{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85f5d67b",
   "metadata": {},
   "source": [
    "# HBOMS epi model example\n",
    "\n",
    "In this notebook, we show a simple SEIR-model example with multiple units. These units might correspond to different communities in which a pathogen spreads, isolated by space (or time, or otherwise).\n",
    "\n",
    "We first have to import the `hboms` package, which provides the building blocks for defining a model.\n",
    "We also import some utilities (for displaying stan models with syntax highlighting), and some other python modules for statistics and plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfb71ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hboms\n",
    "import hboms.utilities as util\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as sts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b717b7a",
   "metadata": {},
   "source": [
    "## Vaccination data\n",
    "\n",
    "Here I assume that the vaccination data can be transformed in a per-capita vaccination rate.\n",
    "This rate is modeled as a smoothed, piece-wise constant function, with levels that are\n",
    "unit-specific, but with change points (knots) that are the same for all units.\n",
    "\n",
    "The smoothed rate function is given by\n",
    "\n",
    "$$ \n",
    "\\eta(t) = \\sum_{i=1}^{n-1} \\eta_i \\mathrm{expit}(t - \\tau_i) [1 - \\mathrm{expit}(t - \\tau_{i+1})] \n",
    "$$\n",
    "\n",
    "where $n$ is the number of knots.\n",
    "This is slightly inefficient, as for $t$ far away from $\\tau_i$ and $\\tau_{i+1}$ the expit terms are practically zero.\n",
    "\n",
    "We set the first and last knot $\\tau_1$ and $\\tau_n$ to values way beyond the observed interval. Notice that outside the interval $[\\tau_1, \\tau_n]$, the vaccination rate is very close to zero.\n",
    "For this example, the time interval is going to be $t \\in [0, 120]$ days, and we choose $\\tau_1 = -20$ and $\\tau_n = 140$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c7e0eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'vaccination rate (per capita)')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## vaccination rates \n",
    "\n",
    "R = 12 # number of units\n",
    "knots = np.array([-20] + [10*i for i in range(1,11)] + [140])\n",
    "num_knots = len(knots)\n",
    "\n",
    "vacc_rates = [\n",
    "    0.001 * np.exp(np.cumsum(sts.norm.rvs(size=num_knots-1, loc=0.1, scale=0.3)))\n",
    "    for _ in range(R)\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "for vr in vacc_rates:\n",
    "    vre = list(vr) + [vr[-1]]\n",
    "    ax.step(knots, vre, where='post', alpha=0.7)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"time (days)\")\n",
    "ax.set_ylabel(\"vaccination rate (per capita)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31eb0b1",
   "metadata": {},
   "source": [
    "## Define an HBOMS model\n",
    "\n",
    "Every HBOMS model has the following obligatory components:\n",
    "\n",
    "* a list of parameters. A parameter has to be defined with the `Parameter` class from the `hboms` module. The `Parameter` class has 3 required arguments: `name`, `value` and `type`. The name must be unique, the value is the initial guess and the type determines if the parameter is a constant, fixed between units, or varies between units.\n",
    "* A list of state variables. Use `Variable` objects from the `hboms` module to create these state variables. It is possible to define vector-valued state variables, but this is still experimental. This should make it easy to define e.g. age classes in epi models.\n",
    "* A string defing the ODE model. This is regular Stan code, and can contain any Stan expression or statement. The ODE model will have access to all the unit-specific or shared parameters used in this piece of code. At this point, non-constant parameters have to be scalars. Constants *are* allowed to be vectors or matrices. In addition to parameters, the ODE function has access to the state variables by name. For each state variable `X`, a variable `ddt_X` is declared, and the user is supposed to define these time derivatives.\n",
    "* A string defining the initial condition. The user has automatic access to the parameters, and for each state variable `X`, an initial value `X_0` is declared, which the user must define.\n",
    "* A list of distributions defining the model likelihood. Distributions are defined using the `StanDist` class, which takes the name of a Stan distribution as its first argument, then the name of the observation and then a list of parameters. The parameters can be arbitrary Stan expressions.\n",
    "* Finally, a list of observations must be given. Use `Observation` from the `hboms` module to create these. The data type (in this case an integer) must be specified, as the default is a real number.\n",
    "\n",
    "In addition to these, we can also specify optional components. In our case\n",
    "\n",
    "* `trans_state`, which is a list of additional state variables, which can be computed from the ODE state variables.\n",
    "* `transform` a string with Stan statements specifying how to compute the transformed state variables.\n",
    "* `options` is a python `dict` with some integration options. It specifies the ODE integrator tolerance parameters etc. The option `unit_obs` is a boolean which must be set to `True` is you have any observations at time `t=0`. This is because Stan by default does not allow evaluation of the ODEs at t=0, and we add some code to circumvent this.\n",
    "\n",
    "Some things to avoid:\n",
    "\n",
    "* Don't use the names `R`, `r`, `N`, `n`, `u_sim` `state`. It's probably a bad idea to start variables with `ddt_` or end them with `_0` or `_sim`. There are currently no warnings when you violate these rules.\n",
    "\n",
    "We can choose from the following parameter types. The \"H\" in HBOMS is currently only justified by the `random` parameter.\n",
    "\n",
    "* `random`: random-effects like parameter. Normal or log-Normal distribution with estimated location and scale parameters.\n",
    "* `fixed`: parameter shared between all units.\n",
    "* `indiv`: Different parameter for each unit. Location and scale not estimated.\n",
    "* `const`: A constant shared by all units\n",
    "* `const_indiv`: A constant that is different for all units.\n",
    "\n",
    "The space in which a parameter lives can be \n",
    "* `real`: this is the default and valid for all parameters\n",
    "* `vector`: Currently only implemented for `const` and `const_indiv` parameters. The dimension of the vector space must be the same for each unit. So you might have to add some padding.\n",
    "* `matrix` The same restrictions hold as for `vector`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "200fea6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:57:36 - cmdstanpy - INFO - compiling stan file /home/chris/Repositories/hboms/notebooks/seir_model.stan to exe file /home/chris/Repositories/hboms/notebooks/seir_model\n",
      "10:58:06 - cmdstanpy - INFO - compiled model executable: /home/chris/Repositories/hboms/notebooks/seir_model\n"
     ]
    }
   ],
   "source": [
    "# parameters are positive by default. set lbound=None to avoid this.\n",
    "params = [\n",
    "    hboms.Parameter(\"R0m1\", 0.5, \"random\", scale=0.15), # R_0 - 1\n",
    "    hboms.Parameter(\"alpha\", 0.5, \"const\"), # rate E -> I\n",
    "    hboms.Parameter(\"gamma\", 0.4, \"random\", scale=0.1), # recovery rate\n",
    "    hboms.Parameter(\"theta\", 300, \"const\"), # scale to population size\n",
    "    hboms.Parameter(\"epsilon\", 1e-4, \"const\"), # inoculum size\n",
    "    hboms.Parameter(\"phi\", 1e3, \"fixed\"), # overdispersion parameter\n",
    "    hboms.Parameter(\"eta\", vacc_rates, \"const_indiv\", space=\"vector\"), # per-capita vaccination rate\n",
    "    hboms.Parameter(\"tau\", knots, \"const\", lbound=None, space=\"vector\"), # vacc-rate change points\n",
    "]\n",
    "\n",
    "# the variable V is the cumulative fraction of vaccinated individuals\n",
    "state = [\n",
    "    hboms.Variable(\"S\"), \n",
    "    hboms.Variable(\"E\"), \n",
    "    hboms.Variable(\"I\"), \n",
    "    hboms.Variable(\"V\")\n",
    "]\n",
    "\n",
    "# beta and gamma are correlated. Does it help to reparameterize with R0m1 = beta / gamma - 1?\n",
    "odes = \"\"\"\n",
    "real beta = gamma * (R0m1 + 1);\n",
    "int n = num_elements(tau);\n",
    "real vacc_rate = sum(eta .* inv_logit(t - tau[1:n-1]) .* inv_logit(tau[2:n] - t));\n",
    "ddt_S = -beta * S * I - vacc_rate * S;\n",
    "ddt_E = beta * S * I - alpha * E;\n",
    "ddt_I = alpha * E - gamma * I;\n",
    "ddt_V = vacc_rate * S;\n",
    "\"\"\"\n",
    "\n",
    "# We define C = theta * I, which is the expected number of cases.\n",
    "trans_state = [hboms.Variable(\"C\")]\n",
    "\n",
    "transform = \"\"\"\n",
    "C = theta * I;\n",
    "\"\"\"\n",
    "\n",
    "init = \"\"\"\n",
    "S_0 = 1.0 - epsilon;\n",
    "E_0 = 0.0;\n",
    "I_0 = epsilon;\n",
    "V_0 = 0.0;\n",
    "\"\"\"\n",
    "\n",
    "obs = [\n",
    "    hboms.Observation(name=\"Cases\", data_type=\"int\")\n",
    "]\n",
    "\n",
    "dists = [\n",
    "    hboms.StanDist(\n",
    "        name=\"neg_binomial_2\", \n",
    "        obs_name=\"Cases\", \n",
    "        params=[\"C\", \"phi\"]\n",
    "    )\n",
    "]\n",
    "\n",
    "\n",
    "options = {\n",
    "    \"integrator\" : \"ode_rk45_tol\",\n",
    "    \"rel_tol\": 1e-9,\n",
    "    \"abs_tol\": 1e-9,\n",
    "    \"max_num_steps\": 100000\n",
    "}\n",
    "\n",
    "# define and compile the HBOMS model\n",
    "\n",
    "hbm = hboms.HbomsModel(\n",
    "    name=\"seir_model\",\n",
    "    state=state,\n",
    "    odes=odes,\n",
    "    init=init,\n",
    "    params=params,\n",
    "    obs=obs,\n",
    "    dists=dists,\n",
    "    trans_state=trans_state,\n",
    "    transform=transform,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2031bbf",
   "metadata": {},
   "source": [
    "## Have a look at the Stan model in full color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8821c84d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01//EN\"\n",
       "   \"http://www.w3.org/TR/html4/strict.dtd\">\n",
       "<!--\n",
       "generated by Pygments <https://pygments.org/>\n",
       "Copyright 2006-2023 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "-->\n",
       "<html>\n",
       "<head>\n",
       "  <title></title>\n",
       "  <meta http-equiv=\"content-type\" content=\"text/html; charset=None\">\n",
       "  <style type=\"text/css\">\n",
       "/*\n",
       "generated by Pygments <https://pygments.org/>\n",
       "Copyright 2006-2023 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "*/\n",
       "pre { line-height: 125%; }\n",
       "td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "body .hll { background-color: #ffffcc }\n",
       "body { background: #f8f8f8; }\n",
       "body .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
       "body .err { border: 1px solid #FF0000 } /* Error */\n",
       "body .k { color: #008000; font-weight: bold } /* Keyword */\n",
       "body .o { color: #666666 } /* Operator */\n",
       "body .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
       "body .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
       "body .cp { color: #9C6500 } /* Comment.Preproc */\n",
       "body .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */\n",
       "body .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */\n",
       "body .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */\n",
       "body .gd { color: #A00000 } /* Generic.Deleted */\n",
       "body .ge { font-style: italic } /* Generic.Emph */\n",
       "body .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */\n",
       "body .gr { color: #E40000 } /* Generic.Error */\n",
       "body .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       "body .gi { color: #008400 } /* Generic.Inserted */\n",
       "body .go { color: #717171 } /* Generic.Output */\n",
       "body .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       "body .gs { font-weight: bold } /* Generic.Strong */\n",
       "body .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       "body .gt { color: #0044DD } /* Generic.Traceback */\n",
       "body .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       "body .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       "body .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       "body .kp { color: #008000 } /* Keyword.Pseudo */\n",
       "body .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       "body .kt { color: #B00040 } /* Keyword.Type */\n",
       "body .m { color: #666666 } /* Literal.Number */\n",
       "body .s { color: #BA2121 } /* Literal.String */\n",
       "body .na { color: #687822 } /* Name.Attribute */\n",
       "body .nb { color: #008000 } /* Name.Builtin */\n",
       "body .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       "body .no { color: #880000 } /* Name.Constant */\n",
       "body .nd { color: #AA22FF } /* Name.Decorator */\n",
       "body .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
       "body .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
       "body .nf { color: #0000FF } /* Name.Function */\n",
       "body .nl { color: #767600 } /* Name.Label */\n",
       "body .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       "body .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       "body .nv { color: #19177C } /* Name.Variable */\n",
       "body .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       "body .w { color: #bbbbbb } /* Text.Whitespace */\n",
       "body .mb { color: #666666 } /* Literal.Number.Bin */\n",
       "body .mf { color: #666666 } /* Literal.Number.Float */\n",
       "body .mh { color: #666666 } /* Literal.Number.Hex */\n",
       "body .mi { color: #666666 } /* Literal.Number.Integer */\n",
       "body .mo { color: #666666 } /* Literal.Number.Oct */\n",
       "body .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       "body .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       "body .sc { color: #BA2121 } /* Literal.String.Char */\n",
       "body .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       "body .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       "body .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       "body .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */\n",
       "body .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       "body .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */\n",
       "body .sx { color: #008000 } /* Literal.String.Other */\n",
       "body .sr { color: #A45A77 } /* Literal.String.Regex */\n",
       "body .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       "body .ss { color: #19177C } /* Literal.String.Symbol */\n",
       "body .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       "body .fm { color: #0000FF } /* Name.Function.Magic */\n",
       "body .vc { color: #19177C } /* Name.Variable.Class */\n",
       "body .vg { color: #19177C } /* Name.Variable.Global */\n",
       "body .vi { color: #19177C } /* Name.Variable.Instance */\n",
       "body .vm { color: #19177C } /* Name.Variable.Magic */\n",
       "body .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"kn\">functions</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* vector field */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">ode_fun</span><span class=\"p\">(</span><span class=\"kt\">real</span> <span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">R0m1</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">gamma</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">eta</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">tau</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack the state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">S</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">E</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">V</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare derivatives of state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">ddt_S</span><span class=\"p\">,</span> <span class=\"n\">ddt_E</span><span class=\"p\">,</span> <span class=\"n\">ddt_I</span><span class=\"p\">,</span> <span class=\"n\">ddt_V</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined ODEs */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">beta</span> <span class=\"o\">=</span> <span class=\"n\">gamma</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">R0m1</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">);</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">num_elements</span><span class=\"p\">(</span><span class=\"n\">tau</span><span class=\"p\">);</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">vacc_rate</span> <span class=\"o\">=</span> <span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">eta</span> <span class=\"o\">.*</span> <span class=\"nb\">inv_logit</span><span class=\"p\">(</span><span class=\"n\">t</span> <span class=\"o\">-</span> <span class=\"n\">tau</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">n</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">])</span> <span class=\"o\">.*</span> <span class=\"nb\">inv_logit</span><span class=\"p\">(</span><span class=\"n\">tau</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"o\">:</span><span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">t</span><span class=\"p\">));</span>\n",
