Composing probabilities in event_based_damage

openquake/calculators/event_based_damage.py

@@ -78,8 +78,8 @@ def damage_from_gmfs(gmfslices, oqparam, dstore, monitor):
     return event_based_damage(df, oqparam, dstore, monitor)
 
 
-def _gen_d3(asset_df, gmf_df, crmodel, dparam):
-    # yields (aids, d3) triples
+def _gen_dd3(asset_df, gmf_df, crmodel, dparam):
+    # yields (aids, dd3) triples
     oq = crmodel.oqparam
     sec_sims = oq.secondary_simulations.items()
     for prob_field, num_sims in sec_sims:
@@ -97,19 +97,19 @@ def _gen_d3(asset_df, gmf_df, crmodel, dparam):
             number = assets['value-number']
         else:
             number = assets['value-number'] = U32(assets['value-number'])
-        d4 = numpy.zeros((L, A, E, dparam.Dc), F32)
+        dd4 = numpy.zeros((L, A, E, dparam.Dc), F32)
         D = dparam.D
         for lti, lt in enumerate(oq.loss_types):
             fractions = out[lt]
             if oq.float_dmg_dist:
-                d4[lti, :, :, :D] = fractions
+                dd4[lti, :, :, :D] = fractions
                 for a in range(A):
-                    d4[lti, a] *= number[a]
+                    dd4[lti, a] *= number[a]
             else:
                 # this is a performance distaster; for instance
                 # the Messina test in oq-risk-tests becomes 12x
                 # slower even if it has only 25_736 assets
-                d4[lti, :, :, :D] = dparam.rng.discrete_dmg_dist(
+                dd4[lti, :, :, :D] = dparam.rng.discrete_dmg_dist(
                     dparam.eids, fractions, number)
 
             # secondary perils and consequences
@@ -118,23 +118,27 @@ def _gen_d3(asset_df, gmf_df, crmodel, dparam):
                     for d in range(1, D):
                         # doing the mean on the secondary simulations
                         if oq.float_dmg_dist:
-                            d4[lti, a, :, d] *= probs
+                            dd4[lti, a, :, d] *= probs
                         else:
-                            d4[lti, a, :, d] *= dprobs
+                            dd4[lti, a, :, d] *= dprobs
 
         df = crmodel.tmap_df[crmodel.tmap_df.taxi == assets[0]['taxonomy']]
         if 'losses' in crmodel.get_consequences():
             loss_types = oq.total_loss_types
         else:
             loss_types = {lt: i for i, lt in enumerate(oq.loss_types)}
+        if L > 1:
+            # compose probabilities
+            dd3 = numpy.zeros((A, E, dparam.Dc), F32)
+            for a in range(A):
+                dd3[a] = general.pprod(dd4[:, a] / number[a], axis=0) * number[a]
+        else:
+            dd3 = dd4[0]
         csq = crmodel.compute_csq(
-            assets, d4[:, :, :, :D], df, loss_types, oq.time_event)
-        d3 = numpy.zeros((A, E, dparam.Dc), F32)
-        for li, lt in enumerate(oq.loss_types):
-            d3[:] += d4[li]
+            assets, dd4[:, :, :, :D], df, loss_types, oq.time_event)
         for name, values in csq.items():
-            d3[:, :, dparam.csqidx[name]] = values
-        yield aids, d3  # d3 has shape (A, E, Dc)
+            dd3[:, :, dparam.csqidx[name]] = values
+        yield aids, dd3  # dd3 has shape (A, E, Dc)
 
 
 def event_based_damage(df, oq, dstore, monitor):
@@ -157,12 +161,9 @@ def event_based_damage(df, oq, dstore, monitor):
     dmg_csq = crmodel.get_dmg_csq()
     csqidx = {dc: i + 1 for i, dc in enumerate(dmg_csq)}
     dmgcsq = zero_dmgcsq(len(assetcol), oq.R, crmodel)
-    _A, R, Dc = dmgcsq.shape
+    _A, _R, Dc = dmgcsq.shape
     D = Dc - len(crmodel.get_consequences())
-    if R > 1:
-        allrlzs = dstore['events']['rlz_id']
-    else:
-        allrlzs = U32([0])
+    rlzs = dstore['events']['rlz_id']
     with mon_risk:
         dddict = general.AccumDict(accum=numpy.zeros(Dc, F32))  # eid, kid
         for sid, asset_df in assetcol.to_dframe().groupby('site_id'):
@@ -172,29 +173,22 @@ def event_based_damage(df, oq, dstore, monitor):
                 continue
             oq = crmodel.oqparam
             eids = gmf_df.eid.to_numpy()
-            if R > 1:
-                rlzs = allrlzs[eids]
-            else:
-                rlzs = allrlzs
             if oq.secondary_simulations or not oq.float_dmg_dist:
                 rng = scientific.MultiEventRNG(
                     oq.master_seed, numpy.unique(eids))
             else:
                 rng = None
             dparam = Dparam(eids, aggids, csqidx, D, Dc, rng)
-            for aids, d3 in _gen_d3(asset_df, gmf_df, crmodel, dparam):
-                if R == 1:
-                    dmgcsq[aids, 0] += d3.sum(axis=1)
-                else:
-                    for e, rlz in enumerate(rlzs):
-                        dmgcsq[aids, rlz] += d3[:, e]
-                tot = d3.sum(axis=0)  # sum on the assets
+            for aids, dd3 in _gen_dd3(asset_df, gmf_df, crmodel, dparam):
+                for e, rlz in enumerate(rlzs[eids]):
+                    dmgcsq[aids, rlz] += dd3[:, e]
+                tot = dd3.sum(axis=0)  # sum on the assets
                 for e, eid in enumerate(eids):
                     dddict[eid, oq.K] += tot[e]
                     if oq.K:
                         for kids in dparam.aggids:
                             for a, aid in enumerate(aids):
-                                dddict[eid, kids[aid]] += d3[a, e]
+                                dddict[eid, kids[aid]] += dd3[a, e]
     try:
         [lt] = oq.loss_types
     except ValueError:

openquake/qa_tests_data/scenario_damage/case_21/expected/aggrisk-parent.csv

@@ -1,8 +1,8 @@
-#,,,,,,,,,"generated_by='OpenQuake engine 3.22.0-git03f37349ed', start_date='2024-10-23T10:42:23', checksum=2614000066, investigation_time=None, risk_investigation_time=None"
+#,,,,,,,,,"generated_by='OpenQuake engine 3.22.0-gitafc5cab7ae', start_date='2024-10-26T05:43:19', checksum=2614000066, investigation_time=None, risk_investigation_time=None"
 loss_type,parent_id,rlz_id,no_damage,slight,moderate,extensive,complete,non_operational_value,non_operational_ratio
 structural,A,0,9.66667E-01,0.00000E+00,0.00000E+00,0.00000E+00,1.00000E-01,1.00000E-01,1.00000E-01
 structural,A,1,9.33333E-01,0.00000E+00,0.00000E+00,0.00000E+00,2.00000E-01,2.00000E-01,2.00000E-01
 structural,B,0,1.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00
 structural,B,1,1.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00
-structural,E1,0,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,3.00000E+00,3.00000E+00,3.00000E+00
-structural,E1,1,0.00000E+00,0.00000E+00,0.00000E+00,0.00000E+00,3.00000E+00,3.00000E+00,3.00000E+00
+structural,E1,0,6.66667E-01,0.00000E+00,0.00000E+00,0.00000E+00,1.00000E+00,3.00000E+00,3.00000E+00
+structural,E1,1,6.66667E-01,0.00000E+00,0.00000E+00,0.00000E+00,1.00000E+00,3.00000E+00,3.00000E+00

openquake/qa_tests_data/scenario_damage/case_21/expected/aggrisk.csv

@@ -1,4 +1,4 @@
-#,,,,,,,,"generated_by='OpenQuake engine 3.22.0-git03f37349ed', start_date='2024-10-23T10:42:23', checksum=2614000066, investigation_time=None, risk_investigation_time=None"
+#,,,,,,,,"generated_by='OpenQuake engine 3.22.0-gitafc5cab7ae', start_date='2024-10-26T05:43:19', checksum=2614000066, investigation_time=None, risk_investigation_time=None"
 loss_type,rlz_id,no_damage,slight,moderate,extensive,complete,non_operational_value,non_operational_ratio
-structural,0,1.96667E+00,0.00000E+00,0.00000E+00,0.00000E+00,3.10000E+00,3.10000E+00,1.03333E+00
-structural,1,1.93333E+00,0.00000E+00,0.00000E+00,0.00000E+00,3.20000E+00,3.20000E+00,1.06667E+00
+structural,0,2.63333E+00,0.00000E+00,0.00000E+00,0.00000E+00,1.10000E+00,3.10000E+00,1.03333E+00
+structural,1,2.60000E+00,0.00000E+00,0.00000E+00,0.00000E+00,1.20000E+00,3.20000E+00,1.06667E+00

openquake/qa_tests_data/scenario_damage/case_22/expected/aggrisk.csv

@@ -1,3 +1,3 @@
-#,,,,,,,"generated_by='OpenQuake engine 3.22.0-git03f37349ed', start_date='2024-10-23T10:42:25', checksum=2138001544, investigation_time=None, risk_investigation_time=None"
+#,,,,,,,"generated_by='OpenQuake engine 3.22.0-gitafc5cab7ae', start_date='2024-10-26T05:43:19', checksum=4025559538, investigation_time=None, risk_investigation_time=None"
 loss_type,no_damage,slight,moderate,extreme,complete,losses_value,losses_ratio
-structural,1.07656E+01,1.41803E+00,1.39786E+00,9.49061E-01,8.93820E+00,1.84217E+04,1.08299E-01
+structural,1.17053E+01,1.41803E+00,1.39786E+00,9.49061E-01,6.11920E+00,1.84217E+04,1.08299E-01

