Loading default parameters into class during initialization

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -20,6 +20,8 @@
 from HARK import HARKobject, Market, AgentType
 from copy import deepcopy
 import matplotlib.pyplot as plt
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+
 
 utility = CRRAutility
 utilityP = CRRAutilityP
@@ -79,6 +81,9 @@ def __init__(self, time_flow=True, **kwds):
         Make a new instance of AggShockConsumerType, an extension of
         IndShockConsumerType.  Sets appropriate solver and input lists.
         '''
+        params = Params.init_agg_shocks.copy()
+        params.update(kwds)
+        kwds = params
         AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_),
                            time_flow=time_flow, pseudo_terminal=False, **kwds)
 
@@ -375,6 +380,9 @@ class AggShockMarkovConsumerType(AggShockConsumerType):
     state is subject to Markov-style discrete state evolution.
     '''
     def __init__(self, **kwds):
+        params = Params.init_agg_mrkv_shocks.copy()
+        params.update(kwds)
+        kwds = params
         AggShockConsumerType.__init__(self, **kwds)
         self.addToTimeInv('MrkvArray')
         self.solveOnePeriod = solveConsAggMarkov
@@ -886,7 +894,11 @@ class CobbDouglasEconomy(Market):
     Note: The current implementation assumes a constant labor supply, but
     this will be generalized in the future.
     '''
-    def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
+    def __init__(self,
+                 agents=[],
+                 tolerance=0.0001,
+                 act_T=1200,
+                 **kwds):
         '''
         Make a new instance of CobbDouglasEconomy by filling in attributes
         specific to this kind of market.
@@ -906,6 +918,9 @@ def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
         -------
         None
         '''
+        params = Params.init_cobb_douglas.copy()
+        params.update(kwds)
+        kwds = params
         Market.__init__(self, agents=agents,
                         sow_vars=['MaggNow', 'AaggNow', 'RfreeNow',
                                   'wRteNow', 'PermShkAggNow', 'TranShkAggNow', 'KtoLnow'],
@@ -1330,7 +1345,11 @@ class CobbDouglasMarkovEconomy(CobbDouglasEconomy):
     productivity growth factor can vary over time.
 
     '''
-    def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
+    def __init__(self,
+                 agents=[],
+                 tolerance=0.0001,
+                 act_T=1200,
+                 **kwds):
         '''
         Make a new instance of CobbDouglasMarkovEconomy by filling in attributes
         specific to this kind of market.
@@ -1350,6 +1369,9 @@ def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
         -------
         None
         '''
+        params = Params.init_mrkv_cobb_douglas.copy()
+        params.update(kwds)
+        kwds = params
         CobbDouglasEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **kwds)
         self.sow_vars.append('MrkvNow')
 
@@ -1773,7 +1795,6 @@ def __init__(self, AFunc):
 ###############################################################################
 
 def main():
-    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from time import clock
     from HARK.utilities import plotFuncs
 
@@ -1791,11 +1812,11 @@ def mystr(number): return "{:.4f}".format(number)
 
     if solve_agg_shocks_micro or solve_agg_shocks_market:
         # Make an aggregate shocks consumer type
-        AggShockExample = AggShockConsumerType(**Params.init_agg_shocks)
+        AggShockExample = AggShockConsumerType()
         AggShockExample.cycles = 0
 
         # Make a Cobb-Douglas economy for the agents
-        EconomyExample = CobbDouglasEconomy(agents=[AggShockExample], **Params.init_cobb_douglas)
+        EconomyExample = CobbDouglasEconomy(agents=[AggShockExample])
         EconomyExample.makeAggShkHist()  # Simulate a history of aggregate shocks
 
         # Have the consumers inherit relevant objects from the economy
@@ -1842,12 +1863,12 @@ def mystr(number): return "{:.4f}".format(number)
 
     if solve_markov_micro or solve_markov_market or solve_krusell_smith:
         # Make a Markov aggregate shocks consumer type
-        AggShockMrkvExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks)
+        AggShockMrkvExample = AggShockMarkovConsumerType()
         AggShockMrkvExample.IncomeDstn[0] = 2*[AggShockMrkvExample.IncomeDstn[0]]
         AggShockMrkvExample.cycles = 0
 
         # Make a Cobb-Douglas economy for the agents
-        MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas)
+        MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample])
         MrkvEconomyExample.DampingFac = 0.2  # Turn down damping
         MrkvEconomyExample.makeAggShkHist()  # Simulate a history of aggregate shocks
         AggShockMrkvExample.getEconomyData(
@@ -1934,14 +1955,14 @@ def mystr(number): return "{:.4f}".format(number)
         PolyMrkvArray[StateCount-1, StateCount-1] += 0.5*(1.0 - Persistence)
 
