Fixed imports in model files (#224)

* Fixed imports in model files

All of the consumption-saving model files used a style of import that didn't work when the file was run directly (rather than called as a module), even though they have a __main__ block (and main() function).  This has now been fixed.

Also mostly removed extraneous file RepAgentModel.py, which seems to be an old name of ConsRepAgentModel.py.  This file now simply imports all of ConsRepAgentModel and warns the user to use that instead.

* import ConsumerParameters -> import HARK.ConsumptionSaving.ConsumerParameters

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -16,7 +16,7 @@
                            CRRAutility_invP, CRRAutility_inv, combineIndepDstns,\
                            approxMeanOneLognormal
 from HARK.simulation import drawDiscrete, drawUniform
-from .ConsIndShockModel import ConsumerSolution, IndShockConsumerType
+from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType
 from HARK import HARKobject, Market, AgentType
 from copy import deepcopy
 import matplotlib.pyplot as plt
@@ -1753,7 +1753,7 @@ def __init__(self,AFunc):
 ###############################################################################
 
 def main():
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from time import clock
     from HARK.utilities import plotFuncs
     mystr = lambda number : "{:.4f}".format(number)
@@ -1793,6 +1793,7 @@ def main():
             mMin = AggShockExample.solution[0].mNrmMin(M)
             c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin,M*np.ones_like(m_grid))
             plt.plot(m_grid+mMin,c_at_this_M)
+        plt.ylim(0.,None)
         plt.show()
 
     if solve_agg_shocks_market:
@@ -1813,6 +1814,7 @@ def main():
             mMin = AggShockExample.solution[0].mNrmMin(M)
             c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin,M*np.ones_like(m_grid))
             plt.plot(m_grid+mMin,c_at_this_M)
+        plt.ylim(0.,None)
         plt.show()
 
     ######### EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType ###########
@@ -1843,6 +1845,7 @@ def main():
                 mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)
                 c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin,M*np.ones_like(m_grid))
                 plt.plot(m_grid+mMin,c_at_this_M)
+            plt.ylim(0.,None)
             plt.show()
 
     if solve_markov_market:
@@ -1861,6 +1864,7 @@ def main():
                 mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)
                 c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin,M*np.ones_like(m_grid))
                 plt.plot(m_grid+mMin,c_at_this_M)
+            plt.ylim(0.,None)
             plt.show()
 
     if solve_krusell_smith:

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -17,7 +17,7 @@
                            CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP,\
                            getPercentiles
 from HARK.simulation import drawLognormal, drawDiscrete, drawUniform
-from .ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType
+from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType
 
 utility       = CRRAutility
 utilityP      = CRRAutilityP
@@ -1276,7 +1276,7 @@ def updatepLvlNextFunc(self):
 ###############################################################################
 
 def main():
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import plotFuncs
     from time import clock
     import matplotlib.pyplot as plt
@@ -1306,6 +1306,7 @@ def main():
         C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp))
         plt.plot(M_temp,C)
     plt.xlim(0.,20.)
+    plt.ylim(0.,None)
     plt.xlabel('Market resource level mLvl')
     plt.ylabel('Consumption level cLvl')
     plt.show()
@@ -1328,6 +1329,7 @@ def main():
         C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp))
         plt.plot(M_temp/p,C/p)
     plt.xlim(0.,20.)
+    plt.ylim(0.,None)
     plt.xlabel('Normalized market resources mNrm')
     plt.ylabel('Normalized consumption cNrm')
     plt.show()
@@ -1382,6 +1384,7 @@ def main():
         C = PersistentExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp))
         plt.plot(M_temp,C)
     plt.xlim(0.,20.)
+    plt.ylim(0.,None)
     plt.xlabel('Market resource level mLvl')
     plt.ylabel('Consumption level cLvl')
     plt.show()

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -2426,7 +2426,7 @@ def constructAssetsGrid(parameters):
 ####################################################################################################
 
 def main():
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import plotFuncsDer, plotFuncs
     from time import clock
     mystr = lambda number : "{:.4f}".format(number)

HARK/ConsumptionSaving/ConsMarkovModel.py

@@ -9,10 +9,8 @@
 from builtins import range
 from copy import deepcopy
 import numpy as np
-from .ConsIndShockModel import ConsIndShockSolver, ValueFunc, MargValueFunc, ConsumerSolution, IndShockConsumerType
-from .ConsAggShockModel import AggShockConsumerType
-from HARK.utilities import combineIndepDstns, warnings  # Because of "patch" to warnings modules
-from HARK import Market, HARKobject
+from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSolver, ValueFunc, \
+                             MargValueFunc, ConsumerSolution, IndShockConsumerType
 from HARK.simulation import drawDiscrete, drawUniform
 from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
 from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \
@@ -974,7 +972,7 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True):
 ###############################################################################
 
 def main():
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import plotFuncs
     from time import clock
     from copy import copy
@@ -1026,7 +1024,7 @@ def main():
     start_time = clock()
     SerialUnemploymentExample.solve()
     end_time = clock()
-    print('Solving a Markov consumer took ' + mystr(end_time-start_time) + ' seconds.')
+    print('Solving a Markov consumer with serially correlated unemployment took ' + mystr(end_time-start_time) + ' seconds.')
     print('Consumption functions for each discrete state:')
     plotFuncs(SerialUnemploymentExample.solution[0].cFunc,0,50)
     if SerialUnemploymentExample.vFuncBool:

HARK/ConsumptionSaving/ConsMedModel.py

@@ -11,14 +11,13 @@
 from HARK.utilities import approxLognormal, addDiscreteOutcomeConstantMean, CRRAutilityP_inv,\
                            CRRAutility, CRRAutility_inv, CRRAutility_invP, CRRAutilityPP,\
                            makeGridExpMult, NullFunc
-from HARK.simulation import drawLognormal
-from .ConsIndShockModel import ConsumerSolution
+from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution
 from HARK.interpolation import BilinearInterpOnInterp1D, TrilinearInterp, BilinearInterp, CubicInterp,\
                                LinearInterp, LowerEnvelope3D, UpperEnvelope, LinearInterpOnInterp1D,\
                                VariableLowerBoundFunc3D
-from .ConsGenIncProcessModel import ConsGenIncProcessSolver, PersistentShockConsumerType,\
-                                     ValueFunc2D, MargValueFunc2D, MargMargValueFunc2D, \
-                                     VariableLowerBoundFunc2D
+from HARK.ConsumptionSaving.ConsGenIncProcessModel import ConsGenIncProcessSolver,\
+                            PersistentShockConsumerType, ValueFunc2D, MargValueFunc2D,\
+                            MargMargValueFunc2D, VariableLowerBoundFunc2D
 from copy import deepcopy
 
 utility_inv   = CRRAutility_inv
@@ -1359,7 +1358,7 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR
 ###############################################################################
 
 def main():
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
     from HARK.utilities import CRRAutility_inv
     from time import clock
     import matplotlib.pyplot as plt

HARK/ConsumptionSaving/ConsPrefShockModel.py

@@ -12,7 +12,7 @@
 from builtins import range
 import numpy as np
 from HARK.utilities import approxMeanOneLognormal
-from .ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \
+from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \
                                    ValueFunc, MargValueFunc, KinkedRconsumerType, ConsKinkedRsolver
 from HARK.interpolation import LinearInterpOnInterp1D, LinearInterp, CubicInterp, LowerEnvelope
 
@@ -594,7 +594,7 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn,
 ###############################################################################
 
 def main():
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
     import matplotlib.pyplot as plt
     from HARK.utilities import plotFuncs
     from time import clock
@@ -618,13 +618,17 @@ def main():
         PrefShk = PrefShockExample.PrefShkDstn[0][1][j]
         c = PrefShockExample.solution[0].cFunc(m,PrefShk*np.ones_like(m))
         plt.plot(m,c)
+    plt.xlim([0.,None])
+    plt.ylim([0.,None])
     plt.show()
 
     print('Consumption function (and MPC) when shock=1:')
     c = PrefShockExample.solution[0].cFunc(m,np.ones_like(m))
     k = PrefShockExample.solution[0].cFunc.derivativeX(m,np.ones_like(m))
     plt.plot(m,c)
     plt.plot(m,k)
+    plt.xlim([0.,None])
+    plt.ylim([0.,None])
     plt.show()
 
     if PrefShockExample.vFuncBool:
@@ -657,13 +661,15 @@ def main():
         PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j]
         c = KinkyPrefExample.solution[0].cFunc(m,PrefShk*np.ones_like(m))
         plt.plot(m,c)
+    plt.ylim([0.,None])
     plt.show()
 
     print('Consumption function (and MPC) when shock=1:')
     c = KinkyPrefExample.solution[0].cFunc(m,np.ones_like(m))
     k = KinkyPrefExample.solution[0].cFunc.derivativeX(m,np.ones_like(m))
     plt.plot(m,c)
     plt.plot(m,k)
+    plt.ylim([0.,None])
     plt.show()
 
     if KinkyPrefExample.vFuncBool:

HARK/ConsumptionSaving/ConsRepAgentModel.py

@@ -11,7 +11,7 @@
 import numpy as np
 from HARK.interpolation import LinearInterp
 from HARK.simulation import drawUniform, drawDiscrete
-from .ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc
+from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc
 
 def solveConsRepAgent(solution_next,DiscFac,CRRA,IncomeDstn,CapShare,DeprFac,PermGroFac,aXtraGrid):
     '''
@@ -331,7 +331,7 @@ def main():
     from copy import deepcopy
     from time import clock
     from HARK.utilities import plotFuncs
-    from . import ConsumerParameters as Params
+    import HARK.ConsumptionSaving.ConsumerParameters as Params
 
     # Make a quick example dictionary
     RA_params = deepcopy(Params.init_idiosyncratic_shocks)