Enable: behavior output to be a map of behavior function to value

engine/configProcessor.py

@@ -1,6 +1,9 @@
 import pandas as pd
 from functools import reduce
 
+def no_state_identity(step, sL, s, _input):
+    return None
+
 def state_identity(k):
     return lambda step, sL, s, _input: (k, s[k])
 
@@ -31,17 +34,27 @@ def create_matrix_field(mechanisms, key):
         return pd.DataFrame({'empty' : []})
 
 
+# Maybe Refactor to only use dictionary BUT I used dfs to fill NAs. Perhaps fill
 def generate_config(state_dict, mechanisms, exo_proc):
-    def no_behavior_handler(bdf, sdf):
-        if bdf.empty == False:
-            sdf_values, bdf_values = sdf.values.tolist(), bdf.values.tolist()
+
+    def no_update_handler(bdf, sdf):
+        if (bdf.empty == False) and (sdf.empty == True):
+            bdf_values = bdf.values.tolist()
+            sdf_values = [ [no_state_identity] * len(bdf_values) for m in range(len(mechanisms)) ]
+            return sdf_values, bdf_values
+        elif (bdf.empty == True) and (sdf.empty == False):
+            sdf_values = sdf.values.tolist()
+            bdf_values = [ [b_identity] * len(sdf_values) for m in range(len(mechanisms)) ]
+            return sdf_values, bdf_values
         else:
             sdf_values = sdf.values.tolist()
-            bdf_values = [ [b_identity] * len(sdf_values) for n in range(mechanisms) ]
-        return sdf_values, bdf_values
+            bdf_values = bdf.values.tolist()
+            return sdf_values, bdf_values
 
     def only_ep_handler(state_dict):
-        sdf_functions = [ lambda step, sL, s, _input: (k, v) for k, v in zip(state_dict.keys(), state_dict.values()) ]
+        sdf_functions = [
+            lambda step, sL, s, _input: (k, v) for k, v in zip(state_dict.keys(), state_dict.values())
+        ]
         sdf_values = [ sdf_functions ]
         bdf_values = [ [b_identity] * len(sdf_values) ]
         return sdf_values, bdf_values
@@ -50,7 +63,7 @@ def generate_config(state_dict, mechanisms, exo_proc):
     if len(mechanisms) != 0:
         bdf = create_matrix_field(mechanisms, 'behaviors')
         sdf = create_matrix_field(mechanisms, 'states')
-        sdf_values, bdf_values = no_behavior_handler(bdf, sdf)
+        sdf_values, bdf_values = no_update_handler(bdf, sdf)
         zipped_list = list(zip(sdf_values, bdf_values))
     else:
         sdf_values, bdf_values = only_ep_handler(state_dict)

engine/mechanismExecutor.py

@@ -1,13 +1,19 @@
 from copy import deepcopy
-from fn.op import foldr, call
 from fn import _
+from fn.op import foldr, call
 from ui.config import behavior_ops
 
 def getColResults(step, sL, s, funcs):
     return list(map(lambda f: f(step, sL, s), funcs))
 
 # Data Type reduction
-def getBehaviorInput(step, sL, s, funcs, ops = behavior_ops[::-1]):
+def getBehaviorInput(step, sL, s, funcs, ops = behavior_ops):
+
+    if len(ops) == 0:
+        ops = [foldr(_ + _)]
+    else:
+        ops = ops[::-1]
+
     return foldr(call, getColResults(step, sL, s, funcs))(ops)
 
 def apply_env_proc(env_processes, state_dict, step):
@@ -72,7 +78,7 @@ def pipe(states_list, configs, env_processes, time_seq, run):
     return simulation_list
 
 
-# Del head
+# Del _ / head
 def simulation(states_list, configs, env_processes, time_seq, runs):
     pipe_run = []
     for run in range(runs):

ui/config.py

@@ -1,6 +1,7 @@
 from engine.utils import bound_norm_random, ep_time_step, proc_trigger, exo_update_per_ts
 from fn.op import foldr
 from fn import _
+from fn.func import curried
 
 import numpy as np
 from decimal import Decimal
@@ -94,6 +95,7 @@ state_dict = {
     'timestamp': '2018-10-01 15:16:24'
 }
 
+# remove `exo_update_per_ts` to update every ts
 exogenous_states = exo_update_per_ts(
     {
     "s3": es3p1,
@@ -102,6 +104,7 @@ exogenous_states = exo_update_per_ts(
     }
 )
 
+
 env_processes = {
     "s3": proc_trigger('2018-10-01 15:16:25', env_a),
     "s4": proc_trigger('2018-10-01 15:16:25', env_b)
@@ -110,7 +113,24 @@ env_processes = {
 # lambdas
 # genesis Sites should always be there
 # [1, 2]
-behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
+# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
+def print_fwd(x):
+    print(x)
+    return x
+
+def behavior_to_dict(v):
+    return dict(list(zip(map(lambda n: 'b_' + str(n), list(range(len(v)))), v)))
+
+
+def foldr_dict_vals(d, f = _ + _):
+    return foldr(f)(list(d.values()))
+
+def sum_dict_values(d):
+    return foldr_dict_vals(d)
+
+# behavior_ops = [ behavior_to_dict, print_fwd, sum_dict_values, lambda x: x + 0 ]
+behavior_ops = []
+
 # need at least 1 behaviour and 1 state function for the 1st mech with behaviors
 mechanisms = {
     "m1": {