bqplot Wealth of Nations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a `bqplot` recreation of Mike Bostock's [Wealth of Nations](https://bost.ocks.org/mike/nations/). This was also done by [Gapminder](http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2013$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj2tPLxKvvnNPA;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=194;dataMax=96846$map_y;scale=lin;dataMin=23;dataMax=86$map_s;sma=49;smi=2.65$cd;bd=0$inds=;modified=60). It is originally based on a TED Talk by [Hans Rosling](http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from bqplot import (\n",
    "    LogScale, LinearScale, OrdinalColorScale, ColorAxis,\n",
    "    Axis, Scatter, Lines, CATEGORY10, Label, Figure, Tooltip\n",
    ")\n",
    "\n",
    "from ipywidgets import HBox, VBox, IntSlider, Play, jslink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "initial_year = 1800"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning and Formatting JSON Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected object or value",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-1325d8e7c38b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data_files/nations.json'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines)\u001b[0m\n\u001b[0;32m    352\u001b[0m         obj = FrameParser(json, orient, dtype, convert_axes, convert_dates,\n\u001b[0;32m    353\u001b[0m                           \u001b[0mkeep_default_dates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 354\u001b[1;33m                           date_unit).parse()\n\u001b[0m\u001b[0;32m    355\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    356\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'series'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    420\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    421\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 422\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    423\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    424\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    637\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    638\u001b[0m             self.obj = DataFrame(\n\u001b[1;32m--> 639\u001b[1;33m                 loads(json, precise_float=self.precise_float), dtype=None)\n\u001b[0m\u001b[0;32m    640\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"split\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             decoded = dict((str(k), v)\n",
      "\u001b[1;31mValueError\u001b[0m: Expected object or value"
     ]
    }
   ],
   "source": [
    "data = pd.read_json(os.path.abspath('../data_files/nations.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_data(data):\n",
    "    for column in ['income', 'lifeExpectancy', 'population']:\n",
    "        data = data.drop(data[data[column].apply(len) <= 4].index)\n",
    "    return data\n",
    "\n",
    "def extrap_interp(data):\n",
    "    data = np.array(data)\n",
    "    x_range = np.arange(1800, 2009, 1.)\n",
    "    y_range = np.interp(x_range, data[:, 0], data[:, 1])\n",
    "    return y_range\n",
    "\n",
    "def extrap_data(data):\n",
    "    for column in ['income', 'lifeExpectancy', 'population']:\n",
    "        data[column] = data[column].apply(extrap_interp)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = clean_data(data)\n",
    "data = extrap_data(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "income_min, income_max = np.min(data['income'].apply(np.min)), np.max(data['income'].apply(np.max))\n",
    "life_exp_min, life_exp_max = np.min(data['lifeExpectancy'].apply(np.min)), np.max(data['lifeExpectancy'].apply(np.max))\n",
    "pop_min, pop_max = np.min(data['population'].apply(np.min)), np.max(data['population'].apply(np.max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(year):\n",
    "    year_index = year - 1800\n",
    "    income = data['income'].apply(lambda x: x[year_index])\n",
    "    life_exp = data['lifeExpectancy'].apply(lambda x: x[year_index])\n",
    "    pop =  data['population'].apply(lambda x: x[year_index])\n",
    "    return income, life_exp, pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the Tooltip to display the required fields\n",
    "\n",
    "`bqplot`'s native `Tooltip` allows us to simply display the data fields we require on a mouse-interaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tt = Tooltip(fields=['name', 'x', 'y'], labels=['Country Name', 'Income per Capita', 'Life Expectancy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the Label to display the year\n",
    "\n",
    "Staying true to the `d3` recreation of the talk, we place a `Label` widget in the bottom-right of the `Figure` (it inherits the `Figure` co-ordinates when no scale is passed to it). With `enable_move` set to `True`, the `Label` can be dragged around. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_label = Label(x=[0.75], y=[0.10], font_size=52, font_weight='bolder', colors=['orange'],\n",
    "                   text=[str(initial_year)], enable_move=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining Axes and Scales\n",
    "\n",
    "The inherent skewness of the income data favors the use of a `LogScale`. Also, since the color coding by regions does not follow an ordering, we use the `OrdinalColorScale`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sc = LogScale(min=income_min, max=income_max)\n",
    "y_sc = LinearScale(min=life_exp_min, max=life_exp_max)\n",
    "c_sc = OrdinalColorScale(domain=data['region'].unique().tolist(), colors=CATEGORY10[:6])\n",
    "size_sc = LinearScale(min=pop_min, max=pop_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax_y = Axis(label='Life Expectancy', scale=y_sc, orientation='vertical', side='left', grid_lines='solid')\n",
    "ax_x = Axis(label='Income per Capita', scale=x_sc, grid_lines='solid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the Scatter Mark with the appropriate size and color parameters passed\n",
    "\n",
    "To generate the appropriate graph, we need to pass the population of the country to the `size` attribute and its region to the `color` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start with the first year's data\n",
    "cap_income, life_exp, pop = get_data(initial_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wealth_scat = Scatter(x=cap_income, y=life_exp, color=data['region'], size=pop,\n",
    "                      names=data['name'], display_names=False,\n",
    "                      scales={'x': x_sc, 'y': y_sc, 'color': c_sc, 'size': size_sc},\n",
    "                      default_size=4112, tooltip=tt, animate=True, stroke='Black',\n",
    "                      unhovered_style={'opacity': 0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nation_line = Lines(x=data['income'][0], y=data['lifeExpectancy'][0], colors=['Gray'],\n",
    "                       scales={'x': x_sc, 'y': y_sc}, visible=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_interval = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = Figure(marks=[wealth_scat, year_label, nation_line], axes=[ax_x, ax_y],\n",
    "             title='Health and Wealth of Nations', animation_duration=time_interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using a Slider to allow the user to change the year and a button for animation\n",
    "\n",
    "Here we see how we can seamlessly integrate `bqplot` into the jupyter widget infrastructure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year_slider = IntSlider(min=1800, max=2008, step=1, description='Year', value=initial_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the `hovered_point` of the `Scatter` plot is changed (i.e. when the user hovers over a different element), the entire path of that country is displayed by making the `Lines` object visible and setting it's `x` and `y` attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hover_changed(change):\n",
    "    if change.new is not None:\n",
    "        nation_line.x = data['income'][change.new + 1]\n",
    "        nation_line.y = data['lifeExpectancy'][change.new + 1]\n",
    "        nation_line.visible = True\n",
    "    else:\n",
    "        nation_line.visible = False\n",
    "        \n",
    "wealth_scat.observe(hover_changed, 'hovered_point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the slider value `callback` (a function that is triggered everytime the `value` of the slider is changed) we change the `x`, `y` and `size` co-ordinates of the `Scatter`. We also update the `text` of the `Label` to reflect the current year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_changed(change):\n",
    "    wealth_scat.x, wealth_scat.y, wealth_scat.size = get_data(year_slider.value)\n",
    "    year_label.text = [str(year_slider.value)]\n",
    "\n",
    "year_slider.observe(year_changed, 'value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add an animation button"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "play_button = Play(min=1800, max=2008, interval=time_interval)\n",
    "jslink((play_button, 'value'), (year_slider, 'value'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Displaying the GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "VBox([HBox([play_button, year_slider]), fig])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "02d32b0ebd854fa2abf35c1321e51e5a": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LinearScaleModel",
      "state": {
       "allow_padding": false,
       "max": 1,
       "min": 0,
       "stabilized": false
      }
     },
     "0fc2f73b9c1841838cb773905d280727": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "AxisModel",
      "state": {
