time\n",
    "\n",
    "As [before](http://composition.al/blog/2018/08/31/understanding-the-regression-line-with-standard-units/), we're using the [datascience](http://data8.org/datascience/) package, and everything else we're using is pretty standard.\n",
    "\n",
    "And, as before, here's the data we'll be working with, [converted to standard units](https://www.inferentialthinking.com/chapters/14/2/Variability#standard-units) and plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Date</th> <th>Height (standard units)</th> <th>Weight (standard units)</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>07/28/2017</td> <td>-1.26135               </td> <td>-1.3158                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>08/07/2017</td> <td>-1.08691               </td> <td>-1.13054               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>08/25/2017</td> <td>-0.912464              </td> <td>-0.808628              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>09/25/2017</td> <td>-0.228116              </td> <td>-0.399485              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11/28/2017</td> <td>0.107349               </td> <td>0.254728               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>01/26/2018</td> <td>0.617255               </td> <td>0.728253               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>04/27/2018</td> <td>1.12716                </td> <td>1.2537                 </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>07/30/2018</td> <td>1.63707                </td> <td>1.41777                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Date       | Height (standard units) | Weight (standard units)\n",
       "07/28/2017 | -1.26135                | -1.3158\n",
       "08/07/2017 | -1.08691                | -1.13054\n",
       "08/25/2017 | -0.912464               | -0.808628\n",
       "09/25/2017 | -0.228116               | -0.399485\n",
       "11/28/2017 | 0.107349                | 0.254728\n",
       "01/26/2018 | 0.617255                | 0.728253\n",
       "04/27/2018 | 1.12716                 | 1.2537\n",
       "07/30/2018 | 1.63707                 | 1.41777"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heightweight = Table().with_columns([\n",
    "    'Date',        ['07/28/2017', '08/07/2017', '08/25/2017', '09/25/2017', '11/28/2017', '01/26/2018', '04/27/2018', '07/30/2018'],\n",
    "    'Height (cm)', [        53.3,         54.6,         55.9,           61,         63.5,         67.3,         71.1,         74.9],\n",
    "    'Weight (kg)', [       4.204,         4.65,        5.425,         6.41,        7.985,        9.125,        10.39,       10.785],\n",
    "                                    ])\n",
    "def standard_units(nums):\n",
    "    return (nums - np.mean(nums)) / np.std(nums)\n",
    "\n",
    "heightweight_standard = Table().with_columns(\n",
    "    'Date', heightweight.column('Date'),\n",
    "    'Height (standard units)', standard_units(heightweight.column('Height (cm)')),\n",
    "    'Weight (standard units)', standard_units(heightweight.column('Weight (kg)')))\n",
    "heightweight_standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heightweight_standard.scatter(\n",
    "    'Height (standard units)',\n",
    "    'Weight (standard units)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data in \"person space\"\n",
    "\n",
    "So far, this is all a recap of [last time](http://composition.al/blog/2018/08/31/understanding-the-regression-line-with-standard-units/).  Now, let's try turning our data sideways.\n",
    "\n",
    "The hacky way I have of doing this is to convert the data first to a numpy [ndarray](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html), then to a [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html), and then [transposing](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.T.html#pandas.DataFrame.T) the DataFrame.  This is kind of silly, but I don't know a better way to transpose a [structured ndarray](https://docs.scipy.org/doc/numpy/user/basics.rec.html).  If you do, let me know."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>07/28/2017</td>\n",
       "      <td>08/07/2017</td>\n",
       "      <td>08/25/2017</td>\n",
       "      <td>09/25/2017</td>\n",
       "      <td>11/28/2017</td>\n",
       "      <td>01/26/2018</td>\n",
       "      <td>04/27/2018</td>\n",
       "      <td>07/30/2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height (standard units)</th>\n",
       "      <td>-1.26135</td>\n",
       "      <td>-1.08691</td>\n",
       "      <td>-0.912464</td>\n",
       "      <td>-0.228116</td>\n",
       "      <td>0.107349</td>\n",
       "      <td>0.617255</td>\n",
       "      <td>1.12716</td>\n",
       "      <td>1.63707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight (standard units)</th>\n",
       "      <td>-1.3158</td>\n",
       "      <td>-1.13054</td>\n",
       "      <td>-0.808628</td>\n",
       "      <td>-0.399485</td>\n",
       "      <td>0.254728</td>\n",
       "      <td>0.728253</td>\n",
       "      <td>1.2537</td>\n",
       "      <td>1.41777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0           1           2           3  \\\n",
       "Date                     07/28/2017  08/07/2017  08/25/2017  09/25/2017   \n",
