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   "source": [
    "# Pydeck + Earth Engine: Lines FeatureCollection\n",
    "\n",
    "This is an example of using [pydeck](https://pydeck.gl) to visualize a Google Earth Engine `FeatureCollection` of lines.\n",
    "To install and run this notebook locally, refer to the [Pydeck Earth Engine documentation](https://earthengine-layers.com/docs/developer-guide/pydeck-integration).\n",
    "\n",
    "To see this example online, view the [JavaScript version][js-example].\n",
    "\n",
    "[js-example]: https://earthengine-layers.com/examples/noaa-hurricanes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydeck_earthengine_layers import EarthEngineLayer\n",
    "import pydeck as pdk\n",
    "import ee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authenticate with Earth Engine\n",
    "\n",
    "Using Earth Engine requires authentication. If you don't have a Google account approved for use with Earth Engine, you'll need to request access. For more information and to sign up, go to https://signup.earthengine.google.com/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    ee.Initialize()\n",
    "except Exception as e:\n",
    "    ee.Authenticate()\n",
    "    ee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOAA Hurricanes dataset\n",
    "\n",
    "This example uses the [NOAA Atlantic Hurricane catalog][noaa], a dataset with positions of hurricanes and related attributes from 1851 to 2018. In this example we'll look only at hurricanes in 2017.\n",
    "\n",
    "[noaa]: https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_atlantic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hurricane tracks and points for 2017.\n",
    "hurricanes = ee.FeatureCollection('NOAA/NHC/HURDAT2/atlantic')\n",
    "\n",
    "year = '2017'\n",
    "points = hurricanes.filter(ee.Filter.date(ee.Date(year).getRange('year')))\n",
    "\n",
    "# Find all of the hurricane ids.\n",
    "def get_id(point):\n",
    "    return ee.Feature(point).get('id')\n",
    "storm_ids = points.toList(1000).map(get_id).distinct()\n",
    "\n",
    "# Create a line for each hurricane.\n",
    "def create_line(storm_id):\n",
    "    pts = points.filter(ee.Filter.eq('id', ee.String(storm_id)))\n",
    "    pts = pts.sort('system:time_start')\n",
    "    line = ee.Geometry.LineString(pts.geometry().coordinates())\n",
    "    feature = ee.Feature(line)\n",
    "    return feature.set('id', storm_id)\n",
    "\n",
    "lines = ee.FeatureCollection(storm_ids.map(create_line))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to create the Pydeck layer. The `EarthEngineLayer` makes this simple. Just pass the Earth Engine object to the class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines_layer = EarthEngineLayer(\n",
    "    lines,\n",
    "    {'color': 'red'},\n",
    "    id=\"tracks\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_layer = EarthEngineLayer(\n",
    "    points,\n",
    "    {'color': 'black'},\n",
    "    id=\"points\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then just pass this layer to a `pydeck.Deck` instance, and call `.show()` to create a map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_state = pdk.ViewState(latitude=36, longitude=-53, zoom=3)\n",
    "r = pdk.Deck(\n",
    "    layers=[points_layer, lines_layer], \n",
    "    initial_view_state=view_state\n",
    ")\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector\n",
    "\n",
    "Let's plot these again, but as a vector dataset, instead of a raster dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines_layer = EarthEngineLayer(\n",
    "    lines,\n",
    "    as_vector=True,\n",
    "    get_line_color=[255, 0, 0],\n",
    "    getLineWidth=1000,\n",
    "    lineWidthMinPixels=3,\n",
    "    id=\"tracks\",\n",
    ")\n",
    "points_layer = EarthEngineLayer(\n",
    "    points,\n",
    "    get_fill_color=[0, 0, 0],\n",
    "    pointRadiusMinPixels=3,\n",
    "    getRadius=100,\n",
    "    getLineColor=[255, 255, 255],\n",
    "    lineWidthMinPixels=0.5,\n",
    "    as_vector=True,\n",
    "    stroked=True,\n",
    "    id=\"points\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_state = pdk.ViewState(latitude=36, longitude=-53, zoom=3)\n",
    "r = pdk.Deck(\n",
    "    layers=[lines_layer, points_layer], \n",
    "    initial_view_state=view_state,\n",
    "    tooltip=True\n",
    ")\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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