From c9e06ba4606ed60608905348a47915b38561de34 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Fri, 30 Jun 2023 15:50:57 -0400
Subject: [PATCH 01/37] basic implementation of generic sim_one_period. see
 #1295

---
 HARK/simulation/__init__.py         |   0
 HARK/simulation/monte_carlo.py      |  33 ++++++++
 HARK/simulation/test_monte_carlo.py | 113 ++++++++++++++++++++++++++++
 3 files changed, 146 insertions(+)
 create mode 100644 HARK/simulation/__init__.py
 create mode 100644 HARK/simulation/monte_carlo.py
 create mode 100644 HARK/simulation/test_monte_carlo.py

diff --git a/HARK/simulation/__init__.py b/HARK/simulation/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
new file mode 100644
index 000000000..2e17e1fac
--- /dev/null
+++ b/HARK/simulation/monte_carlo.py
@@ -0,0 +1,33 @@
+from inspect import signature
+from typing import Callable, Mapping
+
+
+class Control:
+    """
+    Should go in HARK.model
+    """
+
+    def __init__(self, args):
+        pass
+
+def sim_one_period(
+        dynamics : Mapping[str, Callable],
+        pre : Mapping,
+        dr : Mapping[str, Callable]
+):
+    vals = pre.copy()
+
+    for varn in dynamics:
+        # Using the fact that Python dictionaries are ordered
+
+        feq = dynamics[varn]
+
+        if isinstance(feq, Control):
+            vals[varn] = dr[varn](*[
+                vals[var]
+                for var 
+                in signature(dr[varn]).parameters]) # TODO: test for signature match with Control
+        else:
+            vals[varn] = feq(*[vals[var] for var in signature(feq).parameters])
+
+    return vals
\ No newline at end of file
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
new file mode 100644
index 000000000..f8211348e
--- /dev/null
+++ b/HARK/simulation/test_monte_carlo.py
@@ -0,0 +1,113 @@
+"""
+This file implements unit tests for the Monte Carlo simulation module
+"""
+import unittest
+
+from HARK.simulation.monte_carlo import *
+
+
+pre = {
+    'R' : 1.05,
+    'aNrm' : 1,
+    'gamma' : 1.1,
+    'psi' : 1.1, # TODO: draw this from a shock,
+    'theta' : 1.1 # TODO: draw this from a shock
+}
+
+dynamics = {
+    'G' : lambda gamma, psi : gamma * psi,
+    'Rnrm' : lambda R, G : R / G,
+    'bNrm' : lambda Rnrm, aNrm : Rnrm * aNrm,
+    'mNrm' : lambda bNrm, theta : bNrm + theta,
+    'cNrm' : Control(['mNrm']),
+    'aNrm' : lambda mNrm, cNrm : mNrm - cNrm
+}
+
+dr = {
+    'cNrm' : lambda mNrm : mNrm / 2
+}
+
+
+class test_sim_one_period(unittest.TestCase):
+    def test_sim_one_period(self):
+
+        post = sim_one_period(dynamics, pre, dr)
+
+        self.assertAlmostEqual(post['cNrm'], 0.98388429)
+
+
+
+
+
+
+
+
+
+
+###############################################################3
+
+'''
+init_parameters = {}
+init_parameters["PermGroFac"] = 1.05
+init_parameters["PermShkStd"] = 1.5
+init_parameters["PermShkCount"] = 5
+init_parameters["TranShkStd"] = 3.0
+init_parameters["TranShkCount"] = 5
+init_parameters["RiskyAvg"] = 1.05
+init_parameters["RiskyStd"] = 1.5
+init_parameters["RiskyCount"] = 5
+init_parameters["Rfree"] = 1.03
+
+frames_A = [
+    Frame(("bNrm",), ("aNrm",), transition=lambda Rfree, aNrm: Rfree * aNrm),
+    Frame(("mNrm",), ("bNrm", "TranShk"), transition=lambda bNrm: mNrm),
+    Frame(("cNrm"), ("mNrm",), control=True),
+    Frame(
+        ("U"),
+        ("cNrm", "CRRA"),  # Note CRRA here is a parameter not a state var
+        transition=lambda cNrm, CRRA: (CRRAutility(cNrm, CRRA),),
+        reward=True,
+        context={"CRRA": 2.0},
+    ),
+    Frame(("aNrm"), ("mNrm", "cNrm"), transition=lambda mNrm, cNrm: (mNrm - cNrm,)),
+]
+
+
+class test_FrameModel(unittest.TestCase):
+    def setUp(self):
+        self.model = FrameModel(frames_A, init_parameters)
+
+    def test_init(self):
+        self.model.frames.var("aNrm")
+
+        self.assertTrue(
+            isinstance(
+                list(self.model.frames.var("bNrm").parents.values())[0],
+                BackwardFrameReference,
+            )
+        )
+
+        self.assertTrue(
+            isinstance(
+                list(self.model.frames.var("aNrm").children.values())[0],
+                ForwardFrameReference,
+            )
+        )
+
+    def test_make_terminal(self):
+        terminal_model = self.model.make_terminal()
+
+        self.assertEqual(len(self.model.make_terminal().frames.var("aNrm").children), 0)
+
+    def test_prepend(self):
+        double_model = self.model.prepend(self.model)
+
+        self.assertEqual(len(double_model.frames), 10)
+
+    def test_repeat(self):
+        repeat_model = self.model.repeat({"bNrm": {"Rfree": [1.01, 1.03, 1.02]}})
+
+        self.assertEqual(len(repeat_model.frames), 15)
+
+        self.assertEqual(repeat_model.frames.var("bNrm_1").context["Rfree"], 1.03)
+'''
\ No newline at end of file

From 74382c34f8422bdd7a2934d1fbbb3f67edde361b Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Thu, 20 Jul 2023 14:05:16 -0400
Subject: [PATCH 02/37] draw_shocks and docs

---
 HARK/simulation/monte_carlo.py | 46 +++++++++++++++++++++++++++++++---
 1 file changed, 43 insertions(+), 3 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 2e17e1fac..10643e329 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -1,5 +1,10 @@
+"""
+Functions to support Monte Carlo simulation of models.
+"""
+
+from distribution import Distribution
 from inspect import signature
-from typing import Callable, Mapping
+from typing import Any, Callable, Mapping, Union
 
 
 class Control:
@@ -10,11 +15,46 @@ class Control:
     def __init__(self, args):
         pass
 
+def draw_shocks(shocks: Mapping[str, Distribution], N: int):
+    """
+
+    Parameters
+    ------------
+    shocks Mapping[str, Distribution]
+        A dictionary-like mapping from shock names to distributions from which to draw
+
+    N: int
+        Number of draws from each shock
+    """
+    return {
+        shock : shocks[shock].draw(N)
+        for shock in shocks
+    }
+
+
 def sim_one_period(
-        dynamics : Mapping[str, Callable],
-        pre : Mapping,
+        dynamics : Mapping[str, Union[Callable, Control]],
+        pre : Mapping[str, Any],
         dr : Mapping[str, Callable]
 ):
+    """
+
+    Parameters
+    ------------
+
+    dynamics: Mapping[str, Callable]
+        Maps variable names to functions from variables to values.
+        Can include Controls
+        ## TODO: Make collection of equations into a named type
+
+
+    pre : Mapping[str, Any]
+        Bound values for all variables that must be known before beginning the period's dynamics.
+
+
+    dr : Mapping[str, Callable]
+        Decision rules for all the Control variables in the dynamics.
+    """
     vals = pre.copy()
 
     for varn in dynamics:

From f6a672df703b1e4d10648e3db385427a58f605d1 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Thu, 20 Jul 2023 14:18:23 -0400
Subject: [PATCH 03/37] automated test for draw_shock

---
 HARK/simulation/monte_carlo.py      |  2 +-
 HARK/simulation/test_monte_carlo.py | 12 ++++++++++++
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 10643e329..557a9dd45 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -2,7 +2,7 @@
 Functions to support Monte Carlo simulation of models.
 """
 
-from distribution import Distribution
+from HARK.distribution import Distribution
 from inspect import signature
 from typing import Any, Callable, Mapping, Union
 
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index f8211348e..b0e3d75e3 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -3,8 +3,14 @@
 """
 import unittest
 
+from HARK.distribution import MeanOneLogNormal
 from HARK.simulation.monte_carlo import *
 
+shocks = {
+    'psi' : MeanOneLogNormal(1),
+    'theta' : MeanOneLogNormal(1)
+
+}
 
 pre = {
     'R' : 1.05,
@@ -27,6 +33,12 @@
     'cNrm' : lambda mNrm : mNrm / 2
 }
 
+class test_draw_shocks(unittest.TestCase):
+    def test_draw_shocks(self):
+
+        drawn = draw_shocks(shocks, 2)
+
+        self.assertEqual(len(drawn['psi']), 2)
 
 class test_sim_one_period(unittest.TestCase):
     def test_sim_one_period(self):

From 0041d2cb763d2e4e1f4a72f457ac61bd495368ec Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Fri, 21 Jul 2023 12:25:55 -0400
Subject: [PATCH 04/37] AgentTypeMonteCarloSimulator class draft

---
 HARK/simulation/monte_carlo.py      | 472 +++++++++++++++++++++++++++-
 HARK/simulation/test_monte_carlo.py |   6 +-
 2 files changed, 473 insertions(+), 5 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 557a9dd45..8e3895287 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -4,6 +4,7 @@
 
 from HARK.distribution import Distribution
 from inspect import signature
+import numpy as np
 from typing import Any, Callable, Mapping, Union
 
 
@@ -30,9 +31,10 @@ def draw_shocks(shocks: Mapping[str, Distribution], N: int):
         shock : shocks[shock].draw(N)
         for shock in shocks
     }
+    ## TODO: Use time-varying distributions properly with conditions.
 
 
-def sim_one_period(
+def simulate_dynamics(
         dynamics : Mapping[str, Union[Callable, Control]],
         pre : Mapping[str, Any],
         dr : Mapping[str, Callable]
@@ -70,4 +72,470 @@ def sim_one_period(
         else:
             vals[varn] = feq(*[vals[var] for var in signature(feq).parameters])
 
-    return vals
\ No newline at end of file
+    return vals
+
+class Simulator():
+    pass
+
+class AgentTypeMonteCarloSimulator(Simulator):
+    """
+    A Monte Carlo simulation engine based on the HARK.core.AgentType framework.
+    Unlike HARK.core.AgentType, this class:
+      * does not do any model solving
+      * depends on dynamic equations, shocks, and decision rules paased into it
+
+    The purpose of this class is to provide a way to simulate models without
+    relying on inheritance from the AgentType class.
+
+    This simulator makes assumptions about population birth and mortality which
+    are not generic. They are: TODO.
+    
+    Parameters
+    ----------
+    seed : int
+        A seed for this instance's random number generator.
+
+    Attributes
+    ----------
+    AgentCount : int
+        The number of agents of this type to use in simulation.
+
+    state_vars : list of string
+        The string labels for this AgentType's model state variables.
+    """
+
+    state_vars = []
+
+    def __init__(
+        self,
+        parameters,
+        shocks,
+        dynamics,
+        dr,
+        seed=0,
+        agent_count = 1,
+        T_sim = 10
+    ):
+        super().__init__()
+
+        self.parameters = parameters
+        self.shocks = shocks
+        self.dynamics = dynamics
+        self.dr = dr
+
+        self.seed = seed  # NOQA
+        self.agent_count = agent_count
+        self.T_sim = T_sim
+
+        # changes here from HARK.core.AgentType
+        self.vars = list(shocks.keys()) + list(dynamics.keys())
+
+        self.vars_now = {v: None for v in self.vars}
+        self.vars_prev = self.state_now.copy()
+
+        self.read_shocks = False  # NOQA
+        self.shock_history = {}
+        self.newborn_init_history = {}
+        self.history = {}
+
+        self.reset_rng()  # NOQA
+
+    def reset_rng(self):
+        """
+        Reset the random number generator for this type.
+
+        Parameters
+        ----------
+        none
+
+        Returns
+        -------
+        none
+        """
+        self.RNG = np.random.default_rng(self.seed)
+
+    def initialize_sim(self):
+        """
+        Prepares for a new simulation.  Resets the internal random number generator,
+        makes initial states for all agents (using sim_birth), clears histories of tracked variables.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+        """
+        if not hasattr(self, "T_sim"):
+            raise Exception(
+                "To initialize simulation variables it is necessary to first "
+                + "set the attribute T_sim to the largest number of observations "
+                + "you plan to simulate for each agent including re-births."
+            )
+        elif self.T_sim <= 0:
+            raise Exception(
+                "T_sim represents the largest number of observations "
+                + "that can be simulated for an agent, and must be a positive number."
+            )
+
+        self.reset_rng()
+        self.t_sim = 0
+        all_agents = np.ones(self.agent_count, dtype=bool)
+        blank_array = np.empty(self.agent_count)
+        blank_array[:] = np.nan
+        for var in self.vars:
+            if self.vars_now[var] is None:
+                self.vars_now[var] = copy(blank_array)
+
+            # elif self.state_prev[var] is None:
+            #    self.state_prev[var] = copy(blank_array)
+        self.t_age = np.zeros(
+            self.agent_count, dtype=int
+        )  # Number of periods since agent entry
+        self.t_cycle = np.zeros(
+            self.agent_count, dtype=int
+        )  # Which cycle period each agent is on
+        self.sim_birth(all_agents)
+
+        # If we are asked to use existing shocks and a set of initial conditions
+        # exist, use them
+        ### TODO what to do with this?
+        if self.read_shocks and bool(self.newborn_init_history):
+            for var_name in self.state_now:
+                # Check that we are actually given a value for the variable
+                if var_name in self.newborn_init_history.keys():
+                    # Copy only array-like idiosyncratic states. Aggregates should
+                    # not be set by newborns
+                    idio = (
+                        isinstance(self.state_now[var_name], np.ndarray)
+                        and len(self.state_now[var_name]) == self.AgentCount
+                    )
+                    if idio:
+                        self.state_now[var_name] = self.newborn_init_history[var_name][
+                            0
+                        ]
+
+                else:
+                    warn(
+                        "The option for reading shocks was activated but "
+                        + "the model requires state "
+                        + var_name
+                        + ", not contained in "
+                        + "newborn_init_history."
+                    )
+
+        self.clear_history()
+        return None
+
+    def sim_one_period(self):
+        """
+        Simulates one period for this type.  Calls the methods get_mortality(), get_shocks() or
+        read_shocks, get_states(), get_controls(), and get_poststates().  These should be defined for
+        AgentType subclasses, except get_mortality (define its components sim_death and sim_birth
+        instead) and read_shocks.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+        """
+        if not hasattr(self, "solution"):
+            raise Exception(
+                "Model instance does not have a solution stored. To simulate, it is necessary"
+                " to run the `solve()` method of the class first."
+            )
+
+        # Mortality adjusts the agent population
+        self.get_mortality()  # Replace some agents with "newborns"
+
+        # state_{t-1}
+        for var in self.vars:
+            self.vars_prev[var] = self.vars_now[var]
+
+            if isinstance(self.vars_now[var], np.ndarray):
+                self.vars_now[var] = np.empty(self.AgentCount)
+            else:
+                # Probably an aggregate variable. It may be getting set by the Market.
+                pass
+
+        shocks_now = {}
+
+        if self.read_shocks:  # If shock histories have been pre-specified, use those
+            for var_name in self.shocks:
+                shocks_now[var_name] = self.shock_history[var_name][self.t_sim, :]
+        else:  # Otherwise, draw shocks as usual according to subclass-specific method
+            ### BIG CHANGES HERE from HARK.core.AgentType
+            shocks_now = draw_shocks(self.shocks)
+
+        # maybe need to time index the parameters here somehow?
+        pre = self.parameters + shocks_now + self.vars_prev
+        
+        post = simulate_dynamics(self.dynamics, pre, self.dr)
+        
+        self.vars_now = post
+        ### BIG CHANGES HERE
+
+        # 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
+
+    def make_shock_history(self):
+        """
+        Makes a pre-specified history of shocks for the simulation.  Shock variables should be named
+        in self.shock_vars, a list of strings that is subclass-specific.  This method runs a subset
+        of the standard simulation loop by simulating only mortality and shocks; each variable named
+        in shock_vars is stored in a T_sim x AgentCount array in history dictionary self.history[X].
+        Automatically sets self.read_shocks to True so that these pre-specified shocks are used for
+        all subsequent calls to simulate().
+
+        ### TODO: Rethink this for when shocks are passed in.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+        """
+        # Re-initialize the simulation
+        self.initialize_sim()
+
+        # Make blank history arrays for each shock variable (and mortality)
+        for var_name in self.shock_vars:
+            self.shock_history[var_name] = (
+                np.zeros((self.T_sim, self.agent_count)) + np.nan
+            )
+        self.shock_history["who_dies"] = np.zeros(
+            (self.T_sim, self.agent_count), dtype=bool
+        )
+
+        # Also make blank arrays for the draws of newborns' initial conditions
+        for var_name in self.state_vars:
+            self.newborn_init_history[var_name] = (
+                np.zeros((self.T_sim, self.agent_count)) + np.nan
+            )
+
+        # Record the initial condition of the newborns created by
+        # initialize_sim -> sim_births
+        for var_name in self.state_vars:
+            # Check whether the state is idiosyncratic or an aggregate
+            idio = (
+                isinstance(self.state_now[var_name], np.ndarray)
+                and len(self.state_now[var_name]) == self.agent_count
+            )
+            if idio:
+                self.newborn_init_history[var_name][self.t_sim] = self.state_now[
+                    var_name
+                ]
+            else:
+                # Aggregate state is a scalar. Assign it to every agent.
+                self.newborn_init_history[var_name][self.t_sim, :] = self.state_now[
+                    var_name
+                ]
+
+        # Make and store the history of shocks for each period
+        for t in range(self.T_sim):
+            # Deaths
+            self.get_mortality()
+            self.shock_history["who_dies"][t, :] = self.who_dies
+
+            # Initial conditions of newborns
+            if np.sum(self.who_dies) > 0:
+                for var_name in self.state_vars:
+                    # Check whether the state is idiosyncratic or an aggregate
+                    idio = (
+                        isinstance(self.state_now[var_name], np.ndarray)
+                        and len(self.state_now[var_name]) == self.agent_count
+                    )
+                    if idio:
+                        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]
+
+            # Other Shocks
+            self.get_shocks()
+            for var_name in self.shock_vars:
+                self.shock_history[var_name][t, :] = self.shocks[var_name]
+
+            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
+
+        # Flag that shocks can be read rather than simulated
+        self.read_shocks = True
+
+    def get_mortality(self):
+        """
+        Simulates mortality or agent turnover according to some model-specific rules named sim_death
+        and sim_birth (methods of an AgentType subclass).  sim_death takes no arguments and returns
+        a Boolean array of size AgentCount, indicating which agents of this type have "died" and
+        must be replaced.  sim_birth takes such a Boolean array as an argument and generates initial
+        post-decision states for those agent indices.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+        """
+        if self.read_shocks:
+            who_dies = self.shock_history["who_dies"][self.t_sim, :]
+            # Instead of simulating births, assign the saved newborn initial conditions
+            if np.sum(who_dies) > 0:
+                for var_name in self.state_now:
+                    if var_name in self.newborn_init_history.keys():
+                        # Copy only array-like idiosyncratic states. Aggregates should
+                        # not be set by newborns
+                        idio = (
+                            isinstance(self.state_now[var_name], np.ndarray)
+                            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
+                            ]
+
+                    else:
+                        warn(
+                            "The option for reading shocks was activated but "
+                            + "the model requires state "
+                            + var_name
+                            + ", not contained in "
+                            + "newborn_init_history."
+                        )
+
+                # Reset ages of newborns
+                self.t_age[who_dies] = 0
+                self.t_cycle[who_dies] = 0
+        else:
+            who_dies = self.sim_death()
+            self.sim_birth(who_dies)
+        self.who_dies = who_dies
+        return None
+
+    def sim_death(self):
+        """
+        Determines which agents in the current population "die" or should be replaced.  Takes no
+        inputs, returns a Boolean array of size self.AgentCount, which has True for agents who die
+        and False for those that survive. Returns all False by default, must be overwritten by a
+        subclass to have replacement events.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        who_dies : np.array
+            Boolean array of size self.AgentCount indicating which agents die and are replaced.
+        """
+        who_dies = np.zeros(self.agent_count, dtype=bool)
+        return who_dies
+
+    def sim_birth(self, which_agents):
+        """
+        Makes new agents for the simulation.  Takes a boolean array as an input, indicating which
+        agent indices are to be "born".  Does nothing by default, must be overwritten by a subclass.
+
+        Parameters
+        ----------
+        which_agents : np.array(Bool)
+            Boolean array of size self.AgentCount indicating which agents should be "born".
+
+        Returns
+        -------
+        None
+        """
+        print("AgentType subclass must define method sim_birth!")
+        return None      
+
+    def simulate(self, sim_periods=None):
+        """
+        Simulates this agent type for a given number of periods. Defaults to
+        self.T_sim if no input.
+        Records histories of attributes named in self.track_vars in
+        self.history[varname].
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        history : dict
+            The history tracked during the simulation.
+        """
+        if not hasattr(self, "t_sim"):
+            raise Exception(
+                "It seems that the simulation variables were not initialize before calling "
+                + "simulate(). Call initialize_sim() to initialize the variables before calling simulate() again."
+            )
+
+        if not hasattr(self, "T_sim"):
+            raise Exception(
+                "This agent type instance must have the attribute T_sim set to a positive integer."
+                + "Set T_sim to match the largest dataset you might simulate, and run this agent's"
+                + "initalizeSim() method before running simulate() again."
+            )
+
+        if sim_periods is not None and self.T_sim < sim_periods:
+            raise Exception(
+                "To simulate, sim_periods has to be larger than the maximum data set size "
+                + "T_sim. Either increase the attribute T_sim of this agent type instance "
+                + "and call the initialize_sim() method again, or set sim_periods <= T_sim."
+            )
+
+        # Ignore floating point "errors". Numpy calls it "errors", but really it's excep-
+        # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is
+        # -np.inf, np.inf/np.inf is np.nan and so on.
+        with np.errstate(
+            divide="ignore", over="ignore", under="ignore", invalid="ignore"
+        ):
+            if sim_periods is None:
+                sim_periods = self.T_sim
+
+            for t in range(sim_periods):
+                self.sim_one_period()
+
+                # track all the vars -- shocks and dynamics
+                for var_name in self.vars:
+                    self.history[var_name][self.t_sim, :] = self.vars_now[var_name]
+                    
+                self.t_sim += 1
+
+            return self.history
+
+    def clear_history(self):
+        """
+        Clears the histories.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+        """
+        for var_name in self.vars:
+            self.history[var_name] = np.empty((self.T_sim, self.AgentCount))
+            self.history[var_name].fill(np.nan)
\ No newline at end of file
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index b0e3d75e3..b276ba0fe 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -40,10 +40,10 @@ def test_draw_shocks(self):
 
         self.assertEqual(len(drawn['psi']), 2)
 
-class test_sim_one_period(unittest.TestCase):
-    def test_sim_one_period(self):
+class test_simulate_dynamics(unittest.TestCase):
+    def test_simulate_dynamics(self):
 
-        post = sim_one_period(dynamics, pre, dr)
+        post = simulate_dynamics(dynamics, pre, dr)
 
         self.assertAlmostEqual(post['cNrm'], 0.98388429)
 

From 3004643958d5aba008adbff70145408bcbd491ba Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 24 Jul 2023 15:51:15 -0400
Subject: [PATCH 05/37] generic draw_shocks distinguishes between time-varying,
 non-time varying, and Aggregate shocks.

---
 HARK/simulation/monte_carlo.py      | 39 +++++++++++++++++++++--------
 HARK/simulation/test_monte_carlo.py | 24 +++++++++++-------
 2 files changed, 44 insertions(+), 19 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 8e3895287..2abc1fceb 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -2,11 +2,19 @@
 Functions to support Monte Carlo simulation of models.
 """
 
-from HARK.distribution import Distribution
+from HARK.distribution import Distribution, IndexDistribution, TimeVaryingDiscreteDistribution
 from inspect import signature
 import numpy as np
-from typing import Any, Callable, Mapping, Union
+from typing import Any, Callable, Mapping, Sequence, Union
 
+class Aggregate:
+    """
+    Used to designate a shock as an aggregate shock.
+    If so designated, draws from the shock will be scalar rather
+    than array valued.
+    """
+    def __init__(self, dist: Distribution):
+        self.dist = dist
 
 class Control:
     """
@@ -16,7 +24,10 @@ class Control:
     def __init__(self, args):
         pass
 
-def draw_shocks(shocks: Mapping[str, Distribution], N: int):
+def draw_shocks(
+        shocks: Mapping[str, Distribution],
+        conditions: Sequence[int]
+        ):
     """
 
     Parameters
@@ -24,15 +35,23 @@ def draw_shocks(shocks: Mapping[str, Distribution], N: int):
     shocks Mapping[str, Distribution]
         A dictionary-like mapping from shock names to distributions from which to draw
 
-    N: int
-        Number of draws from each shock
+    conditions: Sequence[int]
+        An array of conditions, one for each agent.
+        Typically these will be agent ages.
     """
-    return {
-        shock : shocks[shock].draw(N)
-        for shock in shocks
-    }
-    ## TODO: Use time-varying distributions properly with conditions.
+    draws = {}
+
+    for shock_var in shocks:
+        shock = shocks[shock_var]
+        if isinstance(shock, Aggregate):
+            draws[shock_var] = shock.dist.draw(1)[0]
+        elif isinstance(shock, IndexDistribution) \
+            or isinstance(shock, TimeVaryingDiscreteDistribution):
+            draws[shock_var] = shock.draw(conditions)
+        else:
+            draws[shock_var] = shock.draw(len(conditions))
 
+    return draws
 
 def simulate_dynamics(
         dynamics : Mapping[str, Union[Callable, Control]],
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index b276ba0fe..3533f88fd 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -3,16 +3,20 @@
 """
 import unittest
 
-from HARK.distribution import MeanOneLogNormal
+from HARK.distribution import MeanOneLogNormal, IndexDistribution
 from HARK.simulation.monte_carlo import *
 
-shocks = {
-    'psi' : MeanOneLogNormal(1),
+cons_shocks = {
+    'agg_gro' : Aggregate(MeanOneLogNormal(1)),
+    'psi' : IndexDistribution(
+        MeanOneLogNormal,
+        {
+            'sigma' : [1.0, 1.1]
+        }),
     'theta' : MeanOneLogNormal(1)
-
 }
 
-pre = {
+cons_pre = {
     'R' : 1.05,
     'aNrm' : 1,
     'gamma' : 1.1,
@@ -20,7 +24,7 @@
     'theta' : 1.1 # TODO: draw this from a shock
 }
 
-dynamics = {
+cons_dynamics = {
     'G' : lambda gamma, psi : gamma * psi,
     'Rnrm' : lambda R, G : R / G,
     'bNrm' : lambda Rnrm, aNrm : Rnrm * aNrm,
@@ -29,21 +33,23 @@
     'aNrm' : lambda mNrm, cNrm : mNrm - cNrm
 }
 
-dr = {
+cons_dr = {
     'cNrm' : lambda mNrm : mNrm / 2
 }
 
 class test_draw_shocks(unittest.TestCase):
     def test_draw_shocks(self):
 
-        drawn = draw_shocks(shocks, 2)
+        drawn = draw_shocks(cons_shocks, np.array([0,1]))
 
+        self.assertEqual(len(drawn['theta']), 2)
         self.assertEqual(len(drawn['psi']), 2)
+        self.assertTrue(isinstance(drawn['agg_gro'], float))
 
 class test_simulate_dynamics(unittest.TestCase):
     def test_simulate_dynamics(self):
 
-        post = simulate_dynamics(dynamics, pre, dr)
+        post = simulate_dynamics(cons_dynamics, cons_pre, cons_dr)
 
         self.assertAlmostEqual(post['cNrm'], 0.98388429)
 

From 53a7f651f6bf97ceca077c73b9061f3301026043 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 24 Jul 2023 16:42:16 -0400
Subject: [PATCH 06/37] Working test for AgentTypeMonteCarloSimulator.

---
 HARK/simulation/monte_carlo.py      | 61 ++++++++++++----------
 HARK/simulation/test_monte_carlo.py | 81 +++++++++++++++--------------
 2 files changed, 75 insertions(+), 67 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 2abc1fceb..623d50b4a 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -1,7 +1,7 @@
 """
 Functions to support Monte Carlo simulation of models.
 """
-
+from copy import copy
 from HARK.distribution import Distribution, IndexDistribution, TimeVaryingDiscreteDistribution
 from inspect import signature
 import numpy as np
@@ -47,6 +47,7 @@ def draw_shocks(
             draws[shock_var] = shock.dist.draw(1)[0]
         elif isinstance(shock, IndexDistribution) \
             or isinstance(shock, TimeVaryingDiscreteDistribution):
+            ## TODO  his type test is awkward. They should share a superclass.
             draws[shock_var] = shock.draw(conditions)
         else:
             draws[shock_var] = shock.draw(len(conditions))
@@ -111,16 +112,16 @@ class AgentTypeMonteCarloSimulator(Simulator):
     
     Parameters
     ----------
+    TODO
+
     seed : int
         A seed for this instance's random number generator.
 
     Attributes
     ----------
-    AgentCount : int
+    agent_count : int
         The number of agents of this type to use in simulation.
 
-    state_vars : list of string
-        The string labels for this AgentType's model state variables.
     """
 
     state_vars = []
@@ -131,6 +132,7 @@ def __init__(
         shocks,
         dynamics,
         dr,
+        initial,
         seed=0,
         agent_count = 1,
         T_sim = 10
@@ -141,6 +143,7 @@ def __init__(
         self.shocks = shocks
         self.dynamics = dynamics
         self.dr = dr
+        self.initial = initial
 
         self.seed = seed  # NOQA
         self.agent_count = agent_count
@@ -150,7 +153,7 @@ def __init__(
         self.vars = list(shocks.keys()) + list(dynamics.keys())
 
         self.vars_now = {v: None for v in self.vars}
-        self.vars_prev = self.state_now.copy()
+        self.vars_prev = self.vars_now.copy()
 
         self.read_shocks = False  # NOQA
         self.shock_history = {}
@@ -228,7 +231,7 @@ def initialize_sim(self):
                     # not be set by newborns
                     idio = (
                         isinstance(self.state_now[var_name], np.ndarray)
-                        and len(self.state_now[var_name]) == self.AgentCount
+                        and len(self.state_now[var_name]) == self.agent_count
                     )
                     if idio:
                         self.state_now[var_name] = self.newborn_init_history[var_name][
@@ -262,12 +265,6 @@ def sim_one_period(self):
         -------
         None
         """
-        if not hasattr(self, "solution"):
-            raise Exception(
-                "Model instance does not have a solution stored. To simulate, it is necessary"
-                " to run the `solve()` method of the class first."
-            )
-
         # Mortality adjusts the agent population
         self.get_mortality()  # Replace some agents with "newborns"
 
@@ -276,7 +273,7 @@ def sim_one_period(self):
             self.vars_prev[var] = self.vars_now[var]
 
             if isinstance(self.vars_now[var], np.ndarray):
-                self.vars_now[var] = np.empty(self.AgentCount)
+                self.vars_now[var] = np.empty(self.agent_count)
             else:
                 # Probably an aggregate variable. It may be getting set by the Market.
                 pass
@@ -288,10 +285,10 @@ def sim_one_period(self):
                 shocks_now[var_name] = self.shock_history[var_name][self.t_sim, :]
         else:  # Otherwise, draw shocks as usual according to subclass-specific method
             ### BIG CHANGES HERE from HARK.core.AgentType
-            shocks_now = draw_shocks(self.shocks)
+            shocks_now = draw_shocks(self.shocks, self.t_age)
 
         # maybe need to time index the parameters here somehow?
-        pre = self.parameters + shocks_now + self.vars_prev
+        pre = self.parameters | self.vars_prev | shocks_now
         
         post = simulate_dynamics(self.dynamics, pre, self.dr)
         
@@ -300,17 +297,19 @@ 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
+
+        # What will we do with cycles?
+        #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
 
     def make_shock_history(self):
         """
         Makes a pre-specified history of shocks for the simulation.  Shock variables should be named
         in self.shock_vars, a list of strings that is subclass-specific.  This method runs a subset
         of the standard simulation loop by simulating only mortality and shocks; each variable named
-        in shock_vars is stored in a T_sim x AgentCount array in history dictionary self.history[X].
+        in shock_vars is stored in a T_sim x agent_count array in history dictionary self.history[X].
         Automatically sets self.read_shocks to True so that these pre-specified shocks are used for
         all subsequent calls to simulate().
 
@@ -402,7 +401,7 @@ def get_mortality(self):
         """
         Simulates mortality or agent turnover according to some model-specific rules named sim_death
         and sim_birth (methods of an AgentType subclass).  sim_death takes no arguments and returns
-        a Boolean array of size AgentCount, indicating which agents of this type have "died" and
+        a Boolean array of size agent_count, indicating which agents of this type have "died" and
         must be replaced.  sim_birth takes such a Boolean array as an argument and generates initial
         post-decision states for those agent indices.
 
@@ -424,7 +423,7 @@ def get_mortality(self):
                         # not be set by newborns
                         idio = (
                             isinstance(self.state_now[var_name], np.ndarray)
-                            and len(self.state_now[var_name]) == self.AgentCount
+                            and len(self.state_now[var_name]) == self.agent_count
                         )
                         if idio:
                             self.state_now[var_name][
@@ -454,7 +453,7 @@ def get_mortality(self):
     def sim_death(self):
         """
         Determines which agents in the current population "die" or should be replaced.  Takes no
-        inputs, returns a Boolean array of size self.AgentCount, which has True for agents who die
+        inputs, returns a Boolean array of size self.agent_count, which has True for agents who die
         and False for those that survive. Returns all False by default, must be overwritten by a
         subclass to have replacement events.
 
@@ -465,7 +464,7 @@ def sim_death(self):
         Returns
         -------
         who_dies : np.array
-            Boolean array of size self.AgentCount indicating which agents die and are replaced.
+            Boolean array of size self.agent_count indicating which agents die and are replaced.
         """
         who_dies = np.zeros(self.agent_count, dtype=bool)
         return who_dies
@@ -478,14 +477,20 @@ def sim_birth(self, which_agents):
         Parameters
         ----------
         which_agents : np.array(Bool)
-            Boolean array of size self.AgentCount indicating which agents should be "born".
+            Boolean array of size self.agent_count indicating which agents should be "born".
 
         Returns
         -------
         None
         """
-        print("AgentType subclass must define method sim_birth!")
-        return None      
+
+        initial_vals = draw_shocks(
+            self.initial,
+            np.zeros(which_agents.sum())
+        )
+
+        for varn in initial_vals:
+            self.vars_now[varn][which_agents] = initial_vals[varn]
 
     def simulate(self, sim_periods=None):
         """
@@ -556,5 +561,5 @@ def clear_history(self):
         None
         """
         for var_name in self.vars:
-            self.history[var_name] = np.empty((self.T_sim, self.AgentCount))
+            self.history[var_name] = np.empty((self.T_sim, self.agent_count))
             self.history[var_name].fill(np.nan)
\ No newline at end of file
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index 3533f88fd..83d0b3b24 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -54,10 +54,52 @@ def test_simulate_dynamics(self):
         self.assertAlmostEqual(post['cNrm'], 0.98388429)
 
 
+class test_AgentTypeMonteCarloSimulator(unittest.TestCase):
+    def setUp(self):
 
+        self.shocks = {
+            ## TODO: Add an aggregate shock
+            ## TODO: Add a time varying shock.
+            'theta' : MeanOneLogNormal(1),
+            'agg_R' : Aggregate(MeanOneLogNormal(1))
+        }
+
+        self.initial = {
+            'a' : MeanOneLogNormal(1)
+        }
+
+        self.parameters = { # TODO
+            'G' : 1.05,
+        }
+
+        self.dynamics = {
+            'b' : lambda agg_R, G, a : agg_R * G * a,
+            'm' : lambda b, theta : b + theta,
+            'c' : Control(['m']),
+            'a' : lambda m, c : m - c
+        }
+
+        self.dr = {
+            'c' : lambda m : m / 2
+        }
+
+    def test_AgentTypeMonteCarloSimulator(self):
+        self.simulator = AgentTypeMonteCarloSimulator(
+            self.parameters,
+            self.shocks,
+            self.dynamics,
+            self.dr,
+            self.initial,
+            agent_count = 3
+        )
 
+        self.simulator.initialize_sim()
+        history = self.simulator.simulate()
 
+        a1 = history['a'][5]
+        b1 = history['a'][4] * history['agg_R'][5] * self.parameters['G'] + history['theta'][5]  - history['c'][5]
 
+        self.assertTrue((a1 == b1).all())
 
 
 
@@ -89,43 +131,4 @@ def test_simulate_dynamics(self):
     ),
     Frame(("aNrm"), ("mNrm", "cNrm"), transition=lambda mNrm, cNrm: (mNrm - cNrm,)),
 ]
-
-
-class test_FrameModel(unittest.TestCase):
-    def setUp(self):
-        self.model = FrameModel(frames_A, init_parameters)
-
-    def test_init(self):
-        self.model.frames.var("aNrm")
-
-        self.assertTrue(
-            isinstance(
-                list(self.model.frames.var("bNrm").parents.values())[0],
-                BackwardFrameReference,
-            )
-        )
-
-        self.assertTrue(
-            isinstance(
-                list(self.model.frames.var("aNrm").children.values())[0],
-                ForwardFrameReference,
-            )
-        )
-
-    def test_make_terminal(self):
-        terminal_model = self.model.make_terminal()
-
-        self.assertEqual(len(self.model.make_terminal().frames.var("aNrm").children), 0)
-
-    def test_prepend(self):
-        double_model = self.model.prepend(self.model)
-
-        self.assertEqual(len(double_model.frames), 10)
-
-    def test_repeat(self):
-        repeat_model = self.model.repeat({"bNrm": {"Rfree": [1.01, 1.03, 1.02]}})
-
-        self.assertEqual(len(repeat_model.frames), 15)
-
-        self.assertEqual(repeat_model.frames.var("bNrm_1").context["Rfree"], 1.03)
 '''
\ No newline at end of file

From e8799dae38920ec9068ed9acf7ca09c6130ef456 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 24 Jul 2023 17:07:58 -0400
Subject: [PATCH 07/37] var value updates working for Python 3.8 now

---
 HARK/simulation/monte_carlo.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 623d50b4a..a1e3e735b 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -288,7 +288,10 @@ def sim_one_period(self):
             shocks_now = draw_shocks(self.shocks, self.t_age)
 
         # maybe need to time index the parameters here somehow?
-        pre = self.parameters | self.vars_prev | shocks_now
+        pre = copy(self.parameters)
+        pre.update(self.vars_prev)
+        pre.update(shocks_now)
+        #Won't work for 3.8: self.parameters | self.vars_prev | shocks_now
         
         post = simulate_dynamics(self.dynamics, pre, self.dr)
         

From 98ca16f8d546df3a85a11258dcfcad77bb534c20 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Wed, 26 Jul 2023 10:00:38 -0400
Subject: [PATCH 08/37] tighter make_shock_history, with test

---
 HARK/simulation/monte_carlo.py      | 76 +++--------------------------
 HARK/simulation/test_monte_carlo.py | 12 ++++-
 2 files changed, 19 insertions(+), 69 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index a1e3e735b..1b139569a 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -324,81 +324,21 @@ def make_shock_history(self):
 
         Returns
         -------
-        None
+        shock_history: dict
+            The subset of simulation history that are the shocks for each agent and time.
         """
         # Re-initialize the simulation
         self.initialize_sim()
+        self.simulate()
 
-        # Make blank history arrays for each shock variable (and mortality)
-        for var_name in self.shock_vars:
-            self.shock_history[var_name] = (
-                np.zeros((self.T_sim, self.agent_count)) + np.nan
-            )
-        self.shock_history["who_dies"] = np.zeros(
-            (self.T_sim, self.agent_count), dtype=bool
-        )
-
-        # Also make blank arrays for the draws of newborns' initial conditions
-        for var_name in self.state_vars:
-            self.newborn_init_history[var_name] = (
-                np.zeros((self.T_sim, self.agent_count)) + np.nan
-            )
-
-        # Record the initial condition of the newborns created by
-        # initialize_sim -> sim_births
-        for var_name in self.state_vars:
-            # Check whether the state is idiosyncratic or an aggregate
-            idio = (
-                isinstance(self.state_now[var_name], np.ndarray)
-                and len(self.state_now[var_name]) == self.agent_count
-            )
-            if idio:
-                self.newborn_init_history[var_name][self.t_sim] = self.state_now[
-                    var_name
-                ]
-            else:
-                # Aggregate state is a scalar. Assign it to every agent.
-                self.newborn_init_history[var_name][self.t_sim, :] = self.state_now[
-                    var_name
-                ]
-
-        # Make and store the history of shocks for each period
-        for t in range(self.T_sim):
-            # Deaths
-            self.get_mortality()
-            self.shock_history["who_dies"][t, :] = self.who_dies
-
-            # Initial conditions of newborns
-            if np.sum(self.who_dies) > 0:
-                for var_name in self.state_vars:
-                    # Check whether the state is idiosyncratic or an aggregate
-                    idio = (
-                        isinstance(self.state_now[var_name], np.ndarray)
-                        and len(self.state_now[var_name]) == self.agent_count
-                    )
-                    if idio:
-                        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]
-
-            # Other Shocks
-            self.get_shocks()
-            for var_name in self.shock_vars:
-                self.shock_history[var_name][t, :] = self.shocks[var_name]
-
-            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
+        for shock_name in self.shocks:
+            self.shock_history[shock_name] = self.history[shock_name]
 
         # Flag that shocks can be read rather than simulated
         self.read_shocks = True
+        self.clear_history()
+
+        return self.shock_history
 
     def get_mortality(self):
         """
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index 83d0b3b24..7276bea58 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -83,7 +83,7 @@ def setUp(self):
             'c' : lambda m : m / 2
         }
 
-    def test_AgentTypeMonteCarloSimulator(self):
+    def test_simulate(self):
         self.simulator = AgentTypeMonteCarloSimulator(
             self.parameters,
             self.shocks,
@@ -101,7 +101,17 @@ def test_AgentTypeMonteCarloSimulator(self):
 
         self.assertTrue((a1 == b1).all())
 
+    def test_make_shock_history(self):
+        self.simulator = AgentTypeMonteCarloSimulator(
+            self.parameters,
+            self.shocks,
+            self.dynamics,
+            self.dr,
+            self.initial,
+            agent_count = 3
+        )
 
+        self.simulator.make_shock_history()
 
 
 ###############################################################3

From ddaf18589e46f90a292acc50f3dc0db90cfa44dd Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Wed, 26 Jul 2023 11:01:55 -0400
Subject: [PATCH 09/37] fixing handling of saved newborn inits

---
 HARK/simulation/monte_carlo.py      | 95 ++++++++++++-----------------
 HARK/simulation/test_monte_carlo.py | 11 ++++
 2 files changed, 49 insertions(+), 57 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 1b139569a..63f719b0f 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -218,34 +218,18 @@ def initialize_sim(self):
         self.t_cycle = np.zeros(
             self.agent_count, dtype=int
         )  # Which cycle period each agent is on
-        self.sim_birth(all_agents)
 
-        # If we are asked to use existing shocks and a set of initial conditions
-        # exist, use them
-        ### TODO what to do with this?
+        # Get recorded newborn conditions or initialize blank history.
         if self.read_shocks and bool(self.newborn_init_history):
-            for var_name in self.state_now:
-                # Check that we are actually given a value for the variable
-                if var_name in self.newborn_init_history.keys():
-                    # Copy only array-like idiosyncratic states. Aggregates should
-                    # not be set by newborns
-                    idio = (
-                        isinstance(self.state_now[var_name], np.ndarray)
-                        and len(self.state_now[var_name]) == self.agent_count
-                    )
-                    if idio:
-                        self.state_now[var_name] = self.newborn_init_history[var_name][
-                            0
-                        ]
-
-                else:
-                    warn(
-                        "The option for reading shocks was activated but "
-                        + "the model requires state "
-                        + var_name
-                        + ", not contained in "
-                        + "newborn_init_history."
-                    )
+            for init_var_name in self.initial:
+                self.vars_now[init_var_name] = self.newborn_init_history[init_var_name][self.t_sim, :]
+        else:
+            for var_name in self.initial:
+                self.newborn_init_history[var_name] = (
+                    np.zeros((self.T_sim, self.agent_count)) + np.nan
+                )
+
+        self.sim_birth(all_agents)
 
         self.clear_history()
         return None
@@ -283,7 +267,7 @@ def sim_one_period(self):
         if self.read_shocks:  # If shock histories have been pre-specified, use those
             for var_name in self.shocks:
                 shocks_now[var_name] = self.shock_history[var_name][self.t_sim, :]
-        else:  # Otherwise, draw shocks as usual according to subclass-specific method
+        else:
             ### BIG CHANGES HERE from HARK.core.AgentType
             shocks_now = draw_shocks(self.shocks, self.t_age)
 
@@ -356,45 +340,31 @@ def get_mortality(self):
         -------
         None
         """
+        who_dies = self.sim_death()
+
         if self.read_shocks:
-            who_dies = self.shock_history["who_dies"][self.t_sim, :]
             # Instead of simulating births, assign the saved newborn initial conditions
             if np.sum(who_dies) > 0:
-                for var_name in self.state_now:
-                    if var_name in self.newborn_init_history.keys():
-                        # Copy only array-like idiosyncratic states. Aggregates should
-                        # not be set by newborns
-                        idio = (
-                            isinstance(self.state_now[var_name], np.ndarray)
-                            and len(self.state_now[var_name]) == self.agent_count
-                        )
-                        if idio:
-                            self.state_now[var_name][
-                                who_dies
-                            ] = self.newborn_init_history[var_name][
-                                self.t_sim, who_dies
-                            ]
-
-                    else:
-                        warn(
-                            "The option for reading shocks was activated but "
-                            + "the model requires state "
-                            + var_name
-                            + ", not contained in "
-                            + "newborn_init_history."
-                        )
+                for var_name in self.initial:
+                    self.state_now[var_name][
+                        who_dies
+                    ] = self.newborn_init_history[var_name][
+                        self.t_sim, who_dies
+                    ]
 
                 # Reset ages of newborns
                 self.t_age[who_dies] = 0
                 self.t_cycle[who_dies] = 0
         else:
-            who_dies = self.sim_death()
             self.sim_birth(who_dies)
+
         self.who_dies = who_dies
         return None
 
     def sim_death(self):
         """
+        # TODO: This should mainly just track the 'who_dies' var, which can be a shock or endogenous.
+
         Determines which agents in the current population "die" or should be replaced.  Takes no
         inputs, returns a Boolean array of size self.agent_count, which has True for agents who die
         and False for those that survive. Returns all False by default, must be overwritten by a
@@ -409,6 +379,10 @@ def sim_death(self):
         who_dies : np.array
             Boolean array of size self.agent_count indicating which agents die and are replaced.
         """
+
+        #if self.read_shocks:
+        #    who_dies = self.shock_history["who_dies"][self.t_sim, :]
+
         who_dies = np.zeros(self.agent_count, dtype=bool)
         return who_dies
 
@@ -426,14 +400,21 @@ def sim_birth(self, which_agents):
         -------
         None
         """
-
-        initial_vals = draw_shocks(
-            self.initial,
-            np.zeros(which_agents.sum())
-        )
+        if self.read_shocks:
+            initial_vals = {
+                init_var: self.newborn_init_history[init_var][self.t_sim, :]
+                for init_var
+                in self.initial
+                }
+        else:
+            initial_vals = draw_shocks(
+                self.initial,
+                np.zeros(which_agents.sum())
+            )
 
         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]
 
     def simulate(self, sim_periods=None):
         """
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index 7276bea58..4cc05f032 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -113,6 +113,17 @@ def test_make_shock_history(self):
 
         self.simulator.make_shock_history()
 
+        newborn_init_1 = self.simulator.newborn_init_history.copy()
+        shocks_1 = self.simulator.shock_history.copy()
+
+        self.simulator.initialize_sim()
+        self.simulator.simulate()
+
+        self.assertEqual(newborn_init_1, self.simulator.newborn_init_history)
+        self.assertTrue(
+            np.all(self.simulator.history['theta'] == shocks_1['theta'])
+            )
+
 
 ###############################################################3
 

From dde1e88237ef7a54a920158fbcb5d3c45aab3422 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Wed, 26 Jul 2023 11:09:43 -0400
Subject: [PATCH 10/37] removing warning for very unlikely scenario (T_sim set
 by default)

---
 HARK/simulation/monte_carlo.py | 16 +---------------
 1 file changed, 1 insertion(+), 15 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 63f719b0f..cd84c5523 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -189,13 +189,7 @@ def initialize_sim(self):
         -------
         None
         """
-        if not hasattr(self, "T_sim"):
-            raise Exception(
-                "To initialize simulation variables it is necessary to first "
-                + "set the attribute T_sim to the largest number of observations "
-                + "you plan to simulate for each agent including re-births."
-            )
-        elif self.T_sim <= 0:
+        if self.T_sim <= 0:
             raise Exception(
                 "T_sim represents the largest number of observations "
                 + "that can be simulated for an agent, and must be a positive number."
@@ -437,14 +431,6 @@ def simulate(self, sim_periods=None):
                 "It seems that the simulation variables were not initialize before calling "
                 + "simulate(). Call initialize_sim() to initialize the variables before calling simulate() again."
             )
-
-        if not hasattr(self, "T_sim"):
-            raise Exception(
-                "This agent type instance must have the attribute T_sim set to a positive integer."
-                + "Set T_sim to match the largest dataset you might simulate, and run this agent's"
-                + "initalizeSim() method before running simulate() again."
-            )
-
         if sim_periods is not None and self.T_sim < sim_periods:
             raise Exception(
                 "To simulate, sim_periods has to be larger than the maximum data set size "

From 4ea021aa3a15aede18efd82fe7258d77f378dab4 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Thu, 27 Jul 2023 10:10:19 -0400
Subject: [PATCH 11/37] remove sim_death from MonteCarloSimulator, just use
 'live' state

---
 HARK/simulation/monte_carlo.py      | 45 +++++++++--------------------
 HARK/simulation/test_monte_carlo.py | 11 ++++---
 2 files changed, 21 insertions(+), 35 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index cd84c5523..41d202db9 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -43,7 +43,10 @@ def draw_shocks(
 
     for shock_var in shocks:
         shock = shocks[shock_var]
-        if isinstance(shock, Aggregate):
+
+        if isinstance(shock, (int, float)):
+            draws[shock_var] = np.ones(len(conditions)) * shock
+        elif isinstance(shock, Aggregate):
             draws[shock_var] = shock.dist.draw(1)[0]
         elif isinstance(shock, IndexDistribution) \
             or isinstance(shock, TimeVaryingDiscreteDistribution):
@@ -321,10 +324,15 @@ def make_shock_history(self):
     def get_mortality(self):
         """
         Simulates mortality or agent turnover according to some model-specific rules named sim_death
-        and sim_birth (methods of an AgentType subclass).  sim_death takes no arguments and returns
+        and sim_birth (methods of an AgentType subclass).
+
+        Agents die when their states `live` is less than or equal to zero.
+
         a Boolean array of size agent_count, indicating which agents of this type have "died" and
-        must be replaced.  sim_birth takes such a Boolean array as an argument and generates initial
-        post-decision states for those agent indices.
+        must be replaced.
+
+        sim_birth takes such a Boolean array as an argument and generates initial
+        states for those agent indices.
 
         Parameters
         ----------
@@ -334,13 +342,13 @@ def get_mortality(self):
         -------
         None
         """
-        who_dies = self.sim_death()
+        who_dies = self.vars_now['live'] <= 0
 
         if self.read_shocks:
             # Instead of simulating births, assign the saved newborn initial conditions
             if np.sum(who_dies) > 0:
                 for var_name in self.initial:
-                    self.state_now[var_name][
+                    self.vars_now[var_name][
                         who_dies
                     ] = self.newborn_init_history[var_name][
                         self.t_sim, who_dies
@@ -355,31 +363,6 @@ def get_mortality(self):
         self.who_dies = who_dies
         return None
 
-    def sim_death(self):
-        """
-        # TODO: This should mainly just track the 'who_dies' var, which can be a shock or endogenous.
-
-        Determines which agents in the current population "die" or should be replaced.  Takes no
-        inputs, returns a Boolean array of size self.agent_count, which has True for agents who die
-        and False for those that survive. Returns all False by default, must be overwritten by a
-        subclass to have replacement events.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        who_dies : np.array
-            Boolean array of size self.agent_count indicating which agents die and are replaced.
-        """
-
-        #if self.read_shocks:
-        #    who_dies = self.shock_history["who_dies"][self.t_sim, :]
-
-        who_dies = np.zeros(self.agent_count, dtype=bool)
-        return who_dies
-
     def sim_birth(self, which_agents):
         """
         Makes new agents for the simulation.  Takes a boolean array as an input, indicating which
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index 4cc05f032..12ab20cbb 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -3,7 +3,7 @@
 """
 import unittest
 
-from HARK.distribution import MeanOneLogNormal, IndexDistribution
+from HARK.distribution import Bernoulli, MeanOneLogNormal, IndexDistribution
 from HARK.simulation.monte_carlo import *
 
 cons_shocks = {
@@ -13,7 +13,8 @@
         {
             'sigma' : [1.0, 1.1]
         }),
-    'theta' : MeanOneLogNormal(1)
+    'theta' : MeanOneLogNormal(1),
+    'live' : Bernoulli(p=0.98)
 }
 
 cons_pre = {
@@ -61,11 +62,13 @@ def setUp(self):
             ## TODO: Add an aggregate shock
             ## TODO: Add a time varying shock.
             'theta' : MeanOneLogNormal(1),
-            'agg_R' : Aggregate(MeanOneLogNormal(1))
+            'agg_R' : Aggregate(MeanOneLogNormal(1)),
+            'live' : Bernoulli(p=0.98)
         }
 
         self.initial = {
-            'a' : MeanOneLogNormal(1)
+            'a' : MeanOneLogNormal(1),
+            'live' : 1
         }
 
         self.parameters = { # TODO

From 411703ab73ed8ca15f715ff4c95ac87969bb705c Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Thu, 10 Aug 2023 10:09:07 -0400
Subject: [PATCH 12/37] use age-varying parameters in MonteCarloSimulator

---
 HARK/simulation/monte_carlo.py | 45 +++++++++++++++++++++++++++++++---
 1 file changed, 41 insertions(+), 4 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 41d202db9..43288a401 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -97,6 +97,45 @@ def simulate_dynamics(
 
     return vals
 
+def parameters_by_age(ages, parameters):
+    """
+    Returns parameters for this model, but with vectorized
+    values which map age-varying values to agent ages.
+
+    Parameters
+    ----------
+    ages: np.array
+        An array of agent ages.
+
+    parameters: dict
+        A parameters dictionary
+
+    Returns
+    --------
+    aged_parameters: dict
+        A dictionary of parameter values.
+        If a parameter is age-varying, the value is a vector
+        corresponding to the values for each input age.
+    """
+    def aged_param(ages, p_value):
+        if isinstance(p_value, float) or isinstance(p_value, int):
+            return p_value
+        elif isinstance(p_value, list) and len(p_value) > 1:
+            pv_array = np.array(p_value)
+            return np.apply_along_axis(
+                lambda a: pv_array[a],
+                0,
+                ages
+            )
+        else:
+            return np.empty(ages.size)
+
+    return {
+        p : aged_param(ages, parameters[p])
+        for p
+        in parameters
+    }
+
 class Simulator():
     pass
 
@@ -207,8 +246,6 @@ def initialize_sim(self):
             if self.vars_now[var] is None:
                 self.vars_now[var] = copy(blank_array)
 
-            # elif self.state_prev[var] is None:
-            #    self.state_prev[var] = copy(blank_array)
         self.t_age = np.zeros(
             self.agent_count, dtype=int
         )  # Number of periods since agent entry
@@ -268,8 +305,8 @@ def sim_one_period(self):
             ### BIG CHANGES HERE from HARK.core.AgentType
             shocks_now = draw_shocks(self.shocks, self.t_age)
 
-        # maybe need to time index the parameters here somehow?
-        pre = copy(self.parameters)
+        pre = parameters_by_age(self.t_age, self.parameters)
+
         pre.update(self.vars_prev)
         pre.update(shocks_now)
         #Won't work for 3.8: self.parameters | self.vars_prev | shocks_now

From ea3e7b18cf349a74c7e122ddb909749992ba80d0 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 28 Aug 2023 17:00:09 -0400
Subject: [PATCH 13/37] age-varying decision rules in generic monte carlo

---
 HARK/simulation/monte_carlo.py      | 30 ++++++++---
 HARK/simulation/test_monte_carlo.py | 81 ++++++++++++++++++-----------
 2 files changed, 75 insertions(+), 36 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 43288a401..16634547a 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -88,10 +88,24 @@ def simulate_dynamics(
         feq = dynamics[varn]
 
         if isinstance(feq, Control):
-            vals[varn] = dr[varn](*[
-                vals[var]
-                for var 
-                in signature(dr[varn]).parameters]) # TODO: test for signature match with Control
+            # This tests if the decision rule is age varying.
+            # If it is, this will be a vector with the decision rule for each agent.
+            if isinstance(dr[varn], np.ndarray):
+                ## Now we have to loop through each agent, and apply the decision rule.
+                ## This is quite slow.
+                for i in range(dr[varn].size):
+                    vals_i = {var : vals[var][i] if isinstance(vals[var], np.ndarray) else vals[var]
+                              for var in vals
+                              }
+                    vals[varn][i] = dr[varn][i](*[
+                        vals_i[var]
+                        for var
+                        in signature(dr[varn][i]).parameters])
+            else:
+                vals[varn] = dr[varn](*[
+                    vals[var]
+                    for var
+                    in signature(dr[varn]).parameters]) # TODO: test for signature match with Control
         else:
             vals[varn] = feq(*[vals[var] for var in signature(feq).parameters])
 
@@ -118,10 +132,11 @@ def parameters_by_age(ages, parameters):
         corresponding to the values for each input age.
     """
     def aged_param(ages, p_value):
-        if isinstance(p_value, float) or isinstance(p_value, int):
+        if isinstance(p_value, (float, int)) or callable(p_value):
             return p_value
         elif isinstance(p_value, list) and len(p_value) > 1:
             pv_array = np.array(p_value)
+
             return np.apply_along_axis(
                 lambda a: pv_array[a],
                 0,
@@ -310,8 +325,11 @@ def sim_one_period(self):
         pre.update(self.vars_prev)
         pre.update(shocks_now)
         #Won't work for 3.8: self.parameters | self.vars_prev | shocks_now
+
+        # Age-varying decision rules captured here
+        dr = parameters_by_age(self.t_age, self.dr)
         
-        post = simulate_dynamics(self.dynamics, pre, self.dr)
+        post = simulate_dynamics(self.dynamics, pre, dr)
         
         self.vars_now = post
         ### BIG CHANGES HERE
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index 12ab20cbb..f0df92a3f 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -59,8 +59,6 @@ class test_AgentTypeMonteCarloSimulator(unittest.TestCase):
     def setUp(self):
 
         self.shocks = {
-            ## TODO: Add an aggregate shock
-            ## TODO: Add a time varying shock.
             'theta' : MeanOneLogNormal(1),
             'agg_R' : Aggregate(MeanOneLogNormal(1)),
             'live' : Bernoulli(p=0.98)
@@ -126,33 +124,56 @@ def test_make_shock_history(self):
         self.assertTrue(
             np.all(self.simulator.history['theta'] == shocks_1['theta'])
             )
+        
 
+class test_AgentTypeMonteCarloSimulatorAgeVariance(unittest.TestCase):
+    def setUp(self):
+
+        self.shocks = {
+            'theta' : MeanOneLogNormal(1),
+            'agg_R' : Aggregate(MeanOneLogNormal(1)),
+            'live' : Bernoulli(p=0.98),
+            'psi' : IndexDistribution(
+                MeanOneLogNormal,
+                {
+                'sigma' : [1.0, 1.1]
+                })
+        }
+
+        self.initial = {
+            'a' : MeanOneLogNormal(1),
+            'live' : 1
+        }
+
+        self.parameters = { # TODO
+            'G' : 1.05,
+        }
+
+        self.dynamics = {
+            'b' : lambda agg_R, G, a : agg_R * G * a,
+            'm' : lambda b, theta : b + theta,
+            'c' : Control(['m']),
+            'a' : lambda m, c : m - c
+        }
+
+        self.dr = {
+            'c' : [lambda m : m * 0.5, lambda m : m * 0.9]
+        }
+
+    def test_simulate(self):
+        self.simulator = AgentTypeMonteCarloSimulator(
+            self.parameters,
+            self.shocks,
+            self.dynamics,
+            self.dr,
+            self.initial,
+            agent_count = 3
+        )
+
+        self.simulator.initialize_sim()
+        history = self.simulator.simulate(sim_periods=2)
+
+        a1 = history['a'][1]
+        b1 = history['m'][1] - self.dr['c'][1](history['m'][1])
 
-###############################################################3
-
-'''
-init_parameters = {}
-init_parameters["PermGroFac"] = 1.05
-init_parameters["PermShkStd"] = 1.5
-init_parameters["PermShkCount"] = 5
-init_parameters["TranShkStd"] = 3.0
-init_parameters["TranShkCount"] = 5
-init_parameters["RiskyAvg"] = 1.05
-init_parameters["RiskyStd"] = 1.5
-init_parameters["RiskyCount"] = 5
-init_parameters["Rfree"] = 1.03
-
-frames_A = [
-    Frame(("bNrm",), ("aNrm",), transition=lambda Rfree, aNrm: Rfree * aNrm),
-    Frame(("mNrm",), ("bNrm", "TranShk"), transition=lambda bNrm: mNrm),
-    Frame(("cNrm"), ("mNrm",), control=True),
-    Frame(
-        ("U"),
-        ("cNrm", "CRRA"),  # Note CRRA here is a parameter not a state var
-        transition=lambda cNrm, CRRA: (CRRAutility(cNrm, CRRA),),
-        reward=True,
-        context={"CRRA": 2.0},
-    ),
-    Frame(("aNrm"), ("mNrm", "cNrm"), transition=lambda mNrm, cNrm: (mNrm - cNrm,)),
-]
-'''
\ No newline at end of file
+        self.assertTrue((a1 == b1).all())
\ No newline at end of file

From ce1fe319de80f83b6cb3263a9da46523d8f1a197 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 9 Oct 2023 18:18:43 -0400
Subject: [PATCH 14/37] updating CHANGELOG

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

diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 9ec54333c..79b0a3eb8 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -16,6 +16,7 @@ Release Date: TBD
 
 - 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
 

From 4c0850c3e29b89e8f5a33348d9b40149d7345ccc Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 9 Oct 2023 18:19:56 -0400
Subject: [PATCH 15/37] use HARK.model classes in HARK.simulate functions

---
 HARK/model.py                       | 21 ++++++++++++++++++++-
 HARK/simulation/monte_carlo.py      | 18 +-----------------
 HARK/simulation/test_monte_carlo.py |  1 +
 3 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/HARK/model.py b/HARK/model.py
index 2ea919ea6..63b20d19f 100644
--- a/HARK/model.py
+++ b/HARK/model.py
@@ -2,9 +2,28 @@
 Tools for crafting models.
 """
 
+from HARK.distribution import Distribution
+
+
+class Aggregate:
+    """
+    Used to designate a shock as an aggregate shock.
+    If so designated, draws from the shock will be scalar rather
+    than array valued.
+    """
+    def __init__(self, dist: Distribution):
+        self.dist = dist
+
+
+
 class Control:
     """
-    Should go in different model support module.
+    Used to designate a variabel that is a control variable.
+
+    Parameters
+    ----------
+    args : list of str
+        The labels of the variables that are in the information set of this control.
     """
 
     def __init__(self, args):
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 16634547a..3636a90b4 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -3,27 +3,11 @@
 """
 from copy import copy
 from HARK.distribution import Distribution, IndexDistribution, TimeVaryingDiscreteDistribution
+from HARK.model import Aggregate, Control
 from inspect import signature
 import numpy as np
 from typing import Any, Callable, Mapping, Sequence, Union
 
-class Aggregate:
-    """
-    Used to designate a shock as an aggregate shock.
-    If so designated, draws from the shock will be scalar rather
-    than array valued.
-    """
-    def __init__(self, dist: Distribution):
-        self.dist = dist
-
-class Control:
-    """
-    Should go in HARK.model
-    """
-
-    def __init__(self, args):
-        pass
-
 def draw_shocks(
         shocks: Mapping[str, Distribution],
         conditions: Sequence[int]
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index f0df92a3f..bf999b008 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -4,6 +4,7 @@
 import unittest
 
 from HARK.distribution import Bernoulli, MeanOneLogNormal, IndexDistribution
+from HARK.model import Aggregate, Control
 from HARK.simulation.monte_carlo import *
 
 cons_shocks = {

From 5ef3a19186ea74033aeac01e536eeaf355b3e1a0 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 9 Oct 2023 18:21:05 -0400
Subject: [PATCH 16/37] adjust PF python model so you only need to initialize
 p, not y