       "        <span class=\"n\">ddt_S</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"n\">beta</span> <span class=\"o\">*</span> <span class=\"n\">S</span> <span class=\"o\">*</span> <span class=\"n\">I</span> <span class=\"o\">-</span> <span class=\"n\">vacc_rate</span> <span class=\"o\">*</span> <span class=\"n\">S</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_E</span> <span class=\"o\">=</span> <span class=\"n\">beta</span> <span class=\"o\">*</span> <span class=\"n\">S</span> <span class=\"o\">*</span> <span class=\"n\">I</span> <span class=\"o\">-</span> <span class=\"n\">alpha</span> <span class=\"o\">*</span> <span class=\"n\">E</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_I</span> <span class=\"o\">=</span> <span class=\"n\">alpha</span> <span class=\"o\">*</span> <span class=\"n\">E</span> <span class=\"o\">-</span> <span class=\"n\">gamma</span> <span class=\"o\">*</span> <span class=\"n\">I</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_V</span> <span class=\"o\">=</span> <span class=\"n\">vacc_rate</span> <span class=\"o\">*</span> <span class=\"n\">S</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with derivatives */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">ddt_S</span><span class=\"p\">,</span> <span class=\"n\">ddt_E</span><span class=\"p\">,</span> <span class=\"n\">ddt_I</span><span class=\"p\">,</span> <span class=\"n\">ddt_V</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* initial condition */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">gen_init</span><span class=\"p\">(</span><span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">epsilon</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* declare initial variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">S_0</span><span class=\"p\">,</span> <span class=\"n\">E_0</span><span class=\"p\">,</span> <span class=\"n\">I_0</span><span class=\"p\">,</span> <span class=\"n\">V_0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined initial condition */</span>\n",
       "        <span class=\"n\">S_0</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span> <span class=\"o\">-</span> <span class=\"n\">epsilon</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">E_0</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">I_0</span> <span class=\"o\">=</span> <span class=\"n\">epsilon</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">V_0</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with initival state variables */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">S_0</span><span class=\"p\">,</span> <span class=\"n\">E_0</span><span class=\"p\">,</span> <span class=\"n\">I_0</span><span class=\"p\">,</span> <span class=\"n\">V_0</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* state transform function */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">state_transform</span><span class=\"p\">(</span><span class=\"kt\">real</span> <span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">theta</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare transformed state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">C</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined transformation code */</span>\n",
       "        <span class=\"n\">C</span> <span class=\"o\">=</span> <span class=\"n\">theta</span> <span class=\"o\">*</span> <span class=\"n\">I</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with transformed variables */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">C</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* IVP solver function */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">vector</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">real</span> <span class=\"n\">Time</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">R0m1</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">gamma</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">theta</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">eta</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">tau</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"nb\">num_elements</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">);</span> <span class=\"cm\">/* number of time points */</span>\n",
       "        <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"p\">]</span> <span class=\"n\">init_state</span> <span class=\"o\">=</span> <span class=\"n\">gen_init</span><span class=\"p\">(</span><span class=\"n\">epsilon</span><span class=\"p\">);</span> <span class=\"cm\">/* generate initial state */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">sol</span><span class=\"p\">;</span> <span class=\"cm\">/* allocate space for solution */</span>\n",
       "        <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"mf\">4</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">ode_rk45</span><span class=\"p\">(</span><span class=\"n\">ode_fun</span><span class=\"p\">,</span> <span class=\"n\">init_state</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">],</span> <span class=\"n\">R0m1</span><span class=\"p\">,</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"n\">gamma</span><span class=\"p\">,</span> <span class=\"n\">eta</span><span class=\"p\">,</span> <span class=\"n\">tau</span><span class=\"p\">);</span> <span class=\"cm\">/* solve initial value problem */</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span> <span class=\"p\">)</span> <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">5</span><span class=\"o\">:</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">state_transform</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"mf\">4</span><span class=\"p\">],</span> <span class=\"n\">theta</span><span class=\"p\">);</span> <span class=\"cm\">/* compute transformation of the state and append to the trajectory */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">sol</span><span class=\"p\">;</span> <span class=\"cm\">/* and return the result */</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* auxiliary function for map_rect */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">map_rect_helper_fun</span><span class=\"p\">(</span><span class=\"kt\">vector</span> <span class=\"n\">ppar</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">upar</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">real</span> <span class=\"n\">rdat</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">int</span> <span class=\"n\">idat</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"n\">idat</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">];</span> <span class=\"cm\">/* number of time points */</span>\n",
       "        <span class=\"cm\">/* find shapes of non-scalar parameters */</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">shape_eta</span> <span class=\"o\">=</span> <span class=\"n\">idat</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">shape_tau</span> <span class=\"o\">=</span> <span class=\"n\">idat</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* solve initial value problem */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">sol</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">1</span> <span class=\"o\">+</span> <span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"o\">:</span><span class=\"n\">N</span> <span class=\"o\">+</span> <span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"p\">],</span> <span class=\"n\">upar</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">upar</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">],</span> <span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">],</span> <span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">],</span> <span class=\"nb\">to_vector</span><span class=\"p\">(</span><span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"o\">:</span><span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span><span class=\"p\">]),</span> <span class=\"nb\">to_vector</span><span class=\"p\">(</span><span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">4</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span><span class=\"o\">:</span><span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"p\">]));</span>\n",
       "        <span class=\"cm\">/* concatenate solution into a vector */</span>\n",
       "        <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"n\">res</span><span class=\"p\">;</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span> <span class=\"p\">)</span> <span class=\"n\">res</span><span class=\"p\">[(</span><span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">5</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">n</span> <span class=\"o\">*</span> <span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">];</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">res</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* log-likelihood function */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">loglik_fun</span><span class=\"p\">(</span><span class=\"kt\">int</span> <span class=\"n\">Cases</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">phi</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">C</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare log-lik variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">ll_Cases</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined log-likelihood */</span>\n",
       "        <span class=\"n\">ll_Cases</span> <span class=\"o\">=</span> <span class=\"nb\">neg_binomial_2_lpmf</span><span class=\"p\">(</span><span class=\"n\">Cases</span> <span class=\"p\">|</span> <span class=\"n\">C</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">);</span> <span class=\"cm\">/* neg_binomial_2 log-likelihood */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">ll_Cases</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* rng function */</span>\n",
       "    <span class=\"kt\">int</span> <span class=\"n\">Cases_rng</span><span class=\"p\">(</span><span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">phi</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">C</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare variable to-be-returned */</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">Cases</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined sampler */</span>\n",
       "        <span class=\"n\">Cases</span> <span class=\"o\">=</span> <span class=\"nb\">neg_binomial_2_rng</span><span class=\"p\">(</span><span class=\"n\">C</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">);</span> <span class=\"cm\">/* random neg_binomial_2 sample */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">Cases</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">data</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">R</span><span class=\"p\">;</span> <span class=\"cm\">/* number of units */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">N</span><span class=\"p\">;</span> <span class=\"cm\">/* number of observations per unit */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">Time</span><span class=\"p\">;</span> <span class=\"cm\">/* observation times */</span>\n",
       "    <span class=\"cm\">/* observations */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">int</span> <span class=\"n\">Cases</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">NSim</span><span class=\"p\">;</span> <span class=\"cm\">/* number of simulation time points */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">NSim</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">TimeSim</span><span class=\"p\">;</span> <span class=\"cm\">/* simulation time points */</span>\n",
       "    <span class=\"cm\">/* constants */</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">alpha</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">theta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">epsilon</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">int</span> <span class=\"n\">shape_eta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span><span class=\"p\">[</span><span class=\"n\">shape_eta</span><span class=\"p\">]</span> <span class=\"n\">eta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">int</span> <span class=\"n\">shape_tau</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"n\">shape_tau</span><span class=\"p\">]</span> <span class=\"n\">tau</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">transformed data</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* declarations */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"p\">]</span> <span class=\"kt\">real</span> <span class=\"n\">rdats</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"kt\">int</span> <span class=\"n\">idats</span><span class=\"p\">;</span> <span class=\"cm\">/* integer data */</span>\n",
       "    <span class=\"cm\">/* definitions */</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span> <span class=\"o\">+</span> <span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"o\">:</span><span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Time</span><span class=\"p\">;</span>\n",
       "    <span class=\"n\">idats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">N</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* add constants to rdats */</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"n\">theta</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "    <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">r</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">R</span> <span class=\"p\">)</span> <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"mf\">4</span><span class=\"o\">:</span><span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">to_array_1d</span><span class=\"p\">(</span><span class=\"n\">eta</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]);</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">4</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span><span class=\"o\">:</span><span class=\"mf\">3</span> <span class=\"o\">+</span> <span class=\"n\">shape_eta</span> <span class=\"o\">+</span> <span class=\"n\">shape_tau</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"nb\">to_array_1d</span><span class=\"p\">(</span><span class=\"n\">tau</span><span class=\"p\">),</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "    <span class=\"cm\">/* add shapes to idats */</span>\n",
       "    <span class=\"n\">idats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"n\">shape_eta</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">idats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"n\">shape_tau</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">parameters</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* individual parameters (and their hyper-parameters) */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">gamma</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_gamma</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_gamma</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* fixed parameters */</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">phi</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">transformed parameters</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">upars</span><span class=\"p\">;</span> <span class=\"cm\">/* prepare data structure for map_rect */</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"n\">ppar</span><span class=\"p\">;</span> <span class=\"cm\">/* no population parameters required for map_rect */</span>\n",
       "    <span class=\"cm\">/* assign unit-parameters to array of vectors */</span>\n",
       "    <span class=\"n\">upars</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"n\">upars</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">gamma</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">model</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* solve ODEs in parallel */</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">concat_res</span> <span class=\"o\">=</span> <span class=\"nb\">map_rect</span><span class=\"p\">(</span><span class=\"n\">map_rect_helper_fun</span><span class=\"p\">,</span> <span class=\"n\">ppar</span><span class=\"p\">,</span> <span class=\"n\">upars</span><span class=\"p\">,</span> <span class=\"n\">rdats</span><span class=\"p\">,</span> <span class=\"n\">idats</span><span class=\"p\">);</span>\n",
       "    <span class=\"cm\">/* compute log-likelihood of observations */</span>\n",
       "    <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">r</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">R</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"cm\">/* extract state */</span>\n",
       "            <span class=\"kt\">int</span> <span class=\"n\">idx</span> <span class=\"o\">=</span> <span class=\"mf\">5</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"n\">r</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">;</span>\n",
       "            <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">state</span> <span class=\"o\">=</span> <span class=\"n\">concat_res</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"o\">:</span><span class=\"n\">idx</span> <span class=\"o\">+</span> <span class=\"mf\">4</span><span class=\"p\">];</span>\n",
       "            <span class=\"cm\">/* compute likelihood of observation given state */</span>\n",
       "            <span class=\"k\">target +=</span> <span class=\"n\">loglik_fun</span><span class=\"p\">(</span><span class=\"n\">Cases</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">);</span>\n",
       "        <span class=\"p\">}</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* prior */</span>\n",
