notebooks/Emergence of Political Actors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Emergence of Political Actors in Two Dimensions\n",
    "An extension of the classic model from Robert Axelrod from one to two dimensions\n",
    "\n",
    "* Robert Axelrod, \"The Complexity of Cooperation\", Princeton University Press, 1997 (Chapter 6 \"Building New Political Actors\")\n",
    "* Robert Axelrod, \"A Model of the Emergence of New Political Actors\" \n",
    "in Artificial Societies: The Computer Simulation of Social Life, ed. Nigel Gilbert and Rosaria\n",
    "Conte (London: University College Press, 1995), 19–39.\n",
    "* Lars-Erik Cederman, \"Emergent Actors in World Politics\", Princeton University Press, 1997"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "HARVEST = 1\n",
    "TRIBUTE = 250\n",
    "TAX_RATE = 1/10\n",
    "WAR_COST = 1/4\n",
    "NUMBER_OF_WARS = 33\n",
    "\n",
    "class Agent: \n",
    "  def __init__(self, name, wealth):\n",
    "    self.name = name                    # name of the actor (can be A, B, C or ID like 1,2,3)\n",
    "    self.wealth = wealth                # wealth of the actor, initially between 300 and 500\n",
    "    self.x = 0                          # horizontal location in landscape\n",
    "    self.y = 0                          # vertical location in landscape\n",
    "    self.king = None                    # actor to pay tribute to\n",
    "    self.alliance = []                  # allied actors\n",
    "    self.opponents = []                 # enemy actors\n",
    "\n",
    "  def harvest(self, amount=HARVEST):\n",
    "    self.wealth = self.wealth + amount  # annual harvest\n",
    "    \n",
    "  def description(self):\n",
    "    names = [agent.name for agent in list(set(self.alliance+[self]))]\n",
    "    return ' '.join(names)\n",
    "    \n",
    "  def total_wealth(self):\n",
    "    if len(self.alliance) > 0:\n",
    "      result = sum([agent.wealth for agent in self.alliance])\n",
    "    else:\n",
    "      result = self.wealth\n",
    "    return round(result)\n",
    "\n",
    "  def subtract_total_wealth(self, amount):    \n",
    "    if len(self.alliance) > 0:\n",
    "      members = len(self.alliance)+1\n",
    "      self.subtract_wealth(amount/members)\n",
    "      for agent in self.alliance:\n",
    "        agent.subtract_wealth(amount/members)\n",
    "    else:\n",
    "      self.subtract_wealth(amount)\n",
    "\n",
    "  def add_total_wealth(self, amount):    \n",
    "    if len(self.alliance) > 0:\n",
    "      members = len(self.alliance)+1\n",
    "      self.add_wealth(amount/members)\n",
    "      for agent in self.alliance:\n",
    "        agent.add_wealth(amount/members)\n",
    "    else:\n",
    "      self.add_wealth(amount)\n",
    "    \n",
    "  def subtract_wealth(self, amount):\n",
    "    self.wealth = self.wealth - amount\n",
    "    if self.wealth < 0: self.wealth = 0\n",
    "\n",
    "  def add_wealth(self, amount):\n",
    "    self.wealth = self.wealth + amount\n",
    "\n",
    "  def vulnerability(self, attacker):\n",
    "    if attacker.total_wealth() == 0:\n",
    "      return 0\n",
    "    else:\n",
    "      return (attacker.total_wealth() - self.total_wealth())/attacker.total_wealth();\n",
    "\n",
    "  def possible_tribute(self):\n",
    "    return min(TRIBUTE, self.total_wealth())\n",
    "\n",
    "  def select_target(self, targets):\n",
    "    incentives = [target.vulnerability(self)*target.possible_tribute() for target in targets]\n",
    "    if len(incentives) > 0:\n",
    "      return targets[incentives.index(max(incentives))]\n",
    "    else:\n",
    "      return None\n",
    "\n",
    "  def select_strategy(self, opponent):\n",
    "    cost_fight = round(opponent.wealth*WAR_COST)\n",
    "    cost_pay = self.possible_tribute()\n",
    "    if cost_fight > cost_pay:\n",
    "      strategy = \"PAY\"\n",
    "    else:\n",
    "      strategy = \"FIGHT\"\n",
    "    return strategy\n",
    "\n",
    "  def interact(self, opponent):\n",
    "    strategy = opponent.select_strategy(self)\n",
    "    # print(\"Agent\", self.name, \"demands tribute from agent\", opponent.name, \"who picks\", strategy)\n",
    "    if strategy == \"FIGHT\":\n",
    "      opponent.subtract_total_wealth(round(self.total_wealth()*WAR_COST))\n",
    "      self.subtract_total_wealth(round(opponent.total_wealth()*WAR_COST))\n",
    "      opponent.king = None\n",
    "    if strategy == \"PAY\":\n",
    "      tribute = opponent.possible_tribute()\n",
    "      opponent.subtract_wealth(tribute)\n",
    "      self.add_wealth(tribute)\n",
    "      opponent.king = self\n",
    "    return strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if they work as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agent A wealth 50\n",
      "Agent B wealth 100\n",
      "Agent B vulnerability for A 0.5\n",
      "Agent A vulnerability for B -1.0\n",
      "Agent A strategy for B FIGHT\n",
      "Agent B strategy for A FIGHT\n",
      "Agent A wealth 25\n",
      "Agent B wealth 94\n",
      "Agent D selects target agent B\n"
     ]
    }
   ],
   "source": [
    "a = Agent(\"A\", 50)\n",
    "b = Agent(\"B\", 100)\n",
    "c = Agent(\"C\", 350)\n",
    "d = Agent(\"D\", 500)\n",
    "print(\"Agent A wealth\", a.wealth)\n",
    "print(\"Agent B wealth\", b.wealth)\n",
    "print(\"Agent B vulnerability for A\", a.vulnerability(b))\n",
    "print(\"Agent A vulnerability for B\", b.vulnerability(a))\n",
    "print(\"Agent A strategy for B\", a.select_strategy(b))\n",
    "print(\"Agent B strategy for A\", b.select_strategy(a))\n",
    "b.interact(a)\n",
    "print(\"Agent A wealth\", a.wealth)\n",
    "print(\"Agent B wealth\", b.wealth)\n",
    "target = d.select_target([a,b,c])\n",
    "print(f\"Agent {d.name} selects target agent {target.name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The agent based model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AgentBasedModel:\n",
    "  def __init__(self, size, rate = 0):\n",
    "    self.grid_size = size\n",
    "    self.agents = 0\n",
    "    self.grid = np.empty(size ** 2, dtype=object).reshape((size, size))\n",
    "    self.commitment = np.zeros(size ** 4, dtype=float).reshape((size ** 2, size ** 2))\n",
    "    self.data = []\n",
    "    self.timestep = 0\n",
    "    \n",
    "    for x in range(size):\n",
    "      for y in range(size):\n",
    "        agent = self.new_random_agent()\n",
    "        self.add_agent_to_grid(agent, x, y)\n",
    "        \n",
    "  def new_random_agent(self, wealth = None):\n",
    "    if not wealth: wealth = random.randint(300, 500)\n",
    "    self.agents = self.agents + 1\n",
    "    return Agent(str(self.agents), wealth)\n",
    "\n",
    "  def new_random_position(self):\n",
    "    i = random.randrange(0, self.agents)\n",
    "    return (i // self.grid_size), (i % self.grid_size)\n",
    "        \n",
    "  def add_agent_to_grid(self, agent, x, y):\n",
    "    agent.x = x\n",
    "    agent.y = y\n",
    "    self.grid[y, x] = agent\n",
    "        \n",
    "  def grid_position(self, x, y):\n",
    "    if x >= self.grid_size: x = 0\n",
    "    if y >= self.grid_size: y = 0\n",
    "    if x < 0: x = self.grid_size-1\n",
    "    if y < 0: y = self.grid_size-1\n",
    "    return x, y\n",
    "\n",
    "  def neighborhood(self, x, y):\n",
    "    # Von Neumann neighborhood\n",
    "    top    = self.grid_position(x, y-1)\n",
    "    right  = self.grid_position(x+1, y)\n",
    "    left   = self.grid_position(x-1, y)\n",
    "    bottom = self.grid_position(x, y+1)\n",
    "    return [top, right, left, bottom]\n",
    "\n",
    "  def number(self, agent):\n",
    "    # works because agents are named by their number of creation\n",
    "    return int(agent.name)-1\n",
    "\n",
    "  def friendship(self, agent1, agent2):\n",
    "    return self.commitment[self.number(agent1), self.number(agent2)]\n",
    "\n",
    "  def get_allies(self, agent, opponent):\n",
    "    alliance, opponents = self.get_alliance(agent, agent, [], [])\n",
    "    return [actor for actor in alliance if self.friendship(actor, agent) > self.friendship(actor, opponent)]\n",
    "\n",
    "  def get_alliance(self, demander, agent, alliance, opponents):\n",
    "    # get list of all agents in neighborhood which are allies and have a mutual commitment.\n",
    "    # the boundary of this cluster is the number of possible opponents.\n",
    "    neighbors = [self.grid[y,x] for x,y in self.neighborhood(agent.x, agent.y)] \n",
    "    allies = [neighbor for neighbor in neighbors if self.friendship(demander, neighbor) > 0]\n",
    "    enemies = [neighbor for neighbor in neighbors if self.friendship(demander, neighbor) <= 0]            \n",
    "    new_allies = list(set(allies) - set(alliance + [demander]))\n",
    "    new_enemies = list(set(enemies) - set(opponents + [demander]))\n",