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

diff --git a/HARK/models/perfect_foresight.py b/HARK/models/perfect_foresight.py
index cca33e2ed..6ae8b55af 100644
--- a/HARK/models/perfect_foresight.py
+++ b/HARK/models/perfect_foresight.py
@@ -20,9 +20,9 @@
         'BoroCnstArt' : None,
     },
     'dynamics' : {
+        'y' : lambda p : p,
         'm' : lambda Rfree, a, y : Rfree * a + y,
         'c' : Control(['m']),
-        'y' : lambda p : p,
         'p' : lambda PermGroFac, p: PermGroFac * p,
         'a' : lambda m, c : m - c
     },

From 2cc60bf9f6ef1b919f526cde47bcac6999535558 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 9 Oct 2023 18:22:15 -0400
Subject: [PATCH 17/37] when creating new data arrays for variables NOW in
 generic monte carlo, set to nan to avoid confusion

---
 HARK/simulation/monte_carlo.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 3636a90b4..acfa93578 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -291,6 +291,7 @@ def sim_one_period(self):
 
             if isinstance(self.vars_now[var], np.ndarray):
                 self.vars_now[var] = np.empty(self.agent_count)
+                self.vars_now[var][:] = np.nan
             else:
                 # Probably an aggregate variable. It may be getting set by the Market.
                 pass

From b95b0f4a20092651481413cf400203040d055137 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 9 Oct 2023 18:23:31 -0400
Subject: [PATCH 18/37] adding example notebook for comparing HARK PF and
 Generic Monte Carlo

---
 ...eneric Monte Carlo Perfect Foresight.ipynb | 414 ++++++++++++++++++
 1 file changed, 414 insertions(+)
 create mode 100644 examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb

diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
new file mode 100644
index 000000000..8a547fca3
--- /dev/null
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
@@ -0,0 +1,414 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "be704ca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0698156",
+   "metadata": {},
+   "source": [
+    "## Original Perfect Foresight Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e0f219ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample = PerfForesightConsumerType()\n",
+    "PFexample.cycles = 0\n",
+    "PFexample.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "83e6f76e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SimulationParams = {\n",
+    "    \"AgentCount\": 10000,  # Number of agents of this type\n",
+    "    \"T_sim\": 120,  # Number of periods to simulate\n",
+    "    \"aNrmInitMean\": -6.0,  # Mean of log initial assets\n",
+    "    \"aNrmInitStd\": 1.0,  # Standard deviation of log initial assets\n",
+    "    \"pLvlInitMean\": 0.0,  # Mean of log initial permanent income\n",
+    "    \"pLvlInitStd\": 0.0,  # Standard deviation of log initial permanent income\n",
+    "    \"PermGroFacAgg\": 1.0,  # Aggregate permanent income growth factor\n",
+    "    \"T_age\": None,  # Age after which simulated agents are automatically killed\n",
+    "}\n",
+    "\n",
+    "PFexample.assign_parameters(**SimulationParams)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "66cc08fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mNrm': array([[  1.00095989,   1.00282396,   1.00111932, ...,   1.00185921,\n",
+       "           1.00044451,   1.00448108],\n",
+       "        [ -0.30491013,  -0.30309332,  -0.30475474, ...,  -0.30403362,\n",
+       "          -0.30541244,  -0.30147822],\n",
+       "        [ -1.57766816,  -1.57589742,  -1.57751671, ...,  -1.57681387,\n",
+       "          -1.57815773,  -1.57432327],\n",
+       "        ...,\n",
+       "        [-21.97118426,  -9.62717581, -36.59971086, ...,   1.00376132,\n",
+       "          -2.81712554, -46.01045686],\n",
+       "        [-22.69456637, -10.66355594,   1.00072865, ...,  -0.30217974,\n",
+       "          -4.02618315, -46.12429267],\n",
+       "        [-23.39960622, -11.67365734,  -0.30513551, ...,  -1.575007  ,\n",
+       "          -5.20458357, -46.23524203]])}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.track_vars = [\"mNrm\"]\n",
+    "PFexample.initialize_sim()\n",
+    "PFexample.simulate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b126cc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ead3ec8",
+   "metadata": {},
+   "source": [
+    "## Using the Generic Monte Carlo Simulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "adfbe431",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.distribution import Lognormal\n",
+    "import HARK.models.perfect_foresight as pf\n",
+    "from HARK.simulation.monte_carlo import AgentTypeMonteCarloSimulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5a0c394b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7f86ff5f3cd0>},\n",
+       " 'parameters': {'DiscFac': 0.96,\n",
+       "  'CRRA': (2.0,),\n",
+       "  'Rfree': 1.03,\n",
+       "  'LivPrb': 0.98,\n",
+       "  'PermGroFac': 1.01,\n",
+       "  'BoroCnstArt': None},\n",
+       " 'dynamics': {'y': <function HARK.models.perfect_foresight.<lambda>(p)>,\n",
+       "  'm': <function HARK.models.perfect_foresight.<lambda>(Rfree, a, y)>,\n",
+       "  'c': <HARK.model.Control at 0x7f86ff4d25f0>,\n",
+       "  'p': <function HARK.models.perfect_foresight.<lambda>(PermGroFac, p)>,\n",
+       "  'a': <function HARK.models.perfect_foresight.<lambda>(m, c)>},\n",
+       " 'reward': {'u': <function HARK.models.perfect_foresight.<lambda>(c)>}}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pf.model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e8201f3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.6790219804335322"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.solution[0].cFunc(10).tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7c65d4cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(2.67902198)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## getting the decision rule from the original Perfect Foresight model\n",
+    "PFexample.solution[0].cFunc(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e9d068bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pf_simulator = AgentTypeMonteCarloSimulator(\n",
+    "    pf.model['parameters'],\n",
+    "    pf.model['shocks'],\n",
+    "    pf.model['dynamics'],\n",
+    "    {\n",
+    "        'c' : lambda m: PFexample.solution[0].cFunc(m)\n",
+    "    },\n",
+    "    { # initial states\n",
+    "        'a' : Lognormal(-6, 1),\n",
+    "        'live' : 1,\n",
+    "        'p' : 1.0\n",
+    "    },\n",
+    "    agent_count = 10000,\n",
+    "    T_sim = 120\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "65df3a7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'live': array([[1., 1., 1., ..., 1., 1., 1.],\n",
+       "        [1., 1., 1., ..., 1., 1., 1.],\n",
+       "        [1., 1., 1., ..., 1., 1., 1.],\n",
+       "        ...,\n",
+       "        [1., 1., 1., ..., 1., 1., 1.],\n",
+       "        [1., 1., 1., ..., 1., 1., 1.],\n",
+       "        [1., 1., 1., ..., 1., 1., 1.]]),\n",
+       " 'y': array([[1.        , 1.        , 1.        , ..., 1.        , 1.        ,\n",
+       "         1.        ],\n",
+       "        [1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
+       "         1.01      ],\n",
+       "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
+       "         1.0201    ],\n",
+       "        ...,\n",
+       "        [1.08285671, 1.34784892, 1.69446581, ..., 3.203323  , 2.10912847,\n",
+       "         3.203323  ],\n",
+       "        [1.09368527, 1.3613274 , 1.71141047, ..., 3.23535623, 2.13021975,\n",
+       "         3.23535623],\n",
+       "        [1.10462213, 1.37494068, 1.72852457, ..., 3.2677098 , 2.15152195,\n",
+       "         3.2677098 ]]),\n",
+       " 'm': array([[  1.00289517,   1.00223716,   1.00484398, ...,   1.00168362,\n",
+       "           1.00071965,   1.00716025],\n",
+       "        [ -0.30605416,  -0.3067019 ,  -0.30413577, ...,  -0.3072468 ,\n",
+       "          -0.30819572,  -0.30185566],\n",
+       "        [ -1.58447101,  -1.58510864,  -1.58258257, ...,  -1.58564503,\n",
+       "          -1.58657914,  -1.58033805],\n",
+       "        ...,\n",
+       "        [ -8.63499874, -26.33490532, -33.32979358, ...,  -3.90476234,\n",
+       "         -30.84337272,  -3.90389321],\n",
+       "        [ -9.70981688, -26.86578873, -33.40140489, ...,  -2.91174772,\n",
+       "         -30.53498736,  -2.91089216],\n",
+       "        [-10.75692043, -27.37477187, -33.45478426, ...,  -1.90188034,\n",
+       "         -30.21011365,  -1.90103813]]),\n",
+       " 'c': array([[2.28061766, 2.28058852, 2.28070395, ..., 2.28056401, 2.28052132,\n",
+       "         2.28080652],\n",
+       "        [2.22265556, 2.22262688, 2.22274051, ..., 2.22260275, 2.22256073,\n",
+       "         2.22284147],\n",
+       "        [2.16604548, 2.16601725, 2.1661291 , ..., 2.16599349, 2.16595213,\n",
+       "         2.1662285 ],\n",
+       "        ...,\n",
+       "        [1.8538383 , 1.07006181, 0.76031842, ..., 2.06329975, 0.87042059,\n",
+       "         2.06333824],\n",
+       "        [1.80624386, 1.04655355, 0.75714737, ..., 2.10727182, 0.88407633,\n",
+       "         2.10730971],\n",
+       "        [1.75987666, 1.02401507, 0.75478366, ..., 2.15199016, 0.89846219,\n",
+       "         2.15202745]]),\n",
+       " 'p': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
+       "         1.01      ],\n",
+       "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
+       "         1.0201    ],\n",
+       "        [1.030301  , 1.030301  , 1.030301  , ..., 1.030301  , 1.030301  ,\n",
+       "         1.030301  ],\n",
+       "        ...,\n",
+       "        [1.09368527, 1.3613274 , 1.71141047, ..., 3.23535623, 2.13021975,\n",
+       "         3.23535623],\n",
+       "        [1.10462213, 1.37494068, 1.72852457, ..., 3.2677098 , 2.15152195,\n",
+       "         3.2677098 ],\n",
+       "        [1.11566835, 1.38869009, 1.74580982, ..., 3.30038689, 2.17303717,\n",
+       "         3.30038689]]),\n",
+       " 'a': array([[ -1.27772249,  -1.27835136,  -1.27585997, ...,  -1.27888039,\n",
+       "          -1.27980167,  -1.27364627],\n",
+       "        [ -2.52870972,  -2.52932877,  -2.52687628, ...,  -2.52984955,\n",
+       "          -2.53075645,  -2.52469714],\n",
+       "        [ -3.75051649,  -3.75112588,  -3.74871167, ...,  -3.75163853,\n",
+       "          -3.75253127,  -3.74656654],\n",
+       "        ...,\n",
+       "        [-10.48883704, -27.40496712, -34.090112  , ...,  -5.96806209,\n",
+       "         -31.71379332,  -5.96723145],\n",
+       "        [-11.51606074, -27.91234228, -34.15855226, ...,  -5.01901955,\n",
+       "         -31.41906369,  -5.01820187],\n",
+       "        [-12.51679709, -28.39878694, -34.20956791, ...,  -4.05387049,\n",
+       "         -31.10857584,  -4.05306558]])}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#pf_simulator.track_vars = [\"mNrm\"]\n",
+    "pf_simulator.initialize_sim()\n",
+    "pf_simulator.simulate(sim_periods=120)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9e2c7ad0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(pf_simulator.history['m'], axis=1))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "464f19e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f86f968eb60>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(pf_simulator.history['live'].sum(axis=1))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hark-env",
+   "language": "python",
+   "name": "hark-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0c5296370af638e82f199e80d24ad3abfbc288b5 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Wed, 11 Oct 2023 11:25:59 -0400
Subject: [PATCH 19/37] adding python model configuration for normalized
 perfect foresight; generic monte carlo example shows exact match

---
 HARK/models/__init__.py                       |   0
 HARK/models/perfect_foresight_normalized.py   |  33 ++
 ...eneric Monte Carlo Perfect Foresight.ipynb | 505 ++++++++++++------
 3 files changed, 384 insertions(+), 154 deletions(-)
 create mode 100644 HARK/models/__init__.py
 create mode 100644 HARK/models/perfect_foresight_normalized.py

diff --git a/HARK/models/__init__.py b/HARK/models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/HARK/models/perfect_foresight_normalized.py b/HARK/models/perfect_foresight_normalized.py
new file mode 100644
index 000000000..22211db45
--- /dev/null
+++ b/HARK/models/perfect_foresight_normalized.py
@@ -0,0 +1,33 @@
+from HARK.distribution import Bernoulli
+from HARK.model import Control
+
+# This way of distributing parameters across the scope is clunky
+# Can be handled better if parsed from a YAML file, probably
+# But it would be better to have a more graceful Python version as well.
+CRRA = 2.0,
+LivPrb = 0.98
+
+model = {
+    'shocks' : {
+        'live' : Bernoulli(p=LivPrb),
+    },
+    'parameters' : {
+        'DiscFac' : 0.96,
+        'CRRA' : CRRA,
+        'Rfree' : 1.03,
+        'LivPrb' : LivPrb,
+        'PermGroFac' : 1.01,
+        'BoroCnstArt' : None,
+    },
+    'dynamics' : {
+        'p' : lambda PermGroFac, p: PermGroFac * p,
+        'r_eff' : lambda Rfree, PermGroFac : Rfree / PermGroFac,
+        'b_nrm' : lambda r_eff, a_nrm: r_eff * a_nrm,
+        'm_nrm' : lambda b_nrm: b_nrm + 1,
+        'c_nrm' : Control(['m_nrm']),
+        'a_nrm' : lambda m_nrm, c_nrm : m_nrm - c_nrm
+    },
+    'reward' : {
+        'u' : lambda c : c ** (1 - CRRA) / (1 - CRRA)
+    }
+}
\ No newline at end of file
diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
index 8a547fca3..5583edc51 100644
--- a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
@@ -2,12 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 50,
    "id": "be704ca8",
    "metadata": {},
    "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"
@@ -23,32 +24,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e0f219ec",
+   "execution_count": 13,
+   "id": "83e6f76e",
    "metadata": {},
    "outputs": [],
    "source": [
     "PFexample = PerfForesightConsumerType()\n",
     "PFexample.cycles = 0\n",
-    "PFexample.solve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "83e6f76e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "\n",
     "SimulationParams = {\n",
     "    \"AgentCount\": 10000,  # Number of agents of this type\n",
     "    \"T_sim\": 120,  # Number of periods to simulate\n",
     "    \"aNrmInitMean\": -6.0,  # Mean of log initial assets\n",
-    "    \"aNrmInitStd\": 1.0,  # Standard deviation of log initial assets\n",
+    "    \"aNrmInitStd\": 0, #1.0,  # Standard deviation of log initial assets\n",
     "    \"pLvlInitMean\": 0.0,  # Mean of log initial permanent income\n",
     "    \"pLvlInitStd\": 0.0,  # Standard deviation of log initial permanent income\n",
     "    \"PermGroFacAgg\": 1.0,  # Aggregate permanent income growth factor\n",
-    "    \"T_age\": None,  # Age after which simulated agents are automatically killed\n",
+    "    \"T_age\": None,  # Age after which simulated agents are automatically killed,\n",
+    "    \"LivPrb\": [1.0] # [0.98]\n",
     "}\n",
     "\n",
     "PFexample.assign_parameters(**SimulationParams)"
@@ -56,48 +49,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
+   "id": "e0f219ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "PFexample.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "312a516e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType object at 0x7fa6f7367220.\n",
+       "Parameters:\n",
+       "cycles: 1\n",
+       "CRRA: 2.0\n",
+       "Rfree: 1.03\n",
+       "DiscFac: 0.96\n",
+       "LivPrb: [1.0]\n",
+       "PermGroFac: [1.01]\n",
+       "BoroCnstArt: None\n",
+       "MaxKinks: 400\n",
+       "AgentCount: 10000\n",
+       "aNrmInitMean: -6.0\n",
+       "aNrmInitStd: 0\n",
+       "pLvlInitMean: 0.0\n",
+       "pLvlInitStd: 0.0\n",
+       "PermGroFacAgg: 1.0\n",
+       "T_age: None\n",
+       "T_cycle: 1\n",
+       "PerfMITShk: False\n",
+       "T_sim: 120>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
    "id": "66cc08fb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'mNrm': array([[  1.00095989,   1.00282396,   1.00111932, ...,   1.00185921,\n",
-       "           1.00044451,   1.00448108],\n",
-       "        [ -0.30491013,  -0.30309332,  -0.30475474, ...,  -0.30403362,\n",
-       "          -0.30541244,  -0.30147822],\n",
-       "        [ -1.57766816,  -1.57589742,  -1.57751671, ...,  -1.57681387,\n",
-       "          -1.57815773,  -1.57432327],\n",
+       "{'mNrm': array([[  1.00252784,   1.00252784,   1.00252784, ...,   1.00252784,\n",
+       "           1.00252784,   1.00252784],\n",
+       "        [  0.20624094,   0.20624094,   0.20624094, ...,   0.20624094,\n",
+       "           0.20624094,   0.20624094],\n",
+       "        [ -0.57773444,  -0.57773444,  -0.57773444, ...,  -0.57773444,\n",
+       "          -0.57773444,  -0.57773444],\n",
+       "        ...,\n",
+       "        [-42.18087615, -42.18087615, -42.18087615, ..., -42.18087615,\n",
+       "         -42.18087615, -42.18087615],\n",
+       "        [-42.30949766, -42.30949766, -42.30949766, ..., -42.30949766,\n",
+       "         -42.30949766, -42.30949766],\n",
+       "        [-42.43613053, -42.43613053, -42.43613053, ..., -42.43613053,\n",
+       "         -42.43613053, -42.43613053]]),\n",
+       " 'pLvl': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
+       "         1.01      ],\n",
+       "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
+       "         1.0201    ],\n",
+       "        [1.030301  , 1.030301  , 1.030301  , ..., 1.030301  , 1.030301  ,\n",
+       "         1.030301  ],\n",
        "        ...,\n",
-       "        [-21.97118426,  -9.62717581, -36.59971086, ...,   1.00376132,\n",
-       "          -2.81712554, -46.01045686],\n",
-       "        [-22.69456637, -10.66355594,   1.00072865, ...,  -0.30217974,\n",
-       "          -4.02618315, -46.12429267],\n",
-       "        [-23.39960622, -11.67365734,  -0.30513551, ...,  -1.575007  ,\n",
-       "          -5.20458357, -46.23524203]])}"
+       "        [3.23535623, 3.23535623, 3.23535623, ..., 3.23535623, 3.23535623,\n",
+       "         3.23535623],\n",
+       "        [3.2677098 , 3.2677098 , 3.2677098 , ..., 3.2677098 , 3.2677098 ,\n",
+       "         3.2677098 ],\n",
+       "        [3.30038689, 3.30038689, 3.30038689, ..., 3.30038689, 3.30038689,\n",
+       "         3.30038689]])}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "PFexample.track_vars = [\"mNrm\"]\n",
+    "PFexample.track_vars = [\"mNrm\",\"pLvl\"]\n",
     "PFexample.initialize_sim()\n",
     "PFexample.simulate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 63,
    "id": "3b126cc4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -115,6 +172,66 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "e0e3ac8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(PFexample.history[\"mNrm\"] * PFexample.history[\"pLvl\"], axis=1))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d4a181a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_11254/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "  plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0ead3ec8",
@@ -125,108 +242,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 70,
    "id": "adfbe431",
    "metadata": {},
    "outputs": [],
    "source": [
     "from HARK.distribution import Lognormal\n",
-    "import HARK.models.perfect_foresight as pf\n",
+    "import HARK.models.perfect_foresight_normalized as pfn\n",
     "from HARK.simulation.monte_carlo import AgentTypeMonteCarloSimulator"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 72,
    "id": "5a0c394b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7f86ff5f3cd0>},\n",
+       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7fa6f5a29030>},\n",
        " 'parameters': {'DiscFac': 0.96,\n",
        "  'CRRA': (2.0,),\n",
        "  'Rfree': 1.03,\n",
        "  'LivPrb': 0.98,\n",
        "  'PermGroFac': 1.01,\n",
        "  'BoroCnstArt': None},\n",
-       " 'dynamics': {'y': <function HARK.models.perfect_foresight.<lambda>(p)>,\n",
-       "  'm': <function HARK.models.perfect_foresight.<lambda>(Rfree, a, y)>,\n",
-       "  'c': <HARK.model.Control at 0x7f86ff4d25f0>,\n",
-       "  'p': <function HARK.models.perfect_foresight.<lambda>(PermGroFac, p)>,\n",
-       "  'a': <function HARK.models.perfect_foresight.<lambda>(m, c)>},\n",
-       " 'reward': {'u': <function HARK.models.perfect_foresight.<lambda>(c)>}}"
+       " 'dynamics': {'p': <function HARK.models.perfect_foresight_normalized.<lambda>(PermGroFac, p)>,\n",
+       "  'r_eff': <function HARK.models.perfect_foresight_normalized.<lambda>(Rfree, PermGroFac)>,\n",
+       "  'b_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(r_eff, a_nrm)>,\n",
+       "  'm_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(b_nrm)>,\n",
+       "  'c_nrm': <HARK.model.Control at 0x7fa6f6902380>,\n",
+       "  'a_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(m_nrm, c_nrm)>},\n",
+       " 'reward': {'u': <function HARK.models.perfect_foresight_normalized.<lambda>(c)>}}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pf.model"
+    "pfn.model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "e8201f3e",
+   "execution_count": 73,
+   "id": "08164ab2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6790219804335322"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PFexample.solution[0].cFunc(10).tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7c65d4cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(2.67902198)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "## getting the decision rule from the original Perfect Foresight model\n",
-    "PFexample.solution[0].cFunc(10)"
+    "pfn.model['parameters']['LivPrb'] = 1.0\n",
+    "pfn.model['shocks'] = {'live' : Bernoulli(1.0)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 85,
    "id": "e9d068bd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "pf_simulator = AgentTypeMonteCarloSimulator(\n",
-    "    pf.model['parameters'],\n",
-    "    pf.model['shocks'],\n",
-    "    pf.model['dynamics'],\n",
+    "pfn_simulator = AgentTypeMonteCarloSimulator(\n",
+    "    pfn.model['parameters'],\n",
+    "    pfn.model['shocks'],\n",
+    "    pfn.model['dynamics'],\n",
     "    {\n",
-    "        'c' : lambda m: PFexample.solution[0].cFunc(m)\n",
+    "        'c_nrm' : lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)\n",
     "    },\n",
     "    { # initial states\n",
-    "        'a' : Lognormal(-6, 1),\n",
+    "        'a_nrm' : Lognormal(-6, 0),\n",
     "        'live' : 1,\n",
     "        'p' : 1.0\n",
     "    },\n",
@@ -238,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 86,
    "id": "65df3a7f",
    "metadata": {},
    "outputs": [
@@ -252,45 +338,6 @@
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.]]),\n",
-       " 'y': array([[1.        , 1.        , 1.        , ..., 1.        , 1.        ,\n",
-       "         1.        ],\n",
-       "        [1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
-       "         1.01      ],\n",
-       "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
-       "         1.0201    ],\n",
-       "        ...,\n",
-       "        [1.08285671, 1.34784892, 1.69446581, ..., 3.203323  , 2.10912847,\n",
-       "         3.203323  ],\n",
-       "        [1.09368527, 1.3613274 , 1.71141047, ..., 3.23535623, 2.13021975,\n",
-       "         3.23535623],\n",
-       "        [1.10462213, 1.37494068, 1.72852457, ..., 3.2677098 , 2.15152195,\n",
-       "         3.2677098 ]]),\n",
-       " 'm': array([[  1.00289517,   1.00223716,   1.00484398, ...,   1.00168362,\n",
-       "           1.00071965,   1.00716025],\n",
-       "        [ -0.30605416,  -0.3067019 ,  -0.30413577, ...,  -0.3072468 ,\n",
-       "          -0.30819572,  -0.30185566],\n",
-       "        [ -1.58447101,  -1.58510864,  -1.58258257, ...,  -1.58564503,\n",
-       "          -1.58657914,  -1.58033805],\n",
-       "        ...,\n",
-       "        [ -8.63499874, -26.33490532, -33.32979358, ...,  -3.90476234,\n",
-       "         -30.84337272,  -3.90389321],\n",
-       "        [ -9.70981688, -26.86578873, -33.40140489, ...,  -2.91174772,\n",
-       "         -30.53498736,  -2.91089216],\n",
-       "        [-10.75692043, -27.37477187, -33.45478426, ...,  -1.90188034,\n",
-       "         -30.21011365,  -1.90103813]]),\n",
-       " 'c': array([[2.28061766, 2.28058852, 2.28070395, ..., 2.28056401, 2.28052132,\n",
-       "         2.28080652],\n",
-       "        [2.22265556, 2.22262688, 2.22274051, ..., 2.22260275, 2.22256073,\n",
-       "         2.22284147],\n",
-       "        [2.16604548, 2.16601725, 2.1661291 , ..., 2.16599349, 2.16595213,\n",
-       "         2.1662285 ],\n",
-       "        ...,\n",
-       "        [1.8538383 , 1.07006181, 0.76031842, ..., 2.06329975, 0.87042059,\n",
-       "         2.06333824],\n",
-       "        [1.80624386, 1.04655355, 0.75714737, ..., 2.10727182, 0.88407633,\n",
-       "         2.10730971],\n",
-       "        [1.75987666, 1.02401507, 0.75478366, ..., 2.15199016, 0.89846219,\n",
-       "         2.15202745]]),\n",
        " 'p': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
        "         1.01      ],\n",
        "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
@@ -298,47 +345,99 @@
        "        [1.030301  , 1.030301  , 1.030301  , ..., 1.030301  , 1.030301  ,\n",
        "         1.030301  ],\n",
        "        ...,\n",
-       "        [1.09368527, 1.3613274 , 1.71141047, ..., 3.23535623, 2.13021975,\n",
+       "        [3.23535623, 3.23535623, 3.23535623, ..., 3.23535623, 3.23535623,\n",
        "         3.23535623],\n",
-       "        [1.10462213, 1.37494068, 1.72852457, ..., 3.2677098 , 2.15152195,\n",
+       "        [3.2677098 , 3.2677098 , 3.2677098 , ..., 3.2677098 , 3.2677098 ,\n",
        "         3.2677098 ],\n",
-       "        [1.11566835, 1.38869009, 1.74580982, ..., 3.30038689, 2.17303717,\n",
+       "        [3.30038689, 3.30038689, 3.30038689, ..., 3.30038689, 3.30038689,\n",
        "         3.30038689]]),\n",
-       " 'a': array([[ -1.27772249,  -1.27835136,  -1.27585997, ...,  -1.27888039,\n",
-       "          -1.27980167,  -1.27364627],\n",
-       "        [ -2.52870972,  -2.52932877,  -2.52687628, ...,  -2.52984955,\n",
-       "          -2.53075645,  -2.52469714],\n",
-       "        [ -3.75051649,  -3.75112588,  -3.74871167, ...,  -3.75163853,\n",
-       "          -3.75253127,  -3.74656654],\n",
+       " 'r_eff': array([[1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
+       "         1.01980198],\n",
+       "        [1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
+       "         1.01980198],\n",
+       "        [1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
+       "         1.01980198],\n",
+       "        ...,\n",
+       "        [1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
+       "         1.01980198],\n",
+       "        [1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
+       "         1.01980198],\n",
+       "        [1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
+       "         1.01980198]]),\n",
+       " 'b_nrm': array([[ 2.52783638e-03,  2.52783638e-03,  2.52783638e-03, ...,\n",
+       "          2.52783638e-03,  2.52783638e-03,  2.52783638e-03],\n",
+       "        [-7.93759059e-01, -7.93759059e-01, -7.93759059e-01, ...,\n",
+       "         -7.93759059e-01, -7.93759059e-01, -7.93759059e-01],\n",
+       "        [-1.57773444e+00, -1.57773444e+00, -1.57773444e+00, ...,\n",
+       "         -1.57773444e+00, -1.57773444e+00, -1.57773444e+00],\n",
+       "        ...,\n",
+       "        [-4.31808761e+01, -4.31808761e+01, -4.31808761e+01, ...,\n",
+       "         -4.31808761e+01, -4.31808761e+01, -4.31808761e+01],\n",
+       "        [-4.33094977e+01, -4.33094977e+01, -4.33094977e+01, ...,\n",
+       "         -4.33094977e+01, -4.33094977e+01, -4.33094977e+01],\n",
+       "        [-4.34361305e+01, -4.34361305e+01, -4.34361305e+01, ...,\n",
+       "         -4.34361305e+01, -4.34361305e+01, -4.34361305e+01]]),\n",
+       " 'm_nrm': array([[  1.00252784,   1.00252784,   1.00252784, ...,   1.00252784,\n",
+       "           1.00252784,   1.00252784],\n",
+       "        [  0.20624094,   0.20624094,   0.20624094, ...,   0.20624094,\n",
+       "           0.20624094,   0.20624094],\n",
+       "        [ -0.57773444,  -0.57773444,  -0.57773444, ...,  -0.57773444,\n",
+       "          -0.57773444,  -0.57773444],\n",
        "        ...,\n",
-       "        [-10.48883704, -27.40496712, -34.090112  , ...,  -5.96806209,\n",
-       "         -31.71379332,  -5.96723145],\n",
-       "        [-11.51606074, -27.91234228, -34.15855226, ...,  -5.01901955,\n",
-       "         -31.41906369,  -5.01820187],\n",
-       "        [-12.51679709, -28.39878694, -34.20956791, ...,  -4.05387049,\n",
-       "         -31.10857584,  -4.05306558]])}"
+       "        [-42.18087615, -42.18087615, -42.18087615, ..., -42.18087615,\n",
+       "         -42.18087615, -42.18087615],\n",
+       "        [-42.30949766, -42.30949766, -42.30949766, ..., -42.30949766,\n",
+       "         -42.30949766, -42.30949766],\n",
+       "        [-42.43613053, -42.43613053, -42.43613053, ..., -42.43613053,\n",
+       "         -42.43613053, -42.43613053]]),\n",
+       " 'c_nrm': array([[1.7808741 , 1.7808741 , 1.7808741 , ..., 1.7808741 , 1.7808741 ,\n",
+       "         1.7808741 ],\n",
+       "        [1.75333976, 1.75333976, 1.75333976, ..., 1.75333976, 1.75333976,\n",
+       "         1.75333976],\n",
+       "        [1.72623113, 1.72623113, 1.72623113, ..., 1.72623113, 1.72623113,\n",
+       "         1.72623113],\n",
+       "        ...,\n",
+       "        [0.28766039, 0.28766039, 0.28766039, ..., 0.28766039, 0.28766039,\n",
+       "         0.28766039],\n",
+       "        [0.28321287, 0.28321287, 0.28321287, ..., 0.28321287, 0.28321287,\n",
+       "         0.28321287],\n",
+       "        [0.2788341 , 0.2788341 , 0.2788341 , ..., 0.2788341 , 0.2788341 ,\n",
+       "         0.2788341 ]]),\n",
+       " 'a_nrm': array([[ -0.77834626,  -0.77834626,  -0.77834626, ...,  -0.77834626,\n",
+       "          -0.77834626,  -0.77834626],\n",
+       "        [ -1.54709882,  -1.54709882,  -1.54709882, ...,  -1.54709882,\n",
+       "          -1.54709882,  -1.54709882],\n",
+       "        [ -2.30396557,  -2.30396557,  -2.30396557, ...,  -2.30396557,\n",
+       "          -2.30396557,  -2.30396557],\n",
+       "        ...,\n",
+       "        [-42.46853654, -42.46853654, -42.46853654, ..., -42.46853654,\n",
+       "         -42.46853654, -42.46853654],\n",
+       "        [-42.59271052, -42.59271052, -42.59271052, ..., -42.59271052,\n",
+       "         -42.59271052, -42.59271052],\n",
+       "        [-42.71496463, -42.71496463, -42.71496463, ..., -42.71496463,\n",
+       "         -42.71496463, -42.71496463]])}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#pf_simulator.track_vars = [\"mNrm\"]\n",
-    "pf_simulator.initialize_sim()\n",
-    "pf_simulator.simulate(sim_periods=120)"
+    "pfn_simulator.initialize_sim()\n",
+    "pfn_simulator.simulate(sim_periods=120)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 87,
    "id": "9e2c7ad0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -350,7 +449,7 @@
     }
    ],
    "source": [
-    "plt.plot(np.mean(pf_simulator.history['m'], axis=1))\n",
+    "plt.plot(np.mean(pfn_simulator.history['m_nrm'], axis=1))\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -358,23 +457,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 88,
+   "id": "6b3b4811",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_11254/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "  plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
    "id": "464f19e7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f86f968eb60>]"
+       "[<matplotlib.lines.Line2D at 0x7fa6f68e2410>]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -386,8 +519,72 @@
     }
    ],
    "source": [
-    "plt.plot(pf_simulator.history['live'].sum(axis=1))"
+    "plt.plot(pfn_simulator.history['live'].sum(axis=1))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "3c721410",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(pfn_simulator.history['m_nrm'], axis=1), label = 'Generic monte carlo')\n",
+    "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1), label = 'HARK 0.13 PerfForesightConsumerType')\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "38bce0b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0.])"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(pfn_simulator.history['m_nrm'], axis=1) - np.mean(PFexample.history[\"mNrm\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c0e622a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 799de9a3462c9d00f39d66af4040bf89c20aab7a Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Tue, 17 Oct 2023 18:10:05 -0400
Subject: [PATCH 20/37] put loading initial values to newborns with read_shocks
 into sim_birth

---
 HARK/simulation/monte_carlo.py                |  32 +-
 ...eneric Monte Carlo Perfect Foresight.ipynb | 382 +++++++++++-------
 2 files changed, 256 insertions(+), 158 deletions(-)

diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index acfa93578..93b4605cd 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -384,21 +384,7 @@ def get_mortality(self):
         """
         who_dies = self.vars_now['live'] <= 0
 
-        if self.read_shocks:
-            # Instead of simulating births, assign the saved newborn initial conditions
-            if np.sum(who_dies) > 0:
-                for var_name in self.initial:
-                    self.vars_now[var_name][
-                        who_dies
-                    ] = self.newborn_init_history[var_name][
-                        self.t_sim, who_dies
-                    ]
-
-                # Reset ages of newborns
-                self.t_age[who_dies] = 0
-                self.t_cycle[who_dies] = 0
-        else:
-            self.sim_birth(who_dies)
+        self.sim_birth(who_dies)
 
         self.who_dies = who_dies
         return None
@@ -418,20 +404,26 @@ def sim_birth(self, which_agents):
         None
         """
         if self.read_shocks:
+            t = self.t_sim - 1 if self.t_sim > 0 else 0
             initial_vals = {
-                init_var: self.newborn_init_history[init_var][self.t_sim, :]
+                init_var: self.newborn_init_history[init_var][t, which_agents]
                 for init_var
                 in self.initial
-                }
+            }
+
         else:
             initial_vals = draw_shocks(
                 self.initial,
                 np.zeros(which_agents.sum())
             )
 
-        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]
+        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.t_age[which_agents] = 0
+        self.t_cycle[which_agents] = 0
 
     def simulate(self, sim_periods=None):
         """
diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
index 5583edc51..3894c3da5 100644
--- a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 1,
    "id": "be704ca8",
    "metadata": {},
    "outputs": [],
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "id": "83e6f76e",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
     "    \"pLvlInitStd\": 0.0,  # Standard deviation of log initial permanent income\n",
     "    \"PermGroFacAgg\": 1.0,  # Aggregate permanent income growth factor\n",
     "    \"T_age\": None,  # Age after which simulated agents are automatically killed,\n",
-    "    \"LivPrb\": [1.0] # [0.98]\n",
+    "    \"LivPrb\": [0.98]\n",
     "}\n",
     "\n",
     "PFexample.assign_parameters(**SimulationParams)"
@@ -49,31 +49,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "id": "e0f219ec",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "PFexample.solve()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "312a516e",
+   "execution_count": 4,
+   "id": "c3981c6d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType object at 0x7fa6f7367220.\n",
+       "<HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType object at 0x7f81cd8bae60.\n",
        "Parameters:\n",
        "cycles: 1\n",
        "CRRA: 2.0\n",
        "Rfree: 1.03\n",
        "DiscFac: 0.96\n",
-       "LivPrb: [1.0]\n",
+       "LivPrb: [0.98]\n",
        "PermGroFac: [1.01]\n",
        "BoroCnstArt: None\n",
        "MaxKinks: 400\n",
@@ -89,7 +88,7 @@
        "T_sim: 120>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -100,26 +99,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 5,
    "id": "66cc08fb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'mNrm': array([[  1.00252784,   1.00252784,   1.00252784, ...,   1.00252784,\n",
+       "{'who_dies': array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 1., 0., 0.],\n",
+       "        [0., 0., 1., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]),\n",
+       " 'mNrm': array([[  1.00252784,   1.00252784,   1.00252784, ...,   1.00252784,\n",
        "           1.00252784,   1.00252784],\n",
-       "        [  0.20624094,   0.20624094,   0.20624094, ...,   0.20624094,\n",
-       "           0.20624094,   0.20624094],\n",
-       "        [ -0.57773444,  -0.57773444,  -0.57773444, ...,  -0.57773444,\n",
-       "          -0.57773444,  -0.57773444],\n",
+       "        [ -0.30338194,  -0.30338194,  -0.30338194, ...,  -0.30338194,\n",
+       "          -0.30338194,  -0.30338194],\n",
+       "        [ -1.57617872,  -1.57617872,  -1.57617872, ...,  -1.57617872,\n",
+       "          -1.57617872,  -1.57617872],\n",
        "        ...,\n",
-       "        [-42.18087615, -42.18087615, -42.18087615, ..., -42.18087615,\n",
-       "         -42.18087615, -42.18087615],\n",
-       "        [-42.30949766, -42.30949766, -42.30949766, ..., -42.30949766,\n",
-       "         -42.30949766, -42.30949766],\n",
-       "        [-42.43613053, -42.43613053, -42.43613053, ..., -42.43613053,\n",
-       "         -42.43613053, -42.43613053]]),\n",
+       "        [-21.97112221,  -9.62650475, -36.60139796, ...,   1.00252784,\n",
+       "          -2.81670212, -46.01051785],\n",
+       "        [-22.6945059 , -10.6629019 ,   1.00252784, ...,  -0.30338194,\n",
+       "          -4.02577047, -46.12435211],\n",
+       "        [-23.39954727, -11.67301988,  -0.30338194, ...,  -1.57617872,\n",
+       "          -5.20418135, -46.23529996]]),\n",
        " 'pLvl': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
        "         1.01      ],\n",
        "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
@@ -127,34 +133,36 @@
        "        [1.030301  , 1.030301  , 1.030301  , ..., 1.030301  , 1.030301  ,\n",
        "         1.030301  ],\n",
        "        ...,\n",
-       "        [3.23535623, 3.23535623, 3.23535623, ..., 3.23535623, 3.23535623,\n",
-       "         3.23535623],\n",
-       "        [3.2677098 , 3.2677098 , 3.2677098 , ..., 3.2677098 , 3.2677098 ,\n",
-       "         3.2677098 ],\n",
-       "        [3.30038689, 3.30038689, 3.30038689, ..., 3.30038689, 3.30038689,\n",
-       "         3.30038689]])}"
+       "        [1.26973465, 1.10462213, 1.67768892, ..., 1.01      , 1.04060401,\n",
+       "         2.59927293],\n",
+       "        [1.282432  , 1.11566835, 1.01      , ..., 1.0201    , 1.05101005,\n",
+       "         2.62526565],\n",
+       "        [1.29525631, 1.12682503, 1.0201    , ..., 1.030301  , 1.06152015,\n",
+       "         2.65151831]])}"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "PFexample.track_vars = [\"mNrm\",\"pLvl\"]\n",
+    "PFexample.track_vars = [\"who_dies\",\"mNrm\",\"pLvl\"]\n",
+    "PFexample.make_shock_history()\n",
+    "\n",
     "PFexample.initialize_sim()\n",
     "PFexample.simulate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 6,
    "id": "3b126cc4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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CNSs9SGTE0ieAS1ps0kGKczO47vTBPLF4G2t3VYQdR0QkUHHY4+6z3f296GilTe6+KebJupkvnDWU7LQUfvbymrCjiIgEmlvpu2Z2L/AKUNu8090fj1mqbqhndho3TivlF6+s1YytIhK6IFcONwATgRl80KR0cQwzdVufP7OUnlmp/OiFVWFHEZFuLsiVw2R3HxnzJEJeRio3Tx/GfzyzkrfW7eV0LQgkIiEJcuXwlpmNjnkSAeCzUwfRLz+DHz6/Cve4X/1URLqoIMVhKrDYzFZHh7Eu01DW2MlITeYrHx3Bkq2HeH75zrDjiEg3FaRZaUbMU8jfuWJSf37z+gb+67lVTB9VTEZqctiRRKSbCXKH9KYjbZ0RrrtKSU7i9kvHsHl/NXe/ptnRRaTzBWlWkhCcMayQi8b35c5X17F5nyblE5HOpeIQx7590UkkJxnff3pF2FFEpJtRcYhjffMz+fK5w3l55W5eendX2HFEpBs5anEwswozKz/a1pkhu7Mbp5UyoncOt89eQXVdQ9hxRKSbOGpxcPdcd88Dfg58C+gPDAC+Cfxvp6QTUpOT+M/Lx7Ht4GF+/rJmbRWRzhGkWelSd7/T3Svcvdzdfw1cFutg8oGywQVcNbmEe//6Hit36KJNRGIvSHGoMrNrzCzZzJLM7BpAq9J0sm/OGEV+ZirffmI5TU26c1pEYitIcfgM8ClgV3S7MrrvhJnZlWa2wsyazKysxf7BZnbYzBZHt7vac5yupGd2GrdeMIqFmw7w53e2hR1HRLq4Nu+QdveNdHwz0nLgCuDuIzy33t0ndvDxuoRPnDyAh+Zt5r+eW8VHx/QmLyM17Egi0kUFWSZ0hJm9YmbLo4/Hm9m323NQd1/p7qvb8x7dUVKS8f1Lx7Kvqlad0yISU0GalX4D3ArUA7j7UuCqGGYqNbN3zOw1MzvzaC8ys5vMbIGZLdizZ08M48SXcQPyuWryQB54ayOrd2pJURGJjSDFIcvd531oX5sD7s3sZTNbfoTtWE1UO4CB7j4J+BrwkJnlHemF7n6Pu5e5e1lRUVGA0+g6vn7+SPIyUrj18aXqnBaRmAhSHPaa2VDAAczsk0S+xI/J3c9z97FH2J48xu/Uuvu+6M8LgfXAiEBn0o0UZKfxnYtHs2jzQR58W3MgikjHC1IcbibScTzKzLYBXwG+GIswZlZkZsnRn4cAwwFNS3oEl0/qz5nDC/nh86vZcehw2HFEpIsJMmX3Bnc/DygCRrn7tOgIphNmZpeb2VbgNOAZM3sh+tRZwFIzWww8BnzB3fe351hdlZnxg4+Po6GpiX97coVWjRORDhVktFKjmf03UO3uFdF9i9pzUHf/s7sPcPd0d+/t7udH9//J3ce4+0R3P9ndn2rPcbq6gb2y+Op5I3jp3V08s6zNlj4RkcCCNCutiL7uRTMriO6z2EWS4zFzWinj+ufz3SdXsL+qLuw4ItJFBCkODe7+DeBe4A0zO4Vo57SELyU5iR9fOZ7ymnq+/5TWfRCRjhGkOBiAuz8CfBr4LTAklqHk+Izqk8c/nT2MJxZv58UVO8OOIyJdQJDi8PnmH9x9OXAmcEvMEskJuXn6MMb0y+Prjy1ly34tKyoi7XOsxX7Oif44yMyuaN6Ac4HKTkkngaWlJHHnNSfT1OR86aFF1DU0hR1JRBLYsa4cPhL985IjbBfHOJecgEG9svnxleNZsvUQ//nsyrDjiEgCO+qsrO7+3eifN3ReHGmvGWP7cuMZpdz/5nucNaKQc0b1DjuSiCSgoxYHM/vasX7R3X/a8XGkI3zzgpG8tX4v33hsGS98pQe9ctLDjiQiCeZYzUq5bWwSp9JTkvnfqyZSfrieWx9fprunReS4HatZ6XudGUQ61qg+eXz9/JH84NmVPDJ/C1edOjDsSCKSQNpcCc7MMoCZwBggo3m/u98Yw1zSAWZOK+W1NXv4t9krGN0vj/EDeoQdSUQSRJD7HH4P9AHOB14DBgBaZSYBJCUZv7h6EkU56Xzh9wvZV1kbdiQRSRBBisMwd/8OUOXus4CLgCmxjSUdpSA7jbs+ewp7q+r40kPvUN+o+x9EpG1BikN99M+DZjYWyAeKYxdJOtq4Afn81+XjmLthH9/6kzqoRaRtbfY5APeYWU/gO8BsIAf4t5imkg73iVMGsOVANf/78lr65Kfz9fNHhR1JROJYm8XB3e+N/vgamnAvoX353OHsKq/lV3PWU5STzvVnlIYdSUTiVJDRSj2AzwGDW77e3TX5XoIxM/79sjHsq6zl9qfeJTs9hSvLSsKOJSJxKEifw7NECsMyYGGLTRJQSnISd3xmEmcOL+Sbf1rKU0u2hx1JROJQkD6HDHc/5lQakljSU5K559oyrrt/Hl99ZDGpycaMsX3DjiUicSTQfQ5m9g9m1tfMCpq3mCeTmMpMS+a+68sYPyCfmx96h2eWag1qEflAkOJQB/wYmMsHTUoLYhlKOkduRiqzbjyVSSU9uOXhd9TEJCLvC1Ic/oXIjXCD3b00umnUUhfRXCBOGdiTrz6ymNfW7Ak7kojEgSDFYR2gdSe7sOz0FO69vozhvXP54h8WsmTLwbAjiUjIghSHKmCxmd1tZr9o3mIdTDpXXkYqs26YTEF2Gjc8MJ/39laFHUlEQhSkODwB/AB4Cw1l7dKK8zL4/czItFnX3vc2u8prQk4kImE5ZnEws2Tgenef9eGtk/JJJystzOaBGyZzoKqOz903j0PV9W3/koh0OccsDu7eCDSZWX5HHtTMfmxmq8xsqZn9OXoXdvNzt5rZOjNbbWbnd+RxJZjxA3pw97VlbNhbyY2z5lNZ2xB2JBHpZEGalSqBZWZ2Xwf2ObwEjHX38cAa4FYAMxsNXEVkYaEZwJ3RqxfpZNOGF/LzqyaxeMtBrrt/HhU1uoIQ6U6CFIfHiczI+jod1Ofg7i+6e/M/R/9GZAEhgMuAh9291t3fIzJS6tT2HEtO3IXj+nLH1ZNYEi0Q5SoQIt1GkFlZZ5lZGjAiumu1u3fkt8SNwCPRn/sTKRbNtkb3tWJmNwE3AQwcqPWRY+XCcX1JMuNLDy3is/e+zawbTqVndlrYsUQkxtq8cjCzs4G1wK+AO4E1ZnZWgN972cyWH2G7rMVrbgMagAePN7i73+PuZe5eVlRUdLy/Lsdhxtg+3H3tKazaWcGn75nLbo1iEunygjQr/Q/wMXf/iLufRWQt6Z+19Uvufp67jz3C9iSAmV0PXAxc4x8sTbYNaDmH9IDoPgnZuSf15oHrJ7P1wGE+dfdcth08HHYkEYmhIMUh1d1XNz9w9zVAansOamYzgG8Al7p7y7uvZwNXmVm6mZUCw4F57TmWdJzThxXy+5lT2FdZx6fvnsuW/bpxXqSrClIcFpjZvWZ2dnT7De2feO+XQC7wkpktNrO7ANx9BfAo8C7wPHBzdDitxIlTBvXkwX+YQkVNA5++ey4b9lSGHUlEYsDaWmzezNKBm4Fp0V1vAHe6e22MswVWVlbmCxZootjOtGL7Ia69bx419Y3828Wj+fTkEsws7FgichzMbKG7lx3xubaKQyJQcQjH9oOH+dc/LuGt9fs476RifviJ8fTKSQ87logEdKziEGS00hlm9pKZrTGzDc1bx8eURNOvRyZ/mDmF71w8mtfX7mXGz9/gdU35LdIlBOlzuA/4KZFmpcktNhGSkoyZ00p58uYz6JGZyufun8fts1dQpSk3RBJakOJwyN2fc/fd7r6veYt5MkkoJ/XN46l/nsb1pw9m1tyNfOxnrzNn1e6wY4nICQpSHOZEJ8o7zcxObt5inkwSTkZqMrdfOobHvnAamWnJ3PDAfG5+cBE7D+mmOZFEE2S00pwj7HZ3Pyc2kY6fOqTjT21DI/e8toFfzllHSpLx1Y+O4IYzSklO0ogmkXih0UoSmi37q/nu7BX8ZdVuJgzI578/MZ6T+uaFHUtEaOdoJZH2KCnI4r7ryrjj6klsPXCYS+74K//z4mpqG3Rvo0g8U3GQmDMzLpnQj5e/9hEundiPO/6yjgt//gbzN+4PO5qIHIWKg3Santlp/PRTE5l146nU1Ddx5V1z+YffLWDVzvKwo4nIhwTqczCz04HBtFj/wd1/F7tYx0d9DomnqraB3775Hne/toHKugaumTKQb11wEjnpbS4xIiIdpF0d0mb2e2AosBhobih2d7+lI0O2h4pD4jpYXcfPX1nLA29tpG9eBv95xTjOHlkcdiyRbqG9xWElMNrjeFiTikPiW7T5AN94bCnrdlfy2akDue3C0WSmaflwkVhq72il5UCfjo0k8vdOHtiTp/95Gp+fVsof/raZi+54gwXqsBYJTZDiUAi8a2YvmNns5i3WwaT7yUhN5tsXj+bBz0+huraRT941l+vun8eSLQfDjibS7QRpVvrIkfa7+2sxSXQC1KzU9VTXNTDrrU3c/fp6DlbXM6GkB1dPLuGSCf3IVqe1SIfQHdKSsCpq6nl0wVYenreZtbsr6Zsf6bSerk5rkXZr73oOU81svplVmlmdmTWamQamS6fIzUhl5rRSXvzqWTx801Ry0lO44bfz+fofl7D94OGw44l0WUH6HH4JXA2sBTKBzwO/imUokQ8zM6YO6cXTt0zj5ulDefydbZz5oznc/NAiFqtPQqTDBbpD2t3XAcnu3ujuvwVmxDaWyJGlpyTz9fNH8eq/ns3MaaW8sWYPH//Vm/zTgwt5b29V2PFEuowgPXvVZpYGLDazHwE70LQbErKSgiz+34Unccu5w7n3jQ3c8/oGXlyxiwvG9eXqU0s4bUgvzDQ9uMiJCjJaaRCwC0gDvgrkA3dGrybigjqkZXdFDXe9uoHHFm6hvKaB4cU53HLucC4a15ckrSEhckTtHq1kZpnAQHdf3dHhOoKKgzSrqW/k6aU7uOf19azZVcnI3rlce9ogzh/Th6Lc9LDjicSV9k6fcQnwEyDN3UvNbCLwfXe/tMOTniAVB/mwxibnmWU7uOOVtazdXYkZnD60F9+acRLjBuSHHU8kLrS3OCwEzgFedfdJ0X3L3H1chyc9QSoOcjTuzupdFTy3bCcPvr2ZfVW1XDNlIF85bwSFObqSkO7tWMUhSId0vbsf+lDnXrvunDOzHwOXAHXAeuAGdz9oZoOBlUBz89Xf3P0L7TmWdG9mxqg+eYzqk8fMM0v52UtrmPXWRh6et4Xpo4q5fFJ/zhxeSG5GathRReJKkOKwwsw+AySb2XDgFuCtdh73JeBWd28wsx8CtwLfjD633t0ntvP9RVrJy0jlu5eM4Zopg3h0wRb+/M42Xnp3F8lJxsSSHkwfWcQlE/oxqFd22FFFQhekWSkLuA34GGDAC8C/u3tNhwQwuxz4pLtfE71yeNrdxx7Pe6hZSU5EQ2MT8zce4M11e3lj7R6WbD0EwIQB+VwyoR8Xje9L3/zMkFOKxE5cz61kZk8Bj7j7H6LFYQWwBigHvu3ub7T1HioO0hG2HTzMM0u3M3vJdpZvi8wQM3lwTy4a15cLx/WlOC8j5IQiHeuEikNb03K3NVrJzF7myOtA3ObuT0ZfcxtQBlzh7m5m6UCOu+8zs1OAJ4Ax7t5qLiczuwm4CWDgwIGnbNq06VhxRI7Le3ureGrJdp5ZuoPVuyowgymlBVw6oT8XjO1Dz+y0sCOKtNuJFoc9wBbg/4C3iTQpva+9U3ab2fXAPwLnunv1UV7zKvCv7n7MywJdOUgsrd1VwdNLd/DUku1s2FtFkkUWJzp7ZBEXjOvL0KKcsCOKnJATLQ7JwEeJTLo3HngG+D93X9EBgWYAPwU+4u57WuwvAva7e6OZDQHeAMa5+zGXBFNxkM7g7qzYXs6LK3YyZ/Uelm2L9FFMGtiDK08p4fJJ/bW0qSSUjrhDOp1Ikfgx8D13/2U7A60D0oF90V1/c/cvmNkngO8D9UAT8F13f6qt91NxkDDsKq/hycXbeGzhVtbsqqRnViqfnTqIz0wZqI5sSQgnXByiReEiIoVhMDAbuN/dt8Ug5wlTcZAwuTsLNh3gN69v4KWVu3CHiSU9mDG2D6eWFjCmXx7pKbqikPhzQjfBmdnvgLHAs0SuFpbHKJ9IQjMzJg8uYPLgAjbtq+LppTt4bvkO/vu5VQCkJScxsk8uI3rnMqJ3DsN75zCsKJf+PTNJ1qSAEqeO1efQBDRPkN/yRQa4u+fFOFtgunKQeLS7vIZFmw+waPNB3t1ezppdFeyuqP2712SkJpGTnsKQohzG989nSFEOyUlgGL1y0hjUK4sBPbPISNWVh3S8uL7PoSOoOEiiOFhdx7rdlazbXcmOQzUcrm+k/HA9q3dV8O72cmobmlr9Tkr0Du7ThxXykRFFTCzpoSsO6RAqDiIJoL6xiT3RK4smd3ZX1LJ5XzUrd5Yzd/0+lm07hDsU5qRx9shiJpb0YGz/fHplp1FZ20BtQxOj+uTqKkMCU3EQ6QIOVdfz6prdvLxyN6+v2cOhw/WtXpORmsS0YYWcMayQkb1zGdY7h+Jc3dktR9beWVlFJA7kZ6Vy2cT+XDaxP+7O1gOHWbH9EOWHG8jNSMHMmLt+Ly+vjBSQZiUFmUwbVsjUIb04eWBPBvTM1BKq0iZdOYh0MR5tklq3u5JVOyt4e8M+5m7YR0VNAwAF2Wn0zc8gJz2F3IwUemalUZCdRnZ6CmkpSaQmJ73/PrUNTZQfrqeitoGmpsh3RX5mKmWDC5g8uCc9sjSNSCJTs5JIN9fQ2MSqnRUs2XqQpVsOsaeylsraBipqGjhQVcf+qjrqGlt3hgOkpySRm5Hyfif4gar691+bnZZMTkYKOekp9MhKo0dmKvmZqeRlplKQncaw4hxG9sklNyOFypoG6hqbGFqU834Basuh6nq2HqxmUK9sctLV0NHR1Kwk0s2lJCcxtn8+Y/vnc82UI7+mobGJusYm6hscDMwi92h8uIO7pr6RpVsPsWDTfvZV1lFZ00BFbT2HDtez41ANq3dVcOhw/ftXKh+Wk57C1CEFjOidixk0NsH+qlp2lddyuL6Rnlmp5GaksnJHOe/uKKf536/98jPo3zOTwpx0inPTGdAzi5KCLPrmZ1CQnUavnDSy0vSV1lF05SAiMVFT38jaXZWs2llOTUMTudF/+c/fuJ+/rtvL1gOHMSJFqCA7jd55GWSkJnOoOlJoBhdmcdqQQoYWZ7NpX3V0+O9h9lTUsru8lora1sWntDCbiSU9GN03j8GF2QzulUVxbgZ5mSlx1c9SWdtA+eF6cjJSyE5Loa6hieq6BqrrGqmpb6SmvoneeekU5abHNLealUSkyzlUXc/m/dXsrqhhX1Uduw7VsGzbIRZvOdjqZsOUJKNvjwwmDOjBpIE9Gdk7l9KibPpG1+hodGdvZS1b9h9mZ3kNtfWN1DU2UdcQ2apqG1i/p4rVuypwd6YM6cVpQ3pRWphNUW46+ZmpuIPjZKQkkxRtgtt5qIbFWw6yq7yGipp69lTUsmDTAVbuKKcpwFdvbnoKfXtk4B7JWF3bSEVNPTUNTaSnRK7qLhjbhx9cPu6E/huqWUlEupz8rFTGZeUD+a2eO1hdx3t7q9i0r5q9lbXsq6pj074qFm06wNNLdxz3scxgYEEWI3rn0tjkzF68nYfe3nzE1yYnGT2z0jDj/ftWmmWlJTNhQA++dM5w+uZnUFXbQFVtI2kpSWSlJZOZmkxmWjJpKUnsOHiYDXur2HmohuQkIynJyEpNJjcjlcy0JOoamjhc38iYfq3PvyOoOIhIl9MjK41JA9OYNLBnq+d2l9ewbk8l7+2tYtehGsyM5CSjIDuNkoIs+uVHmreaR26lp0S2lBad6A2NTazcUcH2aDNXeU09Fl3yprK2PtLB3+CM7Z/HxJIeDOqVTXZ6ckJNwKjiICLdSnFeBsV5GZw+tPCE3yMlOYlxA/IZNyA2/2qPB8HGk4mISLei4iAiIq2oOIiISCsqDiIi0oqKg4iItKLiICIirag4iIhIKyoOIiLSSpeYW8nM9gCb2vEWhcDeDooTNp1LfNK5xKfufi6D3L3oSE90ieLQXma24GiTTyUanUt80rnEJ53L0alZSUREWlFxEBGRVlQcIu4JO0AH0rnEJ51LfNK5HIX6HEREpBVdOYiISCsqDiIi0kq3Lg5mNsPMVpvZOjP7Vth5joeZlZjZHDN718xWmNmXo/sLzOwlM1sb/bP1UlhxysySzewdM3s6+rjUzN6Ofj6PmFla2BmDMLMeZvaYma0ys5VmdlqCfy5fjf4dW25m/2dmGYny2ZjZ/Wa228yWt9h3xM/CIn4RPaelZnZyeMlbO8q5/Dj692ypmf3ZzHq0eO7W6LmsNrPzj/d43bY4mFky8CvgAmA0cLWZjQ431XFpAP7F3UcDU4Gbo/m/Bbzi7sOBV6KPE8WXgZUtHv8Q+Jm7DwMOADNDSXX8fg487+6jgAlEzikhPxcz6w/cApS5+1ggGbiKxPlsHgBmfGjf0T6LC4Dh0e0m4NedlDGoB2h9Li8BY919PLAGuBUg+l1wFTAm+jt3Rr/zAuu2xQE4FVjn7hvcvQ54GLgs5EyBufsOd18U/bmCyBdQfyLnMCv6slnAx0MJeJzMbABwEXBv9LEB5wCPRV+SEOdiZvnAWcB9AO5e5+4HSdDPJSoFyDSzFCAL2EGCfDbu/jqw/0O7j/ZZXAb8ziP+BvQws76dEjSAI52Lu7/o7g3Rh38DBkR/vgx42N1r3f09YB2R77zAunNx6A9safF4a3RfwjGzwcAk4G2gt7vviD61E+gdVq7j9L/AN4Cm6ONewMEWf/ET5fMpBfYAv402kd1rZtkk6Ofi7tuAnwCbiRSFQ8BCEvOzaXa0zyLRvxNuBJ6L/tzuc+nOxaFLMLMc4E/AV9y9vOVzHhmnHPdjlc3sYmC3uy8MO0sHSAFOBn7t7pOAKj7UhJQonwtAtD3+MiJFrx+QTeumjYSVSJ/FsZjZbUSamh/sqPfszsVhG1DS4vGA6L6EYWapRArDg+7+eHT3ruZL4eifu8PKdxzOAC41s41EmvfOIdJu3yPalAGJ8/lsBba6+9vRx48RKRaJ+LkAnAe85+573L0eeJzI55WIn02zo30WCfmdYGbXAxcD1/gHN661+1y6c3GYDwyPjrpII9J5MzvkTIFF2+TvA1a6+09bPDUbuC7683XAk52d7Xi5+63uPsDdBxP5HP7i7tcAc4BPRl+WKOeyE9hiZiOju84F3iUBP5eozcBUM8uK/p1rPp+E+2xaONpnMRv4XHTU0lTgUIvmp7hkZjOINMde6u7VLZ6aDVxlZulmVkqkk33ecb25u3fbDbiQSA//euC2sPMcZ/ZpRC6HlwKLo9uFRNrqXwHWAi8DBWFnPc7zOht4OvrzkOhf6HXAH4H0sPMFPIeJwILoZ/ME0DORPxfge8AqYDnweyA9UT4b4P+I9JXUE7mqm3m0zwIwIiMY1wPLiIzQCv0c2jiXdUT6Fpq/A+5q8frboueyGrjgeI+n6TNERKSV7tysJCIiR6HiICIirag4iIhIKyoOIiLSioqDiIi0ouIgchzMrJeZLY5uO81sW/TnSjO7M+x8Ih1FQ1lFTpCZ3Q5UuvtPws4i0tF05SDSAczs7BbrUNxuZrPM7A0z22RmV5jZj8xsmZk9H532BDM7xcxeM7OFZvZCPM0AKqLiIBIbQ4nMEXUp8AdgjruPAw4DF0ULxB3AJ939FOB+4AdhhRX5sJS2XyIiJ+A5d683s2VEFsh5Prp/GTAYGAmMBV6KTFlEMpGpEUTigoqDSGzUArh7k5nV+wede01E/r8zYIW7nxZWQJFjUbOSSDhWA0VmdhpEpl83szEhZxJ5n4qDSAg8sjTtJ4EfmtkSIjNqnh5qKJEWNJRVRERa0ZWDiIi0ouIgIiKtqDiIiEgrKg4iItKKioOIiLSi4iAiIq2oOIiISCv/H0IGlfVCG5KZAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -174,13 +182,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "e0e3ac8d",
+   "execution_count": 7,
+   "id": "633034d3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -200,21 +208,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "d4a181a8",
+   "execution_count": 8,
+   "id": "bb741c54",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_11254/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "/tmp/ipykernel_82097/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n",
       "  plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -232,6 +240,69 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "31ec9e16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., ..., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.history[\"who_dies\"][0,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "603ae6e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(203,)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.newborn_init_history['pLvl'][1,PFexample.history[\"who_dies\"][1,:] > 0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "567440dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(181,)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.newborn_init_history['aNrm'][2, PFexample.history[\"who_dies\"][2,:] > 0].shape"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0ead3ec8",
@@ -242,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 12,
    "id": "adfbe431",
    "metadata": {},
    "outputs": [],
@@ -254,14 +325,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 13,
    "id": "5a0c394b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7fa6f5a29030>},\n",
+       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7f81cd6c33d0>},\n",
        " 'parameters': {'DiscFac': 0.96,\n",
        "  'CRRA': (2.0,),\n",
        "  'Rfree': 1.03,\n",
@@ -272,12 +343,12 @@
        "  'r_eff': <function HARK.models.perfect_foresight_normalized.<lambda>(Rfree, PermGroFac)>,\n",
        "  'b_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(r_eff, a_nrm)>,\n",
        "  'm_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(b_nrm)>,\n",
-       "  'c_nrm': <HARK.model.Control at 0x7fa6f6902380>,\n",
+       "  'c_nrm': <HARK.model.Control at 0x7f81cd53bfa0>,\n",
        "  'a_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(m_nrm, c_nrm)>},\n",
        " 'reward': {'u': <function HARK.models.perfect_foresight_normalized.<lambda>(c)>}}"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -288,18 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "08164ab2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pfn.model['parameters']['LivPrb'] = 1.0\n",
-    "pfn.model['shocks'] = {'live' : Bernoulli(1.0)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 14,
    "id": "e9d068bd",
    "metadata": {},
    "outputs": [],
@@ -313,18 +373,40 @@
     "    },\n",
     "    { # initial states\n",
     "        'a_nrm' : Lognormal(-6, 0),\n",
-    "        'live' : 1,\n",
+    "        #'live' : 1,\n",
     "        'p' : 1.0\n",
     "    },\n",
     "    agent_count = 10000,\n",
     "    T_sim = 120\n",
-    ")\n",
-    "\n"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 15,
+   "id": "36ba1dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfn_simulator.read_shocks = True\n",
+    "pfn_simulator.shock_history['live'] = 1 - PFexample.history[\"who_dies\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bc84d3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfn_simulator.newborn_init_history['a_nrm'] = PFexample.newborn_init_history['aNrm']\n",
+    "pfn_simulator.newborn_init_history['p'] = PFexample.newborn_init_history['pLvl']\n",
+    "#pfn_simulator.newborn_init_history['live'] = np.ones(PFexample.newborn_init_history['pLvl'].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "id": "65df3a7f",
    "metadata": {},
    "outputs": [
@@ -335,8 +417,8 @@
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        ...,\n",
-       "        [1., 1., 1., ..., 1., 1., 1.],\n",
-       "        [1., 1., 1., ..., 1., 1., 1.],\n",
+       "        [1., 1., 1., ..., 0., 1., 1.],\n",
+       "        [1., 1., 0., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.]]),\n",
        " 'p': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
        "         1.01      ],\n",
@@ -345,12 +427,12 @@
        "        [1.030301  , 1.030301  , 1.030301  , ..., 1.030301  , 1.030301  ,\n",
        "         1.030301  ],\n",
        "        ...,\n",
-       "        [3.23535623, 3.23535623, 3.23535623, ..., 3.23535623, 3.23535623,\n",
-       "         3.23535623],\n",
-       "        [3.2677098 , 3.2677098 , 3.2677098 , ..., 3.2677098 , 3.2677098 ,\n",
-       "         3.2677098 ],\n",
-       "        [3.30038689, 3.30038689, 3.30038689, ..., 3.30038689, 3.30038689,\n",
-       "         3.30038689]]),\n",
+       "        [1.25716302, 1.09368527, 1.66107814, ..., 2.54805698, 1.030301  ,\n",
+       "         2.57353755],\n",
+       "        [1.26973465, 1.10462213, 1.67768892, ..., 1.01      , 1.04060401,\n",
+       "         2.59927293],\n",
+       "        [1.282432  , 1.11566835, 1.01      , ..., 1.0201    , 1.05101005,\n",
+       "         2.62526565]]),\n",
        " 'r_eff': array([[1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
        "         1.01980198],\n",
        "        [1.01980198, 1.01980198, 1.01980198, ..., 1.01980198, 1.01980198,\n",
@@ -366,59 +448,59 @@
        "         1.01980198]]),\n",
        " 'b_nrm': array([[ 2.52783638e-03,  2.52783638e-03,  2.52783638e-03, ...,\n",
        "          2.52783638e-03,  2.52783638e-03,  2.52783638e-03],\n",
-       "        [-7.93759059e-01, -7.93759059e-01, -7.93759059e-01, ...,\n",
-       "         -7.93759059e-01, -7.93759059e-01, -7.93759059e-01],\n",
-       "        [-1.57773444e+00, -1.57773444e+00, -1.57773444e+00, ...,\n",
-       "         -1.57773444e+00, -1.57773444e+00, -1.57773444e+00],\n",
+       "        [-1.30338194e+00, -1.30338194e+00, -1.30338194e+00, ...,\n",
+       "         -1.30338194e+00, -1.30338194e+00, -1.30338194e+00],\n",
+       "        [-2.57617872e+00, -2.57617872e+00, -2.57617872e+00, ...,\n",
+       "         -2.57617872e+00, -2.57617872e+00, -2.57617872e+00],\n",
        "        ...,\n",
-       "        [-4.31808761e+01, -4.31808761e+01, -4.31808761e+01, ...,\n",
-       "         -4.31808761e+01, -4.31808761e+01, -4.31808761e+01],\n",
-       "        [-4.33094977e+01, -4.33094977e+01, -4.33094977e+01, ...,\n",
-       "         -4.33094977e+01, -4.33094977e+01, -4.33094977e+01],\n",
-       "        [-4.34361305e+01, -4.34361305e+01, -4.34361305e+01, ...,\n",
-       "         -4.34361305e+01, -4.34361305e+01, -4.34361305e+01]]),\n",
+       "        [-2.22289190e+01, -9.56314476e+00, -3.72398151e+01, ...,\n",
+       "         -4.67738878e+01, -2.57617872e+00, -4.68937221e+01],\n",
+       "        [-2.29711222e+01, -1.06265047e+01, -3.76013980e+01, ...,\n",
+       "          2.52783638e-03, -3.81670212e+00, -4.70105178e+01],\n",
+       "        [-2.36945059e+01, -1.16629019e+01,  2.52783638e-03, ...,\n",
+       "         -1.30338194e+00, -5.02577047e+00, -4.71243521e+01]]),\n",
        " 'm_nrm': array([[  1.00252784,   1.00252784,   1.00252784, ...,   1.00252784,\n",
        "           1.00252784,   1.00252784],\n",
-       "        [  0.20624094,   0.20624094,   0.20624094, ...,   0.20624094,\n",
-       "           0.20624094,   0.20624094],\n",
-       "        [ -0.57773444,  -0.57773444,  -0.57773444, ...,  -0.57773444,\n",
-       "          -0.57773444,  -0.57773444],\n",
+       "        [ -0.30338194,  -0.30338194,  -0.30338194, ...,  -0.30338194,\n",
+       "          -0.30338194,  -0.30338194],\n",
+       "        [ -1.57617872,  -1.57617872,  -1.57617872, ...,  -1.57617872,\n",
+       "          -1.57617872,  -1.57617872],\n",
        "        ...,\n",
-       "        [-42.18087615, -42.18087615, -42.18087615, ..., -42.18087615,\n",
-       "         -42.18087615, -42.18087615],\n",
-       "        [-42.30949766, -42.30949766, -42.30949766, ..., -42.30949766,\n",
-       "         -42.30949766, -42.30949766],\n",
-       "        [-42.43613053, -42.43613053, -42.43613053, ..., -42.43613053,\n",
-       "         -42.43613053, -42.43613053]]),\n",
-       " 'c_nrm': array([[1.7808741 , 1.7808741 , 1.7808741 , ..., 1.7808741 , 1.7808741 ,\n",
-       "         1.7808741 ],\n",
-       "        [1.75333976, 1.75333976, 1.75333976, ..., 1.75333976, 1.75333976,\n",
-       "         1.75333976],\n",
-       "        [1.72623113, 1.72623113, 1.72623113, ..., 1.72623113, 1.72623113,\n",
-       "         1.72623113],\n",
+       "        [-21.22891902,  -8.56314476, -36.23981511, ..., -45.77388776,\n",
+       "          -1.57617872, -45.89372208],\n",
+       "        [-21.97112221,  -9.62650475, -36.60139796, ...,   1.00252784,\n",
+       "          -2.81670212, -46.01051785],\n",
+       "        [-22.6945059 , -10.6629019 ,   1.00252784, ...,  -0.30338194,\n",
+       "          -4.02577047, -46.12435211]]),\n",
+       " 'c_nrm': array([[2.28060139, 2.28060139, 2.28060139, ..., 2.28060139, 2.28060139,\n",
+       "         2.28060139],\n",
+       "        [2.22277389, 2.22277389, 2.22277389, ..., 2.22277389, 2.22277389,\n",
+       "         2.22277389],\n",
+       "        [2.16641268, 2.16641268, 2.16641268, ..., 2.16641268, 2.16641268,\n",
+       "         2.16641268],\n",
        "        ...,\n",
-       "        [0.28766039, 0.28766039, 0.28766039, ..., 0.28766039, 0.28766039,\n",
-       "         0.28766039],\n",
-       "        [0.28321287, 0.28321287, 0.28321287, ..., 0.28321287, 0.28321287,\n",
-       "         0.28321287],\n",
-       "        [0.2788341 , 0.2788341 , 0.2788341 , ..., 0.2788341 , 0.2788341 ,\n",
-       "         0.2788341 ]]),\n",
-       " 'a_nrm': array([[ -0.77834626,  -0.77834626,  -0.77834626, ...,  -0.77834626,\n",
-       "          -0.77834626,  -0.77834626],\n",
-       "        [ -1.54709882,  -1.54709882,  -1.54709882, ...,  -1.54709882,\n",
-       "          -1.54709882,  -1.54709882],\n",
-       "        [ -2.30396557,  -2.30396557,  -2.30396557, ...,  -2.30396557,\n",
-       "          -2.30396557,  -2.30396557],\n",
+       "        [1.29616199, 1.8570201 , 0.63145862, ..., 0.20927661, 2.16641268,\n",
+       "         0.20397018],\n",
+       "        [1.26329619, 1.80993304, 0.61544722, ..., 2.28060139, 2.11148057,\n",
+       "         0.1987983 ],\n",
+       "        [1.23126376, 1.76403993, 2.28060139, ..., 2.22277389, 2.05794134,\n",
+       "         0.19375756]]),\n",
+       " 'a_nrm': array([[ -1.27807355,  -1.27807355,  -1.27807355, ...,  -1.27807355,\n",
+       "          -1.27807355,  -1.27807355],\n",
+       "        [ -2.52615583,  -2.52615583,  -2.52615583, ...,  -2.52615583,\n",
+       "          -2.52615583,  -2.52615583],\n",
+       "        [ -3.74259139,  -3.74259139,  -3.74259139, ...,  -3.74259139,\n",
+       "          -3.74259139,  -3.74259139],\n",
        "        ...,\n",
-       "        [-42.46853654, -42.46853654, -42.46853654, ..., -42.46853654,\n",
-       "         -42.46853654, -42.46853654],\n",
-       "        [-42.59271052, -42.59271052, -42.59271052, ..., -42.59271052,\n",
-       "         -42.59271052, -42.59271052],\n",
-       "        [-42.71496463, -42.71496463, -42.71496463, ..., -42.71496463,\n",
-       "         -42.71496463, -42.71496463]])}"
+       "        [-22.525081  , -10.42016485, -36.87127373, ..., -45.98316437,\n",
+       "          -3.74259139, -46.09769226],\n",
+       "        [-23.2344184 , -11.43643779, -37.21684518, ...,  -1.27807355,\n",
+       "          -4.92818269, -46.20931615],\n",
+       "        [-23.92576966, -12.42694183,  -1.27807355, ...,  -2.52615583,\n",
+       "          -6.08371181, -46.31810967]])}"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,13 +513,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 21,
    "id": "9e2c7ad0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -457,21 +539,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "6b3b4811",
+   "execution_count": 22,
+   "id": "2b471cf1",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_11254/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "/tmp/ipykernel_82097/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n",
       "  plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -491,23 +573,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 23,
    "id": "464f19e7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa6f68e2410>]"
+       "[<matplotlib.lines.Line2D at 0x7f81cd7cac50>]"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -524,13 +606,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "id": "3c721410",
+   "execution_count": 24,
+   "id": "1cc1dc83",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PMW9WNm3axJw5c4iPjyc2Npavv/6aF198kQEDBvDDDz/Qt29fj7t6P9+QIUO4+uqreeCBB0hLS2PixInExcWxevVq4uLiiI+Pp3HjxvTq1YtvvvmGLl268OyzzzJz5kwqV67MCy+8wCuvvMJTTz2V63bkJNfiICItgHeAi1U12N1aqZ+qPpuvNRdTLaN7sGjjfcQkvsaiCc8SMyh/O8B4X8WKFdM/DMB1zeHsH+LSpUupU6cOjRo1okGDBgwZMoT9+/endz89cuRIHnvsMZKTkzP1xNq9e3c+++wzbrvtNipVqlQg2aOjo1m7di3r1q1j8ODB9O7dO8suvy+77DJ8fX0JDQ3l2WefZejQoTl2RX62MGTl+uuv56233kqff/311xkwYEB6z6tXX3018+fPp1+/fjRu3JiYmBgg++7AO3bsyIMPPsgjjzzCFVdcQceOHc9ZX1xcHJ07d07/nV977bVs2LAh/fGrrroKHx8fWrdunWU35LnJazfnISEhOebNSu/evSlbtiwhISGkpqbSq1cvgPTu1tevX5/nrt7PCggIoHbt2qxYsYLdu3cTERFB7dq1AWjfvj1NmjQB4MYbb2TBggVUqFCB+Ph4OnToALgKT8YjqgvlSZfdHwAjgfcAVHWViHwBlMriABB909OseDmOdhteIyEulpbtL3c6UtGUyzd8J0yYMIGEhAQCAgIAOHz4MF9//TV33nkn4Bo1beDAgbz55psMGTKEZcuWpb/24Ycf5tNPP+Xaa69l2rRpmb7VN2jQgF9//TV9Pjk5+ZxurvOiVatWVKlShTVr1qR3N57R2W/+Z2kOXZF7s3vtjO+lOXQHvnz5cn788UeeeOIJunXrlqdvsRk76tNs+n5r06YNy5Yto3///nlIn3U35y1atMgyb5kyZdJP26SkpGT5Pj4+PpQtWzb9FODZ7tY1n129Dx06lHHjxrFr1y6GDBmSvvz8U40igqpy+eWXM2HCBA9/C57x5JxIJVWNO29ZqR44Vnx8aHLnp/zlU4daP97Fvt0FOz6x8Y60tDS+/PJLVq9ezZYtW9iyZQvTpk3L8o9qxIgRpKWlZWqR9Nprr1GtWjXuuOOOTB9cPXv2ZMaMGRw4cIADBw4wY8aMTB/WOUlKSkrv/XTr1q3nFLHc5LUr8px07NiRqVOncvz4cY4dO8aUKVOy/DadXXfgO3bsoFKlStx8882MHDmS5cuXn/O6qKgo5s6dy4EDBzhz5sw5429k5/yuyEeMGMH48eNZvHhx+rJvvvmG3bt357mb8+zyBgQEpH858CRjRhfS1XtGAwYMYPr06SxZsuSc/0NxcXEkJSWRlpbGpEmTuPTSS4mJieG3334jMTERcF1DyXgkdqE8KQ57RaQpoAAiMhDYme81F3PVa9YhZcA4qukRdn10k43/UAzMnz+fBg0aUL9+/fRlnTp1Ij4+Pv1i4VkiwhNPPMGLL76Yafn48ePZuXMnDz/88DmP1apViyeffJKoqCiioqJ46qmn0k+dDB06NP3U1pQpU/D392fhwoX07ds3/Y9/wYIF6d2CDxgwgDFjxpxzdJCTvHZFnpO2bdty22230b59e6Kjoxk6dGj6qaOMsusOfPXq1bRv357w8HCeeeYZnnjiiXNe16BBAx577DHat29Phw4dCAgIoHr16jlmCg0NxdfXl7CwMF599VUuvvhiJk6cyEMPPURQUBCtWrXi559/pmrVqnnu5jy7vE8//TT3338/kZGR+Pr65ul3eKFdvWd8/WWXXcZ11113zrqjoqIYMWIErVq1IjAwkAEDBuDn58e4ceO48cYbCQ0NJTY2loSEhDzlzVJ2zZjOTkATYCZwHFf32QuAgNxeV5hTYTRlzc7ib95Qfbqa/v7eCMcyFCVFoSmrKfqOHDmiqq6mnFdccYV+8803DicqWlJTUzUsLEw3bNiQvuz85rx55fWmrKq6WVW7A35AS1W9VFW35L8slQztB9xLXK0rid3xCStmfOZ0HGOKhdGjRxMeHk5wcDCBgYGZWoaVZvHx8TRr1oxu3brRvHlzx3LkOtiPiPwXeFFVD7rnawIPquoTOb6wEHl7sJ+8SjlxjD//14X6p7exf9B0GrUIdyyL02ywH2OKpoIY7Kf32cIAoKoHgD75CVnSVKhYmWqDJ3BaypI28WaOHj7gdCRH5faFwxhTuC7kb9KT4uArIuntv0SkImCDwp6nbsNmbO/+Ng1Tk9nw3q2ltoO+ChUqsG/fPisQxhQRqsq+ffuyvF8mJ57c5/A5MEtEPnbP3w7kfPm/lAq+tB+LtjxATOKrLPz0SWIHF+oAdkWCv78/ycnJ7Nmzx+koxhi3ChUq4O/vn6fX5HjNQVx3XPgDbfi7J9ZfVDVzd5QOcvqaQ0aalsayVwfS9vBs1nT5kNDLsu5qwRhjnJbTNYccjxxUVUXkR1UNATL3IGYyER8f2gwbT9IrHQmYex/JDVvi3yzY6VjGGJMnnlxzWC4iUQWepASpWLkqFW+eSCo+nPnihlJ/gdoYU/x4UhyigYUisklEVonIahFZVdDBirv6gS3Z3n0M/qnb2fjuTaSlpjodyRhjPOZJcegJNAW6AlcCV7j/NbkIvrQfS1s+RMTx34n7eKTTcYwxxmOeFAfNZjIeiL5+FHE1+hCT/BHLfvw49xcYY0wR4Elx+AH43v3vLGAz8FNBhipJxMeHsGEfkVC2Na0XP0ziHwucjmSMMbnypG+lEFUNdf/bHGgPZN1JuclS+QqVqHPHlxySalSdcit7d2x1OpIxxuQoz2NcqupyXBepTR7UqduQY1d/SlU9yv6xA0k5nv8xXo0xpqB4MkzovzLM+gBtgR0FlqgEaxp6CSt2vULE78NZ9s4ttP3n1zYGtTGmSPLkk6lqhqk8rmsPeRubz6SL6HEzCwNH0O7IbBaNe9TpOMYYk6VcjxxU9RkAEaninrfzIfkUc8t/WPJ6IrF/vsfSH5oT2fdOpyMZY8w5cj1yEJFgEVkBrAXWisgyEbH+IPJBfHwIvedj4ssGExI3ioQlM52OZIwx5/DktNL7wL9UtbGqNgYedC8z+VC+QiXq3/01e3zqcNEPt7MjyQtjvhpjjJd4Uhwqq+qcszOq+itQucASlSI16tQl7caJ+JLK6U+v4dB+6+baGFM0eFIcNovIkyIS4J6ewHUjnPGCRi3CSe7xIfVSd5L87tWcOpnidCRjjPGoOAwB/IBv3FMd9zLjJW0u6cOqds/R5tQq/hhzS6kdRc4YU3R4cof0AVW9T1XbAlHAU+5xpI0XRfa7h4WN7ibq0AwWj33I6TjGmFLOk9ZKX4hINRGpDKwG4kUkX12Misi1IrJWRNJEJPK8x0aJSKKIrBeRnvlZT3ETc9vz6Z30xX39mtNxjDGlmCenlVqr6mHgKlwd7gUCt+RzvWuAq4F5GReKSGvgBlzDkvYCxoiIbz7XVWyIjw8R/xjHqgqRtF31DKvmfOV0JGNMKeVJcSgrImVxFYdvVfU0+eyyW1XXqer6LB7qD0xU1ZOqmgQk4uror9QoW648Tf7xFVvLBNDs13+wccW83F9kjDFe5klxeA/Ygqv56jwRaQwcLqA8DYBtGeaT3csyEZG7RGSpiCzds6dkNQGtUq0m1e+YyiGpRq1pN7N981qnIxljShlPLki/oaoNVLWPumwFLsvtdSIyU0TWZDF5pV8mVX1fVSNVNdLPz88bb1mk1KnfmFM3foUvqein17Bvd7LTkYwxpYgnF6QvFpGPROQn93xrYHBur1PV7qoanMU0LYeXbQcaZpj3dy8rlRoHhbOr73hqp+1j/wdXcfSwNRIzxhQOT04rjQN+Buq75zcADxRQnm+BG0SkvIgEAs2BuAJaV7HQMqo76zu9SeDpTSS9bTfJGWMKhyfFoY6qfgmkAajqGSA1PysVkQEikgzEAj+IyM/u914LfAnEA9OB4aqar3WVBOHdbmB5+DOEnFzO6rduJC211P9KjDEFzJPicExEauNuoSQiMcCh/KxUVaeoqr+qllfVi1W1Z4bHnlPVpqoapKo2VrVb+wH3sbDJfbQ7Mpsl7wy1u6iNMQXKk+LwL1yne5qKyG/AJ8C9BZrKZCnm5mdYdPGNRO/9hkXjHnE6jjGmBMuxOLhvQOvsni4B7gbaqOqqQshmziM+PkTfPYYlNXoT++f7LJrwX6cjGWNKqByLg/t8/42qekZV16rqGvdNcMYh4uNDxPBPWFGpAzHrX2DJtDFORzLGlECenFb6TUTeEpGOItL27FTgyUy2ypQtR6t7J7OmfDgRyx9nxYzPnI5kjClhch1DGgh3//vvDMsU6Or1NMZjFSpWJmD4VDa90ZM2v93P6gpVCenklfsLjTEm9+KgqrneDW2cUaVaTere8x3Jb/eg6aw7SShfkZbRPZyOZYwpATw5rWSKsOq1L6baXd+zz6c2DX68lY0r5zsdyRhTAlhxKAHq1G1I2SHfcVSq4Df1BpLWLnY6kjGmmPOkb6XyniwzzqrbsBlpt37HKcpRbfK1bF2/0ulIxphizJMjh4UeLjMOa9CkFSk3TQGg4oQBJCeucTiRMaa4yrY4iEhdEWkHVBSRiAzNWLsAlQoroMmbRi3COXzd15TlNGU+68+OpASnIxljiqGcjhx6Ai/j6jb7FeB/7umfwGMFH81cqMDWUey/+ksqcgIZfwU7t2Y16J4xxmRPVHMe8VNErlHVrwspzwWJjIzUpUuXOh2jyNm4cj4XT72Oo1IVn9t/oG6j5k5HMsYUISKyTFUjs3rM0zukzxnsR0Tu8GpCUyCah3dkV7+JVNEjpH7cl13bEp2OZIwpJjwpDh9TeIP9GC9r0bYzu/pNoFraYc6MtQJhjPGMI4P9mMLVom0XdvafSDU9TOrYPuz6c6PTkYwxRZwjg/2YwteibRd29ZtIVT1C2sd92LHFLlIbY7Jng/2UIi3admb3VV9SWY/hM64v2zevdTqSMaaI8qQ4HOC8wX4Au0O6mGoe3pE9AyZTnhTKfnIFf25Y6XQkY0wR5Elx+Aq4+OxgP0AsMLZgY5mC1CysAwev/RpfUqn0RX+S4pc4HckYU8R4UhyGAVPdd0z3Ad4E+hRsLFPQAttEc/SGqShCjS8HkPjHb05HMsYUIbkWB1VdAtwHzABGA91VdVsB5zKFoHHLtpy8+XtOUoGLpgwkYekspyMZY4qInPpW+k5EvhWRb4FRuPpTOgl85F5mSgD/ZsEw5CcOS3UafXcjaxbYrjXG5DwS3MuFlsI4qm6j5uy982d2f3glzX8ZwsqUNwnvfqPTsYwxDsq2OKjqXBHxBWbaUKElX536jSnzjxn8+e4VtJk/nKUnDhN55d1OxzLGOCTHaw6qmgqkiUj1QspjHFSjTl3q3juDDeVb03bpIyye9ILTkYwxDvGktdJRYLW78703zk4FHcw4o2r1WjR9YDqrKscQve6/LPz4ETQtzelYxphC5klx+AZ4EpgHLMswmRKqQqUqtHlgGkuq9yB267ssfucu0lKtOy1jSpOcLkgDoKrjCyOIKVrKlitPu/smsui9e4j5axJLXz9A2IgvKFvObo43pjTI9chBRJqLyFciEi8im89OhRHOOMvH15foYe+yMHA4kYdnsu6VPhw7ctDpWMaYQuDpeA7vAGeAy3B1vPdZQYYyRYf4+BA7+L/EhTxDmxPL2PF6d/btTnY6ljGmgHlSHCqq6ixcQ4puVdXRQN+CjWWKmvbXPMDqju/gf3orJ97tbj26GlPCeVIcToqID7BRREaIyACgSgHnMkVQePcb2XrFBCrrUSp+0osNy+c6HckYU0A8KQ734+o64z6gHXALMLggQ5miq2VUd47c9D0pUgH/adfyx+yJTkcyxhQAjzreU9Wjqpqsqrer6tWquqgwwpmiqVGLcMrdPYsdZfwJnjuMxZP/53QkY4yXedJaKVJEpojIchFZdXYqjHCm6KpTtxH1HpjNmkpRRK/9Nwvfv9fuhTCmBPHktNLnuFosXQNcmWEypVzlqjVo868fWFy7P7E7PmHFa9eQcuKY07GMMV7gSXHYo6rfqmqSu7XSVlXdmp+VishLIpLgPgqZIiI1Mjw2SkQSRWS9iPTMz3pMwStTthzth49jUdP7aXdkDlte6c7+v7Y7HcsYk0+eFIenReRDEblRRK4+O+Vzvb8AwaoaCmzANV4EItIauAHXONW9gDHunmFNESY+PsTc8m+WR79GwKmNnHjnMrYmLHc6ljEmHzwpDrcD4bg+rM+eUroiPytV1RmqesY9uwjwd//cH5ioqidVNQlIBNrnZ12m8LTtfTt/9v+K8nqSmhP7snreFKcjGWMuUK59KwFRqhpUgBmGAJPcPzfAVSzOSnYvy0RE7gLuAmjUqFEBxjN50aJtF3bWnsGR8dfRatYQFu/aSPR1DzsdyxiTR54cOfzuPt2TJyIyU0TWZDH1z/Ccx3F1y/F5Xt9fVd9X1UhVjfTz88vry00Bqtc4CL8HfmVNpfZExz/H4rdu58zpU07HMsbkgSdHDjHAShFJwjWGtADqvl6QLVXtntPjInIbrtNT3VRV3Yu3Aw0zPM3fvcwUM1Wq1STkwR9Y9OF9xOz6nNUvJ9HorklUr32x09GMMR7w5MihF9Ac6MHf1xvy1ZRVRHoBDwP9VPV4hoe+BW4QkfIiEuheb1x+1mWc41umDDHDxhAX9h+CUlZx5K3OdqHamGLCkzukt2Y15XO9bwFVgV9EZKWIvOte11rgSyAemA4Mdw9Vaoqx9gPuY3OfiVTU49Sa0IeVs6zLDWOKOvn7jE7xFRkZqUuXLnU6hsnFrm2JHB13HU3ObGZx4DBibv0v4uPJwasxpiCIyDJVjczqMfvLNIWmbsNmNPjXXJZX70bslndY8b/+NniQMUWUFQdTqCpWrkq7ByazqNkDhB2dz55XO7ItcbXTsYwx58m2OIjIERE5nN1UmCFNySI+PsTc/Azx3cZTI20/1T/rYdchjClisi0OqlpVVasBrwOP4roZzR94BHitUNKZEi2kU3+O3zabv3zrET7/bhZ+9CCpZ87k/kJjTIHz5LRSP1Udo6pHVPWwqr6Dq5sLY/KtfkAQ/g/OY0mN3sRu+5C1L/fi4N5dTscyptTzpDgcE5FBIuIrIj4iMgiwfpmN11SoVIXI+75gcesnaHliBSfeupSNK+Y5HcuYUs2T4nATcB2w2z1d615mjNeIjw/R141kS/9vEKDx1AEs/vJFNC3N6WjGlEqe3AS3RVX7q2odVfVT1atUdUshZDOlUIu2nakwYgEJFSOIjn+OZa9da81djXGAJ8OEthCRWSKyxj0fKiJPFHw0U1rVqFOX4JE/syhgOBGHZrH31Q4krV3sdCxjShVPTit9gGswntMAqroK14A8xhQYH19fYm77Lwk9Pqdy2lHqfdmXuK9ftdNMxhQST4pDJVU9v/M7a29oCkWbDn1h2AI2Vgim/erRLHt1IEcO7Xc6ljElnifFYa+INAUUQEQGAjsLNJUxGdSp25DWI2eysPEwIg7P5tBrl7Bx5XynYxlTonlSHIYD7wEtRWQ78ABwT0GGMuZ8vmXKEHv7C6zvPZGyeorGU/qz6PN/k5ZqnfYaUxA8aa202T1wjx/QUlUvtdZKximtY3pRfsTvrK0cTczG/7HmpZ7s3bXN6VjGlDietFZKFZHngeOqesS9zEZsMY6pUacu4Q/9wOJWj9HixErk3Q78MftLp2MZU6J4clpprft5M0SklnuZFFwkY3InPj5EX/8IO6//icM+NQmbdyeL3h5Kygm7ed8Yb/CkOJxR1YeBD4H5ItIO98VpY5wW2DqKeiN/Z9FF1xGzZzK7Xoph0+pFTscyptjzpDgIgKpOAq4HPgaaFGQoY/KiQsXKxPzjA1Z1GUuVtMM0/Koviz59ynp4NSYfPCkOQ8/+oKprgI7AfQWWyJgLFNrlGnyHL2JtlRhiNr3O+he7sCMpwelYxhRLOQ3209X9Y2MRufrsBHQDjhZKOmPyqKZfPcIf/I4l4c/R6GQi1cd1Ju7r1+zOamPyKKcjh87uf6/MYrqigHMZc8HEx4eoq0Zw+Pa5bCnfnParn+aPl3qzd8dWp6MZU2yIavG/thwZGalLly51OoYpgtJSU4mb9H+Er3+NFClPYuTTtOszFPGx4dONEZFlqhqZ5WPZFQcR+VdOb6qqr3ghm1dYcTC52bp+JSmT7yLozHpWVL6Uhre8S526DZ2OZYyjcioOOX19qprLZEyx0TgonGaP/s6iJvfR+uhifN+NZel379m1CGOyYaeVTKmzNWE5KV8Ncx1FVLoE/5vfwa9+gNOxjCl0F3rkcPbFFURkuIiMEZGxZyfvxzSmcDRu2dZ1FNHsn7Q6toTy78daiyZjzuPJVblPgbpAT2Au4A8cKchQxhQ03zJliLl5NHtv+ZVt5ZrSfvXTrH3hMpIT1zgdzZgiwZPi0ExVnwSOqep4oC8QXbCxjCkc/s2CafXIXBa3eZLGKeup82kXFn3yJKdPnXQ6mjGO8qQ4nHb/e1BEgoHqwEUFF8mYwuXj60v0tQ9x4q6FrKvcnpjNb/DnC9FsWD7X6WjGOMaT4vC+iNQEngS+BeKBFws0lTEOuKhBIBEP/8jy2LeomnqIptP6s/jtO2xYUlMqWWslY7Jw+OA+1n32EFF7prBXapIc/RQRPQfbzXOmRLmgm+AyvLgGcCsQAJQ5u1xVi0zne1YcTEHZsPxXfH/4J01TN7OqQhS1r3udBk3aOB3LGK/IV1NW4EdchWE1sCzDZEyJ16JtFxo/uphFLUbS5MQa6ozvzMKxI21QIVPieXLksFxV2xZSngtiRw6mMPy1PYltEx6g3dFf2S4Xs7fjs4R1vc7pWMZcsPweOXwqIneKSD0RqXV28nJGY4q8ixoE0u6haazp9glnpCxh8+5k5Yu92L55rdPRjPE6T4rDKeAlYCF/n1Kyr+mm1Aru2J96jyxjUZP7aHFsuetU04f/5PjRQ05HM8ZrPCkOD+K6ES5AVQPdU76GCRWR/4jIKhFZKSIzRKS+e7mIyBsikuh+vEifzjKlV7nyFYi59T8cvWsxq6t3JjZ5LEdejmDpDx9YNxymRPCkOCQCx7283pdUNVRVw4Hvgafcy3sDzd3TXcA7Xl6vMV51UYNAIv/1NfG9JnHUtzqRSx4i4f8uZePK+U5HMyZfPCkOx4CVIvKe+1v9GyLyRn5WqqqHM8xWBs5eFe8PfKIui4AaIlIvP+sypjC0julFwKglLG7zFBef3kbTKVcS99qNNvqcKbY8KQ5TgeeA3/FiU1YReU5EtgGD+PvIoQGwLcPTkt3Lsnr9XSKyVESW7tmzJ79xjMk33zJliL72Qco8sJK4ejcRfuBnKr0XxcKPHyHluA27boqXHJuyiogvMFNVL8vzG4vMxNWb6/keV9VpGZ43Cqigqk+LyPfA86q6wP3YLOARVc3xArg1ZTVFUXLiGvZMeYSIYwvYTW3+jHiIdlfcjY+vr9PRjAHy0ZRVVVOBNBGpnteVqmp3VQ3OYpp23lM/B65x/7wdyDh2o797mTHFjn+zYCJG/sDaHhM4XKYWUStGsfm/UayZf/6fgDFFjyenlY4Cq0XkI29dcxCR5hlm+wMJ7p+/BW51t1qKAQ6p6s78rMsYp7W5pA9NRy1mabsXqZJ6mOBZt/LH891JWrvY6WjGZMuTO6QHZ7XcPbbDha1U5GsgCEgDtgLDVHW7iAjwFtALVwup23M7pQR2WskUHyknjrHy6xdpnfgBVfQ4y2r0pOE1z1K3UfPcX2yMl+Wr4z33G5QDWrhn16vq6ZyeX9isOJji5tC+3ayb/AwRO78EYEXdgQQNfJqaftY4zxSe/I4h3QXYCLwNjAE2iEgnbwY0prSpXvtiYoaN4cAdC/mj5uVE7ZpI2bciWDj2YY4ePuB0PGM8Oq20DLhJVde751sAE1S1XSHk84gdOZjibmvCcvZ/9yQRxxZwgGqsbz6U8AEPUqFSFaejmRIsvx3vlT1bGABUdQNQ1lvhjDHQuGVbIkb+wIZ+35JcvhkxG1/h8IshLP7yRU6dTHE6nimFPDlyGIvrwvFn7kWDAF9VHVLA2TxmRw6mpFn72w/4/PosrU7HsxM/toWMIOLKeyhbrrzT0UwJkt+R4MoDw4FL3YvmA2NU9aRXU+aDFQdTEmlaGqvnfkOF316gxZkNbJeL2R46goi+d1uRMF6R79ZKRZ0VB1OSaVoaf8z5kiq/v0iz1E1sl4vZETKc8CuGWZEw+ZLf1kodROQXEdkgIpvPTt6PaYzJivj4EN7tBpo+vpSVHd/juE9VolY9xZ7/C2bx5P/ZNQlTIDw5rZQA/BNXZ3upZ5er6r6CjeY5O3IwpYmmpbHq1y+p8PsrBJ1Zz25qkxQ0lPD+91nrJpMn+b3msFhVowskmZdYcTClkaalsWb+VHwX/I/Wp9ewj+psaDKY4P7/pGp1G8nX5C6/xeF5wBf4Bki/CK2qy70ZMj+sOJjSLn7hT5yZ+xKhKcs4TGXWNriOoP4jqXVRlj3eGwPkvzjMyWKxqmpXb4TzBisOxrhsXDmfI7+8QPjRBZykLKv8rqBh30eoH9jS6WimCLLWSsaUMlvXr2T3Ty8QfuBnfEljZbXLqN79IZqFdXA6milCrDgYU0rtTt5E0vf/I3jnN1SRE6wpH05azL2EdL4a8fGkgwRTkllxMKaUO3RgL+u+f4Mmmz7lIvazxacRe4KHEtJ7KBUqVnY6nnGIFQdjDACnTqbwx08fUWv1BzRNTXK1cGp0Pc373Eedug1zfwNTonhjPIdLgACgzNllqvqJtwLmlxUHY/JG09JY+9t3pP7+NmEnFnNKy/BHzcup1fU+moZe4nQ8U0jy21rpU6ApsJK/b4JTVb3PmyHzw4qDMRfuzw0r2fnzq4Ts/YlKcpK15UI41e5OQrreSJmy5ZyOZwpQfovDOqC1FuHzT1YcjMm/Q/v3sO7Ht2ic+Dn12MMu6pAUeD1BvYfb/RIlVH6Lw2TgPlXdWRDhvMGKgzHek3rmDKtmT6Tssg8IPrnSdcqpRleqdbyHFm27WCunEsQbN8GFA3Gce4d0Py9mzBcrDsYUjK3rlrFr1lsE7/mRypJCom9T9re+heCeQ6hUpbrT8Uw+5bc4dM5quarO9UI2r7DiYEzBOnr4AGunf8BFCZ8RmLaVI1qReL8+XNT1HgJbRzkdz1wga8pqjPEKTUtj/ZKZHP3tfUIPzaGcnCGhbGuOtBlE8OWDqVi5qtMRTR7k98ghBngTaAWUw9UJ3zFVrebtoBfKioMxhe/Anp2sn/E+9TdNolHadg5TiXV1elG741DrpqOYyG9xWArcAEwGIoFbgRaqOsrbQS+UFQdjnKNpacQvms6JRR8Rcmgu5eU0ib5N2Rd0Ay0vH0L1mnWcjmiyke/ioKqRIrJKVUPdy1aoakQBZL0gVhyMKRoO7dtNwi9jqbNxEk1Tk0jRsqyp3pnykbfSpsMV+Pj6Oh3RZJBTcSiT1cLzHBeRcsBKEXkR2IkHw4saY0qf6rUvJvqGUWjaIySu/p198z+i1d6fqTZ7Jrtm1yHJvx8Nu9yBf7Ngp6OaXHhy5NAY2I3resM/gerAGFVNLPh4nrEjB2OKrpQTx1g7ewJlV0+kzYml+IqSULY1h1oMpGW3W6ley8/piKWWN/pWqgg0UtX13g7nDVYcjCke/tqexOZZY6m7ZQoBads4pWVYU+USJOx62nQeSLnyFZyOWKrk95rDlcDLQDlVDRSRcODfdhOcMeZCaVoaiat+Y99v42mx52dqcZiDVGF97e5UjbqRllGX2/WJQpDf4rAM6Ar8evYitIisVtUQrye9QFYcjCm+Tp86Sfxv33J6+QRaH15AJTnJLvxIqtebiy4ZRJM27a3LjgKS3wvSp1X1kIhkXFb875wzxhQJZcuVJ+yya+Gyazl25CBL50ykbPxXRO34jDJff8LWKQ3Z4d+HBpfeRKMW4U7HLTU8OXL4CJgFPApcA9wHlFXVYQUfzzN25GBMyXNgz042zPmMKonTaHVyDT6ibPJtwl+NetOo4yAaNGnjdMRiL7+nlSoBjwM9AAF+Bv6jqineDnqhrDgYU7L9tT2JzXM/o+bm7wk6kwBAom9T9jTqTaOON1mhuEDWt5IxpsTYuXU9W+dPoOaWHwk642pAucm3CX/596B+7PU0btnW4YTFxwUVBxH5Nqc3tdZKxhin7dy6nq0LJlFjy0+0PB0PwFafhuyo1506UdfQLLSDXczOwYUWhz3ANmACsBjXKaV01mW3MaYo+Wt7EkkLJlFl04+0PLkKX1F24ccWvy5UCe9Py/Y9bdjT81xocfAFLgduBEKBH4AJqrq2oIJeKCsOxpiMDuzZycYFkym38UdaHVtKeTnNISqzsdol+LTqS4sOV1GlWk2nYzrOG3dIl8dVJF4CnlHVt7wU7EFcN9j5qepecbWXfR3oAxwHblPV5bm9jxUHY0x2jh05yPrfppK67keaH/qNGhzllJYhoWIYJwJ70ChmAPUaBzkd0xEXXBzcRaEvrsIQAHwLjFXV7V4I1RD4EGgJtHMXhz7AvbiKQzTwuqpG5/ZeVhyMMZ44c/oUG5bO4vAf39Fg9xwa6g4AknwC2FW3EzXCrqB528tKzemnCz2t9AkQDPwITFTVNV4O9RXwH2AaEOkuDu/huhN7gvs564Euqrozp/ey4mCMuRDbNv7B9ripVN06kxYn11JWUjlIFRKrRkPzy2kS049aFzVwOmaBudDikAYcc89mfJIAmp+R4ESkP9BVVe8XkS38XRy+B55X1QXu580CHlHVTJ/8InIXcBdAo0aN2m3duvVC4xhjDIcP7mPj79+Stv4nmhxaRG0OkaZCYtnm7K/bkRphfWge0QXfMp50LFE8OHKfg4jMBOpm8dDjwGNAD3e3HFu4gOKQkR05GGO8KS01lU2rfmPvyh+ouX0uzU8n4CvKISqzqUokZwK70qj9FdRt2MzpqPmS376VLoiqds8mTAgQCPzh7q/JH1guIu2B7UDDDE/3dy8zxphC4+PrS/OITjSP6AS4RrhLXPw9aRt+ofHBxVy0ei6sfpqtPv7srB1LhZbdaRbVs0S1gHL8Dunzjhz6AiP4+4L0G6raPrf3sCMHY0xh0bQ0tq5fzq7lP1Bp23yan/iDinKK0+pLYrmWHKzXgRptutMsogtly5V3Om6OHDlyuEA/4ioMibiast7ubBxjjDmX+PgQ0CqSgFauz9SUE8dYs2wWR+J/oc5fC4ne+gE+f77P8R/LE18xlBP+Hagd3J0mwbHF6nqF40cO3mBHDsaYouLQ/j1sWjKd0xtnU3f/EhqnbQPgMJXZVCmMk/4d8AvpTmDrKMcHNLKO94wxxiF7dmxh67KfSds8F/+DS6mvuwE4SBWSKoVx0j+WOm26EtgmutCPLKw4GGNMEbHrz41sWz4D3bKA+oeW46+7ADiiFdlcKYTjddtTs2VnAsMupXyFSgWaxYqDMcYUUbuTN7Ft5SxSkxZQ98Dy9NNQJ7Usm8oFccivHZWaXUpA+GVUr+Xn1XVbcTDGmGJi/1/b2bpyDic3LaDWvmUEnt5EWUklTYU/fRuyu0Y4Po1iqBvcCf8mbfLVJbkVB2OMKaaOHz3E5j/mcWTDAirvXkZAylqqcRyAA1RjfbM7iLl59AW9d3FqymqMMSaDSlWqE9zhSuhwJeC6eztp/XL2xM9HkuMoU7Ng+n6y4mCMMcWIj68vga2jCGwdVbDrKdB3N8YYUyxZcTDGGJOJFQdjjDGZWHEwxhiTiRUHY4wxmVhxMMYYk4kVB2OMMZlYcTDGGJNJieg+Q0T2AFsv8OV1gL1ejOO0krQ9ti1Fk21L0XQh29JYVbPsza9EFIf8EJGl2fUtUhyVpO2xbSmabFuKJm9vi51WMsYYk4kVB2OMMZlYcYD3nQ7gZSVpe2xbiibblqLJq9tS6q85GGOMycyOHIwxxmRixcEYY0wmpbo4iEgvEVkvIoki8qjTefJCRBqKyBwRiReRtSJyv3t5LRH5RUQ2uv+t6XRWT4mIr4isEJHv3fOBIrLYvX8miUg5pzN6QkRqiMhXIpIgIutEJLa47hcR+af7/9caEZkgIhWK034RkbEi8peIrMmwLMt9IS5vuLdrlYi0dS55Ztlsy0vu/2erRGSKiNTI8Ngo97asF5GeeV1fqS0OIuILvA30BloDN4pIa2dT5ckZ4EFVbQ3EAMPd+R8FZqlqc2CWe764uB9Yl2H+BeBVVW0GHADucCRV3r0OTFfVlkAYrm0qdvtFRBoA9wGRqhoM+AI3ULz2yzig13nLstsXvYHm7uku4J1CyuipcWTell+AYFUNBTYAowDcnwU3AG3crxnj/szzWKktDkB7IFFVN6vqKWAi0N/hTB5T1Z2qutz98xFcH0ANcG3DePfTxgNXORIwj0TEH+gLfOieF6Ar8JX7KcViW0SkOtAJ+AhAVU+p6kGK6X7BNZRwRREpA1QCdlKM9ouqzgP2n7c4u33RH/hEXRYBNUSkXqEE9UBW26KqM1T1jHt2EeDv/rk/MFFVT6pqEpCI6zPPY6W5ODQAtmWYT3YvK3ZEJACIABYDF6vqTvdDu4CLncqVR68BDwNp7vnawMEM//GLy/4JBPYAH7tPkX0oIpUphvtFVbcDLwN/4ioKh4BlFM/9klF2+6K4fyYMAX5y/5zvbSnNxaFEEJEqwNfAA6p6OONj6mqnXOTbKovIFcBfqrrM6SxeUAZoC7yjqhHAMc47hVSM9ktNXN9AA4H6QGUyn9Yo1orLvsiNiDyO61Tz5956z9JcHLYDDTPM+7uXFRsiUhZXYfhcVb9xL9599lDY/e9fTuXLgw5APxHZguv0Xldc5+1ruE9nQPHZP8lAsqouds9/hatYFMf90h1IUtU9qnoa+AbXviqO+yWj7PZFsfxMEJHbgCuAQfr3jWv53pbSXByWAM3dLS/K4bp4863DmTzmPif/EbBOVV/J8NC3wGD3z4OBaYWdLa9UdZSq+qtqAK79MFtVBwFzgIHupxWXbdkFbBORIPeibkA8xXC/4DqdFCMildz/385uS7HbL+fJbl98C9zqbrUUAxzKcPqpSBKRXrhOx/ZT1eMZHvoWuEFEyotIIK6L7HF5enNVLbUT0AfXFf5NwONO58lj9ktxHQ6vAla6pz64ztXPAjYCM4FaTmfN43Z1Ab53/9zE/R86EZgMlHc6n4fbEA4sde+bqUDN4rpfgGeABGAN8ClQvjjtF2ACruslp3Ed1d2R3b4ABFcLxk3AalyttBzfhly2JRHXtYWznwHvZnj+4+5tWQ/0zuv6rPsMY4wxmZTm00rGGGOyYcXBGGNMJlYcjDHGZGLFwRhjTCZWHIwxxmRixcGYPBCR2iKy0j3tEpHt7p+PisgYp/MZ4y3WlNWYCyQio4Gjqvqy01mM8TY7cjDGC0SkS4ZxKEaLyHgRmS8iW0XkahF5UURWi8h0d7cniEg7EZkrIstE5Oei1AOoMVYcjCkYTXH1EdUP+AyYo6ohwAmgr7tAvAkMVNV2wFjgOafCGnO+Mrk/xRhzAX5S1dMishrXIDnT3ctXAwFAEBAM/OLqtghfXF0jGFMkWHEwpmCcBFDVNBE5rX9f3EvD9XcnwFpVjXUqoDE5sdNKxjhjPeAnIrHg6n5dRNo4nMmYdFYcjHGAuoamHQi8ICJ/4OpR8xJHQxmTgTVlNcYYk4kdORhjjMnEioMxxphMrDgYY4zJxIqDMcaYTKw4GGOMycSKgzHGmEysOBhjjMnk/wFFsp0Yra8A6AAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -552,24 +634,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "id": "38bce0b9",
+   "execution_count": 25,
+   "id": "dcff94ad",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0.])"
+       "array([ 0.        ,  0.00222005,  0.00809147,  0.0012631 ,  0.00715372,\n",
+       "        0.00472244,  0.01164685, -0.02193753,  0.00705938, -0.00575065,\n",
+       "        0.01159715,  0.01218249,  0.00756169, -0.01088867,  0.00391398,\n",
+       "        0.01271844, -0.03643547, -0.02459725,  0.03015856,  0.01530481,\n",
+       "        0.02311477, -0.03156213,  0.03450044, -0.0074543 , -0.04107368,\n",
+       "       -0.04513277, -0.04755333,  0.01681224,  0.02868896,  0.00597751,\n",
+       "        0.0062148 ,  0.03227652, -0.05788355, -0.03502477, -0.05127585,\n",
+       "       -0.04840637, -0.02264437, -0.01016401, -0.04317002,  0.01554684,\n",
+       "        0.04422656, -0.01825121, -0.05225021,  0.05681161, -0.02034541,\n",
+       "        0.02992888, -0.01631412,  0.03085562, -0.03086612,  0.07806968,\n",
+       "       -0.00208791, -0.03290248,  0.00225374,  0.00643284,  0.00942418,\n",
+       "        0.01848714,  0.00079372,  0.03766846,  0.07001856,  0.02767114,\n",
+       "       -0.00659404, -0.02168669, -0.0207974 , -0.05963123,  0.00159466,\n",
+       "       -0.06902993, -0.01631861,  0.03258728,  0.05195744,  0.01295495,\n",
+       "       -0.07574536,  0.05804067,  0.00946723,  0.04908705,  0.03198564,\n",
+       "       -0.02476253, -0.01308888, -0.02987224, -0.0402046 , -0.05912255,\n",
+       "       -0.03579365,  0.05592895, -0.01209768, -0.00462931, -0.00515618,\n",
+       "        0.03687773, -0.0470187 ,  0.07340151, -0.02497839, -0.06001675,\n",
+       "        0.0057467 , -0.114128  ,  0.03985532,  0.01473989,  0.03048939,\n",
+       "        0.01199857,  0.06194041,  0.03707683,  0.0146343 ,  0.06566299,\n",
+       "        0.04049083, -0.0182422 , -0.05992466, -0.02158895, -0.02265609,\n",
+       "       -0.0748654 ,  0.08375932,  0.02491707, -0.01224103,  0.02084308,\n",
+       "       -0.08846461, -0.02883666, -0.09607532,  0.01259047, -0.02288385,\n",
+       "       -0.02240209,  0.05049241, -0.01061027, -0.02799473,  0.00775624])"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,7 +679,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c0e622a",
+   "id": "6cc8cfd2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7cfd234",
    "metadata": {},
    "outputs": [],
    "source": []

From 77dbbf5f5cb0961659f91f5826a21bd71e888ad0 Mon Sep 17 00:00:00 2001
From: sidd3888 <siddarthv2011@gmail.com>
Date: Mon, 6 Nov 2023 01:39:37 +0530
Subject: [PATCH 21/37] Changes to model description and plot

