fix

bin/inference/pycbc_inference_model_stats

@@ -96,7 +96,10 @@ model.sampling_transforms = None
 def callmodel(arg):
     iteration, paramvals = arg
     # calculate the logposterior to get all stats populated
-    model.update(**{p: paramvals[p] for p in model.variable_params})
+    try:
+        model.update_all_models(**{p: paramvals[p] for p in model.variable_params})
+    except:
+        model.update(**{p: paramvals[p] for p in model.variable_params})
     _ = model.logposterior
     stats = model.get_current_stats()
 

pycbc/inference/models/hierarchical.py

@@ -686,6 +686,9 @@ def total_loglr(self):
         self.primary_model.return_sh_hh = True
         sh_primary, hh_primary = self.primary_model.loglr
         self.primary_model.return_sh_hh = False
+        # set logr, otherwise it will store (sh, hh)
+        setattr(self.primary_model._current_stats, 'loglr',
+                self.primary_model.marginalize_loglr(sh_primary, hh_primary))
 
         margin_names_vector = list(
             self.primary_model.marginalize_vector_params.keys())
@@ -786,6 +789,7 @@ def total_loglr(self):
         print("hh_total: ", hh_total)
 
         loglr = self.primary_model.marginalize_loglr(sh_total, hh_total)
+        setattr(self._current_stats, 'total_loglr', loglr)
 
         return loglr
 

pycbc/inference/models/relbin.py

@@ -596,10 +596,11 @@ def _loglr(self):
             filt += filter_i
             norm += norm_i
 
+        loglr = self.marginalize_loglr(filt, norm)
         if self.return_sh_hh:
             results = (filt, norm)
         else:
-            results = self.marginalize_loglr(filt, norm)
+            results = loglr
         return results
 
     def write_metadata(self, fp, group=None):