Rework parameters to 'reduce_unique_selection'

Author: peanutfun

climada/engine/impact_forecast.py

@@ -315,9 +315,8 @@ def min(self, dim: Literal["member", "lead_time"] | None = None):
             rdim = self._reduce_iter_dim(dim)
             return reduce_unique_selection(
                 self,
-                values=getattr(self, rdim),
-                select=rdim,
-                reduce_attr="min",
+                attr=rdim,
+                reduce_method="min",
                 concat_kws={"reset_event_ids": True},
             )
 
@@ -356,9 +355,8 @@ def max(self, dim: Literal["member", "lead_time"] | None = None):
             rdim = self._reduce_iter_dim(dim)
             return reduce_unique_selection(
                 self,
-                values=getattr(self, rdim),
-                select=rdim,
-                reduce_attr="max",
+                attr=rdim,
+                reduce_method="max",
                 concat_kws={"reset_event_ids": True},
             )
 
@@ -396,9 +394,8 @@ def mean(self, dim: Literal["member", "lead_time"] | None = None):
             rdim = self._reduce_iter_dim(dim)
             return reduce_unique_selection(
                 self,
-                values=getattr(self, rdim),
-                select=rdim,
-                reduce_attr="mean",
+                attr=rdim,
+                reduce_method="mean",
                 concat_kws={"reset_event_ids": True},
             )
 
@@ -431,9 +428,8 @@ def _quantile(
             rdim = self._reduce_iter_dim(dim)
             return reduce_unique_selection(
                 self,
-                values=getattr(self, rdim),
-                select=rdim,
-                reduce_attr="quantile",
+                attr=rdim,
+                reduce_method="quantile",
                 q=q,
                 concat_kws={"reset_event_ids": True},
             )

climada/hazard/forecast.py

@@ -142,9 +142,7 @@ def min(self, dim: Literal["member", "lead_time"] | None = None):
         """
         if dim is not None:
             rdim = self._reduce_iter_dim(dim)
-            return reduce_unique_selection(
-                self, values=getattr(self, rdim), select=rdim, reduce_attr="min"
-            )
+            return reduce_unique_selection(self, attr=rdim, reduce_method="min")
 
         red_intensity = self.intensity.min(axis=0).tocsr()
         red_fraction = self.fraction.min(axis=0).tocsr()
@@ -176,9 +174,7 @@ def max(self, dim: Literal["member", "lead_time"] | None = None):
         """
         if dim is not None:
             rdim = self._reduce_iter_dim(dim)
-            return reduce_unique_selection(
-                self, values=getattr(self, rdim), select=rdim, reduce_attr="max"
-            )
+            return reduce_unique_selection(self, attr=rdim, reduce_method="max")
 
         red_intensity = self.intensity.max(axis=0).tocsr()
         red_fraction = self.fraction.max(axis=0).tocsr()
@@ -209,9 +205,7 @@ def mean(self, dim: Literal["member", "lead_time"] | None = None):
         """
         if dim is not None:
             rdim = self._reduce_iter_dim(dim)
-            return reduce_unique_selection(
-                self, values=getattr(self, rdim), select=rdim, reduce_attr="mean"
-            )
+            return reduce_unique_selection(self, attr=rdim, reduce_method="mean")
 
         red_intensity = sparse.csr_matrix(self.intensity.mean(axis=0))
         red_fraction = sparse.csr_matrix(self.fraction.mean(axis=0))
@@ -240,9 +234,8 @@ def _quantile(
             rdim = self._reduce_iter_dim(dim)
             return reduce_unique_selection(
                 self,
-                values=getattr(self, rdim),
-                select=rdim,
-                reduce_attr="quantile",
+                attr=rdim,
+                reduce_method="quantile",
                 q=q,
             )
 

climada/util/forecast.py

@@ -156,26 +156,33 @@ def _reduce_attrs(self, event_name: str, **attrs) -> dict[str, np.ndarray | list
 
 def reduce_unique_selection(
     forecast,
-    values: np.ndarray,
-    select: str,
-    reduce_attr: str,
+    attr: str,
+    reduce_method: str,
+    *,
+    select_kwarg: str | None = None,
     concat_kws: Mapping[str, Any] | None = None,
     **kwargs,
 ):
     """
-    Reduce an attribute of a forecast object by selecting unique values
-    and performing an attribute reduction method.
+    Select unique values of an attribute and perform a reduction over each selection.
 
     Parameters
     ----------
     forecast : HazardForecast | ImpactForecast
         Forecast object to reduce.
-    values : np.ndarray
-        Array of values for which to select and reduce the attribute.
-    select : str
-        Name of the attribute to select on (e.g. 'lead_time', 'member').
-    reduce_attr : str
+    attr : str
+        Attribute to draw values for which to select and reduce the attribute.
+    reduce_method : str
         Name of the attribute reduction method to call (e.g. 'min', 'mean').
+    select_kwarg : str | None
+        Keyword argument to the select method (e.g. 'lead_time', 'member') of the
+        forecast object. If ``None`` (default), the value of ``attr`` is used. This
+        parameter exists because some parameters to select methods are called
+        differently than the corresponding attributes.
+    concat_kws
+        Mapping of keyword arguments to the concat method of the forecast.
+    kwargs
+        Keyword arguments to the reduction method.
 
     Returns
     -------
@@ -184,10 +191,13 @@ def reduce_unique_selection(
         and selected by the unique values.
     """
     concat_kws = {} if concat_kws is None else concat_kws
+    select_kwarg = attr if select_kwarg is None else select_kwarg
     return forecast.concat(
         [
-            getattr(forecast.select(**{select: [val]}), reduce_attr)(dim=None, **kwargs)
-            for val in np.unique(values)
+            getattr(forecast.select(**{select_kwarg: [val]}), reduce_method)(
+                dim=None, **kwargs
+            )
+            for val in np.unique(getattr(forecast, attr))
         ],
         **concat_kws,
     )