mikes edits

engine/run.py

@@ -1,4 +1,6 @@
-from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+#from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+from ui.toyConfig import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+#from ui.<config_filename> import state_dict, mechanisms, exogenous_states, env_processes, sim_config
 from engine.configProcessor import generate_config
 from engine.mechanismExecutor import simulation
 from engine.utils import flatten
@@ -11,7 +13,8 @@ def main():
     configs = generate_config(mechanisms, exogenous_states)
     # p = pipeline(states_list, configs, env_processes, range(10))
     N = sim_config['N']
-    r = range(5)
+    r = range(sim_config["R"])
+    #r = range(5)
     # Dimensions: N x r x mechs
     s = simulation(states_list, configs, env_processes, r, N)
     result = pd.DataFrame(flatten(s))

ui/toyConfig.py

@@ -0,0 +1,122 @@
+from engine.utils import bound_norm_random, ep_time_step, env_proc
+
+import numpy as np
+from decimal import Decimal
+
+seed = {
+    'z': np.random.RandomState(1),
+    'a': np.random.RandomState(2),
+    'b': np.random.RandomState(3),
+    'c': np.random.RandomState(3)
+}
+
+# Behaviors per Mechanism
+def b1m1(step, sL, s):
+    return s['s1'] + 1
+def b2m1(step, sL, s):
+    return s['s1'] + 1
+
+def b1m2(step, sL, s):
+    return s['s1'] + 1
+def b2m2(step, sL, s):
+    return s['s1'] + 1
+
+def b1m3(step, sL, s):
+    return s['s1'] + 1
+def b2m3(step, sL, s):
+    return s['s2'] + 1
+
+
+# Internal States per Mechanism
+def s1m1(step, sL, s, _input):
+    s['s1'] = s['s1'] + _input
+def s2m1(step, sL, s, _input):
+    s['s2'] = s['s2'] + _input
+
+def s1m2(step, sL, s, _input):
+    s['s1'] = s['s1'] + _input
+def s2m2(step, sL, s, _input):
+    s['s2'] = s['s2'] + _input
+
+def s1m3(step, sL, s, _input):
+    s['s1'] = s['s1'] + _input
+def s2m3(step, sL, s, _input):
+    s['s2'] = s['s2'] + _input
+
+# 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)
+def es4p2(step, sL, s, _input):
+    s['s4'] = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
+def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
+    s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
+
+# Environment States
+def env_a(x):
+    return 10
+def env_b(x):
+    return 10
+# def what_ever(x):
+#     return x + 1
+
+# Genesis States
+state_dict = {
+    's1': Decimal(0.0),
+    's2': Decimal(0.0),
+    's3': Decimal(1.0),
+    's4': Decimal(1.0),
+    'timestamp': '2018-10-01 15:16:24'
+}
+
+exogenous_states = {
+    "s3": es3p1,
+    "s4": es4p2,
+    "timestamp": es5p2
+}
+
+env_processes = {
+    "s3": env_proc('2018-10-01 15:16:25', env_a),
+    "s4": env_proc('2018-10-01 15:16:25', env_b)
+}
+
+# test return vs. non-return functions as lambdas
+# test fully defined functions
+mechanisms = {
+    "m1": {
+        "behaviors": {
+            "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
+            "b2": b2m1
+        },
+        "states": {
+            "s1": s1m1,
+            "s2": s2m1,
+        }
+    },
+    "m2": {
+        "behaviors": {
+            "b1": b1m2,
+            "b2": b2m2
+        },
+        "states": {
+            "s1": s1m2,
+            "s2": s2m2,
+        }
+    },
+    "m3": {
+        "behaviors": {
+            "b1": b1m3,
+            "b2": b2m3
+        },
+        "states": {
+            "s1": s1m3,
+            "s2": s2m3,
+        }
+    }
+}
+
+sim_config = {
+    "N": 1,
+    "R": 100
+}