       "Height (standard units)    -1.26135    -1.08691   -0.912464   -0.228116   \n",
       "Weight (standard units)     -1.3158    -1.13054   -0.808628   -0.399485   \n",
       "\n",
       "                                  4           5           6           7  \n",
       "Date                     11/28/2017  01/26/2018  04/27/2018  07/30/2018  \n",
       "Height (standard units)    0.107349    0.617255     1.12716     1.63707  \n",
       "Weight (standard units)    0.254728    0.728253      1.2537     1.41777  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First convert to a plain old numpy ndarray.\n",
    "heightweight_standard_np = heightweight_standard.to_array()\n",
    "\n",
    "# Now convert *that* to a pandas DataFrame.\n",
    "df = pd.DataFrame(heightweight_standard_np)\n",
    "\n",
    "# Get the transpose of the DataFrame.\n",
    "df = df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas defaults to using `RangeIndex (0, 1, 2, …, n)` for the column labels, but we want the dates from the first row to be the column headers rather than being an actual row.  That's [an easy change to make](https://stackoverflow.com/questions/26147180/convert-row-to-column-header-for-pandas-dataframe), though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Date</th>\n",
       "      <th>07/28/2017</th>\n",
       "      <th>08/07/2017</th>\n",
       "      <th>08/25/2017</th>\n",
       "      <th>09/25/2017</th>\n",
       "      <th>11/28/2017</th>\n",
       "      <th>01/26/2018</th>\n",
       "      <th>04/27/2018</th>\n",
       "      <th>07/30/2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Height (standard units)</th>\n",
       "      <td>-1.26135</td>\n",
       "      <td>-1.08691</td>\n",
       "      <td>-0.912464</td>\n",
       "      <td>-0.228116</td>\n",
       "      <td>0.107349</td>\n",
       "      <td>0.617255</td>\n",
       "      <td>1.12716</td>\n",
       "      <td>1.63707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight (standard units)</th>\n",
       "      <td>-1.3158</td>\n",
       "      <td>-1.13054</td>\n",
       "      <td>-0.808628</td>\n",
       "      <td>-0.399485</td>\n",
       "      <td>0.254728</td>\n",
       "      <td>0.728253</td>\n",
       "      <td>1.2537</td>\n",
       "      <td>1.41777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                    07/28/2017 08/07/2017 08/25/2017 09/25/2017  \\\n",
       "Height (standard units)   -1.26135   -1.08691  -0.912464  -0.228116   \n",
       "Weight (standard units)    -1.3158   -1.13054  -0.808628  -0.399485   \n",
       "\n",
       "Date                    11/28/2017 01/26/2018 04/27/2018 07/30/2018  \n",
       "Height (standard units)   0.107349   0.617255    1.12716    1.63707  \n",
       "Weight (standard units)   0.254728   0.728253     1.2537    1.41777  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.iloc[0]\n",
    "df = df.drop(\"Date\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we're at it, we'll convert the values in our DataFrame to numeric values, so that we can visualize them in a moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Date</th>\n",
       "      <th>07/28/2017</th>\n",
       "      <th>08/07/2017</th>\n",
       "      <th>08/25/2017</th>\n",
       "      <th>09/25/2017</th>\n",
       "      <th>11/28/2017</th>\n",
       "      <th>01/26/2018</th>\n",
       "      <th>04/27/2018</th>\n",
       "      <th>07/30/2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Height (standard units)</th>\n",
       "      <td>-1.261347</td>\n",
       "      <td>-1.086906</td>\n",
       "      <td>-0.912464</td>\n",
       "      <td>-0.228116</td>\n",
       "      <td>0.107349</td>\n",
       "      <td>0.617255</td>\n",
       "      <td>1.127161</td>\n",
       "      <td>1.637068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight (standard units)</th>\n",
       "      <td>-1.315798</td>\n",
       "      <td>-1.130542</td>\n",
       "      <td>-0.808628</td>\n",
       "      <td>-0.399485</td>\n",
       "      <td>0.254728</td>\n",
       "      <td>0.728253</td>\n",
       "      <td>1.253700</td>\n",
       "      <td>1.417773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                     07/28/2017  08/07/2017  08/25/2017  09/25/2017  \\\n",
       "Height (standard units)   -1.261347   -1.086906   -0.912464   -0.228116   \n",
       "Weight (standard units)   -1.315798   -1.130542   -0.808628   -0.399485   \n",
       "\n",
       "Date                     11/28/2017  01/26/2018  04/27/2018  07/30/2018  \n",
       "Height (standard units)    0.107349    0.617255    1.127161    1.637068  \n",
       "Weight (standard units)    0.254728    0.728253    1.253700    1.417773  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.apply(pd.to_numeric)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eight dimensions are too many to try to visualize, but we can pare it down to three.  We'll pick three -- the first (07/28/2017), the last (07/30/2018), and one in the middle (01/26/2018) -- and drop the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Date</th>\n",
       "      <th>07/28/2017</th>\n",
       "      <th>01/26/2018</th>\n",
       "      <th>07/30/2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Height (standard units)</th>\n",
