Merge remote-tracking branch 'origin/staging' into mz-dead

engine/mechanismExecutor.py

@@ -21,8 +21,12 @@ def mech_step(m_step, sL, state_funcs, behavior_funcs, env_processes, t_step):
 
     _input = getBehaviorInput(m_step, sL, last_in_obj, behavior_funcs)
 
-    for f in state_funcs:
+    # OLD: no bueno! Mutation Bad
-        f(m_step, sL, last_mut_obj, _input)
+    # for f in state_funcs:
+    #     f(m_step, sL, last_mut_obj, _input)
+
+    # New
+    last_mut_obj = dict([ f(m_step, sL, last_mut_obj, _input) for f in state_funcs ])
 
     apply_env_proc(env_processes, last_mut_obj, last_mut_obj['timestamp'])
 

test.py

@@ -1,2 +1,4 @@
 from engine import run
-run.main()
+from tabulate import tabulate
+result = run.main()
+print(tabulate(result, headers='keys', tablefmt='psql'))

ui/config.py

@@ -29,29 +29,47 @@ def b2m3(step, sL, s):
 
 # Internal States per Mechanism
 def s1m1(step, sL, s, _input):
-    s['s1'] = s['s1'] + _input
+    y = 's1'
+    x = s['s1'] + _input
+    return (y, x)
 def s2m1(step, sL, s, _input):
-    s['s2'] = s['s2'] + _input
+    y = 's2'
+    x = s['s2'] + _input
+    return (y, x)
 
 def s1m2(step, sL, s, _input):
-    s['s1'] = s['s1'] + _input
+    y = 's1'
+    x =  s['s1'] + _input
+    return (y, x)
 def s2m2(step, sL, s, _input):
-    s['s2'] = s['s2'] + _input
+    y = 's2'
+    x =  s['s2'] + _input
+    return (y, x)
 
 def s1m3(step, sL, s, _input):
-    s['s1'] = s['s1'] + _input
+    y = 's1'
+    x = s['s1'] + _input
+    return (y, x)
 def s2m3(step, sL, s, _input):
-    s['s2'] = s['s2'] + _input
+    y = 's2'
+    x =  s['s2'] + _input
+    return (y, x)
 
 # Exogenous States
 proc_one_coef_A = 0.7
 proc_one_coef_B = 1.3
 def es3p1(step, sL, s, _input):
-    s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
+    y = 's3'
+    x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
+    return (y, x)
 def es4p2(step, sL, s, _input):
-    s['s4'] = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
+    y = 's4'
+    x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
+    return (y, x)
 def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
-    s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
+    y = 'timestamp'
+    x = ep_time_step(s, s['timestamp'], seconds=1)
+    return (y, x)
 
 # Environment States
 def env_a(x):