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

@@ -1956,18 +1956,12 @@ class CorrelatedUniform(Prior):
         Prior.__init__(self, name=name, latex_label=latex_label,
                        minimum=minimum, maximum=maximum, unit=unit)
         if not correlation_func:
-            self.correlation_func = lambda x, **y: x
+            def correlation_func(minimim, maximum, **correlated_variables):
+                return minimum, maximum
+            self.correlation_func = correlation_func
         else:
             self.correlation_func = correlation_func
 
-    def mean(self, **correlated_variables):
-        mean = (self.maximum + self.minimum)/2
-        return self.correlation_func(mean, **correlated_variables)
-
-    @property
-    def width(self):
-        return self.maximum - self.minimum
-
     @property
     def correlated_variables(self):
         from .utils import infer_parameters_from_function
@@ -1991,8 +1985,7 @@ class CorrelatedUniform(Prior):
 
     def rescale(self, val, **correlated_variables):
         Prior.test_valid_for_rescaling(val)
-        minimum = self.mean(**correlated_variables) - self.width/2
-        maximum = self.mean(**correlated_variables) + self.width/2
+        minimum, maximum = self.correlation_func(self.minimum, self.maximum, **correlated_variables)
         return minimum + val * (maximum - minimum)
 
     def prob(self, val, **correlated_variables):
@@ -2006,8 +1999,7 @@ class CorrelatedUniform(Prior):
         -------
         float: Prior probability of val
         """
-        minimum = self.mean(**correlated_variables) - self.width/2
-        maximum = self.mean(**correlated_variables) + self.width/2
+        minimum, maximum = self.correlation_func(self.minimum, self.maximum, **correlated_variables)
         return scipy.stats.uniform.pdf(val, loc=minimum,
                                        scale=maximum - minimum)
 
@@ -2022,7 +2014,6 @@ class CorrelatedUniform(Prior):
         -------
         float: log probability of val
         """
-        minimum = self.mean(**correlated_variables) - self.width/2
-        maximum = self.mean(**correlated_variables) + self.width/2
+        minimum, maximum = self.correlation_func(self.minimum, self.maximum, **correlated_variables)
         return scipy.stats.uniform.logpdf(val, loc=minimum,
                                           scale=maximum - minimum)