Resolve "Allow results to be returned as an ArviZ InferenceData object"

Add the ability for the Result object to return the posterior in an ArviZ InferenceData object.

There shouldn't be anything else to add, but I will need another MR (!773 (merged)) to add arviz to the test docker images.

Resolves #267 (closed).

bilby/core/result.py

@@ -22,7 +22,7 @@ from .utils import (
     check_directory_exists_and_if_not_mkdir,
     latex_plot_format, safe_save_figure,
 )
-from .utils import BilbyJsonEncoder, decode_bilby_json
+from .utils import BilbyJsonEncoder, decode_bilby_json, get_version_information
 from .prior import Prior, PriorDict, DeltaFunction
 
 
@@ -1254,7 +1254,7 @@ class Result(object):
             return self._kde
 
     def posterior_probability(self, sample):
-        """ Calculate the posterior probabily for a new sample
+        """ Calculate the posterior probability for a new sample
 
         This queries a Kernel Density Estimate of the posterior to calculate
         the posterior probability density for the new sample.
@@ -1332,6 +1332,75 @@ class Result(object):
 
         return weights
 
+    def to_arviz(self, prior=None):
+        """ Convert the Result object to an ArviZ InferenceData object.
+
+            Parameters
+            ----------
+            prior: int
+                If a positive integer is given then that number of prior
+                samples will be drawn and stored in the ArviZ InferenceData
+                object.
+
+            Returns
+            -------
+            azdata: InferenceData
+                The ArviZ InferenceData object.
+        """
+
+        try:
+            import arviz as az
+        except ImportError:
+            logger.debug(
+                "ArviZ is not installed, so cannot convert to InferenceData"
+            )
+
+        posdict = {}
+        for key in self.posterior:
+            posdict[key] = self.posterior[key].values
+
+        if "log_likelihood" in posdict:
+            loglikedict = {
+                "log_likelihood": posdict.pop("log_likelihood")
+            }
+        else:
+            if self.log_likelihood_evaluations is not None:
+                loglikedict = {
+                    "log_likelihood": self.log_likelihood_evaluations
+                }
+            else:
+                loglikedict = None
+
+        priorsamples = None
+        if prior is not None:
+            if self.priors is None:
+                logger.warning(
+                    "No priors are in the Result object, so prior samples "
+                    "will not be included in the output."
+                )
+            else:
+                priorsamples = self.priors.sample(size=prior)
+
+        azdata = az.from_dict(
+            posterior=posdict,
+            log_likelihood=loglikedict,
+            prior=priorsamples,
+        )
+
+        # add attributes
+        version = {
+            "inference_library": "bilby: {}".format(self.sampler),
+            "inference_library_version": get_version_information()
+        }
+
+        azdata.posterior.attrs.update(version)
+        if "log_likelihood" in azdata._groups:
+            azdata.log_likelihood.attrs.update(version)
+        if "prior" in azdata._groups:
+            azdata.prior.attrs.update(version)
+
+        return azdata
+
 
 class ResultList(list):