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/prior/base.py

@@ -63,21 +63,52 @@ class Prior(object):
         return self.sample()
 
     def __eq__(self, other):
+        """
+        Test equality of two prior objects.
+
+        Returns true iff:
+
+        - The class of the two priors are the same
+        - Both priors have the same keys in the __dict__ attribute
+        - The instantiation arguments match
+
+        We don't check that all entries the the __dict__ attribute
+        are equal as some attributes are variable for conditional
+        priors.
+
+        Parameters
+        ==========
+        other: Prior
+            The prior to compare with
+
+        Returns
+        =======
+        bool
+            Whether the priors are equivalent
+
+        Notes
+        =====
+        A special case is made for :code `scipy.stats.beta`: instances.
+        It may be possible to remove this as we now only check instantiation
+        arguments.
+
+        """
         if self.__class__ != other.__class__:
             return False
         if sorted(self.__dict__.keys()) != sorted(other.__dict__.keys()):
             return False
-        for key in self.__dict__:
+        this_dict = self.get_instantiation_dict()
+        other_dict = other.get_instantiation_dict()
+        for key in this_dict:
             if key == "least_recently_sampled":
-                # ignore sample drawn from prior in comparison
                 continue
-            if type(self.__dict__[key]) is np.ndarray:
-                if not np.array_equal(self.__dict__[key], other.__dict__[key]):
+            if isinstance(this_dict[key], np.ndarray):
+                if not np.array_equal(this_dict[key], other_dict[key]):
                     return False
-            elif isinstance(self.__dict__[key], type(scipy.stats.beta(1., 1.))):
+            elif isinstance(this_dict[key], type(scipy.stats.beta(1., 1.))):
                 continue
             else:
-                if not self.__dict__[key] == other.__dict__[key]:
+                if not this_dict[key] == other_dict[key]:
                     return False
         return True