{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "from gymnasium.wrappers.jax_to_numpy import JaxToNumpy\n",
    "from gymnasium.wrappers.vector import JaxToNumpy as VJaxToNumpy\n",
    "from solarcarsim.simv1 import SolarRaceV1\n",
    "from stable_baselines3.common.env_checker import check_env\n",
    "from gymnasium.utils.env_checker import check_env as gym_check_env\n",
    "env = SolarRaceV1()\n",
    "wrapped_env = JaxToNumpy(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/common/env_checker.py:271: UserWarning: Your observation wind has an unconventional shape (neither an image, nor a 1D vector). We recommend you to flatten the observation to have only a 1D vector or use a custom policy to properly process the data.\n",
      "  warnings.warn(\n",
      "/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/gymnasium/utils/env_checker.py:384: UserWarning: \u001b[33mWARN: The environment (<JaxToNumpy<SolarRaceV1 instance>>) is different from the unwrapped version (<SolarRaceV1 instance>). This could effect the environment checker as the environment most likely has a wrapper applied to it. We recommend using the raw environment for `check_env` using `env.unwrapped`.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/gymnasium/utils/env_checker.py:434: UserWarning: \u001b[33mWARN: Not able to test alternative render modes due to the environment not having a spec. Try instantiating the environment through `gymnasium.make`\u001b[0m\n",
      "  logger.warn(\n"
     ]
    }
   ],
   "source": [
    "env.reset()\n",
    "check_env(wrapped_env)\n",
    "gym_check_env(wrapped_env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/common/buffers.py:605: UserWarning: This system does not have apparently enough memory to store the complete replay buffer 80.85GB > 53.66GB\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstable_baselines3\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TD3\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m TD3(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMultiInputPolicy\u001b[39m\u001b[38;5;124m\"\u001b[39m, wrapped_env, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m30_000\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/td3/td3.py:222\u001b[0m, in \u001b[0;36mTD3.learn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlearn\u001b[39m(\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28mself\u001b[39m: SelfTD3,\n\u001b[1;32m    215\u001b[0m     total_timesteps: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    220\u001b[0m     progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    221\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m SelfTD3:\n\u001b[0;32m--> 222\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlog_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_interval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    226\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtb_log_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtb_log_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    228\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    229\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/common/off_policy_algorithm.py:347\u001b[0m, in \u001b[0;36mOffPolicyAlgorithm.learn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[38;5;66;03m# Special case when the user passes `gradient_steps=0`\u001b[39;00m\n\u001b[1;32m    346\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m gradient_steps \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 347\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgradient_steps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    349\u001b[0m callback\u001b[38;5;241m.\u001b[39mon_training_end()\n\u001b[1;32m    351\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/td3/td3.py:184\u001b[0m, in \u001b[0;36mTD3.train\u001b[0;34m(self, gradient_steps, batch_size)\u001b[0m\n\u001b[1;32m    182\u001b[0m critic_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(F\u001b[38;5;241m.\u001b[39mmse_loss(current_q, target_q_values) \u001b[38;5;28;01mfor\u001b[39;00m current_q \u001b[38;5;129;01min\u001b[39;00m current_q_values)\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(critic_loss, th\u001b[38;5;241m.\u001b[39mTensor)\n\u001b[0;32m--> 184\u001b[0m critic_losses\u001b[38;5;241m.\u001b[39mappend(\u001b[43mcritic_loss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    186\u001b[0m \u001b[38;5;66;03m# Optimize the critics\u001b[39;00m\n\u001b[1;32m    187\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcritic\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# import a model and try it out!\n",
    "from sbx import TD3\n",
    "model = TD3(\"MultiInputPolicy\", env, verbose=1)\n",
    "model.learn(total_timesteps=30_000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_env = model.get_env()\n",
    "import matplotlib.pyplot as plt\n",
    "import jax.numpy as jnp\n",
    "obs = vec_env.reset()\n",
    "actions = []\n",
    "obs_list = []\n",
    "rewards = []\n",
    "for i in range(1000):\n",
    "    action, _state = model.predict(obs, deterministic=True)\n",
    "    actions.append(action)\n",
    "    obs, reward, done, info = vec_env.step(action)\n",
    "    obs_list.append(obs)\n",
    "    rewards.append(reward)\n",
    "\n",
    "    \n",
    "    # VecEnv resets automatically\n",
    "    if done:\n",
    "        break\n",
    "    #   obs = vec_env.reset()\n",
    "\n",
    "position = jnp.array([x['position'] for x in obs_list]).flatten()\n",
    "energy = jnp.array([x['energy'] for x in obs_list]).flatten()\n",
    "actions = jnp.array(actions).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x79468c67a1b0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z4vsbb7yhsrIyTZw4UUFBQVq1apUee+wx3X333Z11KgAAAAAAdJlOC+qS9OKLL+qOO+7QlClTZLfbdc011+jpp5/2bG9ublZeXp7q6uo86/7nf/7H07axsVFTp07V//7v/3q2BwQE6Nlnn9W8efNkjNHAgQP161//Wrfffvtp13V8hHhGfwcAAAAAdIXj+fN0ZkjvtHnUrezQoUNKTk72dRkAAAAAgHNMYWGhkpKSTtnmnAzqbrdbxcXFCgsLk81m83U5J+VyuZScnKzCwkI5nU5fl4NugD6DM0WfwZmiz+BM0WdwpugzOFPdpc8YY1RdXa3ExETZ7aeegK1Tb323Krvd3uE3GFbidDot3eFgPfQZnCn6DM4UfQZnij6DM0WfwZnqDn0mPDz8tNqd8TzqAAAAAACg8xDUAQAAAACwEIK6hTkcDj344IPMD4/TRp/BmaLP4EzRZ3Cm6DM4U/QZnKme2GfOycHkAAAAAACwKq6oAwAAAABgIQR1AAAAAAAshKAOAAAAAICFENQBAAAAALAQgjoAAAAAABZCULewZ599Vv3791dQUJDS09P18ccf+7ok+Mj777+v7373u0pMTJTNZtNrr73mtd0Yo0WLFikhIUHBwcHKzMzUnj17vNpUVFRoxowZcjqdioiI0G233aaampouPAt0lcWLF+v8889XWFiYYmNjddVVVykvL8+rTUNDg+bMmaOoqCiFhobqmmuuUVlZmVebgoICTZ8+XSEhIYqNjdU999yjlpaWrjwVdJElS5Zo1KhRcjqdcjqdysjI0DvvvOPZTn9BRx5//HHZbDbNnTvXs45+gy976KGHZLPZvJa0tDTPdvoL2lNUVKQbb7xRUVFRCg4O1siRI7Vp0ybP9p78GZigblEvv/yy5s+frwcffFCffPKJRo8eralTp6q8vNzXpcEHamtrNXr0aD377LPtbn/iiSf09NNPa+nSpcrJyVGvXr00depUNTQ0eNrMmDFDO3bs0KpVq/Tmm2/q/fff18yZM7vqFNCF1q1bpzlz5mjDhg1atWqVmpubdfnll6u2ttbTZt68eXrjjTf0yiuvaN26dSouLtb3v/99z/bW1lZNnz5dTU1N+uijj7R8+XItW7ZMixYt8sUpoZMlJSXp8ccf1+bNm7Vp0yZddtlluvLKK7Vjxw5J9Bec2saNG/X73/9eo0aN8lpPv8FXDR8+XCUlJZ5l/fr1nm30F3zVsWPHNGnSJAUEBOidd97Rzp079dRTT6l3796eNj36M7CBJU2YMMHMmTPH83dra6tJTEw0ixcv9mFVsAJJ5tVXX/X87Xa7TXx8vHnyySc96yorK43D4TAvvfSSMcaYnTt3Gklm48aNnjbvvPOOsdlspqioqMtqh2+Ul5cbSWbdunXGmLb+ERAQYF555RVPm127dhlJJjs72xhjzNtvv23sdrspLS31tFmyZIlxOp2msbGxa08APtG7d2/z3HPP0V9wStXV1WbQoEFm1apV5pJLLjF33XWXMYbXGZzowQcfNKNHj253G/0F7bnvvvvMhRdeeNLtPf0zMFfULaipqUmbN29WZmamZ53dbldmZqays7N9WBmsKD8/X6WlpV79JTw8XOnp6Z7+kp2drYiICI0fP97TJjMzU3a7XTk5OV1eM7pWVVWVJCkyMlKStHnzZjU3N3v1mbS0NPXt29erz4wcOVJxcXGeNlOnTpXL5fJcZUXP1NraqhUrVqi2tlYZGRn0F5zSnDlzNH36dK/+IfE6g/bt2bNHiYmJSk1N1YwZM1RQUCCJ/oL2/etf/9L48eN17bXXKjY2VmPHjtUf//hHz/ae/hmYoG5BR44cUWtrq9cLkSTFxcWptLTUR1XBqo73iVP1l9LSUsXGxnpt9/f3V2RkJH2qh3O73Zo7d64mTZqkESNGSGrrD4GBgYqIiPBq+9U+016fOr4NPc+2bdsUGhoqh8OhWbNm6dVXX9WwYcPoLzipFStW6JNPPtHixYtP2Ea/wVelp6dr2bJlWrlypZYsWaL8/HxddNFFqq6upr+gXfv379eSJUs0aNAg/fvf/9bs2bN15513avny5ZJ6/mdgf18XAADoPHPmzNH27du9ngME2jNkyBDl5uaqqqpK//jHP3TTTTdp3bp1vi4LFlVYWKi77rpLq1atUlBQkK/LQTdwxRVXeH4fNWqU0tPT1a9fP/39739XcHCwDyuDVbndbo0fP16PPfaYJGns2LHavn27li5dqptuusnH1XU+rqhbUHR0tPz8/E4Y6bKsrEzx8fE+qgpWdbxPnKq/xMfHnzAQYUtLiyoqKuhTPdgdd9yhN998U2vWrFFSUpJnfXx8vJqamlRZWenV/qt9pr0+dXwbep7AwEANHDhQ48aN0+LFizV69Gj99re/pb+gXZs3b1Z5ebnOO+88+fv7y9/fX+vWrdPTTz8tf39/xcXF0W9wShERERo8eLD27t3L6wzalZCQoGHDhnmtGzp0qOeRiZ7+GZigbkGBgYEaN26cVq9e7Vnndru1evVqZWRk+LAyWFFKSori4+O9+ovL5VJOTo6nv2RkZKiyslKbN2/2tMnKypLb7VZ6enqX14zOZYzRHXfcoVdffVVZWVlKSUnx2j5u3DgFBAR49Zm8vDwVFBR49Zlt27Z5vbmtWrVKTqfzhDdN9Exut1uNjY30F7RrypQp2rZtm3Jzcz3L+PHjNWPGDM/v9BucSk1Njfbt26eEhAReZ9CuSZMmnTC97GeffaZ+/fpJOgc+A/t6NDu0b8WKFcbhcJhly5aZnTt3mpkzZ5qIiAivkS5x7qiurjZbtmwxW7ZsMZLMr3/9a7NlyxZz8OBBY4wxjz/+uImIiDCvv/662bp1q7nyyitNSkqKqa+v9xxj2rRpZuzYsSYnJ8esX7/eDBo0yNxwww2+OiV0otmzZ5vw8HCzdu1aU1JS4lnq6uo8bWbNmmX69u1rsrKyzKZNm0xGRobJyMjwbG9paTEjRowwl19+ucnNzTUrV640MTExZuHChb44JXSy+++/36xbt87k5+ebrVu3mvvvv9/YbDbz7rvvGmPoLzg9Xx713Rj6DbwtWLDArF271uTn55sPP/zQZGZmmujoaFNeXm6Mob/gRB9//LHx9/c3jz76qNmzZ4958cUXTUhIiPnrX//qadOTPwMT1C3smWeeMX379jWBgYFmwoQJZsOGDb4uCT6yZs0aI+mE5aabbjLGtE1P8cADD5i4uDjjcDjMlClTTF5entcxjh49am644QYTGhpqnE6nueWWW0x1dbUPzgadrb2+Ism88MILnjb19fXmJz/5iendu7cJCQkxV199tSkpKfE6zoEDB8wVV1xhgoODTXR0tFmwYIFpbm7u4rNBV7j11ltNv379TGBgoImJiTFTpkzxhHRj6C84PV8N6vQbfNl1111nEhISTGBgoOnTp4+57rrrzN69ez3b6S9ozxtvvGFGjBhhHA6HSUtLM3/4wx+8tvfkz8A2Y4zxzbV8AAAAAADwVTyjDgAAAACAhRDUAQAAAACwEII6AAAAAAAW4u/rAnzB7XaruLhYYWFhstlsvi4HAAAAANDDGWNUXV2txMRE2e2nvmZ+Tgb14uJiJScn+7oMAAAAAMA5prCwUElJSadsc04G9bCwMElt/4GcTqePqwEAAAAA9HQul0vJycmePHoq52RQP367u9PpJKgDAAAAALrM6Tx+3WmDyVVUVGjGjBlyOp2KiIjQbbfdppqamlPus2/fPl199dWKiYmR0+nUf/zHf6isrMyrTf/+/WWz2byWxx9/vLNOAwAAAACALtVpQX3GjBnasWOHVq1apTfffFPvv/++Zs6cedL2tbW1uvzyy2Wz2ZSVlaUPP/xQTU1N+u53vyu32+3V9pFHHlFJSYln+elPf9pZpwEAAAAAQJfqlFvfd+3apZUrV2rjxo0aP368JOmZZ57Rt7/9bf33f/+3EhMTT9jnww8/1IEDB7RlyxbP7ejLly9X7969lZWVpczMTE/bsLAwxcfHd0bpAAAAAAD4VKdcUc/OzlZERIQnpEtSZmam7Ha7cnJy2t2nsbFRNptNDofDsy4oKEh2u13r16/3avv4448rKipKY8eO1ZNPPqmWlpZT1tPY2CiXy+W1AAAAAABgRZ0S1EtLSxUbG+u1zt/fX5GRkSotLW13n4kTJ6pXr1667777VFdXp9raWt19991qbW1VSUmJp92dd96pFStWaM2aNfrP//xPPfbYY7r33ntPWc/ixYsVHh7uWZiaDQAAAABgVWcU1O+///4TBnL76rJ79+6vVUhMTIxeeeUVvfHGGwoNDVV4eLgqKyt13nnneU0GP3/+fE2ePFmjRo3SrFmz9NRTT+mZZ55RY2PjSY+9cOFCVVVVeZbCwsKvVSMAAAAAAJ3tjJ5RX7BggW6++eZTtklNTVV8fLzKy8u91re0tKiiouKUz5Zffvnl2rdvn44cOSJ/f39FREQoPj5eqampJ90nPT1dLS0tOnDggIYMGdJuG4fD4XVLPQAAAAAAVnVGQT0mJkYxMTEdtsvIyFBlZaU2b96scePGSZKysrLkdruVnp7e4f7R0dGefcrLy/W9733vpG1zc3Nlt9tPuNUeAAAAAIDuqFNGfR86dKimTZum22+/XUuXLlVzc7PuuOMOXX/99Z4R34uKijRlyhT9+c9/1oQJEyRJL7zwgoYOHaqYmBhlZ2frrrvu0rx58zxXyrOzs5WTk6NLL71UYWFhys7O1rx583TjjTeqd+/enXEqAAAAAAB0qU4J6pL04osv6o477tCUKVNkt9t1zTXX6Omnn/Zsb25uVl5enurq6jzr8vLytHDhQlVUVKh///76r//6L82bN8+z3eFwaMWKFXrooYfU2NiolJQUzZs3T/Pnz++s0wAAAAAAoEvZjDHG10V0NZfLpfDwcFVVVXnmbAcAAAAAoLOcSQ7tlOnZAAAAAADA10NQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYSKcF9UcffVQXXHCBQkJCFBERcVr7GGO0aNEiJSQkKDg4WJmZmdqzZ49Xm4qKCs2YMUNOp1MRERG67bbbVFNT0wlnAAAAAABA1/PvrAM3NTXp2muvVUZGhp5//vnT2ueJJ57Q008/reXLlyslJUUPPPCApk6dqp07dyooKEiSNGPGDJWUlGjVqlVqbm7WLbfcopkzZ+pvf/tbZ52KTxhjVN/c6usyAAAAAKBbCA7wk81m83UZZ4XNGGM68x9YtmyZ5s6dq8rKylO2M8YoMTFRCxYs0N133y1JqqqqUlxcnJYtW6brr79eu3bt0rBhw7Rx40aNHz9ekrRy5Up9+9vf1qFDh5SYmNjusRsbG9XY2Oj52+VyKTk5WVVVVXI6nWfnRM+yuqYWDVv0b1+XAQAAAADdws5HpioksNOuRX9jLpdL4eHhp5VDLfOMen5+vkpLS5WZmelZFx4ervT0dGVnZ0uSsrOzFRER4QnpkpSZmSm73a6cnJyTHnvx4sUKDw/3LMnJyZ13IgAAAAAAfAOW+bqhtLRUkhQXF+e1Pi4uzrOttLRUsbGxXtv9/f0VGRnpadOehQsXav78+Z6/j19Rt7LgAD/tfGSqr8sAAAAAgG4hOMDP1yWcNWcU1O+//3796le/OmWbXbt2KS0t7RsVdbY5HA45HA5fl3FGbDabpW/bAAAAAAB0jjNKggsWLNDNN998yjapqalfq5D4+HhJUllZmRISEjzry8rKNGbMGE+b8vJyr/1aWlpUUVHh2R8AAAAAgO7sjIJ6TEyMYmJiOqWQlJQUxcfHa/Xq1Z5g7nK5lJOTo9mzZ0uSMjIyVFlZqc2bN2vcuHGSpKysLLndbqWnp3dKXQAAAAAAdKVOG0yuoKBAubm5KigoUGtrq3Jzc5Wbm+s153laWppeffVVSW23es+dO1e//OUv9a9//Uvbtm3Tj370IyUmJuqqq66SJA0dOlTTpk3T7bffro8//lgffvih7rjjDl1//fUnHfEdAAAAAIDupNMegl60aJGWL1/u+Xvs2LGSpDVr1mjy5MmSpLy8PFVVVXna3HvvvaqtrdXMmTNVWVmpCy+8UCtXrvTMoS5JL774ou644w5NmTJFdrtd11xzjZ5++unOOg0AAAAAALpUp8+jbkVnMn8dAAAAAADfVLecRx0AAAAAABDUAQAAAACwFII6AAAAAAAWQlAHAAAAAMBCCOoAAAAAAFgIQR0AAAAAAAshqAMAAAAAYCEEdQAAAAAALISgDgAAAACAhRDUAQAAAACwEII6AAAAAAAWQlAHAAAAAMBCCOoAAAAAAFgIQR0AAAAAAAshqAMAAAAAYCEEdQAAAAAALISgDgAAAACAhRDUAQAAAACwEII6AAAAAAAWQlAHAAAAAMBCCOoAAAAAAFgIQR0AAAAAAAshqAMAAAAAYCEEdQAAAAAALISgDgAAAACAhRDUAQAAAACwEII6AAAAAAAWQlAHAAAAAMBCCOoAAAAAAFgIQR0AAAAAAAshqAMAAAAAYCEEdQAAAAAALISgDgAAAACAhRDUAQAAAACwEII6AAAAAAAWQlAHAAAAAMBCCOoAAAAAAFgIQR0AAAAAAAshqAMAAAAAYCEEdQAAAAAALISgDgAAAACAhRDUAQAAAACwEII6AAAAAAAWQlAHAAAAAMBCCOoAAAAAAFgIQR0AAAAAAAshqAMAAAAAYCEEdQAAAAAALISgDgAAAACAhRDUAQAAAACwkE4L6o8++qguuOAChYSEKCIi4rT2McZo0aJFSkhIUHBwsDIzM7Vnzx6vNv3795fNZvNaHn/88U44AwAAAAAAul6nBfWmpiZde+21mj179mnv88QTT+jpp5/W0qVLlZOTo169emnq1KlqaGjwavfII4+opKTEs/z0pz892+UDAAAAAOAT/p114IcffliStGzZstNqb4zRb37zG/385z/XlVdeKUn685//rLi4OL322mu6/vrrPW3DwsIUHx9/1msGAAAAAMDXLPOMen5+vkpLS5WZmelZFx4ervT0dGVnZ3u1ffzxxxUVFaWxY8fqySefVEtLyymP3djYKJfL5bUAAAAAAGBFnXZF/UyVlpZKkuLi4rzWx8XFebZJ0p133qnzzjtPkZGR+uijj7Rw4UKVlJTo17/+9UmPvXjxYs8VfgAAAAAArOyMrqjff//9Jwzk9tVl9+7dnVWrJGn+/PmaPHmyRo0apVmzZumpp57SM888o8bGxpPus3DhQlVVVXmWwsLCTq0RAAAAAICv64yuqC9YsEA333zzKdukpqZ+rUKOP3NeVlamhIQEz/qysjKNGTPmpPulp6erpaVFBw4c0JAhQ9pt43A45HA4vlZdAAAAAAB0pTMK6jExMYqJiemUQlJSUhQfH6/Vq1d7grnL5VJOTs4pR47Pzc2V3W5XbGxsp9QFAAAAAEBX6rRn1AsKClRRUaGCggK1trYqNzdXkjRw4ECFhoZKktLS0rR48WJdffXVstlsmjt3rn75y19q0KBBSklJ0QMPPKDExERdddVVkqTs7Gzl5OTo0ksvVVhYmLKzszVv3jzdeOON6t27d2edCgAAAAAAXabTgvqiRYu0fPlyz99jx46VJK1Zs0aTJ0+WJOXl5amqqsrT5t5771Vtba1mzpypyspKXXjhhVq5cqWCgoIktd3CvmLFCj300ENqbGxUSkqK5s2bp/nz559RbcYYSWL0dwAAAABAlzieP4/n0VOxmdNp1cMcOnRIycnJvi4DAAAAAHCOKSwsVFJS0inbnJNB3e12q7i4WGFhYbLZbL4u56RcLpeSk5NVWFgop9Pp63LQDdBncKboMzhT9BmcKfoMzhR9Bmequ/QZY4yqq6uVmJgou/3UE7BZZh71rmS32zv8BsNKnE6npTscrIc+gzNFn8GZos/gTNFncKboMzhT3aHPhIeHn1a7M5pHHQAAAAAAdC6COgAAAAAAFkJQtzCHw6EHH3xQDofD16Wgm6DP4EzRZ3Cm6DM4U/QZnCn6DM5UT+wz5+RgcgAAAAAAWBVX1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIahb2LPPPqv+/fsrKChI6enp+vjjj31dEnzk/fff13e/+10lJibKZrPptdde89pujNGiRYuUkJCg4OBgZWZmas+ePV5tKioqNGPGDDmdTkVEROi2225TTU1NF54FusrixYt1/vnnKywsTLGxsbrqqquUl5fn1aahoUFz5sxRVFSUQkNDdc0116isrMyrTUFBgaZPn66QkBDFxsbqnnvuUUtLS1eeCrrIkiVLNGrUKDmdTjmdTmVkZOidd97xbKe/oCOPP/64bDab5s6d61lHv8GXPfTQQ7LZbF5LWlqaZzv9Be0pKirSjTfeqKioKAUHB2vkyJHatGmTZ3tP/gxMULeol19+WfPnz9eDDz6oTz75RKNHj9bUqVNVXl7u69LgA7W1tRo9erSeffbZdrc/8cQTevrpp7V06VLl5OSoV69emjp1qhoaGjxtZsyYoR07dmjVqlV688039f7772vmzJlddQroQuvWrdOcOXO0YcMGrVq1Ss3Nzbr88stVW1vraTNv3jy98cYbeuWVV7Ru3ToVFxfr+9//vmd7a2urpk+frqamJn300Udavny5li1bpkWLFvnilNDJkpKS9Pjjj2vz5s3atGmTLrvsMl155ZXasWOHJPoLTm3jxo36/e9/r1GjRnmtp9/gq4