Correct computation of mf_snr and logL at the end of the trajectory

Codes/main.py

@@ -507,6 +507,11 @@ if __name__ == '__main__':
     snr_dict = likelihood.calculate_network_snrs()
     snr_dict_formatted = cut.dictionary_to_formatted_string(snr_dict, decimal_format=2)
     cut.logger.info(f'SNR at starting point: {snr_dict_formatted}')
+    # Set the first value of the snr of the chain.
+    if likelihood.phase_marginalization:
+        mf_snr = snr_dict['network']['matched_filter_snr_phasemarg']
+    else:
+        mf_snr = snr_dict['network']['matched_filter_snr']
 
 
 
@@ -593,16 +598,17 @@ if __name__ == '__main__':
                     count += 1
                     traj_status = "** ACCEPTED **"
                     fnr.update_train_data(qpos_traj_phase1, dlogL_traj_phase1, qpos_from_traj, dlogL_from_traj)
+
+                    # Very circonvoluted way of setting the matched filter snr value of the new point in the chain. If trajectory was accepted as here, we use the last value computed of the trajectory and otherwise don't modify mf_snr which will keep the previous value.
+                    if sampler.likelihood.phase_marginalization:
+                        mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr_phasemarg']
+                    else:
+                        mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr']
                 Acc = count/traj_index
 
         		# Print the position of the chain
                 sampler.samples.append(q_pos.tolist())
-                if config_dict['analysis']['phase_marginalization']:
-                    mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr_phasemarg']
-                else:
-                    mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr']
-                logL2 = sampler.likelihood.snr_dict['network']['log_l_ratio'] # equal to logL?
-                sampler.chain_metrics.append([mf_snr, logL2, Acc, Accept])
+                sampler.chain_metrics.append([mf_snr, logL, Acc, Accept])
                 idx_Mc = sampler.search_parameter_keys.index('chirp_mass')
                 idx_mu = sampler.search_parameter_keys.index('reduced_mass')
                 eta = paru.Mc_mu_to_eta(q_pos[idx_Mc], q_pos[idx_mu])
@@ -614,7 +620,7 @@ if __name__ == '__main__':
                 flag_strange_proba = ''
                 if abs(proba_acc - 1) > 0.35:
                     flag_strange_proba = ' - ??'
-                cut.logger.info(f'{traj_index:3} - {traj_status} - Acc = {Acc:5.1%} - {a:1.5f} {sup_or_inf} {proba_acc:1.5f}{flag_strange_proba} - mf_snr = {sampler.likelihood_gradient.matched_filter_snr_at_point:1.3f} - eta = {eta:.6f}')
+                cut.logger.info(f'{traj_index:3} - {traj_status} - Acc = {Acc:5.1%} - {a:1.5f} {sup_or_inf} {proba_acc:1.5f}{flag_strange_proba} - mf_snr = {mf_snr:1.3f} - eta = {eta:.6f}')
 
                 if False:
                 # if sampler.n_traj_fit >= 100 and traj_index%int(sampler.n_traj_fit/4)==0:
@@ -1340,12 +1346,12 @@ if __name__ == '__main__':
         		# Record the position of the chain
                 sampler.samples.append(q_pos.tolist())
                 # import IPython; IPython.embed();sys.exit()
-                if config_dict['analysis']['phase_marginalization']:
-                    mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr_phasemarg']
-                else:
-                    mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr']
-                logL2 = sampler.likelihood.snr_dict['network']['log_l_ratio'] # equal to logL
-                sampler.chain_metrics.append([mf_snr, logL2, Acc_phase3, Accept])
+                if Accept == 1:
+                    if sampler.likelihood.phase_marginalization:
+                        mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr_phasemarg']
+                    else:
+                        mf_snr = sampler.likelihood.snr_dict['network']['matched_filter_snr']
+                sampler.chain_metrics.append([mf_snr, logL, Acc_phase3, Accept])
 
                 # Print position of the chain
                 # if traj_index%int(sampler.n_traj_for_this_run/20000)==0: