codacy

Author: christopher-wild

causal_testing/json_front/json_class.py

@@ -77,7 +77,7 @@ def set_variables(self, inputs: dict, outputs: dict, metas: dict, distributions:
                        inputs]
         self.outputs = [Output(i['name'], i['type']) for i in outputs]
         self.metas = [Meta(i['name'], i['type'], populates[i['populate']]) for i in
-                      metas] if metas else list()
+                      metas] if metas else []
 
     def setup(self):
         """ Function to populate all the necessary parts of the json_class needed to execute tests
@@ -134,7 +134,7 @@ def _json_parse(self):
             :param distributions: dictionary of user defined scipy distributions
             :param populates: dictionary of user defined populate functions
         """
-        with open(self.json_path) as f:
+        with open(self.json_path, encoding='UTF-8') as f:
             self.test_plan = json.load(f)
 
         self.data = pd.read_csv(self.data_path)

examples/poisson/run_causal_tests.py

@@ -138,7 +138,7 @@ def populate_num_shapes_unit(data):
 # Create input structure required to create a modelling scenario
 modelling_inputs = [Input(i['name'], i['type'], distributions[i['distribution']]) for i in inputs] + \
                    [Output(i['name'], i['type']) for i in outputs] + \
-                   [Meta(i['name'], i['type'], populates[i['populate']]) for i in metas] if metas else list()
+                   [Meta(i['name'], i['type'], populates[i['populate']]) for i in metas] if metas else []
 
 # Create modelling scenario to access z3 variable mirrors
 modelling_scenario = Scenario(modelling_inputs, None)