49 split control

Creating a split-panel map

This notebook demonstrates how to add a split-panel map with leafmap anf folium. It also supports streamlit. Note that the ipyleaflet SplitControl does not support streamlit.

Uncomment the following line to install leafmap if needed.

In [1]:

# !pip install leafmap

# !pip install leafmap

In [2]:

import folium
import rioxarray
import xarray as xr
import leafmap.foliumap as leafmap

import folium import rioxarray import xarray as xr import leafmap.foliumap as leafmap

The split-panel map requires two layers: left_layer and right_layer. The layer instance can be a string representing a basemap, or an HTTP URL to a Cloud Optimized GeoTIFF (COG), or a folium TileLayer instance.

Using basemaps

In [3]:

m = leafmap.Map(height=500)
m.split_map(left_layer="TERRAIN", right_layer="OpenTopoMap")
m

m = leafmap.Map(height=500) m.split_map(left_layer="TERRAIN", right_layer="OpenTopoMap") m

Out[3]:

Show available basemaps.

In [4]:

# leafmap.basemaps.keys()

# leafmap.basemaps.keys()

Using COG

In [5]:

m = leafmap.Map(height=600, center=[39.4948, -108.5492], zoom=12)
url = "https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif"
url2 = "https://github.com/opengeos/data/releases/download/raster/Libya-2023-09-13.tif"
m.split_map(url, url2)
m

m = leafmap.Map(height=600, center=[39.4948, -108.5492], zoom=12) url = "https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif" url2 = "https://github.com/opengeos/data/releases/download/raster/Libya-2023-09-13.tif" m.split_map(url, url2) m

Out[5]:

Using folium TileLayer

In [6]:

m = leafmap.Map(center=[40, -100], zoom=4)

url1 = "https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2001_Land_Cover_L48/wms?"
url2 = "https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2019_Land_Cover_L48/wms?"

left_layer = folium.WmsTileLayer(
    url=url1,
    layers="NLCD_2001_Land_Cover_L48",
    name="NLCD 2001",
    attr="MRLC",
    fmt="image/png",
    transparent=True,
)
right_layer = folium.WmsTileLayer(
    url=url2,
    layers="NLCD_2019_Land_Cover_L48",
    name="NLCD 2019",
    attr="MRLC",
    fmt="image/png",
    transparent=True,
)

m.split_map(left_layer, right_layer)
m

m = leafmap.Map(center=[40, -100], zoom=4) url1 = "https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2001_Land_Cover_L48/wms?" url2 = "https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2019_Land_Cover_L48/wms?" left_layer = folium.WmsTileLayer( url=url1, layers="NLCD_2001_Land_Cover_L48", name="NLCD 2001", attr="MRLC", fmt="image/png", transparent=True, ) right_layer = folium.WmsTileLayer( url=url2, layers="NLCD_2019_Land_Cover_L48", name="NLCD 2019", attr="MRLC", fmt="image/png", transparent=True, ) m.split_map(left_layer, right_layer) m

Out[6]:

Using xarrays

Download a sample dataset.

In [7]:

url = "https://open.gishub.org/data/raster/srtm90.tif"
dem = leafmap.download_file(url, "srtm90.tif")

url = "https://open.gishub.org/data/raster/srtm90.tif" dem = leafmap.download_file(url, "srtm90.tif")

Downloading...
From: https://open.gishub.org/data/raster/srtm90.tif
To: /home/runner/work/leafmap/leafmap/docs/notebooks/srtm90.tif

  0%|          | 0.00/16.6M [00:00<?, ?B/s]

100%|██████████| 16.6M/16.6M [00:00<00:00, 308MB/s]

Use rioxarray to read the raster as a xarray DataArray and then classify the DEM into 2 elevation classes.

In [8]:

dem_ds = rioxarray.open_rasterio(dem)
dem_class = xr.where(dem_ds < 2000, 0, 1)

dem_ds = rioxarray.open_rasterio(dem) dem_class = xr.where(dem_ds < 2000, 0, 1)

Visualize the DEM and the elevation class image as a split map.

In [9]:

m = leafmap.Map(center=[37.6, -119], zoom=9)
m.split_map(
    dem_ds,
    dem_class,
    left_args={"layer_name": "DEM", "colormap": "terrain"},
    right_args={"layer_name": "Classified DEM", "colormap": "coolwarm"},
)
m

m = leafmap.Map(center=[37.6, -119], zoom=9) m.split_map( dem_ds, dem_class, left_args={"layer_name": "DEM", "colormap": "terrain"}, right_args={"layer_name": "Classified DEM", "colormap": "coolwarm"}, ) m

Out[9]: