update

pycbc/inference/models/base.py

@@ -504,12 +504,16 @@ def _trytoget(self, statname, fallback, apply_transforms=False, **kwargs):
             return getattr(self._current_stats, statname)
         except AttributeError:
             # apply waveform transforms if requested
+            print("[BaseModel _trytoget] apply_transforms: ", apply_transforms)
+            print("[BaseModel _trytoget] self.waveform_transforms: ", self.waveform_transforms)
+            print("[BaseModel _trytoget] self._current_params 1: ", self._current_params)
             if apply_transforms and self.waveform_transforms is not None:
                 self._current_params = transforms.apply_transforms(
                     self._current_params, self.waveform_transforms,
                     inverse=False)
             val = fallback(**kwargs)
             setattr(self._current_stats, statname, val)
+            print("[BaseModel _trytoget] self._current_params 2: ", self._current_params)
             return val
 
     @property
@@ -520,6 +524,7 @@ def loglikelihood(self):
         If that raises an ``AttributeError``, will call `_loglikelihood`` to
         calculate it and store it to ``current_stats``.
         """
+        print("[BaseModel] loglikelihood")
         return self._trytoget('loglikelihood', self._loglikelihood,
                               apply_transforms=True)
 
@@ -546,6 +551,7 @@ def logjacobian(self):
         float :
             The value of the jacobian.
         """
+        print("[BaseModel] logjacobian")
         return self._trytoget('logjacobian', self._logjacobian)
 
     def _logjacobian(self):
@@ -560,6 +566,7 @@ def _logjacobian(self):
     @property
     def logprior(self):
         """Returns the log prior at the current parameters."""
+        print("[BaseModel] logprior")
         return self._trytoget('logprior', self._logprior)
 
     def _logprior(self):

pycbc/inference/models/gaussian_noise.py

@@ -109,6 +109,8 @@ def __init__(self, variable_params, data, low_frequency_cutoff, psds=None,
                                                 static_params=static_params,
                                                 no_save_data=no_save_data,
                                                 **kwargs)
+        print("[BaseGaussianNoise __init__] self.sampling_transforms :", self.sampling_transforms)
+        print("[BaseGaussianNoise __init__] self.waveform_transforms :", self.waveform_transforms)
         self.ignore_failed_waveforms = ignore_failed_waveforms
         self.no_save_data = no_save_data
         # check if low frequency cutoff has been provided for every IFO with
@@ -536,6 +538,7 @@ def from_config(cls, cp, data_section='data', data=None, psds=None,
             All additional keyword arguments are passed to the class. Any
             provided keyword will override what is in the config file.
         """
+        print("[BaseGaussianNoise from_config] cp.sections() :", cp.sections())
         # get the injection file, to replace any FROM_INJECTION settings
         if 'injection-file' in cp.options('data'):
             injection_file = cp.get('data', 'injection-file')
@@ -617,6 +620,7 @@ def from_config(cls, cp, data_section='data', data=None, psds=None,
         if gates:
             args['gates'] = gates
         args.update(kwargs)
+        print("[BaseGaussianNoise from_config] args: ", args)
         return cls(**args)
 
 

pycbc/inference/models/hierarchical.py

@@ -864,6 +864,7 @@ def from_config(cls, cp, **kwargs):
         submodels = {}
         logging.info("Loading submodels")
         for lbl in submodel_lbls:
+            print("lbl: ", lbl)
             logging.info("============= %s =============", lbl)
             # create a config parser to pass to the model
             subcp = WorkflowConfigParser()
@@ -887,6 +888,7 @@ def from_config(cls, cp, **kwargs):
                                      "individual parameter names with the "
                                      "model label. See HierarchicalParam for "
                                      "details.".format(sec))
+                print("[from_config] sec.subname: ", sec.subname)
                 subcp.add_section(sec.subname)
                 for opt, val in cp.items(sec):
                     subcp.set(sec.subname, opt, val)
@@ -915,21 +917,10 @@ def from_config(cls, cp, **kwargs):
                     subcp.set('static_params', param.subname, 'REPLACE')
 
             for section in cp.sections():
-                print("[from_config] section: ", section)
-                print("[from_config] '%s__waveform_transforms' % lbl: ", '%s__waveform_transforms' % lbl)
-                print("[from_config] '%s__waveform_transforms' % lbl in section: ", '%s__waveform_transforms' % lbl in section)
                 # the primary model needs prior of marginlized parameters
                 if 'prior-' in section and lbl in kwargs['primary_lbl']:
                     prior_section = '%s' % section
                     subcp[prior_section] = cp[prior_section]
-                # if `waveform_transforms` has a prefix,
-                # add it into sub-models' config
-                elif '%s__waveform_transforms' % lbl in section:
-                    transforms_section = '%s' % section
-                    subcp[transforms_section] = cp[transforms_section]
-                    print("[from_config] subcp[transforms_section]: ", subcp[transforms_section])
-                else:
-                    pass
 
             # similar to the standard hierarchical model,
             # add the outputs from the waveform transforms if sub-model
@@ -938,6 +929,7 @@ def from_config(cls, cp, **kwargs):
                 for param in wfparam_map[lbl]:
                     subcp.set('static_params', param.subname, 'REPLACE')
 
+            print("[from_config] subcp.sections(): ", subcp.sections())
             # save the vitual config file to disk for later check
             with open('%s.ini' % lbl, 'w', encoding='utf-8') as file:
                 subcp.write(file)

pycbc/inference/models/relbin.py

@@ -427,6 +427,7 @@ def get_waveforms(self, params):
         """
         if self.still_needs_det_response:
             wfs = {}
+            print("[relbin] params: ", params)
             for ifo in self.data:
                 wfs.update(get_fd_det_waveform_sequence(
                         ifos=ifo, sample_points=self.fedges[ifo], **params))