train_drone_pilot.ipynb

{
 "cells": [
  {
   "attachments": {
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     "image/jpeg": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RLDronePilot - Fully Autonomous Line-Follower Drone\n",
    "This project was realized by the [REDS institute](https://reds.heig-vd.ch/) @ [HEIG-VD](https://heig-vd.ch/).\n",
    "\n",
    "Authors: Guillaume Chacun, Mehdi Akeddar, Thomas Rieder, Bruno Da Rocha Carvalho and Marina Zapater<br>\n",
    "REDS. School of Engineering and Management Vaud, HES-SO University of Applied sciences and Arts Western Switzerland<br>\n",
    "Email: {guillaume.chacun, mehdi.akeddar, thomas.rieder, bruno.darochacarvalho, marina.zapater}@heig-vd.ch<br>\n",
    "\n",
    "## Goal\n",
    "This Jupyter notebook is used to train a DDPG (reinforcement learning) model to control a drone, specifically to guide it to follow a predefined line on the ground. A separate deep learning model is used to identify and track the line from images captured by the on-board camera. The pilot module processes real-world normalized coordinates of points A and B (see image below) and outputs forward, lateral, and angular velocities for the drone.\n",
    "\n",
    "![example_line_AB.jpg](attachment:d070880e-9ab1-40a2-bbe9-221438a24ede.jpg)\n",
    "\n",
    "## Content of this repository\n",
    "- This Notebook is used to train the reinforcement learning model.\n",
    "- *Agents.py* : DDPG related classes (OUActionNoise, abstract Agent class, LearningAgent, ExplorationAgent).\n",
    "- *DDPGNetworks.py* : DDPG related classes (CriticNetwork, ActorNetwork).\n",
    "- *SharedReplayBuffer.py* : DDPG replay buffer shared across processes.\n",
    "- *Drone.py* : Class to simulate the behavior of the drone.\n",
    "- *Line.py* : Class to generate a random line for the drone to follow.\n",
    "- *Environment.py* : Class to handle the simulation (episodes). Contains an instance of Drone and Line.\n",
    "\n",
    "## Setup\n",
    "Install dependencies:\n",
    "\n",
    "```bash\n",
    "poetry install\n",
    "```\n",
    "\n",
    "Activate the virtual environment of Poetry:\n",
    "```bash\n",
    "poetry shell\n",
    "```\n",
    "\n",
    "Start your Jupyter Lab server:\n",
    "```bash\n",
    "jupyter lab\n",
    "```\n",
    "\n",
    "## Disclaimer\n",
    "Most of the DDPG code is from [@philtabor's GitHub](https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/PolicyGradient/DDPG/pytorch/lunar-lander/ddpg_torch.py) (last visited on March 5th, 2024)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import time\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiprocessing as mp\n",
    "\n",
    "mp.set_start_method('spawn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# does not work with %matplotlib widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from Agents import ExplorationAgent, LearningAgent\n",
    "from Drone import Drone\n",
    "from Environment import Environment\n",
    "from SharedReplayBuffer import SharedReplayBuffer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = True\n",
    "enable_plots = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# True if existing LearnerAgent weights should be loaded\n",
    "load_weights = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of ExplorationAgent to run in parallel\n",
    "n_process = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 23\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DPPG parameters\n",
    "actor_lr  = 1e-5\n",
    "critic_lr = 1e-4\n",
    "ddpg_tau = 1e-3\n",
    "batch_size = 512\n",
    "layer1_size = 300\n",
    "layer2_size = 150\n",
    "noise_sigma = 0.1 # scale of the noise\n",
    "noise_theta = 0.2 # rate at which the noise reverts to the mean\n",
    "noise_dt = 5e-3   # time step size for the Ornstein-Uhlenbeck process; smaller -> smoother noise\n",
    "memory_size = 0   # size of 0 disables the agent's memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Environment parameters\n",
    "max_allowed_dist = 1.5 # from the drone to the nearest point on the line (meters)\n",
    "# Reward weights\n",
    "alpha = 2    # distance\n",
    "beta = 0.5   # speed\n",
    "gamma = 1.   # p1 centered\n",
    "delta = .5   # angle\n",
    "terminated_reward = -1 * sum([alpha, beta, gamma, delta])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drone parameters\n",
    "allow_x_movement = True\n",
    "z_min_speed = .05        # meters / second\n",
    "z_max_speed = .30        # meters / second\n",
    "x_max_speed = .15        # meters / second\n",
    "max_angular_speed = 45.  # degrees / seconds\n",
    "max_drift = 0.           # meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_dims = 3\n",
    "n_actions = 3 if allow_x_movement else 2\n",
    "input_dims = [state_dims + state_dims*memory_size]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "neptune_params = {\n",
    "    'Model/load_weights': load_weights,\n",
    "    'Model/state_dims': state_dims,\n",
    "    'Model/n_actions': n_actions,\n",
    "    \n",
    "    'Model/actor_lr': actor_lr,\n",
    "    'Model/critic_lr': critic_lr,\n",
    "    'Model/tau': ddpg_tau,\n",
    "    'Model/batch_size': batch_size,\n",
    "    'Model/layer1_size': layer1_size,\n",
    "    'Model/layer2_size': layer2_size,\n",
    "    'Model/noise_sigma': noise_sigma,\n",
    "    'Model/noise_theta': noise_theta,\n",
    "    'Model/noise_dt': noise_dt,\n",
    "    'Model/memory_size': memory_size,\n",
    "    \n",
    "    'Environment/max_allowed_dist': max_allowed_dist,\n",
    "    'Environment/max_drift': max_drift,\n",
    "    \n",
    "    'Reward/alpha': alpha,\n",
    "    'Reward/beta': beta,\n",
    "    'Reward/gamma': gamma,\n",
    "    'Reward/delta': delta,\n",
    "    'Reward/terminated_reward': terminated_reward,\n",
    "\n",
    "    'Pilot/allow_x_movement': allow_x_movement,\n",
    "    'Pilot/z_min_speed': z_min_speed,\n",
    "    'Pilot/z_max_speed': z_max_speed,\n",
    "    'Pilot/x_max_speed': x_max_speed,\n",
    "    'Pilot/max_angular_speed': max_angular_speed,\n",
    "    \n",
    "    'Drone/height': Drone._height,\n",
    "    'Drone/fps': Drone._fps,\n",
    "    'Drone/flight_commands_rate': Drone._flight_commands_rate,\n",
    "}\n",
    "environment_params = {\n",
    "    'seed': seed,\n",
    "    'max_allowed_dist': max_allowed_dist,\n",
    "    'z_min_speed': z_min_speed,\n",
    "    'z_max_speed': z_max_speed,\n",
    "    'x_max_speed': x_max_speed,\n",
    "    'max_angular_speed': max_angular_speed,\n",
    "    'max_drift': max_drift,\n",
    "    'allow_x_movement': allow_x_movement,\n",
    "    'alpha': alpha,\n",
    "    'beta':beta ,\n",
    "    'gamma': gamma,\n",
    "    'delta': delta,\n",
    "    'terminated_reward': terminated_reward,\n",
    "}\n",
    "buffer_params = {\n",
    "    'input_dims': input_dims,\n",
    "    'n_actions': n_actions,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "replay_buffer = SharedReplayBuffer(**buffer_params)\n",
    "new_weights_events = [mp.Event() for _ in range(n_process)]\n",
    "log_lock = mp.Lock()\n",
    "pbar_queue = mp.Queue()\n",
    "weights_shared = mp.Manager().dict()\n",
    "render_queue = mp.Queue(maxsize=100_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner_params = {\n",
    "    'replay_buffer': replay_buffer, \n",
    "    'load_weights': load_weights,\n",
    "    'new_weights_events': new_weights_events,\n",
    "    'actor_lr': actor_lr, \n",
    "    'critic_lr': critic_lr, \n",
    "    'input_dims': input_dims, \n",
