{
 "cells": [
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   "cell_type": "markdown",
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    "Quickstart tutorial\n",
    "======\n",
    "\n",
    "## Summary\n",
    "\n",
    "This notebook provides the basics of the microeconomic, agent-type class which is fundamental for HARK.\n",
    "\n",
    "____\n",
    "### Structure:\n",
    "\n",
    "- **Part 1**: basics of the perfect-foresight agent model\n",
    "- **Part 2**: more advanced methods for the perfect-foresight agent model\n",
    "\n",
    "### Learning outcomes:\n",
    "- **Part 1**:\n",
    "    - Learn how to declare basic agent-type objects\n",
    "    - Learn solution methods for the agent-type objects\n",
    "    - Plot value function and consumption function\n",
    "    - Learn how to simulate the agent-type objects\n",
    "    - Plot value function\n",
    "- **Part 2**:\n",
    "    - Learn how to build life-cycle models\n",
    "    - Learn more advanced simulation techniques\n",
    "    - Learn advanced plots\n",
    "____\n",
    "## Introduction to the consumer problem\n",
    "\n",
    "HARK AgentType classes were designed to solve the consumer problem.\n",
    "\n",
    "In the most basic formulation, the consumer problem is given as follows. The consumer lives T+1 periods (T $\\leq \\infty$) and during her lifetime receives the same income $Y$. In each period t (0$\\leq$ t$\\leq$ T) she can spend it on the consumption $C_t$ or invest in an asset $A_t$ with risk free interest rate R. She maximize the lifetime utility, by solving the following Bellman equation defined on the \"cash in hand\" state space $M_t = C_t +A_t$:\n",
    "\n",
    "For $t<T+1$\n",
    "\\begin{eqnarray*}\n",
    "V_t(M_t) &=& \\max_{C_t} U(C_t) + \\beta V_{t+1}(M_{t+1}), \\\\\n",
    "& s.t. & \\\\\n",
    "A_t &=& M_t - C_t, \\\\\n",
    "M_{t+1} &=& R (M_{t}-C_{t}) + Y, \\\\\n",
    "\\end{eqnarray*}\n",
    "for $t \\geq T+1$\n",
    "\\begin{eqnarray*}\n",
    "V_t(M_t) &=& 0\n",
    "\\end{eqnarray*}\n",
    "\n",
    "Where $\\beta <1 $ is a discount factor and $U(C)$ is a standard CRRA utility function:\n",
    "$$\n",
    "U(C)=\\frac{C^{1-\\rho}}{1-\\rho}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why start with perfect foresight model: HARK class structure\n",
    "<img style=\"float: right;\" src=\"HARK_diagram.png\" width=\"200\" height=\"300\">\n",
    "\n",
    "Obviously, HARK was designed to solve much more complicated consumer problems. However, it was written in the object programming paradigma (OPP). Thus, the class designed to solve such basic problem: $\\texttt{PerfForesightConsumerType}$ is then a foundation (parent/subclass in the OPP language) for the more advanced classes with the heterogeneous agents. In the diagram you can observe the inheritance between some of the HARK Agent-type classes:\n",
    "\n",
    "\n",
    "As you can observe, the $\\texttt{AgentType}$ superclass is the most general type of framework for the microeconomic models implemented in HARK. The child/subclass of $\\texttt{AgentType}$ is $\\texttt{PerfForesightConsumerType}$. There, you will need to define parameters, or **attributes** in OPP (such as $T$, $\\beta$, and so on). Next, there are classes which inherit those attributes, and further incorporate the heterogeneity of agents.\n",
    "\n",
    "In these classes, you will need to *additionally* define parameters of the heterogeneity you wish to model (idiosyncratic shocks to income and aggregate productivity shocks are two common examples). Moreover, **methods** (which define how the object is created, how the solution is presented, etc.) of the subclasses are the same or modified methods of the parent class.\n",
    "\n",
    "Therefore, to master the basics of HARK microclass you will first need to learn the $\\texttt{PerfForesightConsumerType}$ class. Consequently, this tutorial aims to teach you this. However, the majority of the presented methods are general for the HARK agent-type objects (though it may involve assigning more parameters).\n",
    "\n",
    "In the next notebooks, the class $\\texttt{IndShockConsumerType}$ with idiosyncratic income shocks is a more specific example of using the HARK microclass.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I: Basics of the perfect foresight model\n",
    "\n",
    "In this part, you learn basics of the perfect foresight model. We will solve the example of the consumer problem presented in the introduction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "First, you need to import HARK and a few additional libraries. Importantly, to use $\\texttt{PerfForesightConsumerType}$ you also need to import HARK.ConsumptionSaving.ConsIndShockModel sublibrary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# import sys\n",
    "# import os\n",
    "# sys.path.insert(0, os.path.abspath('../../../.'))\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from copy import deepcopy\n",
    "from HARK.ConsumptionSaving.ConsIndShockModel import *\n",
    "from HARK.utilities import plot_funcs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent-type object creation\n",
    "The most basic way of creating HARK object is to call its constructor (in OPP method which create the object, called by the class name).\n",
    "\n",
    "For $\\texttt{PerfForesightConsumerType}$ we need to set:\n",
    "- $T+1$: a consumer's lifespan, called $\\texttt{cycles}$ in the code, if $T= \\infty$, set $\\texttt{cycles}$=0.\n",
    "- $R$: risk free intrest rate, called $\\texttt{Rfree}$ in the code.\n",
    "- $\\beta$: a discount factor, $\\texttt{DiscFac}$ in the code.\n",
    "- $\\rho$: CRRA utility function parameter, $\\texttt{CRRA}$ in the code.\n",
    "\n",
    "Additionally, you need to define two parameters which do not occur in the presented example, but nevertheless can be useful:\n",
    "\n",
    "- Probability of surviving to the next period, called $\\texttt{LivPrb}$ in the code.\n",
    "- Income $Y$ growth factor, $\\texttt{PermGroFac}$ in the code.\n",
    "\n",
    "We call our first HARK object **Example_agent_1** and set the example values of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "Example_agent_1 = PerfForesightConsumerType(\n",
    "    cycles=0, CRRA=2.0, Rfree=1.03, DiscFac=0.99, LivPrb=1.0, PermGroFac=1.0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we did not assume growth in 𝑌 or survival  uncertainty , we set these values to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second method involves creating a **dictionary**: a list of parameters' names and values. Here we define the dictionary with the same values as in the first example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "First_dictionary = {\n",
    "    \"CRRA\": 2.0,\n",
    "    \"DiscFac\": 0.99,\n",
    "    \"Rfree\": 1.03,\n",
    "    \"cycles\": 0,\n",
    "    \"LivPrb\": [1.00],\n",
    "    \"PermGroFac\": [1.00],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create an object with a dictionary, use the constructor with the previously defined dictionary as an argument:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Example_agent_2 = PerfForesightConsumerType(**First_dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the first method is easier, we recommend defining a dictionary whenever you create a HARK object. First, it makes your code cleaner. Second, it enables you to create multiple objects with the same dictionary (the importantance of which will become apparent as we move on to creating macro classes).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The presented here methods work also for the more sophisticated HARK object (however you will need to specify more parameters)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating an agent-type object by copy\n",
    "\n",
    "Once creating an agent-type object, you can use its set of parameters to create another. To do so you need to use **deepcopy** method from copy package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Example_agent_3 = deepcopy(Example_agent_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: **Do not** only use an assignment operator (=) because it does not create new object. For example, a command such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Example_agent_4 = Example_agent_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "does not create a new object. It will only gives a new name to the object Example_agent_2 (this gives a single agent object both names Example_agent_2 and Example_agent_4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying   parameter values\n",
    "\n",
    "You can easily change the parameter value of the object by \".\" operator.\n",
    "\n",
    "For example, to change the discount factor value of the object created in the previous subsection:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Example_agent_3.DiscFac = 0.95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving an agent-type problems\n",
    "\n",
    "To solve agent type problems such as the on presented in the example, you need to find a **value function** from the Bellman equations and **the policy functions**. In our case, the only policy function is a consumption function: a function that for each age t and cash-in-hand $M_t$, specify the optimal consumption level: $c_t(M_t)$.\n",
    "\n",
    "To solve a model in HARK, you need to use $\\texttt{solve}$ method. For example, if we want to solve the model with parameters of the object Example_agent_2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Example_agent_2.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution elements\n",
    "\n",
    "`Solve` method finds the value function and consumption function for each period t of the consumer's life (in case of the infinite T, it specifies only one set of functions; because all the parameters are stable and lifespan is always infinite, the functions are the same for each $t$).\n",
    "\n",
    "Besides consumption and value functions, `solve` method create also a few attributes, the most important is minimal cash-in-hand value for which the problem has a solution.\n",
    "\n",
    "The exact name of these attributes in HARK are:\n",
    "\n",
    "- vFunc: value function\n",
    "- cFunc: consumption function\n",
    "- mNrmMin: Minimum value of $M_t$ such that cFunc and vFunc are defined.\n",
    "\n",
    "To get access to the value/consumption function you need to specify the period t and the object name, using two times operator. So to get access to the value function, consumption function and mNrmMin for the solved example:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-33.33330059492335"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example_agent_2.solution[0].vFunc\n",
    "Example_agent_2.solution[0].cFunc\n",
    "Example_agent_2.solution[0].mNrmMin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, only mNrmMin can be printed as a value. However, the value and consumption functions can be plotted.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the solution\n",
    "\n",
    "After $\\texttt{solve}$ method is used, the value and consumption functions can be plotted. HARK dedicated function for doing so is `plot_funcs`. As arguments, you need to give a function from the solution (possible a few functions) and the limits of interval for which you want to make a plot.\n",
    "\n",
    "For example, we can plot consumption and value functions on the interval from mNrmMin to -mNrmMin.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Consumption function\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value function\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dc/anaconda3/envs/journey_1/lib/python3.10/site-packages/HARK/utilities.py:139: RuntimeWarning: divide by zero encountered in reciprocal\n",
      "  return c ** (1.0 - gam) / (1.0 - gam)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_v = Example_agent_2.solution[0].mNrmMin\n",
    "max_v = -Example_agent_2.solution[0].mNrmMin\n",
    "print(\"Consumption function\")\n",
    "plot_funcs([Example_agent_2.solution[0].cFunc], min_v, max_v)\n",
    "print(\"Value function\")\n",
    "plot_funcs([Example_agent_2.solution[0].vFunc], min_v, max_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "\n",
    "Next step is to simulate the agent behavior. To do so, you first need to set a few parameters for the sake of the simulation:\n",
    "\n",
    "- $\\texttt{AgentCount}$: number of simulated agents\n",
    "- $\\texttt{T_cycle}$: logical parameter which governs the time flow during the simulation (if it is moving forward or backward)\n",
    "- $\\texttt{T_sim}$: number of simulation periods\n",
    "- $\\texttt{T_age}$: Age after which simulated agents die with certainty\n",
    "\n",
    "Moreover, HARK enables simulation of the model with the log-normal distributions of the initial assets and incomes. You need to set the parameters:\n",
    "\n",
    "- $\\texttt{aNrmInitMean}$: Mean of log initial assets\n",
    "- $\\texttt{aNrmInitStd}$: Standard deviation of log initial assets\n",
    "- $\\texttt{pLvlInitMean}$: Mean of log initial permanent income\n",
    "- $\\texttt{pLvlInitStd}$: Standard deviation of log initial permanent income\n",
    "\n",
    "Lastly, using HARK agent type class, you can also set the aggregate income increase (so that the rate of the income increase is common to all agents). You may then set a parameter:\n",
    "\n",
    "- $\\texttt{PermGroFacAgg}$: Aggregate permanent income growth factor\n",
    "\n",
    "In our example, we simulate 1 agent, as it is a representative agent model. Time flow is chronological and there is no initial heterogeneity. Thus, std of the initial assets and income distributions are set to 0. The initial assets and income are set to 1.0. There is no aggregate income increase, so we set the income growth factor to 1. We simulate 1000 periods and assume an infinitely lived agent.\n",
