Vgrid key features¶

You can try out vgrid by using the cloud-computing platforms below without having to install anything on your computer:

Install vgrid¶

In [3]:

#%pip install vgrid --upgrade

#%pip install vgrid --upgrade

DGGS Conversion¶

latlon2dggs¶

In [ ]:

from vgrid.conversion.latlon2dggs import latlon2h3
lat = 10.775276
lon = 106.706797 
res = 10
h3_id = latlon2h3(lat, lon,res)
h3_id   

from vgrid.conversion.latlon2dggs import latlon2h3 lat = 10.775276 lon = 106.706797 res = 10 h3_id = latlon2h3(lat, lon,res) h3_id

dggs2geojson¶

In [ ]:

from vgrid.conversion.dggs2geojson import h32geojson 
h3_geojson = h32geojson(h3_id)
print(h3_geojson)

from vgrid.conversion.dggs2geojson import h32geojson h3_geojson = h32geojson(h3_id) print(h3_geojson)

DGGS visualization with folium¶

Install folium

In [ ]:

%pip install folium

%pip install folium

Initialize folium map and visualize the output GeoJSON

In [ ]:

import folium

m = folium.Map(tiles='CartoDB positron')
h3_layer = folium.GeoJson(
    h3_geojson,
    style_function=lambda x: {
        'fillColor': 'blue',
        'color': 'black',
        'fillOpacity': 0.3,
        'weight': 1
    },
    tooltip=folium.GeoJsonTooltip(
        fields=['h3', 'resolution', 'cell_area'],
        aliases=['H3 ID', 'Resolution', 'Area (m²)'],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """
    )
).add_to(m)

m.fit_bounds(h3_layer.get_bounds()) 


# Display the map
m

import folium m = folium.Map(tiles='CartoDB positron') h3_layer = folium.GeoJson( h3_geojson, style_function=lambda x: { 'fillColor': 'blue', 'color': 'black', 'fillOpacity': 0.3, 'weight': 1 }, tooltip=folium.GeoJsonTooltip( fields=['h3', 'resolution', 'cell_area'], aliases=['H3 ID', 'Resolution', 'Area (m²)'], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """ ) ).add_to(m) m.fit_bounds(h3_layer.get_bounds()) # Display the map m

csv2dggs¶

Loading csv file containing DGGS cell Ids

In [ ]:

import pandas as pd
csv_path = 'https://raw.githubusercontent.com/opengeoshub/opendata/refs/heads/main/csv/point.csv'
csv_file = pd.read_csv(csv_path) # modify the path to the point.csv file containing DGGS cell Ids
csv_file.head()

import pandas as pd csv_path = 'https://raw.githubusercontent.com/opengeoshub/opendata/refs/heads/main/csv/point.csv' csv_file = pd.read_csv(csv_path) # modify the path to the point.csv file containing DGGS cell Ids csv_file.head()

Convert CSV to DGGS and visualize the output GeoJSON with folium

In [ ]:

import json
from vgrid.conversion.csv2dggs import csv2s2
s2_geojson = csv2s2(csv_path,'s2') # csv2s2(csv_path) by default for 's2' column

# Visualize the output GeoJSON
import folium

m = folium.Map(tiles='CartoDB positron')
s2_layer = folium.GeoJson(
    s2_geojson,
    name='s2',
    overlay=True,
    control=True
)

s2_layer = folium.GeoJson(
    s2_geojson,
    style_function=lambda x: {
        'fillColor': 'blue',
        'color': 'black',
        'fillOpacity': 0.3,
        'weight': 1
    },
    tooltip=folium.GeoJsonTooltip(
        fields=['s2', 'resolution', 'cell_area'],
        aliases=['S2 Token', 'Resolution', 'Area (m²)'],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """
    )
).add_to(m)

m.fit_bounds(s2_layer.get_bounds()) 

# Display the map
m

import json from vgrid.conversion.csv2dggs import csv2s2 s2_geojson = csv2s2(csv_path,'s2') # csv2s2(csv_path) by default for 's2' column # Visualize the output GeoJSON import folium m = folium.Map(tiles='CartoDB positron') s2_layer = folium.GeoJson( s2_geojson, name='s2', overlay=True, control=True ) s2_layer = folium.GeoJson( s2_geojson, style_function=lambda x: { 'fillColor': 'blue', 'color': 'black', 'fillOpacity': 0.3, 'weight': 1 }, tooltip=folium.GeoJsonTooltip( fields=['s2', 'resolution', 'cell_area'], aliases=['S2 Token', 'Resolution', 'Area (m²)'], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """ ) ).add_to(m) m.fit_bounds(s2_layer.get_bounds()) # Display the map m

geojson2dggs¶

Loading vector layer in GeoJSON format (Point? Muitipoint, Polyline/ Multipolyline, Polygon/ Multipolygon)

In [10]:

from vgrid.conversion.geojson2dggs.geojson2rhealpix import geojson2rhealpix
import json

with open('../data/polygon.geojson', 'r') as f:
    geojson_data = json.load(f)

from vgrid.conversion.geojson2dggs.geojson2rhealpix import geojson2rhealpix import json with open('../data/polygon.geojson', 'r') as f: geojson_data = json.load(f)

Convert vector layer to DGGS and visualize the output GeoJSON with folium

In [ ]:

geojson_rhealpix = geojson2rhealpix(geojson_data, resolution=10, compact=False) 
# Change compact to True to see the compact version of the rHEALPix cells


# Visualize the output GeoJSON
import folium

m = folium.Map(tiles='CartoDB positron')

rhealpix_layer = folium.GeoJson(
    geojson_rhealpix,
    style_function=lambda x: {
        'fillColor': 'blue',
        'color': 'black',
        'fillOpacity': 0.3,
        'weight': 1
    },
    tooltip=folium.GeoJsonTooltip(
        fields=['rhealpix', 'resolution', 'cell_area'],
        aliases=['rHEALPix ID', 'Resolution', 'Area (m²)'],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """
    )
).add_to(m)

m.fit_bounds(rhealpix_layer.get_bounds()) 

# Display the map
m

geojson_rhealpix = geojson2rhealpix(geojson_data, resolution=10, compact=False) # Change compact to True to see the compact version of the rHEALPix cells # Visualize the output GeoJSON import folium m = folium.Map(tiles='CartoDB positron') rhealpix_layer = folium.GeoJson( geojson_rhealpix, style_function=lambda x: { 'fillColor': 'blue', 'color': 'black', 'fillOpacity': 0.3, 'weight': 1 }, tooltip=folium.GeoJsonTooltip( fields=['rhealpix', 'resolution', 'cell_area'], aliases=['rHEALPix ID', 'Resolution', 'Area (m²)'], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """ ) ).add_to(m) m.fit_bounds(rhealpix_layer.get_bounds()) # Display the map m

dggscompact¶

DGGS Compact

In [ ]:

from vgrid.conversion.dggscompact import rhealpixcompact
from vgrid.utils.rhealpixdggs.dggs import RHEALPixDGGS
rhealpix_dggs = RHEALPixDGGS()
rhealpix_compacted = rhealpixcompact(rhealpix_dggs,geojson_rhealpix,rhealpix_id='rhealpix')

