text: # %%
  """
  <table class="ee-notebook-buttons" align="left">
      <td><a target="_blank"  href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/FeatureCollection/reducing_feature_collection.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
      <td><a target="_blank"  href="https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/FeatureCollection/reducing_feature_collection.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>
      <td><a target="_blank"  href="https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/FeatureCollection/reducing_feature_collection.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a></td>
  </table>
  """
  
  # %%
  """
  ## Install Earth Engine API and geemap
  Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). 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()`.
  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.
  
  **Important note**: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60#issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving).
  """
  
  # %%
  # Installs geemap package
  import subprocess
  
  try:
      import geemap
  except ImportError:
      print('geemap package not installed. Installing ...')
      subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])
  
  # Checks whether this notebook is running on Google Colab
  try:
      import google.colab
      import geemap.eefolium as geemap
  except:
      import geemap
  
  # Authenticates and initializes Earth Engine
  import ee
  
  try:
      ee.Initialize()
  except Exception as e:
      ee.Authenticate()
      ee.Initialize()  
  
  # %%
  """
  ## Create an interactive map 
  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. 
  """
  
  # %%
  Map = geemap.Map(center=[40,-100], zoom=4)
  Map
  
  # %%
  """
  ## Add Earth Engine Python script 
  """
  
  # %%
  # Add Earth Engine dataset
  def areaDiff(feature):
    area = feature.geometry().area().divide(1000 * 1000)
    # Compute the differece between computed area and the area property.
    diff = area.subtract(ee.Number.parse(feature.get('areasqkm')))
    # Return the feature with the squared difference set to the 'diff' property.
    return feature.set('diff', diff.pow(2))
  
  
  # Load watersheds from a data table and filter to the continental US.
  sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06') \
    .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29))
  
  # This function computes the squared difference between an area property
  # and area computed directly from the feature's geometry.
  # areaDiff = function(feature) {
  #   # Compute area in sq. km directly from the geometry.
  #   area = feature.geometry().area().divide(1000 * 1000)
  #   # Compute the differece between computed area and the area property.
  #   diff = area.subtract(ee.Number.parse(feature.get('areasqkm')))
  #   # Return the feature with the squared difference set to the 'diff' property.
  #   return feature.set('diff', diff.pow(2))
  # }
  
  # Calculate RMSE for population of difference pairs.
  rmse = ee.Number(
    # Map the difference function over the collection.
    sheds.map(areaDiff)
    # Reduce to get the mean squared difference. \
    .reduceColumns(ee.Reducer.mean(), ['diff']) \
    .get('mean')
  ) \
  .sqrt()
  
  # Print the result.
  print('RMSE=', rmse.getInfo())
  
  
  
  # %%
  """
  ## Display Earth Engine data layers 
  """
  
  # %%
  Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.
  Map