Ensure all objects sent to requests are json serializable using int.

boptestGymEnv.py

@@ -471,11 +471,11 @@ def find_start_time():
 
         # Initialize the building simulation
         res = requests.put('{0}/initialize'.format(self.url), 
-                           json={'start_time':self.start_time,
-                                 'warmup_period':self.warmup_period}).json()['payload']
+                           json={'start_time':int(self.start_time),
+                                 'warmup_period':int(self.warmup_period)}).json()['payload']
 
         # Set simulation step
-        requests.put('{0}/step'.format(self.url), json={'step':self.step_period})
+        requests.put('{0}/step'.format(self.url), json={'step':int(self.step_period)})
 
         # Set BOPTEST scenario
         requests.put('{0}/scenario'.format(self.url), json=self.scenario)
@@ -718,8 +718,8 @@ def get_observations(self, res):
             for var in self.regressive_vars:
                 res_var = requests.put('{0}/results'.format(self.url), 
                                        json={'point_names':[var],
-                                             'start_time':regr_index[-1], 
-                                             'final_time':regr_index[0]}).json()['payload']
+                                             'start_time':int(regr_index[-1]), 
+                                             'final_time':int(regr_index[0])}).json()['payload']
                 # fill_value='extrapolate' is needed for the very few cases when
                 # res_var['time'] is not returned to be exactly between 
                 # regr_index[-1] and regr_index[0] but shorter. In these cases
@@ -734,8 +734,8 @@ def get_observations(self, res):
         if self.is_predictive:
             predictions = requests.put('{0}/forecast'.format(self.url), 
                                        json={'point_names': self.predictive_vars,
-                                             'horizon':     self.predictive_period,
-                                             'interval':    self.step_period}).json()['payload']
+                                             'horizon':     int(self.predictive_period),
+                                             'interval':    int(self.step_period)}).json()['payload']
             for var in self.predictive_vars:
                 for i in range(self.pred_n):
                     observations.append(predictions[var][i])