ver. 0.2.4

cadCAD/configuration/utils/__init__.py

@@ -1,9 +1,6 @@
 from datetime import datetime, timedelta
-from decimal import Decimal
 from copy import deepcopy
 from functools import reduce
-from pprint import pprint
-
 from fn.func import curried
 from funcy import curry
 import pandas as pd
@@ -175,9 +172,6 @@ def env_trigger(end_substep):
     return lambda trigger_field, trigger_vals, funct_list: \
         curry(trigger)(end_substep)(trigger_field)(trigger_vals)(funct_list)
 
-# trigger = curry(_trigger)
-# print(timestep_trigger)
-
 
 # param sweep enabling middleware
 def config_sim(d):
@@ -185,7 +179,6 @@ def config_sim(d):
         return flatten_tabulated_dict(tabulate_dict(d))
 
     if "M" in d:
-        # print([{"N": d["N"], "T": d["T"], "M": M} for M in process_variables(d["M"])])
         return [{"N": d["N"], "T": d["T"], "M": M} for M in process_variables(d["M"])]
     else:
         d["M"] = [{}]

cadCAD/configuration/utils/policyAggregation.py

@@ -1,6 +1,5 @@
 from fn.op import foldr
 from fn.func import curried
-from collections import Counter
 
 def get_base_value(x):
     if isinstance(x, str):
@@ -39,43 +38,7 @@ def dict_op(f, d1, d2):
 
     key_set = set(list(d1.keys()) + list(d2.keys()))
 
-
     return {k: f(set_base_value(d1, d2, k), set_base_value(d2, d1, k)) for k in key_set}
-#
-# @curried
-# def dict_op(f, d1, d2):
-#     def set_base_value(target_dict, source_dict, key):
-#         if key not in target_dict:
-#             return get_base_value(source_dict[key])
-#         else:
-#             return target_dict[key]
-#
-#     key_set = set(list(d1.keys()) + list(d2.keys()))
-#
-#     norm_d1 = {k: set_base_value(d1, d2, k) for k in key_set}
-#     norm_d2 = {k: set_base_value(d2, d1, k) for k in key_set}
-#
-#     return {k: f(set_base_value(d1, d2, k), set_base_value(d2, d1, k)) for k in key_set}
-
-
-# @curried
-# def dict_op(f, d1, d2):
-#     # d1C = Counter(d1)
-#     # d2C = Counter(d2)
-#     def set_base_value(target_dict, source_dict, key):
-#         if key not in target_dict:
-#             return get_base_value(source_dict[key])
-#         else:
-#             return target_dict[key]
-#     key_set = set(list(d1.keys()) + list(d2.keys()))
-#     norm_d1 = Counter({k: set_base_value(d1, d2, k) for k in key_set})
-#     norm_d2 = Counter({k: set_base_value(d2, d1, k) for k in key_set})
-#     # print(norm_d1)
-#     # print(norm_d2)
-#     print(norm_d1 + norm_d2)
-#     # print(f(norm_d1, norm_d2))
-#     print()
-#     return f(norm_d1, norm_d2)
 
 def dict_elemwise_sum():
     return dict_op(add)

cadCAD/engine/__init__.py

@@ -27,11 +27,8 @@ def single_proc_exec(
         Ts: List[range],
         Ns: List[int]
     ):
-    # print(env_processes_list)
-    # print(configs_structs)
     l = [simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns]
     simulation_exec, states_list, config, env_processes, T, N = list(map(lambda x: x.pop(), l))
-    # print(config.env_processes)
     result = simulation_exec(var_dict_list, states_list, config, env_processes, T, N)
     return flatten(result)
 
@@ -68,7 +65,6 @@ class Executor:
         self.exec_method = exec_context.method
         self.exec_context = exec_context.name
         self.configs = configs
-        # self.main = self.execute
 
     def execute(self) -> Tuple[List[Dict[str, Any]], DataFrame]:
         config_proc = Processor()

cadCAD/engine/simulation.py

@@ -1,9 +1,7 @@
 from typing import Any, Callable, Dict, List, Tuple
-
 from pathos.pools import ThreadPool as TPool
 from copy import deepcopy
 from functools import reduce
-from funcy import compose
 
 from cadCAD.engine.utils import engine_exception
 from cadCAD.utils import flatten
@@ -19,13 +17,10 @@ class Executor:
         state_update_exception: Callable = id_exception
     ) -> None:
 
-        # behavior_ops
         self.policy_ops = policy_ops
         self.state_update_exception = state_update_exception
         self.policy_update_exception = policy_update_exception
-        # behavior_update_exception
 
-    # get_behavior_input # sL: State Window
     def get_policy_input(
                 self,
                 sweep_dict: Dict[str, List[Any]],
@@ -35,10 +30,8 @@ class Executor:
                 funcs: List[Callable]
             ) -> Dict[str, Any]:
 
-        # ops = self.policy_ops[::-1]
         ops = self.policy_ops
 
-
         def get_col_results(sweep_dict, sub_step, sL, s, funcs):
             return list(map(lambda f: f(sweep_dict, sub_step, sL, s), funcs))
 
@@ -119,23 +112,7 @@ class Executor:
                 run: int
             ) -> List[Dict[str, Any]]:
 
