Proposed rwalk change

• Don't add the initial point to the chain (prevents bad estimated of the act)
• Break strictly after maxmcmc and return a random draw from the prior

Closes #477 (closed)

bilby/core/sampler/dynesty.py

@@ -688,7 +688,6 @@ def sample_rwalk_bilby(args):
     u_list = []
     v_list = []
     logl_list = []
-    max_walk_warning = True
 
     while len(u_list) < nact * act:
 
@@ -697,7 +696,7 @@ def sample_rwalk_bilby(args):
         drhat /= linalg.norm(drhat)
 
         # Scale based on dimensionality.
-        dr = drhat * rstate.rand()**(1. / n)
+        dr = drhat * rstate.rand() ** (1.0 / n)
 
         # Transform to proposal distribution.
         du = np.dot(axes, dr)
@@ -749,15 +748,11 @@ def sample_rwalk_bilby(args):
 
         # If we've taken too many likelihood evaluations then break
         if accept + reject > maxmcmc:
-            if max_walk_warning:
-                warnings.warn(
-                    "Hit maximum number of walks {} with accept={}, reject={}, "
-                    "and nfail={} try increasing maxmcmc"
-                    .format(maxmcmc, accept, reject, nfail))
-                max_walk_warning = False
-            if accept > 0:
-                # Break if we are above maxmcmc and have at least one accepted point
-                break
+            warnings.warn(
+                "Hit maximum number of walks {} with accept={}, reject={}, "
+                "and nfail={} try increasing maxmcmc"
+                .format(maxmcmc, accept, reject, nfail))
+            break
 
     # If the act is finite, pick randomly from within the chain
     if np.isfinite(act) and int(.5 * nact * act) < len(u_list):