Merge remote-tracking branch 'upstream/master' into pr/1389

.pre-commit-config.yaml

@@ -2,49 +2,17 @@ exclude: Documentation/example_notebooks/
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.4
+    rev: v0.3.2
     hooks:
       - id: ruff
-        types_or: [jupyter]
+        types_or: [ python, pyi, jupyter ]
+        args:
+          - --fix
       - id: ruff-format
-        args: [--check]
-        types_or: [jupyter]
-
-  - repo: https://github.com/psf/black
-    rev: 23.7.0
-    hooks:
-      - id: black
-        exclude: ^examples/
-
-  - repo: https://github.com/asottile/pyupgrade
-    rev: v3.10.1
-    hooks:
-      - id: pyupgrade
-        args: ["--py38-plus"]
-        exclude: ^examples/
-
-  - repo: https://github.com/asottile/blacken-docs
-    rev: 1.15.0
-    hooks:
-      - id: blacken-docs
-        exclude: ^examples/
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: ["--profile", "black", "--filter-files", "--skip", "__init__.py"]
-        exclude: ^examples/
-
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.1
-    hooks:
-      - id: prettier
-        exclude: ^examples/
+        types_or: [ python, pyi, jupyter ]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace

Documentation/CHANGELOG.md

@@ -8,17 +8,41 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.15.0
+
+Release Date: TBA
+
+### Major Changes
+
+### Minor Changes
+
+- Add option to pass pre-built grid to `LinearFast`. [1388](https://github.com/econ-ark/HARK/pull/1388)
+- Moves calculation of stable points out of ConsIndShock solver, into method called by post_solve [#1349](https://github.com/econ-ark/HARK/pull/1349)
+
+### 0.14.1
+
+Release date: February 28, 2024
+
+#### Major Changes
+
+none
+
+#### Minor Changes
+
+- Fixes a bug in make_figs arising from the metadata argument being incompatible with jpg. [#1386](https://github.com/econ-ark/HARK/pull/1386)
+- Reverts behavior of the repr method of the Model class, so that long strings aren't generated. Full description is available with describe(). [#1390](https://github.com/econ-ark/HARK/pull/1390)
+
 ### 0.14.0
 
 Release Date: February 12, 2024
 
-### Major Changes
+#### Major Changes
 
 - Adds `HARK.core.AgentPopulation` class to represent a population of agents with ex-ante heterogeneous parametrizations as distributions. [#1237](https://github.com/econ-ark/HARK/pull/1237)
 - Adds `HARK.core.Parameters` class to represent a collection of time varying and time invariant parameters in a model. [#1240](https://github.com/econ-ark/HARK/pull/1240)
 - Adds `HARK.simulation.monte_carlo` module for generic Monte Carlo simulation functions using Python model configurations. [1296](https://github.com/econ-ark/HARK/pull/1296)
 
-### Minor Changes
+#### Minor Changes
 
 - Adds option `sim_common_Rrisky` to control whether risky-asset models draw common or idiosyncratic returns in simulation. [#1250](https://github.com/econ-ark/HARK/pull/1250),[#1253](https://github.com/econ-ark/HARK/pull/1253)
 - Addresses [#1255](https://github.com/econ-ark/HARK/issues/1255). Makes age-varying stochastic returns possible and draws from their discretized version. [#1262](https://github.com/econ-ark/HARK/pull/1262)
@@ -35,7 +59,7 @@ Release Date: February 12, 2024
 
 Release Date: February 16, 2023
 
-### Major Changes
+#### Major Changes
 
 - Updates the DCEGM tools to address the flaws identified in [issue #1062](https://github.com/econ-ark/HARK/issues/1062). PR: [1100](https://github.com/econ-ark/HARK/pull/1100).
 - Updates `IndexDstn`, introducing the option to use an existing RNG instead of creating a new one, and creating and storing all the conditional distributions at initialization. [1104](https://github.com/econ-ark/HARK/pull/1104)
@@ -62,7 +86,7 @@ Release Date: February 16, 2023
 - Reorganizes `HARK.distribution`. All distributions now inherit all features from `scipy.stats`. New `ContinuousFrozenDistribution` and `DiscreteFrozenDistribution` to use `scipy.stats` distributions not yet implemented in HARK. New `Distribution.discretize(N, method = "***")` replaces `Distribution.approx(N)`. New `DiscreteDistribution.limit` attribute describes continuous origin and discretization method. [#1197](https://github.com/econ-ark/HARK/pull/1197).
 - Creates new class of _labeled_ models under `ConsLabeledModel` that use xarray for more expressive modeling of underlying mathematical and economics variables. [#1177](https://github.com/econ-ark/HARK/pull/1177)
 
