WIP: Feature/set imp matrix

climada/engine/impact.py

@@ -1111,6 +1111,76 @@ def _cen_return_imp(imp, freq, imp_th, return_periods):
 
         return imp_fit
 
+    def calc_imp_from_mat(self, imp_mat, freq):
+        """
+        Set Impact attributes from the impact matrix. Returns a copy.
+        Overwrites eai_exp, at_event, aai_agg, imp_mat.
+
+        Parameters
+        ----------
+        imp_mat : sparse.csr_matrix
+            matrix num_events x num_exp with impacts.
+        freq : np.array
+            array with the frequency per event
+        Returns
+        -------
+        imp : Impact
+            Copy of impact with eai_exp, at_event, aai_agg, imp_mat set.
+        """
+        eai_exp = self._eai_exp_from_mat(imp_mat, freq)
+        at_event = self._at_event_from_mat(imp_mat)
+        aai_agg = self._aai_agg_from_at_event(at_event, freq)
+        return eai_exp, at_event, aai_agg
+
+    def _eai_exp_from_mat(self, imp_mat, freq):
+        """
+        Compute impact for each exposures from the total impact matrix
+
+        Parameters
+        ----------
+        imp_mat : sparse.csr_matrix
+            matrix num_events x num_exp with impacts.
+        frequency : np.array
+            annual frequency of events
+        Returns
+        -------
+        eai_exp : np.array
+            expected annual impact for each exposure
+        """
+        freq_mat = freq.reshape(len(freq), 1)
+        return imp_mat.multiply(freq_mat).sum(axis=0).A1
+
+    def _at_event_from_mat(self, imp_mat):
+        """
+        Compute impact for each hazard event from the total impact matrix
+
+        Parameters
+        ----------
+        imp_mat : sparse.csr_matrix
+            matrix num_events x num_exp with impacts.
+        Returns
+        -------
+        at_event : np.array
+            impact for each hazard event
+        """
+        return np.squeeze(np.asarray(np.sum(imp_mat, axis=1)))
+
+    def _aai_agg_from_at_event(self, at_event, freq):
+        """
+        Aggregate impact.at_event
+
+        Parameters
+        ----------
+        at_event : np.array
+            impact for each hazard event
+        frequency : np.array
+            annual frequency of event
+        Returns
+        -------
+        float
+            average annual impact aggregated
+        """
+        return sum(at_event * freq)
 
     def select(self,
                event_ids=None, event_names=None, dates=None,
@@ -1226,13 +1296,6 @@ def select(self,
 
         return imp
 
-    def _selected_exposures_idx(self, coord_exp):
-        assigned_idx = u_coord.assign_coordinates(self.coord_exp, coord_exp, threshold=0)
-        sel_exp = (assigned_idx >= 0).nonzero()[0]
-        if sel_exp.size == 0:
-            LOGGER.warning("No exposure coordinates match the selection.")
-        return sel_exp
-
     def _selected_events_idx(self, event_ids, event_names, dates, nb_events):
         if all(var is None for var in [dates, event_ids, event_names]):
             return None
@@ -1278,6 +1341,13 @@ def _selected_events_idx(self, event_ids, event_names, dates, nb_events):
 
         return sel_ev
 
+    def _selected_exposures_idx(self, coord_exp):
+        assigned_idx = u_coord.assign_coordinates(self.coord_exp, coord_exp, threshold=0)
+        sel_exp = (assigned_idx >= 0).nonzero()[0]
+        if sel_exp.size == 0:
+            LOGGER.warning("No exposure coordinates match the selection.")
+        return sel_exp
+
 class ImpactFreqCurve():
     """Impact exceedence frequency curve.