{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import expm\n",
    "from scipy.integrate import quad\n",
    "from scipy.special import factorial\n",
    "from scipy.interpolate import PPoly\n",
    "from scipy.stats import sem\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import msprime as msp\n",
    "import stdpopsim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "matplotlib.rcParams.update({\n",
    "    'font.family': 'Times New Roman',\n",
    "    'text.usetex': True,\n",
    "})\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = '../../../exact_decoding_paper/figures/prior/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exact SMC Simulations\n",
    "### Simulation 1: Comparing renewal prior with Markov prior\n",
    "\n",
    "#### Define necessary functions for simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_expected_coal_times(times, eta):\n",
    "    r = eta[:-1]\n",
    "    a = times[:-1]\n",
    "    b = times[1:]\n",
    "    dt = b - a\n",
    "    # the exact formula is numerically unstable for small r, so use a taylor expansion.\n",
    "    r_small = (a + b) / 2. - dt ** 2 * r / 12\n",
    "    r_normal = b + 1. / r + dt / (np.expm1(-r * dt))\n",
    "    ary = np.where(r * dt < 1e-4, r_small, r_normal)\n",
    "    ret = np.concatenate([ary, times[None, -1] + 1 / eta[-1:]])\n",
    "    return ret\n",
    "\n",
    "def get_emissions(ts, w, L_w, mu, ect, w_end = None):\n",
    "    site_locations = [int(v.position)//w for v in ts.variants()]\n",
    "    loc, y = np.unique(site_locations, return_counts=True)\n",
    "    \n",
    "    if w_end is not None:\n",
    "        counts = np.zeros(L_w)\n",
    "        not_last = np.repeat(2 * mu * w, L_w - 1)\n",
    "        rate = np.r_[not_last, 2 * mu * w_end]\n",
    "        outer = np.outer(rate, ect)\n",
    "        \n",
    "    else:\n",
    "        counts = np.zeros(L_w)\n",
    "        rate = 2 * mu * w\n",
    "        outer = rate * ect[None,:]\n",
    "    \n",
    "    counts[loc] = y\n",
    "    \n",
    "    emissions = (outer ** counts[:,None]) * np.exp(-outer)/factorial(counts)[:,None]\n",
    "    return emissions\n",
    "\n",
    "def initial_distribution(times, eta):\n",
    "    x = np.r_[times, float('inf')]\n",
    "    y = PPoly(eta[None,:],x)\n",
    "    Y = y.antiderivative()\n",
    "    cdf_vals = 1 - np.exp(-np.r_[Y(times), float('inf')])\n",
    "    dist = cdf_vals[1:] - cdf_vals[:-1]\n",
    "    return dist\n",
    "\n",
    "def stationary_distribution(times, eta):\n",
    "    N0 = 1/eta[0]\n",
    "    vt = eta * N0\n",
    "    times_scaled = times / N0\n",
    "    x = np.r_[times_scaled, float('inf')]\n",
    "    y = PPoly(vt[None,:],x)\n",
    "    Y = y.antiderivative()\n",
    "    \n",
    "    def exp_int(tau):\n",
    "        return np.exp(-Y(tau))\n",
    "        \n",
    "    denominator = quad(exp_int, 0, float('inf'))[0]\n",
    "    def pi(t):\n",
    "        return (t * y(t) * exp_int(t))/denominator\n",
    "    \n",
    "    vals = np.r_[Y(times), float('inf')]\n",
    "    cdf_vals = np.r_[np.array([quad(pi, 0 ,t)[0] for t in times_scaled]), 1]\n",
    "    dist = cdf_vals[1:] - cdf_vals[:-1]\n",
    "    return dist\n",
    "\n",
    "def get_transitions_Markov(times, rho, eta):\n",
    "    x = np.r_[times, float('inf')]\n",
    "    y = PPoly(eta[None,:],x)\n",
    "    Y = y.antiderivative()\n",
    "    \n",
    "    A_rho = np.zeros((4,4))\n",
    "    A_rho[0] = [-1, 1, 0, 0]\n",
    "    A_rho[1] = [0, -0.5, 0.5, 0]\n",
    "    A_eta = np.zeros((4,4))\n",
    "    A_eta[1] = [1, -2, 0, 1]\n",
    "    A_eta[2] = [0, 4, -5, 1]\n",
    "    \n",
    "    def q(t, s):\n",
    "        if t > s:\n",
    "            e_j = expm(times[s]*rho*A_rho + Y(times[s])*A_eta)\n",
    "            return (e_j[0,1] + e_j[0,2]) * np.exp(-(Y(times[t-1])-Y(times[s]))) * (1-np.exp(-(Y(times[t])-Y(times[t-1]))))\n",
    "        \n",
    "        else:        \n",
    "            e_k1 = expm(times[t]*rho*A_rho + Y(times[t])*A_eta)\n",
    "            e_k2 = expm(times[t-1]*rho*A_rho + Y(times[t-1])*A_eta)\n",
    "\n",
    "            if t < s:\n",
    "                return e_k1[0,3] - e_k2[0,3]\n",
    "\n",
    "            else:\n",
    "                e_k3 = expm((times[t] - times[t-1])*rho*A_rho + (Y(times[t]) - Y(times[t-1]))*A_eta)\n",
    "\n",
    "                p = e_k1[0,0]\n",
    "                for i in range(3):\n",
    "                    p += e_k2[0,i] * e_k3[i,3]\n",
    "                return p\n",
    "        \n",
    "    transitions = np.empty((T,T))\n",
    "    for i in range(1,T+1):\n",
    "        for j in range(1,T+1):\n",
    "            if j < T:\n",
    "                transitions[i-1,j-1] = q(j,i)\n",
    "            else:\n",
    "                transitions[i-1,j-1] = np.max([1 - transitions[i-1,:j-1].sum(), 0])\n",
    "    return np.maximum(transitions, 0)\n",
    "\n",
    "def get_transitions_Renewal(times, ect, rho, eta):\n",
    "#     rows = stationary_distribution(times, eta)\n",
    "    rows = initial_distribution(times, eta)\n",
    "    cols = -np.expm1(-rho*ect)\n",
    "    diag = np.exp(-rho*ect)\n",
    "    transitions = np.outer(cols, rows) + np.diag(diag)\n",
    "    return transitions\n",
    "\n",
    "def forward(L_w, T, init_dist, emissions, transitions):\n",
    "    alpha = np.empty((L_w, T))\n",
    "    alpha[0] = init_dist * emissions[0]\n",
    "    c = np.empty(L_w)\n",
    "    c[0] = alpha[0].sum()\n",
    "    alpha[0] /= c[0]\n",
    "\n",
    "    for i in range(1, L_w):\n",
    "        alpha[i] = emissions[i] * (alpha[i-1][:,None] * transitions).sum(axis=0)\n",
    "        c[i] = alpha[i].sum()\n",
    "        alpha[i] /= c[i]\n",
    "    return alpha, c\n",
    "\n",
    "def backward(L_w, T, emissions, transitions, c):\n",
    "    beta = np.empty((L_w, T))\n",
    "    beta[-1] = np.ones(T)\n",
    "\n",
    "    for i in range(L_w-2, -1, -1):\n",
    "        beta[i] = (beta[i+1] * emissions[i+1,:] * transitions).sum(axis=1)\n",
    "        beta[i] /= c[i+1]\n",
    "    \n",
    "    return beta\n",
    "        \n",
    "def get_posterior(L_w, T, init_dist, emissions, transitions):\n",
    "    alpha, c = forward(L_w, T, init_dist, emissions, transitions)\n",
    "    beta = backward(L_w, T, emissions, transitions, c)\n",
    "    return np.maximum(alpha * beta,0)\n",
    "\n",
    "def get_viterbi(log_T, log_B, log_pi, L):\n",
    "    K = len(log_pi)\n",
    "    T1 = np.zeros([L, K])\n",
    "    T2 = np.zeros([L, K], dtype=int)\n",
    "    T1[0] = log_pi + log_B[0] \n",
    "    for i in range(1,L):\n",
    "        prob = T1[i-1][:,None] + log_T + log_B[i]\n",
    "        T1[i] = np.max(prob, axis=0)\n",
    "        T2[i] = np.argmax(prob, axis=0)\n",
    "    \n",
    "    Z = np.zeros(L, dtype=int)\n",
    "    Z[L-1] = np.argmax(T1[L-1])\n",
    "    \n",
    "    for i in range(L - 1, 0, -1):\n",
    "        Z[i - 1] = T2[i, Z[i]]\n",
    "    return Z\n",
    "\n",
    "def run_sim(num_sim, N0, mu, r, n, L, w, times, eta, rho, de=None):\n",
    "    L_w = L // w\n",
    "    T = times.size\n",
    "    \n",
    "    gammas_Markov = np.empty((num_sim, L_w, T))\n",
    "    gammas_Renewal = np.empty((num_sim, L_w, T))\n",
    "    viterbis_Markov = np.empty((num_sim, L_w), dtype=int)\n",
    "    viterbis_Renewal = np.empty((num_sim, L_w), dtype=int)\n",
    "    tss = []\n",
    "    \n",
    "    init_dist = initial_distribution(times, eta)\n",
    "    ect = get_expected_coal_times(times, eta)\n",
    "    transitions_Markov = get_transitions_Markov(times, rho, eta)\n",
    "    transitions_Renewal = get_transitions_Renewal(times, ect, rho, eta)\n",
    "    \n",
    "    for i in range(num_sim):\n",
    "        seed = i+1\n",
    "        ts = msp.simulate(Ne=N0, \n",
    "                          mutation_rate=mu, \n",
    "                          recombination_rate=r, \n",
    "                          sample_size=n, \n",
    "                          length=L,\n",
    "                          random_seed=seed,\n",
    "                          demographic_events=de)\n",
    "        tss.append(ts)\n",
    "\n",
    "        emissions = get_emissions(ts, w, L_w, mu, ect)\n",
    "\n",
    "\n",
    "        gammas_Markov[i] = get_posterior(L_w, T, init_dist, emissions, transitions_Markov)\n",
    "        gammas_Renewal[i] = get_posterior(L_w, T, init_dist, emissions, transitions_Renewal)\n",
    "        viterbis_Markov[i] = get_viterbi(np.log(transitions_Markov), np.log(emissions), np.log(init_dist), L_w)\n",
    "        viterbis_Renewal[i] = get_viterbi(np.log(transitions_Renewal), np.log(emissions), np.log(init_dist), L_w)\n",
    "        \n",
    "    return gammas_Markov, gammas_Renewal, viterbis_Markov, viterbis_Renewal, tss, ect\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions to plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_viterbi(viterbis_Markov, viterbis_renewal, tss, seed, ects, L, w, rs, name):\n",
    "    size = 25\n",
    "    L_w = L // w\n",
    "    scale_to_kb = 1000 / w\n",
    "    \n",
    "    fig, axs = plt.subplots(4, figsize=(20, 21), sharey=True)\n",
    "    fig.add_subplot(111, frameon=False)\n",
    "    plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
    "    \n",
    "    plot_labels = [r'Constant, Low $\\rho$', r'Expansion, Low $\\rho$', \n",
    "                   r'Constant, High $\\rho$', r'Expansion, High $\\rho$']\n",
    "    \n",
    "    for i, ax in enumerate(axs):\n",
    "        ts = tss[i][seed]\n",
    "        ect = ects[i]\n",
    "        \n",
    "        pmap = viterbis_Markov[i][seed]\n",
    "        dpmap, = np.diff(pmap).nonzero()\n",
    "        x = np.r_[0, dpmap, L_w - 1]\n",
    "        y = ect[pmap[x]]\n",
    "        ax.plot(x / scale_to_kb, y, drawstyle=\"steps-pre\", color=\"green\", label='Markov')\n",
    "\n",
    "        pmap = viterbis_Renewal[i][seed]\n",
    "        dpmap, = np.diff(pmap).nonzero()\n",
    "        x = np.r_[0, dpmap, L_w - 1]\n",
    "        y = ect[pmap[x]]\n",
    "        ax.plot(x / scale_to_kb, y, drawstyle=\"steps-pre\", color=\"blue\", label='Renewal')\n",
    "\n",
    "\n",
    "        truth = [(tree.interval[0] / w, tree.get_tmrca(0, 1)) for tree in ts.trees()]\n",
    "        x, y = zip(*truth)\n",
    "        x = np.r_[x, L_w]\n",
    "        y = np.r_[y, y[-1]]\n",
    "        ax.plot(x / scale_to_kb, y, drawstyle=\"steps-post\", color=\"red\", label='Truth')\n",
    "        \n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.xaxis.set_tick_params(labelsize=size)\n",
    "        ax.yaxis.set_tick_params(labelsize=size)\n",
    "        ax.legend(loc = 'lower right', prop={'size': size})\n",
    "        \n",
    "        ax.text(0.01, .99, plot_labels[i], transform=ax.transAxes,\n",
    "                fontsize=size, va='top')\n",
    "        \n",
    "        if i < 2:\n",
    "            ax.set_xlim(0, L_w / scale_to_kb)\n",
    "        else:\n",
    "            ax.set_xlim(0, L_w / scale_to_kb * ratio)\n",
    "            \n",
    "        sns.despine()\n",
    "        \n",
    "        if i == 0:\n",
    "            ymin = np.amin(y)\n",
    "            ymax = np.amax(y)\n",
    "        \n",
    "        else:\n",
    "            ylow = np.amin(y)\n",
    "            if ylow < ymin:\n",
    "                ymin = ylow\n",
    "            \n",
    "            yhigh = np.amax(y)\n",
    "            if yhigh > ymax:\n",
    "                ymax = yhigh\n",
    "    \n",
    "    plt.setp(ax.get_xticklabels(), fontsize=size)   \n",
    "    plt.setp(ax.get_yticklabels(), fontsize=size)\n",
    "    plt.setp(ax, ylim=(0.75 * ymin, 1.25 * ymax))\n",
    "    \n",
    "    plt.xlabel('Position (kb)', fontsize=size, labelpad = size)\n",
    "    plt.ylabel('Coalescence time (Generations)', fontsize=size, labelpad = size)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(folder + name, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "def plot_posterior(gammas_Markov, gammas_Renewal, tss, seed, ects, L, w, ratio, name):\n",
    "    size = 25\n",
    "    L_w = L // w\n",
    "    scale_to_kb = 1000 / w\n",
    "    \n",
    "    fig, axs = plt.subplots(8, figsize=(20, 28), sharey=True)\n",
    "    fig.add_subplot(111, frameon=False)\n",
    "    plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
    "    \n",
    "    plot_labels = [r'Constant, Low $\\rho$', r'Expansion, Low $\\rho$', \n",
    "                   r'Constant, High $\\rho$', r'Expansion, High $\\rho$']\n",
    "    \n",
    "    for i, ax in enumerate(axs):\n",
    "        if i % 2 == 0:\n",
    "            gamma = gammas_Markov[i//2][seed]\n",
    "            ax.text(0.01, .99, plot_labels[i//2], transform=ax.transAxes,\n",
    "                fontsize=size, va='top')\n",
    "            \n",
    "        else:\n",
    "            gamma = gammas_Renewal[i//2][seed]\n",
    "            \n",
    "        ts = tss[i//2][seed]\n",
    "        ect = ects[i//2]\n",
    "        \n",
    "        truth = [(tree.interval[0] / w, tree.get_tmrca(0, 1)) for tree in ts.trees()]\n",
    "        x, y = zip(*truth)\n",
    "        x = np.r_[x, L_w]\n",
    "        y = np.r_[y, y[-1]]\n",
    "        ax.plot(x / scale_to_kb, y, drawstyle=\"steps-post\", color=\"red\", label='Truth')\n",
    "        \n",
    "        X, Y = np.meshgrid(np.arange(L_w), ect)\n",
    "        ax.pcolormesh(X / scale_to_kb, Y, gamma.T, cmap=\"Greys\", rasterized=True)\n",
    "        \n",
    "        if i//2 < 2:\n",
    "            ax.set_xlim(0, L_w / scale_to_kb)\n",
    "        else:\n",
    "            ax.set_xlim(0, L_w / scale_to_kb * ratio)\n",
    "        \n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.xaxis.set_tick_params(labelsize=size)\n",
    "        ax.yaxis.set_tick_params(labelsize=size)\n",
    "        \n",
    "        sns.despine()\n",