       "label": "Life Expectancy",
       "orientation": "vertical",
       "scale": "IPY_MODEL_e8722bc3b3684c2889a4ccabcbfdda65",
       "side": "left",
       "tick_values": {
        "type": null,
        "values": null
       }
      }
     },
     "132c46d2c1284fafa026843b84d80f2b": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "FigureModel",
      "state": {
       "_dom_classes": [],
       "animation_duration": 10,
       "axes": [
        "IPY_MODEL_de8ed5d07e324624911ec765f49bca0e",
        "IPY_MODEL_0fc2f73b9c1841838cb773905d280727"
       ],
       "layout": "IPY_MODEL_39e261e9b9e94451be728d190c831a7c",
       "marks": [
        "IPY_MODEL_e96cd2683e6c419d8cf9994d056982a4",
        "IPY_MODEL_87486c6e95a247f3bcc6906cb7824f7e",
        "IPY_MODEL_37aa93264fb244ca88fa3f3526a85180"
       ],
       "max_aspect_ratio": 6,
       "scale_x": "IPY_MODEL_d928baa33f3a4898a854218a33d74db2",
       "scale_y": "IPY_MODEL_02d32b0ebd854fa2abf35c1321e51e5a",
       "title": "Health and Wealth of Nations"
      }
     },
     "2b793bca85ec437783dd7106644499b6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "HBoxModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_6fa915ed411549e0a4da4c8da86016d1",
        "IPY_MODEL_8aef57ec40b04ecfbd8b954de2278cf4"
       ],
       "layout": "IPY_MODEL_8fd46b07dd1240df92c34a9ca829793a"
      }
     },
     "37aa93264fb244ca88fa3f3526a85180": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LinesModel",
      "state": {
       "color": {
        "type": null,
        "values": null
       },
       "colors": [
        "Gray"
       ],
       "display_legend": false,
       "fill_colors": [],
       "labels": [
        "C1"
       ],
       "scales": {
        "x": "IPY_MODEL_9e982787c4514e0e8cb4e540a1411825",
        "y": "IPY_MODEL_e8722bc3b3684c2889a4ccabcbfdda65"
       },
       "selected": [],
       "visible": false,
       "x": {
        "type": "float",
        "values": [
         985.89,
         979.7479999999999,
         973.606,
         967.4639999999999,
         961.322,
         955.18,
         949.038,
         942.896,
         936.754,
         930.612,
         924.47,
         918.328,
         912.1859999999999,
         906.044,
         899.9019999999999,
         893.76,
         887.6179999999999,
         881.476,
         875.334,
         869.192,
         863.05,
         859.02,
         855.01,
         851.02,
         847.04,
         843.09,
         839.15,
         835.23,
         831.33,
         827.45,
         823.59,
         819.74,
         815.92,
         812.11,
         808.32,
         804.54,
         800.78,
         797.05,
         793.32,
         789.62,
         785.93,
         782.26,
         778.61,
         774.98,
         771.36,
         767.76,
         764.17,
         760.6,
         757.05,
         753.52,
         750,
         675.4,
         608.22,
         547.72,
         493.24,
         444.18,
         400,
         420.8,
         442.67,
         465.69,
         489.9,
         515.37,
         542.16,
         570.35,
         600,
         626.77,
         654.74,
         683.95,
         714.47,
         746.35,
         779.65,
         780.42,
         781.18,
         781.94,
         782.71,
         783.47,
         784.24,
         785.01,
         785.78,
         786.55,
         787.32,
         788.09,
         788.86,
         789.63,
         790.4,
         791.18,
         791.95,
         792.73,
         793.5,
         794.28,
         795.06,
         795.78,
         796.5,
         797.22,
         797.94,
         798.66,
         799.38,
         800.11,
         800.83,
         801.56,
         802.28,
         803.06,
         803.84,
         804.62,
         805.4,
         806.18,
         806.96,
         807.74,
         808.53,
         809.31,
         810.09,
         810.88,
         811.66,
         812.45,
         823.4,
         834.5,
         845.75,
         857.15,
         868.7,
         880.41,
         892.28,
         904.31,
         916.5,
         928.85,
         941.37,
         954.06,
         966.92,
         979.96,
         993.16,
         1006.55,
         1015.72,
         1018.48,
         1043.08,
         1035.38,
         940.18,
         1010.72,
         1068.93,
         1037.42,
         1006.21,
         930.68,
         860.82,
         796.21,
         736.44,
         681.16,
         630.03,
         582.74,
         539,
         498.54,
         461.12,
         426.5,
         394.49,
         432.35,
         473.38,
         486.2,
         490.39,
         507.64,
         542.5,
         560,
         607.89,
         604.38,
         583.02,
         486.83,
         484.67,
         519.76,
         567.77,
         617.76,
         657.14,
         622.45,
