refactored

SimCAD/configuration/utils/__init__.py

@@ -6,6 +6,7 @@ import pandas as pd
 from pathos.threading import ThreadPool
 
 from SimCAD.utils import groupByKey, dict_filter, contains_type
+from SimCAD.utils import flatMap
 
 class TensorFieldReport:
     def __init__(self, config_proc):
@@ -126,4 +127,16 @@ def sweep_states(state_type, states, in_config):
     else:
         configs = [in_config]
 
-    return configs
+    return configs
+
+def param_sweep(config, raw_exogenous_states):
+    return flatMap(
+        sweep_states('environmental', config.env_processes),
+        flatMap(
+            sweep_states('exogenous', raw_exogenous_states),
+            flatMap(
+                sweep_mechs('states'),
+                sweep_mechs('behaviors', config)
+            )
+        )
+    )

simulations/validation/config1.py

@@ -7,7 +7,7 @@ from SimCAD import configs
 from SimCAD.utils import flatMap
 from SimCAD.configuration import Configuration
 from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
-    ep_time_step, sweep_states, sweep_mechs
+    ep_time_step, param_sweep
 from SimCAD.engine.utils import sweep
 
 pp = pprint.PrettyPrinter(indent=4)
@@ -28,16 +28,19 @@ def b1m1(step, sL, s):
 def b2m1(step, sL, s):
     return {'param2': 4}
 
+# @curried
+# def b1m2(param, step, sL, s):
+#     return {'param1': 'a', 'param2': param}
 
-@curried
-def b1m2(param, step, sL, s):
-    return {'param1': 'a', 'param2': param}
+def b1m2(step, sL, s):
+    return {'param1': 'a', 'param2': 2}
 
 def b2m2(step, sL, s):
     return {'param1': 'b', 'param2': 4}
 
 def b1m3(step, sL, s):
     return {'param1': ['c'], 'param2': np.array([10, 100])}
+
 def b2m3(step, sL, s):
     return {'param1': ['d'], 'param2': np.array([20, 200])}
 
@@ -49,19 +52,18 @@ def s1m1(step, sL, s, _input):
     return (y, x)
 
 
-# decorator
-# param = Decimal(11.0)
-# def s2m1(step, sL, s, _input):
-#     y = 's2'
-#     x = _input['param2'] + param
-#     return (y, x)
-
-@curried
-def s2m1(param, step, sL, s, _input):
+param = Decimal(11.0)
+def s2m1(step, sL, s, _input):
     y = 's2'
     x = _input['param2'] + param
     return (y, x)
 
+# @curried
+# def s2m1(param, step, sL, s, _input):
+#     y = 's2'
+#     x = _input['param2'] + param
+#     return (y, x)
+
 def s1m2(step, sL, s, _input):
     y = 's1'
     x = _input['param1']
@@ -84,27 +86,17 @@ def s2m3(step, sL, s, _input):
 proc_one_coef_A = 0.7
 proc_one_coef_B = 1.3
 
-# def es3p1(step, sL, s, _input):
-#     y = 's3'
-#     x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
-#     return (y, x)
-
-
-# es3p1 = sweep(
-#     params = [Decimal(11.0), Decimal(22.0)],
-#     sweep_f = lambda param: lambda step, sL, s, _input: (
-#         's3',
-#         s['s3'] + param
-#     )
-# )
-
-
-@curried
-def es3p1(param, step, sL, s, _input):
+def es3p1(step, sL, s, _input):
     y = 's3'
-    x = s['s3'] + param
+    x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
     return (y, x)
 
+# @curried
+# def es3p1(param, step, sL, s, _input):
+#     y = 's3'
+#     x = s['s3'] + param
+#     return (y, x)
+
 def es4p2(step, sL, s, _input):
     y = 's4'
     x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
@@ -119,9 +111,11 @@ def es5p2(step, sL, s, _input):
 
 
 # Environment States
-@curried
-def env_a(param, x):
-    return x + param
+# @curried
+# def env_a(param, x):
+#     return x + param
+def env_a(x):
+    return x
 def env_b(x):
     return 10
 # def what_ever(x):
@@ -138,7 +132,7 @@ genesis_states = {
 
 # remove `exo_update_per_ts` to update every ts
 raw_exogenous_states = {
-    "s3": sweep(beta, es3p1),
+    "s3": es3p1, #sweep(beta, es3p1),
     "s4": es4p2,
     "timestamp": es5p2
 }
@@ -146,9 +140,10 @@ exogenous_states = exo_update_per_ts(raw_exogenous_states)
 
 # ToDo: make env proc trigger field agnostic
 # ToDo: input json into function renaming __name__
+triggered_env_b = proc_trigger('2018-10-01 15:16:25', env_b)
 env_processes = {
-    "s3": sweep(beta, env_a, 'env_a'),
-    "s4": proc_trigger('2018-10-01 15:16:25', env_b)
+    "s3": env_a, #sweep(beta, env_a, 'env_a'),
+    "s4": sweep(beta, triggered_env_b, 'triggered_env_b')
 }
 
 # lambdas
@@ -171,12 +166,12 @@ mechanisms = {
         },
         "states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
             "s1": s1m1,
-            "s2": sweep(beta, s2m1)
+            "s2": s2m1 #sweep(beta, s2m1)
         }
     },
     "m2": {
         "behaviors": {
-            "b1": sweep(beta, b1m2),
+            "b1": b1m2, #sweep(beta, b1m2),
             "b2": b2m2
         },
         "states": {
@@ -201,18 +196,6 @@ sim_config = {
     "T": range(5)
 }
 
-# configs.append(
-#     Configuration(
-#         sim_config=sim_config,
-#         state_dict=genesis_states,
-#         seed=seed,
-#         env_processes=env_processes,
-#         exogenous_states=exogenous_states,
-#         mechanisms=mechanisms
-#     )
-# )
-
-
 c = Configuration(
     sim_config=sim_config,
     state_dict=genesis_states,
@@ -222,23 +205,12 @@ c = Configuration(
     mechanisms=mechanisms
 )
 
-
-l = flatMap(
-    sweep_states('environmental', env_processes),
-    flatMap(
-        sweep_states('exogenous', raw_exogenous_states),
-        flatMap(
-            sweep_mechs('states'),
-            sweep_mechs('behaviors', c)
-        )
-    )
-)
-
+configs = configs + param_sweep(c, raw_exogenous_states)
 
 print()
-print(len(l))
+print(len(configs))
 print()
-for g in l:
+for g in configs:
     print()
     print('Configuration')
     print()