From 7fa0abf8bbdb4bdf37e39d758c268fdb6b6c48f3 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 15 Sep 2023 12:11:55 -0400
Subject: [PATCH 01/28] Move addStablePoints from solver to post-solve

Calculating the "stable points" mNrmTrg and mNrmStE is a costly operation that is not useful in most contexts. This commit moves that code out of the solver and into a method on IndShockConsumerType, which is called as part of post_solve only if it is appropriate. The results get put into both self.bilt and self.solution (for compatibility with tests).
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 259 +++++---------------
 1 file changed, 56 insertions(+), 203 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 37ceaff43..d9d2a8e0f 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -397,158 +397,8 @@ def make_cFunc_PF(self):
 
         # Add two attributes to enable calculation of steady state market resources.
         self.Ex_IncNext = 1.0  # Perfect foresight income of 1
-        # Relabeling for compatibility with add_mNrmStE
         self.mNrmMinNow = mNrmNow[0]
 
-    def add_mNrmTrg(self, solution):
-        """
-        Finds value of (normalized) market resources m at which individual consumer
-        expects m not to change.
-        This will exist if the GICNrm holds.
-
-        https://econ-ark.github.io/BufferStockTheory#Unique-Stable-Points
-
-        Parameters
-        ----------
-        solution : ConsumerSolution
-            Solution to this period's problem, which must have attribute cFunc.
-        Returns
-        -------
-        solution : ConsumerSolution
-            Same solution that was passed, but now with the attribute mNrmStE.
-        """
-
-        # If no uncertainty, return the degenerate targets for the PF model
-        if hasattr(self, "TranShkMinNext"):  # Then it has transitory shocks
-            # Handle the degenerate case where shocks are of size zero
-            if (self.TranShkMinNext == 1.0) and (self.PermShkMinNext == 1.0):
-                # but they are of zero size (and also permanent are zero)
-                if self.GICRaw:  # max of nat and art boro cnst
-                    if type(self.BoroCnstArt) == type(None):
-                        solution.mNrmStE = -self.hNrmNow
-                        solution.mNrmTrg = -self.hNrmNow
-                    else:
-                        bNrmNxt = -self.BoroCnstArt * self.Rfree / self.PermGroFac
-                        solution.mNrmStE = bNrmNxt + 1.0
-                        solution.mNrmTrg = bNrmNxt + 1.0
-                else:  # infinity
-                    solution.mNrmStE = float("inf")
-                    solution.mNrmTrg = float("inf")
-                return solution
-
-        # First find
-        # \bar{\mathcal{R}} = E_t[R/Gamma_{t+1}] = R/Gamma E_t[1/psi_{t+1}]
-        if type(self) == ConsPerfForesightSolver:
-            Ex_PermShkInv = 1.0
-        else:
-            Ex_PermShkInv = np.dot(1 / self.PermShkValsNext, self.ShkPrbsNext)
-
-        Ex_RNrmFac = (self.Rfree / self.PermGroFac) * Ex_PermShkInv
-
-        # mNrmTrg solves Rcalbar*(m - c(m)) + E[inc_next] = m. Define a
-        # rearranged version.
-        def Ex_m_tp1_minus_m_t(m):
-            return Ex_RNrmFac * (m - solution.cFunc(m)) + self.Ex_IncNext - m
-
-        # Minimum market resources plus next income is okay starting guess
-        m_init_guess = self.mNrmMinNow + self.Ex_IncNext
-        try:
-            mNrmTrg = newton(Ex_m_tp1_minus_m_t, m_init_guess)
-        except:
-            mNrmTrg = None
-
-        # Add mNrmTrg to the solution and return it
-        solution.mNrmTrg = mNrmTrg
-        return solution
-
-    def add_mNrmStE(self, solution):
-        """
-        Finds market resources ratio at which 'balanced growth' is expected.
-        This is the m ratio such that the expected growth rate of the M level
-        matches the expected growth rate of permanent income. This value does
-        not exist if the Growth Impatience Condition does not hold.
-
-        https://econ-ark.github.io/BufferStockTheory#Unique-Stable-Points
-
-        Parameters
-        ----------
-        solution : ConsumerSolution
-            Solution to this period's problem, which must have attribute cFunc.
-        Returns
-        -------
-        solution : ConsumerSolution
-            Same solution that was passed, but now with the attribute mNrmStE
-        """
-        # Probably should test whether GICRaw holds and log error if it does not
-        # using check_conditions
-        # All combinations of c and m that yield E[PermGroFac PermShkVal mNext] = mNow
-        # https://econ-ark.github.io/BufferStockTheory/#The-Individual-Steady-State
-
-        PF_RNrm = self.Rfree / self.PermGroFac
-        # If we are working with a model that permits uncertainty but that
-        # uncertainty has been set to zero, return the correct answer
-        # by hand because in this degenerate case numerical search may
-        # have trouble
-        if hasattr(self, "TranShkMinNext"):  # Then it has transitory shocks
-            if (self.TranShkMinNext == 1.0) and (self.PermShkMinNext == 1.0):
-                # but they are of zero size (and permanent shocks also not there)
-                if self.GICRaw:  # max of nat and art boro cnst
-                    #                    breakpoint()
-                    if type(self.BoroCnstArt) == type(None):
-                        solution.mNrmStE = -self.hNrmNow
-                        solution.mNrmTrg = -self.hNrmNow
-                    else:
-                        bNrmNxt = -self.BoroCnstArt * self.Rfree / self.PermGroFac
-                        solution.mNrmStE = bNrmNxt + 1.0
-                        solution.mNrmTrg = bNrmNxt + 1.0
-                else:  # infinity
-                    solution.mNrmStE = float("inf")
-                    solution.mNrmTrg = float("inf")
-                return solution
-
-        def Ex_PermShk_tp1_times_m_tp1_minus_m_t(mStE):
-            return PF_RNrm * (mStE - solution.cFunc(mStE)) + 1.0 - mStE
-
-        # Minimum market resources plus next income is okay starting guess
-        m_init_guess = self.mNrmMinNow + self.Ex_IncNext
-        try:
-            mNrmStE = newton(Ex_PermShk_tp1_times_m_tp1_minus_m_t, m_init_guess)
-        except:
-            mNrmStE = None
-
-        solution.mNrmStE = mNrmStE
-        return solution
-
-    def add_stable_points(self, solution):
-        """
-        Checks necessary conditions for the existence of the individual steady
-        state and target levels of market resources (see above).
-        If the conditions are satisfied, computes and adds the stable points
-        to the solution.
-
-        Parameters
-        ----------
-        solution : ConsumerSolution
-            Solution to this period's problem, which must have attribute cFunc.
-
-        Returns
-        -------
-        solution : ConsumerSolution
-            Same solution that was provided, augmented with attributes mNrmStE and
-            mNrmTrg, if they exist.
-
-        """
-
-        # 0. There is no non-degenerate steady state for any unconstrained PF model.
-        # 1. There is a non-degenerate SS for constrained PF model if GICRaw holds.
-        # Therefore
-        # Check if  (GICRaw and BoroCnstArt) and if so compute them both
-        thorn = (self.Rfree * self.DiscFacEff) ** (1 / self.CRRA)
-        GICRaw = 1 > thorn / self.PermGroFac
-        if self.BoroCnstArt is not None and GICRaw:
-            solution = self.add_mNrmStE(solution)
-            solution = self.add_mNrmTrg(solution)
-        return solution
 
     def solve(self):
         """
@@ -993,51 +843,7 @@ def add_MPC_and_human_wealth(self, solution):
         solution.MPCmin = self.MPCminNow
         solution.MPCmax = self.MPCmaxEff
         return solution
-
-    def add_stable_points(self, solution):
-        """
-        Checks necessary conditions for the existence of the individual steady
-        state and target levels of market resources (see above).
-        If the conditions are satisfied, computes and adds the stable points
-        to the solution.
-
-        Parameters
-        ----------
-        solution : ConsumerSolution
-            Solution to this period's problem, which must have attribute cFunc.
-
-        Returns
-        -------
-        solution : ConsumerSolution
-            Same solution that was passed, but now with attributes mNrmStE and
-            mNrmTrg, if they exist.
-
-        """
-
-        # 0. Check if GICRaw holds. If so, then mNrmStE will exist. So, compute it.
-        # 1. Check if GICNrm holds. If so, then mNrmTrg will exist. So, compute it.
-
-        thorn = (self.Rfree * self.DiscFacEff) ** (1 / self.CRRA)
-
-        GPFRaw = thorn / self.PermGroFac
-        self.GPFRaw = GPFRaw
-        GPFNrm = (
-            thorn / self.PermGroFac / np.dot(1 / self.PermShkValsNext, self.ShkPrbsNext)
-        )
-        self.GPFNrm = GPFNrm
-        GICRaw = 1 > thorn / self.PermGroFac
-        self.GICRaw = GICRaw
-        GICNrm = 1 > GPFNrm
-        self.GICNrm = GICNrm
-
-        if GICRaw:
-            # find steady state m, if it exists
-            solution = self.add_mNrmStE(solution)
-        if GICNrm:
-            # find target m, if it exists
-            solution = self.add_mNrmTrg(solution)
-
-        return solution
+    
 
     def make_linear_cFunc(self, mNrm, cNrm):
         """
@@ -1077,7 +883,6 @@ def solve(self):
         EndOfPrdvP = self.calc_EndOfPrdvP()
         solution = self.make_basic_solution(EndOfPrdvP, aNrmNow, self.make_linear_cFunc)
         solution = self.add_MPC_and_human_wealth(solution)
-        solution = self.add_stable_points(solution)
 
         return solution
 
@@ -1286,7 +1091,6 @@ def solve(self):
             )
 
         solution = self.add_MPC_and_human_wealth(solution)  # add a few things
-        solution = self.add_stable_points(solution)
 
         # Add the value function if requested, as well as the marginal marginal
         # value function if cubic splines were used (to prepare for next period)
@@ -2049,7 +1853,7 @@ def calc_limiting_values(self):
         solution to an infinite horizon problem. This method should only be called
         when T_cycle=1 and cycles=0, otherwise the values generated are meaningless.
         This method adds the following values to the instance in the dictionary
-        attribute auxiliary.
+        attribute called bilt.
             
         APFac : Absolute Patience Factor
         GPFacRaw : Growth Patience Factor
@@ -3261,7 +3065,7 @@ def calc_limiting_values(self):
         solution to an infinite horizon problem. This method should only be called
         when T_cycle=1 and cycles=0, otherwise the values generated are meaningless.
         This method adds the following values to this instance in the dictionary
-        attribute auxiliary.
+        attribute called bilt.
             
         APFac : Absolute Patience Factor
         GPFacRaw : Growth Patience Factor
@@ -3280,6 +3084,8 @@ def calc_limiting_values(self):
         hNrm : Human wealth divided by permanent income.
         ELogPermShk : Expected log permanent income shock
         WorstPrb : Probability of worst income shock realization
+        Delta_mNrm_ZeroFunc : Linear consumption function where expected change in market resource ratio is zero
+        BalGroFunc : Linear consumption function where the level of market resources grows at the same rate as permanent income
 
         Returns
         -------
@@ -3291,7 +3097,8 @@ def calc_limiting_values(self):
         # Calculate the risk-modified growth impatience factor
         PermShkDstn = self.PermShkDstn[0]
         inv_func = lambda x : x**(-1.)
-        GroCompPermShk = expected(inv_func, PermShkDstn)[0]**(-1.)
+        Ex_PermShkInv = expected(inv_func, PermShkDstn)[0]
+        GroCompPermShk = Ex_PermShkInv**(-1.)
         aux_dict['GPFacMod'] = aux_dict['APFac'] / (self.PermGroFac[0] * GroCompPermShk)
         
         # Calculate the mortality-adjusted growth impatience factor (and version
@@ -3353,6 +3160,14 @@ def calc_limiting_values(self):
         aux_dict['hNrm'] = hNrm
         aux_dict['MPCmax'] = MPCmax
         
+        # Generate the "Delta m = 0" function, which is used to find target market resources
+        Ex_Rnrm = self.Rfree / self.PermGroFac[0] * Ex_PermShkInv
+        aux_dict['Delta_mNrm_ZeroFunc'] = lambda m : (1. - 1./Ex_Rnrm) * m + 1./Ex_Rnrm
+        
+        # Generate the "E[M_tp1 / M_t] = G" function, which is used to find balanced growth market resources
+        PF_Rnrm = self.Rfree / self.PermGroFac[0]
+        aux_dict['BalGroFunc'] = lambda m : (1. - 1./PF_Rnrm) * m + 1./PF_Rnrm
+        
         self.bilt = aux_dict
 
 
@@ -3591,11 +3406,49 @@ def calc_stable_points(self):
         None
         """
         infinite_horizon = self.cycles == 0
+        single_period = self.T_cycle = 1
         if not infinite_horizon:
-            _log.warning(
-                "The calc_stable_points method works only for infinite horizon models."
-            )
+            _log.warning("The calc_stable_points method works only for infinite horizon models.")
+            return
+        if not single_period:
+            _log.warning("The calc_stable_points method works only with a single infinitely repeated period.")
+            return
+        if not hasattr(self, 'conditions'):
+            _log.warning("The calc_limiting_values method must be run before the calc_stable_points method.")
             return
+        if not hasattr(self, 'solution'):
+            _log.warning("The solve method must be run before the calc_stable_points method.")
+            return
+        
+        # Extract balanced growth and delta m_t+1 = 0 functions
+        BalGroFunc = self.bilt['BalGroFunc']
+        Delta_mNrm_ZeroFunc = self.bilt['Delta_mNrm_ZeroFunc']
+        
+        # If the GICRaw holds, then there is a balanced growth market resources ratio
+        if self.conditions['GICRaw']:
+            cFunc = self.solution[0].cFunc
+            func_to_zero = lambda m : BalGroFunc(m) - cFunc(m)
+            m0 = 1.0
+            try:
+                mNrmStE = newton(func_to_zero, m0)
+            except:
+                mNrmStE = np.nan
+                
+            # A target level of assets *might* exist even if the GICMod fails, so check no matter what
+            func_to_zero = lambda m : Delta_mNrm_ZeroFunc(m) - cFunc(m)
+            m0 = 1.0 if np.isnan(mNrmStE) else mNrmStE
+            try:
+                mNrmTrg = newton(func_to_zero, m0, maxiter=200)
+            except:
+                mNrmTrg = np.nan
+        else:
+            mNrmStE = np.nan
+            mNrmTrg = np.nan
+            
+        self.solution[0].mNrmStE = mNrmStE
+        self.solution[0].mNrmTrg = mNrmTrg
+        self.bilt['mNrmStE'] = mNrmStE
+        self.bilt['mNrmTrg'] = mNrmTrg
 
 
     # = Functions for generating discrete income processes and

From 562591470817c6297ef2f77b055f124a82de8dd5 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 15 Sep 2023 12:37:17 -0400
Subject: [PATCH 02/28] Remove leftover references to addStablePoints

Somehow missed these on the previous commit.
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 30 ---------------------
 1 file changed, 30 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index d9d2a8e0f..99c2dc3fc 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -428,8 +428,6 @@ def solve(self):
             MPCmax=self.MPCmax,
         )
 
-        solution = self.add_stable_points(solution)
-
         return solution
 
 
@@ -1223,34 +1221,6 @@ def make_cubic_cFunc(self, mNrm, cNrm):
 
         return cFuncNowUncKink
 
-    def add_stable_points(self, solution):
-        """
-        TODO:
-        Placeholder method for a possible future implementation of stable
-        points in the kinked R model. For now it simply serves to override
-        ConsIndShock's method, which does not apply here given the multiple
-        interest rates.
-
-        Discusson:
-
-        - The target and steady state should exist under the same conditions
-          as in ConsIndShock.
-        - The ConsIndShock code as it stands can not be directly applied
-          because it assumes that R is a constant, and in this model R depends
-          on the level of wealth.
-        - After allowing for wealth-depending interest rates, the existing
-          code might work without modification to add the stable points. If not,
-          it should be possible to find these values by checking within three
-          distinct intervals:
-
-          - From h_min to the lower kink.
-          - From the lower kink to the upper kink
-          - From the upper kink to infinity.
-
-          the stable points must be in one of these regions.
-
-        """
-        return solution
 
     def prepare_to_calc_EndOfPrdvP(self):
         """

From c687ba0049d94c55824a09cb025c47f7a3226a55 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 15 Sep 2023 13:04:40 -0400
Subject: [PATCH 03/28] Move methods to PerfForesightConsumerType

I didn't realize that the unit tests include the "stable points" for a perfect foresight model. This commit moves the methods from IndShockConsumerType to PerfForesightConsumerType.
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 178 +++++++++++---------
 1 file changed, 95 insertions(+), 83 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 99c2dc3fc..e487f6126 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -1394,6 +1394,12 @@ def __init__(self, verbose=1, quiet=False, **kwds):
         self.update_Rfree()  # update interest rate if time varying
 
     def pre_solve(self):
+        '''
+        Method that is run automatically just before solution by backward iteration.
+        Solves the (trivial) terminal period and does a quick check on the borrowing
+        constraint and MaxKinks attribute (only relevant in constrained, infinite
+        horizon problems).
+        '''
         self.update_solution_terminal()  # Solve the terminal period problem
 
         # Fill in BoroCnstArt and MaxKinks if they're not specified or are irrelevant.
@@ -1412,6 +1418,23 @@ def pre_solve(self):
                         "PerfForesightConsumerType requires the attribute MaxKinks to be specified when BoroCnstArt is not None and cycles == 0."
                     )
                 )
+                
+    def post_solve(self):
+        """
+        Method that is run automatically at the end of a call to solve. Here, it
+        simply calls calc_stable_points() if appropriate: an infinite horizon
+        problem with a single repeated period in its cycle.
+        
+        Parameters
+        ----------
+        None
+        
+        Returns
+        -------
+        None
+        """
+        if (self.cycles == 0) and (self.T_cycle == 1):
+            self.calc_stable_points()
 
     def check_restrictions(self):
         """
@@ -1834,6 +1857,8 @@ def calc_limiting_values(self):
         MPCmin : Limiting minimum MPC as market resources go to infinity
         MPCmax : Limiting maximum MPC as market resources approach minimum level.
         hNrm : Human wealth divided by permanent income.
+        Delta_mNrm_ZeroFunc : Linear consumption function where expected change in market resource ratio is zero
+        BalGroFunc : Linear consumption function where the level of market resources grows at the same rate as permanent income
 
         Returns
         -------
@@ -1858,6 +1883,14 @@ def calc_limiting_values(self):
         else:
             aux_dict['hNrm'] = np.inf
             
+        # Generate the "Delta m = 0" function, which is used to find target market resources
+        Ex_Rnrm = self.Rfree / self.PermGroFac[0]
+        aux_dict['Delta_mNrm_ZeroFunc'] = lambda m : (1. - 1./Ex_Rnrm) * m + 1./Ex_Rnrm
+            
+        # Generate the "E[M_tp1 / M_t] = G" function, which is used to find balanced growth market resources
+        PF_Rnrm = self.Rfree / self.PermGroFac[0]
+        aux_dict['BalGroFunc'] = lambda m : (1. - 1./PF_Rnrm) * m + 1./PF_Rnrm
+            
         self.bilt = aux_dict
 
     def check_conditions(self, verbose=None):
@@ -1972,6 +2005,66 @@ def check_conditions(self, verbose=None):
         
         if not self.quiet:
             _log.info(self.bilt['conditions_report'])
+
+
+    def calc_stable_points(self):
+            """
+            If the problem is one that satisfies the conditions required for target ratios of different
+            variables to permanent income to exist, and has been solved to within the self-defined
+            tolerance, this method calculates the target values of market resources.
+
+            Parameters
+            ----------
+            None
+
+            Returns
+            -------
+            None
+            """
+            infinite_horizon = self.cycles == 0
+            single_period = self.T_cycle = 1
+            if not infinite_horizon:
+                _log.warning("The calc_stable_points method works only for infinite horizon models.")
+                return
+            if not single_period:
+                _log.warning("The calc_stable_points method works only with a single infinitely repeated period.")
+                return
+            if not hasattr(self, 'conditions'):
+                _log.warning("The calc_limiting_values method must be run before the calc_stable_points method.")
+                return
+            if not hasattr(self, 'solution'):
+                _log.warning("The solve method must be run before the calc_stable_points method.")
+                return
+            
+            # Extract balanced growth and delta m_t+1 = 0 functions
+            BalGroFunc = self.bilt['BalGroFunc']
+            Delta_mNrm_ZeroFunc = self.bilt['Delta_mNrm_ZeroFunc']
+            
+            # If the GICRaw holds, then there is a balanced growth market resources ratio
+            if self.conditions['GICRaw']:
+                cFunc = self.solution[0].cFunc
+                func_to_zero = lambda m : BalGroFunc(m) - cFunc(m)
+                m0 = 1.0
+                try:
+                    mNrmStE = newton(func_to_zero, m0)
+                except:
+                    mNrmStE = np.nan
+                    
+                # A target level of assets *might* exist even if the GICMod fails, so check no matter what
+                func_to_zero = lambda m : Delta_mNrm_ZeroFunc(m) - cFunc(m)
+                m0 = 1.0 if np.isnan(mNrmStE) else mNrmStE
+                try:
+                    mNrmTrg = newton(func_to_zero, m0, maxiter=200)
+                except:
+                    mNrmTrg = np.nan
+            else:
+                mNrmStE = np.nan
+                mNrmTrg = np.nan
+                
+            self.solution[0].mNrmStE = mNrmStE
+            self.solution[0].mNrmTrg = mNrmTrg
+            self.bilt['mNrmStE'] = mNrmStE
+            self.bilt['mNrmTrg'] = mNrmTrg
         
         
 # Make a dictionary to specify an idiosyncratic income shocks consumer
@@ -2124,23 +2217,6 @@ def reset_rng(self):
         if hasattr(self, "IncShkDstn"):
             for dstn in self.IncShkDstn:
                 dstn.reset()
-                
-    def post_solve(self):
-        """
-        Method that is run automatically at the end of a call to solve. Here, it
-        simply calls calc_stable_points() if appropriate: an infinite horizon
-        problem with a single repeated period in its cycle.
-        
-        Parameters
-        ----------
-        None
-        
-        Returns
-        -------
-        None
-        """
-        if (self.cycles == 0) and (self.T_cycle == 1):
-            self.calc_stable_points()
 
     def get_shocks(self):
         """
@@ -2883,7 +2959,6 @@ def J_from_F(F):
         return J_C, J_A
 
 
-
     def make_euler_error_func(self, mMax=100, approx_inc_dstn=True):
         """
         Creates a "normalized Euler error" function for this instance, mapping
@@ -3054,7 +3129,7 @@ def calc_limiting_values(self):
         hNrm : Human wealth divided by permanent income.
         ELogPermShk : Expected log permanent income shock
         WorstPrb : Probability of worst income shock realization
-        Delta_mNrm_ZeroFunc : Linear consumption function where expected change in market resource ratio is zero
+        Delta_mNrm_ZeroFunc : Linear locus where expected change in market resource ratio is zero
         BalGroFunc : Linear consumption function where the level of market resources grows at the same rate as permanent income
 
         Returns
@@ -3131,13 +3206,10 @@ def calc_limiting_values(self):
         aux_dict['MPCmax'] = MPCmax
         
         # Generate the "Delta m = 0" function, which is used to find target market resources
+        # This overwrites the function generated by the perfect foresight version
         Ex_Rnrm = self.Rfree / self.PermGroFac[0] * Ex_PermShkInv
         aux_dict['Delta_mNrm_ZeroFunc'] = lambda m : (1. - 1./Ex_Rnrm) * m + 1./Ex_Rnrm
         
-        # Generate the "E[M_tp1 / M_t] = G" function, which is used to find balanced growth market resources
-        PF_Rnrm = self.Rfree / self.PermGroFac[0]
-        aux_dict['BalGroFunc'] = lambda m : (1. - 1./PF_Rnrm) * m + 1./PF_Rnrm
-        
         self.bilt = aux_dict
 
 
@@ -3361,66 +3433,6 @@ def check_conditions(self, verbose=None):
             _log.info(self.bilt['conditions_report'])
         
 
-    def calc_stable_points(self):
-        """
-        If the problem is one that satisfies the conditions required for target ratios of different
-        variables to permanent income to exist, and has been solved to within the self-defined
-        tolerance, this method calculates the target values of market resources.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
-        """
-        infinite_horizon = self.cycles == 0
-        single_period = self.T_cycle = 1
-        if not infinite_horizon:
-            _log.warning("The calc_stable_points method works only for infinite horizon models.")
-            return
-        if not single_period:
-            _log.warning("The calc_stable_points method works only with a single infinitely repeated period.")
-            return
-        if not hasattr(self, 'conditions'):
-            _log.warning("The calc_limiting_values method must be run before the calc_stable_points method.")
-            return
-        if not hasattr(self, 'solution'):
-            _log.warning("The solve method must be run before the calc_stable_points method.")
-            return
-        
-        # Extract balanced growth and delta m_t+1 = 0 functions
-        BalGroFunc = self.bilt['BalGroFunc']
-        Delta_mNrm_ZeroFunc = self.bilt['Delta_mNrm_ZeroFunc']
-        
-        # If the GICRaw holds, then there is a balanced growth market resources ratio
-        if self.conditions['GICRaw']:
-            cFunc = self.solution[0].cFunc
-            func_to_zero = lambda m : BalGroFunc(m) - cFunc(m)
-            m0 = 1.0
-            try:
-                mNrmStE = newton(func_to_zero, m0)
-            except:
-                mNrmStE = np.nan
-                
-            # A target level of assets *might* exist even if the GICMod fails, so check no matter what
-            func_to_zero = lambda m : Delta_mNrm_ZeroFunc(m) - cFunc(m)
-            m0 = 1.0 if np.isnan(mNrmStE) else mNrmStE
-            try:
-                mNrmTrg = newton(func_to_zero, m0, maxiter=200)
-            except:
-                mNrmTrg = np.nan
-        else:
-            mNrmStE = np.nan
-            mNrmTrg = np.nan
-            
-        self.solution[0].mNrmStE = mNrmStE
-        self.solution[0].mNrmTrg = mNrmTrg
-        self.bilt['mNrmStE'] = mNrmStE
-        self.bilt['mNrmTrg'] = mNrmTrg
-
-
     # = Functions for generating discrete income processes and
     #   simulated income shocks =
     # ========================================================

From f4cf4b8d78d85af65a6ebbccc275795a071b8b1b Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 15 Sep 2023 13:17:11 -0400
Subject: [PATCH 04/28] Make check_conditions called by
 PerfForesightConsumerType

This was done manually in my own test code, hence why I didn't notice it was missing.
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index e487f6126..a95a83802 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -3764,6 +3764,8 @@ def __init__(self, **kwds):
     def pre_solve(self):
         #        AgentType.pre_solve(self)
         self.update_solution_terminal()
+        if not self.quiet:
+            self.check_conditions(verbose=self.verbose)
 
     def calc_bounding_values(self):
         """

From 57cdc2cbba74c787860a3a83e86c4fb7ad5bea71 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 15 Sep 2023 13:30:42 -0400
Subject: [PATCH 05/28] Put the code in the right place

---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index a95a83802..488117d43 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -1401,6 +1401,8 @@ def pre_solve(self):
         horizon problems).
         '''
         self.update_solution_terminal()  # Solve the terminal period problem
+        if not self.quiet:
+            self.check_conditions(verbose=self.verbose)
 
         # Fill in BoroCnstArt and MaxKinks if they're not specified or are irrelevant.
         # If no borrowing constraint specified...

From f92b4f12c1ac3770b83fe20ff261f65fa1ce5b38 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 15 Sep 2023 13:41:48 -0400
Subject: [PATCH 06/28] Remove call to check_conditions in KinkedR

This method is not implemented for KinkedRConsumerType, so I have removed the call to it. Also fixed docstring for the empty method.
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 488117d43..40449926e 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -3766,8 +3766,6 @@ def __init__(self, **kwds):
     def pre_solve(self):
         #        AgentType.pre_solve(self)
         self.update_solution_terminal()
-        if not self.quiet:
-            self.check_conditions(verbose=self.verbose)
 
     def calc_bounding_values(self):
         """
@@ -3874,11 +3872,9 @@ def get_Rfree(self):
 
     def check_conditions(self):
         """
-        This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC),
-        the Return Impatience Condition (RIC), the Growth Impatience Condition (GICRaw), the Normalized Growth Impatience Condition (GIC-Nrm), the Weak Return
-        Impatience Condition (WRIC), the Finite Human Wealth Condition (FHWC) and the Finite Value of
-        Autarky Condition (FVAC). To check which conditions are relevant to the model at hand, a
-        reference to the relevant theoretical literature is made.
+        This empty method overwrites the version inherited from its parent class,
+        IndShockConsumerType. The condition checks are not appropriate when Rfree
+        has multiple values.
 
         Parameters
         ----------

From ee0e663ebf99c89f4e33de191c494071b9e61525 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Mon, 25 Sep 2023 11:26:14 -0400
Subject: [PATCH 07/28] Adjustments so that labeled model and tests work

ConsLabeledModel inherits from IndShockConsumerType, but doesn't use cFunc as an attribute of solution, so add_stable_points can't be run for it; skip post_solve. Added missing T_cycle length in one example notebook, which was generating an error with check_conditions().
---
 HARK/ConsumptionSaving/ConsLabeledModel.py    | 3 +++
 examples/LifecycleModel/Cycles_tutorial.ipynb | 2 ++
 2 files changed, 5 insertions(+)

diff --git a/HARK/ConsumptionSaving/ConsLabeledModel.py b/HARK/ConsumptionSaving/ConsLabeledModel.py
index 5b4bf20d3..05f30256d 100644
--- a/HARK/ConsumptionSaving/ConsLabeledModel.py
+++ b/HARK/ConsumptionSaving/ConsLabeledModel.py
@@ -303,6 +303,9 @@ def update_solution_terminal(self):
             continuation=None,
             attrs={"m_nrm_min": 0.0},  # minimum normalized market resources
         )
+        
+    def post_solve(self):
+        pass # Do nothing, rather than try to run calc_stable_points
 
 
 class ConsPerfForesightLabeledSolver(ConsIndShockSetup):
diff --git a/examples/LifecycleModel/Cycles_tutorial.ipynb b/examples/LifecycleModel/Cycles_tutorial.ipynb
index a3952818c..69a609092 100644
--- a/examples/LifecycleModel/Cycles_tutorial.ipynb
+++ b/examples/LifecycleModel/Cycles_tutorial.ipynb
@@ -90,6 +90,7 @@
     "    \"DiscFac\": 0.98,\n",
     "    \"LivPrb\": [0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90],\n",
     "    \"PermGroFac\": [1.01, 1.01, 1.01, 1.02, 1.00, 0.99, 0.5, 1.0, 1.0, 1.0],\n",
+    "    \"T_cycle\": 10,\n",
     "    \"cycles\": 1,\n",
     "}\n",
     "\n",
@@ -175,6 +176,7 @@
     "    \"LivPrb\": [1.05, 1.1, 0.95, 0.92],\n",
     "    \"PermGroFac\": 4 * [1.0],\n",
     "    \"cycles\": 0,\n",
+    "    \"T_cycle\": 4,\n",
     "}\n",
     "\n",
     "Cyc_agent = PerfForesightConsumerType(**Cyc_dictionary)\n",

From 49266189333fd16bb8e25bdf628f9bce9b651928 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Mon, 25 Sep 2023 11:59:24 -0400
Subject: [PATCH 08/28] Missing assignment to GIC_message fixed

One of the strings was not being assigned to GIC_message, and instead just left dangling. This should fix many test failures.
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 40449926e..d2087bb45 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -1999,7 +1999,7 @@ def check_conditions(self, verbose=None):
         if self.conditions['GICRaw']:
             GIC_message = "\nBecause the GICRaw is satisfed, the ratio of individual wealth to permanent income is expected to fall indefinitely."
         elif self.conditions['FHWC']:
-            "\nBecause the GICRaw is violated but the FHWC is satisfied, the ratio of individual wealth to permanent income is expected to rise toward infinity."
+            GIC_message = "\nBecause the GICRaw is violated but the FHWC is satisfied, the ratio of individual wealth to permanent income is expected to rise toward infinity."
         else:
             pass
             # This can never be reached! If GICRaw and FHWC both fail, then the RIC also fails, and we would have exited by this point.

