Allow ptemcee initialization with array

bilby/core/sampler/ptemcee.py

@@ -92,13 +92,15 @@ class Ptemcee(MCMCSampler):
         is not recommended for cases where tau is large.
     ignore_keys_for_tau: str
         A pattern used to ignore keys in estimating the autocorrelation time.
-    pos0: str, list ("prior")
+    pos0: str, list, np.ndarray
         If a string, one of "prior" or "minimize". For "prior", the initial
         positions of the sampler are drawn from the sampler. If "minimize",
         a scipy.optimize step is applied to all parameters a number of times.
         The walkers are then initialized from the range of values obtained.
         If a list, for the keys in the list the optimization step is applied,
-        otherwise the initial points are drawn from the prior.
+        otherwise the initial points are drawn from the prior. If a numpy array
+        the shape should be (ntemps, nwalkers, ndim).
+
     niterations_per_check: int (5)
         The number of iteration steps to take before checking ACT. This
         effectively pre-thins the chains. Larger values reduce the per-eval
@@ -363,6 +365,17 @@ class Ptemcee(MCMCSampler):
             )
         return pos0
 
+    def get_pos0_from_array(self):
+        if self.pos0.shape != (self.ntemps, self.nwalkers, self.ndim):
+            raise ValueError(
+                "Shape of starting array should be (ntemps, nwalkers, ndim). "
+                "In this case that is ({}, {}, {}), got {}".format(
+                    self.ntemps, self.nwalkers, self.ndim, self.pos0.shape
+                )
+            )
+        else:
+            return self.pos0
+
     def setup_sampler(self):
         """ Either initialize the sampler or read in the resume file """
         import ptemcee
@@ -446,6 +459,8 @@ class Ptemcee(MCMCSampler):
             return self.get_pos0_from_minimize()
         elif isinstance(self.pos0, list):
             return self.get_pos0_from_minimize(minimize_list=self.pos0)
+        elif isinstance(self.pos0, np.ndarray):
+            return self.get_pos0_from_array()
         else:
             raise SamplerError("pos0={} not implemented".format(self.pos0))