# Visualize the output GeoJSON
import folium

m = folium.Map(tiles='CartoDB positron')

rhealpixcompacted_layer = folium.GeoJson(
    rhealpix_compacted,
    style_function=lambda x: {
        'fillColor': 'blue',
        'color': 'black',
        'fillOpacity': 0.3,
        'weight': 1
    },
    tooltip=folium.GeoJsonTooltip(
        fields=['rhealpix', 'resolution', 'cell_area'],
        aliases=['rHEALPix ID', 'Resolution', 'Area (m²)'],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """
    )
).add_to(m)

m.fit_bounds(rhealpixcompacted_layer.get_bounds()) 

# Display the map
m

from vgrid.conversion.dggscompact import rhealpixcompact from vgrid.utils.rhealpixdggs.dggs import RHEALPixDGGS rhealpix_dggs = RHEALPixDGGS() rhealpix_compacted = rhealpixcompact(rhealpix_dggs,geojson_rhealpix,rhealpix_id='rhealpix') # Visualize the output GeoJSON import folium m = folium.Map(tiles='CartoDB positron') rhealpixcompacted_layer = folium.GeoJson( rhealpix_compacted, style_function=lambda x: { 'fillColor': 'blue', 'color': 'black', 'fillOpacity': 0.3, 'weight': 1 }, tooltip=folium.GeoJsonTooltip( fields=['rhealpix', 'resolution', 'cell_area'], aliases=['rHEALPix ID', 'Resolution', 'Area (m²)'], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """ ) ).add_to(m) m.fit_bounds(rhealpixcompacted_layer.get_bounds()) # Display the map m

DGGS expand

In [ ]:

from vgrid.conversion.dggscompact import rhealpixexpand
from vgrid.utils.rhealpixdggs.dggs import RHEALPixDGGS
rhealpix_dggs = RHEALPixDGGS()
rhealpix_expanded = rhealpixexpand(rhealpix_dggs,geojson_rhealpix,resolution=11, rhealpix_id='rhealpix')

# Visualize the output GeoJSON
import folium

m = folium.Map(tiles='CartoDB positron')

rhealpixexpanded_layer = folium.GeoJson(
    rhealpix_expanded,
    style_function=lambda x: {
        'fillColor': 'blue',
        'color': 'black',
        'fillOpacity': 0.3,
        'weight': 1
    },
    tooltip=folium.GeoJsonTooltip(
        fields=['rhealpix', 'resolution', 'cell_area'],
        aliases=['rHEALPix ID', 'Resolution', 'Area (m²)'],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """
    )
).add_to(m)

m.fit_bounds(rhealpixexpanded_layer.get_bounds()) 

# Display the map
m

from vgrid.conversion.dggscompact import rhealpixexpand from vgrid.utils.rhealpixdggs.dggs import RHEALPixDGGS rhealpix_dggs = RHEALPixDGGS() rhealpix_expanded = rhealpixexpand(rhealpix_dggs,geojson_rhealpix,resolution=11, rhealpix_id='rhealpix') # Visualize the output GeoJSON import folium m = folium.Map(tiles='CartoDB positron') rhealpixexpanded_layer = folium.GeoJson( rhealpix_expanded, style_function=lambda x: { 'fillColor': 'blue', 'color': 'black', 'fillOpacity': 0.3, 'weight': 1 }, tooltip=folium.GeoJsonTooltip( fields=['rhealpix', 'resolution', 'cell_area'], aliases=['rHEALPix ID', 'Resolution', 'Area (m²)'], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """ ) ).add_to(m) m.fit_bounds(rhealpixexpanded_layer.get_bounds()) # Display the map m

geojson2csv¶

In [ ]:

from vgrid.conversion.geojson2csv import geojson2csv
import pandas as pd
from io import StringIO

rhealpix_expanded_csv = geojson2csv(rhealpix_expanded)
df = pd.read_csv(StringIO(rhealpix_expanded_csv))
df.head()
# Save the CSV data to a file
# with open('rhealpix_expanded.csv', 'w', newline='', encoding='utf-8') as f:
#     f.write(rhealpix_expanded_csv)

from vgrid.conversion.geojson2csv import geojson2csv import pandas as pd from io import StringIO rhealpix_expanded_csv = geojson2csv(rhealpix_expanded) df = pd.read_csv(StringIO(rhealpix_expanded_csv)) df.head() # Save the CSV data to a file # with open('rhealpix_expanded.csv', 'w', newline='', encoding='utf-8') as f: # f.write(rhealpix_expanded_csv)

raster2dggs¶

Load raster file and convert to DGGS

In [1]:

from vgrid.conversion.raster2dggs.raster2tilecode import raster2tilecode
from vgrid.utils.download import download_file


raster_url  = 'https://raw.githubusercontent.com/opengeoshub/opendata/main/raster/rgb.tif'
raster_file =  download_file(raster_url)

# Convert rasteto GeoJSON raster2h3(raster_file)
raster2isea4t_csv = raster2tilecode(raster_file, format='csv')
print(raster2isea4t_csv)
raster2isea4t_geojson = raster2tilecode(raster_file, format='geojson')

from vgrid.conversion.raster2dggs.raster2tilecode import raster2tilecode from vgrid.utils.download import download_file raster_url = 'https://raw.githubusercontent.com/opengeoshub/opendata/main/raster/rgb.tif' raster_file = download_file(raster_url) # Convert rasteto GeoJSON raster2h3(raster_file) raster2isea4t_csv = raster2tilecode(raster_file, format='csv') print(raster2isea4t_csv) raster2isea4t_geojson = raster2tilecode(raster_file, format='geojson')

rgb.tif already exists. Skip downloading. Set overwrite=True to overwrite.
Nearest tilecode resolution determined: 26

Resampling: 100%|██████████| 8925/8925 [00:00<00:00, 169217.67 cells/s]