    "    alliance = alliance + new_allies\n",
    "    opponents = opponents + new_enemies               \n",
    "    if len(new_allies) > 0:\n",
    "      for neighbor in new_allies:\n",
    "        allies, enemies = self.get_alliance(demander, neighbor, alliance, opponents)\n",
    "        alliance = alliance + allies\n",
    "        opponents = opponents + enemies  \n",
    "    return list(set(alliance)), list(set(opponents))\n",
    "\n",
    "  def change_commitments(self, agent, opponent, strategy):\n",
    "    self.change_commitment(agent, opponent, strategy)\n",
    "    if strategy == \"FIGHT\":\n",
    "      for friend in agent.alliance:\n",
    "        self.change_commitment(agent, friend, \"COMMON_FIGHT\")\n",
    "      for enemy in opponent.alliance:\n",
    "        self.change_commitment(opponent, enemy, \"COMMON FIGHT\")\n",
    "        self.change_commitment(agent, enemy, \"FIGHT\")        \n",
    "                       \n",
    "  def change_commitment(self, agent, opponent, strategy):\n",
    "    logging.info(f\"Creating commitment between {agent.name} and {opponent.name}\")\n",
    "    logging.info(f\"Commitment before {self.friendship(agent, opponent)}\")\n",
    "    if strategy == \"FIGHT\":\n",
    "      value = self.commitment[self.number(agent), self.number(opponent)]\n",
    "      value = max(0, value - 0.1)\n",
    "      logging.info(f\"New commitment {value}\")\n",
    "      self.commitment[self.number(agent), self.number(opponent)] = value\n",
    "      self.commitment[self.number(opponent), self.number(agent)] = value    \n",
    "    \n",
    "    if strategy in [\"PAY\", \"COMMON_FIGHT\"]:\n",
    "      value = self.commitment[self.number(agent), self.number(opponent)]\n",
    "      value = min(1, value + 0.1)\n",
    "      logging.info(f\"New commitment {value}\")\n",
    "      self.commitment[self.number(agent), self.number(opponent)] = value\n",
    "      self.commitment[self.number(opponent), self.number(agent)] = value\n",
    "    \n",
    "    logging.info(f\"Commitment after {self.friendship(agent, opponent)}\")\n",
    "        \n",
    "        \n",
    "  def update_alliances(self):    \n",
    "    for agent in self.grid.flatten():\n",
    "      agent.alliance, agent.opponents = self.get_alliance(agent, agent, [], [])\n",
    "      \n",
    "  def harvest(self):\n",
    "    for agent in self.grid.flatten():\n",
    "      agent.harvest()\n",
    "\n",
    "  def collect_tribute(self):\n",
    "    for agent in self.grid.flatten():\n",
    "      if agent.king:\n",
    "        tribute = min(HARVEST * TAX_RATE, agent.wealth)\n",
    "        agent.subtract_wealth(tribute)\n",
    "        agent.king.add_wealth(tribute)\n",
    "    \n",
    "  def interaction(self): \n",
    "    self.update_alliances()\n",
    "    self.harvest()\n",
    "    self.collect_tribute()\n",
    "    for i in range(NUMBER_OF_WARS):\n",
    "      while True:\n",
    "        x,y = self.new_random_position()\n",
    "        agent = self.grid[x,y]\n",
    "        if agent and not agent.king:\n",
    "          break\n",
    "                \n",
    "      opponent_names = \", \".join([actor.name for actor in agent.opponents])\n",
    "      logging.info(f\"Agent {agent.description()} is selecting an opponent from {opponent_names}\")\n",
    "      opponent = agent.select_target(agent.opponents)\n",
    "      if opponent:\n",
    "        agent.alliance = self.get_allies(agent, opponent)\n",
    "        opponent.alliance = self.get_allies(opponent, agent)\n",
    "        logging.info(f\"Agent {agent.name} is demanding tribute from agent {agent.name}\")\n",
    "        logging.info(f\"Agent {agent.name} belongs to group {agent.description()}, total wealth is {agent.total_wealth()}\")\n",
    "        logging.info(f\"Agent {opponent.name} belongs to group {opponent.description()}, total wealth is {opponent.total_wealth()}\")\n",
    "        strategy = agent.interact(opponent)\n",
    "        logging.info(f\"Oponent {opponent.name} selects {strategy}\")\n",
    "        logging.info(f\"Agent {agent.name} total wealth is {agent.total_wealth()}\")\n",
    "        logging.info(f\"Agent {opponent.name} total wealth is {opponent.total_wealth()}\")\n",
    "        self.change_commitments(agent, opponent, strategy)\n",
    "\n",
    "  def count_class(self, min_wealth, max_wealth):\n",
    "    count = 0\n",
    "    all_agents = list(self.grid.flatten())\n",
    "    for agent in all_agents:\n",
    "      if (agent.wealth >= min_wealth) and (agent.wealth <= max_wealth):\n",
    "        count += 1\n",
    "    if len(all_agents) == 0: return 0\n",
    "    return count / len(all_agents)\n",
    "\n",
    "  def count_colonies(self):\n",
    "    count = 0\n",
    "    all_agents = list(self.grid.flatten())\n",
    "    for agent in all_agents:\n",
    "      if (agent.king):\n",
    "        count += 1\n",
    "    if len(all_agents) == 0: return 0\n",
    "    return count / len(all_agents)\n",
    "\n",
    "  def play(self, timestep):\n",
    "    self.interaction()\n",
    "\n",
    "  def record_statistics(self, timestep):\n",
    "    super_rich = self.count_class(450, 10000) \n",
    "    wealthy = self.count_class(400, 450) \n",
    "    middle = self.count_class(350, 400)    \n",
    "    poor = self.count_class(0, 350)  \n",
    "    colonies = self.count_colonies()\n",
    "    kings = 1-self.count_colonies()\n",
    "    self.data.append([timestep, super_rich * 100, wealthy * 100, middle * 100, poor * 100, colonies * 100, kings * 100])\n",
    "    \n",
    "  def wealth_grid(self):\n",
    "    agents = self.grid.flatten()\n",
    "    wealth_distribution = np.array([agent.wealth if agent else 0 for agent in agents])\n",
    "    return wealth_distribution.reshape((self.grid_size, self.grid_size))\n",
    "\n",
    "  def coalition_grid(self):\n",
    "    result = np.zeros(self.grid_size ** 2, dtype=int).reshape((self.grid_size, self.grid_size))\n",
    "    coalition_sizes = [[len(agent.alliance), agent] for agent in self.grid.flatten()]\n",
    "    coalition_sizes.sort(key=lambda x: x[0], reverse=True)\n",
    "    if coalition_sizes:\n",
    "      i = 0\n",
    "      for coalition in coalition_sizes[:5]:\n",
    "        logging.info(f\"coalition {i+1} for agent {coalition[1].name} which has {coalition[0]} allies\")\n",
    "        i += 1\n",
    "        alliance = coalition[1].alliance+[coalition[1]]\n",
    "        for agent in alliance:\n",
    "          result[agent.x, agent.y] = len(alliance)\n",
    "    return result\n",
    "\n",
    "  def stats(self):\n",
    "    result = [[agent.name, agent.wealth, len(agent.alliance), agent.description()] for agent in self.grid.flatten()]\n",
    "    result.sort(key=lambda x: -x[1])    \n",
    "    return result    \n",
    "    \n",
    "  def run(self, timesteps):\n",
    "    for t in range(self.timestep, self.timestep+timesteps):\n",
    "      logging.info(f\"***** new timestep {t} *****\")   \n",
    "      self.play(t)\n",
    "      self.record_statistics(t)\n",
    "    self.timestep += timesteps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the model for a grid of 10 * 10 agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(format='%(asctime)s [%(levelname)s] : %(message)s',\n",
    "                    filename='simulation.log',\n",
    "                    datefmt='%Y-%m-%d %H:%M:%S',\n",
    "                    filemode=\"w\",\n",
    "                    level=logging.INFO)\n",
    "logging.info('Started')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRID_SIZE = 10\n",
    "TIME_STEPS = 25\n",
    "logging.info(f\"Generated Agent-Based model for {GRID_SIZE}x{GRID_SIZE} agents\")\n",
    "model = AgentBasedModel(GRID_SIZE)\n",
    "\n",
    "grid_time_0 = model.wealth_grid()\n",
    "coalition_grid_time_0 = model.coalition_grid()\n",
    "\n",
    "logging.info(f\"Running {TIME_STEPS} timesteps\")\n",
    "model.run(TIME_STEPS)\n",
    "grid_time_1 = model.wealth_grid()\n",
    "coalition_grid_time_1 = model.coalition_grid()\n",
    "\n",
    "logging.info(f\"Running {TIME_STEPS} timesteps\")\n",
    "model.run(TIME_STEPS)\n",
    "grid_time_2 = model.wealth_grid()\n",
    "coalition_grid_time_2 = model.coalition_grid()\n",
    "\n",
    "logging.info(f\"Running {TIME_STEPS} timesteps\")\n",
    "model.run(TIME_STEPS)\n",
    "grid_time_3 = model.wealth_grid()\n",
    "coalition_grid_time_3 = model.coalition_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(nrows=4, ncols=2, figsize=(8, 8))\n",
    "fig.set_tight_layout(True)\n",
    "\n",
    "colormap = plt.get_cmap('viridis', 8)\n",
    "cmap1 = mpl.colors.ListedColormap(colormap.colors)\n",
    "\n",
    "img1 = ax[0,0].matshow(grid_time_0, cmap=cmap1)\n",
    "ax[0,0].set_xticks([])\n",
    "ax[0,0].set_yticks([])\n",
    "ax[0,0].set_title(f\"Wealth T={TIME_STEPS*1}\")\n",
    "fig.colorbar(img1, ax=ax[0,0])\n",
