Resolve "Introduce conditional prior sets"

Closes #270 (closed)

PP test show that the new capabilities do not interfere with the results we are getting so far. This test is done without any conditional priors, it just shows that everything still works as expected.

https://ldas-jobs.ligo.caltech.edu/~moritz.huebner/conditional_prior_pp/outdir_pp_test_4s/pp.png

bilby/core/sampler/base_sampler.py

@@ -5,7 +5,7 @@ import numpy as np
 from pandas import DataFrame
 
 from ..utils import logger, command_line_args, Counter
-from ..prior import Prior, PriorDict, DeltaFunction, Constraint
+from ..prior import Prior, PriorDict, ConditionalPriorDict, DeltaFunction, Constraint
 from ..result import Result, read_in_result
 
 
@@ -251,13 +251,19 @@ class Sampler(object):
         AttributeError
             prior can't be sampled.
         """
-        for key in self.priors:
-            if isinstance(self.priors[key], Constraint):
-                continue
+        if isinstance(self.priors, ConditionalPriorDict):
             try:
-                self.likelihood.parameters[key] = self.priors[key].sample()
+                self.likelihood.parameters = self.priors.sample()
             except AttributeError as e:
-                logger.warning('Cannot sample from {}, {}'.format(key, e))
+                logger.warning('Cannot sample from prior, {}'.format(e))
+        else:
+            for key in self.priors:
+                if isinstance(self.priors[key], Constraint):
+                    continue
+                try:
+                    self.likelihood.parameters[key] = self.priors[key].sample()
+                except AttributeError as e:
+                    logger.warning('Cannot sample from {}, {}'.format(key, e))
 
     def _verify_parameters(self):
         """ Evaluate a set of parameters drawn from the prior
@@ -276,9 +282,13 @@ class Sampler(object):
                 "Your sampling set contains redundant parameters.")
 
         self._check_if_priors_can_be_sampled()
-        try:
+        if isinstance(self.priors, ConditionalPriorDict):
+            theta = self.priors.sample()
+            theta = [theta[key] for key in self._search_parameter_keys]
+        else:
             theta = [self.priors[key].sample()
                      for key in self._search_parameter_keys]
+        try:
             self.log_likelihood(theta)
         except TypeError as e:
             raise TypeError(
@@ -298,8 +308,12 @@ class Sampler(object):
 
         t1 = datetime.datetime.now()
         for _ in range(n_evaluations):
-            theta = [self.priors[key].sample()
-                     for key in self._search_parameter_keys]
+            if isinstance(self.priors, ConditionalPriorDict):
+                theta = self.priors.sample()
+                theta = list(theta.values())
+            else:
+                theta = [self.priors[key].sample()
+                         for key in self._search_parameter_keys]
             self.log_likelihood(theta)
         total_time = (datetime.datetime.now() - t1).total_seconds()
         self._log_likelihood_eval_time = total_time / n_evaluations