    "        \n",
    "        if i == 0:\n",
    "            ymin = np.amin(y)\n",
    "            ymax = np.amax(y)\n",
    "        \n",
    "        else:\n",
    "            ylow = np.amin(y)\n",
    "            if ylow < ymin:\n",
    "                ymin = ylow\n",
    "            \n",
    "            yhigh = np.amax(y)\n",
    "            if yhigh > ymax:\n",
    "                ymax = yhigh\n",
    "    \n",
    "    plt.setp(ax.get_xticklabels(), fontsize=size)\n",
    "    plt.setp(ax.get_yticklabels(), fontsize=size)\n",
    "    plt.setp(ax, ylim=(0.75 * ymin, 1.25 * ymax))\n",
    "    \n",
    "    plt.xlabel('Position (kb)', fontsize=size, labelpad = size)\n",
    "    plt.ylabel('Coalescence time (Generations)', fontsize=size, labelpad = size)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(folder + name, bbox_inches='tight')\n",
    "    plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions to create tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_truth(tss, w, times):\n",
    "    T = times.size\n",
    "    num_sim = len(tss) \n",
    "    L = tss[0].sequence_length\n",
    "    L_w = int(L // w)\n",
    "    truth = np.empty((num_sim, L_w, T))\n",
    "    tmrca = np.empty((num_sim, L_w))\n",
    "    for i in range(len(tss)):\n",
    "        ts = tss[i]\n",
    "        cts = [(int(tree.interval[0] / w), \n",
    "                int(tree.interval[1] / w), \n",
    "                tree.get_tmrca(0,1)) for tree in ts.trees()]\n",
    "        x1, x2, y = zip(*cts)\n",
    "        for j in range(len(cts)):\n",
    "            left = x1[j]\n",
    "            right = x2[j]\n",
    "            t = y[j]\n",
    "            epoch = np.argmax(t < times) - 1\n",
    "            truth[i, left:right, epoch] = 1\n",
    "            tmrca[i, left:right] = t\n",
    "    return tmrca, truth\n",
    "\n",
    "def get_mean_se(gammas, dist, num_pos):\n",
    "    l1 = (gammas * dist).sum(axis=(1,2)) / num_pos\n",
    "    mean = np.average(l1)\n",
    "    se = sem(l1)\n",
    "    return mean, se\n",
    "\n",
    "def save_table(tab, name):\n",
    "    index_list = ['1','2','3','4']\n",
    "    tab['Scenario'] = index_list\n",
    "    tab = tab.set_index('Scenario').T\n",
    "    tab.to_latex(folder + name)\n",
    "    display(tab)\n",
    "    \n",
    "    \n",
    "def format_entry(mean, se, dec_places):\n",
    "    entry = f'{mean:.{dec_places}f} ({se:.{dec_places}f})'\n",
    "    return entry\n",
    "                \n",
    "mfunc = np.vectorize(format_entry)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create containers for all 4 scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gammas_Markov = [None] * 4\n",
    "gammas_Renewal = [None] * 4\n",
    "viterbis_Markov = [None] * 4\n",
    "viterbis_Renewal = [None] * 4\n",
    "tss = [None] * 4\n",
    "\n",
    "#These parameters are the same for each scenario\n",
    "num_sim = 25 #number of simulation runs\n",
    "N0 = 1e4 #base effective population size\n",
    "mu = 1.4e-8 #mutation rate\n",
    "L = 5_000_000 #sequence length\n",
    "n = 2 #sample size\n",
    "\n",
    "w = 100 #window size\n",
    "T = 32\n",
    "times = np.r_[0.0, np.geomspace(1e1, 1e6, T-1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scenario 1\n",
    "* Constant population (N=1e4)\n",
    "* mutation rate = 1.4e-8\n",
    "* recombination rate = 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:186: RuntimeWarning: divide by zero encountered in log\n",
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:187: RuntimeWarning: divide by zero encountered in log\n"
     ]
    }
   ],
   "source": [
    "r = 1e-9\n",
    "eta = np.repeat(1/(2*N0), T)\n",
    "rho = 2*r*w\n",
    "\n",
    "ret = run_sim(num_sim, N0, mu, r, n, L, w, times, eta, rho)\n",
    "gammas_Markov[0], gammas_Renewal[0], viterbis_Markov[0], viterbis_Renewal[0], tss[0], ect_constant = ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scenario 2\n",
    "* Population Expansion in the recent past (N=1e6 at time=0, N=5e-5 at time 1000, N=1e-4 at time 3000)\n",
    "* mutation rate = 1.4e-8\n",
    "* recombination rate = 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:186: RuntimeWarning: divide by zero encountered in log\n",
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:187: RuntimeWarning: divide by zero encountered in log\n"
     ]
    }
   ],
   "source": [
    "r = 1e-9\n",
    "eta = 1/np.r_[np.repeat(200 * N0, 13), np.repeat(N0, 3), np.repeat(2 * N0, T-16)]\n",
    "rho = 2*r*w\n",
    "\n",
    "#Add demographic event for variable population size\n",
    "de = [msp.PopulationParametersChange(time=0, initial_size=100 * N0),\n",
    "        msp.PopulationParametersChange(time=times[13], initial_size=0.5 * N0),\n",
    "        msp.PopulationParametersChange(time=times[16], initial_size=N0)]\n",
    "\n",
    "ret = run_sim(num_sim, N0, mu, r, n, L, w, times, eta, rho, de)\n",
    "gammas_Markov[1], gammas_Renewal[1], viterbis_Markov[1], viterbis_Renewal[1], tss[1], ect_variable = ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scenario 3\n",
    "* Constant population (N=1e4)\n",
    "* mutation rate = 1.4e-8\n",
    "* recombination rate = 1.4e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:186: RuntimeWarning: divide by zero encountered in log\n",
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:187: RuntimeWarning: divide by zero encountered in log\n"
     ]
    }
   ],
   "source": [
    "r = 1.4e-8\n",
    "eta = np.repeat(1/(2*N0), T)\n",
    "rho = 2*r*w\n",
    "\n",
    "ret = run_sim(num_sim, N0, mu, r, n, L, w, times, eta, rho)\n",
    "gammas_Markov[2], gammas_Renewal[2], viterbis_Markov[2], viterbis_Renewal[2], tss[2], _ = ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scenario 4\n",
    "* Population Expansion in the recent past (N=1e6 at time=0, N=5e-5 at time 1000, N=1e-4 at time 3000)\n",
    "* mutation rate = 1.4e-8\n",
    "* recombination rate = 1.4e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:186: RuntimeWarning: divide by zero encountered in log\n",
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:187: RuntimeWarning: divide by zero encountered in log\n"
     ]
    }
   ],
   "source": [
    "r = 1.4e-8\n",
    "eta = 1/np.r_[np.repeat(200 * N0, 13), np.repeat(N0, 3), np.repeat(2 * N0, T-16)]\n",
    "rho = 2*r*w\n",
    "\n",
    "#Add demographic event for variable population size\n",
    "de = [msp.PopulationParametersChange(time=0, initial_size=100 * N0),\n",
    "        msp.PopulationParametersChange(time=times[13], initial_size=0.5 * N0),\n",
    "        msp.PopulationParametersChange(time=times[16], initial_size=N0)]\n",
    "\n",
    "ret = run_sim(num_sim, N0, mu, r, n, L, w, times, eta, rho, de)\n",
    "gammas_Markov[3], gammas_Renewal[3], viterbis_Markov[3], viterbis_Renewal[3], tss[3], _ = ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Viterbi and posterior for one run under each scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1512 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_seed = 6\n",
    "ects= [ect_constant, ect_variable] * 2\n",
    "ratio = 1e-9/1.4e-8\n",
    "plot_viterbi(viterbis_Markov, viterbis_Renewal, tss, plot_seed, ects, L, w, ratio, 'viterbi1.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X5YuqO8TDGc8rfN+v2p7KJ8y0rOoVqlwrFovwfR/Hjx/HkSNHMDIygqNHj8LzPADQ4chms2hpadHvCxYsQCqV0utX++26LizL0vtkWZauB5n7r84Rtbw6V9S8ra2tel2TzZMblQMzcX6Yx8dMF+qzWSest/xUhTWeVwK1eTTNTWY5Hz+2UsqaMnq2Ms+TeN6q9jEIAhQKBeTzeYRhqPNLdR3m+z6OHTuGQqGAXC6HbDar6+gAdD5rWRbS6TQcx9HbM68BlKRzpN60eH5Trxw250sqQxpdt8zm4zdTJlpfGM98ZjlqlpUA9Lsqj2zb1nW58Vxrx+uTZr1elX2jo6M6Lar51PW9ejmOo8Ogtmmm13plpbktM0xJ16r16jTxa4mjR4/i8OHDOH78OAYGBpBOp5HL5bBs2TJks1l9bTCZtojpZJbHKs8YGhpCsVjE4OCgrrOpfEXF+YIFC9DW1qbjPymfSNqWuc2k65SkZZPiPr4uJan+EC8HJtsmNNfN6jtsJ+ByNG6wVfMMItZtQqUhF1LKsbpcICIiIiIiIiIiIppW86XBNoex75DNVRplF8Wm3wNg3gw4RkRERERERERERHPXfOkSoRHVkLsIAKSUvUKIuyvTOwEclFL2NitwREREREST1tsLPPBA+fOWLUBPT+P5iYiIiGjWOxUabGtIKe+b6DKiPLBZDwCce+65Ux4mIiIiIqIJe+AB4Le/PfGdDbZEREREc94p2WA7GZW7cHsB4PLLLx//yB9ERERERNPp0kubHQIiIiIimkLzpQ9boNyPbaPpR2cqIERERERERERERESTMV/usE0aTExZZPDwBmIAACAASURBVMxzUoQQGwBs6OzsPNlVERHRSXDvvx9n/uM/or1QwAUjIxBCwHEcOI4DL5WC57qQUiKMIkRRBBlF5QWFgJQSwxs2QG7d2tR9ICIiIiIiIkoyX+6w3Ysx7rCVUu492Y1IKR+TUvbkcvU2RUREM8HbuRPeyy9PatnsgQNof+KJKQ4RERERERER0dSYL3fY7gawvc5vV6DcoEtERPOIv2oVfvvNb+Kll16C4zhYuHAhcrkcli5dijPOOANBECCfz2N0dBSlUgkAYFkWOu+6C3aTw05ERERERERUz3y5w/YhAIuEEB0Jv20E8M0ZDg8RERERERERERHRhM2lBtsc6vRTK6UcBLAVwA5zuhBiI4B+KeWuqQiAEGKDEKJ3cHBwKlZHRERERERENP/19gJXXll+JyKiMc3qLhGEEHej3KXBGgAdADqEEM+hPIDYg2ZDrJRylxBiUAhxL4CDONF3bddUhUdK+RiAx9auXbs1DEM1DUD5MdtKmOstq9+FEHXnM+dXr/h61fTxbNNch5pXfY8qg/GEYYhisYhCoYBisQjf95HP55HP5/UynufBtm2k02mk02nYto1MJoN0Og3LsmDbdtV+qe2q9ZdKJeTzefi+DxV3YRgiCAKMjo6iWCzCsixYloVUKoUFCxYgl8shlUrBdV3Ytg3Lsqq20SgOzX1Mmt+MFzMeAMBxHAgh9PbMZc3jqF5hGCKKIhSLRb0e3/dRKpV0uC3L0sdexZXjOFX7pbYT308zLs19iKJIr1PNF0URSqUSgiCAlFKvX82vjsXo6CiOHTuGo0ePAgBc10UqlUJLSwtSqRRs28bixYuRyWTguq7eh3jaHSsdN0PSOTPd25rsduPpKVIDY8XmUdPjaUIdWyWeRgDUHLvxnD9J+zae4x5fRoXdPC/MtKnCp865+P6paea6giBAqVRCLggQhiHa2tpw1VVXIZ1OY8GCBTotp1IpfX6GYVgVNjebRalUQqFQgO/7+pzMZrM6vcfTer1zwAyvmi8eR2Zek1QGmPlJPL9W2x0r3pPiW00LggDFYhH5fB7Hjh1DbudOLPzJT2CHIVBJW1IIhGo9lWlWFCFTCbM0tieMsInK8Ru+5RaM9PQgnU7r8sLM21Q4zbJirLTYqNycTXlPUrqvV9bHz3kguWyqV08wfzeXjaebRnEbT29J6RE4UTaqcmVkZATFYlHPp6aPjo6i9YEHsOipp5COIkBKQAhYRvod+MIXcPjLX8ZpjzyCBT/+MQobNyK8886aMk2d36OjoxgcHITv+zpdq3JU1Rvy+byuu6jlisWizmtUfphKpTAwMFC1fyr9uq4Lx3F02ec4jt6Wqo84Trmq7LouPM/T9RIzDlTeptK3qheoMvSss85CW1tb1blRLz8x62lJxyw+3bIs2JU4N36oWt94Ja1f7aeZB8freGY90cyvzH1RYZ1IOZQUFrVddRzNvDeet0xkG5Mp88azrtmUV8028Tg3y68gCBAEQU19XF0TmMc66Rya6PbN7+NZX3xZNS2efybtYxAE8H0fw8PD+PDDDzE4OIh8Pq/3ReVh6ppM1ZHM6yGVfy1YsACO46CtrU3XYdR6VH1enZO2bVfFnXmdkHRexsurRnWXpLIu6dg4DzwA8fTT5frZXXfN6rJ9IpKuB5RG6We8ZX58nnrHSKU7s5xUZaX6zbIsXVdT134qban0Ed+GuX5Vfx4aGtJdjqnry1KphKGhIZ2+gkpdXaVXz/OQyWTgeV5VuWvbtv6s5lXhMc//+PWSmp5UpsbLH7PM8n0fvu/j2LFjeP/99zEwMADf9wEAvu/j+PHjOHr0KEqlElpbW2viZzLXUknzN1q+Xh4D1NbPfN/HwMAA3n77bQwNDWFgYKDqestxHKRSKWQyGZx++uk488wzdZ1ExXm9Okk8/pLqm/WuU+Jpx7w2UfUkRYXDPNZqHiHK7SfjaRM62TykUV1gKtY/GbO6wVZKed8E5+8D0DdNwaGYzN//PbI//CGgKhibNiG6665mB4uIiBo47amnkHn9dRw7//wpWV/7oUPwn3gCQz09U7I+mnvO6OtDy6FDGDjvvJrfWl9/HdZPf4r3vvxltPzoR/B+/WsIITB6550T28YPf4jFP/2prvi/+fu/jwOf/exU7QIRERER0awyqxtsaXZLP/II7JdeQnjxxbBeeAEA2GBLRDQHDC9bhv/7l3+J0dFRSCn1nYVhGCKfz0NKCd/3USgUav4FV3dFp9NpuK6LDf/5PyPTxH2h2WHo/PPx87/4C30nped5SKfT+Bfbt0+q/63FDz+M3BNPAAAkgPbf/AYAMHjppVhw8CDOCcNpa7Dt3LMH5/3zP8My7uB4/6qr8NGNN07L9oiIiIiI4thgSyclXL0ao088gey11zY7KERERDRPLHzySWReew355csBAMcuuwwfdXXhd9ddhwv/5E+ASjdGSTb/zd8AUuL7De7iveTXv8aFL7yAA5dfjpTnYfkzz5QfoQSw5JVXAACHL7wQANB26BDEz37GBlsiIiIimjFssJ0AIcQGABs6OzubHRQiIiKieS2/fDle/p//s6oPWwTBmMud//rrY86z6re/xTmHDpX7RrMsLHznHQxWunT4cOVKvPUHf4B3r70WjuPg//t3/w5zs2dFIiIiIpqr2GA7AdIYdKzZYSEiIiI6VZ320kvo6OvDy3/wB1OyvoFzz8XP/uN/rB50bErWTERERDQ72X/zN7AfeghAeWDh6NZbIbeyuWu2mEy3YkRERERETfHelVcCAM77P/+nySEhIiIimrvsBx+EeP758pfnn4f1/e83N0BUhXfYEhEREdGc8e4112BJXx9gDIaXZMt3vgPLsqoGzVPOeO89AMDp774LALo7hHoWHDyIS//1v8aRz38eA93dkww5ERER0exg/+3fwnr6aUSf+QzCvj4469Y1O0gUwwZbolPcOT/5Cc7+3/8bh9evx/Hbbmt2cIiIiKbdh2edBa9UQpBOw7YsvPnJT9ad993PfQ7CstD2+usQlsUGWyIiIprznEpXCNHmzU0OCdXDBtsJ4KBjNB+d9fOfY+G+fRCWxQZbIiKactkDB3DOH/4hvFdfnZHtvXX++ZAAHti6FalUCsViUf8mpYQQApZlwXVdOI6DlpYWpFKpupXit6++Gu9v2IDfu/tuDj5GRERE80b06U8juusu1m9mKfZhOwFSyseklD3t7e3NDgoRERHRrPfhunXIX3ABAMBftarJoTl57b/5DU575JGG85z9+OO4/Otfx+qvfQ1n/PCHMxSymefefz8WXH89WjdsgPO3f9vs4BARERHNK7zDloiIiIimxXsbNmBw0ya0t7cjnU7jrC1b5uxdHB9cdRVyzz+PxU89hcM331x3vjP/6Z/QevBg+U5eITDwx388g6GcOd7OnbD27QMAWEKgxFGliYiIiKYMG2wnQUqpH69Tj9apx+sUIUTV/Oa7+s2cJ75+9UoaKEP9ZlmW3naj5dVL/aa+h2EI3/dRLBYxMjKCYrGIQqGAYrEI3/f1PErnnj1Y8stf6n21Dh7E6AUX4ODBg1g+OgohBN556y0A0MsGQQDf91EqlZDP51EoFBAEQc3+2bYNIQQcx0EqlUKhUIDv+0in08hkMkilUnBdt7xdY7/HikO1/qTjEUURpJQ6nGpfHceBbdt6W/XWG4YhSqWS3r/h4WEdp6VSCWEY6u2rQU/M8Kv9cRxH70vSts3jJYTQYTDXHw9TqVSqeuwTAMIwhJRS72sURUin00AlPsIgwJEjR/QjokIItLa2IpvNwnEcOI5TE/8qbpOOQ71jM9XU8az33TSZMNVbn3leq5e5jbHSpvqs0mEURVXnBlB97M28IJ4HKCqNmcfd8zx9POO/NYqP+P7Fj3O9PM58mXmA7/sYHR2F7/tV+YplWTVpXi2r8gVz/1Q6bg1DCACLFy/GokWL4DgOXNet2k/12XGcqrAKy0IghA6LCv/IyIg+L9W2VZhc14Vt23p6Ur6r9icpfsy8Qy0fP9ZmvADQ20vK8+JpKAzDqjxd5bUq/kdHR3Hs2DEMDw9jaT6PSEq88847Ov2VSqWq/FjtS0tLiz7/bduG53lIpVJV8WvbNsIwxOHDh5FKpZBOp6viy4wTNT1ebjY6X9Tv8Xyn0XLKZPOhRvlI/Pd4uldxmrTtseaJ5yXjqUuYzHhqtLyZ75ji5aLv+wiCACMjIzh+/DhGR0d1WimVSjqNnV2pE6n0HYYhhoeHcfz4cZ1G0uk0PM9D28gILCHw0osvwrKsqvyiVCqhWCxidHQUnxoZQRRF+O1vf6vzQBU+AFhVKABC6G04joNcLodUKqXT7fr169HW1qbrbGZ+VCgUMDo6inw+j2PHjmFwcFCfg2obarsqne5buhTZjg5gaAi/+MUvas4DVY6vLBZROvfc8rkbBPjggw9QLBaRyWTgeZ7Ol80ytd4xTaoTmvmAZVlIS1kuyyvTo0p9wUwXjTRKz+rYqFexWEQQBIiiCG4QQK5cCQFAhCGOHz+u9yspn6yXn02kLDLTrzlP0rrHm08kxYG5bKNzrl5YlaR4mKn60WxRr54WP6ZmGlPpDEBVeZxOp6vqymaZD4y/bIiHw3xvdH0xnv0w6+OqXqf2rVQq6Wuc4eFhDA0N6XxVzQOUr0XS6TROO+00tLW14aOPPkI+n0exWKxKn4ODg1Xdu5hlr1kWq3lSqVRV3uO6bk15Ha9nmPlOvE5mlmXmcipviteZWqIIFoCoUu+od842OobNOn+S6gVJacisNzda13jmHWue+Plj1vmGh4f1tbdKNwB0OaWuuc1ySdWB4/VU89wsFAoYGRnRaVdd96r0YFmW/m7WAQFUrdtMe67r6nCodOq6blVZGU97Zh1Spf2k69R4/qLqGepcdBwHS5Ysgeu65etiQJ+HxWIRlmXpcI2V79Q7PmqepPnrXU+Yn+N1gPixLhaLOHbsGIIgQCqVwtKlS6vaERTbthEEAQYGBuD7fjmuSyUIoy61IAwBIZDP5xPTWGjUL4QQNWWveYzM+qRqn1DXu2Z+J6WsOn7mOlS4XddFNpvVZUD8ODQq88dTx6gX5/F1qmukmcQuEWjcPvb002g/dEh/H1m2DEevvrqJISIiIiIiIiIiIppfeIctTcjQ+efjmXvvRSqV0ndSzfz/DERERERERERERPMTG2wnQAixAcCGjo6OZgeFiIiIiIiIiGjaLdy5Ewseewxh5bH2Nz75Sbz6mc80O1hE8xobbCdASvkYgMfWrFnDURWIiIiImuj0d9/FTX/914AQOPh7vwfHdbHs179G21tvIb98ebODR0RENG+0P/44UgcOYHjZMrQfOoRzo4gNtkTTjA22RERERDSn7F+7FhACAsBp77xTHnxCCOTefhvHOjow0NXV7CASERHNK6MXXIAX/st/wap/9a+AhMHRiWhqscGWiOrK/sM/oPWxxxBu2gS5lTeWExHR7PDipz6Fl37/92FZFm76r/8VagzgwfPOwzPf+hZyuRzSTQ3hqcm7/36kHn4YSBiVWVQa2CtfEN16K+Rdd81o+IiIiIjmCjbYElFd2R/+EO6vfgUhBEpssKWTcPqWLQijCId27Gh2UIiIaJp4Dz8Me98+hBdf3HA+8fzzsACEbLAlquucJ5/Eub/8JUqlEqIwhJSydqbK0wXCeEEIfNTVhcM33TTzgSYioinDBluacu0PPogFjz2GwWuuQRiGaH/iCUgpEYUhoiiClBK6uqEqHpW7LoQQEJaFo5//PApf+lKT9oCIplr6l79sdhCIiGgGhKtXI//44+WGI4Nt27AsC0IIuOvXNyl0RHPH2T//OVrfeAMD5503oeXa+vth7dnDBlsiojmODbY05Rb8+MfIPvNMuWFWSqQPHMDIBAb/WHDwIOzdu/E2G2yJiKbEWT/+MVY/9RQO/ot/0eygEBER0TgNL1uGp//iL5DP5xFW7rI1/wyxLAuu68JxHLiuC9u28cl//+9hNTHMRNQ8H3viCZz185+Xvxh35asb42zLgm3b5T9PX30VwYUXNiegNC5ssKVpl7/gArz03/4b8vk8CoUCgiDQjblRpbNylWk4joNP/Yf/ALvJYSYimk+W7NmDhS+9VL7Ya3ZgiIiIiIhoyp31s59hQX8/jnd2jjlvadUqFG++eQZCRZPFBtsJEEJsALCho6Oj2UEhIiIiIiIiIiLSjnd24rm/+it9cxxQvkHO8zy0tLSgpaUFrusilUqV79BvYlipMT4tMQFSyseklD3t7e3NDgoRERERERERERHNQ2xMn4QwDDE0NFQ1zbIsWFa5/VuP0FmhRvSU8T5EYoMxmN0EqMG5QmNEUPM9iiK9vHpXy6tXEAQIKwN9hcaAX+pzFEUIgkC/K67rIpvNwnVdva5SqQTbtiGjCMViEaOjo3pZAFg6MgIA2Lt3L3LHjyMLYLgyDQDeeust2LYNx3H0gBOWZcHzPKTTaaTTab1/jl3uECGfz6NYLGJkZASu68J1XR3PqgsFFef14tGMDzMO1f6rlxk/5rGJx625TBAEOo5LpRJKpZLu7gEo/4tVLBb1PGo96rMZvkZUHFuWpZepR3UroeJIbcuMN8/zkMlk0NbWhsWLFyOdTgMAPM9DLpfT+x+GISIjvgqFgl63OoZJLxWOeHqfavF4ML/H+/cyj+PJbstMS/HXeNZhpk2V9kqlkn5XaSjpPI2n4/g+CiGqzg3btvW5Y9u2fsWPYXxdSfvYKD7j50YYhgjDEEEQoFQqwfd9LKksOzAwULVvZlyY4vmp2i+Vj8gogmXbyGQySKfTen/j+5IUT1IIeC+/jHO/9CUcvfpqfHDDDYiiCMePH69Zzty+2kY87uotY8ZR0vkej2czDtQxTCpbzPWY8W3GufpuxjMAeKmUXs8VV1wB13UhhIDv+zrePc/TeYnneVX7rI6D2q6UEplMBlEUwbZtBEGAkZGRmnxavcfToponng7jcavWkZQ2Gh3zyZ7z8WOU9DnpFS9PzOWSypz4+tR8SfvR6Pw356+XPuPlmBnOpOmq7DLTlrltVZZ4nod0JgPLsnDxxRfDsiyEYQjf93WeViwWdbdIpVIJUkr09/frcJnHVuVbnusCQqCzs1OnLaBcR/E8T6dNz/OQ+973yvteGR39iiuuQKYSJtd1q+LUPGd834fv+8jn8/B9H2EY6rBEUQTf93V9S8VBS2srpJT4xCc+obev6idKtqWlvB6jPqDqTSoPcxynKj9OStfx427Gv5rHtm14UVQevLXyu+/7dc+NJEl5kZmezXxFvavfKhuBiCIMDg6itVQChMDRo0f1Ppn5h9rvBWEICIFCoTBmeR0/x8zjmfq7v0Pq4YchhECwaROCO++sWy+ZSDxMZvl4uNVySXXKRuubjjrTdKmXxybV0RqlM5XnqDqDWXcOgkDnbVEU6fMnXiab55D5Pp7wN6rrNJrf3Bczz4zvh5nvqP31PA9nnXWWLhfDMMTCnTux8MknYVXSTeqttxCuXo01a9agUCjoclfVEwuFAqIowsjIiL7mKJVK5W2EId5//30AqKpDmdcEZpls1jvi+VC8TEmq58evDSzLqrpusCwLqSCAAyAKQ4yOjo55XZF0PKf7+iJ+rJOmNboWMOtz8XiK1wnN63xzfepzvA6XtE3zWkKVW6rsVXWzlpYWACeuJdX8hUIBAwMDet2N6g5SSuQKBUBKDA0N6Xri0qVLAUCnp2w2q8trNU2VI2Z5UigUdBkbRRGGhoZqzkGzjhivT5p10nh5Gk9D8bhVv7mui8WLFyOTySCVSiGdTus4LBaLkFLqeFXlt3kemWmzXlo09ykpXPWWicd9UhuEed2YzWbR0tIC27bLdRAAq1atqspzwjBEsVhEsVjE8PCwDpMZbx3FIgDgzTffrEmHZtpOKifN8MfbneLXt43ix4xn9fI8D77vI5VKIZVKVeVf9Y77yZQF8bAp6lpzJvEOWyIiOrXcdhtKq1YhvX8/Fv7kJ80ODRHRhKW/9z3kbroJbddfD3vfvmYHB6lXX8XZX/wivFdemdntPvww7BdfhL1vH5ydO2d020RTbeFPfoLM/v36e+nCC+HfcksTQ0RERM3EO2yJiOjU0tODj669Fgtvuqlq9FQiorki/YMfwHnxRYQXXYRw9WqUNm6Et2tXU8IydM01UPeg+CtXYnjDhhndfnjRRXPqrlSiRkZXrMA73/seXNdFJpNBJpMBYk92EhHRqYENtkREREQ0p6VeeQUCgH/hhc0OyowJLroIxx99VD8S2KwG28FNm3Ds1lurH09vSkiIiIiI5g822BIRACBz4ABWfPWr+i4Zy7bhvfpqU8NEREQ0lqFrrtF92OZvuKHZwSEiIiIiOmlssCUiAMDoBRfUTPNXrULm179uQmiIZkZm/36s2LYNh9evx3uTeIx34c6daH/88RMTVAf3AIauuw6D3d1TFFIiqmdw0yaM3H67HoAi1ewAERERzXMLDh7EZf/m3wAo13uFZcG2rBMDqVX+SNWDZ6EyqJMaQEtKwJgeJ4w6tVrf7z77WbxzzTXTvWtEswYbbIkIw2vX4sCOHXo0bCEEMpkM0uk0zr79drBnOJqPhq69FlJKZPbvx2JgUg227Y8/jvT+/SisWFE1PfXqq2gH2GBLRERERPPKe5/7XPmacQa32X7oEATABttpltm/H+ffcceJCZVGdf0O4Mj69Th8881NCN2phw22RER0Shro7saHN96IzjvvPKn1FFaswBv33w8A+g+P8/7oj6YghEREREREs8vbX/gC3tuwAbZtAwBs24brushmswiCoGpaGIYIgkC/l0olFAoF+L5fvtM2ihAEAaSUVQNI2rat69VCCPz+N77Bm4im2eA4GsOzBw4AABtsZwgbbCdACLEBwIaPf/zjzQ4KneI+8YtfYNkzzyT+pv7tFJVHUCoT9TRhWbAsC06lEE3t35/YHQIRERERERERzX8DGzdiYONG/V1KqRvV1ecVX/1qE0N46mGD7QRIKR8D8Ngll1yydax5U9/7HtIPP1xers488X+I9HxSIn/jjRi5/fbJBnXCzn78cZz5s5/h/SuvxO+uu67m97N+/GMseuEFHLnoonGvc+GbbwIABs47b8rCSWXLnnkGi99+G0fOOeek11VYsQIDV189BaEiGtvqr32t9tEaKfG7z30Ob3/hC80NHNEcs+D738eCRx89MaHy59zIDTfMaB2CiIiIiIimFhtsp0n64Ydhv/giwgk0cCrOSy8hA8zoxdaZP/sZFr3wAgAkNtgu2bMHAPDu5z437nWqhto3PvWpkw8g1Thyzjn48b/9t1XThBBwHAeWZcG2bYRhCACwKnfV2rYNz/OQyWTQ1taGxYsX68dTUHl8hWimLejvx1kAG2yJJmjBo4/Ce+UV+CtX6mneyy8DmNk6BBERERERTS022E6j8KKLMPSjH+nH0s3RD1VfLCY1gmLuxhtnNJzjdfTii/H21VcDpdKY836wciX2fOMb+rs1nQEjolnv+Nq1iKII+/76rxGGoX68JooiXP7nf97s4M06ix9+GIuefBIQAseuvRYDHLyM6vBXrsTvHngAQLlusXTLliaHiIiIiIiIThYbbImIiGaZhU8+icz+/foRdzbYEhERERERnTrYYDsJQRDgo48+qrpz1rx7FgDShQIA4M1KP67x+ZLe1evCQgGQEocOHdLToiiqmh8AwjCsWg5A4nxq8Ck1Td3da9s2HMcpvyojPKY8D+eeey5SqRRSqZReVyaTAaTEBRdcAN/3USqVUCwWUSgUEIYhHMeBlBKtra16tMiFCxfCdV39ymQy8DxP/64e0XddF57n6f3xPA+RlHBdF0EQYGRkRHd0nRSH9T6ruFDLmp/j8aNGoQQA3/f1SJWqS4H48QXKI1dalgXHceC6LlpaWvR3z/N0+NVdhOo4qP0slUp6pEwVVnMETbVNy7IghIDrunqa57oQloXzzjsPURTpLg8cx0EqlYLrurAsS29bhVcIoY9tNptFW1ubDlMQBDq8Ks1ZlgUIAUipw6S2JYTQ7+Yd4/HP5rsZ5ycjfjzqpYXJbidpfUnnqnl+xtOWed6acWrOG4YhwjDUaUC9qztQ4+d20n6b57U6Hup7/KWOXXz+RvHU6DyL729SXOjwhyFQSceO41TFjbAsQEqdN6iuPdT5p6apPEulb8uykE6n4TjOmPthEkLA8zykUqkTy1XCqs5/dS4o6tjEj8vp+TwA4OXKY+jqt8XDwwCAFypdzZjHwVyv+ZuZD51bKKBw3nmAlEA+r9dvLgOg6hjatq3zVBXPZp4EAJlMBqKy3LJly3Scq3Cr9ahtxNOUefzVfjiVEYBbWlp0HqbSs6LWUyqVavbX3E5SnqLKivhv8Vc8buqlh/h+xI33/DfPa/d//S94Dz8MKSUcKRP7adbvxucjn/88PrjhhqrzO77+pDqGmm9Rpa5x6NAhPT03OgoAePXVV+vupzp2qpyI/66Oj+u6utxob2/X9QJ1rFTaymQysITAeeedV1XWqZfv+xgdHUWxWEQmk4GUEp2dnTX5lzq/M5kMWlpaIITAxRdfrM8bdaxd19XpJ56nqf0yf0/Ku1Q8mHmuWo8ZT+q8UPvU0toKSIk1a9bo9attqXVnMpkT8S4lcrmcLvPDMEShctzM7Y233FRxsKDSjdHAwIA+Xl6pBAiBI0eOVMVLvfwxqQ6ZNMCIiqOk+pf6XiwW6+Zt8XPcsiykSiUIAB9++GHDMDYihECLOm7lCRgeHk7MXxrFbb11J9Vrxrt8fD2Njm/SE3cT3c5MOpl8U31PSl/qpeqa8esXoFwWl0qlmjKjXvlgUuuLhzu+jbHqmI3qdvHzxcxfzbxLdVGWzWaRSqVg2zaklEin0wCAs88+W8/nOA7S6XTVOai2r+om+XwexWJR5zGZdBqRlFi6dKkOg7rmMK9NRkdHq+JdjKJ+ngAAIABJREFU7aMZf2bdRO2HWeeIX9eZv5mfLcvCGZV6XRCGOH78uL4ONeeZyPVF/HPS9/FolKYbvZvldLw8SUon8Xnjr6T549PNaUnhBgDHcZDNZvX1qOu6AKDTWalUgu/7KBaLGB0d1eeVeR1oxqeuT9o2hGXh7LPPxoIFC3RdU82jrkPNOoY6HmofVR27UChUXfea31Vbg+/7On3Gr+eB8nWzEAL5fF7HvRIvc8z6sbpWbmlpQXt7O7LZrA6/lOW2CFVnVeeN7/t10+J4652qLDKnqfexrjfjdUKz3u55HhzHQSaT0fUoAWDx4sVV6SgIAuTzeQwNDSGfz+t4VteiZr5Vr24IAOcXiwCAd955J/GaNb7P5vWJ2X5iXm+obZvltjomKu9WeZzv+/p3c/3xY27G+XjyhXjcJ1F59Ezik+pEREREJyH18MNwXnxxQstkDhzAoqeemqYQERERERHRXMY7bIlmyOk/+AEWP/VU9V1WUiKq3IFTnly+80oa0yDKj0Sb/wy1HzqEY52dM70LRERUR3DRRTiyc2fV3UvxO+rNO8mWb93a7CATEREREdEsxQZbohmy+Kc/Rfa11zCyfPlJr2vo/PPx3mc/OwWhIqL54twnn8THnn66/DiQEFV/9mRee23Gw3PeXXcBAN787ndnfNs0dc567DGc+bOflR8xU38cGmlLWBacl19GtHp1U8NJZEr93d8h9cgj8G+5BcEddzQ7OESnlDMffRRn7NkDoFJOlD8gfeBAU8NFRDTXsMGWaAblly/HS//9v+v+gybbh63uB6tpe0JEs83Zv/gF2g4dwvGEu+9HV6zAgueem9HwtP7qVzO6PZoeS/bsQcvBgxhdsaLuPMFFFyHgwHg0i6QefhjuP/8zALDBlmiGnd7Xh5bXX6+5SaWwYgVann22SaEiIpp72GBLRETTasmjj6L9t7/F0GWXNTso896xjg7s/au/qhqYTQ0CsWLbtmnruN596SWcedttujuX5K76KS574ABW/vEfV8eX6iLH+Pz+VVfhveuum+ngaSPLl+Ngb2/dQcfS6TRSqRQrlUREBKBcbrz8P/5H1YBAruui4447MDuH06O5oP3QIXzynnsAlOtJAie6FIQ5UJQa1KvyWQ9mVXkyyBInBjO2jEEqURk81o0iSHNAWLUuY/0jN9yA/O23T/cu0ymOdWsiIppWZ+zeDQA43NXV5JDQdAi6uyHCEIiNEE+NHb36aiwEqi8wErS+/jqWABNqsF1w8CAu//rXT0yIj3peeRfG5zh18dL6+usYueCCcW+biIiIaKq98+lPl7tjanZAAKR/9Sukf/UrtPzoRzXhyd94I/Jf/GJTwuXefz/sp59G9OlPN2X7NPXYYEtV0vv342N/+IewKnfPAOWLOffll+GvXNncwBHRnDV06aX44IYbpu0OT2qe0le+guHubhQKBd2tSxRFOO+P/qjZQZvVDt98Mz688caqgckAnBiQsjLtkj/7swmt9/0rr5zScA4vW4Yj/LOFiIiImuiN9evx5uc/D8cpN2FJKSGEqBrUVVFdCqqnzWzbhuu6cF0XqVQKnuehtbUV7e3tyGazcBwHjuNASokgCDAyMqK7L1T1MjUAuBACrf/4j2j50Y9qwui+/DKyQNMabL2dOwEA4ebNTdk+TT022FKVwooVif9a+atWYXjDhhkPDxEREY3fu9dei3evvVY3AAOo+gxA95tuWVZVY7EiKo8Kuq4LIUS524PpDzrNYrmHHkL7449XTUvv349Cg76NiYiI5qORLVswsmXLia4UUK47nbZxY5NDBoSf/jTCO+9sdjBoirDBdgKEEBsAbDjnnHOaHZRpcXztWrx1//36nyfVV50aICuKIqBUanIoiYiIiGgmtT/xRE0DbWHFCgxde20TQ0VEREkWP/IITnvqqXKDYuWpWXVTViQlZBQhkhJRGJaf6qncRZrUTZMQAhACrQcPsoskohnGBtsJkFI+BuCxCy+8cGuzw0JERERENFMKK1bgjfvv13dkq7uKiIhodln05JNIHziA4ic+MWXrZBdJRDOPDbZEREREROPkvPQSFt18c/mL8TgkAEBK2C++iHD16uYEbg7xXnkFS7dsKY+2ff31GN6yRf/W+sADaH30UQDAcOw383fvlVfgr1o1o+EmIpoLCitW4J2//3u4rgsAsG1bPznr+z6KxSJGR0dRKpV0f63xbpKEEHAcB5ZlwXVddpFENMPYYEtERERENA6Fm25COuGRUVO4ejX8W26Bu2vXDIVq7jl+3XX68Vzv5ZcBoLrB9tFH9fTW2G/m7/6FF2LkhhvQ+qMfwX3pJSy86SZACNgvvjgTu0FETSS++13Y3/9+zXR5222QW/lALBHNfWywnYQwDDE0NKRHDJRSIgxDPUJhFEU4z/chAbzzzjt6OavSf4w5X3ygDwBYXiwCQuDw4cNVoyCa67EsC6Hqc6YyeIj6TY2I6DgOXNfV86tOsdV3x3HgeZ4eKdGyLJx55pl6Odu29XrNUatV+NW/c1EUIZvNQgJYuXIlstksIARWrlypt2PbNjzPg+M4EELokRZVeNTdKVEUwXFdyChCLpeD7/vI5/PwfV//86fiWoUjHgfxeIjHn/rdtu2qeFJhM/9hNJdV4VXLqQFZ0um0frdtu2pfVbwBQDaTASpxVC8dqO/mNLUP5vEw91FR4VPxrdZnjmyZdPxV/Kljq+ZXcW1uw4xvtb54Z+vm56R3M7wno9GAOknnVaPfk9aVNGI7UB0H9T43ek+aFmeer2acxpnrBKDPLXO9hUJBp2f1rkZRNZeNM7cdH5go/jiseR4nnVuq/6yFCxfC8zwdTiWVSgFSQvUPrtKouc34SK/q336Vd01UpnI+hmEIp3Lnwemnn45isViVx6jt+76PIAj0S8WdXdl2a2trVR5pWRbaDh3CVf/pP6kI1fFZdeeC+VvlM6RE65tv4nhnp95OS0tLzR0P6nxX57zrOBCWhSVLliCVSun4UndEuJ4Hq9IPmQCQzWZr8sh4emt0nqqwqWMlhKiKI2FZgJS6HAOg484Uzz9UmOJlhJqedH4k5UOTyWPMcj3+XeUBZj6pPqeCAJASAwMDOt3G8/j4+Qqg6nfzGFTlLZXppVKpKg3Ff1P1ArNsTEozqpxSaceUNJqyKt+y2aw+f81jY67PPBfjeZ0qU9KpFKSUOP/88xPLV7Uu13UBIbBw4cKqdcbTSTwdmeur95u5v7KSRuMjQKvtxdNH9LWvQf7pn1avz1inWsYFYP/gB+XzubUV+XweABrWY+rlsWaaV86qjCnw4Ycf6mOdKxYBAG+88QbqqbdvSfOYx/qsINB1JNu2q46ZqjOY9Yiku7TM8+mjK68ErrwSALD6T/8UKBbx7rvv6m0v9H0Uly0r54u+j48++qiqPnRaGCJcsQJvfPe7EEKgWCigPQwhgqAcz5/4BLL/7/8hKJVw+PDhxLwlvo/qu5nXxKfVq/PE4y2+7rHqRo3y3UZ52VR2S9EoTUy03hR/j7/q1Z8A1Fx/xOum8fpqvfBMZB/MafXWO9b6gepyy6yPm3Vvlb+mUil9DaHyIqsyfzqdrir7UqlUzbUOcKIe2NLSUnXupdJpSCnR0dGh63yqbDbjLwgCXb8xy4143hyvu5j7pPIAc37btiFQrmslXe9JKVEsFnV+Yl6rqu/mMvXOpewDDwD79iEynmiw9u0rr/9LX6p7nOody/Gk4XhdwLx2i7/iaTxeF4ivt94goPEwmL+Z9W6VtjzPg+e6uk6oynqVzlQaKJVKKBaLOm3E675qG/F6ved5SKfTVeW+WS+Nl2FJcRiPI7V91cYQLyvj1wPmsQiCQI+3o15m+M36hed55TpxpW7T2tqq2z3UeoFyPqTOCzM8SfU5M77afB8A8P7771el1Ub5RpKkOo15LpnHw7ZtpNNpZDIZfc54npcY7yotuK6r77JO2r94WNT2LMuCW1n3kiVLEuuc8TqiCqcKq3qZacdcT3yfAVRdx/qVOI7HVaNrgnrzj3UM4mnZrJPOFDbYEhERTbG3P/MZfAwnBniYqOFly/DBv/yXOKOvbyqDRUQ0K7W8/jou+pM/0Y3fLa+9hpHlywEA2ddew4qvflXnp0IIZA4cqBoAbXDTJgxt3lx1ofixL35xpneDiGZYtHo1Rn/yE92gkr766iaHiIho6rDBlqaM9/LLWLplC/sTI6JT3pvr1+ONrq7Ef5nVP9jm3SPqrhv1b676t5kNtkQ0333Y1YUzYtNGli/H4UoeelrCMqMrVmDommtmInhENMVSr7yCs7/4RQiceErB/ENG/XGjnzyC8Qe4+ZRE7O5aIqL5hg22NCWGr78eLQAgJfyVKzG8YUOzg0REREREs9wH11+PD2+4AUDyY6Af3XSTfgQUgH6cUhiNPEQ0Nwxddx3ap6grj2j1apS6u6dkXUREsxEbbGlKDG/ZgmO33lrdv0yTw0RERERERESzw9CmTTh+6601/a9Opg/bqezDmWiqZA4cwAU9PVV3g5+M+N3l5vgXamwK27Jg2TbECy8Al1wyJdul2YENtkRERESz2IKDB3H5178OoFxRlwAgJRb09+NYR0czg0ZEREREAI5efTUWNXH78uKLgdtua2IIaKqxwZaIiIholnr/yivr/nasowPvfe5zMxcYIiIiIkp0+OabcfjmmwGg6m5w9RTyeCV1D2SOi2FZFhzHgeu6sG0bmUwG2WwWlmXBdV12FzSPsMGWiGiSsv/wD8j84Af6bjfgxJ1vulhWn2O/xz+bzCmNClxzG+WZRfW2AURRVK4kSAkZC8vbn/40DnV1jbWbRNRE7157Ld6pDK6kKutSSkRRhCiKmhw6IiIiIiKaDqd8N6NCiB3NDgMRzU2ZH/4Q7ssvNzsYk9J+6BA+9vTTzQ4GEREREREREcWc0nfYCiE2AljX7HAQ0dxVWrUKHz30kL6Tdaz3pGkmNY+UsmaAhThznQCq7rxT04rFIoIgQBRF+j0MQ/zBN74xvRFDREREdIpauHMnck88Uf4iBASQ+FizqAwaBADi+echp2jAIOfFF7Ho5pt1fVDGnrSSapqqS6oF409+1amD6oGPan4QyOzfj8KKFVOyH0REp7JTtsFWCJFrdhioPvvFF3HmbbdBRhHCKNKViSjh0e7Ex8pVBUJVMpJ+T6g8QYjaiou5mLFus8IlVKVLTVcVMGO79r59CFevnoLYISIiIiKi2Sr3xBNIT7DhUl5yCaLNm0962/4tt8CtXDM1w+iKFRj8wheasm0iovnklG2wRfnO2r5mB4JqBd3dsKMICMNmB2VKhatXw+/ubnYwiIiIiIhomhVWrMAb999fNUiQbdtwXRepVArpdBqu68KyrKonrk52wCD/y1/GyO2366eqACAIgqonraSUCIIAvu/r6eZTXkD1TS3xJ8LUPtm2nTh/vafDiIho/OZMg60QYg2AzVLK7Q3mWQegC8BBADkAkFLeV2ddbKydpUpf+QqGt2zB8ePH4fs+8vk8fN9HqVRCqVRCGIb6ZT5ablKPGFlWuZvmpEqGbdtVIyw6jgMhBEqlkq64mMuqyodaTlW4VGUrnU7Dtm3Ytg3P8+A4Tk2lBwDAQWKIiIiIiIiIiKiOOdFgW+lr9jsAHhpjns1Sym5j2johxG4pZXwY9EVSyr2T7RbBsiwsWLCgalq88dBxXQDA6aefXvNvY7zfSSO8EEIglU4je+AArvrLv6z5d1IY89brCkDPZzz2b/Y/ZK5DzeO8+iqiiy9Ga2trVaOkUtP/kTFCdfzf2Pj+mA2dSf+4mv/cqvWqBlTf99Ha2qobUVU8x7edtE3VqBoX7ztKNbKq8JnbSFrWbAxW/5Srxtv4us24U/ttxqX5bsZvkqR4M5dT3804rndcAFT9K25ZFqIo0u/xcEHKqjQ+EWY4G/3bHt+fJPG4Guv3seI2nq6Tppu/x78vKJUAAMeOHauZr955Ye6r+pPAvPPCPH41fZyh+vjGj4eZv6h33/fh+77+k0P9GeG6LiSAxYsX1z2PzLSetD/xczx+Pqk/QzzPg7AsLFmyRO+jWl5KCc/zAABLly6tip/4cTC35TgOoN4nIZVK6T9VXMeBtW8fOu68s7wttV1zAbMrlsp3APDefBP+qlW48MIL9TExwxwPtxmX8WMaz6/CMEQ2mwWkxKpVqxL/ADLTSDqTgRACH/vYx6ryW53XGelIxV29fGQ8zDtuHMdBKpWqSjOWEJCV7ZjHslE+Xq8cGWu+iWi07FjrrXc+n17JC44fP151nE2qXDXL2EZ3JdWLC1W2JP1RWa9cNvN9c/tmOR8/59U5bJ7LZhqNp+uk8t0sB1V5Y9s2JIC2trbE/TO7GgIAt1KfSor3k5V051ij9Zr7Mdb86nySQiCdTuvvQRDAtm0EQYBSqdSwTmNuwywHoijSdTl1l5w6ryAljh8/XlNfS6KOTzwPj/+prY+vZSGTyei8w0zHruvqO/jMMsjcHxVOdcdfvTpkPG+M/2Eej5t4HqHWFQQBIASGhobqrjOpHIufF/H8Nml6fB0qfhttK76vSe+NPsfj4WRMtN41Vp0JqC4Pk+pFZrqM54Vm3qHOUXVnqOqTP34Dh1pfPC00KjfMY5FU1xkrXcbzTDPvVN2jZbNZnX7Vb47jwPM8fb1Tc4dtLB2Y57853SyD4+dRvI6YlM+ouDPr+PGyw8x34sdKhc1c1pw/KR8w84dSqQTf92uOi7n+eDjiv8WnnV1Z3+9+97vE9caN59qg0fVCvfTV6FrAzNNU+q5Xv0uqU8TThVn/VmnLruTT2Wy2Kt8yw6bOJfV5rGtrMx82y4b4vOOJ73h6MtNr0vgf8fgzl03KB5LSsXntn3TTljoWKi7N/VP11/g5Ez/WScfZ3KeJ5C/xeFfpRYVbpQGVpySF29yOOj/b29uRyWT0dWH8/E/aB7PemEmnAQCdnZ1120xU2Mywm9cm8bTSqB6k6jnFYjHx5r2k8Nc7r+PpMyn9mmnFzNOaxRp7luYRQtwrhNhZ+do/xuzfAbDVnCCl7Kusp8dY50Y1fbY6sn49Ri+4YEa3Ga1ejXDTphndJhHRbBBs2oRokv1L+6tWYeSGG6Y4RERERERERHQqm9V32Eqj+wMhxD315qs0yB6VUg4m/LwbwDYAvUKIDgB7pzygU+yjm27C0Y0b9T8oSS3/tm0n3u1Y7x8k85819Tl+F5xt27O7BZ+IaBoEd9yB4I47arpaMe8WafQbcPL9zREREREREREps7rBdgK6Uf8O3H4Aa0S5+4N1AHJGA+hiAIuEEHcD2Dvb77wlIiIiIiIiIiKi+W2+NNhejvr926qG3MullL3mD5U7bjfKhIHJiIjmu9bXX8clf/ZnAKr7a9V/aQmjD2zVJ1Cd+dR3IU70ja1e3iuvoHTRRQ3DYu/bh5Zrr61aH2JPEJi/Wfv2Ibr44rF3kqo9/3z5/dJLmxsOOqVZL7yA1NVX10yvylOef57plIiIiIhOWfOlwTYHIKk7hPg8REQE4MOuLpwOJDeKTjF/1SoUbryx7u+ljRvLHyYQFtXvNrsiGL9w82Y4avCEW29l3FFThJs3j6/7pUsvBW67bbqDQ0REREQ0K82XBttGVEPuInOiEGIjyn3bdggh7gXwoJSybv+2lX5yewDgrLPOmqagEhHNjA+uvx7vXXdd3RG6k0anHWvkWHOUWrN/bDUScrpOWEpf+QpKX/lKzQie8ZE+k0ZMPRUKsakS3XUXwm3bxjWKL9F0Ce+8E8Edd9Ttg9/MS5hOiYiIiOhUdcpe60opdwHYNYH5ewH0AsCqVaum/5Y0IiIiIiIiIiIiOuWM66k0IiIiIiIiIiIiIpp+8+kO23p91KrpR6dqQ+k338SyrVurJ0pZHoyn8t7y2msYWb58qjZJRERERHOMeP55tFx7LTJRhCiKIKVEZHYxY9QfP+rqwgc33NDsIBMRERHRLDBfGmz7Eeuj1rDImOekCCE2ANiwOpUac96R5ctxuKvrZDdJRERERHNQpAb3C8Mx582+9hpOA9hgS0REREQA5k+D7V6McYdtowHFxktK+RiAx1atWrX19e98p+q3MAxP3DkRRWr+k90kEREREc1BcutWRHfdhXw+j9HRURQKBQRBgFKppN+jyp23n/jqV5sdXCIiIiKaReZLH7a7AXTU+e0KlBt0iYiIiIiIiIiIiGa1+XKH7UMA7hVCdEgp410fbASwfSo35rouli5dCiGEnqb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      "text/plain": [
       "<Figure size 1440x2016 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_posterior(gammas_Markov, gammas_Renewal, tss, plot_seed, ects, L, w, ratio, 'posterior1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['Markov', 'Renewal']\n",
    "df_abs = pd.DataFrame(columns=columns)\n",
    "df_log = pd.DataFrame(columns=columns)\n",
    "\n",
    "for i in range(4):\n",
    "    tmrca, truth = get_truth(tss[i], w, times)\n",
    "\n",
    "    if i%2==0:\n",
    "        dist_abs = np.abs(ect_constant - tmrca[:,:,None])\n",
    "        dist_log = np.abs(np.log10(ect_constant) - np.log10(tmrca[:,:,None]))\n",
    "    else:\n",