    "\n",
    "img2 = ax[0,1].matshow(coalition_grid_time_0, cmap=cmap1, vmin=0, vmax=10)\n",
    "ax[0,1].set_xticks([])\n",
    "ax[0,1].set_yticks([])\n",
    "ax[0,1].set_title(\"Coalitions\")\n",
    "fig.colorbar(img2, ax=ax[0,1])\n",
    "\n",
    "img3 = ax[1,0].matshow(grid_time_1, cmap=cmap1)\n",
    "ax[1,0].set_xticks([])\n",
    "ax[1,0].set_yticks([])\n",
    "ax[1,0].set_title(f\"Wealth T={TIME_STEPS*2}\")\n",
    "fig.colorbar(img3, ax=ax[1,0])\n",
    "\n",
    "img4 = ax[1,1].matshow(coalition_grid_time_1, cmap=cmap1)\n",
    "ax[1,1].set_xticks([])\n",
    "ax[1,1].set_yticks([])\n",
    "ax[1,1].set_title(\"Coalitions\")\n",
    "fig.colorbar(img4, ax=ax[1,1])\n",
    "\n",
    "img5 = ax[2,0].matshow(grid_time_2, cmap=cmap1)\n",
    "ax[2,0].set_xticks([])\n",
    "ax[2,0].set_yticks([])\n",
    "ax[2,0].set_title(f\"Wealth T={TIME_STEPS*3}\")\n",
    "fig.colorbar(img5, ax=ax[2,0])\n",
    "\n",
    "img6 = ax[2,1].matshow(coalition_grid_time_2, cmap=cmap1)\n",
    "ax[2,1].set_xticks([])\n",
    "ax[2,1].set_yticks([])\n",
    "ax[2,1].set_title(\"Coalitions\")\n",
    "fig.colorbar(img6, ax=ax[2,1])\n",
    "\n",
    "img7 = ax[3,0].matshow(grid_time_3, cmap=cmap1)\n",
    "ax[3,0].set_xticks([])\n",
    "ax[3,0].set_yticks([])\n",
    "ax[3,0].set_title(f\"Wealth T={TIME_STEPS*4}\")\n",
    "fig.colorbar(img7, ax=ax[3,0])\n",
    "\n",
    "img8 = ax[3,1].matshow(coalition_grid_time_3, cmap=cmap1)\n",
    "ax[3,1].set_xticks([])\n",
    "ax[3,1].set_yticks([])\n",
    "ax[3,1].set_title(\"Coalitions\")\n",
    "fig.colorbar(img8, ax=ax[3,1])\n",
    "\n",
    "plt.savefig(\"PoliticalActorsCoalition.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Alliances</th>\n",
       "      <th>Allies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>4349.233333</td>\n",
       "      <td>23</td>\n",
       "      <td>36 25 46 37 49 57 55 19 20 48 27 28 59 34 47 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>1114.684656</td>\n",
       "      <td>23</td>\n",
       "      <td>2 73 11 90 12 8 74 80 13 64 100 91 82 21 85 94...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>262.375000</td>\n",
       "      <td>10</td>\n",
       "      <td>71 70 69 87 61 66 78 77 67 79 68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>243.833333</td>\n",
       "      <td>3</td>\n",
       "      <td>46 34 45 35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>95</td>\n",
       "      <td>137.238095</td>\n",
       "      <td>14</td>\n",
       "      <td>62 86 85 54 53 94 73 84 75 96 72 6 52 74 95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29</td>\n",
       "      <td>77.927381</td>\n",
       "      <td>16</td>\n",
       "      <td>71 70 80 20 73 30 8 19 89 100 9 10 28 11 90 29 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>91</td>\n",
       "      <td>55.384656</td>\n",
       "      <td>1</td>\n",
       "      <td>92 91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>82</td>\n",
       "      <td>54.484656</td>\n",
       "      <td>1</td>\n",
       "      <td>82 92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>93</td>\n",
       "      <td>52.684656</td>\n",
       "      <td>1</td>\n",
       "      <td>92 93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>94</td>\n",
       "      <td>48.151323</td>\n",
       "      <td>6</td>\n",
       "      <td>85 94 83 84 96 6 95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45</td>\n",
       "      <td>34.383333</td>\n",
       "      <td>3</td>\n",
       "      <td>46 45 44 35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>20.184656</td>\n",
       "      <td>2</td>\n",
       "      <td>92 3 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>68</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>65 66 56 46 78 77 67 76 68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>78</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>2</td>\n",
       "      <td>67 78 68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>47</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>46 48 58 47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>77</td>\n",
       "      <td>12.314286</td>\n",
       "      <td>3</td>\n",
       "      <td>67 66 77 68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>2</td>\n",
       "      <td>46 37 36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>75</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>4</td>\n",
       "      <td>86 85 87 75 95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>3 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>88</td>\n",
       "      <td>7.505952</td>\n",
       "      <td>1</td>\n",
       "      <td>98 88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>97</td>\n",
       "      <td>7.066667</td>\n",
       "      <td>2</td>\n",
       "      <td>97 98 99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>86</td>\n",
       "      <td>6.447619</td>\n",
       "      <td>6</td>\n",
       "      <td>86 98 87 88 99 89 77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>87</td>\n",
       "      <td>6.280952</td>\n",
       "      <td>3</td>\n",
       "      <td>86 87 75 76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>42</td>\n",
       "      <td>6.273942</td>\n",
       "      <td>5</td>\n",
       "      <td>42 31 52 32 40 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>81</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>71 81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>4.875000</td>\n",
       "      <td>3</td>\n",
       "      <td>9 10 1 99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>98</td>\n",
       "      <td>3.772619</td>\n",
       "      <td>4</td>\n",
       "      <td>86 98 87 88 97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>95 85 75 94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>83</td>\n",
       "      <td>3.233333</td>\n",
       "      <td>2</td>\n",
       "      <td>84 94 83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>48</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>4</td>\n",
       "      <td>47 48 46 59 58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>62</td>\n",
       "      <td>2.782143</td>\n",
       "      <td>3</td>\n",
       "      <td>52 61 62 72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>58</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>58 48 47 57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>34</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5</td>\n",
       "      <td>54 44 53 34 35 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>16 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2</td>\n",
       "      <td>27 28 17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>15</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>26</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>25 27 26 36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>36</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>36 26 25 46 27 37 38 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>63</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>4</td>\n",
       "      <td>65 73 64 63 74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>74</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>3</td>\n",
       "      <td>73 74 64 63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>51</td>\n",
       "      <td>1.832143</td>\n",
       "      <td>3</td>\n",
       "      <td>52 51 31 41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>12</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>4</td>\n",
       "      <td>22 11 23 12 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>44</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>5</td>\n",
       "      <td>34 53 54 44 46 45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>3</td>\n",
       "      <td>22 32 12 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>25</td>\n",
       "      <td>1.433333</td>\n",
       "      <td>6</td>\n",
       "      <td>34 44 36 24 26 25 46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>61</td>\n",
       "      <td>1.382143</td>\n",
       "      <td>3</td>\n",
       "      <td>62 61 52 72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>41</td>\n",
       "      <td>1.146429</td>\n",
       "      <td>4</td>\n",
       "      <td>31 41 52 32 51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>27</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>6</td>\n",
       "      <td>7 36 26 46 27 17 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>54</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>34 44 53 54 64 52 43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>18</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>17 18 8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name       Wealth  Alliances  \\\n",
       "0    46  4349.233333         23   \n",
       "1    92  1114.684656         23   \n",