       "    <span class=\"n\">R0m1</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">lognormal</span><span class=\"p\">(</span><span class=\"n\">loc_R0m1</span><span class=\"p\">,</span> <span class=\"n\">scale_R0m1</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">loc_R0m1</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">scale_R0m1</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">gamma</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">lognormal</span><span class=\"p\">(</span><span class=\"n\">loc_gamma</span><span class=\"p\">,</span> <span class=\"n\">scale_gamma</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">loc_gamma</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">scale_gamma</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">phi</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">generated quantities</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">NSim</span><span class=\"p\">]</span> <span class=\"kt\">real</span> <span class=\"n\">S_sim</span><span class=\"p\">,</span> <span class=\"n\">E_sim</span><span class=\"p\">,</span> <span class=\"n\">I_sim</span><span class=\"p\">,</span> <span class=\"n\">V_sim</span><span class=\"p\">,</span> <span class=\"n\">C_sim</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">int</span> <span class=\"n\">Cases_sim</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">log_lik</span><span class=\"p\">;</span> <span class=\"cm\">/* vector of log-likelihoods for model comparison */</span>\n",
       "    <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">r</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">R</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">NSim</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">u_sim</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">TimeSim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">R0m1</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"n\">gamma</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">theta</span><span class=\"p\">,</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"n\">eta</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">tau</span><span class=\"p\">);</span> <span class=\"cm\">/* solve ODEs at simulation times */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">u_sim_obs</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]],</span> <span class=\"n\">R0m1</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"n\">gamma</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">theta</span><span class=\"p\">,</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"n\">eta</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">tau</span><span class=\"p\">);</span> <span class=\"cm\">/* solve ODEs at observation times */</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">NSim</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"n\">S_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">];</span>\n",
       "            <span class=\"n\">E_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "            <span class=\"n\">I_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">3</span><span class=\"p\">];</span>\n",
       "            <span class=\"n\">V_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">4</span><span class=\"p\">];</span>\n",
       "            <span class=\"n\">C_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">5</span><span class=\"p\">];</span>\n",
       "        <span class=\"p\">}</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"kt\">int</span> <span class=\"n\">idx</span> <span class=\"o\">=</span> <span class=\"mf\">1</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"n\">r</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">;</span>\n",
       "            <span class=\"n\">log_lik</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"o\">:</span><span class=\"n\">idx</span> <span class=\"o\">+</span> <span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">loglik_fun</span><span class=\"p\">(</span><span class=\"n\">Cases</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">u_sim_obs</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">phi</span><span class=\"p\">);</span> <span class=\"cm\">/* record log-likelihood of each observation */</span>\n",
       "            <span class=\"n\">Cases_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Cases_rng</span><span class=\"p\">(</span><span class=\"n\">u_sim_obs</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">phi</span><span class=\"p\">);</span> <span class=\"cm\">/* simulate data at observation times */</span>\n",
       "        <span class=\"p\">}</span>\n",
       "    <span class=\"p\">}</span>\n",
       "<span class=\"p\">}</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "util.show_stan_model(hbm.model_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a462754",
   "metadata": {},
   "source": [
    "## Use the HBOMS model to simulate some data.\n",
    "\n",
    "For this, we create and compile a separate Stan model that generates random effects and observations in the `generated quantities` block. We so have to specify the number of units and time points for each unit.\n",
    "You can add a field \"ID\" to the data to give each unit a name, which is nice for plots.\n",
    "\n",
    "The data has to contain the key `\"Time\"`, with sorted time points for each unit. Integration always starts at time $t_0 = 0$ (this will be relaxed in the future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "946c519d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:58:07 - cmdstanpy - INFO - compiling stan file /home/chris/Repositories/hboms/notebooks/seir_model_simulator.stan to exe file /home/chris/Repositories/hboms/notebooks/seir_model_simulator\n",
      "10:58:28 - cmdstanpy - INFO - compiled model executable: /home/chris/Repositories/hboms/notebooks/seir_model_simulator\n",
      "10:58:28 - cmdstanpy - INFO - CmdStan start processing\n",
      "10:58:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "10:58:28 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01//EN\"\n",
       "   \"http://www.w3.org/TR/html4/strict.dtd\">\n",
       "<!--\n",
       "generated by Pygments <https://pygments.org/>\n",
       "Copyright 2006-2023 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "-->\n",
       "<html>\n",
       "<head>\n",
       "  <title></title>\n",
       "  <meta http-equiv=\"content-type\" content=\"text/html; charset=None\">\n",
       "  <style type=\"text/css\">\n",
       "/*\n",
       "generated by Pygments <https://pygments.org/>\n",
       "Copyright 2006-2023 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "*/\n",
       "pre { line-height: 125%; }\n",
       "td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "body .hll { background-color: #ffffcc }\n",
       "body { background: #f8f8f8; }\n",
       "body .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
       "body .err { border: 1px solid #FF0000 } /* Error */\n",
       "body .k { color: #008000; font-weight: bold } /* Keyword */\n",
       "body .o { color: #666666 } /* Operator */\n",
       "body .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
       "body .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
       "body .cp { color: #9C6500 } /* Comment.Preproc */\n",
       "body .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */\n",
       "body .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */\n",
       "body .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */\n",
       "body .gd { color: #A00000 } /* Generic.Deleted */\n",
       "body .ge { font-style: italic } /* Generic.Emph */\n",
       "body .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */\n",
       "body .gr { color: #E40000 } /* Generic.Error */\n",
       "body .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       "body .gi { color: #008400 } /* Generic.Inserted */\n",
       "body .go { color: #717171 } /* Generic.Output */\n",
       "body .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       "body .gs { font-weight: bold } /* Generic.Strong */\n",
       "body .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       "body .gt { color: #0044DD } /* Generic.Traceback */\n",
       "body .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       "body .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       "body .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       "body .kp { color: #008000 } /* Keyword.Pseudo */\n",
       "body .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       "body .kt { color: #B00040 } /* Keyword.Type */\n",
       "body .m { color: #666666 } /* Literal.Number */\n",
       "body .s { color: #BA2121 } /* Literal.String */\n",
       "body .na { color: #687822 } /* Name.Attribute */\n",
       "body .nb { color: #008000 } /* Name.Builtin */\n",
       "body .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       "body .no { color: #880000 } /* Name.Constant */\n",
       "body .nd { color: #AA22FF } /* Name.Decorator */\n",
       "body .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
       "body .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
       "body .nf { color: #0000FF } /* Name.Function */\n",
       "body .nl { color: #767600 } /* Name.Label */\n",
       "body .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       "body .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       "body .nv { color: #19177C } /* Name.Variable */\n",
       "body .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       "body .w { color: #bbbbbb } /* Text.Whitespace */\n",
       "body .mb { color: #666666 } /* Literal.Number.Bin */\n",
       "body .mf { color: #666666 } /* Literal.Number.Float */\n",
       "body .mh { color: #666666 } /* Literal.Number.Hex */\n",
       "body .mi { color: #666666 } /* Literal.Number.Integer */\n",
       "body .mo { color: #666666 } /* Literal.Number.Oct */\n",
       "body .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       "body .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       "body .sc { color: #BA2121 } /* Literal.String.Char */\n",
       "body .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       "body .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       "body .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       "body .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */\n",
       "body .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       "body .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */\n",
       "body .sx { color: #008000 } /* Literal.String.Other */\n",
       "body .sr { color: #A45A77 } /* Literal.String.Regex */\n",
       "body .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       "body .ss { color: #19177C } /* Literal.String.Symbol */\n",
       "body .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       "body .fm { color: #0000FF } /* Name.Function.Magic */\n",
       "body .vc { color: #19177C } /* Name.Variable.Class */\n",
       "body .vg { color: #19177C } /* Name.Variable.Global */\n",
       "body .vi { color: #19177C } /* Name.Variable.Instance */\n",
       "body .vm { color: #19177C } /* Name.Variable.Magic */\n",
       "body .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"kn\">functions</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* vector field */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">ode_fun</span><span class=\"p\">(</span><span class=\"kt\">real</span> <span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">R0m1</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">gamma</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">eta</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">tau</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack the state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">S</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">E</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">V</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare derivatives of state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">ddt_S</span><span class=\"p\">,</span> <span class=\"n\">ddt_E</span><span class=\"p\">,</span> <span class=\"n\">ddt_I</span><span class=\"p\">,</span> <span class=\"n\">ddt_V</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined ODEs */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">beta</span> <span class=\"o\">=</span> <span class=\"n\">gamma</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">R0m1</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">);</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">num_elements</span><span class=\"p\">(</span><span class=\"n\">tau</span><span class=\"p\">);</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">vacc_rate</span> <span class=\"o\">=</span> <span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">eta</span> <span class=\"o\">.*</span> <span class=\"nb\">inv_logit</span><span class=\"p\">(</span><span class=\"n\">t</span> <span class=\"o\">-</span> <span class=\"n\">tau</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">n</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">])</span> <span class=\"o\">.*</span> <span class=\"nb\">inv_logit</span><span class=\"p\">(</span><span class=\"n\">tau</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"o\">:</span><span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">t</span><span class=\"p\">));</span>\n",
       "        <span class=\"n\">ddt_S</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"n\">beta</span> <span class=\"o\">*</span> <span class=\"n\">S</span> <span class=\"o\">*</span> <span class=\"n\">I</span> <span class=\"o\">-</span> <span class=\"n\">vacc_rate</span> <span class=\"o\">*</span> <span class=\"n\">S</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_E</span> <span class=\"o\">=</span> <span class=\"n\">beta</span> <span class=\"o\">*</span> <span class=\"n\">S</span> <span class=\"o\">*</span> <span class=\"n\">I</span> <span class=\"o\">-</span> <span class=\"n\">alpha</span> <span class=\"o\">*</span> <span class=\"n\">E</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_I</span> <span class=\"o\">=</span> <span class=\"n\">alpha</span> <span class=\"o\">*</span> <span class=\"n\">E</span> <span class=\"o\">-</span> <span class=\"n\">gamma</span> <span class=\"o\">*</span> <span class=\"n\">I</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_V</span> <span class=\"o\">=</span> <span class=\"n\">vacc_rate</span> <span class=\"o\">*</span> <span class=\"n\">S</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with derivatives */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">ddt_S</span><span class=\"p\">,</span> <span class=\"n\">ddt_E</span><span class=\"p\">,</span> <span class=\"n\">ddt_I</span><span class=\"p\">,</span> <span class=\"n\">ddt_V</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* initial condition */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">gen_init</span><span class=\"p\">(</span><span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">epsilon</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* declare initial variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">S_0</span><span class=\"p\">,</span> <span class=\"n\">E_0</span><span class=\"p\">,</span> <span class=\"n\">I_0</span><span class=\"p\">,</span> <span class=\"n\">V_0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined initial condition */</span>\n",
       "        <span class=\"n\">S_0</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span> <span class=\"o\">-</span> <span class=\"n\">epsilon</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">E_0</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">I_0</span> <span class=\"o\">=</span> <span class=\"n\">epsilon</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">V_0</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with initival state variables */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">S_0</span><span class=\"p\">,</span> <span class=\"n\">E_0</span><span class=\"p\">,</span> <span class=\"n\">I_0</span><span class=\"p\">,</span> <span class=\"n\">V_0</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* state transform function */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">state_transform</span><span class=\"p\">(</span><span class=\"kt\">real</span> <span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">theta</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare transformed state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">C</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined transformation code */</span>\n",
       "        <span class=\"n\">C</span> <span class=\"o\">=</span> <span class=\"n\">theta</span> <span class=\"o\">*</span> <span class=\"n\">I</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with transformed variables */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">C</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* IVP solver function */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">vector</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">real</span> <span class=\"n\">Time</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">R0m1</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">gamma</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">theta</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">eta</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">vector</span> <span class=\"n\">tau</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"nb\">num_elements</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">);</span> <span class=\"cm\">/* number of time points */</span>\n",
       "        <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"p\">]</span> <span class=\"n\">init_state</span> <span class=\"o\">=</span> <span class=\"n\">gen_init</span><span class=\"p\">(</span><span class=\"n\">epsilon</span><span class=\"p\">);</span> <span class=\"cm\">/* generate initial state */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">sol</span><span class=\"p\">;</span> <span class=\"cm\">/* allocate space for solution */</span>\n",