         594.27,
         627.78,
         685.35,
         699.98,
         702.94,
         738.22,
         735.36,
         767.09,
         750.79,
         786.85,
         861.1,
         915.22,
         934.27,
         977.54,
         1044.34,
         1107.46,
         1229.08,
         1337.64,
         1406.19,
         1529.5,
         1611.38,
         1615.03,
         1647.38,
         1732.13,
         1877.41,
         2035.59,
         2214.2,
         2521.34,
         2546.44,
         2653.3,
         2635.55,
         2784.39,
         3012.12,
         3309.8,
         3695.65,
         4228.98,
         4551.13,
         4909.2,
         5450.12,
         6127.78,
         6679.2
        ]
       },
       "y": {
        "type": "float",
        "values": [
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         31,
         30,
         29,
         28,
         27,
         26,
         26.625,
         27.25,
         27.875,
         28.5,
         29.125,
         29.75,
         30.375,
         31,
         31.142857142857142,
         31.285714285714285,
         31.428571428571427,
         31.571428571428573,
         31.714285714285715,
         31.857142857142858,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32,
         32.5,
         33,
         33.5,
         34,
         34.5,
         35,
         35.333333333333336,
         35.666666666666664,
         36,
         36.333333333333336,
         36.666666666666664,
         37,
         37.57142857142857,
         38.142857142857146,
         38.714285714285715,
         39.285714285714285,
         39.857142857142854,
         40.42857142857143,
         41,
         39.25,
         39.64,
         40.41,
         44.56,
         46.47,
         48.02,
         50.45,
         50.55,
         50.16,
         38.4,
         31.63,
         34.1,
         44.5,
         51.9,
         53.32,
         55.65,
         56.8,
         58.38,
         59.41,
         60.97,
         62.65,
         63.74,
         63.12,
         62.78,
         62.5,
         62.7,
         62.44,
         63.97,
         64.24,
         65.09,
         66.12,
         66.45,
         66.37,
         66.54,
         66.71,
         66.89,
         67.08,
         67.29,
         67.52,
         67.77,
         68.04,
         68.33,
         68.64,
         68.95,
         69.27,
         69.6,
         69.94,
         70.28,
         70.62,
         70.96,
         71.29,
         71.59,
         71.87,
         72.13,
         72.35,
         72.56,
         72.74,
         72.92,
         73.1
        ]
       }
      }
     },
     "39e261e9b9e94451be728d190c831a7c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "min_width": "125px"
      }
     },
     "39fc1b347dc1451a9a56b1f82b05c6e9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LinkModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "source": [
        "IPY_MODEL_6fa915ed411549e0a4da4c8da86016d1",
        "value"
       ],
       "target": [
        "IPY_MODEL_8aef57ec40b04ecfbd8b954de2278cf4",
        "value"
       ]
      }
     },
     "4139f3ca9f604df39eb7fc6810cd6ed9": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LinearScaleModel",
      "state": {
       "max": 1326856173,
       "min": 2128,
       "stabilized": false
      }
     },
     "45ade275cb57431da84d0a9e42e0ba65": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "6a40bf0571d84adb8a0614b236028a24": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_2b793bca85ec437783dd7106644499b6",
        "IPY_MODEL_132c46d2c1284fafa026843b84d80f2b"
       ],
       "layout": "IPY_MODEL_c0deeac6cbb6476fbe35afbe462a1f06"
      }
     },
     "6fa915ed411549e0a4da4c8da86016d1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "PlayModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "interval": 10,
       "layout": "IPY_MODEL_c3922bc11dab4a9dafca6097b21b1442",
       "max": 2008,
       "min": 1800,
       "value": 1800
      }
     },
     "84532e9c8d344e30bc49531936c652f1": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "OrdinalScaleModel",
      "state": {
       "_view_name": "OrdinalColorScale",
       "colors": [
        "#1f77b4",
        "#ff7f0e",
        "#2ca02c",
        "#d62728",
        "#9467bd",
        "#8c564b"
       ],
       "domain": [
        "Sub-Saharan Africa",
        "South Asia",
        "Middle East & North Africa",
        "America",
        "Europe & Central Asia",
        "East Asia & Pacific"
       ],
       "mid": null,
       "scale_type": "linear",
       "scheme": "RdYlGn"
      }
     },
     "87486c6e95a247f3bcc6906cb7824f7e": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LabelModel",
      "state": {
       "_model_module": "bqplot",
       "_model_module_version": "^0.2.3",
       "_view_module": "bqplot",