       "2    67   262.375000         10   \n",
       "3    35   243.833333          3   \n",
       "4    95   137.238095         14   \n",
       "5    29    77.927381         16   \n",
       "6    91    55.384656          1   \n",
       "7    82    54.484656          1   \n",
       "8    93    52.684656          1   \n",
       "9    94    48.151323          6   \n",
       "10   45    34.383333          3   \n",
       "11    2    20.184656          2   \n",
       "12   68    17.000000          8   \n",
       "13   78    14.400000          2   \n",
       "14   47    14.400000          3   \n",
       "15   77    12.314286          3   \n",
       "16   37    10.800000          2   \n",
       "17   75    10.300000          4   \n",
       "18    3     9.900000          1   \n",
       "19   88     7.505952          1   \n",
       "20   97     7.066667          2   \n",
       "21   86     6.447619          6   \n",
       "22   87     6.280952          3   \n",
       "23   42     6.273942          5   \n",
       "24   81     5.666667          1   \n",
       "25    1     4.875000          3   \n",
       "26   98     3.772619          4   \n",
       "27   85     3.600000          3   \n",
       "28   83     3.233333          2   \n",
       "29   48     2.800000          4   \n",
       "30   62     2.782143          3   \n",
       "31   58     2.500000          3   \n",
       "32   34     2.500000          5   \n",
       "33   16     2.333333          1   \n",
       "34   17     2.250000          2   \n",
       "35   15     2.000000          0   \n",
       "36   26     2.000000          3   \n",
       "37   36     2.000000          7   \n",
       "38   63     1.833333          4   \n",
       "39   74     1.833333          3   \n",
       "40   51     1.832143          3   \n",
       "41   12     1.800000          4   \n",
       "42   44     1.800000          5   \n",
       "43   22     1.700000          3   \n",
       "44   25     1.433333          6   \n",
       "45   61     1.382143          3   \n",
       "46   41     1.146429          4   \n",
       "47   27     1.133333          6   \n",
       "48   54     1.000000          6   \n",
       "49   18     1.000000          2   \n",
       "\n",
       "                                               Allies  \n",
       "0   36 25 46 37 49 57 55 19 20 48 27 28 59 34 47 3...  \n",
       "1   2 73 11 90 12 8 74 80 13 64 100 91 82 21 85 94...  \n",
       "2                    71 70 69 87 61 66 78 77 67 79 68  \n",
       "3                                         46 34 45 35  \n",
       "4         62 86 85 54 53 94 73 84 75 96 72 6 52 74 95  \n",
       "5   71 70 80 20 73 30 8 19 89 100 9 10 28 11 90 29 21  \n",
       "6                                               92 91  \n",
       "7                                               82 92  \n",
       "8                                               92 93  \n",
       "9                                 85 94 83 84 96 6 95  \n",
       "10                                        46 45 44 35  \n",
       "11                                             92 3 2  \n",
       "12                         65 66 56 46 78 77 67 76 68  \n",
       "13                                           67 78 68  \n",
       "14                                        46 48 58 47  \n",
       "15                                        67 66 77 68  \n",
       "16                                           46 37 36  \n",
       "17                                     86 85 87 75 95  \n",
       "18                                                3 2  \n",
       "19                                              98 88  \n",
       "20                                           97 98 99  \n",
       "21                               86 98 87 88 99 89 77  \n",
       "22                                        86 87 75 76  \n",
       "23                                  42 31 52 32 40 21  \n",
       "24                                              71 81  \n",
       "25                                          9 10 1 99  \n",
       "26                                     86 98 87 88 97  \n",
       "27                                        95 85 75 94  \n",
       "28                                           84 94 83  \n",
       "29                                     47 48 46 59 58  \n",
       "30                                        52 61 62 72  \n",
       "31                                        58 48 47 57  \n",
       "32                                  54 44 53 34 35 25  \n",
       "33                                               16 6  \n",
       "34                                           27 28 17  \n",
       "35                                                 15  \n",
       "36                                        25 27 26 36  \n",
       "37                            36 26 25 46 27 37 38 28  \n",
       "38                                     65 73 64 63 74  \n",
       "39                                        73 74 64 63  \n",
       "40                                        52 51 31 41  \n",
       "41                                     22 11 23 12 21  \n",
       "42                                  34 53 54 44 46 45  \n",
       "43                                        22 32 12 21  \n",
       "44                               34 44 36 24 26 25 46  \n",
       "45                                        62 61 52 72  \n",
       "46                                     31 41 52 32 51  \n",
       "47                                7 36 26 46 27 17 28  \n",
       "48                               34 44 53 54 64 52 43  \n",
       "49                                            17 18 8  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(model.stats(), columns=['Name', 'Wealth', 'Alliances', 'Allies'])\n",
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df = pd.DataFrame(model.data, columns=['Time', 'SuperRich', 'Wealthy', 'Middle', 'Poor', 'Colonies', 'Kings'])\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(100,0))\n",
    "plt.ylabel(\"Wealth Distribution\")\n",
    "plt.xlabel(\"Simulation Time\")\n",
    "plt.plot(df.Time, df.SuperRich, color=\"DarkBlue\", label='Rich Empires', linestyle='solid')\n",
    "plt.plot(df.Time, df.Wealthy, color=\"DarkGreen\", label='Wealthy States', linestyle='dashed')\n",
    "plt.plot(df.Time, df.Middle, color=\"DarkRed\", label='Normal States', linestyle='dotted') \n",
    "plt.plot(df.Time, df.Poor, color=\"Black\", label='Poor States', linestyle='dashdot')\n",
    "plt.legend()\n",
    "plt.savefig(\"PoliticalActorsWealth.png\", format=\"png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mJrWTf3bKSM/IkSOxYMGC1D5ymdUqAoC7uztmz56NHj16wMfH57Hm3E8//RRJSUnw9vZG3bp18emnn6ZbRlBQEBo0aIBGjRph2bJlGDNmjMnxZkTl9ZGOvr6+ktLZ0FoWLPgXHh4u6N+/llXvS0REtu/MmTOoXbu21mGQDUjve0EpdVREfNM7n33aLGDIkHpah0BERER5DJtHLeTatQcIDDyjdRhERESURzBps5A5c05i0KCNuHHjkdahEBERUR7ApM1CBg6sDRFg+fKzWodCREQ2Jq/3J6es5eR7gEmbhdSsWRyNGpViEykRET3G3d0dd+7cYeLmwEQEd+7ceWy+OFNwIIIFDRhQCx9+uAcXL95H1apFtQ6HiIhsQIUKFRAeHo6oqCitQyENubu7p67EYCombRY0YEAtfPzx3zhw4AaTNiIiAmCYeLZy5cpah0F2iEmbBVWsWBiRkSNRrFj2qj+JiIiInsQ+bRaWkrCx7wIRERHlBpM2C9Pp9OjSZQW++GK/1qEQERGRHWPSZmHOzoZH/Mcfp1nbRkRERDnGpM0KBg6shUuXonH48E2tQyEiIiI7xaTNCp57rjrc3JwRGBiqdShERERkp5i0WUHRou7o3r0yli0LhU6n1zocIiIiskOc8sNKRo1qhDZtKiApSZ/az42IiIjIVEzarKRTp2fQqdMzWodBREREdopVPlb04EEC/vjjNBITdVqHQkRERHaGSZsV7dkTjlde2YRt28K0DoWIiIjsDJM2K+rSpRKKFXPnKFIiIiLKNiZtVuTm5ox+/apjzZoLiI1N0jocIiIisiNM2qxs4MDaiIlJwoYNF7UOhYiIiOwIkzYra9euAsqWLYB9+25oHQoRERHZEU75YWXOzk44fnwIPD3zax0KERER2RHWtGkgJWHjAvJERERkKiZtGvnww9148cV1WodBREREdoJJm0acnZ2wZs0FREbGaB0KERER2QEmbRp55ZU60OsFP/xwWOtQiIiIyA4wadNI7dolMHx4fQQEBOPs2btah0NEREQ2jkmbhr75pjXy53fBRx/t1ToUIiIisnGc8kNDpUsXQGBgT9SvX1LrUIiIiMjGMWnTWPfuVQAYpv8QAZyclMYRERERkS1i86gNePQoEZ06rcCMGcFah0JEREQ2ikmbDShQwBVubk744ov9iIqK1TocIiIiskFM2myAUgpTp7ZHTEwSPv10n9bhEBERkQ1i0mYjatcugdGjG2H27OMICYnUOhwiIiKyMUzabMhnn7VAiRIe+O67A1qHQkRERDaGo0dtSLFi7tiw4XnUrVtC61CIiIjIxjBpszHNmpUFAOj1Ar1e4OLCylAiIiJi86hNioh4hFq15mLRolNah0JEREQ2gkmbDSpTpgDy5XNGQEAwRETrcIiIiMgGMGmzQUopvPOOD44fj8KePeFah0NEREQ2gEmbjRo0qDaKF3dHQABXSSAiIiImbTYrf35XvP66N9auvYCwsGitwyEiIiKNcfSoDRs9uhF8fcugQoVCWodCREREGmPSZsPKly+Efv2YsBERERGbR21ecrIeX321H4sXn9Y6FCIiItIQa9psnLOzwvr1F/HoURIGDqwNJyeldUhERESkAda02TilFMaMaYzQ0LvYti1M63CIiIhII0za7ED//jVRpkwBTv9BRETkwJi02QE3N2e89VYDbN58GWfP3tU6HCIiItIA+7TZiTfeaICQkCjodHqtQyEiIiINMGmzE6VLF8Dq1X20DoOIiIg0wuZRO3Px4n0EBV3VOgwiIiKyMta02Znhw7fg6tWHuHDhNU7/QURE5EBY02ZnRozwxuXL0di377rWoRAREZEVMWmzM336VIOHhwuWLg3VOhQiIiKyIiZtdqZgQTf06lUVK1acRXIyR5ISERE5CiZtdmjgwFqIjk7EyZNRWodCREREVsKBCHaoe/cqiIwciSJF8mkdChEREVkJkzY75ObmDDc3Z63DICIiIiti86idCg9/iObNF+PPP89rHQoRERFZAZM2O1WmTAFcvhyNwMAzWodCREREVqBJ0qaUelcpdUop9a9SKlAp5a6UqqyUOqiUuqCUWqaUcjOe+7bxvE1p9rVWSk3TInZb4eLihBdfrIH16y/h4cNErcMhIiIiC7N60qaUKg/gHQC+IlIPgDOAAQC+BzBNRKoBuAdguPGSQQC8AewH0FUppQB8CuBra8duawYMqIX4+GSsXXtB61CIiIjIwrRqHnUB4KGUcgGQH0AEgA4AVhqPLwDQ1/haAXA1npcE4GUAm0XkrjUDtkUtW5aHl1chNpESERE5AKuPHhWR60qpyQCuAogD8BeAowDui0iy8bRwAOWNr2cBOADgFIB9ANYC6GrVoG2Uk5PCp5+24BqkREREDsDqSZtSqhiAPgAqA7gPYAWAbhmdLyKLACwyXvsZgBkAnlVKDQZwDcD7IvLY0gBKqdcBvA4AFStWNP8/woaMGOGtdQhERERkBVo0j3YCcFlEokQkCcBqAK0AFDU2lwJABQCPrYiulCoHoKmIrAHwPoCXYEj6Oj55AxGZLSK+IuLr6elpsX+Irbh9OxZr1nDqDyIiorxMi6TtKoDmSqn8xkEFHQGcBrALwAvGc4bA0Aya1tcAPjO+9gAgAPQw9HVzaLNmHcPzz6/F9esPtQ6FiIiILMTqSZuIHIRhwEEwgJPGGGYD+BDAe0qpCwBKAJiTco1SqpHx2mDjriXGa1sB2GK14G3UwIG1IQKsWHFO61CIiIjIQpSIaB2DRfn6+sqRI0e0DsPifHwWwtXVCQcPvqx1KERERJRDSqmjIuKb3jGuiJBHDBxYC4cO3cTFi/e1DoWIiIgsgElbHvHSS7WgFBAUdE3rUIiIiMgCrD7lB1lGxYqFcf36myhbtqDWoRAREZEFsKYtD0lJ2O7ejdM4EiIiIjI3Jm15zIwZwahWbQ6iomK1DoWIiIjMiElbHtO58zN48CABn366T+tQiIiIyIyYtOUxtWuXwOjRjTB79nGEhERqHQ4RERGZCZO2POjzz1uieHEPjB27E3l9Hj4iIiJHwaQtDypWzB3ffNMKhw/fxNmzd7UOh4iIiMyAU37kUSNGeKNXr6ooX76Q1qEQERGRGbCmLY9ydnZC+fKFICKsbSMiIsoDmLTlcV9//Q98fBYiPPyh1qEQERFRLjBpy+MGD64LnU4wYcIerUMhIiKiXGDSlsdVqlQE48Y1weLFZ7B//3WtwyEiIqIcYtLmACZMaIpy5Qrio4/2ah0KERER5RCTNgdQsKAb3n23Mc6du4c7d7guKRERkT1SeX3yVV9fXzly5IjWYWguLi4Jzs5OcHNz1joUIiIiyoBS6qiI+KZ3jPO0OQgPD1cAQFKSDklJeuTP76pxRERERJQdbB51IA8eJKBq1d8xdSprHomIiOwNkzYHUrhwPtSpUwI//RSCxESd1uEQERFRNjBpczBjxvggIiIGK1ee0zoUIiIiygYmbQ6ma9fKqFGjGAICjmodChEREWUDkzYH4+Sk8M47Pjh06CaOHLmpdThERERkIo4edUBDhtRF1apF4eNTWutQiIiIyERM2hxQwYJu6NatstZhEBERUTawedRBiQj+7//24IcfDmkdChEREZmASZuDUkrhzJm7+OGHw4iLS9I6HCIiIsoCkzYHNmaMD+7cicOSJaFah0JERERZYNLmwPz8vODt7YmAgKPI62vQEhER2TsmbQ5MKcP0HydP3kZQ0DWtwyGidIgIHj1K1DoMIrIBTNoc3P/+Vwt9+1ZDvXoltQ6FiJ5w4kQUOnZcjv/9b6PWoRCRDWDS5uA8PFzx55994emZH0lJOsTHJ2sdEpHDu307Fm+9tQ2NGi3E8eNR6NatEkQEBw7cwIMHCVqHR0Qa4TxtBADQ6fTo1etPFCmSD4GBPeHkpLQOicgh7d59DX37rsHDh4kYPboRPv+8BYoX98Dt27Ho1GkFWrUqjw0bnoOrq7PWoRKRlTFpIwCAs7MTOnasiA8+2IPKlYtg0qS2WodElGctWPAvbt2KfWyfl1chDBxYGw0aeKJLl0r4/PMWqFPnv24LJUvmR0BAB7z22laMHLkds2d3gVL8cEX2JSlJBxcXJ37v5hCTNko1blwTXLoUje+/P4TKlYvgjTcaaB0SUZ4UEBCMY8ciH9vn5+eFgQNro2hRdyxb1ivd64YPr4/Ll6Px7bcHULVqUUyY0Mwa4RKZzcSJB/HXX1cQENAejRuX0Tocu8OkjVIppTBzZkdcufIAo0ZtR8WKhfDss1W0DosoT5gz5yQuXbqPL75oiX37BuLJWXZM7ZLw9detcPlyNP7v//aiZctyaNvWywLREplXYqIOrq5OqFy5CM6fv4cmTf7A0KH18N13bVCmTAGtw7MbHIhAj3FxccKyZb3QrVtllC1bUOtwiPKEe/fi8eGHe7Bv33W4uDjBw8MV+fM/vrm7m/YZWimFuXO74qefOqF16woWjpzIPKZPP4rq1eegb9/qOH9+OMaNa4I//jiNGjXmYNkyTvBuKta00VMKFXLDhg3Pp77X6fRwdmZ+T7Zt795w6PWCdu1sr+bpyy/34969eEyf3sEsfXny5XPBW281BABcvHgfv/12Avny/TcwwcPDJbXpdNmyUJw+feex64sUyYf33vMFACxceAoXL95/7LiXVyG89pp3ruMkShEYGIoSJdxRqJAbAOCHH9phxAhvjB+/G9WrFwMAbN16Gfv333jq2i++aMk+cEZM2ihT77yzA7duxWbYx4bIFvzzzw20bbsUK1YYvk9FxGZ+yZ8+fRuzZh3DiBHeaNiwlFnL1usFgwdveuoPXbFi7qlJ24oVZ7Fq1fnHjlesWCg1aVu8+Az++ivsseMtW5bDSy/VSv0DS5QboaF3EBISiWnT2j+2v3r1Ylizpm/q+23brmDKlCNPXf/FFy0BAKtXn0OTJmXg5VXYovHaMpXXly/y9fWVI0ee/iYg03z99T/47LN92LWrP/z8KmodDtFTLl68j+bNF6NIkXw4cOB/KFkyP0aP3o6YmCR8910bzZv5e/RYhX37buD8+eHw9MyvaSymsKWEl/KGzz/fh6+//gfh4W+iXLmc/TzGxCSiYsXZiItLxoQJTTFuXBPkz+9q5khtg1LqqIj4pneMbV6UqXHjfFGxYiGMHbsLOp1e63CIHnPnThy6d18FEWDz5n4oWTI/RARFiuTDkiWhqFFjDiZNOqjppNGTJrXFvHnd7CJhA5CasN248Qi3b8dmcTZR5kQEgYGh8PPzynHCBgAFCrghOPgV9OpVFZ9/vh+1as3FsmWhDrduNmvaKEsrVpxF//7r8euvnfH665wGhGxHjx6rsGPHVezY0R+tWpV/7NiFC/cwbtxurF17AZUrF8G8ed2y1d8tPj4Z06YdTU1cPD3zpzY5/vTTMdy8GYPXX2+AChUKpXu9PddY3bkTh/Llf8H77/vi22/baB2OWc2cGYywsOjH9lWsWBhjxjQGAEyefBgREY8eO16jRnFOgZRDer1g7doLKFzYDR07PmOWMvfuDceYMTsREhKJ06eHoVatEmYp11ZkVtOWZdKmlPoBwDcA4gBsAeAN4F0R+cPcgVoCk7bcExH4+S3DlSsPcOHCa3BxYQUt2YaQkEhcvhyN556rnuE527dfwfffH8L06e1Rt65pa+zq9YKBAzdg+fKzKFjQ0ARTvXoxBAcPBgA8++xK/PXXFbi7O2PChGYYN84XHh6PN9VMmXIYwcGRmDu3K/Lls7/uw337rsHff1/HtWuvP/Vvszfh4Q9Tk+vOnVfgwIHH+wA2aVIGO3e+BABo3nwxTp26/djx9u0rYt265wAA1649cOg+VbZCp9Nj//4baNPGMILaHB+SdDo97t2LT62x1+pDV2ZJG0Qk0w1AiPHrcwDmACgC4HhW19nK1rhxY6HcO3furly9Gq11GEQiIvLPP9dzdJ1er5fo6Pgsz9u3L1ycnCbLDz8czPCcS5fuSb9+awTwl2ee+VWCgq6mHrt585EULhwgPXqsylGctmDnzisC+Mvvv5/QOpQcu307VkaN2ibOzpPl77/Dc13eX39dFheXKTJ27E65ezfODBHmbTqdXvz9D8nly/ctep/5809Khw7LJC4uKVfl/P77CSlSZIZ8++0/0q5doCQmJpspwuwBcEQyyGlMqTJJ+YjVA8AKEYnO7GTKm6pXLwYvr8IQETx8mKh1OOTAfvvtBFq0WIKVK89m+9oPP9yD1q0Ds1x0vWXL8jh2bDDGjWuS4TmVKxfFypV9sGtXf3h6eqBUKUOfNb1e8MknfyM2NhlTp/plO0Zb4efnBW9vTwQEHLW7fkNJSTrMnBmM6tXn4JdfjuPNNxugVq3iuS63QQNPvPpqPQQEHDWWHcK+vpnYt+86xo/fjX37rlv0Pu7uLti58yqGDdsCvT5n36vR0Qn46KO9qFevJOrWLYndu8Pxyy/HzRypGWSUzaVsACYCCAVwDIYEzhPAwayus5WNNW3m9fzza+TZZ1da9Z5xcUkSGRlj1XuSbdq69bI4O0+Wbt1WSFKSLtvXb9sWJi4uU6RLlxXpforesuWSbNx4Mdvl6vX61NcvvbROlPKX99/fle1ybM3vv58QJ6fJcvJkZI7LSEiwbm2FXq+XFi0WC+AvHTsuy1XsGTl27Ja0axcogL+0b7809f//rbf+ktdf35q6jR69XUJCbpn9/vZi5Mht4uExTR4+TLD4vSZNOiCAv3z00Z4cXT9u3C5Ryl+OHIkQvV4vnTsvl6JFZ0hUlPX/9iCTmrasEjYnAC0BFAfgbNxXAECZzK6zpY1Jm3lNnXpYAP8c/WHLLr1eL6tXn5PKlWdL375/Wvx+ZNuOH4+UQoUCpEGD+fLgQc7/CMyZc0IAf3nttS2PJVshIbekYMHp4uu7UHQ6fSYlZEyn08t77+2U1q2XyP37WTfD2rrY2EQJC8tZ05ZOp5dFi05JhQq/yL59uW+azI55807KmjXnH/v/NTe9Xi8rVoTKkiWnU/d5ef0iZcr8lLp5eEyTDz/cbbEYbFlSkk48PWdJ//7rrHI/vV4vI0ZsFcBffvvteLauPXv2jri6TpHhw7ek7vv33yhxdp4sI0duM3eoWcpx0ma4FseyOseWNyZt5pWQkCw1a86RGjV+t+gn6BMnIqVDh2UC+EvdunNl27Yw0en08vHHe+X06dsWuy/ZptjYRKlY8RcpX/5nuXbtQa7L++STvQL4y7ff/iMiIteuPZDy5X+WChV+kfDw3JefF2UnkT148IY0b/6HAP7i67tQDh+OkLVrz8uRIxEWi+/evTjZsuWSxcrPibt341JrmbZtC5OpUw9bveZRK1u2XBLAX/7885zV7pmYmCxdu66QL7/cl63r/P0PSeHCAXLz5qPH9r/99nZxcposly7dM2OUWctt0jYZQD8YR5ra28akzfw2brwogL9MmXLYIuUvW3ZGnJwmS/HiM2XWrODUZrAbNx5K6dI/SqVKvz71w0V534oVoWZrakr5VL527XmJjo4Xb+/5UqhQgBw/bv6mNHun0+mlV6/VJjf3jh69XQB/KVPmJ5k//6TodHqJi0sST89Z0rv3aovEmJCQLB06LJN8+abK9esPLXKP3Bo5cpsA/lKz5hzZtMnyLRVamz79iHh6zsr14IDsStttIjs1rTduPP19c+dOrCYfBHKbtD0EoAeQBOCB8f2DrK6zlY1Jm2U8++xKqVNnbo6bkTJz506sjBu3S+7ciX3q2KFDN8TDY5o0bbpIYmISzX5vR7Zy5VlZtOiU1mE8JilJZ9HaGRGRGTOOirPzZNm69bJF72PPBgxYL0WKzMiwb1JcXFLqH8jp04/IhAm7n2rC/uSTvaKUv1y4cM+ssen1ehk6dJMA/rJgwb9mLduc9Hq9rF9/QapX/10Af3n22ZVy5oz5Wg0WLvxXAgKOWrRJOLvi462bsKX1zz/XpVGjBXL4cMa/PxITk03+P7DmSNJcJW32vjFps4zr1x+avXPpV1/tl+3bw7I8b82a86KUvzz33BpJTs5+Z3R62po15wXwlwIFpueog78l6PV6efPNv8TVdYqcO3fXovc5dsxxO4ub4p9/rgvgL7NmBT+2X6/Xy6pVZ6Vy5dmyePHpDK42uH79Yep0Geb01Vf7BfCXL77IXpOYVhISkmXKlMNSuHCA/PJLiFnKDA29I4C/1Ko1xyzl5ZYt/A4JCbklZcr8JIC/DB26Kd2atGnTjoiz82T599+oTMuaNStYatWaY7Vaw9zWtCkALwP41PjeC0DTrK6zlY1Jm324dy9OlPKXzz//26TzAwKOSr58U+Xo0ZsWjizvS6m9rF9/njx6ZPlRXqb64YeDAvjLhAmO2ZHb1jRtukhq1Pg9tXb9+PFIad9+qQD+Ur/+PNmz51qWZfzvfxukcOGAXA0kSevAgRsC+MvgwRttqobJFLduPUr90Llw4b/yyy8hOf4Q2qPHKilUKCB1Ls3jxyOla9cVWSYjltK//zqbGDwWHR0vH364W9zcpkrBgtNl6tT/uvRERsZIkSIzpGvXFVl+72zfHiaAv3z33QFLhywimSdtpszT9hOAFgD+Z3z/CMCP2ZxZhPKgOXNOolu3lWYp6++/r0MEJi8z9M47PjhzZhh8fEqb5f6OKiwsGr16/YnSpfNj27YXUaCAG27disGxY7c0jWvFirP44IM9eOmlmnluGSV7NWZMY5w7dw9bt17GV1/tR6NGC3HixG389FMnBAcPTp2ZPvMyfFC8uDsuXLhnlpiaNi2DwMCe+O23rna3ZFipUgXg7Gz4E7xmzQW8+eY2NG68CEFBV7NVzubNl7Bx4yV89lmL1JUaLl26j4MHI9CgwQK8/fYO3L0bZ/b4M/LwYSLWrbuI8uVzvs6ouRQunA+TJrXFqVND0bHjM4iK+u85fPrpPjx6lIipU/2y/N7p2PEZ9O1bDd9+ewA3bjzK9FyLyyibS9kABBu/HkuzjysikMyYcVQAf7PMdj1u3C5xc5sqsbHZ76c2f/5J+euvy7mOIa84eTJS3njjL5P6amzZcknKlfv5sRG5zZr9IdWrW3Z0cGaOHbsl+fJNlVatlli9EzNlLCEhWaZOPSy3b8fKqlVnZcyYHTlaFcAcXRouXLhnkfnXtJIyfcgzz/wqgL8MGLDepP5gOp1e6tSZm+7Pa1RUjIwcuS11UNfs2dmbBiOzWNevvyD9+q2R3r1Xy7Zthi4tZ87clt69V0urVksE8DfLChTmlvK9Fxh4RgB/eeedHSZfe+HCPXFzmyqffLLXUuGlQi6bRw8CcE6TvHnCjqYBYdJmOcePRwrgL/Pnn8x1WU2aLJI2bQKzfV1Skk5KlfpR/ve/DbmOIS/Q6fTStOkiAfxNXm7nycRo82bDUP3Jkw9ZMtQMJSXp5NdfQzSZ1JKsIy4uKVdTt/TosUq8vH6xib5T5hQbmyiff/63ODlNNnnU4tGjN2Xv3oybpk+ciJSOHZfJxx/nPtk4dSpKunRZIYC/lC//szRsuEDWrDkvIoY+ZA0bLpCGDRfIiy+utcggNXMJDDwjvXuvzvaHjkOHblilH3Vuk7ZBANYBCAfwLYCzAF7M6jpb2Zi0WY5Op5fixWfKsGGbc1VOYmKyVKv2W44/wfTrt0aqVJmdqxjyin37wkUpf5k69bC88cZfopS/rF17/rFz9Hq9DB++JdNP3t27r0x33iJLs7d+SZR9er1eGjVaIJ07L8/R9Zs2GaYc8vfX5kOFNZw9eyfLc7KTFOn1+tQEd8OGC9K792o5fz57g3t0Or3UqPG7FCkyQ6ZNO6LZupyOILOkLcs+bSKyGMAHMCxnFQGgr4isMFv7LNktJyeFtm0rICjoWq7KcXV1xvnzr+Gzz1rk6Ppmzcri0qVoREXF5iqOvKBly/I4eXIoxoxpjF9+6YyzZ4ejV6+qAAxrdu7adRVff/0P5sw5iYiIjPtmTJ3aHrGxyfjkk7+tFToePEhAw4YLsWHDRavdk6xPKYV+/Wpg27YrOH36drauTUrS4d13g1C9ejG8846PhSLUXo0ahnVSt269jD/+OJ3uOaNGbcfrr/+VUrmSKaUUXFwMf+5v3YrFzp1XUafOPHz44e5M1+FNTtZjzpyTiI1NgpOTwpIlPXD+/HCMHdsYrq7OOfiXUW5lmLQppYqnbAAiAQQCWALglnEfEfr0qYbmzcsiISE512Xl9JdA8+blAAAHD0bkOgZ7lpKE1a1bEk5Oho611asXg1IKycl6TJt2BB06LMfnn+/HkCF18emnGSfJNWsWxzvvNEJcXHKmCzCHht7BG2/8hVmzgpGUpMtV/N9+ewAnTkShTJkCuSqHbN/rr3sjXz5nzJx5LFvX/fhjCM6evYupU/3g5pa3kwYRwfTpRzFs2Bbs2HHlsWMhIZH49dfjyJ/fJdsDMF59tT7OnRuOQYNq44cfDqN69TmYPduwMPqjR4no2HF56la79ly89tpWLF0aCgBo3LgMPD3zm+cfSDmSWU3bUQBHjF+jAJwDcN74+qjlQyN7MHRoPSxZ0hP58rnkuIw+ff7El1/uz/H1jRuXhouLE86evZvjMuzdpUv3UaXK7/j99xPpHndxcUJw8GB8910bvPFGA8ye3SXLX/b+/n74448eqQngk6ZOPYL69Rdg3rx/8fbbO9Gw4ULs3Jm9kW8pzp+/h2nTjmLYsHrw9S2TozLIfnh65segQbWxcOEp3LsXb/J1UVGx6NGjCnr0qGLB6GyDUgpLl/ZCzZrF0K/fOpw6ZaiVFBGMHbsTxYt74PPPW+ao7LJlC2LevGdx6NAgtGhRDjqdGMsGEhN1qdszzxTGqlW9MWxYPbP9uyiXMmo3TdkA/Aage5r3zwL4NavrbGVjnzbL0+v1ORpFJiLy4EGCODtPlo8+2pOrGKKj7X9x7tx47rk1UqDAdIss4XPyZKTs3HlFRAyjr1JG+G7dellGjNgqt249kjVrzkuVKrOfmnzVVL17r5aCBadLRASXJ3MUKQOZsru4t6NNqH3lSrSUKfOTVKz4i0REPJIVK0IF8Jeffz6mdWhkIcjlPG3NRWRTmiRvM4CcpfeUJw0fvhVNmvyRo2v3778OnU7g52fa/GwZKVw4X66ut2c7dlzBn3+ex8cfN0e5cuadG0lEMGzYFgwZshmbN1+Cj88ifPGFoVa0S5dKmD27C0qVKoA+farh9OlheOONBgCABQv+xfjxQYiOzri/TIojR25i3bqL+PTTFmwadSDe3p4IDn4Fw4fXz/LcU6duY+/