Moritz Huebner: Removed the property and created a function to initialise the result

tupak/sampler.py

@@ -59,18 +59,7 @@ class Sampler(object):
         if os.path.isdir(outdir) is False:
             os.makedirs(outdir)
 
-    @property
-    def result(self):
-        self.__result = Result()
-        self.__result.search_parameter_keys = self.__search_parameter_keys
-        self.__result.fixed_parameter_keys = self.__fixed_parameter_keys
-        self.__result.parameter_labels = [
-            self.priors[k].latex_label for k in
-            self.__search_parameter_keys]
-        self.__result.label = self.label
-        self.__result.outdir = self.outdir
-        self.__result.priors = self.priors
-        self.__result.kwargs = self.kwargs
+        self.result = self.initialise_result()
 
     @property
     def search_parameter_keys(self):
@@ -117,7 +106,7 @@ class Sampler(object):
             if user_input not in args:
                 logging.warning(
                     "Supplied argument '{}' not an argument of '{}', removing."
-                    .format(user_input, self.external_sampler_function))
+                        .format(user_input, self.external_sampler_function))
                 bad_keys.append(user_input)
         for key in bad_keys:
             self.kwargs.pop(key)
@@ -140,6 +129,19 @@ class Sampler(object):
         for key in self.__fixed_parameter_keys:
             logging.info('  {} = {}'.format(key, self.priors[key].peak))
 
+    def initialise_result(self):
+        result = Result()
+        result.search_parameter_keys = self.__search_parameter_keys
+        result.fixed_parameter_keys = self.__fixed_parameter_keys
+        result.parameter_labels = [
+            self.priors[k].latex_label for k in
+            self.__search_parameter_keys]
+        result.label = self.label
+        result.outdir = self.outdir
+        result.priors = self.priors
+        result.kwargs = self.kwargs
+        return result
+
     def verify_parameters(self):
         required_keys = self.priors
         unmatched_keys = [r for r in required_keys if r not in self.likelihood.parameters]
@@ -153,7 +155,7 @@ class Sampler(object):
     def log_prior(self, theta):
         return np.sum(
             [np.log(self.priors[key].prob(t)) for key, t in
-                zip(self.__search_parameter_keys, theta)])
+             zip(self.__search_parameter_keys, theta)])
 
     def log_likelihood(self, theta):
         for i, k in enumerate(self.__search_parameter_keys):
@@ -174,7 +176,7 @@ class Sampler(object):
         """
 
         draw = np.array([self.priors[key].sample()
-                        for key in self.__search_parameter_keys])
+                         for key in self.__search_parameter_keys])
         if np.isinf(self.log_likelihood(draw)):
             logging.info('Prior draw {} has inf likelihood'.format(draw))
         if np.isinf(self.log_prior(draw)):