       "_view_module_version": "^0.2.3",
       "apply_clip": true,
       "color": {
        "type": null,
        "values": null
       },
       "colors": [
        "orange"
       ],
       "default_size": 16,
       "display_legend": false,
       "enable_hover": true,
       "enable_move": true,
       "font_weight": "bolder",
       "hovered_style": {},
       "interactions": {
        "hover": "tooltip"
       },
       "labels": [],
       "msg_throttle": 1,
       "opacity": {
        "type": null,
        "values": null
       },
       "preserve_domain": {},
       "rotation": {
        "type": null,
        "values": null
       },
       "scales": {},
       "scales_metadata": {
        "color": {
         "dimension": "color"
        },
        "opacity": {
         "dimension": "opacity"
        },
        "size": {
         "dimension": "size"
        },
        "x": {
         "dimension": "x",
         "orientation": "horizontal"
        },
        "y": {
         "dimension": "y",
         "orientation": "vertical"
        }
       },
       "selected": [],
       "selected_style": {},
       "size": {
        "type": null,
        "values": null
       },
       "text": {
        "type": "|S4",
        "values": [
         "1800"
        ]
       },
       "tooltip": null,
       "tooltip_location": "mouse",
       "tooltip_style": {
        "opacity": 0.9
       },
       "unhovered_style": {},
       "unselected_style": {},
       "visible": true,
       "x": {
        "type": "float",
        "values": [
         0.75
        ]
       },
       "y": {
        "type": "float",
        "values": [
         0.1
        ]
       }
      }
     },
     "8aef57ec40b04ecfbd8b954de2278cf4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "IntSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "Year",
       "layout": "IPY_MODEL_c38efd9ec906448dbe9fd61f037c6f77",
       "max": 2008,
       "min": 1800,
       "style": "IPY_MODEL_9aa04897b9e1463bafaae2a62dc0fb59",
       "value": 1800
      }
     },
     "8fd46b07dd1240df92c34a9ca829793a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "9aa04897b9e1463bafaae2a62dc0fb59": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "SliderStyleModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "9e982787c4514e0e8cb4e540a1411825": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LogScaleModel",
      "state": {
       "max": 119849.29,
       "min": 281.91
      }
     },
     "c0deeac6cbb6476fbe35afbe462a1f06": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c38efd9ec906448dbe9fd61f037c6f77": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c3922bc11dab4a9dafca6097b21b1442": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c6613152924a4053afe3ee0661c7bb68": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "TooltipModel",
      "state": {
       "fields": [
        "name",
        "x",
        "y"
       ],
       "labels": [
        "Country Name",
        "Income per Capita",
        "Life Expectancy"
       ],
       "layout": "IPY_MODEL_45ade275cb57431da84d0a9e42e0ba65"
      }
     },
     "d928baa33f3a4898a854218a33d74db2": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LinearScaleModel",
      "state": {
       "allow_padding": false,
       "max": 1,
       "min": 0,
       "stabilized": false
      }
     },
     "de8ed5d07e324624911ec765f49bca0e": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "AxisModel",
      "state": {
       "label": "Income per Capita",
       "scale": "IPY_MODEL_9e982787c4514e0e8cb4e540a1411825",
       "side": "bottom",
       "tick_values": {
        "type": null,
        "values": null
       }
      }
     },
     "e8722bc3b3684c2889a4ccabcbfdda65": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "LinearScaleModel",
      "state": {
       "max": 82.87,
       "min": 12.7,
       "stabilized": false
      }
     },
     "e96cd2683e6c419d8cf9994d056982a4": {
      "model_module": "bqplot",
      "model_module_version": "^0.2.3",
      "model_name": "ScatterModel",
      "state": {
       "_model_module": "bqplot",
       "_model_module_version": "^0.2.3",
       "_view_module": "bqplot",
       "_view_module_version": "^0.2.3",
       "apply_clip": true,
       "color": {
        "type": "object",
        "values": [
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "Sub-Saharan Africa",
         "South Asia",
         "South Asia",
         "South Asia",
         "South Asia",
         "South Asia",
         "South Asia",
         "South Asia",
         "South Asia",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "Middle East & North Africa",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "America",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "Europe & Central Asia",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific",