         # Make a consumer type to inhabit the economy
-        PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks)
+        PolyStateExample = AggShockMarkovConsumerType()
         PolyStateExample.MrkvArray = PolyMrkvArray
         PolyStateExample.PermGroFacAgg = PermGroFacAgg
         PolyStateExample.IncomeDstn[0] = StateCount*[PolyStateExample.IncomeDstn[0]]
         PolyStateExample.cycles = 0
 
         # Make a Cobb-Douglas economy for the agents
-        PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas)
+        PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample])
         PolyStateEconomy.MrkvArray = PolyMrkvArray
         PolyStateEconomy.PermGroFacAgg = PermGroFacAgg
         PolyStateEconomy.PermShkAggStd = StateCount*[0.006]

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -18,6 +18,7 @@
                            getPercentiles
 from HARK.simulation import drawLognormal, drawDiscrete, drawUniform
 from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType
+import HARK.ConsumptionSaving.ConsumerParameters as Params
 
 utility = CRRAutility
 utilityP = CRRAutilityP
@@ -965,7 +966,7 @@ class GenIncProcessConsumerType(IndShockConsumerType):
     solution_terminal_ = ConsumerSolution(cFunc=cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0)
     poststate_vars_ = ['aLvlNow', 'pLvlNow']
 
-    def __init__(self, cycles=1, time_flow=True, **kwds):
+    def __init__(self, cycles=0, time_flow=True, **kwds):
         '''
         Instantiate a new ConsumerType with given data.
         See ConsumerParameters.init_explicit_perm_inc for a dictionary of the
@@ -982,6 +983,10 @@ def __init__(self, cycles=1, time_flow=True, **kwds):
         -------
         None
         '''
+        params = Params.init_explicit_perm_inc.copy()
+        params.update(kwds)
+        kwds = params
+
         # Initialize a basic ConsumerType
         IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **kwds)
         self.solveOnePeriod = solveConsGenIncProcess  # idiosyncratic shocks solver with explicit persistent income
@@ -1310,7 +1315,6 @@ def updatepLvlNextFunc(self):
 ###############################################################################
 
 def main():
-    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import plotFuncs
     from time import clock
     import matplotlib.pyplot as plt
@@ -1326,7 +1330,7 @@ def mystr(number): return "{:.4f}".format(number)
     print('percentile is ' + str(Params.init_explicit_perm_inc['pLvlPctiles'][-1]*100) + '.\n')
 
     # Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks
-    ExplicitExample = IndShockExplicitPermIncConsumerType(**Params.init_explicit_perm_inc)
+    ExplicitExample = IndShockExplicitPermIncConsumerType()
     t_start = clock()
     ExplicitExample.solve()
     t_end = clock()

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -22,6 +22,7 @@
 import numpy as np
 from scipy.optimize import newton
 from HARK import AgentType, Solution, NullFunc, HARKobject
+import HARK.ConsumptionSaving.ConsumerParameters as Params
 from HARK.utilities import warnings  # Because of "patch" to warnings modules
 from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
 from HARK.simulation import drawDiscrete, drawLognormal, drawUniform
@@ -1596,7 +1597,12 @@ class PerfForesightConsumerType(AgentType):
     poststate_vars_ = ['aNrmNow','pLvlNow']
     shock_vars_ = []
 
-    def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds):
+    def __init__(self,
+                 cycles=1,
+                 time_flow=True,
+                 verbose=False,
+                 quiet=False,
+                 **kwds):
         '''
         Instantiate a new consumer type with given data.
         See ConsumerParameters.init_perfect_foresight for a dictionary of
@@ -1613,6 +1619,11 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds):
         -------
         None
         '''
+
+        params = Params.init_perfect_foresight.copy()
+        params.update(kwds)
+        kwds = params
+
         # Initialize a basic AgentType
         AgentType.__init__(self,solution_terminal=deepcopy(self.solution_terminal_),
                            cycles=cycles,time_flow=time_flow,pseudo_terminal=False,**kwds)
@@ -1969,7 +1980,12 @@ class IndShockConsumerType(PerfForesightConsumerType):
     time_inv_.remove('MaxKinks') # This is in the PerfForesight model but not ConsIndShock
     shock_vars_ = ['PermShkNow','TranShkNow']
 