    "        dist_abs = np.abs(ect_variable - tmrca[:,:,None])\n",
    "        dist_log = np.abs(np.log10(ect_variable) - np.log10(tmrca[:,:,None]))\n",
    "\n",
    "    mean1, se1 = get_mean_se(gammas_Markov[i], dist_abs, L // w)\n",
    "    mean2, se2 = get_mean_se(gammas_Renewal[i], dist_abs, L // w)\n",
    "    mean3, se3 = get_mean_se(gammas_Markov[i], dist_log, L // w)\n",
    "    mean4, se4 = get_mean_se(gammas_Renewal[i], dist_log, L // w)\n",
    "\n",
    "    df_abs.loc[i] = [f'{mean1:.2f} ({se1:.2f})', f'{mean2:.2f} ({se2:.2f})']\n",
    "    df_log.loc[i] = [f'{mean3:.4f} ({se3:.4f})', f'{mean4:.4f} ({se4:.4f})']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Scenario</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <td>6075.50 (215.59)</td>\n",
       "      <td>5187.24 (187.27)</td>\n",
       "      <td>12037.25 (298.71)</td>\n",
       "      <td>12112.81 (106.44)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <td>6068.04 (209.11)</td>\n",
       "      <td>5187.49 (184.34)</td>\n",
       "      <td>11476.92 (282.36)</td>\n",
       "      <td>11571.28 (97.99)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Scenario                 1                 2                  3  \\\n",
       "Markov    6075.50 (215.59)  5187.24 (187.27)  12037.25 (298.71)   \n",
       "Renewal   6068.04 (209.11)  5187.49 (184.34)  11476.92 (282.36)   \n",
       "\n",
       "Scenario                  4  \n",
       "Markov    12112.81 (106.44)  \n",
       "Renewal    11571.28 (97.99)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_table(df_abs, 'table1_1.tex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Scenario</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <td>0.1362 (0.0035)</td>\n",
       "      <td>0.1281 (0.0024)</td>\n",
       "      <td>0.3051 (0.0013)</td>\n",
       "      <td>0.3006 (0.0014)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <td>0.1437 (0.0026)</td>\n",
       "      <td>0.1359 (0.0025)</td>\n",
       "      <td>0.3451 (0.0046)</td>\n",
       "      <td>0.3422 (0.0014)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Scenario                1                2                3                4\n",
       "Markov    0.1362 (0.0035)  0.1281 (0.0024)  0.3051 (0.0013)  0.3006 (0.0014)\n",
       "Renewal   0.1437 (0.0026)  0.1359 (0.0025)  0.3451 (0.0046)  0.3422 (0.0014)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_table(df_log, 'table1_2.tex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = pd.MultiIndex.from_tuples([('Markov', 'Q1'), ('Renewal', 'Q1'), \n",
    "                                     ('Markov', 'Q2'), ('Renewal', 'Q2'),\n",
    "                                     ('Markov', 'Q3'), ('Renewal', 'Q3'),\n",
    "                                     ('Markov', 'Q4'), ('Renewal', 'Q4')])\n",
    "df_abs = pd.DataFrame(columns=columns)\n",
    "df_log = pd.DataFrame(columns=columns)\n",
    "\n",
    "count = np.empty((4, 4, num_sim))\n",
    "\n",
    "def get_row(l1, dec_places):\n",
    "    mean = np.average(l1, axis=0)\n",
    "    se = sem(l1, axis=0)\n",
    "    row = [f'{m:.{dec_places}f} ({s:.{dec_places}f})' for m, s in zip(mean, se)]\n",
    "    return row                       \n",
    "                                  \n",
    "\n",
    "for i in range(4):\n",
    "    df_abs.loc[i] = [None] * 8\n",
    "    df_log.loc[i] = [None] * 8\n",
    "    tmrca, truth = get_truth(tss[i], w, times)\n",
    "    l11 = np.empty((num_sim, 4))\n",
    "    l12 = np.empty((num_sim, 4))\n",
    "    l13 = np.empty((num_sim, 4))\n",
    "    l14 = np.empty((num_sim, 4))\n",
    "    \n",
    "    if i%2==0:\n",
    "        dist_abs = np.abs(ect_constant - tmrca[:,:,None])\n",
    "        dist_log = np.abs(np.log10(ect_constant) - np.log10(tmrca[:,:,None]))\n",
    "    else:\n",
    "        dist_abs = np.abs(ect_variable - tmrca[:,:,None])\n",
    "        dist_log = np.abs(np.log10(ect_variable) - np.log10(tmrca[:,:,None]))\n",
    "            \n",
    "    \n",
    "    for j in range(num_sim):\n",
    "        full_Markov_abs = gammas_Markov[i][j] * dist_abs[j]\n",
    "        full_Renewal_abs = gammas_Renewal[i][j] * dist_abs[j]\n",
    "        full_Markov_log = gammas_Markov[i][j] * dist_log[j]\n",
    "        full_Renewal_log = gammas_Renewal[i][j] * dist_log[j]\n",
    "\n",
    "        uniq_t = np.unique(tmrca)\n",
    "        breaks = np.quantile(uniq_t, [0, 0.25, 0.5, 0.75])\n",
    "        \n",
    "        for k in range(4):\n",
    "            if k != 3:\n",
    "                inds_k = (tmrca[j] >= breaks[k]) & (tmrca[j] < breaks[k+1])\n",
    "            else:\n",
    "                inds_k = (tmrca[j] >= breaks[3])\n",
    "                \n",
    "            num_pos = inds_k.sum()\n",
    "            count[i,k,j] = num_pos\n",
    "            l11[j,k] = full_Markov_abs[inds_k].sum() / num_pos\n",
    "            l12[j,k] = full_Renewal_abs[inds_k].sum() / num_pos\n",
    "            l13[j,k] = full_Markov_log[inds_k].sum() / num_pos\n",
    "            l14[j,k] = full_Renewal_log[inds_k].sum() / num_pos\n",
    "\n",
    "    df_abs.loc[i][::2] = get_row(l11, 2)\n",
    "    df_abs.loc[i][1::2] = get_row(l12, 2)\n",
    "    df_log.loc[i][::2] = get_row(l13, 4)\n",
    "    df_log.loc[i][1::2] = get_row(l14, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Scenario</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>25878.20 (951.37)</td>\n",
       "      <td>28103.76 (1075.15)</td>\n",
       "      <td>27807.96 (580.26)</td>\n",
       "      <td>27659.64 (233.24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>12037.56 (646.81)</td>\n",
       "      <td>10589.40 (702.38)</td>\n",
       "      <td>10931.92 (331.94)</td>\n",
       "      <td>11149.32 (143.83)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>7917.24 (461.61)</td>\n",
       "      <td>7231.24 (509.98)</td>\n",
       "      <td>7200.76 (178.59)</td>\n",
       "      <td>7119.12 (95.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q4</th>\n",
       "      <td>4167.00 (360.71)</td>\n",
       "      <td>4075.60 (272.09)</td>\n",
       "      <td>4059.36 (124.89)</td>\n",
       "      <td>4071.92 (90.72)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Scenario                  1                   2                  3  \\\n",
       "Q1        25878.20 (951.37)  28103.76 (1075.15)  27807.96 (580.26)   \n",
       "Q2        12037.56 (646.81)   10589.40 (702.38)  10931.92 (331.94)   \n",
       "Q3         7917.24 (461.61)    7231.24 (509.98)   7200.76 (178.59)   \n",
       "Q4         4167.00 (360.71)    4075.60 (272.09)   4059.36 (124.89)   \n",
       "\n",
       "Scenario                  4  \n",
       "Q1        27659.64 (233.24)  \n",
       "Q2        11149.32 (143.83)  \n",
       "Q3          7119.12 (95.21)  \n",
       "Q4          4071.92 (90.72)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = np.average(count, axis=2)\n",
    "se = sem(count, axis=2)\n",
    "entries = mfunc(mean, se, 2)\n",
    "df = pd.DataFrame(data=entries, columns=['Q1', 'Q2', 'Q3', 'Q4'])\n",
    "df['Scenario'] = ['1', '2', '3', '4']\n",
    "df = df.set_index('Scenario').T\n",
    "df.to_latex(folder + 'locicounts.tex')\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scenario</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q1</th>\n",
       "      <td>2840.79 (124.42)</td>\n",
       "      <td>2219.61 (101.38)</td>\n",
       "      <td>6820.01 (245.65)</td>\n",
       "      <td>6672.23 (88.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q1</th>\n",
       "      <td>2887.14 (129.53)</td>\n",
       "      <td>2273.91 (100.00)</td>\n",
       "      <td>5292.75 (180.86)</td>\n",
       "      <td>5256.89 (55.12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q2</th>\n",
       "      <td>6524.06 (131.61)</td>\n",
       "      <td>6106.74 (122.89)</td>\n",
       "      <td>13346.56 (35.50)</td>\n",
       "      <td>13465.23 (57.68)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q2</th>\n",
       "      <td>6604.09 (117.57)</td>\n",
       "      <td>6123.72 (107.90)</td>\n",
       "      <td>11463.27 (38.05)</td>\n",
       "      <td>11665.01 (35.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q3</th>\n",
       "      <td>10543.82 (239.47)</td>\n",
       "      <td>9716.04 (219.67)</td>\n",
       "      <td>18831.10 (64.05)</td>\n",
       "      <td>18957.97 (67.16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q3</th>\n",
       "      <td>10454.31 (249.99)</td>\n",
       "      <td>9528.27 (225.84)</td>\n",
       "      <td>19538.07 (72.65)</td>\n",
       "      <td>19682.76 (71.76)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q4</th>\n",
       "      <td>17242.02 (460.92)</td>\n",
       "      <td>16362.35 (471.86)</td>\n",
       "      <td>33016.17 (212.12)</td>\n",
       "      <td>33526.33 (226.10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q4</th>\n",
       "      <td>16959.09 (505.93)</td>\n",
       "      <td>16341.50 (601.40)</td>\n",
       "      <td>40181.58 (263.39)</td>\n",
       "      <td>40141.12 (279.70)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Scenario                    1                  2                  3  \\\n",
       "Markov  Q1   2840.79 (124.42)   2219.61 (101.38)   6820.01 (245.65)   \n",
       "Renewal Q1   2887.14 (129.53)   2273.91 (100.00)   5292.75 (180.86)   \n",
       "Markov  Q2   6524.06 (131.61)   6106.74 (122.89)   13346.56 (35.50)   \n",
       "Renewal Q2   6604.09 (117.57)   6123.72 (107.90)   11463.27 (38.05)   \n",
       "Markov  Q3  10543.82 (239.47)   9716.04 (219.67)   18831.10 (64.05)   \n",
       "Renewal Q3  10454.31 (249.99)   9528.27 (225.84)   19538.07 (72.65)   \n",
       "Markov  Q4  17242.02 (460.92)  16362.35 (471.86)  33016.17 (212.12)   \n",
       "Renewal Q4  16959.09 (505.93)  16341.50 (601.40)  40181.58 (263.39)   \n",
       "\n",
       "Scenario                    4  \n",
       "Markov  Q1    6672.23 (88.84)  \n",
       "Renewal Q1    5256.89 (55.12)  \n",
       "Markov  Q2   13465.23 (57.68)  \n",
       "Renewal Q2   11665.01 (35.60)  \n",
       "Markov  Q3   18957.97 (67.16)  \n",
       "Renewal Q3   19682.76 (71.76)  \n",
       "Markov  Q4  33526.33 (226.10)  \n",
       "Renewal Q4  40141.12 (279.70)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_table(df_abs, 'table1_3.tex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scenario</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q1</th>\n",
       "      <td>0.1479 (0.0056)</td>\n",
       "      <td>0.1328 (0.0033)</td>\n",
       "      <td>0.3212 (0.0020)</td>\n",
       "      <td>0.3080 (0.0022)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q1</th>\n",
       "      <td>0.1563 (0.0045)</td>\n",
       "      <td>0.1413 (0.0034)</td>\n",
       "      <td>0.3299 (0.0057)</td>\n",
       "      <td>0.3119 (0.0018)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q2</th>\n",
       "      <td>0.1279 (0.0023)</td>\n",
       "      <td>0.1253 (0.0023)</td>\n",
       "      <td>0.2755 (0.0016)</td>\n",
       "      <td>0.2863 (0.0016)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q2</th>\n",
       "      <td>0.1373 (0.0024)</td>\n",
       "      <td>0.1336 (0.0024)</td>\n",
       "      <td>0.3277 (0.0019)</td>\n",
       "      <td>0.3480 (0.0022)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q3</th>\n",
       "      <td>0.1255 (0.0029)</td>\n",
       "      <td>0.1242 (0.0036)</td>\n",
       "      <td>0.2826 (0.0020)</td>\n",
       "      <td>0.2857 (0.0022)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q3</th>\n",
       "      <td>0.1324 (0.0033)</td>\n",
       "      <td>0.1293 (0.0039)</td>\n",
       "      <td>0.3769 (0.0024)</td>\n",
       "      <td>0.3857 (0.0024)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markov</th>\n",
       "      <th>Q4</th>\n",
       "      <td>0.1197 (0.0036)</td>\n",
       "      <td>0.1216 (0.0032)</td>\n",
       "      <td>0.3168 (0.0026)</td>\n",
       "      <td>0.3178 (0.0021)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renewal</th>\n",
       "      <th>Q4</th>\n",
       "      <td>0.1247 (0.0040)</td>\n",
       "      <td>0.1278 (0.0036)</td>\n",
       "      <td>0.4546 (0.0029)</td>\n",
       "      <td>0.4568 (0.0023)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Scenario                  1                2                3                4\n",
       "Markov  Q1  0.1479 (0.0056)  0.1328 (0.0033)  0.3212 (0.0020)  0.3080 (0.0022)\n",
       "Renewal Q1  0.1563 (0.0045)  0.1413 (0.0034)  0.3299 (0.0057)  0.3119 (0.0018)\n",
       "Markov  Q2  0.1279 (0.0023)  0.1253 (0.0023)  0.2755 (0.0016)  0.2863 (0.0016)\n",
       "Renewal Q2  0.1373 (0.0024)  0.1336 (0.0024)  0.3277 (0.0019)  0.3480 (0.0022)\n",
       "Markov  Q3  0.1255 (0.0029)  0.1242 (0.0036)  0.2826 (0.0020)  0.2857 (0.0022)\n",
       "Renewal Q3  0.1324 (0.0033)  0.1293 (0.0039)  0.3769 (0.0024)  0.3857 (0.0024)\n",
       "Markov  Q4  0.1197 (0.0036)  0.1216 (0.0032)  0.3168 (0.0026)  0.3178 (0.0021)\n",
       "Renewal Q4  0.1247 (0.0040)  0.1278 (0.0036)  0.4546 (0.0029)  0.4568 (0.0023)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_table(df_log, 'table1_4.tex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation 2: Effect of Demographic Prior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Redefine some functions for second simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_viterbi(viterbis, tss, ects, L_w, w, name):\n",
    "    size = 25\n",
    "    scale_to_kb = 1000 / w\n",
    "    width = 3600\n",
    "    \n",
    "    fig, axs = plt.subplots(3, figsize=(20, 13.5), sharex=True, sharey=True)\n",
    "    fig.add_subplot(111, frameon=False)\n",
    "    plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
    "    \n",
    "    labels = ['Africa', 'Zigzag', 'Constant']    \n",
