Merge branch 'master' into i583-bl

Documentation/packaging.md

@@ -43,7 +43,7 @@ Follow the directions within https://packaging.python.org/tutorials/packaging-pr
 Deactivate the virtualenv, and make or use a different Python 2.7 virtualenv to test that you can `pip install` the `sdist` you've just made. One thing you can do to quickly verify that the package installs OK:
 
 `>>> import HARK.simulation`
-`>>> HARK.simulation.drawBernoulli(5)`
+`>>> HARK.simulation.Bernoulli().draw(5)`
 
 You should get something like:
 

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -15,7 +15,7 @@
  VariableLowerBoundFunc2D, BilinearInterp, LowerEnvelope2D, UpperEnvelope
 from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv,\
  CRRAutility_invP, CRRAutility_inv 
-from HARK.simulation import drawUniform
+from HARK.distribution import Uniform
 from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType, init_idiosyncratic_shocks
 from HARK import HARKobject, Market, AgentType
 from copy import deepcopy
@@ -89,7 +89,7 @@ class AggShockConsumerType(IndShockConsumerType):
  evolves over time and take aggregate shocks into account when making their
  decision about how much to consume.
  '''
- def __init__(self, time_flow=True, **kwds):
+ def __init__(self, **kwds):
  '''
  Make a new instance of AggShockConsumerType, an extension of
  IndShockConsumerType. Sets appropriate solver and input lists.
@@ -98,7 +98,7 @@ def __init__(self, time_flow=True, **kwds):
  params.update(kwds)
 
  AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_),
- time_flow=time_flow, pseudo_terminal=False, **params)
+ pseudo_terminal=False, **params)
 
  # Add consumer-type specific objects, copying to create independent versions
  self.time_vary = deepcopy(IndShockConsumerType.time_vary_)
@@ -1629,7 +1629,7 @@ def makeMrkvHist(self):
 
  # Add histories until each state has been visited at least state_T_min times
  while go:
- draws = drawUniform(N=self.act_T_orig, seed=loops)
+ draws = Uniform().draw(N=self.act_T_orig, seed=loops)
  for s in range(draws.size): # Add act_T_orig more periods
  MrkvNow_hist[t] = MrkvNow
  MrkvNow = np.searchsorted(cutoffs[MrkvNow, :], draws[s])

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -17,7 +17,7 @@
 from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \
  CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP,\
  getPercentiles
-from HARK.simulation import drawLognormal, drawUniform
+from HARK.distribution import Lognormal, Uniform
 from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType, init_idiosyncratic_shocks
 
 __all__ = ['ValueFunc2D', 'MargValueFunc2D', 'MargMargValueFunc2D', 'pLvlFuncAR1',
@@ -985,7 +985,7 @@ 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=0, time_flow=True, **kwds):
+ def __init__(self, cycles=0, **kwds):
  '''
  Instantiate a new ConsumerType with given data.
  See ConsumerParameters.init_explicit_perm_inc for a dictionary of the
@@ -995,8 +995,6 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
  ----------
  cycles : int
  Number of times the sequence of periods should be solved.
- time_flow : boolean
- Whether time is currently "flowing" forward for this instance.
 
  Returns
  -------
@@ -1006,7 +1004,7 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
  params.update(kwds)
 
  # Initialize a basic ConsumerType
- IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **params)
+ IndShockConsumerType.__init__(self, cycles=cycles, **params)
  self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income
 
  def preSolve(self):
@@ -1109,13 +1107,12 @@ def updatepLvlGrid(self):
  -------
  None
  '''
- orig_time = self.time_flow
- self.timeFwd()
  LivPrbAll = np.array(self.LivPrb)
 
  # Simulate the distribution of persistent income levels by t_cycle in a lifecycle model
  if self.cycles == 1:
- pLvlNow = drawLognormal(self.AgentCount, mu=self.pLvlInitMean, sigma=self.pLvlInitStd, seed=31382)
+ pLvlNow = Lognormal(self.pLvlInitMean,
+ sigma=self.pLvlInitStd,).draw(self.AgentCount, seed=31382)
  pLvlGrid = [] # empty list of time-varying persistent income grids
  # Calculate distribution of persistent income in each period of lifecycle
  for t in range(len(self.PermShkStd)):
@@ -1129,14 +1126,15 @@ def updatepLvlGrid(self):
  # Calculate "stationary" distribution in infinite horizon (might vary across periods of cycle)
  elif self.cycles == 0:
  T_long = 1000 # Number of periods to simulate to get to "stationary" distribution
- pLvlNow = drawLognormal(self.AgentCount, mu=self.pLvlInitMean, sigma=self.pLvlInitStd, seed=31382)
+ pLvlNow = Lognormal(mu=self.pLvlInitMean,
+ sigma=self.pLvlInitStd).draw(self.AgentCount, seed=31382)
  t_cycle = np.zeros(self.AgentCount, dtype=int)
  for t in range(T_long):
  LivPrb = LivPrbAll[t_cycle] # Determine who dies and replace them with newborns
- draws = drawUniform(self.AgentCount, seed=t)
+ draws = Uniform().draw(self.AgentCount, seed=t)
  who_dies = draws > LivPrb
- pLvlNow[who_dies] = drawLognormal(np.sum(who_dies), mu=self.pLvlInitMean,
-  sigma=self.pLvlInitStd, seed=t+92615)
+ pLvlNow[who_dies] = Lognormal(self.pLvlInitMean,
+ self.pLvlInitStd).draw(np.sum(who_dies),  seed=t+92615)
  t_cycle[who_dies] = 0
 
