Prediksi_Beras.ipynb

{
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      "cell_type": "code",
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      "metadata": {
        "id": "ETSkimQV0ura"
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "# tensorflow\n",
        "import tensorflow as tf\n",
        "\n",
        "# visualisasi\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio\n",
        "\n",
        "import warnings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pj8IeCETkkr-"
      },
      "source": [
        "# Dataset"
      ]
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              "            Beras  Beras Kualitas Bawah I (kg)  Beras Kualitas Bawah II (kg)  \\\n",
              "Date                                                                           \n",
              "2021-01-04  12350                        10850                         10700   \n",
              "2021-01-05  12350                        10850                         10700   \n",
              "2021-01-06  12350                        10850                         10700   \n",
              "2021-01-07  12350                        10850                         10700   \n",
              "2021-01-08  12350                        10850                         10700   \n",
              "\n",
              "            Beras Kualitas Medium I (kg)  Beras Kualitas Medium II (kg)  \\\n",
              "Date                                                                      \n",
              "2021-01-04                         12650                          12400   \n",
              "2021-01-05                         12650                          12400   \n",
              "2021-01-06                         12650                          12400   \n",
              "2021-01-07                         12650                          12400   \n",
              "2021-01-08                         12650                          12400   \n",
              "\n",
              "            Beras Kualitas Super I (kg)  Beras Kualitas Super II (kg)  \n",
              "Date                                                                   \n",
              "2021-01-04                        13950                         13400  \n",
              "2021-01-05                        13950                         13400  \n",
              "2021-01-06                        13950                         13400  \n",
              "2021-01-07                        13950                         13400  \n",
              "2021-01-08                        13950                         13400  "
            ],
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              "      <th>Beras Kualitas Bawah I (kg)</th>\n",
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              "      <th>Beras Kualitas Super II (kg)</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2021-01-04</th>\n",
              "      <td>12350</td>\n",
              "      <td>10850</td>\n",
              "      <td>10700</td>\n",
              "      <td>12650</td>\n",
              "      <td>12400</td>\n",
              "      <td>13950</td>\n",
              "      <td>13400</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-05</th>\n",
              "      <td>12350</td>\n",
              "      <td>10850</td>\n",
              "      <td>10700</td>\n",
              "      <td>12650</td>\n",
              "      <td>12400</td>\n",
              "      <td>13950</td>\n",
              "      <td>13400</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-06</th>\n",
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              "      <th>2021-01-07</th>\n",
              "      <td>12350</td>\n",
              "      <td>10850</td>\n",
              "      <td>10700</td>\n",
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              "      <td>12400</td>\n",
              "      <td>13950</td>\n",
              "      <td>13400</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-08</th>\n",
              "      <td>12350</td>\n",
              "      <td>10850</td>\n",
              "      <td>10700</td>\n",
              "      <td>12650</td>\n",
              "      <td>12400</td>\n",
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      ],
      "source": [
        "dateparse = lambda x: datetime.strptime(x, '%d/%m/%Y')\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/gemparpanggih/Beras_Kaltim/main/beras_kaltim.csv', parse_dates=[\"Komoditas(Rp)\"],\n",
        "                 date_parser=dateparse,\n",
        "                 index_col=[\"Komoditas(Rp)\"])\n",
        "#Ubah nama index\n",
        "df.index.names = ['Date']\n",
        "\n",
        "df.head()"
      ]
    },
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            "text/plain": [
              "              Beras  Beras Kualitas Bawah I (kg)  \\\n",
              "count    429.000000                   429.000000   \n",
              "mean   12309.906760                 10957.575758   \n",
              "std      146.735475                   269.812495   \n",
              "min    12050.000000                 10500.000000   \n",
              "25%    12150.000000                 10750.000000   \n",
              "50%    12350.000000                 10850.000000   \n",
              "75%    12350.000000                 11100.000000   \n",
              "max    14050.000000                 11600.000000   \n",
              "\n",
              "       Beras Kualitas Bawah II (kg)  Beras Kualitas Medium I (kg)  \\\n",
              "count                    429.000000                    429.000000   \n",
              "mean                   10881.002331                  12515.734266   \n",
              "std                      206.679743                    198.218077   \n",
              "min                    10650.000000                  11400.000000   \n",
              "25%                    10750.000000                  12350.000000   \n",
              "50%                    10750.000000                  12550.000000   \n",
              "75%                    11100.000000                  12650.000000   \n",
              "max                    11400.000000                  12850.000000   \n",
              "\n",
              "       Beras Kualitas Medium II (kg)  Beras Kualitas Super I (kg)  \\\n",
              "count                     429.000000                   429.000000   \n",
              "mean                    12431.934732                 13812.470862   \n",
              "std                       157.762496                   141.014191   \n",
              "min                     12150.000000                 13500.000000   \n",
              "25%                     12250.000000                 13650.000000   \n",
              "50%                     12450.000000                 13850.000000   \n",
              "75%                     12500.000000                 13950.000000   \n",
              "max                     12950.000000                 14050.000000   \n",
              "\n",
              "       Beras Kualitas Super II (kg)  \n",
              "count                    429.000000  \n",
              "mean                   13203.496503  \n",
              "std                      551.706688  \n",
              "min                    12650.000000  \n",
              "25%                    13000.000000  \n",
              "50%                    13100.000000  \n",
              "75%                    13500.000000  \n",
              "max                    23650.000000  "
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              "      <th></th>\n",
              "      <th>Beras</th>\n",
              "      <th>Beras Kualitas Bawah I (kg)</th>\n",
              "      <th>Beras Kualitas Bawah II (kg)</th>\n",
              "      <th>Beras Kualitas Medium I (kg)</th>\n",
              "      <th>Beras Kualitas Medium II (kg)</th>\n",
              "      <th>Beras Kualitas Super I (kg)</th>\n",
              "      <th>Beras Kualitas Super II (kg)</th>\n",
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              "      <th>count</th>\n",
              "      <td>429.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>429.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12309.906760</td>\n",
              "      <td>10957.575758</td>\n",
              "      <td>10881.002331</td>\n",
              "      <td>12515.734266</td>\n",
              "      <td>12431.934732</td>\n",
              "      <td>13812.470862</td>\n",
              "      <td>13203.496503</td>\n",
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              "      <th>std</th>\n",
              "      <td>146.735475</td>\n",
              "      <td>269.812495</td>\n",
              "      <td>206.679743</td>\n",
              "      <td>198.218077</td>\n",
              "      <td>157.762496</td>\n",
              "      <td>141.014191</td>\n",
              "      <td>551.706688</td>\n",
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              "      <th>min</th>\n",
              "      <td>12050.000000</td>\n",
              "      <td>10500.000000</td>\n",
              "      <td>10650.000000</td>\n",
              "      <td>11400.000000</td>\n",
              "      <td>12150.000000</td>\n",
              "      <td>13500.000000</td>\n",
              "      <td>12650.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12150.000000</td>\n",
              "      <td>10750.000000</td>\n",
              "      <td>10750.000000</td>\n",
              "      <td>12350.000000</td>\n",
              "      <td>12250.000000</td>\n",
              "      <td>13650.000000</td>\n",
              "      <td>13000.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12350.000000</td>\n",
              "      <td>10850.000000</td>\n",
              "      <td>10750.000000</td>\n",
              "      <td>12550.000000</td>\n",
              "      <td>12450.000000</td>\n",
              "      <td>13850.000000</td>\n",
              "      <td>13100.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12350.000000</td>\n",
              "      <td>11100.000000</td>\n",
              "      <td>11100.000000</td>\n",
              "      <td>12650.000000</td>\n",
              "      <td>12500.000000</td>\n",
              "      <td>13950.000000</td>\n",
              "      <td>13500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14050.000000</td>\n",
              "      <td>11600.000000</td>\n",
              "      <td>11400.000000</td>\n",
              "      <td>12850.000000</td>\n",
              "      <td>12950.000000</td>\n",
              "      <td>14050.000000</td>\n",
              "      <td>23650.000000</td>\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0dp0tpmhFWV"
      },
      "outputs": [],
      "source": [
        "df_resample_month = df.resample('M').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "AZLJak1jiYFX",
        "outputId": "90f00f9b-58e9-48ee-e5b5-cb165fca9814"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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GQRAwY8aMaIvFwr766quSXxpkPd/hw4fzAeDUqVPyDRs2+F+NQCsArF279vRdd91lrKmpEcfHx/ebP39+g0Kh4Fej76vpvffeC3zooYd0EskvD2sqFAo+YsSI5o8++sj/N7/5zVW5bq6ua0SIMaYE8CKApdf5vHMZYzmMsZy6urrreeqbEhcENNfX4kzuIRza/i/s+ng1vlr2Ij74za/x9ZvLnPuJxGIUHvgJ9eWl0FWehaW9HQDARCKExiUiND4RKSMyIZHJIJZKoQoK7nQeVVAw/MLCsWDd15j/6Qbc/9Sic9sCgzFl6WuY8MIf8cAz/9fpuLQHxmPwuElIHfMgvPwDnO1+oeEIS9QgWB0Lv7AIeAcEQeGtcma4OrgGcQFAKnPZZnIJ8MrPBX4NjQ1orChHzekiVOTnITKlvzPICtiDxl+/uQzGZj3+9fbr+O79d3F4x79g1Ddd+oXvAVzgsNS0ovavR1D9xv9Q+9cjsNS0ggs33L+jhBBCCCGEEELIFdHr9WIfHx+r4/mSJUtCtFptckJCgubZZ58NA4CCggKZWq3WTpgwQZ2QkJBSXFwsmz59epRWq02Oi4tLcewHAPPmzQuPjY1NSUhI0MydOzeiq/POmjUrsrGxUbJ58+YSsViM5557Lmzp0qUhju3x8fEpBQUFMgDIzMyMTUlJSY6Li0tZvnx5oLv+lEplKgAsXrw4PCcnxyspKUnz8ssvBxcUFMjS0tISNRpNskajSd65c6cnAJSWlkrT09MTk5KSNPHx8Sk7duzw6u46NTc3iz08PASJRMIBwN389+7dq7z33ntjAeDzzz/3VSgUA9vb25nRaGQRERH9AGDFihWBWq02OTExUXPffffFtrS0OOOQe/fu9UpNTU2KiIjo9+mnn/p1N57zffnllwFTpky5IOCyd+9eZXJysiYvL09eWVkpGTZsWHxcXFzK1KlTo8PCwvpVVVVJAGDSpElN69ev9/8l57xU1zujNRZAXwCObNYIAIcYY4MBVACIdNk3oqOtAvbyAa7tezraI9zsfwHO+YcAPgSA9PR0iiB1sJhN0FVWoLHyLKL7pzqzS79+cxlOH/qf22PELmnV3oFB8A+PhE9wCPzDwmG1WiCFPWg5ZelrnY7jgoDxi5Z0qtE6ftES5+324vO+hZAqFIhM6e92DIPHTXLbPnzKdAyfMt3tNntFinN9P/rWalhNJljMJsiUHs5tQyZMhkHXCEt7OwIj1c72oOi+SBgyHBazCVaTCZ6+fm4zdK0WM04d+C84ty/0FaUdAKWPLwDg0PZ/wWo2IbhvLELjEiBXerod6/UktFrQsPYEbDp7gNmmM6Fh7QkEz7sNYm9ZD4+OEEIIIYQQQkiv8uHdiW7b5+62Z5h+/dtI1J5QXrB9zOtliBzShgPvByB3Q+AFx3XDZDKJkpKSNCaTidXX10u3bdtWCACbN29WFRUVKY4ePXqSc47MzMy47du3e8XExJjLysrkH3/8cUlGRsYZAHjrrbcqQkJCbFarFcOGDUs8cOCAR3R0tHnbtm1+p0+fPi4SiVBfX+827fWbb77xj42Nbb/UW9XXrVt3JiQkxGYwGFhqaqpmxowZuj59+tjc7fvqq69WuN4239LSIvrhhx8KlUolP3bsmPyRRx6JOX78+MlPPvnEPyMjQ//GG29UW61WuAY8Xc2cOTNGJpMJZWVlimXLlpU5MkbdzX/YsGHGEyfsr1V2drZXXFxcW3Z2ttJisbDU1FQDAEyfPl23cOHCegB4+umnw1auXBm4ePHiWgCoqamR5uTk5B85ckQxYcKEuIuVEXBob29n5eXl8sTERLNr+86dOz0XLFgQtWXLlqL4+HjzzJkzo0aMGNHy5z//uXrjxo2qL7/80vlzM2jQoLajR49ek6DMdQ20cs6PAXCmNTLGzgBI55zXM8a2AJjPGFsP+2JYes55FWPsOwCvMcYc0e17Afyec97IGGtmjN0O+2JYMwG8dz3nczPgnKO1SYfGirNoqq5Ev4z7nHVHP3nGXusUALIWL4O6fyoAQBUU0qkPqcID/mER8A8LR0BElLMeq0gkxqy3Vl/SOJhIhMDIaEx7ZUWP1DR1LVMhEokREB7pdr+E2+9w254yIgMpIzKcz1ubdFAFBXcKtqqCgsHAEJGcgtozp2GzWuEXdq6axZHvvoWuyv5dwNgFzyNx6J0AgLMnjqOttQUh6lh4BwZ1Wxf2auNWwRlkdbDpTOBW4bqNgRBCCCGEEEIIuVZcSwfs2rXLc9asWX0LCwvzduzYocrOzlZpNBoNABiNRlF+fr4iJibGHBoaas7IyHDWcf3ss8/816xZE2i1WlldXZ00NzdXMXDgwDa5XC5MnTpVPXbs2KapU6fq3Z0/JSXFWFxcrOjIAL1obdg33ngjZOvWrb4AUF1dLc3Ly1P06dPnkmrKms1m9thjj0WfOHHCQyQSobS0VA4At99+e+sTTzyhtlgsokmTJumGDRvW5u54R+mAyspKydChQ5PGjRvXnJCQYHY3/yFDhrRFRUW1Hzp0SHHo0CHPp556qmb37t3eNpuNDR8+3AAABw8e9Fi6dGl4S0uLuLW1VTxixAjnNXrooYeaxGIx0tLS2hsaGi65WGp1dbXE29vb6tpWVFSkmDdvnnrnzp2FarXaAgA///yz19dff10EAJMmTWpWqVTOYLVEIoFUKuU6nU7k5+d3VQMg1zTQyhj7AvZs1EDG2FkAf+Scf9zF7tsA3A+gCIARwCwA6AioLgPgSLH8k2NhLADzAKwB4AH7Ili3xEJY7hZhstls0NdUobHiLExtRmhHZgIATK2t+ODJmc5jY9IGw8vPnh3tFxruDLTqKs86A62Jt98B//AIe3A1PAJefgFXJfjHRCJ4+v6ibPAbllLl4zZD1zsgEFP++Gd7gFvX6Kwha7NaYHEpWRCsjnE+PrR9C079/F8AgMLLG8HqvghSxyJEHYOQ2Hj4h3V598EV4TYOq64dYj95p2Cr2E8Ood3azZE3tt6ySFlvmYdDb5sPIYQQQggh5DJcLAN1/F/Ku90+5MkGDHmy4XJPn5mZ2arT6SRVVVUSzjkWLFhQtWjRok4LPhUUFMiUSqUz+Jafny9btWpVyMGDB08GBQXZsrKy1O3t7SKpVIojR46c3LJli2rjxo1+q1evDt6/f3/h+eeMi4trf/nllytmzJgRu3Xr1sL09PR2iUTCBeFcfM9kMjEA+Pbbb7337t3rnZOTk+/t7S0MHjw4sa2t7ZJ/cXr11VdDgoODLZs2bSoRBAEeHh5pADBmzBhDdnZ2waZNm3xmz57dd/78+TXz58/v8jqGhYVZtVqtMTs721MQBLibPwAMHz7csGXLFh+pVMoffPDB5mnTpqltNhtbsWLFWQCYO3du340bNxYNHTq0beXKlQF79+51LtTjWvvV9S7ki/H09BTMZnOnaxIcHGwxmUyi/fv3K9VqtduA9/ksFgtTKpVX/a73axpo5Zw/cpHtapfHHMBvu9jvEwCfuGnPAaC9slHeXLggoL68tFOAb/RvFuCHL9ai6lQ+AEDh6YWUERlgjEHh5QWlj6+zVmhjRbkz0Jr2wHj0y7gP/qHhnYJ5ERotIjS31GX9xS6WocsY61Q/ViyR4om/roFR34TakmL4hoQ6t9WWnnY+bje0oOz4UeeiXmEJyXhkmX3hLnObEfn/zUawOhaBkdGQyC7/1n7OOZq+KYKlqhV+WQnQbSqETWeC2E8Ov6wEWOqMkIV2W7LlhsIFATarFWKJ5IL3x9hnX0CfvnFgIhEEwYYTe//TZT/BfWOdQfDGyrOoLDgJAPDy84f6tjQAgMnYilMH7IFxJhJ1ynTO/2kvrGYz3PELDUd4kgYA0NJYj9Jc+8KJMqUSCUOGA4BzfApvFTx9ffHtO2845/Hgsy/AoNOhvaX5gr4vZ3wxAwc5y1qUnzgGfU2123G7G59D4rA7Ie2of1x88Ge0Ndv/P4tI1sK3j/1nvOZ0EVoaGy6Yz826eBwhhBBCCCHk5nX48GGFIAgICQmxjhkzpvmll14Kmzt3bqOPj49QUlIilclkFwTedDqd2MPDQ/D397eVl5dL9uzZ4zNixIgWvV4vMhgMoqlTp+ozMzMNsbGx/bo676hRo1rffvvt0nHjxsXv2bOnQK1Wm7Zt2+YLAD/++KOyoqJCDgBNTU1iHx8fm7e3t3D48GFFbm5ut7e3+/j42AwGg7NkgV6vF0dERJjFYjFWrVoVYLPZkzgLCwtlMTEx5oULF9abTCZ26NAhJYAuA60tLS2ivLw85QsvvFDd1fwBYMSIEYY5c+aoJ0+e3BAWFmbV6XSS+vp6aXp6ehtgzxKOioqymEwmtn79ev/Q0FBLd/O5FEFBQTabzcaMRqMzUKpSqWxr164tzsjISPDy8hLGjh3bMmjQIMPf//53/1dffbV68+bNqubmZud1qq6uFvv6+lrlcvnNFWglV5+xWe8MIgH2uqA7Vr+DkTPnYMuKVwEA7a0GtLU0Q6nyAQDcM+sJyD2U8AuLgCowyNlXbNrg6z+BXuRyMnSVPr7OgJjDzDdWora0BLUlxagtOY3aM8VoOFsGwWZDcN9zma+1Jaex88NVAACFtwrz/rbOmWlcUXASgZFRl1z3tXlXGVp/tgfWWnOqEfTkAEDg9izXhjZ49rdX+OA2Dia+fqUMHDjnMBlbYdTrYWxuQluz3vk4YchwBEREAQD2b1qPw999i7bmZqSOeRCDx0264P3x7duvY9orK+Dp6wcuCPju/Xe7PO8dD890BlrPnjiOnX+zX+/o/qnO1621qcnZh0gs6RTI3PP3j9Gqc79oYb+M+5yB1obyMmcffqFhzkCmY3wPLVzsDEo65vGvt1/HyJlz3I7/csb38J/edAZaj+7agfyf9rodt7vxOagHDHQGWg98/SWqCu1f9tz/1O+cgdbCAz+hT2zCBfP5+s1lGPPbhQiK7gu58sISTIQQQgghhBByNThqtAL23zVXr159RiKRYOLEic15eXmKQYMGJQGAUqkU1q1bV+JYAMph6NChbVqt1hgbG6sNDQ01p6WlGQB7UHTs2LFxjmzUZcuWdZuNO23aNH1dXV3l6NGj43ft2lW4bt26gLi4uJTU1NTW6OjodgDIysrSf/jhh0ExMTEpMTEx7QMGDOi2ZMDgwYPbxGIxT0xM1EybNq1+wYIFtVlZWbHr168PuOeee/QeHh4CAHz33XfeK1eu7CORSLhSqbStW7euxF1/M2fOjFEoFILZbGYPP/xw/Z133mkEAHfzB4CRI0caGhoapCNHjjQAgEajaaupqbGKOhJqXnjhhcrBgwcn+/v7WwcOHGhwDQpfibvuukv//fffe40fP77F0RYZGWndunVr0ZgxY+KVSuWZ119/vXLSpEkx8fHxAWlpaYbAwECLr6+vDQC2b9+uyszMvKTM11+KAq03GZvF4nYRJp/gEIye96zzln/XgJujFii5Mck8lIhISkFEUoqzzWqxoKG8FFKFwtnmmvkaGBnlDLK2tTRj/dJFAADfkFBMe+0t58JmJqPxgiCWYX8VWv5dBsBeJsD3/hiIVeeyY6WB9sXBLNWtaPj7CfhNSYQ8WnVFc+Scw2JqBxe4czz15aUozjmAthY9PH39MeihLACArqoCaxb+FoLNffkCVWCwM9AqCIIzW9uob+ry/WGzXPGXZteVwsvL7TwUXjdPlrGrruYDADKXn/F/vf06VEHBCItPQvSAVMgUHiCEEEIIIYSQK2Gz2Q52tW3JkiW1S5YsqT2//dSpU3muzzdt2nTG3fHHjh072d25n3766Qa4ZI4+88wzDc8880wDAPz000+n3B2TnZ3ttr2iouKY47HRaDwMAHK5nJ9frqCwsPCE4/Hq1asrAOCpp55qeOqpp7otufDzzz93Wdahq/l7eXlxs9l8yPH8iy++KHXd/vzzz9c9//zzdRfrzzGf83XV/vTTT9ctX748ZPz48S1jx45tGTt2bAsAxMfHm4uKivIAoK2tjWVnZxdKpVLs2rXL88iRI54eHh68Y5z+y5cvP9vVfK8EBVpvMmKp1O0iTJ6+fp3qfpKbm0QqRUhMXKe22+57ANH9U1F75nSn4FTtGZfSA60GKDzPBePWPPckOLshoIIAACAASURBVOcIVscguG8sQmV9Ie+odizylCBwthYiLwlam3SdSiBws4C6vx2F0GpF/ad5CHqiP2ShnbNlLWZTp0zTtuZmGPVNMDbroR05CgER9gXH9vz9Y+R+txVWixlDJkzBHQ/bawbXl53Bj+vXAgBCYuKdgVaFl3eXQVYAaDc4vzxDaHwi+mXcB6XKFyExsV2+P8QdKzuKxBLMWXVBFRInuee5OSYNvwvqAQMBwHk8APgEh3TZx/RX3wIX3NfRdg2ahyenOPtg4nO3zruOz908fILcn/tyxufIZgWAux+dizsf+bXbY7oa3/l9jFu42BnQ9vA+F5gf9GAWzG1Gt/MBg7N0QGuTDoX7f3Run/OXT52B1uKDB6D08UWwOgZiySXXSCeEEEIIIYQQ0gvdcccdxpycnGar1QqJxH1os6ioSDZlypRYQRAglUr5Bx98cAYA2tvb2UMPPdTUv39/k9sDrxAFWm8yXS3C5CgTQHovkUiMgPBIBIRHdmoP6RuHcYuWoLakGJwLzkzX1iYdDB23iZccOQhDfg0iQyIBkQRWwYKWJANCAxQX1DQd97s/IChKDZ/RfaHbdAq83YqKVQfQ2E+PtIcnALDX3fz89wu6HGuf2ARnoFUkEsFqsdcFdWSfAoBHx8+sVK6ARHYueKbw9MKgcZOg9FZB6eMLpcoHHo6/VT6QuAQV+96Whr4upRi4IHT7/mCM2QN8l0DmoYTM48Jb2sUSSZd9eAcEXlLfUpkcUjd9OMbX1Ty8AwIvWtP0csZ3qf9+dHf9uiqjofDyglypdDufwMho535GfROC+8airrQEnr5+zjInnHN8/8F7MOqbIJHKMOkPrzhLMFjMJkhl8ksaOyGEEEIIIYSQ3mPBggXdZuj269fPdPLkyRPntysUCt7dQmBXiv2Slb16g/T0dJ6Tk9PTw7gitHo3uRRtLc04vmdXR+3XYgzE3QiQh0HgNvxQswmpj01EdL/b8I8/LLwg09BR07Tw0/9AWWAPbLYzI/q+MBJiHzma6+vwt9/OcntesVSKzMd/C+3ITABAyeEclB7PhVLlg5CYOET3uw0AYLNaIdiszhqfV0tveX/0lnk4XOp8LO3taK6vdZaH0NdW46OnHndun/fRP5wZs1+98gfoqioQFp+EsIQkhCYkUdYrIYQQQgghPYQxdpBznu7alpube2bAgAH1PTUmQq6V3NzcwAEDBqjPb6eM1pvQ5SzCRG49Ht4qDHpwovO5qaEFdWuOozWwDUEx8QiNT7xoTVNxf0+c+HkvND5DoeBK1H18HEFP9IfSxxd3PDwTHioVlCpfKH3s2aZKlS9kHh7OrFoA6Juajr6pnf6vtfctkUDcRYr/legt74/eMg+HS52PVKFwBlkBQBUUgsdWfoSqwpNorKp0BlkFwYbqogKY29pQUF+Hgn0/AAAkUhmCY+KQMiID/TPuuzaTIYQQQgghhBBC3KBAKyG9mK3VAuOhWnjdEQZ5gDfCn70dTMSQjHsB2MsLdFfTtG9qOvr+ZRD0W06jdX8VrLVG1K/JQ9DjWgyZMKVH5kRuLYwx+Ib0gW9In07tNqsVQyZMRWVhPqpO5TvLUlgtZlQWnEBUSj/nvuV5R5G7czvCEpIQnqxFSN/Y6zoHQgghhBBCCCG3Bgq0EtJLCWYbGj7Lg7msBZbqVvhNjAcTs077XKzmr0gkBgD4PhQLod2KtiN1sJS3oGHtCQQ+qgWT3ry3spObm1Qmx+BxkwDY67g219WgsjDfGXgNT9Q49y09louCfT+gYN8PiE0fgvGLlgAAjM16nD15HGHxSfDyD+iReRBCCCGEEEII6T0oSkJIL8RtHI3/yIe5rMX+3CoA7ML9mEiEwMhoTHtlBeas+gTTXlmBwMjoC2pnMhGD/+QEKJL8AQCWGiOs+muyQB8hvxhjDD7BfZB8x0hkzH4SM/78DtQui6QxkQhKH18AQGh8krO9PO8Y/vXWn/HBb36N9X983tnOOYfNarl+EyCEEEIIIbccLghobdKhua4WrU06cEHo6SH1WmKxOC0pKUmTmJio0Wg0yTt37vS8XudeuXJlwMyZM6MAwGazYeLEierJkyerhct4vcPDw/tVVVVJACA1NTUJAAoKCmTvv/++/9UY6+DBgxPVarU2KSlJExMTk7J8+fJLW+35MmRlZak//fTTi9aWUyqVqYB9nvHx8Snu9iktLZXefffdcUDn630pKisrJXfeeWf8pe5/KSijlZBehnMO3T9PoT2/EQAgj/OF/+QEMJGbSCsuvXYmE4sQMD0Jus1FUGVEQRLocVXHTci1MnzKdAybPA3NdTWQyOTO9qpTJ52PvQPOfYbQVVVg7f89hZC+cQhNsC+0dX7Wa29brKy3oNeFEEIIITcDLgioPXMaW956rdOdhe6SXsiVk8vlQn5+/gkA2LRpk+rFF1+MGDVqVMGlHCsIAjjnEIvFVzQGQRAwY8aMaIvFwr766qsS0RW+zocPH84HgFOnTsk3bNjg/+STTzZeUYcd1q5de/quu+4y1tTUiOPj4/vNnz+/QaFQ8KvR97Xy2muvhTz22GOXteBaWFiYNSQkxPL999973nvvva1XYzz0Diakl2n+vhTGnBoAgDTcCwG/SgaTXJ23OpOK4T810RlkNeyvQtO3p8H5Df3vLiHOrFfXLxWGT/0Vpv7xddw57VEk3znS2V5ZmA+bxYLKwpM4+O0/nVmvH/52Fv71zhtorq9DfXkp/vGHhfjb/Nn4xx8Wor689KbMQuhNmRRcEHrN60IIIYSQ3qe91YDm+joA9hJWjiArYF+Q+Os3l8HYrO/JId4S9Hq92MfHx+p4vmTJkhCtVpuckJCgefbZZ8MAe/akWq3WTpgwQZ2QkJBSXFwsmz59epRWq02Oi4tLcewHAPPmzQuPjY1NSUhI0MydOzeiq/POmjUrsrGxUbJ58+YSsViM5557Lmzp0qUhju3x8fEpBQUFMgDIzMyMTUlJSY6Li+syq9SR6bl48eLwnJwcr6SkJM3LL78cXFBQIEtLS0vUaDTJrtm7paWl0vT09MSkpCRNfHx8yo4dO7y6u07Nzc1iDw8PQSKRcABwN/+9e/cq77333lgA+Pzzz30VCsXA9vZ2ZjQaWURERD8AWLFiRaBWq01OTEzU3HfffbEtLS3O4MTevXu9UlNTkyIiIvpdSnZrV7Zu3eqXlZV1wZtn/fr1PrfddltSVVWVJC8vTz5gwICkhIQEzdNPPx3muH4AMH78+Ka1a9detVpylNFKSC9i+KkCLbvLAQDiAAUCH02BSH5t3uaGA1Vo+roIACBSiKHKjL4m5yHkWpHKFYjQaBGh0XZqD4iIROroB1FZmI+60tMQbDYAQEt9HVrq6zBixmxnXWPg3AfjyX94Fb59QgEAe9b+DSVHDoExhoH3j0P/jPsAAIX7f8SBf37VkWHOwBjAmMhe2oMxTPi/pfDwVgEAdn30VzRWlAOM4faJDyNK2x8AcGz39yjc9yMYY+ibmo7U0Q8CAGpOF2HfpvVgjDn/wOXv4VOmwy80HABw5LutaGtpRpR2ALatWu7MpHjw2d/j9MGf0VRbjaAoNQY9lGWfe0M99qz9COAcHBzgAOcC7N+xcKQ/OBERSfY7eY7953sU7v8RnHPEDByMgWPs46sszMcPX6wBOKAKDMKY+QsBABazCV+9/GJHv9zZJxfs5xo45iFoR2YCAPJ/2ov9mzeAc46IpBSMmjsfgD0LWVddhX9//NcLXpcx8xfi4LdfQyqXQypXQKqQQyJTQCqXIyQmDuoBAwEAbS3NqC05DalCDrnSCwERkQDsdwlwQYDoCrMYfgnKzCWEEEJ6h+a6WhTlHEBxzn6cPXkcScPuwpj5C2GzWDotSOzY12bp/eWrHvn2kcSxsWPrpydPb7hajy92TpPJJEpKStKYTCZWX18v3bZtWyEAbN68WVVUVKQ4evToSc45MjMz47Zv3+4VExNjLisrk3/88cclGRkZZwDgrbfeqggJCbFZrVYMGzYs8cCBAx7R0dHmbdu2+Z0+ffq4SCRCfX292w+M33zzjX9sbGz7vn37CqQdC093Z926dWdCQkJsBoOBpaamambMmKHr06ePzd2+r776asWKFStCdu/eXQQALS0toh9++KFQqVTyY8eOyR955JGY48ePn/zkk0/8MzIy9G+88Ua11WqFa8DT1cyZM2NkMplQVlamWLZsWZlEYo8nuJv/sGHDjCdOnFACQHZ2tldcXFxbdna20mKxsNTUVAMATJ8+Xbdw4cJ6AHj66afDVq5cGbh48eJaAKipqZHm5OTkHzlyRDFhwoS4WbNm6S56cc6Tn58v8/HxsXp4eHTK/lq7dq3vu+++G7Jz585TQUFBtmnTpqnnzZtX+8QTTzT+v//3/4Jc9x0+fHjrn/70pzBcJRRoJaSXMB6tQ9O3pwEAIi8pgmZrIfaWXbPzKRL9IPaRw6Y3oXlXGUQeEngND79m5yPkegmNS0RoXCIAwGJqR83pIuciW61NOoBztx+MOT+XOdnS0GAPkgJoN7Q4243Nzag9U9zluR1BXQCoOX0K1cWnAAD9M0c72xsrzuJM7iEAgCoo2NneqtehOGd/l32n3T/O+bj8xDEkDR/hDLI65vCvt/+MkTPnYN+mL9B2W5oz0GpuM6Jw/49d9p00fMS58VWeG59vSB9ne3trC86eOA4A8A9z+bKfc1QVdX3nllHfdK4PgwENZ8sumLtgs0Eql7t9XQCg6H/73PbdP3O0M9BaXXwKm//8RwCAX2g4Zr/zAQDAZrXi3RkTIJZIIJUr8Ohbq52Z0bs++guaaqrPBXHlCkg6HgdERiG547qY24woyzvm3C8s4VytYHObERKZ3BnIdWTmnr9IId1KSAghhNwc6kpLUHjgvyjO2Y+60pJO204fzoFgs0EslUIVFNzps4sqKBjiSwjCkV/OtXTArl27PGfNmtW3sLAwb8eOHars7GyVRqPRAIDRaBTl5+crYmJizKGhoeaMjAznreSfffaZ/5o1awKtViurq6uT5ubmKgYOHNgml8uFqVOnqseOHds0depUtynJKSkpxuLiYkVHBuhFb09/4403QrZu3eoLANXV1dK8vDxFnz59Lum2drPZzB577LHoEydOeIhEIpSWlsoB4Pbbb2994okn1BaLRTRp0iTdsGHD2twd7ygdUFlZKRk6dGjSuHHjmhMSEszu5j9kyJC2qKio9kOHDikOHTrk+dRTT9Xs3r3b22azseHDhxsA4ODBgx5Lly4Nb2lpEbe2topHjBjhvEYPPfRQk1gsRlpaWntDQ8Nl/fCXl5dL/f39ra5tP/30k3dubq5y9+7dhf7+/gIAHD582Ov7778vAoDHH3+84aWXXnL+QhIWFmatra29asETCrQS0gtYqlvRuKEA4ACTiRH4aAokAde2hqrEV4HAx7Woe/8ohFYLmv51GsxDAs+BIRc/mJCbhFSuQESyFhHJ57JeW5t0bj8Yi8Tn/kt1BNI4F+AXFu6yXxBiBg6ybxME2JM3uT1rknOIJec+XwSrYyGWygBwKFU+znbvgCD0iUsAOIcq6Nz7TabwQFCUGryjb3vX9n7BOSSyc58dPFQ+UKp83AYmlSof+IT0gafPubt3xFKZPTjqyJCFvb4zAwDGIFeeW09AFWgfH2Os0/g8vFT268gAVeC5IKlILEZ0/9RzGbgdfaOjb0eWMGAP3MYPGQbGRAhWxzjb5UpPtLW0uH1dBKsVgVFqWEztsLS3w2IywWJqBziHVK5w7msxtTsfu7ZbTfaF/2xWK2xWQ6dfgOxZz51/gXKIGTjIGWjV19bgmzeX2ftWeODpz75y7ve3px5He0szxBIJHlm2HF7+AW4zph9c8HtUnsqHUqWCd0AQwpM0bs9LCCGEkOvLZrVAsNogVdg/Pxz+7lsc+/d3nfbxDghCbPoQxKYPARigVPlg/KIlF3yx6vqZr7f6YuwXBVf78S+RmZnZqtPpJFVVVRLOORYsWFC1aNGiTvU9CwoKZEql0plFkZ+fL1u1alXIwYMHTwYFBdmysrLU7e3tIqlUiiNHjpzcsmWLauPGjX6rV68O3r9/f+H554yLi2t/+eWXK2bMmBG7devWwvT09HaJRMJdF8QymUwMAL799lvvvXv3eufk5OR7e3sLgwcPTmxra7vkb9tfffXVkODgYMumTZtKBEGAh4dHGgCMGTPGkJ2dXbBp0yaf2bNn950/f37N/Pnzu8wGDgsLs2q1WmN2dranIAhwN38AGD58uGHLli0+UqmUP/jgg83Tpk1T22w2tmLFirMAMHfu3L4bN24sGjp0aNvKlSsD9u7d6+04h2vt18stR6hUKgWTydTp+kRHR5vKysrkx48fV9x1113Gi/VhNBqZXC6/avXGKNBKSC8gCVbCa3g4DD9VIOBXyZBFeF/8oKtAGqRE4Gwt6j48Cm6yQbexECKFBB6aq1behJAbTlcfjFWB5+5ASXtgPNIeuPDYmNRBiEkddEnncdwWf76BYx503o7vKiJZi5lvrrqkvjMf+02XAWPfPqF4fOVHnfb3DemDWW+/f0l9p45+0FnOwFVofCKmvvT6Be1iiRSTFi+7pL7Vt6VBfVvaBe1e/gHw9PVz+7oERkbj1+ddF845bBaLvVxBh0hNP0x96XVYTCZIXIKpIokYw6ZMh8VkgtVk6hSEDYpSQyKV2YO4JkcQ1x7I7TqIe25BNgCwttu32axWSGSyLm8ltJjbsXvNB87zOl5rk9GID+c9Cg+VCkpvH2Qt/pMz8H0i+z8QbDZ4qFQIjU+6JX55I4QQQq6XkiMHkbdnF0qOHMSwydOR9oD97qHYtCE49u/vEKSOQVz6EMSm345gdYzzy2qHwMhoTHtlBZUKus4OHz6sEAQBISEh1jFjxjS/9NJLYXPnzm308fERSkpKpDKZ7IKIn06nE3t4eAj+/v628vJyyZ49e3xGjBjRotfrRQaDQTR16lR9ZmamITY2tl9X5x01alTr22+/XTpu3Lj4PXv2FKjVatO2bdt8AeDHH39UVlRUyAGgqalJ7OPjY/P29hYOHz6syM3N9eyqTwDw8fGxGQwGZ8kCvV4vjoiIMIvFYqxatSrA1nHHXGFhoSwmJsa8cOHCepPJxA4dOqQE0GWgtaWlRZSXl6d84YUXqruaPwCMGDHCMGfOHPXkyZMbwsLCrDqdTlJfXy9NT09vA+xZwlFRURaTycTWr1/vHxoaelXrY/Tr189UUVHRKRs1IiLCvGLFirOTJk2K3bBhQ3F6enr7bbfdZlizZo3fnDlzdJ988om/6/7Hjx9XJCQkuM3wvRwUaCXkJmbVtUMkF0OklML3/r7wHBQCaZDyuo5BFu6FwF+noO6T44BVQMM/TiJwlhaKWN/rOg5CrhcmEvWKD8a9LZPil7wujLFOGb4A4OGt6pS57CBTeGBo1iNuz+moM3s+znmnMhBBUX0x48/vwGJqv+Db+uFTZ9iDs2YTlD6+EGw2twHwdoPh3FhdXqO2lmaY24wwtxmhr61xZtMAwH83/gP6mmoAQNaLf3KWSfjPpx+g8MBP8PBWQalSwcPbBx4df3sHBqLf3fc6+2ht0kHh5Q2xhD4yEkIIubU119VCJJHAy88eozl74hgK9v0AACjO2e8MtEb3T8Wcv3zS6Q4ed5hI1GmhVnLtOGq0AvbPaatXrz4jkUgwceLE5ry8PMWgQYOSAHt25Lp160ocC0A5DB06tE2r1RpjY2O1oaGh5rS0NANgD4qOHTs2zpGNumzZsvLuxjFt2jR9XV1d5ejRo+N37dpVuG7duoC4uLiU1NTU1ujo6HYAyMrK0n/44YdBMTExKTExMe0DBgzotmTA4MGD28RiMU9MTNRMmzatfsGCBbVZWVmx69evD7jnnnv0Hh4eAgB899133itXruwjkUi4Uqm0rVu3zu1tWTNnzoxRKBSC2WxmDz/8cP2dd95pBAB38weAkSNHGhoaGqQjR440AIBGo2mrqamxijo+g7/wwguVgwcPTvb397cOHDjQ4BoUvhpUKpUQFRVlOn78uFyr1Zoc7ampqe1r1649PXXq1NgtW7YUvffee+XTp0/v++abb4bec889zV5eXs4P6zt37vQePXr0VVuJjt1qq4Wnp6fznJycnh4GIVfMZjCj7v2jgIghcLYWEl/5xQ+6htryG9Gw9gQgcDCZGEFz+123zFpCyOWhRZduPF3VaPUPj4Sp1QBjsx6MMQRERAEADI0N+HnLRrQ1N8NqNmPc7xY7+3rv0Skwt9nvlprx+rsI6RsLANjy1ms4deC/bs/v2ycUj737NwD2LNt3po8HAMg9PfHrN/8C7wD7wrf/27IJbYYWeHirEJs2BP4dJTJMxlYwxiBVeDgzd+jnjBBCyM0sb++/cXDr16grLcHtE6di+NRfAbCXENr02lL0vS0N8UOGIXHonT080p7HGDvIOU93bcvNzT0zYMCA+q6OIeRKrV271jcnJ0e5cuXKyq72aWlpEXl6egoikQgffvih34YNG/z//e9/FwNAenp64vbt24uCgoLcLjjWldzc3MABAwaoz2+n9ARCblKGfVWw1tuz242HaqC6J6pHx+OR5A//KQlo3FAAbrah5YcKBDySdPEDCSE9hjIpbjzdZeYqfXyh9Ol8t4CXfwDuefQJt309sXoN2lqaYWzWOwOhAKDuP7Cjrq19W3vH36bW1k7Zsq4LuZlaW6Hw9HI+P5H9H9SXlwKwB2cd/e/b+A8c3PoNxFKpPUs4pT8GZNyH7X95ixb3IoQQcsOzWS0oP3EcXr5+CIxSAwDamvXOmuxFOQecgdbQuATM+2hdpxr7hJDrb+bMmU319fXdxjd/+ukn5TPPPBPFOYdKpbKtWbPmDABUVlZKnnnmmZpfGmTtDgVaCblJqTKiwNutEIxWeI+M7OnhAACUtwVDaLfCVKyH/+SEnh4OIYTclK5WAFzmoYTMQwmf4D6d2vtnjkb/zNEX7G+zWjvVk5XI5MiY/ZuOIKyhU1kCJhZDJJZAsFmh9HYpZdDcbO/LYoGhsQEJg4c5g6zAucW9pr2yAlazGaqg4Atq1hFCCCHXU3urASVHDqL4f/tRcuQgzG1GDBg1BpmP/xYAEDvodpz4cY+z3qoDE4kgpi8NCbkhPPfcc91mTY8ePdpQUFBw4vz2sLAw669+9aumqzkWCrQSchPhnMOQXQHPIX0gUkjgMzYG4AAT3Ti/pHrdHgbPIaFgjIFbBOi3l8D77kiIvWUXP5gQQkiPEUskEEvOZa3KlUrcdp+bVd0AzHxjJTjnMLcZO9W7Tb7rHgRERqOtpRltzc3w8g9wu7iX1WzCx0/PhW9oGBKGDMOQCVM6LSBGCCGEXGvHd+/EyR/34OzJ451qqwNAyZFD4JyDMQa/PmGY+cbKHholIeRmQ4FWQm4izTvOoGXvWRgP1yJwdgrEKjlw48RYnexBVhvqP82D6bQephI9gub0g0hJt9UQQkhvwRiDXNl5IVx1/1So+6c6n7c26dwu7mVqawPnAnSVZ5G3998YPmWGc3vN6SL76syUJUQIIeQq4ZyjtqQYquAQeHjZ15EoPXYEZcdznfvIPJTom5qOuPQh6JuaTndcEEIuCwVaCblJtPxYgZa9ZwEA3CoAN1AWq1sSEcT+CuC0HkwhufHHSwgh5KpTqnwwftGSCxb38vBW4c5pj+LUz/9FaFyiM6iqq6rA579fAC8/f8QNHoqRM+dALKGPq4QQQi7f/k3rcfTf36GloQ6j5s5H/wx7+Zy4QbejIv8EYtOHIDZ9CCI1Wqq3Sgi5YvTJlZCbgDG3FvpvTwMARN5SBM7WQux1Y9+KzxiD38R4SIOV8BoaCiYVgwscEDiYhLKUCCHkVtDd4l6Dx03C4HGTIAjnbtcsPPBfAIBB14izJ/OcQVYuCCg9dgSRKf3ol2BCCCFdam81oORwDsISkpw1yo0terQ01AEAinMOOAOt8UOGIeH2OyhzlRByVVG0g5AbXPspHRq/LAQAMLkYgbO0kPjfHHXsmIjB+64Ie5DVKqBxfT4aNxTYA66EEEJuCY7FvVRBwfD09bugJIBIJHY+1o7MRObjv0V0/1QkDr3T2V5VVIBNry3F6jkzsG3VCpz63z5YzKbrNgdCCCE3Di4IaG3SobmuFq1NOnBBAAD8+5P3sXrOdGx7bznyf8p27h8/eBiitANwz6wnkPHYb5ztIpGYgqy3CLFYnJaUlKRJTEzUaDSa5J07d3pe/KirY+XKlQEzZ86MAgCbzYaJEyeqJ0+erBY6fm5/ifDw8H5VVVUSAEhNTU0CgIKCAtn777/vfzXGOnjw4MTQ0NB+rmPLzMyMVSqVqd0cdoHnnnsubOnSpSEAsGDBgrCvv/7a+2qMz1VWVpb6008/dbt67OzZsyO3b9/uBXS+Zpdi7ty5EVu2bLmi8VJGKyE3MHOFAQ1/PwnYOCBmCJipgSzM6+IH3oCad5ai7ah9IcAmRRF8J8bRBxtCCCGdePr6YcCoMRgwakyn9lM/7wMAmIytOPnDbpz8YTckcjlibktH/JBhiBs0tNOiXIQQQnonLgioKzuDb5a/0qkkTWBkNJQqH+eiVmXHj2DIhCkAgEhNP0Rq+vXksEkPk8vlQn5+/gkA2LRpk+rFF1+MGDVqVMGlHCsIAjjnEIvFF9/5Iv3MmDEj2mKxsK+++qpEdIW16A8fPpwPAKdOnZJv2LDB/8knn2y8og47eHt723bu3Ol13333Gerr68W1tbVXdCvRO++8U3k1xnWpqqurxQcPHvT85JNPyi/n+N/97ne1s2bNin7ooYdaLncMlNFKyA3K2tCG+k+Pg5ttAAP8pyZCEevb08O6bN4jIiDtY//isPV/1dDvONOzAyKEEHLTuH3iw7j/6UWIHzIMErkcAGA1mVB44CfsWP0OBJvVua/F1N5TwyTXUVcZbYSQ3oVzjqKcA9i3iXB/jwAAIABJREFU6Qtsees1nDl6yBlkBYDmulp8/eYyGJv1SLh9OG677wFkLV6Gib9/uYdHTm5Uer1e7OPj4/zgsGTJkhCtVpuckJCgefbZZ8MAe5aoWq3WTpgwQZ2QkJBSXFwsmz59epRWq02Oi4tLcewHAPPmzQuPjY1NSUhI0MydOzeiq/POmjUrsrGxUbJ58+YSsVjcKesTAOLj41MKCgpkgD2LNCUlJTkuLi5l+fLlge76c2SZLl68ODwnJ8crKSlJ8/LLLwcXFBTI0tLSEjUaTbJr9m5paak0PT09MSkpSRMfH5+yY8cOtxlcEydObFy3bp0/AHz++ef/n707j4vizPbH/6nqlYam2ZtNdmhoQESQRFxiBIwY44aJ0RAmmhuXjJOoGe/MxG8yo46Z8eeWeJ3oZK5GY0g0USdxd/RG0cQlQRFFBBQBEUH2Zu216vdHSwvaCG5B8bxfr7xCP11Vz1PVLdqnT53j8NJLL9W3f97a9QKAP/zhD+5+fn4RMTExqkuXLknaxttnnrbPLj169KgsLi5OBZgzYCdMmOAXExOj8vT0jNy0aZPDzJkzvUNCQtRDhgwJ1ul03c7Q+vLLLx0TEhIabh9vampihg4dGrxixQoXAJg/f75H23pfeukl/7bXIiQkRF9fXy+8evXqfSemUkYrIY8h3sSheuMFcE0GAIDD6ADI+rr28KoeDCsTweXNCFSuy4apRoumjGtgbYSwH9anp5dGCCHkMSeRyRA26DmEDXoOBp0WxdlncOnUcRSe/hne6giIbWQAAF1LC/45Mw2eqjAEx8Uj/LkEynTthXiOQ3VpSYcma2PfWwCXPr5gqXkaIU8kk9GIyqJCVJUUoaasFMPS/gsMw4BhGPywYZ2lxmr0yJcsQdY2DVWVMBkMcPb2QcK0WdYOTx4jRS+/omr/WDHmpWqn11+vqVy5yr35xAmF7cCBGrd5cytqN2921uzc1SHI6P/tN/mG8nLhtXfeDQQA79WfFIo8PIzogk6nY0NDQ9U6nY6prq4W7d27twAAduzYYX/58mXpuXPnLvI8j8TExKB9+/bZBQQE6K9evSpZv359UUJCQjEArFy5skypVJqMRiPi4+NVp06dsvH19dXv3bvX8cqVKzksy6K6utpq2uv333/vFBgYqD1x4kS+SNR1gmh6enqxUqk0NTU1MdHR0erU1NQ6d3d3k7VtlyxZUrZixQrl4cOHLwNAY2Mje+zYsQKZTMafP39eMnny5ICcnJyLGzZscEpISNAsXbq0wmg0orGx0Wri5YgRIxpnzpzpazQa8e233zpt2LChZNWqVR53u152dnbcv//9b6fz58/nGgwG9OvXTx0dHd3S5Ym2U1JSIjl+/HjBmTNnpMOHDw/dtGlT4bp1664lJSUFfvPNN4rXX3+9vuujAMePH7ebOHFiXfuxhoYGNiUlJWDKlCk1s2fPrsnIyJDt2rXLMTc394JOp2NuX29kZGTLDz/8YPfGG290a87b0b9ECHkMMQIWilH+qP0qD3aDvGA3yKunl/RQCORiuL4Zicp12eAa9GjYXwzWRgi7Zzx6emmEEEKeECKJFMFx8QiOi4fRYIC28VbSwpWsX2DQaVFyLgs3igoROXyE5bnm+jrYOlgt5UWeIDzP40bxFexa+VGHjLbvVyzB87+ZjtxjP2DMvPcB3AzcFBfCydMbEtmvVo6PEHIXPM+joaoSVSVFaG1ssPyeNup1+Or/vWfZrn/yGCjczMl+rr5+aKypgr2rGwDA3tWtQ7DV3tUNgm4Er8jTq33pgEOHDtlOnTrVv6Cg4ML+/fvtjx49aq9Wq9UA0NLSwubl5UkDAgL0Hh4e+oSEhOa2Y2zatMlp48aNLkajkamqqhJlZ2dL+/fv3yqRSLhJkyb5jR49un7SpEkaa/OHh4e3FBYWSjMyMmQjRoxotrZNe0uXLlXu2bPHAQAqKipEFy5ckLq7u3e5HwDo9XrmzTff9M3NzbVhWRYlJSUSAHj22WebZ8yY4WcwGNiJEyfWxcfHt1rbXygU8nFxcU3/+te/nLRaLatSqfRtz3V2vRobG9lRo0bVy+VyDgBGjBhxzwHKxMREjUQi4ePi4lpNJhMzceLEBgAIDw9vLSoq6va35jdu3BAplcoOwfcxY8YEzZkzp2LWrFm1AJCRkWGXnJxcL5PJeJlMxiclJXVYr6urq7GsrOy+v6mnQCshjxHeyMFY0wqR0hY2Yc5we7c/hM5PRuOr7hI6SeH6ZgSq/nkOXIsR9d9dBisVQBbl1tNLI4QQ8oQRikSwc3K2PHb26oOoES/i8i8n4N8vBuzNemqNNdX47LdT4R4YjOC4eMS8OA4Cynx8YpiMBjAMC1ZgblzDc5zVjDaJrW2Hhpv1FeX4aoE5cGPn7IK31qy3NF8rOX8WcmcXKNzc6b1AyCNi0GpRdbUYnNEIb3UEAKD8Uh6+/mA+AEAktUHEsEQwLAuJzBb2rko0VN2AUCJBQ3WlJdCa8OYsJM9+D1JbO/Ach3HzP+iQ0T5u/geQ2St67DzJvfH/9hurtVHd5s2tAOZWtD12ev31GqfXX6+5fTuRh4exs2N0R2JiYnNdXZ2wvLxcyPM85syZUz5//vzq9tvk5+eLZTKZpSZNXl6eeM2aNcrTp09fdHV1NaWkpPhptVpWJBLh7NmzF3fu3Gm/bds2x7Vr17qdPHmy4PY5g4KCtAsXLixLTU0N3LNnT0FsbKxWKBTy7ZtOtd0ev3v3bnlGRoY8MzMzTy6Xc3FxcarW1tZul/1csmSJ0s3NzbB9+/YijuNgY2MTAwDJyclNR48ezd++fbti2rRp/rNnz74xe/bsO64vALz22mu1kydPDpo/f36H+qqdXa9FixZ168O8QCCwnPPt5ySRSPib20AoFPJtNWxZloXRaOx26QCpVMrdfuwBAwY0HThwQDFjxoza7tTG1Wq1jI2NzX3XJKIarYQ8JniOR+22AlT+4yy0BeZMd5GLTa9sGCVS2sJlagQYsQDggdqtBWjNeyi1uwkhhDzF3PwCkPjmLMz4dCOGpf2XZfzSz8cBnkfF5QKc/78DlgAsz/OoKSsFz/OdHZL0oJprpTiyeT3+OesNXM48aRkXSaSWzLY29q5uEAhFCIyJs4zVlZfd2kcstgRZOZMJO/72F3w+dyZWp6Wg9vqt7UrOncW13Bxz3Vd6XxDSbQ3VVbj8y0mc2Gaupbr+3bew+o2X8fUHv8fR9M8t27n08bX8bNC2oqG6yvJ41Oz3MO3jf+J3G7/p0LzK3sUNUltzOUmGZeHSxxdT/roCb63ZgCl/XQGXPr5gHrCxEHl6ZGVlSTmOg1KpNCYnJzds3rzZRaPRsABQVFQkKisru+Pbt7q6OoGNjQ3n5ORkKi0tFR45ckQBABqNhq2trRVMmjRJs27dutK8vDxZZ/MmJSU1r1q1qmTs2LHBly5dEvv5+enOnj1rCwA//vijrKysTAIA9fX1AoVCYZLL5VxWVpY0Ozv7rrdkKBQKU1NTk6VkgUajEXh4eBgEAgE+/fRTZ9PNBnEFBQVib29vw3vvvVedlpZWdebMmU7X+sILLzS988475dOmTevwIb2z6zV8+PCmvXv3OjQ1NTF1dXXswYMHrTaX8fb21v/0008yAPjmm28eyW1GKpVKW1BQIGk/tmzZsusODg7GtLQ0HwB47rnnmg4cOKBoaWlhNBoNe+jQoQ7rLSwslEZFRVnN+O0O+vqWkMeEZm8RWs+a/6HR8MNVSIIdemWQtY24jxzOaWpUb8wBjDyMN5qBUKeeXhYhhJBeoC07qo1vZDSeGf8KCk4dR1DsM5a/X6uvFuOL//4dHD29EfKMuRyBm39gr/7790lycscW5P2UAQDI+eE/CHlmEADAycPTakabSx9feIaEWvb3DAnFmHnvo/b6tQ61ejVVNywN1DiOg73LrTr4Rzb/L6qvFgMAJDJbOHp4wtHTG44envBSqeETEfWoT/uJxnMcWho0MBkMEIhEkNkrKADWy+i1rai+WoKqkiIE9B8AubO5hGbGlxtQcOKY1X2qSovBcSawrABiGxlGvj0XCjclXH39O/yu9gpVd2sNDMtSKRhyT9pqtALmL1nXrl1bLBQKMWHChIYLFy5IBwwYEAoAMpmMS09PLxIKhR2+aRs4cGBrRERES2BgYISHh4c+JiamCTAHRUePHh3Ulo26ePHiu3a6nzJliqaqqur6yJEjgw8dOlSQnp7uHBQUFB4dHd3s6+urBYCUlBTNZ5995hoQEBAeEBCgjYqKumvJgLi4uFaBQMCrVCr1lClTqufMmVOZkpISuGXLFufhw4dr2jIzDxw4IF+9erW7UCjkZTKZKT09vaizY7Isi0WLFt24fbyz6zV48OCW8ePH10ZERIQ7Ozsb+vbta3XNH3744fWZM2f6LVq0yBQfH994t/O6X2PGjNGsXbvWdd68eR2ybjds2FD6yiuv+M2cOdN73bp110aOHKlRq9Xhzs7OBpVK1apQKEyAObO4uLhYMnTo0G6VarCGedq+qY2NjeUzMzN7ehmEdNB49Bo0e82/54SuNnCdGQWB7dNRZ6j1Qg1MDTrYDTQ3LOR5nj7gEkIIeWRMRgMEQvPfsT99k46T27/u8Ly9qxLBcQMR/MwgeAarKEj0K+B5HtcL8pBz+D/oo46EeuhwAMDVnGx8u3gBvNUR6JswEmGDh93a5wECeka9HjeKClF3/Rqa6+vwzPhXLMdcnTYRRoPe6n7hwxIxctYcAMCNK5dx9KuNcPL0gqtvAPomvPAAV6B3sNakrC0ATn+Onjw8x6GhuhKVJUUIiI61/N7c9Pvforq0BAAw6p35CBv0HADg5I6t+GnrZgDm36Ouvv5w9fUz/9/HDw7unvRv/KcAwzCneZ6PbT+WnZ1dHBUVVd3ZPoQ8bDExMaoDBw5cdnFxsdpADDBnIysUCq6xsZEdOHCgat26dSWDBw9u+eKLLxxOnz4t++STT653tm+b7Oxsl6ioKL/bxymjlZAe1pxVaQmysvZiuEyLeGqCrABgE36rtp6xphW1W/Ph+HIIRK6d3slACCGE3Le2YAEARAxLgEgiwaWfj6PisrmkWkPVDZze8x3O7NuJWZ99CRu5PYBbXwRSxt7Dd3L7Fhz/Nh0AUFN61RJo7aOOxJuf/AsO7nc2zXyQjDahWAwvVRi8VGG3HZTBb5b/A7Xl11B3vQx15WWovV6GuuvX0FRXCydPb8umVSVFuHr+LK6ePwv3oBBLoFXX0oItH86Ho6cXHD28MDBlcods2ts9Lu8njjOhtaEBRr0ORr0Bzt59LM8VnT0NbXMTjDodjHodDDodjHo9jAY9lP6BUA0cAgDQVN6wBFkBc93c75YtxpS/rsCJbV+BYVkIxRIMeGkCZArzXZrF57LQ2tgAkVgCoUQCoVhs+Vkis4WdY8/c7fS4vC4PS1fno9e2or6iHG5+AQDM74e101+3NBtMW7YGrj5+AAAXHz9LoLXm5v8BIGzwc/BWR8DVx4+azxFCetSyZcuuFRYWil1cXDq9/T81NdX30qVLNjqdjnn11VdrBg8e3AIARqOR+eCDD+7I5r0XFGglpAdpC+pQ9635gx0jFcB1WgSEjr2r+VV3cS0GVK7LBtdoQPX6HLjOioJQIel6R0IIIeQ+KdzcETd2IuLGTjTXF/z5OApOHUdZfi76qCMtQVaDXoeN895GxPAR8I/qj12r/kYZe/eJ40wozj4Dk9GI4AEDAQCBsc/g+LfpYFgWMgdHGPV6CMViMCxrNcj6qDAMAwd3D/Oc0QM6PKdvbelQs1UoFsPNPxB15dfh6OFlGa+7fg3VpSWoLi2BSCLF4FfTLM9t/sO7EEokcLoZhHUPCoFEZoudK5bc8X7ieR4GnQ4MA4htzF8+61qaUVVSBKNOB7As/PpGAzB/CfDLzu3m4KfeHAQ13AyKGvU6hAwcYsk6LDj1E45+uQFGvR7uQSEYN/8DAEBDZSXWv/tW24XAvK93WrIPD362Bo01t+pothf+XIIl0MpxJqtNykwGA7IP7rOMRSWNguxm36Jfvv8WV3POWT22p0qNyYv+PwBAY201Nrwz3RKMfWvNBkut5X3/WAltUyOEEikGTpgEl5sBwcuZp1Bz7SpEEgmEYglEYjGEEglEYgkktnbwCFZZ5mrR1FuOzYDpVZm51jKNX5r7R1QWF6H47GlUlRSh/kY5WIEAv9u0DUKRCCwrgJ2jkyXQWlVSZAm0RiUlI/iZeLj6+sPBzd0yj8LNHYp2jwkhpKcMHz68y9v+d+3aZbV0wrRp0+oedH4KtBLSQ/Sljaj5MhfgeEDIwCUtHCL3p/fbX1Ymgm2cBxr/7ypM9TpU/+95uM7oC4Fd51kghBBCyMNi7+KK/qPGov+osWiur4O26VbpsJLsLDRU3YCLt48lyArcytib/NcVsKN6gXdVf6MCOYcP4kLGITTV1sDRwxNBsc+CYRi4+QXghZnvwj869rGtu9gW7GwTOug5hA56DjzPw6jXWcYZloV/dCzqrpdBamdnCVYatFpUFhcCAK7n5wIAxry3AAfWfnzH++mluX9C+vtzAQDqIc8jefZ7AMzBrq1/+SMAwM7JGTPWbjLPyTD4ccsX4DnrDZKdvX0sP5sMBmgqzYk6LZp6y3iHrFueh8lgsIwJJda/+BaIRABu3QrOCoSwd3XrEGy1d3UDKxTCztnFnBGr03WYy6DToTOidvMadToYDeYsWoZlLUFWALh6/iya6sz9WqJHjraMF5z8ERePHbZ6bIWbEv/1P+vNp8txWDs91fLcW2s2WM3MnfDHhZZM3192bse5Q/utHpsVCjF15VrL468WvIfWmwHL0XP/CKV/IADgh43/RNEZc0m72JfGIyppFADg4o9HcPyb9E6vy+S/LofM3hyp3rnyI1QVm2MFw37zlqUZXPv1vfjOfOz6+O8dzmfXqr9jWNpb5kaBN3EmE2rLSi1ZrTGjxsKo15tv/ffzt2znHRbR6doIIYRQoJWQHmGobkX1xgvg9RzAAM6vhkISoOjpZfU4+0Qf8K1GNB2/DmNVK6o/vwDXtyLBSulXFSGEkF+PrYNjh4Cf1M4OATFxsJHLrWbsaSrKcXzrZgyfOvOut4k/bQx6HS6fOo7zhw+i9ELHrMXG2hrUlV+Hk6c5GzTi+aSeWOIDYxgGIsmtu5GUAUGY8Me/AECHwKdBr0PE80mWUgStjQ2Q2tlZfT+ZA5i39mvTfh6jvmMdWaFYAoO2FQKRyHzr/c3sTaFYYsnMBgAHdw+EDXkeIrGkQ7awxM4OCdNmWfZrn7k59vcLwDCM+TmxOSNUIDZnPbanuJn5eXsmqK3CATM+3Wj1+o15bwH0rS3mDFydDgb9rf+3X7fYRoYBY1Jg0OnuCCi7+vpDpnCEQa/rcMu68S5BXKG4XRD3tmvJc5zV1wW4ldHc2tSI+hvlVo/d/vUDzM3X2oLaJoPBMt5SX285hrb5VvKVvrWl02O3ra9NU02NZVuD9tYdsu3Xx7Cs1fOR2tlZGlK5+PjDzdcf9q5ulm2e1D+ThBDS0yh6QUgPYIQsWFsRuGYDHMYGwSbCpaeX9FhgGAaK0QHgWo1oyaqEoawJ1Zty4TotHIxI0PUBCCGEkEfAOywC3mERaKqtsZqx19KgQcn5s5YAC8/zOLHta3iGhMIrVN0hQPY0uXDk//B/6z/tMOYRpELE8CSoBg6FRNa767G3D1bK7BV4Yea7lsetTY3QNjVafT8JxWLEv/IaRGIJnLxu1Up19PDExAV/NdcxlXZ8T836bDOEInGXt7Z7BKngMVt1x7hILEG/F160uo9zuzXcDcOycOnjiyl/XdHt2qZ2jk5AN+qw2jo4YuhrU60+N+FPC62OvzTvT+YsWF27urI3a8yygltrYgQCJEybZQnyCkTiTjNz27j5+ndoztYeK+j4ETv4mUHQt5gDqTb2t4LHXqFqS2auS59bWccO7p6dHhvoGMj1jepvCZi3D5K2Xx8rEFg9Hwd3T0tWLyGEkIeHaV9r6GkQGxvLZ2Zm9vQyyFOKN3JghOZ/2HEtBrReqIHtAKpldDvexKHmy4vQXjTfBiYNc4JzahgYwZNXF4sQQkjvYa3W4Yvv/jfyfjoK8DyGT50BAKgpK8XGebMAAAKhEJ4qNXwj+8E3sh/cAgLvyATsDbRNTbj442HompvxbMqrlrF1M1+HWGoD9dDnEfH8CLj08e3hlT4+rL2fnuRaoL1Fb3tdetv5kMcbwzCneZ6PbT+WnZ1dHBUVVd1TayLkUcnOznaJioryu32cAq2E/Ep4I4fqz3Mg8rKDYqQ/GJbpeqenGG8wXy/dFQ0AQBbtBseXQ+i6EUII6VHd6Uae91MG9v7PCvD8nTUzpbZ2GPvfH8A7NPzXWvIjV5Z/Ed8uft9c11Miwcx1my3ZquWX8uHmHwCBUNTFUZ5Ova27fW/R216X3nY+5PH1uAZaBQJBTHBwcCvP8xAIBPwnn3xyNSkpqcuGSQ/D6tWrnTMzM22/+OKLqyaTCS+//LKfQCDA1q1bi9l7/HPo5eUVmZmZedHDw8MYHR0dmpWVlZefny8+fPiw3cyZM2sfdK1xcXGq0tJScVlZ2fm2tSUmJgYeP37cvqWlJau7x5k3b56nnZ2dadGiRTfmzJnjOWzYsMZx48Y1dr1n96WkpPiNHj1aM3Xq1Lq4uDjV8uXLS4cOHdpy+3YjR44MWLly5TW1Wq2XyWTR93Ieo0ePDvjb3/5WFhkZabUWTWeBVvrtSsivpH7PFegKNWg6WoamE9d7ejmPPUbEwvk3aoi87QAArRdrYarV9vCqCCGEPO0YloWtgyPsXd1g6+BoNVgROug5vP2/X2HMe+8jasSLHbrSa5ub4OTpbXl88F9rcGDdJ8j7KQPapqZf5RweVGNNNfJ+yrA8VvoHQnSz5qXM3qFDfUmPYBUFWe+iO+8n8uvrba9LbzsfQu6VRCLh8vLycvPz83MXL15c9v7773t3vZcZx3EwmUwPvAaO45CamuprMBiYLVu23HOQ9XZZWVl5AHDp0iXJ1q1bu67B0k1yudx08OBBOwCorq4WVFZWPtBf4h9//PH1hx1k7a7MzEypyWRi1Gq1vuut7zRr1qzKJUuW3PMtyPQblpBfiXyIN4QuNhD72lO5gG5iJUK4TI2AJFAB1xl9IXSx6eklEUIIId0itbNDcFw8Et+chWkf/xNv/WMDXpj5LuLGTrR0DDcZjcj7KQM5hw9iz+plqCgssOxff6MCBt3j9QVjS4MGO/72Z/zrt9Owd80KS6d3oViMYb95CxP/31/xX6v/ZemqTgghhDxuNBqNQKFQGNsef/DBB8qIiIiwkJAQ9dy5cz0BID8/X+zn5xcxfvx4v5CQkPDCwkLxa6+95hMREREWFBQU3rYdALz99ttegYGB4SEhIerp06d3GsCdOnVqn9raWuGOHTuKBAIB5s2b5/nhhx8q254PDg4Oz8/PFwPmLNLw8PCwoKCg8OXLl1tt6CKTyaIBYMGCBV6ZmZl2oaGh6oULF7rl5+eLY2JiVGq1OkytVocdPHjQFgBKSkpEsbGxqtDQUHVwcHD4/v377awdd8KECbXp6elOAPDll186vPTSS/Xtn7d2vQDgD3/4g7ufn19ETEyM6tKlS5aOgykpKX6ff/65I2DOyC0vLxcCwNGjR2VxcXEqwJwBO2HCBL+YmBiVp6dn5KZNmxxmzpzpHRISoh4yZEiwTqe7r9taN27c6Hz7+gGgvLxc2K9fv9AtW7YoTCYTUlNTffz9/cPj4+ODn3vuuaC29Y4cObLp2LFj9oZ2jQy7g5phEfKI6cuaIPayg9BJCteZfcGwDFhx76vN9qgIbEVwfauv5XFLdiVMjQbIB3vdZS9CCCHk8WLv4nZHF29tUyM8VWpcu5gD3mSCV6ja8tz+T1eh4nI+PEPCMGjS6x2e+zXV36iAg9L8BbGNnRzV166aSyLwwJXTP6Nv4kgAQPhzCT2yPkIIIU+Wb//2y50d+W5SPeNe3Xd4n5pv//aLqv3PXW3f1Zw6nY4NDQ1V63Q6prq6WrR3794CANixY4f95cuXpefOnbvI8zwSExOD9u3bZxcQEKC/evWqZP369UUJCQnFALBy5coypVJpMhqNiI+PV506dcrG19dXv3fvXscrV67ksCyL6upqqx/0v//+e6fAwEDtiRMn8kWirhNE09PTi5VKpampqYmJjo5Wp6am1rm7u1tNq12yZEnZihUrlIcPH74MAI2NjeyxY8cKZDIZf/78ecnkyZMDcnJyLm7YsMEpISFBs3Tp0gqj0YjGxkariZcjRoxonDlzpq/RaMS3337rtGHDhpJVq1Z53O162dnZcf/+97+dzp8/n2swGNCvXz91dHT0Hbfx301JSYnk+PHjBWfOnJEOHz48dNOmTYXr1q27lpSUFPjNN98oXn/99TsCpl05deqUXVpaWoeSCqWlpcIXX3wxaOHChdfHjx/f8PnnnzuWlpaKL1++fKGsrEwYERER8cYbb9QAgEAggK+vr/bkyZOyIUOGdPt8KNBKyCPUnHkDddsKIH++D+xH+EJgJ+7pJT3RmjNvoG57AcADrFQA21jKDCaEEPLksnVwRMqfFsJoMKC2rBQiibmTvL61BeWX8sCZTCjNPQ9Bu27nOUcOwajTwSeyHxw9PMEwD792ub61BXnHjyLnh4Mov5yPN1ashbN3HzAsi/7JY1BfUY7I4SPgRpmrhBBCngBtpQMA4NChQ7ZTp071LygouLB//377o0eP2qvVajUAtLS0sHl5edKAgAC9h4eHPiEhwVLHddOmTU4bN250MRqNTFVVlSg7O1vav3//VolEwk2aNMlv9OjR9ZMmTdJYmz88PLylsLBQmpGRIRsxYkSXtWGXLl2q3LNnjwMAVFRUiC5cuCB1d3fvVk1ZvV7PvPnmm765ubk2LMuipKREAgDPPvts84zhtFgXAAAgAElEQVQZM/wMBgM7ceLEuvj4+FZr+wuFQj4uLq7pX//6l5NWq2VVKpXltvvOrldjYyM7atSoerlczgHAiBEj7jkompiYqJFIJHxcXFyryWRiJk6c2AAA4eHhrUVFRfcVSKmqqhK5u7tb0lGNRiMzfPhw1ccff1zy4osvNgHAsWPH7CZMmFAnEAjg4+NjfPbZZzuUOXBxcTGWlpbeU/kECrQS8oi05tWibof5FsCmE+WwfdYDQoWki73I3Yi87MBIhADHQ+Ao7enlEEIIIQ+FUCSCm1+A5TErEGL03D/i6vmzqLhcALeAWwHN07v/jerSEgCA3MUVvpH94BPZD74RUZApHO57DTzP43r+RZz/4T/IP3kMRt2tvg85Rw7iudRpAIDY0ePvew5CCCHk5T8NyL+Xbbqz/b1ITExsrqurE5aXlwt5nsecOXPK58+f36FZV35+vlgmk1k6Wubl5YnXrFmjPH369EVXV1dTSkqKn1arZUUiEc6ePXtx586d9tu2bXNcu3at28mTJwtunzMoKEi7cOHCstTU1MA9e/YUxMbGaoVCIc9xt5pmtt0ev3v3bnlGRoY8MzMzTy6Xc3FxcarW1tZul/1csmSJ0s3NzbB9+/YijuNgY2MTAwDJyclNR48ezd++fbti2rRp/rNnz74xe/Zsq9nAr732Wu3kyZOD5s+f36G5TGfXa9GiRW7dWZtAILCc8+3nJJFI+JvbQCgU8m01bFmWhdFovK9vlSUSCdd+HoFAwEdGRjbv27dP0RZo7YpOp2Pbvxe6g2q0EvII6K42oDb9IsABELJweUNNQdaHQOxhC5ep4XB9KxLSQPOHSU5n7GIvQggh5MkiFIsRPGAgEqbNwmsfrQLLmu9E1Le2oKXhVrJMY3UVcg4fxN7Vy7B2eiqOfPGv+5rPoNdh0+9/iy1//m9cyDhkCbLaOTrhmfGvICox+cFPihBCCHkMZGVlSTmOg1KpNCYnJzds3rzZRaPRsABQVFQkKisruyMhsa6uTmBjY8M5OTmZSktLhUeOHFEAgEajYWtrawWTJk3SrFu3rjQvL0/W2bxJSUnNq1atKhk7dmzwpUuXxH5+frqzZ8/aAsCPP/4oKysrkwBAfX29QKFQmORyOZeVlSXNzs62vdv5KBQKU1NTk6VkgUajEXh4eBgEAgE+/fRT57ZGXgUFBWJvb2/De++9V52WllZ15syZTtf6wgsvNL3zzjvl06ZN63DbfWfXa/jw4U179+51aGpqYurq6tiDBw9a/ebX29tb/9NPP8kA4JtvvnG823k9DMHBwdqLFy9aAjEMw+Cbb74pLigokC5YsMAdAAYPHtz03XffOZpMJpSWlgpPnTolb3+MoqIiSf/+/a1m/3aGMloJecgMVS2o2XgBvIEDGMB5SigkfoqeXlavIfG1t/zcmlONuh2X4PxGOCQ+9nfZixBCCHnyiW1kmPnPzaguLcHV82dRci4LpRdzLIFRR49b9cvzfspA9qF98I3oB//+A+Dm64+WBg1MBgNYoRD1N8rhEaSCQCiESCyBraMTaq5dBSsQIKB/HCKHj4BfVH+wAqorTwgh5MnWVqMVMGdlrl27tlgoFGLChAkNFy5ckA4YMCAUAGQyGZeenl4kFAr59vsPHDiwNSIioiUwMDDCw8NDHxMT0wSYg6KjR48OastGXbx4cend1jFlyhRNVVXV9ZEjRwYfOnSoID093TkoKCg8Ojq62dfXVwsAKSkpms8++8w1ICAgPCAgQBsVFXXXkgFxcXGtAoGAV6lU6ilTplTPmTOnMiUlJXDLli3Ow4cP19jY2HAAcODAAfnq1avdhUIhL5PJTOnp6UWdHZNlWSxatOjG7eOdXa/Bgwe3jB8/vjYiIiLc2dnZ0LdvX6tr/vDDD6/PnDnTb9GiRab4+PhGa9s8TMnJyfU//PCDfNy4cZa5hEIhvv/++ytJSUlBf//7303z58+vOnTokDwoKCjcw8NDHx4e3uLg4GACzPVcJRIJ7+Pjc0/ZXQzP811v1YvExsbymZmZPb0M0kuZGnSo/DQbpvqbH3gmBMM2juqIPgrGeh0qlv0CmHgwNkK4zegLkftdv+wjhBBCeh2T0YDygnyU5JxFxLAkKNzMDYz3/WMlco/+AAAYNOl1BMbE4btli9FQVQl7VzeMmPEuWJZFn/BIAEDR2dOoLi2BesjzsHV45EkmhBBCeiGGYU7zPB/bfiw7O7s4KiqqurN9CHlUmpqamEGDBqlOnz6dJxR2nmeq0WhYhULBVVRUCAYMGBD2008/5fn4+BgXLlzoZm9vz82dO9fq+zc7O9slKirK7/ZxKh1AyEPCtRpRvSHHEmS1T/KlIOsjJHSQwHFsEACAbzWiav15GGvuKaOfEEIIeeIJhCJ4qyMw6JVUS5AVAByUHnDy6gMACBrwrCXICgANVZX4zz8/Ado10vLvF4MBL02gICshhBBCegU7Ozv+ww8/vN5VM62kpKTg0NBQ9aBBg0Lnz59f3pbB6uDgYJo9e/Y9f0lApQMIeQh4A4fqL3JhqGgBANg+6wH58D49vKrezzbOHVyrEZp9ReAaDahanwO3mX0hsKd6uIQQQp5uAydOxsCJk9FYWw3OaLIEWds0VFXC3sW1h1ZHCCGEEPLopaSkNHS1zc8//2y14dq7775rtVlYVx5ZRivDMBsYhqlkGCan3dhihmHOMQxzlmGY/zAM43lzfBjDMJqb42cZhvmw3T4jGYbJZxjmMsMwf2w37s8wzKmb41sZhrlrhJqQR4oxZ1gCgE24MxzGBIJh7qsxHrlH8ue8IR9mDmqbarWoWp8DrsXQw6sihBBCHg9yJxcIxWLYu3ZsCGzv6gahmP75TAghhBDyMD3K0gEbAYy8bWwZz/N9eZ7vB2A3gA/bPXeM5/l+N/9bBAAMwwgA/ANAMgA1gMkMw6hvbr8UwCqe54MA1AF489GdCnkYeI6HqVEPY50WpkY9eO7Jrg/c/ny4ViMcUoLhMC4QTq+GgmEpyPprsn/BF7bPmMs0sGIB9GVNMNY+2e+z3vbnhRBCSM+R2Sswbv4HlmCrvasbxs3/ADJ7atZJCCGEEPIwPbLSATzPH2UYxu+2sfYpu7YAuoocxAG4zPP8FQBgGGYLgLEMw1wEMBzAlJvbbQLwFwBrH3jh5L7xHA9eZwLXagQrFYCVicAbTGg5Vw0IGQgdpKjdkgdTnQ4CRwmcJqmgLdKAq9MBLAPHceZ6my3nq6G7VGd9rAs24c6QqpzAGznU7yzsetH3OS/rKIHUX4HarfmW83FOU8M2zoOCrD2AYRg4jA0CIxNCGuiIuu0FHV6X1vw6cLXaLo/jMDYQjIBFa34ttBdqOh27G2mII2wiXAAAdd9dBroRIL19DtZRAhuVE2o253Y4D5HSlt5fhBBC7hnDsnDp44spf10Bk8EAgUgEmb0CDEvtGgghhBBCHqZfvUYrwzBLAKQB0AB4vt1TAxmGyQZwHcDveZ6/AMALQGm7ba4BeAaAM4B6nueN7ca9HvXaezvexIHXmcDKRAAAQ2ULDDdawBs52EabMyBazlVBe7EWnNYITmsE32q69bPOZAmdK0YHQD7YC7yJR923BXB+PcwSZAUAU50OtVvz4TA6ADUHSgAhawlu6q81ovnnCutjXRA4SSFVOQE8363t73de59fDLEHWtvOp+SIXbm/3g0BOt+H1BIZlIB/oicq12Xe8Lg6jA1Czv7jLYziMCQQAGK43W153a2N3w8qElkBr8y8VgKkbgdbb5nB+PcwSZG1/HvT+IoQQcr8YlqVGV4QQQgghj9ivHmjleX4BgAUMw/wJwGwAfwZwBoAvz/NNDMOMAvAdgOCHNSfDMNMBTAcAHx+fh3XYHsNzPLhmA3gjB0bIgrUVAQwAIwdOa84oNQdBjRA4SSFylYE3cWg4eNUcFG01gtcaO26rNYLXc4CQgfdfBwMAWs7cQOORa4CAgayfKxiGgaG8GS1ZlXdfIACu1RwDZ8QCgAFYG5ElaNTGVKcDaysCKxeDEd3KqGDFgk7HusJKBLd+7sb29zsva2v9fHgj1+Wc5NHhTfxd32fdxYrZO7a3NmYN0+49KJCLwXcj0Hr7HJ29v0wNOrBSARiRoJMjEEIIIYQQQghpTyAQxAQHB7fyPA+BQMB/8sknV5OSkpp/jblXr17tnJmZafvFF19cNZlMePnll/0EAgG2bt1azN7jnSVeXl6RmZmZFz08PIzR0dGhWVlZefn5+eLDhw/bzZw5s/ZB1/r1118rFi1a5MVxHIxGIzNz5swb8+fPr37Q43bXvHnzPO3s7EyLFi26cftzixYtcnNycjLNnj27Ji4uTrV8+fLSoUOHtnTnuB999JGrTCbj5syZc1/Nre7Vrx5obScdwF4Af25fUoDn+b0Mw3zKMIwLgDIA7Vu3e98cqwHgwDCM8GZWa9u4VTzPfwbgMwCIjY19ogsd8hwPw41m1Hxx65Zix4khaPhPMfQljXdsLx/eB4oRfgDLoDGjtOtiDUYevIEDI2LBSG++PUw3x8QCCORiCBwkYG2EYKQCsFIhWBshWOnNxzd/FnnZATBnGbr/PhYQsBA4SjoEjwSOEgidbeC54JkOS7BP8IF9gk+XY3fDiAR3HLcr9zKvqVFv9XwYId2C15MYYfffZ3djN8gLdoO8uhzriscf4+5p+7Y5Ont/mep1qN1aAKfJoRB72N7TsQkhhBBCCCHkaSSRSLi8vLxcANi+fbv9+++/752UlGS10/ztOI7DzQDtA62B4zikpqb6GgwG5ttvvy261yDr7bKysvIA4NKlS5KtW7c6PWigVafTMe+++67viRMnLgYGBhpaW1uZgoKCR3o7pdFohFDYdVjSYDDgyy+/dLlw4ULu/czzu9/9riYuLi701wq0/qpRIYZh2mepjgWQd3PcnbnZop1hmLib66oB8AuAYIZh/BmGEQN4FcBOnud5AIcBTLx5rN8A+P7XOYuexTUbLEFWwJzlVretAPKh3la357UmAOYalqytCKxMCIGzFCIvO0iCHGAT7gxZrBJ2g71gn+gDxUsBln1t+yvh9k403P8wwBJAtIv3hMcf46B8tz/cZkTB5TfhcHpFBYcxgVCM8IN8iDdsB7hD7GlnOY7Q2QYCezGc09QQOEoAwFJzkrUVPZLr9KixtqJedT69RW95Xaydh+PLKjQeuQZjZQsq12Sh8ccyapBFCCGEEEIIIfdAo9EIFApFWxlKfPDBB8qIiIiwkJAQ9dy5cz0BID8/X+zn5xcxfvx4v5CQkPDCwkLxa6+95hMREREWFBQU3rYdALz99ttegYGB4SEhIerp06dbD8wAmDp1ap/a2lrhjh07igQCAebNm+f54YcfKtueDw4ODs/PzxcDQGJiYmB4eHhYUFBQ+PLly12sHU8mk0UDwIIFC7wyMzPtQkND1QsXLnTLz88Xx8TEqNRqdZharQ47ePCgLQCUlJSIYmNjVaGhoerg4ODw/fv327U/Xn19PWs0GhmlUmkEABsbGz4qKkoHACkpKX6ff/654+1z7969Wx4bG6saNmxYkJ+fX8SUKVN8TCZzDGrHjh32/fr1C1Wr1WHJyckBGo2GBcxZubNmzfJSq9VhGzZs6FZNo127dtlHRka2iEQdP9ebTCakpKT4vfPOO54AsGrVKhc/P7+IyMjIsFdffdU3LS3NBwDkcjnn7e2tO3z4sKw78z2oR5bRyjDM1wCGAXBhGOYazCUCRjEMowLAASgBMPPm5hMBzGIYxgigFcCrN4OpRoZhZgM4AEAAYMPN2q0A8AcAWxiG+SuALADrH9W5PE54I2f1lmKBoxT2L/iBtRHczC41Z5oKHCSW7TwWPIOb8exuEdiLIbB/OF9gMCwDkdIWbm/361Dy4Elt7NPbzqe36C2vi7XzYGyEkKocob/WCJh4aHZfgbagDk4vh1DdVkIIIYQQQsgTIf39uar2j8OGPF/dP3lMzbGvN7lfPX9W8fwb0696hoS17vj7woDWhnrRax+tym+orhLuWvlRYPv9BoxJKQ95dnADukGn07GhoaFqnU7HVFdXi/bu3VsAmIOBly9flp47d+4iz/NITEwM2rdvn11AQID+6tWrkvXr1xclJCQUA8DKlSvLlEqlyWg0Ij4+XnXq1CkbX19f/d69ex2vXLmSw7Isqqurraa9fv/9906BgYHaEydO5N8eLLR6jdLTi5VKpampqYmJjo5Wp6am1rm7u5usbbtkyZKyFStWKA8fPnwZABobG9ljx44VyGQy/vz585LJkycH5OTkXNywYYNTQkKCZunSpRVGoxGNjY0dEi+VSqUpKSmp3sfHp++gQYMaRo0apZk+fXptV5m858+ft83KysoJCQnRDx06NPiLL75wHDlyZONHH33kcfTo0QJ7e3tuwYIF7osXL1YuX768HACcnZ2Nubm5F7u8EDcdO3bMrn///h3KBBgMBmbcuHH+arW6denSpRXFxcWi5cuXe5w5cybXwcGBi4+PDwkPD29t275///7NR44ckT///PPdKjfwIB5ZoJXn+clWhq0GQ3meXwNgTSfP7YW5xMDt41cA3Nt9ub1AZ7dGC+Ri2D/f5y574p6CrI8CwzK9KiDU286nt+gtr4u187BP9IUk2NHSWE5XUIcbH5+B22/7Qegk7aGVEkIIIYQQQsjjq33pgEOHDtlOnTrVv6Cg4ML+/fvtjx49aq9Wq9UA0NLSwubl5UkDAgL0Hh4e+oSEBEsd102bNjlt3LjRxWg0MlVVVaLs7Gxp//79WyUSCTdp0iS/0aNH10+aNEljbf7w8PCWwsJCaUZGhmzEiBFd1oZdunSpcs+ePQ4AUFFRIbpw4YLU3d29WzVl9Xo98+abb/rm5ubasCyLkpISCQA8++yzzTNmzPAzGAzsxIkT6+Lj41tv33fr1q0lP//8c+W+ffvkq1evdj906JD99u3bi+82X2RkZLNardYDwCuvvFJ77NgxO6lUyhUWFkrj4uJCAXNQNCYmpqltn7S0tLrunEubiooKUVhYWIf1vv32277jxo2rXbp0aQUAHDt2zPaZZ55pVCqVJgAYP358XUFBgeVDspubmzEvL+9X+dDckzVayX1ou6W4fY3WJ/HWaELI/ZH42kP5bn/Uf1+IlqxKiH3klhIDhBBCCCGEEPI4e+2jVVZrow6Z/JsKTP5NRdvjCX/885W2n+1dXI2d7XevEhMTm+vq6oTl5eVCnucxZ86c8tsbPuXn54tlMpml03VeXp54zZo1ytOnT190dXU1paSk+Gm1WlYkEuHs2bMXd+7cab9t2zbHtWvXup08ebLg9jmDgoK0CxcuLEtNTQ3cs2dPQWxsrFYoFPIcd6uZtk6nYwDz7fgZGRnyzMzMPLlczsXFxalaW1u7XfZzyZIlSjc3N8P27duLOI6DjY1NDAAkJyc3HT16NH/79u2KadOm+c+ePfvG7Nmz76hZGhcX1xoXF9c6ffr02qCgoEgAxUKhkG8rCWAymWAwGCxZfLcn9DEMA57nMXjw4IZdu3YVWVujXC6/py7iUqmU02q1Ha5BbGxs07Fjx+xbWlpuyGSyLmvqabVa1sbG5lfpXk6de54w7W8pdv/DALi93Q8ipe0Td2s0IeT+sVIhnCap4DQ5FI4pwWAYBlyLAVUbcqC/3tT1AQghhBBCCCHkKZSVlSXlOA5KpdKYnJzcsHnzZpe2+qFFRUWisrKyOxIS6+rqBDY2NpyTk5OptLRUeOTIEQUAaDQatra2VjBp0iTNunXrSvPy8jqtAZqUlNS8atWqkrFjxwZfunRJ7Ofnpzt79qwtAPz444+ysrIyCQDU19cLFAqFSS6Xc1lZWdLs7Oy7dkFWKBSmpqYmy/39Go1G4OHhYRAIBPj000+d2wKkBQUFYm9vb8N7771XnZaWVnXmzJkOa9VoNOzu3bvlbY9PnTpl4+npqQcAX19f/enTp2UA8NVXXzkYjUZLAOr8+fO2eXl5YpPJhG3btjkNGTKkcdiwYc2ZmZl2OTk5EgBoaGhgz507d9/ZQWFhYdrLly932H/GjBnVI0aM0IwePTrQYDBg8ODBzadOnZJXVVUJDAYDvv/++w71XwsKCiQRERF3ZPE+CpTR+gTqLbdGE0IejCzKFQDA8zzq/n0ZuoI6VBbWQzHSH3aDPOkLGEIIIYQQQshTr61GK2D+7LR27dpioVCICRMmNFy4cEE6YMCAUACQyWRcenp6kVAo7JAhOXDgwNaIiIiWwMDACA8PD33bbfD19fWC0aNHB7Vloy5evLj0buuYMmWKpqqq6vrIkSODDx06VJCenu4cFBQUHh0d3ezr66sFgJSUFM1nn33mGhAQEB4QEKCNioq6a8mAuLi4VoFAwKtUKvWUKVOq58yZU5mSkhK4ZcsW5+HDh2vasjgPHDggX716tbtQKORlMpkpPT29Q7Ypx3FYtmyZcvbs2b5SqZSTyWTc+vXriwDgd7/7XdXo0aODVCqVuv0xASAiIqJ55syZPsXFxdL4+PiG119/vV4gEOCf//xn8auvvhqg1+sZAPjzn/9c1rdv344Nh7pp3LhxmilTpvjfPv6Xv/zlxty5cwUTJkzw/+6774rmzp1bHhsbG6ZQKIxBQUFahUJhqWv7yy+/2C1duvT6/cx/rxhzz6mnR2xsLJ+ZmdnTyyCEkIeG53g0/nAVDT9cNbcaBCAJdoDTy6qH1tCOEEIIIYQQQu6GYZjTPM/Hth/Lzs4ujoqKqu5sH/Lk2r17t7x9I65HKSkpKXDlypXXIiMjOw3WajQaVqFQcAaDAS+88ELQG2+8UZ2Wllb/008/2Sxbtsz9u+++s1rK4H5lZ2e7REVF+d0+TqUDCCHkCcewDOwTfeE6IwqCm02xdJfqceOT02jNvaPsDiGEEEIIIYQQ8sRYvnz5tWvXrt21OdH8+fM9Q0ND1SEhIeE+Pj661NTUegCorKwULV26tOzXWSlltBJCSK/CaY2WRlltbJ/1gGKUP1ix4C57EkIIIYQQQsj9o4xW8jShjFZCCHkKWBplvaoCIzEHVptPlqNyTRYMN+5a3ocQQgghhBBCCCEPgAKthBDSC8n6uUH5bn+Ife0BACaNHoyIMloJIYQQQgghhJBHRdjTCyCEEPJoCJ2kcJ3eF41HSiF0kUJ4s35r08ly2KidqVEWIYQQQgghhBDyEFFGKyGE9GKMgIF9gg9kUW4AgNbcGtR/dxk3Pj4N3ZX6Hl4dIYQQQgghhBDSe1CglRBCniK6QnNwlTfxEDhIe3g1hBBCCCGEEPJoCQSCmNDQULVKpVKr1eqwgwcP2v5ac69evdo5LS3NBwBMJhMmTJjg9/LLL/txHHfPx/Ly8oosLy8XAkB0dHQoAOTn54vXrVvn9DDW+vXXXyvCwsLUKpVKHRgYGL5s2TKXh3Hc7po3b57nhx9+qASAlJQUv88//9zR2nbTpk3rs2/fPjug4zXpjunTp3vv3LlT/nBWbB0FWgkh5Cni8FIgnCar4Dgx2FJKoOFIKfTXm3p4ZYQQQgghhBDy8EkkEi4vLy83Pz8/d/HixWXvv/++d3f35TgOJpPpgdfAcRxSU1N9DQYDs2XLlmKWfbBwXFZWVh4AXLp0SbJ169YHDrTqdDrm3Xff9d29e/el/Pz83JycnNwRI0Y0Puhx78ZoNN7zPhUVFYLTp0/bJicn39cH2N///veVS5cudb+ffbuLAq2EEPKUkUW5QRbpCsBcSqBhfzEq/3EWjceugef4Hl4dIYQQQgghhDwaGo1GoFAoLBG+Dz74QBkREREWEhKinjt3ridgzhL18/OLGD9+vF9ISEh4YWGh+LXXXvOJiIgICwoKCm/bDgDefvttr8DAwPCQkBD19OnTOw3gTp06tU9tba1wx44dRQKBoEP2JgAEBweH5+fniwEgMTExMDw8PCwoKCh8+fLlVrNKZTJZNAAsWLDAKzMz0y40NFS9cOFCt/z8fHFMTIxKrVaHtc/eLSkpEcXGxqpCQ0PVwcHB4fv377drf7z6+nrWaDQySqXSCAA2NjZ8VFSUDrgzu7Rt7t27d8tjY2NVw4YNC/Lz84uYMmWKT1tQeseOHfb9+vULVavVYcnJyQEajYYFzBmos2bN8lKr1WEbNmywmrF6N19++aVjQkJCw+3jTU1NzNChQ4NXrFjhAgDz58/38PPzi4iJiVG99NJL/m3XOiQkRF9fXy+8evXqI+tZRc2wCCHkKWas05q/cjPx0OwpgragDk4vh0BgL+nppRFCCCGEEEJ6mRtrslTWxpWzo/MbfypzbsmqvCOwKIt2q5YP8qq5sSZL1f7ntv26mlOn07GhoaFqnU7HVFdXi/bu3VsAmIOBly9flp47d+4iz/NITEwM2rdvn11AQID+6tWrkvXr1xclJCQUA8DKlSvLlEqlyWg0Ij4+XnXq1CkbX19f/d69ex2vXLmSw7IsqqurBdbm//77750CAwO1J06cyBeJRF1eo/T09GKlUmlqampioqOj1ampqXXu7u5W02qXLFlStmLFCuXhw4cvA0BjYyN77NixAplMxp8/f14yefLkgJycnIsbNmxwSkhI0CxdurTCaDSisbGxQ+KlUqk0JSUl1fv4+PQdNGhQw6hRozTTp0+vFQisnpLF+fPnbbOysnJCQkL0Q4cODf7iiy8cR44c2fjRRx95HD16tMDe3p5bsGCB++LFi5XLly8vBwBnZ2djbm7uxS4vhBXHjx+3mzhxYl37sYaGBjYlJSVgypQpNbNnz67JyMiQ7dq1yzE3N/eCTqdj+vXrp46Ojm5p2z4yMrLlhx9+sHvjjTceSdMSCrQSQshTTD7IC+I+ctRuyYepVgvdpXrc+PgMHFNCYBPu3NPLI4QQQgghhJAH0lY6AAAOHTpkO3XqVP+CgoIL+/fvtz969Ki9Wq1WA0BLSwubl5cnDQgI0Ht4eOgTEhKa246xadMmp40bN7oYjUamqhpuoqAAACAASURBVKpKlJ2dLe3fv3+rRCLhJk2a5Dd69Oj6SZMmaazNHx4e3lJYWCjNyMiQjRgxotnaNu0tXbpUuWfPHgcAqKioEF24cEHq7u7e5X4AoNfrmTfffNM3NzfXhmVZlJSUSADg2WefbZ4xY4afwWBgJ06cWBcfH996+75bt24t+fnnnyv37dsnX716tfuhQ4fst2/fXny3+SIjI5vVarUeAF555ZXaY8eO2UmlUq6wsFAaFxcXCgAGg4GJiYmx3OqflpZW19nxunLjxg1RW9ZtmzFjxgTNmTOnYtasWbUAkJGRYZecnFwvk8l4mUzGJyUldQiourq6GsvKysT3u4auUKCVEEKechIfeyjfiUb9zkK0nKkE12JEzeZc2D7jDsWLAWDFd/8WkxBCCCGEEEK6424ZqPJBXjXyQV413dm3O5ms1iQmJjbX1dUJy8vLhTzPY86cOeXz58+vbr9Nfn6+WCaTWbpV5eXlidesWaM8ffr0RVdXV1NKSoqfVqtlRSIRzp49e3Hnzp3227Ztc1y7dq3byZMnC26fMygoSLtw4cKy1NTUwD179hTExsZqhUIh374hlk6nYwDz7fgZGRnyzMzMPLlczsXFxalaW1u7XfZzyZIlSjc3N8P27duLOI6DjY1NDAAkJyc3HT16NH/79u2KadOm+c+ePfvG7Nmz77jWcXFxrXFxca3Tp0+vDQoKigRQLBQK+baSACaTCQaDgWnbnmGYDvszDAOe5zF48OCGXbt2FVlbo1wuv/dOYDdJpVLu9usxYMCApgMHDihmzJhR253at1qtlrGxsbnvNXSFarQSQggBKxXC6RUVnCaHgpGaA6vNpypQ+T9Z0JdRoyxCCCGEEELIky8rK0vKcRyUSqUxOTm5YfPmzS5t9UOLiopEZWVldyQk1tXVCWxsbDgnJydTaWmp8MiRIwoA0Gg0bG1trWDSpEmadevWlebl5ck6mzcpKal51apVJWPHjg2+dOmS2M/PT3f27FlbAPjxxx9lZWVlEgCor68XKBQKk1wu57KysqTZ2dm2dzsfhUJhampqsmTGaDQagYeHh0EgEODTTz91bguQFhQUiL29vQ3vvfdedVpaWtWZM2c6rFWj0bC7d++Wtz0+deqUjaenpx4AfH199adPn5YBwFdffeVgNBot0dXz58/b5uXliU0mE7Zt2+Y0ZMiQxmHDhjVnZmba5eTkSADzrf3nzp17KLXpVCqVtqCgoMOxli1bdt3BwcGYlpbmAwDPPfdc04EDBxQtLS2MRqNhDx065NB++8LCQmlUVNQdGb0PC2W0EkIIsZBFuULsI0ft1nzoixtgrGqFvqQBYi+7rncmhBBCyP/P3p3HV1bX9x9/fc85d783ucnNMplkJpnMxswkDMuwCoigSBGxPymCFHChICL+XGv1p5ZWa22Ltu6KVkQsCLW1bqVF0VZZRJ0Bhtm3zJZJZrJvd7/nfH9/nJuTXHJnZWYyyXyej8eQm7N+T0K29/mcz1cIIcQpZrxHK4DWmq9//eu7LMvizW9+88iGDRuC55133hkA4XDYefjhh3dallUyQ/BFF12UbmtrSy1cuLCtoaEhN/4Y/NDQkHnttdcuGq9G/fSnP733UOO4+eabh3t7e7uuvvrqxU8++eTWhx9+OLFo0aIVZ599drK5uTkDcP311w9/85vfrG1tbV3R2tqaWbly5SFbBpx//vlp0zT10qVLl998881973//+3uuv/76hY8++mjiiiuuGB6v3HziiSdiX/rSl+ZYlqXD4bD98MMPl1SbOo7DfffdV3/PPfc0B4NBJxwOO9/+9rd3Arz3ve/tvfbaaxctXbp0+eRjArS1tSXvuuuu+bt27QpefPHFI7feeuuQaZrcf//9u2666abWXC6nAO699959Z555ZvZIPl+Hct111w1//etfr/3gBz9YUoX8wAMP7H3LW97SctdddzV94xvf6Lz66quHly9fviKRSOSXLl2arqystMGtHN61a1fgsssuO6JWDMdCaX16zTC9atUqvXr16ukehhBCnNK0oxn9n73k9o2RuHUZSily+8YwYz6ZKEsIIYQQQggxhVJqjdZ61eRla9eu3bVy5cq+g+0jZq6f/exnsckTcZ0s55577tInnnhie01NTdkJwsCt0K2srHRGR0eNiy66aOk3vvGN3ZdccknqoYceiq9Zsyb8xS9+seuVjmPt2rU1K1eubHn5cqloFUIIMYUyFBVXzkdrjVIKJ+32bdU5m6rrFxNaMWUyUCGEEEIIIYQQ4oS67777Onfs2OGvqak56OP/t9xyS/O2bdtC2WxW3XTTTf2XXHJJCqBQKKhPfvKTB07k+KSiVQghxGGN/a6bof+YuFEZOX8OldfKRFlCCCGEEEIIl1S0itPJwSpaZTIsIYQQhxW9oKF0oqzfy0RZQgghhBBCCCHEZBK0CiGEOCLhlbXUv/8c/C0VABR60/R87UVGf9OJdk6vpyOEEEIIIYQQR8RxHEcdfjMhZo7i/9NOuXUStAohhDhiVjxI7Z1nUnFVs/sTxNYMP76TvgfWY4+84kkkhRBCCCGEELPL+t7e3koJW8Vs4TiO6u3trQTWl1svk2EJIYQ4KspQVFwxn8CiOAOPbcHuz5DdPsTgD7dT8/YV0z08IYQQQgghxCmiUCj82f79+/95//79bUixn5gdHGB9oVD4s3IrJWgVQghxTALzK6j/v2cz9JMO0uv7iF+3EACdt9GaWT1RlnY0TjKPLjgoy8CI+FDGzLtJP1uuQwghhBBCnJrOPffcHuC66R6HECeLBK1CCCGOmRGwqL5hCYXXNWPFAwAMPb6T7PYhqm86A39jdJpHeHyN96LNH0jS/9BG7MEsZlWAxK3L8c2JYI/kKPSnixsX/zO5fW3xtRkP4KsLA5DZOugeV5fbdmJZcFk1yjTI96bIdycBCK1IuMt6UuS7xkr2dXfXJef11YXxz4sBkNrcjxnxMfDI5onruG05vvqIhK1CCCGEEEIIcQwkaBVCCPGKjYes2Z3DJH/bDcDwf3ZQc0c7Sp16oZ0uODjpAhgKM+IDILnmAE6qgJPOF98WcFJ59226gJMqoDMFGj52vheyAtiDWfq/t5G6u88i/VIvw4/vPOz5o5c0Er+2FYD+f9mEztmH3WfuX12EMg0ymwa8c3jLNg8c8XnHg1bl4IWs3nU8tJHqm87AHskRWFCBGfUf9phCCCGEEEIIIVwStAohhDhu/M0VVLy+mbGn9lH1J0tQSqHzNk66gFkROCHndNIFCoMZnHSBQEslylRkd4+Q3tiPTk0NS510Hp1zJ4icHHgO/WQHOnv4wFMXHC+cHGcPZtGFspNOHt7R5tDltj+GLNsIWWWvA1sz8PAmAKy6MHXvWYkRsNCOlkpXIYQQQgghhDgECVqFEEIcN8pQVLxmPtGL5mIE3R8xQ4/vJN+bouqNC1F+c0ovUO1osB2Uz+3pmtk+hDOWmwhGywSlTqpA/QfOxYz4SL3Yw9CPdwDQ8PELMGN+8vuTjP2687DjddIF77URtrCzNig3hDRCFirsc1+HreJbH5gGZlWgJKQ0qwIoyyB0Zi2+xigTRbzKDUEn55NKYcYmKkVr72ifaBnwsu0mL1PFnrfhc+oJLq5yD11cFjm3nuDS6in7q5cdUwUnfuyb8UDZ63DSee99XXAwAu4+w//ZQWbrIMHFVVS+sfWUrFQWQgghhBBCiOkkQasQQojjbjxkzXUnyXeOUfH6Fvoe3OD1Aq26YQmjz+wj1zGCzhQIn1NP9Q1LABj8t63YQ9lDHR4AJ5XHjPgwwhM/ypx0ATPmxwhZYKpiQOorCUonB6e+ORFv39q7VmL4DFTQOmTlpnY0iduWl/ZovW25Fx6Pt1E4Uv6m2FFtb0Z8XruDcUbY54bAR3OcykDZ67ASQWruaCfbMVwyoVm2Y5hCb5pc2OeFrEP/2YHO2PhbKwksqDzqaxdCCCGEEEKI2USCViGEECeMvyFC/PrF9H93Q0kv0MEfbCV+bSv9GwaAl1WWhqySoFX5zUlB6URYOl4B62+pJHHLMlTIwiwGfaG2Ghrba46q6tKqPLKQUBkKX32EurvPQhecKRW6M8WhriO4ME5wYdzbVmtNqL0GI+IjsKDSW5Z6oQdnLE/yD/sBMKuDBBZUEmh1/1lVwWm5NiGEEEIIIYSYDhK0CiGEOKGMgFm2F6hZGSByUcOUytLqm5aCKlajBi2UZRzy+FZlYEpIeqJDT2WUPv4/Ux3pdSilqLhiPlwxsUxnbHwNEXK7RtB5tz+tPZAhNZAhteYA4LYnCLRWEv/jRSXVsUIIIYQQQggxG0nQKoQQ4oRSVvmepmZlgKo3LZqyva8+MmWZOPUYIYva29vRtkOuc4zszmGyHcNu8JpzJxWzh7Jktg2ifG5YPvpUJ/mupFvxuqASMxGUXq9CCCGEEEKIWUOCViGEECeUEfEdtKepmPmUaRBoriDQXAGXz0PbmnzXGNmOYbI7hzEr/F6Yml7XR27PKKkXegAwKvylrQZqQhK8CiGEEEIIIWYsCVqFEEKcULOlp6k4MspU+OfF8M+LEXt1k7dca40Z86OCFjrj9uR1RnKk1/aSXtsLgBFze8BWXtMqE2sJIYQQQgghZhwJWoUQQpxws6WnqTh2SikSty5HO5p8d9JrNZDdOYwuTobmjOZJr++j6vrFAKTW9pBe309gQSWR8+cctl+vEEIIIYQQQkwnCVqFEEIIcdIoQ+FvjOJvjBK7pNENXg+kyHUMke0YRhccjID760lm0wDpdX1ktg8RubABgNS6XuzhHIHWSnxzIlIZLcQR0o7GSeblyQIhhBBCiBNIglYhhBBCTBtlKPwNEfwNEaKvaixZZ4R9GFEf/nkxLxBKPtdNdsewu2/IItBSQaA17vZ4rQ+j0wUJkoR4GSdvU+hJ0f8vm7xe2dVvPQM7VcCK+vA3xQCwR3Ion4EKmPK1c5JIAC6EEELMLhK0CiGEEOKUFL9uIZVvbEVnbcANJOyhrLdepwtkNg2Q2TSAf16MiqtbGPy3rSWTrvnqpepVnJ6yO4dJ/mE/+a4kFa+bz9DPOrAH3a8fezDLwPc3E7+2lbHfdlN9gxu09nztRe9rTAVMjKCJCloYQWvSa5OKq1owIz7yvSnynWOooElwURzlM3FyNtj6tA1rtaPRBQeds9E5B51335oVfszKALrgkHqxF523UQEDKxFm4NHNpQF4Mo/SEDyjGmUo92MKbgguEwYKIYQQpzQJWoUQQghxylJKoYLuryvKUNR/eBWF/gy5jmGyHUNkdw5jD+eIXd7khazgBkn9D20k8bYVjP5qD/6mGL5iywIjKL/+iJlPaw24XyPZPSOM/aaTXFeSurvOxKwIYA9lST3fA4AR8nlfG+PswSxGyIcRNL1lTsaeOH7Wxs7aMJybcu6K1zUDkN0+xNCPdwDQ8PELMH0mqRd6GPqP7e7YDhHWBhbFCbfXApBe3wemwgha3nojZKH8Bw9rX2kl6PiNm/Eg1MnZ6PzUgFQ7morXzAMg9WIP6Q39oDWJW5YDMPK/e0k+1z2xfd4pe77KaxYQu6wJbWsG/20rAIlbl3khK5QG4P3/sonGz1wCwOivOxn95R6wDBo/dTHKUCRX7ye9rs/9OIUsjJDlfj5DFkZ4/H0LszKAEZLveUIIIcTJIj91hRBCCDFjKKXw1YTw1YSInD8HrTX2QAadd8oGSdgO6Zf6SL/U5y23akMTwWtTFN/cKIbffPmphDhlaEdT6E+T70qS7xoj1zVGvjtJzdtX4G+KofMO6fX9AOS6koQqAvjmRjDCFr65UbAUZlWg5GvErApgxgPELmvylsWvbcVJF3AyBXSmgJOxS16Pvx2/WTE5mB0PbHWmMDHuQ4S1yjK8oHXgB1u9yvXSjUD5zWIAa1J310qMkEVmzzDKgYHHtkxUgt68jMyWfgoH0uicjTM5LJ0UosaunE/F5fPA0ez/hz8c/oNvKC9ozR9IkV7XB4YbdCul3Gscyh7mIKAnVaV6hz5UAB6yvOB4fMJAZSlvWb47SWbL4GHPW3F1CxWXz0PnHbr//vdeADv+b0pIG7EILUsA4GQLoNRRVdJKKwQhhBCnOwlahRBCCDFjKaWwEiHs0VzZIEnbGiPmwxnNe8sLvWkKvWl4oad4EKi+aSnhlXU4OZv8/iT+hmhJICLEyZTvSZHbNeIGql1j5Pcn0bmplZK5rjH8TTH8DRHMeMC9aRBwA0+rLkzDJy90w0BHk7htOf0PbSxprWHGAyUhWGRV/VGNM3pRA6G2BDpjg+V+vQQWxql8w4KSYLZsWBvxAcVH7XNlQlYAPTmsnQgpDcOg//ubSitBH9lE/NpWRp/ce8gxe+cyFSj3HIdUbAWgLAOzwo9VG0L5TXA0mO7kfuGz61B+w63A9Zson4HhN91lPvetrzYMuJX5cz5yHspvoB1dPgCvClB755nesuDyBEaFv2SsZoUfX2PUDcZThZKAe7LxalYnnccZc/8dihG2CP3lRQCMPLGbsWe7MKI+5n7iQgBGn9lHdutg2ZDWrApgBEz6H94kLVyEEEKctiRoFUIIIcSMZ0R8ZYMkX32EuR+/EHskS65zjFznKPl97lsnWQwmNFiJEAC5vaP0fWsdGIqa29sILozjpPIUBjL45kRQloSv4vhx0gWvOtUezhJ/QysAY0/vI/n7/eV3MsCqDeOfG8WqDrqLwj4aPnp+yWaTKxCVofDVR6i7+6zjWmloFFsCTOZvinmTax2p+g+eiy4Gsm4oO/56IpjVeXvi689U5StBIz4vCFU+N/g0JoWdym8SaK4A3I9P1Q1LUJbhbTsRkJoly8Y/TtGL5hK9aG7JeUNtNYTaao7qesc/bwcNwCtLA/DgojjBRfGSY8RePY/Yq+d572tHux+rdOk/f2PU3cBQRM6fM7Eulfde68mVyZPaDDgpN5SdfNMpv2/soJW0iVuX0V+mhUv1jUvJ7RnFqgvjqwtPCfiFEEKI2USCViGEEELMeIcLksyKAKHlAULL3UditdbYw1nynWPkOsfwzYkAbogAgKPx1brha2brIAOPbgFT4WuI4G+Muq0HmmL46sIoUwIDcWhaa5yRXLFCtfj4f3cSeyBTsl3FlfMxgsXH/XEDLl9DBN/cqPv/3twovjlhlO/oW10oQ2HG/Mfleo4nZSiv2vNImVF/2UpQKxFizodWHfFxIuccXQXv8XY8A3BlKFTYhxH2lV1vRv1UvXlx2XWTQ1ptT5TNhs+uw3rZDSa3cjpykJC2fCsEHBh+fOfEWH0GVk0Iqz6MrzbsBrBzwkf9/4EQQghxKpKgVQghhBCzwtEESUoprHgQKx4sqUYLn1WLGQ+QP5DCrAgAkOsshq+2Jt85Rr5zjOTvitWGloF/bgR/U4xQWw2B1srjek1i5tGOptCXRtsaf4Mb4B/4/BoKfelD7meELQqDWfwNFqG2BIHWSqyakFT+lXGwCvbxdgQzyakQgB8spA0urSa4tLpkWeVVLVRe1eK9XxLSQtkA3MmWtjXQeYd8d5J8d5LxrwpfY5T6954NwOhvOt1q3Hkx7+aYEEIIMVNI0CqEEEIIUWRWBAifWVuyLHrxXPyNUXKdo+T2jZHfNzYxs3jBIbdnlNyeUcx4gEBrJbrg0PfAenxzi5NtNUaxEhKYzRYlk/2YCjtVQKfyBFrdR7t7vvIC+a4kwaVV1LyjDQAj5oNJQatZ5fZT9RcrVX2NUcwKv/e4vxn1Y0ZPverTU8WJaoUgjt7kkPZgrRCsujANn7iAQk+KfE+6+DZFoSeFPeJOlOarm6hmTa4+QKEnRfCMai9o7X94E07WxlcXxqoLuW9rw5gzMFwXQggxu0nQKoQQQghxCFZ1EKs6SPjsOqBYsdibKu352pX0+lLmu5NkO4bJdgx7x1ABE39jFF9TDH9TFH9jFLM6eMQzeYtTg50pUOhNM/DIxGQ/VdcvIbl6vxe0WrVh8l1Jct1Jb7/oBQ3Yy2vwzXUf/5/cB1Mcm1OhElSUOmQAXrx5MP51Ms4pfk2N94HVjnYnKVPuhG7gtt7IbBtCZwpkt5b2hzUivpLgNXJePUZAvr6EEEJMH6X14abanF1WrVqlV69ePd3DEEIIIcQsom0HUChTke0YZujxDvLdSbAP/ntWYGEltXe4M4vn9o5ixPyYle7M4l7FpFTqTTs7mSezqZ/0+n4i59Uz9LOOKY9Gx69tJbi0GmUZZLYNUhjMuNWqjVEJ04U4BjrvoG0HI2ih8zYDP9hKoSdNvi8FhYN/X537qYsx/CZjv+8m+fv9xQrY8V6wIazqkPTVFuIEUkqt0VofebNsIWahE3q7Tyn1AHAt0KO1bisu+zTwJsABeoC3a627lPtb6BeBa4BUcfnzxX3eBnyieNi/0Vp/t7j8XOBBIAQ8DrxPn27JsRBCCCGmnTInJosJtFZSf8/Z6IJDfn+ypPI1fyDp/gYEWDUhb5+BRzdT6M8Qu6KJUHvtlEdvffURCVtPIu1olKFwsgW6P/s7L9iJXdZUdrIfa04Eip+f4OKqkz5eIWYb5TO8KlflM0ncvAxwvzbtwUyx9UDaa0GQ70lhhCwMvztRXH7fmNdTu4SpsGrGK2BDhNpq8Bcnn5uspEXILLjhNduuRwghTmUn+rmKB4GvAA9NWnaf1vqTAEqp/wv8JXAX8EfA4uK/C4CvAxcopaqBe4FVgAbWKKV+orUeLG5zB/A73KD1auC/TvA1CSGEEEIclrIM/E2xYkuBBgB03ibXnSTfOYZvjvtYrJPKU+h3Z58PLa/xQlZwQ7z+hzaSuGU5GMqdcV4qJE+IQl+a9IY+0uv7sWpDVL9lKUbAIjC/gmzHMEbEAkOVnezH8JsSWghxEihDYSVCWIkQLJtYrrVGpycm3bJqwwRaK8n3pHDG8hMb2prCgRSFAyl3u+og/rlR7NEcPV9fi68uTOx181GGKr3hdetyzKoAqlxJj6m8dgVOpgDO4et+VMBCmQptO+isfdBlh3SE59Wq+LPke3IDTwghToYTGrRqrX+jlGp52bKRSe9GcMNTcKtcHypWpD6nlIorpRqAy4FfaK0HAJRSvwCuVkr9L1ChtX6uuPwh4I+RoFUIIYQQpyjlMwnMryAwv2Jimd+k9q4zyXWOoYJW2YpJnbXp/eZLGDE/wcVxgkuqCCyKy4RJr4DWmvz+FOn1fWQ29JHfn/LW5XvTXuVXxWubAY2/uRIUs2a2eyFmE6XcSbnGxS5pJHZJI+DezMr3lk7Cle9JYQ9lvT6w+Z4U9kAGeyBD5TUL6PvO+tIbXt/bSPzaVvq/t2nKuSdPfNf7z+umVtGWUfees/DPi5HZNkT/gxsOuuxQjvS8iVuXlbQ8Gb+BV/XmxYw900XF61vwN0SwR3Nktg9hRnwYUR9m1OdWvk56YkMIIcThTUuncKXUZ4DbgGHgNcXFjcDeSZt1FpcdanlnmeXlzncncCfA/PnzX/kFCCGEEEIcJ8oyCLRUEmipxB7Nla2YdNJuRZYzmiP1fA+p53tAQfCMamretgJwg0Opdj00rTW5PaOkN/ST3tCHXawknsyqCRFqS3hBa6C1smS9zHYvxMxihH0Emn0EmitKljs52wsRDb9JqC1BvieNMlXZG15GaGbeUDFCvrLXo0yDzOYBKl7r/n2c2zfG4GNbpuyvQpYXuppRH/6WSi/EznYMAcoLZlXQku+HQojT3rQErVrrjwMfV0p9DLgHtzXAiTzfN4FvgjsZ1ok8lxBCCCHEsTIivvIVk0GL+JsWktk6SHbHMDpng3YDhHF931qHCpgEl1QRXFxV0gP2dKZtB3s0hxUPgob+f9mIM5ov2cY3N0JoRQ2htgRW3aHbM8hs90LMDuP9XAH882JuixY46A0vFTCpvLZ1ynGsqqD3OnZpI/bLvr+UY8YDAPjqwt4xyy07lCM9rwqYZa9Ha42vIeJ9PytpsTCJThcopAvQmy4eUEExaB388Q6vDQMAhvICWSPqK1bH+old1oRZ4ccey1EYyGBG/ZiVAZmYTAgxK01L0DrJw7i9Ve8F9gHzJq1rKi7bh9s+YPLy/y0ubyqzvRBCCCHEjKQMddCKyehFc4leNBddcMjtGSGzdYjAQrfa0k7mye4cBg2ZTQMAmNXBYugaJ7AwjhGc7l/7Tr7hX+wm+dsuzOog9fecjTIUoRU1JH/XjX9+BaG2BKEVNVjVwcMfTAhxWjjYDS9ffQR/49SJsyYLr6w7qnNZ1UGvOvRQyw7nUOfVjj7o9dS/7xxvu9CZNfibYzhjeeyxPE4yN+l1HnvMfX/y98sp4ayjcUZzOKO5ksXRi+cCkNkyyOAPtgIw58OrsGpCpDf2M/I/e1/WssDvhbVucOvHiFheBbJM7iWEOJWd9N+4lVKLtdbbiu++CdhcfP0T4B6l1KO4k2ENa627lVJPAH+rlBqfwvUq4GNa6wGl1IhS6kLcybBuA7588q5ECCGEEOL4O1zFpPs4e5xAa9xbpnM24bPryGwb9Ko17YEMyee6ST7XDYbCPz9GaFk1sVfPO9ihZzQnUyCzZYDszhHib1roVqU6GidVwEmNURjKYMWDxC6fR8UV8zErpCpVCDHVoW54zURHej2G38SoDUPtkR+75s/acYoBrJ3MF4PZHM7462QeZzSHEXWfvnCSE8Hs+LLCQIb83lEOVwccOX8OVW9ejHY0ud0jDPzrFi84rr7pDHL7RnHG8ijLQFkGmIrohQ0o0yDfkyJ/IIUyFcGl1W57iJEc9mgOZSk3wC2+VaaC4jFO9OdcAmMhZqcTGrQqpb6PW41ao5TqxK1cvUYptRRwgN3AXcXNHweuAbYDKeAdAMVA9dPAH4rbfWp8YizgbuBBIIQ7CZZMhCWEEEKI045VFaT6LUu9CZ6yWwfJbBt0q1xtDY4mt2sEFF7QOvbMPq/VgFkRmOYrODZ2Mk9mYz/pDf1ktg261wpEgfjJnwAAIABJREFULmjA3xAh1F5D/kCKUFvC669oxWfmtQohTp7Z1iLkRF2PvyGCO7/1kQm112DVhtxANOC2bjAr/AQWx0uqZ3GmdvubHNaOh6zg9psdeHSzO1nZTzpK9ole0ABAekMfI0/sBqDx068CU5Fcc4CRJ3YdesAKYpfPo/L1LQDs/9xq0NoNYk1VDHQNL6xVlhvSVv/JEpRlkNnm/ixWpkHFa+ejTINc5yi5zlGMmB+zIsDAI5umVBpL2CrEzHZCg1at9VvLLP72QbbVwHsOsu4B4IEyy1cDba9kjEIIIYQQs4VSCn9DBH9DhNirm3ByNtmdw27wunWQ4GL3ASHtaEZ+uQcnVSDUXkPiT5cBkNs7im9OGOUzD3WaaVUYzpLZ0E96fZ/XLmEyFTAp9KXcj8PcKDW3LZ+egQohhChhVQVLessChM+sJXzmRBmtdjQ6U5jUssBtY+Cb67Zt0AWn/GRl4TKTlRV7wOqCLrPMOfyANVDMPLXWFPrTU37mlPWWpQDkdo8w9hu3u2HF65oBt33CyC92k7h1mReyjl9D/0Mbqbv7rFkV8gtxOjr9mnUJIYQQQpwmDL9JaGk1oaXVgPsHLIA9mEEX/8YcD1+drE3PN9aCUgRaKwkujhNcUnXYyaFOpqH/2snYrzunLDeiPkLLE4RWJAgsjLtVRkIIIWYcZShU2Fc+OMVtn1Nuci+rJkTjZy8BR6ML2n0cv/izK3pRA6G2GrAdr1o0fFYt/rlRtO2gbQ0Fx31d3Nd9XxNodXuhoyF8Tn3JOnd7Bwra2xdHT1SkKoXyGehJy8YDXiPkKxsYH1EALIQ4pSm3kPT0sWrVKr169erpHoYQQgghxLTStia3bxQrEcKM+EhvHqD/wQ1TtjMr/QQWVxFcXEVgURwzUv6P3+M6Nq3JdydJr+8js2WQ2jvbMQIWyRd6GHxsizuueIDQigShthr8zRXyqKUQQpwGtKPJH0iWndxrJvwccHI2OmujCw6933xpSmA80ytalVJrtNarpnscQkwnCVqFEEIIIYQ7mdS2IbLb3DYD9lB26kYKwmfXUV18LFJrfdyqXbWjye0dJb2+j/SGfuyBjLeu+uYzCJ9Zi5MuMPrrTkJtCXyN0VOm0lYIIcTJMxsmkZrpgfHBSNAqhLQOEEIIIYQQgBG0CLfXEG6vcXvR9aXJbB0ku3WQbMcwOu+Axqu00VrT88XnMatDBJdUuW0GqoOHOUspbTtkO4bdcHVjP87o1HmnfY1RdxZowAhZVF7d8oqvVQghxMw1GyYrU4bCVx+h7u6zZnRgLISYSoJWIYQQQghRQimFrzaMrzZM7FWN6IJDdtcImW2Dbp87oNCbJr8/RX5/iszGfgCsmhCBxXG3zcDCOEbAnFJ5pAIGymeilGLkl3sY/dXel50c/M0VhNpqCC1PHHV4K4QQQswEsyEwFkJMJUGrEEIIIYQ4JGUZBBfFCS6KlywPn1VLZtsQTtKtRC30pSn0pUn+thtMRXhVPZFz6xn4/mbv0ciq65dgxv34asKElifcoNVQBBbF3Z6ryxPyh6cQQgghhJiRJGgVQgghhBBHzVcXpvqmM9w+c91JMtuKbQZ2j4CtwdaEllR5ISu4MyoP/vtWEn+6zD1GY5Tqt55BcEkVRkh+LRVCCCGEEDOb/EYrhBBCCCGOmTIU/sYo/sYoXD4PJ2uT7Rgis3UQI+ovmVEZ3LBVBUx3X6UIr6ydjmELIYQQQghx3EnQKoQQQgghjhsjYBJaliC0LIE9msOsCpSErWZVACMov4IKIYQQQojZx5juAQghhBBCiNnJiPhI3LYcsyoAuCFr4rblGBHfNI9MCCGEEEKI40/KCYQQQgghxAmhDIWvPkLd3WehCw7KMjAiPpShpntoQgghhBBCHHcStAohhBBCiBNGGQoz5p/uYQghhBBCCHHCSesAIYQQQgghhBBCCCGEeIUkaBVCCCGEEEIIIYQQQohXSFoHCCGEEEIIIYQQQpxE2nGwBwbQuRzK78esrkYZUgsnxEwnX8VCCCGEEEIIIYQQJ4l2HLJbt7HrxhvZfsWV7LrxRrJbt6EdZ7qHJoR4hSRoFUIIIYQQQgghhDiBdKFAZutWhv7jR6RfeonO99xNfl8XAPl9XXS+527sgYFpHqUQ4pWS1gFCCCGEEEIIIYQQJ8Dor35F//3fJLNlCzqTAWD+9x7CrKkl/pd/i6pKoAf7Gf3aP6FzuWkerRDilZKgVQghhBBCCCGEEOIY2UNDjD75JOkNG8hs2EjF619P4vZ3AqBzOdJr15buoCH8l5/n8Ud2M9q/h1giyOv/8nOoYGgaRi+EOJ4kaBVCCCGEEEIIIYQ4DCeXI7t1G5kNG0Brqm66EXCD1u5PfNLbzqqrJYEbtAbb2gitXElwxQp8Z6wg1L4c3dDCE/c9z2i/W+E62p/hiUf2cP1HzpWQRogZTr6GhRBCCCGEEEIIIV4mu307qdWryWzYQHrDBrLbtkM+D4CvsdELWn3z52NEo2jbJrhsGaEVK7xjGPUNOB/7Mi+t6WHXs31c3VZLla29kHXcaH8Gp6BP3sUJIU4ICVqFEEIIIYQQQghx2nIyGbJbtpDesIHIhRcRaF0AwOAj32fwkUfK7mMPDmKPJTGjEZRh0PrjH2HNmYMyTQp5m44Xe9m+podd6/rIZ2xvv+1rerjgulZiiWBJ2BpLBDEsdWIvVAhxwknQKoQQQgghhBBCiNOCk07jJJNYNTUAjPz3E+z70IfAdsPQ+o991AtagyuWA2BEIgSXLye4YkXx33L8LS0ow/COq+rmsHPdADue72HnS6XhKoDlN2hpr6H1rFrCMT/XvLudx7++jtH+DLFEkGve3U445j8ZHwIhxAkkQasQQgghhBBCCCFmHSeZJLN5M5kNG9x/GzeS3dFB5XXXMffvPguAf/48L2QFyGzb5r2OXXkl4XPPxTd/fkmoOtneTQNs/m33IcPVhefU0dyewOc3vXWJuVGu/8i5OAWNYSnCMT/KkIpWIWY6CVqFEEIIIYQQQggxI2jHwR4YQOdyKL8fs7oaZRjYY2MYoRDKdMPMvffcw9gvfwV6at/TzIYN3uvAokUk7vgzt1J1+XJ88+Z568x4HDMeL9nXzjt0dwzTtLQKgF0v9bH19we89YcKVydThiJSGTj2D4QQ4pQkQasQQgghhBBCCCFOedpxyG7dRud77ia/rwtf41zm/sM/0P/dhxj7+c9Z8MN/J7jcfdzfjMZKQlazqsoLU0NntnvLld9P3Yc+dETn3/F8D796aBO5jM0tn76IytoQC8+tY+MzXTS31bDo3Dqa2xL4AuXDVSHE7CdBqxCnuYPdEZ6pZsv1zJbrEEIIIYQQ4pVwUikymzaRXreOYFsb3X/xF+T3dQGQ39dF10c+Qv1HP8bYz39OZuNGL2iNXXUV1px6gitWEFqxAquhAaWO/NF8O++wZ9MA29cc4OzXzaemKUa8Pkyu2B5gx/M9nPP6ZhpaK3nnfZdKuCqEACRoFeK0obXGGRvDHhrCHhrGHhpCa41VFWff+98/cUf4c58j/dI6Ct3d+ObPo/rmmwGwh4bo+8b9Bz1+xRuuIdTu3hkee+ppks88462reutN+JubARj+2X+SWb++7DHMygpq3v1u7/0Df/f33usjGYvV0EDozHa6Pvxh73oav/BFUs8/T6G7+6SOZdyxfFzKXUfTV79GYMliCVuFEEIIIcSsprVm8JFHyKzfQGb9OrI7OsBxAJj/vYe8kHVcfl8XVn09NXffTbCtzVseu+I1xK54zVGde3K4umttnxeqRuNBappiVM+N0P6aJuYuitPclgDcFgASsgohxknQKsQM5KTT2MPDGNEoZjQKQOqFF0j9/g/FIHWImnfdib+lBYCuT3yC4R/9GAqFkuM0ffnLXsgKxTvCH/4w9R/9GD2f/Szh886bCBTHkgw8+OBBxxRYssQLFNMvvliybfQ1r/ECxeTTTzP8ox+VPYZv7tyScHPyMY5kLE1f/rIXTo5fz773v8+7npM5lnHH8nEpdx2d77mblsce82ZHFUIIIYQQYibT+TzZ7dtJr19PdstW6j/+/1BKoZRi4NsPkO/qmrKPM5bE1zi3JGz1Nc7FN7eB2v/73mMax3i4umNNDzvX9nrh6jjLZ2AX3KBXKcVlNy45pvMIIU4PErQKcYyO56Pd2R07vIBU+f1EL70UgMLgIPvv/St33fCwt43OZgFo+JtPE/+TPwEg9dxz9H7xS94xK665xgtalWlNCVkBjHhl2TvCZlUcIxxGBYPecqXACIcPeg3KmriLq3y+km2VOfFxUYHAQY+jwqHS8U0+xhGMxayKH/J6TuZYvPXH8HE52HXoXO6g5xFCCCGEEOJUpW2b3M6dWPX1mLEYAPs+9GFGf/5zb5vqW/7U+/sl2N6OnUwSWrGCYFsbwfY2Qm1tmHV1NH31ayU9Wpu++jXM6uqjGs+RhKvN7QkWnlNHS3uNVKwKIY6Y0mVm4JvNVq1apVevXj3dwxAzXLkm7E1f+Qq+BQswiyFc+sUXSb+0zgtHJ4elKMWCf/uBd7zNZ5+DTqcBCCxfRusPfwiAPTzM1gsuPOg46v78wyRuvx2AgUce4cCnPo0KBjHjcRr++q+IvvrVAIz+8pek177kzppZWYlZ5c6eadXVsedtb5tyR3imVk4W+vrYdeONM/56DnUdZlWVN5OqEEIIIYQQpxqtNfk9e0ivX09m3Xoy69eT2bgRJ5Wi8QtfoOLq1wPQ941v0PuFLwJu4ULjl79E9FWvAtynxoxIuGxP1WMteHEcjWG4x/vlgxvZ/Nz+kvWWz6C5LcHC4oRW/qDUpR0tpdQarfWq6R6HENNJvnPMQDN1kpzpHrcuFFDWxP/y2e3bccbGcNJpUAaRCy9wt7NtBr7zHZx0BiedRmfS7utMGp1KU/HGNxK58AIvZIXio9333MO8++/HXLQIgJFf/IKBbz9QfjCWhdba+8XBrKykUAxa7aEhbzMjFiOweBFGrMINScf/VVZixuOEzjrL2zZ+/fXE3/xmjEnVluNiV15J7Morp35MHOe43BE+VZjV1bPieg52Hfj97LzhBhLveCeVb7x2uocphBBCCCFOc1prnOFhzHgcgEJ/Pzv+6BqckZGy22fWr/OC1thrX4uvoYFgWxv+lpaSYgIzGjnoOZVhHFURRV/nGC8+uYc9G/q5+a8uJBjx0bKyhs3P7ccshquLJFwVQhwn8l1khilbSTkDJsk53Li11uhUCidTDDfTaZy0G3DqTBpfUxOBhQsByGzZyuiTv0BnMjipNBXXvoHw2WcDMPTD/2DoX//V3bcYjDqZDE4mA/k8Z2za6IWbe+68k0KXO0GSNbeBxb/6lTtYw6Dnc58/6LUEli8jfM7Z5R/tnlQhPv7LBoBRUeGFo+P/dD6P8vsBaPjUX4NhFqtNq7z9lGHQ+tOfHtHH2AgEjmi7yZRhEFiymJbHHptxwX05s+V6yl2HUVVF553vIrtxE11//ucUDuyn+vbbj2rmVCGEEEIIIY6H/ge+Q/J3z5FZvwFlmiz+za8Bt2Cg5OkryyK4ZAnB9naCbSuInHeetyqwaBGBYpHK8WQXHPZuGqB2foxIZYBMMs+WYvVqx4u9LH/VXJpXJLjq9hU0t0u4KoQ4vuQ7ygxjDwxMraR8z93M+dSn6P3Hf/K2a/7eQ16/x84PfID8nr2AWyHZ/OB3vO123XILOp0pe66KN15L4u1vByD529+WhI+J299JxTXXADD8k58w8N2HDjrm5u89hJNKlR335Ee6t5x/Adh22WMk7ryTug9+AIDs1q30ffkr3rrA4sVe0Fro6yP94osHHYvOZFAht/emEZrolTn5Y6CUQoVC6HQaFQxiBIOoUAgjFMIIBrESNSi/v2wT9snVpFU33UT8+usxKypKKmnLiV522SHXn0hHe0f4VDdbrqfcdSTuuIP02rU4Y2P0fO7zhC+4wJtoSwghhBBCiOPJHhoivX6D++j/hvXM+eu/xio+KZZ8+imSz/7W2zZ/oAdffR1KKRJ33okK+Am1txNYsuSYCkKOeqzFcHX7mh52ru0jly7wqj9ZxFmvnc/cxXFi1UFqm2NU1rp/C1p+k8Xn1Z/wcQkhTj8StM4wOpcrW0mp/H4yGzZMbOdMVFbmtu8gu20b4D6iPll24yacVKrsucKT7jbaI6Mlxy/0D0y87usvWTdlzI4+6LjHJ/dRSmEEgzjJZNljOMXH6gGM4iRFyu/3QtNx/vnziVx8sRuMBoMY4RAqGCqGpUF35qKiOZ/4uFtVGgxiREofTVnyzNOoYPCglZAHe+Te19jobTPe5F2I4yVy4QU0P/wwe9/1Lqr+9GYJWYUQQgghxHGT7+lh5Kc/I7NhPel168nv3VuyPn7DDV6BSLD9TPJd3QTb2gi1t6H8Pm+7xDvefkLHqR1NajSHXXAo5By2rznA2l92kkuXTv7b8WIvZ712PoahuOXTF2KYM+spNyHEzCRB6wxzsEpKHO1NfASls5mHz1uFb+5cgCmBYuTSS9GZ8hWt/oWt3murtqbk+JMDRd+8ppJ1U8ZsGgcd9/ij8wC1H/qgO8ZgCCNUrCItvrbqJ+42Ri+/nDPWrytbJVpx9eu9nj+HE7noooOuO9Qs8jB7HlEXM09w6RJaf/wjjIoKb1nq+efxL1iANanthBBCCCGEEDB1rgwViZDdtJnM+vUon0XVW98KgDM8TM9995U9hq+xsaRAp/b976PuA+8/KeOfLJvKM9ST5olvrWe0P0MsEeSKW89gz4YBDuwcmei5ek4dze0Jbz8JWYUQJ4ua3FPydLBq1Sq9evXq6R7GMZutPVqFEMcms2kTu//0FqzaWuZ965v458+f7iEJIYQQQohTgNaaQl8fhQMH2Pe+93l/hzV85jP0/NMXyKxdi7+5mYVP/Le7vW2z5bzzMaNRr1I12NZGcMUKr2XAdHruxzuoa67g6R9sY7R/olgolghy5duWkRrOSc/VaaaUWqO1XjXd4xBiOsl3oBlmplZSztRxC3GqG/7Rj3BSKXK7d7Prprcy7/5vSEsBIYQQU4w/ausUHAzLIBzzowyZUFGI2aLQ20vqxRfJdewk17GD7I4Och0dzP37v+fA3322ZK6M7o9/nPqPfozO974Xe3QUJ5NxW62ZJot++eQp8ZRUX+cYG5/aR+eWQW74f+fh85vEqoMEwlZJyAow2p+hIhGiccn0j1sIISRonYFm6mQ/M3XcQpzK6v7iL1D+AP3f+hb2wAC7b3sbjf/4eWKvec10D00IIcQpQjua/q4xHv/6Ou9R22ve3U5iblTCVnFcvfwRdSmsOL6cXI7slq1ekBo6s53Ya18LwNhvnqL74x+fso8Rryw7V4a/dQELn3zSbec2aR6L6QpZRwcy7F7fz8KzawnF/Iz0pVn3630A7NsySEt7Dc1tNYwNut/DXl7RaljyvUwIcWqQoFUIIWYwZRjUfeiDWA1zOPA3n0Gn03S+5x7m3HsvVTe+ZbqHJ4QQ4gTKJPMM96TJpPJkk3kyyQLZVJ5MMk82WfCWr3rDAn79yBYvmBjtz/D419dx1TtX0LNnlMXn1RGK+g9zNiEOrWyrsK98Ff+SxRimOd3DmzG01th9fW5F6s4OVCBI/M3/B4BcRwe7brjB27byzW/2glZ/6wJvuVVbi7+1lcDCVsxotOxcGWZl5bQWwTi2w/6dI+xe18/u9X3073MnRTYtg2UXN9B0RhVWwKS+pQLT54b10aoAkUo/17y7fcqNo3BMvocJIU4NErQKIcQsUH3zzfjq69n3oQ+jMxn233svobPOIrh0yXQPTQghZpXj+Qi+bTtkvXC04IalqWJImnRD0vOvayUY8dHxQi/P/HA72WSemz55PtGqIB0v9vI/39t82PNYPqPso7a27fDUY1tpbksQisLOtb384T93UTUnTNWcMPH6CFUNYeK1YS/oEOLlnHSa7JYt+JqavJAV3KrJznveQ/0nP0ns8su97Xv+6QuYFTGsujqs2lr3X10dRjRaUll5Ohn+6U9J/vY5cjt2kN25E2dkxFsXWLrUC1r9LS2gFGgNhlEyOVXwjDNoefT7+FtbMSdNmqodh6avfm3KXBnmNPRczYzl2b2hn93r+9mzoZ9sqjBlm67tQyy7uAF/0OL2z12C5SsN6ZWhSMyNcv1HzsUpaAxLSSsUIcQpRYJWIYSYJWJXXknzg99h77vvJvFnt0vIKoQQx9nBHsGP14bIpm0yyTyVdSEsn8nQgRQ71/aRSeU568p5hGJ+9m4c4Nn/2O5Vm+Yz9mHPueLVjQQjPhxHM9KbBiCTLBCtgmDYV3YfX9AkGPYRiFgEIz6Uouyjttl0AdMyiCWCgNsTsXfPKL17RkuOpxRU1ITc8HVOxA1i68NUzYkQjJYfg5i9tNbkdu4i+fRTjP3mKVJ/+APatln43/9V9hF1s7JyYt9cjv777y97XBUMUvvee0jcfjsAmY0bGXvmGXzFQDawbNkp0Tv0WKXXrWfsf37l9U5t+Nu/JdTeBkDymWcZ/tGPyu5X2L8f7Tgow8AIBmn62lfxNTbib2nB8E9UcRqhEKGzzpqy/3TPldHXOcqul9yq1QM7Ryg3F3ddc4zm9hpa2hPUzot5y18eso5ThiJSGThRQz5pHEfTn8yRK9j4LZNExI8hgbEQM54ErUIIMYuEzjqL1p/9tKRKIf3ii/gXLcKMRqdxZEIIMfOlRnNeyAoTj+BfcsNi/usb6wC48RPnUdMUY3B/kmd/uB3A6zloFxz69o4d2ckUBMIWhawDQGVdiMXn1RMMW/iDbvhQ31rBG+4+k0DERzBiESiGq6ZZGqBoR5d91La6IcKtn7nI+8M+WhWkcWkVQ/uTJIdzE/trGO5NM9ybhnX9E0M0FO/60qsxLYPdG/oZ6EpSNSdMc1vitK1MnM2yHTsZ+N5DJJ96mnxn55T1OpMt+4i6EQx67xcGh1DBIDqTKbN/BhWY2Db1hz/Q+/l/9N5v/MI/UXH11QD0f/sBRp98cqIq1ntbi6+ujsDixcflmo9GYXDQrUgtBqnZjg4qr7uOyjdeC7i/j/V97eve9tlt27yg1b+wFbO6Gn/rAgKtCwksbHUf/W9txWpoKAlFj6UP/8mcKyOXKTDck6Z2vhuY/vqRLezvGCnZxh80mbc8QUt7gvkrEoQrTr/H/h1Hs3n/KHd+bzWdg2maqkJ867ZVLK2PSdgqxAwnQasQQswyViLhvU5v2MCed96Ob/585t1/P776umkcmRBCzFz5rE1qOFv2EfxAeOJX6kzSfRQ2EHErPQ1DkStWrsYSQVraE24wOqniNBCxiu9PCkxDVsmjsLXzYlx1+4qSc0cqA0TOPHxV16EetZ1cFbbs4gaWXdwAQC5dYPBAiqH9SQb3pxg8kGJwf4rhnhSO7ZakVdaGMC03ANq+pofNz3YTivl4532XAvDCz/dwYNeI14qgak6EyroQ/qD8CXKq01qT276dwsAgkQvOB8BJJhn6/qMl21kNDUQvvZTIpZdgNswp+4h6YMlE6Omrr2PpC8/jjI1R6O2l0NMz8ban1wseAQr9A6Xnqq31Xme3biX9wgtlx24mEix55mnv/d1vfwdmzG1VEFy+jPj117vXaNvYIyOY8fiUGwMHm9RLOw6F3l589fXeMfa87e1kt2/HHhqaMhZ/c7MXtAYWtoJSbjXqwlbM6onq3MQ730nNHXeUvZ6Z5Pc/28ma/95FIGTxjr+/BGUomttq2N/hfh9obq+hpS3BnEWVU24InQ7ytsNLnUM8s72fc5ur+It/f4nOQfdJhc7BNHc8tJr/uPtV1MZmfrWuEKcz+S1HCCFmsaFHH8VJpchu3syum25i/re+SWDRoukelhBCzCjd24d48rubeNX1i8o+gu8LmLz65qUEIz6qGyIA1C+o4I4vXIYvYHohTqIxyhves3JaruFoH7X1hyzqWyqob6koWe7YDiN9GQYPpHBsx1ueHMoCUDUn4i3r3DLAng2lYRm4E9p4bQjqJ0LYcKVfKmGnmXYc9v/1pxj7zW8odHfjX7SQhT/7GQDBFcux5szBv6CF6KWXEb30EvyLFpV8zswjeERdKYUZi2HGYgRaWw86lroPfoDEnXd6YWxg6VJvnb+1lfD553vrnGTSW2fVTdxUtseSpJ57zns/csklXtCa37uXHVf/Efh8WLU1WLVuNWxw5UrCq1bR9aEPeYHx3M99joFHvs/Yz38OwNIXnkcZBso0ye/fPyVkVX4//pYWrJqJm9/hVatY+vwajFBoyrWqGTZZmF1w6N4+xK71/XRuHuT6j5yLz28SqfTjFDTp0Tw9u0epX1DB8kvmsuT8eipqpl73bOc4mk37R/jtjn6e2d7H73cOkMy5N94eu/NCL2Qd1zmYJlc4fEsZIcSpTYJWIYSYxebcey+YJkOPPub+EVKuMZYQQoiD+t1POlj9X7tAw/NP7ObKty/jlw9uKnkEPzE3Sl1zaSBpmsasrNgyTIN4fZh4fbhk+Rvfu5LMWJ5semJym0g8QEVNkJH+DEz68TM2mGVsMMveTYMlx2hcEuePP3gO4FbIKsMNp+N1peeC4zsp2elKa01282bGnn6axNvehvL7UYZBZvMmCt3dAOS27yDf3Y2v+Pj6oid/gbIO9SekIuuL4aji54VX9jkxoxHM6AICrQtKlte8605q3nWn976TTFLo7SXf01N6jekU4Qsu8KpmJ1fFFnp73Rf5PIWubgpd3WSAyuve5IWs4PaZ7frwh6n/6McY/elP3WVdXfibmgCIXXUV9vAQgdaF+Be6j/v7GhunhKfK73+FH43plRzOsnu9O5HV3k0DJT2m920ZpKW9hua2GlZcNkZzW4LqRvemy+nUFkBrza7+FM/u6OPZ7f38tqOfgWSu7LaZvE1TVagkbG2qCuG3ZlboLoSYSoJWIYSYxZRlMefee/G67KIEAAAgAElEQVQ1NhJauXJaepYJIcRM5g9ZoMG0DBadW0fDgkqZ7boMpRShmJ9QbCJUueLWZQAUcjZDPWkG9ycZKrYgGH9dyE1UxcaqJ/pz/u4nHQwdSLFgZQ3XvPtMAJ75t20opWhYVEEkHuS/v7l+SuAtn4tDs4eHST77LGO/eYqxp5/C7u0DINR+JpELLwAgduVrMSsqiV56CZFLL8WaM8fb/1Ah68EmizsZnxcjEsEfieBvaSlZbtXW0vzdByfGaE+Eg77GRuo/9lEvoHXD2F7MqnjZSb2s+jqqbruVQGsrRmSicrv+I39+Qq5pumlH07N7lF3r+9i9rn/KJHngTpQ3Z2Elhjne5znA5TcvnbLdbHZgJMOzO/p4Zns/z27vo2t4av9hgMZ4iIsXJnjVohouWpigNhrgW7et4o6HSnu0JiKnTzAtxGyl9GlW3bRq1Sq9evXq6R6GEEJMq96vfBUjHKb6HW+XxzSFEGIS23ZY8/gu5i1P0LCwEsfRPP3YVtoub/LaAojjQzuasaEsg8U+sFX1YeavSGDbDve/99doR3PO65u56P8sRGvNP3/gN+QyNn90VztP/2DblBYOV9x2Bhuf6vImBWs6o5qmpW4fzK7tQ/iDFtGqAMFi/9zTSb6ri30f+jDptWvBcaasr/3AB0oqRCfLZQoMdCfJJgvUL6ggGPExdCDF+qf2kU0VyCbzZFMFzv2jZv734S1TPi+vueUMXvqfTlZd00J9SwWpkRzb1/QQjFg0Lq0iUhnALjjkMgUCIQtjmivBC3197LrxximTerU89thJm1BqOhXyNr9+eAu7N/STHs1PWR+M+Ji/oprm9gTzlydOu6+n4VSe33b0F8PVPnb0JstuVx3xc9HCBK9aWMPFCxM0J8JTfud2HE1/MkeuYOO3TBIR/4yfCEsptUZrvWq6xyHEdDphFa1KqQeAa4EerXVbcdl9wBuBHLADeIfWekgp1QJsArYUd39Oa31XcZ9zgQeBEPA48D6ttVZKVQOPAf+fvfMOj6M69/9nyvaiLeqSVSzJvYBtwMYQILkJEEJoSUggIeWGEhLS7v2RSkLCTSMhjUDuheTm0i4QLimkAyEkNAd3jLssV/Vetu/O+f0xq5FkrWwZS7Ykn8/z7DM7Z2bOnLOzK81+932/bxWwD3iPEGJk/pFEIpFIRtHz5K/o+MlPAEi1NFP0uc9NO28wiUQimQyEIfjNXRtpaehl16utXP3lM7E5NN70vlMrQutEoagKvpATX8hJxYIhL0tNU/nXu86lpyWK02t+XUnGM3gCDtJtMRxuPWdRMkVR2L1uKHVcVRXK5wYRhuDXd20AASveXsVZ75yNEIIHvvAydqdmCbMOd7YomdssSGYVJnPrODw2PAEHDtfUTwhMd3UReellkvv2UXDLJwAwfEFaD8VIBeaR0t2k3XmoVXMQZdWIcAl7DTuJuzaYwmnUFE4v+fgSyuYEad/fz29+YBaeuuzTp1E+L0S0L8nmZw+OOO+Zl1bnvC6qprDvtQ6WvNlMte9pi/LC47sAuPSTS/HkOWg/0M+Td64HzChyhztbKO7wa+EZai+tC+Dy2slkDIy0QLerE/LjsRYK5SzqpYVCx933VKS7JcK+1zqxuzQWnluGbtNo3tM7QmQNl3upWhSmcnE+RdX+aS8GHguxZIa1+7p4KWsH8HpTb04nLo9d46zZYc6uCXN2TT7zin1HfZ1UVZGFrySSGchk3in8D/AT4MFhbc8AXxBCpBVF+Q7wBeBz2W17hBCn5ejnp8D1wD8xhdaLgD8Bnwf+KoT4tqIon8+ufy7H8RKJRCIZhnPRQvSiItKtrXQ/+BDp5hZKv3snqtN59IMlEolkBiKEQFEUFFVh9mkFtDT0ottVYv1JbI5Tr4DLVMDh0imq9o9Yv+b2lWQyBpHuRM6iZJm0IK/AZYmFjmykXTKetjxiHW7z608qniHSkyB3LFpuzrpsNisuriKTMXj4y6/gcOuc9i8VzFtVgpExePnXe3C6hwmCbpsl1Do9NuxufZTwMh6v2UzGIBlNk4imiWejRwfF0EQ0RTy7bckF5SR/+DUa/76Z1xbdSFqfzdvW7Gf2ykq6O5KsW3jL6Ek1Ao19OeebiJh+uw7P0FfGRDRtvY66TbVE6MHXNdd1EYYgXO7FnbWVGOwDsKIh45EhUS8ZS5OMpUeJtodzxb8tw1Vnp3l3D7/94SZUXeHyT59OSW2A9gP9vPq7Bmtsw0XbwfWhtpFRtIqqYq+tpeSXT5HJCDRNwZ7nHFXUazox4n2mqaSSaQKFZoT+cw9up6Whj2Cxm4XnlgFQu7yQzqYIVYvDVC4K4w2eOveIqYzB5oM9vFTfyUt7Oth4oJtUZrSyatdUllUGzIjV2jBLygPYZqAvt0QiOXYmTWgVQvwjG6k6vO3pYatrgHcdqQ9FUUoAvxBiTXb9QeByTKH1MuD87K4PAM8jhVaJRCI5Ks65c6l67FEO3nADid319D/zDAc+/BHK770HPRg82cOTSCSSE0pPa5TnHtrO2VfWUjw7j6X/MgvdrrJgdSmaTX5pnmpomoovZHp/5vICff8dqwBTWBq0SNPtGu/89GkkImnC2QI9AlhwTqklWFoCZiRFMp676rczKyYmImmroNfgvolYelSEZy7sTs0S/1ZdUYPbbx8xj4tuWETBLB89bVGe+tEmEtE0qcT4qpBXLAjhz89HNVIknGb0ZXRfI6ysxOnOnd6t29WhqN3B6NHs0pdvimv+fBeXfHwJDreNYLFZmCxU6uHGu88f0ZcwxJjX5b1fPtPar3JhiI989xzikZTlyxsq8XDOe+qsaxCPDl2PeCQrLEfSGMaQ4DUo7g4Kt0ZaYHOaGTr9nXH2bekc1+sGYHNqXPHZZRRU+OhsGiARTfPsL7aNuC7NDb1EuhPj7vP0t1bg8tlpP9DP7nWtY7ZNdn+eoIPCCj/P/PdWaz5vvm4+/pALVVepXJRPS0MfmbRBImbaN6y8vOaYxjadMQzB9pY+Xs4Kq6/u7SKaHP2ZUxVYXJbH2bWmFcCKyhAuu8wIk0gkozmZuS8fwUz9H6RaUZSNQB/wZSHEC0AZcGjYPoeybQBFQojm7PMWoGiSxyuRSCQzBltJCZWPPMKhT9xC9NVXiW3cyP73XcOsn91vVdGVSCSSmYwwBFv+3sgrv6onnTL46wPbec+XzsBm11h8vvw7OJVRVIVwqfeIRckUVUHJ1njXdJVZ80amfTtcOhe8f17O/o2MQTKWscRXU/RLUVhhRtgqCsw7u4REJGUJj6l4Bt2ujijulYtkPEMynqG/E9x+B3/86WtW5GZ/Z5w/3/c6V926HE1XGRinqKepBk6/GXntu+hCijUnNaqOp7yQwlWlgFmkaFAsHRRVnW7buH5MsDt1qhaP9CbNlaI/nusCoGrqqMJp/nwXS98864jjEEKQSmQsYTyv0JxzqNTDqitqSERTeAOmcKvZVPJneUlEzOuXGkM8HyQVz2BzDIlmgyIrDF2Xc95dx4uP7z5iP8NZsLoUl89Od0uEjU8fGLNtsvu7+KbFlsg6OJ/nHtzOOz91GoFCN/NXl1CzrIBA0WgP0ZmIEIJ9nVFequ/g5T0dvLKnk+7oaC9agLpCr1W8auXsMHmuU8uPViKRvDFOitCqKMqXgDTwSLapGagQQnRmPVl/oyjKwvH2l/VsHbOql6IoNwA3AFRUVLzxgUskEskMQvP7mfWz+2n+whfp+8MfSHd1IRLjj9SQSCSS6UpfZ4znHtxB484he/+KhSFmvsQwc1BUBU/e5HgbqpqK06vi9OYWVVw+O2+5bv6INn++ixt/fD6ZlDEiGtNK748MpfsPbtd0JaenqZEWOL025q0sNoVRp0Lvffeg9XWip6PY0lFzmYpid+vkv/8ay4sVinCffjqH/1Sg27VRYulkMJnXRVEU7E4du1O3ImEBgsUegsUjC9VVLjJT3gcZtF+wxPPDr00khctvt86T67o4POMTpkeNW1VGHZerbbL6c3hsub1zswK4J88xaddsqtDSG88Wr+rklT0dNPXmtqUoC7g4uybM6mzUaqH/1LFMkEgkE8cJF1oVRfkQZpGst4hsPo8QIgEkss/XK4qyB5iD6Ro0/D6hPNsG0KooSokQojlrMdDGGAgh7gPuA1ixYsWYgqxEIpGcaqh2O6XfvRNbeTnec8/BUXPqpIpJJJJTDyEE219u5sUndlsRbr6Qkzd/cL5VnX6mIgyDTFcXIplEsdvRQqFp7Tk5VdFs6riFq0hvbq9Zo62Zxttu5YIHH0B1mxGz+5/oJFq/FgDnwoV43nQh3nPPxbVkCYo+9Qt0nWy0HFG0Y+Fw6zmvS6DQxU2H2SWMh7oVRdStKDpq22T1N9b7bCZbo/REk6xp6LR8Vhvac7sxhzx2VtWEWV2Tz+raMBWhUyOqVyKRTC4n9L+yoigXAbcC5wkhosPaC4AuIURGUZTZQB3QIIToUhSlT1GUlZjFsK4D7s4e9hTwQeDb2eVvT+BUJBLJFGU8RSWmAydyHoqqUviZT49oa//JPdiKiwi864hW2hKJRDJtiPQmeP7hHSN8GxecU8rqq2qxT4NK8seKkUwikik0rwdhGMS3bafxU5+0qqiX/fCHOBcuRFFVIq++Sv9fnkax2bIPHcVmA91cBq++GtVlpmkPvPAiRiRi7uNw4j1ntXXO+I4doChmH9ljrecuF6pj4qLmToRwbCSTiGgUkU4jUqnscvB5Cuf8+dY5o+vWkenvR6TTKJqG781vtvrpfvRRjEQC0mmzj6TZl33ePC6+fhV/un/IC/Tij85n4Od3EX/9daJr1+I97zwAQh/5MIF3XYXnnHPQw+Gc45VMDG6fPafXrHscIu1UZKbNB0xf1c5IkmQ6g13X8Ng11u7v5uX6Dl7a08HWpj5EjvAqr0PnrOqQKa7W5jO3yDeqQJ1EIpEcL4rI9RdoIjpWlEcxi1XlA63AV4EvAA5g8A53jRDiJkVRrgK+DqQAA/iqEOJ32X5WAP8DuDCLYN2StQoIA78EKoD9wHuEEF1HG9eKFSvEunXrJmqaEolkCiEMQWfTQM4iDNNJbD3Z8+h+4glabvsKAPk330z+LZ+Qv+5LJJJpze51rfz90Z1WFXV3np0L3j/vhKRSTzb9zz5LqqmJVFMzqebBRxOZ9g7CN9xA4Wc/Q7qjg31XX02qsck6zlZWStXjj6Pn59P1wAO0fuvbY55jzppX0AIBABquvJLEtu0AqF4vc9ettfbbeeZZGH25K9gHr72W4tu+bI75+edp/MxnLTG24JOfJPjeqwHo/d3v6fzv/x4p0g5blnzrW6huF4lduzn08Zst4bj0u9/DddpSS/g8dMstGNGYKWymh8RRUmnyrriC8Ec+DEDfM8/Q8uXbLCG16ItftMbScd/9tH//+2O+LnM3bUR1mqnFey5+O8m9ewHQwmHmvPSitd+OpafltOYpv/tuen77G1zv/gBqMIzR3UnsiYcIXHY5nT/7Gfkfvxnvm9405vklk8fQD95je81OJ2bSfOKpNPVtEW56eD2HumOUB11856olfO8vO9l4sGfEvnZNZVllgNU1+Zxdm8+S8jxs2syN5J0KKIqyXgix4mSPQyI5mUzaz/dCiPflaP75GPs+CTw5xrZ1wKIc7Z3AW45njBKJZPojstVnFVWhrzNuiZNg+k/98adbOP/aefzzqYacx59/zVwKKny07e/j74/uGrNtvExEf2e9czbPP7Jj1DyuunX5CfHQctTWogUCZHp66Lj3XrRAgNB1H5j080okEslEExtI8o9Hd1G/fshhqu6MIt703jk4PVO3qIkRj6PY7ZZo2P3YY8S2bCHd3Ey6p4fqJ5+0fgBr/urtZDpzV1dPNZt1Y0UyOUJkBUg1NiGSSXNF11Hz8hCpFKRS5nIYim3YazVs2+Ep6yKdHnNOw/sQiSQiFmMw3GP4+dLt7SS2bx+zn5JvfZNMV5clsg7Open//bslHAMMvPQyIhrN2Ue6bZjjWMYg09s7NLbB1yTH/A5n+HyH73v466Do+mihVddRAwEGnv0rA8/+dcSmos9/nqrHHzviuSWTy2R6zZ4MptN8+uMpGntiNHbHrOWhYev/cfki7vj9Ng51xwA41B3jc0++xm3vWMDHHl7P4rI8zq7NZ3VNPiuqgjht2lHOKJFIJBPLzMuTkkgkx8ThqTdhj33KpdAIIYhHUvS0xgiVuHG4bbQ09PL8IzvobYtxxb8vo7DSj5Excpr9a7pC277cETbJeDq7zFj75GobLxPR31jFMQa64ux7rYPa5YU43JMnELhPP53KR/+Xg9ffgOb3E7jqykk7l0QikUwmkZ4kDZvaAXB6bZx/zVxqlhWe5FGZpNvbia7fYEWgppubrajUTFcXNX/5M/bKSgD6//ockRdesI41+vvR/H4AbCUlZDo7URwObCUl6CXF2EpKsZWU4FqyGADFbsdWVjoqolWxm6nDoWuvJXTttdY2IcRQmnsqhZL1CQUo/+lPMaLRUWIsQNn37zJF1Gz0qEilzH5SZpr9IPbKCkIf+Yi5XyqJY86coXGVl+G94IJhqfqpYdGoKRSbDaOv78jCMeBauBAjmUDRD4+K1XHU1Q2NpaqS4DXXWJYJzkVD9XjdZ51J0Re/YFkojOjLpqPah1KvS797p/la6br1ug5S8+wzZnv2ga6jKArpjo6c12UwSlYimWkIYX7vGC6iNvbEOGStR+mLj/2DDUDAZbNE1kEOdceoKfCw8StvI881dX9Ek0gkpwZSaJVITmE6I3Eau+Pc/MgGK/Xmp9cuY3a+F4/zxP95SMbT9LbF6GmL0tMaHfE8ETVvui75+BKqFuej2zU6G01j+562KIWVfnSbmtPsXwhBxcJQznMOCpZOj27tk6ttvExEf0KInPOI9CZ5/pGdFM/Ow+G2ERtIomoqjknwFnRUV1P12KMgBKrHrOSbGRhAxGLoBQUTfj6JRCKZKBKxNI07u5l9WgH55V7OvLSa1r19nH/tPNz+4/ckPJo3qEinrejGdFcX3Q8/bAmonlWryL/pRgBir79O46c/nfMcYEajDgqtttJStIJ8S0A14nFLaC37/l2oXi9aMDimzYsWClF+z70jUu3L77kXLZT7f5KiKJBN3SfrzTqIvfzwevZD+M4/f8xtw3HOm4dz3ryc2/xvexv+t73tiMcbRxGOASofenDcYyn+ym05t7kWLsS1cGHObbn6GQs9mLvQ2rFeF4lkqpPOGLT2J7ICanSUkNrUEyOeMo6pT5umUJLnoizgoizowmnTKA+6Roit5UEXeS67FFklEsmUYNI8Wqcq0qNVciqSzhg0dETY3tzHtqY+tjX3sb25j29csXhE6g2YNyq3vWMB3/zjduYV+5hX7Gd+ibmsCLmPO9o1kzYwDIHNrhHrT7LmqQZ6W00xNdKbPOrx57y7jqVvmUUqmeHP//U6gSIXc84spqjKjGjtao7MSI/Wt310Iev+sJe+zgTv+8qZKIrCK7/Zw+ZnD1K5KEztikKqluRjs09OepRIJjl4000k9+1n1v334aipmZTzSCQSyfHQXN/D0z/fSqQ3ybs+t5zCSr9pMaMwIV7TwjByeoP2Pfss0TWvkG5qxlFbS+XDDwGQam2jPlvMCMD31rdSfvePAYjv3Mneyy4HQHG5sJWUmI/SEvSSEvIuucQSWoUQxz3+E1E86kSR6zqU33Mvjjl1025OM+m6wPTIlJK8ceKpDE09sTFT+1v64mSMY9MX3HbNElGHL8uDLsoCbgp9jhHvIcMQ7Gzt5/oH11mBIvdft0IWtpoiSI9WiURGtE5LpmtV9ek67ulGXzzFjuZ+S1Td3tLHzpZ+EunRvx6PlXoTcNnY3xllf2eUv2xttba57RpzinyW8DooxOYdlsYuDMFAT4Ke1ijeoINgsYd4JMUT315Hf0eMc94zhyUXlKPpKtteGJn6NxxPwEGg0EVekZtAoZtAkZvCCh8ANrvGpbcsHbG/qqmES71cdevyaW32r6hKznlccvNSIr0JFEVBCEH9ulYyaYOGTe00bGpHd2hUL8mnbkUhFQvCaLaJ+6LW+4c/Enn5FQD2XXMts+69B/fy5RPWv0QikUwENqdGtC+JMATbX26msNL/hv8HGJEIifp687G7ntBHPoyiqjm9QYs+/wW6//u/zbasLyqAXpBvimeBALaSEuzV1dY2e3U11b96Er2kBC0QOKKQOhEisaKqln/pdEdRVRxz6qh6/PFpL1DOpOsykwSwmSYYj3c+ffGUKaAOiqiHCakdA6OLuh2NoNs2JKIG3IcJqS4Cbtsx/Y1TVYW5RT5+ffPqGXN9JBLJzEIKrdOMsaqRBwpdcJR/UJquoigKRsaMKByrbbwcU39C0NMWm/aRhlMJIQSNPTFTTG3uZ1tzL9ua+zjYFTvicZqqUFvgZUGpH49Dz5l647RpXLiwiB0t/ezvHCokEU1m2HSwh00He0CAS0DQUJntsFPlcFCAiisuyPSZVU0Bll9UycrLa3C4daK9CYQwU/0B7C6dYLEbu0vPCqku8rKCal6BC/sbsC+YTmb/R2KseXiDWd82AeddM5fda1tp2NhOMp4hnciwe20ru9e2YnfpzD69gLoVhZTPDaIeZ4XVvMsvI93aQvsPf4TR28uBD3+E0jvvxH/RhcfVr0QikRwvTfU9dB4aYPH55eSX+1h1RQ26XWPhuaXjOt6IREjs2UNidz3ORQtxzp0LQMfPfkbnT//T2s+zaiWOurqc3qBafhjPueeaEamzhlLrFVVl7vp1I4tJZVHtdpwLFryRKUuYWQLldMcwBPu7oqQyhiWygvnj/fUPruM7Vy3h3ufr0VQVXVXQVOWwpdmuHt6ujdw+/LjDj9VUBV3L3a6NOqeaY3+zXVXNSvVNPTFueGioqv19H1jB7Hz3Ub9vTUmEoKEjyg0PDQng91yzjL3tA2w82DMitb//KP6oh6MoUORzjopGLQu6KA+4KA248DgmXnJQVYUC3/S/35dIJDMTaR0wzYj0JnjyzvWjvBvPeXcdf/rPLUc89vofvgm7U2fjMwd4+cn6MdvGy7H0d/FNi3nxid2jxn3h9Yvo64gRLvUSKD7+tPSZSiKdYXfrANsGo1Szqf9HM4v3OXUWlPiZX+JnQamfBSV+agu9VvVNwxDs74zQ3hHDa9MYSGUoyHdRGfZY16K7N87Oxj4a+uPsaOkj82onWl+KvLSCUxz9ekUK7YTeWsq8Yj/xdR14XDbK5wSZtUD6j00UmZTB/q2d1K9vY+/mdtLJkdHLTq+NmmWFLD6vjHCZ97jO1fPr39B8222QToOiUPT5zxH64AePq0+JRCJ5I6RTGf751F42PXsARVEsq4AjIVIpen//BxL1u0nU15PcXU+qaUg4zb/lExR8/OMA9P7hDzT9278DoNhsFN/+Vbznnce+q68e5Q06vNq9RDKTiSTS7Gjpt+5Ftzf3saOln2gyw+M3rOTq+9aMOmas9qnKf31g+ZjWWjc+tP4kjuyNcTzzsWkKpYHBaNTDhVQ3xXlO7Pr0iySXTB7SOkAiOYaIVkVRgkAdYJXBFEL8YzIGJRkbI527qrrDPbWDkx1uPee408kMT/9sKwAfvvMc3H47jbu6aa7vJVTqoXJRGO0U++fdOZBg+2Dqf/YGtr5tgPRRIo5nhVxDompWWC0LuI6cigj4EoIXHt5tRRpXXb8IJWxuf+Lb62jb10fdGUVc869mMYhHN/+TrlRmVF8G0KsadKmCbk3QnX3eGY8R+UOvtV++18H8zg7m7xuyHqgp9ODQJ8db9FRAs6nMPq2A2acVkEpk2Lelg/p1bex/vZNM2iA+kGLrPxqZNT9IuMxLIpamuyVCUZX/mNNRA1dcjl5YQOMnP4URidD6rW+jejwE3vWuSZqdRCKRjKZtfx/P/s92upvNooiqptDdHLGE1vjOXSR2bCdRX0+6vYPSb3/LPFDXab3jDoxoNGe/yT0N1nPPmWdS9qMf4airw14xC0XXEYYhixdJTgmEEDT1xtk++AN/i5lBta8zwlhxOj2xVM5MqbQhWFEZJG0IMobILg1rPTOiXZDOGCPXjzHr7ng5krXWdORI8/HYtcOiUUem9hd4HTIQRiKRSI6RcalziqJ8FPgUUA5sAlYCrwBvnryhSXKh6rmrqtscGisvn33EY7Vs6nBpbcDaN1fbeDmW/mwOLee4ByvJu3w2qxLw3tc62PzsQVRd4YYfmQUkdrzSTOPObkKlXkJlHsKlHjwBx4R4lp0sMtlo0uFRqtua+2jtO7L3kV1XmVvks8TU+SV+5pX48DuPfPOXSmTobY/R2x6lty1Gb0eMuuWFPPfQDuu69HfG+fP9r3PVrcvx5DmwO03xs6d16AtpSU0eLp/N8kwNFLrJK3Thz3eREoLdrQNsb+ljR3M/O1r66G/uIxJNWcd3DCR4YXeCF3Z3WG26qlBT4GXeoPdriY/5xX6K/NP7Gp8MbA6NuhVF1K0oIhFLs3dzO7vXttG6r5fKhaaC3rCxjece3IE/38k7P3U6eQWuo/Q6Eu/q1VQ+/BAHb7gRvbAQ/8UXT8ZUJBKJZBSZjMH6P+5j3Z/2m0WugFBehjMCO6ied5q1X8sdXye2bihSq+jLX0LzelEUBXttLfFt27BXVeKorcNRW2s+6mqxV1RYx+gFBfgvfNuI888kb1CJZJDhmVNDkar99MZSRzzOY9eYV2IWTZ1f4qcy5OK+DywfkXI/6NG6+mNnv+HxCSEwBKQNI7cwawgyGWFtP1zQzRiM3JYZvs/oY3zO3NZabofGrRfNfcPzOFm4HVrO+VSE3bz+tQvlvbZEIpFMMOOyDlAUZQtwBrBGCHGaoijzgG8KIa6c7AFONNPdOmAsj9ap7nU61rjzClz0tMaI9ictEejpn29l99pWwmVe3nvbmSPahmN36YRLPYRKPYRKvebzMg8ur8r98g0AACAASURBVP2Ez+9oDE+zGryJ3dHcTyxHZOhw8r32ERGq80v8zM73oI/ht5mIpuhpi5GMp5k1z4yueeGXu6hf10a0Lzlq/8s/ezq/+f7GUe3XfeNsfGEnO9Y0M9CVIFTqYfZpBW9g5ubNcXt/gu0t/ezIppdtb+5jT/sAqcyR//4E3DYr6nWwANecIh8uu4x+PVbSyQx69nV76sebOLitC5ffzoe+vRpVVdj1ags9bTHqVhQSLPaMq89UUxOKzYZeYL43Mv39kMmgBQKTNg+JRHLq0rKhgb8+sJ2ehPnjkGJkqNr/RyoPPI0qDCoefADPmeZ9Q/Ptt9Pz2OOgadirqph1z0+wV1UBkGprQw8EUOxT735BIpls2vsTI9L+tzf3U98+cNRK8WUBV/ae1Gfdk84Kjrb9mglFpGZSUS+YefORTG2kdYBEMn6hda0Q4gxFUTYBZwkhEoqibBVCLJz8IU4s011oBcikDQb6khgZA1VT8frt0yK9/ljGnYimiPWnCBS5AfjbQ9tp2NxBfODIv6wDuPx2KheEeMuHzAITfR0xnB4bdtfE2ivkupFUFGjtS5iFqawiVX1HTLMCUBWYXeC1RNX52ZvYQp9zxH5CCKJ9STMytS1GX0eMVDLDOe+qA4YEaW/IwQe/uRqAvz2yg20vNI06p8Ot87aPLuL5R3aMijQejGidTJJpg4aOAXY094+IgD1aRK+iQHXYMxT9WmxGUZQFXKiqMiNu8GFyv6hEehLUb2gDAUvfMguA//vOOlr39gGQP8tL3YoiapcX4s8fX7SrSCY5cMONpNvamHXffdjLyyZkrBKJ5NTCSCbpf+YZ0z+1fg/2qkryP/NZNj1zgH/+dg9G1oLaM9DIgh0P4hs4ZDaoKqXf+iZ5l10GQHL/foxEAkdVlRRUJack6YxBQ0dk2I/8/Wxr6jtq1fjBzKnBKNUFJX7mlfjJm6Zp82+UmXI/OchMm49k6iKFVolk/ELrr4EPA5/GtAvoBmxCiLdP7vAmnukutBqGYGtzHx97eCgl595rl5HKGAwkjhwdeTLxOjRsmsrNj2w4rnGno2niHXESHXHinQkSnXHiHXGMw4r/+Gp8VF5WBUDDY3uINkXx1/qpeGclAAMHBtCcGo6gA9V27CJ1rvl8/z1Lufuvu3mhvvOIx3rsGvOHFaiaX+Jn7rAoTcMQDHTH6W2P0ZcVVHs7hpbpw14vTVe58cfnoagK//xdA+v+sA8UuPHH56HbNHatbeHg1i4zvb/ARV6Bm7wCF06PbUpGSHdFkuwYJrzuaOlnZ0s/ibRxxOO8Dp13LCnmXctn8enHN02rz8fh5Hp/TWbkQSqZ4VffXU/HwYFR24qq/Zbo6gmMLb53/e//0vr1OwDQCvKp+K//ktW0JRIJAMIwyHR1Wan2qs9H6sABEvX1JHbXk+nvp/hLXzT3TaXYsWw5pMwfVtOLVrLrzE/Q0tA72BnVvf9kQagVd93soZT/6mpUh6xALTk16Y2lRkSpbmvuY1frAMmj3DsV+BzZe9KsJVWJn+ojZE5JJBLJ0ZBCq0QyTqF1xAGKch6QB/xZCDE6F3mKM92F1vb+BFfc+9K0q4I5qdU7BfiEQn5GIT+jkp9RaNYNNjkyIOCTvU4cKGyyp3nGbX5xu77PQcBQMRD0qIIOTdChGrRrBh2a2WYcQc8a73xK85yWmDqY/j88zaqrKcKhnV30dcRZfVWtKZY+1cC6P+4b39wV8AWdvPsLK3D57HQ2DdDTGiWvwE2o1DMuUU4Ygmh/EiMtUHUFt88+5WwoMoZgX2fEEl+3Z5eHG/vPlCqxY83jnmuWEUtlOL0iMCnFw7pbItSvb2P32la6Ww4rFKNAWV2A2hVF1CwrGGXRIQyDtu9+j65f/AKAgn/7LPnXXz/hY5RIJNMLYRgkdu0eUTyq5D++QdsPf0h882ZzJ11n3ob1VuRpw6WXkqjfgz5rFq/W3ECPCAIQKHJzwTW1lM7NP1nTkUhOKoYhONAVHSaomnZMjT2xIx6nqQq1Bd6hKNXsvWm+V/44IZFIJhYptEok4y+GNbyU6pbs8sSWf5QAkExnpmUVzEmt3qlAvyLoVwV7bcbhm/iTO0m+odKsmdtsAgKG+Uu9ikLIUAgZMIch4SqNoEsVdGSF1716hjZ96C0fcNko8Dr41tsXEHbZ6Ywl+f4/9lCaZ4p680t8zAl5UKIZMxK1PUbva71s+msLf2+PcfWXz8Th0jm0s5sXHt8NwGn/UoE36BiVqq1qCv58s9hUXqGLvIKhhz/sQhsWkRsu9RIu9R7by6cqk24TcLxo2WJZNQVeLllSYrX3xVPsaum3/F8LfY5p+fk4nLE+L/FUhvfetwaHrrK8Msiq2WFW1YRZUh7APgH2IcFiD2dcUs2Kt1fR2Rhh97pW6te10tcRBwGNu3po3NXDPx7bxcU3LaZ6yZDYoagqRZ+7FVtJMYm9ewl/9KPHPR6JRDL9SLW1EduwkeiG9XjPOw/n3LmWyAqQamyi+ctfoujzX+DQLbcAYCsuJt3Rga20FIDAd+7GX1GI7nWTt7+PJ+9cz6Lzylh5eQ026dEtmWGMldIdTQ75+2/PFk7d2dJPJHnkDJ08l21E2v/8Ej91Rd5J+YFWIpFIJBLJaMZrWrkBmIVpGaAAAaBFUZRW4HohxPQJFZvm2PXcVSML/U6ePI5qnpNNyGOfMuMWhiDWESfaESfWHiPaHifaHiPRMxSgraNQaCgUGiqk4NrzKyhbVYyREWx7ZDfxzd3c9db5vPjADl7Lptz/4Lr5HFrfTnB3LzvbG9nYO3bAd197jIIKH3mFpqiq21QivQm8QQeldQHOu2auJaZ6Q07poTQGfqeNFVUhVlSZvwW19yemzPvseBjr89KTrf6bSBu8vKeTl/d0wjPgsmmsqAqyqibMqtlhFpflHVfan6Io5Jd7yS/3svKy2bTt67dE10j2fV1c7QfgwNZOXv9HI3VnFDF7aQGh665DCGFVsI3v3MnA838nfMP1sqqtRDLDEIZBprcXPWhGnEb++SoHPvjBoR3SGRzV1ZbIOkiqsQlbdRVVTzyBo2Y2qtv0YxdCsOOVZl745X6WXQgrLq6isNLP++9YhS800rNcIpnuJNMGkUSKxp44Nw2zBPvBe07jFy/t5U9bW47o768oUBX2mKJq8VCUakmeU/6/lUgkEonkJDJeofUZ4P+EEH8BUBTlbcBVwC+Ae4GzJmd4ksMJe+zcf92KUVUjK0NuqvPHVyn8ZGAYYmqNu3p0UyqRobslQmdjhK6mAbqaInQ2RYj0JFiyoICqyiCdTQO82hhh1qWzefGBoSJS/Z1x/vHgDs55dx2b/3Qg5ykdbt2MQi1woWrmDXBpXYAPfWc1br/duik2BVZZSOiNMF0/H4cz1uelyO/gJ9eczit7OnmloZOG9ggAsVSGF3Z38MLuDsD0qz3DEl7zWVDqR3uDYr2iKBRV+ymq9rP6qlqa9/TQcWgAl89M8d35agt7N3dwcFsXld8No9lUIj1JXF4bRkcrB2+4kXRrK4YwCF55FaRTKHY7WiiEokoPuJPN4d6Z8rpIjoSRSBB//XWiGzYQW7+B6MaNOOfOpfLBBwBwLlwAqgqGATYbRjSKYrdjKysdIbbaykrRAwH02pEWAEZasPHpA6TiGdb+fi/zVhbjDTqlyCqZcqQzBgOJNP3xNL2xFP3xNP1xc9kXz73el23ri5nLRNoYZRV0qDvGZ365idvesYA/vt5inc9t16zin4Op/3OLfHgcE1voVSKRSCQSyfEz3mJYW4QQiw9re00IsURRlE1CiNMmbYQTzHT3aIXpWzVyuo47Hkmh21R0u0b7gX5efGI3Z71zNr++a8Oofa/492W88qs9h6X4u8krNItPSSaf6fo+O5zxzKO1L86ahk5LeN3fGc3Zl9+pc2Z12Ip4nVc8cUW1Xnh8FzvWtFCxIMSF1y8C4A/3vkbT7h4qa5z4/3I/Be4BSr70BeKRDMLjQ4n04y4O4qiskKLeSSSXd2b5PffimFMnr4tkBD1P/oqeJ58kvmULIlukahDF6WTuq/+0/FW7f/lLHNXVOBcvRnU6x/U+27elg4oFIVRNpW1/H3/6ry2c9765VC2WXqwTyUz5/zjIG52PYQgGkmlLDB0UPq31EWJpmr5Yath2c1v0KOn74+XxG1Zy9X1rRrX/9uOreXZ7q5X+XxFyT+trJZFITh2kR6tEMn6h9Wngr8Bj2aargbcCFwFrhRDLJm2EE8xMEFolJ59Ib4In71xvRbQC+MJOrrp1+ZT3O5XMXJp6Ypbo+sqezjGLYwTdNs4aFF5rwtQVeo8rzTCTMohHU3jyHMQjKX5x64sYmaH/LeVz/JxxcQXPPlxPf9Zq48JrKgiXeNBDQdPTcd061Lw8tEAAW2mplYosOTaEEIhEAtVpRgBmBgbof/oZMt1dZLq7ccydS96llwKQ2LuPgx/911GRhsVfv4OuBx+g4GMfw3Wa+Ttq7LXXSDU1owWDaIEA9uoqVLt91Pkl0xchBKmDB4mu30Bswwb8l1yCZ6WZsNR+7710/PjuEfvbyspwLVuGe/ky8i67DNXlytWt2fcYkdOxgST/eHQX9evbWHn5bJZfVAVAJm2gTYDvtGQIwxDsaOnjhoeGUtR/eu1ybLpCPDXkbz/8e8Hh3xBGfmUQY2470nHj7V8cvvWw/j0ODVVRuPmRDdZ87n7f6eztGKC+LTJGhKn5fCCRPmJK/kThtKn4nDZ8Th3/4UuXDZ9D583zC7kxe00GKQ+6+PXNqynwyftJiUQy/ZBCq0QyfqE1H/gqcA7m/c1LwNeBXqBCCFE/mYOcSKTQKpkIhCHobBrgjz/dYglHb//YYsKlXhQZcSCZIhzsio4QXlv64jn3y/faOWt22CquNTvf84aF13Qqw97NHdSva2P/651k0gYX37SYF5/YPeqHiQuuncuWvzcyv6CD+Jc/TtLmpa3gdMIXXsDcm67Ek+eg69e/pfXnD+HwO7Hl+dECAbSsIKsF8si7/HJLVEx3dqI6nShu94zxpzMSCTLd3WS6u7HPno3qML949zz5JPGtW0l3dVvb091dZLp7cJ22lKqHHwYg1dpK/XnnW/35Lr6I8h/8AIDEvn00XHTxqHNWPPQgBz5wHbN+/jO8q1cD0PyVr9Lzy19a+8z+/e9w1Naa2267jfj2HaYIGwygBQLowSBaMIht1iyrD2EYkE5bkY+Sk4tIpUx/1XwzarT/ub9x6Oabre2hf/0IRf/v/wEQefVV2r79HUtYdS1bhq2o6LjOv3dzO397ZCexPtP32ZNn59o7VsliV8dAOmPQHU3RGUnQOZCkYyBBx0CSzgFzvTOSoD27/tVLF/C1320bJejd9o4F3PjQ9Cu1cHjKPUzsfOyais+pD4miTh2fwzZyfZh46h++7rLhdejjKlJpGIKdrf2jrILmFk1c5olEIpGcSKTQKpGMw6NVURQN+JEQ4toxdpk2IqtEMlEoqkK41MtVty7HSAtUXcHts0uRVTKlmBVyMyvk5j1nzEIIwb7OkcJrx0ACgI6BJH94rZk/vNYMQKHPYdkMrKoJUxEav3Cp2zTqVhRRt6KIRCzN3s3tePLsI0RWMH2NVV1l7+YOqpdnvWZdBeya817YC+FDA3jyHLQ1dPNcuSn+aKkYenMM28EoeiqKLd1IOLYbp9+Fw63T/8DPUJr2ERxowFsUoOqp35NKZLC7ddr+4z9QHI5RQq0WCKAXFR1zBO0b9TYVySTJQ4dMYbSrKyuS9pDp6iLT003w/R/Atdi0YGj91rfoeuBB69jq3/wa57x55uv33N8Y+Otfc54j09VtPdeGz8tmA2Pox1XV4cjpnUnGwLlgAXpBwVCf3UN9Ht5vfNcu4q+/nnMs7rPOsoTWVFMze/7lX1A9HrRgkLx3XkrBJz8JQPLgQfp+/3vzugSDaIFB0dZcjjd6VnrOjk1mYIDYps3ENqw3o1Zfew33GSuouO8+AFxLl1j7Km43pNPWuufMM6n+1ZMTMo5ELM2Lv9zFjleG/Cerl+Zz/rXzTnmRVQhBNJkZKZhGzGVHVkgdFFA7B5J0RZPjjsz0O20jREkw/UADrulpaxRwjT0fTVUskdTnsOF3jS2K+pwjtw/u47SdmPeiqirMLfLx65tXzxhLB4lEIpFITnWOKrQKITKKolQqimIXQoxdRl0iOcVQVEXaBEimDYqiUJ3voTrfwzVnVSCEYE/7gCW8rmnooiti/olv60/w201N/HaTKcCV5jlZOUx4LQ+6x3VOh0tn3soSIr0JfGHnqIhWIyMIlXoIn7eYvPOfYt+WDnja/OLscJv/nkR+Cew2j8noLjK6iwQhq5/2f7YPnTDwZgjA6Rt/gCedoLmhl9/+YCMAp29aS7BnN/3ecuprrkRPHcSWjqKno+QtW0zobefjcOsYO7cQffJRHB47Dr+Tog+8F282fTq+cyfJ/fvRS0pR3S6ibX1Zz9lW3AU9OGbPRlFVRDJJyx3/Qaanm3RXN466Wkpuvx2A5IEDNLzj0jFfM8/ZZ1tCq+r1jdg2XOzUQyFTMA4G0UIhK5pUD4awlZZY+6l2OzXPPI0WDKJ6RkYq60VFlN9zb07vzMNFteLbv0r+x28m09NDprsbLS/P2uZethzN5zdF4+x2I2r6BQ8XZDM9PQAYkQhGJEJmYMDalti9m/Yf/XjM16Xq8cdwLV0KQMf995PYtduKng1/6EOobjfCMIjv2EHjLbdIz9lhdD34ED2//jWJnTvNIlXDiG3chDAMFFVFD4cp+cY3cMybi3PuXBR94orsCEMQ7U+SjKXpbY/R3WK+P+wunTddXcecs4qnVRT6sXiDpjMGXdGkKZBaUadDAuqISNRIYkQa/xtFUxVCHjv5Xgf5Xjthjx27rlIedI2KAA177fziQ2eM7EDJ+dRcH3adRm8bfpxyhG2HH5j7uMPfEsNXC/2OnPOpDHuo/8bF0+r9pKqKtAmQSCQSiWQGMd676AbgJUVRngIig41CiO9PyqgkEolEMqkoikJtoY/aQh8fWFWFYQh2tfWbwuueTv65t4vemFn0pqk3zq82NPKrDY0AzAq5WDnM47Ukb2xvRgC3z87bP7Y4p9XG+75yVnavAupm1zDrX9IkoimrynjZJW/i3IoO4hGzPRFNk4imifcnSAwkSSQFiWiazDBxIv+yiwgEVXqjQ0V7HH4XStRO3BGiOzh35AA7gP/dmV1RoTCbwJEB/meAq4p6Ka7OY+//PcfGzQazLgxStzLE079vpb+zG1/YyVuvLSG+7hDC5kKzKzQ99xpaIopqJHFnHAQGkuh2DTWQO3JWcbnMqNph4oBn1UoUm25GdYaCOObMsbYVf/1rlNzx9SO+7oPYZ83KfU5VxTGnjqrHHz9qBKgeDqOHwzn7KfrcraPajGSSTHcPw40V9XCI/E/ekhVje3AtWWpty/T2HXEOWiBgPY++sobIyy9b6+EPf9jso6uLjnvvJfCVb6IEw4juTtp/cjfB976Xzvt/hvfccwh/9KOAaTMx8PzzVnSzbVYFtqLCI47hRDIoTBppA1VXj5oxITIZEvX1xDZsILp+A8Fr3od7mWmfn+7oILF9+4j9HXW1uJYtx718mRm5mo0YDlx15RHHZRiCTMogncqYy6SBv8CFqir0d8Xpbo6QThlULg6jaSrtB/rpbB4gUODm6Z9vtT7/b/7APBo2t7PsbZV4g87jfLVOLLlSvX/yvtPZ1dLP1uY+OiLDU/eTdB9D1OmR8Dl0wl47Ya+DsMdOvs9Bvie77rUT9jgo8JnLPJdtlPBrGIL7r1sxKkV9dr6X2kLfGGeduow1n0KfY1qJrBKJRCKRSGYe4/Vo/WqudiHE1yZ8RJOM9GiVSCSSo2MYgm3NfazJ2gy8ureL/kQ6575VYTerasKszEa8FvpGCydDwtHkWG2kkxlTgI2myCtwods0etqi7N/SSSKaYvEF5bi8dg681sKrv9tLIpIiHsuQTBgYxpHHcc3tZxEs9rD2/93Nq/0Lx/ScPefddfzpP7ccdazveFOMcEWApl4Xa1+JojttXHTjYvIK3LQ09LLl+UPodg3drqLbNWzZpdVmM5c2h9kWLvWg2zUyKYNMxjAF3XG+tscq6E0mRjJpibBWdGyP6T8buu46VI8HgKbPfY7o+g1kursRhsG8jRsASLW10dUa5y+PHRoquvbecnxqP/uvehd5V11J6Te+AUBkzT858KEPWecOf+wmCj/1KQD6/vQnWu/87kibiWHLwLvfheYzhalUSwuKzYbm96PYJiYFezwe4EYsRqavDyWUTyKapvvPf6XlO98lo9owVBu+K96F55JLyaQMIlt30PPc39HLK1FKZ7H83UtxFIRo3NnNrrWtpFMZznvfXOxOnT0b2tj07AHSKcMSUi1RNWWMKHI3yL9+71ycXhuv/e0gLzy+e1SbN+jM+XmZTsUjhRDs74zySkMnNQVePvvLTcftDaqpCuGsUJrvNaNPR60PE1YnIpX9WCJxpwMzbT4SiUQyE5AerRLJOCNap6OgKpFIJJI3jqoqLCrLY1FZHh89dzbpjMHWpj7L33Xtvi6iyQwA+zqj7OuM8uirBwGoKfBkPV7zWTk7RNjrQABRFZKqgV3VcJEjffQ4GBQiPYEh4SZQ6CbwlpE2BxVLiqlYUmytCyFIpwwSVsRsKhs9OxRB686KQaEr3kHB04dweW05PWcH7Q6ORuCtF+AtcCPWNNPTsR0YinjrbY+x69XWY5r7oBC8Z1Mbz/x824i2vZvbeenJ+lGCrc2uEizxUD4vyDP/vc0S9N76rwsJlXhwuHRa9vayZ30bAjjj7VU43DZaGnrZtbYVRLZ6uMjGrAphzkGYtcIHnyNg9bvrcHpsNO7sZuuLTSAEb3rvXJxeGwd3dLH1743mMVn/WPO1cCGEE0Sxuf7z3dZ53vrlOyj12dm3pYONf9nP9rs2cNENizA0D395rN66Nv2dpuh6/vvq2HjOV9ASeeh3mkJYpmeA5OmfBWDR1p+hBwLse62D9X/eT7od5nZGcDQ30xFexP6Ks8G6JGmcLVtRdFNUTezaiRHJJvpoOq4li7noxsV48hxs+MLd7BqYBbrGOfO78Rb6aY74eX2XhmJ3jCnMnnlpNX97eMeIefzxp1u4+EO1/OYHm0hnBGe/ejvhc86g+6pb+cdjuwAnnHnbUCf7gXtfG1p3rjYjtzsES6/14AC6WyJse9G0Bzn7ylrsTp3YQIqWhiNHFx9OOpUBbGjDCv8Mb3O49ZyfFyN9Asq+HweDxQTXNJj2Ks295hwev2HlmN6gPodOvm9QMB0UTQfT903hdFBE9TtHR51ONjMtRX2mzUcikUgkEsnMYFzfChVFKQBuBRYCVqiSEOLNkzQuiUQikUwhdE1l6awAS2cFuOm8GlIZg9cO9bKmwRQi1u7rsrwF97RH2NMe4eE1BwB459ISPriqik89vmnKVVVWFAWbXcNm1/AGj/yFvebsamrOrh7Tc9YXcnL1l88gnTRIJTKkkxnzeXaZTmZIJTM4vWaatjfopHZ5IalkxhJpNV3FX+Cyjk0nMzmjCIejZwsIpZPGqLbYQIretljO4y6+abElsoIpfj3z861c8W/LcLh0upoibHrWFM+XvnkWDreNrqYIW/526IjjOZwz3zkbp8dGb3uM3WtNxXLVlbU4sdHfEWfPxvaj9DCSdPZ9Fu1L0lTfa7UJ1JyCnmbX6daLYAAY6M1uUSCvBoDi792Fd0E1LfuTtDT0Ag6WX3gxjr4WOmJF9LprRvTZeyA6tKIXQ96wbQ191vXqWfc6nTULIQXt93yL/kQ3TcWraJv3fiDKWKiaknMeGBmShg4KZAyV6IYNaO89du/ZQZsNu1vH7bej21VrzL6Qk/J5QXSbipaNnNZsKnr2odm07DLbZtewu8z3btWSfK4q86LpKi6f+R6fu7KY+EA65+dF1adW5GFTT2xEscDGntyfm/54Oqc3aFW+hy1fu/BEDVcikUgkEolEMkUZr0frI8DjwDuAm4APAsf2zUgikUgkMwabprK8MsjyyiAfv6CWZNpg86Eey+N1/YFukmlT0LlkSaklsoIZ/XX9g+u455pltPbFWVDqpyzgmja+emN5zvpCzmNKuy+bG6Rs7kjP1trlhdQuH+kVmskYlug6QrxNmM9dPjMysrDSz9lX1o4Qbn1hJ3UrCkkNO37wuXOMyFwjY143VVPQ7eoI31jNZkYoomSL1ijZzUq2hI1iitfWIcpQ8Ry7SyevwAUKlsDu8OgESzwog/0M63PwOKv/bJ+qZj5x++2U1OahKAqariKEyCnoCUNQPi+3Ny6Af+VC7H47nt5Oa7/Sj3wRt9+OsbWTrmcOWPuKTBpFG7p1Snd1IZJJSKcAFVt5mTmWVAq3RyXUX49Ip1EN0y/Ykewl2LUde0UFtvJys4+2NhL19VafRnpJznloNo2ypn9gD+aR/54rCZyxmHSVj/OumYumq6Yoml3qNm2YGKqi6YO2E6ZICjDnjGLmnDEU3Q1QuShM5aLcXrxHw5PnGGUFoNs0PHlqzs+LOyvGnixa++LW36s1ezvZ35lb/PY5dc6qDlnWKHMLfTm9QQu8MrJSIpFIJBKJRDJ+j9b1QojliqK8JoRYkm1bK4Q442jHTjWkR6tEIpFMPvFUho0HeniloZML5hZwxb0vj9rn8RtWcvV9awAIuG0sKPGzsNTPglI/C0vzmJ3vQdemZrX4yfacPRFEehM8eef6ae2dOZzxeJuenHEZGP39pudsby+Znh5s5bNwzK4GILJmDT3/96S5rbeX0u/fRU+3McprNhDSsIVCqK4jF5+bikyFz0t7f8KyAVizp5OGjkjO/Tx2jTOHCasLS/PQchSWkt6gEolEIpGMRnq0SiTjF1rXCCFWKoryF+DHQBPwf0KImqMcOuWQQqtEIpGcWNr7E1xxNdKhVwAAIABJREFU70vHXDzGoavMK/FbAuzCUj/ziv247MdfFEYydYXJ42EqCHrHS7qjg+bbb8f17g+gBsMY3Z3EnniIkttvR8/PP9nDmzZ0RZJWMb9XGjqpbxvIuZ/LprGiKpj1lQ6zuCxvyv7AI5FIJBLJVEcKrRLJ+IXWdwAvALOAuwE/8DUhxFOTO7yJRwqtEolEcmIxDMHO1v5Rqbazgi62NfezramXrU19bG3qY3dbP6kjeJKqCswu8FrC64KSPBaW+gl6Tm4a8nRlJgiTMw1hGCR27ebQx28m1diErayU8nvuxTGnDkWVAuBY9ESTrGnosnyjd7T059zPoaumsDo7zMrZYZaUB7Dr8nWVSCQSiWQikEKrRDJOoXUmIYVWiUQiOfGMN9U2mTbY3dbP1qY+tjX1sbWpl21NfUSSmSP2X5rnZEFpXtZ2wHxMJ99XiWQ4wjDIZP1fFbsdLRSSIuth9MVTvNrQZRWv2t7SR65bWrumcnpFwIpYPa0igEOXUfESiUQikUwGUmiVSI4itCqKcjcw5g5CiE9OxqAmEym0SiQSyfTCMAQHuqLZqNdetjWb0a/t/YkjHpfnGvJ9XVhmRr/WFExd31eJRDI2A4k0a/eawuqahk5eb+zFyHGHatMUTpsVMCNWa8IsqwjitElhVSKRSCSSE4EUWiUS0I+yfbgi+TXgq5M4FolEIpFIRqGqClX5HqryPVyypMRqb+uPW5Gvg9Gv+4ZVDu+Npcxot4ZOq82hq8wr9o2Ifp0vfV8lkilHNJlm3b5uK2J1S2MvmRzKqqYqLCnPY1W2eNXyyiBu+9FubyUSiUQikUgkkslh3NYBiqJsFEKcPsnjmXRkRKtEIpHMXAYSabY397G10fR93dbcx67W8fm+DhXdMkXYkPR9nXbIavDTl3gqw/r93Vbxqs0He0jnEFZVBRaX5bEyawWwoiqE1yGFVYlEIpFIpgIyolUiOXpE63BOLTNXiUQikUw7vA6dM6pCnFEVstoO933dlhVgBxJpAAwB9W0D1LcN8NTmJuu4kjynWXCrNM8SYcuDpu+rFPRODEII0oYgkTZIpDLEs8tE2iCeXQ5uc9hU8lx2PvG/G6yia//5/uVUhd14HLr06z2J5Pq8JDMGmw72WMLqpgM9JDPGqGMVBRaU+K2I1TOqQ/idtpMwC4lEIpFIJBKJ5OgcS0TrBiHEskkez6QjI1olEolEMuj7avq99mb9X8fn+3rpkhKuWFbOpx7baAl6P712GaqqkEgbaIqCqiioqpnWrGbXzecMe57dx9p/jH0UJk0kHK9gnMoYOcXOEYJnLgE0nSGeMpeJlDFivxHb0gaJlEHc2m+or1w+nLn4rw8s547fb+NQd8xqKw+6uO0dC/jM45so8jsp9DlGLv3m0nw4ZMr5JGAYgp2t/Vz/4Drr83LXe5byvb/sZO2+7pzHzCv2WcWrzqoOk+eWwqpEIpFIJNMBGdEqkRwlolVRlH6GIlndiqL0DW4ChBDCP5mDk0gkEolkMhju+/r2xSN9X7dlRddtzWb0696OiLW9N5bi3DkFlsgKcKg7xsce2cBt71jAjQ+tn5zxZsVXVVWywizDhNlBIZfRoq61v3msqgyJv3OLvFy5rJx/e2KzJYB9791LeeClvWw82DskgKaNnN6YU42AyzZCZAXz2gRcNqLJDHs7IiOuZS58Dn2E+Froc1CYFWGL/E6KfKY4eyoXV0pnDPriaXqiSXpiKXqjKXpiSbojqey62d7z/9m77/gqy/v/46/rrGyyA4Q9gjIE1ADiREBREK1aBBX3am3Fn7bVVm3tt35r7fpacbWurwvFgdsvKmKtrbhYKoJARFmBJJCQnZxx378/7pMzIBAwQAbv5+NxHufc17nHdZ2ToLy57s9V52zfMLGA215ZEff78rPnP+fXZwzhs++c35dBXVOdxav6ZzOmf7bKdoiIiIhIh7XHoNW27bSD1REREZG2lpeWSN5hiYw7LC/S1lT3tWnBrdzUhN0GegeKZYNl2+z19M698ONxAyIhKzhj+PkLTgA2/6uS/XYdcBYhS/C4SPC6SfC4SAw/Ow83iV7nOcHrIjH83Nx78cc2tTnb6UleemYm7TKjNSXBw+XH9aOkuoHSqgZKqhopqWqgMbjrberVjUGqy4J8U7bnQDY9yRsJX3PTmkLYplmyTjCbm5ZAgqf9BrKBkEVlOBCtrPc7wWhdgIo6f6TdCUyj2xV1fqobgvt0nSSvu9nflx4ZSdx3wZEc0z+bnNSE/Tk0EREREZE2o3vkRERE9mDnuq9l1Y3NBno5qQk8ftkoLNsmZDnBqGXZhGzbCUotm5BlO+2x+9hN7cTsHz62uX3smPM0e/6d9mn2/NC1S2KzAVj39ETOK+wZH3I2BZzNBaAeVyTwjA9Fnfd8btdBqV9rWTYPX1wYd4v6wxcXcljXNIZNTY/b17ZtquqDlFQ3UFLVQGlVYziIdULYknAgW1rd0OxCapX1ASrrA6wpqdljn7JSfJEyBV27JJCXFn6OKVeQk5qA1+3aZSx7WwPYH7TYUe8PzyyNCUvDM02bAtO47bpApEbx/tYl0UNGso+MZC/pSV6MMc3+vnTtksiwHul7OJOIiIiISMez1zVaOwvVaBURkdZoruZkU6DXkRbEKqtu5OwHPtwlAHv52uPITeuYMwz39yJltm1TUReIhK9NQWxpdTiQrW6kNLz9fcsrGAPZKQmRGbJH98lgbP8cZsXUAL5nxpF8vG47XxVXRoLSplv36/yh7z2+PfUpPclLRpKX9GQfGUleMpKj25nJTds+0sPtGck+uiR68DQTGneG3xcRERFpmWq0iihoFRER2Wf7O9BrCwrA9p+QZVNe6w+HsNHyBCVVjZTFbG+raWyx+sOeFvXa1xrALoMzuzTJGxeIpoeD08yYmacZMYFqWqIX9378GegMvy8iIiLSMgWtIiodICIiss9cLtNhZ302cbkMh3VN4+Vrj1MA1kru8M+D8zOx+9vhgyGL7eFAtil8jdSNDZcuyErxNVvSISvFx4DclJ2CU184MN155qkz0zQtwdMuvs/O8PsiIiIiIrI3FLSKiIgcohSAHVwetytcmzVxt/vsrgZw3+wUFv5s3EHopYiIiIiIfF+ulncRERERkYMhO8XHwxcX0jMzCSBS0iE7xdfGPRMRERERkZZoRquIiIhIO6GSDiIiIiIiHZeCVhEREZF2RCUdREREREQ6JpUOEBEREREREREREWklBa0iIiIiIiIiIiIiraSgVURERERERERERKSVFLSKiIiIiIiIiIiItJKCVhEREREREREREZFWOmBBqzHmMWNMqTFmRUzbn40xXxtjvjDGvGyMyYh571fGmCJjzGpjzKSY9tPCbUXGmF/GtPczxnwSbn/OGOM7UGMRERERERERERER2ZMDOaP1ceC0ndoWAMNs2x4OrAF+BWCMGQLMAIaGj3nAGOM2xriB+4HTgSHA+eF9Af4I3G3b9kCgArjiAI5FREREREREREREZLcOWNBq2/YHQPlObe/Yth0Mb34M9Ay/PguYa9t2o23b3wJFwOjwo8i27XW2bfuBucBZxhgDjAdeDB//BPCDAzUWERERERERERERkT1pyxqtlwPzw697ABtj3tsUbttdezawIya0bWpvljHmamPMYmPM4rKysv3UfRERERERERERERFHmwStxphbgSAw52Bcz7bth2zbLrRtuzA3N/dgXFJEREREREREREQOIZ6DfUFjzKXAGcAE27btcPNmoFfMbj3DbeymfTuQYYzxhGe1xu4vIiIiIiIiIiIiclAd1BmtxpjTgJuAM23brot56zVghjEmwRjTDygAPgU+AwqMMf2MMT6cBbNeCwe0/wR+GD7+EuDVgzUOERERERERERERkVgHLGg1xjwLfAQcZozZZIy5ArgPSAMWGGOWG2P+DmDb9lfA88BK4C3gJ7Zth8KzVX8KvA2sAp4P7wtwM3CjMaYIp2browdqLCIiIiIiIiIiIiJ7YqJ37x8aCgsL7cWLF7d1N0REREREREREOg1jzBLbtgvbuh8ibalNFsMSERERERERERER6UwUtIqIiIiIiIiIiIi0koJWERERERERERERkVZS0CoiIiIiIiIiIiLSSgpaRURERERERERERFpJQauIiIiIiIiIiIhIKyloFREREREREREREWklBa0iIiIiIiIiIiIiraSgVURERERERERERKSVFLSKiIiIiIiIiIiItJKCVhEREREREREREZFWUtAqIiIiIiIiIiIi0koKWkVERERERERERERaSUGriIiIiIiIiIiISCspaBURERERERERERFpJQWtIiIiIiIiIiIiIq2koFVERERERERERESklRS0ioiIiIiIiIiIiLSSglYRERERERERERGRVlLQKiIiIiIiIiIiItJKClpFREREREREREREWklBq4iIiIiIiIiIiEgrKWgVERERERERERERaSUFrSIiIiIiIiIiIiKtpKBVREREREREREREpJUUtIqIiIiIiIiIiIi0koJWERERERERERERkVZS0CoiIiIiIiIiIiLSSgpaRURERERERERERFpJQauIiIiIiIiIiIhIKyloFREREREREREREWklBa0iIiIiIiIiIiIiraSgVURERERERERERKSVFLSKiIiIiIiIiIiItJKCVhEREREREREREZFWUtAqIiIiIiIiIiIi0koKWkVERERERERERERaSUGriIiIiIiIiIiISCspaBURERERERERERFpJQWtIiIiIiIiIiIiIq2koFVERERERERERESklRS0ioiIiIiIiIiIiLSSglYRERERERERERGRVlLQKiIiIiIiIiIiItJKClpFREREREREREREWklBq4iIiIiIiIiIiEgrKWgVERERERERERERaSVPW3dAREREREREROSQYllQVwZBP3h8kJwLLs2FE+noDthvsTHmMWNMqTFmRUzbNGPMV8YYyxhTGNPe1xhTb4xZHn78Pea9o40xXxpjiowxs40xJtyeZYxZYIxZG37OPFBjERERERERERHZLywLSr+CRybC34Y5z6UrnXYR6dAO5D+XPA6ctlPbCuAc4INm9v/Gtu2R4cePYtofBK4CCsKPpnP+Elho23YBsDC8LSIiIiIiIiLSftWVwdwLYMcGZ3vHBph7vtMuIh3aAQtabdv+ACjfqW2Vbdur9/YcxpjuQBfbtj+2bdsGngR+EH77LOCJ8OsnYtpFRERERERERNqPqi3QWO28DvqjIWuTHRucdhHp0NpTAZB+xphlxph/GWNOCLf1ADbF7LMp3AbQ1bbtLeHXW4GuB6mfIiIiIiIiIiIt2/4NvHYd3DMcPnvEafP4IKN3/H4ZvZ12EenQ2kvQugXobdv2kcCNwDPGmC57e3B4tqu9u/eNMVcbYxYbYxaXlWkqvoiIiIiIiIgcQMXL4PlL4N6jYemTEPLDRw9AsNFZ+GrGs9GwNaO3s52c27Z9FpFW87R1BwBs224EGsOvlxhjvgEGAZuBnjG79gy3AZQYY7rbtr0lXGKgdA/nfwh4CKCwsHC3gayIiIiIiIiIyPdi2/Ddv+Hf/wPr/hn/3mGT4bj/B54EZztvCFz5rlMuwONzQlZXe5kLJyLfV7sIWo0xuUC5bdshY0x/nEWv1tm2XW6MqTLGHAN8AlwM3Bs+7DXgEuCu8POrbdB1ERERERERETnUrXoD/vM/sHlJtM24Yfh5cNz1kDc4fn+XC1JVAVGkszlgQasx5llgHJBjjNkE3I6zONa9QC7wpjFmuW3bk4ATgd8ZYwKABfzItu2mhbSuBR4HkoD54Qc4AevzxpgrgPXAeQdqLCIiIiIiIiIiu7X0yWjI6kmCoy+BsT/ZtRariHRqxilveugoLCy0Fy9e3NbdEBEREREREZGOqLEGNn4MAydG2zZ8DM9MhzHXwOirISWn7frXRowxS2zbLmzrfoi0pXZROkBEREREREREpF2rK4dP/gGf/gMaq2HWcsjo5bzX+xi4cRX4ktu2jyLSphS0ioiIiIiIiIjsTuUm+Oh+WPI4BOqi7cvnwLhfRrcVsooc8hS0ioiIiIiIiIjsrGw1fHgPfPEcWMFoe/eRcMKNcPgZbdc3EWmXFLSKiIiIiIiIiDQp/xbeuQ2+fhOIWdem/zg4/gbodxIY00adE5H2TEGriIiIiIiIiEgTTyKsfQcnZDUweKoTsPY4qq17JiLtnIJWERERERERETk0WSFY+Spk9IaehU5bl+5w1CUQbIDjroecgrbto4h0GApaRUREREREROTQEmiAz5+FRbOhfB0MGA8XvRx9f/KfVR5ARPaZglYREREREREROTQ0VMHix+DjB6CmJNq+5XOo3Q4p2c62QlYR+R4UtIqIiIiIiIhI51ZTCp/8HT59BBoro+3pveDY6+DImeBLabv+iUinoKBVRERERERERDqv9++C/9zt1Fxtknu4s8DVsHPB7W27volIp6KgVUREREREREQ6L19KNGTtOQqOvxEGnQYuV9v2S0Q6HQWtIiIiIiIiItI5rP8Ilj8NZ9wD7nDkcfSlsOkzGH0N9DlW9VdF5IBR0CoiIiIiIiIiHZdtw5q3nfIAGz922vqfDEf80HmdkAbnPdl2/RORQ4aCVhERERERERHpGCwL6sog6Hdqq279Ehb8BkpXRvdxJ0Dlprbr416wbIvyhnL8IT8+t4+sxCxcRqUMRDo6Ba0iIiIiIiIi0v5ZlhOozj0fdmyAjN5w5n3gS3beT+gCo66AMT+GtK5t29c9sGyLNRVruP696ymuLSY/JZ/Z42dTkFmgsFWkgzO2bbd1Hw6qwsJCe/HixW3dDREREREREWmPYmdMenyQnNuxF03an+OxbbCCEPI7C0w1qSuHiu+c9mADBBtjHg0QCr/OGQQFp0SPW/oUrF8UfT9ybEP88afeAUPOgpoSeGSiE7I2yegNp//ZCWBHXQGJ6d9vbAfRd5Xfcc2CayiuLY605afkM2fKHHKSctqwZ61jjFli23ZhW/dDpC1pRquIiIiIiIi0XlsHlJblBH0hP4QC8a+t8HaXHpASE2R98x40VEIoHB4mpEGXfHjxsuiMyXMfg5wCSMpwjvHXwqcPt9wfbzKMuTq6XbYaVs9v+bicQXD45Oh20ULn9viWDDgZuo+Ibi95Atw+yB4I8y6PGc8jsPEz2F7kBJmn3hH9TKpL4LkL40POSFgaDlFtC9J7wQ0rotda8xa88uOW+zh8RnzQuvFj+PyZlo+r3+E8B/3xISs4212HwGGntXyeNlLZWMnr37zO4VmHU9itEK/LGxeyAhTXFuMP+duohyKyvyhoFRERERERkdZp7pbuHz4ONVuhesuuwWfTtjcJxt8WPc/6RbDoPuc9K9DMceHXBZNgyl+ix71+PSx5vOV+nvE3KLwsuv3mz6H8m+j29KejISs4z/Muh0veiA9a37295Wul5MYHrSUr9u64oWfHB61fvwGLH2v5uITU+KD1o/tgwm+iIWtkPFfCpDvhnVudthNujAmfbdj0WcvXCjbEb3sSWj6mueNSciG9txPMexKd87gTnOembU8CZPYNX8fn/GztPKN1b6/fBgJWgKkvT6WisYJxvcZR2K2QBE8C+Sn5u8xo9bl9bdhTEdkfFLSKiIiIiIjI92dZUL4uGrKC8/zipU6g9+bPdn9sck580Fq9FVa/2fI1a0vjt117+VfbUCB+e+dgKymz+RmTdmjvzt/e7G48ydmQkueEmbHlBL3JMGD8TmFnTAjqSXTeS+wSf87eY+H8uTFBaeKuYaknATxJ8cdN/K3z2FvJuTDj2fhAf8azTns7UdFQwWvfvMbmms3cMuYWvC4v43uPZ97aeXxX+R3+kJ+sxCxmj5/NrPdmxdVozUrMauvui0grqUariIiIiIiI7Lv6Clg2Bz57BM66Dx6fsus+l77ZfLvL64ScydlwQ8xt8eveh7dvdVaTd/vCD2/0uem4HkfHzxZd8w4ULwvv693puJjXXYdGZ0eCczu/FYruY9zw+Om7zpi84t3o4kq2DYH6vfuMmhZpgmh5gpa43PEzNIN+py5qS5rG3iRQ79xy/9ipu47nynchtf0uFrVHbV2iohm2bWOMAeC/P/5vnlv9HAbD2+e+TffU7qyrXEdZXRmjuo2KLHZl2RblDeX4Q358bh9ZiVkdfiEs1WgVUdAqIiIiIiIi+2LrCvj0IfjieQiGA8fpT8Pbt+wa6F36f+FZjjGhp8sD4VCq3WmuBMKMZyFvSJuHed9LZxtPO1PRUMG8tfN4pegV/j7x7/RM68mq7as4743z6NulL3ccdwcj80a2dTcPGgWtIgpaRUREREREpCWhgFMr9JOHYMOi+PeyB8K4W5xFnJ67oOMHeu1wxmSrdLbxtLGQFaIx1EiyN5k1FWs497VzAbjqiKuYddQsAFZsW8HQ7KGRWa6HCgWtIqrRKiIiIiIiIntSugqeOttZ1CrCwGGnw6grof/JTnBnWc4t6R090HO5Ou5t9c3pbONpI5Zt8fAXD/PS2peY1G8SNx59I4MyBzE8dzh1gTr6dOkT2XdYzrA27KmItCUFrSIiIiIiIhJl22BbTq1QgKwBTh1TgMQMOOpiGHVFfK1TUKAnnU7QCrKhagP9M/rjMi4WFS+iuLaYV4te5bqR1+F1e3lgwgN08XU55GavikjzFLSKiIiIiIiIs3jSinlO/dWRM6OLTXl8MP5WZ6GoYefGL/Ak0km9/s3r3LP0HhpDjbw77V0S3An8cNAPqQvWcW7BuVhYAKQnpLdxT0WkPVHQKiIiIiIiciirWA+LH4WlT0J9hdPmr4PRV0UXrTr60jbrnsjBELACfLDpA47LP45