#!/usr/bin/env python
# coding: utf-8

# ## Using ALOS Digital Elevation Models (DEMs) with the Planetary Computer STAC API
# 
# The Advanced Land Observing Satellite (ALOS) is a Japanese satellite that was operational from 2006 to 2011. It generated a global digital elevation model using its PRISM panchromatic optical sensor. These data are available from the Planetary Computer as Cloud Optimized GeoTIFFs.

# For this example, we'll find the DEM tile that includes the Columbia Glacier in Alaska and plot the elevation data with some hillshade.

# In[1]:


from pystac_client import Client

columbia_glacier = [-146.88, 61.21]
client = Client.open(
    "https://planetarycomputer.microsoft.com/api/stac/v1",
    ignore_conformance=True,
)
search = client.search(
    collections=["alos-dem"],
    intersects={"type": "Point", "coordinates": columbia_glacier},
)
items = list(search.get_items())
print(f"Returned {len(items)} items")


# ALOS DEM STAC Items include the actual elevation data as well as some additional assets for quick visualization.

# In[2]:


item = items[0]
print(item.assets)


# Plotting the elevation data is straightforward using `xarray`, after signing the asset HREF using the `planetary_computer.sign` method.
# We also coarsen the data to reduce the final image size for this notebook.

# In[3]:


import planetary_computer
import xarray
import xrspatial
from datashader.transfer_functions import shade, stack
from datashader.colors import Elevation

signed_asset = planetary_computer.sign(item.assets["data"])
data = (
    xarray.open_dataset(signed_asset.href, engine="rasterio")
    .band_data.squeeze()
    .drop("band")
    .coarsen({"y": 5, "x": 5})
    .mean()
)
hillshade = xrspatial.hillshade(data)
stack(shade(hillshade, cmap=["white", "gray"]), shade(data, cmap=Elevation, alpha=128))