-### Minor Changes
+#### Minor Changes
 
 - Updates the lognormal-income-process constructor from `ConsIndShockModel.py` to use `IndexDistribution`. [#1024](https://github.com/econ-ark/HARK/pull/1024), [#1115](https://github.com/econ-ark/HARK/pull/1115)
 - Allows for age-varying unemployment probabilities and replacement incomes with the lognormal income process constructor. [#1112](https://github.com/econ-ark/HARK/pull/1112)

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -4,6 +4,7 @@
 basic solver.  Also includes a subclass of Market called CobbDouglas economy,
 used for solving "macroeconomic" models with aggregate shocks.
 """
+
 from copy import deepcopy
 
 import numpy as np

HARK/ConsumptionSaving/ConsBequestModel.py

@@ -1,5 +1,4 @@
-"""
-Classes to solve consumption-saving models with a bequest motive and
+"""Classes to solve consumption-saving models with a bequest motive and
 idiosyncratic shocks to income and wealth. All models here assume
 separable CRRA utility of consumption and Stone-Geary utility of
 savings with geometric discounting of the continuation value and
@@ -11,7 +10,6 @@
 """
 
 import numpy as np
-
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsIndShockSolver,
     IndShockConsumerType,
@@ -37,51 +35,44 @@
 
 
 class BequestWarmGlowConsumerType(IndShockConsumerType):
-    time_vary_ = IndShockConsumerType.time_vary_ + [
+    time_inv_ = IndShockConsumerType.time_inv_ + [
         "BeqCRRA",
-        "BeqFac",
         "BeqShift",
     ]
 
+    time_vary_ = IndShockConsumerType.time_vary_ + [
+        "BeqFac",
+    ]
+
     def __init__(self, **kwds):
         params = init_wealth_in_utility.copy()
         params.update(kwds)
 
         super().__init__(**params)
 
-        self.solve_one_period = make_one_period_oo_solver(BequestWarmGlowConsumerSolver)
+        self.solve_one_period = make_one_period_oo_solver(
+            BequestWarmGlowConsumerSolver,
+        )
 
     def update(self):
         super().update()
         self.update_parameters()
 
     def update_parameters(self):
-        if isinstance(self.BeqCRRA, (int, float)):
-            self.BeqCRRA = [self.BeqCRRA] * self.T_cycle
-        elif len(self.BeqCRRA) == 1:
-            self.BeqCRRA *= self.T_cycle
-        elif len(self.BeqCRRA) != self.T_cycle:
-            raise ValueError(
-                "Bequest CRRA parameter must be a single value or a list of length T_cycle"
-            )
+        if not isinstance(self.BeqCRRA, (int, float)):
+            raise ValueError("Bequest CRRA parameter must be a single value.")
 
         if isinstance(self.BeqFac, (int, float)):
             self.BeqFac = [self.BeqFac] * self.T_cycle
         elif len(self.BeqFac) == 1:
             self.BeqFac *= self.T_cycle
         elif len(self.BeqFac) != self.T_cycle:
             raise ValueError(
-                "Bequest relative value parameter must be a single value or a list of length T_cycle"
+                "Bequest relative value parameter must be a single value or a list of length T_cycle",
             )
 
-        if isinstance(self.BeqShift, (int, float)):
-            self.BeqShift = [self.BeqShift] * self.T_cycle
-        elif len(self.BeqShift) == 1:
-            self.BeqShift *= self.T_cycle
-        elif len(self.BeqShift) != self.T_cycle:
-            raise ValueError(
-                "Bequest Stone-Geary parameter must be a single value or a list of length T_cycle"
-            )
+        if not isinstance(self.BeqShift, (int, float)):
+            raise ValueError("Bequest Stone-Geary parameter must be a single value.")
 
