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