       "        <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"mf\">4</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">ode_rk45</span><span class=\"p\">(</span><span class=\"n\">ode_fun</span><span class=\"p\">,</span> <span class=\"n\">init_state</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">],</span> <span class=\"n\">R0m1</span><span class=\"p\">,</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"n\">gamma</span><span class=\"p\">,</span> <span class=\"n\">eta</span><span class=\"p\">,</span> <span class=\"n\">tau</span><span class=\"p\">);</span> <span class=\"cm\">/* solve initial value problem */</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span> <span class=\"p\">)</span> <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">5</span><span class=\"o\">:</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">state_transform</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"mf\">4</span><span class=\"p\">],</span> <span class=\"n\">theta</span><span class=\"p\">);</span> <span class=\"cm\">/* compute transformation of the state and append to the trajectory */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">sol</span><span class=\"p\">;</span> <span class=\"cm\">/* and return the result */</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* rng function */</span>\n",
       "    <span class=\"kt\">int</span> <span class=\"n\">Cases_rng</span><span class=\"p\">(</span><span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">phi</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">C</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare variable to-be-returned */</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">Cases</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined sampler */</span>\n",
       "        <span class=\"n\">Cases</span> <span class=\"o\">=</span> <span class=\"nb\">neg_binomial_2_rng</span><span class=\"p\">(</span><span class=\"n\">C</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">);</span> <span class=\"cm\">/* random neg_binomial_2 sample */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">Cases</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">data</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">R</span><span class=\"p\">;</span> <span class=\"cm\">/* number of units */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">N</span><span class=\"p\">;</span> <span class=\"cm\">/* number of observations per unit */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">Time</span><span class=\"p\">;</span> <span class=\"cm\">/* observation times */</span>\n",
       "    <span class=\"cm\">/* non-random parameters and hyper-parameters */</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">alpha</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_gamma</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_gamma</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">theta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">epsilon</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">phi</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">int</span> <span class=\"n\">shape_eta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span><span class=\"p\">[</span><span class=\"n\">shape_eta</span><span class=\"p\">]</span> <span class=\"n\">eta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">int</span> <span class=\"n\">shape_tau</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"n\">shape_tau</span><span class=\"p\">]</span> <span class=\"n\">tau</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* hyper parameters for correlated parameters */</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">parameters</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* no parameters */</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">model</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* no model */</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">generated quantities</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* random parameter declarations */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">R0m1</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">gamma</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* simulated observation declarations */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">int</span> <span class=\"n\">Cases</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* simulate for each unit */</span>\n",
       "    <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">r</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">R</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* sample random parameters */</span>\n",
       "        <span class=\"n\">R0m1</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">lognormal_rng</span><span class=\"p\">(</span><span class=\"n\">loc_R0m1</span><span class=\"p\">,</span> <span class=\"n\">scale_R0m1</span><span class=\"p\">);</span>\n",
       "        <span class=\"n\">gamma</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">lognormal_rng</span><span class=\"p\">(</span><span class=\"n\">loc_gamma</span><span class=\"p\">,</span> <span class=\"n\">scale_gamma</span><span class=\"p\">);</span>\n",
       "        <span class=\"cm\">/* sample random correlated parameters */</span>\n",
       "        <span class=\"cm\">/* solve initial value problem */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">5</span><span class=\"p\">]</span> <span class=\"n\">sol</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]],</span> <span class=\"n\">R0m1</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"n\">gamma</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">theta</span><span class=\"p\">,</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"n\">eta</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">tau</span><span class=\"p\">);</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"n\">Cases</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Cases_rng</span><span class=\"p\">(</span><span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">phi</span><span class=\"p\">);</span> <span class=\"cm\">/* simulate data at observation times */</span>\n",
       "        <span class=\"p\">}</span>\n",
       "    <span class=\"p\">}</span>\n",
       "<span class=\"p\">}</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = [120 for _ in range(R)]\n",
    "\n",
    "Time = [list(range(1, Nr+1)) for Nr in N]\n",
    "ID = [f\"Epi {i}\" for i in range(1, R+1)]\n",
    "\n",
    "data = {\"Time\" : Time, \"ID\" : ID}\n",
    "\n",
    "sims = hbm.simulate(data, 10) ## 10 simulated data sets\n",
    "util.show_stan_model(hbm.simulator_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a0229c",
   "metadata": {},
   "source": [
    "## Check intial guesses. \n",
    "\n",
    "Fitting Bayesian ODE models is (in my experiance) hopeless if you don't have a reasonable initial parameter guess. I know that is is good practice to start the MCMC from a random state, but with ODEs that just does not work that well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf4f0a56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:58:28 - cmdstanpy - INFO - CmdStan start processing\n",
      "10:58:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "10:58:29 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data, sim_draws = sims[-1] ## pick one of the simulated data sets\n",
    "state_var_names = [\"C\"]\n",
    "fig = hbm.init_check(sim_data, state_var_names=state_var_names, ppd=\"envelope\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff6463c",
   "metadata": {},
   "source": [
    "## Now fit the model\n",
    "\n",
    "The `sample` method passes keyword arguments to the `sample` method from `cmdstanpy`. It is a good idea to choose a small `step_size` (which is used as an initial step size for HMC), otherwise you might ruin your carfully chosen initial parameter guess instantly. Increase `threads_per_chain` to the number of units if you have a large computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b58435f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:58:31 - cmdstanpy - INFO - CmdStan start processing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7c128f17a784eada8adc9305a7e0783",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11:00:15 - cmdstanpy - INFO - CmdStan done processing.\n",
      "11:00:15 - cmdstanpy - WARNING - Non-fatal error during sampling:\n",
      "Exception: Exception: neg_binomial_2_lpmf: Location parameter is -1.10936e-08, but must be positive finite! (in 'seir_model.stan', line 74, column 8 to column 55) (in 'seir_model.stan', line 155, column 12 to column 58)\n",
      "Consider re-running with show_console=True if the above output is unclear!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hbm.sample(\n",
    "    sim_data, chains=1, threads_per_chain=R,\n",
    "    step_size=0.01, refresh=1,\n",
    "    iter_sampling=300, iter_warmup=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26894b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check out the fits...\n",
    "\n",
    "fig = hbm.post_pred_check(sim_data, state_var_names=['C'], ppd=\"envelope\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae16667",
   "metadata": {},
   "source": [
    "## Look at the parameter estimates\n",
    "\n",
    "The CmdStanPy fit object can be found in `hbm.fit`. The method `stan_variable` extracts the trace of a single parameter, while `stan_variables` extracts a python `dict` with all samples. You might explore further statistics with the `arviz` package. For instance, the generated quantity `log_lik` contains the log-likelihood of all observations and can be used for model comparison, using `arviz.loo` and `arviz.compare`.\n",
    "\n",
    "The method simulate returns a list of pairs of simulated data sets and the corresponding random parameter draws, both in a dictionary. We can now compare the conditional distribution of the random parameters with the ground-truth values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4304be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\gamma$')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R0m1_hats = hbm.fit.stan_variable(\"R0m1\")\n",
    "gamma_hats = hbm.fit.stan_variable(\"gamma\")\n",
    "\n",
    "R0m1_gt = sim_draws[\"R0m1\"]\n",
    "gamma_gt = sim_draws[\"gamma\"]\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(7,5), sharex=True)\n",
    "\n",
    "pos = list(range(R))\n",
    "axs[0].violinplot(R0m1_hats + 1, positions=pos)\n",
    "axs[0].scatter(pos, R0m1_gt + 1, color='r')\n",
    "\n",
    "axs[1].violinplot(gamma_hats, positions=pos)\n",
    "axs[1].scatter(pos, gamma_gt, color='r')\n",
    "\n",
    "axs[1].set_xticks(pos)\n",
    "axs[1].set_xticklabels(ID, rotation=90)\n",
    "\n",
    "axs[0].set_ylabel(\"$R_0$\")\n",
    "axs[1].set_ylabel(\"$\\\\gamma$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0e20e9",
   "metadata": {},
   "source": [
    "## Vaccination coverage\n",
    "\n",
    "In the posterior predictive checks above, we could also include the variable $V$, which corresponds to the total fraction of vaccinated individuals. However, $V \\in [0,1]$ and the observed cases are much larger, so we'll plot $V$ separately. The variable $V$ should pretty much correspond to the vaccination data. However, in the model we only vaccinate $S$-individuals, and the rate is per-capita, so there might be some discrepancies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4569b192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'total fraction vaccinated')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+wAAALECAYAAACSUbpnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAADjIklEQVR4nOzdd3iV5f3H8fc52XvvhIQMSMJIgDBVcCCIE1eprULpr7a2WgetsxVXK26prZXaVuvee4EQBQd7jwxm9p4ne5zz/P4IRhFQAiHnJPm8rotL8pznhC9H7pPzeZ77/t4mwzAMRERERERERMShmO1dgIiIiIiIiIgcToFdRERERERExAEpsIuIiIiIiIg4IAV2EREREREREQekwC4iIiIiIiLigBTYRURERERERByQAruIiIiIiIiIA1JgFxEREREREXFAzvYu4GSz2WyUlJTg4+ODyWSydzkiA5ZhGDQ0NBAZGYnZfOLXAjV2RfqOxq9I/6SxK9J/Hev4HfCBvaSkhJiYGHuXITJoFBYWEh0dfcLfR2NXpO9p/Ir0Txq7Iv3Xj43fAR/YfXx8gK4XwtfX187ViAxcFouFmJiY7jF3ojR2RfqOxq9I/6SxK9J/Hev4HfCB/ZvpPL6+vnrjEekDvTWFTmNXpO9p/Ir0Txq7Iv3Xj41fNZ0TERERERERcUAK7CIiIiIiIiIOSIFdRERERERExAEpsIuIiIiIiIg4IAV2EREREREREQekwC4iIiIiIiLigBTYRURERERERByQAruIiIiIiIiIA1JgFxEREREREXFACuwiIiIiIiIiDkiBXURERERERMQBKbCLiIiIiIiIOCAFdhEREREREREHpMAuIiIiIiIi4oAU2EVEREREREQckAK7iIiIiIiIiANSYBcRERERERFxQArsIiIiIiIiIg5IgV1ERERERETEASmwi4iIiIiIiDggBXYRERERERERB6TALiIiIiIiIuKA+jywP/nkk8TFxeHu7s7EiRNZv379Uc99++23ycjIwN/fHy8vL9LT03nhhRf6sFoRERERERER++jTwP7aa6+xYMEC7rrrLjZv3kxaWhozZ86koqLiiOcHBgbypz/9iTVr1rB9+3bmz5/P/PnzWbZsWV+WLSIiIiIiItLn+jSwP/bYY1x99dXMnz+f1NRUlixZgqenJ88888wRzz/99NO5+OKLSUlJISEhgRtuuIHRo0fz1Vdf9WXZIiIiIiIiIn3Oua/+oPb2djZt2sTtt9/efcxsNjN9+nTWrFnzo883DIPPPvuM3NxcHnzwwaOe19bWRltbW/fXFovlxAoXkT6hsSvSf2n8ivRPGrsijq/P7rBXVVVhtVoJCws75HhYWBhlZWVHfV59fT3e3t64urpy3nnn8fe//52zzz77qOcvWrQIPz+/7l8xMTG99ncQkZNHY1ek/9L4FemfNHZFHJ/Dd4n38fFh69atbNiwgb/+9a8sWLCAlStXHvX822+/nfr6+u5fhYWFfVesiBw3jV2R/kvjV6R/0tgVcXx9NiU+ODgYJycnysvLDzleXl5OeHj4UZ9nNptJTEwEID09nezsbBYtWsTpp59+xPPd3Nxwc3PrtbpFpG9o7Ir0Xxq/Iv2Txq6I4+uzO+yurq6MGzeOzMzM7mM2m43MzEwmT558zN/HZrMdstZGREREREREZCDqszvsAAsWLGDevHlkZGQwYcIEFi9eTFNTE/Pnzwdg7ty5REVFsWjRIqBrXU1GRgYJCQm0tbXx8ccf88ILL/DUU0/1ZdkiIiIiIiIifa5PA/ucOXOorKxk4cKFlJWVkZ6eztKlS7sb0RUUFGA2f3vTv6mpid/97ncUFRXh4eFBcnIyL774InPmzOnLskVERERERET6nMkwDMPeRZxMFosFPz8/6uvr8fX1tXc5IgNWb481jV2RvqPxK9I/aeyK9F/HOt4cvku8iIiIiIiIyGCkwC4iIiIiIiLigBTYRURERERERByQAruIiIiIiIiIA1JgFxEREREREXFACuwiIiIiIiIiDkiBXURERERERMQBKbCLiIiIiIiIOCAFdhERERERB9Pc3mnvEkTEASiwi4iIiIg4CKvNYEVWOTuLLfYuRUQcgAK7iIiIiIgDaGjt4PWNheworscwDHuXIyIOQIFdRERERMTOCmuaeXldAWX1rbR2WPnPl/upb+6wd1kiYmcK7CIiIiIidrS5oJa3NxfT3G6lrL6Vl9cXsDy7gjve3WHv0kTEzpztXYCIiIiIyGDUabWxIruc7NIGDMNga2EdX+2twmZAmK8bv52WYO8SRcTOFNhFRERERPqYpbWDD7eVUm5ppa3DyvLscvZVNgGQFOrNHecmMzLKz85Vioi9KbCLiIiIiPSh4roWPtxWQnO7lYqGVj7eUUZ9SwdOJhOnDQtmdJQfnq76mC4iCuwiIiIiIn1mZ3E9n+VU0Gm1savEwsrdlVhtBr7uzswaFUG4rzv+ni4MDfGyd6ki4gAU2EVERERETjKbzWDVnkq2FtTRYbXxeW4F2aUNAAwN9mJGahjuLk6kRvpy+vAQ3Jyd7FyxiDgCBXYRERERkZOotcPKxztKya9upq65nY92lFLV2I4JmJIQxLjYANxdnTgrOYzh4T72LldEHIgCu4iIiIjISVLX3M57W0uoaWpnf2Ujy7LKae+04eHixKyR4cQEehLp7845IyPw83Cxd7ki4mAU2EVEREREToLCmmY+3F5Kc3sn6/bXsD6vBoAIP3fOHRmBj4czE+ICmRQfhNlssnO1IuKIFNhFRERERHrZN83lmto6WbqzjPyaZgDSov04LSkEXw9nzhkRwZAgTztXKiKOTIFdRERERKSXGIbBV3ur2JhXS2VDGx9uL8HS2omz2cRZyaEkR/gSG+TJOSPDtXWbiPwovUuIiIiIiPSCDquNpTvL2FvRSG5ZAyuyy+k8uGXb+aMjCfN1Z3JCEOPjAjCZNAVeRH6cAruIiIiIyAlqauvk/W0llNS1sHpfNZvyawGIDey6mx7s7casUeFEB2gKvIgcOwV2EREREZETUN3YxrtbS6iwtB6yXj0jNoDJCUEMDfbSFHgROS561xAREREROU6FNc18sL2E0rpW3t9WQn1LB85mE2endu2pPik+iIlDAzUFXkSOiwK7iIiIiMhxyCmz8OmucvZUNLBsZzntVhs+7s5cMDqS2CBPZo1UF3gROTEK7CIiIiIiPbQhr4av9lSyMa+Wr/dVAxDl78G5o8JJDPXm3FER+Li72LlKEenvFNhFRERERI6RYRiszK1kY14NmTkV5JQ1ADAyypfTh4UyLi6AqUkhOJk1BV5ETpwCu4iIiIjIMei02vhkZxnbCuv4aEcppfWtmEwwbVgIGbEBnJUSRkqEr73LFJEBRIFdRERERORHtHZYeX9rCduK6nh/WwkNrZ24OZs5d1QEo6L8uCAtkhAfN3uXKSIDjAK7iIiIiMgPaGjt4N0txWzIq+WTnaV0WA38PVy4MD2SMUP8mTUyAncXJ3uXKSIDkAK7iIiIiMhRVDe28c6WYr7cU8UXuysxgOgAD84fHcHUpBAmJwRpyzYROWkU2EVEREREjqC4roV3NxfzaVYZ24rqARgR6cvMEeEHu8H72LlCERnoFNhFRERERL5nb0Uj720p5v3tJeRXNwNwamIwZyWHcGF6FEHeWq8uIiefAruIiIiIyHdsLazj/W3FvL+1hKrGdpzNJmaOCGd6aqjWq4tIn1JgFxERERGha4/1L/dU8dGOUj7YVkJzuxVPVycuSIvkvFERnJoYjFn7q4tIH1JgFxEREZFBr8NqY+nOMj7aXsry7HKsNoMgb1cuSY/i4rHRpEZqf3UR6XsK7CIiIiIyqDW2dfLulmLe29q1dRtAXJAnl42N5rKMGML93O1coYgMVgrsIiIiIjJolVtaeX1DIW9tLiLvYHO5cUMCuCwjigvSovB208dlEbEfvQOJyI9q77Th6my2dxkiIiK9and5Ay+vy+fdLSXUtXTgZDZxVnIoP8mI4YzkUJy0Xl1E7EyBXUSOqLXDSnaphe1FdTiZzVwyNgpPV71liIjIwLB2fzUvrMnj06xyOqwG3m7OzE6P5KcThjAyys/e5YmIANDnt8yefPJJ4uLicHd3Z+LEiaxfv/6o5/773//mtNNOIyAggICAAKZPn/6D54vIiSu3tPLprjIWL9/NQ8tyeWz5Hv787k4+3lFm79JEREROWKfVxkfbSnh4WS4f7Sijw2oQHeDBb6bGc/1ZSQrrIuJQ+vR22WuvvcaCBQtYsmQJEydOZPHixcycOZPc3FxCQ0MPO3/lypVcccUVTJkyBXd3dx588EFmzJjBrl27iIqK6svSRQa0TquN3PIGthTUsXZ/NbtKLORVNWEcfNzDxYm65na71igiInKimts7eXV9Af9bnU9BTdd69bFD/LlqchyzRoZrf3URcTh9Gtgfe+wxrr76aubPnw/AkiVL+Oijj3jmmWe47bbbDjv/pZdeOuTr//znP7z11ltkZmYyd+7cPqlZZCCrb+5gW1EdX++tYlN+LdmlFprard2PR/q5MyLSj19PjWf80EA7VioiInJiqhvbWPLFfl5bX4CltRNns4mZI8L55alDGRcbYO/yRESOqM8Ce3t7O5s2beL222/vPmY2m5k+fTpr1qw5pu/R3NxMR0cHgYEKDiLHyzAM8qub2ZhXw2e5FeworqewpqX7cQ8XJ1IifBgzxJ/J8cGkRPoS5e9hx4pFREROTEF1M498msPHO8rotBn4ebgwJyOa+acOJcJPP+NExHH1WWCvqqrCarUSFhZ2yPGwsDBycnKO6XvceuutREZGMn369KOe09bWRltbW/fXFovl+AoWGWBaO6xklVr4PKeC1fuqySqx0NLx7d302EBPRkX5MW14MKOjA0gI8cLZqe/aXGjsivRfGr/iyLYX1fGXj7JZf6AGgCGBnvxmajyXjI3Gw3VwT4HX2BVxfP2m5fMDDzzAq6++ysqVK3F3dz/qeYsWLeKee+7pw8pEHFtNUzub8mv4YFspWwpqKaz99m66l5sTIyL8mJIYxCmJwaRE+Nptv1mNXZHe1d5pY/2BGk5NCj7pf5bGrzgiwzBYkV3O/R/ncKCqCYBxsQH88exhTEoIwmTSlm0auyKOz2QYhvHjp5249vZ2PD09efPNN5k9e3b38Xnz5lFXV8d777131Oc+8sgj/OUvf2HFihVkZGT84J9zpCuFMTEx1NfX4+vre8J/D5H+wDAM8qqbycwu59NdZewssdD8nbXpsUGejBniz6wREYyO8euV6YAWiwU/P7/jHmsauyK9J7+6iRXZFbS0dzL/lKF4/ciFOI1fGWg6rTZeXlfA4sw91DS142Q2ce7IcG6emcyQIE97l9drNHZF+q9jHb99divN1dWVcePGkZmZ2R3YbTYbmZmZXHfddUd93kMPPcRf//pXli1b9qNhHcDNzQ03N7feKlukX2nvtLGzpJ63Nxfx9d7qQzq9e7o6MTLSl+mp4ZyWFExiqDcufTjl/cdo7IqcuNYOK6t2V5JVYqG2uZ0vdleyPq+W5+aPP6l3EzV+xZE0t3fy2Ke7eWFtPm2dNrzcnPjF5DiuOT0BH3cXe5fnUDR2RRxfn859XbBgAfPmzSMjI4MJEyawePFimpqaurvGz507l6ioKBYtWgTAgw8+yMKFC3n55ZeJi4ujrKxrH2hvb2+8vb37snQRh9bQ2sGXeyp5fWMRWwrqqG/p6H4sOsCDCUMDmZ0eSVp0AH6e+rAiMhDtq2zks+wK6prb2Zhfy8b8Wqw2g5L6VvZVNpIY6mPvEkVOuqqGVu54ZyfLs8oxgDBfN245Zziz06NxMmsKvIj0P30a2OfMmUNlZSULFy6krKyM9PR0li5d2t2IrqCgALP52zt+Tz31FO3t7Vx22WWHfJ+77rqLu+++uy9LF3FI5ZZW3t1SzPtbS8gpb8Bq67qf7uZsZkSkL+eOiuCslDDigjy1Vk9kgGrtsLIyt5LsUguFNc18lltBXXPXRbu4IE+enT+BocFedq5S5OTLKbWw4I1tZJV0NU5LjfDl/otHkT7E376FiYicgD7vLnXdddcddQr8ypUrD/k6Ly/v5Bck0s8YhsHu8gaeW53H57mVlNa3dj8W4u3G5IRA5mTEMCY2AE/XftNXUkSOw77KRjKzy6lsaOOrPVVklzUAXUtgpiaFMDLKV2FdBoVPdpSw8P0sKhvaMJlgRkoY984eQZivtmwTkf5Nn+ZF+gmrzWDNviqe+foA6/bX0HSwiZzZBMPCfDh/dAQXpkUSE6i76SID3Td31bNK6skua+DLPZW0dtgAGB3VtfNDgKcrpySe/A7xIvbUabXxt8w9/PuL/bR22vBwceKXp8bx+zOTcHcZ3Fu2icjAoMAu4uA6rDY+3FbC/1bnsbPE0j3t3dPViQlxgVw5eQinJIQM+r1kRQaL/OomlmeVd01/z6no3qoxyNuVs5JDifL3JC3Gj0nxQQosMqBZWjr4w+tbWZ5dAXStV7/nwhHMHBGuC9ciMmAosIs4qLYOK69sKOD51fnsP7h/LHR9IDlnZDhzJ8URH+KlDyUig0SH1caXeyrZnF/HloJa1h6owWozcDabmDg0kDFDAogL9uL04SEEe6vrswxse8ob+O1Lm9lb0QjA2CH+PPqTdC0BEZEBR4FdxMF0dFp5dnU+z63Oo7iu686ZCRge7sMVE4ZwydgobUsjMsiU1rewdGcZu8saWJFTQWVD177JMYEenDk8lJhAT05LCmF4uDrBy8D3yY5Sbnt7B/UtHTiZTczJiObO80doppmIDEgK7CIOwmq18dyafP795f7uRnJOZhPjYwP49bR4pg0L1ZY0IoOM1Wawbn81a/ZVs3p/NZsLajGMrp0gvmkqNzY2gIlDg3B1Nv/4NxTp5x5ZlsuSVfvotBn4ujtz5/mpXDYuWrPNRGTAUmAXsTPDMHhlfQFPfr6v+466i5OJqUkh3Dg9iVHR/vYtUETsoqapnaU7y9hcUMuK7PLurdqSQr2ZNiyE5AhfzhgeQpCmv8sg0NreybWvbCHz4Hr1+GAv/vnzsSRH+Nq5MhGRk0uBXcSO3t9WwqOf5pJf3Qx0BfWzkkO5eWYyCaHedq5OROxle1EdK7LKWbW7km1F9QB4uTpxRnIoo6P9mDoshORwBRUZHMrqW7nqv+vYc3C9+vSUUP720zF4ueljrIgMfHqnE7GDL3dXct9HWewu7/rw4Ww2cVZKKLfNSmZosIK6yGDV0m7l06wyVuVWsiK7HEtrJwCpEb5MHRbMxKFBTE5Q93cZPDbn1/Kr5zdS09SOs9nE9Wcl8fszEzUFXkQGDQV2kT60vaiOu9/fxeaCOqBrD/UzkkP507kpxIcoqIsMZgXVzXywrYRlWWVsP3hX3dvNmekpoUwYGsRZKaGE+brbuUqRvvPBthL++MY22jpt+Lo787efjuGM5FB7lyUi0qcU2EX6QF5VI3e9n8UXuysx6Or6fmpSMAvPTyUpTF2dRQYzq81g9b4q3t9awqdZ5dS3dK1VHxnpy5kpoZwxPJS0aH/Majopg8iSlft4aFkONgNigzx5/pcTiA3Slm0iMvgosIucROX1Lfz14xw+3F6Czeg6lhEbwMILUhmtZnIig159cwfvbSvmnS3FbDk48+abu+pnpoRxxvAQbeMog4phGNz/cTb//vIAABPiAvnf/PF4ar26iAxSevcTOQmqG9t4aFkub28uosPaldRHRPry5/NSmJwQbOfqRMQR7C5v4IU1+Xy0vZSa5nag633i3JHhzBwZQaIaT8ogYxgGt729g9c2FAJwyZgoHv1Jmtari8igpsAu0ouqGtt4bPlu3thY2B3UE0K8uOPcFM5KCbNzdSLiCDqtNjKzK3h29QHWH6jBZoCnqxPTU0KZPSaKUxKDcXNWUzkZXAzD4I53vg3r10yL57ZZKXauSkTE/hTYRXpBhaWVx1fs5s1N395RHxrsyc0zhzNrZITuDogI0LW3+vNr8nh1fSFlllaga1/1S8ZGcWF6FFH+HnauUMQ+7v0wi1fWd4X1W88Zzm9PT7RzRSIijkGBXeQElNa38Pjy3by9uZhO27d31P8wYzizRoYrqItIt5xSC4+v2M1nORV0WA1cnc2cOTyUKycNYVJ8EM5OZnuXKGIXT36+l2e/zgPg5hkK6yIi36XALnIciuu6gvq7W74N6sPDfbhl5nDOTA5VUBeRblabwcfbS1mcuZt9lU0ARPt78NMJMfwkI4ZQbdUmg9g7W4p5eFkuANedkci1Zyqsi4h8lwK7SA8U17WwePlu3t5SjPVgUB8V5cst5yRzamKwgrqIHKKhtYMnMvfwyvpCGts6MZvglMRgfnt6AhOHBuGkrdpkEFt/oJqb39gGwE8yovnDjGF2rkhExPEosIscg7L6Vhav2M0bm4q6g3p6jD+3njNcXd9F5IgOVDXy53d28vW+agD8PV342cQhzJ8ylBAfNztXJ2Jf+dVN/PJ/G+m0GUxNCmbRJaN10VtE5AgU2EV+QHVjG098toeX1xV0N5MbE+PPbbOSmRgfZOfqRMRRZWaXs/C9XRTXtQBd27X9ccZwpg4L0V11GfTqWzq46r/raGzrZFiYN/+6KkPjQkTkKBTYRY6gsa2Tp1ft4+kv99PaYQNgdLQft89KYXKCgrqIHJnVZvD3z/bwr1X7aemw4upk5sL0SG6eOZwwrVUXwWoz+O2LmyioaSHIy5UX/28iHq7axlBE5GgU2EW+o8Nq49X1BTy6fDd1zR0ADA/z4Y7zUpiapDXqInJ0ja0d/OGNbSzbVQ5AiLcbC2YM47Jx0bioA7wIAH/9KIvV+6pxdTLz3C8nqOmiiMiPUGAXAQzD4PPcCu79IIu86mYAovw9uOPcFM4dpe3ZROSH5VU18esXNrK7vBGAtGg/Hrx0NMkRvnauTMRxvL25iGcObt/26E/SGBnlZ9+CRET6AQV2GfT2lDdw9we7+HpvV2MoPw8XbpqexM8nxequmIj8qFW5Fdz42lZqmztwMpu4bGwUCy8YgZebfsSKfGN7UR23vrUdgN9OS+CCtEg7VyQi0j/o04QMWvXNHTy+YjfPr8nDZoCz2cT8U4by+7MS8XV3sXd5ItIPPP3FPh5Ztpt2qw0fN2f+dF4Kc8bHaFaOyHdUNbbxq+c20mE1mDosmJtnDrd3SSIi/YYCuww6VpvBaxsKeWhpDnUtXevUp6eEcuf5qcQGedm5OhHpD2w2G398cztvby4GYEigJ//8+VhN8RX5nk6rjd++uImKhjaGBHryj5+NxayO8CIix0yBXQaVTfk13PneLrJKLAAkhHhx70UjOSVRe6mLyLGxtLQz79kNbCmoA+DUxCD++fNx+HpoZo7I993/cTYb8mrxcHHimV9kaAabiEgPKbDLoFBhaWXRJzm8s6Xrbpi3mzN/mDGMqybF4qx16iJyjPZXNnLlf9dRUteK2QS/OjWe289N1hR4kSN4f1tJd5O5x+ekkRjqY9+CRET6IQV2GdDaOq387+s8/pa5h+Z2Kybg8oxobjknmWBvN3uXJyL9yJe7K7nmpU00tVnxdHXigUtGcWF6lL3LEnFIOWUWbnljGwDXTIvnnJERdq5IRKR/UmCXAckwDJbuLGPRJzkU1HRt0zY62o97LxpJeoy/fYsTkX7nf18f4L6PsrHaDMJ93Xl2/nhStGWbyBHVt3Tw6+c30dppY0pCEDfPTLZ3SSIi/ZYCuwwohmHwxZ4qHl2Wy/biegCCvV259ZxkLh0brUY3ItIjNpvBn97dwSvrC4Gu/dWf++UE/D1d7VyZiGOy2QxufHULBTXNhPu684+fjcVJP3tFRI6bArsMCJ1WG0t3lbFk1T52Fnc1lHN3MXP1afFcMy1B+yGLSI81tnUy/9kNbMirAeCSsVE8clmaLvyJ/IAnPtvD57mVuDiZ+PfcDAK9dHFLROREKMVIv1bb1M6rGwp5fk0epfWtALg5m7lyUiy/PT1B69RF5LjsKW9g7jPrKa1vxcls4k/npvDLU4fauywRh7Y8q5zFK/YAsOiS0YyK1jaHIiInSoFd+qWdxfU8vyaP97aW0NZpAyDQy5WrJsUyb0qcruiLyHF7Z0sxt7+1ndZOG77uzvzrqnFMTtDWjyI/JLesgRte3QLAVZNiuWxctJ0rEhEZGBTYpd/osNr4ZGcZz63OY1N+bffxEZG+zJsSx4Vpkbi7ONmxQhHpz1o7rNzxzg7e3ty1/ePwcB+enz+BMD93O1cm4thqmtr5v+c20NxuZVJ8IAsvSLV3SSIiA4YCuzi8uuZ2XlpXwHOr86hoaAPAyWzivFERzJsSy9ghAdoDWUROSHZpPb95YXP3rhJXjI/h3tkjcXEy27kyEcfW1mnlNy9spKi2hegAD/7583EaNyIivUiBXRxWuaWVf63azyvrC2jpsAJdHd+vnBTLzyYMIdRXd71E5MTYbAb/+mIfj366m06bgY+bM49cnsbMkeH2Lk3E4RmGwe1v7WBDXi1ebk4884vxWpImItLLFNjF4VhaO/jHZ3t59usDdFgNAFIjfPnVaUM5f3Qkrs66ci8iJ66kroVrX97MloI6ACbEBfLkz8cS4qNmlSLH4onMvby9pRgnEzz183EMC/Oxd0kiIgOOArs4DMMweHdrMfd+kEVtcwcA4+MC+P2ZSZyWFKxp7yLSKwzD4N0txdzx7k5a2q24Opm5bVYy80+J0/uMyDF6a1MRj6/YDcC9s0cydViInSsSERmYFNjFIVQ1tnH72ztYnlUOQEKIF38+L5XTh4foA7SI9Jr65g5ueWsby3Z1vdcMD/NhyVXjGBrsZefKRPqP1fuquPWt7QBcMy2Bn0+MtXNFIiIDlwK72N3a/dVc/8oWKhracDabuHF6Er+ZlqCmNSLSq9bsq+b3r2ymqrEdswl+e3oCN00fhrPea0SO2Z7yBn7z/CY6bQbnj47glpnD7V2SiMiApsAudmMYBk+t2scjy3KxGZAY6s3frxhDSoSvvUsTkQGkvdPGw8ty+M+XBzCASD93/nnlONJj/O1dmki/UtHQyi+eXU9DWyfjYgN45PI0zGbNghMROZkU2MUuGlo7+OMb305LvXRsNPfNHoGnq/5Jikjv2VPewHUvbyG3vAGAS8ZGcd9FI/Fy03uNSE+0tFv5v/9tpLiulbggT/49NwN3Fyd7lyUiMuDpE4v0uf2VjVz9/Eb2VTbh4mTi3otGcsWEIfYuS0QGEJvN4H+rD/DAJ7m0W234uDvz8GVpnKPt2kR6zGozuOHVLeworsff04X/zZ+g7dtERPqIArv0qc9zKvj9K1tobOsk3Nedp64cy5ghAfYuS0QGkPzqJha8vo1N+bUAnJIQxONz0gn1dbdzZSL906KPs/k0qxwXJxP/mZtBnJo0ioj0mT7vtPPkk08SFxeHu7s7EydOZP369Uc9d9euXVx66aXExXVttbN48eK+K1R6lWEY/HPlXn753AYa2zoZHxfA+78/RWFdRHpNe6eNv3+2h7Mf+4JN+bW4O5u576IRvPiriQrrIsfphbX5/OerAwA8cnkaGXGBdq5IRGRw6dPA/tprr7FgwQLuuusuNm/eTFpaGjNnzqSiouKI5zc3NxMfH88DDzxAeLimMfZXLe1Wrn91Kw8tzcUw4GcTh/DSryYR6qMP0CJy4gzDIDO7nOmPreLRT3fTbrUxcWggn940jasma291keO1MreCu9/bBcAfZwzjovQoO1ckIjL49OmU+Mcee4yrr76a+fPnA7BkyRI++ugjnnnmGW677bbDzh8/fjzjx48HOOLj4viK61r49fMb2VViwdls4u4LR3DlJO3XKiK9I6vEwl8+ymL1vmoAAj1dWXhBKhelRyqoi5yArBILv3tpM1bD4NKx0Vx7RqK9SxIRGZT6LLC3t7ezadMmbr/99u5jZrOZ6dOns2bNmr4qQ/rQuv3V/O6lzVQ3tRPo5cqSK8cxYaim0onIiSurb+WRT3N5a1MRBuBsNvGr04Zy7RmJ+Li72Ls8kX6ttL6F+f9bT3O7lSkJQSy6ZJQugImI2EmfBfaqqiqsVithYWGHHA8LCyMnJ6fX/py2tjba2tq6v7ZYLL32veXYGIbBi2vzueeDLDptBqkRvjw9dxzRAZ72Lk0cmMauHIvGtk6eXrWPf32xn7ZOGwDnjYrg1nOSGRKk9xh70fgdOOqa25n3zHrKLW0khXrz1JXjcHXu85ZH0kc0dkUc34B7B160aBF+fn7dv2JiYuxd0qDS2mHl5je3c+d7u+i0GZw/OoK3fjtFYV1+lMau/BCrzeCV9QVMe+hznvhsL22dNsYO8eft303hyZ+PVVi3M43fgaGl3cqvntvI7vJGwnzceHb+ePw8NGNlINPYFXF8fRbYg4ODcXJyory8/JDj5eXlvdpQ7vbbb6e+vr77V2FhYa99b/lh+dVNXPrUat7cVITZBHecm8zfrxiDh6uTvUuTfkBjV47m671VzPrbF9z+9g6qm9oZEujJUz8fy1u/ncJY7TThEDR++7+mtk5+8ex6NubX4uvuzPP/N1EX2wcBjV0Rx9dnU+JdXV0ZN24cmZmZzJ49GwCbzUZmZibXXXddr/05bm5uuLm59dr3k2OzdGcZN7+5jYbWTgI9XXniijGcmhRs77KkH9HYle8rqG7mrx9nsWxX14VeH3dnbjgribmT4zRF18Fo/PZv9c0d/PK5DWzKr8XLzYln549neLiPvcuSPqCxK+L4+rRL/IIFC5g3bx4ZGRlMmDCBxYsX09TU1N01fu7cuURFRbFo0SKgq1FdVlZW9++Li4vZunUr3t7eJCaqW6kjaO2wcv/H2Ty/Jh+AcbEB/ONnY4jw87BzZSLSX7V2WHn6i/08+XnX1HezCa6aFMuN04cR4OVq7/JEBpSsEgvXvLiJgppmfN2deeH/JpIW42/vskRE5KA+Dexz5syhsrKShQsXUlZWRnp6OkuXLu1uRFdQUIDZ/O1dk5KSEsaMGdP99SOPPMIjjzzCtGnTWLlyZV+WLkewt6KB617eQk5ZAwC/mRrPH2cOx8VJd75E5Phsyq/l5je3sb+yCYDJ8UHcc9EIhoXpbp9Ib7LZDF5cl8/9H2fT2mEjOsCDf8/NICXC196liYjId/RpYAe47rrrjjoF/vshPC4uDsMw+qAq6QnDMHh1QyH3fLCL1g4bQV6uPPKTNM4YHmrv0kSkn2rrtPLYp7t5+ov9GECwtysLLxjBBaMjtJ2USC/bW9HIHe/sYP2BGgCmDgvhiZ+m4++pGSwiIo6mzwO79G/1zR3c/s52Pt5RBsCpicE89pM0Qn3d7VyZiPRXeysaueHVLewq6dpO6NKx0dx5forCg0gva2rr5InP9vDMVwfosBp4ujpx26xkrpwYi9msC2MiIo5IgV2O2ca8Gm54dSvFdS04m038ceZwfn1avH7Ii8hxe2tTEX9+dwctHTb8PV146NLRzBjRezuHiEjXzLj3tpaw6JNsyi1de25PTwnlrgtGEBOoTvAiIo5MgV1+lNVm8OTne1m8Yjc2A2KDPHnip2PUlEZEjltrh5WF7+3k9Y1FAExJCOLxOemEabaOSK/aXlTH3e/vYnNBHdD1M3zh+amclRJm38JEROSYKLDLDyqtb+HGV7ey7uA6t4vHRHHf7JF4u+mfjogcn/zqJq55cRPZpQ2YTXDj9GFce0YiTpqtI9JrqhrbeHhpLq9vKsQwwNPViWvPSORXpw3FzdnJ3uWJiMgxUuqSo8rMLuePb2yjtrkDL1cn7r1oJJeOi7Z3WSLSjy3PKmfB61tpaO0k0MuVv18xhlMSg+1dlsiA0WG18fyafB5fvpvGtk6g62L7bbOSNYNFRKQfUmCXw3RYbTy0NId/f3kAgJFRvvz9irEMDfayc2Ui0l9ZbQaPLc/lyc/3ATAuNoAnfzaWcD8FCJHesmZfNQvf28meikYARkX5cfeFIxgXG2DnykREBi7DMKhuaie/uom9FY3sKW+koa2TBy8d3SvfX4FdDlFc18J1L29my8G1bvNPieO2WcmaPicix62mqZ0bXt3Cl3uqAPjFlDjuODcFV2eznSsTGRgqLK385aNs3t9WAkCgpyu3nDOcn2TEqDGsiEgvMAyDioY2DlQ1sa+ikd0VDeyvbKKwppnS+lbaOm2HPef2Wcm9suONArt0+2J3Jde/soW6lg583Z155PI0dWsWkROyo6iea17cRHFdC+4uZh68dDQXpUfZuyyRAcFqM3hpXT4PLc2lsa0TkwmunBjLH2YM07aIIiI9ZLMZlDe0klfVzP6qRnLLukJ5QU0zZfWttFsPD+XfMAFB3q5E+XswJMiT1Ag/TPTOBVMFdsFmM/jnyr08unw3htE1he6fPx+rrV5E5IS8vrGQP7+7k/ZOG7GBniy5ahwpEb72LktkQMgps3DbWzvYWlgHwOhoP/46exSjov3sW5iIiAP7Zvp6XlUT+6uayC1rYG9FI3nVTZTW/XAoN5sgxMeNIYGexAV5kRTmTXywN3HBXkQHeODucnJmJCuwD3KNbZ384fWtLNtVDsAVE4Zw1wWpJ+0fnIgMfG2dVu5+P4tX1hcAcFZyKI/NScfPw8XOlYn0f60dVv7x2V6WrNpHp83A282Zm2cO58pJsdppQUTkoOb2TvZXNnGgqmtdeW55Awcqmyisbaa53XrU55lNEObrTkygJwkh3iSFejM0xIu4oK5Q7uLU98v5FNgHsfzqJq5+fiO7yxtxdTJz70Uj+OmEIfYuS0T6scKaZq59eTPbi+oxmeCm6cO47oxEraMV6QUb82q45a3t7K9sAmBGahj3XjRSzRtFZFCy2QxKLa3sq2hkf2Uju8u71pbnVTVR1dh+1OeZ6LpTHhvUFcqHhXkz9Dt3yu0Ryn+IAvsgtXpvFb97aTN1LR2E+rix5KpxjB2iLrIicvyW7Srj5je2YWntxN/Dhb9dMYZpw0LsXZZIv9fY1slDS3N4fk0+0PVB876LRnDOyAg7VyYicvK1d9rIO9iBfW9F19ry3eUNFNQ0H7HZ2zd83J2JDfIkMcSbYeE+xAd7ER/izZBAz341m1iBfRB6YU0ed3+QhdVmkBbtx9NzM7Q3q4gct5Z2K3/5KIuX1nVNgR87xJ+//2wsUf4edq5MpP/7Ynclt7+9g+K6FgB+khHNn85Nxc9TS0xEZGBp67Syv7KJ3eVd68qzS7uCeVFtMzbjyM9xNpuI9PcgIdSb4WHexId4kxDiRXywNwFeA6P5pgL7INJhtXHPB7t4cW3Xh+rZ6ZE8cOnofnWFSUQcy87iem58bSt7D+77/Oup8dw8c7jDTScT6W/qmzv4y0dZvLGpCIDoAA8evHQ0pyQG27kyEZETY7MZFNY2k13aQG5ZA7llFrLLLORXHz2Ye7o6MTTYi+RwHxJDfUgM7QrmQwI9cR7gnzkU2AeJ2qZ2fvfSZtbsr8ZkgltmJnPNtHhMJq0rFZGe67Ta+NcX+3l8+W46bQYhPm489pM0TkvSFHiRE7V0Zxl3vreTyoY2TCb4xZQ4bp45HE9XfWwTkf6lsa2TnFIL2aUWskobyCqpZ3d5Iy0dR2785u3mRGKoDykRXcF8WJg3iaHehPu6D9rconf+QWBvRQP/99xG8qub8XJ14m8/HcP01DB7lyUi/dSBqib+8PpWNhfUAXDOiHDuv2QUgQNk6pmIvVQ0tHLXe7v4ZGcZAPEhXjx82WjGxQbauTIRkR9mGAZlltauYF5iIavUws5iCwU1zUc839XJTEKoF6kRfgwP92Z4uC/DwgZ3MD8aBfYBbtXuSq57aTMNbZ1EB3jwn3kZJIdrH2QR6TnDMHhxbT73f5xDS4cVbzdn7r5wBJeOjdIPV5ETYLMZvLKhgAc+yaGhtRMns4nfTI3n+rOStGxNRBxOdWMbeyoa2VPewO7yri3TckotWFo7j3h+qI8bIyJ9SYno+pUc7sPQYK8BP5W9tyiwD1CGYfDs13n85aMsbAZMiAvkqSvHEuTtZu/SRKQfKq1v4ZY3t/PlnioAJscH8chP0tRYTuQEbSus4673d7G1sA6AUVF+PHDpKEZE+tm3MBEZ9OpbOthd3tDdlf2b/9Y2dxzxfCeTifgQL1IjfUmN8O3+r/LHiVFgH4DaOq3c9d4uXt1QCMDl46L568WjcHXWVSwR6RnDMHhvawkL39uJpbUTN2czt89KZu7kOO2tLnICSupaeGz5bt7aXIRhgJerE3+YMZx5U+Jw0tgSkT5kGAZFtS3sLK5nV0nXevPsMgslda1HPN8ERAd6kBTqw7CwrnXmw8N9SAjx1qygk0CBfYCpsLRyzYub2FxQh9kEd5ybwv+dOlTTVUWkx6ob2/jzuzu719OmRfvx6E/SSQz1tnNlIv1XbVM7S77Yx/++zuveP/iSsVHcdk4yodpiVUT6QEu7la2FdWzKr2FzQR1bC+qoaW4/4rkRfu4MD/dheJgPSWFd/00M9cbDVcG8ryiwDyAb8mq49qXNVDS04evuzBNXjOH04aH2LktE+hnDMPh4RxkL39tJdVM7zmYT15+VxO9OT9B6M5Hj1NDawTNf5fGfL/fT0Na1znPC0EDuODeF9Bh/+xYnIgNafUsHG/NqWH+ghnUHathZXE/n9/ZPczabGB7uw8hIP0ZE+ZIc7svwcB/8PFzsVLV8Q4F9ALDZDP771QEeWJqD1WaQFOrNv+dmEBfsZe/SRKSfKa5r4d4PdrFsVzkAw8N8ePQnaYyM0npakePR3N7Jc6vz+dcX+6g7uO4zJcKXm2cO44zhoZoBJyK9ztLawfr9NazdX83aA9XsKrFgfG9/8zBfNzLiAhk7JIAxQ/xJjfDVdHYHpcDez1U0tPKH17d1N4K6KD2S+y8ehZeb/teKyLFrbOvk2a8O8OTKvbR22HA2m/jdGYlce0YCbs76AS7SU60dVl5cm8+SVfuoauyaapoQ4sWN04dx3qgI9YAQkV7T2mFlQ14NX++tZs2+KnYU1/O9G+gMDfZiQlwgE+MDGR8XSHSAhy4Y9hNKdf3YxztK+dM7O6ht7sDdxcyd56fyswlDNPhE5JjVNLXzyvoC/vPl/u6urxPiArnnohGkRGgLSJGeauu08tqGQp78fC/lljYAhgR6csNZScweE6WGciJywgzDILu0gS/2VPLlnko25NXSfrAnxjeGBnsxKT6QSfFBTIoPIkw9MvotBfZ+qKapnbve38UH20oASI3w5Ykr0kkM9bFzZSLSHxiGwboDNby+sZAPt5d2/5AfGuzFjdOTuDAtUhf+RHqovdPGG5sKefKzvZTUd3VWjvRz5/dnJXHZuGhc1P9BRE5AXXM7X+6pYmVuJV/sqaSyoe2Qx8N93TklMZhTEoOYnBBEhJ+2XR0oFNj7maU7y/jzuzuoamzHyWzi2tMTuO7MJG3ZJiI/Kr+6ibc3F/POlmIKapq7j4+M8uWXpwzlwrRINZUT6aHWDitvbCpiycp9FNe1AF1rQ689I5E542O0pEREjovNZrCrxMLK3ApW7q5kS0HtIdPcPVycmJwQxNSkYE5NCiEhxEsX2wcoBfZ+orqxjbve38WH20sBGBbmzcOXpZGmzrIi8gNqm9r5cEcp72wuYnNBXfdxbzdnzh8dwU/GxzAmxl8/5EV6qL65g5fW5/PMV3lUNXbd6Qr1ceOaaQn8bOIQNW8SkR6rb+7gy72VrMzt+vXNe8s3hoV5c/rwUKYNCyEjLkAXBAcJBfZ+4KPtpd3bKzmZTfxmajw3TE/SIBWRI2ppt7Iiu5z3tpawMreie+sWswlOSQzm0rHRzBgRhqerfgSI9NTeigaeW53Pm5uKaOmwAl1T3389NZ6fTlBQF5Fj19rRtR/66r1VfLW3iq2FdYfcRfdydeKUxGBOHx7K6cNDiPTXNPfBSJ/WHFhlQxsL39vJJzvLgK7tlR6+fDSjo/3tW5iIOJzWDitf7aniw+0lLM8qp6nd2v3YyChfZqdHcWFaJKFqOiPSY51WG5k5FbywJp+v9lZ1H08O9+FXp8VzYVqklqaJyI8qrW9ha0EdWwvr2FxQy7ai+sOaxSWFejNtWAhnJIfqLroACuwOyTAM3t9Wwt3v76K2uaNre6XTE7j2zEQNWhHpVt/cwcrdFSzPKufznIpDQnp0gAcXpUcyOz2KpDA1pBQ5HpUNbby2oYCX1xV0N5Izm2B6ShjzpsQxJSFIy0lE5IjqmzvYXlzHtsI6thXVs72ornvniO8K8XFj4tBATju4Fj1Kd9HlexTYHUxZfSt/emcHmTkVAKRE+PLwZaMZGeVn58pExN5sNoOsUgurdleyMreCTfmHNqAJ93XnnJHhXJAWydghWpcucjwMw2BDXi0vrM1n6c5SOqxdgyzQy5U542P4+cQhRAd42rlKEXEkrR1WdpVYDobzrpCeV9182HlmEwwP9yU9xp+xQ/wZFxvA0GA1i5MfpsDuIGw2g5fXF/DgJzk0tHXi4mTiujOS+N0ZCdoKRmQQq25s46u9Vaw6uI1LVWP7IY8PC/NmekoYZ6eGkRbtj1l7PIscl8a2Tt7dUsyLa/PJKWvoPj5miD9zJ8cya2SE1qeLCDabwYHqpu6p7VsKa8kpbejuF/NdsUGejI72Jy3aj7QYf0ZE+qp/jPSY/sU4gJwyC39+Zycb82sBSIvx5+HLRjNM01hFBp0Oq42thXV8sbuSL3ZXsr24HuM7nwE8XZ2YkhDMtOEhnDE8RHf6RE7QnvIGXlybz1ubi2ls6wTA3cXMRWlRXDU5VjPcRAY5S2sH2wrr2JzfFc63FNRR39Jx2HnB3q6MjvYnPcaftBh/Rkf5EeDlaoeKZaBRYLcjS2sHT6zYw7Or87DaDDxdnbh55nDmTo7DSXfJRAYFq80gt6yBNfurWbOvirX7a7pDwzeSw32YNiyEacNDGBerBjQiJ6q908ayXWW8uDafdQdquo/HB3vx80mxXDYuGj8PFztWKCL2YLMZ7K1sZEtBLZvzuxrD7a1sPOTCOYCbs5lRUX6kx/iTPsSftGh/ogM8NLVdTgoFdjuw2Qze3FzEQ0tzuqe3zhoZzp3np2q7BpEBztLawfbCerYU1LKpoJZN+bU0tB4a0AM8XTg1KYTTkoKZNiyEMHV2F+kVxXUtvLKugFc3FHbvb2w2wdmpYVw5KZZTEoK1rERkEKlsaGN7UdfU9q2FdWwtqKPhexfNAWICPRg7JIAxMf6MjQ0gOdxXO0NIn1Fg72Mb82q454MsdhTXA11X8xdekMrpw0PtXJmI9LZOq43c8obuDwFbC+uOeKXey9WJjLhAJicEcUpCMCMifRUaRHqJzWbwxZ5KXlxbwGc55d2NGkN83LhifAw/nTBEF8tFBjibzaCotoWsUgvZpRZ2lVjYVVJP6cHdH77Lw8WJ0dF+jI3tCuhjhgQQ4uNmh6pFuiiw95Gi2mYe+CSHD7eXAuDt5szvz0xk/ilDdYVOZIAot7SyOb+WLYV1bCmoZUdxPa0dtsPOiw7wYMyQAMYO8ScjNpCUCB+c1VxSpFfVNLXz+sZCXl5XQEHNt92aJ8cHceWkWGaMCFNTV5EBqKG1g9yyBrLLGsgptZBT1kBuWcNhy80ATCZICPEmLbpravvYIf4MD9PPZHEsCuwnWVNbJ0+t3Me/v9xPW6cNkwnmZMTwhxnDdbVOpB8zDIO86mbW7q9m3f5qNubXUlTbcth5Pm7OpMV0NaH5Zq1bsLfGvsjJYBgGmwvqeGltPh/uKKW9s+uCmY+7M5eNi+bnE2NJDPW2c5Ui0hsMw6C4roWdxRaySi1klVjIKbMc8WcxgKuTmaQwb1IifBkR6cuISD9SI33xdlMcEsemf6EnidVm8NbmIh5ZlktFQ9c6uUnxgdx5fiojItVxVqQ/qmxo4+u9VXy5p4rV+6oOm0r3zf6qY4b4H5xG5098sLemt4ucZE1tnby3tYQX1+aTVWrpPj4qyo+rJsVyQVokHq5q1ijSnzW2dbKtsI5N+bVsKahlW1E9NU3tRzw3ws+d4eE+pET4knzwv0ODvTSrRvolBfaT4Ou9Vfz1o+zuDw2xQZ7ccW4KM1LD1D1SpB9p7bCyKb+2a4u1PVVkfycIQNfV+vQh/kwaGsj4oYGMGRKgK/UifWhvRWPXlmybirobRbk5m7kgLZIrJ8WSHuNv3wJF5LhVN7axIa+W9Qdq2JBXQ1apBev39jp3NptICvNhZKQvKRHf/PLB31PbqcnAoU+WvWhHUT2PLs9lZW4l0DUF7/ozk5g7JVbbMIn0A4ZhsK+ykVW7q/hidyXrDlQftgZ9RKQvpyYFc2piMBmxgbprJ9LHOq02VmRX8MLaPL7eW919PC7IkysPbsmmD+si/U9ZfSvrDlSz7kAN6w/UsLei8bBzovw9GBfb1QMmLcaflAhf3F30c1gGNgX2E2QYBpvya1myaj8rssuBrqt9V06K5fqzkgj00ocGEUfW2NbJV3uqWLW7ki92V1Jcd+jat1AfN05LCmHqsGBOSQzW+nMRO6lqbOO1DYW8tDafkoPLUcwmODM5jLmTYzk1UVuyifQn5ZZW1uyrZs2+atYeqCa/uvmwc4aH+TB+aADj4wIZHxeoHR1kUFJgP071zR18sL2E1zcWsr2oa4s2kwlmp0dxw1lJxAV72blCETmawppmVmSXk5ldwboD1XRYv51i5+psZuLQQKYmhXDasGCGh/loKYuIHW0trOP51Xl8uL2UdmvXjJdAL1fmjI/h5xOHEB3gaecKReRYtHVaWX+ghpW5lXy5p5Ld5YfeQTebYESkH+PjApkYH8iEuEACdONLRIG9J+pbOsjMLufjHaV8sbuq+4ODq7OZS8ZE8avT4tV9VsQBGYbBrhILn2aV8+muMnLKGg55PC7Ik9OHhzJteAiThgZpmruInbV1WvloeynPrc5j28GL4gBpMf7MnRTLeaMjNA1WpB+obGjj85wKVmSX89XeKprbrd2PmUwwMtKPyQlBTI4PIiMuAB93FztWK+KY+jywP/nkkzz88MOUlZWRlpbG3//+dyZMmHDU89944w3uvPNO8vLySEpK4sEHH+Tcc8/ts3orLK0szy5n2a5yVu+tovM7zS6Gh/lweUY0s8dEaZqsiIPptNrYkFfLp1llfLqr/JCp7k5mExmxAUxPCeOslFDiQ3ShTcQRlNW38uLafF5ZX0D1we7Prk5mzh8dwdwpcWoiJ+LgvukF82lWOSuyytlSWIfxnT5xIT5unDE8hGnDQjklMUj9JkSOQZ8G9tdee40FCxawZMkSJk6cyOLFi5k5cya5ubmEhoYedv7q1au54oorWLRoEeeffz4vv/wys2fPZvPmzYwcOfKk1bn/4BvNsl1lbCmoO+SxYWHenDMygvNHRzAszOek1SAiPdfaYeXLPVUs21VGZnY5tc0d3Y95uDgxdVgwM1LDOTM5VNPsRByEYRhszK/lf6vzWLqzrLsLdISfO1dOimXO+BhdFBdxYJ1WG5vya1meVc6K7HLyvrcWfWSUb9cF8uQwRkT6qteESA+ZDMMwfvy03jFx4kTGjx/PP/7xDwBsNhsxMTH8/ve/57bbbjvs/Dlz5tDU1MSHH37YfWzSpEmkp6ezZMmSY/ozLRYLfn5+1NfX4+vre8RzDMNge1E9n2aVsWxX+WFdKdNj/Jk5IpwZI8JI0J04kSM6lrF2Mr5ffUsHn+WUs2xnOat2V9LS8e10uwBPF85KCWPmiHBOSwrWFFqRo7DH+G3tsPLe1mKeW33o3ukThgbyiylxzEgNw1l7Jov8IHv97G1u7+SL3ZV8mlXO5zkVh1wgd3UyMyUxiLNSwpieEkqEnxrFiRzJsY63PrvD3t7ezqZNm7j99tu7j5nNZqZPn86aNWuO+Jw1a9awYMGCQ47NnDmTd99996h/TltbG21tbd1fWyyWo577jX9/uZ/7P87p/trZbGJyQhAzUsM4OzWccD/3H/0eInJijmfs2mwGZz6ysnvqLHRt+XJ2aldIHx8XoA/8In3geMbvm5uK+PO7OwFwdzEzOz2KeVPiSIk48dAhIsfmeMYuwPlPfMX+qqbur/09XThzeChnp4Zx2rAQvN3UJkukt/TZaKqqqsJqtRIWFnbI8bCwMHJyco74nLKysiOeX1ZWdtQ/Z9GiRdxzzz09qm3qsBD+tmIPpw8PZcaIME4fHoqfh5peiPSl4xm7ZrOJ05KCySq1MHNEODNHhDMi0ldd3UX62PGM34vHRPHSugJmp0cyZ3yM1rKK2MHxjF2AU5OC6bQZnJ0axvSUMF0gFzmJ+mxKfElJCVFRUaxevZrJkyd3H7/llltYtWoV69atO+w5rq6uPPfcc1xxxRXdx/75z39yzz33UF5efsQ/50hXCmNiYn50Snxbp03TZUVOwIlOyzuesQtd3aTdnDV2RU6EvcaviJwYe43d1g4rbs5mXSAXOQEONyU+ODgYJyenw4J2eXk54eHhR3xOeHh4j84HcHNzw82tZ81pTCaTwrqInR3P2AUU1kUcwPGOXxGxr+Mdu/rcLNJ3+mzuiqurK+PGjSMzM7P7mM1mIzMz85A77t81efLkQ84HWL58+VHPFxERERERERko+rQjxIIFC5g3bx4ZGRlMmDCBxYsX09TUxPz58wGYO3cuUVFRLFq0CIAbbriBadOm8eijj3Leeefx6quvsnHjRp5++um+LFtERERERESkz/VpYJ8zZw6VlZUsXLiQsrIy0tPTWbp0aXdjuYKCAszmb2/6T5kyhZdffpk///nP3HHHHSQlJfHuu++e1D3YRURERERERBxBn++5cN1113Hdddcd8bGVK1ceduzyyy/n8ssvP8lViYiIiIiIiDgW7b8gIiIiIiIi4oD6/A57X/tm1zqLxWLnSkQGtm/GWG/tFKmxK9J3NH5F+ieNXZH+61jH74AP7A0NDQDExMTYuRKRwaGhoQE/P79e+T6gsSvSlzR+RfonjV2R/uvHxq/J6K1Lcg7KZrNRUlKCj48PJpPpqOdZLBZiYmIoLCz8wY3r5cfptew9/em1NAyDhoYGIiMjD2keebw0dvueXsve1Z9eT43f/k+vZe/pT6+lxu7AoNez9/Sn1/JYx++Av8NuNpuJjo4+5vN9fX0d/n9uf6HXsvf0l9eyN67uf0Nj1370Wvau/vJ6avwODHote09/eS01dgcOvZ69p7+8lscyftV0TkRERERERMQBKbCLiIiIiIiIOCAF9oPc3Ny46667cHNzs3cp/Z5ey96j1/LH6TXqPXote5dezx+n16j36LXsPXotf5xeo96l17P3DMTXcsA3nRMRERERERHpj3SHXURERERERMQBKbCLiIiIiIiIOCAFdhEREREREREHpMAuIiIiIiIi4oAU2EVEREREREQckAK7iIiIiIiIiANSYBcRERERERFxQArsIiIiIiIiIg5IgV1ERERERETEASmwi4iIiIiIiDggBXYRERERERERB6TALiIiIiIiIuKAnO1dwMlms9koKSnBx8cHk8lk73JEBizDMGhoaCAyMhKz+cSvBWrsivQdjV+R/kljV6T/OtbxO+ADe0lJCTExMfYuQ2TQKCwsJDo6+oS/j8auSN/T+BXpnzR2RfqvHxu/Az6w+/j4AF0vhK+vr52rERm4LBYLMTEx3WPuRGnsivQdjV+R/kljV6T/OtbxO+AD+zfTeXx9ffXGI9IHemsKncauSN/T+BXpnzR2RfqvHxu/ajonIiIiIiIi4oAU2EVEREREREQckAK7iIiIiIiIiANSYBcRERERERFxQArsIiIiIiIiIg5IgV1ERERERETEASmwi4iIiIiIiDggBXYRERERERERB6TALiIiIiIiIuKAFNhFREREREREHJACu4iIiIiIiIgDUmAXERERERERcUAK7CIiIiIiIiIOSIFdRERERERExAEpsIuIiIiIiIg4IAV2EREREREREQekwC4iIiIiIiLigBTYRURERERERByQAruIiIiIiIiIA1JgFxEREREREXFACuwiIiIiIiIiDkiBXURERERERMQB9Xlgf/LJJ4mLi8Pd3Z2JEyeyfv36o5779ttvk5GRgb+/P15eXqSnp/PCCy/0YbUiIiIiIiIi9tGngf21115jwYIF3HXXXWzevJm0tDRmzpxJRUXFEc8PDAzkT3/6E2vWrGH79u3Mnz+f+fPns2zZsr4sW0RERERERKTP9Wlgf+yxx7j66quZP38+qampLFmyBE9PT5555pkjnn/66adz8cUXk5KSQkJCAjfccAOjR4/mq6++6suyRURERERERPqcc1/9Qe3t7WzatInbb7+9+5jZbGb69OmsWbPmR59vGAafffYZubm5PPjgg0c9r62tjba2tu6vLRbLiRUuIn1CY1ek/9L4FemfNHZFHN8xBfb333//mL/hhRdeeMTjVVVVWK1WwsLCDjkeFhZGTk7OUb9ffX09UVFRtLW14eTkxD//+U/OPvvso56/aNEi7rnnnmOuV0Qcg8auSP+l8SvSP2nsijg+k2EYxo+dZDYfOnPeZDLx3aeZTKbu