ETyJu46akrgSAhesXMrn/ZKb0n8IZ/c/Q7FUR2S0FrSIiIiIiIoca24Z1/4RPH4bV84GYRZJTu8ER0yDkB09Cm3VR5GApqijiqgVXsa1+G3cefydTB0xlYp+JjC0ay6l9T+WkXicB4DIdsOawiBxUClpFREREREQOFbbthKufPgTb18a/1/tYZxbr4Kng9rZN/0QOgkAowMKNC0nzpnFcj+Po06UPlu2UAnhz3ZtMHTAVn9vHQ6c+1MY9FZGORkGriIiIiIjIocIYWPVaNGT1JMHw85yAtdsRbds3kYPAtm3Of/N8VlesZnjucI7rcRxet5efHvlTvC4vp/Y5ta27KCIdmIJWERERERGRzigUhDXzYcdGGHtttH30VVC5EUZdBUdeCEmZbddHkQOsMdTIu+vfZWnJUn499tcYYzix54msrljNd5Xfsb1+O9lJ2UwbNK2tuyoinYCCVhERERERkc6kdhssfQI+ewyqNjmzVkfMgOQs5/3Dz4DDp4JL9Sal83t8xePct/w+AM4ddC5DsocwbdA0+mf0Z2LviSR6Etu4hyLSmei/rCIiIiIiIp3B5iXw8o/gfwbDwt85ISuA2wclK6L7udwKWaVTagg28GrRq1w8/2K+KPsCgDMHnInBkJGQweaazQB0T+3OGf3PUMgqIvudZrSKiIiIiIh0VMFG+OplZ3GrzUvi38sb6pQJGH4e+FIOeFcs26K8oRx/yI/P7SMrMatDrtLeWcbRpLONpzlNY6v2V3P7otsJ2SHmrZ3H8NzhdE/tzqOTHmVE7gh8bl9bd1VEOjkFrSIiIiIiIh3VP++ED/8W3TZuGDwVRl8NfY51Fr86CAKhAOsq1zHrvVkU1xaTn5LPXSfeRb8u/chIzGDl9pW89e1bGGO4bOhlZCRmsLp8NQs3LMQYwwWHX0B6QjpFFUV8sPkDXLg4u+Bs0hPS+bbyWz4q/ghjDFP6T6GLrwsbqjawuGQxBsMpfU4h1ZfKpupNfFH2BS7j4oSeJ5DiTWFr7VZWbl+Jy7gY3W00yd5kSutKKdpRhMu4GJE7giRPEtvrt7O+aj3J3mQMJm4cd598NwPSB5DgSWBHww421zqzIgsyCvC5fe26zePysKZ8Ddf/8/rIeGaPn02P1B6U1pXiMi56pPbA6/ZSF6ijyl+F27jJTMzE4/IQCAVoCDXgNm4SPYm4jAvLtrBtG5dxYQ7Sz1es2OC4NlDLk189iYXF74//PbnJuZzY80S+KPuC/JT8yDGjuo066P0UkUNT5/pnLBERERERkc7KtmHjZ85zk6Mudp5TcuHEm+CGFXDeE9D3uIMSsn64+UMufetS/rP5P5FwEqC4tphffvBL6oJ1ABTtKOJ/v/pfHlvxGNWBagBWV6zmwc8f5IHlD1DVWAXAqvJV3L3kbv665K+RthXbVvCHT//AnZ/cSWVDJQCfl33O7Ytu5zeLfkNFQwUAy0qXcfO/b+YXH/yC8vpyAD7b+hnX//N6rnvvOrbXbwfgky2fcM2Ca7jqnavYVrcNgEXFi7jkrUvYXLN5l3Hc8M8b2N7gHPvB5g+Y8cYMZrwxg5K6knbfVt5QHglZm8Yz671ZbKndwlmvnsXUV6aypXYLAAs3LOSUF09h/Avj2VLjtL313Vsc++yxjHlmDJurnQD3zXVvMvKpkQx/cjgbqjZE2kY9PYoxc8awsXojAG9/9zYnPXcS458fH7llf+GGhUx+aTJTX54aucYHmz5g2uvTmP7GdLbWbo38XF0y/xIuf/tySmpLIt/bnz79E8tKl3Hhmxcyad4kfrrwp0wZMIXN1Zup8js/L78Z+xsWTFvANSOu2aufYRGR/UkzWkVERERERNqzxhr4Yi58+jCUfQ1XLoSehc572QPg4leh91jwJBzQbnxb+S3PrHqGZaXLuHvc3fTq0ougFWRJyRJ+OvKnkTCvSXFtMSE7BIDBkOhOxLKtyG3rlm1F9m2aGdnWbem+9ObHYYX2+Nm0V/6Qv9nxxM5Ebc330XRs0ArSEGpw9sPZryHYQHmDE3jb4X8cqPHXRILYpp+NioYKvi7/GoCAFQBgW/02lpYudcZg+QHnZx9MAAAgAElEQVTYWruVo7sdzS3/viUuOL79w9v567i/Rq6Rk5SzD5+QiMj+paBVRERERESkPdpWBJ89DMufgfDsTgA+eyQatAL0H7ffL13lr+Lt795mWckyLhx8IUNzhlIXrGPu6rkALCldQq8uvRiZN5JBmYPwuDzkp+THhXr5KfkkeZIAmDpgKlMHTI27xg8G/oCzBpyFjR0J56b0n8KpfU/Ftu3IQkWn9zudk3qehIVFui8dgFP6nMKY7mOwbIvc5FwAxvcez2u5r2HbNl2TuwIwrtc4nj/jeSws8pLzADi+x/E8efqTcceOzR/LQ6c8RHZSdrPjSHA7IfbobqO5d/y9AGQnZrf7trpgXbPjSfYk86cT/4RlW2QlZgEwIncEt4+9Hcu2yEzMBGBo9lB+UfgLLNsiPcH57A/POpxrR16LZVuk+dIAGJgxkEuHXkrIDkXa+nTpw7RB07BsixSvUyO4R2oPJvebjGVbJHuTAeia0pVxPccRskORn5fspGxGdRuFZVuRtvSEdDITMpsNjrMSsyL9ExFpS8aOve3kEFBYWGgvXry4rbshIiIiIiKyKysEaxc4i1t9szD+vfTeMOoKOPIiSMneb5cMWSG+3PYly0qXMSJ3BEd1PYqyujLGvzAegOuPup4rj7iSoBVk/PPj6Zfej4uHXMyEPhOi3bYt1lasjattOnv8bAoyCzrUwkudZRxNOtt4ttVv48I3L9wlOJ4zZY5msrYDxpgltm0XtrynSOeloFVEREREpLOzLKgrg6AfPD5IzgVXxwtZOp2dvxd/LTz5A9ixPn6//ic7i1sNmgQud6svW+2v5vOyz0n1pjIybyT+kJ+xz4zFb/m54PAL+NWYXwEw+aXJVDZWMnPwTH488seAE8q6d9OHzrK6fWcZR5PONJ7OFhx3NgpaRVQ6QERERESkc7MsKF0Jc8+HHRsgozdMnwPZA52anvshuJNmBBqgoRKC9c7rQB0EGyBQ7zx7EiAxA168LPq9zHgGsgY4QasvDUZeAKOuhNxBrepKSW0JOxp3cFjWYQCc/erZlNSVcEqfUxiZNxKf28ewnGEsLV0aqZ8J8MRpT5CdlB0XYO0uZAWnXmdnmFXYWcbRpDONx2VcFGQWMGfKnE4RHItI56OgVURERESkMwgFwR3zv/dLn4Kid+HoS+H1WU6YB87zcxfCpDvhuZkw+S8w+qrocU+fCzs2OjMsPYngTnBCQU8CuMNtHh+MuwW6dHeO8dc5tURj9/UkhLcTo+fyJkO3YdFrBf0QanTec3kgZoGePfq+M3TrK5ygs+nRFHwG6uMD0REzwOvUhaR6K3zwl133CYafAw1Oe0YfuPSNmM//CZh/0+77Mv1peOOG+O9l7gVOCL5xstOHhLS9+zxiPxrbYnv99kjt0esWXsf7m95nZO5Inpr8FODU4nxn/Tt8Xvo5tm1jjOGWMbeQnpBOt5RukXM1nUOkPelMwbGIdD4KWkVERERE2jvbhrpyqNzgBHI7NkLlxvBzuG3Q6XDOP6LHbF4CK19xQtSmMK/Jjg2Q5Cx2g9sX/972b6Di25b7dNz/i75uqIQFv2n5mJQ8+MXa6Paq12DeFeENEw1ldw5oB02CCeHzWxYUL42fCXr2P+DLF2HrF/FB6Ol/gsFnRK9332ioLW25n4NOiwatjTVOiNwSs9NMz/BCTruVlLn77yU2+G5B7K38//j8Hzy16ilSvam8de5bAPRI6wHAiu0raAg2kOhJ5KIhF3HWwLMYmTcyspJ802xXERER+f4UtIqIiIiItDXLgpqtTnBaUwJDzoy+V7TQmXkaqNvzOSo3xm9nD4DMfs7iShm940O9jN7gS4WTb4X8kfHHDToNqrdAsNGZbRqMeYQanQAz6I8PEoMNezfOncPHYGPMhu3MDA3W73pctyOir+vKoiErOM8vX+PM0F38aPxxDTvit70thJ9NYj9rXzIkZTnBa9OsXG9i+HVSuD0J0rrGn6P3Mc5s4cj+SdH9vUlO2YDmvhfPTsH3bry34T2eWvkU6yrXsXDaQjwuDx6Xh8rGSiobKympLaFrSldO7XMquUm5HN31aDwu569/I/NGtnB2ERER+T4UtIqIiIiIHAy2Dd/9JzoTdceG8GzUjVC1GUL+6L63lkRDweSs5kNWbzKk94KMXk5AFxtGAhx7nfOwLJjxbHyN1hnPQt6QXUNWgNPv2vexZfSBX4XHEGzYKZiNeexc3zP/SDjljl1D3J2P6x7Tz6C/+Zmgad2cRaNiA9HMfvH7jbvFuUZsQOoN7xsbnKbkRY/pkg8378UM353lHuY8dmd330szt+uvqVjDi2teZFnpMh4+5WEyEjOoCdSwuMRZ5Hd1xWqGZg/lmPxjmLJjCkflHUViONQ+qutRHNX1qH3vv4iIiOwzBa0iIiIiIntrd7VBA/VQuSkcnm6M3to/6kroNdo51hhnZurOsyybU7XZmZEKTlg45kdOENcUrKb3dgLYvalp6nI5oeqV7+57TdO95XJBQuq+H9d1iPPYFx5f8zNBM3rDxa/s+diR5+97Hw8Ulwsr93DKr3wHvxXE5/KQlZRLpb+Sdze8y7KSZVw9/Gr6pvdlW/02nv36WQCWly1nXK9xHJl3JIMyB3Fk3pEkeZwyB0Ozh3LXCd8jKBcREZH9QkGriIiIiMjesCwoXRk/A/EHf4d/3w3fLGj+mJ6jokErOCHp1nDQmpK7a3jaNDs1vVf0mKQMOP2Preu7ywWpXVveryNIzt3rmaDtmWVbrK38hlnvzaK4tpj8lHxmj59NojuR3330OwCO7HokfdP7MiJ3BEmeJAZnDY7c/t8rrRfzzpzXlkMQERGRnShoFRERERHZG3Vl0XAPnOdXfuTUBt05aDUuSMvf9RxnPeDcmp7eM7rYkuybgzFDdz8IWkFK6krwGA9dU7oStILc+p9b2VyzmSn9p3BKn1MiIStAcW0xs96bxZzJc0jzpWEw1IVLRqR4U/jw/A/xurxtOSQRERFpgYJWEREREZGWlK5yFo9qrjZoei+n9mdGr+js1C49wN1MKNZ9+MHpb2fXzmbovvXdW2yo2sCA9AFM6DMB27Y5Ye4J1ARqmDl4JjePvhmPy8Oi4kXsaNzBwIyBnNTzpEjI2qS4thi/5Wfe1Hl0TemKy0TDY4WsIiIi7Z+CVhERERGR3QkFYMFv4OMH4SefNF8btEt3GHdz2/VRDpgafw2bazZjjGFQ5iAAbv3PrazcvpIx3cfwy9G/BODuxXdTXFvM6f1OZ0KfCRhjyEvOo6ayhk01myLnG5k3khp/DX279MXn9pGfkh8Xtuan5ONz+8hJyjm4AxUREZH94oAFrcaYx4AzgFLbtoeF26YBvwUGA6Nt214cs/+vgCuAEDDLtu23w+2nAfcAbuAR27bvCrf3A+YC2cAS4CLbtmOWahURERERaYWqLfDCpbDxY2d74R0w4xmYe0GHqw1q2RblDeX4Q358bh9ZiVlxsyUPVZZtRT6H+d/OZ1X5KvKS8pg5ZCYAF82/iKIdRYzrOY57J9wLwLod6yjaUUR2UnbkPD3SelBcW0xVY1Wk7bJhlxGwApGAFuDe8ffGXXv2+Nm71GjNSsw6oGMWERGRA+dAzmh9HLgPeDKmbQVwDvCP2B2NMUOAGcBQIB941xjT9H8k9wOnAJuAz4wxr9m2vRL4I3C3bdtzjTF/xwlpHzxwwxERERGRQ8Z3/4EXLoPaUme7+wiY9N/OglXtvDbozizbYm3F2l0CvYLMgg4btu5LcLytfhvrq9YTsAIc0/0YAO746A7+telf9Evvx8OnPgzAi2te5NOtnzI8Z3gkaO2R2oOiHUVxs1JHdR9FTnIOw3OiZSD+cPwfSPWlkuJNibT9YOAP9jgGl3FRkFnAnClzFICLiIh0EgcsaLVt+wNjTN+d2lYBGGN23v0sYK5t243At8aYIqBpedYi27bXhY+bC5xljFkFjAcuCO/zBM5MWQWtIiIiIvL92TYsuhfe/S3YIaftyItg8l/Am+hst6PaoHujtK602UWXbj3mVk7ocQLGGOZ+PZeX1r6E1+Xl6clPY4zh9W9e563v3sLr8nL3uLsxxrBww0L+venfeFwebh1zK8YYPt7yMUtLluJ1eblq+FUAfFn2JV9XfI3X5Y0Ejusq17GxaiNel5djexwLQEltCdsbtuNxeSIzP6v91TQEG/C6vGQkZgBOsGowGGP2GBy/s/4dPt3yKT63L3Jb/38t+i/e3/Q+AzMG8vJZLwNQ5a9yFqpyRf861CO1Bx7jgZi/qlww+AKm9J9C7y69I203Hn3jLp9x15Tv9zPhMi6VCRAREelE2kuN1h7AxzHbm8JtABt3ah+DUy5gh23bwWb234Ux5mrgaoDevXvvbjcREREROZQ1VMGrP4FVrznb7gSY8hc46uK27Vcr3L/8fsZ0G9PsokvJnuTIBIittVtZVb4Kj8sTaVtXuY4PNn2Ax0TbVmxbwby18/AYD7cdcxsAn2z5hEe+fASP8USC1vc2vhdpawpa3/jmDR7+8mHcxs3yi5cD8Nzq53Zp+98V/7tL233L7uPhLx8m0Z3I/HPnNxscz5kyh4+KP+KltS+RlZgVCVp7pDl/TdhcsxnbtjHGMLr7aDwuT1yA+svRv+T2sbfjdrkjbcfmH7tfvgcRERE5NLSXoPWAsm37IeAhgMLCQruNuyMiIiIi7U11CTw+BbavdbYzesN5T0L+kW3br32wuWYzrxS9wr82/ouHT32Y9IR0BqQPoNJf2eyiS7GzOful9+OknifF3XmWk5TD4KzBcbeyJ3mSyEnKiWsLhWf+xp4vaAUPSJuNjT/kbzY49of89EjtQZovja7JXQmEAnjdXs4eeDbH5R8XCVwBpg2axrRB0+LOkexNRkRERKQ1jG0fuNwxXDrgjabFsGLa3wd+3rQYVnghLGzb/kN4+22cUgAAv7Vte1LsfsBdQBnQzbbtoDFmbOx+e1JYWGgvXry4pd1ERERE5FBiWfDMNCh6FwaeAuc8BMnte1GikBViedly+nTpQ05SDh8Vf8TVC64G4M7j72TqgKnUBmp5de2rjMgbwY3v33jAarRatkXQCuJz+wCoDdRS468hZIfIT80HnDIBZfVlWLbF8Fynvum6HetYX7UeC4sJvScA8HnZ56wuXw3AeYedB8CizYtYWroUYwzTD5vOhW9euEtwPGfKHDITMuNmpIqIyMFjjFli23ZhW/dDpC21l6B1KPAMTl3WfGAhUIBTIWkNMAHYDHwGXGDb9lfGmBeAeTGLYX1h2/YDLfVJQauIiIiINKuuHJY/A8dc2+4XuCqpLeG8N86jvKGcm0fdzMwhMwlYAcY9N4781HyuOuIqTu17amT/fVk8qr3rjIt7iYh0BgpaRQ5g0GqMeRYYB+QAJcDtQDlwL5AL7ACWx8xWvRW4HAgC/8+27fnh9snA3wA38Jht278Pt/cH5gJZwDJgZngxrT1S0CoiIiIiVBXDa7Pg9D9C9oC27s0e2bbN29+9zbsb3mVo9lAuG3YZtm1z2rzTKK4tZlS3UTw26TEAKhsrSU9Ib+MeH3idKTgWEeksFLSKHOAZre2RglYRERGRQ9y3H8CLl0NtGeQNhSsXgC+lrXsVZ1v9NjZVb2Jk3kgAznv9PFaVr6Igs4CXznwJgJfWOs8n9TyJ7KTsNuuriIgIKGgVgUNkMSwREREREWwbPrwHFv4X2JbT1msUmPZV0/PPn/2Zp1Y+RW5yLgt+uACXcTG+93g2Vm9kYMbAyCzOcwrOaeuuioiIiEgMBa0iIiIi0vk1VMIr18LXbzjbnkSY8j9w5IVt2q2NVRt5Y90bfLzlYx459RG8bi8903piY1NaV8rK7SsZljOMi4ZcxOXDLo8sNiUiIiIi7Y+CVhERERHp3EpWwnMzofwbZzujD0x/GroPP+hdCVkhvtj2BQUZBaT6Uvly25c88LmznusnWz/h+B7HM77XeDZXb2ZCnwkMzhoMQIq3fZU2EBEREZFdKWgVERERkc7ri+fh9eshUOdsF0yCc/4BSZkHvSvrKtdx2VuXUd5Qzl0n3MWU/lM4oecJeF1e+qf3J2SFAOia0pWfj/r5Qe+fiIiIiLSOglYRERER6ZwCDfDPO8Mhq4GTb4UTfgauA786fcgKsWDDAt5b/x5j88dydsHZ9ErrRSAUAGDhhoVM6T+FNF8a7/zwHXKScg54n0RERETkwFLQKiIiIiKdkzcRpj8Fz8yAM2fDwAkH9HLb67dTWlfK4OzBuIyLvy7+K1trt1LeUM7ZBWfjdXm5ZsQ1pHhTGNdrXOQ4hawiIiIinYOCVhERERHpPNYvgm5HQEKas93tCJi1DDz7ZxEpy7YobyjHH/Ljc/vISszCZVz8dtFvebnoZQoyCnjxzBcxxjC+13heKXqFnOQcLNvCZVxcMvSS/dIPEREREWl/FLSKiIiISMdnWfDh3+C9O2DwVJj2BBjjvLcfQ9a1FWuZ9d4simuLyU/JZ/b42RRkFtA1uSuWbbG6YjUbqzfSK60X1468lp8V/gyfe/9cX0RERETaNwWtIiJy4FkW1JVB0O8EHsm5B6VGoogcIup3wCs/htX/52yveRvKVkPe4ft0mhp/DasrVlNWX8aYbmPITMxkxbYV3L3kbkrrSnlg4gORkBWguLaYWe/NYs6UOUzqO4nqQDUTe08kPyUfgPSE9P06TBERERFp3xS0dkQdNbDoqP3enc4yns4yjiadbTydgWVB6UqYez7s2AAZvWHGs5A3RN+NiLTe1hXw3Eyo+NbZzuzn1GWNCVnrAnWsrlhNaV0pR+UdRW5yLmsr1vLHz/5IWV0Zvzvud4zIHcGq8lVc/vblADx0ykOMzR9L0Ary6dZPAQhYgUjI2qS4thh/yE//jP7cNOqmgzNmEREREWmXFLR2NM0FFmc/BKvnQ/k3u+5/2GQ48sLo9gd/geJlLV/nhBuhx9HR7RevgGBDy8ed+wh4k5zX5evgnV87r7MGwGGnw8tX79pvOwSTfh89x9dvwvJnWr5WW46tciPs2NT8eGK/h8y+7XtszX0v05+GL+c1//MUqz2OrdnxPANdhzqBXk0ZLH0CPInOAimenR5NbQldIHdQ9LyhoPPsPoh/ZB7swDgUhGC9s0J3oM75bAN10e3MvpA9ILr/53OhYn3z+wYbIFDvPM66H1Kyo39mgfM893y48l1I7XrgxiQinV7Dsqf4esGvKCPIMLeb7gNOZcPE27hj+T1s+2QbN426ibH5Y/m26lsunn8xAH8b9zcm9HEWxfpkyycAbKnZwojcEeQm5UbOXVZfBkC3lG6MyB1BXnIebuMmPyU/LmzNT8lXaQARERERARS0djx1ZbsGFi9fDZPuhEX37Lp/Zt/47U2fwZq3Wr7OkTPjt9e8Bf6alo+zgtHXDZXw9RvO6+lPR8Ovnfv9wZ/jz1G+LnrcnrTl2EKB3Y8n9nvoPiL+HO1tbM19L8/N3P3PU6z2OLZmx3NBNNCr2uzU7mtJ1yPgx/+Jbn/6ELz9KzBuJ2z3JICn6TkmoB12Doy6Mnrc4sdg29qYMDeh+eN7HA2p0b/cU1kMddvguQvjA/CELtCwwwk0Q43Qf1z0mB0b4KuXowFnoD4cnMZuNzih8+SY37n373LCbiuw58/k5NvgpF/EjO1/YePHLX+W9RXOgjRN30lsf+srNONY5BCwu8Wj9sQf8rOqfBXb6rYxKGsQvdJ6UVJbwu2Lbqe0vpSfHnEN4796i63Ln+SiXs5t+r/POZ4zT78fd90WPt7i/PlUXOMEonlJeZFzl9aXOm3JeQzPGU5uci6ZiZkA5Kfm84+J/yA3OZceqT0AJ2h9evLTkbHMHj97lxqtWYlZ+/ETExEREZGOSkFrRxP0Nx9YpHaFvKG77r/zbLGM3s3vtzNfavx27uFOUNOS2L84eZKi10rtuvt+Z/WPb0/O3rs+tuXYbPbue2jvY9vT99LS9drj2HY3nqDfeR1sbPk64ASgsYLh69ghJ9zdXXjdszB+++s3oejdlq93wfMwaFJ0e8tyeOvm5gPw52IC599UREPKHRtgwW9avpZtxW8bV8shKzgzVWM1ze52+5zfdW+SEzh7k8Phc7gtIdWZkZvRO/67yejthNAvXAYn3wKHnxFdtEZEOo3mFo+6e9zd1ARqyE3OpV96PyobK7n53zdTVlfG5cMuZ0r/KVQ2VjLz/5w/724dcyszDp9BgjuBD4s/BGDTB7+Hbz8nN+bPjdLeR4PLRW5SLkfkHEFuUi65yc4/YmUlZvHgxAfJTYoGqOkJ6cyZMieuvz63j2N7HLvb8biMi4LMAuZMmbNPwbGIiIiIHBoUtHY0uwssMvvAtYtaPn7yn1vepzlXLdz3Y/IOj/appmT3/Z72ePxxIy9wHvvqYI4tIeX7fQ/tbWx7+l725ucpVnsY2+7G07TadK/RcFtp+Nb2Buc58mgMz/psBF9K/Hn7HOfM6AyG39/d8TuHz55E8KU5x8XOiN5ZbLBr25DYpfnAOCkzvi3YAL7k8DmSou27DT+TnRmtsXofA8ffsGtA2rS/N9E5V0av+OPOnwtuL7jcux9XE8tyarLGljw5635Y+F9OKZTnZkL3kTD+1zBwggJXkQ7Otm2MMdQGainaUcRN/7opbvGoG96/gZtG38SXG77kiiOuIMmTxIebnQB1Q7XzZ19TeGnZFqV1zgzU9IR0hucOJysxi3xvHnz7OSn5R/HA2OvIzRkcCVB9bh/PTIkvZeN2uTm+x/H7ZXwu4yInKWe/nEtEREREOhcFrR1Ncu6ugcWMZ5329qyj9nt3Ost4Oss4mrQ0HmPCt+snQOI+rATd+xjnsa9mxMyUCgWd2/13CWgbIHtgdD/bdgLV5gLjpCxnPE3haWxNwO4j4FebnZB0b8LPJv1OdB77ypu49/u6XFi5h1N+5Tv4rSA+l4csXxdch58B24qccghblsOcc6HXMTD1nn1eKVxEDr66QB2NoUYyEzMJWAGue+86iiqKmH7YdK4afhUJ7gQCoeYXj0r3pbOtfhvgBKOju40myZNErzTnH3XcLjcPTniQjMQMJ0C1LAwwZ3LMn6vZw2HQaZyw810IIiIiIiJtxNi23dZ9OKgKCwvtxYsXt3U3WqejrqreUfu9O51lPJ1lHE06w3iaW/RuxrOQN6TjjYXmbx2ePX42BZkFuBqr4aMH4KP7wV/tzKSdtRy6dG/rbotIWEOwgXWV63AbN4dlHYZt25zz2jkU7ShixmEzuPWYWwGY+MJESupKOL3f6fzpxD8B8FHxR/x20W93WTzq4VMfJs2XFqmNukf1FfDyj527Ek648YCMUURERFrPGLPEtu3ClvcU6bw0o7Ujcrk65krdHbXfu9NZxtNZxtGkM4zH5XJC1Svf7diBMc4txOUN5ZGQFZzZbLPem8WcKXN49MtHCSSFGHXuPUza8AV4Etju9ZFuBfG4PLD1S6eu7M6Lr4nIAVFUUcTqitUkuhOZ0GcCAOe+di4bqjcwsfdE7j75bowxJLqdWe1FO4oix47rNY6qxirGdBsTaRvdbXSzi0f1TOu5d3VNt3wBz18EFd/B2redsLXv/ikBICIiIiKyvyloFRFpjzp4YPxK0SvM/3Y+AzIGMHPwzGZvHfaH/Ly57k0qGisIFpzLpFP+C4AZL55CaV0p5x82g1+u/Des/5BHC8aQNOh0hvc7hWE5w9piSCKdzoebP2RZ6TIS3AlcNfwqAO74+A6Wli5leO7wSNDaP70/G6o3xIWqPxj4A07seSJDc6J1n2875rZdruF2ub//4lHLn4E3bnBKrAAMOh266vdfRERERNovBa0iItJqVf4q1las5eiuRwPwxro3+GTLJ6ypWMNlQy8jPyV/l1uHvS4vucm5BO0g3VOcUgFBK0hpXSmWbZEcaICNn2ADD/o307jyMS5eO59hkx6E7AFMfGEiyd5kph82nQsHXwjA/G/nk5WYRf/0/pHVxkUOVU2LUgG8/s3rvL/xfQD+Ou6vALy09iXeWf8O+Sn5kaB1QMYAlpYu5bvK7yLHnz/4fCb3n0xBRkHk3NMPn77X/djnxaOCjTD/JljyuLNtXDDhN3Ds9R1yZr+IiIiIHDoUtIqISKs8+uWj3L/8fhLcCbw//X0S3AlM6juJoooiJvaeSLInudlbh7OTspl35jwAQlYIcOq53nbMbWyp2cKY7mNgyCVUvf97QlWfANB960q4bxS1I6dTUlcCQH2wHgB/yM9NH9wEwI9G/IifjPwJlm1x0fyLyEvK46yBZzGu1zhs2+ar7V/RLaXb3s+sE2nHQlaI0rpSuqc6/2Ax9+u5vLDmBWoDtbx17lsAfLX9K95Z/w4J7gRCVgi3y83AjIEsWL8At8tNIBTA6/Zy0ZCLmH7YdPqm942EtMfmH3vwBrNjAzx/MRQvc7aTc+CHj0H/kw5eH0REREREvicFrSIistfqAnX8a9O/WLB+Ab899rd08XWhR1oPAlaAgBVg0eZFnNz7ZM4acBbnDDwHt8sN0OKtw037+dw+pg2aFnfN9HMeZUnpKra//9/4at4E2yb4+bPMzMxiS05/Dk90Siw0Ba9AZIbs9vrtfFH2BQCju48GoDpQzflvng/AjUffyGXDLqMh2MAdH99Bt5RuTOg9gSHZQ7Bsi4ZgA8ne5F0+B8u2KG8o3/dboduZzjKOJp1lPLsbh2VbFNcU823ltxzf43iMMTz51ZPMXjabkBXi0ws/xev2UhOoYU3FGgAqGytJT0hnYMZA8lPyGZAxgJpADekJ6cwcMpNLh11Kkicpcu1+6f3aathQtBDmXeEsfgXQcxRMewLSe7Rdn0RERERE9oGCVhER2aP6YD22bZPsTWZZ6bLIrNFxvcZx5oAzObHHiUwbNI2JfSYyupsTZvrcvrhz7POtwztx5Q0m97w5zsI47/+B9NX/x83bt8P27dB3IxQ44eobZ62pZqcAACAASURBVL/Bltot9OvihEWNoUbGdh/Lltot9EztCcCWmi2R8zYFsltrt/LaN68B0DutN0Oyh1BaV8opL55CF18Xfj3215zW9zSq/dW8t+E9+qf35+f/+nlkhu7fTv4bqd5UspOySfYm0xBsoLSuFGMMuUm5JHoS8Yf8lDeUA5CVmIXP7SMQClDlr8IYQ5o3Da/bS9AKUhesw4WLRE8iHpeHkBUiYAUwxuB1eXEZF7ZtY2NjMJGZh/vCsi3WVqzdZaZxQWbBPoeTTX2xbRuXcWGMwbItAlYA27bxury4XW6CVpCGYAM2NomeRLwuLwErQLW/Gtu26eLrgtftxR/yU9FQgY1NZmImCe4EGkONlNaVgg25yc5nWh+sZ0vtFrChW0o3NlZvjBvPPePvYVDmIBqCDWyq2QRAz9SeJHuTqQvUtdu2ncfR9L08s+oZ/vjZHwH453n/JCcph/SEdBpDjQCsr1rPwMyBDMsZxv9v777jrKrO/Y9/nmm0oQooRYqK2MWIKPaGIhZssRu9iZrEeDWxxJLoTbvGGDW/xBa9MTEq9mjsUaNGjSYqAnYRY8FCR6TKwMzz+2PtM3NmOMDAAHvvNd/367VfM2efM8PzZe89Z82atdfauffObNJlE5bWLQXgiEFHcOSmRzY6bh2rOq7ScV7rPny2oZN12Ldhv1+ExQBFRERERHIif8M8RERknZhfM58fPvdD9rhrD+5//34gjArt0qYLHSs7MnfxXADaV7bnkuGXsHPvnakoW8t/v+u1DRx7B5zyNGy8N3TsDUO/CUBFWQX9O/Rhpy6bsX6HMMq1b8e+3LjfjTx02EPs1ne38C2qe/HbvX7LBcMuqF9Ya/6S+fRs3xPDGnW+Qph/tn1FGNX6ybxPqK6qru9khbCw1/ef+T7vzXmP12eG0bNvznyTA+8/kFH3jWLCjAkAvD7jdUbcO4IR945g3PRxALw24zX2vHtP9rhrD16d/ioAE6ZPYJc7dmH4HcMZO20sAOOnj2eHMTsw9LahvDz1ZQBenfYq296yLdvcsg3/nvLv+n1DbhnCdrdux0tTXqr/2mFjhjFszDBemfpK/b/x3KfP1XfmFXKc+fSZPPfpc4ydGv7dcdPGMWzMMHa4bYf6r3112qtsd8t2DLllCC9PCbWMnTaWbW/ZliG3DmlU39DbhrLDmB0a5Rh+x3B2vmNnxk0bV1/LHnftwZ5379no/2Xfe/dlxL0jmDC94f9v1H2jGHX/KF6b8Vr9//Pov45m9AOjmb5w+jJ5znr6LGZ/NZu3Z73NEQ8ewREPHsEbM98AyOy+UjnOfPpMZn81m406b1R/KUz6YhIA2/TYhuM3P57/Gf4/dGvXDYCdeu3EDSNu4LwdzmO9dusBrFZn/Dq39yUwaH84/A8w6nJ1soqIiIhI7mhEq4iIALCkdgkvfv4itV7L3v32pkNlB16b/hqLli7iiY+e4PjNj6eyrJI/7PcHBnYeuMyo1XWq7/Zw4v2wYCZUtm3Y//pd8PiFsPN/w47fgTbLjtjrVNWJvfvt3WjfVt234qmvPxVGjRI6pHq078FZXzuLKfOn1HdwTVswjc5VnRst7AWhM6xzVWdqPcw163j9c4Xvty721XldqKHhKZbWLa2fx7bO6wBYUreE6srqkjmqK6tZ6kvrX1/42vps7g3PU9fo3y+uK419pfLU1NaQJyvKscV6W/DjHX/Mxl02Zov1tgDCrf4XDLsgjVJbbsprsGhOw/yr5RVw3F2Qh05hEREREZES1NEqsppimQtQWrfiVclPeeIUxk0fxyZdNmHvfntjZhy6yaF8+OWH7D9w//qvGdxtcFrlLqtD0XQES2vg2cvgqy/h6V/Av6+HXX8AO5wCle2W/z2KVJZV1n/ep7oPp2x9SqPn9+q3F9MWTKN3h96NOsN6d+hNlzZd6NK2CxA6vy7d9VIcr++kHdBpAD8Z/hMcr58Hs1/Hfly040W4O/079Qdgw44bct7Q83Ccfh37AeF28rO+dlb98wC9q3tz+ran4zh9qsMclr069OLUrU/FcXp36A3ABu034KQtTsLx+tG667dfny+++qJkjsqyStZrE0ZBrt9hfU7a4iTMGkb69qruxSlbn4Jh9OnQp1EtGPVTNPTtuGzNfTv25dyh52IY/TuGvP069ePCYRdiZgzoNACA/p36c8nwSyijrH4aiAGdB/CLXX6BmbFx543r/59/tduvKLMy2lW0K5mnqryKjbpsxFV7XgXAJl02AcjsvhXl6NK2C0dvdjRRGHcrPHIOVLWH056FruF8UCeriIiIiOSZufvKXxWRoUOH+tixY9MuQ3JuTc5tKJKGN2e+yb3v3cvbs97mroPuwsy48fUbuXr81bQtb8vDhz1cf/t9brjDxMfgmf+FaW827K/eAHY/F772Daho0+J/JpbrP5YcBbHkiSXHci35Ch47D8bdEh5bGRz6e9g2kg5kERGRVszMXnX3oWnXIZImdbTmUF5HUq6Nuuu8jpraGtqUt8HM+HLxl8z6ahY1tTUM7joYM2Py3Mn8Z85/WFy3mP3774+Z8fqM13l12qssrl3Mt7f5NmbGc58+x1OTn2Jx7WJ+uesvMTMe/M+D/OW9v7C4djG3H3g7ZVbGLW/dQv9O/bn0pUuXGXF0xR5X0L9zfzpVdWJp3VLKrTwf8+JJ9Grrahk3fRxDeg6hsqySO969g0tfuhSAWw+4lSE9hzBl/hQmzJjAHn33oH1l+5QrboG6Onj7r/DMpTBrUsP+zv1gjx/CtseGW5Rb8k/k9OdwU7HkKIglTyw56tXVwcIZULMQZn8I//hf+HQsdOgBR/4JBu6WdoUiIiKyBqijVURTB+TOika6TJw9kfHTxwPw9U2/TmV5Je/MeicT+8rLynl39rv84JkfNKp7Se0SHvvoMRbXLubCYRdSXlbOkx8/yQPvP8Di2sVcv+/1VJRVcM979/Dnt/7M4trFPHr4o2GeyDf+wG/H/RaAcSeOo9IquWviXVw9/uqw74RxVJZX8vhHj/O78b8DYJ8T9qGyvJKXprxUv++bW32TqvIq3vviPe6bdB8AP9v5Z1SVVzF94fT6xVmW1C2hTXkbaupq6FDZoeQceotrF9ffejzmnTHc8PoN9O/Yn5v2v4n2le35ZO4nfFnzJf069aNTVac1d2KIrMBbM9/ijKfPYOaimVy/7/Xs2mdXRvQfwZVjr2R4r+H1C1j1qu5Fr+peKVe7BpSVwVaHw+aHwBt3wz8ugzkfw5eT4cEzYNLjcPRtLfsnrIzu7bqv/IUZF0uOgljyxJIDCJ2s09+CO4+DOZOhSz845Bp44y+w1wXQqXfaFYqIiIiIrDHqaM2Z2V/NLrka8ZgDx/Dy1Je5YuwVAIzeZDSV5ZWZ2beoZlF9J2tx3b/Z6zfc8na4ffDs7c+mfVl7Pp33Kc9++iwANbU1VJRVsKBmAR/P/RgIC/ZUllVSYQ2nb01tDZVllbQpb7gteHHtYirLKxst2LO8fVXlVXSq6kTP9j1pU96GJXVLqCqvom91X4ZtMIyq8ipq62qhHLbotgUVZRUl59BbtHQR7SrCXJCT505mXs08Ppr7Uf2+eybdw5/e/BMVVsErJ7xCRVkFz37yLO998R79O/VnRP8RGgErLeLuvD7zdf724d8Y3ns4u/fdnf6d+jN38VwA/vbh39i1z650b9ed545+Lt8jV1emvAKGHAdbHQkTboNnfw3zPochJ6RdmUgc6upg4UyY+znMmxI+VnWAbY9peM2UCXDPSaGTFcLHB8+Abz0JHTdIp24RERERkbVEHa05U1Nbk8tVlZdXd2VZJe0q2tV3bgL0bN+TzbttTpvyNvUrZG/UZSMOGHhAo47UIT2HcPq2p1NVXkW5lQOwW5/d6NGuB23K29S/9sCNDmSHDXagTXmb+g7PYzY7hq9v+nWqyqvqR/MdNfgojhp8VKMaRw4cyciBIxvt27nPztR5Hb/b+3fLjCwuLNACsFPvnXAcw+o7TyfPDb9o9u3Yt/7ffeLjJ3jwPw/So10P9huwHwDXTbiOsdPGMrjrYM4fdj4Ac76aQ/vK9umu9C6Z5O7MrZlL5zadWVK3hO8++V3mLZnHlAVT2L3v7lRXVXPqNqfSo10P9um3T/3XRd3JWqyiCoZ+E7Y9Dt5+ADZtWNiLRV/AXSfCzmfCoBFaiEekYMlXYU7jwjXx5afwr+tg7mdJp+qU8DF57663wTaNO1or2jZ0shbMmQy1Tb5ORERERCQCmqM1Z2Yumsnxjxy/zEjKMQeOobqymkVLFwHQuU1nyqyMr5Z+lYl9s7+avdy683p75OrMoffRlx/x/pz3WepLGTkgdOCe/vfTef6z59l+/e25eeTNAHz7yW/z4ucvslm3zbjn4HsAOPsfZ/PU5KfYfv3t+eP+fwTgX5//i6V1SxnYeSB9O/Zde2Els/781p+589076V3dm5v2vwmAH/3zRzz0n4fYpc8uXLfPdRolvSJP/RyeD6Pv6TsM9v4xbLRHujWJrG0LZzcehdro45TQmbpoNpz7PlT3CF8z8324ZvuVf+/q9eHc9xoez/4QbjmkcWdrl35wyt/Da0VERCQamqNVRCNac6db224lR1IWOvnaVrRt9Pq2FW0zsW9FdefV6syhN6DzAAZ0HtBo33X7Xsf8mvnMq5lXv2/Trpsyv2Y+g7oOqt83ee5k6ryu0YjW37/2+7DAUY8h3DrqVgCun3A985bMY0iPIfUjZCUO7s6kOZN4ZeorHL/58QBMXTCVT+d/yucLPmfmopl0b9ed04eczg+2/0Fu/4ixTrXtBFUdoWYefPpy6BAauDvs9WPot2Pa1YmsmtolMH9aQ2dpoQN18CjoP7zhdTfusewo01Lmfd7Q0dqpF1S0Cx879YGOvcLnHXs3/ti087RLfzjmDrjz2IY5Wo+5A9r3WHO5RUREREQyQh2tOVNmZQzqOogxB47J1WrEea17Xamuqqa6qrr+8TlDz1nmNadsfQqT5kxiw44b1u8rzFvbv1P/+n0Pf/Awk+dNZkq/KfUdrd947BvMq5nHfgP247vbfheAibMnsl679ejapitfLP4imuMSy2rdpXKMeWcMl79yORCmyejXqR8jB47kjZlvMHLAyPpO+D7VfdIsPV92OQu2OxFe/B28dAMsWQgfPgcf7geD9oO9fgS9h6RdpTRXYXX7pTVhyoj2PcLiaHlTKseSBVCzEDoWdWS+cS+8+ZeG0ajzpwMl7lRq17VxR2unPst2tLbv3rjDtFMfaL9ew/NVHeBHU1Z9eo2yMui5RRjBmvfjIiIiIiKyEupozaG8rkac17qzYuTAkYyk8Xyx9xx8Dx/P/bi+k7bO68I8rmVV9OvUDwijICfOnsjCpQvZflHDbZ+n//10NuiwAefucC4XPHdB/UjjX+72SxbXLmZ47+G4O+c8Gzp99x+wP/sP2D/T+0b0H8GkLyYtM3J6ky6bcN5z56VeX0tz7NFnj/qO1penvky/Tv3Ytse23DbqtlU+n6RI+26w709gp9Ph+atg7E1QWwOTngjb5ofAkX+E8sq0K5VSapfCFx+FjsaKtvCXbzYeOblkAfzr2pV/n433hu1Pbnj84jVhlPPK7PS9xqOfH/geLJ63/NcXHHI1tO0cPv/yU3j8ovB5t41g0P5w/2kNOQ65Fp7+aRhRevLDDd9j9ocw8dEV/zvlVbBkUeN9u3wfhp1aNBq1V5iPdWVWdxqSsjJNEyAiIiIirYI6WkVyrEf7HvQouv2yzMq45+B7qPM6FtcuBmBJ3RIOH3Q4k+dNZtse2wKwcMlCpi+azkU7XVTfyQphgbILn7+Qq/a8qv57PvnxkwAM6jIo8/tmrz+7vnOykOfMp89kzKgxmaivxTkOHMPFO13M8N7DG41sljWkuicccBnsfAY8dwWMvxXqlobn1MmavtolMPM9mPEuzEg+znwPZr0fOsaPvi10Vhavbn/nsXDM7WERtJVp3+QPgZ++0ryv2+LQxo8n/g0Wzlz51426suHzxfMb/q2jb2voZIXw8cHvwf6XwpOXNP4e3QZCj82LRqL2XnZUartuy44eHdz4j3YiIiIiIrJmqKNVJEJlVka7inYAVJVXcf6w8xs9X1FWwbX7XEuvDr0aLVAGoVOveB7YjTtvDEDXtl0zv6+mtqZknpq6mkzU1+IctTUcNfgoZC3r3BcO/n9hWoFnL4ddzmz8/PgxYfRjWZluhV4bFs9LOlQnhtHEbZJpVb74GK7feflf165r6dXtyyuhx2Yr/3c7btD4cac+zfu6Np0aP+4+CBY14+6NsvKGzyuqGv6t6p6lc3TbCPa+uPH+rY8Mm4iIiIiIZIK5l5jLK2JDhw71sWPHpl2GSCbMXDST4x85vlGnXu8OvRlz4JhcTvMQS55YckRpxnvwwOmhw+vBMxrfot5zC3W2rooFs2DmxIYRqjMnhs7VuZ81vOaUp6BvsnBt7VK4tFcYvQrh/737YOiRbP13gVsPzf/q9vOnwR/2zX8OERERaXXM7FV3H5p2HSJpUkerSCtW53Ul5wId1HVQbheQiiFPLDmi9OLV0HVA41vUIXSEHXglPPvrsFjRlofDVoc3PD/nkzB/aPv1WldnrHtYqMnroEvRdBe3HwPvPbbyrx99LWx3QsPjiY+F+US7DwqLMxWrq4Ppby+7un3eOsBjySEiIiKtjjpaRdTRKtLqlVrdPs+debHkiSVHlGa9D1dvv+z+kx+Bmw8Mn+/9Y9j9vIbn/jQKPn4ByiqgQ8/QGVu9QdHHZOs3HNp1WTc51qS62rAgVfEcqjMnho8182C7E2H0NQ2vf+j78OqfGh5XtA2dp90Hh1voe2waPu+2Ubitvtl11MHCGfmf0iGWHCIiItKqqKNVRHO0irR6ZVYW1e3oseSJJUeU2nQMowybjmi1MhiwW7j1u8uAxl8zb2r4WLcU5n0etlJO+we02y58vmQR/GFEiU7ZJh8r267hgCtQu6TxwmAf/wsePRdmToJkAb6SZkxs/HiL0WEhp8Kt/136NZ6zdHXFsrp9LDlERERERFoZdbSKiIisivY9wq3cpW7tPvnh0l+z38/D9AHzp8K8aY0/LpzV8LrqogWZ5k2FaW+EbUW6bQRnjm94PO1t+OCZ0FHXcYOGTtk2HZf92uWNnFw8P4xObTpCdc4ncOGnDaNMK9vBtDeX/b4dejSeP3X9rRo/v/FeYRMREREREYmIOlpFRERWRVlZ6FQ95e/Nv7V7swOX/9zSGlgwPXS8VvcsesJh05Ewb0p4bsH0MNdpUxXtGj/++IUwh2xTlR0aj4YdcnyY77S4w/iwG8I8tBMfXX69s/8DPTcPn3cfBBvv0/h2/x6DoX235X+9iIiIiIhIpNTRKiIisqrW5K3dFVXQuW/YinXbCI67q+FxXS0smNlkVOxUaNu58dfNn17631myAGZ/EDaA4Wc0dLJC+Hj/t2H/Sxt3tFp549v8ixehquoAJ963erlFREREREQio45WERGRPCgrDyNRO64PvVbwur0ugp2+Gzphm05VMG9KmEN23lSobN94nlkIjzv1hT0vahihut7GUNFmrUYTERERERGJgTpaRUREYmIWbt1v3w3W32L5r5s/rfSiXp17w57nr/06RUREREREIrOCCeVEREQkWoVFvbr0C48Li3q175FuXSIiIiIiIjmlEa0iIiKt0eos6iUiIiIiIiLLpY5WERGR1mpNLuolIiIiIiLSyq21YStm9kczm25mbxbt62ZmT5rZpORj12T/nmb2pZlNSLZLir5mpJlNNLP3zeyCov0DzeylZP9dZla1trKIiIiIiIiIiIiIrMjavD/wZmBkk30XAE+5+yDgqeRxwfPuPiTZfgZgZuXAtcABwBbAsWZWWNnjV8Bv3H0T4AvgW2stiYiIiIiIiIiIiMgKrLWOVnd/DpjdZPdo4M/J538GDl3JtxkGvO/uH7h7DXAnMNrMDNgbuHcVvpeIiIiIiIiIiIjIWrGuV7xY392nJJ9PBYonhhtuZq+Z2WNmtmWyrw/wSdFrPk32rQfMcfelTfaLiIiIiIiIiIiIrHOpLYbl7m5mnjwcB/R39/lmNgr4KzBoTf1bZnYacBpAv3791tS3FREREREREREREQHW/YjWaWbWCyD5OB3A3ee6+/zk80eBSjPrDnwGbFj09X2TfbOALmZW0WR/Se5+o7sPdfehPXr0WNOZREREREREREREpJVb1x2tDwInJZ+fBDwAYGYbJPOuYmbDkrpmAa8Ag8xsoJlVAccAD7q7A88ARzb9XiIiIiIiIiIiIiLr2lrraDWzO4B/AYPN7FMz+xZwGTDCzCYB+yaPIXSYvmlmrwG/A47xYClwBvA48A5wt7u/lXzN+cDZZvY+Yc7Wm9ZWFhEREREREREREZEVsTA4tPUYOnSojx07Nu0yRERERERERESiYWavuvvQtOsQSdO6njpAREREREREREREJDrqaBURERERERERERFpIXW0ioiIiIiIiIiIiLSQOlpFREREREREREREWkgdrSIiIiIiIiIiIiItpI5WERERERERERERkRYyd0+7hnXKzGYAH6ddxxrSHZiZdhGrIa91L08seWLJURBLnlhyFChPNsWSoyCWPLHkKFCebMhr3csTS55YchTEkieWHAXKk02x5ADo7+490i5CJE2trqM1JmY21t2Hpl3Hqspr3csTS55YchTEkieWHAXKk02x5CiIJU8sOQqUJxvyWvfyxJInlhwFseSJJUeB8mRTLDlEJNDUASIiIiIiIiIiIiItpI5WERERERERERERkRZSR2u+3Zh2Aaspr3UvTyx5YslREEueWHIUKE82xZKjIJY8seQoUJ5syGvdyxNLnlhyFMSSJ5YcBcqTTbHkEBE0R6uIiIiIiIiIiIhIi2lEq4iIiIiIiIiIiEgLqaNVRFrMzKzU5yIiIiKydqkdJiIikh3qaJXcUkMyU6oLn7i7m5l+tmSAfvGStU3nWPbpuMjaonMrU9QOyyC9R8q6oPNMJHv0JtwK5bnxZWYbm9lQqG9I6s0kZWZ2IPCwmV1mZv8D4O51OT/Pclt7E13NrBzy/4tXnmuPXDTnGMRznplZTzPrAuG4pF1PS5lZZdo1rEl5Ps/UDssetcMyTe+Rsi5EdZ6JxECLYbUyZrY30Bt4xN2/SLueVZE0JH8NTAPauftOyX7L4y+SZrY7UAe86O51adezOsxsa+A+4PvAXOACAHc/MHm+LG/Z8nyNFDOzQ4CfAC8BFe5+aroVrb5YjklBDNc+xHWOQTznmZkdClwKvAGUA6e5++x0q1p9ZjYC2BEY4+4fpl1PS+X5PFM7LHvUDssuvUdmVwzXfkFs55lILPTXjlbEzHYB/g6cBOxnZl1TLqnZzGxL4BfACe6+FzDdzDaCfI7WMbM9gH8QfmHZMcd/eVwAvODuj7j780nDvo2ZPQRhREW65a2aPF8jxcxsEPBz4JzkYz8ze9DMqpPnc3O+xXJMCmK59mM6xyCe88zM+gDnAf/l7kcDi4BrzGybdCtbPWa2I/AosD1whJkNTLmkFsnzeaZ2WGapHZZBeo/Mroiu/ejOM5GY6OJrJcysAugKHA3cABwEjCx+o8z47V/zgPHADDNbD9gF+F8z+1vS+M96/fXMrA2wCXAEcCtwCTA8p2+GS4BeZrZDYYe770s4HOenV9aqi+AaKTYHmAS84+6fu/v+wHzgDqi/pTDzWSI7JrFd+1GcYxDdefYlYVRbHYC7nwh8AlxkZp0gV1kAHPgG8P+APsBRxZ2tecoSwXmmdlg2qR2WTXqPzKDIrn2I6DwTiU1F2gXIuuHuS83scaDK3RckbzQHAGVm9pi7z874iIQaoJZwO+QOwGXu/mszuwr4DbBfxuuv5+6Lzew+YIm7zzez9sCPgF+a2YvuXptyiStkZjsDXwPecvdnzOxu4AEz28vdJyYvu5Zwq2duRHCNFFtAaHztABRGtRxnZk+Y2TXufkYeskR2THJ/7TcRxTkGcZ1nyXn1b2CImb3v7l+4+/lmdjtwI3BMXrIAuPvLZvaGuy9Kftk/EDjGzO7M2zQCEZxnaodlhNphuaD3yAzK+7VfQjTnmUhs1NEaOTPbE9iT8NeuF9z9IwB3H5P89W5/wu1f2wFd3f3ClEpdhoXbVHYE3gP+CVwEdAEuBh4AcPezkzeTQe4+KbVimyFpGG8PvAlMcPf5AO5+RfLXxguBM81sN8KxuCq9akszs5HAlcDjwGgza+fuN5lZN+AZM/u6u78ADCTcjtMGqMnym3yer5FiSY49gPeBxwjXyE/N7At3/2fystOBM9KpsPliOSYFMVz7ENc5BvGcZ2a2L2GE0RTgfsKxuRiYY2ZPeZif9b+Am5Kf2YvSq3blmuR53N0nALj7U8lxOQAYYWb9gW7Ad1MrthnyfJ6pHZa9n8Vqh2XrGimm98hsHheI49oviO08E4mVOlojZmYHAJcBfwFGAh0II1oAcPdbzWwqcAXhl5VD0qizFDM7iFD7k8CWhL/c/9rdZ5jZe8B2ZjYb2A1YH8j0pOxNGsYHAb8FHjWzcnevTUaFzCDMf2SEUTuZYmZDCPMZnebuL5jZz4GtzWxsUv/HwPlmtgDYBjja3RenWfPK5PkaKdYkxyigg7vfaGY9gd+a2aXAK8DuwDAza+/uC9OrePliOSYFMVz7ENc5BvGcZ0mOS4GbCB0rJ7r7xWZ2I3AasL6ZjQcGAFuR8XZfkzwDCLd3Tig87+5PmtlnhNtXBwCj132VzZfn80ztsOz9LFY7LFvXSDG9R2bzuEAc135BbOeZSNTcXVuEGzAYeB7YNXl8FnA5sCnQr+h1hxHm3doi7ZqLahoEjAWGJY/3Ibz5dUsejyYsjnEv4c1k67RrXkmeIYTVn3dJHv+csFhJz0KmomPxZZaORZMclcDQ5PNewGeE0VO3AL8DqpNMvYHeadfbw8TncAAAG8FJREFUjDy5vUaakePKJEcVobF1bXKsXga2Sbvm2I9JUZ2xXPvRnGMxnWdAf8Ktgrsnj48HbibMnblBkvNC4GHgaWBI2jWvRp4/Jnm2anJcFmT1uMRwnqF2WGaORZMcaodloO5m5tB7ZAa2WK79GM8zbdpi38w9s3eSSAtYmMtsc3d/I/kr1wuEN5rJwEbAj5LnRgCfufvbKZbbSFL7McC97v5Vsu+vwO/c/enkcR/Cm8pid/88tWKbwcwqgW3dfayZ9SL88vIyYU6decBVhMby8cAr7v5WasUuh5mVedHKtWY2Cujv7tdbWM36h8D/ufuzqRW5ivJ8jRRbQY5PCSO+/hv4HGhPmF9rRlq1rkwsx6Qghmsf4jrHIJ7zLLl1cxN3f8/C4kRPA+8C7wCHAye5+3gzawu0dfc5KZa7UivI8xZwJPAdd3/RzLYgLHT/TorlrlSezzO1w7L3s1jtsGxdI8X0HpnN4wJxXPsFsZ1nIrFTR2uESjTGdgc29DCnTnfgx8C/3f1OMzPP0ElQovZyd6+1MHH5n9z9ITMbRnhj/yy9SpunGQ3jc4Gb3P3ZQtbUim0BM/sT8Hd3H5N2Lc2R52ukWDNyXAK86O53plZkM8VyTApiufZjOscgnvOsaW0W5sjb0N0fTB5fAsz3DM8zV6wZeS4GFuQoT27PM7XDsvmzeGXUDkuH3iOzeVwgrms/tvNMpDXI9FxdsnqKfxAnj58r+nymmZUTbpkga2+QTWsHygir3H4CTDGzg4EfEP7ymHkljsWjRZ+/bma1wIbJ40y+wRc3Pko1qszsKGA74Gdp1Lc68nyNFGtGDiPJkWVNG5CQ32NSEMO1D/GcYwWxnGdNa3P38cD4ol1t121FLdOMPO0I7YFcyPN5pnZY9qgdlq1rpFhM75GxtcViuPYLYjrPRFqL3DRaZdUlP3Sb7jsK2JUwt1bmFGoufHT3JclTs4D/I/wl9Sx3n5JOhaumaZ4mzxUaxi+s67pWxsxGmNnNEBofZlaRNMDczLYxs9HJvhMIDfvj3f3DVIteCTPrUGJfHq+RfmZW3WRf7nJAWNjDzDZo8lf6XGZpysJt0Lm79ptqxs+w2I5LLvM02Xc0YTXou9Z9Raun6DxbUZ7cjdTJ43mmdlg2qB2W6WskmnYYxNsWi6UdBvG1xURaBc/ARLHaWrYBG5TYV5gWYijwreTzEwhzt22Zds1Fde4InAzsQcMiC+VFtZ+afH4FsBjYOO2aV5JnN8IcOYcC3ZN9ZcnHbQgLSFQkx+LdLB2LwnmT1PcboA64pcnze9F4Uvk+wIC0625GroOA64F2xVmLzrPMXiNNcowGHqFogvui8ys3OZIa9yOMkDo3eVyex2NSlGcEYbGIC4C+TY5N5q/9ohw7A/8FDAd6Jvsqc3xc9iEsCHUsySIehLnLcpWHMP9auyb7CtfLzsD5yedHAC9lNUdR7bsBZxNGRfZO9hXe+/OYR+2wjGyoHZbJDbXDMrkRUVuMSNphSb1RtcW0aWuNW+oFaGvhAQwNyTrg5BLPDQcmAPsmj9sQ5nNJve6knoOSxuIfCCsLnwNUNKl9RFHtm6Zd80ryHAC8BvwKuAM4rui5vfPUMCb84vVtwirDjxTtPwo4Mvnc0q6zmVlGAuOAvUo8t2uWr5EmtW6TNAx3KfHcbsm5l/kcSX37JcfkJuD2Js9l+ufWcvIcSLjN+RzgOuC0ouf2ycu1X/Qz+VLgp4QV4AcWnWN5Oy57AxMJc5ddnlw/WxflycU1AxySvM+fDXRs8tzXgFeBg5LHFUCvtGtuxnn2WnJMriGsklyd4zxqh2VkQ+2wTG6oHZbJjYjaYkTSDkvqi6otpk1ba920GFaOmVlv4GrgY8JtdZe5+61Fzx8PfOHuj5pZhbsvTanUZZjZlsAY4L88rIp8GHAScLS7L25Se6U33LqWSWa2NXAjcI6HVZEvBmoIGb8ERgF17n5Pqfm1sqLolpT9gMPc/Ttm9g/C/HhL3H3XwuuymqGYmQ0C7gd+7+7XmFk3wi/y8wkdMMMIo9seyto10pSF1V2Pd/eTzawf4S/z5YQGWF+gs7s/mIMcuwA3A8d6WAX2ZeAhd/958nxmf26VksxXdh3wF3d/wsy+TViV9zFgEuGX5Qp3vzvL101yi911wN3u/rSZ9QduJXR0HQNsQcjxcB6OC4CZnQ10cfdLksf/DVxMGLnXC+iQ9WvfzNYjdEjOBDYHniQsSDQ/ef4gwiJRz2Q5R4GZbUy4/s9291fM7GuE1dK/5+6zcphH7bCMUDssm9QOy6aY2mKxtMMgzraYSGulxbDybQ5wbfKDeC/gJjOj0Mj3ZOVRC5PoZ+0H8VTCG8nrAO5+v5mdDmwNjG1Se6Yb94lPgTPc/VULqz+eTLiVox/QlTCf2YysvsGb2QZArbvPSHY9nvzCC/Aj4G/Ae4XXZzHDcswCngfamdlowi3EHwOebP/tySTyGbxGmpoOLEzmBbsFeJkwz/bzwB4eVk3NQ473Cb/Ij0se/ww4wMy6uPscL1oxOQdZINzm2QkYYWbTCaMOXwW+DuxAGHk0OavXfpEyQufjcOBpd//YzF4EuhBGVJxW+Fmc9eNS9H/9CdC/sN/dr076MG4HRrr7tBxcM3OB65NfhIcSOl3dzG519y/d/WGAHP3CNR34PeEawd3HmVknwm2SDxXlyWK7pRS1w7JD7bBsUjssm2Jqi8XSDoOI2mIirZ0Ww8ohM9vAwqTlC939aQB3fwb4JvBTM/tG8rrdzay7Z2glxaT2Xu4+y91v9DDBf5vk6aUknf8WJmbvnKXaSyk6Fl+4+6vJ7t2An7n7QYTbPhYBO0E2G8ZmdgRwD/CQmf3QzA4oeu5qwi2FxwMVZnZ7SmWukqLzbDahUd+bcCz+5O5HA+cBCwgNsEweF6jP0SN5+AHhF+BbgPvd/Yfufi6h0+JkyG4OaHRMphU17AHeIoxoGZlSaaslybN+0tC9ANiE5Jdhdz/O3b8HPAscDNk9NiVyHGNm15jZdYQRlL8m/FzunGadq6Lo//o5YG8zuxDqO2CvJhyXoU1em0nJL1Tjks/HAucDhwOF9/kDkusqF79wufs84C53rzOzwh/7ZxDe/zGzXbPWbilF7bDsUDssm9QOy6aY2mKxtMMgzraYSGunEa05kzTGvg9Umtn9wAR3fxzA3f9hZt8CrjWzkYTbCw5a/ndbt5ZXu7svTl4yBZhuZocDpxJuYcusJnnuA15L8txfeI27f2ZmTvhLZOZYuC31x4RfDpcQJpI/zMKwr7tJVhh29weBB81sYGrFNlOT4/Ig8BShc+Ixd38CwN0/sXB7Trf0Kl2xJjkeINwCdRjwItDFzK5NGmQLCHM0ZVaJa2VC0bH40Mx+Bfy3mb3o7pPTrLU5muR5iNCoP8zMjiQ09AucDDeKS1wrzxBuVz2WcMvtGUmHWEfCbZEzUyu2GZLOiQPc/UyAZMTqaOCfySjDXyYvrQA2TqvOlWmagzBaBwsrjr9iZucDFycjKIcA+6ZUarOUyONNPk4FZpvZoYT59Y5ZxyWuErXDskPtsGxSOyybYmqLxdIOg/jaYiISaI7WHEkaY3+ncWNsMPCMu99V9LprgKOBvd39jTRqbao5tZvZVcB2QDVhzrA3Uyp3pVaQ51l3v6PodUcQGtBHuPsHadS6ImbWkzCKYrS7zzGzXoRf2ocDTwP/Sn5Jyfz8bLDc47Il8LC7/7XodUcS/uqd1eNSKsdWhFEUbxFWvH2C0LDfFzjR3d9Kp9oVW8G18g93vzN5TQ/CiJBrklFhmbWcPJsT5mj7d/Lcg4RbI78DnODu76ZT7fI1ybGUcB5tSRil80jR675BmENzH3eflkatzWFmOwP3Ap8D77r7CUXPbZQ89yKhk3V34PCMHpeSOSzcvl2bjMr15P3yBML7fJbfK5eXp8zd65LPfwocCSwkv+/9aoetY2qHZZPaYdkUU1sslnYYxNcWE5EGGtGaL+WE+do+TBpjswg/kPc0sxke5gjbnDAB+L5ZadwnVlT7LHf/O+Gv2tsDX3P391OstTmWl2d3M5uWHIvTgB8Q5gbKXCMSwN2nm9kE4AozO8vdp5jZU4QVOfsmjXvLQ+M+sbzjsr+ZzU2OywmE23KOzupxYfk5TgR+Q1jUY3tgQ+AP7v7ecr9T+paXZQ8zm+7uT3uYN+9Fwm15Wbe8PAcTRuYdR1jlvjuhoyKTjXuWzTGTJIeZLUqulcI5d1wOGvYdgJ8AfwZuM7Pb3f04AHf/IBlduB1hJOtv3H1iapWuWMkcSSdrobO1D+Fn9H5Z7ghLLC9PXVHHUQ2wHqGjKa/v/WqHrXtqh2WT2mHZFFNbLJZ2GMTXFhORhEa05oyZ/Zbwi8tZ7r7Awoq33wBq3P0qM2sPtHH3L1IttIQV1L7E3a+0sIJntbu/nWqhzdSMY7EB0D6rjcjCiCIz25TwF9/5wK+SLNsB/49khEWqha6iZhyXnoTVxj9MtdCVWEGOWnf/dbrVrZqVHZN0q1t1Kzs2FuaerCuM2MuqZlwrnYB2eWnYm1knd59rYVXr3xOOx7HFz6VbYfOsJEc3d59tZh3cfUG6lTbPSvK0JZyDXXPQsQeoHZYlaodlk9ph2RRTWyyWdhjE1xYTkUCLYeWEhXmMAK4l/OXr/OQXrc+Bx4HRZraeh4UZMtW4b0bth5hZD3efnIfGfTOPRXd3n5rVxj1AUePjP8B9QDvg9xZW692UcAtL5hsoBatwXKZnuXHfjBwHmVnX1ApcBc08JrnIAs0+Nt3cfWmWG/er8H4yN08N+0JHqofFV04nLBzzezM7Gbgg6dTLvJXkOM/MqvLSyQorzfNTYE4eOlnVDsuOvLfDzMxA7bDUCl2JmNphEFdbLJZ2GMTbFhORQB2tOdHMxlgmbytqZu2Ll/PlmdPMPDUplbdSZlZe/LmHFYU/Am4FpicfzwbOy8sIMMj/cSloZo5MrwJdEFMWaHaezK/+nuf3k+Zy95nu/nVgL8Jtnne6+1cpl7XKSuS4w90z/3NseUrkGePZX9V+VTrFMnXdrGLtmW+HrWKezF0nZtbFzNq7N9xOaGYVeW+H5f24FKxijkz/3IK48qxilky3w/L8niIizaepAzKuqCOseDGMvoR5tE4irGjbDfiuu49LsdRl5Ln2UvKcx8z2A7b3ZNVta7wQyV6EUUbnuPtkM+sMLM3LiKk8H5diseSAuLJAPHliyVHQJE/hFtxewHx3n2dhFfurgIM924uURJGjIJY8ZtaFcOvmwqJ9Fe6+NOvXTZ5rLyWGPGZ2MPBtoCNwA2Hhnvnu7nlth8VwXCCeHAUx5VGWbGYRkWZwd20Z24D9gAuLHpcVfb4XYXXSfsnjzoR5jlKvO++1x5qHsML2dOBd4Iqi/eWEN/KXCCtwp15razouMeWILUtMeWLJ0cw8exBGhQxMHo8CBqddc8w5Is5zMPAw8CxhYZWONAxOyPR1k+faY81DWBX9TWAo8HXgUWDH5Ln1yGc7LPfHJaYcMeZRlvTr1qZN2+pvqRegrckByXGnWJ5rjzkPcCxwatKYfwq4ssnzvZKPZeu6tlZ+XKLIEVuWmPLEkiO2PLHkiDhPbjvF8lx7rHmS6+CnwMlF+y5ocq3krR2W++MSU44Y8yiLNm3a8r5p6oCMMbNjgWrC6I+7gQnufk7R873cfUrxrd9ZkefaS4kpj4VFLmaY2QDgD8Cb7v795LkunqMVbWM5LrHkgLiyQDx5YslRsCp5APeMNnBiyVEQUx4Lc5hfAnzo7jcn+y4Aurv7ucnjTF43ea69lJjymFk3wIDZya4TgX3d/RvJ8209J3NIx3JcYslREFMeZRGRGKijNYPy3CmW59pLyWseM9sFWB9o6+63J/vM3d3MNgJuBJ4D3gY2Bq5y99xMuJ7X49JULDkgriwQT55YchTEkieWHAUx5clzp1ieay8lz3mK2mEV7n53sq/QDtsROM3dv5X8oaKSHCwMV5Dn41IslhwFMeVRFhHJO3W0ZkCeO8XyXHspMeQxs1HAr4G7gKOBR9z9h01eUwV8CLQHdnf3N9Z5oasghuMC8eSAuLJAPHliyVEQS55YchREnCd3nWJ5rr2UGPKsrB1mZkMJC2O9AJwPHObu76ZRa3PFcFwgnhwFMeVRlmxmEZHV5BmYv6A1b4RFId4i3FbwFnB5iddUAZ8BXwBbp11zDLXHmgcYBIwFdk0eDwAeAHqQ/GEl2X8k8BGwZdo1t4bjElOO2LLElCeWHLHliSVHa81DmFfv/4CTgXeAzdKuOYbaY83DitthZYSRbdsC84AXs5ghxuMSU44Y8yhL+nVr06ZtzW6pF9Cat5U0xjLdKZbn2mPOk+Q4Kvm8nDDJ+r8K9RayJG/um6ddbys7LrnPEVuWmPLEkiO2PLHkaKV5Mt0plufaY87DStphyf52hAVxMv1HiAiPS+5zxJhHWdKvW5s2bWt+K0PSdrm7/9PCZNnzgJ5AT3d3M7PkNdXAAe7+VmpVlpbn2kvJbR4z62dmlcBHntyiAtS5+yzgP8DCZN+2AO5+s7u/k0KpqyO3x6WJWHJAXFkgnjyx5CiIJU8sOQpaS546d3fgPeB54Nuevdu781x7KbnN09x2mJkNcfdFhOkCMj1tU5HcHpcmYslREFMeZRGRuKTd09saN6AfYT6WyqJ9hZGGtwEDk8+HpF1rTLXHmgc4EHiTcBvKXSR/HS1kIoya2Ao4AZhEeMO3tOptLcclphyxZYkpTyw5YssTSw7lCXmANmnXHEPtseZh1dph7wM90q65NRyXmHLEmEdZ0q9bmzZta2/TiNZ1zMwOJDS4rgNuM7PNkqcqko/dgA5mdgJwj5n1LBohkqo8115K3vNYsCFwGXAG8GPgZeAZM9vSGxYemQr8BPgOcKi7T3d3T6Pm5sj7cSmIJQfElQXiyRNLjoJY8sSSo6CV57nXzHq4++IUSl1GnmsvJe95VrMdNtrdZ6RRb3Pl/bgUxJKjIKY8ypLNLCKyBqXd09taNsKcLBsCbwB7ElYiPAeYQuN5m/4I3Av8k4zMbZbn2mPPQ5j/60agDw1/OT2LsAjJ4OTxlYQRFJmeByiW4xJLjtiyxJQnlhyx5Yklh/JkL0+ea489D2qHZe64xJIjxjzKks0s2rRpW/Nb4S8tspa5u5vZ54QJ8ScB0939SjNbCjxhZnu7+0TCCr27Awd5RuZtyXPtpcSQx8w2AboCHwCdgePd/XIAd/+tmbUBLjKzbwJ/B6519w9SK7gZYjguEE8OiCsLxJMnlhwFseSJJUeB8mQnT55rLyWGPGqHZfO4QDw5CmLKoyzZzCIia56mDlgHzGwTM9sB6EJDY8whNMaA3xIaY+WExth+WflBnOfaS4khj5kdBNwHXAH8FBgDnG5mFxa97G6gxt1r3f2xrDfuYzguEE8OiCsLxJMnlhwFseSJJUeB8mQnT55rLyWGPGqHZfO4QDw5CmLKoyzZzCIia4lnYFhtzBtwEPA68CxwDXAI8BFwYdFrBgD/l3atMdUeax5gZ+AdYLvk8Y3AL4DewGTC/GCbACcDY4FuadfcGo5LTDliyxJTnlhyxJYnlhzKk708ea491jyoHZZ6vbHniDGPsmjTpq21bZo6YC0ys52BXwPHuft4M7sRGEZopP07+SvXncCuwHZm1s3dZ6dXcYM8115KZHl+5e7jk89/BNzs7p+b2Z6EBv7ZwI7AyRnOAMRzXGLJAXFlgXjyxJKjIJY8seQoUJ7s5Mlz7aVElkftsIyJJUdBTHmUJZtZRGQtS7unN+aN8EP35KLHPYBHks83IkyOfR3wKrB12vXGUnvMeQiLLnQq+rwvMB7olezrT1jlsnPatbay4xJFjtiyxJQnlhyx5Yklh/JkL0+ea485D2qHpV5zzDlizKMs2rRpa41bYXVMWQuSv2p1cPe5yee9gIeAUe4+xcz6E1Yl7eDuX6ZZa1N5rr2U2PIAmFkF0BZ4wN33MbMTgN2A77v7onSra55YjkssOSCuLBBPnlhyFMSSJ5YcBcqTHXmuvZTY8oDaYVkSS46CmPIoi4i0RloMay3yMAH+3OShAXOA2ckP4hOAi4DKLP4gznPtpcSWB8Ddl7r7fOATM/sl8APgmrw07iGe4xJLDogrC8STJ5YcBbHkiSVHgfJkR55rLyW2PKB2WJbEkqMgpjzKIiKtkUa0rmNmdjMwBdiPcOvBG+lW1Hx5rr2UvOcxMwMqCYsyVAL7uPukdKtqubwfl4JYckBcWSCePLHkKIglTyw5CpQnO/Jceyl5z6N2WLbFkqMgpjzKIiKxU0frOpLnxlieay8lwjwnA6+4+1tp19ISsRyXWHJAXFkgnjyx5CiIJU8sOQqUJzvyXHspEeY5GbXDMiOWHAUx5VEWEWkt1NG6juW5MZbn2kuJJY+ZmUd0IUd0XE4mghwQVxaIJ08sOQpiyRNLjgLlyY48115KLHnUDsumWHIUxJRHWUQkdupoXcfy3BjLc+2lxJYnFrEcl1hyQFxZIJ48seQoiCVPLDkKlCc78lx7KbHliUUsxyWWHAUx5VEWEYmdOlpFREREREREREREWqgs7QJERERERERERERE8k4drSIiIiIiIiIiIiItpI5WERERERERERERkRZSR6uIiIjIGmJm65nZhGSbamafFT2uSqGePc3s4XX974qIiIiItEYVaRcgIiIiEgt3nwUMATCznwDz3f2KVIsSEREREZF1QiNaRURERNYiMzvVzF4xs9fM7C9m1j7Zf7OZ/c7MXjSzD8zsyGR/mZldZ2bvmtmTZvZo0XOjkv2vJl/7cLJ/mJn9y8zGJ99vcHqJRURERERaJ3W0ioiIiKxd97n7Du6+LfAO8K2i53oBuwIHAZcl+w4HBgBbACcCwwHMrC1wA3CAu28P9Cj6Pu8Cu7n7dsAlwKVrLY2IiIiIiJSkqQNERERE1q6tzOwXQBegGni86Lm/unsd8LaZrZ/s2xW4J9k/1cyeSfZvBnzg7h8mj+8ATks+7wz82cwGAQ5Urr04IiIiIiJSika0ioiIiKxdNwNnuPvWwE+BtkXPLS763Frwb/wceMbdtwIObvJviIiIiIjIOqCOVhEREZG1qyMwxcwqgeOb8foXgCOSuVrXB/ZM9k8ENjKzAcnjo4u+pjPwWfL5yS2sV0REREREVoM6WkVERETWrouBlwgdqO824/V/AT4F3gZuA8YBX7r7IuB04G9m9iowD/gy+ZrLgV+a2Xg0NZSIiIiISCrM3dOuQURERESKmFm1u883s/WAl4Fd3H1q0X4DrgUmuftv0q1WRERERERAIx5EREREsuhhM+sCVAE/d/epyf5TzeykZP944Ia0ChQRERERkcY0olVERERERERERESkhTRHq4iIiIiIiIiIiEgLqaNVREREREREREREpIXU0SoiIiIiIiIiIiLSQupoFREREREREREREWkhdbSKiIiIiIiIiIiItJA6WkVERERERERERERa6P8DNBVGzyDCgsUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(20, 8))\n",
        "ax = sns.lineplot(data=df_resample_month, linewidth=2.5, marker=\"o\")\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga beras', xticks=df_resample_month.index)\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yH1Gmi-gOi5"
      },
      "source": [
        "### Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NLDnnJyggOi5",
        "outputId": "00b59c6d-2ebb-4bca-ce6c-6a146f103812"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train:  321\n",
            "test:  108\n"
          ]
        }
      ],
      "source": [
        "train, test = train_test_split(df, test_size=.25, shuffle = False, stratify = None)\n",
        "print(\"train: \", len(train))\n",
        "print(\"test: \", len(test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LjuG-PibgFYg"
      },
      "outputs": [],
      "source": [
        "train_beras = train[['Beras']]\n",
        "test_beras = test[['Beras']]\n",
        "test_beras_2 = test[['Beras']]\n",
        "\n",
        "train_beras_kualitas_bawah_1 = train[['Beras Kualitas Bawah I (kg)']]\n",
        "test_beras_kualitas_bawah_1 = test[['Beras Kualitas Bawah I (kg)']]\n",
        "\n",
        "train_beras_kualitas_bawah_2 = train[['Beras Kualitas Bawah II (kg)']]\n",
        "test_beras_kualitas_bawah_2 = test[['Beras Kualitas Bawah II (kg)']]\n",
        "\n",
        "train_beras_kualitas_medium_1 = train[['Beras Kualitas Bawah I (kg)']]\n",
        "test_beras_kualitas_medium_1 = test[['Beras Kualitas Bawah I (kg)']]\n",
        "\n",
        "train_beras_kualitas_medium_2 = train[['Beras Kualitas Medium II (kg)']]\n",
        "test_beras_kualitas_medium_2 = test[['Beras Kualitas Medium II (kg)']]\n",
        "\n",
        "train_beras_kualitas_super_1 = train[['Beras Kualitas Super I (kg)']]\n",
        "test_beras_kualitas_super_1 = test[['Beras Kualitas Super I (kg)']]\n",
        "\n",
        "train_beras_kualitas_super_2 = train[['Beras Kualitas Super II (kg)']]\n",
        "test_beras_kualitas_super_2 = test[['Beras Kualitas Super II (kg)']]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-P3d7d-gOi6"
      },
      "source": [
        "### Standarisasi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dt41ATkbgOi6"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaled_train_beras = scaler.fit_transform(train_beras)\n",
        "scaled_test_beras = scaler.fit_transform(test_beras) \n",
        "\n",
        "scaled_train_beras_kualitas_bawah_1 = scaler.fit_transform(train_beras_kualitas_bawah_1)\n",
        "scaled_test_beras_kualitas_bawah_1 = scaler.fit_transform(test_beras_kualitas_bawah_1)\n",
        "\n",
        "scaled_train_beras_kualitas_bawah_2 = scaler.fit_transform(train_beras_kualitas_bawah_2)\n",
        "scaled_test_beras_kualitas_bawah_2 = scaler.fit_transform(test_beras_kualitas_bawah_2)\n",
        "\n",
        "scaled_train_beras_kualitas_medium_1 = scaler.fit_transform(train_beras_kualitas_medium_1)\n",
        "scaled_test_beras_kualitas_medium_1 = scaler.fit_transform(test_beras_kualitas_medium_1)\n",
        "\n",
        "scaled_train_beras_kualitas_medium_2 = scaler.fit_transform(train_beras_kualitas_medium_2)\n",
        "scaled_test_beras_kualitas_medium_2 = scaler.fit_transform(test_beras_kualitas_medium_2)\n",
        "\n",
        "scaled_train_beras_kualitas_super_1 = scaler.fit_transform(train_beras_kualitas_super_1)\n",
        "scaled_test_beras_kualitas_super_1 = scaler.fit_transform(test_beras_kualitas_super_1)\n",
        "\n",
        "scaled_train_beras_kualitas_super_2 = scaler.fit_transform(train_beras_kualitas_super_2)\n",
        "scaled_test_beras_kualitas_super_2 = scaler.fit_transform(test_beras_kualitas_super_2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgZue_DcNR9B"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7aWm8-VgOi6"
      },
      "source": [
        "### Time Series Generator\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46Hs7PIQgOi6"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "\n",
        "scaled_train = scaled_train_beras[:,0]\n",
        "scaled_test = scaled_test_beras[:,0]\n",
        "\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=30, batch_size=8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XinGIowwALWs"
      },
      "source": [
        "### Callback Function untuk earlystop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wq0s_0aYARME"
      },
      "outputs": [],
      "source": [
        "monitor_loss_callbacks = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68tGvYXdmv9F"
      },
      "source": [
        "## Percobaan training\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q86mOrh3jwPi"
      },
      "source": [
        " ### Testing untuk mencari Jumlah layer & unit layer terbaik"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gKIstKIjwPs"
      },
      "source": [
        "#### Add Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-H4iWd1djwPs"
      },
      "outputs": [],
      "source": [
        "model_test_1 = tf.keras.models.Sequential()\n",
        "model_test_1.add(tf.keras.layers.Dense(units=1, activation='relu'))\n",
        "model_test_1.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
        "model_test_1.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
        "model_test_1.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
        "model_test_1.add(tf.keras.layers.Dense(units=1))\n",
        "model_test_1.compile(loss='mean_absolute_error', optimizer='adam')\n",
        "model_test_2 = tf.keras.models.Sequential()\n",
        "model_test_2.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "model_test_2.add(tf.keras.layers.Dense(units=256, activation='sigmoid'))\n",
        "model_test_2.add(tf.keras.layers.Dense(units=256, activation='sigmoid'))\n",
        "model_test_2.add(tf.keras.layers.Dense(units=256, activation='sigmoid'))\n",
        "model_test_2.add(tf.keras.layers.Dense(units=1))\n",
        "model_test_2.compile(loss='mean_absolute_error', optimizer='adam')\n",
        "model_test_3 = tf.keras.models.Sequential()\n",
        "model_test_3.add(tf.keras.layers.Dense(units=1, activation='relu'))\n",
        "model_test_3.add(tf.keras.layers.Dense(units=512, activation='relu'))\n",
        "model_test_3.add(tf.keras.layers.Dense(units=512, activation='relu'))\n",
        "model_test_3.add(tf.keras.layers.Dense(units=512, activation='relu'))\n",
        "model_test_3.add(tf.keras.layers.Dense(units=1))\n",
        "model_test_3.compile(loss='mean_absolute_error', optimizer='adam')\n",
        "model_test_4 = tf.keras.models.Sequential()\n",
        "model_test_4.add(tf.keras.layers.Dense(units=1, activation='relu'))\n",
        "model_test_4.add(tf.keras.layers.Dense(units=1024, activation='relu'))\n",
        "model_test_4.add(tf.keras.layers.Dense(units=1024, activation='relu'))\n",
        "model_test_4.add(tf.keras.layers.Dense(units=1024, activation='relu'))\n",
        "model_test_4.add(tf.keras.layers.Dense(units=1))\n",
        "model_test_4.compile(loss='mean_absolute_error', optimizer='adam')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jsbai-gDjwPs"
      },
      "source": [
        "  #### Training Model "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j39QfEhfjwPt",
        "outputId": "68d852ed-11a5-4bd2-ffdc-01d5207daaf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "37/37 [==============================] - 1s 3ms/step - loss: 0.0488\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0407\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0362\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0308\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0230\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0233\n",
            "Epoch 7/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0192\n",
            "Epoch 8/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0197\n",
            "Epoch 9/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0232\n",
            "Epoch 10/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0212\n",
            "Epoch 11/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0193\n",
            "Epoch 12/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0207\n",
            "Epoch 13/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0186\n",
            "Epoch 14/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0234\n",
            "Epoch 15/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0217\n",
            "Epoch 16/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0197\n",
            "Epoch 17/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0172\n",
            "Epoch 18/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0208\n",
            "Epoch 19/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0231\n",
            "Epoch 20/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0194\n",
            "Epoch 21/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0188\n",
            "Epoch 22/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0170\n",
            "Epoch 23/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0168\n",
            "Epoch 24/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0171\n",
            "Epoch 25/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0164\n",
            "Epoch 26/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0147\n",
            "Epoch 27/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0147\n",
            "Epoch 28/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0185\n",
            "Epoch 29/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0171\n",
            "Epoch 30/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0169\n",
            "Epoch 31/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0160\n",
            "Epoch 32/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0180\n",
            "Epoch 33/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0187\n",
            "Epoch 34/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0149\n",
            "Epoch 35/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0188\n",
            "Epoch 36/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0179\n",
            "Epoch 1/100\n",
            "37/37 [==============================] - 1s 3ms/step - loss: 0.2502\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.1652\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.1098\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.1036\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.1102\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.1507\n",
            "Epoch 7/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0813\n",
            "Epoch 8/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0569\n",
            "Epoch 9/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0739\n",
            "Epoch 10/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0542\n",
            "Epoch 11/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0593\n",
            "Epoch 12/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0635\n",
            "Epoch 13/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.1071\n",
            "Epoch 14/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0664\n",
            "Epoch 15/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0646\n",
            "Epoch 16/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0664\n",
            "Epoch 17/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0671\n",
            "Epoch 18/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0606\n",
            "Epoch 19/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0717\n",
            "Epoch 20/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0789\n",
            "Epoch 1/100\n",
            "37/37 [==============================] - 1s 4ms/step - loss: 0.0544\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0545\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0468\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0498\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0500\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0466\n",
            "Epoch 7/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0380\n",
            "Epoch 8/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0389\n",
            "Epoch 9/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0359\n",
            "Epoch 10/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0341\n",
            "Epoch 11/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0269\n",
            "Epoch 12/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0272\n",
            "Epoch 13/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0275\n",
            "Epoch 14/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0290\n",
            "Epoch 15/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0271\n",
            "Epoch 16/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0266\n",
            "Epoch 17/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0290\n",
            "Epoch 18/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0248\n",
            "Epoch 19/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0285\n",
            "Epoch 20/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0289\n",
            "Epoch 21/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0241\n",
            "Epoch 22/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0289\n",
            "Epoch 23/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0256\n",
            "Epoch 24/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0244\n",
            "Epoch 25/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0255\n",
            "Epoch 26/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0255\n",
            "Epoch 27/100\n",
            "37/37 [==============================] - 0s 4ms/step - loss: 0.0251\n",
            "Epoch 28/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0300\n",
            "Epoch 29/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0250\n",
            "Epoch 30/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0244\n",
            "Epoch 31/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0250\n",
            "Epoch 1/100\n",
            "37/37 [==============================] - 1s 3ms/step - loss: 0.0344\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0334\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0477\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0410\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0302\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0294\n",
            "Epoch 7/100\n",
            "37/37 [==============================] - 0s 4ms/step - loss: 0.0239\n",
            "Epoch 8/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0211\n",
            "Epoch 9/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0233\n",
            "Epoch 10/100\n",
            "37/37 [==============================] - 0s 4ms/step - loss: 0.0193\n",
            "Epoch 11/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0226\n",
            "Epoch 12/100\n",
            "37/37 [==============================] - 0s 4ms/step - loss: 0.0199\n",
            "Epoch 13/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0213\n",
            "Epoch 14/100\n",
            "37/37 [==============================] - 0s 4ms/step - loss: 0.0205\n",
            "Epoch 15/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0177\n",
            "Epoch 16/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0220\n",
            "Epoch 17/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0184\n",
            "Epoch 18/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0304\n",
            "Epoch 19/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0196\n",
            "Epoch 20/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0244\n",
            "Epoch 21/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0196\n",
            "Epoch 22/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0205\n",
            "Epoch 23/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0221\n",
            "Epoch 24/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0191\n",
            "Epoch 25/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0182\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f1fddd8ce90>"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ],
      "source": [
        "#Latih Model\n",
        "model_test_1.fit(generator, epochs=100, batch_size=32, callbacks=monitor_loss_callbacks)\n",
        "model_test_2.fit(generator, epochs=100, batch_size=32, callbacks=monitor_loss_callbacks)\n",
        "model_test_3.fit(generator, epochs=100, batch_size=32, callbacks=monitor_loss_callbacks)\n",
        "model_test_4.fit(generator, epochs=100, batch_size=32, callbacks=monitor_loss_callbacks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "goiZrHfxjwPt"
      },
      "source": [
        "#### Visualisasi prediksi 30 Hari terakhir untuk mengevaluasi model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmYHixf9k8ob",
        "outputId": "1dbd0bf4-3344-4b52-edaa-abac529a0429"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
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            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n"
          ]
        }
      ],
      "source": [
        "def predict_last_30days(model):\n",
        "  test_pred = []\n",
        "  first_eval_batch = scaled_train_beras[-30:]\n",
        "  cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "  for i in range(len(scaled_test_beras)):\n",