ecABIndvMUVSsWBgbNz6PO3fiMWfOSXzxxX54e3tixAhvrUMjDZjy3X9DKfWJUqqScfsYwA1LB0b2o169krh48T6uX3+Y7Wt37w6Hi4sTWrYsl6sYTp6MQu/efzpcE2lysh5jx+5C5cpF8O67jc1evlIKU6b44dq1h+jefTWioxPQrFnZdM/Nl88ltbPzqVN3MGXKEdSoMQdz5pyETqfP8B6+vmWwZUs/jBmTdzuWU/oaNSoNpRQ2bbqExMT0+0SKCEaN2o7nn1+L2NgkK0doG3x8SiM4+BV89FEzbN36AhYt6u5wySsZmPK/PhCGudn+NG6ljPuIACC1lmz37vBsXxsUdA1NmpRBgQJuuYrB1dUJ69dfxP7913NVjr05cSIKly9HY8oUP7i757xfYWbatvXCxIltMGlSG5w5MwzPP18jy2t++KEdDh9+GdWrF8NrrxlqYtMbKBIfbxjA0rVr5Vz1iyT7debMHfTosRr168/Hxo0XnxoNuWrVOezeHY5vvmmN/PldNYpSezVqFIdSCuXLF4K3t6fW4ZBGTJny466IjBGRRsZtjIg4VnUGZapBA08UKZIPu3dnb+oPEUGrVuUwaFDtXMdQo0ZxFC2aDwcOONYIUh+f0rh06TX07VvNoveZMKEZPvywGTw8TP+j2bhxGezdOwCBgT0RFRWHO3ceX0rn9u1YVKnyGxYuPGXucMmO1K5dAhs3Pg+lFHr2/BPdu6/CmTN3AABxcUkYN243vL098dprWTejEuV1WX60VUrVADAOQKW054tIB8uFRfbE2dkJrVuXz/Z8bUop+Pv7mSUGJyeFpk3LOtS0HyEhkWjQwBOlStluPzClFAYMqIW+faul1gR+880/SEzU4caNGERGxqJxY64f6+i6d6+CTp2ewY8/HsOXX/6DVq0Cce3a65g27SiuXHmAnTv7szmQCCYkbQBWAPgFwO8AcjcRE+VZY8b44M6deIiIyfMG3bjxCKVK5U/tB5VbzZuXxTffHMCjR4koWDB3za227sKFe/D1XYTvv2+L999vonU4WUpJ2EQEly5FY968fwEAb7/dCHXrltQyNLIRbm7OePddX7z8ch0cPXoLBQq4oXhxd7z6aj20b19R6/CIbIIpSVuyiPxs8UjIrnXuXCnb17z00no4OysEBQ0wSwytW5dHixblEBkZm+eTtvXrL0KnE7z4Yk2tQ8kWpRTmzu2G4cPrY9myUHz5ZSutQyIb4+mZH926VQYAjBzZSONoiGyLKUnbeqXUSBgGIaSO32e/NnrSyZNRiIqKQ4cOWX8qjo1NwsGDEWYd8di5c6UcJY/2aPfucFStWhQVKxbWOpQcadWqPFq1Kq91GEREdsWUpG2I8ev4NPsEQN6fkpqy5cMP9yAsLBqnT7+a5bkHDkQgKUmPdu1yNz9bevR6yXAW/7xArxfs2ROO556z7OADIiKyLaaMHq2czsaEjZ7i5+eFM2fu4tatmCzPDQq6CicnhdatzVvb8sUX+1Ct2u8mLaJsr06ejMK9e/EWSXiJiMh2mdQDXClVTynVXyk1OGXLzU2VUkWVUiuVUqFKqTNKqRbGxem3KaXOG78WM57bTyl1Sim1VylVwrivqlJqWW5iIPNLSSL27Ml6vrbdu8Ph41PK7CsZlCzpgcuXoxEenv2Jfu1FjRrF8NdfL+DZZytrHQoREVlRlkmbUupzADONW3sAPwDoncv7BgDYIiK1ADQAcAbABAA7RKQ6gB3G9wDwNoAmAH4F8D/jvm8AfJLLGMjMfHxKoWBBV5Pma/vqq1b4+uvWZo8hZbb+vDz1h4eHKzp3rgRPz/xah0JERFZkSk3bCwA6ArgpIsNgSLKK5PSGSqkiANoCmAMAIpIoIvcB9AGwwHjaAgB9ja/1APIByA8gSSnVxhjL+ZzGQJbh6uqMVq3Km1TT1q6dV+oIMXNq0KAU8uVzzrOT7Or1gq+//genTt3WOhQiIrIyUwYixImIXimVrJQqDCASQG4601QGEAVgnlKqAYCjAMYAKC0iKX9pbwJImXFzIoDtMKx3+jIM88aZZ44IMruff+6EEiU8Mj1n586rEBF07PiM2e/v5uYMH5/SuappO3PmDipXLmKxZaFy49Sp2/jss33w8irE+c2IiByMKX+VjiiligL4DYYE6xGAf3J5Tx8Ab4vIQaVUAP5rCgUAiIgopcT4ehuAbQBg7Eu3CUANpdQ4APcAjBGR2LTXK6VeB/A6AFSsyEkZraly5aJZnvP11//gwYNEHD36ikViGDGiPqKjE3N0bVxcErp1W4lGjUpjzZq+5g3MDFKanlPWeyUiIsdhyujRkSJyX0R+AdAZwBBjM2lOhQMIF5GDxvcrYUjibimlygKA8Wtk2ouUUvkBDAXwI4AvYZiK5G8Ag9KJebaI+IqIr6cnF9a1tsmTD+PHH4+leywhIRkHDkSgXbsKFrv/sGH1MXZszuZ/mzz5CK5efYixY30QFHQVvXqtTl3U3Bbs3h2OihULoVKlHPdQICIiO2Xq6FFvpVRvGJKrakqp53N6QxG5CeCaUiplKveOAE4DWIf/5oQbAmDtE5eOBzBDRJIAeMAwV5wehr5uZEO2bg3D7Nkn0j126NBNxMcnW7ymKCoqFleuRGfrmmvXHmDixIN44YUa8POriMjIWGzYcAlDh26GXq/9FCIigt27r7GWjYjIQZmyYPxcAN4ATsGQJAGGhGl1Lu77NoDFSik3AJcADIMhgVyulBoO4AqA/mliKAegqYh8adw1E8BhAPfx34AFshF+fl745JO/kS/fNADAxx83w2eftURExCO0b78MSgFt2liupk1E4O29AF26PIMFC7qbfN2ECXuh1wv8/dsBAPr3r4WwsAf48MM9qFy5CCZObGupkE0SHv4QcXHJnJ+NiMhBmdKnrbmI1DHnTUUkBIBvOoc6ZnD+DQA90rxfAcOABLJBI0bUR0JCMpKSDDl+ixblAAAFCrhi/PgmqF27BIoVc7fY/ZVSaNasbLZGkMbFJeHSpfsYP77JY02P48c3waVL0Zg06RCqVCmKESO8LRGySby8CuPevbeRnKzP+mQiIspzVFYzxyul5gCYIiKnrROSefn6+sqRI0e0DoOsbOLEg/joo724c2cUihfPfDRrCr1ekJysh5ub82P7k5P16NVrNUqWzI+FC5+FUnl3iSwiItKWUuqoiKRXsWVSn7aFAP5RSp1VSp1QSp1USqXfYYnIRjRvbphk99Chm1meu2/fdURGxsDJST2VsAGAi4sTVq/ugwULtEvYRASdOi3HH3/Y5WcnIiIyA1OStjkAXgHQDUAvAD2NX4lslq9vGSiV9coIDx4koF+/tXjllc2Znufh4QonJ4XLl++jR49VVm+iPHv2LnbsuIq4ONsZyUpERNZlSp+2KBFZZ/FIiMyoUCE3LF/eCz4+pTM977vvDuLWrVh8/XUrk8o9cuQWNm26jKCga+jUyfyTA2dk927DKhMcOUpE5LhMSdqOKaWWAFgPICFlp4jkZvQokcW98ELNTI9fuHAP06YdxZAhddG0aVmTyuzZswoKFXLD0qWhVk3agoKuoWzZAqhWrajV7klERLbFlOZRDxiStS4wNIumNJES2bS7d+Mwf/6/OHfuLu7fj0/dUuZcGzduN9zcnDBxYhuTy/TwcEXfvtWwatU5JCRYp6ky7fxsHARBROS4sqxpy+XqB0SaiYiIwbBhW57af/nyCJQrVxAeHi74+OPmKFu2YLbKHTiwFhYtOo2tW8PQu3c1c4WboZiYJLRpUwG9elW1+L2IiMh2mTK5bg0AP8OwoHs9pZQ3gN4i8o3FoyPKhbp1S2LJkh64deuxpWlRvLg73NycERjYE1lNeZOeTp2eQfPmZZGYqDNXqJkqWNANy5Zx7A8RkaMzZZ623TAsIfWriDQy7vtXROpZIb5c4zxtZO/u3Yu36GTERERkO3I7T1t+ETn0xD7OO0AOLzFRh4iIRxa9R8qSXKNGbbfofYiIyPaZkrTdVkpVhWG9USilXgBg+vpARHmUr+8ivPnmNove49KlaISHP0S9eiUteh8iIrJ9piRtowD8CqCWUuo6gLEA3rRkUET2oFOnZ7B582XcuxdvsXvs3n0NANCuXQWL3YOIiOxDlkmbiFwSkU4APAHUEpHWInLF8qER2baBA2shKUmP1avPW+weQUHX4Onpgdq1S1jsHkREZB8yTdqUUjWVUlOUUhsBLAfwuXE0KZHD8/Utg6pVi2Lp0lCLlJ8yP1u7dpyfjYiIMknalFItAAQBeAhgNoDfAMQACFJKNbdKdEQ2TCmFgQNrYefOq7h1K8bs5YsAEye2xciRDc1eNhER2Z8Mp/xQSm0G8L2IBD2xvx2ACSLyrOXDyz1O+UGWdPXqA1y79hAtWpSDk1Pua8Pi45MxbdpR3LoVg+nTO5ghQiIisic5nfKj6pMJGwCIyG4AVcwUG5Fdq1ixMFq1Kp/rhE1EsHr1OdSpMw8ffbQX4eEPodPpzRQlERHlBZklbQ8zOWb+tiAiO3XlSjRGj96OsLDoHF1/4cI9dOy4HP36rUOBAq7Yvv1FrFzZB87OpgzuJiIiR5HZMlZeSqkZ6exXAMpbKB4iu6PXC378MQQVKhTChAnNsn29u7sLLly4j59+6oQRI7zh4sJkjYiInpZZn7YhmV0oIgssEpGZsU8bWUOLFosRF5eMkJBMf2wec/duHIoVc4dSCsnJeiZrRESUaZ+2DGva7CUpI7IFAwfWxpgxO3HmzB2T5lS7cycOLVoswXPPVcP337djwkZERFniXwoiM+jfvyacnBQWLz6T5bnx8cno23cNrl59gN69q1khOiIiygsy69NGRCYqU6YAOnV6BrVrF8/0PL1eMGzYFvz993UsXdoTrVqxeygREZmGSRuRmSxZ0gPFi7sDAH75JQRubs4YOrTeY9OBfPbZPixdGopJk9rgpZdqaRUqERHZoSybR5VSPyilCiulXJVSO5RSUUqpl60RHJE9KVHCA0opiAjWrLmA4cO3omnTP7Bv3/XUc5o3L4uxYxvjgw+aahgpERHZI1P6tHURkQcAegIIA1ANwHhLBkVkz5RS2Ly5H5Ys6YFbt2LRunUgBg7cgPj4ZPTsWRXTprXnWqJERJRtpiRtKU2oPQCsEJGczSBK5EAM65LWRmjoMHz+eQts2HARa9de0DosIiKyY6b0aduglAoFEAfgLaWUJ4B4y4ZFlDcUKOCGL75ohdGjG8HDg11IiYgo57L8KyIiE5RSPwCIFhGdUioWQB/Lh0aUd5QsmV/rEIiIyM6ZMhDhBIA3AVQCABGJEZGbFo6LiIiIiNIwpU9bLwDJAJYrpQ4rpcYppSpaOC4iIiIiSiPLpE1ErojIDyLSGMD/AHgDuGzxyIiIiIgolUk9o5VSzwB4ybjpAHxgyaCIiIiI6HFZJm1KqYMAXAEsB/CiiFyyeFRERERE9BhTatoGi8hZi0dCRERERBkypU8bEzYiIiIijZkyepSIiIiINMakjYiIiMgOmDK57otKqULG158opVYrpXwsHxoRERERpTClpu1TEXmolGoNoBOAOQB+tmxYRERERJSWKUmbzvi1B4DZIrIRgJvlQiIiIiKiJ5mStF1XSv0Kw8S6m5RS+Uy8jogA6PWC1avPITj4ltahEBGRHTMl+eoPYCuAriJyH0BxAOMtGRRRXnHwYARatlyCfv3WoX37Zfj33yitQyIiIjuVadKmlHIGECwiq0XkPACISISI/GWV6Ijs1PXrDzF48CY0b74YV648wIwZHVCggCt69FiNiIhHWodHRER2KNOkTUR0AM4qpSpaKR4iuxYXl4Rvvz2AGjXmYNmys/i//2uGc+eG4+23fbBhw/O4cycePXv+iUePErUOlYiI7Iwpy1gVA3BKKXUIQEzKThHpbbGoiOzQnj3XMGTIZoSFPcDzz1eHv387VKlSNPW4j09pLFvWE717r8HAgRuwZk1fODuzeygREZnGlKTtU4tHQWTnRARvvrkNALBjR3906JB+5XSPHlUxc2YHjBq1A2PG7MTMmR2hlLJmqEREZKeyTNpEZLdS6hkA1UVku1IqPwBny4dGZD9OnryNM2fu4qefOmWYsKUYObIRLl+OxuTJR1C1alG8+66vlaIkIiJ7ZsqKCCMArATwq3FXeQBrLBgTkd0JDDwDZ2eFF16oYdL533/fDv36Vcf77wdh9epzlg2OiIjyBFM61IwC0ArAAwAwjiItZcmgiOyJiGDp0lB07vwMPD3zm3SNk5PCokXd0axZWQwatAkbN160cJRERGTvTEnaEkQkdaibUsoFgFguJCL7cuBABMLCHmDgwNrZus7DwxXr1j2HypULo2fPP9G79584f/6ehaIkIiJ7Z0rStlsp9READ6VUZwArAKy3bFhE9mPp0lDky+eMvn2rZftaT8/8OHZsML7/vi127bqKunXnYfz4IERHJ1ggUiIismemJG0TAEQBOAngDQCbAHxiyaCI7IVOp8fy5WfRo0cVFC6cL0dl5Mvngg8+aIrz51/DK6/UwZQpR1Cjxhz8/vsJ6HR6M0dMRET2ypSkrQeAOSLyooi8ICK/iQibR4kABAVdw82bMRg4sFauyypTpgDmzOmGQ4deRrVqRTFixF9o0uQPXL/+0AyREhGRvTMlaXsJwHml1A9Kqdz/ZSLKQwIDQ1GwoCt69KhitjJ9fcvg778HYsmSHggJicTcuf+arWwiIrJfWSZtIvIygEYALgKYr5T6Ryn1ulKqkMWjI7JhCQnJWLXqHPr2rQ4PD1ezlq2UwsCBteHt7Yndu6+ZtWwiIrJPJq2hIyIPYJirbSmAsgCeAxCslHrbgrER2bS//rqC+/cTzNI0mhE/Py/s338DiYk6i92DiIjsgymT6/ZWSv0JIAiAK4CmIvIsgAYA3rdseES2KzDwDEqU8EDnzs9Y7B7t2nkhLi4Zhw/ftNg9iIjIPphS09YPwDQRqS8i/iISCQAiEgtguEWjI7JRMTGJWLv2Al54oQZcXS23qlubNuUBGAY8EBGRYzOlT9sQEdmTwbEd5g+JyPatX38JsbHJGDCgpkXvU7JkftSvX5L92oiIyKTm0eZKqcNKqUdKqUSllE4p9cAawRHZqsDAMyhXriDatKlg8Xu1a+eFffuuIymJ/dqIiByZiwnnzAIwAIaVEHwBDAZg2qrYRBpKTNRhxIitiIyMTfd48eIemDrVD6VLF8hWuffuxWPz5ssYPboRnJ1NGsuTK35+Xpg16xiOHLmFFi3KWfx+RERkm0wdPXoBgLOI6ERkHoBulg2LKPcOH76JhQtP48qVB7h7N/6pbcWKs/jgg93ZLvfPP88jKUlv0VGjabVta6jNYxMpEZFjM6WmLVYp5QYgRCn1A4AImJjsEWnpwIEbAIBdu15Ktzbt//5vDyZNOoSRIxuhWbOyJpcbGBiKqlWLwte3jNlizYynZ37UqVMCQUHXMGFCM6vck4iIbI8pydfLxvNGA4gB4AXDiFIim3bwYAQqVSqcYfPnRx81R5kyBTBmzE7o9aatzHbzZgx27ryKAQNqQSllznAz5efHfm1ERI4uw6RNKVVdKbUWhgXi5wEoJCJfish7xuZSIpt24EAEmjfPuA9YoUJumDSpDQ4ejMDixadNKnPBgn+h14vVmkZTtGtXAY8eJSE4ONKq9yUiItuRWU3bXAAbYKhVCwYw0yoREZnBjRuPcO3awyybPV95pS6aNCmDDz/cg0ePEjM9d8eOK/jkk33o1q0S6tYtac5ws9SunRcA9msjInJkmSVthUTkNxE5KyL+ACpZKSaiXDt4MAIA0Lx55kmbk5NCQEAHRETEYOLEgxmed+rUbfTrtw41axbD0qW9zBqrKUqXLoBatYpzkl0iIgeWWdLmrpRqpJTyUUr5APB44j2RzTp4MAKurk5o2LBUlue2aFEOL79cB1OmHMHly/efOn7zZgy6d18FDw8XbNrUD0WK5LNAxFlr184Lf/99HcnJek3uT0RE2sps9GgEgKlp3t9M814AdLBUUGTfjh27hTfe2IakpPSTi7p1S2DRou4W7ch/4MANNGpUCu7upgyQBiZNaoPVq89h/PjdWLmyT+r+mJhE9Oy5GnfuxGPPngGoWLGwpULOkp+fF3799ThCQiKtNnKViIhsR4Y1bSLSPpMt1wmbUspZKXVMKbXB+L6yUuqgUuqCUmqZcZoRKKXeVkr9q5TalGZfa6XUtNzGQJbxxx+ncfx4FCpWLPTU5uyssHjxGYSFRVvs/snJehw5citb03iUL18IH33UHKtWnceuXVcBADqdHgMHbsSxY5FYurQnfHxKWypkk7RrZ5ivjU2kRESOScv51sYAOJPm/fcwLExfDcA9/LcY/SAA3gD2A+iqDNUznwL42oqxUjbs3h2OFi3KYu3a557a5s/vlnqOpZw6dRsxMUmZjhxNz3vvNUalSoUxduwuJCfr8d57QVi//iJmzuyAnj2rWiha05UtWxA1ahTjYAQiIgelSdKmlKoAoAeA343vFQzNrSuNpywA0DfldACuAPIDSIJh3rjNInLXiiGTiaKjE3DsWCT8/LzSPV6nTkmULOlh0dqilEEI2alpAwAPD1dMnuyHEyei0K3bSsyYEYz33/fFyJGNLBFmjrRr54U9e8Kh07FfGxGRo9Gqpm06gA8ApPzlKQHgvogkG9+HAyhvfD0LwAEAFQHsAzAMwI9Wi5Sy5e+/w6HXS+oUFU9yclJo27aCRWuLDhyIQMmSHqhSpUi2r33++erw8/PCjh1X0a9fdfzwQzsLRJhz7dpVwIMHiTh+PErrUIiIyMqyTNqUwctKqc+M7ysqpZrm9IZKqZ4AIkXkqCnni8giEWkkIi8DeBfADADPKqVWKqWmKaWe+jcopV5XSh1RSh2JiuIfN2vavTscbm7OmU614efnhbCwB7hyxTL92g4ejECzZmVzNNBBKYU5c7rik0+aY9Gi7nByst6qB6ZISYbZr42IyPGYUtP2E4AWAAYa