Fix unused arguments

src/modelskill/comparison/_collection.py

@@ -573,7 +573,7 @@ def gridded_skill(
         by: str | Iterable[str] | None = None,
         metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
         n_min: Optional[int] = None,
-        **kwargs: Any,
+        **_kwargs: Any,
     ) -> SkillGrid:
         """Skill assessment of model(s) on a regular spatial grid.
 

src/modelskill/comparison/_collection_plotter.py

@@ -343,15 +343,14 @@ def hist(
         self,
         bins: int | Sequence = 100,
         *,
-        model: str | int | None = None,
         title: Optional[str] = None,
         density: bool = True,
         alpha: float = 0.5,
         ax=None,
         figsize: Optional[Tuple[float, float]] = None,
         **kwargs,
     ):
-        """Plot histogram of specific model and all observations.
+        """Plot histogram of all models and observations.
 
         Wraps pandas.DataFrame hist() method.
 

src/modelskill/comparison/_comparer_plotter.py

@@ -459,7 +459,6 @@ def box(self, *, ax=None, title=None, figsize=None, **kwargs):
     def scatter(
         self,
         *,
-        model=None,
         bins: int | float = 120,
         quantiles: int | Sequence[float] | None = None,
         fit_to_quantiles: bool = False,

src/modelskill/comparison/_comparison.py

@@ -1052,7 +1052,7 @@ def gridded_skill(
         by: str | Iterable[str] | None = None,
         metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
         n_min: int | None = None,
-        **kwargs: Any,
+        **_kwargs: Any,
     ):
         """Aggregated spatial skill assessment of model(s) on a regular spatial grid.
 

src/modelskill/configuration.py

@@ -66,13 +66,13 @@ def from_config(
     for name, data in conf["observations"].items():
         if data.pop("include", True):
             data["name"] = name
-            observations.append(_obs_from_dict(name, data, dirname, relative_path))
+            observations.append(_obs_from_dict(data, dirname, relative_path))
 
     return match(obs=observations, mod=modelresults)
 
 
 def _obs_from_dict(
-    name: str, obs_dict: dict, dirname: Path, relative_path: bool
+    obs_dict: dict, dirname: Path, relative_path: bool
 ) -> PointObservation | TrackObservation:
     fp = Path(obs_dict.pop("filename"))
     if relative_path:

src/modelskill/metrics.py

@@ -753,11 +753,11 @@ def _linear_regression(
     return slope, intercept
 
 
-def _std_obs(obs: ArrayLike, model: ArrayLike) -> Any:
+def _std_obs(obs: ArrayLike, _model: Any) -> Any:
     return obs.std()
 
 
-def _std_mod(obs: ArrayLike, model: ArrayLike) -> Any:
+def _std_mod(_obs: Any, model: ArrayLike) -> Any:
     return model.std()
 
 

src/modelskill/plotting/_scatter.py

@@ -428,13 +428,13 @@ def _scatter_plotly(
     x_trend,
     show_density,
     show_points,
-    norm,  # TODO not used by plotly, remove or keep for consistency?
+    _norm,  # Not used by plotly, kept for API consistency
     show_hist,
     nbins_hist,
     reg_method,
     xlabel,
     ylabel,
-    figsize,  # TODO not used by plotly, remove or keep for consistency?
+    _figsize,  # Not used by plotly, kept for API consistency
     xlim,
     ylim,
     title,

src/modelskill/plotting/_taylor_diagram.py

@@ -28,7 +28,7 @@ def taylor_diagram(
     figsize: tuple[float, float] = (7, 7),
     obs_text: str = "Observations",
     normalize_std: bool = False,
-    ax: Axes | None = None,
+    _ax: Axes | None = None,  # Not yet implemented
     title: str = "Taylor diagram",
 ) -> matplotlib.figure.Figure:
     """

src/modelskill/skill.py

@@ -656,12 +656,11 @@ def query(self, query: str) -> SkillTable:
         return self.__class__(self.data.query(query))
 
     def sel(
-        self, query: str | None = None, reduce_index: bool = True, **kwargs: Any
+        self, reduce_index: bool = True, **kwargs: Any
     ) -> SkillTable | SkillArray:
         """Filter (select) specific models or observations.
 
-        Select a subset of the SkillTable by a query,
-           (part of) the index, or specific columns
+        Select a subset of the SkillTable by (part of) the index.
 
         Parameters
         ----------