        "      #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "      current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "      #Append hasil prediksi ke array\n",
        "      test_pred.append(current_pred)\n",
        "\n",
        "      #Update nilai tiap batch\n",
        "      cur_batch = np.append(\n",
        "          cur_batch[:,1:,:],\n",
        "          [[current_pred]],\n",
        "          axis=1\n",
        "          )\n",
        "      \n",
        "  return test_pred;\n",
        "\n",
        "arr_pred_model_1 = predict_last_30days(model_test_1)\n",
        "arr_pred_model_2 = predict_last_30days(model_test_2)\n",
        "arr_pred_model_3 = predict_last_30days(model_test_3)\n",
        "arr_pred_model_4 = predict_last_30days(model_test_4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "mBB3CiY_k8od",
        "outputId": "99c1f29c-cc48-4774-9bf0-47f579f73458"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras  Prediksi Model 1  Prediksi Model 2  Prediksi Model 3  \\\n",
              "Date                                                                      \n",
              "2022-09-26  12550      12890.303707      12894.247605      12888.830858   \n",
              "2022-09-27  12550      12890.303439      12894.247605      12888.830925   \n",
              "2022-09-28  12550      12890.310398      12894.247605      12888.830932   \n",
              "2022-09-29  12550      12890.310930      12894.247605      12888.830943   \n",
              "2022-09-30  12550      12890.309686      12894.247605      12888.830992   \n",
              "\n",
              "            Prediksi Model 4  \n",
              "Date                          \n",
              "2022-09-26      12880.284768  \n",
              "2022-09-27      12880.284563  \n",
              "2022-09-28      12880.284164  \n",
              "2022-09-29      12880.284004  \n",
              "2022-09-30      12880.283984  "
            ],
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              "      <th>Beras</th>\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ],
      "source": [
        "prediction_model_1 = scaler.inverse_transform(arr_pred_model_1)\n",
        "prediction_model_2 = scaler.inverse_transform(arr_pred_model_2)\n",
        "prediction_model_3 = scaler.inverse_transform(arr_pred_model_3)\n",
        "prediction_model_4 = scaler.inverse_transform(arr_pred_model_4)\n",
        "test_beras_2[\"Prediksi Model 1\"] = prediction_model_1\n",
        "test_beras_2[\"Prediksi Model 2\"] = prediction_model_2\n",
        "test_beras_2[\"Prediksi Model 3\"] = prediction_model_3\n",
        "test_beras_2[\"Prediksi Model 4\"] = prediction_model_4\n",
        "\n",
        "test_beras_2.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xF9LZXmSk8oh"
      },
      "outputs": [],
      "source": [
        "test_beras_2_M = test_beras_2.resample('M').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "p3pYidbLk8oi",
        "outputId": "55ff9739-fd58-4b0d-d3b5-2d1ab54b54b2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 8))\n",
        "ax = sns.lineplot(data=test_beras_2, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras (Kg)', xticks=test_beras_2.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYKCa8-9jeXh"
      },
      "source": [
        "## Training Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Oucbje3gOi7"
      },
      "source": [
        "### Add Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gsq-uaZ4gOi7"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Dense(units=1, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=256, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=256, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=1))\n",
        "model.compile(loss='mean_absolute_error', optimizer='adam')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLePTJGogOi7"
      },
      "source": [
        "### Fit Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOq2sJUggOi7",
        "outputId": "5ad22242-c269-4277-963a-240d842d0d92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "37/37 [==============================] - 1s 3ms/step - loss: 0.0572\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0443\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0419\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0323\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0326\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0356\n",
            "Epoch 7/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0305\n",
            "Epoch 8/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0286\n",
            "Epoch 9/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0258\n",
            "Epoch 10/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0219\n",
            "Epoch 11/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0251\n",
            "Epoch 12/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0232\n",
            "Epoch 13/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0222\n",
            "Epoch 14/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0261\n",
            "Epoch 15/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0213\n",
            "Epoch 16/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0245\n",
            "Epoch 17/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0216\n",
            "Epoch 18/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0212\n",
            "Epoch 19/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0200\n",
            "Epoch 20/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0181\n",
            "Epoch 21/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0200\n",
            "Epoch 22/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0236\n",
            "Epoch 23/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0209\n",
            "Epoch 24/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0192\n",
            "Epoch 25/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0203\n",
            "Epoch 26/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0193\n",
            "Epoch 27/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0197\n",
            "Epoch 28/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0215\n",
            "Epoch 29/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0180\n",
            "Epoch 30/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0190\n",
            "Epoch 31/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0181\n",
            "Epoch 32/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0233\n",
            "Epoch 33/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0199\n",
            "Epoch 34/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0185\n",
            "Epoch 35/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0199\n",
            "Epoch 36/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0210\n",
            "Epoch 37/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0166\n",
            "Epoch 38/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0181\n",
            "Epoch 39/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0186\n",
            "Epoch 40/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0180\n",
            "Epoch 41/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0210\n",
            "Epoch 42/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0176\n",
            "Epoch 43/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0166\n",
            "Epoch 44/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0183\n",
            "Epoch 45/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0162\n",
            "Epoch 46/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0163\n",
            "Epoch 47/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0169\n",
            "Epoch 48/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0162\n",
            "Epoch 49/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0161\n",
            "Epoch 50/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0175\n",
            "Epoch 51/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0204\n",
            "Epoch 52/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0218\n",
            "Epoch 53/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0169\n",
            "Epoch 54/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0172\n",
            "Epoch 55/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0158\n",
            "Epoch 56/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0160\n",
            "Epoch 57/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0169\n",
            "Epoch 58/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0216\n",
            "Epoch 59/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0176\n",
            "Epoch 60/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0160\n",
            "Epoch 61/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0184\n",
            "Epoch 62/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0196\n",
            "Epoch 63/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0158\n",
            "Epoch 64/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0162\n",
            "Epoch 65/100\n",
            "37/37 [==============================] - 0s 3ms/step - loss: 0.0182\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f1fdd968150>"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ],
      "source": [
        "#Latih Model\n",
        "model.fit(generator, epochs=100, batch_size=32, callbacks=monitor_loss_callbacks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dzlc2g1in1Py"
      },
      "source": [
        "#### Visualisasi Loss setiap epoch ketika training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "2GPHKsISkNEV",
        "outputId": "20163a8f-0da8-4687-e6df-ce2a02cce567"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "loss_per_epoch = model.history.history[\"loss\"]\n",
        "n_epoch = list(range(len(loss_per_epoch)))\n",
        "\n",
        "plt.figure(figsize=(30, 8))\n",
        "sns.lineplot(data=loss_per_epoch, linewidth=2.5, marker=\"o\", linestyle='solid').set(xlabel='n_epoch', ylabel='loss', title='LOSS', xticks=n_epoch)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbsrQ1cubmh3"
      },
      "source": [
        "# Test Predict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOSF7pbXX5yy"
      },
      "source": [
        "### Test Model dengan memprediksi satu hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPD0YCUzgOi8",
        "outputId": "fa521e98-4bd4-4176-d428-4c36b2955e99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 52ms/step\n",
            "[[0.13912798]]\n"
          ]
        }
      ],
      "source": [
        "last_line_train_beras = scaled_train_beras[-30:]\n",
        "last_line_train_beras = last_line_train_beras.reshape(1,30,1)\n",
        "model_pred = model.predict(last_line_train_beras)\n",
        "\n",
        "print(model_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdvxTBO3LwyD",
        "outputId": "2fc7708c-1585-44de-ff40-229343c10b3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = scaled_train_beras[-30:]\n",
        "data = np.append(data, model_pred)\n",
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=data, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AabZPj6YX_ud"
      },
      "source": [
        "### Test Model dengan memprediksi 30 hari terakhir lalu divalidasi oleh test_beras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxRRpsvigOi8",
        "outputId": "ae93c385-b1c3-4868-f526-43e9e4fcf132"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n"
          ]
        }
      ],
      "source": [
        "test_pred_beras = []\n",
        "\n",
        "first_eval_batch = scaled_train_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "for i in range(30):\n",
        "    #Prediksi dari nilai pertama\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    # Append hasil prediksi\n",
        "    test_pred_beras.append(current_pred)\n",
        "\n",
        "    # Perbarui nilai batch dengan menghapus nilai index pertama dari batch\n",
        "    cur_batch = np.append(\n",
        "        cur_batch[:,1:,:],\n",
        "        [[current_pred]],\n",
        "        axis=1\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ixLRsdp0gOi8",
        "outputId": "676d6ef3-8aef-4aae-bbdd-3ad61047c897"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras  Prediksi Beras\n",
              "Date                             \n",
              "2022-08-22  12450    12884.781996\n",
              "2022-08-23  12450    12883.694223\n",
              "2022-08-24  12450    12885.375282\n",
              "2022-08-25  12500    12886.001142\n",
              "2022-08-26  12500    12885.884809\n",
              "2022-08-29  12550    12885.210133\n",
              "2022-08-30  12550    12885.100121\n",
              "2022-08-31  12550    12885.896666\n",
              "2022-09-01  12550    12886.605913\n",
              "2022-09-02  12550    12886.592223\n",
              "2022-09-05  12550    12886.511656\n",
              "2022-09-06  12550    12886.516145\n",
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              "2022-09-12  12550    12887.204087\n",
              "2022-09-13  12550    12886.998864\n",
              "2022-09-14  12600    12887.768073\n",
              "2022-09-15  12600    12887.768431\n",
              "2022-09-16  12600    12886.847610\n",
              "2022-09-19  12600    12887.102681\n",
              "2022-09-20  12600    12887.526913\n",
              "2022-09-21  12600    12887.769943\n",
              "2022-09-22  12600    12887.751872\n",
              "2022-09-23  12600    12887.284315\n",
              "2022-09-26  12550    12887.125517\n",
              "2022-09-27  12550    12887.670527\n",
              "2022-09-28  12550    12887.752617\n",
              "2022-09-29  12550    12887.674405\n",
              "2022-09-30  12550    12887.661232"
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              "      <th>Beras</th>\n",
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              "      <td>12600</td>\n",
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              "      <td>12550</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ],
      "source": [
        "temp_df_beras_sep = test_beras[-30:]\n",
        "prediction = scaler.inverse_transform(test_pred_beras)\n",
        "temp_df_beras_sep[\"Prediksi Beras\"] = prediction\n",
        "temp_df_beras_sep"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "CZCgKprugOi8",
        "outputId": "31bc7b8d-584d-4675-fc7f-cab468fa952d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
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            "image/png": 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awrlH7ME5RwyiQ7ndgSRJkiRJ2prVAqnIRVHEA68u52eTZrF0XUXD+ONG9uGiY4bRv3uHFLOTJEmSJEktmcVDqYi9tnQ9l0+YwfOL1jSMG9GnC5eOH8HHBvVIMTNJkiRJklQILB5KRWjVpkqu+dds/vLCm0RRPG6njuV8/6ihnDxqAG1KvK+hJEmSJEl6bxYPpSJSVVPHHc8u4rpH5rKxsgaA0pLAlz8+kG+NHULX9mXpJihJkiRJkgqKxUOpSDw+ewVXTJzBgpWbG8aNHtqLHx03gsG9O6WYmSRJkiRJKlQWD6UCN3/lJn4ycQaPz17ZMG5Qz45cMm4ERw7rnWJmkiRJkiSp0Fk8lArUhi3V/PqRudz+n0XU1MU3NuzctpRvf2IIpx88kPLSkpQzlCRJkiRJhc7ioVRgausi/jr1Ta5+eDarN1cBEAKccsAALvjUUHp2aptyhpIkSZIkqVhYPJQKyPML13DZhNd5fdmGhnEHDOzOpeP3Yu9+XVPMTJIkSZIkFSOLh1IBWLqugp9NmsnE6csbxvXt2o6Ljh3OuJF9CCGkmJ0kSZIkSSpWFg+lFqyiqpYbn5zPjU/Op7KmDoB2ZSWce8QenHP4HrQvb5NyhpIkSZIkqZhZPJRaoCiKmDB9OVdNmsmy9Vsaxo//SF9+eMww+nVrn2J2kiRJkiSptbB4KLUwry1dz2UTXueFRWsbxu3VtwuXjt+LA3ffKcXMJEmSJElSa2PxUGohVm2q5OqHZ3PP1DeJonhcz07lXPipoZw0agBtSryvoSRJkiRJ2rEsHkopq6qp44//WcSvH53LxsoaAEpLAmceMpBvjh1Cl3ZlKWcoSZIkSZJaK4uHUooem/U2V0ycycJVmxvGjRnWm4uPG84evTqlmJkkSZIkSZLFQykV81Zs4oqJM3hyzsqGcYN6deSScSM4cmjvFDOTJEmSJElqZPFQ2oHWV1Rz3SNzuePZRdTUxTc27NyulG+PHcKXPz6QsjYl6SYoSZIkSZKUweKhtAPU1kXc88KbXP2v2azZXAVACHDKAbtywaf2pGentilnKEmSJEmS9G55Kx6GEG4FxgEroijaOxn3S2A8UAXMB86MomhdCGEgMBOYnbx8ShRF5yav2R+4HWgPTAK+HUVRFELYCbgHGAgsAk6OomhtvpZH+rCmLFjNZRNmMHP5hoZxB+6+E5eOH8FefbummJkkSZIkSdL25fMayduBo5uM+zewdxRFI4E5wEUZ0+ZHUbRv8jg3Y/zvgLOAIcmjfp4/BB6NomgI8Gjyv7RNdXURKzdWsnTtO6zcWEldctlwvuIsXr2Z5xes5ucPzmooHPbr1p4bvrgf95x9kIVDSZIkSZLU4uXtzMMoiiYnZxRmjvtXxr9TgM9tbx4hhD5AlyiKpiT/3wF8BngQOB4YnTz1j8ATwA+an7mKUV1dxKy3NnL2nVNZsraC/t3bc/0X92PVpkrWvVOdszjdOpTRs1Nbzv/ziw1xfn7iSK57ZA6HDunF2YcPol1Zm5zFkyRJkiRJyqc073n4FeLLjuvtHkJ4CdgA/CiKoqeAfsCSjOcsScYB7BxF0fJk+C1g5zznqwK2enNVQ+EQYMnaCs7/84tcMm4EF/7tlZzFuem0/RsKh/VxfnDfdO45+yD6de+QsziSJEmSJEk7QirFwxDCxUANcFcyajmwaxRFq5N7HP5fCGGv9zu/5B6I27wGNYRwNnA2wK677vrhE1fB2riluqGgV2/J2gq6tS/LaZxu7cuyxpEkSZIkSSpEO7x4GEI4g/iHVMZGURQBRFFUCVQmw9NCCPOBPYGlQP+Ml/dPxgG8HULoE0XR8uTy5hXbihlF0c3AzQCjRo3Kz43u1GLV1UUsWxdfQpxZyOvfvT39urfnqf86MmexQiBrnPJSL1WWJEmSJEmFZ4cWD0MIRwP/BRwRRdE7GeN7AWuiKKoNIQwi/mGUBVEUrQkhbAghHAQ8B5wO/CZ52f3Al4Grkr//3IGLogLyfy8v5c5nF/PzE0fyg/umN9yL8JbTR9G3a3tKSkLOYtXVRdxy+ijOumPqVnF6dCzPWQxJkiRJkqQdJSQn/+V+xiHcTfyDJj2Bt4FLiX9duS2wOnnalCiKzg0hnAhcDlQDdcClURRNSOYziviXm9sT/1DKN5PLlHsAfwV2BRYDJ0dRtOa98ho1alQ0derUXC2mWrjNlTWMueYJ3t5QyeihvbjqsyOpraujvLQNPTqW57RwWK+uLmL15iqqamrzGkeSJEmSpJYihDAtiqJRaeeh3Mvnry1/IcvoP2zjufcB921j2lRg7yzjVwNjm5Ojit+NT87n7Q2VABy3Tx926dou7zFLSgK9OrfNexxJkiRJkqR8K0k7ASlf3lzzDjdNXgDAyP5dOXG//u/xCkmSJEmSJGWyeKiiddWDs6iqqQPg0vEjvHRYkiRJkiTpA7J4qKI0ZcFqHnh1OQDH79uX/XfbKeWMJEmSJEmSCo/FQxWd2rqIyyfMAKBdWQk/OHpYyhlJkiRJkiQVJouHKjp/nfomM5ZvAODrRwymb7f2KWckSZIkSZJUmCweqqhs2FLN1Q/PBqBv13acffiglDOSJEmSJEkqXBYPVVR+8+hcVm+uAuCiY4fTvrxNyhlJkiRJkiQVLouHKhoLVm7i9v8sAuCAgd0ZN7JPuglJkiRJkiQVOIuHKhpXPjCT6tqIEODH4/YihJB2SpIkSZIkSQXN4qGKwpNzVvLorBUAnLR/f/bp3zXljCRJkiRJkgqfxUMVvOraOq6YOAOATm1LufCooSlnJEmSJEmSVBwsHqrg3TVlMfNWbALg/DGD6d25XcoZSZIkSZIkFQeLhypoazdXce0jcwHYrUcHzjxkYLoJSZIkSZIkFRGLhypo1z4yh/UV1QBcfOxw2pa2STkjSZIkSZKk4mHxUAVr9lsb+dOUxQAcOrgnnxyxc8oZSZIkSZIkFReLhypIURRx+cTXqYugJMAl40YQQkg7LUmSJEmSpKJi8VAF6d8z3uaZeasB+NJBuzF0l84pZyRJkiRJklR8LB6q4FTW1HLlpJkAdG1fxnc/sWfKGUmSJEmSJBUni4cqOLc9s4jFq98B4LufGEL3juUpZyRJkiRJklScLB6qoKzYuIXrH5sHwODenTj1oN1SzkiSJEmSJKl4WTxUQbn64dlsqqwB4h9JKWtjE5YkSZIkScoXKy8qGK8uWc/fpi0BYOyw3hyxZ6+UM5IkSZIkSSpuFg9VEKIo4rIJrxNFUNYmcPFxw9NOSZIkSZIkqehZPFRBmDh9OVMXrwXgjI8PZFCvTilnJEmSJEmSVPwsHqrFq6iq5WeTZgLQo2M53xw7JOWMJEmSJEmSWgeLh2rxbp68gGXrtwBw4VFD6dKuLOWMJEmSJEmSWgeLh2rRlq2r4HdPzgNgeJ8unDxqQMoZSZIkSZIktR4WD9Wi/fyhWWyprgPg0vEjaFMSUs5IkiRJkiSp9bB4qBZr2uI1/PPlZQAcu88uHDSoR8oZSZIkSZIktS4WD9Ui1dVFXDZhBgDlpSVcdMzwlDOSJEmSJElqfSweqkX6+0tLmb5kPQBnHzaIATt1SDkjSZIkSZKk1sfioVqcTZU1/PyhWQDs3KUtXx+9R8oZSZIkSZIktU4WD9Xi/PbxeazcWAnAD44eRse2pSlnJEmSJEmS1DpZPFSL8sbqd/j9UwsB2HdANz6zb7+UM5IkSZIkSWq9LB6qRfnppJlU1dYBcOn4EZSUhJQzkiRJkiRJar0sHqrF+M/8VTz0+lsAfPaj/fjort1TzkiSJEmSJKl1s3ioFqGmto7LJ8wAoH1ZG/7r6GEpZyRJkiRJkiR/iaIA1dVFrN5cRVVNLeWlbejRsTwvl/fuyDhz3t7EZZ/ei3UV1ax/p4pdurbLeRxJkiRJkiR9MBYPC0xdXcSM5Rs490/TWLK2gv7d2/O7U/ejtE2gorouZ3Hal5VQUxvx9bte3OFxbj5tFHV1kfc7lCRJkiRJSlmIoijtHHaoUaNGRVOnTk07jQ9t5cZKTvjtMyxZW9Ewrn/39lwybgTn3DktZ3FuOm1/rpg4I7U4/zjvEHp1bpuzOJIkSZIkKX9CCNOiKBqVdh7KPe95WGCqamq3KrQBLFlbQbf2ZTmN0619WapxqmpqcxpHkiRJkiRJH5yXLReY8tI29O/e/l1n6vXo1JbbzzwgZ3F6dGqbapzy0jY5iyFJkiRJkqQPx8uWC0xdXcTstzdy1h1TG+4ReMvpoxi6c+ec3iOw2OJIkiRJkqT88bLl4mXxsAAV468t74g4kiRJkiQpPyweFi8vWy5AJSVhh/yYSLHFkSRJkiRJ0gfjD6ZIkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJysrioSRJkiRJkqSsLB5KkiRJkiRJyipEUZR2DjtUCGElsDjtPHKkJ7DKOK0+TrEptu1jHEHxbR/jCIpv+xhHUHzbxziC4ts+xmm5douiqFfaSSj3Wl3xsJiEEKZGUTTKOK07TrEptu1jHEHxbR/jCIpv+xhHUHzbxziC4ts+xpF2PC9bliRJkiRJkpSVxUNJkiRJkiRJWVk8LGw3G8c4RajYto9xBMW3fYwjKL7tYxxB8W0f4wiKb/sYR9rBvOehJEmSJEmSpKw881CSJEmSJElSdlEU+cjBAxgAPA7MAF4Hvp2M3wn4NzA3+ds9GX8qMB14FfgP8JHtzWcbcRYDbwPzgMsy4kwDXgFeBmYDs7LEGUL8c/CVwBbgkYwY3wPmAJuAzcCSPMU5N3nNRuAdYFk+4mSst9eACFiep+U5A1iZvP5l4NpkPvOAH2Y8byzwYvKcp4HBWdpTB+CBJIfXgauaTD85o438udDbdZPnLE7W70rghxmxlgAbknk/TdxOt4qVrLdHidvtlmQe9TnvlkybCawlbuPNeQ8NAFYncbYAzwBtMtbPZOJ2vQl4M09xTkpeFwELtrM8U5M4ryV5bWsbPZvkXEncRsuTaYcn/9cAnwOOJnvbHpM87zXgj0DpNtrV7sBzyevvyYjzPeI2Mj3ZVrsVetvm/fXZzd0+ZyRtZGPSRlbnI04y/es0vr8252l5rk3W5cZkWm2e4uyabJuXktdeQn7adX3fMx14Auhvu94qzhygirgf+++MGKcS99eb2P4+QnPjHJ/E2QhUAG/lI07G9KnJtJV5Wp7RwHoa90XuoHnt+vzktRHQs8m00UmM14Enbdfva/t0Bx4ibtcVNL9dv0XjPukUoCyZFoBbid87W/IYZxjxvkxdMv/mrrdtxWm6Tb9O89r1XcnrX0vWU32c45M4Lyc5H2q7fu/tk0w/icb9kC15agffp3H/YAuNfWmu43RN5rUpmbYqT8vTHfhH8rrngXNoXrv+Q5LPdOBeoFMyvi3xfsk84v2Ugflq1z6K+5F6AsXyAPoA+yXDnYl3GkYAv6h/8xMXQn6eDH+cxg+DY4DntjefpnGANsTFgoXERZMVwP8mz1kJ/D4Z/iVJcalJnEHA2cnwTsQ7F+ck/x+ZTN8POC/pOPMRp0vG8nwmmZbzOMm4wUmnOwVYmqflOQO4PhluA8xPnl9O3JGPSKbNAYYnw+cBt2dpTx2AI5PhcuAp4Jjk/yHEX3br20/vQm/XGc8Zlay3fZLnzCL+IPxh8v81wM+T9fZA01jJejsxaVPlxB/eS5Kc/wZ8OYlzDvAnmvce6gMclrFMG4ELkv9/AVyZ5HERcbEyH3GGA4cS77wcmG15iM8wXwfcnLzmVuCb24jzL+CUJM464LJk2kBgJPGX0JPJ0raTOG8CeyavuRz46jba1V+BU5LhG4GvZ/Q9HZLhrwP3FHrb5j367BxtnzOS1+U7zhDineIjkjiL8xGnyXr7FvFBg3wsz800tr29gWry067/Bnw5GR4D3Gm73irOF4j7mMXEX2xGZOQ2LIlzHPGXrnzE6ZSxPPsSF3xyHieZ3o/4C+Ik4i+i+Vie0cDEZDjrvggfrF1/NImziIziIdCNuBixa/J/we+LsGPa9S+Tx37JMrxr3/cDxvkmcaGwM3FfWd+/HQs8lsT5OM1//2wrTm/iA4p/IC7sNHe9bStO5jat7w+a066PTeIE4G4a++tONN7WayQwy3b9vrZPtyTuseRm/yBrnCbLczz5e//8N3B9Emdn4oOY+YjzS+DSZLi+P2hOu+6SMfy/NLaj84Abk+FTyOP+tY/ifnjZco5EUbQ8iqIXk+GNxEex+xF3bH9MnvZH4iIZURT9J4qitcn4KUD/95hP0zgHEh/xmE78wR2IP0ghPuL8yWR4DfEOX9M4C6IoujkZXkN8Nt7Q5P/Hk+kvEp9ZUp2nOBsylmdkEivncRLnE59VUg68kcc49Q4E5iXPrwL+QtwWID5K1iUZ7kp8NHgrURS9E0XR48lwFXHhs38y+Szghvr2E0XRiizxc2JHtev65wBlxOvt1eQ5zwCfTmJExGfvfIZ4vb3SNFay3u6LoujFZL29QHy0sB/xB/BjSZybgRNo3ntoeRRFTyXj68/C6p78fzxxIflF4mJLeT7iRFE0M4qip4kL/HttY3l6EO/cHJHM4zbinZqt4hAfFd0PuDfZRtOJd9CJomhRFEXTic8sGEz2tt0DqIqiaE4yv38TF3K3EkIIxAWUe5NRme3n8SiK3smSW861oD672dsn8c4OiHMWcF0URU8mcWbla3ky1tvXiL+Q5CNOZl98MLApH+2apO9Jhh+n8bMg5wqtXSfzuTuKokXE22NufZwkt1lJnOrktfmIsyljeQ4gLh7mPE7iJBo/z5bnMU69be2LvK92ncR6KYnT1BeBv0dR9EbyvILfF9kR7Zq4P7g/idM1GTe7GXF+E8U2EhdshiTPOx64JYkTERc/ch4niqIVURQ9RFzc2DUH621bcTK3aXUyrjntelISJyIu6NfnsCkZB9AxWXd5UaDtelvt7YvAX6MomkQz9w/eI07m8nyDuKCXjzgRcdHuJeAQ4rMG8xEnc/+gvj/Y3Ix2vQEa9kna09h+M9vUvcDY5DnSB2LxMA9CCAOJj9Q+B+wcxcUKiL/E7JzlJV8FHnyP+TTVj/hspvrpHWj8EnQmMCCEsAQ4DbjqPeKMJL6s6vYscU4h3iHIS5wQwjdCCPOJzy55Lh9xQgj7AQOiKHqA+LTtjflaHuDEEMJ04qM9azLGL6Hxg/xrwKQscbIKIXQDxhMXzgD2BPYMITwTQpgSQjh6e6/PlR3Yrt/MeM4U4qNoy4nX253EH7hN19u7YiXr7QSgVxLrFeCzyeQTiLf9ZprZFkIIDxMXKDsDVyejM9dPO6A0T3Ey9SJ7n7CKeEenb/K8zxFfQtE0Tg9gXRRFNcn63yOZR1M7EfcJ9erb9iqgNIQwKkucTA1xmry+qaztJx9S7rNztX0y+57KPMVp6HuId0Lb5XF5CCHslrx2fp7i/A/wpeT9dy3xJcX1ctmum/Y9nUMIPbLMI6cKpF1nKiU+6zxbnFOJrxrIS5wQwgkhhFnE759n8xEnhNCPePv/LpnvpnwtD3BwCOEV4AbifZ56H7Rdb8+eQPcQwhMhhGkhhNM/4Os/lCJo15n9wRHE/ehHmhsnhDCY+GB2fYGgH42f1f2I28G+eYiTqQs5Wm/vEedUGg+2QjPadQihjHj/66GMcfX9wQPAV7b3+lwptHadZfs09AfEV/Z0zVOc+vEdiA/6zc5TnOuJr+5ZRnyJ+wt5ipOtP6g/+Pmh2nUI4TbidjMM+E0yuqE/SPZT1hPvt0gfTNQCTn8spgfx6e7TgM8m/69rMn1tk/+PJD5C1GN788kS50vEnUl9nM00XjL7d2BDMvx94PfbidOVuAO5dRsx5uY7TjL9BuKjODmNQ1wgf4Lk3g7Ja9ah/MTQAAAK70lEQVTlY3mIO+G2yfBNwLKMaac1ifOxzDjbaU+lxB8+38kYN5H4/hhlxPfYehPoViTt+nPExdhpxB+mpwGVmeuNeMenYb1li5Wst38l66Y+577JPF4CriO+L9zqHLSFTsRnhj4LfLLp+iF+H9XlI07GtCeAH7Ht9+rBxEfqnwd+Qnwvn63iAD2JL62q30ZnAa81iXM7ceHy9xnjTmsS56nMOFm2cU/iM2Hq/x+QJc6XiAvHbfPZrndw295en92s7cPWfc8fiM+cykeczL7nu8Rnsnwpj+3tB0B90Twfy/M9Gm8B8N/EfUtJrts17+57llA8fXaz2nWTOJUkl3dnyW0p2+6vcxInmX4FzXj/bC8O8eXrByXDk4kvXctHnC403ufqSmB9xrQP1K6bxFrE1pctX0/cT3ckbv9zSS6rs12/5/a5LXndU8m8vpeDOCuBCRnjJpLcry9Znirg+7mOkzHtf4iLLLlab9uKcyRxH3pnxrjmtOtbgF9tY9rhNLmHuu16m+0tsz84l3j/4Bt5bAefJ94nztfyfI74oGIg3leoBU7NQ5zM/uDJZPq+OWjXbYDfAmcm/79Gxv2WiQ/K9tzePHz4yPZIPYFiehB/oXqYZCcgGTcb6JMM9wFmZ0wbmbx593wf8xlA482vz0s66FkZ01cCPyU++2hRfRziSwjmJ4+Zyesvz4izBJicJc484ktqpuU5Tv3yPAfU5iHOCOIfeagi/uJRR9zJj8rj8pxLfIp7dcb0i5JHL2B+xvhdie8Z1Cbj9ZdnTL8V+HWT9nEjyYdB8v+jwAFF0K7PJb5/3yoad6QvSrbRXsk8+ySx69fbvsQ75jObrLfbiC8L2Fas7yavy0Xbfph4x+J04p2nAcSF8NeI2/aLJAXQPMSpX545xJdHv2t5mm4j4FPJvOYT74i8TFy0DMm6/1cS5+DkeZlxniUusjzctG1naTefIr6EhWQ+TeOUJtMObjK/TyTLn7f7Z7WkPjvH22ebfWmO4qwivu1D/fJsJul78rQ8bxDfGD1f660imf+5yfh36tsdOW7XGa/vBCyxXcfrrUmcNSRfZjLizKLxh8jyGSdzeaoypucyTiWN+yK1xPsmn8nj8tS364qM6R+oXTd5ziK2Lh7+kK3vQ/YH4CTb9Qdub9Uk9ylrRpx5xPsAJRlxViXbrD5OZca6ymWc+uV5gHi/KxfrbVtx6vuDk2nGfkjG9EuB/6uPs412t4A8FlkozHadbfssIz7brT7OOpL+IE/t7SXiAxb5Wp71yfwz93cOzOPyZPbX9f3Bh+6vk2mH03j/24eBg5PhUpKzJvPVrn0U7yP1BIrlQfzF4Q6aHL0ivhFq5k1vf5EM75p0Ih9/P/PJMv064g+03Ynvp1Z/89bSpIO7JXn+94kvjckWZ3rSMZY0mfbRpOP7R57jDMlYnvuJd2xyHqfJeluSPPKxPH0yhk8kLiDVx3mFuABW32HX3/T2q8B929jWPwHuyxLnaOCPyXBP4rPremSbR6G064zn3En8gZ253m4lLlitIj7r7RfJepu0jVg/Id6BbZpzT+IzUeu34XM0r213Ij6T5FfJa+4Bzs9cP8kyP0O8E5XzOBnr7S3ie6O8a3mS51yRrLe2xL9UvXQb22gRMCn5/0bgvCbPuZ14pz0zzivAXsn0+sJLW+Ki9phtbOu/sfUPS5zXpO8Zko/2nEbb5j367FxsH+Kd1/o4E9m678llnKOJL7W5g/i+oTXJNst5eyO+3GZTntfbg8AZyfDeyfLko133pPHLwpVkHOiwXTfG4d3FqfrcHsxznMEZ0+9u0g5yFqfJ9BnEP5iSj+XZhcYffDiYZrbrjPk2jTM8eV0p8WV8rwF7267fc/t0S+Z5R7L+NjYzzn+ICzftm0w7jvi9cwfxfkNzPxeyxsmY/gpxwaS5621by9OwTZM219z9kK8lsZrGGUzj+2e/JM+8FFm21R5p2e16W9unvj+4k/hqsirgqDy1t67E3xl/m8fl+R3x2bR3EJ+dWkPjjzHmMk43oDwZPoet+4MP1K6TOIMzhq8Grk7+/wZb/2DKX/PRpn0U/yP1BIrlQXzGVERclKg/inAs8eVkjxIXmx4Bdkqe/3viS6Tqnzt1e/PZRpz5xDsDy4kLJvVxXiHeMX0lmbY+S5wTkvlsIT7KUUF8I3ySPNck0yuICxz5iHMd8Y1uI+IviW/mI06T9baJuHiYj+X5GfFZLK8Q3xT/bOIzwuYDF2fkcgLxkadXiC83HZSlPfVP4szMyOFrybRAXHyakcznlEJv102eszBZv5XEhYr6WMuS7fdast7uaRorY73Vt90K4rOXjiW+BGFu8n+UrLvmvIfGN4mzEhiXTOuR5BMR7wgsylOcE4h3YiLisxfe2UacRcRHsmcTnz24vW30TrLu1wGfTqYdQPy+2Ux81sxisrftXxK32dlkXGqfpV0NIr70Yh5xwaX+kttHiL9Q1+d2f6G3bd5fn93c7fMz3l9f2tw4gbhIXd8e385HnGT6rTtgvY0gLu6/krzmYvLTruv7njnE7Shvl+NTuO26isZ+7KGM3DbQ2N4q8xTnBzS+fzYTf/nLeZwm09cQH/TJx/KcT+O+yBTgAprXrr9F3P/XEH8OZ5659X3iz7DXtjcP2/VW2+dgGvdD1rPt/YMPEqeSxn2EOzP667/T+P7Z1r5vc+PsQuN+SP0ZtZV5iNN0m9b3qR+2Xdckr62f348z+oPXabza4lDb9Xtvn2T6DTR+R1qVxzhX5nt5iG838nzG8qzIU5yDidvxbOL368l8yHZNfILEM8Tfb14jvo1A/VmM7Yj3S+Yly/Wu754+fLyfR/2RFUmSJEmSJEnair+2LEmSJEmSJCkri4eSJEmSJEmSsrJ4KEmSJEmSJCkri4eSJEmSJEmSsrJ4KEmSJEmSJCkri4eSJEk5EkLoEUJ4OXm8FUJYmvF/eQr5jA4hTNzRcSVJklQ8StNOQJIkqVhEUbQa2BcghPA/wKYoiq5ONSlJkiSpGTzzUJIkKY9CCGeFEF4IIbwSQrgvhNAhGX97COHXIYT/hBAWhBA+l4wvCSH8NoQwK4Tw7xDCpIxpxybjpyWvnZiMPzCE8GwI4aVkfkPTW2JJkiQVE4uHkiRJ+fX3KIoOiKLoI8BM4KsZ0/oAhwLjgKuScZ8FBgIjgNOAgwFCCO2Am4BjoijaH+iVMZ9ZwGFRFH0U+DHw07wtjSRJkloVL1uWJEnKr71DCD8BugGdgIczpv1fFEV1wIwQws7JuEOBvyXj3wohPJ6MHwYsiKJoYfL/3cDZyXBX4I8hhCFABJTlb3EkSZLUmnjmoSRJUn7dDpwfRdE+wGVAu4xplRnDoRkxrgAej6Job2B8kxiSJEnSh2bxUJIkKb86A8tDCGXAqe/j+c8AJyb3PtwZGJ2Mnw0MCiEMTP7/fMZrugJLk+EzmpmvJEmS1MDioSRJUn5dAjxHXBSc9T6efx+wBJgB/Al4EVgfRVEFcB7wUAhhGrARWJ+85hfAz0IIL+FtaSRJkpRDIYqitHOQJElShhBCpyiKNoUQegDPA4dEUfRWxvgA3ADMjaLo2nSzlSRJUjHzyLQkSVLLMzGE0A0oB66IouitZPxZIYQvJ+NfIv71ZUmSJClvPPNQkiRJkiRJUlbe81CSJEmSJElSVhYPJUmSJEmSJGVl8VCSJEmSJElSVhYPJUmSJEmSJGVl8VCSJEmSJElSVhYPJUmSJEmSJGX1/8l3ONV/aKa7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 8))\n",
