Reduce lead time and member and update test

Author: luseverin

climada/engine/impact_forecast.py

@@ -194,18 +194,23 @@ def _reduce_attrs(self, reduce_method: str):
         - event_id: set to [0]
         - event_name: set to [reduce_method]
         - date: set to the minimum value
-        - frequency: set to 0
+        - frequency: set to 1
 
         Parameters
         ----------
         reduce_method : str
             The reduction method used to reduce the attributes.
         """
-        red_event_id = np.asarray([0])
-        red_event_name = np.asarray([reduce_method])
-        red_date = np.array([self.date.min()])
-        red_frequency = np.array([0])
-        return red_event_id, red_event_name, red_date, red_frequency
+        reduced_attrs = {
+            "lead_time": np.array([np.timedelta64("NaT")]),
+            "member": np.array([-1]),
+            "event_id": np.array([0]),
+            "event_name": np.array([reduce_method]),
+            "date": np.array([self.date.min()]),
+            "frequency": np.array([1]),
+        }
+
+        return reduced_attrs
 
     def min(self):
         """
@@ -222,16 +227,14 @@ def min(self):
         """
         red_imp_mat = sparse.csr_matrix(self.imp_mat.min(axis=0))
         red_at_event = np.array([red_imp_mat.sum()])
-        red_event_id, red_event_name, red_date, red_frequency = self._reduce_attrs(
-            "min"
-        )
+        reduced_attrs = self._reduce_attrs("min")
         return ImpactForecast(
-            lead_time=self.lead_time,
-            member=self.member,
-            event_id=red_event_id,
-            event_name=red_event_name,
-            date=red_date,
-            frequency=red_frequency,
+            lead_time=reduced_attrs["lead_time"],
+            member=reduced_attrs["member"],
+            event_id=reduced_attrs["event_id"],
+            event_name=reduced_attrs["event_name"],
+            date=reduced_attrs["date"],
+            frequency=reduced_attrs["frequency"],
             frequency_unit=self.frequency_unit,
             coord_exp=self.coord_exp,
             crs=self.crs,
@@ -259,16 +262,14 @@ def max(self):
         """
         red_imp_mat = sparse.csr_matrix(self.imp_mat.max(axis=0))
         red_at_event = np.array([red_imp_mat.sum()])
-        red_event_id, red_event_name, red_date, red_frequency = self._reduce_attrs(
-            "max"
-        )
+        reduced_attrs = self._reduce_attrs("max")
         return ImpactForecast(
-            lead_time=self.lead_time,
-            member=self.member,
-            event_id=red_event_id,
-            event_name=red_event_name,
-            date=red_date,
-            frequency=red_frequency,
+            lead_time=reduced_attrs["lead_time"],
+            member=reduced_attrs["member"],
+            event_id=reduced_attrs["event_id"],
+            event_name=reduced_attrs["event_name"],
+            date=reduced_attrs["date"],
+            frequency=reduced_attrs["frequency"],
             frequency_unit=self.frequency_unit,
             coord_exp=self.coord_exp,
             crs=self.crs,
@@ -298,16 +299,14 @@ def mean(self):
         """
         red_imp_mat = sparse.csr_matrix(self.imp_mat.mean(axis=0))
         red_at_event = np.array([red_imp_mat.sum()])
-        red_event_id, red_event_name, red_date, red_frequency = self._reduce_attrs(
-            "mean"
-        )
+        reduced_attrs = self._reduce_attrs("mean")
         return ImpactForecast(
-            lead_time=self.lead_time,
-            member=self.member,
-            event_id=red_event_id,
-            event_name=red_event_name,
-            date=red_date,
-            frequency=red_frequency,
+            lead_time=reduced_attrs["lead_time"],
+            member=reduced_attrs["member"],
+            event_id=reduced_attrs["event_id"],
+            event_name=reduced_attrs["event_name"],
+            date=reduced_attrs["date"],
+            frequency=reduced_attrs["frequency"],
             frequency_unit=self.frequency_unit,
             coord_exp=self.coord_exp,
             crs=self.crs,

climada/engine/test/test_impact_forecast.py

@@ -189,15 +189,21 @@ def test_impact_forecast_mean_min_max(impact_forecast):
     npt.assert_array_equal(imp_fcst_max.at_event, impact_forecast.at_event.max())
 
     # check that attributes where reduced correctly
+    assert np.isnat(imp_fcst_mean.lead_time[0])
+    assert np.isnat(imp_fcst_min.lead_time[0])
+    assert np.isnat(imp_fcst_max.lead_time[0])
+    assert imp_fcst_mean.member[0] == -1
+    assert imp_fcst_min.member[0] == -1
+    assert imp_fcst_max.member[0] == -1
     assert imp_fcst_mean.event_name[0] == "mean"
     assert imp_fcst_min.event_name[0] == "min"
     assert imp_fcst_max.event_name[0] == "max"
     assert imp_fcst_mean.event_id[0] == 0
     assert imp_fcst_min.event_id[0] == 0
     assert imp_fcst_max.event_id[0] == 0
-    assert imp_fcst_mean.frequency == 0
-    assert imp_fcst_min.frequency == 0
-    assert imp_fcst_max.frequency == 0
+    assert imp_fcst_mean.frequency == 1
+    assert imp_fcst_min.frequency == 1
+    assert imp_fcst_max.frequency == 1
     assert imp_fcst_mean.date == impact_forecast.date.min()
     assert imp_fcst_min.date == impact_forecast.date.min()
     assert imp_fcst_max.date == impact_forecast.date.min()