---
 .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb   | 207 +++++-------------
 1 file changed, 50 insertions(+), 157 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index 6657619dc..df21b9e31 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -15,7 +15,8 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "code_folding": []
+    "code_folding": [],
+    "is_executing": true
    },
    "outputs": [],
    "source": [
@@ -42,23 +43,23 @@
     "\n",
     "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n",
     "\n",
-    "\\begin{equation}\n",
+    "\\begin{align*}\n",
     "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n",
-    "\\end{equation}\n",
+    "\\end{align*}\n",
     "\n",
-    "has perfect foresight about everything except the (stochastic) date of death, which occurs with constant probability implying a \"survival probability\" $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb < 1$.  Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$.  At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. Between periods, the agent dies with probability $\\mathsf{D}_t$, ending his problem.\n",
+    "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\aleph_t$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$ and hold the rest in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. With probability $1-\\aleph_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n",
     "\n",
     "The agent's problem can be written in Bellman form as:\n",
     "\n",
-    "\\begin{eqnarray*}\n",
-    "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) + \\beta \\aleph V_{t+1}(M_{t+1},P_{t+1}), \\\\\n",
-    "& s.t. & \\\\\n",
-    "%A_t &=& M_t - C_t, \\\\\n",
-    "M_{t+1} &=& R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
-    "P_{t+1} &=& \\Gamma_{t+1} P_t, \\\\\n",
-    "\\end{eqnarray*}\n",
+    "\\begin{align*}\n",
+    "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n",
+    "&\\text{s.t.} \\\\\n",
+    "A_t &= M_t - C_t \\\\\n",
+    "M_{t+1} &= R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
+    "P_{t+1} &= \\Gamma_{t+1} P_t, \\\\\n",
+    "\\end{align*}\n",
     "\n",
-    "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\mathsf{\\aleph}_t\\}$.  To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an $\\textit{infinite horizon}$ consumer with constant income growth and survival probability.\n",
+    "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\aleph_t\\}$.  To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability.\n",
     "\n",
     "## Representing Agents in HARK\n",
     "\n",
@@ -162,9 +163,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$.  In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (paramterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem.\n",
+    "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$.  In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (parameterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem.\n",
     "\n",
-    "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem.  The consumption function stored as the first element (element 0) of the solution list can be retrieved by:"
+    "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem.  The consumption function stored as the first element (index 0) of the solution list can be retrieved by:"
    ]
   },
   {
@@ -175,7 +176,7 @@
     {
      "data": {
       "text/plain": [
-       "<HARK.interpolation.LinearInterp at 0x7f42bdc43e80>"
+       "<HARK.interpolation.LinearInterp at 0x15a9687cf40>"
       ]
      },
      "execution_count": 6,
@@ -191,7 +192,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One of the results proven in the associated [the lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$.\n",
+    "One of the results proven in the associated [lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$.\n",
     "\n",
     "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation.  It can be plotted between an $m$ ratio of 0 and 10 using the command below."
    ]
@@ -203,7 +204,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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2Pys59w9n7oRBWK16+Ee6ESwej4cFCxYwceJEUlNTr3u5/fv385//+Z8cOnSow7edm5vL888/39VpIiLSi9Q5L/Psu2XsrKgDYEzCbeTNSWdw/xCTl4k36XKwZGdnc/jwYfbv33/dy1y8eJHvfve7bNiwgcjIyA7f9rPPPsuiRYuuvu10OomPj+/qVBER8UKGYVBUepZlhYdpaG4jwGZl4dRkHp+UhE2nKvJnuhQs8+fP57333mPv3r0MHDjwupf7/PPPOXHiBDNnzrz6Po/Hc+UD+/lRWVnJ4MGD/+J6drsdu93elWkiItILnGtysbTwMFvLagEYGRvG+qzRDIsJNXmZeKtOBYthGDz55JMUFBSwe/duEhMTb3j54cOHU1ZW9rX3/eu//isXL17kP/7jP3RqIiLig7YfqWVJQRn1Ta34WS3Mv3cI2fcMwd/WqR9cFR/TqWDJzs5m06ZNFBYWEhoaSm3tlTJ2OBwEBV35UbPHHnuMuLg4cnNzCQwM/Ivnt9x2220AN3zei4iI9D2NzW0sLzpCwadnAEiODmF91mhS4xwmL5PeoFPB8uqrrwJw9913f+39b7zxBnPnzgXg1KlTWK2qZBER+V+7K+vI2VLKl04XVgs8MXkwC6YMxe5nM3ua9BIWwzAMs0d8E6fTicPhoLGxkbCwMLPniIhIBzW52ln1fjlvHTwNQFJkMHlZ6WQkhJu8TG6Fnrz/1msJiYjITXHg83qeyS+l5kILAPMmJvL0tGEEBehURTpPwSIiIj2qpdXNmm0VbDxwAoD4iCDWzU7nzqTbzR0mvZqCRUREekzxyfMs3lxKdf2V15h7ZHwCzz0wghC77m6ke/Q3SEREuu1ym5sXdxxlw77jeAyICQtkzew0Jif3N3ua9BEKFhER6ZaymkYWvXOIY3VNADyUMZBlM1NwBPmbvEz6EgWLiIh0SWu7h5d3VfHKrircHoPIEDu5maOYmhJt9jTpgxQsIiLSaRW1Tha9XUL5WScAM9IGsOLBVCKCA0xeJn2VgkVERDqs3e3htb3HeenDo7S5DcL7+bNyVioz0mLNniZ9nIJFREQ6pKquiac2l1ByugGAKSOiWZ2ZSlRooLnDxCcoWERE5IY8HoPXP6pm3fZKXO0eQgP9WD5zJJkZcVgsFrPniY9QsIiIyHWdOtfM4vwSDlafB+CuoZGsnZ3GAEeQycvE1yhYRETkLxiGwZufnGL11s9obnUTHGBjyfQUHh4Xr1MVMYWCRUREvuaLhhZytpSy71g9AOMTI8ibk058RD+Tl4kvU7CIiAhw5VQlv7iGFUXlXHS1Y/ezknP/cOZOGITVqlMVMZeCRUREqHNe5tl3y9hZUQfAmITbyJuTzuD+ISYvE7lCwSIi4sMMw6Co9CzLCg/T0NxGgM3KwqnJPD4pCZtOVcSLKFhERHzUuSYXSwsPs7WsFoCRsWGszxrNsJhQk5eJ/CUFi4iID9p+pJYlBWXUN7XiZ7Uw/94hZN8zBH+b1expItekYBER8SGNzW0sLzpCwadnAEiODmF91mhS4xwmLxO5MQWLiIiP2F1ZR86WUr50urBa4InJg1kwZSh2P5vZ00S+kYJFRKSPa3K1s+r9ct46eBqApMhg8rLSyUgIN3mZSMcpWERE+rADn9fzTH4pNRdaAJg3MZGnpw0jKECnKtK7KFhERPqgllY3a7ZVsPHACQDiI4JYNzudO5NuN3eYSBcpWERE+pjik+dZvLmU6vpLADwyPoHnHhhBiF3f8qX30t9eEZE+4nKbmxd3HGXDvuN4DIgJC2TN7DQmJ/c3e5pItylYRET6gLKaRha9c4hjdU0APJQxkGUzU3AE+Zu8TKRnKFhERHqx1nYPL++q4pVdVbg9BpEhdnIzRzE1JdrsaSI9SsEiItJLVdQ6WfR2CeVnnQDMSBvAigdTiQgOMHmZSM9TsIiI9DLtbg+v7T3OSx8epc1tEN7Pn5WzUpmRFmv2NJGbRsEiItKLVNU18dTmEkpONwAwZUQ0qzNTiQoNNHeYyE2mYBER6QU8HoPXP6pm3fZKXO0eQgP9WD5zJJkZcVgsFrPnidx0ChYRES936lwzi/NLOFh9HoC7hkaydnYaAxxBJi8TuXUULCIiXsowDN785BSrt35Gc6ub4AAbS6an8PC4eJ2qiM9RsIiIeKEvGlrI2VLKvmP1AIxPjCBvTjrxEf1MXiZiDgWLiIgXMQyD/OIaVhSVc9HVjt3PSs79w5k7YRBWq05VxHcpWEREvESd8zLPvlvGzoo6AMYk3EbenHQG9w8xeZmI+RQsIiImMwyDotKzLCs8TENzGwE2KwunJvP4pCRsOlURARQsIiKmOtfkYmnhYbaW1QIwMjaM9VmjGRYTavIyEe+iYBERMcn2I7UsKSijvqkVP6uF+fcOIfueIfjbrGZPE/E6ChYRkVussbmN5UVHKPj0DADJ0SGszxpNapzD5GUi3kvBIiJyC+2urCNnSylfOl1YLfDE5MEsmDIUu5/N7GkiXk3BIiJyCzS52ln1fjlvHTwNQFJkMHlZ6WQkhJu8TKR3ULCIiNxkBz6v55n8UmoutAAwb2IiT08bRlCATlVEOkrBIiJyk7S0ulmzrYKNB04AEB8RxLrZ6dyZdLu5w0R6IQWLiMhNUHzyPIs3l1JdfwmAR8Yn8NwDIwix69uuSFfoX46ISA+63ObmxR1H2bDvOB4DYsICWTM7jcnJ/c2eJtKrKVhERHpIWU0ji945xLG6JgAeyhjIspkpOIL8TV4m0vspWEREuqm13cPLu6p4ZVcVbo9BZIid3MxRTE2JNnuaSJ+hYBER6YaKWieL3i6h/KwTgBlpA1jxYCoRwQEmLxPpWxQsIiJd0O728Nre47z04VHa3Abh/fxZOSuVGWmxZk8T6ZMULCIinVRV18RTm0soOd0AwJQR0azOTCUqNNDcYSJ9mIJFRKSDPB6D1z+qZt32SlztHkID/Vg+cySZGXFYLBaz54n0aQoWEZEOOHWumcX5JRysPg/AXUMjWTs7jQGOIJOXifgGBYuIyA0YhsGbn5xi9dbPaG51ExxgY8n0FB4eF69TFZFbSMEiInIdXzS0kLOllH3H6gEYnxhB3px04iP6mbxMxPcoWERE/oxhGOQX17CiqJyLrnbsflZy7h/O3AmDsFp1qiJiBgWLiMhX1Dkv8+y7ZeysqANgTMJt5M1JZ3D/EJOXifg2BYuICFdOVYpKz7Ks8DANzW0E2KwsnJrM45OSsOlURcR0ChYR8XnnmlwsLTzM1rJaAEbGhrE+azTDYkJNXiYif6JgERGftv1ILUsKyqhvasXPamH+vUPIvmcI/jar2dNE5CsULCLikxqb21hedISCT88AkBwdwvqs0aTGOUxeJiLXomAREZ+zu7KOnC2lfOl0YbXAE5MHs2DKUOx+NrOnich1KFhExGc0udpZ9X45bx08DUBSZDB5WelkJISbvExEvomCRUR8woHP63kmv5SaCy0AzJuYyNPThhEUoFMVkd5AwSIifVpLq5s12yrYeOAEAPERQaybnc6dSbebO0xEOkXBIiJ9VvHJ8yzeXEp1/SUAHhmfwHMPjCDErm99Ir2N/tWKSJ9zuc3NizuOsmHfcTwGxIQFsmZ2GpOT+5s9TUS6SMEiIn1KWU0ji945xLG6JgAeyhjIspkpOIL8TV4mIt2hYBGRPqG13cPLu6p4ZVcVbo9BZIid3MxRTE2JNnuaiPQABYuI9HoVtU4WvV1C+VknADPSBrDiwVQiggNMXiYiPUXBIiK9Vrvbw2t7j/PSh0dpcxuE9/Nn5axUZqTFmj1NRHpYp14sIzc3l7FjxxIaGkpUVBSzZs2isrLyhtd59913ueOOO7jtttsIDg5m9OjR/OpXv+rWaBGRqromHvrF71m3vZI2t8GUEdFsXzhJsSLSR3XqhGXPnj1kZ2czduxY2tvbee6557jvvvsoLy8nODj4mteJiIhgyZIlDB8+nICAAN577z2+//3vExUVxbRp03rkkxAR3+HxGLz+UTXrtlfiavcQGujH8pkjycyIw2KxmD1PRG4Si2EYRlev/Mc//pGoqCj27NnDpEmTOny9jIwMpk+fzsqVKzt0eafTicPhoLGxkbCwsK7OFZFe7tS5Zhbnl3Cw+jwAdw2NZO3sNAY4gkxeJiLX0pP33916DktjYyNw5RSlIwzD4He/+x2VlZWsWbPmupdzuVy4XK6rbzudzu7MFJFezjAM3vzkFKu3fkZzq5vgABtLpqfw8Lh4naqI+IguB4vH42HBggVMnDiR1NTUG162sbGRuLg4XC4XNpuNn//850ydOvW6l8/NzeX555/v6jQR6UO+aGghZ0sp+47VAzA+MYK8OenER/QzeZmI3Epdfkjohz/8IR988AH79+9n4MCBN7ysx+Ph+PHjNDU1sXPnTlauXMlvf/tb7r777mte/lonLPHx8XpISMSHGIZBfnENK4rKuehqx+5nJef+4cydMAirVacqIr1BTz4k1KVgmT9/PoWFhezdu5fExMROf9Af/OAHnD59mu3bt3fo8noOi4hvqXNe5tl3y9hZUQfAmITbyJuTzuD+ISYvE5HOMO05LIZh8OSTT1JQUMDu3bu7FCtw5cTlqycoIiJw5XtMUelZlhUepqG5jQCblYVTk3l8UhI2naqI+LROBUt2djabNm2isLCQ0NBQamtrAXA4HAQFXXmW/mOPPUZcXBy5ubnAleej3HHHHQwePBiXy8XWrVv51a9+xauvvtrDn4qI9GbnmlwsLTzM1rIr31dGxoaxPms0w2JCTV4mIt6gU8Hyp8j48+eevPHGG8ydOxeAU6dOYbX+7++ju3TpEv/8z/9MTU0NQUFBDB8+nF//+tf83d/9XfeWi0ifsf1ILUsKyqhvasXPamH+vUPIvmcI/rZO/W5LEenDuvV7WG4VPYdFpG9qbG5jedERCj49A0BydAjrs0aTGucweZmI9ASv+T0sIiJdtbuyjpwtpXzpdGG1wBOTB7NgylDsfjazp4mIF1KwiMgt1eRqZ9X75bx18DQASZHB5GWlk5EQbvIyEfFmChYRuWUOfF7PM/ml1FxoAWDexESenjaMoACdqojIjSlYROSma2l1s2ZbBRsPnAAgPiKIdbPTuTPpdnOHiUivoWARkZuq+OR5Fm8upbr+EgCPjE/guQdGEGLXtx8R6Th9xxCRm+Jym5sXdxxlw77jeAyICQtkzew0Jif3N3uaiPRCChYR6XFlNY0seucQx+qaAHgoYyDLZqbgCPI3eZmI9FYKFhHpMa3tHl7eVcUru6pwewwiQ+zkZo5iakq02dNEpJdTsIhIj6iodbLo7RLKzzoBmJE2gBUPphIRHGDyMhHpCxQsItIt7W4Pr+09zksfHqXNbRDez5+Vs1KZkRZr9jQR6UMULCLSZVV1TTy1uYSS0w0ATBkRzerMVKJCA80dJiJ9joJFRDrN4zF4/aNq1m2vxNXuITTQj+UzR5KZEYfFYjF7noj0QQoWEemUU+eaWZxfwsHq8wDcNTSStbPTGOAIMnmZiPRlChYR6RDDMHjzk1Os3voZza1uggNsLJmewsPj4nWqIiI3nYJFRL7RFw0t5GwpZd+xegDGJ0aQNyed+Ih+Ji8TEV+hYBGR6zIMg/ziGlYUlXPR1Y7dz0rO/cOZO2EQVqtOVUTk1lGwiMg11Tkv8+y7ZeysqANgTMJt5M1JZ3D/EJOXiYgvUrCIyNcYhkFR6VmWFR6mobmNAJuVhVOTeXxSEjadqoiISRQsInLVuSYXSwsPs7WsFoCRsWGszxrNsJhQk5eJiK9TsIgIANuP1LKkoIz6plb8rBbm3zuE7HuG4G+zmj1NRETBIuLrGpvbWF50hIJPzwCQHB3C+qzRpMY5TF4mIvK/FCwiPmx3ZR05W0r50unCaoEnJg9mwZSh2P1sZk8TEfkaBYuID2pytbPq/XLeOngagKTIYPKy0slICDd5mYjItSlYRHzMgc/reSa/lJoLLQDMm5jI09OGERSgUxUR8V4KFhEf0dLqZs22CjYeOAFAfEQQ62anc2fS7eYOExHpAAWLiA8oPnmexZtLqa6/BMAj4xN47oERhNj1LUBEegd9txLpwy63uXlxx1E27DuOx4CYsEDWzE5jcnJ/s6eJiHSKgkWkjyqraWTRO4c4VtcEwEMZA1k2MwVHkL/Jy0REOk/BItLHtLZ7eHlXFa/sqsLtMYgMsZObOYqpKdFmTxMR6TIFi0gfUlHrZNHbJZSfdQIwI20AKx5MJSI4wORlIiLdo2AR6QPa3R5e23uclz48SpvbILyfPytnpTIjLdbsaSIiPULBItLLVdU18dTmEkpONwAwZUQ0qzNTiQoNNHeYiEgPUrCI9FIej8HrH1WzbnslrnYPoYF+LJ85ksyMOCwWi9nzRER6lIJFpBc6da6ZxfklHKw+D8BdQyNZOzuNAY4gk5eJiNwcChaRXsQwDN785BSrt35Gc6ub4AAbS6an8PC4eJ2qiEifpmAR6SW+aGghZ0sp+47VAzA+MYK8OenER/QzeZmIyM2nYBHxcoZhkF9cw4qici662rH7Wcm5fzhzJwzCatWpioj4BgWLiBerc17m2XfL2FlRB8CYhNvIm5PO4P4hJi8TEbm1FCwiXsgwDIpKz7Ks8DANzW0E2KwsnJrM45OSsOlURUR8kIJFxMuca3KxtPAwW8tqARgZG8b6rNEMiwk1eZmIiHkULCJeZPuRWpYUlFHf1Iqf1cL8e4eQfc8Q/G1Ws6eJiJhKwSLiBRqb21hedISCT88AkBwdwvqs0aTGOUxeJiLiHRQsIibbXVlHzpZSvnS6sFrgicmDWTBlKHY/m9nTRES8hoJFxCRNrnZWvV/OWwdPA5AUGUxeVjoZCeEmLxMR8T4KFhETHPi8nmfyS6m50ALAvImJPD1tGEEBOlUREbkWBYvILdTS6mbNtgo2HjgBQHxEEOtmp3Nn0u3mDhMR8XIKFpFbpPjkeRZvLqW6/hIAj4xP4LkHRhBi1z9DEZFvou+UIjfZ5TY3L+44yoZ9x/EYEBMWyJrZaUxO7m/2NBGRXkPBInITldU0suidQxyrawLgoYyBLJuZgiPI3+RlIiK9i4JF5CZobffw8q4qXtlVhdtjEBliJzdzFFNTos2eJiLSKylYRHpYRa2TRW+XUH7WCcCMtAGseDCViOAAk5eJiPReChaRHtLu9vDa3uO89OFR2twG4f38WTkrlRlpsWZPExHp9RQsIj2gqq6JpzaXUHK6AYApI6JZnZlKVGigucNERPoIBYtIN3g8Bq9/VM267ZW42j2EBvqxfOZIMjPisFgsZs8TEekzFCwiXXTqXDOL80s4WH0egLuGRrJ2dhoDHEEmLxMR6XsULCKdZBgGb35yitVbP6O51U1wgI0l01N4eFy8TlVERG4SBYtIJ3zR0ELOllL2HasHYHxiBHlz0omP6GfyMhGRvk3BItIBhmGQX1zDiqJyLrrasftZybl/OHMnDMJq1amKiMjNpmAR+QZ1zss8+24ZOyvqABiTcBt5c9IZ3D/E5GUiIr5DwSJyHYZhUFR6lmWFh2lobiPAZmXh1GQen5SETacqIiK3lIJF5BrONblYWniYrWW1AIyMDWN91miGxYSavExExDcpWET+zPYjtSwpKKO+qRU/q4X59w4h+54h+NusZk8TEfFZChaR/6exuY3lRUco+PQMAMnRIazPGk1qnMPkZSIiomARAXZX1pGzpZQvnS6sFnhi8mAWTBmK3c9m9jQREUHBIj6uydXOqvfLeevgaQCSIoPJy0onIyHc5GUiIvJVChbxWQc+r+eZ/FJqLrQAMG9iIk9PG0ZQgE5VRES8jYJFfE5Lq5s12yrYeOAEAPERQaybnc6dSbebO0xERK5LwSI+pfjkeRZvLqW6/hIAj4xP4LkHRhBi1z8FERFvpu/S4hMut7l5ccdRNuw7jseAmLBA1sxOY3Jyf7OniYhIByhYpM8rq2lk0TuHOFbXBMBDGQNZNjMFR5C/yctERKSjOvWbsHJzcxk7diyhoaFERUUxa9YsKisrb3idDRs2cNdddxEeHk54eDhTpkzh4MGD3Rot0hGt7R7W7zjKrJ9/xLG6JiJD7Gx47A7+PStdsSIi0st0Klj27NlDdnY2H3/8MTt27KCtrY377ruPS5cuXfc6u3fv5uGHH2bXrl38/ve/Jz4+nvvuu48zZ850e7zI9VTUOpn1ykf8dOcx3B6DGWkD+L8LJzE1JdrsaSIi0gUWwzCMrl75j3/8I1FRUezZs4dJkyZ16Dput5vw8HBefvllHnvssQ5dx+l04nA4aGxsJCwsrKtzxQe0uz28tvc4L314lDa3QXg/f1bOSmVGWqzZ00REfE5P3n936zksjY2NAERERHT4Os3NzbS1td3wOi6XC5fLdfVtp9PZ9ZHiM6rqmnhqcwklpxsAmDIimtWZqUSFBpo7TEREuq3LweLxeFiwYAETJ04kNTW1w9fLyckhNjaWKVOmXPcyubm5PP/8812dJj7G4zF4/aNq1m2vxNXuITTQj+UzR5KZEYfFYjF7noiI9IAuPyT0wx/+kA8++ID9+/czcODADl3nhRdeYO3atezevZu0tLTrXu5aJyzx8fF6SEj+wqlzzSzOL+Fg9XkA7hoaydrZaQxwBJm8TERETH9IaP78+bz33nvs3bu3w7GSl5fHCy+8wIcffnjDWAGw2+3Y7fauTBMfYRgGb35yitVbP6O51U1wgI0l01N4eFy8TlVERPqgTgWLYRg8+eSTFBQUsHv3bhITEzt0vbVr17Jq1Sq2b9/OHXfc0aWhIn/yRUMLOVtK2XesHoDxiRHkzUknPqKfyctERORm6VSwZGdns2nTJgoLCwkNDaW2thYAh8NBUNCVI/jHHnuMuLg4cnNzAVizZg3Lli1j06ZNDBo06Op1QkJCCAkJ6cnPRfo4wzDIL65hRVE5F13t2P2s5Nw/nLkTBmG16lRFRKQv69RzWK531P7GG28wd+5cAO6++24GDRrExo0bARg0aBAnT578i+v85Cc/Yfny5R36uPqxZqlzXubZd8vYWVEHwJiE28ibk87g/opeERFvZdpzWDrSNrt37/7a2ydOnOjMhxD5GsMwKCo9y7LCwzQ0txFgs7JwajKPT0rCplMVERGfodcSEq91rsnF0sLDbC278jDiyNgw1meNZlhMqMnLRETkVlOwiFfafqSWJQVl1De14me1MP/eIWTfMwR/W6deTUJERPoIBYt4lcbmNpYXHaHg0yuvNZUcHcL6rNGkxjlMXiYiImZSsIjX2F1ZR86WUr50urBa4InJg1kwZSh2P5vZ00RExGQKFjFdk6udVe+X89bB0wAkRQaTl5VORkK4yctERMRbKFjEVAc+r+eZ/FJqLrQAMG9iIk9PG0ZQgE5VRETkfylYxBQtrW7WbKtg44ETAMRHBLFudjp3Jt1u7jAREfFKCha55YpPnmfx5lKq6y8B8Mj4BJ57YAQhdv11FBGRa9M9hNwyl9vcvLjjKBv2HcdjQExYIGtmpzE5ub/Z00RExMspWOSWKKtpZNE7hzhW1wTAQxkDWTYzBUeQv8nLRESkN1CwyE3V2u7h5V1VvLKrCrfHIDLETm7mKKamRJs9TUREehEFi9w0FbVOFr1dQvlZJwAz0gaw4sFUIoIDTF4mIiK9jYJFely728Nre4/z0odHaXMbhPfzZ+WsVGakxZo9TUREeikFi/SoqromntpcQsnpBgCmjIhmdWYqUaGB5g4TEZFeTcEiPcLjMXj9o2rWba/E1e4hNNCP5TNHkpkRh8ViMXueiIj0cgoW6bZT55pZnF/CwerzANw1NJK1s9MY4AgyeZmIiPQVChbpMsMwePOTU6ze+hnNrW6CA2wsmZ7Cw+PidaoiIiI9SsEiXfJFQws5W0rZd6wegPGJEeTNSSc+op/Jy0REpC9SsEinGIZBfnENK4rKuehqx+5nJef+4cydMAirVacqIiJycyhYpMPqnJd59t0ydlbUATAm4Tby5qQzuH+IyctERKSvU7DINzIMg6LSsywrPExDcxsBNisLpybz+KQkbDpVERGRW0DBIjd0rsnF0sLDbC2rBWBkbBjrs0YzLCbU5GUiIuJLFCxyXduP1LKkoIz6plb8rBbm3zuE7HuG4G+zmj1NRER8jIJF/kJjcxvLi45Q8OkZAJKjQ1ifNZrUOIfJy0RExFcpWORrdlfWkbOllC+dLqwWeGLyYBZMGYrdz2b2NBER8WEKFgGgydXOqvfLeevgaQCSIoPJy0onIyHc5GUiIiIKFgEOfF7PM/ml1FxoAWDexESenjaMoACdqoiIiHdQsPiwllY3a7ZVsPHACQDiI4JYNzudO5NuN3eYiIjIn1Gw+Kjik+dZvLmU6vpLADwyPoHnHhhBiF1/JURExPvo3snHXG5z8+KOo2zYdxyPATFhgayZncbk5P5mTxMREbkuBYsPKatpZNE7hzhW1wTAQxkDWTYzBUeQv8nLREREbkzB4gNa2z28vKuKV3ZV4fYYRIbYyc0cxdSUaLOniYiIdIiCpY+rqHWy6O0Sys86AZiRNoAVD6YSERxg8jIREZGOU7D0Ue1uD6/tPc5LHx6lzW0Q3s+flbNSmZEWa/Y0ERGRTlOw9EFVdU08tbmEktMNAEwZEc3qzFSiQgPNHSYiItJFCpY+xOMxeP2jatZtr8TV7iE00I/lM0eSmRGHxWIxe56IiEiXKVj6iFPnmlmcX8LB6vMA3DU0krWz0xjgCDJ5mYiISPcpWHo5wzB485NTrN76Gc2tboIDbCyZnsLD4+J1qiIiIn2GgqUX+6KhhZwtpew7Vg/A+MQI8uakEx/Rz+RlIiIiPUvB0gsZhkF+cQ0risq56GrH7mcl5/7hzJ0wCKtVpyoiItL3KFh6mTrnZZ59t4ydFXUAjEm4jbw56QzuH2LyMhERkZtHwdJLGIZBUelZlhUepqG5jQCblYVTk3l8UhI2naqIiEgfp2DpBc41uVhaeJitZbUAjIwNY33WaIbFhJq8TERE5NZQsHi57UdqWVJQRn1TK35WC/PvHUL2PUPwt1nNniYiInLLKFi8VGNzG8uLjlDw6RkAkqNDWJ81mtQ4h8nLREREbj0FixfaXVlHzpZSvnS6sFrgicmDWTBlKHY/m9nTRERETKFg8SJNrnZWvV/OWwdPA5AUGUxeVjoZCeEmLxMRETGXgsVLHPi8nmfyS6m50ALAvImJPD1tGEEBOlURERFRsJispdXNmm0VbDxwAoD4iCDWzU7nzqTbzR0mIiLiRRQsJio+eZ7Fm0uprr8EwCPjE3jugRGE2PVlERER+SrdM5rgcpubF3ccZcO+43gMiAkLZM3sNCYn9zd7moiIiFdSsNxiZTWNLHrnEMfqmgB4KGMgy2am4AjyN3mZiIiI91Kw3CKt7R5e3lXFK7uqcHsMIkPs5GaOYmpKtNnTREREvJ6C5RaoqHWy6O0Sys86AZiRNoAVD6YSERxg8jIREZHeQcFyE7W7Pby29zgvfXiUNrdBeD9/Vs5KZUZarNnTREREehUFy01SVdfEU5tLKDndAMCUEdGszkwlKjTQ3GEiIiK9kIKlh3k8Bq9/VM267ZW42j2EBvqxfOZIMjPisFgsZs8TERHplRQsPejUuWYW55dwsPo8AHcNjWTt7DQGOIJMXiYiItK7KVh6gGEYvPnJKVZv/YzmVjfBATaWTE/h4XHxOlURERHpAQqWbvqioYWcLaXsO1YPwPjECPLmpBMf0c/kZSIiIn2HgqWLDMMgv7iGFUXlXHS1Y/ezknP/cOZOGITVqlMVERGRnqRg6YI652WefbeMnRV1AIxJuI28OekM7h9i8jIREZG+ScHSCYZhUFR6lmWFh2lobiPAZmXh1GQen5SETacqIiIiN42CpYPONblYWniYrWW1AIyMDWN91miGxYSavExERKTvU7B0wPYjtSwpKKO+qRU/q4X59w4h+54h+NusZk8TERHxCQqWG2hsbmN50REKPj0DQHJ0COuzRpMa5zB5mYiIiG9RsFzH7so6craU8qXThdUCT0wezIIpQ7H72cyeJiIi4nMULH+mydXOqvfLeevgaQCSIoPJy0onIyHc5GUiIiK+S8HyFQc+r+eZ/FJqLrQAMG9iIk9PG0ZQgE5VREREzKRgAVpa3azZVsHGAycAiI8IYt3sdO5Mut3cYSIiIgJAp37MJTc3l7FjxxIaGkpUVBSzZs2isrLyhtc5cuQIDz30EIMGDcJisfDSSy91Z2+PKz55ngd+uu9qrDwyPoEPfjRJsSIiIuJFOhUse/bsITs7m48//pgdO3bQ1tbGfffdx6VLl657nebmZpKSknjhhReIiYnp9uCecrnNTe7Wz5jzi99TXX+JmLBA/s+8caz+21GE2HXwJCIi4k06dc+8bdu2r729ceNGoqKiKC4uZtKkSde8ztixYxk7diwAP/7xj7s4s2eV1TSy6J1DHKtrAuChjIEsm5mCI8jf5GUiIiJyLd06SmhsbAQgIiKiR8bcbK3tHl7eVcUru6pwewwiQ+zkZo5iakq02dNERETkBrocLB6PhwULFjBx4kRSU1N7chMulwuXy3X1bafT2e3brKh1sujtEsrPXrmtGWkDWPFgKhHBAd2+bREREbm5uhws2dnZHD58mP379/fkHuDKk3uff/75HrmtdreH1/Ye56UPj9LmNgjv58/KWanMSIvtkdsXERGRm69LwTJ//nzee+899u7dy8CBA3t6E88++yyLFi26+rbT6SQ+Pr7Tt1NV18RTm0soOd0AwJQR0azOTCUqNLCnpoqIiMgt0KlgMQyDJ598koKCAnbv3k1iYuJNGWW327Hb7V2+vsdj8PpH1azbXomr3UNooB/LZ44kMyMOi8XSg0tFRETkVuhUsGRnZ7Np0yYKCwsJDQ2ltrYWAIfDQVBQEACPPfYYcXFx5ObmAtDa2kp5efnV/33mzBkOHTpESEgIQ4YM6cnPBYBT55pZnF/CwerzANw1NJK1s9MY4Ajq8Y8lIiIit4bFMAyjwxe+zunEG2+8wdy5cwG4++67GTRoEBs3bgTgxIkT1zyJmTx5Mrt37+7Qx3U6nTgcDhobGwkLC7vmZQzD4M1PTrF662c0t7oJDrCxZHoKD4+L16mKiIiICTpy/91RnX5I6Jv8eYQMGjSoQ9frji8aWsjZUsq+Y/UAjE+MIG9OOvER/W7qxxUREZFbo1f/SlfDMMgvrmFFUTkXXe3Y/azk3D+cuRMGYbXqVEVERKSv6LXBUue8zLPvlrGzog6AMQm3kTcnncH9Q0xeJiIiIj2t1wWLYRgUlZ5lWeFhGprbCLBZWTg1mccnJWHTqYqIiEif1KuC5XyTix8XHWNr2ZWfThoZG8b6rNEMiwk1eZmIiIjcTL0qWP725x9xod0fP6uF+fcOIfueIfjbOvWC0yIiItIL9apgOXepjeEJ4azPGk1qnMPsOSIiInKL9Kpg+ce/TuTHD47B7mcze4qIiIjcQr3q8ZSFU5MVKyIiIj6oVwWLiIiI+CYFi4iIiHg9BYuIiIh4PQWLiIiIeD0Fi4iIiHg9BYuIiIh4PQWLiIiIeD0Fi4iIiHg9BYuIiIh4PQWLiIiIeD0Fi4iIiHg9BYuIiIh4PQWLiIiIeD0Fi4iIiHg9P7MHdIRhGAA4nU6Tl4iIiEhH/el++0/3493RK4Ll3LlzAMTHx5u8RERERDrr3LlzOByObt1GrwiWiIgIAE6dOtXtT1i6x+l0Eh8fz+nTpwkLCzN7jk/T18J76GvhXfT18B6NjY0kJCRcvR/vjl4RLFbrlafaOBwO/eXzEmFhYfpaeAl9LbyHvhbeRV8P7/Gn+/Fu3UYP7BARERG5qRQsIiIi4vV6RbDY7XZ+8pOfYLfbzZ7i8/S18B76WngPfS28i74e3qMnvxYWoyd+1khERETkJuoVJywiIiLi2xQsIiIi4vUULCIiIuL1FCwiIiLi9bw+WF555RUGDRpEYGAg48eP5+DBg2ZP8km5ubmMHTuW0NBQoqKimDVrFpWVlWbP8nkvvPACFouFBQsWmD3FZ505c4Z/+Id/4PbbbycoKIhRo0bx3//932bP8jlut5ulS5eSmJhIUFAQgwcPZuXKlT3yGjZyY3v37mXmzJnExsZisVj47W9/+7X/bhgGy5YtY8CAAQQFBTFlyhSOHTvW6Y/j1cHy9ttvs2jRIn7yk5/wP//zP6SnpzNt2jTq6urMnuZz9uzZQ3Z2Nh9//DE7duygra2N++67j0uXLpk9zWf94Q9/4LXXXiMtLc3sKT7rwoULTJw4EX9/fz744APKy8v593//d8LDw82e5nPWrFnDq6++yssvv8xnn33GmjVrWLt2LT/72c/MntbnXbp0ifT0dF555ZVr/ve1a9fy05/+lF/84hd88sknBAcHM23aNC5fvty5D2R4sXHjxhnZ2dlX33a73UZsbKyRm5tr4ioxDMOoq6szAGPPnj1mT/FJFy9eNIYOHWrs2LHDmDx5svGjH/3I7Ek+KScnx/jrv/5rs2eIYRjTp0835s2b97X3ZWZmGo8++qhJi3wTYBQUFFx92+PxGDExMca6deuuvq+hocGw2+3GW2+91anb9toTltbWVoqLi5kyZcrV91mtVqZMmcLvf/97E5cJXHlBK6BHXtBKOi87O5vp06d/7d+H3Hr/9V//xR133MGcOXOIiopizJgxbNiwwexZPmnChAns3LmTo0ePAlBSUsL+/fv5m7/5G5OX+bbq6mpqa2u/9r3K4XAwfvz4Tt+Xe+2LH9bX1+N2u4mOjv7a+6Ojo6moqDBplQB4PB4WLFjAxIkTSU1NNXuOz/nNb37D//zP//CHP/zB7Ck+7/jx47z66qssWrSI5557jj/84Q/8y7/8CwEBAXzve98ze55P+fGPf4zT6WT48OHYbDbcbjerVq3i0UcfNXuaT6utrQW45n35n/5bR3ltsIj3ys7O5vDhw+zfv9/sKT7n9OnT/OhHP2LHjh0EBgaaPcfneTwe7rjjDlavXg3AmDFjOHz4ML/4xS8ULLfYO++8w5tvvsmmTZsYOXIkhw4dYsGCBcTGxupr0Ud47UNCkZGR2Gw2vvzyy6+9/8svvyQmJsakVTJ//nzee+89du3axcCBA82e43OKi4upq6sjIyMDPz8//Pz82LNnDz/96U/x8/PD7XabPdGnDBgwgJSUlK+9b8SIEZw6dcqkRb7r6aef5sc//jF///d/z6hRo/jud7/LwoULyc3NNXuaT/vT/XVP3Jd7bbAEBATw7W9/m507d159n8fjYefOnfzVX/2Vict8k2EYzJ8/n4KCAn73u9+RmJho9iSf9J3vfIeysjIOHTp09c8dd9zBo48+yqFDh7DZbGZP9CkTJ078ix/vP3r0KN/61rdMWuS7mpubsVq/fpdms9nweDwmLRKAxMREYmJivnZf7nQ6+eSTTzp9X+7VDwktWrSI733ve9xxxx2MGzeOl156iUuXLvH973/f7Gk+Jzs7m02bNlFYWEhoaOjVxx4dDgdBQUEmr/MdoaGhf/G8oeDgYG6//XY9n8gECxcuZMKECaxevZqsrCwOHjzIL3/5S375y1+aPc3nzJw5k1WrVpGQkMDIkSP59NNPWb9+PfPmzTN7Wp/X1NREVVXV1berq6s5dOgQERERJCQksGDBAv7t3/6NoUOHkpiYyNKlS4mNjWXWrFmd+0A99JNMN83PfvYzIyEhwQgICDDGjRtnfPzxx2ZP8knANf+88cYbZk/zefqxZnMVFRUZqampht1uN4YPH2788pe/NHuST3I6ncaPfvQjIyEhwQgMDDSSkpKMJUuWGC6Xy+xpfd6uXbuuef/wve99zzCMKz/avHTpUiM6Otqw2+3Gd77zHaOysrLTH8diGPo1gCIiIuLdvPY5LCIiIiJ/omARERERr6dgEREREa+nYBERERGvp2ARERERr6dgEREREa+nYBERERGvp2ARERERr6dgEREREa+nYBERERGvp2ARERERr6dgEREREa/3/wOBOabCH8MH+AAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -268,7 +269,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -315,7 +316,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -353,7 +354,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -383,16 +384,16 @@
     "\n",
     "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines.  Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n",
     "\n",
-    "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$.  (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\n",
+    "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n",
     "\n",
-    "\\begin{eqnarray*}\n",
-    "v_t(m_t) &=& \\max_{c_t} ~ U(c_t) ~ +  \\phantom{\\LivFac} \\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ], \\\\\n",
-    "a_t &=& m_t - c_t, \\\\\n",
-    "a_t &\\geq& \\underset{\\bar{}}{a}, \\\\\n",
-    "m_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n",
-    "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1, \\\\\n",
-    "u(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n",
-    "\\end{eqnarray*}\n",
+    "\\begin{align*}\n",
+    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
+    "a_t &= m_t - c_t \\\\\n",
+    "a_t &\\geq \\underline{a} \\\\\n",
+    "m_{t+1} &= R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
+    "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n",
+    "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n",
+    "\\end{align*}\n",
     "\n",
     "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$.  To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks?  Can't the joint distribution of permanent and transitory shocks be just about anything?\n",
     "\n",
@@ -497,75 +498,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "GPFRaw                 = 0.985648 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFNrm                 = 0.994897 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFAggLivPrb           = 0.965935 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Thorn = APF            = 0.995505 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "PermGroFacAdj          = 1.000611 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "uInvEpShkuInv          = 0.988401 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "VAF                    = 0.929888 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WRPF                   = 0.289257 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DiscFacGPFNrmMax       = 0.972357 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "GPFRaw                 = 0.985648 \n",
+      "GPFNrm                 = 0.994897 \n",
+      "GPFAggLivPrb           = 0.965935 \n",
+      "Thorn = APF            = 0.995505 \n",
+      "PermGroFacAdj          = 1.000611 \n",
+      "uInvEpShkuInv          = 0.988401 \n",
+      "VAF                    = 0.929888 \n",
+      "WRPF                   = 0.289257 \n",
+      "DiscFacGPFNrmMax       = 0.972357 \n",
       "DiscFacGPFAggLivPrbMax = 1.015641 \n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -601,69 +548,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "GPFRaw                 = 0.985648 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFNrm                 = 1.023116 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFAggLivPrb           = 0.965935 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Thorn = APF            = 0.995505 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "PermGroFacAdj          = 0.973012 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "uInvEpShkuInv          = 0.954556 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "VAF                    = 0.898046 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WRPF                   = 0.289257 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DiscFacGPFNrmMax       = 0.906690 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "GPFRaw                 = 0.985648 \n",
+      "GPFNrm                 = 1.023116 \n",
+      "GPFAggLivPrb           = 0.965935 \n",
+      "Thorn = APF            = 0.995505 \n",
+      "PermGroFacAdj          = 0.973012 \n",
+      "uInvEpShkuInv          = 0.954556 \n",
+      "VAF                    = 0.898046 \n",
+      "WRPF                   = 0.289257 \n",
+      "DiscFacGPFNrmMax       = 0.906690 \n",
       "DiscFacGPFAggLivPrbMax = 1.015641 \n"
      ]
     }
@@ -716,7 +609,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.18"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

From cb66d463dd5a82f95d6dbcf63469f88da44ea520 Mon Sep 17 00:00:00 2001
From: Siddarth Venkatesh <72447516+sidd3888@users.noreply.github.com>
Date: Mon, 6 Nov 2023 18:39:42 +0530
Subject: [PATCH 22/37] Remove time subscript on PermGroFac

The code and the earlier model assumes $\Gamma_t$ to be a constant, so changed the notation in the later model as well
---
 examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index df21b9e31..57c311e47 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -387,10 +387,10 @@
     "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n",
     "\n",
     "\\begin{align*}\n",
-    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
+    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
     "a_t &= m_t - c_t \\\\\n",
     "a_t &\\geq \\underline{a} \\\\\n",
-    "m_{t+1} &= R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
+    "m_{t+1} &= R/(\\Gamma \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
     "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n",
     "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n",
     "\\end{align*}\n",

From 1a2a1af4652f09559c8879c9d15000b365e9b5c8 Mon Sep 17 00:00:00 2001
From: Alan Lujan <alanlujan91@gmail.com>
Date: Mon, 6 Nov 2023 08:34:49 -0500
Subject: [PATCH 23/37] notation review

---
 .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb   | 1321 ++++++++---------
 1 file changed, 644 insertions(+), 677 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index 57c311e47..49463f273 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -1,677 +1,644 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# A Gentle Introduction to HARK\n",
-    "\n",
-    "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users.  It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK.  Our aim is to convey a feel for how the toolkit works.\n",
-    "\n",
-    "For readers for whom this is your very first experience with Python, we have put important Python concepts in **boldface**. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in _italics_. Only cursory definitions (if any) are provided here.  If you want to learn more, there are many online Python and Jupyter tutorials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "code_folding": [],
-    "is_executing": true
-   },
-   "outputs": [],
-   "source": [
-    "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n",
-    "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n",
-    "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n",
-    "#   The most common problem beginners have is to execute a cell before all its predecessors\n",
-    "#   If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import HARK\n",
-    "from copy import deepcopy\n",
-    "\n",
-    "mystr = lambda number: \"{:.4f}\".format(number)\n",
-    "from HARK.utilities import plot_funcs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Your First HARK Model: Perfect Foresight\n",
-    "\n",
-    "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n",
-    "\n",
-    "\\begin{align*}\n",
-    "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n",
-    "\\end{align*}\n",
-    "\n",
-    "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\aleph_t$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$ and hold the rest in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. With probability $1-\\aleph_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n",
-    "\n",
-    "The agent's problem can be written in Bellman form as:\n",
-    "\n",
-    "\\begin{align*}\n",
-    "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n",
-    "&\\text{s.t.} \\\\\n",
-    "A_t &= M_t - C_t \\\\\n",
-    "M_{t+1} &= R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
-    "P_{t+1} &= \\Gamma_{t+1} P_t, \\\\\n",
-    "\\end{align*}\n",
-    "\n",
-    "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\aleph_t\\}$.  To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability.\n",
-    "\n",
-    "## Representing Agents in HARK\n",
-    "\n",
-    "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$.  To make agents of this class, we must import the class itself into our workspace.  (Run the cell below in order to do this)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/).\n",
-    "\n",
-    "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have.  In the hidden cell below, we define a $\\textbf{dictionary}$ named $\\texttt{PF_dictionary}$ with these parameter values:\n",
-    "\n",
-    "| Param | Description | Code | Value |\n",
-    "| :---: | ---  | --- | :---: |\n",
-    "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n",
-    "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n",
-    "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n",
-    "| $\\aleph$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n",
-    "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n",
-    "\n",
-    "\n",
-    "For now, don't worry about the specifics of dictionaries.  All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n",
-    "PF_dictionary = {\n",
-    "    \"CRRA\": 2.5,\n",
-    "    \"DiscFac\": 0.96,\n",
-    "    \"Rfree\": 1.03,\n",
-    "    \"LivPrb\": [0.98],\n",
-    "    \"PermGroFac\": [1.01],\n",
-    "    \"T_cycle\": 1,\n",
-    "    \"cycles\": 0,\n",
-    "    \"AgentCount\": 10000,\n",
-    "}\n",
-    "\n",
-    "# To those curious enough to open this hidden cell, you might notice that we defined\n",
-    "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n",
-    "# worry about these for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make an **object** named $\\texttt{PFexample}$ which is an **instance** of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in $\\texttt{PF_dictionary}$.  Such a bundle is created passing $\\texttt{PF_dictionary}$ to the class $\\texttt{PerfForesightConsumerType}$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PFexample = PerfForesightConsumerType(**PF_dictionary)\n",
-    "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem.\n",
-    "\n",
-    "## Solving an Agent's Problem\n",
-    "\n",
-    "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ **method**. (A method is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n",
-    "\n",
-    "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PFexample.solve()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$.  In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (parameterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem.\n",
-    "\n",
-    "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem.  The consumption function stored as the first element (index 0) of the solution list can be retrieved by:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<HARK.interpolation.LinearInterp at 0x15a9687cf40>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PFexample.solution[0].cFunc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One of the results proven in the associated [lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$.\n",
-    "\n",
-    "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation.  It can be plotted between an $m$ ratio of 0 and 10 using the command below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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iA0YdLIZhsGHDBrKyskhLS7vr5ZqamigsLOQ3v/kNwcHD+3JFRUVs3759tNNERMSPdPcPsePIWcprLgKQHBNGaX4mmQkPmDtMfMqog+XZZ5+ltraW6urqu17G4/Hw3e9+l+3bt/PQQw8N+7Y3b97Mhg0b7nzc1dVFQkLCaKeKiIiPevvTdjYdqOVSRx8WC6xZlETBkjnYQ3SqIl82qsewrFu3jsOHD3Pq1CmSkpLuermOjg4iIyMJCvp/f/G8Xi+GYRAUFMSxY8f47//9v3/j19NjWEREJpfeQTfFRxvY+24LALOjplKS5+TRpHs/xED8i2mPYTEMg3Xr1nHo0CFOnjx5z1gBiIiIoK6u7kuf++d//mdOnDjBgQMHvvH6IiIy+Zw+f4OC/Wdoud4LwOqFiRTmziXMppcGk7sb0d+OtWvX8uqrr1JRUUF4eDhXrlwBwOFwMGXKFOD2j3MuXbrE3r17sVqtX3l8y4wZM7Db7fd83IuIiEw+/UMeSo818nL1OQwD4hx2dq1ykpUSbfY08QMjCpaysjIAsrOzv/T5PXv28NRTTwFw+fJlPv/883EZJyIik4PrQgcby100X7v9ul158+PZuvRhIuwhJi8TfzGm12GZKHoMi4iIfxp0e9n9ZhNlVc14vAYx4TaKV6aTMy/W7GkyAXzmdVhERETu5mxrFxvKXTRc6QZgmTOO7ctSiQwLNXmZ+CMFi4iIjCu3x0vZyWZ2n2hiyGMQFRbKzhVp5KbPMnua+DEFi4iIjJumtm427j9D7cXbr4S+JDWWnU+mEz3NZvIy8XcKFhERGTOP1+CV6s8oOfYJg24vEfZgXliexvLMOCwWvbOyjJ2CRURExuR8ew8F+89Q03ITgOw5MRSvzGCmw27yMplMFCwiIjIqXq/BvvdbKHq9gb4hD9NswWx9Yh75CxJ0qiLjTsEiIiIjdvFmL5sO1PJO83UAHkuezq5VGcRHTjV5mUxWChYRERk2wzAor7nAjiMfc2vAjT3EyubceaxemIjVqlMVuX8ULCIiMixtXf0UHqzlrcZrAMxPjKQkz0lSdJjJyyQQKFhEROSeDMOgwtXKtsp6OvuGCA22UrD4IdZkPUiQTlVkgihYRETkrtpvDbDlUB1v1LcBkBHvoDTPSUpsuMnLJNAoWERE5GsdrbvMlsMfcaNnkGCrhfU5KTydnUxIkNXsaRKAFCwiIvIlHb2DbKusp8LVCsDcmeGU5jtJjXOYvEwCmYJFRETuONHQRuHBOq52D2C1wDPZyTyXk4ItOMjsaRLgFCwiIkJ3/xA7jpylvOYiAMkxYZTmZ5KZ8IC5w0T+k4JFRCTAvf1pO5sO1HKpow+LBdYsSqJgyRzsITpVEd+hYBERCVC9g26Kjzaw990WAGZHTaUkz8mjSVEmLxP5KgWLiEgAOn3+BgX7z9ByvReA1QsTKcydS5hNdwvim/Q3U0QkgPQPeSg91sjL1ecwDIhz2Nm1yklWSrTZ00TuScEiIhIgXBc62FjuovlaDwB58+PZuvRhIuwhJi8T+WYKFhGRSW7Q7WX3m02UVTXj8RrEhNsoXplOzrxYs6eJDJuCRURkEjvb2sWGchcNV7oBWOaMY/uyVCLDQk1eJjIyChYRkUnI7fFSdrKZ3SeaGPIYRIWFsnNFGrnps8yeJjIqChYRkUmmqa2bjfvPUHuxE4AlqbHsfDKd6Gk2k5eJjJ6CRURkkvB4DV6p/oySY58w6PYSYQ/mheVpLM+Mw2KxmD1PZEwULCIik8D59h4K9p+hpuUmANlzYihemcFMh93kZSLjQ8EiIuLHvF6Dfe+3UPR6A31DHqbZgtn6xDzyFyToVEUmFQWLiIifunizl00Hanmn+ToAjyVPZ9eqDOIjp5q8TGT8KVhERPyMYRiU11xgx5GPuTXgxh5iZXPuPFYvTMRq1amKTE4KFhERP9LW1U/hwVrearwGwPzESErynCRFh5m8TOT+UrCIiPgBwzCocLWyrbKezr4hQoOtFCx+iDVZDxKkUxUJAAoWEREf135rgC2H6nijvg2AjHgHpXlOUmLDTV4mMnEULCIiPuxo3WW2HP6IGz2DBFstrM9J4ensZEKCrGZPE5lQChYRER/U0TvItsp6KlytAMydGU5pvpPUOIfJy0TMoWAREfExJxraKDxYx9XuAawWeCY7medyUrAFB5k9TcQ0ChYRER/R3T/EjiNnKa+5CEByTBil+ZlkJjxg7jARH6BgERHxAW9/2s6mA7Vc6ujDYoE1i5IoWDIHe4hOVURAwSIiYqreQTfFRxvY+24LALOjplKS5+TRpCiTl4n4FgWLiIhJTp+/QcH+M7Rc7wVg9cJECnPnEmbTP80i/5X+rxARmWD9Qx5KjzXycvU5DAPiHHZ2rXKSlRJt9jQRn6VgERGZQK4LHWwsd9F8rQeAvPnxbF36MBH2EJOXifg2BYuIyAQYdHvZ/WYTZVXNeLwGMeE2ilemkzMv1uxpIn5BwSIicp+dbe1iQ7mLhivdACxzxrF9WSqRYaEmLxPxHwoWEZH7xO3xUnaymd0nmhjyGESFhbJzRRq56bPMnibidxQsIiL3QVNbNxv3n6H2YicAS1Jj2flkOtHTbCYvE/FPChYRkXHk8Rq8Uv0ZJcc+YdDtJcIezAvL01ieGYfFYjF7nojfUrCIiIyT8+09FOw/Q03LTQCy58RQvDKDmQ67yctE/J+CRURkjLxeg33vt1D0egN9Qx6m2YLZ+sQ88hck6FRFZJwoWERExuDizV42HajlnebrADyWPJ1dqzKIj5xq8jKRyUXBIiIyCoZhUF5zgR1HPubWgBt7iJXNufNYvTARq1WnKiLjTcEiIjJCbV39FB6s5a3GawDMT4ykJM9JUnSYyctEJi8Fi4jIMBmGQYWrlW2V9XT2DREabKVg8UOsyXqQIJ2qiNxXChYRkWFovzXAlkN1vFHfBkBGvIPSPCcpseEmLxMJDAoWEZFvcLTuMlsOf8SNnkGCrRbW56TwdHYyIUFWs6eJBAwFi4jIXXT0DrKtsp4KVysAc2eGU5rvJDXOYfIykcCjYBER+RonGtooPFjH1e4BrBZ4JjuZ53JSsAUHmT1NJCApWEREfkt3/xA7jpylvOYiAMkxYZTmZ5KZ8IC5w0QCnIJFROQ/vf1pO5sO1HKpow+LBdYsSqJgyRzsITpVETGbgkVEAl7voJviow3sfbcFgNlRUynJc/JoUpTJy0TkCwoWEQlop8/foGD/GVqu9wKwemEihblzCbPpn0cRX6L/I0UkIPUPeSg91sjL1ecwDIhz2Nm1yklWSrTZ00TkayhYRCTguC50sLHcRfO1HgDy5sezdenDRNhDTF4mInejYBGRgDHo9rL7zSbKqprxeA1iwm0Ur0wnZ16s2dNE5BsoWEQkIJxt7WJDuYuGK90ALHPGsX1ZKpFhoSYvE5HhULCIyKTm9ngpO9nM7hNNDHkMosJC2bkijdz0WWZPE5ERULCIyKTV1NbNxv1nqL3YCcCS1Fh2PplO9DSbyctEZKQULCIy6Xi8Bq9Uf0bJsU8YdHuJsAfzwvI0lmfGYbFYzJ4nIqOgYBGRSeV8ew8F+89Q03ITgOw5MRSvzGCmw27yMhEZCwWLiEwKXq/BvvdbKHq9gb4hD9NswWx9Yh75CxJ0qiIyCShYRMTvXbzZy6YDtbzTfB2Ax5Kns2tVBvGRU01eJiLjRcEiIn7LMAzKay6w48jH3BpwYw+xsjl3HqsXJmK16lRFZDKxjuTCRUVFPPLII4SHhzNjxgxWrFhBY2PjPa9TXV3NokWLmD59OlOmTGHu3Ln8/d///ZhGi4i0dfXzw1+c5kcH67g14GZ+YiRH1z/ODx77HcWKyCQ0ohOWqqoq1q5dyyOPPILb7WbLli0sXryYs2fPEhYW9rXXCQsL49lnnyUjI4OwsDCqq6v5i7/4C8LCwvjzP//zcflDiEjgMAyDClcr2yrr6ewbIjTYSsHih1iT9SBBChWRSctiGIYx2itfu3aNGTNmUFVVxeOPPz7s661cuZKwsDD+5V/+ZViX7+rqwuFw0NnZSURExGjnioifa781wJZDdbxR3wZARryD0jwnKbHhJi8Tka8znvffY3oMS2fn7RdjioqKGvZ1PvzwQ9555x1+8pOf3PUyAwMDDAwM3Pm4q6tr9CNFZFI4WneZLYc/4kbPIMFWC+tzUng6O5mQoBH9ZFtE/NSog8UwDDZs2EBWVhZpaWnfePn4+HiuXbuG2+3mxz/+MX/2Z39218sWFRWxffv20U4TkUmko3eQbZX1VLhaAZg7M5zSfCepcQ6Tl4nIRBr1j4TWrl3La6+9RnV1NfHx8d94+XPnznHr1i3ee+89CgsLefHFF/nTP/3Tr73s152wJCQk6EdCIgHmREMbhQfruNo9gNUCz2Qn81xOCrbgILOnicgwmP4joXXr1lFZWcmpU6eGFSsASUlJAKSnp9PW1saPf/zjuwaLzWbDZtN7fYgEqu7+IXYcOUt5zUUAkmPCKM3PJDPhAXOHiYhpRhQshmGwbt06Dh06xMmTJ+9EyEgZhvGlExQRkS+8/Wk7mw7UcqmjD4sF1ixKomDJHOwhOlURCWQjCpa1a9fy6quvUlFRQXh4OFeuXAHA4XAwZcoUADZv3sylS5fYu3cvAP/0T//E7NmzmTt3LnD7dVlKSkpYt27deP45RMTP9Q66KT7awN53WwCYHTWVkjwnjyYN/0H9IjJ5jShYysrKAMjOzv7S5/fs2cNTTz0FwOXLl/n888/v/J7X62Xz5s2cO3eO4OBgkpOTKS4u5i/+4i/GtlxEJo3T529QsP8MLdd7AVi9MJHC3LmE2fRi3CJy25heh2Wi6HVYRCan/iEPpccaebn6HIYBcQ47u1Y5yUqJNnuaiIwD0x90KyIyVq4LHWwsd9F8rQeAvPnxbF36MBH2EJOXiYgvUrCIyIQadHvZ/WYTZVXNeLwGMeE2ilemkzMv1uxpIuLDFCwiMmHOtnaxodxFw5VuAJY549i+LJXIsFCTl4mIr1OwiMh95/Z4KTvZzO4TTQx5DKLCQtm5Io3c9FlmTxMRP6FgEZH7qqmtm437z1B78fZ7jy1JjWXnk+lET9OLQ4rI8ClYROS+8HgNXqn+jJJjnzDo9hJhD+aF5Wksz4zDYrGYPU9E/IyCRUTG3fn2Hgr2n6Gm5SYA2XNiKF6ZwUyH3eRlIuKvFCwiMm68XoN977dQ9HoDfUMeptmC2frEPPIXJOhURUTGRMEiIuPi4s1eNh2o5Z3m6wA8ljydXasyiI+cavIyEZkMFCwiMiaGYVBec4EdRz7m1oAbe4iVzbnzWL0wEatVpyoiMj4ULCIyam1d/RQerOWtxmsAzE+MpCTPSVJ0mMnLRGSyUbCIyIgZhkGFq5VtlfV09g0RGmylYPFDrMl6kCCdqojIfaBgEZERab81wJZDdbxR3wZARryD0jwnKbHhJi8TkclMwSIiw3a07jJbDn/EjZ5Bgq0W1uek8HR2MiFBVrOnicgkp2ARkW/U0TvItsp6KlytAMydGU5pvpPUOIfJy0QkUChYROSeTjS0UXiwjqvdA1gt8Ex2Ms/lpGALDjJ7mogEEAWLiHyt7v4hdhw5S3nNRQCSY8Iozc8kM+EBc4eJSEBSsIjIV7z9aTubDtRyqaMPiwXWLEqiYMkc7CE6VRERcyhYROSO3kE3xUcb2PtuCwCzo6ZSkufk0aQok5eJSKBTsIgIAKfP36Bg/xlarvcCsHphIoW5cwmz6Z8JETGf/iUSCXD9Qx5KjzXycvU5DAPiHHZ2rXKSlRJt9jQRkTsULCIBzHWhg43lLpqv9QCQNz+erUsfJsIeYvIyEZEvU7CIBKBBt5fdbzZRVtWMx2sQE26jeGU6OfNizZ4mIvK1FCwiAeZsaxcbyl00XOkGYJkzju3LUokMCzV5mYjI3SlYRAKE2+Ol7GQzu080MeQxiAoLZeeKNHLTZ5k9TUTkGylYRAJAU1s3G/efofZiJwBLUmPZ+WQ60dNsJi8TERkeBYvIJObxGrxS/Rklxz5h0O0lwh7MC8vTWJ4Zh8ViMXueiMiwKVhEJqnz7T0U7D9DTctNALLnxFC8MoOZDrvJy0RERk7BIjLJeL0G+95voej1BvqGPEyzBbP1iXnkL0jQqYqI+C0Fi8gkcvFmL5sO1PJO83UAHkuezq5VGcRHTjV5mYjI2ChYRCYBwzAor7nAjiMfc2vAjT3EyubceaxemIjVqlMVEfF/ChYRP9fW1U/hwVrearwGwPzESErynCRFh5m8TERk/ChYRPyUYRhUuFrZVllPZ98QocFWChY/xJqsBwnSqYqITDIKFhE/1H5rgC2H6nijvg2AjHgHpXlOUmLDTV4mInJ/KFhE/MzRustsOfwRN3oGCbZaWJ+TwtPZyYQEWc2eJiJy3yhYRPxER+8g2yrrqXC1AjB3Zjil+U5S4xwmLxMRuf8ULCJ+4ERDG4UH67jaPYDVAs9kJ/NcTgq24CCzp4mITAgFi4gP6+4fYseRs5TXXAQgOSaM0vxMMhMeMHeYiMgEU7CI+Ki3P21n04FaLnX0YbHAmkVJFCyZgz1EpyoiEngULCI+pnfQTfHRBva+2wLA7KiplOQ5eTQpyuRlIiLmUbCI+JDT529QsP8MLdd7AVi9MJHC3LmE2fS/qogENv0rKOID+oc8lB5r5OXqcxgGxDns7FrlJCsl2uxpIiI+QcEiYjLXhQ42lrtovtYDQN78eLYufZgIe4jJy0REfIeCRcQkg24vu99soqyqGY/XICbcRvHKdHLmxZo9TUTE5yhYRExwtrWLDeUuGq50A7DMGcf2ZalEhoWavExExDcpWEQmkNvjpexkM7tPNDHkMYgKC2XnijRy02eZPU1ExKcpWEQmSFNbNxv3n6H2YicAS1Jj2flkOtHTbCYvExHxfQoWkfvM4zV4pfozSo59wqDbS4Q9mBeWp7E8Mw6LxWL2PBERv6BgEbmPzrf3ULD/DDUtNwHInhND8coMZjrsJi8TEfEvChaR+8DrNdj3fgtFrzfQN+Rhmi2YrU/MI39Bgk5VRERGQcEiMs4u3uxl04Fa3mm+DsBjydPZtSqD+MipJi8TEfFfChaRcWIYBuU1F9hx5GNuDbixh1jZnDuP1QsTsVp1qiIiMhYKFpFx0NbVT+HBWt5qvAbA/MRISvKcJEWHmbxMRGRyULCIjIFhGFS4WtlWWU9n3xChwVYKFj/EmqwHCdKpiojIuFGwiIxS+60Bthyq4436NgAy4h2U5jlJiQ03eZmIyOSjYBEZhaN1l9ly+CNu9AwSbLWwPieFp7OTCQmymj1NRGRSUrCIjEBH7yDbKuupcLUCMHdmOKX5TlLjHCYvExGZ3BQsIsN0oqGNwoN1XO0ewGqBZ7KTeS4nBVtwkNnTREQmPQWLyDfo7h9ix5GzlNdcBCA5JozS/EwyEx4wd5iISABRsIjcw9uftrPpQC2XOvqwWGDNoiQKlszBHqJTFRGRiaRgEfkavYNuio82sPfdFgBmR02lJM/Jo0lRJi8TEQlMChaR/+L0+RsU7D9Dy/VeAFYvTKQwdy5hNv3vIiJiFv0LLPKf+oc8lB5r5OXqcxgGxDns7FrlJCsl2uxpIiIBT8EiArgudLCx3EXztR4A8ubHs3Xpw0TYQ0xeJiIioGCRADfo9rL7zSbKqprxeA1iwm0Ur0wnZ16s2dNEROS3KFgkYJ1t7WJDuYuGK90ALHPGsX1ZKpFhoSYvExGR/0rBIgHH7fFSdrKZ3SeaGPIYRIWFsnNFGrnps8yeJiIid6FgkYDS1NbNxv1nqL3YCcCS1Fh2PplO9DSbyctEROReFCwSEDxeg1eqP6Pk2CcMur1E2IN5YXkayzPjsFgsZs8TEZFvoGCRSe98ew8F+89Q03ITgOw5MRSvzGCmw27yMhERGS7rSC5cVFTEI488Qnh4ODNmzGDFihU0Njbe8zq//vWv+fa3v01MTAwRERH83u/9Hm+88caYRosMh9drsPfd8+T+w2+oabnJNFswf/s/0tnz1COKFRERPzOiYKmqqmLt2rW89957HD9+HLfbzeLFi+np6bnrdU6dOsW3v/1tXn/9dT744AP+23/7byxdupQPP/xwzONF7ubizV6+98r7/E1FPX1DHh5Lns6/Pf/7/PEjs/UjIBERP2QxDMMY7ZWvXbvGjBkzqKqq4vHHHx/29VJTU/njP/5j/uZv/mZYl+/q6sLhcNDZ2UlERMRo50oAMAyD8poL7DjyMbcG3NhDrGzOncfqhYlYrQoVEZGJNJ7332N6DEtn5+1nWkRFDf8N4bxeL93d3fe8zsDAAAMDA3c+7urqGv1ICRhtXf0UHqzlrcZrAMxPjKQkz0lSdJjJy0REZKxGHSyGYbBhwwaysrJIS0sb9vVKS0vp6ekhPz//rpcpKipi+/bto50mAcYwDCpcrWyrrKezb4jQYCsFix9iTdaDBOlURURkUhj1j4TWrl3La6+9RnV1NfHx8cO6zi9/+Uv+7M/+jIqKCv7wD//wrpf7uhOWhIQE/UhIvqL91gBbDtXxRn0bABnxDkrznKTEhpu8TERETP+R0Lp166isrOTUqVPDjpVf/epXrFmzhv37998zVgBsNhs2m17IS+7taN1lthz+iBs9gwRbLazPSeHp7GRCgkb0WHIREfEDIwoWwzBYt24dhw4d4uTJkyQlJQ3rer/85S/54Q9/yC9/+Uu+853vjGqoyBc6egfZVllPhasVgLkzwynNd5Ia5zB5mYiI3C8jCpa1a9fy6quvUlFRQXh4OFeuXAHA4XAwZcoUADZv3sylS5fYu3cvcDtWvv/97/MP//APLFy48M51pkyZgsOhOxgZmRMNbRQerONq9wBWCzyTncxzOSnYgoPMniYiIvfRiB7DcrfXr9izZw9PPfUUAE899RTnz5/n5MmTAGRnZ1NVVfWV6/zgBz/gF7/4xbC+rp7WLN39Q+w4cpbymosAJMeEUZqfSWbCA+YOExGRuxrP++8xvQ7LRFGwBLa3P21n04FaLnX0YbHAmkVJFCyZgz1EpyoiIr7M9AfdikyE3kE3xUcb2PtuCwCzo6ZSkufk0aThv+6PiIhMDgoW8Umnz9+gYP8ZWq73ArB6YSKFuXMJs+mvrIhIINK//uJT+oc8lB5r5OXqcxgGxDns7FrlJCsl2uxpIiJiIgWL+AzXhQ42lrtovnb7zTTz5sezdenDRNhDTF4mIiJmU7CI6QbdXna/2URZVTMer0FMuI3ilenkzIs1e5qIiPgIBYuY6mxrFxvKXTRc6QZgmTOO7ctSiQwLNXmZiIj4EgWLmMLt8VJ2spndJ5oY8hhEhYWyc0UauemzzJ4mIiI+SMEiE66prZuN+89Qe7ETgCWpsex8Mp3oaXr/KBER+XoKFpkwHq/BK9WfUXLsEwbdXiLswbywPI3lmXF3fRVlERERULDIBDnf3kPB/jPUtNwEIHtODMUrM5jpsJu8TERE/IGCRe4rr9dg3/stFL3eQN+Qh2m2YLY+MY/8BQk6VRERkWFTsMh9c/FmL5sO1PJO83UAHkuezq5VGcRHTjV5mYiI+BsFi4w7wzAor7nAjiMfc2vAjT3EyubceaxemIjVqlMVEREZOQWLjKu2rn4KD9byVuM1AOYnRlKS5yQpOszkZSIi4s8ULDIuDMOgwtXKtsp6OvuGCA22UrD4IdZkPUiQTlVERGSMFCwyZu23BthyqI436tsAyIh3UJrnJCU23ORlIiIyWShYZEyO1l1my+GPuNEzSLDVwvqcFJ7OTiYkyGr2NBERmUQULDIqHb2DbKusp8LVCsDcmeGU5jtJjXOYvExERCYjBYuM2ImGNgoP1nG1ewCrBZ7JTua5nBRswUFmTxMRkUlKwSLD1t0/xI4jZymvuQhAckwYpfmZZCY8YO4wERGZ9BQsMixvf9rOpgO1XOrow2KBNYuSKFgyB3uITlVEROT+U7DIPfUOuik+2sDed1sAmB01lZI8J48mRZm8TEREAomCRe7q9PkbFOw/Q8v1XgBWL0ykMHcuYTb9tRERkYmlex75iv4hD6XHGnm5+hyGAXEOO7tWOclKiTZ7moiIBCgFi3yJ60IHG8tdNF/rASBvfjxblz5MhD3E5GUiIhLIFCwCwKDby+43myirasbjNYgJt1G8Mp2cebFmTxMREVGwCJxt7WJDuYuGK90ALHPGsX1ZKpFhoSYvExERuU3BEsDcHi9lJ5vZfaKJIY9BVFgoO1ekkZs+y+xpIiIiX6JgCVBNbd1s3H+G2oudACxJjWXnk+lET7OZvExEROSrFCwBxuM1eKX6M0qOfcKg20uEPZgXlqexPDMOi8Vi9jwREZGvpWAJIOfbeyjYf4aalpsAZM+JoXhlBjMddpOXiYiI3JuCJQB4vQb73m+h6PUG+oY8TLMFs/WJeeQvSNCpioiI+AUFyyR38WYvmw7U8k7zdQAeS57OrlUZxEdONXmZiIjI8ClYJinDMCivucCOIx9za8CNPcTK5tx5rF6YiNWqUxUREfEvCpZJqK2rn8KDtbzVeA2A+YmRlOQ5SYoOM3mZiIjI6ChYJhHDMKhwtbKtsp7OviFCg60ULH6INVkPEqRTFRER8WMKlkmi/dYAWw7V8UZ9GwAZ8Q5K85ykxIabvExERGTsFCyTwNG6y2w5/BE3egYJtlpYn5PC09nJhARZzZ4mIiIyLhQsfqyjd5BtlfVUuFoBmDsznNJ8J6lxDpOXiYiIjC8Fi5860dBG4cE6rnYPYLXAM9nJPJeTgi04yOxpIiIi407B4me6+4fYceQs5TUXAUiOCaM0P5PMhAfMHSYiInIfKVj8yNuftrPpQC2XOvqwWGDNoiQKlszBHqJTFRERmdwULH6gd9BN8dEG9r7bAsDsqKmU5Dl5NCnK5GUiIiITQ8Hi406fv0HB/jO0XO8FYPXCRApz5xJm07dOREQCh+71fFT/kIfSY428XH0Ow4A4h51dq5xkpUSbPU1ERGTCKVh8kOtCBxvLXTRf6wEgb348W5c+TIQ9xORlIiIi5lCw+JBBt5fdbzZRVtWMx2sQE26jeGU6OfNizZ4mIiJiKgWLjzjb2sWGchcNV7oBWOaMY/uyVCLDQk1eJiIiYj4Fi8ncHi9lJ5vZfaKJIY9BVFgoO1ekkZs+y+xpIiIiPkPBYqKmtm427j9D7cVOAJakxrLzyXSip9lMXiYiIuJbFCwm8HgNXqn+jJJjnzDo9hJhD+aF5Wksz4zDYrGYPU9ERMTnKFgm2Pn2Hgr2n6Gm5SYA2XNiKF6ZwUyH3eRlIiIivkvBMkG8XoN977dQ9HoDfUMeptmC2frEPPIXJOhURURE5BsoWCbAxZu9bDpQyzvN1wF4LHk6u1ZlEB851eRlIiIi/kHBch8ZhkF5zQV2HPmYWwNu7CFWNufOY/XCRKxWnaqIiIgMl4LlPmnr6qfwYC1vNV4DYH5iJCV5TpKiw0xeJiIi4n8ULOPMMAwqXK1sq6yns2+I0GArBYsfYk3WgwTpVEVERGRUFCzjqP3WAFsO1fFGfRsAGfEOSvOcpMSGm7xMRETEvylYxsnRustsOfwRN3oGCbZaWJ+TwtPZyYQEWc2eJiIi4vcULGPU0TvItsp6KlytAMydGU5pvpPUOIfJy0RERCYPBcsYnGhoo/BgHVe7B7Ba4JnsZJ7LScEWHGT2NBERkUlFwTIK3f1D7DhylvKaiwAkx4RRmp9JZsID5g4TERGZpBQsI/T2p+1sOlDLpY4+LBZYsyiJgiVzsIfoVEVEROR+UbAMU++gm+KjDex9twWA2VFTKclz8mhSlMnLREREJj8FyzCcPn+Dgv1naLneC8DqhYkU5s4lzKb/fCIiIhNB97j30D/kofRYIy9Xn8MwIM5hZ9cqJ1kp0WZPExERCSgKlrtwXehgY7mL5ms9AOTNj2fr0oeJsIeYvExERCTwKFj+i0G3l91vNlFW1YzHaxATbqN4ZTo582LNniYiIhKwFCy/5WxrFxvKXTRc6QZgmTOO7ctSiQwLNXmZiIhIYFOwAG6Pl7KTzew+0cSQxyAqLJSdK9LITZ9l9jQREREBRvRGN0VFRTzyyCOEh4czY8YMVqxYQWNj4z2vc/nyZb773e8yZ84crFYrzz///Fj2jrumtm5Wlr1D6fFPGPIYLEmN5dhfPq5YERER8SEjCpaqqirWrl3Le++9x/Hjx3G73SxevJienp67XmdgYICYmBi2bNmC0+kc8+Dx4vEavHSqme/8YzW1FzuJsAfzsz/O5Offm0/0NJvZ80REROS3WAzDMEZ75WvXrjFjxgyqqqp4/PHHv/Hy2dnZZGZm8rOf/WxEX6erqwuHw0FnZycRERGjXPv/nG/voWD/GWpabt7eNSeG4pUZzHTYx3zbIiIictt43n+P6TEsnZ2dAERF+cervXq9Bvveb6Ho9Qb6hjxMswWz9Yl55C9IwGKxmD1PRERE7mLUwWIYBhs2bCArK4u0tLTx3MTAwAADAwN3Pu7q6hrzbV682cumA7W803wdgMeSp7NrVQbxkVPHfNsiIiJyf406WJ599llqa2uprq4ezz3A7Qf3bt++fVxuyzAMymsusOPIx9wacGMPsbI5dx6rFyZitepURURExB+MKljWrVtHZWUlp06dIj4+frw3sXnzZjZs2HDn466uLhISEkZ8O21d/RQerOWtxmsAzE+MpCTPSVJ02LhtFRERkftvRMFiGAbr1q3j0KFDnDx5kqSkpPsyymazYbON/pk6hmFQ4WplW2U9nX1DhAZbKVj8EGuyHiRIpyoiIiJ+Z0TBsnbtWl599VUqKioIDw/nypUrADgcDqZMmQLcPh25dOkSe/fuvXM9l8sFwK1bt7h27Roul4vQ0FAefvjhcfpj/D/ttwbYcqiON+rbAMiId1Ca5yQlNnzcv5aIiIhMjBE9rfluz6TZs2cPTz31FABPPfUU58+f5+TJk/e8XmJiIufPnx/W1x3u06KO1l1my+GPuNEzSLDVwvqcFJ7OTiYkaEQvNyMiIiLjwLSnNQ+nbX7xi1+M6npj0dE7yLbKeipcrQDMnRlOab6T1DjHff26IiIiMjH8/r2ETjS0UXiwjqvdA1gt8Ex2Ms/lpGALDjJ7moiIiIwTvw2W7v4hdhw5S3nNRQCSY8Iozc8kM+EBc4eJiIjIuPPLYHn703Y2HajlUkcfFgusWZREwZI52EN0qiIiIjIZ+VWw9A66Kan4iL3vtgAwO2oqJXlOHk3yj7cGEBERkdHxq2D5H2XvcKnn9jOOVi9MpDB3LmE2v/ojiIiIyCj41b39hRt9xM+IYtcqJ1kp0WbPERERkQniV8GyIjOOn/zxo0TYQ8yeIiIiIhPIr15R7SdPpitWREREApBfBYuIiIgEJgWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+LxgswcMh2EYAHR1dZm8RERERIbri/vtL+7Hx8IvguX69esAJCQkmLxERERERur69es4HI4x3YZfBEtUVBQAn3/++Zj/wDI2XV1dJCQkcOHCBSIiIsyeE9D0vfAd+l74Fn0/fEdnZyezZ8++cz8+Fn4RLFbr7YfaOBwO/eXzEREREfpe+Ah9L3yHvhe+Rd8P3/HF/fiYbmMcdoiIiIjcVwoWERER8Xl+ESw2m41t27Zhs9nMnhLw9L3wHfpe+A59L3yLvh++Yzy/FxZjPJ5rJCIiInIf+cUJi4iIiAQ2BYuIiIj4PAWLiIiI+DwFi4iIiPg8nw+Wf/7nfyYpKQm73c78+fP5zW9+Y/akgFRUVMQjjzxCeHg4M2bMYMWKFTQ2Npo9K+AVFRVhsVh4/vnnzZ4SsC5dusT3vvc9pk+fztSpU8nMzOSDDz4we1bAcbvd/K//9b9ISkpiypQpPPjgg7zwwgt4vV6zpwWEU6dOsXTpUuLi4rBYLBw+fPhLv28YBj/+8Y+Ji4tjypQpZGdnU19fP6Kv4dPB8qtf/Yrnn3+eLVu28OGHH/L7v//75Obm8vnnn5s9LeBUVVWxdu1a3nvvPY4fP47b7Wbx4sX09PSYPS1gnT59mpdeeomMjAyzpwSsmzdvsmjRIkJCQjh69Chnz56ltLSUBx54wOxpAedv//Zv+fnPf86LL77Ixx9/zK5du/jf//t/84//+I9mTwsIPT09OJ1OXnzxxa/9/V27dvF3f/d3vPjii5w+fZqZM2fy7W9/m+7u7uF/EcOHPfroo8bTTz/9pc/NnTvXKCwsNGmRfOHq1asGYFRVVZk9JSB1d3cbKSkpxvHjx40/+IM/MNavX2/2pID0ox/9yMjKyjJ7hhiG8Z3vfMf44Q9/+KXPrVy50vje975n0qLABRiHDh2687HX6zVmzpxpFBcX3/lcf3+/4XA4jJ///OfDvl2fPWEZHBzkgw8+YPHixV/6/OLFi3nnnXdMWiVf6OzsBBiXN7SSkVu7di3f+c53+MM//EOzpwS0yspKFixYQF5eHjNmzOBb3/oW/+f//B+zZwWkrKws3nzzTT755BMAzpw5Q3V1NX/0R39k8jI5d+4cV65c+dL9uc1m4w/+4A9GdH/us29+2N7ejsfjITY29kufj42N5cqVKyatErj9s8gNGzaQlZVFWlqa2XMCzr/+67/yH//xH5w+fdrsKQHvs88+o6ysjA0bNvDXf/3X/Pu//zvPPfccNpuN73//+2bPCyg/+tGP6OzsZO7cuQQFBeHxeNi5cyd/+qd/ava0gPfFffbX3Z+3tLQM+3Z8Nli+YLFYvvSxYRhf+ZxMrGeffZba2lqqq6vNnhJwLly4wPr16zl27Bh2u93sOQHP6/WyYMECfvrTnwLwrW99i/r6esrKyhQsE+xXv/oV+/bt49VXXyU1NRWXy8Xzzz9PXFwcP/jBD8yeJ4z9/txngyU6OpqgoKCvnKZcvXr1K5UmE2fdunVUVlZy6tQp4uPjzZ4TcD744AOuXr3K/Pnz73zO4/Fw6tQpXnzxRQYGBggKCjJxYWCZNWsWDz/88Jc+N2/ePA4ePGjSosD1V3/1VxQWFvInf/InAKSnp9PS0kJRUZGCxWQzZ84Ebp+0zJo1687nR3p/7rOPYQkNDWX+/PkcP378S58/fvw4jz32mEmrApdhGDz77LP8+te/5sSJEyQlJZk9KSDl5ORQV1eHy+W682vBggX8z//5P3G5XIqVCbZo0aKvPL3/k08+ITEx0aRFgau3txer9ct3aUFBQXpasw9ISkpi5syZX7o/HxwcpKqqakT35z57wgKwYcMGVq9ezYIFC/i93/s9XnrpJT7//HOefvpps6cFnLVr1/Lqq69SUVFBeHj4nZMvh8PBlClTTF4XOMLDw7/yuKGwsDCmT5+uxxOZ4C//8i957LHH+OlPf0p+fj7//u//zksvvcRLL71k9rSAs3TpUnbu3Mns2bNJTU3lww8/5O/+7u/44Q9/aPa0gHDr1i0+/fTTOx+fO3cOl8tFVFQUs2fP5vnnn+enP/0pKSkppKSk8NOf/pSpU6fy3e9+d/hfZLyexnS//NM//ZORmJhohIaGGr/7u7+rp9GaBPjaX3v27DF7WsDT05rN9X//7/810tLSDJvNZsydO9d46aWXzJ4UkLq6uoz169cbs2fPNux2u/Hggw8aW7ZsMQYGBsyeFhDeeuutr72P+MEPfmAYxu2nNm/bts2YOXOmYbPZjMcff9yoq6sb0dewGIZhjFdhiYiIiNwPPvsYFhEREZEvKFhERETE5ylYRERExOcpWERERMTnKVhERETE5ylYRERExOcpWERERMTnKVhERETE5ylYRERExOcpWERERMTnKVhERETE5ylYRERExOf9/3pK1A6IkNumAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mPlotTop = 10\n",
-    "plot_funcs(PFexample.solution[0].cFunc, 0.0, mPlotTop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis).  How can this be?\n",
-    "\n",
-    "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth.  How much is that?  What's the minimum value of $m_t$ where the consumption function is defined?  We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This agent's human wealth is 50.49994992551661 times his current income level.\n",
-      "This agent's consumption function is defined (consumption is positive) down to m_t = -50.49994992551661\n"
-     ]
-    }
-   ],
-   "source": [
-    "humanWealth = PFexample.solution[0].hNrm\n",
-    "mMinimum = PFexample.solution[0].mNrmMin\n",
-    "print(\n",
-    "    \"This agent's human wealth is \"\n",
-    "    + str(humanWealth)\n",
-    "    + \" times his current income level.\"\n",
-    ")\n",
-    "print(\n",
-    "    \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n",
-    "    + str(mMinimum)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Yikes! Let's take a look at the bottom of the consumption function.  In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7dkJDQ3tmThwOBw888ADw1cc5Z8+eZdOmTcBX3wh67bXXWLRoET/84Q85fPgwGzdu5He/+10/vxQRERkOGq50sGRrJYdqLwEwOXk06+ZkEB8+wuRkMpB6VVg2bNgAQHZ29g3L33rrLb7//e8DcO7cOc6cOdOzLikpiV27dvHKK6/wy1/+kri4ONavX89zzz13f8lFRGRYMQyDkrJ6Vu88xrUuN/YgK0tzU5k3KRGrVbMqQ919XYdlsOg6LCIiw1tTaycFpZXsO34RgKzEcIrynCRFhpicTO7EZ67DIiIiMpAMw2B7eSMrdlTTcr2b4EAr+VMfZv6UsQRoVmVYUWERERGf1Hyti2XbqnivugmAjHgHxXlOUqJDTU4mZlBhERERn7O76hzL3vmcy+0uAq0WXs5JYUF2MkEBvfoJPBlCVFhERMRnXO1wsWJHNdvLGwEYHxNK8VwnaXEOk5OJ2VRYRETEJ+ytaaKgtIoLbV1YLfBCdjILc1KwBQaYHU18gAqLiIiYqq2zm9U7j1JS1gBAclQIxXMzyUwYZW4w8SkqLCIiYpqPTzazZGslZ69ex2KB+Y8nkT9tHPYgzarIjVRYRERk0HW43BTurmHT4ToAxkSMoCjPyaNJESYnE1+lwiIiIoPq09OXyd9SQd2lDgDmTUqkIHc8ITYdkuT29NchIiKDorPbQ/Ge47x58BSGAXEOO+vmOJmSEml2NPEDKiwiIjLgyuuvsriknNqL7QDkZcWzfPojhNmDTE4m/kKFRUREBozL7WX9ByfYcKAWj9cgKtRG4ewJ5KRGmx1N/IwKi4iIDIijja0sKimn5nwbADOccayckUZ4SLDJycQfqbCIiEi/cnu8bNhfy/q9J+j2GESEBLNmVjq5E2LNjiZ+TIVFRET6zYmmNhZvqaCyoQWAaWnRrHl2ApEjbSYnE3+nwiIiIvfN4zXYePBLivZ8gcvtJcweyKqZ6czMjMNisZgdT4YAFRYREbkvp5vbyd9SQVndFQCyx0VRODuDGIfd5GQylKiwiIhIn3i9Bps/qWPtrhqud3sYaQtk+TOpzJ2YoFkV6XcqLCIi0msNVzpYsrWSQ7WXAJicPJp1czKIDx9hcjIZqlRYRETknhmGQUlZPat3HuNalxt7kJWluanMm5SI1apZFRk4KiwiInJPmlo7KSitZN/xiwBkJYZTlOckKTLE5GQyHKiwiIjIHRmGwfbyRlbsqKblejfBgVbypz7M/CljCdCsigwSFRYREbmt5mtdLNtWxXvVTQBkxDsoznOSEh1qcjIZblRYRETklnZXnWPZO59zud1FoNXCyzkpLMhOJijAanY0GYZUWERE5AZXO1ys2FHN9vJGAMbHhFI810lanMPkZDKcqbCIiEiPvTVNFJRWcaGtC6sFXshOZmFOCrbAALOjyTCnwiIiIrR1drN651FKyhoASI4KoXhuJpkJo8wNJvKfVFhERIa5j082s2RrJWevXsdigfmPJ5E/bRz2IM2qiO9QYRERGaY6XG4Kd9ew6XAdAGMiRlCU5+TRpAiTk4ncTIVFRGQY+vT0ZfK3VFB3qQOAeZMSKcgdT4hNhwXxTfrLFBEZRjq7PRTvOc6bB09hGBDnsLNujpMpKZFmRxO5IxUWEZFhorz+KotLyqm92A5AXlY8y6c/Qpg9yORkInenwiIiMsS53F7Wf3CCDQdq8XgNokJtFM6eQE5qtNnRRO6ZCouIyBB2tLGVRSXl1JxvA2CGM46VM9IIDwk2OZlI76iwiIgMQW6Plw37a1m/9wTdHoOIkGDWzEond0Ks2dFE+kSFRURkiDnR1MbiLRVUNrQAMC0tmjXPTiBypM3kZCJ9p8IiIjJEeLwGGw9+SdGeL3C5vYTZA1k1M52ZmXFYLBaz44ncFxUWEZEh4HRzO/lbKiiruwJA9rgoCmdnEOOwm5xMpH+osIiI+DGv12DzJ3Ws3VXD9W4PI22BLH8mlbkTEzSrIkOKCouIiJ9quNLBkq2VHKq9BMDk5NGsm5NBfPgIk5OJ9D8VFhERP2MYBiVl9azeeYxrXW7sQVaW5qYyb1IiVqtmVWRoUmEREfEjTa2dFJRWsu/4RQCyEsMpynOSFBlicjKRgaXCIiLiBwzDYHt5Iyt2VNNyvZvgQCv5Ux9m/pSxBGhWRYYBFRYRER/XfK2LZduqeK+6CYCMeAfFeU5SokNNTiYyeFRYRER82O6qcyx753Mut7sItFp4OSeFBdnJBAVYzY4mMqhUWEREfNDVDhcrdlSzvbwRgPExoRTPdZIW5zA5mYg5VFhERHzM3pomCkqruNDWhdUCL2QnszAnBVtggNnRREyjwiIi4iPaOrtZvfMoJWUNACRHhVA8N5PMhFHmBhPxASosIiI+4OOTzSzZWsnZq9exWGD+40nkTxuHPUizKiKgwiIiYqoOl5vC3TVsOlwHwJiIERTlOXk0KcLkZCK+RYVFRMQkn56+TP6WCuoudQAwb1IiBbnjCbHprVnkz+n/ChGRQdbZ7aF4z3HePHgKw4A4h511c5xMSYk0O5qIz1JhEREZROX1V1lcUk7txXYA8rLiWT79EcLsQSYnE/FtKiwiIoPA5fay/oMTbDhQi8drEBVqo3D2BHJSo82OJuIXVFhERAbY0cZWFpWUU3O+DYAZzjhWzkgjPCTY5GQi/kOFRURkgLg9Xjbsr2X93hN0ewwiQoJZMyud3AmxZkcT8Tu9/jGKDz/8kOnTpxMXF4fFYuGdd9654/b79+/HYrHcdKupqelrZhERn3eiqY3ZGw5R/P4XdHsMpqVFs+eVJ1VWRPqo1zMs7e3tOJ1O/vt//+8899xz97zf8ePHCQsL67kfFRXV26cWEfF5Hq/BxoNfUrTnC1xuL2H2QFbNTGdm5lf/yBORvul1YcnNzSU3N7fXT/Tggw8yatSoXu8nIuIvTje3k7+lgrK6KwBkj4uicHYGMQ67yclE/N+g/T75t771LWJjY8nJyWHfvn2D9bQiIgPO6zXYdPg0uf/PR5TVXWGkLZCfPTeBt77/HZUVkX4y4CfdxsbG8sYbb5CVlUVXVxe//e1vycnJYf/+/Tz55JO33Kerq4uurq6e+62trQMdU0SkTxqudLBkayWHai8BMDl5NOvmZBAfPsLkZCJDy4AXlnHjxjFu3Lie+4899hj19fUUFRXdtrCsXbuWlStXDnQ0EZE+MwyDkrJ6Vu88xrUuN/YgK0tzU5k3KRGrVeeqiPS3QftI6JsmTZrEiRMnbrt+6dKltLS09Nzq6+sHMZ2IyJ01tXbyP37zKT8preJal5usxHB2v/wkz09+SGVFZICYch2WI0eOEBt7+6/22Ww2bDbbICYSEbk7wzDYXt7Iih3VtFzvJjjQSv7Uh5k/ZSwBKioiA6rXheXatWucPHmy5/6pU6coLy8nIiKCMWPGsHTpUs6ePcumTZsAePXVV3nooYdIS0vD5XKxefNmSktLKS0t7b9XISIywJqvdbFsWxXvVTcBkBHvoDjPSUp0qMnJRIaHXheWsrIynnrqqZ77ixYtAuD555/nN7/5DefOnePMmTM9610uF/n5+Zw9e5YHHniAtLQ03n33XZ5++ul+iC8iMvB2V51j2Tufc7ndRaDVwss5KSzITiYowJRP1UWGJYthGIbZIe6mtbUVh8NBS0vLDRefExEZSFc7XKzYUc328kYAxseEUjzXSVqcw+RkIv6hP4/f+i0hEZFb2FvTREFpFRfaurBa4IXsZBbmpGALDDA7msiwpMIiIvINbZ3drN55lJKyBgCSo0IonptJZsIoc4OJDHMqLCIi/+njk80s2VrJ2avXsVhg/uNJ5E8bhz1IsyoiZlNhEZFhr8PlpnB3DZsO1wEwJmIERXlOHk2KMDmZiHxNhUVEhrVPT18mf0sFdZc6AJg3KZGC3PGE2PT2KOJL9H+kiAxLnd0eivcc582DpzAMiHPYWTfHyZSUSLOjicgtqLCIyLBTXn+VxSXl1F5sByAvK57l0x8hzB5kcjIRuR0VFhEZNlxuL+s/OMGGA7V4vAZRoTYKZ08gJzXa7GgichcqLCIyLBxtbGVRSTk159sAmOGMY+WMNMJDgk1OJiL3QoVFRIY0t8fLhv21rN97gm6PQURIMGtmpZM74fY/wCoivkeFRUSGrBNNbSzeUkFlQwsA09KiWfPsBCJH6tfgRfyNCouIDDker8HGg19StOcLXG4vYfZAVs1MZ2ZmHBaLxex4ItIHKiwiMqScbm4nf0sFZXVXAMgeF0Xh7AxiHHaTk4nI/VBhEZEhwes12PxJHWt31XC928NIWyDLn0ll7sQEzaqIDAEqLCLi9xqudLBkayWHai8BMDl5NOvmZBAfPsLkZCLSX1RYRMRvGYZBSVk9q3ce41qXG3uQlaW5qcyblIjVqlkVkaFEhUVE/FJTaycFpZXsO34RgKzEcIrynCRFhpicTEQGggqLiPgVwzDYXt7Iih3VtFzvJjjQSv7Uh5k/ZSwBmlURGbJUWETEbzRf62LZtireq24CICPeQXGek5ToUJOTichAU2EREb+wu+ocy975nMvtLgKtFl7OSWFBdjJBAVazo4nIIFBhERGfdrXDxYod1WwvbwRgfEwoxXOdpMU5TE4mIoNJhUVEfNbemiYKSqu40NaF1QIvZCezMCcFW2CA2dFEZJCpsIiIz2nr7Gb1zqOUlDUAkBwVQvHcTDITRpkbTERMo8IiIj7l45PNLNlaydmr17FYYP7jSeRPG4c9SLMqIsOZCouI+IQOl5vC3TVsOlwHwJiIERTlOXk0KcLkZCLiC1RYRMR0n56+TP6WCuoudQAwb1IiBbnjCbHpLUpEvqJ3AxExTWe3h+I9x3nz4CkMA+IcdtbNcTIlJdLsaCLiY1RYRMQU5fVXWVxSTu3FdgDysuJZPv0RwuxBJicTEV+kwiIig8rl9rL+gxNsOFCLx2sQFWqjcPYEclKjzY4mIj5MhUVEBs3RxlYWlZRTc74NgBnOOFbOSCM8JNjkZCLi61RYRGTAuT1eNuyvZf3eE3R7DCJCglkzK53cCbFmRxMRP6HCIiID6kRTG4u3VFDZ0ALAtLRo1jw7gciRNpOTiYg/UWERkQHh8RpsPPglRXu+wOX2EmYPZNXMdGZmxmGxWMyOJyJ+RoVFRPrd6eZ28rdUUFZ3BYDscVEUzs4gxmE3OZmI+CsVFhHpN16vweZP6li7q4br3R5G2gJZ/kwqcycmaFZFRO6LCouI9IuGKx0s2VrJodpLAExOHs26ORnEh48wOZmIDAUqLCJyXwzDoKSsntU7j3Gty409yMrS3FTmTUrEatWsioj0DxUWEemzptZOCkor2Xf8IgBZieEU5TlJigwxOZmIDDUqLCLSa4ZhsL28kRU7qmm53k1woJX8qQ8zf8pYAjSrIiIDQIVFRHql+VoXy7ZV8V51EwAZ8Q6K85ykRIeanExEhjIVFhG5Z7urzrHsnc+53O4i0Grh5ZwUFmQnExRgNTuaiAxxKiwicldXO1ys2FHN9vJGAMbHhFI810lanMPkZCIyXKiwiMgd7a1poqC0igttXVgt8EJ2MgtzUrAFBpgdTUSGERUWEbmlts5uVu88SklZAwDJUSEUz80kM2GUucFEZFhSYRGRm3x8spklWys5e/U6FgvMfzyJ/GnjsAdpVkVEzKHCIiI9OlxuCnfXsOlwHQBjIkZQlOfk0aQIk5OJyHCnwiIiAHx6+jL5Wyqou9QBwLxJiRTkjifEprcJETGf3olEhrnObg/Fe47z5sFTGAbEOeysm+NkSkqk2dFERHqosIgMY+X1V1lcUk7txXYA8rLiWT79EcLsQSYnExG5kQqLyDDkcntZ/8EJNhyoxeM1iAq1UTh7Ajmp0WZHExG5JRUWkWHmaGMri0rKqTnfBsAMZxwrZ6QRHhJscjIRkdtTYREZJtweLxv217J+7wm6PQYRIcGsmZVO7oRYs6OJiNyVCovIMHCiqY3FWyqobGgBYFpaNGuenUDkSJvJyURE7o0Ki8gQ5vEabDz4JUV7vsDl9hJmD2TVzHRmZsZhsVjMjicics9UWESGqNPN7eRvqaCs7goA2eOiKJydQYzDbnIyEZHeU2ERGWK8XoPNn9SxdlcN17s9jLQFsvyZVOZOTNCsioj4LRUWkSGk4UoHS7ZWcqj2EgCTk0ezbk4G8eEjTE4mInJ/rL3d4cMPP2T69OnExX31Gfg777xz130OHDhAVlYWdrudsWPH8vrrr/clq4jchmEYvP3pGf7q1Y84VHsJe5CVlTPS2Dz/uyorIjIk9LqwtLe343Q6ee211+5p+1OnTvH000/zxBNPcOTIEX7605+ycOFCSktLex1WRG7W1NrJ//jNp/yktIprXW6yEsPZ/fKTPD/5IaxWfQQkIkNDrz8Sys3NJTc39563f/311xkzZgyvvvoqAKmpqZSVlVFUVMRzzz3X26cXkf9kGAbbyxtZsaOaluvdBAdayZ/6MPOnjCVARUVEhpgBP4fl8OHDTJ069YZl06ZNY+PGjXR3dxMUdPNvlnR1ddHV1dVzv7W1daBjiviV5mtdLNtWxXvVTQBkxDsoznOSEh1qcjIRkYHR64+Eeuv8+fNER9/4+yTR0dG43W6am5tvuc/atWtxOBw9t4SEhIGOKeI3dledY+rPP+S96iYCrRYW/+XDlL4wWWVFRIa0QfmW0J9/ldIwjFsu/9rSpUtZtGhRz/3W1laVFhn2rna4WLGjmu3ljQCMjwmleK6TtDiHyclERAbegBeWmJgYzp8/f8OyCxcuEBgYyOjRo2+5j81mw2bTJcNFvra3pomC0ioutHVhtcAL2ckszEnBFhhgdjQRkUEx4IXlscce449//OMNy/bs2cPEiRNvef6KiPz/2jq7Wb3zKCVlDQAkR4VQPDeTzIRR5gYTERlkvS4s165d4+TJkz33T506RXl5OREREYwZM4alS5dy9uxZNm3aBMCCBQt47bXXWLRoET/84Q85fPgwGzdu5He/+13/vQqRIejjk80s2VrJ2avXsVhg/uNJ5E8bhz1IsyoiMvz0urCUlZXx1FNP9dz/+lyT559/nt/85jecO3eOM2fO9KxPSkpi165dvPLKK/zyl78kLi6O9evX6yvNIrfR4XJTuLuGTYfrABgTMYKiPCePJkWYnExExDwW4+szYH1Ya2srDoeDlpYWwsLCzI4jMmA+PX2Z/C0V1F3qAGDepEQKcscTYtOvaIiI/+nP47feBUV8QGe3h+I9x3nz4CkMA+IcdtbNcTIlJdLsaCIiPkGFRcRk5fVXWVxSTu3FdgDysuJZPv0Rwuw6KV1E5GsqLCImcbm9rP/gBBsO1OLxGkSF2iicPYGc1Oi77ywiMsyosIiY4GhjK4tKyqk53wbADGccK2ekER4SbHIyERHfpMIiMojcHi8b9teyfu8Juj0GESHBrJmVTu6EWLOjiYj4NBUWkUFyoqmNxVsqqGxoAWBaWjRrnp1A5Ehd1VlE5G5UWEQGmMdrsPHglxTt+QKX20uYPZBVM9OZmRl329/TEhGRG6mwiAyg083t5G+poKzuCgDZ46IonJ1BjMNucjIREf+iwiIyALxeg82f1LF2Vw3Xuz2MtAWy/JlU5k5M0KyKiEgfqLCI9LOGKx0s2VrJodpLAExOHs26ORnEh48wOZmIiP9SYRHpJ4ZhUFJWz+qdx7jW5cYeZGVpbirzJiVitWpWRUTkfqiwiPSDptZOCkor2Xf8IgBZieEU5TlJigwxOZmIyNCgwiJyHwzDYHt5Iyt2VNNyvZvgQCv5Ux9m/pSxBGhWRUSk36iwiPRR87Uulm2r4r3qJgAy4h0U5zlJiQ41OZmIyNCjwiLSB7urzrHsnc+53O4i0Grh5ZwUFmQnExRgNTuaiMiQpMIi0gtXO1ys2FHN9vJGAMbHhFI810lanMPkZCIiQ5sKi8g92lvTREFpFRfaurBa4IXsZBbmpGALDDA7mojIkKfCInIXbZ3drN55lJKyBgCSo0IonptJZsIoc4OJiAwjKiwid/DxyWaWbK3k7NXrWCww//Ek8qeNwx6kWRURkcGkwiJyCx0uN4W7a9h0uA6AMREjKMpz8mhShMnJRESGJxUWkT/z6enL5G+poO5SBwDzJiVSkDueEJv+dxERMYvegUX+U2e3h+I9x3nz4CkMA+IcdtbNcTIlJdLsaCIiw54KiwhQXn+VxSXl1F5sByAvK57l0x8hzB5kcjIREQEVFhnmXG4v6z84wYYDtXi8BlGhNgpnTyAnNdrsaCIi8g0qLDJsHW1sZVFJOTXn2wCY4Yxj5Yw0wkOCTU4mIiJ/ToVFhh23x8uG/bWs33uCbo9BREgwa2alkzsh1uxoIiJyGyosMqycaGpj8ZYKKhtaAJiWFs2aZycQOdJmcjIREbkTFRYZFjxeg40Hv6Rozxe43F7C7IGsmpnOzMw4LBaL2fFEROQuVFhkyDvd3E7+lgrK6q4AkD0uisLZGcQ47CYnExGRe6XCIkOW12uw+ZM61u6q4Xq3h5G2QJY/k8rciQmaVRER8TMqLDIkNVzpYMnWSg7VXgJgcvJo1s3JID58hMnJRESkL1RYZEgxDIOSsnpW7zzGtS439iArS3NTmTcpEatVsyoiIv5KhUWGjKbWTgpKK9l3/CIAWYnhFOU5SYoMMTmZiIjcLxUW8XuGYbC9vJEVO6ppud5NcKCV/KkPM3/KWAI0qyIiMiSosIhfa77WxbJtVbxX3QRARryD4jwnKdGhJicTEZH+pMIifmt31TmWvfM5l9tdBFotvJyTwoLsZIICrGZHExGRfqbCIn7naoeLFTuq2V7eCMD4mFCK5zpJi3OYnExERAaKCov4lb01TRSUVnGhrQurBV7ITmZhTgq2wACzo4mIyABSYRG/0NbZzeqdRykpawAgOSqE4rmZZCaMMjeYiIgMChUW8Xkfn2xmydZKzl69jsUC8x9PIn/aOOxBmlURERkuVFjEZ3W43BTurmHT4ToAxkSMoCjPyaNJESYnExGRwabCIj7p09OXyd9SQd2lDgDmTUqkIHc8ITb9yYqIDEd69xef0tntoXjPcd48eArDgDiHnXVznExJiTQ7moiImEiFRXxGef1VFpeUU3uxHYC8rHiWT3+EMHuQyclERMRsKixiOpfby/oPTrDhQC0er0FUqI3C2RPISY02O5qIiPgIFRYx1dHGVhaVlFNzvg2AGc44Vs5IIzwk2ORkIiLiS1RYxBRuj5cN+2tZv/cE3R6DiJBg1sxKJ3dCrNnRRETEB6mwyKA70dTG4i0VVDa0ADAtLZo1z04gcqTN5GQiIuKrVFhk0Hi8BhsPfknRni9wub2E2QNZNTOdmZlxWCwWs+OJiIgPU2GRQXG6uZ38LRWU1V0BIHtcFIWzM4hx2E1OJiIi/kCFRQaU12uw+ZM61u6q4Xq3h5G2QJY/k8rciQmaVRERkXumwiIDpuFKB0u2VnKo9hIAk5NHs25OBvHhI0xOJiIi/kaFRfqdYRiUlNWzeucxrnW5sQdZWZqbyrxJiVitmlUREZHeU2GRftXU2klBaSX7jl8EICsxnKI8J0mRISYnExERf6bCIv3CMAy2lzeyYkc1Lde7CQ60kj/1YeZPGUuAZlVEROQ+qbDIfWu+1sWybVW8V90EQEa8g+I8JynRoSYnExGRocLal51+9atfkZSUhN1uJysri48++ui22+7fvx+LxXLTraamps+hxXfsrjrH1J9/yHvVTQRaLSz+y4cpfWGyyoqIiPSrXs+wvP322/z4xz/mV7/6FY8//ji//vWvyc3N5ejRo4wZM+a2+x0/fpywsLCe+1FRUX1LLD7haoeLFTuq2V7eCMD4mFCK5zpJi3OYnExERIYii2EYRm92+O53v8u3v/1tNmzY0LMsNTWVWbNmsXbt2pu2379/P0899RRXrlxh1KhRfQrZ2tqKw+GgpaXlhtIj5thb00RBaRUX2rqwWuCF7GQW5qRgCwwwO5qIiPiQ/jx+9+ojIZfLxWeffcbUqVNvWD516lQOHTp0x32/9a1vERsbS05ODvv27bvjtl1dXbS2tt5wE/O1dXazZGsF/+M3ZVxo6yI5KoQ//P3j/MO08SorIiIyoHr1kVBzczMej4fo6OgblkdHR3P+/Plb7hMbG8sbb7xBVlYWXV1d/Pa3vyUnJ4f9+/fz5JNP3nKftWvXsnLlyt5EkwH28clmlmyt5OzV61gsMP/xJPKnjcMepKIiIiIDr0/fEvrzS6obhnHby6yPGzeOcePG9dx/7LHHqK+vp6io6LaFZenSpSxatKjnfmtrKwkJCX2JKvepw+WmcHcNmw7XATAmYgRFeU4eTYowOZmIiAwnvSoskZGRBAQE3DSbcuHChZtmXe5k0qRJbN68+bbrbTYbNputN9FkAHx6+jL5Wyqou9QBwLxJiRTkjifEpm/Di4jI4OrVOSzBwcFkZWXx/vvv37D8/fffZ/Lkyff8OEeOHCE2NrY3Ty2DqLPbw5p3jzL314epu9RBnMPO5vnfZfWsdJUVERExRa+PPosWLWLevHlMnDiRxx57jDfeeIMzZ86wYMEC4KuPc86ePcumTZsAePXVV3nooYdIS0vD5XKxefNmSktLKS0t7d9XIv2ivP4qi0vKqb3YDkBeVjzLpz9CmD3I5GQiIjKc9bqw/PVf/zWXLl1i1apVnDt3jvT0dHbt2kViYiIA586d48yZMz3bu1wu8vPzOXv2LA888ABpaWm8++67PP300/33KuS+udxe1n9wgg0HavF4DaJCbRTOnkBO6r1/1CciIjJQen0dFjPoOiwD62hjK4tKyqk53wbADGccK2ekER4SbHIyERHxZ/15/NYJCcOY2+Nlw/5a1u89QbfHICIkmDWz0smdoPOLRETEt6iwDFMnmtpYvKWCyoYWAKalRbPm2QlEjtS3s0RExPeosAwzHq/BxoNfUrTnC1xuL2H2QFbNTGdmZtxtr6UjIiJiNhWWYeR0czv5Wyooq7sCQPa4KApnZxDjsJucTERE5M5UWIYBr9dg8yd1rN1Vw/VuDyNtgSx/JpW5ExM0qyIiIn5BhWWIa7jSwZKtlRyqvQTA5OTRrJuTQXz4CJOTiYiI3DsVliHKMAxKyupZvfMY17rc2IOsLM1NZd6kRKxWzaqIiIh/UWEZgppaOykorWTf8YsAZCWGU5TnJCkyxORkIiIifaPCMoQYhsH28kZW7Kim5Xo3wYFW8qc+zPwpYwnQrIqIiPgxFZYhovlaF8u2VfFedRMAGfEOivOcpESHmpxMRETk/qmwDAG7q86x7J3PudzuItBq4eWcFBZkJxMU0Ksf4xYREfFZKix+7GqHixU7qtle3gjA+JhQiuc6SYtzmJxMRESkf6mw+Km9NU0UlFZxoa0LqwVeyE5mYU4KtsAAs6OJiIj0OxUWP9PW2c3qnUcpKWsAIDkqhOK5mWQmjDI3mIiIyABSYfEjH59sZsnWSs5evY7FAvMfTyJ/2jjsQZpVERGRoU2FxQ90uNwU7q5h0+E6AMZEjKAoz8mjSREmJxMRERkcKiw+7tPTl8nfUkHdpQ4A5k1KpCB3PCE2/acTEZHhQ0c9H9XZ7aF4z3HePHgKw4A4h511c5xMSYk0O5qIiMigU2HxQeX1V1lcUk7txXYA8rLiWT79EcLsQSYnExERMYcKiw9xub2s/+AEGw7U4vEaRIXaKJw9gZzUaLOjiYiImEqFxUccbWxlUUk5NefbAJjhjGPljDTCQ4JNTiYiImI+FRaTuT1eNuyvZf3eE3R7DCJCglkzK53cCbFmRxMREfEZKiwmOtHUxuItFVQ2tAAwLS2aNc9OIHKkzeRkIiIivkWFxQQer8HGg19StOcLXG4vYfZAVs1MZ2ZmHBaLxex4IiIiPkeFZZCdbm4nf0sFZXVXAMgeF0Xh7AxiHHaTk4mIiPguFZZB4vUabP6kjrW7arje7WGkLZDlz6Qyd2KCZlVERETuQoVlEDRc6WDJ1koO1V4CYHLyaNbNySA+fITJyURERPyDCssAMgyDkrJ6Vu88xrUuN/YgK0tzU5k3KRGrVbMqIiIi90qFZYA0tXZSUFrJvuMXAchKDKcoz0lSZIjJyURERPyPCks/MwyD7eWNrNhRTcv1boIDreRPfZj5U8YSoFkVERGRPlFh6UfN17pYtq2K96qbAMiId1Cc5yQlOtTkZCIiIv5NhaWf7K46x7J3Pudyu4tAq4WXc1JYkJ1MUIDV7GgiIiJ+T4XlPl3tcLFiRzXbyxsBGB8TSvFcJ2lxDpOTiYiIDB0qLPdhb00TBaVVXGjrwmqBF7KTWZiTgi0wwOxoIiIiQ4oKSx+0dXazeudRSsoaAEiOCqF4biaZCaPMDSYiIjJEqbD00scnm1mytZKzV69jscD8x5PInzYOe5BmVURERAaKCss96nC5Kdxdw6bDdQCMiRhBUZ6TR5MiTE4mIiIy9Kmw3INPT18mf0sFdZc6AJg3KZGC3PGE2DR8IiIig0FH3Dvo7PZQvOc4bx48hWFAnMPOujlOpqREmh1NRERkWFFhuY3y+qssLimn9mI7AHlZ8Syf/ghh9iCTk4mIiAw/Kix/xuX2sv6DE2w4UIvHaxAVaqNw9gRyUqPNjiYiIjJsqbB8w9HGVhaVlFNzvg2AGc44Vs5IIzwk2ORkIiIiw5sKC+D2eNmwv5b1e0/Q7TGICAlmzax0cifEmh1NREREUGHhRFMbi7dUUNnQAsC0tGjWPDuByJE2k5OJiIjI14ZtYfF4DTYe/JKiPV/gcnsJsweyamY6MzPjsFgsZscTERGRbxiWheV0czv5Wyooq7sCQPa4KApnZxDjsJucTERERG5lWBUWr9dg8yd1rN1Vw/VuDyNtgSx/JpW5ExM0qyIiIuLDhk1habjSwZKtlRyqvQTA5OTRrJuTQXz4CJOTiYiIyN0M+cJiGAYlZfWs3nmMa11u7EFWluamMm9SIlarZlVERET8wZAuLE2tnRSUVrLv+EUAshLDKcpzkhQZYnIyERER6Y0hWVgMw2B7eSMrdlTTcr2b4EAr+VMfZv6UsQRoVkVERMTvDLnC0nyti2XbqnivugmAjHgHxXlOUqJDTU4mIiIifTWkCsvuqnMse+dzLre7CLRaeDknhQXZyQQFWM2OJiIiIvdhSBSWqx0uVuyoZnt5IwDjY0IpnuskLc5hcjIRERHpD35fWPbWNFFQWsWFti6sFnghO5mFOSnYAgPMjiYiIiL9xG8LS1tnN6t3HqWkrAGA5KgQiudmkpkwytxgIiIi0u/6dHLHr371K5KSkrDb7WRlZfHRRx/dcfsDBw6QlZWF3W5n7NixvP76630K+7WPTzbzV69+RElZAxYL/GBKEu8ufEJlRUREZIjqdWF5++23+fGPf8yyZcs4cuQITzzxBLm5uZw5c+aW2586dYqnn36aJ554giNHjvDTn/6UhQsXUlpa2uuwHS43//f2z/nbNz/h7NXrjIkYwdv/12P84zOPYA/SR0AiIiJDlcUwDKM3O3z3u9/l29/+Nhs2bOhZlpqayqxZs1i7du1N2//kJz9hx44dHDt2rGfZggULqKio4PDhw/f0nK2trTgcDiav+iNn27+6jsq8SYkU5I4nxOa3n2qJiIgMaV8fv1taWggLC7uvx+rVDIvL5eKzzz5j6tSpNyyfOnUqhw4duuU+hw8fvmn7adOmUVZWRnd39y336erqorW19YYbQP3l68Q57Gye/11Wz0pXWRERERkmelVYmpub8Xg8REdH37A8Ojqa8+fP33Kf8+fP33J7t9tNc3PzLfdZu3YtDoej55aQkADArMw4/uOVJ5mSEtmb2CIiIuLn+nTSrcVy4+XtDcO4adndtr/V8q8tXbqUlpaWnlt9fT0A//zsBMLsQX2JLCIiIn6sV5+pREZGEhAQcNNsyoULF26aRflaTEzMLbcPDAxk9OjRt9zHZrNhs9l6E01ERESGsF7NsAQHB5OVlcX7779/w/L333+fyZMn33Kfxx577Kbt9+zZw8SJEwkK0myJiIiI3F2vPxJatGgRb775Jv/6r//KsWPHeOWVVzhz5gwLFiwAvvo45+/+7u96tl+wYAF1dXUsWrSIY8eO8a//+q9s3LiR/Pz8/nsVIiIiMqT1+ms2f/3Xf82lS5dYtWoV586dIz09nV27dpGYmAjAuXPnbrgmS1JSErt27eKVV17hl7/8JXFxcaxfv57nnnuu/16FiIiIDGm9vg6LGfrze9wiIiIyOEy7DouIiIiIGVRYRERExOepsIiIiIjPU2ERERERn6fCIiIiIj5PhUVERER8ngqLiIiI+DwVFhEREfF5KiwiIiLi83p9aX4zfH0x3tbWVpOTiIiIyL36+rjdHxfV94vC0tbWBkBCQoLJSURERKS3Ll26hMPhuK/H8IvfEvJ6vTQ2NhIaGorFYqG1tZWEhATq6+v120L3QePYPzSO/UPj2D80jv1D49g/WlpaGDNmDFeuXGHUqFH39Vh+McNitVqJj4+/aXlYWJj+kPqBxrF/aBz7h8axf2gc+4fGsX9Yrfd/yqxOuhURERGfp8IiIiIiPs8vC4vNZmPFihXYbDazo/g1jWP/0Dj2D41j/9A49g+NY//oz3H0i5NuRUREZHjzyxkWERERGV5UWERERMTnqbCIiIiIz1NhEREREZ/nd4XloYcewmKx3HArKCi4YZszZ84wffp0QkJCiIyMZOHChbhcLpMS+7auri4yMzOxWCyUl5ffsE7jeHczZsxgzJgx2O12YmNjmTdvHo2NjTdso3G8s9OnTzN//nySkpJ44IEHSE5OZsWKFTeNkcbx7tasWcPkyZMZMWLEba8qqnG8u1/96lckJSVht9vJysrio48+MjuSz/vwww+ZPn06cXFxWCwW3nnnnRvWG4bBP/3TPxEXF8cDDzxAdnY21dXVvXoOvyssAKtWreLcuXM9t3/8x3/sWefxePje975He3s7Bw8e5Pe//z2lpaUsXrzYxMS+a8mSJcTFxd20XON4b5566ilKSko4fvw4paWl1NbWMmfOnJ71Gse7q6mpwev18utf/5rq6mp+/vOf8/rrr/PTn/60ZxuN471xuVzk5eXxwgsv3HK9xvHu3n77bX784x+zbNkyjhw5whNPPEFubi5nzpwxO5pPa29vx+l08tprr91y/bp16/iXf/kXXnvtNT799FNiYmL4y7/8y57fCrwnhp9JTEw0fv7zn992/a5duwyr1WqcPXu2Z9nvfvc7w2azGS0tLYOQ0H/s2rXLGD9+vFFdXW0AxpEjR25Yp3Hsve3btxsWi8VwuVyGYWgc+2rdunVGUlJSz32NY++89dZbhsPhuGm5xvHuHn30UWPBggU3LBs/frxRUFBgUiL/Axjbtm3rue/1eo2YmBijsLCwZ1lnZ6fhcDiM119//Z4f1y9nWH72s58xevRoMjMzWbNmzQ3TmYcPHyY9Pf2GWYNp06bR1dXFZ599ZkZcn9TU1MQPf/hDfvvb3zJixIib1msce+/y5cv8r//1v5g8eTJBQUGAxrGvWlpaiIiI6LmvcewfGsc7c7lcfPbZZ0ydOvWG5VOnTuXQoUMmpfJ/p06d4vz58zeMq81m4y/+4i96Na5+V1hefvllfv/737Nv3z5eeuklXn31Vf7+7/++Z/358+eJjo6+YZ/w8HCCg4M5f/78YMf1SYZh8P3vf58FCxYwceLEW26jcbx3P/nJTwgJCWH06NGcOXOG7du396zTOPZebW0tv/jFL1iwYEHPMo1j/9A43llzczMej+emMYqOjtb43Ievx+5+x9UnCss//dM/3XQi7Z/fysrKAHjllVf4i7/4CzIyMvjBD37A66+/zsaNG7l06VLP41kslpuewzCMWy4fSu51HH/xi1/Q2trK0qVL7/h4Gse7/z0C/MM//ANHjhxhz549BAQE8Hd/93cY37iAtMbx3sYRoLGxkb/6q78iLy+PH/zgBzes0zje+zjeyXAdx97487HQ+PSP+x3XwP4O1BcvvfQSf/M3f3PHbR566KFbLp80aRIAJ0+eZPTo0cTExPDJJ5/csM2VK1fo7u6+qd0NNfc6jv/8z//Mn/70p5t+22HixIn87d/+Lf/2b/+mcezF32NkZCSRkZE8/PDDpKamkpCQwJ/+9Ccee+wxjWMvxrGxsZGnnnqKxx57jDfeeOOG7TSOfXt//HPDeRzvRWRkJAEBATf9q//ChQsan/sQExMDfDXTEhsb27O81+PaD+fXmOqPf/yjARh1dXWGYfz/J5U1Njb2bPP73/9eJ5V9Q11dnVFVVdVze++99wzA2Lp1q1FfX28Yhsaxr86cOWMAxr59+wzD0Djeq4aGBiMlJcX4m7/5G8Ptdt+0XuPYO3c76VbjeHuPPvqo8cILL9ywLDU1VSfd9gK3Oen2Zz/7Wc+yrq6uXp9061eF5dChQ8a//Mu/GEeOHDG+/PJL4+233zbi4uKMGTNm9GzjdruN9PR0Iycnx/g//+f/GP/7f/9vIz4+3njppZdMTO7bTp06ddO3hDSOd/fJJ58Yv/jFL4wjR44Yp0+fNvbu3WtMmTLFSE5ONjo7Ow3D0Djei7Nnzxr/5b/8F+O//tf/ajQ0NBjnzp3ruX1N43hv6urqjCNHjhgrV640Ro4caRw5csQ4cuSI0dbWZhiGxvFe/P73vzeCgoKMjRs3GkePHjV+/OMfGyEhIcbp06fNjubT2traev7egJ5j9deTCYWFhYbD4TD+8Ic/GFVVVcZ/+2//zYiNjTVaW1vv+Tn8qrB89tlnxne/+13D4XAYdrvdGDdunLFixQqjvb39hu3q6uqM733ve8YDDzxgREREGC+99FLPAURudqvCYhgax7uprKw0nnrqKSMiIsKw2WzGQw89ZCxYsMBoaGi4YTuN45299dZbBnDL2zdpHO/u+eefv+U4fj3jZxgax3vxy1/+0khMTDSCg4ONb3/728aBAwfMjuTz9u3bd8u/veeff94wjK9mWVasWGHExMQYNpvNePLJJ42qqqpePYfFML5xdqCIiIiID/KJbwmJiIiI3IkKi4iIiPg8FRYRERHxeSosIiIi4vNUWERERMTnqbCIiIiIz1NhEREREZ+nwiIiIiI+T4VFREREfJ4Ki4iIiPg8FRYRERHxeSosIiIi4vP+PzMeIqMQjZOVAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_funcs(PFexample.solution[0].cFunc, mMinimum, mPlotTop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Changing Agent Parameters\n",
-    "\n",
-    "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does.  We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NewExample = deepcopy(PFexample)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "nbsphinx-thumbnail": {}
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "NewExample.assign_parameters(DiscFac=0.90)\n",
-    "NewExample.solve()\n",
-    "mPlotBottom = mMinimum\n",
-    "plot_funcs(\n",
-    "    [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(Note that you can pass a **list** of functions to `plot_funcs` as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n",
-    "\n",
-    "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income.  We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n",
-    "\n",
-    "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income __shrinks__ by 99 percent each period?  In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n",
-    "\n",
-    "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept.  Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a __time-varying__ attribute, it must be specified as a **list** (with a single element in this case)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Revert NewExample's discount factor and make his future income minuscule\n",
-    "# print(\"your lines here\")\n",
-    "\n",
-    "# Compare the old and new consumption functions\n",
-    "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n",
-    "\n",
-    "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes.  However, keep in mind that *no solution exists* for some combinations of parameters.  HARK should let you know if this is the case if you try to solve such a model.\n",
-    "\n",
-    "\n",
-    "## Your Second HARK Model: Adding Income Shocks\n",
-    "\n",
-    "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines.  Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n",
-    "\n",
-    "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n",
-    "\n",
-    "\\begin{align*}\n",
-    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
-    "a_t &= m_t - c_t \\\\\n",
-    "a_t &\\geq \\underline{a} \\\\\n",
-    "m_{t+1} &= R/(\\Gamma \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
-    "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n",
-    "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n",
-    "\\end{align*}\n",
-    "\n",
-    "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$.  To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks?  Can't the joint distribution of permanent and transitory shocks be just about anything?\n",
-    "\n",
-    "_Yes_, and HARK can handle whatever correlation structure a user might care to specify.  However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment.  The distributions are independent of each other by default, and by default are approximated with $N$ point equiprobable distributions.\n",
-    "\n",
-    "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n",
-    "\n",
-    "\n",
-    "| Param | Description | Code | Value |\n",
-    "| :---: | --- | --- |  :---: |\n",
-    "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n",
-    "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n",
-    "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n",
-    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n",
-    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n",
-    "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n",
-    "| $\\underset{\\bar{}}{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
-    "\n",
-    "IndShockDictionary = {\n",
-    "    \"CRRA\": 2.5,  # The dictionary includes our original parameters...\n",
-    "    \"Rfree\": 1.03,\n",
-    "    \"DiscFac\": 0.96,\n",
-    "    \"LivPrb\": [0.98],\n",
-    "    \"PermGroFac\": [1.01],\n",
-    "    \"PermShkStd\": [\n",
-    "        0.1\n",
-    "    ],  # ... and the new parameters for constructing the income process.\n",
-    "    \"PermShkCount\": 7,\n",
-    "    \"TranShkStd\": [0.1],\n",
-    "    \"TranShkCount\": 7,\n",
-    "    \"UnempPrb\": 0.05,\n",
-    "    \"IncUnemp\": 0.3,\n",
-    "    \"BoroCnstArt\": 0.0,\n",
-    "    \"aXtraMin\": 0.001,  # aXtra parameters specify how to construct the grid of assets.\n",
-    "    \"aXtraMax\": 50.0,  # Don't worry about these for now\n",
-    "    \"aXtraNestFac\": 3,\n",
-    "    \"aXtraCount\": 48,\n",
-    "    \"aXtraExtra\": [None],\n",
-    "    \"vFuncBool\": False,  # These booleans indicate whether the value function should be calculated\n",
-    "    \"CubicBool\": False,  # and whether to use cubic spline interpolation. You can ignore them.\n",
-    "    \"aNrmInitMean\": -10.0,\n",
-    "    \"aNrmInitStd\": 0.0,  # These parameters specify the (log) distribution of normalized assets\n",
-    "    \"pLvlInitMean\": 0.0,  # and permanent income for agents at \"birth\". They are only relevant in\n",
-    "    \"pLvlInitStd\": 0.0,  # simulation and you don't need to worry about them.\n",
-    "    \"PermGroFacAgg\": 1.0,\n",
-    "    \"T_retire\": 0,  # What's this about retirement? ConsIndShock is set up to be able to\n",
-    "    \"UnempPrbRet\": 0.0,  # handle lifecycle models as well as infinite horizon problems. Swapping\n",
-    "    \"IncUnempRet\": 0.0,  # out the structure of the income process is easy, but ignore for now.\n",
-    "    \"T_age\": None,\n",
-    "    \"T_cycle\": 1,\n",
-    "    \"cycles\": 0,\n",
-    "    \"AgentCount\": 10000,\n",
-    "    \"tax_rate\": 0.0,\n",
-    "}\n",
-    "\n",
-    "# Hey, there's a lot of parameters we didn't tell you about!  Yes, but you don't need to\n",
-    "# think about them for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
-    "\n",
-    "IndShockExample = IndShockConsumerType(**IndShockDictionary)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFRaw                 = 0.985648 \n",
-      "GPFNrm                 = 0.994897 \n",
-      "GPFAggLivPrb           = 0.965935 \n",
-      "Thorn = APF            = 0.995505 \n",
-      "PermGroFacAdj          = 1.000611 \n",
-      "uInvEpShkuInv          = 0.988401 \n",
-      "VAF                    = 0.929888 \n",
-      "WRPF                   = 0.289257 \n",
-      "DiscFacGPFNrmMax       = 0.972357 \n",
-      "DiscFacGPFAggLivPrbMax = 1.015641 \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "IndShockExample.solve()\n",
-    "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Changing Constructed Attributes\n",
-    "\n",
-    "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the **constructor** or **initialization** method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n",
-    "\n",
-    "Suppose you were interested in changing (say) the amount of permanent income risk.  From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n",
-    "\n",
-    "That's _almost_ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you _actually_ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again).  In the cell below, we invoke the method $\\texttt{update_income_process}$ so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFRaw                 = 0.985648 \n",
-      "GPFNrm                 = 1.023116 \n",
-      "GPFAggLivPrb           = 0.965935 \n",
-      "Thorn = APF            = 0.995505 \n",
-      "PermGroFacAdj          = 0.973012 \n",
-      "uInvEpShkuInv          = 0.954556 \n",
-      "VAF                    = 0.898046 \n",
-      "WRPF                   = 0.289257 \n",
