Merge branch '194-plot-multiple-overwrites-labels-with-evidences' into 'master'

Resolve "Plot multiple overwrites labels with evidences"

Closes #194

See merge request Monash/tupak!204

CHANGELOG.md

@@ -31,6 +31,7 @@ Changes currently on master, but not under a tag.
 - Modified how sampling in non-standard parameters is done, the
   `non_standard_sampling_parameter_keys` kwarg has been removed
 - .prior files are no longer created. The prior is stored in the result object.
+- Fix label creation in plot_multiple, evidences and repeated plots.
 - Changed the way repr works for priors. The repr can now be used to
 re-instantiate the Prior in most cases
 - Users can now choose to overwrite existing result files, rather than creating

tupak/core/result.py

@@ -701,11 +701,12 @@ def plot_multiple(results, filename=None, labels=None, colours=None,
 
     if evidences:
         if np.isnan(results[0].log_bayes_factor):
-            template = ' $\mathrm{{ln}}(Z)={:1.3g}$'
+            template = ' $\mathrm{{ln}}(Z)={lnz:1.3g}$'
         else:
-            template = ' $\mathrm{{ln}}(B)={:1.3g}$'
-        for i, label in enumerate(labels):
-            labels[i] = label + template.format(results[i].log_bayes_factor)
+            template = ' $\mathrm{{ln}}(B)={lnbf:1.3g}$'
+        labels = [template.format(lnz=result.log_evidence,
+                                  lnbf=result.log_bayes_factor)
+                  for ii, result in enumerate(results)]
 
     axes = fig.get_axes()
     ndim = int(np.sqrt(len(axes)))

tupak/core/sampler/__init__.py

@@ -157,10 +157,9 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         result.log_evidence = \
             result.log_bayes_factor + result.log_noise_evidence
     else:
-        if likelihood.noise_log_likelihood() is not np.nan:
-            result.log_noise_evidence = likelihood.noise_log_likelihood()
-            result.log_bayes_factor = \
-                result.log_evidence - result.log_noise_evidence
+        result.log_noise_evidence = likelihood.noise_log_likelihood()
+        result.log_bayes_factor = \
+            result.log_evidence - result.log_noise_evidence
     if injection_parameters is not None:
         result.injection_parameters = injection_parameters
         if conversion_function is not None: