ipynb/mario_silva-krusell_smith.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "\n",
                "Created on Sat Aug 19 21:42:38 2017\n",
                "\n",
                "@author: Mario Silva\n",
                "Code for solving Krusell-Smith model\n",
                "(Aiyagari model with aggregate productivity shocks)\n",
                "\n",
                "Implements algorithm by Lilia Maliar, Serguei Maliar, and Fernando Valli (2008)\n",
                "combines\n",
                "    1) stochastic simulation approach in Krusell-Smith\n",
                "    2) Euler equation method to solve for HH policy functions\n",
                "\n",
                "State variables:\n",
                "    two individual states are $(a, \\epsilon) \\in S$\n",
                "    two aggregate states are $(z, \\lambda) \\in Z x \\Lambda$ where\n",
                "    lambda is the measure of households across states\n",
                "lambda represents beginning-of-period distribution of wealth and\n",
                "employment status (epsilon), after this period employment status\n",
                "is realized\n",
                "\n",
                "Individual states are directly budget relevant\n",
                "Aggregate states are necessary to compute prices\n",
                "Law of motion psi is necessary to forecast prices\n",
                "\n",
                "Recursive competitive equilibrium:\n",
                "    (1) individual saving rule is a time invariant function $g(a, \\epsilon, z, \\lambda)$\n",
                "    (2) next period cross sectional distributon is a time invariant function\n",
                "    $\\Psi(\\lambda, z, z')$\n",
                "Key complication: decision rule depends on lambda, which is a distribution\n",
                "To solve problem, HH need to forecast prices next period\n",
                "Prices depend on aggregate capital, which depends on how assets are distributed\n",
                "in the population\n",
                "\n",
                "For individual problem, uses Euler-equation based algorithm:\n",
                "    1) Fix some capital function $k'(k,\\epsilon,m,a)$ on grid\n",
                "       Set initial capital function at $k'(k, \\epsilon, m, a)=0.9k$ for all k, eps, m, a\n",
                "    2) For each grid point $(k, \\epsilon, m, a))$:\n",
                "        a) substitute assumed capital function $k'(k,eps,m,a)$ in RHS of (5)\n",
                "        b) set Lagrange multiplier h=0\n",
                "        c) compute new capital function $\\tilde{k}(k,\\epsilon,m,a)$ in LHS of (5)\n",
                "        d) Restrict $\\tilde{k}$ on $[0, k_max]$\n",
                "    3) Update capital function\n",
                "        $\\tilde{k}_2 = \\eta*\\tilde{k} + (1-\\eta)*k_{\\prime}$, where $\\eta$ is updating parameter\n",
                "    4) Iterate until $|\\tilde{k}_2-k'|<10e-8$\n",
                "        "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\mariors\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
                        "  from pandas.core import datetools\n"
                    ]
                }
            ],
            "source": [
                "\n",
                "import numpy as np\n",
                "from numpy.random import randn\n",
                "import statsmodels.api as sm\n",
                "from scipy.optimize import  brentq, root\n",
                "from scipy.interpolate import RectBivariateSpline, interpn\n",
                "np.set_printoptions(precision=4, suppress=True)\n",
                "import pickle\n",
                "import quantecon as qe\n",
                "import pandas as pd\n",
                "import matplotlib.pyplot as plt\n",
                "from itertools import cycle"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "# Saving options\n",
                "def save_object(obj, filename):\n",
                "    with open(filename, 'wb') as output:\n",
                "        pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)\n",
                "\n",
                "\n",
                "def gen_params():\n",
                "    beta = 0.99  # discount factor\n",
                "    gamma = 1  # utility function parameter\n",
                "    alpha = 0.36  # share of capital in production function\n",
                "    delta = 0.025  # depreciation rate\n",
                "    mu = 0.15  # unemployment benefits as a share of the wage\n",
                "    l_bar = 1/0.9  # time endowment; normalizes labor supply to 1 in bad state\n",
                "    k_ss = ((1/beta-(1-delta))/alpha)**(1/(alpha-1))\n",
                "    return alpha, beta, gamma, delta, mu, l_bar, k_ss\n",
                "\n",
                "\n",
                "def gen_grid():\n",
                "    N = 10000  # number of agents for stochastic simulation\n",
                "    J = 1000  # number of grid points for stochastic simulation\n",
                "    k_min = 0\n",
                "    k_max = 1000\n",
                "    burn_in = 100\n",
                "    T = 1000 + burn_in\n",
                "    ngridk = 100\n",
                "    x = np.linspace(0, 0.5, ngridk)\n",
                "    tau = 7\n",
                "    y = (x/np.max(x))**tau\n",
                "    km_min = 30\n",
                "    km_max = 50\n",
                "    k = k_min + (k_max-k_min)*y\n",
                "    ngridkm = 4\n",
                "    km = np.linspace(km_min, km_max, ngridkm)\n",
                "    return (N, J, k_min, k_max, T, burn_in, k, km_min, km_max,  km,\n",
                "            ngridk, ngridkm)\n",
                "\n",
                "\n",
                "def shocks_parameters():\n",
                "    nstates_id = 2    # number of states for the idiosyncratic shock\n",
                "    nstates_ag = 2    # number of states for the aggregate shock\n",
                "    ur_b = 0.1        # unemployment rate in a bad aggregate state\n",
                "    er_b = (1-ur_b)   # employment rate in a bad aggregate state\n",
                "    ur_g = 0.04       # unemployment rate in a good aggregate state\n",
                "    er_g = (1-ur_g)   # employment rate in a good aggregate state\n",
                "    epsilon = np.arange(0, nstates_id)\n",
                "    delta_a = 0.01\n",
                "    a = np.array((1-delta_a, 1+delta_a))\n",
                "    prob = np.array(([0.525, 0.35, 0.03125, 0.09375],\n",
                "                 [0.038889, 0.836111, 0.002083, 0.122917],\n",
                "                 [0.09375, 0.03125, 0.291667, 0.583333],\n",
                "                 [0.009115, 0.115885, 0.024306, 0.850694]))\n",
                "    return nstates_id, nstates_ag, epsilon, ur_b, er_b, ur_g, er_g, a, prob\n",
                "\n",
                "\n",
                "def shocks():\n",
                "    (N, J, k_min, k_max, T, burn_in, k, km_min, km_max,  km, ngridk,\n",
                "        ngridkm) = gen_grid()\n",
                "    nstates_id, nstates_ag, epsilon, ur_b, er_b, ur_g, er_g, a, prob = shocks_parameters()\n",
                "    ag_shock = np.zeros((T, 1))\n",
                "    id_shock = np.zeros((T, N))\n",
                "    np.random.seed(0)\n",
                "\n",
                "    # ag_shock = np.zeros((T, 1))\n",
                "    # Transition probabilities between aggregate states\n",
                "    prob_ag = np.zeros((2, 2))\n",
                "    prob_ag[0, 0] = prob[0, 0]+prob[0, 1]\n",
                "    prob_ag[1, 0] = 1-prob_ag[0, 0] # bad state to good state\n",
                "    prob_ag[1, 1] = prob[2, 2]+prob[2, 3]\n",
                "    prob_ag[0, 1] = 1-prob_ag[1, 1]\n",
                "\n",
                "    P = prob/np.kron(prob_ag, np.ones((2, 2)))\n",
                "    # generate aggregate shocks\n",
                "    mc = qe.MarkovChain(prob_ag)\n",
                "    ag_shock = mc.simulate(ts_length=T, init=0)  # start from bad state\n",
                "    # generate idiosyncratic shocks for all agents in the first period\n",
                "    draw = np.random.uniform(size=N)\n",
                "    id_shock[0, :] = draw>ur_b #set state to good if probability exceeds ur_b\n",
                "\n",
                "    # generate idiosyncratic shocks for all agents starting in second period\n",
                "    draw = np.random.uniform(size=(T-1, N))\n",
                "    for t in range(1, T):\n",
                "        # Fix idiosyncratic itransition matrix conditional on aggregate state\n",
                "        transition = P[2*ag_shock[t-1]: 2*ag_shock[t-1]+2, 2*ag_shock[t]: 2*ag_shock[t]+2]\n",
                "        transition_prob = [transition[int(id_shock[t-1, i]), int(id_shock[t-1, i])] for i in range(N)]\n",
                "        check = transition_prob>draw[t-1, :] #sign whether to remain in current state\n",
                "        id_shock[t, :] = id_shock[t-1, :]*check + (1-id_shock[t-1, :])*(1-check)\n",
                "    return id_shock, ag_shock\n",
                "\n",
                "def convergence_parameters(nstates_ag=2):\n",
                "    dif_B = 10**10 # difference between coefficients B of ALM on succ. iter.\n",
                "    criter_k = 1e-8\n",
                "    criter_B = 1e-8\n",
                "    update_k = 0.77\n",
                "    update_B = 0.3\n",
                "    B = np.array((0,1))*np.ones((nstates_ag, 1))\n",
                "    return B, dif_B, criter_k, criter_B, update_k, update_B"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Solve individual problem by iterating on policy function"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "def individual(k_prime, B):\n",
                "    dif_B, criter_k, criter_B, update_k, update_B = convergence_parameters()[1:]\n",
                "    (N, J, k_min, k_max, T, burn_in, k, km_min, km_max,  km, ngridk,\n",
                "        ngridkm) = gen_grid()\n",
                "    alpha, beta, gamma, delta, mu, l_bar, k_ss = gen_params()\n",
                "    nstates_id, nstates_ag, epsilon, ur_b, er_b, ur_g, er_g, a, prob = shocks_parameters()\n",
                "    e = np.array((er_b, er_g))\n",
                "    u = 1-e\n",
                "    replacement = np.array((mu, l_bar)) #replacement rate of wage\n",
                "    #Tax rate depending on aggregate and idiosyncratic states\n",
                "\n",
                "    #Transition probabilities by current state (k,km, Z, eps) and future (Z', eps')\n",
                "    n = ngridk*ngridkm*nstates_ag*nstates_id\n",
                "    P = np.zeros((ngridk, ngridkm, nstates_ag, nstates_id, nstates_ag*nstates_id))\n",
                "    for z in range(nstates_ag*nstates_id):\n",
                "        for i in range(nstates_ag):\n",
                "            for j in range(nstates_id):\n",
                "                P[:, :, i, j, z] = prob[2*i+j, z]*np.ones((ngridk, ngridkm))\n",
                "    P = P.reshape((n, nstates_ag*nstates_id))\n",
                "\n",
                "    k_indices = np.zeros(n)\n",
                "    km_indices = np.zeros(n)\n",
                "    ag = np.zeros(n)\n",
                "    e_i = np.zeros(n)\n",
                "    for s_i in range(n):\n",
                "        k_indices[s_i], km_indices[s_i], ag[s_i], e_i[s_i] = np.unravel_index(s_i, (ngridk, ngridkm, nstates_ag, nstates_id))\n",
                "    k_indices = np.asarray([int(x) for x in k_indices])\n",
                "    km_indices = np.asarray([int(x) for x in km_indices])\n",
                "    ag = np.asarray([int(x) for x in ag])\n",
                "    e_i = np.asarray([int(x) for x in e_i])\n",
                "\n",
                "    \"\"\"\n",
                "    Using indices, generate arrays for productivity, employment, aggregate\n",
                "    capital, individual capital\n",
                "    \"\"\"\n",
                "\n",
                "    Z = np.asarray([a[int(i)] for i in ag])\n",
                "    L = np.asarray([e[int(i)] for i in ag])\n",
                "    K = np.asarray([km[int(i)] for i in km_indices])\n",
                "    k_i = np.asarray([k[int(i)] for i in k_indices])\n",
                "    irate = alpha*Z*(K/(l_bar*L))**(alpha-1)\n",
                "    wage = (1-alpha)*Z*(K/(l_bar*L))**alpha\n",
                "    wealth = irate*k_i + (wage*e_i)*l_bar + mu*(wage*(1-e_i))+(1-delta)*k_i-mu*(wage*(1-L)/L)*e_i\n",
                "    # Transition of capital depends on aggregate state\n",
                "    K_prime = np.asarray([np.exp(B[ag[i], 0] + B[ag[i], 1]*np.log(K[i])) for i in range(n)])\n",
                "\n",
                "    # restrict km_prime to fall into bounds\n",
                "    K_prime = np.minimum(K_prime, km_max)\n",
                "    K_prime = np.maximum(K_prime, km_min)\n",
                "\n",
                "    # future interest rate and wage conditional on state (bad or good aggregate state)\n",
                "    irate = np.zeros((n, nstates_ag))\n",
                "    wage = np.zeros((n, nstates_ag))\n",
                "    for i in range(nstates_ag):\n",
                "        irate[:, i] = alpha*a[i]*((K_prime/(e[i]*l_bar))**(alpha-1))\n",
                "        wage[:, i] = (1-alpha)*a[i]*((K_prime/(e[i]*l_bar))**alpha)\n",
                "\n",
                "    # Tax rate\n",
                "    tax = np.zeros((n, nstates_ag, nstates_id))\n",
                "    for i in range(nstates_ag):\n",
                "        for j in range(nstates_id):\n",
                "            tax[:, i, j] = j*(mu*wage[:, i]*u[i]/(1-u[i]))\n",
                "    # Solving the individual problem\n",
                "\n",
                "    def iterate_policy(k_prime):\n",
                "        dif_k = 1\n",
                "        while dif_k > criter_k:\n",
                "            \"\"\"\n",
                "             interpolate policy function k'=k(k, km) in new points (k', km')\n",
                "            \"\"\"\n",
                "            k2_prime = np.zeros((n, nstates_ag, nstates_id))\n",
                "            c_prime = np.zeros((n, nstates_ag, nstates_id))\n",
                "            mu_prime = np.zeros((n, nstates_ag, nstates_id))\n",
                "            #reshape k_prime for interpolation\n",
                "            k_prime_reshape = k_prime.reshape((ngridk, ngridkm, nstates_id, nstates_ag))\n",
                "            K_prime_reshape = K_prime.reshape((ngridk, ngridkm, nstates_id, nstates_ag))\n",
                "            for i in range(nstates_ag):\n",
                "                for j in range(nstates_id):\n",
                "                    # capital in aggregate state i, idiosyncratic state j as a function of current states\n",
                "                    k2_prime[:, i, j] = RectBivariateSpline(k, km, k_prime_reshape[:, :, i, j]).ev(k_prime_reshape, K_prime_reshape).reshape(n)\n",
                "                    c_prime[:, i, j] = (irate[:, i]*k_prime + replacement[j]*(wage[:, i])\n",
                "                    +(1-delta)*k_prime-k2_prime[:, i, j]-tax[:, i, j])\n",
                "                    # replace negative consumption by very low positive number\n",
                "            c_prime = np.maximum(c_prime, 10**(-10))\n",
                "            mu_prime = c_prime**(-gamma)\n",
                "\n",
                "            #Expectation term in Euler equation\n",
                "            #Components in terms of all possible transitions\n",
                "            expec_comp = np.zeros((n, nstates_ag, nstates_id))\n",
                "            for i in range(nstates_ag):\n",
                "                for j in range(nstates_id):\n",
                "                    expec_comp[:, i, j] = (mu_prime[:, i, j]*(1-delta + irate[:, i]))*P[:, 2*i+j]\n",
                "            \"\"\"\n",
                "            Expectation term in the Euler equation\n",
                "             sum over various transitions (which have already been scaled by their probability)\n",
                "            \"\"\"\n",
                "            expec = sum(expec_comp[:, i, j] for i in range(nstates_ag) for j in range(nstates_id))\n",
                "            # current consumption from Euler equation if borrowing constraint is not binding\n",
                "            cn = (beta*expec)**(-1/gamma)\n",
                "            k_prime_n = wealth-cn\n",
                "            k_prime_n = np.minimum(k_prime_n, k_max)\n",
                "            k_prime_n = np.maximum(k_prime_n, k_min)\n",
                "            \"\"\"\n",
                "            difference between new and previous capital functions\n",
                "            \"\"\"\n",
                "            dif_k = np.linalg.norm(k_prime_n-k_prime)\n",
                "            k_prime = update_k*k_prime_n + (1-update_k)*k_prime  # update k_prime_n\n",
                "        c = wealth - k_prime\n",
                "        return k_prime, c\n",
                "    return iterate_policy(k_prime)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "def aggregate_st(k_cross, k_prime, id_shock, ag_shock):\n",
                "    (N, J, k_min, k_max, T, burn_in, k, km_min, km_max,  km, ngridk,\n",
                "     ngridkm) = gen_grid()\n",