From 441318b94a00742e4c3c60181887061575ce1e35 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Mon, 25 Sep 2023 12:43:18 -0400
Subject: [PATCH 09/28] Add changelog entry

---
 Documentation/CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 479a5814f..73701aa4a 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -24,6 +24,7 @@ Release Date: TBD
 - Fixes bug in the calc_jacobian method. [#1342](https://github.com/econ-ark/HARK/pull/1342)
 - Fixes bug that prevented risky-asset consumer types from working with time-varying interest rates `Rfree`. [1343](https://github.com/econ-ark/HARK/pull/1343)
 - Overhauls and expands condition checking for the ConsIndShock model [#1294](https://github.com/econ-ark/HARK/pull/1294). Condition values and a description of their interpretation is stored in the bilt dictionary of IndShockConsumerType.
+- Moves calculation of stable points out of ConsIndShock solver, into method called by post_solve [#1349](https://github.com/econ-ark/HARK/pull/1349)
 
 ### 0.13.0
 

From d50512c180adeb3b95d2a8aad4228df584439e33 Mon Sep 17 00:00:00 2001
From: sidd3888 <siddarthv2011@gmail.com>
Date: Wed, 22 Nov 2023 18:50:43 +0530
Subject: [PATCH 10/28] Update KinkedRconsumerType.ipynb

---
 .../KinkedRconsumerType.ipynb                 | 116 +++++++++---------
 1 file changed, 59 insertions(+), 57 deletions(-)

diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
index d98d74d20..ce423c351 100644
--- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
+++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "# Consumption-Saving model with Idiosyncratic Income Shocks and Different Interest Rates on Borrowing and Saving\n",
     "\n",
-    "**The `KinkedRconsumerType` class**"
+    "**The** `KinkedRconsumerType` **class**"
    ]
   },
   {
@@ -57,25 +57,27 @@
    "source": [
     "## Statement of \"kinked R\" model\n",
     "\n",
-    "Consider a small extension to the model faced by `IndShockConsumerType`s: that the interest rate on borrowing $a_t < 0$ is greater than the interest rate on saving $a_t > 0$.  Consumers who face this kind of problem are represented by the $\\texttt{KinkedRconsumerType}$ class.\n",
+    "Consider a small extension to the model faced by the `IndShockConsumerType`: that the interest rate on borrowing ($a_t < 0$) is greater than the interest rate on saving ($a_t > 0$).  Consumers who face this kind of problem are represented by the `KinkedRconsumerType` class.\n",
     "\n",
     "For a full theoretical treatment, this model analyzed in [A Theory of the Consumption Function, With\n",
-    "and Without Liquidity Constraints](https://www.econ2.jhu.edu/people/ccarroll/ATheoryv3JEP.pdf)\n",
-    "and its [expanded edition](https://www.econ2.jhu.edu/people/ccarroll/ATheoryv3NBER.pdf).\n",
+    "and Without Liquidity Constraints](https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.15.3.23)\n",
+    "and its [expanded edition](https://www.nber.org/system/files/working_papers/w8387/w8387.pdf).\n",
     "\n",
     "Continuing to work with *normalized* variables (e.g. $m_t$ represents the level of market resources divided by permanent income), the \"kinked R\" model can be stated as:\n",
     "\n",
-    "\\begin{eqnarray*}\n",
-    "v_t(m_t) &=& \\max_{c_t} {~} U(c_t) + \\DiscFac (1-\\DiePrb_{t+1})  \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n",
-    "a_t &=& m_t - c_t, \\\\\n",
-    "a_t &\\geq& \\underline{a}, \\\\\n",
-    "m_{t+1} &=& \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n",
-    "\\Rfree_t &=& \\cases{\\Rfree_{boro} \\texttt{ if } a_t < 0 \\\\\n",
-    "\\,\\!                \\Rfree_{save} \\texttt{ if } a_t \\geq 0},\\\\\n",
-    "\\Rfree_{boro} &>& \\Rfree_{save}, \\\\\n",
-    "(\\psi_{t+1},\\theta_{t+1}) &\\sim& F_{t+1}, \\\\\n",
-    "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1.\n",
-    "\\end{eqnarray*}"
+    "\\begin{align*}\n",
+    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\DiscFac (1-\\DiePrb_{t+1})  \\mathbb{E}_{t} \\left[(\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n",
+    "a_t &= m_t - c_t, \\\\\n",
+    "a_t &\\geq \\underline{a}, \\\\\n",
+    "m_{t+1} &= \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n",
+    "\\Rfree_t &= \\begin{cases}\n",
+    "    \\Rfree_{boro} & \\text{if } a_t < 0\\\\\n",
+    "    \\Rfree_{save} & \\text{if } a_t \\geq 0,\n",
+    "\\end{cases}\\\\\n",
+    "\\Rfree_{boro} &> \\Rfree_{save}, \\\\\n",
+    "(\\psi_{t+1},\\theta_{t+1}) &\\sim F_{t+1}, \\\\\n",
+    "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &= 1.\n",
+    "\\end{align*}"
    ]
   },
   {
@@ -95,37 +97,37 @@
    "source": [
     "## Example parameter values to construct an instance of KinkedRconsumerType\n",
     "\n",
-    "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`.  The only difference is that the parameter $\\texttt{Rfree}$ is replaced with $\\texttt{Rboro}$ and $\\texttt{Rsave}$.\n",
+    "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`.  The only difference is that the parameter `Rfree` is replaced with `Rboro` and `Rsave`.\n",
     "\n",
-    "While the parameter $\\texttt{CubicBool}$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model.  In the future, this restriction will be lifted.\n",
+    "While the parameter `CubicBool` is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model.  In the future, this restriction will be lifted.\n",
     "\n",
     "| Parameter | Description | Code | Example value | Time-varying? |\n",
     "| :---: | --- | --- | --- | --- |\n",
-    "| $\\DiscFac$ |Intertemporal discount factor  | $\\texttt{DiscFac}$ | $0.96$ |  |\n",
-    "| $\\CRRA$ |Coefficient of relative risk aversion | $\\texttt{CRRA}$ | $2.0$ | |\n",
-    "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\texttt{Rboro}$ | $1.20$ | |\n",
-    "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\texttt{Rsave}$ | $1.01$ | |\n",
-    "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\texttt{LivPrb}$ | $[0.98]$ | $\\surd$ |\n",
-    "|$\\PermGroFac_{t+1}$|Permanent income growth factor|$\\texttt{PermGroFac}$| $[1.01]$ | $\\surd$ |\n",
-    "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\texttt{PermShkStd}$ | $[0.1]$ |$\\surd$ |\n",
-    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | $7$ | |\n",
-    "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\texttt{TranShkStd}$ | $[0.2]$ | $\\surd$ |\n",
-    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | $7$ |  |\n",
-    "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\texttt{UnempPrb}$ | $0.05$ |  |\n",
-    "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | $0.3$ |  |\n",
-    "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\texttt{UnempPrb}$ | $0.0005$ |  |\n",
-    "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\texttt{IncUnemp}$ | $0.0$ |  |\n",
-    "| $(none)$ | Period of the lifecycle model when retirement begins | $\\texttt{T_retire}$ | $0$ | |\n",
-    "| $(none)$ | Minimum value in assets-above-minimum grid | $\\texttt{aXtraMin}$ | $0.001$ | |\n",
-    "| $(none)$ | Maximum value in assets-above-minimum grid | $\\texttt{aXtraMax}$ | $20.0$ | |\n",
-    "| $(none)$ | Number of points in base assets-above-minimum grid | $\\texttt{aXtraCount}$ | $48$ | |\n",
-    "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\texttt{aXtraNestFac}$ | $3$ | |\n",
-    "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\texttt{aXtraExtra}$ | $None$ | |\n",
-    "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\texttt{BoroCnstArt}$ | $None$ | |\n",
-    "| $(none)$ |Indicator for whether $\\texttt{vFunc}$ should be computed | $\\texttt{vFuncBool}$ | $True$ | |\n",
-    "| $(none)$ |Indicator for whether $\\texttt{cFunc}$ should use cubic splines | $\\texttt{CubicBool}$ | $False$ |  |\n",
-    "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T_cycle}$| $1$ | |\n",
-    "|(none)| Number of times the \"cycle\" occurs |$\\texttt{cycles}$| $0$ | |\n",
+    "| $\\DiscFac$ |Intertemporal discount factor  | `DiscFac` | $0.96$ |  |\n",
+    "| $\\CRRA$ |Coefficient of relative risk aversion | `CRRA` | $2.0$ | |\n",
+    "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | `Rboro` | $1.20$ | |\n",
+    "| $\\Rfree_{save}$ | Risk free interest factor for saving | `Rsave` | $1.01$ | |\n",
+    "| $1 - \\DiePrb_{t+1}$ |Survival probability | `LivPrb` | $[0.98]$ | $\\surd$ |\n",
+    "|$\\PermGroFac_{t+1}$|Permanent income growth factor|`PermGroFac`| $[1.01]$ | $\\surd$ |\n",
+    "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | `PermShkStd` | $[0.1]$ |$\\surd$ |\n",
+    "| $N_\\psi$ | Number of discrete permanent income shocks | `PermShkCount` | $7$ | |\n",
+    "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | `TranShkStd` | $[0.2]$ | $\\surd$ |\n",
+    "| $N_\\theta$ | Number of discrete transitory income shocks | `TranShkCount` | $7$ |  |\n",
+    "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | `UnempPrb` | $0.05$ |  |\n",
+    "| $\\underline{\\theta}$ | Transitory shock when unemployed | `IncUnemp` | $0.3$ |  |\n",
+    "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | `UnempPrbRet` | $0.0005$ |  |\n",
+    "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | `IncUnempRet` | $0.0$ |  |\n",
+    "| $(none)$ | Period of the lifecycle model when retirement begins | `T_retire` | $0$ | |\n",
+    "| $(none)$ | Minimum value in assets-above-minimum grid | `aXtraMin` | $0.001$ | |\n",
+    "| $(none)$ | Maximum value in assets-above-minimum grid | `aXtraMax` | $20.0$ | |\n",
+    "| $(none)$ | Number of points in base assets-above-minimum grid | `aXtraCount` | $48$ | |\n",
+    "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | `aXtraNestFac` | $3$ | |\n",
+    "| $(none)$ | Additional values to add to assets-above-minimum grid | `aXtraExtra` | $None$ | |\n",
+    "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | `BoroCnstArt` | $None$ | |\n",
+    "| $(none)$ |Indicator for whether `vFunc` should be computed | `vFuncBool` | $True$ | |\n",
+    "| $(none)$ |Indicator for whether `cFunc` should use cubic splines | `CubicBool` | $False$ |  |\n",
+    "|$T$| Number of periods in this type's \"cycle\" |`T_cycle`| $1$ | |\n",
+    "|(none)| Number of times the \"cycle\" occurs |`cycles`| $0$ | |\n",
     "\n",
     "These example parameters are almost identical to those used for `IndShockExample` in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to `None`.  The cell below defines a parameter dictionary with these example values."
    ]
@@ -226,7 +228,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -243,7 +245,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -270,16 +272,16 @@
     "\n",
     "| Description | Code | Example value |\n",
     "| :---: | --- | --- |\n",
-    "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n",
-    "| Number of periods to simulate | $\\texttt{T_sim}$ | $500$ |\n",
-    "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n",
-    "| Stdev of initial log  (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n",
-    "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n",
-    "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n",
-    "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n",
-    "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n",
+    "| Number of consumers of this type | `AgentCount` | $10000$ |\n",
+    "| Number of periods to simulate | `T_sim` | $500$ |\n",
+    "| Mean of initial log (normalized) assets | `aNrmInitMean` | $-6.0$ |\n",
+    "| Stdev of initial log  (normalized) assets | `aNrmInitStd` | $1.0$ |\n",
+    "| Mean of initial log permanent income | `pLvlInitMean` | $0.0$ |\n",
+    "| Stdev of initial log permanent income | `pLvlInitStd` | $0.0$ |\n",
+    "| Aggregrate productivity growth factor | `PermGroFacAgg` | $1.0$ |\n",
+    "| Age after which consumers are automatically killed | `T_age` | $None$ |\n",
     "\n",
-    "Here, we will simulate 10,000 consumers for 500 periods.  All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\texttt{pLvlInitMean})$, as $\\texttt{pLvlInitStd}$ has been set to zero; they will have essentially zero assets at birth, as $\\texttt{aNrmInitMean}$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n",
+    "Here, we will simulate 10,000 consumers for 500 periods.  All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp($ `pLvlInitMean` $)$, as `pLvlInitStd` has been set to zero; they will have essentially zero assets at birth, as `aNrmInitMean` is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n",
     "\n",
     "These example parameter values were already passed as part of the parameter dictionary that we used to create `KinkyExample`, so it is ready to simulate.  We need to set the `track_vars` attribute to indicate the variables for which we want to record a *history*."
    ]
@@ -358,7 +360,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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U1yWmY/7285i+5qhussxyLU5cyTcKaA27Zgz/v+10ttiFCgA7z1wVi58B4M0Hw2v8vc/3bobfX+yB/uG6CVv/PWm6S/JK3k1M+OUwTl4pwO5zV/Hl5rO4WarBwYu5OHo5H2sT08WM5drEy+j58TYxmMotLkWfz3bgoa/3IDO/BAO+3IkP1p/Cp5tOA9C9Fw/UEMCmXDP/hFQ1e3ylSr1d/s0yMQtgSB+g6dvdq6LuxPBEXNVTPx7EQ9/swcjv4/DYwjh8u63mOcCs2Ykr+Yj5ahf+OnIFX26uXgagV/X7Qv8Z1HfpA5XHTxAEnMooQGm5Fqk5N7AuMb3i4i3F6Oen/34Uc/45jVeWH67SJt13VrdQ3XJSvyVcvqvXeL24VHzOaYNa4n89QsXHmld0BzMDRHfE8CqpatdN/o0ysep+xxnjk0dVWq2Aod/sEU/knYI98c1o3Ui8mt6chpO5jesZKv6/hb8LAtycoBWAXWdN/17DAKiZjy6o6RLiabRPqUaLrIIS8eohupUfOgZV7tO9mZdYT6FQKPDew22N2gHoUu6CoAsC/tOxcbV2PNS+EX5/sQcaONohNfcGFu+5gPwqXReRTb0xoW+YeP/ElQJsNJhPqLCkDE//dEi837axG9o2dgNgXH8EAD/tu4i+n+1Aszc3oNP7m/HB3yeNvmB2n9Mdr5yi6gFVabkWvydcxr7kmq8Uq/6tfhzbBSue647GHs4o1Wjx/a7b1yXpr8oA4FpRKdYfzcDi3Rdw/mqRWCg7rmcoFArd3EvmZLkSU6+bfG2mLNiRjIe+2SMGPj9XCdQMZ1OesPwwftp3sdbt0Pv9sO7YZxWocfn6TXz27xkM+XoP1hlkVGsKBv5IuoJvtlVOprpkb4p4fL4b0wnjK+b+MUWhUKBziKdYZFpTQfSMNcew/lgGHvx6N5788SDmbT2HVjM3YuySg9Vew5RVR5CedxMfbjgJQNelVFquRWZBCT7ZeBp5Fdmp9UczUK7RbS8sMd1tYpgBKinT1Kq278JV4zqUqu/9P5PSjTK5eteKSqEu14hTWPy3cxMANWeAyjVasTbxQEouDl7Mxbyt51Cm0eJakRpx53OqjfbUO56ejz6fbjcKXOWiLtcYZfAOXbxeY+1VTnGpUaY9u0D3+jIMAiD9hd6iXRcweN5uRH60Bb0/3Y7Jq5LQ8+Nt2GdwIVukLsfWiqzn9jNXxe7HwpLKc8ZrMS0B6I7x3YzK23c+B4Kg+155qU8YGqoqx1m1EAMgZoDoDhheJVXNNhxOrezfL6jhi04v90apeFWhVAAh3g3RvGLStqwCtfiBM6QvlH3v4TaY+VBrcXvbxu4YWZExmfb7UZMnbH0AtPK57tj2Wh+sfakHFj7Zudp+PT7ehrNZRbBXKtCnhR9i2ui6vPxcVXjzwVbV9revMpJgQ8XQ/AGt/fHFyA54sc994mOH3orGt6M7IdDDGQNa6573ow26q+OBrf3RyN0Jj3cNQkOVvTiDr955gwBuxcFUHE/XHfu+LX3x9yv3o3dz3RQLl6rM+Gt4xV1arsXiPSl47dcjuJRTjJwitfg8uVUyTGUaLR77Pg6v/noEoxcfqPFq8aJBvUrPMG/0b+UPF5U9+lVkGvRfot2beaFvy+rTQHg1dBQn69N7/bej+GD9KQyfvxeXK05qA1r7I7Kp7gqxtsNY1x/NwIjv9iH6i51INHhvVnUqowCnMwuM5nwCdGn02X+dwNVCNa4VqXG9Sp1ZrJkBUFruDaNRhwmXrmNRRYD4+q9HUaQuR7lGW+1EXpW+u9hQm0C3246eBCqHxG88kYlFu84b1RsB1bMqpvyZdMUo+6Xr9i4y6lZYk1gZ0BWXajD7r5O3POnoi2oFQcB/F+5D38923HYW9aqF84aBsUYriN3WLirj41WkLseJKwXQaAW4Odmjb7gfFApd94uperwsg2yrPptQphHw6MI4dPtwC0b9sB9Rc7bh5/2XxOyx3sQVibiYcwMvLzddoySl3xIu42xWEbwbOorbaurWy6ry/fvOH8fR7cMt2JdcGdSsOZyO+Iu5Yjd91c8HAHQI8oCTQ/VT/H8XxuHQxVwx2x/k5YyuoZ66C6dyLeIu1L57DtDVCX7w90msOXwZRy/nAQC6hnpV209/sXX+apHFRx/WFgOge4jhVdLJDOMviwSDAseU24wSyC6o/FL5elRHONor4erkIHaDdftoK7afzoYgCFi8+wJ+jrsoXrFFNPGAQqHAx/9phw5BHpgWE44HDE6uE1cm4ujlPHHxO422cu6dEG9dYNEx2BM+Lip8OTICTg5KsVtKr3cLX7g3cMDANgHYPa0vdk3ri/ZNPKq9jie7h8LZoXLkjT7zpQ9wHmqvm/3bq6EjfF0r13p7uEOg0fO83C8McTP64+NHKofifjiirfj/UwaTkRmm9Qe11Q2TFuclyilGkbocT/54AD/uSUFaRdYizM9FnMEa0F09G86Qalh3AujqoAxH1vwab7rAWn/lPqR9I3w3ujKgfLRLE/H/jT2c8cv47lg6rhtOzI7B1AEt8OqAFvBxUWHBmE4I9mpY7XkBXbem/m8e5NkAA1vrXuvmGrpvDJWWa/FJRdfL9RtleHZZPA5cyMHXW8/hn2MZYpfT2axCPPj1bgz6ajeKK646g7wqh5Iv3XsRw+fvxT8mRrGZO03T2kTjujnDbqhyrYAuH2zGjjNXxfW79AxPWI72ShyZNRBxM/oZ7ePvVrv1jZr7uYr//2jDafT/fKfRxYbhewSozI4Yyiwowe8GWcSzWUWI/mKnyck79Rc1P++/JAbRPi6Vn4MvHosAAMRdyMG7f57A/gu5OJ5egGtFpeLFRE303YOtGumynynXirHpRCZulmrwW0IaTmcWwlVlb5RN1dtb8RkKD3CDu7MDBlVMNxDz1S58s/Ucpv9+VOziyaj47mji6Yx3h7VBzzBvAEBSWp5Yd6XRCnhn3XE881O8uBwJYHqi1JUHUzHnn1OSnoA1Wl1BMqD7rtFf2H2/8wLe+O1otUEkVQOg81eLkV2oxq9Vuqf+uzAOmRX7ju/VFIPbBuBRg/dM92be8HM1/d48kpaHzRUXM/3D/aFQKNCn4nt811njAKhco8V7f53E2sTq3WOrDqVi2Ld7sXhPCqb9dhTxFechfVe8oSDPBnByUKK0XFst+JcLA6B7RP5N4+JQwwxQSZlGLC4ETE8QaEifMm7VyA0Pta8MBjwMJnF77dcjiDufgw/Wn8I7f5wQu8uaVtTlPN4tGOsm9ESAuxM6Bnng+Qd0XQDXikox7Nu9aPfuJry59hiOp+ejXCvATqmo9mEc0bEJjr8bg33T++Edg6zSsIjKNgV5NYCTg+lFTYO9G+DkezFG8/UEeTkjPEB3omkT6I51E3pi3Us9jX7u/ua+cHfWvdb/dGpsMrgaExmCv17uBQA4eaUAgiCgpEwjjobaMPF+jOwaLLYD0HUDrD18GbvPXcP7f58UuzoWjOmE/TP6i4Feau4No0CqUF0OdbkGs/86ga4fbsGEivlR9P3yV/JLMGVVUrVaKv3fuX+4n9EEfO0auyOiYqHYD0e0FefcaKiyx8T+zfFK/+aIfzsakc28xaC0JnZKBRq5O4lB5cGU3Bq7SErLtfjlwCU8vigOl3JuoKGjHYK9GuBaUSlGLtqPLzafxYu/HBbT88fT840Cmed7N8Puaf3wekxLMTOVnncT71SMnBoaESheRabn3ayWOavJ5pNZ+KIiANCfbDccMw6qSsq0+HDDKQBA5xBP8T3Uq7kPZj7UGj4ujlj9fBQUCgUauTujW9PKK9ya3p9V+bsZL7hcrhWwLO4SLlRcERvWq93f3AefPRqBH8d2EbfpF/+d88/pWv2+/xsfKXbP6gPq4R0C8XLfMHw5MgJ9W1YuIh277yJG/bBfvL/5Fpm+kjKN2CX5QAvdSfPHPSl4/ucEvLX2GJYf1AXsL/cLQ6BH5Wfex0UXTOprD8Mb6Y7xS30qg6TPN5/FykNp+M+CvQB0730ACKyYYyk8wE3c992hrfHlyAijts3ZUHNwU1KmwfQ1x/D9zgv49xbL5BSry2vsur2TwOlcdiHScm+ioaMdHu8aLM4U/u/JLKyKT8PMPytHBgqCcNvs5jO9mhp9R06Obo63H2qNBU90xuPdgsTtkU29qr3n9InKM5mFYrfYwIrPtr6LdvnBVCzdmwKNVsDfR69gwvLDWLI3BVNWHRGzVmUaLbaczMIbvx8Tn7tcK4gX4m1NBEBKpUL8XPf7fCdW13BhJyUGQPeIl5cfRo7BF/6x9HzxS2jhzvNGVzsXrxVDEATk3ywTZ1I2pM8A+bkafzhGdQsW/59TXIrRiw8YPe7jooKbk/FMt4CuxmHG4FZi1wsAqMu1WH4gVZzzx99VZXLyK3s7JRQKBZ7uGYroVn5o19gdA9vcfhI/w99t+CU7oFUAFAbjkzsEeYgBip6DnRJfj+qIKdEtMOc/7Wp87ub+LrBTKnD9RhkyC0qw/0IOSsq08HNVoVWjyqt5fQboUs4NvPPHCaNjoFDogjjPho5iRio19wb2VCkQ3XX2GpbuvYirhWrxqm5Aa3/xpLE2MR1P/ngAZRqtmFHTd4FVnRZAoVBg6f+6Yv3EXuhjcJIzJcSgu695le4wAAj0cIK9nRJBXg0Q7NUAWgHidAKGBEHA+GXxeGvtcXGqg5f7NcdPT3ertq9+6G16xQK3wzsEYsvU3ni9og5hQt8wbJn6gFGNl4OdArOGtsa/Ux5A04o6smO1GBmkLteIheK+rirM+U87NDAxXw9QmTmNauaNdRN6Yt7jHfDWkFZ4uldTxL89QAxAAGBMpO6zov/71Ibh+1Lv2+3JGPrNHrz31wlxHqoJfe/DN6N0NXm9mvugdSM3dA7xxAsP6Lp0q9atmRIe4Ap/NyfMHtbGaPt9fi54LaYlRnRsAs+GjkYZVEN7k68ZFcTfLK0Mek5mFEAr6F57t6bGtXxrEtORWvG+7N3CF50qRgE52ivR2FP3XtMPatAHM+2auOOX8ZFGz1OmEVBYUiZmgBpVfMYNA88x3UPE59e7kl+ClGvF1b73yjVaowy6YV3f8fR88bUKgoDRiw+g9yfbjWpuAODbbefQZtbGWwZPhrIKSnAwJVe8cGkR4ApnR7tqF1x/JF3B5pNZmP77UbSauRG7z12Do70SE/reZ+JZdd3dX4/qiC1Te2Pn630wObqF+FhEEw+EejeAn6sK3Zp6GV109m7hi7kVWe5fEy4jt7gUfq4qdK04pvpjW1quxey/TiJ230W8vDwRm05UBsMfrj8l/jt+Wbz4vPpAG9BNKKuv96mqhUEWdNpvR00ODpGSrDNBU+0cvZyH3eeuwcFOgT8m9MKHG05ib3IOes3djpkPtRa/UGY+1Brvrz+JQnU5jl7Ox+OL9qNnmDcWj+1q9Hz6DFDVAOiJ7iEY0bExLuXcwGPfx1V7cz7SuXphsaFeYT7YVmWIsf5qv9EtZlUGdCeHqu2sLZV95Ze4fqTY7TzQwle8eq2Jk4MdWjVyxfH0Auy/kIP1R3VffIPbGgdZfq4qeDd0NApQ9QLdncUMgb62aOfZq8gsKIHKXgknBzvk3yzDJxurX9V3CfXE5lMuuFak+/tm5Jdg7JKDiLuQg6d7NhVHIekDAkNeDR3h1fD2J2fD4PCdh1oj7kIOwgNcMWllEgBdsKjXOcQTqbk3kHDpOnpXOXZHLucbFcG7OdljdGQw3J0d8NPT3fDttnNwc3LA1tPZYmZMP4lgqE9DhPlV/8Ic3LaRWOPwXO9mYvdNu8buSLlWjOPp+UZ/w4z8m7hRqsHn/57BCw/cB19XFX6OuyQucfL3K73g2dARD3cIxIqKLMXnj0Zg+cFUoy7kziGecHKww8Mdan4vPdyhMVT2dmgZYPqLviY/PNUFKw+momOwBz77V3dxUFyqEUfn+bup8HpM5Ygylb0d1k/sBYVCAY1WMPk+a+LpjCnRLaByUCKqmTe+2ZYsXtVHNPGAq8penFG4WZX3ys0qgYKTgxIlZVqUaQQkpuahZ5gPft5/CR/8fRLqci2+f7Kz2G3XtrG70UAFPX09SmNPZ7g5OeCfSffDq6Ej3lp7zGi/iKDKLEHPMB/0uM/bqHh33/kc8T2un2V7YGt/vD+8LbqEeMLBTolgr+oZzCt5N6uNErxapDaqRdt8MgslZRqsOZyON9ceQxNPZ/w4tityi0vFTPuWk1kY1S0Yfx65goz8EvHvtepQGgaamCU85VoxXv/1CCb0DUPvFr4Ys/gAkrMr6370FximsiPPVgQTepP6N0f3Zt6Yv736yMsAN92xMPWZsbdT4o+Xe0EQBDRU2Rt1/bcNdBNH1uq993Ab8TNu2D0KAF/8W71bdX9KDjLyb2LXucrP+qyhrfHl5rNiTWN4I1c42pvOrYT5G19k/RqfhnE9m5rcVwoMgO4B+onqhrRrhNaBbni+933YW1EQ98H6k2JfeLsm7gh0d0Z63k18v+s8bpZpsOVUNq4VqY3e3NkV3Vl+VdKjgK6bpHWgG1Y93x3ztyfDyd4O/Vv5o6HK7rYBw+jIYGTk30S/cH/kFKvx8vJEsRaoap1PXdLP3QOY/nK5G/c398Xx9AKsOpQmFjU/GRVqtI9SqcDPz0Tiwa93V/t5fVcbUBkAiVeE/q4oLdci/2aZWMS66MnOWB2fhpIyLdo2dodnlbWl9CeIHysmlHusS5NaBTo1MVy+IczPRQxs9AGQYTdTpxBPrE1MNyq4B3RXzT9UrCU3omNjPN2zKRqo7MTXrg82/zmWga2ns8VpGvQBUE1LjnQN9cQr/cLg5uSA8fdXfkm2a+yOP49cMZpP52xWIR6ct1uc62nDsUx0b+YlFj7HtPEXa3Um9W+BwpJyjIkMQdR93hgaEYj2szehpEz3Xq2aVaiJvgbMHANa+2NAa3+cziwQT6iGTH3G9MG2nVKBr0d1xAs/J+DVgS3w7l+6EWDuzg54xKD2412DrI+9nRLP9m6Gn/dfQscgD6N5WfQ/q88o/flyTwR7NcCsP0/gj6QrGLP4AHqF+RjVq/2ecFnMPLYNdIdnQ0d4NHAQR50ZPq8+W6yvEzL8DnJyUKJllSxBr+Y+RgFQ3PkcMdupz/IqFAo82b1yCgyFQoGX+tyH1fGX4dHAAcnZRUjPu1ltDqJf4y/jpEEG6EapBkcv52PeVt3f4PL1mxi/7JA4WR+g+6ztPHsVW04ZX9Qdr5g6oWpGb+KKRBxLz8e42EP4dnRH8XOuD1j13T9VC8Or6hbqhed6NzMatXefb0Pc39wX14rUtw26Db9zDKd4GNUt2GhUVjPfhhjUtpHRz04d0ELsLi42GA3m56pCY09nJKbm4df4y2L3+8G3+sPP1clobraoZt41ti3I0zhgXZd0hQEQ3ZrYf18xtLt3C1/89kIU3lx7zGh0R5BnAzTzbYj0vJtG8/nsOnsV/+lU+QVZ2QVWc1DSJtAd342pPlLrVpwc7PDWEF0tT9URDoG3yQDdjTcGhyP/ZpnRlXNdub+5DxbsOC+eSDsFe1QbOQUArQPdjL48Hu4QiD+Sroi1M0D1D39jD+dq3RlNfRoaZcJu9TfydVXh47tcQ8lOqcCq57qjsKTc6G/0ySPtMe33o/hweGUXYeeKk2dSah7yb5Qhq7AE9/m6YFncRaw/qqtBG9sjFO2amA5CmxtMhCYIgtgF1tjT9HtDoVDg1YEtq23XB7mGXWCrD6UZTXQJwGjUV2eDaRcC3J3w7ehO4n1HeyU+f7QDDl3MxdCIwGoLmlpCcz9XBHk5G9X9/K9HqDgcuSY9w3xwbHYMAIgBUEPHW3+NT+zf3GgyRkOLnuyM2X+dxMePtBO7ZrqGeuGPisk19cGPo72ueNVwFKD+79DI3blaANTExN/UMABq19gd9nbGWYLxvZqhtFyLq4Vq/HIgFcnZRWLwYGqdNb1pg8IxbVA43l53DMnZRfj76BXxAlHvC4ORlPqZuZ9YfAClGi2cHezg5myPtNybRn8PU8X3QOU0CoajRQVBMHo/LttXfb4twyL4NoFuOHGlAG0C3dAhyEMcNTfv8Q54qH0g7JQKo4sfhUJhFNjW1uB2jfBT3CU83CEQQV4NjAKiQSayWC/3DUPbxm54OrYyIzV7WBv0C/fDzrNXkZiaJx5L74aO8K34mxrWEvZqXvOaYoPaBmBUt2C0r/iOGH6LLKsUWAN0D9AXIBteKXcJ9cLMhyo/EI72Svi5qsRI3HBY5G8Jl5FdWCK++bMqusCqFsjVpap1N5bMAIUHuGHNSz0RdV/NVx53qnOIJ1wNrppuVVNjWFT61cgO2De9HyYZnHiaeDobLZ/QyMMJXlVqSKoGA1MGtEB0K3+M79UU7Rq7G43yiGrmXavh17cT2cwb0a2N664e6xqEE7Nj8GC7yi/JMD8X2CsVKFSXI+K9fzHwy134YvMZrI7XjQ6ZNqilUZ1MVSHeDWGvVOBGqQZpuTfFDFATj1sXYlfVpqLeID3vJp5bFg+NVjA5DFhPqbj13w3QjaR7d1gbo0DJkuyUCvz1ci9M7FdZAPxSn/tumx0wpO/Cfiii0W32rFlkM29smHS/UV1KdxNX8C/1Ma5HsVcq0DVUd6zmPtIOTg5KsXAcMB0AGT6vYS2PnqO9EpOjW4gXa4cu5uJ0xQjM9jUE1YYaV7yP9MHPoDYBRm0CdBc0+toyfc3V16M64vGuwbgdRzul+P7eW2W6j6rzGOlnuv7EYGSp4YXTj2O7YmL/5ljxXHe8/3BbdA31RDPfhhjQunKhUMMM051M/AnojvnuaX3x+aMR4nPOGByO/uF+eMnECD2lUoHIppV/JxeVPcZEBiPIqwEe7dLEqAu1hb+r2MYAg+93U0Pg9RzslJjzn3YY1S0Yo7oFm1w/T0rMAFk5dblGzBL4VqnZMRxq6OZkD6VSYbIeZN/5HHT7cKv4xtNngHxvkV24W25ODmI9AWDZDJAlqeztMCoyWJwzpmrti6F2Tdzx8zPd4OOiqijONn7NTg52CPVuKBbbNvZwNhpV4tXQEQ2qXM17NXTEYoORQCVlGnE4rKmi5brUsMrJ2NFeiVCfhkaj0fYk54gTMo4wMfmkIQc7Jdo3ccfh1DzM3XhaLBIPMDM4dnNyQDPfhrhwtRj/nsxC/MXcGpcGGBoRiOd7N6uxKFNOHg0c8VjXICzceQFhfi7wq+Vwer3fX+yBAym5tz3u5grzc8GCMZ3E1doBIKZNAL7aopuB2dnBDvvf7C92tbRv4oHT7w/GtSI1unywBYBxXZ5er+Y+2DL1AcRdyMEjt6jVC6vohtJPpKgv6L4dw8EQgK6GroHKTly2Y1hEIN4a0sqoiP+J7sEY0NrfaN0/hQL4cHg7vFlRs7Rh4v345cAlDO/YGIcu5iIpLQ/T1xyDV0NH9G/ljymrkkwuBRTirQsazmUXolwrGAWFAe5OmDqgsnj51xd6mHxN0a38sOVUNp6+i26iqvOaPf/AfXj+AdMF1oDuc6+vG3u5X5iYqVPZ22Huf9vj0YVxAGBUT9TjPh880T0YLf1daz0q0howALJy1yrmiHG0Uxr17QIwStXrg6SqAdCsoa0xuyJVvjf5Gga09hevvE0FS3XJcE6VHhbIzkhlfK+mWB2fBs8GjibntzB0f/Nb10m18HcRA6DAKgGQqavmqpwc7PBIpybYk3wVj3e7/VVrXQvzdTEKgPQFo36uKgTU4iT1WkxLjP7hgLjmUKC7c40Fk7fywfC2GP2DbpTi6cxCnDNY/mVoRCAeat8I/7f/Et4Y1BJNPM3LMEmpiWcDbHvtAbiqzO92C/JqUO3kVlcGt2uEnmHeYjYlzM8FbRu74Xh6ASZHN6/2XQQYd3FVLajVC/NzMdmFbMi9gQN8XFTishm3uugwVLWWrFOIJ4a0bwQfFxWe7tlUDLQ7BXvA0U6JUo1WHEFl+B7xd3XCqG5BECDA3dkBrQPd8OEIXVdwq0ZuWLw7BbnFpXju5wR88kh7o+BnUJsAcYRZ7+a+UCgUYlnAnfjq8Y44mpaHyFvU1VjCT890w9G0vGr1jl1DvTB/dCf8sPsCxkRW1mLZKRX4YHjNI2qtFQMgK6cfceHrqjI5jFZPf4VkGNRENHHHuJ5N4dnAEZNXJeFSzg2DIaiud1U8WxuvDmyBBdvPY8ETneFqYvj8vcLPzQlbpz4AezulyaH85mjp7yoOKw30cEYLf1dxXhdXE7MMm/L5YxG338lCwvxcgBPVt7evmCDzdnrc54MwPxeDQvA7y2L1uM8Hz/Rqih/3pGDFwVSUaXQzC//0dDeE+bnA1ckBMSZqHKyRtQZobRu7iwGQg50S3z/ZBYdSco3moKlq1XPdseJgqlHX750wnOVdP33E7Rh2HzvaKdEm0A0qe7tqs8j7uTnh1xei4OJkLwZqVbMzCoXC6ASv56Kyx5cjO4jLkxhOGDmxf3NM6Hsfvvj3LBJT86ot1XMnXFT26BFWc02NpXQK9qxxMMCQ9o0wpJZ/E2vHAMjK6et/fFxNX1EtHx+J99efwvsP6+qBmng6o5lvQ6jLtJg/RlfoqR+FcSw9Hy9VpLVN9fPXtZf6hOH53vfdddBgDbxruKI1130GV7+BHk5GRc767kJr5uZs+iujQ1DtR9+1DXQTA6CWBhPbmUs/YkffxfHfzkHVRjnRnZtcMVruoXa6k11jD2c0vk13W2Qz7zrJVjzWpQm+3paMV/qFmZyo1JQANyeM6haMY+l5eLRzkMluOL2IKrVqht2wjna3zkg+0MJXHOSgX7tubFSI2KU1w8SyPWSdGABZuasVaWDfGk7APcJ88M+k+8X79nZK/Du5NzSCIH4BmJorwxIFw6bUh+CnLhkOs/VpqPubfjkyAjP/OIHpg+t+FFtdG96hMb7Zlow+Lf1w9HKeuP7ZrQofq2oT6I51FaOMWgbceR2TYVeKi8oeL/erXtRJd87Z0Q4fjZCnW+OV/s0xNCJQXEG8NhQKxS0nNr0Vw/mutLVYZ0Xf3abfNcwKa8zo9hgAWbFyjVacF8PUnD01sbdTGv1hq1bav9TnPgxoVfvZlqnutG3sjjcfDIe/m5M4gmtExyYY0bH6uk/WyM/NCYffGQClQoFec7eJ26teUd9Km8DKrE9L/7vJAFV29z7fu5nFu3RJOg52SrOCn7pUm+/aqgMcLD0ggSyDAZAVW7DjvDi/Sk1FhXdi2iDrzzTUZ8/1rnkExr1Af7VsOPeLOSM/2gRWdpdVnZnWHF4NHRHdyh9XC0vwdC/5JlOj+mHe4x2wZO9FzBh8+y6sqtNV3K6wm6wTAyArZjgD693OozO8QyDWJV3BeJ4oqI588t/2eGVFIuY+Yl63g3sD3fIIdkrFXQ2Z1S2f0uX2OxLVwsMdGt9y+RNDTQwyQOEBrnV6gUrSYQBkxfTDQFv6u+LBdndXdT97WFvEtAkwuYYN0Z14qH0jPNDS1+QCubejL8wnuhcZdoHxO/XexZmgrZRWKyCtYqmAxWO7mJx3wxzuDRwwuF0jFiVTnVEoFHcU/BDd6xqq7NEm0A2uTrqZkunexAyQlcoqLEFpuRb2SoVFl5EgIiLzrXo+CuoyTZ1NkUHSYwBkpVIrhhc39nSutmggERHJy0Vlb9babWR9eGa1UpdydQGQqTl8iIiI6O4wALJS+gX7QrwZABEREdU1BkBWqLCkDH8kpQMAHmxbP9ZcISIisiYMgKzQ5pNZuFGqwX2+DSVbsoKIiMiWMACyQqkV9T/dmnrVaoVtIiIiMg8DICuUXbECvOFK4URERFR3GABZoeyCEgDmLYBKREREtccAyAplFegyQP7MABEREVkEAyArlFWRAfJ3YwBERERkCQyArIxGK4iLoPqzC4yIiMgiGABZmZwiNbQCoFSAa8wQERFZCAMgK6Ov//FxUXHldiIiIgthAGRlsjgCjIiIyOIYAFmZnOLKDBARERFZBgMgK5NTXAoA8GroKHNLiIiI6i8GQFYmt0gXADEDREREZDkMgKwMM0BERESWxwDIyjAAIiIisjwGQFYmVyyCZgBERERkKQyArExOkT4DxBogIiIiS2EAZEUEQRC7wLzZBUZERGQxDICsSHGpBqXlWgCAN7vAiIiILIYBkBXRD4F3clCigaO9zK0hIiKqvxgAWZGsQt0yGJwDiIiIyLIYAFmRc1lFAIBmvi4yt4SIiKh+YwBkRc5mFQIAWvgxACIiIrIkBkBWRAyAAlxlbgkREVH9xgDIipyt6AJr4c8AiIiIyJIYAFmJvBuluFakmwW6ObvAiIiILIoBkJW4VjEE3s3JHg1VHAJPRERkSQyArERhSRkAwNXJQeaWEBER1X+yBkC7du3C0KFDERgYCIVCgXXr1t1y/zVr1mDAgAHw9fWFm5sboqKisGnTpmr7zZ8/H6GhoXByckJkZCQOHjxooVdQdwpLygEArk7M/hAREVmarAFQcXExIiIiMH/+/Frtv2vXLgwYMAAbNmxAQkIC+vbti6FDhyIxMVHcZ9WqVZg6dSpmzZqFw4cPIyIiAjExMcjOzrbUy6gTRWoGQERERFJRCIIgyN0IAFAoFFi7di2GDx9u1s+1adMGI0eOxMyZMwEAkZGR6Nq1K7799lsAgFarRVBQEF555RVMnz69Vs9ZUFAAd3d35Ofnw83Nzaz23KlVh1Lxxu/H0C/cD0v+11WS30lERFSfmHP+vqdrgLRaLQoLC+Hl5QUAKC0tRUJCAqKjo8V9lEoloqOjERcXV+PzqNVqFBQUGN2kpu8Cc2EBNBERkcXd0wHQZ599hqKiIjz22GMAgGvXrkGj0cDf399oP39/f2RmZtb4PHPmzIG7u7t4CwoKsmi7TWENEBERkXTu2QBo+fLlmD17NlavXg0/P7+7eq4ZM2YgPz9fvKWlpdVRK2uvMgDiKDAiIiJLuyfTDStXrsT48ePx66+/GnV3+fj4wM7ODllZWUb7Z2VlISAgoMbnU6lUUKnkXYG9SK0fBn9P/kmIiIjuKfdcBmjFihUYN24cVqxYgSFDhhg95ujoiM6dO2Pr1q3iNq1Wi61btyIqKkrqppqFXWBERETSkfVsW1RUhOTkZPF+SkoKkpKS4OXlheDgYMyYMQPp6elYtmwZAF2319ixYzFv3jxERkaKdT3Ozs5wd3cHAEydOhVjx45Fly5d0K1bN3z11VcoLi7GuHHjpH+BZmAAREREJB1Zz7bx8fHo27eveH/q1KkAgLFjxyI2NhYZGRlITU0VH1+0aBHKy8sxYcIETJgwQdyu3x8ARo4ciatXr2LmzJnIzMxEhw4dsHHjxmqF0damUD8PkIo1QERERJZmNfMAWRM55gHq9/kOXLhajJXPdUf3Zt6S/E4iIqL6xGbmAapP2AVGREQkHQZAVqKoIgBy4zB4IiIii2MAZAXKNFrcLNMA4EzQREREUmAAZAXybujmAFIoADdnZoCIiIgsjQGQFci7UQoAcHd2gJ1SIXNriIiI6j8GQFYgt1gXAHk1cJS5JURERLaBAZAVuF7RBebRgN1fREREUmAAZAWuV3SBeTVkBoiIiEgKDICsgD4A8mAXGBERkSTMDoDS0tJw+fJl8f7BgwcxefJkLFq0qE4bZkuuV9QAebILjIiISBJmB0CjR4/G9u3bAQCZmZkYMGAADh48iLfeegvvvfdenTfQFuhrgDzZBUZERCQJswOg48ePo1u3bgCA1atXo23btti3bx9++eUXcUFSMk9lBogBEBERkRTMDoDKysqgUqkAAFu2bMGwYcMAAOHh4cjIyKjb1tkIfQ0QAyAiIiJpmB0AtWnTBgsXLsTu3buxefNmDBo0CABw5coVeHtzFfM7IXaBsQaIiIhIEmYHQHPnzsX333+PPn36YNSoUYiIiAAA/Pnnn2LXGJknj6PAiIiIJGX2ypt9+vTBtWvXUFBQAE9PT3H7c889hwYNGtRp42yFfiHUBo52MreEiIjINtzRPECCICAhIQHff/89CgsLAQCOjo4MgO6AIAgoKdMCAFQOnJaJiIhICmZngC5duoRBgwYhNTUVarUaAwYMgKurK+bOnQu1Wo2FCxdaop31lrpcK/7fyYEZICIiIimYnXKYNGkSunTpguvXr8PZ2VncPmLECGzdurVOG2cL1GUGAZA9AyAiIiIpmJ0B2r17N/bt2wdHR+OC3dDQUKSnp9dZw2xFSbmu/kepABzsFDK3hoiIyDaYnQHSarXQaDTVtl++fBmurq510ihbUlJRAO3kYAeFggEQERGRFMwOgAYOHIivvvpKvK9QKFBUVIRZs2bhwQcfrMu22QR9ATTrf4iIiKRjdhfY559/jpiYGLRu3RolJSUYPXo0zp07Bx8fH6xYscISbazXxAyQPUeAERERScXsAKhJkyY4cuQIVq1ahSNHjqCoqAjPPPMMxowZY1QUTbVj2AVGRERE0jA7AAIAe3t7jBkzBmPGjKnr9ticknL9HEAMgIiIiKRidr/LnDlzsGTJkmrblyxZgrlz59ZJo2yJWswAsQuMiIhIKmafdb///nuEh4dX265fJJXMo88AcQ4gIiIi6ZgdAGVmZqJRo0bVtvv6+iIjI6NOGmVL9DVAXAaDiIhIOmafdYOCgrB3795q2/fu3YvAwMA6aZQtEbvAmAEiIiKSjNlF0M8++ywmT56MsrIy9OvXDwCwdetWTJs2Da+++mqdN7C+q5wHiBkgIiIiqZgdAL3++uvIycnBSy+9hNLSUgCAk5MT3njjDcyYMaPOG1jfcRg8ERGR9MwKgDQaDfbu3Yvp06fjnXfewalTp+Ds7IzmzZtDpVJZqo31mn4tMAZARERE0jErALKzs8PAgQNx6tQpNG3aFF27drVUu2yGvguMRdBERETSMfus27ZtW1y4cMESbbFJJSyCJiIikpzZAdAHH3yA1157DX///TcyMjJQUFBgdCPzcDFUIiIi6ZldBK1f8X3YsGFQKBTidkEQoFAooNFo6q51NqCyBohdYERERFIxOwDavn27Jdphs9QcBUZERCQ5swOgBx54wBLtsFmcB4iIiEh6ZgdAu3btuuXjvXv3vuPG2CIWQRMREUnP7ACoT58+1bYZ1gKxBsg86nIWQRMREUnN7H6X69evG92ys7OxceNGdO3aFf/++68l2livcTFUIiIi6ZmdAXJ3d6+2bcCAAXB0dMTUqVORkJBQJw2zFZwJmoiISHp1lnbw9/fHmTNn6urpbIZYBM0aICIiIsmYnQE6evSo0X1BEJCRkYGPP/4YHTp0qKt22Qx2gREREUnP7ACoQ4cOUCgUEATBaHv37t2xZMmSOmuYrVBzJmgiIiLJmR0ApaSkGN1XKpXw9fWFk5NTnTXKVmi0Ako1+i4wZoCIiIikYnYAFBISYol22CR1eeWUAcwAERERSeeO0g47d+7E0KFDERYWhrCwMAwbNgy7d++u67bVe/oCaIABEBERkZTMDoD+7//+D9HR0WjQoAEmTpyIiRMnwtnZGf3798fy5cst0cZ6S18A7WCngJ1ScZu9iYiIqK6Y3QX24Ycf4pNPPsGUKVPEbRMnTsQXX3yB999/H6NHj67TBtZnXAaDiIhIHmZngC5cuIChQ4dW2z5s2LBqBdJ0a/ouMBW7v4iIiCRldgAUFBSErVu3Vtu+ZcsWBAUF1UmjbEXlLNAcAUZERCQls7vAXn31VUycOBFJSUno0aMHAGDv3r2IjY3FvHnz6ryB9ZnYBcYMEBERkaTMDoBefPFFBAQE4PPPP8fq1asBAK1atcKqVavw8MMP13kD67PKleCZASIiIpKS2QEQAIwYMQIjRoyo67bYHDWLoImIiGRhduohLS0Nly9fFu8fPHgQkydPxqJFi+q0YbaghMtgEBERycLsAGj06NHYvn07ACAzMxPR0dE4ePAg3nrrLbz33nt13sD6rLIGiF1gREREUjL7zHv8+HF069YNALB69Wq0a9cO+/btwy+//ILY2FiznmvXrl0YOnQoAgMDoVAosG7dulvun5GRgdGjR6NFixZQKpWYPHlytX1iY2OhUCiMbta6TlnlSvDMABEREUnJ7ACorKwMKpUKgG7o+7BhwwAA4eHhyMjIMOu5iouLERERgfnz59dqf7VaDV9fX7z99tuIiIiocT83NzdkZGSIt0uXLpnVLqmU6IugWQNEREQkKbOLoNu0aYOFCxdiyJAh2Lx5M95//30AwJUrV+Dt7W3Wcw0ePBiDBw+u9f6hoaHiUPslS5bUuJ9CoUBAQECtn1etVkOtVov3CwoKav2zd6MyA8QuMCIiIimZfeadO3cuvv/+e/Tp0wejRo0SMzF//vmn2DUmt6KiIoSEhCAoKAgPP/wwTpw4ccv958yZA3d3d/Em1YSOYhE0M0BERESSMjsD1KdPH1y7dg0FBQXw9PQUtz/33HNo0KBBnTbuTrRs2RJLlixB+/btkZ+fj88++ww9evTAiRMn0KRJE5M/M2PGDEydOlW8X1BQIEkQxCJoIiIiedzRPECCICAhIQHnz5/H6NGj4erqCkdHR6sIgKKiohAVFSXe79GjB1q1aoXvv/9e7K6rSqVSiXVNUlKXcyZoIiIiOZgdAF26dAmDBg1Camoq1Go1BgwYAFdXV8ydOxdqtRoLFy60RDvvmIODAzp27Ijk5GS5m1JN5TxAzAARERFJyewz76RJk9ClSxdcv34dzs7O4vYRI0aYXCRVbhqNBseOHUOjRo3kbko1XAuMiIhIHmZngHbv3o19+/bB0dHRaHtoaCjS09PNeq6ioiKjzExKSgqSkpLg5eWF4OBgzJgxA+np6Vi2bJm4T1JSkvizV69eRVJSEhwdHdG6dWsAwHvvvYfu3bsjLCwMeXl5+PTTT3Hp0iWMHz/e3JdqcSVcCoOIiEgWZgdAWq0WGo2m2vbLly/D1dXVrOeKj49H3759xfv6QuSxY8ciNjYWGRkZSE1NNfqZjh07iv9PSEjA8uXLERISgosXLwIArl+/jmeffRaZmZnw9PRE586dsW/fPjFAsib6xVA5DJ6IiEhaCkEQBHN+YOTIkXB3d8eiRYvg6uqKo0ePwtfXFw8//DCCg4OxdOlSS7VVMgUFBXB3d0d+fj7c3Nws9nseXbgPhy5ex4IxnTC4nfV10REREd1LzDl/m50B+uyzzzBo0CC0bt0aJSUlGD16NM6dOwcfHx+sWLHijhtti0orMkCO9swAERERScnsACgoKAhHjhzBqlWrcOTIERQVFeGZZ57BmDFjjIqi6fZKNbrkm4MdAyAiIiIpmRUAlZWVITw8HH///TfGjBmDMWPGWKpdNqG0Yh4gZoCIiIikZdaZ18HBASUlJZZqi80pYwaIiIhIFmafeSdMmIC5c+eivLzcEu2xKfoaIBUzQERERJIyuwbo0KFD2Lp1K/7991+0a9cODRs2NHp8zZo1dda4+q5MowuAmAEiIiKSltkBkIeHBx555BFLtMXmcBQYERGRPMwOgOrDPD/WolTMAClkbgkREZFtYepBJoIgiAEQM0BERETS4plXJhqtAP0c3I6sASIiIpIUz7wy0Wd/AGaAiIiIpMYzr0zKyiuXYOMoMCIiImmZfeZdtmwZ1Gp1te2lpaVYtmxZnTTKFqg1ulmgFQrAXskiaCIiIimZHQCNGzcO+fn51bYXFhZi3LhxddIoW2A4C7RCwQCIiIhISmYHQIIgmDxhX758Ge7u7nXSKFsgzgLN7i8iIiLJ1XoeoI4dO0KhUEChUKB///6wt6/8UY1Gg5SUFAwaNMgijayPxFmgWQBNREQkuVoHQMOHDwcAJCUlISYmBi4uLuJjjo6OCA0N5QzRZhBngWYGiIiISHK1DoBmzZoFAAgNDcXIkSPh5ORksUbZAnEWaHvW/xAREUnN7PTD2LFjUVJSgsWLF2PGjBnIzc0FABw+fBjp6el13sD6ihkgIiIi+Zi9FtjRo0cRHR0Nd3d3XLx4Ec8++yy8vLywZs0apKamcih8LZWJy2DYydwSIiIi22N2+mHKlCn43//+h3Pnzhl1gz344IPYtWtXnTauPqvMALELjIiISGpmZ4Di4+OxaNGiatsbN26MzMzMOmmULSjjQqhERESyMfvsq1KpUFBQUG372bNn4evrWyeNsgXqigwQl8EgIiKSntln32HDhuG9995DWVkZAEChUCA1NRVvvPEGh8GbQT8TNDNARERE0jP77Pv555+jqKgIfn5+uHnzJh544AGEhYXB1dUVH374oSXaWC+VMgNEREQkG7NrgNzd3bF582bs3bsXR44cQVFRETp16oTo6GgIgnD7JyAArAEiIiKSk9kB0KefforXX38dPXv2RM+ePcXtGo0GTzzxBFasWFGnDayvOA8QERGRfMw++3766af48ccfjbZpNBo8/vjjSEpKqqt21Xv6maAZABEREUnP7AzQ+vXrMXDgQLi7u+O///0vysvL8dhjj+H06dPYvn27JdpYL4k1QFwKg4iISHJmB0Bdu3bF77//juHDh8PR0RE//vgjkpOTsX37dvj7+1uijfWSWANkx5mgiYiIpHZH/S/9+vXDsmXL8MgjjyAlJQU7d+5k8GMmZoCIiIjkU6sM0H/+8x+T2319feHh4YHnnntO3LZmzZq6aVk9p68BUrEGiIiISHK1CoDc3d1Nbo+JianTxtgSfRcY5wEiIiKSXq0CoKVLlwIABEFAWloafH194ezsbNGG1Xel5bo5kxw4DxAREZHkzDr7CoKAsLAwXL582VLtsRnMABEREcnHrLOvUqlE8+bNkZOTY6n22IzKUWAsgiYiIpKa2emHjz/+GK+//jqOHz9uifbYDP1iqPbMABEREUnO7HmAnnrqKdy4cQMRERFwdHSsVguUm5tbZ42rz9gFRkREJB+zA6CvvvrKAs2wPeVafQDELjAiIiKpmR0AjR071hLtsDliF5iSGSAiIiKpmR0AGSopKUFpaanRNjc3t7tqkK2o7AJjBoiIiEhqZqcfiouL8fLLL8PPzw8NGzaEp6en0Y1qp7wiA8QaICIiIumZffadNm0atm3bhgULFkClUmHx4sWYPXs2AgMDsWzZMku0sV5iETQREZF8zO4C++uvv7Bs2TL06dMH48aNw/3334+wsDCEhITgl19+wZgxYyzRznpHHwDZswuMiIhIcmanH3Jzc9GsWTMAunof/bD3Xr16YdeuXXXbunqsXKvvAmMAREREJDWzA6BmzZohJSUFABAeHo7Vq1cD0GWGPDw86rRx9VlZObvAiIiI5GL22XfcuHE4cuQIAGD69OmYP38+nJycMGXKFLz++ut13sD6qkzLYfBERERyMbsGaMqUKeL/o6Ojcfr0aSQkJCAsLAzt27ev08bVZ+X6tcDs2QVGREQktbuaBwgAQkJCEBISUhdtsSmcCJGIiEg+dxQAHTp0CNu3b0d2dja0FUs66H3xxRd10rD6jqPAiIiI5GN2APTRRx/h7bffRsuWLeHv7w+FovIEbvh/ujV9AOTIImgiIiLJmR0AzZs3D0uWLMH//vc/CzTHNmi1AipqoGHPAIiIiEhyZp99lUolevbsaYm22Iwyg25DdoERERFJz+wAaMqUKZg/f74l2mIz9AXQALvAiIiI5GB2F9hrr72GIUOG4L777kPr1q3h4OBg9PiaNWvqrHH1lX4IPADYK5kBIiIikprZAdDEiROxfft29O3bF97e3ix8vgOlFQGQQgHYMQAiIiKSnNn9Lz/99BN+//13/PPPP4iNjcXSpUuNbubYtWsXhg4disDAQCgUCqxbt+6W+2dkZGD06NFo0aIFlEolJk+ebHK/X3/9FeHh4XByckK7du2wYcMGs9plaeUVXWAOSiUDSCIiIhmYHQB5eXnhvvvuq5NfXlxcjIiIiFrXFKnVavj6+uLtt99GRESEyX327duHUaNG4ZlnnkFiYiKGDx+O4cOH4/jx43XS5rqgD4BYAE1ERCQPhSAIwu13q7R06VJs3LgRS5cuRYMGDequIQoF1q5di+HDh9dq/z59+qBDhw746quvjLaPHDkSxcXF+Pvvv8Vt3bt3R4cOHbBw4cJaPXdBQQHc3d2Rn58PNze32r6EWkvOLkL0Fzvh7uyAI7MG1vnzExER2SJzzt9m1wB9/fXXOH/+PPz9/REaGlqtCPrw4cPmPmWdiouLw9SpU422xcTE3LJ7Ta1WQ61Wi/cLCgos1TwAQLlWvxI8M0BERERyMDsAqm2GRi6ZmZnw9/c32ubv74/MzMwaf2bOnDmYPXu2pZsmEmuAOASeiIhIFmYHQLNmzbJEO2Q1Y8YMo6xRQUEBgoKCLPb7SrkOGBERkazuejV4axMQEICsrCyjbVlZWQgICKjxZ1QqFVQqlaWbJjIcBUZERETSq3dn4KioKGzdutVo2+bNmxEVFSVTi6rTL4TKLjAiIiJ5yJoBKioqQnJysng/JSUFSUlJ8PLyQnBwMGbMmIH09HQsW7ZM3CcpKUn82atXryIpKQmOjo5o3bo1AGDSpEl44IEH8Pnnn2PIkCFYuXIl4uPjsWjRIklf262UsQuMiIhIVrIGQPHx8ejbt694X1+HM3bsWMTGxiIjIwOpqalGP9OxY0fx/wkJCVi+fDlCQkJw8eJFAECPHj2wfPlyvP3223jzzTfRvHlzrFu3Dm3btrX8C6olFkETERHJS9YAqE+fPrjVNESxsbHVttVm2qJHH30Ujz766N00zaIqu8CYASIiIpKD2QGQRqNBbGwstm7diuzsbGi1WqPHt23bVmeNq6/KtBUzQbMImoiISBZmB0CTJk1CbGwshgwZgrZt23ItqztQVl6RAbJnAERERCQHswOglStXYvXq1XjwwQct0R6bIM4EzZXgiYiIZGF2CsLR0RFhYWGWaIvNKONiqERERLIyOwB69dVXMW/evFoVI5NpnAeIiIhIXmZ3ge3Zswfbt2/HP//8gzZt2lRbDHXNmjV11rj6isPgiYiI5GV2AOTh4YERI0ZYoi02o4yrwRMREcnK7ABo6dKllmiHTSkr19cAMQNEREQkB56BZcBRYERERPK6o5mgf/vtN6xevRqpqakoLS01euzw4cN10rD6rFRcC4zxJxERkRzMPgN//fXXGDduHPz9/ZGYmIhu3brB29sbFy5cwODBgy3RxnpHwyJoIiIiWZl9Bv7uu++waNEifPPNN3B0dMS0adOwefNmTJw4Efn5+ZZoY71TLi6FwS4wIiIiOZgdAKWmpqJHjx4AAGdnZxQWFgIAnnzySaxYsaJuW1dPlYldYAyAiIiI5GB2ABQQEIDc3FwAQHBwMPbv3w8ASElJ4eSItaRhBoiIiEhWZgdA/fr1w59//gkAGDduHKZMmYIBAwZg5MiRnB+oliqXwmANEBERkRzMHgW2aNEiaCuGcU+YMAHe3t7Yt28fhg0bhueff77OG1gfaSqOHzNARERE8jA7AFIqlVAqKzMXjz/+OB5//PE6bVR9V8YuMCIiIlndUR/M7t278cQTTyAqKgrp6ekAgJ9//hl79uyp08bVV+WcB4iIiEhWZp+Bf//9d8TExMDZ2RmJiYlQq9UAgPz8fHz00Ud13sD6iEXQRERE8jI7APrggw+wcOFC/PDDD0Yrwffs2ZOzQNcSi6CJiIjkZfYZ+MyZM+jdu3e17e7u7sjLy6uLNtV7+gwQV4MnIiKSxx3NA5ScnFxt+549e9CsWbM6aVR9p58I0Y5dYERERLIwOwB69tlnMWnSJBw4cAAKhQJXrlzBL7/8gtdeew0vvviiJdpY71QuhcEuMCIiIjmYPQx++vTp0Gq16N+/P27cuIHevXtDpVLhtddewyuvvGKJNtY7XAuMiIhIXmYHQAqFAm+99RZef/11JCcno6ioCK1bt4aLi4sl2lcvlXMtMCIiIlmZHQDpOTo6onXr1nXZFptRrmEXGBERkZxqHQA9/fTTtdpvyZIld9wYW1GuZQaIiIhITrUOgGJjYxESEoKOHTty1fe7VM5h8ERERLKqdQD04osvYsWKFUhJScG4cePwxBNPwMvLy5Jtq7f0XWB27AIjIiKSRa3PwPPnz0dGRgamTZuGv/76C0FBQXjsscewadMmZoTMVM7V4ImIiGRlVgpCpVJh1KhR2Lx5M06ePIk2bdrgpZdeQmhoKIqKiizVxnpHLIJmFxgREZEs7rgPRqlUQqFQQBAEaDSaumxTvceJEImIiORl1hlYrVZjxYoVGDBgAFq0aIFjx47h22+/RWpqKucBMoN+HiAWQRMREcmj1kXQL730ElauXImgoCA8/fTTWLFiBXx8fCzZtnpLnwHiWmBERETyqHUAtHDhQgQHB6NZs2bYuXMndu7caXK/NWvW1Fnj6qvKYfDsAiMiIpJDrQOgp556CgoFMxZ3SxAEaJgBIiIikpVZEyHS3dNnfwDAgUXQREREsuAZWGL6IfAAYMciaCIiIlkwAJKYfhJEgBMhEhERyYUBkMQMM0AsgiYiIpIHz8ASKzPIADEBREREJA8GQBLTGKwEz1F1RERE8mAAJLHKleAZ/BAREcmFAZDExEkQOQSeiIhINjwLS0y/DhhXgiciIpIPAyCJlYldYDz0REREcuFZWGKGRdBEREQkDwZAEtMPg2cRNBERkXwYAElMw5XgiYiIZMezsMTK9EXQzAARERHJhgGQxDgPEBERkfwYAEmMXWBERETy41lYYvouMGaAiIiI5MMASGLlHAZPREQkOwZAEtMHQMwAERERyYcBkMT0S2GwBoiIiEg+sp6Fd+3ahaFDhyIwMBAKhQLr1q277c/s2LEDnTp1gkqlQlhYGGJjY40ef/fdd6FQKIxu4eHhlnkBd0CfAeIweCIiIvnIGgAVFxcjIiIC8+fPr9X+KSkpGDJkCPr27YukpCRMnjwZ48ePx6ZNm4z2a9OmDTIyMsTbnj17LNH8O1LOtcCIiIhkZy/nLx88eDAGDx5c6/0XLlyIpk2b4vPPPwcAtGrVCnv27MGXX36JmJgYcT97e3sEBATUeXvrQrlW3wXGDBAREZFc7qk0RFxcHKKjo422xcTEIC4uzmjbuXPnEBgYiGbNmmHMmDFITU295fOq1WoUFBQY3SyFEyESERHJ754KgDIzM+Hv72+0zd/fHwUFBbh58yYAIDIyErGxsdi4cSMWLFiAlJQU3H///SgsLKzxeefMmQN3d3fxFhQUZLHXoGENEBERkezuqQCoNgYPHoxHH30U7du3R0xMDDZs2IC8vDysXr26xp+ZMWMG8vPzxVtaWprF2icWQXMUGBERkWxkrQEyV0BAALKysoy2ZWVlwc3NDc7OziZ/xsPDAy1atEBycnKNz6tSqaBSqeq0rTXRVNQA2SmYASIiIpLLPZWGiIqKwtatW422bd68GVFRUTX+TFFREc6fP49GjRpZunm1UjENEOxYBE1ERCQbWQOgoqIiJCUlISkpCYBumHtSUpJYtDxjxgw89dRT4v4vvPACLly4gGnTpuH06dP47rvvsHr1akyZMkXc57XXXsPOnTtx8eJF7Nu3DyNGjICdnR1GjRol6WuriT4DxBogIiIi+cjaBRYfH4++ffuK96dOnQoAGDt2LGJjY5GRkWE0gqtp06ZYv349pkyZgnnz5qFJkyZYvHix0RD4y5cvY9SoUcjJyYGvry969eqF/fv3w9fXV7oXdgtcCoOIiEh+sgZAffr0gSAINT5edZZn/c8kJibW+DMrV66si6ZZjH4UGGuAiIiI5HNP1QDVB2IAxBogIiIi2TAAkhjXAiMiIpIfAyCJiRkgrgVGREQkG56FJVbOGiAiIiLZMQCSmFacCZoBEBERkVwYAEmMw+CJiIjkxwBIYpwIkYiISH4MgCSmzwApWQNEREQkGwZAEtMKrAEiIiKSGwMgiZVrWANEREQkNwZAEtNwIkQiIiLZMQCSmEZgDRAREZHcGABJTMN5gIiIiGTHAEhilTVAPPRERERy4VlYYqwBIiIikh8DIImxBoiIiEh+DIAkVs4MEBERkewYAElMvxSGHYugiYiIZMMASGL6ImhmgIiIiOTDAEhi+qUw7FgDREREJBsGQBLT1wBxKQwiIiL5MACSGCdCJCIikh8DIIlxIkQiIiL58SwsMdYAERERyY8BkMRYA0RERCQ/BkASYw0QERGR/BgASaxcUzERIjNAREREsmEAJLGKBBBrgIiIiGTEAEhi5VpmgIiIiOTGAEhirAEiIiKSHwMgiXEUGBERkfwYAElIqxUgsAaIiIhIdgyAJKTRRz8A7DkTNBERkWx4FpaQvv4HAOxYA0RERCQbBkASMgyA7FkDREREJBsGQBIqN8wAMQAiIiKSDQMgCRl1gbEImoiISDYMgCSknwRRoQCUzAARERHJhgGQhCriH9b/EBERyYwBkIS4DAYREZF1YAAkIX0NEOt/iIiI5MUASEJcBoOIiMg6MACSkFZcCJWHnYiISE48E0uIGSAiIiLrwABIQqwBIiIisg4MgCTEDBAREZF1YAAkIY1YA8QAiIiISE4MgCSkYQaIiIjIKjAAkpA4ESJrgIiIiGTFAEhCzAARERFZBwZAEmINEBERkXVgACShygwQDzsREZGceCaWkDgMngkgIiIiWTEAkpC4FAYzQERERLLimVhCnAiRiIjIOjAAkpiTgxIqBx52IiIiOdnL3QBbMjQiEEMjAuVuBhERkc2TNRWxa9cuDB06FIGBgVAoFFi3bt1tf2bHjh3o1KkTVCoVwsLCEBsbW22f+fPnIzQ0FE5OToiMjMTBgwfrvvFERER0z5I1ACouLkZERATmz59fq/1TUlIwZMgQ9O3bF0lJSZg8eTLGjx+PTZs2ifusWrUKU6dOxaxZs3D48GFEREQgJiYG2dnZlnoZREREdI9RCIIgyN0IAFAoFFi7di2GDx9e4z5vvPEG1q9fj+PHj4vbHn/8ceTl5WHjxo0AgMjISHTt2hXffvstAECr1SIoKAivvPIKpk+fbvJ51Wo11Gq1eL+goABBQUHIz8+Hm5tbHbw6IiIisrSCggK4u7vX6vx9T1XjxsXFITo62mhbTEwM4uLiAAClpaVISEgw2kepVCI6Olrcx5Q5c+bA3d1dvAUFBVnmBRAREZFVuKcCoMzMTPj7+xtt8/f3R0FBAW7evIlr165Bo9GY3CczM7PG550xYwby8/PFW1pamkXaT0RERNaBo8AAqFQqqFQquZtBREREErmnAqCAgABkZWUZbcvKyoKbmxucnZ1hZ2cHOzs7k/sEBARI2VQiIiKyYvdUF1hUVBS2bt1qtG3z5s2IiooCADg6OqJz585G+2i1WmzdulXch4iIiEjWAKioqAhJSUlISkoCoBvmnpSUhNTUVAC62pynnnpK3P+FF17AhQsXMG3aNJw+fRrfffcdVq9ejSlTpoj7TJ06FT/88AN++uknnDp1Ci+++CKKi4sxbtw4SV8bERERWS9Zu8Di4+PRt29f8f7UqVMBAGPHjkVsbCwyMjLEYAgAmjZtivXr12PKlCmYN28emjRpgsWLFyMmJkbcZ+TIkbh69SpmzpyJzMxMdOjQARs3bqxWGE1ERES2y2rmAbIm5swjQERERNah3s4DRERERFQXGAARERGRzWEARERERDbnnpoHSCr6sqiCggKZW0JERES1pT9v16a8mQGQCYWFhQDANcGIiIjuQYWFhXB3d7/lPhwFZoJWq8WVK1fg6uoKhUJRp8+tX2k+LS2NI8wsiMdZGjzO0uGxlgaPs3QscawFQUBhYSECAwOhVN66yocZIBOUSiWaNGli0d/h5ubGD5cEeJylweMsHR5rafA4S6euj/XtMj96LIImIiIim8MAiIiIiGwOAyCJqVQqzJo1CyqVSu6m1Gs8ztLgcZYOj7U0eJylI/exZhE0ERER2RxmgIiIiMjmMAAiIiIim8MAiIiIiGwOAyAiIiKyOQyAJDR//nyEhobCyckJkZGROHjwoNxNuqfs2rULQ4cORWBgIBQKBdatW2f0uCAImDlzJho1agRnZ2dER0fj3LlzRvvk5uZizJgxcHNzg4eHB5555hkUFRVJ+Cqs35w5c9C1a1e4urrCz88Pw4cPx5kzZ4z2KSkpwYQJE+Dt7Q0XFxc88sgjyMrKMtonNTUVQ4YMQYMGDeDn54fXX38d5eXlUr4Uq7dgwQK0b99enAguKioK//zzj/g4j7NlfPzxx1AoFJg8ebK4jcf67r377rtQKBRGt/DwcPFxqzvGAkli5cqVgqOjo7BkyRLhxIkTwrPPPit4eHgIWVlZcjftnrFhwwbhrbfeEtasWSMAENauXWv0+Mcffyy4u7sL69atE44cOSIMGzZMaNq0qXDz5k1xn0GDBgkRERHC/v37hd27dwthYWHCqFGjJH4l1i0mJkZYunSpcPz4cSEpKUl48MEHheDgYKGoqEjc54UXXhCCgoKErVu3CvHx8UL37t2FHj16iI+Xl5cLbdu2FaKjo4XExERhw4YNgo+PjzBjxgw5XpLV+vPPP4X169cLZ8+eFc6cOSO8+eabgoODg3D8+HFBEHicLeHgwYNCaGio0L59e2HSpEnidh7ruzdr1iyhTZs2QkZGhni7evWq+Li1HWMGQBLp1q2bMGHCBPG+RqMRAgMDhTlz5sjYqntX1QBIq9UKAQEBwqeffipuy8vLE1QqlbBixQpBEATh5MmTAgDh0KFD4j7//POPoFAohPT0dMnafq/Jzs4WAAg7d+4UBEF3XB0cHIRff/1V3OfUqVMCACEuLk4QBF2wqlQqhczMTHGfBQsWCG5uboJarZb2BdxjPD09hcWLF/M4W0BhYaHQvHlzYfPmzcIDDzwgBkA81nVj1qxZQkREhMnHrPEYswtMAqWlpUhISEB0dLS4TalUIjo6GnFxcTK2rP5ISUlBZmam0TF2d3dHZGSkeIzj4uLg4eGBLl26iPtER0dDqVTiwIEDkrf5XpGfnw8A8PLyAgAkJCSgrKzM6FiHh4cjODjY6Fi3a9cO/v7+4j4xMTEoKCjAiRMnJGz9vUOj0WDlypUoLi5GVFQUj7MFTJgwAUOGDDE6pgDf03Xp3LlzCAwMRLNmzTBmzBikpqYCsM5jzMVQJXDt2jVoNBqjPyoA+Pv74/Tp0zK1qn7JzMwEAJPHWP9YZmYm/Pz8jB63t7eHl5eXuA8Z02q1mDx5Mnr27Im2bdsC0B1HR0dHeHh4GO1b9Vib+lvoH6NKx44dQ1RUFEpKSuDi4oK1a9eidevWSEpK4nGuQytXrsThw4dx6NChao/xPV03IiMjERsbi5YtWyIjIwOzZ8/G/fffj+PHj1vlMWYAREQ1mjBhAo4fP449e/bI3ZR6q2XLlkhKSkJ+fj5+++03jB07Fjt37pS7WfVKWloaJk2ahM2bN8PJyUnu5tRbgwcPFv/fvn17REZGIiQkBKtXr4azs7OMLTONXWAS8PHxgZ2dXbVq96ysLAQEBMjUqvpFfxxvdYwDAgKQnZ1t9Hh5eTlyc3P5dzDh5Zdfxt9//43t27ejSZMm4vaAgACUlpYiLy/PaP+qx9rU30L/GFVydHREWFgYOnfujDlz5iAiIgLz5s3jca5DCQkJyM7ORqdOnWBvbw97e3vs3LkTX3/9Nezt7eHv789jbQEeHh5o0aIFkpOTrfL9zABIAo6OjujcuTO2bt0qbtNqtdi6dSuioqJkbFn90bRpUwQEBBgd44KCAhw4cEA8xlFRUcjLy0NCQoK4z7Zt26DVahEZGSl5m62VIAh4+eWXsXbtWmzbtg1NmzY1erxz585wcHAwOtZnzpxBamqq0bE+duyYUcC5efNmuLm5oXXr1tK8kHuUVquFWq3mca5D/fv3x7Fjx5CUlCTeunTpgjFjxoj/57Gue0VFRTh//jwaNWpkne/nOi+rJpNWrlwpqFQqITY2Vjh58qTw3HPPCR4eHkbV7nRrhYWFQmJiopCYmCgAEL744gshMTFRuHTpkiAIumHwHh4ewh9//CEcPXpUePjhh00Og+/YsaNw4MABYc+ePULz5s05DL6KF198UXB3dxd27NhhNJz1xo0b4j4vvPCCEBwcLGzbtk2Ij48XoqKihKioKPFx/XDWgQMHCklJScLGjRsFX19fDhmuYvr06cLOnTuFlJQU4ejRo8L06dMFhUIh/Pvvv4Ig8DhbkuEoMEHgsa4Lr776qrBjxw4hJSVF2Lt3rxAdHS34+PgI2dnZgiBY3zFmACShb775RggODhYcHR2Fbt26Cfv375e7SfeU7du3CwCq3caOHSsIgm4o/DvvvCP4+/sLKpVK6N+/v3DmzBmj58jJyRFGjRoluLi4CG5ubsK4ceOEwsJCGV6N9TJ1jAEIS5cuFfe5efOm8NJLLwmenp5CgwYNhBEjRggZGRlGz3Px4kVh8ODBgrOzs+Dj4yO8+uqrQllZmcSvxro9/fTTQkhIiODo6Cj4+voK/fv3F4MfQeBxtqSqARCP9d0bOXKk0KhRI8HR0VFo3LixMHLkSCE5OVl83NqOsUIQBKHu80pERERE1os1QERERGRzGAARERGRzWEARERERDaHARARERHZHAZAREREZHMYABEREZHNYQBERERENocBEBEREdkcBkBEVO/873//w/Dhw+VuBhFZMXu5G0BEZA6FQnHLx2fNmoV58+aBk9wT0a0wACKie0pGRob4/1WrVmHmzJk4c+aMuM3FxQUuLi5yNI2I7iHsAiOie0pAQIB4c3d3h0KhMNrm4uJSrQusT58+eOWVVzB58mR4enrC398fP/zwA4qLizFu3Di4uroiLCwM//zzj9HvOn78OAYPHgwXFxf4+/vjySefxLVr1yR+xURkCQyAiMgm/PTTT/Dx8cHBgwfxyiuv4MUXX8Sjjz6KHj164PDhwxg4cCCefPJJ3LhxAwCQl5eHfv36oWPHjoiPj8fGjRuRlZWFxx57TOZXQkR1gQEQEdmEiIgIvP3222jevDlmzJgBJycn+Pj44Nlnn0Xz5s0xc+ZM5OTk4OjRowCAb7/9Fh07dsRHH32E8PBwdOzYEUuWLMH27dtx9uxZmV8NEd0t1gARkU1o3769+H87Ozt4e3ujXbt24jZ/f38AQHZ2NgDgyJEj2L59u8l6ovPnz6NFixYWbjERWRIDICKyCQ4ODkb3FQqF0Tb96DKtVgsAKCoqwtChQzF37txqz9WoUSMLtpSIpMAAiIjIhE6dOuH3339HaGgo7O35VUlU37AGiIjIhAkTJiA3NxejRo3CoUOHcP78eWzatAnjxo2DRqORu3lEdJcYABERmRAYGIi9e/dCo9Fg4MCBaNeuHSZPngwPDw8olfzqJLrXKQROl0pEREQ2hpcxREREZHMYABEREZHNYQBERERENocBEBEREdkcBkBERERkcxgAERERkc1hAEREREQ2hwEQERER2RwGQERERGRzGAARERGRzWEARERERDbn/wEm5a3VGA3z5wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -388,7 +390,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -414,7 +416,7 @@
    "source": [
     "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n",
     "\n",
-    "The smaller point masses in this distribution are due to $\\texttt{HARK}$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks.  For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$.  You can verify this by changing $\\texttt{TranShkCount}$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye."
+    "The smaller point masses in this distribution are due to `HARK` drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks.  For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$.  You can verify this by changing `TranShkCount` to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye."
    ]
   }
  ],
@@ -439,7 +441,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

