24 publish maps

Uncomment the following line to install geemap if needed.

In [1]:

# !pip install geemap

# !pip install geemap

To follow this tutorial, you will need to sign up for an account with https://datapane.com, then install and authenticate the datapane Python package. More information can be found here.

• pip install datapane
• datapane login
• datapane ping

In [2]:

import ee
import geemap.foliumap as geemap

import ee import geemap.foliumap as geemap

In [3]:

# Create a map centered at (lat, lon).
Map = geemap.Map(center=[40, -100], zoom=4)

# Use an elevation dataset and terrain functions to create
# a custom visualization of topography.

# Load a global elevation image.
elev = ee.Image('USGS/GMTED2010')

# Zoom to an area of interest.
Map.setCenter(-121.069, 50.709, 6)

# Add the elevation to the map.
Map.addLayer(elev, {}, 'elev')

# Use the terrain algorithms to compute a hillshade with 8-bit values.
shade = ee.Terrain.hillshade(elev)
Map.addLayer(shade, {}, 'hillshade', False)

# Create a "sea" variable to be used for cartographic purposes
sea = elev.lte(0)
Map.addLayer(sea.mask(sea), {'palette': '000022'}, 'sea', False)

# Create a custom elevation palette from hex strings.
elevationPalette = ['006600', '002200', 'fff700', 'ab7634', 'c4d0ff', 'ffffff']
# Use these visualization parameters, customized by location.
visParams = {'min': 1, 'max': 3000, 'palette': elevationPalette}

# Create a mosaic of the sea and the elevation data
visualized = ee.ImageCollection(
    [
        # Mask the elevation to get only land
        elev.mask(sea.Not()).visualize(**visParams),
        # Use the sea mask directly to display sea.
        sea.mask(sea).visualize(**{'palette': '000022'}),
    ]
).mosaic()

# Note that the visualization image doesn't require visualization parameters.
Map.addLayer(visualized, {}, 'elev palette', False)

# Convert the visualized elevation to HSV, first converting to [0, 1] data.
hsv = visualized.divide(255).rgbToHsv()
# Select only the hue and saturation bands.
hs = hsv.select(0, 1)
# Convert the hillshade to [0, 1] data, as expected by the HSV algorithm.
v = shade.divide(255)
# Create a visualization image by converting back to RGB from HSV.
# Note the cast to byte in order to export the image correctly.
rgb = hs.addBands(v).hsvToRgb().multiply(255).byte()
Map.addLayer(rgb, {}, 'styled')

states = ee.FeatureCollection('TIGER/2018/States')
Map.addLayer(ee.Image().paint(states, 0, 2), {}, "US States")

# Create a map centered at (lat, lon). Map = geemap.Map(center=[40, -100], zoom=4) # Use an elevation dataset and terrain functions to create # a custom visualization of topography. # Load a global elevation image. elev = ee.Image('USGS/GMTED2010') # Zoom to an area of interest. Map.setCenter(-121.069, 50.709, 6) # Add the elevation to the map. Map.addLayer(elev, {}, 'elev') # Use the terrain algorithms to compute a hillshade with 8-bit values. shade = ee.Terrain.hillshade(elev) Map.addLayer(shade, {}, 'hillshade', False) # Create a "sea" variable to be used for cartographic purposes sea = elev.lte(0) Map.addLayer(sea.mask(sea), {'palette': '000022'}, 'sea', False) # Create a custom elevation palette from hex strings. elevationPalette = ['006600', '002200', 'fff700', 'ab7634', 'c4d0ff', 'ffffff'] # Use these visualization parameters, customized by location. visParams = {'min': 1, 'max': 3000, 'palette': elevationPalette} # Create a mosaic of the sea and the elevation data visualized = ee.ImageCollection( [ # Mask the elevation to get only land elev.mask(sea.Not()).visualize(**visParams), # Use the sea mask directly to display sea. sea.mask(sea).visualize(**{'palette': '000022'}), ] ).mosaic() # Note that the visualization image doesn't require visualization parameters. Map.addLayer(visualized, {}, 'elev palette', False) # Convert the visualized elevation to HSV, first converting to [0, 1] data. hsv = visualized.divide(255).rgbToHsv() # Select only the hue and saturation bands. hs = hsv.select(0, 1) # Convert the hillshade to [0, 1] data, as expected by the HSV algorithm. v = shade.divide(255) # Create a visualization image by converting back to RGB from HSV. # Note the cast to byte in order to export the image correctly. rgb = hs.addBands(v).hsvToRgb().multiply(255).byte() Map.addLayer(rgb, {}, 'styled') states = ee.FeatureCollection('TIGER/2018/States') Map.addLayer(ee.Image().paint(states, 0, 2), {}, "US States")