       "      <td>-1.261347</td>\n",
       "      <td>0.617255</td>\n",
       "      <td>1.637068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight (standard units)</th>\n",
       "      <td>-1.315798</td>\n",
       "      <td>0.728253</td>\n",
       "      <td>1.417773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                     07/28/2017  01/26/2018  07/30/2018\n",
       "Height (standard units)   -1.261347    0.617255    1.637068\n",
       "Weight (standard units)   -1.315798    0.728253    1.417773"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3dim = df.drop(df.columns[[1, 2, 3, 4, 6]],axis=1)\n",
    "df_3dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize the data with a three-dimensional scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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('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",
       "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 = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(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 (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\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=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "scatter_3d = plots.figure().gca(projection='3d')\n",
    "scatter_3d.scatter(df_3dim.iloc[:, 0], df_3dim.iloc[:, 1], df_3dim.iloc[:, 2])\n",
    "scatter_3d.set_xlabel(df_3dim.columns[0])\n",
    "scatter_3d.set_ylabel(df_3dim.columns[1])\n",
    "scatter_3d.set_zlabel(df_3dim.columns[2])\n",
    "height_point = df_3dim.iloc[0]\n",
    "weight_point = df_3dim.iloc[1]\n",
    "origin = [0,0,0]\n",
    "\n",
    "X, Y, Z = zip(origin,origin) \n",
    "U, V, W = zip(height_point, weight_point)\n",
    "\n",
    "scatter_3d.quiver(X, Y, Z, U, V, W, arrow_length_ratio=0.09)\n",
    "\n",
    "plots.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's going on here?  We're in the \"person space\", where, as Rodgers and Nicewander explained, each axis represents an observation -- in this case, three observations.  And there are two points, as promised -- one for each of height and weight.\n",
    "\n",
    "If we look at the difference between the two points on the z-axis -- that is, the axis for 07/30/2018 -- the darker-colored blue dot is higher up, so it must represent the height variable, with coordinates (-1.26135, 0.617255, 1.63707)  That means that the other, lighter-colored blue dot, with coordinates (-1.3158, 0.728253, 1.41777), must represent the weight variable.\n",
    "\n",
    "I've also plotted vectors going from the origin to each point.  These are the \"variable vectors\" for the two points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The angle between the variable vectors\n",
    "\n",
    "Finally, we want to figure out the angle between the two vectors.  There are [various ways](https://stackoverflow.com/questions/2827393/angles-between-two-n-dimensional-vectors-in-python) to do that in Python; we'll use a simple one that works for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11140728370937446"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dotproduct(v1, v2):\n",
    "  return sum((a*b) for a, b in zip(v1, v2))\n",
    "\n",
    "def length(v):\n",
    "  return math.sqrt(dotproduct(v, v))\n",
    "\n",
    "def angle(v1, v2):\n",
    "  return math.acos(dotproduct(v1, v2) / (length(v1) * length(v2)))\n",
    "\n",
    "angle_between_vvs = angle(height_point, weight_point)\n",
    "angle_between_vvs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can take the cosine of that to get the correlation coefficient $r$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9938006245545371"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.cos(angle_between_vvs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost 1!  That means that, just like [last time](http://composition.al/blog/2018/08/31/understanding-the-regression-line-with-standard-units/), we have an almost perfect linear correlation.\n",
    "\n",
    "It's a bit different from what we had last time, though, which was 0.9910523777994954.  That's because, for the sake of visualization, we decided to only look at three of the observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The angle between the _actual_ variable vectors\n",
    "\n",
    "We can, however, go back to all eight dimensions.  We may not be able to visualize them, but we can still measure the angle between them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1338730551963976"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_point_8dim = df.iloc[0]\n",
    "weight_point_8dim = df.iloc[1]\n",
    "angle_between_8dim_vvs = angle(height_point_8dim, weight_point_8dim)\n",
    "angle_between_8dim_vvs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the cosine of this slightly bigger angle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9910523777994951"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.cos(angle_between_8dim_vvs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This turns out to be the same as what we had previously calculated $r$ to be, modulo a little numerical imprecision.  And so, that's way number eight of looking at the correlation coefficient -- as the angle between two variable vectors in \"person space\"."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}