         "East Asia & Pacific"
        ]
       },
       "colors": [
        "DeepSkyBlue"
       ],
       "default_size": 4112,
       "display_legend": false,
       "display_names": false,
       "enable_hover": true,
       "hovered_style": {},
       "interactions": {
        "hover": "tooltip"
       },
       "labels": [],
       "msg_throttle": 1,
       "names": {
        "type": "object",
        "values": [
         "Angola",
         "Benin",
         "Botswana",
         "Burkina Faso",
         "Burundi",
         "Cameroon",
         "Cape Verde",
         "Chad",
         "Comoros",
         "Congo, Dem. Rep.",
         "Congo, Rep.",
         "Cote d'Ivoire",
         "Equatorial Guinea",
         "Eritrea",
         "Ethiopia",
         "Gabon",
         "Ghana",
         "Guinea",
         "Guinea-Bissau",
         "Kenya",
         "Lesotho",
         "Liberia",
         "Madagascar",
         "Malawi",
         "Mali",
         "Mauritania",
         "Mauritius",
         "Mozambique",
         "Namibia",
         "Niger",
         "Nigeria",
         "Reunion",
         "Rwanda",
         "Sao Tome and Principe",
         "Senegal",
         "Sierra Leone",
         "Somalia",
         "South Africa",
         "Sudan",
         "Swaziland",
         "Tanzania",
         "Togo",
         "Uganda",
         "Zambia",
         "Zimbabwe",
         "Afghanistan",
         "Bangladesh",
         "Bhutan",
         "India",
         "Maldives",
         "Nepal",
         "Pakistan",
         "Sri Lanka",
         "Algeria",
         "Bahrain",
         "Djibouti",
         "Iraq",
         "Israel",
         "Jordan",
         "Kuwait",
         "Lebanon",
         "Libya",
         "Morocco",
         "Oman",
         "Qatar",
         "Saudi Arabia",
         "Tunisia",
         "United Arab Emirates",
         "West Bank and Gaza",
         "Yemen, Rep.",
         "Argentina",
         "Aruba",
         "Barbados",
         "Belize",
         "Bolivia",
         "Brazil",
         "Canada",
         "Chile",
         "Colombia",
         "Costa Rica",
         "Cuba",
         "Ecuador",
         "El Salvador",
         "French Guiana",
         "Grenada",
         "Guadeloupe",
         "Guatemala",
         "Guyana",
         "Haiti",
         "Honduras",
         "Jamaica",
         "Martinique",
         "Mexico",
         "Netherlands Antilles",
         "Nicaragua",
         "Panama",
         "Paraguay",
         "Peru",
         "Puerto Rico",
         "Suriname",
         "Trinidad and Tobago",
         "United States",
         "Uruguay",
         "Albania",
         "Armenia",
         "Austria",
         "Azerbaijan",
         "Belarus",
         "Belgium",
         "Bosnia and Herzegovina",
         "Bulgaria",
         "Croatia",
         "Cyprus",
         "Denmark",
         "Estonia",
         "Finland",
         "France",
         "Georgia",
         "Germany",
         "Greece",
         "Hungary",
         "Iceland",
         "Ireland",
         "Italy",
         "Kazakhstan",
         "Latvia",
         "Lithuania",
         "Luxembourg",
         "Macedonia, FYR",
         "Malta",
         "Moldova",
         "Montenegro",
         "Netherlands",
         "Norway",
         "Poland",
         "Portugal",
         "Romania",
         "Serbia",
         "Slovak Republic",
         "Slovenia",
         "Spain",
         "Sweden",
         "Switzerland",
         "Tajikistan",
         "Turkey",
         "Turkmenistan",
         "Ukraine",
         "United Kingdom",
         "Uzbekistan",
         "Australia",
         "Brunei",
         "Cambodia",
         "China",
         "Fiji",
         "French Polynesia",
         "Hong Kong, China",
         "Indonesia",
         "Japan",
         "Korea, Dem. Rep.",
         "Korea, Rep.",
         "Macao, China",
         "Malaysia",
         "Micronesia, Fed. Sts.",
         "Mongolia",
         "Myanmar",
         "New Caledonia",
         "New Zealand",
         "Papua New Guinea",
         "Philippines",
         "Samoa",
         "Singapore",
         "Solomon Islands",
         "Taiwan",
         "Thailand",
         "Timor-Leste",
         "Tonga",
         "Vietnam",
         "Vanuatu"
        ]
       },
       "opacity": {
        "type": null,
        "values": null
       },
       "preserve_domain": {},
       "rotation": {
        "type": null,
        "values": null
       },
       "scales": {
        "color": "IPY_MODEL_84532e9c8d344e30bc49531936c652f1",
        "size": "IPY_MODEL_4139f3ca9f604df39eb7fc6810cd6ed9",
        "x": "IPY_MODEL_9e982787c4514e0e8cb4e540a1411825",
        "y": "IPY_MODEL_e8722bc3b3684c2889a4ccabcbfdda65"
       },
       "scales_metadata": {
        "color": {
         "dimension": "color"
        },
        "opacity": {
         "dimension": "opacity"
        },
