Revert "testing fun"

This reverts commit 8f35856348.

engine/run.py

@@ -1,6 +1,5 @@
 #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.toyConfig import state_dict, mechanisms, exogenous_states, env_processes, sim_config
-from ui.simpleBC_Config 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

ui/simpleBC_Config.py

@@ -1,168 +0,0 @@
-from engine.utils import bound_norm_random, ep_time_step, env_proc
-
-import numpy as np
-from decimal import Decimal
-
-alpha = Decimal('.7') #forgetting param
-theta = Decimal('.75') #weight param for rational price
-beta = Decimal('0.5') #agant response gain
-gamma = Decimal('.03') #action friction param
-delta = Decimal('.3') #bounds on price change
-omega = Decimal('.5') #bound on burn frac per period
-
-seed = {
-    'z': np.random.RandomState(1),
-    'a': np.random.RandomState(2),
-    'b': np.random.RandomState(3),
-    'c': np.random.RandomState(3)
-}
-
-# Behaviors per Mechanism
-
-#arbit X Bond
-def b1m1(step, sL, s):
-    #returns "delta p"
-    if s['Price']< s['Pool']/s['Supply']-gamma:
-        return  (s['Pool']/s['Supply']-s['Price'])/s['Price']*s['Pool']*beta
-    else :
-        return 0
-    
-
-#invest X Bond
-def b2m1(step, sL, s):
-    #returns "delta p"
-    if s['Belief']< (alpha*s['Belief']+s['Pool']/s['Supply'])*(1-alpha):
-        return  s['Supply']*((alpha*s['Belief']+s['Pool']/s['Supply'])*(1-alpha)-s['Belief'])*beta
-    else :
-        return 0
-
-#arbit X Burn
-def b1m2(step, sL, s):
-    #returns "delta s"
-    if Decimal('1')/s['Price']< s['Supply']/s['Pool']-gamma:
-        return  (s['Supply']/s['Pool']-Decimal('1')/s['Price'])*s['Price']*s['Supply']*beta
-    else :
-        return 0
-
-#invest X Burn 
-def b2m2(step, sL, s):
-    #returns "delta s"
-    if Decimal('1')/s['Belief']< Decimal('1')/s['Price']:
-        return  np.min([ s['Pool']*(Decimal('1')/s['Price']-Decimal('1')/s['Belief'])*beta, omega*s['Supply']])
-    else :
-        return 0
-
-#
-#def b1m3(step, sL, s):
-#    return s['s1']
-#def b2m3(step, sL, s):
-#    return s['s2']
-
-
-# Internal States per Mechanism
-
-#Pool X Bond
-def s1m1(step, sL, s, _input):
-    #_input = "delta p"
-    s['Pool'] = s['Pool']+_input
-
-#Supply X Bond     
-def s2m1(step, sL, s, _input):
-    #_input = "delta p"
-    s['Supply'] = s['Supply']+_input*s['Supply']/s['Pool'] 
-
-# Pool X Burn   
-def s1m2(step, sL, s, _input):
-    #_input is "delta s"
-    s['Pool'] = s['Pool']- _input*s['Pool']/s['Supply'] 
-
-# Supply X Burn    
-def s2m2(step, sL, s, _input):
-    s['Supply'] = s['Supply'] - _input
-
-#def s1m3(step, sL, s, _input):
-#    s['s1'] = s['s1']+Decimal(.25)*(s['s2']-s['s1']) + Decimal(.25)*(_input-s['s1'])
-#
-#def s2m3(step, sL, s, _input):
-#    s['s2'] = s['s2']+Decimal(.25)*(s['s1']-s['s2']) + Decimal(.25)*(_input-s['s2'])
-
-# Exogenous States
-proc_one_coef_A = -delta
-proc_one_coef_B = delta
-def es3p1(step, sL, s, _input):
-    rv = bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
-    s['Price'] = theta*s['Price'] * (Decimal('1')+rv) +(Decimal('1')-theta)*s['Pool']/s['Supply'] 
-def es4p2(step, sL, s, _input):
-    s['Belief'] = alpha*s['Belief']+s['Pool']/s['Supply']*(Decimal('1')-alpha)
-
-def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
-    s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
-
-# Environment States
-#from numpy.random import randn as rn
-def env_a(x):
-    return 3
-def env_b(x):
-    return 7
-# def what_ever(x):
-#     return x + 1
-
-# Genesis States
-state_dict = {
-    'Pool': Decimal(10.0),
-    'Supply': Decimal(5.0),
-    'Price': Decimal(.01),
-    'Belief': Decimal(10.0),
-    'timestamp': '2018-10-01 15:16:24'
-}
-
-exogenous_states = {
-    "Price": es3p1,
-    "Belief": es4p2,
-    "timestamp": es5p2
-}
-
-env_processes = {
-    "Price": env_proc('2018-10-01 15:16:25', env_a),
-    "Belief": env_proc('2018-10-01 15:16:25', env_b)
-}
-
-# test return vs. non-return functions as lambdas
-# test fully defined functions
-mechanisms = {
-    "bond": {
-        "behaviors": {
-            "arbit": b1m1, # lambda step, sL, s: s['s1'] + 1,
-            "invest": b2m1
-        },
-        "states": {
-            "Pool": s1m1,
-            "Supply": s2m1,
-        }
-    },
-    "burn": {
-        "behaviors": {
-            "arbit": b1m2,
-            "invest": b2m2
-        },
-        "states": {
-            "Pool": s1m2,
-            "Supply": s2m2,
-        }
-    },
-#    "m3": {
-#        "behaviors": {
-#            "b1": b1m3,
-#            "b2": b2m3
-#        },
-#        "states": {
-#            "s1": s1m3,
-#            "s2": s2m3,
-#        }
-#    }
-}
-
-sim_config = {
-    "N": 1,
-    "R": 1000
-}

ui/toyConfig.py

@@ -83,7 +83,7 @@ env_processes = {
 # test return vs. non-return functions as lambdas
 # test fully defined functions
 mechanisms = {
-    "mech1": {
+    "m1": {
         "behaviors": {
             "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
             "b2": b2m1