Expose temporal interpolation method

src/modelskill/matching.py

@@ -181,6 +181,7 @@ def match(
     gtype: Optional[GeometryTypes] = None,
     max_model_gap: Optional[float] = None,
     spatial_method: Optional[str] = None,
+    temporal_method: str = "linear",
     obs_no_overlap: Literal["ignore", "error", "warn"] = "error",
 ) -> Comparer: ...
 
@@ -195,6 +196,7 @@ def match(
     gtype: Optional[GeometryTypes] = None,
     max_model_gap: Optional[float] = None,
     spatial_method: Optional[str] = None,
+    temporal_method: str = "linear",
     obs_no_overlap: Literal["ignore", "error", "warn"] = "error",
 ) -> ComparerCollection: ...
 
@@ -208,6 +210,7 @@ def match(
     gtype=None,
     max_model_gap=None,
     spatial_method: Optional[str] = None,
+    temporal_method: str = "linear",
     spatial_tolerance: float = 1e-3,
     obs_no_overlap: Literal["ignore", "error", "warn"] = "error",
 ):
@@ -243,6 +246,11 @@ def match(
         'inverse_distance' (with 5 nearest points), by default "inverse_distance".
         - For GridModelResult, passed to xarray.interp() as method argument,
         by default 'linear'.
+    temporal_method : str, optional
+        Temporal interpolation method passed to xarray.interp(), by default 'linear'
+        Valid options are: "akima", "barycentric", "cubic", "krogh", "linear",
+        "makima", "nearest", "pchip", "polynomial", "quadratic",
+        "quintic", "slinear", "spline", "zero".
     spatial_tolerance : float, optional
         Spatial tolerance (in the units of the coordinate system) for matching
         model track points to observation track points. Model points outside
@@ -271,6 +279,7 @@ def match(
             gtype=gtype,
             max_model_gap=max_model_gap,
             spatial_method=spatial_method,
+            temporal_method=temporal_method,
             spatial_tolerance=spatial_tolerance,
             obs_no_overlap=obs_no_overlap,
         )
@@ -328,6 +337,7 @@ def _match_single_obs(
     gtype: GeometryTypes | None,
     max_model_gap: float | None,
     spatial_method: str | None,
+    temporal_method: str = "linear",
     spatial_tolerance: float,
     obs_no_overlap: Literal["ignore", "error", "warn"],
 ) -> Comparer | None:
@@ -346,7 +356,10 @@ def _match_single_obs(
 
     raw_mod_data = {
         m.name: (
-            m.extract(observation, spatial_method=spatial_method)
+            m.extract(
+                observation,
+                spatial_method=spatial_method,
+            )
             if isinstance(m, (DfsuModelResult, GridModelResult, DummyModelResult))
             else m
         )
@@ -358,6 +371,7 @@ def _match_single_obs(
         raw_mod_data=raw_mod_data,
         max_model_gap=max_model_gap,
         obs_no_overlap=obs_no_overlap,
+        temporal_method=temporal_method,
         spatial_tolerance=spatial_tolerance,
     )
     if matched_data is None:
@@ -385,6 +399,7 @@ def _match_space_time(
     max_model_gap: float | None,
     spatial_tolerance: float,
     obs_no_overlap: Literal["ignore", "error", "warn"],
+    temporal_method: str = "linear",
 ) -> Optional[xr.Dataset]:
     idxs = [m.time for m in raw_mod_data.values()]
     period = _get_global_start_end(idxs)
@@ -404,7 +419,9 @@ def _match_space_time(
                     observation, spatial_tolerance=spatial_tolerance
                 )
             case PointModelResult() as pmr, PointObservation():
-                aligned = pmr.align(observation, max_gap=max_model_gap)
+                aligned = pmr.align(
+                    observation, max_gap=max_model_gap, method=temporal_method
+                )
             case _:
                 raise TypeError(
                     f"Matching not implemented for model type {type(mr)} and observation type {type(observation)}"

src/modelskill/model/point.py

@@ -97,12 +97,16 @@ def align(
         observation: Observation,
         *,
         max_gap: float | None = None,
+        method: str = "linear",
         **kwargs: Any,
     ) -> xr.Dataset:
         new_time = observation.time
 
         dati = self.data.dropna("time").interp(
-            time=new_time, assume_sorted=True, **kwargs
+            time=new_time,
+            assume_sorted=True,
+            method=method,  # type: ignore
+            **kwargs,
         )
 
         pmr = PointModelResult(dati)

tests/test_match.py

@@ -616,3 +616,26 @@ def test_multiple_models_same_name(tmp_path: Path) -> None:
 
     with pytest.raises(ValueError, match="HKZN_local_2017_DutchCoast"):
         ms.match(obs, [mr1, mr2])
+
+
+def test_directional_data_use_nearest_temporal_interpolation():
+    mod = ms.PointModelResult(
+        name="mod",
+        data=pd.Series(
+            [359, 5], index=pd.date_range("2023-01-01", periods=2, freq="3H")
+        ),
+    )
+
+    obs = ms.PointObservation(
+        name="obs",
+        data=pd.Series(
+            np.zeros(5), index=pd.date_range("2023-01-01", periods=5, freq="1H")
+        ),
+    )
+
+    cmp = ms.match(
+        obs=obs,
+        mod=mod,
+        temporal_method="nearest",
+    )
+    assert cmp.data["mod"].values[1] == pytest.approx(359.0)