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/prior.py

@@ -602,15 +602,16 @@ class ConditionalPriorDict(PriorDict):
         list: List of floats containing the rescaled sample
         """
 
-        res = [_ for _ in range(len(theta))]
+        rescale_dict = {}
         conditional_keys, conditional_idxs, _ = np.intersect1d(keys, self.conditional_keys, return_indices=True)
         unconditional_keys, unconditional_idxs, _ = np.intersect1d(keys, self.unconditional_keys, return_indices=True)
-        all_keys = np.concatenate((conditional_keys, unconditional_keys))
-        all_indexes = np.concatenate((conditional_idxs, unconditional_idxs))
-        for key, index in zip(all_keys, all_indexes):
-            required_variables = self._get_required_variables(key)
-            res[index] = self[key].rescale(theta[index], **required_variables)
-        return res
+        for key, ind in zip(unconditional_keys, unconditional_idxs):
+            rescale_dict[key] = self[key].rescale(theta[ind])
+        for key, ind in zip(conditional_keys, conditional_idxs):
+            conditional_variables = dict([(k, rescale_dict[k]) for k in self[key].required_variables])
+            rescale_dict[key] = self[key].rescale(theta[ind], **conditional_variables)
+        ls = [rescale_dict[key] for key in keys]
+        return ls
 
     def get_subset_sorted_keys(self, subset_keys):
         subset_unconditional_keys = [key for key in self.unconditional_keys if key in subset_keys]