-      "DiscFacGPFNrmMax       = 0.906690 \n",
-      "DiscFacGPFAggLivPrbMax = 1.015641 \n"
-     ]
-    }
-   ],
-   "source": [
-    "OtherExample = deepcopy(\n",
-    "    IndShockExample\n",
-    ")  # Make a copy so we can compare consumption functions\n",
-    "OtherExample.assign_parameters(\n",
-    "    PermShkStd=[0.2]\n",
-    ")  # Double permanent income risk (note that it's a one element list)\n",
-    "OtherExample.update_income_process()  # Call the method to reconstruct the representation of F_t\n",
-    "OtherExample.solve()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use the line(s) below to plot the consumptions functions against each other"
-   ]
-  }
- ],
- "metadata": {
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-  "latex_envs": {
-   "LaTeX_envs_menu_present": true,
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-   "autocomplete": true,
-   "bibliofile": "biblio.bib",
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Gentle Introduction to HARK\n",
+    "\n",
+    "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users.  It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK.  Our aim is to convey a feel for how the toolkit works.\n",
+    "\n",
+    "For readers for whom this is your very first experience with Python, we have put important Python concepts in **boldface**. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in _italics_. Only cursory definitions (if any) are provided here.  If you want to learn more, there are many online Python and Jupyter tutorials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "code_folding": [],
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n",
+    "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n",
+    "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n",
+    "#   The most common problem beginners have is to execute a cell before all its predecessors\n",
+    "#   If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import HARK\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "mystr = lambda number: \"{:.4f}\".format(number)\n",
+    "from HARK.utilities import plot_funcs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your First HARK Model: Perfect Foresight\n",
+    "\n",
+    "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n",
+    "\n",
+    "\\begin{align*}\n",
+    "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n",
+    "\\end{align*}\n",
+    "\n",
+    "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb \\le 1$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$ and hold the rest in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. With probability $\\mathsf{D}_t = 1-\\LivPrb_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n",
+    "\n",
+    "The agent's problem can be written in Bellman form as:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n",
+    "&\\text{s.t.} \\\\\n",
+    "A_t &= M_t - C_t \\\\\n",
+    "M_{t+1} &= R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
+    "P_{t+1} &= \\Gamma_{t+1} P_t, \\\\\n",
+    "\\end{align*}\n",
+    "\n",
+    "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\aleph_t\\}$.  To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability.\n",
+    "\n",
+    "## Representing Agents in HARK\n",
+    "\n",
+    "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$.  To make agents of this class, we must import the class itself into our workspace.  (Run the cell below in order to do this)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/).\n",
+    "\n",
+    "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have.  In the hidden cell below, we define a $\\textbf{dictionary}$ named `PF_dictionary` with these parameter values:\n",
+    "\n",
+    "| Param | Description | Code | Value |\n",
+    "| :---: | ---  | --- | :---: |\n",
+    "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n",
+    "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n",
+    "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n",
+    "| $\\aleph$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n",
+    "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n",
+    "\n",
+    "\n",
+    "For now, don't worry about the specifics of dictionaries.  All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n",
+    "PF_dictionary = {\n",
+    "    \"CRRA\": 2.5,\n",
+    "    \"DiscFac\": 0.96,\n",
+    "    \"Rfree\": 1.03,\n",
+    "    \"LivPrb\": [0.98],\n",
+    "    \"PermGroFac\": [1.01],\n",
+    "    \"T_cycle\": 1,\n",
+    "    \"cycles\": 0,\n",
+    "    \"AgentCount\": 10000,\n",
+    "}\n",
+    "\n",
+    "# To those curious enough to open this hidden cell, you might notice that we defined\n",
+    "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n",
+    "# worry about these for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make an **object** named $\\texttt{PFexample}$ which is an **instance** of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in `PF_dictionary`.  Such a bundle is created passing `PF_dictionary` to the class $\\texttt{PerfForesightConsumerType}$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample = PerfForesightConsumerType(**PF_dictionary)\n",
+    "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem.\n",
+    "\n",
+    "## Solving an Agent's Problem\n",
+    "\n",
+    "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ **method**. (A method is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n",
+    "\n",
+    "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$.  In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (parameterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem.\n",
+    "\n",
+    "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem.  The consumption function stored as the first element (index 0) of the solution list can be retrieved by:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HARK.interpolation.LinearInterp at 0x1d5359c3ee0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.solution[0].cFunc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the results proven in the associated [lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$.\n",
+    "\n",
+    "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation.  It can be plotted between an $m$ ratio of 0 and 10 using the command below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mPlotTop = 10\n",
+    "plot_funcs(PFexample.solution[0].cFunc, 0.0, mPlotTop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis).  How can this be?\n",
+    "\n",
+    "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth.  How much is that?  What's the minimum value of $m_t$ where the consumption function is defined?  We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This agent's human wealth is 50.49994992551661 times his current income level.\n",
+      "This agent's consumption function is defined (consumption is positive) down to m_t = -50.49994992551661\n"
+     ]
+    }
+   ],
+   "source": [
+    "humanWealth = PFexample.solution[0].hNrm\n",
+    "mMinimum = PFexample.solution[0].mNrmMin\n",
+    "print(\n",
+    "    \"This agent's human wealth is \"\n",
+    "    + str(humanWealth)\n",
+    "    + \" times his current income level.\"\n",
+    ")\n",
+    "print(\n",
+    "    \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n",
+    "    + str(mMinimum)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yikes! Let's take a look at the bottom of the consumption function.  In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7dkJDQ3tmThwOBw888ADw1cc5Z8+eZdOmTcBX3wh67bXXWLRoET/84Q85fPgwGzdu5He/+10/vxQRERkOGq50sGRrJYdqLwEwOXk06+ZkEB8+wuRkMpB6VVg2bNgAQHZ29g3L33rrLb7//e8DcO7cOc6cOdOzLikpiV27dvHKK6/wy1/+kri4ONavX89zzz13f8lFRGRYMQyDkrJ6Vu88xrUuN/YgK0tzU5k3KRGrVbMqQ919XYdlsOg6LCIiw1tTaycFpZXsO34RgKzEcIrynCRFhpicTO7EZ67DIiIiMpAMw2B7eSMrdlTTcr2b4EAr+VMfZv6UsQRoVmVYUWERERGf1Hyti2XbqnivugmAjHgHxXlOUqJDTU4mZlBhERERn7O76hzL3vmcy+0uAq0WXs5JYUF2MkEBvfoJPBlCVFhERMRnXO1wsWJHNdvLGwEYHxNK8VwnaXEOk5OJ2VRYRETEJ+ytaaKgtIoLbV1YLfBCdjILc1KwBQaYHU18gAqLiIiYqq2zm9U7j1JS1gBAclQIxXMzyUwYZW4w8SkqLCIiYpqPTzazZGslZ69ex2KB+Y8nkT9tHPYgzarIjVRYRERk0HW43BTurmHT4ToAxkSMoCjPyaNJESYnE1+lwiIiIoPq09OXyd9SQd2lDgDmTUqkIHc8ITYdkuT29NchIiKDorPbQ/Ge47x58BSGAXEOO+vmOJmSEml2NPEDKiwiIjLgyuuvsriknNqL7QDkZcWzfPojhNmDTE4m/kKFRUREBozL7WX9ByfYcKAWj9cgKtRG4ewJ5KRGmx1N/IwKi4iIDIijja0sKimn5nwbADOccayckUZ4SLDJycQfqbCIiEi/cnu8bNhfy/q9J+j2GESEBLNmVjq5E2LNjiZ+TIVFRET6zYmmNhZvqaCyoQWAaWnRrHl2ApEjbSYnE3+nwiIiIvfN4zXYePBLivZ8gcvtJcweyKqZ6czMjMNisZgdT4YAFRYREbkvp5vbyd9SQVndFQCyx0VRODuDGIfd5GQylKiwiIhIn3i9Bps/qWPtrhqud3sYaQtk+TOpzJ2YoFkV6XcqLCIi0msNVzpYsrWSQ7WXAJicPJp1czKIDx9hcjIZqlRYRETknhmGQUlZPat3HuNalxt7kJWluanMm5SI1apZFRk4KiwiInJPmlo7KSitZN/xiwBkJYZTlOckKTLE5GQyHKiwiIjIHRmGwfbyRlbsqKblejfBgVbypz7M/CljCdCsigwSFRYREbmt5mtdLNtWxXvVTQBkxDsoznOSEh1qcjIZblRYRETklnZXnWPZO59zud1FoNXCyzkpLMhOJijAanY0GYZUWERE5AZXO1ys2FHN9vJGAMbHhFI810lanMPkZDKcqbCIiEiPvTVNFJRWcaGtC6sFXshOZmFOCrbAALOjyTCnwiIiIrR1drN651FKyhoASI4KoXhuJpkJo8wNJvKfVFhERIa5j082s2RrJWevXsdigfmPJ5E/bRz2IM2qiO9QYRERGaY6XG4Kd9ew6XAdAGMiRlCU5+TRpAiTk4ncTIVFRGQY+vT0ZfK3VFB3qQOAeZMSKcgdT4hNhwXxTfrLFBEZRjq7PRTvOc6bB09hGBDnsLNujpMpKZFmRxO5IxUWEZFhorz+KotLyqm92A5AXlY8y6c/Qpg9yORkInenwiIiMsS53F7Wf3CCDQdq8XgNokJtFM6eQE5qtNnRRO6ZCouIyBB2tLGVRSXl1JxvA2CGM46VM9IIDwk2OZlI76iwiIgMQW6Plw37a1m/9wTdHoOIkGDWzEond0Ks2dFE+kSFRURkiDnR1MbiLRVUNrQAMC0tmjXPTiBypM3kZCJ9p8IiIjJEeLwGGw9+SdGeL3C5vYTZA1k1M52ZmXFYLBaz44ncFxUWEZEh4HRzO/lbKiiruwJA9rgoCmdnEOOwm5xMpH+osIiI+DGv12DzJ3Ws3VXD9W4PI22BLH8mlbkTEzSrIkOKCouIiJ9quNLBkq2VHKq9BMDk5NGsm5NBfPgIk5OJ9D8VFhERP2MYBiVl9azeeYxrXW7sQVaW5qYyb1IiVqtmVWRoUmEREfEjTa2dFJRWsu/4RQCyEsMpynOSFBlicjKRgaXCIiLiBwzDYHt5Iyt2VNNyvZvgQCv5Ux9m/pSxBGhWRYYBFRYRER/XfK2LZduqeK+6CYCMeAfFeU5SokNNTiYyeFRYRER82O6qcyx753Mut7sItFp4OSeFBdnJBAVYzY4mMqhUWEREfNDVDhcrdlSzvbwRgPExoRTPdZIW5zA5mYg5VFhERHzM3pomCkqruNDWhdUCL2QnszAnBVtggNnRREyjwiIi4iPaOrtZvfMoJWUNACRHhVA8N5PMhFHmBhPxASosIiI+4OOTzSzZWsnZq9exWGD+40nkTxuHPUizKiKgwiIiYqoOl5vC3TVsOlwHwJiIERTlOXk0KcLkZCK+RYVFRMQkn56+TP6WCuoudQAwb1IiBbnjCbHprVnkz+n/ChGRQdbZ7aF4z3HePHgKw4A4h511c5xMSYk0O5qIz1JhEREZROX1V1lcUk7txXYA8rLiWT79EcLsQSYnE/FtKiwiIoPA5fay/oMTbDhQi8drEBVqo3D2BHJSo82OJuIXVFhERAbY0cZWFpWUU3O+DYAZzjhWzkgjPCTY5GQi/kOFRURkgLg9Xjbsr2X93hN0ewwiQoJZMyud3AmxZkcT8Tu9/jGKDz/8kOnTpxMXF4fFYuGdd9654/b79+/HYrHcdKupqelrZhERn3eiqY3ZGw5R/P4XdHsMpqVFs+eVJ1VWRPqo1zMs7e3tOJ1O/vt//+8899xz97zf8ePHCQsL67kfFRXV26cWEfF5Hq/BxoNfUrTnC1xuL2H2QFbNTGdm5lf/yBORvul1YcnNzSU3N7fXT/Tggw8yatSoXu8nIuIvTje3k7+lgrK6KwBkj4uicHYGMQ67yclE/N+g/T75t771LWJjY8nJyWHfvn2D9bQiIgPO6zXYdPg0uf/PR5TVXWGkLZCfPTeBt77/HZUVkX4y4CfdxsbG8sYbb5CVlUVXVxe//e1vycnJYf/+/Tz55JO33Kerq4uurq6e+62trQMdU0SkTxqudLBkayWHai8BMDl5NOvmZBAfPsLkZCJDy4AXlnHjxjFu3Lie+4899hj19fUUFRXdtrCsXbuWlStXDnQ0EZE+MwyDkrJ6Vu88xrUuN/YgK0tzU5k3KRGrVeeqiPS3QftI6JsmTZrEiRMnbrt+6dKltLS09Nzq6+sHMZ2IyJ01tXbyP37zKT8preJal5usxHB2v/wkz09+SGVFZICYch2WI0eOEBt7+6/22Ww2bDbbICYSEbk7wzDYXt7Iih3VtFzvJjjQSv7Uh5k/ZSwBKioiA6rXheXatWucPHmy5/6pU6coLy8nIiKCMWPGsHTpUs6ePcumTZsAePXVV3nooYdIS0vD5XKxefNmSktLKS0t7b9XISIywJqvdbFsWxXvVTcBkBHvoDjPSUp0qMnJRIaHXheWsrIynnrqqZ77ixYtAuD555/nN7/5DefOnePMmTM9610uF/n5+Zw9e5YHHniAtLQ03n33XZ5++ul+iC8iMvB2V51j2Tufc7ndRaDVwss5KSzITiYowJRP1UWGJYthGIbZIe6mtbUVh8NBS0vLDRefExEZSFc7XKzYUc328kYAxseEUjzXSVqcw+RkIv6hP4/f+i0hEZFb2FvTREFpFRfaurBa4IXsZBbmpGALDDA7msiwpMIiIvINbZ3drN55lJKyBgCSo0IonptJZsIoc4OJDHMqLCIi/+njk80s2VrJ2avXsVhg/uNJ5E8bhz1IsyoiZlNhEZFhr8PlpnB3DZsO1wEwJmIERXlOHk2KMDmZiHxNhUVEhrVPT18mf0sFdZc6AJg3KZGC3PGE2PT2KOJL9H+kiAxLnd0eivcc582DpzAMiHPYWTfHyZSUSLOjicgtqLCIyLBTXn+VxSXl1F5sByAvK57l0x8hzB5kcjIRuR0VFhEZNlxuL+s/OMGGA7V4vAZRoTYKZ08gJzXa7GgichcqLCIyLBxtbGVRSTk159sAmOGMY+WMNMJDgk1OJiL3QoVFRIY0t8fLhv21rN97gm6PQURIMGtmpZM74fY/wCoivkeFRUSGrBNNbSzeUkFlQwsA09KiWfPsBCJH6tfgRfyNCouIDDker8HGg19StOcLXG4vYfZAVs1MZ2ZmHBaLxex4ItIHKiwiMqScbm4nf0sFZXVXAMgeF0Xh7AxiHHaTk4nI/VBhEZEhwes12PxJHWt31XC928NIWyDLn0ll7sQEzaqIDAEqLCLi9xqudLBkayWHai8BMDl5NOvmZBAfPsLkZCLSX1RYRMRvGYZBSVk9q3ce41qXG3uQlaW5qcyblIjVqlkVkaFEhUVE/FJTaycFpZXsO34RgKzEcIrynCRFhpicTEQGggqLiPgVwzDYXt7Iih3VtFzvJjjQSv7Uh5k/ZSwBmlURGbJUWETEbzRf62LZtireq24CICPeQXGek5ToUJOTichAU2EREb+wu+ocy975nMvtLgKtFl7OSWFBdjJBAVazo4nIIFBhERGfdrXDxYod1WwvbwRgfEwoxXOdpMU5TE4mIoNJhUVEfNbemiYKSqu40NaF1QIvZCezMCcFW2CA2dFEZJCpsIiIz2nr7Gb1zqOUlDUAkBwVQvHcTDITRpkbTERMo8IiIj7l45PNLNlaydmr17FYYP7jSeRPG4c9SLMqIsOZCouI+IQOl5vC3TVsOlwHwJiIERTlOXk0KcLkZCLiC1RYRMR0n56+TP6WCuoudQAwb1IiBbnjCbHpLUpEvqJ3AxExTWe3h+I9x3nz4CkMA+IcdtbNcTIlJdLsaCLiY1RYRMQU5fVXWVxSTu3FdgDysuJZPv0RwuxBJicTEV+kwiIig8rl9rL+gxNsOFCLx2sQFWqjcPYEclKjzY4mIj5MhUVEBs3RxlYWlZRTc74NgBnOOFbOSCM8JNjkZCLi61RYRGTAuT1eNuyvZf3eE3R7DCJCglkzK53cCbFmRxMRP6HCIiID6kRTG4u3VFDZ0ALAtLRo1jw7gciRNpOTiYg/UWERkQHh8RpsPPglRXu+wOX2EmYPZNXMdGZmxmGxWMyOJyJ+RoVFRPrd6eZ28rdUUFZ3BYDscVEUzs4gxmE3OZmI+CsVFhHpN16vweZP6li7q4br3R5G2gJZ/kwqcycmaFZFRO6LCouI9IuGKx0s2VrJodpLAExOHs26ORnEh48wOZmIDAUqLCJyXwzDoKSsntU7j3Gty409yMrS3FTmTUrEatWsioj0DxUWEemzptZOCkor2Xf8IgBZieEU5TlJigwxOZmIDDUqLCLSa4ZhsL28kRU7qmm53k1woJX8qQ8zf8pYAjSrIiIDQIVFRHql+VoXy7ZV8V51EwAZ8Q6K85ykRIeanExEhjIVFhG5Z7urzrHsnc+53O4i0Grh5ZwUFmQnExRgNTuaiAxxKiwicldXO1ys2FHN9vJGAMbHhFI810lanMPkZCIyXKiwiMgd7a1poqC0igttXVgt8EJ2MgtzUrAFBpgdTUSGERUWEbmlts5uVu88SklZAwDJUSEUz80kM2GUucFEZFhSYRGRm3x8spklWys5e/U6FgvMfzyJ/GnjsAdpVkVEzKHCIiI9OlxuCnfXsOlwHQBjIkZQlOfk0aQIk5OJyHCnwiIiAHx6+jL5Wyqou9QBwLxJiRTkjifEprcJETGf3olEhrnObg/Fe47z5sFTGAbEOeysm+NkSkqk2dFERHqosIgMY+X1V1lcUk7txXYA8rLiWT79EcLsQSYnExG5kQqLyDDkcntZ/8EJNhyoxeM1iAq1UTh7Ajmp0WZHExG5JRUWkWHmaGMri0rKqTnfBsAMZxwrZ6QRHhJscjIRkdtTYREZJtweLxv217J+7wm6PQYRIcGsmZVO7oRYs6OJiNyVCovIMHCiqY3FWyqobGgBYFpaNGuenUDkSJvJyURE7o0Ki8gQ5vEabDz4JUV7vsDl9hJmD2TVzHRmZsZhsVjMjicics9UWESGqNPN7eRvqaCs7goA2eOiKJydQYzDbnIyEZHeU2ERGWK8XoPNn9SxdlcN17s9jLQFsvyZVOZOTNCsioj4LRUWkSGk4UoHS7ZWcqj2EgCTk0ezbk4G8eEjTE4mInJ/rL3d4cMPP2T69OnExX31Gfg777xz130OHDhAVlYWdrudsWPH8vrrr/clq4jchmEYvP3pGf7q1Y84VHsJe5CVlTPS2Dz/uyorIjIk9LqwtLe343Q6ee211+5p+1OnTvH000/zxBNPcOTIEX7605+ycOFCSktLex1WRG7W1NrJ//jNp/yktIprXW6yEsPZ/fKTPD/5IaxWfQQkIkNDrz8Sys3NJTc39563f/311xkzZgyvvvoqAKmpqZSVlVFUVMRzzz3X26cXkf9kGAbbyxtZsaOaluvdBAdayZ/6MPOnjCVARUVEhpgBP4fl8OHDTJ069YZl06ZNY+PGjXR3dxMUdPNvlnR1ddHV1dVzv7W1daBjiviV5mtdLNtWxXvVTQBkxDsoznOSEh1qcjIRkYHR64+Eeuv8+fNER9/4+yTR0dG43W6am5tvuc/atWtxOBw9t4SEhIGOKeI3dledY+rPP+S96iYCrRYW/+XDlL4wWWVFRIa0QfmW0J9/ldIwjFsu/9rSpUtZtGhRz/3W1laVFhn2rna4WLGjmu3ljQCMjwmleK6TtDiHyclERAbegBeWmJgYzp8/f8OyCxcuEBgYyOjRo2+5j81mw2bTJcNFvra3pomC0ioutHVhtcAL2ckszEnBFhhgdjQRkUEx4IXlscce449//OMNy/bs2cPEiRNvef6KiPz/2jq7Wb3zKCVlDQAkR4VQPDeTzIRR5gYTERlkvS4s165d4+TJkz33T506RXl5OREREYwZM4alS5dy9uxZNm3aBMCCBQt47bXXWLRoET/84Q85fPgwGzdu5He/+13/vQqRIejjk80s2VrJ2avXsVhg/uNJ5E8bhz1IsyoiMvz0urCUlZXx1FNP9dz/+lyT559/nt/85jecO3eOM2fO9KxPSkpi165dvPLKK/zyl78kLi6O9evX6yvNIrfR4XJTuLuGTYfrABgTMYKiPCePJkWYnExExDwW4+szYH1Ya2srDoeDlpYWwsLCzI4jMmA+PX2Z/C0V1F3qAGDepEQKcscTYtOvaIiI/+nP47feBUV8QGe3h+I9x3nz4CkMA+IcdtbNcTIlJdLsaCIiPkGFRcRk5fVXWVxSTu3FdgDysuJZPv0Rwuw6KV1E5GsqLCImcbm9rP/gBBsO1OLxGkSF2iicPYGc1Oi77ywiMsyosIiY4GhjK4tKyqk53wbADGccK2ekER4SbHIyERHfpMIiMojcHi8b9teyfu8Juj0GESHBrJmVTu6EWLOjiYj4NBUWkUFyoqmNxVsqqGxoAWBaWjRrnp1A5Ehd1VlE5G5UWEQGmMdrsPHglxTt+QKX20uYPZBVM9OZmRl329/TEhGRG6mwiAyg083t5G+poKzuCgDZ46IonJ1BjMNucjIREf+iwiIyALxeg82f1LF2Vw3Xuz2MtAWy/JlU5k5M0KyKiEgfqLCI9LOGKx0s2VrJodpLAExOHs26ORnEh48wOZmIiP9SYRHpJ4ZhUFJWz+qdx7jW5cYeZGVpbirzJiVitWpWRUTkfqiwiPSDptZOCkor2Xf8IgBZieEU5TlJigwxOZmIyNCgwiJyHwzDYHt5Iyt2VNNyvZvgQCv5Ux9m/pSxBGhWRUSk36iwiPRR87Uulm2r4r3qJgAy4h0U5zlJiQ41OZmIyNCjwiLSB7urzrHsnc+53O4i0Grh5ZwUFmQnExRgNTuaiMiQpMIi0gtXO1ys2FHN9vJGAMbHhFI810lanMPkZCIiQ5sKi8g92lvTREFpFRfaurBa4IXsZBbmpGALDDA7mojIkKfCInIXbZ3drN55lJKyBgCSo0IonptJZsIoc4OJiAwjKiwid/DxyWaWbK3k7NXrWCww//Ek8qeNwx6kWRURkcGkwiJyCx0uN4W7a9h0uA6AMREjKMpz8mhShMnJRESGJxUWkT/z6enL5G+poO5SBwDzJiVSkDueEJv+dxERMYvegUX+U2e3h+I9x3nz4CkMA+IcdtbNcTIlJdLsaCIiw54KiwhQXn+VxSXl1F5sByAvK57l0x8hzB5kcjIREQEVFhnmXG4v6z84wYYDtXi8BlGhNgpnTyAnNdrsaCIi8g0qLDJsHW1sZVFJOTXn2wCY4Yxj5Yw0wkOCTU4mIiJ/ToVFhh23x8uG/bWs33uCbo9BREgwa2alkzsh1uxoIiJyGyosMqycaGpj8ZYKKhtaAJiWFs2aZycQOdJmcjIREbkTFRYZFjxeg40Hv6Rozxe43F7C7IGsmpnOzMw4LBaL2fFEROQuVFhkyDvd3E7+lgrK6q4AkD0uisLZGcQ47CYnExGRe6XCIkOW12uw+ZM61u6q4Xq3h5G2QJY/k8rciQmaVRER8TMqLDIkNVzpYMnWSg7VXgJgcvJo1s3JID58hMnJRESkL1RYZEgxDIOSsnpW7zzGtS439iArS3NTmTcpEatVsyoiIv5KhUWGjKbWTgpKK9l3/CIAWYnhFOU5SYoMMTmZiIjcLxUW8XuGYbC9vJEVO6ppud5NcKCV/KkPM3/KWAI0qyIiMiSosIhfa77WxbJtVbxX3QRARryD4jwnKdGhJicTEZH+pMIifmt31TmWvfM5l9tdBFotvJyTwoLsZIICrGZHExGRfqbCIn7naoeLFTuq2V7eCMD4mFCK5zpJi3OYnExERAaKCov4lb01TRSUVnGhrQurBV7ITmZhTgq2wACzo4mIyABSYRG/0NbZzeqdRykpawAgOSqE4rmZZCaMMjeYiIgMChUW8Xkfn2xmydZKzl69jsUC8x9PIn/aOOxBmlURERkuVFjEZ3W43BTurmHT4ToAxkSMoCjPyaNJESYnExGRwabCIj7p09OXyd9SQd2lDgDmTUqkIHc8ITb9yYqIDEd69xef0tntoXjPcd48eArDgDiHnXVznExJiTQ7moiImEiFRXxGef1VFpeUU3uxHYC8rHiWT3+EMHuQyclERMRsKixiOpfby/oPTrDhQC0er0FUqI3C2RPISY02O5qIiPgIFRYx1dHGVhaVlFNzvg2AGc44Vs5IIzwk2ORkIiLiS1RYxBRuj5cN+2tZv/cE3R6DiJBg1sxKJ3dCrNnRRETEB6mwyKA70dTG4i0VVDa0ADAtLZo1z04gcqTN5GQiIuKrVFhk0Hi8BhsPfknRni9wub2E2QNZNTOdmZlxWCwWs+OJiIgPU2GRQXG6uZ38LRWU1V0BIHtcFIWzM4hx2E1OJiIi/kCFRQaU12uw+ZM61u6q4Xq3h5G2QJY/k8rciQmaVRERkXumwiIDpuFKB0u2VnKo9hIAk5NHs25OBvHhI0xOJiIi/kaFRfqdYRiUlNWzeucxrnW5sQdZWZqbyrxJiVitmlUREZHeU2GRftXU2klBaSX7jl8EICsxnKI8J0mRISYnExERf6bCIv3CMAy2lzeyYkc1Lde7CQ60kj/1YeZPGUuAZlVEROQ+qbDIfWu+1sWybVW8V90EQEa8g+I8JynRoSYnExGRocLal51+9atfkZSUhN1uJysri48++ui22+7fvx+LxXLTraamps+hxXfsrjrH1J9/yHvVTQRaLSz+y4cpfWGyyoqIiPSrXs+wvP322/z4xz/mV7/6FY8//ji//vWvyc3N5ejRo4wZM+a2+x0/fpywsLCe+1FRUX1LLD7haoeLFTuq2V7eCMD4mFCK5zpJi3OYnExERIYii2EYRm92+O53v8u3v/1tNmzY0LMsNTWVWbNmsXbt2pu2379/P0899RRXrlxh1KhRfQrZ2tqKw+GgpaXlhtIj5thb00RBaRUX2rqwWuCF7GQW5qRgCwwwO5qIiPiQ/jx+9+ojIZfLxWeffcbUqVNvWD516lQOHTp0x32/9a1vERsbS05ODvv27bvjtl1dXbS2tt5wE/O1dXazZGsF/+M3ZVxo6yI5KoQ//P3j/MO08SorIiIyoHr1kVBzczMej4fo6OgblkdHR3P+/Plb7hMbG8sbb7xBVlYWXV1d/Pa3vyUnJ4f9+/fz5JNP3nKftWvXsnLlyt5EkwH28clmlmyt5OzV61gsMP/xJPKnjcMepKIiIiIDr0/fEvrzS6obhnHby6yPGzeOcePG9dx/7LHHqK+vp6io6LaFZenSpSxatKjnfmtrKwkJCX2JKvepw+WmcHcNmw7XATAmYgRFeU4eTYowOZmIiAwnvSoskZGRBAQE3DSbcuHChZtmXe5k0qRJbN68+bbrbTYbNputN9FkAHx6+jL5Wyqou9QBwLxJiRTkjifEpm/Di4jI4OrVOSzBwcFkZWXx/vvv37D8/fffZ/Lkyff8OEeOHCE2NrY3Ty2DqLPbw5p3jzL314epu9RBnMPO5vnfZfWsdJUVERExRa+PPosWLWLevHlMnDiRxx57jDfeeIMzZ86wYMEC4KuPc86ePcumTZsAePXVV3nooYdIS0vD5XKxefNmSktLKS0t7d9XIv2ivP4qi0vKqb3YDkBeVjzLpz9CmD3I5GQiIjKc9bqw/PVf/zWXLl1i1apVnDt3jvT0dHbt2kViYiIA586d48yZMz3bu1wu8vPzOXv2LA888ABpaWm8++67PP300/33KuS+udxe1n9wgg0HavF4DaJCbRTOnkBO6r1/1CciIjJQen0dFjPoOiwD62hjK4tKyqk53wbADGccK2ekER4SbHIyERHxZ/15/NYJCcOY2+Nlw/5a1u89QbfHICIkmDWz0smdoPOLRETEt6iwDFMnmtpYvKWCyoYWAKalRbPm2QlEjtS3s0RExPeosAwzHq/BxoNfUrTnC1xuL2H2QFbNTGdmZtxtr6UjIiJiNhWWYeR0czv5Wyooq7sCQPa4KApnZxDjsJucTERE5M5UWIYBr9dg8yd1rN1Vw/VuDyNtgSx/JpW5ExM0qyIiIn5BhWWIa7jSwZKtlRyqvQTA5OTRrJuTQXz4CJOTiYiI3DsVliHKMAxKyupZvfMY17rc2IOsLM1NZd6kRKxWzaqIiIh/UWEZgppaOykorWTf8YsAZCWGU5TnJCkyxORkIiIifaPCMoQYhsH28kZW7Kim5Xo3wYFW8qc+zPwpYwnQrIqIiPgxFZYhovlaF8u2VfFedRMAGfEOivOcpESHmpxMRETk/qmwDAG7q86x7J3PudzuItBq4eWcFBZkJxMU0Ksf4xYREfFZKix+7GqHixU7qtle3gjA+JhQiuc6SYtzmJxMRESkf6mw+Km9NU0UlFZxoa0LqwVeyE5mYU4KtsAAs6OJiIj0OxUWP9PW2c3qnUcpKWsAIDkqhOK5mWQmjDI3mIiIyABSYfEjH59sZsnWSs5evY7FAvMfTyJ/2jjsQZpVERGRoU2FxQ90uNwU7q5h0+E6AMZEjKAoz8mjSREmJxMRERkcKiw+7tPTl8nfUkHdpQ4A5k1KpCB3PCE2/acTEZHhQ0c9H9XZ7aF4z3HePHgKw4A4h511c5xMSYk0O5qIiMigU2HxQeX1V1lcUk7txXYA8rLiWT79EcLsQSYnExERMYcKiw9xub2s/+AEGw7U4vEaRIXaKJw9gZzUaLOjiYiImEqFxUccbWxlUUk5NefbAJjhjGPljDTCQ4JNTiYiImI+FRaTuT1eNuyvZf3eE3R7DCJCglkzK53cCbFmRxMREfEZKiwmOtHUxuItFVQ2tAAwLS2aNc9OIHKkzeRkIiIivkWFxQQer8HGg19StOcLXG4vYfZAVs1MZ2ZmHBaLxex4IiIiPkeFZZCdbm4nf0sFZXVXAMgeF0Xh7AxiHHaTk4mIiPguFZZB4vUabP6kjrW7arje7WGkLZDlz6Qyd2KCZlVERETuQoVlEDRc6WDJ1koO1V4CYHLyaNbNySA+fITJyURERPyDCssAMgyDkrJ6Vu88xrUuN/YgK0tzU5k3KRGrVbMqIiIi90qFZYA0tXZSUFrJvuMXAchKDKcoz0lSZIjJyURERPyPCks/MwyD7eWNrNhRTcv1boIDreRPfZj5U8YSoFkVERGRPlFh6UfN17pYtq2K96qbAMiId1Cc5yQlOtTkZCIiIv5NhaWf7K46x7J3Pudyu4tAq4WXc1JYkJ1MUIDV7GgiIiJ+T4XlPl3tcLFiRzXbyxsBGB8TSvFcJ2lxDpOTiYiIDB0qLPdhb00TBaVVXGjrwmqBF7KTWZiTgi0wwOxoIiIiQ4oKSx+0dXazeudRSsoaAEiOCqF4biaZCaPMDSYiIjJEqbD00scnm1mytZKzV69jscD8x5PInzYOe5BmVURERAaKCss96nC5Kdxdw6bDdQCMiRhBUZ6TR5MiTE4mIiIy9Kmw3INPT18mf0sFdZc6AJg3KZGC3PGE2DR8IiIig0FH3Dvo7PZQvOc4bx48hWFAnMPOujlOpqREmh1NRERkWFFhuY3y+qssLimn9mI7AHlZ8Syf/ghh9iCTk4mIiAw/Kix/xuX2sv6DE2w4UIvHaxAVaqNw9gRyUqPNjiYiIjJsqbB8w9HGVhaVlFNzvg2AGc44Vs5IIzwk2ORkIiIiw5sKC+D2eNmwv5b1e0/Q7TGICAlmzax0cifEmh1NREREUGHhRFMbi7dUUNnQAsC0tGjWPDuByJE2k5OJiIjI14ZtYfF4DTYe/JKiPV/gcnsJsweyamY6MzPjsFgsZscTERGRbxiWheV0czv5Wyooq7sCQPa4KApnZxDjsJucTERERG5lWBUWr9dg8yd1rN1Vw/VuDyNtgSx/JpW5ExM0qyIiIuLDhk1habjSwZKtlRyqvQTA5OTRrJuTQXz4CJOTiYiIyN0M+cJiGAYlZfWs3nmMa11u7EFWluamMm9SIlarZlVERET8wZAuLE2tnRSUVrLv+EUAshLDKcpzkhQZYnIyERER6Y0hWVgMw2B7eSMrdlTTcr2b4EAr+VMfZv6UsQRoVkVERMTvDLnC0nyti2XbqnivugmAjHgHxXlOUqJDTU4mIiIifTWkCsvuqnMse+dzLre7CLRaeDknhQXZyQQFWM2OJiIiIvdhSBSWqx0uVuyoZnt5IwDjY0IpnuskLc5hcjIRERHpD35fWPbWNFFQWsWFti6sFnghO5mFOSnYAgPMjiYiIiL9xG8LS1tnN6t3HqWkrAGA5KgQiudmkpkwytxgIiIi0u/6dHLHr371K5KSkrDb7WRlZfHRRx/dcfsDBw6QlZWF3W5n7NixvP76630K+7WPTzbzV69+RElZAxYL/GBKEu8ufEJlRUREZIjqdWF5++23+fGPf8yyZcs4cuQITzzxBLm5uZw5c+aW2586dYqnn36aJ554giNHjvDTn/6UhQsXUlpa2uuwHS43//f2z/nbNz/h7NXrjIkYwdv/12P84zOPYA/SR0AiIiJDlcUwDKM3O3z3u9/l29/+Nhs2bOhZlpqayqxZs1i7du1N2//kJz9hx44dHDt2rGfZggULqKio4PDhw/f0nK2trTgcDiav+iNn27+6jsq8SYkU5I4nxOa3n2qJiIgMaV8fv1taWggLC7uvx+rVDIvL5eKzzz5j6tSpNyyfOnUqhw4duuU+hw8fvmn7adOmUVZWRnd39y336erqorW19YYbQP3l68Q57Gye/11Wz0pXWRERERkmelVYmpub8Xg8REdH37A8Ojqa8+fP33Kf8+fP33J7t9tNc3PzLfdZu3YtDoej55aQkADArMw4/uOVJ5mSEtmb2CIiIuLn+nTSrcVy4+XtDcO4adndtr/V8q8tXbqUlpaWnlt9fT0A//zsBMLsQX2JLCIiIn6sV5+pREZGEhAQcNNsyoULF26aRflaTEzMLbcPDAxk9OjRt9zHZrNhs9l6E01ERESGsF7NsAQHB5OVlcX7779/w/L333+fyZMn33Kfxx577Kbt9+zZw8SJEwkK0myJiIiI3F2vPxJatGgRb775Jv/6r//KsWPHeOWVVzhz5gwLFiwAvvo45+/+7u96tl+wYAF1dXUsWrSIY8eO8a//+q9s3LiR/Pz8/nsVIiIiMqT1+ms2f/3Xf82lS5dYtWoV586dIz09nV27dpGYmAjAuXPnbrgmS1JSErt27eKVV17hl7/8JXFxcaxfv57nnnuu/16FiIiIDGm9vg6LGfrze9wiIiIyOEy7DouIiIiIGVRYRERExOepsIiIiIjPU2ERERERn6fCIiIiIj5PhUVERER8ngqLiIiI+DwVFhEREfF5KiwiIiLi83p9aX4zfH0x3tbWVpOTiIiIyL36+rjdHxfV94vC0tbWBkBCQoLJSURERKS3Ll26hMPhuK/H8IvfEvJ6vTQ2NhIaGorFYqG1tZWEhATq6+v120L3QePYPzSO/UPj2D80jv1D49g/WlpaGDNmDFeuXGHUqFH39Vh+McNitVqJj4+/aXlYWJj+kPqBxrF/aBz7h8axf2gc+4fGsX9Yrfd/yqxOuhURERGfp8IiIiIiPs8vC4vNZmPFihXYbDazo/g1jWP/0Dj2D41j/9A49g+NY//oz3H0i5NuRUREZHjzyxkWERERGV5UWERERMTnqbCIiIiIz1NhEREREZ/nd4XloYcewmKx3HArKCi4YZszZ84wffp0QkJCiIyMZOHChbhcLpMS+7auri4yMzOxWCyUl5ffsE7jeHczZsxgzJgx2O12YmNjmTdvHo2NjTdso3G8s9OnTzN//nySkpJ44IEHSE5OZsWKFTeNkcbx7tasWcPkyZMZMWLEba8qqnG8u1/96lckJSVht9vJysrio48+MjuSz/vwww+ZPn06cXFxWCwW3nnnnRvWG4bBP/3TPxEXF8cDDzxAdnY21dXVvXoOvyssAKtWreLcuXM9t3/8x3/sWefxePje975He3s7Bw8e5Pe//z2lpaUsXrzYxMS+a8mSJcTFxd20XON4b5566ilKSko4fvw4paWl1NbWMmfOnJ71Gse7q6mpwev18utf/5rq6mp+/vOf8/rrr/PTn/60ZxuN471xuVzk5eXxwgsv3HK9xvHu3n77bX784x+zbNkyjhw5whNPPEFubi5nzpwxO5pPa29vx+l08tprr91y/bp16/iXf/kXXnvtNT799FNiYmL4y7/8y57fCrwnhp9JTEw0fv7zn992/a5duwyr1WqcPXu2Z9nvfvc7w2azGS0tLYOQ0H/s2rXLGD9+vFFdXW0AxpEjR25Yp3Hsve3btxsWi8VwuVyGYWgc+2rdunVGUlJSz32NY++89dZbhsPhuGm5xvHuHn30UWPBggU3LBs/frxRUFBgUiL/Axjbtm3rue/1eo2YmBijsLCwZ1lnZ6fhcDiM119//Z4f1y9nWH72s58xevRoMjMzWbNmzQ3TmYcPHyY9Pf2GWYNp06bR1dXFZ599ZkZcn9TU1MQPf/hDfvvb3zJixIib1msce+/y5cv8r//1v5g8eTJBQUGAxrGvWlpaiIiI6LmvcewfGsc7c7lcfPbZZ0ydOvWG5VOnTuXQoUMmpfJ/p06d4vz58zeMq81m4y/+4i96Na5+V1hefvllfv/737Nv3z5eeuklXn31Vf7+7/++Z/358+eJjo6+YZ/w8HCCg4M5f/78YMf1SYZh8P3vf58FCxYwceLEW26jcbx3P/nJTwgJCWH06NGcOXOG7du396zTOPZebW0tv/jFL1iwYEHPMo1j/9A43llzczMej+emMYqOjtb43Ievx+5+x9UnCss//dM/3XQi7Z/fysrKAHjllVf4i7/4CzIyMvjBD37A66+/zsaNG7l06VLP41kslpuewzCMWy4fSu51HH/xi1/Q2trK0qVL7/h4Gse7/z0C/MM//ANHjhxhz549BAQE8Hd/93cY37iAtMbx3sYRoLGxkb/6q78iLy+PH/zgBzes0zje+zjeyXAdx97487HQ+PSP+x3XwP4O1BcvvfQSf/M3f3PHbR566KFbLp80aRIAJ0+eZPTo0cTExPDJJ5/csM2VK1fo7u6+qd0NNfc6jv/8z//Mn/70p5t+22HixIn87d/+Lf/2b/+mcezF32NkZCSRkZE8/PDDpKamkpCQwJ/+9Ccee+wxjWMvxrGxsZGnnnqKxx57jDfeeOOG7TSOfXt//HPDeRzvRWRkJAEBATf9q//ChQsan/sQExMDfDXTEhsb27O81+PaD+fXmOqPf/yjARh1dXWGYfz/J5U1Njb2bPP73/9eJ5V9Q11dnVFVVdVze++99wzA2Lp1q1FfX28Yhsaxr86cOWMAxr59+wzD0Djeq4aGBiMlJcX4m7/5G8Ptdt+0XuPYO3c76VbjeHuPPvqo8cILL9ywLDU1VSfd9gK3Oen2Zz/7Wc+yrq6uXp9061eF5dChQ8a//Mu/GEeOHDG+/PJL4+233zbi4uKMGTNm9GzjdruN9PR0Iycnx/g//+f/GP/7f/9vIz4+3njppZdMTO7bTp06ddO3hDSOd/fJJ58Yv/jFL4wjR44Yp0+fNvbu3WtMmTLFSE5ONjo7Ow3D0Djei7Nnzxr/5b/8F+O//tf/ajQ0NBjnzp3ruX1N43hv6urqjCNHjhgrV640Ro4caRw5csQ4cuSI0dbWZhiGxvFe/P73vzeCgoKMjRs3GkePHjV+/OMfGyEhIcbp06fNjubT2traev7egJ5j9deTCYWFhYbD4TD+8Ic/GFVVVcZ/+2//zYiNjTVaW1vv+Tn8qrB89tlnxne/+13D4XAYdrvdGDdunLFixQqjvb39hu3q6uqM733ve8YDDzxgREREGC+99FLPAURudqvCYhgax7uprKw0nnrqKSMiIsKw2WzGQw89ZCxYsMBoaGi4YTuN45299dZbBnDL2zdpHO/u+eefv+U4fj3jZxgax3vxy1/+0khMTDSCg4ONb3/728aBAwfMjuTz9u3bd8u/veeff94wjK9mWVasWGHExMQYNpvNePLJJ42qqqpePYfFML5xdqCIiIiID/KJbwmJiIiI3IkKi4iIiPg8FRYRERHxeSosIiIi4vNUWERERMTnqbCIiIiIz1NhEREREZ+nwiIiIiI+T4VFREREfJ4Ki4iIiPg8FRYRERHxeSosIiIi4vP+PzMeIqMQjZOVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_funcs(PFexample.solution[0].cFunc, mMinimum, mPlotTop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing Agent Parameters\n",
+    "\n",
+    "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does.  We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NewExample = deepcopy(PFexample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "nbsphinx-thumbnail": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "NewExample.assign_parameters(DiscFac=0.90)\n",
+    "NewExample.solve()\n",
+    "mPlotBottom = mMinimum\n",
+    "plot_funcs(\n",
+    "    [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Note that you can pass a **list** of functions to `plot_funcs` as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n",
+    "\n",
+    "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income.  We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n",
+    "\n",
+    "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income __shrinks__ by 99 percent each period?  In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n",
+    "\n",
+    "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept.  Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a __time-varying__ attribute, it must be specified as a **list** (with a single element in this case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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d2d7jnH2YiVld306iDwMgyBFqgEBz78M4d+q9etlzjEcfJktKzZKs4wMyXQAYrgg1wM3WYr/mvKSj1+/DpMyRIq2BmS8AjBCEGuBGs1d7HjVw3T5MtjT+q/RhAMBPhBpgKBlGVx/m7KHr9GEmux01kC0lTqUPAwCDRKgBBqO9Vao+4bYS46UPkzSj56gB+jAAcEMQagB/tNilL//Sc9QAfRgAGDYINYAv9mrPowb66sNExXseNZA8UwqPCMx8ASCIhfozeNOmTZo5c6asVqusVquys7O1b98+r+OLi4sVEhLS6/bZZ595jCssLFR6erosFovS09NVVFQ0sFcDDIZhSBdPSce2SW//P9JrM6VfTZf+z3elv7wh1ZzsCjRjJktf/Wfp0fVS/hHp//1C+ucdUs6yrhUaAg0ABIRfKzUpKSl6+eWXdeutt0qSfve732nhwoU6fvy4MjIyvF536tQpWa09y+9jx451/e+ysjItXrxYL774ov7hH/5BRUVFWrRokQ4ePKi5c+f6+3qA/nP1Ycp6VmO89WHcv5lEHwYAhqUQwzCMwTxBfHy8XnnlFS1ZsqTXnxUXF+vee+/VlStXNHr06D6vX7x4sex2u8eKz4MPPqgxY8Zo586d/Z6H3W6XzWZTfX29R4ACXJx9GGeh98ujUvtVzzGuPkx3gKEPAwA31FC+fw+4U9PR0aG33npLTU1Nys7O9jl21qxZamlpUXp6up5//nnde++9rj8rKyvTs88+6zH+gQce0GuvvebzOR0OhxwOh+u+3W73/0XA3Dz6MGXS+f/rvQ/jPGqAPgwAjFh+h5qTJ08qOztbLS0tio2NVVFRkdLT0/scO378eG3evFmzZ8+Ww+HQ73//e913330qLi5Wbm6uJKmmpkZJSUke1yUlJammpsbnPAoKCrRmzRp/pw+zcu4P4x5irpzpPW7MZM+PkhL/jv1hAMAk/A4106ZNU3l5uerq6lRYWKgnnnhCJSUlfQabadOmadq0aa772dnZqqys1C9/+UtXqJGkkGveVAzD6PXYtVatWqWVK1e67tvtdqWmpvr7cjBSefRhukPMdfswWZJ1QmDmCwC44fwONREREa6icGZmpo4cOaJ169bpjTfe6Nf1WVlZevPNN133k5OTe63KXLhwodfqzbUsFossFoufs8eI1a8+TGRXB8YZYFLmSJG2wMwXAHDTDXqfGsMwPLot13P8+HGNH9/z7ZHs7GwdOHDAo1fz3nvvKScnZ7BTw0jmbx/GeV4SfRgACFp+hZrVq1froYceUmpqqhoaGrRr1y4VFxdr//79kro+EqqqqtL27dslSa+99pomT56sjIwMtba26s0331RhYaEKCwtdz7l8+XLl5uZq7dq1Wrhwofbs2aP3339fBw8eHMKXiWHNMKTazz0PfeyrDzN6Us9RAxOzpYSpUqhfWy0BAEzMr1Bz/vx55eXlqbq6WjabTTNnztT+/fs1f/58SVJ1dbUqKipc41tbW/XDH/5QVVVVioqKUkZGht555x09/PDDrjE5OTnatWuXnn/+eb3wwgu65ZZbtHv3bvaoMbP2VqnmI89DH6/twyhESp7hdugjfRgAgG+D3qdmuGCfmmGsxS59eaQnwHjrw3wls/uoAfowABAshsU+NYBXDTU9fZizh7z0YcZ092Gy6cMAAIYEoQaD4+rDuB366K0P477JHX0YAMAQI9TAP84+jPs3k5ovXTPI2YdxPy+JPgwA4MYi1MA3R4NU+Zf+9WGcASaVPgwA4OYj1MCTex+mokyqOemjD+PcH+Z2+jAAgIAj1ASzXn2YMunK6d7j3PswzvOS6MMAAIYZQk0w6WjrfV7S9fowqVmS7SsBmS4AAP4g1JgZfRgAQBAh1JhJQ01PgOl3H+arUjgHgwIARj5CzUhlGNKlv3WFl7NlPvowE92OGqAPAwAwL0LNSNHRJlV/1LMK460PkzSje4O7bPowAICgQqgZrhwNPeclnT3Udx8mzCKlOPswOfRhAABBjVAzXDSc9zxqwFsfJjWr56gB+jAAALgQagLBvQ/jXInx2ofJ7lmJoQ8DAIBXhJqboVcf5rDUXHvNILc+jLPUSx8GAIB+I9TcCO59GOf+MG3NnmM8+jDZUuqd9GEAABgEQs1Q8OjDOPeH6fAcEznac3+YCbfThwEAYAgRavxlGNKl/6+rzOsMMZe/6D3Oow+TLSVOow8DAMANRKi5nn73YTI8Qwx9GAAAbipCzbUcjd19mDLffZivzO7a4G5itpQyR4oaHZDpAgCALoSahvNS5eGeowbowwAAMCIFV6hx9WHcjhroqw9jm9hz1AB9GAAARgRzh5qONqnmo54N7nz2YbJ6VmNsKQGZLgAAGDjzhZovSqUrJ7u+neSrD+M8aoA+DAAApmC+ULP7XyRLSM/9yNFuu/Tm0IcBAMCkzBdqrCnS393VsxJDHwYAgKBgvlCTf1iyWgM9CwAAcJOxhAEAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEzBr1CzadMmzZw5U1arVVarVdnZ2dq3b5/X8W+//bbmz5+vsWPHusb/93//t8eYbdu2KSQkpNetpaVlYK8IAAAEJb9CTUpKil5++WUdPXpUR48e1de//nUtXLhQH3/8cZ/jS0tLNX/+fL377rs6duyY7r33Xj366KM6fvy4xzir1arq6mqPW2Rk5MBfFQAACDohhmEYg3mC+Ph4vfLKK1qyZEm/xmdkZGjx4sX6yU9+IqlrpWbFihWqq6sbzDRkt9tls9lUX18vK5vvAQAwIgzl+/eAOzUdHR3atWuXmpqalJ2d3a9rOjs71dDQoPj4eI/HGxsbNWnSJKWkpOgb3/hGr5WcvjgcDtntdo8bAAAIXn6HmpMnTyo2NlYWi0VLly5VUVGR0tPT+3Xtq6++qqamJi1atMj12PTp07Vt2zbt3btXO3fuVGRkpO666y59/vnnPp+roKBANpvNdUtNTfX3pQAAABPx++On1tZWVVRUqK6uToWFhfrNb36jkpKS6wabnTt36vvf/7727Nmj+++/3+u4zs5O3XHHHcrNzdX69eu9jnM4HHI4HK77drtdqampfPwEAMAIMpQfP/l9oGVERIRuvfVWSVJmZqaOHDmidevW6Y033vB6ze7du7VkyRK99dZbPgONJIWGhmrOnDnXXamxWCyyWCz+Th8AAJjUoPepMQzDY8XkWjt37tSTTz6pHTt26JFHHunX85WXl2v8+PGDnRoAAAgifq3UrF69Wg899JBSU1PV0NCgXbt2qbi4WPv375ckrVq1SlVVVdq+fbukrkDz+OOPa926dcrKylJNTY0kKSoqSjabTZK0Zs0aZWVlaerUqbLb7Vq/fr3Ky8u1cePGoXydAADA5PwKNefPn1deXp6qq6tls9k0c+ZM7d+/X/Pnz5ckVVdXq6KiwjX+jTfeUHt7u/Lz85Wfn+96/IknntC2bdskSXV1dXrqqadUU1Mjm82mWbNmqbS0VHfeeecQvDwAABAsBr1PzXDBPjUAAIw8w2KfGgAAgOGEUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEzBr1CzadMmzZw5U1arVVarVdnZ2dq3b5/Pa0pKSjR79mxFRkZqypQpev3113uNKSwsVHp6uiwWi9LT01VUVOTfqwAAAEHPr1CTkpKil19+WUePHtXRo0f19a9/XQsXLtTHH3/c5/jTp0/r4Ycf1t13363jx49r9erVeuaZZ1RYWOgaU1ZWpsWLFysvL08nTpxQXl6eFi1apA8++GBwrwwAAASVEMMwjME8QXx8vF555RUtWbKk158999xz2rt3rz799FPXY0uXLtWJEydUVlYmSVq8eLHsdrvHis+DDz6oMWPGaOfOnf2eh91ul81mU319vaxW6yBeEQAAuFmG8v17wJ2ajo4O7dq1S01NTcrOzu5zTFlZmRYsWODx2AMPPKCjR4+qra3N55hDhw75/PkOh0N2u93jBgAAgpffoebkyZOKjY2VxWLR0qVLVVRUpPT09D7H1tTUKCkpyeOxpKQktbe3q7a21ueYmpoan/MoKCiQzWZz3VJTU/19KQAAwET8DjXTpk1TeXm5Dh8+rH/913/VE088oU8++cTr+JCQEI/7zk+73B/va8y1j11r1apVqq+vd90qKyv9fSkAAMBEwv29ICIiQrfeeqskKTMzU0eOHNG6dev0xhtv9BqbnJzca8XlwoULCg8PV0JCgs8x167eXMtischisfg7fQAAYFKD3qfGMAw5HI4+/yw7O1sHDhzweOy9995TZmamRo0a5XNMTk7OYKcGAACCiF8rNatXr9ZDDz2k1NRUNTQ0aNeuXSouLtb+/fsldX0kVFVVpe3bt0vq+qbThg0btHLlSv3gBz9QWVmZtmzZ4vGtpuXLlys3N1dr167VwoULtWfPHr3//vs6ePDgEL5MAABgdn6FmvPnzysvL0/V1dWy2WyaOXOm9u/fr/nz50uSqqurVVFR4Rqflpamd999V88++6w2btyoCRMmaP369frmN7/pGpOTk6Ndu3bp+eef1wsvvKBbbrlFu3fv1ty5c4foJQIAgGAw6H1qhgv2qQEAYOQZFvvUAAAADCeEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAp+hZqCggLNmTNHcXFxGjdunB577DGdOnXK5zVPPvmkQkJCet0yMjJcY7Zt29bnmJaWloG9KgAAEHT8CjUlJSXKz8/X4cOHdeDAAbW3t2vBggVqamryes26detUXV3tulVWVio+Pl7f+ta3PMZZrVaPcdXV1YqMjBzYqwIAAEEn3J/B+/fv97i/detWjRs3TseOHVNubm6f19hsNtlsNtf9P/zhD7py5Yq++93veowLCQlRcnKyP9MBAABwGVSnpr6+XpIUHx/f72u2bNmi+++/X5MmTfJ4vLGxUZMmTVJKSoq+8Y1v6Pjx4z6fx+FwyG63e9wAAEDwGnCoMQxDK1eu1Lx58zRjxox+XVNdXa19+/bp+9//vsfj06dP17Zt27R3717t3LlTkZGRuuuuu/T55597fa6CggLXKpDNZlNqaupAXwoAADCBEMMwjIFcmJ+fr3feeUcHDx5USkpKv64pKCjQq6++qnPnzikiIsLruM7OTt1xxx3Kzc3V+vXr+xzjcDjkcDhc9+12u1JTU1VfXy+r1erfiwEAAAFht9tls9mG5P3br06N07Jly7R3716Vlpb2O9AYhqHf/va3ysvL8xloJCk0NFRz5szxuVJjsVhksVj8mjcAADAvvz5+MgxDTz/9tN5++2396U9/UlpaWr+vLSkp0d/+9jctWbKkXz+nvLxc48eP92d6AAAgiPm1UpOfn68dO3Zoz549iouLU01NjaSubzhFRUVJklatWqWqqipt377d49otW7Zo7ty5ffZv1qxZo6ysLE2dOlV2u13r169XeXm5Nm7cONDXBQAAgoxfoWbTpk2SpHvuucfj8a1bt+rJJ5+U1FUGrqio8Pjz+vp6FRYWat26dX0+b11dnZ566inV1NTIZrNp1qxZKi0t1Z133unP9AAAQBAbcFF4uBnKohEAALg5hvL9m7OfAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKfgVagoKCjRnzhzFxcVp3Lhxeuyxx3Tq1Cmf1xQXFyskJKTX7bPPPvMYV1hYqPT0dFksFqWnp6uoqMj/VwMAAIKWX6GmpKRE+fn5Onz4sA4cOKD29nYtWLBATU1N17321KlTqq6udt2mTp3q+rOysjItXrxYeXl5OnHihPLy8rRo0SJ98MEH/r8iAAAQlEIMwzAGevHFixc1btw4lZSUKDc3t88xxcXFuvfee3XlyhWNHj26zzGLFy+W3W7Xvn37XI89+OCDGjNmjHbu3NmvudjtdtlsNtXX18tqtfr9WgAAwM03lO/fg+rU1NfXS5Li4+OvO3bWrFkaP3687rvvPv35z3/2+LOysjItWLDA47EHHnhAhw4dGsz0AABAEAkf6IWGYWjlypWaN2+eZsyY4XXc+PHjtXnzZs2ePVsOh0O///3vdd9996m4uNi1ulNTU6OkpCSP65KSklRTU+P1eR0OhxwOh+u+3W4f6EsBAAA3WHtHp6rqruqL2iadvtik07Vdt8+/vDBkP2PAoebpp5/WRx99pIMHD/ocN23aNE2bNs11Pzs7W5WVlfrlL3/p8ZFVSEiIx3WGYfR6zF1BQYHWrFkzwNkDAIChZhiGztsdrsByurZRp2ub9EVtkyovN6uto3fjpdPRMmQ/f0ChZtmyZdq7d69KS0uVkpLi9/VZWVl68803XfeTk5N7rcpcuHCh1+qNu1WrVmnlypWu+3a7XampqX7PBQAA+KeuudUtuDS5Vl/OXGpSc2uH1+ss4aFKS4zxuI21dOje14ZmXn6FGsMwtGzZMhUVFam4uFhpaWkD+qHHjx/X+PHjXfezs7N14MABPfvss67H3nvvPeXk5Hh9DovFIovFMqCfDwAAfGtubdeZ2madudQdXC72rLxcaW7zel1YaIhSx0R1h5ZYpY2N0ZTEGE1OjNF4a6RCQz0/hRnK+ohfoSY/P187duzQnj17FBcX51pdsdlsioqKktS1glJVVaXt27dLkl577TVNnjxZGRkZam1t1ZtvvqnCwkIVFha6nnf58uXKzc3V2rVrtXDhQu3Zs0fvv//+dT/aAgAAA9fW0anKy13B5Qu3nsvp2iZV1/v+WCjZGtkVXLpDi3PlJWVMtCLCA7O3r1+hZtOmTZKke+65x+PxrVu36sknn5QkVVdXq6KiwvVnra2t+uEPf6iqqipFRUUpIyND77zzjh5++GHXmJycHO3atUvPP/+8XnjhBd1yyy3avXu35s6dO8CXBQAAJKmz01CNvcUjsDhvFZeb1dHpfWeX0dGjXGFlinPlJTFGkxOjFR0x4FruDTOofWqGE/apAQAEK8MwdKW5rfvjoeaegm53z6WlrdPrtVGjwjQ50XO1JW1sjNISYjQmJuKGz30o37+HX8wCAAB9anK063RtV1Bxfi36i+5Vl/qr3nsu4aEhmhgf7RlaEmM0JTFWSVaLz28bjySEGgAAhpHW9k5VXG7WGfdvFnWvvJy3O3xeO8EW6QosaYmxrtWXr4yJ0qgw859hTagBAOAm6+w0dK7+ateqi9tqy+nu/Vx81FwUHxPh8ZXoKd0rL5PiYxQVEXbzXsQwRKgBAOAGMAxDl5o893NxfmR05lKTHO3eey4xEV09F/fQkpYYq7SEGNmiR93EVzGyEGoAABiEhpY2nalt1he1jTrjXtKtbVJDS7vX60aFOXsusZoy1nPlZWyceXouNxOhBgCA63C0d6jiUrO+6P64yL2ge7HBe88lJESaYIvyCC3Ogu6E0ZEKD4Key81EqAEAQFJHp6Fz3QcuXlvSrbpy1WfPJTHWorTE6J5ddBNjNGVsjCbGRytyVHD3XG4mQg0AIGgYhqGLjY6eU6Ldvhp99lKzWju891xiLeGeqy3dqy+TE2NkjaTnMhwQagAApmNvaesJLtfcGh3eey4RYaGalBB9zfb/XSsvibER9FyGOUINAGBEamnr0NlLzb120T1d26Taxlav14WGSCljonvvopsYowmjoxQWSnAZqQg1AIBhq72jU1XdPZfT3Vv+O7f/P1d/Vb4O+hkXZ+kVXKaMjVFqfLQs4fRczIhQAwAIKMMwdKHB4ToluufE6EZVXG5WW4f35BIXGa4pY2OVltD11WjnR0aTE2MUa+EtLtjwGwcA3BT1zW36wu0jIvdbc2uH1+ss4aFdhdwE9zOLuv4ZH0PPBT0INQCAIXO1tcP1EdG1t8tN3nsuYaEhSh0T5bmLbvfKy3hrpELpuaAfCDUAAL+0dXTqyytXdbq20eMjo9MXm3SuvsXntcnWSNfXoF1dl7ExSh0TrYhwNqLD4BBqAAC9dHYaOt/QotMXe3bOdW5IV3G5We0+dqKzRY3q2sMloSe0OD8+iqHnghuIv10AEMSuNLW6nRDd2H2GUVeAudrmvecSOSq0e/+Wa3bRTYzRmJiIm/gKgB6EGgAwuebW9l6nRJ/u7r3UNbd5vS481HngYkyvE6OT4ui5YPgh1ACACbS2d6rySrMrtLifX1Rj991zmWCLVNrY7m8Xubb/j1XKmCiN4sBFjCCEGgAYITo7DVXbW7qDS6NHcKm8clUdPnou8TERrl6L+4nRkxNiFBXBRnQwB0INAAwjhmHoclOr2wnRnjvpOtq9H7gYHRHmseW/+210ND0XmB+hBgACoNHRrjPO4NK98nL6UrNOX2yUvcX7gYujwpw9l1hNueYjo3FxFjaiQ1Aj1ADADeJo71Dl5WbXXi7uXZcLDQ6v14WESBNsUa6PiZw76U5JjNFXRkcpnJ4L0CdCDQAMQkenoXN1Vz12znUGly+vNMtHzUWJsRE93Ra3XXQnJUQrchQ9F8BfhBoAuA7DMFTb2Oray8X9xOgzl5rV6qPnEmsJ77PjMjkxRraoUTfxVQDmR6gBgG72ljbXt4ncPzI6U9ukBof3nktEWKgmJUT37J7rtpPu2Fh6LsDNQqgBEFRa2jpU4dFzcZ4a3azaRt89l5QxUV0F3cQYTU6IVtrYrv89YXSUwtiIDgg4Qg0A0+noNFR15aq+cAWWnltV3VUZPnouY+MsbqdE93RdUuPpuQDDHaEGwIhkGIYuNjjczi1yfmTUqMrLV9Xa4b3nEmcJd9uALlaTE6M1pfufcZH0XICRilADYFirb27rPqeosdeJ0U2t3g9cjAgP7XVKtPOWEBNBzwUwIUINgIBraevo2jHXLbQ4b5ebWr1eFxoipcZHu8LKFLeDFyfYojhwEQgyhBoAN0V7R6e+vHLVbfv/RtcRAOfqfR+4mGS1uD4qcg8uE+OjFRHORnQAuhBqAAwZwzB03u7oKei6fS264nKz2n3sRGeNDNeU7m8TOT8ych4BEGPhP1UAro//UgDwW11zq9uZRZ676F5t895ziRwVes0p0bFKS+w6x2hM9Ch6LgAGxa9QU1BQoLffflufffaZoqKilJOTo7Vr12ratGler3n77be1adMmlZeXy+FwKCMjQz/72c/0wAMPuMZs27ZN3/3ud3tde/XqVUVGRvozRQBDpLm1XWdqmz130e2+1TW3eb0uLNR54GLvXXSTrZH0XADcMH6FmpKSEuXn52vOnDlqb2/Xj3/8Yy1YsECffPKJYmJi+rymtLRU8+fP10svvaTRo0dr69atevTRR/XBBx9o1qxZrnFWq1WnTp3yuJZAA9xYbR2dqrzc7LHa4lx9qbH77rmMt0V6BBbnidGp8dEaxYGLAAIgxDB8bUPl28WLFzVu3DiVlJQoNze339dlZGRo8eLF+slPfiKpa6VmxYoVqqurG+hUZLfbZbPZVF9fL6vVOuDnAcyms9NQtb1FZzxCS1fnpfLKVXX46LmMiR7VU9B1OzF6cmK0oiP49BrA4A3l+/eg/qtUX18vSYqPj+/3NZ2dnWpoaOh1TWNjoyZNmqSOjg7dfvvtevHFFz1Wcq7lcDjkcPRsaW632/2cPWAehmHoclOrzlzyPLPIeXP4OHAxalSYq5g75ZqPi0ZHR9zEVwEAgzPgUGMYhlauXKl58+ZpxowZ/b7u1VdfVVNTkxYtWuR6bPr06dq2bZtuu+022e12rVu3TnfddZdOnDihqVOn9vk8BQUFWrNmzUCnD4xIjY5214GLnh8ZNcre4v3AxfDQEE1MiHYLLbGu4JJk5cBFAOYw4I+f8vPz9c477+jgwYNKSUnp1zU7d+7U97//fe3Zs0f333+/13GdnZ264447lJubq/Xr1/c5pq+VmtTUVD5+wojnaO/o7rk0uz4mcq6+XGjwfuCiJH1ldJRnObf7xOiUMVEKp+cCYBgK+MdPy5Yt0969e1VaWtrvQLN7924tWbJEb731ls9AI0mhoaGaM2eOPv/8c69jLBaLLBaLX/MGhouOTkPn6q527aJb6/mR0ZdXmuWj5qKEmAiP0OLcjG5yQgwHLgIIan6FGsMwtGzZMhUVFam4uFhpaWn9um7nzp363ve+p507d+qRRx7p188pLy/Xbbfd5s/0gGHFMAzVNrZes/1/18rLmUvNavXRc4mJCOs+ryi214nRtigOXASAvvgVavLz87Vjxw7t2bNHcXFxqqmpkSTZbDZFRUVJklatWqWqqipt375dUlegefzxx7Vu3TplZWW5romKipLNZpMkrVmzRllZWZo6darsdrvWr1+v8vJybdy4ccheKHCjNLS09SrmOnfTbXB477mMCgvRpIQYj9DivI2No+cCAP7yK9Rs2rRJknTPPfd4PL5161Y9+eSTkqTq6mpVVFS4/uyNN95Qe3u78vPzlZ+f73r8iSee0LZt2yRJdXV1euqpp1RTUyObzaZZs2aptLRUd9555wBeEjD0Wto6VOG2n4tzL5cvaptU2+i95xIS0tNz6dn+v+sogAmjoxTGRnQAMGQGtU/NcMI+NRisjk5DVVeu6ovaRtc3jJy76FbVXZWv/6ckxlo8zixyhpjU+Gh6LgDgQ8CLwsBIZRiGLjY4XOcUuQeXikvNau3w3nOJs4S7AotrF93EWE1OjFZcJD0XAAg0Qg1Mqf5qV8/ljFtoOV3bqNMXm9TU6v3AxYjwUE1OiO7ZRddt5SUhJoKeCwAMY4QajFgtbR2ubxadvtTkcWL0paZWr9eFhkgpY3oOXJzitvoy3kbPBQBGKkINhrX2jk59eeVqn98uOlfvu+cyLs5yTWjp+np0anyULOH0XADAbAg1CDjDMHTe7ugu6Lrtotvdc2n3sROdNTLc9W0i967L5MQYxVr46w0AwYT/6uOmqWtudZ0SfeZSk8f/bvbRc4kcFarJCdcUdLs3phsTPYqeCwBAEqEGQ6y5tb17taXJ7cTorpWXK81tXq8LCw3RxPjo7pJurMeJ0cnWSIXScwEAXAehBn5r6+jsPnCxd8+lur7F57XjbZFdqy5jPXfRTY2P1igOXAQADAKhBn3q7DRUY29xdVvOuAWXisvN6vDRcxkTPUqTE923/4/t7rlEKzqCv3IAgBuDd5ggZhiGrjS36XRto77o7rY4T4w+c6lJLW3eN6KLGhWmydeeWTQ2RmkJMRoTE3ETXwUAAF0INUGgydHu8RGR+4Z09Ve991zCQ0M0MSFaaQnXbv8fqyQrBy4CAIYXQo1JtLZ3uh242KjTbl+NPm/3fuCi1HXg4uTEa3bRTYxRypgohdNzAQCMEISaEaSz09C5+r43oqu83CwfNRclxES49m9xdV3GxmhSfIyiItiIDgAw8hFqhhnDMHSpqbUrrFz0LOmeudQkR7v3nktMRJjSxsZockKM25lFsUpLiJEtmgMXAQDmRqgJkIaWNp2pbdYX3R8RuZ8Y3dDS7vW6UWEhmpTQHVzcziyakhijsXH0XAAAwYtQcwM52jtUcam555Ro58GLtU262OC95xIS0tVzcd9B11nQnTA6kp4LAAB9INQMUkenoXN1V7u3/O9adTl9qaukW3Xlqs+eS2KsRVO6929x7uUyZWyMJsZHK3IUPRcAAPxBqOkHwzB0sdHRtdLi9jHRmdomnb3UrNYO7z2XWEu4pnT3XNxPjJ6cGCNrJD0XAACGCqHGTf3VNo9ui3vXpdHhvecSER7afWZRjNuGdF0rL4mxEfRcAAC4CYIu1LS0dehs98dD7qdEn65tUm1jq9frQkOklDHRvXouaYkxmjA6SmEcuAgAQECZMtS0d3SqytVz8dzP5Vz9VRk+ei7j4iyuj4ncPzJKjY+WJZyeCwAAw5XpQs2j//m/OtccorYO78klLjJcU8Z27ZzrfmL05MQYxVpM968EAICgYLp38NO1zQq1RMsSHurxEZH74YvxMfRcAAAwG9OFmv96PFMz0pI13hqpUHouAAAEDdOFmuxbEmS1RgV6GgAA4CZja1oAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKfoWagoICzZkzR3FxcRo3bpwee+wxnTp16rrXlZSUaPbs2YqMjNSUKVP0+uuv9xpTWFio9PR0WSwWpaenq6ioyJ+pAQCAIOdXqCkpKVF+fr4OHz6sAwcOqL29XQsWLFBTU5PXa06fPq2HH35Yd999t44fP67Vq1frmWeeUWFhoWtMWVmZFi9erLy8PJ04cUJ5eXlatGiRPvjgg4G/MgAAEFRCDMPwfpz1dVy8eFHjxo1TSUmJcnNz+xzz3HPPae/evfr0009djy1dulQnTpxQWVmZJGnx4sWy2+3at2+fa8yDDz6oMWPGaOfOnf2ai91ul81mU319vaxW60BfEgAAuImG8v17UJ2a+vp6SVJ8fLzXMWVlZVqwYIHHYw888ICOHj2qtrY2n2MOHTrk9XkdDofsdrvHDQAABK8BhxrDMLRy5UrNmzdPM2bM8DqupqZGSUlJHo8lJSWpvb1dtbW1PsfU1NR4fd6CggLZbDbXLTU1daAvBQAAmMCAT+l++umn9dFHH+ngwYPXHRsSEuJx3/mJl/vjfY259jF3q1at0sqVK1336+vrNXHiRFZsAAAYQZzv24Now7gMKNQsW7ZMe/fuVWlpqVJSUnyOTU5O7rXicuHCBYWHhyshIcHnmGtXb9xZLBZZLBbXfeeqDys2AACMPJcuXZLNZhvUc/gVagzD0LJly1RUVKTi4mKlpaVd95rs7Gz98Y9/9HjsvffeU2ZmpkaNGuUac+DAAT377LMeY3Jycvo9N2evp6KiYtD/UjA4drtdqampqqyspLQdYPwuhg9+F8MLv4/hw/lJi69+bn/5FWry8/O1Y8cO7dmzR3Fxca7VFZvNpqioKEldHwtVVVVp+/btkrq+6bRhwwatXLlSP/jBD1RWVqYtW7Z4fKtp+fLlys3N1dq1a7Vw4ULt2bNH77//fr8+2nIKDQ11zYW/oMOD1WrldzFM8LsYPvhdDC/8PoYP5/v4oJ7Dn8GbNm1SfX297rnnHo0fP9512717t2tMdXW1KioqXPfT0tL07rvvqri4WLfffrtefPFFrV+/Xt/85jddY3JycrRr1y5t3bpVM2fO1LZt27R7927NnTt30C8QAAAEh0HtUzOcsE/N8MHvYvjgdzF88LsYXvh9DB/DZp+a4cRiseinP/2pR3kYgcHvYvjgdzF88LsYXvh9DB9D+bswzUoNAAAIbqZZqQEAAMGNUAMAAEyBUAMAAEyBUAMAAEzBFKHm17/+tdLS0hQZGanZs2frf//3fwM9paBTUFCgOXPmKC4uTuPGjdNjjz2mU6dOBXpaUNfvJiQkRCtWrAj0VIJWVVWVvvOd7yghIUHR0dG6/fbbdezYsUBPK+i0t7fr+eefV1pamqKiojRlyhT9+7//uzo7OwM9NdMrLS3Vo48+qgkTJigkJER/+MMfPP7cMAz97Gc/04QJExQVFaV77rlHH3/8sd8/Z8SHmt27d2vFihX68Y9/rOPHj+vuu+/WQw895LEBIG68kpIS5efn6/Dhwzpw4IDa29u1YMECNTU1BXpqQe3IkSPavHmzZs6cGeipBK0rV67orrvu0qhRo7Rv3z598sknevXVVzV69OhATy3orF27Vq+//ro2bNigTz/9VL/4xS/0yiuv6D//8z8DPTXTa2pq0le/+lVt2LChzz//xS9+oV/96lfasGGDjhw5ouTkZM2fP18NDQ3+/SBjhLvzzjuNpUuXejw2ffp040c/+lGAZgTDMIwLFy4YkoySkpJATyVoNTQ0GFOnTjUOHDhgfO1rXzOWL18e6CkFpeeee86YN29eoKcBwzAeeeQR43vf+57HY//4j/9ofOc73wnQjIKTJKOoqMh1v7Oz00hOTjZefvll12MtLS2GzWYzXn/9db+ee0Sv1LS2turYsWNasGCBx+MLFizQoUOHAjQrSF0HlEkakgPKMDD5+fl65JFHdP/99wd6KkFt7969yszM1Le+9S2NGzdOs2bN0n/9138FelpBad68efqf//kf/fWvf5UknThxQgcPHtTDDz8c4JkFt9OnT6umpsbjvdxisehrX/ua3+/lfh1oOdzU1taqo6NDSUlJHo8nJSW5DtvEzWcYhlauXKl58+ZpxowZgZ5OUNq1a5c+/PBDHTlyJNBTCXpffPGFNm3apJUrV2r16tX6y1/+omeeeUYWi0WPP/54oKcXVJ577jnV19dr+vTpCgsLU0dHh37+85/rn//5nwM9taDmfL/u67387Nmzfj3XiA41TiEhIR73DcPo9RhunqefflofffSRX6esY+hUVlZq+fLleu+99xQZGRno6QS9zs5OZWZm6qWXXpIkzZo1Sx9//LE2bdpEqLnJdu/erTfffFM7duxQRkaGysvLtWLFCk2YMEFPPPFEoKcX9IbivXxEh5rExESFhYX1WpW5cOFCr8SHm2PZsmXau3evSktLlZKSEujpBKVjx47pwoULmj17tuuxjo4OlZaWasOGDXI4HAoLCwvgDIPL+PHjlZ6e7vHY3//936uwsDBAMwpe//Zv/6Yf/ehH+qd/+idJ0m233aazZ8+qoKCAUBNAycnJkrpWbMaPH+96fCDv5SO6UxMREaHZs2frwIEDHo8fOHBAOTk5AZpVcDIMQ08//bTefvtt/elPf1JaWlqgpxS07rvvPp08eVLl5eWuW2Zmpr797W+rvLycQHOT3XXXXb22N/jrX/+qSZMmBWhGwau5uVmhoZ5ve2FhYXylO8DS0tKUnJzs8V7e2tqqkpISv9/LR/RKjSStXLlSeXl5yszMVHZ2tjZv3qyKigotXbo00FMLKvn5+dqxY4f27NmjuLg41+qZzWZTVFRUgGcXXOLi4np1mWJiYpSQkEDHKQCeffZZ5eTk6KWXXtKiRYv0l7/8RZs3b9bmzZsDPbWg8+ijj+rnP/+5Jk6cqIyMDB0/fly/+tWv9L3vfS/QUzO9xsZG/e1vf3PdP336tMrLyxUfH6+JEydqxYoVeumllzR16lRNnTpVL730kqKjo/Uv//Iv/v2gofh6VqBt3LjRmDRpkhEREWHccccdfI04ACT1edu6dWugpwbD4CvdAfbHP/7RmDFjhmGxWIzp06cbmzdvDvSUgpLdbjeWL19uTJw40YiMjDSmTJli/PjHPzYcDkegp2Z6f/7zn/t8j3jiiScMw+j6WvdPf/pTIzk52bBYLEZubq5x8uRJv39OiGEYxlCkMAAAgEAa0Z0aAAAAJ0INAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwhf8fEXL2MAjXwlMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Revert NewExample's discount factor and make his future income minuscule\n",
+    "# print(\"your lines here\")\n",
+    "\n",
+    "# Compare the old and new consumption functions\n",
+    "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n",
+    "\n",
+    "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes.  However, keep in mind that *no solution exists* for some combinations of parameters.  HARK should let you know if this is the case if you try to solve such a model.\n",
+    "\n",
+    "\n",
+    "## Your Second HARK Model: Adding Income Shocks\n",
+    "\n",
+    "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines.  Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n",
+    "\n",
+    "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n",
+    "\n",
+    "\\begin{align*}\n",
+    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
+    "a_t &= m_t - c_t \\\\\n",
+    "a_t &\\geq \\underline{a} \\\\\n",
+    "m_{t+1} &= R/(\\Gamma \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
+    "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n",
+    "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n",
+    "\\end{align*}\n",
+    "\n",
+    "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$.  To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks?  Can't the joint distribution of permanent and transitory shocks be just about anything?\n",
+    "\n",
+    "_Yes_, and HARK can handle whatever correlation structure a user might care to specify.  However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment.  The distributions are independent of each other by default, and by default are approximated with $N$ point equiprobable distributions.\n",
+    "\n",
+    "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n",
+    "\n",
+    "\n",
+    "| Param | Description | Code | Value |\n",
+    "| :---: | --- | --- |  :---: |\n",
+    "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n",
+    "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n",
+    "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n",
+    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n",
+    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n",
+    "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n",
+    "| $\\underset{\\bar{}}{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
+    "\n",
+    "IndShockDictionary = {\n",
+    "    \"CRRA\": 2.5,  # The dictionary includes our original parameters...\n",
+    "    \"Rfree\": 1.03,\n",
+    "    \"DiscFac\": 0.96,\n",
+    "    \"LivPrb\": [0.98],\n",
+    "    \"PermGroFac\": [1.01],\n",
+    "    \"PermShkStd\": [\n",
+    "        0.1\n",
+    "    ],  # ... and the new parameters for constructing the income process.\n",
+    "    \"PermShkCount\": 7,\n",
+    "    \"TranShkStd\": [0.1],\n",
+    "    \"TranShkCount\": 7,\n",
+    "    \"UnempPrb\": 0.05,\n",
+    "    \"IncUnemp\": 0.3,\n",
+    "    \"BoroCnstArt\": 0.0,\n",
+    "    \"aXtraMin\": 0.001,  # aXtra parameters specify how to construct the grid of assets.\n",
+    "    \"aXtraMax\": 50.0,  # Don't worry about these for now\n",
+    "    \"aXtraNestFac\": 3,\n",
+    "    \"aXtraCount\": 48,\n",
+    "    \"aXtraExtra\": [None],\n",
+    "    \"vFuncBool\": False,  # These booleans indicate whether the value function should be calculated\n",
+    "    \"CubicBool\": False,  # and whether to use cubic spline interpolation. You can ignore them.\n",
+    "    \"aNrmInitMean\": -10.0,\n",
+    "    \"aNrmInitStd\": 0.0,  # These parameters specify the (log) distribution of normalized assets\n",
+    "    \"pLvlInitMean\": 0.0,  # and permanent income for agents at \"birth\". They are only relevant in\n",
+    "    \"pLvlInitStd\": 0.0,  # simulation and you don't need to worry about them.\n",
+    "    \"PermGroFacAgg\": 1.0,\n",
+    "    \"T_retire\": 0,  # What's this about retirement? ConsIndShock is set up to be able to\n",
+    "    \"UnempPrbRet\": 0.0,  # handle lifecycle models as well as infinite horizon problems. Swapping\n",
+    "    \"IncUnempRet\": 0.0,  # out the structure of the income process is easy, but ignore for now.\n",
+    "    \"T_age\": None,\n",
+    "    \"T_cycle\": 1,\n",
+    "    \"cycles\": 0,\n",
+    "    \"AgentCount\": 10000,\n",
+    "    \"tax_rate\": 0.0,\n",
+    "}\n",
+    "\n",
+    "# Hey, there's a lot of parameters we didn't tell you about!  Yes, but you don't need to\n",
+    "# think about them for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
+    "\n",
+    "IndShockExample = IndShockConsumerType(**IndShockDictionary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "IndShockExample.solve()\n",
+    "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing Constructed Attributes\n",
+    "\n",
+    "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the **constructor** or **initialization** method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n",
+    "\n",
+    "Suppose you were interested in changing (say) the amount of permanent income risk.  From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n",
+    "\n",
+    "That's _almost_ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you _actually_ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again).  In the cell below, we invoke the method `update_income_process` so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OtherExample = deepcopy(\n",
+    "    IndShockExample\n",
+    ")  # Make a copy so we can compare consumption functions\n",
+    "OtherExample.assign_parameters(\n",
+    "    PermShkStd=[0.2]\n",
+    ")  # Double permanent income risk (note that it's a one element list)\n",
+    "OtherExample.update_income_process()  # Call the method to reconstruct the representation of F_t\n",
+    "OtherExample.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the line(s) below to plot the consumptions functions against each other"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "collapsed,code_folding",
+   "formats": "ipynb"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 2d212ae101edc3f4ee68672a7d1c69aeb233409e Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 13 Nov 2023 11:23:48 -0500
Subject: [PATCH 24/37] track 'who_dies' to t-1 spot in history for HARK 0.13
 core; generic monte carlo now matches exact