  for j in range(self.T_cycle): # Update persistent income
@@ -1161,8 +1159,6 @@ def updatepLvlGrid(self):
  # Store the result and add attribute to time_vary
  self.pLvlGrid = pLvlGrid
  self.addToTimeVary('pLvlGrid')
- if not orig_time:
- self.timeRev()
 
  def simBirth(self, which_agents):
  '''
@@ -1181,10 +1177,13 @@ def simBirth(self, which_agents):
  '''
  # Get and store states for newly born agents
  N = np.sum(which_agents) # Number of new consumers to make
- aNrmNow_new = drawLognormal(N, mu=self.aNrmInitMean, sigma=self.aNrmInitStd,
+ aNrmNow_new = Lognormal(self.aNrmInitMean,
+ self.aNrmInitStd).draw(
+ N,
  seed=self.RNG.randint(0, 2**31-1))
- self.pLvlNow[which_agents] = drawLognormal(N, mu=self.pLvlInitMean, sigma=self.pLvlInitStd,
- seed=self.RNG.randint(0, 2**31-1))
+ self.pLvlNow[which_agents] = Lognormal(self.pLvlInitMean,
+ self.pLvlInitStd).draw(N, 
+ seed=self.RNG.randint(0, 2**31-1))
  self.aLvlNow[which_agents] = aNrmNow_new*self.pLvlNow[which_agents]
  self.t_age[which_agents] = 0 # How many periods since each agent was born
  self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in
@@ -1278,18 +1277,12 @@ def updatepLvlNextFunc(self):
  -------
  None
  '''
- orig_time = self.time_flow
- self.timeFwd()
-
  pLvlNextFunc = []
  for t in range(self.T_cycle):
  pLvlNextFunc.append(LinearInterp(np.array([0., 1.]), np.array([0., self.PermGroFac[t]])))
 
  self.pLvlNextFunc = pLvlNextFunc
  self.addToTimeVary('pLvlNextFunc')
- if not orig_time:
- self.timeRev()
-
 
 ###############################################################################
 
@@ -1330,7 +1323,6 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
 
  GenIncProcessConsumerType.__init__(self,
  cycles=cycles,
- time_flow=time_flow,
  **params)
 
  def updatepLvlNextFunc(self):
@@ -1348,8 +1340,6 @@ def updatepLvlNextFunc(self):
  -------
  None
  '''
- orig_time = self.time_flow
- self.timeFwd()
 
  pLvlNextFunc = []
  pLogMean = self.pLvlInitMean # Initial mean (log) persistent income
@@ -1360,5 +1350,3 @@ def updatepLvlNextFunc(self):
 
  self.pLvlNextFunc = pLvlNextFunc
  self.addToTimeVary('pLvlNextFunc')
- if not orig_time:
- self.timeRev()

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -24,7 +24,7 @@
 from HARK import AgentType, Solution, NullFunc, HARKobject
 from HARK.utilities import warnings # Because of "patch" to warnings modules
 from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
-from HARK.simulation import drawLognormal, drawUniform
+from HARK.distribution import Lognormal, Uniform
 from HARK.distribution import DiscreteDistribution, approxMeanOneLognormal, addDiscreteOutcomeConstantMean, combineIndepDstns 
 from HARK.utilities import makeGridExpMult, CRRAutility, CRRAutilityP, \
  CRRAutilityPP, CRRAutilityP_inv, CRRAutility_invP, CRRAutility_inv, \
@@ -1621,7 +1621,6 @@ class PerfForesightConsumerType(AgentType):
 
  def __init__(self,
  cycles=1,
- time_flow=True,
  verbose=False,
  quiet=False,
  **kwds):
@@ -1634,8 +1633,6 @@ def __init__(self,
  ----------
  cycles : int
  Number of times the sequence of periods should be solved.
- time_flow : boolean
- Whether time is currently "flowing" forward for this instance.
 