    "    colors = ['purple', 'green', 'orange']\n",
    "    \n",
    "    for i, ax in enumerate(axs):\n",
    "        ts = tss[i]\n",
    "        for j in range(3):\n",
    "            ect = ects[j]\n",
    "        \n",
    "            pmap = viterbis[i,j]\n",
    "            dpmap, = np.diff(pmap).nonzero()\n",
    "            x = np.r_[0, dpmap, L_w-1]\n",
    "            y = ect[pmap[x]]\n",
    "            ax.plot(x / scale_to_kb, y, drawstyle=\"steps-pre\", color=colors[j], label=labels[j])\n",
    "\n",
    "        truth = [(tree.interval[0] / w, tree.get_tmrca(0, 1)) for tree in ts.trees()]\n",
    "        x, y = zip(*truth)\n",
    "        x = np.r_[x, L_w]\n",
    "        y = np.r_[y, y[-1]]\n",
    "        ax.plot(x / scale_to_kb, y, drawstyle=\"steps-post\", color=\"red\", label='Truth')\n",
    "        \n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.xaxis.set_tick_params(labelsize=size)\n",
    "        ax.yaxis.set_tick_params(labelsize=size)\n",
    "        ax.legend(loc = 'lower right', prop={'size': size})\n",
    "        \n",
    "        ax.text(0.01, .99, labels[i], transform=ax.transAxes,\n",
    "                fontsize=size, va='top')\n",
    "            \n",
    "        sns.despine()\n",
    "        \n",
    "        if i == 0:\n",
    "            ymin = np.amin(y[:width])\n",
    "            ymax = np.amax(y[:width])\n",
    "        \n",
    "        else:\n",
    "            ylow = np.amin(y[:width])\n",
    "            if ylow < ymin:\n",
    "                ymin = ylow\n",
    "            \n",
    "            yhigh = np.amax(y[:width])\n",
    "            if yhigh > ymax:\n",
    "                ymax = yhigh\n",
    "    \n",
    "    plt.setp(ax.get_xticklabels(), fontsize=size)   \n",
    "    plt.setp(ax.get_yticklabels(), fontsize=size)\n",
    "    plt.setp(ax, xlim=(0, width/scale_to_kb))\n",
    "    plt.setp(ax, ylim=(0.75 * ymin, 1.25 * ymax))\n",
    "    \n",
    "    plt.xlabel('Position (kb)', fontsize=size, labelpad = size)\n",
    "    plt.ylabel('Coalescence time (Generations)', fontsize=size, labelpad = size)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(folder + name, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_posterior(gammas, tss, ects, L_w, w, name):\n",
    "    size = 25\n",
    "    scale_to_kb = 1000 / w\n",
    "    width = 3600\n",
    "    \n",
    "    fig, axs = plt.subplots(9, figsize=(20, 27), sharex=True, sharey=True)\n",
    "    fig.add_subplot(111, frameon=False)\n",
    "    plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
    "    \n",
    "    labels = ['Africa', 'Zigzag', 'Constant'] \n",
    "    \n",
    "    for i, ax in enumerate(axs):\n",
    "        ind1 = i//3\n",
    "        ts = tss[ind1]\n",
    "        \n",
    "        ind2 = i % 3\n",
    "        if ind2 == 0:\n",
    "            ax.text(0.01, .99, labels[ind1], transform=ax.transAxes,\n",
    "                fontsize=size, va='top')\n",
    "\n",
    "        ect = ects[ind2]\n",
    "        gamma = gammas[ind1,ind2,:width]\n",
    "        \n",
    "        truth = [(tree.interval[0] / w, tree.get_tmrca(0, 1)) for tree in ts.trees()]\n",
    "        x, y = zip(*truth)\n",
    "        x = np.r_[x, L_w]\n",
    "        y = np.r_[y, y[-1]]\n",
    "        ax.plot(x / scale_to_kb, y, drawstyle=\"steps-post\", color=\"red\", label='Truth')\n",
    "        \n",
    "        X, Y = np.meshgrid(np.arange(width), ect)\n",
    "        im = ax.pcolormesh(X / scale_to_kb, Y, gamma.T, cmap=\"Greys\", rasterized=True)\n",
    "#         if unique_clim:\n",
    "#             im.set_clim(np.amin(gamma), np.amax(gamma))\n",
    "        \n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.xaxis.set_tick_params(labelsize=size)\n",
    "        ax.yaxis.set_tick_params(labelsize=size)\n",
    "        \n",
    "        sns.despine()\n",
    "        \n",
    "        if i == 0:\n",
    "            ymin = np.amin(y)\n",
    "            ymax = np.amax(y)\n",
    "        \n",
    "        else:\n",
    "            ylow = np.amin(y)\n",
    "            if ylow < ymin:\n",
    "                ymin = ylow\n",
    "            \n",
    "            yhigh = np.amax(y)\n",
    "            if yhigh > ymax:\n",
    "                ymax = yhigh\n",
    "    \n",
    "    plt.setp(ax.get_xticklabels(), fontsize=size)\n",
    "    plt.setp(ax.get_yticklabels(), fontsize=size)\n",
    "    plt.setp(ax, ylim=(0.75 * ymin, 1.25 * ymax))\n",
    "    plt.setp(ax.set_xlim(0, width/scale_to_kb))\n",
    "    \n",
    "    plt.xlabel('Position (kb)', fontsize=size, labelpad = size)\n",
    "    plt.ylabel('Coalescence time (Generations)', fontsize=size, labelpad = size)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(folder + name, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_truth(ts, w, times):\n",
    "    T = times.size\n",
    "    L = tss[0].sequence_length\n",
    "    L_w = int(np.ceil(L / w))\n",
    "    tmrca = np.empty(L_w)\n",
    "    \n",
    "    cts = [(round(tree.interval[0] / w), \n",
    "            round(tree.interval[1] / w), \n",
    "            tree.get_tmrca(0,1)) for tree in ts.trees()]\n",
    "    x1, x2, y = zip(*cts)\n",
    "    for j in range(len(cts)):\n",
    "        left = x1[j]\n",
    "        right = x2[j]\n",
    "        t = y[j]\n",
    "        tmrca[left:right] = t\n",
    "    return tmrca, truth\n",
    "\n",
    "def create_table(err, name, log=False):\n",
    "    columns = ['Africa', 'Zigzag', 'Constant']\n",
    "    mean = np.average(err, axis=2)\n",
    "    se = sem(err, axis=2)\n",
    "    if log:\n",
    "        dec_places = 4\n",
    "    else:\n",
    "        dec_places = 2\n",
    "    entries = mfunc(mean, se, dec_places)\n",
    "    df = pd.DataFrame(data=entries, index=columns, columns=columns)\n",
    "    df.to_latex(folder + name)\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set simulation and model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sim = 25\n",
    "w = 100\n",
    "\n",
    "species = stdpopsim.get_species(\"HomSap\")\n",
    "chrom = 22\n",
    "contig = species.get_contig(f'chr{chrom}')\n",
    "r = contig.recombination_map.get_rates()[0]\n",
    "mu = contig.mutation_rate\n",
    "\n",
    "T = 64\n",
    "times = np.r_[0.0, np.geomspace(1e1, 1e6, T-1)]\n",
    "\n",
    "plot_seed = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "africa = species.get_demographic_model(\"Africa_1T12\")\n",
    "zigzag = species.get_demographic_model(\"Zigzag_1S14\")\n",
    "constant = stdpopsim.PiecewiseConstantSize(species.population_size)\n",
    "models = [africa, zigzag, constant]\n",
    "\n",
    "demo_africa = africa.get_demography_debugger()\n",
    "demo_zigzag = zigzag.get_demography_debugger()\n",
    "\n",
    "eta_africa = np.squeeze(1/(2*demo_africa.population_size_trajectory(steps=times)))\n",
    "eta_zigzag = np.squeeze(1/(2*demo_zigzag.population_size_trajectory(steps=times)))\n",
    "eta_constant = np.repeat(1/(2*species.population_size), T)\n",
    "etas = [eta_africa, eta_zigzag, eta_constant]\n",
    "\n",
    "L = species.genome.chromosomes[chrom-1].length\n",
    "L_w = int(np.ceil(L / w))\n",
    "\n",
    "w_end = None\n",
    "if L % w > 0:\n",
    "    w_end = L % w\n",
    "rho = 2*r*w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Population size trajectories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "x = np.geomspace(1, 1e6, 1000)\n",
    "y = np.squeeze(2*demo_africa.population_size_trajectory(steps=x))\n",
    "plt.plot(x, y, color='red', label='Africa')\n",
    "\n",
    "y = np.squeeze(2*demo_africa.population_size_trajectory(steps=times))\n",
    "plt.plot(times, y, color=\"blue\", drawstyle='steps-post', label='Africa Piecewise')\n",
    "\n",
    "x = np.geomspace(1, 1e6, 1000)\n",
    "y = np.squeeze(2*demo_zigzag.population_size_trajectory(steps=x))\n",
    "plt.plot(x, y, color='orange', label='Zigzag')\n",
    "\n",
    "y = np.squeeze(2*demo_zigzag.population_size_trajectory(steps=times))\n",
    "plt.plot(times, y, color=\"green\", drawstyle='steps-post', label='Zigzag Piecewise')\n",
    "\n",
    "plt.plot([0,1e6], [2*species.population_size]*2, color='purple', label='Constant')\n",
    "\n",
    "size = 15\n",
    "plt.xticks(fontsize=size)\n",
    "plt.yticks(fontsize=size)\n",
    "\n",
    "plt.xlabel('Time (Generations)', fontsize=size)\n",
    "plt.ylabel('Effective Population Size', fontsize=size)\n",
    "sns.despine()\n",
    "\n",
    "plt.legend(fontsize=size)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.savefig(folder + 'sizehistory.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caleb/Desktop/Git/HMM-EM/venv/lib/python3.7/site-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "0 2\n",
      "0 3\n",
      "0 4\n",
      "0 5\n",
      "0 6\n",
      "0 7\n",
      "0 8\n",
      "0 9\n",
      "0 10\n",
      "0 11\n",
      "0 12\n",
      "0 13\n",
      "0 14\n",
      "0 15\n",
      "0 16\n",
      "0 17\n",
      "0 18\n",
      "0 19\n",
      "0 20\n",
      "0 21\n",
      "0 22\n",
      "0 23\n",
      "0 24\n",
      "1 0\n",
      "1 1\n",
      "1 2\n",
      "1 3\n",
      "1 4\n",
      "1 5\n",
      "1 6\n",
      "1 7\n",
      "1 8\n",
      "1 9\n",
      "1 10\n",
      "1 11\n",
      "1 12\n",
      "1 13\n",
      "1 14\n",
      "1 15\n",
      "1 16\n",
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      "1 18\n",
      "1 19\n",
      "1 20\n",
      "1 21\n",
      "1 22\n",
      "1 23\n",
      "1 24\n",
      "2 0\n",
      "2 1\n",
      "2 2\n",
      "2 3\n",
      "2 4\n",
      "2 5\n",
      "2 6\n",
      "2 7\n",
      "2 8\n",
      "2 9\n",
      "2 10\n",
      "2 11\n",
      "2 12\n",
      "2 13\n",
      "2 14\n",
      "2 15\n",
      "2 16\n",
      "2 17\n",
      "2 18\n",
      "2 19\n",
      "2 20\n",
      "2 21\n",
      "2 22\n",
      "2 23\n",
      "2 24\n"
     ]
    }
   ],
   "source": [
    "ids = [None] * 3\n",
    "ects = [None] * 3\n",
    "transitions = [None] * 3\n",
    "\n",
    "gammas = np.empty((3,3,L_w,T))\n",
    "viterbis = np.empty((3,3,L_w), dtype=int)\n",
    "tss = []\n",
    "\n",
    "engine = stdpopsim.get_engine('msprime')\n",
    "\n",
    "err_A = np.empty((3, 3, num_sim))\n",
    "err_B = np.empty((3, 3, num_sim))\n",
    "\n",
    "for j in range(3):\n",
    "    ids[j] = initial_distribution(times, etas[j])\n",
    "    ects[j] = get_expected_coal_times(times, etas[j])\n",
    "    transitions[j] = get_transitions_Renewal(times, ects[j], rho, etas[j])\n",
    "\n",
    "for i in range(3):\n",
    "    model = models[i]\n",
    "    samples = model.get_samples(2)\n",
    "    for k in range(num_sim):\n",
    "        print(i, k)\n",
    "        ts = engine.simulate(model, contig, samples, seed=k+1)\n",
    "        if k+1 == plot_seed:\n",
    "            tss.append(ts)\n",
    "        \n",
    "        tmrca, truth = get_truth(ts, w, times)           \n",
    "\n",
    "        for j in range(3):\n",
    "            emissions = get_emissions(ts, w, L_w, mu, ects[j], w_end)\n",
    "            gamma = get_posterior(L_w, T, ids[j], emissions, transitions[j])\n",
    "            dist_abs = np.abs(ects[j] - tmrca[:,None])\n",
    "            dist_log = np.abs(np.log10(ects[j]) - np.log10(tmrca[:,None]))\n",
    "            err_A[i,j,k] = (gamma * dist_abs).sum()/L_w\n",
    "            err_B[i,j,k] = (gamma * dist_log).sum()/L_w\n",
    "\n",
    "            if k+1 == plot_seed:\n",
    "                gammas[i,j] = gamma\n",
    "                viterbis[i,j] = get_viterbi(np.log(transitions[j]), np.log(emissions), np.log(ids[j]), L_w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x972 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_viterbi(viterbis, tss, ects, L_w, w, 'viterbi2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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GSPdcF8mf/ATpb3wDsWh0ziPB9wwAALfTQds/DR27807sf+tb8Wt/8zfwPA//eBnS0w2Wj03T0/jfnn4asCy8eMUVeML3sLlQqF97LV77679Gs9nEzX/wB2bD1sDAwMDAAIC3axeazz+P2P33A2fhFbTp+99HdmwM9e3b0dixA7WHHup57c+uvRb2gQPLfpZEfN8+2E8/jYbZyDUwMDAwMHjDwWzkLgN02+6VHbEfdTLs+osdi3Ez7+VWvhzX8n735j1J9SqXyzhz5gx+8YtfYPfu3SgWi6jVaory9qZvfQu5mRkcSKXQ9jdy260WOoJWAPhZNbuFVBSoKKlmlgXPddHudADPg+v3/1XFIgYKBTzfaODjo6MAgJdffhnpdBqpVAqpVAqDg4PI5/NIJpMBykcYlWg5bv3LoX6fLXSqBl/1fm82m6hUKqhUKmqstFqtAJVOpzyQ3iepIfp1verdr1yktvTK1CmpQY1GQ1FIONaZgbhUKmF0dBT79+/H1u98B9dPTuJfYzG88MILKJfLgWyr6wE81unAmpjA9e02rh4fx7ctS1GFmDlX0jc7nQ4GBwcxPj6OgYEBJBIJrFmzBul0GrZtY926dchmswEaDuvJUBaVSgWlUgnZRgMd18Xo6ChqtRq2+xmjR0dHMeBT9Q4cOIDBahV2JIIjR46oEAMyzICkqLFdm82mouNv89uwUqnAcZwAPSiM9tiPQterD+W4lzSefvSjdrsN2/d4mpycxPHjx1Eul9XhzG2NBlzPw+TkpApdIcdpL9pNv3KH0Q8BhNKgJOVt4KmnkP7mN7vPtrox1aiP1DMeewzeJz4xr666jNfrdZTLZdVOpEDq/dlqtVAsFnHq1CkVNmR6ehqu62JwcFDRwEiPYtv0oq71G5u9+livh9TtuvzIOtZqNTUeJW1NhlZgiB2WVz6LtEQ9XAIP+njYByBA96Pepn7jPYBgtnVdjvplUl4sBfhSsxsWg36ywL/LIXzVUmwo+T5s/qpWq7D9EAGTk5NKj0xOTuLadhuWbQdCjnBelVTLMJ22mDJeiDZfqo0SJkN81ecOju30F74A+wc/QPvuu1GtVpHxww9NTEx0Y9kePhyYK+r1OmZnZ1VohXq9ruaiaDSKNxUKKA4N4cuPPYbVq1cjm83C+ulP0W63USqVMD4+joMHD+LgwYP4fqUCa+1atNttFItFNBoNNJtNlBoNFS4MAFK2jaSvc9aOjOCKK67A4OCg0kW7du3CB//u7xBtNlEoFNTcK3UydT3D6JC+TH3J8DkMQyN1WbPZRDweV+3K8Aik8zM8Q1jYMCmzDK/F0BQAAtTuTCaj5hrPd5hgCAdgzmakDDN0leM4fefqxc5HveSJ8sP2YFtKqnjYnC7XJUePHkWj0cD09LQKQ3BjtYq2CPNVrVZx8OBBFZ6g1Wqpa9vtNiqVCsrlMoCuF3Y6ncbAwAA2tNuIRiKo1WqBjPd6+wOYF7JMb5d+Y65XO8hySvo/n6PPebo9p/+mn62l60Y5pimLeogB2sP1eh2FQkGFkOIYm5mZQaFQUCHVKMt6v9KG0sN4SDug2WyiVCopewSep0KoWZaFpOt2YyD7dkevOnqeB9vrhlYgSMHX205fy+jjjWOA47ZarSIWiyGXyylbCejadclkUo1jvZ1t254XLkOOL+oPyoT+OccLbU+2WblcxsTEBE6ePIlyuYx0Oo1isYhkMjlvLEQiEdVP1B2tVguVSkXZ2JFIROkSllm3URdad7DOLDt1UdJvb67pE4lEN/xdoYCIbeOtb30rLMtCLpdDpVLBmTNn1HqIoRXa7baSMbU+sW1Eo1Fks1k4jhMarkbvj7A1rKTxS13QS8Yo0/paW+9b6igZWk/aESyPbG99DDIsWZj+6bcWXCzC5nv2X68QFNSHskxnU55++kkP48C696rDcsKQmo1cg8sC2777Xbzln/85YCC9dOWVAICxfB7/+86daqKdmZlRMacAKOOQk8369euxbds25HI5ZDIZ2Lat4ix5nqc2y/7Lj35kPBzfwLh/agoA8LQfX1nH56NR/IPjIJFI