Minor polishing to MCMC searches

- Adds chains to saved data
- Add catch for when corner plots error

pyfstat/mcmc_based_searches.py

@@ -492,6 +492,7 @@ class MCMCSearch(core.BaseSearchClass):
             self.lnprobs = d['lnprobs']
             self.lnlikes = d['lnlikes']
             self.all_lnlikelihood = d['all_lnlikelihood']
+            self.chain = d['chain']
             return
 
         self._initiate_search_object()
@@ -533,21 +534,27 @@ class MCMCSearch(core.BaseSearchClass):
         logging.info('Running final burn and prod with {} steps'.format(
             nburn+nprod))
         sampler = self._run_sampler(sampler, p0, nburn=nburn, nprod=nprod)
+
         if create_plots:
-            fig, axes = self._plot_walkers(sampler, nprod=nprod, **kwargs)
-            fig.tight_layout()
-            fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label),
-                        )
+            try:
+                fig, axes = self._plot_walkers(sampler, nprod=nprod, **kwargs)
+                fig.tight_layout()
+                fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label))
+            except RuntimeError as e:
+                logging.warning("Failed to save walker plots due to Erro {}"
+                                .format(e))
 
         samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
         lnprobs = sampler.logprobability[0, :, nburn:].reshape((-1))
         lnlikes = sampler.loglikelihood[0, :, nburn:].reshape((-1))
         all_lnlikelihood = sampler.loglikelihood[:, :, nburn:]
         self.samples = samples
+        self.chain = sampler.chain
         self.lnprobs = lnprobs
         self.lnlikes = lnlikes
         self.all_lnlikelihood = all_lnlikelihood
-        self._save_data(sampler, samples, lnprobs, lnlikes, all_lnlikelihood)
+        self._save_data(sampler, samples, lnprobs, lnlikes, all_lnlikelihood,
+                        sampler.chain)
         return sampler
 
     def _get_rescale_multiplier_for_key(self, key):
@@ -1215,11 +1222,13 @@ class MCMCSearch(core.BaseSearchClass):
                  maxStartTime=self.maxStartTime)
         return d
 
-    def _save_data(self, sampler, samples, lnprobs, lnlikes, all_lnlikelihood):
+    def _save_data(self, sampler, samples, lnprobs, lnlikes, all_lnlikelihood, 
+                   chain):
         d = self._get_data_dictionary_to_save()
         d['samples'] = samples
         d['lnprobs'] = lnprobs
         d['lnlikes'] = lnlikes
+        d['chain'] = chain
         d['all_lnlikelihood'] = all_lnlikelihood
 
         if os.path.isfile(self.pickle_path):
@@ -1254,6 +1263,7 @@ class MCMCSearch(core.BaseSearchClass):
         old_d.pop('lnprobs')
         old_d.pop('lnlikes')
         old_d.pop('all_lnlikelihood')
+        old_d.pop('chain')
 
         for key in 'minStartTime', 'maxStartTime':
             if new_d[key] is None:
@@ -1616,7 +1626,7 @@ class MCMCGlitchSearch(MCMCSearch):
             'multiplier': 1/86400.,
             'subtractor': 'minStartTime',
             'unit': 'day',
-            'label': '$t^{g}_0$ \n [days]'}
+            'label': '$t^{g}_0$ \n [d]'}
             )
 
     @helper_functions.initializer
@@ -2108,6 +2118,7 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
             self.lnprobs = d['lnprobs']
             self.lnlikes = d['lnlikes']
             self.all_lnlikelihood = d['all_lnlikelihood']
+            self.chain = d['chain']
             self.nsegs = run_setup[-1][1]
             return