From fd573293edcd7f3d7b87588c3bbef2f048390729 Mon Sep 17 00:00:00 2001
From: sidd3888 <siddarthv2011@gmail.com>
Date: Thu, 23 Nov 2023 12:56:56 +0530
Subject: [PATCH 11/28] indentation fix

---
 examples/ConsIndShockModel/KinkedRconsumerType.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
index ce423c351..9b51a6e89 100644
--- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
+++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
@@ -71,8 +71,8 @@
     "a_t &\\geq \\underline{a}, \\\\\n",
     "m_{t+1} &= \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n",
     "\\Rfree_t &= \\begin{cases}\n",
-    "    \\Rfree_{boro} & \\text{if } a_t < 0\\\\\n",
-    "    \\Rfree_{save} & \\text{if } a_t \\geq 0,\n",
+    "\\Rfree_{boro} & \\text{if } a_t < 0\\\\\n",
+    "\\Rfree_{save} & \\text{if } a_t \\geq 0,\n",
     "\\end{cases}\\\\\n",
     "\\Rfree_{boro} &> \\Rfree_{save}, \\\\\n",
     "(\\psi_{t+1},\\theta_{t+1}) &\\sim F_{t+1}, \\\\\n",

From 7c99c8ac86ba1ee75803ee76d21c9c3aa67366fc Mon Sep 17 00:00:00 2001
From: sidd3888 <siddarthv2011@gmail.com>
Date: Thu, 23 Nov 2023 13:16:19 +0530
Subject: [PATCH 12/28] parameter formatting

---
 .../KinkedRconsumerType.ipynb                 | 74 +++++++++----------
 1 file changed, 37 insertions(+), 37 deletions(-)

diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
index 9b51a6e89..a0cb080de 100644
--- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
+++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
@@ -97,37 +97,37 @@
    "source": [
     "## Example parameter values to construct an instance of KinkedRconsumerType\n",
     "\n",
-    "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`.  The only difference is that the parameter `Rfree` is replaced with `Rboro` and `Rsave`.\n",
+    "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`.  The only difference is that the parameter $\\verb|Rfree|$ is replaced with $\\verb|Rboro|$ and $\\verb|Rsave|$.\n",
     "\n",
-    "While the parameter `CubicBool` is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model.  In the future, this restriction will be lifted.\n",
+    "While the parameter $\\verb|CubicBool|$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model.  In the future, this restriction will be lifted.\n",
     "\n",
     "| Parameter | Description | Code | Example value | Time-varying? |\n",
     "| :---: | --- | --- | --- | --- |\n",
-    "| $\\DiscFac$ |Intertemporal discount factor  | `DiscFac` | $0.96$ |  |\n",
-    "| $\\CRRA$ |Coefficient of relative risk aversion | `CRRA` | $2.0$ | |\n",
-    "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | `Rboro` | $1.20$ | |\n",
-    "| $\\Rfree_{save}$ | Risk free interest factor for saving | `Rsave` | $1.01$ | |\n",
-    "| $1 - \\DiePrb_{t+1}$ |Survival probability | `LivPrb` | $[0.98]$ | $\\surd$ |\n",
-    "|$\\PermGroFac_{t+1}$|Permanent income growth factor|`PermGroFac`| $[1.01]$ | $\\surd$ |\n",
-    "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | `PermShkStd` | $[0.1]$ |$\\surd$ |\n",
-    "| $N_\\psi$ | Number of discrete permanent income shocks | `PermShkCount` | $7$ | |\n",
-    "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | `TranShkStd` | $[0.2]$ | $\\surd$ |\n",
-    "| $N_\\theta$ | Number of discrete transitory income shocks | `TranShkCount` | $7$ |  |\n",
-    "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | `UnempPrb` | $0.05$ |  |\n",
-    "| $\\underline{\\theta}$ | Transitory shock when unemployed | `IncUnemp` | $0.3$ |  |\n",
-    "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | `UnempPrbRet` | $0.0005$ |  |\n",
-    "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | `IncUnempRet` | $0.0$ |  |\n",
-    "| $(none)$ | Period of the lifecycle model when retirement begins | `T_retire` | $0$ | |\n",
-    "| $(none)$ | Minimum value in assets-above-minimum grid | `aXtraMin` | $0.001$ | |\n",
-    "| $(none)$ | Maximum value in assets-above-minimum grid | `aXtraMax` | $20.0$ | |\n",
-    "| $(none)$ | Number of points in base assets-above-minimum grid | `aXtraCount` | $48$ | |\n",
-    "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | `aXtraNestFac` | $3$ | |\n",
-    "| $(none)$ | Additional values to add to assets-above-minimum grid | `aXtraExtra` | $None$ | |\n",
-    "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | `BoroCnstArt` | $None$ | |\n",
-    "| $(none)$ |Indicator for whether `vFunc` should be computed | `vFuncBool` | $True$ | |\n",
-    "| $(none)$ |Indicator for whether `cFunc` should use cubic splines | `CubicBool` | $False$ |  |\n",
-    "|$T$| Number of periods in this type's \"cycle\" |`T_cycle`| $1$ | |\n",
-    "|(none)| Number of times the \"cycle\" occurs |`cycles`| $0$ | |\n",
+    "| $\\DiscFac$ |Intertemporal discount factor  | $\\verb|DiscFac|$ | $0.96$ |  |\n",
+    "| $\\CRRA$ |Coefficient of relative risk aversion | $\\verb|CRRA|$ | $2.0$ | |\n",
+    "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\verb|Rboro|$ | $1.20$ | |\n",
+    "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\verb|Rsave|$ | $1.01$ | |\n",
+    "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\verb|LivPrb|$ | $[0.98]$ | $\\surd$ |\n",
+    "|$\\PermGroFac_{t+1}$|Permanent income growth factor| $\\verb|PermGroFac|$ | $[1.01]$ | $\\surd$ |\n",
+    "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\verb|PermShkStd|$ | $[0.1]$ |$\\surd$ |\n",
+    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\verb|PermShkCount|$ | $7$ | |\n",
+    "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\verb|TranShkStd|$ | $[0.2]$ | $\\surd$ |\n",
+    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\verb|TranShkCount|$ | $7$ |  |\n",
+    "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\verb|UnempPrb|$ | $0.05$ |  |\n",
+    "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\verb|IncUnemp|$ | $0.3$ |  |\n",
+    "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\verb|UnempPrbRet|$ | $0.0005$ |  |\n",
+    "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\verb|IncUnempRet|$ | $0.0$ |  |\n",
+    "| $(none)$ | Period of the lifecycle model when retirement begins | $\\verb|T_retire|$ | $0$ | |\n",
+    "| $(none)$ | Minimum value in assets-above-minimum grid | $\\verb|aXtraMin|$ | $0.001$ | |\n",
+    "| $(none)$ | Maximum value in assets-above-minimum grid | $\\verb|aXtraMax|$ | $20.0$ | |\n",
+    "| $(none)$ | Number of points in base assets-above-minimum grid | $\\verb|aXtraCount|$ | $48$ | |\n",
+    "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\verb|aXtraNestFac|$ | $3$ | |\n",
+    "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\verb|aXtraExtra|$ | $None$ | |\n",
+    "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\verb|BoroCnstArt|$ | $None$ | |\n",
+    "| $(none)$ |Indicator for whether $\\verb|vFunc|$ should be computed | $\\verb|vFuncBool|$ | $True$ | |\n",
+    "| $(none)$ |Indicator for whether $\\verb|cFunc|$ should use cubic splines | $\\verb|CubicBool|$ | $False$ |  |\n",
+    "|$T$| Number of periods in this type's \"cycle\" | $\\verb|T_cycle|$ | $1$ | |\n",
+    "|(none)| Number of times the \"cycle\" occurs | $\\verb|cycles|$ | $0$ | |\n",
     "\n",
     "These example parameters are almost identical to those used for `IndShockExample` in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to `None`.  The cell below defines a parameter dictionary with these example values."
    ]
@@ -272,16 +272,16 @@
     "\n",
     "| Description | Code | Example value |\n",
     "| :---: | --- | --- |\n",
-    "| Number of consumers of this type | `AgentCount` | $10000$ |\n",
-    "| Number of periods to simulate | `T_sim` | $500$ |\n",
-    "| Mean of initial log (normalized) assets | `aNrmInitMean` | $-6.0$ |\n",
-    "| Stdev of initial log  (normalized) assets | `aNrmInitStd` | $1.0$ |\n",
-    "| Mean of initial log permanent income | `pLvlInitMean` | $0.0$ |\n",
-    "| Stdev of initial log permanent income | `pLvlInitStd` | $0.0$ |\n",
-    "| Aggregrate productivity growth factor | `PermGroFacAgg` | $1.0$ |\n",
-    "| Age after which consumers are automatically killed | `T_age` | $None$ |\n",
+    "| Number of consumers of this type | $\\verb|AgentCount|$ | $10000$ |\n",
+    "| Number of periods to simulate | $\\verb|T_sim|$ | $500$ |\n",
+    "| Mean of initial log (normalized) assets | $\\verb|aNrmInitMean|$ | $-6.0$ |\n",
+    "| Stdev of initial log  (normalized) assets | $\\verb|aNrmInitStd|$ | $1.0$ |\n",
+    "| Mean of initial log permanent income | $\\verb|pLvlInitMean|$ | $0.0$ |\n",
+    "| Stdev of initial log permanent income | $\\verb|pLvlInitStd|$ | $0.0$ |\n",
+    "| Aggregrate productivity growth factor | $\\verb|PermGroFacAgg|$ | $1.0$ |\n",
+    "| Age after which consumers are automatically killed | $\\verb|T_age|$ | $None$ |\n",
     "\n",
-    "Here, we will simulate 10,000 consumers for 500 periods.  All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp($ `pLvlInitMean` $)$, as `pLvlInitStd` has been set to zero; they will have essentially zero assets at birth, as `aNrmInitMean` is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n",
+    "Here, we will simulate 10,000 consumers for 500 periods.  All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\verb|pLvlInitMean|)$, as $\\verb|pLvlInitStd|$ has been set to zero; they will have essentially zero assets at birth, as $\\verb|aNrmInitMean|$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n",
     "\n",
     "These example parameter values were already passed as part of the parameter dictionary that we used to create `KinkyExample`, so it is ready to simulate.  We need to set the `track_vars` attribute to indicate the variables for which we want to record a *history*."
    ]
@@ -416,7 +416,7 @@
    "source": [
     "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n",
     "\n",
-    "The smaller point masses in this distribution are due to `HARK` drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks.  For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$.  You can verify this by changing `TranShkCount` to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye."
+    "The smaller point masses in this distribution are due to $\\verb|HARK|$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks.  For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$.  You can verify this by changing $\\verb|TranShkCount|$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye."
    ]
   }
  ],

From fc93dcce953d361a6af836e6b3d2dd9119fcea5f Mon Sep 17 00:00:00 2001
From: sidd3888 <siddarthv2011@gmail.com>
Date: Thu, 23 Nov 2023 13:33:20 +0530
Subject: [PATCH 13/28] verb compatibility fix

---
 .../KinkedRconsumerType.ipynb                 | 74 +++++++++----------
 1 file changed, 37 insertions(+), 37 deletions(-)

diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
index a0cb080de..71dbdb9eb 100644
--- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
+++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
@@ -97,37 +97,37 @@
    "source": [
     "## Example parameter values to construct an instance of KinkedRconsumerType\n",
     "\n",
-    "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`.  The only difference is that the parameter $\\verb|Rfree|$ is replaced with $\\verb|Rboro|$ and $\\verb|Rsave|$.\n",
+    "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`.  The only difference is that the parameter $\\verb!Rfree!$ is replaced with $\\verb!Rboro!$ and $\\verb!Rsave!$.\n",
     "\n",
-    "While the parameter $\\verb|CubicBool|$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model.  In the future, this restriction will be lifted.\n",
+    "While the parameter $\\verb!CubicBool!$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model.  In the future, this restriction will be lifted.\n",
     "\n",
     "| Parameter | Description | Code | Example value | Time-varying? |\n",
     "| :---: | --- | --- | --- | --- |\n",
-    "| $\\DiscFac$ |Intertemporal discount factor  | $\\verb|DiscFac|$ | $0.96$ |  |\n",
-    "| $\\CRRA$ |Coefficient of relative risk aversion | $\\verb|CRRA|$ | $2.0$ | |\n",
-    "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\verb|Rboro|$ | $1.20$ | |\n",
-    "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\verb|Rsave|$ | $1.01$ | |\n",
-    "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\verb|LivPrb|$ | $[0.98]$ | $\\surd$ |\n",
-    "|$\\PermGroFac_{t+1}$|Permanent income growth factor| $\\verb|PermGroFac|$ | $[1.01]$ | $\\surd$ |\n",
-    "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\verb|PermShkStd|$ | $[0.1]$ |$\\surd$ |\n",
-    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\verb|PermShkCount|$ | $7$ | |\n",
-    "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\verb|TranShkStd|$ | $[0.2]$ | $\\surd$ |\n",
-    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\verb|TranShkCount|$ | $7$ |  |\n",
-    "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\verb|UnempPrb|$ | $0.05$ |  |\n",
-    "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\verb|IncUnemp|$ | $0.3$ |  |\n",
-    "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\verb|UnempPrbRet|$ | $0.0005$ |  |\n",
-    "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\verb|IncUnempRet|$ | $0.0$ |  |\n",
-    "| $(none)$ | Period of the lifecycle model when retirement begins | $\\verb|T_retire|$ | $0$ | |\n",
-    "| $(none)$ | Minimum value in assets-above-minimum grid | $\\verb|aXtraMin|$ | $0.001$ | |\n",
-    "| $(none)$ | Maximum value in assets-above-minimum grid | $\\verb|aXtraMax|$ | $20.0$ | |\n",
-    "| $(none)$ | Number of points in base assets-above-minimum grid | $\\verb|aXtraCount|$ | $48$ | |\n",
-    "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\verb|aXtraNestFac|$ | $3$ | |\n",
-    "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\verb|aXtraExtra|$ | $None$ | |\n",
-    "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\verb|BoroCnstArt|$ | $None$ | |\n",
-    "| $(none)$ |Indicator for whether $\\verb|vFunc|$ should be computed | $\\verb|vFuncBool|$ | $True$ | |\n",
-    "| $(none)$ |Indicator for whether $\\verb|cFunc|$ should use cubic splines | $\\verb|CubicBool|$ | $False$ |  |\n",
-    "|$T$| Number of periods in this type's \"cycle\" | $\\verb|T_cycle|$ | $1$ | |\n",
-    "|(none)| Number of times the \"cycle\" occurs | $\\verb|cycles|$ | $0$ | |\n",
+    "| $\\DiscFac$ |Intertemporal discount factor  | $\\verb!DiscFac!$ | $0.96$ |  |\n",
+    "| $\\CRRA$ |Coefficient of relative risk aversion | $\\verb!CRRA!$ | $2.0$ | |\n",
+    "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\verb!Rboro!$ | $1.20$ | |\n",
+    "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\verb!Rsave!$ | $1.01$ | |\n",
+    "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\verb!LivPrb!$ | $[0.98]$ | $\\surd$ |\n",
+    "| $\\PermGroFac_{t+1}$|Permanent income growth factor| $\\verb!PermGroFac!$ | $[1.01]$ | $\\surd$ |\n",
+    "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\verb!PermShkStd!$ | $[0.1]$ | $\\surd$ |\n",
+    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\verb!PermShkCount!$ | $7$ | |\n",
+    "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\verb!TranShkStd!$ | $[0.2]$ | $\\surd$ |\n",
+    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\verb!TranShkCount!$ | $7$ |  |\n",
+    "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\verb!UnempPrb!$ | $0.05$ |  |\n",
+    "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\verb!IncUnemp!$ | $0.3$ |  |\n",
+    "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\verb!UnempPrbRet!$ | $0.0005$ |  |\n",
+    "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\verb!IncUnempRet!$ | $0.0$ |  |\n",
+    "| $(none)$ | Period of the lifecycle model when retirement begins | $\\verb!T_retire!$ | $0$ | |\n",
+    "| $(none)$ | Minimum value in assets-above-minimum grid | $\\verb!aXtraMin!$ | $0.001$ | |\n",
+    "| $(none)$ | Maximum value in assets-above-minimum grid | $\\verb!aXtraMax!$ | $20.0$ | |\n",
+    "| $(none)$ | Number of points in base assets-above-minimum grid | $\\verb!aXtraCount!$ | $48$ | |\n",
+    "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\verb!aXtraNestFac!$ | $3$ | |\n",
+    "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\verb!aXtraExtra!$ | $None$ | |\n",
+    "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\verb!BoroCnstArt!$ | $None$ | |\n",
+    "| $(none)$ |Indicator for whether $\\verb!vFunc!$ should be computed | $\\verb!vFuncBool!$ | $True$ | |\n",
+    "| $(none)$ |Indicator for whether $\\verb!cFunc!$ should use cubic splines | $\\verb!CubicBool!$ | $False$ |  |\n",
+    "| $T$| Number of periods in this type's \"cycle\" | $\\verb!T_cycle!$ | $1$ | |\n",
+    "| $(none)$ | Number of times the \"cycle\" occurs | $\\verb!cycles!$ | $0$ | |\n",
     "\n",
     "These example parameters are almost identical to those used for `IndShockExample` in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to `None`.  The cell below defines a parameter dictionary with these example values."
    ]
@@ -272,16 +272,16 @@
     "\n",
     "| Description | Code | Example value |\n",
     "| :---: | --- | --- |\n",
-    "| Number of consumers of this type | $\\verb|AgentCount|$ | $10000$ |\n",
-    "| Number of periods to simulate | $\\verb|T_sim|$ | $500$ |\n",
-    "| Mean of initial log (normalized) assets | $\\verb|aNrmInitMean|$ | $-6.0$ |\n",
-    "| Stdev of initial log  (normalized) assets | $\\verb|aNrmInitStd|$ | $1.0$ |\n",
-    "| Mean of initial log permanent income | $\\verb|pLvlInitMean|$ | $0.0$ |\n",
-    "| Stdev of initial log permanent income | $\\verb|pLvlInitStd|$ | $0.0$ |\n",
-    "| Aggregrate productivity growth factor | $\\verb|PermGroFacAgg|$ | $1.0$ |\n",
-    "| Age after which consumers are automatically killed | $\\verb|T_age|$ | $None$ |\n",
+    "| Number of consumers of this type | $\\verb!AgentCount!$ | $10000$ |\n",
+    "| Number of periods to simulate | $\\verb!T_sim!$ | $500$ |\n",
+    "| Mean of initial log (normalized) assets | $\\verb!aNrmInitMean!$ | $-6.0$ |\n",
+    "| Stdev of initial log  (normalized) assets | $\\verb!aNrmInitStd!$ | $1.0$ |\n",
+    "| Mean of initial log permanent income | $\\verb!pLvlInitMean!$ | $0.0$ |\n",
+    "| Stdev of initial log permanent income | $\\verb!pLvlInitStd!$ | $0.0$ |\n",
+    "| Aggregrate productivity growth factor | $\\verb!PermGroFacAgg!$ | $1.0$ |\n",
+    "| Age after which consumers are automatically killed | $\\verb!T_age!$ | $None$ |\n",
     "\n",
-    "Here, we will simulate 10,000 consumers for 500 periods.  All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\verb|pLvlInitMean|)$, as $\\verb|pLvlInitStd|$ has been set to zero; they will have essentially zero assets at birth, as $\\verb|aNrmInitMean|$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n",
+    "Here, we will simulate 10,000 consumers for 500 periods.  All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\verb!pLvlInitMean!)$, as $\\verb!pLvlInitStd!$ has been set to zero; they will have essentially zero assets at birth, as $\\verb!aNrmInitMean!$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n",
     "\n",
     "These example parameter values were already passed as part of the parameter dictionary that we used to create `KinkyExample`, so it is ready to simulate.  We need to set the `track_vars` attribute to indicate the variables for which we want to record a *history*."
    ]
@@ -416,7 +416,7 @@
    "source": [
     "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n",
     "\n",
-    "The smaller point masses in this distribution are due to $\\verb|HARK|$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks.  For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$.  You can verify this by changing $\\verb|TranShkCount|$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye."
+    "The smaller point masses in this distribution are due to $\\verb!HARK!$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks.  For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$.  You can verify this by changing $\\verb!TranShkCount!$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye."
    ]
   }
  ],

From a7f018df9bdce88c75c6324083a46409eeef828b Mon Sep 17 00:00:00 2001
From: Mateo VG <mateovegi@gmail.com>
Date: Thu, 22 Feb 2024 19:02:38 -0500
Subject: [PATCH 14/28] allow for a pre-built grid

---
 HARK/econforgeinterp.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/HARK/econforgeinterp.py b/HARK/econforgeinterp.py
index efc8c6ba8..af5d8bdd2 100644
--- a/HARK/econforgeinterp.py
+++ b/HARK/econforgeinterp.py
@@ -20,10 +20,10 @@ class LinearFast(MetricObject):
 
     distance_criteria = ["f_val", "grid_list"]
 
-    def __init__(self, f_val, grids, extrap_mode="linear"):
+    def __init__(self, f_val, grids, extrap_mode="linear", prebuilt_grid=None):
         """
         f_val: numpy.array
-            An array containing the values of the function at the grid points.
+        An array containing the values of the function at the grid points.
             It's i-th dimension must be of the same lenght as the i-th grid.
             f_val[i,j,k] must be f(grids[0][i], grids[1][j], grids[2][k]).
         grids: [numpy.array]
@@ -32,11 +32,17 @@ def __init__(self, f_val, grids, extrap_mode="linear"):
         extrap_mode: one of 'linear', 'nearest', or 'constant'
             Determines how to extrapolate, using either nearest point, multilinear, or
             constant extrapolation. The default is multilinear.
+        prebuilt_grid: CGrid, optional
+            A prebuilt CGrid object to be used as the grid. If None, a new one
+            will be created using the grids provided. By default None.
         """
         self.dim = len(grids)
         self.f_val = f_val
         self.grid_list = grids
-        self.Grid = CGrid(*grids)
+        if prebuilt_grid is None:
+            self.Grid = CGrid(*grids)
+        else:
+            self.Grid = prebuilt_grid
 
         # Set up extrapolation options
         self.extrap_mode = extrap_mode

From 82830e354290f40152294989fc728067a2de7ae3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Mateo=20Vel=C3=A1squez-Giraldo?= <mateovegi@gmail.com>
Date: Thu, 22 Feb 2024 20:13:25 -0500
Subject: [PATCH 15/28] Update CHANGELOG.md

---
 Documentation/CHANGELOG.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 3ac0ab23c..4ae68133c 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -8,6 +8,16 @@ 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)
+
 ### 0.14.0
 
 Release Date: February 12, 2024

From 8123924423c896f531aa849a3909def10a747da0 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Wed, 28 Feb 2024 10:40:46 -0500
Subject: [PATCH 16/28] Change repr to describe()

When the "parameters code" was added, it specified new behavior for `__repr__()`, listing *all* of the parameters whenever the object was returned to stdout. This is ok sometimes, but generates a massive amount of printed output in lifecycle models. Often the user just wants to do a quick check that an object is the class they think it is, or that there are the right number and kind of things in a list, and this behavior makes that impossible.

This commit *only* changes the function name `__repr__` to describe. I.e. the "list everything" behavior is still there, it just needs to be explicitly requested rather than assumed as the default.
---
 HARK/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HARK/core.py b/HARK/core.py
index 71a79e2a9..bae538f1a 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -323,7 +323,7 @@ def __str__(self):
         s += ">"
         return s
 
-    def __repr__(self):
+    def describe(self):
         return self.__str__()
 
 

From 28b72a5b7c311aaa27258d0f610bae083e5e497b Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Wed, 28 Feb 2024 10:48:46 -0500
Subject: [PATCH 17/28] Update CHANGELOG

Fixed tiny formatting errorS, and missing entry for prior pr.
---
 Documentation/CHANGELOG.md | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 3ac0ab23c..5f3482d89 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -8,17 +8,30 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.14.1 (IN DEVELOPMENT)
+
+Release date: ???
+
+#### 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 +48,7 @@ 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 +75,7 @@ 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)

From 366578fe262bd56497ab3993404d8e948b49c72a Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Wed, 28 Feb 2024 10:59:33 -0500
Subject: [PATCH 18/28] Change repr test to look at describe instead.

---
 HARK/tests/test_core.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py
index 0cda93381..8dacf071b 100644
--- a/HARK/tests/test_core.py
+++ b/HARK/tests/test_core.py
@@ -109,8 +109,8 @@ def test_solve(self):
         self.assertEqual(len(self.agent.solution), 4)
         self.assertTrue(isinstance(self.agent.solution[0], MetricObject))
 
-    def test___repr__(self):
-        self.assertTrue("Parameters" in self.agent.__repr__())
+    def test_describe(self):
+        self.assertTrue("Parameters" in self.agent.describe())
 
     def test___eq__(self):
         agent2 = AgentType(cycles=1)

From ca038693725d8b52c1e24d77a7c4e1094df68301 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Wed, 28 Feb 2024 11:39:26 -0500
Subject: [PATCH 19/28] Adjustments for 0.14.1 minor release

---
 Documentation/CHANGELOG.md | 4 ++--
 HARK/__init__.py           | 2 +-
 pyproject.toml             | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 5f3482d89..4166ac4bf 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -8,9 +8,9 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
-### 0.14.1 (IN DEVELOPMENT)
+### 0.14.1
 
-Release date: ???
+Release date: February 28, 2024
 
 #### Major Changes
 
diff --git a/HARK/__init__.py b/HARK/__init__.py
index ba1e4a763..bc0da6990 100644
--- a/HARK/__init__.py
+++ b/HARK/__init__.py
@@ -1,6 +1,6 @@
 from .core import *
 
-__version__ = "0.14.0"
+__version__ = "0.14.1"
 
 """
 Logging tools for HARK.
diff --git a/pyproject.toml b/pyproject.toml
index 94aa901c9..c8da30f69 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "econ-ark"
-version = "0.14.0"
+version = "0.14.1"
 authors = [{name = "Econ-ARK team", email = "econ-ark@jhuecon.org"}]
 classifiers = [
     "Development Status :: 3 - Alpha",

From bb44496316334e1726810d6edb677a0b70035b52 Mon Sep 17 00:00:00 2001
From: alanlujan91 <alanlujan91@gmail.com>
Date: Fri, 1 Mar 2024 14:48:31 -0500
Subject: [PATCH 20/28] fix kink in cons Bequest model

---
 HARK/ConsumptionSaving/ConsBequestModel.py    |  65 +++++---
 HARK/ConsumptionSaving/ConsIndShockModel.py   |  27 ++--
 .../example_AccidentalBequest.ipynb           | 117 +++++++-------
 .../example_ConsIndShockComp.ipynb            | 150 +-----------------
 .../example_TerminalBequest.ipynb             | 117 +++++++-------
 5 files changed, 182 insertions(+), 294 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsBequestModel.py b/HARK/ConsumptionSaving/ConsBequestModel.py
index d64c3b5d4..e8dc83ffe 100644
--- a/HARK/ConsumptionSaving/ConsBequestModel.py
+++ b/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,
@@ -37,33 +35,32 @@
 
 
 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
@@ -71,17 +68,11 @@ def update_parameters(self):
             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 = (
@@ -127,7 +120,7 @@ def __init__(self, **kwds):
         super().__init__(**params)
 
         self.solve_one_period = make_one_period_oo_solver(
-            BequestWarmGlowPortfolioSolver
+            BequestWarmGlowPortfolioSolver,
         )
 
     def update(self):
@@ -212,6 +205,28 @@ 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()
 
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 1d80b1362..02fd8efec 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -12,19 +12,10 @@
 See NARK https://github.com/econ-ark/HARK/blob/master/Documentation/NARK/NARK.pdf for information on variable naming conventions.
 See HARK documentation for mathematical descriptions of the models being solved.
 """
+
 from copy import copy, deepcopy
 
 import numpy as np
-from scipy import sparse as sp
-from scipy.optimize import newton
-
-from HARK import (
-    AgentType,
-    NullFunc,
-    _log,
-    make_one_period_oo_solver,
-    set_verbosity_level,
-)
 from HARK.Calibration.Income.IncomeTools import (
     Cagetti_income,
     parse_income_spec,
@@ -71,6 +62,16 @@
     jump_to_grid_2D,
     make_grid_exp_mult,
 )
+from scipy import sparse as sp
+from scipy.optimize import newton
+
+from HARK import (
+    AgentType,
+    NullFunc,
+    _log,
+    make_one_period_oo_solver,
+    set_verbosity_level,
+)
 
 __all__ = [
     "ConsumerSolution",
@@ -2221,8 +2222,8 @@ def check_conditions(self, verbose=None):
 
 
 # Make a dictionary to specify an idiosyncratic income shocks consumer
-init_idiosyncratic_shocks = dict(
-    init_perfect_foresight,
+init_idiosyncratic_shocks = {
+    **init_perfect_foresight,
     **{  # assets above grid parameters
         "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
         "aXtraMax": 20,  # Maximum end-of-period "assets above minimum" value
@@ -2255,7 +2256,7 @@ def check_conditions(self, verbose=None):
         # Whether Newborns have transitory shock. The default is False.
         "NewbornTransShk": False,
     },
-)
+}
 
 
 class IndShockConsumerType(PerfForesightConsumerType):
diff --git a/examples/ConsBequestModel/example_AccidentalBequest.ipynb b/examples/ConsBequestModel/example_AccidentalBequest.ipynb
index d53946d70..c17c6778a 100644
--- a/examples/ConsBequestModel/example_AccidentalBequest.ipynb
+++ b/examples/ConsBequestModel/example_AccidentalBequest.ipynb
@@ -53,13 +53,20 @@
     "\n",
     "# Initial distribution of wealth and permanent income\n",
     "dist_params = income_wealth_dists_from_scf(\n",
-    "    base_year=adjust_infl_to, age=birth_age, education=education, wave=1995\n",
+    "    base_year=adjust_infl_to,\n",
+    "    age=birth_age,\n",
+    "    education=education,\n",
+    "    wave=1995,\n",
     ")\n",
     "\n",
     "# We need survival probabilities only up to death_age-1, because survival\n",
     "# probability at death_age is 1.\n",
     "liv_prb = parse_ssa_life_table(\n",
-    "    female=True, cross_sec=True, year=2004, min_age=birth_age, max_age=death_age - 1\n",
+    "    female=True,\n",
+    "    cross_sec=True,\n",
+    "    year=2004,\n",
+    "    min_age=birth_age,\n",
+    "    max_age=death_age - 1,\n",
     ")\n",
     "\n",
     "# Parameters related to the number of periods implied by the calibration\n",
@@ -98,7 +105,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solving a lifecycle consumer took 0.25531482696533203 seconds.\n"
+      "Solving a lifecycle consumer took 0.2072460651397705 seconds.\n"
      ]
     }
    ],
@@ -125,7 +132,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -152,45 +159,45 @@
     {
      "data": {
       "text/plain": [
-       "{'aNrm': array([[  0.29308355,   0.9570509 ,   0.35897356, ...,   1.7684826 ,\n",
-       "           2.84407737,   0.24751228],\n",
-       "        [  0.24560297,   0.938678  ,   0.63105756, ...,   1.70153542,\n",
-       "           1.81677469,   0.15850134],\n",
-       "        [  0.60598111,   1.8494616 ,   0.60580686, ...,   1.37491855,\n",
-       "           2.55948322,   0.96661499],\n",
+       "{'aNrm': array([[ 0.32380522,  0.68955532,  0.34197775, ...,  1.46693047,\n",
+       "          3.42320775,  0.23104866],\n",
+       "        [ 0.13306755,  0.54144539,  1.17330302, ...,  1.17214556,\n",
+       "          3.05300486,  1.1052612 ],\n",
+       "        [ 0.11375969,  0.78364427,  1.170144  , ...,  0.48155715,\n",
+       "          2.09446397,  0.95931916],\n",
        "        ...,\n",
-       "        [  3.9841464 , 109.62976182,   3.4988143 , ...,   4.64289002,\n",
-       "           9.43144884,  14.43281143],\n",
-       "        [  4.69003931, 102.77957996,   4.47177149, ...,   3.47631164,\n",
-       "          10.18674707,  18.65258919],\n",
-       "        [  7.06787067,  95.98954284,   5.19840374, ...,   3.32887517,\n",
-       "           8.4678008 ,  16.20440513]]),\n",
-       " 'cNrm': array([[ 0.82973632,  0.94980982,  0.85095005, ...,  1.01988478,\n",
-       "          1.09574156,  0.81281457],\n",
-       "        [ 0.80242792,  0.93338416,  0.89571756, ...,  0.99728864,\n",
-       "          1.00571056,  0.76141644],\n",
-       "        [ 0.8840081 ,  0.9951231 ,  0.88398171, ...,  0.96072933,\n",
-       "          1.04254393,  0.92625936],\n",
+       "        [ 1.98329928, 47.11068145,  5.60926002, ...,  5.47982373,\n",
+       "          2.97252781,  5.08850217],\n",
+       "        [ 2.09397696, 44.00732908,  6.38008957, ...,  4.99716316,\n",
+       "          2.57089877,  5.84933883],\n",
+       "        [ 2.22702697, 40.92172852,  5.32578927, ...,  5.61152821,\n",
+       "          2.92852871,  6.35404606]]),\n",
+       " 'cNrm': array([[0.84083875, 0.91732219, 0.84644168, ..., 0.99649981, 1.13416322,\n",
+       "         0.80614124],\n",
+       "        [0.74584474, 0.88143084, 0.95562365, ..., 0.95552339, 1.08946728,\n",
+       "         0.94958006],\n",
+       "        [0.72833189, 0.9074526 , 0.94451529, ..., 0.86333317, 1.01188232,\n",
+       "         0.92575319],\n",
        "        ...,\n",
-       "        [ 1.13200592, 11.44170176,  0.90100823, ...,  1.03606976,\n",
-       "          1.26575515,  1.83811696],\n",
-       "        [ 1.15678012, 11.17591682,  0.94968419, ...,  0.9756263 ,\n",
-       "          1.32703547,  2.16470374],\n",
-       "        [ 1.27302649, 10.91026663,  0.98582206, ...,  0.9865864 ,\n",
-       "          1.2413397 ,  2.05323406]]),\n",
-       " 'pLvl': array([[ 44.17755524,  20.35577683,   9.62156414, ...,  22.58783801,\n",
-       "           8.69202078,  11.39484156],\n",
-       "        [ 42.69570423,  17.65771873,  15.2555234 , ...,  29.27932939,\n",
-       "          13.78168087,   8.1685812 ],\n",
-       "        [ 54.99138173,  15.21967918,  13.14916017, ...,  37.71130629,\n",
-       "           9.81667739,   7.84428065],\n",
+       "        [1.00435376, 5.66117289, 1.02024892, ..., 1.09216373, 0.86590282,\n",
+       "         1.19905103],\n",
+       "        [0.99736924, 5.55351492, 1.05682324, ..., 1.08182569, 0.84861422,\n",
+       "         1.29003044],\n",
+       "        [0.99063141, 5.44266253, 0.99309475, ..., 1.14812129, 0.88529626,\n",
+       "         1.36624392]]),\n",
+       " 'pLvl': array([[  32.95524294,   30.41775995,   10.71966635, ...,   27.60319592,\n",
+       "            7.18310446,   18.48391768],\n",
+       "        [  34.93462894,   48.22904486,    9.2988273 , ...,   26.67729987,\n",
+       "            8.35288028,   13.25050302],\n",
+       "        [  44.99524133,   46.31430523,    8.92965488, ...,   30.82406664,\n",
+       "           13.15956943,   11.42097732],\n",
        "        ...,\n",
-       "        [168.67772028, 332.10427744, 629.7416812 , ..., 121.36453854,\n",
-       "         264.89759274, 915.62596279],\n",
-       "        [146.1438155 , 332.10427744, 498.99941378, ..., 155.10185875,\n",
-       "         242.88199398, 676.9427477 ],\n",
-       "        [106.82942396, 332.10427744, 442.15292568, ..., 172.65416349,\n",
-       "         309.43504077, 754.08506963]]),\n",
+       "        [ 455.76963893,  789.08391721,  145.10373655, ...,   70.93733888,\n",
+       "          978.91843042,  133.64358289],\n",
+       "        [ 394.88270242,  789.08391721,  145.11513823, ...,   79.0007559 ,\n",
+       "         1241.89709256,  112.80446244],\n",
+       "        [ 379.50719585,  789.08391721,  183.0391237 , ...,   71.83164114,\n",
+       "         1055.20521815,  102.6405975 ]]),\n",
        " 't_age': array([[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
        "        [ 2.,  2.,  2., ...,  2.,  2.,  2.],\n",
        "        [ 3.,  3.,  3., ...,  3.,  3.,  3.],\n",
@@ -198,19 +205,19 @@
        "        [ 3., 50., 17., ..., 31., 27., 36.],\n",
        "        [ 4., 51., 18., ..., 32., 28., 37.],\n",
        "        [ 5., 52., 19., ..., 33., 29., 38.]]),\n",
-       " 'mNrm': array([[  1.12281987,   1.90686072,   1.2099236 , ...,   2.78836738,\n",
-       "           3.93981893,   1.06032685],\n",
-       "        [  1.04803089,   1.87206216,   1.52677513, ...,   2.69882406,\n",
-       "           2.82248525,   0.91991778],\n",
-       "        [  1.48998921,   2.84458471,   1.48978857, ...,   2.33564787,\n",
-       "           3.60202715,   1.89287436],\n",
+       " 'mNrm': array([[ 1.16464396,  1.60687751,  1.18841943, ...,  2.46343028,\n",
+       "          4.55737096,  1.03718989],\n",
+       "        [ 0.87891229,  1.42287623,  2.12892667, ...,  2.12766895,\n",
+       "          4.14247214,  2.05484126],\n",
+       "        [ 0.84209158,  1.69109687,  2.11465929, ...,  1.34489032,\n",
+       "          3.10634629,  1.88507235],\n",
        "        ...,\n",
-       "        [  5.11615232, 121.07146358,   4.39982253, ...,   5.67895978,\n",
-       "          10.69720399,  16.27092838],\n",
-       "        [  5.84681943, 113.95549678,   5.42145567, ...,   4.45193794,\n",
-       "          11.51378254,  20.81729293],\n",
-       "        [  8.34089716, 106.89980947,   6.1842258 , ...,   4.31546157,\n",
-       "           9.70914051,  18.25763919]])}"
+       "        [ 2.98765303, 52.77185433,  6.62950894, ...,  6.57198746,\n",
+       "          3.83843063,  6.28755319],\n",
+       "        [ 3.0913462 , 49.56084399,  7.43691281, ...,  6.07898885,\n",
+       "          3.41951299,  7.13936927],\n",
+       "        [ 3.21765838, 46.36439105,  6.31888402, ...,  6.7596495 ,\n",
+       "          3.81382496,  7.72028999]])}"
       ]
      },
      "execution_count": 6,
@@ -262,7 +269,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -281,7 +288,7 @@
     "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Age\")\n",
-    "plt.ylabel(\"Thousands of {} USD\".format(adjust_infl_to))\n",
+    "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n",
     "plt.title(\"Variable Medians Conditional on Survival\")\n",
     "plt.grid()"
    ]
@@ -306,7 +313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/ConsBequestModel/example_ConsIndShockComp.ipynb b/examples/ConsBequestModel/example_ConsIndShockComp.ipynb
index e3108fba0..18d192e4e 100644
--- a/examples/ConsBequestModel/example_ConsIndShockComp.ipynb
+++ b/examples/ConsBequestModel/example_ConsIndShockComp.ipynb
@@ -18,78 +18,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFRaw                 = 0.984539 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFNrm                 = 0.993777 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFAggLivPrb           = 0.964848 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Thorn = APF            = 0.994384 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "PermGroFacAdj          = 1.000611 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "uInvEpShkuInv          = 0.990704 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "VAF                    = 0.932054 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WRPF                   = 0.213705 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DiscFacGPFNrmMax       = 0.972061 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DiscFacGPFAggLivPrbMax = 1.010600 \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "beq_agent = BequestWarmGlowConsumerType(\n",
     "    **init_idiosyncratic_shocks, TermBeqFac=0.0, BeqFac=0.0\n",
@@ -102,78 +31,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFRaw                 = 0.984539 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFNrm                 = 0.993777 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFAggLivPrb           = 0.964848 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Thorn = APF            = 0.994384 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "PermGroFacAdj          = 1.000611 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "uInvEpShkuInv          = 0.990704 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "VAF                    = 0.932054 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WRPF                   = 0.213705 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DiscFacGPFNrmMax       = 0.972061 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DiscFacGPFAggLivPrbMax = 1.010600 \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ind_agent = IndShockConsumerType(**init_idiosyncratic_shocks)\n",
     "ind_agent.cycles = 0\n",
@@ -187,7 +45,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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3Na1eoD1ZDMMwvn2Ya7NarQoMDFRNTY3DAn8A0NHZZyjt/EQBpWsUX79bPudPs5ZU4B2ryqAM+Q+YqLi0LHUL6GlStYBzWvvzmxupAOBCDJtNJQU7VLplmXyKPlNc3RYl6tS5AS3TrHumy6v/BMWO+K7igvuyVgwgQg0AmM7eF5P/qSKrNypClTp/kQqruiq/6zCdifqOwodNVr/YJKZZAxdBqAGAdvZV3Ukd2PCxTu1doT7H1imuqdChL6bB6KQDvoN0MnyseiVfp7jkMcxQAi4D/5UAQBtramxUwfY1qtq+TAGlXyi+freSLY0OYwq8Y1TZZ5S6DJio/sOzlNQt0KRqgY6LUAMArcyw2VRycJdKt3wkn8OrFVe3VQmqOzfAIpUrSEU90uUVf41ihk9WXEg/+mKAq0SoAYBWUFVRosJNH8l2YOUl+mK6qKDrMDVEfkfhad+lLwZoA4QaALgC9adP6cCmFTq5a7mCj65RXNPBr/XFeOuAb5Ks4aPVa/B1iksZo9TOPqbVC3gCQg0AXIaWqdZHNn0o/6JVij+Vp8GWeocxBd4xqgwaqS4DstR/xLX0xQDtjFADAJdgra5S/vr/6Mz+5Yo4vlYRxtFzt5TOPtG6MDBdlriJik6forjQCPpiABMRagDgLPsspW0fqUfp54pv2KNhFpv9ePMtpcGy9hun4KGTFZOUoSBvbxMrBnA+Qg0Aj3b0SKEObXhf3gdXKvbkJiXo5LmDFqnYEq7SoFHyG3it4tMnc0sJcGGEGgAe5fRXdTqw8RPV7fpYIZVfKsZ2WMHnHT9p+Cu/W5oaoicoYsRURUQnOsxiAuC6CDUA3Jphs6lo31aVbflQXYpXK+GrbRpiOWM/bjMsyu8cr+OhY9VjyCTFpY5Tqo+viRUDuFKEGgBup+Z4pfLXfaCmA8sVeWK9onRMUS0HLdJR9dKhHiPlHT9RcRnfU0JQqJnlAmglhBoAHZ5hs+ngznWq3PKBAo+sUkLDbqVZDPvxeqOz9vkn61TEOIUOm6KoxGEKZuE7wO0QagB0SNbqKuWve1+N+z5R9Im1itPxc9OpLdIhrwiV9xmtLgOvU0J6tpK7dDOzXADtgFADoEMwbDYd2rtZFZv+re4lq5RQv0vDLE3246cMX+3rmqaG6GsUmXG9oqMSFW1euQBMQKgB4LLqTlZr/9oP1LD3Y0Ud/1IxOqaYloMWqcirr0r7jFXXpElKSM9Wql8XM8sFYDJCDQCXYdhsKjqwXWWb3lO3opVKOL1TqZZG+/HTRmft65Kq01HXqF/69YqMTVKkifUCcC2EGgCm+qrupPav/1Cnd3+siKovFGVUOMxUOmIJUUnvMfJLmqzEjMlKoTcGwCUQagC0uyMHd6l4/bvqcnilEr/appTz1o1pMDppr3+KTkVOUN8R16tf3BD1ZaYSgMtAqAHQ5hrPNGj/5pWq2fa+witWK8pWrL4tBy1SufrocO/R8h04SQkjv6tkHkUA4AoQagC0CWt1lQ58+a6MvR+pv3WtBqnWfuyM4a39vkk6GXmNwtKuV2RiqkL5NgbAVSLUAGg1Rw7uUvG6t9Xt8Aolnt6htPOmXNeoqw4EZMqSOFn9R01TUs8gEysF4I4INQCuWMttJWvevxV29LMLbisd9uqnsuBxCkiZqoThEzW8s4+Z5QJwc4QaAE75tttK+/yGqDZyovpl3Kio/oPPzWQCgDZGqAHwrb7ptlK1uik/IFOWxEnqP2qaBnNbCYBJCDUALmDYbMrftkbHNr2tsLJcRduKvnZbKUKlIeMUmDJVCWnXcFsJgEsg1ACQJJ1pqNfedct0avu7ij62WvGqUnzLsZbbSlFZihh5o6Jik7itBMDlEGoAD1Z3slr7vnhXtt0fKMH6pYao7twxw0/7umfIljhF8WN+wG0lAC6PUAN4mONHjyj/87fkk/+RBp7arGHnreZbpUAV9Bwr3yE3KDFziob5dzWxUgBwDqEG8ABHDu5R8do3FXDoEyU27FK6xWg+YJFKLKEqCZmoHqnTFJ92jdI78ccCgI6JP70AN2TYbCrYsVbHNv5LIaW5irEdcmj0PeDdX8f6ZSls5E2KShymfqzmC8ANEGoAN2HYbDqQ97mqNr6pyPLl6m9UqP/ZY42Gl/b6JasuZpKiRv1Q8ZHx9iZgAHAXhBqgA7M1NWn/lk9Vven/FFWxQgmqtB/7yvDR3m7pakyYooQxP9Dg3iEmVgoAbY9QA3QwTY2N2rdphayb/0+xlbkaoOP2Y6cMX+0JGCXLoBs0YOyNSuVp1wA8CKEG6AAazzRo74aPVbf1bcUdW6lBqrYfqzX8tTdwtLwHf18Dx0xTWpdu5hUKACYi1AAuqnkxvP/oVN7bij++SoNltR+zqqv2BY5V5+Tva+Do6zXcr4uJlQKAa3B6ysNnn32mqVOnKjw8XBaLRe++++43jn/77bd17bXXqk+fPgoICFBmZqY+/vhjhzG//e1vZbFYHLYBAwY4WxrQ4TU1NmrnF+9rw3M/Vt0TsRqy8jZlHP+3esmqE+quDT2naPu4l+Q3+6BG3L9UQyfeLF8CDQBIuoJvaurq6pSSkqLbb79dN95447eO/+yzz3TttdfqiSeeUI8ePfTyyy9r6tSpWr9+vVJTU+3jkpKStGLFinOFsVYGPIRhs2nf5pWq3vCG+lcu1+Dzbi1VKVD5vSeo69AbNWDkZKXzjCUAuCSnk8PkyZM1efLkyx6/YMECh9dPPPGE3nvvPb3//vsOoaZTp04KDQ11thygQ2pZR6Zy3WuKLvtYA86btVStbtrfa4K6DPuRBo78rjII+ABwWdr9T0ubzaaTJ0+qV69eDvsPHDig8PBw+fn5KTMzU/PmzVNkZGR7lwe0qcN7t6j0i3+qX8l/1N8ota8jU2f4aXeP78gn5SYNHH290n39TK0TADqidg81f/jDH1RbW6sf/ehH9n0ZGRlasmSJEhMTVVZWpscee0xjx47Vzp071b179wuuUV9fr/r6evtrq9V6wRjAVZQW7tXhz15VyOEPFWs7ZH+69Wmjs3Z3z5RlyA81cOwPNIJZSwBwVdo11Lz22mt67LHH9N577yk4ONi+//zbWcnJycrIyFBUVJTefPNN3XHHHRdcZ968eXrsscfapWbgStRUVWhv7qsKOPC2Bp7ZrfCz+xsMb+3uMkKNA7+vAeOna1hAT1PrBAB30m6h5o033tCdd96pt956S1lZWd84tkePHkpISFB+fv5Fj8+ePVs5OTn211arVREREa1aL+Cs+tOntHv1W9L2N5VUu04ZlkZJUpNh0R6/FJ1K+L4Sx8/QUFb2BYA20S6h5vXXX9ftt9+uN954Q1OmTPnW8bW1tSooKNBPfvKTix739fWVr69va5cJOM2w2bR343JZN/xTA6pWKFV1zQcsUoF3jCpjpinumts0ODza1DoBwBM4HWpqa2sdvkEpLCxUXl6eevXqpcjISM2ePVtHjhzRq6++Kqn5ltPMmTP17LPPKiMjQ+Xl5ZIkf39/BQY2L+H+q1/9SlOnTlVUVJRKS0s1d+5ceXt7a8aMGa3xHoFWV3xgm46sfkWRRz7QQKPCvv+oeqkg7LsKHXOr4pIyFGdijQDgaZwONZs2bdKECRPsr1tuA82cOVNLlixRWVmZioqK7MdfeOEFNTY26p577tE999xj398yXpJKSko0Y8YMVVVVqU+fPhozZozWrVunPn36XOn7AlpdTVWF9q54WT0P/EsJjfvVcsOzeebSePkP/38amDlFwUzBBgBTWAzDMMwu4mpZrVYFBgaqpqZGAQEBZpcDN9LU2Khda97Tmc1/1xDr5/I52yfTaHhpl3+azgz+kZImzJB/1wtn6QEAvllrf37zv5TARRw5uEdFK19QbMl7SlZV886WPpm4H6j/NbcpJZTmdABwJYQa4Kyv6k5q54q/q8uu15XUsF19z+6vUVftDZqk3mPvUP+U0fTJAICLItTAoxk2m/ZvWaXqL19WUtVyjbB8JUmyGRbt9B+mM0P+n5KumaEM/64mVwoA+DaEGngka3WV9nz8ooL3va5E26HmnRap1BKiw5HfV8zEO5UcGW9qjQAA5xBq4FEO5H2uE6sXafDx5cqwND9q47TRWTt6TFCX9Ns0cOQkhXt7m1wlAOBKEGrg9k7V1mjnx4vVc/c/FN90do0li3TYK0Jl8TM0MPunGtGL5QMAoKMj1MBtFe5ar6OfLtKgyo+UfrZXpsHopO2B49Vl1F0amH6dory8TK4SANBaCDVwK2ca6rV9xWvqsvVFDTyzSzGSZJFKLGEqiZuuxOyfaXifMLPLBAC0AUIN3MKJyjLt/c+fFVv4htLOritzxvDWju6j1Tn9TiWN/p760SsDAG6NUIMOrWDHOlWtfE7Jxz9RpuWMJKlKgdofcZP6T/6FhvEgSQDwGIQadDiNZxq0Y+Xr8tn8opIadjQvhmeRDnj3V3XyHUrOvk2Zfl3MLhMA0M4INegwTtXWaMcH/6uIfYuVahyV1PwMpm0B49R17D1KHD5RFhp/AcBjEWrg8o4fPaJ978/XwOI3lKFaSdIJddfevj9Q7ORfKq0fDy4AABBq4MKOHNylkv/8QSmV79v7ZY5YQlQy8E4lT7lbmTwZGwBwHkINXM6BrZ/pZO4flHLyM/W1GM39Mp3idTLtHqVc+xP17cS/tgCAC/HpAJexd8NyNax8UsmnNzXvsEjb/Eao09j7NShzMv0yAIBvRKiB6fas/1iNK+dpSP1WSc3Nv1t7ZKn3tb9SyuAMk6sDAHQUhBqYZteX/5FWPamkhm2SmhfL29prsvpO/Y1GxA40uToAQEdDqEG72/Xlf2RZ9YSSGnZIkhoMb23tPUUR1/9G6dGJJlcHAOioCDVoN/nbvtCpj+Yq+fRGSc0Pl9wa9D1F3fAbZUTGm1wdAKCjI9SgzZXk71TFe48q7eRKSc23mbYETVX09+cogzVmAACthFCDNlNZekgH/zVXw469r36WJknSpoAshU37H2XEJplcHQDA3RBq0OpO1dZo29LHNbToFWVYGpqnZvunq9vkxzQ8eZTZ5QEA3BShBq3G1tSkLR++oMgtTytTxyWLtLfzINmumaOUzMlmlwcAcHOEGrSKvRtXyOvj2RreuF+SVGoJVnn6I0rNnsmieQCAdkGowVUpLzqgI289aG8CrjP8tD3up0q96WGF+3c1uToAgCch1OCKNNSf1ualv1dKwV8VaqmXzbBoU68pip0+T5mhkWaXBwDwQIQaOG3XFx+qW+5DyrQVSxZpd+fB8p36jNJpAgYAmIhQg8t2rLxIh17L0XDrcknScQWoYOjDGn793fTNAABMR6jBtzJsNm185zkN2PGUhuuUbIZFG4Nu0IBb/qARvfqYXR4AAJIINfgWpYV7VfXGz5R+9gnaB7z7y5gyXxnDxplcGQAAjgg1uChbU5M2vPmUkvcuULilXqeNzsqLv0fDp/+3OnX2Mbs8AAAuQKjBBYr256nurbs18szu5kZgnyHq/qPnNbL/ELNLAwDgkgg1sDNsNm148yml7Pmj/CxnVGf4aWfSAxrxgwfk5e1tdnkAAHwjQg0kScfKi3VkySxlnN4oWaTtfmkK/n9/VUZkvNmlAQBwWQg1UF7uG4r8/NdKkVX1RmflDXxA6T96iGnaAIAOhVDjwb6qO6ntL/9SGcfeliQd9IqW100vKWPgcJMrAwDAeYQaD1VauFdf/eNmZTQVSpLWhcxQ6qz58vXrYnJlAABcGUKNB9qx+m1FfPoLhatWVQpU6TXPauR3vm92WQAAXBWnmyY+++wzTZ06VeHh4bJYLHr33Xe/9ZxVq1Zp2LBh8vX1Vf/+/bVkyZILxixcuFDR0dHy8/NTRkaGNmzY4Gxp+BaGzaa1rz6qQStvVw/Van+nBDXe+amGEGgAAG7A6VBTV1enlJQULVy48LLGFxYWasqUKZowYYLy8vJ033336c4779THH39sH7N06VLl5ORo7ty52rJli1JSUpSdna2jR486Wx4uoe5ktbbOn6bMg8/J22JoQ88pinxglUL6xZldGgAArcJiGIZxxSdbLHrnnXc0bdq0S4556KGH9OGHH2rnzp32fTfffLOqq6u1bNkySVJGRoZGjBihv/zlL5Ikm82miIgI/eIXv9DDDz/8rXVYrVYFBgaqpqZGAQEBV/p23FZNVYUqnv+eEhr3q8Hw1tbBjyj9BznMbgIAmKq1P7/b/FNt7dq1ysrKctiXnZ2ttWvXSpIaGhq0efNmhzFeXl7Kysqyj8GVO1Z6WMcXXquExv2qVjcd/O4byrjpVwQaAIDbafNG4fLycoWEhDjsCwkJkdVq1VdffaUTJ06oqanpomP27t170WvW19ervr7e/tpqtbZ+4W6g9NA+2V65XjFGuSrVU3XT/08DmK4NAHBTHfJ/1+fNm6fAwED7FhERYXZJLufwvjx5L5msfka5Si3Barj1I0UTaAAAbqzNQ01oaKgqKioc9lVUVCggIED+/v4KCgqSt7f3RceEhoZe9JqzZ89WTU2NfSsuLm6z+jui4vwd6vb69QpRlQ57RajTnZ+ob+xAs8sCAKBNtXmoyczMVG5ursO+5cuXKzMzU5Lk4+OjtLQ0hzE2m025ubn2MV/n6+urgIAAhw3Njh4pVKd/fF+9VaMC7xh1/9nHCu4bY3ZZAAC0OadDTW1trfLy8pSXlyepecp2Xl6eioqKJDV/i3Lrrbfax//sZz/TwYMH9eCDD2rv3r363//9X7355pu6//777WNycnL04osv6pVXXtGePXt09913q66uTrNmzbrKt+dZaqoqdOqlqQpTpYot4erx0w/UK7iv2WUBANAunG4U3rRpkyZMmGB/nZOTI0maOXOmlixZorKyMnvAkaSYmBh9+OGHuv/++/Xss8+qX79++tvf/qbs7Gz7mOnTp6uyslJz5sxReXm5hg4dqmXLll3QPIxLO1Vbo/Lnr1eirVhH1UveM99V75B+ZpcFAEC7uap1alyFp69T09TYqO3zpyr11JeqUVedmP5vmoIBAC6vw61Tg7a38cWfK/XUl6o3OqtsyqsEGgCARyLUdHAb/m++Rla8LknakT5PA0ZkfcsZAAC4J0JNB7Zzzb+VuuN3kqS1kf+fhk+5y+SKAAAwD6Gmgyo+sE2RK36mzpYmbeo+USNve9LskgAAMBWhpgOqO1mtptdvUYDqtLfTQA3+r7/zLCcAgMfjk7CDMWw27XnhdkXbinVMPRR055vy8+9qdlkAAJiOUNPBbHjzKQ0/matGw0tHJ/1VQaGRZpcEAIBLINR0IHs3rlDqnmckSZsS7tegkZNMrggAANdBqOkgqipK1OvDu+RjadKWbuOUMeM3ZpcEAIBLIdR0AIbNppKXb1OwjuuwVz8l/PQVGoMBAPgaPhk7gPVL5ynl9EbVG51l++ESdQvoaXZJAAC4HEKNizu4c71S9/5JkpQ38AHFDBphckUAALgmQo0LO32qVl5v3yFfyxlt889Q+o8eMrskAABcFqHGhW1b/Ev7ejT9bltMHw0AAN+AT0kXtfPz95Rx7F+SpNLx89U7pJ/JFQEA4NoINS6o1npCvXMfkCStD7pRyeN/YHJFAAC4PkKNC9r1yn0KU6VKLcEaPPNPZpcDAECHQKhxMTvX/FsZVe9Kko5P/KO6du9haj0AAHQUhBoXUneyWr1abjv1nqbBY643uSIAADoOQo0L2fH3BxVuHFWZ+ihp5gKzywEAoEMh1LiI/G1faETFm5KkyvFPsWowAABOItS4gKbGRtnev0/eFkObu09gthMAAFeAUOMCNr09XwmN+3XS8FfkjAVmlwMAQIdEqDHZsfIiDdzdPG1798B71Sc82tyCAADooAg1Jjv02v0K0Ckd8O6v4T/8tdnlAADQYRFqTLR3/Scabl0hm2GRvvcneXfqZHZJAAB0WIQak9iamuS9/BFJ0qZeUxSf+h2TKwIAoGMj1Jhk8/uLFN94QLWGv2KnP2l2OQAAdHiEGhPUnaxWVN4fJEk74+5SUGiEyRUBANDxEWpMsH3pYwrWcR2xhCj1R7PNLgcAALdAqGln5UUHlFr8d0lSxchH5evXxeSKAABwD4Sadlb8r/+Wn+WMdvkkK/XaW8wuBwAAt0GoaUcHd65XWvUnkiSfyb+XxYt//AAAtBY+VdvRyQ8flZfF0OZu45nCDQBAKyPUtJPdaz9SylfrdcbwVvC035ldDgAAbodQ0w4Mm01euY9JkrYETVVE/yEmVwQAgPsh1LSDHavf1oDGPfrK8FHcD/7H7HIAAHBLhJo2Zths8vviKUnSttAfKCg8yuSKAABwT4SaNrbt0zeV0Lhfpwxfxd/4G7PLAQDAbV1RqFm4cKGio6Pl5+enjIwMbdiw4ZJjx48fL4vFcsE2ZcoU+5jbbrvtguOTJk26ktJcimGzqduXT0uStoVPV++QfiZXBACA++rk7AlLly5VTk6OFi1apIyMDC1YsEDZ2dnat2+fgoODLxj/9ttvq6Ghwf66qqpKKSkpuummmxzGTZo0SS+//LL9ta+vr7OluZy8Fa8ptalAdYafBtz4iNnlAADg1pz+pmb+/Pm66667NGvWLA0aNEiLFi1Sly5dtHjx4ouO79Wrl0JDQ+3b8uXL1aVLlwtCja+vr8O4nj17Xtk7ciFdNzwrSdre72b17BNmcjUAALg3p0JNQ0ODNm/erKysrHMX8PJSVlaW1q5de1nXeOmll3TzzTera9euDvtXrVql4OBgJSYm6u6771ZVVdUlr1FfXy+r1eqwuZrD+/KU0LhfZwxvJd7woNnlAADg9pwKNceOHVNTU5NCQkIc9oeEhKi8vPxbz9+wYYN27typO++802H/pEmT9Oqrryo3N1dPPfWUVq9ercmTJ6upqemi15k3b54CAwPtW0REhDNvo12Ubfq3JGmPf6p6Bfc1uRoAANyf0z01V+Oll17SkCFDlJ6e7rD/5ptvtv/9kCFDlJycrLi4OK1atUoTJ0684DqzZ89WTk6O/bXVanW5YON7pPmbq1P9xphcCQAAnsGpb2qCgoLk7e2tiooKh/0VFRUKDQ39xnPr6ur0xhtv6I477vjWnxMbG6ugoCDl5+df9Livr68CAgIcNlfS1NiouFPbJEm9ky4MZQAAoPU5FWp8fHyUlpam3Nxc+z6bzabc3FxlZmZ+47lvvfWW6uvr9eMf//hbf05JSYmqqqoUFtYxm2sLd61XgOpUa/grZvBIs8sBAMAjOD37KScnRy+++KJeeeUV7dmzR3fffbfq6uo0a9YsSdKtt96q2bNnX3DeSy+9pGnTpql3794O+2tra/XrX/9a69at06FDh5Sbm6sbbrhB/fv3V3Z29hW+LXMd29kc+gq6DFGnzj4mVwMAgGdwuqdm+vTpqqys1Jw5c1ReXq6hQ4dq2bJl9ubhoqIieXk5ZqV9+/ZpzZo1+uSTTy64nre3t7Zv365XXnlF1dXVCg8P13XXXafHH3+8w65V09JP81X4N397BQAAWo/FMAzD7CKultVqVWBgoGpqakzvr7E1Nenk4xEKVJ32X/9vJQwbZ2o9AAC4qtb+/ObZT62scNd6BZ7tp4kdwjc1AAC0F0JNK6uknwYAAFMQalqZb8mXkuinAQCgvRFqWpGtqUmxZ9en6ZV0jcnVAADgWQg1railn6bO8FPskFFmlwMAgEch1LSiln6afH/6aQAAaG+EmlbkW8L6NAAAmIVQ00qa+2nyJEm9BvO8JwAA2huhppUU7t5IPw0AACYi1LSSyh0rJNFPAwCAWQg1raRlfZpT9NMAAGAKQk0rcOinGTTB3GIAAPBQhJpW4NBPkzza7HIAAPBIhJpWYH/ek/8QdfbxNbkaAAA8E6GmFbT009TRTwMAgGkINVfJ1tSkmLo8SfTTAABgJkLNVTq0Z5N6qJZ+GgAATEaouUpHz65PU+A/mH4aAABMRKi5SvZ+mrCRJlcCAIBnI9RchfP7aXomXWNuMQAAeDhCzVU4vLe5n+aU4au45DFmlwMAgEcj1FyFiu3N69Pksz4NAACmI9RcBR/6aQAAcBmEmitEPw0AAK6FUHOFDu/dpJ46ST8NAAAuglBzhc7107A+DQAAroBQc4XopwEAwLUQaq5Acz/NNklSz0H00wAA4AoINVfg8L4t6ilrcz9NylizywEAACLUXJGj25uf90Q/DQAAroNQcwU6F9NPAwCAqyHUOMlhfZqBE8wtBgAA2BFqnHR+P00s/TQAALgMQo2TWvppCvyS5OPrZ3I1AACgBaHGSZ3t69NkmlwJAAA4H6HGCYbNppjaPElSj0H00wAA4EoINU6gnwYAANdFqHFChb2fZhD9NAAAuBhCjRM6F38hSaplfRoAAFzOFYWahQsXKjo6Wn5+fsrIyNCGDRsuOXbJkiWyWCwOm5+f47cchmFozpw5CgsLk7+/v7KysnTgwIErKa3NnN9P03Mgz3sCAMDVOB1qli5dqpycHM2dO1dbtmxRSkqKsrOzdfTo0UueExAQoLKyMvt2+PBhh+NPP/20nnvuOS1atEjr169X165dlZ2drdOnTzv/jtpISz/NV4aPYod+x+xyAADA1zgdaubPn6+77rpLs2bN0qBBg7Ro0SJ16dJFixcvvuQ5FotFoaGh9i0kJMR+zDAMLViwQL/5zW90ww03KDk5Wa+++qpKS0v17rvvXtGbagsV23MlSfmsTwMAgEtyKtQ0NDRo8+bNysrKOncBLy9lZWVp7dq1lzyvtrZWUVFRioiI0A033KBdu3bZjxUWFqq8vNzhmoGBgcrIyLjkNevr62W1Wh22tkY/DQAArs2pUHPs2DE1NTU5fNMiSSEhISovL7/oOYmJiVq8eLHee+89/eMf/5DNZtOoUaNUUlIiSfbznLnmvHnzFBgYaN8iIiKceRtOM2w2RddulUQ/DQAArqrNZz9lZmbq1ltv1dChQzVu3Di9/fbb6tOnj/76179e8TVnz56tmpoa+1ZcXNyKFV+oaN9W9aKfBgAAl+ZUqAkKCpK3t7cqKioc9ldUVCg0NPSyrtG5c2elpqYqPz9fkuznOXNNX19fBQQEOGxtqZz1aQAAcHlOhRofHx+lpaUpNzfXvs9msyk3N1eZmZf3LKSmpibt2LFDYWFhkqSYmBiFhoY6XNNqtWr9+vWXfc221tJPczKUfhoAAFxVJ2dPyMnJ0cyZMzV8+HClp6drwYIFqqur06xZsyRJt956q/r27at58+ZJkv7nf/5HI0eOVP/+/VVdXa1nnnlGhw8f1p133impeWbUfffdp9/97neKj49XTEyMHn30UYWHh2vatGmt906vkGGzKcr+vCf6aQAAcFVOh5rp06ersrJSc+bMUXl5uYYOHaply5bZG32Liork5XXuC6ATJ07orrvuUnl5uXr27Km0tDR9+eWXGjRokH3Mgw8+qLq6Ov30pz9VdXW1xowZo2XLll2wSJ8ZivbnKUo1zf00PO8JAACXZTEMwzC7iKtltVoVGBiompqaVu+vWf/m08rY/Xvt9B2qwbNXt+q1AQDwZK39+c2zn75FpyL6aQAA6AgINd/g/PVpAgdOMLkaAADwTQg136Bof556q0anjc6KY30aAABcGqHmG7SsT5PvlyRfvy4mVwMAAL4JoeYb0E8DAEDHQai5hPPXp6GfBgAA10eouYSiA9sVpGr6aQAA6CAINZdQvm25JPppAADoKAg1l9Cp5XlPIRkmVwIAAC4HoeYiDJtNUSdZnwYAgI6EUHMR5/fTxNJPAwBAh0CouYiWfpoC30Hy8+9qcjUAAOByEGouolPxl5IkK+vTAADQYRBqvqa5n2aLJPppAADoSAg1X1OcTz8NAAAdEaHma8q2NT/vqcB3IP00AAB0IISar2l53hP9NAAAdCyEmvOc308TMIB+GgAAOhJCzXla+mnqjc6KSx1ndjkAAMAJhJrzlG3LlSTl008DAECHQ6g5jzf9NAAAdFiEmrPopwEAoGMj1JxVUrBDfXSCfhoAADooQs1ZpXnN69Pk+w6gnwYAgA6IUHOWvZ8mhH4aAAA6IkKN6KcBAMAdEGoklRzcda6fZth4s8sBAABXgFAjqSxvuST6aQAA6MgINZK87P00GSZXAgAArpTHhxrDZlOktbmfpvuA8eYWAwAArpjHh5qSg7sUrONqMDqpfypNwgAAdFQeH2rO9dMMlF+XbiZXAwAArpTHh5qWfpoa+mkAAOjQPDrU0E8DAID78OhQc+TgbvppAABwEx4dakq3ne2n8RlAPw0AAB2cR4car8NrJLE+DQAA7sBjQ835/TTd6KcBAKDDu6JQs3DhQkVHR8vPz08ZGRnasGHDJce++OKLGjt2rHr27KmePXsqKyvrgvG33XabLBaLwzZp0qQrKe2ynd9PE0c/DQAAHZ7ToWbp0qXKycnR3LlztWXLFqWkpCg7O1tHjx696PhVq1ZpxowZ+vTTT7V27VpFRETouuuu05EjRxzGTZo0SWVlZfbt9ddfv7J3dJnO76fx79q9TX8WAABoe06Hmvnz5+uuu+7SrFmzNGjQIC1atEhdunTR4sWLLzr+n//8p/7rv/5LQ4cO1YABA/S3v/1NNptNubm5DuN8fX0VGhpq33r27Hll7+gyeR3meU8AALgTp0JNQ0ODNm/erKysrHMX8PJSVlaW1q5de1nXOHXqlM6cOaNevXo57F+1apWCg4OVmJiou+++W1VVVc6U5hTDZlME/TQAALiVTs4MPnbsmJqamhQSEuKwPyQkRHv37r2sazz00EMKDw93CEaTJk3SjTfeqJiYGBUUFOiRRx7R5MmTtXbtWnl7e19wjfr6etXX19tfW61WZ96GSg/tUV9VqcHwpp8GAAA34VSouVpPPvmk3njjDa1atUp+fn72/TfffLP974cMGaLk5GTFxcVp1apVmjhx4gXXmTdvnh577LErruPI1uXqK6nAZ4AG0k8DAIBbcOr2U1BQkLy9vVVRUeGwv6KiQqGhod947h/+8Ac9+eST+uSTT5ScnPyNY2NjYxUUFKT8/PyLHp89e7ZqamrsW3FxsTNvw74+TTX9NAAAuA2nQo2Pj4/S0tIcmnxbmn4zMzMved7TTz+txx9/XMuWLdPw4cO/9eeUlJSoqqpKYWFhFz3u6+urgIAAh+1ynd9P0z1x/GWfBwAAXJvTs59ycnL04osv6pVXXtGePXt09913q66uTrNmzZIk3XrrrZo9e7Z9/FNPPaVHH31UixcvVnR0tMrLy1VeXq7a2lpJUm1trX79619r3bp1OnTokHJzc3XDDTeof//+ys7ObqW3eU7poT0KaemnGXZNq18fAACYw+memunTp6uyslJz5sxReXm5hg4dqmXLltmbh4uKiuTldS4rPf/882poaNAPf/hDh+vMnTtXv/3tb+Xt7a3t27frlVdeUXV1tcLDw3Xdddfp8ccfl6+v71W+vQuV5q2gnwYAADdkMQzDMLuIq2W1WhUYGKiamppvvRW18U83aUTNJ1rb73Zl3vmndqoQAAB8nTOf35fDo579ZNhsiqg5uz5NwnhziwEAAK3Ko0JN6aF9CtWxs/00480uBwAAtCLPCjV5n0iSCnwS1aVboMnVAACA1uRRoUYt69MEsz4NAADuxmNCjUM/TSKPRgAAwN14TKgpO7yffhoAANyYx4SaEvppAABwax4TaiyHWvpp0k2uBAAAtAWPCDWO/TTjzS0GAAC0CY8INc39NJU6w/OeAABwWx4RauinAQDA/XlEqGnppzlBPw0AAG7LI0JNP/ppAABwe24fakoP7VMY/TQAALg9tw81JVvP9tN0TqCfBgAAN+b2oYZ+GgAAPIPbh5p+NZsl0U8DAIC7c+tQc34/TSz9NAAAuDW3DjXn99N07d7D3GIAAECbcutQYzn8hST6aQAA8ARuHWr6nu2n6ZowzuRKAABAW3PbUFN6aJ/CjaNqNLwUlzbR7HIAAEAbc9tQcyRvuST6aQAA8BRuG2p0dn2a4/TTAADgEdw21JzrpxlvbiEAAKBduGWoKTtMPw0AAJ7GLUNNydYVkuinAQDAk7hlqDFa+mn60E8DAICncMtQ04/1aQAA8DhuF2qa+2kq1Gh4KZZ+GgAAPIbbhZpz/TTx6hbQ0+RqAABAe3G7UHOunybD5EoAAEB7crtQQz8NAACeya1CTUVxAf00AAB4KLcKNSXbV0qSDnbuTz8NAAAexq1CjXF4rSSpivVpAADwOG4VasJrtkqSuvC8JwAAPI57hRpVNvfTDKOfBgAAT+NWoUZq7qfpHtjL7DIAAEA7u6JQs3DhQkVHR8vPz08ZGRnasGHDN45/6623NGDAAPn5+WnIkCH6z3/+43DcMAzNmTNHYWFh8vf3V1ZWlg4cOHAlpdFPAwCAh3I61CxdulQ5OTmaO3eutmzZopSUFGVnZ+vo0aMXHf/ll19qxowZuuOOO7R161ZNmzZN06ZN086dO+1jnn76aT333HNatGiR1q9fr65duyo7O1unT592+g3RTwMAgGeyGIZhOHNCRkaGRowYob/85S+SJJvNpoiICP3iF7/Qww8/fMH46dOnq66uTh988IF938iRIzV06FAtWrRIhmEoPDxcDzzwgH71q19JkmpqahQSEqIlS5bo5ptv/taarFarAgMDdfyhAHWaXcjtJwAAOoCWz++amhoFBARc9fWc+qamoaFBmzdvVlZW1rkLeHkpKytLa9euveg5a9eudRgvSdnZ2fbxhYWFKi8vdxgTGBiojIyMS16zvr5eVqvVYZOkwk5xBBoAADyUU6Hm2LFjampqUkhIiMP+kJAQlZeXX/Sc8vLybxzf8ldnrjlv3jwFBgbat4iICEnSid7DnHk7AADAjXTI2U+zZ89WTU2NfSsuLpYkBaR81+TKAACAWZwKNUFBQfL29lZFRYXD/oqKCoWGhl70nNDQ0G8c3/JXZ67p6+urgIAAh02SEodnXXQ8AABwf06FGh8fH6WlpSk3N9e+z2azKTc3V5mZmRc9JzMz02G8JC1fvtw+PiYmRqGhoQ5jrFar1q9ff8lrAgAAfF0nZ0/IycnRzJkzNXz4cKWnp2vBggWqq6vTrFmzJEm33nqr+vbtq3nz5kmS7r33Xo0bN05//OMfNWXKFL3xxhvatGmTXnjhBUmSxWLRfffdp9/97neKj49XTEyMHn30UYWHh2vatGmt904BAIBbczrUTJ8+XZWVlZozZ47Ky8s1dOhQLVu2zN7oW1RUJC+vc18AjRo1Sq+99pp+85vf6JFHHlF8fLzeffddDR482D7mwQcfVF1dnX7605+qurpaY8aM0bJly+Tn59cKbxEAAHgCp9epcUWtPc8dAAC0PVPXqQEAAHBVhBoAAOAWCDUAAMAtEGoAAIBbINQAAAC3QKgBAABugVADAADcAqEGAAC4BUINAABwC04/JsEVtSyKbLVaTa4EAABcrpbP7dZ6uIFbhJqqqipJUkREhMmVAAAAZ1VVVSkwMPCqr+MWoaZXr16Smh+m2Rr/UHB1rFarIiIiVFxczLO4TMbvwnXwu3Ad/C5cR01NjSIjI+2f41fLLUJNy1PBAwMD+RfUhQQEBPD7cBH8LlwHvwvXwe/CdbR8jl/1dVrlKgAAACYj1AAAALfgFqHG19dXc+fOla+vr9mlQPw+XAm/C9fB78J18LtwHa39u7AYrTWPCgAAwERu8U0NAAAAoQYAALgFQg0AAHALhBoAAOAW3CLULFy4UNHR0fLz81NGRoY2bNhgdkkeZ968eRoxYoS6d++u4OBgTZs2Tfv27TO7LEh68sknZbFYdN9995ldisc6cuSIfvzjH6t3797y9/fXkCFDtGnTJrPL8jhNTU169NFHFRMTI39/f8XFxenxxx9vtecO4dI+++wzTZ06VeHh4bJYLHr33XcdjhuGoTlz5igsLEz+/v7KysrSgQMHnP45HT7ULF26VDk5OZo7d662bNmilJQUZWdn6+jRo2aX5lFWr16te+65R+vWrdPy5ct15swZXXfddaqrqzO7NI+2ceNG/fWvf1VycrLZpXisEydOaPTo0ercubM++ugj7d69W3/84x/Vs2dPs0vzOE899ZSef/55/eUvf9GePXv01FNP6emnn9af//xns0tze3V1dUpJSdHChQsvevzpp5/Wc889p0WLFmn9+vXq2rWrsrOzdfr0aed+kNHBpaenG/fcc4/9dVNTkxEeHm7MmzfPxKpw9OhRQ5KxevVqs0vxWCdPnjTi4+ON5cuXG+PGjTPuvfdes0vySA899JAxZswYs8uAYRhTpkwxbr/9dod9N954o3HLLbeYVJFnkmS888479tc2m80IDQ01nnnmGfu+6upqw9fX13j99dedunaH/qamoaFBmzdvVlZWln2fl5eXsrKytHbtWhMrQ01NjSS12kPK4Lx77rlHU6ZMcfjvA+3v3//+t4YPH66bbrpJwcHBSk1N1Ysvvmh2WR5p1KhRys3N1f79+yVJ27Zt05o1azR58mSTK/NshYWFKi8vd/izKjAwUBkZGU5/lnfoB1oeO3ZMTU1NCgkJcdgfEhKivXv3mlQVbDab7rvvPo0ePVqDBw82uxyP9MYbb2jLli3auHGj2aV4vIMHD+r5559XTk6OHnnkEW3cuFG//OUv5ePjo5kzZ5pdnkd5+OGHZbVaNWDAAHl7e6upqUm///3vdcstt5hdmkcrLy+XpIt+lrccu1wdOtTANd1zzz3auXOn1qxZY3YpHqm4uFj33nuvli9fLj8/P7PL8Xg2m03Dhw/XE088IUlKTU3Vzp07tWjRIkJNO3vzzTf1z3/+U6+99pqSkpKUl5en++67T+Hh4fwu3ESHvv0UFBQkb29vVVRUOOyvqKhQaGioSVV5tp///Of64IMP9Omnn6pfv35ml+ORNm/erKNHj2rYsGHq1KmTOnXqpNWrV+u5555Tp06d1NTUZHaJHiUsLEyDBg1y2Ddw4EAVFRWZVJHn+vWvf62HH35YN998s4YMGaKf/OQnuv/++zVv3jyzS/NoLZ/XrfFZ3qFDjY+Pj9LS0pSbm2vfZ7PZlJubq8zMTBMr8zyGYejnP/+53nnnHa1cuVIxMTFml+SxJk6cqB07digvL8++DR8+XLfccovy8vLk7e1tdokeZfTo0Rcsb7B//35FRUWZVJHnOnXqlLy8HD/2vL29ZbPZTKoIkhQTE6PQ0FCHz3Kr1ar169c7/Vne4W8/5eTkaObMmRo+fLjS09O1YMEC1dXVadasWWaX5lHuuecevfbaa3rvvffUvXt3+33QwMBA+fv7m1ydZ+nevfsFvUxdu3ZV79696XEywf33369Ro0bpiSee0I9+9CNt2LBBL7zwgl544QWzS/M4U6dO1e9//3tFRkYqKSlJW7du1fz583X77bebXZrbq62tVX5+vv11YWGh8vLy1KtXL0VGRuq+++7T7373O8XHxysmJkaPPvqowsPDNW3aNOd+UCvN0DLVn//8ZyMyMtLw8fEx0tPTjXXr1pldkseRdNHt5ZdfNrs0GAZTuk32/vvvG4MHDzZ8fX2NAQMGGC+88ILZJXkkq9Vq3HvvvUZkZKTh5+dnxMbGGv/93/9t1NfXm12a2/v0008v+hkxc+ZMwzCap3U/+uijRkhIiOHr62tMnDjR2Ldvn9M/x2IYLKUIAAA6vg7dUwMAANCCUAMAANwCoQYAALgFQg0AAHALhBoAAOAWCDUAAMAtEGoAAIBbINQAAAC3QKgBAABugVADAADcAqEGAAC4BUINAABwC/8/8fhOhYiVxxsAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -227,7 +85,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/ConsBequestModel/example_TerminalBequest.ipynb b/examples/ConsBequestModel/example_TerminalBequest.ipynb
index 07c965240..48cf2b2eb 100644
--- a/examples/ConsBequestModel/example_TerminalBequest.ipynb
+++ b/examples/ConsBequestModel/example_TerminalBequest.ipynb
@@ -53,13 +53,20 @@
     "\n",
     "# Initial distribution of wealth and permanent income\n",
     "dist_params = income_wealth_dists_from_scf(\n",
-    "    base_year=adjust_infl_to, age=birth_age, education=education, wave=1995\n",
+    "    base_year=adjust_infl_to,\n",
+    "    age=birth_age,\n",
+    "    education=education,\n",
+    "    wave=1995,\n",
     ")\n",
     "\n",
     "# We need survival probabilities only up to death_age-1, because survival\n",
     "# probability at death_age is 1.\n",
     "liv_prb = parse_ssa_life_table(\n",
-    "    female=True, cross_sec=True, year=2004, min_age=birth_age, max_age=death_age - 1\n",
+    "    female=True,\n",
+    "    cross_sec=True,\n",
+    "    year=2004,\n",
+    "    min_age=birth_age,\n",
+    "    max_age=death_age - 1,\n",
     ")\n",
     "\n",
     "# Parameters related to the number of periods implied by the calibration\n",
@@ -98,7 +105,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solving a lifecycle consumer took 0.27190065383911133 seconds.\n"
+      "Solving a lifecycle consumer took 0.23761677742004395 seconds.\n"
      ]
     }
    ],
@@ -125,7 +132,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -152,45 +159,45 @@
     {
      "data": {
       "text/plain": [
-       "{'aNrm': array([[0.29180317, 0.97445584, 0.36040378, ..., 1.79332645, 2.87342647,\n",
-       "         0.24466457],\n",
-       "        [0.24197944, 0.97645683, 0.6442841 , ..., 1.74578226, 1.86103899,\n",
-       "         0.14921818],\n",
-       "        [0.61664841, 1.91975561, 0.63314946, ..., 1.43299487, 2.6504932 ,\n",
-       "         0.97807016],\n",
+       "{'aNrm': array([[0.32426629, 0.70206002, 0.34318066, ..., 1.48985662, 3.45438995,\n",
+       "         0.22773623],\n",
+       "        [0.12491832, 0.55855075, 1.19638637, ..., 1.21659401, 3.11015491,\n",
+       "         1.12183257],\n",
+       "        [0.0994806 , 0.8181565 , 1.21644532, ..., 0.52555426, 2.15858826,\n",
+       "         0.99831787],\n",
        "        ...,\n",
-       "        [2.57121847, 0.23619627, 0.23789935, ..., 0.51954935, 5.05662923,\n",
-       "         0.44677126],\n",
-       "        [3.01728285, 0.22740195, 0.43379262, ..., 1.01764029, 5.0196123 ,\n",
-       "         0.38871327],\n",
-       "        [3.6414104 , 0.39773907, 0.76229044, ..., 2.21032915, 3.25887889,\n",
-       "         0.4625948 ]]),\n",
-       " 'cNrm': array([[0.8310167 , 0.93240488, 0.84951983, ..., 0.99504093, 1.06639246,\n",
-       "         0.81566228],\n",
-       "        [0.80468688, 0.91627163, 0.88342012, ..., 0.97278283, 0.98051186,\n",
-       "         0.76660798],\n",
-       "        [0.87044306, 0.96997463, 0.87244477, ..., 0.93803717, 1.0155411 ,\n",
-       "         0.90484725],\n",
+       "        [1.3049415 , 0.42555252, 1.09378696, ..., 0.23810415, 2.77931762,\n",
+       "         0.17060085],\n",
+       "        [1.01614121, 0.10904969, 0.23944278, ..., 0.54243046, 3.24416828,\n",
+       "         0.50733475],\n",
+       "        [1.05863018, 0.56999085, 0.40866293, ..., 0.17076358, 3.31047178,\n",
+       "         0.79032783]]),\n",
+       " 'cNrm': array([[0.84037767, 0.90481749, 0.84523877, ..., 0.97357366, 1.10298102,\n",
+       "         0.80945367],\n",
+       "        [0.75444197, 0.87244871, 0.93396861, ..., 0.93550845, 1.05993699,\n",
+       "         0.92824936],\n",
+       "        [0.73609404, 0.89128728, 0.92297277, ..., 0.85895892, 0.9851216 ,\n",
+       "         0.90655719],\n",
        "        ...,\n",
-       "        [1.01069925, 0.79677493, 0.7973765 , ..., 0.85792327, 1.15653244,\n",
-       "         0.8468094 ],\n",
-       "        [1.02069239, 0.78696088, 0.83549934, ..., 0.89505671, 1.13496128,\n",
-       "         0.82765242],\n",
-       "        [1.03878448, 0.81945519, 0.8613672 , ..., 0.9559697 , 1.01704522,\n",
-       "         0.82938239]]),\n",
-       " 'pLvl': array([[  44.17755524,   20.35577683,    9.62156414, ...,   22.58783801,\n",
-       "            8.69202078,   11.39484156],\n",
-       "        [  42.69570423,   17.65771873,   15.2555234 , ...,   29.27932939,\n",
-       "           13.78168087,    8.1685812 ],\n",
-       "        [  54.99138173,   15.21967918,   13.14916017, ...,   37.71130629,\n",
-       "            9.81667739,    7.84428065],\n",
+       "        [0.92924216, 0.8433773 , 0.91393767, ..., 0.79750399, 1.02342963,\n",
+       "         0.77149929],\n",
+       "        [0.89500363, 0.73681956, 0.79107485, ..., 0.8510354 , 1.03400455,\n",
+       "         0.84650523],\n",
+       "        [0.88398787, 0.84294421, 0.82143585, ..., 0.7588883 , 1.01998935,\n",
+       "         0.86383387]]),\n",
+       " 'pLvl': array([[  32.95524294,   30.41775995,   10.71966635, ...,   27.60319592,\n",
+       "            7.18310446,   18.48391768],\n",
+       "        [  34.93462894,   48.22904486,    9.2988273 , ...,   26.67729987,\n",
+       "            8.35288028,   13.25050302],\n",
+       "        [  44.99524133,   46.31430523,    8.92965488, ...,   30.82406664,\n",
+       "           13.15956943,   11.42097732],\n",
        "        ...,\n",
-       "        [ 334.37065892,  580.46586375,   73.4222415 , ...,  471.44813581,\n",
-       "         1340.40284941, 2943.53409086],\n",
-       "        [ 383.4323394 ,  502.92057514,  113.23140697, ...,  408.46668584,\n",
-       "         1287.81626232, 3744.83739958],\n",
-       "        [ 333.81288451,  483.3384092 ,   98.57828538, ...,  298.58438145,\n",
-       "         1945.53387791, 4707.55109494]]),\n",
+       "        [ 811.26681528,  697.76308319,  117.93351531, ...,  694.28951589,\n",
+       "         2423.72567536, 1444.68167622],\n",
+       "        [1032.11385264, 1076.08667385,  181.87646678, ...,  601.53835815,\n",
+       "         2099.93659706, 1042.81119073],\n",
+       "        [ 991.9265433 , 1034.18719936,  174.79476178, ...,  908.75794077,\n",
+       "         2018.17158498, 1002.20736025]]),\n",
        " 't_age': array([[1., 1., 1., ..., 1., 1., 1.],\n",
        "        [2., 2., 2., ..., 2., 2., 2.],\n",
        "        [3., 3., 3., ..., 3., 3., 3.],\n",
@@ -198,19 +205,19 @@
        "        [3., 3., 3., ..., 3., 3., 3.],\n",
        "        [4., 4., 4., ..., 4., 4., 4.],\n",
        "        [5., 5., 5., ..., 5., 5., 5.]]),\n",
-       " 'mNrm': array([[1.12281987, 1.90686072, 1.2099236 , ..., 2.78836738, 3.93981893,\n",
-       "         1.06032685],\n",
-       "        [1.04666632, 1.89272845, 1.52770422, ..., 2.71856508, 2.84155086,\n",
-       "         0.91582616],\n",
-       "        [1.48709147, 2.88973024, 1.50559423, ..., 2.37103204, 3.6660343 ,\n",
-       "         1.88291741],\n",
+       " 'mNrm': array([[1.16464396, 1.60687751, 1.18841943, ..., 2.46343028, 4.55737096,\n",
+       "         1.03718989],\n",
+       "        [0.87936029, 1.43099946, 2.13035498, ..., 2.15210246, 4.1700919 ,\n",
+       "         2.05008193],\n",
+       "        [0.83557464, 1.70944379, 2.13941809, ..., 1.38451318, 3.14370986,\n",
+       "         1.90487506],\n",
        "        ...,\n",
-       "        [3.58191771, 1.0329712 , 1.03527585, ..., 1.37747262, 6.21316167,\n",
-       "         1.29358066],\n",
-       "        [4.03797525, 1.01436284, 1.26929197, ..., 1.912697  , 6.15457359,\n",
-       "         1.21636569],\n",
-       "        [4.68019488, 1.21719426, 1.62365764, ..., 3.16629884, 4.27592411,\n",
-       "         1.29197719]])}"
+       "        [2.23418365, 1.26892982, 2.00772463, ..., 1.03560814, 3.80274725,\n",
+       "         0.94210013],\n",
+       "        [1.91114484, 0.84586926, 1.03051763, ..., 1.39346586, 4.27817283,\n",
+       "         1.35383997],\n",
+       "        [1.94261805, 1.41293506, 1.23009878, ..., 0.92965188, 4.33046113,\n",
+       "         1.6541617 ]])}"
       ]
      },
      "execution_count": 6,
@@ -262,7 +269,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -281,7 +288,7 @@
     "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Age\")\n",
-    "plt.ylabel(\"Thousands of {} USD\".format(adjust_infl_to))\n",
+    "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n",
     "plt.title(\"Variable Medians Conditional on Survival\")\n",
     "plt.grid()"
    ]
@@ -306,7 +313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

From eeaae5c4a5d21a47335839feb4d2c11ae8ecaf65 Mon Sep 17 00:00:00 2001
From: alanlujan91 <alanlujan91@gmail.com>
Date: Fri, 1 Mar 2024 17:44:40 -0500
Subject: [PATCH 21/28] fix Portfolio Bequest?

