first commit

2024-08-28 05:10:02 +00:00
commit 5daf9882a7
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+import xarray as xr
+import numpy as np
+
+# function to load soil moisture data
+
+def load_soil_moisture(lat, lon, time, product = 'surface', grid = 'nearest'):
+    product_baseurl = 'https://dapds00.nci.org.au/thredds/dodsC/ub8/global/GRAFS/'
+    assert product in ['surface', 'rootzone'], 'product parameter must be surface or root-zone'
+    # lat, lon grid
+    if grid == 'nearest':
+        # select lat/lon range from data; snap to nearest grid
+        lat_range, lon_range = None, None
+    else:
+        # define a grid that covers the entire area of interest
+        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)
+        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)
+    # split time window into years
+    day_range = np.array(time).astype("M8[D]")
+    year_range = np.array(time).astype("M8[Y]")
+    if product == 'surface':
+        product_name = 'GRAFS_TopSoilRelativeWetness_'
+    else: product_name = 'GRAFS_RootzoneSoilWaterIndex_'
+    datasets = []
+    for year in np.arange(year_range[0], year_range[1]+1, np.timedelta64(1, 'Y')):
+        start = np.max([day_range[0], year.astype("M8[D]")])
+        end = np.min([day_range[1], (year+1).astype("M8[D]")-1])
+        product_url = product_baseurl + product_name +'%s.nc'%str(year)
+        print(product_url)
+        # data is loaded lazily through OPeNDAP
+        ds = xr.open_dataset(product_url)
+        if lat_range is None:
+            # select lat/lon range from data if not specified; snap to nearest grid
+            test = ds.sel(lat=list(lat), lon=list(lon), method='nearest')
+            lat_range = slice(test.lat.values[0], test.lat.values[1])
+            lon_range = slice(test.lon.values[0], test.lon.values[1])
+        # slice before return
+        ds = ds.sel(lat=lat_range, lon=lon_range, time=slice(start, end)).compute()
+        datasets.append(ds)
+    return xr.merge(datasets)