Forecast classes

climada/engine/__init__.py

@@ -22,3 +22,4 @@
 from .cost_benefit import *
 from .impact import *
 from .impact_calc import *
+from .impact_forecast import ImpactForecast

climada/engine/impact.py

@@ -1431,6 +1431,8 @@ def write_attribute(group, name, value):
 
         def write_dataset(group, name, value):
             """Write a dataset"""
+            if name == "lead_time":
+                value = value.astype("timedelta64[ns]").astype("int64")
             group.create_dataset(name, data=value, dtype=_str_type_helper(value))
 
         def write_dict(group, name, value):
@@ -1618,7 +1620,9 @@ def read_excel(self, *args, **kwargs):
         self.__dict__ = Impact.from_excel(*args, **kwargs).__dict__
 
     @classmethod
-    def from_hdf5(cls, file_path: Union[str, Path]):
+    def from_hdf5(
+        cls, file_path: Union[str, Path], *, add_scalar_attrs=None, add_array_attrs=None
+    ):
         """Create an impact object from an H5 file.
 
         This assumes a specific layout of the file. If values are not found in the
@@ -1663,6 +1667,10 @@ def from_hdf5(cls, file_path: Union[str, Path]):
         ----------
         file_path : str or Path
             The file path of the file to read.
+        add_scalar_attrs : Iterable of str, optional
+            Scalar attributes to read from file. Defaults to None.
+        add_array_attrs : Iterable of str, optional
+            Array attributes to read from file. Defaults to None.
 
         Returns
         -------
@@ -1691,17 +1699,27 @@ def from_hdf5(cls, file_path: Union[str, Path]):
             # Scalar attributes
             scalar_attrs = set(
                 ("crs", "tot_value", "unit", "aai_agg", "frequency_unit", "haz_type")
-            ).intersection(file.attrs.keys())
+            )
+            if add_scalar_attrs is not None:
+                scalar_attrs = scalar_attrs.union(add_scalar_attrs)
+            scalar_attrs = scalar_attrs.intersection(file.attrs.keys())
             kwargs.update({attr: file.attrs[attr] for attr in scalar_attrs})
 
             # Array attributes
             # NOTE: Need [:] to copy array data. Otherwise, it would be a view that is
             #       invalidated once we close the file.
             array_attrs = set(
                 ("event_id", "date", "coord_exp", "eai_exp", "at_event", "frequency")
-            ).intersection(file.keys())
+            )
+            if add_array_attrs is not None:
+                array_attrs = array_attrs.union(add_array_attrs)
+            array_attrs = array_attrs.intersection(file.keys())
             kwargs.update({attr: file[attr][:] for attr in array_attrs})
-
+            # correct lead_time attribut to timedelta
+            if "lead_time" in kwargs:
+                kwargs["lead_time"] = np.array(file["lead_time"][:]).astype(
+                    "timedelta64[ns]"
+                )
             # Special handling for 'event_name' because it should be a list of strings
             if "event_name" in file:
                 # pylint: disable=no-member
@@ -2208,9 +2226,12 @@ def stack_attribute(attr_name: str) -> np.ndarray:
         imp_mat = sparse.vstack(imp_mats)
 
         # Concatenate other attributes
-        kwargs = {
-            attr: stack_attribute(attr) for attr in ("date", "frequency", "at_event")
-        }
+        concat_attrs = {
+            name.lstrip("_")  # Private attributes with getter/setter
+            for name, value in first_imp.__dict__.items()
+            if isinstance(value, np.ndarray)
+        }.difference(("event_id", "coord_exp", "eai_exp", "aai_agg"))
+        kwargs = {attr: stack_attribute(attr) for attr in concat_attrs}
 
         # Get remaining attributes from first impact object in list
         return cls(

climada/engine/impact_calc.py

@@ -29,6 +29,8 @@
 
 from climada import CONFIG
 from climada.engine.impact import Impact
+from climada.engine.impact_forecast import ImpactForecast
+from climada.hazard.forecast import HazardForecast
 
 LOGGER = logging.getLogger(__name__)
 
@@ -217,7 +219,7 @@ def _return_impact(self, imp_mat_gen, save_mat):
 
         Returns
         -------
-        Impact
+        Impact or ImpactForecast
             Impact Object initialize from the impact matrix
 
         See Also
@@ -230,12 +232,31 @@ def _return_impact(self, imp_mat_gen, save_mat):
             at_event, eai_exp, aai_agg = self.risk_metrics(
                 imp_mat, self.hazard.frequency
             )
+            if isinstance(self.hazard, HazardForecast):
+                eai_exp = np.full_like(eai_exp, np.nan, dtype=eai_exp.dtype)
+                aai_agg = np.full_like(aai_agg, np.nan, dtype=aai_agg.dtype)
+                LOGGER.warning(
+                    "eai_exp and aai_agg are undefined with forecasts. "
+                    "Setting them to NaN arrays."
+                )
+
         else:
+            if isinstance(self.hazard, HazardForecast):
+                raise ValueError(
+                    "Saving impact matrix is required when using HazardForecast."
+                    "Please set save_mat=True."
+                )
             imp_mat = None
             at_event, eai_exp, aai_agg = self.stitch_risk_metrics(imp_mat_gen)
-        return Impact.from_eih(
+
+        impact = Impact.from_eih(
             self.exposures, self.hazard, at_event, eai_exp, aai_agg, imp_mat
         )
+        if isinstance(self.hazard, HazardForecast):
+            return ImpactForecast.from_impact(
+                impact, self.hazard.lead_time, self.hazard.member
+            )
+        return impact
 
     def _return_empty(self, save_mat):
         """
@@ -248,21 +269,37 @@ def _return_empty(self, save_mat):
 
         Returns
         -------
-        Impact
+        Impact or ImpactForecast
             Empty impact object with correct array sizes.
         """
         at_event = np.zeros(self.n_events)
-        eai_exp = np.zeros(self.n_exp_pnt)
-        aai_agg = 0.0
+        if isinstance(self.hazard, HazardForecast):
+            eai_exp = np.full(self.n_exp_pnt, np.nan)
+            aai_agg = np.nan
+        else:
+            eai_exp = np.zeros(self.n_exp_pnt)
+            aai_agg = 0.0
+
         if save_mat:
             imp_mat = sparse.csr_matrix(
                 (self.n_events, self.n_exp_pnt), dtype=np.float64
             )
         else:
+            if isinstance(self.hazard, HazardForecast):
+                raise ValueError(
+                    "Saving impact matrix is required when using HazardForecast. "
+                    "Please set save_mat=True."
+                )
             imp_mat = None
-        return Impact.from_eih(
+
+        impact = Impact.from_eih(
             self.exposures, self.hazard, at_event, eai_exp, aai_agg, imp_mat
         )
+        if isinstance(self.hazard, HazardForecast):
+            return ImpactForecast.from_impact(
+                impact, self.hazard.lead_time, self.hazard.member
+            )
+        return impact
 
     def minimal_exp_gdf(
         self, impf_col, assign_centroids, ignore_cover, ignore_deductible