     def update_solution_terminal(self):
         if self.TermBeqFac == 0.0:  # No terminal bequest
@@ -90,7 +81,9 @@ def update_solution_terminal(self):
             utility = UtilityFuncCRRA(self.CRRA)
 
             warm_glow = UtilityFuncStoneGeary(
-                self.TermBeqCRRA, factor=self.TermBeqFac, shifter=self.TermBeqShift
+                self.TermBeqCRRA,
+                factor=self.TermBeqFac,
+                shifter=self.TermBeqShift,
             )
 
             aNrmGrid = (
@@ -117,7 +110,16 @@ def update_solution_terminal(self):
             self.solution_terminal.mNrmMin = 0.0
 
 
-class BequestWarmGlowPortfolioType(PortfolioConsumerType, BequestWarmGlowConsumerType):
+class BequestWarmGlowPortfolioType(PortfolioConsumerType):
+    time_inv_ = IndShockConsumerType.time_inv_ + [
+        "BeqCRRA",
+        "BeqShift",
+    ]
+
+    time_vary_ = IndShockConsumerType.time_vary_ + [
+        "BeqFac",
+    ]
+
     def __init__(self, **kwds):
         params = init_portfolio_bequest.copy()
         params.update(kwds)
@@ -127,46 +129,94 @@ def __init__(self, **kwds):
         super().__init__(**params)
 
         self.solve_one_period = make_one_period_oo_solver(
-            BequestWarmGlowPortfolioSolver
+            BequestWarmGlowPortfolioSolver,
         )
 
     def update(self):
-        PortfolioConsumerType.update(self)
+        super().update()
         self.update_parameters()
 
+    def update_parameters(self):
+        if not isinstance(self.BeqCRRA, (int, float)):
+            raise ValueError("Bequest CRRA parameter must be a single value.")
+
+        if isinstance(self.BeqFac, (int, float)):
+            self.BeqFac = [self.BeqFac] * self.T_cycle
+        elif len(self.BeqFac) == 1:
+            self.BeqFac *= self.T_cycle
+        elif len(self.BeqFac) != self.T_cycle:
+            raise ValueError(
+                "Bequest relative value parameter must be a single value or a list of length T_cycle",
+            )
+
+        if not isinstance(self.BeqShift, (int, float)):
+            raise ValueError("Bequest Stone-Geary parameter must be a single value.")
+
     def update_solution_terminal(self):
-        BequestWarmGlowConsumerType.update_solution_terminal(self)
-
-        # Consume all market resources: c_T = m_T
-        cFuncAdj_terminal = self.solution_terminal.cFunc
-        cFuncFxd_terminal = lambda m, s: self.solution_terminal.cFunc(m)
-
-        # Risky share is irrelevant-- no end-of-period assets; set to zero
-        ShareFuncAdj_terminal = ConstantFunction(0.0)
-        ShareFuncFxd_terminal = IdentityFunction(i_dim=1, n_dims=2)
-
-        # Value function is simply utility from consuming market resources
-        vFuncAdj_terminal = self.solution_terminal.vFunc
-        vFuncFxd_terminal = lambda m, s: self.solution_terminal.vFunc(m)
-
-        # Marginal value of market resources is marg utility at the consumption function
-        vPfuncAdj_terminal = self.solution_terminal.vPfunc
-        dvdmFuncFxd_terminal = lambda m, s: self.solution_terminal.vPfunc(m)
-        # No future, no marg value of Share
-        dvdsFuncFxd_terminal = ConstantFunction(0.0)
-
-        # Construct the terminal period solution
-        self.solution_terminal = PortfolioSolution(
-            cFuncAdj=cFuncAdj_terminal,
-            ShareFuncAdj=ShareFuncAdj_terminal,
-            vFuncAdj=vFuncAdj_terminal,
-            vPfuncAdj=vPfuncAdj_terminal,
-            cFuncFxd=cFuncFxd_terminal,
-            ShareFuncFxd=ShareFuncFxd_terminal,
-            vFuncFxd=vFuncFxd_terminal,
-            dvdmFuncFxd=dvdmFuncFxd_terminal,
-            dvdsFuncFxd=dvdsFuncFxd_terminal,
-        )
+        if self.TermBeqFac == 0.0:  # No terminal bequest
+            super().update_solution_terminal()
+        else:
+            utility = UtilityFuncCRRA(self.CRRA)
+
+            warm_glow = UtilityFuncStoneGeary(
+                self.TermBeqCRRA,
+                factor=self.TermBeqFac,
+                shifter=self.TermBeqShift,
+            )
+
+            aNrmGrid = (
+                np.append(0.0, self.aXtraGrid)
+                if self.TermBeqShift != 0.0
+                else self.aXtraGrid
+            )
+            cNrmGrid = utility.derinv(warm_glow.der(aNrmGrid))
+            vGrid = utility(cNrmGrid) + warm_glow(aNrmGrid)
+            cNrmGridW0 = np.append(0.0, cNrmGrid)
+            mNrmGridW0 = np.append(0.0, aNrmGrid + cNrmGrid)
+            vNvrsGridW0 = np.append(0.0, utility.inv(vGrid))
+
+            cFunc_term = LinearInterp(mNrmGridW0, cNrmGridW0)
+            vNvrsFunc_term = LinearInterp(mNrmGridW0, vNvrsGridW0)
+            vFunc_term = ValueFuncCRRA(vNvrsFunc_term, self.CRRA)
+            vPfunc_term = MargValueFuncCRRA(cFunc_term, self.CRRA)
+            vPPfunc_term = MargMargValueFuncCRRA(cFunc_term, self.CRRA)
+
+            self.solution_terminal.cFunc = cFunc_term
+            self.solution_terminal.vFunc = vFunc_term
+            self.solution_terminal.vPfunc = vPfunc_term
+            self.solution_terminal.vPPfunc = vPPfunc_term
+            self.solution_terminal.mNrmMin = 0.0
+
+            # Consume all market resources: c_T = m_T
+            cFuncAdj_terminal = self.solution_terminal.cFunc
+            cFuncFxd_terminal = lambda m, s: self.solution_terminal.cFunc(m)
+
+            # Risky share is irrelevant-- no end-of-period assets; set to zero
+            ShareFuncAdj_terminal = ConstantFunction(0.0)
+            ShareFuncFxd_terminal = IdentityFunction(i_dim=1, n_dims=2)
+
+            # Value function is simply utility from consuming market resources
+            vFuncAdj_terminal = self.solution_terminal.vFunc
+            vFuncFxd_terminal = lambda m, s: self.solution_terminal.vFunc(m)
+
+            # Marginal value of market resources is marg utility at the consumption function
+            vPfuncAdj_terminal = self.solution_terminal.vPfunc
+            dvdmFuncFxd_terminal = lambda m, s: self.solution_terminal.vPfunc(m)
+            # No future, no marg value of Share
+            dvdsFuncFxd_terminal = ConstantFunction(0.0)
+
+            # Construct the terminal period solution
+            self.solution_terminal = PortfolioSolution(
+                cFuncAdj=cFuncAdj_terminal,
+                ShareFuncAdj=ShareFuncAdj_terminal,
+                vFuncAdj=vFuncAdj_terminal,
+                vPfuncAdj=vPfuncAdj_terminal,
+                cFuncFxd=cFuncFxd_terminal,
+                ShareFuncFxd=ShareFuncFxd_terminal,
+                vFuncFxd=vFuncFxd_terminal,
+                dvdmFuncFxd=dvdmFuncFxd_terminal,
+                dvdsFuncFxd=dvdsFuncFxd_terminal,
+            )
 
 
 class BequestWarmGlowConsumerSolver(ConsIndShockSolver):
@@ -212,6 +262,28 @@ def def_utility_funcs(self):
 
         self.warm_glow = UtilityFuncStoneGeary(self.BeqCRRA, BeqFacEff, self.BeqShift)
 
+    def def_BoroCnst(self, BoroCnstArt):
+        self.BoroCnstNat = (
+            (self.solution_next.mNrmMin - self.TranShkMinNext)
+            * (self.PermGroFac * self.PermShkMinNext)
+            / self.Rfree
+        )
+
+        self.BoroCnstNat = np.max([self.BoroCnstNat, -self.BeqShift])
+
+        if BoroCnstArt is None:
+            self.mNrmMinNow = self.BoroCnstNat
+        else:
+            self.mNrmMinNow = np.max([self.BoroCnstNat, BoroCnstArt])
+        if self.BoroCnstNat < self.mNrmMinNow:
+            self.MPCmaxEff = 1.0
+        else:
+            self.MPCmaxEff = self.MPCmaxNow
+
+        self.cFuncNowCnst = LinearInterp(
+            np.array([self.mNrmMinNow, self.mNrmMinNow + 1]), np.array([0.0, 1.0])
+        )
+
     def calc_EndOfPrdvP(self):
         EndofPrdvP = super().calc_EndOfPrdvP()