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nghiadang
39 lines
2.0 KiB
Python
39 lines
2.0 KiB
Python
import xarray as xr
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import numpy as np
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# function to load soil moisture data
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def load_soil_moisture(lat, lon, time, product = 'surface', grid = 'nearest'):
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product_baseurl = 'https://dapds00.nci.org.au/thredds/dodsC/ub8/global/GRAFS/'
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assert product in ['surface', 'rootzone'], 'product parameter must be surface or root-zone'
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# lat, lon grid
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if grid == 'nearest':
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# select lat/lon range from data; snap to nearest grid
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lat_range, lon_range = None, None
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else:
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# define a grid that covers the entire area of interest
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lat_range = np.arange(np.max(np.ceil(np.array(lat)*10.+0.5)/10.-0.05), np.min(np.floor(np.array(lat)*10.-0.5)/10.+0.05)-0.05, -0.1)
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lon_range = np.arange(np.min(np.floor(np.array(lon)*10.-0.5)/10.+0.05), np.max(np.ceil(np.array(lon)*10.+0.5)/10.-0.05)+0.05, 0.1)
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# split time window into years
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day_range = np.array(time).astype("M8[D]")
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year_range = np.array(time).astype("M8[Y]")
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if product == 'surface':
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product_name = 'GRAFS_TopSoilRelativeWetness_'
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else: product_name = 'GRAFS_RootzoneSoilWaterIndex_'
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datasets = []
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for year in np.arange(year_range[0], year_range[1]+1, np.timedelta64(1, 'Y')):
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start = np.max([day_range[0], year.astype("M8[D]")])
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end = np.min([day_range[1], (year+1).astype("M8[D]")-1])
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product_url = product_baseurl + product_name +'%s.nc'%str(year)
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print(product_url)
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# data is loaded lazily through OPeNDAP
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ds = xr.open_dataset(product_url)
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if lat_range is None:
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# select lat/lon range from data if not specified; snap to nearest grid
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test = ds.sel(lat=list(lat), lon=list(lon), method='nearest')
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lat_range = slice(test.lat.values[0], test.lat.values[1])
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lon_range = slice(test.lon.values[0], test.lon.values[1])
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# slice before return
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ds = ds.sel(lat=lat_range, lon=lon_range, time=slice(start, end)).compute()
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datasets.append(ds)
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return xr.merge(datasets) |