YPH66SkhLPsn79es82+gu+6tixY5o0aZICAgL0zjvvaOfOnXrqqafUu3dvT5se/RnYwJImTJhg5syZ4/m7tbXVJCYmmsWLF/uwKliBJPPqq696/na73SY+Pt48+eSTnnWVlZXG4XCYl156yRhjzM6dO40ks3HjRk+bd955x9hsNlNUVNRltcM3ysvLjSSzbt06Y0xb/wgICDCvvPKKp82uXbuMJJOdnW2MMebtt982drvdlJaWetosWbLEOJ1O09jY2LUnAJ/o3bu3ee655+gvOKXq6mozaNAgs2rVKnPJJZeYu+66yxjD6wxO9OCDD5rRo0e3u43+gvbcd9995sILLzzp9p7+GZgr6hbU1NSkzZs3KzMz07PObrcrMzNT2dnZPqwMVpSfn6/S0lKv/hIeHq709HRPf8nOzlZERITGjx/vaZOZmSm73a6cnJwurxldq6qqSpIUGRkpSdq8ebOam5u9+kxaWpr69u3r1WdGjhypuLg4T5upU6fK5XJ5rrKiZ2ptbdWKFStUW1urjIwM+gtOac6cOZo+fbpX/5B4nUH79uzZo8TERKWmpmrGjBkqKCiQRH9B+/71r39p/PjxuvbaaxUbG6uxY8fqj3/8o2d7T/8MTFC3oCNHjqi1tdXrhUiS4uLiVFpa6qOqYFXH+8Sp+ktpaaliY2O9tvv7+ysyMpI+1cO53W7NnTtXkyZN0ogRIyS19YfAwEBFRER4tf1qn2mvTx3fhp5n27ZtCg0NlcPh0KxZs/Tqq69q2LBh9Bec1IoVK/TJJ59o8eLFJ2yj3+Cr0tPTtWzZMq1cuVJLlixRfn6+LrroIlVXV9Nf0K79+/dryZIlGjRokP79739r9uzZuvPOO7V8+XJJPf8zsL+vCwAAdJ45c+Zo+/btXs8BAu0ZMmSIcnNzVVVVpX/84x+66aabtG7dOl+XBYsqLCzUXXfdpVWrVikoKMjX5aAbuOKKKzy/jxo1Sunp6erXr5/+/ve/Kzg42IeVwarcbrfGjx+vxx57TJI0duxYbd++XUuXLtVNN93k4+o6H1fULSg6Olp+fn4njHRZVlam+Ph4H1UFqzreJ07VX+Lj408YiLClpUUVFRX0qR7sjjvu0Jtvvqk1a9YoKSnJsz4+Pl5NTU2qrKz0av/VPtNenzq+DT1PYGCgBg4cqHHjxmnx4sUaPXq0fvvb39Jf0K7NmzervLxc5513nvz9/eXv769169bp6aeflr+/v+Li4ug3OKWIiAgNHjxYe/fu5XUG7UpISNCwYcO81g0dOtTzyERP/wxMULegwMBAjRs3TqtXr/asc7vdWr16tTIyMnxYGawoJSVF8fHxXv3F5XIpJyfH018yMjJUWVmpzZs3e9pkZWXJ7XYrPT29y2tG5zLG6I477tCrr76qrKwspaSkeG0fN26cAgICvPpMXl6eCgoKvPrMtm3bvN7cVq1aJafTecKbJnomt9utxsZG+gvaNWXKFG3btk25ubmeZfz48ZoxY4bnd/oNTqWmpkb79u1TQkICrzNo16RJk06YXvazzz5Tv379JJ0Dn4F9PZod2rdixQrjcDjMsmXLzM6dO83MmTNNRESE10iXOHdUV1ebLVu2mC1bthhJ5te//rXZsmWLOXjwoDHGmMcff9xERESY119/3WzdutVceeWVJiUlxdTX13uOMW3aNDN27FiTk5Nj1q9fbwYNGmRuuOEGX50SOtHs2bNNeHi4Wbt2rSkpKfEsdXV1njazZs0yffv2NVlZWWbTpk0mIyPDZGRkeLa3tLSYESNGmMsvv9zk5uaalStXmpiYGLNw4UJfnBI62f3332/WrVtn8vPzzdatW839999vbDabeffdd40x9Becni+P+m4M/QbeFixYYNauXWvy8/PNhx9+aDIzM010dLQpLy83xtBfcKKPP/7Y+Pv7m0cffdTs2bPHvPjiiyYkJMT89a9/9bTpyZ+BCeoW9swzz5i+ffuawMBAM2HCBLNhwwZflwQfWbNmjZF0wnLTTTcZY9qmp3jggQdMXFyccTgcZsqUKSYvL8/rGEePHjU33HCDCQ0NNU6n09xyyy2murraB2eDztZeX5FkXnjhBU+b+vp685Of/MT07t3bhISEmKuvvtqUlJR4HefAgQPmiiuuMMHBwSY6OtosWLDANDc3d/HZoCvceuutpl+/fiYwMNDExMSYKVOmeEK6MfQXnJ6vBnX6Db7suuuuMwkJCSYwMND06dPHXHfddWbv3r2e7fQXtOeNN94wI0aMMA6Hw6SlpZk//OEPXtt78mdgmzHG+OZaPgAAAAAA+CqeUQcAAAAAwEII6gAAAAAAWAhBHQAAAAAAC/H3dQG+4Ha7VVxcrLCwMNlsNl+XAwAAAADo4Ywxqq6uVmJiouz2U18zPyeDenFxsZKTk31dBgAAAADgHFNYWKikpKRTtjkng3pYWJiktv9ATqfTx9UAAAAAAHo6l8ul5ORkTx49lXMyqB+/3d3pdBLUAQAAAABd5nQev2YwOQAAAAAALISgDgAAAACAhZyTt74DAAAAALqnuqYWlbkaVeZqUHl1o8pdDQrws+umC/r7urSzhqAOAAAAAPA5Y4xc9S0qcdWrpLJBxVX1Kq1qUHFlg0qq6lXqatBhV6OqG1tO2LdvZAhBHQAAAACAM9Hc6tahY/U6eLRWJVUNbUtl/ee/t/2sa2o9rWOFBPopzhmk2DCH4pxBSo4M7uTquxZBHQAAAABwVjQ0t6qwok4Hj9bpwNFar59FlfVqdZsOj9E7JEAJ4cFKjAhSfHiQ5/c45xdLqKNnR9mefXYAAAAAgLOusq5Je8prtKesRnvKq7W3vEb7ymtU4mqQOUUWDw7wU9/IECVGBCkhIliJnwfxhPC2vxPCgxQU4Nd1J2JRBHUAAAAAQLuaWtzaW16jnSUu7Sx2aXepS3vKa3S4uvGk+4Q5/NUvOkT9onqpf9Txn22/x4Q5Tmse8XMdQR0AAAAAoIbmVu0ortKnhVWeYL6nvFrNre1fIu8TEayBsaEaHBeqQbFhGhDbFsgjewUSxr8hgjoAAAAAnGNa3UZ7yqv1aWGlPj1UpU8LK7W7tLrdZ8jDgvw1LMGpYYlODU1wakhcmAbEhvb458R9if+yAAAAANDDNba0atuhKuXkV+jj/AptPnhMNe1McxYdGqjRSREa0SdcwxKdGpbgVFLvYK6QdzGCOgAAAAD0MI0trdp88Jg27K/Qx/lHtaWgUo0tbq82vQL9NDIpXKOTIjQ6uW1JDA8ilFtAlwb1Rx99VG+99ZZyc3MVGBioysrKk7Y9evSoRo8eraKiIh07dkwRERGebWvXrtX8+fO1Y8cOJScn6+c//7luvvnmTq8fAAAAAKzI7TbaWeLSh3uPaP3eI9p4oEINzd7BPKpXoCakRHqWtHin/OyEcivq0qDe1NSka6+9VhkZGXr++edP2fa2227TqFGjVFRU5LU+Pz9f06dP16xZs/Tiiy9q9erV+vGPf6yEhARNnTq1M8sHAAAAAMs4XN2oNXnlWvfZYX2094iO1TV7bY8Jc+iCAVFKT4nShJRIDYjpxdXybqJLg/rDDz8sSVq2bNkp2y1ZskSVlZVatGiR3nnnHa9tS5cuVUpKip566ilJ0tChQ7V+/Xr9z//8D0EdAAAAQI9ljNGOYpeydpdr9e5yfVpY6bU91OGviamRmjQwWpMGRmtQbCjBvJuy3DPqO3fu1COPPKKcnBzt37//hO3Z2dnKzMz0Wjd16lTNnTv3pMdsbGxUY+MX8/y5XK6zVi8AAAAAdJbmVrc+2ndUK7eXas3ucpW6Gry2j0oK16VDYnXx4GiNSopQgJ/dR5XibLJUUG9sbNQNN9ygJ598Un379m03qJeWliouLs5rXVxcnFwul+rr6xUcHHzCPosXL/ZczQcAAAAAK2tsadX6PUf09rZSrdpZKlfDF6OzBwf46aJB0ZoyNFaXDolVrDPIh5Wis3zjoH7//ffrV7/61Snb7Nq1S2lpaR0ea+HChRo6dKhuvPHGb1rWCcedP3++52+Xy6Xk5OSz+m8AAAAAwNfV0NyqtXmH9c72Eq3eVe41dVp0qENTh8fpW8PiNDE1SkEBfj6sFF3hGwf1BQsWdDjiempq6mkdKysrS9u2bdM//vEPSW3PYEhSdHS0/uu//ksPP/yw4uPjVVZW5rVfWVmZnE5nu1fTJcnhcMjhcJxWDQAAAADQFVrdRhv2H9XruUV6Z3upqr905TzeGaRpI+J1xYh4je8fyejs55hvHNRjYmIUExNzNmrRP//5T9XX13v+3rhxo2699VZ98MEHGjBggCQpIyNDb7/9ttd+q1atUkZGxlmpAQAAAAA6izFG24tcej23SG9sLVaZ64uxtBLCgzR9ZIKuGJmgsckRshPOz1ld+ox6QUGBKioqVFBQoNbWVuXm5kqSBg4cqNDQUE8YP+7IkSOS2kZ2Pz6P+qxZs/S73/1O9957r2699VZlZWXp73//u956662uPBUAAAAAOG3l1Q169ZMivbL5kPaW13jWO4P8NX1Ugq4c00cT+kcSziGpi4P6okWLtHz5cs/fY8eOlSStWbNGkydPPq1jpKSk6K233tK8efP029/+VklJSXruueeYmg0AAACApTS3upW1u1yvbCrUmrzDanW3Pdrr8Lcrc2icrhyTqEuGxMjhzzPn8GYzxx8EP4e4XC6Fh4erqqpKTqfT1+UAAAAA6EH2llfr5Y2FenVLkY7UNHnWn9c3QteOT9b0UQlyBgX4sEL4wpnkUEtNzwYAAAAA3VFTi1v/3lGqv244qJz8Cs/66FCHrjmvj64dn6SBsWE+rBDdCUEdAAAAAL6mosp6vZRToBUbC3Wkpm1gOLtNmjI0TteNT9YlQ2IU4Gf3cZXobgjqAAAAAHAGjDFav/eIln90UFm7y/T5o+eKCXPohvOTdf2EvkqMaH/qaOB0ENQBAAAA4DTUN7Xqtdwi/Wl9vvZ8aeT2jNQo3Tixny4fHsfVc5wVBHUAAAAAOIWSqnr9Jfug/vZxgSrrmiVJIYF+unZckv5fRj+ePcdZR1AHAAAAgHZsL6rSH97fr7e3lajl8/vbk3oH6+YL+uva8ckKD2bkdnQOgjoAAAAAfM4Yo+z9R7Vk7T59sOeIZ316SqRumZSibw2Lk5/d5sMKcS4gqAMAAAA457ndRu/uLNOSdfv0aWGlJMnPbtN3RiXo9otSNaJPuG8LxDmFoA6gyxljVN/cqqM1TaqobVJFXZNc9c2qaWxRbWOLahtb2342tf1sanGrxW3U6m772dJq1Oo2anG75We3yc9uU4CfXX52m/ztNvnb7fL3s6lXoL96OfzVy+H3+U9/hTr8FOYIUGRooKJ7ORQVGqiQQD/ZbHwzDgDAuaipxa3Xc4u0dN0+7TtcK0ly+Nv1H+OTNfPiVCVHhvi4QpyLCOoAzqr6plaVVNWrpKpBJVUNKv3899KqBpVVN6iipklHa5vU2OL2dakeDn+7okPbQntMqEN9egcrMSJYfSLafib1DlZMqEN2bnMDAKDHqG1s0YqNhXrug/0qqWqQJIUF+etHGf108wUpiglz+LhCnMsI6gDOWFVdsw4crdWBo7U6eLTu86VWByvqdLi68bSPE+hvV1SvQEX2ClRESMCJV8AD/RUS6KegAD/5f37l3N/PJj+7Xf52m+w2m4wxbVfZ3W7PlfZmt1Fzi1v1za2eq/RfvlrvamjW0ZomHa1tVEOzW40tbhVV1quosv6ktQb42ZTUO0QDYnopNSZUqdGf/4zppahegVyRBwCgm6huaNayDw/o+Q/zPSO4x4Q59OMLU/TD9L4KC2KAOPgeQR3ASR2rbdKe8hp9VlatveU12lNerc/KajoM4yGBfkoID1JiRLDinUFKCA9SfHiw4pwORYU6POHcCrec1zW16GhNk47UNOpoTZPKqhtUXFmvomP1Kq5sUFFlvUpdDWpuNco/Uqv8I7XSrnKvY4QHB2hIfJiGJTjblkSnBsWFyuHv56OzAgAAX3U8oD+3Pl9V9W0BvX9UiP7zkgG6emwfBQXwvg3rIKgDkNttVHisTjuKXdpRXKUdxS7tLHap/BSBPDbMof5RvdQ3KkT9o0LUN6qX+keFqF9kLzmD/X0ewE9XSKC/QiL9T/n8WUurW2XVjTp4pFb7Dtdo3+Fa7T9Sq/2Ha1RUWa+q+mZ9nF+hj/MrPPv4220aGBuqYQlOjU6O0Ni+EUqLdyrQ394VpwUAAD5X3dCs5R8d0B8/+CKgD4jppTunDNJ3RiUygjssyWaMMb4uoqu5XC6Fh4erqqpKTqfT1+UAXcoYo5KqBm0pqNSWgmPaeqhKu0pcqm5sabd9n4hgDY4L1aC4MA2Kbfs5MDZUoQ6+55OkhuZW7T9cq92lbV9u7CxpW47fSvdlDn+7RvYJ15jkCI3t21vn9YtQQniwD6oGAKDnq25o1p+zD+qPH+z3vC8T0OFLZ5JDCeoEdfRwdU0t2naoSlsK24J5bmGlylwnXikP9LNrSHyYhic6NTzRqWGJ4UqLD1MvAvkZM8ao1NWgncUubS9yKbfwmLYUVrYb3vtFhWhiSpQmDohUekqUEiMI7gAAfBM1jS2fX0H/IqCnxvTSXQR0+BhBvQMEdfRkxZX1ysk/qs0Hj2lLQaV2l1ar1e39f3M/u01DE8I0Nrm3RidHaHiiUwNjQxXgx23ZncUYowNH67SloO1/l08KjrX7v83x4J4xIEoXDYpWVCgjzgIAcDrqm1q17KMD+v37+wjosCSCegcI6uhJCivqtGH/UeXkVygn/6gKK04cuTzO6dB5fXtrbN+2W65HJIYrOJABU3ytuqFZmw4e04b9R7Vhf4W2F1V5BXebTRrZJ1yXDI7RJYNjNCY5Qv58mQIAgJfmVrf+vqlQv31vj2d8ndTotlvcvzuagA7rIKh3gKCO7soYo4KKOuXsr/CE869OKeZnt2lEn3BN6N9bYz8P5zwH3T14gvu+o/pgzxHtLHF5bQ8L8tdFg6I1eUispqTFcrUdAHBOc7uN3tpWoqfezdOBo3WSpKTewZqXOVhXje1DQIflENQ7QFBHd3K0plHr9x7RB3uO6MO9R1RS1eC13d9u06ikcKWnRmliapTG9evNQG89RLmrQe/vOaJ1nx3WB3sOez3jbrdJ4/tF6lvD4vStYXHqH93Lh5UCANB1jDF6f88RPbFyt3YUt32pHdUrUD+9bKBuSO/L9KiwLIJ6BwjqsLKG5lZtOnBMH+w53O5V1QA/m8YkRyg9pS2Yn9cvQiGBBPOertVttPVQpdbkHdZ7O8tO6BeD40L1rWFxumJEgoYnOrvN9HgAAJyJLQXH9KuVu7Vhf9uUqKEOf828OFW3XpjChQpYHkG9AwR1WIkxRrtKqvXBnsNav/eIPs6vUGOL26vNsASnLhoUrQsHRWt8v0ieL4cOHavTezvLtGpXmTbsr/B6tj0lupemj0zQ9FEJSosPI7QDALq9veXVevLfefr3jjJJbbPV/L+Mfppz6UBF9gr0cXXA6SGod4CgDl9raG7Vh3uPaPXucmXtKlepy/t29jinQxcNitFFg6J1wYBoxYTxLDJOrqquWWvyyvXvHaVak1euhuYvvuhJjeml74xM0HdGJ2pwXJgPqwQA4Mwdrm7Ub977TCs2FqrVbWS3Sdecl6S53xqsPkxpim6GoN4Bgjp8oaSqXlmfB/MP9x3xClPBAX6amBrpCecDY0O5CoqvpbaxRat3l+utrcVak3dYTV+6O2NoglNXj03UlWP6KM4Z5MMqAQA4tYbmVj2/Pl9L1u5TTWOLJOlbw+J079QhGsQXz+imCOodIKijK7jdRluLqpS1q0zv7So/4ZniPhHBuiwtVpcNjVVGapSCAridHWdXdUOzVu8q15tbS7Tus3I1t7a93Nts0qQB0bpqbB9NGxHPM30AAMtwu41e/7RIT67MU/HnA+iOSgrXf317qNJTo3xcHfDNENQ7QFBHZ6lpbNH6PUeUtbtMWbsP60hNo2ebzSaNTY7QlKFxmjI0VkPieHYYXaeyrklvbSvRa1uKtPHAMc/6oAC7pg6P13XjkzUxNUp2prIBAPjIhv1H9ehbu7StqEqSlBgepHunpel7oxN5f0KPQFDvAEEdZ1NhRZ1W7yrT6t3lytlfoabWL241DnX465LBMbosLVaTh8Qw7zUsobCiTq9tKdKrW4q0/0itZ33fyBBdd36yfjAuiVvjAQBdZv/hGi1+Z7dW7WwbKC7U4a+fXDpAt05K4Y5D9CgE9Q4Q1PFNtLqNPik4ptW7ypW1u0yfldV4be8XFaIpaW1Xzc/vH6lAf7uPKgVOzRijTw9V6ZVNhfpXbrGqP38G0G6TLkuL1XXn99WlQ2Lk70cfBgCcfZV1TfrNe3v01w0H1eI28rPbdMOEZM3NHKxoLm6gByKod4CgjjNVVd+s9z87rKzd5VqTV67KumbPNj+7TeP79daUobG6LC1OA2J6cUs7up26pha9va1UL28s8Lo1PjbMoR+MS9J15yerX1QvH1YIAOgpWlrdejGnQL9e9Zmq6ts+U01Ji9XCb6dpYCwDxaHnsmxQf/TRR/XWW28pNzdXgYGBqqysPGnbo0ePavTo0SoqKtKxY8cUEREhSVq7dq0uvfTSE9qXlJQoPj7+tOogqON07D9co6zd5Vq9q1wbD1So5UvzVIcHB2jykM9vaR8cq/CQAB9WCpxde8tr9PdNhfrn5kM6WtvkWZ+RGqUbJ/bT5cPjFMBVdgDA17B+zxE98uYOzx2JafFheuA7wzRpYLSPKwM635nk0C4d6repqUnXXnutMjIy9Pzzz5+y7W233aZRo0apqKio3e15eXleJxcbG3tWa8W5p7nVrY0HKpS1q1yrd5cr/0vP7krSwNhQTUmL1WVpsRrXrze3A6PHGhgbqp99e6juvnyIVu8q04qNhXp/z2Fl7z+q7P1HFed06IcT+umGCcmK5Vl2AMBpOHCkVr98a5fe29X2HHrvkADNv3yIbjg/mc9UQDu6NKg//PDDkqRly5adst2SJUtUWVmpRYsW6Z133mm3TWxsrOcqe0caGxvV2PjF6Nsul+sUrXEuOVbbpLWfleu9XeV6P++w5xldSQrwsyk9JUqXpcVqytBYbvvFOSfQ364rRiboipEJOnSsTi9vLNRLHxeozNWo/3nvMz2TtUfTRsTrpgv6a3y/3jzyAQA4QXVDs363Zq/+tD5fza1G/nab/l9GP82dMpg7EoFTsNzkuTt37tQjjzyinJwc7d+//6TtxowZo8bGRo0YMUIPPfSQJk2adNK2ixcv9nxJgHObMUZ7ymv03q4yZe0q1ycFx/SlO9oV1StQl6bFakparC4cFK2wIN5AAElK6h2iBZcP0R2XDdTK7aX6c/ZBbT54TG9uLdGbW0uUFh+mH2X011VjExUSaLm3FgBAF3O7jf6x+ZCe+HeeZ7raiwfHaNF3hvIcOnAafDKY3LJlyzR37twTnlFvbGzUhAkTdM899+jGG2/0PI/+5WfU8/LytHbtWo0fP16NjY167rnn9Je//EU5OTk677zz2v332ruinpyczDPq54jGllZt2F+hrM+nUDt0rN5r+9AEZ9st7UNjNTopQn7M0wmclu1FVfpL9kG9/mmRGprbpiUMC/LXD8Yl6eYL+nMXCgCcozYdqNDDb+z0zIeeEt1LD3xnqC4dEsvdVzindelgcvfff79+9atfnbLNrl27lJaW5vn7ZEF9/vz5Ki4u1ooVKySp3aDenksuuUR9+/bVX/7yl9OqmcHker7y6gat3X1Yq3eX6YM9R1TX1OrZFuhv16QBUbpsaJwuS4tVn4hgH1YKdH9Vdc16ZXOh/rLhoA4erZMk2WzSt4bG6ccXper8/twWDwDngqLKej3+zm698WmxJCnM4a+7MgfpRxn9ma4WUBcH9cOHD+vo0aOnbJOamqrAwEDP3ycL6mPGjNG2bds8H+iMMXK73fLz89N//dd/nfT29XvuuUfr169Xdnb2adVMUO953G6jrUVVytpdrrV55dp6qMpre2yYwzN92qSBUdyaC3QCt9vo/T2HteyjA1qbd9izfmSfcN12YYqmj0pgtHgA6IEamlv1+3X7tWTdXjU0u2WzSdefn6wFlw9hPnTgS7p01PeYmBjFxMR808NIkv75z3+qvv6L25I3btyoW2+9VR988IEGDBhw0v1yc3OVkJBwVmpA91FZ16T39xzR2t3lWvfZYa9ppCRpVFK4LkuLVebQOA1PdHJFD+hkdrtNk4fEavKQWO0tr9bz6w/o/z45pG1FVZr7cq4ef2e3fnRBP/1wQl9FhAR2fEAAgKUZY/TernI98uYOFVa0fYafkBKpRd8ZphF9wn1cHdC9dellxYKCAlVUVKigoECtra3Kzc2VJA0cOFChoaEnhPEjR45IkoYOHeq59f03v/mNUlJSNHz4cDU0NOi5555TVlaW3n333a48FfiAMUa7Sqq1Jq/tqvnmg94DwYU5/HXR4Oi2oDA4hmmjAB8aGBumxd8fqXumDtGLGw7qzxsOqtTVoCdW5umZ1Xv1g3FJumVSf6XGhPq6VADA15B/pFYPv7HDcwdVQniQfvbtofrOqAQujgBnQZcG9UWLFmn58uWev8eOHStJWrNmjSZPnnxax2hqatKCBQtUVFSkkJAQjRo1Su+9954uvfTSzigZPlbT2KIP9x7Rmt3lWpt3WKWuBq/tg+NCdemQWF36+dzm3FYLWEtkr0D9dMogzbwkVW98WqLn1+drV4lLf9lwUH/NOajMoXGadUmqxvWL9HWpAIDTUNfUot9l7dVzH+SrqdWtAD+bbr8oVXMuHaheDh4tBM4Wn4z67ms8o25drW6jbUVV+uCzw/pg7xF9cvCYWr502Tw4wE+TBkZ9fnttjJJ6h/iwWgBnyhij7H1H9fz6fK3eXe5ZP75fb826ZIAuS4uVnZkXAMByjDF6e1upfvnWTpVUtV04uWRwjB787jDujgJOU5cOJtcdEdStpbCiTh/sOaIP9hzWR/uOqqq+2Wt7v6gQz1Xz9JRIBQX4+ahSAGfT3vIa/fH9/Xp1S5GaWtumdxsYG6qZF6fqyjGJcvjz/3UAsII9ZdV68F879NG+tgGkk3oHa9F3hulbw+K4zR04AwT1DhDUfcvV0KzsfUf1wZ7DWr/niA58Pp3TcWFB/rpgQJQuGhSjiwZFMxcz0MOVuRr0wocH9OKGg6pubJEkxTkdunVSin6Y3ldhQQE+rhAAzk3VDc367Xt7tOyjA2pxGzn87Zo9eYBmXTKACyfA10BQ7wBBvWs1trTq08Iqfbj3iNbvPaLcwkq1ful2dn+7TWP7RujCgTG6aHC0RvUJlz/PmgPnnOqGZr30cYGeX5+vMlejpLZBImdM7KdbJ/VngEgA6CLGGL26pUiPvb1bR2raXo8vHxanB74zTMmRPHYIfF0E9Q4Q1DvX8WC+Yf9Rbdh/VJsPHlNji9urTWp0L100KFoXDorRxNRIrpgB8Ghqceu13CL94f392lteI0kK9LPr6rF9NPOSVA3gWUgA6DQ7iqv04Os7tOngMUlSSnQvPfjdYZo8JNbHlQHdH0G9AwT1s6uxpVVbD1Vpw76j2pDfFswbmr2DeXRooNJTo3TRwGhdOCiaQeAAdMjtNsraXa7fv79PGw+0fWC02aRvDY3TrMkDdF7f3j6uEAB6jsq6Jv161Wf664aDcpu2AXx/OmWgbrswhTFDgLOEoN4Bgvo342po1paCSm0+UKGNB45pS+HJg/nE1ChlpEZqQEwog40A+No2H6zQ79ft17s7yzzr0lMiNXvyAF0yOIbXFwD4mtxuo79vKtQT/85TRW2TJOk7oxL0s28PVWJEsI+rA3oWgnoHCOpnpqiyXpsOVGjTgWPadPCYdpe69NVeE9UrUBNTozQxNVITU6M0MJZgDuDs21teoz+8v0+vbilSc2vbC9HQBKdmTx6gb4+IZ3wLADgDuYWVevD17fr0UJUkaVBsqB6+crguGBDt48qAnomg3gGC+sk1trRqV0m1cguOaXNBpTYdqPDMlfllfSNDNL5fb43r31vn94/UIII5gC5UUlWv5z/I198+LlBdU6ukttelmRen6gfjkhiNGABO4WhNo55YmaeXNxVKkkId/pqbOUg3XdBfAXzhCXQagnoHCOpt3G6j/Udq9WlhpT49VKlPCyu1s8TluUp1nJ/dphGJTo3rF6nx/XtrfL/ejL4MwBIq65r05+yDeuHDfB2ra5YkRYc6dOuF/XXjxH5yMlAlAHi0tLr1Yk6Bnno3T66Gtukwv39eH91/RZpiw/hsB3Q2gnoHztWgXu5qUG5hpXI/D+ZbC6s8cxZ/WVSvQI1OjtDY5AiN699bY5IjFBLo74OKAeD01DW16O8bC/XHD/JVVFkviandAODLPs6v0KLXt2t3abUkaViCU49cOVzj+0f6uDLg3EFQ78C5ENQPVzdqR3GVdhS7tO1QlT49VNnuLexBAXaN7BOu0UkRGtM3QqOTIpTUO5jb2AF0S82tbr25tVhL1u7TZ2WfT+3mb9cPxiVp5kWp6h/dy8cVAkDXKqmq1+K3d+tfnxZLkpxB/rpn6hD9ML2f/Ox83gO6EkG9Az0pqBtjVFRZrx3FLu0oagvm24urVOZqPKGt3SYNjgvzCuWD40IZfAlAj+N2G63JK9f/rt2nzZ/PBWy3Sd8emaBZlwzQiD7hPq4QADpXQ3Ornl+fr99l7VV9c6tsNun68/vq7ssHKyrU4evygHMSQb0D3TWou91GB47WavtXQnnl589lfpnNJqVG99LwxHCN6OPU6KQIjegTrl4ObmEHcG7ZeKBCS9buU9bucs+6iwZFa/bkAcpIjeIOIgA9ijFG7+0q1y/e3KmCijpJ0rh+vfXw94bzJSXgYwT1DnSXoL6nrFq5hZVtV8uLq7Sz2KXaz0c3/jJ/u02D48I0PNGpEX3CNTzRqaEJTkI5AHzJrhKXfr9un97YWqJWd9tb3+jkCM2+ZIAuHxYnO7eAAujm9h2u0SNv7NS6zw5LkmLDHPrZt4fqyjGJfCkJWABBvQPdJajPeG6DPtx71GtdUIBdQxOcbaE8MVzDE8M1OD5UDn+mIgKA01FYUac/frBfL28sVGOLW5I0IKaX/vOSAbpqTB8F+vM4EIDupbqhWc9k7dWf1uerxW0U4GfTbRem6o7LBiqUCzeAZRDUO9Bdgvpv39uj7P1HPLevD08MV2p0L54pB4Cz4EhNo5Z9eEDLsw+o+vNpiuKdQfrxRSm6YUJf7koCYHlut9H/bSnS4+/s1pGatvGJLkuL1QPfGaYUBs8ELIeg3oHuEtQBAJ2vuqFZL31coOc+yFd5ddsH3fDgAN10QX/dfEF/RfYK9HGFAHCiTwsr9dAbO7SloFKS1D8qRIu+O0yXpcX5tjAAJ0VQ7wBBHQDwVY0trXr1kyL9/v39yj9SK6ntcaPrz++rH1+UoqTeIT6uEADa7gZ6cmWe/r65UMZIIYF++ullg3Trhf15FBKwOIJ6BwjqAICTaXUb/XtHqZas3adtRVWS2gbt/N6YRM26ZIAGx4X5uEIA56LmVrf+nH1Qv3nvM8/jOleP7aP7r0hTnDPIx9UBOB0E9Q4Q1AEAHTHG6MO9R7Vk3V6vgT0zh8Zq9uQBGtcv0ofVATiXfLj3iB761w7tKa+RJA1PdOrh7w3X+P68DgHdCUG9AwR1AMCZ+LSwUkvX7dPKHaU6/q45oX+kZk8eoMlDYpj2CECnKKyo06Nv7dLKHaWSpN4hAbpnapquOz9ZfkwpCXQ7BPUOENQBAF/HvsM1+sO6/fq/LYfU3Nr29pkWH6bZkwdo+sgEZuUAcFbUNrbo9+v26ffv71dji1t+dpv+38R+mpc5WOEhAb4uD8DXRFDvAEEdAPBNlFY16Pn1+/W3nALVNrVKkpIjgzXzolRdOz5ZQQEM6ATgzLndRv/45JD++995nlkoJqZG6qHvDVdaPJ9Zge6OoN4BgjoA4GyoqmvWXzYc0J8+PKCK2iZJUlSvQN16YYpunNhP4cFc+QJwerL3HdUv39qpHcUuSW1f/i28YqiuGBHP4zVAD0FQ7wBBHQBwNtU3teqVzYX6/br9KqqslySFOvw1I72vbr0whRGZAZxU/pFaLX57l97dWSZJCnP466dTBuqmC5huDehpCOodIKgDADpDc6tbb20t0ZK1+5RXVi1JCvSz63tjEnXLpP4anhju4woBWEVVXbN+u3qP/px9QC1uIz+7TT+c0FdzMwcpKtTh6/IAdAKCegcI6gCAzmSM0Zq8ci1Zu08bDxzzrJ+YGqlbJ6VoytA4RmwGzlHNrW79dcNB/Xb1HlXWNUuSLh0So599e6gGxYX5uDoAnYmg3gGCOgCgq3xScEwvfHhAb28rUau77S23b2SIbrqgv/5jfJLCgniOHTgXGGO0cnupnvx3nvYfqZUkDY4L1c+nD9PFg2N8XB2ArnAmObRL55F59NFHdcEFFygkJEQRERHttrHZbCcsK1as8Gqzdu1anXfeeXI4HBo4cKCWLVvW+cUDAPA1nNe3t565