openquake/qa_tests_data/scenario_damage/case_22/expected/dmg.csv

@@ -1,17 +1,17 @@
-#,,,,,,,,,,"generated_by='OpenQuake engine 3.22.0-gita2d5dedadb', start_date='2024-10-25T07:10:31', checksum=4025559538"
+#,,,,,,,,,,"generated_by='OpenQuake engine 3.22.0-gitafc5cab7ae', start_date='2024-10-26T05:43:19', checksum=4025559538"
 asset_id,NAME_1,taxonomy,lon,lat,no_damage,slight,moderate,extreme,complete,losses
-a1,a,Wood1,83.31382,29.46117,9.267137E-01,4.338550E-02,2.389134E-02,4.983470E-03,1.026009E-03,1.492097E+02
-a2,a,Wood1,83.31382,29.23617,9.323701E-01,4.003692E-02,2.204736E-02,4.598836E-03,9.468192E-04,1.376935E+02
-a3,a,Wood1,83.53882,29.08617,8.998597E-01,5.549773E-02,3.362454E-02,8.460128E-03,2.557828E-03,2.299460E+02
+a1,a,Wood1,83.31382,29.46117,9.267137E-01,4.338550E-02,2.389134E-02,4.983467E-03,1.026009E-03,1.492097E+02
+a2,a,Wood1,83.31382,29.23617,9.323701E-01,4.003692E-02,2.204736E-02,4.598836E-03,9.468218E-04,1.376935E+02
+a3,a,Wood1,83.53882,29.08617,8.998597E-01,5.549773E-02,3.362454E-02,8.460126E-03,2.557830E-03,2.299460E+02
 a4,a,Wood1,80.68882,28.93617,1.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00
-a5,a,Wood1,83.53882,29.01117,8.605580E-01,7.386525E-02,4.770378E-02,1.326894E-02,4.604037E-03,3.436947E+02
+a5,a,Wood1,83.53882,29.01117,8.605580E-01,7.386524E-02,4.770378E-02,1.326893E-02,4.604037E-03,3.436947E+02
 a6,a,Wood1,81.13882,28.78617,1.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00
-a7,a,Wood1,83.98882,28.48617,7.521410E-01,8.566735E-02,8.970005E-02,4.155788E-02,3.093375E-02,9.213160E+02
-a8,a,Concrete1,83.23882,29.38617,4.697937E-01,2.247916E-02,3.195053E-02,3.080165E-02,4.449750E-01,5.401368E+02
-a9,a,Concrete1,83.01382,29.08617,4.619936E-01,2.497276E-02,3.440397E-02,3.081745E-02,4.478122E-01,5.061439E+02
-a10,a,Concrete1,83.31382,28.71117,1.616082E-01,4.471088E-02,6.344895E-02,6.095122E-02,6.692807E-01,1.065684E+03
-a11,a,Concrete1,86.91382,27.73617,6.076256E-01,1.408595E-02,1.940564E-02,1.738266E-02,3.415002E-01,2.854917E+02
-a12,a,Concrete1,83.16382,29.31117,5.451100E-01,2.104819E-02,2.899724E-02,2.597436E-02,3.788701E-01,4.266012E+02
+a7,a,Wood1,83.98882,28.48617,7.524759E-01,8.566736E-02,8.970005E-02,4.155788E-02,3.059878E-02,9.213160E+02
+a8,a,Concrete1,83.23882,29.38617,5.814353E-01,2.247916E-02,3.195053E-02,3.080165E-02,3.333333E-01,5.401368E+02
+a9,a,Concrete1,83.01382,29.08617,5.764725E-01,2.497276E-02,3.440397E-02,3.081745E-02,3.333333E-01,5.061439E+02
+a10,a,Concrete1,83.31382,28.71117,4.975556E-01,4.471088E-02,6.344895E-02,6.095122E-02,3.333333E-01,1.065684E+03
+a11,a,Concrete1,86.91382,27.73617,6.157925E-01,1.408595E-02,1.940564E-02,1.738266E-02,3.333333E-01,2.854917E+02
+a12,a,Concrete1,83.16382,29.31117,5.906469E-01,2.104818E-02,2.899724E-02,2.597436E-02,3.333333E-01,4.266012E+02
 a13,a,Concrete1,80.61382,28.93617,1.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00
-a14,a,Concrete1,83.91382,29.01117,1.478413E-01,4.692676E-02,7.078027E-02,7.755719E-02,6.568946E-01,1.534645E+03
+a14,a,Concrete1,83.91382,29.01117,4.714025E-01,4.692676E-02,7.078026E-02,7.755719E-02,3.333333E-01,1.534645E+03
 a15,a,Concrete1,82.03882,30.28617,1.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00