26_croston.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81a34b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1cf585c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97dfdc88",
   "metadata": {},
   "source": [
    "# Croston's method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67466e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 00:00:00</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 01:00:00</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 02:00:00</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 03:00:00</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 04:00:00</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id                   ds   y\n",
       "0          1  2023-01-01 00:00:00  43\n",
       "1          1  2023-01-01 01:00:00  87\n",
       "2          1  2023-01-01 02:00:00  89\n",
       "3          1  2023-01-01 03:00:00  87\n",
       "4          1  2023-01-01 04:00:00  73"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/intermittent_time_series.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9bc1753",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsforecast import StatsForecast\n",
    "from statsforecast.models import CrostonClassic\n",
    "\n",
    "models = [CrostonClassic()]\n",
    "\n",
    "sf = StatsForecast(\n",
    "    df=df,\n",
    "    models=models,\n",
    "    freq='H',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "cv_df = sf.cross_validation(\n",
    "    df=df,\n",
    "    h=1,\n",
    "    step_size=1,\n",
    "    n_windows=50\n",
    ")\n",
    "\n",
    "cv_df.index = np.arange(50, 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ddc8e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "ax.bar(df.index, df['y'], color='lightgray')\n",
    "ax.plot(cv_df.index, cv_df['CrostonClassic'], ls='--', label='Croston')\n",
    "ax.set_ylabel('Value')\n",
    "ax.set_xlabel('Time steps')\n",
    "ax.legend(loc='best')\n",
    "plt.xlim(40, 100)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2389581d",
   "metadata": {},
   "source": [
    "## Optimized Croston's Method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d15babc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsforecast.models import CrostonOptimized\n",
    "\n",
    "models = [CrostonOptimized()]\n",
    "\n",
    "sf = StatsForecast(\n",
    "    df=df,\n",
    "    models=models,\n",
    "    freq='H',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "cv_df = sf.cross_validation(\n",
    "    df=df,\n",
    "    h=1,\n",
    "    step_size=1,\n",
    "    n_windows=50\n",
    ")\n",
    "\n",
    "cv_df.index = np.arange(50, 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f07a97af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dq9TUVM2bN09nnnlmg17b6sXSAQAAAG9lmqays7NVUFBgdymApaKiopSUlHTEL5eszoReu5zYtm3blJ2drTFjxni2ORwOjRw5UkuWLNHNN9+sFStWqLq6uk6blJQUZWRkaMmSJQ0O3gAAAEBbZRiGkpOTlZCQoOrqarvLASwRGBho64horw3e2dnZkqTExMQ62xMTE7Vjxw5Pm6CgIEVHRx/W5uDxR1JZWanKykrP86KiouYqGwAAAPBJ/v7+3KoJWMTrZzX/7TAA0zSPed/JsdpMnTpVkZGRnkdqamqz1AoAAAAAwG95bfBOSkqSpMN6rnNycjy94ElJSaqqqlJ+fn69bY7kvvvuU2Fhoeexa9euZq4eAAAAAIBaXhu809LSlJSUpAULFni2VVVVadGiRRo+fLgkadCgQQoMDKzTJisrS5mZmZ42R+JwOBQREVHnAQAAAACAFWy9x7ukpEQ///yz5/m2bdu0evVqxcTEqEOHDpo4caKmTJmi9PR0paena8qUKQoJCdEVV1whSYqMjNQNN9ygO++8U7GxsYqJidFdd92lPn366IwzzrDrbQEAAAAA4GFr8F6+fLlOPfVUz/NJkyZJkq655hq9/PLLuueee1ReXq5x48YpPz9fQ4cO1WeffeZZw1uSnn76aQUEBOjSSy9VeXm5Tj/9dL388stMDAEAAAAA8Apes463nVjHGwAAAADaLqszodfe4w0AAAAAQGtA8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALBRgdwEAAAAAgLYnMzOzUe0zMjIsqsR69HgDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWYjkxAAAAAK1OW1qqCt6PHm8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACwUYHcBAAAAALxbZmZmo9pnZGRYVAngm+jxBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsFGB3AQAAAADQWmRmZjaqfUZGhkWVwJvQ4w0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQLsLgBA25KZmdmo9hkZGRZVAgAAALQMerwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALsZwYWhWWqgIAAEBbxOdg70aPNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGWEwPQaCxXAQAAADQcPd4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhbw6eNfU1OjBBx9UWlqanE6nOnfurEceeURut9vTxjRNTZ48WSkpKXI6nRo1apTWrVtnY9UAAAAAAPzKq4P3448/rueee04zZ87Uhg0bNG3aND3xxBN65plnPG2mTZum6dOna+bMmVq2bJmSkpI0evRoFRcX21g5AAAAAAC1vDp4f/fdd7rgggt0zjnnqFOnTrr44os1ZswYLV++XFJtb/eMGTP0wAMP6KKLLlJGRobmzJmjsrIyvfHGGzZXDwAAAACAlwfvk046SQsXLtSmTZskSWvWrNHixYs1duxYSdK2bduUnZ2tMWPGeI5xOBwaOXKklixZUu95KysrVVRUVOcBAAAAAIAVAuwu4GjuvfdeFRYWqkePHvL395fL5dJjjz2myy+/XJKUnZ0tSUpMTKxzXGJionbs2FHveadOnaqHH37YusIBAECzyczMbFT7jIwMiyoBAOD4eHWP99tvv63XXntNb7zxhlauXKk5c+boySef1Jw5c+q0MwyjznPTNA/bdqj77rtPhYWFnseuXbssqR8AAAAAAK/u8b777rv15z//WX/4wx8kSX369NGOHTs0depUXXPNNUpKSpJU2/OdnJzsOS4nJ+ewXvBDORwOORwOa4sHAAAAAEBe3uNdVlYmP7+6Jfr7+3uWE0tLS1NSUpIWLFjg2V9VVaVFixZp+PDhLVorAAAAAABH4tU93uedd54ee+wxdejQQb1799aqVas0ffp0XX/99ZJqh5hPnDhRU6ZMUXp6utLT0zVlyhSFhIToiiuusLl6AAAAAAC8PHg/88wz+stf/qJx48YpJydHKSkpuvnmm/XXv/7V0+aee+5ReXm5xo0bp/z8fA0dOlSfffaZwsPDbawcAAAAAIBaXh28w8PDNWPGDM2YMaPeNoZhaPLkyZo8eXKL1QUAAAAAQEN5dfAGAAAAAFiLZRut59WTqwEAAAAA4OsI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQLsLsCbrF+/XmFhYXW2ZWRk2FQNAAAAgLYkMzOzUe3JKr6DHm8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCLCcGoE1geQ4AAADYhR5vAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALMSs5rAEM0gDgHfi5zMAAC2PHm8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACwUYHcBviQzM7NR7TMyMiyqBAAAAADgK+jxBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsFGB3AQAAAFbJzMxsVPuMjAyLKgEAtGX0eAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYKEAuwsAAMBOmZmZjWqfkZFhUSUAAKC1oscbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsF2F0AAABNlZmZ2aj2GRkZFlUCAGgN+L2C5kaPNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGWE4PXYfkGAAAAAK0JPd4AAAAAAFjI64P3nj17dNVVVyk2NlYhISHq37+/VqxY4dlvmqYmT56slJQUOZ1OjRo1SuvWrbOxYgAAAAAAfuXVwTs/P18jRoxQYGCgPvnkE61fv15PPfWUoqKiPG2mTZum6dOna+bMmVq2bJmSkpI0evRoFRcX21c4AAAAAAC/8Op7vB9//HGlpqZq9uzZnm2dOnXy/Nk0Tc2YMUMPPPCALrroIknSnDlzlJiYqDfeeEM333xzS5cMAAAAAEAdXt3j/cEHH2jw4MG65JJLlJCQoAEDBujFF1/07N+2bZuys7M1ZswYzzaHw6GRI0dqyZIl9Z63srJSRUVFdR4AAAAAAFjBq3u8t27dqlmzZmnSpEm6//77tXTpUt1+++1yOBy6+uqrlZ2dLUlKTEysc1xiYqJ27NhR73mnTp2qhx9+2NLagYZiFnfAd/H/FwAANIRX93i73W4NHDhQU6ZM0YABA3TzzTfrpptu0qxZs+q0MwyjznPTNA/bdqj77rtPhYWFnseuXbssqR8AAAAAAK8O3snJyerVq1edbT179tTOnTslSUlJSZLk6fk+KCcn57Be8EM5HA5FRETUeQAAAAAAYAWvDt4jRozQxo0b62zbtGmTOnbsKElKS0tTUlKSFixY4NlfVVWlRYsWafjw4S1aKwAAAAAAR+LV93jfcccdGj58uKZMmaJLL71US5cu1QsvvKAXXnhBUu0Q84kTJ2rKlClKT09Xenq6pkyZopCQEF1xxRU2Vw8AAAAAgJcH7xNOOEFz587Vfffdp0ceeURpaWmaMWOGrrzySk+be+65R+Xl5Ro3bpzy8/M1dOhQffbZZwoPD7excgAAAAAAanl18Jakc889V+eee269+w3D0OTJkzV58uSWKwoAAAAAgAby6nu8AQAAAADwdQRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACwUcDwH1dTU6KuvvtKWLVt0xRVXKDw8XHv37lVERITCwsKau0Y0QWZmZqPaZ2RkWFQJAAAAmqKxn+skPtsB3qLRwXvHjh0666yztHPnTlVWVmr06NEKDw/XtGnTVFFRoeeee86KOgEAAAAA8EmNHmo+YcIEDR48WPn5+XI6nZ7tv/vd77Rw4cJmLQ4AAAAAAF/X6B7vxYsX69tvv1VQUFCd7R07dtSePXuarTAAAAAAAFqDRvd4u91uuVyuw7bv3r1b4eHhzVIUAAAAAACtRaOD9+jRozVjxgzPc8MwVFJSooceekhjx45tztoAAAAAAPB5jR5q/vTTT+vUU09Vr169VFFRoSuuuEKbN29WXFyc3nzzTStqBAAAAADAZzU6eKekpGj16tV68803tXLlSrndbt1www268sor60y2BgAAAAAAjnMdb6fTqeuvv17XX399c9cDAAAAAECr0ujg/corrxx1/9VXX33cxQAAAAAA0No0OnhPmDChzvPq6mqVlZUpKChIISEhBG8AAAAAAA7R6FnN8/Pz6zxKSkq0ceNGnXTSSUyuBgAAAADAbzQ6eB9Jenq6/v73vx/WGw4AAAAAQFvXLMFbkvz9/bV3797mOh0AAAAAAK1Co+/x/uCDD+o8N01TWVlZmjlzpkaMGNFshQEAAAAA0Bo0OnhfeOGFdZ4bhqH4+Hiddtppeuqpp5qrLgAAAAAAWoVGB2+3221FHQAAAAAAtErNdo83AAAAAAA4XIN6vCdNmtTgE06fPv24iwEAAAAAoLVpUPBetWpVg05mGEaTigEAAAAAoLVpUPD+8ssvra4DAAAAAIBWiXu8AQAAAACwUKNnNZekZcuW6Z133tHOnTtVVVVVZ997773XLIUBAAAAANAaNLrH+6233tKIESO0fv16zZ07V9XV1Vq/fr2++OILRUZGWlEjAAAAAAA+q9HBe8qUKXr66af10UcfKSgoSP/4xz+0YcMGXXrpperQoYMVNQIAAAAA4LMaHby3bNmic845R5LkcDhUWloqwzB0xx136IUXXmj2AgEAAAAA8GWNDt4xMTEqLi6WJLVr106ZmZmSpIKCApWVlTVvdQAAAAAA+LgGB+/Vq1dLkk4++WQtWLBAknTppZdqwoQJuummm3T55Zfr9NNPt6RIAAAAAAB8VYNnNR84cKAGDBigCy+8UJdffrkk6b777lNgYKAWL16siy66SH/5y18sKxQAAAAAAF/U4B7vb7/9VgMHDtSTTz6pLl266KqrrtKiRYt0zz336IMPPtD06dMVHR1tZa0AAAAAAPicBgfvYcOG6cUXX1R2drZmzZql3bt364wzzlCXLl302GOPaffu3VbWCQAAAACAT2r05GpOp1PXXHONvvrqK23atEmXX365nn/+eaWlpWns2LFW1AgAAAAAgM9qdPA+VJcuXfTnP/9ZDzzwgCIiIjR//vzmqgsAAAAAgFahwZOr/daiRYv00ksv6d1335W/v78uvfRS3XDDDc1ZGwAAAAAAPq9RwXvXrl16+eWX9fLLL2vbtm0aPny4nnnmGV166aUKDQ21qkYAAAAAAHxWg4P36NGj9eWXXyo+Pl5XX321rr/+enXv3t3K2gAAAAAA8HkNDt5Op1Pvvvuuzj33XPn7+1tZEwAAAAAArUaDg/cHH3xgZR0AAAAAALRKTZrVHAAAAAAAu23aV6xdeWV2l1EvgjcAAAAAwKelRDn18dosfbkxx+5Sjui4lxMDAAAAAMAbhDkCdMvILnaXUS96vAEAAAAAPsk0TZmmaXcZx0SPNwAcQ2ZmZqPaZ2RkWFQJAAAADvX5hhw988Vm3X1md52cHm93OfWixxsAAAAA4HPcblNPfbZRP+4u1Hdbcu0u56gI3gAAAAAAnzMvM0s/ZRcr3BGgP53S2e5yjorgDQAAAABoFi11v3WNy63pCzZJkm48ubOiQoJa5HWPF8EbAAAAANBkpmnqvvfW6tkvf7Y8gP9v9V5t3V+qqJBAXX9SJ0tfqzkwuRoAAAAAoMleXrJdby3bJT9DGtktXhntIi15nWqXWzMW1vZ23zKyi8KDAy15neZEjzcAAAAAoEm+/fmAHv14gyTp/rE9ldEuUqZp6kBJZbO/1ry1WdqVV664MIeuHtax2c9vBXq8AQAAAHgllvT0DTtySzXu9ZVyuU1dNLCdbjgpTYXl1br/vbVavatAn0w8WRHN2Ct9Xt8UBfj5qcbtVkiQb0RaerwBAAAAAMelpLJGN72yXIXl1eqXGqUpv+sjwzAU4Gdo7Z5C7Sko1+T/rWvW1/TzM3RO32Rd0L9ds57XSgRvAAAAAECjmaapO95erU37SpQQ7tALfxyk4EB/SVKoI0BPX9ZPfob03qo9+vjHrCa/XkW1S2VVNU0+jx0I3gAAAACARjMMQ6d2T1CYI0DP/3GQEiOC6+wf1DFG40Z1lSTdP3etsgsrmvR6c5Zs1ynTvtTcVbubdB47ELwBAAAAAMfliqEdtPjeUzWgQ/QR9084I1192kWqsLxad/93jdzu41tmrKzKrecWbdGBkiq53E2p2B4EbwAAAABAg/2cU6z80irP86iQoHrbBvr76enL+skR4KdvNh/QK99tP67X/N9PRcovq1bn+FBd2D/luM5hJ4I3AAAAAKBBCitcuualZbrg2W+1dX9Jg47pmhCu+8f2VEpksLolhTf6NYsrXZq7oUiSdMcZ3RTg73sx1jfmXgcAAAAA2KraZWrq1/u1p6BSaXGhig11NPjYq4d11EUD2yn8OJYVm7uhSGXVpnokheucPsmNPt4b+N5XBYAPqah2Hfd9LAAAAIA3eX55njJzKhXmCNCLVw9SZEjDQ7RhGHVCd1FFdYOOK6xw6YOfiiVJk0Z3k5+f0biivQTBG7DI1E82aMhjn+uHbXl2lwIAAAA0ybxNxfp0c4kMSf+8vL+6JjR+yLhUuwTZW0t3asTfv9CG/cee5Xz53nJV1JhKjwnS6F6Jx/Wa3oCh5oBFisqrVVRRo3dX7tawLrF2lwMAAAAcl7X7KvT8strOpKv7R+m0HscfgA3D0NJteSquqNFT3+bqn+ckKySw/v7g0zuHKS06SDUuU4bhm73dEj3egGV+P7C9JOmTtVkqq6qxuRoAAACg8UzT1Mur8uUypZGdQnRx74gmn3PyBb3VLsqp7JIa/d/y/GO27xwdpG5xDb+f3BsRvAGLDOoYrY6xISqtcunTzGy7ywEAAAAazTAMPXRqgs7rHq7bToxtll7niOBAPXVpPxmSPttSou93lR3WJresRvtKWk/nFUPNAYsYhqGLBrTX059v0rsrd+uiX3rAAaAtyszMbFT7jIwMiyoBADRWhMNfN58Q06znPLFzrH7XM0LvbSjSM9/nqnucQ9FOf8/+19cU6ottJbpxUIzO7X5895N7E3q8AQtdNLCdJGnJllztLSi3uRoAQHPYX1ypvQXlnkfxITPzVtW46+z77aOwvGGz+AKA3d7JLNSnm4stfY0/9o9Sp6hAFVa69c/vc2WatasB7Smq1udbS1TjlrrEBFlaQ0uhxxuwUGpMiIamxeiHbXmau2qPxp/a1e6SAADHqaLapUn/Wa15a+vePnTXmG669bR0SdLmnGKd88/F9Z4jKMBPH9w6Qj2Smn6PJABYZcXecs1ZXSBJ6hgVqJ7xwZa8TqC/obtGxOnOT7PVKSpQblPyN6Q3fyyU25ROaOdUz3jfvrf7III3YLFrh3fS4E7ROqdPst2lAACOU2F5tW6as1xLt9fO6hvk/+ugwUPXlDVk1Nl3qMRIh9760zC1i3JaWywANEFJpUv/+C5XkjS2W5hlofugTtFB+r8L23mGme8oqNKi7aWSpCv7Rlr62i2J4A1Y7Ow+yTqb0A0APm3c6yu0dHuewh0BevGawTqx85GXieyVEqFNj53dwtUBQPN5cUW+8spdahceoOsHRrfIax56b/crqwtkShreIURdY1tHb7fEPd4AAADHdM+ZPZQWF6q3bx5Wb+huDJfbbIaqAKB5/bC7TAu3lsqQNHF4rIIDWjYu/rS/Uj/srp0XqTX1dksEb6BFuNymPl+/T3e8vVqVNS67ywEANEBF9a8/r/ulRmnBHaeoV0rT7s3+ZvN+/e5f3+rB9xs3yzsAWK2k0qVnf6i9neZ3PSMsH2J+JDmlNfIzpDO7hqljVOuYVO0ghpoDLeTB9zOVXVSh0b0SNZah5wB8UFtaEmzx5gOa9J/VevHqweqXGiVJCqjn3u3GMGRo1c4C7cwt06MXZsjfr+nr4QJAc1iRVaH8cpfaRwToyn729Daf0ilUfZOCFR7U+vqHW987AryQv5+h3/2ytNi7K3bbXA0A4Gg++nGvrnt5qXKKK/XiN1ub9dxDO8co0hmo3NIqrdiR36znBoCmGNkpVI+PSdSdI+LkaOEh5oeKCvZvlV9KEryBFvL7ge0lSV9t2q/9xZU2VwMAOJI5S7brtjdXqdpl6py+yXrq0n7Nev5Afz+d3iNBkvRpZvYxWgNAy+qVEKz0VjShmTcheAMtpGtCmPqlRsnlNvW/1XvsLgcAcAjTNPXamgI99ME6maZ09bCO+ucfBsgR4H/sgxvpzIwkSdL8ddkyTSZZA2CvZ7/8WbsLq+0uo9UjeAMt6OJfhpu/t5LgDQDewuU29ewPeXprbaEk6Y4zuunh83tbNtTxlPR4BQf6aU9BudbtLbLkNQCgIeavy9YT8zfqjk+yVFjBBMBWIngDLei8fikK8vfT+qwibcjiwxYAeIvccpcMSY9emKEJZ6TLMKy7v9AZ5K9R3RhuDsBe+aVVemBu7aSZ53QPV2Rw84/wwa+Y1RxoQVEhQTq9Z4K255apsJwhPQDgDfz9DN17cpw27K/UVSd2bJHXPL9/iiRpUMfoFnk9APithz5YpwMllUpPCNMVfaPsLqfVI3gDLWz6pf3lDOIbRQCwU365Swu2lOiS3hEyDEPBAX4akOxssdcf2yeZpSUB2GbJzjJ9sGa//P0MPXlJP/kXsuqO1QjeQAsjdAOAvbKKq/WXhTnKLqmRIemSDHvWqwUAOxRWuPSvpbmSpFtGdla/1ChlErwtxz3egE1KKmu0ZMsBu8sAgDalymXq0UX7lV1So6SwAJ3UMcTWerbuL9E7y3fZWgOAtuX9DUUqqHCre2K4bj893e5y2gx6vAEb7C0o1+lPLZLLNPXK71IU5qAXHABawqurC7SjoFpRwX6aNiZRMSH2fRTKLanU6dMXyTSlk9LjlBzZckPdAbRdV/aLUpC/octOybBkyUQcGT3egA2SI4PVMTZEVTVufbOjzO5yAKBNWLuvQu9vqF1R4vYTY20N3ZIUG+bQoA61k6t9tm6frbUAaDsC/Axd3jdKfdtH2V1Km0LwBmxgGIZ+P7C9JGnh1lKbqwGA1q+syq2nlxyQKWlM1zANaW/vEPODzuydJKl2LV0AsNKibaWqdpl2l9FmEbwBm1zQP0V+hvTTgUrtKWJpMQCw0rr9lcordykxLEA3DvKeJbwOBu8ftuUpv7TK5moAtFbf7CjVE98e0N3zs+VyE77twD3egE0SIoJ1Srd4fbVxv77YWqI/9veeD4IArJeZmdmo9hkZGRZV0jac0M6p6Wcnq9plKiTQe/odOsSGqGdyhDZkFenzDft0yeBUu0sC0Mrkl7s0a2mepNqfhf5+hs0VtU3e85sHaIMODjf/Ylup3CbfPgKAlTpHB6l7nMPuMg5zFsPNAVjENE3NWpqnokq30qIDdSnLJ9qG4A3YaHSvRIUGGjpQ6tLWPIYYAkBzMk1TLy7P08+5lXaXclRnZiRKklbsyFdVjdvmagC0Jl/vKNOSXWXyN6Q7hsUp0J/ebrsw1BywUXCgv+4+KV4dogKVEMp/x6Z4f9UePfPF5nr339g/TH0SgyVJS3aW6ZXV+ZKkyGB/DW0fohEdQpQYxr8B0Jr8Z/ku/e+nYs3/uUSzf9dO4V66dGP3xHDNvu4EDescq6AA+kQANI/8cpeeX1Y7xPyyPpHqHBNkc0VtG58yAZsNbte867a63KZySyvlbxiKDft1SGVeaZVq3EfuSfEzDMUd0ja/tErV9bQ1VPeb0uJKlw520IQ7/BRg031DheXV2rK//hniK2pCPX8urXZrd1GNJGl3UY3W5VTqpZX5So8J0oiOITq9c5iltf6UXaSqGjfLeAAW2plbpkc+XC9JurxPpNeGbql2pYtTuyfYXQaAVubgEPPODDH3CgRvwIu43GaTJrz4cmOOHpybqT0F5UqNceqbe07z7Lv6pR+UuafoiMfFhQVp+YOjPc9vfnWFlm7PO2Lb0CB/vX1pe8/zJ789oBV7KyRJiWEBmnJGYov0HO/KK9MzX2zWA2N7KTIkUGf2TlL3pPB627vzd3v+PDjFqamjE2Wa0o7CKi3ZWaZ1OZXanFelzXlVGpzy65chFdWuJtW5r6hCy7fna1iXWMWE1n7TvHBDjp6Yv1GXDm6vh8/PkDPIewMB4ItcblN3vrNapVUu9U5w6MKeEXaX1CimacowGA4KoGluHByt4iqXxg2Jta1jBL8ieANeYEdBlV5amS+3Kf3t9MRGH59fWqW/fbRe763a49nm95sPbX6Gofp+5v72A55hqN62vz2vodrzuk1pX0mN/v71fk07M8nSe4hcblN3vL1ay3fkq7TKpWevGKikyGAlRQbXe0xmZpbnz9FOf0U7a8Nu36Rgndc9QgUVLn2/q0w/7a9Ux6hAT9t7/vujVm/fr+Edaoejd4oKrPcDscttantBtVZ+t13Lt+drxY587SkolyTNvGKAzu2bIknKaFf7rfN/lu/W6l0FevaKgUpPrP9LAwCN8+I3W7Vse75Cg/x1x/BYn5nB9/++2aq3l+3S/WN76tQe9IADaJqE0ABNHZ1kdxn4BcEb8ALOAD9Pr3FOSY0SGthjbJqm5q3N1kMfZOpASZX8DOn6EWm6c0z3w3pRP7j1pAbX8/bNw466/9BlkCafVvvhMKe0RhM+ztLmvCr934p8/b8hMQ1+vcZ6btEWLd+RrzBHgP58Vo9mOWdUsL/OSg/XWem/BmCX29Tinw8or7RaO9cW6q21hUoOD9CIX0J415ggTwhfk12uR7/ar/IaU9KvId/PkHokRdT5wmJkt3i9cdNQTXhrtTbtK9H5M7/Voxdm6PeDfh1JAOD4bMuv0vTPdkmSHjq/t5KCC22uqOG255Zqc06J5q/LJngDOC4b9leosMKtE1ND7C4Fv8EMHoAXSAgLUN/E2nusv9hW0uDjVu7M1/g3VupASZXSE8L07v8brgfP7WXL0OWE0ADdOSJOkvTxpmJ9vb3++62bInNPoZ5esEmS9NB5vZQaY90vFn8/Q1/eNUp3Do/Vie2dCvSTsopr9N91Rbrjk2y9nfnrB/rk8ECV15gKCTR0Srd43XFGN71+41D9OPlMzZtwssb2Sa5z7uFd4jTv9pM1omusyqtduvOdNbr7nTUqr2ra0Hagrft0c7GqXG6N7pWoS3zsy6yzetf+nFiwfp9cbpaYBNA4B0pr9Nii/Xp00X59u9Oaz2E4fvR4A17i9M5h+nFfpb7YWqrLMiIbdH/foI4xOr9fijrFhmj8aV3lCLD3XuHB7Zy6NCNC/8ksUnZJTbOfv7zKpQlvrVKN29TZGUm6uAU+VEc6A3Vq5zCd2jlMZdVuLd9TriU7y7RsT7k25/66BFx8iL9mnpus1IhA9evbp0Hnjg936JXrh+rZL3/WjM836b1Ve3TliR3VPzXKoncDtH43nxCjE3t21Nl9kn3uPumhnWMU6QxUbmmVlm/P09DOsXaXBMBHVFS79NjX+1VQ4VanqEANTG7eyXvRdARvwEsM7xCiWcvytLe4Rj8dqFLPeMdhbfaV1GjWGyv1yPm9PTOW/+MP/b3qw+WVfaM0OMWpXgn13299vP7+yQZt2V+qhHCHpvyuT4u/75BAP53SKVSndApVZY1bla5fe6QMw1CnqMYv0+HvZ+j209M1uFO0tu4vJXQDTeRnGPrjsE52l3FcAv39dHrPBL23co/mr9tH8AbQIKZp6v65a7U5t0rhQX56cFS8nIEMbPY2/IsAXsIZ6KfhHWqHTS/cWne4uds09eFPRRr/0V59/GOWHvt4g2efN4VuqTZIHhq6a9ymTLPpQyYLy6s1f90+SdITl/RTdKi9a1E6AvwU0YzLEw3vEqerTuzoeb4xu1j3z13L0HOgAcqq3JqzKl/l1UdeBtGXnNW7diKk+euym+VnJ4DW76Vvt+u9lXvkZ0j3nhynpLDAYx+EFkfwBrzI6Z1r15r+ZkeZqn7pTd1VWK17P9un55fnq6LG1JBOMbr1tK52ltlg2SXVuvvTbL29bFeTzxXpDNSnE0/Wk5f008hu8c1QnfdyuU3d/uYqvfHDTl3w7GJt3ldsd0mAV3thRZ7eWVekKV/vt7uUJjulW7ycgf7aU1CudXuPvAQkABz07c8HNGVebYfMDQOj1Z8h5l6L4A14kT6JwRqeGqLrBkSpxm3qP5mFuu3jvdqwv1LOAEPjhsTorT+dqM7xYXaX2iBLdpZrc16V/vrBOq3b2/SZhaNCglrkvm67+fsZmnx+b8WHOzyznv93xe5jHwi0Qd/vKtPnW0plSLqsT6Td5TRZcKC/LhyQot8PbC9HAB/TABzd91tz5XKb+v3A9jq/B0uTejPu8Qa8iJ9h6P6Rtb25b60t0GtrasPqoJRgjR8aq4TQAPn5yHq0knRhz3Ct3VehZXvKNe71lfrwtoYvaXbQ/1bvUbXL1O8HtvO6YfVWGtYlVvNuP1l3vL1ai38+oLveWaPvt+bqkQt6KySIH91t3aFL+jVERkaGRZXYq6DCpWe+z5Uk/a5XhDIsmFvCDlMv6mt3CQB8xJ1juqt3SqRGdY/Xzxs3HPsA2IavUgEvdV73CKVFB2rS8FhNPjVBCaG+F7b8DEOThseqXZRTO3LLdM87PzbqnsXskmo9MDdTd72zRp9mZltYqXeKD3dozvVDdOfobvIzpP+u2K0LZn6rfUUVdpcG2M40TT3zfa4KK2tn8P1jvyi7SwKAFmGaZp0lB8/KSFJwoL0r2+DYfCp4T506VYZhaOLEiZ5tpmlq8uTJSklJkdPp1KhRo7Ru3Tr7igSaSWiQn/4xNlmndQ7z6Z7ecIe/nr1yoAL9DX26Llsf/NSw+5VdblPTv81VSWWNTugUrTG/TDjU1vj7Gbrt9HS9fuOJig93KDYsSHFhv854X1HN5Gtom57+Llc/7C5XgJ80aUScAv199+fkkZimqR93F+j7rbl2lwLAy7ydWagHF+5TYQWfAXyJzwTvZcuW6YUXXlDfvnWHX02bNk3Tp0/XzJkztWzZMiUlJWn06NEqLmYyIvg+Px8O3IfqnxqlB8/pJUl6aWW+NuyvPOYx764v0vr9lQpzBGj6pf3l70ND7K0wrEusPplwsv55+QDP38WuvDINeGSBbntzlT5fv09VNb4/ozNwqJyiCn22LltzVuXr8W/qTpx28APnVf2i1Dna3lUOrPDuyj06f+a3mjqPoaMAfvXD7jK9vqZQa/dVasXecrvLQSP4xNjVkpISXXnllXrxxRf16KOPerabpqkZM2bogQce0EUXXSRJmjNnjhITE/XGG2/o5ptvtqtkAL9x9bCOWro9Txt35yraefTv/H7OrdTrawokSZPP763UmJAWqND7HdrTLUkLN+xTebVLH67Zqw/X7FWkM1Bj+yTrgv4pGtIpxqfmAwCk2v/7a7IrtDG3Sts/XKi9hXVvq7hpUI1iQmo/ulzYM0Lndo/Q4JTWcV/3b43sFi/DkNbsLtTegnKlRDFTMdDW7Sqs1pPfHpApaWy3MJ3W2Tcm20Utn+jxHj9+vM455xydccYZdbZv27ZN2dnZGjNmjGebw+HQyJEjtWTJkpYuE8BRGIahab/vqyfOTDrq+pIVNW49+e0BuUxpeIcQ/X5guxas0rdcM7yT/jd+hK4fkab4cIcKy6v15tKd+sML32vE4180y0zygBXKq93asL9S8zYVq+yQtbc/31qq2asKtGRnmfYWVsgwpO6J4RrTJUy3Do1R0CGzfA9IduqEdk6fvhXnaOLDHRrUIVqS9Nm6tjfHBYC6SqrcevSrHJVXm+qd4NBNg2LsLgmN5PU93m+99ZZWrlypZcuWHbYvO7v2F1FiYmKd7YmJidqxY0e956ysrFRl5a9DXYuKWCcTaAmhjoA692HuK6lRYljdH0Orsyq0p6hGMU5/3To0ptV+qG4OhmGoX2qU+qVG6YFzeur7rbn63+o9+iQzWwVl1UqLC/W0Xb49T/HhDnWMDT3KGYHml1dapTW7CvRlZqG25Vdpa16V9hbX6OC0QB2jAtX7l9nI+ycFK6/MpW5xQTpzcA/1aR+pMEdAo2dxby3OykjS8h35mr9un64dkWZ3OQBs4nKbenLxAe0prlF8iL/uOyW+1c1r0RZ4dfDetWuXJkyYoM8++0zBwfUPJfvtB3PTNI/6YX3q1Kl6+OGHm61OAI1jmqZeW1Oo/64r1MOnJah/8q9DKE9MDdFjZyTKlKkIBzN0NpS/n6ERXeM0omucHrkgQxuzi+ssO/bA3Ext3FestLjQw35Z906J1NOX9fc8v+z575RfVnXYa6REOfXM5QMUHlz/iAW0XS63qd1F1dqWX62MBIfiflmJ4cM1e/XQB4dPehrj9FdadGCduSxOTA3Riam1t5ZkdIltmcK92Jm9k/Toxxv0w7Zc5ZVWKSa09d3LDuDY3lpbqOV7y+XwN/TAyHhFBfP5yBd5dfBesWKFcnJyNGjQIM82l8ulr7/+WjNnztTGjRsl1fZ8Jycne9rk5OQc1gt+qPvuu0+TJk3yPC8qKlJqaqoF7wDAkRiGobzyGrlM6clvD+gfY5MVG/Lrj6O+Sa3zns2WEhzor36pUZ7npZU1SohwaHNOsbYdKD2sfcRvgvTWA6XaX3z4BHib9pXo9R926paRXZq9ZviuH3aV6a3MQu0oqFaVq7Yf+45hsTq9S+29hxntIpSeEKaUELfSooPUOTpIadFBinbywfFYUmNC1Cs5QuuzivT5hn26dDCfVYC26KSOIfpqe6mu6hulrrGOYx8Ar+TVwfv000/X2rVr62y77rrr1KNHD917773q3LmzkpKStGDBAg0YMECSVFVVpUWLFunxxx+v97wOh0MOBxctYKdbTojR5twqbS+o1rTFB5SREKzTu4QqJZze1OYW6gjQqzcM1f7iSm3OOXzFh98G71lXDlSVq+4M6cu25euDNXvUjgmecIgN+ys09Zv9OjihfnCAobToIDkCfu3FHtQxRgsmjWyzw8Wb6szeSVqfVaSvN+0neANtVMeoID17boqCGF7u07w6eIeHhysjI6POttDQUMXGxnq2T5w4UVOmTFF6errS09M1ZcoUhYSE6IorrrCjZAAN5Ajw032nxGviJ1lal1OpdTmV+nhTsV66sJ1Cgnxi3kefEx/uUHz4sb90HNzp8AlbhnSK0W2ndWWmdHjsL63RY4tqQ/eJqU5dNyBayeEBrWYZRG9x8eD2OiEtWkOO8P8SQOvmcpueJUQJ3b7Pq4N3Q9xzzz0qLy/XuHHjlJ+fr6FDh+qzzz5TeHi43aUBOIZ2EYGaOCxWU78+IEn6fa8IQreXCvDn3wV1fbmtVAUVbnWKCtSdw+PkDOQasUK7KCcjTYA2qKLGrds/ztJJHUN0aUakggP4GevrfC54f/XVV3WeG4ahyZMna/LkybbUA6BpRnQI1fghbh0oq9FFvSLsLgfHUFHt0rsrd6tHUrgGdaQHri27pHeEwoL8NDAlmNANAM3so43F2ltco6+2leryPlF2l4Nm4HPBG0Drc3Y3Rqj4iukLNumFr7dqZLd4zbl+iN3lwAYHVw4xDENj+b/bIkora/TE/I1asuWAPrztJDkCmJgOaM2KK116J7NQknRVvyiWDmsl+IoaANBgVw7tID9DWrRpvzbtO3yiNrRuy3aX6ZGv9qu0yn3sxmg2zkB/fZKZpU37SnTliz/o8U9/0qeZ2coqLJdpmsc+AQCf8k5mkUqrTXWKCtTITqF2l4NmQvAGADRYx9hQndk7SZL0f99stbkatKSfc4r1xLcHtGxPud7fUGR3OW2Kn5/hmdF8+Y58zfpqi255bYWGTf1CQ6cs1NrdhZ62BHHAt+WU1ujDjbU/Y68bGO2ZXA2+j6HmAIBGuemUzvokM1vvr9qru87sroRw1l1v7QrLqnXTKytUVm2qd4JDl2ZE2l1SmzNpdDednZGsNbsL9OPuAq3eVahN+4qVU1ypdtG/Tr725GcbNW9ttvq1j1Tf9lHqlxql3ikRCg5keDrgC15fU6Bqt9Q30aGByfx+bU0I3gCARhnYIVqDOkZrxY58vbJkh+46s7vdJcFCLrep295apW0HShUf6q/7TonnfkMbGIahXikR6pUSocuHdJAklVe5tHFfsWJCgzzt1uwq1LYDpdp2oFTvr94rSQrwM5QWF6oIZ6De/X/DPW0nf7BOy3fk1fuaH912skXvBsCR5JVWafGOMknStQOiZbA8Y6tC8AYANNpNJ6dpxY58vfr9Do07tYtCgvh10lr9/ZMN+nrTfgUH+ukvIxMUFUzPqbdwBvmrf2pUnW3PXD5AP+4p1JpdBbWP3QU6UFKlzTklCv7N7PM7ckuVuefYtw2YpqmfsovVM5mVJwArxYQG6bnzU7R0d7m6xTnsLgfNjE9KAIBGG90rSWlxoUqLC1VBWTXBu5X6YmuJXlySK0l68pJ+6uRXf+8ovEN0aJBGdovXyG7xkmpD897CCv2cU3JY2ztGd9PVwzsd9XxVNW794YXvtHJngeZPPEXdk5jJHrBSfGiAzunO/7PWiE9KAIBG8/cz9NFtJynUwa+R1qxDZKBSIoP1+0HtdW7fFGVmErx9jWEYahflVLso52H7+raPOubxQQF+Soyovc/0uUVb9PRl/Zu5QgCmaWpDVrF6pTCqpDVjVnMAwHEhdLd+XWMd+vj2k3XHGd3sLgU2GjeqqyTpgzV7tSuvzOZqgNZn/rpsjf3nN5r0n9V2lwILEbwBAE2yt6BcLy3eZncZaCZVLlNb86o8z6NDg+THcjZtWp/2kTo5PU4ut6nnv95idzlAq+Jym5r26UZJUvsjjExB60HwBgAct5LKGp0xfZEe+Wi9VuzIt7scNJFpmpr5Q67u/DRLi7aX2l0OvMj4U2t7vf+zfLdyiitsrgZoPRZsKdHWA6WKCQ3STad0trscWIjgDQA4bmGOAJ3TJ1mS9H/fbLW5GjTV+xuK9cXWUrlMKdLBRwT8amhajAZ2iFJVjVv/ZoQL0Cwqatx648dCSdJtp3VVeHCgzRXBSvxWBQA0yY0n135DP39dtnbk0kvqq1bsLdfsVbWjFm4cFK3+yQx5xK8Mw/D0en+3JVemadpcEeD7PvipWHnlLqXGOHXl0I52lwOLEbwBAE3SPSlcI7vFy22Ke719UH65Syv2lmva4gNym9LoLqE6j6VscASn9UjQ7GtP0NxxI2QY3PcPNEVhhUv/XVfb233XmO4KCiCWtXb8CwMAmuymX3q9/7N8twrKqo7RGi3JNE3tK6rQ91tz9faynZqzKl/uQ3orX1iep4e+yFFplVs94hwaNySWUIUjMgxDp/ZIkD+T7QFNtre4RsEBfuoSHaTz+qbYXQ5aAGvBAACabETXWPVMjtCGrCK9/sNOz5BU2OOrbaVasqtMe4uqlVVco0rXzjr7x3YLV3xo7UeA9hGBSg4LUHpskG4aHKNAf0IVjq2i2qXtuaXqkcS6w8Dx6Bnv0AsXpCi/3MXKEW0EwRsA0GSGYeimk9P0l/czufezBbjcpnYUVOunA5Xa+MvjiTOTFO7wlyRtL6jSkp2/rrfsZ0jto0PUKS5UEUaFDv2Id2W/KF3ZL6pl3wB82oasIl390lL5G4a+vudUhsgCxyk4wE/J4fz/aSsI3gCAZnFu3xSd3jNRkU5mZbVC5p5CffjjXq3aWaA1O/NV6ar7Bcem3CoNSqmdEG1o+xBFO/2VEh6olPAAnTqknyccZWZmtnjtaF06x4fKkJRdVKG5q3brshM62F0S4DO25VdpW36VRnYK5baNNobgDQBoFkEBfvR8NYPKGre25FXppwNVGtreqXYRtV9kZO4p1POLfl2yLSTQUPc4R+0jNkg94hyefT3jHeoZ/+tz/l3QnBwB/rrp5M56bN4GPbdoqy4elEqAABpo9sp8rcyq0PaCal0/MNructCCCN4AgGZlmqaWbc9XcKCf+raPsrscn1HlMvXC8jwt+LlEBzuzg/wNT/Aekhajy4d00IAOUQqr2K/2kYHyYxI02OSKoR0088ufte1AqT7JzNK5TA4FHNPqrHKtzKpQgJ90dnqY3eWghfEVOACgWb34zVZd+vx3emL+RrtL8Rk5pTW6Z362Pt1cG7qjgv10YqpTCaH+njad48M09aI+unRwqjpEBRG6YatQR4CuHd5JkvSvL7cwtwNwDG7T1MurCiRJZ6eHKzmc27LaGoI3AKBZnZ2RLD9D+mbzAa3fW2R3OV5vTXa5Js7L0s95VYpw+OmR0xL06u/b68GRCRrSPsTu8oB6XTu8k0KC/LU+q0hfbdpvdzmAV/t2Z5l+zquSM8DQZX0i7S4HNiB4AwCaVWpMiM7ukyxJ+r/FW4/RGt/tKldRpVtdYoI04+xkDUxxso42fEJ0aJCuGFI7sdqybXk2VwN4rxq3qVd+6e2+qFeEooL9j34AWiXu8QYANLubTu6sj3/M0odr9ures3ooMSLY7pK81g0DoxXj9NcFPcLlYBI0+Jg/jeys3w1sp94p9OAB9Xkns1BZJTWKCvbThT0j7C4HNuE3PACg2fVPjdKQTjGqdpl6ecl2u8vxKtsOlOqBuWvlctfeExvob+jSjEhCN3xSQngwoRs4htFdwpQYFqCbBsXIGcjP+raKf3kAgCVuPDlNkvT69ztUWlljczXe4fP1+3T+M4v1+g879dbaQrvLAZpVVmG5duWV2V0G4BUOnXAwLjRAz56brJFpoTZWBLsRvAEAljijZ6LS4kIVHRqknW38w7jbNPX6mgLd+MpyFVfWaHDHaJaSQavy1tKdOmXal/r7pz/ZXQpgu4oatyZ/maNvdpR6tgUzqqnN4x5vAIAl/PwMvXrDECVHOuXvZ2j7gVK9u3J3ve1HdY/XoI4xkqQ9BeV6a+nOetuO6BqnEzvHSpJyiir06uqCI7ZLCQ/QqLRQ+fvZN1lZSZVbTy4+oOV7yyVJ1wzrqAfO6aVNP623rSagufVLjVK1y9S8tVnaur9EneP5YgltU2F5tf6yMEcb9ldq44EqDUx2KjSI0A2CNwDAQu2jf10Oa1d+mZ754ud620Y6Az3BO7uw/KhtHQF+nuB9oKRKb2fWP2x7V1G1rh0Q3djSm8WOgio9+tV+ZZXUKMjf0JSL+uriQe1tqQWwUs/kCJ3eI0ELf8rR84u26vGL+9pdEtDiDpRU6o//XqoN+ysVFuSnh09LIHTDg+ANAGgRyZFOXTOsY737e6X8OtNrfFjwUdv2aR/l+XNMaJDO7R5+WJuKarfWZFfopA72rYVtSsqvcCkh1F/3nxKvCwndaMXGndpFC3/K0Xurdmvi6HQlRzrtLgloMXsLynXV//2grQdKFRXsp7+dnqi06CC7y4IXIXgDAFpE14QwPXxBRoPadogNaXDbpMhg3XJCzBH3VdS4W/y+OtM0Petwd4oK0l9HJahTdKAiHKzbitZtUMcYDU2L0Q/b8vTi19v01/N62V0S0CL2FFXr5ue+056CcrWLcuqvp0SrXUSg3WXByzD2AQDQah0aun/OrVRBhcvS16tymXps0X5l5lR4tvVNCiZ0o80Yd2pXSdKbS3cqr7TK5mqAlvHVtlLtKShX57hQvXPLMEI3jojgDQBo9b7fVaZ7PtunqV/vV7XLPPYBx6HaZeqxRTn6fne5ZizJ9azTDbQlp6THKaNdhAxD+nF3gd3lAC3i8r6RunN0N7198zClRHGLBY6MoeYAgFavXUSgAvykdTmVenFFnv7Vr3nPX+0yNeXr/Vqxt0IOf0O3nxhr60zqgF0Mw9BTl/RXQrhD0aHc34rWa3NupTpFBSnQ35CfYei209PtLglejh5vAECrlxoZqLtGxMmQNG9Tid48ylJljXUwdC/bUy6Hv6G/nhqvvknBzXZ+wNd0TwondKNV+35Xme6Zn60nFh9gdBMajB5vAECbMKR9iK7qF6VX1xTor//LVHpCmAZ3OvKkbA1V7TL1929qQ3eQv6G/jIpXvySGGQIHfbYuWwkRwXIcYZLDtLhQBQfWzn+wv7hSB0oq6z1Px9gQhQTVfmzNLalUTnH9bVNjQhTmqG2bX1ql7KKKetu2i3YqIrj2ftzCsmrtLSyvt21KlFORztq2RRXV2pNff9vkyGBFhdR++VBSWaNdeWX1tk2MCFYMX1T4jC+3lejpJblym5IpU25TYhYPNATBGwDQZlyaEaEt+VVasrNMt7y2Uh/ddlKTzvfe+iL9sLs2dP91VLz6JxO6gYNuf3OVPlizt979n0w4WT2Ta5cRfHPpTk1fsKnetu+NG66BHaIlSXNX7dGjH2+ot+3rNw7ViK5xkqSP12bpwfcz62374tWDNbpXoiRp4U/7NOk/a+pt+8/LB+j8fimSpCU/H9Atr62st+3jv++jy07oIElavj1P185eVm/bh87rpetGpNW7H95j3qZizVqaJ1PSaZ1DNYHbitAIBG8AQJthGIbuGBarvCp//ZRdrJeXbNe5qcd/vgt7hmtzbqXGdgsndAO/cXZGklbsyFeVy33E/QGHBJaQIH/FhzvqPVeg36895s5jtfU/pG3g0dsGHdITH3yMtof22jsCjt72YE/+wdc4WlvnIW2ratx1aoL3+HZnqf61NE+SdG73cP1pcLT8DEI3Go7gDQBoU5yBfnrhj4P1/uo9uvXUrlq/fl2jjne5TfkZtSHeEeCnB0clWFQp4NvO7pOss/skN6jtjSd31o0nd25Q2yuHdtSVQzs2qO3vB7XX7we1b1DbsX2SNbaB9Z7aI0HLHjijQW2Hd4k7Ztufc4r1p1dWqNrt1jf3nNag86LluE1Tr68plFQbum8eHC2D0I1G4is1AECb0yE2RLefni6/Rg4RdLlN3f7mKv39059kmkyoA6B5JEQEa+uBUu3KK1c+6597nV2F1dpXUiNnoKGr+kURunFc6PEGALRpVS5Ts5bmamSn0KMOF3e5TT357QF9s6NMgf6GLh7YsF40ADiWiOBAdYoN0fbcMmXuLdTJ6fF2l4RDdIwK0r9/105b86oUFkS/JY4PVw4AoE17d12hFmwp1ePfHFB2SfUR27jcpp46JHQ/d9UgpSeGt3ClAFqz3u0iJUmZe4psrgRHEhXsr4EpzOWB40ePNwCgTft970gt3VOuzblVevSr/XryrCQFHzK5kcttavqSA/p6R5kC/KRZVw7S6T0TbawYrV1mZv2zcB9JRkaGRZWgJfVpF6mPf8xS5p5Cu0vBIXYVVis1MtDuMtAK0OMNAGjTgvwNPXBKvKKC/bS9oFozluR67t92uU3N+C5Xi7aXyd+Q/nxyvM7oRegG0PwyUn7p8d5L8PYWW/Kq9P8+3KsHPt8nl5t5PdA0BG8AQJsXFxqg+0+JV4CftHhnmd5ZVzvUc/3+Sn21rVT+hnTvyfE6MTXE5koBtFYZ7WrXNN+RW6bC8iPf9oKW9d91tV+CRAX7sV43moyh5gAASOqVEKxbTojRzB/y9OrqAqVFBeqE9iGaMCxWIYF+Gt6B0A3AOlEhQTq1e7ziwhyqqHYp0snwZjvtKarWtzvLJEkX9460uRq0BgRvAAB+cVZ6uLbkVWnxL5OoSdIZXcJsrgpAWzH7uiF2l4BfvLe+SG5TOqGdU2nRQXaXg1aA4A0AwCH+NDhGl2REKiGUX5EA0BbtK6rQwq0lkqSLe0fYXA1aC+7xBgDgEIH+BqEbgG2qatzavK/Y7jLatP/7Zqtq3FKveId6JwTbXQ5aCYI3AAAA4AWKKqqVMXm+Rj/9tYormGDNDm63qa83HZAkXZLBvd1oPgRvAAAAwAtEBAcqLrT2fuL1e4tsrqZt8vMz9NHtJ+n+U+I1OIXebjQfgjcAAADgJXq3O7ieN8HbLoH+tStZGAZLiKH5ELwBAAAAL9HnYPDeU2hzJW3Pzzklqna57S4DrRSzxwAAAABeIqNd7SzaBO+WVe0y9ad//yDDMDT7uhPsLgetED3eAAAAgJfI+KXHe8v+EpVV1dhcTduxaHup9hZWqMrlVoeYELvLQStE8AYAAAC8REJ4sBLCHXKb0oYs7vNuCW7T1H/X1Y4wuOGkNAUH+ttcEVojhpoDAAAAXuTaEZ1kmrUhHNb7fle5dhfVKDw4QFcO7WB3OWilCN4AAACAFxk3qqvdJbQZ5iG93VcP66jw4ECbK0JrxVBzAAAAAG3Sj/sqtCm3SkH+hq4bkWZ3OWjFCN4AAACAFzFNU7vyyjRvbZYqql12l9OqrcqqkCSN6RqmuDCHzdWgNWOoOQAAAOBlfvevb3WgpEpzxw3XgA7RdpfTal07IFrDUkMU62RCNViLHm8AAADAixiGod4ptcuKZe5lZnOrdY9zKC6U/khYi+ANAAAAeJk+v6znnbm70OZKWqfcshoVVDCMHy2Hr3ZQr8zMzEa1z8jIsKgSoG3i/yAAtF0Z7SIkSZl7Cd5WeG1Ngb7eXqabT4jWmK7hdpeDNoAebwAAAMDLZPzS471pX7Eqa+iZbU4HSmv05bZSVbpMdYgMsrsctBEEbwAAAMDLtItyKiokUNUuU5uyS+wup1WZu6FINW6pT6JDPeKZyRwtg+ANAAAAeBnDMDz3ea/dw3Dz5lJU6dKnm2u/yLi4d6TN1aAt4R5vAAAAwAtdf1KaLjshVUM6xdhdSqvx4U/FqnSZ6hIdpIHJwXaXgzaE4A0AAAB4oVO7J9hdQqtSVu3WhxuLJUmXZETIMAybK0JbQvAGAAAA0OrVuE05AgxFOAI0LDXE7nLQxhC8AQAAAC/1w9ZcrdxZoHP6JNtdik+qdpkK8Ku9Zz7C4a/xQ2LkZxjy96O3Gy2L4A0AAAB4qRmfb9Z3W3MVGxqkXk67q/Et63IqNPOHPF3UK0Kju4RJkoa0p6cb9mBWcwAAAMBL9Wnfemc2X7hhn7YfKG328xaWV+v+uWt172f7tKuwWu+tL5LbNJv9dYDGoMcbAAAA8FK9UyIkSZl7C3VZ19az/FVFtUt3vrNGBWXVOjk9TlcO7ajTeyYo0P/4+wVN09S8tVl66IN12l9cKUka0zVM1w+Ikh8TqcFmBG8AAADASx1cy3tDVpFc7ohWc29yXmmV+qdGadGm/fpm8wF9s/mAEiMcuuyEDvrDCalKiWrcuPr9pTWatTRPS/fslCR1jgvVTQPC1CeRJcPgHRhqDgAAAHipTrGhCnMEqKLarV1F1XaX02RvLd2pJVsOKDkyWC9fN0Rf332qxo3qoriwIO0rqtQ/F27WSY9/obeX7WzUeXPLXFq2p1yB/oZuP62r5k04mdANr0LwBgAAALyUn5+hXr8MN9+SW2VzNU1TWF6tRz5aryte/EErd+ZLklJjQnTPWT205M+na+YVAzSsc6zcpjS4U4znuOziauWXuw47X3Hlr9t6xDv0p8HR+vj2kzVpTHcFB/pb/4aARmCoOQAAAODFMlIitXRbnn7Oq9LpXeyu5vi9s3yXyqpc6pEUroEdouvsCwrw07l9U3Ru3xTtKShXu0OGms9ZXaAlO8s0vEOIzk4PV7e4IL21tlAfbyzWjLHJahcRKEk6r0eEuiWGt+h7AhqK4A0AAAB4sT8O66iLBrZT9YHGDb/2Ji63qTnfbZckXTu8k4yjTHZ2aOh2uU3ll7vkMqVvdpTpmx1lCvI3VOWqnaX8mx2l+kOfKCtLB5oFwRsAAADwYmlxoZKkzHzfnVjti59ytCuvXFEhgbqgf7sGH+fvZ+jvY5K0Nb9Kn2wq1lfbSlVeYyo2xF/jTojR0FTW5YZvIHgDAAAAsNTsb7dJkv5wQgc5gxp//3Xn6CCNHxqr6wZGa31OpXolOBQSyHRV8B1crQAAAICX+2xdtv75fa6W7ym3u5RG25hdrCVbcuVn1A6bb4qQQD8NbuckdMPn0OMNAAAAeLklW3L12c8lcgYYGtyucWtc2y2vtEqd40PVPTG8zv3bQFtC8AYAAAC8XO+DS4rl+d6SYsO6xOrzO0aquLLG7lIA2zBGAwAAAPByfdpHSpK25FfJbZo2V9N4fn6GIp2BdpcB2IbgDQAAAHi5rvFhCvI3VF5tKqvYN3qOa1xuvbdytyqqXXaXAtiO4A0AAAB4uQB/P6VF1/YY/+wjw80XrN+nSf9Zo3P++Y1MH+ylB5oTwRsAAADwAV1igiT5zn3es5dslySdnZEsw/DdNciB5kDwBgAAAHxA11+Cd3659w/dXre3UEu35cnfz9BVJzZtCTGgNWBWcwAAAMAHnNQxVMM7hCosyPv7zuZ4eruTlBQZbG8xgBcgeAMAAAA+ICTQ+wO3JBVWuPT+6r2SpOtGdLK3GMBL+Mb/XgAAAAA+Yf