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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapboxgl Python Library for location data visualization\n",
    "\n",
    "https://github.com/mapbox/mapboxgl-jupyter\n",
    "\n",
    "### Requirements\n",
    "\n",
    "These examples require the installation of the following python modules\n",
    "\n",
    "```\n",
    "pip install mapboxgl\n",
    "pip install pandas\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import os\n",
    "from mapboxgl.utils import *\n",
    "from mapboxgl.viz import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data from sample csv\n",
    "data_url = 'https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/data/points.csv'\n",
    "df = pd.read_csv(data_url).round(3)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your Mapbox access token.  \n",
    "Set a  `MAPBOX_ACCESS_TOKEN` environment variable or copy/paste your token  \n",
    "If you do not have a Mapbox access token, sign up for an account at https://www.mapbox.com/  \n",
    "If you already have an account, you can grab your token at https://www.mapbox.com/account/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Must be a public token, starting with `pk`\n",
    "token = os.getenv('MAPBOX_ACCESS_TOKEN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a visualization from a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a geojson Feature Collection from the current dataframe\n",
    "# optionally create a geojson file export by including the argument \n",
    "# filename='../data/healthcare_points.geojson',\n",
    "geodata = df_to_geojson(df, \n",
    "              properties=['Avg Medicare Payments', 'Avg Covered Charges', 'date'], \n",
    "              lat='lat', \n",
    "              lon='lon', \n",
    "              precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just show a map of the data\n",
    "viz = CircleViz(geodata, \n",
    "                access_token=token, \n",
    "                radius=2, \n",
    "                center=(-95, 40), \n",
    "                zoom=3)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add data-driven styles and a legend to the viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Generate data breaks using numpy quantiles and color stops from colorBrewer\n",
    "measure = 'Avg Medicare Payments'\n",
    "color_breaks = [round(df[measure].quantile(q=x*0.1), 2) for x in range(1, 9)]\n",
    "color_stops = create_color_stops(color_breaks, colors='YlGnBu')\n",
    "\n",
    "# Create the viz from the dataframe\n",
    "viz = CircleViz(geodata,\n",
    "                access_token=token, \n",
    "                color_property=\"Avg Medicare Payments\",\n",
    "                color_stops=color_stops,\n",
    "                radius=2.5,\n",
    "                stroke_color='black',\n",
    "                stroke_width=0.2,\n",
    "                center=(-95, 40),\n",
    "                zoom=3,\n",
    "                below_layer='waterway-label')\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add a scale bar to the viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz.scale = True\n",
    "viz.scale_unit_system = 'imperial'\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add labels to the viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz.label_property = \"Avg Medicare Payments\"\n",
    "viz.stroke_width = 0\n",
    "viz.label_size = 8\n",
    "viz.legend_text_numeric_precision = 2\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change viz data property and color scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a new data domain breaks and a new color palette from colorBrewer2\n",
    "measure = 'Avg Covered Charges'\n",
    "color_breaks = [round(df[measure].quantile(q=x*0.1), 1) for x in range(1, 9)]\n",
    "color_stops = create_color_stops(color_breaks, colors='YlOrRd')\n",
    "\n",
    "# Show the viz\n",
    "viz.color_property = 'Avg Covered Charges'\n",
    "viz.color_stops = color_stops\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the viz map style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Map settings\n",
    "viz.style = 'mapbox://styles/mapbox/dark-v9?optimize=true'\n",
    "viz.label_color = 'hsl(0, 0%, 70%)'\n",
    "viz.label_halo_color = 'hsla(0, 0%, 10%, 0.75)'\n",
    "\n",
    "# Legend settings\n",
    "viz.legend_gradient = False\n",
    "viz.legend_fill = '#343332'\n",
    "viz.legend_header_fill = '#343332'\n",
    "viz.legend_text_color = 'hsl(0, 0%, 70%)'\n",
    "viz.legend_key_borders_on = False\n",
    "viz.legend_title_halo_color = 'hsla(0, 0%, 10%, 0.75)'\n",
    "\n",
    "# Scale bar settings\n",
    "viz.scale = True\n",
    "viz.scale_border_color = 'hsla(0, 0%, 10%, 0.75)'\n",
    "viz.scale_position = 'bottom-left'\n",
