earthengine-py-notebooks/GetStarted/09_a_complete_example.ipynb

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        "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
        "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/GetStarted/09_a_complete_example.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/GetStarted/09_a_complete_example.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/GetStarted/09_a_complete_example.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
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      "source": [
        "## Install Earth Engine API and geemap\n",
        "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
        "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Installs geemap package\n",
        "import subprocess\n",
        "\n",
        "try:\n",
        "    import geemap\n",
        "except ImportError:\n",
        "    print('Installing geemap ...')\n",
        "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import ee\n",
        "import geemap"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an interactive map \n",
        "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "# This function gets NDVI from a Landsat 8 image.\n",
        "\n",
        "\n",
        "def addNDVI(image):\n",
        "    return image.addBands(image.normalizedDifference(['B5', 'B4']))\n",
        "\n",
        "# This function masks cloudy pixels.\n",
        "\n",
        "\n",
        "def cloudMask(image):\n",
        "    clouds = ee.Algorithms.Landsat.simpleCloudScore(image).select(['cloud'])\n",
        "    return image.updateMask(clouds.lt(10))\n",
        "\n",
        "\n",
        "# Load a Landsat collection, map the NDVI and cloud masking functions over it.\n",
        "collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \\\n",
        "    .filterBounds(ee.Geometry.Point([-122.262, 37.8719])) \\\n",
        "    .filterDate('2014-03-01', '2014-05-31') \\\n",
        "    .map(addNDVI) \\\n",
        "    .map(cloudMask)\n",
        "\n",
        "# Reduce the collection to the mean of each pixel and display.\n",
        "meanImage = collection.reduce(ee.Reducer.mean())\n",
        "vizParams = {'bands': ['B5_mean', 'B4_mean', 'B3_mean'], 'min': 0, 'max': 0.5}\n",
        "Map.setCenter(-122.262, 37.8719, 10)\n",
        "Map.addLayer(meanImage, vizParams, 'mean')\n",
        "\n",
        "# Load a region in which to compute the mean and display it.\n",
        "counties = ee.FeatureCollection('TIGER/2016/Counties')\n",
        "santaClara = ee.Feature(counties.filter(\n",
        "    ee.Filter.eq('NAME', 'Santa Clara')).first())\n",
        "Map.addLayer(ee.Image().paint(santaClara, 0, 2), {\n",
        "             'palette': 'yellow'}, 'Santa Clara')\n",
        "\n",
        "# Get the mean of NDVI in the region.\n",
        "mean = meanImage.select(['nd_mean']).reduceRegion(**{\n",
        "    'reducer': ee.Reducer.mean(),\n",
        "    'geometry': santaClara.geometry(),\n",
        "    'scale': 30\n",
        "})\n",
        "\n",
        "# Print mean NDVI for the region.\n",
        "print('Santa Clara spring mean NDVI:', mean.get('nd_mean').getInfo())\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
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