3z9E7mq6WgHorZQKA7AUhmbRAABFlVIpQ/0qALie9iKlVDkATUVkDYD3AbwE4D6Ajk/eQERmi4iviPh6enrmIlTKrqCga2jWrCw8PFwzPOe/iWLN368tOjoBZ87cyXJ+tsxUqVIUX3/dOtN/g1YqVCiEqlWLsl8bEZEDMiVpayYiowDEA4CI3APgltMbisj/iUgFEakEYACAnSIyCMAuAC8YTxsCYO0Tl34N4DPjaw8Yph3Rw9DXjWzAgwcJCA6+lTrKMSP16pVE8eLuFkk8Dh++CZHs92ezJ35+Xti797rJ66USEVHeYErSlqSUcoYhSYJSyhP/9UUzpw8BvKeUugBDH7c5KQeUUo0AQESCjbuWADgJQ63dFgvEQjmwb9916HSS4SCEFCn92izRxHfwYASUApo2zbtJW7t2FXDvXjxOnGDTPxGRIzElaZsB4E8ApZRS3wL4G8BEc9xcRIJEpKfx9SURaSoi1UTkRRFJSHPeMREZnub9dBGpKyLd0p5H2tq9Oxyurk5o0SLrqTbatfPCpUvRuHbtgVljOHDgBmrVKq7ZqgXWwHVIiYgcU5ZJm4gshmGk50QYVknoKyLLLR0Y2Z+goGto0qQM8ufPui9YSm2cOfu1iQgOHozI9vxs9qZixcKoXLkIByMQETkYU0aPLhKRUBH5UURmicgZpdQiawRH9uPRo0QcOXIzy6bRFPXrl0TRovnMWlt0+XI0oqLi8nR/thTt2lXAnj3h7NdGRORATGkerZv2jbF/W2PLhEP2av/+G9DpMp6f7UnOzk7G+drMV9OWMqlubkaO2gs/Py/cvRuPQ4citA4l22JiEvHpp3+jRo05OHXqttbhkA1KTNRh8uTDqF79d+zda7nVU4jsTYZJm1Lq/5RSDwF4K6UeKKUeGt9H4umRneTggoKuwcXFCS1bmt402a6dF86fv4cbNx6ZJYYDByKQP78L6tYtaZbybFmfPtVQooQHJkzYAxH7qG3T6wV//HEaNWvOxTffHMDVqw8wZsxOu4mfLE9EsH79RdSrNx/jx+9GePgjjBq1HcnJXAGECMh8wfiJIlIIgL+IFBaRQsathIj8nxVjJDuwe/c1+PqWRsGCps8G81+/NvM0kR48GIEmTcrAxUXLJXWto2hRd3zzTSvs3h2OVavOaR1Olg4dikCrVkvwyiubULZsAezbNxCTJ7fDjh1XsW7dRa3DIxtw+vRtdOu2Cr17/wlnZ4XNm/th8eLuOHnyNn777YTW4RHZBFP+um1WSrV9crN4ZGQ3YmIScejQTZObRlM0aOCJIkXymaVDfUJCMo4di3SI/mwpRozwhre3J8aN2424uCStw0nXjRuPMGTIJjRrthhhYQ8wb143HDz4Mlq2LI8332yIunVL4L33diE+PjnrwihPuns3Du+8swPe3gtw6FAEpk9vjxMnhqBbt8p47rnqaN/eC5988jfu3o3TOlQizZmStI1Ps30KYD2ALywYE9mZf/6JQHKy3uRBCCmcnZ3QunV5s/RrCwmJQmKiLs+PHE3L2dkJ06e3x5UrDzBlyhGL3Uen06N9+2Xw81uKo0dvmnRNfHwyJk48iBo15mDp0rOYMKEpzp0bjqFD66UuDebi4oTp0zvg0qVoTJ9u0qp2lMc8eJCA+vUX4McfQ/D66944f344xoxpDFdXZwCGZeWmT++A+/cT8MUX+zWOVlt6vWDEiK1o334Zm4sdmClTfvRKs3UGUA/APcuHRvYiKOganJ0VWrUqn+1r/fy8cPbsXURE5K5f24EDNwDk7ZUQ0tO+fUX061cdEycexPXrDy1yjzlzTiIo6BqCgyPRpMkfeO21rbh1Kybdc0UEq1efQ5068/DRR3vRqdMzOH16GCZObItChZ5uOu/U6Rn06VMN33xzwGx9G8l+zJv3L27ceIRt217ETz91RsmSTy9w4+3tiTfe8MZPP4U49MCVTz75G7//bvhZZHOx48pJ559wALXNHQjZr927r6Fx49Lp/lHOSsqSV3v25K627eDBCHh5FUK5cgVzVY498vdvB51OMGHCXrOXff9+PD7++G+0aVMB1669gfff98XChadQvfoc+PsfQkLCf82aJ05EoWPH5ejXbx0KFHDF9u0vYs2avqhatWim95g8uR2SkvT46CPzx0+2S6fTY+bMY2jRohw6dKiY6blffdUKhQq54d13dznkwJXffjuBiRMP4vXXveHn54VPP93H5mIHZco8bTOVUjOM2ywAewEEZ3UdOYbY2KQc9WdL0aiRIdnLbb+2AwciHK6WLUXlykXx/vu++OOP0/jnnxtZnp+dP3pfffUP7tyJQ0BAexQpkg/+/n44dWoY/Py88MEHe1C37nwsWxaKt97ahkaNFuL48Sj8+GNHHDs2GB07PmPSPapVK4Z3322MBQtO2eUUJpQzmzZdxsWL9zFmjE+W55YsmR9fftkK27Zdwfr1jjVwZevWy3jrrW3o1q0SfvyxE6ZPb4979+Lx5Zf/mKX8pCQdYmIS093Sfigj26Cy+gWulBqS5m0ygDAR2WfRqMzI19dXjhyxXH8fR7dz51V07LgcGzY8hx49quaojO7dVyEsLBqnT7+ao+sjI2NQuvTPmDy5Hd5/v0mOyrB3jx4lokaNOahQoRAOHBiU2m8sreDgW3j33V24evUB9u4diAoVCmVaZmjoHdSvvwDDhtXD7Nldnjq+detlvPvuLpw5cxfOzgqjRjXC55+3QPHiHtmO/+FDQ/zPPFMY+/f/L934KW/p1Gk5QkPv4vLlEal92DKTlKRDgwYLkJiox6lTQ5Evn4sVotTW8eORaNNmKapUKYK9ewemtma89dY2/PbbCZw4MQR16uRsiqO4uCRMnXoUEyceRExM+gOZnJwUXn21Hr79tjVKlSqQ438HZY9S6qiI+KZ3LMvvehFZoJRyA1ALhkXjz5o5PrJju3dfg5OTQuvWFXJcRrt2FbB582VERsbk6BfDwYOGzvGOWtMGAAULuuH779ti8ODNWLToFIYMqZd67NatGHz88d+YO/ckSpb0QHy8Dj16rMbevQNQuHDGa7S+914Q8ud3wTfftEr3eNeulXH8eEWsX38RtWuXQO3aJXIcf6FCbpg4sQ2GDduCJUvO4OWX6+S4LLJ9//4bhR07ruK779qYlLABgKurM6ZNa49u3VYhICAYH3zQ1MJRauv69Yfo0WM1Chd2w8aNzz/W/eSrr1ph6dJQjB27C1u3vgClTP+QIyJYteocxo/fjbCwB+jbt1qG82tevhyN3347ieXLz+Kzz1rg7bd94OZm2v8XWYiIZLoB6A7gGoAgALsBXAXwbFbX2crWuHFjIctp2zZQGjdemKsyDhy4IYC/LF8emqPrP/54rzg7T5aYmMRcxWHvdDq9NGv2h5Qp85M8eJAg8fFJ8sMPB6VQoQBxcZki77+/S+7fj5etWy+Ls/Nk6dp1hSQmJqdb1saNFwXwlylTDls1fl/fhVKu3M/y8GGC1e5L1jdixFZxd58mt2/HZvvanj1XScGC0yUi4pEFIrMNDx4kSIMG86VQoQA5fjwy3XOmTz8igL+sW3fB5HKPHbsl7doFCuAv9evPk507r2R5TWjoHenefaUA/lK9+u+yfv0F0ev1Jt+Tsg/AEckoJ8voQOoJQCiAamneVwUQmtV1trIxabOcuLgkyZdvqrz33s5clZOYmCwFCkyXUaO25ej6Tp2Wi49P7hLHvCIlAX7uuTVSrdpvAvhLjx6rJDT0zmPn/fbbcQH85bXXtjz1CzghIVlq1PhdatT4XRIS0k/qLGX//usC+MvHH++16n3JNDqdXpKTdbkq4/btWHF3nyavvbYlR9efPXtHXF2nyLBhm3MVh61KStJJt24rxNl5smzZcinD8xITk6VWrTlSrdpvEh+flGmZkZEx8vrrW8XJabKUKDFLfv75mCQlZe//cdOmi1Kz5hwB/KVLlxVy9OhNuXnzUbpbbKztf4DO6ANrZm7ceCj378dbIJrH5TZpO/zEe/XkPlvemLRZzq5dV7L9SS8jXbuukLp152b7unPn7kr+/NNk5MicJXx50eDBGwXwl1q15sjmzRn/0v/ooz0C+MvEiQce2z9lymEB/GXjxouWDjVdgwZtkHz5psqBAzc0uT9lbMyYHVKs2EyZMeNojv7oiYhMnHhAAH85cSL9GiRTjBu3SwB/2bAh9797bMmRIxHSsuViAfxl9uzjWZ6/ZcslAfzlhx8Opns8ISFZpk49LEWKzBAXlykyduxOuXs3LsfxJSYmy7RpR6RIkRkC+Ge4lSr1o80mbmfP3pEePVZJ/vzT5N9/o7J1bZ8+f0rVqr9lO+HNrsyStgwHIiilnje+7AzgGQDLYejT9iKAqyIy0swttRbBgQiW8+WX+/Hll/tx9+5oFC3qnquyJk48iI8+2ovIyJHw9Hx6rqb0REXFokWLJXjwIAEHDgxClSpFcxVDXvHwYSJ27LiCHj2qZNpfSK8XvPzyRgQGhiIwsCcGDKiFyMgYVK8+B61alcemTf2sGPV/oqJi0bz5Yjx8mMj/Vxty+3YsvLxmo0ABV9y5E4c6dUpg+vT26Ny5ksllJCXpUKXK76hRoxh27Oif41hiYhLRtu0ynD17F3v3DkCjRqVzXJYtuHkzBh99tBfz5/8LT8/8+OGHto/1S81Mr16rsXt3OM6dG44yZf7rE7xp0yW8++4unDt3D926VcLUqe1z1e80raioWKxdewFJSU9P8nvt2kNMnHgQK1b0wgsv1DTL/czh/v14fP31P5gx4xg8PAzd+Zs3L2tyn8Bt28LQpctKTJrUBh9+2MyisWY2ECGzGrZ5mWxzM7rO1jbWtFmOn99SadRogVnKSmkWW7nyrEnnx8YmSosWi8XdfZr88891s8TgiOLjk6RNm0Bxc5sqe/dekxEjtoqLyxQ5c+a2pnGFht6RYsVmSs2ac+TOnez3eyLz++47Qw3ZyZORsmbNeala1dD83qvXajl37q5JZSxbdkYAf1m79nyu47lx46F4ef0iZcv+JFevRue6PC3ExyfJ998b+p26uk6RceN2Zbv5LaW5+NVXDc3Fp0/flm7dVgjgLzVq/G71GvPkZJ2UKfOTPP/8GqveNyPJyTqZPfu4eHrOEqX8ZfjwLXLz5iMJCDhq8vdiUpJO6tSZK1WrZt0UbQ7ITfOovW9M2iwjPj5J3N2nydixuevPliIxMVny558mb7+9PctzdTq9vPjiWlHK9CSPMnb7dqzUqPG7FC06Q5Tyl3ffNc//aW7t3n1V3NymSrt2gRb9RRkTk8iO1VlITEyW8uV/lo4dl6XuS0k4ChacbnLC0bLlYqlSZXau+8WlOHEiUgoXDpD69edJdLTl+xqZi16vz3Him55x43aJUv4yePBGcXGZIkWKzJCpUw9bvU9qinfe2SH58k21Sv+vzOzefVUaNlwggL+0br1Ejh69mXosMTFZateeY1IiNnOmIcFbsyb3HzZMkaOkDcAHxq8zAcx4csvoOlvbmLRZxp9/njP7N3HXriukUKEACQjIvL/MBx8EWX1kY1534cI9KVlylpQsOUvu3ct5nxdzW7z4tAD+MmjQBoskVocPR0ihQgEydOgmJm6ZWLr0TIb9VyMiHsmwYZtFKUNfpt9/P5FuUnb4cIQA/jJt2hGzxrZtW5i4uEyRLl0yHg1tS06ejJSOHZcJ4C916syVrVsv57rM+/fjpVSpH0Upf3njjb8kMjIm94HmQkrLyYIF/2oWw9y5JwTwFy+vX2Tp0jPp/nyn9An8/vv0+wSKGD7UFis2Uzp1Wm613xE5Tdp6Gb8OSW/L6Dpb25i0mV9CQrJUr/671Kw5x6yf5C5evCedOy8XwF9q156T7sipn38+JoC/jBq1jX9kzezKlehcfdq3lG+//UcAf/n0U/OOKA0Luy+lS/8oBQpMF8Bfvvhin1nLz0tatFgsVav+Jjpdxj9zhw//14nex2eh7N177bHjL7+8UQoWnG6R2pc5c05kOBraVty+HSujRm0TJ6fJuR7MkZ7Q0Dty+rS23RpS6PV6qVTpV+nWbYUm909J5Dt1Wp7lVFC9eq3OdAqZUaO2ibPzZDl5MucDZ7Irx82jAJwBTM7sHFvfmLSZ3+TJhwTwl02bzN9XQq/Xy9q1/zUb9Oy5Ss6eNUxXsXHjRXFymiw9e66y+Ogdsh16vV6GD98igL/Mm3fSLGXeuxcnderMlSJFZsipU1EyZMgmAfxl/nzzlJ+XHDpkmEZm+vSsa8j0er0sWXJaKlT4RQB/eemldXLlSrTcuPFQXF2nmNT9Iac++WSvAP7y3XcHsj7ZihITk2XGjKNSrNhMcXKaLKNGbcvR/HT2ZsKE3eLsPNnqtX4nTxqazOvVm2fSB4Rz5+5mOIXMiROR4uQ0WUaPttz3bXpyWtPmYvz6T0bn2MPGpM28bt58JIULB0j37istep+0E8O6uk6RN974SwoUmC4+Pgs58aoDSkxMls6dl4uLyxTZvj0sV2UlJCRLhw7LxNV1iuzYceWxfS4u/+3LqaQknURFads8ZU6DBm2QQoUCstVn7NGjBPn887/F3X2auLtPk5YtF4tS/hatydXr9fK//20QwF+WLDltsfs86erVaDl+PDLdbe3a81KnzlwB/KVjx2W5mubE3hw/HimAv/z00zGr3TPt4JQrV0wfnDJ+vKHLzeHDEan79Hq9dOiwTIoXn2n1wVA5TdqCjV9/BrAOwCsAnk/ZMrrO1jYmbeb12mtbxMVlylOTtVpKRMQjefVVQ38ZL69f5MaNh1a5L9me+/fjpV69eVK4cECOmyr0er0MHbop3f429+7FSd26/9W+5cT27WFSr948cXefZrWfEUtKqSEbM2ZHjq6/ciVaBgxYn1prbmnx8UnStm2gODlNtnjfrrS/mzKbs6xKldmyZs15m222tRS9Xi+1a8+RNm0CrXK/hw8TxMdnoRQoMP2xAQemiI429Als2XJx6v9TSr/tWbOCLRFupjJL2jKbpy1YRHyUUvPSzhACw+S6IiI5W93byjhPm/kEB9+Cr+8ivPtuY0yZ0t6q9z5z5g6KFMmHcuUKWvW+ZFuuXXuAZs0Ww9XVCQcODELZstn7fvj663/w2Wf78MUXLfH55y2fOn7lSjSaN1+CfPmcceDAoMfmvcrMxYv3MW5cENasuYBKlQrj7t14tG5dHhs3ajPXnbl89tnf+OabAzh//jVUrVo0x+X8+28UypUriOLFPcwXXAYePEjAZ5/tw6xZx1CwoBs+/7wFRo1qZLY1MxMSkjFjRjC+/voA4uOT8fbbjdCqVfl0z82XzxmdOj3jEIvbpyfl5+3atTdQoUIhi91Hp9Ojb9812LTpMtat64sePapmu4y5c09i+PCtWLKkB557rjrq1p0HDw8XhIQMgYuLkwWizlhO52kLB/AegPfTfE3Z3svoOlvbWNNmHnq9Xlq3XiKenrY1upAcz9GjN3PUVL5o0SkBDNMiZFbrceRIhOTPP018fRfKo0eZl//gQYJMmLBb3NymSoEC0+W77w5IXFyS5qtKmENcXJJ4es6SXr1Wax1Kjpw6FSVduxrmK6tZc06u++Dq9XpZt+5C6vJwafvbUvrOn78rgL9MnnzIYvfQ6/UyatQ2Afzlxx9zXium0+mlceOFUqHCL6n9I3PbFSOnkMOatggYmkbTmypYROSrXCaTVsGaNvNYtiwUAwZswOzZXTBihLfW4ZCD27jxInr3XoPu3StjzZq+cHbO/JPwrl1X0bXrSrRuXR5btryQZa3L+vUX0bfvGvToUQUTJjRN95zTp+/g00/34ebNGAweXAcTJ7ZNrQlOTNShfv35UErhxIkhZqvlsab58//FsGFbsH37i+jY8Rmtw8kRETGuDBCE8+fvoXv3ynjvPd/UGfFNFRubjB9+OIRt266gdu3imDatPbp2rWyhqPOWJk0WQQQ4cuQVi5Q/deoRvP9+EMaN84W/v1+uytq37zpatw4EAPTtWw1//tk31/HlRGY1bVk2j1o0Mitg0pZ7sbFJqFVrLkqU8MCRIy9n+QeSyBp++ukYRo3agVGjGmLmzI7pLkWTdumaGjWKYd++gSYvufbjj8cwevSOTM9p3rwsAgI6oGnTsk8d27TpEnr0WI2pU/3w7rvpt3TYKhGBj88iJCfrceLEEJOW+bFliYk6zJp1DF9+uR8PHiTmqIyiRfPhyy9b4a23GmS6PBw9bsqUwxg3bjfOnRuO6tWLma3cS5fuY/z43Vi9+jz69auO5ct7w8kp99+ngwZtxKpV53Dq1LBcdQnIjZwmbcdEpJFFI7MCJm259+WX+/HFF/uxe/dLaNvWS+twiFKNHx+EyZOPPJUY6XR6zJlzEp988jdu347Dq6/Wx8SJbUxe1zZFcPAt3L4dl+6xAgVc0aJFuUz/UHTvvgr799/AuXOvolQp0/rH2YI9e66hXbtlea5m/fbtWAQHR+boWl/f0lbpk5fXhIc/RMWKv+KLL1ris8+e7keaXY8eJWLixIOYMuUInJ0VPvqoOcaN8zVbv8GkJB1u3oyBl1dhs5SXEzlN2oqLyF2LRmYFTNpy5+rVB6hVay569qyC5ct7ax0O0WP0ekH//uuwevV5rFzZG88/XwO7d1/DmDE7cfx4FFq3Lo+AgA7w8dFmQfHQ0DuoX38BXn21Hn79tYsmMeREv35rERR0DdeuvYH8+V21DofsXLt2SxEZGYvTp4fluNZWrxf88cdpTJiwBxERMXjllTqYOLENype33AAHrWSWtGWYmuaFhI1y78MP90AE+OGHdlqHQvQUJyeFRYu64/r15Rg0aBM6dz6F9esvwsurEJYt64kXX6ypadNerVol8PbbjTB9+lG89VZDNGxYyuz3OHPmDq5de2i28h4+TMSaNRfwwQdNmLCRWQwcWAtvvbUdJ05EoUGD9H8GwsKice7cvXSPPXqUiO+/P4RDh26iadMyWL26D5o3L2fJkG1WhjVteQVr2nJu4cJTGDJkMz79tDm++