add tensor field to output

README.md

@@ -44,12 +44,15 @@ from SimCAD import configs
 exec_mode = ExecutionMode()
 
 
+exec_mode = ExecutionMode()
+
+
 print("Simulation Execution 1")
 print()
 first_config = [configs[0]] # from config1
 single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
 run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
-run1_raw_result, tensor_field  = run1.main()
+run1_raw_result, tensor_field = run1.main()
 result = pd.DataFrame(run1_raw_result)
 # result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
 print()

SimCAD/engine/__init__.py

@@ -1,5 +1,4 @@
 from pathos.multiprocessing import ProcessingPool as Pool
-from tabulate import tabulate
 
 from SimCAD.utils import flatten
 from SimCAD.configuration import Processor
@@ -12,8 +11,8 @@ class ExecutionMode:
     multi_proc = 'multi_proc'
 
 
+# ToDo: switch / rename self.name & context ??
 class ExecutionContext:
-
     def __init__(self, context=ExecutionMode.multi_proc):
         self.name = context
         self.method = None
@@ -37,7 +36,6 @@ class ExecutionContext:
 
 
 class Executor:
-
     def __init__(self, exec_context, configs):
         self.SimExecutor = SimExecutor
         self.exec_method = exec_context.method
@@ -72,7 +70,7 @@ class Executor:
         if self.exec_context == ExecutionMode.single_proc:
             tensor_field = create_tensor_field(mechanisms.pop(), eps.pop())
             result = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
-            return (result, tensor_field)
+            return result, tensor_field
         elif self.exec_context == ExecutionMode.multi_proc:
             if len(self.configs) > 1:
                 simulations = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)

SimCAD/engine/simulation.py

@@ -27,7 +27,7 @@ class Executor:
         for state in state_dict.keys():
             if state in list(env_processes.keys()):
                 env_state = env_processes[state]
-                if env_state.__name__ == '_curried': # might want to change
+                if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'): # might want to change
                     state_dict[state] = env_state(step)(state_dict[state])
                 else:
                     state_dict[state] = env_state(state_dict[state])

simulations/sim_test.py

@@ -20,7 +20,7 @@ print()
 first_config = [configs[0]] # from config1
 single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
 run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
-run1_raw_result, tensor_field  = run1.main()
+run1_raw_result, tensor_field = run1.main()
 result = pd.DataFrame(run1_raw_result)
 # result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
 print()

simulations/validation/config1.py

@@ -84,7 +84,7 @@ def es5p2(step, sL, s, _input):
 
 # Environment States
 def env_a(x):
-    return 10
+    return 5
 def env_b(x):
     return 10
 # def what_ever(x):
@@ -108,9 +108,10 @@ exogenous_states = exo_update_per_ts(
     }
 )
 
-#	make env proc trigger field agnostic
+# ToDo: make env proc trigger field agnostic
+# ToDo: input json into function renaming __name__
 env_processes = {
-    "s3": proc_trigger('2018-10-01 15:16:25', env_a),
+    "s3": env_a,
     "s4": proc_trigger('2018-10-01 15:16:25', env_b)
 }
 
@@ -126,6 +127,7 @@ env_processes = {
 
 # need at least 1 behaviour and 1 state function for the 1st mech with behaviors
 # mechanisms = {}
+
 mechanisms = {
     "m1": {
         "behaviors": {