    "\n",
    "To declare the values of these parameters, we create a new dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "Simulation_dictionary = {\n",
    "    \"AgentCount\": 1,\n",
    "    \"aNrmInitMean\": 0.0,\n",
    "    \"aNrmInitStd\": 0.0,\n",
    "    \"pLvlInitMean\": 0.0,\n",
    "    \"pLvlInitStd\": 0.0,\n",
    "    \"PermGroFacAgg\": 1.0,\n",
    "    \"T_cycle\": 1,\n",
    "    \"T_sim\": 1000,\n",
    "    \"T_age\": None,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you need to update the object. To do so we use **setattr** function, which adds the parameters' values to the defined agent object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in Simulation_dictionary.items():\n",
    "    setattr(Example_agent_2, key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can start our simulation. First, you need to decide which variables you want to track, we choose an assets level and consumption level, in the code they are called: $\\texttt{aNrmNow}$ and $\\texttt{cNrmNow}$. Next, you need to initialize the simulation by $\\texttt{initialize_sim}$ method. Lastly, run the simulation with the $\\texttt{simulate}$ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [9.43675155e+03],\n",
       "        [9.52923258e+03],\n",
       "        [9.62261994e+03],\n",
       "        [9.71692251e+03],\n",
       "        [9.81214925e+03],\n",
       "        [9.90830921e+03],\n",
       "        [1.00054116e+04],\n",
       "        [1.01034655e+04],\n",
       "        [1.02024804e+04],\n",
       "        [1.03024657e+04],\n",
       "        [1.04034308e+04],\n",
       "        [1.05053854e+04],\n",
       "        [1.06083391e+04],\n",
       "        [1.07123018e+04],\n",
       "        [1.08172834e+04],\n",
       "        [1.09232937e+04],\n",
       "        [1.10303430e+04],\n",
       "        [1.11384414e+04],\n",
       "        [1.12475992e+04],\n",
       "        [1.13578267e+04],\n",
       "        [1.14691344e+04],\n",
       "        [1.15815330e+04],\n",
       "        [1.16950331e+04],\n",
       "        [1.18096455e+04]])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example_agent_2.track_vars = [\n",
    "    \"aNrm\",\n",
    "    \"cNrm\",\n",
    "]  # should these be 'aLvl, cLvl' since the normalized versions of these variables isn't introduced until the next section?\n",
    "Example_agent_2.initialize_sim()\n",
    "Example_agent_2.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Plotting the simulation\n",
    "\n",
    "Plotting the simulation is a little bit more complicated than plotting the solution, as you cannot use a dedicated function. Instead, we will use the **matplot** library in the following way.\n",
    "\n",
    "To see the consumption and asset history, we can use objects created by the simulation which contain the history of every agent in each of the simulation periods. These objects have the same naming as the tracked variables with a **\\_hist** ending. Thus, from the previous example, the history of assets and consumption are called $\\texttt{aNrmNow_hist}$ and $\\texttt{cNrmNow_hist}$.\n",
    "\n",
    "Let's make a plot of the assets level and consumption level during the simulated periods. First, define the vectors of mean assets and consumption. Here, there is only one consumer, so we do not need to use a mean function (although it is done so here). However, if you want to plot the mean asset/consumption level for many agents, you will need to use this method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "periods = np.linspace(0, 1000, 1000)\n",
    "asset_level = np.mean(Example_agent_2.history[\"aNrm\"][0:1000], axis=1)\n",
    "cons_level = np.mean(Example_agent_2.history[\"cNrm\"][0:1000], axis=1)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(periods, asset_level, label=\"Assets level\")\n",
    "plt.plot(periods, cons_level, label=\"Consumption level\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot the mean asset and consumption increase:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "increase_assets = asset_level[1:1000] / asset_level[0:999]\n",
    "increase_cons = cons_level[1:1000] / cons_level[0:999]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(periods[1:1000], increase_assets, label=\"Assets increase\")\n",
    "plt.plot(periods[1:1000], increase_cons, label=\"Consumption increase\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Congratulations! You've just learned the basics of the agent-type class in HARK. It is time for some exercises:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1: create the agent-type object\n",
    "\n",
    "Define a dictionary and then use it to create the agent-type object with the parameters:\n",
    "\n",
    "- $\\beta = 0.96$\n",
    "- $\\rho = 2.0$\n",
    "- $T = \\infty$\n",
    "- Risk free interest rate $R= 1.05$\n",
    "Assume no survival uncertainty  and income growth factor 1.01\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your solution here\n",
    "\n",
    "# fill the dictionary and then use it to create the object\n",
    "\n",
    "# First_dictionary = {\n",
    "#    'CRRA' : ,\n",
    "#    'DiscFac' : ,\n",
    "#    'Rfree' : ,\n",
    "#    'cycles' : ,\n",
    "#    'LivPrb' : [],\n",
    "#    'PermGroFac' : [],\n",
    "# }\n",
    "# Exercise_agent ="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden",
    "solution_first": true
   },
   "source": [
    "**Solution**: click on the box on the left to expand the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "solution": "hidden"
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "First_dictionary = {\n",
    "    \"CRRA\": 2.0,\n",
    "    \"DiscFac\": 0.96,\n",
    "    \"Rfree\": 1.05,\n",
    "    \"cycles\": 0,\n",
    "    \"LivPrb\": [1.0],\n",
    "    \"PermGroFac\": [1.0],\n",
    "}\n",
    "Exercise_agent = PerfForesightConsumerType(**First_dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: Solve the model and plot the value function\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your solution here, use methods from \"solving the model\" subsection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden",
    "solution_first": true
   },
   "source": [
    "**Solution**: click on the box on the left to expand the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false,
    "solution": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value function\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "Exercise_agent.solve()\n",
    "\n",
    "min_v = Exercise_agent.solution[0].mNrmMin\n",
    "max_v = -Exercise_agent.solution[0].mNrmMin\n",
    "print(\"Value function\")\n",
    "plot_funcs([Exercise_agent.solution[0].vFunc], min_v, max_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: Prepare the simulation\n",
    "\n",
    "Next prepare the simulation. Assume that **there exsists the initial assets and income heterogenity**. Assume, the initial income and assets distributions are log-normal, have mean 1 and std 1. Simulate 1000 agents for 1000 periods.\n",
    "\n",
    "Add the new parameters to the object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Write your solution here.\n",
    "\n",
    "# Fill the dictionary\n",
    "# Simulation_dictionary = {  'AgentCount': ,\n",
    "#                           'aNrmInitMean' : ,\n",
    "#                           'aNrmInitStd' : ,\n",
    "#                           'pLvlInitMean' : ,\n",
    "#                           'pLvlInitStd' : ,\n",
    "#                           'PermGroFacAgg' : 1.0, #assume no income aggregate growth\n",
    "#                           'T_cycle' : 1, #assume forward time flow\n",
    "#                           'T_sim' : ,\n",
    "#                           'T_age' : None #assume immortal agents\n",
    "#                          }\n",
    "\n",
    "# for key,value in Simulation_dictionary.items():\n",
    "#    setattr(Exercise_agent,key,value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden",
    "solution_first": true
   },
   "source": [
    "**Solution**: click on the box on the left to expand the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "solution": "hidden"
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "Simulation_dictionary = {\n",
    "    \"AgentCount\": 1000,\n",
    "    \"aNrmInitMean\": 1.0,\n",
    "    \"aNrmInitStd\": 1.0,\n",
    "    \"pLvlInitMean\": 1.0,\n",
    "    \"pLvlInitStd\": 1.0,\n",
    "    \"PermGroFacAgg\": 1.0,\n",
    "    \"T_cycle\": 1,\n",
    "    \"T_sim\": 1000,\n",
    "    \"T_age\": None,\n",
    "}\n",
    "\n",
    "for key, value in Simulation_dictionary.items():\n",
    "    setattr(Exercise_agent, key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4: Simulate\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Write your solution here. Use the commands from \"simulation\" subsection, track consumption values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden",
    "solution_first": true
   },
   "source": [
    "**Solution**: click on the box on the left to expand the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "solution": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aNrm': array([[1.98852329e+01, 2.43139510e+00, 3.80130444e+00, ...,\n",
       "         2.17778682e+00, 9.03301714e+00, 1.32631458e+00],\n",
       "        [2.00444516e+01, 2.52093985e+00, 3.89631773e+00, ...,\n",
       "         2.26631919e+00, 9.14891491e+00, 1.41144796e+00],\n",
       "        [2.02043060e+01, 2.61084205e+00, 3.99171031e+00, ...,\n",
       "         2.35520497e+00, 9.26527533e+00, 1.49692118e+00],\n",
       "        ...,\n",
       "        [2.09754758e+03, 1.17091038e+03, 1.24363989e+03, ...,\n",
       "         1.15744613e+03, 1.52139540e+03, 1.11224084e+03],\n",
       "        [2.10600062e+03, 1.17566438e+03, 1.24868421e+03, ...,\n",
       "         1.16214638e+03, 1.52754850e+03, 1.11676063e+03],\n",
       "        [2.11448741e+03, 1.18043736e+03, 1.25374868e+03, ...,\n",
       "         1.16686539e+03, 1.53372616e+03, 1.12129847e+03]]),\n",
       " 'cNrm': array([[ 1.82774668,  1.0279216 ,  1.09069793, ...,  1.01629996,\n",
       "          1.33044205,  0.9772811 ],\n",
       "        [ 1.83504291,  1.03202501,  1.09505193, ...,  1.02035697,\n",
       "          1.33575309,  0.98118235],\n",
       "        [ 1.84236826,  1.03614479,  1.09942331, ...,  1.02443018,\n",
       "          1.34108532,  0.98509918],\n",
       "        ...,\n",
       "        [97.03697611, 54.57366942, 57.90651123, ..., 53.9566678 ,\n",
       "         70.6347045 , 51.88512758],\n",
       "        [97.42433815, 54.79152215, 58.13766833, ..., 54.17205751,\n",
       "         70.91667128, 52.09224791],\n",
       "        [97.8132465 , 55.01024452, 58.36974818, ..., 54.38830704,\n",
       "         71.19976365, 52.30019505]])}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "Exercise_agent.track_vars = [\"aNrm\", \"cNrm\"]\n",
    "Exercise_agent.initialize_sim()\n",
    "Exercise_agent.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden"
   },
   "source": [
    "### Exercise 5: Plot the simulations\n",
    "\n",
    "Plot mean consumption level and consumption increase:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Write your solution here.\n",
    "\n",
    "# Firstly prepare the vectors which you would like to plot:\n",
    "# periods= np.linspace(0,1000,1000)\n",
    "# cons_level = np.mean(Exercise_agent.cNrmNow_hist[0:1000], axis = 1)\n",
    "# increase_cons = cons_level[1:1000]/cons_level[0:999]\n",
    "\n",
    "# next plot your solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden",
    "solution_first": true
   },
   "source": [
    "**Solution**: click on the box on the left to expand the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "lines_to_next_cell": 2,
    "scrolled": true,
    "solution": "hidden"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "periods = np.linspace(0, 1000, 1000)\n",
    "cons_level = np.mean(Exercise_agent.history[\"cNrm\"][0:1000], axis=1)\n",
    "increase_cons = cons_level[1:1000] / cons_level[0:999]\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(periods, cons_level, label=\"Consumption level\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(periods[1:1000], increase_cons, label=\"Consumption increase\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART II: advanced methods for the perfect foresight agent\n",
    "\n",
    "In this part we focus on more complicated cases of the deterministic agent model.\n",
    "\n",
    "In the previous example survival probability (in the code **LivPrb**) and income increase factor (in the code **PermGroFac**) were stable and set to 1. However, if you want to build deterministic life-cycle model you need to add a age-dependent survival probability or income growth.\n",
    "\n",
    "Consumer problem in this setting is:\n",
    "\n",
    "\\begin{eqnarray*}\n",
    "V_t(M_t,Y_t) &=& \\max_{C_t}~U(C_t) + \\beta \\pi_t V_{t+1}(M_{t+1},Y_{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",
    "Y_{t+1} &=& \\Gamma_{t+1} Y_t, \\\\\n",
    "\\end{eqnarray*}\n",
    "\n",
    "Where $Y_t$ is an age-dependent income, $\\pi_t$ is a survival  probability and $\\Gamma_{t+1}$ is an income growth rate. Also $\\pi_{T+1} =0$\n",
    "\n",
    "While it does not reduce the computational complexity of the problem (as permanent income is deterministic, given its initial condition $Y_0$), HARK represents this problem with normalized variables (represented in lower case), dividing all real variables by permanent income $Y_t$ and utility levels by $Y_t^{1-\\rho}$. The Bellman form of the model thus reduces to:\n",
    "\n",
    "\\begin{eqnarray*}\n",
    "v_t(m_t) &=& \\max_{c_t}~U(c_t) ~+ \\beta_{t+1}\\pi_{t+1} \\Gamma_{t+1}^{1-\\rho} v_{t+1}(m_{t+1}), \\\\\n",
    "& s.t. & \\\\\n",
    "a_t &=& m_t - c_t, \\\\\n",
    "m_{t+1} &=&  R / \\Gamma_{t+1} a_t + 1.\n",
    "\\end{eqnarray*}\n",
    "\n",
    "To solve this problem we need to study the **cycles** parameter more carefully. There is a notebook dedicated to solving and simulating life-cycle models which can be found here: [Cycles_tutorial](https://github.com/econ-ark/HARK/blob/master/examples/LifecycleModel/Cycles_tutorial.ipynb).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methods of plotting the solution\n",
    "\n",
    "$\\texttt{plot_funcs()}$ enables to plot many functions at the same graph. You need to declare them as vector of functions.\n",
    "\n",
    "To see this, just follow an example. We plot the consumption functions for each age $t$ of the consumer.\n",
    "\n",
    "To get better access to the consumption functions, you can use $\\texttt{unpack('cFunc')}$ method, which will create the attribute $\\texttt{cFunc}$ of the object (so you do not have to use it is as a solution attribute).\n",
    "\n",
    "We illustrate this with the solution for \"Exercise_agent_3\". Recall that this agent was given a different time preference value ($\\beta = .95$). Here, we also changed the length of the life-cycle of this agent to $10$ periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Example_agent_3.cycles = 1\n",
    "Example_agent_3.LivPrb = [0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90]\n",
    "Example_agent_3.PermGroFac = [1.01, 1.01, 1.01, 1.02, 1.00, 0.99, 0.5, 1.0, 1.0, 1.0]\n",
    "\n",
    "\n",
    "Example_agent_3.solve()\n",
    "Example_agent_3.unpack(\"cFunc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we set the minimal value of the gird such that at least one of the consumption functions is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Consumption functions\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_v = min(Example_agent_3.solution[t].mNrmMin for t in range(11))\n",
    "max_v = -min_v\n",
    "print(\"Consumption functions\")\n",
    "plot_funcs(Example_agent_3.cFunc[:], min_v, max_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to compare a few functions (eg. value functions), you can also construct the vector by yourself, for example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value functions\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Value functions\")\n",
    "plot_funcs(\n",
    "    [\n",
    "        Example_agent_3.solution[0].vFunc,\n",
    "        Example_agent_3.solution[5].vFunc,\n",
    "        Example_agent_3.solution[9].vFunc,\n",
    "    ],\n",
    "    min_v,\n",
    "    max_v,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced simulation techniques\n",
    "Here we present more advanced simulation techniques with the mortal agents and income dynamics.\n",
    "\n",
    "We will also present how to plot the distribution of assets among the agents.\n",
    "\n",
    "First, as in the part 1 of the tutorial, you need to define the simulation dictionary. However, you need to be careful with T_age parameter: because a maximal lifespan is 11 (T=10), T_age is set to 10, to ensure that all agents die after this age.\n",
    "\n",
    "For the rest of the parameters, we set the number of consumers alive in each period to 1000. Initial asset level is near 0 (log of -10). The initial income level is given by the log-normal distribution with mean 0 and std 1. We set the rest of parameters as in the previous example.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Simulation_dictionary = {\n",
    "    \"AgentCount\": 1000,\n",
    "    \"aNrmInitMean\": -10.0,\n",
    "    \"aNrmInitStd\": 0.0,\n",
    "    \"pLvlInitMean\": 0.0,\n",
    "    \"pLvlInitStd\": 1.0,\n",
    "    \"PermGroFacAgg\": 1.0,\n",
    "    \"T_cycle\": 1,\n",
    "    \"T_sim\": 200,\n",
    "    \"T_age\": 10,\n",
    "}\n",
    "\n",
    "for key, value in Simulation_dictionary.items():\n",
    "    setattr(Example_agent_3, key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we simulate the economy and plot the mean asset level. However, be careful! $\\texttt{aNrmNow}$ gives the asset levels normalized by the income. To get the original asset level we need to use $\\texttt{aLvlNow}$ (unfortunately, cLvlNow is not implemented)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Example_agent_3.track_vars = [\"aNrm\", \"cNrm\", \"aLvl\"]\n",
    "Example_agent_3.initialize_sim()\n",
    "Example_agent_3.simulate()\n",
    "\n",
    "\n",
    "periods = np.linspace(0, 200, 200)\n",
    "assets_level = np.mean(Example_agent_3.history[\"aLvl\"][0:200], axis=1)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(periods, assets_level, label=\"assets level\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, for the first 10 periods the asset level much more fluctuate. It is because in the first periods the agents which were born in period 0 strictly dominate the population (as only a small fraction die in the first periods of life).\n",
    "\n",
    "You can simply cut the first observations, to get asset levels for more balanced population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "after_burnout = np.mean(Example_agent_3.history[\"aLvl\"][10:200], axis=1)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(periods[10:200], after_burnout, label=\"assets level\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the distribution of assets\n",
    "\n",
    "When you plot similar simulations, often the main interest is not to get exact assets/consumption levels during the simulation but rather a general distribution of assets.\n",
    "\n",
    "In our case, we plot the asset distribution.\n",
    "\n",
    "First, get one vector of the asset levels:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_wealth = np.reshape(Example_agent_3.history[\"aLvl\"], -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot simple histogram of assets level using a standard **hist** function from matplotlib library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wealth distribution histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Wealth distribution histogram\")\n",
    "n, bins, patches = plt.hist(sim_wealth, 100, density=True, range=[0.0, 10.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With HARK, you can also easily plot the Lorenz curve. To do so import some HARK utilities which help us plot Lorenz curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "from HARK.utilities import get_lorenz_shares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, use $\\texttt{get_lorenz_shares}$ to plot the Lornez curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pctiles = np.linspace(0.001, 0.999, 15)\n",
    "# SCF_Lorenz_points = get_lorenz_shares(SCF_wealth,weights=SCF_weights,percentiles=pctiles)\n",
    "sim_Lorenz_points = get_lorenz_shares(sim_wealth, percentiles=pctiles)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.title(\"Lorenz curve\")\n",
    "plt.plot(pctiles, sim_Lorenz_points, \"-b\", label=\"Lorenz curve\")\n",
    "plt.plot(pctiles, pctiles, \"g-.\", label=\"45 Degree\")\n",
    "plt.xlabel(\"Percentile of net worth\")\n",
    "plt.ylabel(\"Cumulative share of wealth\")\n",
    "plt.legend(loc=2)\n",
    "plt.ylim([0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Let's make a model with a little more realistic assumptions.\n",
    "\n",
    "In files 'life_table.csv' you find the death-probablities for Americans in age 25-105 in 2017 from Human Mortality Database. The age-dependent income for American males in file 'productivity_profile.csv' are deduced from Heathcote et al. (2010). Try to build a model with this data, assuming additionaly CRRA parameter to be 2.0, discount rate set to 0.99 and interest rate set to 1.05. Moreover, assume that initial income is given by log-normal distribution with mean 0 and std 0.05. Assume that initial asset is near 0 for all agents.\n",
    "\n",
    "Do the following tasks:\n",
    "- Build a dictionary and create an object with a given data and parameters\n",
    "- Solve model and plot a consumption functions for each age\n",
    "- Simulate 1000 agents for 2000 periods\n",
    "- Plot a histogram of the assets distribution and the Lorenz curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "lines_to_next_cell": 2,
    "solution": "hidden"
   },
   "outputs": [],
   "source": [
    "# Write your solution here\n",
    "\n",
    "# Firstly import data, you can use this part of code (however, there are other ways to do this)\n",
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.insert(0, os.path.abspath(\"..\"))\n",
    "\n",
    "\n",
    "prob_dead = np.genfromtxt(\"life_table.csv\", delimiter=\",\", skip_header=1)\n",
    "prob_surv = 1 - prob_dead\n",
    "\n",
    "# The HARK argument need to be a list, thus convert it from numpy array\n",
    "prob_surv_list = np.ndarray.tolist(prob_surv[:79])\n",
    "\n",
    "income_profile = np.genfromtxt(\"productivity_profile.csv\", delimiter=\",\", skip_header=1)\n",
    "income_profile_list = np.ndarray.tolist(income_profile[:79])\n",
    "\n",
    "# Continue your solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden",
    "solution_first": true
   },
   "source": [
    "**Solution**: click on the box on the left to expand the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "solution": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Consumption functions\n"
     ]
    },
    {
     "data": {
      "image/png": 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q+fjhD38IwIYNG6a8/pOf/ITf//3fB+C73/0uWq2W3/iN38Dv97N9+3befvvtWQlWCCGEeFj9uL2PQwMuTFoNPy7LI1avi3ZID637qvPxIEidDyGEEI+aK64RPnapnqCq8o3CLH4/MynaIc25OavzIYQQQjzpXKEwL1Q1E1RVdiU7+GRGYrRDeuhJ8iGEEELcI1VV+UJtGy2+AFlmA98pykajkfLpdyLJhxBCCHGPftY1yK96h9Fr4EeleTgM91TB4okjyYcQQghxD2o8o7xW3w7Al+als9wRG+WIHh2SfAghhBB3aSSs8EJVCz5FZWOCjU/npEQ7pEeKJB9CCCHEXXqtvp26ER+pRj3fL8lBK+s87ookH0IIIcRd+EXPEO90DaIB3irNJdloiHZIjxxJPoQQQogZahrx8/naSAPUV3JTWRtvi3JEjyZJPoQQQogZ8CsKL1Q14w0rPOOI5XN50zdMFXcmyYcQQggxA19v7OSqZ5QEg463S3PRa2Wdx72S5EMIIYS4g4p+J3/X3g/Am8U5ZJiNUY7o0SbJhxBCCPEROnwBXqlpBeCFrGS2JTmiHNGjT5IPIYQQ4jZCisqL1S0MhcIstln40/z0aIf0WJDkQwghhLiNbzV3c97pxarT8qOyPIxaeWzOBvktCiGEENM4Mejmey09AHyrKJs8iynKET0+JPkQQgghbtIXCLK7pgUV+N30RD6eGh/tkB4rknwIIYQQkyiqysvVrfQFQhTFmvlaQWa0Q3rsSPIhhBBCTPJWay/HhtxYtBp+XJZHjE4elbNNfqNCCCHEmAtOL99o6gLgLwuyKIo1Rzmix5MkH0IIIQQwHAzxQlUzYRV+PSWO305PiHZIjy1JPoQQQjzxVFXl1ettdPiD5FmMfLMoG41Gyqc/KJJ8CCGEeOL9pKOfd/udGDQaflSWh02vi3ZIjzVJPoQQQjzRKt0jfLWhE4Cv5Kez2BYT5Ygef5J8CCGEeGJ5Q2FeqGohoKpsS7TzqazkaIf0RJDkQwghxBPri3XtNI76yTAZeLMkR9Z5zBFJPoQQQjyR/rlrkJ/3DKEF3i7NJcGgj3ZITwxJPoQQQjxx6r0+vlTXDsAX5qXxTJw1yhE9WST5EEII8UQZDSu8UNXMqKKwNs7KZ3JTox3SE0eSDyGEEE+UrzZ0UO31kWjQ81ZpLjpZ5zHnJPkQQgjxxNjTO8xPOwcA+EFJDqkmQ5QjejJJ8iGEEOKJ0DLq59XaVgBeyklhY6I9yhE9uST5EEII8dgLKiovVrfgCimssMfwxXnp0Q7piSbJhxBCiMfeGze6uOQawaHX8cOyPAxaWecRTZJ8CCGEeKwdHnDxdlsvAN8pzibbbIxyREKSDyGEEI+tbn+Ql2taAPiDzCR2JcdFNyABSPIhhBDiMRVWVXZXtzAYDFNutfDn+RnRDkmMkeRDCCHEY+nN5h5ODXuI0Wn5UVkuZp088h4W8pcQQgjx2Dk95OHbzd0A/HVhFvkx5ihHJCaT5EMIIcRjZSAQ4tPVLSjAb6XF85tpCdEOSdxEkg8hhBCPDVVV+aPrrXQHgiyIMfFGQVa0QxLTkORDCCHEY+PH7X0cGnBh0mr4cVkesXpdtEMS05DkQwghxGPhimuErzd2AfD6gkxKrZYoRyRuR5IPIYQQjzxXKMwLVc0EVZVdyQ4+mZEY7ZDER5DkQwghxCNNVVW+UNtGiy9AltnAd4qy0WikfPrDTJIPIYQQj7SfdQ3yq95h9Br4UWkeDoM+2iGJO5DkQwghxCOrxjPKa/XtAHxpXjrLHbFRjkjMhCQfQgghHkkjYYUXqlrwKSobE2x8Oicl2iGJGZLkQwghxCPptfp26kZ8pBr1fL8kB62s83hkSPIhhBDikfOLniHe6RpEA7xVmkuy0RDtkJ54g4NnZnysJB9CCCEeKU0jfj5f2wbAK7mprI23RTmiJ5uqKjQ1/YCr116c8TmyJFgIIcQjw68ovFDVjDes8Iwjls/lpUU7pCdaMDhMVfXnGBg4BqgzPk9GPoQQQjwyvt7YyVXPKAkGHW+X5qLXyjqPaHG5Kzn//q8xMHAMrdZEUeHrMz5XRj6EEEI8Eir6nfxdez8AbxbnkGE2RjmiJ1dn579QW/fnKEoAizmHhQvfQlVn3sTvrkc+Tpw4wcc+9jEyMjLQaDT88pe/nPL+7//+76PRaKZ87dix425vI4QQQozr8AV4paYVgBeyktmW5IhyRE+mcNhHdc2XqLn+ZRQlQFLiJp566pfYbKV3dZ27Hvnwer0sXryY//Jf/guf+MQnpj1mx44d/OQnPxn/3mQy3e1thBBCCABCisqL1S0MhcIstln40/z0aIf0RBodbeXatZdwe6oALfnzP0tu7n9Do7n7FRx3nXzs3LmTnTt3fuQxJpOJtDRZBCSEEOL+fau5m/NOL1adlh+V5WHUynLFudbff4Sq6s8RCrkwGBIoL3uThIQ193y9B7Lm49ixY6SkpBAfH8+mTZv4+te/TmLi9B0G/X4/fr9//HuXy/UgQhJCCPEIOjHo5nstPQB8qyibPIuMpM8lVQ1z48abNLe8DYDdvpSF5d/HbM64r+vOevq4Y8cO/vEf/5HDhw/z13/91xw/fpydO3cSDoenPf6NN97A4XCMf2VnZ892SEIIIR5BfYEgu2taUIHfTU/k46nx0Q7piRIIDHDlyh+MJx5ZWb/H8mXv3HfiAaBRVXXmG3NvPlmj4Re/+AUf//jHb3vMjRs3yM/P59ChQ2zevPmW96cb+cjOzsbpdGK32+81NCGEEI8wRVX5nQ9ucGzITVGsmX3LC4nRyXTLXHE6r3Ctcjd+fzdarYWS4r8iLe3/+shzXC4XDodjRs/vB77Vdv78+SQlJdHQ0DBt8mEymWRBqhBCiCneau3l2JAbi1bDj8vyJPGYI6qq0tHxM+rqv46qBomJmcfC8rewWotm9T4PPPlob29nYGCA9HRZnSyEEOLOLji9fKOpC4C/LMiiKNYc5YieDOHwCNevv0Z3z68ASE7eQWnJN9DrZ798/V0nHx6Ph4aGhvHvm5qauHLlCgkJCSQkJPD666/zG7/xG6SlpdHY2Mgf//Efs2DBArZv3z6rgQshhHj8DAdDvFDVTFiFX0+J47fTE6Id0hNhZKSJq9c+jddbh0ajIz//j8nJ/kM0D6hT8F0nHxcuXGDjxo3j37/66qsAfPKTn+SHP/whV69e5ac//SnDw8NkZGSwbds2/uIv/kKmVoQQQnwkVVV59XobHf4geRYj3yzKfmAPPzGht7eC6po/Jhz2YDQmU172feLjn36g97zr5GPDhg181BrVioqK+wpICCHEk+knHf282+/EoNHw30vzsOl10Q7psaYoIRpv/A2trX8PQJzjKcrLv4/JlPLA7y29XYQQQkRdpXuErzZ0AvCV/HSW2GOiHNHjze/vo7LqMwwPnwcgJ/sPyc//AlqtYU7uL8mHEEKIqPKGwrxQ1UJAVdmWaOdTWcnRDumxNjT8PpWVLxMI9KHTWSkp+QapKR9duXy2SfIhhBAiqr5Y107jqJ8Mk4E3S3JknccDoqoqbW0/oaHxG6hqmNjYAhaWv01s7Pw5j0WSDyGEEFHzz12D/LxnCC3wdmkuCQZ5LD0IoZCHmpov0du3D4DU1I9RXPSX6PWxUYlH/spCCCGiot7r40t17QB8YV4az8RZoxzR48njrefatU8zMnIDjcZAQcGfkJX5e1EdYZLkQwghxJwbDSu8UNXMqKKwNs7KZ3JTox3SY6m7+9+5XvunhMMjmExpLCz/WxyOZdEOS5IPIYQQc++rDR1Ue30kGvS8VZqLTtZ5zCpFCVDf8Abt7f8IQHz8asrL3sRonL7D/FyT5EMIIcSc2tM7zE87BwD4QUkOqaa52d75pPD5uqisfBmn6zIAebkvMn/+Z9FoHp66KZJ8CCGEmDMto35erW0F4KWcFDYmSvfy2TQ4eJrKqj8iGBxEr7dRWvptkpNubeoabZJ8CCGEmBNBReXF6hZcIYUV9hi+OE8ajs4WVVVoafkxjTe+DShYrSUsLH+LmJjcaIc2LUk+hBBCzIk3bnRxyTWCQ6/jh2V5GLSyzmM2BIMuqmu+QH//IQDS036DoqKvodM9vN2AJfkQQgjxwB0ecPF2Wy8A3ynOJttsjHJEjwe3u5prlbsZHW1FozFSVPjnZGT8/x76Qm2SfAghhHiguv1BXq5pAeAPMpPYlRwX3YAeE11d/4frtV9BUfyYzZksLP8BdvuiaIc1I5J8CCGEeGDCqsru6hYGg2HKrRb+PD8j2iE98sJhP3X1X6Oz858ASExcT1npdzAY4qIb2F2Q5EMIIcQD82ZzD6eGPcTotPyoLBezThvtkB5po6PtXKt8Cbf7GqBh3rw/Yl7ebjSaR+v3KsmHEEKIB+L0kIdvN3cD8NeFWeTHPLwLIB8FAwPHqax6lVBoGL0+jvKy75KYuC7aYd0TST6EEELMuoFAiE9Xt6AAv5UWz2+mJUQ7pEeWqio0Nf+ApqbvAyo220IWlr+FxZIZ7dDumSQfQgghZpWqqvzR9Va6A0EWxJh4oyAr2iE9soLBIaqqXmVg8AQAmZm/Q2HBa2i1pihHdn8k+RBCCDGrftzex6EBFyathh+X5RGrf3jKej9KXK6rXLu2G5+/E63WRHHR10lP/0S0w5oVknwIIYSYNVdcI3y9sQuA1xdkUmq1RDmiR4+qqnR2/hO1dV9DVQNYLDksLH8bm60k2qHNGkk+hBBCzApXKMwLVc0EVZVdyQ4+mfFwdFB9lITDo9TW/hld3f8GQFLSFkpL/gaD4fHqgSPJhxBCiPumqipfqG2jxRcgy2zgO0XZD32VzYfNyEgz1yp34/FcB7Tk53+e3JxPPXLbaGdCkg8hhBD37Wddg/yqdxi9Bn5UmofDII+Xu9HXd5Dqmi8QCrkxGBIpL/8eCfGroh3WAyP/OoQQQtyXGs8or9W3A/Cleeksd8RGOaJHh6KEuNH0XVpa/jsADscyysv/FrMpLcqRPViSfAghhLhnI2GFF6pa8CkqGxNsfDonJdohPTICgX4qq15haOgMANlZv8+CBV9CqzVEObIHT5IPIYQQ9+y1+nbqRnykGvV8vyQHrazzmJFh50UqKz+D39+NThdDcfFfkZb6sWiHNWck+RBCCHFPftEzxDtdg2iAt0pzSTY+/p/Y75eqqrS3/yP1DX+FqoaIicln4cK3sMYWRDu0OSXJhxBCiLvWNOLn87VtALySm8raeFuUI3r4hUJerl//E3p69wCQkvIcJcVvoNdboxzZ3JPkQwghxF3xKwovVDXjDSs844jlc3mP9+LI2eD1NnKtcjdebz0ajZ4FC75EdtbvP7HbkSX5EEIIcVe+3tjJVc8oCQYdb5fmotc+mQ/QmerpfZeami8RDnsxGlNYWP63xMWtiHZYsyoUCnHmzJkZHy/JhxBCiBmr6Hfyd+39ALxZnEOG2RjliB5eihKkofGbtLX9DwDi4lZSXvY9TKbkKEc2e1RVpba2loqKCrq7u2d8niQfQgghZqTDF+CVmlYAXshKZluSI8oRPbz8/h6uVX4Gp/MCALk5/5X58z+HVvv4PHZ7e3upqKigsbERgNjYmdd30aiqqj6owO6Fy+XC4XDgdDqx2x+vWvZCCPGoCikqn7jSwHmnl8U2C/+xrACj9vEr+z0bhobOUVn1GQKBfnQ6K6Wl3yQleXu0w5o1o6OjHDt2jPPnz6OqKjqdjlWrVrF48WJSUlJm9Px+fFIwIYQQD8y3mrs57/Ri1Wn5UVmeJB7TUFWV1ra/p7Hxb1DVMLGxhSxa+DYxMfOiHdqsUBSFS5cuceTIEUZGRgAoKipi+/btJCQk4HK5ZnwtST6EEEJ8pBODbr7X0gPAt4qyybOYohzRwycUclNd80X6+ioASEv9OMXFf4FOFxPlyGZHc3Mz+/fvH1/XkZSUxM6dO8nPz7+n60nyIYQQ4rb6AkF217SgAr+bnsjHU+OjHdJDx+Op5eq1TzM62oxGY6Cw4CtkZv7OY7GNdnh4mIMHD1JVVQWA2Wxmw4YNPPXUU+h0unu+riQfQgghpqWoKi9Xt9IXCFEUa+ZrBZnRDumh0939K2qu/ymKMorJlM7ChW/hsC+Odlj3LRAIcPr0ad577z1CoRAAy5cvZ9OmTXe1sPR2JPkQQggxrbdaezk25Mai1fDjsjxidLLO40OK4qeu/q/o6PhfACTEr6Ws7LsYjQlRjuz+qKpKVVUVBw8exOl0ApCbm8uOHTtIT0+ftftI8iGEEOIWF5xevtHUBcBfFmRRFGuOckQPD5+vk2uVL+NyXQEgL+8l5s/7DBrNvU9DPAy6u7vZt28fLS0tANjtdrZt20ZZWdmsTyFJ8iGEEGKK4WCIF6qaCavw6ylx/Hb6o/1pfjYNDL5HVdUrBIND6PUOykq/TVLSxmiHdV+8Xi9Hjhzh0qVLqKqKXq9n7dq1rF69GqPxwRSRk+RDCCHEOFVVefV6Gx3+IHkWI98syn4sFk7eL1VVaG75ITdufBdQsVnLWLjwLSyW7GiHds/C4TDvv/8+x44dw+fzAVBWVsbWrVuJi4t7oPeW5EMIIcS4n3T0826/E4NGw38vzcOmf7SnEmZDMOikqvpzDAwcBSAj/bcoLPwqOt2ju+W4sbGR/fv309fXB0Bqaio7d+4kLy9vTu4vyYcQQggAKt0jfLWhE4Cv5KezxP541Ki4H253FVev7cbna0OrNVJU+DUyMn4z2mHds8HBQSoqKqitrQXAYrGwefNmli1bhnYOC8dJ8iGEEAJvKMwLVS0EVJVtiXY+lfX4ND+7V52d/0pt3Z+hKAHM5mwWLXwLm60s2mHdE7/fz8mTJzlz5gzhcBiNRsPTTz/Nhg0bsFgscx6PJB9CCCH4Yl07jaN+MkwG3izJeaLXeYTDfurqvkpn178AkJi4kbLSb2MwPHqN9BRF4dq1axw8eBCPxwPA/Pnz2bFjBykpKVGLS5IPIYR4wv1z1yA/7xlCC7xdmkuC4cl9NIyOtnGtcjdudxWgYf78z5KX+yIazaNX46S9vZ39+/fT3t4OQHx8PNu3b6eoqCjqyeWT+y9MCCEE9V4fX6qLPJy+MC+NZ+KsUY4oevr7j1JV/SqhkAuDIYGysu+SmLA22mHdNbfbzeHDh7ly5QoABoOBdevWsWrVKvT6h+Ox/3BEIYQQYs6NhhVeqGpmVFFYG2flM7mp0Q4pKlQ1zI2m79Pc/AMA7PbFLCz/AWZzRpQjuzuhUIizZ89y4sQJAoEAAIsXL2bz5s13bHE/1yT5EEKIJ9RXGzqo9vpINOh5qzQX3RO4ziMQGKSq6rMMDr0HQFbm71FQ8GW02kdnG62qqtTV1VFRUcHg4CAAmZmZ7Nixg+zsh7MOiSQfQgjxBNrTO8xPOwcA+EFJDqkmQ5QjmntO5xWuVb6E39+FVmumpPivSEv7tWiHdVf6+vrYv38/jY2NAFitVrZs2cKiRYvmdOvs3ZLkQwghnjAto35erW0F4KWcFDYmPlxD8g+aqqp0dPyMuvqvo6pBLJY8Fi18G6u1KNqhzdjo6CjHjx/n/PnzKIqCVqtl1apVrFu3DpPp4R+1keRDCCGeIEFF5cXqFlwhhRX2GL44b/Y6lT4KwuERrl//Ct09vwQgOXk7pSV/jV5vi25gM6QoCpcvX+bw4cOMjIwAUFhYyPbt20lMTIxaXKqiMlLZP+PjJfkQQognyBs3urjkGsGh1/HDsjwM2idnncfISBNXr30ar7cOjUZHfv4XyMn+f6O+7XSmWlpa2LdvH93d3QAkJSWxY8cOFixYELWY1LDCyJU+3MfaGGqfefJx1xNCJ06c4GMf+xgZGRloNBp++ctfTg1EVfmzP/sz0tPTsVgsbNmyhfr6+ru9jRBCiFl2eMDF2229AHynOJts84PpWPow6u2r4Pz7H8frrcNoTGLpkv9Jbs6nHonEw+l08vOf/5yf/OQndHd3YzKZ2L59Oy+++GLUEg81qOA520X3ty4w9K91hPpG0ZhnPp5x1yMfXq+XxYsX81/+y3/hE5/4xC3vf/Ob3+T73/8+P/3pT5k3bx5f+cpX2L59O9XV1ZjN5ru9nRBCiFnQ7Q/yck0LAH+QmcSu5LjoBjRHFCXEjRvfpqX1xwA4HCtYWP59TKaHf1txMBjk9OnTvPfeewSDQQCWLVvGpk2bsFqjU49FCYTxnuvGfbIdxRXZzqu1GrA9m4m1NBa+MbPr3HXysXPnTnbu3Dnte6qq8uabb/Laa6/xa78WWTH8j//4j6SmpvLLX/6S//yf//Pd3k4IIcR9Cqsqu6tbGAyGKbda+PP8R6t+xb3y+/uorPojhofPAZCT/Yfk538Brfbh3tmjqirV1dUcOHAAp9MJQE5ODjt37iQ9PTprdBRfCM+ZTjzvdaB4QwDoHEZs67KIfToNjUGHy+Wa8fVmdc1HU1MT3d3dbNmyZfw1h8PBypUrOXPmzLTJh9/vx+/3j39/N8ELIYS4szebezg17CFGp+VHZbmYdQ/vFszZMjx8gWuVLxMI9KLTxVJS8g1SU56Ldlh31N3dzf79+2lubgbAbrezdetWysvLozJFFPYG8ZzqwHO6E9UXBkCXaMa+PpuYZSlo9Pf2b2lWk48PF8Gkpk4dzkpNTR1/72ZvvPEGr7/++myGIYQQYszpIQ/fbo78//evC7PIj3m8p79VVaWt7Sc0NH4DVQ0TG1vAwvK3iI3Nj3ZoH2lkZISjR49y4cIFVFVFr9ezZs0a1qxZg9E492tzwq4A7pPteM91oQYUAPQpFuwbc7AsSkaju79EKOq7Xb785S/z6quvjn/vcrke2opsQgjxKBkIhPh0dQsK8Ftp8fxmWkK0Q3qgQiEPNde/TG/vuwCkpjxPcfFfodfHRjmy2wuHw1y4cIGjR4/i8/kAKC0tZevWrcTHx895PKFhH+7j7Xjf74aQCoAhIxb7phzMpYloZml31KwmH2lpaQD09PRMmZfq6elhyZIl055jMpkeiYIoQgjxKFFVlT+63kp3IMiCGBNvFGRFO6QHyuOt59q13YyMNKLR6ClY8CdkZf0/D/Vulhs3brBv3z76+vqAyCzBjh07mDdv3pzHEuwfxX2sjZFLvaBEkg5jrh3bxmzMRfGz/nuc1eRj3rx5pKWlcfjw4fFkw+Vyce7cOV588cXZvJUQQoiP8OP2Pg4NuDBpNfy4LI9YvS7aIT0w3T3/wfXrf0I4PILJlMbC8r/F4VgW7bBua2hoiIqKCq5fvw6AxWJh06ZNLFu2DJ1ubv9OwW4vrqNtjF7tg0jOgWlBHLaN2ZjmOx5Y8nbXyYfH46GhoWH8+6amJq5cuUJCQgI5OTm88sorfP3rX6egoGB8q21GRgYf//jHZzNuIYQQt3HFNcLXG7sAeH1BJqVWS5QjejAUJUB9wzdob/8pAPHxqygvexOjMSnKkU3P7/fz3nvvcfr0acLhMBqNhqeeeooNGzYQExMzp7EE2t24jrThqx4Yf81cnIBtUzamnAdfbv+uk48LFy6wcePG8e8/XK/xyU9+kn/4h3/gj//4j/F6vfzX//pfGR4eZu3atezfv19qfAghxBxwhcK8UNVMUFXZlezgkxnRK7n9IPn83VRWvozTeQmA3NwXyZ//WTSah2+ER1VVrl27xsGDB3G73UBkpmDHjh23bNB40PxNTlxH2/DXDUVe0IClPAnbxmyMGXNXO0Sjqqo6Z3ebAZfLhcPhwOl0Yrc/Wc2OhBDifqiqyn+rbuFXvcNkmQ0cXlGEwxD1fQWzbnDwNJVVrxAMDqDX2ygt+RuSk7dGO6xpdXR0sG/fPtrb2wGIi4tj+/btFBcXz9l6FFVV8TcM4zrSSqBprJyFFmKWpGDbkI0hZXZGXe7m+f34/asUQogn1M+6BvlV7zB6DfyoNO+xSzxUVaWl9cc0Nn4LULBai1lY/hYxMXnRDu0WHo+Hw4cPc/nyZQAMBgPPPvssq1atwmCYmyJnqqriqxnEdaSVYLsn8qJOQ+zyVGzrs9Anzu50nH+s0d1MPF7/MoUQ4glV4xnltfrIp+svzUtnuePh3V56L4JBF9U1X6C//xAA6WmfoKjoa+h0D9d6llAoxLlz5zh+/DiBQKT8+KJFi9iyZcucjearisrotX7cR1sJdkcSAo1BS+zTaVjXZaF3zO4O04H2Vi5X7OXSoYoZnyPJhxBCPOJGwgovVLXgU1Q2Jtj4dE5KtEOaVW7Pda5de5HR0VY0GiNFhX9GRsZ/fui20dbV1VFRUcHAQGQRZ0ZGBjt37pyz2lVqWGHkcqTDbKh/FACNSYd1VTrWtZnorLNXrEwJh2m8eI4rFXtorbwKMN5/ZiYk+RBCiEfca/Xt1I34SDXq+X5JDtqH7KF8P7q6/o3rtV9BUXyYzZksLP8BdvuiaIc1RX9/PxUVFeMd3GNjY9myZQuLFy9Gq33wpezVoIL3YjfuY+2EhyPtSrQxeqyrM7CuzkAbM3vTPCMuJ9cOV/DBwX24ByL1STQaLfkrVpK/Zj2v/eLAjK4jyYcQQjzCftEzxDtdg2iAt0pzSTY+3E3TZkpR/NTV/QUdnf8bgMSEdZSVfQeDYe6rft6Oz+fj+PHjnDt3DkVR0Gq1PPPMM6xbt25OdnhGOsx24T7RgeKe3GE2i9hn0tCaZu8R391Qx+WKPdSePkE4FGksZ7HZWbh5O4u37sSelBK9xnJCCCHmTtOIn8/XtgHwSm4qa+NtUY5odoyOdnCtcjdu9zVAw7x5n2Fe3ktoNA9HQzxFUbhy5QqHDx/G6/UCUFBQwPbt20lKevA1RhRfCM/pTjynbuowuz6b2KdS0RhmZ7txKBik7sxJLlfsobuhbvz11PkFLN3xPEWrnkV/j31nJPkQQohHkF9ReKGqGW9Y4RlHLJ/LS4t2SLNiYOAElVWfJRQaRq+Po7zsOyQmro92WONaW1vZt28fXV2RIm6JiYns2LGDgoKCB37vsDeI570OPGdu6jC7IZuYpffeYfZmrv4+rh7ax9XDFYy6nJH76PUUrXqWJTueJ31B0X3fQ5IPIYR4BH29sZOrnlESDDreLs1FP0sNv6JFVRWamt+iqel7gIrNtpCF5W9hsWRGOzQAnE4nhw4d4tq1a0CkL9n69et5+umn0esf7KN0vMPs2S7U4IcdZmOwb8rGsvD+O8zCWDfgqmtcqdhDw4WzqErkPtbEJJZsfY6Fm7YR44i77/t8SJIPIYR4xFT0O/m79n4A3izOIcM89y3XZ1MwOERV9ecYGDgOQEbGf6aw4M/Q6aLfdDQYDHLmzBlOnjw5vptj2bJlbNq0Cav1wVYEDQ2NdZi9MKnDbKYV+8bsWeswG/CNUn3iKFcq9jDQ3jr+enbZIpZuf578FSvRPoB+M5J8CCHEI6TDF+CVmshD4oWsZLYlOaIc0f1xua5xrXI3Pl8HWq2JoqKvkZH+n6IdFqqqUlNTw4EDBxgeHgYgOzubnTt3kpGR8UDvHewbwX2snZHLN3WY3ZSNuXB2OswOdrZz5cBeqo4dJjAaqQViMJkpXbeJJdt3kZSde9fXVJWZF0yX5EMIIR4RIUXlxeoWhkJhFtss/Gl+erRDumeqqtLZ+c/U1r2OqgawWHJYWP42NltJtEOjp6eH/fv309TUBIDNZmPr1q0sXLjwgdYWuV2HWfumbIzz7r/DrKKEabp8gcv799By9fL46/HpGSzZ/jxl6zdjirn74nSu/lGun+3m8rGGOx88RpIPIYR4RHyruZvzTi9WnZYfleVhnIMaEg9COOyjtvbP6Or+PwAkJW2htORvMBii289rZGSEo0ePcuHCBVRVRafTsWbNGtauXYvxHnd1zESgzY3r6E0dZksSIm3tZ6HD7KjbReXRg1w58C6uvp7IixoN85c9xdLtz5O7cAmau/y3FAyEuXG5j5rTXXTURprUjQZ8Mz5fkg8hhHgEnBh0872WyIPjW0XZ5Fmivx7iXoyMtHCtcjceTw2gJX/+58jN/a9R3UYbDoe5ePEiR48eZXQ0Uhm0pKSEbdu2ER//4OqK+G84cR1txV8/HHlBA5aFSdg2zE6H2Z6mRq5U7OH6e8cJBSN1QMyxVso3bWPJtudwpNzdDilVVelpclFzuouGCz0ExnbcoIGsoniyF2XDT2Z2LUk+hBDiIdcXCLK7pgUV+N30RD6e+vAU2robff2Hqa7+HKGQG4MhgfKy75GQsDqqMTU1NbFv3z56e3sBSElJYceOHcyfP/+B3E9VVfz1Yx1mm2e/w2w4FKTu3Gmu7N9DZ13N+OvJefNZuuN5ilevw2C6uwJoXqef2rPdXD/TxVD3RPM4e5KZ4lXpFD2Thj3RIkXGhBDicaGoKi9Xt9IXCFEUa+ZrBQ/H1tO7oSghbjS9SUvLDwFw2JdSvvAHmE3Rq00yNDTEgQMHqKmJPKDNZjObNm1i+fLl6B7A7g5VGeswe/SmDrMrUrGtz0afcH8VUT2DA3xwaD9XD+1jxDkMgFano/CZtSzZ/jwZhcV3tWYkHFJovtpPzZkuWqsGxxeT6o1a8pelULIqnYyCuHvecSPJhxBCPMTeau3l2JAbi1bDj8vyiNE9Wus8AoF+KqteYWjoDABZWZ+kYMGX0Gqjsz04EAjw3nvvcerUKcLhMBqNhhUrVrBx40ZiYu5v1GE6kQ6zfbiPtt3SYda2LgvdfXSYVVWVjutVXK7YS8P50yjhyDRIbHwCi7fsZOHm7VjjE+7qmn1tbq6f7qLufA8+70SjuPR8B8Wr01mwLAWj5f5TB0k+hBDiIXXB6eUbTZFKmn9ZkEVR7IPvFzKbnM5LXKt8Gb+/G50uhuLivyIt9WNRiUVVVSorKzl48OD49EBeXh47d+4kNTV19u8XVhi53Iv7WPtNHWYzsK7NuK8Os0Gfj5pTx7iyfw99rc3jr2cWl7F0x/MseGoVursofObzBKl7v5ua0130t3nGX491GCl6Jp3iVWnEp915F0zY453xPSX5EEKIh9BwMMQLVc2EVfj1lDh+O/3uPsFGk6qqtLf/I/UNf4WqhoiJmc/C8rewWgujEk9nZyf79u2jrS3SBycuLo5t27ZRUlIy61tn1aCC90I37uM3dZhdk4l1Vfp9dZgd7u7iyoG9VB47iH+sp4zeaKLk2Q0s2baLlLyZr1NRwgqt1YNcP9NF09V+lLEiZlq9hnmLkilZnU52STzaO4y0KX4/nuPHce19l+7Dh2d8f0k+hBDiIaOqKq9eb6PDHyTPYuSbRdkPtL7EbAqHR6i5/if09PwHACnJOykp+QZ6/YOtBjodj8fDkSNHuHTpEgAGg4G1a9eyevVqDIbZ7f6r+Mc6zJ5sR3FHpiu0VgO2dVnErkxHa7q3dSSqotD8wSUuV+yh6cpFUCNJgiM1jSXbdlG+YSvmu6i0OtTt5fqZLmrPduN1BsZfT8q2UrI6g8KnUjFbP/p3o4ZCeM+cxbV3L+5Dh1A8kdESdWzaZyYk+RBCiIfMTzr6ebffiUGj4b+X5mHTz/4CyAfB673BtcpP4/XWo9HoWJD/JbKz/2DOE6dQKMT777/PsWPH8Psjow8LFy5ky5YtOByzWxFWGQ3hOdOJ570OlJEPO8yasG3IInbFvXeY9Xk9VB07xJUDexnu7hp/fd6S5SzZ8TzzFi+fcW2OwGiIhou91JzuovuGc/x1c6yBwpWplKxOJynrozsiq4rC6KVLOPfuxb2/gvDQ0Ph7+rQ07M89R8L6dfDMMzOKSZIPIYR4iFS6R/hqQycAX8lPZ4l99hdBPgg9vfuoqfki4bAXozGF8vLvEx/31JzHUV9fz/79+xkYiBTsSk9PZ+fOneTk5MzqfcY7zJ7uRPVHPvHrE83Y7rPDbF9LE1cq9lL93lFCY4mTKSaW8o1bWLz1OeLTZ7bbSVVUOuqHuX66i8ZLvYTGGtJptBpyyxIoXp1O3sIkdB8Rp6qq+Kqrce19F9e+fYS6JpIgXXw8th3bcezahWXZMjRarWy1FUKIR5E3FOaFqhYCqsq2RDufykqOdkh3pChBGhv/hta2/w+AuLiVlJd9D5NpbmMfGBhg//791NfXAxAbG8vmzZtZsmQJ2lmsBBt2+XGf6MB7blKH2dQY7BvvvcNsOBSi4f2zXKnYQ3tN5fjrSdm5LN3xMUrWbsBgntliY9fA6HhNDlf/RMXR+LSY8ZocsXfYYeO/0YRr715ce/cSaG4ef11rtWLbsgX7rl3EPrMSzX1MXUnyIYQQD4kv1rXTOOonw2TgzZKch36dh9/fS2XlZxh2vg9ATs6nyJ//ebTauXu0+Hw+Tpw4wdmzZ1EUBa1Wy8qVK1m/fj3mGT6wZyI06MN9oh3v+90QntRhdlM25pJ76zDrHR7i6uH9XD24D8/QIAAarZaCp1ezdPvzZJaUzejfwIelzq+f6aK9dmi8L4zRrGPBU6mUrEondZ79I68V7OzE9e67OPe+i79mojiZxmTCumED9l3PYV2/Hq1p+sSlxdXCnuo9M/7ZJfkQQoiHwD93DfLzniG0wNuluSQYHu7/PQ8Nnaey6mUCgX50OiulJd8kJWX7nN1fURQ++OADDh06hHds58eCBQvYsWMHSUlJs3afaTvM5tmxb8zGdA8dZlVVpav+Opf376Hu7CmUcGSdSIwjjkVbdrBo8w5siXeOf7zU+ZkuGt6fVOocyCqOp3hVOvOXJmMw3n7NSWhgANf+/bj2vsvo2KJcAPR6YlevwrFrF9bNm9FNs6BVVVXqhuo43HqYQ62HqB+qJzwqC06FEOKRUe/18aW6dgC+MC+NZ+LmfmfITKmqSmvb39PY+DeoapjY2EIWLXybmJh5cxZDW1sb+/bto7MzsjYmISGBHTt2UFg4e1t5A11e3EdbGb3WP9FhtiAO+8YcTPPvftFqMOCn9tQJLlfsobepcfz19MJilm5/noKVa9DPYBrD6/RTe66b66enljq3JUZKnRc/k4Y9yXLb88NuN+6Dh3Dt3Yv37Fn4cIeKRkPMihXYd+3Ctn0b+ml62iiqQmV/JYdaD3G45TCt7tbx9/QaPctTl1NDzS3nTUeSDyGEiKLRsMILVc2MKgpr46x8Jnf2C17NllDITXXNF+nrqwAgLfXXKC7+Ojrd3CyKdblcHDp0iKtXrwJgNBpZv349K1euRH8XRbU+SqDNjetIK76awfHXzCUJ2DflYMz+6B0h03H29vDBwXe5duQAPo8bAJ3BQPGa9Szd/jyp8xfc8RrhkELztX6un+6iZXKpc0Ok1Hnx6nQyP6LUuTI6iufYMZx79+I9fgI1OFG51Fxejn3XLuzP7cQwTbG1kBLiUs+lSMLRepjekd7x90w6E6szVrMldwvrs9aj8Wv4KT+d0e9Fkg8hhIiirzZ0UO31kWjQ81ZpLrqHdJ2Hx1PL1WufZnS0GY3GQGHBa2Rm/t9zsi4lGAxy9uxZTpw4QXDswblkyRI2b96MzXb3CcF0ZrPDrKqqtFy7wpWKPTRePD9em8OenMLirc9RvnErMfY7j570t7up+bDUuWciYUibb6dkdQYLlt++1LkaCOA5fRrX3nfxHD6MMjIxSmJckI9j1y7sO3dizMu75dxAOMDZrrMcbj3M0dajDPknttXG6GNYn7WezbmbeTbzWWIME4mnyy+7XYQQ4qG3p3eYn3ZGtoT+oCSHVNPsFr6aLd3d/07N9T9BUUYxmdJYWP4WDseSB35fVVW5fv06Bw4cYGisrkRWVhY7d+4kM/P+G+x9ZIfZjdkYku9uRMc/MkLV8cNcObCXoc728ddzFy1lyfbnmb9sBVrtR9f98HmD1J3voeZ055RS5zEOI8XPpFG8Kv22pc7VcJiR9y/gevdd3BUVhJ0TNT0MmZnYn3sO+/O7MBUW3pI0jgRHONV5ioMtBznRfgJvcKJUepwpjo3ZG9mSu4WV6Ssx6W5adDrUDLX74YP/uNOvaJwkH0IIEQUto35erY3Mmb+Uk8LGRHuUI7qVogSob/gr2tv/JwAJ8WsoK/suRmPiA793b28v+/fv58aNGwDYbDa2bNnCokWL7nu0JdJhdgDXkTaCHfffYXagvZXLFXupPnGEoC/Sx8VosVC2fguLtz1HYmb2R56vKCpt1YPUnO6i6WrfRKlznYZ5i5MoXpVOTmnCtKXOVVXFd+1aZGvsvv2EeiemRXRJSdh37MC+6zksS5bc8ntz+p2caD/BoZZDnOo8hT/sH38vxZLCppxNbMndwvLU5egn72BSwtB2Hur2R776rkde96sz/p1J8iGEEHMsqKi8WN2CK6Swwh7DF+elRzukW/h8nVyrfBmX6woAeXm7mT/vj9BoHmy11dHRUY4dO8b58+dRVRWdTsfq1atZu3Ytptts85wpVVEZvdqH62gboZ5JHWZXpmNbl4nOPvPrK+EwjRfPcaViD62VV8dfT8jMZun25yldtxGj5aNHToZ7Rqg53UXt2a5pSp2nU/hU2m1Lnfvr63Hu3Yvr3X0EWycWfmrtdmzbtuLYtYuYp59Go5v69+of7edo21EOtxzmXNc5Qmpo/L0saxZbc7eyOXczC5MWotVMSnZ8Tmg4HEk26g/C6MSaGDQ6yFkFWevhG1/8yJ/5Q5J8CCHEHHvjRheXXCM49Dp+WJaH4R5qRDxIg4OnqKx6hWBwEL3eTlnpd0hK2vhA76koChcvXuTIkSOMjkZGD4qLi9m2bRsJCffXVE8NjXWYPX5Th9nVGVjX3F2H2RGXk2uHK/jg4D7cA32Ra2m05K9YydIdz5Nd9tEjMx9Z6vzpVIpXp5N8m4Wtgfb2SLXRvXvx19WNv66xWLBt3Ij9+V3Erl2L1jj15+n0dEa2xLYc4nLvZVQmRigWxC1gS+4WtuRsoTD+pumYgUaoq4C6fdByGpSJRAVzHBRshcIdsGAzWOLB5QIk+RBCiIfO4QEXb7dFhsa/U5xNtvneW6vPNlVVaG75ITdufBdQsVnLWLjwB1gss1ua/GbNzc3s27ePnp4eAJKTk9mxYwf5+fn3dd2P7DC7OgPtbRZrTqe7oY7LFXuoPX2CcCjyELbY7CzcvJ3FW3diT0q5fRyKSmf9MDVnxkqdB8ZKnWsgpzyRklVjpc4Nt06rBHt7ce/fj3PvXnwfTIywYDBgXbs2sjV200a0MVNHWZqcTeMJR9VA1ZT3yhPL2Zy7mS05W8hz5E28EQ5B27lIslFXAf11U84jqRAKt0PhTsheCbrI7y8YVrh0Y4CKy013+jWOk+RDCCHmSLc/yMs1LQD8QWYSu5LjohvQJMGgk+rqz9M/cASA9PTfpKjwq+h0s1cl9GbDw8McOHCA6upqAMxmMxs3bmTFihXodPc+vTNth1mbAduzd9dhNhQMUnfmJJcr9tDdMPEgTp1fwNIdz1O06ln0xtsnj7crdR6XGkPJ6tuXOg87nbgOHMC1911Gzp8HJZKsoNUSs/JpHLt2Ydu6Fd2kJnmqqlI7VMuhlkMcajlEo3OilogGDctSl7E1dyubsjeRbp00zTc6NHU6xTc88Z5WD7mrI6MbhTsgcSIZbBsc4UR9B8dr+zjdOIDHH0LxT+youRNJPoQQYg6EVZXd1S0MBsOUWy38eX5GtEMa53ZXcfXabny+NrRaI0WFr5OR8VsP7H6BQIBTp05x6tQpQqEQGo2G5cuXs3HjRmJjp9/JMRPKaAjP6U48pyZ1mI0zYVufReyKNDTTjCxMx9Xfx9VD+7h6uIJRV2RqRKfXU7TqWZbseJ70BUW3PTcUCHPjSh81p6eWOjeYdRSsiHSQna7UueL14j5yFNfevXhOnYJJtTgsixdHanHs3IE+eaJnjqIqXO27Gkk4Wg/R4ekYf0+v1bMyfSVbcrawIXsDSZZJVVP7GyZGN1pOgzqpMqklHgq2RZKN/E1giQNgNBDmbG0vJ+r6OF7Xx42+id0wAAmxRp4utPOjGf2GJfkQQog58WZzD6eGPcTotPyoLBfzNDsXoqGz8+fU1v0ZiuLHbM5i4cK3sNvKH8i9VFWlqqqKAwcOjHdAzc3NZefOnaSlpd3zdcOeAJ5Tnbd2mN2YTcySmXWYVVWVtqprXKnYQ8OFs6hjow3WxCSWbH2OhZu2EeOIu+25Pc0urp/uov5CL4HRibURmUXxlKyevtS5EgjgPXkS1969uI8eQx1b6wJgKioaL/5lzMoafz2oBLnYc5FDLYc40nqEvtG+8ffMOjNrMtewJXcL67LWYTeO7aAKB6HpZGR0o3YfDE6MigCQXDwxupH9NGh1qKpKQ6+H4xducLyuj3NNgwRCyvgpOq2GZTlxrC9MZl1hMuUZDjweNz/6wzv+qgFJPoQQ4oE7PeTh283dAPx1YRb5MQ9uKmOmwmE/dXVfpbPrXwBITNxAWem3MRjiHsj9urq62LdvH61jOzMcDgfbtm2jtLT0nrfO3rbD7KaxDrMzWMgb8I1SfeIoVyr2MNA+sWsku2wRS7c/T/6KlWhvMwU0Xur8TDdDXRMjAbYEM8WrIjU5bi51robDjJw7h3PvXtwHD6FMakNvyMnBvus