7PJaqqcatPu0gN7BBtdzmAV6DHGwAAAPARi3eU6uNNxeqf5NRlfSLtLueIsoqrJdX2djOpGlCL4A0AAAD4iOJKt9buq1SAn6HL5J3Be8KwOP35wsFqH+20uxTAaxC8AQAAAB/RNfbXJcVM0/TaHuUu8WF2lwB4Fe7xBgAAAHxEx6gg+RtSUaVb+0u9a1mxnJIaHSirsbsMwCsRvAEAAAAfEeRvqGNUoCTp57wqm6up67UfC3TD3D36aGOx3aUAXofgDQAAAPiQLjEHh5tXNts5swsrmnR8frlLX28vlcuU0n8ZDg/gVwRvAAAAwId0jXFIOv4e77KqGt3z3zXaV1QbtjdmF2vkE19q8gfrVF51fMPXP91crBq31D0uSN3jHMd1DqA1I3gDAAAAPqRLTJAiHH4KD/Jv9LHlVS7d8PJy/Wf5bt30ynKZpqmvN+1XZY1bLy/ZrnOe+UY/7i5o1DmrXabmbSqRJJ3fPaLRNQFtAcEbAAAA8CHd44L0+sXtdddJcY06rqLapT+9ulzfbc1VmCNAk8/vLcMwdNMpnTXn+iFKCHdo6/5SXfSvJfrnws2qcbkbdN5vd5Ypv8KlGKe/hncIOZ63BLR6BG8AAADAhxiG0ehlxCprXLrltRX6ZvMBhQT5a/Z1J2hgh2jP/pHd4jV/4ik6p0+yatympi/YpIuf+07bDpQe89wfbiySJI3tFqZAf+9c3gywG8EbAAAA8FHVLvOYbapq3Br/+kp9tXG/ggP99NK1J+iETjGHtYsODdLMKwZoxmX9FR4coNW7CvRpZvZRz72/tEY7CqoV4CedlR5+3O8DaO0C7C4AAAAAQOP8sLtMs5bmKS06SA+dmnDUtlM/2aDPN+TIEeCnf19zgk7sHFtvW8MwdOGAdhqSFqPZ327Tn07p7NlnmuZhPe3xoQF6+aL2+ml/paKCG3/POdBW0OMNAAAA+JiwID8dKHNpSwNmNr/5lC7qkRSuF64erBFdG3ZfeEqUUw+c00v+frVBu7LGrfsW7NOSnWVHrGVwO2fj3gDQxtDjDQAAAPiYztFBMiTllbuUX+5StLP+3uakyGB9fPvJnhB9PD7cWKzMnEpl5uzX6Z1D9afBMaqscSsmhDgBNAT/UwAAAAAf4wz0U7uIAO0uqtHPeVU64ZAeZ7dpauYPeeqXGKyMjNptTQndknRBjwiVVrn17voiLdxaqrX7KlRRYyopLEB/PiVeCaHECuBoGGoOAAAA+KCuMQ5J0s+5lZ5tbtPUsz/k6bOfS/T0dwe0t6C8WV4r0N/QNQOi9ffRiUoKC1BOqUtFlW7lldcuIwbg6AjeAAAAgA/qEhMkSZ77vE3T1PPL8jT/5xL5GdIdw+KUEtW89173SgjWP89J1piuYQrwk67oG6WAJvamA20BY0IAAAAAH9Q19tfgbZqmXlyRr483lciQNHFYrEamhVryuiGBfrr9xFiNHxLT5CHsQFtB8AYAAAB8UJfoIHWPC1LXGIf+b0W+PvipWJJ024mxOq1zmOWvT+gGGo6h5gAAAIAPCgny01NnJWtgSrD+90voHj8kRmO6Wh+6ATQOPd4AAACADxvSzqkLeoQrKTxAZ3cLt7scAEdA8AYAAAB8mGEYumlwjN1lADgKhpoDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABby6uA9depUnXDCCQoPD1dCQoIuvPBCbdy4sU4b0zQ1efJkpaSkyOl0atSoUVq3bp1NFQMAAAAAUJdXB+9FixZp/Pjx+v7777VgwQLV1NRozJgxKi0t9bSZNm2apk+frpkzZ2rZsmVKSkrS6NGjVVxcbGPlAAAAAADUCrC7gKP59NNP6zyfPXu2EhIStGLFCp1yyikyTVMzZszQAw88oIsuukiSNGfOHCUmJuqNN97QzTffbEfZAAAAAAB4eHWP928VFhZKkmJiYiRJ27ZtU3Z2tsaMGeNp43A4NHLkSC1ZsqTe81RWVqqoqKjOAwAAAAAAK/hM8DZNU5MmTdJJJ52kjIwMSVJ2drYkKTExsU7bxMREz74jmTp1qiIjIz2P1NRU6woHAAAAALRpPhO8b731Vv3444968803D9tnGEad56ZpHrbtUPfdd58KCws9j127djV7vQAAAAAASF5+j/dBt912mz744AN9/fXXat++vWd7UlKSpNqe7+TkZM/2nJycw3rBD+VwOORwOKwrGAAAAACAX3h1j7dpmrr11lv13nvv6YsvvlBaWlqd/WlpaUpKStKCBQs826qqqrRo0SINHz68pcsFAAAAAOAwXt3jPX78eL3xxhv63//+p/DwcM9925GRkXI6nTIMQxMnTtSUKVOUnp6u9PR0TZkyRSEhIbriiitsrh4AAAAAAC8P3rNmzZIkjRo1qs722bNn69prr5Uk3XPPPSovL9e4ceOUn5+voUOH6rPPPlN4eHgLVwsAAAAAwOG8OnibpnnMNoZhaPLkyZo8ebL1BQEAAAAA0EheHbwBwNdlZmY2qv3B5RIBAADQenj15GoAAAAAAPg6gjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYKEAuwsAAACA/TIzMxvVPiMjw6JK0NpwbQH0eAMAAAAAYCmCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFgqwuwAAAAAA1svMzGxU+4yMDIsqAdoeerwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALsZwY0AxYngMAAABAfejxBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALMRyYgCAOlgeDwCsw89YtDZc0w1DjzcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWYlZzH8BMgQAAAADgu+jxBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALBRgdwEAAACtTWZmZqPaZ2RkWFQJAMAb0OMNAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFio1QTvf/3rX0pLS1NwcLAGDRqkb775xu6SAAAAAABoHcH77bff1sSJE/XAAw9o1apVOvnkk3X22Wdr586ddpcGAAAAAGjjWkXwnj59um644QbdeOON6tmzp2bMmKHU1FTNmjXL7tIAAAAAAG2czwfvqqoqrVixQmPGjKmzfcyYMVqyZIlNVQEAAAAAUCvA7gKa6sCBA3K5XEpMTKyzPTExUdnZ2Uc8prKyUpWVlZ7nhYWFkqTS0tLD2hYVFXn+XFJS0qjaDj22KZryuhzb8GObwhdrbgpffL++eE364t+VXXzx34hjvf/YpmhrNaPhWvLaaOrxHMux3nxsczt4btM0LTm/zwfvgwzDqPPcNM3Dth00depUPfzww4dtP+OMMyypDQAAAADg/XJzcxUZGdns5/X54B0XFyd/f//DerdzcnIO6wU/6L777tOkSZM8zwsKCtSxY0ft3LnTkr9ktE1FRUVKTU3Vrl27FBERYXc5aCW4rmAFritYgesKVuHaghUKCwvVoUMHxcTEWHJ+nw/eQUFBGjRokBYsWKDf/e53nu0LFizQBRdccMRjHA6HHA7HYdsjIyP5z4tmFxERwXWFZsd1BStwXcEKXFewCtcWrODnZ800aD4fvCVp0qRJ+uMf/6jBgwdr2LBheuGFF7Rz507dcsstdpcGAAAAAGjjWkXwvuyyy5Sbm6tHHnlEWVlZysjI0Lx589SxY0e7SwMAAAAAtHGtInhL0rhx4zRu3LjjOtbhcOihhx464vBz4HhxXcEKXFewAtcVrMB1BatwbcEKVl9XhmnVfOkAAAAAAEDW3DkOAAAAAAAkEbwBAAAAALAUwRsAAAAAAAu12eA9depUGYahiRMneraZpqnJkycrJSVFTqdTo0aN0rp16+wrEl5v8uTJMgyjziMpKcmzn2sKx2vPnj266qqrFBsbq5CQEPXv318rVqzw7OfawvHo1KnTYT+zDMPQ+PHjJXFd4fjU1NTowQcfVFpampxOpzp37qxHHnlEbrfb04ZrC8ejuLhYEydOVMeOHeV0OjV8+HAtW7bMs5/rCsfy9ddf67zzzlNKSooMw9D7779fZ39DrqHKykrddtttiouLU2hoqM4//3zt3r270bW0yeC9bNkyvfDCC+rbt2+d7dOmTdP06dM1c+ZMLVu2TElJSRo9erSKi4ttqhS+oHfv3srKyvI81q5d69nHNYXjkZ+frxEjRigwMFCffPKJ1q9fr6eeekpRUVGeNlxbOB7Lli2r8/NqwYIFkqRLLrlEEtcVjs/jjz+u5557TjNnztSGDRs0bdo0PfHEE3rmmWc8bbi2cDxuvPFGLViwQK+++qrWrl2rMWPG6IwzztCePXskcV3h2EpLS9WvXz/NnDnziPsbcg1NnDhRc+fO1VtvvaXFixerpKRE5557rlwuV+OKMduY4uJiMz093VywYIE5cuRIc8KECaZpmqbb7TaTkpLMv//97562FRUVZmRkpPncc8/ZVC283UMPPWT269fviPu4pnC87r33XvOkk06qdz/XFprLhAkTzC5duphut5vrCsftnHPOMa+//vo62y666CLzqquuMk2Tn1k4PmVlZaa/v7/50Ucf1dner18/84EHHuC6QqNJMufOnet53pBrqKCgwAwMDDTfeustT5s9e/aYfn5+5qefftqo129zPd7jx4/XOeecozPOOKPO9m3btik7O1tjxozxbHM4HBo5cqSWLFnS0mXCh2zevFkpKSlKS0vTH/7wB23dulUS1xSO3wcffKDBgwfrkksuUUJCggYMGKAXX3zRs59rC82hqqpKr732mq6//noZhsF1heN20kknaeHChdq0aZMkac2aNVq8eLHGjh0riZ9ZOD41NTVyuVwKDg6us93pdGrx4sVcV2iyhlxDK1asUHV1dZ02KSkpysjIaPR11qaC91tvvaWVK1dq6tSph+3Lzs6WJCUmJtbZnpiY6NkH/NbQoUP1yiuvaP78+XrxxReVnZ2t4cOHKzc3l2sKx23r1q2aNWuW0tPTNX/+fN1yyy26/fbb9corr0ji5xWax/vvv6+CggJde+21kriucPzuvfdeXX755erRo4cCAwM1YMAATZw4UZdffrkkri0cn/DwcA0bNkx/+9vftHfvXrlcLr322mv64YcflJWVxXWFJmvINZSdna2goCBFR0fX26ahAppQq0/ZtWuXJkyYoM8+++ywb84OZRhGneemaR62DTjo7LPP9vy5T58+GjZsmLp06aI5c+boxBNPlMQ1hcZzu90aPHiwpkyZIkkaMGCA1q1bp1mzZunqq6/2tOPaQlP8+9//1tlnn62UlJQ627mu0Fhvv/22XnvtNb3xxhvq3bu3Vq9erYkTJyolJUXXXHONpx3XFhrr1Vdf1fXXX6927drJ399fAwcO1BVXXKGVK1d62nBdoamO5xo6nuuszfR4r1ixQjk5ORo0aJACAgIUEBCgRYsW6Z///KcCAgI833T89puLnJycw74FAeoTGhqqPn36aPPmzZ7Zzbmm0FjJycnq1atXnW09e/bUzp07JYlrC022Y8cOff7557rxxhs927iucLzuvvtu/fnPf9Yf/vAH9enTR3/84x91xx13eEYYcm3heHXp0kWLFi1SSUmJdu3apaVLl6q6ulppaWlcV2iyhlxDSUlJqqqqUn5+fr1tGqrNBO/TTz9da9eu1erVqz2PwYMH68orr9Tq1avVuXNnJSUleWZ4lWrvf1u0aJGGDx9uY+XwJZWVldqwYYOSk5M9vxS4ptBYI0aM0MaNG+ts27Rpkzp27ChJXFtostmzZyshIUHnnHOOZxvXFY5XWVmZ/PzqfqT09/f3LCfGtYWmCg0NVXJysvLz8zV//nxdcMEFXFdosoZcQ4MGDVJgYGCdNllZWcrMzGz8dXZ8c8K1DofOam6apvn3v//djIyMNN977z1z7dq15uWXX24mJyebRUVF9hUJr3bnnXeaX331lbl161bz+++/N88991wzPDzc3L59u2maXFM4PkuXLjUDAgLMxx57zNy8ebP5+uuvmyEhIeZrr73macO1hePlcrnMDh06mPfee+9h+7iucDyuueYas127duZHH31kbtu2zXzvvffMuLg485577vG04drC8fj000/NTz75xNy6dav52Wefmf369TOHDBliVlVVmabJdYVjKy4uNletWmWuWrXKlGROnz7dXLVqlbljxw7TNBt2Dd1yyy1m+/btzc8//9xcuXKledppp5n9+vUza2pqGlULwfuQ4O12u82HHnrITEpKMh0Oh3nKKaeYa9euta9AeL3LLrvMTE5ONgMDA82UlBTzoosuMtetW+fZzzWF4/Xhhx+aGRkZpsPhMHv06GG+8MILdfZzbeF4zZ8/35Rkbty48bB9XFc4HkVFReaECRPMDh06mMHBwWbnzp3NBx54wKysrPS04drC8Xj77bfNzp07m0FBQWZSUpI5fvx4s6CgwLOf6wrH8uWXX5qSDntcc801pmk27BoqLy83b731VjMmJsZ0Op3mueeea+7cubPRtRimaZpN66QHAAAAAAD1aTP3eAMAAAAAYAeCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwDgZSZPnqz+/fvbXQYAAGgmBG8AAFqQYRhHfVx77bW66667tHDhQrtLrWP79u0yDEOrV6+2uxQAAHxOgN0FAADQlmRlZXn+/Pbbb+uvf/2rNm7c6NnmdDoVFhamsLAwO8oDAAAWoMcbAIAWlJSU5HlERkbKMIzDtv12qPm1116rCy+8UFOmTFFiYqKioqL08MMPq6amRnfffbdiYmLUvn17vfTSS3Vea8+ePbrssssUHR2t2NhYXXDBBdq+fXu9teXn5+vKK69UfHy8nE6n0tPTNXv2bElSWlqaJGnAgAEyDEOjRo3yHDd79mz17NlTwcHB6tGjh/71r//f3t2ERLXGcRz/OmNiRRaNL1FTFBQ1TS+YUiRkRCS5ydBN0KLQhKJoERgYVFAt7F2kbpgGLgqmTRAUuZCQCemFTGhhi+zNdrVwpwmmdxHOvZPevPfCcHHu9wMD55znPM/vzPLPf84zvyXGxjvlsViMkpISsrOziUajdHZ2/q1cSZLSgR1vSZKmgcePHxMOh4nH43R1dVFTU8PTp08pLS3l+fPn3L17l4MHD7Jjxw4WL17M4OAg27ZtY8uWLcTjcTIzMzl37hw7d+7k9evXZGVlTcg4efIkvb29PHr0iNzcXPr6+hgaGgLgxYsXbNy4kY6ODqLRaGJ+S0sLp0+f5tq1axQWFtLT00NtbS2zZ89m3759ibXr6upobGxk9erVXLlyhV27dvHhwwdCodAvcyVJSgcW3pIkTQPz58+nqamJQCDAypUruXDhAoODg5w4cQKA+vp6Ghoa6OrqYs+ePcRiMQKBAK2trWRkZAA/OtPz5s2js7OTsrKyCRn9/f0UFhZSXFwMwNKlSxNjeXl5AIRCIRYsWJC4fvbsWS5fvkxlZSXwozPe29tLc3NzUuF95MgRqqqqALhx4wbt7e3cunWL48eP/zJXkqR0YOEtSdI0EI1GCQT+eEOsoKCANWvWJM6DwSChUIgvX74A0N3dTV9fH3PmzEla59u3b7x7927SjEOHDlFVVcWrV68oKytj9+7dlJSU/OUzff36lc+fP1NTU0NtbW3i+sjICHPnzk26d/PmzYnjzMxMiouLefPmzb/KlSRpurHwliRpGpgxY0bSeUZGxqTXRkdHARgdHaWoqIg7d+5MWGu8e/2z8vJyPn36xMOHD+no6GD79u0cPnyYS5cuTXr/eFZLSwubNm1KGgsGg1N+p/FO/D/NlSRpunFzNUmS0tCGDRt4+/Yt+fn5LF++POnzczf6z/Ly8ti/fz+3b9+msbGRmzdvAiTe6f7+/Xvi3oKCAhYtWsT79+8nZIxvxjbu2bNnieORkRG6u7tZtWrVlLmSJKUDO96SJKWhvXv3cvHiRSoqKjhz5gzhcJj+/n7u3btHXV0d4XB4wpxTp05RVFRENBpleHiYBw8eEIlEAMjPz2fmzJm0t7cTDofJzs5O7MB+9OhRcnJyKC8vZ3h4mJcvXzIwMMCxY8cSa1+/fp0VK1YQiUS4evUqAwMDVFdXT5krSVI6sOMtSVIamjVrFvF4nCVLllBZWUkkEqG6upqhoSFycnImnZOVlUV9fT3r1q2jtLSUYDBILBYDfryX3dTURHNzMwsXLqSiogKAAwcO0NraSltbG2vXrmXr1q20tbVN6Hg3NDRw/vx51q9fz5MnT7h//z65ublT5kqSlA4yxsbGxv7rh5AkSenp48ePLFu2jJ6enqT/Jpck6f/EjrckSZIkSSlk4S1JkiRJUgr5U3NJkiRJklLIjrckSZIkSSlk4S1JkiRJUgpZeEuSJEmSlEIW3pIkSZIkpZCFtyRJkiRJKWThLUmSJElSCll4S5IkSZKUQhbekiRJkiSlkIW3JEmSJEkp9DtBUmviw45P+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "ax.bar(df.index, df['y'], color='lightgray')\n",
    "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls='--', label='Croston (optimized)')\n",
    "ax.set_ylabel('Value')\n",
    "ax.set_xlabel('Time steps')\n",
    "ax.legend(loc='best')\n",
    "plt.xlim(40, 100)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c116ec6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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