                "    nstates_id, nstates_ag, epsilon, ur_b, er_b, ur_g, er_g, a, prob = shocks_parameters()\n",
                "    km_series = np.zeros((T,1))\n",
                "    for t in range(T):\n",
                "        \"\"\"\n",
                "        find t-th obs. by computing mean of t-th period cross sectional\n",
                "        distribution of capital\n",
                "        \"\"\"\n",
                "        km_series[t] = np.mean(k_cross)\n",
                "        km_series[t] = np.minimum(km_series[t], km_max)\n",
                "        km_series[t] = np.maximum(km_series[t], km_min)\n",
                "        \"\"\"\n",
                "        To find km_series[t+1], we should compute a new cross sectional distribution\n",
                "        at t+1.\n",
                "        1) Find k_prime by interpolation for realized km_series[t] and agg. shock\n",
                "        2) Compute new kcross by interpolation given previous kcross and id.shock\n",
                "        \"\"\"\n",
                "        # Stack sampling points for interpolation as len(k)*len(epsilon) x 4\n",
                "        # arr stores the coordinates of the sampling points: k rows, 4 columns\n",
                "        interp_points = np.array(np.meshgrid(k, km_series[t], ag_shock[t], epsilon))\n",
                "        interp_points = np.rollaxis(interp_points, 0, 5)\n",
                "        interp_points = interp_points.reshape((len(k)*len(epsilon), 4))\n",
                "\n",
                "        k_prime_t4 = interpn(points=(k, km, epsilon, epsilon), values=k_prime.reshape(ngridk, ngridkm, nstates_ag, nstates_id),\n",
                "                             xi=interp_points).reshape(ngridk, nstates_id)\n",
                "        # 4-dimensional capital function at time t is obtained by fixing known\n",
                "        # km_series[t] and ag_shock\n",
                "        interp_points = np.vstack((k_cross, id_shock[t,:])).T\n",
                "        \"\"\"\n",
                "        given k_cross and idiosyncratic shocks, compute k_cross_n\n",
                "        \"\"\"\n",
                "        k_cross_n = interpn(points=(k, epsilon), values= k_prime_t4.reshape(ngridk, nstates_id),\n",
                "                            xi= interp_points)\n",
                "        # restrict k_cross to be within [k_min, k_max]\n",
                "        k_cross_n = np.minimum(k_cross_n, k_max)\n",
                "        k_cross_n = np.maximum(k_cross_n, k_min)\n",
                "        k_cross = k_cross_n\n",
                "    return km_series, k_cross"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "def solve_ALM():\n",
                "    # generate shocks, grid, parameters, and convergence parameters\n",
                "    id_shock, ag_shock = shocks()\n",
                "    (N, J, k_min, k_max, T, burn_in, k, km_min, km_max,  km, ngridk,\n",
                "        ngridkm) = gen_grid()\n",
                "    alpha, beta, gamma, delta, mu, l_bar, k_ss = gen_params()\n",
                "    nstates_id, nstates_ag, epsilon, ur_b, er_b, ur_g, er_g, a, prob = shocks_parameters()\n",
                "    B, dif_B, criter_k, criter_B, update_k, update_B = convergence_parameters()\n",
                "\n",
                "    k_prime = 0.9*k\n",
                "    n = ngridk*ngridkm*nstates_ag*nstates_id\n",
                "    k_prime = k_prime.reshape((len(k_prime), 1, 1, 1))\n",
                "    k_prime = np.ones((ngridk, ngridkm, nstates_ag, nstates_id))*k_prime\n",
                "    k_prime = k_prime.reshape(n)\n",
                "    k_cross = np.repeat(k_ss, N)\n",
                "    \n",
                "    \"\"\"\n",
                "    Main loop\n",
                "    Solve for HH problem given ALM\n",
                "    Generate time series km_ts given policy function\n",
                "    Run regression and update ALM\n",
                "    Iterate until convergence\n",
                "    \"\"\"\n",
                "    iteration = 0\n",
                "    while dif_B > criter_B:\n",
                "        # Solve for HH policy functions at a given law of motion\n",
                "        k_prime, c = individual(k_prime, B)\n",
                "        # Generate time series and cross section of capital\n",
                "        km_ts, k_cross_1 = aggregate_st(k_cross, k_prime, id_shock, ag_shock)\n",
                "        \"\"\"\n",
                "        run regression: log(km') = B[j,1]+B[j,2]log(km) for aggregate state\n",
                "        \"\"\"\n",
                "        x = np.log(km_ts[burn_in:(T-1)]).flatten()\n",
                "        X = pd.DataFrame([np.ones(len(x)), ag_shock[burn_in:(T-1)], x, ag_shock[burn_in:(T-1)]*x]).T\n",
                "        y = np.log(km_ts[(burn_in+1):]).flatten()\n",
                "        reg = sm.OLS(y, X).fit()\n",
                "        B_new = reg.params\n",
                "        B_mat = np.array((B_new[0], B_new[2], B_new[0]+B_new[1], B_new[2]+B_new[3])).reshape((2, 2))\n",
                "        dif_B = np.linalg.norm(B_mat-B)\n",
                "        print(dif_B)\n",
                "\n",
                "        \"\"\"\n",
                "        To ensure that the initial capital distribution comes from the ergodic set,\n",
                "        we use the terminal distribution of the current iteration as the initial distribution for\n",
                "        subsequent iterations.\n",
                "\n",
                "        When the solution is sufficiently accurate, we stop the updating and hold the distribution\n",
                "        k_cross fixed for the remaining iterations\n",
                "        \"\"\"\n",
                "        if dif_B > (criter_B*100):\n",
                "            k_cross = k_cross_1 #replace cross-sectional capital distribution\n",
                "\n",
                "        B = B_mat*update_B + B*(1-update_B) #update the vector of ALM coefficients\n",
                "        iteration += 1\n",
                "    return B, km_ts, k_cross, k_prime, c, id_shock, ag_shock"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "# Figure comparing implied law of motion for aggregate capital stock and\n",
                "# actual law of motion\n",
                "\n",
                "\n",
                "def accuracy_figure(km_ts, ag_shock):\n",
                "    T = len(km_ts)\n",
                "    km_alm = np.zeros((T))\n",
                "    km_alm[0] = km_ts[0]\n",
                "    for i in range(0, T-1):\n",
                "        km_alm[i+1] = np.exp(B[ag_shock[i], 0] + B[ag_shock[i], 1]*np.log(km_alm[i]))\n",
                "\n",
                "    fig, ax = plt.subplots(figsize=(9, 6))\n",
                "    ax.plot(km_ts, label='Implied by policy rule')\n",
                "    ax.plot(km_alm, label='Aggregate law of motion')\n",
                "    ax.set_xlabel('Time')\n",
                "    ax.set_ylabel('Aggregate capital stock')\n",
                "    ax.legend(loc='best')\n",
                "    plt.show()\n",
                "\n",
                "\n",
                "#Capital accumulation policy function\n",
                "\n",
                "def plot_policy(k_prime, km_ts):\n",
                "    (N, J, k_min, k_max, T, burn_in, k, km_min, km_max,  km,\n",
                "            ngridk, ngridkm) = gen_grid()\n",
                "    nstates_id, nstates_ag, epsilon, ur_b, er_b, ur_g, er_g, a, prob = shocks_parameters()\n",
                "    k_prime = k_prime.reshape((ngridk, ngridkm, nstates_ag, nstates_id))\n",
                "    nstates_id, nstates_ag = shocks_parameters()[0:2]\n",
                "    percentiles = [0.1, 0.25, 0.75, 0.9]\n",
                "    km_percentiles = np.percentile(km_ts, percentiles)\n",
                "    km_cycler = cycle(km_percentiles)\n",
                "    fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))\n",
                "    for a in range(len(km_percentiles)):\n",
                "        m, n = np.unravel_index(a, (2, 2))\n",
                "        for i in range(nstates_ag):\n",
                "            for j in range(nstates_id):\n",
                "                ax[m, n].plot(k[0: 40], RectBivariateSpline(k, km, k_prime[:, :, i, j]).ev(k[0:40],\n",
                "                        next(km_cycler)), label='Aggregate state = %s, Employment = %s' % (i,j))\n",
                "                ax[m, n].set_xlabel('Capital accumulation: percentile = %s' % (percentiles[a]))\n",
                "                ax[m, n].legend(loc='best', fontsize=8)\n",
                "    plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "0.673011591188\n",