tilecode,band_1,band_2,band_3
z26x53446372y31526797,33,44,72
z26x53446274y31526763,36,54,54
z26x53446273y31526785,214,207,201
z26x53446356y31526815,103,88,91
z26x53446352y31526775,36,51,58
z26x53446322y31526813,136,130,140
z26x53446360y31526771,62,79,86
z26x53446325y31526817,186,181,177
z26x53446307y31526759,41,41,53
z26x53446340y31526773,49,63,74
z26x53446324y31526768,103,100,111
z26x53446377y31526767,80,85,88
z26x53446351y31526784,74,52,38
z26x53446328y31526770,74,77,94
z26x53446319y31526758,135,119,120
z26x53446381y31526798,42,52,77
z26x53446371y31526804,94,98,110
z26x53446340y31526780,29,31,54
z26x53446292y31526771,64,68,95
z26x53446314y31526759,113,98,103
z26x53446275y31526793,122,112,102
z26x53446279y31526815,137,137,139
z26x53446345y31526783,63,52,58
z26x53446303y31526791,211,207,206
z26x53446362y31526820,91,97,111
z26x53446298y31526762,61,80,86
z26x53446283y31526788,132,134,129
z26x53446312y31526787,107,106,112
z26x53446356y31526790,175,175,167
z26x53446352y31526800,205,190,185
z26x53446295y31526752,153,155,168
z26x53446353y31526816,176,169,163
z26x53446353y31526797,214,199,192
z26x53446325y31526798,138,124,123
z26x53446364y31526756,59,77,79
z26x53446355y31526789,182,173,166
z26x53446351y31526771,28,34,48
z26x53446337y31526814,199,189,188
z26x53446276y31526805,196,188,185
z26x53446327y31526798,132,122,123
z26x53446277y31526792,96,93,86
z26x53446309y31526764,42,55,61
z26x53446329y31526792,115,105,104
z26x53446284y31526763,100,100,110
z26x53446312y31526795,114,108,108
z26x53446341y31526787,105,91,91
z26x53446327y31526804,195,189,175
z26x53446331y31526762,60,64,73
z26x53446351y31526824,178,180,177
z26x53446325y31526804,192,188,176
z26x53446357y31526784,100,100,98
z26x53446303y31526793,201,196,193
z26x53446357y31526824,85,87,99
z26x53446376y31526813,177,121,122
z26x53446270y31526815,229,229,231
z26x53446276y31526763,16,27,29
z26x53446301y31526804,196,211,208
z26x53446334y31526774,25,39,48
z26x53446342y31526760,81,95,98
z26x53446308y31526756,112,109,116
z26x53446338y31526799,207,196,192
z26x53446355y31526819,108,102,104
z26x53446270y31526752,77,86,95
z26x53446283y31526821,148,145,152
z26x53446299y31526815,224,214,213
z26x53446374y31526816,161,115,115
z26x53446315y31526785,176,167,168
z26x53446341y31526771,46,54,73
z26x53446291y31526788,170,174,173
z26x53446385y31526796,97,99,114
z26x53446294y31526794,201,210,209
z26x53446312y31526809,150,140,139
z26x53446328y31526793,138,129,124
z26x53446360y31526813,110,77,88
z26x53446286y31526797,108,97,95
z26x53446354y31526799,189,179,177
z26x53446336y31526756,113,124,130
z26x53446381y31526771,83,91,94
z26x53446355y31526797,182,174,171
z26x53446338y31526762,64,82,82
z26x53446383y31526783,170,155,160
z26x53446311y31526800,179,167,169
z26x53446381y31526765,57,68,74
z26x53446303y31526771,46,66,65
z26x53446303y31526781,125,103,105
z26x53446295y31526825,157,146,150
z26x53446273y31526818,149,147,150
z26x53446328y31526804,198,190,177
z26x53446299y31526811,220,207,199
z26x53446319y31526753,149,149,149
z26x53446332y31526800,243,228,221
z26x53446274y31526794,197,183,172
z26x53446301y31526812,160,148,148
z26x53446286y31526798,121,107,104
z26x53446371y31526764,97,95,106
z26x53446296y31526754,19,29,39
z26x53446267y31526762,21,27,51
z26x53446305y31526797,111,97,96
z26x53446365y31526798,101,98,115
z26x53446307y31526816,120,118,123
z26x53446277y31526821,127,112,109
z26x53446365y31526805,42,51,60
z26x53446341y31526780,28,32,57
z26x53446274y31526802,104,104,112
z26x53446385y31526769,54,68,69
z26x53446368y31526759,65,81,80
z26x53446323y31526822,202,203,208
z26x53446289y31526807,235,234,239
z26x53446345y31526775,54,64,73
z26x53446328y31526758,82,82,90
z26x53446319y31526787,204,189,182
z26x53446310y31526755,135,123,127
z26x53446317y31526785,174,163,159
z26x53446375y31526761,49,52,67
z26x53446325y31526794,194,186,184
z26x53446358y31526822,92,95,100
z26x53446364y31526770,80,87,97
z26x53446333y31526785,197,167,169
z26x53446340y31526767,62,74,86
z26x53446304y31526755,73,79,93
z26x53446268y31526794,168,148,150
z26x53446269y31526753,99,106,114
z26x53446304y31526769,35,55,56
z26x53446343y31526791,229,213,200
z26x53446305y31526805,208,222,207
z26x53446383y31526811,123,100,110
z26x53446385y31526765,54,70,70
z26x53446320y31526777,47,49,70
z26x53446385y31526818,177,138,139
z26x53446305y31526764,13,23,33
z26x53446294y31526770,96,97,117
z26x53446290y31526799,179,194,191
z26x53446353y31526776,28,45,52
z26x53446276y31526797,135,131,132
z26x53446283y31526813,96,90,100
z26x53446268y31526765,45,62,70
z26x53446273y31526813,187,183,182
z26x53446297y31526805,202,216,216
z26x53446343y31526754,102,116,127
z26x53446359y31526799,54,70,69
z26x53446302y31526802,182,186,185
z26x53446383y31526796,99,102,119
z26x53446269y31526759,89,94,113
z26x53446362y31526781,82,83,87
z26x53446288y31526765,99,93,103
z26x53446345y31526817,201,203,202
z26x53446331y31526800,232,217,210
z26x53446352y31526755,97,90,97
z26x53446347y31526816,209,209,209
z26x53446319y31526818,176,175,183
z26x53446287y31526768,102,100,113
z26x53446292y31526807,201,208,214
z26x53446282y31526772,31,37,71
z26x53446376y31526769,114,115,119
z26x53446372y31526766,101,100,108
z26x53446281y31526804,173,169,170
z26x53446363y31526812,74,58,69
z26x53446350y31526761,58,77,75
z26x53446315y31526811,130,129,137
z26x53446372y31526763,94,91,102
z26x53446285y31526812,119,109,117
z26x53446332y31526806,238,224,211
z26x53446354y31526768,20,34,43
z26x53446347y31526773,59,69,81
z26x53446350y31526758,56,65,72
z26x53446354y31526795,185,175,166
z26x53446371y31526812,55,47,68
z26x53446372y31526764,95,93,104
z26x53446318y31526772,100,104,116
z26x53446333y31526757,92,99,107
z26x53446287y31526780,125,112,119
z26x53446289y31526754,165,169,178
z26x53446352y31526788,238,213,206
z26x53446330y31526818,235,222,216
z26x53446370y31526765,90,93,102
z26x53446283y31526807,150,145,151
z26x53446356y31526793,191,192,186
z26x53446380y31526753,45,47,59
z26x53446367y31526779,96,94,105
z26x53446308y31526805,153,149,148
z26x53446331y31526761,61,66,72
z26x53446348y31526764,59,80,83
z26x53446297y31526752,126,130,141
z26x53446354y31526825,96,94,105
z26x53446269y31526807,187,177,175
z26x53446310y31526756,148,133,136
z26x53446323y31526813,195,190,197
z26x53446269y31526819,162,158,159
z26x53446382y31526771,78,88,90
z26x53446306y31526774,51,79,80
z26x53446274y31526788,177,174,169
z26x53446359y31526798,77,93,92
z26x53446280y31526792,113,112,108
z26x53446314y31526792,117,118,123
z26x53446345y31526764,62,75,83
z26x53446313y31526815,102,96,100
z26x53446299y31526768,59,70,76
z26x53446326y31526818,195,190,184
z26x53446371y31526821,150,128,131
z26x53446303y31526796,153,144,139
z26x53446357y31526818,89,78,86
z26x53446325y31526809,214,225,219
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Nearest tilecode resolution determined: 26

