Allow sampling in aligned spin and spin magnitude

This MR will allow us to sample in the spin magnitude and the aligned component of the spin, rather than magnitude and tilt.

The reason to do this is that the aligned component of the spin is the best measured per component spin parameter. From preliminary tests, it looks like this will improve convergence with at least MultiNest.

Currently, this will only support the vanilla flat in magnitude, isotropic in orientation prior. It would take more effort to figure out the generic case.

This required making the CBCPriorDict a subclass of the ConditionalPriorDict, I know that people have reported seeing issues with this in the past, but I don't remember what they were. @moritz.huebner @rory-smith?

EDIT I think the issue with the conditional priors was the _check_if_prior_can_be_sampled method which I have removed. That was falling over for some conditions/constraints. I think the current version should be suitably robust.

bilby/core/sampler/base_sampler.py

@@ -251,22 +251,6 @@ class Sampler(object):
 
         return result
 
-    def _check_if_priors_can_be_sampled(self):
-        """Check if all priors can be sampled properly.
-
-        Raises
-        ------
-        AttributeError
-            prior can't be sampled.
-        """
-        for key in self.priors:
-            if isinstance(self.priors[key], Constraint):
-                continue
-            try:
-                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
 
@@ -323,7 +307,13 @@ class Sampler(object):
         Checks if use_ratio is set. Prints a warning if use_ratio is set but
         not properly implemented.
         """
-        self._check_if_priors_can_be_sampled()
+        try:
+            self.priors.sample_subset(self.search_parameter_keys)
+        except (KeyError, AttributeError):
+            logger.error("Cannot sample from priors with keys: {}.".format(
+                self.search_parameter_keys
+            ))
+            raise
         if self.use_ratio is False:
             logger.debug("use_ratio set to False")
             return