# %pip install "leafmap[raster]"

import leafmap
import rasterio
import rioxarray
import xarray as xr

m = leafmap.Map()
url = "https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif"
m.add_cog_layer(url, name="Libya")
m.add("inspector")
m

m = leafmap.Map()
url = "https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json"
m.add_stac_layer(url, bands=["B3", "B2", "B1"], name="SPOT Image")
m.add("inspector")
m

m = leafmap.Map()
collection = "landsat-8-c2-l2"
item = "LC08_L2SP_047027_20201204_02_T1"
m.add_stac_layer(
    collection=collection,
    item=item,
    assets="SR_B7,SR_B5,SR_B4",
    name="Landsat Band-754",
)
m.add("inspector")
m

url = "https://opengeos.org/data/raster/landsat.tif"
satellite = leafmap.download_file(url, "landsat.tif", overwrite=True)

m = leafmap.Map()
m.add_raster(satellite, indexes=[4, 1, 2], vmin=0, vmax=120, layer_name="Landsat 7")
m.add("inspector")
m

dataset = rasterio.open(satellite)
nir = dataset.read(4).astype(float)
red = dataset.read(1).astype(float)
ndvi = (nir - red) / (nir + red)
ndvi_image = leafmap.array_to_image(ndvi, source=satellite)

m = leafmap.Map()
m.add_raster(satellite, indexes=[4, 1, 2], vmin=0, vmax=120, layer_name="Landsat 7")
m.add_raster(ndvi_image, colormap="Greens", layer_name="NDVI")
m.add("inspector")
m

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

ds = rioxarray.open_rasterio(dem)
ds

array = ds.sel(band=1)
masked_array = xr.where(array < 2000, 0, 1)

m = leafmap.Map()
m.add_raster(dem, colormap="terrain", layer_name="DEM")
m.add_raster(masked_array, colormap="coolwarm", layer_name="Classified DEM")
m

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