Fixed interpolation methods for tides

adcircpy/cmd/best_track_run.py

@@ -54,6 +54,4 @@ def main():
     )
     logging.Formatter.converter = lambda *args: datetime.now(
         tz=timezone('UTC')).timetuple()
-    # if len(args.constituents) == 0:
-    #     args.constituents = ['all']
     BestTrackRunCommand(args).run()

adcircpy/forcing/tides/hamtide.py

@@ -134,13 +134,21 @@ def _get_interpolation(
         yi = yi.flatten()
         dataset = self._get_dataset(variable, constituent)
         zi = dataset[netcdf_variable][yidx, xidx].flatten()
-
-        return griddata(
+        values = griddata(
                 (xi[~zi.mask], yi[~zi.mask]),
                 zi[~zi.mask],
                 (xq, yq),
-                method='linear'
+                method='linear',
+                fill_value=np.nan
+            )
+        nan_idxs = np.where(np.isnan(values))
+        values[nan_idxs] = griddata(
+                (xi[~zi.mask], yi[~zi.mask]),
+                zi[~zi.mask],
+                (xq[nan_idxs], yq[nan_idxs]),
+                method='nearest',
         )
+        return values
 
     def _prepend_path(self, filename: str) -> str:
         if self.path is None:

adcircpy/forcing/tides/tpxo.py

@@ -118,9 +118,18 @@ def _get_interpolation(self, tpxo_array: np.ndarray, constituent: str,
         )
 
         # "method" can be 'spline' or any string accepted by griddata()'s method kwarg.
-        return griddata(
+        values = griddata(
                 (x[_idx], y[_idx]),
                 array[_idx],
                 (_x, _y),
-                method='linear'
+                method='linear',
+                fill_value=np.nan,
         )
+        nan_idxs = np.where(np.isnan(values))
+        values[nan_idxs] = griddata(
+                (x[_idx], y[_idx]),
+                array[_idx],
+                (_x[nan_idxs], _y[nan_idxs]),
+                method='nearest',
+        )
+        return values