SkillTable is a collection of SkillArrays

docs/api/skill.md

@@ -2,9 +2,11 @@
 
 ## Aggregated Skill
 
-::: modelskill.skill.AggregatedSkill
+::: modelskill.skill.SkillTable
 
-::: modelskill.skill.AggregatedSkillPlotter
+::: modelskill.skill.SkillArray
+
+::: modelskill.skill.SkillArrayPlotter
 
 ## Gridded Skill
 

modelskill/comparison/_collection.py

@@ -12,7 +12,7 @@
 from ..plotting import taylor_diagram, TaylorPoint
 
 from ._collection_plotter import ComparerCollectionPlotter
-from ..skill import AggregatedSkill
+from ..skill import SkillTable
 from ..skill_grid import SkillGrid
 from ..settings import options, reset_option
 
@@ -415,7 +415,7 @@ def skill(
         by: Optional[Union[str, List[str]]] = None,
         metrics: Optional[List[str]] = None,
         **kwargs,
-    ) -> Optional[AggregatedSkill]:
+    ) -> Optional[SkillTable]:
         """Aggregated skill assessment of model(s)
 
         Parameters
@@ -491,7 +491,7 @@ def skill(
 
         res = _groupby_df(df.drop(columns=["x", "y"]), by, metrics)
         res = cmp._add_as_col_if_not_in_index(df, skilldf=res)
-        return AggregatedSkill(res)
+        return SkillTable(res)
 
     def _add_as_col_if_not_in_index(
         self, df, skilldf, fields=["model", "observation", "variable"]
@@ -695,7 +695,7 @@ def mean_skill(
         weights: Optional[Union[str, List[float], Dict[str, float]]] = None,
         metrics: Optional[list] = None,
         **kwargs,
-    ) -> Optional[AggregatedSkill]:  # TODO raise error if no data?
+    ) -> Optional[SkillTable]:  # TODO raise error if no data?
         """Weighted mean of skills
 
         First, the skill is calculated per observation,
@@ -718,7 +718,7 @@ def mean_skill(
 
         Returns
         -------
-        AggregatedSkill
+        SkillTable
             mean skill assessment as a skill object
 
         See also
@@ -789,14 +789,14 @@ def weighted_mean(x):
 
         # output
         res = cmp._add_as_col_if_not_in_index(df, res, fields=["model", "variable"])
-        return AggregatedSkill(res.astype({"n": int}))
+        return SkillTable(res.astype({"n": int}))
 
     def mean_skill_points(
         self,
         *,
         metrics: Optional[list] = None,
         **kwargs,
-    ) -> Optional[AggregatedSkill]:  # TODO raise error if no data?
+    ) -> Optional[SkillTable]:  # TODO raise error if no data?
         """Mean skill of all observational points
 
         All data points are pooled (disregarding which observation they belong to),
@@ -816,7 +816,7 @@ def mean_skill_points(
 
         Returns
         -------
-        AggregatedSkill
+        SkillTable
             mean skill assessment as a skill object
 
         See also

modelskill/comparison/_comparison.py

@@ -32,7 +32,7 @@
     TimeTypes,
     IdOrNameTypes,
 )
-from ..skill import AggregatedSkill
+from ..skill import SkillTable
 from ..skill_grid import SkillGrid
 from ..settings import options, register_option, reset_option
 from ..utils import _get_name
@@ -911,7 +911,7 @@ def skill(
         by: Optional[Union[str, List[str]]] = None,
         metrics: Optional[list] = None,
         **kwargs,
-    ) -> AggregatedSkill:
+    ) -> SkillTable:
         """Skill assessment of model(s)
 
         Parameters
@@ -926,7 +926,7 @@ def skill(
 
         Returns
         -------
-        AggregatedSkill
+        SkillTable
             skill assessment object
 
         See also
@@ -969,7 +969,7 @@ def skill(
         df = cmp.to_dataframe()  # TODO: avoid df if possible?
         res = _groupby_df(df.drop(columns=["x", "y"]), by, metrics)
         res = self._add_as_col_if_not_in_index(df, skilldf=res)
-        return AggregatedSkill(res)
+        return SkillTable(res)
 
     def _add_as_col_if_not_in_index(self, df, skilldf):
         """Add a field to skilldf if unique in df"""