31ut1iN+j5KSEqKioli9ejWTJ0/uPn7LLbewatUq1q1bd8Tn2Ww29u/fT2NjI5mZmdx33328++67nH766Uc8/0hXCmNiYqivr8fX1/fH/qoicpwsFgt+fn7HPdY0dkXsR+NXpH/S2BXpv451/B7THXabzdb9+xUrVnDrrbdy//33dwfvNWvW8Oc//5n777//qN8jODgYJycnysvLDzleXl5OeHj4UZ9nNptJTEwEID09nezsbBYtWnTUwO7m5oabm9ux/LVExIFo7Ir0Xxq/Iv2Txq6I4+tx07kbb7yRv/3tb8ycORNfX198fX2ZOXMmjz32GNdff/1Rn+fq6sq4cePIzMzsPmaz2cjMzDzkjvuPsdlsh1wJFBERERERERmIetx0bt++ffj7+x923M/Pj7y8vB987oIFC5g3bx4ZGRlMmDCBxYsX09TUxPz58wGYO3cuUVFRLFq0COhaV5ORkUFCQgJtbW18/PHHvPDCCzz11FM9LVtERERERESkX+lxYB8/fjwLFizghRde6G4gV15ezs0338yECRN+8Llz5syhsrKShQsXUlZWRnp6OkuXLu3+PgUFBYesl29qauJ3v/sdRUVFeHh4kJyczIsvvsicOXN6WraIiIiIiIhIv3JMTee+a+/evVx88cXs3r2bmJgYAAoLC0lKSuLdd9/tXm/uKE60GYeIHJveHmsauyJ9R+NXpH/S2BXpv3q16dx3JSYmsn37dpYvX969HVtKSgrTp08/pFu8iIiIiIiIiBy/Hgd26NrGbcaMGUydOhU3NzcFdREREREREZFe1uMu8Tabjfvuu4+oqCi8vb05cOAAAHfeeSf//e9/e71AERERERERkcGox4H9L3/5C//73/946KGHcHV17T4+cuRI/vOf//RqcSIiIiIiIiKDVY8D+/PPP8/TTz/Nz3/+c5ycnLqPp6Wlda9pFxEREREREZET0+PAXlxcfMRO8DabjY6Ojl4pSkRERERERGSw63FgT01N5csvvzzs+JtvvsmYMWN6pSgRERERERGRwa7HXeIXLlzIvHnzKC4uxmaz8fbbb5Obm8vzzz/Phx9+eDJqFBERERERERl0enyH/aKLLuKDDz5gxYoVeHl5sXDhQrKzs/nggw84++yzT0aNIiIiIiIiIoPOce3Dftppp7F8+fLerkVEREREREREDurxHfb4+Hiqq6sPO15XV0d8fHyvFCUiIiIiIiIy2PU4sOfl5WG1Wg873tbWRnFxca8UJSIiIiIiIjLYHfOU+Pfff7/798uWLcPPz6/7a6vVSmZmJnFxcb1anIiIiIiIiMhgdcyBffbs2QCYTCbmzZt3yGMuLi7ExcXx6KOP9mpxIiIiIiIiIoPVMQd2m80GwNChQ9mwYQPBwcEnrSgRERERERGRwa7HXeIPHDhwMuoQERERERERke84rm3dmpqaWLVqFQUFBbS3tx/y2PXXX98rhYmIiIiIiIgMZj0O7Fu2bOHcc8+lubmZpqYmAgMDqaqqwtPTk9DQUAV2ERERERERkV7Q423dbrrpJi644AJqa2vx8PBg7dq15OfnM27cOB555JGTUaOIiIiIiIjIoNPjwL5161b+8Ic/YDabcXJyoq2tjZiYGB566CHuuOOOk1GjiIiIiIiIyKDT48Du4uKC2dz1tNDQUAoKCgDw8/OjsLCwd6sTERERERERGaR6vIZ9zJgxbNiwgaSkJKZNm8bChQupqqrihRdeYOTIkSejRhEREREREZFBp8d32O+//34iIiIA+Otf/0pAQAC//e1vqays5Omnn+71AkVEREREREQGox7fYc/IyOj+fWhoKEuXLu3VgkRERERERETkOO6wi4iIiIiIiMjJ1+PAXl5ezlVXXUVkZCTOzs44OTkd8ktERERERERETlyPp8T/4he/oKCggDvvvJOIiAhMJtPJqEtERERERERkUOtxYP/qq6/48ssvSU9PPwnliIiIiIiIiAgcx5T4mJgYDMM4GbWIiIiIiIiIyEE9DuyLFy/mtttuIy8v7ySUIyIiIiIiIiJwHFPi58yZQ3NzMwkJCXh6euLi4nLI4zU1Nb1WnIiIiIiIiMhg1ePAvnjx4pNQhoiIiIiIiIh8V48D+7x5805GHSIiIiIiIiLyHccU2C0WC76+vt2//yHfnCciIiIiIiIix++YAntAQAClpaWEhobi7+9/xL3XDcPAZDJhtVp7vUgRERHpuZwyC8nhupAuIiLSXx1TYP/ss88IDAwE4PPPPz+pBYmIiMiJ6bDa+CyngpW5FcxIDeeM5FB7lyQiIiLH4ZgC+7Rp0474exEREXEsdc3tvLethKU7y1i7v5qPtpfy8Q2nER3gae/SREREpId63HTu2Wefxdvbm8svv/yQ42+88QbNzc1qSiciImIneysaeXtLER9sLaGwtgWAM5JD8fNw+ZFnioiIiCMy9/QJixYtIjg4+LDjoaGh3H///b1SlIiIiBw7m83gqz1VPPn5Hp79Ko/C2haczSZmjQxn8Zx0fNwV2EVERPqjHt9hLygoYOjQoYcdj42NpaCgoFeKEhERkWPT0m7lg20lvLGpkA15tQAEebty3qgIpqeEHbFRrIiIiPQPPQ7soaGhbN++nbi4uEOOb9u2jaCgoN6qS0RERH5EuaWVl9bl89amYorruqbAj4zy5dyREZw3OoLYIC87VygiIiInoseB/YorruD666/Hx8eHqVOnArBq1SpuuOEGfvrTn/Z6gSIiInK4XSX1PPPVAT7eUUZLhxVXJzNnpYRy7sE76x6uTvYuUURERE5QjwP7fffdR15eHmeddRbOzl1Pt9lszJ07V2vYRURETjKbzeDznAr+9eV+1h+oASDY25WL0iK5eGw0I6P87FyhiIiI9JYeN51zdXXltddeIzc3l5deeom3336bffv28cwzz+Dq6vqjz3/yySeJi4vD3d2diRMnsn79+qOe++9//5vTTjuNgIAAAgICmD59+g+eLyIiMpA1t3fyvzV53P3hru6wPjLSl+vOTOTaM5MU1kVERAaYHt9h/0ZSUhJJSUk9es5rr73GggULWLJkCRMnTmTx4sXMnDmT3NxcQkNDDzt/5cqVXHHFFUyZMgV3d3cefPBBZsyYwa5du4iKijre0kVERPqdCksrT36+l7c2F9PY1omz2cRZKaH8bGIspyYG42RWczkREZGBpsd32C+99FIefPDBw44/9NBDh+3N/n2PPfYYV199NfPnzyc1NZUlS5bg6enJM888c8TzX3rpJX73u9+Rnp5OcnIy//nPf7DZbGRmZva0bBERkX4rp9TCbW9t54W1+TS2dRLg6cL8KXHcPiuFacNCFNZFREQGqB4H9i+++IJzzz33sOOzZs3iiy++OOrz2tvb2bRpE9OnT//2DzebmT59OmvWrDmmP7u5uZmOjg4CAwN7WraIiEi/YxgGK7LKuf7VLXyWW4nNgKRQb/44Yzg3nj2MuGB1gRcRERnIejwlvrGx8Yhr1V1cXLBYLEd9XlVVFVarlbCwsEOOh4WFkZOTc0x/9q233kpkZOQhof/72traaGtr6/76h2oSEcehsStyqLZOK8+vyWfJyn1UN7VjNsFpSSH87vQEJgwNdKj91TV+RfonjV0Rx9fjO+yjRo3itddeO+z4q6++Smpqaq8UdSQPPPAAr776Ku+88w7u7u5HPW/RokX4+fl1/4qJiTlpNYlI79HYFflWfXMH936QxcNLc6luasfT1YkrJ8Wy6JJRTIwPcqiwDhq/Iv2Vxq6I4zMZhmH05AkffPABl1xyCT/72c8488wzAcjMzOSVV17hjTfeYPbs2Ud8Xnt7O56enrz55puHnDNv3jzq6up47733jvpnPvLII/zlL39hxYoVZGRk/GB9R7pSGBMTQ319Pb6+vsf+FxWRHrFYLPj5+R33WNPYFelSUNPE7W/v4Ou91QBE+Lnz29MT+ElGDO4uJ2dvdY1fkf5JY1ek/zrW8dvjKfEXXHAB7777Lvfffz9vvvkmHh4ejB49mhUrVjBt2rSjPs/V1ZVx48aRmZnZHdi/aSB33XXXHfV5Dz30EH/9619ZtmzZj4Z1ADc3N9zc3Hr61xIRO9PYFYH1B6q57a0d7K9qAiA9xp/bZiUzKT7IzpX9MI1fkf5JY1fE8R3Xtm7nnXce5513Xo+ft2DBAubNm0dGRgYTJkxg8eLFNDU1MX/+fADmzp1LVFQUixYtAuDBBx9k4cKFvPzyy8TFxVFWVgaAt7c33t7ex1O6iIiIwzEMgzc2FrHok2xqmztwMps4d2Q4d5yXQoSfh73LExERETs57n3Yj8ecOXOorKxk4cKFlJWVkZ6eztKlS7sb0RUUFGA2f7us/qmnnqK9vZ3LLrvskO9z1113cffdd/dl6SIiIidFh9XGY8tz+e9XebR32vB2c+bXp8Xz62nxJ20KvIiIiPQPPQ7sVquVxx9/nNdff52CggLa29sPebympuYHn3/dddcddQr8ypUrD/k6Ly+vp+WJiIj0G01tHfzxje18srNrBlmknzt3XZDKjBHhDtdYTkRERPpej7vE33PPPTz22GPMmTOH+vp6FixYwCWXXILZbNZdbxERkWNUWt/CT59e1x3W02P8eOH/JjJzZITCuoiIiADHEdhfeukl/v3vf/OHP/wBZ2dnrrjiCv7zn/+wcOFC1q5dezJqFBERGVC2FtRyyT9Xs6O4HpMJLkqL5PlfTiAhVP1ZRERE5Fs9DuxlZWWMGjUK6Gr+Vl9fD8D555/PRx991LvViYiIDDAfbi/hyv+up7S+FTdnM3+cMYzH5qTj6+Fq79JERETEwfQ4sEdHR1NaWgpAQkICn376KQAbNmzQthAiIiI/4B+f7eHGV7fS2NZJgKcLf//ZGK49Iwkns6bAi4iIyOF63HTu4osvJjMzk4kTJ/L73/+eK6+8kv/+978UFBRw0003nYwaRURE+jWr1cYf39zOO1uKAYgP9uLpueNIDPWxc2UiIiLiyHoc2B944IHu38+ZM4fY2FhWr15NUlISF1xwQa8WJyIi0t9ZWtqZ/7+NbMqvBeCUxCCW/HwcPh4udq5MREREHF2PA3trayvu7u7dX0+aNIlJkyb1alEiIiIDwe6yBub/bwPFdS2YTDB3chx3nZ+KWVPgRURE5Bj0eA17aGgo8+bNY/ny5dhstpNRk4iISL/3zuYiLv7n1xTXteDuYub+i0dyz4UjFNZFRETkmPU4sD/33HM0Nzdz0UUXERUVxY033sjGjRtPRm0iIiL9TkenjT++sZUFr2+jqd1KmK8br/56MldMiLV3aSIiItLP9DiwX3zxxbzxxhuUl5dz//33k5WVxaRJkxg2bBj33nvvyahRRESkX9hWWMv0x1bx5qZiDGBSfBBLbzyN9Bh/e5cmIiIi/VCPA/s3fHx8mD9/Pp9++inbt2/Hy8uLe+65pzdrExER6Rc6Oq3c88EuLn1qDfk1zbg6mVlw9jBe/fUkAjy15amIiIgcnx43nftGa2sr77//Pi+//DJLly4lLCyMm2++uTdrExERcXhr91Xxxze3U1TbAkBSqDd//9kYksN97VyZiIiI9Hc9DuzLli3j5Zdf5t1338XZ2ZnLLruMTz/9lKlTp56M+kRERBxSY2sHt7+zgw+3lWIA7s5mfjMtgRunJ2EyqbGciIiInLgeB/aLL76Y888/n+eff55zzz0XFxftIysiIoPLe1uLuev9XdQ1dwAwPi6Ax36STkygp50rExERkYGkx4G9vLwcHx+fk1GLiIiIQyuvb+Wm17eyel81AAGeLiw8P5WLx0bbuTIREREZiHoc2BXWRURksLHZDJ75+gCPfJpLa4cNkwkuHhPFfReNwMtNM81ERETk5DjupnMiIiKDwZ6yBm54bQtZpQ0AxAV58thP0hkbG2DnykRERGSgU2AXERE5grZOK49+upv/fnUAq83AxcnEtWck8vszk3Ayq6mciIiInHwK7CIiIt+TmVXOHe/uoNzSBkBGbACPz0kjJtDLzpWJiIjIYKLALiIictDeigb+9O5O1u2vAcDPw4W7L0hl9pgobdUmIiIifa7Hgb2pqYkHHniAzMxMKioqsNlshzy+f//+XitORESkL5TVt/Lg0hze21qMzQCzCX46YQi3z0rGx11N5URERMQ+ehzYf/WrX7Fq1SquuuoqIiIidMdBRET6rZqmdp7I3MNL6/LpsBoAnJoYxL0XjSQ+xNvO1YmIiMhg1+PA/sknn/DRRx9xyimnnIx6RERETrqG1g7+tWof//nqAK0dXTPFRkb5ctf5qYwfGmTn6kRERES69DiwBwQEEBgYeDJqEREROalaO6z8b3UeT36+l4bWTgASQ725Y1YyZySHataYiIiIOJQeB/b77ruPhQsX8txzz+Hp6XkyahIREelVnVYbb2wq4tFPc6lqbAcgJtCDW89J5tyREZi1TZuIiIg4oB4H9kcffZR9+/YRFhZGXFwcLi6HNuPZvHlzrxUnIiJyImw2g493lvLQ0lwKapoBCPFx4+YZw7hkbDTOTmY7VygiIiJydD0O7LNnzz4JZYiIiPQewzD4LKeCh5flklPWAICfhzO/PzOJKyfF4u7iZOcKRURERH5cjwP7XXfddTLqEBEROWFWm8HyrDKeyNxLVqkFAA9XJ64+dShXT43XFm0iIiLSr/Q4sH9j06ZNZGdnAzBixAjGjBnTa0WJiIj0RH1zB29uLuLZrw9QVNsCgLuLmbmT47hmWgKBXq52rlBERESk53oc2CsqKvjpT3/KypUr8ff3B6Curo4zzjiDV199lZCQkN6uUURE5DCGYbD+QA2vbSjkw+2ltFu7tmfzdXdm7uQ45p8SR5C3m52rFBERETl+PQ7sv//972loaGDXrl2kpKQAkJWVxbx587j++ut55ZVXer1IERGRb5RbWnlrcxGvrS8k/2AjOYBhYd7MnRzHJWOj8HQ97glkIiIiIg6jx59oli5dyooVK7rDOkBqaipPPvkkM2bM6NXiRMT+Oqw2mts6cXNxUqMusZsOq43Pcyp4dUMhK3MrsBldxz1cnLgwPZI542MYE+OvfdRFRERkQOlxYLfZbIdt5Qbg4uKCzWbrlaJExD7aOq3kljawIb+WXcX17K1spLi2hdrmdh6+LI1Lx0Xbu0QZZAqqm3l1QwGvbyzs3j8dYOyQAH46IYbzRkXg5aa76SIiIjIw9fhTzplnnskNN9zAK6+8QmRkJADFxcXcdNNNnHXWWb1eoIicHE1tnWwpqGNjfg1ZJRb2VzZSUt9Kc7v1iOfvq2zs4wplsOq02liRXcFL6/L5ck9V9/FAL1cuHxfN5RkxJIZ627FCERERkb7R48D+j3/8gwsvvJC4uDhiYmIAKCwsZOTIkbz44ou9XqCInLjapnbW7q9mS0EdWaUWDlQ1UW5ppfObecXfE+DpQpS/B/Eh3qRG+HJGcgjDwnz6uGoZbKoa23hlXQEvrSugzNIKgAk4LSmYKyYM4ayUMFydzfYtUkRERKQP9Tiwx8TEsHnzZlasWEFOTg4AKSkpTJ8+vdeLE5GeK7e0snpfFZvz68gutZBX3UR1YztHiubOZhNhvm4MCfRieLgPo6L8yIgLIDrAEyez1gJL39hVUs+zX+fx3tZiOqxd/1IDvVyZMz6GK8YPYUiQp50rFBEREbGP41r4ZzKZOPvsszn77LN7ux4R6YGSumZW761mc0EtWaUN5FU3UdfcccRzPVyciArwICHEi9QIP8bFBpAW44eP++E9KURONpvNYOXuCv79xQHW7K/uPp4e488vpsQxa1Q4bs5qcigiIiKD2zEF9ieeeIJf//rXuLu788QTT/zguddff32vFCYih6psaOOrvZVsyOtqCHegqglLa+cRz/XzcGFIoCfDwnwYHe3L+LhAhof54OSk6cRiX22dVt7dUsy/vzjA3oN9EZxMJmaNCueXpw5l7JAAO1coIiIi4jiOKbA//vjj/PznP8fd3Z3HH3/8qOeZTCYFdpFeYLMZ7CiuIzOnkg0Hasgta6Cmuf2I5wZ7uxIX5EVqpC9jYvyZGB9IpL+mEItjqW/p4KV1+Tz7VR6VjW0AeLs587OJQ5g3JY4ofw87VygiIiLieI4psB84cOCIvxeR3mEYBjllDSzdWcZXe6vIKrHQ0nF4t/YQbzcSQr0YEenH+LgAJsUH4e/paoeKRY5NhaWV/351gBfX5dPU1vVvOtzPnf87ZSg/nRCjJRkiIiIiP6DHa9jvvfde/vjHP+LpeegdvJaWFh5++GEWLlzYa8WJDGTlllY+3VVGZnYFmwtqD5ve7upsJinUm/QYfyYnBHFqQjD+Xgrn0j8U1jTz1Kp9vLGxsLuR3LAwb34zNYEL0iLV7V1E5ChK6lpYkVXOtqI6rp4aT3K4r71LEhE76nFgv+eee7jmmmsOC+zNzc3cc889CuwiR9HSbmXN/mqW7ixl9b5qimpbDnncxcnE8DAfpiQEMz01lLFDAnDWmnPpZwqqm3nisz28s7kYq9EV1DNiA/jt6QmcmRyKyaTdB0REvq+prZO1+6t5Y1MRm/NrqWjoWjrk7+nKneen2rk6EbGnHgd2wzCO+IFr27ZtBAYG9kpRIgOB1Wawq6SezOwKVuZWsLPEgvU7+56bgLhgL6YkBDFzRDgT4wPVFVv6rbL6Vv6WuZs3NhbRefDf+WlJwVx3RiIT44PsXJ2IiOOx2Qxyyxt4bUMhq3ZXklfdxMHrnJhNXbtmnJKo90+Rwe6YA3tAQAAmkwmTycSwYcMOCe1Wq5XGxkauueaak1KkSH/QabWRVWrh671VfLG7im1FdTS3H7oOPdDLlUnxgZydGsbpw0IJ0BR36ecsrR388/N9PPv1Ado6bQBMGxbCjdOTGKOO7yIih6ltauOdLSV8sK2ErFJL93snQJivGxOHBnLpuBgmxAXi4aoL+SKD3TEH9sWLF2MYBr/85S+555578PPz637M1dWVuLg4Jk+e/KPf58knn+Thhx+mrKyMtLQ0/v73vzNhwoQjnrtr1y4WLlzIpk2byM/P5/HHH+fGG2881pJFTqrWDivbCutYva+ar/ZWsau4ntbv/NCFrr3Pxwzx54zhoZyZEkp8sJemBMuA0GG18fK6Ahav2E1tcwcA4+MCuPWcZDLiNNtKROS7Oq02Vu+r4uV1Baw9UEPdwfdN6NoxY+wQfy4eE82ZKaH4eagZp4h865gD+7x58wAYOnQop5xyCs7OPZ5Nz2uvvcaCBQtYsmQJEydOZPHixcycOZPc3FxCQ0MPO7+5uZn4+Hguv/xybrrpph7/eSK9qbm9k035tazeV83XBzu5d35nijuAp6sTo6P9ODUxhNOSghkR6at16DLgrMyt4C8fZbO3omsf9cRQb26flaw16iIi31NU28zza/L5dFcZedXN3cddnEykRPhy3qgIZo+JIszX3Y5Viogj63HqbmpqIjMzk5kzZx5yfNmyZdhsNmbNmnXU5z722GNcffXVzJ8/H4AlS5bw0Ucf8cwzz3Dbbbcddv748eMZP348wBEfFzmZGts62ZhXw9r91Xy1p4rs0obuJlrf8Pd0YVxsAKcmBjMpPojhYT6YzQosMjDtrWjkLx9lsTK3EoBAT1dumjGMK8bH6MKUiMhB7Z1WPthWwmsbi9haUEe79dvZd0MCPTkrJZSfTRxCYoi3LnKKyI/qcWC/7bbbeOCBBw47bhgGt91221EDe3t7O5s2beL222/vPmY2m5k+fTpr1qzpaRkiva7C0srG/FrWH+gK6bnlDXwvnxPi7cb4oYFMTQpmYnwQcUGe+mErA159SwdPZO7hudV5dNoMXJxMzJscx+/PStLUTRGRg3LLLPz3qwNkZldQ3dTefdzX3ZlTk4K5YsIQpiQE46QL+yLSAz0O7Hv27CE19fDtJZKTk9m7d+9Rn1dVVYXVaiUsLOyQ42FhYeTk5PS0jKNqa2ujra2t+2uLxdJr31sGDsMw2FfZyIa8roC+7kA1JXWth50X7uvOpPhATjl4Bz0m0PMI3016g8au47HaDN7YWMjDy3K7P3yelRzKn89PZWiwl52rE0ei8SuDVWt7J69tLOT1jUVkl1r4ZqWcs9nE6Gg/LhsXzez0KDzder6UtC9o7Io4vh6/e/j5+bF//37i4uIOOb537168vOz/AW7RokXcc8899i5DHIzVZpBVYmHdgWrW7q9mQ14t9S0dh5xjAuJDvJgUH8SEoYFMGBpIhJ+HfQoehDR2Hcum/Brufj+LHcX1ACSEeLHwghFMGxZi58rEEWn8ymCzs6iep7/cx2c5lTS2dXYfjw7wYNbIcH5xylCi/B3/M4TGrojj63Fgv+iii7jxxht55513SEhIALrC+h/+8AcuvPDCoz4vODgYJycnysvLDzleXl5OeHh4T8s4qttvv50FCxZ0f22xWIiJiem17y/9g2EY7K1o5Ms9VXy1t4p1+6tp+t4Wa27OZkZF+TElIYhxcYGMGeKPr7um99qLxq5jKKtv5YFPsnl3awnQ1b34prOHMXdyLC5apy5HofErg0FLeycvrivg9Q2F7DnYdBO6doQ5bVgw8ybHckpi/7qoqbEr4vh6HNgfeughzjnnHJKTk4mOjgagqKiI0047jUceeeSoz3N1dWXcuHFkZmYye/ZsAGw2G5mZmVx33XXHV/0RuLm54ebm1mvfT/qPTquN9QdqWLarjOXZ5YdNcfdyc2J8XCCT44MYPzSQkZF+uDorgDgKjV37au2w8t+vDvDk53tpbrdiMsGcjBj+OHM4wd76/yI/TONXBrJthXU8/UXX3fSWjm8v/ieH+/CTjGiumBDbb/dL19gVcXzHNSV+9erVLF++nG3btuHh4cHo0aOZOnXqjz53wYIFzJs3j4yMDCZMmMDixYtpamrq7ho/d+5coqKiWLRoEdDVqC4rK6v798XFxWzduhVvb28SExN7WroMQDabwfq8Gt7fVsInO0q794OGri1TxscFMnVYCKckBJMa6atGLyLfYxgGn+ws4/6PsymqbQFgXGwAd12Qyuhof/sWJyJiJ7VN7Ty/Np93Nhcdsh2br7szM0aE85up8SSF+dixQhEZLI6rA4bJZGLGjBnMmDGjR8+bM2cOlZWVLFy4kLKyMtLT01m6dGl3I7qCggLM5m/veJaUlDBmzJjurx955BEeeeQRpk2bxsqVK4+ndBkgskstvLulmHe3FlNu+bZZip+HCzNHhHF2ajinJAbh6eqYTV5EHMGOonru+zCL9Xk1QFeTxdtmJXNReqR2PxCRQae2qY13thTz/rZSthfVdTeQM5lgTIw/V06K5aL0KF38F5E+dVxppqmpiVWrVlFQUEB7e/shj11//fU/+NzrrrvuqFPgvx/C4+LiML6/r5YMWhUNrby3pYS3NheRU9bQfdzbzZlzR4VzQVokk+ODtB+0yI8ot7Ty8LJc3tpchGGAu4uZX09N4Jpp8brIJSKDhmEY7Cyu5/1tJazaXcmeisZDtnON9HfngrRI5k8ZSrifu/0KFZFBrcefzLZs2cK5555Lc3MzTU1NBAYGUlVVhaenJ6GhoT8a2EV6osNqIzO7gjc2FrIytxLrwZ+kLk4mzkoOY/aYKE4fHoK7S/9cOybSl1rarfz7y/0sWbWP5oNNGGenR3LLOclE9oNuxiIiJ6q2qZ3lWWUs3VXOxrwaLK2dhzweHeDB6cNC+NnEIaRG+tmpShGRb/U4sN90001ccMEFLFmyBD8/P9auXYuLiwtXXnklN9xww8moUQah4roWXlqbz+sbC6lq/HYWx5gYfy4dF835oyPw93S1Y4Ui/YdhGHywvZQHPs6mpL6rGePYIf7ceX4qY4YE2Lk6EZGTx2oz2FZYy0c7yli1u4J9FU18d+6mi5OZ0dF+nJUcyvmjIxgSZP8tikVEvqvHgX3r1q3861//wmw24+TkRFtbG/Hx8Tz00EPMmzePSy655GTUKYPE9qI6/rVqP0t3lnXfTQ/2duXScdFcPi6GxFBvO1co0r9sL6rjng+y2JRfC0Cknzu3nZvCBaMjtE5dRAYcwzDIr27miz2VrMgqZ2N+bfeMom9EB3hwamIw54wMZ3JCEG7OmqUnIo6rx4HdxcWluzFcaGgoBQUFpKSk4OfnR2FhYa8XKIPDpvxaFq/YzZd7qrqPTUkIYu7kWM5KCdP+zyI9VNnQxkNLc3hjUxHQtU/w705P4Oqp8VpCIiIDRku7lV0l9WzKr2Xt/mq2FtYdsmMMdL3/jY8L4OzUcKanhhLhpyVAItJ/9Diwjxkzhg0bNpCUlMS0adNYuHAhVVVVvPDCC4wcOfJk1CgD2K6Seh5ZlsvnuZUAOJlMXJQeya+nxZMc7mvn6kT6nw6rjedW5/G3FXtoaOtam3nJmChuOSdZTZNEpF9r7bCSXWphW2EdG/Nr2VlcT35NM9/vT+xkNpEc7sNpicGcPSKMtGh/NaQVkX6rx4H9/vvvp6Ghq0P3X//6V+bOnctvf/tbkpKSeOaZZ3q9QBmY8qubePTT3by/rQToCuqXjoviujOSGBLkaefqRPqnTfk1/Omdnd27KIyO9uPuC0cwVuvURaSfsdkM9lc1saWglg15tWwpqGV/ZVP3crnv8vd0YUSELxlxgUyKDyI9xh8PV80kEpGBoUeB3TAMQkNDu++kh4aGsnTp0pNSmAxMNU3tPJG5hxfX5dNp7fqhe2FaJDedPYyhwWr0InI8Gts6eWhpDs+vyQcgwNOFW89J5icZMZi1X7CI9AOGYbCvspEvdlfx5Z5KNuXXHtbBHcDX3ZnUSF/GDgkgPcaftBh/wnw1e0hEBq4eB/bExER27dpFUlLSyapJBqDWDivPfH2Af36+j8aD03SnDgvh1nOGM0Lbpogct3X7q1nw+jaK61oA+ElGNLfPSiHAS7soiIhj67DaWLOvmuVZ5XyaVUa5pe2Qx12dTCRH+DI+LpBxsQGMjvYjyt9DDTNFZFDpUWA3m80kJSVRXV2twC7HxGYzeG9bMQ8vze3eTmpEpC+3z0rh1KRgO1cn0n+1d9p4dHkuT3+xH8Po6nr8wCWjNa5ExKG1d9r4am8lH2wrZXlWefdFfAAXJxPjYgOYNiyUifGBjIz0w9VZa89FpH+xtHZQYWnrtd2teryG/YEHHuDmm2/mqaeeUpM5+UGb8mu494MsthXVA13bSd18znAuSovSNF2RE3CgqokbXt3C9oNja05GDHdekIq3W4/f0kVETrpOq401+6v5YFsJn+wo626ICV1LeGaMCGdGahhTEoK19lxEHFpzeyclda2U1LVQWt9CcW0LhbUtFNU2U2ZppaqhnZYOKyYT5N43q1cuOvb4093cuXNpbm4mLS0NV1dXPDwO3RqjpqbmhIuS/q2svpX7P87ubijn5erE785I5P9OHartpERO0Htbi7nj7R00tVvx93ThgUtGc87IcHuXJSJyCMMw2FZUz7tbivlgWwnVTe3djwV7u3LuqAjOGxVBRlwgTrqILyIOwDAMKhvbKK5t6Q7lBbXNFFQ3U1LXQpmllYYj9NY4El93F2qb23ulx0aPA/vixYtP+A+VganDauPZrw+weMUemtu7rizNyYhhwYxhhPqoIYzIiWhpt3L3+7t4bWMhABOGBvK3n6ZrP2ERcSgF1c28s6WYt7cUkV/d3H3cz8OF80ZHcGFaJOMV0kXEDmw2g4qGNopqmymqbaG4roWCmmbyq5soru0K5B3Ww3ei+D5PVyfCfN2J9HcnOsCTKH8Pwv3cifTzIMLfnQg/dzxde2/W4zF9pwULFnDffffh5eXF0KFDmTJlCs7Omnop39pcUMsdb+/o3k5qXGwA91w4gpFRaigncqL2lDdw7cub2V3eiMkEvz8zievPTNS+wiLiEGqb2vlwRynvbi5mU0Ft93E3ZzMzR4Qze0wkpyWF4KL3LBE5yeqbOyioaaagppnC2mYKDwbygpquKew/FsjNJgj2diPS34OYAA+iAjyJCvAgyt+dSH8PIvw88PNw6aO/TZdjSt1///vfufXWW/Hy8uKMM86gtLSU0NDQk12b9ANNbZ08vCyX59bkYRhda9FuPzeFy8dFq4urSC94Y2MhC9/bRUuHlRAfN/42J50piWosJyL21dph5bOcCt7ZUsznORV02ro+BJtNMCUhmEvGRjFjRLh6a4hIr+q02r5zZ/xgIK9pJq+qiaLalkMaWR6J2QThvu5EB3oSE+BJTKAHUf4eRAd4Eh3Qdafc0S4uHtO7aFxcHE888QQzZszAMAzWrFlDQEDAEc+dOnVqrxYojuvLPZXc9taO7u2kLh0bzZ/P03ZSIr2hub2TO9/dxVubiwA4NTGYx+ekE+LjZufKRGSwMgyDTfm1vLW5mA+3lxyyljM1wpeLx0RxYXqk9kUXkRPS1mmlsKaZA1Vdd8fzqpvIr27mQFUTpXWtWI0fvkse5OXKkCBPhgR2/YoJ8CQ60IOYAE8i/Nz73QzFYwrsDz/8MNdccw2LFi3CZDJx8cUXH/E8k8mE1Wrt1QLF8VhaO7j/o2xe3dC1ljbK34MHLh3FaUkhdq5MZGDILrVw3cub2VfZhNkEN00fxu/OSNSaTxGxi6LaZt7eXMxbm4rIr/l2XXqEnzuzx0QxOz2K4eE+dqxQRPqbbxq87a1oZF9lE/sqGtlf1cT+ykaKa1v4oUju6mQmJtCD2CCv7lAeG+RJTGDXXfLeXD/uCI7pbzN79mxmz55NY2Mjvr6+5Obmakr8IPXF7kpufWs7pQf3VJ83OZZbzknGS1PeRE6YYRi8uDafv3yUTVunjTBfN/720zFMig+yd2kiMsi0tFtZuquUNzYWsXpfdfdxT1cnZo2M4NJxUUwaGqRtWkXkBxmGQZmlld3ljewpb2BvRSN7Krp+b/mBjuuerk4MDfYiLtiL2EBP4oK8GBLU9d9QH7dB9d7To5Tl7e3N559/ztChQ9V0bpBpaO3g/o+zeWV911312CBPHrp0NBMVJER6RWVDG7e/vZ0V2RUAnD48hEcvTyPIW1PgRaRvGIbBxvxa3tpUxAfbS2hq+3bW5JSEIC4dG82sUeED7u6ViPSOhtYOsksbyC61kFPWwO7yBnLLGo66rtxsgpgATxJCvUkM9SY+2Iv4EG/igj0J8XZTP6yDevyOO23atJNRhziwr/dWccub27vXqv9iShy3nDNcP7BFeoFhGHy4vZSF7+2ktrkDVyczt81K5hdT4gbV1WMRsQ/DMMgubeCjHSW8t7WEotqW7sdiAjy4bFwMl4yNIibQ045Vioijae+0kVVqYUtBLVsL69hWWEfed7Zy/C4nk4m4YE+GhfmQFOZDYqg3SaHeDA32wt3FqY8r73+UuOSomto6WfRJNi+uLQAgJtCDhy9L0/RckV5yoKqJu9/fxardlQCkRPjy6OVppEb62rkyERnIrDaDrYW1fJpVzqe7yjlQ1dT9mJerE7NGRXDZuGgmxAXqwqGIAFDX3M6m/Fo25NWyKb+G7UX1tHXaDjsv0s+dlAhfkiN8GBbmQ3K4L0ODvXB17l+N3hyJArsc0ep9XXfVv7nSftWkWG6bpbXqIr2hvqWDf36+l2e/zqPdasPVycxvT0/g2jMS9QNNRE6KlnYrX+2tYkVWOZk55VQ1tnc/5upsZtqwEC5Mi2R6ShgerrrjJTKYGYZBcV0LG/Jq2JBXy8a8GnaXNx52XoCnC2OGBJAe409ajD+jovwI1G5RvU7pSw7R2NbJA9+5qx7l78HDl43Wvs8ivaCt08qLawv4x2d7qG3uAOC0pGDuuXAE8SHedq5ORAaayoY2PsspZ3lWBV/traS149u7Yd5uzpyZHMrZqWGckRyq/dJFBrFOq42csgY25deyMb+WTXk1lBxsMP1d8SFejI8NZFxcABmxAQwN9tI68z6gd2fptiKrnDvf29ndAf5nE4dwx7kp+iEucoKsNoN3txTz2PLd3b0gEkO9+dO5KZw+PEQ/7ESkVxiGwZ6KRlZkl7Miq5wthXV8d7viKH8Pzk4N4+zUMMbHBWpGj8gg9E3X9m2FdWwtrGdrYS3bi+ppbj90a25ns4kRUX5MiAsgIy6QjNgANcK1k2NKYpdccskxf8O33377uIsR+yitb+HeD7L4ZGcZ0LVW/cFLdFdd5EQZhsGK7AoeXpbTPZUszNeNm6YP47Jx0Tg76cOyiJyY+uYO1uyv5ss9lazMrey+KPiNUVF+nJ0axvSUMFIifHSBUGSQqW/uYFtRV1O4bUX1bCuqo7Kh7bDzfNycSR/iz/iD4Tx9iL8aTDuIY/q/4Ofnd7LrEDto77Txv9UHWLxiD83tVpzMJn512lBuPGuY1q+JnKB1+6t5cGkOmwvqAPDzcOG3pycwb3KcxpeIHLeqxjY25tWyIa+GdQeqySqxYPvOXXRXZzOnJARxVkpXSA/3c7dfsSLSp9o6rWSVWLq7tm8rqj+kqeQ3nMwmhof5kBbjR3qMP+kxASSFeqvJpIM6psD+7LPPnuw6pI99taeKu97fyb7KrkE8LjaA+y4aqe7UIicop8zCQ0tz+Synaz91dxczvzxlKL+ZmoCfp4udqxOR/sQwDPZVNrEpv+ZgZ+baI374Tgjx4pTEYE4fHsLk+GBdFBQZJL65gLcpv4aN+bXsKrbQbj28c3tskCdp0f6Mju4K6CMi/fQ+0Y9onsMgU1jTzF8/ymbprq7p70Fertw6K5nLxkbrqprICSitb+HRT3fz1uYiDKPr6vVPx8dw/VlJhPnqDpeI/LgOq41dJRbWH6ju7sz8TYPKb5hMMDzMh3GxAUyMD2Li0EC9x4gMEhWWVtYeqGHt/mrW7a/uvvH2XYFergfvmnd1bk+L9sPfU53b+7PjCuxvvvkmr7/+OgUFBbS3tx/y2ObNm3ulMOldrR1Wnlq5jyWr9tHWacPJbOKqSbHcdPYw/Dx010/keLW0W1myah//+mJfdwfm80ZF8MeZwxka7GXn6kTEkdlsBtllFr7aU8XqfdVszKuh6XuNn9yczaTF+JMRG8D4uEDGDgnQbB2RQaKyoY11B6pZs6+aNfur2X+EgD48zIeMuADGxQYwdkgAsUGe6lUxwPQ4sD/xxBP86U9/4he/+AXvvfce8+fPZ9++fWzYsIFrr732ZNQoJ8AwDJbuLOMvH2V3N6KZFB/I3ReOIDlc099FjpdhGHyys4y/fJjVvfXJ+LgA7jg3hTFDAuxcnYg4qobWDr7cU8VnORWszK2kqvHQ5k9+Hi6MjwtkfFwA44cGMjLST93cRQaJ6sY21h28g75mXzV7Kg7d+9xkgtQIXyYdnF0zPi6QAO17PuD1OLD/85//5Omnn+aKK67gf//7H7fccgvx8fEsXLiQmpqak1GjHKecMgv3fpDF6n3VAET4ufOn81I4b1SErryJnIC8qibufG8nX+6pArq2SvrTeSnMGhmusSUihympa2FFdjnLs8pZu7+aDuu3XeI8XZ2YHB/ElMRgpiQEMTzMR0vURAaJsvpWNuTVsP5A16/c8obDzkkO92FyQhCT44OYODRIM2wGoR4H9oKCAqZMmQKAh4cHDQ1d/7CuuuoqJk2axD/+8Y/erVB6rKKhlb+t2MMr6wuwGV0dY38zNZ7fnp6g7RlETkB7p41/f7mfv2Xuob3ThquzmWumJfDbaQlq3iIi3QzDIKvUwvKsrpC+q8RyyOPxwV6ckRzKWcmhZGg/dJFBocNqI6e0gc0FtWwu6GoiWVTbcth5yeE+TBwayOSEICYMDSJQd9AHvR6nt/DwcGpqaoiNjWXIkCGsXbuWtLQ0Dhw4gGEYP/4N5KSpbWrnma8P8N+vDtB8cA3crJHh3HFuCjGBnnauTqR/21xQy+1v7ei++n1qYjD3zR6pdeoiAnR9GF9/oKY7pH93P3SzCcYOCejaDz01jIQQbztWKiJ9oaKhlc35dWwprGVLfh3bi+u6e918w2yC1EhfMmIDmRTfNcU9yNvNThWLo+pxYD/zzDN5//33GTNmDPPnz+emm27izTffZOPGjVxyySUno0b5ETllFl5dX8jrGwu7g3pajD93zEpmYnyQnasT6d8a2zp5ZFkuz63JwzC6uq8uPD+Vi9IjNf1dZJCztHawKreSFdnlfJ5TgaW1s/sxdxczU5NCODs1jDOTQ/UhXGQAa27v7N7/fGthHVsK6g65aPcNX3dnxgwJYMwQfzJiA0mL8cPHXVPc5Yf1OLA//fTT2GxdV4euvfZagoKC+P/27js8qjL9//h7Jr33XkgCgSQkJHRREZUgCKiIBVl3USxbXNRddF1d+3dd0XX1h2vdouiyKlixY4mAIL230AnpnfQ+c35/RINZQSGEzEzyeV1XrkvOnJm5eeTOzH3O89zP6tWrufTSS/nVr37V7QHK8eVWNPDJziI+3l7EjoLqjuODI32Zc8EAJmktrchp+2pPCfe9t7OjqdwVw6K5d0qypqeJ9GF5lQ1kZZfwZXYp6w53Xo8e5OXK+ORQLkoJ59zEYNxdtFRGpDdpaGnjSEUDh8vrOVRWx57iWvYU13KorA7r/0w0NplgYKgPw/r5MzQmgGH9/EkI9laPCjllp1yw5+fnExMT0/Hna665hmuuuQbDMMjLyyM2NrZbA5R2VqvB1vyq9i8Ju0s7NaVwNpvITA7jZ6NjGZsYrEJd5DSV1zXzfx/u5oNthQDEBHrw6OVpjE0MsXFkItLTrFaD7QXVfLm7hC+zS9hT3LkpVEKIFxNSwpiQHMbQ2ACc9GVcxGEZhkFZbTO5lQ3HfiqO/XdpbfMJnxvm60ZalD9DY9v3QB8Srbvn0j1OuWCPj4+nqKiI0NDQTscrKyuJj4/HYrGc4Jlyquqb21i5v4ys7FKW7S2lvO7YnvdOZhOj4wOZnBbBpNRwgjXVTuS0GYbBu5sL+PPHu6lqaMVsgpvGJvC7zEQ1bBTpQxpbLKw6UE5WdglZe0op+96XdLMJRsQFkpkcSmZyGAlajy7iUAzDoLS2mYOldRwqryenvJ6cigaOVNSTd7ThB+vM/5efhwvxwV4khHiRGOpDUoQPgyN8CfV176G/gfQ1p/wN1DCM497Braurw91d/1BPV/7RBr7aU8qX2aWsPVhBi+XYLw0fN2fOGxRCZnIoFwwKxd9T03JFusuRinrufW8nqw60b9WWEuHL41cMIS3az8aRiUhPKK1tIiu7lC93l7DqQDnNbcc+f73dnDlvYDATUsI4f2Co9j0WcQDf3S3fW1LLvpI69hXXsq+0lgMlddQ2t53weWYTRPp7EBPgSb8gT2ICPYn99qdfkKe+f0uPO+mCfe7cuQCYTCbuv/9+PD2PdR23WCysW7eOjIyMbg+wtzMMg12FNXy+q5gvskvJLuq89Uu/IE8uTGq/ij9SW7+IdLvmNgv/XHGIZ5YdoKXNipuzmd9lDuSmsfG4OCnfRHorwzDYX1rX0dV9a15Vp8ej/D2YkBLG+ORQRscH6fNXxI7VN7d9u568hr3FtewtrmVfSS1HG1qPe77ZBLGBniSEeBMX5EV8sCexQV7EBXkS6e+hz3+xKyddsG/ZsgVo/4DbsWMHrq7Hri65urqSnp7OnXfe2f0R9kJtFisbco7y2a5iPt9V3NHQCtp/gQzvF8D45DAyk0PpH+KtNekiZ8hXe0r4vw93k1PRALRv1fbItFTitFWbSK/UZrGy8cjRjiI9t7Kh0+PpMf5kJoWSmRJGUriPPn9F7NB3F9vWHapgS24VW/OrOFxez/F2lzabIC7Ii4FhPgwM8yYxzIfEMG/ig71wc1ZTSHEMJ12wL1u2DIDZs2fz9NNP4+vre8aC6o2a2yysPlDB0p3FfJFdQmX9sfXoHi5OjBsYQua3W7+oA7XImZVdVMOjn2Szcn/79PcQHzfum5LMpenaqk2kt2lssbByfxmf7y4hK7uk0x03V2cz5/QPYkJKOOOTQwnTGlQRu1Td2MrX+8pYtreUr/eVderr9J1QHzeSInxJCvchKdyHgWE+DAj11m4N4vBOeQ37ggULOv47Pz8fgOjo6O6LqBdpaGljxd4yPt1ZzFd7Sqn73noZf08XMpPDmDg4nLHa+kWkR+QfbWD+l/t5Z3M+hgEuTiZuODeeWy9MxNtNTeVEeouKumay9pTyxe4SVu4v69REys/DhfFJoUxICeO8gSF4KfdF7FJRdSNf7C7h810lrD1UQdv39k1zdzEzol8gI+ICyIjxJzXKTw2Ypdc65U8pq9XKI488wpNPPkldXR0APj4+3HHHHdx7772YzX17zUdVQwtZ2aUs3VXM1/vKOjWtCfVxY1JqOBMHhzM6PhBnrY8R6REVdc08v/wgC9cc6WjkOHVIBHdNTCI2yPMnni0ijuBIRX37l/vdJWzMqey0J3J0QPt69ItSwhkZF6DPXxE7ZBgGB0rr2peM7i5he351p8f7h3iRmRzGuEEhDO8XoCnt0meccsF+77338tJLL/HYY49xzjnnALBq1Soeeughmpqa+Mtf/tLtQdq7IxX1ZGW3X8lfn1OJ5XvfEmIDPZk4OIxJqREMjfHHrP1ZRXpMbVMr/155mH+vPER9S/uWk2MSgvjjxUlkxPjbNjgROS1tFitb8qrIyi4lK7uE/aV1nR5PifDlosFhTEgJIyXCV8tdROyQxWqwOfdYX4nD5fUdj5lMMDw24Ns8Dide/WWkjzrlgv3VV1/l3//+N5deemnHsSFDhhAVFcUtt9zSJwr2hpY21h2qZMW+Mr7eX8ahsvpOjyeF+zBxcPud9OQINa0R6WlNrRb+u/YIzy070LFeNTXKlz9MTOK8xGDlpIiDyqtsYOX+clYdKGPV/nJqmo4tNXMymxgdH8iElPYiPTpAs2dE7FFRdSPfHKhg5f4yVuwro+r7fSWczJwzIIiJg8MZnxxGiI+muYuccsFeWVlJUlLSD44nJSVRWVn5k89/7rnneOKJJyguLiY9PZ1nnnmGUaNGnfD8t956i/vvv5+cnBwSExN5/PHHmTx58qmGfVosVoMdBdWs2l/GqgPlbDpylFbLsbvozmYTo+IDuTAplItSwjXFVsRG2ixW3tqUz9Nf7qe4pn33hYQQL+6YMIiLU8M1w0XEwZTWNLHmUAWrD1TwzcFy8o82dnrc39OF8xJDGJ8cyvkDQ/HzdLFRpCJyPG0WK/tK6tiSd5RNR46yMefoD3Zn8HV35sKkUCakhDNuUIh6yoj8j1POiPT0dJ599ln+/ve/dzr+7LPPkp6e/qPPXbx4MXPnzuXFF19k9OjRzJ8/n4kTJ7J3715CQ0N/cP7q1auZOXMm8+bNY+rUqbz++utMmzaNzZs3k5qaeqqhnzTDMMipaGDVgXK+2V/O6oOdr+JD+3q4sYnBjBsYwtkDgvF115cEEVuxWg0+3lHEU1/s65hOF+nnzu8yBzJ9WJTWq4o4iKP1Law9VMHqgxWsPljOwf+ZweZsNpER48+5icGcNzCE9Gh/nHQhTsQuGIZBYXUTW3Or2JZfxda8KnbkV9PYaul0ntkEadH+nDsgiPMHhTI0xl+f0yI/wmQYx9u18MRWrFjBlClTiI2NZcyYMQCsWbOGvLw8PvnkE8aOHXvC544ePZqRI0fy7LPPAu0N7GJiYrj11lu5++67f3D+jBkzqK+v56OPPuo4dtZZZ5GRkcGLL754UvHW1NTg5+dHdXX1j25FV17XzOqDFazaX8Y3ByooqOp8Fd/H3ZkxCUGMTQzm3MQQ4oI8Na1W5HtONte68/UMw2DFvjKe+GwvuwprAAjycuW3Fwzg2rNi1ZBG5CTZIn8BappaWX+osv0u+sEKsotqOj1uMsHgSF/O7h/MmP5BjIoLVFd3ke+xVe5Ce6Pl7fnVbMv7rkCvpryu+Qfnebs5kxHjz9BYf0bEBTI01l83ukQ4+Xw75U+9cePGsW/fPp577jn27NkDwPTp07nllluIjIw84fNaWlrYtGkT99xzT8cxs9lMZmYma9asOe5z1qxZw9y5czsdmzhxIkuWLDnh+zQ3N9PcfOyXRU1NzQnP/c7CtUe4f8nOTsdcnEwMiw1gbGIw5wwIJi3KT1f/RM6gruRuc5uVP7y9nbLaZrzdnLl5bAI3jo3XdDqRHtaV/P1gWyG/W7SlUzd3gIFh3pzdP5izEoIYkxCkae4iZ1BXchfg2n+v5ZsDFT847mw2MSjch4wYf9Jj/Bka40//EG8tSRM5Daf8rTY3N5eYmJjjNpfLzc0lNjb2uM8rLy/HYrEQFhbW6XhYWFhH4f+/iouLj3t+cXHxCeObN28eDz/88E/9NTpJifABIDnCl3MHBHFuYggj4wLwdNWXfpGe0pXcdXdx4s6LBnKgtI7fnD+AQC/XMxSdiPyYruRvcrgPVgPig73ai/P+7QW6mkyJ9Jyu5C5AgGf7521ckCdDov2/LdD9GBzph7uLZreJdKdTnhLv5OREUVHRD9acV1RUEBoaisViOe7zCgsLiYqKYvXq1R1T6QHuuusuVqxYwbp1637wHFdXV1599VVmzpzZcez555/n4YcfpqSk5Ljvc7wrhTExMT861aDNYqWqsZVgb31JEOmq052W15XcFZHuYYv8NQyD4pomIvw8uhy3SF9nq8/e/KMNeLk6E6AL5SJddsamxBuGcdy123V1dbi7u5/wecHBwTg5Of2g0C4pKSE8PPy4zwkPDz+l8wHc3Nxwczu1wtvZyaxiXcTGupK7ImIfupK/JpNJxbqIjXX1s1fbJor0nJMu2L9bS24ymbj//vvx9DyWqBaLhXXr1pGRkXHC57u6ujJ8+HCysrKYNm0a0N50Lisrizlz5hz3OWPGjCErK4vf/e53Hce++OKLTnfoRURERERERHqjky7Yt2zZArTfYd+xYweursemwLi6upKens6dd975o68xd+5crrvuOkaMGMGoUaOYP38+9fX1zJ49G4BZs2YRFRXFvHnzALj99tsZN24cTz75JFOmTGHRokVs3LiRf/7zn6f8FxURERERERFxJCddsC9btgyA2bNn8/TTT3dpncyMGTMoKyvjgQceoLi4mIyMDJYuXdrRWC43Nxez+Vgn9rPPPpvXX3+d++67jz/96U8kJiayZMmSM7oHu4iIiIiIiIg9OOWmc46mu/enFJHjs+VesCJyepS/Io5JuSviuE4237SxuIiIiIiIiIgd6vUbjX83gaCmpsbGkYj0bt/lWHdN2lHuivQc5a+IY1Luijiuk83fXl+w19bWAhATE2PjSET6htraWvz8/LrldUC5K9KTlL8ijkm5K+K4fip/e/0adqvVSmFhIT4+PsfdP/47NTU1xMTEkJeXpzU7p0lj2X0caSwNw6C2tpbIyMhOzSO7Srnb8zSW3cuRxlP56/g0lt3HkcZSuds7aDy7jyON5cnmb6+/w242m4mOjj7p8319fe3+f66j0Fh2H0cZy+64uv8d5a7taCy7l6OMp/K3d9BYdh9HGUvlbu+h8ew+jjKWJ5O/ajonIiIiIiIiYodUsIuIiIiIiIjYIRXs33Jzc+PBBx/Ezc3N1qE4PI1l99FY/jSNUffRWHYvjedP0xh1H41l99FY/jSNUffSeHaf3jiWvb7pnIiIiIiIiIgj0h12ERERERERETukgl1ERERERETEDqlgFxEREREREbFDKthFRERERERE7JAKdhERERERERE7pIJdRERERERExA6pYBcRERERERGxQyrYRUREREREROyQCnYRERERERERO6SCXURERERERMQOqWAXERERERERsUMq2EVERERERETskLOtAzjTrFYrhYWF+Pj4YDKZbB2OSK9lGAa1tbVERkZiNp/+tUDlrkjPUf6KOCblrojjOtn87fUFe2FhITExMbYOQ6TPyMvLIzo6+rRfR7kr0vOUvyKOSbkr4rh+Kn97fcHu4+MDtA+Er6+vjaMR6b1qamqIiYnpyLnTpdwV6TnKXxHHpNwVcVwnm7+9vmD/bjqPr6+vfvGI9IDumkKn3BXpecpfEcek3BVxXD+Vv2o6JyIiIiIiImKHVLCLiIiIiIiI2CEV7CIiIiIiIiJ2SAW7iIiIiIiIiB1SwS4iIiIiIiJih1Swi4iIiIiIiNghFewiIiIiInbkSEU9h8vrbR2GiNiBXr8Pu4iIiIiIvbNaDfaX1rHxSCWlNc2c3T+I+GAvW4clIjamgl1ERERExEZaLVZ2F9aw6chRqhtbbR2OiNgZFewiIiIiIj2sqdXCtrwqtuZV0dBi+cHjB8vqSI3yw8tNX9dF+jL9BhARERER6SF1zW1sPnKUHQXVtLRZOz1mNQwOldWzJfcohdVNtFoMrjs7zjaBiohdUMEuIiIiInKGVTW0sDHnKNlFNbRZjU6PtbRZ2V1Uw5bco9Q0tQHgZDJRUtNki1BFxI6oYBcREREROUPKapvZkFPJ/pI6rEbnQr22qZVtedXsKDx2t93d2UxatB83nBPPxWkRtghZROyICnYRERERkW5WXN3EusMVHC6v53/qdEprmticW8X+0lq+u9nu7+nC0Bh/kiN8cXEyE+jl2vNBi4jdUcEuIiIiItJN8o82sO5QJbmVDZ2OG4ZBTkUDm48cJb+qseN4tL8HQ/v5Ex/khclk6ulwRcTOqWAXERERETlNRyrqWXe4koKjjZ2Ot1mt7CmuZcuRKiobWgAwmyAxzIdhMf6E+rrbIlwRcRAq2EVEREREuuhQWR3rDldSXN25QVxTq4UdBdWdtm1zdTKTGuVLRow/Pu4uJ3xNf08XIvw8zmjcIuIYVLCLiIiIiJwCwzA4WFbPusMVlNY0d3qspqmVrblV7CysptXSvkDd282ZjBh/UqN8cXN2Ou5rmkwQH+xFerQ//YI8NT1eRAAV7CIiIiIiJ6W9UK9j7aFKymo7F+rldc1sOnKUfSXHGskFebkyvF8AA8N8cDIfvwB3d3FicKQv6dH++Hme+K67iPRNKthFRERERH7CgdI61h6q6FSoG4ZBYVUTG49UklNxrMlcdIAHw2MDfvROebCPGxnR/iRF+ODiZD7j8YuIY1LBLiIiIiJyAkcq6vnmQAUlNcfWqBuGweGKejbmHKXoe2vXB4R6M7xfAOEnaCRnNplICPEiI8afmEDPMx67iDg+FewiIiIiIv+jrLaZlfvLOPK9O+dWq8G+0lo2HjlKRV17x3cnk4nkCB+G9QsgwPP4e6e7uziRGuVLeow/vj/SbE5E5H/1+Pyb5557jri4ONzd3Rk9ejTr168/4bnvvvsuI0aMwN/fHy8vLzIyMli4cGEPRisiIiIifUlzm4Vle0t5fV1uR7FusRrsLKjmP2uP8NmuEirqWnBxMjE8NoDZ58QxPjnsuMV6sLcrmclh3DQ2nrGJISrWReSU9egd9sWLFzN37lxefPFFRo8ezfz585k4cSJ79+4lNDT0B+cHBgZy7733kpSUhKurKx999BGzZ88mNDSUiRMn9mToIiIiItLLHamo54vdJdQ2tQHte6jvKqxhY85R6prbj3m4OJER48+QaD/cXX7Y8f27bu8ZMf70C/Lq0fhFpPfp0YL9qaee4uabb2b27NkAvPjii3z88ce8/PLL3H333T84//zzz+/059tvv51XX32VVatWqWAXERERkW7RZrGycn852/KrMAywGgZ7impZe7iio3j3cnViWL8A0qL8jtskztXZTEpE+x7rAV7HnxovInKqeqxgb2lpYdOmTdxzzz0dx8xmM5mZmaxZs+Ynn28YBl999RV79+7l8ccfP+F5zc3NNDcf695ZU1NzeoGLSI9Q7oo4LuWvOLLK+hY+3lFE+bfd3wuONrJsX2nHGnUvNydG9gtkcKQvzscp1H3cv9tj/fh33O2ZclfE/vVYwV5eXo7FYiEsLKzT8bCwMPbs2XPC51VXVxMVFUVzczNOTk48//zzTJgw4YTnz5s3j4cffrjb4haRnqHcFXFcyl9xVHuLa/kyu4SWNitNrRZW7CtjT3EtAG7OZkbGBZIe7XfcQj3cz51hsQEkhnpjPsEe6/ZOuSti/0yGYRg98UaFhYVERUWxevVqxowZ03H8rrvuYsWKFaxbt+64z7NarRw6dIi6ujqysrL485//zJIlS34wXf47x7tSGBMTQ3V1Nb6+vt36dxKRY2pqavDz8+tyril3RWxH+St9jcVq8PX+MrbmVgFwqKyOrD2lNLRYAEiL8mNM/yA8/ueOuckE/UO8GdYvgCh/j54O+weUuyKO62Tzt8fusAcHB+Pk5ERJSUmn4yUlJYSHh5/weWazmQEDBgCQkZFBdnY28+bNO2HB7ubmhpubW7fFLSI9Q7kr4riUv+JIapta+WRHEYVVTbRarHy9r4ydhe1TwQM9XZmQEka4X+d91F2cTKRE+jIsNgD/E2zd5oiUuyL2r8cKdldXV4YPH05WVhbTpk0D2u+eZ2VlMWfOnJN+HavV2ulKoIiIiIjIycirbOCTHUU0tFgorW3i053FVDW0AjA8NoCzEgI7TX93czGTEe1PRqw/nq492qtZRATo4S7xc+fO5brrrmPEiBGMGjWK+fPnU19f39E1ftasWURFRTFv3jygfV3NiBEj6N+/P83NzXzyyScsXLiQF154oSfDFhEREREHtzn3KCv3lWOxWtmWX82q/eVYDANvN2cuSgkjJtCz41xXZzNDY/0ZFhvgcI3kRKR36dGCfcaMGZSVlfHAAw9QXFxMRkYGS5cu7WhEl5ubi9l87KpmfX09t9xyC/n5+Xh4eJCUlMR///tfZsyY0ZNhi4iIiIiDarNY+TK7lOyiGppaLXyZXcLBsnoAEoK9yEwJ61irbjJBamT7+nUvN91RFxHb67Gmc7Zyus04ROTkdHeuKXdFeo7yV3qruuY2PtxWSHF1EyU1TXyyo4iapjacTCbOTQwmPdoPk6m9w3uIjxuZyT9cv27PlLsijsvums6JiIiIiPSU0tomPthaSE1jK9vzq1n57RR4X3dnJqdFEObbXpg7mU2clRDEiH4BDrs9m4j0XirYRURERKRXOVxezyc7iqhvbiMru5S9Je17q/cP8WJCchhu306BD/V1Y+LgcIK91SldROyTCnYRERER6TV2FVbz5e5SKuub+Wh7ERX1LZhMcO6AYIbG+GMymTCbTIyMD2B0fBBOuqsuInZMBbuIiIiI9Apbco+yYl8ZOeX1fLqzmOY2K56uTkxOjSAqwAMAf08XJqWGE+HnYeNoRUR+mgp2EREREXF4m45U8vW+crbnV7F8XxmGAeG+7kwZEoH3tx3fB0f6cv6gUFydzT/xaiIi9kEFu4iIiIg4tE1HKlmxt4xVB8rZnFsFQHK4DxcmheLsZMbdxYnM5FASw3xsG6iIyClSwS4iIiIiDmtrXhXL95Tx5Z4Ssovam8uNSQhiZFwAJpOJKH8PJqWF4+vuYuNIRUROnQp2EREREXFIuwqr+TK7mE93FnOwrB6TCSYkh5Ec4YvJBCPjAhmTEKTt2kTEYalgFxERERGHc6C0lqU7ivloexE5FQ04mU1cnBpO/xBv3F2cuDg1nLhgL1uHKSJyWlSwi4iIiIhDyats4MOthXywvZAj3xbrl6ZHEhvoSbCPG5cOicTPU1PgRcTxqWAXEREREYdRWtPEe1sKeH9be7Hu/G2xHhPoSUKIFxenRqgLvIj0GirYRURERMQhVDW08PamfN7fWtAxDf67Yj0jxp/zB4VgMmm9uoj0HirYRURERMTuNbS08c7mfJZsLeBgWT1OZhOXDIkgJtCTcwYEMyo+0NYhioh0OxXsIiIiImLXWtqsvLu5gHc3F7CvpA6zCaakRRAX7MWFSaEMifa3dYgiImeECnYRERERsVtWq8EnOwp5e1M+uwprMAGTBrd3g79ocPsWbiIivZUKdhERERGxW8v2lvLG+jy25lUBkJkSRlKEL5PTwhkQ6mPb4EREzjAV7CIiIiJilzYdqWThmiOsO1wJwLiBIQyJ8mPKkAgSQrxtHJ2IyJmngl1ERERE7M7Bsjr+vfIwy/eVATA6PpAR/QK4JD2SuGAvG0cnItIztEmliIiIiNiVstpm/rHiIJ/tKgYgPdqPc/oHqVgXkT5HBbuIiIiI2I3GFgv/WnmIJVsKsRowMNSb8UmhXJoRpWJdRPocFewiIiIiYhesVoPX1x3htXVHaLFYifb3YFJaOFN1Z11E+igV7CIiIr1Qm8XKyv1ltg5D5JR8taeUF1ccor7ZQpCXK5ekR3JpepQazIlIn6WmcyIiIr1MTVMrH28v4ut9ZeRWNnDt6H62DknkJx0oreXRT7Ipq2vGw8WJyzIimTY0igGhKtZFpO9SwS4iItKL5JTX88G2Qj7ZUcSe4lqysksZFRdIYpj2qxb7VdPUyv3v7+JQeT1OZhOXpEdw+dBoBoXr362I9G0q2EVERHoBwzBYf7iyo1g/2tCKCbht/ABNJxa7ZhgGf/tsL2sOVgAwPimUq0bEkBbtZ+PIRERsTwW7iIiIg2tqtbB0ZxEfbCtixb4yLFYDbzdnLkmPYM6FibYOT+RHLdlSwBvrcwHIiPFn5uhYRsYF2jgqERH7oIJdRETEgZXVNvPu5nze3VzA3pJaAOKCPLkoJZxwP3cbRyfy43LK6/nLJ9m0Wgyi/D2YfU4c5w8MsXVYIiJ2QwW7iIiIg9pbXMsb63N5f2tB+xR4E5zdP4jhsQEkhvmQmRxm6xBFTqjVYmXum1spr2vBw8WJX4zpx+S0CEwmk61DExGxGyrYRUREHIzVavDNwXL+s+YIy/aU0vbtFPiLU8OJC/Zi3MAQUqO0/lfs25Of7WVzbhUA04dF8fOz+uHipB2HRUS+TwW7iIiIA2lqtbBkSwEL1x5hV2ENAP0CPZk4OJz4EC8mDQ4nwMvVxlGK/Li1Byt4+ZscAM6KD+TWCxPxdtPXUhGR/6XfjCIiIg6ivK6ZV77JYfGGPMrqmgE4KyGQ0fGBjIwLYkz/IJzMmk4s9q2+uZU739pGi8VKhJ8790xOVr8FEek1SmqaCPVx67blPSrYRUREHMCB0lqeW3aQj3cU0dJmxcPFiUmp4aRE+DIpNZyYQE9bhyhyUu5+Zwf5VY24Opu5fXwi6TH+tg5JROS0Wa0G6w5X8MrqHAaEePOHSUnd8roq2EVEROyYYRisPlDB01n7WJ9zFIAIP3cuTg0nPcafi1LC8XB1snGUIifn4+2FfLS9CIDLh0Zx5fBoG0ckInL6qhpaWLwhjzfW55JT0cBnlJCZEsbQ2IDTfm0V7CIiInaqpc3Ku5vzeX75QXIrGwDIiPbn/EEhjBsU0i1fBER6SmV9M/cu2YkBpET48qfJyTiryZyIOLgd+VU8t/wgX+0ppaXNipPJxB0XDSStm5q/qmAXERGxQzVNrbyw/CD/XXuE2qY2nM0mMpPDOCshkMlDIgj10ZpfcSy/X7yVqoZWfNydefTyNPw8XGwdkohIlzW2WHh7Ux4vrTpMTkX7RfUwXzcmDQ7n1+P6Y+6mnjIq2EVEROxMYVUjj36ym093lmCxGvh5uDB1SARjE0O4MCkUV2fdlRTH8tbGPFbsKwfglvP7kxHrb9uAREROQ055HU9+vo/Pdpe031U3mzgrIZApaRFcNDi824p1UMEuIiJiV7bnV/HA+zvZmlcNQHywF1PSwrk4LYLBkdpbXRxPWW0TD3+4G4Bz+gfxy/P62zgiEZGusVgNPt5RxPwv93GorB5ov6s+OS2CyzKiSI/267bu8N9RwS4iImIHDMPg0x3FPPLJbgqrmoD2/akvTgtn6pBIgrzdbByhSNfctmgrdc1tBHq58uTV6dp6UEQcUmV9C39duoclWwtoarViNsHohCCuGh7NhJQwfNzPzDIfFewiIiI21mqx8s+vD/HC8oPUNbfh6mRm4uAwpqZHcmFSKC5qzCUOatGGXNYcrMBkggemphDu52HrkERETtmaAxX830e7yC6uBSDY25XL0iO5emQsg8J9zuh7q2AXERGxofrmNh7+cBfvbC7AYjUI8HTh8qFRTB8WTWo3dZgVsYXy2ib+/FH7VPiJKWFMGxpl44hERE5NU6uFZ77az6urj1DX3IYJGBEXwPVnxzE+OQx3lzO/raoKdhERERsprm7i94u3sOZQJQBxQZ5cMzKGK0fEEKwp8OLgbl+8lfpmCyE+bvz1ynRbhyMickoOltVx9zvb2ZBzFAB/DxemDY3i+rPjiAv26rE4VLCLiIjYwK7Caua8voXD5e1Na0b0C+C6MXFclBqGm/OZv2Ivcia9tyWfbw60T4Wfd3kqvtrCTUQchGEYLFqfyxOf7aOyoQWAIdF+/GZcAhcm9/xntAp2ERGRHvbZzmLueXcHlQ0tOJtNTBwczo1j4xkWG2Dr0EROW1VDCw+8vwuAqWkRZKaE2zgiEZGTU9PYyh/f2c5nu4qxGuDl6sS0oVH85vz+RAd42iQmFewiIiI9xDAM/vH1If7fF/tobrPi7ebM1SOiuXFsAlH+asYlvcNdb2+ntqmNEG83Hr9iiK3DERE5KWsPlXPnW9vJP9oIQGKoN7ePT2RiarhNm7/2+Ds/99xzxMXF4e7uzujRo1m/fv0Jz/3Xv/7F2LFjCQgIICAggMzMzB89X0RExF61Wazc/e4OHl+6h+Y2K+G+7vw+M5E7LhqkYl16jS92F/P57hIAHr8yDU833RsSEftmtRr8dekeZr20gfyjjbg6mZmWEcmC2SOZmh5p851aevTdFy9ezNy5c3nwwQfZvHkz6enpTJw4kdLS0uOev3z5cmbOnMmyZctYs2YNMTExXHTRRRQUFPRk2CIiIqelurGFX7y0jsUb8jAMGBTuw5+npTL7nHi8VNBIL1HX3Mbd7+wAYEpaOBcmhdk4IhGRH1dY1cCVL67m+eUHabFYifBz55HLU3niqnSbTYH/Xz1asD/11FPcfPPNzJ49m5SUFF588UU8PT15+eWXj3v+a6+9xi233EJGRgZJSUn8+9//xmq1kpWV1ZNhi4iIdNmB0loue/abjk7wYxODeXpGBhNSwjCbTTaOTqT7PPj+TirqWwj0cuWvV2oqvIjYt4+3FzL56VVszq3CBIwbGMIbvxzN1SNibH5X/ft67LJ+S0sLmzZt4p577uk4ZjabyczMZM2aNSf1Gg0NDbS2thIYGHjCc5qbm2lubu74c01NTdeDFpEeo9yV3ujL7BLueHMb1Y2tuDiZmD4smjsvGkSIT+/ask35K2sPVvDO5vYZkI9dnoaXm7rCOwLlrvRFrW1W7n1vB29tzscwwNfdmd9eMIDZ58Tj6mw/hfp3eiyi8vJyLBYLYWGdp0eFhYVRXFx8Uq/xxz/+kcjISDIzM094zrx58/Dz8+v4iYmJOa24RaRnKHelNzEMgyc+28uvF26iurEVX3dnfj9hIA9fOrjXFeug/O3rmlot/P7NrQBclBLGRanqCu8olLvS1xwqq2PS01/z5qb2Yn1wpC+v33wWvxrX3y6LdbBB07mueuyxx1i0aBHvvfce7u7uJzzvnnvuobq6uuMnLy+vB6MUka5S7kpvUVbbxDX/XMtzyw7QZjWIDfTkyavT+c24/ri79M791ZW/fdtfl+6hqLoJX3dnnrgq3dbhyClQ7kpfsnhDLlP+voqDZfW4OJmYNaYfb/96DKlRfrYO7Uf12JT44OBgnJycKCkp6XS8pKSE8PAfvxL7t7/9jccee4wvv/ySIUN+fE2Um5sbbm697+6FSG+n3JXe4OPthdy3ZCdHG1oBOHdAEH++LI34EC8bR3ZmKX/7rp0F1byyOgeA/7ssFT8PTYV3JMpd6QuaWtu4483tfLyjCIAwXzfmXT6EC5NDbRzZyemxgt3V1ZXhw4eTlZXFtGnTADoayM2ZM+eEz/vrX//KX/7yFz777DNGjBjRQ9GKiIicvJrGFu56ewef7S7GMMDbzZlfjIllzgWJ6gIvvVarxcrti7ZgNdqbKU4bGmXrkEREOtlbXMNN/9lIXmX73upjE4N5+pqhBHq52jiyk9ej3yLmzp3Lddddx4gRIxg1ahTz58+nvr6e2bNnAzBr1iyioqKYN28eAI8//jgPPPAAr7/+OnFxcR1r3b29vfH29u7J0EVERI7r422FPPDBLirqWwBIDvfh3ikpnDMgCJNJXeCl93ph+QEOltXj6erEU1dn2DocEZFOFq7J4c8fZ9PSZsXdxczcCQO5eWyCw30292jBPmPGDMrKynjggQcoLi4mIyODpUuXdjSiy83NxWw+tqz+hRdeoKWlhSuvvLLT6zz44IM89NBDPRm6iIhIJ5X1Ldz55la+2lsGgKerE1ePiOH3EwZqWrD0egfL6vj7VwcAuG9Kcq9spigijqmhpY3b39jKF9ntS7Hjgjx5/tphpETa91r1E+nxeXpz5sw54RT45cuXd/pzTk7OmQ9IRETkFL25IY9HPt5NTVMbAEOi/Lj/kmRGxgXZODKRM89qNbjjzW20WQyGxfozc1SsrUMSEQFga14Vv164keKa9u0Kp2VE8vgVQ3Bz4KavWlgnIiJykkprm7j9ja2sOVQBgK+HMzefm8DN5yX02g7wIv/rjQ25bM2rwtXZzNPXDHW46aUi0vtYrQZPf7WfZ7L2YzXAx82ZR6alclkv6K2hgl1EROQkvLb2CI9+kk19iwWAs/sH8Zdpvb8DvMj3ldY08ejH2QD8PjORmEBPG0ckIn1dYVUjv1q4iR0F1QAMifbjHz8fToS/h40j6x4q2EVERH5EaU0Tt76xhXWHKwEI8nLlD5MGcfXwGMxm3VmUvuX+93dS32JhYJg3vzyvv63DEZE+zDAMFm3I4+EPd9HUasXFycQt5/fnd5kDe9XMHxXsIiIiJ/Dmhlwe/nA39S0WTCa4KCWMRy9PI8hbDbak7/lqTwmf7SrBbIKnrs7ASResRMRGKuqa+d3irazcXw5811huOCmRvjaOrPupYBcREfkf1Q2t3PrGZr7+9otAqI8b/3fZYCalRtg4MhHbaGhp40/v7gTgujFxpEY5ZrdlEXF8n+4o4o/vbKemqQ2zCa47O457Jyfj7GT+6Sc7IBXsIiIi37N8bym/W7SVqsZWTMCUIRHMm56Gj7u2apO+a/6X+ymuaSLEx40/TBpk63BEpA+qaWrlnnd38PH2IgAi/d15duYwhvULsHFkZ5YKdhEREaDVYuX/PtzNwrVHAAj0cmXe9DQmDg63cWQitrWnuIaXVh0GYN7laXi66uujiPSs1QfKuW3RFsrrWgCYOTKGhy4bjJtz79+hRb9xRUSkz8urbOCGVzawv7QOgHEDQ3j6mgz8PV1tHJmIbVmtBn96dwcWq8GFSaFkpoTZOiQR6UOaWi385ePsjovpId5uzL8mg3MGBNs4sp6jgl1ERPq0j7YX8oe3ttPYasHd2cyfpiQza0ycrcMSsQvvbM5nc24V7s5mHpmWautwRKQP2Z5fxZzXt5Bb2QDAZemRPDo9DS+3vlXC9q2/rYiIyLda2qw8+MFO3lifB0B8sBf/mjWcAaE+No5MxD5UNbTwl2/3XP9d5kAie8mexiJi31rarDydtZ8Xlx/EYhj4ebjwxJVDuKiPLlFTwS4iIn1OQVUjN76ygT3FtQBcOTyaedPTcOmlHWZFuuKJz/ZS1dhKfLAXN46Nt3U4ItIHbM+vYu7ibRwoa1+idmFSKE9elU6AV99doqaCXURE+pRle0qZ88Zm6psteLg48dj0NC4bGmXrsETsyra8Kl5flwugi1kicsbVNLXyt8/2snDtEQwDfNyd+cu0VC7N0OezCnYREekTLFaDJz/fywvLD2LQPgX+ldkj6RfkZevQROyKxWpw75IdGMCl6ZGclRBk65BEpJdqtVh5Y10uf/tiLzWNbQBMGhzOI5enEuztZuPo7IMKdhER6fWO1rfwm9c2sfZQJdBehDxx1ZA+sR2MyKlatCGXnQU1eLk6cd/UZFuHIyK9UJvFygfbCvnb53sprGoCIDrAg0cvT+W8gaE2js6+qGAXEZFebWdBNTe8soHS2mZcnEw8fFkqPxsVa+uwROxSeV0zj326B4A/TBxEqI+7jSMSkd7ku0J9/pf7O7q/+3m48PvMRK49q5+W3xyHCnYREem1Fm/I5b4lO2m1GIT5uvHydSMZHOVn67BE7Na8T/ZQ29RGcrgPPz+rn63DEZFeos1iZcnWQp7O2kdeZSMA3m7O/HpcAjecG4+nq8rSE9HIiIhIr9PcZuHB93exaEP7lm1jEoJ48RfD8fNwsXFkIvZr3aEK3tmcjwl4dHoazrrTJSKnqdVi5d3N+fw96wAFVe2Fuo+7M786L4Hrz4nHu4/tqd4VGiEREelViqubuPk/G9lRUI0JuOWC/twxYRBms8nWoYnYraZWC398ZzsAM0fFMDQ2wMYRiYgja2mz8s7mfJ7J2k9hdfsadT8PF349LoFZY+LwUqF+0jRSIiLSa6w9VMGv/7uJqoZWPF2deGbmUMYnh9k6LBG79+xXB8ipaCDY25U/XqxGcyLSNc1tFt7amM+zyw5Q/G2hHuDpwm/O78/Pz+qnqe9doBETERGHZxgG/155iHmf7sFqQP8QL16+Xlu2iZyM7KIaXlhxEIBHpqVq6YiInLKmVguL1ufywoqDlNQ0AxDo5cpvLxjAz0bF4uGqXVm6SgW7iIg4tPrmNu58axuf7iwGYEpaBH+7Kl1fDkROQkubld8v3orFajAhJYxJqRG2DklEHEhtUyv/XZvLv1YeorK+BYBg7/ZCfeaoWNxd9Fl8ulSwi4iIwzpYVsfNr27kUHk9TiYT901N5vqz4zCZtF5d5GQ8nbWPPcW1+Hu68OjlabYOR0QcRGlNEy9/k8PCtTnUN1sAiPBz55YLBnDV8GgV6t1IBbuIiDikz3cV8/vFW6lvsRDo5cq/Zg1neL9AW4cl4jA2HankheXtU+HnXZ5GiI+bjSMSEXt3sKyOf319iHc259NqMQBICPZizoUDuCQ9UvuonwEq2EVExKFYrAbzv9zHM18dAGBojD//mDWcUB93G0cm4jiqG1u59Y0tWA24fGgUF6dpKryInNimI0f5x4qDfLG7BOPbY0Nj/LnlggGMTwrVTixnkAp2ERFxGNWNrdz2xhZW7CsDYNaYftw/NUVX9EVOgWEY3PveDgqrmogJ8OD/Lhts65BExA4ZhsGyvaU8v+wgG48c7TiemRzKr8f1Z0ScZrX1BBXsIiLiEPaX1HLTqxs5UtmAq5OZx69M4/Kh0bYOS8ThvLkxj4+2F+FkNvHMz4bh466u8CJyjMVq8PGOIp5fdoA9xbUAOJtNTB8WxS/P68+AUG8bR9i3qGAXERG79/muYm5fvJXGFgthvm68dN1IUqP8bB2WiMPZV1LLg+/vAuDOiwaREeNv24BExG5YrAYfbivk6az9HC6vB8DT1Ymfn9WPG8+NJ8xXS89sQQW7iIjYLcMweOarAzz1xT4ARsYF8OLPhxPkreZYIqeqscXCb1/bTFOblfMSg/nVeQm2DklE7MSyPaXM+zSbfSV1APi6O3PjuQlcf3Ycfp6ahWNLKthFRMQuNbVauPOtbXy0vQiAX5zVjwcu0Xp1ka56+MNd7C+tI9jbladmZKhJlIiQW9HAwx/uImtPKQA+7s78elx/rjs7Dm83lYr2QP8XRETE7pTWNnHTqxvZnl+Nk9nEI9NSmTkq1tZhiTisD7YVsmhDHibg79cMJVizVET6NKvV4JXVOfz1sz00tVpxMpu48dx4fnv+AN1RtzMq2EVExK7sK6nl+gXrKaxqwtfdmX/OGsFZCUG2DkvEYR2pqOeed7cDMOfCAZw9INjGEYmILRVUNTJ38VbWHa4E4KyEQB6ZlqZmcnZKBbuIiNiNjTmVzH5lA7VNbfQL9OSVG0YRH+xl67BEHFZLm5VbX99CfbOFkXEB3D4+0dYhiYgNfbitkD+9t4PapjY8XJy4d0oyPxsVqyUydkwFu4iI2IWs7BJueW0zzW1Whsb48/L1IwnwcrV1WCIO7W+f72V7QTW+Hs48fc1QnNUDQqRPqm9u48EPdvH2pnwAhsb4M/+aDPoF6aK4vVPBLiIiNvfx9iJuW7QFi9XggkEhPH/tcDxcnWwdlohDW7GvjH9+fQiAv12ZTqS/h40jEhFb2F1Yw5zXN3OovB6zCeZcMIDbxifqAp6DUMEuIiI2tWRLAXPf3IrVgEvTI3jy6gx1ghc5TWW1zcxdvBVo32HhosHhtg1IRHqcYRj8d+0R/vxxNi1tVsJ83Xj6mqHqC+NgVLCLiIjNfL9Yv2p4NI9dMQQnraMTOS1Wq8Edb22jor6FQeE+3Dsl2dYhiUgPq25s5Z53t/PJjmIAxieF8sRV6QRqqZnDUcEuIiI28f7WY8X6zFEx/GVampreiHSDl1Yd5ut9Zbg5m3l25lDcXbS8RKQv2Z5fxW9f20ze0UaczSbuvjiJG8+Nx2TSZ6wjUsEuIiI97qPthfx+cXuxfs1IFesi3WV7fhWPf7YHgPunppAY5mPjiESkpxiGwX/WHOHPH++mzWIQHeDBcz8bRnqMv61Dk9Oggl1ERHrUZ7uKuf2N9mL96hHRPHq5inWR7lDX3MZtb2yhzWJwcWo4146OtXVIItJDaptaufudHXy8owiAiYPD+OuV6fh5uNg4MjldKthFRKTHLNtTym9f24zFMJg+NIp504eoWBfpBoZhcO97O8ipaCDS353Hpg/R9FeRPmJfSS2/WriJw+X1OJtN3DM5mRvOidPvgF5CBbuIiPSIbw6U86uFm2izGkwZEsFfr1SDOZHu8vr6XN7fWoiTycQzM4fi56m7aiJ9wYfbCrnr7e00tloI93PnuZ8NY3i/AFuHJd1IBbuIiJxxG3MquenVjbRYrExICWP+jAzt/yrSTXYWVPPwh7sBuGvSIIb3C7RxRCJyprVZrPz1s7388+tDAJwzIIi/XzOUIG83G0cm3U0Fu4iInFE7C6q5fsEGGlstnDcwhGd/NlT7rIt0k8r6Fn61cBMtbVbGJ4Vy89gEW4ckImdYVUMLt76xhZX7ywH49bj+/GHiIM1a66V6/BvTc889R1xcHO7u7owePZr169ef8Nxdu3ZxxRVXEBfXvgZj/vz5PReoiIictv0ltfz8pXXUNbcxKj6Qf/x8OG7O2mJKpDu0WazMeX0zBVWN9Av05KmrM9QTQqSX219Sy2XPfcPK/eV4uDjx7M+GcvfFSSrWe7EeLdgXL17M3LlzefDBB9m8eTPp6elMnDiR0tLS457f0NBAQkICjz32GOHh4T0ZqoiInKbcigau/fc6qhpaGRLtx0vXjcDDVcW6SHd55ONsVh+swNPViX/OGqF16yK93LI9pUx7/huOVDQQ5e/BO785m6lDIm0dlpxhPVqwP/XUU9x8883Mnj2blJQUXnzxRTw9PXn55ZePe/7IkSN54oknuOaaa3Bz03oMERFHUVzdxM/+vZbS2mYGhnnznxtG4eOuYkKkuyxck8Mrq3MAeOrqdAaFa791kd7KMAz+vfIQN766gfpmC6PiA/lgzjmkRPraOjTpAT1WsLe0tLBp0yYyMzOPvbnZTGZmJmvWrOmpMERE5AyrqGvm5y+tI/9oI/2CPPnvTaPx93S1dVgivcbyvaU89G2TuT9MHMSk1AgbRyQiZ0pLm5U/vrOdRz7OxmrANSNj+O+No9Vcrg/psaZz5eXlWCwWwsLCOh0PCwtjz5493fY+zc3NNDc3d/y5pqam215bRM4c5W7vUNPUyvULNnCgtI5wP3deu2k0oT7utg5LzjDlb8/ZVVjNb1/bjMVqcMWwaG45v7+tQxIHpty1b5X1Lfx64SbW51RiNsF9U1KYrf3V+5xe16Z33rx5+Pn5dfzExMTYOiQRh9fUaqGxxXJG30O56/gaWyzc9MpGdhRUE+jlyn9vHE10gKetw5IeoPztGYVVjdzwygbqWyyc3T+IedPT9MVdToty134dKK1l2nPfsD6nEm83Z16+fiQ3nBuvnO+DeqxgDw4OxsnJiZKSkk7HS0pKurWh3D333EN1dXXHT15eXre9tkhv19RqYXt+Ff9Zk8Ndb29j+vPfMOKRL0h5YCkfbS88o++t3HVsLW1WbnltU8cXi//cMIoBod62Dkt6iPL3zKtubGX2gg2U1LT3hXjh58Nxde51912khyl37dPX+8qY9txqcisbiA305L1bzub8QaG2DktspMemxLu6ujJ8+HCysrKYNm0aAFarlaysLObMmdNt7+Pm5qYGdSI/oc1iJaeini25VWzPr2ZfSS05FfWU1TZjNY7/nCMVDWc0JuWu47JYDe54axvL9pbh5mzm5etHkhrlZ+uwpAcpf8+sljYrv164ib0ltYT6uLFg9ij8PNTEUU6fctf+LFx7hIc+2IXFajAqLpAXfzGcQC/1genLeqxgB5g7dy7XXXcdI0aMYNSoUcyfP5/6+npmz54NwKxZs4iKimLevHlAe6O63bt3d/x3QUEBW7duxdvbmwEDBvRk6CIOyWo1KKhqZFdhNVvyqsgurOFQeT1F1U1YTlCZu7uYifL3IC7Ii6RwH1Kj/RgWE0Corz7Q5YcMw+CB93fy4bZCnM0mXvzFcEbFB9o6LJFewzAM7n5nO2sOVeDl6sSC2SOJ8vewdVgi0s0sVoO/fJzNy98cBmD6sCjmTU/DzVnbofZ1PVqwz5gxg7KyMh544AGKi4vJyMhg6dKlHY3ocnNzMZuPTe8qLCxk6NChHX/+29/+xt/+9jfGjRvH8uXLezJ0EbtXWd/CnqIatuRVsaugmn2ldeRVNtDcZj3u+a5OZiL93UkI8WJQmA9Dov3JiPEn3M9d66PkpD3x2V5eW5eLyQTzr8ngAk3ZE+lW/++Lfby7pQAns4nnfz6cwZGavSLS29Q3t3H7oi18mV0KtO/+cMv5/fV9TIAeLtgB5syZc8Ip8P9bhMfFxWEYJ5ifK9JHNbdZOFBax86CGrblVbG7qIbD5fVUN7Ye93wnk4lwv/bCPCn8WGEe5e+B2awPAum6f6w4yPPLDwLw6OVpTB0SaeOIRHqXxRty+ftXBwCYd3ka4waG2DgiEeluJTVN3PjqBnYW1ODmbOapqzOYMkRbNcoxPV6wi8jJsVgN8iob2FdSy46CanYWVLO/tI7CqsYTrjMP9nYlPtibpHDvjsI8LtgLFyc1JpLutXhDLvM+bd+S8+6Lk5g5KtbGEYn0Liv2lfGn93YCcOuFA7h6pLp3i/Q2+0pquf7l9RRWNxHk5cq/rhvBsNgAW4cldkYFu4iNVTe2cqisjsPl9WQX1bCvpJbD5Q0UVjXSdoLK3NPVifhgLwaG+ZAW7UdGjD8Dw3zwdlNKy5m3dGcx97y7A4BfjUvg1+O0D7RId9pVWM0t/92ExWowfWgUcycMtHVIItLN1hys4JcLN1Lb1EZCsBcLZo+kX5CXrcMSO6Rv9yI9wGo1yD/ayMGyOvaX1JJdXMuB0jpyKxtOOJUdwMXJRHSAJwkhXqRG+pEe7UdypC/hvlpnLrax+kA5t76xGasBM0bEcPekJFuHJNKr5FU2cP2CY3utP3bFEP2+F+llPtxWyB1vbqPFYmVEvwD+NWsEAeoELyeggl2kGxmGQVF1E3uKa9hTVMvOwmr2FteRW1lPq+XE/RgCPF2IDfQkPsSLpHBfBoZ5MyDEh6gAD5y0zlzsxPb8Km7+z0ZaLQYTB4fxl8tTVUiIdKOqhhauX7CestpmksJ9tNe6SC/075WHeOTjbAAuTg3n/83IwN1FneDlxFSwi3SRxWpwuLy9+duOgmq25Vext7iW2qa2457v4mQiyt+DhBBvBoV5Myjcl4QQLxJCvDWVXezewbI6rn+5/a7fmIQgnr5mKM7qjSDSbeqb25j9ygYOltUT6efOK9prXaRXsVoNHlu6h39+fQiA68+O4/6pKboxIz9JVYLISbBYDQ6V1bGjoJrt+dVszatiT3ENTa0/3DLNbIKYQE+Swtr3MB8U5kNimA+xgZ76pSwOqai6kV+8tI7KhhZSo3z556zhuhsg0o2aWi38cuFGtuRW4efhwis3jCLcz93WYYlIN2m1WLnr7e28t6UAgLsmDeI347Rtm5wcFewi/6PVYuVAaR27C2vYWVjdsXXa8YpzdxcziaE+DIn2Iz3an5RIXwaEequYkV6jqqGFWS+tp7CqiYRgL16dPQofd931E+kujS0WfvPaJr45UIGXqxOv3jCKgWE+tg5LRLpJfXMbv3ltM1/vK8PJbOLxK4Zw5fBoW4clDkQFu/RZ360331tcy96SWvYW17K7qIaDpXXH7c7u5mwmOcKXoTH+pEX7MSTaj/hgb901l16rvrmN6xdsYH9pHWG+bvznxlEEebvZOiyRXqO2qZUbX93I+sOVuLuY+dd1I8iI8bd1WCLSTSrrW5j9yga25VXh4eLE89cO44KkUFuHJQ5GBbv0CU2tlo6CfE9RDdlFtWQX15xwvbmXqxPJEb6kRvmRFuVHWrQf/UNUnEvf0dxm4df/3cTWvCr8PVz4742jiQ7wtHVYIr1GXmUDN/9nI3uKa/F2c2bB7JGMjAu0dVgi0k0KqtqXkx0qq8ffw4WXZ4/UHuvSJSrYpdepb25jV2EN2/Or2FVYw86Cag6V1WMxfnjX3MlsIi7Ik5RIv45GcEnhPkQHeGhdkfRZbRYrt72xhZX7y/FwceLl2SNJ1BRdkW6zan85c17fTFVjKyE+biy4fiSpUX62DktEusm+klpmvbSe4pomIv3c+c+NoxgQqs9R6RoV7OLQ2ixW9pbUsiW3iq15VWzLq+JAaR3H20AtwNOFlEhfUiJ8SY7wZVC4DwNCvXFz1npzke9YrAZ/eHs7n+0qwcXJxL9mjdAdAZFu0maxMv/L/Ty3/ACGAenRfvzjFyPUYE6kF9mYU8kNr2ygpqmNAaHe/OeGUUT6e9g6LHFgKtjFoRytb2Fz7lE2HWn/2Z5fTWOr5Qfnhfu6kxbdPp19cKQvgyP9CPN1011zkR/RarFyx5vb+GBbIU5mE89fO5xzE4NtHZZIr5BX2cDvF29l45GjAMwcFcODlwxWk1KRXuSzXcXc9sYWmtusDIv15+XrR+Lv6WrrsMTBqWAXu5ZX2cD6w5VsPFLJhpyjHCit+8E53m7ODI31JyPGn/Rof4bE+BHqo7sVIqeiqdXC7Yu28NmuEpzNJp6ZOZQJKWG2DkukV1iypYD7l+yktrkNbzdnHp2exqXpkbYOS0S60cI1OTz4wS6sBoxPCuXZnw3Dw1UX5OT0qWAXu1JW28yqA2V8c6CCNQcrKKhq/ME5CSFejOgXwLDYAIb1C2BAiDdmNYMT6bKSmiZu/s9GtudX4+pk5vlrh5GpYl3ktFU3tnL/kp18sK0QgOH9Apg/I4OYQDVwFOkt2ixW/vJJNgu+yQHaZ8/8+bJUnJ3Mtg1Meg0V7GJTVqvB9oJqvsou4au9pewsqOn0uLPZRGqUH6PjAxkRF8jwfgEEemlqkUh3Wbm/jLlvbqOstpkATxde+PlwzkoIsnVYIg5v9cFy7nxzG4XVTTiZTdx2YSK/vaC/vsSL9CKV9S38bvFWvt5XBsAdEwYy58IBWoIp3UoFu/S4plYL3xwo54vdJXyZXUJ5XUunx1MifBmbGMzZA4IZ0S8ALzf9MxXpbo0tFv72+V5eWnUYgIFh3vx71khig3TnT+R0NLdZePLzffxr5SEMA+KCPPl/MzIYquaNIr3KukMV3L5oK8U1Tbi7mHnq6gwmp0XYOizphVQJSY+obmxl2Z5SPttVzIp9ZTS0HGsU5+3mzHkDg7lgUCjjBoVo/bnIGbb2UAV3v7OdnIoGAH5xVj/+NDlZa+1ETlN2UQ2/X7yVPcW1QPvU2PumpOjCs0gvUtPUyt8+28vCtUcwjPalms/MHMrgSG3NKGeGPkHkjKmsb+GL3cV8sqOY1QfLabUc22wtws+dCSlhTEgJY3R8EK7OmiIocqZVN7Qy79NsFm3IA9p3U3h0eioXJmm9usjpaLNY+efKQ/y/L/bRajEI8nLlsSuGqHGjSC9itRq8szmfJz7bS2ltMwBXj4jmwUsG66KcnFH61yXdqq65jaU7i/lgWyHfHCjHYj1WpPcP8WJSajgTB4eTFuWn9T0iPcQwDD7YVsifP9rdsQTlZ6NjufviJHzdXWwcnYhj21dSy11vb2drXhUAmclhzJueRoiPm20DE5Fus2p/OfM+zWZXYXuvpbggTx69PI2zB2jrUznzVLDLaTMMgzWHKnhrYz6f7iyiqdXa8VhKhC+T08KZlBrOgFAfG0Yp0jfllNdz//s7Wbm/HIABod7Mm57GyLhAG0cm4tiaWi28sPwgzy8/QKvFwMfNmQcuSeHK4dG6IC3SS+wsqObxpXs6PkN93JyZc+EArj8nDjdnLSOTnqGCXbqsoq6Ztzfl88b63I61sAAJwV5clhHFpRmRxAd72TBCkb6ruc3CP1Yc4tllB2hps+LqbOa2Cwfwy/P6awmKyGn6ak8JD32wm9zK9s++8UmhPHJ5KhF+HjaOTES6Q055PU9+sY8Pv92S0cXJxLWj+3HrhQMI8tbsGelZKtjllBiGwebcoyxcc4RPdhTTYmm/m+7t5swl6ZFcPSKajBh/3V0QsaGV+8t44P1dHC6vB2BsYjB/viyVOF1AEzktB0rr+PNHu1nx7RZO4b7u3D81hclp4frcE+kFSmua+PtX+1m0Po+2b5d1XpYRyR0TBmkXFbEZFexyUhpa2liypZCFa4+QXXRsr/Qh0X78bFQsl6RHquGGiI0dKqvj0U+y+TK7FIAQHzfum5LMpemRKiZETkNlfQtPf7mP/67LxWI1cHEyccM58dw6PhFvffaJOLyqhhb+8fUhFnxzuGNp5/mDQrjzokGkRqn7u9iWPmXkRx0sq2PhmiO8symf2uY2ANyczVyaHsnPz+pHeoy/bQMUEfIqG3j2qwO8szmfNquBk9nErDH9+P2EgWoqJ3IamtssvPJNDs8uO0BtU/tnYGZyGPdOSdaSL5FeoLaplQXf5PCvrw91fM8dFuvPXZOSOCshyMbRibRTwS4/0Gqx8sXuEl5bd4RvDlR0HI8L8uTnZ/XjyuHR+Hu62jBCETEMg/WHK3l1TQ6f7Srp2JHhgkEh3DslWU0eRU6DYRh8sqOYx5Zmk1fZCLQ3Ub1vSrK6Qov0AtWNrby6OoeXVh2murEVgKRwH+68aBDjk0M1K03sigp26XCgtJa3NxXw9qZ8yuva95c0m+DCpFB+MSaOsQOCMZv1C0zElkprmnh3SwFvbszjUFl9x/HzBoZw+/hEhvcLsGF0Io5va14Vf/5oN5uOHAUg1MeNP0wcxPRh0TjpM1DEoZXUNPHyN4d5bW0udd/eUe8f4sXtmQOZmhah77lil1Sw93HF1U18tL2QD7YVsj2/uuN4iI8bV4+IZuaoWKID1GRDxJaaWi18sbuEdzbn8/W+Mr69mY6HixOXZURy/TlxJIX72jZIEQdXWNXIX5fuYcnW9q7QHi5O/PK8BH41LgFPV31dEnFk2UU1vLTqMO9vLaDV0v4hOijMh1su6M/UIZG6GCd2TZ9AfVBRdSNLdxbz8fYiNn57BwHAyWzigkGhXDk8mvHJobg4aesnEVuxWg02HjnKu5vz+Xh7UcfaOmhfX3fViBimDonAR2vURU5LY4uFF1cc5B9fH+xoNnXFsGj+MHEQ4X7uNo5ORLrKMAy+3l/Ov1ce6thHHWBUXCC/PC+BC5NCdUddHIIK9j4i/2gDS3cW88mOIjbnVnV6bES/AC7LiOTitAiCtbekiE0dLq/nvc35vLe1oGPtLECUvwfTh0Vx+dAoEkK8bRihSO9gGAYfbS9i3ifZFFY3Ae1f5O+fmkJatLpCiziqplYL728t4KVVh9lXUge0L/G8ODWCG8fGMyxWS8fEsahg78VyKxr4ZGcRn+4oYtv3prubTO1F+sWpEVycFk6En4cNoxSR6oZWPtpRyLubCzrWzQJ4uzlzcWo4VwyPZlRcoO4EiHSTfSW1PPj+LtYcam+sGuXvwZ8mJ2s/dREHVlHXzH/X5rJwbQ7ldS1A++fo1SNimH1OHDGBWuIpjkkFey+TV9nAxzuK+Hh7ETsKOhfpI+MCmZIWwaTUcMJ8Nc1PxJbaLFZW7i/n7U35fJFdQktb+1RcswnGJoYwfVgUF6WE4+HqZONIRXqP+uY2ns7az8urDtNmNXBzNnPL+QP41bgE3F2UayKOKKe8nn+tPMTbm/Jp/vazNMrfg9nnxHH1yBhtbyoOTwV7L1Ba08SH24v4cFshW/OqOo6bTXBWQhCT0yKYODicEB9Ndxextb3FtbyzOZ/3thRQVtvccXxQmA9XDI/isowoXVAT6WaGYfDZrhIe/nAXRd9Of5+QEsYDU1N0103EQe0qrOb55Qf5ZEcRxrfNWIdE+3HT2AQmp4bjrF5M0kuoYHdQtU2tLN1ZzPtbC1l9sLyja/R3RfqUIe1Futaki9heZX0LH2wt4O3N+ewsqOk4HujlyqXpkVw5PJrBkb6aiityBuRVNvDQB7vI2lMKQEygBw9fOpgLk8JsHJmIdMWO/GqeztrHl9mlHccuGBTCr8b1Z3R8oD5LpddRwe5AWi1WVu4v493NBXyxu6Rj2g+0d42+ND2SyUMiCPXR3TkRW2u1WFmxt4y3N+WTtaekYxsZF6djuzGcPygUV2fdARA5E1otVl5adZinv9xPY6sFFycTvzqvP7+9YICWmog4oH0ltTz5+V4+21UCtN+kmjokklsu6K+tTaVXU8Fu5wzDYHt+Ne9tKeDDbYVU1Ld0PNY/xIvLh7ZPodWUPhH7kF1Uw9ub8nl/a0FH0xuA1ChfrhgWzWUZUQR6udowQpHeb9ORo9z73g72FNcCMDo+kL9cnsqAUB8bRyYipyr/aAP/74v9vLslH8NoL9Qvy4ji1gsHaNcU6RNUsNup/KMNLNlSwHtbCjhYVt9xPNjblUvSI5k+NJrUKE2hFbEHpbVNfLC1vcv77qJjU96DvV2ZlhHFFcOjSY7Q1X+RM626oZXHlu7hjfW5AAR4uvCnyclcOTxan5ciDqayvoXnlh1g4ZojtFjaZ5VOGhzOHRcNJDFMF9+k71DBbkeqG1v5dEcR724pYP3hyo7jbs5mJg4O5/JhUYwdEKwmGiJ2oK65jc93FbNkayHfHCjH8m0jCVcnM+OT26e8nzcwBBflq8gZZxgGS7YW8JePsztmtlw1PJp7JidrRouIg2lssfDyN4d5cflBapvbADi7fxB/nJREeoy/bYMTsQEV7DbW1Gph+d5Slmwp5Ks9pR1XEE0mOCs+iMuHRXFxajg+2pJCxOaa2yws31vGB9sKycouoan1WB+JobH+TB8axSXpkfh7qkAQ6Sn/u6d6/xAvHr08jdEJQTaOTERORZvFytub8vl/X+6jpKZ9F5WUCF/uvjiJsYnBmiUjfZYKdhtos1j55mAFH24r5LOdxR1XDwEGhnlz+dBoLsuIJNLfw4ZRigi05+vqb/N16a5iapuO5Wt8sBeXpkdyWUak1tGJ9LDqhlaeztrPq2tysHy7p/pt4xO5eWyCmjmKOJDvtl184rM9HctAowM8uPOiQVyaHonZrEJd+jYV7D3EajXYkFPJh9sL+WRHMZXfax4X6efOJRmRXJoeSUqE1qWL2NqP5Wu4rztTh0RwWUaU+kiI2EBzm4XX1uby96/2U9XQCsBFKWHcrz3VRRyKYRisOlDO3z7fx7a8KqC978RvLxjAL8b0w81ZuzmIgAr2M+q7Du8fbivko+1FFNc0dTwW6OXK5LRwLk2PYkS/AF09FLExwzDY9m2+fnyCfL1kSCQj4wKVryI20Gax8u6WAp7+cj8FVY1A+6y0e6ekMG5giI2jE5GTZRgGK/aV8exXB9h45CgAHi5O3HhuPL8cl4CvloGKdKKCvZsZhkF2US0f7yjkw21F5FY2dDzm4+7MpMHhTE2P5Jz+QWoeJ2JjhmGwq7CGj3cU8dH2QvIqGzse83F3ZuLgcC5Nj+Rs5auIzbRZrCzZWsgzX+3nSEX7Z2qYrxu3jx/I1SOilZsiDqKp1cIH2wp5edXhji0XXZ3N/GxULL+9YAAhPm42jlDEPqlg7wZWq8HOwmqW7ixm6c5iDpUf24bNw8WJ8cmhXJoeyXkDQ3B30fQeEVv6fpH+6Y4iciqOXVTzcHFiQkoYU4dEMG5QiKbjidhQq8XKe1sKeG7ZgY5CPcjLlV+P688vxvTT56mIg9hbXMviDXm8szmf6sb2ZSyerk5cMzKWX56XQLifu40jFLFvKti7qLqhlW8OlvP1vjK+2lNKaW1zx2OuzmYuGBTClCGRZCaH4umqYRaxJavVYGt+FUt3FvPpzqJOd9LdnM1cMCiUS9IjuTApFA9XFQEittTSZuXdzfk8t/xAR64Gernyy/MSmDWmnz5TRRxAdWMrH20v5M2N+R3r0wGi/D34+Vn9mDkqRjuqiJykHv/Ue+6553jiiScoLi4mPT2dZ555hlGjRp3w/Lfeeov777+fnJwcEhMTefzxx5k8eXIPRtyuvrmNDTmVrDlUwZqDFewsqObbbZeB9iuF4waGMCk1nAuTQrUNm4iNWawGG3Mq+XRnMZ/tKqao+tiadHcXMxcmhXJxagQXJoXi5aYCQMTWmtssvL0pn+eXHexYox7s7crNYxP4hQp1EbtnsRqsPljOWxvz+WxXMc1t7VufOptNjE8OZcbIGMYNDMVJfWBETkmPfvotXryYuXPn8uKLLzJ69Gjmz5/PxIkT2bt3L6GhoT84f/Xq1cycOZN58+YxdepUXn/9daZNm8bmzZtJTU09o7E2tLSx6chR1hysYO2hCrbnV9P2/QodGBDqzXmJIYwbFMJZCYGaPitiY60WK+sOVfLpziI+21VCed2xmS9erk5cmBzG5NRwxg0K0Zd/ETvR3GbhzY35vLDsAIXfXlgL8XHjV+clcO3ofpr1ImLncsrreXtTPu9uzu/IYWhvCnnV8BimDY3S+nSR02AyDMP46dO6x+jRoxk5ciTPPvssAFarlZiYGG699VbuvvvuH5w/Y8YM6uvr+eijjzqOnXXWWWRkZPDiiy+e1HvW1NTg5+dHdXU1vr6+JzyvqdXC5iNHO+6gb8uvotXSeWii/D04u38QZw8I4uz+wYT5as2NyHdONte6+/Wa2yysPlDBpzuL+Hx3Scc2TwC+7s5MSAnn4tRwzk0M1ppXkROwRf42tVpYvCGPF5Yf7NiVIdTHjd+c35+Zo2KVryInwVafvXXNbXyyvYi3N+WzPqey47ivuzOXZURx1Yho0qL8tPWpyI842XzrsVtMLS0tbNq0iXvuuafjmNlsJjMzkzVr1hz3OWvWrGHu3Lmdjk2cOJElS5ac8H2am5tpbj52V62mpuYnY1u8IZf7l+yixWLtdDzSz52z+gdxVkIQYxKCtL+ryBnUldy1WA3G/XV5py3YgrxcuWhwGJNSIxiTEISrszpIi5xpXcnfNzfm8eAHuwAI93XnN+f3Z8bIGBXqIj2oK7lrGAZT/r6yoxmk2QRjE0O4cng0E1LClMMi3azHCvby8nIsFgthYWGdjoeFhbFnz57jPqe4uPi45xcXF5/wfebNm8fDDz98SrHFBnrRYrES5uvGmIQgxvQPYkxCMDGBHroyKNJDupK7TmYTw+MC2HC4kkmp4UxKDWdUXKC2eRLpYV3J36uGx/D2pnyuGhHD1SOitaxMxAa6krsmk4mJg8P5cncJV46IZvrQaHV6FzmDemxKfGFhIVFRUaxevZoxY8Z0HL/rrrtYsWIF69at+8FzXF1defXVV5k5c2bHseeff56HH36YkpKS477P8a4UxsTE/OhUg5Y2K4VVjfQL8lSBLtJFpzstryu5C+07Nvi4O2NWExuRLrNV/hqGoc9dkdNgq9xtarXg5mxW/oqcBrubEh8cHIyTk9MPCu2SkhLCw8OP+5zw8PBTOh/Azc0NN7dTa2zh6mwmLtjrlJ4jIt2rK7kL4OepHRlEbK2r+asv+yK21dXc1bR3kZ7TY/NGXV1dGT58OFlZWR3HrFYrWVlZne64f9+YMWM6nQ/wxRdfnPB8ERERERERkd6iR/c1mjt3Ltdddx0jRoxg1KhRzJ8/n/r6embPng3ArFmziIqKYt68eQDcfvvtjBs3jieffJIpU6awaNEiNm7cyD//+c+eDFtERERERESkx/VowT5jxgzKysp44IEHKC4uJiMjg6VLl3Y0lsvNzcVsPnbT/+yzz+b111/nvvvu409/+hOJiYksWbLkjO/BLiIiIiIiImJrPVqwA8yZM4c5c+Yc97Hly5f/4NhVV13FVVdddYajEhEREREREbEv2vtIRERERERExA71+B32nvbdrnU1NTU2jkSkd/sux7prp0jlrkjPUf6KOCblrojjOtn87fUFe21tLQAxMTE2jkSkb6itrcXPz69bXgeUuyI9Sfkr4piUuyKO66fy12R01yU5O2W1WiksLMTHx+dH93utqakhJiaGvLy8H924Xn6axrL7ONJYGoZBbW0tkZGRnZpHdpVyt+dpLLuXI42n8tfxaSy7jyONpXK3d9B4dh9HGsuTzd9ef4fdbDYTHR190uf7+vra/f9cR6Gx7D6OMpbdcXX/O8pd29FYdi9HGU/lb++gsew+jjKWyt3eQ+PZfRxlLE8mf9V0TkRERERERMQOqWAXERERERERsUMq2L/l5ubGgw8+iJubm61DcXgay+6jsfxpGqPuo7HsXhrPn6Yx6j4ay+6jsfxpGqPupfHsPr1xLHt90zkRERERERERR6Q77CIiIiIiIiJ2SAW7iIiIiIiIiB1SwS4iIiIiIiJih1Swi4iIiIiIiNghFezAc889R1xcHO7u7owePZr169fbOiS799BDD2EymTr9JCUldTze1NTEb3/7W4KCgvD29uaKK66gpKTEhhHbl6+//ppLLrmEyMhITCYTS5Ys6fS4YRg88MADRERE4OHhQWZmJvv37+90TmVlJddeey2+vr74+/tz4403UldX14N/C/ug/D11yt+uU+52H+XuqVPunh7lb/dR/p465W/X9fXc7fMF++LFi5k7dy4PPvggmzdvJj09nYkTJ1JaWmrr0Oze4MGDKSoq6vhZtWpVx2O///3v+fDDD3nrrbdYsWIFhYWFTJ8+3YbR2pf6+nrS09N57rnnjvv4X//6V/7+97/z4osvsm7dOry8vJg4cSJNTU0d51x77bXs2rWLL774go8++oivv/6aX/7ylz31V7ALyt+uU/52jXK3eyh3u06523XK3+6h/O065W/X9PncNfq4UaNGGb/97W87/myxWIzIyEhj3rx5NozK/j344INGenr6cR+rqqoyXFxcjLfeeqvjWHZ2tgEYa9as6aEIHQdgvPfeex1/tlqtRnh4uPHEE090HKuqqjLc3NyMN954wzAMw9i9e7cBGBs2bOg459NPPzVMJpNRUFDQY7HbmvK3a5S/3UO523XK3a5R7nYf5W/XKX+7RvnbPfpi7vbpO+wtLS1s2rSJzMzMjmNms5nMzEzWrFljw8gcw/79+4mMjCQhIYFrr72W3NxcADZt2kRra2uncU1KSiI2NlbjehIOHz5McXFxp/Hz8/Nj9OjRHeO3Zs0a/P39GTFiRMc5mZmZmM1m1q1b1+Mx24Ly9/Qof7ufcvfkKHdPj3L3zFD+nhzl7+lR/na/vpC7fbpgLy8vx2KxEBYW1ul4WFgYxcXFNorKMYwePZpXXnmFpUuX8sILL3D48GHGjh1LbW0txcXFuLq64u/v3+k5GteT890Y/di/y+LiYkJDQzs97uzsTGBgYJ8ZY+Vv1yl/zwzl7slR7nadcvfMUf6eHOVv1yl/z4y+kLvOtg5AHNPFF1/c8d9Dhgxh9OjR9OvXjzfffBMPDw8bRiYiP0X5K+KYlLsijkv5K13Vp++wBwcH4+Tk9IMOjCUlJYSHh9soKsfk7+/PwIEDOXDgAOHh4bS0tFBVVdXpHI3ryflujH7s32V4ePgPmru0tbVRWVnZZ8ZY+dt9lL/dQ7l7cpS73Ue5232UvydH+dt9lL/doy/kbp8u2F1dXRk+fDhZWVkdx6xWK1lZWYwZM8aGkTmeuro6Dh48SEREBMOHD8fFxaXTuO7du5fc3FyN60mIj48nPDy80/jV1NSwbt26jvEbM2YMVVVVbNq0qeOcr776CqvVyujRo3s8ZltQ/nYf5W/3UO6eHOVu91Hudh/l78lR/nYf5W/36BO5a+uud7a2aNEiw83NzXjllVeM3bt3G7/85S8Nf39/o7i42Nah2bU77rjDWL58uXH48GHjm2++MTIzM43g4GCjtLTUMAzD+PWvf23ExsYaX331lbFx40ZjzJgxxpgxY2wctf2ora01tmzZYmzZssUAjKeeesrYsmWLceTIEcMwDOOxxx4z/P39jffff9/Yvn27cdlllxnx8fFGY2Njx2tMmjTJGDp0qLFu3Tpj1apVRmJiojFz5kxb/ZVsQvnbNcrfrlPudg/lbtcod0+P8rd7KH+7RvnbdX09d/t8wW4YhvHMM88YsbGxhqurqzFq1Chj7dq1tg7J7s2YMcOIiIgwXF1djaioKGPGjBnGgQMHOh5vbGw0brnlFiMgIMDw9PQ0Lr/8cqOoqMiGEduXZcuWGcAPfq677jrDMNq3qLj//vuNsLAww83NzRg/fryxd+/eTq9RUVFhzJw50/D29jZ8fX2N2bNnG7W1tTb429iW8vfUKX+7TrnbfZS7p065e3qUv91H+XvqlL9d19dz12QYhtFz9/NFRERERERE5GT06TXsIiIiIiIiIvZKBbuIiIiIiIiIHVLBLiIiIiIiImKHVLCLiIiIiIiI2CEV7CIiIiIiIiJ2SAW7iIiIiIiIiB1SwS4iItIDli9fjslkoqqqyibvn5WVRXJyMhaL5YTnPPTQQ2RkZPRYTHfffTe33nprj72fiIiIo1HBLiIi0s3OP/98fve733U6dvbZZ1NUVISfn59NYrrrrru47777cHJyssn7H8+dd97Jq6++yqFDh2wdioiIiF1SwS4iItIDXF1dCQ8Px2Qy9fh7r1q1ioMHD3LFFVf0+Hv/mODgYCZOnMgLL7xg61BERETskgp2ERGRbnT99dezYsUKnn76aUwmEyaTiZycnB9MiX/llVfw9/fno48+YtCgQXh6enLllVfS0NDAq6++SlxcHAEBAdx2222dprE3Nzdz5513EhUVhZeXF6NHj2b58uU/GtOiRYuYMGEC7u7unY4/9thjhIWF4ePjw4033khTU1Onxzds2MCECRMIDg7Gz8+PcePGsXnz5o7Hb7jhBqZOndrpOa2trYSGhvLSSy8B8Pbbb5OWloaHhwdBQUFkZmZSX1/fcf4ll1zCokWLTnp8RURE+hIV7CIiIt3o6aefZsyYMdx8880UFRVRVFRETEzMcc9taGjg73//O4sWLWLp0qUsX76cyy+/nE8++YRPPvmEhQsX8o9//IO333674zlz5sxhzZo1LFq0iO3bt3PVVVcxadIk9u/ff8KYVq5cyYgRIzode/PNN3nooYd49NFH2bhxIxERETz//POdzqmtreW6665j1apVrF27lsTERCZPnkxtbS0AN910E0uXLqWoqKjjOR999BENDQ3MmDGDoqIiZs6cyQ033EB2djbLly9n+vTpGIbRcf6oUaPIz88nJyfnpMdYRESkr3C2dQAiIiK9iZ+fH66urnh6ehIeHv6j57a2tvLCCy/Qv39/AK688koWLlxISUkJ3t7epKSkcMEFF7Bs2TJmzJhBbm4uCxYsIDc3l8jISKB9HfjSpUtZsGABjz766HHf58iRIx3nf2f+/PnceOON3HjjjQA88sgjfPnll53usl944YWdnvPPf/4Tf39/VqxYwdSpUzn77LMZNGgQCxcu5K677gJgwYIFXHXVVXh7e7Nv3z7a2tqYPn06/fr1AyAtLa3Ta34X15EjR4iLi/vR8RIREelrdIddRETERjw9PTuKdYCwsDDi4uLw9vbudKy0tBSAHTt2YLFYGDhwIN7e3h0/K1as4ODBgyd8n8bGxh9Mh8/Ozmb06NGdjo0ZM6bTn0tKSrj55ptJTEzEz88PX19f6urqyM3N7TjnpptuYsGCBR3nf/rpp9xwww0ApKenM378eNLS0rjqqqv417/+xdGjRzu9h4eHB9A+20BEREQ60x12ERERG3Fxcen0Z5PJdNxjVqsVgLq6OpycnNi0adMPur1/v8j/X8HBwT8olE/GddddR0VFBU8//TT9+vXDzc2NMWPG0NLS0nHOrFmzuPvuu1mzZg2rV68mPj6esWPHAuDk5MQXX3zB6tWr+fzzz3nmmWe49957WbduHfHx8QBUVlYCEBIScsrxiYiI9Ha6wy4iItLNXF1df3S/864aOnQoFouF0tJSBgwY0Onnx6bfDx06lN27d3c6lpyczLp16zodW7t2bac/f/PNN9x2221MnjyZwYMH4+bmRnl5eadzgoKCmDZtGgsWLOCVV15h9uzZnR43mUycc845PPzww2zZsgVXV1fee++9jsd37tyJi4sLgwcPPqWxEBER6Qt0h11ERKSbxcXFsW7dOnJycvD29iYwMLBbXnfgwIFce+21zJo1iyeffJKhQ4dSVlZGVlYWQ4YMYcqUKcd93sSJE3n11Vc7Hbv99tu5/vrrGTFiBOeccw6vvfYau3btIiEhoeOcxMREFi5cyIgRI6ipqeEPf/hDxxT277vpppuYOnUqFouF6667ruP4unXryMrK4qKLLiI0NJR169ZRVlZGcnJyxzkrV65k7Nixx31dERGRvk532EVERLrZnXfeiZOTEykpKYSEhHRa8326FixYwKxZs7jjjjsYNGgQ06ZNY8OGDcTGxp7wOddeey27du1i7969HcdmzJjB/fffz1133cXw4cM5cuQIv/nNbzo976WXXuLo0aMMGzaMX/ziF9x2222Ehob+4PUzMzOJiIhg4sSJnZrb+fr68vXXXzN58mQGDhzIfffdx5NPPsnFF1/ccc6iRYu4+eabT2dIREREei2T8f29VURERKRX+sMf/kBNTQ3/+Mc/uv216+rqiIqKYsGCBUyfPv2kn/fpp59yxx13sH37dpydNelPRETkf+kOu4iISB9w77330q9fv44Gdt3BarVSWlrKn//8Z/z9/bn00ktP6fn19fUsWLBAxbqIiMgJ6A67iIiIdElOTg7x8fFER0fzyiuvMH78eFuHJCIi0quoYBcRERERERGxQ5oSLyIiIiIiImKHVLCLiIiIiIiI2CEV7CIiIiIiIiJ2SAW7iIiIiIiIiB1SwS4iIiIiIiJih1Swi4iIiIiIiNghFewiIiIiIiIidkgFu4iIiIiIiIgdUsEuIiIiIiIiYof+PwKesSaX+MU6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 4, figsize=(10,7), sharex=True, sharey=True)\n",
    "\n",
    "for r, ax in enumerate(axs.flatten()):\n",
    "    Vsim = hbm.fit.stan_variable(\"V_sim\")[:,r,...]\n",
    "    Vsim_mean = np.mean(Vsim, axis=0)\n",
    "    Vsim_lo, Vsim_hi = np.percentile(Vsim, axis=0, q=[2.5, 97.5])\n",
    "    tsim = np.linspace(0, 120, Vsim.shape[1])\n",
    "    ax.plot(tsim, Vsim_mean)\n",
    "    ax.fill_between(tsim, Vsim_lo, Vsim_hi, alpha=0.5, linewidths=0)\n",
    "    \n",
    "fig.tight_layout()\n",
    "\n",
    "fig.text(0.5, 0, \"time (days)\", ha='center')\n",
    "fig.text(0, 0.5, \"total fraction vaccinated\", rotation=90, va='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7394910",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}