update timeseries section in RunWindFarmSimulation.ipynb + add time interpolation support in XRSite

py_wake/site/xrsite.py

@@ -96,7 +96,7 @@ class XRSite(Site):
             ix = tuple([(slice(None), indices)[dim == i] for dim in range(data.ndim)])
             return data[ix]
 
-        ip_dims = [n for n in ['i', 'x', 'y', 'h', 'wd', 'ws'] if n in var.dims]  # interpolation dimensions
+        ip_dims = [n for n in ['i', 'x', 'y', 'h', 'time', 'wd', 'ws'] if n in var.dims]  # interpolation dimensions
         data = var.data
         data_dims = var.dims
 
@@ -121,7 +121,10 @@ class XRSite(Site):
 
         # pre select, i.e. reduce input data size in case only one ws or wd is needed
         data, k_indices = pre_sel(data, 'ws')
-        data, l_indices = pre_sel(data, 'wd')
+        if 'time' in coords:
+            data, l_indices = pre_sel(data, 'time')
+        else:
+            data, l_indices = pre_sel(data, 'wd')
 
         if 'i' in ip_dims:
             if 'i' in coords and len(var.i) != len(coords['i']):
@@ -165,8 +168,12 @@ class XRSite(Site):
 #         if 'i' in var.dims:
 #             ip_data_dims.insert(0, 'i')
         if l_indices is not None:
-            ip_data_dims.append('wd')
-            ip_data = sel(ip_data, ip_data_dims, l_indices, 'wd')
+            if 'time' in coords:
+                ip_data_dims.append('time')
+                ip_data = sel(ip_data, ip_data_dims, l_indices, 'time')
+            else:
+                ip_data_dims.append('wd')
+                ip_data = sel(ip_data, ip_data_dims, l_indices, 'wd')
         if k_indices is not None:
             ip_data_dims.append('ws')
             ip_data = sel(ip_data, ip_data_dims, k_indices, 'ws')

py_wake/tests/test_wind_farm_models/test_enginering_wind_farm_model.py

@@ -377,13 +377,17 @@ def test_time_series_values():
 
 def test_time_series_dates():
     import pandas as pd
+    import xarray as xr
     d = np.load(tfp + "time.npz")
-    wd, ws = [d[k][:6 * 24] for k in ['wd', 'ws']]
+    wd, ws, ws_std = [d[k][:6 * 24] for k in ['wd', 'ws', 'ws_std']]
+    ti = np.minimum(ws_std / ws, .5)
+    t = pd.date_range("2000-01-01", freq="10T", periods=24 * 6)
     wt = V80()
     site = Hornsrev1Site()
+    site.ds['TI'] = xr.DataArray(ti, [('time', t)])
+
     x, y = site.initial_position.T
     wfm = NOJ(site, wt)
-    t = pd.date_range("2000-01-01", freq="10T", periods=24 * 6)
     sim_res = wfm(x, y, ws=ws, wd=wd, time=t, verbose=False)
     npt.assert_array_equal(sim_res.WS, ws)
     npt.assert_array_equal(sim_res.WD, wd)