Add dynesty sample dump

This adds a dump of the current samples (downsampled using the effective sample size) to the standard write. As such, there is no need for a "convert resume" facility which was broken anyway: this closes #481 (closed).

bilby/core/result.py

@@ -158,8 +158,8 @@ def rejection_sample(posterior, weights):
 
     Parameters
     ----------
-    posterior: pd.DataFrame
-        The dataframe containing posterior samples
+    posterior: pd.DataFrame or np.ndarray of shape (nsamples, nparameters)
+        The dataframe or array containing posterior samples
     weights: np.ndarray
         An array of weights
 
@@ -170,7 +170,7 @@ def rejection_sample(posterior, weights):
 
     """
     keep = weights > np.random.uniform(0, max(weights), weights.shape)
-    return posterior.iloc[keep]
+    return posterior[keep]
 
 
 def reweight(result, label=None, new_likelihood=None, new_prior=None,