{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from ipywidgets import Dropdown\n",
    "\n",
    "from bqplot import Lines, Figure, LinearScale, DateScale, Axis\n",
    "\n",
    "from ipyleaflet import Map, GeoJSON, WidgetControl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_json(os.path.abspath(\"nations.json\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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",
    "\n",
    "def extrap_interp(data):\n",
    "    data = np.array(data)\n",
    "    x_range = np.arange(1800, 2009, 1.0)\n",
    "    y_range = np.interp(x_range, data[:, 0], data[:, 1])\n",
    "    return y_range\n",
    "\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": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_start = pd.datetime(1800, 12, 31)\n",
    "date_end = pd.datetime(2009, 12, 31)\n",
    "\n",
    "date_scale = DateScale(min=date_start, max=date_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_data = pd.date_range(start=date_start, end=date_end, freq=\"A\", normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_name = \"Angola\"\n",
    "data_name = \"income\"\n",
    "\n",
    "x_data = data[data.name == country_name][data_name].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scale = LinearScale()\n",
    "\n",
    "lines = Lines(x=date_data, y=x_data, scales={\"x\": date_scale, \"y\": x_scale})\n",
    "\n",
    "ax_x = Axis(label=\"Year\", scale=date_scale, num_ticks=10, tick_format=\"%Y\")\n",
    "ax_y = Axis(\n",
    "    label=data_name.capitalize(), scale=x_scale, orientation=\"vertical\", side=\"left\"\n",
    ")\n",
    "\n",
    "figure = Figure(\n",
    "    axes=[ax_x, ax_y],\n",
    "    title=country_name,\n",
    "    marks=[lines],\n",
    "    animation_duration=500,\n",
    "    layout={\"max_height\": \"250px\", \"max_width\": \"400px\"},\n",
    ")\n",
    "figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_figure(country_name, data_name):\n",
    "    lines.y = data[data.name == country_name][data_name].values[0]\n",
    "    ax_y.label = data_name.capitalize()\n",
    "    figure.title = country_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_name = \"Benin\"\n",
    "data_name = \"income\"\n",
    "\n",
    "update_figure(country_name, data_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_name = \"Angola\"\n",
    "data_name = \"population\"\n",
    "\n",
    "update_figure(country_name, data_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./countries.geo.json\") as f:\n",
    "    countries = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Map(zoom=3)\n",
    "\n",
    "geo = GeoJSON(\n",
    "    data=countries,\n",
    "    style={\"fillColor\": \"white\", \"weight\": 0.5},\n",
    "    hover_style={\"fillColor\": \"#1f77b4\"},\n",
    "    name=\"Countries\",\n",
    ")\n",
    "m.add(geo)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "widget_control1 = WidgetControl(widget=figure, position=\"bottomright\")\n",
    "\n",
    "m.add(widget_control1)\n",
    "\n",
    "\n",
    "def on_hover(event, feature, **kwargs):\n",
    "    global country_name\n",
    "\n",
    "    country_name = feature[\"properties\"][\"name\"]\n",
    "    update_figure(country_name, data_name)\n",
    "    print(\"feature\", feature)\n",
    "\n",
    "\n",
    "geo.on_hover(on_hover)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropdown = Dropdown(\n",
    "    options=[\"income\", \"population\", \"lifeExpectancy\"],\n",
    "    value=data_name,\n",
    "    description=\"Plotting:\",\n",
    ")\n",
    "\n",
    "\n",
    "def on_click(change):\n",
    "    global data_name\n",
    "\n",
    "    data_name = change[\"new\"]\n",
    "    update_figure(country_name, data_name)\n",
    "\n",
    "\n",
    "dropdown.observe(on_click, \"value\")\n",
    "\n",
    "widget_control2 = WidgetControl(widget=dropdown, position=\"bottomleft\")\n",
    "\n",
    "m.add(widget_control2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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