Computing all loss types together

openquake/risklib/riskmodels.py

@@ -256,10 +256,11 @@ def loss_types(self):
         """
         return sorted(self.risk_functions)
 
-    def __call__(self, loss_type, assets, gmf_df, rndgen=None):
+    def __call__(self, assets, gmf_df, rndgen=None):
         meth = getattr(self, self.calcmode)
-        res = meth(loss_type, assets, gmf_df, rndgen)
-        return res  # for event_based_risk this is a DataFrame (eid, aid, loss)
+        res = {lt: meth(lt, assets, gmf_df, rndgen) for lt in self.loss_types}
+        # for event_based_risk this is a map loss_type -> DataFrame(eid, aid, loss)
+        return res
 
     def __toh5__(self):
         return self.risk_functions, {'taxonomy': self.taxonomy}
@@ -440,6 +441,8 @@ def get_riskcomputer(dic, alias):
     rc.asset_df = pandas.DataFrame(dic['asset_df'])
     rc.wdic = {}
     rfs = AccumDict(accum=[])
+    steps = dic.get('lrem_steps_per_interval', 1)
+    mal = dic.get('minimum_asset_loss', {lt: 0. for lt in dic['loss_types']})
     for rlk, func in dic['risk_functions'].items():
         riskid, lt = rlk.split('#')
         rf = hdf5.json_to_obj(json.dumps(func))
@@ -451,16 +454,14 @@ def get_riskcomputer(dic, alias):
             rf.retro.init()
             rf.retro.loss_type = lt
         rfs[riskid].append(rf)
-    steps = dic.get('lrem_steps_per_interval', 1)
-    mal = dic.get('minimum_asset_loss', {lt: 0. for lt in dic['loss_types']})
-    for rlt, weight in dic['wdic'].items():
-        riskid, lt = rlt.split('#')
         rm = RiskModel(dic['calculation_mode'], 'taxonomy',
                        group_by_lt(rfs[riskid]),
                        lrem_steps_per_interval=steps,
                        minimum_asset_loss=mal)
         rm.alias = alias
-        rc[riskid, lt] = rm
+        rc[riskid] = rm
+    for rlt, weight in dic['wdic'].items():
+        riskid, lt = rlt.split('#')
         rc.wdic[riskid, lt] = weight
     rc.loss_types = dic['loss_types']
     rc.minimum_asset_loss = mal

openquake/risklib/scientific.py

@@ -1668,7 +1668,7 @@ def __init__(self, crm, asset_df):
             for lt in self.minimum_asset_loss:
                 if loss_type in ('*', lt):
                     if country == '?' or country_str in country:
-                        self[riskid, lt] = crm._riskmodels[riskid]
+                        self[riskid] = crm._riskmodels[riskid]
                         self.wdic[riskid, lt] = weight
 
     def output(self, haz, sec_losses=(), rndgen=None):
@@ -1682,11 +1682,10 @@ def output(self, haz, sec_losses=(), rndgen=None):
         """
         dic = collections.defaultdict(list)  # lt -> outs
         weights = collections.defaultdict(list)  # lt -> weights
-        for riskid, lt in self:
-            rm = self[riskid, lt]
-            out = rm(lt, self.asset_df, haz, rndgen)
-            weights[lt].append(self.wdic[riskid, lt])
-            dic[lt].append(out)
+        for riskid, rm in self.items():
+            for lt, res in rm(self.asset_df, haz, rndgen).items():
+                weights[lt].append(self.wdic[riskid, lt])
+                dic[lt].append(res)
         out = {}
         for lt in self.minimum_asset_loss:
             outs = dic[lt]