Resampling: 100%|██████████| 8925/8925 [00:00<00:00, 142742.28 cells/s]
Converting to GeoJSON: 100%|██████████| 8806/8806 [00:00<00:00, 9019.09 cells/s] 

Visualize the output with folium

In [2]:

import folium

m = folium.Map(tiles='CartoDB positron',max_zoom=28)

h3_layer = folium.GeoJson(
    raster2isea4t_geojson,
    style_function=lambda x: {
        'fillColor': f'rgb({x["properties"]["band_1"]}, {x["properties"]["band_2"]}, {x["properties"]["band_3"]})',
        'fillOpacity': 1,
        'color': 'black',
        'weight': 1
    },
    popup=folium.GeoJsonPopup(
        fields=['tilecode', 'resolution', 'band_1', 'band_2', 'band_3', 'cell_area'],
        aliases=['H3 ID', 'Resolution', 'Band 1', 'Band 2', 'Band 3', 'Area (m²)'],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """
    )
).add_to(m)

m.fit_bounds(h3_layer.get_bounds()) 

# Display the map
m

import folium m = folium.Map(tiles='CartoDB positron',max_zoom=28) h3_layer = folium.GeoJson( raster2isea4t_geojson, style_function=lambda x: { 'fillColor': f'rgb({x["properties"]["band_1"]}, {x["properties"]["band_2"]}, {x["properties"]["band_3"]})', 'fillOpacity': 1, 'color': 'black', 'weight': 1 }, popup=folium.GeoJsonPopup( fields=['tilecode', 'resolution', 'band_1', 'band_2', 'band_3', 'cell_area'], aliases=['H3 ID', 'Resolution', 'Band 1', 'Band 2', 'Band 3', 'Area (m²)'], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """ ) ).add_to(m) m.fit_bounds(h3_layer.get_bounds()) # Display the map m

Out[2]:

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

DGGS Generator¶

Generate DGGG within a bounding box at a specified resolution

In [1]:

from vgrid.generator.h3grid import h3grid
h3grid_csv = h3grid(resolution=2, format='csv')
print (h3grid_csv)
h3grid_geojson = h3grid(resolution=2, format='geojson')

from vgrid.generator.h3grid import h3grid h3grid_csv = h3grid(resolution=2, format='csv') print (h3grid_csv) h3grid_geojson = h3grid(resolution=2, format='geojson')

Processing base cells: 5882 cells [00:00, 8176.71 cells/s]        

