Merge pull request #10018 from gem/totloss

Fix extract_agg_curves to handle '+' separated total loss types

openquake/calculators/event_based_risk.py

@@ -244,6 +244,19 @@ def gen_outputs(df, crmodel, rng, monitor):
             yield out
 
 
+def check_tot_loss_unit_consistency(units, total_losses, loss_types):
+    total_losses_units = set()
+    for separate_lt in total_losses.split('+'):
+        assert separate_lt in loss_types
+        for unit, lt in zip(units, loss_types):
+            if separate_lt == lt:
+                total_losses_units.add(unit)
+    if len(total_losses_units) != 1:
+        logging.warning(
+            'The units of the single components of the total losses'
+            ' are not homogeneous: %s" ' % total_losses_units)
+
+
 def set_oqparam(oq, assetcol, dstore):
     """
     Set the attributes .M, .K, .A, .ideduc, ._sec_losses
@@ -264,6 +277,9 @@ def set_oqparam(oq, assetcol, dstore):
 
     ideduc = assetcol['ideductible'].any()
     if oq.total_losses:
+        units = dstore['exposure'].cost_calculator.get_units(oq.loss_types)
+        check_tot_loss_unit_consistency(
+            units.split(), oq.total_losses, oq.loss_types)
         sec_losses.append(
             partial(total_losses, kind=oq.total_losses, ideduc=ideduc))
     elif ideduc:

openquake/calculators/extract.py

@@ -15,7 +15,7 @@
 #
 # You should have received a copy of the GNU Affero General Public License
 # along with OpenQuake.  If not, see <http://www.gnu.org/licenses/>.
-from urllib.parse import parse_qs
+from urllib.parse import parse_qs, quote_plus
 from functools import lru_cache
 import operator
 import logging
@@ -120,11 +120,17 @@ def parse(query_string, info={}):
     {'kind': ['mean'], 'k': [0], 'rlzs': False}
     >>> parse('kind=rlz-3&imt=PGA&site_id=0', {'stats': {}})
     {'kind': ['rlz-3'], 'imt': ['PGA'], 'site_id': [0], 'k': [3], 'rlzs': True}
+    >>> parse(
+    ...    'loss_type=structural+nonstructural&absolute=True&kind=rlzs')['lt']
+    ['structural+nonstructural']
     """
     qdic = parse_qs(query_string)
     for key, val in sorted(qdic.items()):
         # convert site_id to an int, loss_type to an int, etc
         if key == 'loss_type':
+            # NOTE: loss types such as 'structural+nonstructural' need to be
+            # quoted, otherwise the plus would turn into a space
+            val = [quote_plus(lt) for lt in val]
             qdic[key] = [LOSSID[k] for k in val]
             qdic['lt'] = val
         else:
@@ -793,8 +799,11 @@ def extract_agg_curves(dstore, what):
     if qdic['absolute'] == [1]:
         pass
     elif qdic['absolute'] == [0]:
-        evalue, = dstore['agg_values'][agg_id][lts]
-        arr /= evalue
+        evalue_sum = 0
+        for lts_item in lts:
+            for lt in lts_item.split('+'):
+                evalue_sum += dstore['agg_values'][agg_id][lt]
+        arr /= evalue_sum
     else:
         raise ValueError('"absolute" must be 0 or 1 in %s' % what)
     attrs = dict(shape_descr=['kind', 'return_period', 'ep_field'] + tagnames)

openquake/calculators/post_risk.py

@@ -35,6 +35,7 @@
 U16 = numpy.uint16
 U32 = numpy.uint32
 
+
 class FakeBuilder:
     eff_time = 0.
     pla_factor = None
@@ -305,7 +306,7 @@ def store_aggcurves(oq, agg_ids, rbe_df, builder, loss_cols,
                      limit_states=' '.join(oq.limit_states),
                      units=units, ep_fields=ep_fields)
 
-    
+
 # aggcurves are built in parallel, aggrisk sequentially
 def build_store_agg(dstore, oq, rbe_df, num_events):
     """
@@ -385,10 +386,11 @@ def build_store_agg(dstore, oq, rbe_df, num_events):
                         agg * tr if oq.investigation_time else agg/ne)
     fix_dtypes(acc)
     aggrisk = pandas.DataFrame(acc)
-    dstore.create_df('aggrisk', aggrisk, limit_states=' '.join(oq.limit_states))
+    dstore.create_df('aggrisk', aggrisk,
+                     limit_states=' '.join(oq.limit_states))
     if oq.investigation_time and loss_cols:
-        store_aggcurves(
-            oq, agg_ids, rbe_df, builder, loss_cols, events, num_events, dstore)
+        store_aggcurves(oq, agg_ids, rbe_df, builder, loss_cols, events,
+                        num_events, dstore)
     return aggrisk