Fill output NANs with previous value

engine/mechanismExecutor.py

@@ -3,6 +3,7 @@ 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))
 
@@ -15,7 +16,6 @@ def getBehaviorInput(step, sL, s, funcs, ops = behavior_ops):
         ops = ops[::-1]
 
     return foldr(call, getColResults(step, sL, s, funcs))(ops)
-    # return getColResults(step, sL, s, funcs)
 
 def apply_env_proc(env_processes, state_dict, step):
     for state in state_dict.keys():
@@ -36,13 +36,22 @@ def mech_step(m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, ru
 
     _input = exception_handler(getBehaviorInput, m_step, sL, last_in_obj, behavior_funcs)
 
+    # print(sL)
+
+    # *** add env_proc value here as wrapper function ***
     last_in_copy = dict([ exception_handler(f, m_step, sL, last_in_obj, _input) for f in state_funcs ])
+
+    for k in last_in_obj:
+        if k not in last_in_copy:
+            last_in_copy[k] = last_in_obj[k]
+
     del last_in_obj
 
     #	make env proc trigger field agnostic
     apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
 
     last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
+    # print(last_in_copy)
     sL.append(last_in_copy)
     del last_in_copy
 
@@ -52,6 +61,7 @@ def mech_step(m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, ru
 def block_gen(states_list, configs, env_processes, t_step, run):
     m_step = 0
     states_list_copy = deepcopy(states_list)
+    # remove copy
     genesis_states = states_list_copy[-1]
     genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step
     states_list = [genesis_states]
@@ -60,8 +70,6 @@ def block_gen(states_list, configs, env_processes, t_step, run):
     for config in configs:
         s_conf, b_conf = config[0], config[1]
         states_list = mech_step(m_step, states_list, s_conf, b_conf, env_processes, t_step, run)
-        # print(states_list)
-        # print(b_conf)
         m_step += 1
 
     t_step += 1
@@ -91,7 +99,6 @@ def simulation(states_list, configs, env_processes, time_seq, runs):
             head[-1]['mech_step'], head[-1]['time_step'], head[-1]['run'] = 0, 0, 0
             simulation_list = [head] + tail
             pipe_run += simulation_list
-            # print(pipe_run)
         else:
             transient_states_list = [pipe_run[-1][-1]]
             _, *tail = pipe(transient_states_list, configs, env_processes, time_seq, run)

engine/run.py

@@ -3,7 +3,6 @@ from engine.configProcessor import generate_config, create_tensor_field
 from engine.mechanismExecutor import simulation
 from engine.utils import flatten
 from tabulate import tabulate
-#from tabulate import tabulate
 import pandas as pd
 
 def main():
@@ -13,17 +12,13 @@ def main():
     # print(len(configs))
     print(tabulate(create_tensor_field(mechanisms, ep), headers='keys', tablefmt='psql'))
     print
-    # print(configs)
-    # print(states_list)
-    # print(configs)
     # p = pipeline(states_list, configs, env_processes, range(10))
     T = sim_config['T']
     N = sim_config['N']
 
     # Dimensions: N x r x mechs
     s = simulation(states_list, configs, env_processes, T, N)
+
     result = pd.DataFrame(flatten(s))
-    # print('Test')
+
-#    print(tabulate(result, headers='keys', tablefmt='psql'))
-# remove print and tabulate functions, so it returns a dataframe
     return result

setup.py

@@ -6,6 +6,6 @@ setup(name='SimCAD',
       url='https://github.com/BlockScience/DiffyQ-SimCAD',
       author='Joshua E. Jodesty',
       author_email='joshua@block.science',
-      license='MIT',
+      # license='MIT',
       packages=['engine'],
       zip_safe=False)

ui/config.py

@@ -149,7 +149,7 @@ def dict_elemwise_sum(f = _ + _):
 
 # [1, 2] = {'b1': ['a'], 'b2', [1]} =
 # behavior_ops = [ behavior_to_dict, print_fwd, sum_dict_values ]
-behavior_ops = [ print_fwd, foldr(dict_elemwise_sum()) ]
+behavior_ops = [ foldr(dict_elemwise_sum(_ + _)) ]
 # behavior_ops = []
 
 # need at least 1 behaviour and 1 state function for the 1st mech with behaviors
@@ -162,7 +162,7 @@ mechanisms = {
         },
         "states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
             "s1": s1m1,
-            "s2": s2m1
+            # "s2": s2m1
         }
     },
     "m2": {
@@ -172,7 +172,7 @@ mechanisms = {
         },
         "states": {
             "s1": s1m2,
-            "s2": s2m2
+            # "s2": s2m2
         }
     },
     "m3": {