5HLt2YVowUfnUH/ZztvMsB1sOcqz9GE7/xEJVq8HK+uz1bMnZwuqM1RNFv0YG4fq/RBKOhkOR3Sof0hogb01k7UbhdkiIlMZ3jgY5XdXL8bo+TtT10emcmCoCyHCYWV+UzPrCZFblJ+GwGFBVlfoRPz/p7OdIW9cdf98fkuRDCCEeoIFAiE9Xt6AAv5UWz2+m3d/OjdkwOtrGtcrduN1VgIb5814hL+/TaDSzPxrj9Xo5cuQIFy9eBECv17N27VrWrFmD4R5bsocGfbiPt+G90DPRYTbLin1jDuaShBklHYOd7Vw5sJeqY4cJjEbWKhhMZkrXbWLJ9l0kZedOe144pNBybYCaM120VA5MKXU+f1kyJavSySyMnxKDqqqMXr4SqcVRUUG4v3/8PX1KCvadO7E/vwtzefl4IuYNejnZcZLDLYc50X6CkdDEeop4UzybcjaxOWczK9NXYtQZI1VU++sj0ym1+6HtLKgTIxXEJE6dTjHbURSVyk4nxy/Xc7yuj8ttw4SViZ0wRr2WlfMSWF+YzIaiZPKTrWg0Gjp9AfYPuTnZ5Oa9IQ/dgcgUkeKdKIp2J5J8CCHEA6KqKn90vZXuQJAFMSbeKMi680kPWH//UaqqP0co5MRgiKes9LskJj476/cJh8OcP3+eY8eO4fdHdpqUl5ezdetWHI67b8wGYx1mj7YxcqUXxp6rxjw79k05mAri7jiCoihhmi5f4PL+PbRcvTz+enx6Bku2P0/Z+s2YYqZfc9Lf7uH66S5qz3ffUuq8eFU6C1akYpq0e0ZVVfy1tZGEY++7BMea4AHoHA5sO3Zgf+45YlYsH6/F4fQ7OdZ2jEOthzjdcZqAMlH7IzUmlS25W9ics5mlKUsjRb9CgciajbqKyHTK0E27TVJKJ6ZTslaAVkef28/J6j6O1zVysr6fQW9gyin5ybGsK4yMbqycl4jFqGM4GOLUsIe/r+/gvSE3DSP+KeeYtBqedsTyVLJ1hhttJfkQQogH5sftfRwacGHSavhxWR6x+gdboOujqGqYG03fp7n5BwDY7YtZWP4DzObZX/ja0NDA/v376R/7hJ+WlsbOnTvJzZ1+NOFO7rfD7KjHTeXRg3xwYC/O3sh2XjQa5i97iqXbnyd34RI02ltHfT4sdX79TBd9re7x12PsRoqeSaNk9a2lzgPNzTjffRfX3ncJNE6srdDGxGDdshnHrl3Erl6NZmzUp2+kjyOtRzjUeoj3u98nPGnxZ44tZ7wGR1lSWaTo18ggXPv52HTKYZjcT0VnhLy1E9Mp8bkEwwoXW4Y4cSAyulHVObX/itWkZ82CRNYVJrOuIJnshBhGwwrvO718u62Hk0NurrlHmTSGghZYbIvh2Xgr6xJsrLDHYtSE6eg4J8mHEEJE0xXXCF9vjMyBv74gk1Lr7ducP2iBwCBV1a8yOHgSgMzM/5vCgj9Fq72/iqE3GxgY4MCBA9TW1gIQExPD5s2bWbp0KdppHu534m914T7aNrXDbGki9o3ZM+ow29PUyJWKPVx/7zihYOQTvjnWSvmmbSzZ9hyOlFsXuSqKSlvNINdPd3Hjg5tKnS9Konj1raXOg93duN7dh2vvXnxVEzU1NEYj1vXrsO/ahXX9erSWyL+Bdnc7h+sOc7j1MFd6r0wp+lUYX8iWnC1szt1MQVwBGoiULz/1vch0Svv5qdMpsclQsD2SbORvBJONtsERjtf2caLuwvg22MnKM+2RhaIFySzLjUej0XDVPcL/GXJxsqWTCy4vfmVqqfSCGBPPxttYF29jVVwsNp2Cy32N4aFfUdNyDqfzIi6XTLsIIUTUuEJhXqhqJqiq7Ep28MmMB98L5Xacrg+4dm03fn8XWq2Z4qKvk57+67N6D7/fz4kTJzh79izhcBitVsvTTz/N+vXrsVjuLulSVZVAkxPXkTb8DcORFz/sMLsxB2P6R2/FDYeC1J07zZX9e+isqxl/PTlvPkt3PE/x6nUYTLcu+B3uGaHmTBe1Z7vxDk9MKyRlWylelU7h06lYJlVCDQ0N4a6owLVnLyMXL453rkWnI3bVqkjxry2b0Y113b0xfIND9ZEaHDWDNVPuvSh5USThyNlMjj1nbDrlPTjz95ERjuGWqcGmlkemUop2QsYyRkMqZ5sGOF7Ryon66bfBritIYn1RMmsXJJNkNVI34ufkkJu3q5s5PeTBHVamnJNhMrA23sqz8TaejbeRYlBwua4xNPwrbrSfY9h5CUUZnXKOXj/z/kSSfAghxCxSVZUv1LbR4guQZTbwnaLsOWk7P10cHZ3/m7q6v0BVA1gsuSxa+EOs1tvXqLhbiqJw9epVDh06hMcT+dSbn5/Pjh07SJ5Uj2Km8frrhnAdaSPQ8mGHWQ0xS1Owbci6Y4dZz+AAHxzaz9VD+xhxDkdO1+kofGYtS7Y/T0Zh8S1/h4AvUur8+ukuuhondoOYYvUUPp1Gyap0knMmRljCHg/uQ4dwvfsu3tNnIDQxomBZvhz7ruewb9+OPjERVVWpHqzm8KXDHGo9RJNzYj2GVqNlReoKNudsZlPOJtJi08DbD/UHIms3Go9CYGKaB50J5q0bqy66A9WRFdkGW9fH8Yr3P3Ib7PrCFMoy7HQFgpwccvN6Wzcnh9z0BKaOhjj0OtbGW1kbb2NdvJU8E7hcVxke/nc6u85R47yMokzd/WIwJBAX9xTxcSuJi1+Jy2kFPrqJ3ock+RBCiFn0s65BftU7jF4DPyrNw2GY+//NhsOjXK99je7uXwKQnLyN0pJvotffeapiptrb29m3bx8dHZHCVvHx8ezYsYPCadq1fxRVUfFVD+A6elOH2afSsK3L+sgOs6qq0nG9issVe2k4fxolHFkvERufwOItO1m4eTvW+IRbzumsH+b66S4abi51XpZI8ap05i2aKHWu+Hx4jp+IFP86fhzVPzEqYi4tHS/+ZcjIIKyE+aDvAw69//9xuOUwnd6JRaZ6rZ5V6avYkhsp+pVgiofearj4vyILRtvfh0nTL8SmRJKNop0wbz1OxcTphn6OH+7jeF0dXR+xDXb1giTCOg2nhjz8ryEX753vpHF06iJRs1bDSod1fHSjNEaL132FoaF/Z+j6OU64LqMoUxejGgwJ44lGnONpfL542trauXixldbWA3R1yVZbIYSYczWeUV6rbwfgS/PSWe6492qd92pkpIlr13bj8dYCWhbkf4GcnE/N2uiL2+3m0KFDfPDBBwAYjUbWrVvHM888g14/80eKGlYZvXZvHWaDPh81p45xZf8e+lqbx1/PLC5j6Y7nWfDUKnQ3xeIe9HH9TNftS52vTCM2LnJPNRjEc/IUrj17cR86hOKdmMYwzps3VvzrOUzz5xFUgrzf/T6Hzvw9R1qPMOAbGD/WorewNnMtW3K28GzWs9i0Rmg+CYf/KpJwOCfqigCQtmhsOmUHStoSrnW6IxVFj16ddhvsM/MTWVeQxIaiZNITYnjf5eXkkIfvVN7gmnt0ciqDFlhij2FdvI218VaWWrX43R8wNPzvDDec5z3nFVR1arJhNCYRF7eS+LiV2O0rcDpjaGtro6qqldbWXzEyMvNy6jfTqKqq3vmwueNyuXA4HDidTuz2mc8fCSFENI2EFXZcqKNuxMfGBBs/WzQf7RxPt/T1HaCq+guEwx6MxiTKy75HfPwzs3LtUCjE2bNnOXHiBIFA5CG1ZMkSNm/ejM028xGV8Q6zx9oIDUSSgPEOs2sz0cXevvbHcHcXVw7spfLYQfxjCYHeaKLk2Q0s2baLlLz5U2MOhLnxQR81p6Ypdb48heLVGaTNj5Q6VxWF0YsXI8W/Kg4QHhoav44+PR37czsjxb9KSvCH/ZzuPM3h1sMcbTuKe9IUic1gY0P2BjbnbmZNxhrMPtdYZ9j9kemU4KT1GHozzFsPRTugYDu92kRO1vVzor7vtttg1xemsK4wieW5CdT6I+s2Tg55uOD0ErjpcV4YYx7fkfK0TYvi/YDhoXMMDZ/D5bqKqganHG8yphIX/zRxcU8TE7OUgX49bW1ttLa20t7eTig0dapGr9eTmZlJTk4OOTk52O120tLSZvT8luRDCCFmwavXW3mna5BUo55DTxWRbLy3Alr3QlFC3LjxbVpafwyAw7GcheV/i8mUet/XVlWVuro6KioqGByM7DrJzMxk586dZGXNvG6JGgzjvdCD+1g7YeekDrNrM7Guun2HWVVRaP7gEpcr9tB0ZWJhpyM1jSXbdlG+YStmq3VKvL3NbmrOdFH/fs9Npc7jKFmVzvylKRhMkRLivqrqSC2OffsIdXePH6tLSMC+Ywf2Xc9hWboUb2iEkx0nOdRyiJMdJxkNTSy2TDAnsClnE1tztvJU6goM/bWRnSl1+6Dj4tQfyJo2Pp0SyHmWS13+8Yqit9sGu74whWcLkvCaNJwc9HByyM2Z4VsXiWaaDKyNt/FsvJVnbDosvg8YGj7P8NBZXO5rqOrU5MFkShufRjHoS+npYTzZ6Onp4eb0wGKxjCcaOTk5pKekEe4ZJdDqJtDqYqCui6LXN8/o+S3TLkIIcZ9+0TPEO12DaIC3SnPnNPHwB/qprPwMw8PnAMjO/gMW5H8Rrfb+Y+jr62P//v00jtWrsFqtbNmyhUWLFs146+x4h9kT7SieSR1m12UR+/TtO8z6vB6qjh3iyoG9DHdPrCWYt2Q5S3Y8z7zFy6fU5hhxBag9203Nma5bSp0XrYosHv2w1Ln/xg369uzFtXcvgZaJnSRaqxXb1q3Yd+0i9pmVDIfcVLQd49DR/8GZzjMElYmRgvTYdDbnbGZL7haWxBWjazkFl38OdZ8CV/vUHyZ9SWTtRuF22kwFHK8f4Pi5Ps68c+q222DXF6aQkhLDmbGplG/U3KD3pkWi8Xodq8fWbKy2a4j3X8M5vIeh9vPUua+hTm4YB5hNGWPrNZ5CVQvp6gpRWxtJNoaHp+7Agcg6nsnJRrzBRqDNTaDFTeDqIN0dLRCaSFDC/sAt17gdGfkQQoj70DTiZ8uFWrxhhc/mpvLF+el3PmmWDA9f4FrlywQCveh0sZQUv0Fq6q77vu7o6CjHjh3j/PnzqKqKTqdj1apVPPvss5hMM6sNctsOsxuyiF1++w6zfS1NXKnYS/V7RwmNLe40xcRSvnELi7c+R3x65vixtyt1rjNoyV+aTPHqdLLGSp0HOzrGi3/5r18fv4bGZMK6cSP2Xc9hXbeOvtAwR9qOcLjlMBd6Lkwp+pVnzxsv+lVqTEBTfyAypXLjKAQnrX/QW2D+BijawWjeFs72G8fqbvRxo3/qNtjEWCPPjm2DLc+L53owODaV4qZpdOrD3DJpkehqu47M0DVczrMMD53H5a4Epo6EmM1Z4+s1/P48uroCtLa20tbWxujo1G2yGo2GtLS08UQjOyMLk0szPqoRaHURdt6aXGhj9Bhz7BhzbPjiIXVZrox8CCHEg+RXFF6oasYbVnjGEcvn8u69M+vdUFWVtvZ/oKHhG6hqiJiYBSxa+BaxsQvufPJHUBSFS5cuceTIkfHFhEVFRWzbto3ExJnVKgl7Anje68RzZlKH2SQLtg3ZxCxNRjNNN99wKETD+2e5UrGH9prK8deTcvJYuv15StZuwGCe2PUy0OGh5nQXdee7GXVPjEakzrNTsnqi1Hmov5+hd97BtXcvo5cnyqmj12Ndswb787uwbtxEpzrIv7Uc5tDhP+SDvg+mxFaSUBIZ4cjZzPwRdyTh+Lfd0Hl5ynHYMqBoB2rBdhqsyzh+w8PxK32c+8W1W7bBLs+JZ11hEk8vSMQdo+PUsJe3h9xUXumfskhUp4GlthiejbexyqYhX6lkxLWXod5zDDVWM3RTsmGx5BAXt5LY2KV4PZl0dvqovd5KR0ct4XD1lGMNBgNZWVnjyUaaLRlNt59Ai4vAcTeujmvjfXPGacCQFosxN5JsGLJicY720XWjnp7Gem5UXbvlb3s7MvIhhBD36Cv17fxdez8JBh2HVhSRYTbe+aT7FAp5qLn+ZXp73wUgJWUXJcVvoNff386alpYW9u3bR/fYuoekpCR27NjBggUzS2jCTj/uE+14z3fPuMOsd3iIq4f3c/XQfjyDkV0iGq2WgqdXs3T782SWlI3v0vF5g9S/30PN6WlKna9Mo3h1OgnpsYRdLtwHD+La+y7es2dBGXtAazTEPPVUpPjX1q00afo51HqIwy2HqR2qnRLXkuQlbMndwqaMNWT3NkQWi9ZVgLtzynFkLIOinbhzN3PSlc6J+n6O1/Xdsg02M87CusJk1ixIwpoaw6XRUU4OubngHCF40yO4KNbMungrq2xaSqgi6D7H0NA5PJ4apmzFBSyWPOLjnsZoWojLmUZHxwitra309vbe8ruOjY2dMqqRoFgJt3nHRjXchF3TjGrEGjDm2MZGNqz4TKP0tDXS1VBHT2M9PTcaCPonflZfMMhrvzggC06FEOJBqeh38slrkcJR/7hwHtuS7q1Z2t3wehu4em03IyMNaDR6ChZ8maysT97XNtrh4WEOHjxI1VhZcJPJxMaNG3nqqafQ3aaV/GR322FWVVW66q9zef8e6s6eQglHpmRiHHEs2rKDRZt3YEtMAiKlzttrBqmZptR53qIkSlalk1OWAAE/nqNHce59F++JE6jBidEQ86JF2J/biW3HDur0/RxqOcTh1sM0u5rHj9FpdKxIW8HWnK1sjC8lpe1CJOG4cRwmLSzFEAP5m1AKtlFjXcXhdg0npukGa9JrWTk/kfUFSWRl22nWhnlv2MuZYQ+eaRaJfrhmo5zr6LynGR4+j8dTy83JRkzMfOIcT6PVFjM0lEx7u4fW1lZcrqkLVQESExMjiUZ2NllxacS6dATbPPhb3QQ7PbeOamjBkG4dTzaUBOgbaKa7sT7y1VDHqPvW+xjMFtLmLyBtQSHWtAyWb9kh0y5CCPEgdPgCvFITqdHwQlbynCQePT17qbn+JcLhEUzGVMoX/i1xjuX3fL1gMMipU6d47733xrdQLl++nE2bNhEbe+dRlGDvCO5jN3WYnWePNHubpsNsMOCn9tQJLlfsobdpouFaemExS7c/T8HKNejHmq0N94xw/UwXtee68QxNFMdKzLRSsjqdwpWpmI3gOXWKri++i/vIEdRJNSdMBQsii0Z3bKfKPMA/tx7i8Hu/R7d3YjeLUWtkdcZqNudsYoM+kbjmU3D8LeiaOu2CPQuKdjCcvZmjviKONrp4791+Br31Uw77cBts6bw4vHYDZ91e3hz20Nc09YEdr9exJt7KaruGhZo6bN7TDDvP4e2q4+YSXTExC3A4nkIJ5zMwkERjg5O2tjb8/kZgUtM6rZb09PTxUY00fQKG3jD+VheBfW4C7kZuHtfQWg3jazW0aSaG/T20tkaSjO6D9bj6em7+k6PV6UnJm0dqfiHpCwpxpOYRCtrpb/PS1+qm5lL3Lefcjox8CCHEXQgpKp+40sB5p5fFNgv/sawA4z00TZspRQnQ0PDXtLX/AwDxcc9QXv49jMake7qeqqpUV1dz4EBkeBwgJyeHnTt3kp5+58WygU4P7qNtjFZO6jBbGI99YzamebcmYc7eHj44+C7XjhzA54lMl+gMBorXrGfp9udJnR+Z1hkvdX6mi66Gm0qdPxXpIJuYYWH0wgVce9/FdeAAinPiOENWVqRF/c5tXLEPcajlEEfbjjLom2hKZ9FbWJe1ji0Za3k2oBLbeATqDoBn8kNTA1krCC3YRrVtNft6Ezle109119QkwmbSs3pBIivyEzGkWKgKRxaKNt+ySFTLM3GxPGPTsFhbT4rvFC7nebw3JS8AsbGFWK3LCAby6OuLp7V1mM7OThRl6miJ0WgkOzs7MqoRn05S0AqdPvytLoKdXlBuHtXQYMiIxZRjR58Zg0fnpKf3Bl2NkemTgfY2VHXqPdBoSMjIIi2/gLSxRENVExns9NHX6qav1T0lMQQYDXj5wk/+r+hMu3z1q1/l9ddfn/JaUVER1yetLv4oknwIIR5m37jRxZstPVh1Wg49VUSeZXY7w07m83dTWfkyTuclAHJz/xvz530WrfbeBq27u7vZt28fLWPbS+12O9u2baOsrOyOUzf+VhfuI234rt+5w6yqqrRcu8KVij00Xjw/XpvDnpzC4q3PUb5xKzF2R2QKpmGYmtNdNFzqIzS2QFWjgezSREpWp5O3MJFgTWWk+Ne+/YT6+sbvo0tOwr5zJ6btm7mU6OZQ22GOtx3HHZxYE2I32tmQvYGtSUt5xtmPueEwNJ2A0KR1GUYr5G9kIHMzR8OLqWhRON3QjzcwdavqwkwHqxYkkpBhpcui4ZTTS6Vn6q4RnQaW2WJZZdewSNtITuAUXucZRkZu3PI7tcYWY7EsZtSXQ0+3ndbWQfr7+289zmolNzeX7Iws0o1JONxGQu2R9Rofbl+eTGsbG9XItuK3BujztNHdVEd3Yx29zTcIB289x5aYTNqCAlLnFxCXNg+NNoXhnhB9rW56W92MTLPTBQ3EpcSQnGMjOceG0eSlfH1p9KZdysrKOHTo0MRN7qLkrhBCPKxODLr5XktkOPpbRdkPNPEYHDpDZeUfEQwOoNNZKSv9FsnJW+/pWl6vl6NHj3Lx4kVUVUWv17NmzRrWrFmD0Xj7RbKqquK/4cR99KYOs4uSsW/MxpA2dXrGPzJC9YnDXK7Yy1DnRK2L3EVLWbL9eeYvW4FWq8M96OPCu03UnOnG1Tfx8I5LjaF4VRpFK9PR97Xg2vsOzV96l2Bb2/gxWocD+7at6Ldt5Fyah8PtR3mv5iV84YlkIsmSxObsTWyOzWVFXzOGygPQ/YOpP5wjh9CC7VRaV/EfzvkcbXBy47IXJk1+JMYaWVuQRHaWndEEIxd9Pn7oGiE4ODDlUiWxZlbZtSzRNTM/cJKA6zSjbc0ATKRKGqzWYgyGcka8WXR1xXL50sBYQ77usa+I5OTkyKhGYjqpajyWfpVgm4fgZQ8o3UxpXK/TYMiwYsqxEU6AoWAPXZ21dDfW0XOkAf/I1K29AOZYK2kLCscTDa0+Dfeglr5WN1Wn3Yy6XMBN6zs0EJ8aQ3KujeRsG/EWH1ZnC0rD+/guXsf3sxqG29pvudftPJCsQK/Xk5Y2N1vOhBBiLvQFguyuaUEFfjc9kY+nxj+Q+6iqSkvrj2ls/BagYLUWs7D8LWJi8u76WuFwmAsXLnD06FF8vsjDuaysjK1btxIXF/eRMfjqhnDPsMPsQHsrlyv2Un3iCEFfJJkwWiyUrd/C4m3PkZiZTSgQpuFiH9dPd9F2fVKpc5OOBStSKFmdQYLBifvdd+l961389RPTEhqLBdumTWi3reN01iiHuo5xtvlzhG5Mql5qzWRz5jq2au0s6qxG+94/gnfyrg8NavbT9Gds5Li6nF912jl3bmhsG2xkF8uH3WBL8+LRJJup1yvscXrxBt0waQlEltnAGruOxfpWCoOn0LpPMNoZWQM0MRGkxRpbjFZXgsedQXu7hba2gbHS9P1jX5H1GpmZmWRnZpFuTiYlYEPXFSDwgRvF60Oli8kdVLR2I6YcG5o0Ey7NED3DN+i6UUfP3jo8Q4PcTG80kTIvn7T5C3CkzUNnSGPEZaG/zUPdRTej7gAwtceMRgPx6bGk5NhIzIwhXjNMzMANwg1n8R+uwVdby4jbzb13dnlAyUd9fT0ZGRmYzWZWrVrFG2+8QU5OzrTH+v1+/JO6BE63alcIIaJJUVVerm6lLxCiKNbM1woy73zSPQiF3FRXf4G+/oMApKX9OsVFf4FOZ7nrazU2NrJ//376xqYpUlNT2blzJ3l5ebc9Z9oOs3oNsSvSsK3PQh8/UWtDCYdpvHiOKxV7aK28Ov56QmY2S7c/T+m6jRjMFnpb3Bx/p5b6Cz34RyYlC4VxFK9OJydTg+9IBc4vv8uNqxPX0RgMxK5bh7plNafy/BzsPcnFnq+gdE+sTZjvmM+W1KfZEoTilotoDn4XwpOmB4w2gvM2Um1bza88pexrCtFV7wOCQGT0IjPOwrL5CdjSY+mxajnrHeVk0A+uiedSgkHHMzYdS43tFAfPYnEfxt8T+ZQ/cZSW2NgSUAtxOlNpazPR0TE0VqJ8eOwrspsoJyeHzMR00rXxJHgsKG0jBI97QfUSxMv4pIhOgzHTij4zhlHTCH2jbXS219J9oZ6hro5b/n4arZak7FxS8wuIS81Db8rAP2Kjr22Exmtu/GdDTB6LiZyjISE9luRcG0nJeuyhfmL76gnV1eD75XX8jY2MBIO3JBoagwFTQQGmkmJ0hQvoy7LSYHLBs//vLXFNZ9bXfOzbtw+Px0NRURFdXV28/vrrdHR0UFlZOW3zoenWiACy5kMI8dD425Ye/vJGFxathv0riiiKvX2b93vl9lzn2rVPMzragkZjpLDwK2Rm/PZdb6MdHBykoqKC2tpI7QqLxcLmzZtZtmzZbUuiq2GV0atjHWZ7J3WYfSYd27NZ6OwTUzMjLifXDlfwwcF9uAciDzKNRkv+ipUs3fE82WWLGHUHqT3XzfUzXQx2Tgz7WxNMFK9Kp7A0Fi4ex7X3XUbOT6wJQasl9pmVBDc9w8kFQQ4MnOJa/9TCVaUJpWyJK2az18v8G6eht2rqzxKXS3/GJt7TruCferO50O69ZRvssrx4UjNtjMQbuaIEaPVPXQNh0Wp52qZjqbGTkvB5EtwVBAJTH/YajQ6LpZhweAFDQ8m0NOvp7791isPhcJCdmUWGNYXUsAN7v55gm3u86utkOocJQ7aVoCPMUKibzr46uhrr6G9rRgmHbzk+LjV9ItEwZxD0JzDQ6ae/zT0l0fuQVqchISOW5BwbCfYwDn83lq5agnU1+GtqCHZ23nIORKa6zCUlmIqL8Oal0J6mp8bqos5zg7qhOlpdraiohEfD1LxY83DU+RgeHiY3N5fvfOc7/OEf/uEt70838pGdnS3JhxDioXDB6eXXLtcTVuE7Rdn8TsbMKn3eja6uX3C99jUUxYfZlMHChW9hty+6q2v4/X5OnjzJmTNnCIfDaDQann76aTZs2IDFMv3IiRpSGLnUi+t4G+HJHWbXZGBdM7XDbHdDHZcr9lB7+gThsa25FpudhZu3s3jrTmLjkyKlzk930Vo5gDKp1Pn8JckUL4vH3nYR9969eE6dgkkdUi1LluDbuIL3ihTedZ2hfmjSlAsaliYtYoslk83DfWQ0HIeRSYsyNVoCGU9Ra1/Df4wu4l9bYhganfrgnZ8cS36OAzXZTKNZpeamHSl6DSyO1bHM1ENp+AJp3ndRbkk29JhNRQSD8+kfSKTphgaP59YHfGpqKlnJGaQbEkn2WTF3KQR7vDeX7AB9ZFRDTdLh0gzR7Wqio6Wa3qbGKYW7PhTjiCMtv5C4tDwMlgyCwSSGuxX629wEfLcmJlq9hsQMK8lZscSbR7F5OzB3VhG6XoPv+nUUt/uWcyCya8hcUoy6IC8ympEcplrXQ91wPQ3DDVMa6k2WYE4g15DL//yN//lw1PmIi4ujsLCQhoaGad83mUwz7hUghBBzaTgY4oWqZsIq/HpKHL+dnjCr11cUP3X1X6ej4x0AEhKepbzsuxgMM19PoigK165d4+DBg2OLF2H+/Pns2LGDlJSUac9Rg2G87/fgPn5Th9lnxzrMmiOPhlAwSN2Zk1yu2EN3Q934+anzC1i643mKVj2Lsy/A1WNd1J2rvaXUedHTyWSM1jN68O/x/O0xvL6Jh6qpuIiRDcs5WaywZ+Q8re5/gLEeb3qNnqeTFrJZ62BTTxNJF/fDpKZuqsnOQNo6zuhX8L/6Czk3UfICCGE16SnNjSMmNYYeu45r4QDVKqD6YezZWWzRstzUR4l6mVzvXrTuVhh7HitEkg2jsQi/P4++3jiamrRMfE6OPOx1Oh2ZGZlk2lNJI44klwVNhx+lJUQk23CPT6Ho4kzo0s2MmLz0+9pp66yi6+L0hbuMFgup8wpwpOVhiskgHE5huE9LX7uXzqYPE42J9R06vZbELCtJaSbidC5s7hbMLZUEztfg/9+NEAwSgCm1PjQGA8aCBRiLCvHkJtOWrqcmboTqYAv1g9X0jh4DD0xd3Rqpj5Ifl09BXD6FhngKFCj0ukkabMLVcI3/ectPM70Hnnx4PB4aGxv5vd/7vQd9KyGEmDWqqvLq9TY6/EHyLEa+WZR9X5VEb+bzdXLt2m5c7quAhnl5LzNv3ktoNHeuKvqhjo4O9u3bR3t7ZP1BfHw827dvp6ioaNpYFX8I79lu3Ccnd5g1RjrMrkxDa4zc29Xfx9VD+7h6uIJRV2QJpU6vp2jVsyzZ8Tzx6fOpf7+HX3z7A3pbJj5BW+xGip5OIdfSg/bUL3D/8UF6J33CNuTk4Fm/lJOlKr8MXaB35F/GF3KadCZWx5ewJaxnfVs1jhu/mhJ70DGP+ri17Ass5h870nHWTv35FqTbSMmw4oozUGVQOKGqQBhCkYd1llHDCvMgpeoV5o++i2XkBpMXMmg0BvT6Qnyj2XR3O2hp0REOT/1bWCwWslIzyTAnkRq04+jTozb4x0Y1FFS8kf/UazFkxBCwBRkO9dI+UEd7cyWuy7eWPdfp9STlzCM+fR6m2EzCSgruQTMDHV76uj5c3zKRAegNWhKzrSQmaIljEOtQE8amqwQPVI9PmwTHvj6kdTgwFxcTzs+mL8tKY4rC1ZgBat2NNDsrCKmhyctSxmVaMymIW0ChOZkCRUuhb4ScwQ70rbVw+TAoN438+Gc+kTLr0y6f//zn+djHPkZubi6dnZ38+Z//OVeuXKG6uprk5OQ7ni91PoQQD4P/0d7Hn9R3YNBo+I9lBSyxx9z5pBkaGDhBVfWrBIND6PUOysq+Q1Lihhmf73a7OXz4MFeuXAEiTcLWrVvHqlWrpi1toIwE8ZzuxH2qE3V0cofZbGKXp6IxaCPN6qqucaViDw0XzqKOFbayJiaxZOtzlG3YymCnQs2ZLpqu9BMea5am1WrIXZjI/LRR7FcP4jmwn/DAxFZUXWoK7nWLOVms8m/aKwwFhsffi9XHsM6ez5YRP2tbLhEzMvFpXtXoGE5azvvGp/nZUAnHB+OAiYQjLsZAbrYDJclEQwwM3bScJV4PK8zDlFPJ/NF3SQhN7d+i0RjRaQvwejPo7LTR2WlCUab+7uIccWTGp5GuTyDZayW2C5hmikMXZ0JJ1ODWDtPjbqK57SoDHa23Ldz1YaKhkoLXaWOw00coqNxyXb1RS1KWlQR7CEewn9iBeoyNVwhcr/nIaRN90QLcuUm0pxmoThzlqqaD+uGGKfVPJrMZbBTEF1BgSaVQY6TQ52eBqxdrXz0M1E9dxDuZ0YaaUsxoXAHdxnl8MBLPJ377v0Zn2qW9vZ3f/u3fZmBggOTkZNauXcvZs2dnlHgIIcTDoNI9wlcbIp8iv5KfPmuJh6oqNDW/RVPT9wAVm62cheVvYbFkzej8UCjEuXPnOH78+NiWTVi0aBFbtmyZ9n/2kQ6zHXjOdN22w2zAN0r10aNcqdjDQPvElsvsskUs3f48iTkLqTvfy//5ZvVNpc5jWZCvI7XtPXz/+u+EOrvGt5lq4xy41pRzokTl55ZKPKGjMPYhOc5gY2NMJlucQ6xsvIxJmShAGTY6aIpfRUVwCf/Qu4C+tonfu06rITfdhiXVQqdNR5cJuieN7sRoYanZRZmmigW+A6QHr6ENTny21miMaFiAy51Oe1sM/f02VFU36X0NqUnJZMamkKI4SBqyYO5VJ22xHSuAZtCiSTYyavbS7+uktbuSjsqa2xTuSiI+Yz5mWxaQyojbwVB3iI7GyYlGZPjFYNKRlGEh3uLHHugmtqcWQ/0lAkcbYOzaCjA+cWUwYFqwgGB+Fv1ZVhqTFT6wD1MVaKbD817kGJUPd/RG/vYaPXmOPApiMyjUxlIYDFDoGiR1oBHNjf1TC69NZohFTS7CY19Al3Ee9WoWl3xpXBiKobHNi6ch8sdV/DPffCvl1YUQYhJvKMy2C3U0jvrZlmjnpwvnzcp0SzA4TFX1qwwMHAcgI+M/U1jwZ+h0d17zpqoqdXV1VFRUMDg4OHZ+Bjt37iQ7O/uW40NOP56bOswa0mKwbczBsjAJjVbDYGc7Vw7sperYYQKjHz4AzZSu20TZhh0M95qpOX1TqfMYPfnFMWS6PkB75N8I3pio2qmJjcG5spiTJSr/aq9lZNIKgxRjHJv08Wzt72BZT8OUT70e6zwumVbyv51lHPTkEpr0boLNRFKGFWecnpZYLRgmhjf0GigzeVikqWGB/xB54SvomZgG0GhMqMp8hodTaW+PYXg4bkqyodfryUhII92YSIrfRkKvEYP/1r+zNs5AwBZiONxL52A9TS1X8I3cOoJgttpIyJiH2Z4N2hR8ngSG+xhvhjeZ0awjMdVIvHEE20gHsV3V6K+/T+i2u03s6AoX4M5JpCPDSE2Cj0vmbuo8TQSU6UclUmJSKLBmU6i3UxBWKPQ4mT/QgqG/HoK3SRL0FpSkQly2BXQac6lVsrg0ms77gzHcGBglEL51dAYiiWF2QgwJFoVfvLT54djtcrck+RBCRNNL1S38vGeIDJOBQ08VkWC4/wFil+sa1yp34/N1oNWaKCr6Ghnp/2lG5/b19VFRUTG+aD82NpYtW7awePHiW7bOhgZGcR9vx3txUofZbBv2jdmYixNQUWi6fIHL+/fQcvXy+Hnx6Rks3raLxOwVNF5y3lLqPCs/lmy1CduZfyNYPWnrq9GIc8UC3iuBf0loZFQ/MSWRZYxnq2pmc3cDC10DfBipqtHTbl/CofAy/udgETeUiX4yBr2W9HQrwUQTbVYNoRh9JIAxBcYRFmrrKAwcpSB8AfPEOAAazITCeQwOJtPREYPHnTgl2Ygxx5DpSCGVeJLdMcQPGtFy01yNQYsSr8GjG6bH00JT+2WGBm9NCPRGI/HpecTEZYM2Ff9IAs4+I9M9TU0WHYlJOhw6NzZ3K7FtV9HWXECdZqEpgD4rk+D8TPqzbNxIUfnA4eIizVOmqyaz6C0U2HIpMMZRoOgoHHVTONiBo68eAp5pz0FnJJxYyLA1n05DLteVLC6OpHJ20ErLsH/anwPAbNCSlRhDQpwFk91IKFbPkAk6dCp9oTCK10Pfx559OHa7CCHEo+Kfuwb5ec8QWuDt0txZSTw6Ov+ZurqvoigBLOYcFi58C5ut9I7n+Xw+jh8/zrlz51AUBa1Wy6pVq3j22Wcxm6fWGQn2juA+2sbIB5M7zDqwb8rGtCAOn9fDhb2/4IMDe3H2js0jaDTMX/YUxau34XGmUHOmB+e/T6yLcCSayLP3k1S5F/XvT0buA6DT4lyUx8kS+HlaKyOmiV0wC4wJbPGH2dJVT6G/dXyFRsBg5wPz0/zcU86+0TJcoxNl2RPjzRhTLHTZdLjjjLh1E8lGpt7HIl0jhYHjFCvvY/dPfmCbCQaL6O9LoKfHgceTiKpOJBPxtjgyLEmkBB0kDZqxD5vQDN80smHXMmoeZcDfSVtvFW1N1ag37YnVaLXEpWYRE5eDVp+GfyQB11AsbpeGm/MHU4yOxDhwMIRtuAlLyxW0dZfR3DQlowIY9Gjn5+HJTaIj3cj1JD/nY3uoC3agTi6nOjawodVoyYnNoMCURAEGCn2jFDp7yOxsQOufup5l/D5aA+H4+QxZF9Cuz+V6OJP3R1I5PWinu/XWbcIflkyzW/SkJ8RgizOhtRrwWXT0GaFdo1A55fhQ5IcZu1SSQXNTCbPbk5EPIYQA6r0+tl2oY1RR+OK8ND6bd38tIsJhH7V1f05X188BSEraTGnJtzAYPvr/a4qicPnyZQ4fPszIWJv4wsJCtm/fTmLi1Bojd+ow29PUyJWKPVx/7zihYOQpZo61UrZhK3HpK2mtCdFWMzip1LmWnCQfaa0nMJ7+dzSTCls5S7J4r0Tl37K7ccdMPMTLDXFscbvZ3NdG3qTaHf3mPI6qy/kXVxmX1ALCREYhTEYdjtQYhuMMuOINYJlI8OK1QRbpmygKvkeJ8j4pTN4dYsHny6avN47+/kQ8ngQYG7nQaDSk2pNI0yWQMmIladhCDDdNZxk0BG0hnEo/nUMNNHVcwRe8dWTAmphKbHw2On06fn8i3mEbYLjlOHOMjgRbCEe4n9jBRmIa3kfXep3pJug0dhvB/CwGsuw0p6hciXNxztSOR51+jUWCKY4CSyoFGguFgQCFrn7m9zViGR2a9nhVoyMUN4+h2Hxa9blUhzI5703l1JCdodss4wBIsplIirdgcRhRYvW4zVq6DCpDGnXKiNNkJg3kmcJk6z1kaHpJU5pJClWT4L+C4hnm1/6vZhn5EEKImRgNK7xQ1cyoorA2zspnclPv73qjrVy9thuPpxrQkj//VXJzX0Cjmb7C6IdaW1vZt28fXV2R5mZJSUls376dgoKCKcfdtsPspmx0aWbqzp3myv/YQ2ddzfj7yXnzyV+xBf/oPBquDOE/PfFgT01UyBi+jOP4v6D1TTyQXfOSOVkC/zFvkEF7pPGZFi0rtFa2DPWyyTlIejiySFXR6Kk2L+NXIwvZF1xCq2/idxiXaMGfaGQ4zoDPYcSpjTzYYjRhyvU3KAqdoVS5SFa4Fe3YdJGqxjAyUkBPbxzDQ8l4PPF8mGwY9QZyHMmkKQ6SnDEkB2wYRqdui1ViVTx6F73eFpo7rzLo7bxlVMNsdWBNyEFnTCfoT8TrjiOkWHBO7RuHJUZLfIwfe6AHa28t5rrzGAbbp000yEjFk5tMV4aJmkQf71v7qDUOgKZ+6nHqWM2MmDQKdLEUhhQKPEMU9jeT5Gmd7sqoGi1Bey6DMfNp0edSHczknDeFk0NxeLum36Kt1UBKnIW4ODN6m4FAjI5Bk4Yug0q7TsvUVnAf/n40pBtUcgwjZGoHSFNaSQ7Xkuj/gHilE+3o9GMWI5pbk7TbkeRDCPHE+2pDB9VeH4kGPW+V5qK7jwWmff2Hqa7+PKGQC4MhgfKyN0lIWPOR5zidTg4ePEhlZWRQ22QysWHDBp5++ml0ushD5U4dZv1GH+8f+neuHtrHiDPyvlanY/7y1TjSnqazwcKVwyN82NsjNgaywo0kvv+vmAcnusZ6MhycLIH9+R66EiOftPXoWKMa2TLQxUavl8SxbbgjOgcH9Ev55cgiTiqLcI9GdqeYzXpik80MxRlQEk10m3Rj11Eo1nVQHD5LqXqJfLUB/VjbelWNweOZT29vPM7hVLzeOD5MNmJNFhbYkkj120h2x5Lgs6L1TCRyqh5GLaMMBrpo7a2he7iBgDL1I7/eZMaakIPBFKkOOuqJR9VY8bgn/tYaDcTEaIg3jWAb6SSmuxpLw/uYvDdlIwB6PcG8dAaz7TSlwNU4N+dje3CZBviwb8wEDZnmJAoMDgrDGgpGnBQOdpDjbEXPrQU4VTQEbNkMxMynWZtDdSiDs+4UTjkTGB2dvguxQaclJd6MNc4MVj1es5YeI7hNWpq1047HEKNVyTUEyNINkU4nyeEGkgLXSFEaMQf8U6uSTfzgaLUpKEoSPp8DtzuGwQE9TqcJl0sH/PW08d16FSGEeILt6R3mp52Rh8UPSnJINc3809tkqhrmxo3v0tzyQwDs9qUsLP9bzOb0254TDAY5ffo07733HsGxdQHLli1j06ZNWK3Wseuq+GqHcB+9qcPsshRs67Po6b/B6X/6AQ3nT4/3/4iNSyBn4TqCoWI664K016vACDodpBt6Sancg6P9EpqxT7rexFhOlcDBQh8tKR7QaDCjY0tQZfNQH+tGR7GPlUtv0+fy8+ASDoSWcFktQEEbeWgnWQjHGwglmfDZDOPD9vN1vZSGz1OqXqaIGsyhyLoCRYnB5cploD+BYWcaI944PqzjkWBxkBsTT7InltSgA5vPjGbSOEPIEmJI7aNzuIGO/uu4ggNTRjW0Oj22pFwM5gxCoSR8IwlotAn4fBo+LLKq0UKMRSVO78LmaSe2/SqWlg8wBW5dCKraYvHmpdCVYaI2McD7tj7qHCOEdV1A15RjbXoLBcZEClQ9hT4vhUNdLBjuwqpOP5rhi82k3zKfZm02lcEMzriTOe9JZtQ3ff+gGJOOhHgLJpuBYKwep0lLrxF8Fh3uaZJmDZBhCJGtd5FBN6lKE0nBKlLC9cSHB9HcWrYE0KLVphIOJ+Lz2XG7YhgcNOB2W/D5YuHmhbpjLIaZJ+2SfAghnlgto35erY08FF7KSWFj4r2tMwsEBqiseoWhodMAZGX9PxQs+DJa7fSfUlVVpaamhgMHDjA8PAxATk4OO3bsICMjI3KMojJaNYD7aCvBD5uz6TXEPpWG5Zlk6irPcuVv/pa+lqbx66bOL8aW8jT9HSk0VSp8+NE13ughreU4SY1HMYz15hi1mThTouFIUZD6TB+qRoMVHbtGfGxxO1k96iNGVQlp9FxkEe8Gl3BYWUq7L1Ky3RSrR0kwEUg0oSSYGB3bBpuuHaJUuUSZeplSKrGFIttSw+EYhofTaR9KxjmcyshIHKBBq9GSYo6nQO8gZcRKquLA7Jv4vSk6FY/BRZ+3lbb+6wz6Om8a1dAQE5eK0ZJBKJRMwJeIRpdEMKwnOPZr0+og1qzg0Axic7Zgaf0Aa891jNOs+QimJTCY7aAlRcPVeDcX7YMM2H2gaZtynF6jZ4EpgQJMFPp9FLr6KBzqJDUcnnY6xmdJo88yjyZNNlcDkSTjii8Nr2/6vjv2WAN2hxmdzcCoRUu/UcNIjA6fUcvgNEmGTauQbfCQqekjTW0lOVhDcriONLoxBgLTjmJoNIljCYYDt8vC4KABrzcWn886ZafQlPuYYnEYrNhVC7aAGeuIAbtiwa7+/9v77zDJzvO8E/6dnCp17p48A8wMMAE5MwMgQZqSReX12jIty7ZEUfxWpuxPkmWTpr6Ppix+tnmJpixqd03Zn3ctLbWWZMtM4JAEQAQiDTCDycDkzqnyie/77h+nurp7ZkATNohBOL/rqquqTp+aOnO6us/d93M/z+sRxTGfvOKrLqcQHwUFBW9JUqn4yNFzNDPJbRWfX9/+8g7F96PROMjhF36FOJ5B1z2uv+6fMT7+V192/9nZWb761a9y9uxZACqVCu9973vZt28fmqZdeYVZO19hVu62eP7Rr/PCbz5I3MmvrKZtM37tHWRiL/W5gMYygMQxMiYWn2H05IOUuvlf57Fn8vBei4euFxzZmiF1jQFl8BPtJvd1utwVRlhAQ6vylex2vilu4RG5nw4euqHBsEM65CCHHKIgb4OtaR32qMfZq55jL4cZEXlZJ8sC6ssjzNavp9EYo9utAhq2YTFuDjIqy4ylFUZUBXNNXiO2I6bTs0wunmS+e4FmumZKFuAEA5T9a5FihCQZRjdHkZrTdzR0E0pORkXOUlp6Cf/cc5QaZ7HT9SvOKtOgs32M6Q0uJ4cTnqks8dJwRtdtAmvdD41Rq8xO3WdXmrGrtcjO5Rl2pAkWZ7iUyB1hzt3OS9pmDsUTPN4c4Wi2gWYUwCV5UQ0YqDp4FQcZGLRcnSVHRwUmkaVz6TB2A8UGK2KTvsS4mmRUnGIkO84GJqmIxsu4GFWE2NxzMDyWly263TJRVL5soivkXTVVp0RF96lIj3LkUE6cXGwoDyO63PVQmiKxYxriCuWpl6EQHwUFBW9JPnN6mmebXaqmwb/Zuw3rinXxl0cpxcXJ/8CpU59GqRTf387+fb9PqbTrivt3u12+/e1v8/TTT6OUwjRN7rnnHt7+9rdj2zYqk7SfnaH1nYuIpd4Ks65B6e4JlqoLfO87/54z//6Z/vLzwcAI5ZE7aC5uZ2E6dwo0FKPRaUZPPcjQ0hF0JUktncf3GDxyveK5HYrMhDGp89daLe7rdrk5ijGBk2zli9lNHBC38Ly6BomOUbKIh2zksIsccMDQ8Ii5jkPsVc+zVx1ms8pbatM0YHl5hFONHTTqY4RhBdAomR4bVI3RtMKYrDKgSug9b0AYghZLTNdPM9s5y1I8TSpXp6iatk8wuAspR8jSEXRzDE0PSHtNNYYFJTuhkl6gNHcSf/II5fb5vruzgghc5q8d59yYzuFaiyNDXSaHQBjrL5aebnODWWZnBjs7dXbVZ9iVJFTl5cO1InuQKXcbL7GF5+NxnmiPcVxspBGVLtvXMDQGqw5W2SbxDZYdjdg3Ub5JaFz+uavqGZuNZSa0acbEaUayY0wwyRizmMmVWmRLCLGVMMwFRr3hEIZlorCCEJeXEU3dYMAuUcGnnLmUIpuK9Kgqn0A56OHlAiMzM0JatNNl6p05GuE87bROJ6sTitxBiq4w5fXlKMRHQUHBW44Di01+/0L+d+W/vG4zm90rl0deDiG6HDv+W8zO/mcARkbez57rfwfTLF9hX8HTTz/Nt7/9baLen+d79uzhve99LwMDA6hU0H50ktbDFxGN3BvXAxP3jhHORkf42le+RH1mNVcwsGEPQu4ljjZRn88vXGWxyNiZbzM++xR22kYYGgev0fjuHp2nd2rEtsaWTPI32y3u74TsSxIyTB4Ve/mUvJlviZuZZATN1MlGcrEhhh3wTEwEuznOXnWIveowO3gRE0GSBNSXR3mxcRf1+hhRVAY0Bs0K25IKY1mVcVmjRJ5dUChCo8OF7nFmm2dYiKdopasXf92wcEobMBhFiBE0YxxNryKUBhoYtqJsRpSj0wQzxygtnKLUvniZ0AhHK1zcUOHkcMLztSZnRmGxkoK26qDo6GwxS+yUOjvDDrsas+yKYzZm2WVphtiqcs7fxotqMwfjCZ7ujnFSbmIpqqw3SADb0hmsOlCy6Ho6TVdHBRbKN+hcUioxkWw022zU5hiTZxkRJ5hQF5lgkrJor0xzX4NHlo3Tjio0my7NpkcYVojCMll2+ZRc27AYNEtUDI9y4lBO3X55xMdB664/HqlJYj1iWczSCOdpdOdpZ3U6aZ121kCoy4WFaZcw3BqOthmRlTFNG/jGZftdiUJ8FBQUvKWYiVM+dixfu/3nNw7zwZHaK3p9p3Oawy/8Mp3OKTTN4Nprfp3Nm//2FUewnz59mq997WvMzeVCZ3R0lA984ANs374dGWe0HrpA65HJ1RVmKzbafp8jk49w5P84QNZbw920PYKBmwjD6wjDAQBsYkYnv8fE9GOU2xdAgyNbNB7do/PEbo2Op7ErTviFTpf750KuTVMWVZUD4h4+L2/mu3I/XVxkxeqLDVW10XTYyhn2qkPsU4fZxTFcYuI4oF4f43T9DhqNMaKohK4ZjFJld1phTNYYk1Wc3kyMTMtYFjOcaZ5hMZrMXQ3VCx5oOo4/hu3ciJSj6MY4mjGE6rUiG4airHcod04QTB+j1DhLuX0BU6y6ItLUaWyocG7M44WBDqdGMs6NQtftsrpcrcag7nAnNjujkF3NBXbFETvSFO+SEVexWeK8v41TahMHo3GeizdwSm5iPqpCa/331vNMahWbzM9nY6SBgQwsIkenecnnYFCP2ajPMa4uMCpOMa7Os4EpRpjFuGQxOaVshBikHW6k2XRpt33CsEIYlklTFy5Jk3iGw7ARUDY8yrFNRfpUVC4wHKx1IV2AVE/o0mYyvkC9M0snq9PuiYtIXJ5/MUwPw65hWDtAltC0CppRRdOraHoFbU1rrWmBmVx54borUQwZKygoeMsglOJnnnuJR+tt9pU8/vKWnbjG95+9sZa5ua9x9NivI0Qb2x5h377PM1C7/bL9lpeX+cY3vsGxY/mcDc/zuPfee7nlllvQYnnFFWbDrTFPn/gqF44d6v87bnkcpd0A+u7eL3rF4PJxJqYeY3jxEIbMOLkBHt2j8/j1GvWSxg1RzP3dLvd1QrZkGUfkVr4pb+Fb4mYOqR0o20QMO4ghFznkgGMwxjT71CH2cigPidImiko06qPUG+M0GqPEUQlHsxkVVcZFlTFZY1iVMTFQKNqqwVz7LAvRJIvxFK10dQaJ5Q6h6WNINYpujqMZI/0Ll4aiRJNy8xzB7AnKrfOUOpPrhEbq28xs9Dg1nHFsMOTsmMbFYRBrSha2pnON5rIzSfNcRhSxK0kYvqRkkhg+U9ZWTsp8cbQj2UZOyk3MMsClF3e/ZGGUbULPIPR0ZGCiSta6NWYAbAQbjToTTDEqXuoJjEnGmSJg/ToqSpkIMUgYlmk1PTqdoCcwKiSJd9kxlEyvXx6ppC5l5VFRuciwL/EPpCaJ6OSlke487XS571x0ruBe6IaNYdVAqyBlqScqesLCqKJp6x0VHYGXNXHDRZzWDG60hBst5vfxEml3iTtfPFkMGSsoKChYy+fOzvJovY1v6Hxx79YfWHhImfHSS7/L+Qv/OwC16u3s2/d7OM7ouv2SJOGRRx7hscceQwiBpmncfvvtvPvd78aRJu1vnM9XmO3NttAHbearM3zvuT+jdbBXFtB0nNJulNqPMjaiaRpeNM/E1OOMz34PN65zfgT+r3foPHq9wUINbotiPtLpct9SSC3TeVTu5Ys9wTGtDSFrNmLYRQ47qLJFlQZ7eZp95KWUYRYIwxKNxhhT9RtoNMaI4xJlPMZElZ0yL6HUVICGRkrCQnSRo91DLMaTLMUzZD1Xw7BKaMY4pnEdmjmOboyh6XlHh4GiJJcpLT5Peeklyu0LlNoXMeTqRbE15HHiOotjg4LTo5KzYxoLFQHaSmBUZ6Pm8I5MsKvRYGccsitJ2JJm6y5oie4yZe3kabGR56JxjsuNnJSbmWKItRd4XddwqzZeYNLxdLLARPVu0SWfj2G9wwZtkjFxhnF1jgkmmWCKYRbQs1WRo5SOEAOE4ShTTY9ut0wYlgnDCnHss7ZVVUOjbPiMKo+ycNa5F2XlYbK+6yTRIjpZk+noPK1k+fu6F5pmYlhV0CtgbcA0esKiJzLQ3L5jZwCGSvHSOk5rDrd7DDdawlsRF9ESVtpaJ40SW2exqnN2WLBQgUlX4wpjS65I4XwUFBS8JXhsuc1PPfciEvj89Vv46fHBH+h1cTzHC0f+F+r1JwHYsuXvcM2Of4Cur1rOSikOHz7Mgw8+SKuVW8/bt2/n/e9/P0NujfZDF+k8tbrCLAMGZznGU8//JVLkF17DCtCMfRjODWh6GUPEjM49y8TM41QbLzFbg0f3aDy2R2dmGO4OI+7vdHl3NyQRFQ6Im/mWvIVH5V66boAYdvKg6JCDa0Ts4Qh7Ocw+DrGRC0RhmXp9jEYjvyVxwJAqMSZqjMka47JKgItC0cgWWOheYDGeYiGaop3lbRua4aCbY8AYujmObo6DVkLTNDQk5XSBYPElys1zVFrnCTqTGLK3/LqhMz/ucmo449RIxtkxrVc2Wb28lTHYKTV2ddvsjLrsShKuTVJKay5bqWYzaW7huNjI8/EEJ9VGTqpNXFQjqDUXecPUscsWsW8S+waq1BMZnpmPAe3hkrFBX2BcnWNcnmWCqZ7ImF63kJ1SGkJUcwej5RF2K/0SSRyX1q0zY2g6Fc2nLD0qmdsTFz5l5VFW7roF7iSCrmrTihdpxUu0e+Kik9V77sXawKmOYVVAqwDlVefCqPTERbCuHGjJCDdewmnP4fXcCneNuDCzbl9cKA0aJYP5imS+AgsVWKhoLFRgvprfd1z681xMBUPtlAMfO1GsaltQUFAAsJhk3PfUCWaSlJ8ZH+D3rt/6A71uuf4UL7zwMZJkHsMosef6f87o6PvX7TM1NcVXv/pVLlzI50DUajUeeOABrh3ZSvvhyXUrzGZVydHG4xw7893+6w17A7p1I7q1E00zqdVPMTHzBCPzB2l6MY9fnwdHp8Ykb49i7u90eUc35KzYygF5CwfELRzWtiEGvVxsDDsYnmKndpK9HGIfh9jBS8Td3Nlo9ASHTEqMiApjqsa4rDEqq9iYxDJkIbyYC414kuV4hkyloBno5iiaPpZnNMxxNH0ATdPQEZSiOUpLL1Fune85GlPovQtl5JlcGDc4OZxwdlS7rGxiorFN6uyKunk2I0nYlaTrZmZkmsVFYxPHxEZeSDZwqicyzqsx5JqLt+UYULKIPB1ZslCBiQxMcI3+hVJDMaI18g6SnsDY0BMZAyyhrxlYlmW5a7Gav8hDnmFYXjcLw9JMKvhUspXSSO8mfQKcdfmLWIW00mVa8SKdrLEm2FknEmtbgjV0s5SLC62yWhLRq+h6FfTSupH9tujgdhdww8U1omKxJzKW1pWyEktjoaIxV1EsVFeFxYrIWCpDZvbOl4IRKRlP8+/JeJYxnvXuhWAiyxgSknasqP5OqxAfBQUFBUopfu7wGb652ORa3+Hrt+4iMK88QGntay5c+Le8+NI/RylBEOxk/77fJwh29Pdpt9scOHCAgwfzpekty+Id73gHt197E+Ej0+tWmA2DkIOTD3JhubfWimZgWNdhODehm2M40RITM99jfOYJJAs8cZ3Go3s0LmxQvCuKuL/b5ZZuxtNiHwdWyinBKLLnbqiaxTbjDHs5zF4Os5tjiI5HozFGvTFGozGKEVf7odBxWWNIldEU1JM5FuNJFqMpFuIpOlmd/KI3iKaPoxlja3IaBroSlLpTlOtnKLcv5MvEd6bRVV5KWh6weGlEcnpUcXaMXtmE/oV/VOnsjON8VdYkYWeSsiNN+0u3CQwmjY0cyTZyLMsFxim1kbNqvL84HYDpm4jAJPUNVGAhe04G9uo+HjEbtJmegzHZExhTjDONvWbqVpb5hGGZTqfUb1ENewJDylWHy9XyltSydPPchfT6Loa7JuApyOhkzXXi4uXcC83w0bRqLi6MyvrchV5G08yVDyVO1szFxdqsRT9zsbyufFUv5cJi8RK3YkVktL3V78mgkIxl2aqoECviIn88kon+96elPKbVIDNqkGk1xAyD/ecXI48D/+rXCvFRUFBQ8MULc3zyxSkcXeMrt+5ib+nKEyVXyLIWR4/9BvPzXwNgbOyvcv11n8Yw/N7XM5588kkeeugh4l43yv79+3n3/nvgqfq6FWYb1iJPn/8aC1G+fJemVzCcGzHsfRiYjMw/x8TME3jtEzy9C767R+PiFsm7o5D7uyGbuj6PiJs5IG/mUX0f3aEKcijvTJlwZ/tiYw8voHUMGvXxXhllFC8ezMsnKhccVeUTi24/ELoYT7EUzyBU2gsYroiM8Xyehmajq5RSa5Jy61zuaLQuEHSn0ZVEGBqTIwYvjQjOjmmXlU08pbEzzdgZhexMcydjV5L2Z2YIdCb1CY5lG/t5jJNqE2fVOGkvvaFpoAf5YmiqZPUCn/l8DMz8L34dySiLjKsLvfLIZL9UUqPe9xuEcAm7pV7+otLPYIRhGSFWW619nFxUyNXSSLWXv3DWrG4byQ6tZKknKl7evdB0t+daVNaHOS/pGNGUwInrq65Fz61YyVw4cb0v8BIT5npuxeK6csjlrkVZyvVORSYYX+NejIkMp/d5XValnqgYXL0nFxnTapBZNUCb/OdAmRrKMVCugXIMcA2EDJn5yPsL8VFQUPDW5rlmlx999hSpUnxm1yZ+fuPw992/3T7J4Rc+Srd7Gk2z2Lnzt9i08W/06+anTp3ia1/7GouL+XyKiYkJ7r/5XVSPinUrzM6K8zw/8y2Wk1kAdHNr7nJY26m0zjMx8wSDi0/zwtaIR/doTG6TvDPpcn8nRA838G1xKwfkLRwuXYsc8RDDDpVKh3364X5uw22LnqsxRrM+TjUZZlzW+u6Gq8zc1Yjy8sliPEUna4Dm5SFQcxx9RWjoAYZMKLUu9EVGuX0evzuLriRdV+fMipNxSdlEV7BFSHbGETuTVZGxMjNDojGpjXMs28BJtakvMk6rCZLexVwzNFSQOxkrpRIV9ERGL49Ross4F9mgLq7JYUwxxgwWuZMghJWLissERqU/C0MDAuWuC3audI9U1gQ8hcpop8trxMVyP9jZXeteaNaaFtTKy3aM6DLNRcTaEGdPYLjRIk7c6K+1sxzkQmKxssat6AmL+TWuhSfl5U5FrxSyIi783iV+XlWYUYPM9ITEenGRP49wUBrgGChHXyculGvk2918O6aOpWIGWGaAJWosE3Sm+I9/9Z8V4qOgoOCtSzMTvPepE5yLEj44UuV/27vtirM4VpiZ+c8cO/6PkDLEccbZv+9fU63eDMDCwgJf//rXOXUqXxY9CALeuf8etp8vkZzOJ00pFBfCExxdfJRGugDYGM4eDOcmHGEyPvskYzNPcHp0hsf2aMxsE7xDdnlbO2Mh2s235K18y7iFmeFxxJCLPSTYYx/rd6RUOx2avbxGpz7BYDLGuKwypmqMygpZFrEYT7IQT7EYTbGczCAUaObYqsjoDe4yZbJOZJRb5/G7c2go5msaZ0ZXRMb6ssmgVOyM43UiY+3MjIuMclxs5JTaxEm5iZNqMy+qDcT0JrDaei4wAjMfvlVan8cwEIwxw7iaXJfDmGCKMk00QEqjJzB64iLqCYxupT8LQ0fLw52XiQufknIxehmRbtZa51istqWudS+MnqjoCYuX6RgxRHx5KWTlFi9hJy00FLHJZYJioUL/8WLPtbCU6pVC1jsVa0sjFalQSmOO2uWORU9kTDPInBogwVrnVqwVEusEhq1jIKixTK0nLAb690vrnvt0yFKbNPFIEo963eLv/J2HCvFRUFDw1kQpxS8dPcdfzNXZ5FocuG03VevKkwWkTDj14j/j4sX/PwADA/ewb+/nsO0hoiji4Ycf5oknnkBKia7r3LbrJvYvTKBdzDsfJJKzrcMcqz9BO6uj6YMYzk2Y1m6Gl08yMf04C94RHtsDizsy7qLL/rbNS9FNfFPdwmPlG+mOVNGGDHaWX2Kfdpi96hAj7WXajXwRtqSxiZFkrJ/XqEqPZjzfC4VOsRhP0s1aaMZwPwy6MrjLFPEakZE7G344jzAUF4ZXnYyzYxrnR6DjadhKcU2S9kXGziRdNzNjSg1zQq7kMXKh8aLaSHdlmqlr9ASGua5cspLHqNDsTfOcYsOaMskIc5gIpNSJotI69yIKy3TDCknsAxqm0vtlkYrKR4OXVV4uCXDR0chkul5crGlLXXUvtDXiYo1zcUnHiJl21rgVq+LC63eKdNDIXYtcTFzuWixUoOXlba0jQlwmJlbci7EsY1BKhDKYZaDnWFwqLvJyyDxVUs0EW18nIq4kMjQDqjSorREQK48H+4+XKNNCpDZJ4pHEubBYvfnrnpvCxVM2vrIhlvzSZ4vMR0FBwVuU/zC1yD84cQFTg7+4eSe3VoMr7hdF0xx+4WM0m3lodNvWX2bHjl9FKY3nnnuOAwcO0Okt4LZjfCt3htcQzPZChSrjdPN5jje+R1e00a1rMJybKEc6G2a/RyKf5IndXeo7Um4zQja2RzgU38I3rdt4YWgXasRl8+AU+81cbGxozxHWB2k0xlD1rYwmY73MRhUjzViKp1ns5TWWk1mkVlpTOskDoVaW9kOgK10nXrhA11WcGdU4d4WyycY0WxUZabpuZsasGuCE3JSXS9QmTsk8/NnGBw2kvzoTY225BFPHJGVcTa8rkayEPgM6KKURRUFfYERrhUZv2XZbmZeVRVbKJR42GtrLuBe5yIhlb8CXVrrEsbi8Y8RKmusFxaVtqCJ6edeiJzZWXIvhdWJiJVux+nhYCIQyc1HBeqdiRVxMq0EWqSKMtbmKy50K5Rjg6JRorXMo1rsWiwywTEXVkSuiYu0tXi8o0tjHVV5fVHjKwWfNY2Xj42BLg0xERKJNlHVYjBb54P/2/y7ER0FBwVuPY+2QDzxzkkgq/vGOCX5l69gV91taepQXjvwqabqEaZbZs+dfMDJ8H+fPn+erX/0q09P5eioDQY275E42Lue/jzKZcKp5kJPNp4ikxHD24+g7mVg4jtl9goPbLtC6JmWfneC0d/BUdhsHKncyNzrO0FCD/f4h9vECm9uTZPUKzfoYdn0HI+kY47LGsAgI4+V+KHQhmiSUMp+jYYz3yihjWEJQbl2g0hMZ5dZ53GiR+WpeKrm0bFKWqh/8XHEyVmZmzKsqJ2TPxeg5GafURpqUULrWczDWl0tW8hg1tbRGWKyWS4aZR0cSRX7PuVgf8lxZtt1T9qrAkKvDtVY6SDKZ0M4a+bTOrNHvGmn3OkekEqD5a8oiV+gYQcdJGutLIZcIDEOm1HuuxaWdIStio+VBbSVnkWU9QbE+ZzGWCVLl9IXE2kzFWpGxSLmXrbiCmFgjMjwjXJerGLjEtRhkiYpqQGJc2aGIVx+TBLjSxcfpCwtfOXj07pWNKy0QKYnoEonOFW+h6BCJmAwjd4Z0H03ziTKd3/zjTxfio6Cg4K1FV0je//RJTnYj3jNY5v+4YQf6JTkPpSTnzn2Rl07/S0BSKu3hhv1fIE1rPPjggxw+fBgA27S5hR1c357AQCcRESebz3Cq+TSpVsV0bmS4oyjXn+LE+CE6O7ps8XXi9l6+a97FY8O3Ygwr9g8cZZ9+iG3ti+jLDu36BF59F6PpCOOyRpBCPZrN213jKZaTZTCG17kaVqqotC+scTUuYKZLXBy5vGwSu7AtTdeJjJWZGUuqzCm1aVVo9FyNOmWUpeUtqysdJSsiwzWwSRjvtamuzWGMM4VPSBJ764TFqsAoI4VJiXxRs/KlLobysDDpZs0rdo303QvNWedYXNoxoqPjRMtrApxLXNqGmhniioOy+nMtKmDral1HyNgl8yzGM0EqvcsCmzOXOBYNo4RyzfVC4hKRYVkpg/rluYq1JZGqbKAnvGzJI4l9ktjDTMv4yu27EmvFxIq4MDKBEHHuUlxJTGQdIpGSoqF6YiIXFQGseW45ZfxqjaAW4JVtvIqNX7bxyjZCi7jlPTsL8VFQUPDW4uPHz/N/Ti8xZpt88/bdjNjrlxNP0yZHj/0DFhYOADAx8VPs2P6PefLJgzzyyCOkvSXBd2sbuTXcjo9DJLqcaDzJi81DSGsbAdsYWTrFZPV7dLfNU66UWOzeykOVt/HiyFZ2jpzhJud5trYv4C0r4vomSvVdjKUjjGRliFssxdMsRFMsxjNEeGtcjXGczMiFxhpHI9XqnLtC2WRIiXUiY2VmRkcF/TLJiTW5jEWqKGd1+FY/jxHkmYEhFi7LYUwwxSCLiMTORUVUXhP4rOQCI7MpK/cSYZE/LikXJUU/b3Fl90JfIygu7xjRlX6JW7G+DdVOGjR9eUW3Yr53nziS8Z5rMXaleRZZRiqD79sNMq0G6TjBFdwKHXrPdUdRMxsvKygGWKYq65iJJL2k3JEkHnFPYIg4wE4qa1yK3j1Ov/xhCUAkxKJL3BMQ4aVOhUxJJEjdReu7FGsEheHjlqoE1Rp+NcAvW3iVXEz4K/dlG6+33bJffkbOK7l+F+KjoKDgTcGfzS7zkaPn0IAv33QNbx9Yv7x9q3WUw4c/ShidR9dtdu38JM3mjXz961+nXq8DMKqq3JPsYlhV6GYtTjSe5KX2i+jWbkZagpbzNNGmE6QDY5yXb+Oh4bvxR7vcXHmOa8LzVBYTWN5Mqb6b8WyESqoThgv9EkojzcAc7a954mQW1fYkpXavfNK6QN1rcO6SskmnJPOZGSsio+dsaMLpC4tTKhcaJ+Um5qih/PUdJSuPXSNek8FY62JMY2byMvdiJeyp0vXdI+U1jwNpE4tOL8xZv0xoxDL+vh0jutTw4+X1jsWakgiixWJFXSHEmT9ulBWDSMbEy8+zSEX5kuDm0DpxMa0PETluf2bF5cFNjYrdYlDPBcSlgqLGElXRwE4EWeKuCWquFxhaXMFJy70chbM+V6FsHGmgZxmZCF+27BHJjFgqpOb2Sk5rBIXuY5glvGqVUrWGX/PXCQj/EmHhlix0/eW7wF4JhfgoKCh4S3GmG3P/0yfoCMnf3zrGr++YWPf1qek/5cSJTyBljOtuYuOG3+ahh85y5swZAHxlc0e6k2vkGJ20wfHGE5wL6/hiBF2dJRp7joXRCY679/Li2Bb2DJ9gV3qGkcU29vIWqvXdDCcD2HFEK5zNO1DSJgnl/uAuL3OodKb7ZRO/c4HZWnNd2eTiiGLIEP3g54rYqKYWp9XGfplkJZcxrQ+iArs/Qlz1HA18nWF9YV0OY8XRKIvmJfmL1XHhJAEVFax3L3o5DEtqdNPGZV0j+dTOJkoLXrZjxBQaXl9cLF4mMjpWh8UKLFa13joiq4HOpbLCduSqkLikS2QkEwhRZe6K3SCDTDPEnDVI7LpXdCqUa+DbXQbMOoPa4mWCYoAlylkLL0kQiXPFgGYWBxhxFVeUVgOZa7IUrrTQRQZZ8n2yFLmgyDR7XZljJVPheGW8So3SwAB+1c3FxBWcCb9sY7nG920r/2FRiI+CgoK3DLGU/OgzpzjUDrmrGvCnN12L2ftLToiYk6c+xdTUnwBQq72D2ZkP8tRTR1FKoSuNG8RWbsy2EiYNjjWeZCpK8URMOnCYsxvHOTL0TsyxiP3GSTYuNyktbGKgsZtK7KG6DerxFIvxIg1hgJkvGe9lNtX2fK/F9QJGfJ7JofY6odEdEOwQ2ZqZGQkbEo0LcuO6PMZJuYlJcxhZcta1r6rAxHVDNmjT6wTGBJOMyDlE6K5xLlbFhh5XVgWGXC2PlKWLENFlAqPvXij9ZTtGrEzhxfUrtKEuoqdL1IMo7wapXB7oVL5gUFszhXNNKWQ4VUhRYX7tOO814mJKG2LOGUS49hXaTHUcO2HAafTcivWCYoBlSmkLP4lRiXWFPIUHcQWzJyp87H75YyVXYWQSLcsQIuyXPVbFRJdIZkRKkSpznaDQtADdCHBLFfxajdJADb/i9cSEdUm5I99mmD/YCsxXk0J8FBQUvGX4J6cu8r9eXGDQMvjmbbvZ4OYDrcLwIodf+Cit1guAhmP/FA8/FJCk+YjqbWKEO7OdZFGTY81DLHYbqNIsx7ZXuLDperaVJ7m2scTIwgaqy9fihoqkO89SPMdSlpJoNXRzHDe1qHaWqbQvUGldINIuMDnU7ZdMJkeg6mXrOk22JYrlbLyfy1hxM87bY4jAWV1xNbAg0Bix59eIjN5kTzmFHUniK5RJzGiAsgqoSL8/GryifDyhk6adS9yL/L4rUpReumLHiJNKvLixmrNYM5kz0pdZLiUsXqFDJCxJAueSIVm9x4OphsyqLL3M/Ippa5h5ewDpWfm0zf7MCgPDFgy4DWpmnUHt8uFXpbSDH0doazpA0sQjTjyy2MdIqphxDU/4l4U0LQF6dmWXIhQdYimJlCSR+iXBTB/TKeNV8vxEMFBbdSj6ZQ6r/9jxTLRXqdzxw0QJgWg0EIuLZItLiKXe/fISycIC0fwMyeI8YmmZ9sISdz9/uBAfBQUFb26+vtDgw4fz0sm/37+d9w1XAVhY/A5HjnycLGsgpcuJw3ew0MhXsh2QAXdlu3C7Ecebx5iTk7y426WzfYg90QJb54YpLW3G6EZ0urMspSFNaYE5hpfa1LpNKq2L+J0LNO3zXBiJ+vmMZFCySV+dmbE9FmTJKC9dkss450yQld01LawWgd9lg3XJXAw1RTVukYbB6kTPnpNhhcOUZbAu3FmWHkaWEqXNKwQ7QxLsyzpGdK2Em0q8uHVJh8gSZrJEy1lmqSzWZSwWKtAqKyxPMqxfPtq7muqotEpdDjLDJfMrtEGmrBGW3VreEbLGsdAdRdlpM+gsM6ivbyetqWVKWYcgijAS+lM1V24yLmPGFcx4AF95q6Kil6MwRJa7FFmXWHTXBDNDIiGIlSSWGkr3VnMUuo8TVPDKNYLaAKXBKn7FudyZqOSi4vuFMV8vKKWQ7XYuJpaWEEtLfVGRLCwQzk+TLMwjlpZQ9SZGs4MmfzCZ0BaCO148VYiPgoKCNy+TUcL9T51gORP84qYRPrVzI0oJnjr0aZoL/w5Ng059kCMn3kUcl3CUya3ZDgbailPtYxzb2MTb7nD9XBlvfgTZadKMmywJSLUaXmZT63Sotqaw4vMsBBc5PxxxdkxjflgRVFKukbmTsSMW2PEgF9YskHaSTZzxNpKWvX4mQws0Rv0FNhpT62ZjDMbLGKG+zr1IutVcYIjKugyGJwxIIsKskWcu+u5Fm1CCMsrrw5xagJcIvKR9WeYi1ZZouvU8zLlmKme9ArovKNk9p2JNkLOUWai0SksMMNNf1TQvh0yZw0zbIzTcta2mBsrRKDldBtw6g+YyA9qasKZaJki7BEmIFct1Yc008dHjCkZcw05qeGtmVLjSxBQSLUtRWUK64lJkPUEhM2KliJWGwAY9QNN8DKuUlzsqNYKBAUoDawWFta519NUMY/4wkVG0TkSsiol5uj1nIltcguU6er2NnolX/B4tFxoBNH1o+hoNH5oBtHzIXAWeRKiMz/9/Thfio6Cg4M1JJhU/8dyLPNnosC9w+dDiIRZf+lNuGHmOykj+i3V6chcvnb4NpMF1YiOb2jYvqtO4w1BarJF0WtQzQUs6uJlNtRMx0J4ikxeYL01ybjTiwqhGNpgx6uZOxrVxihvXWM5WchmbOaFv5rS/ibjk98slpaDLBm+aDdqqyBhOFgnCiCRcXbY97dawuqOURGWde2FlApl01sy/qNNOm3SkIMVZ1zGi4+fiIm73Qp25uLCSJTrWEnW/tU5cLFVABhLHEwxr63MWXupAVqGTDTKrhlYzFtog09YwM+4Ibbe0Llfh2RE1t8mAs8SQviasKZcppV38JMSJs9WwZuIh4xJ6nJc+nKTan1HhKQtTKPQsQ8vSvkvRdyiUJBaSSEGG1RcUllvGK1fxqwOUBgYoDZTxq86qO7EmS2E5VyeM+UpQWYZYXs6diTXljnhxge7cNMnCHNniIqonJowoecXvEdq5kGisiImesOiLCVdiuhLLEfiWpKYJ3MzEFA66CBBZQJJViWSVJa3KolFlOnP4vz79TwvxUVBQ8OYjSiN+/vFn+bbwsUTEzz3xG1yz0GXzu7sYlRAhDF48dRdzczuYEDV2dEp0O0t00oi6NDASnVo3otKeJTIuMF2Z5NxoSmNY4tZStui5m1GKy0TxBk73SiYnzK28GGwmKgWowEQPFCPBEhudSTZoeSfJSDZHrduG0OqHPLPuIHZ3lFJW63eP+NJETxKytEWnH/Bs0BGCUGqr2Qu9iqEcvFTgx128aLlfEhFqkZazxFKQd4qsuBZZIDEDQdleFRWjmcDNPFRaIe4Ji34ZxBhm2h5mxh0mdP1+F4jtpJTdFoNOnSFrkUGt104q65R7mQovTpCJ3R/JTVLGiGtYK6KiN5PCFBqGEGhZCmm86lKIkEgKIqmIFaTKROkeuhHg+BW8So2gWqM0OEgwUMavXDp74o0RxlRSIpvNVTGxtJyLiYV5unNTxAtzpIuLqKU6WqOF1Qpf8Xtkeu5MvKyY8CS6I7EdgWdLSrrEFTaGcCHzkaJMklXoyCrLRo1Fo8qCVWPZrrFg12jZPoYj8a2IwO5QtltUrSZVvUmJLl4WQ6PFp//GfyjER0FBwRufOO7wlecf4r8eO0d14SVCv8V/2v2LoOn87f/8l9xcPcHA255AMyRhWObo0Xehtya4plOjNb+E3ewSdOdoOReZqk4yNZrCQEatnHKtSKjFAVk8zpTYki+WZm/lZGkrYanUczE6TASzbLLyhdDGxCy1sInfTYl7LaqqO5w7GMkgZeVRVi5OqtDSiCRtrWlLjekoVt0LvYIlbbw0w49DvKjedy0iY5Gmt8xiKey7FlFJovsSz8sY0kVfWDiZB2mVKB1kQQ33VzOdtoaZdkaZdYdIXKcX1lSU3TY1p8Gws8SgsdifpllKuwRxiBclvbCmj4gDtLiCmdTykGZaxlMujtQxhOyHM+WKS5FFRCojkhArSKSB1F1Mu4wb9ARFrUZ5aDB3KCqrImLlseObr3t3Qna7lzkT0cIc3blpovlZsqVF5NIyWr2J2eyii1d2qZXkwiEvb2h9l6LtQ+oqlCvRPYntSFxb4BpgS6cnJgKEKJFkFdoMsGQMsGhWWbKqLNg16naZ2LHx7IjADilbLSpWi6rRokwHT0R4aYyTpASpJIg0rMRFT0sYSYAee+ixhxZb6EKiSYHKFN2ww//8b/9JIT4KCgreeLSbdR45+A3+8+QcC3XBnvpxho2nWRJLRDMj/N/v/SzNUpmbT57nl43fw9+RB04XFjZz+vjb2dIYgMlnOR28QGcoxh5M2eAkDCUuWjTKYrolD3662zgZbKVTqmAEkuFgmU3BFBuNi4zLaQajBpVuF9l18iFb3WHs7jhBPNQbrGVhJCkq6RKnzZ57EdGRgq40wCij6VVsYeImGUES9cWFlEt07UUa3jKL5YSFikarrNACgeULKrZgXGYMpwon81FZhSQdZFEO546FPsSUPcK0M8KcO4TwbHCg5HSpOk0G3WWGrQUGtWUqoicqkhA/jtEjnTTxIC6jJXlnjB3X8ES5l6NQ6JlAyxJUFpNmXWIR5Q6FksQSYgyk5mJ7ZZyg191RG8gFxWBwebmjbGO+zsOYKknIlpcvExOduWmihVnShflVMdHoYCSvPDfRcVaciVxQ9MWEp5CuRHcVliNwbYFh6djSQZceZAFZViKWNdraIEtGLRcTdo0lu0zb8dEchW+HlK0OZatJ2WhT1dsEIsRNE7xE4MeCUqzhJC56UkJPfLTYQYs89NRAyyRIiRISlYGQgkxIRJYiswyZxSiZoqkMqacoLUIaEcrsoKw2bdXiH/zRU4X4KCgoeH2jlGL+wgWefOYveTha5mA8yshigzvbTxF4h3hRS9DPOtx2qoLDnXzm536CFzdVmWgu8dt8Er88hVIaZ8/cTHh2D5r/DcrWPEY8QifezGm1hRPuVk6WttEsVykFHcaDebZ4F5jQphiKl6mGbZyOJO5W0brD2N0xgmiEsvRxMoWeJqik05t/0emJC0jx0PQytjDwEokfRwRRHStZItaXaDtLLPvLLFYy6mVQJYnhCzwvY0QTDGUKOwsgrZBkQ9RFz7Gw8tDmlDPKgpd3hHhORMVpM+AuM+wsMmwsUhbNVVERJZiRQvYmaOpJFTOq4cSDuMLHEvTLHlqWINIusUiIZdYTFBqx0hGGi+NXcEuVvNwxMEhpeIDyQKnf0fFGCWNe1iK6vEQ0P0dnbpJwfoZkcQG5tIS21MBodrC66St+j8TMxURjjZho+ZB5uTOhuRLTyXMTum1hKAdDeEgRkIkKkarR0gapG3lpY8mq0HJ8IsfCdlJKVoey2aZidqhoLUoqxEtj/EQQxFCKwU1ctNhDi330xIHQQct0NCFRQoFQiEwhRYbIMoRIkFmCEDGKDKXHKD1BGV2wOkizi7BClBWROYrUcogdn9AJSI0SqV4m0wMyo0RieiSWiTBNlAFm1OU//OInC/FRUFDw+iJLUk498QxHT36LF/wmzxqbmK8Pcsv8ce7KnkCWTvG0YWCct7nzmMlYZx8z43cxX/Z4dLfGQ3fchi0yPiV/gy3mGZLE5dzRdzG7PMAhb4zj5R10Sj6DpWU2BtNssS8wki0xEDYotSNUN0DrjmB1xwiiMQLhYCUZWhqTxS26WZuOSOlIRahMNK2EJXT8VBBEMX7UQMklQnOJlrvEUlBnqSzJSgotyHB9QdXOGBQaVpoLizQdpC6HmdGGmbZGmLJHmPJGqLs1bDel7HZ6JZBFRuwFKmtEhRcn2JFAj320uIoRV7GTGnY8iJVamELmkzPTBJVFpFlMIgWRlERSI0ZDWm5e7ugHMmtURgYJasGaUdu9VtHXcRhTKYXsdNY5E+H8LJ3ZScKFmX57KEt1jEYHqx2jv8Krm9B6IcxgtaNjRUxIV4IrsVyJYSuUY6Fjo8s8L5HKChEDtLRBlswBlqwKTcena7ukrkZgR5TNDmWjTVlvU6ZLkCYEiSBIFKVQw03snohwIXLRIhst06EnJFQmcwdCZIgsIctiMhmhtBS0BKVHKLOLsnIBIa2IzEpJHZPE8QitErFTIjVKCD0g0wMSy0OaJsIwwdAokVGWKbUsoZQIvBC8LpiRgYpMVKSjUokS+XsLLSTWuiRWQpuYf/yHf16Ij4KCgqvL0rlpnn/wEeZaz3B2JOGFYJSj7WspzYW8t/EEN5tPsxBM84jjYp63uOcoXDu3ifnRu5ge3U8sz6Liw4itN/Iv3nsvUtf4u+r3eTcHaDRG+cbij9HelLFBm2MgalDtdHHaJkY4jN0Zx+uO4aQGZppCGpHGTboipSMlXamTKQc70/AySRBFuHGDTFumYy3T9BZZKjUJyzIvh3iCkp9RRsfKVoVFU4wwa44wZY0w7Qwz5Y4SuQ6+F1J1Wgy5S4w4CwyoRr/7w4tj/FBgxD7GSitpPIAVVTAyvTeTIg9niiwlFXnJI1KQKAPlONhBGb9Sw6/VKA8OUhkeJBjw1g21ckuv7zDmpS2i4fws7dlJuvPTRAtzZAsLsFTHbHZwWjHGK8xNwOUtoq1emUO4Es2TGI5EczSUZaFMB00GSFEiUVVCNUjTGGLZqtG0A9q2Q+jaGI4gMLuUjQ4Vo0NVtSmJNBcSMZQiDTcy0WMXLc6FBJGdqxspUamEVKCyjEzEpDImFSFSi9H0VRGBFZJZEZmdkNqK2HWJbJ/YKZFYZYQeIHSfzPQQhokyLHRDo6xSKllKNU0JInBDidPRIDIRkQapRMqYTIakekRshiRWRuZA6kHiGAjbBbuMp5coWSWGgmE2jYyxdeNWxrbtIKiWLjvXxYTTgoKC15yw1eHEf32CM8ePEAVnmRkPOVUb5HC0m+mFUW6dP8L9ydPsdp7lZKnLtx0Pa8ri7UcUN5wNqA/dzvT4XTQ9BxE9B+kpdpT3Mjp0O3/7XcMsuS53q0f4KJ9jYXYH0ewuvGgcpz2KnRhoaYKKOqRZQltkdCVEQscW4CaSIIkw0yaJsUzHXmbZX6RbaiEDiRHk5RDHNrCFj0orZOkgLTHKrDWauxXuKPPuAMrTqDgtBtwGw/YCQ8Yy5Wyl/BETRBIncvutpGZUw4x8jEz1w5kqzUh7q41GElJdzwVFf/5EHsisjAwSVL2+M/F6DGMqpUiEpBsLWp2I7vwi4fwCydwC4dxF4vlziKUZtOUFzGYdu93G6cS4ocBJXvnl59IW0ZYPiaeQnkS5EsOW4OoI20JaHkoFSFEmpkao5WKiYZVp2x5dxyZ2DTwrpqR3KBtdalqHchZTTiWlWBFEGl5ooPXdCBdiC5UBmYQ0Q2UpQqakWUiiQhQR6DHSjFBGiHQihJWQWCmJaxA5AZFTInJKCKOE0n2k4SEMC6Ub2JpGWWZU05RSLAhCiduVaF0DFYNIFUJEZFpEYoREZkLmKBJHR7oG0vUwvAoVb5jRygSbN2xhx9ZtjIxvwLpkpedXk0J8FBQU/FCJw5iTX32Mc08fpSM6yJFplsbqnB2scUTu4uTiNXgLXe6tP8l92tNscI/yRKBzwPPwpk3edlRx+0mNONjD9PjdzA9ejxDnyOLnMMQc11ZuZlf1Ns7bTX77NpcTta2MqWl+O/tN1DO3kby0na5UhKnEyCRuKvDiGE01icw6LWeJlr9AFnTQSxLbExiegYGPllVJ0kHajDFnjDJtjzDtDdPwAmwvpeo0GbKXGLKWGZAt/CQkiBLKocSLHIy4ihXX0MMSRuRhZHmWQqUCkWWkK22jhgGegxWU8Wo1ygODlIcHcoei5q52eLzGYcwkk3STjE4i6MQZnTijmwi6YZe42yTttsjCFlnURnTrZK15tPosemsJ2suobhOj28XpxrhxhhsK3Ai8CPzolR/PpS2iK2JC9MocuKAcA2lbZLZLppXIVIWEGh1tiI5Ro20HdGyHyLGQDgRGRNnoUKVLTUWUk56Q6CrcyMDouREqdNESAzIFWYZMUqRMSGVEJrtkdMGIUUaMtCKEnZDYKYktiT03FxFuicwqoTQfDB9pWGgYWEqjKlOqSUoplLgdgdFVqERHJpJMRcRaRGLEJE6GcA2Eb2H6JfyBMcaGt7F543au3bqNwYERDOP1HdiFQnwUFBS8iiTtkENf+SZTTx4liQ1ESSFGpmiNzHJhoMop+xoOL++hOe+xa/El7k+e5j3Gs9juWb5d8vim5+PPGbz9qOTu4wpLjTE9fjczY3cQWyYiPkwWH8LWMnZWbmNX5VbqZszj1gme3ZvxX0cfwFApv9X6NEN/7tNppbS8RVJvEUpdDE+i+SbS9EBUycQgbTXOvDXOtD3Molel6+VlkAG7zqBZZ0ivU0ojSlFMJVSUQhszqmLEZfRugBZZ+RTITKAySSoEKRrCtFCeg1kqEQzkEzKrw0NURmsEFXd13Y7g1Qljpj1HoZPkIqGTCLpxRjeKibtN4m6TLGwjoiYiapPEDdJ4mSRrkmUtMtlGZh20pIOWhehJjB3HOB2F1zUwYgMt1dATsDKFnQmcROAkEjeW+JHCkK/smC9tEW17EHsK4UmUq5BOLiYyxyK1PFKzREqNiBqxPkjHrNCxPSLHJrZNDCujrIfUtIgBFVJJBUEsKYUKr6ujxy4qclFdBy3VIc0gyxC9PESqOqSqA2aEMlMyKyZzUmJbErsQuwGhWyKzSygjyEeraxY6Jo6SVLOMapjhhQK7I5CxQqSKVMUkekxiJwjPQCv5eEODDGzcwvbN13PN1l0MVQbeEKLh1aIQHwUFBf9dNGanefbP/xPThy5gJWV0PyArZaQjF4gHLzIzWOFFbxuH2jcwuzCEvdDhrsYh7tef5Z36QRb8Ot8MfL7leXjLBm87KnnbUcVg22V29DamJ+6iUd6GEtNk8XPI5CSu4bGrcjvXVm4m0RVPWS9y2rqAv+ccnxv8X8g0i59s/zu2H32GKBuho8aZtyZYcAao+wGJZ1Jx2wz0REVNdCjHKdVQUelamFEZIwqg46PHev5XrpC5da1pCMsEz8UolQgGa5QGB6iODFEZqRFU3f5iYP+tMGYmZC4OVoRCTzR0o5S42yLutsiiXCzIqEUWN0iS/JZlTTLRRsg2ZB30JERLYwyRYKQZutDRUhstszCEg8wcNOGCyLdZicLPdAwl0UjQVYypOpgiwkpjrDjFjTO8UGJnr/xz0XFYN2ci8kD0QpjC1RCOibAtEscnssokWi2/GVW6Zpmu7RLbFsLW8cyEKhE1FVHLEsqRpBSC2zHQYwcV2dC1IVWQZnlnhuySqi4JbaQRI+2EzEpIHUHsKmLXJvJKJE4JZfgYmo+muWjKwEsltTSl3E2wQoERSpJMkpGSOALhatgjFWobNrJpx3Xs2LGXDbWxt5RoeLUoxEdBQcH3RYiME09+nUPfeJjWuRYlOYzvVpGBQxwkREOnUQPnWR70ecnbxqHsBs4sbobFjOGFOd6THuQ+41nuNA5x1FM8GPh8x/dwGzpvP6p421HJxkWN5doupifuYn74JoSuIZITiPg5lJjDM8pcX72T7ZUbQNM5YlzgWfMMtr/Mjj1P8M+Cf8y0tpGt2Uvsbx5iiBYDcUI1gkrHwA59tNCHjoeWmGiZRAqFQkfZdu5QVAL8gXz+RG10iPJwFb/qYPsmytWJMrXqKsSCbpwShW2SbpOk20JEbUTcRIQt0qRBmjZIsyapaKFklyzrYKRdtDTEyBIMkaGnAl1YaJmFljlomYPK3FwsSBtN2mjSxNA8LGxM3UHXbTTDANMCQ0NXMRYdkA0ETYRoYaRtjDjEDFPs3l/iQVfgxa/8+7+2RbTtQ9Sbgik8iXAMhKOTOg6R4xM6VWKjSqZXSPQqoVUish1Sy0QzoaLH1FTMgIipxBK/q/DbOkZko0IH1c0Fn0pjhAxJVZtU65CZEcJKSe2MxJWEnk7ieSRuCWmV0fExpYOhdPxUUo1ivG6KEWZkUkNYCkoW3niNsW3b2XbdDWzfsBPHtl/9H5iCH4hCfBQUFOQoxYtnn+eJr/8JraNTaMsOjqxRcQexfQ/pObT9LuHgSayBC3QGTF7ytvEC+znR2E22qNDnQ3a1zvJe/VnuNZ5lt/4ijwUuB3yPh30Pp6NxzzHF245Irp2B0B1ievwupsfvJHaHkKKBiJ9HJIdBxZTMGrtrd7O9tBdd0zmvL/CofZwuCcPDZ9m163H+0PwI39XezUDU4de/8SylRKFsC+HYSN+DSgWtVEYLAqSl8u4A1UZkbVSy6ipkWYMsayFkByE7aFkXLY0wshhDpBhphpZpaJmFLhzIejfhgbDRhYMmLcDCwsXUbAzdwdBNMEywTDANlA66I8BOwcoQTpY3ODiKyIRQQhZnqG4Xq9PC7zTxOw38Thevm+CFGX6YBwv9kP+hFtGWB3FvCqZwFZmr91otHSLXJ3QrRFaFVK+RGhViKyCzLDLLxNYlAz0hUYsFQVfhdzX0jgmhjYw0VJaispCUDrHWITFDhJ2RuILY1Yg8k9Qrk9kBpgowlYWdGfippByG2IlA0yzMkkdpYpiNu3dx7fU3MDQwBq+jIG3BK6cQHwUFbzGklBx88Xkef+LLRMdfwJu3sDuDaLKC79YoOT6W75G4DnVviWjgJH5tkrSWcSbYxgvcwAvRHrpLDvpCjLvQ4k55jPv0Z7lPf5aquch3fI9v+h6PeR5OBHeeyB2O68+D0i3mRm5meuJu6rVd+UyG7DxZ8iwqySeQVqxh9gzcwxb/OjRNY1lr84hxjDmziaZJtm97ho2bj/Mw7+aL2sfQlOSnDn2BLcsvYIoEPcswUoHWdxXcnlhYdRUQNpqyMJSJqbmYmoOh22i6gWZaaKaJMnUwQFkKzU7Q7RRpZcSeIrbzjsiOZdAyDNqaRaKBlApkBiJDEzFm3KbSrFNqtvA7HYIwwotSrERiJzLPTEQSP5T4ocJ65cMw+y2ibS93JlJPkbqQ9sRE7DjETkDXq9B1K2RmlUwvk1oewrTBNCmRMpjFDCQppa7Ca2kYXQMZmhCLvGNCdUj0LpEZkjmS2FVEnk7qOgi3jEGALWysTKeUCipK4nslBsc2sHn3Lrbu2Ytfrr26H+iCNySF+CgoeJOy2Fri69/7DidffAK5eILqfJ2gXsMKJ1CqhO74VByPiu2BX6bj6Cw6MySDpyjXptGrHc4GWznCfg7L/SzWB9EXIvSFiJH2Iu8xnuNe/SDv1A8Rmgnf8n2+GXg85bpYCdx+UvG2o4obzuZBxGZlO1PjdzM7dhvScFAqRsRHEfEzKNkEYMAeY8/APWzydwEQk/JdnueMUwdNwzY77N31EKXhRabYyG/J/x+JYfPBgyd439GzYOqga2BJsDM0JwUzIXUlsQ2xAx1Lo2OatHSLDjoZGggJUqCLBE1GaCLCTkLcuIMTxziRxEoUVqJhpQZuV8cSBrrKMInQVIimEowkwYpTvCjD70qCUOG+8kVE+y2iLQ/CXkdH4mqkrk7iWiSOTeQFdNwSoVcjtapIo4Q0PaRuY+kaNZEyFMeUQ4XfkugdHRnqyDQmUyGxHhLaIYkjSTxF6tmkTp6BcJSHj8Ow7bBxcIytO3axcd9e/Gr11fuAFrylKcRHQcEbmPrsLI88+TBPT52nG82gsklq9UkqixF+cwIz3oBSFSLHJHAsBk2bkjdI5vksmTGL7hSy9hLV2hxObZnz/iaOsJ8j7Od8uKUnNmL0pYjr5Pnc3TAOcrP2IlOWzgHf50Dg8ZzjYAq4+aVccNx6SmELiO0q0+N3MDXxNiJvBAApFpHRM2TJcSBPNA67m9k/+E5GnU35Piieso9x1JhmZVZUeWyG6699BMeIaEuf35T/iiVrmOHOLPecexBDdHGSLk7cxYlD7ETiJmAkGmZqYGQWtvAx8DF0D1230R0D4UBqZYR6RJY1MOI6RtjCiSK8MMGNMrwwD2AGXYUXwSsdxbXSItryIex1dCSuRuIaJK7VC0EGdLwSoV8jtStglFC6i6Y5+FIylMRUOoKgk6F3IItAyIhY7xJZEZGrSD0N6bhobpmqPch4aZRdW7ZxzZ6bqIyPvK5mfhS8tXkl12/zNTqmgoK3DEopolTmnQ6XtklGKZ2lBcLnniJun2G6ZDJju+iijZNN48UXqCzP49cNNjc348QbUdoOWu5OTFNSc3QGylUcf4SuYzBnNDntTGIMHKdamyWoLqD8sZ7YeIAXs2tQ8zIXGwsRlfACd+nHuFd/lvvMg2zSFjhtmXzT9/nnwRjHHBtdKvafVXzkqOTOE+AlCqmZLAzv5/zmt9Ms7wJNRylJlp4gFs9ghjP9//9gdQd7Bu5mo7YiOiTPq4McsmZJdQcUmEnENcPPM7LzFJquSOtV/n3rH7K0ZZggififTzyLokpilknchNQJydpNrLiJEbVwkgxDpFgiy12MMMULBV4o8ENFKeS/u0W05UPXg9iD2MvFROyaxG6emegGJUKvSupWwShj4mJIg0oqGehGBO0UpyNxIokbR7hhRBxq4Gd4NYuJke1cv3MfO3fuwgvKr+Inr6DgjUMhPgre0iiliDPZH7SUC4V8+FI3TgnDiDRskHTbZFELGbWQcRsRtxBpE5E2yGQLIdpI1UWqLqKT4nSGqSQTOIFFe9jiQq3EtO5ix10q4SyuuEBJTlG60GHPso/b2kYp3kRq3UPTsxB6B9uVDJYDBpwJ8CosWwmzep1z3kXc2nPUqrNUanOk3gAvcANHeCfH1fXEbavvbpjLC4yoBu8xDnK//ixvdw7jazFHbYv/FPg86E9w1rbQlGL3RfiFI4J7jivKYX5+WqVNHN1+D3OjtyMNPz9nsosMnyRLjqJU1P8lsmVwP7urdzHIIAACyUH/DM8ZpyHVAQeUwMlmue7656lsXABg6cIg35p9D4/efj0A/9OBP+CaU8/jhRluKAlCSRCC88rX/aLjrIqJqCcmYs8gcq1cTHgeoVci8nMxYehlLOngpFALE4JujAozUPkiYaWaz+atm9l5/a1cv/MGKqVCPBQU/PdQiI+CNwwrY5z74iAR69okwygk6bVJijAfuiSTNipqI5ImWdZAZC2EbCNlB0EXpcI8zChiTJGhZRmakOiphMyCzM1vwkVkNp3KENI3cSIoxT6BqmCXh0mGa8wMmpz3ysyIgKAbMtKZpyzPYsuLDF9oMNIQWEs1BlpbKKU76Lh30PRsEr2F6XUwyjajbo3rnHFi12bOaHBBb3DEPUlpYJpqdZbR6iye53OE/TzIj3KU/TTTMvpihL4YYyw0cGLBHu0c9+oHud96lpv0lxDA847DFwKPb/rDTFsGKMX2Wfi5I5K3H9MYaOWpyMQKOL3tbi5uehuZOdo//6k4g4gfh3jV5dB1k03DN7G7dCuDqgZApjJeFM/xvH6KlhgCmb/X4PwZdjSew/uxDgwDKVT/1ICjLt/4Rz8GwF/7+l/wN/7z9172M5CYeW6i28tNRK5G7JlEK86E7xF7JSKvTOLVsFQJRzh4UUo5ijGFhl0rs2XXVq67+S62bd6JYRa/BgsKXmuKn7qCHxpJz1HoJD2hsHbwUhyTdHOhkIX5PAUZtyBpI+M2WVJHyHzwkpJdJF2kCtFV3BMKKbrIelMoJVoKZC4q8/Luh8xFky566qBLG1PZOMoCaaJpA+iMYhgOumEhDY3OkMHygKDrzWOHjV6ngkMpG8SyK2g1DTXQwRiWTJctjuk1FtIqpXbIhvYCI+kF3OULbFtaZGszRdZNKosDjHY24Yq30QpGqXsOid5iuTSPMhXDNux0hnG93SybuatxSK/T9Z6iVpulWp1lc22WxDU4wj4e5r0c4QbmGUVrpBi9oKjTaOOScLd+hPv1Z7nPOciEtkQKPOW6/HYwwDf9gOXeAmMbFhU/+4LknUc0Rhq54JCazuzoDZzZ/i663k4gH7AkVUwmn0CGRyBdnZ1tkbHbvYZrBt6N646DApVFxKe/zenoBZ7bu5uunwuXkbk5bn72IM6uRRo/J1A2GEsw8L+aqIsWn/qH/y86fsCOiye588T/zdM3WkSemY+v9lxit0TilRB2FZMSgXQZsmw2jm3kpltvY/OuPYWAKCh4g1EETguAfIzz2vHNK/ftXvkhDtuk3RZJ2EJGTWTcQcW5YBBpE5E1kTKfpyBVF0UXRYwpY4wsxRAZCIGWCfRUooSFSl0QHlqat0pqmYMuXAxlo0sbXRpoWICOrtnoho1mmuiGhabraLoBlo6m590Qmk5e6LdSMiMBDUJbMO+btB1BTB2zO0+pGeK3DKyOSyAHcY0auhNASUKtSacWMlszmHJ9LoghFuIBqo2QLZ1ZtiQvEZhnEfY8URIR1i2sJZMNizWGO5sx9M00yhM0PJdEbyGzGXxChpwaw84GBp0NSMdnzmgyq9eZ0ZcR3gLVntioVmfBlRxnDy+wnyPcwHltG0QCYzHqZze0TDHCMvcaz+XlFP0wnpYQaRqPeS7f8AO+43t0jDyMONRQvPOIxjuPaGxcWK1ftIIxzmy7h6WB25HmateD0z6O1XiEhl0nNXMhokvFhkbI9c711Lbdjx4MA6CSDsnpbzG78BQHb7ie+dFcdNhRh/HJQ9jJFKV3ZpSvz1tEGgsBp07sQlqjPHntfTw+dA0VXePAndez2S0GRBUUvFEpul3e5KyMcc5LD6tuwuqExi5p2CRZKT1EbVSc32TaQmQNhMjLD0p1kERAiCFjdJX2pjSuiAUFKajMgbWugvDQMxtduhjSQlcWujLRMFEY6LqNYVhgmmi62RvKpKOZGug6mrEiFhRY+WAm7CQf0GT2BjTZisjSiCzoajpR5GC2XIKOgUfeall3TBIjQagWerxAqblIuSlxuzZmGGBlFVxjENsOwHVRvkCrNkgrTRaqktnAZtqsMZmMstgdpNrocE3nItfGZxgwz6KcWZpmh+WOiVi2GFzQ2Lw4wEB3M5m7mWZpIw0/INGaSDGDIZYYsByGnI0MuRuoOmO0zIxZvcGMXmdOr2P4y7nQqM1Qrc5hOAkvspMj7OcFbuAldiKUgb6c9Ntg9XYGKPZqZ7lff5b7jWfZr+fzM9qaxiO+x1f9Eo/5DnFvTZFKR/Guw5J7Dyk2Lq75/Bgus6O35ivIVnf0t5txk+rit4nFSRYDHdXronDSjLFOly2DNzGy4b3YVg2AWLY4lT7OCesE3dJmYpnnH0zT5O1vfzv33HMPUi5w+IVfodl8HoDt2z7G9u0fQ9MMDiw2+euHTgPwv+/bxgdHaj+0n5mCgoIfPq+LbpcvfOELfPazn2VmZoYbb7yRz3/+89xxxx0/rLd73ZIJSTddLTl01wQaO0lGFIYkYZO021scKm6heq4CSTvPKYg2SjaRsoskBC0CIkyZYogETWTomcyXd84UKjV7YsFHEw566qFJBz1zcGRehsiFwhAwCj2xoBlWPohJN9ANE03XwFwRChqaTb7CpJWhWWlvmmOKslJiR/XEAkSWRmhK2rqgoxl0gFAZpNJAyxRmJjEzgRMJvDCj2hVUY51SouE4GnHJYlGzWcQmSxK0bgdNLlBqzRO0G5TaBla3RJCUGchK2HoV1xrAdCfAMZGOhM1NRHmZZjnkTHmJRTdmUo0xHY6z2L6OoVaT3RfPsis6wy7zMIYzQ9tpckEZdGOLxUWDYA42Lg9yTXIjob+ZRnkzTb/E3GiTWTGLzGYo6XOMmhMMORsYdu7CtqvMG01m9DrP6HUWtHM4fqPvbNxUncVyIs6zlad6zsZx9hBrLlony4XGYgNrMQIJLjH36Ee433yG9+rPMKLnszOWdZ0/8wO+bgU8WXZIew6HHyl+5DnBfc8rNizBShOmYv2oc2n0HAYlsKLDiPR7dGWTtmewUnKpbBjjtvd9iB3ODXQenUZ2csfEqNiU3rUJ95YR5p8fJPxOQBzlJZm9e/fy3ve+l1qtxuLiIxw5+vdJ02VMs8rePf+C4eH3ADATp3zs2DkAfn7jcCE8CgreYvxQnI8/+ZM/4W/+zb/JH/zBH3DnnXfyuc99ji9/+cucOHGC0dHR7/vaq+l8CKno9vIJebmhl09IMtqxIAwjkrBFFjZJoxYy6uQ5hbiNStqotEkmWsheoFHRRWkhGjG6SjB7QkETvZxCJlGphszsnqvgoWcuunDRhYMuHExpY0gLTVl9VwHNQDcsMPKJjbpuousGmqHnN518fQgdlA6YGdhZ32FQVkpqi1ws2BBbEFrQ1XU6mkUHmy42kbQRQsfIFKaQWJnAylLMLMRQIZbsYMgQJ+vgpm3sJMZKDMzYw0qquGIIU69iWiaUEuIKLFd0Zh2XZSx0kWCmHcxsCS9eoNxZxG93cbo+ZljBSAZAljDwcewylu1gOjbYOtIGVW6TlReJgiUWyxpLvs+iXeNCspHp9hj1doXx1gK7u2fZHZ9li34ex50ksuucsXVmEguxZDE2r7F1TrFheRhHbaZV3kyzvJmmP0CqNZHZTM/VWGbIGWbI3ciQs4FBZ4LE1JjV6szodWb1OnW9g+83+iWUam0W246YYyyfIsp+jrKPllaFTKIvxXlXynwXovxHcYwl7jUO8n6e5C7jGI6ez82YNQwOOD7fcAIOVixkz+GwU8V9z0nec0ixZX79aO7FkXEu7ryPZnALaer2tw+M+2zZq9NZepqTj3+HJOwCYDkue955Lze+6/3YZ3Taj02jovz9jUGX8rs3EdwyxulzZ/ja177G/Pw8AOPj43zgAx9g69atKCU5e/b3OX3mc4CiXN7L/n1fwPM25z9nSvEzz73Eo/U2+0oef3nLTlzjlU7ZKCgoeL1x1csud955J7fffjv/+l//ayAf/bx582Y+9rGP8Ru/8Rvf97U/6MFLqeima3IJyWoHRC4cEpJuiyxskYRtZNxERh1I2pC0kEk7zymoFkq2kSoELUTTY1AxhkrRRYqeip5YUCgBMtHzRaJ6YsHInDzYmDlY0slLEDIXCmCh0NEwwLLBMNFME0M3eoJBy0sRBrmzoAMaKFOCnaLZuVDQzJTMSols+oIhsiA0NDqaSUez6WIRKZdIWuiZQu85DFYmsEWCKbqYIsRQXWzZwRYdnLSLnYbYWYyRCIzMRM9MDOFCaucLYkkfJSqAj675OIaHadnopobmZWilLlE5pV6CRd9iVg/oSAMzSfGTDna6iC0WKEWLBJ0GTkdghlX0aBAjHUAXJdBsDDvAclxc08K0dZRlIm2DrNQmLc2T+XO0SinLgc+yV2FGG+FCZxNznRHabZeN7Vl2d86yOz3HTu0CJfsiibPIeUfjJd2i2bbxFnW2zSm2zmqMN4aR9mZa5S20SltoBUMkWhPVExoym6Fs+gw7GxhyNjDkbqRsDbKsd5jRG8z2xEZXi/H9OtXabN76Wp3FtmMaVPuDvY6wn3ltDJRCa+fuhjXXRaunKDQ0JPu0s7xXf5q/or7HtfZ0/3N+wTT5qhPwDT/gRGnVqDSE4oHJQR54qcLE85MQroZBzV3X0bjrpziv7WD6XNjfbrsG1942QnlwlrPPfZtzzz/b/9rAxAZueuBHuP7WdxI/vUTniWlUkg/KMEc9yu/Zgn/DCMuNZb7+9a9z4sQJAHzf59577+WWW25B13XStM6Ro7/G4uJ3ANiw4WfZtfOTGIbTf69/cWaGz56dwTd0HrxtF9f4q6KooKDgjctVFR9JkuD7Pn/6p3/Khz70of72D3/4w9Trdf7iL/5i3f5xHBPHq8syNptNNm/ezK/80XcRprumRJGXLEaic3wo/QqODPG1iBIRvhYREOMT8V9qkkVTkeiKrq7R0TW6mk5X1xiZ0bnhGJiphp2CF4Ob5qOShXUN3fItaJqD0hyE4SBMF2E4hCySqCmkJlFkoBKUShl3xhj3NmBpBqZuYmoWlm6DbnDEnUdqIDRFpkkSTeCNnMCrTaIZMZqRopsJHdPjQfe9xLpNrDnEukOEx/KkT9SwyISByAykgL8ef4M94ixl1SVQISUV8mi1y6ydkuqSWJeUlhV7XjDQUzATDSuFxL8PjBE0XDQcEi2iq00jNYXUBFW7xrC/i5aq0ugeJFVtrivtJHFgwYkwhiZxRl8Ep8OfV99H0yhx7vQoYeRwT/cw70+eoOHMcMZv07YShs/C1tMGQdtF+n8FTR+kZcWEeh2hhUzYA4y7IwRmlbqnWLRi2lpE11mktvlZXL+B57V40ruNI9p+ptjITDhGdi7LSxOdjJ9IvsOd+jF26RfZqU3yYFnnGdfhlG3zkmUxtgAffEqybU6xeR5iZ4Lpibt7YmMTwvTIomeR2TRKzKBkA4A9tXsYdnJnwzZcJIrnjDPM6g3m9AaplneH1AammJg4SbUyh2Xnn98Qlz/lf+KIuoEL+tb+Z1pfjDAnO1hzXYRY/Qv/bv0IP6M/xHvM56nR6m9f0A2+vGkXBzybE8nyup+Xd+nX89NPGow++RI0Vl9jbdqEfv+HOF26jdMvRqRRbzERDTbtHuC6u8dpTD/GoQNfoTE32/uaxo5bbufmB36EjZuvp/3IJJ2nZvIl5wFrIqB87xa8vUNkIuOhhx7i8ccfRwiBruvccccdvOtd78LzvPxnt/UChw9/lCi6iK477N7122zY8FPrjv+x5TY/9dyLSODz12/hp8cHv+/vk4KCgjcOVzXzsbCwgBCCsbGxddvHxsY4fvz4Zft/5jOf4VOf+tRl2//iuSl0x79s+wZ9jr9hf/1lZyE/XBrnuHPlxPxEQ3LfsxK4XG9d3LCBc+PvvuLrsmiaLDx22fZB+1p2VW64bHtKxin76GXbrxk4x8iGk+v/bUb4pvbAZftaC0sYsyEg0BAYwP3WU9xvHMwL+b1i/r8tjfI9z2WlTr9/TvILB9ePdnzq1ltplVcvhiI5Sdr5bv80lPSAIXcb57qCKDkFssnW0Xs577Q4Yx1nonKRic2HAHiCO+loZZwLU2hCcZ1xlr9mfZs/dCt8pVYDHH5qSXL3CxKppXznne8BTSftPoyI83MyUt7OtZWbADhtHue4OQWArYfs3bx63k5wPY9p7wRAUynO+Ub/a++3nuIB4+n+828EIzzqe/3n1QjuPbT6fV72a1zYfF//uaYlKHEQma7+m8tmlR3l3QS92RapStAWz3Ji9Hy/a8RKEoYXFpjIThDsvwiASkDO2OhLAY/sv4+OmX9ud2kZ7yj7NJcU/2V6EYGOa+ncvWOId+0a4cfnvkP1+e/mb26X4Jp7Ydf7EZtu5ve/8rOQdDA0g9vHb+f+Lfdz75Z7qS7FvPTp9wJgjAxT+cAHqP6Vv4J7440sXGxz4tNPAVAZdrnu7gl23zVOZSg/L1/+/x6kMTeLG5TYd+/7uOl9f4Xq6DgA3YNzdB7PHRd7S5nyvVtwdw/0R3frSuf48eMIIbjmmmt4//vfz8jIyLrP2dLiw0TRRVx3Mzfs/wLl8l4u5ZuLTSTwM+MDhfAoKHgLc9Wb43/zN3+Tj3/84/3nK87Hr96/k+HBGr5tEtgGgWMSOAaVeDuNk21Mr4ztVzDdEppTyn952yV+fPq71GWM71Tx3Vp+b/kEVoC/ax5j6CnschW7VMGp1LCCCnoQEHRsvEmwSw52ycUpe1iBg+Oa1GfLzJ4exQ183FKAVw5wAh+7bqEtgOFZmIGDFbiYgYvumtz9QojjOji+i+u7OL6LUNeSiRexrDKOXcG2K2y3avztpZSyZVM2bSqWQ8XyOGYFLC7H1DyLqmsy6NqMzP81LoZvx/AqWF4V26/w/vAUt6gugVOl5A5Q2QZdeQSrMoBdqWGXq+xYGkWi4ZV97JJDqvYyc17iVcu4pRIlrQSzOrtaBvXGnWR6zEUWSVyN7dYYgaFIWl0sp8JPeMe5qA0yu8uj5Hi0ojv4P5dNFiomd1lt9sRzbN3p0CgpjlRCTHUIc9ChtpSg5oap1AJMx2NG1dHKDrLRZKPh4wc+G8QY9nITT9/AZEfj9mArm4JTbLZnGKmZfOmaLYwPmKjM5MXGe3DVdibEHMvlCSLzPDeZFnundHbXfZz0Zpb3n6JVsokGK3ijO9m4LHCriqazTG3bQZoXHU6KG+hu2Mqk2IhulVmYOs6CNsmTlbOUZZedx1tsK2/BqQ2xdct2tl9zPf7YBKnTYXbuLxkYuJNyeR+6bgEwP7lAzTJ4W63MsJ3/eB2ZaDDuOrxr1yi3bRvAtXKhyIWfBkeDXQ/AtreDmZcmxoC/tfdvcU3tGt696d3U3NrqD4wPI7/6q3g33Yh/++1ohtH/0sjmMrd+YCubrxtkw85aHhhew10//jNc97Z3ct0978Ry1pc6vBtG8E8u4982hrOjetl6IYZh8CM/8iPEccyuXbuuuJ7I1q2/BMDGjX8dy7ryYmWfuHYDN1V87hssJoMWFLyVuepll0spWm0LCgoKCgreeLyS6/erHjG3bZtbb72VAwcO9LdJKTlw4AB33333q/12BQUFBQUFBW8wfihll49//ON8+MMf5rbbbuOOO+7gc5/7HJ1Oh5//+Z//YbxdQUFBQUFBwRuIH4r4+Nmf/Vnm5+f5xCc+wczMDDfddBNf+9rXLguhFhQUFBQUFLz1KMarFxQUFBQUFPwPc1UzHwUFBQUFBQUF349CfBQUFBQUFBS8phTio6CgoKCgoOA1pRAfBQUFBQUFBa8phfgoKCgoKCgoeE0pxEdBQUFBQUHBa0ohPgoKCgoKCgpeUwrxUVBQUFBQUPCaUoiPgoKCgoKCgteUH8p49f8RVgauNpvNq3wkBQUFBQUFBT8oK9ftH2Rw+utOfLRaLQA2b958lY+koKCgoKCg4JXSarWoVqvfd5/X3douUkqmpqYol8tomna1D+eHTrPZZPPmzVy4cKFYy+Y1pDjvV4fivF8divN+9XgrnXulFK1Wiw0bNqDr3z/V8bpzPnRdZ9OmTVf7MF5zKpXKm/6D+XqkOO9Xh+K8Xx2K8371eKuc+/+W47FCETgtKCgoKCgoeE0pxEdBQUFBQUHBa0ohPq4yjuPwyU9+EsdxrvahvKUozvvVoTjvV4fivF89inN/ZV53gdOCgoKCgoKCNzeF81FQUFBQUFDwmlKIj4KCgoKCgoLXlEJ8FBQUFBQUFLymFOKjoKCgoKCg4DWlEB9XibNnz/ILv/ALbN++Hc/zuOaaa/jkJz9JkiTr9jt06BDveMc7cF2XzZs387u/+7tX6YjfPHz605/mnnvuwfd9arXaFfc5f/48H/zgB/F9n9HRUf7hP/yHZFn22h7om5AvfOELbNu2Ddd1ufPOO3nyySev9iG9qXj44Yf50R/9UTZs2ICmafz5n//5uq8rpfjEJz7BxMQEnudx//33c+rUqatzsG8iPvOZz3D77bdTLpcZHR3lQx/6ECdOnFi3TxRFfPSjH2VoaIhSqcRP/uRPMjs7e5WO+OpTiI+rxPHjx5FS8sUvfpEjR47wr/7Vv+IP/uAP+Ef/6B/192k2m7zvfe9j69atPPPMM3z2s5/ln/7Tf8of/uEfXsUjf+OTJAk//dM/zUc+8pErfl0IwQc/+EGSJOGxxx7j3/27f8cf/dEf8YlPfOI1PtI3F3/yJ3/Cxz/+cT75yU/y7LPPcuONN/LAAw8wNzd3tQ/tTUOn0+HGG2/kC1/4whW//ru/+7v83u/9Hn/wB3/A9773PYIg4IEHHiCKotf4SN9cPPTQQ3z0ox/liSee4MEHHyRNU973vvfR6XT6+/z9v//3+S//5b/w5S9/mYceeoipqSl+4id+4ioe9VVGFbxu+N3f/V21ffv2/vPf//3fVwMDAyqO4/62X//1X1e7d+++Gof3puNLX/qSqlarl23/yle+onRdVzMzM/1t/+bf/BtVqVTWfS8KXhl33HGH+uhHP9p/LoRQGzZsUJ/5zGeu4lG9eQHUn/3Zn/WfSynV+Pi4+uxnP9vfVq/XleM46j/+x/94FY7wzcvc3JwC1EMPPaSUys+zZVnqy1/+cn+fY8eOKUA9/vjjV+swryqF8/E6otFoMDg42H/++OOP8853vhPbtvvbHnjgAU6cOMHy8vLVOMS3BI8//jj79+9nbGysv+2BBx6g2Wxy5MiRq3hkb1ySJOGZZ57h/vvv72/TdZ3777+fxx9//Coe2VuHM2fOMDMzs+57UK1WufPOO4vvwatMo9EA6P8+f+aZZ0jTdN25v+6669iyZctb9twX4uN1wosvvsjnP/95fvEXf7G/bWZmZt0FEOg/n5mZeU2P761Ecd5ffRYWFhBCXPG8Fuf0tWHlPBffgx8uUkp+9Vd/lbe97W3s27cPyM+9bduXZczeyue+EB+vMr/xG7+Bpmnf93b8+PF1r5mcnOT9738/P/3TP83f/bt/9yod+Rub/57zXlBQUPBq89GPfpQXXniBP/7jP77ah/K6xrzaB/Bm49d+7df4W3/rb33ffXbs2NF/PDU1xXve8x7uueeey4Kk4+Pjl6WhV56Pj4+/Ogf8JuGVnvfvx/j4+GVdGMV5/x9jeHgYwzCu+Hkuzulrw8p5np2dZWJior99dnaWm2666Sod1ZuLX/mVX+Ev//Ivefjhh9m0aVN/+/j4OEmSUK/X17kfb+XPfyE+XmVGRkYYGRn5gfadnJzkPe95D7feeitf+tKX0PX1RtTdd9/Nb/3Wb5GmKZZlAfDggw+ye/duBgYGXvVjfyPzSs77f4u7776bT3/608zNzTE6Ogrk571SqbBnz55X5T3eati2za233sqBAwf40Ic+BOT29IEDB/iVX/mVq3twbxG2b9/O+Pg4Bw4c6IuNZrPJ9773vZft/Cr4wVBK8bGPfYw/+7M/4zvf+Q7bt29f9/Vbb70Vy7I4cOAAP/mTPwnAiRMnOH/+PHfffffVOOSrz9VOvL5VuXjxorr22mvVfffdpy5evKimp6f7txXq9boaGxtTP/dzP6deeOEF9cd//MfK9331xS9+8Soe+Rufc+fOqYMHD6pPfepTqlQqqYMHD6qDBw+qVqullFIqyzK1b98+9b73vU8999xz6mtf+5oaGRlRv/mbv3mVj/yNzR//8R8rx3HUH/3RH6mjR4+qv/f3/p6q1WrruooK/sdotVr9zzOg/uW//Jfq4MGD6ty5c0oppX7nd35H1Wo19Rd/8Rfq0KFD6sd+7MfU9u3bVRiGV/nI39h85CMfUdVqVX3nO99Z97u82+329/mlX/oltWXLFvWtb31LPf300+ruu+9Wd99991U86qtLIT6uEl/60pcUcMXbWp5//nn19re/XTmOozZu3Kh+53d+5yod8ZuHD3/4w1c879/+9rf7+5w9e1Z94AMfUJ7nqeHhYfVrv/ZrKk3Tq3fQbxI+//nPqy1btijbttUdd9yhnnjiiat9SG8qvv3tb1/xs/3hD39YKZW32/6Tf/JP1NjYmHIcR913333qxIkTV/eg3wS83O/yL33pS/19wjBUv/zLv6wGBgaU7/vqx3/8x9f9sflWQ1NKqdfQaCkoKCgoKCh4i1N0uxQUFBQUFBS8phTio6CgoKCgoOA1pRAfBQUFBQUFBa8phfgoKCgoKCgoeE0pxEdBQUFBQUHBa0ohPgoKCgoKCgpeUwrxUVBQUFBQUPCaUoiPgoKCgoKCgteUQnwUFBQUFBQUvKYU4qOgoKCgoKDgNaUQHwUFBQUFBQWvKYX4KCgoKCgoKHhN+X8AYRikTF2Q1JAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wealth distribution histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.insert(0, os.path.abspath(\"..\"))\n",
    "\n",
    "\n",
    "prob_dead = np.genfromtxt(\"life_table.csv\", delimiter=\",\", skip_header=1)\n",
    "prob_surv = 1 - prob_dead\n",
    "prob_surv_list = np.ndarray.tolist(prob_surv[:79])\n",
    "\n",
    "income_profile = np.genfromtxt(\"productivity_profile.csv\", delimiter=\",\", skip_header=1)\n",
    "income_profile_list = np.ndarray.tolist(income_profile[:79])\n",
    "\n",
    "Ex_dictionary = {\n",
    "    \"CRRA\": 2.0,\n",
    "    \"Rfree\": 1.05,\n",
    "    \"DiscFac\": 0.99,\n",
    "    \"LivPrb\": prob_surv_list,\n",
    "    \"PermGroFac\": income_profile_list,\n",
    "    \"cycles\": 1,\n",
    "    \"T_cycle\": 79,\n",
    "}\n",
    "\n",
    "Ex_agent = PerfForesightConsumerType(**Ex_dictionary)\n",
    "Ex_agent.solve()\n",
    "\n",
    "Ex_agent.unpack(\"cFunc\")\n",
    "\n",
    "min_v = min(Ex_agent.solution[t].mNrmMin for t in range(11))\n",
    "max_v = -min_v\n",
    "print(\"Consumption functions\")\n",
    "plot_funcs(Ex_agent.cFunc[:], min_v, max_v)\n",
    "\n",
    "\n",
    "Simulation_dictionary = {\n",
    "    \"AgentCount\": 1000,\n",
    "    \"aNrmInitMean\": -10.0,\n",
    "    \"aNrmInitStd\": 0.0,\n",
    "    \"pLvlInitMean\": 0.0,\n",
    "    \"pLvlInitStd\": 0.05,\n",
    "    \"PermGroFacAgg\": 1.0,\n",
    "    \"T_cycle\": 79,\n",
    "    \"T_sim\": 2000,\n",
    "    \"T_age\": 80,\n",
    "    \"BoroCnstArt\": 0.0,\n",
    "}\n",
    "\n",
    "for key, value in Simulation_dictionary.items():\n",
    "    setattr(Ex_agent, key, value)\n",
    "\n",
    "Ex_agent.track_vars = [\"aNrm\", \"cNrm\", \"aLvl\"]\n",
    "Ex_agent.initialize_sim()\n",
    "Ex_agent.simulate()\n",
    "\n",
    "\n",
    "sim_wealth = np.reshape(Ex_agent.history[\"aLvl\"], -1)\n",
    "print(\"Wealth distribution histogram\")\n",
    "n, bins, patches = plt.hist(sim_wealth, 50, density=True, range=[-1.0, 2.0])\n",
    "\n",
    "pctiles = np.linspace(0.001, 0.999, 15)\n",
    "# SCF_Lorenz_points = get_lorenz_shares(SCF_wealth,weights=SCF_weights,percentiles=pctiles)\n",
    "sim_Lorenz_points = get_lorenz_shares(sim_wealth, percentiles=pctiles)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.title(\"Lorenz curve\")\n",
    "plt.plot(pctiles, sim_Lorenz_points, \"-b\", label=\"Lorenz curve\")\n",
    "plt.plot(pctiles, pctiles, \"g-.\", label=\"45 Degree\")\n",
    "plt.xlabel(\"Percentile of net worth\")\n",
    "plt.ylabel(\"Cumulative share of wealth\")\n",
    "plt.legend(loc=2)\n",
    "plt.ylim([0, 1])\n",
    "plt.show()"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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