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deafrica_tools/load_isda.py

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+"""
+Functions to retrieve iSDAsoil data.
+"""
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import rasterio as rio
+from pyproj import Transformer
+import matplotlib.pyplot as plt
+import os
+import numpy as np
+
+from urllib.parse import urlparse
+import boto3
+from pystac import stac_io, Catalog
+
+#this function allows us to directly query the data on s3, adapted from iSDA tutorial https://github.com/iSDA-Africa/isdasoil-tutorial/blob/main/iSDAsoil-tutorial.ipynb
+def my_read_method(uri):
+    parsed = urlparse(uri)
+    if parsed.scheme == 's3':
+        bucket = parsed.netloc
+        key = parsed.path[1:]
+        s3 = boto3.resource('s3')
+        obj = s3.Object(bucket, key)
+        return obj.get()['Body'].read().decode('utf-8')
+    else:
+        return stac_io.default_read_text_method(uri)
+
+stac_io.read_text_method = my_read_method
+
+catalog = Catalog.from_file("https://isdasoil.s3.amazonaws.com/catalog.json")
+
+assets = {}
+
+for root, catalogs, items in catalog.walk():
+    for item in items:
+        str(f"Type: {item.get_parent().title}")
+        # save all items to a dictionary as we go along
+        assets[item.id] = item
+        for asset in item.assets.values():
+            if asset.roles == ['data']:
+                str(f"Title: {asset.title}")
+                str(f"Description: {asset.description}")
+                str(f"URL: {asset.href}")
+                str("------------")
+
+# define load_isda() function
+
+def load_isda(var, lat, lon):
+    """
+    Download and return iSDA variable with number of bands corresponding to number of iSDA layers.
+    Parameters
+    ----------
+    var : string
+        Name of the iSDA variable to download, e.g "ph"
+    lat: tuple or list
+        Latitude range for query.
+    lon: tuple or list
+        Longitude range for query. 
+    """
+
+    bands = assets[var].assets["image"].extra_fields.get('eo:bands')
+    bands = [val['description'] for val in bands]
+    
+    if len(np.unique(bands)) > 1:
+    
+        ds = xr.open_dataset(assets[var].assets["image"].href, engine="rasterio").rio.clip_box(
+                minx=lon[0],
+                miny=lat[0],
+                maxx=lon[1],
+                maxy=lat[1],
+                crs="EPSG:4326",
+            )
+
+        ds_layered = ds.drop_dims('band')
+        for x in np.unique(ds.band):
+            ds_layered[bands[x-1]] = ds.sel(band=x).to_array(dim='band').squeeze()
+            
+    else:
+        
+        ds_layered = xr.open_dataset(assets[var].assets["image"].href, engine="rasterio").rio.clip_box(
+                minx=lon[0],
+                miny=lat[0],
+                maxx=lon[1],
+                maxy=lat[1],
+                crs="EPSG:4326",
+            ).squeeze()
+
+    return ds_layered