-    def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds):
+    def __init__(self,
+                 cycles=1,
+                 time_flow=True,
+                 verbose=False,
+                 quiet=False,
+                 **kwds):
         '''
         Instantiate a new ConsumerType with given data.
         See ConsumerParameters.init_idiosyncratic_shocks for a dictionary of
@@ -1986,9 +2002,18 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds):
         -------
         None
         '''
+
+        params = Params.init_idiosyncratic_shocks.copy()
+        params.update(kwds)
+        kwds = params
+
         # Initialize a basic AgentType
-        PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,
-                                           verbose=verbose,quiet=quiet, **kwds)
+        PerfForesightConsumerType.__init__(self,
+                                           cycles=cycles,
+                                           time_flow=time_flow,
+                                           verbose=verbose,
+                                           quiet=quiet,
+                                           **kwds)
 
         # Add consumer-type specific objects, copying to create independent versions
         self.solveOnePeriod = solveConsIndShock # idiosyncratic shocks solver
@@ -2324,7 +2349,10 @@ class KinkedRconsumerType(IndShockConsumerType):
     time_inv_.remove('Rfree')
     time_inv_ += ['Rboro', 'Rsave']
 
-    def __init__(self,cycles=1,time_flow=True,**kwds):
+    def __init__(self,
+                 cycles=1,
+                 time_flow=True,
+                 **kwds):
         '''
         Instantiate a new ConsumerType with given data.
         See ConsumerParameters.init_kinked_R for a dictionary of
@@ -2341,6 +2369,10 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
         -------
         None
         '''
+        params = Params.init_kinked_R.copy()
+        params.update(kwds)
+        kwds = params
+
         # Initialize a basic AgentType
         PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds)
 
@@ -2669,15 +2701,14 @@ def constructAssetsGrid(parameters):
 ####################################################################################################
 
 def main():
-    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import plotFuncsDer, plotFuncs
     from time import time
     mystr = lambda number : "{:.4f}".format(number)
 
     do_simulation           = True
 
     # Make and solve an example perfect foresight consumer
-    PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight)
+    PFexample = PerfForesightConsumerType()
     PFexample.cycles = 0 # Make this type have an infinite horizon
 
     start_time = time()
@@ -2701,7 +2732,7 @@ def main():
 ###############################################################################
 
     # Make and solve an example consumer with idiosyncratic income shocks
-    IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
+    IndShockExample = IndShockConsumerType()
     IndShockExample.cycles = 0 # Make this type have an infinite horizon
 
     start_time = time()
@@ -2795,7 +2826,7 @@ def main():
 ###############################################################################
 
     # Make and solve an agent with a kinky interest rate
-    KinkyExample = KinkedRconsumerType(**Params.init_kinked_R)
+    KinkyExample = KinkedRconsumerType()
     KinkyExample.cycles = 0 # Make the Example infinite horizon
 
     start_time = time()

HARK/ConsumptionSaving/ConsMedModel.py

@@ -18,6 +18,7 @@
 from HARK.ConsumptionSaving.ConsGenIncProcessModel import ConsGenIncProcessSolver,\
                             PersistentShockConsumerType, ValueFunc2D, MargValueFunc2D,\
                             MargMargValueFunc2D, VariableLowerBoundFunc2D
+import HARK.ConsumptionSaving.ConsumerParameters as Params
 from copy import deepcopy
 
 utility_inv   = CRRAutility_inv
@@ -509,7 +510,7 @@ class MedShockConsumerType(PersistentShockConsumerType):
     '''
     shock_vars_ = PersistentShockConsumerType.shock_vars_ + ['MedShkNow']
 
-    def __init__(self,cycles=1,time_flow=True,**kwds):
+    def __init__(self,cycles=0,time_flow=True,**kwds):
         '''
         Instantiate a new ConsumerType with given data, and construct objects
         to be used during solution (income distribution, assets grid, etc).
@@ -527,6 +528,10 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
         -------
         None
         '''
+        params = Params.init_medical_shocks.copy()
+        params.update(kwds)
+        kwds = params
+
         PersistentShockConsumerType.__init__(self,cycles=cycles,**kwds)
         self.solveOnePeriod = solveConsMedShock # Choose correct solver
         self.addToTimeInv('CRRAmed')
@@ -1361,7 +1366,6 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR
 ###############################################################################
 
 def main():
-    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import CRRAutility_inv
     from time import clock
     import matplotlib.pyplot as plt
@@ -1370,7 +1374,7 @@ def main():
     do_simulation = True
 
     # Make and solve an example medical shocks consumer type
-    MedicalExample = MedShockConsumerType(**Params.init_medical_shocks)
+    MedicalExample = MedShockConsumerType()
     t_start = clock()
     MedicalExample.solve()
     t_end = clock()