---
 HARK/ConsumptionSaving/ConsBequestModel.py  | 125 ++++++++++++++------
 HARK/ConsumptionSaving/ConsIndShockModel.py |   1 -
 2 files changed, 91 insertions(+), 35 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsBequestModel.py b/HARK/ConsumptionSaving/ConsBequestModel.py
index e8dc83ffe..f097a8d0d 100644
--- a/HARK/ConsumptionSaving/ConsBequestModel.py
+++ b/HARK/ConsumptionSaving/ConsBequestModel.py
@@ -110,7 +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)
@@ -124,42 +133,90 @@ def __init__(self, **kwds):
         )
 
     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):
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 02fd8efec..25612a864 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -36,7 +36,6 @@
 )
 from HARK.interpolation import CubicHermiteInterp as CubicInterp
 from HARK.interpolation import (
-    CubicInterp,
     LinearInterp,
     LowerEnvelope,
     MargMargValueFuncCRRA,

From fed3ca761fa8710be912cc083a4bcdb5ad20c742 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Tue, 5 Mar 2024 14:38:33 -0500
Subject: [PATCH 22/28] Run black on ConsIndShockModel

---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 178 ++++++++++----------
 1 file changed, 93 insertions(+), 85 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index eb2886e76..19495dcec 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -12,6 +12,7 @@
 See NARK https://github.com/econ-ark/HARK/blob/master/Documentation/NARK/NARK.pdf for information on variable naming conventions.
 See HARK documentation for mathematical descriptions of the models being solved.
 """
+
 from copy import copy, deepcopy
 
 import numpy as np
@@ -399,7 +400,6 @@ def make_cFunc_PF(self):
         self.Ex_IncNext = 1.0  # Perfect foresight income of 1
         self.mNrmMinNow = mNrmNow[0]
 
-
     def solve(self):
         """
         Solves the one period perfect foresight consumption-saving problem.
@@ -841,7 +841,6 @@ def add_MPC_and_human_wealth(self, solution):
         solution.MPCmin = self.MPCminNow
         solution.MPCmax = self.MPCmaxEff
         return solution
-    
 
     def make_linear_cFunc(self, mNrm, cNrm):
         """
@@ -1221,7 +1220,6 @@ def make_cubic_cFunc(self, mNrm, cNrm):
 
         return cFuncNowUncKink
 
-
     def prepare_to_calc_EndOfPrdvP(self):
         """
         Prepare to calculate end-of-period marginal value by creating an array
@@ -1394,12 +1392,12 @@ def __init__(self, verbose=1, quiet=False, **kwds):
         self.update_Rfree()  # update interest rate if time varying
 
     def pre_solve(self):
-        '''
+        """
         Method that is run automatically just before solution by backward iteration.
         Solves the (trivial) terminal period and does a quick check on the borrowing
         constraint and MaxKinks attribute (only relevant in constrained, infinite
         horizon problems).
-        '''
+        """
         self.update_solution_terminal()  # Solve the terminal period problem
         if not self.quiet:
             self.check_conditions(verbose=self.verbose)
@@ -1420,17 +1418,17 @@ def pre_solve(self):
                         "PerfForesightConsumerType requires the attribute MaxKinks to be specified when BoroCnstArt is not None and cycles == 0."
                     )
                 )
-                
+
     def post_solve(self):
         """
         Method that is run automatically at the end of a call to solve. Here, it
         simply calls calc_stable_points() if appropriate: an infinite horizon
         problem with a single repeated period in its cycle.
-        
+
         Parameters
         ----------
         None
-        
+
         Returns
         -------
         None
@@ -1561,9 +1559,9 @@ def sim_birth(self, which_agents):
         if (
             self.PerfMITShk is False
         ):  # If True, Newborns inherit t_cycle of agent they replaced (i.e. t_cycles are not reset).
-            self.t_cycle[
-                which_agents
-            ] = 0  # Which period of the cycle each agent is currently in
+            self.t_cycle[which_agents] = (
+                0  # Which period of the cycle each agent is currently in
+            )
 
         return None
 
@@ -1897,16 +1895,18 @@ def calc_limiting_values(self):
         if aux_dict["FHWFac"] < 1.0:
             aux_dict["hNrm"] = 1.0 / (1.0 - aux_dict["FHWFac"])
         else:
-            aux_dict['hNrm'] = np.inf
-            
+            aux_dict["hNrm"] = np.inf
+
         # Generate the "Delta m = 0" function, which is used to find target market resources
         Ex_Rnrm = self.Rfree / self.PermGroFac[0]
-        aux_dict['Delta_mNrm_ZeroFunc'] = lambda m : (1. - 1./Ex_Rnrm) * m + 1./Ex_Rnrm
-            
+        aux_dict["Delta_mNrm_ZeroFunc"] = (
+            lambda m: (1.0 - 1.0 / Ex_Rnrm) * m + 1.0 / Ex_Rnrm
+        )
+
         # Generate the "E[M_tp1 / M_t] = G" function, which is used to find balanced growth market resources
         PF_Rnrm = self.Rfree / self.PermGroFac[0]
-        aux_dict['BalGroFunc'] = lambda m : (1. - 1./PF_Rnrm) * m + 1./PF_Rnrm
-            
+        aux_dict["BalGroFunc"] = lambda m: (1.0 - 1.0 / PF_Rnrm) * m + 1.0 / PF_Rnrm
+
         self.bilt = aux_dict
 
     def check_conditions(self, verbose=None):
@@ -2018,7 +2018,7 @@ def check_conditions(self, verbose=None):
         # Report on the consequences of the Growth Impatience Condition
         if self.conditions["GICRaw"]:
             GIC_message = "\nBecause the GICRaw is satisfed, the ratio of individual wealth to permanent income is expected to fall indefinitely."
-        elif self.conditions['FHWC']:
+        elif self.conditions["FHWC"]:
             GIC_message = "\nBecause the GICRaw is violated but the FHWC is satisfied, the ratio of individual wealth to permanent income is expected to rise toward infinity."
         else:
             pass
@@ -2026,69 +2026,76 @@ def check_conditions(self, verbose=None):
         self.log_condition_result(None, None, GIC_message, verbose)
 
         if not self.quiet:
-            _log.info(self.bilt['conditions_report'])
-
+            _log.info(self.bilt["conditions_report"])
 
     def calc_stable_points(self):
-            """
-            If the problem is one that satisfies the conditions required for target ratios of different
-            variables to permanent income to exist, and has been solved to within the self-defined
-            tolerance, this method calculates the target values of market resources.
+        """
+        If the problem is one that satisfies the conditions required for target ratios of different
+        variables to permanent income to exist, and has been solved to within the self-defined
+        tolerance, this method calculates the target values of market resources.
 
-            Parameters
-            ----------
-            None
+        Parameters
+        ----------
+        None
 
-            Returns
-            -------
-            None
-            """
-            infinite_horizon = self.cycles == 0
-            single_period = self.T_cycle = 1
-            if not infinite_horizon:
-                _log.warning("The calc_stable_points method works only for infinite horizon models.")
-                return
-            if not single_period:
-                _log.warning("The calc_stable_points method works only with a single infinitely repeated period.")
-                return
-            if not hasattr(self, 'conditions'):
-                _log.warning("The calc_limiting_values method must be run before the calc_stable_points method.")
-                return
-            if not hasattr(self, 'solution'):
-                _log.warning("The solve method must be run before the calc_stable_points method.")
-                return
-            
-            # Extract balanced growth and delta m_t+1 = 0 functions
-            BalGroFunc = self.bilt['BalGroFunc']
-            Delta_mNrm_ZeroFunc = self.bilt['Delta_mNrm_ZeroFunc']
-            
-            # If the GICRaw holds, then there is a balanced growth market resources ratio
-            if self.conditions['GICRaw']:
-                cFunc = self.solution[0].cFunc
-                func_to_zero = lambda m : BalGroFunc(m) - cFunc(m)
-                m0 = 1.0
-                try:
-                    mNrmStE = newton(func_to_zero, m0)
-                except:
-                    mNrmStE = np.nan
-                    
-                # A target level of assets *might* exist even if the GICMod fails, so check no matter what
-                func_to_zero = lambda m : Delta_mNrm_ZeroFunc(m) - cFunc(m)
-                m0 = 1.0 if np.isnan(mNrmStE) else mNrmStE
-                try:
-                    mNrmTrg = newton(func_to_zero, m0, maxiter=200)
-                except:
-                    mNrmTrg = np.nan
-            else:
+        Returns
+        -------
+        None
+        """
+        infinite_horizon = self.cycles == 0
+        single_period = self.T_cycle = 1
+        if not infinite_horizon:
+            _log.warning(
+                "The calc_stable_points method works only for infinite horizon models."
+            )
+            return
+        if not single_period:
+            _log.warning(
+                "The calc_stable_points method works only with a single infinitely repeated period."
+            )
+            return
+        if not hasattr(self, "conditions"):
+            _log.warning(
+                "The calc_limiting_values method must be run before the calc_stable_points method."
+            )
+            return
+        if not hasattr(self, "solution"):
+            _log.warning(
+                "The solve method must be run before the calc_stable_points method."
+            )
+            return
+
+        # Extract balanced growth and delta m_t+1 = 0 functions
+        BalGroFunc = self.bilt["BalGroFunc"]
+        Delta_mNrm_ZeroFunc = self.bilt["Delta_mNrm_ZeroFunc"]
+
+        # If the GICRaw holds, then there is a balanced growth market resources ratio
+        if self.conditions["GICRaw"]:
+            cFunc = self.solution[0].cFunc
+            func_to_zero = lambda m: BalGroFunc(m) - cFunc(m)
+            m0 = 1.0
+            try:
+                mNrmStE = newton(func_to_zero, m0)
+            except:
                 mNrmStE = np.nan
+
+            # A target level of assets *might* exist even if the GICMod fails, so check no matter what
+            func_to_zero = lambda m: Delta_mNrm_ZeroFunc(m) - cFunc(m)
+            m0 = 1.0 if np.isnan(mNrmStE) else mNrmStE
+            try:
+                mNrmTrg = newton(func_to_zero, m0, maxiter=200)
+            except:
                 mNrmTrg = np.nan
-                
-            self.solution[0].mNrmStE = mNrmStE
-            self.solution[0].mNrmTrg = mNrmTrg
-            self.bilt['mNrmStE'] = mNrmStE
-            self.bilt['mNrmTrg'] = mNrmTrg
-        
-        
+        else:
+            mNrmStE = np.nan
+            mNrmTrg = np.nan
+
+        self.solution[0].mNrmStE = mNrmStE
+        self.solution[0].mNrmTrg = mNrmTrg
+        self.bilt["mNrmStE"] = mNrmStE
+        self.bilt["mNrmTrg"] = mNrmTrg
+
+
 # Make a dictionary to specify an idiosyncratic income shocks consumer
 init_idiosyncratic_shocks = dict(
     init_perfect_foresight,
@@ -2981,7 +2988,6 @@ def J_from_F(F):
 
         return J_C, J_A
 
-    
     def make_euler_error_func(self, mMax=100, approx_inc_dstn=True):
         """
         Creates a "normalized Euler error" function for this instance, mapping
@@ -3174,11 +3180,11 @@ def calc_limiting_values(self):
 
         # Calculate the risk-modified growth impatience factor
         PermShkDstn = self.PermShkDstn[0]
-        inv_func = lambda x : x**(-1.)
+        inv_func = lambda x: x ** (-1.0)
         Ex_PermShkInv = expected(inv_func, PermShkDstn)[0]
-        GroCompPermShk = Ex_PermShkInv**(-1.)
-        aux_dict['GPFacMod'] = aux_dict['APFac'] / (self.PermGroFac[0] * GroCompPermShk)
-        
+        GroCompPermShk = Ex_PermShkInv ** (-1.0)
+        aux_dict["GPFacMod"] = aux_dict["APFac"] / (self.PermGroFac[0] * GroCompPermShk)
+
         # Calculate the mortality-adjusted growth impatience factor (and version
         # with Modigiliani bequests)
         aux_dict["GPFacLiv"] = aux_dict["GPFacRaw"] * self.LivPrb[0]
@@ -3239,14 +3245,16 @@ def calc_limiting_values(self):
             MPCmax = np.maximum(MPCmax, 0.0)
 
         # Store maximum MPC and human wealth
-        aux_dict['hNrm'] = hNrm
-        aux_dict['MPCmax'] = MPCmax
-        
+        aux_dict["hNrm"] = hNrm
+        aux_dict["MPCmax"] = MPCmax
+
         # Generate the "Delta m = 0" function, which is used to find target market resources
         # This overwrites the function generated by the perfect foresight version
         Ex_Rnrm = self.Rfree / self.PermGroFac[0] * Ex_PermShkInv
-        aux_dict['Delta_mNrm_ZeroFunc'] = lambda m : (1. - 1./Ex_Rnrm) * m + 1./Ex_Rnrm
-        
+        aux_dict["Delta_mNrm_ZeroFunc"] = (
+            lambda m: (1.0 - 1.0 / Ex_Rnrm) * m + 1.0 / Ex_Rnrm
+        )
+
         self.bilt = aux_dict
 
         self.bilt = aux_dict

From 5678676711e9d282b025a3a1cd88cd79e0b6b808 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Tue, 5 Mar 2024 14:45:53 -0500
Subject: [PATCH 23/28] Rectify differences with black on remote

This needs to get resolved at some point; it's happening to Alan too. One of these "changes" is adding an extra space before a comment-- two spaces rather than one. Come on.
---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 10 ++++------
 HARK/ConsumptionSaving/ConsLabeledModel.py  |  2 +-
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 19495dcec..a41724c7e 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -1556,12 +1556,10 @@ def sim_birth(self, which_agents):
             self, "PerfMITShk"
         ):  # If PerfMITShk not specified, let it be False
             self.PerfMITShk = False
-        if (
-            self.PerfMITShk is False
-        ):  # If True, Newborns inherit t_cycle of agent they replaced (i.e. t_cycles are not reset).
-            self.t_cycle[which_agents] = (
-                0  # Which period of the cycle each agent is currently in
-            )
+        if not self.PerfMITShk:
+            # If True, Newborns inherit t_cycle of agent they replaced (i.e. t_cycles are not reset).
+            self.t_cycle[which_agents] = 0
+            # Which period of the cycle each agent is currently in
 
         return None
 
diff --git a/HARK/ConsumptionSaving/ConsLabeledModel.py b/HARK/ConsumptionSaving/ConsLabeledModel.py
index 05f30256d..5bc785454 100644
--- a/HARK/ConsumptionSaving/ConsLabeledModel.py
+++ b/HARK/ConsumptionSaving/ConsLabeledModel.py
@@ -305,7 +305,7 @@ def update_solution_terminal(self):
         )
         
     def post_solve(self):
-        pass # Do nothing, rather than try to run calc_stable_points
+        pass  # Do nothing, rather than try to run calc_stable_points
 
 
 class ConsPerfForesightLabeledSolver(ConsIndShockSetup):

From 1d707c7d8c9941dcab77320b6b01e090477b3f03 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Tue, 5 Mar 2024 14:53:01 -0500
Subject: [PATCH 24/28] Thanks for catching this critical whitespace error,
 black

---
 HARK/ConsumptionSaving/ConsLabeledModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HARK/ConsumptionSaving/ConsLabeledModel.py b/HARK/ConsumptionSaving/ConsLabeledModel.py
index 5bc785454..585389b0e 100644
--- a/HARK/ConsumptionSaving/ConsLabeledModel.py
+++ b/HARK/ConsumptionSaving/ConsLabeledModel.py
@@ -303,7 +303,7 @@ def update_solution_terminal(self):
             continuation=None,
             attrs={"m_nrm_min": 0.0},  # minimum normalized market resources
         )
-        
+
     def post_solve(self):
         pass  # Do nothing, rather than try to run calc_stable_points
 

From 09195b13bae501b715b271d7decf2c5bb7c59f60 Mon Sep 17 00:00:00 2001
From: Mridul Seth <git@mriduls.com>
Date: Sat, 9 Mar 2024 19:01:47 +0100
Subject: [PATCH 25/28] update pre-commit

---
 .pre-commit-config.yaml | 44 ++++++-----------------------------------
 ruff.toml               |  3 +--
 2 files changed, 7 insertions(+), 40 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 30117c837..d2360c4e3 100644
--- a/.pre-commit-config.yaml
+++ b/.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
diff --git a/ruff.toml b/ruff.toml
index 84d7322cf..cc569fbce 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,3 +1,2 @@
 include = ["*.ipynb"]
-# ignore F401 for now: https://github.com/astral-sh/ruff/issues/8354
-ignore = ["E731", "E721", "E402", "F841", "F821", "F405", "F403", "F401"]
+lint.ignore = ["E731", "E721", "E402", "F841", "F821", "F405", "F403", "E722", "E741", "F811"]

From 1bdfca2ac4929f13c4703c95fc533b19d2a9aa3d Mon Sep 17 00:00:00 2001
From: Mridul Seth <git@mriduls.com>
Date: Sat, 9 Mar 2024 19:02:13 +0100
Subject: [PATCH 26/28] run linter

---
 HARK/ConsumptionSaving/ConsAggShockModel.py   |   1 +
 .../ConsGenIncProcessModel.py                 |   1 +
 HARK/ConsumptionSaving/ConsIndShockModel.py   |   9 +-
 HARK/ConsumptionSaving/ConsLabeledModel.py    |   4 +-
 HARK/ConsumptionSaving/ConsLaborModel.py      |  20 +-
 HARK/ConsumptionSaving/ConsMarkovModel.py     |   8 +-
 HARK/ConsumptionSaving/ConsMedModel.py        |   1 +
 .../ConsPortfolioFrameModel.py                |   2 +-
 HARK/ConsumptionSaving/ConsPortfolioModel.py  |   1 +
 HARK/ConsumptionSaving/ConsPrefShockModel.py  |   1 +
 HARK/ConsumptionSaving/ConsRepAgentModel.py   |   3 +-
 HARK/ConsumptionSaving/ConsRiskyAssetModel.py |   1 +
 .../ConsRiskyContribModel.py                  |   9 +-
 .../TractableBufferStockModel.py              |   7 +-
 .../tests/test_ConsAggShockModel.py           |   5 +-
 .../tests/test_ConsMarkovModel.py             |   6 +-
 .../tests/test_IndShockConsumerType.py        |  10 +-
 .../tests/test_IndShockConsumerTypeFast.py    |   6 +-
 .../tests/test_SmallOpenEconomy.py            |   6 +-
 .../tests/test_modelInits.py                  |   8 +-
 .../tests/test_modelcomparisons.py            |   2 +-
 HARK/core.py                                  |  39 +--
 HARK/dcegm.py                                 |   1 +
 HARK/distribution.py                          |   4 +-
 HARK/estimation.py                            |   1 +
 HARK/frame.py                                 |  15 +-
 HARK/interpolation.py                         |   1 +
 HARK/models/fisher.py                         |   1 -
 HARK/parallel.py                              |   2 +-
 HARK/simulation/monte_carlo.py                |   6 +-
 HARK/simulation/test_monte_carlo.py           |   1 +
 HARK/tests/OpenCLtest.py                      |   6 +-
 HARK/tests/test_HARKutilities.py              |   1 +
 HARK/tests/test_core.py                       |   1 +
 HARK/tests/test_dcegm.py                      |   1 +
 HARK/tests/test_discrete.py                   |   1 +
 HARK/tests/test_frame.py                      |   1 +
 HARK/tests/test_interpolation.py              |   1 +
 HARK/tests/test_simulation.py                 |   3 -
 HARK/utilities.py                             |  10 +-
 HARK/validators.py                            |   1 +
 .../example_ConsMarkovModel.ipynb             |  12 +-
 examples/Journeys/JourneyPhDparam.py          | 264 ++++++++++--------
 examples/LifecycleModel/LifecycleModel.ipynb  |  36 +--
 ...eneric Monte Carlo Perfect Foresight.ipynb |   1 -
 45 files changed, 275 insertions(+), 246 deletions(-)

diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py
index 2d14f81c7..5827a26b9 100644
--- a/HARK/ConsumptionSaving/ConsAggShockModel.py
+++ b/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
diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
index b586299ff..31083d011 100644
--- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
+++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
@@ -4,6 +4,7 @@
 ConsIndShockModel by explicitly tracking persistent income as a state variable,
 and allows (log) persistent income to follow an AR1 process rather than random walk.
 """
+
 import numpy as np
 
 from HARK import AgentType, make_one_period_oo_solver
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index a41724c7e..b1f1e015f 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -46,7 +46,6 @@
 )
 from HARK.interpolation import CubicHermiteInterp as CubicInterp
 from HARK.interpolation import (
-    CubicInterp,
     LinearInterp,
     LowerEnvelope,
     MargMargValueFuncCRRA,
@@ -2569,9 +2568,7 @@ def calc_transition_matrix(self, shk_dstn=None):
             if not hasattr(shk_dstn, "pmv"):
                 shk_dstn = self.IncShkDstn
 
-            self.cPol_Grid = (
-                []
-            )  # List of consumption policy grids for each period in T_cycle
+            self.cPol_Grid = []  # List of consumption policy grids for each period in T_cycle
             self.aPol_Grid = []  # List of asset policy grids for each period in T_cycle
             self.tran_matrix = []  # List of transition matrices
 
@@ -2952,9 +2949,7 @@ def J_from_F(F):
         else:
             peturbed_list = [getattr(self, shk_param) + dx] + (
                 params["T_cycle"] - 1
-            ) * [
-                getattr(self, shk_param)
-            ]  # Sequence of interest rates the agent
+            ) * [getattr(self, shk_param)]  # Sequence of interest rates the agent
 
         setattr(ZerothColAgent, shk_param, peturbed_list)  # Set attribute to agent
 
diff --git a/HARK/ConsumptionSaving/ConsLabeledModel.py b/HARK/ConsumptionSaving/ConsLabeledModel.py
index 585389b0e..ca49deea6 100644
--- a/HARK/ConsumptionSaving/ConsLabeledModel.py
+++ b/HARK/ConsumptionSaving/ConsLabeledModel.py
@@ -897,7 +897,9 @@ class ConsRiskyAssetLabeledSolver(ConsIndShockLabeledSolver):
     """
 
     solution_next: ConsumerSolutionLabeled  # solution to next period's problem
-    ShockDstn: DiscreteDistributionLabeled  #  distribution of shocks to income and returns
+    ShockDstn: (
+        DiscreteDistributionLabeled  #  distribution of shocks to income and returns
+    )
     LivPrb: float  # survival probability
     DiscFac: float  # intertemporal discount factor
     CRRA: float  # coefficient of relative risk aversion
diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py
index 3a47115f4..7ffbde762 100644
--- a/HARK/ConsumptionSaving/ConsLaborModel.py
+++ b/HARK/ConsumptionSaving/ConsLaborModel.py
@@ -8,6 +8,7 @@
 productivity shocks.  Agents choose their quantities of labor and consumption after
 observing both of these shocks, so the transitory shock is a state variable.
 """
+
 import sys
 from copy import copy
 
@@ -343,7 +344,6 @@ def uPinv(X):
 
 
 class LaborIntMargConsumerType(IndShockConsumerType):
-
     """
     A class representing agents who make a decision each period about how much
     to consume vs save and how much labor to supply (as a fraction of their time).
@@ -737,13 +737,13 @@ def plot_LbrFunc(self, t, bMin=None, bMax=None, ShkSet=None):
 init_labor_intensive["LbrCostCoeffs"] = [-1.0]
 init_labor_intensive["WageRte"] = [1.0]
 init_labor_intensive["IncUnemp"] = 0.0
-init_labor_intensive[
-    "TranShkCount"
-] = 15  # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks
+init_labor_intensive["TranShkCount"] = (
+    15  # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks
+)
 init_labor_intensive["PermShkCount"] = 16  # Crank up permanent shock count
-init_labor_intensive[
-    "aXtraCount"
-] = 200  # May be important to have a larger number of gridpoints (than 48 initially)
+init_labor_intensive["aXtraCount"] = (
+    200  # May be important to have a larger number of gridpoints (than 48 initially)
+)
 init_labor_intensive["aXtraMax"] = 80.0
 init_labor_intensive["BoroCnstArt"] = None
 
@@ -791,6 +791,6 @@ def plot_LbrFunc(self, t, bMin=None, bMax=None, ShkSet=None):
 init_labor_lifecycle["LbrCostCoeffs"] = np.array([-2.0, 0.4])
 init_labor_lifecycle["T_cycle"] = 10
 # init_labor_lifecycle['T_retire']   = 7 # IndexError at line 774 in interpolation.py.
-init_labor_lifecycle[
-    "T_age"
-] = 11  # Make sure that old people die at terminal age and don't turn into newborns!
+init_labor_lifecycle["T_age"] = (
+    11  # Make sure that old people die at terminal age and don't turn into newborns!
+)
diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py
index 958220fdf..2b096fb0b 100644
--- a/HARK/ConsumptionSaving/ConsMarkovModel.py
+++ b/HARK/ConsumptionSaving/ConsMarkovModel.py
@@ -470,9 +470,7 @@ def calc_EndOfPrdvP(self):
                     np.logical_and(self.possible_transitions[:, j], which_states)
                 ):  # only consider a future state if one of the relevant states could transition to it
                     EndOfPrdvP_all[j, :] = self.EndOfPrdvPfunc_list[j](aGrid)
-                    if (
-                        self.CubicBool
-                    ):  # Add conditional end-of-period (marginal) marginal value to the arrays
+                    if self.CubicBool:  # Add conditional end-of-period (marginal) marginal value to the arrays
                         EndOfPrdvPP_all[j, :] = self.EndOfPrdvPfunc_list[j].derivativeX(
                             aGrid
                         )
@@ -606,9 +604,7 @@ def make_solution(self, cNrm, mNrm):
             solution_cond = ConsumerSolution(
                 cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=self.mNrmMinNow
             )
-            if (
-                self.CubicBool
-            ):  # Add the state-conditional marginal marginal value function (if desired)
+            if self.CubicBool:  # Add the state-conditional marginal marginal value function (if desired)
                 solution_cond = self.add_vPPfunc(solution_cond)
 
             # Add the current-state-conditional solution to the overall period solution
diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py
index 0c3fb8912..c32c00bd8 100644
--- a/HARK/ConsumptionSaving/ConsMedModel.py
+++ b/HARK/ConsumptionSaving/ConsMedModel.py
@@ -1,6 +1,7 @@
 """
 Consumption-saving models that also include medical spending.
 """
+
 from copy import deepcopy
 
 import numpy as np
diff --git a/HARK/ConsumptionSaving/ConsPortfolioFrameModel.py b/HARK/ConsumptionSaving/ConsPortfolioFrameModel.py
index 249462fcc..7fa32d95e 100644
--- a/HARK/ConsumptionSaving/ConsPortfolioFrameModel.py
+++ b/HARK/ConsumptionSaving/ConsPortfolioFrameModel.py
@@ -161,7 +161,7 @@ def birth_pLvlNow(self, N):
                     {
                         "mean": init_portfolio["RiskyAvg"],
                         "std": init_portfolio["RiskyStd"],
-                    }
+                    },
                     # seed=self.RNG.integers(0, 2 ** 31 - 1) : TODO: Seed logic
                 ).discretize(init_portfolio["RiskyCount"], method="equiprobable"),
                 aggregate=True,
diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py
index 731f603de..9778d0f99 100644
--- a/HARK/ConsumptionSaving/ConsPortfolioModel.py
+++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py
@@ -3,6 +3,7 @@
 agents who must allocate their resources among consumption, saving in a risk-free
 asset (with a low return), and saving in a risky asset (with higher average return).
 """
+
 from copy import deepcopy
 
 import numpy as np
diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py
index 4dcb61992..fcedbe2f3 100644
--- a/HARK/ConsumptionSaving/ConsPrefShockModel.py
+++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py
@@ -6,6 +6,7 @@
 2) A combination of (1) and ConsKinkedR, demonstrating how to construct a new model
    by inheriting from multiple classes.
 """
+
 import numpy as np
 
 from HARK import make_one_period_oo_solver
diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py
index e0c9f8ca9..c365b7c6e 100644
--- a/HARK/ConsumptionSaving/ConsRepAgentModel.py
+++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py
@@ -4,6 +4,7 @@
 take a heterogeneous agents approach.  In RA models, all attributes are either
 time invariant or exist on a short cycle; models must be infinite horizon.
 """
+
 import numpy as np
 
 from HARK.ConsumptionSaving.ConsIndShockModel import (
@@ -12,7 +13,7 @@
     init_idiosyncratic_shocks,
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
-from HARK.distribution import MarkovProcess, Uniform
+from HARK.distribution import MarkovProcess
 from HARK.interpolation import LinearInterp, MargValueFuncCRRA
 
 __all__ = ["RepAgentConsumerType", "RepAgentMarkovConsumerType"]
diff --git a/HARK/ConsumptionSaving/ConsRiskyAssetModel.py b/HARK/ConsumptionSaving/ConsRiskyAssetModel.py
index a18fce177..e02a8b0fc 100644
--- a/HARK/ConsumptionSaving/ConsRiskyAssetModel.py
+++ b/HARK/ConsumptionSaving/ConsRiskyAssetModel.py
@@ -5,6 +5,7 @@
 simulation methods. It is meant as a container of methods for dealing with
 risky assets that will be useful to models what will inherit from it.
 """
+
 from dataclasses import dataclass
 
 import numpy as np
diff --git a/HARK/ConsumptionSaving/ConsRiskyContribModel.py b/HARK/ConsumptionSaving/ConsRiskyContribModel.py
index 368aea69d..0baa243bb 100644
--- a/HARK/ConsumptionSaving/ConsRiskyContribModel.py
+++ b/HARK/ConsumptionSaving/ConsRiskyContribModel.py
@@ -23,6 +23,7 @@
    }
 
 """
+
 from copy import deepcopy
 
 import numpy as np
@@ -425,9 +426,9 @@ def sim_one_period(self):
         # Advance time for all agents
         self.t_age = self.t_age + 1  # Age all consumers by one period
         self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
-        self.t_cycle[
-            self.t_cycle == self.T_cycle
-        ] = 0  # Resetting to zero for those who have reached the end
+        self.t_cycle[self.t_cycle == self.T_cycle] = (
+            0  # Resetting to zero for those who have reached the end
+        )
 
     def get_states_Reb(self):
         """
@@ -513,7 +514,7 @@ def get_states_Sha(self):
 
         # Post-states are assets after rebalancing
 
-        if not "tau" in self.time_vary:
+        if "tau" not in self.time_vary:
             mNrmTilde, nNrmTilde = rebalance_assets(
                 self.controls["dfrac"],
                 self.state_now["mNrm"],
diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py
index f1e563beb..94846dc70 100644
--- a/HARK/ConsumptionSaving/TractableBufferStockModel.py
+++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py
@@ -20,6 +20,7 @@
 Despite the non-standard solution method, the iterative process can be embedded
 in the HARK framework, as shown below.
 """
+
 from copy import copy
 
 import numpy as np
@@ -621,9 +622,9 @@ def sim_birth(self, which_agents):
         self.shocks["eStateNow"][which_agents] = 1.0
         # How many periods since each agent was born
         self.t_age[which_agents] = 0
-        self.t_cycle[
-            which_agents
-        ] = 0  # Which period of the cycle each agent is currently in
+        self.t_cycle[which_agents] = (
+            0  # Which period of the cycle each agent is currently in
+        )
         return None
 
     def sim_death(self):
diff --git a/HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py b/HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py
index 0bce3ffbc..31d44393e 100644
--- a/HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py
+++ b/HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py
@@ -23,7 +23,10 @@ def setUp(self):
 
         # Make agents heterogeneous in their discount factor
         self.agents = distribute_params(
-            agent, "DiscFac", 3, Uniform(bot=0.90, top=0.94)  # Impatient agents
+            agent,
+            "DiscFac",
+            3,
+            Uniform(bot=0.90, top=0.94),  # Impatient agents
         )
 
         # Make an economy with those agents living in it
diff --git a/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py
index b557f81ae..ce435bccb 100644
--- a/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py
+++ b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py
@@ -56,9 +56,9 @@ def setUp(self):
 
         init_serial_unemployment = copy(init_idiosyncratic_shocks)
         init_serial_unemployment["MrkvArray"] = [MrkvArray]
-        init_serial_unemployment[
-            "UnempPrb"
-        ] = 0.0  # to make income distribution when employed
+        init_serial_unemployment["UnempPrb"] = (
+            0.0  # to make income distribution when employed
+        )
         init_serial_unemployment["global_markov"] = False
         self.model = MarkovConsumerType(**init_serial_unemployment)
         self.model.cycles = 0
diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
index 45a202259..f7f0caaa1 100644
--- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
+++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
@@ -169,9 +169,9 @@ def test_GICRawFails(self):
         GICRaw_fail_dictionary = dict(self.base_params)
         GICRaw_fail_dictionary["Rfree"] = 1.08
         GICRaw_fail_dictionary["PermGroFac"] = [1.00]
-        GICRaw_fail_dictionary[
-            "cycles"
-        ] = 0  # cycles=0 makes this an infinite horizon consumer
+        GICRaw_fail_dictionary["cycles"] = (
+            0  # cycles=0 makes this an infinite horizon consumer
+        )
 
         GICRawFailExample = IndShockConsumerType(**GICRaw_fail_dictionary)
 
@@ -896,9 +896,7 @@ def test_calc_tran_matrix(self):
         asset = example1.aPol_Grid  # Normalized Asset Policy Grid
 
         example1.calc_ergodic_dist()
-        vecDstn = (
-            example1.vec_erg_dstn
-        )  # Distribution of market resources and permanent income as a vector (m*p)x1 vector where
+        vecDstn = example1.vec_erg_dstn  # Distribution of market resources and permanent income as a vector (m*p)x1 vector where
 
         # Compute Aggregate Consumption and Aggregate Assets
         gridc = np.zeros((len(c), len(p)))
diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerTypeFast.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerTypeFast.py
index 5bebe1ba7..130cf2177 100644
--- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerTypeFast.py
+++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerTypeFast.py
@@ -127,9 +127,9 @@ def test_GICRawFails(self):
         GICRaw_fail_dictionary = dict(self.base_params)
         GICRaw_fail_dictionary["Rfree"] = 1.08
         GICRaw_fail_dictionary["PermGroFac"] = [1.00]
-        GICRaw_fail_dictionary[
-            "cycles"
-        ] = 0  # cycles=0 makes this an infinite horizon consumer
+        GICRaw_fail_dictionary["cycles"] = (
+            0  # cycles=0 makes this an infinite horizon consumer
+        )
 
         GICRawFailExample = IndShockConsumerTypeFast(**GICRaw_fail_dictionary)
 
diff --git a/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py b/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py
index aee2d3860..9aeea94a0 100644
--- a/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py
+++ b/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py
@@ -1,7 +1,6 @@
 import copy
 import unittest
 
-import numpy as np
 
 from HARK import distribute_params
 from HARK.ConsumptionSaving.ConsAggShockModel import (
@@ -20,7 +19,10 @@ def test_small_open(self):
 
         # Make agents heterogeneous in their discount factor
         agents = distribute_params(
-            agent, "DiscFac", 3, Uniform(bot=0.90, top=0.94)  # Impatient agents
+            agent,
+            "DiscFac",
+            3,
+            Uniform(bot=0.90, top=0.94),  # Impatient agents
         )
 
         # Make an economy with those agents living in it
diff --git a/HARK/ConsumptionSaving/tests/test_modelInits.py b/HARK/ConsumptionSaving/tests/test_modelInits.py
index 032f40da5..616e789ec 100644
--- a/HARK/ConsumptionSaving/tests/test_modelInits.py
+++ b/HARK/ConsumptionSaving/tests/test_modelInits.py
@@ -2,7 +2,6 @@
 This file tests whether HARK's models are initialized correctly.
 """
 