        "size": {
         "dimension": "size"
        },
        "x": {
         "dimension": "x",
         "orientation": "horizontal"
        },
        "y": {
         "dimension": "y",
         "orientation": "vertical"
        }
       },
       "selected": [],
       "selected_style": {},
       "size": {
        "type": "float",
        "values": [
         1567028,
         636559,
         121000,
         1665421,
         899097,
         1860054,
         55716,
         1432000,
         56346,
         5163819,
         314465,
         1088530,
         80377,
         205010,
         2948990,
         158227,
         2016034,
         983959,
         218167,
         2574000,
         276361,
         313543,
         1683000,
         737000,
         1403398,
         382696,
         181000,
         2096000,
         229000,
         1244861,
         12100855,
         92744,
         928368,
         22731,
         1009886,
         794264,
         1000000,
         1550000,
         5156000,
         86000,
         3019768,
         445985,
         2101397,
         747000,
         1085814,
         3280000,
         20000000,
         392287,
         175349000,
         42378,
         3881000,
         13651000,
         1213000,
         2689000,
         64474,
         22848,
         1093000,
         209954,
         217000,
         81280,
         332000,
         538000,
         2689000,
         318000,
         14092,
         2091000,
         875000,
         40153,
         165944,
         2593000,
         534000,
         19286,
         81729,
         25526,
         1100000,
         4507000,
         816000,
         771447,
         1206000,
         53000,
         572000,
         500000,
         248000,
         9899,
         29409,
         80726,
         595000,
         166034,
         723000,
         130000,
         401000,
         84039,
         6587000,
         42536,
         186000,
         78842,
         143000,
         1317000,
         155000,
         80721,
         60000,
         9980510,
         55000,
         437000,
         413326,
         3369000,
         879960,
         2355081,
         3434000,
         851806,
         2187000,
         1227886,
         184392,
         1155000,
         334136,
         1169000,
         31250000,
         1072178,
         24905000,
         2312000,
         4146000,
         61428,
         5052603,
         20176000,
         2041282,
         590588,
         778655,
         127030,
         391865,
         134072,
         712558,
         126582,
         2333000,
         970000,
         10426000,
         3297000,
         6389000,
         2147197,
         2140651,
         469664,
         12203000,
         2585000,
         1986000,
         466629,
         10074000,
         367215,
         11215490,
         16186397,
         1919159,
         334000,
         2128,
         2090000,
         381000000,
         130533,
         62530,
         20000,
         17927000,
         31000000,
         4345000,
         9395000,
         35000,
         287000,
         16416,
         619000,
         3506000,
         29432,
         100000,
         754894,
         2176000,
         47300,
         30000,
         56998,
         2000000,
         4665000,
         137262,
         18658,
         6551000,
         27791
        ]
       },
       "skew": {
        "type": null,
        "values": null
       },
       "stroke": "Black",
       "tooltip": "IPY_MODEL_c6613152924a4053afe3ee0661c7bb68",
       "tooltip_location": "mouse",
       "tooltip_style": {
        "opacity": 0.9
       },
       "unhovered_style": {
        "opacity": 0.5
       },
       "unselected_style": {},
       "visible": true,
       "x": {
        "type": "float",
        "values": [
         359.93,
         553.72,
         407.36,
         454.33,
         447.59,
         517.46,
         340,
         400.69,
         800.61,
         394.03,
         387.38,
         549.19,
         379.13,
         583.95,
         619.21,
         377.54,
         505.14,
         375.96,
         345,
         512.93,
         364,
         544.65,
         434.14,
         360,
         427.43,
         374.38,
         799.61,
         358,
         350,
         371.26,
         576.38,
         692.33,
         369,
         674.7,
         420.73,
         508.4,
         498.83,
         759.05,
         484.74,
         414.04,
         470.64,
         535.59,
         461.08,
         364.46,
         372.82,
         472.05,
         609,
         539.56,
         563,
         472.12,
         419.18,
         666,
         453.6,
         766.25,
         808.99,
         380.73,
         700,
         882.54,
         528.69,
         661.9,
