Expose temporal interpolation method

src/modelskill/matching.py

@@ -172,6 +172,7 @@ 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: ...
 
@@ -186,6 +187,7 @@ 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: ...
 
@@ -199,6 +201,7 @@ def match(
     gtype=None,
     max_model_gap=None,
     spatial_method: Optional[str] = None,
+    temporal_method: str = "linear",
     obs_no_overlap: Literal["ignore", "error", "warn"] = "error",
 ):
     """Match observation and model result data in space and time
@@ -233,6 +236,11 @@ 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".
     obs_no_overlap: str, optional
         How to handle observations with no overlap with model results. One of: 'ignore', 'error', 'warn', by default 'error'.
 
@@ -256,6 +264,7 @@ def match(
             gtype=gtype,
             max_model_gap=max_model_gap,
             spatial_method=spatial_method,
+            temporal_method=temporal_method,
             obs_no_overlap=obs_no_overlap,
         )
 
@@ -308,6 +317,7 @@ def _match_single_obs(
     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",
 ) -> Optional[Comparer]:
     observation = _parse_single_obs(obs, obs_item, gtype=gtype)
@@ -324,7 +334,10 @@ 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
         )
@@ -336,6 +349,7 @@ 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,
     )
     if matched_data is None:
         return None
@@ -361,6 +375,7 @@ def match_space_time(
     raw_mod_data: Mapping[str, Alignable],
     max_model_gap: float | None = None,
     obs_no_overlap: Literal["ignore", "error", "warn"] = "error",
+    temporal_method: str = "linear",
 ) -> Optional[xr.Dataset]:
     """Match observation with one or more model results in time domain.
 
@@ -380,6 +395,10 @@ def match_space_time(
     max_model_gap : Optional[TimeDeltaTypes], optional
         In case of non-equidistant model results (e.g. event data),
         max_model_gap can be given e.g. as seconds, by default None
+    obs_no_overlap : Literal['ignore', 'error', 'warn'], optional
+        How to handle observations with no overlap with model results. One of: 'ignore', 'error', 'warn', by default 'error'.
+    temporal_method : str, optional
+        Temporal interpolation method passed to xarray.interp(), by default 'linear'
 
     Returns
     -------
@@ -399,7 +418,7 @@ def match_space_time(
 
     for mr in raw_mod_data.values():
         # TODO is `align` the correct name for this operation?
-        aligned = mr.align(observation, max_gap=max_model_gap)
+        aligned = mr.align(observation, max_gap=max_model_gap, method=temporal_method)
 
         if overlapping := set(aligned.filter_by_attrs(kind="aux").data_vars) & set(
             observation.data.filter_by_attrs(kind="aux").data_vars

src/modelskill/model/point.py

@@ -98,12 +98,16 @@ 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)