fix

bin/inference/pycbc_inference

@@ -141,7 +141,12 @@ with ctx:
     if pool.is_main_process():
         for fn in [sampler.checkpoint_file, sampler.backup_file]:
             with loadfile(fn, 'a') as fp:
-                fp.write_config_file(cp)
+                # some models will interally modify original cp for sampling,
+                # such as joint_primary_marginalized, we need to save original
+                if hasattr(model, 'original_config'):
+                    fp.write_config_file(model.original_config)
+                else:
+                    fp.write_config_file(cp)
 
     # Run the sampler
     sampler.run()

pycbc/inference/models/hierarchical.py

@@ -693,6 +693,8 @@ def total_loglr(self):
             margin_names_vector.remove('logw_partial')
         margin_params = {}
 
+        print("self.primary_model.current_params: ", self.primary_model.current_params)
+
         if self.static_margin_params_in_other_models:
             # Due to the high precision of extrinsic parameters constrined
             # by the primary model, the mismatch of wavefroms in others by
@@ -704,6 +706,7 @@ def total_loglr(self):
             # waveform. Using SNR will cancel out the effect of amplitude.err
             i_max_extrinsic = numpy.argmax(
                 numpy.abs(sh_primary) / hh_primary**0.5)
+            print("i_max_extrinsic: ", i_max_extrinsic)
             for p in margin_names_vector:
                 if isinstance(self.primary_model.current_params[p],
                               numpy.ndarray):
@@ -740,12 +743,14 @@ def total_loglr(self):
             # not using self.primary_model.current_params, because others_model
             # may have its own static parameters
             current_params_other = other_model.current_params.copy()
+            print("[total_loglr] current_params_other 1: ", current_params_other)
             if not self.static_margin_params_in_other_models:
                 for i in range(nums):
                     current_params_other.update(
                         {key: value[i] if isinstance(value, numpy.ndarray)
                          else value for key, value in margin_params.items()})
                     other_model.update(**current_params_other)
+                    print("[total_loglr] current_params_other 2: ", current_params_other)
                     other_model.return_sh_hh = True
                     sh_other, hh_other = other_model.loglr
                     sh_others[i] += sh_other
@@ -757,6 +762,7 @@ def total_loglr(self):
                     {key: value[0] if isinstance(value, numpy.ndarray)
                      else value for key, value in margin_params.items()})
                 other_model.update(**current_params_other)
+                print("other_model.current_params: ", other_model.current_params)
                 other_model.return_sh_hh = True
                 sh_other, hh_other = other_model.loglr
                 other_model.return_sh_hh = False
@@ -772,6 +778,13 @@ def total_loglr(self):
         sh_total = sh_primary + sh_others
         hh_total = hh_primary + hh_others
 
+        print("sh_primary: ", sh_primary)
+        print("hh_primary: ", hh_primary)
+        print("sh_others: ", sh_others)
+        print("hh_others: ", hh_others)
+        print("sh_total: ", sh_total)
+        print("hh_total: ", hh_total)
+
         loglr = self.primary_model.marginalize_loglr(sh_total, hh_total)
 
         return loglr
@@ -828,8 +841,9 @@ def from_config(cls, cp, **kwargs):
         # we need the read from config function from the init; to prevent
         # circular imports, we import it here
         from pycbc.inference.models import read_from_config
-        # store the original config file
-        kwargs['original_config'] = cp
+        # store the original config file, here use deeocopy to avoid later
+        # changes of cp affect it
+        kwargs['original_config'] = cp.__deepcopy__(cp)
         # get the submodels
         kwargs['primary_lbl'] = shlex.split(cp.get('model', 'primary_model'))
         kwargs['others_lbls'] = shlex.split(cp.get('model', 'other_models'))
@@ -839,9 +853,7 @@ def from_config(cls, cp, **kwargs):
                                        submodel_lbls))
         sparam_map = map_params(hpiter(cp.options('static_params'),
                                        submodel_lbls))
-        print("cp: ", cp)
-        print("vparam_map: ", vparam_map)
-        print("sparam_map: ", sparam_map)
+
         # get the acceleration label
         kwargs['static_margin_params_in_other_models'] = shlex.split(
             cp.get('model', 'static_margin_params_in_other_models'))
@@ -893,7 +905,6 @@ def from_config(cls, cp, **kwargs):
             # set the static params
             subcp.add_section('static_params')
             for param in sparam_map[lbl]:
-                print("[set the static params] param: ", param)
                 subcp.set('static_params', param.subname,
                           cp.get('static_params', param.fullname))
 
@@ -907,7 +918,6 @@ def from_config(cls, cp, **kwargs):
             subcp.add_section('variable_params')
             for param in vparam_map[lbl]:
                 if lbl in kwargs['primary_lbl']:
-                    print("[set the variable params] param: ", param)
                     # set variable_params for the primary model
                     subcp.set('variable_params', param.subname,
                               cp.get('variable_params', param.fullname))
@@ -971,12 +981,10 @@ def from_config(cls, cp, **kwargs):
             marginalized_params.append('distance')
         if primary_model.marginalize_phase:
             marginalized_params.append('coa_phase')
-        print("marginalized_params: ", marginalized_params)
 
         for p in primary_model.static_params.keys():
             p_full = '%s__%s' % (kwargs['primary_lbl'][0], p)
             if p_full not in cp['static_params']:
-                print("p_full: ", p_full)
                 cp['static_params'][p_full] = "%s" % \
                     primary_model.static_params[p]