Yaw+uPdSzZ48QOHBASqoqNMv3typjMVZeviNHTp4tNbXZQLoRDn7j+rq//1Is1/8RPuP1Co6NFCPXT1Sb995ESEdQLu69Ir6gw8+qIiICB06dEjPP/+8KisrTyzIZtMLL7ygadOmedZFREQoKKhtIJ78/HyNGDFCs2bN0o9//GOtXr1ac+fO1VtvvaWpU6eeVh1cUQcA+Ep9U6v+b8shvfDhAe0tr5Ek2WxS5tA43TopRRNTIxnhGegh8kqr9cTK3Vq9u1ySFBzgp9svStHtF6dyNw1wDrL8re/Lli3T3LlzTxrUX331VV111VXt7nvffffprbfe0vbt2z3rrr/+elVWVmrlypXt7tPY2KjGxkbP3y6XS8nJyQR1AIDPGGP0wZ4j+tOH+Vqbd9izfmiCU7dO6q/vjk5kPnagmyqurNevV32mf35ySMZIfnabrj8/WXdlDlJsGLNAAOcqy976frrmzJmj6OhoTZgwQX/605/05e8SsrOzlZmZ6dV+6tSpys7OPunxFi9erPDwcM+SnJzcabUDAHA6bDabLh4co2W3TNB78y/RjRP7KjjAT7tKXLrnH1s16fEsPf7ObhVW1Pm6VACnqaquWYvf3qXJ/71W/9jcFtK/PTJeq+ZdrEevHklIB3Da/H1dwFc98sgjuuyyyxQSEqJ3331XP/nJT1RTU6M777xTklRaWqq4uDivfeLi4uRyuVRfX6/g4OATjrlw4ULNnz/f8/fxK+oAAFjBwNhQ/fKqkbr78iFasbFQf/7ogIqrGrR03T79/v19unRIrG6c2FeXDI5ltHjAghqaW7X8owN6ds1euRpaJEkTUiK18Io0je3b28fVAeiOvnFQv//++/WrX/3qlG127dqltLS00zreAw884Pl97Nixqq2t1ZNPPukJ6l+Hw+GQw8F8lAAAa4sICdSsSwboxxemaPXucv11w0F9sOeIsnaXK2t3uZJ6B+uH6X31H+OTFc08y4DPNba0asXHhXp2zV6VV7c9ZjkkLkz3XTFElw6JZbwJAF/bNw7qCxYs0M0333zKNqmpqV/7+Onp6frFL36hxsZGORwOxcfHq6yszKtNWVmZnE5nu1fTAQDobvz97Jo6PF5Th8cr/0itXtxwUK9sPqRDx+r1xMo8/WbVHl0xMl7/b2I/jevXmzAAdLHmVrf+sfmQnlm9R8VVDZKkPhHBuitzkK45L4k7XwB8Y984qMfExCgmpvOmlcjNzVXv3r09V8QzMjL09ttve7VZtWqVMjIyOq0GAAB8JSW6l37+nWG6e+oQvfFpsf664aA+PVSl13OL9XpusdLiw3T9+cm6amwfRYQE+rpcoEdrdRu9tqVIv129RwWfjx8R53TojssG6brxyQr0t+TwTwC6oS59Rr2goEAVFRUqKChQa2urcnNzJUkDBw5UaGio3njjDZWVlWnixIkKCgrSqlWr9Nhjj+nuu+/2HGPWrFn63e9+p3vvvVe33nqrsrKy9Pe//11vvfVWV54KAABdKijAT9eOT9a145O19VCl/rrhoP71abF2l1broTd26rF3duuKEfG6bnyyJqZGyc4VPeCscbuN3txWot+895n2H66VJEWHBmr25IGakd6XGRoAnHVdOj3bzTffrOXLl5+wfs2aNZo8ebJWrlyphQsXau/evTLGaODAgZo9e7Zuv/122e1ffEO5du1azZs3Tzt37lRSUpIeeOCBDm+//zLmUQcA9ARVdc16LbdIKzYWaleJy7O+b2SIrjs/WT8Yl6Q4J6NMA1+XMUbv7izT/6z6TLtLqyVJESEB+s+LB+imC/opJNBy4zIDsDDLz6PuawR1AEBPYozR9iKXVmws0Ou5xappbBt12m6TLkuL1X+MT9alabEK8OO2XOB0tLqN3tleot9l7fUE9DCHv358UapuvbC/woICfFwhgO6IoN4BgjoAoKeqa2rR29tK9fLGAm08cMyzPibMoavH9tE15yVpSHyYDysErKu51a3Xc4v1v2v3em5x7xXop5su6K+ZF6cyDgSAb4Sg3gGCOgDgXLC3vEavbCrUPzYf0tHaJs/64YlOXXNekr43JpFp3gC1zYP+j82HtHTdPh06Vi9JCg8O0M0X9Nctk/oT0AGcFQT1DhDUAQDnkqYWt9bklev/PjmkrN3lam5te+v3s9s0eXCMrhmXpMvSYhkQC+ccV0Oz/pZToD+tz/fMgx4dGqgfX5SqGyf2U6iDZ9ABnD0E9Q4Q1AEA56qK2ia9ubVY//ykSJ8WVnrWO4P89Z3RibrmvCSd1zeCudnRo5VWNehPH+brbzkFnjEdEsKD9J8Xp+q68/sqOJAvrQCcfQT1DhDUAQCQ9pZX6/8+KdKrW4pUUtXgWd83MkTfG52o741J1OA4nmdHz/FZWbX+8P5+vZ5b5LmzZFBsqGZenKorx/RhHnQAnYqg3gGCOgAAX2h1G23Yf1T//OSQVm4vVV1Tq2dbWnyYvjcmUd8dlajkyBAfVgl8PcYYrd97RC98eEBZu8s96yekRGrWJamaPDhWdjt3kADofAT1DhDUAQBoX11Ti97bVa5/5RZr3WdfPM8uSeP69db3Ridq+qgEBqGD5dU1tej/PinSso8OaG95jSTJZpOmDY/XzItTNbZvbx9XCOBcQ1DvAEEdAICOVdY1aeX2Uv3r02Jl7z+q458Y/Ow2XTAgSt8dnajLh8UxIjYspbCiTn/ZcFArPi6Qq6Ht+fNQh79+MC5JN13QXynRvXxcIYBzFUG9AwR1AADOTJmrQW9uLdG/cov06aEqz3p/u02TBkZr+sgEfWtYnHr3IrSj67W6jd7/7LD+9nGBVu8qk/vzT7f9o0J00wX99YNxSQoLCvBtkQDOeQT1DhDUAQD4+g4cqdUbnxbrrW0l2l1a7Vnvb7cpY0CUpo9M0NTh8YR2dLrSqgb9fVOhXt5YqKLKes/6iwZF65ZJ/Xn+HIClENQ7QFAHAODs2He4Rm9vLTkhtB+/Pf7bn4f2SEI7zpIvXz3P2l2u1s8vn0eEBOia85J0w4RkDYxltgIA1kNQ7wBBHQCAs2//4Rq9s71Ub20t0c4Sl2e93SaN7xepy4fH6VvD4tQvimeEceb2lFXrH58c0mtbilTmavSsn5ASqR9O6KtpI+IVFMD85wCsi6DeAYI6AACdK/9Ird7eVqK3t5VoR7HLa9vguFB9a1icvjUsXqP6hHNrMk7qWG2T/vVpsf75ySFt/dLYCFw9B9AdEdQ7QFAHAKDrHDpWp/d2lundnWXKya/w3KosSXFOhzKHtl1pzxgQJYc/V0TPdXVNLcra3TZF4Jq8L6YI9LfbNHlIrH4wro8uTYulrwDodgjqHSCoAwDgG1V1zVqTV653d5ZqXd5h1Ta1erb1CvRTxoBoTR4So0sGxyg5MsSHlaIr1Te1ak1eud7aWqLVu8vU0Oz2bBvRx6lrzkvS90YnKirU4cMqAeCbIah3gKAOAIDvNTS3Knv/Ua3aWaZVO8t0uLrRa3tqTC9dMrgttE9MjeL54x6mtrFF7392WG9tK9HqXeWqb/7iS5u+kSH69sgEXT22j4bEc2s7gJ6BoN4BgjoAANbidhvtLHFp3WeHtS7vsDYXHPO6Rd7hb1d6apQmD47RxYNjNCCml2w2nm3vbkqq6vXernK9t7NM2fuOqqn1iyvnyZHBmj4yUdNHJmhEHyf/+wLocQjqHSCoAwBgbVX1zfpo75G24P7ZYZVUNXhtj3M6NDE1ShmpUcoYEKW+kSEEOwtqdRvtKK5S1u5yvberTNuLvAcW7BcVoqnD4/WdUQka2Sec/w0B9GgE9Q4Q1AEA6D6MMdpTXqN1eW2h/eP8Cq8rsZLUJyJY6amRmpgapfH9eislmivuvmCM0cGjdVq/94g+3HtEH+07qqr6Zs92m006r2/vzwcQjNWAmFD+dwJwziCod4CgDgBA99XQ3KpPDh5T9v6jyt53VJ8eqvSMDH5cVK9Andevt8b1663x/XprRJ9wnnHvBMYYHTpWr40HKpSzv0Lr9x5RUWW9V5swh78uGBilKUPjdFlarKIZEA7AOYqg3gGCOgAAPUddU4s2Hzym7H1HtfFAhT49VKWmFu8r7oF+dg1NdGpUn3CN7BOukUnhGhQbKn8/u4+q7p6aW93aWezSpoPHtPlghTYdOKbyrwwCGOBn03l9e+vCgdGaNChao/qE898ZAERQ7xBBHQCAnquxpVU7il3afOCYNh88pk0Hj+lITeMJ7Rz+dg1NcGpUUriGJjg1OC5Ug+LC5AwK8EHV1tPY0qrPSmu0o7hKO4pd2lFcpZ0lLq+p06S2YD6iT7jO7x+pSQOjdX7/3goJ9PdR1QBgXQT1DhDUAQA4dxhjVFhRr08PVWpbUZW2HarS9qIqVTe2tNs+ITxIg+LCNDg2VIPjwpQS00v9okIUE+rokc9TN7e6VVBRp33lNdp3uFZ7y2u0s8SlPWXVanGf+DExPDhA4z5/rOD8/pEalcRjBQBwOgjqHSCoAwBwbnO7jQ5W1GnroUptL6pSXlmN9pRVnzC6/JeFBPqpb2SI+kWFqH9ULyVHhigxIkhxziAlhAerd0iAJYO8MUaVdc0qqqxX8fGlqkH5R2q173CNCo7WtRvIJSkiJEDDE50anhju+Zka3Ut2u/XOEwCsjqDeAYI6AABoT1V9s/aWV+uzshrllVZrb3mNDlbUquhYvU6SZT0C/e2KdwYp3hmkGKdDEcEBiggJUERwoMJDAj7/O1C9HH4KCvCTw9+uoIC234P87ad8jtsYo8YWtxqaW1Xf3Kr6prafDc2tqmlsVWVdkypqm3SstkkVdU06VtusitomlVc3qLiyQfXNraesPSTQT6kxvTQgJlSp0aEamhCm4X3ClRgeZMkvHwCgOyKod4CgDgAAzkRTi1uHjtXpYEWdDh6p1cGKOhVW1KnU1aDSqgYdqWn6xv+Gn90mu006/snMqC2gS+rwS4LTER0aqMSIYCWGByshIkj9o9qC+YDYXop3EsgBoLOdSQ5lpA8AAIAOBPrblRoTqtSYUGnIidubWtwqczWozNWgkqoGHalpVGVds6rqm1VZ16TK+mZV1rX9XtvUdiW8scXtNTp9q9vo1Ne92/jbbQoO8FNQoJ+CA/wUEuinyF6B6t0rUJEhx38GqHevQEWHOtQnIljx4UE8Rw4A3UiXBvVHH31Ub731lnJzcxUYGKjKysoT2rT3be5LL72k66+/XpK0du1aXXrppSe0KSkpUXx8/FmvGQAAoCOB/nYlR4YoOTLkjPZzu7+4pb2xxS2jtkvnNrV9HrLZ1PabTW3hPMBPAUx1BgA9XpcG9aamJl177bXKyMjQ888/f9J2L7zwgqZNm+b5OyIi4oQ2eXl5XrcLxMbGntVaAQAAOpvdblNwoJ+CA7naDQD4QpcG9YcffliStGzZslO2i4iI6PDqeGxsbLsBHgAAAACA7syS907NmTNH0dHRmjBhgv70pz+pvfHuxowZo4SEBH3rW9/Shx9+eMrjNTY2yuVyeS0AAAAAAFiR5QaTe+SRR3TZZZcpJCRE7777rn7yk5+opqZGd955pyQpISFBS5cu1fjx49XY2KjnnntOkydPVk5Ojs4777x2j7l48WLP1fwvI7ADAAAAALrC8fx5WhOvmW/ovvvuM/p8BpGTLbt27fLa54UXXjDh4eGndfwHHnjAJCUlnbLNxRdfbG688caTbm9oaDBVVVWeZefOnR3WzMLCwsLCwsLCwsLCwsJytpfCwsIOc/A3vqK+YMEC3Xzzzadsk5qa+rWPn56erl/84hdqbGyUw+Fot82ECRO0fv36kx7D4XB47RsaGqrCwkKFhYVZes5Ql8ul5ORkFRYWMt87LIt+iu6AforugH4Kq6OPojuwcj81xqi6ulqJiYkdtv3GQT0mJkYxMTHf9DAnlZubq969e580pB9vk5CQcNrHtNvtSkpKOhvldQmn02m5TgZ8Ff0U3QH9FN0B/RRWRx9Fd2DVfhoeHn5a7br0GfWCggJVVFSooKBAra2tys3NlSQNHDhQoaGheuONN1RWVqaJEycqKChIq1at0mOPPaa7777bc4zf/OY3SklJ0fDhw9XQ0KDnnntOWVlZevfdd7vyVAAAAAAA6BRdGtQXLVqk5cuXe/4eO3asJGnNmjWaPHmyAgIC9Oyzz2revHkyxmjgwIH69a9/rdtvv92zT1NTkxYsWKCioiKFhIRo1KhReu+993TppZd25akAAAAAANApujSoL1u27JRzqE+bNk3Tpk075THuvfde3XvvvWe5MmtyOBx68MEHT3nbP+Br9FN0B/RTdAf0U1gdfRTdQU/ppzZjTmdseAAAAAAA0BXsvi4AAAAAAAB8gaAOAAAAAICFENQBAAAAALAQgjoAAAAAABZCUAcAAAAAwEII6hb27LPPqn///goKClJ6ero+/vhjX5eEc9RDDz0km83mtaSlpXm2NzQ0aM6cOYqKilJoaKiuueYalZWV+bBinAvef/99ffe731ViYqJsNptee+01r+3GGC1atEgJCQkKDg5WZmam9uzZ49WmoqJCM2bMkNPpVEREhG677TbV1NR04Vmgp+uon958880nvL5+dapa+ik60+LFi3X++ecrLCxMsbGxuuqqq5SXl+fV5nTe5wsKCjR9+nSFhIQoNjZW99xzj1paWrryVNCDnU4/nTx58gmvp7NmzfJq0536KUHdol5++WXNnz9fDz74oD755BONHj1aU6dOVXl5ua9Lwzlq+PDhKikp8Szr16/3bJs3b57eeOMNvfLKK1q3bp2Ki4v1/e9/34fV4lxQW1ur0aNH69lnn213+xNPPKGnn35aS5cuVU5Ojnr16qWpU6eqoaHB02bGjBnasWOHVq1apTfffFPvv/++Zs6c2VWngHNAR/1UkqZNm+b1+vrSSy95baefojOtW7dOc+bM0YYNG7Rq1So1Nzfr8ssvV21tradNR+/zra2tmj59upqamvTRRx9p+fLlWrZsmRYtWuSLU0IPdDr9VJJuv/12r9fTJ554wrOt2/VTA0uaMGGCmTNnjufv1tZWk5iYaBYvXuzDqnCuevDBB83o0aPb3VZZWWkCAgLMK6+84lm3a9cuI8lkZ2d3UYU410kyr776qudvt9tt4uPjzZNPPulZV1lZaRwOh3nppZeMMcbs3LnTSDIbN270tHnnnXeMzWYzRUVFXVY7zh1f7afGGHPTTTeZK6+88qT70E/R1crLy40ks27dOmPM6b3Pv/3228Zut5vS0lJPmyVLlhin02kaGxu79gRwTvhqPzXGmEsuucTcddddJ92nu/VTrqhbUFNTkzZv3qzMzEzPOrvdrszMTGVnZ/uwMpzL9uzZo8TERKWmpmrGjBkqKCiQJG3evFnNzc1e/TUtLU19+/alv8Jn8vPzVVpa6tUvw8PDlZ6e7umX2dnZioiI0Pjx4z1tMjMzZbfblZOT0+U149y1du1axcbGasiQIZo9e7aOHj3q2UY/RVerqqqSJEVGRko6vff57OxsjRw5UnFxcZ42U6dOlcvl0o4dO7qwepwrvtpPj3vxxRcVHR2tESNGaOHChaqrq/Ns62791N/XBeBER44cUWtrq1cnkqS4uDjt3r3bR1XhXJaenq5ly5ZpyJAhKikp0cMPP6yLLrpI27dvV2lpqQIDAxUREeG1T1xcnEpLS31TMM55x/tee6+jx7eVlpYqNjbWa7u/v78iIyPpu+gy06ZN0/e//32lpKRo3759+tnPfqYrrrhC2dnZ8vPzo5+iS7ndbs2dO1eTJk3SiBEjJOm03udLS0vbfb09vg04m9rrp5L0wx/+UP369VNiYqK2bt2q++67T3l5efq///s/Sd2vnxLUAXToiiuu8Pw+atQopaenq1+/fvr73/+u4OBgH1YGAN3b9ddf7/l95MiRGjVqlAYMGKC1a9dqypQpPqwM56I5c+Zo+/btXuPQAFZzsn765bE7Ro4cqYSEBE2ZMkX79u3TgAEDurrMb4xb3y0oOjpafn5+J4ymWVZWpvj4eB9VBXwhIiJCgwcP1t69exUfH6+mpiZVVlZ6taG/wpeO971TvY7Gx8efMEBnS0uLKioq6LvwmdTUVEVHR2vv3r2S6KfoOnfccYfefPNNrVmzRklJSZ71p/M+Hx8f3+7r7fFtwNlysn7anvT0dEnyej3tTv2UoG5BgYGBGjdunFavXu1Z53a7tXr1amVkZPiwMqBNTU2N9u3bp4SEBI0bN04BAQFe/TUvL08FBQX0V/hMSkqK4uPjvfqly+VSTk6Op19mZGSosrJSmzdv9rTJysqS2+32vLkDXe3QoUM6evSoEhISJNFP0fmMMbrjjjv06quvKisrSykpKV7bT+d9PiMjQ9u2bfP6UmnVqlVyOp0aNmxY15wIerSO+ml7cnNzJcnr9bRb9VNfj2aH9q1YscI4HA6zbNkys3PnTjNz5kwTERHhNUoh0FUWLFhg1q5da/Lz882HH35oMjMzTXR0tCkvLzfGGDNr1izTt29fk5WVZTZt2mQyMjJMRkaGj6tGT1ddXW22bNlitmzZYiSZX//612bLli3m4MGDxhhjHn/8cRMREWFef/11s3XrVnPllVealJQUU19f7znGtGnTzNixY01OTo5Zv369GTRokLnhhht8dUrogU7VT6urq83dd99tsrOzTX5+vnnvvffMeeedZwYNGmQaGho8x6CfojPNnj3bhIeHm7Vr15qSkhLPUldX52nT0ft8S0uLGTFihLn88stNbm6uWblypYmJiTELFy70xSmhB+qon+7du9c88sgjZtOmTSY/P9+8/vrrJjU11Vx88cWeY3S3fkpQt7BnnnnG9O3b1wQGBpoJEyaYDRs2+LoknKOuu+46k5CQYAIDA02fPn3MddddZ/bu3evZXl9fb37yk5+Y3r17m5CQEHP11VebkpISH1aMc8GaNWuMpBOWm266yRjTNkXbAw88YOLi4ozD4TBTpkwxeXl5Xsc4evSoueGGG0xoaKhxOp3mlltuMdXV1T44G/RUp+qndXV15vLLLzcxMTEmICDA9OvXz9x+++0nfClPP0Vnaq9/SjIvvPCCp83pvM8fOHDAXHHFFSY4ONhER0ebBQsWmObm5i4+G/RUHfXTgoICc/HFF5vIyEjjcDjMwIEDzT333GOqqqq8jtOd+qnNGGO67vo9AAAAAAA4FZ5RBwAAAADAQgjqAAAAAABYCEEdAAAAAAALIagDAAAAAGAhBHUAAAAAACyEoA4AAAAAgIUQ1AEAAAAAsBCCOgAAAAAAFkJQBwAAAADAQgjqAAAAAABYCEEdAAAAAAAL+f/5DiE6L+PaHwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(12,6))\n",
    "ax1.plot(position, label=\"position\")\n",
    "ax2.plot(actions, label=\"energy\")\n",
    "ax3.plot(rewards[0:250])\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       -1., -1., -1.], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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