    "    'tau': ddpg_tau,\n",
    "    'n_actions': n_actions, \n",
    "    'layer1_size': layer1_size,\n",
    "    'layer2_size': layer2_size, \n",
    "    'batch_size': batch_size, \n",
    "    'allow_x_movement': allow_x_movement,\n",
    "    'weights_shared': weights_shared,\n",
    "}\n",
    "explorer_params = {\n",
    "    'replay_buffer': replay_buffer, \n",
    "    'environment_params': environment_params,\n",
    "    'actor_lr': actor_lr, \n",
    "    'input_dims': input_dims, \n",
    "    'noise_sigma': noise_sigma,\n",
    "    'noise_theta': noise_theta,\n",
    "    'noise_dt': noise_dt,\n",
    "    'n_actions': n_actions,\n",
    "    'layer1_size': layer1_size, \n",
    "    'layer2_size': layer2_size, \n",
    "    'batch_size': batch_size, \n",
    "    'memory_size': memory_size, \n",
    "    'allow_x_movement': allow_x_movement,\n",
    "    'pbar_queue': pbar_queue,\n",
    "    'log_lock': log_lock,\n",
    "    'weights_shared': weights_shared,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = LearningAgent(**learner_params)\n",
    "explorers = [ExplorationAgent(**explorer_params,\n",
    "                              new_weights_event=new_weights_events[i], \n",
    "                              render_queue=(render_queue if i == 0 else None),\n",
    "                              is_plot_process=i==0 and enable_plots,\n",
    "                              neptune_params=(neptune_params if i == 0 else None),) \n",
    "             for i in range(n_process)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5321db6b67024798842936f07cee10e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "256f15a5f6074eeabdeab1e27a811f00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(layout=Layout(border_bottom='1px solid black', border_left='1px solid black', border_right='1px solid b…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[neptune] [info   ] Neptune initialized. Open in the app: https://app.neptune.ai/chacungu/Drone-Controller-with-Reinforcement-Learning/e/RLCONTROL-180\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start the processes\n",
    "processes = [learner, *explorers]\n",
    "for p in processes:\n",
    "    p.start()\n",
    "\n",
    "time.sleep(3) # wait for processes to be fully initialized\n",
    "\n",
    "pbar = tqdm(position=0) # display the progress bar\n",
    "out = widgets.Output(layout={'border': '1px solid black'})\n",
    "display(out) # display the plot area\n",
    "\n",
    "while True:\n",
    "    # update the progress bar\n",
    "    try:\n",
    "        pbar_update = pbar_queue.get_nowait()\n",
    "        pbar.update(1)\n",
    "    except mp.queues.Empty:\n",
    "        pass\n",
    "\n",
    "    # update the plot\n",
    "    try:\n",
    "        render_dict = pickle.loads(render_queue.get_nowait())\n",
    "        with out:\n",
    "            Environment.render(dict(render_dict))\n",
    "            clear_output(wait=True)\n",
    "            plt.show(block=True)\n",
    "            display(plt.gcf())\n",
    "            plt.clf()\n",
    "    except mp.queues.Empty:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interrupt the training\n",
    "for p in processes:\n",
    "    p.join()\n",
    "\n",
    "# stop neptune\n",
    "if log:\n",
    "    run.stop()\n",
    "# close the progress bar\n",
    "pbar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reward func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, FloatSlider\n",
    "import ipywidgets as widgets\n",
    "\n",
    "slider_a = FloatSlider(value=1., min=0, max=1, step=0.1, description='speed:')\n",
    "slider_b = FloatSlider(value=0, min=0, max=1, step=0.1, description='dist_p1_C:')\n",
    "slider_c = FloatSlider(value=0, min=-1, max=1, step=0.1, description='drone_angle:')\n",
    "slider_d = FloatSlider(value=0, min=-1, max=1, step=0.1, description='line_angle:')\n",
    "\n",
    "def update_plot(speed_z, dist_p1_C, drone_angle, line_angle):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.stackplot(\n",
    "        dists,\n",
    "        np.array([get_reward_(d, speed_z, dist_p1_C, drone_angle, line_angle) for d in dists]).T,\n",
    "        labels=['travelled distance', 'speed', 'p1 centered', 'angle']\n",
    "    )\n",
    "    plt.xlabel('Distance')\n",
    "    plt.ylabel('Reward')\n",
    "    plt.ylim(0,10)\n",
    "    plt.legend(loc=[1.05,0])\n",
    "    plt.title('Stackplot of Rewards')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ba371458c8e43e584a4f1bc65cb32da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.0, description='speed:', max=1.0), FloatSlider(value=0.0, descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_plot(speed_z, dist_p1_C, drone_angle, line_angle)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_reward_(travelled_distance, speed_z, dist_p1_C, drone_angle,\n",
    "                line_angle,\n",
    "                alpha=1.5, beta=.5, gamma=1., delta=.75,):\n",
    "    # travelled_distance in meters\n",
    "    # speed_z in range [ 0., 1.]\n",
    "    # dist_p1_C in range [0., 1.]\n",
    "    # drone_angle and line_angle in range [-1., 1.]\n",
    "    travelled_distance_percentage = travelled_distance / path_length\n",
    "\n",
    "    reward_A = travelled_distance_percentage * alpha\n",
    "    reward_B = travelled_distance_percentage * (speed_z * beta)\n",
    "    reward_C = travelled_distance_percentage * ((1 - dist_p1_C) * gamma)\n",
    "    reward_D = travelled_distance_percentage * ((1 - (abs(drone_angle - line_angle) / 2)) * delta)\n",
    "    return reward_A, reward_B, reward_C, reward_D\n",
    "    \n",
    "path_length = 15.\n",
    "dists = np.arange(0., path_length, .25)\n",
    "\n",
    "interact(update_plot, speed_z=slider_a, dist_p1_C=slider_b, drone_angle=slider_c, line_angle=slider_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def get_reward_(travelled_distance, dist_p1_C, \n",
    "                speed_z, speed_z_activation_dist=5, target_dist_p1_C=.15, \n",
    "                alpha=.75, beta=3, gamma=2):\n",
    "    # speed_z in range [ 0., 1.]\n",
    "    # dist_p1_C in range [-1., 1.]\n",
    "    reward_A = travelled_distance * (alpha if travelled_distance < speed_z_activation_dist else 1.)\n",
    "    reward_B = max(0, travelled_distance - speed_z_activation_dist) * beta * speed_z\n",
    "    reward_C = (reward_A + reward_B) * np.interp(dist_p1_C, [ 0., 1.], [0.1, 1.]) * gamma\n",
    "    return reward_A, reward_B, reward_C\n",
    "    \n",
    "fig, axes = plt.subplots(1,10, figsize=(25,8), sharey=True)\n",
    "dists = np.arange(0.,15., .25)\n",
    "for ax, speed in zip(axes, np.arange(0., 1.11, .1)):\n",
    "    ax.set_title('%.1f' % speed)\n",
    "    rewards = list(map(lambda d: get_reward_(d, 0, speed), dists))\n",
    "    ax.bar(dists, list(map(sum, rewards)), color='black', label='total')\n",
    "    ax.bar(dists, list(map(lambda r: r[0], rewards)), color='orange', label='reward: dist')\n",
    "    ax.bar(dists, list(map(lambda r: r[1], rewards)), color='blue', label='reward: speed')\n",
    "    ax.bar(dists, list(map(lambda r: r[2], rewards)), color='yellow', label='reward p1 centered')\n",
    "    ax.grid(axis='y')\n",