-        # def dp_psu(d):
-        #     for k, v in deepcopy(d).items():
-        #         yield k, deepcopy(v)
-        #
-        # def dp_psub(l):
-        #     for d in l:
-        #         yield dict(dp_psu(d))
-
-        # last_in_obj: Dict[str, Any] = dict(dp_psu(sL[-1]))
-
         last_in_obj: Dict[str, Any] = deepcopy(sL[-1])
-        # last_in_obj: Dict[str, Any] = sL[-1]
-
-        # last_in_obj: Dict[str, Any] = sH[-1]
-        # print(last_in_obj)
-        # print(sH[-1])
-
         _input: Dict[str, Any] = self.policy_update_exception(self.get_policy_input(sweep_dict, sub_step, sH, last_in_obj, policy_funcs))
 
         # ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
@@ -152,22 +129,13 @@ class Executor:
             return destination
 
         last_in_copy: Dict[str, Any] = transfer_missing_fields(last_in_obj, dict(generate_record(state_funcs)))
-        # ToDo: Remove
-        # last_in_copy: Dict[str, Any] = self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestep'])
-        # print(env_processes)
-        # print()
         last_in_copy: Dict[str, Any] = self.apply_env_proc(sweep_dict, env_processes, last_in_copy)
-
-
         # ToDo: make 'substep' & 'timestep' reserve fields
         last_in_copy['substep'], last_in_copy['timestep'], last_in_copy['run'] = sub_step, time_step, run
 
         sL.append(last_in_copy)
         del last_in_copy
 
-        # print(sL)
-        # print()
-
         return sL
 
     # mech_pipeline - state_update_block
@@ -182,23 +150,7 @@ class Executor:
             ) -> List[Dict[str, Any]]:
 
         sub_step = 0
-        # states_list_copy: List[Dict[str, Any]] = deepcopy(states_list)
-        # states_list_copy: List[Dict[str, Any]] = states_list
-        # ToDo: flatten first
-        # states_list_copy: List[Dict[str, Any]] = simulation_list[-1]
         states_list_copy: List[Dict[str, Any]] = deepcopy(simulation_list[-1])
-
-        # def dp_psu(d):
-        #     for k, v in deepcopy(d).items():
-        #         yield k, deepcopy(v)
-        #
-        # def dp_psub(l):
-        #     for d in l:
-        #         yield dict(dp_psu(d))
-
-        # states_list_copy: List[Dict[str, Any]] = list(dp_psub(simulation_list[-1]))
-        # print(states_list_copy)
-
         # ToDo: Causes Substep repeats in sL:
         genesis_states: Dict[str, Any] = states_list_copy[-1]
 
@@ -211,7 +163,7 @@ class Executor:
         del states_list_copy
         states_list: List[Dict[str, Any]] = [genesis_states]
 
-        # ToDo: Causes Substep repeats in sL, use for yield
+        # ToDo: Was causing Substep repeats in sL, use for yield
         sub_step += 1
 
         for [s_conf, p_conf] in configs: # tensor field
@@ -219,11 +171,7 @@ class Executor:
             states_list: List[Dict[str, Any]] = self.partial_state_update(
                 sweep_dict, sub_step, states_list, simulation_list, s_conf, p_conf, env_processes, time_step, run
             )
-            # print(sub_step)
-            # print(simulation_list)
-            # print(flatten(simulation_list))
             sub_step += 1
-            # print(sub_step)
 
         time_step += 1
 
@@ -244,10 +192,6 @@ class Executor:
         # ToDo: simulation_list should be a Tensor that is generated throughout the Executor
         simulation_list: List[List[Dict[str, Any]]] = [states_list]
 
-        # print(simulation_list[-1])
-        # print()
-        # pipe_run = simulation_list[-1]
-        # print(simulation_list)
         for time_step in time_seq:
             pipe_run: List[Dict[str, Any]] = self.state_update_pipeline(
                 sweep_dict, simulation_list, configs, env_processes, time_step, run
@@ -255,8 +199,6 @@ class Executor:
 
             _, *pipe_run = pipe_run
             simulation_list.append(pipe_run)
-            # print(simulation_list)
-            # print()
 
         return simulation_list
 

cadCAD/utils/sys_config.py

@@ -1,12 +1,6 @@
-from copy import deepcopy
-
-from fn.func import curried
+from funcy import curry
 
 from cadCAD.configuration.utils import ep_time_step, time_step
-from funcy import curry
-import pprint as pp
-# from fn import _
-from functools import reduce
 
 def increment(y, incr_by):
     return lambda _g, step, sL, s, _input: (y, s[y] + incr_by)
@@ -26,7 +20,6 @@ def update_timestamp(y, timedelta, format):
         ep_time_step(s, dt_str=s[y], fromat_str=format, _timedelta=timedelta)
     )
 
-
 def apply(f, y: str, incr_by: int):
     return lambda _g, step, sL, s, _input: (y, curry(f)(s[y])(incr_by))
 

setup.py

@@ -11,7 +11,7 @@ long_description = "cadCAD is a differential games based simulation software pac
     monte carlo analysis and other common numerical methods is provided."
 
 setup(name='cadCAD',
-      version='0.2.3',
+      version='0.2.4',
       description="cadCAD: a differential games based simulation software package for research, validation, and \
         Computer Aided Design of economic systems",
       long_description=long_description,