[{'h3': '820007fffffffff'}, {'h3': '82000ffffffffff'}, {'h3': '820017fffffffff'}, {'h3': '82001ffffffffff'}, {'h3': '820027fffffffff'}, {'h3': '82002ffffffffff'}, {'h3': '820037fffffffff'}, {'h3': '820047fffffffff'}, {'h3': '82004ffffffffff'}, {'h3': '820057fffffffff'}, {'h3': '82005ffffffffff'}, {'h3': '820067fffffffff'}, {'h3': '82006ffffffffff'}, {'h3': '820077fffffffff'}, {'h3': '820087fffffffff'}, {'h3': '82008ffffffffff'}, {'h3': '820097fffffffff'}, {'h3': '82009ffffffffff'}, {'h3': '8200a7fffffffff'}, {'h3': '8200affffffffff'}, {'h3': '8200b7fffffffff'}, {'h3': '8200c7fffffffff'}, {'h3': '8200cffffffffff'}, {'h3': '8200d7fffffffff'}, {'h3': '8200dffffffffff'}, {'h3': '8200e7fffffffff'}, {'h3': '8200effffffffff'}, {'h3': '8200f7fffffffff'}, {'h3': '820107fffffffff'}, {'h3': '82010ffffffffff'}, {'h3': '820117fffffffff'}, {'h3': '82011ffffffffff'}, {'h3': '820127fffffffff'}, {'h3': '82012ffffffffff'}, {'h3': '820137fffffffff'}, {'h3': '820147fffffffff'}, {'h3': '82014ffffffffff'}, {'h3': '820157fffffffff'}, {'h3': '82015ffffffffff'}, {'h3': '820167fffffffff'}, {'h3': '82016ffffffffff'}, {'h3': '820177fffffffff'}, {'h3': '820187fffffffff'}, {'h3': '82018ffffffffff'}, {'h3': '820197fffffffff'}, {'h3': '82019ffffffffff'}, {'h3': '8201a7fffffffff'}, {'h3': '8201affffffffff'}, {'h3': '8201b7fffffffff'}, {'h3': '820207fffffffff'}, {'h3': '82020ffffffffff'}, {'h3': '820217fffffffff'}, {'h3': '82021ffffffffff'}, {'h3': '820227fffffffff'}, {'h3': '82022ffffffffff'}, {'h3': '820237fffffffff'}, {'h3': '820247fffffffff'}, {'h3': '82024ffffffffff'}, {'h3': '820257fffffffff'}, {'h3': '82025ffffffffff'}, {'h3': '820267fffffffff'}, {'h3': '82026ffffffffff'}, {'h3': '820277fffffffff'}, {'h3': '820287fffffffff'}, {'h3': '82028ffffffffff'}, {'h3': '820297fffffffff'}, {'h3': '82029ffffffffff'}, {'h3': '8202a7fffffffff'}, {'h3': '8202affffffffff'}, {'h3': '8202b7fffffffff'}, {'h3': '8202c7fffffffff'}, {'h3': '8202cffffffffff'}, {'h3': '8202d7fffffffff'}, {'h3': '8202dffffffffff'}, {'h3': '8202e7fffffffff'}, {'h3': '8202effffffffff'}, {'h3': '8202f7fffffffff'}, {'h3': '820307fffffffff'}, {'h3': '82030ffffffffff'}, {'h3': '820317fffffffff'}, {'h3': '82031ffffffffff'}, {'h3': '820327fffffffff'}, {'h3': '82032ffffffffff'}, {'h3': '820337fffffffff'}, {'h3': '820347fffffffff'}, {'h3': '82034ffffffffff'}, {'h3': '820357fffffffff'}, {'h3': '82035ffffffffff'}, {'h3': '820367fffffffff'}, {'h3': '82036ffffffffff'}, {'h3': '820377fffffffff'}, {'h3': '820387fffffffff'}, {'h3': '82038ffffffffff'}, {'h3': '820397fffffffff'}, {'h3': '82039ffffffffff'}, {'h3': '8203a7fffffffff'}, {'h3': '8203affffffffff'}, {'h3': '8203b7fffffffff'}, {'h3': '820407fffffffff'}, {'h3': '82040ffffffffff'}, {'h3': '820417fffffffff'}, {'h3': '82041ffffffffff'}, {'h3': '820427fffffffff'}, {'h3': '82042ffffffffff'}, {'h3': '820437fffffffff'}, {'h3': '820447fffffffff'}, {'h3': '82044ffffffffff'}, {'h3': '820457fffffffff'}, {'h3': '82045ffffffffff'}, {'h3': '820467fffffffff'}, {'h3': '82046ffffffffff'}, {'h3': '820477fffffffff'}, {'h3': '820487fffffffff'}, {'h3': '82048ffffffffff'}, {'h3': '820497fffffffff'}, {'h3': '82049ffffffffff'}, {'h3': '8204a7fffffffff'}, {'h3': '8204affffffffff'}, {'h3': '8204b7fffffffff'}, {'h3': '8204c7fffffffff'}, {'h3': '8204cffffffffff'}, {'h3': '8204d7fffffffff'}, {'h3': '8204dffffffffff'}, {'h3': '8204e7fffffffff'}, {'h3': '8204effffffffff'}, {'h3': '8204f7fffffffff'}, {'h3': '820507fffffffff'}, {'h3': '82050ffffffffff'}, {'h3': '820517fffffffff'}, {'h3': '82051ffffffffff'}, {'h3': '820527fffffffff'}, {'h3': '82052ffffffffff'}, {'h3': '820537fffffffff'}, {'h3': '820547fffffffff'}, {'h3': '82054ffffffffff'}, {'h3': '820557fffffffff'}, {'h3': '82055ffffffffff'}, {'h3': '820567fffffffff'}, {'h3': '82056ffffffffff'}, {'h3': '820577fffffffff'}, {'h3': '820587fffffffff'}, {'h3': '82058ffffffffff'}, {'h3': '820597fffffffff'}, {'h3': '82059ffffffffff'}, {'h3': '8205a7fffffffff'}, {'h3': '8205affffffffff'}, {'h3': '8205b7fffffffff'}, {'h3': '820607fffffffff'}, {'h3': '82060ffffffffff'}, {'h3': '820617fffffffff'}, {'h3': '82061ffffffffff'}, {'h3': '820627fffffffff'}, {'h3': '82062ffffffffff'}, {'h3': '820637fffffffff'}, {'h3': '820647fffffffff'}, {'h3': '82064ffffffffff'}, {'h3': '820657fffffffff'}, {'h3': '82065ffffffffff'}, {'h3': '820667fffffffff'}, {'h3': '82066ffffffffff'}, {'h3': '820677fffffffff'}, {'h3': '820687fffffffff'}, {'h3': '82068ffffffffff'}, {'h3': '820697fffffffff'}, {'h3': '82069ffffffffff'}, {'h3': '8206a7fffffffff'}, {'h3': '8206affffffffff'}, {'h3': '8206b7fffffffff'}, {'h3': '8206c7fffffffff'}, {'h3': '8206cffffffffff'}, {'h3': '8206d7fffffffff'}, {'h3': '8206dffffffffff'}, {'h3': '8206e7fffffffff'}, {'h3': '8206effffffffff'}, {'h3': '8206f7fffffffff'}, {'h3': '820707fffffffff'}, {'h3': '82070ffffffffff'}, {'h3': '820717fffffffff'}, {'h3': '82071ffffffffff'}, {'h3': '820727fffffffff'}, {'h3': '82072ffffffffff'}, {'h3': '820737fffffffff'}, {'h3': '820747fffffffff'}, {'h3': '82074ffffffffff'}, {'h3': '820757fffffffff'}, {'h3': '82075ffffffffff'}, {'h3': '820767fffffffff'}, {'h3': '82076ffffffffff'}, {'h3': '820777fffffffff'}, {'h3': '820787fffffffff'}, {'h3': '82078ffffffffff'}, {'h3': '820797fffffffff'}, {'h3': '82079ffffffffff'}, {'h3': '8207a7fffffffff'}, {'h3': '8207affffffffff'}, {'h3': '8207b7fffffffff'}, {'h3': '820807fffffffff'}, {'h3': '820817fffffffff'}, {'h3': '82081ffffffffff'}, {'h3': '820827fffffffff'}, {'h3': '82082ffffffffff'}, {'h3': '820837fffffffff'}, {'h3': '820887fffffffff'}, {'h3': '82088ffffffffff'}, {'h3': '820897fffffffff'}, {'h3': '82089ffffffffff'}, {'h3': '8208a7fffffffff'}, {'h3': '8208affffffffff'}, {'h3': '8208b7fffffffff'}, {'h3': '8208c7fffffffff'}, {'h3': '8208cffffffffff'}, {'h3': '8208d7fffffffff'}, {'h3': '8208dffffffffff'}, {'h3': '8208e7fffffffff'}, {'h3': '8208effffffffff'}, {'h3': '8208f7fffffffff'}, {'h3': '820907fffffffff'}, {'h3': '82090ffffffffff'}, {'h3': '820917fffffffff'}, {'h3': '82091ffffffffff'}, {'h3': '820927fffffffff'}, {'h3': '82092ffffffffff'}, {'h3': '820937fffffffff'}, {'h3': '820947fffffffff'}, {'h3': '82094ffffffffff'}, {'h3': '820957fffffffff'}, {'h3': '82095ffffffffff'}, {'h3': '820967fffffffff'}, {'h3': '82096ffffffffff'}, {'h3': '820977fffffffff'}, {'h3': '820987fffffffff'}, {'h3': '82098ffffffffff'}, {'h3': '820997fffffffff'}, {'h3': '82099ffffffffff'}, {'h3': '8209a7fffffffff'}, {'h3': '8209affffffffff'}, {'h3': '8209b7fffffffff'}, {'h3': '820a07fffffffff'}, {'h3': '820a0ffffffffff'}, {'h3': '820a17fffffffff'}, {'h3': '820a1ffffffffff'}, {'h3': '820a27fffffffff'}, {'h3': '820a2ffffffffff'}, {'h3': '820a37fffffffff'}, {'h3': '820a47fffffffff'}, {'h3': '820a4ffffffffff'}, {'h3': '820a57fffffffff'}, {'h3': '820a5ffffffffff'}, {'h3': '820a67fffffffff'}, {'h3': '820a6ffffffffff'}, {'h3': '820a77fffffffff'}, {'h3': '820a87fffffffff'}, {'h3': '820a8ffffffffff'}, {'h3': '820a97fffffffff'}, {'h3': '820a9ffffffffff'}, {'h3': '820aa7fffffffff'}, {'h3': '820aaffffffffff'}, {'h3': '820ab7fffffffff'}, {'h3': '820ac7fffffffff'}, {'h3': '820acffffffffff'}, {'h3': '820ad7fffffffff'}, {'h3': '820adffffffffff'}, {'h3': '820ae7fffffffff'}, {'h3': '820aeffffffffff'}, {'h3': '820af7fffffffff'}, {'h3': '820b07fffffffff'}, {'h3': '820b0ffffffffff'}, {'h3': '820b17fffffffff'}, {'h3': '820b1ffffffffff'}, {'h3': '820b27fffffffff'}, {'h3': '820b2ffffffffff'}, {'h3': '820b37fffffffff'}, {'h3': '820b47fffffffff'}, {'h3': '820b4ffffffffff'}, {'h3': '820b57fffffffff'}, {'h3': '820b5ffffffffff'}, {'h3': '820b67fffffffff'}, {'h3': '820b6ffffffffff'}, {'h3': '820b77fffffffff'}, {'h3': '820b87fffffffff'}, {'h3': '820b8ffffffffff'}, {'h3': '820b97fffffffff'}, {'h3': '820b9ffffffffff'}, {'h3': '820ba7fffffffff'}, {'h3': '820baffffffffff'}, {'h3': '820bb7fffffffff'}, {'h3': '820c07fffffffff'}, {'h3': '820c0ffffffffff'}, {'h3': '820c17fffffffff'}, {'h3': '820c1ffffffffff'}, {'h3': '820c27fffffffff'}, {'h3': '820c2ffffffffff'}, {'h3': '820c37fffffffff'}, {'h3': '820c47fffffffff'}, {'h3': '820c4ffffffffff'}, {'h3': '820c57fffffffff'}, {'h3': '820c5ffffffffff'}, {'h3': '820c67fffffffff'}, {'h3': '820c6ffffffffff'}, {'h3': '820c77fffffffff'}, {'h3': '820c87fffffffff'}, {'h3': '820c8ffffffffff'}, {'h3': '820c97fffffffff'}, {'h3': '820c9ffffffffff'}, {'h3': '820ca7fffffffff'}, {'h3': '820caffffffffff'}, {'h3': '820cb7fffffffff'}, {'h3': '820cc7fffffffff'}, {'h3': '820ccffffffffff'}, {'h3': '820cd7fffffffff'}, {'h3': '820cdffffffffff'}, {'h3': '820ce7fffffffff'}, {'h3': '820ceffffffffff'}, {'h3': '820cf7fffffffff'}, {'h3': '820d07fffffffff'}, {'h3': '820d0ffffffffff'}, {'h3': '820d17fffffffff'}, {'h3': '820d1ffffffffff'}, {'h3': '820d27fffffffff'}, {'h3': '820d2ffffffffff'}, {'h3': '820d37fffffffff'}, {'h3': '820d47fffffffff'}, {'h3': '820d4ffffffffff'}, {'h3': '820d57fffffffff'}, {'h3': '820d5ffffffffff'}, {'h3': '820d67fffffffff'}, {'h3': '820d6ffffffffff'}, {'h3': '820d77fffffffff'}, {'h3': '820d87fffffffff'}, {'h3': '820d8ffffffffff'}, {'h3': '820d97fffffffff'}, {'h3': '820d9ffffffffff'}, {'h3': '820da7fffffffff'}, {'h3': '820daffffffffff'}, {'h3': '820db7fffffffff'}, {'h3': '820e07fffffffff'}, {'h3': '820e0ffffffffff'}, {'h3': '820e17fffffffff'}, {'h3': '820e1ffffffffff'}, {'h3': '820e27fffffffff'}, {'h3': '820e2ffffffffff'}, {'h3': '820e37fffffffff'}, {'h3': '820e47fffffffff'}, {'h3': '820e4ffffffffff'}, {'h3': '820e57fffffffff'}, {'h3': '820e5ffffffffff'}, {'h3': '820e67fffffffff'}, {'h3': '820e6ffffffffff'}, {'h3': '820e77fffffffff'}, {'h3': '820e87fffffffff'}, {'h3': '820e8ffffffffff'}, {'h3': '820e97fffffffff'}, {'h3': '820e9ffffffffff'}, {'h3': '820ea7fffffffff'}, {'h3': '820eaffffffffff'}, {'h3': '820eb7fffffffff'}, {'h3': '820ec7fffffffff'}, {'h3': '820ecffffffffff'}, {'h3': '820ed7fffffffff'}, {'h3': '820edffffffffff'}, {'h3': '820ee7fffffffff'}, {'h3': '820eeffffffffff'}, {'h3': '820ef7fffffffff'}, {'h3': '820f07fffffffff'}, {'h3': '820f0ffffffffff'}, {'h3': '820f17fffffffff'}, {'h3': '820f1ffffffffff'}, {'h3': '820f27fffffffff'}, {'h3': '820f2ffffffffff'}, {'h3': '820f37fffffffff'}, {'h3': '820f47fffffffff'}, {'h3': '820f4ffffffffff'}, {'h3': '820f57fffffffff'}, {'h3': '820f5ffffffffff'}, {'h3': '820f67fffffffff'}, {'h3': '820f6ffffffffff'}, {'h3': '820f77fffffffff'}, {'h3': '820f87fffffffff'}, {'h3': '820f8ffffffffff'}, {'h3': '820f97fffffffff'}, {'h3': '820f9ffffffffff'}, {'h3': '820fa7fffffffff'}, {'h3': '820faffffffffff'}, {'h3': '820fb7fffffffff'}, {'h3': '821007fffffffff'}, {'