        "ax = sns.lineplot(data=temp_df_beras_sep, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras (Kg)', xticks=temp_df_beras_sep.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dq3H0xARYc5G"
      },
      "source": [
        "### Prediksi Beras untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SZLUncvWgOi9"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras = test_beras.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sinl3MAUgOi9"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nifPwmPrgOi9",
        "outputId": "b99f86b9-2a5b-4904-b15e-95f64bf9a64b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3UbI0IHegOi9"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7EXckbCzgOi9",
        "outputId": "940a5b5b-6c73-432c-8018-d49b4f5c426d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Beras  Prediksi Beras\n",
              "2022-04-20  12350.0             NaN\n",
              "2022-04-21  12350.0             NaN\n",
              "2022-04-22  12350.0             NaN\n",
              "2022-04-25  12350.0             NaN\n",
              "2022-04-26  12350.0             NaN\n",
              "...             ...             ...\n",
              "2022-10-26      NaN    12458.149260\n",
              "2022-10-27      NaN    12467.542583\n",
              "2022-10-28      NaN    12471.937861\n",
              "2022-10-29      NaN    12468.617157\n",
              "2022-10-30      NaN    12466.338070\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d2ac864e-cbbe-4927-944d-44312e50f498\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Beras</th>\n",
              "      <th>Prediksi Beras</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12458.149260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12467.542583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12471.937861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12468.617157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12466.338070</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2ac864e-cbbe-4927-944d-44312e50f498')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d2ac864e-cbbe-4927-944d-44312e50f498 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d2ac864e-cbbe-4927-944d-44312e50f498');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras,temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "h2-qcU_4gOi-",
        "outputId": "b450bcaf-71ce-467a-dc08-cfc1097480f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras')\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA7Ure9LCov_"
      },
      "source": [
        "# Predict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxsAWxH0bcuv"
      },
      "source": [
        "### Prediksi Beras Kualitas Bawah 1 untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eF6L7fRPbcu4"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras_kualitas_bawah_1 = test_beras_kualitas_bawah_1.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xiXAIk5Lbcu5"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras_kualitas_bawah_1.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-73yhh3bcu8",
        "outputId": "589e28dd-099d-44a7-ad46-2b811c0079dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rmpNQFpAbcu9"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "S6zOLn--bcu9",
        "outputId": "9ebf72ee-58a8-46c0-c0b3-256768e7b8a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras Kualitas Bawah I (kg)  Prediksi Beras Kualitas Bawah 1\n",
              "2022-04-20                      11100.0                              NaN\n",
              "2022-04-21                      11100.0                              NaN\n",
              "2022-04-22                      11100.0                              NaN\n",
              "2022-04-25                      11100.0                              NaN\n",
              "2022-04-26                      11100.0                              NaN\n",
              "...                                 ...                              ...\n",
              "2022-10-26                          NaN                     11316.285613\n",
              "2022-10-27                          NaN                     11339.275900\n",
              "2022-10-28                          NaN                     11345.227252\n",
              "2022-10-29                          NaN                     11340.474449\n",
              "2022-10-30                          NaN                     11343.986359\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <th>Beras Kualitas Bawah I (kg)</th>\n",
              "      <th>Prediksi Beras Kualitas Bawah 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>11100.0</td>\n",
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              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11316.285613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11339.275900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11345.227252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11340.474449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11343.986359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-488e10d9-deb9-4b98-959f-79e11fd531bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-488e10d9-deb9-4b98-959f-79e11fd531bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras Kualitas Bawah 1\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras_kualitas_bawah_1,temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_beras_M = test_beras.resample('M').mean()"
      ],
      "metadata": {
        "id": "NqFEl2dL0DPh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Cyw740vRbcu9",
        "outputId": "4b83f8c0-c1e8-412e-b336-11a0aa77e85d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras', xticks=test_beras_M.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tlB8GrCfOZD"
      },
      "source": [
        "### Prediksi Beras Kualitas Bawah 2 untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tmVjd3TFfOZN"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras_kualitas_bawah_2 = test_beras_kualitas_bawah_2.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MUeQYKdQfOZN"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras_kualitas_bawah_2.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ucok04z3fOZO",
        "outputId": "761c8f3e-8ee3-41d5-99ca-c76ec991b1ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PwSom9N8fOZO"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "u7-E8L-nfOZO",
        "outputId": "30a38eb0-310c-4cff-c1ac-dbd6ac2e31e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras Kualitas Bawah II (kg)  Prediksi Beras Kualitas Bawah 2\n",
              "2022-04-20                       11100.0                              NaN\n",
              "2022-04-21                       11100.0                              NaN\n",
              "2022-04-22                       11100.0                              NaN\n",
              "2022-04-25                       11100.0                              NaN\n",
              "2022-04-26                       11100.0                              NaN\n",
              "...                                  ...                              ...\n",
              "2022-10-26                           NaN                     11163.571344\n",
              "2022-10-27                           NaN                     11182.502633\n",
              "2022-10-28                           NaN                     11187.700370\n",
              "2022-10-29                           NaN                     11183.564240\n",
              "2022-10-30                           NaN                     11186.405172\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a974cc8-a9a2-44d2-b9c7-9d0715a0dcd7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Beras Kualitas Bawah II (kg)</th>\n",
              "      <th>Prediksi Beras Kualitas Bawah 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11163.571344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11182.502633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11187.700370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11183.564240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11186.405172</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a974cc8-a9a2-44d2-b9c7-9d0715a0dcd7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8a974cc8-a9a2-44d2-b9c7-9d0715a0dcd7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8a974cc8-a9a2-44d2-b9c7-9d0715a0dcd7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras Kualitas Bawah 2\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras_kualitas_bawah_2, temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "BcbrcQKpfOZP",
        "outputId": "f616d94b-1fbe-4d41-c945-877762c8ad71"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras', xticks=test_beras_M.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o3Iyeyh4f3io"
      },
      "source": [
        "### Prediksi Beras Kualitas Medium 1 untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZX_Yxld-f3iu"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras_kualitas_medium_1 = test_beras_kualitas_medium_1.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8atexsUaf3iu"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras_kualitas_medium_1.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jj093q-_f3iv",
        "outputId": "9d167bbb-7d97-4e92-8178-8c76e0e8001d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yTQFuYnUf3iv"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "P9pkFD7Uf3iv",
        "outputId": "d3fd7bba-6d1f-4a63-8ed9-17d241c4f55b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras Kualitas Bawah I (kg)  Prediksi Beras Kualitas Medium 1\n",
              "2022-04-20                      11100.0                               NaN\n",
              "2022-04-21                      11100.0                               NaN\n",
              "2022-04-22                      11100.0                               NaN\n",
              "2022-04-25                      11100.0                               NaN\n",
              "2022-04-26                      11100.0                               NaN\n",
              "...                                 ...                               ...\n",
              "2022-10-26                          NaN                      11316.285613\n",
              "2022-10-27                          NaN                      11339.275900\n",
              "2022-10-28                          NaN                      11345.227252\n",
              "2022-10-29                          NaN                      11340.474449\n",
              "2022-10-30                          NaN                      11343.986359\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ba9cb4f-d300-470c-961c-7795d01eac52\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Beras Kualitas Bawah I (kg)</th>\n",
              "      <th>Prediksi Beras Kualitas Medium 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>11100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11316.285613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11339.275900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11345.227252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11340.474449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11343.986359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ba9cb4f-d300-470c-961c-7795d01eac52')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9ba9cb4f-d300-470c-961c-7795d01eac52 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9ba9cb4f-d300-470c-961c-7795d01eac52');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras Kualitas Medium 1\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras_kualitas_medium_1, temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "LnBrJlgyf3iw",
        "outputId": "9ab8a753-a2be-4f64-f7af-4bd89721393c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras', xticks=test_beras_M.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sieSxDaXf3u0"
      },
      "source": [
        "### Prediksi Beras Kualitas Medium 2 untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WxisAfJlf3u1"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras_kualitas_medium_2 = test_beras_kualitas_medium_2.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rimdpelCf3u1"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras_kualitas_medium_2.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Rya_l8rf3u1",
        "outputId": "81552065-732d-4be2-f0a3-2a383a451549"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lhor9Fxgf3u2"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pV38pv0mf3u2",
        "outputId": "a3af269f-d5f4-46f0-e0e6-160864b7ec47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras Kualitas Medium II (kg)  Prediksi Beras Kualitas Medium 2\n",
              "2022-04-20                        12500.0                               NaN\n",
              "2022-04-21                        12500.0                               NaN\n",
              "2022-04-22                        12500.0                               NaN\n",
              "2022-04-25                        12500.0                               NaN\n",
              "2022-04-26                        12500.0                               NaN\n",
              "...                                   ...                               ...\n",
              "2022-10-26                            NaN                      12615.361288\n",
              "2022-10-27                            NaN                      12632.764103\n",
              "2022-10-28                            NaN                      12637.790701\n",
              "2022-10-29                            NaN                      12631.398803\n",
              "2022-10-30                            NaN                      12632.212451\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66914443-f026-472e-a5e0-b61a5a6ab7b8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Beras Kualitas Medium II (kg)</th>\n",
              "      <th>Prediksi Beras Kualitas Medium 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>12500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>12500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>12500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>12500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>12500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12615.361288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12632.764103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12637.790701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12631.398803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12632.212451</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66914443-f026-472e-a5e0-b61a5a6ab7b8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-66914443-f026-472e-a5e0-b61a5a6ab7b8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-66914443-f026-472e-a5e0-b61a5a6ab7b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras Kualitas Medium 2\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras_kualitas_medium_2, temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "3Q23Z6kXf3u2",
        "outputId": "5713b068-2951-4010-dfb9-69239a4dcd07"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras', xticks=test_beras_M.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQsmk31tf380"
      },
      "source": [
        "### Prediksi Beras Kualitas Super 1 untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QnbcsdWkf380"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras_kualitas_super_1 = test_beras_kualitas_super_1.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JFSoQyL1f381"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras_kualitas_super_1.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lqB7gx0f381",
        "outputId": "be4b482f-f57d-43b0-975d-0ff11d605ff2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zsrrbwa4f381"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "A7chhkN2f382",
        "outputId": "a1815f34-3c62-4532-b6ce-a5ceb6573212"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras Kualitas Super I (kg)  Prediksi Beras Kualitas Super 1\n",
              "2022-04-20                      13650.0                              NaN\n",
              "2022-04-21                      13650.0                              NaN\n",
              "2022-04-22                      13650.0                              NaN\n",
              "2022-04-25                      13650.0                              NaN\n",
              "2022-04-26                      13650.0                              NaN\n",
              "...                                 ...                              ...\n",
              "2022-10-26                          NaN                     13769.706491\n",
              "2022-10-27                          NaN                     13760.593362\n",
              "2022-10-28                          NaN                     13767.483564\n",
              "2022-10-29                          NaN                     13768.468951\n",
              "2022-10-30                          NaN                     13760.120171\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-703e2939-f555-4ff6-b159-e2d27b30c5f1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Beras Kualitas Super I (kg)</th>\n",
              "      <th>Prediksi Beras Kualitas Super 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>13650.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>13650.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>13650.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>13650.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>13650.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>13769.706491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>13760.593362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>13767.483564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>13768.468951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>13760.120171</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-703e2939-f555-4ff6-b159-e2d27b30c5f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
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              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-703e2939-f555-4ff6-b159-e2d27b30c5f1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-703e2939-f555-4ff6-b159-e2d27b30c5f1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras Kualitas Super 1\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras_kualitas_super_1, temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "S1umkCPOf382",
        "outputId": "3342f7ba-6bda-46ca-c850-8ea5c8de431c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras', xticks=test_beras_M.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nsfl_aff4WS"
      },
      "source": [
        "### Prediksi Beras Kualitas Super 2 untuk 30 hari kedepan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BroPAUkgf4WT"
      },
      "outputs": [],
      "source": [
        "#Ambil 30 data terakhir\n",
        "forecast_beras_kualitas_super_2 = test_beras_kualitas_super_2.iloc[-30:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LKk0e6oNf4WT"
      },
      "outputs": [],
      "source": [
        "#Lakukan Scaling\n",
        "scaled_forecast_beras = scaler.fit_transform(forecast_beras_kualitas_super_2.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmBm6LMjf4WT",
        "outputId": "fd129f08-8df9-4510-8040-70e58b001302"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n"
          ]
        }
      ],
      "source": [
        "#Lakukan prediksi\n",
        "test_pred_forecast = []\n",
        "\n",
        "first_eval_batch = scaled_forecast_beras[-30:]\n",
        "cur_batch = first_eval_batch.reshape(1,30,1)\n",
        "\n",
        "\n",
        "for i in range(30):\n",
        "    #Dapatkan Prediksi dari nilai pertama untuk tiap batch\n",
        "    current_pred = model.predict(cur_batch)[0]\n",
        "\n",
        "    #Append hasil prediksi ke array\n",
        "    test_pred_forecast.append(current_pred)\n",
        "\n",
        "    #Update nilai tiap batch\n",
        "    cur_batch = np.append(cur_batch[:,1:,:],[[current_pred]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OdGB9alIf4WT"
      },
      "outputs": [],
      "source": [
        "prediction_forecast = scaler.inverse_transform(test_pred_forecast)\n",
        "prediction_forecast = prediction_forecast.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ap1Q2-Maf4WT",
        "outputId": "20f16411-06aa-4f32-f4cc-6c0fd33a13e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Beras Kualitas Super II (kg)  Prediksi Beras Kualitas Super 2\n",
              "2022-04-20                       13100.0                              NaN\n",
              "2022-04-21                       13100.0                              NaN\n",
              "2022-04-22                       13100.0                              NaN\n",
              "2022-04-25                       13100.0                              NaN\n",
              "2022-04-26                       13100.0                              NaN\n",
              "...                                  ...                              ...\n",
              "2022-10-26                           NaN                     12855.428538\n",
              "2022-10-27                           NaN                     12863.433106\n",
              "2022-10-28                           NaN                     12865.033947\n",
              "2022-10-29                           NaN                     12863.310559\n",
              "2022-10-30                           NaN                     12864.589834\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ebc36e7-8a30-4060-afec-077ab3e52cf8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Beras Kualitas Super II (kg)</th>\n",
              "      <th>Prediksi Beras Kualitas Super 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-04-20</th>\n",
              "      <td>13100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-21</th>\n",
              "      <td>13100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-22</th>\n",
              "      <td>13100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>13100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-26</th>\n",
              "      <td>13100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12855.428538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12863.433106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12865.033947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12863.310559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12864.589834</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ebc36e7-8a30-4060-afec-077ab3e52cf8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ebc36e7-8a30-4060-afec-077ab3e52cf8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2ebc36e7-8a30-4060-afec-077ab3e52cf8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ],
      "source": [
        "#Buat DataFrame Baru\n",
        "date = pd.date_range(start='2022-10-01', periods=30)\n",
        "\n",
        "temp_df = pd.DataFrame({\n",
        "    \"Prediksi Beras Kualitas Super 2\" : prediction_forecast\n",
        "}, index=date)\n",
        "\n",
        "\n",
        "#Gabungkan Data\n",
        "df_pred = pd.concat([test_beras_kualitas_super_2, temp_df])\n",
        "\n",
        "df_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "ZxWGfisHf4WU",
        "outputId": "9570bd85-7873-4b9d-d3b6-8ee7dc5d2751"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x576 with 1 Axes>"
            ],
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AAPifmLXW3jkAAAD8q5k5t9b6bWbuq76uHl9rnRzEp3qv+n6t9e6+2QIAAMB/44tTAADgbvbZzFyo7qkur7VOtvhLM/PCFr9WXdkrQQAAADgtJ+wAAAAAAABgR+6wAwAAAAAAgB0p2AEAAAAAAMCOFOwAAAAAAABgRwp2AAAAAAAAsCMFOwAAAAAAANiRgh0AAAAAAADs6C//8AuomDl+IwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(27, 8))\n",
        "ax = sns.lineplot(data=df_pred, linewidth=2.5, marker=\"o\", linestyle='solid')\n",
        "ax.set(xlabel='Tanggal', ylabel='Harga', title='Tren Harga Beras', xticks=test_beras_M.index)\n",
        "ax.legend(title='Beras', title_fontsize = 13)\n",
        "plt.legend(bbox_to_anchor=(1.005, 1), loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "GOSF7pbXX5yy"
      ],
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}