    "    plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Drone & Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "image/png": 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8Xqr4k+JICCGEXqSkpBAfH4+bmxvW1tY5tk2/9GZpaWnQf9Ql48t7mYxOTk7cvHmTlJSUlxoGwDDfGSGEEIVeen+Sl70EIkS69O/Sy/ankuJICCGEXhlq3xdhfHT1XZLiSAghhFF5mvw0X9sLIcWREEIIo/Ht8W+psbAGUTFRuWofFRNFjYU1+PbYt/mcTBQmUhyJgqMocOcOHDqU9rMQQuTB05SnzAqbxYUHF2i8vPFzC6SomCgaL2/MhQcXmHlgpk7PIIWEhKBSqVCpVJiZmVGqVClatmzJjz/++NxhCYThk+JIFJzbt6F3b3j/fTh2TN9phBBGxsrUiu29tlO+RHkuPbyUY4GUXhhdeniJ8iXKs7PPTqzMrHSaJygoiFu3bnHlyhU2bdpEkyZNGDduHO3atSMlJSXL1yQnJ+s0g8gfUhyJghEdDcuXQ2IiWFtDuXL6TiSEMELuxd3Z3Xd3jgXSfwuj3X13417cXedZLCwscHFxoXTp0vj5+TFu3DhWrFjB5s2bCQ0NBdI6CC9YsID27dtjY2PDJ598AsCCBQuoUKEC5ubmVKpUiR9++CHDtlUqFUuWLKFjx45YW1tTsWJFfv/99wxtIiMjad26Nba2tpQqVYrevXtz7949nR9nUSTFkch/N25AaCg8eQK2tlCrFtjZ6TuVEMJI5VQgFVRhlJ2GDRtSs2ZN1q1bp102ZcoUOnbsyMmTJ+nfvz+//vor7733HqNGjSIyMpI333yTfv36sWvXrgzbmjp1Km+88QYnTpygTZs29OzZkwcPHgDw6NEjmjZtiq+vL0ePHmXz5s1ER0fzxhtvFNixFmZSHIn8deVK2hmjp0+hTJm0wkjGNBFCvKSsCqQDUQf0Whilq1SpEleuXNE+79GjB/369aN8+fJ4eHgwa9YsQkJCGDJkCN7e3owcOZJOnToxa9asDNsJCQkhODgYLy8vPv30U+Li4jh8+DAA8+fPx9fXl08//RQfHx98fX1ZunQpu3bt4vz58wV5uIWSFEci/1y4AD/+CElJ4OkJPXuCqQzKLoTQjf8WSPWW1tN7YQRp06I8O95O7dq1M6w/c+YM9erVy7CsXr16nDlzJsOyGjVqaH+2sbHBzs6OO3fuABAREcGuXbuwtbXVPnx8fAC4ePGiTo+nKJK/VCJ/nD4Na9dCaip4e8Mbb4DcwSGE0DH34u780PEH6i39t9j4oeMPeiuMAM6ePYunp6f2uY2NzQtt57/TX6hUKu2dcHFxcbRr144ZM2Zkep2rq+sL7U/8S84cCd0LD4c1a9IKo2rVoFs3OWMkhMgXUTFR9P61d4ZlvX/tnetxkHTtr7/+4uTJk3Tu3DnbNpUrV2b//v0Zlu3fv58qVarkej9+fn6cOnWKcuXK4eXlleHxosWY+JcUR0K3jhyB9evTxjHy9YVOncDERN+phBCF0H87X+/vvz9Xt/nrSmJiIrdv3+bGjRscP36c6dOn07NnT9q2bUufPn2yfd2YMWMIDQ1lwYIF/PPPP8yZM4d169YxevToXO/7nXfe4cGDBwQHB3PkyBEuXrzIli1b6Nev30vPKyakOBK6tH8//Pln2s9160L79mCgsz4LIYxbVnelBboHPvc2f13avHkzrq6ulCtXjqCgIHbt2sVnn33G+vXrMcnhH4UdOnRg3rx5zJo1i6pVq7Jo0SKWLVtG48aNc71vNzc39u/fT2pqKi1btqR69eoMHz4ce3v7PM9kLzKTax3i5SkK7NoFf/2V9rxhQ2jSBGQySSFEPsjpdv30Ttrp6xsvb5wvnbNDQ0O1Yxml02g0xMbGZihOlGxmA3j77bd5++23s91+Vq979OhRhucVK1bMMGSA0J1CVV5OmTJFO5x7+iO99z5AQkIC77zzDiVLlsTW1pbOnTsTHR2tx8SFgKLAli3/FkbNm0PTplIYCSHyRW7GMcrNQJFC5KRQFUcAVatW5datW9rHvn37tOtGjBjBH3/8wZo1a9izZw83b96kU6dOekxr5DQa+OMPOHgw7XnbtlC/vn4zCSEKracpT2n+Y/Nc3a7/3wKp6fdNdTq3mijcCt1lNVNTU1xcXDItj4mJ4bvvvmPlypU0bdoUgGXLllG5cmUOHjzIq6++WtBRjVtqKvz2G0RGpp0l6tABatbUdyohRCFmZWrF6IDRzAqbxc4+O597qSy9QGr6fVPGBo7V+dxqovAqdMXRP//8g5ubG5aWlgQEBDB9+nQ8PDw4duwYycnJNG/eXNvWx8cHDw8PwsLCsi2OEhMTSUxM1D6PjY0F0iYP1PUEgunbM+SJCZOTk1GlpqL56SdSL10CExM0HTpAlSrwvNzJyaj/fxeFJjk53y69Gcv7+Oz/GyLJqBuSMef9KoqCRqN57kz26X1wBvoOpFf1XliZWT33NQCli5UmfHB4rtu/jPSM6cdkiAp7Ro1Gg6IoJCcnZ+oUn5fvt0rJrreYEdq0aRNxcXFUqlSJW7duMXXqVG7cuEFkZCR//PEH/fr1y1DoALzyyis0adIky4G0IK0f09SpUzMtX7lyJdbW1vlyHIZMlZxMmb17sY6ORjEx4Ub9+jxxc8v1a71/+QWA8126oPxngDMhRNGSfqbf3d0dc5lWSOhAUlISUVFR3L59m5SUlAzr4uPj6dGjBzExMdg9Z37PQnXmqHXr1tqfa9SoQd26dSlbtiyrV6/GyurFTqeOGzeOkSNHap/Hxsbi7u5Oy5Ytn/vm5lVycjLbtm2jRYsWmUZGNQgJCWh+/JHL0dGUr1wZVY8eeJUrl/vXJyWhjogAoELr1vk2x5rBv49IRl2RjLqhr4wJCQlERUVha2uLpaVljm0VReHx48cUK1Ysw9QchkQy6sbLZExISMDKyoqGDRtm+k6lX/nJjUJVHP2Xvb093t7eXLhwgRYtWpCUlMSjR4+wt7fXtomOjs6yj1I6CwsLLCwsMi03MzPLt18i+bntF/bkCaxYQerNm6RaWKDq2xezvBRGkHZn2/9Pc5qYmUE+H6NBvo//IRl1QzLqRkFnTE1NRaVSoVarnzs2T/rllfT2hkgy6sbLZFSr1ahUqiy/y3n5bhvmO6MjcXFxXLx4EVdXV/z9/TEzM2PHjh3a9efOnePatWsEBAToMaURiI2FZcvg9m2wsSGqaVMoXVrfqYQQQoh8UajOHI0ePZp27dpRtmxZbt68yeTJkzExMSE4OJjixYszYMAARo4ciYODA3Z2drz77rsEBATInWo5efgQvv8+7f+LF0cTHEzioUP6TiWEEELkm0J15uj69esEBwdTqVIl3njjDUqWLMnBgwdxcnICYO7cubz22mt07tyZhg0b4uLiIqOL5uTuXVi6NK0wcnCAfv2gZEl9pxJCCKOhUqlYv359kdu3sStUZ45+/vnnHNdbWlry9ddf8/XXXxdQIiN26xb88APEx4OzM/TuDcWKPf92fSGEKAJCQkJYvnw5kHbXnYODA9WrV6dDhw689dZb2r4yt27dokSJEvmaZcqUKaxfv57w8PAMywti34VVoTpzJHQkKgqWL08rjNzcICQkrTASQgihFRQUxK1bt7hy5QqbNm2iSZMmjBs3jnbt2mlvI3dxccnypp50+Tm21PP2LbInxZHI6PLltDNGCQng4QF9+kARHM9JCFHwFBSe6Ol/Cnkf8s/CwgIXFxdKly6Nn58f48aNY8WKFWzevFk7Ke2zl7auXLmCSqVi1apVNGrUCEtLS1asWAHAkiVLqFy5MpaWlvj4+PDNN99k2Fd6txEHBwdsbGyoXbs2hw4dIjQ0lKlTpxIREaGdUzSrfQOcPHmS5s2b4+rqipOTE4MHDyYuLk67PiQkhA4dOjBr1ixcXV0pWbIk77zzjkEPYJpfCtVlNfGSzp+H1ashJQUqVIBu3fJtLCIhhPiveOKxxTbrlWrAPv/2HUccNti89HYaNmxIzZo1WbduHQMHDsyyzQcffMDs2bPx9fXVFkiTJk1i/vz5+Pr68vfffzNo0CBsbGzo27cvcXFxNGrUiNKlS/P777/j4uLC8ePH0Wg0dOvWjcjISDZv3sz27dsBKF68eKZ9PnnyhFatWvHqq6+yY8cO4uPjGTx4MEOHDtUWUwC7du3C1dWVXbt2ceHCBbp160atWrUYNGjQS783xkSKI5EmMhLWrUubTLZyZejcGUzl6yGEEHlVqVIlTp48me364cOHZ5j0fPLkycyePVu7zNPTk9OnT7No0SL69u3LypUruXv3LkeOHMHBwQEALy8v7ettbW2znVc03cqVK0lISGD58uWkpqZiZ2fH/PnzadeuHTNmzKBUqVIAlChRgvnz52NiYoKPjw9t27Zlx44dUhyJIuj4cfjjj7RBGmvUSJtE1kAHBxNCFF7WWBNHXJbrNBoNsbGx2NnZ5cvghdborvuAoig5juxcu3Zt7c9Pnjzh4sWLDBgwIEMBkpKSoj0DFB4ejq+vr7YwehFnzpyhZs2a2NjYaEeKrlevHhqNhnPnzmmLo6pVq2aYk8zV1TXHQq+wkuKoqDt4EDZvTvu5dm1o2zbfJoQVQoicqFBle2lLg4ZUUrHBBrWBd5c9e/Ysnp6e2a63sfn3GNP7/Hz77bfUrVs3Q7v0IuVFp796Ef8dRVqlUhnsBLX5ybC/YSL/KAr89de/hVFgoBRGQgjxkv766y9OnjxJ586dc9W+VKlSuLm5cenSJby8vDI80gusGjVqEB4ezoMHD7Lchrm5OampqTnup3LlykRERPDkyRPtsv3796NWq6lUqVIuj67okOKoKFIU2LEDdu5Me96kCbRoIYWREELkQWJiIrdv3+bGjRscP36c6dOn07NnT9q2bUufPn1yvZ2pU6cyffp0vvzyS86fP8/JkydZtmwZc+bMASA4OBgXFxc6dOjA/v37uXTpEmvXriUsLAyAcuXKcfnyZcLDw7l37x6JiYmZ9tGzZ08sLS0JCQnh9OnT7Nq1i3fffZfevXtrL6mJf0lxVNQoCmzcCPv2pT1v1QoaNZLCSAgh8mjz5s24urpSrlw5goKC2LVrF5999hnr16/P0G/neQYOHMiSJUtYtmwZ1atXp1GjRoSGhmrPHJmbm7N161acnZ1p06YN1atX57PPPtPuo3PnzgQFBdGkSROcnJz46aefMu3D2tqaLVu28PDhQ5o1a8Ybb7xBs2bNmD9/vm7ejEJG+hwVJRoN/PYbRESkFUOvvQb+/vpOJYQQRic0NDTDLfDwb6fxZzuMK8q/4yeVK1cuw/Nn9ejRgx49emS7v7Jly/LLL79kuc7CwiLLdf/dV/Xq1dm+fXu2Hdv/ezwAX3zxRbaZCjMpjoqK1FRYuxZOn067E61jR6heXd+phBBCCIMjxVFRkJwMq1bBhQtgYgJdu4KPj75TCSGEEAZJiqPCLjERVq6Eq1fBzAy6d08b/VoIIYQQWZLiqDB7+hR+/BFu3AALC+jZM22+NCGEEEJkS4qjwiouLm0C2ejotIlje/cGV1d9pxJCCCEMnhRHhVFMDHz/Pdy/D8WKpRVGzs76TiWEEEIYBSmOChNFSTtT9MMPaWeOSpSAPn3gJebjEUIIIYoaKY4Kk7t304qhpKS0yWP79wc7O32nEkIIIYyKFEeFiZ1d2uPpU+jbVwojIYQQ4gXI9CGFiVoNVapArVpgk/XM1kIIIYqm3bt3Y2JiQkxMTIHsLzQ0FHt7+wLZl65JcVTYqNVp4xkJIYTIF3fv3uXtt9/Gw8MDCwsLXFxcaNWqFfv379d3NKEjcllNCCGEyIPOnTuTlJTE8uXLKV++PNHR0ezYsYP79+/rO5peJCUlYW5uni/bTk5OxkwP/+CXM0dCCCEMg6Kk3VCij0c2E8L+16NHj9i7dy8zZsygSZMmlC1blldeeYVx48bRvn37DO3efPNNSpUqhaWlJdWqVWPDhg3a9fv27aNBgwZYWVnh7u7OsGHDePLkiXZ9uXLl+PTTT+nfvz/FihXDw8ODxYsXZ8gSFRXFG2+8gb29PQ4ODrz++utcuXLlucdw8OBBatWqhaWlJa+++iqRkZEZ1q9du5aqVatiYWFBuXLlmD17dob15cqVY9q0afTp0wc7OzsGDx4MpF1G8/DwwNramo4dO2ZZLP7222/4+flhaWlJ+fLlmTp1KikpKdr1KpWKBQsWEBwcTLFixfjkk094+PAhPXv2xMnJCSsrKypWrMiyZcuee5wvQ84cCSGEMAzJyfDpp1muUikKlomJqCwsQKXS/b7Hj4dcnP2wtbXF1taW9evX8+qrr2JhYZGpjUajoXXr1jx+/Jgff/yRChUqcPr0aUxMTAC4ePEiQUFBfPzxxyxdupS7d+8ydOhQhg4dmuGP/uzZs5k2bRrjx4/nl19+4e2336ZRo0ZUqlSJ5ORkWrVqRUBAAHv37sXU1JSPP/6YoKAgTpw4keOZnMmTJzNv3jzc3NwYP3487dq14/z585iZmXHs2DHeeOMNpkyZQrdu3Thw4ABDhgyhZMmShISEaLcxa9YsJk2axOTJkwE4dOgQAwYMYPr06XTo0IHNmzdr16Xbu3cvffr04csvv6RBgwZcvHhRW1g92/ajjz5i0qRJfPXVV5ibmzNx4kROnz7Npk2bcHR05MKFCzx9+vS5n9XLkOJIFIi4uCTmfX4Qq7lHefw4ieVLFtPvTX9GjPDH1jZ/TscKIYSumZqaEhoayqBBg1i4cCF+fn40atSI7t27U61aNQC2b9/O4cOHOXPmDN7e3gCUL19eu43p06fTs2dPhg8fDkDFihX58ssvadSoEQsWLMDS0hKANm3aMGTIEADef/995s6dy65du6hUqRKrVq1Co9GwZMkSVP8vFpctW4a9vT27d++mZcuW2R7D2LFjadGiBWq1muXLl1OmTBl+/fVX3njjDebMmUOzZs2YOHEiAN7e3pw+fZrPP/88Q3HUtGlTRo0apX0+ceJEgoKCGDt2rPZ1Bw4cYPPmzdo2U6dO5YMPPqBv377a92TatGmMHTs2Q3EUHBxMz549sbOzQ61Wc+3aNXx9falduzaQduYqv0lxJPJdXFwSjRqt4tTfN/lASQLg+o04pkw5wPr1F9izp5sUSEKItJtJxo/PcpWi0ZAQG4u5nR0qdT70CMlDv5bOnTvTtm1b9u7dy8GDB9m0aRMzZ85k8eLFdOrUiYiICMqUKaMtjP4rIiKCEydOsGLFCu0yRVHQaDRcvnyZypUrA1CjRg3tepVKhYuLC3fu3NFu48KFCxQrVizDthMSErh48WKO+evUqaP92cHBgUqVKnHmzBkAzpw5w+uvv56hfb169fjiiy9ITU3Vnv1KL1TSnTlzho4dO2ZYFhAQkKE4ioiIYP/+/XzyySfaZampqSQkJBAfH4+1tTUA/v7+Gbbz9ttv07lzZ44fP07Lli3p0KEDgYGBOR7jy5LiSE+eJj/Fyswq39obkrlzjxEefgeT/1zT1ygK4SfvMnvOUSZPyt8vuhDCCKhU2V/a0mjS1pmbp92Vq2eWlpa0aNGCFi1aMHHiRAYOHMjUqVPp1KkTVlY5/66Oi4vjzTffZNiwYZnWeTwzOfh/OyKrVCo0Go12G/7+/hkKrHROTk4vckh5YvMCw8XExcVp36P/Sj9bltW2W7duzdWrV9m4cSPbtm2jWbNmvPPOO8yaNSvvwXNJiiM9+PbYt8w8MJOdfXbiXtz9ue2jYqJo+n1TxgaOZZD/oAJIqFuLF0egsjXBzrsYVvdsQAXOfu6oy9qhNlOzNO4BOz/fRXlHGzwdbSnvZEN5RxtKl7CitL0Vpib6/0UohBA5qVKlCuvXrwegevXqXL9+nfPnz2d59sjPz4/Tp0/j5eX1wvvz8/Nj1apVODs7Y5fHAX+PHj1K1apVAXj48CHnz5/Xnq2qXLlypiEJ9u/fj7e3t/asUVYqV67MoUOHMiw7ePBgpsznzp17oeN2cnKib9++9O3blwYNGjBmzBgpjgqTp8lPmXlgJhceXKDx8sbs7rs7xwIpKiaKxssbc+nhJWYemEmvGr2M6gxSQnIqcZ5WlK5bBtOkJEz3mmvvConZd5fUJ6mozdUMnhbApbtP2HXuDsvD4knVpLWxtzajmU8p3mxUHu9SxXLalRBC5Lv79+/TtWtX+vfvT40aNShWrBhHjx5l5syZ2rvVGjVqRMOGDencuTNz5szBy8uLs2fPolKpCAoK4v333+fVV19l6NChDBw4EBsbG06fPs22bduYP39+rnL07NmTzz//nNdff52PPvqIMmXKcPXqVdatW8fYsWMpU6ZMtq+dOXMmZcqUwdXVlQkTJuDo6EiHDh0AGDVqFHXq1GHatGl069aNsLAw5s+fzzfffJNjnmHDhlGvXj1mzZrF66+/zpYtWzJcUgOYNGkSr732Gh4eHnTp0gW1Wk1ERASRkZF8/PHH2W570qRJ+Pv7U7VqVRITE9mwYYO2mMsvUhwVMCszK3b22akteHIqkJ4tjMqXKM/OPjuNpjBK1Sis//sGc7adx65uSWIO3uPpwWgepzwE4E54FMn///qVKWPLhLZVtK9NTtVw7UE8Nx895fDlB/z69w1+/fs6XfzLMLy5N272xvEeCCEKH1tbW+rWrcvcuXO5ePEiycnJuLu7M2jQID744AOSk5OBtNvhR48eTXBwME+ePMHLy4vPPvsMSOtLtGfPHiZMmECDBg1QFIUKFSrQrVu3XOewtrbmr7/+4v3336dTp048fvyY0qVL06xZs+eeSZo8eTIjRozgn3/+oVatWvzxxx/au9v8/PxYvXo1kyZNYtq0abi6uvLRRx9l6IydlVdffZVvv/2WyZMnM2nSJJo3b86HH37ItGnTtG1atWrFhg0b+Oijj5gxYwZmZmb4+PgwcODAHLdtbm7OuHHjuHLlClZWVjRo0ICff/45d2/Ui1KKqPnz5ytly5ZVLCwslFdeeUU5dOhQrl4XExOjAEpMTMxL7f/ao2tK+XnlFaaglJ9XXrn26JqSlJSkrF+/XklKSspyfU4eP05UPp60R5ldrK0yhRaKZ+mvlI8+OqA8fpz4Ujn/69mM2Tl5/ZES9MVfStn3Nyhv/XBUGTVlr6JWz1LMmK5MpoUymRaKGdMV+FxRq2cpH310IMd9JianKsv2XVL8PtqqVJywUXn/lwjlTmzCS2XUN8moG5JRN/SV8enTp8rp06eVp0+fPrdtamqq8vDhQyU1NbUAkr0YyagbL5Mxp+9UXv5+F8nOHKtWrWLkyJFMnjyZ48ePU7NmTVq1aqW9C6AguBd3Z3ff3ZQvUV57BulG7A0AbsTeyHDG6HmX3tLvBps2LYzYx0ko/Hs3WKNGq4iLSyqQY0pMSeXLHf/Q8Zv9qIBfhwSyoJc/U0a9Qq1azqj/MzaJWq2iVi1nRozwz3qD/2duqiaknid7xjZhWFMvtp+JpsvCA0Q9iM/HoxFCCFFUFcniaM6cOQwaNIh+/fpRpUoVFi5ciLW1NUuXLi3QHP8tkIJWB5FgkkDQ6iAuxV2inFM5NvbdiENxB57k8L8Z8/fx9/koNJaJYJqa9rBKQmOZyN/no5gxf1+Or8/r/xJMEjI8v58cy4q/z9Py66188ddJ+jZ2YcU7tfD2MOcJT1DZJrNxz2t88FEtijmoUZml4uZlxvhPfNm45zVUtskoPH90WlsLU4Y2rcivQ+qhArouDON2TEL+f1BCCCGKlCLX5ygpKYljx44xbtw47TK1Wk3z5s0JCwvL1D4xMZHExETt89jYWCBtvpf0a8svw8XahR09dxC0Oohzb5+jO921665wBR98nr+RD/7/SALSB5cdvxPMQQE+/v9DJ8yA17JY5vv/BzDx/48MbMFsLIz//yjxN8fv5GPzf3M9TH6IDbm7NdSlmBkrBtShy6JDDFh+mJ8GvIKV+b93UaR/Lrr4fPKLZNQNyagb+sqYnJysHd8n/Rb17Cj/v5Ejvb0hkoy68TIZNRoNiqKQnJyc6e66vHy/i1xxdO/ePVJTUylVqlSG5aVKleLs2bOZ2k+fPp2pU6dmWr5161btgFW6MNV9aobCqKjZsmULlqmWz2/4jF5lYV6kCYMXbSO4Qub/gLZt26arePlGMuqGZNSNgs5oamqKi4sLcXFxJCXl7vL/48eP8znVy5OMuvEiGZOSknj69Cl//fVXhjnbAOLjc98Vo8gVR3k1btw4Ro4cqX0eGxuLu7s7LVu2zPPYEtm5EXuD1itbU2JmCRZUWcDbp9/GuZgz695Yh5ud23Nf7+//PTdvPsFMScH06W4ALL9qjIkq7eN1c7Ph2LE+OsmanJzMzp07adq0ad5nSk5OwjJ1DgDDk0eC6t/B3qxbWaMi7/Ml2ZaNYvIfZxjevi7+ZUtoM27bto0WLVroZTbn3JCMuiEZdUNfGRMSEoiKisLW1jbDIIBZURSFx48fU6xYMe10GYZGMurGy2RMSEjAysqKhg0bZvpOpV/5yY0iVxw5OjpiYmJCdHR0huXR0dG4uLhkam9hYZHlxIJmZmY6+SUSFRNFsxXNuPTwEpUdKmOZaomLlQtn7pyh3Yp2z+2MDRDStQ5TphwgVaMihbTTiAlPzUjGFLVaRch7dbA3s3/prADJJGOZaom9mX3ej19JApO0L6ulmT2YvfyUIb0DPFkXfovJf5xlw7D6mD0zYKSuPqP8JBl1QzLqRkFnTE1NRaVSoVarUT9n1Ov0yyvp7Q2RZNSNl8moVqtRqVRZfpfz8t02zHcmH5mbm+Pv78+OHTu0yzQaDTt27CAgIKBAs/x3HKONPTYCsLHHxgx3sUXFROW4nREj/F/qbjBjplar+KRDNf6585hl+y/rO44QQohCoMgVRwAjR47k22+/Zfny5Zw5c4a3336bJ0+e0K9fvwLL8N/CaHff3ZS2Kw1AabvSmW7zz6lAsrU1Z8+ebkycGIBdMXNUQJnStkyZElgkJnWtVro4fQLK8cX2f7j56Km+4wghhDByRe6yGkC3bt24e/cukyZN4vbt29SqVYvNmzdn6qSdX7IqjNyLu2foSZ9+m39uRtKGtAJpwoRXQZU2U/Lk8YOzn8CxEBrV0puNJ28x9Y9TzO9eU99xhBBCGLEieeYIYOjQoVy9epXExEQOHTpE3bp1C2S/T5Of0vT7prka4PG/4yA1/b4pT5PlzEhWilmaMfG1Kmw5Fc3Oc3f1HUcIUYSZmJhoJ6EVxqnIFkf6YmVmxdjAsXg5eOWqs3V6geTl4MXYwLFGM7eaPrxWw5UGFR35dOM5Ugxz+A4hRCEQEhKinag1Kzdu3KB169YFF+glJCQkEBISQvXq1TE1Nc3xuIoSKY70YJD/IE68deK5hVE69+LunHjrBIP8B+VzMuOmUqn4sG0Voh7Gsy/aMG9RFUIUfi4uLlne5WyIUlNTsbKyYtiwYTRv3lzfcQyGFEd6ktczQHLGKHcquRSjq38ZtkSpeRRvuCMSCyF0Iy4uiWnTwnB3X4iJyWzc3RcybVpYgc0pmZVnL6tduXIFlUrFunXraNKkCdbW1tSsWTPTjAz79u2jQYMGWFlZ4e7uzrBhw3jy5MlL5di9ezcqlYo///yTGjVqYGlpSWBgIKdPn9a2sbGxYcGCBQwaNCjL4WyKKimORKEzvFkFUhX4evdFfUcRQuSj9Em3p0w5wPXrcWg0CtevF/yk27kxYcIERo8eTXh4ON7e3gQHB2tHcL548SJBQUF07tyZEydOsGrVKvbt28fQoUN1su8xY8Ywe/Zsjhw5gqOjI8HBwQY9nY0hkOJIFDqOthY0L61hxeEoLt97uX95CSEM19y5xwgPv4NGk3Hiao1GITz8DnPnHtNTssxGjx5N27Zt8fb2ZurUqVy9epULFy4AadNU9ezZk+HDh1OxYkUCAwP58ssv+f7770lIePnJtSdPnkyLFi2oXr06oaGh3L17l19//fWlt1uYSXEkCqXGrgqOthZ8tumMvqMIIfLJ4sURmQqjdBqNwuLFEQWcKHs1atTQ/uzq6grAnTt3AIiIiCA0NBRbW1vto1WrVmg0Gi5fzjy47bVr1zK0/fTTTzO1edazAxw7ODjg5eWV5Vyi4l9FcpwjUfiZm8CoFhUZ/ctJDl26T93yJfUdSQihYzdv5nxm+HnrC9KzU1ekzxeWPk1GXFwcb775JsOGDcv0Og8Pj0zL3NzcCA8P1z53cHDQcVohxZEotNpVd+GHg9f4ZOMZ1g+ph1otd7AJUZi4udlw/XpcjuuNgZ+fH6dPn8bLyytX7U1NTXPdFuDgwYPaIuvhw4dcvHgRHx+fF8paVMhltcLm/n24fl3fKQyCWq3iw9eqcOJ6DL9F3NB3HCGEjg0eXDPbf/So1SoGD86/0fJjYmIIDw/P8IiKynkezOy8//77HDhwgKFDhxIeHs4///zDb7/9prMO2R999BE7duwgMjKSfv364eDgkGE8o9OnTxMeHs6DBw8yHFdRJmeOCpMHD+DUKdBo4K+/oFkzUBXtsyV1yjnQqmopZm89T9vqbpibyr8HhCgsRozwZ/36C5k6ZRfEpNu7d+/G19c3w7L+/fsze/bsPG+rRo0a7NmzhwkTJtCgQQMURaFChQp069ZNJ1k/++wz3nvvPf755x9q1arFTz/9hPkz00u1adOGq1evap+nH5eiZN2fqyiQ4qgwKVECypaFy5dhzx5QFGjevMgXSKNbVqLlF3+x6sg1egeU03ccIYSOpE+6PXfuMRYvjuDmzSe4udkweHBNRozwz7dJt0NDQwkNDc20XKPREBsbS2pqKmp12j/EypUrl6nIsLe3z7SsTp06bN26NV/y1q9fn8jIyAwZn3XlypV82a8xk+KoMDE3h0WL4NAh2L4d9u+HxERo27ZIF0gVSxWjo29pvtx5gS7+7liZm+g7khBCR2xtzZk4MYCJEwOe31iIXJJrDIWJSpVWIDVoAK+/nvb86FFYvz7tUlsRNqK5N4/ik1gedkXfUYQQQhg4KY4KKz8/6NwZ1GqIiIA1a+D/o7EWRe4O1nSv48GC3ReJTZCRYYUQhV/jxo1RFAV7e3t9RzE6UhwVZtWqQbduYGoKZ87Azz9DER4y/t2mXiSmpLLkr0v6jiKEEMKASXFU2FWqBD16gJkZXLgAP/6Y1g+pCHK2syQk0JPFey9x6W72Y6MIIYQo2qQ4KgrKl4c+fcDSEq5eheXLIT5e36n0YlgzL1zsLBm9JoLUbKYdEEIIUbRJcVRUuLtD375gbQ03b0JoKMQVvbMn1uamzOpak7+jHvHtXrm8JoQQIjMpjooSV1fo1w+KFYM7d2DpUnj0SN+pClztcg4MalCeOVvPc/pm7PNfIIQQokiR4qiocXKC/v3B3j5tRO1ly9KmHCliRrbwpmIpWwYuP8Kdxwn6jiOEEMKASHFUFJUokVYgOTpCTExagRQdre9UBcrSzIQlfWuTqigMWn6Up0mp+o4khCgkTExMWL9+vb5jiJcgxVFRZWeXdonNxSWt71FoKNwoWpOzuha34ru+dTgfHcfI1eEZ5mYSQojshISEZJi49b9u3LhB69atCy7QS9i9ezevv/46rq6u2NjYUKtWLVasWKHvWHonxVFRZmOT1km7TBl4+hS+/z7tbrYipFrp4szrXovNp24za+s5fccRQuRRXFwS06aF4e6+EBOT2bi7L2TatDDi4pL0lsnFxQULCwu97T8vDhw4QI0aNVi7di0nTpygX79+9OnThw0bNug7ml5JcVTUWVml3ebv6Zk2/tGPP6aNh1SEtKzqwrjWPnyz+yLrjl/XdxwhRC7FxSXRqNEqpkw5wPXrcWg0CtevxzFlygEaNVqltwLp2ctqV65cQaVSsW7dOpo0aYK1tTU1a9YkLCwsw2v27dtHgwYNsLKywt3dnWHDhvHkyZOXyrF7925UKhV//vknNWrUwNLSksDAQE6fPq1tM378eKZNm0ZgYCAVKlTgvffeIygoiHXr1r3Uvo2dFEcibT62nj3B2zttBO2ffoJn/uMpCgY1KE9X/zJ8sPYkx64+1HccIUQuzJ17jPDwO5kuiWs0CuHhd5g795iekmU2YcIERo8eTXh4ON7e3gQHB5Py/ymdLl68SFBQEJ07d+bEiROsWrWKffv2MXToUJ3se8yYMcyePZsjR47g6OhIcHAwyTnMlhATE4ODg4NO9m2spDgSaUxN06YaqVoVUlPT5mKLiNB3qgKjUqn4uGM1aroX580fjsodbEIYgcWLI7LtK6jRKCxebDi/w0aPHk3btm3x9vZm6tSpXL16lQv/P0s/ffp0evbsyfDhw6lYsSKBgYF8+eWXfP/99yQkvPzvosmTJ9OiRQuqV69OaGgod+/e5ddff82y7erVqzly5Aj9+vV76f0aMymOxL9MTNImq/X1BUWBX3+FI0f0narAWJiasKCXP4oCE36NRFGkg7YQhuzmzZwvOz1vfUGqUaOG9mdXV1cA7ty5A0BERAShoaHY2tpqH61atUKj0XD58uVM27p27VqGtp9++mmO+w4ICND+7ODggJeXF2fPns3UbteuXfTr149vv/2WqlWrvtBxFham+g4gDIxaDe3bp11qO3QI/vwTkpKgXj19JysQjrYWfNKxGm/9eJxf/75BJ78y+o4khMiGm5sN169nP9K/m5tNAabJmZmZmfZnlUoFgEajASAuLo4333yTYcOGZXqdh4dHpmVubm6Eh4drn+viEtiePXto164dc+fOpU+fPi+9PWMnxZHITKWCoCCwsIC//oJt29I6azdpou9kBSKomiuv13Jj8u+nCKzgiEtxS31HEkJkYfDgmkyZciDLS2tqtYrBg2vqIVXe+fn5cfr0aby8vHLV3tTUNNdtAQ4ePKgtsh4+fMjFixfx8fHRrt+9ezevvfYaM2bMYPDgwXkLX0jJZTWRNZUKmjaF5s3Tnv/1F2zZkna5rQiY2r4qVmYmDPvpb5JSNPqOI4TIwogR/tSq5YxarcqwXK1WUauWMyNG+OfbvmNiYggPD8/wiIqKeqFtvf/++xw4cIChQ4cSHh7OP//8w2+//aazDtkfffQRO3bsIDIykn79+uHg4KAdp2nXrl20bduWYcOG0blzZ27fvs3t27d58OCBTvZtrApVcVSuXDlUKlWGx2effZahzYkTJ2jQoAGWlpa4u7szc+ZMPaU1EvXrQ5s2aT8fPIhqwwbQFP5iwd7anG96+vF31EO+2vmPvuMIIbJga2vOnj3dmDIlkDJlbFGrVZQpY8uUKYHs2dMNW1vzfNv37t278fX1zfD46KOPXmhbNWrUYM+ePZw/f54GDRrg6+vLpEmTcHNz00nWzz77jPfeew9/f3+io6P56aefMDdPe2+WL19OfHw806dPx9XVVfvo1KmTTvZtrArdZbWPPvqIQYMGaZ8XK1ZM+3NsbCwtW7akefPmLFy4kJMnT9K/f3/s7e3lVGJOXnklrQ/Sb7+h+vtvXJOSoHVreOYaemFUu5wDbzf2YsHuC7Sr6YZ3qWLPf5EQokDZ2pozcWIAEycGPL+xjoSGhhIaGpppuUajITY2ltTUVNTqtHMP5cqVy3Rzh729faZlderUYevWrfmSt379+kRGRmbImC67YynqCtWZI0grhlxcXLQPG5t/O+StWLGCpKQkli5dStWqVenevTvDhg1jzpw5ekxsJGrVgq5dwcQEu6tXUa1ZA/8fo6MwG9K4Au4O1oxbd1KmFxFCiCKi0J05+uyzz5g2bRoeHh706NGDESNGYGqadphhYWE0bNhQezoRoFWrVsyYMYOHDx9SokSJTNtLTEwkMTFR+zy94k5OTs5xEK0Xkb49XW9XZypWJKVTJ5QZM1DOniXl++9RunVLO6uUG8nJqFPTJnjVJCen9WvKB7p8H02Aj9pVptfSo6w8dIVutXVz95rBf9ZIRl2RjDnvV1EUNBqN9s6t7KSfaUlvb4gMLWN6hmffX0PLmJWXyajRaFAUheTkZExMTDKsy8v3W6UUosFc5syZg5+fHw4ODhw4cIBx48bRr18/7Zmhli1b4unpyaJFi7SvOX36NFWrVuX06dNUrlw50zanTJnC1KlTMy1fuXIl1tbW+XcwBswqOpoyf/2FOiWFp46OXG/UCE0uCiRVcjLev/wCwPkuXVCM6LLcigtqTj5QMb5WKnb5141BiCLF1NQUFxcX3N3dM/yjVYgXlZSURFRUFLdv39aOQJ4uPj6eHj16EBMTg52dXY7bMfji6IMPPmDGjBk5tjlz5kyG2xLTLV26lDfffJO4uDgsLCxeqDjK6syRu7s79+7de+6bm1fJycls27aNFi1aZBgTw5CkZ2xZpQrma9akTVjr6oqmR4+0iWxzkpSE+v8d5DUffJD7M04vmFGX7+ODJ0kEfbmf+l4lmdO1xvNf8BzG9FlLxpcjGbOXkJBAVFQU5cqVw9Iy5yEzFEXh8ePHFCtWTDtOkKGRjLrxMhkTEhK4cuUK7u7umb5TsbGxODo65qo4MvjLaqNGjSIkJCTHNuXLl89yed26dUlJSeHKlStUqlQJFxcXoqOjM7RJf+7i4pLlNiwsLLKcXdnMzCzffonk57Z1xbRcOUwGDIDvv4c7dzBZsQJ694acvnCKkjYKN2BiZpbvHbp1+T6Wsjfjw7ZVGLUmgo5+ZWjqU0on2zWGz1oy6oZkzCw1NVV7Z3F6B+bspF9eyU1bfZGMuvEyGdO/T1l9l/Py3Tb44sjJyQknJ6cXem14eDhqtRpnZ2cgbQj1CRMmkJycrH2Ttm3bRqVKlbLsbySeo1Qp6NcvrUC6exeWLYM+faCQvped/EqzPvwGE36NZOsIB4pZGvYfOiEMXXqfkKSkJKysrPScRhQGSUlJAJn6G+WVwRdHuRUWFsahQ4do0qQJxYoVIywsjBEjRtCrVy9t4dOjRw+mTp3KgAEDeP/994mMjGTevHnMnTtXz+mNmKMj9O+fViA9ePBvgeToqO9kOqdSqfi0Y3Vazv2L2VvPM6V90Z57SIiXZWpqirW1NXfv3sXMzCzHswQajYakpCQSEhIM+oyHZHx5L5pRo9Fw9+5drK2ttTdivahCUxxZWFjw888/M2XKFBITE/H09GTEiBGMHDlS26Z48eJs3bqVd955B39/fxwdHZk0aZKMcfSy7O3TziD98APcuZNWIPXuDdlcqjRm7g7WjGzhzfRNZ2jk7UQTH2d9RxLCaKlUKlxdXbl8+TJXr17Nsa2iKDx9+hQrKyuD7isjGV/ey2RUq9V4eHi89LEVmuLIz8+PgwcPPrddjRo12Lt3bwEkKmKKFYOQkLQC6dYtCA2Fnj3B3V3fyXSuf31PDl1+wNCVx1nzViBV3HTbMV+IosTc3JyKFStqL4dkJzk5mb/++ouGDRsabN8tyagbL5PR3NxcJ2fECk1xJAyAtTX07QsrV8K1a2mFUnAweHrqO5lOmahVzOtei26Lwxiw/Ajr36lHKTuZnFaIF6VWq597t5qJiQkpKSlYWloa7B91yagbhpDRMC84CuNlaQm9ekGFCpCUBCtWwPnz+k6lczYWpnzXtw4A/UOP8CSx8I8WLoQQRYUUR0L3zM3Tzhj5+KRNMfLzz/D/eX0Kk1J2lnzXtw5X7j1h4PKjPE1K1XckIYQQOiDFkcgfpqZpc7FVrw4aDaxdC+Hh+k6lc1Xc7FjW7xXCox4x+IejJCRLgSSEEMZOiiORf0xMoGNH8PdPGwDyjz/g+nV9p9K5VzwdWBpShyNXHjBw+VFi4g13Di0hhBDPJ8WRyF9qNbz2GgQGpj2/cAGuXk0rlgqRgAolWRpSh5M3Ymj1xV9sOx39/BcJIYQwSFIcifynUkGLFtCwYdrzy5fh+HH9ZsoHgRUc2fReAyq7FmPQ90d5Z8VxYhPkLJIQQhgbKY5EwVCpoFGjtLvYbG2hauEcXdrN3oqlIXX4MtiXv/65S5cFB4h6EK/vWEIIIfJAiiNRcMzMYMECWL06bdDIQkqlUtG+phu/DgkkIVlDx2/2c/DSfX3HEkIIkUtSHImCo1Kl3eZvbZ32cyHn5VyM9e/Uo7yTLd0XH+SDtSeks7YQQhgBKY6EyEcONub8POhVpr1elT9P3KLF3D2cj36s71hCCCFyIMWREPlMrVbRO6Ac20Y2oqStBcGLD0qBJIQQBkyKIyEKiEtxS1YOrIuznSW9lh7llvTTFkIIgyTFkRAFqISNeVqBVMyCb8+a8Ej6IAkhhMGR4kiIAlbCxpyFPX1JSIXhq0+QkqrRdyQhhBDPkOJICD0oU8KKEG8NBy8/4PMt5/QdRwghxDOkOBJCT7yLK7zfyptFf13it/Ab+o4jhBDi/0z1HUCIoiwkwIMzt+N4f+0JvJxtqepWXN+RhBCiyJMzR0LokUqlYnqn6ng52zL4+2M8eJKk70hCCFHkSXEkhJ5ZmpmwqHdtEpJTGbryuHTQFkIIPZPiSAgDUNreivk9/Dh0+QGfbjyr7zhCCFGk5anPkUajYc+ePezdu5erV68SHx+Pk5MTvr6+NG/eHHd39/zKKUShF1ChJBPbVmbKH6cxN1XzflAlVEVgDjohhDA0uTpz9PTpUz7++GPc3d1p06YNmzZt4tGjR5iYmHDhwgUmT56Mp6cnbdq04eDBg/mdWYhCK6SeJx+2rczCPRcZ88sJklLkEpsQQhS0XJ058vb2JiAggG+//ZYWLVpgZmaWqc3Vq1dZuXIl3bt3Z8KECQwaNEjnYYUoCgY2KI+jrQVjfznB9YfxzOvuSyk7S33HEkKIIiNXxdHWrVupXLlyjm3Kli3LuHHjGD16NNeuXdNJOCGKqg6+pXGzt2LIimM0+nwX7Wu60TewnNzqL4QQBSBXl9WeVxg9y8zMjAoVKrxwICFEmlc8Hdg5ujFDm3ix/8J92n65j0m/RZIsd7MJIUS+eqFBIBMSEjhx4gR37txBo8n4i7p9+/Y6CSaEADtLM4Y2rchbjSrw48GrfPznGS7ciWN+Dz8cbMz1HU8IIQqlPBdHmzdvpk+fPty7dy/TOpVKRWpqqk6CCSH+ZWqiJqSeJ5Vc7Hhn5XHazNvLT4NfxdPRRt/RhBCi0MnzOEfvvvsuXbt25datW2g0mgwPKYyEyF8BFUqycVgDbCxM6LXkELdinuo7khBCFDp5Lo6io6MZOXIkpUqVyo88QojncCluyQ8D6gLQa8khmXJECCF0LM/FUZcuXdi9e3c+RMnZJ598QmBgINbW1tjb22fZ5tq1a7Rt2xZra2ucnZ0ZM2YMKSkpGdrs3r0bPz8/LCws8PLyIjQ0NP/DC6FjbvZW/DiwLjFPk+m37DBPElOe/yIhhBC5kuc+R/Pnz6dr167s3buX6tWrZxrzaNiwYToL96ykpCS6du1KQEAA3333Xab1qamptG3bFhcXFw4cOMCtW7fo06cPZmZmfPrppwBcvnyZtm3b8tZbb7FixQp27NjBwIEDcXV1pVWrVvmSW4j84uloQ2i/V+i++CBv/XiMb/vUxtLMRN+xhBDC6OW5OPrpp5/YunUrlpaW7N69O8P0BiqVKt+Ko6lTpwJke6Zn69atnD59mu3bt1OqVClq1arFtGnTeP/995kyZQrm5uYsXLgQT09PZs+eDaQNUbBv3z7mzp0rxZEwStVKF2dxH3/6LTtCn6WH+b7/K1IgCSHES8pzcTRhwgSmTp3KBx98gFptOPPWhoWFUb169Qx9oVq1asXbb7/NqVOn8PX1JSwsjObNm2d4XatWrRg+fHi2201MTCQxMVH7PDY2FoDk5GSSk5N1egzp29P1dnVJMuqGLjPW8SjO8hB/+oYeY9y6E8zoWFUnc7IVtfcxv0hG3ZCMulGUM+Zle3kujpKSkujWrZtBFUYAt2/fztRJPP357du3c2wTGxvL06dPsbKyyrTd6dOna89aPWvr1q1YW1vrKn4G27Zty5ft6pJk1A1dZuxaTsWPf99E9TCKRq6KzrZb1N7H/CIZdUMy6kZRzBgfH5/rtnkujvr27cuqVasYP358Xl+ayQcffMCMGTNybHPmzBl8fHxeel8vaty4cYwcOVL7PDY2Fnd3d1q2bImdnZ1O95WcnMy2bduynb/OEEhG3ciPjG0As03n+OHgNbo2r03tsiUMLqOuSUbdkIy6IRl1I78ypl/5yY08F0epqanMnDmTLVu2UKNGjUzB58yZk+ttjRo1ipCQkBzblC9fPlfbcnFx4fDhwxmWRUdHa9el/3/6smfb2NnZZXnWCMDCwgILC4tMy83MzPLti5Wf29YVyagbus44oW0VTt96zLBVJ9jwbn2dTFhbFN/H/CAZdUMy6kZRzJiXbeW5ODp58iS+vr4AREZGZliX134OTk5OODk55TVClgICAvjkk0+4c+cOzs7OQNopOTs7O6pUqaJts3Hjxgyv27ZtGwEBATrJIIS+mZqomd/Dj3Zf7WPIiuOsHFQXC1PpoC2EEHmR5+Jo165d+ZHjua5du8aDBw+4du0aqamphIeHA+Dl5YWtrS0tW7akSpUq9O7dm5kzZ3L79m0+/PBD3nnnHe2Zn7feeov58+czduxY+vfvz86dO1m9ejV//vmnXo5JiPzgVMyCb3r50X3xQUavOcG8brVQq1++g7YQQhQVee5VHRMTw4MHDzItf/DgQZ6u5+XVpEmT8PX1ZfLkycTFxeHr64uvry9Hjx4FwMTEhA0bNmBiYkJAQAC9evWiT58+fPTRR9pteHp68ueff7Jt2zZq1qzJ7NmzWbJkidzGLwodP48SzOtWiw0nbjJj81l9xxFCCKOS5zNH3bt3p127dgwZMiTD8tWrV/P7779numylK6Ghoc8dzbps2bLP3X/jxo35+++/dZhMCMPUurorE9tW4aMNp7GzMmNI4wo6ucVfCCEKuzyfOTp06BBNmjTJtLxx48YcOnRIJ6GEELrRv74n7zWryOdbzvHh+kiSUjT6jiSEEAYvz2eOEhMTM81XBmm33j19KjOEC2FoRrTwprS9FeN/PcnJGzF80a0W5Z1s9R1LCCEMVp7PHL3yyissXrw40/KFCxfi7++vk1BCCN16o44764YE8jghhbZf7mPVkWsoiu4GihRCiMIkz2eOPv74Y5o3b05ERATNmjUDYMeOHRw5coStW7fqPKAQQjdqlLFnw7v1mbbhNO+vPcmus3eZ3qk6JWzM9R1NCCEMSp7PHNWrV4+wsDDc3d1ZvXo1f/zxB15eXpw4cYIGDRrkR0YhhI7YWJjyWecaLOzlx8HL92n5xV98vesCdx8nPv/FLyAhOZXIGzFsOnmLnWejOXUzhpRU6fckhDBseT5zBFCrVi1WrFih6yxCiAISVM2VWu4lmLPtHF/t/Icvtp+nnpcjDSo60bCiI17OL9YnKeZpMrvP3eHE9RhOXH9ERFQMSf8phmzMTfArW4IKTra4FLfExc6SUnaWeJeypaRt5tHohRCioOWqOHry5Ak2Nja53mhe2wshCp5LcUtmdqnJhDZVWHv8OjvP3mHG5rNM26DB3ESNa3FLXE3VJIffJLCiM272/06xk5Kq4dqDeC7efcKFO3Fpj7txnL4ZQ3KqQtmS1lR1s2N8Gx9quNtT1sGaFI3CtQfxHLnygKNXHrL/wj1uxybwOOHfGzy8nG1pUaUUrau5UL10cRl6QAihF7kqjry8vHjvvffo27cvrq6uWbZRFIXt27czZ84cGjZsyLhx43QaVAiRP4pbm9G/vif963vyNCmVw1cecOluHBfvPGbHiSeMXps2TZCHgzXuDlbcikng2v14UjRpHbptLUyp4GyLl5MtnXxL07JqKVyLZz1XYSk7S+qUc8iwLD4phVsxCZy8HsOBi/f4+fA1Fuy+SGl7K9pUdyGomiu+7vYyyrcQosDkqjjavXs348ePZ8qUKdSsWZPatWvj5uaGpaUlDx8+5PTp04SFhWFqasq4ceN488038zu3ECIfWJmb0MjbiUbeTiQnJ1NHfZm6jZoTfj2Wg5cecPdxIt6lilHe0QZPR1u8nG0pZWfxUmd4rM1NqeBkSwUnWzr4liYlVcOhyw/YePIWv/59k2/3XsbFzpLXfd3oX89TJ5PpCiFETnJVHFWqVIm1a9dy7do11qxZw969ezlw4ABPnz7F0dERX19fvv32W1q3bo2JiUxyKURhUtLGnKBqrgRVy/qssa6Zmqip5+VIPS9HPnq9GkevpBVKKw9eY9m+KwxpUoGhTbwwNcnz/SRCCJEreeqQ7eHhwahRoxg1alR+5RFCCC0TtYq65UtSt3xJRrWqxLd/XeKrnRfYfe4uX3SrReniMgyBEEL35J9eQgijYGdpxqiWlVj9ZgAP45No8+Vefou4pe9YQohCSIojIYRR8S9bgo3DGhBU1YXRv5xk9y3pqC2E0C0pjoQQRsfGwpTZb9RkUP1y/HrFhBWHo/QdSQhRiEhxJIQwSiqVirGtvGnoomHan2c5cPGeviMJIQqJFxohWwghDEWHchpSbZx464dj/DCgLjXd7Qtkv4qicPneE27HJhCfmIpGUXAsZoGTrQVOxSywNJM7d4UwVnkujjZv3oytrS3169cH4Ouvv+bbb7+lSpUqfP3115QoUULnIYUQIjsmKviqew0G/vA3vZYc4ptefjSo6JQv+9JoFM7feczGE7fYcOIWl+49ybKdSgWVShWjWWVnBgR65EsWIUT+yXNxNGbMGGbMmAHAyZMnGTVqFCNHjmTXrl2MHDmSZcuW6TykEELkpJilGd8PqMubPxyl93eHaV3NhfFtKuPuYP3S2z59M5Z1x69z+MoD/omO42lyKsUsTWlV1YUPX6tMeUdbbCzSfpXef5LI3ceJ3Hj4lOPXHrJ03xV+PhxFi1Iqgv4/orgQwvDluTi6fPkyVapUAWDt2rW89tprfPrppxw/fpw2bdroPKAQQuSGrYUpPw6oy/rwG3y26SxNZu2mWWVn3qjtTiNvp1wPGqnRKNx5nMifJ2/xy7HrnLkVS0kbcxpXcqZdDTd8XIvxiqcDFqaZL5s5FbPAxyXt5+6veDCihTef/nman0/c5vTiQyzuUweX4jLCtxCGLs/Fkbm5OfHx8QBs376dPn36AODg4EBsbKxu0wkhRB6oVCo6+pahZRUX1hyNYvXR6wxYfhQHG3PaVnelWx13qpUunuE1j+KT+PHgVQ5eesDZ27Hci0sCwMxERTOfUoxs4U3jSk6YvcCI3K7FrZjTtQblU6+z5noS3RaHsXLQq5S2z3ruOSGEYchzcVS/fn1GjhxJvXr1OHz4MKtWrQLg/PnzlClTRucBhRAir2wsTAmp50lIPU9O3Yzh9/Cb/BZ+kx8OXqV66eL4ly2BqVpFzNNkNp68RaqiUN/LiR51y+JW3JLiVma8Wr4kJWx0MwJ3eTtYMaAOfUKP8sbCMH4a9CoeJV/+kp8QIn/kuTiaP38+Q4YM4ZdffmHBggWULl0agE2bNhEUFKTzgEII8TKquhWnqltxxrSqxO5zd1l1NIqDl+6TnKrB3NSEkHrl6FfPE0dbi3zNUaaEFasGB9BzySG6LQ7j58GvUrakTb7uUwjxYvJcHHl4eLBhw4ZMy+fOnauTQEIIkR9MTdQ0r1KK5lVK6S2Dm70Vqwa/SrfFB+kXeoT179TDztJMb3mEEFnL1UX0Z/sSxcbG5vgQQgiRPWc7S5aG1OFubCIf/hqJoshdbEIYmlydOSpRogS3bt3C2dkZe3t7VKrMcxkpioJKpSI1NVXnIYUQojDxdLTh447VeO/ncBpXcqKTn/TXFMKQ5Ko42rlzJw4ODtqfsyqOhBBC5N7rtUqz5/xdJq6PpKa7PRWcbPUdSQjxf7kqjho1aqT9uXHjxvmVRQghipRpr1cjIuoR76w4zq9D6mFlLlOOCGEI8jxwx5QpU9BoNJmWx8TEEBwcrJNQQghRFNhYmPJNT3+u3H/C5N8j9R1HCPF/eS6OvvvuO+rXr8+lS5e0y3bv3k316tW5ePGiTsMJIURhV8mlGB93qM7qo9f55dh1fccRQvACxdGJEycoU6YMtWrV4ttvv2XMmDG0bNmS3r17c+DAgfzIKIQQhVoX/zK8UbsMH64/ycW7cfqOI0SRl+fiqESJEqxevZqhQ4fy5ptvMm/ePDZt2sQnn3yCqWmeh03KtU8++YTAwECsra2xt7fPso1Kpcr0+PnnnzO02b17N35+flhYWODl5UVoaGi+ZRZCiNya2r4arsWtGLMmglSZpFYIvcr7ZEHAV199xbx58wgODqZ8+fIMGzaMiIgIXWfLICkpia5du/L222/n2G7ZsmXcunVL++jQoYN23eXLl2nbti1NmjQhPDyc4cOHM3DgQLZs2ZKv2YUQ4nmszE2Y2aUGf0c9Yum+y/qOI0SRludTPUFBQRw9epTly5fTpUsXnj59ysiRI3n11VeZOnUqY8eOzY+cTJ06FeC5Z3rs7e1xcXHJct3ChQvx9PRk9uzZAFSuXJl9+/Yxd+5cWrVqpdO8QgiRV3XKOdC/niefbz1HEx8nvJyL6TuSEEVSnouj1NRUTpw4gZubGwBWVlYsWLCA1157jYEDB+ZbcZRb77zzDgMHDqR8+fK89dZb9OvXTzsuU1hYGM2bN8/QvlWrVgwfPjzb7SUmJpKYmKh9nj4KeHJyMsnJyTrNnr49XW9XlySjbkhG3SiMGYc3Lc+us9GMXBXOqkGvYGryQif486Qwvo/6IBl1I78y5mV7KkWHY9ffu3cPR0dHXW0uS6GhoQwfPpxHjx5lWjdt2jSaNm2KtbU1W7duZfLkycycOZNhw4YB4O3tTb9+/Rg3bpz2NRs3bqRt27bEx8djZWWVaZtTpkzRnrV61sqVK7G2llm1hRC6d+UxfBFpQht3DS3LSP8jIXQhPj6eHj16EBMTg52dXY5tddqDOq+F0QcffMCMGTNybHPmzBl8fHxytb2JEydqf/b19eXJkyd8/vnn2uLoRYwbN46RI0dqn8fGxuLu7k7Lli2f++bmVXJyMtu2baNFixaYmRnmZJSSUTcko24U5oxPHf7hu/1XeKdDABWd83f07ML8PhYkyagb+ZUxL/O/vtBltblz57J69WquXbtGUlJShvUPHjzI9bZGjRpFSEhIjm3Kly+f14hadevWZdq0aSQmJmJhYYGLiwvR0dEZ2kRHR2NnZ5flWSMACwsLLCwsMi03MzPLty9Wfm5bVySjbkhG3SiMGUe0rMTGyGhmb7vAdyF18jHZvwrj+6gPklE3dJ0xL9vKc3E0depUlixZwqhRo/jwww+ZMGECV65cYf369UyaNClP23JycsLJySmvEXItPDycEiVKaIubgIAANm7cmKHNtm3bCAgIyLcMQgjxIixMTRjTqhLv/vQ3YRfvE1ChpL4jCVFk5Lmn34oVK/j2228ZNWoUpqamBAcHs2TJEiZNmsTBgwfzIyMA165dIzw8nGvXrpGamkp4eDjh4eHExaUNmPbHH3+wZMkSIiMjuXDhAgsWLODTTz/l3Xff1W7jrbfe4tKlS4wdO5azZ8/yzTffsHr1akaMGJFvuYUQ4kW9VsOVmmWKM33TGTQy9pEQBSbPxdHt27epXr06ALa2tsTExADw2muv8eeff+o23TMmTZqEr68vkydPJi4uDl9fX3x9fTl69CiQdrrs66+/JiAggFq1arFo0SLmzJnD5MmTtdvw9PTkzz//ZNu2bdSsWZPZs2ezZMkSuY1fCGGQVCoV49pU5sT1GP44cVPfcYQoMvJ8Wa1MmTLcunULDw8PKlSowNatW/Hz8+PIkSNZ9s3RldDQ0BzHOAoKCiIoKOi522ncuDF///23DpMJIUT+ebV8SZr5OPPF9n94rYYbJmqVviMJUejl+cxRx44d2bFjBwDvvvsuEydOpGLFivTp04f+/fvrPKAQQhR1w5pV5PK9J/x58pa+owhRJOT5zNFnn32m/blbt254eHgQFhZGxYoVadeunU7DCSGEgJru9jT0duLrnRd4rborajl7JES+eulxjgICAuRuLyGEyGfvNvWi68Iwtp+JpmXVrKdIEkLoxkuNS29nZ8elS5d0lUUIIUQ26pRz4BVPB77edQEdTmwghMjCSxVH8h+oEEIUnHebehFxPYa9/9zTdxQhCrX8n9FQCCGETtT3cqSmuz3zd13QdxQhCrWXKo569eql8/nFhBBCZE2lUjG0iReHLz/g6JXcT9UkhMibXBdHEydOJCUlJcOyBQsWaCebvXbtGi1atNBtOiGEEBk083GmorMtC3Zf1HcUIQqtXBdHy5cvp06dOkRGRmZat2jRIqpVq4ap6Uvf/CaEECIHarWKtxtXYMfZO5y9nftZxoUQuZfr4igyMpLq1atTu3Ztpk+fjkaj4dq1azRv3pyxY8cya9YsNm3alJ9ZhRBCAO1qulHa3oqFcvZIiHyR6+LIzs6O77//nlWrVjFv3jz8/PyoXr06KpWKEydOMHjw4PzMKYQQ4v/MTNQMblieP07c4tr9eH3HEaLQyXOH7FdffZXq1atz4sQJNBoNH374IWXLls2PbEIIIbLxRm13SlibMWfbOX1HEaLQyVNx9NNPP1GlShU0Gg1nzpzh7bffpmXLlowYMYKEhIT8yiiEEOI/rMxNGNvKh/XhN+XONSF0LNfFUefOnRk0aBBTpkxhx44dVKpUiZkzZ7Jr1y42btxIzZo1CQsLy8+sQgghntHFvww1yhRn8u+nSNXIoLxC6Equi6Pbt2/z999/8+6772ZYHhgYSHh4OEFBQTRq1EjnAYUQQmRNrVYxpX1VTt2MZdWRKH3HEaLQyHVxtHfvXipWrJjlOisrK+bNm8f27dt1FkwIIcTz+XmUoLNfGT7fcpaY+GR9xxGiUMh1caRWP79pw4YNXyqMEEKIvHs/qBLJqQqztkrnbCF0QeZWE0III+dsZ8nIFt78cPAqhy7d13ccIYyeFEdCCFEIhASWo065Eoxde4L4pJTnv0AIkS0pjoQQohBQq1XM7FKT2zEJfL5FLq8J8TKkOBJCiELC09GGMa0qEXrgCocvy9hHQrwoKY6EEKIQ6VfPE3+PEoz9JYKE5FR9xxHCKElxJIQQhYiJWsVnnWtw49FTFu25pO84QhglKY6EEKKQ8XK2ZWCD8nyz+4JMTCvEC5DiSAghCqF3m3rhYGPORxtO6TuKEEZHiiMhhCiErM1NmfhaFbafucO209H6jiOEUZHiSAghCqnW1VxoXMmJD9eflKlFhMgDKY6EEKKQUqlUTO9UnfjEVD7acFrfcYQwGlIcCSFEIeZa3IqJr1Vh7fHr7Dwrl9eEyA0pjoQQopDrWrsMjbydGL8ukieJMrWIEM9jFMXRlStXGDBgAJ6enlhZWVGhQgUmT55MUlJShnYnTpygQYMGWFpa4u7uzsyZMzNta82aNfj4+GBpaUn16tXZuHFjQR2GEELohUql4uMO1XgYn8TXuy7oO44QBs8oiqOzZ8+i0WhYtGgRp06dYu7cuSxcuJDx48dr28TGxtKyZUvKli3LsWPH+Pzzz5kyZQqLFy/Wtjlw4ADBwcEMGDCAv//+mw4dOtChQwciIyP1cVhCCFFg3B2seatRBZbsvcyVe0/0HUcIg2YUxVFQUBDLli2jZcuWlC9fnvbt2zN69GjWrVunbbNixQqSkpJYunQpVatWpXv37gwbNow5c+Zo28ybN4+goCDGjBlD5cqVmTZtGn5+fsyfP18fhyWEEAXqrUYVcCpmwbh1J9FoFH3HEcJgmeo7wIuKiYnBwcFB+zwsLIyGDRtibm6uXdaqVStmzJjBw4cPKVGiBGFhYYwcOTLDdlq1asX69euz3U9iYiKJiYna57GxsQAkJyeTnKzbW2PTt6fr7eqSZNQNyagbkjFvTFXwaYcq9A09xtJ9F+kbUBYwrIzZkYy6UZQz5mV7KkVRjO6fDxcuXMDf359Zs2YxaNAgAFq2bImnpyeLFi3Stjt9+jRVq1bl9OnTVK5cGXNzc5YvX05wcLC2zTfffMPUqVOJjs76Lo4pU6YwderUTMtXrlyJtbW1jo9MCCHy39rLasKiVYyonkppG32nEaJgxMfH06NHD2JiYrCzs8uxrV7PHH3wwQfMmDEjxzZnzpzBx8dH+/zGjRsEBQXRtWtXbWGUn8aNG5fhbFNsbCzu7u60bNnyuW9uXiUnJ7Nt2zZatGiBmZmZTretK5JRNySjbkjGF9MkKZXg7w7zw9Vk1r5ZF3tLtcFl/C9DfB//SzLqRn5lTL/ykxt6LY5GjRpFSEhIjm3Kly+v/fnmzZs0adKEwMDADB2tAVxcXDKd/Ul/7uLikmOb9PVZsbCwwMLCItNyMzOzfPti5ee2dUUy6oZk1A3JmDdmZmYs6VuH1+fv552fI/ghxF+73FAyZkcy6kZRzJiXbem1OHJycsLJySlXbW/cuEGTJk3w9/dn2bJlqNUZ+5IHBAQwYcIEkpOTtW/Atm3bqFSpEiVKlNC22bFjB8OHD9e+btu2bQQEBOjmgIQQwki4Frfi2z61eWNRGOPXn6ap9BIQQsso7la7ceMGjRs3xsPDg1mzZnH37l1u377N7du3tW169OiBubk5AwYM4NSpU6xatYp58+ZluCT23nvvsXnzZmbPns3Zs2eZMmUKR48eZejQofo4LCGE0Kua7vbM6lqT30/cYvtNlb7jCGEwjOJutW3btnHhwgUuXLhAmTJlMqxL709evHhxtm7dyjvvvIO/vz+Ojo5MmjSJwYMHa9sGBgaycuVKPvzwQ8aPH0/FihVZv3491apVK9DjEUIIQ9Guphvnb8fw1a5LBJ2K5rVaZZ7/IiEKOaMojkJCQp7bNwmgRo0a7N27N8c2Xbt2pWvXrjpKJoQQxm9o4wrsO3GBMWtPUs6pGNVKF9d3JCH0yiguqwkhhMg/arWKHhU0eDnbMvj7o8Q8NdwxcIQoCFIcCSGEwNwE5nevSWxCCtM2nNZ3HCH0SoojIYQQALjZWzHxtcr8cuw6W0/dfv4LhCikpDgSQgih9UZtd1pWKcWIVeGcuhmj7zhC6IUUR0IIIbRUKhVzu9XC08mG/qFHuPHoqb4jCVHgpDgSQgiRgY2FKUv71sHMRE3vJYe4F5f4/BcJUYhIcSSEECITZztLVgysy+PEFPp8d5jYBLmDTRQdUhwJIYTIUtmSNvww4BWuP4xn8PdHSUhO1XckIQqEFEdCCCGy5eNix3chdfj72iPe+/lvUjWKviMJke+kOBJCCJGjOuUc+LqHH9vP3OHD9Se10zYJUVhJcSSEEOK5mlcpxWedqvPT4ShmbT0nBZIo1IxibjUhhBD617W2O4/ik/lk4xniElKY1K4qJmqVvmMJoXNSHAkhhMi1QQ3LY2NhyofrTxKXmMqsrjVQqaRAEoWLFEdCCCHypEddD2wsTHjv53Dc7C0Z1bKSviMJoVNSHAkhhMiz12uV5nZMAtM3ncW1uBU96nroO5IQOiPFkRBCiBcyuGF5bj56yofrT1LKzoJmlUvpO5IQOiF3qwkhhHghKpWKSe2q0qJKKYasOM5f5+/qO5IQOiHFkRBCiBdmolbxZbAvgRVKMvD7o+yRAkkUAlIcCSGEeCkWpiYs7O1PfS9HBn1/lAMX7+k7khAvRYojIYQQL83C1IQFvfx4pZwDb/1wjIt34/QdSYgXJsWREEIInbAwNeHrnn6UsrOkf+gRHjxJ0nckIV6IFEdCCCF0priVGUtD6hCXkMKbPxwlMSVV35GEyDMpjoQQQuiUu4M1i/vUJuJ6DO//ckLmYRNGR4ojIYQQOudftgSzu9ZkffhN5u34R99xhMgTGQRSCCFEvmhX042r958wa+t5ypW0oYNvaX1HEiJXpDgSQgiRb95p4sXle/GM/eUElVyKUdnVTt+RhHguuawmhBAi36hUKj7tVI3yTjaMWBVOQrJ00BaGT4ojIYQQ+crC1IQvutfi0t0nzNl2Xt9xhHguKY6EEELkOx8XO0a08GbJ3ktE3ojRdxwhciTFkRBCiAIxsIEn3qWKMW7dSVJSNfqOI0S2jKI4unLlCgMGDMDT0xMrKysqVKjA5MmTSUpKytBGpVJlehw8eDDDttasWYOPjw+WlpZUr16djRs3FvThCCFEkWRmomZ6p+pE3oxhedhVfccRIltGURydPXsWjUbDokWLOHXqFHPnzmXhwoWMHz8+U9vt27dz69Yt7cPf31+77sCBAwQHBzNgwAD+/vtvOnToQIcOHYiMjCzIwxFCiCLL16MEvV8ty+yt57jx6Km+4wiRJaMojoKCgli2bBktW7akfPnytG/fntGjR7Nu3bpMbUuWLImLi4v2YWZmpl03b948goKCGDNmDJUrV2batGn4+fkxf/78gjwcIYQo0sa0qkQxS1Mm/xYpo2cLg2S04xzFxMTg4OCQaXn79u1JSEjA29ubsWPH0r59e+26sLAwRo4cmaF9q1atWL9+fbb7SUxMJDExUfs8NjYWgOTkZJKTk1/yKDJK356ut6tLklE3JKNuSEbdKOiMliYwsY0PQ3+O4M+IG7SqWuq5r5H3UTeKcsa8bE+lGGHZfuHCBfz9/Zk1axaDBg0C4N69e3z//ffUq1cPtVrN2rVrmTlzJuvXr9cWSObm5ixfvpzg4GDttr755humTp1KdHR0lvuaMmUKU6dOzbR85cqVWFtb58PRCSFE4acosOScmqg4FeNrpWJptP9UF8YiPj6eHj16EBMTg51dzoOR6rU4+uCDD5gxY0aObc6cOYOPj4/2+Y0bN2jUqBGNGzdmyZIlOb62T58+XL58mb179wIvVhxldebI3d2de/fuPffNzavk5GS2bdtGixYtMlwONCSSUTcko25IRt3QV8abj57S+qsDdPYrzaS2Pjm2lfdRN4pyxtjYWBwdHXNVHOm1Vh81ahQhISE5tilfvrz255s3b9KkSRMCAwNZvHjxc7dft25dtm3bpn3u4uKSqQiKjo7GxcUl221YWFhgYWGRabmZmVm+fbHyc9u6Ihl1QzLqhmTUjYLOWNbJjJEtvPlk4xm6+LtT093+ua+R91E3imLGvGxLr8WRk5MTTk5OuWp748YNmjRpgr+/P8uWLUOtfn5f8vDwcFxdXbXPAwIC2LFjB8OHD9cu27ZtGwEBAXnOLoQQ4uWFBJbj179vMG7dSX4fWg9TE6O4T0gUckZxlffGjRs0btyYsmXLMmvWLO7evatdl37WZ/ny5Zibm+Pr6wvAunXrWLp0aYZLb++99x6NGjVi9uzZtG3blp9//pmjR4/m6iyUEEII3TM1UfNpx+p0/GY/C/dcZGjTivqOJIRxFEfbtm3jwoULXLhwgTJlymRY92yXqWnTpnH16lVMTU3x8fFh1apVdOnSRbs+MDCQlStX8uGHHzJ+/HgqVqzI+vXrqVatWoEdixBCiIxqutvzVqMKfLH9H6qXsaeRd+6uKOS3xwnJRMcmoFapMDNR8zghhf0X7vEkKQVHWwtiE5KJehDP1fvxJKVocCtuiXWciiZJqQZ/yUrkzCiKo5CQkOf2Terbty99+/Z97ra6du1K165ddZRMCCGELoxo4c25249564djLA2pQ0CFknrLcvX+Ez7bdJYtp26j+c8tS1ZmJthamnI/LhEbC1PKlrTGw8EaC1MTLt+LIyJKzY65exnW3Juer3igVqv0cxDipRhFcSSEEKJwMzNR83VPP/ouPUzwtwfp5Fuaia9VoYSNeYFlePgkiSX7LvHt3ss42VowpX1VfFzsUBSFVI0CKvDzKIGlmQkajYJKBSrVv8VPcnIy36/bSKRSkonrI/nr/F3m9/DFwtSkwI5B6IYUR0IIIQyCpZkJKwbWZc2x60zfeIY95+8y8bUqtKvplq/7vR+XyJJ9l/n+wBU0Cgys78nQpl5Ym2f/JzK7M0KOljCzTXVeq1mat1cc5+0fj7Ogl58USEZGiiMhhBAGw9RETfArHjSr7MyU308xfFU4X+74h/71ymKlefHtxiWmsP10NPsv3OPag3hinqaNlqxSqbh4Jw5TExV9AsoxsIEnjraZh2/Jq2aVS7GkT20Gfn9UCiQjJMWREEIIg+NczJJvevrz97WHLP7rEhN/P42dmQmnTc8RVN0NP48SmOSiP4+iKPx58hYf/XGaO48T8XEphnepYvi4FAMgVVHo6l+GDr6lcdDxJbyG3k7aAumtH46xoJc/lmZSIBkDKY6EEEIYLF+PEizo5c+5m4+Y8vNe1off4rv9VylpY05Nd3vc7C2xtzLHr6w9tcs5YGf5711iV+8/YdJvp9hz/i6tqpbiw7ZVcHco2GmfGno78V3f2gxcfpRB3x/l655+GTIKwyTFkRBCCINX3smG7hU0tApqxKnbcWw/c4fztx9z7Ooj7j5OZP6uC6hVULqEFW7FrUhITuXkjRhci1uxpE9tmld5/uS2+aVBRSeW9avDmz8co/M3B1jY258KTrZ6yyOeT4ojIYQQRsNErcK/rAP+ZR20yxRF4er9eA5fecDFu3HcjknA3ERNtzoedPQtjZW5/i9lBVZw5Nch9Rj0/VFaz9vLsKZeDG5YAXNTGRHcEElxJIQQwqipVCrKOdpQztFG31Fy5OVsy8ZhDZi34x/mbv+HDSdu8VWwLxVLFdN3NPEfUrIKIYQQBcTK3IQPWvvw+9B6pGoU2s3fx8+Hr2WY7UHonxRHQgghRAGr6lac34fWp6NvaT5Yd5Luiw9y9MoDfccS/yfFkRBCCKEHVuYmTO9Ug2UhdYhNSKHLwjD6LTvMuduP9R2tyJPiSAghhNCjJj7O/Plufb4K9uXq/XjafbWP1Uei9B2rSJMO2UIIIYSeqdUq2tV0o2XVUkz5/TRj154gPimFkHqe+o5WJElxJIQQQhgIC1MTPu1YDVsLE6b8cZrEFA1vNqqg71hFjhRHQgghhAFRqVSMb1MZC1MTpm86i1MxCzr5ldF3rCJFiiMhhBDCwKhUKka19ObO4wQ+WHsSdwdr6pRzeP4LhU5Ih2whhBDCAKlUKj7uUB2/svYM/v4oV+8/0XekIkOKIyGEEMJAmZuqWdjLH3trcwZ/f4wniSn6jlQkSHEkhBBCGDB7a3MW9/bn+sN4Rq+JIFUjo2nnNymOhBBCCANXsVQx5nSrxZZTtxmzJoKUVI2+IxVqUhwJIYQQRqBVVRfmdfflt4ibjP/1pMzHlo/kbjUhhBDCSLSr6UZiiobRayKoXdaBN+q46ztSoSRnjoQQQggj0sW/DMGvuDPxt0jO3o7Vd5xCSYojIYQQwshMblcVT0cbhv8cTkJyqr7jFDpSHAkhhBBGxtLMhC+61+LS3SfM2nJO33EKHSmOhBBCCCPk42LH2KBKLNl3mf0X7uk7TqEixZEQQghhpPrX8ySwQklGrY4gJj5Z33EKDSmOhBBCCCOlVquY1bUm8UkpDP3puPQ/0hEpjoQQQggj5mZvxYJe/hy+/IAhK46TmCIF0suS4kgIIYQwcvW8HPm2T232XbjHOyvkDNLLMpriqH379nh4eGBpaYmrqyu9e/fm5s2bGdqcOHGCBg0aYGlpibu7OzNnzsy0nTVr1uDj44OlpSXVq1dn48aNBXUIQgghRL5p6O3E4t7+7Ltwj26LwrgTm6DvSEbLaIqjJk2asHr1as6dO8fatWu5ePEiXbp00a6PjY2lZcuWlC1blmPHjvH5558zZcoUFi9erG1z4MABgoODGTBgAH///TcdOnSgQ4cOREZG6uOQhBBCCJ1qXMmZNW8GEh2bSPv5+zlzSwaJfBFGUxyNGDGCV199lbJlyxIYGMgHH3zAwYMHSU5O652/YsUKkpKSWLp0KVWrVqV79+4MGzaMOXPmaLcxb948goKCGDNmDJUrV2batGn4+fkxf/58fR2WEEIIoVPVyxTnt6H1KGlrTu/vDnPtfry+Ixkdo5xb7cGDB6xYsYLAwEDMzMwACAsLo2HDhpibm2vbtWrVihkzZvDw4UNKlChBWFgYI0eOzLCtVq1asX79+mz3lZiYSGJiovZ5bGxaFZ6cnKwtzHQlfXu63q4uSUbdkIy6IRl1QzLqhiFldLAy4bs+fnRbfJje3x1i1aA6lLS1MKiM2cmvjHnZnkoxoml933//febPn098fDyvvvoqGzZsoGTJkgC0bNkST09PFi1apG1/+vRpqlatyunTp6lcuTLm5uYsX76c4OBgbZtvvvmGqVOnEh0dneU+p0yZwtSpUzMtX7lyJdbW1jo+QiGEEEJ37iXAF5Em2JvDkCqpWBvlKRHdiI+Pp0ePHsTExGBnZ5djW72+TR988AEzZszIsc2ZM2fw8fEBYMyYMQwYMICrV68ydepU+vTpw4YNG1CpVPmWcdy4cRnONsXGxuLu7k7Lli2f++bmVXJyMtu2baNFixbaM2KGRjLqhmTUDcmoG5JRNww1Y+2AWEJCj/F9lCXf9qzBsQN7DC7js/LrfUy/8pMbei2ORo0aRUhISI5typcvr/3Z0dERR0dHvL29qVy5Mu7u7hw8eJCAgABcXFwynf1Jf+7i4qL9/6zapK/PioWFBRYWFpmWm5mZ5dsXKz+3rSuSUTcko25IRt2QjLphaBlrepTk58EB9FxyiJDvw+njYXgZs6LrjHnZll6LIycnJ5ycnF7otRqNBkDbHyggIIAJEyaQnJysfQO2bdtGpUqVKFGihLbNjh07GD58uHY727ZtIyAg4CWOQgghhDBslVyKserNV+nx7UG+PGVCo8ZPKetk2MWRPhnF3WqHDh1i/vz5hIeHc/XqVXbu3ElwcDAVKlTQFjY9evTA3NycAQMGcOrUKVatWsW8efMyXBJ777332Lx5M7Nnz+bs2bNMmTKFo0ePMnToUH0dmhBCCFEgKjjZ8tPAOigKBC85wpV7T/QdyWAZRXFkbW3NunXraNasGZUqVWLAgAHUqFGDPXv2aC95FS9enK1bt3L58mX8/f0ZNWoUkyZNYvDgwdrtBAYGsnLlShYvXkzNmjX55ZdfWL9+PdWqVdPXoQkhhBAFxr2ENcOqpmJppuaNRWH8E/1Y35EMklH0W69evTo7d+58brsaNWqwd+/eHNt07dqVrl276iqaEEIIYVTsLWDFgDr0W36cNxaF8cOAulQrXVzfsQCIT0ph3NqTlNfz7CdGceZICCGEELrjaGvBz4NfxcPBmuBvD3Ls6kN9R+La/Xg6fXOAbWfuEJecf3eh54YUR0IIIUQRZG9tzo8D61LZ1Y7e3x0i8kaM3rLsv3CP9l/v42lyKmsGv0I1B/0OwSjFkRBCCFFEFbM0Y3m/V/BytuXNH47x4ElSge4/OVXDF9vP02fpYaqXLs7v79THu1SxAs2QFSmOhBBCiCLMytyEhb38SUhOZejK46Skagpkv+ejH9Pxm/18tfMC7zTxYllIHYpbG8bwAlIcCSGEEEWcm70V83v4cejyAyb+dgqNJn8va605GsVrX+0jIVnDr0MCGdnCG1MTwylJDCeJEEIIIfQmoEJJpneqzs9HrjF27Yl8O4P00+FrjPnlBJ18S7Ph3frUKGOfL/t5GUZxK78QQggh8t8btd2xMFUzcnUE0bEJzO/hR3Er3V3qWnnoGuN/PUmfgLJMbV81X+dGfRly5kgIIYQQWq/XKs33/V/hxPUYOn69n0t343Sy3RWHrjL+15P0NfDCCKQ4EkIIIcR/1PNy5Ld36qFWq3j96/2sPHSNuMSUF97eikNXmfBrJH0DyjLFwAsjkOJICCGEEFko52jDuiGBNPR2YsL6k9T5eDuj10Rw5MoDFCV3HbaTUzXM3/mPURVGIH2OhBBCCJENO0szvu7hx81HT1l77Dqrj0Xxy7HreJeypYmPMyWszTE3UaNRFGITUkhMTsXdwRpHWwuOXX3AnyducSs2gWFNvRjRwtsoCiOQ4kgIIYQQz+Fmb8W7zSryThMvDly8z89HrrEh4hZxiSkkpWhQq9IGlDQ3VXPj0VNSNQqOtha0rFqKPgFl8XGx0/ch5IkUR0IIIYTIFbVaRf2KjtSv6Jhtm8SUVOISUihhbY5abRxniv5LiiMhhBBC6IyFqQkWtib6jvFSpEO2EEIIIcQzpDgSQgghhHiGFEdCCCGEEM+Q4kgIIYQQ4hlSHAkhhBBCPEOKIyGEEEKIZ0hxJIQQQgjxDCmOhBBCCCGeIcWREEIIIcQzpDgSQgghhHiGFEdCCCGEEM+Q4kgIIYQQ4hky8WweKYoCQGxsrM63nZycTHx8PLGxsZiZmel8+7ogGXVDMuqGZNQNyagbklE38itj+t/t9L/jOZHiKI8eP34MgLu7u56TCCGEECKvHj9+TPHixXNso1JyU0IJLY1Gw82bNylWrBgqlUqn246NjcXd3Z2oqCjs7Ox0um1dkYy6IRl1QzLqhmTUDcmoG/mVUVEUHj9+jJubG2p1zr2K5MxRHqnVasqUKZOv+7CzszPYL206yagbklE3JKNuSEbdkIy6kR8Zn3fGKJ10yBZCCCGEeIYUR0IIIYQQz5DiyIBYWFgwefJkLCws9B0lW5JRNySjbkhG3ZCMuiEZdcMQMkqHbCGEEEKIZ8iZIyGEEEKIZ0hxJIQQQgjxDCmOhBBCCCGeIcWREEIIIcQzpDgSQgghhHiGjJCtR/fu3WPp0qWEhYVx+/ZtAFxcXAgMDCQkJAQnJyc9JxRCCCGKHrmVX0+OHDlCq1atsLa2pnnz5pQqVQqA6OhoduzYQXx8PFu2bKF27dp6TiqKkpSUFE6dOpWhWK9SpYpBzd5tDBnFyzOGz9kYMooXI8WRnrz66qvUrFmThQsXZprAVlEU3nrrLU6cOEFYWJieEv7r8OHDmc5uBQQE8Morr+g52b9u377NoUOHMmSsW7cuLi4uek72L0POqNFomDRpEl9//TUxMTEZ1hUvXpyhQ4cyderU507WmJ+MIWM6Y/ijaagZjeFzNoaMzzLUz/pZBpdREXphaWmpnDlzJtv1Z86cUSwtLQswUWbR0dFK/fr1FZVKpZQtW1Z55ZVXlFdeeUUpW7asolKplPr16yvR0dF6zRgXF6f07NlTMTExUUxNTRVnZ2fF2dlZMTU1VUxMTJRevXopT548kYzPMWbMGMXJyUlZuHChcvnyZSU+Pl6Jj49XLl++rCxatEhxdnZWxo4dKxmfIzU1VZkwYYJib2+vqFSqDA97e3vlww8/VFJTUyVjDozhczaGjIpi+J+1IWeU4khPypUrpyxfvjzb9cuXL1fKli1bcIGy0LlzZyUgIEA5e/ZspnVnz55VAgMDlS5duugh2b8GDBigVKxYUdm8ebOSkpKiXZ6SkqJs2bJF8fb2VgYOHKjHhMaRsVSpUsrmzZuzXb9582bF2dm5ABNlZgwZjeGPpqFnNIbP2RgyKorhf9aGnFGKIz2ZP3++YmFhoQwbNkz57bfflIMHDyoHDx5UfvvtN2XYsGGKlZWV8vXXX+s1o62trXL8+PFs1x89elSxtbUtwESZ2dvbK/v37892/b59+xR7e/sCTJSZMWS0trZWTpw4ke36iIgIxcbGpgATZWYMGY3hj6ahZzSGz9kYMiqK4X/WimK4GQ3jgmgR9M4777B8+XIOHTpE586dCQgIICAggM6dO3Po0CFCQ0MZMmSIXjNaWFgQGxub7frHjx/rffJCjUaDubl5tuvNzc3RaDQFmCgzY8jYuHFjRo8ezb179zKtu3fvHu+//z6NGzcu+GDPMIaMjx8/xs3NLdv1rq6uPHnypAATZWboGY3hczaGjGD4nzUYcMYCL8dEJklJScrNmzeVmzdvKklJSfqOozVkyBClbNmyyrp165SYmBjt8piYGGXdunVKuXLllKFDh+oxoaL06NFD8fX1zfIM1/HjxxV/f3+lZ8+eekj2L2PIeO3aNaVatWqKqamp4uvrqwQFBSlBQUGKr6+vYmpqqtSoUUO5du2aZHyONm3aKC1btlTu3r2bad3du3eVoKAgpW3btnpI9i9Dz2gMn7MxZFQUw/+sFcVwM8rdaiJbiYmJDB8+nKVLl5KSkqI9+5GUlISpqSkDBgxg7ty5ej179PDhQ3r06MGWLVsoUaIEzs7OANy5c4dHjx7RqlUrVq5cib29vWR8Do1Gw5YtWzh48GCmOxNbtmxpEHfeGHrGqKgo2rRpw9mzZ6levXqGITpOnjxJlSpV2LBhA+7u7pIxB4b+OYNxZDSGz9pQM0pxJJ4rNjaWY8eOZfgF4O/vj52dnZ6T/evMmTNZ/pLy8fHRc7J/nT17NsshEQwpo3h5xvBH0xgyCt0whs/aEDNKcSRy7cmTJ6xevZoLFy7g5uZG9+7dKVmypL5jCR3KakyrwMBA6tSpo+dkWbt8+TIXLlzA1dWVatWq6TuO0CH5Lgp9kuJIZKtKlSrs27cPBwcHoqKiaNiwIQ8fPsTb25uLFy9iamrKwYMH8fT01GvOpKQk1q9fn+Uv0tdffz3HztD6oCgKu3fv1v4ibdWqld4HY7tz5w6dO3dm//79eHh4ZDi1fe3aNerVq8fatWu1lwT1YciQIcycORNbW1uePn1K7969WbduHQAqlYpGjRrx+++/Y2trq7eM6eQP+4uT76LuyffxBRR4LydhNFQqlXaQx549eyqBgYHKo0ePFEVRlMePHyvNmzdXgoOD9RlR+eeff5Ty5csrlpaWSqNGjZQ33nhDeeONN5RGjRoplpaWipeXl/LPP//oNWPr1q2179v9+/eVunXrKiqVSnFyclLUarXi4+Oj3LlzR68ZjWFMK7Varf0+jhs3TilTpoyyc+dO5cmTJ8q+ffuUChUqKB988IFeMxrDwKlvv/228vjxY0VRFCU+Pl7p3LmzdtA9tVqtNGnSRLteH+S7qDvyfXxxUhyJbD1bHJUvX17ZunVrhvX79+9X3N3d9RFNq3nz5srrr7+e4W66dDExMcrrr7+utGzZUg/J/vXs+/j2228rVapUUS5duqQoiqJERUUp/v7+yltvvaXPiEYxptWz72O1atWUlStXZlj/22+/Kd7e3vqIpiV/2F+efBd1R76PL06KI5EtlUqlPaPh5uamnDx5MsP6K1eu6H2KEysrq0y5nnXixAnFysqqABNl9uwv0kqVKim//fZbhvXbt29XPD099RFNq2TJksru3buzXb9r1y6lZMmSBZgos2e/j46OjkpkZGSG9VeuXNH7Zy1/2F+efBd1R76PL07/3dSFQWvWrBl+fn7ExsZy7ty5DOuuXr2q9w7Z9vb2XLlyJdv1V65c0fst8oB2cuGHDx9SoUKFDOu8vLy4efOmPmJpdevWjb59+/Lrr79mGPgzNjaWX3/9lX79+hEcHKzHhGkmTpzIyJEjUavVmd6z+/fvY2Njo6dkaYxh4FT49/t4+/ZtatSokWFdzZo1iYqK0kcsQL6LuiTfxxdnWuB7FEZj8uTJGZ7/t3PhH3/8QYMGDQoyUiYDBw6kT58+TJw4kWbNmmXovLljxw4+/vhj3n33Xb1mBAgJCcHCwoLk5GQuX75M1apVtetu376t9wJuzpw5aDQaunfvnu2YVrNmzdJrxoYNG2oL9CpVqnD16tUM6zdu3JjhfdWH9D/sc+fOpVmzZtrhLmJjY9mxYwcjR440mD/s1tbW2j/sz75v+v7Dnt13MTExETMzM/ku5oF8H1+c3K0mjN6MGTOYN28et2/f1v4LRFEUXFxcGD58OGPHjtVrvn79+mV43rp1a9544w3t87Fjx3LixAk2b95c0NEyMYYxrbJz6dIlzM3NKVOmjN4yGMPAqY0bN9b+dwLQs2dPBg4cqH3+8ccfs337dnbv3q2HdP+KjY3l6NGjREdHA1CqVClq165t0N9FRVFQqVQG8V0E+T6+DCmORKFx+fLlDH/U9T3EQG49efIEExMTLC0t9R1F6IgUmbpnbm5OREQElStX1neUbBlqRvk+5p0UR6JQi4qKYvLkySxdulTfUbJlKBmfPn3KsWPHcHBwoEqVKhnWJSQksHr1avr06aOndGmMIWP6aO3po5+fPXuWefPmkZiYSK9evWjatKle8z2bMTAwkEqVKhlUxpEjR2a5fN68efTq1Uvbz3HOnDkFGSsDY8iYFWMYyPfZjK6urgQHB+sloxRHolCLiIjAz8+P1NRUfUfJliFkPH/+PC1btuTatWuoVCrq16/PTz/9pJ0tOzo6Gjc3N4PL+PPPP+Pq6mowGTdv3szrr7+Ora0t8fHx/Prrr/Tp04eaNWui0WjYs2cPW7du1WvxYegZ1Wo1NWvWzNQPb8+ePdSuXRsbGxtUKhU7d+7USz4wjoxgHAP5/jdjgwYNePTokd4zSnEkjNrvv/+e4/pLly4xatQovf7BNIaMHTt2JDk5mdDQUB49esTw4cM5ffo0u3fvxsPDwyAKD2PIGBgYSNOmTfn444/5+eefGTJkCG+//TaffPIJAOPGjePYsWNs3bpVMmbjs88+Y/HixSxZsiRDgWZmZkZERESmM4b6YAwZIa2Iu337Ns7OzvTq1YvLly+zceNGihcvTlxcHB07dsTJyYmVK1dKxv8q8MEDhNCh9FFU00dUzeqhVqsl43M4OzsrJ06c0D7XaDTKW2+9pXh4eCgXL15Ubt++LRlzwc7OTjsie2pqqmJqapphnJmTJ08qpUqV0lc8RVGMI+Phw4cVb29vZdSoUUpSUpKiKIpiamqqnDp1Sq+5nmUMGY1hIF9DzSjjHAmj5urqyrp169BoNFk+jh8/ru+IRpHx6dOnmJr+O7KHSqViwYIFtGvXjkaNGnH+/Hk9pktjDBnh3zFb1Go1lpaWFC9eXLuuWLFixMTE6CualqFnrFOnDseOHePu3bvUrl2byMjIDHc0GQJjyAj/ftYJCQnaS9DpSpcuzd27d/URKwNDzCjFkTBq/v7+HDt2LNv1KpUKRc9Xjo0ho4+PD0ePHs20fP78+bz++uu0b99eD6kyMoaM5cqV459//tE+DwsLw8PDQ/v82rVrmX75FzRjyAhp46otX76ccePG0bx5c4PsN2gMGQ19IF8wzIwyCKQwamPGjOHJkyfZrvfy8mLXrl0FmCgzY8jYsWNHfvrpJ3r37p1p3fz589FoNCxcuFAPyf5lDBnffvvtDH8g/zuj+KZNm/R+t5oxZHxW9+7dqV+/PseOHaNs2bL6jpMlQ81oDAP5GmpG6ZAthBBCCPEMuawmhBBCCPEMKY6EEEIIIZ4hxZEQQgghxDOkOBJCCCGEeIYUR0KIIqdx48YMHz78pbbRu3dvPv30U90Eykb37t2ZPXt2vu5DCJGZ3K0mhChwu3fvpkmTJtmub9y4cb4Ob/DgwQPMzMwoVqzYC70+IiKCpk2bcvXq1Uy3HutSZGQkDRs25PLlyxkGahRC5C85cySEKHCBgYHcunUr02PRokWoVCqGDBmSr/t3cHB44cII4KuvvqJr1675WhhB2hhEFSpU4Mcff8zX/QghMpLiSAhR4MzNzXFxccnwePjwIaNHj2b8+PF07do129ceOXKEFi1a4OjoSPHixWnUqFGGKVh2796Nubk5e/fu1S6bOXMmzs7OREdHA5kvq33zzTdUrFgRS0tLSpUqRZcuXbLdf2pqKr/88gvt2rXLsDwxMZH3338fd3d3LCws8PLy4rvvvtNmUqlUbNnyv/buJSSqNo7j+DdMh6mcJDQxAkMabJLCpAtuUqQaEqIW4aKchi5D0GKibVRIEUUElQVmFwLDtMA0aqp3ijIyu1LjEIkLybFNmVlmeQPnvIuXpjnvaGVZbX4feOCc/zn/5znzrP48cy7/MG/ePKxWKwUFBXR0dHDt2jUcDgc2m401a9bQ29tr6nfFihVUV1d/f1JFZMyoOBKRv+7Dhw+sXLmS/Px89uzZ881ze3p6cLvdNDQ08ODBA+x2O4WFhfT09ABfCx+Xy0V3dzfPnj1j586dnDp1itTU1Jj+njx5gtfrZffu3bS0tHD9+nUWL1484vjBYJDu7m7mz59viq9bt46qqipKS0tpbm6mvLw8ZmWppKSEY8eO0djYyKtXrygqKuLw4cOcO3cOn8+H3+/n6NGjppyFCxfy6NEjBgYGvjkvIjKG/vinbkVEogwNDRnLly83HA6H8fHjx5/KT0xMNC5fvhyJDQwMGNnZ2UZRUZExe/Zsw+PxmHLy8vKMrVu3GoZhGDU1NYbNZvvhsWtra424uDgjHA5HYi0tLQZg3LhxY9ic27dvG4Bx8+bNSGzfvn0GYLS2tkZimzdvNpxOpym3qanJAIy2trYfuj4R+XVaORKRv2r79u3cv3+fS5cume4Dam9vZ9KkSZH25cmwN2/e4PF4sNvtTJ48GZvNxqdPn2hvb4/kJiQkUFlZSU1NDf39/Rw6dGjE8ZcuXUp6ejoZGRm4XC4qKytj/tqK1tfXh8ViMX2BPRAIEBcXR15e3jd/69y5cyPbqampTJgwgYyMDFOso6PDlGO1WgG+eU0iMrb04VkR+Wuqq6s5ePAgPp8Pu91uOjZt2jQCgUBkf8qUKQC43W7evXvHkSNHSE9Px2KxkJuby+DgoCm/sbER+O/JtK6uLiZOnDjsNSQmJvL06VPq6+vx+/3s2rWLkpISHj9+TFJSUsz5ycnJ9Pb2Mjg4SEJCAvC1gPme+Pj4yPa4ceNM+19i4XDYFOvq6gIgJSXlh8YQkV+nlSMR+SsCgQAbN25k//79OJ3OmOPjx49n5syZkfalOLp37x5er5fCwkKysrKwWCx0dnaacltbW9m2bRsnT55k0aJFuN3umKLj/2MtWbKEAwcOEAwGaWtr49atW8Oem52dDcCLFy8isTlz5hAOh7lz585op+G7nj9/zvTp00lOTh7zvkVkeCqOROSP6+zsZNWqVeTn51NcXMzr169N7e3btyPm2u12zp49S3NzMw8fPmTt2rWmlZuhoSGKi4txOp2sX7+eM2fOEAwGR3yZ4pUrVygtLSUQCBAKhaioqCAcDpOZmTns+SkpKeTk5NDQ0BCJzZgxA7fbzYYNG6irq+Ply5fU19dz4cKFn5yhr+7evcuyZct+uR8R+XEqjkTkj/P5fIRCIa5evUpaWlpMW7BgwYi5p0+f5v379+Tk5OByufB6vUydOjVyfO/evYRCIcrLywFIS0vjxIkT7Nixg6amppj+kpKSuHjxIgUFBTgcDo4fP05VVRVZWVkjXsOmTZuorKw0xcrKyli9ejVbtmxh1qxZeDwePn/+PNqpMenv76eurg6Px/NL/YjI6OgN2SIio9TX10dmZibnz58nNzf3t41TVlZGbW0tfr//t40hIrG0ciQiMkpWq5WKioqYe53GWnx8fMx7j0Tk99PKkYiIiEgUrRyJiIiIRFFxJCIiIhJFxZGIiIhIFBVHIiIiIlFUHImIiIhEUXEkIiIiEkXFkYiIiEgUFUciIiIiUVQciYiIiET5F3yRNU1eU/W8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from Line import Line\n",
    "from Drone import Drone\n",
    "import matplotlib.pyplot as plt\n",
    "seed = 23\n",
    "fig = plt.figure()\n",
    "line = Line(seed)\n",
    "line.plot(show=False)\n",
    "drone_params = {\n",
    "    'seed': seed,\n",
    "    'z_min_speed': z_min_speed,\n",
    "    'z_max_speed': z_max_speed,\n",
    "    'x_max_speed': x_max_speed,\n",
    "    'max_angular_speed': max_angular_speed,\n",
    "    'max_drift': max_drift,\n",
    "    'allow_x_movement': allow_x_movement\n",
    "}\n",
    "drone = Drone(**drone_params)\n",
    "drone.find_line(line)\n",
    "drone.plot(show=False)\n",
    "step = 50\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_major_locator(plt.MultipleLocator(step))\n",
    "ax.yaxis.set_major_locator(plt.MultipleLocator(step))\n",
    "ax.set_aspect('equal')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from Environment import Environment\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testenv.drone.p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "environment_params = {\n",
    "    'seed': seed,\n",
    "    'max_allowed_dist': max_allowed_dist,\n",
    "    'z_min_speed': z_min_speed,\n",
    "    'z_max_speed': z_max_speed,\n",
    "    'x_max_speed': x_max_speed,\n",
    "    'max_angular_speed': max_angular_speed,\n",
    "    'max_drift': max_drift,\n",
    "    'allow_x_movement': allow_x_movement,\n",
    "    'speed_z_activation_dist': speed_z_activation_dist,\n",
    "    'target_dist_p1_C': target_dist_p1_C,\n",
    "    'alpha': alpha,\n",
    "    'beta':beta ,\n",
    "    'gamma': gamma\n",
    "}\n",
    "testenv = Environment(**environment_params)\n",
    "testout = widgets.Output(layout={'border': '1px solid black'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observation [-2, 1.0, 0.32272052081883285],\n",
      " reward 1.885, terminated True, truncated False\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9019e89150e44ff08612d7b0aedf8166",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(layout=Layout(border_bottom='1px solid black', border_left='1px solid black', border_right='1px solid b…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "action = {\n",
    "    'speed_z': 0, # range [ 0,1] (0 = slow forward speed, 1 = fast forward speed)\n",
    "    'speed_x': 0, # range [-1,1] (-1 = move left fast, 0 = no lateral movement, 1 = move right fast)\n",
    "    'speed_a': -1, # range [-1,1] (-1 = turn left fast, 0 = no turn, 1 = turn right fast)\n",
    "}\n",
    "\n",
    "print('observation %s,\\n reward %.3f, terminated %s, truncated %s' % testenv.step(list(action.values())))\n",
    "testfig, testax = plt.subplots(figsize=(12, 8))\n",
    "display(testout)\n",
    "testenv.render(testout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}