lstm_approach.ipynb

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  "metadata": {
    "colab": {
      "name": "btp.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "038P0XRnMdww",
        "colab_type": "code",
        "outputId": "54b67558-645f-41ed-a779-65e62edc9d27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import Dense, Dropout, LSTM, Activation\n",
        "import keras\n",
        "import os\n",
        "import keras.backend as K"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Pm8GCD52Mdw3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "os.chdir('/content/drive/My Drive/btp')\n",
        "train = pd.read_csv('pred_train.csv').drop(labels = ['label1', 'label2', 'cycle'], axis = 1)\n",
        "test = pd.read_csv('pred_test.csv').drop(labels = ['label1', 'label2', 'cycle'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mUpmLfUkMdw9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ryLfQLGLMdxB",
        "colab_type": "code",
        "outputId": "21d85a11-14aa-4a33-fd33-01146a16b199",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "print(train.shape, test.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20631, 27) (13096, 27)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "klPwW6AtMdxI",
        "colab_type": "code",
        "outputId": "af57d4b2-a7f1-4254-9970-3541922abd07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>setting1</th>\n",
              "      <th>setting2</th>\n",
              "      <th>setting3</th>\n",
              "      <th>s1</th>\n",
              "      <th>s2</th>\n",
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              "      <th>s16</th>\n",
              "      <th>s17</th>\n",
              "      <th>s18</th>\n",
              "      <th>s19</th>\n",
              "      <th>s20</th>\n",
              "      <th>s21</th>\n",
              "      <th>RUL</th>\n",
              "      <th>cycle_norm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.166667</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>191</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.609195</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.283133</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.731014</td>\n",
              "      <td>190</td>\n",
              "      <td>0.00277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.252874</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.343373</td>\n",
              "      <td>0.369523</td>\n",
              "      <td>0.370527</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.710145</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.140043</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.795309</td>\n",
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              "      <td>0.357445</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.627907</td>\n",
              "      <td>0.621375</td>\n",
              "      <td>189</td>\n",
              "      <td>0.00554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.540230</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.343373</td>\n",
              "      <td>0.256159</td>\n",
              "      <td>0.331195</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.740741</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.124518</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.889126</td>\n",
              "      <td>0.294118</td>\n",
              "      <td>0.174889</td>\n",
              "      <td>0.166603</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573643</td>\n",
              "      <td>0.662386</td>\n",
              "      <td>188</td>\n",
              "      <td>0.00831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.390805</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.349398</td>\n",
              "      <td>0.257467</td>\n",
              "      <td>0.404625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.668277</td>\n",
              "      <td>0.242424</td>\n",
              "      <td>0.149960</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.255952</td>\n",
              "      <td>0.746269</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.174734</td>\n",
              "      <td>0.402078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.589147</td>\n",
              "      <td>0.704502</td>\n",
              "      <td>187</td>\n",
              "      <td>0.01108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   id  setting1  setting2  setting3   s1        s2        s3        s4   s5  \\\n",
              "0   1  0.459770  0.166667       0.0  0.0  0.183735  0.406802  0.309757  0.0   \n",
              "1   1  0.609195  0.250000       0.0  0.0  0.283133  0.453019  0.352633  0.0   \n",
              "2   1  0.252874  0.750000       0.0  0.0  0.343373  0.369523  0.370527  0.0   \n",
              "3   1  0.540230  0.500000       0.0  0.0  0.343373  0.256159  0.331195  0.0   \n",
              "4   1  0.390805  0.333333       0.0  0.0  0.349398  0.257467  0.404625  0.0   \n",
              "\n",
              "    s6        s7        s8        s9  s10       s11       s12       s13  \\\n",
              "0  1.0  0.726248  0.242424  0.109755  0.0  0.369048  0.633262  0.205882   \n",
              "1  1.0  0.628019  0.212121  0.100242  0.0  0.380952  0.765458  0.279412   \n",
              "2  1.0  0.710145  0.272727  0.140043  0.0  0.250000  0.795309  0.220588   \n",
              "3  1.0  0.740741  0.318182  0.124518  0.0  0.166667  0.889126  0.294118   \n",
              "4  1.0  0.668277  0.242424  0.149960  0.0  0.255952  0.746269  0.235294   \n",
              "\n",
              "        s14       s15  s16       s17  s18  s19       s20       s21  RUL  \\\n",
              "0  0.199608  0.363986  0.0  0.333333  0.0  0.0  0.713178  0.724662  191   \n",
              "1  0.162813  0.411312  0.0  0.333333  0.0  0.0  0.666667  0.731014  190   \n",
              "2  0.171793  0.357445  0.0  0.166667  0.0  0.0  0.627907  0.621375  189   \n",
              "3  0.174889  0.166603  0.0  0.333333  0.0  0.0  0.573643  0.662386  188   \n",
              "4  0.174734  0.402078  0.0  0.416667  0.0  0.0  0.589147  0.704502  187   \n",
              "\n",
              "   cycle_norm  \n",
              "0     0.00000  \n",
              "1     0.00277  \n",
              "2     0.00554  \n",
              "3     0.00831  \n",
              "4     0.01108  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "ffzzeNAnMdxR",
        "colab_type": "code",
        "outputId": "43a93c58-db36-4f83-f34a-dab7d5c86e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>id</th>\n",
              "      <th>setting1</th>\n",
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              "      <th>s20</th>\n",
              "      <th>s21</th>\n",
              "      <th>cycle_norm</th>\n",
              "      <th>RUL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.545181</td>\n",
              "      <td>0.310661</td>\n",
              "      <td>0.269413</td>\n",
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              "      <td>0.558140</td>\n",
              "      <td>0.661834</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>142</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.146684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.386905</td>\n",
              "      <td>0.739872</td>\n",
              "      <td>0.264706</td>\n",
              "      <td>0.204768</td>\n",
              "      <td>0.213159</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.682171</td>\n",
              "      <td>0.686827</td>\n",
              "      <td>0.00277</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.517241</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.376506</td>\n",
              "      <td>0.346632</td>\n",
              "      <td>0.322248</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.685990</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.158081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.386905</td>\n",
              "      <td>0.699360</td>\n",
              "      <td>0.220588</td>\n",
              "      <td>0.155640</td>\n",
              "      <td>0.458638</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.728682</td>\n",
              "      <td>0.721348</td>\n",
              "      <td>0.00554</td>\n",
              "      <td>140</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.741379</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.370482</td>\n",
              "      <td>0.285154</td>\n",
              "      <td>0.408001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.679549</td>\n",
              "      <td>0.196970</td>\n",
              "      <td>0.105717</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.255952</td>\n",
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              "      <td>0.250000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.662110</td>\n",
              "      <td>0.00831</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.580460</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.391566</td>\n",
              "      <td>0.352082</td>\n",
              "      <td>0.332039</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.694042</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.102396</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.273810</td>\n",
              "      <td>0.737740</td>\n",
              "      <td>0.220588</td>\n",
              "      <td>0.152751</td>\n",
              "      <td>0.300885</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.658915</td>\n",
              "      <td>0.716377</td>\n",
              "      <td>0.01108</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  setting1  setting2  setting3   s1        s2        s3        s4   s5  \\\n",
              "0   1  0.632184  0.750000       0.0  0.0  0.545181  0.310661  0.269413  0.0   \n",
              "1   1  0.344828  0.250000       0.0  0.0  0.150602  0.379551  0.222316  0.0   \n",
              "2   1  0.517241  0.583333       0.0  0.0  0.376506  0.346632  0.322248  0.0   \n",
              "3   1  0.741379  0.500000       0.0  0.0  0.370482  0.285154  0.408001  0.0   \n",
              "4   1  0.580460  0.500000       0.0  0.0  0.391566  0.352082  0.332039  0.0   \n",
              "\n",
              "    s6        s7        s8        s9  s10       s11       s12       s13  \\\n",
              "0  1.0  0.652174  0.212121  0.127614  0.0  0.208333  0.646055  0.220588   \n",
              "1  1.0  0.805153  0.166667  0.146684  0.0  0.386905  0.739872  0.264706   \n",
              "2  1.0  0.685990  0.227273  0.158081  0.0  0.386905  0.699360  0.220588   \n",
              "3  1.0  0.679549  0.196970  0.105717  0.0  0.255952  0.573561  0.250000   \n",
              "4  1.0  0.694042  0.166667  0.102396  0.0  0.273810  0.737740  0.220588   \n",
              "\n",
              "        s14       s15  s16       s17  s18  s19       s20       s21  \\\n",
              "0  0.132160  0.308965  0.0  0.333333  0.0  0.0  0.558140  0.661834   \n",
              "1  0.204768  0.213159  0.0  0.416667  0.0  0.0  0.682171  0.686827   \n",
              "2  0.155640  0.458638  0.0  0.416667  0.0  0.0  0.728682  0.721348   \n",
              "3  0.170090  0.257022  0.0  0.250000  0.0  0.0  0.666667  0.662110   \n",
              "4  0.152751  0.300885  0.0  0.166667  0.0  0.0  0.658915  0.716377   \n",
              "\n",
              "   cycle_norm  RUL  \n",
              "0     0.00000  142  \n",
              "1     0.00277  141  \n",
              "2     0.00554  140  \n",
              "3     0.00831  139  \n",
              "4     0.01108  138  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "ULsqiInQMdxX",
        "colab_type": "code",
        "outputId": "4f9190e1-44cf-4f5d-ebaf-6b6e3434c54a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "print(train.shape, test.shape)\n",
        "train = train.loc[:, train.std()>0].reset_index(drop = True)\n",
        "test = test.loc[:, test.std()>0].reset_index(drop = True)\n",
        "print(train.shape, test.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20631, 27) (13096, 27)\n",
            "(20631, 20) (13096, 20)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "srqOKGG6Mdxc",
        "colab_type": "code",
        "outputId": "14396196-b795-42ed-ee35-c7a286a1d1bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>setting1</th>\n",
              "      <th>setting2</th>\n",
              "      <th>s2</th>\n",
              "      <th>s3</th>\n",
              "      <th>s4</th>\n",
              "      <th>s6</th>\n",
              "      <th>s7</th>\n",
              "      <th>s8</th>\n",
              "      <th>s9</th>\n",
              "      <th>s11</th>\n",
              "      <th>s12</th>\n",
              "      <th>s13</th>\n",
              "      <th>s14</th>\n",
              "      <th>s15</th>\n",
              "      <th>s17</th>\n",
              "      <th>s20</th>\n",
              "      <th>s21</th>\n",
              "      <th>RUL</th>\n",
              "      <th>cycle_norm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.459770</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.183735</td>\n",
              "      <td>0.406802</td>\n",
              "      <td>0.309757</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.726248</td>\n",
              "      <td>0.242424</td>\n",
              "      <td>0.109755</td>\n",
              "      <td>0.369048</td>\n",
              "      <td>0.633262</td>\n",
              "      <td>0.205882</td>\n",
              "      <td>0.199608</td>\n",
              "      <td>0.363986</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.713178</td>\n",
              "      <td>0.724662</td>\n",
              "      <td>191</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.609195</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.283133</td>\n",
              "      <td>0.453019</td>\n",
              "      <td>0.352633</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.628019</td>\n",
              "      <td>0.212121</td>\n",
              "      <td>0.100242</td>\n",
              "      <td>0.380952</td>\n",
              "      <td>0.765458</td>\n",
              "      <td>0.279412</td>\n",
              "      <td>0.162813</td>\n",
              "      <td>0.411312</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.731014</td>\n",
              "      <td>190</td>\n",
              "      <td>0.00277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.252874</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.343373</td>\n",
              "      <td>0.369523</td>\n",
              "      <td>0.370527</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.710145</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.140043</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.795309</td>\n",
              "      <td>0.220588</td>\n",
              "      <td>0.171793</td>\n",
              "      <td>0.357445</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.627907</td>\n",
              "      <td>0.621375</td>\n",
              "      <td>189</td>\n",
              "      <td>0.00554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.540230</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.343373</td>\n",
              "      <td>0.256159</td>\n",
              "      <td>0.331195</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.740741</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.124518</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.889126</td>\n",
              "      <td>0.294118</td>\n",
              "      <td>0.174889</td>\n",
              "      <td>0.166603</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.573643</td>\n",
              "      <td>0.662386</td>\n",
              "      <td>188</td>\n",
              "      <td>0.00831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.390805</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.349398</td>\n",
              "      <td>0.257467</td>\n",
              "      <td>0.404625</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.668277</td>\n",
              "      <td>0.242424</td>\n",
              "      <td>0.149960</td>\n",
              "      <td>0.255952</td>\n",
              "      <td>0.746269</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.174734</td>\n",
              "      <td>0.402078</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.589147</td>\n",
              "      <td>0.704502</td>\n",
              "      <td>187</td>\n",
              "      <td>0.01108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  setting1  setting2        s2        s3        s4   s6        s7  \\\n",
              "0   1  0.459770  0.166667  0.183735  0.406802  0.309757  1.0  0.726248   \n",
              "1   1  0.609195  0.250000  0.283133  0.453019  0.352633  1.0  0.628019   \n",
              "2   1  0.252874  0.750000  0.343373  0.369523  0.370527  1.0  0.710145   \n",
              "3   1  0.540230  0.500000  0.343373  0.256159  0.331195  1.0  0.740741   \n",
              "4   1  0.390805  0.333333  0.349398  0.257467  0.404625  1.0  0.668277   \n",
              "\n",
              "         s8        s9       s11       s12       s13       s14       s15  \\\n",
              "0  0.242424  0.109755  0.369048  0.633262  0.205882  0.199608  0.363986   \n",
              "1  0.212121  0.100242  0.380952  0.765458  0.279412  0.162813  0.411312   \n",
              "2  0.272727  0.140043  0.250000  0.795309  0.220588  0.171793  0.357445   \n",
              "3  0.318182  0.124518  0.166667  0.889126  0.294118  0.174889  0.166603   \n",
              "4  0.242424  0.149960  0.255952  0.746269  0.235294  0.174734  0.402078   \n",
              "\n",
              "        s17       s20       s21  RUL  cycle_norm  \n",
              "0  0.333333  0.713178  0.724662  191     0.00000  \n",
              "1  0.333333  0.666667  0.731014  190     0.00277  \n",
              "2  0.166667  0.627907  0.621375  189     0.00554  \n",
              "3  0.333333  0.573643  0.662386  188     0.00831  \n",
              "4  0.416667  0.589147  0.704502  187     0.01108  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "vM4WXC08Mdxo",
        "colab_type": "code",
        "outputId": "35bb81c8-f780-48e9-ecfe-0c4c835083ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        }
      },
      "cell_type": "code",
      "source": [
        "data = train[(train.id==1)].reset_index(drop = True)\n",
        "\n",
        "fig = plt.figure(figsize = (15,10))\n",
        "fig.subplots_adjust(hspace=1.2, wspace=0.8)\n",
        "for i in range(len(data.columns)-1):\n",
        "    ax = fig.add_subplot(6, 4, i+1)\n",
        "    ax.plot(data.loc[:,data.columns[i+1]])\n",
        "    ax.set_xlabel(data.columns[i])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EvbHszWz011x+KDgrqsjYK8uA034jKFmvwp1X9sGXv51BSaUFz7y5BQBgSJBD\nwUT+w1ubHDBolNzN25So5gw7t90Js8MFRzMlWvUqGZQhmot7CY++udaGj789AgDITdfzDLtfdhZj\nh6AXztWjusNm5aefVtX4end9v72Yt92UlIBSf48tsf5j7OLx+DlfTaDJoOI8n6+u2AVzrQ05aTrR\n/li0L1bnhTXy3W6vqJw/yY87S6L+XgmCBYXCUVYVHLHyKd7V8raJ1eGx41bIpTzlWC/DcM6MJJ2K\nSycWg3UKJajkkEiC00uEEbsMo4ZrQ2B3evDV1rP4TjDngMC8epuog/V4Amq+6ckarg5XDJlUEjZi\nl6hVimYC0D6U7UNHFWOYu2wrbA43V+NSUduE9btKcOukvLg19oTROBYbL2IX+Dfp7LA73VApZSg4\nXI6jxbWY/8FO3Httf/z7h+MoOFyB+28YiE37SrGtsBzPzxmJRK0Se45X+tubtGydI6yvp7QvsVoH\ns5hMepjNjWAYBr/sLcWgnkak+/sUPvb6rwD4CuUPvbqR93lyrdWS/rDvfFEIpVyK6WMDzbrLKhpQ\nfL4OWamaoFo7h9MDpULKc5yKCe0Jqa0LnP/Dr/6C956dCrO5kRdprKhsgFQiQRUhGMb2XU1NVKOq\n3o6SC/XQKQJjOkZoXNiINeuOQxdEx7Hy+/BBGwBosjpg8N8PX/pgB9RKGa//NQn7m8uIHKX27A8b\nMQ8wXK+dxsZG3HvvvXA6fT/Url270Lt376j680SiPQN2g3oZAYg3M89MCfxhNCo5hvcxcduK/MZR\ngloOiUSCbJM26PMkbg/DaxeQQXw3e4FHaoyaLuhMn5WiCZniJZdJ8dZjE7nXDONTpsxK5Y+TbcIO\nAH+8fgBee2g8hvVODRIzYWvsdhz1KdeM7JeGx2cMBTk3o+mflZKYAJn/PHce9UUbNHG6QIg1ixYt\nwowZMzBz5kwcPMhPL54yZQp+//vfY/bs2Zg9ezYqKioifqY5sLUdFn9xdZ82yiF3ecTVXQFg9lV9\ng7ZdEKQi/uvJyVyPvUiwgkNyGT+lyeMJROzEPPCvPTQej9zCX2TLpBJ88PSUoDGSht2saX2w8L5L\nubnncHlCijOJ1dh5vF4ubUSbIMcDNw4MeW5qpSxsxG5U/zS88sDYoO3R1hZSoocUVli4cKFo24Ll\ny5dj5cqVWLlyJdLT06P6TFsgFN16e10hNu4txfc7zrXL8aKloqYJF0ScNi2FjKCEUsMlf4tTpfX4\n788nsW7zaU7NEgika5Jpm2ytz4mSOsz/YAe+3nYW1Q12vPtFId79shBvrSvEx98fi+gAi2bslPYn\nVutgwKdKvPCjHSgub8TJ8/UaaCACAAAgAElEQVT49KcTWPDRzqD9wmW1kLVfYqJ6XoZBncXBBR6E\n7DpWia2F5bzrtaCwHAs+3IlVv/D7LjtcHjz4j1/x+uoD3LZP1h/Ho0t/a1Z/1wtVVu65T253+Z0w\nTbxoum/b+MGZ3DmSc4SNrgP8LKJw6c6R0Kp9NXYsdqfHJ/RIcEnvVNx3/QDuNbt/ewsHRlxdR+q1\nM3HiRMyYMQMqlQoDBgzA1VdfDYlEEvSZ5pKgkuO+6wZg+TdHWnRi4Rial4rC0z4LXgJ+nU8WEaZl\nvZNCD3s3k29yPjdrBJZ/fURUVh3wLcSMhGGXIPLHjFQ7lJWqRQXhHTRolbwUr745STjuF2ZRyKVI\nUMnRp1siVwyqT1CGbYswZmCgEFUYwWRVMessTqQnJ+BP/oLS/0PAMKxtjNwItXuajuddAcQXx5TW\nEWvlLrf/ps2mKSaJ9EBsCU6XN+Q8UYtc20LjRSaVRF13woqnyAURO7fHy9W8aVXBKZYatTyo5yPr\nzBD2jFQSnkSFzOfVZM/D4fSEVOTyeJmg38FDpL1q1YqQDcgB38MklKASOxYF8XsmqOSwOdxh++JR\nWkZnEGNwe7yQy6RcxDZc+vHF4Nl/bQcg3jtViNvjhdXmQqIu9D2IdFiETMUk5ktZdRPndLl5Qk9u\nO2vQkX0zybpUMip3XCCiJnwuRou7gzWIjnditQ4GgK+3nsX2wnKcvdCA3030pd86XV489c42zLkm\n0B7FYnPhx53nME2khRSZyvjQ65vxygNjeX0bH359M3cd/+vJySEDDuS1fNgvIlJQWI7fT+0DL8PA\n7fZyx2LX2AC48gCrzR1Uo04ae8K2R9X1NkgAXo9pu8sDlVLG6ynLRtMT/QKATXY3L8JuCdGjmmVw\nr5SoetSyLLh7FLRqRUiH8asPjoNKKQtqO9I9XY8HbxqEHu0sHBjV6jpcr505c+Zgzpw5ET/TElqq\nmviXu0ZAJpWElAo2JQVq0QyC9KNMwrBj/yikYXTt2B4YP9hnDCWo5Oiergtr2KUYAsca3T8d67ac\nwQ3jc7ltkRxvky/Jxr6TVVApZLj98jxIJBLexDAS389eZA/eNAiPvbkVgM+rQC4ko0UmlcDS5ILb\n40Wj1YlMY6D+jrT/IqWS9O+RjBF9TThYxJ801LBre2K9WHQLiqpDRZabi8vjhUfEuJDLojPYfKIC\nURp2djZix/+Mkyga1yYEX7tymSRoO/t54bHJ+4nCPzfJiF24OSUUovF4AoqYGrU8bAaASiELe7+R\ny6S8B5VaKYPL7fX3w2x7aeuuTFVVFQYODERXWWEFct4tWLAApaWlGDFiBJ544omoPtOW2J0e6BKk\nULC1s53IwP+/jafw8+7zGD8oA93T9Vy/1Ca7C1/8egqj+5h4LQ5CyZTbQpSEkHW8320vxqRhWSgn\n0sXOm6OLLFrtbnQz6VDbaBed91OGZ4vWzIrdDyntS6zWwayTwO3x8oIQVfV2FBFqjl9sOY1N+y/g\n2LngdPx/ruFn4hSercH4QRlQyGU8FWvAF9ESOilZSFERNkLoZRiUVVux4ofjOF5ShydmDAt5Lnan\nGyqFDDWNdm6tTQqJCA27iuom/LDtDDYfCKRMNtldqLc48Pmvp7ltTrcHMqkEOn+/VZvDHTIaLuag\nmnN1X05hMxIyqQQ9MnypkkqFDCqlDA6nB3nZBhSV+iKDKYTehZBR/dJCvtdWdOjVtVCYI1p6Zhgg\nlfoMILE/Itnt3WhQcYbdQzcP4v1BdP7IGBk2vXJUDm+BI5brfsP4XHy19SzGDEjn6vUAIN2owftP\nX84TIw4VPv/d5HzkpGowuFcK3n/6cl40jVwwGw2BCcgqbCbqVEhLTkBlrQ0atRy5GYF83Zf/cCkW\nrtwdsb9WWnICyqqbsGlfKRi0PPoyfnAGJBJJkBCERiTqQWkdsV4sChd+2jZqkrtu82nIRQyznLRg\nYZ9QRBuxYz30CkEqptNv4ACARqRZt1QiCWrizRp0wmOThp3SXyivJCJ2Ys3aWYS/sYcQT9GqFREN\nu3DI5VKeE0ilkMEud+NseSMWf7IXf7lrZNjPU1pOe4kxAC0TZAAAjU4Nk1EDlVIOwIH6JheMRi2q\n6+0wJSdErJcR44LZgjc+24dpl3bH1NE9In+gmWNmOX3BlxK1tbAcWwvLccc1/SGRSPDYG7/iVEkd\n1LcOxYh+6dz+SpUi6HvrLQ786yvxjKEyomn4rmOV2EUIGgHgatCj4YZJeXA4Pfjgq0Le9hH90jD3\n9yN4ht2UkTnYuKcE10/KhyklfCkIJT6o8xs+ugRF0HqRdEjU+lXQz5QF124Jr8cVPxzHih+O44kZ\nw4KeNw1WJ8+wIyNqtYTSOlv37vEy+MvyQC9poROfxObw4Kutx7GtsBzz7x6J3AwDT72dHcuQvBQc\nLKpGWbWVZ9QBwF+W7whylhaVNkCtlHEBA5vDHbKPs1jELpTQmRjCgNP0MT2wbvNpDOqZguvG5oZ0\nEl1MOrRhF21djBB2IZUYwrBLJYw3XxNi30S4pI+JJ/HP9pxQEgsiYWjVoA28HpqXgnSjBjde1hNT\nR+ZAl6DgDLJrxnT3jU3wMAyZYqaSY2h+quhn0oi6uyQi1YRc1LGf0KoVSEvW4LbJeTh6rhYZKRos\nuHsUnnlvu+hxuWMk+Qy7//zsU/gj++Y053GeqPWNTyb4W8rlVJK5vbnYyl1CgZO01LZbeKwm6kIB\n4MaJebjl8nzsPxlZPtpk0kMXJiULCDQTd/kfnEajFnbifKRSKSDxRQjFFlRpaYag306vU8Fk0iM5\nsYG33UA8NFNTtNyCUqWUwcMwvLQTYc87QyK/OFupkkPudy6lm3SiRieLLkKbBmOSBhnpBkglPkVb\nnUYBm9MNm8MDc72dChu1IRdLjAGILMhAQi4cS8vqIfV4uAyNw6ercef872G1u3HX1X0x2d90l2EY\nmOtsSAshHECydNV+HD1bg6Nna5CVnMBzsoaDTJUsr6iHTCpFTYMdBYfLcdXo7kEODbWgv9b5C3VQ\nymU45U+FrKu3oawiEO2orWsK+i2+2nom5HhOl9aHfA9AUD+r/G6JqG1woLohuHShd4bO54RuzONU\n/ACg0epEVVWg5umZO4ejT04SZk3tDXi9zRbBoPO3c8JmglhsrqBaUFJevyWOltdW7Q/aJhTLItsH\nkdevx/+8E7YXcrgCBtWLH+3C9USG2ptrD3Hfsf1wBXIzDLw1OmvYZaVocbCoGsv+L3h8QLBqPeDL\nMGDLp5oc7pDBC/Z4ugQF7rt+AOosjmb1+btubK7gdQ/0SNejf4+kNu0X2Bo6dG5NOO/zmIHpmHlF\nbyy879KQ+yTqxBcyCrmM80iQNXBSiYSL0gFExI4w7IRG1rD8wIP1od8NxswrekMikXAG4KBeKXjp\n3tG4dVKe6FiyBYvfsQN9XsSeWaEbAieo5Lj7mn5IMajQNyeQIkn2xGMve7a/xzVjeuDx24dBKpGE\nrTt45s7huPuafkg28EPJpEqpMEf6qtHBOd0s7O8gjNhFISpKaSbRLBZTUlIgl8tbrdxlNjcG/RfU\nZ60dRTduHNcDbocL9VHUqJjNjXCEiYIBgch7vd972GRxwE58xtLkgNXmhEIuhd0W7Cwymxt5izAA\ncDjcMJsbYRWozTLEQ8lmdXC/n1IuRW2Dg5ciJlz4ni/jLygtFgfq/L9Bk9WBxobQv0ekm73b6UJV\nlYW770olEu5+J5dJRP/mlJYRSzGGcJAGFHvPlxMZKmy64LbCgKry9iMVeOa97di493zE7ydrzwoJ\npbpIkCmR7EJy6ZqD+PzX03hr7SFO2h0ADhZVBanD2hz8SLjT7cHXW89yr/ecMOPfPxzDih+OYfex\nSjRYnVGnU4Yij3iGqxUyrgQk26TFFcO7ce/51HMlmDYqB9eO7SHacxMIPMspXYu5tw5Fn+5JqLM4\neO03AH6v4VDNuZvLtsIyfPDNETz1zjb87eNdPMEito8zEDrbjEzrLK5oxJtrDwU+T8xTtgcyWf/H\nzlEyE605aAjDrskh/sxnDbtnZw3H4F4pmDAki/c+K3Zy2eBMngDc1aO742/3jMYlffjrI4lEgiF5\nKR3GqAM6sWHXzaTDtFE5vJo4IYN6pnD/HtjTyHvP5L95GgUGDGm4BWrsQo9Do5bjvusH4LbJeSHH\nm23ShfSmDMg18pTs7r6mP56dNRxjBmWGPCbga1T+6p/G86J3vOOHCbyoFDJo1XIMyUsJeq9PThIm\nDs0KikxeRlz8c67ux4vahfsbJPmNa+Fv425Bo2RKeGK9WBSmCV6MOspQDxfh9RspFZMVHWEfBnJ5\ncI2dy+2FUi6FMsoUcXnIVExCPIV4GKgUMl6qCxBs2AnrBjxehlvoKuRSnnNHSKRUTPZ99idVK2Vc\n1kRLsyco4pBiDC+//DInxvDTTz9Br9dzYgwzZ86E0WjE1VdfLfqZtsbhJtXzPNi0vzRI6Q0Abw7s\nPeGLmn8r0qJDiNXm4ubVcZFaoOp6O06erwvabifSqljjs8Sv4HegqBrz3t7GpV69sTpY2dfmcPMM\nO5vDje1HKrjXpWYrft1/AZv2X8DbXxRi7rLfUBQhKic2n0i15+7pgQiZWinDteNyAfgWiEoleQ9g\nRZakuGVSHv58yxDoEhSYdWUf/pdTvZQuiVQqQUpiAtweJqhHK2kUkUqtLMPyU3HduOalPO886lPA\nrKq342x5I94n+jWTDhRniJRDYbQ6FOcqLD4NB8I4DRh2oWvUwpHApmLa3SGFwhqsvmOEeh4a9Sp8\n+MwU3HNtfzwzawS3XamQolta+4qetBUdOxUzzCIlmpSxK0d2Q4pBjUG9jPjtYBkOEx5CU1ICTpyv\nD5tbK1wchmIsoSzZEkgDVCGXone3pDB78yHTRBUiHr1QBuWyuRNFt7PoiXMX1vilJKpx/40D8e6X\nhwGAJ/mqS1DAYnPhhvG56N8jmYsOCnOio03BoURPLJW7gGBhD7LmTKyfW1sQKpU5SafkLeQiiaew\nCzI2fUNYY3fqfD0SVDIkqORRe+ZYD3s48RRygaxWyoLORzhPth/h95/cuK+Uu08p5LKwzrBIBilb\nS8zeW5UKGVfn3JG8kfFCrMQYxFi7uQhGvZprBQQAh8/U4Kfd4v0mlcT1oPfXfNc0OLD7WCVG9DXx\nnjv1Focvi0WjQJPdjfxuiaisteHk+WDD6al3toEBsGzuBN79g0yr2nzgAvrkJEEqkXDpYICvEXMo\nwTWb081zAllskdsMsE6W0f3TuDY9JN1MWq4FEkuWSYtT/vMiWxWplXIMzDXizbkTkaCS4ZttZ7n3\nhM/onDQdlj46gXv94j2jceBUFXqFyeKhxDesxkGQYRcmE2Xi0Ez8fmofbNgTOZIeDjISSEbcLKHE\nSZrCZ8ewuD1eXKiyitaUC8VbMowaTBudgxU/HOdtH9TLyFPfZJ/je06YoVaJP7PYZ2y0LQcUcilc\nbm+HqJ2Llg5t2AnT90iiUfuVSCQY0dcXNhWu60z+m65WrcATM4aJGnGslzpU89K2IpwB2xzIica0\noXtPrJE76cEnU14NWt+COjNFi77dk7nt5IIzO5WfikJpO2K5WBRG7HhpzQkKnmcOYBtmt+46zc0Q\nX+wk6lS8VCrSSJPLpEFj5XLzOVVMaZBBZnN4YNCqws7XDKMG5TW+miZWZEmoJkk2eSa/S1gfl6CS\nYXT/NN6D+efdwQ9p9sGolEvDGnaRUrm4iJ3/b6JWyLh7cEuUdSmdg1Ol9fhmmy/aRpY2nCoNjpyx\nkNethYgavP1FIZ6cOQz9cwMG4lPvFsDl9mLpoxPAwHcv0KjkOFBUjc9/LcLQ/FTkZ/tSnti7wdpf\nT+Pw2RqoFDI8dvtQnhDC2s0+NTzh+qCu0cErrSCxOdywE8ZhjUit2+RhWdi0ny/UMKpfGh64cRB2\nHv0laP/M1GDDLjuVMOyMGu4exy4i2SyGSNFzkpw0HXI6SaSA0j6wWgXmOv512ximBUlediKUChnU\nrewZzO+B5xHdTlJvjT4ltKTSwos6sgj7HKckqpEgIlSYIojskc+/rYd8TtBQ6wylwFmpVcthtQe3\nY0hQyeFyOyMKDnYkOvTTOlwxKNm/6o9EA8Bov2vyJdm4aUJPDOplxMCeRk6+FADysn2LRbbPB/sA\nMTRDOac5hDNgo6GHP+WDDI0n630XvL6FyoSspz4xhOAC+WBn90nUKrnfSOgNISfcFSO6Ra1SSOk8\nCPvHkTddMVEPnch8mjYqR7QPTyh6ZOix+P4xePXBcXj+rpFI8HvphD0jyetNI+LJYx9+rDdPzLAD\nEDEVc/7dAeVI9uPCa51Mnybn0Z9uHsT9+6YJPbHwvjHo3S0Ji+8fE9VvooyQiikUMBLCReyI13Ka\nihn37DwaSEkkhRDE1PVYdh2rxJ7jvhTMOsHikq3DYz3cbOokm7KpVSu45+23BcX4h4iAw8Z9pais\ntaGk0oI1m4pwuqwhaB/hM73W4ggSfmA5VlyHt78IqE6Shp1SIUX/Hsm8nq4sbK1Pkki9fqpIuhh5\nz8vN0HPPUaFztL0bFFPiC9Z5bq4VpmKGjo6xyuNi/ZPbE7J9QcR9rU7Rc1AKHB8qhUzUGSK27T6B\nPcD2nRYiXHf/7d5L8cgtQ4JKi9gyDXuI1icdkahM+UWLFuHAgQOQSCR47rnnMGTIEO697du34x//\n+AekUil69uyJhQsXYteuXXj00UfRu3dvAECfPn3wwgsvtNmgJ1+SjUnDAjVfYwZmoMRswffbz4VO\nuRJsNmiUuGF8T9FdH799GKob7FyK4cShWaisteHy4dltMv6goUkkUCtlyMtOjLyzCHNvH4qvt57B\nNZcGcqkfuHEgvt1WjN9N6tWi75wwJBPl1U2YMkL8nBW8FDI5Fv1xDHQJCnyy3hcqFzaOJqMF4aIK\nlM6LMApGXiNidar6BEXQQyA9OYEnfxwNrBqfr1WJJOjYAD8dMkGtCEoXET785HKpqPMhkvFEtj9h\nnYTCexKZukw+wMg2Jkk6Fad4m5asCdt4nEURYsws8gh96IRzlux719LWM5SOy5kL9fht73leFJjs\nUxWJt9YdwvKnJqOu0QGlQsoZhU0ON3YercC7Xx7G3NuGcvvv8Ne0aRPkyCdECexOD9web5C6Hsuu\nY5WiDcSFV3pto4O3SCQ99b/u5/eCq/Y3E79iRDfcfnkeZDIpquqChYeMfgfpy3+41B+xV+D+Jb8C\n8Am41VocqG10cBLvpGBKok4FhUwKBzxwCdQMhQtXCiUcbBRJmKofLt+FLTNKaGXErrlEk4XTK8uA\n0xcacPhMDSprg+ed2PO7X48kTBmejeMldSg1W5Gs52fPXH2pT3l+7MAMbDtUhsN+AaXrx+fihx3n\nYNAqOecSEOwYStarRPv33TyhF9776jAmDs0Keq+jEvEvvnPnThQXF2PVqlUoKirCc889h1WrVnHv\nz58/HytWrEBGRgYeeeQRbNmyBWq1GqNHj8bSpUvbdLDZJi1evGe0aGrgJfkmfL/9HG6eKG7IiH0m\nFAkqOc/Kl8ukmHlF7+YPuBm8+djEZrURIEnUKjFrWl/etiSdCndO6xPiE5GRy6S4Y2r058wuVtkH\noTC1lTTmaKuD+ESsgbEEvoeP2EJGLP1ZIpU0a66GQlgTRn6nMM0DCH74KWTiTc0VcmnUizL2ISw0\nttg54juOuMEkjApG4wxRyGUI99NFygwQej+VCilXb9cWfxNKx6HJ7sYTb/4WZDBdaEb/NcBXe1pv\ndSIrVYsbx/fE0s8Pwmp3YZ0/XfI7QlCFFUXRqhUYmGvE4zOG4h+rDgAAHlv2m2iDbgCiRh2AIMvu\n24JifFsQOF6SToU7pvbGm2sPBS3i2NROsmY2SUQtmhVx0KgVXNbBPx4ej7NljUhL1mDO1b5Ud4bx\nFT8wDIPLhmRizACfujW78Ax5DpROQywDHGwqZnNgjcGLbdiFI9ukxSO3DIHb48Vflu/A0WKf8cVq\nM7AIn38Mw0CtlGPWtL545T97AfgEEMnn0u2X5xPH0XGG3bDeqRjex4SSSgvPsIuWSwekY0RfU6cK\nSEQcaUFBAaZOnQoAyMvLQ319PSyWgKz32rVrkZHhS2EwGo2orQ1WumorlHJpyAVGfrdEvPXYREwf\nI64A1NGXJVKJpEV9SGKFM8SD6qpLu+P+GwYGqQeRDaYVMuqtjEeE4ilAwCumEon46DXB6U0SRBY6\nCQeXiqkKk4opotYpNPbkMqlo6qJCLos6LZH1XArPhoxeh4r+CVtHRPNQkYcwRgPHjS4Vk9tfKuE8\nwp3o1kSJgpLKRlFjg+1bKoaYY+BcpQUutxdGvYrrt9Vkd3MpkSdKAnV6bKq2Vu2T9x/UM4VTqxYz\n6pRyKVcWIUaoCB+Ll2G4SLyYQAPAv08oFTIYtEr075GMxfePwQ3jczG4lzHoM0k6FYb1TuVtk/hb\ng8ikUtwzvT8G+GsMOcNOMJ9DqflSOiZkgGPhwoVYuHAh7/358+dj6dKl+Oyzz2C1WrFlyxYAwOjR\no7Fy5UqsXLmyVVlrhhCtu4SQZUlsxC6abA/Ap6AppAeh7Dp5mHjE6rLBmbjer/gajtH90/DcrBEw\nJSUEGap/vCEwbgl882ZgbkCjgQxUzpjSG9mpWtw9vT/nPBU+99KJrBjWZiCftWTJRDR0JqMOiMKw\nq6qqQnJy4Ac2Go0wmwNWLyuNXllZia1bt2LSpEkAgFOnTuGBBx7AHXfcga1bt7Z6oAkqOS9VKdQ+\noRje1wRTkjoo/5bSMtheO8IHb6JWiUv93koScmIoaMQuLnF7vOiRrseIPiY8O2s4gIBBEG3EjmH4\nhk+SToncjOgb6869bSiG5qXgunG5uHx4Nu70S4bzUjFF7hNaYYRZLg0SXAL8LQCiFBJh+9UJl3BS\nqQR3X9MP00blBEXznps1AsP7mDC6P38ORSOwJIngHIqk8iuM2EklEhqxa0cWLVrEtTQ4eJAv0b99\n+3bcfvvtmDlzJp599ll4vV7s2LEDY8aMwezZszF79my89NJLLT72uQqfc5ZUbiS5/JLgFHxhDS0A\n/NdvCKYkqrmIVlV9sDAJCTnXksP0VH39z5fxekx1M4VuqyOG18tw8uehEN4L3vjzZXjyjkuQlqzB\nTRN6tTplko0iTB3Jr5FtD4VgSvsR6wBHuN7DLM/NGsFzWrLOSlI8ZfEDY9Gvu7jq+jVjuvNeSyTA\n3NsCUclskw49MvQYLujjds+1/aNSOe9m0nHzjXSozL6qLwb0CDhQ2L6Oj9w6hHP+kimoPTL0eOkP\nlyLDqOGcp0GGnch9zahXQauW44oR3UKKrsULzY7RirUZqK6uxgMPPIAFCxYgOTkZubm5ePjhh3HN\nNdegpKQEd911F9avXw+lMrRhlpysgVxEUvujF6bByzBwub3QJSiiusDFMAH48IWrWvTZWGEyRb+g\nvdiYTHosfWIyMlK0UYX6k5MDxe+pRl2HPrfOTKzSRbxeBl7GFw176HeDue3hDDsxY4VhGJ6xM2ta\nXwzvY8I9i4NV6cToZtLhUX9dz2wiPZn8TjHxE6ESVqionDZBEXUfO67WQHDLlEokIfP187sl4uFu\ng4O2t1ZgCQDSiIfvFcO7YeexCl5NkvDvIZFIuAUotevalliXOJRU+hal/XONqKgtDXq/ue1oUg1q\n6PyLylMifehIyDqWJJGalpF9Tbhjah8kqOQY2TcNxRWNUCtlyErR4gOipxbv+InqIIPS42WCDMeZ\nU/KRmW7A6//1pXOJKe21JcP7mPDhM1OCto/oa8LGfaW4dmzzeoxRYkNVVRUGDgz0G2YDHGxgQxjg\nePTRR3HixAkuwFFfX4+HH34Y48ePb9HxDVolVEoZHE4PBuQm48hZvuF4y6ReyO+WyDX9BgLZMqyA\nXl62AWlJCSGjTyq/CjLpwNETz0WVQoYFd49Cdb09KKUxmucD6TgkHZApBjWkUp/OhN3p4YxThVyG\nlEQ1GpucIR0hbORb6CAVu38pFTIsmzsxqlZpnZ2Id7W0tDRUVVVxrysrK2EyBSx2i8WC++67D3Pn\nzsVll10GAEhPT8f06dMBAN27d0dqaioqKiqQkxNa2a22tkl0u8mkh9ncCCUAp80Jsy16xZ3ODHve\nHRmdQgpLgw2WyLvC7SCaUFrsIc+NGnwtJ5aLRY+/4bzQc+a7gTOiRpxYFIgBICO2N0cWPBzkuMQe\nbBq1nCe2IJdJRR8mWrU8YvSM/Z5QPfZaQlukgpgIL6ZYnasw2ieVSrrEQzAWhIoAsAvEtWvXcv9m\nIwCZmZltdvzKOhskEmBQTyM27Qs27GwON9KSE3jCBg//bjA27SvFdeNycbykjqujA/gRO1aYhCRZ\nr+L6wpGGXYoh2LCbfVVfzlOvUcs5B83uY8G95FjEnIsMwyBRp0Juhp5r3jx1VA7S0wKGXazUKRNU\ncrwwp3npYJSOw8UOcADA0scnw+Zwo1d2IhgGuPHJr7j3Rg/Ogsmkh53I+CXXUiv/ejU0armv/YGI\nQjUA9OiWjP99eAKe+OdmAL6UyPS0QGQr1aiFyaSHyaTHc3ePxqKPd3LH6dcrONW5Z5YBSoUMx/11\ndPm5RtH1XW//doNOBXtNEww6Fbcfu0aQK2Sin1X6571cJuW97xvjKHTPMMAUQhUznolo2I0fPx7L\nli3DzJkzcfjwYaSlpXEPHABYvHgx5syZg4kTAw2vv/rqK5jNZtx7770wm82orq5Genpweh6l60DW\nU7VV3z4Kn1guFlkvn9AAYW0FlcjDSkzB0ZeKSahpttHCizQixa4/hUwKrVrOqWWGipD5DMDw16/U\nb9h5uFTM1htHbTFnTIkB0ZZoDEWpJBBspKmYbUusIwA1DXYk61W4pHcq7r6mH3QJCry59hD3fv8e\nybh5Yi8cLKrGG6t9AifD+5i4NKxsk5Zn2Bm0Sr+wUEAdkxRHyU7VcoYdKVKSlRpIr7xtch56ZhpE\na2+B8FEBsfdYx8ro/qCVR3IAACAASURBVOmcYSe8jkP14qJQSDpCgEMBBgqVDFVVPlf6n24ahL0n\nzbj98nwk6ZQwmxvRZAlErYXO83q7LyhySV4KdhMtTm67PA/9uieDcbmRnCDHdeNy8c22s2AY/ndY\nrQ7udX6GDqYkNbqZdDCbG5GiVeDJmcNwoKga63eVAADund4fqYlqPPCaT0U2Xa8UdehLPB6YzY1c\nHb5SJuH265Gpx+kL9UjWin/W6q/llUiCzzc/Qw+A6fABEjFaG+CIaNgNHz4cAwcOxMyZMyGRSLBg\nwQKsXbsWer0el112Gb744gsUFxdjzZo1AIDrrrsO1157LebNm4cNGzbA5XLhr3/9a1gvBSX+IVPd\naE+s9iGWi0W2h6JQFn9IrxTsPm5GbmbgRjUkLwUHi6rRPT3Yk+ZlGF6qIxuxa20zczJixxo4STol\n6vztFmQyCTT+NghyWehaNa2It1NofA7JS8Ge42Zk+xetoXpBNoe2MOyUChmUcimcbi+0agVSExNE\newjld0vEqfP1yEzR0lTMi0R7RQCA4CiA18ugzuJAXnYS0tIMuGWqAdX1NsBv2N197QBMGOmrt0kn\nom/kYiOVGO/AXikYNTgLCrkMiToV12vr0iHZgN+wSzVqgDM1AIDMjECrAzUhonDzFX1CGnUAID1d\nI7r9738ajw++Kgzant8tGSaTHrdd2Rf/t/EUBuelcufwwM2D8e/vjmLCiJwWl3dQug4dMcAxsl8a\nRvZL421L1qswbVQO+uaI19EBwNhBGcjrlohn3i0A4Guz0zMzcs2ZsI/b4vvH8p6T/XONvGi9UEFa\n2Mv2b/eORk2DnWsRFBA5IXpU3zQYJoOa196MxBMiFbOrE1WC+bx583iv+/Xrx/27sDD4hgoA7777\nbiuGRYk39EQzatoT6+JwMdNFqv0GQrd0PW8B+NSc0ThUVIWR/dLRPy8ViToVDFolDp+uxsj+6cjr\nbsSTy7Zw+2s0KuQQBl9WhgGmFC0Ucik8hHe9uR6tJHNAxn1QnzS83DsNPTINmP3XHwAAxmQtkvQq\nlNc0QSH3pX3oLwQ3Rc5M853f63MnIUEtR02DHVmpWqQkBtIcnybOWSqVwGTS4+UHxuH5d7e1aOwA\nkO8M5NiwtRYA8PBtQ/GmP6IS6nuf+P1w9Ms1wpSixZJHJ+J0aT3GDs7ElWNzcehUFUzJCTAa1Nzn\nX7p/HI4V12JEvzSs2njKd0y1gqZJtyEXKwIABEcB6iwOuD0MUpMSOG822arEoJZz2y2NoSMALE/c\nPhR1/mNMG5mDT386gSuGd4O10Y5h+anYf6oKeRl6bIrwPTaLHXZr6B6W/bINGNTTiGvH9kB1gx3v\nf3MUE4ZkIt2ggoeoC/rTTYNQZ3FgVP907lhvzp0ImT8SYDLpMbqvCaP7mrpceQelZXSWAIdEIomq\nNRdZby10mE4eloWCwjLceaUvBZp93giXE2LOz56EA5d1Rr764DhRwbFuJh2vrRibCEOuW3QaJdef\nToyrRudgz/FK3DO9f8h9uiIdp8EFJa7h9bFrAyEISjCxTBc5ccbXoFevkgUt3HqatKiutsCoUQBe\nL6yNduSatKiqsiBFy/fiNTbaocoI3OytjXaYvV7/9RMw7JqbXkEuUJusDuRnJ8JlDyzoamqtXKTQ\n7fHCbG5EfX1w41SP0w2zuRGJahkABhkGFVISE0KeM0tWUiANsiWpIXImsPC+tH86Nh+4AABIJhwm\nob63h0kLmdd3TjqFFENyk2FttEMGYBgh505+vkeqBlVVFk4S3+U/byF0sdgyYhkBqG7wzYVUYnFH\n3p9JZb1wnvD7bxgYJNs/ZXg2slK1yPerJT9w40CcN1vRI0OH9btKMCQvJeh7br88Hw1NzojtfhJU\ncjw+Yxj3elh+KhctZyXdh+WnBkUxhOdEobSEeAtwaNVyWO1u2AXpyEaDGq/+KZC1s+DuUdi0rxRj\nBka+12QThhqbbZNClACEg53/zalNz0zRYtnciZF37GLQux3lokNTMduHWC4W2fSr5qrpiUE+CNiH\nQ2udAZFUMcEEFn/hmgnHaoFIikOQUtHR1L6JNWSPGv9DtjP12OwMxDICoJBJIYEv5VYMLXGNh+uB\nJdbWRiKRoH+PQHskpUKGXlk+I+/Fe0aLfk84j3w4yNSu/7mmH1b8eByzpvVp0XdRKF2Nv9w1Et8V\nFIdMc2TJMGqiigKyvHTvaJRWWZvdGJ19xNAej62HGnaUi06kRsmUlhHLxWJlnd+wC9EXK1oYhuH3\nPPSncLRWFVIWQTyFgXj9nJCO0KiUVAqNpragNfUHXI1di7+BEopYRQC6p+vxz0cnIDcnmRNiIFET\nLQAyjBrceFlPnrHWEUlNSuBF8ygUSngyjBrcc23bpzBmm3S8yF20sE5KKsTceqhhR7lo3DA+F4fP\n1LQugkAJS6wWi9mpWvTOSeIa1zeHyy/JxsZ9pVApZVwUYOrIbqiqs3M3+zlX98Nrq/YD8MmhN5fM\nFA0SdUqoFDIYDYEx3jo5D+t3lSAnTYd6iwNbDl7Apf7m4AN7GqFVy3HzxF5wuDzYcbgCaa0wXIfk\npbSqBcLvJvbCodPVmHxJNn7afR4zp+Qjw6hBikGNiYTXddqoHBwoqkY3k7bVqn+zr+qLt9YdwjVj\naL+teEKXoAiKwt4yqRf2n6xCkj7g2JFIJLjxsp4Xe3gUCqWLceeVffD66gO4Y2r00UGKOBKmgzQq\nCtfXrDPKlbaWrnzelI4Nnat8uup5A3S+dgbofOXTlc+b0rGhc5VPVz7v1tBhDDsKhUKhUCgUCoVC\nobSM2BeMUCgUCoVCoVAoFAqlVVDDjkKhUCgUCoVCoVA6OdSwo1AoFAqFQqFQKJRODjXsKBQKhUKh\nUCgUCqWTQw07CoVCoVAoFAqFQunkUMOOQqFQKBQKhUKhUDo5HdawW7RoEWbMmIGZM2fi4MGDsR5O\nu3DixAlMnToVn3zyCQCgrKwMs2fPxu9//3s8+uijcDqdAICvvvoKt9xyC2677TasXr06lkNuE155\n5RXMmDEDt9xyC9avX99lzjueofM1fq9bOl/jCzpX4/eapXM1/qDzNX6v23abr0wHZMeOHcwf//hH\nhmEY5tSpU8ztt98e4xG1PVarlZk1axbz/PPPMytXrmQYhmGeeeYZ5rvvvmMYhmFee+015tNPP2Ws\nViszbdo0pqGhgbHZbMy1117L1NbWxnLoraKgoID5wx/+wDAMw9TU1DCTJk3qEucdz9D5Gr/XLZ2v\n8QWdq/F7zdK5Gn/Q+Rq/1217ztcOGbErKCjA1KlTAQB5eXmor6+HxWKJ8ajaFqVSieXLlyMtLY3b\ntmPHDlxxxRUAgMsvvxwFBQU4cOAABg8eDL1eD7VajeHDh2Pv3r2xGnarGTVqFP75z38CAAwGA2w2\nW5c473iGztf4vW7pfI0v6FyN32uWztX4g87X+L1u23O+dkjDrqqqCsnJydxro9EIs9kcwxG1PXK5\nHGq1mrfNZrNBqVQCAFJSUmA2m1FVVQWj0cjt09l/C5lMBo1GAwBYs2YNJk6c2CXOO56h8zV+r1s6\nX+MLOlfj95qlczX+oPM1fq/b9pyvHdKwE8IwTKyHcNEJdc7x8lv8/PPPWLNmDebPn8/bHu/n3RXo\nin+reL9u6XyNT7ri3yner1k6V+OXrvi3ivfrtj3ma4c07NLS0lBVVcW9rqyshMlkiuGILg4ajQZ2\nux0AUFFRgbS0NNHfggxZd0a2bNmCd999F8uXL4der+8y5x2v0Pka39ctna/xA52r8X3N0rkaX9D5\nGt/XbXvN1w5p2I0fPx4//vgjAODw4cNIS0uDTqeL8ajan3HjxnHnvX79ekyYMAFDhw7FoUOH0NDQ\nAKvVir1792LkyJExHmnLaWxsxCuvvIL33nsPSUlJALrGecczdL7G73VL52t8Qedq/F6zdK7GH3S+\nxu91257zVcJ00HjmkiVLsHv3bkgkEixYsAD9+vWL9ZDalMLCQvzv//4vSktLIZfLkZ6ejiVLluCZ\nZ56Bw+FAVlYW/v73v0OhUOCHH37ABx98AIlEglmzZuGGG26I9fBbzKpVq7Bs2TL07NmT27Z48WI8\n//zzcX3e8Q6dr/F53dL5Gn/QuRqf1yydq/EJna/xed2253ztsIYdhUKhUCgUCoVCoVCio0OmYlIo\nFAqFQqFQKBQKJXqoYUehUCgUCoVCoVAonRxq2FEoFAqFQqFQKBRKJ4cadhQKhUKhUCgUCoXSyaGG\nHYVCoVAoFAqFQqF0cqhhF2OOHj2Kl156ibetuLgYU6ZMidGIKBRKRUUFCgoKAAA2mw3r168HAGze\nvBnvvPNOi7/34MGDmDJlCl5//fU2GSeFQmmf+WqxWPDnP/8Zd955J26++WZ8+eWXbTZeCoUizvHj\nxzFr1izMmjULt99+Ow4fPhzrIXU6qGEXY/r3748XXngh1sOgUCgEO3bswPbt2wEAR44c4RaKEydO\nxIMPPtii7zx37hzeeOMNXHbZZW02TgqF0j7z9a233kJeXh4+/fRTvP/++3jppZfgdDrbbMwUCiWY\n5557Dg899BA++eQT3H///Vi8eHGsh9TpkMd6AF2dHTt24I033sCTTz6JBQsWwGg0YuDAgbEeFoUS\nd1RUVGDevHkAALvdjhkzZmDcuHF48cUXYbPZ0NTUhMcffxw5OTl44403wDAM1Go1vvzySzQ0NOCV\nV15Bfn4+tm3bhiVLlmDKlCm46667sHnzZpw/fx4vvvgixo4di8LCQsyfPx8ajQYTJ07EsmXLsG/f\nPphMJixfvhxvv/023G53jH8NCqVjE+v5+uijj0Iq9fm+ExMT4fF4YLVaoVQqY/mzUChxg9gc//jj\nj6HT6QAAKSkpqKuri+UQOyXUsOsgvPLKK5g3bx4mTZqEjz76KNbDoVDiju+//x69evXCiy++CIfD\ngdWrV+Ovf/0r7rnnHowZMwZmsxkzZszA+vXrcfPNN8PtduPBBx9Eeno6tm3bhqeeegpr167lfadK\npcKHH36IdevWYcWKFRg7diwWLlyIhx56CFdccQU+++wzzsufkJAQi9OmUDolsZ6varWa+9wnn3yC\nMWPGIDk5+aL+BhRKPCM2x/V6PQCAYRgsX74ct9xyS4xH2fmgqZgdhOPHj2PEiBEAgDFjxsR4NBRK\n/DFhwgQUFBTgmWeewS+//IIZM2Zgx44dWLZsGWbPno3HH38ccrkc1dXVUX/n6NGjAQBZWVmor68H\nABw7dgyXXnopAOCqq65q+xOhULoAHWW+rlixAl999RVNCaNQ2hixOQ4ALpcL8+bNg8FgwJw5c2I8\nys4Hjdh1INi0D4/HE+ORUCjxR15eHr799lvs2rULP/zwA/79739DqVRi2bJlMBqNLfpOuTxwC2UY\nBgDg9XohkUgAADKZrPUDp1C6IB1hvv7rX/9CQUEBVqxYwaWHUSiUtkFsjn/66af485//jPz8fDzx\nxBPc3KRED43YdRDy8vKwf/9+AMC2bdtiPBoKJf74+uuvcejQIYwbNw4LFixAWVkZhg4diu+//x4A\nUFNTg4ULFwIAJBIJVwcnlUqbVRPXq1cv7Nu3DwA4EQcKhdI8Yj1ft2/fjk2bNuG9996jRh2F0g6I\nzfGlS5eiZ8+emDdvHjXqWgiN2HUQnnzySbz00kvIzMzEgAEDYj0cCiXuyM/Px4IFC6BUKsEwDO67\n7z5MmjQJ8+fPx7fffgun08kp6I0cORKPPfYYFAoFrr/+eixZsgTPPvssRo0aFfE4Tz31FF566SWk\npaVh8uTJkEgkkEql2LBhAz7++GOUlpaCYRjs3bsXDzzwAMaPH9/ep06hdDpiPV8//PBD1NTU4N57\n7+X2ffrppzFo0KB2O2cKpSshNseXLFmCPn36YPbs2f/P3p0HNlHm/wN/5+6VHmmTtPSgpdzlvqFc\nsiCIiLqoVJdDRV1c8VjBhS8eqCgKi+4Kq6Ks/lxhVcQFBQ9AFJSjtBzlKkdp6X2k6ZU0PXLO748k\nk7sHtKRJP69/SCYzk2foTGY+z/F52PU+++wz6v3SDhzG2h+BEELITTtx4gTCw8PRv39/ZGdn4/nn\nn8f+/fu9XSxCiBt0vRJC/Am12BFCSAfi8/l48cUXIRKJoNfr8frrr3u7SIQQD+h6JYT4E2qxI4QQ\nQgghhBAfR8lTCCGEEEIIIcTHUWBHCCGEEEIIIT6OAjtCCCGEEEII8XEU2BFCCCGEEEKIj6PAjhBC\nCCGEEEJ8HAV2hBBCCCGEEOLjKLAjhBBCCCGEEB9HgR0hhBBCCCGE+DgK7AghhBBCCCHEx1FgRwgh\nhBBCCCE+jgI7QgghhBBCCPFxFNgRQgghhBBCiI+jwI4QQgghhBBCfBwFdoQQQgghhBDi4yiwI4QQ\nQgghhBAfR4EdIYQQQgghhPg4CuwIIYQQQgghxMdRYEcIIYQQQgghPo4CO0IIIYQQQgjxcRTYEUII\nIYQQQoiPo8COEEIIIYQQQnwcBXaE+JGcnBxMnz4d27dvd/ns+PHjuO+++zB//ny8//77AICMjAyM\nGzcOCxcuxMKFC7F27dpbXWRCCCGEENIB+N4uACGkYzQ2NmLt2rUYP36828/feOMNfPLJJ5DL5Viw\nYAFmzpwJABgzZgw2bdp0K4tKCCGEEEI6WJcJ7JTKerfLIyKCUFvbeItL433+dNzl1Q2ICguAgM9r\ndd2IiCDw27AecSUUCrF161Zs3brV5bPi4mKEhYUhJiYGADBlyhSkp6ejb9++7f4eulYdddfjBgCp\nVOztIpBWdPb1Wq1qhkjIQ0ig4Kb3dSt01+uVrtWuj+6tjvz9uCtrGxEUIHD57bzZ5+Au3xWzuz7k\n+8txF1bU48WtGdjyXXab1veX4/YGPp+PgIAAt58plUpIJBL2vUQigVKpBADk5uZi6dKlePDBB3Hs\n2LGb+P7u+bfrrsdNfFtHnLcmhsFrn53Ev7+/1AElujXoeiW+prues/583EaTCa9/dgqf/XTF5bOb\nPe4u02JH/FOJUgMAyLpW5eWSEHcSExOxbNky3HHHHSguLsaiRYtw4MABCIVCj9u0VJvUXWuFu+tx\nk5uzbt06nDt3DhwOB6tXr8aQIUMAAAqFAitWrGDXKy4uxvLly6HX6/Hee+8hISEBADBhwgQ8+eST\nnVrGIkU9dh7Ow5/nprjULDc2G6Bp0iOnuA4mhgGXw+nUshBCiC/LuqbE9gM5+PPcFDRqDeDzOv43\nkwI70qkE/C7fKNwtyGQyVFXZgmuFQgGZTAa5XI7Zs2cDABISEhAVFQWFQoH4+HiP+/LUNUIqFXvs\nSuLPuutxAxTQ3ozMzEwUFhZix44dyMvLw+rVq7Fjxw4AgFwux7Zt2wAABoMBCxcuxLRp07B//37M\nnj0bK1eu7LhyXFagX3w4wkJEbj//x85zUGl0+PFEIR64rTcAQKXR4vjFCgzpHQUAaNYZoaxtAofL\nwc5fczFnQiJ6Rt/8uVGjboaAz4U4yHNFEyGE+IrN/7sAAPjxRCEAIDLMfS+rm9GmwM4XahVJ11FY\nUY/XPjuJZ+4bQjW4XURcXBw0Gg1KSkoQHR2NQ4cOYePGjdizZw+USiWWLFkCpVKJ6upqyOVybxeX\nEL+Xnp6O6dOnAwCSk5OhUqmg0WgQEhLisN7u3bsxc+ZMBAcHd3gZrpXUYct32YgMFeHvf0lFnUYL\nHpfjEEg1NRsAACYTwy774NuLuFaiQn6FrUIjt1SFT364DAAoVNTjmXlDECdzPBZ31A06vPbZScyb\n0gsTBsWwyxmGwYoPjoPP4+LjF6ayy7fuvYR4WQhmjU244eNuicnE4H+/5WFgogQpSZLWNyCEkFaY\nTAyyrinZ9+fzqgEAUWGBHf5drQZ2XaVWkfiO748XAAC+/jUXd47v6fBZ5mUF4mUhiIns+IeU7u7i\nxYtYv349SktLwefzsX//fkybNg1xcXGYMWMGXn31VSxfvhwAMHv2bCQlJUEqlWLFihX45ZdfoNfr\n8eqrr7bYDZMQ0jGqqqqQkpLCvreOe3UO7Hbu3IlPP/2UfZ+ZmYklS5bAYDBg5cqVGDhw4A19v9Fo\nQublSgBAtVoLrd6I1R+fQLPOiJF9pZg3NRnRkiAYLQGdfSVdibIBgDkwtPp/P9rGilSpmvHKp5n4\ndNU0j9+vbtShrl6L3FIVauu1+Pf3lxEZGoDk2DDweVw0WAJKg9HEbqM3GJGeXYH0bLgEdgzD4Pv0\nQgxNjkSCvG2thQajCUYjA5HQ1rX8cmEtfsoowk8ZRS2WnxBC2mrPsXzsOVbgsjwy1Astdl2hVpH4\nFk2THgAQEiiAVm9kl//9yyxcLqwFANw+Oh4TB8e0qUaXtM2gQYPYihZ3Ro8ezVbKWIWEhGDLli2d\nXTRCSCsYhnFZlpWVhV69erH326FDh0IikWDq1KnIysrCypUrsXfv3hb36zwmVm8w4tdTJTj70xUc\nPVdm+34eD8068+/16RwlLhXW4Ot1c2CylovHZbveBor4aNIaoNLo2O1NbspvXT/7ejXO51YhbUZf\ncCwB4t/WHkBVXRPmWbp3AsD6L7Iwf0ZfLJg1AA3lanZ5ZGQIuFwOatTNLvtmGAb/+PIMmnVGpF8o\nx+7fr2PvO3e3+H9i3XbNx+k4c7USO9fdiQCR+XHo0m/X2fWiokLY8hJCyI26UlTndnmUN7piertW\nkfgeTbM5sMstVSG3VMUutwZ1AHDgZDEiwwIosCOEdEvO414rKyshlUod1jl8+LDDvJTJyclITk4G\nAAwfPhw1NTUwGo3g8TxnUXMeE3u1qBb/2nnWZb2TF8oc3jdpjVAq62GN1xRVGnYsKc/D0OmkmFDk\n2wVk1vVXvX8UABAWyMfo/jIAQFVdEwDgXI7SYR9ZlxUY20+K5/9ly9CbX1yD0CAhSqsaXPatbtDh\n0OkSh324G/NqMjEwmhj0iAljPz9z1dximVdUA1l4IHYeysVPGUXsNlfylJ3SVcobaDwsId6jNxhd\nlt09MQkxkUEd/l3tTp5yq2oV7XXXHyRfOu6f0guQGB2KAUkSNGkNbdpmVmovSDqhGZoQQrq61NRU\nbN68GWlpacjOzoZMJnOpML1w4QKb3AgAtm7dipiYGMyZMwc5OTmQSCQtBnXuBIrc3/YLKlyDoT1H\n89nX9Y3mCjsTw0BteW01bqAcxUoNZo1NwIffXmSXa5r0uGJXoffhtxfBnzfYoVunfeUfAEhCA9gu\nolZ19VqEBgnR2Oz4vQdPFSOvTI222LInG6euVOLrdXe6fGbeb6BDUAcAJZUNrQZ2Wr0R2w9cxR1j\ne6JHFPVYIsSf1Dfq8NvZMvxhZJzH305Pjp4vR89oMa4U1aKipoldzuVw8IeRcbh7YlJHFxdAGwI7\nb9UqWnXXjHO+dNxNWgM++OYcAOBfz01GjVrb4vq9Y8MwfVQcjFo9lErHG7UvBbOEEHKjRowYgZSU\nFKSlpYHD4WDNmjXYtWsXxGIxZsyYAcA8/2RkZCS7zV133YUXXngBX331FQwGA9588812f2+E2H32\nS3eB3amrtgBL3WDudvmv/12AVmeELDwQlZZWt3smJUEWYa55/tBu+/e+OYe8UsfA6+TlSpy4pPBY\nPi6XA2Vdk8OyfZlFuH9qb5RUathlWr0RXxy85nYfJhODH08UIl4WgqGWzJ2nrpiPpUbdDOdRxBu+\nyMJbT4xj30eGilCt1rqUw52Dp4px7EIFLlyvwT+fntjq+sQ3eUoiCADTpk1DdHQ0+4y7ceNGyOXy\nFrchvmHbgRyculKJ+kY9Hpzex+06JZUa9IgKBpdrq7DKvKzApz9edrv+gpl9MXVYbKeUF2hDYOet\nWkXS9Rw6UwIOh4Opwx1PSPsWui9/yWl1Pw9O74OkmNAOLx8hhPgS+6zSANC/f3+H9849XaKjo1sc\nR9sWznPRWRW6CexKlA3stAWFFfX46+ajUFkCvNTB0Zg4pAcKytVsUOfMOagDgDpNyxV/V4pqUVev\nc1h2IluBE9mOweDF6zUe95FXpsKu381j5T58fopDchRNow6SIAGadbb7VrPOiG8O57HvH78rBW//\n9wwbuLZEbzAnd7EGvsT/tJRE0Grr1q0OOSbasg3p+sqrzd2/q+3G99rLuKTAR3uyce+kJNyVam6B\nS79Yga3fX/K4z6B2tvy1V6uTjNnXKr7xxhtsreLPP//MruOuVnHHjh1YsGABXnnllRuqVSRdz7YD\nOfh8/1U0NOuxP7OIvaFZB9wDwLELFZCGu3av3LDU1qIb7mG+JEIIIZ3LPhnIqP4ytjuQu+QnACCP\nCIQ8wtwd0RrULb07BXelJiFCLMLwvo49eOZN6eV2PzxLbba7JAI8LgeThpinOqhRa2FiGNwxLgHL\n5w/zeBzv777g8TP7FsEn3/2NTegF2AIwlVMgZu1eOj5Fjjip+QHducVOqzM6JASrqmvy+P9G/Ien\nJIIdvQ3pgiyXt6cUSictPQF2H8nH8vePoayqAenZFS3usrMDuzbt3Ru1isT7Gpv1UNY1o2e0mA3i\nAODNz0+joqYRVXXN+NPtfR0Cu5BAAf40ox8+2nMRTVrbcqGQh6fnDUZeqRrhIZROnxBCvG32uAQk\nRofi93NlqK1335IWHChAcIBjK5/cQwsdANw5PhHKuib8fq7cYfnkoT0g4HNx4GQxAKBffDhyiuvA\nABjWOwqPzB6AI+fN24wbKMf9U83ZMpenDcM7X7kme2nJGaeELIUKW2tkfaMOQLBDRk8AbMAmEvIR\nFCBAcAAfippGGIwm8HlcNDYb8Py/jiJWGoKXF49CVV0T/rYl3e33qxp02H7gKuamJiGeEoT5vLYk\nEVyzZg1KS0sxcuRILF++vM2JB0nXZq24cZccN69U5fBbU1uvxeufnYTO7nnZnfaO1Wuvzt078TkG\nown/+PocJgyKxr7MIpQqG/D8A0PZTJcAUFFjHg9ZojTXPlm7tNw9MQlzUxPB4XAg4HHRBFtgFyDg\nYXgfKYb3cazdtGBfTgAAIABJREFUJYQQcmuNGSBD5uVKNkAbnxKNH08Uul13zvhEdnyaVZSbXhkO\nnzslHAkQ8jAwUYKwYCEb2PWICkZ+hRo6vQniYMfKPmv3TwBI7hEKSagImka9ywPTPZOS8O2RfDhT\naXQIEvExqr8Uv58rR55dgpbMSwqkny9zqKwEgErLOP8AgbnbZu/YMJzLq8YTfz+MfyxLxYlLCugM\nJuSXq3HxejXe/fqcx+P/eE82LhfWwmRi8PQ8Glflb5yTCD7zzDOYNGkSwsLC8NRTT2H//v2tbuMO\nJRF05e3jto6bCwwUOpSFYRg8vuGQy/qtBXUAEBsT1qnHRYEdcVCoqMflwlqHqQk83cB0Tl0xA4U8\ntpuPpskxM6aA32qvX0IIIbfAE3el4G+Lx0BdZw5mpo2IdRvYrV0yBhFikcuY6Na6Es0ckwBJqAhZ\nOVWYNLQHBvWSgMvhWFrLzAJFfPC5XOhgcunmFBNpG6sUIOTj709OwKWCWryzw7HlTmj3EJw6OBoJ\ncjG+tCRU6RMXxgaY1gnVAeDYOcdpHazqLC141vF4993WG+fyqgEAlwprHcbQ7bcEp55Y75/W/6cr\nhbXYl1mEJ+8e5DDej/iG1pII3nPPPezryZMnIycnp02JB51REkFHXeG4DZbnXK1WjwqFCgwD8Hlc\nFFbUw2hyH6yPS5Hj8TkD8ea207juJmtvc6MWSqWbDS1uNuijp20/YzCacOxCuUta6LZqz3AB67wc\n1ha7ALubvfO4A5rklRBCugYulwORwBZgSEID8Ox9Q3DXhER22T+enohYqbnbWO+4MLy8eBT7WWu/\n5wI+FxMGxeCpPw7GkORIdnqDYLvELYEiHvg883K90bGWWy5xbPHjcDjolxCOuamJSO5hDjJjIoMg\nEpgfYSJDA7DkzoGIlti6iPaKDYMk1Dye+4IlQHOWkiTBvZMdxwRa/19io4Lx0iLzMV8rrnMYcuDp\n8A1GE744aEsg1mhJLLbhyyycz6vGiUstj70hXVNqairbCuecRLC+vh5LliyBTmcO/E+ePIk+ffq0\nuA3xHdZnWS6Hg+XvH8eLW08AAEqrzD3WZo1JQKzUVhH19B8HY9HMfuBwOAgNcj/siLpiknb59XQJ\nvvo1FyP7SfHUvYPbvX1jc9vmoAPMtaDL3z+G/gnhAMzdbZyNGSBD3/jwdpeDEELIrTO0dxSG9o5C\nxiUFlKomhDl1j0yKCcWiWf0QeRNzj9rPXxco4mPCoBjsyyxi7yFPzxuMayUqyMJd547j87i4Z1Iv\nTBwSg09/uIxFs/ojKiwAVepmNnW4fUuiRCyCRGwuq33CE3vS8EDcNSERBeVqZF0zt67Y38cS5CHg\n8zgoVGgQbR9seqgAvZBXjYOnbJOl1zvN90dpVnxTa1OTTJ48GfPnz4dIJMLAgQMxa9YscDgcl22I\n77G2UdgnZNq69xIuFZqz8g5JjkSEWIQvfzH3FLBPJrVoVj8knhNjWO8oXCmqQ68eoaisbXSoVOsM\nFNj5mWLLuDd3zb9t0d6Wvtp6LdItaajdBXZzxicijgaPE0KIg646L9abT4z12HOjI+deChTyMW9q\nL4zoJ2Vb4doyDjsqLBB/e2gE+96aZAUAggJsjzThISL0kDpOGB4bFYyJw2Ox4+ccy77MgV9ybBgb\n2Nl3leTzuBAHCVHfqHNI+tXgoQJ08y7HTJ3qRscELdaw9kyOEpomPUb1kzmUmXRdLSURXLx4MRYv\nXtzqNsT3uBsbaZ/1MiYqGL16hOJCfjVGOGUIDg8RYa5lCoQEubl7Ze/YsE4srRn9ovgZk6XPL497\nY10fPd2wosICMGNUPPIr1C7zCVkFCG2n04q0YcgprnNooiadLycnB3/5y1/w8MMPY8GCBQ6fHT9+\nHO+++y54PB4mT56Mp556CkDLD5iEkI7XlefF4nFvzQgNDsf8XR35oGPfYhcWIkRokBCLZ/XDf/Zd\nBQA8e/8QDOgtYwM7a2WkNbAEbMlTrMRBAihqmhy6Ynqa08qZukGHGrt1rc+I/7IEgJ/9dAVvPTEO\nconnLKOEkFtP1aADhwN4GEYHwJwFPjRIAA6Hg+cf8Dw1y61GY+z8jLGNgd2GL87g4z3ZLss9tdj1\niArGjNHxHie3BRxb7AYmSnDPpF40tu4WamxsxNq1azF+/Hi3n7/xxhvYvHkzvvzySxw7dgy5ubkO\nD4tvvvkmzTlJyC1A82K1LXtcezm32AHm7pbs5yLz/WtY7ygAtuybidG2wM45uYk4SAit3ujQ+mZN\npNI3znNQKuBz0awzYsUHx9llzTqjQwIZwDyZOiGka/nr5qN4btNRtrHEnciwgC75jNumwG7dunWY\nP38+0tLScP78eYfPpk2bhoceeggLFy7EwoULoVAoWt2GdLwzOUocyiq1BXY8z39avcGEK0V1OHFJ\ngQanQM5Ti511sGeI03xGz91va91x1xWT3DpCoRBbt26FTCZz+ay4uBhhYWGIiYkBl8vFlClTkJ6e\n7ncPi4T4gqqqKkRERLDvrXNc2VuzZg0efPBBbNy4EQzDtGkbX/DwHf0RHMBng6uOJLDLkhlsCfLC\nLAEeAASIzJ//eW4K1jw8Gsk9zIGZfTDnXCkqtlRmVtU5ttIJ+VysWjASy+cPw+JZ/Rw+mzqsB0b3\nd/0dbtQaUKRw/H1t74PhtZI6XCtxneSdEOJeiVKDZ947gly7aU9KKjV4+p+/I69Uha17L2HrXnND\nx/YDV7Fu+2l2PfuWemexUV2zR1qrXTG7cpcRX5RXpsKx8+WYNLQHFDWNGJcSfdP7NDEM27XDerPk\ncTmordfig90XMGtsT4zsZ+77++kPl5FdUMNue+F6NcYNtJXBmjzlgdt6Y8wAGVvbaA3s7GtEN/5l\nAiShAQgQ8tCsMyIkkCYe9yY+nw8+3/0lrVQqIZFI2PcSiQTFxcWora3tkElUv/41F2euKWE0dr/0\nADwex++Pe3R/GR6Y1rv1FckN6ax5sYCuNzfWvOn9MG96v9ZXvEkymbkVLjDYFtjJLcviYsMRF+uY\n1GtkfxlOX6lE78RISO2mW5BZXjsnYAkJEkAqFWOqVAy9wcR29wSAMYN6oKxag+MXHbNgcnhcVDtN\njN5kMKGyXoeUXpEoKFcjJiq4xeQKj779KwBg7zt3t/wfQAgBAOz67To0TXps238Vrz06BgCw+8h1\nNDQb8Nm+Kyi1TIcye3wifj1T6rCtp8RLIYGCLntPbDWw81Sj39KD341s0128+bm5JuDwWfNcOoN6\nRbbYvbEtiu1qAK0nIZfLwZbvLiKvTI0f0gvYwO7ohXKHbWvrtQCAGnUzcktVbAvexCExDi1wgZbX\nwXYtdhJLdrSNf0lFTX0zDQL3Azc6iWqgJa0vj9f1uiXcCv5+3IFBQq9PFOtPbtW8WED3mxtr+fxh\n4HI57LHZ/6YplfUej/uJOQNRO6UXeCaTw+eeLm2RgOew3sqHhmP9F1kAAJ1WB7GblOaKKg2u5DtO\nvbD9pysAgAen98GXB69hZF8p5k5MQn65GnqDCbcNj2UnSbbX3r8dXb+kK8sprkNYiBDyCNt405yi\nWhh1enY+ytZomvQoUtRjYKKk1XWtU69UVNt+Hw9nlXpa3cULDw73OJ2Bt7X6JF5VVdVqjf6aNWtQ\nWlqKkSNHYvny5W3axllXq1W8VQKCRJDaNedeyq/Gp3uy8dKjY9t83OmXK9nXWr153EKAiI98S2ZM\nmSTY8764XEilYqz/IgtXi8yTqvJ5HCTERTh0SZFEBEEqFSNGbpvo1X6fPdtUUuItzg+FCoUCMpkM\nAoGgQyZRvWtcAh69K8UvHxRb468PyM7cHaM//zZ3ptTUVGzevBlpaWlu58V67rnn8OGHH0IoFOLk\nyZOYOXMm5HK5x22ITUqS40Mdh8PBPROTHOZZdUfA50IW4ZrERBxkq8wMDuCzwxWcJ2mPsbuPiwQ8\nRIhdp4XItLtXO7NOrH46R4nTObYuthwOMG1EnMv6JhMDLpeDxmY9RELeLUt6Q0hHMBhN4FuGDOkN\nRrz93zMAgE9XTWM/X/7e7y7LeFyOx+7L7++6gKvFdXghbRgGuAnuGMY8FAkA26BiP8l4+sW2zzNp\nnyW3q2l3E0tndRnpbrWKVsVldaiu0eBSfg1uGxGH1R8cg9HE4Ptj1zFzpOuPuTt5xbXs6zqNeRxA\nY5MeOkvrXealCvx+qgh93Az0rqppRHFpLRvUAUC8TIyaanMrYKCIjyatAYWlKiiV9TDobGPwOuPv\nQg+KnSMuLg4ajQYlJSWIjo7GoUOHsHHjRtTW1tLDIiG3GM2LdWvNnZh0w9vaz9uXGC1GdoH5Xumc\n8dk+0AsQ8iGLaFsrQ2usvWoA84OtVZPOAK0lOcvYgXL8eW6Ku80J6XLO51XhnzvPY9kfB0MWEYhX\nPsl0WadJ65jvoaBCjdc/O4UFt/d1W9EBAFeLzWNPc8vUbgO7EqUGf9542GO5GrWtz+N87+ReGNFX\nCnEXba0D2hDY3couI91RQ5Mea/9zCoB5ngtr7YGA71j7lmM5Ye0n+/72yHVcKaxFRW2T3f7MJ2Zl\nXZPD9n//Mgv/WJbq8v2Hz5bikFPzs33X0Iem98EnP1zGqP7mv18wdbfssi5evIj169ejtLQUfD4f\n+/fvx7Rp0xAXF4cZM2bg1VdfxfLlywEAs2fPRlJSEpKSkuhhkRAvoHmxfIN9goRAu6EIfeIcx+jx\n7RKWBYh4EAl42PiXCeBxOfh8/1V2nrw/Tu4FdYMOB0+XoC0Edvu1T+TQ1GxAiWVsUMYlBQV2xGf8\nkF4IANifWYTBvSLdruOctCTzkrm1e/sB81Ql7oI7kZAHrc6I3b9fR7GiHokxoci8rHBJWNSS0GAh\nm/V2wqBoyCMC0TsuHH//0tzNOkjE77JJU6xafUqnLiOdyzpbPeCYkZLr1NRsbaZ+5r4hbIKUPccK\nXPZnHWOndZPJR+Mm46V9Y+rDd/THtv1XcfuYeHZZ6uAYDO0dxQZ7QQE3Nx6QdJ5BgwZh27ZtHj8f\nPXq02yRG9LBICCHuRYhtyVdio4LRb0Zf/HqmBMP7eM7qaZ0LzzoO/el5Q/DaZyfBmBjMmZCII+fL\n2vz9lwtrUaVqxuI7+qHZrsdMo9bg0IJHup8mrQG/nyvDtBFxLo0B1apmXMyvxuShPZBxWYF4mbjL\nBCTWoC1QxPc4Pt2+xY5hGDajLWAO7kb1l7mMcQsPEUFRY+79d+qqEqeutj9r8Mi+Urax47E5A9nl\ny+cPw97jBZgw6OYTHna2VgM76jLSdiaGAQftS19cbjdw8587z7GvG5rMSUzqG3V4dtNRdvmmb87j\njcfG3lA3jzqNuUtHSKAAAUIeqlS29M1P3TsYI/tJkTo42qWvvn0LnrXFjlruCCGE+Dv7+/m4FDnk\nEUH4QyvDJAKErvfHVxaPYitSR/SV4ofjhS49a9y5WlyHq8V1mDYyFnmlanZ5k9YAPQV23UZtvRYB\nQh6boRwAPt9/FRmXFCisqMcjs/tDwOfBxDBQ1DTire1noGnSg8vl4P/9aE7QYx2rdrP0BhNq1M2Q\nSxzHpKo0WvB43FYTAlorKAKEPPA9jA11aJ3WGlyeqy8V1GBATwl4XA77fYIWpvlqzbDeUbiYX4Op\nw2NxKKsUYwY4TleSkiRxGb/bVbXp6dzbXUZq67UoqFBjeJ/O6c5ZrWqGUMC96T6zy98/hqjQALy4\naJTDcoZh8M3hPGTn13jY0pW6QYcL16txtch1vpqX/p2BQFHrc8YlRotRUGEbB/fOV2cBAHeO74mZ\nYxLw3KYjUDfqERkqYrNmtjYAm8/j4q0nxiEkiFruCCGE+L9XHh4FRU2TQ8a+lggFrvdRDocD67Np\ncIAAby8dz05dEC8LQXFly93FLhXU4pvDeez7Rq3Bbc8c4n90eiOWv38MMZFBePPxcezy65bJ7U9c\nUkDA5+KR2QPw/bECfHs0n13HfoxmR/nxRCG+O5qPp+cNdngu/+u/jkHA5+KjFVNb3N4atLWUfsO+\nxa6+SY9GpzmX6xv0+Ovmo+ByOPj3ytvYbYID+BjVX4bfzra9VRwA/nLvIDRqDQgNEmLTs5Pa9Izd\nVflEs8tL/85Ak9aAtY+N9diUzDAMzl6rwsBEicNko87UjTp8dfAa7pncC7LwQDAMgxc+PN6mkxEA\nMi8roKhpxF2pjoOxm3UGqDQ6qJzmqAGA+kY9fsooanXf9pS1TfhHpudtmrSOP+jJsaEOtXmAeXB3\nkUIDk9PVE+zUnTJa0rablZVzLQ0hhBDirxKjQ5EYHdrqes/PH4oqVXObe+0Eifho1BogiwhsNbDL\nvKRweN+kNbDTExH/Vqo0nxv2PbycHTlfjkdmD8Dhs445EzpjjtXMy+ZzcfP/LiApRox4mRi/nzMH\nUtask/b0BhNe/X+ZUDfo8NKiUWxgd/JKJU5ecZ8p1r7Frr5Rz+aPsLIOYzIxDJa8/Ss4HA5MDIM4\naQjCQ0RwtvTuFJy+qvT4fXwel+3aebNTkHlbl8+PW1HdwEbu1u6J9s7nVeFQVimOnC/H5l0X8J/9\nV1rc338P5ODEJQW+/Nk8ALO+0bxPdyejO1u+y8buI/lsxsmGZj32Hi9AYYVrhkhlXRMam/VQN7oG\ne605n9u+vsG9YlwzXooDhXhx0UiX5dY0rWrLsdsnZCGEEEJI+w1KisTUYbFtXl9qGVJh/2zzycrb\n8M5TqexYeqsip8Dvk+8vs88vAPDi1hM3UmTiA0rcJP9gGMbh72/N3urcCtaWTI9tpdJosW3/VQjt\npibLL69ngzpPFLWNKK9uREOzAaeuVrb4vP31oVzklqrQZDee9OM92Tiba04+1FPumjmdAdgWNi7X\ndSoSABgzQI55U5Ndlt82PBbL5w9rsfy+pksHdo3NBjy+7qDtvZsT9J87z2Pb/qu4VGDu5ni5oNZl\nHXv55eZWLeuEn9aTBTDPpQEAuSUqfHnwGowmx5PPvhtEXYMO+eVqbPrmPHb/fh2f77/KfmYwmlBZ\n14RVH6Xj2U1HUdJKTZwzkYAHUzsrWfr3dA3OQoIESIoJxUy7ZChJMWL07xkBAOhhaf0cn9L1B4MS\nQggh/mTK0B4AgIGJEgzrHYW+8eHgcDiIEIvwzH1DWtyWAZBbqmLft9SaQ27eunXrMH/+fKSlpeH8\n+fMOn504cQIPPPAA0tLS8H//938wmUzIyMjAuHHjsHDhQixcuBBr16694e8urrQ1HFgT5qgb9Q6t\nWtaxd849tFQaW1fMi9erHbo0llY1oKRSA5PJ3OOtsVmPC9erodJocbnQ9Vl6x6+5OJRVikJFy1Nd\n2U9xVlhR77Cv83nVLW67L6MI67adRp7duV2laobGUvnx3P3urwtrRYhKo3MYhwgAi2b2A+A+4Fs4\ns5/PjJ1rqy7dFdO5FsC5xa5KZRt4bO3G4KkbJsMwOHmlkk0YIhLwcOF6NT77ydbC9/u5chw8VQyF\nZfqABHkI+saHQyTk4YNdF5BTYjvRDmeVYp9d90r7H9WGZgPySlVgGMDIMMizTBQ+Y1Q8fj5V7FK2\nBHkIm471tUfH4LOfrrABaGsWzuyHSUNi3P6oW5uTG+2yYf7fgpFsWubl84ehTqNFVHjHzLdDCCGk\nbdatW4dz586Bw+Fg9erVGDLE9sBy4sQJvPvuu+ByuUhKSsKbb76JkydP4tlnn0WfPn0AAH379sXL\nL7/sreKTDjB1eCx6RouRIA+5oQnG3fUUIh0vMzMThYWF2LFjB/Ly8rB69WqHDNOvvPIKPv/8c0RH\nR+OZZ57BkSNHEBAQgDFjxmDTpk03/f2ldo0D6gYdJKEBqLY8y84YFY+zuUpomsw9w0xOrQI1dmPs\n3v36HKYM64HFs/qjSWvAy//OAADMHtcTP54odPneVX8a4dCjy5p1vTXmjK0MhHwuXvvspMNn1+ye\no1ty7IL7ycLd5cKYPioOIksmWlWDDkF2yf3e+vM4dmysL4+ba48uHdhdKXKsMdA4BXbXim0nSIUl\nsAmwC+x2HspFUAAfd45PRHZ+DbZ8l81+pmrQYe/xAof9/dfSPdPqkx8ueyzb0fPlHj/LvKxArdp2\nMf1ima8mOTYUaX+4DbuPXMf3x20XkX0tgkjIw18fGIrqBj2qqhtgMJrw0R5buZ1xOea+wbFRweBx\nOew8eIAtsJs1NgGZlyvx1L2DHObaiRCLHFI5E0II6XzeflAkXUdSjOexeymJEaip17pU3MZJQ1Ci\n1Djc7wFza43zVEnk5qWnp2P69OkAgOTkZKhUKmg0GnYar127drGvJRIJamtrERMT02Hfr6i1/f1V\n1sBObQ7sIsMCIA4SorCiHgzDuPT2ul6mdvv+hN2YTU9dKb9PL8Dz8bZuii0lO7H39D+PuF0eEihw\neY5vzTP3DQEYYNP/zK2k1t52VjPHxOO+qcnsPHeRoSKHZ2qJOIB9fSOVJ76oywZ2JoZBbokKwYEC\naHXm6P/rQ7m4eL0aKUmRuFZSx074CZi7JQBAkUKD0qoG9IgMYhOWnM+rdhn47K6ZuS3kkiAoahrd\nnpzjUuQ4ka3Alwevsd0c7YkDBeBwOLh7YhKk4YFsClr7lMUBAh5CAgVISpBAqTTXxjkHdg/+oQ8G\n9IzAL2dKMGGQ+ceDy+Xg3WWpyCmuw/u7LwKwBYwxkcH4cPmUGzpeQgghHcvbD4rENyxPGw4AyMpR\nIlwswskrldiXUYSJg6Px1a+5LutrdUaXbmjk5lVVVSElxTYBvEQigVKpZK9R67+VlZU4duwYnn32\nWeTk5CA3NxdLly6FSqXCsmXLkJqa2uL3REQEgc93bFX6/MdLuGr3vMpwuZBKxdBmmwOzXvHhyC1T\n43qZGntPFMH2NOxegIgPqVSMerueXJ6CrYvXa6DWGpEcZ261q2vwnC/ij1N7Y9dh13PS3qiBchy2\nNHS0VUofKXpEhUAg4iPQUnarB6b3xZ9m9geXy8EcaSi0JgapQ3qweTkkoSL0cJN/wp79/vxFm34B\nvNFlxGg0QW804fZRPTFugAwv/zsDDANkF9Qiu5VxdC//OwOrF9iShlwrUbkMPLZ6Zt4QqBt1Dl0y\nW5IUI2YnQLS6e2ISesrFKK2yfUdZVYPLtiGWJmQel4tJQ3ogOECAj/Zk4/bRCfiw1ByMtZTRUx4R\niJcWj2KzWi6e1d/hc3GQECP72ebeCOgmzc6EEOJLbtWDIvEPw/uaU8r3lIsxY1Q8uBy4DeyaKbC7\nJRg3TVfV1dVYunQp1qxZg4iICCQmJmLZsmW44447UFxcjEWLFuHAgQMQCj1Pq1Vb6zqkJt2pd1hB\naR3U6mb8etLccCEAUFhu7r327W95zpu7UFQ3QKmsh7La9RnVKkIsQv+EcKRnK7DveD7Cg4WYNjIO\nippGhAYLIYsIxOh+MjYzJQD0ig7BneN74od01y6dVknyEBy2vI6WBKHC7ln69tHxOHDSPFQpKSYU\nQ3tHIq+sHhyDEUplPVISzMGlUlmPeyclobKuCbNGxaG62vbcPXVIDAAGdZZuqREhIraBxGr2uJ7Q\nGYxoaNIjXiZ2+bwruNlgs9VfAG91GRHwedjw5AT0jAtHYYnrXG6BIp5Lyn9767afdniv1RkRExmE\nx+YMxOb/nUedRofkHuaTB4DHwK5vXBhW/mkElqw/BABIig7FiWzHtMOpg6MRFRboMtlorDQYpUrb\nxeOcQnVEXyk+WjEV5XYXmJDv2lQ8rHcUzuZW4f7bertMVeBOcAAfDc0GRLhJ+UoIIaRr6awHRcB9\nK4CVP9ZWt4UvH7cc7s8XANi86wLum9YHE4b0gMDNswS5MTKZDFVVth5ilZWVkEpt87dpNBo8/vjj\neO655zBx4kQAgFwux+zZswEACQkJiIqKgkKhQHx8PNojMlSEEqUteKlWNePbI/nQNOkhFHAhiwjE\n1OGx2Hmo9aAOMCddKatqcEmAwoG5rS85NhQvLhyFvFKVObCz9Hy7VqpCk9aAPnGReO7+oQCAXj1C\n8eY287N2TGQwcopdn9Xt2fdkG9lP6hAEWrsQ83kcvLzYPBe0VOo+8HKebsxZaLAQwQF8JPVw7eZ8\nn5vMmP6m1cDOm11GwoKFEPB5CA5wLea7T03EDycKkCAT478Hc9zOH+csKSYUSTGhmDIsFunZFVgy\nZyA738xT9w5CQUU9LlyvZhOZAEBQgMBhTpoeUtuJOTAxAg/c1htRYebkI2qnZuphvaPYwG5Y7yh2\nmgFn1kGfANzOf/PnuSnILVMhJbFtmXvWPjYWlbVNCKPAjhBCupxb+aDorhUA8PzQ5O/85bjdVW4X\nlKux8b+n8Ve9AYN7RTp85svBrLelpqZi8+bNSEtLQ3Z2NmQyGfvcCwBvv/02Fi9ejMmTJ7PL9uzZ\nA6VSiSVLlkCpVKK6uhpyubzd3z2oVyTO5VWjd1wYcktUqLAMBerVIxTL/jgYgSI+Zo5OwISUaKz6\n6ITHBCcr0obh5JVK/Ha2DC9ZkqbYs1YVSC3Ps5LQAIfPT181T8EVabc82K6xIjxE2GJjy7iBcvZZ\nGQBiIoOw6dlJeOY983g86/QGHdHiLBLwsH7peAg8VGj5u1b/B7tClxE+j4s/jIxjk5AA5i6Lf5xs\njrwPniqGSqND3/hwlxqD8SnRSM82Z9eJtQRld09MwtzURIcgamQ/GUb2k6F/QgTe2XGWXW4dnPzw\nHf3x29ky9I619dftKRcjwW5OjclDYxwyC9kPiv7TjL4eJy0VClo++URCXpuDOgAIDxG5naCREEKI\n93nzQZH4hyCRwOODdFRYgNvl5MaMGDECKSkpSEtLA4fDwZo1a7Br1y6IxWJMnDgR3377LQoLC/HN\nN98AAObMmYM777wTK1aswC+//AK9Xo9XX3211dZ1d24bEQu5NASJ0mD8dfNRXLdkTJdHBLLPeVwu\nB2EhIrzy8CiUVTUgKiwQ6kYd/vH1OXY/0vBAzBmfiIxLCodpEpyNGWD+TQmza4iw730WaXdu2Scp\n4XA48JRw7agPAAAgAElEQVS355HZ/TF2gNyhFTlQyEdIoAAvLx4FhgH2Z5pbBjuqK3FQG3q3+at2\n/w92VpeR1rqLPPfQSPSKC8fW7y6yy6wSYsKQU6LC/dP74tvf8pB9vdrus1A2sBs5MKbVWqupUjEG\n95Phrc9O4mpRLcDhQCoVY970fpg3vZ9juSKDHfYnlYrx96cn4YXN5hqIIf3kAC4AAHrGR3g8YUPt\nalic90dIe7Q0HvbgwYP48MMPIRQKceedd2LBggXIyMigFOqE3GLefFAk/iEmKgjV6maEBgmgbnRM\nfmFN7046zooVKxze9+9vy3Fw8eJFt9ts2bLlpr+Xy+HgtpHxUCrrESEWsVN2ueuRFRMZjJhIcwOG\n87O6SMBDaLAQrz86Bn/bku6y7RN3DUR+eT07PMk+u+pjdw5kpy2wb7ETBwkwc0w82+BxV2oiauu1\n0BtMMDEMO2fdpCE9XL4vwPI8bG0AsSY8cTfXHGmfVv8Hb1WXkbZ0FwkPtBXXvivFoMRw5BTWQCYW\nQtNo6w4pDQ/AuP5SpF8oQ9/4cMjEwjZ3wUiQheBqUS2iwlwHX1pTthr1RpfPjHpbpiFdk23Kg3pV\nIzQeqjPsL0Dr/vylu0h7UTB741oaD2symbB27Vrs3r0b4eHhePzxx9ku1pRCnZBbz1sPisQ/LJk9\nADsP5+GB23rj5JVKh+manFPCE/+QHBvGBnbhwS1X6nA4HDaLe6CIz87tFhUeiGfmDWGnDwCARbP6\nYVxKNMalRDvsY+rwWFzKr0G83NabwD6w43A4mD+tD/s+OECAJ+8ZBACorddi+fvH8NB02+f2nE/R\nO8f3xMX8mm4xBq6ztRrYdaUuI5GWibQT7E4yABiSHIUhyeZZ51MSJSisqMcDt/XGrLEJAIBXHxnT\n7u+6b2oyZBGBmDTEdbzgyoeG45fTJZg81PWzMMvFFhosRIDQsZnaEw6Hg95xYUiQhXhch5DWtDQe\ntra2FqGhoZBIzF16x40bh+PHjyM2NtabRSaEEHIDwkJEeGzOQABAs85Wobx2Sfufd4hveOKugciw\nzD8X2kpgBwBvPTHO7fI4y7CkBFkIXn3U8/myaKatl5o1i2VkG7v5RohF+HTVNJflPaPFKKyod2lx\n7JcQ4XZ90n6tBnZdqcuILDwQrz4yusX+4/dMSkLf+HAM7tX2MWnuCPjmcX3uxEpDsMhpqgErkYCH\ndU+MQ0igoF21ZvbTMxByI1oaDyuRSNDQ0ICCggLExsYiIyMDY8aMQWxsLKVQJ4QQH2ZNwBYTGYRY\nKVUQ+ysOh8MmzWktN0NLosID8dqjYyAJbXsuhhcXjUSNWosI8c3lb/jbg8NRWduEaAl1F+4sberM\n2pW6jNgnK3GHz+NiSHJki+t0NvsTdundKQ4td4TcKvZdfDkcDt5++22sXr0aYrEYcXHmSovOGA/b\nHXXX4yaEeN/U4bGob9Rj6nDqgeHv1jwyBscvlGNY76ib2k98O3uIBQcI2jTdVmsCRXz0jKb7ZWei\niKOTWTMMEdLZWhsPO2bMGHzxxRcAgHfeeQexsbGdNh62O+muxw1QQEtIV8DncXHv5F7eLga5BWTh\ngbhnEv2tiWddJrBr6QGhuz48dNfjJjemtfGwjz32GNavX4/AwEAcOnQIjzzyyA2Nh6Vr1VV3PW7S\n9dH16qq7Hjfp2uhaddVdj/tmcBh38xcQQnzSxo0bcerUKXY87KVLlyAWizFjxgwcOHAA77//Pjgc\nDh599FHMnTsXGo0GK1asgFqthl6vx7JlyzBlyhRvHwYhhBBCCGknCuwIIYQQQgghxMdxW1+FEEII\nIYQQQkhXRoEdIYQQQgghhPg4CuwIIYQQQgghxMdRYEcIIYQQQgghPq7LBnbr1q3D/PnzkZaWhvPn\nz3u7OJ0iJycH06dPx/bt2wEA5eXlWLhwIR566CE8++yz0Ol0AIA9e/Zg3rx5uP/++7Fz505vFrlD\nbNiwAfPnz8e8efNw4MCBbnPc/oyuV/89b+l69S90rfrvOUvXqv+h69V/z9tOu16ZLigjI4N54okn\nGIZhmNzcXOaBBx7wcok6XkNDA7NgwQLmpZdeYrZt28YwDMOsWrWK+fHHHxmGYZh33nmH+e9//8s0\nNDQwt99+O6NWq5mmpibmzjvvZGpra71Z9JuSnp7OPPbYYwzDMExNTQ0zZcqUbnHc/oyuV/89b+l6\n9S90rfrvOUvXqv+h69V/z9vOvF67ZItdeno6pk+fDgBITk6GSqWCRqPxcqk6llAoxNatWyGTydhl\nGRkZ+MMf/gAAuO2225Ceno5z585h8ODBEIvFCAgIwIgRI3DmzBlvFfumjR49Gu+99x4AIDQ0FE1N\nTd3iuP0ZXa/+e97S9epf6Fr133OWrlX/Q9er/563nXm9dsnArqqqChEREex7iUQCpVLpxRJ1PD6f\nj4CAAIdlTU1NEAqFAIDIyEgolUpUVVVBIpGw6/j6/wWPx0NQUBAA4JtvvsHkyZO7xXH7M7pe/fe8\npevVv9C16r/nLF2r/oeuV/89bzvzeu2SgZ0zphvOoe7pmP3l/+LgwYP45ptv8Morrzgs9/fj7g66\n49/K389bul79U3f8O/n7OUvXqv/qjn8rfz9vO+N67ZKBnUwmQ1VVFfu+srISUqnUiyW6NYKCgtDc\n3AwAUCgUkMlkbv8v7JusfdGRI0ewZcsWbN26FWKxuNsct7+i69W/z1u6Xv0HXav+fc7Stepf6Hr1\n7/O2s67XLhnYpaamYv/+/QCA7OxsyGQyhISEeLlUnW/ChAnscR84cACTJk3C0KFDceHCBajVajQ0\nNODMmTMYNWqUl0t64+rr67FhwwZ89NFHCA8PB9A9jtuf0fXqv+ctXa/+ha5V/z1n6Vr1P3S9+u95\n25nXK4fpou2ZGzduxKlTp8DhcLBmzRr079/f20XqUBcvXsT69etRWloKPp8PuVyOjRs3YtWqVdBq\ntejRowfeeustCAQC7Nu3D5988gk4HA4WLFiAuXPnerv4N2zHjh3YvHkzkpKS2GVvv/02XnrpJb8+\nbn9H16t/nrd0vfofulb985yla9U/0fXqn+dtZ16vXTawI4QQQgghhBDSNl2yKyYhhBBCCCGEkLaj\nwI4QQgghhBBCfBwFdoQQQgghhBDi4yiwI4QQQgghhBAfR4EdIYQQQgghhPg4vrcLQGxOnDiBf/7z\nnxAIBNDr9Vi+fDlGjx7t7WIRQjwoLy/HnDlz8MEHH2Ds2LHeLg4hxIlWq8Xq1auRn58Pk8mEl156\nyafnvyLEnz3zzDOora1l3589exYHDx6EXC73Yql8CwV2XciHH36IDRs2ICEhAenp6XjjjTfw3Xff\nebtYhBA3GIbBK6+8gl69enm7KIQQDz7++GMEBwdj165dyM7Oxv/+9z8K7AjpojZt2sS+Tk9Px44d\nOyioaycK7LxEoVBgxYoVAIDm5mbMnz8f//nPf9jPKyoqEBMT463iEUIs3F2r9913H7788ksMHjwY\nZWVlXi4hIQRwf60eOHAAGzZsAACkpKQgJSXFm0UkhFh4urcCgMlkwvr16/HBBx94s4g+icbYeclP\nP/2EXr16Ydu2bdi+fTuam5sBAJmZmZg7dy4+/fRTrFmzxsulJIS4u1aLi4uxd+9ePPnkk94uHiHE\nwt21WlhYiLNnz2Lx4sVYuHAhsrKyvF1MQgg8PwdbPxs0aBB69OjhxRL6Jt6rr776qrcL0R2JxWJs\n2bIF2dnZYBgGc+fOBY/HQ2xsLB588EHExMTgxRdfRFpaGjgcjreLS0i35Xyt3nXXXXj++efx4osv\nQi6X4+DBgxgwYADi4uK8XVRCujV399WPP/4Y06dPx+rVqxEfH4/nn38eDz/8MN1XCfEyT8/BAPDy\nyy/j2WefRVRUlJdL6Xs4DMMw3i5Ed6XX63Hy5Ens27cPFy9exNKlS3H77bezn48ePRr79++HRCLx\nYikJIfbX6v79+yEWi9nrsqioCBKJBK+99holOyLEy+yv1ZycHFRVVeHzzz9na/7Hjh2Ln376ie6r\nhHQBztfrV199BYVCgQULFuDnn3/2dvF8Eo2x85K9e/ciNjYWEyZMwNixYzFt2jSsXbsWcXFxGDhw\nIK5duwaRSISIiAhvF5WQbs35Wv3tt9+wb98+8Pnmn89Vq1bh3nvvpaCOEC9zd1+944478Msvv2Dh\nwoXIy8uDQCCg+yohXYC769VgMCArKwuDBg3ydvF8FgV2XtK7d2+sWbMGQqEQDMPg8ccfx4ABA/D6\n669DIBCgqakJGzdupO4ihHiZu2vVGtQRQroOd9fqPffcg1WrVuGHH34AwzB0XyWki/B0b62oqKAu\nmDeBumISQgghhBBCiI+jrJiEEEIIIYQQ4uMosCOEEEIIIYQQH0eBHSGEEEIIIYT4OArsCCGEEEII\nIcTHUWBHCCGEEEIIIT6OAjtCCCGEEEII8XEU2BFCCCGEEEKIj6PAjhBCCCGEEEJ8HAV2hBBCCCGE\nEOLjKLAjhBBCCCGEEB9HgR0hhBBCCCGE+DgK7AghhBBCCCHEx1FgRwghhBBCCCE+jgI7QgghhBBC\nCPFxFNgRQgghhBBCiI+jwI4QQgghhBBCfBwFdoQQQgghhBDi4yiwI4QQQgghhBAfR4EdIYQQQggh\nhPg4CuwIIYQQQgghxMdRYEcIIYQQQgghPo4CO0IIIYQQQgjxcRTYEUIIIYQQQoiPo8COEEIIIYQQ\nQnwc39sFsFIq690uj4gIQm1t4y0ujfd15+Pm83neLgZpAV2rjrrrcQOAVCr2dhFIK+h6ddRdj5uu\n1a6PrlVH3fm4b+Y5uMu32LX34PZnFuFyYW0nlebW6a7BTXc9bn/QXf92/nzcv54pQW6pytvFIJ2g\nodmA//2WB63e6O2i3FL+fL0S/9Rdz1l/PW69wYRvj1yHsq7J7ec3e9xdPrBrjyatATt+zcXfv8xq\n97bqBh3SL1agtl7bCSUjhBDfom7QYfuBHKzbdtrbRSGdYOP20/ghvRB7jxV02ndo9UaYTEyn7Z8Q\nQnzNvoxC7DlWgE++v9Qp+/erwM5gNN3wtp/+eBlbv7+Ebw7ntWs7E0M3LUKI/2lo1rOvb+a3lXRN\nBeVqAICmSdcp+6+t1+LJd37Dfw/mdMr+CSHEF1l7FdY36VtZ88b4TGDX2KzH98cLHB42nOkNnh8+\nWqs1VDeYb2717bjJffXLNTy2/hCatIY2b0MIIV1dXqkKlwpsXdrf2n7Gi6UhnaFZZ75vCTupu1Nh\nhXm80KEzpe3etklrwFvbT+NifnVHF4sQQryqStUMAFDWNaFK5b475s3oMslTWvPVL7k4eqEcyrom\nPDJ7gNt19B5qlTMvK7Dlu2ys+tMI9I0Pd7sOh8MBAGh1bR9vcOBkMQCgtKoBvWPD2rwdIZ1l3bp1\nOHfuHDgcDlavXo0hQ4YAABQKBVasWMGuV1xcjOXLl0Ov1+O9995DQkICAGDChAl48sknvVJ20jWY\nGAZvOnW/tFZ8Ef9hHVvnrsrTZGLQrDMiKMDxEaGiphEf7L6IUf2k2JdZhNULRiJOFuJ2/57ux54w\nDMPeh49dKMe1EhXe3XEOn66a1q79EEJIV2ZtDDIYGXz1Sy6W/XFwh+7fZwK78poGALZI1x1PLXa7\nfr8OADiUVeo2sPvuaD7yLd1SmtsR2FnRGALSFWRmZqKwsBA7duxAXl4eVq9ejR07dgAA5HI5tm3b\nBgAwGAxYuHAhpk2bhv3792P27NlYuXKlN4tOuhB3v7GhwUIvlIR0JusogoYmPXYeykWEWITpo+IB\nAJ/vv4Lfz5Xj7aXjIQsPhFZnBAMGu3+/jhKlBiVKDQBgx6/XsDxtuMN+VRotxMFC1Gk8j1c3MQxO\nXanEoTOlGNVfBgGfiy9+zsHrS8ZAFhHkNtgkhBBfxzAMmrS2OCMmMqjDv6NNgZ2nVgAA0Gq1eOWV\nV3Dt2jXs2rULAJCRkYFnn30Wffr0AQD07dsXL7/88k0V1GA0/9TzeZ57j3oK7KzbuBsncqmgBt8d\nzWffW7untIfRT8affHnwGnpEBWHKsFhvF4XcgPT0dEyfPh0AkJycDJVKBY1Gg5AQxxr13bt3Y+bM\nmQgODvZGMUkX1tisx6ot6S7LZRGBXiiN//NWC7vGbmzH9TI1Ki3Z2ayB3e/nygGYx+HJwgPxwofH\n0aQ1YMwAucN+8srUDu+vFNZiw5dZ+NOMvlBpbK28Wr0RIoGty+fv58rw+b6rAICrxXUQ8rnQGUz4\nPr0Qj84e4L4ZsYMYTQy+PpSLcQPlSJDTFACEkFtHpzc55OYY0DOiw7+j1cCupVYAANiwYQMGDBiA\na9euOWw3ZswYbNq0qcMKag2e+DyOx3U8BnZcjmUfrneL01eVDu+dW+zySlWQRgQiNMhzjbXBqcWu\nvLoB3xzOw8KZ/RAeIvK4XWdhGAYf7cnGoKRITBwS0+Ztfj5l7lpKgZ1vqqqqQkpKCvteIpFAqVS6\nBHY7d+7Ep59+yr7PzMzEkiVLYDAYsHLlSgwcOPCWlZl0DZomPWrrtaiocZ0zKDRIgLRpvb1QKv/m\nzRb2U1cr2deVHlJuAwCXw4GJYdhAUOc0NUKzzoiGZj2CAwS4mF+Nd3ecAwDs+DUXYwbI2PVq1M2I\niQzGP3eew/UyNcYOdAwQ+8aH42J+Dc7nmcfUue0eyjDgwByYnbpSiVH9ZWyl7b6MIsREBmFo7yi3\nx/HtkesIEPIxa2wCTl9WYF9GEfZlFFE3T0LILcFYgrlGSzfMpBgxpo+M905g11orwF//+lfU1dVh\nz549HV44e0ZL8MS7gRY76zYGk/lzE8NAbzBBJOCx/8lW1sCupFKDVz7NBABIwwOwfukEj9/rfLP7\n5IfLuF6mRpCIjyVzbA/Jb3x+CjXqZry7bKLHfXWE2notMi9XIvNypdvALq9MhfSLFXhweh/wuOb/\nGyN1J/U7jJuMrVlZWejVqxd7/Q4dOhQSiQRTp05FVlYWVq5cib1797a435Ymz+yuk+D6ynEzDIOs\nq0r0T4xAUICAXf76xkMoKFdjzMBol23eeW4KoiOpdbejebOFPft6jdvlJoYBl2OrPOVwgFq1rUtl\nXpnrnIb5ZWoM6hWJc7n2iU4Yhzma6uq1iIkMZgM3514uF/PN5VE36FBZ1wS9wXZPzbyswJgBcrzx\nn1PgcTlISZJgz7EC5JWp8acZfaHTG/H1oVwAwDP3DUFPuRgRYscK1T2WKR1mjU2A0eQfPWwIId6R\nnV+Dd3acxcuLRyEpJtTjeqevKvH+7gt4/dEx+PTHywgK4LNJyRLkYowf5Hq/7QitBnattQKEhISg\nrq7OZbvc3FwsXboUKpUKy5YtQ2pq6g0XMrdUhfJqc02ytfXNXl6pCtGRQS0Edo4tdl/9cg0HT5Ug\n7Q990OwU2OkNJhhNJnzzm23aA2Wd53F9gGsrnzUBS7NTwHfd0m2lTqPF1r2XsOD2vojphAem1oK0\no+fL8dvZMqQOjmFPyhtJZ369TI0DJ4vwyOwBDt1siHfIZDJUVVWx7ysrKyGVSh3WOXz4MMaPH8++\nT05ORnJyMgBg+PDhqKmpgdFoBI/n+e9ZW+vaqgOYgxulsv5mDsEn+dJx/3SiEDsP5+GOsQm4/7be\n0OqMEAq4bOr7zEsVEAl4mDE6Ht8fLwAAGLV6j8fnKwFtV3SrWtjdVcQ06ozgcjlIkIvZvz0ABIUE\nQGzXO0UYIITW7tZQp3FNolNR14zbpGLoLffXYX2kOHtNiWsldkEgn+dwrpy5VuW8G1ZBZQPAtVXg\nbvkuG1NG90SBJctmZLh5TEpOiQpSqRjVdlnlNn1zHuEhImx7bRa7zH4CdqlUjOsKjcN7Qghpjx2/\nmiuSvjuaj+fuH+pxve0/m7ubf3s0n/39sgoUdV6Kk3bv2V0rgLPExEQsW7YMd9xxB4qLi7Fo0SIc\nOHAAQqHn7owttQLYT5AbHCx0+DEuLFfjzW2nkRgTigem92WX68CBLCIQAj6PrZnm8riQSsUorzHf\nCM7lVYPrJlAMEQdC7NSFsqUbgDBAAKlUjCNZpRAKuBBYghyBgOd2ux8zinG5sBYffHsRW1ZN97jf\nG73pNBhsfyN3++BYWz3tbrb2We/a+r2PbTgEk4nBiAHRuDM16YbKSjpOamoqNm/ejLS0NGRnZ0Mm\nk7k8JF64cAGzZ89m32/duhUxMTGYM2cOcnJyIJFIWgzqiG/77ph5PPHV4jrU1muxcstxdvyy1eSh\nPRBkd9MRUqXNLdFZLezuKmJq1M0IDRIiJNDxEaCwuBZyiW0wv7JagypL4jJnckkQFDWNOJ+rxPQR\nsai0rJcUI8bZa+YhDpJQEWrUWpy5rEC/WFvNdktZVksr1C7zO/1+qoh9rWk0tyA2NZsrHEqrHMtX\np9E6VETUqG0Vs5WVaofpiazrGYymFsfv2zMYTaip10IW7lvjTimIJaRjWH83rV3Uj5wvww/phRAH\nCvDkPYMgCQ0AAAQK+VBB5/IbZf6s8+6rrQZ2bWkFcCaXy9mHx4SEBERFRUGhUCA+Pt7jNi21AtjT\n64wOP9o5lnluCsrV2LDtFLt86du/YFAvCZ5/YBhMltYo641Aa0mQUlXX6DZqLilTAU4tWPbfeTa3\nCj3tBl1X1TSgoLgGG7abvz9Bbr4JNzXZarrtu5ZoGsw3plq1tsWa8BttBShX2Gpg3e2jvt78/cVl\nKiRJzS2G9hnM2vq91myg1bUNHdZiQTefGzdixAikpKQgLS0NHA4Ha9aswa5duyAWizFjxgwAgFKp\nRGRkJLvNXXfdhRdeeAFfffUVDAYD3nzzTW8Vn3QyE8NApzf/rl0vU2PllnSXoA4AZoyOwwVLl7nk\nHp67mZCbc6ta2N3RNOogCQtEeLBjBaamSQ/70W9anRHFSo3DOlFhAahSNSMuKhiMiUFOcR1MJgb1\njXoEB/ARbRcYThgUg++PF+CX0yUIcPMgIxT8f/bOOz6qMuvjv+nJJJkkkzLpIYUEAoTeO9IVX8sq\noERcWV3bqruyyqor+u5aF91Vd9eK7lp2reiLBQFRqQFCJ0BIQkIaKZMySWYyfeb9Y2bu3Ds1CQnJ\nTM738/HjzG3zXHKf+zznOef8Dp95JtltcK0Nu/mbs8xnjda2T280o6G1C6cuePf+Oa7nwGS2uF17\n/+l6bP72HB4vmIgsP2WLzBYLHnvrIFradfjrb2aRWixBDEHC7M4ijf3d8t53JQCApjYtvtxbgXVX\n26IoHPZFo4fc9QH12HXHC+DK1q1boVQqsW7dOiiVSrS0tEChUPg8xxsXarlhngIX8RSLDw9isT2P\nQGD3yjnCDR05cd5CLHUGk9dV6urGTrz6+SnEyJwDYm2TGr/5217muyNHgd0ytdY5mDhSGLQ+FDi3\nFV7Et/sq8IdbJ3Da0tKuwyc/luHWRTmIdPEqNrdrcaREiZQ43+GderuR2c5aNTV5CWP9tvAiEuRh\nmJjr3Zincg+DB7aSHgCMGDGC8911dT8hIYERaSCCm04XL4mn8OusJBliI0Mxe2wS9EZLt8WXiJ4z\nUB52s8UCjc6EjGQxoiK4holaa+QsQn728wXX05E3TA6JSIBx2TEoPNuIfafq8bu/70NHlxGJMVLE\nRYUwx47LjmVCer8trOJcJyk2DFKJEOV13Ly9zi4DDC7jEft7fYtt9VtvNOPlT054LM9ReKYBReea\nEB0hwU/HnQXSvy2sQiNr3NcbzExY1TMfHMVjayYiO8W7cXehroNTXJgMu6GDL3X4BQsWICEhgemL\nmzZtgkKh8HkOEbg40p3ULpEFANDZ5dwWKuG+mxPkUkagrD+nzX4NO39egAceeAANDQ2orKxEQUEB\nbr75ZixYsADr16/Hrl27YDQa8dRTT/kMw/TFQ3/dzfnOtuPMFgsKixv8XsNsP8mRY+e6QuiKr1p2\nTW22MM4WVkK5a76AozAr2+BhPwCOXARvNmlpjQr//NymLnapRYNhCc5V89e+OIXqJjVkYWKsWZzL\nOe9P/z6Czi4jpo/ybUQ77p8dDsNW9nQUijWZLfhit60GoC/1MBJeIYjBT2un97pigE398pFbbDXJ\nhAI+lk5NuxLNGrIMlIfdscgYGSbh5NMBtlqv720rcTtnXHYsTpTbxjlZmBg3zMkEACTHh6OhtQvl\n9ny6CKkYsZHOEMUEuedwxTuWj8SEnDj85ePjbvuOuChVO8hMkqHiUgdj5BmMFq91bd/++qzH7Q4R\nFQftXQbOAsezHx71OdaxI1vafYSTEsGFP3V4wLbowhY46s45ROBQp1Sj8Ewjrp05DGqdbT6v0Znc\n3gMqtR46gwnfFlahQ8M1/G5dlIOXPjkBAG6RA31Jt3yBvrwA3koavPHGG5fRLO80tGjw5Z4KrJg5\nDN8cuIijpZ4HATYObxTjsTP5LkKuM5g5CdeAzUjr0pvwlocBQ+9iCDqMOLMXw67dR+FWAHj+o2PM\nZ3aJhpKqNlQ32cJiPLlxHSsFHV3uqwhsHB5Lbx47s8UKoYDX7WLt3fHY6Q1mCAS8bucxEMRQ58s9\nFchKjkR+Voz/gz2w/3Q9ymrbESIW4Ma5WThS0uTxuPioUDSptBAJ+RB5yXMm+oeB8LB3dtne+7Jw\nMUQu72OHaqUrS6emOQ07qVNNVSYV47c3jcV9f91j+x4mRnioCNfNzkBybLjHcWp2fiLjCe6JaNf4\n4bGMAFlf4almY0u7DjGRIR6O5q7G+xvHieChuwq2l3sOMTho7dDhyHklFk5MAZ/Pg8VixQv/OQ61\n1gihgMeEYAJAcQX3nVndqMa9L+/xeN00RThWLxyO//5Qhok5vlPaLof+C/LsJ0qqVSipVkEs4nNV\nt3zgUMt0GGv+PHZdepObUXP2YisOlzR1ayBy/NHZyfAajmFnYH3Wu4VUsnEoa36x+wInlIXHsxmL\n728/j2tnDENKvPNlwTZKLVYrzl5sRahEiKwkW4iJ49+jQ2NgvHNG1n0ZTbZEcrbB6im53FHjqDse\nu3vMknYAACAASURBVHte3g25TIJN9/ZeHZUghgqdXQZ8bQ9h62mtLYvVCovFis3fnmO2ldaoGFWu\nqyakYNexWmZffLTNsNP7eS8SwYHabpxEhkkQFuo00hJjpIz6NJvJI+IxnBWe6OrlC5UIkRYfjuom\nNcbb68hdyxLTSosPh0DAQ2V9J3O8A28pAGyiwsVQqQ2YMToRW3ZX9GftcgDAf3eV4f4bxnjc5zCK\nAZv4mixMgom5cejoMqCmSY289GjweN5r7RKBSXcUbDdu3Ii6ujpMnDgRDz/8cLdVb9lQKSF3rvR9\nW61W3P3SbhiMZuRmxGDSSAUqL7UzzplD55o4c+zKRrW3SwEAoiIkePeJxeDxbJEwGWlyrFwykkkR\n6w8GtWHnq97MxYbObiUf7j5Rhwv2VT611gSr1QqD0YyMxAhkJkVi19Fat3Ne/6rYbdvLn57sdrsd\nggRsTxZb5YtdEPaLPRW4Y/lI5rtrzqBOb3uAfj5eB4lYgBCRAO0aA745UIXDZ5vQpNKiQ63HhjUT\nmXNUrJAro8nCFI0FgDfXz2MeytIaFX739/144e7pHM+gwWRBqIRbrqFdbXBbxeTzebCYrX49dg4D\nt7WDVjgJojt4EjVhU1qjwu4Tl/DL5SPcFlz+va0Ee0/Vc7axpZaXTUvD5JHxTGTArPxEFFe2Yvqo\n/qmpQwwuYiNDoJBLMSY7BooICZZOScO0UQqUVLXhY3u+2fCUSGbh9M4VeRxjRRriPu7euSIPLR06\n5Ge5Fwh/6o4psFqt+PWm3TCZLWDbPTmpUWhs81wgfVx2LG5fPgIhIgEMJgvCQ0VIT4hwkw3vS0RC\nPo6VKvGHNwvxp19NhVDAR3VjJ57+VxGGJURw+uWpCy04daEFrz88F3/99CSqGjqxdmku5o5L7rf2\nEYMDVwXbBx54ALNnz0ZkZCTuu+8+bN++3e85nqBSQlwG4r4b27qYqLbisiakx0pRftFZ99MhhJIW\nH46aJjXO2QUcR6ZH44a5mXjm/aOc64WIBFC1eVYW9sblGrODOi5Ob/Bu2FVc6kB37N1/f3+e+Wwy\nW9CpNcJgskAsFHhU6WIj4PNw1cSU7jbXDba9U+GhsCtgK+7KxrWu3tb9lXj181PQ6EzITpLhj2sn\nMfscBmKIRMiJ12UbUK5xvEUljZy8hHaNAWcutnI8dlq9CWW1KvxwpIbZ1mY3FtkvJ8eKgz+PXXdD\nOgmCsOEaCs7m+0PVeP6jYyg804CjLvlIFqvVzagDgILFOVi9cDg2PzofclkIclKjcO91o7FmcQ6m\njFRg4+2TcdP8rD6/D2LwERsViufumob87Djw+TzcvCAbaYoIxLLk+zfcOoH57Lpw4Bq+CQDJceEe\njToHPB4PEpHtPHbEzK2LcpCbGsV8H8sKOx6dKYdMKoZYJEC43bM4f0IyEuRSLGPlf4azvI6eeObO\nqfjlshE+j3EwMj0aANDYpkV5bTuUKi1+PFYLqxWorO9ETZP76vw9L+1Gld3Y3N+NnH8i8PCnYHvd\nddchJiYGQqEQc+bMQWlpaa8U5YmBp401f66ye+Na7CVTxELnuy8uOhRymYSJclBEhyIrKZIzRwfg\ntb52fzKoPXa+JjdtnXoc7fSfX+eKQ/xELBL49fhJRAJOPafc1CjcsigHpy40M6Ii6QkReGT1eCbH\ngI3D+2a2WHCy3D13IStJhsr6TjS3a1FW044xWTGMISYW8mEwWVDTpGYGk6hwicfVUo3OyCjtsH8X\n4IZ9AsA735yDK8fLmjF5RDzzfcfhavx84hLnmOZ2LWRhIvzp30cwOz8JNy/IZmoAWixWnK9uQ2xk\nqMfcBDLsrhyk3BX4dHQZ0NbhWRQCAD79qZz5rHNR1q1Tel4ZnD/BfYFqEqvPpycMzTAfwkmMzPnu\n5vF4SIyRcmq1bbx9Mo6WKpGbFuXpdL+IRQJodCbOuC4WCTBlZDzO19jUr++7YQzUWiPKa9s9KjHP\nzk/C7PwkWK1WCAQ8jMmMQXxUKC41a/CXj22iBKnx4TCaLMyYmCCXMtdnc82MdNQ0qpGmiEDesGjo\njWYcOuvMQ33xvzZhl+XT0t3OFQr4HtMyOjwUcCcCH18Ktp2dnXjooYfw+uuvQywWo6ioCEuWLIFC\noeixojxxZTCZLejSmSALE6NDY4A0RMgsYLG1J8rs9V5bO23jcVZyJM5VtQGwLUq9/MkJADZD0JGf\nnpEow59+NRU7i2qw5+Qlxii8kgxqw84xaUmODcMf1kzE/X/znJD462tH4eSFZhw80+j3mg1261os\n4mNSbhw+Z8k5C/g8jvepS2/iGFJzxiUhNT4cF1jyzLGyEK8GoiN/4OCZRqi1RsTIJBw1zVRFBC5c\n6sA/thSjqrETmUkyFNiVLmPtgxWbqAgJJKzSBzfPz8anP5XjQl0H/vTvI/BEWzcSvKsbOjF+uHO1\ntdDDv+PRUiWa2rTQ6Ez4/nA1Fk9JZTx2XXoTXviPbRD0lA/Un+o/hBNS7goOHnp1n9d9ruHp9S1d\neOrdw4iLCsXaZSPwIyt3LixECI2O+h7RPeKjbUacYyx45s5pnP3pCRGXtQCQnRyJopImxHoI6Xcg\nFPARFS7hLDp4gsfj4YY5Tg9zZLgE664eifqWLtwwNxM8AP/9oQzDEiPA4/EwNisWqfF1SIkLQ+GZ\nRsTIQjjnOyipcjcAvztY5bbthbun49UvTjGeOgeklBmc+FOwnTNnDlauXAmJRIK8vDwsXboUPB7P\n7Ryi/zFbLOCBx3mvuPL+9+ex73Q91q8ah00fn8CsMYm4bWkuhAI+I4rE5/HQrjHg4X/sZ2pTZyXL\ncK6qDTmpUYgKlyAuKhS19sVUdgRgcmwY5oxNwp6TlxAh9R1R0B8McsPOtrI3KkPOqQehkEvR1qGD\nwWRBYowUU/MUaOvUd8uwa7THMIuFAsRHS/HuhgV45bOTOHmhBaESoVtdCrbR5vjDsY09odB7NGt1\nkxrr/7kfMnuy+dKp6fhoZymzPy89Gj8fr0NVo21wqFWq0WWXUfVo2IVLOLkOGYn+B1lVNwy7Sy1d\nnFVUT57So+eVnLAvrd7EdBxPtTwctHXq8cQ7h/y2gbh8SLkrsDlR3gyll3wjB64e+B1FtnDp6iY1\nzlW1ocu+iPL83dNx6EwDvtxb2T+NJS6bweZdD5UI8c/fzek35eI7lo9ETmoU5o5L4mzPTbOFP86f\ncHm5aTPHcGsu3rIoh/kcHSHB03dMgclswVVT0pEU7Vn1csXMYThfo0Jlvbv65u3LRmDX0VoMT4lE\ndIQEv5iXhZfsXkIAkEqE6NKb8OyHR/GHWyd4FVHRG804dl6JqXkKn5NPYnDhS8F27dq1WLt2rd9z\niL7DIfznyoOv7INCLnULiWSz77QtXeE/P5Qx389VteIv985kFmemj1IwodXVjWrweMCKGcMgk4ox\nfbQtH/2WhTnM+2vGaG6OemaSDL++dhRjFF5JBrVh51BlDBELwOPxGBVGmVSEVrt7M9JeINRfvpwD\nR1ijI94fAPNwhEoEbkaKlGPY2T6zlcSEAt8v5tYOPVo79JCFid3CFCfmxiE3NYoJEzEYLai3h4/E\nRbvX/3F9huUyz4MTG5Wf2lWAzS3takSycfVkArbByeGx6/RRXuEsK+mU6F+ulHIX0T+8+vkpj9t3\nFNVg8eRUAL4FiLpYnvG4yBBMzVPgy72VWLs01+s5xMAwWL3rjjGuP5CIBR5z1hPkUvz9odndEkO7\nXIQCPqaOTvQqyBAqEeLBX+TjoddsXvNrZw5jat9NHhGPOWOdRmmiXMo5NzU+HOdrVCivbUdjmxYy\nqQi1Sg1S48Oh1hoRHSGBUMDHB9vP40BxA9o1BqoVSQQ9z7x/BFEREtx3vWel2d6g1hrxxNsHsWBi\nCkeBV280o0tvYhZm3vi/YjS367DqquF47sOjePSWCchh5fSy570tHXooVVqo7Iuny6alIypCgp+P\n10GjM2FUhhwioQALJ6Uy58REhjBjsyem5vmuKd1fDGrD7ge7YqVjsHHkjoWHijBzdAJ+PnEJiyfb\nXoyeFIfiokKwemEOM2EKCxEydXrErJDGySPicaK8GfPHpzD5K/lZMRiZHs3xzjmMxzDWNk+J5J5Q\nRIciPysG18/OYFbReTweUhXhnPj/cnuYJzuRvWBJLnYcrsYEl7oX0RHeyyRcPycTX+6pYB5Sfxw+\n67nGFWCLGS6v44q/6A1m8HkOj53zNxpbu7C/uAGjM+TISY3i/DsDNje5gD+oNXuChv5S7gpWSeby\nWhW27rmA+24axwl59obVasVrn55AbrocSzzk4egMJkhEAq8r92qtEcfPNwFWYLiPvKWPd5Vh5RKb\n+uU5DyVeYiNDOIJIjxRMQny8DPHxMnz90v/4vQ/iykPedS7SkCsfruQNWZgYG26dAIVcihJ7Pg3g\nvngsl4Vg4+2T8fS/igBwo3v2nLiE3SfroNU7o1+umTEMN8zJRKl9vK9uHHoqh8TQwmyxMKr03jxs\nvaGhpQsdXUZ8tbcSc8YmIcpeMqyVlc+m0Rlx+JxtXrv523OwWoEteyqw4dYJEAp4HpWnt+6vROEZ\nm5cuNjIEN87NwuyxSfh6f6XH0O3BSrcMO1/hH3q9Hk8++STKysqwZcuWbp3THfQGM47Zi49LXF6o\nEVIRbl6QjUWTU5EYY1vR9DQd3XDrRI64wE3zs/GvbSUAbDl2DqaPTkBuWhSiIySMYffAjfng83mc\nkAxnKCbbY2e7zqgMOc5UtiJELPAoFqKIloLP42HFzAyMzY5l7ikukuuZc4STxrEMuzljEzF/vDNM\n5ferxqGlQ+8zZCbUfn1fiZvXz8nEzNEJeOKdQ5wSDK6kKyLcDTsvHrvn/3MM7WoDDp1twAt3z3Cr\nU2QyWSHglkEi+ojuKHc5uBzlrmCVZN7wj33QG8xQRIX6XIVz0Nqhw87D1dh5uBpRoUJkJslgsVjx\n5tYziAwT44ejtbh1UQ7HS6FUaXG+WgWTxYK9Jy8xtb38cbqkEXFRoSircveAZydHQqU2wGS2ICcl\nEiOSZVfs7xDIhvxAQt71wY1jVZ89DnualLLDrNiG3/eHq92OrbQrYzsu41raiCCCDXaOd3O7jtOf\nekt1Yyee/dBZUqCstp0R/2NHtLBrcjpKFERHSGCxWuGt6+0/bTPqclIiGadEfFQo1l2dd9ntvpL4\nNez8hX+8+OKLGDlyJMrKyrp9TndgG3MhLqvnYaEihIiFSIxxNn92fhLaOvXITJThtS2nbdewyyRL\nxALMHpOIWfmJjGHnWqTcNazREfvO9djZQzE95Njde91oVDV04uh5Jaf4rwOF3PlApymck6G545JQ\n16zBnLFJeH97Cart8qp5GU7ZZ1cP18hhcubzE7dNwp/fdwqn/OaGMchJi2JWBc9U2iaCqfaaG2xW\nzBgGwJboeeGSe06Bg4ykCOAYdxu7mDHbkHXkAClVOrRrDJzwMMBWG08k4jPePqLvIOUu72jsuath\nrEUZtdaIf355GjfOy0JWUiQT+u3Ic/UHe0Fj87dn8fDKcTCZLSgqcXq/P9pZikPnGvHLZSOQGBOG\nR98o7FX7n3z3sNd9MZEhSI4LQ1VDJ+XsBCj95V0HgtfDfjl0977Foc5VSH/n3P2LsTB9dhJHztkW\nZ6PCbQXg65S2cffMxTb8cKyO+VscPteEqWNasXiqu7efIPqKIyVNKK1RYfXC4X3mMXNgtVrx8a5y\n5KRGYmKuu+CRhpXaVN3Y2SeG3V/sarXs6zoNO6cjo9xDdMuhs43ISYn0W6Lrvhv6Lmx0IPBr2PkL\n//jtb38LlUqFrVu3dvucnqJzEfOICHV3+YiEfNw4N4vjoROL+BAK+Hj9d3OZbZNGxONISROnIDeb\n9avGcf7oUg/iKeyQC4fXLFQixIj0aFQ2eDaQspMjPW4XiwS43V5j57c3j8PnP5Vj3PA4pMR3798q\nM0mG0ZlyFFfYDLj46FCEhYg4MtUAkJMSxTHs2LkCsjDfLrS4qFA89cvJeOq9Imab3mD2WkA+LioE\nSpUOFXXtbobdb1/bh2l5Ctx17SiP5xK9h5S7vPObv+0FwFVt3X64GiXVKrz08Qn883dzmVzS7tad\n6exyhiDXt3Th9a+KsXphjttx5bXt+Ghnqd96W70hK1mGSbnxmD8+Ge98cxbLp9MkMRC4Ut51IHg9\n7L2lJ/dttVohFPAxIi3K6zmvPTQbPAAWgwl3X5uHX9kNu/joUCyfloZXPj/FeAj+s+M84qKci8iv\nfXoCKfLQPpnw+mOoGvFDnX9+VQwAWDo1rVu6DD2hQ2PAziM12HmkBu9uWIAdRTWQSUWYNsomJML2\n2FU1qj0afz3BYrW6KT1XNXTiyz0VGJ4SyYlQO1rqOb3ogx2lHrc7iJCKECEN7LAyv4adv/CP8PBw\nqFSqHp3TXaaPUqDwTCMjw+zA1wSJnfztKVRx3fKRSIkNwzwvClx5LG8YwDXiHF5EtrcpzKWunDzC\nc8fJTPJs2LGJDBNj3TU2ly+Px8OffzXVY906V0JZ9+xor+tAwRZjmZqnwJrFzgko+yGeMjKeiUt2\nyLUmxoQhPFSEVVcNx7FSJUprVNAbzR5jlAFgUm48th2qxqUWDbQe5NYPnm0kw66fGEjlru56EK4k\nVqsVW/ZUeNznqENlsS/kCIV8mA1mn/Uz2biKBjW2ab0qxJ692OZxuwMeD7BabQtJ8dGhuNjQvYnn\n4wVO5a9Hbpng40hiMEHe9cCAx+PhzfVzfXo62FEA7LmBLEyM/KxYvLl+Hu59eQ/zvulweW+U1qg4\n43Vjm22R6I7lIznRPQRxObSp9X1u2LGjtTq6DPh4ly1yb9qoBJyuaMHhs06l+r7IKT1oz39jU1zZ\nimJ7ZNo8luLuhTrvUWiecIzBk/2UWgkEeiye0pvJW28FGR5ZOwUVde3IscuJOkhOlPlcffrNzePQ\notJ6PWbd9b0rsKqIlzGfF01JQ4fGgOsX5HBUMjPT3MVKbpyfjaRE/4adK2NHJvg/CEA0K08vNTnK\nLRH9sdsnQxYmYTpdVmo0EhOc7UmMc04Orp8/HFNGJyEvQ46U+HD8bg2YXLpbl+chZ1gD/nfzIU7Z\nBlcmjUrEtkPVMFl5sHoJDVOqDZxwUyKwqVWq8cgbhZg1JoGjUnU5lNaokBwXxkyc1FojHn3jAK6b\nnYlFLGWq89VtSFNEeFTVU6q0+LbQvQ4VADgczjz7MyoW8qE3mN1qUXXpTAiRCMDn8fDDkRoYzRYs\nnZLG8dgBthV6h5DQqgXZ+PjHcrhSsDjH44phdIQErR165GfFIDpC4tGwk4gE+P3q8ahv0WDzt+fc\nVHKJwIG864FDb8PXHIrdrsXM9S45+BcudWDmmES0derx4Y7zKK1RQaMz4d/fn/cp2U4QPaG1Q4+s\nJP/HdZcT5c1MXWgAOFPhzP/+/lA1o1fhwJthV1zZAh54GJXhdKqcvdgKHoCwiBDsOFyN2WOTECoR\n4nhps8drONhnz5HLTo5004VgkxQbBrGQzxlnn/rlFCTIQ5lC44GMX8OuN+EffSnIkJMW7RYCYTGY\nfIZSjM+0PSB9HWbCvt7qBdkAgC61Dl1qp/vXYnT3UF09Na3HbelRmAwrJFLdoYWm09aeZVPT0NKh\nQ3ZCBKeQqtnI/fcTssYttVqHCVm2f7/mZm5OHgDouvyrbMrsNQcbW9SMV29angIHWas3j/59n8di\n5hQuEphsP1SNZpUWB4obPBp27RoDqho6kJ8Vy9luMltwrFSJiblxnFzS6sZOPP/RMaQrIrDxl5MB\nACVVbdDqzfjvD2U4daEFq68ajs4uA174z3GMSIvy6LFy9aqZzBbGk+8QL9AbzGhs7WJW29m1H386\nXocPtp/H+OGxCJUIccBe12bfqXomOXvJtHRsP1gFvcGMn49fAmDL2eWBK+oUFiLE6EzuYsak3DiM\nyYxB0fkmtHboIQ0RuolFAcCs/EQULM6BSChAekI4qho6MXtsH47SxBWH6mIFN74ii36/ahxS4sPx\n+38eQFmtLeLphf8cQxOrjqXFTx4QQfiDLdDjyD8zmiw4er4J43PiOOrPja1daNcYGOEgk9mCIyVN\nmDwy3k3noa5Z41aep6TaGZXiatQBgEptQJfOCGmICKU1KkRIRRAK+Hj5k5MAuGkSm+z1IdeodPj4\nx3LsPV2Pq6eno9MlIiZBLkVDq9N2cCyg5A2L9mrYbbx9MpJiw7DpY1uuXm5qFNavHhdUau1+DTtf\nISN9eU5PCL/CldwLluR221MZFxWKiblxyEmJQlmtCvPGX17R1e7ADjllry7eND+b+cyeLLpOHNk5\ndv7KN3iadHLbwoMszPb3aVcb0GQ32P3l8RGBzS/mZ2N/cQNEQs/Pz8Z3D6NDY8Azd06FQi6FTm+C\nNESEbQer8OXeSiyenIrxw21GX5tajxa7fH8Va5UvROJ89s5UtuJYqZIZuEqqbZMjtdaIUIkAAj4f\n5y62ur3cOzQGHD7XBEV0KGfi9Ie3DjKfVZ3OxQtHHcbjZdyVQrbi1v/MycLx802oY9XEifDwjspM\ninQrUXLz/GzERoVi3PBYfH3gIq6fnYm9p+o5x4zNisEdy0cy3wV8Pqf4MkEQgw+BDyEjhwBaTmoU\niitb0aTScow6wCFMUQadwYTbl430dBliCFHTpEZ4qIgzhpgtFpRWq8Dj8ZCeYItaMZktKKtRYUR6\nNLpYqTCO/LOv9lVg28Fq/HLZCOSmRcFitRlIjjHwz7+aCgGfhz0nL2HboWrUNWtw41yb1H91Yydk\nYWLsdxmjALiNW2ySYsNwqVmD1g49xCIBnv/IpsY3Mdfp8Dl3sRUjh8kZoTMA+PB7m9hhnVKDt7ae\nBWBbIHXk2bkadg4yk2Ru2xwkxkghEvLh6J1WuAsUBjp+DTt/ISMPPPAAGhoaUFlZiYKCAtx8881Y\nsWJFv4aMRPSDCIEv5vfAOOPzeEwhxkXdkEzvCxwrMcmxYV6PYa/MuNboYr8oBH4MO7HLxF0iFnBC\nS+aNS4ZIKECoRIBzrBpAUg9hchaLlVT8goTIMDGS48LQqXH36OoMJnTYtze2aXH0vBJb9lTgj2sn\n4ZLdQNpRVIMdRTU+f8NVyfbrAxc5QidnKlvx0icnsGBCMlYvHI6/2Ff92Kz/5wHms2tdSAftGj0K\nixug0uhx9LySs8+1ZhwAREVIIJOKOROzsFAR462LChdjdGYM5o9PhlDAxwt3T8fGdw9DZzAzOQ8R\nUjFusQuvuNbLSvTRrwmCGJywDTvXiBUHozPkKK5sxQYParnKdi2qi2xRM7ctHcFEFFgsVrz73Tk0\ntWmxftU4t1qxRP/gq4TXwYMH8fLLL4PP5yMjIwPPPPMMioqK8OCDD2L48OEAgJycHPzxj3/s1W+X\n16iw8d3DEPB5ePuR+cz2z3++gO2HbeNmeKgIj6wej90nL2HX0VqsXZrLKcbd0NqFprYu7LSPsyq1\nHhvetBlzmx91XvOJdw7Z2ptiS9f58VgdrpkxDLACT71XhBCxAGOzuZE3DiKkItx3/RjGcHOQpgjH\npWYNWjp0nMVf9vj6l49P4MFf5Pt1AoSFiBjDTi5zzl3TWZFp6fbc1OgICdo6bRE4ty8bgbxh0c7+\n4nCCDEJtgMulWzl2vkJGXn311W6d0xdkJMpQWd+BEA9GwlBm+fR0KFVa/Ooa77U22B4EV8GXrCQZ\nFkxIxvkaFWL9JNe6GoVhIULGsFs8ORU3zc+y/54YWr1tohsXFeLRk6M3mj3mRRG9ZyAHn6iIEFxq\n1sBiseK9becQHy3FihnDcJFVq62pTcuImRwrVUIi8r9S5ihs6pqb4qpe+dU+23WPnldiyZQ0zj5P\nBpmjJIgrJrMVb39z1uO+OWOTsHV/JRNirJBLESEVu3no2ItPU/MUWLlgOPM9LioUm+6dCaPJ7HFh\ng31fN8/PxsJJKW7HEAQxOPnl8hH417YSTBmp4Gzj8YDCM41IT3CmG8wYk4iqRjVTFJkNu7j5/75X\nhPzsWGQnR+Jvn51ktl9q0WBYgnfvBNE3+Cvh9eSTT+L9999HQkICHnjgAezduxchISGYMmWK1zly\nd3EsWALgKLa3depxpMRpGKm1Rk5ZnHNVbUydZwAorVbhw52lzNjFziX35PVqsC9UavUmPLn5EFPL\nTWcwo52VrsAmNy0aOalRCJUIOM9vVlIkDp5pRGunnjG0PPHdwSrkZ7lrL/z25rH466e25z4sVIS/\nrB4PvdGMPSdtqQ9CAQ9jMuWMYRcZLsEf105CjCwEz310DI2tXUiJC0csS4/CMfJ2TwM7sAioWfXj\nBRNhNFmoBpoLKXHhePw230nWQgEfL903E8fLlBiZzlX+5PF4WLM4t1u/JXbxJrC9KPMnJDMubbYH\n46lfTsG+0+5uejLs+paBHHwAW90mq9U24DgKfa6YMYyTs9bU1sXknlmttrh7f3RoDGjt1Hs1thw4\nakCaLVYoVdywprioUMawi48ORZOLguXMMQlMm30xJjMGs8cmQSzkY9/peky3yzpHhXNDLNmCSp6k\nk21qt56ffYcSbt6waCydmubxGIIgBiez85MwO5+b/yoSCrB26QhEhkmweIozkic8VIQ7V+ThRHkz\ntHr3/HwH1U1qVDe557w3q3Rk2F0B/JXw2rJlC/NZLpejra0NiYmJffLb7Nw1ADAYzSgqacLmb8/5\nPE9nMDORMjweYDBZUFzRivioUDSptBzhEHbtVQcdLMNPqdJxvHCui6QObl1oW8B0naOnxNkMzAOn\n691qJg9LiGDaUlbbjjJ7/bmlU9Lw/eFqALZxNzkuDHVKDaQSAWIibQ4IR06dSMiHVMJdXM1ItPWL\nDbdOwMX6DrfwTB47FjPICKjAUj6f5zfHi/BOdIQECyakeM2D6g6uxeLZcrfs/Dx2eGeoRMj5zYxE\n24pld2Xlie7hbfBxsGXLFiQk2AwRx+DTl0TZ/+Y/Hqtltn2x+wLHeKtv6WKWygxGM1Q+Vu8cw/kj\nqgAAIABJREFUvPTJSfzp30f8HufwdKm1RvxrWwlnH7t21F0r3Ett5KZG4x+/nYPrZjuFX26an4V7\nrhuNB3+RDx5sk7CEGCkiw8QIlQixaFIqI5Aw1y6zHB0hwRsPz+Xkvfa0ft3UPAXuvCYPv7kh3//B\nBEEEBGKRADcvyHZbBAKAx9ZMwCRWvlF3JdfZdbuI/qO5uRnR0U51dkcJLwcOo66pqQn79+/H3Lm2\n2snl5eW4++67sXr1auzfv79Xv+2qytqk0vo16gDg1IUWfGhXL89niXZNybM9WxUsA8tR4qq7eDLs\n5o9PRqSHZxsAYuyRYK5GHQAMS+QaXKESIW6cm4klU7ipTA51bD3LmSCzL5qmxoV7nddGhok9ho46\nvHcOIzGYIHcJ0SPEIgEKFufgaKkSZy+2wWS24I9rJ6Gstp1TI2XSiHjOKhDb6LMNbJ1uoXXE5dGd\nmpOAc/B58MEHUVpaygw+7e3tuP/++zFz5sxe/b7jpb7tUDWz7dvCKswZ61y5LKttZ0LaWzv1HMER\nb9QquSvVv189Hu98c9ZnSIfrwMMOwUhThHNi7wGbIFOoRMhZkIiNDGUmWK8+NBtCAd8tFNl5zQg8\nVjARYSFCt5wX17xUf/B5PEwf3b1SJwRBBD7JceG49/oxuOP5HwHYRFU8eVFcaW7XobVDh/BQEcQi\nASxWKxpbu5Agl/a6TAPhH09iei0tLbj77ruxceNGREdHY9iwYbj//vuxbNky1NTU4LbbbsOOHTsg\nFnvPIfNU9kvvknJw2k9NVDYOr9uEkQqcvNACABieLgcOcEsAXXIZh1MVEaixC5c9cPM4nCxrxu7j\ntfDGoilpuOv6MUwdadcUg9ws56JFhFSMa+dk4iO7MMod/zMaSXHhOHC6HhfrOxAbFYLbr7XpVNx/\nk009Ni4uAvLIEKAG0BnNjHr6bStGQRIqwopZmTheajO0hQJ+t9TVH1g9AcmKMtwwP9utRFigQ4Yd\n0WPmT0iByWJlii5nJMoYt7eD8cNjMS1PgTH2lSL2ZNexekMeu/7lSg4+gC3E0RONKpuRtWhKGnYe\ndhp9x+wv4vzsWLR26GA2W1Hf4hxglkxLx87D1W6y30kJMkSGSzwadsumD8O2wosAbCUH/vTr6dAb\nzShn5dMlJkTif389A9sPXsQ3+yptbY+xDR7ZrDDl7HQ5M0D4LtZiK9PhbTCJjQmjMh6EGwOZD0sM\nbkIl3YtMOnuxFbuO1mLyiHjMGZuEd745i3aNAQ+vGodRw+T+L0B0C38lvNRqNe6880489NBDmDVr\nFgBAoVBg+fLlAIC0tDTExsaisbERqaneRfU8lf265LKw+Zm9HrEr00cpUHjGXaAHABKjnGMzvxtl\nNDbcMh67jtYiJS4c4zLluFjnOR/dwcp5Wehs18IR3Ok69WhpUTMpEDNHJ+CqcUmQh4lQp9TApDNi\n4YRknCyzzQd4VmdpsSXT0qFUdkKp7ESYI23HauWU61oyMQUGrQEjkiOweHIqZoxO6HapsCWTUqDp\n1DElwgYLlztfIMOO6BWuyn2uCAV83HWt03s0JlOOBROSMTozBjX2XAEy7PqWgRx8AO/qjRfrbeEX\no4dFcww7B7ctzoFcFgKT2YKt+ysxZ2wSVGoDspJkmD8uCTuLarDrqHO1UKvRo1Pj2Vs3YXgMbpqb\nCYvVCh5sYSyhAiE6WXl+SmUnwoQ83DArA6VVbSitUSFEYNuukEkYb56YZ+3WAOGt5uToTDmKK1oR\nIRH0eU3NwQIZrL1joPNhicGJQz0zMymS2Xb97AzUKDU4UtLEkXrn83hM2ZWikqZuefiI3uGvhNfz\nzz+PtWvXYs6cOcy2rVu3QqlUYt26dVAqlWhpaYFCofB0eZ84VM/njE2CQh6KonNNCJeKcKGug5OX\nuWJmBhRyKb7aa1usZAuGxbJSESLDnYu2d12bh//bW4nGNi3CQ0VM3nmoRGhTwrTjKVIlXRHBlCPy\npW7+WMFEAMC9143G/+2rxNJptrzx8cPjMH64c37iUJGVhng2S66bnYF2jQH/M8u9Tq7tfD5WXTXc\n476hBhl2RK+I7GFduhCxkBFoUdpFVSgUs28ZyMEHcDfsspMjUV7XDr3BDJlUhDSFuxEg4POYEF6h\ngI8b5thUVR2hk/FRochNjeIYdiFiAZZOTUfRuUasWZyL97adQ6VdeTPSHnPvmrztqqDp4Lc3jUWn\n1sC0gc/j4dm7pkHVqWdi+nvLfdePQYfGgLgoz55MYugykGIMxODljqtHuuXhmS1WmOzvr8hwCWPY\nDU+JxHkvyr5RVDe2T/FV9mvWrFn46quvUFVVhc8//xwAcM011+Dqq6/G+vXrsWvXLhiNRjz11FM+\nI2G8MSYzBjPyk3C1XUhr2dR0AGDCdh2Eh4qwdEoaKi91YHxOHGblJ2LzN2eREBPGUW1mz93S4iOg\nkEvR2KbF1JEK7DrmOdxyxphEnChvxk3zsnGuqhV1zRrERYVy6syycQy/88YlITvZtkiRpojAb270\nnje+euFwdOlMWLt0hMf9YSEi3HvdaK/nE07IsCN6xeiMGCyalMok4vYEhwAOeez6loEcfAB3ZcgR\n6dFMgfDZY5MgYw0uk+05mKMz/IcLuRpGEpEAV01MwVUTbWUAFHIpY9j5q4HjavBJxAJIxO7XV8il\nftvlD4lIQEYd4ZErmQ/rLXQaGLoe10C478VT07HjUBXG5ipQZ/fMhbK8Gdlp0V4Nu+xhMQj3oMZL\n9B5fZb+Ki4s9nvPGG29c9u+uumq4x6iQWxYOx4HiBkZRUioRgs/n4cGbxjLH3OlBKCwsVISb5mXh\nzMVWJMilmJgbh3aNAWOHx3g17CLDxPjDGpvnLdte3661Q4ej55VYu9S7onpPBCcV0VLGu0dcHt0y\n7HzlAhw4cAAvv/wyBAIB5syZg/vuuw+HDh2iXIAgh8/nYfXC3rm9xfbaZXpjMFYQGVgGavABbGGP\nafHhaFPr8dxd03H2Yiuzb1Z+IieZf+3SXORnxXgtdMrGVbXKVRmX7VnzVqx37tgklNeqsHx6erfu\nhSCuJP2VDwt4D532FkIc7ATKff9iTgYm58YiIz4MN8zOQF2TGrctzmVyji+1eBee6lLroHUJVw8E\nY5boPgsnpWLhpFT848vTuNSs8RkOCQD5WTFQa43g83hYNi0dy6bZxkJHeQ6t3mRXe+5e3VS5LATP\n3jXN4z4S7hlY/Bp2/nIB/vznP2Pz5s1QKBRYs2YNlixZAgCUC0B4JURke+woFDP4ePL2ybDCCgGf\nzwn/iLOHVj7362loVxsgDRFh5pjuhZaFh4rw6C3j8cJ/jgNw97qNzYrhhGp6QiIW4N7rx/TkVgii\n37hS+bBE4CIU8JFlz7VLjAlzm0SnxIchRhaCd74566YCTBProcN93RzXHmJ58jwRKhHiH7+d4/OY\n7sKUiAvCGnGBgF8dbl+1sWpqahAZGYnExETw+XzMnTsXhYWF/dtiIuCRMB47MuyCDT6fxxSpZ3vS\nHKuJimgpclKjenzd3LRovHj3dPzvuilu+/Iy5MhIlGHe+ORetpogriwzZ87E9u3bAaBH+bCbN28G\ngMvOhyUCHwGfj5zUKHR2GQe6KQThBbLsBgK/HjtfuQBKpRJyuZyzr6amBjk5OX1WG4sIPhyFrC+n\nUDox+JHZ1bdS48P9HNk9Yr3kq/F5PPxx7aQ++Q2CuBIMdD4sETzMzk/ED0drkRIXhrioUEwe2fO8\nd4LoS6bmKbCjqAa5adH+Dyb6nB6Lp3jKBXClL2tjAUM3NjxY7zsuLgKvPjwPKfHhEHn5mxOBj0wq\nxvO/noYISuInCDcGMh+WCB5ump+NueOTkeyl3AxBXGl+MS8L00clIE3RN4u6RM/wa9j5ygVw3dfY\n2Ij4+Phe5QJ4M+qI4CSDVaeHCCx8LTi47gvWxQlXhsp9EoFHT/rrUCHY7jspkcbTYCCY+mpiQt88\nk4F234MBv7FwvnIBUlJSoFarUVtbC5PJhJ9++gkzZ86kXACCIAiCIAiCIIgrCM/ajdjKTZs24ciR\nI0wuwNmzZxEREYFFixahqKgImzZtAgAsXrwY69atg1qtxvr169HR0QGj0Yj7778fc+fO7febIQiC\nIAiCIAiCGIp0y7AjCIIgCIIgCIIgBi8kS0gQBEEQBEEQBBHgkGFHEARBEARBEAQR4JBhRxAEQRAE\nQRAEEeCQYUcQBEEQBEEQBBHgDFrD7tlnn8XKlSuxatUqnDp1aqCb0y+UlpZi4cKF+PDDDwEA9fX1\nKCgowC233IIHH3wQBoMBALB161bceOONuOmmm/DZZ58NZJP7hBdffBErV67EjTfeiB07dgyZ+w5m\nqL8G73NL/TW4oL4avM8s9dXgg/pr8D63/dZfrYOQQ4cOWe+66y6r1Wq1lpeXW2+++eYBblHfo9Fo\nrGvWrLE+8cQT1g8++MBqtVqtGzZssH733XdWq9Vqfemll6wfffSRVaPRWBcvXmzt6OiwarVa69VX\nX21ta2sbyKZfFoWFhdZf/epXVqvVam1tbbXOnTt3SNx3MEP9NXifW+qvwQX11eB9ZqmvBh/UX4P3\nue3P/jooPXaFhYVYuHAhACArKwvt7e1Qq9UD3Kq+RSwW4+2330Z8fDyz7dChQ7jqqqsAAPPnz0dh\nYSFOnjyJMWPGICIiAiEhIZgwYQKOHTs2UM2+bCZPnoxXXnkFACCTyaDVaofEfQcz1F+D97ml/hpc\nUF8N3meW+mrwQf01eJ/b/uyvg9Kwa25uRnR0NPNdLpdDqVQOYIv6HqFQiJCQEM42rVYLsVgMAIiJ\niYFSqURzczPkcjlzTKD/WwgEAkilUgDA559/jjlz5gyJ+w5mqL8G73NL/TW4oL4avM8s9dXgg/pr\n8D63/dlfB6Vh54p1CNZQ93bPwfJv8cMPP+Dzzz/Hk08+ydke7Pc9FBiKf6tgf26pvwYnQ/HvFOzP\nLPXV4GUo/q2C/bntj/46KA27+Ph4NDc3M9+bmpoQFxc3gC26MkilUuh0OgBAY2Mj4uPjPf5bsF3W\ngcjevXvxxhtv4O2330ZERMSQue9ghfprcD+31F+DB+qrwf3MUl8NLqi/Bvdz21/9dVAadjNnzsT2\n7dsBAGfOnEF8fDzCw8MHuFX9z4wZM5j73rFjB2bPno2xY8fi9OnT6OjogEajwbFjxzBp0qQBbmnv\n6ezsxIsvvog333wTUVFRAIbGfQcz1F+D97ml/hpcUF8N3meW+mrwQf01eJ/b/uyvPOsg9Wdu2rQJ\nR44cAY/Hw8aNGzFixIiBblKfUlxcjBdeeAF1dXUQCoVQKBTYtGkTNmzYAL1ej6SkJDz33HMQiUT4\n/vvvsXnzZvB4PKxZswbXXnvtQDe/13zyySd47bXXkJGRwWx7/vnn8cQTTwT1fQc71F+D87ml/hp8\nUF8NzmeW+mpwQv01OJ/b/uyvg9awIwiCIAiCIAiCILrHoAzFJAiCIAiCIAiCILoPGXYEQRAEQRAE\nQRABDhl2BEEQBEEQBEEQAQ4ZdgRBEARBEARBEAEOGXYEQRAEQRAEQRABDhl2g4hz586hoKAABQUF\nWLVqFfbv3z/QTSIIwgPFxcVYvXo11qxZg3vuuQdtbW0D3SSCIFhs2bIF48aNw4EDBzjbd+/ejalT\np+Kzzz4boJYRBOGKp/7a0NCAdevWYc2aNbjhhhuwb9++AWxh4CAc6AYQTl588UXcc889mDFjBkpL\nS3Hvvffihx9+GOhmEQThwuOPP45HHnkEM2fOxLZt2/DKK6/gqaeeGuhmEQQB4KuvvkJxcbFbza/D\nhw/jyy+/xJQpUwaoZQRBuOKtvz777LNYunQpbrrpJpSUlODee+/Fjz/+OECtDBzIsBsgGhsbsX79\negCATqfDypUrER0djdbWVgBAR0cH5HL5QDaRIAh47qsXL17EuHHjAADz5s3Diy++OJBNJIghi6f+\nuXTpUlx33XUoKCjgHJuXl4e//e1v2LBhw0A0lSCGPD3pr8899xwkEgkAICYmhiJjugmFYg4Q27Zt\nQ2ZmJj744AN8+OGH0Ol0ePTRR/HSSy9h+fLluP/++/HEE08MdDMJYsjjqa/m5eVh165dAICff/4Z\nzc3NA9xKghiaeOqf4eHhHo/1tp0giCtDT/prWFgYhEKb/+nNN9/EjTfeeCWbGrCQYTdAzJ49G4WF\nhdiwYQN+/PFHrFy5Ek8++SQeeughfPfdd3jrrbfwyCOPwGKxDHRTCWJI46mvPvPMM/j6669RUFCA\n+vp6mjASxADhqX8SBDE46Wl/tVqteOGFF1BZWYlHHnnkCrUysCHDboDIysrCt99+i2uvvRaFhYUo\nKCjAwYMHsWjRIgBAfn4+1Go1eQIIYoDx1FczMzPx9ttv44MPPsDy5cspbJogBghP/ZMgiMFJT/vr\n008/jebmZrzxxhsQi8VXqJWBDeXYDRBff/01kpOTMWPGDEydOhULFixAZmYmjh8/jpkzZ6KmpgYW\niwUxMTED3VSCGNJ46qtPP/005s2bh7lz5+Ljjz/GwoULB7qZBDEk8dQ/TSYTE8JFEMTgoSf9dcuW\nLWhra8Pf/vY38Hi8AWhtYMKzWq3WgW7EUOTcuXPYuHEjxGIxrFYrli1bhgkTJuDZZ58Fj8eD0WjE\n/fffj1mzZg10UwliSOOtrz7++OMQCoVISkrCCy+8gJCQkIFuKkEMOTz1T5VKhUOHDuHcuXNISkpC\nZGQkXnnlFezcuRPffPMNKioqEB4ejvj4eDzxxBPIzc0d6NsgiCFBT/prQUEBhEIhZDIZc/6mTZug\nUCgG8A4GP2TYEQRBEARBEARBBDiUY0cQBEEQBEEQBBHgkGFHEARBEARBEAQR4JBhRxAEQRAEQRAE\nEeCQYUcQBEEQBEEQBBHgkGFHEARBEARBEAQR4JBhRxAEQRAEQRAEEeCQYUcQBEEQBEEQBBHgkGFH\nEARBEARBEAQR4JBhRxAEQRAEQRAEEeCQYUcQBEEQBEEQBBHgkGFHEARBEARBEAQR4JBhRxAEQRAE\nQRAEEeCQYUcQBEEQBEEQBBHgkGFHEARBEARBEAQR4JBhRxAEQRAEQRAEEeCQYUcQBEEQBEEQBBHg\nkGFHEARBEARBEAQR4JBhRxAEQRAEQRAEEeCQYUcQBEEQBEEQBBHgkGFHEARBEARBEAQR4JBhRxAE\nQRAEQRAEEeCQYUcQBEEQBEEQBBHgkGFHEARBEARBEAQR4AgHugEEQfQdzz77LE6ePAkej4fHHnsM\n+fn5AIDGxkasX7+eOa6mpgYPP/wwjEYjXnnlFaSlpQEAZsyYgXvuuWdA2k4QBEEQBEH0Hp7VarUO\ndCMAQKns9Lg9OlqKtrauK9yagWco37dQKBjoZgQkhw8fxubNm/Hmm2/iwoULeOyxx/DJJ5+4HWcy\nmVBQUIB33nkH27dvR1lZGR599NFu/w71VS5D9b4BIC4uYqCbQPiB+iuXoXrf1FcHP9RXuQzl+76c\neXCvPXbePAMAcPDgQbz88svg8/nIyMjAM888Az6/d1Gfnm7ura1nYAXw62tH9fh6lfUdCBELkBgT\n1qv2XCmGqnEzVO+7LygsLMTChQsBAFlZWWhvb4darUZ4eDjnuC+//BJLlixBWFjf9oGh+rcLtvs+\nUtKET38qx5jMGNQo1Zg8Ih6LJqUOdLOIPibYntvuEqz3/W3hRew6Wovnfz0dYlFw3uNQxdcz+5+d\npWho7cLvVo67gi26MgRrX/XH5d53rwy7w4cPo6qqCp988olHz8CTTz6J999/HwkJCXjggQewd+9e\nzJ0797Iayubg2UYAvTPs/vTvIwCAdzcs6LP2EMRgoLm5GaNGOfuEXC6HUql0M+w+++wzvPvuu8z3\nw4cPY926dTCZTHj00UeRl5d3xdpMDB5MZgt4POB0RQua23X46XgdAKC8th3T8hSIkIoHuIUEQXjj\ni90VAIBLLRoMS5ANcGuCj8Ga5vDD0VoAgMVqBZ/H6/PrE4FHrww7f56BLVu2MJ/lcjna2tp61bhm\nlRayKKnX/VarFTx6kAnCI56irI8fP47MzEymf44dOxZyuRzz5s3D8ePH8eijj+Lrr7/2eV1fYQJD\nNdxnMN53Q4sGViuQGOvfM2s2W3DdI19DLpNAZzADABZPTceOQ1UAgBMVbbhhfna/tncoMlgni0Tg\nwgPNifoaX84MhUKBDz74AIAzzWHBggXYvn07li9f3qM0h8tBpzdBGiLq1rFVDZ1obOvClJEKALbF\nvMgwMdIUg28cI3pOrww7f54Bx/+bmpqwf/9+PPjggz3+Da3ehD+8dRDLZgzDDbMymO3syapWb4Y0\nxP8tdOlMEIv4cLUBrVYrDp1rRF66HLIwWo0mApv4+Hg0Nzcz35uamhAXF8c55ueff8b06dOZ71lZ\nWcjKygIAjB8/Hq2trTCbzRAIvIcCeIt5j4uL8JojEMwM1vu+8/kfAbhHJ6i1RjS2dSErKZLZ1tll\nAAC0dugBAAI+DyvnZeKaaWn43d/34YfDVZg9WuH2G4PRoA0UAmGySAQetNbd9wx0mkN3UOu6b9g9\n/a8iAMDY7FiIhHz89dOTAAYmku18dRssVmBkevQV/+1gpU9UMT15BlpaWnD33Xdj48aNiI72/wdz\n9QJ06YwwW6xoaOniTB6MJjPzWSIVIy4mDCUXW5EcH+4xVMhgNON3f96JWWOTcNvVzhCz2NhwFJ1r\nxFtbzyItIQL/+P3gC82kSRPRE2bOnInXXnsNq1atwpkzZxAfH+828Jw+fRrLly9nvr/99ttITEzE\nNddcg9LSUsjlcp9GHREYdNgNNU/86d9FUKp0ePHu6YiNCgUAxkvnwGyxRUOEh4pw/ZxMdGqM/dre\noUggTBaJwMNotgx0E4KOK5Xm4C8apkNjwNqnt+PWpSPwiwXDOXNvSYgYcXERUGuN2HGwCitmZ0Ik\n9K1tESELhYSVjzkQc8477AuQX7/0Px730zy45/TKsPPnGVCr1bjzzjvx0EMPYdasWd26pqsXwGK1\ngscDNFojZzVco3NOMKrrVCitaMZfPj6B8cNj8ZsbnQIuJ8qakZEkg8FohkqtR2WdCvUN7cz+nQcq\nUVZr+17d0NmnK+4WqxVtHXrERIb0+hqD1QvQ31An7j0TJkzAqFGjsGrVKvB4PGzcuBFbtmxBREQE\nFi1aBABQKpWIiYlhzlmxYgV+//vf4+OPP4bJZMIzzzwzUM0n+pCL9R3MZ4PRzBFTUKp0AIB2jYEx\n7LR6k9drLZua3k+tHNpQTizRG9RaI/6x5TQkYgHuuW40/rWtBNNHOb3pJhMZdv1Nf6U5eIqGMVss\niI+ToaVFjSMlTTCZLfj3t2cxd0wCx9FR29COyBABXvzPMZRUq6DVGrB4sm/Rq9r6doSwxobGpg6v\neXr9kfpkYf071je0QyjgGqKOefCek5fwr20lePauaYiNDIGAzwvqNKzLnQf3yrDz5xl4/vnnsXbt\nWsyZM6fXDePzeJBKhBxDDgAMRudLS6MzYv/pBgDAiXKnoVlS1YZXvziFtPhwFCzNtZ1nssDIeuG9\ntuV0r9vmj0NnG/H212fxy+UjMDs/qd9+hyBcYeflAMCIESM4310HloSEBCbkiwgeapUa5rNGZ/Ks\nkscaF109dsSVh3JirxyBfN/VJY04X6MCAJysaMWhs404ZBeUAwBpeEhA399g5EqlOXjib5+dglDI\nx4M35sNkcc5hT5Q149UvTjHfu3S2xbmSatuzoTdy3+nFlS0wGC2YkONs94Y3ChEqcbbnVy/8hGtm\npOO62Zm488WfMCEnDvddPwZVDZ14+l9FWHf1SMwck9ij9vtCrXXO7zs0Bshlnp0hH2w/DwB455uz\nuNSswczRibh1cU6ftSPY6JVh58szMGvWLHz11VeoqqrC559/DgC45pprsHLlyh7/TqhECI2Wa9ix\nH1aN1oTGVtsKR3Ks07CsarR5uqqb1EwIkath540unQl/33IK187MwAh7zO9Xeytw9LwSqxcOR94w\nud9rnCizvQDe+64ExRWtuG1pLsJCRDhR1owuvRHTRiXgeGkz8rNi/LrKfWEyW7Dn5CVMyo2nHEGC\nIBjYHji11ojoCInbMRaLzZAorVHhTGUrAGDm6ATsL27geACI/oFyYgeGQL/vhiZn26sutbvtb25R\nQ6kMddtOxl7vGcg0B6PRjHMXW6EzmNCudobYs406wOboYC8OufqzXv7Elkf31u/ncbZr9VwD8JsD\nVbhqQgqsVuDoeSUA4IcjNQCALXsqGMPuSEkTJGIBxmTGwJWfj9chPSECybFhEAn5Xr1r7PtRqb0b\ndvHRoahv6ULFJVskSlFJI25dnAOjyYJvCy9iwYSUXs2BLVYrjCYLJxw1GOh1jp0vz0BxcXHvW8RC\nGiJEU5uWs83AMuxOXWhGrVINgJtT0qFxft59wibZbTRZcPJCi9/f/Ol4LUqqVSipPo6nfjkZGp0J\nW/dfBAAcK1V6NewsFitaOnSIiwrlTKKKSpogEQlwx9UjmY7YrjHgs58uICtJhvtvzEdkLx5ItdaI\nv285jdIaFY6eV+L3q8f3+BoEQQQn7MiGv/z3OBZPTkV5XTtn8HMc8/xHx5htmUkyXDc7kxaKrgCU\nE0v0BodnBuB65h10ZwGb6BkDmeaQmRSJ0tp2VDV0oq1T7/W4Lp0JGtaz0dllhFZvgkQswLeFVcz2\nu/7ys9/fbFM7f0dvsKUzAeCMC//8yjbPdwiuWKxWGI0WaHRGvG/3sAHAsqlpuImlqFzbpMbJC81Y\nNi0d7azfUan1sFqt+O5gFcZkxnAUOuOjbIadK1/urcD3h6pxuqIVecOisWLGsG7VcLRYrDCZLXjx\nv8dRcakD7zw6P6hKRfSJeEp/ERYigs6gxld7KyARC7BkShpnwlJ4xhl+0