demo_usage/argoverse_forecasting_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Argoverse-forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple tutorial that will show you how to interact with the Argoverse-forecasting dataset using our python package. See [github page](https://github.com/argoai/argoverse-api) for instructions on how to install the package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Argoverse dataset can be download at [https://www.argoverse.org](https://www.argoverse.org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to create argoverse loader. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of sequences: 5\n"
     ]
    }
   ],
   "source": [
    "from argoverse.data_loading.argoverse_forecasting_loader import ArgoverseForecastingLoader\n",
    "\n",
    "##set root_dir to the correct path to your dataset folder\n",
    "root_dir = '../../forecasting_sample/data/'\n",
    "\n",
    "afl = ArgoverseForecastingLoader(root_dir)\n",
    "\n",
    "print('Total number of sequences:',len(afl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq : /Users/jsingh/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3828.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 45\n",
      "        ----------------------\n"
     ]
    }
   ],
   "source": [
    "print(afl[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to go through each log in our dataset is by iterating through our data loader. For example, we can see statistics for each log with simple iteration and printing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq : /Users/jsingh/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/2645.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 24\n",
      "        ----------------------\n",
      "Seq : /Users/jsingh/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/4791.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 60\n",
      "        ----------------------\n",
      "Seq : /Users/jsingh/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3700.csv\n",
      "        ----------------------\n",
      "        || City: PIT\n",
      "        || # Tracks: 32\n",
      "        ----------------------\n",
      "Seq : /Users/jsingh/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3861.csv\n",
      "        ----------------------\n",
      "        || City: PIT\n",
      "        || # Tracks: 5\n",
      "        ----------------------\n",
      "Seq : /Users/jsingh/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3828.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 45\n",
      "        ----------------------\n"
     ]
    }
   ],
   "source": [
    "for argoverse_forecasting_data in (afl):\n",