         932,
         570,
         497.25,
         882.54,
         588.36,
         441.27,
         518.72,
         845.76,
         735.45,
         661.9,
         871.63,
         575.86,
         1017.81,
         542.97,
         695.05,
         509.2,
         1159.5,
         702.1,
         522.98,
         670.12,
         1123.88,
         1085.13,
         804.31,
         819.27,
         775.67,
         760.98,
         682.75,
         910.69,
         503.81,
         583.8,
         1350.4,
         944.05,
         1053.6,
         698.1,
         726.97,
         686.66,
         564.16,
         697.3,
         775.1,
         1004.13,
         774.21,
         1912.62,
         784.46,
         601.22,
         502.64,
         1434.51,
         639.95,
         717.05,
         1554,
         490.82,
         836.31,
         1227.06,
         572.78,
         1342.84,
         906.39,
         1037.69,
         1388.32,
         571.2,
         1695.68,
         934.41,
         1390.67,
         801.9,
         1213.16,
         1339.84,
         585.03,
         865.19,
         988.79,
         1145.57,
         800.76,
         515.5,
         617.12,
         1112.36,
         2412.43,
         950,
         1198.39,
         1284.41,
         1093.92,
         1308.87,
         1197.6,
         1357.95,
         1443.02,
         1100,
         1612.48,
         428.48,
         869.92,
         513.35,
         807.51,
         2716.87,
         346.43,
         671.48,
         1011.68,
         809.23,
         985.89,
         472.12,
         809.34,
         790.15,
         514.12,
         1055.06,
         698,
         596,
         607.01,
         750.13,
         553.05,
         606.93,
         569.07,
         944.24,
         541.62,
         573.29,
         626.97,
         1308.44,
         861.82,
         387.81,
         871,
         496.98,
         514.12,
         667.71,
         459.71,
         829.58
        ]
       },
       "y": {
        "type": "float",
        "values": [
         26.98,
         31,
         33.6,
         29.2,
         31.5,
         28.75,
         33.8,
         30.9,
         32.1,
         31.6,
         32.7,
         31.9,
         29.8,
         30.2,
         29.7,
         30.6,
         28,
         29.5,
         29.3,
         25.5,
         32.8,
         31.1,
         30.5,
         30.3,
         29.6,
         32,
         31.2,
         30.28,
         32.4,
         30.8,
         30.4,
         51.83,
         31.8,
         45.62,
         25.2,
         25.1,
         29.4,
         33.5,
         31.4,
         32.3,
         32.2,
         31.3,
         25.3,
         32.6,
         33.7,
         28.21,
         25.5,
         36.04,
         25.4,
         37.63,
         32.8,
         25.9,
         32.6,
         28.8,
         30.3,
         29.9,
         31.2,
         64.17,
         31.7,
         26,
         29.7,
         33,
         33.1,
         32.3,
         30.8,
         32.1,
         28.8,
         30.7,
         42.12,
         33,
         33.2,
         58.43,
         54.59,
         56.67,
         33,
         32,
         39,
         32,
         32,
         30.21,
         32.2,
         32.9,
         28.7,
         52.21,
         62.44,
         54.06,
         25.8,
         31.12,
         36.27,
         33.9,
         34.2,
         54.74,
         26.9,
         58.45,
         25.4,
         32.9,
         35.5,
         35.7,
         30.43,
         32.9,
         32.9,
         39.41,
         32.9,
         35.4,
         61.94,
         34.4,
         57.15,
         36.2,
         40,
         35.1,
         35.8,
         36.1,
         38.5,
         35,
         36.5,
         31.8,
         32.4,
         59.8,
         38.37,
         36.6,
         36,
         32.4,
         37.7,
         29.01,
         26.2,
         33,
         28.9,
         36.9,
         53.09,
         64.7,
         57.46,
         35.4,
         39.86,
         38.8,
         35.9,
         35.6,
         35.7,
         35.5,
         36.4,
         36.6,
         29.5,
         32.16,
         38,
         52.31,
         35,
         50.5,
         36.6,
         40.02,
         54.96,
         34.05,
         29.2,
         35,
         32,
         26.1,
         45.51,
         59.22,
         30,
         36.4,
         26,
         25.8,
         52.98,
         30.6,
         53.78,
         31.8,
         30.8,
         51.93,
         34.05,
         31.5,
         30.9,
         44.9,
         29.1,
         25.7,
         28.3,
         30.4,
         28.97,
         57.91,
         32,
         40.8
        ]
       }
      }
     }
    },
    "version_major": 1,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}