h3': '82100ffffffffff'}, {'h3': '821017fffffffff'}, {'h3': '82101ffffffffff'}, {'h3': '821027fffffffff'}, {'h3': '82102ffffffffff'}, {'h3': '821037fffffffff'}, {'h3': '821047fffffffff'}, {'h3': '82104ffffffffff'}, {'h3': '821057fffffffff'}, {'h3': '82105ffffffffff'}, {'h3': '821067fffffffff'}, {'h3': '82106ffffffffff'}, {'h3': '821077fffffffff'}, {'h3': '821087fffffffff'}, {'h3': '82108ffffffffff'}, {'h3': '821097fffffffff'}, {'h3': '82109ffffffffff'}, {'h3': '8210a7fffffffff'}, {'h3': '8210affffffffff'}, {'h3': '8210b7fffffffff'}, {'h3': '8210c7fffffffff'}, {'h3': '8210cffffffffff'}, {'h3': '8210d7fffffffff'}, {'h3': '8210dffffffffff'}, {'h3': '8210e7fffffffff'}, {'h3': '8210effffffffff'}, {'h3': '8210f7fffffffff'}, {'h3': '821107fffffffff'}, {'h3': '82110ffffffffff'}, {'h3': '821117fffffffff'}, {'h3': '82111ffffffffff'}, {'h3': '821127fffffffff'}, {'h3': '82112ffffffffff'}, {'h3': '821137fffffffff'}, {'h3': '821147fffffffff'}, {'h3': '82114ffffffffff'}, {'h3': '821157fffffffff'}, {'h3': '82115ffffffffff'}, {'h3': '821167fffffffff'}, {'h3': '82116ffffffffff'}, {'h3': '821177fffffffff'}, {'h3': '821187fffffffff'}, {'h3': '82118ffffffffff'}, {'h3': '821197fffffffff'}, {'h3': '82119ffffffffff'}, {'h3': '8211a7fffffffff'}, {'h3': '8211affffffffff'}, {'h3': '8211b7fffffffff'}, {'h3': '821207fffffffff'}, {'h3': '82120ffffffffff'}, {'h3': '821217fffffffff'}, {'h3': '82121ffffffffff'}, {'h3': '821227fffffffff'}, {'h3': '82122ffffffffff'}, {'h3': '821237fffffffff'}, {'h3': '821247fffffffff'}, {'h3': '82124ffffffffff'}, {'h3': '821257fffffffff'}, {'h3': '82125ffffffffff'}, {'h3': '821267fffffffff'}, {'h3': '82126ffffffffff'}, {'h3': '821277fffffffff'}, {'h3': '821287fffffffff'}, {'h3': '82128ffffffffff'}, {'h3': '821297fffffffff'}, {'h3': '82129ffffffffff'}, {'h3': '8212a7fffffffff'}, {'h3': '8212affffffffff'}, {'h3': '8212b7fffffffff'}, {'h3': '8212c7fffffffff'}, {'h3': '8212cffffffffff'}, {'h3': '8212d7fffffffff'}, {'h3': '8212dffffffffff'}, {'h3': '8212e7fffffffff'}, {'h3': '8212effffffffff'}, {'h3': '8212f7fffffffff'}, {'h3': '821307fffffffff'}, {'h3': '82130ffffffffff'}, {'h3': '821317fffffffff'}, {'h3': '82131ffffffffff'}, {'h3': '821327fffffffff'}, {'h3': '82132ffffffffff'}, {'h3': '821337fffffffff'}, {'h3': '821347fffffffff'}, {'h3': '82134ffffffffff'}, {'h3': '821357fffffffff'}, {'h3': '82135ffffffffff'}, {'h3': '821367fffffffff'}, {'h3': '82136ffffffffff'}, {'h3': '821377fffffffff'}, {'h3': '821387fffffffff'}, {'h3': '82138ffffffffff'}, {'h3': '821397fffffffff'}, {'h3': '82139ffffffffff'}, {'h3': '8213a7fffffffff'}, {'h3': '8213affffffffff'}, {'h3': '8213b7fffffffff'}, {'h3': '821407fffffffff'}, {'h3': '82140ffffffffff'}, {'h3': '821417fffffffff'}, {'h3': '82141ffffffffff'}, {'h3': '821427fffffffff'}, {'h3': '82142ffffffffff'}, {'h3': '821437fffffffff'}, {'h3': '821447fffffffff'}, {'h3': '82144ffffffffff'}, {'h3': '821457fffffffff'}, {'h3': '82145ffffffffff'}, {'h3': '821467fffffffff'}, {'h3': '82146ffffffffff'}, {'h3': '821477fffffffff'}, {'h3': '821487fffffffff'}, {'h3': '82148ffffffffff'}, {'h3': '821497fffffffff'}, {'h3': '82149ffffffffff'}, {'h3': '8214a7fffffffff'}, {'h3': '8214affffffffff'}, {'h3': '8214b7fffffffff'}, {'h3': '8214c7fffffffff'}, {'h3': '8214cffffffffff'}, {'h3': '8214d7fffffffff'}, {'h3': '8214dffffffffff'}, {'h3': '8214e7fffffffff'}, {'h3': '8214effffffffff'}, {'h3': '8214f7fffffffff'}, {'h3': '821507fffffffff'}, {'h3': '82150ffffffffff'}, {'h3': '821517fffffffff'}, {'h3': '82151ffffffffff'}, {'h3': '821527fffffffff'}, {'h3': '82152ffffffffff'}, {'h3': '821537fffffffff'}, {'h3': '821547fffffffff'}, {'h3': '82154ffffffffff'}, {'h3': '821557fffffffff'}, {'h3': '82155ffffffffff'}, {'h3': '821567fffffffff'}, {'h3': '82156ffffffffff'}, {'h3': '821577fffffffff'}, {'h3': '821587fffffffff'}, {'h3': '82158ffffffffff'}, {'h3': '821597fffffffff'}, {'h3': '82159ffffffffff'}, {'h3': '8215a7fffffffff'}, {'h3': '8215affffffffff'}, {'h3': '8215b7fffffffff'}, {'h3': '821607fffffffff'}, {'h3': '82160ffffffffff'}, {'h3': '821617fffffffff'}, {'h3': '82161ffffffffff'}, {'h3': '821627fffffffff'}, {'h3': '82162ffffffffff'}, {'h3': '821637fffffffff'}, {'h3': '821647fffffffff'}, {'h3': '82164ffffffffff'}, {'h3': '821657fffffffff'}, {'h3': '82165ffffffffff'}, {'h3': '821667fffffffff'}, {'h3': '82166ffffffffff'}, {'h3': '821677fffffffff'}, {'h3': '821687fffffffff'}, {'h3': '82168ffffffffff'}, {'h3': '821697fffffffff'}, {'h3': '82169ffffffffff'}, {'h3': '8216a7fffffffff'}, {'h3': '8216affffffffff'}, {'h3': '8216b7fffffffff'}, {'h3': '8216c7fffffffff'}, {'h3': '8216cffffffffff'}, {'h3': '8216d7fffffffff'}, {'h3': '8216dffffffffff'}, {'h3': '8216e7fffffffff'}, {'h3': '8216effffffffff'}, {'h3': '8216f7fffffffff'}, {'h3': '821707fffffffff'}, {'h3': '82170ffffffffff'}, {'h3': '821717fffffffff'}, {'h3': '82171ffffffffff'}, {'h3': '821727fffffffff'}, {'h3': '82172ffffffffff'}, {'h3': '821737fffffffff'}, {'h3': '821747fffffffff'}, {'h3': '82174ffffffffff'}, {'h3': '821757fffffffff'}, {'h3': '82175ffffffffff'}, {'h3': '821767fffffffff'}, {'h3': '82176ffffffffff'}, {'h3': '821777fffffffff'}, {'h3': '821787fffffffff'}, {'h3': '82178ffffffffff'}, {'h3': '821797fffffffff'}, {'h3': '82179ffffffffff'}, {'h3': '8217a7fffffffff'}, {'h3': '8217affffffffff'}, {'h3': '8217b7fffffffff'}, {'h3': '821807fffffffff'}, {'h3': '82180ffffffffff'}, {'h3': '821817fffffffff'}, {'h3': '82181ffffffffff'}, {'h3': '821827fffffffff'}, {'h3': '82182ffffffffff'}, {'h3': '821837fffffffff'}, {'h3': '821847fffffffff'}, {'h3': '82184ffffffffff'}, {'h3': '821857fffffffff'}, {'h3': '82185ffffffffff'}, {'h3': '821867fffffffff'}, 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{'h3': '82ee07fffffffff'}, {'h3': '82ee0ffffffffff'}, {'h3': '82ee17fffffffff'}, {'h3': '82ee1ffffffffff'}, {'h3': '82ee27fffffffff'}, {'h3': '82ee2ffffffffff'}, {'h3': '82ee37fffffffff'}, {'h3': '82ee47fffffffff'}, {'h3': '82ee4ffffffffff'}, {'h3': '82ee57fffffffff'}, {'h3': '82ee5ffffffffff'}, {'h3': '82ee67fffffffff'}, {'h3': '82ee6ffffffffff'}, {'h3': '82ee77fffffffff'}, {'h3': '82ee87fffffffff'}, {'h3': '82ee8ffffffffff'}, {'h3': '82ee97fffffffff'}, {'h3': '82ee9ffffffffff'}, {'h3': '82eea7fffffffff'}, {'h3': '82eeaffffffffff'}, {'h3': '82eeb7fffffffff'}, {'h3': '82eec7fffffffff'}, {'h3': '82eecffffffffff'}, {'h3': '82eed7fffffffff'}, {'h3': '82eedffffffffff'}, {'h3': '82eee7fffffffff'}, {'h3': '82eeeffffffffff'}, {'h3': '82eef7fffffffff'}, {'h3': '82ef07fffffffff'}, {'h3': '82ef0ffffffffff'}, {'h3': '82ef17fffffffff'}, {'h3': '82ef1ffffffffff'}, {'h3': '82ef27fffffffff'}, {'h3': '82ef2ffffffffff'}, {'h3': '82ef37fffffffff'}, {'h3': '82ef47fffffffff'}, {'h3': '82ef4ffffffffff'}, {'h3': '82ef57fffffffff'}, {'h3': '82ef5ffffffffff'}, {'h3': '82ef67fffffffff'}, {'h3': '82ef6ffffffffff'}, {'h3': '82ef77fffffffff'}, {'h3': '82ef87fffffffff'}, {'h3': '82ef8ffffffffff'}, {'h3': '82ef97fffffffff'}, {'h3': '82ef9ffffffffff'}, {'h3': '82efa7fffffffff'}, {'h3': '82efaffffffffff'}, {'h3': '82efb7fffffffff'}, {'h3': '82f007fffffffff'}, {'h3': '82f00ffffffffff'}, {'h3': '82f017fffffffff'}, {'h3': '82f01ffffffffff'}, {'h3': '82f027fffffffff'}, {'h3': '82f02ffffffffff'}, {'h3': '82f037fffffffff'}, {'h3': '82f047fffffffff'}, {'h3': '82f04ffffffffff'}, {'h3': '82f057fffffffff'}, {'h3': '82f05ffffffffff'}, {'h3': '82f067fffffffff'}, {'h3': '82f06ffffffffff'}, {'h3': '82f077fffffffff'}, {'h3': '82f087fffffffff'}, {'h3': '82f08ffffffffff'}, {'h3': '82f097fffffffff'}, {'h3': '82f09ffffffffff'}, {'h3': '82f0a7fffffffff'}, {'h3': '82f0affffffffff'}, {'h3': '82f0b7fffffffff'}, {'h3': '82f0c7fffffffff'}, {'h3': '82f0cffffffffff'}, {'h3': '82f0d7fffffffff'}, {'h3': '82f0dffffffffff'}, {'h3': '82f0e7fffffffff'}, {'h3': '82f0effffffffff'}, {'h3': '82f0f7fffffffff'}, {'h3': '82f107fffffffff'}, {'h3': '82f10ffffffffff'}, {'h3': '82f117fffffffff'}, {'h3': '82f11ffffffffff'}, {'h3': '82f127fffffffff'}, {'h3': '82f12ffffffffff'}, {'h3': '82f137fffffffff'}, {'h3': '82f147fffffffff'}, {'h3': '82f14ffffffffff'}, {'h3': '82f157fffffffff'}, {'h3': '82f15ffffffffff'}, {'h3': '82f167fffffffff'}, {'h3': '82f16ffffffffff'}, {'h3': '82f177fffffffff'}, {'h3': '82f187fffffffff'}, {'h3': '82f18ffffffffff'}, {'h3': '82f197fffffffff'}, {'h3': '82f19ffffffffff'}, {'h3': '82f1a7fffffffff'}, {'h3': '82f1affffffffff'}, {'h3': '82f1b7fffffffff'}, {'h3': '82f207fffffffff'}, {'h3': '82f20ffffffffff'}, {'h3': '82f217fffffffff'}, {'h3': '82f21ffffffffff'}, {'h3': '82f227fffffffff'}, {'h3': '82f22ffffffffff'}, {'h3': '82f237fffffffff'}, {'h3': '82f247fffffffff'}, {'h3': '82f24ffffffffff'}, {'h3': '82f257fffffffff'}, {'h3': '82f25ffffffffff'}, {'h3': '82f267fffffffff'}, {'h3': '82f26ffffffffff'}, {'h3': '82f277fffffffff'}, {'h3': '82f287fffffffff'}, {'h3': '82f28ffffffffff'}, {'h3': '82f297fffffffff'}, {'h3': '82f29ffffffffff'}, {'h3': '82f2a7fffffffff'}, {'h3': '82f2affffffffff'}, {'h3': '82f2b7fffffffff'}, {'h3': '82f2c7fffffffff'}, {'h3': '82f2cffffffffff'}, {'h3': '82f2d7fffffffff'}, {'h3': '82f2dffffffffff'}, {'h3': '82f2e7fffffffff'}, {'h3': '82f2effffffffff'}, {'h3': '82f2f7fffffffff'}, {'h3': '82f307fffffffff'}, {'h3': '82f30ffffffffff'}, {'h3': '82f317fffffffff'}, {'h3': '82f31ffffffffff'}, {'h3': '82f327fffffffff'}, {'h3': '82f32ffffffffff'}, {'h3': '82f337fffffffff'}, {'h3': '82f347fffffffff'}, {'h3': '82f34ffffffffff'}, {'h3': '82f357fffffffff'}, {'h3': '82f35ffffffffff'}, {'h3': '82f367fffffffff'}, {'h3': '82f36ffffffffff'}, {'h3': '82f377fffffffff'}, {'h3': '82f387fffffffff'}, {'h3': '82f38ffffffffff'}, {'h3': '82f397fffffffff'}, {'h3': '82f39ffffffffff'}, {'h3': '82f3a7fffffffff'}, {'h3': '82f3affffffffff'}, {'h3': '82f3b7fffffffff'}]

Processing base cells: 5882 cells [00:00, 6932.42 cells/s]        

Visualize the output with leafmap[maplibre]¶

Install leafmap[maplibre]

In [ ]:

%pip install "leafmap[maplibre]"

%pip install "leafmap[maplibre]"

Initialize leafmap map and visualize the output GeoJSON

In [5]:

import leafmap.maplibregl as leafmap
m = leafmap.Map(style="positron")
m.add_geojson(
    h3grid_geojson,
    layer_type="fill",
    name="H3",
    paint={"fill-color": "blue", "fill-opacity": 0.3},
)
m

import leafmap.maplibregl as leafmap m = leafmap.Map(style="positron") m.add_geojson( h3grid_geojson, layer_type="fill", name="H3", paint={"fill-color": "blue", "fill-opacity": 0.3}, ) m

Container(children=[Row(children=[Col(children=[Map(calls=[['addControl', ('NavigationControl', {'showCompass'…