    "    print(argoverse_forecasting_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get all the track_ids for a sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00000000-0000-0000-0000-000000000000', '00000000-0000-0000-0000-000000023211', '00000000-0000-0000-0000-000000023327', '00000000-0000-0000-0000-000000023356', '00000000-0000-0000-0000-000000023366', '00000000-0000-0000-0000-000000023430', '00000000-0000-0000-0000-000000023435', '00000000-0000-0000-0000-000000023457', '00000000-0000-0000-0000-000000023463', '00000000-0000-0000-0000-000000023464', '00000000-0000-0000-0000-000000023465', '00000000-0000-0000-0000-000000023466', '00000000-0000-0000-0000-000000023470', '00000000-0000-0000-0000-000000023475', '00000000-0000-0000-0000-000000023476', '00000000-0000-0000-0000-000000023478', '00000000-0000-0000-0000-000000023481', '00000000-0000-0000-0000-000000023484', '00000000-0000-0000-0000-000000023487', '00000000-0000-0000-0000-000000023490', '00000000-0000-0000-0000-000000023491', '00000000-0000-0000-0000-000000023492', '00000000-0000-0000-0000-000000023493', '00000000-0000-0000-0000-000000023494']\n"
     ]
    }
   ],
   "source": [
    "argoverse_forecasting_data = afl[0]\n",
    "print(argoverse_forecasting_data.track_id_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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wb0N0LSIikhJKBipvHrDHEB1LBYQiIlIyJQOVNx8Y7+7jhuBY5SYDfehvQkSk7uiDv8LMrA+4gfJGAeRcDTxYRvxDwNeG4DpERCRFNJqgCpjZ6UN0qMcor5t/JbBgiK5FRERSQj0DVcDdT3f3k4fgUJ8HvlFG/FTgmiG4DhERSRElA9VhC+CQIThOuZMOqYBQRKQOKRmoDi8zNLMQDsVoAv1NiIjUGX3wV4dXCIsElesl4Mky4l8jLJwkIiJ1REsYVwF3zwDELj0sIiJSDvUMVIEkCfiYu29WznHcfWd3v7KM+Le7+7XlXIOIiKSPkoHq8UnKn4mwETi8jPiRwM5lXoOIiKSMkoHqMRRLGauAUERESqYP/urxKrB9mcdYQXlDA7uAN8q8BhERSRkVEFYJd98R6DGzuRW+FBERqTPqGageHcCYcg/i7p9y99GRsVu7++fLvQYREUkXJQPVY0/g+iE4znnA5pGxk4AzhuAaREQkRZQMVI95DM3EQ+VMSdyHpiMWEak7Sgaqx0Jgors3l3mcckYUaG0CEZE6pGSgSphZL/A7YOsyD/V34msPetFoAhGRuqNkoLpMBrYp8xgNwKjI2C7CokkiIlJHNLSwirj7PsArZra80tciIiL1Qz0D1WU+ZQ4vdPcj3f0dkbFbufuPyjm/iIikj5KB6vJJ4FNlHuNI4tcnGAMcVeb5RUQkZZQMVJc3KL9moNz1CTJlnl9ERFJGyUB1qXQyoAISEZE61FTpC5A3eQa4ucxjXEf8aII5wK5lnl9ERFJGyUAVMbPZwI/LPMxqYGxk7BjgGOCGMq9BRERSRMlAFXH3RsJSxjskkxDFeBdwDnGFgJOA76BkQESkrqhmoIokCUAzsGUZh1mJagZERKQESgaqzxuUt2CRRhOIiEhJlAxUnwcp7/HN68BPImMXA2eWcW4REUkhTUcs/+DuDUCLmS2p9LWIiMjwUc9AlXH3Y9395DLiR7r77MjwrYFnY88tIiLppGSg+mxP/HTCAOuAKe4+IiJW3UQiInVIyUD1KWsWQjPLEooIx0UeQgWEIiJ1RslA9RmKKYn/RBiiWKrVaI4BEZG6owLCKuPuzUCzma2u9LWIiEh9UM9AlTGzbuD4pLI/iruf6+67RcRNcvc7Ys8rIiLppGSgOn0XaC0j/khgx4i4JuDAMs4rIiIppGSgOr0GbFtGfOwshHpmJCJSh5QMVKfXgO3KiF8BTIiI6wOWlnFeERFJIa1aWJ2+SRhVEOscYE2pQWa2GCi51kBERNJNPQPV6UlgVRnxWxFRM+DuY9z9c2WcV0REUkjJQHU6BrimjPiTgNMi4kYD55dxXhERSSElA9XpFeAtZcSXs4yxZiAUEakzSgaq02xgR3ePbZg1mkBERAZNyUAVMrNO4MdAzGJDAA8BP42IWw4cFXlOERFJKY0mqFJmdk4Z4bOAVyPispRXuCgiIimknoEq5e4Xu/sxkeEHAw9GxI0HHo48p4iIpJSSgerVAOwRGbsSLWEsIiKDpGSges0mfkSBCghFRGTQlAxUr+cI0wPHWArcHxG3FvhO5DlFRCSllAxUr/uAn0TGLgdmRyyDvA54RxlDGkVEJIWUDFSvzYA/xASaWRY4G2gpMa4POJwwE6GIiNQJJQPVaxEw1t1jG+bFwKSIuHKKD0VEJIWUDFSp5O7+dWDbyEMsIS4ZWA6MiTyniIikkCYdqm4XEu7UY5wCzIuI2yVJREREpE6oZ6CKmdk1ZvZGZHgvccMLT3D3KZHnFBGRFFIyUMXc/QJ3/2Jk+BeAkyPiPgXsFXlOERFJISUD1W0ZsENk7BJgYkTcKmBs5DlFRCSFlAxUt7nEFxAuBjaPiFuJkgERkbqiAsLq9iLwWmTsTOD5iLhvERIJERGpE5lsVoXjkufuLUDGzJZW+lpERGR4qGegiiXTAv8KOM3M1pUYewBwkZn9c4mn/Vzy3UqMExGRlFLNQBVLxvsfAmwTEb6SuHqD5cCEiDgREUkpJQPVby6wXUTcEuIKCGNnLhQRkZRSMlD95gKTI+KWAq9GrEA4jzC8UERE6oSSgeoXlQyYWTewO6UvOrQC2K/U84mISHopGah+C4CtI2PfoPQpiecB7ZHnExGRFNLQwirn7iOBHjPrrfS1iIhIbdLQwurXAhwA3F