-
 # Bring in modules we need
 import unittest
 from copy import copy
@@ -18,7 +17,6 @@
     init_lifecycle,
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
-from HARK.utilities import plot_funcs, plot_funcs_der
 
 
 class testInitialization(unittest.TestCase):
@@ -92,9 +90,9 @@ def test_MarkovConsumerType(self):
             # Make a consumer with serially correlated unemployment, subject to boom and bust cycles
             init_serial_unemployment = copy(init_idiosyncratic_shocks)
             init_serial_unemployment["MrkvArray"] = [MrkvArray]
-            init_serial_unemployment[
-                "UnempPrb"
-            ] = 0.0  # to make income distribution when employed
+            init_serial_unemployment["UnempPrb"] = (
+                0.0  # to make income distribution when employed
+            )
             init_serial_unemployment["global_markov"] = False
             SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment)
         except:
diff --git a/HARK/ConsumptionSaving/tests/test_modelcomparisons.py b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py
index 8c8dba980..b1a1cb78a 100644
--- a/HARK/ConsumptionSaving/tests/test_modelcomparisons.py
+++ b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py
@@ -19,7 +19,7 @@
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
 from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType
-from HARK.distribution import DiscreteDistribution, DiscreteDistributionLabeled
+from HARK.distribution import DiscreteDistributionLabeled
 
 
 class Compare_PerfectForesight_and_Infinite(unittest.TestCase):
diff --git a/HARK/core.py b/HARK/core.py
index bae538f1a..2f292a870 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -6,15 +6,16 @@
 model adds an additional layer, endogenizing some of the inputs to the micro
 problem by finding a general equilibrium dynamic rule.
 """
+
 # Set logging and define basic functions
 # Set logging and define basic functions
 import logging
 import sys
-from collections import defaultdict, namedtuple
+from collections import namedtuple
 from copy import copy, deepcopy
 from dataclasses import dataclass, field
 from time import time
-from typing import Any, Dict, List, NewType, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 from warnings import warn
 
 import numpy as np
@@ -707,9 +708,9 @@ def sim_one_period(self):
         # Advance time for all agents
         self.t_age = self.t_age + 1  # Age all consumers by one period
         self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
-        self.t_cycle[
-            self.t_cycle == self.T_cycle
-        ] = 0  # Resetting to zero for those who have reached the end
+        self.t_cycle[self.t_cycle == self.T_cycle] = (
+            0  # Resetting to zero for those who have reached the end
+        )
 
     def make_shock_history(self):
         """
@@ -779,13 +780,13 @@ def make_shock_history(self):
                         and len(self.state_now[var_name]) == self.AgentCount
                     )
                     if idio:
-                        self.newborn_init_history[var_name][
-                            t, self.who_dies
-                        ] = self.state_now[var_name][self.who_dies]
+                        self.newborn_init_history[var_name][t, self.who_dies] = (
+                            self.state_now[var_name][self.who_dies]
+                        )
                     else:
-                        self.newborn_init_history[var_name][
-                            t, self.who_dies
-                        ] = self.state_now[var_name]
+                        self.newborn_init_history[var_name][t, self.who_dies] = (
+                            self.state_now[var_name]
+                        )
 
             # Other Shocks
             self.get_shocks()
@@ -795,9 +796,9 @@ def make_shock_history(self):
             self.t_sim += 1
             self.t_age = self.t_age + 1  # Age all consumers by one period
             self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
-            self.t_cycle[
-                self.t_cycle == self.T_cycle
-            ] = 0  # Resetting to zero for those who have reached the end
+            self.t_cycle[self.t_cycle == self.T_cycle] = (
+                0  # Resetting to zero for those who have reached the end
+            )
 
         # Flag that shocks can be read rather than simulated
         self.read_shocks = True
@@ -831,11 +832,11 @@ def get_mortality(self):
                             and len(self.state_now[var_name]) == self.AgentCount
                         )
                         if idio:
-                            self.state_now[var_name][
-                                who_dies
-                            ] = self.newborn_init_history[var_name][
-                                self.t_sim, who_dies
-                            ]
+                            self.state_now[var_name][who_dies] = (
+                                self.newborn_init_history[
+                                    var_name
+                                ][self.t_sim, who_dies]
+                            )
 
                     else:
                         warn(
diff --git a/HARK/dcegm.py b/HARK/dcegm.py
index 131f96c35..ba0d7c060 100644
--- a/HARK/dcegm.py
+++ b/HARK/dcegm.py
@@ -6,6 +6,7 @@
 
 Example can be found in https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb
 """
+
 import numpy as np
 from interpolation import interp
 from numba import njit
diff --git a/HARK/distribution.py b/HARK/distribution.py
index 3b5ac0463..8a1be8882 100644
--- a/HARK/distribution.py
+++ b/HARK/distribution.py
@@ -407,9 +407,7 @@ def _approx_equiprobable(
             lower_CDF_vals = [0.0]
             if lo_cut > 0.0:
                 for x in range(tail_N - 1, -1, -1):
-                    lower_CDF_vals.append(
-                        lower_CDF_vals[-1] + lo_cut * scale**x / mag
-                    )
+                    lower_CDF_vals.append(lower_CDF_vals[-1] + lo_cut * scale**x / mag)
             upper_CDF_vals = [hi_cut]
             if hi_cut < 1.0:
                 for x in range(tail_N):
diff --git a/HARK/estimation.py b/HARK/estimation.py
index dd58fc342..4b4c84a42 100644
--- a/HARK/estimation.py
+++ b/HARK/estimation.py
@@ -2,6 +2,7 @@
 Functions for estimating structural models, including optimization methods
 and bootstrapping tools.
 """
+
 import csv
 import multiprocessing
 import warnings
diff --git a/HARK/frame.py b/HARK/frame.py
index 143e96ca2..968df180e 100644
--- a/HARK/frame.py
+++ b/HARK/frame.py
@@ -1,14 +1,13 @@
 import copy
 import itertools
 from collections import OrderedDict
-from sre_constants import SRE_FLAG_ASCII
 
 import matplotlib.pyplot as plt
 import networkx as nx
 import numpy as np
 
 from HARK import AgentType, Model
-from HARK.distribution import Distribution, TimeVaryingDiscreteDistribution
+from HARK.distribution import Distribution
 
 
 class Frame:
@@ -537,9 +536,9 @@ def sim_one_period(self):
         # Advance time for all agents
         self.t_age = self.t_age + 1  # Age all consumers by one period
         self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
-        self.t_cycle[
-            self.t_cycle == self.T_cycle
-        ] = 0  # Resetting to zero for those who have reached the end
+        self.t_cycle[self.t_cycle == self.T_cycle] = (
+            0  # Resetting to zero for those who have reached the end
+        )
 
     def sim_birth(self, which_agents):
         """
@@ -584,9 +583,9 @@ def sim_birth(self, which_agents):
 
         # from ConsIndShockModel. Needed???
         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
+        self.t_cycle[which_agents] = (
+            0  # Which period of the cycle each agent is currently in
+        )
 
         ## simplest version of this.
 
diff --git a/HARK/interpolation.py b/HARK/interpolation.py
index 4ba7021aa..62b08b0e2 100644
--- a/HARK/interpolation.py
+++ b/HARK/interpolation.py
@@ -6,6 +6,7 @@
 convergence.  The interpolator classes currently in this module inherit their
 distance method from MetricObject.
 """
+
 import warnings
 from copy import deepcopy
 
diff --git a/HARK/models/fisher.py b/HARK/models/fisher.py
index bc5aa83fd..b676963b7 100644
--- a/HARK/models/fisher.py
+++ b/HARK/models/fisher.py
@@ -2,7 +2,6 @@
 A model file for a Fisher 2-period consumption problem.
 """
 
-from HARK.distribution import Bernoulli
 from HARK.model import Control
 
 # This way of distributing parameters across the scope is clunky
diff --git a/HARK/parallel.py b/HARK/parallel.py
index 61df0d596..0bde05d08 100644
--- a/HARK/parallel.py
+++ b/HARK/parallel.py
@@ -1,5 +1,5 @@
 import multiprocessing
-from typing import Any, List, Type
+from typing import Any, List
 
 from joblib import Parallel, delayed
 
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 13a3e34af..ecfd986ba 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -386,9 +386,9 @@ def sim_birth(self, which_agents):
         if np.sum(which_agents) > 0:
             for varn in initial_vals:
                 self.vars_now[varn][which_agents] = initial_vals[varn]
-                self.newborn_init_history[varn][
-                    self.t_sim, which_agents
-                ] = initial_vals[varn]
+                self.newborn_init_history[varn][self.t_sim, which_agents] = (
+                    initial_vals[varn]
+                )
 
         self.t_age[which_agents] = 0
         self.t_cycle[which_agents] = 0
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index bb1620c3c..ec0a183db 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests for the Monte Carlo simulation module
 """
+
 import unittest
 
 from HARK.distribution import Bernoulli, IndexDistribution, MeanOneLogNormal
diff --git a/HARK/tests/OpenCLtest.py b/HARK/tests/OpenCLtest.py
index 665d508b3..78fccd458 100644
--- a/HARK/tests/OpenCLtest.py
+++ b/HARK/tests/OpenCLtest.py
@@ -7,9 +7,9 @@
 import numpy as np
 import opencl4py as cl
 
-os.environ[
-    "PYOPENCL_CTX"
-] = "0:0"  # This is where you set which devices are in the context
+os.environ["PYOPENCL_CTX"] = (
+    "0:0"  # This is where you set which devices are in the context
+)
 # EVERY machine will have a device 0:0
 from time import time
 
diff --git a/HARK/tests/test_HARKutilities.py b/HARK/tests/test_HARKutilities.py
index a21ed3712..37873faf1 100644
--- a/HARK/tests/test_HARKutilities.py
+++ b/HARK/tests/test_HARKutilities.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests to check HARK/utilities.py
 """
+
 # Bring in modules we need
 import unittest
 from types import SimpleNamespace
diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py
index 8dacf071b..c105648ab 100644
--- a/HARK/tests/test_core.py
+++ b/HARK/tests/test_core.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests for core HARK functionality.
 """
+
 import unittest
 
 import numpy as np
diff --git a/HARK/tests/test_dcegm.py b/HARK/tests/test_dcegm.py
index 7632c4fae..9b93d7262 100644
--- a/HARK/tests/test_dcegm.py
+++ b/HARK/tests/test_dcegm.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests to check discrete choice functions
 """
+
 # Bring in modules we need
 import unittest
 
diff --git a/HARK/tests/test_discrete.py b/HARK/tests/test_discrete.py
index 8ee9afec7..eb987177d 100644
--- a/HARK/tests/test_discrete.py
+++ b/HARK/tests/test_discrete.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests to check discrete choice functions
 """
+
 # Bring in modules we need
 import unittest
 
diff --git a/HARK/tests/test_frame.py b/HARK/tests/test_frame.py
index fbe5169b5..50110759c 100644
--- a/HARK/tests/test_frame.py
+++ b/HARK/tests/test_frame.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests for the frame.py module.
 """
+
 import unittest
 
 from HARK.frame import BackwardFrameReference, ForwardFrameReference, Frame, FrameModel
diff --git a/HARK/tests/test_interpolation.py b/HARK/tests/test_interpolation.py
index ad0008330..200b49d0e 100644
--- a/HARK/tests/test_interpolation.py
+++ b/HARK/tests/test_interpolation.py
@@ -1,6 +1,7 @@
 """
 This file implements unit tests for interpolation methods
 """
+
 import unittest
 
 import numpy as np
diff --git a/HARK/tests/test_simulation.py b/HARK/tests/test_simulation.py
index fc321956b..e69de29bb 100644
--- a/HARK/tests/test_simulation.py
+++ b/HARK/tests/test_simulation.py
@@ -1,3 +0,0 @@
-import unittest
-
-import HARK.simulation as simulation
diff --git a/HARK/utilities.py b/HARK/utilities.py
index 96181fbb9..f44aede12 100644
--- a/HARK/utilities.py
+++ b/HARK/utilities.py
@@ -3,6 +3,7 @@
 continuous distributions with discrete ones, utility functions (and their
 derivatives), manipulation of discrete distributions, and basic plotting tools.
 """
+
 import cProfile
 import functools
 import os
@@ -574,9 +575,7 @@ def jump_to_grid_2D(m_vals, perm_vals, probs, dist_mGrid, dist_pGrid):
     # For instance, if mval lies between dist_mGrid[4] and dist_mGrid[5] it is in bin 4 (would be 5 if 1 was not subtracted in the previous line).
     mIndex[
         m_vals <= dist_mGrid[0]
-    ] = (
-        -1
-    )  # if the value is less than the smallest value on dist_mGrid assign it an index of -1
+    ] = -1  # if the value is less than the smallest value on dist_mGrid assign it an index of -1
     mIndex[m_vals >= dist_mGrid[-1]] = (
         len(dist_mGrid) - 1
     )  # if value if greater than largest value on dist_mGrid assign it an index of the length of the grid minus 1
@@ -606,8 +605,9 @@ def jump_to_grid_2D(m_vals, perm_vals, probs, dist_mGrid, dist_pGrid):
             mlowerIndex = mIndex[i]
             mupperIndex = mIndex[i] + 1
             # Assign weight to the indices that bound the m_vals point. Intuitively, an mval perfectly between two points on the mgrid will assign a weight of .5 to the gridpoint above and below
-            mlowerWeight = (dist_mGrid[mupperIndex] - m_vals[i]) / (
-                dist_mGrid[mupperIndex] - dist_mGrid[mlowerIndex]
+            mlowerWeight = (
+                (dist_mGrid[mupperIndex] - m_vals[i])
+                / (dist_mGrid[mupperIndex] - dist_mGrid[mlowerIndex])
             )  # Metric to determine weight of gridpoint/index below. Intuitively, mvals that are close to gridpoint/index above are assigned a smaller mlowerweight.
             mupperWeight = 1.0 - mlowerWeight  # weight of gridpoint/ index above
 
diff --git a/HARK/validators.py b/HARK/validators.py
index 792832abc..c3acbe87c 100644
--- a/HARK/validators.py
+++ b/HARK/validators.py
@@ -1,6 +1,7 @@
 """
 Decorators which can be used for validating arguments passed into decorated functions
 """
+
 from functools import wraps
 from inspect import signature
 
diff --git a/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb b/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb
index f48d7948d..d991e9f20 100644
--- a/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb
+++ b/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb
@@ -320,13 +320,13 @@
     "MrkvArray[0, 0] = (\n",
     "    1.0 - ImmunityPrb\n",
     ")  # Probability of not becoming immune in ordinary state: stay in ordinary state\n",
-    "MrkvArray[\n",
-    "    0, ImmunityT\n",
-    "] = ImmunityPrb  # Probability of becoming immune in ordinary state: begin immunity periods\n",
+    "MrkvArray[0, ImmunityT] = (\n",
+    "    ImmunityPrb  # Probability of becoming immune in ordinary state: begin immunity periods\n",
+    ")\n",
     "for j in range(ImmunityT):\n",
-    "    MrkvArray[\n",
-    "        j + 1, j\n",
-    "    ] = 1.0  # When immune, have 100% chance of transition to state with one fewer immunity periods remaining"
+    "    MrkvArray[j + 1, j] = (\n",
+    "        1.0  # When immune, have 100% chance of transition to state with one fewer immunity periods remaining\n",
+    "    )"
    ]
   },
   {
diff --git a/examples/Journeys/JourneyPhDparam.py b/examples/Journeys/JourneyPhDparam.py
index d03e3e3dd..d095f5100 100644
--- a/examples/Journeys/JourneyPhDparam.py
+++ b/examples/Journeys/JourneyPhDparam.py
@@ -1,6 +1,7 @@
-'''
+"""
 Set if parameters for the first journey
-'''
+"""
+
 from copy import copy
 import numpy as np
 
@@ -8,150 +9,171 @@
 # --- Define all of the parameters for the perfect foresight model ------------
 # -----------------------------------------------------------------------------
 
-CRRA = 2.0                          # Coefficient of relative risk aversion
-Rfree = 1.03                        # Interest factor on assets
-DiscFac = 0.96                      # Intertemporal discount factor
-LivPrb = [1.0]                     # Survival probability
-PermGroFac = [1.0]                 # Permanent income growth factor
-AgentCount = 10000                  # Number of agents of this type (only matters for simulation)
-aNrmInitMean = 0.0                  # Mean of log initial assets (only matters for simulation)
-aNrmInitStd  = 1.0                  # Standard deviation of log initial assets (only for simulation)
-pLvlInitMean = 0.0                  # Mean of log initial permanent income (only matters for simulation)
-pLvlInitStd  = 0.0                  # Standard deviation of log initial permanent income (only matters for simulation)
-PermGroFacAgg = 1.0                 # Aggregate permanent income growth factor (only matters for simulation)
-T_age = None                        # Age after which simulated agents are automatically killed
-T_cycle = 1                         # Number of periods in the cycle for this agent type
+CRRA = 2.0  # Coefficient of relative risk aversion
+Rfree = 1.03  # Interest factor on assets
+DiscFac = 0.96  # Intertemporal discount factor
+LivPrb = [1.0]  # Survival probability
+PermGroFac = [1.0]  # Permanent income growth factor
+AgentCount = 10000  # Number of agents of this type (only matters for simulation)
+aNrmInitMean = 0.0  # Mean of log initial assets (only matters for simulation)
+aNrmInitStd = 1.0  # Standard deviation of log initial assets (only for simulation)
+pLvlInitMean = 0.0  # Mean of log initial permanent income (only matters for simulation)
+pLvlInitStd = 0.0  # Standard deviation of log initial permanent income (only matters for simulation)
+PermGroFacAgg = (
+    1.0  # Aggregate permanent income growth factor (only matters for simulation)
+)
+T_age = None  # Age after which simulated agents are automatically killed
+T_cycle = 1  # Number of periods in the cycle for this agent type
 
 # Make a dictionary to specify a perfect foresight consumer type
-init_perfect_foresight = { 'CRRA': CRRA,
-                           'Rfree': Rfree,
-                           'DiscFac': DiscFac,
-                           'LivPrb': LivPrb,
-                           'PermGroFac': PermGroFac,
-                           'AgentCount': AgentCount,
-                           'aNrmInitMean' : aNrmInitMean,
-                           'aNrmInitStd' : aNrmInitStd,
-                           'pLvlInitMean' : pLvlInitMean,
-                           'pLvlInitStd' : pLvlInitStd,
-                           'PermGroFacAgg' : PermGroFacAgg,
-                           'T_age' : T_age,
-                           'T_cycle' : T_cycle
-                          }
+init_perfect_foresight = {
+    "CRRA": CRRA,
+    "Rfree": Rfree,
+    "DiscFac": DiscFac,
+    "LivPrb": LivPrb,
+    "PermGroFac": PermGroFac,
+    "AgentCount": AgentCount,
+    "aNrmInitMean": aNrmInitMean,
+    "aNrmInitStd": aNrmInitStd,
+    "pLvlInitMean": pLvlInitMean,
+    "pLvlInitStd": pLvlInitStd,
+    "PermGroFacAgg": PermGroFacAgg,
+    "T_age": T_age,
+    "T_cycle": T_cycle,
+}
 
 # -----------------------------------------------------------------------------
 # --- Define additional parameters for the idiosyncratic shocks model ---------
 # -----------------------------------------------------------------------------
 
 # Parameters for constructing the "assets above minimum" grid
-aXtraMin = 0.001                    # Minimum end-of-period "assets above minimum" value
-aXtraMax = 20                       # Maximum end-of-period "assets above minimum" value
-#aXtraExtra = [None]                   # Some other value of "assets above minimum" to add to the grid, not used
-aXtraNestFac = 3                    # Exponential nesting factor when constructing "assets above minimum" grid
-aXtraCount = 48                     # Number of points in the grid of "assets above minimum"
+aXtraMin = 0.001  # Minimum end-of-period "assets above minimum" value
+aXtraMax = 20  # Maximum end-of-period "assets above minimum" value
+# aXtraExtra = [None]                   # Some other value of "assets above minimum" to add to the grid, not used
+aXtraNestFac = (
+    3  # Exponential nesting factor when constructing "assets above minimum" grid
+)
+aXtraCount = 48  # Number of points in the grid of "assets above minimum"
 
 # Parameters describing the income process
-PermShkCount = 7                    # Number of points in discrete approximation to permanent income shocks
-TranShkCount = 7                    # Number of points in discrete approximation to transitory income shocks
-PermShkStd = [0.1]                  # Standard deviation of log permanent income shocks
-TranShkStd = [0.2]                  # Standard deviation of log transitory income shocks
-UnempPrb = 0.005                     # Probability of unemployment while working
-UnempPrbRet = 0.005                 # Probability of "unemployment" while retired
-IncUnemp = 0.3                      # Unemployment benefits replacement rate
-IncUnempRet = 0.0                   # "Unemployment" benefits when retired
-tax_rate = 0.0                      # Flat income tax rate
-T_retire = 0                        # Period of retirement (0 --> no retirement)
+PermShkCount = (
+    7  # Number of points in discrete approximation to permanent income shocks
+)
+TranShkCount = (
+    7  # Number of points in discrete approximation to transitory income shocks
+)
+PermShkStd = [0.1]  # Standard deviation of log permanent income shocks
+TranShkStd = [0.2]  # Standard deviation of log transitory income shocks
+UnempPrb = 0.005  # Probability of unemployment while working
+UnempPrbRet = 0.005  # Probability of "unemployment" while retired
+IncUnemp = 0.3  # Unemployment benefits replacement rate
+IncUnempRet = 0.0  # "Unemployment" benefits when retired
+tax_rate = 0.0  # Flat income tax rate
+T_retire = 0  # Period of retirement (0 --> no retirement)
 
 # A few other parameters
-BoroCnstArt = 0.0                  # Artificial borrowing constraint; imposed minimum level of end-of period assets
-CubicBool = True                  # Use cubic spline interpolation when True, linear interpolation when False
-vFuncBool = False                  # Whether to calculate the value function during solution
+BoroCnstArt = 0.0  # Artificial borrowing constraint; imposed minimum level of end-of period assets
+CubicBool = (
+    True  # Use cubic spline interpolation when True, linear interpolation when False
+)
+vFuncBool = False  # Whether to calculate the value function during solution
 
 # Make a dictionary to specify an idiosyncratic income shocks consumer
-init_idiosyncratic_shocks = { 'CRRA': CRRA,
-                              'Rfree': Rfree,
-                              'DiscFac': DiscFac,
-                              'LivPrb': LivPrb,
-                              'PermGroFac': PermGroFac,
-                              'AgentCount': AgentCount,
-                              'aXtraMin': aXtraMin,
-                              'aXtraMax': aXtraMax,
-                              'aXtraNestFac':aXtraNestFac,
-                              'aXtraCount': aXtraCount,
-                              #'aXtraExtra': [aXtraExtra],
-                              'PermShkStd': PermShkStd,
-                              'PermShkCount': PermShkCount,
-                              'TranShkStd': TranShkStd,
-                              'TranShkCount': TranShkCount,
-                              'UnempPrb': UnempPrb,
-                              'UnempPrbRet': UnempPrbRet,
-                              'IncUnemp': IncUnemp,
-                              'IncUnempRet': IncUnempRet,
-                              'BoroCnstArt': BoroCnstArt,
-                              'tax_rate':0.0,
-                              'vFuncBool':vFuncBool,
-                              'CubicBool':CubicBool,
-                              'T_retire':T_retire,
-                              'aNrmInitMean' : aNrmInitMean,
-                              'aNrmInitStd' : aNrmInitStd,
-                              'pLvlInitMean' : pLvlInitMean,
-                              'pLvlInitStd' : pLvlInitStd,
-                              'PermGroFacAgg' : PermGroFacAgg,
-                              'T_age' : T_age,
-                              'T_cycle' : T_cycle
-                             }
+init_idiosyncratic_shocks = {
+    "CRRA": CRRA,
+    "Rfree": Rfree,
+    "DiscFac": DiscFac,
+    "LivPrb": LivPrb,
+    "PermGroFac": PermGroFac,
+    "AgentCount": AgentCount,
+    "aXtraMin": aXtraMin,
+    "aXtraMax": aXtraMax,
+    "aXtraNestFac": aXtraNestFac,
+    "aXtraCount": aXtraCount,
+    #'aXtraExtra': [aXtraExtra],
+    "PermShkStd": PermShkStd,
+    "PermShkCount": PermShkCount,
+    "TranShkStd": TranShkStd,
+    "TranShkCount": TranShkCount,
+    "UnempPrb": UnempPrb,
+    "UnempPrbRet": UnempPrbRet,
+    "IncUnemp": IncUnemp,
+    "IncUnempRet": IncUnempRet,
+    "BoroCnstArt": BoroCnstArt,
+    "tax_rate": 0.0,
+    "vFuncBool": vFuncBool,
+    "CubicBool": CubicBool,
+    "T_retire": T_retire,
+    "aNrmInitMean": aNrmInitMean,
+    "aNrmInitStd": aNrmInitStd,
+    "pLvlInitMean": pLvlInitMean,
+    "pLvlInitStd": pLvlInitStd,
+    "PermGroFacAgg": PermGroFacAgg,
+    "T_age": T_age,
+    "T_cycle": T_cycle,
+}
 
 # Make a dictionary to specify a lifecycle consumer with a finite horizon
 
 # -----------------------------------------------------------------------------
 # ----- Define additional parameters for the aggregate shocks model -----------
 # -----------------------------------------------------------------------------
-MgridBase = np.array([0.1,0.3,0.6,0.8,0.9,0.98,1.0,1.02,1.1,1.2,1.6,2.0,3.0])  # Grid of capital-to-labor-ratios (factors)
+MgridBase = np.array(
+    [0.1, 0.3, 0.6, 0.8, 0.9, 0.98, 1.0, 1.02, 1.1, 1.2, 1.6, 2.0, 3.0]
+)  # Grid of capital-to-labor-ratios (factors)
 
 # Parameters for a Cobb-Douglas economy
-PermGroFacAgg = 1.00          # Aggregate permanent income growth factor
-PermShkAggCount = 1           # Number of points in discrete approximation to aggregate permanent shock dist
-TranShkAggCount = 1           # Number of points in discrete approximation to aggregate transitory shock dist
-PermShkAggStd = 0.00        # Standard deviation of log aggregate permanent shocks
-TranShkAggStd = 0.00        # Standard deviation of log aggregate transitory shocks
-DeprFac = 0.025               # Capital depreciation rate
-CapShare = 0.36               # Capital's share of income
-DiscFacPF = DiscFac           # Discount factor of perfect foresight calibration
-CRRAPF = CRRA                 # Coefficient of relative risk aversion of perfect foresight calibration
-intercept_prev = 0.0          # Intercept of aggregate savings function
-slope_prev = 1.0              # Slope of aggregate savings function
-verbose_cobb_douglas = True   # Whether to print solution progress to screen while solving
-T_discard = 200               # Number of simulated "burn in" periods to discard when updating AFunc
-DampingFac = 0.5              # Damping factor when updating AFunc; puts DampingFac weight on old params, rest on new
-max_loops = 20                # Maximum number of AFunc updating loops to allow
+PermGroFacAgg = 1.00  # Aggregate permanent income growth factor
+PermShkAggCount = (
+    1  # Number of points in discrete approximation to aggregate permanent shock dist
+)
+TranShkAggCount = (
+    1  # Number of points in discrete approximation to aggregate transitory shock dist
+)
+PermShkAggStd = 0.00  # Standard deviation of log aggregate permanent shocks
+TranShkAggStd = 0.00  # Standard deviation of log aggregate transitory shocks
+DeprFac = 0.025  # Capital depreciation rate
+CapShare = 0.36  # Capital's share of income
+DiscFacPF = DiscFac  # Discount factor of perfect foresight calibration
+CRRAPF = CRRA  # Coefficient of relative risk aversion of perfect foresight calibration
+intercept_prev = 0.0  # Intercept of aggregate savings function
+slope_prev = 1.0  # Slope of aggregate savings function
+verbose_cobb_douglas = (
+    True  # Whether to print solution progress to screen while solving
+)
+T_discard = 200  # Number of simulated "burn in" periods to discard when updating AFunc
+DampingFac = 0.5  # Damping factor when updating AFunc; puts DampingFac weight on old params, rest on new
+max_loops = 20  # Maximum number of AFunc updating loops to allow
 
 # Make a dictionary to specify an aggregate shocks consumer
 init_agg_shocks = copy(init_idiosyncratic_shocks)
-del init_agg_shocks['Rfree']        # Interest factor is endogenous in agg shocks model
-del init_agg_shocks['CubicBool']    # Not supported yet for agg shocks model
-del init_agg_shocks['vFuncBool']    # Not supported yet for agg shocks model
-init_agg_shocks['PermGroFac'] = [1.0]
-init_agg_shocks['MgridBase'] = MgridBase
-init_agg_shocks['aXtraCount'] = 24
-init_agg_shocks['aNrmInitStd'] = 0.0
-init_agg_shocks['LivPrb'] = LivPrb
+del init_agg_shocks["Rfree"]  # Interest factor is endogenous in agg shocks model
+del init_agg_shocks["CubicBool"]  # Not supported yet for agg shocks model
+del init_agg_shocks["vFuncBool"]  # Not supported yet for agg shocks model
+init_agg_shocks["PermGroFac"] = [1.0]
+init_agg_shocks["MgridBase"] = MgridBase
+init_agg_shocks["aXtraCount"] = 24
+init_agg_shocks["aNrmInitStd"] = 0.0
+init_agg_shocks["LivPrb"] = LivPrb
 
 
 # Make a dictionary to specify a Cobb-Douglas economy
-init_cobb_douglas = {'PermShkAggCount': PermShkAggCount,
-                     'TranShkAggCount': TranShkAggCount,
-                     'PermShkAggStd': PermShkAggStd,
-                     'TranShkAggStd': TranShkAggStd,
-                     'DeprFac': DeprFac,
-                     'CapShare': CapShare,
-                     'DiscFac': DiscFacPF,
-                     'CRRA': CRRAPF,
-                     'PermGroFacAgg': PermGroFacAgg,
-                     'AggregateL':1.0,
-                     'act_T':1200,
-                     'intercept_prev': intercept_prev,
-                     'slope_prev': slope_prev,
-                     'verbose': verbose_cobb_douglas,
-                     'T_discard': T_discard,
-                     'DampingFac': DampingFac,
-                     'max_loops': max_loops
-                     }
+init_cobb_douglas = {
+    "PermShkAggCount": PermShkAggCount,
+    "TranShkAggCount": TranShkAggCount,
+    "PermShkAggStd": PermShkAggStd,
+    "TranShkAggStd": TranShkAggStd,
+    "DeprFac": DeprFac,
+    "CapShare": CapShare,
+    "DiscFac": DiscFacPF,
+    "CRRA": CRRAPF,
+    "PermGroFacAgg": PermGroFacAgg,
+    "AggregateL": 1.0,
+    "act_T": 1200,
+    "intercept_prev": intercept_prev,
+    "slope_prev": slope_prev,
+    "verbose": verbose_cobb_douglas,
+    "T_discard": T_discard,
+    "DampingFac": DampingFac,
+    "max_loops": max_loops,
+}
diff --git a/examples/LifecycleModel/LifecycleModel.ipynb b/examples/LifecycleModel/LifecycleModel.ipynb
index e57fab58b..ac94fb46e 100644
--- a/examples/LifecycleModel/LifecycleModel.ipynb
+++ b/examples/LifecycleModel/LifecycleModel.ipynb
@@ -59,25 +59,25 @@
    "outputs": [],
    "source": [
     "# Set up default values for CRRA, DiscFac, and simulation variables in the dictionary\n",
-    "Params.init_consumer_objects[\n",
-    "    \"CRRA\"\n",
-    "] = 2.00  # Default coefficient of relative risk aversion (rho)\n",
-    "Params.init_consumer_objects[\n",
-    "    \"DiscFac\"\n",
-    "] = 0.97  # Default intertemporal discount factor (beta)\n",
-    "Params.init_consumer_objects[\n",
-    "    \"PermGroFacAgg\"\n",
-    "] = 1.0  # Aggregate permanent income growth factor\n",
+    "Params.init_consumer_objects[\"CRRA\"] = (\n",
+    "    2.00  # Default coefficient of relative risk aversion (rho)\n",
+    ")\n",
+    "Params.init_consumer_objects[\"DiscFac\"] = (\n",
+    "    0.97  # Default intertemporal discount factor (beta)\n",
+    ")\n",
+    "Params.init_consumer_objects[\"PermGroFacAgg\"] = (\n",
+    "    1.0  # Aggregate permanent income growth factor\n",
+    ")\n",
     "Params.init_consumer_objects[\"aNrmInitMean\"] = -10.0  # Mean of log initial assets\n",
-    "Params.init_consumer_objects[\n",
-    "    \"aNrmInitStd\"\n",
-    "] = 1.0  # Standard deviation of log initial assets\n",
-    "Params.init_consumer_objects[\n",
-    "    \"pLvlInitMean\"\n",
-    "] = 0.0  # Mean of log initial permanent income\n",
-    "Params.init_consumer_objects[\n",
-    "    \"pLvlInitStd\"\n",
-    "] = 0.0  # Standard deviation of log initial permanent income"
+    "Params.init_consumer_objects[\"aNrmInitStd\"] = (\n",
+    "    1.0  # Standard deviation of log initial assets\n",
+    ")\n",
+    "Params.init_consumer_objects[\"pLvlInitMean\"] = (\n",
+    "    0.0  # Mean of log initial permanent income\n",
+    ")\n",
+    "Params.init_consumer_objects[\"pLvlInitStd\"] = (\n",
+    "    0.0  # Standard deviation of log initial permanent income\n",
+    ")"
    ]
   },
   {
diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
index 2699e7125..9fae87065 100644
--- a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
@@ -8,7 +8,6 @@
    "outputs": [],
    "source": [
     "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n",
-    "from HARK.distribution import Bernoulli\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"

From bca2586e6a098e0ebb00868e829c806c234252b3 Mon Sep 17 00:00:00 2001
From: alanlujan91 <alanlujan91@gmail.com>
Date: Sun, 10 Mar 2024 20:33:51 -0400
Subject: [PATCH 27/28] fix failing test

---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index b1f1e015f..604528bc5 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -44,8 +44,8 @@
     combine_indep_dstns,
     expected,
 )
-from HARK.interpolation import CubicHermiteInterp as CubicInterp
 from HARK.interpolation import (
+    CubicInterp,
     LinearInterp,
     LowerEnvelope,
     MargMargValueFuncCRRA,

From 291414992e4f9a7338e77e549ccfe0b2ef3d9331 Mon Sep 17 00:00:00 2001
From: alanlujan91 <alanlujan91@gmail.com>
Date: Mon, 11 Mar 2024 12:33:24 -0400
Subject: [PATCH 28/28] fix failing check

---
 HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 25612a864..03000ca6e 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -34,8 +34,8 @@
     combine_indep_dstns,
     expected,
 )
-from HARK.interpolation import CubicHermiteInterp as CubicInterp
 from HARK.interpolation import (
+    CubicInterp,
     LinearInterp,
     LowerEnvelope,
     MargMargValueFuncCRRA,