                        "0.0272453471851\n",
                        "0.00621594530294\n",
                        "0.00367831811199\n",
                        "0.00176765680718\n",
                        "0.0010982009191\n",
                        "0.000792942868968\n",
                        "0.000584418458347\n",
                        "0.000439749770803\n",
                        "0.000332329624831\n",
                        "0.000253264303282\n",
                        "0.000194483025288\n",
                        "0.000150581404414\n",
                        "0.000117462591906\n",
                        "9.22776656205e-05\n",
                        "7.29397777335e-05\n",
                        "5.79718447986e-05\n",
                        "4.63289128792e-05\n",
                        "3.72162471849e-05\n",
                        "3.00355487071e-05\n",
                        "2.43365815177e-05\n",
                        "1.97776821757e-05\n",
                        "1.6120307933e-05\n",
                        "1.31824964844e-05\n",
                        "1.08178279391e-05\n",
                        "8.90883917688e-06\n",
                        "7.36223174602e-06\n",
                        "6.10360819094e-06\n",
                        "5.07802923163e-06\n",
                        "4.23803215667e-06\n",
                        "3.54672735806e-06\n",
                        "2.97526279603e-06\n",
                        "2.50133706989e-06\n",
                        "2.10742564813e-06\n",
                        "1.77927585034e-06\n",
                        "1.50543414271e-06\n",
                        "1.27655799133e-06\n",
                        "1.08461502784e-06\n",
                        "9.23406042522e-07\n",
                        "4.54013494811e-07\n",
                        "2.43213725123e-07\n",
                        "1.35989204764e-07\n",
                        "7.85650257874e-08\n",
                        "4.70680623513e-08\n",
                        "2.91975661835e-08\n",
                        "1.85052315621e-08\n",
                        "1.19591030046e-08\n",
                        "7.88561495799e-09\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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kSd0xoEiSpO7MGFCSbE5yIMnzQ7VzkuxI8lJ7P7vVk+TeJJNJnk1yydA+G1r7l5JsODWH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\n",
                        "text/plain": [
                            "<matplotlib.figure.Figure at 0x25188bade80>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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77/nDl/4MwzBa8vmdjLKkKA4K/vOJfeimzamr+/h+8KuQBLi81cMipxsMyFFs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\n+cSRUt4hpfwAsEVK+YGKfx+TUt7fxDE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dF9AV1OdWomxlsJW/mCQrElVbztciHJthZd0A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\nfAAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<matplotlib.figure.Figure at 0x2518fd62550>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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gjIrClJSEZmeHnb8/tt7e/HPypOQwLNsGqEyTZrGFU6abxZqyIDkSUrI7xrj6\notwCMCenkXXuAiojAxsnJ+wDH8DGzU3GPIhiITmscMpsDlMK0mIh6Zo+M9HeFbwfwIwDxsgoTAkJ\naLa22AUEYOfjI10w7pJc6RLiXpmMkJJdbCkzuGQXW2mZGCOjMKenoTk6Yl+uHDYeHnmKLXlv6+RK\nlxBWohSkx+tLP5gywN4Z3AMx2zhhjI7GFBcPNhp2vr7Y+fndVGzJ+1snV7qEKGpmkz6LJzlaX/rB\n2Rvcy2PKMGG8FIE5NRXN3h77ihWx9fKSK1tCiJJDKUhPhKSrYEwDOyfwroKyc8VoMGCMvQyAna8P\ndv7+aHZSLliCvIpCFJbZpF/VSo7Uiy0nT3CvgNkIWeHXMCcno9nZYV+hArbe3jK4VAhRciiVvTDz\nVchKBVtH8HoA5eCBMSYGY8wVMCtsvb2w8/fHRtqNWZQUXUIUlNkMqdnFltmo90d0D8SsbDFejcKU\nqI95sA8IwNbXV4otIUTJcr3YykwBWwfwrIxy8sYYE4sp5jTKZMLW0xO7cuWwcXS0drT3Jflf4QbS\nLLbgykyz2PPn2LR6BUQdh8QI/TK8b3XM7kFkRsWScfo05uQk7Pz9caxRQ78Un11wHTx4kHr16hEc\nHFzscYuySXJYwZWZHHbqOJt++VbvgmHMBM9KKL9aGNM0Mk6fxhgViY2LC47VquFQuXKegktymGVJ\n0ZWLNIstnPu+WaxSkBrDhYNb2LT+D/2ToW81zJ7BZMUkknH6NKaEBOx8/XCsUQP7gICbBplWq1aN\n3bt356z+LURRkhxWOPd9DstMhZizXDi8g01btutdMPxrY8yw1dcKvHYVzdERxwcfxOGBB7DJpw+l\n5DDLKlVfL16bMIGME/fWMNaxdi3Kv/12vvukWaw0i81pFpuVQawhknWLZzJv8c/s2HuYXUfOsXbJ\nEowxet81Wy9v7Mr5Y2Nvf8uf093d/a5eH3H/Ker8BZLDJIdl57DPpmLOSiM2JoZ1S+cy78c17Phr\nPzsPnuSP+fNRmZnYODvrk3zc3G77c0oOsyy50pWLNIv9V5lsFjtnDmQkQ0YSvy/4lK4d2rDx4EVe\nHjaGZ3v3ZvXMmRgNBmw9PXGsXp3Q1ato//jjt20WK0Rxkhz2rzKZw2bNhMRrkJnM74u+oGuXzvx5\n5ApDBg2hf7durJ41C83GBoegIBwefJCvlyy5Y8NrYVml6krX7T7h3StpFptXmWsWm5lB80YPQQ1/\n6taqDt7BBFavjyEyBue0LMwpKdi4uetXtrI/GRekWawQ1xVl/gLJYTcqUzls7RqMGWk0b1Qbaleg\nbr36qHIPUSEwGMPpszj7+4HAB0AQAAAgAElEQVRCH6+Va61AyWHF745Fl6ZpC4EngSilVN189rcF\nfgPOZ29aoZT6MHtfZ2AaYAssUEpNvPHxJYU0iy2DzWJr1GBg/768PuRpyEoly6yx4+glcPXFmGLG\nGBmJMplwCAxE8/TEIShvj7qCNIsV1ic5THLYdfddDqv2IAP7dOf1F58GIMvBix2HzqBsTpF58RJG\ngwHN1han8uXB0wNbT8888UsOK34F+XoxFOh8h2O2KaUaZt+uJytbYBbQBXgI6K9p2kP3EmxRkmax\nZaxZbGYaPR6rzYVTR2jfcyDt+4/ij30XMGWCKTaOrGtXIbu/WKOOHdm5Z0/hG14Dly9fpmPHjhw9\nepSOHTvm/FuIYhWK5DDJYfdTDjObICWaHs2qcuHcGdr3G07758awevNeMqMNmOLjUZmZ2Hp5YRcQ\nQMMWLdi5c6fksJLg+kyV292AYODoLfa1BVbls705sC7X/beAtwryfI0bN1a5HT9+XFlLVlaWUkqp\n8PBw1bVr1zzbkpOTVYsWLQp9rokTJ6olS5ZYONKSYfny5Wrjxo33fJ5Fixap+fPnF+oxRqNRdezY\n8fYHZSQrFX1aqYgDSl09olRylDKbjCorLk6lnTypUo8cUelnzihjUtI9RF9w1nxvlyTAPlWA3HC3\nt+LMYSUpfyklOaywSnQOM2YqFR+uVMTfeg6Lu6hUVoYypaWpjIsXVeqRIyrt+HGVFR2tzCbTPf4E\nBWPt93dJUdAcZqkxXc01TTsEXAHeUEodAyoCl3MdEw40u9UJNE0LAUIAgoKCLBTWvZNmsYVTYpvF\nZqbqiwJmJIKNnT512sUXc1IyxrPnMF9vRh0UhI27u7TsKXvuKYeV1PwFksMKq0TmMLMRkqMgJVrv\n7+rsA+7lMZvAeDVSb0ZtY4tduXLY+fpKM+oSrEANrzVNC0b/JJjfeAgPwKyUStY0rSswTSlVXdO0\nPsATSqmXso8bADRVSo280/NJw1hhMVlperGVngCaLbiVQ7n6Y05J/bcZtYMj9gE3N6MuDvLe1hV1\nw+vizGGSv4TFZH+NSHJUdssxL73lmLLJ24zaxxc7P1+r9EeU97eu2BpeK6USc/39D03TZmua5of+\nqTD3YIFK6J8i74rJZMJWqndRUFnpkHQN0uNAswG38uDmjyktA+OFiyWiGfX1AcHCuoojh6WlpeHk\n5CRXUEXB3Njf1dETPCpgxg5TdDTG7NmRdr4+2Pn5od1mrcCiopQiPT292J+3tLvnokvTtPJApFJK\naZrWFH1wfgwQD1TXNK0KEAH0A569m+fw8fHh1KlT9xqqKAvMRv2qVmYqaBo4uoGDByoqClPSWVRG\nBtjYYOvurs/UunZNv1mJj4+P1Z5b6Io6h1WoUIGIiIibZtcJcROlIDMZ0hP1YsvOGZw8UTZpmE8f\nwpyaCkph4+KiD4OIj4f4eKuFa29vT4UKFaz2/KVRQZaMWIo+0NRP07RwYBxgD6CUmgs8DQzVNM0I\npAH9sgeVGTVNGwGsQ59uvTB7nEShBQQEFGj6rijDEsJh61Q4+J3+NeIjL0HLMaRHxBE9fTrJGzfi\n6O2Nb0gI3v375dvuQtyfrJ3DvLy88PLyssjPIu5Txkw9d22dCklXoEpraPcuJp86xC4KJTY0FC01\nFe/uT+I/YgQOJWzcoCi4Ao3pKm43jokQ4pYSr8L2z2F/qP4psfEL0Op1MmIyMMyYSeKaNdi4ueH7\n4mC8BwzE1s3V2hGLfBT1mK7iJPlLFJgpCw4thS1TIOESVG4G7d7BXKEpcYsXEzN/AaaEBNw7dcJ/\n1Egcq1WzdsTiFoptTJcQVpEcBdu/gH0L9a8UGz4Hrd8gK8WW6EmzSPj1NzQHB3xffhnfFwdjK1ca\nhBAlhckIR36CLZMg7gJUbAzdv8BcuRXxPy3D8NW7mAwGXFu3wn/UazjXrWPtiIWFSNElSpeUGNjx\nJfw1H0wZ0KA/tB5LltGVmBnziPvpJzRNw+f55/ANCcHO19faEQshhM5sgqMrYMtEiDkD5etD/x9R\nVdqTsHIl0SFdMF65issjj+A/fRouhWzgLUo+KbpE6ZAaC7tmwp6vIDMF6vWBNv/DaONDzIKviVu8\nGGUy4dWrF35DX8VeBncKIUoKsxlO/AZhEyH6HyhXB575HlW9C4lr1hA9qjtZFy/hVL8+gR9/jEvz\n5jLT9T4lRZco2dLiYfcc2D1bX9i0Tk9o+xYmxwrELFpE3DffYk5Px7N7d/yGD5MBpkKIkkMp+Gc1\nhH0KkUfBryb0CUXV7E7Sxk1Ej32KzDNncaxVi0qzZ+PWrq0UW/c5KbpEyZSRBHvmws4Z+hIQtbvr\nxZZrMHHffUvMwkWYk5Jw79IZ/xEjcMzVTFYIIaxKKTi9HjZ/AlcPgU9V6LUAVacnyVu3Ef1uXzJO\nnMChalUqfvkF7p06odkUpBWyKO2k6BIlS2aKPl5rxzRIi4UaXaDdW5i9ahC3ZCkx81/CFB+PW4cO\n+I8cgVOtWtaOWAghdErB2U2weQJE7APvYHhqDqpuH1L/2kvUs8+Rfugw9kFBBE6ehEe3btKyp4yR\nokuUDFlp+kzE7V/obS+qdYS2b2MuVy97Ns8ITNEGXFu2xH/USJzr17d2xEII8a9zW/Ri6/Ju8KwM\n3adDw2dJPXiI6MFDSN27F7sKFSj/0Yd4PfWUVVaRF9YnRZewrqx0OPANbPsckq9BlTbQ7m1UhcbE\n//orhtljMV69ikuTJvh/8QUuTe6LpZyEEPeLizv1YuvCNnAPhG6fQaMBpJ04RfQrw0jZvh1bfz8C\n3n0Xr759sHFwsHbEwoqk6BLWcX0F5m2fQWIEBLWAp79GVW5O4qpVRA/pRtblyzg1qE/gJzKbRwhR\nwlzeq4/ZOrcZXMtB50nQeBDpZy8QPeq/JG/ahK23N+XefFPvguHsbO2IRQkgRZcoXjeuwFypKTw1\nG/VAK5LWryd6WA8yz53DsXZtKs2dg1ubNlJsCSFKjogD+mzE0+vBxQ86fQxNhpBx+SrRY98iae1a\nbNzd8R/9Gt7PD5AuGCIPKbpE8TAZ4ciy7BWYz0NgI3jyC1TV9iSHhRE9pjcZJ0/iUK0qFadNw/3x\njjKbRwhRclw9rBdbJ/8AZ2/oMA6ahpAZGYvhvQ9I+H0VNk5O+A59Fd9Bg7D19LR2xKIEkqJLFC2z\nCY4s14ut2LNQvh70/wFV/QlSdu4i+u1+pB85gv0DQQROmYxH164ym0cIUXJcO6oXW/+sAkdPaPcO\nNHuVrNhkDB9PJn7FCjQ7O3wGDcL3pSHY+fhYO2JRgknRJYqG2QTHftGLLcMpCKgLz3wPNbuRun8/\nUQMHkrZvP3aBFajwycd4/uc/aHbydhRClBCRx/V2Pcd/A0cPaPN/8OhQspIyiZk6g/gffwTAu39/\nfENexr5cOSsHLEqDO/4vp2naQuBJIEopVTef/c8B/8u+mwwMVUodyt53AUgCTICxIB24RSlnNsPx\nX/ViK/of8K8Nfb6B2j1IO3KE6JdeJmXnTuz8/Ql4/z28nn5aZvOIIiU5TBRK9Em9Xc+xX8DBDVqP\nhebDMaZDzMz5xC1egsrK+rflWGCgtSMWpUhBLi2EAjOBb2+x/zzQRikVp2laF2Ae0CzX/nZKKcM9\nRSlKPrMZ/vldT1ZRx/V2F08vhId6knbiBIahw0jeskWfzfO//+mzeZycrB21KBtCkRwm7sRwWv+w\neGQ52LtAyzHQYiSmTBti5oYS9913mNPS8OzRHb9hw3B44AFrRyxKoTsWXUqprZqmBd9m/85cd3cD\nle49LFFqKKWPdQibqPcW860Ovb+GOj1JP32G6FGjSP5zIzaenviPGYPP889h4yqzeUTxkRwmbivm\nLGyZDEd+AjsneOw1aDEKk9mB2IXfEhsaijk5WVqOCYuw9CCaIcCaXPcVsF7TNAV8pZSaZ+HnE9ai\nFJxcow8wvXYYfB6EnvOg3tNknL9A9OtvkLRmLTZubviNGIHPCwOxdXe3dtRC3InksLIi9py+dM3h\nH8HWAZoPhxavYdZciP1+MTELF2JOSMD98Y74jRiJU80a1o5Y3AcsVnRpmtYOPWG1zLX5MaXUFU3T\nygEbNE37Rym19RaPDwFCAIKCgiwVlrC0641cwz6FKwdzeotRry+Zl8OJ/r+3SFy1Wp86/eor+A4e\nLFOnRalwLzlM8lcpEncBtk6Bv5eCrT00exUeew2znYfe33XBAkxxcbi1aYPfqJE416lj7YjFfcQi\nRZemafWBBUAXpVTM9e1KqSvZf0ZpmvYL0BTIt+jK/gQ5D6BJkybKEnEJC1IKzmyEsAkQsR+8gqDH\nTGjQj8yrkRjeG0fCb7+h2dvjM3gQvi+9hJ23t7WjFqJA7jWHSf4qBeIvwdap8Pdi0Gyh6cvQcgxm\nB2/if/wJw/x5en/Xxx7Df+QInBs2tHbE4j50z0WXpmlBwApggFLqVK7troCNUiop+++dgA/v9flE\nMVNKb3OxeQKE783TyDUryoDhw0+I//lnNBsbfJ5/Ti+2/P2tHbUQBSY57D6XEK63GzvwHWgaNHkR\nWo5BOfkR//PPGOZ+hTEyEpemTaW/qyhyBVkyYinQFvDTNC0cGAfYAyil5gLvA77A7Ox2LdenVQcA\nv2RvswOWKKXWFsHPIIqCUnB+q/414qVd4FERnvwCGj5PVmw8MRMmEf/TTyjAu28ffF95BfuAAGtH\nLcRNJIeVUYlXYNvncOAbPZ89PBBa/RflEkDCb79hmD2HrCtXcG7UiMBJE3F99FFrRyzKgILMXux/\nh/0vAS/ls/0c0ODuQxNWkVNsTYRLO8G9AnSdCg8PxJiQTMzUL4hbuhRlNOLVqyd+r76KfcWK1o5a\niFuSHFbGJF6B7V/A/lBQZmj4HLR+A+VekcRVq4ieNZusS5dwqleP8h98gGvLx6S/qyg2sgS4+Nf1\nYuviDr3Y6jJFL7aS04idNovYxYtR6el49uiB37ChOMiAYSFESZF4NVexZdKLrVavozwrk7hmDYaZ\nr5J5/jyOtWtTafZs3Nq1lWJLFDspugSc35ZdbG3PU2yZ0jKJnTOf2G++wZyaikfXrvgNH47jg1Ws\nHbEQQugSr8KOL2Hfouxi69nsYiuIpD//xDBjJBmnT+NYvRoVp03D/fGOaDY21o5alFFSdJVlF7bD\n5k/1YsutPHSZDA+/gCnDSOz8hcQuCsWclIR7p074jRiOUw1Zp0YIUUIkXdOvbO1bBGajXmy1fgPl\n9QDJmzcTPeO/ZJw4gUOVKgR+NhWPLl2k2BJWJ0VXWXRhhz5A/sI2cAuAzpOg8QuYMszELfw2Z1FA\ntw4d8B8xHKfata0dsRBC6JKuwfYvYf8iMGVBw/7Q6g2UdzApW7cSPf0N0o8dwz4oiMBJE/Ho1g3N\nTv6rEyWDvBPLkpuKrYnQeBDmLEXcN4uJ+Xqhvihg27b4jRiBc11ZFFAIUUIkXYMd02DfQr3YatBf\nv7LlHUzK9h1Ez/w/0g8dxr5SJSpMmIBnj+5SbIkSR96RZcHFnXqxdX4ruJaDJz6FJoMxGyHu+x/1\nFZhjYnBt1UpfFLB+fWtHLIQQuqTI7GLr6+xiq192sVWF1N27iZ7+DmkHD2IXWIHyH32I11NPodnb\nWztqIfIlRdf97OKu7GJrS3axNQEaD8asbIlf+iOG+fP1FZhbtMBv5AhcGjWydsRCCKFLjtKLrb1f\ngylTL7ZavQ6+VUn56y8M08eRum8fduXLU378OLx69UJzcLB21ELclhRd96OLO/XZiOe3gKs/dPoE\nmryIGTvily0j5qt5GKOicGnWTFZgFkKULEmRsHN6drGVAfX1K1v4ViV1/36i/zuI1D17sCtXjoD3\n3sWrTx9spNgSpYQUXfeTPF8jXi+2BqOwJ37FCr3dxbVrODdpTOCUKbg2a2rtiIUQQpfna8RMqP8M\ntB6rF1sHD2IYO4SUnTux9fMj4O238OrbFxsnJ2tHLUShSNF1P7iwA7ZM/HfMVvaVLaXZE//rr8TM\nmftvu4tPJ+Dy6KOyKKAQomS4ccxW/WdyrmylHT5M9P+FkLJtG7Y+PpT73//w7vcMNs7O1o5aiLsi\nRVdplns2Yq4xW0qzJ2Hl7xjmziXr8mWcGtSXdhdCiJLlptmI/47ZSjt6DMM7Q0kOC8PWy4tyb7yO\n97PPYuPiYu2ohbgnUnSVRhe262O2coqtT6HxIJSNw7/F1qVLONWtS8A7b+PWpo0UW0KIkuF2xdax\nYxjeHUby5s3YenriP3o03s8/j62bq7WjFsIipOgqTc5vgy2T/l1nK3extWoVhjlzyLp4CaeHHiJA\neosJIUqSGxc1bdAfWr8OPg+SfuIE0e+PIHnjRmw8PfEf/Vp2seVm7aiFsCgpukqDG4ut7EVNlY0D\niatXY5g1m8yLF3F8qDaVZs/CrV07KbaEECVD4lX9ylZ+xdbJkxjGjSRpw5/YeHjgN2okPgMGYOvu\nbu2ohSgSBSq6NE1bCDwJRCml6uazXwOmAV2BVGCQUupA9r4XgHezD/1YKfWNJQK/7ymlD4zfMgku\n7ri52PpjDYbZs8k8fx7HWrWoNHMGbh06SLElxA0kf1lJ4pXsK1uh2b0R++tfI/o8SPrJUxjGv0bS\n+vXYuLvjN2IEPgMHYOvhYe2ohShSBb3SFQrMBL69xf4uQPXsWzNgDtBM0zQfYBzQBFDAfk3TViql\n4u4l6PuaUvr6WmET4dIuvRF1dm9EZeNA4pq1erF17hyONWpQcfo03Dt2lEauQtxaKJK/ik9ChN6I\n+sA3oMz6la1Wr4NPFdJPncLwwWiS1q3Dxs0Nv2HD8Bn0ghRboswoUNGllNqqaVrwbQ75D/CtUkoB\nuzVN89I0rQLQFtiglIoF0DRtA9AZWHovQd+XlIJzmyFsElzeDe6B0GUKPDwQZetA0tq1RM+aTebZ\nszhWr07FL7/EvdPjUmwJcQeSv4pJQjhs+xwOfqcXWw2fg1b/Be9gMs6cIfqjMSStXYeNiwu+Q1/F\n94UXsPXysnbUQhQrS43pqghcznU/PHvbrbbfRNO0ECAEICgoyEJhlQJKwdmNerEV/hd4VISuU6HR\nAL3YWr8ew6xZZJw+g0O1qlT84nPcn3hCii0hLEfy172Iv5RdbH2v32/0vF5seQWRceYMho9eJ3HN\nGmycnfENCcF38CAptkSZZamiK7+BROo222/eqNQ8YB5AkyZN8j3mvqIUnPlT/xoxYh94VIJun0Oj\n51E29nmLrapVCfxsKh6dO6PZ2lo7ciHuN5K/7kbcRdj2Gfy9BDQNHh4ILceAV2W92PowV7H10kv4\nvDgYO29va0cthFVZqugKByrnul8JuJK9ve0N28Ms9Jylk1JweoO+gnzEfvCsDE9+CQ2fQ9nYSbEl\nRPGT/FUYsedh++fZxZYNNH5BL7Y8K91cbL38Mj6DB0mxJUQ2SxVdK4ERmqb9gD4QNUEpdVXTtHXA\nBE3Trv/GdQLestBzli5Kwck1+mzEq3+DVxB0nwYNnpViSwjrkvxVEDFn9a8RDy0FGzto8iI8Nho8\nK+rF1gd6saVJsSXELRV0yYil6J/4/DRNC0ef0WMPoJSaC/yBPt36DPqU68HZ+2I1TfsI2Jt9qg+v\nD0otM8xmOPmHXmxdOwzewdBjJjToh9JspdgSoohJ/rpHhjOwbSoc/gls7aFpCDz2GnhUuLnYkq8R\nhbgtTZ+wU7I0adJE7du3z9ph3BuzGf75HbZMhsij4PMgtB4L9frcXGw9+CB+w4dJsSXKLE3T9iul\nmlg7Dku4L/IXQPQp2DoFji4HW0d4ZAi0GAnu5ck4exbDrNk5xZbPc89JsSXKtILmMFmR3tLMJjj+\nm56soo6DbzXo+RXUfRql2ZC0bh2G2bNziq3AqVPx6CLFlhCihIj6B7ZOhqMrwN4Zmg+HFqPArRwZ\np09jGDeZxDVr5cqWEHdBii5LMZvg2C96sRX9D/jVgF4LoG4vlIKkdeuInj2bzDNn9WJryhQ8unaR\nYksIUTJEHtPz17Ffwd5F/wqxxUhw9SPj9GmiZ39K0tp1/xZbgwdh5+Nj7aiFKFWk6LpXJqN++X3r\nFIg5A/61oPfXUKcnSkHiH2sxzJlD5tmzOFSTMVtCiBLm6mH9ytaJ38HBXZ+J2HwEuPrqK8jP/kRf\nQV4GyAtxz6ToulumLDj8I2ydCnHnIaAu9PkGavdAKUXi6jV6sXXunCxqKoQoeSIO6B8WT/4Bjp7Q\n5n/Q7FVw8dF7I87+SC+2XFzwDQnBZ9ALUmwJcY+k6CosY6Y+ZXrbZxB/EcrXh2cWQ82u2cXWagyz\n5+iNqKtXo+KXX+DeqZMUW0KIkiF8nz6b+vR6cPKEtm9Ds1fA2Yv0kycxzPpAb0Tt6orvq6/g84IU\nW0JYihRdBWXM0HuKbf8SEi5D4MPQZTLUeAJlMpH4++8Y5swl88IFvRG19EYUQpQkl3brxdbZTeDs\nDe3f05d/cPIg/cQJDLPfI2nDn9mNqIfiM3CgtOsRwsKk6LqTrDQ48K1ebCVdgUpN4ckvoFpHlMlE\nwi+/YvhqLlkXL+FYqxYVp0/DvWNHKbaEECXDhe16sXV+K7j4Qcfx8MhL4OhO2rFjGGbNJnnTJmzc\n3fEbPhyfgQOw9fS0dtRC3Jek6LqVzBTYtwh2TofkSAhqAT3nQJU2KKORhBUrMMz9iqzLl3GsXZtK\nM2fg1r69FFtCCOtTCs6F6esEXtoJbgHwxARoPAgcXEk7chTDrFkkh4Vh4+GB34gRerHl4WHtyIW4\nr0nRdaOMJNj7NeycAakGqNIanl4IwS1RmZnEL19OzFfzyAoPx+mhhwiYPQu3du3QtPx64wohRDFS\nCs78qRdb4X+Be6A+DOLhgWDvTNrhw0TPmkXKlq3YeHri/9oovJ9/Hlt3d2tHLkSZIEXXdekJsGce\n7J4FaXFQtT20fhMeaI45M5OEH34kZt48sq5cwaluXQLeeRu3tm2l2BJCWN/13q5bJ8OVg+BZGbp9\nBo0GgJ0jqQcPYpg1m5Tt27H19MR/9Gi8n38OWzc3a0cuRJkiRVdqLOyZC7vnQkYC1OisF1uVGmPO\nyCB+8WJi5i/AeO0aTg3qU378OFxbtZJiSwhhfTntxqZA5JHs3q4zoH4/sHMgdd8+DLNnk7JzF7be\n3vi//l+8+z+LrZurtSMXokwqu0VXigF2zYK/5kNmEtR6Uu+NGNgQc3o68d9+R8yCBRijonBu1IgK\nH3+M62MtpNgSQlhfTgeMqRB9Qm839tRcvberjS2pf+3FMHs2qXv2YOvrS7mxY/Hu3w8bFxdrRy5E\nmVb2iq6kSH1w/L6F+szEOk/pxVZAHcypqcQtCiXm668xGQy4NGlC4ORJuDRrJsWWEML6TEY4sgy2\nTb25A4ZmQ+quXUTPnk3avv3Y+vsR8Nb/4dW3LzbOztaOXAhBAYsuTdM6A9MAW2CBUmriDfu/ANpl\n33UByimlvLL3mYAj2fsuKaV6WCLwQksIhx3T4cA3YMqEen2g1evgXxNzSgpxCxYQs3ARpthYXB59\nFL/PP8O1aVOrhCqEsJz7In8ZM+HQEtj2ub4oc0C9fztgaBop27djmDWbtL//xi4ggIB338Xr6d7Y\nODlZJVwhRP7uWHRpmmYLzAIeB8KBvZqmrVRKHb9+jFJqTK7jRwKNcp0iTSnV0HIhF1LsedjxJRxc\nDCho0A9a/hd8q2JKTiZu7lfEhoZiio/H9bHH8Bs2FJfGja0WrhDCckp9/spKg4Pf6+sEJoZnL8o8\nCWp0RgHJm8MwzJlD+pEj2AVWoPz4cXj26oWNg4PVQhZC3FpBrnQ1Bc4opc4BaJr2A/Af4Pgtju8P\njLNMePfAcFr/VHj4R7Cx1adMtxwNXkGYEhKInTmL2G+/xZyYiGub1vgPHYpzQ+vlViFEkSid+Ssz\nRR8CsXOGvk5g5UehxzSo2gGlFEkbNmCYO5eM4yewr1SJ8h9+gNdTT6FJsSVEiVaQoqsicDnX/XCg\nWX4Hapr2AFAF2JRrs5OmafsAIzBRKfXrLR4bAoQABAUFFSCsW4g8pvdFPLoC7Jz0NhePjQKPQIxx\nccR++SVx3y/GnJyMW4cO+A0dinPdOnf/fEKIkqx05a/0BH1yz65ZkBarrxPY+2t9nUCzmaQ1azDM\nmUvG6dM4PPAAFSZMwLP7k2j29nf/nEKIYlOQoiu/EeTqFsf2A5YrpUy5tgUppa5omvYgsEnTtCNK\nqbM3nVCpecA8gCZNmtzq/Ld25aA+k+efVeDgBo+9Bs1HgJs/RoOBmClTiFv6AyotDfdOnfAb+ipO\ntWoV+mmEEHdmNJkxK3Cws3qHhtKRv1JjYfcc+OsrvfCq3kmf4FO5Kcpo1Hu7zv2KzHPncKhalcAp\nk/Ho0gXNruzNhRKiOKRnmXCyt7X4eQvyGxsOVM51vxJw5RbH9gOG596glLqS/ec5TdPC0MdL3JS0\n7tql3bB1ir4Ks5MntPk/aPYKuPiQFRlJzPQJxP+0DJWZiUfXrvi9+gqO1apZ7OmFEP86FZnE8v3h\nrDgQwdtda9Hr4UrWDqlk56+kSNg1U/8qMTM5z9I1KjOThOXLMcybT9alSzjWrEnFL7/AvVMnaTcm\nRBFISMvi90NXWLY/HA8nO74bku9F8XtSkKJrL1Bd07QqQAR6Ynr2xoM0TasJeAO7cm3zBlKVUhma\npvkBjwGTLRE4AL8Og78Xg4svdHgfHnkZnDzIiojAMHk8CT+vQJnNePbogW/IyzhWqWKxpxZC6BJS\ns1h5+ArL913mUHgCdjYa7WuVI8inRKwJVXLz18m1sOwFfTZ1nV76bOqAh/QOGEuXYpg/H+OVqzjV\nqUOA9HYVokiYzIodZwws2x/OumPXyDSaqVXenXYNAlFKWXy5qDsWXUopo6ZpI4B16FOuFyqljmma\n9iGwTym1MvvQ/sAPSqncl9Zr/z979x0fR3Uu/v9z1CzLtmT1Lltyk6skS5Z7kw3YlgsJ1fSS64TQ\nEhJyLxeSACEEkhvuL6UoeLkAACAASURBVO1CHL4kpBAIhoALYMAFcLdkq7hjy0XNVu9tpT2/P2a0\nrIVsr+XVqvh5v156eXd2ZvbZ0erxM2fOnAP8USllBdww+kRcqAPr5Ru5EEInmJO4+tBy+jRlq1+k\n+v21oBRDv/ENAlf9B15RPX62LUS/0mbVbDtextsZ+Xx86JwtUf146TiuT4wgcPCAng4R6OX5K2oK\nJKyEGQ9D4AisjY1Uvf465f/vNWNQ5sREwp9+WmbAEKIbnCyrZ01mPu/uK6S4uomhPp6snBLNTSnR\njI/w7ba/OXV+jukdUlJSdEZGhsPrNx8/TtkfV1OzYQPK05OhN99M4H334hke3o1RCnH1ySuts10+\nPFtjJKrrEyO5MTnqihKVUipTa53i5HB7xOXmr7a6eir/+QYVf3mdtvJyfFJTCXrgO/hMmybFlhBO\nVNfcyoacIt7OKCDjdCVuCuaODubG5GgWjgthgEfX+3A5msP6dC/MpsOHKXv5FWo/+QQ1cCAB99xD\n4L334BEc3NOhCdFv1DZZ2JBTzNuZBWSaiWremBB+smwcC8ZeWaK6mrVVV1Pxt79T8be/Ya2uZtCs\nWUaxJeMECuE0Vqtm18ly1mQU8OGBszRa2ogLHsR/Lornm5MjCfV17QDCfbroKvvjaup37CDw26sI\nuPtuPPz9ezokIfoFq1WzK6+ctzML+PBAMU0WKyOCB/Ffi+P5ZlIkIS5OVP1RQ+Y+yn7/ewbPn0/Q\ndx9g4MSJPR2SEP1GfkUDazILeGdfAQWVjQwZ4MH1SZHclBJFUvTQHmtF7tNFV+gT/4XbwIG4+/r2\ndChC9Av5FQ28nVnAO5kFFFY1MsTbg29OjuKm5CgSezBR9UeD588jbv06uZtaCCdpaGnlw9yzrMks\nYGdeOUrBzBFBPH7dGK4bH9YtQ0Bcrj5ddHmGhvZ0CEL0eQ0trXyQe5Y1mfnsyqtAKZg1MogfLeo9\niao/UkpJwSXEFdJak3G6kjUZBWzILaauuZVhgT784JrRfDM5isihvWuy9z5ddAkhuqY9Ub2dkc+G\nnGLqW9oYHujDD68dzTcnRxHRyxKVEELYK65u5N19hazJLOBkWT0+Xu6kTwznppRopgz377Wt8lJ0\nCXEVKapq5N19BazJLOBUeQODvNxJn2QkqpRhvTdRCSFEk6WNjw+d4+2MfLYdL0NrmBobwHfnjWDJ\nxHAGDej9JU3vj1AIcUWaLG1sPGj0c2hPVNPiAngobRSLJ4T1iUQlhLg6aa3JLqjm7Yx81mUXUdPU\nSuTQgTw8fyQ3JEcxLHBQT4d4WSTbCtEPaa3Jyq/i7cwC1mUXUdueqNJGcePkKGICe8Vo8UII0amS\n2ib+bV4+/LKkjgEebiyeEMZNKdFMjwvEza1vtspL0SVEP1JS08S7+41EdbykDm9PNxZPCOem5Cim\n9eFEJYTo/1parWw6fI41mQVsPVZKm1UzOWYov/jmRNInhePr7dnTIV4xKbqE6OOaW9vYdLiEtzPy\n+exYKVYNycP8ecFMVENcnKjya/PZkLeBedHziA+Id+l7CyH6ngOF1azJLOD9rEIqGyyE+g5g1Zw4\nbkyOYkTwYJfG0mBpYNOZTTS2NnLzmJudvn8puoTog7TW5BRU8+6+At7PLqKqwUKYrzffmTuCG5Oj\niHNxoqpsqmTjqY2sz1tPdmk2CsVgz8FSdAkhOlVS28S67GLWZBZwuLgGL3c3rhkfyo3JUcwZFYy7\nC1vlW62t7Czayfq89WzJ30JjayMTAidw0+ibXD/htRCi98grreO9rCLWZhVyqrwBLw83rh1nJKrZ\nLk5Uja2NfJb/Gevz1rO9cDutupXR/qP5fvL3WRK7hLBBYS6LRQjR+9U2WfjowFnWZhex/XgZVg2T\novx4dsV4lidEMNTHy2WxaK05UHaA9Xnr+ejUR1Q0VeDr5cuyuGUsHbGUxODEbrmbW4ouIXq5czVN\nrMsu4v2sInILq1EKpscF8sC8ESyaEI7fQNddPmyztrHn7B7W563n09Of0tDaQIhPCHeOv5P02HTG\nBIxxWSxCiN6vubWNrUdLeT+rkE8Pl9DSaiUmwIcH549keUIEo0KHuDSe/Jp81p9cz4a8DZyuOY2X\nmxfzouexNG4psyJn4enevfnUoaJLKbUI+A3gDryqtX6hw+v3AL8CCs1Fv9dav2q+djfwlLn8Oa31\n606IW4h+rabJwke5Z3kvq5CdeeVoDRMj/XgqfSzLEiJcOkmr1pojFUdYn7eeD09+SGljKYM9B7Mo\ndhFL45aSHJqMm3JzWTyXS/KXEK7VZtXsPlnO+/uL+OBAMbVNrQQO8mLllGhWJEW6fO7DiqYKW/eH\nnNIcFIrUsFTun3A/C4ctZIiX6wq/SxZdSil34A/ANUABsFcptVZrfajDqm9prR/qsG0A8FMgBdBA\nprltpVOiF6IfabK0seVICe9nFbH5qHFGOCzQh4fTRrE8IYKRIa7tp1VYV8gHeR+wIW8DJ6pP4OHm\nwZzIOaTHpTM3ei4D3Ae4NJ6ukPwlhGtorTlYVMP7WYWsyy7mbE0Tg7zcuW58GMsTI5g1MggPd9ed\nnDW2NrI1fyvr89azo3CHrfvDY8mPsTh2cY91f3CkpSsVOK61zgNQSr0JrAA6Jq3OXAd8orWuMLf9\nBFgE/LNr4QrRv7RZNbvyynlvfyEfHThLbXMrQYO9uC01huuTIkmI8nPpGWF1czUfn/6Y9SfWs69k\nHwCTQybz42k/5rrh1+E3wM9lsTiJ5C8hutHp8nrezyri/axCTpTW4+GmmDcmmCfTx7JwbCgDvVw3\nd2ubtY3dZ3ezIW+DrftDqE8od42/i/S4dEb7j3ZZLBfiSNEVCeTbPS8Apnay3g1KqTnAMeD7Wuv8\nC2wb2dmbKKVWAasAYmJiHAhLiL5Ja01uYTXvZxWxLruIktpmBg/w4LrxYaxIjGDGiECXnhE2tzXz\necHnrD+xns8LP6fV2kqcXxyPJD3CkrglRA7u9E+2r5D8JYSTldY2syGniPeyisjKrwIgNTaA+2fF\nsXhCGP6DXNsh/nDFYaND/MmPKG0sZYjnEBbHLiY9Lr3XdX9wpOjq7DRbd3i+Dvin1rpZKfUd4HUg\nzcFtjYVarwZWA6SkpHS6jhB92cmyet7PKmRtVhF5ZfV4uivmjwlhRWIkC8aG4O3pujNCq7aSeS6T\n9Xnr+eTUJ9RaagkaGMRt8bexNG4p8QHx/WUeRslfQjhBbZOFjw+e472sQtudh2PDffmvxfEsT4gg\nYuhAl8ZTWFfIhrwNbMjbQF51Hh5uHsyNmkt6XDpzoub02u4PjhRdBUC03fMooMh+Ba11ud3TPwEv\n2m07r8O2Wy83SCH6qvaxaNZmFZJdYNx5ODU2gFVz4lg8IRw/H9cOXHqs8hjr89bzQd4HnGs4h4+H\nDwuHLWRp3FJSw1Jxd3Nd4ecikr+E6KKWVitbj5bwfnYRnx46R3OrlSj/gTwwbwQrEiMZ7eI7D6ub\nq9l4aiMb8jbYuj8khybzk3E/4dph1/aJ7g+OFF17gVFKqViMu3tuBW6zX0EpFa61LjafLgcOm483\nAs8rpfzN59cCT1xx1EL0YjVNFjYeOMv7WUXsOGGcEY6P8OW/l8SzLCGCcD/XnhGerT/LByeNDvHH\nKo/hoTyYGTmTH6b8kLnRcxno4dp4XEzylxCXwWrV7D5ZwdrsQj7IPUt1o4WAQV7cMiWaFYkRTI7x\nd2kreHNbs208wC8Kv6DV2soIvxE8OvlRlsQuIWJwhMticYZLFl1a61al1EMYCcgdeE1rfVAp9SyQ\nobVeCzyilFoOtAIVwD3mthVKqZ9hJD6AZ9s7pQrRnzS3trHliDEWzaYj549FsyIxgpEhrj0jrG2p\n5dPTn7I+bz17z+5Fo0kITuC/p/431w2/jgDvAJfG01MkfwlxaVprDhXX8H5WEWuzijhb04SPlzvX\njgtlRVIks0YG4enCfqZWbSXjbIbR/eH0J9RZ6ggeGMzt8bezdMRSxviP6bPdH5TWva/7QUpKis7I\nyOjpMIS4qDarZndeOe9nfTUWTdBgL5ZOimB5YoTLx6KxtFn4ovAL1uet57P8z2ixtjDMdxjpceks\njV1KtG/0pXfSQ5RSmVrrlJ6Owxkkf4m+4kx5A2uzC3kvq4jjJXV4uCnmjg5meWIE14wLxcfLteOn\nH604yoa8DXxw0uj+MMhzEAtjFrJ0xFKmhE7p1d0fHM1hMiK9EJdBa012QTXrsotYn1PEuZpm21g0\nK5IimeniOw+t2kpWSRbr89az8dRGalpqCPAO4KYxN7E0binjA8f32TNCIYTzldQ0sSG3mLXZRew/\nY9x5OGW4P89dP4ElE8MJcOGdh2B0f9iQt4ENJzfwZeWXeCgPZkXO6rfdH6ToEsIBR8/Wsi67iHU5\nRZwub8DTXTF3dAhPpUf0yFg0OWU5bDq9iU9Of0JRfREDPQaSFpPG0rilTAufhoeb/GkLIQxVDS22\nOQ935ZVj1RAfNoQfLRrD8oQIovx9XBpPUV0RW/K38OnpT8k8l2nr/vDk1Ce5bvh1+Hv7X3onfZRk\nZiEu4HR5vVFoZRdz9FwtbgpmjgziwXkjuW58mEvvPGxpa2F38W42ndnE1vytlDeV4+HmwbTwaTyU\n9BALYhbg4+naxCmE6L3qm1v59PA51mYV8fmXpVjaNMMDfXho/kiWuXjOQ601x6uOs/nMZjad2cTh\nCuNelZFDR/LdxO+SHpveq7s/OJMUXULYaZ9cel1OMdnmoH8pw/x5dsV4Fk8IJ3iI68Z+qWupY1vh\nNjad2cQXhV9Qb6nHx8OH2VGzWRCzgNmRsxns5dqpgYQQvVeTxZhcel1OEZsOn6PJYiXcz5t7Zgxn\neUIkEyJ9XdbdwKqt5JTm2AqtM7VnAEgITuCx5MdIi0ljmO8wl8TSm0jRJa56lfUtfHCgmHXZRew+\nWYE2h3h4YnE86ZPCXdr0XtZYxtb8rWw6s4ndxbuxWC0EeAewaPgi0mLSmBo+tdcO+ieEcL3WNivb\nT5SzLruIjeZUYoGDvLgpOZplCRGkDPPHzc01hZalzcKes3vYdGYTW/K3UNZYhoebB1PDpnL3+LuZ\nHz2fYJ9gl8TSW0nRJa5Kdc2tfHzQ6OOw7csyWq2auOBBPLpgFMsSIhgR7LoWpPzafDaf2czmM5vZ\nX7IfjSZycCQr41eyIGYBCcEJvfquHSGEa1mtmozTlazLLuKD3GLK61sYMsCD6yaEsSwhwqU39NRb\n6r9qkS/4gjpLHQM9BjI7cjZpMWnMjpqNr5evS2LpC6ToEleNJksbW46UsDa7iM1HSmhutRI5dCD3\nz45leUIE48Jd0/SuteZo5VE2ndnE5jObOVZ5DIAx/mN4IOEB0mLSGO0/Wu46FELYaK05UFjDupwi\n1mcXUVTdhLenGwvGhrI8IYK5o4NdNpVYeWM5nxV8xqYzm9hVtIsWawv+A/y5Ztg1LIhZwLSIadIi\nfwFSdIl+zdJmZdvxMtZlFfHxoXPUNRtjad06JdocS8s1Te9t1jb2l+xnc77RolVYV4hCkRSSxOMp\njzM/Zj7RQ66OjqRCCMcdL6llbbbR/eGkOWfrnFHB/OfieBaODWXQANf8N15QW2Drn5VVmoVVW4kc\nHMkt8beQFp1GUkiStMg7QIou0e9YrZo9pypYm13Eh7nFVDZY8PX2IH1iOMsSIpgWF+CSpvfmtmZ2\nFe1ic/5mtuZvpaKpAk83T6ZHTGfVpFXMjZpL4MDAbo9DCNG35Fc0sC7HuHP6cHENSsH0uEC+PSeO\nRRPCGOrT/WNpaa05VnnMVmgdrTwKwGj/0Xx70rdJi0nr0yPD9xQpukS/oLUmp6CatXaDlg70dOea\ncaEsS4hgzuggBnh0/1lYbUstXxR8waYzm9hWuI2G1gYGew623XE4K3IWgzwHdXscQoi+paS2iQ05\nRovWPnPQ0skxQ/npsnGkTwwnxNe722Nos7aRXZpt6/pQUFdga5H/YcoPSYtOu2qGduguUnSJPqu9\nj8P63CI25BRTUNmIl7sbc8cEszwhggVjQ1wyjUVpQylb8rew+cxmdp/dTau1laCBQaTHpbMgZgGp\nYal4urtuTC8hRN9QWtvMRweKWZ9TzJ5Txp3TY8N9+dGiMSybFEF0QPffOd3S1sKu4l1sPrOZLflb\nbC3y08Kncf/E+5kXPY+ggUHdHsfVQoou0ae0T8y6IaeYDbnFnC5vwMNNMWtUEI8uGMW147p/0FKt\nNV9Wfcm2wm1sPrOZnNIcNJqYITHcOfZO0mLSmBQ8CTfluumAhBB9Q3ldMx8dPMuGnGLb6PAjQwbz\n6IJRLJ0UzsiQ7h+0tKKpgp1FO9mSv4UvCr6gobWBQZ6DmB35VYu8jAHYPaToEr2e1pojZ2tthdbJ\nsnrc3ZRtdPhrx4d2ex+HyqZKdhbtZEfRDnYW7aSksQSAsQFjeTDxQdJi0hg5dKT0bxBCfE1FfQsb\nzUJrZ145beYQNQ+lGYXW6G4eHd7SZiGrNIsdRTvYXrjdNiJ8gHcAi2MXsyBmAVPDp+Ll7tp5F69G\nDhVdSqlFwG8Ad+BVrfULHV5/DPgW0AqUAvdprU+br7UBueaqZ7TWy50Uu+jnjp6tZUNOEetzi8kr\nrcdNwYwRQXx7ThzXjg/r1olZLVYLOaU5bC/czo6iHRwqP4RG4+vly/SI6cyMmMn0iOmEDQrrthiE\nc0j+Ej2hqqGFjw+eY11OETtOGIXW8EAfHpg7gvRJ4cSHDenWk7T8mny2F21ne9F29hTvoaG1AXfl\nTkJwAg8lPsTMyJmMDRgrdxy62CWLLqWUO/AH4BqgANirlFqrtT5kt9p+IEVr3aCUegD4JXCL+Vqj\n1jrRyXGLfup4SS3rc4rZkFPMlyV1uCmYFhfI/bNiWTQ+jMDB3Tf2S0Ftge1McM/ZPdRZ6nBX7kwM\nmsgDiQ8wM2Im4wPHS5LqQyR/CVeqbrDw8aGzbMgttg26HBPgw7fnxJE+KbxbxwKst9Szp3gP24uM\nE8X82nwAIgdHsjRuKTMiZzA1bKpcNuxhjrR0pQLHtdZ5AEqpN4EVgC1paa232K2/C7jDmUGK/u1E\naZ1x6TDHmFhaKZgaG8BdMyawaHxYt8132GBpYO/ZvbYkdbrmNAARgyJYFLuIGREzmBo+VUZT7tsk\nf4luVdNk4ZOD59iQW8wX5sTSUf4D+dbsOJZOCmd8RPcUWlZt5UjFEduJYlZpFq3WVgZ6DCQ1LJU7\nxt7BzMiZxAyJkW4PvYgjRVckkG/3vACYepH17wc+tHvurZTKwGi6f0Fr/V5nGymlVgGrAGJiYhwI\nS/RlJ8vqjUuHOcUcOWsUWlOGBfDM8vEsnhDWLbdHt48E337JcF/JPluSSglNYWX8SmZEzGC473BJ\nUv2H5C/hdLVNFjYdLmF9TjGfHyulpc2Y3eLembGkTwxnUpRft+SQssYydhbtZHvRdnYW7aSiqQKA\n+IB47hp3FzMjZpIYkih9s3oxR4quzr45utMVlboDSAHm2i2O0VoXKaXigM1KqVyt9Ymv7VDr1cBq\ngJSUlE73L/ourTUnSuuNpvecYg4W1QCQMsyfny4bx+IJ4YT5Ob/QKm8sZ2fxTnYU7mBH0Q7Km8oB\nY4C/O8feyYzIGUwOmSxJqv+S/CWcorrBwtZjJWzIKWbrsVJaWq2E+3lz1/RhpE8KJzF6qNMLLUub\nhf0l+22t8UcqjgBGB3j7vqUypEPf4UjRVQDYj4YWBRR1XEkptRB4EpirtW5uX661LjL/zVNKbQWS\ngK8lLdH/NFna2H2ygi1HSth8pIQzFQ0AJMUM5cdLx7FkYhjhfgOd+p4XukvHf4A/0yKmMTNiJjMi\nZlz1M91fRSR/iS7RWnPsXB2bj5Sw5UgJmWcqabNqQn0HcPvUGJZOCnf6NGJaa07XnGZHkXGSuOfs\nHhpbG/FQHiSGJPLo5EeZETGD+IB4GZKmj3Kk6NoLjFJKxQKFwK3AbfYrKKWSgD8Ci7TWJXbL/YEG\nrXWzUioImInRSVX0U8XVjWw5UsrmIyVsP15Go6UNb083ZowI4j/mxJEWH0LkUOcVWlZt5VjlMfYU\n72Hv2b3sOWvcpeOhPJgUPImHkx5mZsRMxgaOlSR1dZL8JRzW2NLGzrwys9AqpbCqEYBx4b48MHcE\n8+NDSIoe6tRCq7Sh1Ja7dhXvorCuEIDoIdEsH7GcmREzSQ1PlZks+olLFl1a61al1EPARoxbrl/T\nWh9USj0LZGit1wK/AgYDb5vNq+23Vo8F/qiUsgJuGH0iDnX6RqJParNqsvIr2XykhM1HSjlcbFw2\njBw6kBuTo0iLD2H6iEC8PZ1zx5/WmuNVx9lz1iiyMs5lUN1cDUDMkBi5S0ecR/KXuJSCygZba/yO\nE+U0t1rx8XJn5sggHkobyfwxIU7t+lDeWE7GuQxboXWy+iQAQzyHkByWzD3j72FmxEyZbqefUlr3\nvu4HKSkpOiMjo6fDEBdQ1dDCZ8dK2XKkhM+OlVLZYMHdTZE8zJ+0+BDS4kMYFTLYKf0btNacrDnJ\n3mIjQWWcy7B1Ho0cHMmUsCmkhqUyJWyKjJnVhymlMrXWKT0dhzNI/urdWtusZJ6uZPNR47LhsXN1\nAAwL9GH+GCN/TY0LcNpcrVVNVWScy7CdKB6vOg6Aj4cPk0MnkxqWSmpYKvEB8TIcTR/maA6TEenF\nJbWPCN/et2HfmUqsGgIGeTF/TAjz40OYMyrYKdPvaK3Jr81nz9k9tiRV1lgGQKhPKDMjZhqFVngq\nkYMjr/j9hBD9X3ldM1uPlrL5aAmfHyultqkVDzdFamwAN6dEMz8+hLigQU45UaxpqSHzbKYtfx2r\nPIZGM9BjIInBiaTHpTMlbArjAsfh6SZzsl5tpOgSnaqob2F3XjlfHC9j65ESiqqbABgf4cuD80cy\nPz6EhKihuDuhb0NBbYGtqX3v2b2cazgHQNDAIFtLVmpYKtFDomUoByHEJbW0WskuqGLH8XK2HC0h\nu6AKrSFo8AAWjQ8jLT6EWaOCGOJ95UVPvaWezHOZthx2pOIIVm3Fy82LxJBEvpv4XVLDUpkYNFEm\nvhdSdAlDZX0Lu09WsCuvnF155Rw5WwuAj5c7s0YG8ciCUcyPDyHUCeNnna0/a7RkmZ3fi+qNm8kC\nvANICU0xLheGTyHWN1aKLCHEJVnarOQUVLErr4KdJ8rJOF1Bk8UKQEKUH48uGEVafAgTIvyuuBN8\ng6WBrJIs20niwfKDtOk2PNw8mBQ0iVWTVpEalsqk4EkMcO++GTRE3yRF11WqusHC7pPl7MwrZ1de\nBUfO1qA1eHu6MWV4AMsSIpgWF8ikKD883bt+15/WmqL6IjLPZdrOBtunp/Ab4EdKaAp3jb+L1LBU\nmTBaCOEQS5uV3MJqduWVG0XWqUoaLW0AxIcN4dYpMUyLC2RqbAD+VzhHa11LHVmlWbYclluWS6u1\nFQ/lwYSgCdw34T5Sw1NJCE5goIdzh8AR/Y8UXVeJ6kYLe09WmEVWOYeKjSJrgIcbKcP9eWzhaKaP\nCGRS1FC8PK6syDpZfZKMcxm2JNV+udDXy5fJIZO5dcytTA2fyij/UTKMgxDiklrbrBwoqrErsiqo\nbzGKrNGhg7k5JcoosuICCbjCIquyqZJ95/aRWWLkr/bLhe7KnXGB47hrnHGSmBSShI+njzM+nriK\nSNHVT9U2Wdh7qsLW3H6wqBqrBi8PN5Jj/PneAqPISoj2u6K7dNqsbRytPGorsPad20dlcyVg9MlK\nDk22/YwcOlKKLCHEJbVZNYeKatiZV8bOE+XsPVVJXXMrACNDBvPNye1FVgBBg6/sEt7Z+rNGkWXm\nsBPVxti3A9wHMCnYuFw4OWQyCcEJUmSJKyZFVz9R22Qh41SlrU9WbqFZZLm7kRQzlIfTRjF9RCCJ\n0UOvaMyslrYWDpYftCWorJIs6izGLdeRgyOZHTWblNAUkkOTpeO7EMIh7UVWe/7ac7KCWrPIGhE8\niBWJRneHaXGBBA/pepHVfnd05rlMW2t8+2CkgzwHkRSSxNIRS0kOTWZ84HiZHkw4nRRdfdTFiqzE\nmKE8lDaKabEBTB7mf0VFVoOlgZyyHFuRlVOaQ3ObMUvKCL8RLIldQnJoMpNDJ8s4WUIIh1ysyIoL\nHsSy9iIrNoCQK7h5x6qtHK86fl5LVmljKWBMDZYcmsztY28nOTSZMf5jZJws0e2k6OojHCqy4gKY\nHHNlRVZ5YzlZJVnsK9nH/pL9HC4/TKtuxU25ER8Qz81jbjaKrJDJ+Hv7O/ETCiH6q68VWacqqG0y\ni6ygQSxNiGD6iCsvsprbmjlUfoh954z8tb9kPzUtxiwZoT6hTAmbQnJoMimhKcT6yd3RwvWk6Oql\nqhstZJyqYPfJCnbbFVme7oqkaH8emj+SaXGBJMX4M9Cra0VW++Sq+0v224qs0zWnAaM/w4SgCdwz\n4R5SQlNICE6QaXWEEA5pbbNysKiG3SfL2Z1X8fUia1IE0+ICmBYXeEXD0FQ3V593knig7AAWqwWA\nWL9YFg5baOtTGjEoQoos0eOk6Oolqhpa2HPSLLJOlnOoqOarlqzooTw4fyTTr7DIsrRZOFxx2Ciy\nzu0jqzTLNqXO0AFDSQpJ4sZRN5IYksi4wHHSn0EI4ZD2IRx25xn5K8Ou47tRZIXb+mR1tcjSWlNY\nV/jVSeK5/bZO7x5uHowPHM8dY+8gKSSJxJBEaYkXvZIUXT2kor6FPSeNMbJ25ZVz9Fwt2ry7cLLZ\n8X3qFV4urG2pJbs029bUfqDsAE1txsjyMUNimB05m6SQJJJCk2QgUiGEw1pajcFI2wdUzjxdSYM5\nhMPIkMGsSIxg6hX2yWq1tnKs8pjtJHF/yX5bf6whnkNIDDGm1EkKSWJC0AS8PZw3KbUQ3UWKLhcp\nqWli98kKszWr3DbJqrenG8nD/Pn+xNFMjQ0goYt3F2qtKagtIKs0i/0l+8kqzeJ45XE0GnflTnxA\nPDeOvpHJoZNJ5ajZFwAAIABJREFUCkkiaGCQsz+iEKKfarK0sf9MFbtPGp3e952ptI34PiZ0CDcm\nRzE1NpDU2IAu311Y01JDTmkO+0v2k12STU5ZDo2tjQCEDwpnStgUJodMJik0SYafEX2WQ0WXUmoR\n8BvAHXhVa/1Ch9cHAH8FkoFy4Bat9SnztSeA+4E24BGt9UanRd+LFVY1stu8K2f3yQpOltUDMMjL\nneThAaxIjGRaXAATI7s2GGlzWzOHyw+TVfJVkdV+qXCw52ASghO4Ztg1TA6ZzMSgiTK+jLiqSQ67\nPPXNrWSerrQVWdn51bS0WVEKxob52kZ8T40N6NJgpFprztSeseWv7NJsTlSdsJ0kjvYfzfUjrycx\nOFHujBb9yiWLLqWUO/AH4BqgANirlFqrtT5kt9r9QKXWeqRS6lbgReAWpdQ44FZgPBABfKqUGq21\nbnP2B+lJ9c2tHCyqIbewmtyCKvaeqqSwyjhD8/X2IDU2gJWp0UyNDWR8hC8eXZhWp6yxjOySbFtL\n1qHyQ7YOozFDYpgVOYvEkEQSgxMZMXSEnAUKYZIcdnFtVs2J0jpyC6rJLaxmf34VBwqrabNq3N0U\nEyL9uHfmcFJjA0gZHoDfwMuftLm5rZmDZQfJKs0iqySL7NJs20niEK8hJAQnsGj4IhJDEuUkUfRr\njrR0pQLHtdZ5AEqpN4EVgH3CWgE8bT5eA/xeGR2EVgBvaq2bgZNKqePm/nY6I/jDJQWU1dc4Y1eX\nZN/fqbyumWPnavnyXC1Hz9WRX9mANl8LGuzFuAg/bpjqx6SoocQFD8LNtm0VBXVVgNFfodZSS01z\nDdUt1dQ011DTYv50eFzdUk1ZYxkAXm5ejA8yOowmhiSSEJxA4MBAlxwDIfqoXpnDyhtqOXQu/0p3\n47D2HNZkaeNEaZ2Zw+r4sqSOplajhvT2cGd06GBumzmUSVF+TIjws7txp5HKlkIqW0CjqbfUn5+r\nWmqobq7uNIeVNJbQajU61g/3Hc7syNkkhiSSFJJErF+snCSKq4YjRVckYJ8ZCoCpF1pHa92qlKoG\nAs3luzpsG9nlaDt4eOOznLM6pX7rugAYFPDV0yZgn4Z9Z4Azl7+7wZ6D8fXyxXeAL75evsT6xeI7\nwJc4vzgSQxIZGzBW7ioU4vL0yhz2Vu5nvHzkCWfsqusGgscwsB8MJg/IK4P3yoCsy9vdAPcBRv4y\nc1ioTyijho4ixCeEhOAEEkISCPAOuPSOhOinHCm6OrulTTu4jiPbGjtQahWwCiAmJsaBsOD+Sbfz\nZcUch9a9ErpDxD5e7kQH+DDEu+v3Ibgr9/OKK18vXwZ7DcbDTe5tEMLJuj2HdSV/zR2eQFnDfzq0\n7pWyz2HubhDuN5AQ3wF2rfCXb5DnIHy9fPEb4GfLZQPcr2weRCH6O0f+hy8Aou2eRwFFF1inQCnl\nAfgBFQ5uC4DWejWwGiAlJaXTwqyjlQlzHVlNCHF16/Yc1pX8NT40mvGhdzj4EYQQ/YEjF9L3AqOU\nUrFKKS+MTqVrO6yzFrjbfHwjsFlrrc3ltyqlBiilYoFRwB7nhC6EEA6RHCaE6BUu2dJl9m94CNiI\ncbv1a1rrg0qpZ4EMrfVa4P8BfzM7mVZgJDXM9f6F0WG1FXiwP931I4To/SSHCSF6C6U7dljqBVJS\nUnRGRkZPhyGEcBGlVKbWOqWn43AGyV9CXH0czWFyn64QQgghhAtI0SWEEEII4QJSdAkhhBBCuIAU\nXUIIIYQQLtArO9IrpUqB0w6uHgSUdWM4V0Ji67reHJ/E1jUXi22Y1jrYlcF0l36Uv6B3xyexdY3E\n1jWXis2hHNYri67LoZTK6K13PUlsXdeb45PYuqY3x9ZTevsx6c3xSWxdI7F1jbNik8uLQgghhBAu\nIEWXEEIIIYQL9Ieia3VPB3ARElvX9eb4JLau6c2x9ZTefkx6c3wSW9dIbF3jlNj6fJ8uIYQQQoi+\noD+0dAkhhBBC9Hq9uuhSSi1SSh1VSh1XSv1XJ68PUEq9Zb6+Wyk13O61J8zlR5VS1/VAbI8ppQ4p\npXKUUpuUUsPsXmtTSmWZP2t7ILZ7lFKldjF8y+61u5VSX5o/d/dAbP9rF9cxpVSV3WvdfdxeU0qV\nKKUOXOB1pZT6rRl7jlJqst1r3X3cLhXb7WZMOUqpHUqpBLvXTimlcs3j5vRJAR2IbZ5Sqtrud/cT\nu9cu+n3oyyR/dVtskr86j03yV/fE5tz8pbXulT+AO3ACiAO8gGxgXId1vgu8Yj6+FXjLfDzOXH8A\nEGvux93Fsc0HfMzHD7THZj6v6+Hjdg/w+062DQDyzH/9zcf+roytw/oPA6+54riZ+58DTAYOXOD1\nJcCHgAKmAbtdcdwcjG1G+3sCi9tjM5+fAoJ68LjNA9Zf6fehL/1I/urW2CR/df5+kr+6Jzan5q/e\n3NKVChzXWudprVuAN4EVHdZZAbxuPl4DLFBKKXP5m1rrZq31SeC4uT+Xxaa13qK1bjCf7gKinPj+\nVxTbRVwHfKK1rtBaVwKfAIt6MLaVwD+d+P4XpbX+HKi4yCorgL9qwy5gqFIqnO4/bpeMTWu9w3xv\ncO33zZHjdiFX8l3t7SR/dVNsFyH5S/LXZXN1/urNRVckkG/3vMBc1uk6WutWoBoIdHDb7o7N3v0Y\nZxjtvJVSGUqpXUqp650Y1+XEdoPZlLtGKRV9mdt2d2yYlzNigc12i7vzuDniQvF393G7XB2/bxr4\nWCmVqZRa1UMxTVdKZSulPlRKjTeX9bbj5kySv7o3Nslfl0/yV9c5LX95dEd0TqI6WdbxVssLrePI\ntlfC4f0rpe4AUoC5dotjtNZFSqk4YLNSKldrfcKFsa0D/qm1blZKfQfjbDvNwW27O7Z2twJrtNZt\ndsu687g5oqe+bw5TSs3HSFqz7BbPNI9bCPCJUuqIeXbnKvswpsioU0otAd4DRtGLjls3kPzVfbFJ\n/uoayV9d49T81ZtbugqAaLvnUUDRhdZRSnkAfhjNhI5s292xoZRaCDwJLNdaN7cv11oXmf/mAVuB\nJFfGprUut4vnT0Cyo9t2d2x2bqVD03w3HzdHXCj+7j5uDlFKTQJeBVZorcvbl9sdtxLg3zj3UtUl\naa1rtNZ15uMPAE+lVBC95Lh1E8lf3RSb5K8uk/zVBU7PX5fq9NVTPxitcHkYTbTtndTGd1jnQc7v\niPov8/F4zu+ImodzO6I6ElsSRie7UR2W+wMDzMdBwJc4sfOwg7GF2z3+BrDLfBwAnDRj9DcfB7gy\nNnO9MRidJ5Wrjpvd+wznwh0q0zm/I+oeVxw3B2OLwej7M6PD8kHAELvHO4BFLo4trP13iZEwz5jH\n0KHvQ1/8kfzVrbFJ/rpwjJK/nB+bU/OXUwPvhgOxBDhm/vE/aS57FuPMC8AbeNv8Ze0B4uy2fdLc\n7iiwuAdi+xQ4B2SZP2vN5TOAXPMXlAvc3wOx/QI4aMawBYi32/Y+83geB+51dWzm86eBFzps54rj\n9k+gGLBgnMXcD3wH+I75ugL+YMaeC6S48LhdKrZXgUq771uGuTzOPGbZ5u/8yR6I7SG779su7BJr\nZ9+H/vLjwN+h5K+uxSb5q/PYJH91T2xOzV8yIr0QQgghhAv05j5dQgghhBD9hhRdQgghhBAuIEWX\nEEIIIYQLSNElhBBCCOECUnQJIYQQQriAFF0dKKXClFJvKqVOKKUOKaU+UEqN7uK+XlVKjTMf/7eD\n25wyB17rM5RS9yilfn+JdeYppWbYPf+OUuqu7o+uZ1zs8yql/qKUurEb33uRusTM90qpOUqpfUqp\n1u6MRXQ/yVmXT3LW1/WBnPWY+f3OUUptMqdaan+tTSmVZf6s7a44naE3TwPkcuZks/8GXtda32ou\nSwRCMcbiuCxa62/ZPf1v4HlnxNlHzQPqMAa3Q2v9So9G0wmllIc25sBzhnn0wOdVSrljjMVzDcaY\nM3uVUmu11oc6rHoGuAf4oSviEt1Dcla3mofkrG53GTlrP8bYYg1KqQeAXwK3mK81aq0TXRHvlZKW\nrvPNByz2XzatdZbW+gul1GCzut6nlMpVSq0AUEoNV0odUUq9rr6agNXHfG2rUipFKfUCMNCswv9h\nvvaeMibwPKgcmMRTKfWyMiZLPaiUesZu+RSl1A5lTMa5Ryk1RCnlrpT6HzPOHKXUw+a6tjNSM66t\n5uOnzfg/Ntf5plLql+b2HymlPC+2fYc4lymldiul9iulPlVKhSqlhmMMNvd98xjMNt/zh+Y2icqY\nBDZHKfVvpZS/3fF70fxcx5RSsx04TnVKqV+bv6dNSqlgc/kI87NkKqW+UErFm8v/opR6SSm1BXjR\n/D3/2e7Y3WCud61Saqe537eVUoPtjskzdt+L+Et93g7xJiulPjPj2qiUCr/UZ7yEVLj0zPda61Na\n6xzAeoXvJ3qW5CzJWVdLztqitW4wn+7CmHanz5Gi63wTgMwLvNYEfENrPRkj0f1aKdU+4eUYYLXW\nehJQA3zXfkOt9X9hVuJa69vNxfdprZMxJpN9RCkVeInYntRapwCTgLlKqUlKKS/gLeBRrXUCsBBo\nBFZhTE2QZMb0Dwc++wiMaSJWAH8HtmitJ5r7S3dg+3bbgGla6ySMP54faa1PAa8A/2segy86bPNX\n4D/NWHOBn9q95qG1TgW+175cKRWhlPrgAu8/CNhn/p4+s9vXauBh85j/EPg/u21GAwu11j8AfgxU\na60nmvFsNpP2U+Y6k4EM4DG77cvM5S8DP3Tg82J+Dk/gd8CNZlyvAT/vZL3b1VdN5/Y/azrZbSRd\nmPle9FmSsyRnXY05636MKY3aeZsF/i6l1PWX2LZHyeVFxyngeaXUHIzWgUiMJnyAfK31dvPx34FH\ngP+5xP4eUUp9w3wcjTFreflF1r9ZGWeXHkA4MA5jRvNirfVeMCbmBNtEta+0NztrrSsc+Hwfaq0t\nSqlcwB34yFyeizEvlaOigLfMsx8vjHm8Lkgp5QcM1Vp/Zi56HWNqlHbvmv9mtsehjQlQl1xgl1aM\npA7G7+Jd8wxvBvD2V//nMMBum7e11m3m44UY8+BhvlelUmopxvHebm7vBey8QIzfvMjH7WgMxn+a\nn5j7dceYjuI8Wut/4Nh/QmB8T7+2i8uISfQfkrMcIznLcT2es5RSd2AU/nPtFsdorYuUUnEYRWeu\n1vqEg+/vUlJ0ne8gcKHOgrcDwUCy+Yd+CmPuNPj6F+Si/8kppeZh/KFMN69Pb7XbV2frx2Kc6Uwx\n/6D+Yq6vLvBeF1reyletmx3frxlAa21VSln0V/NDWfnqe3Kx7dv9DnhJa73W/JxPX+hzOajZ/LeN\nrn1fNUbMVRe55l9v97izY6eAT7TWK50cowIOaq2nX3QlpW4HHu/kpeNa647f1wKM/xDbReHIzPei\nr5KcJTnrqslZZnH+JDBXa93+GdqLWrTWeeZ3s33C9l5HLi+ebzMwQCn1H+0LlNH/YC7gB5SYyWs+\nMMxuuxilVPuXcCVGc3VHFrNpFnNflWbyiseY8f1ifDH+yKqVUqHAYnP5ESBCKTXFjHWIUsoD+Bj4\njvkYpVSAuf4pINl8fMMl3rMzjmzvBxSaj++2W14LDOm4sta6GqhUX/V9uBOjib2r3PjqP6HbgG3m\n2fRJpdRNYHQ+VkolXGD7jzEmOMVc1x+j/8BMpdRIc5mPuvTdYZ1+3g6OAsHt3x2llKdSanzHlbTW\n/zCb/Dv+dPaf7V5glFIq1ryUcyvQq+/mEVdEctbFObK95CxDr85ZSqkk4I8Yk4uX2H9epdQA83EQ\nMBPo2Am/15Ciy455pvQN4Bpl3H59EOOspwijqTRFKZWBcQZ5xG7Tw8DdSqkcIADjOnlHq4EcZXRK\n/QjwMNf/GcYfyMXiysa4c+MgxjX07ebyFoy7N36nlMoGPsE4m3sV4+60HHP5beaungF+o5T6AuMM\n53I5sv3TGE3iXwBldsvXAd8wr+t37Fx6N/Ar83gkAs9eLAh18f4R9cB4pVQmkGa3r9uB+83jcZBO\nOmqangP8lVIHzHXna61LMe70+6cZ4y4g/mIxcvHPC9h+fzdidIbNBrIwLil0mXl55iFgI8b38l9a\n64MASqlnlVLLzcdTlFIFwE3AH83vuuhjJGddkuSsfpKzgF8BgzF+V/ZDQ4wFMsx4tgAv6K/f+dhr\nqK9aZEVXKOOuj/Va6wk9HIrAuBNIaz24p+MQoreSnNW7SM66ukhLlxBCCCGEC0hLlxBCCCGEC0hL\nlxBCCCGEC0jRJYQQQgjhAlJ0CSGEEEK4gBRdQgghhBAuIEWXEEIIIYQLSNElhBBCCOECUnQJIYQQ\nQrhAr5zwOigoSA8fPrynwxBCuEhmZmaZ1jq4p+NwBslfQlx9HM1hvbLoGj58OBkZGT0dhhDCRZRS\np3s6BmeR/CXE1cfRHCaXF4UQQgghXECKLiGEEEIIF+iVlxc7qqqqori4uKfDEMLpwsPDGTp0aE+H\nIbqRxWKhoKCApqamng5FCKfy9vYmKioKT0/Png6lz+gTRVdZWRnDhw9n4MCBPR2KEE7T2NhIYWGh\nFF39XEFBAUOGDGH48OEopXo6HCGcQmtNeXk5BQUFxMbG9nQ4fUafuLxosVjw9vbu6TCEcCpvb28s\nFktPhyG6WVNTE4GBgVJwiX5FKUVgYKC04F6mPlF0AZKwRL8j3+mrh/yuRX8k3+vLd8miSykVrZTa\nopQ6rJQ6qJR6tJN1lFLqt0qp40qpHKXUZLvX7lZKfWn+3O3sD+BsI0eO5M033+zRGF577bUubXfq\n1Ck2b958wderqqp49913uxpWp77//e8ze/ZsHn30a18Lm7/85S+MGTOGefPmMW/ePPbs2dOl99q6\ndStPPfVUV0Ptsksd14tpbW3lzjvvZNasWbzwwgtOjqyXKj8B7/wHFGT2dCSA5LCeIDmsc5LD+gBL\nI2z/LWx5vlt270hLVyvwA631WGAa8KBSalyHdRYDo8yfVcDLAEqpAOCnwFQgFfipUsrfSbE7XXZ2\nNrNnz2bdunVd3ofWGq31FcXRVxLWvn37qK+v54svvqClpYW9e/decN3HH3+crVu3snXrVlJTU50W\ngytcScJau3YtY8eOZdu2bWzbto2zZ886ObpepLoA1j4Cv58Ch9dB6eGejqid5LDLIDmsc5LD+nkO\na22Bva/CbxLhkx/D2VywWp3+NpcsurTWxVrrfebjWuAwENlhtRXAX7VhFzBUKRUOXAd8orWu0FpX\nAp8Ai5z6CZzo3Xff5bvf/S4NDQ00NzcDxhcuOTmZVatWMWvWLAB2797N5MmTWblyJZMnGyfETz/9\nNPfeey/XXXcdZWVlPPvss8ybN4+0tDROnToFwL333svChQu57777ePrppwG4+eabmTt3Ltdeey01\nNTWsXr2a3Nxc5s2bR25uLuvXr2fOnDnMmDGDjz766Lx4//3vf5OamkpaWhoffPABq1ev5m9/+xsL\nFizAYrGwYMEC5syZww033EBbWxurV6/mk08+Yd68eZSWlvLqq68ye/ZsZs+ezb59+y77eO3cuZOF\nCxcCsHDhQnbt2uXwtn/5y1+47bbbSE9PZ/ny5bz88svMmTOHb33rW7bXb775Zq677jpWrFhBS0vL\nedu/+OKLzJw5k7S0NM6cOcNbb73FH/7wBwCysrJ4+OGH2bp1K9dffz0rVqxg1qxZ/OMf/2DBggWk\np6fb/mN54IEHSEtLIz09ncrKSrZu3cqKFStYtmwZM2fOpK6u7rzjeiXHaP78+RdN6n1WXSl89AT8\ndjJkvQFTvkXLDR9iHXtjT0cGSA6THHZhksMu7xj1yxxmbTPy1u+TYcMPICAW68r3aJn5Irg5vwfW\nZd29qJQaDiQBuzu8FAnk2z0vMJddaHln+16FcYZJTExMp+//zLqDHCqquZyQv2ZchC8/XTa+09f2\n7dvHM888w6JFi/j0009JT0/nxRdf5PPPP6eqqop58+YB8LOf/Yy1a9fi7+/PsGHDbNuPHj2aP//5\nz+Tm5lJYWMjWrVs5fPgwv/jFL7jvvvsYMGAAn376KS+++CKNjY2A8Yfp4+PDq6++yltvvcWqVav4\n61//ytatW7FarTz88MNs3rwZq9XK4sWLWbToq3z/zjvv8K9//Yvhw4ejtcbHx4e4uDiee+45tNas\nX7+egQMH8tRTT7F582ZWrVrFmTNn+Pvf/05ZWRlr167l888/p7Kykvvuu4/33nvPtu+1a9fy0ksv\nnXd80tPTefzxx23Pq6qqGDFiBAB+fn4cPHjwgsf9V7/6FX//+98B+Otf/wpAcHAwb7zxBqtWraKp\nqYnPP/+ca6+9loqKCts+//Wvf/Hiiy/y7rvvEhYWBsDZs2fZvHkz27dvZ9u2bfziF7/gpZde4oYb\nbuDBBx/krbfeYuXKlbS0tKC15v333+f5559nz549bNq0iVWrVrF//34KCwuJiYnh5Zdf5sMPP+SV\nV15h+vTpAKxbt46f//zntvXbj6u91atX88Ybb5y37P777+fOO+887xj5+vraPk9lZeUFj1Gf01gJ\nO34Hu16B1kZIvA3LuG9R9o/3qfrRXYR8//sE3n9fT0d5nu7KYb0hf4HkMMlhksMcZrXC4bXGZcSy\noxCegL7uf6jKqaJs1TO4+/sT++47KCcXXg4XXUqpwcA7wPe01h0zR2e96fRFln99odargdUAKSkp\nV9a23QUnTpzgwIEDLFq0iObmZkaPHk16ejru7u4MGjSIQYMGERQUBEBNTQ1RUVEAjBo1yraP5ORk\nAA4fPszWrVttCS48PJyTJ08yadIkABITE9m5cydtbW08/vjj5ObmUlNTwze+8Y3zYiorK+Pw4cO2\ns4ySkhK01rbOi0899RTPPfccra2tPPnkk+dtW19fz6pVqygsLOTcuXOMGjXqvFjz8vLIzs5m/vz5\nnR6P5cuXs3z58oses6FDh1JTU2M7Jhcb+uDxxx+3nQG2mzBhAgARERHnPW7/o05KSgKM47V3715b\nwjp16pTtWKakpPDMM88wcOBAQkJCOHPmDLt37+b555/ns88+O2+/wcHB573H4cOHefPNN9m4cSOt\nra22ZNW+TWRkJFVVVfj5+XX6mVatWsWqVasu6xiNHDnyouv3Cc11sPsV2PFbaKqG8d+kdfJDVPx7\nCxU/vQ/d2srQm27Ed+nSno70PN2Zw3o6f4HksI4kh0kO65TWcPxT2PwzKM6GoDHoG/9CzSlPSr/3\nEpbTZxiYkEDwY485veACB4supZQnRrL6h9a6swvqBUC03fMooMhcPq/D8q1dCRS46BnelXrnnXd4\n9dVXbc2vy5cvp62tDavVSkNDA1VVVZSVlQHg6+tLUVERQ4cO5fjx47Z9uJm/oDFjxnDttdfyu9/9\nDjCGvNi3bx+fffYZADk5OYDRhFxfX8/nn3/On/70JwoLC4Gv7ggJCgpi4sSJbNy4EXd3dywWy3l3\niwwbNoxXX32VHTt28NJLL3HHHXfQ1tYGwMaNGxk9ejRvvPEGTz75JFprPD09ba/HxsYyZcoU1qxZ\nY4vRniNnidOnT+ePf/wjN998M59++in33HMPra2tlJeXExoaesljbv9Z7B+39yfJzs62/dt+NgrG\n3Hbtr2VkZNheu+222/jBD35AamqqbX8Xe48xY8Zw11138YMf/MB2DLZv3/619eyPmz1HzhKnT5/O\npk2bSE1NZcuWLaxcufKSx6XXsjRB5p/hi19DfSmMXox12g+o2JhB+S3fwVpfj++ypQQ//DBe0dGX\n3p8L9YYc1p35CySHSQ6THHZJp7bDpmchfxcMHYZe8TJ1VWGUPvU7mo8cYcDo0UT93/8xeP68brsz\n85JFlzLe+f8Bh7XWL11gtbXAQ0qpNzE6nFZrrYuVUhuB5+06nl4LPOGEuJ1uw4YNPPzww7bn48aN\nY9u2bfzoRz9izpw5JCYm2v4If/zjH7Ns2TJGjhxJdCf/uSQkJBAWFsa8ecYvbuXKlaxatYpXXnmF\nBQsWEBERQXx8PGPGjOH48eMsWrSI6OhoIiONqxbR0dHccMMN/PznP+exxx5jwYIFKKUYN26c7Zo/\nGH0wdu3aRV1dHb/+9a+ZMGECTzzxBLfccgu//vWv+fnPf05GRgZ+fn6MGjWKsLAwKioquPHGG1m9\nejXp6enMmTMHd3d30tLS+PGPf2zbtyNniZMnT8bb25vZs2eTkJBAamoqx48f58UXX+RPf/rTeeva\nN813PKO9kPLycq699lq8vb15++232blzJwBhYWHMnz+fGTNm4OXlxeuvvw7AggULuPvuux2+O2j5\n8uU88sgjpKWlAfC9733P1oxuz/64vvXWW7bljpwlLlu2jHfeeYdZs2axZMkSwsPDHYqtV2mzGH0e\nPnsRagohdg7WWX+lavtxyu74Hm0VFQxesIDgRx/Be/Tono72aySHSQ67EMlhV0kOK8yEzc/Bic0w\nJBzSX6KhbRwlv/o9jfv24RkTQ8SvfoVv+pJuad06T3tnvAv9ALMwmtNzgCzzZwnwHeA75joK+ANw\nAsgFUuy2vw84bv7ce6n301qTnJys7R06dEj3FIvForXWuqCgQC9ZsuS8ZXV1dXrGjBmXva8XXnhB\nv/HGG06OtHdYs2aN3rRp0xXv589//rP+05/+dFnbtLa26oULF17xe7tST363L6mtTevsf2n9m0St\nf+qr9eo0bT32qa5cs0Yfmz9fHxoTr0/ddbdu2L//it8KyNAO5Iau/Lg6h/Wm/KW15LDLJTns8vT0\n9/uizh3S+p+3GfnrheFab/+tbsjK1Ke/9R/60Jh4fWzWbF3xz39qa0vLFb+Voznski1dWuttdN6v\nwX4dDTx4gddeA7p2/3AvsGbNGl5++WXq6+v57W9/C8D27dv5yU9+Qm1tLT/5yU8c3tf999/PyZMn\n8fX1tTWJ9zc33HBDj7xvRUUFN9xwA9/+9rd75P37Fa3h6Aew+edQchBCJ6BveYPa0+6UPvoSLXl5\neE+cSMQsrnMmAAAgAElEQVRzz+EzfXqvHyBRcpjksMshOawfKD8BW1+A3LdhwBCY9980hy2h9JXX\nqP3o/3D38yPk8R/if9ttuLl4ekGlr3A8lu6QkpKiMzIybM8PHz7M2LFjezAiIbpHr/puaw15W4xm\n+MJMCBiBnvcE9VWhlP5/v6Hp0CG8Ro4g+NFHGbJwoVOLLaVUptY6xWk77EGSv8TVpFd9v6sL4fNf\nwv6/g5snTP02lrhbKH3tH1T/+z2UtzeB99xNwL334j5kiFPf2tEc1icmvBZCdLMzu427eU59Ab5R\nsPz3NFjHUvrL39CQkYFnZCThL/wCv2XLUO7uPR2tEEJ8pa4Utv2vMbiptkLKfbSOv4+yf/ybqsdu\nBSDgzjsIXLUKj8DAHg1Vii4hrmbFOUbL1pcbYVAILP4lTYNnUPq7/6Nu6/O4BwUR+tRT/z979x0e\nVbU1cPh3Jr33QhJ67y2C9K6UUESUYgEEQSmKXb/rtRcUGxZAQMBybWBDAiKi0gQ1dARECIEUIJlM\nep2yvz9OCKFJAkkmZb3Pk0fmTFsJcbHOmb3XwvfWWzA4O9s7WiGEOCcvvahX4EK9V2D7CVg7z8T0\nzU+YnrwdW34+PjeNImjmTJzCwuwdLSBFlxC1U8oR+OVFOPgtuPrCgKcprDOUlIVLyVw7H4OXF0EP\nPID/HbdjcHe3d7RCCHFOYY7eK3Db/KJegTdh6/YQaev/IHXMXVgzMvAaPJig+2bj0qiRvaM9TwXv\njax+ZFhs2ciw2H+3e/du2rZtS4MGDco3qKuVdgK+nQELusI/G6D3I5jHb+DUhgyOjRxD1s8/E3D3\n3TTZ8COB06dJwVUNSQ4rG8lh/65K5TBLgT4BY357vd9WvW6oKb+QZh3MsQmzSJ73Gq7t2tFg1Soi\n3nqzyhVcIEXXeWRYbNnIsNgra9KkCTt27Cju/m03Wach+mF4pzPsXwXXz8AycTNn/nTm2IhbSf/m\nG/zGjaPJj+sJfvABHC7TwVpUbZLDykZy2JVViRxmtcDOD/X5rj88BkEtUJPXk+F1O8fuepzTzzyL\nU3g49T/+iHpLFuPWpmIbEV8LKbpKkGGxZSPDYq/My8sLDw+PMj+v3OSa4Mf/wvwOejf5jrdjnbKN\nlKNhHBs5DtOKFXgPHkzjdWsJ/e+TOBaNGRHVk+SwspEcdmV2zWE2m36S+F4X+P4+8ApB3f4NWfUe\n5Pjsl0l65FEM7u5ELFpI/U//h/t119knzjKoXmu61j0Op/df22uEtoUhcy95lwyLlWGx5T0s1m7y\nM2HHAvjtXSjMhna3Yuv2IGnrd5B680SsaWl4DRpI0H334VJinp2oQBWcv0BymOSwGpLDlIK/1+mb\nfJL/guDWMO4zcjICSHnyLfL27MGpfj3CXnsN76FDKr6LfDmqXkVXBZJhseeTYbHlMyy20pnz4I8l\n+vbpPBO0HI7q9Tjp2w5hHH8vltOn8ejejaA5c3Ar+hmKmkFy2Pkkh1XTHBb7q75eq6hXIDd/QJ5q\nRsq8t8nZtg3HkBBCn30W39E3oTk52TvaMqteRde/nOFdKxkWK8NiK2JYbKWxFMKuD2Hza5B9GhoP\nQPX9P7IOpJBy1yMUxsXh2q4dYXNfxuP66ys/PlGh+Qskh0kOq+Y5LP4Pvdgq7hX4DgWe15Py7ntk\n/fhfHHx9CX70UfwmjMfg6lr58ZWT6nNNroJFR0fTvXv34tsXDot96qmnLhoWO3ny5CsOi+3Xrx/L\nly+na9eu5OfnM2DAAPbt24eTk9N5w2JL7oY5Oyz2yJEjxcNi+/Xrx5w5c857n2eeeYY+ffowe/Zs\nxo4dS5s2bdi2bRtjx46la9eurF69mqioqOL1GCWHxTo4OBQPi+3Xrx9z557/D8KIESOKF42e/SqZ\nrOD8YbEGg4EuXboQFxd3yd058+bNK975s2HDhlL9nZwdFrt161ZGjx5dfLzksNgnn3ySJ57Q5w8P\nGDCArVu3Mn78+FK9/ogRI4iLi6N///7079+fdevWXfJxJX+uJU2bNu2in9GFySo+Pp6BAwdy4MAB\nBg4cWPx3UW5sVn0Y9budYe3D4N8QNTGa7PpzOH7/SyQ+8CCakyMR771Lgy8+l4KrBpMcJjmsWuaw\nU/vgf7fCB4Mg5TAMfgXzzd+TtPIwsaNuImfrVgJnzqTxTxsIuGtytS64gCsPvLbHV1UaGCvDYstG\nhsWWzVX/blutSh34Rql3rtOHuS7qpdSRDSrnzz/V8Qm3qYPNW6h/BgxU6d9+q2wWS/kGXQGowIHX\nlf1VlfKXUpLDykpyWNlc9e938t9KfTlRz18v11Vq82vKnBinTj3/gjrYpq061LadOv3Sy8qcmlqu\n8VaU0uaw6vXxoh3IsNiykWGxFUwpOLIefnlBX5Qd2Bxu/Yh8mpL82nxyNm3GISiQkKf+i9+YMWjS\nRb7WkxxWNpLDKlhaHGx6FfZ+Bo5u0OthrG0nkfrpV5juH40qKMB39E0EzpiBU5069o623F1x4LWm\nacuAKCBZKdXmEvc/AtxWdNMRaAkEKaVMmqbFAVmAFbCoUg60lYGxorYo0+927K/6bp6EP8GvAfT9\nPwq8IjG++x6Za9dh8PEhYOoU/G+7rdo1Na3IgdeVncMkf4napNS/35lJsHke7PoINAfocje2zvdi\n+mYdqUs/wJaRgffQIQTOno1Lw4YVH3g5K8+B1yuAd4GPLnWnUmoeMK/oTYcDDyilTCUe0k8pZSzF\n+wghLuW8YdThMHw+5pABGN9fTPrXz6M5OxMwfToBU+7Cwdvb3tFWRSuQHCaEfVw4jLrTRFS3+0n7\nYSvGm8ZjTTHi0bsXwXPm4Nqqlb2jrXBXLLqUUps1TWtQytcbD3x2LQEJIYok7dHnI/7zI3gEweBX\nsDQYgXHZCtI/GwaA3/jxBN4zHceiVgDiYpLDhLCDvLSiYdSLioZRj0f1fIiMLfsx3joFc2Iibp07\nE/zmm7hHVshF7iqp3NZ0aZrmDgwGZpU4rIAfNU1TwPtKqcX/8vxpwDSAevXqlVdYQlQ/yYfgl5fg\n0Gp9GPXAZ7C2GE/qJ59jmjEclZ+Pz02jCJoxA6fwcHtHW2NcSw6T/CVEkYIsvdD67R0oyIDWo1F9\nHidrVxwpE++n8NgxXFu1IvSZp/Ho2fO89ha1QXm2jBgObLvgsnwPpVQnYAgwU9O03pd7slJqsVIq\nUikVGWTHUSQyLLb0kpKSirdcWyyWyz6uNg+LtVgs3HHHHfTs2fOiLe0XST0GX90NC7rBsV+gz+PY\npv+O8S8Pjg4dSeqi9/Hq24dGa9YQ9uKLUnCVv6vOYVUlf4HksLKQHHZlpc5h5jy90JrfXt/o06AH\navpWskPvIu6e/yOxqF1I+Pz5NPhqFZ69etW6ggvKt+gaxwWX5ZVSSUX/TQa+Aar0lFAZFls2/v7+\nbNy4ketL0fuptg6LXb16NS1btmTr1q1s3bqV06dPX/yg9HhYfR+8ex0c+h563IdtRgymhLocHX4L\nKW++iXunTjT85mvC33gDl0bVb5FpNSE5DMlhlyM57DI5TCl9Csb8DvDjk1CnPUz9mdymD3HikVeI\nv3sa1owM6sx9mUarv8P7xhtqZbF1VrkUXZqm+QB9gO9KHPPQNM3r7J+BG4AD5fF+FUWGxZaNq6sr\nfn5+V/Wzri3DYksO1O3Xrx9//vnnuTutZn3dwzud9O3T101FzdxJemZ7jo2ewJkXX8SlYUPqf/op\ndRctxFV2wFUYyWGSw8qq1ucwpSA3FbJOFTdmZtJa8jq+wMln3+fE7XdgPnGSkKf+S+N1a/EdNQrN\nweGqftY1yRXXdGma9hnQFwjUNC0BeBpwAlBKLSp62E3Aj0qpnBJPDQG+KapoHYFPlVLn/x9XRq/8\n8QqHTYev5SVo4d+Cx7o8dsn7ZFhs2YbFlkVtHRabnp6Od9GOQh8fH30mm9UCOWcg2wgF2dB+HKrn\nw2T9fpCU8VP1kT1t21Ln+efx6N69Vp8VloeqksMqOn+B5DDJYZWQw0wmyDVB1mmwFoBmgNu/ooAG\npLz5Llnr1+Pg40PwIw/jN2ECBje3q/p511Sl2b14xXkESqkV6NuySx6LBdpfbWCVTYbFnq80w2LL\norYOiz1voG5GOk0iAiH5ICgruPmBl0ZWwc2k3DWHgsOHcWnahIh338FzwAAptsqJ5DDJYeWh1ucw\npchMPUOTOr6QfgIcXcGvISo1nqSln5KxejUGV1cCZ8zAf/IkHLy8yvDTrT2qVUf6fzvDu1YyLLbs\nw2IvRYbFnn+W2K1bNzb+tIEuLevzyw/fM/7V/4CLJ3jVwVpgw2KKJ+Gee3GqW5ewea/iPXSoXIKv\noSoyf4HkMMlhFZTDrr+ejeuj6dLQh19+Ws/4158H3/rYnLywpqRgSU4mc906/CdOJGDa3The5ce1\ntUW1KroqUnR0NLNnzy6+feGw2A4dOlw0LLZJkyZXHBaraRrjx49n2rRpLFq0iAEDBhAWFkaLFi3O\nGxZbt25dwot2o50dFvviiy8WD4vVNI1WrVoVf+YP+hqMHTt2kJ2dzeuvv06bNm144oknGDt2LK+/\n/jovvvgiMTEx+Pj40LRp0/OGxS5evLh4WKyDgwP9+/fnv//9b/Frl+Ys0Ww2M2TIEPbu3cuNN97I\nSy+9REBAAK+88gpLliw577ElL81feEZ7OWeHxbq6urJy5Uq2b98OnD8s1tnZmQ8//BDQh8VOnDix\n1LuDRowYwX333Uf//v0BmDNnTvFl9JJK/ly/+OKL4uNXPEtUNob3u56vPvmAngNWMfSG/tRp3ROb\nB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NjeEYsqQIouUflO7dP7bP0drXdgHvA0dJlG4elUUp58hszv12Bwdydw5kz8J03EwcvL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\nGLvOGWNGP6+3m+3+caxkyx7sfwQYYy7Yz6Y74bEye1Bdprrsj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                        "text/plain": [
                            "<matplotlib.figure.Figure at 0x2518fe06780>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "\n",
                "\n",
                "\n",
                "\n",
                "B, km_ts, k_cross, k_prime, c, id_shock, ag_shock = solve_ALM()\n",
                "fig, ax = plt.subplots(figsize=(9, 6))\n",
                "ax.hist(k_cross, label='Cross sectional capital distribution', bins=50)\n",
                "accuracy_figure(km_ts, ag_shock)\n",
                "plot_policy(k_prime, km_ts)\n",
                "\n",
                "# Saving increases substantially with employment status, and increases modestly\n",
                "# with low aggregate state\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": []
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.3"
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    "nbformat": 4,
    "nbformat_minor": 2
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