qq11uEQZSgqKhYtWizBjRuPMGFCU4wbZzsJx/378ahefQ7q1CmBoKCXzJpErlt3Ac89txZ6vXl/j7u5OePCheGaNhFR3hEVFYuyZX/GuHFNMGlS28eO3b8fjy+//AezZh1DcrI+wzLKli2ASZPa4uWX65il75oty1HzaF7BpC1ndu40jLZr27YCNm/uZ5ej38ix3L8fj6Qkfbb7rVnDr78ex5tvbsPy5b3w4os1zVLmkSM30a7dUtStWxLTprWHOSsUS5XKj2rVzNdpnOjZZ1fizJm7uHx5BJRS0On0+P13Q7/TO3fiMHx4fQwZUjfdhEwpBW/vkihQwE2DyK2PSRuTtmw5ffo2WrYMRPnyBbM12o6I0qfT6eHjswj378cjNPRVeHjkrhbwypVoNGu2GB4eLjhwYBBKl7afQQ7kmFJabvbtG4iEBB3Gjt2FEyei0LZtBUyf3h6NGmnT79QW5ahPGzmmmzdj0L37anh4uGDTpueZsBGZgbOzEwIC2qN9++V4770gdO6c/rxn9eqVRI0axTMt6/79eHTvvhoJCTrs2vUSEzayC337VoO7uwtefHE9btx4hIoVC2H58l544YUadj+ljDUxaaNUMTGJ6NVrNaKiYrF79wA880wRrUMiyjP8/Crif/+rjV9+OY5ffjme7jlOTgpvvtkAX33VCiVKPD29RGKiDs8/vxbnz9/D1q0voHbtEpYOm8gsChfOh379quPPP8/j669b4f33fXNd4+yI2DxKAAzNN88/vxYbNlzCmjV90atX9tduI6LM6fWCU6duI71fuzqdHnPn/ouffw5B4cL58MUXLR+byFVEMHToZixceBoLFz6LV16pa+XoiXInPj4ZiYk6FC6cT+tQbBr7tDFpy9KYMTsxY0YwZs7sgNGj7X4hDCK79e+/UXj33SBs3/74kkkpk1xntNg9EeUNTNqYtGUqIOAoxo7dhXffbYypU9trHQ6RwxMRrF9/Ee+9F4SLF++jRYty+OefGxg8uA7mz3+WfYCI8jAmbUzaMhQR8QjPPDMbzz5bGatX9+G6okQ2JCEhGTNmBOPrrw+gadMy2LSJ0+8Q5XUcPUoZ+vnnECQn6zFlih8TNiIbky+fC8aPb4qRIxsiXz4XuLjwZ5TIkTFpc2Dx8cn45Zfj6NGjCifSJLJhjjKpKBFljh/bHNjSpaGIiorDmDGNtQ6FiIiIssCkzUGJCAICglG3bgl07FhR63CIiIgoC0zaHNTeveEICYnEO+/4cCQaERGRHWDS5qACAoJRvLg7Xn65jtahEBERkQmYtDmgsLBorFlzASNGeCN/fi4jQkREZA+YtDmgn34KgVLAqFENtQ6FiIiITMSkzcHExCTit99O4Pnnq8PLq7DW4RAREZGJmLQ5mEWLTuP+/QRO80FERGRnmLQ5EBHBjBnBaNy4NFq2LKd1OERERJQNXBHBgWzbdgVnztzFwoVccJqIiMjesKbNgQQEHEXp0vnRv39NrUMhIiKibGLS5iDOnbuLTZsu4623DAtPExERkX1h0uYgZs48BldXJ7z5ZgOtQyEiIqIcYNLmAC5duo/ffjuBl1+ug9KlC2gdDhEREeUAkzYHMH78bri4OOHrr1tpHQoRERHlEJO2PG7nzqtYvfo8PvqoGcqXL6R1OERERJRDTNrysORkPcaO3YlKlQrjvfd8tQ6HiIiIcoHDCPOw3347gZMnb2PVqt5wd+d/NRERkT1jTVsedfduHD79dB/at/fCc89V1zocIiIiyiUmbXnUl1/+g3v34jF9egeufkBERJQHMGnLg06fvo0ffzyGN97whre3p9bhEBERkRkwactjRARjx+5CoUJu+OorTvFBRESUV7B3eh6zYcMlbNt2BQEBHVCyZH6twyEiIiIzYU1bHpKQkIz33tuF2rWL4623uFwVERFRXsKatjxk1qxjuHDhPrZs6QdXV2etwyEiIiIzYk1bHiEi+OmnEPj5eaFr18pah0NERERmxqQtjzh8+CYuXYrGK6/U0ToUIiIisgAmbXnE0qWhcHNzxvPPcyJdIiKivIhJWx6g0+mxbNlZPPtsZRQt6q51OERERGQBVk/alFJeSqldSqnTSqlTSqkxxv3FlVLblFLnjV+LGff3M563VylVwrivqlJqmbVjt1V794bjxo1HGDiwltahEBERkYVoUdOWDOB9EakDoDmAUUqpOgAmANghItUB7DC+B4C3ATQB8CuA/xn3fQPgE6tGbcMCA0ORP78LevasonUoREREZCFWT9pEJEJEgo2vHwI4A6A8gD4AFhhPWwCgr/G1HkA+APkBJCml2gC4KSLnrRm3rUpK0mHlynPo06caChRw0zocIiIishBN52lTSlUC0AjAQQClRSTCeOgmgNLG1xMBbAdwA8DLAFYAGGDdSG3Xtm1XcPduPAYOrK11KERERGRBmg1EUEoVBLAKwFgReZD2mIgIADG+3iYijUWkFwy1cZsA1FBKrVRK/aaUemqtJqXU60qpI0qpI1FRUZb/x2goMDAURYvmQ9eulbQOhYiIiCxIk6RNKeUKQ8K2WERWG3ffUkqVNR4vCyDyiWvyAxgK4EcAXwIYAuBvAIOeLF9EZouIr4j4enp6WuzfobXY2CSsWXMe/frVgJsbV0AgIiLKy7QYPaoAzAFwRkSmpjm0DoZEDMava5+4dDyAGSKSBMADhpo4PQx93RzSxo2X8OhREkeNEhEROQAt+rS1AvAKgJNKqRDjvo8ATAKwXCk1HMAVAP1TLlBKlQPQVES+NO6aCeAwgPv4b8CCw1m6NBRlyhSAn5+X1qEQERGRhVk9aRORvwGoDA53zOCaGwB6pHm/AoYBCQ4rOjoBGzdewhtvNICzM+dIJiIiyuv4195OrVlzHgkJOgwYwKZRIiIiR8CkzU4FBoaiUqXCaN68rNahEBERkRUwabNDUVGx2L79CgYMqAXDuA4iIiLK65i02aGVK89BpxNOqEtERORAmLTZocDAUNSpUwL165fUOhQiIiKyEiZtdubatQfYuzecTaNEREQOhkmbnQkMDAUATqhLRETkYJi02RGdTo+ffw5BmzYVUK1aMa3DISIiIiti0mZH1q+/iLCwBxgzxkfrUIiIiMjKmLTZkYCAYFSsWAh9+lTTOhQiIiKyMiZtduLEiSgEBV3D6NGN4OLC/zYiIiJHw7/+diIg4Cjy53fBa695ax0KERERaYBJmx2IiorF4sVnMHhwXRQr5q51OERERKQBJm12YPbsE0hI0OGddzgAgYiIyFExabNxSUk6/PRTCLp0qYTatUtoHQ4RERFphEmbjVu58hxu3HjEaT6IiIgcHJM2GxcQEIwaNYqhW7fKWodCREREGmLSZsMOHozAwYMRePvtRnBy4jqjREREjoxJmw0LCDiKwoXdMGRIPa1DISIiIo0xabNR168/xIoV5/Dqq/VRqJCb1uEQERGRxpi02aiffz4OnU6Pt99upHUoREREZAOYtNmg+Phk/PrrcfTqVRVVqhTVOhwiIiKyAUzabNCcOSdx+3YcxoxprHUoREREZCOYtNmYu3fj8Pnn++Hn54X27b20DoeIiIhsBJM2G/Pll//g3r14BAR0gFKc5oOIiIgMmLTZkNOnb+PHH4/hjTe84e3tqXU4REREZEOYtNkIEcHYsbtQqJAbvvqqldbhEBERkY1x0ToAMtiw4RK2bbuCgIAOKFkyv9bhEBERkY1hTZsNSEhIxnvv7ULt2sXx1lsNtA6HiIiIbBBr2mzAjBnBuHDhPrZs6QdXV2etwyEiIiIbxJo2jd28GYOvvz6Anj2roGvXylqHQ0RERDaKSZvGPv54L+LjkzFlip/WoRAREZENY9KmoSNHbmLevH8xZowPatQornU4REREZMOYtGlERDBmzE54eubHJ5+00DocIiIisnEciKCR7duvYP/+G5g9uwuKFMmndThERERk41jTppElS86gcGE3vPJKHa1DISIiIjvApE0DCQnJWL36PJ5/vjrc3VnZSURERFlj0qaBzZsv48GDRAwcWFvrUIiIiMhOMGnTQGBgKDw9PdChQ0WtQyEiIiI7waTNyh49SsT69Rfx4os14eLCx09ERESmYdZgZevWXURcXDIGDqyldShERERkR5i0WVlg4Bl4eRVCy5bltQ6FiIiI7AiTNiu6ezcOW7eG4aWXasLJSWkdDhEREdkRJm1WtGrVeSQl6TlqlIiIiLKNSZsVBQaeQY0axdCoUSmtQyEiIiI7w6TNSiIiHiEo6BoGDKgFpdg0SkRERNnDpM1Kli8/CxFw1CgRERHlCJM2KwkMDEXDhqVQq1YJrUMhIiIiO8SkzQouXbqPgwcjWMtGREREOcakzQqWLg0FALz0Uk2NIyEiIiJ7xaTNCpYuDUXLluXwzDNFtA6FiIiI7BSTNgs7deo2Tp68zaZRIiIiyhUmbRYWGBgKJyeFF19k0ygRERHlHJM2CxIRBAaeQYcOFVG6dAGtwyEiIiI7xqTNgg4fvolLl6LZNEpERES5xqTNgpYuDYWbmzOef7661qEQERGRnWPSZiE6nR7Llp3Fs89WRtGi7lqHQ0RERHaOSZuF7N0bjhs3HmHAADaNEhERUe4xabOQwMBQ5M/vgl69qmgdChEREeUBTNosIClJh5Urz6FPn2ooUMBN63CIiIgoD2DSZgHbtl3B3bvxGDiwttahEBERUR7BpM0CAgNDUbRoPnTtWknrUIiIiCiPYNJmZrGxSViz5jz69asBNzdnrcMhIiKiPIJJm5lt3HgJjx4lcUJdIiIiMismbWa2dGkoypQpAD8/L61DISIiojyESZsZRUcnYOPGS+jfvyacnfloiYiIyHyYWZjRmjXnkZCg44S6REREZHZM2swoMDAUlSoVRvPmZbUOhYiIiPIYJm1mEhUVi+3br2DAgFpQSmkdDhEREeUxTNrMZOXKc9DphBPqEhERkUXYVNKmlOqmlDqrlLqglJpg3LdYKXVCKfVdmvM+UUr11SzQdAQGhqJOnRKoX7+k1qEQERFRHmQzSZtSyhnAjwCeBVAHwECllDeAOBHxBtBEKVVEKVUWQDMRWaNdtI+7du0B9u4NZ9MoERERWYyL1gGk0RTABRG5BABKqaUAegDwUEo5AXAFoAPwFYDPNYsyHcuWnQUATqhLREREFmMzNW0AygO4luZ9uHFfFIBgAOsBVAPgJCLB1g8vY0uXhsLXtzSqVSumdShERESUR9lSTVu6RGRsymul1HoAbyilPgbQAMA2EfntyWuUUq8DeB0AKlasaNH44uKSULKkB7p3r2LR+xAREZFjs6Wk7TqAtGs/VTDuAwAopfoAOAqgIICqItJfKbVVKbVYRGLTFiQiswHMBgBfX1+xZNAeHq7YsuUFS96CiIiIyKaaRw8DqK6UqqyUcgMwAMA6AFBKuQIYC+AHAB4AUhIxZwBu1g+ViIiIyLpsJmkTkWQAowFsBXAGwHIROWU8PArAAmON2gkA+ZVSJwEcFZH7WsRLREREZE221DwKEdkEYFM6+6eneS0ABloxLCIiIiLN2UxNGxERERFljEkbERERkR1g0kZERERkB5i0EREREdkBJm1EREREdoBJGxEREZEdYNJGREREZAeYtBERERHZASZtRERERHaASRsRERGRHWDSRkRERGQHmLQRERER2QEmbURERER2gEkbERERkR1g0kZERERkB5SIaB2DRSmlogBcscKtSgK4bYX72As+j6fxmTyOz+NpfCZP4zN5HJ/H0/LaM3lGRDzTO5DnkzZrUUodERFfreOwFXweT+MzeRyfx9P4TJ7GZ/I4Po+nOdIzYfMoERERkR1g0kZERERkB5i0mc9srQOwMXweT+MzeRyfx9P4TJ7GZ/I4Po+nOcwzYZ82IiIiIjvAmjYiIiIiO8CkLZeUUt2UUmeVUheUUhO0jkcLSqm5SqlIpdS/afYVV0ptU0qdN34tpmWM1qSU8lJK7VJKnVZKnVJKjTHud+Rn4q6UOqSUOm58Jl8a91dWSh00/vwsU0q5aR2rNSmlnJVSx5RSG4zvHf15hCmlTiqlQpRSR4z7HPbnBgCUUkWVUiuVUqFKqTNKqRaO+kyUUjWN3xsp2wOl1FhHeh5M2nJBKeUM4EcAzwKoA2CgUqqOtlFpYj6Abk/smwBgh4hUB7DD+N5RJAN4X0TqAGgOYJTx+8KRn0kCgA4i0gBAQwDdlFLNAXwPYJqIVANwD8Bw7ULUxBgAZ9K8d/TnAQDtRaRhmikcHPnnBgACAGwRkVoAGsDw/eKQz0REzhq/NxoCaAwgFsCfcKDnwaQtd5oCuCAil0QkEcBSAH00jsnqRGQPgLtP7O4DYIHx9QIAfa0Zk5ZEJEJEgo2vH8LwS7Y8HPuZiIg8Mr51NW4CoAOAlcb9DvVMlFIVAPQA8LvxvYIDP49MOOzPjVKqCIC2AOYAgIgkish9OPAzSaMjgIsicgUO9DyYtOVOeQDX0rwPN+4joLSIRBhf3wRQWstgtKKUqgSgEYCDcPBnYmwKDAEQCWAbgIsA7otIsvEUR/v5mQ7gAwB64/sScOznARgS+b+UUkeVUq8b9znyz01lAFEA5hmb0X9XShWAYz+TFAMABBpfO8zzYNJGFieGIcoON0xZKVUQwCoAY0XkQdpjjvhMRERnbNaoAEMtdS1tI9KOUqongEgROap1LDamtYj4wNDlZJRSqm3agw74c+MCwAfAzyLSCEAMnmj6c8BnAmNfz94AVjx5LK8/DyZtuXMdgFea9xWM+wi4pZQqCwDGr5Eax2NVSilXGBK2xSKy2rjboZ9JCmPzzi4ALQAUVUq5GA850s9PKwC9lVJhMHSr6ABD3yVHfR4AABG5bvwaCUNfpaZw7J+bcADhInLQ+H4lDEmcIz8TwJDUB4vILeN7h3keTNpy5zCA6sYRX24wVNeu0zgmW7EOwBDj6yEA1moYi1UZ+ybNAXBGRKamOeTIz8RTKVXU+NoDQGcY+vrtAvCC8TSHeSYi8n8iUkFEKsHwe2OniAyCgz4PAFBKFVBKFUp5DaALgH/hwD83InITwDWlVE3jro4ATsOBn4nRQPzXNAo40PPg5Lq5pJTqDkPfFGcAc0XkW20jsj6lVCAAPwAlAdwC8DmANQCWA6gI4AqA/iLy5GCFPEkp1RrAXgAn8V9/pY9g6NfmqM/EG4YOws4wfFhcLiJfKaWqwFDTVBzAMQAvi0iCdpFan1LKD8A4EenpyM/D+G//0/jWBcASEflWKVUCDvpzAwBKqYYwDFZxA3AJwDAYf4bggM/EmNBfBVBFRKKN+xzme4RJGxEREZEdYPMoERERkR1g0kZERERkB5i0EREREdkBJm1EREREdoBJGxEREZEdYNJGRDZLKfWxUuqUUuqEUipEKdXMuP93pVQdM90jTClVMotzPnri/X4z3PdH47/ptFIqzvg6RCn1glJqU8q8dkREKTjlBxHZJKVUCwBTAfiJSIIxsXITkRtmvk8YAF8RuZ3JOY9EpKA575um7EoANohIPUuUT0R5B2vaiMhWlQVwO2VyWRG5nZKwKaWClFK+xtePlFL+xhq57Uqppsbjl5RSvY3nDFVKzUopWCm1wTip7WOUUmuMi5WfSlmwXCk1CYCHsRZscco9jV+V8d7/KqVOKqVeMu73M8awUikVqpRabFwpwyQptX9KqUrG6+crpc4Zy+mklNqnlDqvlGpqPL+AUmquUuqQcWHxPtl/3ERk65i0EZGt+guAlzFZ+Ukp1S6D8wrAsAxUXQAPAXwDwzJZzwH4Kpv3fFVEGgPwBfCOUqqEiEwAECciDY1LTaX1PICGABoA6ATAP2UNRACNAIwFUAdAFRjWG82JagCmAKhl3P4HoDWAcTCstAEAH8PwDJoCaG+Mo0AO70dENopJGxHZJBF5BKAxgNcBRAFYppQams6piQC2GF+fBLBbRJKMrytl87bvKKWOAzgAwAtA9SzObw0gUER0xsWrdwNoYjx2SETCRUQPICQHsaS4LCInjeWcArBDDP1a0v77ugCYoJQKARAEwB2GJX2IKA9x0ToAIqKMiIgOhiQkSCl1EobFoOc/cVqS/Nc5Vw8gpTlVr5RK+R2XjMc/pLo/eS9jc2knAC1EJFYpFZTeedmQds1QHXL++zZtOfo07/VpylQA+onI2Rzeg4jsAGvaiMgmKaVqKqXS1nQ1hGEx6JwIA9BQKeWklPIC0DSdc4oAuGdM2GoBaJ7mWJJSyjWda/YCeEkp5ayU8gTQFsChHMaYG1sBvJ3Sb04p1UiDGIjIwljTRkS2qiCAmcapL5IBXIChqTQn9gG4DOA0gDMAgtM5ZwuAN5VSZwCchaGJNMVsACeUUsFP9Gv7E0ALAMcBCIAPROSmMemzpq8BTDfG6ATDv7WnlWMgIgvjlB9EREREdoDNo0RERER2gEkbERERkR1g0kZERERkB5i0EREREdkBJm1EREREdoBJGxEREZEdYNJGREREZAeYtBERERHZgf8HKBw7wJL1LtkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(100,0))\n",
    "plt.ylabel(\"Tribute Payers vs Demanders\")\n",
    "plt.xlabel(\"Simulation Time\")\n",
    "plt.plot(df.Time, df.Colonies, color=\"DarkBlue\", label='Tribute Payers', linestyle='solid')\n",
    "plt.plot(df.Time, df.Kings, color=\"DarkBlue\", label='Tribute Demanders', linestyle='dashed')\n",
    "plt.legend()\n",
    "plt.savefig(\"PoliticalActorsKings.png\", format=\"png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}