5qoLt/CPijmc0pIaYBuAE4WasXiojUBz0mqH5TgG9Exn4M2LuUADPrA44ibsVDERFJISUD1a8DaI2MXQZsFhGn4YUiInVEyUD1WwBskXTflyp2AqHYFQ9FRCSFlAxUuWSI4InE/a6+DdwWEXegmT0ZESciIimkZCAdHiau2HMh0BcR9y533zUiTkREUkjJQDrcSFijoFSnAedFxJ0KHB4RJyIiKaRkIB1iiwhVQCgiIhulZCAdKpEMqIBQRKROaNKhdLiHuGf/fwNmRMT9BhgVESciIimk6YhlPe4+AphgZosqfS0iIrLp6TFBCrj7u9z9yoi47d39gYhT/hNhSmIREakDSgbSYR1hfYJSrQH2jIhTAaGISB1RMpAOZRUQunumxLilKBkQEakbSgbSYQGwstRG3czWAu1Ac4nnW0goIhQRkTqgAkIREZE6p56BlHD3c9x9SkTcV919m4i4P7j7FqXGiYhI+igZSI8TgN0i47aLiNsO2DwiTkREUkbJQHpUYhZCJQMiInVAyUB6lJMMTIiIexEYEREnIiIpo+mI0+NCYG1E3FnAylKDzOxfI84lIiIppJ6B9MgC20fEtQCTSw1y96PdfWrE+UREJGWUDKTHwcB/R8SdmnzFnO/wiDgREUkZJQPpMZ/hrRlYipYxFhGpC0oG0qMSowk0JbGISB1QAWF6zAe+ERH3APBKRNytwB8i4kREJGXUM5ASyToDV0asT/ACcF/EKbuBrSPiREQkZZQMpEs7Jc5C6O4HAX+MONf2wC8j4kREJGWUDKTLUkqvG1hBGF4Ycy7VDIiI1AElA+kSU0QYW0C4FJhY6mMJERFJHyUD6fIAsLzEmE7gwVJPlNQofAloLDVWRETSJZPNZit9DSIiIlJB6hlIEXf/Z3f/fETcL9295LoBd3/Y3fctNU5ERNJFyUC6jAcOi4h7J3HFgN2RcSIikiJKBtIldhbC5cTPQqgpiUVEapySgXTpIO5OvZO4ZGAWMDYiTkREUkTJQLp0AFMihvstBjaPOF8PsF1EnIiIpIiSgRQxs1VAltLv1hcBW0acMvaxhIiIpIgWKkqfVwl3+StLiHkWWBNxrmcIyYeIiNQwzTMgIiJS5/SYIGXc/Vh337PEmP3c/dMR55ri7j8tNU5ERNJFyUD6HA8cUmLM5sCJEedaCxwbESciIimiZCB9FlB6MWBMDIR5Bsa7+4iIWBERSQklA+mzANiqxJiFRCQDZtYHzAW2KDVWRETSQ6MJ0uc6YFSJMfOBtsjz7WRmqjIVEalh6hlIn6WUuKxwcod/sLuPjjjf+919t4g4ERFJCSUD6bMncENE3CXA9hFxH6D0gkUREUkRJQPps4C4WQEXUnqtAYRZCGPiREQkJZQMpM9CYIuI9QmiiggJycDWEXEiIpISSgZSxszWAhcCzSWGfgd4MuKU1wGXR8SJiEhKaDRBCpnZf0WEPQzEzBewGNg2Ik5ERFJCPQMp5O43ufs7Swz7DPCtiNPtBtwcESciIimhZCCdMsDkEmPKKSBUzYCISA1TMpBOMdMLxxYQLgJa3F2PlEREapSSgXRqB9aUGPMy8MdST2RmvcDXKb1gUUREUiKTzWqmWRERkXqmrt8Ucve3A4ea2aUlxDQCfwAOL3WtAXf/GfBTM7u3tCsVEZE00GOCdJoInFhKQNLd/3agJeJ8DWh4oYhIzVLPQDptdEri9qnTRwMnADsDs4CbOOKA3IiCpSWer2Nj5xMRkfRSMpBOGxxN0D51+oHAXYSiv7HAKuCyLZcsf3HhpAkTIs73N0pfNllERFJCyUA6LQJ2L/ZC0iNwF+FRQs44gHc/9cKuwF+x0k5mZj+OukoREUkF1QykkJn1Ae9097FFXj6BAYYB9sGo+ZMmfLHU87n77u5+bqlxIiKSDkoG0ut/gLcU2b4z4dHAejIwqrup8d0R5xoLnBIRJyIiKaBkIL0WUHx64VmEGoH19GUy65aPHd0bcS4VEIqI1DAlA+k1UDJwE9A9QEzvi9u1dkaea4tkrgIREakxSgbS67vAY/03ts2csQaYRhg+mJuyeAWwtHP8mON6mxq/WuqJzKwb2Aboi79cERGpVkoG0usxYH6xF9pmzngcmAKcS3hkcBYw+f4D9nwQGBN5vrcBkyJjRUSkiikZSK8zgEsGerFt5oyutpkzLiUsd/zbpMdgK8KwwxjnA/tGxoqISBVTMpBeg12SeAX5KYgXEp79ZyLOpyJCEZEapWQgvTY6JXHiWWAfADNbQ6gj2CzifEoGRERqlJKB9JoNPDiI/f4CvKvg3/8JxKxb/X/A7yPiRESkymWy2Zh2QdKgfer0DDAP+HrbzBk/KOdY7t4AjDazonMYiIhIeqlnIMXc/X53HznQ620zZ2SBzwH/0T51+uZJzE/d/f0Rpzsa+E3clYqISDVTMpBuuwFbbGiHtpkzriNMRJTrGeglzBlQKtUMiIjUKCUD6TbQLIT9GXB4+9TpezH4UQjFzqVkQESkBikZSLeXgQkb26lt5oxVwIXADzN9fX8DlkWcqwO4MyJORESqnAoI60T71OmNwBxgatvMGS9V+npERKR6qGcgxdz9cHcf7JLEfcDk17aadJC7l7w+QXK+X7j7zjGxIiJSvZQMpNvBwPsGs2MysuDDUxYu/f7oNWs/HHm+7YHtImNFRKRKKRlIt8EWEALQNnPG9d1Njbfs+8KcnZPHBqXSiAIRkRqkZCDdFlJCMgDw8pQtv9DU0zcqC1e2T51e6u9/NvGrHoqISJVSAWGKuftmwFZm9mIpcdd+7JOT3zpr7nUZmAv8ezLaQERE6pR6BtJtNTC+1KAXd9h63z/vseOHgbXAY+1Tp+82mDh3f6u7n1Tq+UREpLopGUi3kQxusaL+7NXJW+zQNnPGvwHfAx5qnzp9MFMU7wR8NOJ8IiJSxZQMpNsqoMHdx5YY949ZCNtmzrgSOA74UfvU6d9snzp9Q8dSAaGISA1SMpBiZpalxBEFiTcVHrbNnPFH4EBgB+D59qnTpw8Qp2RAROpSe0fn6PaOzlPaOzovSL6PqqXrUDKQft8g9BCU4nLg/sINbTNnvNE2c8YpwIeBH7ZPnf7d9qnT+ycZc4H57r537MWKiKRNe0fngcDrwBWAJ9/nJdtr4jqUDKTfDEIhYClWMsAdftvMGQ8B+xP+Nv7ePnX6f7ZPnT4GwMy6k912ibxWkbpUDXeV1XANadTe0TkauAuYCIwDMsn3icBdw/U+DuI6NrpOzYYoGUi/i4HBFP8VegfwiYFebJs5o6Nt5ozPAgcAewMvtU+dfkH71OmtwGTgKXefEnvBUh/U+ATVcFdZDdeQYicAzQO81gScOIzXMXGA1yYStwDdPygZSL8lwOYlxixiEMsYt82c8UrbzBkfAqYRpiF+/l1Pvzhuj1fmnUU2+5i771H65Uo9UOMTVMNdZTVcQ8rtDAxUWD2WMMpquK5jk2nalAeXYbGYTZQM5LTNnPFX4Mz2qdO/tHTC2D/uNmf+R3aZu2Ddk7tv//B/n3/Boedd+PW/l3h+qWH9Gp+cccn3u9o7Oie3tbasGf4rq4jB3FVeWwfXkBrungEaCe9N0/FnfHp2Q2PjqkwmM67/vtlstmvRG693+4++93YgY2Z/cfftCA13U/L1GNADHFmw7e/Jvh8h/H/SCCwzs5+5+5GEdWeaku2XAJvvsf9B79rj7Qd2NzY1rfe77O3p7ml/9JGn2048LvrnVjKQfvdT+hTBfwe+WOqJ2mbOWHqje9ter8zro7fvuH2fn3NxQzb7aPvU6Zd2Nzb8vrm3709tM2eUWr8gtWdD3Zn11vhUw13lkF+Du48ARpA0WGa2OJkRdXNC4tEMvEzohXhb8u8mYI6ZveTu0wgTpjUDy83sdnf/p2TfpmT7FcDWwKkFx7zFzB529x8WnL/dzP7H3b8CvKvgXO8FjgEuIN8InwM8AzxRsN+vzOwcd39gOoH6AAAgAElEQVSI8Ai1iTByqgXo+d0vrznjmNPO6Cn2PvT29DQ/fPtv/5VQeL0MOAz4J+BMQgLQC3wB6AROSf7dnXz/C9AGTEr2nZccdmTy1QOsAbLAmlntT9+7x9sPnEqRxC6TaVj72ksvXFT8tzU4SgZSzszuiwhbATwfecr9bzzigLVmdjNw89OHH7vP2uamM7tGNt89ftWa3qcPO/bmxmz2KuD+ZKVEqT8b6s4czm7VYZHcSeaMJd9wrTvprLNnZbPZ1ZlMZr3GuK+vb8282bNG3/ijO44CHiUsM1549/i35O7xXwnJVROw1MyucfdjyN89NgHfJDScnyXfmP7CzO5+5pEHjtn7oHf2NjY1r/d539PdzTOPPHDGjX9vvwg4FvgW+UbyTMJd7ayCbT83s48D9wD7ERqsBcBuwGnA55Jt3YTGbzXhzrY72f5z4CXgZJLGlpA03E54FLl3wb656+0jjJjqJj9y6inyDetrybb7gPaC85Nc/2cKjjk7OcZhyb97Co75nuSYvWbWV/g+tXd0ziL0djURfsergJ6m5uZp55//1ccL9zWzayme7K43e6uZrbecvJndBtzWb/MS4H/aOzrvL3YdDY2N07587hcfpwxKBlIuyabPMLPTSwx92d0nmllXiXHHEP6HeQpgn/tufRr4tLtfMaZr7X9tu2Dp9D1mz3tHU2/fuj+/5/ifjuzuvRV4TolBXZlFGLGyXrcq4cPr5U19AUkD3WRm3e4+iTCvxtjk63FgM8KdY27bTDN73N2/l7w2FnjOzC5w928TinTHEu5GdwQ+AFxFvuE9GbgXmEO+MboW+FJvT/dPm5pHrHeNvT09I5649/eHEAp1P0d4bz5MvpFaS7h73D25pm7CnTaEu8V1hMY2dwe6EniafMP3GsCcF5+/pO0dh/yYIp/3DY2Nqxa8PvdDSfw9hCXRc9e/OLmGHQqO2Q1gZu/ufywzuxS4dL0fNCQt/fc9o8i2Yo3oUkLNSf99ryiy7U9Fzt2RfPU3u0j8gI+u2lpbHm/v6JxC6PXaifA3fNNwP+7alNehZCD9uoGSCvnMLOvuuVkI55R4vkXArkWO+QxwvLvv+vKULRcf8PdX3t/T0HD1Vp0rvLm7t+8vRxz/yIje3juBh4G/6HFCTbsJuGyA13qS13H3RsIjrrGEu7GF7t5GuMMdA4wws9+4+wG8ueG+nNBw/qBg26/N7BJ3f4TQ9ToGeC75708B/5rErAI+QrgrPbJg28jk+v5GaGRXEebVAPgR8FNCw7s2ef3XwC0kjWTBneR6CVB7R+chFLmbax4xYtr5X/1q/7u5D/SPN7Pzi2y7A7ij3+ZlwA/77/vlc794fXtH58vFrqGhoWHaf3zu7McL4otVpHcW2VZ32lpbuqiCx1ub6jqUDKTfEsIzp1LlighLTQYWUyTTzylYQfFn7n4DcMhmK1efsNur85dvu2DpW1aMGfXNcV1rGv541AcW9TY2PNWysuu3neNGj2zq6Vs3Zs3alQ3hDmUeIXN/XUnDppe7iybfvZ0lNGrNhDvh+YRnl/sl/x5BuNvaidCojiV0X1/n7h8G/mnr7Xd8+B1HHj2tsal5LTC2t6cnm8328fDtv127uOONY2+EWwmNaxehYboR+CSha7ot2dYJ/IbQsE9Iti1OrmU58H/JttXk/46PI7ljzs2LYWYXAhf2+7HnEhKENxngjrNYT8a65GujquGushquQaqbljDeiKRQZiT5Z3OLgdGEu5fcc7TXCR9OBxVsW0LomuxLvjrM7NnkzmdCsq036ZrcijB+P7fvbMIHzY7Jv7NAZ1KkszWhwrSPcFfSR5h46P0F8blnYM0F2/qS6YtzP9fngdvM7IUS349tgG3M7C+lxCWxGWDL8au69mtdvOzILTtXtExetKy3Y+L4I9c1N22TzWSam3p7l09etOyZnoaGPRuyfZv3NjR09TQ2Lm3u6flrBuasHD2qJ5PNvt7Y1zd3RHfPq829ffMJzyyXVeJRRHJ3O4p8w7mM8Pt6C/mGc6GZzXH3QwjdvSMIv8NRwN2Eu+UPk//b+bOZ3e/unyP8DTUDi8zsW0ljO418w302sA3hmXGuQb/MzH7t7o8SGvUm4FEzO83dryIU8OX23ZzQxf0Twt9TN6H7+1/IN3ifIjx7/X3BttsJz3ebCX9vzyd35kcRJqVaNWrMmHXv/fBpTX19fbstXdCx5plHHrhtRefSpYTfVxf8Y0ptEamwmu0ZcPefmNnH3P0ThMKWwmd7LcB15D98v2Vml7n7HMLdcu7D81BCt9uHyD8z2xU4BPg++Wd7XyZ8qH+H/HO1BwiVrQ3J1wPAs8AZhKShIdn3UELhyrkF+55JuHO5rWDb/xGenV1LeIbYCLxmZge6+93AK4TniQ3J8ScSRhrktn0V+Ja7LyI0SH3AdOBwd78y+RlzicdOwLsJ1by5ZOKc5HoeAjLu3gP8zszOThqYdyT7rTGzg5NG6xzCnX42+e95wC9WjB3dt2Ls6L6Xtt/6ZjP74Y3uPwC2JZ/0nO7uXyGbPXH02nVjxqxZN3b3V+ff35DN7rJs7OiPj+jpaRqxrqdh/Oo1i5q71q7obmzYqbEvm3niyBP6uhsbu8Z3rb37he1at1ncMq61sbevt6m3t2/PV964bFHLuH1e32rS2Dc232xZX2NDE6Gw6O2Eat9cw/0/yc/4IPkG/vfJz3kjcHiyrdvMJgKfJhRddRO6kE9Mfne5mSHXAb8gFFF9nJBEriOfVP6ZcAf8VvKN8XMEPYRiz25CAwrwanJtuX1XEZ4Nf6/gby/XO3Mm+SKrlcm2c4HzCuLXmNnPgJ/xZl9gfQcV2fYmZnZn0Rd22Ib3HLjvxsJFpEJqNhkgVK1CaKSfJf9BOS/5KiyUyT0TayPfwPcAJJWzH+937NuTr/7eubGLMrNzimwbqPp0tyL7HlFkv1HA9ma2vN/29auWQmOUIRQsLU22nU1oJHKJw3LC+7Yf+WRkKeG9+lhyrUeTb2D+mzBMqIF8gdNMQsPVmGybRejevbDgmLkq4BsIvSWNhC5fgDvIZF7pGjWyoWvUyIZHWsbflvycfyqIf8zMnnL3LzT19Ewa27Vu0vjVa7re8ezLT2y+bOVnt1y6YkxvY0O2t7EhO6Kn58CWlau32nLpiraDenondTc1LhrR3fOnvkymsbexYXQ2k1mXhbXNvb0XNPRlG3obG8Zlstnc8J792++d/qtjGxsWrBo98tQ/HLR3rjHGzL5PSAz7K/a7+1iR/XI+WWT/9YqxzOyPwB/7bV4B/K7Ivk8X2ba0/zYRET0mqAHuPguYZmYvlRBzPjDGzM4r8VzjgTfMrFileCq0T50+iTCMKZe4NBIeBY0g9AZ0EZKS1cm/Wwu+/pis8igiUjNquWegnuSmJB50MkBYxnj/iHOtBEa4+6gNDcWpZm0zZywhvGeD9ddNdS0ismkltUq5Xs+smfW6+2jyPYx9ZrYqudEZVbC9qeDrtWTbbuRnBpxnZq+5+6GEItcmYKWZPZCMgNm1IP56ws3E+8nPbng3YQIkK9j2QjKPxFnAviSPspNHp0cCZyX7NgJfJ/RGT89tKzbkc7CUDNSGxZQ+omAuYbavkiTDEr9D+CNNZTIgtavfB38mmWcgV/uR+5DPFdhOLNxmZsuTBbhy+3YnhZ9bE5Lt3HGfI9R77FoQ/6qZvZE0DI3JtqXJY6y3Ex7P5fbNTbCTWx20EXiEkKCfTP5x2ywze9Ddjy+IX2tmP3b3gwi1S7lj/pLwSOvfC7Y9ZGZ/cPdzC37WDjP7jrufRKgLyv1M/0koRD278JhJ/E8J/783AH896ayzL3n+qSd+3NTUvO/KZZ2rX/l7e0dvT89xwBGEWpNc/NcIifSMgp/pDjO70N2vAfZJti8zs6lJfdc5BfEfJHzG3Fmw7dKkkPap5D1sIEzOdIi7X0JoLHM/0y7AXoRHaFlCTdNnCUNSFyT79BHmVziBMBz2aPK1U7mRTN2EWqBewhDT3uS9vppQbHs2oQ6rB3iBUB+2H6G+KFezcwuhmHf3gm2jk+NnyM8ZkXvUOzfZ3g10J3/XLwO/SuJ7CSNoVhGGw/Yk1x1NyUBtOI38H9GgmNlAdQ+Dif1yTJyUpt9IhWZCr8wawkiFXPHrKjOb7e57AVuQnx72bnffifChlNv3XsLfyWkFx3zazH7v7p8mFHE2ET5YxpMvuv0BoSjxavJ3Ovcmx841UrPN7IxkBMQHyH8gn0AYFXM5+Q/0b5nZte7+DOHxTAPwsJl91N2vINTz5PbdPjnepeQb2X8lzFcxl3wjk5sZ7xFCMWuWMHy2lVAfY+SLYY9M3suZBdu+TxiR8WvCyJ5eQqN/DKHo9xTyjcRRyXVdWhB/cRL7dfKNzGOEybmmE+7gcvF3EmZpPDX5dy9hBFEnYT6FXMPVRCgW3YP8yKIVyZ/HZsk15M6fGzk0puCYucdgq8mPQMrVRy0lFB3n4nsIfxtPFGx7I9n37uR7314HHjwBeH3Xt+03MpPJjM5m+9a+9eBD98hms/vdcvXlfyMUUefinyc0VucV/Ey5QthvExrD3KgoCPNPPFAQPzf5Od5dsC338/9z7pqSa4dQyP3Vgn1zsxM29B+1Ymbj6WeQE7etVwVrZicX2XYlcGW/zUuB/1fkmFYkvv8MhBDqrmb12zafUBNXNtUMFCiYVjQ3GUruw3Al4X+onXnzh/DLyYfwlgXbtyJfsLgr4Q9+NaFq+3Z335FQOZ7zaHL8Iwu2vWJmz7j7EYTCOoAuM7srOd/uBfveS/iwOIDwhwHwrJk95+7TyU+mssTM7k3uKHYkjA1/D6E4cgvCXUbOY4Tivw8WbJtrZg+5++GEub5nErq0fuXuuwGFq9HdR/hgOZ78B9ILZvZock25eetXmdmN7r5f8p7k9r01eS+PKTjmX8zsaXc/hdBAQhiyN8Pd382bC/Z+SWik3lOwbYGZ/TaZsXF/8l19VxE+WD9asO325I7sYsLvrzG5/ovd/f8l71Wuq+5fCB/055Lv6rsweQ/vKdh2p5l9xd1/SWisGgkN+ytm9j53v5RQnJlrbCcn+11HvvL/7OTfL5Ivfr0nGenwneR30A2sM7Oj3P1oQkOWi/9fwt3ENwq2PWRmtyR3ZZsn27qS30WukPZuQsNwfMG22YRROSNJPqST3+/OhDvM3Afyk8k+uxRsm2dmi5JVL3ONxCozm+fuWya/31zD+Uby77HJtl7ys+6NKojvTbp/MxquuGkkC1C9TvF1J5YC9bQAVc2p2Z6BZCayXAN9tZldnmybkmz/m5m9x92/TxiClWsItiV0n19PvjvnPwijE+4k/yF8H6HL6XTCJDy57ZMI3WInJ8caQ+jGf8LdTyU0HGMIH7jPkB++djr5SUx+l7x2HGEqUAh3OXcR7