---
 HARK/core.py                                  |    5 +-
 HARK/simulation/monte_carlo.py                |    2 +-
 ...eneric Monte Carlo Perfect Foresight.ipynb | 1623 ++++++++++++++---
 3 files changed, 1418 insertions(+), 212 deletions(-)

diff --git a/HARK/core.py b/HARK/core.py
index d7db51b30..09309e362 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -1061,7 +1061,10 @@ def simulate(self, sim_periods=None):
                     elif var_name in self.controls:
                         self.history[var_name][self.t_sim, :] = self.controls[var_name]
                     else:
-                        self.history[var_name][self.t_sim, :] = getattr(self, var_name)
+                        if var_name is 'who_dies' and self.t_sim > 1:
+                            self.history[var_name][self.t_sim - 1, :] = getattr(self, var_name)
+                        else:
+                            self.history[var_name][self.t_sim, :] = getattr(self, var_name)
                 self.t_sim += 1
 
             return self.history
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 93b4605cd..2326186ed 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -404,7 +404,7 @@ def sim_birth(self, which_agents):
         None
         """
         if self.read_shocks:
-            t = self.t_sim - 1 if self.t_sim > 0 else 0
+            t = self.t_sim
             initial_vals = {
                 init_var: self.newborn_init_history[init_var][t, which_agents]
                 for init_var
diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
index 3894c3da5..e4749ea1d 100644
--- a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
@@ -33,7 +33,7 @@
     "PFexample.cycles = 0\n",
     "\n",
     "SimulationParams = {\n",
-    "    \"AgentCount\": 10000,  # Number of agents of this type\n",
+    "    \"AgentCount\": 3,  # Number of agents of this type\n",
     "    \"T_sim\": 120,  # Number of periods to simulate\n",
     "    \"aNrmInitMean\": -6.0,  # Mean of log initial assets\n",
     "    \"aNrmInitStd\": 0, #1.0,  # Standard deviation of log initial assets\n",
@@ -66,7 +66,7 @@
     {
      "data": {
       "text/plain": [
-       "<HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType object at 0x7f81cd8bae60.\n",
+       "<HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType object at 0x7f6482eab190.\n",
        "Parameters:\n",
        "cycles: 1\n",
        "CRRA: 2.0\n",
@@ -76,7 +76,7 @@
        "PermGroFac: [1.01]\n",
        "BoroCnstArt: None\n",
        "MaxKinks: 400\n",
-       "AgentCount: 10000\n",
+       "AgentCount: 3\n",
        "aNrmInitMean: -6.0\n",
        "aNrmInitStd: 0\n",
        "pLvlInitMean: 0.0\n",
@@ -106,39 +106,486 @@
     {
      "data": {
       "text/plain": [
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-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 1., 0., 0.],\n",
-       "        [0., 0., 1., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]]),\n",
-       " 'mNrm': array([[  1.00252784,   1.00252784,   1.00252784, ...,   1.00252784,\n",
-       "           1.00252784,   1.00252784],\n",
-       "        [ -0.30338194,  -0.30338194,  -0.30338194, ...,  -0.30338194,\n",
-       "          -0.30338194,  -0.30338194],\n",
-       "        [ -1.57617872,  -1.57617872,  -1.57617872, ...,  -1.57617872,\n",
-       "          -1.57617872,  -1.57617872],\n",
-       "        ...,\n",
-       "        [-21.97112221,  -9.62650475, -36.60139796, ...,   1.00252784,\n",
-       "          -2.81670212, -46.01051785],\n",
-       "        [-22.6945059 , -10.6629019 ,   1.00252784, ...,  -0.30338194,\n",
-       "          -4.02577047, -46.12435211],\n",
-       "        [-23.39954727, -11.67301988,  -0.30338194, ...,  -1.57617872,\n",
-       "          -5.20418135, -46.23529996]]),\n",
-       " 'pLvl': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
-       "         1.01      ],\n",
-       "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
-       "         1.0201    ],\n",
-       "        [1.030301  , 1.030301  , 1.030301  , ..., 1.030301  , 1.030301  ,\n",
-       "         1.030301  ],\n",
-       "        ...,\n",
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-       "         2.59927293],\n",
-       "        [1.282432  , 1.11566835, 1.01      , ..., 1.0201    , 1.05101005,\n",
-       "         2.62526565],\n",
-       "        [1.29525631, 1.12682503, 1.0201    , ..., 1.030301  , 1.06152015,\n",
-       "         2.65151831]])}"
+       "{'who_dies': array([[ 0.,  0.,  0.],\n",
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+       "        [ -6.08371181, -17.01522664, -28.86144954],\n",
+       "        [ -7.20994102, -17.8642734 , -29.41012026],\n",
+       "        [ -8.30761326, -18.69179151, -29.94487875],\n",
+       "        [ -9.37745262, -19.49832683, -30.46607775],\n",
+       "        [-10.42016485, -20.28441144, -30.97406109],\n",
+       "        [-11.43643779, -21.05056386, -31.46916387],\n",
+       "        [-12.42694183, -21.79728952, -31.95171268],\n",
+       "        [-13.39233038, -22.525081  , -32.42202586],\n",
+       "        [-14.33324028, -23.2344184 , -32.88041365],\n",
+       "        [-15.25029222, -23.92576966, -33.32717843],\n",
+       "        [-16.14409113,  -1.27807355, -33.76261493],\n",
+       "        [-17.01522664,  -2.52615583, -34.18701038],\n",
+       "        [-17.8642734 ,  -3.74259139, -34.60064474],\n",
+       "        [-18.69179151,  -4.92818269, -35.00379088],\n",
+       "        [-19.49832683,  -6.08371181, -35.39671474],\n",
+       "        [-20.28441144,  -7.20994102, -35.77967551],\n",
+       "        [-21.05056386,  -8.30761326, -36.15292583],\n",
+       "        [-21.79728952,  -9.37745262, -36.51671191],\n",
+       "        [-22.525081  , -10.42016485, -36.87127373],\n",
+       "        [-23.2344184 , -11.43643779, -37.21684518],\n",
+       "        [-23.92576966, -12.42694183,  -1.27807355],\n",
+       "        [-24.59959082, -13.39233038,  -2.52615583]])}"
       ]
      },
      "execution_count": 5,
@@ -147,7 +594,7 @@
     }
    ],
    "source": [
-    "PFexample.track_vars = [\"who_dies\",\"mNrm\",\"pLvl\"]\n",
+    "PFexample.track_vars = [\"who_dies\",\"mNrm\",\"pLvl\",\"aNrm\"]\n",
     "PFexample.make_shock_history()\n",
     "\n",
     "PFexample.initialize_sim()\n",
@@ -162,7 +609,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -188,7 +635,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -216,13 +663,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_82097/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "/tmp/ipykernel_14820/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n",
       "  plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -243,37 +690,16 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "31ec9e16",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 0., 0., ..., 0., 0., 0.])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PFexample.history[\"who_dies\"][0,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
    "id": "603ae6e5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(203,)"
+       "(0,)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -284,17 +710,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "567440dd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(181,)"
+       "(0,)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "adfbe431",
    "metadata": {},
    "outputs": [],
@@ -325,14 +751,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "5a0c394b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7f81cd6c33d0>},\n",
+       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7f6482cab160>},\n",
        " 'parameters': {'DiscFac': 0.96,\n",
        "  'CRRA': (2.0,),\n",
        "  'Rfree': 1.03,\n",
@@ -343,12 +769,12 @@
        "  'r_eff': <function HARK.models.perfect_foresight_normalized.<lambda>(Rfree, PermGroFac)>,\n",
        "  'b_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(r_eff, a_nrm)>,\n",
        "  'm_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(b_nrm)>,\n",
-       "  'c_nrm': <HARK.model.Control at 0x7f81cd53bfa0>,\n",
+       "  'c_nrm': <HARK.model.Control at 0x7f6482cc5b40>,\n",
        "  'a_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(m_nrm, c_nrm)>},\n",
        " 'reward': {'u': <function HARK.models.perfect_foresight_normalized.<lambda>(c)>}}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -359,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "e9d068bd",
    "metadata": {},
    "outputs": [],
@@ -376,131 +802,888 @@
     "        #'live' : 1,\n",
     "        'p' : 1.0\n",
     "    },\n",
-    "    agent_count = 10000,\n",
+    "    agent_count = 3,\n",
     "    T_sim = 120\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "36ba1dda",
    "metadata": {},
    "outputs": [],
    "source": [
     "pfn_simulator.read_shocks = True\n",
+    "#pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n",
+    "\n",
     "pfn_simulator.shock_history['live'] = 1 - PFexample.history[\"who_dies\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "bc84d3e5",
    "metadata": {},
    "outputs": [],
    "source": [
     "pfn_simulator.newborn_init_history['a_nrm'] = PFexample.newborn_init_history['aNrm']\n",
     "pfn_simulator.newborn_init_history['p'] = PFexample.newborn_init_history['pLvl']\n",
-    "#pfn_simulator.newborn_init_history['live'] = np.ones(PFexample.newborn_init_history['pLvl'].shape)"
+    "#pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "id": "65df3a7f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'live': array([[1., 1., 1., ..., 1., 1., 1.],\n",
-       "        [1., 1., 1., ..., 1., 1., 1.],\n",
-       "        [1., 1., 1., ..., 1., 1., 1.],\n",
-       "        ...,\n",
-       "        [1., 1., 1., ..., 0., 1., 1.],\n",
-       "        [1., 1., 0., ..., 1., 1., 1.],\n",
-       "        [1., 1., 1., ..., 1., 1., 1.]]),\n",
-       " 'p': array([[1.01      , 1.01      , 1.01      , ..., 1.01      , 1.01      ,\n",
-       "         1.01      ],\n",
-       "        [1.0201    , 1.0201    , 1.0201    , ..., 1.0201    , 1.0201    ,\n",
-       "         1.0201    ],\n",
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-       "          -6.08371181, -46.31810967]])}"
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+       "        [1.01980198, 1.01980198, 1.01980198],\n",
+       "        [1.01980198, 1.01980198, 1.01980198]]),\n",
+       " 'b_nrm': array([[ 2.52783638e-03,  2.52783638e-03,  2.52783638e-03],\n",
+       "        [-1.30338194e+00, -1.30338194e+00, -1.30338194e+00],\n",
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+       "        [ 2.52783638e-03, -1.26730199e+01, -2.64092098e+01],\n",
+       "        [-1.30338194e+00, -1.36575250e+01, -2.70454164e+01],\n",
+       "        [-2.57617872e+00, -1.46170668e+01, -2.76654912e+01],\n",
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+       "        [-5.02577047e+00, -1.64637761e+01, -2.88588711e+01],\n",
+       "        [-6.20418135e+00, -1.73521618e+01, -2.94329634e+01],\n",
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+       "        [-8.47212045e+00, -1.90619260e+01, -3.05378466e+01],\n",
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+       "        [-1.06265047e+01, -2.06860829e+01, -3.15874088e+01],\n",
+       "        [-1.16629019e+01, -2.14674067e+01, -3.20923156e+01],\n",
+       "        [-1.26730199e+01, -2.22289190e+01, -3.25844199e+01],\n",
+       "        [-1.36575250e+01, -2.29711222e+01, -3.30640462e+01],\n",
+       "        [-1.46170668e+01, -2.36945059e+01, -3.35315109e+01],\n",
+       "        [-1.55522782e+01,  2.52783638e-03, -3.39871226e+01],\n",
+       "        [-1.64637761e+01, -1.30338194e+00, -3.44311816e+01],\n",
+       "        [-1.73521618e+01, -2.57617872e+00, -3.48639809e+01],\n",
+       "        [-1.82180214e+01, -3.81670212e+00, -3.52858060e+01],\n",
+       "        [-1.90619260e+01, -5.02577047e+00, -3.56969353e+01],\n",
+       "        [-1.98844323e+01, -6.20418135e+00, -3.60976398e+01],\n",
+       "        [-2.06860829e+01, -7.35271213e+00, -3.64881839e+01],\n",
+       "        [-2.14674067e+01, -8.47212045e+00, -3.68688253e+01],\n",
+       "        [-2.22289190e+01, -9.56314476e+00, -3.72398151e+01],\n",
+       "        [-2.29711222e+01, -1.06265047e+01, -3.76013980e+01],\n",
+       "        [-2.36945059e+01, -1.16629019e+01,  2.52783638e-03],\n",
+       "        [-2.43995473e+01, -1.26730199e+01, -1.30338194e+00]]),\n",
+       " 'm_nrm': array([[  1.00252784,   1.00252784,   1.00252784],\n",
+       "        [ -0.30338194,  -0.30338194,  -0.30338194],\n",
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+       "        [-30.46607775, -30.46607775, -25.25632639],\n",
+       "        [-30.97406109, -30.97406109, -25.89640959],\n",
+       "        [-31.46916387, -31.46916387, -26.52026268],\n",
+       "        [-31.95171268, -31.95171268, -27.12829717],\n",
+       "        [-32.42202586, -32.42202586, -27.72091418],\n",
+       "        [-32.88041365, -32.88041365, -28.29850463],\n",
+       "        [-33.32717843, -33.32717843, -28.86144954],\n",
+       "        [-33.76261493, -33.76261493, -29.41012026],\n",
+       "        [-34.18701038, -34.18701038, -29.94487875],\n",
+       "        [-34.60064474, -34.60064474, -30.46607775],\n",
+       "        [-35.00379088, -35.00379088, -30.97406109],\n",
+       "        [-35.39671474, -35.39671474, -31.46916387],\n",
+       "        [-35.77967551, -35.77967551, -31.95171268],\n",
+       "        [-36.15292583, -36.15292583, -32.42202586],\n",
+       "        [-36.51671191, -36.51671191, -32.88041365],\n",
+       "        [-36.87127373, -36.87127373, -33.32717843],\n",
+       "        [-37.21684518,  -1.27807355, -33.76261493],\n",
+       "        [-37.55365424,  -2.52615583, -34.18701038],\n",
+       "        [-37.88192307,  -3.74259139, -34.60064474],\n",
+       "        [-38.20186823,  -4.92818269, -35.00379088],\n",
+       "        [-38.51370077,  -6.08371181, -35.39671474],\n",
+       "        [-38.8176264 ,  -7.20994102, -35.77967551],\n",
+       "        [-39.11384561,  -8.30761326, -36.15292583],\n",
+       "        [-39.4025538 ,  -9.37745262, -36.51671191],\n",
+       "        [-39.68394144, -10.42016485, -36.87127373],\n",
+       "        [-39.95819413, -11.43643779, -37.21684518],\n",
+       "        [-40.2254928 , -12.42694183, -37.55365424],\n",
+       "        [-40.48601376, -13.39233038, -37.88192307],\n",
+       "        [-40.73992889, -14.33324028, -38.20186823],\n",
+       "        [-40.98740568, -15.25029222, -38.51370077],\n",
+       "        [-41.22860738, -16.14409113, -38.8176264 ],\n",
+       "        [-41.46369311, -17.01522664,  -1.27807355],\n",
+       "        [-41.69281793, -17.8642734 ,  -2.52615583],\n",
+       "        [-41.91613301, -18.69179151,  -3.74259139],\n",
+       "        [-42.13378565, -19.49832683,  -4.92818269],\n",
+       "        [-42.34591942, -20.28441144,  -6.08371181],\n",
+       "        [-42.55267428, -21.05056386,  -7.20994102],\n",
+       "        [-42.75418661, -21.79728952,  -8.30761326],\n",
+       "        [-42.95058933, -22.525081  ,  -9.37745262],\n",
+       "        [-43.14201201, -23.2344184 , -10.42016485],\n",
+       "        [-43.32858093, -23.92576966, -11.43643779],\n",
+       "        [-43.51041916, -24.59959082, -12.42694183],\n",
+       "        [ -1.27807355, -25.25632639, -13.39233038],\n",
+       "        [ -2.52615583, -25.89640959, -14.33324028],\n",
+       "        [ -3.74259139, -26.52026268, -15.25029222],\n",
+       "        [ -4.92818269, -27.12829717, -16.14409113],\n",
+       "        [ -6.08371181, -27.72091418, -17.01522664],\n",
+       "        [ -1.27807355, -28.29850463, -17.8642734 ],\n",
+       "        [ -2.52615583,  -1.27807355, -18.69179151],\n",
+       "        [ -3.74259139,  -2.52615583, -19.49832683],\n",
+       "        [ -4.92818269,  -3.74259139, -20.28441144],\n",
+       "        [ -6.08371181,  -4.92818269, -21.05056386],\n",
+       "        [ -7.20994102,  -6.08371181, -21.79728952],\n",
+       "        [ -8.30761326,  -7.20994102, -22.525081  ],\n",
+       "        [ -9.37745262,  -8.30761326, -23.2344184 ],\n",
+       "        [-10.42016485,  -9.37745262, -23.92576966],\n",
+       "        [-11.43643779, -10.42016485, -24.59959082],\n",
+       "        [-12.42694183, -11.43643779, -25.25632639],\n",
+       "        [-13.39233038, -12.42694183, -25.89640959],\n",
+       "        [ -1.27807355, -13.39233038, -26.52026268],\n",
+       "        [ -2.52615583, -14.33324028, -27.12829717],\n",
+       "        [ -3.74259139, -15.25029222, -27.72091418],\n",
+       "        [ -4.92818269, -16.14409113, -28.29850463],\n",
+       "        [ -6.08371181, -17.01522664, -28.86144954],\n",
+       "        [ -7.20994102, -17.8642734 , -29.41012026],\n",
+       "        [ -8.30761326, -18.69179151, -29.94487875],\n",
+       "        [ -9.37745262, -19.49832683, -30.46607775],\n",
+       "        [-10.42016485, -20.28441144, -30.97406109],\n",
+       "        [-11.43643779, -21.05056386, -31.46916387],\n",
+       "        [-12.42694183, -21.79728952, -31.95171268],\n",
+       "        [-13.39233038, -22.525081  , -32.42202586],\n",
+       "        [-14.33324028, -23.2344184 , -32.88041365],\n",
+       "        [-15.25029222, -23.92576966, -33.32717843],\n",
+       "        [-16.14409113,  -1.27807355, -33.76261493],\n",
+       "        [-17.01522664,  -2.52615583, -34.18701038],\n",
+       "        [-17.8642734 ,  -3.74259139, -34.60064474],\n",
+       "        [-18.69179151,  -4.92818269, -35.00379088],\n",
+       "        [-19.49832683,  -6.08371181, -35.39671474],\n",
+       "        [-20.28441144,  -7.20994102, -35.77967551],\n",
+       "        [-21.05056386,  -8.30761326, -36.15292583],\n",
+       "        [-21.79728952,  -9.37745262, -36.51671191],\n",
+       "        [-22.525081  , -10.42016485, -36.87127373],\n",
+       "        [-23.2344184 , -11.43643779, -37.21684518],\n",
+       "        [-23.92576966, -12.42694183,  -1.27807355],\n",
+       "        [-24.59959082, -13.39233038,  -2.52615583]])}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -513,13 +1696,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "id": "9e2c7ad0",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -539,7 +1722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "id": "2b471cf1",
    "metadata": {},
    "outputs": [
@@ -547,13 +1730,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_82097/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "/tmp/ipykernel_14820/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n",
       "  plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -573,23 +1756,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "id": "464f19e7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f81cd7cac50>]"
+       "[<matplotlib.lines.Line2D at 0x7f6482c7c670>]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -606,13 +1789,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "id": "1cc1dc83",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -624,8 +1807,8 @@
     }
    ],
    "source": [
-    "plt.plot(np.mean(pfn_simulator.history['m_nrm'], axis=1), label = 'Generic monte carlo')\n",
-    "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1), label = 'HARK 0.13 PerfForesightConsumerType')\n",
+    "plt.plot(np.mean(pfn_simulator.history['a_nrm'], axis=1), label = 'Generic monte carlo')\n",
+    "plt.plot(np.mean(PFexample.history[\"aNrm\"], axis=1), label = 'HARK 0.13 PerfForesightConsumerType')\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.legend()\n",
@@ -634,60 +1817,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "id": "dcff94ad",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.        ,  0.00222005,  0.00809147,  0.0012631 ,  0.00715372,\n",
-       "        0.00472244,  0.01164685, -0.02193753,  0.00705938, -0.00575065,\n",
-       "        0.01159715,  0.01218249,  0.00756169, -0.01088867,  0.00391398,\n",
-       "        0.01271844, -0.03643547, -0.02459725,  0.03015856,  0.01530481,\n",
-       "        0.02311477, -0.03156213,  0.03450044, -0.0074543 , -0.04107368,\n",
-       "       -0.04513277, -0.04755333,  0.01681224,  0.02868896,  0.00597751,\n",
-       "        0.0062148 ,  0.03227652, -0.05788355, -0.03502477, -0.05127585,\n",
-       "       -0.04840637, -0.02264437, -0.01016401, -0.04317002,  0.01554684,\n",
-       "        0.04422656, -0.01825121, -0.05225021,  0.05681161, -0.02034541,\n",
-       "        0.02992888, -0.01631412,  0.03085562, -0.03086612,  0.07806968,\n",
-       "       -0.00208791, -0.03290248,  0.00225374,  0.00643284,  0.00942418,\n",
-       "        0.01848714,  0.00079372,  0.03766846,  0.07001856,  0.02767114,\n",
-       "       -0.00659404, -0.02168669, -0.0207974 , -0.05963123,  0.00159466,\n",
-       "       -0.06902993, -0.01631861,  0.03258728,  0.05195744,  0.01295495,\n",
-       "       -0.07574536,  0.05804067,  0.00946723,  0.04908705,  0.03198564,\n",
-       "       -0.02476253, -0.01308888, -0.02987224, -0.0402046 , -0.05912255,\n",
-       "       -0.03579365,  0.05592895, -0.01209768, -0.00462931, -0.00515618,\n",
-       "        0.03687773, -0.0470187 ,  0.07340151, -0.02497839, -0.06001675,\n",
-       "        0.0057467 , -0.114128  ,  0.03985532,  0.01473989,  0.03048939,\n",
-       "        0.01199857,  0.06194041,  0.03707683,  0.0146343 ,  0.06566299,\n",
-       "        0.04049083, -0.0182422 , -0.05992466, -0.02158895, -0.02265609,\n",
-       "       -0.0748654 ,  0.08375932,  0.02491707, -0.01224103,  0.02084308,\n",
-       "       -0.08846461, -0.02883666, -0.09607532,  0.01259047, -0.02288385,\n",
-       "       -0.02240209,  0.05049241, -0.01061027, -0.02799473,  0.00775624])"
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0.])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.mean(pfn_simulator.history['m_nrm'], axis=1) - np.mean(PFexample.history[\"mNrm\"], axis=1)"
+    "np.mean(pfn_simulator.history['a_nrm'], axis=1) - np.mean(PFexample.history[\"aNrm\"], axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6cc8cfd2",
+   "execution_count": 22,
+   "id": "70de1058",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8,  50,  65,  76,  81,  82,  93, 107, 117]),\n",
+       " array([2, 1, 2, 0, 0, 1, 0, 1, 2]))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(pfn_simulator.history['live'] < 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0e37528d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8,  50,  65,  76,  81,  82,  93, 107, 117]),\n",
+       " array([2, 1, 2, 0, 0, 1, 0, 1, 2]))"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(1 - PFexample.history['who_dies'] < 1)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a7cfd234",
+   "id": "7e5cf6a1",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -709,7 +1912,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

From 1bd53cfb08b4f18fff5bdcd15f99b6426d7d00d7 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 13 Nov 2023 11:36:03 -0500
Subject: [PATCH 25/37] black

---
 HARK/core.py                                |  16 ++-
 HARK/model.py                               |   1 +
 HARK/models/perfect_foresight.py            |  12 +-
 HARK/models/perfect_foresight_normalized.py |  40 +++---
 HARK/simulation/monte_carlo.py              | 111 ++++++++--------
 HARK/simulation/test_monte_carlo.py         | 136 +++++++++-----------
 6 files changed, 151 insertions(+), 165 deletions(-)

diff --git a/HARK/core.py b/HARK/core.py
index 5cdd1bc82..ab1c4a548 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -29,6 +29,12 @@
 from HARK.parallel import multi_thread_commands, multi_thread_commands_fake
 from HARK.utilities import NullFunc, get_arg_names
 
+<<<<<<< HEAD
+=======
+# Set logging and define basic functions
+import logging
+
+>>>>>>> 7c443df5 (black)
 logging.basicConfig(format="%(message)s")
 _log = logging.getLogger("HARK")
 _log.setLevel(logging.ERROR)
@@ -1061,10 +1067,14 @@ def simulate(self, sim_periods=None):
                     elif var_name in self.controls:
                         self.history[var_name][self.t_sim, :] = self.controls[var_name]
                     else:
-                        if var_name is 'who_dies' and self.t_sim > 1:
-                            self.history[var_name][self.t_sim - 1, :] = getattr(self, var_name)
+                        if var_name is "who_dies" and self.t_sim > 1:
+                            self.history[var_name][self.t_sim - 1, :] = getattr(
+                                self, var_name
+                            )
                         else:
-                            self.history[var_name][self.t_sim, :] = getattr(self, var_name)
+                            self.history[var_name][self.t_sim, :] = getattr(
+                                self, var_name
+                            )
                 self.t_sim += 1
 
             return self.history
diff --git a/HARK/model.py b/HARK/model.py
index 67632a8ef..44e47261b 100644
--- a/HARK/model.py
+++ b/HARK/model.py
@@ -11,6 +11,7 @@ class Aggregate:
     If so designated, draws from the shock will be scalar rather
     than array valued.
     """
+
     def __init__(self, dist: Distribution):
         self.dist = dist
 
diff --git a/HARK/models/perfect_foresight.py b/HARK/models/perfect_foresight.py
index 644f351f5..38bedd97d 100644
--- a/HARK/models/perfect_foresight.py
+++ b/HARK/models/perfect_foresight.py
@@ -19,12 +19,12 @@
         "PermGroFac": 1.01,
         "BoroCnstArt": None,
     },
-    'dynamics' : {
-        'y' : lambda p : p,
-        'm' : lambda Rfree, a, y : Rfree * a + y,
-        'c' : Control(['m']),
-        'p' : lambda PermGroFac, p: PermGroFac * p,
-        'a' : lambda m, c : m - c
+    "dynamics": {
+        "y": lambda p: p,
+        "m": lambda Rfree, a, y: Rfree * a + y,
+        "c": Control(["m"]),
+        "p": lambda PermGroFac, p: PermGroFac * p,
+        "a": lambda m, c: m - c,
     },
     "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)},
 }
diff --git a/HARK/models/perfect_foresight_normalized.py b/HARK/models/perfect_foresight_normalized.py
index 22211db45..79fab78dc 100644
--- a/HARK/models/perfect_foresight_normalized.py
+++ b/HARK/models/perfect_foresight_normalized.py
@@ -4,30 +4,28 @@
 # This way of distributing parameters across the scope is clunky
 # Can be handled better if parsed from a YAML file, probably
 # But it would be better to have a more graceful Python version as well.
-CRRA = 2.0,
+CRRA = (2.0,)
 LivPrb = 0.98
 
 model = {
-    'shocks' : {
-        'live' : Bernoulli(p=LivPrb),
+    "shocks": {
+        "live": Bernoulli(p=LivPrb),
     },
-    'parameters' : {
-        'DiscFac' : 0.96,
-        'CRRA' : CRRA,
-        'Rfree' : 1.03,
-        'LivPrb' : LivPrb,
-        'PermGroFac' : 1.01,
-        'BoroCnstArt' : None,
+    "parameters": {
+        "DiscFac": 0.96,
+        "CRRA": CRRA,
+        "Rfree": 1.03,
+        "LivPrb": LivPrb,
+        "PermGroFac": 1.01,
+        "BoroCnstArt": None,
     },
-    'dynamics' : {
-        'p' : lambda PermGroFac, p: PermGroFac * p,
-        'r_eff' : lambda Rfree, PermGroFac : Rfree / PermGroFac,
-        'b_nrm' : lambda r_eff, a_nrm: r_eff * a_nrm,
-        'm_nrm' : lambda b_nrm: b_nrm + 1,
-        'c_nrm' : Control(['m_nrm']),
-        'a_nrm' : lambda m_nrm, c_nrm : m_nrm - c_nrm
+    "dynamics": {
+        "p": lambda PermGroFac, p: PermGroFac * p,
+        "r_eff": lambda Rfree, PermGroFac: Rfree / PermGroFac,
+        "b_nrm": lambda r_eff, a_nrm: r_eff * a_nrm,
+        "m_nrm": lambda b_nrm: b_nrm + 1,
+        "c_nrm": Control(["m_nrm"]),
+        "a_nrm": lambda m_nrm, c_nrm: m_nrm - c_nrm,
     },
-    'reward' : {
-        'u' : lambda c : c ** (1 - CRRA) / (1 - CRRA)
-    }
-}
\ No newline at end of file
+    "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)},
+}
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 2326186ed..eaf544148 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -2,16 +2,18 @@
 Functions to support Monte Carlo simulation of models.
 """
 from copy import copy
-from HARK.distribution import Distribution, IndexDistribution, TimeVaryingDiscreteDistribution
+from HARK.distribution import (
+    Distribution,
+    IndexDistribution,
+    TimeVaryingDiscreteDistribution,
+)
 from HARK.model import Aggregate, Control
 from inspect import signature
 import numpy as np
 from typing import Any, Callable, Mapping, Sequence, Union
 
-def draw_shocks(
-        shocks: Mapping[str, Distribution],
-        conditions: Sequence[int]
-        ):
+
+def draw_shocks(shocks: Mapping[str, Distribution], conditions: Sequence[int]):
     """
 
     Parameters
@@ -32,8 +34,9 @@ def draw_shocks(
             draws[shock_var] = np.ones(len(conditions)) * shock
         elif isinstance(shock, Aggregate):
             draws[shock_var] = shock.dist.draw(1)[0]
-        elif isinstance(shock, IndexDistribution) \
-            or isinstance(shock, TimeVaryingDiscreteDistribution):
+        elif isinstance(shock, IndexDistribution) or isinstance(
+            shock, TimeVaryingDiscreteDistribution
+        ):
             ## TODO  his type test is awkward. They should share a superclass.
             draws[shock_var] = shock.draw(conditions)
         else:
@@ -41,10 +44,11 @@ def draw_shocks(
 
     return draws
 
+
 def simulate_dynamics(
-        dynamics : Mapping[str, Union[Callable, Control]],
-        pre : Mapping[str, Any],
-        dr : Mapping[str, Callable]
+    dynamics: Mapping[str, Union[Callable, Control]],
+    pre: Mapping[str, Any],
+    dr: Mapping[str, Callable],
 ):
     """
 
@@ -78,23 +82,25 @@ def simulate_dynamics(
                 ## Now we have to loop through each agent, and apply the decision rule.
                 ## This is quite slow.
                 for i in range(dr[varn].size):
-                    vals_i = {var : vals[var][i] if isinstance(vals[var], np.ndarray) else vals[var]
-                              for var in vals
-                              }
-                    vals[varn][i] = dr[varn][i](*[
-                        vals_i[var]
-                        for var
-                        in signature(dr[varn][i]).parameters])
+                    vals_i = {
+                        var: vals[var][i]
+                        if isinstance(vals[var], np.ndarray)
+                        else vals[var]
+                        for var in vals
+                    }
+                    vals[varn][i] = dr[varn][i](
+                        *[vals_i[var] for var in signature(dr[varn][i]).parameters]
+                    )
             else:
-                vals[varn] = dr[varn](*[
-                    vals[var]
-                    for var
-                    in signature(dr[varn]).parameters]) # TODO: test for signature match with Control
+                vals[varn] = dr[varn](
+                    *[vals[var] for var in signature(dr[varn]).parameters]
+                )  # TODO: test for signature match with Control
         else:
             vals[varn] = feq(*[vals[var] for var in signature(feq).parameters])
 
     return vals
 
+
 def parameters_by_age(ages, parameters):
     """
     Returns parameters for this model, but with vectorized
@@ -115,29 +121,24 @@ def parameters_by_age(ages, parameters):
         If a parameter is age-varying, the value is a vector
         corresponding to the values for each input age.
     """
+
     def aged_param(ages, p_value):
         if isinstance(p_value, (float, int)) or callable(p_value):
             return p_value
         elif isinstance(p_value, list) and len(p_value) > 1:
             pv_array = np.array(p_value)
 
-            return np.apply_along_axis(
-                lambda a: pv_array[a],
-                0,
-                ages
-            )
+            return np.apply_along_axis(lambda a: pv_array[a], 0, ages)
         else:
             return np.empty(ages.size)
 
-    return {
-        p : aged_param(ages, parameters[p])
-        for p
-        in parameters
-    }
+    return {p: aged_param(ages, parameters[p]) for p in parameters}
 
-class Simulator():
+
+class Simulator:
     pass
 
+
 class AgentTypeMonteCarloSimulator(Simulator):
     """
     A Monte Carlo simulation engine based on the HARK.core.AgentType framework.
@@ -150,7 +151,7 @@ class AgentTypeMonteCarloSimulator(Simulator):
 
     This simulator makes assumptions about population birth and mortality which
     are not generic. They are: TODO.
-    
+
     Parameters
     ----------
     TODO
@@ -168,15 +169,7 @@ class AgentTypeMonteCarloSimulator(Simulator):
     state_vars = []
 
     def __init__(
-        self,
-        parameters,
-        shocks,
-        dynamics,
-        dr,
-        initial,
-        seed=0,
-        agent_count = 1,
-        T_sim = 10
+        self, parameters, shocks, dynamics, dr, initial, seed=0, agent_count=1, T_sim=10
     ):
         super().__init__()
 
@@ -255,7 +248,9 @@ def initialize_sim(self):
         # Get recorded newborn conditions or initialize blank history.
         if self.read_shocks and bool(self.newborn_init_history):
             for init_var_name in self.initial:
-                self.vars_now[init_var_name] = self.newborn_init_history[init_var_name][self.t_sim, :]
+                self.vars_now[init_var_name] = self.newborn_init_history[init_var_name][
+                    self.t_sim, :
+                ]
         else:
             for var_name in self.initial:
                 self.newborn_init_history[var_name] = (
@@ -309,13 +304,13 @@ def sim_one_period(self):
 
         pre.update(self.vars_prev)
         pre.update(shocks_now)
-        #Won't work for 3.8: self.parameters | self.vars_prev | shocks_now
+        # Won't work for 3.8: self.parameters | self.vars_prev | shocks_now
 
         # Age-varying decision rules captured here
         dr = parameters_by_age(self.t_age, self.dr)
-        
+
         post = simulate_dynamics(self.dynamics, pre, dr)
-        
+
         self.vars_now = post
         ### BIG CHANGES HERE
 
@@ -323,10 +318,10 @@ def sim_one_period(self):
         self.t_age = self.t_age + 1  # Age all consumers by one period
 
         # What will we do with cycles?
-        #self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
-        #self.t_cycle[
+        # 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
+        # ] = 0  # Resetting to zero for those who have reached the end
 
     def make_shock_history(self):
         """
@@ -382,7 +377,7 @@ def get_mortality(self):
         -------
         None
         """
-        who_dies = self.vars_now['live'] <= 0
+        who_dies = self.vars_now["live"] <= 0
 
         self.sim_birth(who_dies)
 
@@ -407,20 +402,18 @@ def sim_birth(self, which_agents):
             t = self.t_sim
             initial_vals = {
                 init_var: self.newborn_init_history[init_var][t, which_agents]
-                for init_var
-                in self.initial
+                for init_var in self.initial
             }
 
         else:
-            initial_vals = draw_shocks(
-                self.initial,
-                np.zeros(which_agents.sum())
-            )
+            initial_vals = draw_shocks(self.initial, np.zeros(which_agents.sum()))
 
         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
@@ -468,7 +461,7 @@ def simulate(self, sim_periods=None):
                 # track all the vars -- shocks and dynamics
                 for var_name in self.vars:
                     self.history[var_name][self.t_sim, :] = self.vars_now[var_name]
-                    
+
                 self.t_sim += 1
 
             return self.history
@@ -487,4 +480,4 @@ def clear_history(self):
         """
         for var_name in self.vars:
             self.history[var_name] = np.empty((self.T_sim, self.agent_count))
-            self.history[var_name].fill(np.nan)
\ No newline at end of file
+            self.history[var_name].fill(np.nan)
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index bf999b008..9b5a5dad2 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -8,82 +8,73 @@
 from HARK.simulation.monte_carlo import *
 
 cons_shocks = {
-    'agg_gro' : Aggregate(MeanOneLogNormal(1)),
-    'psi' : IndexDistribution(
-        MeanOneLogNormal,
-        {
-            'sigma' : [1.0, 1.1]
-        }),
-    'theta' : MeanOneLogNormal(1),
-    'live' : Bernoulli(p=0.98)
+    "agg_gro": Aggregate(MeanOneLogNormal(1)),
+    "psi": IndexDistribution(MeanOneLogNormal, {"sigma": [1.0, 1.1]}),
+    "theta": MeanOneLogNormal(1),
+    "live": Bernoulli(p=0.98),
 }
 
 cons_pre = {
-    'R' : 1.05,
-    'aNrm' : 1,
-    'gamma' : 1.1,
-    'psi' : 1.1, # TODO: draw this from a shock,
-    'theta' : 1.1 # TODO: draw this from a shock
+    "R": 1.05,
+    "aNrm": 1,
+    "gamma": 1.1,
+    "psi": 1.1,  # TODO: draw this from a shock,
+    "theta": 1.1,  # TODO: draw this from a shock
 }
 
 cons_dynamics = {
-    'G' : lambda gamma, psi : gamma * psi,
-    'Rnrm' : lambda R, G : R / G,
-    'bNrm' : lambda Rnrm, aNrm : Rnrm * aNrm,
-    'mNrm' : lambda bNrm, theta : bNrm + theta,
-    'cNrm' : Control(['mNrm']),
-    'aNrm' : lambda mNrm, cNrm : mNrm - cNrm
+    "G": lambda gamma, psi: gamma * psi,
+    "Rnrm": lambda R, G: R / G,
+    "bNrm": lambda Rnrm, aNrm: Rnrm * aNrm,
+    "mNrm": lambda bNrm, theta: bNrm + theta,
+    "cNrm": Control(["mNrm"]),
+    "aNrm": lambda mNrm, cNrm: mNrm - cNrm,
 }
 
-cons_dr = {
-    'cNrm' : lambda mNrm : mNrm / 2
-}
+cons_dr = {"cNrm": lambda mNrm: mNrm / 2}
+
 
 class test_draw_shocks(unittest.TestCase):
     def test_draw_shocks(self):
 
-        drawn = draw_shocks(cons_shocks, np.array([0,1]))
+        drawn = draw_shocks(cons_shocks, np.array([0, 1]))
+
+        self.assertEqual(len(drawn["theta"]), 2)
+        self.assertEqual(len(drawn["psi"]), 2)
+        self.assertTrue(isinstance(drawn["agg_gro"], float))
 
-        self.assertEqual(len(drawn['theta']), 2)
-        self.assertEqual(len(drawn['psi']), 2)
-        self.assertTrue(isinstance(drawn['agg_gro'], float))
 
 class test_simulate_dynamics(unittest.TestCase):
     def test_simulate_dynamics(self):
 
         post = simulate_dynamics(cons_dynamics, cons_pre, cons_dr)
 
-        self.assertAlmostEqual(post['cNrm'], 0.98388429)
+        self.assertAlmostEqual(post["cNrm"], 0.98388429)
 
 
 class test_AgentTypeMonteCarloSimulator(unittest.TestCase):
     def setUp(self):
 
         self.shocks = {
-            'theta' : MeanOneLogNormal(1),
-            'agg_R' : Aggregate(MeanOneLogNormal(1)),
-            'live' : Bernoulli(p=0.98)
+            "theta": MeanOneLogNormal(1),
+            "agg_R": Aggregate(MeanOneLogNormal(1)),
+            "live": Bernoulli(p=0.98),
         }
 
-        self.initial = {
-            'a' : MeanOneLogNormal(1),
-            'live' : 1
-        }
+        self.initial = {"a": MeanOneLogNormal(1), "live": 1}
 
-        self.parameters = { # TODO
-            'G' : 1.05,
+        self.parameters = {  # TODO
+            "G": 1.05,
         }
 
         self.dynamics = {
-            'b' : lambda agg_R, G, a : agg_R * G * a,
-            'm' : lambda b, theta : b + theta,
-            'c' : Control(['m']),
-            'a' : lambda m, c : m - c
+            "b": lambda agg_R, G, a: agg_R * G * a,
+            "m": lambda b, theta: b + theta,
+            "c": Control(["m"]),
+            "a": lambda m, c: m - c,
         }
 
-        self.dr = {
-            'c' : lambda m : m / 2
-        }
+        self.dr = {"c": lambda m: m / 2}
 
     def test_simulate(self):
         self.simulator = AgentTypeMonteCarloSimulator(
@@ -92,14 +83,18 @@ def test_simulate(self):
             self.dynamics,
             self.dr,
             self.initial,
-            agent_count = 3
+            agent_count=3,
         )
 
         self.simulator.initialize_sim()
         history = self.simulator.simulate()
 
-        a1 = history['a'][5]
-        b1 = history['a'][4] * history['agg_R'][5] * self.parameters['G'] + history['theta'][5]  - history['c'][5]
+        a1 = history["a"][5]
+        b1 = (
+            history["a"][4] * history["agg_R"][5] * self.parameters["G"]
+            + history["theta"][5]
+            - history["c"][5]
+        )
 
         self.assertTrue((a1 == b1).all())
 
@@ -110,7 +105,7 @@ def test_make_shock_history(self):
             self.dynamics,
             self.dr,
             self.initial,
-            agent_count = 3
+            agent_count=3,
         )
 
         self.simulator.make_shock_history()
@@ -122,44 +117,33 @@ def test_make_shock_history(self):
         self.simulator.simulate()
 
         self.assertEqual(newborn_init_1, self.simulator.newborn_init_history)
-        self.assertTrue(
-            np.all(self.simulator.history['theta'] == shocks_1['theta'])
-            )
-        
+        self.assertTrue(np.all(self.simulator.history["theta"] == shocks_1["theta"]))
+
 
 class test_AgentTypeMonteCarloSimulatorAgeVariance(unittest.TestCase):
     def setUp(self):
 
         self.shocks = {
-            'theta' : MeanOneLogNormal(1),
-            'agg_R' : Aggregate(MeanOneLogNormal(1)),
-            'live' : Bernoulli(p=0.98),
-            'psi' : IndexDistribution(
-                MeanOneLogNormal,
-                {
-                'sigma' : [1.0, 1.1]
-                })
+            "theta": MeanOneLogNormal(1),
+            "agg_R": Aggregate(MeanOneLogNormal(1)),
+            "live": Bernoulli(p=0.98),
+            "psi": IndexDistribution(MeanOneLogNormal, {"sigma": [1.0, 1.1]}),
         }
 
-        self.initial = {
-            'a' : MeanOneLogNormal(1),
-            'live' : 1
-        }
+        self.initial = {"a": MeanOneLogNormal(1), "live": 1}
 
-        self.parameters = { # TODO
-            'G' : 1.05,
+        self.parameters = {  # TODO
+            "G": 1.05,
         }
 
         self.dynamics = {
-            'b' : lambda agg_R, G, a : agg_R * G * a,
-            'm' : lambda b, theta : b + theta,
-            'c' : Control(['m']),
-            'a' : lambda m, c : m - c
+            "b": lambda agg_R, G, a: agg_R * G * a,
+            "m": lambda b, theta: b + theta,
+            "c": Control(["m"]),
+            "a": lambda m, c: m - c,
         }
 
-        self.dr = {
-            'c' : [lambda m : m * 0.5, lambda m : m * 0.9]
-        }
+        self.dr = {"c": [lambda m: m * 0.5, lambda m: m * 0.9]}
 
     def test_simulate(self):
         self.simulator = AgentTypeMonteCarloSimulator(
@@ -168,13 +152,13 @@ def test_simulate(self):
             self.dynamics,
             self.dr,
             self.initial,
-            agent_count = 3
+            agent_count=3,
         )
 
         self.simulator.initialize_sim()
         history = self.simulator.simulate(sim_periods=2)
 
-        a1 = history['a'][1]
-        b1 = history['m'][1] - self.dr['c'][1](history['m'][1])
+        a1 = history["a"][1]
+        b1 = history["m"][1] - self.dr["c"][1](history["m"][1])
 
-        self.assertTrue((a1 == b1).all())
\ No newline at end of file
+        self.assertTrue((a1 == b1).all())

From c4feeb3bc5e50b091a5ba82b9ed512fe76d53d40 Mon Sep 17 00:00:00 2001
From: Sebastian Benthall <sbenthall@gmail.com>
Date: Mon, 13 Nov 2023 13:09:31 -0500
Subject: [PATCH 26/37] Update core.py -- fixing bad merge

---
 HARK/core.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/HARK/core.py b/HARK/core.py
index ab1c4a548..ab2560859 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -29,12 +29,8 @@
 from HARK.parallel import multi_thread_commands, multi_thread_commands_fake
 from HARK.utilities import NullFunc, get_arg_names
 
-<<<<<<< HEAD
-=======
 # Set logging and define basic functions
 import logging
-
->>>>>>> 7c443df5 (black)
 logging.basicConfig(format="%(message)s")
 _log = logging.getLogger("HARK")
 _log.setLevel(logging.ERROR)

From 1a33be6badf25b6747de5051e7889c0102b839c6 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 13 Nov 2023 13:20:03 -0500
Subject: [PATCH 27/37] pre-commit fixes

---
 HARK/core.py                        | 5 ++---
 HARK/simulation/monte_carlo.py      | 8 +++++---
 HARK/simulation/test_monte_carlo.py | 6 +-----
 3 files changed, 8 insertions(+), 11 deletions(-)

diff --git a/HARK/core.py b/HARK/core.py
index ab2560859..71a79e2a9 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -7,6 +7,7 @@
 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
@@ -29,8 +30,6 @@
 from HARK.parallel import multi_thread_commands, multi_thread_commands_fake
 from HARK.utilities import NullFunc, get_arg_names
 
-# Set logging and define basic functions
-import logging
 logging.basicConfig(format="%(message)s")
 _log = logging.getLogger("HARK")
 _log.setLevel(logging.ERROR)
@@ -1063,7 +1062,7 @@ def simulate(self, sim_periods=None):
                     elif var_name in self.controls:
                         self.history[var_name][self.t_sim, :] = self.controls[var_name]
                     else:
-                        if var_name is "who_dies" and self.t_sim > 1:
+                        if var_name == "who_dies" and self.t_sim > 1:
                             self.history[var_name][self.t_sim - 1, :] = getattr(
                                 self, var_name
                             )
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index eaf544148..6ec22a196 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -2,15 +2,17 @@
 Functions to support Monte Carlo simulation of models.
 """
 from copy import copy
+from inspect import signature
+from typing import Any, Callable, Mapping, Sequence, Union
+
+import numpy as np
+
 from HARK.distribution import (
     Distribution,
     IndexDistribution,
     TimeVaryingDiscreteDistribution,
 )
 from HARK.model import Aggregate, Control
-from inspect import signature
-import numpy as np
-from typing import Any, Callable, Mapping, Sequence, Union
 
 
 def draw_shocks(shocks: Mapping[str, Distribution], conditions: Sequence[int]):
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
index 9b5a5dad2..bb1620c3c 100644
--- a/HARK/simulation/test_monte_carlo.py
+++ b/HARK/simulation/test_monte_carlo.py
@@ -3,7 +3,7 @@
 """
 import unittest
 
-from HARK.distribution import Bernoulli, MeanOneLogNormal, IndexDistribution
+from HARK.distribution import Bernoulli, IndexDistribution, MeanOneLogNormal
 from HARK.model import Aggregate, Control
 from HARK.simulation.monte_carlo import *
 
@@ -36,7 +36,6 @@
 
 class test_draw_shocks(unittest.TestCase):
     def test_draw_shocks(self):
-
         drawn = draw_shocks(cons_shocks, np.array([0, 1]))
 
         self.assertEqual(len(drawn["theta"]), 2)
@@ -46,7 +45,6 @@ def test_draw_shocks(self):
 
 class test_simulate_dynamics(unittest.TestCase):
     def test_simulate_dynamics(self):
-
         post = simulate_dynamics(cons_dynamics, cons_pre, cons_dr)
 
         self.assertAlmostEqual(post["cNrm"], 0.98388429)
@@ -54,7 +52,6 @@ def test_simulate_dynamics(self):
 
 class test_AgentTypeMonteCarloSimulator(unittest.TestCase):
     def setUp(self):
-
         self.shocks = {
             "theta": MeanOneLogNormal(1),
             "agg_R": Aggregate(MeanOneLogNormal(1)),
@@ -122,7 +119,6 @@ def test_make_shock_history(self):
 
 class test_AgentTypeMonteCarloSimulatorAgeVariance(unittest.TestCase):
     def setUp(self):
-
         self.shocks = {
             "theta": MeanOneLogNormal(1),
             "agg_R": Aggregate(MeanOneLogNormal(1)),

From 62e8d5feff5e8c72f82d894901ffef99e40c9316 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 13 Nov 2023 13:47:51 -0500
Subject: [PATCH 28/37] ruff format the example notebook

---
 ...eneric Monte Carlo Perfect Foresight.ipynb | 75 +++++++++++--------
 1 file changed, 44 insertions(+), 31 deletions(-)

diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
index e4749ea1d..2699e7125 100644
--- a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb	
@@ -36,12 +36,12 @@
     "    \"AgentCount\": 3,  # Number of agents of this type\n",
     "    \"T_sim\": 120,  # Number of periods to simulate\n",
     "    \"aNrmInitMean\": -6.0,  # Mean of log initial assets\n",
-    "    \"aNrmInitStd\": 0, #1.0,  # Standard deviation of log initial assets\n",
+    "    \"aNrmInitStd\": 0,  # 1.0,  # Standard deviation of log initial assets\n",
     "    \"pLvlInitMean\": 0.0,  # Mean of log initial permanent income\n",
     "    \"pLvlInitStd\": 0.0,  # Standard deviation of log initial permanent income\n",
     "    \"PermGroFacAgg\": 1.0,  # Aggregate permanent income growth factor\n",
     "    \"T_age\": None,  # Age after which simulated agents are automatically killed,\n",
-    "    \"LivPrb\": [0.98]\n",
+    "    \"LivPrb\": [0.98],\n",
     "}\n",
     "\n",
     "PFexample.assign_parameters(**SimulationParams)"
@@ -594,7 +594,7 @@
     }
    ],
    "source": [
-    "PFexample.track_vars = [\"who_dies\",\"mNrm\",\"pLvl\",\"aNrm\"]\n",
+    "PFexample.track_vars = [\"who_dies\", \"mNrm\", \"pLvl\", \"aNrm\"]\n",
     "PFexample.make_shock_history()\n",
     "\n",
     "PFexample.initialize_sim()\n",
@@ -681,7 +681,12 @@
     }
    ],
    "source": [
-    "plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n",
+    "plt.plot(\n",
+    "    np.log(\n",
+    "        np.mean(PFexample.history[\"mNrm\"], axis=1)\n",
+    "        - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))\n",
+    "    )\n",
+    ")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -705,7 +710,7 @@
     }
    ],
    "source": [
-    "PFexample.newborn_init_history['pLvl'][1,PFexample.history[\"who_dies\"][1,:] > 0].shape"
+    "PFexample.newborn_init_history[\"pLvl\"][1, PFexample.history[\"who_dies\"][1, :] > 0].shape"
    ]
   },
   {
@@ -726,7 +731,7 @@
     }
    ],
    "source": [
-    "PFexample.newborn_init_history['aNrm'][2, PFexample.history[\"who_dies\"][2,:] > 0].shape"
+    "PFexample.newborn_init_history[\"aNrm\"][2, PFexample.history[\"who_dies\"][2, :] > 0].shape"
    ]
   },
   {
@@ -791,19 +796,17 @@
    "outputs": [],
    "source": [
     "pfn_simulator = AgentTypeMonteCarloSimulator(\n",
-    "    pfn.model['parameters'],\n",
-    "    pfn.model['shocks'],\n",
-    "    pfn.model['dynamics'],\n",
-    "    {\n",
-    "        'c_nrm' : lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)\n",
-    "    },\n",
-    "    { # initial states\n",
-    "        'a_nrm' : Lognormal(-6, 0),\n",
+    "    pfn.model[\"parameters\"],\n",
+    "    pfn.model[\"shocks\"],\n",
+    "    pfn.model[\"dynamics\"],\n",
+    "    {\"c_nrm\": lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)},\n",
+    "    {  # initial states\n",
+    "        \"a_nrm\": Lognormal(-6, 0),\n",
     "        #'live' : 1,\n",
-    "        'p' : 1.0\n",
+    "        \"p\": 1.0,\n",
     "    },\n",
-    "    agent_count = 3,\n",
-    "    T_sim = 120\n",
+    "    agent_count=3,\n",
+    "    T_sim=120,\n",
     ")"
    ]
   },
@@ -815,9 +818,9 @@
    "outputs": [],
    "source": [
     "pfn_simulator.read_shocks = True\n",
-    "#pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n",
+    "# pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n",
     "\n",
-    "pfn_simulator.shock_history['live'] = 1 - PFexample.history[\"who_dies\"]"
+    "pfn_simulator.shock_history[\"live\"] = 1 - PFexample.history[\"who_dies\"]"
    ]
   },
   {
@@ -827,9 +830,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pfn_simulator.newborn_init_history['a_nrm'] = PFexample.newborn_init_history['aNrm']\n",
-    "pfn_simulator.newborn_init_history['p'] = PFexample.newborn_init_history['pLvl']\n",
-    "#pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']"
+    "pfn_simulator.newborn_init_history[\"a_nrm\"] = PFexample.newborn_init_history[\"aNrm\"]\n",
+    "pfn_simulator.newborn_init_history[\"p\"] = PFexample.newborn_init_history[\"pLvl\"]\n",
+    "# pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']"
    ]
   },
   {
@@ -1689,7 +1692,7 @@
     }
    ],
    "source": [
-    "#pf_simulator.track_vars = [\"mNrm\"]\n",
+    "# pf_simulator.track_vars = [\"mNrm\"]\n",
     "pfn_simulator.initialize_sim()\n",
     "pfn_simulator.simulate(sim_periods=120)"
    ]
@@ -1714,7 +1717,7 @@
     }
    ],
    "source": [
-    "plt.plot(np.mean(pfn_simulator.history['m_nrm'], axis=1))\n",
+    "plt.plot(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -1748,7 +1751,12 @@
     }
    ],
    "source": [
-    "plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n",
+    "plt.plot(\n",
+    "    np.log(\n",
+    "        np.mean(pfn_simulator.history[\"m_nrm\"], axis=1)\n",
+    "        - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n",
+    "    )\n",
+    ")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -1784,7 +1792,7 @@
     }
    ],
    "source": [
-    "plt.plot(pfn_simulator.history['live'].sum(axis=1))"
+    "plt.plot(pfn_simulator.history[\"live\"].sum(axis=1))"
    ]
   },
   {
@@ -1807,8 +1815,11 @@
     }
    ],
    "source": [
-    "plt.plot(np.mean(pfn_simulator.history['a_nrm'], axis=1), label = 'Generic monte carlo')\n",
-    "plt.plot(np.mean(PFexample.history[\"aNrm\"], axis=1), label = 'HARK 0.13 PerfForesightConsumerType')\n",
+    "plt.plot(np.mean(pfn_simulator.history[\"a_nrm\"], axis=1), label=\"Generic monte carlo\")\n",
+    "plt.plot(\n",
+    "    np.mean(PFexample.history[\"aNrm\"], axis=1),\n",
+    "    label=\"HARK 0.13 PerfForesightConsumerType\",\n",
+    ")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.legend()\n",
@@ -1840,7 +1851,9 @@
     }
    ],
    "source": [
-    "np.mean(pfn_simulator.history['a_nrm'], axis=1) - np.mean(PFexample.history[\"aNrm\"], axis=1)"
+    "np.mean(pfn_simulator.history[\"a_nrm\"], axis=1) - np.mean(\n",
+    "    PFexample.history[\"aNrm\"], axis=1\n",
+    ")"
    ]
   },
   {
@@ -1862,7 +1875,7 @@
     }
    ],
    "source": [
-    "np.where(pfn_simulator.history['live'] < 1)"
+    "np.where(pfn_simulator.history[\"live\"] < 1)"
    ]
   },
   {
@@ -1884,7 +1897,7 @@
     }
    ],
    "source": [
-    "np.where(1 - PFexample.history['who_dies'] < 1)"
+    "np.where(1 - PFexample.history[\"who_dies\"] < 1)"
    ]
   },
   {

From 4f4b9bae581e2c974d4f1a4c6934a4635c77e8ec Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 27 Nov 2023 11:22:01 -0500
Subject: [PATCH 29/37] improving documentation for Generic Monte Carlo

---
 Documentation/CHANGELOG.md                   |  2 +
 Documentation/reference/index.rst            |  1 +
 Documentation/reference/tools/simulation.rst |  2 +-
 HARK/simulation.py                           |  7 --
 HARK/simulation/monte_carlo.py               | 91 ++++++--------------
 5 files changed, 32 insertions(+), 71 deletions(-)
 delete mode 100644 HARK/simulation.py

diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 79b0a3eb8..d91d204f1 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -27,6 +27,8 @@ Release Date: TBD
 - 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.
 - Creates a `models/` directory with Python model configurations for perfect foresight and Fisher 2-period models. [1347](https://github.com/econ-ark/HARK/pull/1347)
+- Fixes bug in AgentType simulations where 'who_dies' for period t was being recorded in period t-1in the history Carlo simulation functions using Python model configurations.[1296](https://github.com/econ-ark/HARK/pull/1296)
+- Removes unused `simulation.py` .[1296](https://github.com/econ-ark/HARK/pull/1296)
 
 ### 0.13.0
 
diff --git a/Documentation/reference/index.rst b/Documentation/reference/index.rst
index 1d9cced2f..de65cad62 100644
--- a/Documentation/reference/index.rst
+++ b/Documentation/reference/index.rst
@@ -13,6 +13,7 @@ API Reference
    tools/frame
    tools/helpers
    tools/interpolation
+   tools/model
    tools/numba_tools
    tools/parallel
    tools/rewards
diff --git a/Documentation/reference/tools/simulation.rst b/Documentation/reference/tools/simulation.rst
index 55e17756f..a56040159 100644
--- a/Documentation/reference/tools/simulation.rst
+++ b/Documentation/reference/tools/simulation.rst
@@ -1,7 +1,7 @@
 Simulation
 ------------
 
-.. automodule:: HARK.simulation
+.. automodule:: HARK.simulation.monte_carlo
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/HARK/simulation.py b/HARK/simulation.py
deleted file mode 100644
index 918e4e377..000000000
--- a/HARK/simulation.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""
-Currently empty. Will be used for future simulation handling code.
-"""
-
-import warnings  # A library for runtime warnings
-
-import numpy as np  # Numerical Python
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
index 6ec22a196..13a3e34af 100644
--- a/HARK/simulation/monte_carlo.py
+++ b/HARK/simulation/monte_carlo.py
@@ -1,6 +1,7 @@
 """
 Functions to support Monte Carlo simulation of models.
 """
+
 from copy import copy
 from inspect import signature
 from typing import Any, Callable, Mapping, Sequence, Union
@@ -17,6 +18,7 @@
 
 def draw_shocks(shocks: Mapping[str, Distribution], conditions: Sequence[int]):
     """
+    Draw from each shock distribution values, subject to given conditions.
 
     Parameters
     ------------
@@ -26,6 +28,11 @@ def draw_shocks(shocks: Mapping[str, Distribution], conditions: Sequence[int]):
     conditions: Sequence[int]
         An array of conditions, one for each agent.
         Typically these will be agent ages.
+
+    Parameters
+    ------------
+    draws : Mapping[str, Sequence]
+        A mapping from shock names to drawn shock values.
     """
     draws = {}
 
@@ -53,6 +60,8 @@ def simulate_dynamics(
     dr: Mapping[str, Callable],
 ):
     """
+    From the beginning-of-period state (pre), follow the dynamics,
+    including any decision rules, to compute the end-of-period state.
 
     Parameters
     ------------
@@ -144,19 +153,27 @@ class Simulator:
 class AgentTypeMonteCarloSimulator(Simulator):
     """
     A Monte Carlo simulation engine based on the HARK.core.AgentType framework.
-    Unlike HARK.core.AgentType, this class:
-      * does not do any model solving
-      * depends on dynamic equations, shocks, and decision rules paased into it
+
+    Unlike HARK.core.AgentType, this class does not do any model solving,
+    and depends on dynamic equations, shocks, and decision rules paased into it.
 
     The purpose of this class is to provide a way to simulate models without
     relying on inheritance from the AgentType class.
 
     This simulator makes assumptions about population birth and mortality which
-    are not generic. They are: TODO.
+    are not generic. All agents are replaced with newborns when they expire.
 
     Parameters
-    ----------
-    TODO
+    ------------
+    parameters: Mapping[str, Any]
+
+    shocks: Mapping[str, Distribution]
+
+    dynamics: Mapping[str, Union[Callable, Control]]
+
+    dr: Mapping[str, Callable]
+
+    initial: dict
 
     seed : int
         A seed for this instance's random number generator.
@@ -166,6 +183,8 @@ class AgentTypeMonteCarloSimulator(Simulator):
     agent_count : int
         The number of agents of this type to use in simulation.
 
+    T_sim : int
+        The number of periods to simulate.
     """
 
     state_vars = []
@@ -201,14 +220,6 @@ def __init__(
     def reset_rng(self):
         """
         Reset the random number generator for this type.
-
-        Parameters
-        ----------
-        none
-
-        Returns
-        -------
-        none
         """
         self.RNG = np.random.default_rng(self.seed)
 
@@ -216,14 +227,6 @@ def initialize_sim(self):
         """
         Prepares for a new simulation.  Resets the internal random number generator,
         makes initial states for all agents (using sim_birth), clears histories of tracked variables.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
         """
         if self.T_sim <= 0:
             raise Exception(
@@ -270,14 +273,6 @@ def sim_one_period(self):
         read_shocks, get_states(), get_controls(), and get_poststates().  These should be defined for
         AgentType subclasses, except get_mortality (define its components sim_death and sim_birth
         instead) and read_shocks.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
         """
         # Mortality adjusts the agent population
         self.get_mortality()  # Replace some agents with "newborns"
@@ -328,18 +323,12 @@ def sim_one_period(self):
     def make_shock_history(self):
         """
         Makes a pre-specified history of shocks for the simulation.  Shock variables should be named
-        in self.shock_vars, a list of strings that is subclass-specific.  This method runs a subset
+        in self.shock, a mapping from shock names to distributions.  This method runs a subset
         of the standard simulation loop by simulating only mortality and shocks; each variable named
-        in shock_vars is stored in a T_sim x agent_count array in history dictionary self.history[X].
+        in shocks is stored in a T_sim x agent_count array in history dictionary self.history[X].
         Automatically sets self.read_shocks to True so that these pre-specified shocks are used for
         all subsequent calls to simulate().
 