In [4]:

# Display the map.
Map

# Display the map. Map

Out[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [5]:

Map.publish(
    name='Terrain Visualization',
    description='A folium map with Earth Engine data layers',
)

Map.publish( name='Terrain Visualization', description='A folium map with Earth Engine data layers', )

Bokeh version 2.4.2 is not supported, these plots may not display correctly, please install version ~=2.2.0

Uploading report and associated data - please wait...

Your report only contains a single element - did you know you can include additional plots, tables and text in a single report? More info here

---------------------------------------------------------------------------
InvalidTokenError                         Traceback (most recent call last)
File ~/.local/lib/python3.9/site-packages/geemap/foliumap.py:1573, in Map.publish(self, name, description, source_url, tags, source_file, open, formatting, **kwargs)
   1571 try:
-> 1573     dp.Report(dp.Plot(self)).upload(
   1574         name=name,
   1575         description=description,
   1576         source_url=source_url,
   1577         tags=tags,
   1578         source_file=source_file,
   1579         open=open,
   1580         formatting=formatting,
   1581     )
   1583 except Exception as e:

File ~/.local/lib/python3.9/site-packages/datapane/client/api/report/core.py:420, in Report.upload(self, name, description, source_url, visibility, tags, project, source_file, open, formatting, **kwargs)
    418 display_msg("Uploading report and associated data - *please wait...*")
--> 420 self._upload_report(
    421     name, description, source_url, visibility, tags, project, source_file, formatting=formatting, **kwargs
    422 )
    424 if open:

File ~/.local/lib/python3.9/site-packages/datapane/client/api/report/core.py:381, in Report._upload_report(self, name, description, source_url, visibility, tags, project, source_file, formatting, **kwargs)
    380         files["source_file"] = [output_file.file]
--> 381     res = Resource(self.endpoint).post_files(files, document=report_str, **kwargs)
    383 # Set dto based on new URL

File ~/.local/lib/python3.9/site-packages/datapane/client/api/common.py:162, in Resource.__init__(self, endpoint)
    161 self.endpoint = endpoint.split("/api", maxsplit=1)[-1]
--> 162 config = c.check_get_config()
    163 self.url = up.urljoin(config.server, f"api{self.endpoint}")

File ~/.local/lib/python3.9/site-packages/datapane/client/config.py:183, in check_get_config()
    182         webbrowser.open(url=str(f), new=2)
--> 183     raise InvalidTokenError(
    184         "Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token"
    185     )
    186 return _config

InvalidTokenError: Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token
Please run with `dp.enable_logging()`, restart your Jupyter kernel/Python instance, and/or visit https://www.github.com/datapane/datapane to raise issue / discuss if error repeats

During handling of the above exception, another exception occurred:

Exception                                 Traceback (most recent call last)
Input In [5], in <cell line: 1>()
----> 1 Map.publish(
      2     name='Terrain Visualization',
      3     description='A folium map with Earth Engine data layers',
      4 )

File ~/.local/lib/python3.9/site-packages/geemap/foliumap.py:1584, in Map.publish(self, name, description, source_url, tags, source_file, open, formatting, **kwargs)
   1573     dp.Report(dp.Plot(self)).upload(
   1574         name=name,
   1575         description=description,
   (...)
   1580         formatting=formatting,
   1581     )
   1583 except Exception as e:
-> 1584     raise Exception(e)

Exception: Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token
Please run with `dp.enable_logging()`, restart your Jupyter kernel/Python instance, and/or visit https://www.github.com/datapane/datapane to raise issue / discuss if error repeats

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Last update: 2022-03-14