sYK57h+knCndARhASIIXUnPEbq2chnwi4TE4QDCvNwjCF1in0zek8I1uRcQMurjC7Y9Thhy9w7CB3tz8v1XhOTifQX7tic/y1HJv7OEu6VHk59/22T7YsJkMzsl15/b9w/J9RU+/1pEmGr1YPKzvL1MeK935s0TIV1P+MAqHAqXG663BeHOOve7bSw4b1eyPdc9+FJy3b3kZ6N7mlBv0Fvw9QLhLjHXffgsoaHPzdPem/ysAF8hNI65fXMfnl9KXsv9HfWZ2a0UX4Rqvbn9zezzRbb9jiKjDAjzBvTfd73Jdoq4fmM7mFmxu5fctLr9932uyLaFRQ7blXwV294/XonApnMC+ZuL/kYRPoeuGr7LkaFUsz0DyQQvubufN8xsfnJXDvnx1YvdfRT5ZzO9m/LDJJkjfTtC47uFheUq3094TnYAoRE+ljD0cRvC3ehGr8fdzwZ2MrOzS7iWRsKH9CgzK7oi1wZif0ToXl5v6lMRqU3tHZ1OqC0YyK1trS3xa+hKRdVsMpA2SeO8N+EOPtczMQa4GfifDQ0bTFY1m2Zmp5Z4zkXAXma2YKM7vznuXMKyoz8qJU5E0qu9o/MywiRbA7m8rbWl2DNxSYGafUyQNmbWS3g0AKE7fcekJ+MjwEh3355Q+PVzM3u1X/jD5OsFSnEwpQ2xy13rtyPOJSLptngjry8alquQTaKh0hcgAzOz2Wb2TTPLVYtOBv7s7te5+xYF+71MqAco1WiKFwNtkLvv4+6nRZxPRNLrefIzhPa3ilA7IymlnoGUMLM5wKfc/QuEoTqN7p4rRNyFMExwvSlwN+JrhALA35QYN4XQY9F/PnuRYZdUuZ9AKCSdhVbj21RyS1MXK2pdl7wuKaVkIGXMrIswtI9kTPYWhGFtm0ccbiFhWGSpFifnFamo9o7OAwmjbJoJww9XAZe1d3ROa2ttebyiF1dj2lpb1rR3dE4jvN9N5N/vHmCaErB002OCdDubMAmHAZu5e6m/z0XENeqLiEs+RIZM0iNwF+FR1zjCqKBxyb/vau/oHLWBcImQJFhTCDP9WfJ9shKv9FPPQIqZ2ZpkEpynCI8JRjHwM71ibiXcUZVqDm+e0Edk2LR3dDYR5s84kYH/fpuS14utBiplaGtt6ULva81Rz0DKmdlKYG8zO87MSkkEMLPHCNO3lnrObmB7d1cyKZWwM2Eq3cvJT0DV31iKTM4kIsUpGagBZrbW3Z9x95Lu1pOphe+MPO31qG5AKqCtteX5ttaWFsL0zSsH2G0VYYZKERkEJQO1YzvCKmCl6CQs5BJjMaobkMq6ifwiN/31oOp2kUFTMlA7VpKf83+wFhE3mgA0okAqLKlen0ZYJGcF+VXtlqLqdpGS6Jlv7eik9Dv1hYQlS2NcTFjZUKRi2lpbHm/v6JxCmGdgJ8KjAc0zIFIirU1QIyqxdKuWixURqQ16TFA7jnf3/yg1yN1/4O6lPl7A3R34cqlxIiJSfZQM1I6jKbJO/SAcQOm1BhCezapmQESkBigZqB3zGHjM9YZoSmIRkTqnZKB2zCWsQliqhcQ16vOBtRFxIiJSZTSaoHY8BNwcEXcPG1+nvJh2YEJEnIiIVBmNJpBoGk0gIlIb9Jighrj7Ve5eUpe/u+/r7j+IOFcGWOTuMXUKIiJSRZQM1JYDgO1LjOkCjiz1REmPwGLCNMgiIpJiSgZqy1xKb5znANu5e2PE+eZQevIhIiJVRslAbXkNmFxKgJl1AbOIW3TobmBdRJyIiFQRFRDWEHdvMrOeUmIyX/rCaMK87jsTkoKbshddrHndRUTqiHoGasu27v6+we6c+dIXDgRez8DVgANXAPOS7Rvl7u9293PjLlVERKqFkoHashMwqMY56RG4C5iYDZMVZQgzGE4E7sp86QujBnGYkYQlZAetvaNzdHtH5yntHZ0XJN8Hcx4REdmENOlQbSmlgPAEoHmA15qAE4Fr+7/Q3tH5j8cKhx3/wdUPzrhph8FeXHtH54GEBKQZGAusAi5r7+ic1tba8vhgjyMiIkNLPQO1ZS4wJZkDYGN2JjTIxYwl9DK8SdKYv054nOCTWrf+2vtO+/hOz7yxZKOPFZIk4i5Cz8M4+vVEqIdARKRylAzUEDNbDUxncL/XWYQ782JWAS8XbijWmGcymXEjRo7KNDQ0DKYxPymJLSbXEyEiIhWgxwS1px0YxcANfc5NwGUDvNaTvF5owMcK2Wx2RCaT+VB7R2c3BaMS2lpbCkclvGUD11K0J0JERIaHegZqz1XAP29sp2T44DRgKbAi2bwy+fe0IsMLN/RYYQzwI5LHB8n3ecljhZxZDLzK4Xo9ESIiMnyUDNSeQRcRZi+6+HFgCnDWO5tGvLZbQ9PFwORke38beqyQIfRGbKgWYD/C6INiivVEiIjIMFEyUHvmEhr4QcledHFX9qKLr502YtTDp4wa8+IGJhy6idBol6KwFuALyfdlhJ6IvuT7UmBav0cKIiIyjFQzUHvuA7aKiLsKWLiB17cCWgr+nQVWZbPZJsIdf7ERDP+oBWhrbcnAm4Ym7kR4NNC/tkBERIaZpiOWf3D3RjPrLdzW3tGZAX4O/EuRkB/39nQ/0tDYdGkmkxlT5PUVwFltrS3rzVcgIiLVQ48Jaoy7b+/uD5Qad+kPrzjuoH8+6pX2js572js6L2vv6NwseenbFE8EALZ5YMZND3SvWzd6gNdVCyAikgJKBmrPYuDAQU48BEB7R+fph53wwZu33WW37QgjET4NLG3v6DwdeGwDoROWdMyf/dBtN/dms9lOoJvw+EC1ACIiKaJkoMaY2SpgDTBpMPu3d3ROBK7JBIUvZYBrgA31Mhx60llnT1i6sGPe/bf8+mDgNuB+4CxgsqYYFhFJByUDtel+YLON7ZT4OsWL/0i2XwC8awPxBwAXLel4YwVhhsKX21pbrlWPgIhIemg0QW16DpjM4Cby2WMjr+8OHJv89w+SY85Kvr/S1tqy4kbYF3groUdioImJRESkSqlnoDZNBnYd5L7PbeT154EvJ/+9L/B2YP/cV3tH5zhga0IyMA/YpuSrFRGRilIyUJvmA62D3PerhKK/YrLJ6w3A9sBXgHsIEwYdDvwB+DUhCZhMmPBIyYCISMroMUFtmssgJx5qa21Z1t7ReSZh0iGy2SxJIWEW+Le21pZlya6vJV//KChs7+i8DTgvOd+ewBIGWbgoIiLVQ5MO1blkMaHc0sQAvYT5AY5qa225fyOxEwiJwLZtrS3L2zs6RwCr21pblGSKiKSIHhPUIHef7O7/trH9kqmBCxMBgEbC9MI3FSwyVFRba8ty4E/Llyxuc/f/R/h7yiazFoqISEooGahNLcCXBrHfCUDzAK8VLjK0IdPuvuEXTwH/u3zJ4l7CcEQlAyIiKaJkoDYNtoBwZwYeCviPRYY2pK21JWtmq4Guu2/4xQTCqoSDLV4UEZEqoGSgNi0Fxrj7QGsG5MwCVg3w2ioGN09BzuuEpZNXARt8vFBMe0fn6PaOzlPaOzovSL6XfAwREYmjZKAGmVkWeCdhrYANuWkD+5S6yNAphOThWeBtJcTlihhfB64APPk+L9kuIiKbmJKB2rUCGLehHZIpg6cRehJWAH3ZbHZVsuhQqYsMLQTGAy8BWw42qF8R4zhCvcG45N93qYdARGTTUzJQuy4Gpm5sp2QxoSmExYXsqYfuf/V3v/jJhyMWGfo0cAbw2bbWlqtLiBuKIkYRESmDxoPXrvmEaYI3qq21pQu4FuDGZ5/ZG9gi4nyvA/u0tbaUOnFF2UWMIiJSHvUM1K75xE0N/EZk3DxCD0OphrKIUUREIigZqF13Ag9FxN0KPBYR9xTwm4i4oSxiFBGRCJqOWCquYErkJsKjgVWERGBaRO2CiIiUSMlAjXL3/YFvmdmRJcYdCnzJzKaXGNdAqBvY3sw2NqRxPcmoghMINQIvAzeVOJpBREQiqYCwdnUCuwxXnJn1uXsvod5gTqnxhUWMIiIyvFQzULs6gFZ3L3WdgDeIn054HjA5MlZERCpEyUCNMrOVQDthIqBSLAFmu/vIiNM+QwkTDomISHVQMlDbtqLExjmZyngr4noHeokbXigiIhWkZKC2LQY2j4hbQEgISvUcsDYiTkREKkgFhLXtHMJaAaU6grBWQUnM7JKIc4mISIWpZ6C2Pc3GVy4sZifCNMElcfcD3P3fI84nIiIVpJ6B2vbfwPPApSXGnUJYhfCiEuO2AY4Hrhpoh4L5BHYmTEWs+QRERCpMyUBtW0J8zUBMAeGiDZ2vYKbBZvIzDV7W3tGpmQZFRCpIjwlq23AXEA54vqRH4C5gIjAOyCTfJwJ3tXd0joo4n4iIDAH1DNS2J4ClEXH3EVd4+DJwyACvnUDoESimCTgRzUAoIlIR6hmoYWb2sJn9LCJ0DmECoVLP1wMc5O6NRV7emfBooJixhKJFERGpACUDNczd93T36yJCtwX+FnnanwKTimyfRagRKGYVoVdBREQqQMlAbesGDoyIWwhsFbGuAYS6gS2KbL+JgYc59iSvi4hIBSgZqG1RBYRmtgboAlqG6pzJ8MFphBqGFUBf8n0pME3DC0VEKkfJQG1bBixz95hC0fsofoe/MX8BJhR7IRk+OAU4C7Dk+2QNKxQRqSyNJqhhZtbn7j8HRhC64ktxBWHegFI5G5jKuK21pQuNGhARqSpKBmqcmZ0fGXofYS6AUk0gzFHw7MZ21GyEIiLVQY8Japy7/8TdD4sIvQT4WETc+4BPbmynZDbC1wk9EJ58n5dsFxGRYaRkoPaNBLaLiOsgbkrijcZpNkIRkeqiZKD2xU4tHJsMLAC23sg+g5mNUEREholqBmrf8wzc8G7Ik5Hnawf+dyP7aDZCEZEqkslms5W+Bqkx7j7SzNYO9Hp7R+cphGWOxxR5eQVwVltri0YciIgMEz0mqHHuvpu7nxMRt427/zYiLgMsd/cNPffXbIQiIlVEyUDtmwCcFhG3CnhPqUFmliWZznigfZLhg0cm/1yJZiMUEakoJQO1L7aAcAXQ4O7jImIHU3z4BGFI4SfQbIQiIhWlZKD2LSBi0aHkDv9ZwnC/Ut0CDFgzANDW2pJta235Wltry7VtrS0XJt/VIyAiUgFKBmpcsujQWyLDDyHc5ZfqJkL3v4iIpICSgfrwY2CHiLgbgGMi4k4HTo6IExGRClAyUB8msYlmExzAgsg4ERGpACUD9WERccsRdxBXfLgA2DIiTkREKkAzENaHvxOX+P0RaImIexCYFxEnIiIVoBkIZZNw92YzG2hiIRERqSJ6TFAH3H2qux8fEbe3u18VEbctMLvUOBERqQwlA/VhD+B9EXE9wGERcQuBLUud20BERCpDyUB9WERcQV/UaIJkkaIu4uoNRERkmCkZqA+LgM0j4pYBK9x9ZETstcCIiDgRERlmKiCsA+4+GphoZqrwFxGR9ahnoD50A3vGBLr76e6+Y0Tc+e5+RMw5RURkeCkZqA8Z4E53j/l9nwy8LSJuO2C3iDgRERlmSgbqQDLefyWwWUS4piQWERf8SsMAAAWiSURBVKlxSgbqx2KGcUQBMB8YGxEnIiLDTNMR14/zgSURcZcDfaUGmdnlEecSEZEKUM9A/bieuGRgBRHzBbj7Nu5+asT5RERkmCkZqB9XAR+NiNsf+FFE3CTgvIg4EREZZkoG6scy4iYeiq0ZWIiWMRYRSQUlA/UjdhbC2GRgMdDi7qpLERGpckoG6sdfgTkRcUuAb5e66JCZ9QJTAU1xKSJS5TQdsWwy7r4TsMDMVlb6WkREZGDqGagT7r67u38nMvZed98/IvRq4OCYc4qIyPBRMlA/GoBjImPXAltHxC1ARYQiIlVPyUD9mE/89MDlTEm8VeQ5RURkmCgZqB+dQJO7j4yIfQkYFxH3IjAiIk5ERIaRkoE6YWZZwlwDMXfqq4CdIuLWArtHxImIyDBSMlBfFhNmBixVB/E1A3pMICJS5ZQM1JfFwBYRcR2RcbOBVyPiRERkGGl2uPryOeImHroPuLfUIDN7CvhMxPlERGQYqWegvswl7nfeCJxRapC7j3D3n0WcT0REhpGSgfryeeCsiLhe4HJ3L3VkQDfwQXcfE3FOEREZJkoG6kvUSoJm1kdY6KikYsBkBIOKCEVEqpySgfoyn7hRARAa9ZiJhzpQMiAiUtWUDNSXF4CXI2NvA0ZHxP0RGBt5ThERGQZKBurLKuCkyNjtgF0j4sYDb4k8p4iIDAMlA/XlDWCbyNhy1ieIXRNBRESGgZKB+rIcaHT3mHUGYusNZqO/MxGRqqZJh+qImWXd/UwgGxH+EyIadTP7YcS5RERkGOmOrf5cS1hAqFRZYI9Sg9x9R3ePmdtARESGiZKB+nMdcGJE3LbA1RFxLcCnIuJERGSYKBmoP7ErEJYTp3kGRESqmJKB+jOfuBEFS4Bx7j6yxLiFwObu3hhxThERGQZKBurPE8ArpQYlUxJ/AsiUGNcDvI24okURERkGGk1QZ8zsrjLCf0pcAjmSMPnQsjLOLSIim4h6BuqMu+/g7tdEht8KHB0RdwlwQOQ5RURkE1MyUH/WAsdExi4kfhbCkldLFBGR4aFkoP4sBCa6e3NErKYkFhGpQUoG6oyZ9QKvAltEhD8OvB4RdwNh9UIREalCmWxWRd6y6bl7xsz0xyYiUoXUM1CH3H26u+8VEbeXu38vIu5o4KZS40REZHgoGahPxwGHRsRlgaMi4jqJXzpZREQ2MSUD9ekN4hrncgoINSWxiEiVUjJQn2KTgaUAEVMSdwBPR5xPRESGgQoI65C7twLjzGxWRKwKAUVEaox6BupTJ2GK4Bgnu/t2pQa5+w/cfUrkOUVEZBNSMlCftgFi1yg4Ddg3Im5/oOQkQkRENj0lA/VpPrCVu5e0AmGiA9g6Ik5FhCIiVUrJQB0yszXAKmBSRPh8hml9gvaOztHtHZ2ntHd0XpB8HxVxXhER2QgtYVy/HOiLiLuKMN9AqT5hZj2D3bm9o/NAwqOMZmAsIXm5rL2jc1pba8vjEecXEZEBqGegfvUBO0TETQROjIib6u5nDGbH9o7O0YREYCIwDsgk3ycCd6mHQERkaCkZqF8HAftExI0nLhnYCjhykPueQOgRKKYp8vwiIjIAzTMgVae9o/MCwmOMYgWOfYC1tbZcOLxXJSJSu5QMiIiI1Dk9JhAREalzSgZERETqnJIBERGROqdkQEREpM4pGRAREalzSgZERETqnJIBERGROqdkQEREpM4pGRAREalzSgZERETqnJIBERGROqdkQEREpM4pGRAREalzSgZERETqnJIBERGROqdkQEREpM4pGRAREalzSgZERETqnJIBERGROqdkQEREpM4pGRAREalzSgb+f7t1IAAAAAAgyN96kIsiAJiTAQCYkwEAmJMBAJiTAQCYkwEAmJMBAJiTAQCYkwEAmJMBAJiTAQCYkwEAmJMBAJiTAQCYkwEAmAs0msE9KnFRwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from argoverse.visualization.visualize_sequences import viz_sequence\n",
    "seq_path = f\"{root_dir}/2645.csv\"\n",
    "viz_sequence(afl.get(seq_path).seq_df, show=True)\n",
    "seq_path = f\"{root_dir}/3828.csv\"\n",
    "viz_sequence(afl.get(seq_path).seq_df, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using map_api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting candidate centerlines for the agent's trajectory is a simple function call. Below we use the first 2 secs of the trajectory to compute candidate centerlines for the next 3 secs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from argoverse.map_representation.map_api import ArgoverseMap\n",
    "\n",
    "avm = ArgoverseMap()\n",
    "\n",
    "obs_len = 20\n",
    "\n",
    "seq_path = f\"{root_dir}/2645.csv\"\n",
    "agent_obs_traj = afl.get(seq_path).agent_traj[:obs_len]\n",
    "candidate_centerlines = avm.get_candidate_centerlines_for_traj(agent_obs_traj, afl[1].city, viz=True)\n",
    "\n",
    "seq_path = f\"{root_dir}/3828.csv\"\n",
    "agent_obs_traj = afl.get(seq_path).agent_traj[:obs_len]\n",
    "candidate_centerlines = avm.get_candidate_centerlines_for_traj(agent_obs_traj, afl[4].city, viz=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So is getting the lane direction of the trajectory's coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq_path = f\"{root_dir}/2645.csv\"\n",
    "agent_traj = afl.get(seq_path).agent_traj\n",
    "lane_direction = avm.get_lane_direction(agent_traj[0], afl[1].city, visualize=True)\n",
    "\n",
    "seq_path = f\"{root_dir}/3828.csv\"\n",
    "agent_traj = afl.get(seq_path).agent_traj\n",
    "lane_direction = avm.get_lane_direction(agent_traj[0], afl[4].city, visualize=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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