  Returns
  -------
@@ -1648,7 +1645,8 @@ def __init__(self,
 
  # Initialize a basic AgentType
  AgentType.__init__(self,solution_terminal=deepcopy(self.solution_terminal_),
- cycles=cycles,time_flow=time_flow,pseudo_terminal=False,**kwds)
+ cycles=cycles,
+ pseudo_terminal=False,**kwds)
 
  # Add consumer-type specific objects, copying to create independent versions
  self.time_vary = deepcopy(self.time_vary_)
@@ -1745,9 +1743,11 @@ def simBirth(self,which_agents):
  '''
  # Get and store states for newly born agents
  N = np.sum(which_agents) # Number of new consumers to make
- self.aNrmNow[which_agents] = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1))
+ self.aNrmNow[which_agents] = Lognormal(mu=self.aNrmInitMean,
+ sigma=self.aNrmInitStd).draw(N,seed=self.RNG.randint(0,2**31-1))
  pLvlInitMeanNow = self.pLvlInitMean + np.log(self.PlvlAggNow) # Account for newer cohorts having higher permanent income
- self.pLvlNow[which_agents] = drawLognormal(N,mu=pLvlInitMeanNow,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1))
+ self.pLvlNow[which_agents] = Lognormal(pLvlInitMeanNow,
+ self.pLvlInitStd,).draw(N,seed=self.RNG.randint(0,2**31-1))
  self.t_age[which_agents] = 0 # How many periods since each agent was born
  self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in
  return None
@@ -1769,7 +1769,7 @@ def simDeath(self):
  # Determine who dies
  DiePrb_by_t_cycle = 1.0 - np.asarray(self.LivPrb)
  DiePrb = DiePrb_by_t_cycle[self.t_cycle-1] # Time has already advanced, so look back one
- DeathShks = drawUniform(N=self.AgentCount,seed=self.RNG.randint(0,2**31-1))
+ DeathShks = Uniform().draw(N=self.AgentCount,seed=self.RNG.randint(0,2**31-1))
  which_agents = DeathShks < DiePrb
  if self.T_age is not None: # Kill agents that have lived for too many periods
  too_old = self.t_age >= self.T_age
@@ -2045,7 +2045,6 @@ class IndShockConsumerType(PerfForesightConsumerType):
 
  def __init__(self,
  cycles=1,
- time_flow=True,
  verbose=False,
  quiet=False,
  **kwds):
@@ -2072,7 +2071,6 @@ def __init__(self,
  # Initialize a basic AgentType
  PerfForesightConsumerType.__init__(self,
  cycles=cycles,
- time_flow=time_flow,
  verbose=verbose,
  quiet=quiet,
  **params)
@@ -2094,15 +2092,11 @@ def updateIncomeProcess(self):
  -----------
  none
  '''
- original_time = self.time_flow
- self.timeFwd()
  IncomeDstn, PermShkDstn, TranShkDstn = constructLognormalIncomeProcessUnemployment(self)
  self.IncomeDstn = IncomeDstn
  self.PermShkDstn = PermShkDstn
  self.TranShkDstn = TranShkDstn
  self.addToTimeVary('IncomeDstn','PermShkDstn','TranShkDstn')
- if not original_time:
- self.timeRev()
 
  def updateAssetsGrid(self):
  '''
@@ -2558,7 +2552,7 @@ 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,**kwds):
  '''
  Instantiate a new ConsumerType with given data.
  See ConsumerParameters.init_kinked_R for a dictionary of
@@ -2568,8 +2562,6 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
  ----------
  cycles : int
  Number of times the sequence of periods should be solved.
- time_flow : boolean
- Whether time is currently "flowing" forward for this instance.
 
  Returns
  -------
@@ -2579,7 +2571,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
  params.update(kwds)
 
  # Initialize a basic AgentType
- PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**params)
+ PerfForesightConsumerType.__init__(self,
+ cycles=cycles,
+ **params)
 
  # Add consumer-type specific objects, copying to create independent versions
  self.solveOnePeriod = solveConsKinkedR # kinked R solver
@@ -2901,6 +2895,7 @@ def constructAssetsGrid(parameters):
 
  return aXtraGrid
 
+
 # Make a dictionary to specify a lifecycle consumer with a finite horizon
 init_lifecycle = copy(init_idiosyncratic_shocks)
 init_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02]
@@ -2918,4 +2913,3 @@ def constructAssetsGrid(parameters):
 init_cyclical['TranShkStd'] = [0.1,0.1,0.1,0.1]
 init_cyclical['LivPrb'] = 4*[0.98]
 init_cyclical['T_cycle'] = 4
-