4OmZmbPy+DG4vJH/2tcQ379/XvIiwnrllW5GerGRa2BgYGCwdCS+/GUA6IYluMSR3L8fI48+iggXinKR6HlAuQw3lULOdeH6TLTAwtLfOCq/+90of+hDK1UNA4PLGvaePYjffz86H/gA3N/6rRUpw5qvfhVrv/MdWHRcwlxOi9ajj6Lz8Y+vSLkMDAyWD7ORuwzYBw4gfv/9cB97zGShvEhw1Y9+hPzkJI4MDgIArigUYI+NrWyhDC57vJRI4B9SKaDRWOmiGFziaF53HarPPKMSwUgPkMg73mFirBkYGBicI7TuvhvNj30M0NhZlxIm77uvyxzr8X1kzx5YxSLcXA6dHTt63if98stIv/wy1vyH/4DaHXd0N3tdF5bvHVR617sw+fDD574CBgZvADQfeQQJy4K9ezciwIpt5K761reQfP11NDRdEHn1VViWhZrZyDUwuORgNnKXAc/zYO3eDQtA+/HHl5QNlL9fCs4lnW2pz17sfcKoCb1cyxdDVdbvqdOVSTEsFAo4ffo0BhsNHM7n8XvXXYfZ2Vn8P5UKtk9OAgD2xePYv3+/oirxPryvZVmo1WqIRqNIpVI4c+aMym5Pigep9aQkdDodlIpFWJaFH//4x6rMBw8exPDwMI4dO4aNGzcG3Pgl5UPSGfq1w1JpyucSve4t+4Wvsn9Iw6jX66hUKigWi6oNms2mypoqKc9Al4pB2qAehgLAPEpGv/KG0bVJ/QmjEJFGQ5qqpGIwS3WhUMDMzAyOHz+uqEvMlEqKYMOnR7INSFkn/SeRTiOZTCpaWblcVpSnWCyGZDKJer2OkydPolQqIZVKodFoIJVKIR6Po1gsYtWqVd3kJz4dim3Hcs7MzGBqagrrymV0XBf79u1Do9FAyafbv/jii7i2WIQH4KWXXsK1pRLWjY9j82/8RnfTMBpFNBKBZduwLUt58Mgsp57ndT15RJsXCgV0Op1AeIVeWaWXEqZAly1JudepR7yWYR+ifl9MTU2hXC6jUqmo53dcF3ang5mZGWQyGTUumcVXymc/any/cdDwN/hZZvk9KbqkuwJArVZTVDRJsYr4XlOUI0JSePg8Zj6Hf32r1VLjTcpKs9nE1NQUTp06pf4nZXh2dlaFQJE0q1gsFhi7YdS1pdDI9T7m2Gk0GoE5Q37farVQq9VQLBZRKpXUGGM4A5aF9GEZDkK2lS6b1BEMrUMaLWnHkiLGckhKGT/ne6nLdH2/HJ2/ULte7KEGFqLv8lXKQ5g86VRdHRe6HZY7B/ezoQAEbBSOY1JSk74NcubMGUWdLZVKaLdaiPihFWzbVvTzMLpzLyrhYnTzSmEp8z7/OBeTpt5oNJDz9ejU1FTXHmk04LquCn8wNjam7D2GcikWi/il117DL585o+jTAADLwtpGAwfSabzwwgsYGBiA4zhK3zQaDRXCZnp6GtVqVdlBrVZLzauRSERRn9lPnAcajQYmJibQaDTgOA5SqRROnDiBf73+egy/9a1YtWoV0um00nfswzUf+ACcH/8Y1W3b8Mpf/AVmZ2fRarXQarWQTCbVnLTh61/HTf/tvwEAavW6CiFmWxayhw4hVq/jyK23otFoIBaLoV6vo9lswvEZR3I+0Oc4tl2xWMTp06fRbrcDdks2m1W2sWS8UV7b7Tby+bwKJ5VKpdQzljIfLVau9bUMgEB+DYYqkeF6pL1Xr9cxMzODo0ePYmxsDLVaDbOzs2g0Gl27xA9RxXuXSiW89NJLaq7lPEwKNcd9JBJBOp1GJpPB4OAgbmw04MZimJ6eVu0gx7hc5+ghkfrZM3q7hM3R0l5guAK2W1h/6PfTy7aYubGfrRBWb2lfFYtF1Ot1JJNJ1X/VahUzMzNKN9BulX3LMjLUk/xOykSj0VCMT3q4M8SZbdtovf/9SH7kI8g/9BAg7qeHcHBdt2vreZ4KrdVoNObZQNQfUm5p99ZqtW45/DJyzqhWq9hRr6N9xRXY8+d/ruzLVCqFbb/927DbbRQKBWUnskzS9gnrT9pCDBFDWeCY6HQ66rdsM66nqBPL5TIikQiOHz+u5IdjodlsBkIRsjwAVHgCyrfrukin06E2fNjeQ9jcIcN7NRoNNPy5wQKwdu1aDA8PK3nL/fjHiMdiuOeee9T8AgCTk5MoFouqnGwjrtnYPrFYDI7joF6vq7ApDC0hZVrKtd6mUjYsy1r0+JMhScJC4+jXsh94nW5L6+tqfidDnIXZwYvRz2EI24fQ10OUDz20AssqbaNeMrKUcoStWeV1/UIr8D6UmaXAbOQuA+4118DNZFa6GAZ98I2BAaBQgAfg2VRqpYtjYHDRYs/11yNi2yZMiIGBgYHBGxKpAwfw5n//73Fjva420rgpwwS3u/wYrz/WbMr9joNvr1q1AqU+NzjywANqI/enn/lMIEbu7X/0RytcOgMDAwMDA4MwmI3cZcDzjTvLm/M+XUrA5OWePKwUej0/7EREnjRLj1x5ErEUrzz9XjwZYiKt2dlZFIvF7smubatkEP+cSuGffI+HTqeD9X7/xONx5HI59WwG/OZrPp/HzMxMwHOBnhP8TSwW63oQ+Kee9C4AoJKbSa82emf0Ot3sdRq9WO+28ylP/byGwk6g2FYMEl8ul1WiBgDq9JpB3/UA6fRKlSfji/Xk1k9XZdBzyo1MohR24sgA+vI7fjY7O4tyuYx2u43BwUGkfc+Qt7zlLcpjgh6C9Czh6XRm3z7YloX/etVV+KXjx7v39ssG18W3BwfxL1u3YmBgQJ0Csz2YZOzYsWOoVCrKg4VtCEAluWI5S6US7qxW4bkuDh06pDzYAWDPnj0o+yfHe/bswd5kEl+84w51P8dxkE6nEY1GVfIHtiNPXtkH0WgUH/nc55B0HExOTgZOnvVT8aWOff1kk54TAHomG2F/0TMh027D7XRQKpXUab7y6rW6CcWYHIweMfRu0T3D+3mehnmKMFGDnA/kiTHHQq1WU3qO8kU9wZPjmOsi8uqriN53Hx8491x0abCe5yHuukh1OhhotbpeVUIH62NDT5rhAbDgx0zj2LO68cNj8Xi3LJaF5iOPoPH44wuOz4W8l/WyRD73OSSeegqe5yEhPd4A5RXOenZcF6t9z4Sj99yDk+96V8BTg22t65mRp5/G8PPPBzxeJu+7D+MPPaRkp1qtolarKe8rJmyQyQV5bTQaDXjBSd2SSCTmefmE6fuF7AViMd5Ty8VyvFjP1i7p5QUn/wDMsyEk+snYxYSwMi1Ufz3pCmWyWCxioN1Gp93GxMSE8thrtVrdOcV1A4m6FvIMX6z8ARdHsrNe7cZX6cks2UHSa261rx+PHz/eZVvdeis2lMtoVqtoCs8y13XhUkd1Ovix4+Cr6TSeyGYD3ja2bSNi20gcPIhUKhVgB7Ac0WgUmzZtUvMNPYPoiUtPXiY25WaybduBxC3RaBTJZBLVahUnTpzA7OysYpUwWQ6fm6vV4ACoVirYs2cPZmdnlY2ml+8hvy4vv/xywGNsR6kEWBZeffVVAF2Py3w+j6GhIZXIV87z/C29JUulEqanp7Hh61/HL730En6+cydevukmAEEWlvQso/cXvabS6TTi8bhKNgTMMS6SyWTAK7jXfE2EeUOFvereXXqSOylvtB8rlQrq9TrOnDmDEydOKC9q6YXNfnR9Fk69Xsdrr72m6k87R8oXxzHlo1KpdO2GTgenTp1S7cBxzt/wftFoFHHO4SGe+f3Gmm4vSO9LehkrG0I8j3KoJ++R99KZR728JnW9KBl09Xq9Z3/UajWVqNDzPORyOaXv2u02pqenlb2dTCZDk6NyrgcQsH31dYNc69AjN5lMKlug0Wgg49ueZPTp5e50Ooj7iRfh26csO2WedWb95W/pfVsoFOB5c97cMjmx3WrhwIEDyl7KZDLY4ns1nzp1al794/G4apdeCfVox1IeZLswKSxlg+upcrmskpNXq1VVF3l/ep9KhgL1JctP2U8mkwCgGJ9yDUI5DJN3Od6l7qL9zvWcZdvYtGkTbNtW7IdsJoPUgQN491/8BVxvzoOYsch5EEjZtfw+BYBf3HILXrvnHiSTSbWuy+Vyin3Zr705n8g5nPtQYaxWuV6R95Ce+jpLlW1BfSz3QPTy6B7/+j24rmJ9FtLRi4FuJ4atv+iRK8Fr6Bkt7SKWY7FlCps7ZDkkqyxsj0dvS8/zlBwvBWYj18DAwOACYGu5jJv+7d8AAHuF987WchkYH8e/bN26UkV7w2LViRN425/+KQoPPojGRz96zu6b+Pu/R/ZLX+pOzuhukJbf8x4UH3tsWfdrvv/9iF+ATZSFEN+3D/ZXvoLK44+f+3t/6UuwX30VrZ07F/2bgSNHAMvCqXe9a1HXr3r+eaQOHEDZH2vpAwcAAOMPPdTvZwYGBpcxDt93H1696y6cOXMGJ0+exKlTp1Aul9UmT71eR61WU4eE8pDJYHHY9pOfYMPYGLaMjWHXnj0AuhtelmVh34034ud33IGbX34ZO3fvxs937MDPbrttZQtsYHCBMfzYY0i89BJqd9yx0kUxWASmHnig60zjLi3O+sjJkwCA1+6553wUy+ANBrORa2BgYHCe8a1Vq2D7XhX/esUVeG7LFnV6/n//7GfYWi7jz158EdFoVHkBAXOxVW3Lwp4bbsDovfeubEUuI7y6axei+/Zh5MgRxL/9bRw7lxu5X/4yInv3ouknlYjv24cMsOyN3NbHPob2b/6m+l+edvP0WcZHm5qaUl7a9Xo9wBqQsfzK5XI3tqbwhkgmk4pJEI/HsWHDBqxZswb5fB6bf+M3zmsIjvb112PqqaeU55OsL7082u02qtUqZmdncccyaL+Va67Bnr/8S1iWhRt+//fPZfENDAwMLhqcetvb4Loujtx550oXReHwli2BjfC1p0/Dtiz8/I47cP3u3dgyNgbPdS/7jdx/veIKAMCL/quBQeIHP1jpIhgsAZMPP4zO44+rQ77Tp0/j1KlTimUnWYLSc/XRv/3blS66wWUEs5G7DEh3bun2DyAQ8D+MtrYYuvyFoK9JLIaKqF/Ty6Vcp+JISvZC7uX6/cOo1ZIqUq/Xlet8LBqFlUxi27ZtivZD5WnbtqLNOI6DVatWBYKQ27atqFz5fB4nTpxQyphJHeiCz0D58Xgc+eeegwfgjjvuAHzPrr8bHe2W55VXMD41hcKjjyqvDia/AKDu049m0I/yqFMczga9fr8Q9ZP1YD+zf9j+pH/VajWVzIlUFSZmkLQqJg7rdDqKWhJGze9HT5B/MukYy9VqteZRaaRctdttlMtlRYvgJhlpZO12G5lMRv1ZloW77roLnuchlUqhVCopGhZDK7TbbVQB/JPjYGRki40h/gAAIABJREFUBKlkEr/qe/gUCgUcTaWQf/VV5Pz26IgkKKTlbJqagr13L763fTsmJiYCicRYVtaRbdxsNuG5rkp80Go24XrdEA3NRgOwLBw9elSNBdJyZNgGSbthf0nafyqVQtundJ3xk8A0m01kMhlF8ZEhAsJkvVc/hlGdJMVUvz6sHda22+i4rkoSE4/HVf8cesc7MP7QQ3jXZz6DqE9HJpWMiQ5lUPx+yQMknabdbiPpumhfey0OfvazaLfb2PpbvwWv3cbMzExALtvtNlZ/9avI//znKNx4IwqFgqKq6vSqMKqhLtsMrTE1NaV0JJM1yHZiW7IMpGWl0+lAsrFOp6PKBADtTgcRz0O5XA6UTw8VA/SmvYeN03a7jXinA9e/d6VSmRsDosysU6VSQaVSQdP3jjt48GAoZY79xvJt8vv45z//OQDgylIJq06cwJaPfnSuH6WO8zy8snMnfnLLLaqeEjK0ggzJAQCO4yj6r0x0GUZt7af3Zb+H4VzYC2c7hyz3Of3siOWW60LbTxL9ytuv7r3Gha7PmLzHdV10XFclSiRF3bIsZA8exNWPP45INIqIbaP+8MNoPv546FwaRrEOszsWksHzgbBnhckLX8NojbQVacsVCgVFp4bnYWxsTLUpk3LVajVEIpEAxTEajSKXy6nwKnpSRdJHGfrHsizV3qT+5vN5DA8Po9PpqKQ41PWJREKFVsjn87AsS5WF72dnZxVNOZFIwPXndib91ZP7dDodtFavRuPhh7tzw759ipItNxnkgRkA7N27V9XBtu1uSCbLwujoqNJvyWQS6XQajuOocA6UPwBqXmHbVyoV3Fmv48D69fiv99+v7DDP8/Dvn3sOVq2G119/vZtoDUCj2VQ0byY4Y2iFZrOpDrwdx0Emk1HxfKXNqNuL/dZhYdRYOZ/LMajPS5KGzaRSMzMzKnwGAGXvAkA0EkE0FsNPbrkF39m6tUsBnp0NyJEsJ6nt7FMmXm41mxg+dgzXfOITOPHWt+L4Qw/NS5QUiURU27DvdLuuH91cH088MNZtYvnHsB3sA33OlLYdbcqwRGzyen0dyDACbHfeT18rku7PMT00NATL6iZq4mEwbSY9nBYwR8NmYlOdOq7XW1KpOca53kkkEhjx6em04XXa/Bq/zk3fRk0fOICB976XDYHTb387jjzwgArVIZMmUjbIHqA8SZnifQ8fPqzGViKRwG1+csG9e/cGdBxlN5FIBBLmEnw2bTK2N8c+x48cT5KqzzBndDrg3Eb9IG19lte2bSXP6XQalmWhXC5jcnISkUhEhXajzmCfhoUQ0GVSypfca/AA2JaF1atXq+cypEs2m1V7Ee12G5OTkyq5ofxc6tlmqwULwMTEBBKJBKrVKhzHQTabRTabVfOCXB9Lu0DattJ25BpaX3OF1ZUby2F6IEy/cd0UNiblOkBf47E92Rdh66kw/RwGXWf32ieifuC+gw7WS5a9117MQgiz3WQ5gPmhAOX8o+vYXC63qOdKmI1cA4PzgJETJ5D44Q9RePTRlS6KwUWOn956K35yyy1qEVcsFjE5Oaniq7bbbXz62WexctsTBucT6/7lXwAAZ97xjhUuyRsPowt4fa0dH4cH4Ce33HJhCmRgcBYYu+uu7qaa/3983z5Yto3yeQiFYmBgsHLYe+ONiO7di5GjR2G/8IIJD3QZYZK5EATSr7+ONZ6HIw88sAIlOn+4e+9e3LJ/P9xOBx0/N4LneSrGLLTYpRYAy7bx46uvNnaZgQHMRq6BwTnB/rVrYdk2/vaRR+A4Dn7nqaeQWOlCGRgYXBKY3rULp979biw9zL3B2WDv3Xdj7913A5hLJCK9jH7z7/9+hUtoYLB4HHz72zH18MMYGhpCLpfDVR//+Bv+ADD9hS9g6CtfUV4y7VYLubExFLZsWemiGRgsG/92++04fN99eOSv//q8hjsyuHAo33orPM/D+EMPwbIsnPm1XwPQ9drb+bu/e1nG5r5tdBQbpqZwZGBg0b/ZMjMD+9Ahs5FrYACzkbts0C1aZuvUXcNltsB+tDVer997pdCP0ibf96IGSpoLqROst067DaPs9aLJSQo8MxJaloVMJoO4T/244YYbAlQn13URj8eRy+UUHWxoaAiJRHeblVQYZmRMpVIYHx/HkSNHUCwWUalU1HOYgZRZg/MvvQTX83DHHXcg+/LLsC0Lt99+e/f7Z59V9DxSRyTtjvWTISfCaI69aMq95GipWExf6/0tw0PotCvSrUiBllQsACrMBGkfkj4SiUTU96T2k3a1GCpYGMVCL5ek53Ps6jQxhkSQY5t1j8fjyGazGBoa6oZWsG3cfvvtsCwLjuMoKg1lStIrSJ+Mx+OKsjM7O6soWclkEplMBsViEUePHkWxWES73UapVEI8FkNbbDTJkBZ6P1CmbcsColEMDw93vbTicbidDoaHhxX9m9+RokVqEvuLVEVmPOZ7YC6MDDwPbqeDmZkZJeuSTqZTasJoj7qs6RmjJYWN9eZvwq7l+G/7GYBt28aAbyiybqQhWpYFz3VRq9XU+GaZJU2xF01JUp6oe/J+BvmZmRlVHg/AmTNnApS3TqeDjq8XpqenA/R8tr2k/Og6ln3FEArMBszQBJRlyr2UafZhMplUYTUGBwdRq9UAzGVobrVaKJVK3TAe7TY820axWFQyITNPh9Ejw2hE8o/tk/b7dmpqStHsSI/ieOEY5v+dTgdup4Pjx48r+e9Fzfc8D2/z6Y779u0L1WssHz9vNBrYPDODD3/2s3OeId1KBejpfLVtGwmf9mdHIogKvVV/+GE0Pv7xAPVX12eLtRmWYi+sRKiBxYYYWMiW0CnQvKafDXWu22M59lhYmfX3/eynsLBADK1A/UX6PuWLGe1jsZia91zPg+2HlnFddx6tWrblQvbZYtptMdcstj0XajPZdrrNyXm30Wgg85WvIL5vHyrXXKPadWrjRozefLOiwNKmtG0bmUwGqVQKAALhuRKJBFKpVGBeALp6g3qW1GJSgmkDZrNZrFu3Dps3b4bneSqMj+M4ytbJZrMq2zoAVKtVFeKmUCjg9OnTKJVKqh+np6cVlZ+f6+1A3U95kPZItVoN0GWJY8eOqfmAbQjLwpkzZ5BKpVS8dT30VViGdb0csKxgJnjfXoDnYXZ2VtnInuepEAq2baNer6u1RDwex9TUFACosAqO4yCZTAYy2uthoXrRZfXxJ9cbUo7k+JN0Y35PCj/npkgkglwuF6CHR6NRJP0QT1deeWUghBv7SZ9HKRNsX9at0WigWCyi2WrBbrdx/PhxFUKO7ZBMJlGr1QK2GL+X4ReA4NwTZofJuVq3KwCofupls4e1ubQJe9Gaw2RargHlwau0q2T/AF17b3BwUNk0tGtk6AG9nHJM9dKBui1FMPzT9PS0CrFylR8q5dChQ0ompNyN+Dbo6OjovDG0pVzGqhMnsONTn5oL/yTK5ol7wRMerd2KwbYsrJucxLGhIYyPj6uwF7Zto+aHT9m9e/e8/qAshYX4kuOE/aHbmrIN+TsZBsCORHBqzRr81Tvf2ZVn366jfESjUfWe65Y//ta3sHlmBn/wzDNdGRJryYiYE23aon65y+95D4of+lBoP+r6StbJ87qewQx7wNAKHGPRaBSdTqe7FxGPw/PmcjpwPgAwFzKi0wEsC5VKJRD6I5FI4Laf/Qw7Xnllzsb0+0+HmqPF2D36lrdg7MEHA2FUdHtc6jpdP+rtoe+9hM3JlH0ZXkeO61gspuY5GeZCDwUk9z/ka9hYC9tv0tdVcuzr4zsSiajrFpor+tk0/Ww3uR7R66XrWNnO69at6/m8XjAbuQYGBgYC13z3u7jyxRfnJiq58PQNomgshvTYGKrbtq1MIQ0MDM47XrzqKuDw4bP2hInt2wfLslD6+MfPUckMDAwWi9r27dj7V38ViJHbarWAZnOli2ZgYGCwIEZvu627OavFtF4Kjg8P40dXXXXOyrRSeOmqq2CPjSGy8KUKiV/8AhaAyoc+dL6KdU6w4+c/x8jJk5jYuHFJvxs4cgSwLBx/8MHzVDKDixVmI9fAwMBA4KoXX8Tg0aOY2rSp73WVrVsxff/9F6hUwIaJCXz6mWfU6Ts3l/g+8Lll4YrZWYwtga5kYHApYu34OD715JNz8g+Ebrx6ADZOTuL4qlWLvvf3tm3DD669VnmRSM9bmSyELA3HcbB+/XqsWbMG6XQa2WwWjuNg44c//IanuBsYGBgYGJwthr/8ZQx885sBjzzliUrvVI2dQ69B+Ayspu+x15choNsUYd550uvVtjF48iSmF1g7LAf73vIW7LnzzoBHtEx2JtkI0ssPmEsUpjzjz/JgeqXxve3b8dKuXcjlcnAcB6lUCvl8HgMDA+ozJpllMtotH/vYJWODTWzYgKd///fnMRild6dkdUUiEdz/n//zCpfaYKVgNnKXATU5WJaiI0saoE4PCctWt1R637miRy5Eawv7vhdNtZ+bu6QpsPwLhVUIo7jqtB49GyEpaYl4HJZlYevWrQAQmNBk1ltmmSQ9jH1BOjxpTI7jKMpTu91GNBqF4zjqmfF4HJlsFp7n4bbbbuvS7AFcd9118DxPhW5wHEfRDeREy2dLSlov2SH6haNYinz06+Ow/gijD0hajU5lqtfrAeqTvC9paJJWTJqFpLQzy28YxbwXVasXvZIUFxo9smyyDqTQdFwXExs24Cu/+7uqbWWm1KGhIaxdu1aF6Fjv91cikQhkrpVtAwCxWExRX9leAwMDypgkBaVcLiOVSil6/NTUFE7eey/sH/wA0XYbHc1w9QvZpdigmwjAtiycXrMGo9dfjx07diCRSCD97W/DdV3s3LkT6X37AMvCzp07Ydv2nIcSoKjrNAJpAEtKHsdJJpNBxDeOW62WCgchx5ukooZlTtdp4pKKJWlOuoEaRt+cd73IGprNZlXoCM/zkEql1JhzPQ+lUgm1Wk1RhHSKmU65CaPU8Lnrmk14rotTp06h2WxiR6MBD13aqqyfZVlo+9TSM2fOBGhnvSihYRlldXqbLAsA1Z+yT0n7zefzasytWbMGMzMzKtQMqWwMt9But2FHIpiZmQmMTUnxDiuz1CV6f7Hcq/yxMz4+rrLIk1JL3SLHrGVZ2HPDDdjBcCkardAC4Go0w0PZLL6/ejXGx8dVG8pswjLcC8vP8cq+oVxQxwMI0H5JdVay5bro+DqhXq+HZlHuRy/TDXi2Yy+dH6YbVxL68xcz18jvw8IrLBb96Hlni373WaiOfO1HEZTZs6XsS3nJZDJq7shkMl2qqj+2JeWWup3PWShzdNjrYuyNhWzbpdqf/doMmG8rSn1CW9F1XXgMNeGPQ1kPzk2kRfNQJhaLqd/HYjHkcjkV/oDtzDIwPEOpVEK5XO6GI0B33kmlUhgYGMDatWsxMjICAKjX6wCgdAFDY1D3AAjMyfl8Hul0GtVqVYV6YB8DUGFopM0hbWfWKx6Pq/6PRqNqfpDU23Q6rdqD4Sbg2zeO48CyLDQaDZVhPkwupDzRXuD7bDaLRCIRsH/hz8ek+Uf9+Vra3dyMoh6VmeEldZeU4l5zUi+af5j9KOWIz6ItyXEpqcecJ6LRqJpXLasbdovlT/u20fXXX6/sPNp6bBNZdob0oE3peV6A6uy6LjxAhYLjb+VGFm1LGSKrlw7Qx5m0ueS6imXR5Uza9fp99ZBL/KyXXc+y6OvAW555Bs7BgyhccUX3e23uD2xU+iw627dV4OsyhrUKUP/F7+dpqjDdRbvbspTdXdiyBYfe9CbUajXUarXAGujOahUAsGfPnnnt3Ol0cGOlAs/rhlbQN2X1vgAwb41B/SttFklll6EQ5Nq36TMTjh07Fhi3HI/62JZ9I18t0bYAlAzI/yXdfmBgQK3dt2zZgpmZGRUqA5jTWZ1OR/2WOjwejyOfzwfsMOrHSqWiwoxwLbm+2VQhYnrpAT08jQyzZfvjmHqQG8TU19ThnDu41onFYoF5seOHVdg4OYnfeeopJVuUrlUTEzg5MoJDhw7N06Pyle3NsXNPrYY1p0/jlk9/OjQsQ6C2/M5/f/COOzB6772B9tDDYaiy+5AODXJtxzrTySEWi6lwiTLsi7xeX1/1WhuyXPyfsiZteco2dbO07/Vyyz/qrDB92MumDrPddH2pz5FhcibH+nJgNnINDAwMLnKM3nsvvrd9O4rFIqrVamBjUyp/TpI02OPxOLIXoHwbX38dO37wA4xdQA9lA4N/u+02fPeaa1SsMRlD2bKswAZ2YMNhBctsYGBgYHDpYM1Xv4qh555ThwKBGKWYf8AAT+QE0TYpckeOoL59+4rW53LA7JVX4nv/8T8GDrDlhq90jEkkEhgaGsLIyIg6zD969ChOnz4dcNiRm0Jhh4r6oQU3oKLRKBKJBAYHB7F27druQU6hsBLNYnAJ4Kfbt/c88DwxMoKfLUM/vLprV3CDdpEYPnYMAOZt5BpcOjAbucuFf4IiT8Jk8gjpWdPP04HvdY+b+Y87f8mten3Xy5OE78P+9NPDsBORXs8Mu5880dCDSAMIbFpZto2RkRHVnjyZoecZT6KZOIDPpNcU0N0Iy+fz6kSn2WwG7sFrYrEYEvE4PAAbNmxAwk8AtXr16q6HjB+gPZVKBTxo5GklZUZ62YTJTa9ToTB5WKo3TFif9/IY0j1f5Omu7vlK7wTLstRJMNtUBiFnmXUvBOmxxs/1U369DmHe23rgc8/zlKeLNPj0E2XKj/TAi8fjSKVSyGQyyOfzyOVySCQSoZ57LI80CunFx1NlnlZKjxnKcyQSUd6I+XwerVYLiUQCU1NTKJfLqq2lx7v03mAwfsdxMDg4qDzRXc/Dm970JmSfew6WZeFNb3oTLMsKeD/W63XUarWABxPLRvlkOVOpFE780i9h08GDuOYnP8HovffCdV3U63V1Ws3TV/0Et59ntdQj0kON/0ujW55s6n3J8sdiMWSzWcRiMXWfeDyu+th1XeVFKeVSP/km2Gf6SSzHw3W1GjwAJ0+eDOgReoICc96fHd8jt1AoKK8qfdEg5Vx+p5+as5z0BJKJtdiu/D4ajSKTyWBkZATxeByO42DVqlXKq5Rt5/rebGz3wUOHsP23fzuwUO17+g8EvFtU+aVe8Tykx8YwuWkTTp48qZKYcCOWsijbjmWWic9kmzChgWw7/lZ6duj9IT0H6PnB5+vXySR+TILB5Bzsdz7L9j3JOp1OwENdsjJYpqV4SC7VLjgXdoSO5dgV8n0/W6PfM6RddK5spMVioTL2q1s/W0f3yJW6Tmc1DA4OAphje0i5VjYO5jzGqSdpd8j5VPf00efZXu/1//vJwmLbr9cGSi97U5/3pSclN9v0dtI907jhk8lk1FwhmV+pVEp5jXLcAgjox0qlopKI8b6cJ4eGhgIJN6mj2O7U2fS0VXOD/3zaBLL/PK+bMG12dlYlEZNzJzDngZRMJpW9Eo1G1Rwv7U4A2LZtm7IlGo0GnJ/9DABw1VVXIZPJoNPpoFQqoVKpqPlKzsHSluVhciqVQtwPQXP11VerZGfUm57nYfPmzcq7LplMYvPmzcruooee9IDjwV2z2US5XA4cXusJbMLkW8qZXKfINQY3CK/4+teRPnwYhS1b1Hyl5EraDN2bz9mwWrJS27LQ3LED7UcewZYtW5BMJpUXN20E2laUSVLH6fnN5EiFQqHLbvOf3Ww2A+O20WgEZEpnz4QxQvRxrNtWOoOFOkYyB6RtT+ieuJRBfQ4L09/62sPzPFznM4ROnToFy7Lmed/p8kfGQj6fVxu5uVwO09PTARuD7/WQC9SrYeNK2gBMhhyNRlEsFgO2dLvdRqPZBDwPR48eDbQr60lP/jNnzgTWVrpdy77jOoWf096mvFCvpNNpZDIZ1Ot1FItFpSc8z1NyI/Up60w9I5MkcozLssi1i7Q7pScwP6eXfS6Xw5o1a+CkUojYNm644QZMTk4qu49lTyQSav6ybTvAtCULpVKpKN3I31erVVWHSCSChn/PkydPzpvbdHlj3ShDntdlYOvJiOW6lDo2l8thYGBA7RPIxNE8MHjtnnvw+tveptqWjIqAM8LMTOh6hO2qr/2+vmEDvrFxY+hv9D/Kred5+O0nngCqVezfvz/wG/2Zcj7UdYru0SrXhxwXMhGjTGSuMyd0Rl8v20LfX5L2U5jOkGNZtoPOTOjFUtJlRD4jbG9E2iVyH6vXPor0rF8qzEaugYHBRYstzz+PzT/4wdwGUchGkVLRnjcvXiw3jCTFmh4V6nr/N/4DMHLyJM6sX3+ea3b54MDb3obNP/whho8dwzs/85mexgMXOfqm3zzIvsNcH3p6//J78Rv+Hv79h44dw8zmzeel3m9EjN1111z/nWNMb96Mg7fffh7ubGBgYGBgsHwUrrgC3/mTPwmE5wrbZNMPBLhxxU1Fem4aj00DAwMDg7OF2cg1MDC4aLHp+99HbmwMxQuYaXViwwbsv/nmC/a8ywEH77hjpYsQiulNm3D4zW9e8LqBsTHc95/+U8BztGdMKfQ4PMDcSe2qEycwsWFD4BlrTp/GR/7H/wjc37IsjJw+jYlL5ODg9be/HWP33z/Pu0eeqoedYstNeellSE8e6ckP34PBwMDAwODCYHTz5kV7UxsYGBiE4d7RUbz58GF14G9ZlspZID0hI/7BRu74cVT83DbnGyN79+Itf/Ini