    "viz.scale_background_color = '#343332'\n",
    "viz.scale_text_color = 'hsl(0, 0%, 70%)'\n",
    "\n",
    "# Render map\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Create a graduated cricle viz based on two data properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate data breaks and color stops from colorBrewer\n",
    "measure_color = 'Avg Covered Charges'\n",
    "color_breaks = [round(df[measure_color].quantile(q=x*0.1), 2) for x in range(2, 10)]\n",
    "color_stops = create_color_stops(color_breaks, colors='Blues')\n",
    "\n",
    "# Generate radius breaks from data domain and circle-radius range\n",
    "measure_radius = 'Avg Medicare Payments'\n",
    "radius_breaks = [round(df[measure_radius].quantile(q=x*0.1), 2) for x in range(2, 10)]\n",
    "radius_stops = create_radius_stops(radius_breaks, 0.5, 10)\n",
    "\n",
    "# Create the viz\n",
    "viz2 = GraduatedCircleViz(geodata, \n",
    "                          access_token=token,\n",
    "                          color_property=\"Avg Covered Charges\",\n",
    "                          color_stops=color_stops,\n",
    "                          radius_property=\"Avg Medicare Payments\",\n",
    "                          radius_stops=radius_stops,\n",
    "                          stroke_color='black',\n",
    "                          stroke_width=0.5,\n",
    "                          center=(-95, 40),\n",
    "                          zoom=3,\n",
    "                          opacity=0.75,\n",
    "                          below_layer='waterway-label')\n",
    "viz2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a heatmap viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "measure = 'Avg Medicare Payments'\n",
    "heatmap_color_stops = create_color_stops([0.01, 0.25, 0.5, 0.75, 1], colors='RdPu')\n",
    "heatmap_radius_stops = [[0, 3], [14, 100]] # increase radius with zoom\n",
    "\n",
    "color_breaks = [round(df[measure].quantile(q=x*0.1), 2) for x in range(2, 10)]\n",
    "color_stops = create_color_stops(color_breaks, colors='Spectral')\n",
    "\n",
    "heatmap_weight_stops = create_weight_stops(color_breaks)\n",
    "\n",
    "# Create the heatmap \n",
    "viz3 = HeatmapViz(geodata, \n",
    "                  access_token=token,\n",
    "                  weight_property=\"Avg Medicare Payments\",\n",
    "                  weight_stops=heatmap_weight_stops,\n",
    "                  color_stops=heatmap_color_stops,\n",
    "                  radius_stops=heatmap_radius_stops,\n",
    "                  opacity=0.8,\n",
    "                  center=(-95, 40),\n",
    "                  zoom=3,\n",
    "                  below_layer='waterway-label')\n",
    "viz3.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a clustered circle map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a clustered circle map\n",
    "color_stops = create_color_stops([1, 10, 25, 50, 75, 100], colors='YlOrBr')\n",
    "\n",
    "viz4 = ClusteredCircleViz(geodata, \n",
    "                          access_token=token,\n",
    "                          color_stops=color_stops,\n",
    "                          stroke_color='black',\n",
    "                          radius_stops=[[1, 5], [10, 10], [50, 15], [100, 20]],\n",
    "                          radius_default=2,\n",
    "                          cluster_maxzoom=10,\n",
    "                          cluster_radius=30,\n",
    "                          label_size=12,\n",
    "                          opacity=0.9,\n",
    "                          center=(-95, 40),\n",
    "                          zoom=3)\n",
    "viz4.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save our viz to an HTML file for distribution\n",
    "### Note\n",
    "Viz export contains a reference to the data in this visualization.  Serve data from the same directory as the HTML file to vis your visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('viz4.html', 'w') as f:\n",
    "    f.write(viz4.create_html())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run exported HTML example\n",
    "\n",
    "Python2: `python -m SimpleHTTPServer 8080`\n",
    "\n",
    "Python3: `python3 -m http.server 8080`\n",
    "\n",
    "Now navigate your browser to `http://localhost:8080/viz4.html` to see the viz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download the map canvas to a PNG file\n",
    "### Note\n",
    "Screen captures of the map and legend are separate; additional image processing is required to export a map with visible legend and other Mapbox control elements.  Click either the `Map` or `Legend` links to download the corresponding PNG file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz4.add_snapshot_links = True\n",
    "viz4.show()"
   ]
  }
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