-        ### TODO: Rethink this for when shocks are passed in.
-
-        Parameters
-        ----------
-        None
-
         Returns
         -------
         shock_history: dict
@@ -360,24 +349,8 @@ def make_shock_history(self):
 
     def get_mortality(self):
         """
-        Simulates mortality or agent turnover according to some model-specific rules named sim_death
-        and sim_birth (methods of an AgentType subclass).
-
+        Simulates mortality or agent turnover.
         Agents die when their states `live` is less than or equal to zero.
-
-        a Boolean array of size agent_count, indicating which agents of this type have "died" and
-        must be replaced.
-
-        sim_birth takes such a Boolean array as an argument and generates initial
-        states for those agent indices.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
         """
         who_dies = self.vars_now["live"] <= 0
 
@@ -471,14 +444,6 @@ def simulate(self, sim_periods=None):
     def clear_history(self):
         """
         Clears the histories.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
         """
         for var_name in self.vars:
             self.history[var_name] = np.empty((self.T_sim, self.agent_count))

From a3294738aa60c5ff2e9b248ff7566e6a5783b3f5 Mon Sep 17 00:00:00 2001
From: sb <sbenthall@gmail.com>
Date: Mon, 27 Nov 2023 11:28:28 -0500
Subject: [PATCH 30/37] adding model.rst file to docs

---
 Documentation/reference/tools/model.rst | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 Documentation/reference/tools/model.rst

diff --git a/Documentation/reference/tools/model.rst b/Documentation/reference/tools/model.rst
new file mode 100644
index 000000000..141b2ac93
--- /dev/null
+++ b/Documentation/reference/tools/model.rst
@@ -0,0 +1,7 @@
+Model
+-------------
+
+.. automodule:: HARK.model
+   :members:
+   :undoc-members:
+   :show-inheritance:

From 674050780e0f4ab7e75672f88d5c1456db16c932 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 8 Dec 2023 15:23:15 -0500
Subject: [PATCH 31/37] Very light editing of Gentle Intro

Added one sentence and a couple words, adjusted commas.
---
 .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb   | 1222 ++++++++---------
 1 file changed, 578 insertions(+), 644 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index 49463f273..bd4e204c5 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -1,644 +1,578 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# A Gentle Introduction to HARK\n",
-    "\n",
-    "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users.  It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK.  Our aim is to convey a feel for how the toolkit works.\n",
-    "\n",
-    "For readers for whom this is your very first experience with Python, we have put important Python concepts in **boldface**. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in _italics_. Only cursory definitions (if any) are provided here.  If you want to learn more, there are many online Python and Jupyter tutorials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "code_folding": [],
-    "is_executing": true
-   },
-   "outputs": [],
-   "source": [
-    "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n",
-    "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n",
-    "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n",
-    "#   The most common problem beginners have is to execute a cell before all its predecessors\n",
-    "#   If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import HARK\n",
-    "from copy import deepcopy\n",
-    "\n",
-    "mystr = lambda number: \"{:.4f}\".format(number)\n",
-    "from HARK.utilities import plot_funcs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Your First HARK Model: Perfect Foresight\n",
-    "\n",
-    "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n",
-    "\n",
-    "\\begin{align*}\n",
-    "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n",
-    "\\end{align*}\n",
-    "\n",
-    "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb \\le 1$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$ and hold the rest in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. With probability $\\mathsf{D}_t = 1-\\LivPrb_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n",
-    "\n",
-    "The agent's problem can be written in Bellman form as:\n",
-    "\n",
-    "\\begin{align*}\n",
-    "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n",
-    "&\\text{s.t.} \\\\\n",
-    "A_t &= M_t - C_t \\\\\n",
-    "M_{t+1} &= R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
-    "P_{t+1} &= \\Gamma_{t+1} P_t, \\\\\n",
-    "\\end{align*}\n",
-    "\n",
-    "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\aleph_t\\}$.  To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability.\n",
-    "\n",
-    "## Representing Agents in HARK\n",
-    "\n",
-    "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$.  To make agents of this class, we must import the class itself into our workspace.  (Run the cell below in order to do this)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/).\n",
-    "\n",
-    "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have.  In the hidden cell below, we define a $\\textbf{dictionary}$ named `PF_dictionary` with these parameter values:\n",
-    "\n",
-    "| Param | Description | Code | Value |\n",
-    "| :---: | ---  | --- | :---: |\n",
-    "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n",
-    "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n",
-    "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n",
-    "| $\\aleph$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n",
-    "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n",
-    "\n",
-    "\n",
-    "For now, don't worry about the specifics of dictionaries.  All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n",
-    "PF_dictionary = {\n",
-    "    \"CRRA\": 2.5,\n",
-    "    \"DiscFac\": 0.96,\n",
-    "    \"Rfree\": 1.03,\n",
-    "    \"LivPrb\": [0.98],\n",
-    "    \"PermGroFac\": [1.01],\n",
-    "    \"T_cycle\": 1,\n",
-    "    \"cycles\": 0,\n",
-    "    \"AgentCount\": 10000,\n",
-    "}\n",
-    "\n",
-    "# To those curious enough to open this hidden cell, you might notice that we defined\n",
-    "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n",
-    "# worry about these for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make an **object** named $\\texttt{PFexample}$ which is an **instance** of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in `PF_dictionary`.  Such a bundle is created passing `PF_dictionary` to the class $\\texttt{PerfForesightConsumerType}$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PFexample = PerfForesightConsumerType(**PF_dictionary)\n",
-    "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem.\n",
-    "\n",
-    "## Solving an Agent's Problem\n",
-    "\n",
-    "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ **method**. (A method is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n",
-    "\n",
-    "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PFexample.solve()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$.  In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (parameterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem.\n",
-    "\n",
-    "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem.  The consumption function stored as the first element (index 0) of the solution list can be retrieved by:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<HARK.interpolation.LinearInterp at 0x1d5359c3ee0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PFexample.solution[0].cFunc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One of the results proven in the associated [lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$.\n",
-    "\n",
-    "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation.  It can be plotted between an $m$ ratio of 0 and 10 using the command below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mPlotTop = 10\n",
-    "plot_funcs(PFexample.solution[0].cFunc, 0.0, mPlotTop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis).  How can this be?\n",
-    "\n",
-    "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth.  How much is that?  What's the minimum value of $m_t$ where the consumption function is defined?  We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This agent's human wealth is 50.49994992551661 times his current income level.\n",
-      "This agent's consumption function is defined (consumption is positive) down to m_t = -50.49994992551661\n"
-     ]
-    }
-   ],
-   "source": [
-    "humanWealth = PFexample.solution[0].hNrm\n",
-    "mMinimum = PFexample.solution[0].mNrmMin\n",
-    "print(\n",
-    "    \"This agent's human wealth is \"\n",
-    "    + str(humanWealth)\n",
-    "    + \" times his current income level.\"\n",
-    ")\n",
-    "print(\n",
-    "    \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n",
-    "    + str(mMinimum)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Yikes! Let's take a look at the bottom of the consumption function.  In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_funcs(PFexample.solution[0].cFunc, mMinimum, mPlotTop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Changing Agent Parameters\n",
-    "\n",
-    "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does.  We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NewExample = deepcopy(PFexample)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "nbsphinx-thumbnail": {}
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "NewExample.assign_parameters(DiscFac=0.90)\n",
-    "NewExample.solve()\n",
-    "mPlotBottom = mMinimum\n",
-    "plot_funcs(\n",
-    "    [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(Note that you can pass a **list** of functions to `plot_funcs` as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n",
-    "\n",
-    "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income.  We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n",
-    "\n",
-    "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income __shrinks__ by 99 percent each period?  In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n",
-    "\n",
-    "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept.  Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a __time-varying__ attribute, it must be specified as a **list** (with a single element in this case)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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d2d7jnH2YiVld306iDwMgyBFqgEBz78M4d+q9etlzjEcfJktKzZKs4wMyXQAYrgg1wM3WYr/mvKSj1+/DpMyRIq2BmS8AjBCEGuBGs1d7HjVw3T5MtjT+q/RhAMBPhBpgKBlGVx/m7KHr9GEmux01kC0lTqUPAwCDRKgBBqO9Vao+4bYS46UPkzSj56gB+jAAcEMQagB/tNilL//Sc9QAfRgAGDYINYAv9mrPowb66sNExXseNZA8UwqPCMx8ASCIhfozeNOmTZo5c6asVqusVquys7O1b98+r+OLi4sVEhLS6/bZZ595jCssLFR6erosFovS09NVVFQ0sFcDDIZhSBdPSce2SW//P9JrM6VfTZf+z3elv7wh1ZzsCjRjJktf/Wfp0fVS/hHp//1C+ucdUs6yrhUaAg0ABIRfKzUpKSl6+eWXdeutt0qSfve732nhwoU6fvy4MjIyvF536tQpWa09y+9jx451/e+ysjItXrxYL774ov7hH/5BRUVFWrRokQ4ePKi5c+f6+3qA/nP1Ycp6VmO89WHcv5lEHwYAhqUQwzCMwTxBfHy8XnnlFS1ZsqTXnxUXF+vee+/VlStXNHr06D6vX7x4sex2u8eKz4MPPqgxY8Zo586d/Z6H3W6XzWZTfX29R4ACXJx9GGeh98ujUvtVzzGuPkx3gKEPAwA31FC+fw+4U9PR0aG33npLTU1Nys7O9jl21qxZamlpUXp6up5//nnde++9rj8rKyvTs88+6zH+gQce0GuvvebzOR0OhxwOh+u+3W73/0XA3Dz6MGXS+f/rvQ/jPGqAPgwAjFh+h5qTJ08qOztbLS0tio2NVVFRkdLT0/scO378eG3evFmzZ8+Ww+HQ73//e913330qLi5Wbm6uJKmmpkZJSUke1yUlJammpsbnPAoKCrRmzRp/pw+zcu4P4x5irpzpPW7MZM+PkhL/jv1hAMAk/A4106ZNU3l5uerq6lRYWKgnnnhCJSUlfQabadOmadq0aa772dnZqqys1C9/+UtXqJGkkGveVAzD6PXYtVatWqWVK1e67tvtdqWmpvr7cjBSefRhukPMdfswWZJ1QmDmCwC44fwONREREa6icGZmpo4cOaJ169bpjTfe6Nf1WVlZevPNN133k5OTe63KXLhwodfqzbUsFossFoufs8eI1a8+TGRXB8YZYFLmSJG2wMwXAHDTDXqfGsMwPLot13P8+HGNH9/z7ZHs7GwdOHDAo1fz3nvvKScnZ7BTw0jmbx/GeV4SfRgACFp+hZrVq1froYceUmpqqhoaGrRr1y4VFxdr//79kro+EqqqqtL27dslSa+99pomT56sjIwMtba26s0331RhYaEKCwtdz7l8+XLl5uZq7dq1Wrhwofbs2aP3339fBw8eHMKXiWHNMKTazz0PfeyrDzN6Us9RAxOzpYSpUqhfWy0BAEzMr1Bz/vx55eXlqbq6WjabTTNnztT+/fs1f/58SVJ1dbUqKipc41tbW/XDH/5QVVVVioqKUkZGht555x09/PDDrjE5OTnatWuXnn/+eb3wwgu65ZZbtHv3bvaoMbP2VqnmI89DH6/twyhESp7hdugjfRgAgG+D3qdmuGCfmmGsxS59eaQnwHjrw3wls/uoAfowABAshsU+NYBXDTU9fZizh7z0YcZ092Gy6cMAAIYEoQaD4+rDuB366K0P477JHX0YAMAQI9TAP84+jPs3k5ovXTPI2YdxPy+JPgwA4MYi1MA3R4NU+Zf+9WGcASaVPgwA4OYj1MCTex+mokyqOemjD+PcH+Z2+jAAgIAj1ASzXn2YMunK6d7j3PswzvOS6MMAAIYZQk0w6WjrfV7S9fowqVmS7SsBmS4AAP4g1JgZfRgAQBAh1JhJQ01PgOl3H+arUjgHgwIARj5CzUhlGNKlv3WFl7NlPvowE92OGqAPAwAwL0LNSNHRJlV/1LMK460PkzSje4O7bPowAICgQqgZrhwNPeclnT3Udx8mzCKlOPswOfRhAABBjVAzXDSc9zxqwFsfJjWr56gB+jAAALgQagLBvQ/jXInx2ofJ7lmJoQ8DAIBXhJqboVcf5rDUXHvNILc+jLPUSx8GAIB+I9TcCO59GOf+MG3NnmM8+jDZUuqd9GEAABgEQs1Q8OjDOPeH6fAcEznac3+YCbfThwEAYAgRavxlGNKl/6+rzOsMMZe/6D3Oow+TLSVOow8DAMANRKi5nn73YTI8Qwx9GAAAbipCzbUcjd19mDLffZivzO7a4G5itpQyR4oaHZDpAgCALoSahvNS5eGeowbowwAAMCIFV6hx9WHcjhroqw9jm9hz1AB9GAAARgRzh5qONqnmo54N7nz2YbJ6VmNsKQGZLgAAGDjzhZovSqUrJ7u+neSrD+M8aoA+DAAApmC+ULP7XyRLSM/9yNFuu/Tm0IcBAMCkzBdqrCnS393VsxJDHwYAgKBgvlCTf1iyWgM9CwAAcJOxhAEAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEzBr1CzadMmzZw5U1arVVarVdnZ2dq3b5/X8W+//bbmz5+vsWPHusb/93//t8eYbdu2KSQkpNetpaVlYK8IAAAEJb9CTUpKil5++WUdPXpUR48e1de//nUtXLhQH3/8cZ/jS0tLNX/+fL377rs6duyY7r33Xj366KM6fvy4xzir1arq6mqPW2Rk5MBfFQAACDohhmEYg3mC+Ph4vfLKK1qyZEm/xmdkZGjx4sX6yU9+IqlrpWbFihWqq6sbzDRkt9tls9lUX18vK5vvAQAwIgzl+/eAOzUdHR3atWuXmpqalJ2d3a9rOjs71dDQoPj4eI/HGxsbNWnSJKWkpOgb3/hGr5WcvjgcDtntdo8bAAAIXn6HmpMnTyo2NlYWi0VLly5VUVGR0tPT+3Xtq6++qqamJi1atMj12PTp07Vt2zbt3btXO3fuVGRkpO666y59/vnnPp+roKBANpvNdUtNTfX3pQAAABPx++On1tZWVVRUqK6uToWFhfrNb36jkpKS6wabnTt36vvf/7727Nmj+++/3+u4zs5O3XHHHcrNzdX69eu9jnM4HHI4HK77drtdqampfPwEAMAIMpQfP/l9oGVERIRuvfVWSVJmZqaOHDmidevW6Y033vB6ze7du7VkyRK99dZbPgONJIWGhmrOnDnXXamxWCyyWCz+Th8AAJjUoPepMQzDY8XkWjt37tSTTz6pHTt26JFHHunX85WXl2v8+PGDnRoAAAgifq3UrF69Wg899JBSU1PV0NCgXbt2qbi4WPv375ckrVq1SlVVVdq+fbukrkDz+OOPa926dcrKylJNTY0kKSoqSjabTZK0Zs0aZWVlaerUqbLb7Vq/fr3Ky8u1cePGoXydAADA5PwKNefPn1deXp6qq6tls9k0c+ZM7d+/X/Pnz5ckVVdXq6KiwjX+jTfeUHt7u/Lz85Wfn+96/IknntC2bdskSXV1dXrqqadUU1Mjm82mWbNmqbS0VHfeeecQvDwAABAsBr1PzXDBPjUAAIw8w2KfGgAAgOGEUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEyBUAMAAEzBr1CzadMmzZw5U1arVVarVdnZ2dq3b5/Pa0pKSjR79mxFRkZqypQpev3113uNKSwsVHp6uiwWi9LT01VUVOTfqwAAAEHPr1CTkpKil19+WUePHtXRo0f19a9/XQsXLtTHH3/c5/jTp0/r4Ycf1t13363jx49r9erVeuaZZ1RYWOgaU1ZWpsWLFysvL08nTpxQXl6eFi1apA8++GBwrwwAAASVEMMwjME8QXx8vF555RUtWbKk158999xz2rt3rz799FPXY0uXLtWJEydUVlYmSVq8eLHsdrvHis+DDz6oMWPGaOfOnf2eh91ul81mU319vaxW6yBeEQAAuFmG8v17wJ2ajo4O7dq1S01NTcrOzu5zTFlZmRYsWODx2AMPPKCjR4+qra3N55hDhw75/PkOh0N2u93jBgAAgpffoebkyZOKjY2VxWLR0qVLVVRUpPT09D7H1tTUKCkpyeOxpKQktbe3q7a21ueYmpoan/MoKCiQzWZz3VJTU/19KQAAwET8DjXTpk1TeXm5Dh8+rH/913/VE088oU8++cTr+JCQEI/7zk+73B/va8y1j11r1apVqq+vd90qKyv9fSkAAMBEwv29ICIiQrfeeqskKTMzU0eOHNG6dev0xhtv9BqbnJzca8XlwoULCg8PV0JCgs8x167eXMtischisfg7fQAAYFKD3qfGMAw5HI4+/yw7O1sHDhzweOy9995TZmamRo0a5XNMTk7OYKcGAACCiF8rNatXr9ZDDz2k1NRUNTQ0aNeuXSouLtb+/fsldX0kVFVVpe3bt0vq+qbThg0btHLlSv3gBz9QWVmZtmzZ4vGtpuXLlys3N1dr167VwoULtWfPHr3//vs6ePDgEL5MAABgdn6FmvPnzysvL0/V1dWy2WyaOXOm9u/fr/nz50uSqqurVVFR4Rqflpamd999V88++6w2btyoCRMmaP369frmN7/pGpOTk6Ndu3bp+eef1wsvvKBbbrlFu3fv1ty5c4foJQIAgGAw6H1qhgv2qQEAYOQZFvvUAAAADCeEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAp+hZqCggLNmTNHcXFxGjdunB577DGdOnXK5zVPPvmkQkJCet0yMjJcY7Zt29bnmJaWloG9KgAAEHT8CjUlJSXKz8/X4cOHdeDAAbW3t2vBggVqamryes26detUXV3tulVWVio+Pl7f+ta3PMZZrVaPcdXV1YqMjBzYqwIAAEEn3J/B+/fv97i/detWjRs3TseOHVNubm6f19hsNtlsNtf9P/zhD7py5Yq++93veowLCQlRcnKyP9MBAABwGVSnpr6+XpIUHx/f72u2bNmi+++/X5MmTfJ4vLGxUZMmTVJKSoq+8Y1v6Pjx4z6fx+FwyG63e9wAAEDwGnCoMQxDK1eu1Lx58zRjxox+XVNdXa19+/bp+9//vsfj06dP17Zt27R3717t3LlTkZGRuuuuu/T55597fa6CggLXKpDNZlNqaupAXwoAADCBEMMwjIFcmJ+fr3feeUcHDx5USkpKv64pKCjQq6++qnPnzikiIsLruM7OTt1xxx3Kzc3V+vXr+xzjcDjkcDhc9+12u1JTU1VfXy+r1erfiwEAAAFht9tls9mG5P3br06N07Jly7R3716Vlpb2O9AYhqHf/va3ysvL8xloJCk0NFRz5szxuVJjsVhksVj8mjcAADAvvz5+MgxDTz/9tN5++2396U9/UlpaWr+vLSkp0d/+9jctWbKkXz+nvLxc48eP92d6AAAgiPm1UpOfn68dO3Zoz549iouLU01NjaSubzhFRUVJklatWqWqqipt377d49otW7Zo7ty5ffZv1qxZo6ysLE2dOlV2u13r169XeXm5Nm7cONDXBQAAgoxfoWbTpk2SpHvuucfj8a1bt+rJJ5+U1FUGrqio8Pjz+vp6FRYWat26dX0+b11dnZ566inV1NTIZrNp1qxZKi0t1Z133unP9AAAQBAbcFF4uBnKohEAALg5hvL9m7OfAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKRBqAACAKfgVagoKCjRnzhzFxcVp3Lhxeuyxx3Tq1Cmf1xQXFyskJKTX7bPPPvMYV1hYqPT0dFksFqWnp6uoqMj/VwMAAIKWX6GmpKRE+fn5Onz4sA4cOKD29nYtWLBATU1N17321KlTqq6udt2mTp3q+rOysjItXrxYeXl5OnHihPLy8rRo0SJ98MEH/r8iAAAQlEIMwzAGevHFixc1btw4lZSUKDc3t88xxcXFuvfee3XlyhWNHj26zzGLFy+W3W7Xvn37XI89+OCDGjNmjHbu3NmvudjtdtlsNtXX18tqtfr9WgAAwM03lO/fg+rU1NfXS5Li4+OvO3bWrFkaP3687rvvPv35z3/2+LOysjItWLDA47EHHnhAhw4dGsz0AABAEAkf6IWGYWjlypWaN2+eZsyY4XXc+PHjtXnzZs2ePVsOh0O///3vdd9996m4uNi1ulNTU6OkpCSP65KSklRTU+P1eR0OhxwOh+u+3W4f6EsBAAA3WHtHp6rqruqL2iadvtik07Vdt8+/vDBkP2PAoebpp5/WRx99pIMHD/ocN23aNE2bNs11Pzs7W5WVlfrlL3/p8ZFVSEiIx3WGYfR6zF1BQYHWrFkzwNkDAIChZhiGztsdrsByurZRp2ub9EVtkyovN6uto3fjpdPRMmQ/f0ChZtmyZdq7d69KS0uVkpLi9/VZWVl68803XfeTk5N7rcpcuHCh1+qNu1WrVmnlypWu+3a7XampqX7PBQAA+KeuudUtuDS5Vl/OXGpSc2uH1+ss4aFKS4zxuI21dOje14ZmXn6FGsMwtGzZMhUVFam4uFhpaWkD+qHHjx/X+PHjXfezs7N14MABPfvss67H3nvvPeXk5Hh9DovFIovFMqCfDwAAfGtubdeZ2madudQdXC72rLxcaW7zel1YaIhSx0R1h5ZYpY2N0ZTEGE1OjNF4a6RCQz0/hRnK+ohfoSY/P187duzQnj17FBcX51pdsdlsioqKktS1glJVVaXt27dLkl577TVNnjxZGRkZam1t1ZtvvqnCwkIVFha6nnf58uXKzc3V2rVrtXDhQu3Zs0fvv//+dT/aAgAAA9fW0anKy13B5Qu3nsvp2iZV1/v+WCjZGtkVXLpDi3PlJWVMtCLCA7O3r1+hZtOmTZKke+65x+PxrVu36sknn5QkVVdXq6KiwvVnra2t+uEPf6iqqipFRUUpIyND77zzjh5++GHXmJycHO3atUvPP/+8XnjhBd1yyy3avXu35s6dO8CXBQAAJKmz01CNvcUjsDhvFZeb1dHpfWeX0dGjXGFlinPlJTFGkxOjFR0x4FruDTOofWqGE/apAQAEK8MwdKW5rfvjoeaegm53z6WlrdPrtVGjwjQ50XO1JW1sjNISYjQmJuKGz30o37+HX8wCAAB9anK063RtV1Bxfi36i+5Vl/qr3nsu4aEhmhgf7RlaEmM0JTFWSVaLz28bjySEGgAAhpHW9k5VXG7WGfdvFnWvvJy3O3xeO8EW6QosaYmxrtWXr4yJ0qgw859hTagBAOAm6+w0dK7+ateqi9tqy+nu/Vx81FwUHxPh8ZXoKd0rL5PiYxQVEXbzXsQwRKgBAOAGMAxDl5o893NxfmR05lKTHO3eey4xEV09F/fQkpYYq7SEGNmiR93EVzGyEGoAABiEhpY2nalt1he1jTrjXtKtbVJDS7vX60aFOXsusZoy1nPlZWyceXouNxOhBgCA63C0d6jiUrO+6P64yL2ge7HBe88lJESaYIvyCC3Ogu6E0ZEKD4Key81EqAEAQFJHp6Fz3QcuXlvSrbpy1WfPJTHWorTE6J5ddBNjNGVsjCbGRytyVHD3XG4mQg0AIGgYhqGLjY6eU6Ldvhp99lKzWju891xiLeGeqy3dqy+TE2NkjaTnMhwQagAApmNvaesJLtfcGh3eey4RYaGalBB9zfb/XSsvibER9FyGOUINAGBEamnr0NlLzb120T1d26Taxlav14WGSCljonvvopsYowmjoxQWSnAZqQg1AIBhq72jU1XdPZfT3Vv+O7f/P1d/Vb4O+hkXZ+kVXKaMjVFqfLQs4fRczIhQAwAIKMMwdKHB4ToluufE6EZVXG5WW4f35BIXGa4pY2OVltD11WjnR0aTE2MUa+EtLtjwGwcA3BT1zW36wu0jIvdbc2uH1+ss4aFdhdwE9zOLuv4ZH0PPBT0INQCAIXO1tcP1EdG1t8tN3nsuYaEhSh0T5bmLbvfKy3hrpELpuaAfCDUAAL+0dXTqyytXdbq20eMjo9MXm3SuvsXntcnWSNfXoF1dl7ExSh0TrYhwNqLD4BBqAAC9dHYaOt/QotMXe3bOdW5IV3G5We0+dqKzRY3q2sMloSe0OD8+iqHnghuIv10AEMSuNLW6nRDd2H2GUVeAudrmvecSOSq0e/+Wa3bRTYzRmJiIm/gKgB6EGgAwuebW9l6nRJ/u7r3UNbd5vS481HngYkyvE6OT4ui5YPgh1ACACbS2d6rySrMrtLifX1Rj991zmWCLVNrY7m8Xubb/j1XKmCiN4sBFjCCEGgAYITo7DVXbW7qDS6NHcKm8clUdPnou8TERrl6L+4nRkxNiFBXBRnQwB0INAAwjhmHoclOr2wnRnjvpOtq9H7gYHRHmseW/+210ND0XmB+hBgACoNHRrjPO4NK98nL6UrNOX2yUvcX7gYujwpw9l1hNueYjo3FxFjaiQ1Aj1ADADeJo71Dl5WbXXi7uXZcLDQ6v14WESBNsUa6PiZw76U5JjNFXRkcpnJ4L0CdCDQAMQkenoXN1Vz12znUGly+vNMtHzUWJsRE93Ra3XXQnJUQrchQ9F8BfhBoAuA7DMFTb2Oray8X9xOgzl5rV6qPnEmsJ77PjMjkxRraoUTfxVQDmR6gBgG72ljbXt4ncPzI6U9ukBof3nktEWKgmJUT37J7rtpPu2Fh6LsDNQqgBEFRa2jpU4dFzcZ4a3azaRt89l5QxUV0F3cQYTU6IVtrYrv89YXSUwtiIDgg4Qg0A0+noNFR15aq+cAWWnltV3VUZPnouY+MsbqdE93RdUuPpuQDDHaEGwIhkGIYuNjjczi1yfmTUqMrLV9Xa4b3nEmcJd9uALlaTE6M1pfufcZH0XICRilADYFirb27rPqeosdeJ0U2t3g9cjAgP7XVKtPOWEBNBzwUwIUINgIBraevo2jHXLbQ4b5ebWr1eFxoipcZHu8LKFLeDFyfYojhwEQgyhBoAN0V7R6e+vHLVbfv/RtcRAOfqfR+4mGS1uD4qcg8uE+OjFRHORnQAuhBqAAwZwzB03u7oKei6fS264nKz2n3sRGeNDNeU7m8TOT8ych4BEGPhP1UAro//UgDwW11zq9uZRZ676F5t895ziRwVes0p0bFKS+w6x2hM9Ch6LgAGxa9QU1BQoLffflufffaZoqKilJOTo7Vr12ratGler3n77be1adMmlZeXy+FwKCMjQz/72c/0wAMPuMZs27ZN3/3ud3tde/XqVUVGRvozRQBDpLm1XWdqmz130e2+1TW3eb0uLNR54GLvXXSTrZH0XADcMH6FmpKSEuXn52vOnDlqb2/Xj3/8Yy1YsECffPKJYmJi+rymtLRU8+fP10svvaTRo0dr69atevTRR/XBBx9o1qxZrnFWq1WnTp3yuJZAA9xYbR2dqrzc7LHa4lx9qbH77rmMt0V6BBbnidGp8dEaxYGLAAIgxDB8bUPl28WLFzVu3DiVlJQoNze339dlZGRo8eLF+slPfiKpa6VmxYoVqqurG+hUZLfbZbPZVF9fL6vVOuDnAcyms9NQtb1FZzxCS1fnpfLKVXX46LmMiR7VU9B1OzF6cmK0oiP49BrA4A3l+/eg/qtUX18vSYqPj+/3NZ2dnWpoaOh1TWNjoyZNmqSOjg7dfvvtevHFFz1Wcq7lcDjkcPRsaW632/2cPWAehmHoclOrzlzyPLPIeXP4OHAxalSYq5g75ZqPi0ZHR9zEVwEAgzPgUGMYhlauXKl58+ZpxowZ/b7u1VdfVVNTkxYtWuR6bPr06dq2bZtuu+022e12rVu3TnfddZdOnDihqVOn9vk8BQUFWrNmzUCnD4xIjY5214GLnh8ZNcre4v3AxfDQEE1MiHYLLbGu4JJk5cBFAOYw4I+f8vPz9c477+jgwYNKSUnp1zU7d+7U97//fe3Zs0f333+/13GdnZ264447lJubq/Xr1/c5pq+VmtTUVD5+wojnaO/o7rk0uz4mcq6+XGjwfuCiJH1ldJRnObf7xOiUMVEKp+cCYBgK+MdPy5Yt0969e1VaWtrvQLN7924tWbJEb731ls9AI0mhoaGaM2eOPv/8c69jLBaLLBaLX/MGhouOTkPn6q527aJb6/mR0ZdXmuWj5qKEmAiP0OLcjG5yQgwHLgIIan6FGsMwtGzZMhUVFam4uFhpaWn9um7nzp363ve+p507d+qRRx7p188pLy/Xbbfd5s/0gGHFMAzVNrZes/1/18rLmUvNavXRc4mJCOs+ryi214nRtigOXASAvvgVavLz87Vjxw7t2bNHcXFxqqmpkSTZbDZFRUVJklatWqWqqipt375dUlegefzxx7Vu3TplZWW5romKipLNZpMkrVmzRllZWZo6darsdrvWr1+v8vJybdy4ccheKHCjNLS09SrmOnfTbXB477mMCgvRpIQYj9DivI2No+cCAP7yK9Rs2rRJknTPPfd4PL5161Y9+eSTkqTq6mpVVFS4/uyNN95Qe3u78vPzlZ+f73r8iSee0LZt2yRJdXV1euqpp1RTUyObzaZZs2aptLRUd9555wBeEjD0Wto6VOG2n4tzL5cvaptU2+i95xIS0tNz6dn+v+sogAmjoxTGRnQAMGQGtU/NcMI+NRisjk5DVVeu6ovaRtc3jJy76FbVXZWv/6ckxlo8zixyhpjU+Gh6LgDgQ8CLwsBIZRiGLjY4XOcUuQeXikvNau3w3nOJs4S7AotrF93EWE1OjFZcJD0XAAg0Qg1Mqf5qV8/ljFtoOV3bqNMXm9TU6v3AxYjwUE1OiO7ZRddt5SUhJoKeCwAMY4QajFgtbR2ubxadvtTkcWL0paZWr9eFhkgpY3oOXJzitvoy3kbPBQBGKkINhrX2jk59eeVqn98uOlfvu+cyLs5yTWjp+np0anyULOH0XADAbAg1CDjDMHTe7ugu6Lrtotvdc2n3sROdNTLc9W0i967L5MQYxVr46w0AwYT/6uOmqWtudZ0SfeZSk8f/bvbRc4kcFarJCdcUdLs3phsTPYqeCwBAEqEGQ6y5tb17taXJ7cTorpWXK81tXq8LCw3RxPjo7pJurMeJ0cnWSIXScwEAXAehBn5r6+jsPnCxd8+lur7F57XjbZFdqy5jPXfRTY2P1igOXAQADAKhBn3q7DRUY29xdVvOuAWXisvN6vDRcxkTPUqTE923/4/t7rlEKzqCv3IAgBuDd5ggZhiGrjS36XRto77o7rY4T4w+c6lJLW3eN6KLGhWmydeeWTQ2RmkJMRoTE3ETXwUAAF0INUGgydHu8RGR+4Z09Ve991zCQ0M0MSFaaQnXbv8fqyQrBy4CAIYXQo1JtLZ3uh242KjTbl+NPm/3fuCi1HXg4uTEa3bRTYxRypgohdNzAQCMEISaEaSz09C5+r43oqu83CwfNRclxES49m9xdV3GxmhSfIyiItiIDgAw8hFqhhnDMHSpqbUrrFz0LOmeudQkR7v3nktMRJjSxsZockKM25lFsUpLiJEtmgMXAQDmRqgJkIaWNp2pbdYX3R8RuZ8Y3dDS7vW6UWEhmpTQHVzcziyakhijsXH0XAAAwYtQcwM52jtUcam555Ro58GLtU262OC95xIS0tVzcd9B11nQnTA6kp4LAAB9INQMUkenoXN1V7u3/O9adTl9qaukW3Xlqs+eS2KsRVO6929x7uUyZWyMJsZHK3IUPRcAAPxBqOkHwzB0sdHRtdLi9jHRmdomnb3UrNYO7z2XWEu4pnT3XNxPjJ6cGCNrJD0XAACGCqHGTf3VNo9ui3vXpdHhvecSER7afWZRjNuGdF0rL4mxEfRcAAC4CYIu1LS0dehs98dD7qdEn65tUm1jq9frQkOklDHRvXouaYkxmjA6SmEcuAgAQECZMtS0d3SqytVz8dzP5Vz9VRk+ei7j4iyuj4ncPzJKjY+WJZyeCwAAw5XpQs2j//m/OtccorYO78klLjJcU8Z27ZzrfmL05MQYxVpM968EAICgYLp38NO1zQq1RMsSHurxEZH74YvxMfRcAAAwG9OFmv96PFMz0pI13hqpUHouAAAEDdOFmuxbEmS1RgV6GgAA4CZja1oAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKhBoAAGAKfoWagoICzZkzR3FxcRo3bpwee+wxnTp16rrXlZSUaPbs2YqMjNSUKVP0+uuv9xpTWFio9PR0WSwWpaenq6ioyJ+pAQCAIOdXqCkpKVF+fr4OHz6sAwcOqL29XQsWLFBTU5PXa06fPq2HH35Yd999t44fP67Vq1frmWeeUWFhoWtMWVmZFi9erLy8PJ04cUJ5eXlatGiRPvjgg4G/MgAAEFRCDMPwfpz1dVy8eFHjxo1TSUmJcnNz+xzz3HPPae/evfr0009djy1dulQnTpxQWVmZJGnx4sWy2+3at2+fa8yDDz6oMWPGaOfOnf2ai91ul81mU319vaxW60BfEgAAuImG8v17UJ2a+vp6SVJ8fLzXMWVlZVqwYIHHYw888ICOHj2qtrY2n2MOHTrk9XkdDofsdrvHDQAABK8BhxrDMLRy5UrNmzdPM2bM8DqupqZGSUlJHo8lJSWpvb1dtbW1PsfU1NR4fd6CggLZbDbXLTU1daAvBQAAmMCAT+l++umn9dFHH+ngwYPXHRsSEuJx3/mJl/vjfY259jF3q1at0sqVK1336+vrNXHiRFZsAAAYQZzv24Now7gMKNQsW7ZMe/fuVWlpqVJSUnyOTU5O7rXicuHCBYWHhyshIcHnmGtXb9xZLBZZLBbXfeeqDys2AACMPJcuXZLNZhvUc/gVagzD0LJly1RUVKTi4mKlpaVd95rs7Gz98Y9/9HjsvffeU2ZmpkaNGuUac+DAAT377LMeY3Jycvo9N2evp6KiYtD/UjA4drtdqampqqyspLQdYPwuhg9+F8MLv4/hw/lJi69+bn/5FWry8/O1Y8cO7dmzR3Fxca7VFZvNpqioKEldHwtVVVVp+/btkrq+6bRhwwatXLlSP/jBD1RWVqYtW7Z4fKtp+fLlys3N1dq1a7Vw4ULt2bNH77//fr8+2nIKDQ11zYW/oMOD1WrldzFM8LsYPvhdDC/8PoYP5/v4oJ7Dn8GbNm1SfX297rnnHo0fP9512717t2tMdXW1KioqXPfT0tL07rvvqri4WLfffrtefPFFrV+/Xt/85jddY3JycrRr1y5t3bpVM2fO1LZt27R7927NnTt30C8QAAAEh0HtUzOcsE/N8MHvYvjgdzF88LsYXvh9DB/DZp+a4cRiseinP/2pR3kYgcHvYvjgdzF88LsYXvh9DB9D+bswzUoNAAAIbqZZqQEAAMGNUAMAAEyBUAMAAEyBUAMAAEzBFKHm17/+tdLS0hQZGanZs2frf//3fwM9paBTUFCgOXPmKC4uTuPGjdNjjz2mU6dOBXpaUNfvJiQkRCtWrAj0VIJWVVWVvvOd7yghIUHR0dG6/fbbdezYsUBPK+i0t7fr+eefV1pamqKiojRlyhT9+7//uzo7OwM9NdMrLS3Vo48+qgkTJigkJER/+MMfPP7cMAz97Gc/04QJExQVFaV77rlHH3/8sd8/Z8SHmt27d2vFihX68Y9/rOPHj+vuu+/WQw895LEBIG68kpIS5efn6/Dhwzpw4IDa29u1YMECNTU1BXpqQe3IkSPavHmzZs6cGeipBK0rV67orrvu0qhRo7Rv3z598sknevXVVzV69OhATy3orF27Vq+//ro2bNigTz/9VL/4xS/0yiuv6D//8z8DPTXTa2pq0le/+lVt2LChzz//xS9+oV/96lfasGGDjhw5ouTkZM2fP18NDQ3+/SBjhLvzzjuNpUuXejw2ffp040c/+lGAZgTDMIwLFy4YkoySkpJATyVoNTQ0GFOnTjUOHDhgfO1rXzOWL18e6CkFpeeee86YN29eoKcBwzAeeeQR43vf+57HY//4j/9ofOc73wnQjIKTJKOoqMh1v7Oz00hOTjZefvll12MtLS2GzWYzXn/9db+ee0Sv1LS2turYsWNasGCBx+MLFizQoUOHAjQrSF0HlEkakgPKMDD5+fl65JFHdP/99wd6KkFt7969yszM1Le+9S2NGzdOs2bN0n/9138FelpBad68efqf//kf/fWvf5UknThxQgcPHtTDDz8c4JkFt9OnT6umpsbjvdxisehrX/ua3+/lfh1oOdzU1taqo6NDSUlJHo8nJSW5DtvEzWcYhlauXKl58+ZpxowZgZ5OUNq1a5c+/PBDHTlyJNBTCXpffPGFNm3apJUrV2r16tX6y1/+omeeeUYWi0WPP/54oKcXVJ577jnV19dr+vTpCgsLU0dHh37+85/rn//5nwM9taDmfL/u67387Nmzfj3XiA41TiEhIR73DcPo9RhunqefflofffSRX6esY+hUVlZq+fLleu+99xQZGRno6QS9zs5OZWZm6qWXXpIkzZo1Sx9//LE2bdpEqLnJdu/erTfffFM7duxQRkaGysvLtWLFCk2YMEFPPPFEoKcX9IbivXxEh5rExESFhYX1WpW5cOFCr8SHm2PZsmXau3evSktLlZKSEujpBKVjx47pwoULmj17tuuxjo4OlZaWasOGDXI4HAoLCwvgDIPL+PHjlZ6e7vHY3//936uwsDBAMwpe//Zv/6Yf/ehH+qd/+idJ0m233aazZ8+qoKCAUBNAycnJkrpWbMaPH+96fCDv5SO6UxMREaHZs2frwIEDHo8fOHBAOTk5AZpVcDIMQ08//bTefvtt/elPf1JaWlqgpxS07rvvPp08eVLl5eWuW2Zmpr797W+rvLycQHOT3XXXXb22N/jrX/+qSZMmBWhGwau5uVmhoZ5ve2FhYXylO8DS0tKUnJzs8V7e2tqqkpISv9/LR/RKjSStXLlSeXl5yszMVHZ2tjZv3qyKigotXbo00FMLKvn5+dqxY4f27NmjuLg41+qZzWZTVFRUgGcXXOLi4np1mWJiYpSQkEDHKQCeffZZ5eTk6KWXXtKiRYv0l7/8RZs3b9bmzZsDPbWg8+ijj+rnP/+5Jk6cqIyMDB0/fly/+tWv9L3vfS/QUzO9xsZG/e1vf3PdP336tMrLyxUfH6+JEydqxYoVeumllzR16lRNnTpVL730kqKjo/Uv//Iv/v2gofh6VqBt3LjRmDRpkhEREWHccccdfI04ACT1edu6dWugpwbD4CvdAfbHP/7RmDFjhmGxWIzp06cbmzdvDvSUgpLdbjeWL19uTJw40YiMjDSmTJli/PjHPzYcDkegp2Z6f/7zn/t8j3jiiScMw+j6WvdPf/pTIzk52bBYLEZubq5x8uRJv39OiGEYxlCkMAAAgEAa0Z0aAAAAJ0INAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwBUINAAAwhf8fEXL2MAjXwlMAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Revert NewExample's discount factor and make his future income minuscule\n",
-    "# print(\"your lines here\")\n",
-    "\n",
-    "# Compare the old and new consumption functions\n",
-    "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n",
-    "\n",
-    "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes.  However, keep in mind that *no solution exists* for some combinations of parameters.  HARK should let you know if this is the case if you try to solve such a model.\n",
-    "\n",
-    "\n",
-    "## Your Second HARK Model: Adding Income Shocks\n",
-    "\n",
-    "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines.  Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n",
-    "\n",
-    "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n",
-    "\n",
-    "\\begin{align*}\n",
-    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
-    "a_t &= m_t - c_t \\\\\n",
-    "a_t &\\geq \\underline{a} \\\\\n",
-    "m_{t+1} &= R/(\\Gamma \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
-    "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n",
-    "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n",
-    "\\end{align*}\n",
-    "\n",
-    "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$.  To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks?  Can't the joint distribution of permanent and transitory shocks be just about anything?\n",
-    "\n",
-    "_Yes_, and HARK can handle whatever correlation structure a user might care to specify.  However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment.  The distributions are independent of each other by default, and by default are approximated with $N$ point equiprobable distributions.\n",
-    "\n",
-    "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n",
-    "\n",
-    "\n",
-    "| Param | Description | Code | Value |\n",
-    "| :---: | --- | --- |  :---: |\n",
-    "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n",
-    "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n",
-    "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n",
-    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n",
-    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n",
-    "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n",
-    "| $\\underset{\\bar{}}{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
-    "\n",
-    "IndShockDictionary = {\n",
-    "    \"CRRA\": 2.5,  # The dictionary includes our original parameters...\n",
-    "    \"Rfree\": 1.03,\n",
-    "    \"DiscFac\": 0.96,\n",
-    "    \"LivPrb\": [0.98],\n",
-    "    \"PermGroFac\": [1.01],\n",
-    "    \"PermShkStd\": [\n",
-    "        0.1\n",
-    "    ],  # ... and the new parameters for constructing the income process.\n",
-    "    \"PermShkCount\": 7,\n",
-    "    \"TranShkStd\": [0.1],\n",
-    "    \"TranShkCount\": 7,\n",
-    "    \"UnempPrb\": 0.05,\n",
-    "    \"IncUnemp\": 0.3,\n",
-    "    \"BoroCnstArt\": 0.0,\n",
-    "    \"aXtraMin\": 0.001,  # aXtra parameters specify how to construct the grid of assets.\n",
-    "    \"aXtraMax\": 50.0,  # Don't worry about these for now\n",
-    "    \"aXtraNestFac\": 3,\n",
-    "    \"aXtraCount\": 48,\n",
-    "    \"aXtraExtra\": [None],\n",
-    "    \"vFuncBool\": False,  # These booleans indicate whether the value function should be calculated\n",
-    "    \"CubicBool\": False,  # and whether to use cubic spline interpolation. You can ignore them.\n",
-    "    \"aNrmInitMean\": -10.0,\n",
-    "    \"aNrmInitStd\": 0.0,  # These parameters specify the (log) distribution of normalized assets\n",
-    "    \"pLvlInitMean\": 0.0,  # and permanent income for agents at \"birth\". They are only relevant in\n",
-    "    \"pLvlInitStd\": 0.0,  # simulation and you don't need to worry about them.\n",
-    "    \"PermGroFacAgg\": 1.0,\n",
-    "    \"T_retire\": 0,  # What's this about retirement? ConsIndShock is set up to be able to\n",
-    "    \"UnempPrbRet\": 0.0,  # handle lifecycle models as well as infinite horizon problems. Swapping\n",
-    "    \"IncUnempRet\": 0.0,  # out the structure of the income process is easy, but ignore for now.\n",
-    "    \"T_age\": None,\n",
-    "    \"T_cycle\": 1,\n",
-    "    \"cycles\": 0,\n",
-    "    \"AgentCount\": 10000,\n",
-    "    \"tax_rate\": 0.0,\n",
-    "}\n",
-    "\n",
-    "# Hey, there's a lot of parameters we didn't tell you about!  Yes, but you don't need to\n",
-    "# think about them for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
-    "\n",
-    "IndShockExample = IndShockConsumerType(**IndShockDictionary)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "IndShockExample.solve()\n",
-    "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Changing Constructed Attributes\n",
-    "\n",
-    "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the **constructor** or **initialization** method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n",
-    "\n",
-    "Suppose you were interested in changing (say) the amount of permanent income risk.  From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n",
-    "\n",
-    "That's _almost_ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you _actually_ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again).  In the cell below, we invoke the method `update_income_process` so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "OtherExample = deepcopy(\n",
-    "    IndShockExample\n",
-    ")  # Make a copy so we can compare consumption functions\n",
-    "OtherExample.assign_parameters(\n",
-    "    PermShkStd=[0.2]\n",
-    ")  # Double permanent income risk (note that it's a one element list)\n",
-    "OtherExample.update_income_process()  # Call the method to reconstruct the representation of F_t\n",
-    "OtherExample.solve()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use the line(s) below to plot the consumptions functions against each other"
-   ]
-  }
- ],
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Gentle Introduction to HARK\n",
+    "\n",
+    "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users.  It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK.  Our aim is to convey a feel for how the toolkit works.\n",
+    "\n",
+    "For readers for whom this is your very first experience with Python, we have put important Python concepts in **boldface**. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in _italics_. Only cursory definitions (if any) are provided here.  If you want to learn more, there are many online Python and Jupyter tutorials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     0
+    ],
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n",
+    "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n",
+    "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n",
+    "#   The most common problem beginners have is to execute a cell before all its predecessors\n",
+    "#   If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import HARK\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "mystr = lambda number: \"{:.4f}\".format(number)\n",
+    "from HARK.utilities import plot_funcs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your First HARK Model: Perfect Foresight\n",
+    "\n",
+    "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n",
+    "\n",
+    "\\begin{align*}\n",
+    "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n",
+    "\\end{align*}\n",
+    "\n",
+    "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb_t \\le 1$, and a complementary death probability of $\\mathsf{D}_t = 1 - \\LivPrb_t$; death ends the consumer's decision problem. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$ and hold the rest in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$.\n",
+    "\n",
+    "The agent's problem can be written in Bellman form as:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n",
+    "&\\text{s.t.} \\\\\n",
+    "A_t &= M_t - C_t \\\\\n",
+    "M_{t+1} &= R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
+    "P_{t+1} &= \\Gamma_{t+1} P_t, \\\\\n",
+    "\\end{align*}\n",
+    "\n",
+    "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\aleph_t\\}$.  To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability: $\\Gamma_t = \\Gamma$ and $\\LivPrb_t = \\LivPrb$ for all $t$.\n",
+    "\n",
+    "## Representing Agents in HARK\n",
+    "\n",
+    "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$.  To make agents of this class, we must import the class itself into our workspace.  (Run the cell below in order to do this)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the Python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/).\n",
+    "\n",
+    "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have.  In the hidden cell below, we define a $\\textbf{dictionary}$ named `PF_dictionary` with these parameter values:\n",
+    "\n",
+    "| Param | Description | Code | Value |\n",
+    "| :---: | ---  | :---: | :---: |\n",
+    "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n",
+    "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n",
+    "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n",
+    "| $\\aleph$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n",
+    "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n",
+    "\n",
+    "\n",
+    "For now, don't worry about the specifics of dictionaries.  All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n",
+    "PF_dictionary = {\n",
+    "    \"CRRA\": 2.5,\n",
+    "    \"DiscFac\": 0.96,\n",
+    "    \"Rfree\": 1.03,\n",
+    "    \"LivPrb\": [0.98],\n",
+    "    \"PermGroFac\": [1.01],\n",
+    "    \"T_cycle\": 1,\n",
+    "    \"cycles\": 0,\n",
+    "    \"AgentCount\": 10000,\n",
+    "}\n",
+    "\n",
+    "# To those curious enough to open this hidden cell, you might notice that we defined\n",
+    "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n",
+    "# worry about these for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make an **object** named $\\texttt{PFexample}$ which is an **instance** of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$ and the specific set of parameter values defined in `PF_dictionary`.  Such a bundle is created passing `PF_dictionary` to the class $\\texttt{PerfForesightConsumerType}$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample = PerfForesightConsumerType(**PF_dictionary)\n",
+    "# The asterisks ** basically say \"here come some arguments in a dictionary\" to PerfForesightConsumerType."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem.\n",
+    "\n",
+    "## Solving an Agent's Problem\n",
+    "\n",
+    "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ **method**. (A method is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n",
+    "\n",
+    "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$.  In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (parameterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem.\n",
+    "\n",
+    "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem.  The consumption function stored as the first element (index 0) of the solution list can be retrieved by:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample.solution[0].cFunc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the results proven in the associated [lecture notes](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$.\n",
+    "\n",
+    "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation.  It can be plotted between an $m$ ratio of 0 and 10 using the command below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mPlotTop = 10\n",
+    "plot_funcs(PFexample.solution[0].cFunc, 0.0, mPlotTop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis).  How can this be?\n",
+    "\n",
+    "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- their \"human wealth\".  How much is that?  What's the minimum value of $m_t$ where the consumption function is defined?  We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "humanWealth = PFexample.solution[0].hNrm\n",
+    "mMinimum = PFexample.solution[0].mNrmMin\n",
+    "print(\n",
+    "    \"This agent's human wealth is \"\n",
+    "    + str(humanWealth)\n",
+    "    + \" times his current income level.\"\n",
+    ")\n",
+    "print(\n",
+    "    \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n",
+    "    + str(mMinimum)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yikes! Let's take a look at the bottom of the consumption function.  In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_funcs(PFexample.solution[0].cFunc, mMinimum, mPlotTop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing Agent Parameters\n",
+    "\n",
+    "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does.  We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NewExample = deepcopy(PFexample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "nbsphinx-thumbnail": {}
+   },
+   "outputs": [],
+   "source": [
+    "NewExample.assign_parameters(DiscFac=0.90)\n",
+    "NewExample.solve()\n",
+    "mPlotBottom = mMinimum\n",
+    "plot_funcs(\n",
+    "    [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Note that you can pass a **list** of functions to `plot_funcs` as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n",
+    "\n",
+    "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income.  We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n",
+    "\n",
+    "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income __shrinks__ by 99 percent each period?  In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n",
+    "\n",
+    "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept.  Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a __time-varying__ attribute, it must be specified as a **list** (with a single element in this case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Revert NewExample's discount factor and make his future income minuscule\n",
+    "# WRITE YOUR CODE HERE!\n",
+    "\n",
+    "# Compare the old and new consumption functions\n",
+    "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n",
+    "\n",
+    "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes.  However, keep in mind that *no solution exists* for some combinations of parameters.  HARK should let you know if this is the case if you try to solve such a model.\n",
+    "\n",
+    "\n",
+    "## Your Second HARK Model: Adding Income Shocks\n",
+    "\n",
+    "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines.  Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n",
+    "\n",
+    "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$.  Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$.  As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly, the normalized utility and continuation value are $u$ and $v_t$.\n",
+    "\n",
+    "\\begin{align*}\n",
+    "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n",
+    "a_t &= m_t - c_t \\\\\n",
+    "a_t &\\geq \\underline{a} \\\\\n",
+    "m_{t+1} &= R/(\\Gamma \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n",
+    "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n",
+    "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n",
+    "\\end{align*}\n",
+    "\n",
+    "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$.  To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks?  Can't the joint distribution of permanent and transitory shocks be just about anything?\n",
+    "\n",
+    "_Yes_, and HARK can handle whatever correlation structure a user might care to specify.  However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment.  The distributions are independent of each other by default, and by default are approximated with $N$ point equiprobable distributions.\n",
+    "\n",
+    "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n",
+    "\n",
+    "\n",
+    "| Param | Description | Code | Value |\n",
+    "| :---: | --- | --- |  :---: |\n",
+    "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n",
+    "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n",
+    "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n",
+    "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n",
+    "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n",
+    "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n",
+    "| $\\underset{\\bar{}}{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     0,
+     2
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
+    "\n",
+    "IndShockDictionary = {\n",
+    "    # The dictionary includes our original parameters...\n",
+    "    \"CRRA\": 2.5,\n",
+    "    \"Rfree\": 1.03,\n",
+    "    \"DiscFac\": 0.96,\n",
+    "    \"LivPrb\": [0.98],\n",
+    "    \"PermGroFac\": [1.01],\n",
+    "    # ... and the new parameters for constructing the income process.\n",
+    "    \"PermShkStd\": [0.1], \n",
+    "    \"PermShkCount\": 7,\n",
+    "    \"TranShkStd\": [0.1],\n",
+    "    \"TranShkCount\": 7,\n",
+    "    \"UnempPrb\": 0.05,\n",
+    "    \"IncUnemp\": 0.3,\n",
+    "    \"BoroCnstArt\": 0.0,\n",
+    "    \"aXtraMin\": 0.001,  # aXtra parameters specify how to construct the grid of assets.\n",
+    "    \"aXtraMax\": 50.0,  # Don't worry about these for now\n",
+    "    \"aXtraNestFac\": 3,\n",
+    "    \"aXtraCount\": 48,\n",
+    "    \"aXtraExtra\": [None],\n",
+    "    \"vFuncBool\": False,  # These booleans indicate whether the value function should be calculated\n",
+    "    \"CubicBool\": False,  # and whether to use cubic spline interpolation. You can ignore them.\n",
+    "    \"aNrmInitMean\": -10.0,\n",
+    "    \"aNrmInitStd\": 0.0,  # These parameters specify the (log) distribution of normalized assets\n",
+    "    \"pLvlInitMean\": 0.0,  # and permanent income for agents at \"birth\". They are only relevant in\n",
+    "    \"pLvlInitStd\": 0.0,  # simulation and you don't need to worry about them.\n",
+    "    \"PermGroFacAgg\": 1.0,\n",
+    "    \"T_retire\": 0,  # What's this about retirement? ConsIndShock is set up to be able to\n",
+    "    \"UnempPrbRet\": 0.0,  # handle lifecycle models as well as infinite horizon problems. Swapping\n",
+    "    \"IncUnempRet\": 0.0,  # out the structure of the income process is easy, but ignore for now.\n",
+    "    \"T_age\": None,\n",
+    "    \"T_cycle\": 1,\n",
+    "    \"cycles\": 0,\n",
+    "    \"AgentCount\": 10000,\n",
+    "    \"tax_rate\": 0.0,\n",
+    "}\n",
+    "\n",
+    "# Hey, there's a lot of parameters we didn't tell you about!  Yes, but you don't need to\n",
+    "# think about them for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
+    "\n",
+    "IndShockExample = IndShockConsumerType(**IndShockDictionary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IndShockExample.solve()\n",
+    "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing Constructed Attributes\n",
+    "\n",
+    "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the **constructor** or **initialization** method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n",
+    "\n",
+    "Suppose you were interested in changing (say) the amount of permanent income risk.  From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n",
+    "\n",
+    "That's _almost_ true-- there's one small extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you _actually_ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again).  In the cell below, we invoke the method `update_income_process` so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OtherExample = deepcopy(\n",
+    "    IndShockExample\n",
+    ")  # Make a copy so we can compare consumption functions\n",
+    "OtherExample.assign_parameters(\n",
+    "    PermShkStd=[0.2]\n",
+    ")  # Double permanent income risk (note that it's a one element list)\n",
+    "OtherExample.update_income_process()  # Call the method to reconstruct the representation of F_t\n",
+    "OtherExample.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, agents in HARK have an $\\texttt{update}$ method that calls *all* of their various internal $\\texttt{update_X}$  methods (e.g. $\\texttt{update\\_income\\_process}$). If you change a parameter that describes a constructed attribute of your agent, invoking the $\\texttt{update}$ method will re-construct that attribute using the new parameter value.\n",
+    "\n",
+    "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the line(s) below to plot the consumptions functions against each other"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "collapsed,code_folding",
+   "formats": "ipynb"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From fa8bc418b6b3db30be66759f2b91cb6c88ddd1de Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 8 Dec 2023 15:34:18 -0500
Subject: [PATCH 32/37] Run Gentle Introduction to satisfy Sphinx

---
 .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb   | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index bd4e204c5..1f6e38361 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "code_folding": [
      0
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "code_folding": [
      0
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "nbsphinx-thumbnail": {}
    },

From 174a152d6348ccafdbeaba7b0980800fa0aa75e0 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 8 Dec 2023 15:58:30 -0500
Subject: [PATCH 33/37] Try again with additional output

Output was mysteriously missing in some notebook cells; trying again for tests.
---
 .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb   | 108 +++++++++++++++---
 1 file changed, 95 insertions(+), 13 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index 1f6e38361..0d97f377f 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -176,7 +176,18 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HARK.interpolation.LinearInterp at 0x117b36d17d0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "PFexample.solution[0].cFunc"
    ]
@@ -194,7 +205,18 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mPlotTop = 10\n",
     "plot_funcs(PFexample.solution[0].cFunc, 0.0, mPlotTop)"
@@ -213,7 +235,16 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This agent's human wealth is 50.49994992551661 times his current income level.\n",
+      "This agent's consumption function is defined (consumption is positive) down to m_t = -50.49994992551661\n"
+     ]
+    }
+   ],
    "source": [
     "humanWealth = PFexample.solution[0].hNrm\n",
     "mMinimum = PFexample.solution[0].mNrmMin\n",
@@ -239,7 +270,18 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_funcs(PFexample.solution[0].cFunc, mMinimum, mPlotTop)"
    ]
@@ -275,7 +317,18 @@
    "metadata": {
     "nbsphinx-thumbnail": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "NewExample.assign_parameters(DiscFac=0.90)\n",
     "NewExample.solve()\n",
@@ -300,9 +353,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Revert NewExample's discount factor and make his future income minuscule\n",
     "# WRITE YOUR CODE HERE!\n",
@@ -355,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "code_folding": [
      0,
@@ -416,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -434,9 +498,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "IndShockExample.solve()\n",
     "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)"
@@ -457,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -482,12 +557,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Use the line(s) below to plot the consumptions functions against each other"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 6ca702545fb25dee061039f5a7cf9fea0cf5f7a3 Mon Sep 17 00:00:00 2001
From: "Matthew N. White" <mnwhite@gmail.com>
Date: Fri, 8 Dec 2023 16:08:47 -0500
Subject: [PATCH 34/37] Try to satisfy lint

---
 examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index 0d97f377f..8d460eed8 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -422,8 +422,7 @@
    "execution_count": 13,
    "metadata": {
     "code_folding": [
-     0,
-     2
+     0
     ]
    },
    "outputs": [],
@@ -431,15 +430,13 @@
     "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
     "\n",
     "IndShockDictionary = {\n",
-    "    # The dictionary includes our original parameters...\n",
-    "    \"CRRA\": 2.5,\n",
+    "    \"CRRA\": 2.5, # The dictionary includes our original parameters...\n",
     "    \"Rfree\": 1.03,\n",
     "    \"DiscFac\": 0.96,\n",
     "    \"LivPrb\": [0.98],\n",
     "    \"PermGroFac\": [1.01],\n",
-    "    # ... and the new parameters for constructing the income process.\n",
     "    \"PermShkStd\": [0.1], \n",
-    "    \"PermShkCount\": 7,\n",
+    "    \"PermShkCount\": 7, # and the new parameters for constructing the income process.\n",
     "    \"TranShkStd\": [0.1],\n",
     "    \"TranShkCount\": 7,\n",
     "    \"UnempPrb\": 0.05,\n",

From 53d54ac34b24a266a8d66a5b45388d61d6c7ec2b Mon Sep 17 00:00:00 2001
From: Alan Lujan <alanlujan91@gmail.com>
Date: Fri, 8 Dec 2023 19:38:04 -0500
Subject: [PATCH 35/37] fix ruff format

---
 examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
index 18b34a54a..94f33e9b3 100644
--- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
+++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
@@ -427,13 +427,13 @@
     "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
     "\n",
     "IndShockDictionary = {\n",
-    "    \"CRRA\": 2.5, # The dictionary includes our original parameters...\n",
+    "    \"CRRA\": 2.5,  # The dictionary includes our original parameters...\n",
     "    \"Rfree\": 1.03,\n",
     "    \"DiscFac\": 0.96,\n",
     "    \"LivPrb\": [0.98],\n",
     "    \"PermGroFac\": [1.01],\n",
-    "    \"PermShkStd\": [0.1], \n",
-    "    \"PermShkCount\": 7, # and the new parameters for constructing the income process.\n",
+    "    \"PermShkStd\": [0.1],\n",
+    "    \"PermShkCount\": 7,  # and the new parameters for constructing the income process.\n",
     "    \"TranShkStd\": [0.1],\n",
     "    \"TranShkCount\": 7,\n",
     "    \"UnempPrb\": 0.05,\n",

From d47c7ffd839b7cfb8ed2f86d42e0df3116d5b477 Mon Sep 17 00:00:00 2001
From: Adam Turner <9087854+AA-Turner@users.noreply.github.com>
Date: Tue, 2 Jan 2024 12:58:21 +0000
Subject: [PATCH 36/37] Remove unused .gitmodules file (#1368)

---
 .gitmodules | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 .gitmodules

diff --git a/.gitmodules b/.gitmodules
deleted file mode 100644
index e69de29bb..000000000

From 0d16951f76092486d83540533767dc56994ab9e1 Mon Sep 17 00:00:00 2001
From: Adam Turner <9087854+AA-Turner@users.noreply.github.com>
Date: Tue, 2 Jan 2024 13:15:18 +0000
Subject: [PATCH 37/37] Improve ARKitecture (#1369)

* Style (Capitalise, grammar)

* Remove mention of Spyder, mention mamba

* Fix the module for CRRAutility

* Syntax highlighting

* Update Documentation/overview/ARKitecture.md

---------

Co-authored-by: Mridul Seth <mail@mriduls.com>
---
 Documentation/overview/ARKitecture.md | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/Documentation/overview/ARKitecture.md b/Documentation/overview/ARKitecture.md
index 94aeeaef7..ca458c6ed 100644
--- a/Documentation/overview/ARKitecture.md
+++ b/Documentation/overview/ARKitecture.md
@@ -1,8 +1,11 @@
 # ARKitecture of Econ-ARK
 
-This document guides you through the structure of econ-ark and explain the main ingredients. Note that it does not explain _how_ to use it. For this, please follow the example notebooks which you can find on the left.
+This document guides you through the structure of Econ-ARK,
+and explains the main ingredients.
+Note that it does not explain _how_ to use it---for this,
+please follow the example notebooks which you can find on the left.
 
-[Econ-ark](https://github.com/econ-ark) contains the three main repositories [HARK](https://github.com/econ-ark/HARK), [DemARK](https://github.com/econ-ark/DEMARK), and [REMARK](https://github.com/econ-ark/REMARK). On top of that, the [website](https://econ-ark.org/) combines all of them. Hence, if you want to find a notebook search them in [materials](https://econ-ark.org/materials).
+[Econ-ARK](https://github.com/econ-ark) contains the three main repositories [HARK](https://github.com/econ-ark/HARK), [DemARK](https://github.com/econ-ark/DEMARK), and [REMARK](https://github.com/econ-ark/REMARK). On top of that, the [website](https://econ-ark.org/) combines all of them. Hence, if you want to find a notebook search them in [materials](https://econ-ark.org/materials).
 
 - [HARK](https://github.com/econ-ark/HARK): Includes the source code as well as some example notebooks.
 - [DemARK](https://github.com/econ-ark/DemARK): Here you can find *Dem*onstrations of tools, AgentTypes, and ModelClasses.
@@ -12,20 +15,21 @@ Before describing each repository in detail, some preliminary remarks.
 
 HARK is written in Python, an object-oriented programming (OOP) language that has experienced increasing popularity in the scientific community in the past several years. A significant reason for the adoption of Python is the **_numpy_** and **_scipy_** packages, which offer a wide array of mathematical and statistical functions and tools; HARK makes liberal use of these libraries. Python's object-oriented nature allows models in HARK to be easily extended: more complex models can inherit functions and methods from more fundamental ''parent'' models, eliminating the need to reproduce or repurpose code.
 
-As implied in the previous section, we strongly encourage HARK users to use the Anaconda distribution of Python, which includes all commonly used mathematical and scientific packages, an interactive development environment for iPython (Spyder), and a package manager that allows users to quickly install or update packages not included in the default distribution (conda).
+We strongly encourage HARK users to use the `conda` or `mamba` package managers,
+which includes all commonly used mathematical and scientific Python packages.
 
 For users unfamiliar with OOP, we strongly encourage you to review the background material on OOP provided by the good people at [QuantEcon](https://python.quantecon.org/intro.html) (for more on them, see below) at this link: [Object Oriented Programming](https://python-programming.quantecon.org/oop_intro.html). Unlike non-OOP languages, OOP bundles together data and functions into _objects_. These can be accessed via: **_object_name.data_** and **_object_name.method_name()_**, respectively. For organizational purposes, definitions of multiple objects are stored in _modules_, which are simply files with a **_.py_** extension. Modules can be accessed in Python via:
 
-```
+```python
 import module_name as import_name
 ```
 
-This imports the module and gives it a local name of **_import_name_**. We can access a function within this module by simply typing: **_import_name.function_name()_**. The following example will illustrate the usage of these commands. **_CRRAutility_** is the function object for calculating CRRA utility supplied by **_HARK.utilities_** module. **_CRRAutility_** is called _attributes_ of the module **_HARK.utilities_**. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:
+This imports the module and gives it a local name of **_import_name_**. We can access a function within this module by simply typing: **_import_name.function_name()_**. The following example will illustrate the usage of these commands. **_CRRAutility_** is the function object for calculating CRRA utility supplied by the **_HARK.rewards_** module. **_CRRAutility_** is called _attributes_ of the module **_HARK.rewards_**. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:
 
-```
-import HARK.utilities as Hutil
+```python
+from HARK.rewards import CRRAutility
 
-Hutil.CRRAutility(1,2)
+CRRAutility(1, 2)
 ```
 
 Python modules in HARK can generally be categorized into three types: tools, models, and applications. **Tool modules** contain functions and classes with general purpose tools that have no inherent ''economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. Tool modules generally reside in HARK's root directory and have names like **_HARK.simulation_** and **_HARK.interpolation_**; they do not necessarily do anything when run.