3dE8MubOXQItW3bzmfRDAyWDbORezbwXaS7b4O0IknX0t3U+Z4415S1xRd/YcqjfL8QxU2PT6THG9J/J+8vv5cu6GH0Hra5dPm3IxFE/EQKbH+6rdO1X6fCSJD6QUpZNpsN0IRkchtJm4Lndek6Pt0r6ydAk/T5sHaWVMleyQEWotfq73t9thQKqOyLMCpAGGVA0hn1pA9se96PlEL+Ru9LPTh4vdFAfd06PP2JT/Ss70KUVZ0KRhq2/FxSRZLJZHcsIyhjyWRS0StlUHv5ezn2w+RVbnhJ3cGyUQZImeQrE5zkcjmVTEXSnbix5jgOstksMpmMorIwwVoqlULitdfwzs98Bs6xY6hfey127twJy+pSvklPq9frKJVK8+SfVBxJz08kEnBdFyff9S4cesc7QtuW0Ol7vXRbmA6Q/SRDZsi+5/1kSA4mJhh0HKTT6QBNMxaLoV6v4/Qv//Lc/fhclkmWKyh0wc/Ehi4AnBwZwe5t2zAzM4NOp4OfbN+OG9ttgNQkzCXImFi/Hq/dcgtarZYKcaGPM72N2BaS+sRwAwyZQP2SSCTUPdk+9AzK5XJYt26d6t98Pg/btgPJV6rVKqrVqrpHWDn0/tTRT5dLOpQcKzKhi9SVLH8mk+kmy0gkkEwmFW2N5SN9WNITJRVQPpe6nxQwhuKhdxVpoywHZYsyzTJxrFE3UB9QPmSiS84Rkg6vb4YvZQ7Q6cL9qIP9+mo56GWbLNXG6Pcb6kV+1msu6KVXzrbOC82j8v+F5td+tpMMr8BxotsHEV/ucrmcmleYpIryLj3n5diiDPM5YfTzfjbrQvbHUtGrrfQ2k/UI0x96QiZJVbQARTOn7pO6jDohkUggnU4rT0r5vcyALmn60p5JpVKBuZR9Q5uSNiTvLe8jk76wbfX+oE1g27ai7M7OziKbzaJWqynKtKSYyzkhk8koXU/dyPr82803o9Vq4TZt3sh885uAZeHmm29GJpNBq9VCoVBAoVBAtVpFo9GYZ7dJPU26cTqVQjQWw65duxSF3LZtpNNpeK6L6667DulUCgCQSqWwfft2eF431IBMrEb6tEx+LNtKhqqRG0cL0WSlzaGHbWr6bc3wS3qoH2knkso+PDysEqymUilFx47H48jlcqrdBwYGVBtSDiirwFwYKz20QiwWw+zsLGw/FAfHNPue7/WysrzyVX/fTwezPSnXDFXA0B5hnspSL/P3pGrrZVvM/MT+YNJVOY/LvuD4Y7gx9ken08Hg4CCKxaIKDyHtAmlHWJalQqCFfc6EWrlcDqtXr8bw8LBKjKfbH/DbRCVh9OavhS0EEzLJtpDjSibR5vUcb/l8PrD2zWazyOfzqNfrmJ6eDthK1WpV6cO1a9fOo+zLNZ2cj+RYo82TTCbx1u99D+sKBZxeuxa2f928jVy/rePxOJrXXYfWQw/hqquuwsjISCBJL0M2MAQaMJfgjfWNRCIolUqYmZlRYW3kWpPt2/ZlJnfoEKY2bgxlBCrWoP9q+3UsXX01Zh94AHF/fMu1GtvB8zxlU69evRqZTAa1Wi0Q4oLJ2ChvXDdTl3JtJ9tbX0uxHaT8UM6pm+TcJ+VVt6H5fA/A+Pi4uk72rb7W4zXsd5kYWL8/kyTroV309zIERlgonF4IWyvKuY/yItuAoXnkdWFzhd7e8plEWBhRzh+8P8ePHmJhKSFIF4LZyDUwMDB4A2D2Xe9Czp+E6tu3Y/bBB1e4RBcHTjz4IPa8+c0olUpqQaXH4g7bUNCNB32zxLIs8Ljo5Ztuwo927QoYwVwI5XK57n3L5ZWovoGBgYGBgYGBgcFZ49SaNfj7j35UHWzIzUxurA8MDGDt2rUYHh7u5p/xD3fON6Y2bsSzf/iHoYcZ+mEQN8Ydx8HAwAA29LqpgcEKwmzkGhgYGLwBMPvoo5h6+OGgJ9BZnAIaGBgYGBgYGJxL5A4fxt1//McAfBaO6wZYMKHMkO7FwdeQ9/Mg2TW6R7j/2dCxY5jatOkc1MzAwMDAwODcwWzkniWkezSpLb2okeeCrrZcKlsv6qP+XRi1ja/SI02n3fNVpyRJN3Pper4Q3VC/Jqwd6NUWj8cVBSKZTCoKpk47kLQbvR0lNYAncaTZAMFsuSq7otXN2J5IJLrvAUWBsmwbljeXeVhvK1JipDs/MwgvJDvyVX8v/+9Fj+rVr7Jv+ar3maQGMLumDJPAz3RqH/uEiSJId5D0eP6etDnSRIBuFtcwmegn12wLGVIjGo0rrCY5AAAgAElEQVTOo1qrUBniBFb2TTweh+M4yGQy88IqSFli30mvS26aymfJ/2UdWA4ASCQSKmyDpI5JCjnbnM+VGaIdx1H0q2QyiUgkojJOs10ogwAUncl1XTSbTfUM9rUst+xb27bVPcvlMkqlEur1esCrVc/Y2W989wrJwHGrh02R/SwpTqS1k0ZLWh29A2T56/W6anfSYBivVZdTOZZ70QEl9VCG3iDVR9LgUqkUMpmMGjNsc0kLC9P50nNAZupOJpPIZDIYHh4GAEXrZJZw/jaRSCjK3eDgoLpPOp2G53ldyptP/9PpdXxl24SFwdBfZV/10ueSlsX+o87VqVzpdBpDQ0MYHBzE0NCQyhAt5TsejysqG8sq20tSJfl8OX5IXWTmYWYV57XJZBIAApnl4/F44KBEzRtirOttxr7vRXFfylyg/x8mO9RL5xNh9w+bf5by/WKeFVa3XvPhcu6/mM8Wsp169b98r1OTKbfUbdRX2WxW6X7KI8eqX3n1TN1WlTK2UEgPPSyO/j7s/8W26WLsEvkX1l5hYSlU+/kbefQO8zxPhbyScxvbkDRs3f6TNFLZNp5v57muG6ACy3AppF+zD6VtQLBPeV/Od3yG4ziq3LZtK32UzWYxNDSkQjpI24asD4YYYhZx0uF5HcO7kNIs9V46kwEA7NixQ2WQL5fLKBaLKls87Qd5P6lPs9ksHMdBJBrFli1bAvqY4SK2bdsGx3EAAI0dO5Dw/NBGljVvjmm323C1udjtNlS3vRYnhMF/Qy6hRJ9Zvx6jN92kbHTKqb4OYVvn83lks1mVvZ6hejgfMAyP53lIp9OKest+57wHQFGQaZsxtALg6wW/7xzHmdcPwJw+oM3I7/vZYar+2pqVdjT/WPdWq4VGo6FCbUgbW7djpD3O8oSFGZBlkHMibU7bstBsNgM2gu5NyVBouVwOmUwG6XRayfrg4CDq9bqy5/XQJFJPhoVWkOEl2Odr1qzByMiIsp0AqHvXajVEff0zODgYWPuw/LFYDLZlYd26dfPWKRxXUs6kTSlDwwwODqrxJW2aZrM5LxxKpVJBOp2G64c30W0i9m0gRATmbDI+V4aeswBcd911ag0ibSTS9NPpNAYGBpDL5ZBIJNBsNtUaRYYfYH9RRqXtL2WC/8vwCrLP5LqW40eXMdZJ2qBSp3EOkPOlHCcMTwYgsH7gPaVcRCIRNV6np6dRKpUwOzsb6J8wGWC5KKtSHj3PU/OAbp+znJQVpWMsS4X8kJDjkeNcyoasO5/D+YNrBrkfELZGku3ez/6QbRwG+Z1cR7CN9XrIMEy6/uv3nF62Stir1FVSHsPWj4upYz+YjVwDAwMDAwMDAwMDAwODC4rU6Ciu/dSnkDpwAKVbbsHhz30ucFAtY422Wi214SE3KfsdKIZB/67XITJfbdtG7BzW+WLHna++itv275/7wLICsf25uaU2cjoddMRmBTwvkEOAh0rKY1psIElv6FBYQa/qDZOTOLV69bmtsMFljf233goAeO2WW1a4JAYG5xZmI9fAwMDAwMDAwMDAwMAgAGf/flz9+OMBRky704Grs20QDHFg2TYidjdDvfP662hed928e8/8yq+oTdXqNddg+oEHLmTVDHrg1v37sWFiAidGRla6KPNwYmQEu0NkyWD5GD5+HO/9y7+cN44914Xbw4tQeVOS1RGJYOjYMcxs3rwSVeiLV++6C6/eddd5ZyNditg8PY3/8/nnAQAvXXklvrd9+wqXyGApMBu5ywRVQS+q/GIoamHUyHNFXZtX3hC3+bD/dWqbfK/TE/T/5Wfy99KtPMy9vBcFUZZHp+XwPakdPCEm9U3es5fbfhgk9UCnNkrqm6LRw6fECTqcJU+uBf1O0kNknYD58tCLPrtcmehHmwijLwLB0Ar6/3p2Rikfepvx5J7XMKM2MybLMSRfFSXC8wKUp14eFzptQ2bAlDRDSZfR6UGJRAKZTCZQB4YnSKVSKvA9s6VSJngtdQBlQfa7Lnusp3wWyyYzZvO+nU4H0WgUzWazb5Zo0uZZRlKB5D11uZH3I41cp8TI0AqSJkMvGUkHlDLEPmef9hr/OiTlRoY1ke0t30taPsNh5HI5FVYhk8mEhlZoNBrI5XKqXSV1UtaX3kGyXrKseuZVlkFeTzlkFnRmt/Y8D8ViEalUSlHDwjyNdPmWssts2fl8HsPDw6pMiURC0WhZb1LcGDKE90wmk+h0Oiq0AtuDZWCIBr19dDpzv37V+5btJUNEOI6jxo+kdMoxmsvlkMvlkM/nVaZf2UaxWEzJMal6st10WZYUWRk2RVLgpOxI3cV7MBNx2Ji3MEefljYDIamc0paQOkzek+/DXvu9D+uLc4WFFkmLtUMW89uwcp/LuvR7dpiNIt/3m1t1G0nXMfr4oQzo3oEA4DhOYJzL+ZMyL8sh5Yr36mWbLlbmiMW0/XLtzrD2AzCv3XrZoBB617Ks0NBK1KOO4wT0D+umZyqX0PWItJuo22RooLA269eu0n7iZ7RT0un0vLAKfC7nIepU2j/UU7LMrAfBazof+AA6Tz+NuP9cD0Ck00FEt8dk30ob2C9H+/rr4b7//RgaGgq0V/NTn0Lhk59U97FtG8O+bqW8SrpwvV5XbdDwEyTJ0Ec6nVdfqyw0L0m6tgy7wTlT2n2Sek8KO8MbZbNZReVPJBKq/6WdkkqlAuEOpB0hxyDlhvMfw3dYloWob9MMDg4GwpZJe0WG6CDtWW8jvW08z8PxVavwV+99b6CenIfj8biixTPcA0NtSBtV93SWtirtHN2elfalDFMkfxuPx5HybagwmXccB+l0Gul0WoVVkPZ/NptFs9lUoRdYbmkvSFuOtjGfRTlg2AyGChgYGFDXRSIR5PN51Go1VKtVJH0dvWXLllDKu5NMwrJtbNu2TXmg83vaNKwbQ6bpcijXMPq6QIaukzZd7b3vhfNP/4S05wVCjFhAdxPX38yF/r0/zlWoAdtG/dprUXvgAWzdulXpVWl/yfGSTqdVuBd+pq9raA9RLmT/SL0l+4zh0prNptL30t7qt+YIW0uwXTn+dZuMv41Go8hkMoEwBFLm9c8YjiCfz6NaraJUKs0LV6PPVQxpwrEu5xnXdVGtVgOh8fQQHiwLw1ns3bUL0X37EPM8bJiYQCwaxZ4771TyJsNz6GEK2EYy5JpsDwDzZG4p6wR5H/1/3TYJW3/J0JayzCyvrgflfNFPTnqVR84dnPNZ57DwMVK/S7tuqTAbuQYGBgYGBgYGBgYGBgYKtQ9/GLUPf1gt7DudjtqYkhvI+mY2F7OMn8mDSwMDg4sLE+99L068852h45gxcqVzBxA83OL45oGFZVlY3paUwYXGj2+6Cf96/fVwXRe/+9RTK10cg2XAbOQaGBgYGBgYGBgYGFwSyP7jPyLzzDPdf+gpRk8a8Zmzfz9qhipqYGBgYGBgcJnBbOQuF77BKF3JAYS624dR1BaiVC2HIjm/iP1pbGGf9aO1AQhQE/pRBuW9SA/oFVpB3i8MvWh9kiaru6jr91tsaAXeW2bK5O91Gj0sC5ZPn7Hk592bILp3L0YefRSeHg6gW+G5973K0ec7YuZXfxXTjzzS8/vF9DXf9/rr971+n27V5/qB9AJJJ7EsS9F8ZLZey7JQq9UQiUQUVToiKGWUL53WLp+rU2NIiSNlktRxeb3MpEpakiwT75FMJhWtieEKFkMvlXQJKb86rVG+6rROz/MUBUnPgCmpJZKerlNNwmidks7H79gWOm2c1+tUOVJnJBWdtHae5JMmRI8enWYi6UG6DElKXzweB4BQuowMGZBIJJBKpZDJZJBMJlWIBRkyhf3XbrfRbDYBAPF4XNEEWW6GDegle3r78zWZTCKdTqt2Y/10mcz4WcHz+TwGBwdVaAVJnZLtrlM+STMk/YnhFXgt21ynq7GfGP6Bn5EaRjkjJc22bdTrdcTjcdRqNXVfPbtuL2qSTpXUaauk/5GaKmmDLCv/WM9MJqPomXrIDxnmQJZDZoMP+40cO+xbKQOyz/X60oOFOiZwPYK6QMo+y0B68GIo77pNobfzQrppMbbEQtcshnrW67qz+W0vLMU+WsqzFrKl+tlPwMJ2k35tGP2O76lnYVmIx+MBnUsaX6AdvCAlOAy95qHFyNJybdJ+bdqrHeX/a55+Gon9+1GVm7Qhdaxt347Cr/5qYGzLOUDakdRBMmM566jow9p443083xYEME8nyN8CvW0CiTBbV36XSCRUGBy+l3Oz9JiT4Xd4vzBdptvMun5kPTnH6lR+/Vppk/CP84msR1h7yTmPoQQ6nY6yBW3bVvaibdtoNptotVqwbTsQYkGXNf2zXvMS506OLc7n0lbis6X9GI/HkclkkEql4DiO+l4PjUJ5c10XsdhcGjVdxmR7SBnmPBGLxZAeHcU7P/OZubWGkKOOoA/LWKeep1HltbGzbmoKp9aswcDAwDy7hbbL4OCgkoNyuRwIraDLhZQ1PbSCnL+lHOre3dLmkeEddNuG/cVwItlsNmC3055ut9sqBAPDQcjnSxuFc7MEZYNeqel0Wj2TNhPv3Ww24TgOPM/D1q1b59nWkUgEKd9evPrqq+e1G5+nh3/S25V1l7Ijad6O4wQ8a8NsTTmOJW2+V0gxWSZpn8jQCHpdac9Lnayv3aVukuNXL6MMBwcAiUQCtVpNrSNZN8pkPxte1sdxHBUuJZFIzLMNddi2reRM1pm/0duBMpVKpdBsNgNllpR8WUaGj5Rjht93Oh1UKhW17goLb8LyMCQKr+NYWHXiBP7w2Wfn5M7yYyBT73nzY6F7nqdCcMj9Dfi6fl4sdf6Jaxe7LwI+27+/+l+zjW2rG9aHZYa4hnoyEPu5hy4MK0ug5625RIxsL/V8v3yBNgn7rX89LAv40z9doPbzYTZyDQzOBq+8AvvtbwdeeQW48Ub1ceuRRxDpdPpnYj1LOH5G2X4buQYGBgYGBgYGlxtq27fj9f/+30MPjgi1oXChC2dgcAEwed998Fz3nK81Tq1ejd07d57TexoYGFy8GL3ttrlNWoNLBmYjdxngTj4ANJvN0BMeYjGeMmGfna1niY6z9YTQP+vnMSHBwPq6R1vYvXSEnezpHgM82VanGtpptjz9kvcMe5Z8z/vI38/rvw9+cO4GN90E77HH1HXtj38cM+9/P5rNpjqh5gmaflIdFgQ7rC11T8hb//APgVYLZ86cCW2/xd5vMbIRBt27hCeZYaeuhIypRHmQgfBrtRpSqZQ6lYzFYvA8D6tWrVLtxOv1k2HpSSG9KeLxeCBZWdgJp/T444m1vCYWiymPBCZCCZMl3VNnIU+mXvqBsi5Pbpnwi55Xehl1L2jKsPLgCuk/fXzIE2RdJsO8doDuiX0mkwk8L5lMotVqKc9WKft6wHu9raQHEtueciQ9enQPHhnknh4y9I6QiS7k8zyv662Wz+dV37bb7YBHrix3WOIO+XyZ2EQml5EeHroXD700mMArzBO4l5eK9CCQ8QhTqVTgmjCvGOnpKtuRbUw5o45i8gjHcQJeN/TWkAln9PlA9q3uKS4TqDiOozxuZfI81pHjUCaWofeL7nVAzywAoWNFfi77U/eeo7yFeaPocxrrpydRsCwreMqveePwftKbUp+zwnTHQvZF2L3CcK7tj7B5o99c0qtMuhwtZX5aDvrNhWHlCLu+l90U9r7XvXTo3oL0/OB8K+0j9p3ruspDRvc2W8qzJc61XCy2P8PamWO7UqkEyibtEunJyrFJ7zpeRx1KW0EmRZPg/eQzCDk3hs37/X7bD/qcKD8n66PT6QR0tbxG92SUdouum2Xb8pow/Sj1XDweDzAx9LLr8xN1um7H63aAbmtIL1t6o1qWpbxDq9Wqshn1RKG9WIB6WcOYItLbkh6XeiJaOY/S9qA3rvSY15kV0sahvSX1uq7/+b1kEtm2jcZv/iaOf+QjaozLcW5ZXZZbvV5Xc3aj0VDXyevD+t+yLGzy5YZ1JNuJzBkmFCKTSbKwdHtAtzvoSSm9C8NYrtJmYDvTIzeVSs0rt+6FLpODsV2ZbIsyJNcisi/k2A0bD9Lu5PNc11XxoMk+bLfbKmna+vXrQ+2xeCwGy7axevXqeTYfMOcBzrGky4j0gtf7kTKm2/ZcH+nzA3+nM9J0yDKFyXkvPSLtV8mA1XWY1FXsA30M0+b1vK5Xb6vVUgl7e3nk6m2r29Ncb1B2ODeEsTJkvZhkOqx/9bbl//ROp5d4P53KtpPeuryn67qqzv1YhKwnn6XYCzt34sAnPzmvn9ge0mtbloesxjDPbSZxY0x1enizX8IYj/qf3gYsm9SHco6RCRllO7GP6SHPMkuWq5TzXs8PK4Mcl1L3hMms/I0+13xinlQtDLORa2CwXHzyk/A+sZxhd+6QOXgQt/67f7e4i/VFE4BT996Lo7/yK+e+YAYGBgYGBgYGBgYGBgYGBgYG5xRmI9fA4BLF6be/HWvP4ve5Q4cAwGzkGhgYGBgYGBgYGBgYGBgYGFwCMBu5y4Byefc8VCqVUHdweW3Y+/NdvqVcczbvdehUMLrdS9pMP4qTvIekEfZ6vnJz154bRuldLHRak/7ZQvfjd6wvaQcyqDj/ZHiBXtQCIDwMwulbbwVuvXXe50QYDUbigT/7Mwy9/jpu+fSnWfBgPeZXTIWwUN+TPsM2ErQTBki3Rf8U3/lOTL3vfeHUYwDpdBq5XE7RHZjYYtOmTX1lR6cW6aEVJNVCp1nL60mN0al/kjoUj8d7BrvXyxRGs9Sv6feZlGfSwsJCceg0MEmh1Kk98hlyrOnlDmvjsM9JPeQ1TLZA2r2k3ofRtMLoY7JfSDthf+r6QLaTpJbpScBkogtZH8dx0Ol0FFW13W4jk8nMSzS2WLnjc0i109tWUoEolwDQaDRUaIew5JASOqVHyjCfK/tep67KsoQlzGA/sO6u6yIajaLZbKqwGSynHjKGf1L/SDqapNzqtFsmlGD4koX6VqcU6/Xr12dhdFL5PHl9v/A3+nd81cM68Dv9urAy6OXt9dli9Ee/+yzmWecavajN+nvZf/3arNc9l1KGhX6/0HMXKt9C9yHC7A6p/wM6H/PphWwzNS49D/A8RWnUExLKObUXnXAx7byQrXEuoIcmaLfbgGWhVCrNC3HD9zKMgvyc9+Hn1NnUo2Hzey86rZzn+1GwFzu2wxBmf8q5RpcN/bmSNizvo9snYXRjnSKs02rDbFN5vWxX6nTdvpIyp9OOCUk/ZWiFZrOpqOG0ryVtl3IdJud6G+lzkx7KhxR8mXSOvw2bx3itlL2wttXbNKyvpYzxOyZSI324F/29Xq8H2kSGt9LtG/nMMNmRiUkZQoK0eJk0KyxJq7w35UEmSu0V/kK3bXUKswyXJssubfZkMqnGCsEwWLQt9ZBtsp9kOIKw76VtIkMtAAgkhY34IQ8GBwfn2Sq0CS3LQj6fDw2HpY8nWWdZdxmqQ8opr5H1YDv1Cm3QS0YI3RYNm8t72WcyBJysTy+dI8sv39OOpl4Io/ovJjRf2LqDYRVoy/db+8n5R0LO42Ftw+fJpJX9xsH/39697Eh2pIcB/qPY7CY59KBNSQttBLippS0JJGdhwAvBIg1DtgFb4sXWzhbYXAreTIMLrwfkG7DnCUTyDUg9gTkNeAzINmy2tTEMwxKnDQ1EimR3eJEnqqOi41wyK6vqZPb3AYXKy7nEOScyTmRk/BER/Ukbc85nhito607tuW+/Z/SOJ+LxUAy9Yd4i4ky7Rvud5Ztvvomvv/46/uZv/ia+/vrr08mxy/fDtjxqy42xelJbJrTDI5WhbXoTY5chZuphYMbaYdpzXO+/d3+rh3OsJ+eur0F9z6knyK6Hk9uGhlx4St3/h//wUvd3/c//PP5OzvFXf/iHl7pfAAAAgGOgIReeUv/9d383/vvv/u7p8/oX0Yjx3kH1r0l1b5a6B0P5dbFM+JRSil//oz/a+8y6AAAAAE8LDbk7ePToUTx6+DByRPzyl7/shsG2YZW9ruK9cLx6nWIsnG2bcMA5c+GOvdfbBr66ca9u2GtnMhwLqY44G+bUhpDU4Qm9EIUzYf97CGOrw5mmttkLj4qIM8MmlLCDhw8fng6zUIdAlPPThj62YcPl8VjYwdw174V8RJwNWyyvteH5JbSiDdOrr1UdBn/jxo3TWeVPTk7imeHavfjii6OhIyUkpoQ6lGEOfv3Xf33081IfR5tv2tD8esbhNjSqPoZ6P20YRx0ONKcNC9r22rShML3PQBvu0Yb/9EKdevsr6hCg3me0l+eee+65033XIUJtmF07Q3L3czwSNtMOrdALO+vlxToksz3mEgZTQgTrWU6nwv3a61Ontd1/LxynLSfrtCwJMeodb/3DSjtr8Vh4VR2qVaerzgfPPfdclNDu8pnuhWgunem1vbZ1qGQdytqGvPaWL+dwLAxtLC1jebneX/t6Lx/UZX/5q9ety/TIOb799ttu+T5Wtm1zD9/W0vvhZWnPfbmuvXM0VYfqnbNd61D7Wqc1VtbXeb3+bNczjteh3W0ZX85XO9TJ3/7t356Znbn+7JZ12qGLep/n3vnex7AKY2HC9bkpy5XzUurYv/zlL58IbSzlSFv+1eVb7x7T/lDdXqMxY2Vq/f6Se++Udv06zXP3iPpYy3u9um0vhLndb13ejZWL29RJxsro3veikv4XXnghTk5OToffquvTdXhvXXaMhYbXn7eIOHNe21nsS122HHu7Thve2xvOo913mz/r19vl6nNQ6hZleKFeXSWlFM8991x3aKte2drub6wsaodCKmVKOyxUm6faPNELZe6VJ718XA8nNbaPdpi1+pzWQ0eV7za9/ZbrWx/LWD2q/k5UyqHy+Sz7ieE7UKusH8M1m9rXVD2lLX/qZXPOT4T4l/S1x1iXCb17QL3vXpk5Vc8q748NxTD2v05Peb3cF8uweWXIgFKXLvfBXptMu486f9XfG8uQZXNDK/SOqd52+1pJR1mvDGHWu8+O5cVWex+f+mzX9YX6/LbnpRz3WFlf1ynasvbbb7+Nb7755nSoi3poybH2j14dpD0H9ees/K+HXCl1gN5QOqVNqh5ypj6Gue999Xms6w3t9862LaF3HdrvQrvYbS0AAAAAAC6NhlwAAAAAgJUztMIO6q7Xf/3Xf31m5u42dKXXVbwNU1gy5MIuIZe9bvljxrr9T4W31WFqbdfyEs5y/fr1M7PHlvNX64V71WH8Y13c67Sm+vFImNg2xkLPxpat/9dhAL3125DF8jc2o3RZth1moT3GqTzRhnGMHWMbTtV2/a9nSS6hQ3UYQbnmZdbGMk5u2U6OiB/84AejoYBtqMW1Z5+NyDl+9Vd/dfS42/M8lu6SN6dCak7TORNqtk0IxJK8M/V6HRJVjn0qVGMq7Gof6WiXzTmfhpCUc13KwzbEcdvZQMs261DHko6p42/LojrMtlaWK2FTbZhi7zPXOy913qnLwt5s1W052ob69sJ6xo53bP/1Nsv5mgp9bdNV9plzPg1dLKGQU7Nez5XT9XH30lw+pyUksZcv2vM89XmYSstcOpes1wsFLa+Vcuy7776LPJyfr7/+unvu2vPXO59jeXEsj4yFvC+pO5xn+YjlZU0bctzmiXIf7IVIt8fdCw8873EsMVXOtsOWlMdj99c6VK8eeqXct0oYabu/tsxvwxTLjPIlpHBsaJT63PaGwZmqf/Seb3O+xsqkXj3zdDiBlE7vNW09sr639Mrk+vzXw7Tscv+sl2nLpCV1yCV66anD/JfcE8fS0ru399JbL9ce51QI6lxI8th6ZZtl3fL8xo0bERFnhvHqDU82dy+dqzfWn8VSv+0Nm9H7DLf5aWyfvXM9dh3qdeqQ4qnrUELOx8KX565FW1bVQ0iU+lV9vttzPlaXroccWFp/aK9RffxTy9dD9BV1fbKct6m8Mff9qs3jbX015xwnKUWkFDdu3Oh+3tKQxvp7ytgxjX0+y77H0ln/L8ddH19bbi2ph06lqd5Pb702bWNprtParlfq7hFxOrTCs88+2x0ebSpP9vJMPTzbkjJs7N7RK2Pr4+uVqXPbbvNrqR9MXa96n2WZJWVjrV2uNwxcWeb777+P559//swQm/V3/LnvW2NpqtPW+95VD0nVHns71ExviJf2OHrnsP1r622l3jKV5yLO1nt3oUcuAAAAAMDK6ZG7o0c5R3706HQSiXbg5vKrZ+8XofZXh7FfIXq9bsaeLzX3y2/vF4+2l0LvV5DyC0T9i9PYL/RjPUra/dS/qrS/jrcTUwwbmjzW856rbdar01961Dx8+DBS2gxk/+23357+Il16x5Rl2h4x7S9EY5MIjP1a2ns+1tutN4h479y3x3ZychLXr18/7ZFbT3Z2/fr101+bn/n5z+Pmv/yXm/S2aS2v5Rw5Iv72D/7gNK/88Ic/XNR7oD6eXi/JurfhWN7v9chtz9N589a26mMa+5Ww9xnddV/bvlc+/+XxXM/WJb849/Lj3C+WU2XU2C/l5VfblNJpGTY3CVV7Lnqfn7FeSG0+rXvPPnz4cLTnde+X6bFzVp+ntkzvna/2tfozkHM+M2nH2IQAS3v4TP2S3fa87qW/d25Lb4TeMU/1FFtSZtav93q/9ZYvvQ5KT8hyrr755pszE07VkyzknM9Er4z1ZB+rR5THvfvDksdTr51Hm1frx73Pet2zondOevfE3v9dj2nsc9J7PpdHx+6tdf4tZWevV3qZrKPuidT2tO7dx9vPdb2vR48enXnc0+aRqV4qc/mpTWvb42tq2bHzdnJyEtGpJ9YTjbRRQ3XPqvoatX/1+Vua1rG0X6S2PjBVzvXyxFieXvJary6y5P40dhxzr7X7KnWNMjnQWBRkWaZN39gx9T6v9b2pRPb00jpVX5m6p9Rpm7pXt++3k9/2jrHUKXr3kLL81D2w/K+Po+0NW5YZ61U7d/8dKwVfdaYAACAASURBVM/n0lRfp7n91MvV79c9vZech3Y/U/m8t/2UUqSf/zxe+P3f7x/jz38e+bd/e7KXcW9f7TJTZddUXak+vvb53Oen3Vd7zqbKkrm0tc/rNOacn/j+0UaazJUFY/m8/b7bK0enjmfqOGtT5fjYtsaW2+Z7w67Xtt3mVLtEuef2er7u8h2i3m+v7G2jKXpKGpZEFc6VRxH98rFtb5g7jvPUGTTkApfi+7ffjmsLC+ln/vN/jhs5P9HYCwAAcCgevvNOPDPxfv6t34pH77xzaekBDp+G3HP44f/8n/GP/sN/ONuKXx5HnPYsjLpBqm2c6jw/fTi18117zUz9Mj6yXKqep+r91P4fHp/+unByEif1rz5z+y3bqh6nlM48PvNXfnk5OYmTP//ziN/5nSVngCvy8N/9u/j+3/7byd4D5ZeyH/zzf757HgcAAFiBh3/8x/Hwj/949P3T3sOXmCbgsGnI3dH/+cf/eBMu8OhR5HjcGJkjNo2eOW8aIIeG2tOw8ebxE8/Luu02Wzt2wa73Oba91Lw21dDaa8Atr52UsJ+yTq9hbqrBuNpHxJMNuSfV40e/9Vtx8m/+zc5d059M1u5hdRFxJizj+vXrp9t6+PDhaYhWCRmtBwCfCg8bCz9aEorRPh4LB2uf12HO9XAWvbCTNoTxxo0bcf369SfC0ebS++jRo9NJASI2+f/5559fctpHw696YVi9sJ25sJXzhD+c11RoTv36PtJXhzAt2V4ZHqCe/GIsbG6bMJ5evlyS9jZ/j61blm0npRj7sWEs37Rprfe9zfJTE5dMhUi121uSV8beq7dXwtbq81PS2JvYYEmYbZ3WiHiivOmdtzZf9IZV6J3nqZC+qbzde31qW+3jcn5OJ0IdfuT99ttvTyedKkMr1BPI1RNQ1feGco6XTIo2dz16r/VC7JeGuPX0Qgfr18fybH3PKeXI1CQ6c8c1dXxt2qbS30v31HG0oXbtfbUdLqrUC+qJWuq8XofaljpWVNtpz93pBI4pRY44Hd6ozXP1sBVTYYa90Mmx8907172w5t657Q35VIe0tsM9pdhMnlqHvteTJZYhnm7cuBHXrl07Hfap3n5vCKaxfDCnV89p88q+tOdw11Dsfe177l6y7X2o93p5XvLBo0ePTidGXlIeTu1/qs7RDr0xdZwRT96f5iy9X7fpnRpWoV5urF6z9LvDWDlX1wvqPDj1/aS9b49dn7E6RHts9T23t5+IGK2H1een3sZYvps7nvY6Tg0/MraN3ramlh2z9PPWfnbn6lFTx79LWnZZtnee23pqPSzRkrKgVwb0ht3ZJf3bnKNeWT52v+xZ8tmeys9zy4/tc6wsqu/XvfrbNuV0L11tmduW371zOVaXXPJZ7aWh/t+mY8m2zltH0JC7o//1z/5Z/MU/+SenX8zK+B/1WLl1BXnsi1e5wFOz9U3dFLcxVRi0lenyuPfBqG/i9ZeRMl5qqVBfv349bty4MTlGVa/g7BWiKaUzlfTyReX69evxwgsvxAsvvLC4sQ8ALtMP/sf/iN/59/8+8lglMoZ7ZF4Y0dM8Pn1Y77T3BbmXuKXLLfREdbT9cth5L1XPU0pPjJle0jj2w/DOEUxLvpzNrJOa188cS+dxSunx87qOFVW9qzQipcfRRycpxckzz8S1//Jf4uE/+Aez6QYA4DhpyAUAuCB/+cYbm8bEc/wQC8X3f//vx3d/+IdXnQwAAK6IhtwdpJTixo0bZ2bJLTNRl964dY/cNiyw192/1yN36vEuaW4fz4UDtT1k61CaNuSgDWurz83SrvtjvX/bWQhL+Pazzz57Gro/Njvheezazb2kv/RKLj2Sy2yavV7bdV4ZCxudm418yXFMhRvN9cBuZ9puh1Yo16T0zL5+/fqZ2T5rY72zS4jGmXRFnIZDLjGW1+sZZJcsP7X9XfPGeS0Np9pX+paGErVlw1yYylTv/N7jNuxpSdhQm5fHlq3Ljl3L216Z2m57Ko0Rj8uH+vi2+Xz3ttkuU7a5JMSrPK7DDesQql2v7VSa23Jn6ljH8svUfpe+N7XsXChiXU6Wv//7r/5V/O9/8S/iu+++OzO0wnffffdEJE891E7vvjA3zMBYCG0d8j6Vz89Tz+idk97j+vyU19shCK5duxY55+6s61PH2TrPsApj94ap/DsW4leXk3V9phfNVNdtSuTRc889F9evX4/nn38+XnzxxU09qzp35XNZhhPIOcfJM8/Ec//1v8Zv/8mfnPb6jqoHeK/3d0T0e0CPvda8N2rsM9p5f7JH8/D/xb/4i/jlb/5m/PCHPzwz/FNdF+2dy3qop6nrdF6XVUdYUvbtOy298nyuXnWeNLT11vK5LffMsfrGLvfwNr1L88Y29Y7ePrdRb3+sLO/VuXc5L/W22mNr07G0/lS2t886xFz6p+oTS59P7W+qTlLOz9LPant+xpbbh7F9lfeWpnsuTUuu6a7qunZvaLex/Y9tq/xv6yVL0tx+Jpfmz/o8z21jrLyd2sY225pafkn6I84OU1LO31TdrX28ZJ/t41653VMPsTh3z9jm3CzZ90XQkAsAAEfk//3+72++iEw0Zh+qX/7mb8b/+b3fu+pkAABcCQ25AABwRB68/Xb85R/8wWkP8HZi1aWRY6VXytiEZ+3jnrkedb25GeYiwlJK8cwezhMAwKHRkLuDk5OTeOGFF87MNl3P+FseL60YT3Xt3jZUsE5ja6wrevk/VpEu26tfO61Ed4ZaKEMd9IZWqLvbj6VrqiLfDrFQhnG47K7sc0r6yrUuz0ueKCG0ETEaKhuxv4nv5q59/d7YNSjH1Q6xUA99Uc8YXfJBL0x6LGSoHHtK6Uz45jPPLP+61vvC2IZcLFlvbebCnOrlLlsJcVwyS+9Yvl0Ssrb02Kby+Nj263CbubSOpXlJaM9cWsr/Jdd6bN1t0jy2TDn29l4yF0K5NIRurNzp7XPqPF+WsWvShvLV4d3PPvvsadnehtc/evToTANbO7RC+1e2X0+e2htqYOyeMTbp6L6GVpi6xr3GuvK/d68p9/iS7vYc9I7vPMcx1sg49njsc96rr7TXvT6+dliqNu+cnJycGVqhDA/Qq/eU81WWiYjTfPb999+f1lXbBty2blrOXe/51Pne1tLz2ns+NiluXQ8pj+vhnsrjsf0unZl8yXH16rr7VG937F4xlo/3nYYl+9/XviIef85O64rnrCdP1Y/L47qOsGQbF31/Wnq+23rN0nJyrEwc+3yW17bJ9+1nZRttXWlu2akyfm4fuxjb31zY/ZLP7EV+lsden7quF30ul2x7m3D5Jdsr/3e5N+xyrG0+3iVvLL33LLl3jC2/VHtvGKufto932Uf9uFeXaPXqNb10bHvv6KXjMux/YFEAAAAAAPZKj9wdnJycxIsvvvhEmFrp8TI3adVF9LicMte7pDwe+yWj7TVT//o11jOz7p27za8avV/dy+u9fZWeLBcx2dl5lHSW3h8nJyenvWDaHlV1T6m5fLHPX4zqx2PXvH7cywO9a99OPLLtr+Gn+61ee+aZZxZ/Hs7zC1m7zNwvpJdtrvfBVaWx5IOxMm+X7bXPtz22bXo21Glvf63eNp1L0zy17ra9cev195UH2l/Uez0eil17PNSPp3r6rMXc9Sw9b8vkjOXcPfPMM/Hw4cMzk5u1dYe2PtHrgTsWudFG9tTXY8kkmfuoayy5z4z1yB3rYVnSPzWh0dgkbj279D6p6xa9vFr+zw0N0JsstGy/PK/vm+2EXaU3aT2JaHsOIzY9cm/cuHFa/2h7ebd1j6ne3fU53Xcdda4MqI+tXq43mVwv79T1kRIdVOqKU3WgfZefl+EielQt2efS3pf73n+JACppuMhe+bvkj8u4f5VjX7qf3vJz6/fem/ps7lJvqdfddp2pdO66ran3d6l7z53vqXO8JmtMU1GX/+ft9dmrw19WffQq7j0Xtc+xvL7vNq6pcrv93LbPp3ps7+PecVnW1foFAAAAAMATNOQC6/Sf/tPmDwAAAABDK+yiTHbWmyhi6q+2j5D5pZaEPM51T++FF9ShhG0IYR0iuE14ci89Y2F0bbjimpTjr9M5NrFIHRYb0Q9r7DlP1//ee3Pnvn4ecXZYjbHw0Z3DUv7ojyJKGMS//tfnCm05TwjW2vJVxDrTFNE/z7uG2rXb6z3fJT1zy7ehP+cJURorW+e2cZ5zdhHmwqMizl8WTZVBS9a/Sm16ygRL5fUS2l1PcllPNlXC3iOmhxGYC4EvekMO1K+PPV/63jbXpldfaF+fur/XaRkbWmEsvfsY0qWX1vbx0vvnXB2mHXahnQytnrCrHlqh3k895FSpg4xNvBvx5LAbU8Pi7Dsksj2PSx5P1Uvrv94kvPVQC2NpWFvZMqf9jIy9f0z7L/efEkbd3o/2Ha67zb2pt/5FqvfTHvdFpXPbe/Pc8BtLh+fYdn9Ltjl2/sbW26UOumt62M7Y94/atvWa3vdelpm6ly8tE8a2t83+tn2+z/Jo7v64LxpygfW5fTvyu+9edSoAAAAAVsPQCgAAAAAAK6dH7g5SSvH888+PDqkQMR0ev0sY0JLl9hl6vDRcsH3eC7PfZbbIqTSOhSauSZumMrRCRD9EtJcnLiqUsVgaCt671u3y7fVoX982TfsOLxcWc7FKuMx5Q0u2Dds77zbb98+b7+ZCeLbZxnlmcd6niw4lnQpjXbs239eh8aXsu3bt2ulQCmUYhTbMfW4Yhan7w5L7xb6GHhg7B3OvLwlVbu8nvRmotzmOXY9vl7DauWOZeq13/0zp7PAAZUiAMmxCr87TqwvV57AeUmGbITrax1P2EWI/Vy+dOp+9ukd9TtphuKa2f0iuOs2Xvf+x+kablvPWO6byyjbbuQxTx3CecOFt9rXLsr33dwmt3na5fa13VdvlsSV5f2q5qe0dWp10babK6ql1dtnPNtuYun/sa1iupWk5r3W1fgEAAAAA8AQNuQAAAAAAK2dohR2cnJzE9evXI2J8JuWls/1e9Gx2PVNd0KdCldrllgx7sM8w5bF9rW1YhaIO86tn2Y3YbviEi84jS0NJ58Jje9dlH2FO+w6R5WKMhdDsM6xmLgzvvNf8KkIPL3Ib+yT0sK+kv+TDEgpfh8i3Qyi0wzKV9XvDKIwN0VS/tk0ZeRnD9CxZbiysvb6PLKlHXXYdai6Eb+z53P+6vlAPUVUPD9A+bvdT6hnFXP101yE4LnJYjqXl/5L6SH1ue3WSQy97nlYXHWY/tv6h5pf2HnXe7Vy0Qz3PXK2p+8m+hnhke+01uMjzucu9oc0ba/keudQ6W8AAAAAAADilR+6Orl3bnLptejZMvX5VdhmIftteEfsw1ctln/vZl7onzdSENe3j2lX3NBp7fUmPo31el7VdW8b1Boq/6F8395nHzvOZO+Z82l5XvXOfVPf+K+V9Sum0923Edj0kl/S23TbCZ5/3lPPWHXrv9eoTl91LdM7S++TYe7vUn+rJuupepVO9mneJALrM/FNb8rlfkp/a87m0TnLI5c7Tqr4nzV2/80yetevya3QMxwA9pTxo8/iuvUCPpTf+mkz1gt3Xdq9i/aumRy4AAAAAwMppyAUAAAAAWDlDK+wgpccTmPRcVXjaNvY1WcDSiT3Oayocbs1DK9TP1zbp3RJLwkinwhl5+kxd97F8vpa8spZ0rNFaw6LWph1SZ6zsP8/9YK33iznb1CeWDHeylvNwnvrU3PAA5X/vr7etNv9FTA/TsZZzuI1th/za93BPrMPSIZF2vebyChyOixjabV/b4CzndH/0yAUAAAAAWDkNuQAAAAAAK2dohR2VWYQPIexvG7vOHty+fhnh9Wvvml+fg3o2zd7MmsVa8swu+WBq1m2IWP9nlmVcx3FtaPvcsAq959tay32jOE/+2GVolrXbdYiq+vHS+2pvSKe5EPRDOq/bHPuSdTh8ri8Q8WRZsOu9TZnCodAjFwAAAABg5TTkAgAAAACsnKEVdlCHTi7pfr/2sLVdQgiWDK+w67b3lZartDT87yJm2LwoS49pblmAY1dC4OvhFHYJ+1t7/WFbS+8NS2ekP1S73k/3NfxVfV4P9X697TALh3qcAGxPmc+x0yMXAAAAAGDlNOQCAAAAAKycoRV2VM8cPBf6dyxd+88T0ndZ+1+bJbNVrz10dNtQ2G3XAzgWc2Ht24a0r/3+sNSu94On5T6y7TABU8uWYTymhv86hLrHmF3rJAAAx0KPXAAAAACAldMjd0d175qn6Vf/bY71aTovPXWPl965mHpvrfSEAZg2V/Zvu62n0bFPdlbb9311rjfuIdY9xlx1pBgAwFXQIxcAAAAAYOWeyh65KaXbEXEzIl6OiAc55ztXnCQAAAAAgFFPXUNuSul2zvlu9fyDlNInOee3dtze/hJ34JyLJ02Fhx7j+TrGYwLYVl0WbjvBGY89zefrPMc+NZnZ03JOn5bjBACePk9VQ25K6WZEfNW8/JOI+EVK6WbO+cEVJAsAAAAAYNLTNkburYj4pH6hary9dfnJAQAAAACYdzA9clNKr0TEO1Pj2aaUXo+INyLiy9iMgRs55w/L+znneymlV5t1bg4P72+ZntPHxzyrstC0/eidx0PMN/IDwHamys1DvA/sw9ruJdtch7WlfcxYOo8pzx3KtQAA2KeDaMhNKb0ZET+NiI9nlnmnHus2pfR6SumznPMb5bWc871m1fcj4q5hFQAAAACAtVr10AplIrLh6VyP2Z9GxLv1Cznnz4ft3B7Z/q2IeCXn/N550woAAAAAcFFW3SO3HkYhpfT+2HJDQ+1XI71qP4uI9yLibue9DyLirc7rWxHaxS7kG4Cnm/vAOjxN1+FpOlYAgGO06h65W3grxnvs3o+IV6qxcCNi09s3It41pAIAAAAAsHbH0pD7Wkw35JZlIuK0B+9HpRE3pXRzGGYBAAAAAGB1jqUh92ZEzPWsvRmxmQAtIr7IOd8fnt+MiNsR8dWFphAAAAAAYEerHiN3T0oD70tDr9vPIp4YI+xBzvnDy04YAAAAAMAST0ND7qmhF+5OszwMwzHcjoj4jd/4jX0mCwAAAABg0rEMrXDhcs53c86v5Zxf+7Vf+7WrTg4AAAAA8BQ5pobcmzOvGwMXAAAAADhIx9KQez8iXhp576VqGQAAAACAg3MsDbn3YqZHbs753uUlBwAAAABgf46lIfeziLg18t6PYtPQCwAAAABwkI6lIffjiHgppdRrzH0zIn5yyekBAAAAANibQ2rIvRkj4+DmnB9ExLsR8VH9ekrpzYi4n3P+9OKTBwAAAABwMa5ddQKmpJR+HJuhEV6JzdAJt1JKP4vNxGV/WjfQ5pw/TSk9SCl9EBFfxuOxcd+4/JQDAAAAAOzPqhtyc84fbrn85xHx+QUlBwAAAADgShzS0AoAAAAAAE8lDbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAldOQCwAAAACwchpyAQAAAABWTkMuAAAAAMDKacgFAAAAAFg5DbkAAAAAACunIRcAAAAAYOU05AIAAAAArJyGXAAAAACAlUs556tOw8FJKf11RPy3q04H7NGvRsRfXnUiYI/kaY6NPM0xkq85NvI0x0ae5tisLU//Zc75n26zwrWLSsmR+28559euOhGwLymlL+Rpjok8zbGRpzlG8jXHRp7m2MjTHJtjyNOGVgAAAAAAWDkNuQAAAAAAK6chdzd3rzoBsGfyNMdGnubYyNMcI/maYyNPc2zkaY7Nwedpk50BAAAAAKycHrkAAAAAACunIRcAAAAAYOWuXXUCDkVK6fWIeCMivoyImxEROecPrzRRMCOl9OOIeBARn+ec76eUbkbEaxHxXkT8JOd8r1lePmdVUkqvRMQ7Oec7E8sszrfyOFdtLk8rtzkkKaXbEfFyRNwa/j6fyNvKag7C0nytvOaQpJTejE3+exCb/PdSRNzJOd/vLKu8ZvWW5uljLKs15C4wZJB3cs5vVa+9nlL6LOf8xhUmDeb8KCLejIhIKZXXHkTEW50CSz5nVYY8+dOI+HhmmUX5Vh7nqi3J06Hc5kCklD6IiI9yzneH5zcj4pOU0i8i4u/lnB9UyyqrOQjb5OtQXnMghnz9Wc75veq12xHxZUrp1Tq/Kq85BNvk6TjCstpkZwuM3LgjpfRZRHxSbvSwNqWAi4hXhpfu55w/HVlWPmcVhnx7KyL+NCLej4gv6pt0s+zifCuPc1W2zNPKbVZv+KJzv/0CNLz3i9jk8Tea15TVrNoO+Vp5zeoNkUCv9fJYSinHppei8pqDsUOePrqy2hi5M4ZW/a/aCzn4LDbdsWG1cs6f55w/HP7GCiz5nNXIOd/JOb81ll+LbfKtPM5VWpqnq+WV26zdG73GrsHHEfH60JNRWc0hWZyvC+U1B+D1iPhgaPxq3Y/ND80RobzmYCzO08WxldUacue9FZvM0HM/Il5pb+hwgORzDtE2+VYe59jI01ylt4feKT0/G/6/NvxXVnMotsnX25CvuUr3IuKr2ISSt24N7xfKaw7BNnl6GweTpzXkznstpi9mWQYOmXzOIdom38rjHBt5mqv0RWwmFekpX3K+Gv4rqzkU2+TrbcjXXJmhJ+LLnQmgbg8P64n8lNes3pZ5ehsHk6dNdjbvZvRb+ttlYJVSSrdiM7h3yccvx2Z2xjpfy+ccom3yrTzOwVBus3YzE378aFim9IhRVnMQtszXEaG85jAN+fZObIYTqRuulNccpIk8Xb9/NGW1htzzKRd57JdbuGq3IuL1nPOH5YVhLJmfDbM5zhVUEfI5h2mbfCuPsybKbQ7dmxGxdDIQZTWHopevldccjCEk/HZsfpS4FSMNXhOU16zKFnn66MpqDblw3N5tC6ac872U0v2I+CBWNGA3ABGh3OaApZQ+ik344a5hjbA6E/laec3BGPJq3ZD1SUrpfs5Zec1B2iJPH11ZbYxcOGITvy59FptfrwBYEeU2hyql9HpEvB2bHjFLerfA6k3la+U1hyzn/FZE3B5+qICDN5anj7Gs1pC7zNg4GOcZ9B6u0oOI05CCQj7nEG2Tb+VxDplym9Uawhs/iojfGwlrVFZzcBbk6zHKaw7F3dg0fO2aV+Vr1qaXp8ccbFmtIXfe/RgfB+OlahlYlZTSRymlD0beLnn2VvVcPufQbJNv5XFWT7nNAfskIt5rJ4IaKKs5VKP5WnnNkfhy+P/a8F95zaE7k6ePtazWkDvvXsy0yo9UWuGqvR2PC6VWydP3qv/yOYdmm3wrj3MIlNscnCGE8YOc8+cjiyirOTgL8rXymtVLKd1MKf0ipfTZzKJ1nlVes1o75OmjLKs15M77LMYv/I/i8UWHtbk7jBPT80ZERBUmJp9ziLbJt/I4h0C5zUFJKf04Ij5rG7tSSreGsUUjlNUcmIX5WnnNobgZQwh5x8vD/5IHldccgm3y9FGW1Rpy530cES+llHoX9M2I+MklpweW+o8TY8O8HWdnZ5TPOUTb5Ft5nEOg3OZgpJTejIj7OedPO2+/Eo/DD5XVHIwt8rXymtUbJnmaasgq+b38aKG8ZtV2yNNHWVZryJ0xZJR3YzPQ/amZmzxcuSFvvtcWXCmlTyLi85zz3WpZ+Zy1uhkjYxVtk2/lcVZkKk8rtzkIQx59LzZfeG5Xfz8eejO+X3q4KKs5FFvma+U1h+LOME7omZDxIU+/FBGnDWLKaw7ENnn6KMvqlHO+6jQchCGM5o3YDJ5cxsf48EoTBQsMBdqvxOPGg8/qAqtZVj7nyg159kex6flSfhG9F5teMH/a3kS3ybfyOFdhhzyt3GbVUkq/iPFx5CI2X3herl9QVrN2O+Zr5TUHoZrwqeTVryLiztB41S6rvGb1tszTR1VWa8gFAAAAAFg5QysAAAAAAKychlwAAAAAgJXTkAsAAAAAsHIacgEAAAAAVk5DLgAAAADAymnIBQAAAABYOQ25AAAAAAArpyEXAAAAAGDlNOQCAAAAAKychlwAAAAAgJXTkAsAAAAAsHIacgEAAAAAVk5DLgAAAADAymnIBQAAAABYOQ25AAAAAAArpyEXAAAOXErplZTSK5e8zzdTSjcvc58AAE+zlHO+6jQAAAA7GhpwX885f9i89lJERM7584XbWLx8td5HEXEn5/xg23QDALAdDbkAAOxVSunHEfFORJQeop9HRN3QV3pxfpJzvnsJ6fkyIj7NOd+5zHUvw9Aj9pOc8xvNax9ExNsR8UX93sQ2Fi/frHsrIj7aZh0AAHZz7aoTAADAcRl6hn6YUvpZRNzqNfKVBsiU0p2IePUSenR2hwBIKb2Sc763y7or8X5sGmFPDefyvZRSRMStuQ1su3yz7v2U0mcppTdzzp9usy4AANsxRi4AABflq+HvCTnnB0MD70sRaTAjkwAABeFJREFU8WcXmYic88s55/dG3h57fcm6V2poDH99YiiEbRvHd21MvxtNYzIAAPunIRcAgKv0cURc+kRdla16oK7M+xHxp1ediKFH7/2U0utXnRYAgGOmIRcAgKv05fD/0htUU0q3L3ufe/ZmRKxlOINPYqZ3MwAA56MhFwCAq/Ty8H9unNqdpJRupZReqXuLppRuDo24H2277loMk4xFzvn+Vadl8HFErO48AQAcE5OdAQBwJYYxXt+OiE/bBsmU0puxGT83YphsbJhErV7mVmwaD8s4vC8Nj3+Uc74zvPbRsMy9iHh1eO12RPwoIu5HxGsppU+qzd6p0tJbd3Eah/R9Epvexl/knN+o1rk5pOHOjo2xJV1baRqw70XEW51zX65L8WpEfDCVzpzzg5RSpJRurahxGQDgqGjIBQDg0lWNnJ/nnN9q3vsoIn6Wc75bL59S+lkMDY9DY+MHnXVvRzVMw9B4+kFUvUVLY+vw+ivtNqbW3SaNQ4Pmq0NDcekF/PEwpmxpCP5ZRPzd+TP2hJdj0xC9rc+H9d7KOfcagm9FxNv1cUVEpJS+TCm9NzGxWkTEF8P6GnIBAC6AhlwAAC7SraExtHYzIh5ExLttY+LQ2PlazvnMeKtD4+1HsWn8fTUiXovHPXHr5e6mlNres391jvQ/se4WaSz+Y2wmJvugNOIOPo9NA+8rI42qU27GphF4saHx+72c88sTi73UNuIO3ouIT1JKf685htqDIV0AAFwADbkAAFykB9UwB0t8EBE/GXnv44j4aBiz9n5EvJ1S+qTTS/STJ1fdq0VpbNJ1s01nGY4gHg/PsI0yjMQiQyPu+wuuRbc3bc7582EbtyPiw94yQ3oufdI6AICnhcnOAABYhWG4hZsx3pj4IDa9Pt8Yhi34OCI+Syn9IqX00TBUQcyE/19aGpu39j3cwOKer0MD7J/F+RtZ78VmXN8pv3LOfQAAMEJDLgAAa1EaGsdC988sNwxt8GpE3I3NOLafDI26r1xcErdL4wWa239xKzbDOrwVEW8OvZnPY+64zjOMBQAAEzTkAgCwFqXX6lRv05sRcb801uac7+Wc7wzjvv7d2PTS/bNdE1B69e4jjbumYaGvYtmQDA+G83M/Iu7EprF713Fs547rpZn3AQA4Bw25AACswtDYGDHS63MY1iBiM3nYrbZ3ac75wdBL96tq2TltY+jkelum8SI9iIipScuK03F0c84fDs/byeeWuhXTx1UmsQMA4AJoyAUAYE3eG/563oyIeznnT6tle+7FsonA/iqe7Fm7pCFymzRelC9jt+Eb3oqI2xNDLIw1UL8Zm969YxOdRUS8FnrkAgBcGA25AABclJdii0m5IiJyzncj4l5K6Uyv0aGn6zuxaYgsXm/Hwy3DBgyTjtV66fg0Nj17bw7rvhIRX3SWO7PulmmMmJ8AbJehDj6PiLmxgJ/Ybs753rDu2BALd1NKt+sXhuXej4jfG9tRdd415AIAXJCUc77qNAAAcERSSj+OiDdiMwFZxKaH7BfDsAdLt3E7NkMHlMmzfiUiflIaaIcepWWc2LoX6c3Sa3RoXPzpkI6bsWnAvDM0Zpb9vBmb3rWfxabH6d0t1p1LY28bH+WcPx3O0TuxaYy9H5tevG0D8Nw5+jIi3mgbT4cG5Q96aR/e+1k8Hgbhbs75zrDeBxHxk2Ezt+Nx7+RXh/VHeysP5/GdbY8BAIDlNOQCAMABGhqDY2a4g8tKy2cR8UHO+fOrTgsAwLHSkAsAAAdo6PH7ZznnV1eQli9zzksmXwMAYEfGyAUAgAM0DHXwRTtO8GUbegbfuco0AAA8DfTIBQCAAzX0yv0k5/zGFe7/p8bGBQC4eHrkAgDAgRp65d4p4+Vegfcj4t0r2jcAwFNFj1wAADhwZXiFnPO9S9znmxHx+dCYDADABdOQCwAAAACwcoZWAAAAAABYOQ25AAAAAAArpyEXAAAAAGDlNOQCAAAAAKychlwAAAAAgJXTkAsAAAAAsHL/H8imAh1zp35cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1944 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_posterior(gammas, tss, ects, L_w, w, 'posterior2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Africa</th>\n",
       "      <th>Zigzag</th>\n",
       "      <th>Constant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>10144.40 (19.76)</td>\n",
       "      <td>10359.75 (20.84)</td>\n",
       "      <td>10663.13 (20.19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zigzag</th>\n",
       "      <td>5507.79 (132.03)</td>\n",
       "      <td>4962.75 (120.63)</td>\n",
       "      <td>5700.60 (137.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Constant</th>\n",
       "      <td>11584.24 (259.28)</td>\n",
       "      <td>11764.86 (263.18)</td>\n",
       "      <td>11898.49 (266.09)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Africa             Zigzag           Constant\n",
       "Africa     10144.40 (19.76)   10359.75 (20.84)   10663.13 (20.19)\n",
       "Zigzag     5507.79 (132.03)   4962.75 (120.63)   5700.60 (137.21)\n",
       "Constant  11584.24 (259.28)  11764.86 (263.18)  11898.49 (266.09)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_table(err_A, 'table2_1.tex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Africa</th>\n",
       "      <th>Zigzag</th>\n",
       "      <th>Constant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>0.3293 (0.0004)</td>\n",
       "      <td>0.3934 (0.0005)</td>\n",
       "      <td>0.3549 (0.0004)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zigzag</th>\n",
       "      <td>0.3678 (0.0041)</td>\n",
       "      <td>0.3548 (0.0042)</td>\n",
       "      <td>0.3788 (0.0038)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Constant</th>\n",
       "      <td>0.3496 (0.0068)</td>\n",
       "      <td>0.3955 (0.0059)</td>\n",
       "      <td>0.3679 (0.0064)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Africa           Zigzag         Constant\n",
       "Africa    0.3293 (0.0004)  0.3934 (0.0005)  0.3549 (0.0004)\n",
       "Zigzag    0.3678 (0.0041)  0.3548 (0.0042)  0.3788 (0.0038)\n",
       "Constant  0.3496 (0.0068)  0.3955 (0.0059)  0.3679 (0.0064)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_table(err_B, 'table2_2.tex', log=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tsmc",
   "language": "python",
   "name": "tsmc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}