KkxoPBMA46XNXOMP4cx\nd6lZg493lfn9TfZL8an3ijj7mlRcI9NqteK1L04jO8WmLvf5zxdw9fR0Ny/jvtP1nJAdj5WbAAAg\nAElEQVTSk+W2Nl241IEnNx/CKw/M9tiWJpUW/95WgrXLRiA+irsC98pnJ3HBvnpxrqoNb399Bosm\np6K0ph2LJqUEdfwxQRC+MbByL9RaI7bsqXA75qVPTuDe60ZztoWIhZeVG0x0H8qJJXoD2xtf0+Tu\neTSReEq/MFBpDhlJtpqEFfUdjIHlCY3WiC7Ws7HzSA12HqnB4smp2FFU0+3fC5UI8dbWs8z3e17e\nzXx2OCFcwzwB4NMfy7GjqAa/u3ksZ/u2Q9W4bnYGRPZw8Oc/OoYuvQmJMWGcRQqVWo9apQZf7K7A\nF7srOAqdfD53PtulN8NqtaKmyebYqazvQGV9B9IVEZg0It7vPX60s5Sp0wog6Lx2g9qwk0pszXN4\nzBTRUkjEnv/xrQDe/vqsx309wZeiVFObFtWNnUiNDwePx8OJ8mbmv8n2h+nbwiqMzbINxGIRnzFE\nj5c5Q25a2p0GYmeXET8fr8O88cluv/f6l8WoauzE/+2twJ0ruMXYHUadg8IzjYyhywOQGCPFaA8u\ncoIggh+Dh4HXE45VVwchkuAZ3AIByokdOLp0RnyxpwLLpqQxIkJXGr3BjC17KrB8Whoiw93DpdkU\nV7agvqWLM3lXqd3Vb9/7rgSjM2Mg86ASTgQeGYk2z1XlpQ6oNN7Vjg+fa8T4nDi37T0x6gDbwoE3\nMa0Iqa2cQodLO4pKmpjfOVftXre6pkmDTLuB6nh+qxs7OeIpKrWeE3nnWKAwmszo7OI6S0xmC3QG\nMzpd2qHWdk+9mW3UATZDNZgMu94neF0BXPPPPvupHCofrujLwRGbbDK5J7ADQIwsBE1tWjz1XhH+\n/P5R7Dpai9e+cAqwnL3Yynx2eAlf/91c5KZGuV2rpYN7D+9vP49yu0JnVUMn3v76LLp0RlTbV+MK\nzzSiqqET1Y2d+P5QNS7UucfVs/nvrjK8/OlJv8cRBBH46AwmHCtVcvIrDL0MxwoVD+q1PoLoM745\nUIWfjtXhza1nBqwNW/ZUYOeRGrz+f/7b8PInJ/HfH8rQ2qHzeZzZYkWZXVyFCHxiZCGICpegsr6D\n0ZTwREuHHuc9GFXdpWBxDsL81IV2aLewDbvaJjVeZy0QtnW4z9HZz2yMzLaAcalZw4gfAoCq08CZ\n39c0qaHWGvHYWwdR7mEu26ExoNPFkHMVW2RzorwZX+11j1wBur8QGigM6lHcEVM7PCUSWcmR+P5Q\nNb4/VN0vv1VW247hKZEeXcwAkJUsQ4v94XS4fdmwY5sd8Hg8hEu7VzDyo52lePy2iUzhyPllzWCL\npD39ryIkyKVo8NGxXfnxWC2ykm1hoiazxU1KliCIwGfrvov4/nA1rpuVgWtnZQDg5tj1BPLYEUMF\ntX0S2NpPi8XdodPuoWjr9G2ssalpsuXViYR8r/l0IbRAEzTweDwMT4tCkV35ND8rBisXZOPxtw+5\nHat0SRfqCSFiIcJCRR7nsg7qWzR455uznNSgJ9897HKM+xyV3ceiZSFo6dDjiF2YZVqeAgfPNkKl\n1nNCTf/66UmfHrh2jcHNc9jlo+2vfm7TuFgyJc1tn76X4+VgZVDP9LOSba7bGaMTcM30YRAK+Khr\ndk8WFvB7lk+WmSSDXMYNe3j+o2P4trCKedECNiUeT597gr8VEAdVjZ3YdtCZ4Or6wALokVEHgBGe\n2VFUg3te2s0oHLHp7DJg19FamC3B9WATRDBTXtuO1744hWOlSmYw//6wc9HLsQL5WMFEn9dxhPk4\nII8dMVRwiCVYPJSZGMw4BONS4pxCO6ESbr91/U4ENjlp0cznpJgwJMaE4fHbJmLKSFsKkKNenaNG\nqSckYgHnmXFFLBIwNe14gEdHwMWGThwobsBX+yrd9j28ahwAm/HnSklVG6PC7LoYMT4nDqESod2w\nc857/YVVHilpgtnC7bsanQkdGgO6dEY0tnZh8ze26Dc2npw3weaxG9SG3U3zsvG/d03HnLFJkIYI\nkZMa6fE4T8n+keHe48vnjkvCX+6ZgVHDojnb95y8xHHtJsilzOe4SKdhN8FDHLN3nEbnZA9JnbPz\nE/HnX00Fjwd8udfZWf7+2Yke/IZn2jUGGE1mfLyrDGaLFf/48jR+PlGHOqUaW/dXwmq14qOdpfh/\n9s47Poo6//+v2b6b3c1mk82mk0ISEkih94CAVAuoFAvqiWfDw4KnHqdyzYaed54/yx1nOfUKJ1/0\n9OQEFQtqIEgvgUACIQkk2bRNNmU3W35/7M5kZlsSSM/7+Xj4cHd2ZvczZD4zn3d7vf/+eRE+8dQx\nEgTR95RcaESNueve1h+OXcTB0zV47cNjCFG6swL4vehsdgekEpFAcXfehDjMyhX21bz/umz8cd0M\nAG4HmYbqcohhAusPHmR2HQCAYYDYiBDuvbfjWUmR9yEF37CLjnCvS1NiQnHPtWPw1uNzkOFpzVVY\n6k7FnDc+Dg8uz0aaR9hvZGwoXn94Fm5fNAqBkMtEXPnTyLhQzMzuer86lVwCgyeK568M4NCZGmx8\nqwCbPzkuUMIEgPR4HXRqGRostqDiMN58sb/cZ1tTiw0PvvIdnn5vP17/zzF8f6wSn/ICJgBQa/Y1\nfsmw60PkMjHGpkdyCo+B2gLo/fRompgeiYV+Qq4AuL4aD6/MxfIrUrjt7XanoBjTGOaeQCKG4S5a\nABibGhHwe1niDO6bLlv3EhoiwzXTE32OWTozGTERIQJp157C3GzD2YtC1ax3PzuFJ98swEe7z+LE\nuXouRM4qdbIEK6AlBi7PPPMMVq5ciVWrVuHIEWGfmzlz5uCmm27C6tWrsXr1alRVVXV6DNF1LtQ0\nY/2r36PkQiOaWnzTRAJhbXfgd+/+iEdfz/f7eZvNzvXvKTxXhxpzK1o9RpzT5UI1r08ZW3Buszsh\nk4gE8tQapRQSkfCWr5JLoFXJ8P8ezMOv75gEVRczDAhisMN4LDuns3csu4qaZnx35CL33uF04qsD\n5Whpc8/n47y6fJZacxt+OOY+5tvDF7B9Tym+OlDus+BVyiSI4Dm0jV6GnUrRtRIQYnCQxtNqGO8n\nsOBd8jMmWY/slAjIPGKDbJQqWAaZXCrmomlKuYSLmClkYtx5VQb4QutqpfD3Nqwej1A/TkH+mrii\nphn5x6sEUbm0uFBoQ2TQqeWwtLajJkjEsSuwIoUXa1tQVed2lHrX/FX56evZW6mYTqfLb5ZhbzOo\nnuKBvMn+0g4UcklA2V+ZR3aVYRiE8xoimpttMPP+BiPjQpEUo0FStBZSMd9o8x/OfvLWCdh95CLG\npxu4fdg0D4YBYg1qPLwyByOMGhwprsXJ0nroPJHF1NhQlFYGbpqqkIkFHnl/hGsVXB2gUi5Gq9WB\nY2cD9+6zO5zQeZS4vC++tX/4FgDw6kN5lNYxSCgoKEBpaSm2bNmC4uJibNiwAVu2bBHss3nzZoSE\nhHTrmKHM/lPV2HO8CvcsHQ2x6PL8XB98dQb1TVa8+9lJnPfIMPMlm/3RYLHiF3/Zw7232hwoM1kw\n0lMbu+tAOd7fWQQAWH5Firv/ZawWat7C7eT5DqEES2s7dGo5bO0OyKRiyKViyCQi2OxOKOQSgaIe\n4FbuBdw9Q8moI4YTbCqmq5dCdk/+1V0DlZ6gg0GnxPY95/HhtyUoLK3n6ovYVDqWz/aex5cHyqFW\nSvHO/05y27d7RR0MOiXiI91rDKVcgrycGBQUVnOfqyhiN6RQq2R4cHk29FqFX6Nd42VosTWW6fE6\nHCup47QWgt3j5VIxZwDKpWIuhTE6PATTxkTjvR1FsLY7EK6V44X7pmPbt8X47w/u6zLGEz32Xqdq\nVTLcsXgUPtx9ViCAYtSrsHhyAiZ6rn/WSVFa1RS0dtT9nVI0elQyn7xtAp7/xwG/NeXsuZwqa0Cb\nreO599f/Fvrs21sRu39+cRpfHijHwytzMCap71TqB3TEzhtNACESp9OF2xeNwm0L07l2CM2t7QEN\nO77nISI0cO2cRMxgSmYUjGEq6DxRwZyUcESHqwT7LZ2RhBvnpiIuUo0b56UiLV7HTSC2UPPWhe4Q\n+JikcGhUMkzPisaaqzK5aGR6gjAt1BvWAFXIxJiSafT5XCET4xe3jOPeJ0S6I4BHPAqdv7hlHJeH\nzSISMVzPPbvD6ffiXvuHb4M2xSQGDvn5+Zg3bx4AICUlBWazGRaLpcePGUq8+uEx7C8y4VwAp8qx\nklps+scBTm3L7nDif3tLBepkLW3taGlrh8OzQOT33Pn4u7P48WQ1AvH5j2Ww8h6Er//nGJ55bz8K\nPd581qgDgA++KgYAFFc0BnTyWDwPPFu7k2vU+tw9U7FoSgKmZBqh1wjT1qnfJTFcYS/9XgrYcbCp\naazjlu9EZecx4ynZYOXeT5wTqhuyStrp8Tr8cvV4/PzGXOSmRuA3aybh2bumIDNRj8duGsvtz/YM\nI3qO/s6GyU6JCBhU8A56sM74RZNH4K5rMrFqjrtBON+wyxgRJmizJZcJDbuVc1MRHa7CHYvda1d2\nvrDZa97ZHwAQFykcnzZEioxEPXJGCo0atVKCmTkxnAG6ZFoi9xk/xdgvvGdWfKS60zYF9U1WfN5J\ny4dAooktbXaupvVS+PKAO100WNCmNxhULlr+xZuXE41vD7tTFkQiBnk57tqRClMzvthfjnijGuer\n/P9BHI6OO3mwZrz8RY+IYbD50dlgGAYihsGoBB2KLzRi6mgjrpqeGLBrfZxB3anXHgByU4Nb8zqN\nHBU1zXA6XZjsURHio5CJBZG1eKMap8oacL7KArlMjJTYUIyMDUVjiw17PP3urDaHQCymuqEVKrkE\nNV45yC/88yAMOiUeXJ5NC8EBTE1NDUaP7uh3qNfrYTKZoFZ33Gw3btyIiooKjB8/HuvXr+/SMT3J\n/lPVOF9lwbK85B793q8OlCNMo0BugDTpzjhyphYjjBqfgvFXPzoGq82Bz/eVYenMZHy29zy2fVuC\no8W1ePQmtyPl1l/vgNXmQKanZpcv5sQWmQe6B3hHCVlHTGmVBRmJer/HRIer0GZzQCYVITZCLVDo\nZWuEbXYHl7quU8uxfLb7wT5nfCwq61p8+vgQfc8zzzyDw4cPg2EYbNiwAdnZ2dxnc+bMQVRUFMRi\n96LlxRdfhNFoDHoM0T36SjyFTfVkIyB8wQc2XZvdxpY/nArQriAuUs1FXwBh9lAwJzVxeQz0bBiF\nV39npee9SOQOTrDwnzfp8TpBX2iNUsalJMqlbqGVp386hfucdUCy6f12j+Aef0UYpVdxrbuAjjV7\nmFe5lLcxFqlTIkwjR32TFWEaeUBHKwDA5cLTP52MFqsdErHIk4HnX2glKzkcR0tqUVnnW7+eFK3l\nnp2BIna/ersANeY2vLxuxmXVn3uff28zyAy7jojd7YsysHjKCPzts1O4+co0bvvKuSORPTIcmYl6\nnDhXh6/9LGD4kTxtkHYEbJ0cC39SsIu6nkIsEuHF+6ah3mLF0+/u9/k8OyUcx8/WIXtkBEYn6TE9\nK0rQA0QplwgmaQzP6xEXEcI9xPgytTv3laHc1OE9rKprwasfChsWA241zsq6Fqx5/is8e9cUNFis\n0KnlMOpVPvsSAwfvFKN169Zh5syZCA0Nxdq1a7Fjx45Oj/FHWJgKkgAeYYMheK3oq8/tAgD85Nox\nPeZV3neiEu95Iluf/P7aS/qOT344B61WgVVXpgNwL8I+yy+FTCKG1eZAbZMNBoOGc6g40XGu7AOP\n8dwfFH5SZQL9u4QEqBtWqWRosvnPOGizOcAwDEIUUozPMAoMO5FUDINBA1u7EyEqmd/f/el12Zxh\n19nfi+gdBvpCcTjAGnSuXg7ZsdGAViv7f36DcatgG795sz9UQcoivPv+Ej1HoMyWYA7QSznmUmEY\nBrNzY/D1oQsAAGWQlMsnb5uAdz87hdzUCEGvY6VczKmj+2t7w84S1sBh0x/5111qbKigrjQr2R2w\nCFMHN+wAcBkmAPCbNZPw1JsFPvsA7gh7dHjHfVEuC7yOSDCqcbSkFuZm36yzu67JxKf5pfjuyEW8\nt7MI49IjBToebTY7F+Soa7Ry593UYkN9k7Vbuhh9HRAZVIZdiNeCKTJMhZ/fOFawTSwScbmsY5LC\n8fu10/H6R8cE+b2J0VruNcMweO6eqZBLRHj6vf2oMbdh6YwkzB4XC20fK8TptQrotf4jiFeMjUWU\nXoW0OB0kYhHWLMkUGHYKmUQQNUyK6jhHfnicH9XzbvrYlXYKO/aV4euDFRCLGGx+9ArBZ6aGVlht\nDp9wPNE3REZGoqamhntfXV0Ng6Gj0Hrp0qXc67y8PBQVFXV6jD/q/RQfA24jwWTqWspB+QWzTwH2\npfLN/o40i9KyOqgUUnx35CJ2HSjHYzeP6zRVg+XoaRPmelQjX//oGPbxUijPVzbCZGriFmAupwvP\nvbNXMAfrPfWtDruv9y/Qv4vJjzQ0AHy+txTvfHrC72dNLTY4nC4oZWLoVMJb+IXKRlw0qt0RAJfL\n7++288bX1b9XIMgwvDQG+kIxGPVNVjS12HpF8Ku+yYpacxtGxvlXwObvZ2lt5+rM7A4nTpyrQ2ai\nHiIRg2MldUiP18HS2o5Wm91vCpvd3iE0ZG62oaquhYu2S8QiKOVidy2Py11vX3KhERE6BbQqGcqq\nLQhRSFDfZIVRr4LL5UJVXSus7Q7IpWJE8co1bO0OHC2p5erfm1o6oguskESL1Y6isgZOJCmQfy1Y\nvTsZdr1HX2XDXI7TdP3qifjJtW2oaWhFYnzg0h6DQYNJ2e4UzPqWDidDZKQWv717Gt7dXoibFmUK\nhLf4GCPUMBg0nANTpZByY1s6Jw1x0aEYkxKOk+fqMTbdAIZhfJyUfMcoC7e+ZxiMzQysyMkwjOBY\nVhVaIhb5lF/FR2mhVkph8dPfLj05Ag6IOEP0s31laGqx4UxZA9JH6CERd6ynf/3OPvzhoVlotNjw\nyr8Posbchnd/tQCnyxrwwRdFuHtZNkbyBG4AYXqnKoCTtbcYVIYdG7ELVGvnjzCNnFuMjUsz4PZF\no3wWlGwUa/3KXHy6pxTzJ8X3a4PPtHgdijypGDdfmYbocBUkYhHn/WCZPzEeOz25w2wo/sHlOZBL\nRYiL7PBo8FWUAqWMAu4m7Z3B1v44nC4UV5ix60A5Vs5NhUYpxWNvuFX9/vLz2dQMvR+YPn06Xnnl\nFaxatQrHjx9HZGQk9xBpamrCgw8+iNdffx0ymQz79u3DggULYDQaAx7Tm9jaHUAnhl1pZRO+OliB\nm69MC7po4TclPV9lwagRYXhru7tA+tzFxk7rV1n4c+OCl8HF3kPYWrvGlnaf+cLKOIv89NVkF3ws\nLpcLp8vNfpu5Ar5iRnzsDhcam20IU2t80q+aWtrx2V53P7tAU10qEeOKcbFI5jm4iL6lL9OmL2ex\n6I87N30Fp9OF/3vuKoGXvSdY8/wuuFzAv363mFuw+eMOT+T/o01XQywW4W+fnsDWXadx88JRiA4P\nwR8/OIzpOTFotNhQWtmIv/9mkeB4g0EDEe/f5KFXvgs6rl/fNRXPvr8fV04egbuXZXG/DwAJURpk\nJOqxgydwkspb5Jlb7dj8H99MGG+e+/uBTvdJiAkN+DfjC06Qw6V36a1smJ5wmoYpJV3et621oxTH\nZGqCQS3D+hU5sLZYYWrxr60gcjlhMjVhTm4MTp2rw6q5qYLfS4oMQXNTG+LDlaipcZdDqSTA+HQD\n10u5otriM8acFD1KLpgxMlbrd/zhWjlqG60QMUKHJGt/GfVKVJiEz02XwwltiAw19b6pmA31LWjh\nneOeoxdh9qRGV9f7Zvo99IdvBO+Lz9Xhi33ncbK0Hhv/8gP+uG6m4HN+oKS+obVbTtTLnb+DyrCL\nDg/Bwytyuh0RWjUvFS/96xCumZ4YNEpg1Ktwx+KMyx3mZfP4zeO4B0d0uAqZAWptVs1N9TQXd3GC\nLtkpHcZfXk4MqupakJnUcby38AsAPLQiB//84jRX3xOMKt4Eefo9d8po/vEqrF6Qzm0vq7YgiRaN\nfc64ceMwevRorFq1CgzDYOPGjdi2bRs0Gg2uvPJK5OXlYeXKlZDL5cjMzMTChQvBMIzPMX1BZwqv\ngNtLBgBjkvSYMCoSTpcLTqeLcxo4nS5cqG0WpDZV1DRj1IgOQ07iMQh/uXkPHA4XnrtnasDf4xtk\nDIRWkbXdgXa7g0s/8dfKoNljYPo7t6YWG+Q8I2zbtyX4NL/UZ7/uIJOKkByjxbg0A+IMIfj4+3PI\nP17JzVF/SmEsq+enB/yM6Ht6a6EI9MxikQ9bM3aqpEbQ67UnYE+p4qK5S3Up5ysaoFZKse+EO3J+\nuKga9R7hsD1HLyJEIUFjSzsuXGzgUr/Z87b4Sc8KxHcH3M/ZC9VNOF1SI/jsfGUTZF6OzNO8GrmS\ncvfr3JEROHRGeCzgTgn7y8f+o/N8QhQSpMUE/5s9edsE6NRyv/uQsXfp9FU2TF8TSGAwGOwaWhsi\nw2M3d60kSSwSYe2yLHy0uwQff3/Or1jJkmmJGBmnQ3qC2ymydtkYrjQoOyUcP1k0Cm/85zhu4LUo\nAzpSOKP1Kh/DTq2UQq2U4oKXo/TZu921g3xnq7mL7YlYGltsXHp1m9f5lFY24WxlR4lE+yX8O18O\ng8qwA4Axyd2XDB2dqMebXRAwGUhMyDDix8IqrpdeINiia35BNYu/ZpRjksOxdGYSPvI0Q79v6Rhk\nJYfj3OhGQYN0PjdfmYa/f17k9zOWj7/rOPZ0udnHsGtps0MqYUitq5d55JFHBO9Hjeq4Bm677Tbc\ndtttnR7TkzicTpy92MTJ97MEUqFi4T9w2LqT377zI0qrmjghko+/P4uPvz8nOK6+ycrVCQDu+rf3\ndpwKGBnjww+0eUe7rO0OWFo7DMhgPR5ZQaIxyXrEhIdg574y7CusxqIpI7h9vvHUQvARixg4nS4E\nW66HKCScAWlpbYdELML912XB5XJh14EKzqgbnRiGq6cnBfkmoj8ZrAtFvtBIrbmtxw07lq4uhFqs\ndqiVUk4QTSIScemUDqeLk0VvabMjVC189gSTVPem1LNIa7HauZRKPv62sbB1dKlxoZxhJxYx3LM7\nKzncnS7WKhSAiA5XCe5bebkxnWbCkEO1dxhM2TDdoSsOVm+8hVq6w4JJCSirtmDhZN8e0yKG4Rqt\nA8D49Egkx4aixFMyFKqW+zUkp2dFQxsiQ4RWwbURYVErpYJWByzsutpfUktKjBbFnpRofmsFbxos\nVjR5Ip58H5vL5eKc0ix8wca+gPLlBii/uG0ifr92elDVTsAdtYsMUyJ3ZNfVACfz2iWEad1e0at4\ncrPe8B/eWpXUb4oX2w8LAMqqfb2F9//xW6x/9Ycuj5EYXDhdLrz58TEcKa6F0+XiogmvbnPL9xd5\nqbxt+udBn4XMv786g//7xi3pz48Mmxrcr0s9ggJsjdheL2VYwH2z5Tc5PVNu9lGAZMfmrYYniNh5\nXeO2dqdAQTYYbA2NRCTi0sk++LqYM1bb7Q7BuUeHqzAqQQdHAKNutqfuDxA+VPlNixmGQXKMe1G3\naHIC1q8aK2hqSwwspk+fzkXh/C0U16xZA5vNfb3t27cPqampQY/pK8y85sLBjJnLpat9pVo9Tg7W\nmSMWMRCLfR9Q3v0bge550Us8AkWtVruPajQANARpCcTOU359HD9zRhmg7CPGS/bdW4CC6Dv42TC/\n+93vuGyYzz//HBqNhsuGWbVqFfR6PRYuXOj3mIEGW6M62U8LrUB0tWbdH0q5BD+7PhupcV17NrHt\nGWRBSjEmjorEHYsz/ArGhCgluGFWip+j3MREhGD6mCju2QkAOby1dDCBQLPFhqZm93O83e7EvpPV\nqKpv8WssX0pk9HIYdBG74YJMKu5SKsr8ifE+/ek6g18/yPa1YhgGdyzO4GqThPt3TOQ//GwG/vDB\nYRwrqRPsw18j15rb8Nne8zhw2oTHbhrL1S55L+SJoUOFqRkfeYwyAFg6MwmLJo/gPNS15ja44jou\nEqvNgU++P4cb56UCcF8bu/aXAwywLC9ZsLCr9VpINVhsMOiUggcMK5XcYLHiQFGH1+7EOeF1eqS4\nFq99dBQzs2Jw3SxhywV+jZ0/Fau6LvZzZG/sEomIS1sD3Ombeq0Cx716VGlVsi5HspfOTMabn7rn\nKJsGwrJocgLCNHJcQ5G6Ac9ASZtutdrhcLqgVkphbXegzeaAVMzgYm0LEqM1Pu04+HPxfFUTLK3t\nnYogNbbYUGtuwwijBiIRg/omKzQqqSD6ZHc4BaIitnYn6hrbEKqW+YyB72CpMbfBoFNw0S+xmPEb\n1Wqx2lFjboVeq0B1XQscDieq/dTdBMLu8bibGlp9RMcABI2yF1e4jUJ+D7EovYpTpPZXkwt09K5l\nCdZcmuh9Blo2TE+QFq/Db9ZM6jQzDOhoD9CXaug/W56L17cexgpPH75g8NsMGXQKmBraoFHKEJGs\nhEou4Zw7CcYOZ5hIxGDNVZkoKKzCG/85DgAYm2bAtm9LALgNu0DaE2aLjes7CbgF1yJCFXh4Za7P\nvmTYEb2Ogrcg5su7zsiOhqW1Hf/+6oxg/+QYLVZcMRKjk/RgGMav5DLfi1ljbuO+o77RilB136qL\nEn2PdxPPj3afxbTRHf1z2h1ObnHEcrG2GV/uL4dCJuaMFQAw1bcKDDuTWbgAa7BY3YYdz+GgDZHB\n1u5Ag8Um6PFY5HVT3rLrNGztTnx5oBwpccK0pcYWG17ddhQJURo4/NyI2UVtRKjCr9feG6nYLUH9\nH0+a8iOv/YANq8fjT1uFjWo1KmnQJsntDiduvjINKrkEU0YbEWsIwW/e+RHXefUCTE8I67JQDNH/\nDISF4sOvfg+rzYG3Hp+Df35xGvtPVUOndvdMvX5WMpZMTRTsX9PYMRd3HajArgMVnfZp/e07P6K2\nsQ23LkhHWrwOT/x1L6aNicKdV2Vy+7z5aaEgAl98wYx/fnEaM7Oj8RNe3bvL5fThu/oAACAASURB\nVMIDf+oQOnn1w6MAOowgsUjoTGE5WVqP//umBCOiNCitbIJSLgmaTh0IW7vTbxp1V+BH7LwXx2nx\nOoFDCgAiw5Re76m9ENHzBGp67s3Pb8yFpaUduj6MHMcY1Fh3Q9d6dfIds7/6ySQ0t7VzawSpRARY\n3U3Zf3Z9ls+xWcnhGJ9uwIKJCTDwsuS0KhleuHcaDhSZUFBYxaVoAvAbnasxt+GInzpa77VPb3PJ\nhl2wRqlWqxVPPfUUTp8+jW3btvXIQImeg5826e0t9FczwTCMICeajfjJpCJMHxPtk+pWz4ts2OzO\nS8rjJgYX/vouWXk1LC1tduQfrxR8Xm6y4NjZOu/DUFZtEajtlZuaBYu1HQVlaGmzCyJ2KrkEOo0c\n9Y3WoN7zi7UtYBh3hPnrg8IF2glPJG1/kQl6bcfDK86gRrnJgjpP6lmCUSMw7KZmRSP/6EV4IxGL\nEKqWY+mMJK5R+TPv+fao1ITIuJQywJ12opCJudx+h8OFuePjuM8To7TY/OjsoAq3BNEV2B6MTpcL\n5yob0dxm52o4TQ1+asm64NDg0253cimb5SYLNzd/OFYpMOy806rZ1O3dRy4KDDtHAA8Im4opEgE2\nP+1Gzl50359KPY2P+Ubd5Ewj9/vzJ8bjQJGJm995OTHIP14pqMfTqWVcm4LuoJJL8OxdU3Chphmt\nXnU/a5ZkICtZD4lYhIwRYThSXIupY6IwIkoDU0MrRAzjU6dMEH2JQibpV7X4zuBnlinlEoEjhVXV\n1qllfs9BKZdg7TJfg0+lkCA8VIErJ8YjKUYreH77W7sAQAGvTRJLX0fsLqnGjt8o9emnn8bTTz8t\n+HzTpk3IyOh/dUnCPwzDwKBT+LRPANxNKvn4a/7IRlM0SqlADZOF//Bttdq5xQPQ9xc40Tc0++kT\nw4+6FZbW453/nRR8HmhxVFZtEUgFW20ObOWleR4oMuHlrUcE15lKLkGYWo4Wq71TTzx73VcF6dvY\nzBNKGRHl9miytX4jeKkcE9INeOTm8dx7vrHJKnL6a89y5YR4ro5PKZMI6oI23TcNf1w3Extvn4g4\ng9onMge4IxN93fSUGFq08eZJm9XhY7T5m0e1jV1XkgSAuqaO76w1t11SlIxPoNo71iNud7j8qsEG\nM0jnjIvlXs8dH4creaUN8ybEISlKqCYZHR6CMcn+laoBdxoYAMR61cgp5RIY9SqMTTP4pF4r5RLM\nyo3F9Kxo6LUKzB4bC7lUjJSYUEzJjMKkjK7XQBHEcGRiRiTmjIvFxtsn+nzGGna2bggmAcIoO99w\nHJUQuEaQ7UUpFjG4d+kYAINEPCVQo1SWhx56iPucGJg8f880PLQix2c735AbmxqBn68a67MPWyun\nVrpTLPl98rx5+r39KOOl6V3ug50YmNw4NxVXzxQaIGzPNwA4WtJ5Kw2WT344h399eRoAkBLrTpdk\ne7Px4ad/tnk1IQ5WnxquVUAmEQWVN2ZVO68YF8s1aq2scxt2yTEdnnO5TCyILmbx2o1IRKxh55uK\nrFPLuLpUsYjhbvyROiW0nv1HRGnwmzWTEKFT+hxPEJcL66gAgPqmNh/njD/BkTo/gineIkSC/XkG\nVW1jm+CeEIymAHPTGqCFB9vL0tbu8Gv8BRN64deyqRQSn0wAbwEzlVwCeZCaWLb2Ved1D+LXyHW1\nVQVBEF1DLBLhlvnpGBHl29ZD5pmv3VHCBYRK2XyRoyU8scHMRHf5g1QigpbnxF2/MhdxBrdzx+4c\nBDV2nTVKVavVaGhoCHS4X3q6iepQoD/O28wTZPjNPdP97hMfpcWxs3UYMzICBoMGj/9kEp76cz4K\nz9UhOiIEF716huw/3ZFzrAxRwODlySQGP0q5RKAsBQAFhb4pCd1lRlY0ovUh+M5PqiNfbCEuUo2E\nqA7DzqBTClKC+YQopdCopJ1GH25fNAp5OTH45Ht3GmW1px8Yv/jaO60jLS4UP3pSMSQS91PBX8SO\nX6cgFjGcQp8kiPoXQfQUrVY7LvBSLcu8amQBt0iJudkGlVwCm90Bu8MFU0MrlHIJbO0OLmJe19gG\nqUSMEIUEbTaHIJJeznsW1Da2cYIlcpnY891iv+mV/Gg+K9DidLoCzmnWuGxqbfcbyQ4m3KVTy7n2\nA0qZRFCqoPRj2CkVEq4Gl99+hIU1MnVeteV8739fe/AJYjjDRuy6a9jxHUkKXjZbtF6Fn16VCXOz\nDSOiNDhxrh7Xz0pBfVMbdhSUAXDX0bL3iYqaZnx1sAKzc2P6JNOmRxJme8L71NNNVAc7/XXeWpkI\n8yfGI3dkRMDfv3pqAkJVUlwxNpbbZ931Wcg/XgmdWu4jDnGQ11vkVEkNJK7Ak2u4GvFDAYWXqM53\nR3yNse6iVclwx5IMv4YdABjDlLh1gdtLx+83Y9ApUFTm/zvVSik0Klmnhl1ejrvNAOvBt9mdkEpE\nAhVAsVeNapimYxEo9ajz+VPpC1XLMD7dgP2nTEj0qI2596X0SqJ3uVjbjCf+ulegZOyvQfb5Kgse\neuU7n+1xBjUu1nYYbI++nt/pb8okIrRaHdjveRZYbQ6/381SzYsmPvDybjy4Igc7Cs5zdbCBOOMl\nlsTW0wZDJGKg18phaW2HSMQIonFymRgRocKIuUGn5M5fpZCAYdxGIZuNotfKUWNu82lXwJdsZ41H\nqpMliN6nIxWza3oPV00bgf/+UIpMXl89NhWTgVusbeqYDnG4l9fNgFop5dLAxySFc0rdgPu+dKbc\njNiIkD5pQ3RJht1AaJRK9A4Mw2DV3NSg+yhkEp8WC3KpGLNzYwUPfBZ++uXvtxzCyjkjsWCSb4NK\nYnDTE4XV185Iwo8nq1Hh8fTLvHrmpMRqMcKowa4DbsEepVyCjEQ995pFy1N7XTxlBLbvKeXehygk\nUHuiaAyEUuX+GpLKeOnJOrUMDMMgNiIEFTXNqPAT6WBhDboRURqMSdZj6ugobP7EvYBWyCS4c0km\nrpzQhLR4HXbuc6eaSjtpQEwQl4tOLe/U2PHHmCQ9lHIJpmQa8dpHxwSfhaplXI+77JRwQTqjRiXF\nuDQDvj18AS4XsM+PuEAgtCEyNDbbUFxh9jHqZufG4OsgCpVjUyMwKzcWf/n4uE9a6ZRMI7QhMozy\nLNxWzU3lnlP8e46IYTBxVCQu1jZj6ugo7DtZjUWTE7h57E7/SgHDMLC02BAVHgK9Vo7vj1Zi/sR4\nyCRi1DW5G7nzPfW5qRFYOClBsDgkBjbBBAP37NmDl156CSKRCElJSXj66aexb98+PPDAA0hNda+n\n0tLS8OSTT/bX8Ic1Bp0ShaX1CA3pmqLn0pnJWDR5hGBNIRaJIJOIIJeJfZy1bLmFXCbGyjkd62dv\nR219kxUulwvtdqfP2qYnuaSV2PTp0/HKK69g1apV/dYolRiYePfe8cexs3Vk2PUS/fnwUfppgxGI\nx28eB4fThRf+eZDb9vDKHIxJCse4NAM2vlUAQKjgCrh76dw0Lw1HS2phamgT/CbDMHj0xrH44Osz\nmJAeif/tcRtL189KRmxECDb/170YUyulXGNRfrTM/XtiAO2CQml+exC2SfD1s1Pwp61HMNMT1Vu7\nbAy++LEcY5I6RBUkvIjdwyvcvW0MOiUOn6lBUrQGDMNw3ju7J0XEX3SPGJr011xVyiWYOz4OX+4v\n79Zxd16dydV/ejMuzYCvPM6We68d41d0K9PjgCn6f98JGp0H46dXZeL3Ww6hrFroQFk6MwnXTE9C\nhE6JrV8X+z32/uuywDAMbl2YzvWoAtxG6F3XjBbsOza1wzHt3YBZKZdwi7UEozujhN83z5+wCSt4\nxFez5SMWibrUm4sYGPAFA4uLi7FhwwZs2bKF+/ypp57Cu+++i6ioKKxbtw67d++GQqHApEmT8Kc/\n/akfR04AwA2zU6CSSzB/Utd6PosYxu96JsGo6bRvJx/v5/mfPz6OvSeqcOhMDR5ekYMxfgQMe4JL\nMuw6a666bt06VFZW4uzZs1i9ejVWrFiBq6++uqfHTgxAZFIxkqI1nLy0PxqDiFYQl05/P3wUfhZz\n/sgYEcYZNEumjsCn+e5oGrto4iuzskXPbA0MG9EakxyOrw5U+PSDGjUiDE/eNlEg0sAwjGChqVZK\nuWLm1LhQzMqN4RQ7r56eiL0nqnDD7BRuf/4NPtkjOZ47MgKvPpTHnfP49EiMT48UjMVfWuXI2FC/\nsuXtnpobKdXYDQv6e64GcsJE6pSCNEh+OpEmyIImitdjzZ9Rx4dt7aFWSoPWvgFArEd8gG1/wMLe\nF4I5k9gImbezMUQRfGHm7UzyB6vu7J2KTQxNAgkGsgGNbdu2ca/1ej3q6+sRHR3db+MlhKiV0h5x\npDx+87hu7e/PUXvI0+fupX8fhlGvwso5I5E7MuKyxyb43Us9MFhzVfJQDG9+cct41Da24Rd/3uP3\nczLseof+fvjwF1nrbsj2qbWcmR2N3UcuYmZOx2/yjUG28b2K9z3sIksiFsHh7BBmuOXKNCyYGA99\ngAixzEuIib/YDFXLcOeSTPxvbymumZ4ElULCGXbGMBUe8VKCzRgRhjnjYlHfZMW105P8nq8/uhN9\nmzs+Fn/9byNmj43tfGdi0NPfc1UV4NpNjtUKDLv4SDVn2AUr+g80D/3BSo7HR6pRWBq8Zk6rkiE0\nROajYMvOZ0cX2uf4qFp24nHvyrzlInYicsQMB7oiGAi4y5K+//57PPDAAygqKsKZM2dwzz33wGw2\n4/7778f06f4F6YjBgXff584Qd1IzX1XXgr0nqgaOYUcQgZCIRQJpWJYlU0fgZGk9ii804t3PTuLW\nhaP8HE1cKn318AmkYMuXQs8c2ZHa9MLPZiI1XgexWIR7W2wC+f+kOHeNS/qIMERHuSNZfJW8aGMo\nDGFKSCUiWNsdkEjFnMBOZKRQhZMPX9DJYNBgnEqOMSllmDw6Ghkj3ZG1CVkxPsfFx4T6FfB56OYJ\nAX+L/Q1vFEpZl8WArr1Cg/nTkruVzkoMXvp7ociX3udj8BIKiY0IwZHizluVhAT4Pn+wgiYJxs4N\nO7ewicLHsGOFSJyee4VELArYI5Vfb9vdsQaCzRxQyXuvToYYuPgTDKytrcU999yDjRs3IiwsDImJ\nibj//vuxaNEilJWV4dZbb8XOnTshk/lPZwZIHd4fQ/W89VoF6hrbMH9qYo+fI60iiF5BGyLDHYsz\nEB+pxq/f2QfAHfm44BHF+PrQBdyyIJ1UwXqR3nr4BFKwDeEpQrZaOoy81hYr6uo6RHXamjvUKNNi\nNHhoRQ4yRoT5VWG1NLUCdjuX8tRksXZZLfbaGUmIDFNy+z+83N23MdjxtlZbt9VoAynY1jW0dPu7\nAkuxDEyG6kO3r+mtuQr4XyxG8f5ueWNjUd9oxfTsaOSkGVBtbsPcifH44chFrFmWDQcYTMgwCv7W\n3rftaWPjMOd0DfLGxnZ6TTy/dib++10J7lyWjcZWO/I9ireZSXo4nS6IxSKIRQyyPe10rslLxiff\nnYVaKcURTxqTIVwNg0GD6+al43xNM1bMTcO5i404X9kEtUoKGc8BBAC3LBwFpUKCi6ZmjE4JDzrG\n8HA18sbGYmpWdMD97lueiz9/eAR3L8um9j3DgM4EAy0WC37605/iwQcfxIwZMwAARqMRixcvBgAk\nJCQgIiICVVVViI8PXOdF6vBChvJ5P7wiB6fO1yMxQuVzjpf7XCXDjug1ZmS7U4cWTkrAZwXnEWtQ\nC9IwzRZb0EbSRPfoq4dPIOQyfgolTzI8iPqTRCxCVpACYtYzz6ZH2bvR/+naGUmd7+SFqpP6m+7g\nr1EyQQB9O1f9LRbbrR21bbcvSBd8dvfVmQCAkVEaNDa0YNUV7npT/uLD2w6tq2vGLfNSffbzR4Ra\nitsXpqPJ3IrbF6Rxht3N81IRHS40kkymJuQk6ZHjESW647ldAIA2ngPmzsUZAIDsxDBkJ4YJjmWZ\nk+uJzo/q2mKR/TcJtJ8EwNqlYwCXc9AsPMkJc+l0Jhj43HPP4bbbbkNeXh637eOPP4bJZMKaNWtg\nMplQW1sLo9FXaIcYHoxJ1mPRpAT87bNTmD02FjERIT4tUXoKMuyIXmf5FSm4blayO0WTlwZTa24j\nw64H6e+HD19IQCro2XTp6Urs9+TlROPD3WeRlaLv5IhLIy8nGkVl5h4RL5GIGdgdrm43QyWGD/09\nVwOlYvY1Ul4ksTtpyDISGSL6kGCCgTNmzMBHH32E0tJSbN26FQBw1VVXYcmSJXjkkUfw5Zdfor29\nHb/61a86ja4TQ5f5E+KRkajHc/dM7fXfGhh3d2JIwzAMpxB4+8JR2LB5D2ztTtSYWzEyzlchkLg0\nBtLDhy9AcCmLsMzEMJy92MQJNiyZlojJmUYYdMpOjrw0bl+U0WPftXZZFv722UkSQiEC0t9zdSDW\ncgYSdCGIgUAwwcBjx4557w4AeOONN3p1TMTgITKsd9Yu/qA7KdGn6LUK3HvtGLy89QhqeWIbRM8w\nkB4+a5dloeSC+ZIWketX5grSvUQMg8gwVeADBhA5IyPw0v0z+nsYxACnP+fqQIx4dSdazrYrIQiC\nGAx4q/P2JmTYEX1OUowWUXoVYsKp6HyoMSMrGk0t7jrK8ekGjE83dHKEfxiG8RFoIAiiZ9Cp5chK\niUBGgu6Sjl+7LAt/+r8jntdjLmss8yfGo77JGrSdAsvqBenYWXAeqbGXNm6CIIi+ZP7EeJibbX3a\nGoVx+ZPj6gcCFSAPZVWcYAzn8yYGNjRXhQzX8wZovg4GaL4KGc7nTQxsaK4KGc7nfTkMGMOOIAiC\nIAiCIAiCuDQGXqI9QRAEQRAEQRAE0S3IsCMIgiAIgiAIghjkkGFHEARBEARBEAQxyCHDjiAIgiAI\ngiAIYpBDhh1BEARBEARBEMQghww7giAIgiAIgiCIQc6ANeyeeeYZrFy5EqtWrcKRI0f6ezi9QlFR\nEebNm4f3338fAHDx4kWsXr0aN910Ex544AHYbO5Gzx9//DGuv/56LF++HB988EF/DrlH2LRpE1au\nXInrr78eO3fuHDbnPZSh+Tp0r1uar0MLmqtD95qluTr0oPk6dK/bXpuvrgHI3r17XXfddZfL5XK5\nzpw541qxYkU/j6jnaW5udt1yyy2uJ554wvXee++5XC6X6/HHH3dt377d5XK5XL///e9df//7313N\nzc2u+fPnuxobG12tra2uJUuWuOrr6/tz6JdFfn6+684773S5XC5XXV2da9asWcPivIcyNF+H7nVL\n83VoQXN16F6zNFeHHjRfh+5125vzdUBG7PLz8zFv3jwAQEpKCsxmMywWSz+PqmeRyWTYvHkzIiMj\nuW179+7F3LlzAQBXXHEF8vPzcfjwYWRlZUGj0UChUGDcuHE4cOBAfw37spk4cSJefvllAIBWq0Vr\na+uwOO+hDM3XoXvd0nwdWtBcHbrXLM3VoQfN16F73fbmfB2Qhl1NTQ3CwsK493q9HiaTqR9H1PNI\nJBIoFArBttbWVshkMgBAeHg4TCYTampqoNfruX0G+7+FWCyGSqUCAGzduhV5eXnD4ryHMjRfh+51\nS/N1aEFzdeheszRXhx40X4fuddub83VAGnbeuFyu/h5CnxPonIfKv8UXX3yBrVu34qmnnhJsH+rn\nPRwYjn+roX7d0nwdmgzHv9NQv2Zprg5dhuPfaqhft70xXwekYRcZGYmamhrufXV1NQwGQz+OqG9Q\nqVRoa2sDAFRVVSEyMtLvvwU/ZD0Y2b17N9544w1s3rwZGo1m2Jz3UIXm69C+bmm+Dh1org7ta5bm\n6tCC5uvQvm57a74OSMNu+vTp2LFjBwDg+PHjiIyMhFqt7udR9T7Tpk3jznvnzp2YOXMmcnJycPTo\nUTQ2NqK5uRkHDhzAhAkT+nmkl05TUxM2bdqEP//5z9DpdACGx3kPZWi+Dt3rlubr0ILm6tC9Zmmu\nDj1ovg7d67Y35yvjGqDxzBdffBE//vgjGIbBxo0bMWrUqP4eUo9y7NgxPP/886ioqIBEIoHRaMSL\nL76Ixx9/HFarFTExMXj22WchlUrx2Wef4c033wTDMLjllltwzTXX9PfwL5ktW7bglVdeQVJSErft\nueeewxNPPDGkz3uoQ/N1aF63NF+HHjRXh+Y1S3N1aELzdWhet705XwesYUcQBEEQBEEQBEF0jQGZ\nikkQBEEQBEEQBEF0HTLsCIIgCIIgCIIgBjlk2BEEQRAEQRAEQQxyyLAjCIIgCIIgCIIY5JBhRxAE\nQRAEQRAEMcghw24AsG3bNuTm5uKHH37gtp08eRK33HILVq9ejeXLl3O9LQiC6D/8zVUWi8WCOXPm\nYNu2bf0wMoIg+Pibq48//jiuvfZarF69GqtXr8Yvf/nLfhwhQRAs/ubrb3/7W26url69GhMmTMDB\ngwf7cZSDA0l/D2C489FHH+HYsWM+vUneeustrFu3DpMmTUJZWRmWLVuGBQsW9NMoCYIINFdZnn32\nWRiNxj4eFUEQ3gSbqxs2bMDkyZP7YVQEQfgj0Hx98sknudfFxcV48sknkZub29fDG3SQYdeHVFVV\n4ZFHHgEAtLW1YeXKlVi4cCGWLl2K1atXC/bdtGkT9/rixYuIjo7u07ESxHCmO3MVAL755hs4HA5M\nmzatr4dKEMOa7s5VgiD6j0udr8888wweffRRMAzTV0MdtFAqZh/yv//9D8nJyXjvvffw/vvvo62t\nDWq1OuD+JSUluOGGG/CLX/wCL7zwQh+OlCCGN92Zq2azGX/605+wYcOGPh4lQRDdfa6+/fbb+MlP\nfoJbb70V+fn5fThSgiC6O18B4NChQ3C5XBSt6yIUsetDZs6ciX/84x94/PHHMWvWLKxcuTLo/snJ\nydi6dSuOHj2K++67D5988glCQkL6aLQEMXzpzlz93e9+h5/97GfQarV9OEKCIIDuzdVrr70WOp0O\nGRkZOHHiBO644w7s3LmT5i5B9BHdXQcDwLvvvoubb765D0Y3NKCIXR+SkpKCTz/9FNdccw3y8/OD\nhp23b98Oh8MBAMjKyoJWq0VxcXFfDZUghjVdnasWiwX79+/Ha6+9hhUrVuCDDz7Aa6+9hu3bt/fx\niAlieNKd5+rUqVORkZEBAMjMzERUVBRKS0v7aqgEMezpznwFAJvNhoKCAkyfPr2PRjj4oYhdH/LJ\nJ58gNjYW06ZNw+TJkzFnzhzY7XZIJL5/hrfffhsymQzz5s1DTU0NqqqqkJCQ0A+jJojhR1fnqlqt\nxq5du7j3r7zyCmJjY7F48eK+HjJBDEu681y97777cO+99yIrKwsXLlxAdXU1EhMT+37QBDFM6c58\nBYBTp04hJiYGCoWij0c6eGFcLpervwcxXCgsLMTGjRshk8ngcrmwaNEiNDQ0YO/evSgsLERMTAxC\nQ0Px8ssvo76+Hr/+9a/hcrnQ3NyMO++8kxaLBNFHdGeu6vV67jjWsLvuuuv6cfQEMXzozlwtKSnB\n888/D7lcDqvVirVr12L27Nn9fQoEMWzo7rP1888/x4cffojXXnutv4c+aCDDjiAIgiAIgiAIYpBD\nNXYEQRAEQRAEQRCDHDLsCIIgCIIgCIIgBjlk2BEEQRAEQRAEQQxyyLAjCIIgCIIgCIIY5JBhRxAE\nQRAEQRAEMcghw44gCIIgCIIgCGKQQ4YdQRAEQRAEQRDEIIcMO4IgCIIgCIIgiEEOGXYEQRAEQRAE\nQRCDHDLsCIIgCIIgCIIgBjlk2BEEQRAEQRAEQQxyyLAjCIIgCIIgCIIY5JBhRxAEQRAEQRAEMcgh\nw44gCIIgCIIgCGKQQ4YdQRAEQRAEQRDEIIcMO4IgCIIgCIIgiEEOGXYEQRAEQRAEQRCDHDLsCIIg\nCIIgCIIgBjlk2BEEQRAEQRAEQQxyyLAjCIIgCIIgCIIY5JBhRxAEQRAEQRAEMciR9PcACILoHTZt\n2oT9+/fDbrfj7rvvRlZWFh599FE4HA4YDAa88MILkMlk+Pjjj/G3v/0NIpEIK1aswPLly/t76ARB\nEARBEEQ3YVwul6u/B0EQRM+yZ88evPnmm9i8eTPq6+uxbNkyTJ06FXl5eVi0aBFeeuklREVFYenS\npVi2bBm2bt0KqVSKG264Ae+//z50Ol1/nwJBEARBEATRDSgVkyCGIBMnTsTLL78MANBqtWhtbcXe\nvXsxd+5cAMAVV1yB/Px8HD58GFlZWdBoNFAoFBg3bhwOHDjQn0MnCIIgCIIgLgEy7AhiCCIWi6FS\nqQAAW7duRV5eHlpbWyGTyQAA4eHhMJlMqKmpgV6v547T6/UwmUz9MmaCIAiCIAji0hkwNXYmU5Pf\n7WFhKtTXt1z299c3WWGzO2AMU132d/UFPXXeg42wMBUkEnF/D2PI8MUXX2Dr1q146623MH/+fG57\noAzsrmRm2+0On79RY7MNF2ssGBkfBrGIubxBE8Qw5plnnsHhw4fBMAw2bNiA7Oxs7rM5c+YgKioK\nYrF7/r344oswGo1Bv6+3n62DjaF83i1t7Sg3NSM1LhQMI7wPGwyafhoVQRB9yYAx7ALRU4v89a9+\nDwB46/E5AfdptztRWtmEkXGhPfKbl8NwNW6G63n3Brt378Ybb7yBv/71r9BoNFCpVGhra4NCoUBV\nVRUiIyMRGRmJmpoa7pjq6mrk5uYG/V5/i6J3/ncS3x6+gNAQGSaOisTkTCOSY7Q+i4uhiMGgCbh4\nHurQYrFnKSgoQGlpKbZs2YLi4mJs2LABW7ZsEeyzefNmhISEXPZvDdd77VA7b6vNgUNnalBQWIWj\nJbWwO1x49MaxGDUirL+HRhBEPzDgDTtvXC4XdhSUQSkXY1Zu7CUdzzAM9hyvRHykGrEGNZxOFw4U\nmXDoTA1+OFaJe5eOwcRRkb0w+g6cLhecThckYsqGJXqepqYmbNq0Ce+88w4nhDJt2jTs2LED1157\nLXbu3ImZM2ciJycHTzzxBBobGyEWi3HgwAFs2LCh27+3ZOoIKJVSfHeo7YG6kgAAIABJREFUAl/s\nL8cX+8sREarApAwjJmcaEWcIGRZGHkFcDvn5+Zg3bx4AICUlBWazGRaLBWq1up9HRgwk2u1OHDtb\ni4LCahw8bYKt3QkAiDOEYOroqAHhnCYIon/okmEXKDWkqqoKjzzyCLdfWVkZ1q9fj/b2drz88stI\nSEgA4F5Q3nvvvT0y4L0nqvDvr84AwCUZdtZ2B+qbrPjLJycAuCN4O/adxwdfFXP7FFeYe92we3Xb\nURw8XYM/PzIbUgkZd0TPsn37dtTX1+PBBx/ktj333HN44oknsGXLFsTExGDp0qWQSqVYv3491qxZ\nA4ZhsHbtWmg03Y/CGHRK3L88F9fPTMKJc3XYe6IKB07XYPueUmzfU4rocBUmZ7qNvMGSDk0QfU1N\nTQ1Gjx7NvWdrXvmG3caNG1FRUYHx48dj/fr15DAZJjidLhSer0fBiSrsP2VCi9UOADDoFO57a4YR\nsQZyABDEcKdTwy5YaojRaMR7770HALDb7Vi9ejXmzJmDHTt2YPHixXjsscd6fMBFZQ3cazZ6d6Gm\nGXcsyQAAnCk349UPj+KRG8dCKRNDLBYhNETGHdNqdaDc1Cz4zvJqi+D9D8cqsWTqCGhUMnjDRvy8\nt+0oKEN2SjhiIrqWInPwtDv9zWyxIkKn7NIxBNFVVq5ciZUrV/psf/vtt322LVy4EAsXLuyR35WI\nRchOiUB2SgSs7Q4cLa7F3hNVOFxci492n8VHu88iMUqDSRlGTMqIhF6r6JHfJYihiHfN67p16zBz\n5kyEhoZi7dq12LFjR6dzN1jd8nBNpR0s5+1yuXDyXD2+PVSO7w5fQEOTFQCg1ypw5eQRyBsbi9R4\nHRn3BEFwdGrYdTU15MMPP8SCBQt6JPc/GKaGVu61ze7kone3LkyHRCzCm9sLYW624cNvS3CgyAS5\nVIxXH8rjjmmz2QXG4R3P7fL5DUtrOz75/hxuujKN2+ZyubD2D98iLV6HB5fnCPYvKmvAv786gw++\nPoM3H5uDM+VmvP6fY3j0prGdRicaW9rJsCOGJHKpGBNGRWLCqEi0tNlx8LQJewurcOJsPc5VNuGD\nr84gNV6HyRmRGD8qElo/jhSCGE74q3k1GAzc+6VLl3Kv8/LyUFRU1KlhF0goZLjWhg7083a5XCir\ntmBvYRUKTlSjtrENAKBWSjE7NwaTM41IjdNB5BGpqqmxBPs6jsFizBIEcXl0ath1JTUEAD744AO8\n9dZb3PuCggKsWbMGdrsdjz32GDIzM4P+jj+vYllVE74oKMXciQmcR6rOYuM+lyvl3GtliAKlFxvR\nbnfnmh8ocku2W9sdUGs7DKeiC034cn95Z6eNc9UWwY2wzWpHm82BI8W1PjfIM5XuG6vL5b55Pv6X\nPahvsmLHj+VYf9P44D8kFvm94e4+WIEde8/hqTVTIJMGL/Z2OJw4cqYGuWkG8twRAxKVQoLpWdGY\nnhWNxhYb9p8yYe+JKhSVNaCorAF///w0MpPCMDnDiHFpBijlg678lyAum+nTp+OVV17BqlWrcPz4\ncURGRnLP2qamJjz44IN4/fXXIZPJsG/fPixYsKCfR0z0FJV1LSg4UYW9hVW4WOs2xuUyMaaOjsLk\nTCMyE8OoJp8giE7p9urJnxz6wYMHkZyczD2AcnJyoNfrMXv2bBw8eBCPPfYYPvnkk6Df6+1VbLc7\ncd+LX7sHyQCjE/VwOl2oruvY73xFPff63ztP4pMfzvn97g93FXGv//bpCTAAOhN1P1thRsWFBs6o\narBYuc8uVpoFN9jySjP32mRqQmtbOwDA5XB26hk8f9GMZGOHkdxqteMfnxfh+2OVAIDn/1YAALhx\nbipC1XK/3/HvXWfwWcF53Dg3FVdOjO/kzAY25FUc+mhVMlwxNhZXjI1FXWMbCgqrUVBYhWMldThW\nUoe/fXYKOSnhmJRpRE5KeKeODYIYKowbNw6jR4/GqlWrwDAMNm7ciG3btkGj0eDKK69EXl4eVq5c\nCblcjszMzB5LoSb6B/b+t/dEFUqr3GsFiViE8ekGTM4wIpvufwRBdJNODbvOUkMA4Ouvv8bUqVO5\n9ykpKUhJSQEAjB07FnV1dXA4HFzvnS4NTMwgwajG+SoL/vF5EdpsDjy0PAcOZ4dJxqYoAAho1AHA\nll1nBO+TY7TQhsi4Ojd/OJwunKtsQlq8W1GwzeYQ/C4/xbK+scPo21FwHk0tbsNO7ueGfLG2GRGh\nHXVFjbwIJADsPnKRM+oAoKCwGgAQopBi9YJ0v2M9XOw+j9PlDYPesCOGF3qtAgsnJ2Dh5ARU1bVg\nb2EV9p6owv4iE/YXmSCXiTEuNQKTMowYnaQnjzUx5OELkgHAqFGjuNe33XYbbrvttr4eEtGDNLbY\n8ONJtzF3utztFBaLGGQlh2NyZiTGplLGAkEQl06nd49gqSEsR48exeLFi7n3mzdvRnR0NK666ioU\nFRVBr9d3y6gDAIZhsHz2SPx+yyEuLWHPiSoA7pugw+nCPo/RE/DkxCLYHU6f7RqVDBqVtNMxFF8w\nc4Zdq0eBCgCq6lpR32iFC0DGiDDU86J5fCPSW+3yfFUTfvX2PoxJ1nPbzM1Cw87cbIU/+EasN06P\nsSuixtDEIMaoV+Ga6Um4eloiyk3N2HuiCgWFVcg/7v4vRCHBhFGRmJRhRHq8jq53giAGBS1tdhwo\nctcYF56rh9PlAgMgPV6HyZlGjE83+BVrIwiC6C6dGnadpYYAgMlkQnh4OHfM1VdfjZ///Of417/+\nBbvdjqeffvqSBqdTC2902/eUAgCiw1UoNzVzhl4gEoxqlFxo9NmuVkmhkndu2H3wVTGOnKnFozeN\nFUTsCkvrsKOgDADw2E1jOaUqb9rtTjS22LA9vxTzJ8aj0pNGeqykjtuntKoJL/7rIJZMTUTGiDBY\neb/Dx9Lazr3+6kA5LtS24GaPuIvTkx4r6kJ9nd3hRIPFivNVFoxLM3S6P0H0NQzDID5SjfhINa6f\nlYySC43YW1iFfYXV+ObQBXxz6AJC1TJMGmXEpMxIJEcPj0boBEEMHqztDhw+U4O9JzoahwNAUrQG\nkzOMmJhhRJjGf3kFQRDEpdKleH+w1BAAPvVzUVFRXBuEyyHQTS8qPMSnZYE/jGEq/4adUgqDHyVK\nNvUTAELVMpgtNpwqa0BBYTUKSzuMMdaoA9xtC2rM/qNpbTYHnn73R5ga2iARixAf6dtjhh3fiXP1\neOvxOQENu5ILjaiub0FkmArv7XTXDN4wOwVyqbhbEbuXthzCyfNuVdDf3DEJcX7GRBADBYZhkBIb\nipTYUKyak4pT5+uxt7Aa+09V4/Mfy/D5j2Uw6PiN0Ol6Jgiif7A7nDh2tg4FJ6pw8HQNrO3u53ls\nRAgmZRoxOSMSkdTHkyCIXmRAJ3Lz88xzR0bg0Bl3LVlsRAh+DHLc7NwYLJiUgM9/LPP7uUYpxYys\naMQZQlBV14q3thdCLhXjVz+ZhD3HK9Fud+Lbwxdg9tS//fnj4wF/q7KuRdCCgc+3hy9wr+0OpyDq\nFog2z4Mge2QEjnjON0qvQmVdCx7/8x68+dgV3L73/v4b/HHdDLBlh966NsfO1qKkohHzJsRDpXD/\nW7JGHQCuwSlBDAZEIgYZiXpkJOpxy/w09wKqsAoHi2rwaX4pPs0vpQUUQRB9itPp8jic3I3Dm9vc\nz9WIUAXmZcZhcoaRHKgEQfQZA9qwYxgGMokINrsTd16Vgfv/uBsAEGcI3isvKUYLo14Fhcz/6amV\nUkglIqTG6bhol0LmrgGcMjoKAHCqrAHFfqJ97HhYTp6vh8PpQqwhBBWeKGJeTozAqAPc/fdkUmHN\nnU4tQwNPPMXpdHEevl/cPgk3PrFdMDYAgv0B4NDpGk5Qps0mNNT+/vlpVNW1oMbchjuWZPgompJh\nRwxWJGIRckdGIHdkBJfyVFBYjSPFNfjw2xJ8+G0JpTwRBNEruFwud4r4iSrsO1nN1cqHqmW4ckI8\npYgTBNFvDGjDDgDefGI+amqaoFJ01MTF8tKtFk5KwGcF5zEuzcD1rmMXcXyDKDJMiep6d2RNrez4\nLjYlk78vAKycMxI/8NQpuf3DlJwBF6KQcN659Hgdt331gjQfw+7g6RofFU5jmEpgqL320TFYbQ4w\njPu7b12YjohQBd7bcYrb5w//Piz4DnOzDY2eh0qrl6FW7xFcOVBkwq0L0+FwCA27Vs/YP/7uLFpt\ndqyck+pzvgQx0JFLxZiUYcSkDCMnUlBQWIUT5+px9mITtuw6gzSPSMGEUZGC+U8QBNEVXC6XQNSJ\nLcEIUUgwKzeGRJ0IghgQDHjDTqeRo71NGKUK4/Vzizeq8cefzYBKIcFdL3wNwN0aABDWnD1391Tc\n8dwu9xvefVenlkMmEUHtpZKpUclwXV4ytn1bItgeqesw7NLidZyxNjIuFLsOVAAAxKKuSbJPGxOF\nU2UdqZFHS2oRrlVAIRODYRjMzo0FIBROKTdZBN/xIW98rVYHKkwWSCQi6ELkXGSxxWrH+SoLtCHu\nc2TVQtmI3UffnQXgrtnr6tjb7U7YHU6SZSYGFCqFBDOyozEjOxqNzTb8eMotK36qrAGnyhrw98+L\nkJmoJ1lxgiC6RFV9i8eYq8aFGvez39043EhtWAiCGHB0aVXzzDPP4PDhw2AYBhs2bEB2djb32Zw5\ncxAVFcW1M3jxxRdhNBqDHnOp3LYwHWXVFsh50TWNUgptiFs9865rMrH/pAkJnobfbASLzYa4b+kY\nfLqnFKMSwrjjRSIGP7s+268X39+iLyK0Q3QlOyWcM+xGJ+qRkxLu8z3JMVofAZeRcaGICVchKVrL\nbRudpMfxs3WorGtBqJcaaFZyONfPLhilVU148k13Q/Nn754i+KzG3Aqxx9CNDFPiQk0zWtqENX+1\n5jafuiSXy4WyagtarXb8b+953H3NaCjlEvzp/47g+Nk6/PmR2T5tHQhiIKANkWHOuDjMGRcnaAR8\ntKQWR0tqIZWcQnZKODUCJghCQF1jG/Z5es2dq+Q1Dk8zYFKm+37hr08tQRBEf9OpYVdQUIDS0lJs\n2bIFxcXF2LBhA7Zs2SLYZ/PmzQgJCenWMZfCLE8Ei4+CZ3xNyYzClMwo7j3bw07r6Q8zYVQkJoyK\n9PmO0Ul6n20AoJSLBa9brQ5EhnUYduPSDPjbZ6cQGaaERiXDA8tzfL5j3fXZePCV7wTbfnHzODAM\ngwZe/7vx6QYcP+tW3lR4PTBuXzQK0eEh+I8nsjZvfBxabXZ8f9SdKnrVtBH47w+lgmMOFnmEZjy1\nf2/85zgWTUkA4I46Xqhpxoe7z2JGdgx3TGVdq8CwO1Nuxoe7S1BYWs9tO11uRnZKODfW42frkJsa\n4fffjyAGCvxG6JV1LSg4UcWJHew/ZYJCJsbYVAMmZxqRmRhGHniCGGY0ttiw/5TJ3Ti8rAEuuFsI\njUnWY3KGEWNTDZwIGUEQxECl07tUfn4+5s2bBwBISUmB2WyGxWLxaVJ+ucd0FzadMCTIjXbRlBE4\nX2XBqrmXVjvGj9j97s4pKDdZuKgX4E7X/N2dk4PW7HineErEDFdQzT8uM7HDuJR71fspZBJMGxPF\nGXZqlRS5qRGcYRepUyEzMQwnznUYYP/+yt0ofWRsKJc6+r89593784zTt7cXcq8r61qQneLuR1he\nbcEz7+/3OR9bu7Adw9GSWjLsiEFFlF6Fa2Yk4erpiSirtmBvYRUKTlQj/3gl8o9XQq2UYkK628hL\njdd1qT8kQRCDj1ZrR+PwE2fruZ6wabzG4VpqHE4QxCCiU8OupqYGo0eP5t7r9XqYTCaBkbZx40ZU\nVFRg/PjxWL9+fZeOuVyeuWsyyqosiA4PrJCpVcnw8xvHXvJvKHmqmmEaOcI0crTbncgdGYHpWe7I\nYEyE/9//5erxaG6zQ8QwuOfa0XjjP+6WCXwjkx8ViAhVQK+Vo67R6jfFg9+sXa2UCoy/EIUEP7su\nG/e+9I3PcaMT9fjmkFDIhd/D72JtC/f6X1+ehlQiwhVjY3HiXB384S3Qwo86AoDZYkWIUkoRD2LA\nwzAMEowaJBg1uGFWCop5KndfH7qArw9dgE4t43rkJUZpSOWOIAY51nYHfvSkWR7+/+3de1CTd7oH\n8O9LQgiXJBBIAogX5CIEb0Ultii2lWrV1nXW04oWarvd9ri92bOH6bhsW7bTXev2sD2ndedox+1O\np9jtOrVOj91u1anHXo7SgLVVuamgIijmwk3CNcB7/ggEEBRakZDk+5lxJG/yJr8fvA/Jw+/yVNY5\nZ/ZMC1f0bsKkhVopd3EriYh+mh89r+D6LfOff/55LF68GCqVCs888wwOHjw44jnDCQkJgFQ6/Jx1\njUYx7LHE2KHTKseS1da/Bm1gG177VeqI5w58/CqNAvenTke12YZpA9bVDaTTKhE3OQTGkquw9O62\nNVy/ASBSp0SErv++SREqRE0KHvK4159ORdL0UPz3J8WDjs+IDnV+3dk1eATu74fPwTA7EhW1zcO+\ntkQmRVhYECQ+Arp7RNTWt+I/PzqFXzyYhAC5FP/256O4e14U/n3DvGHPJ5qIBEFA7CQVYiepsH5p\nHMovNcBY6piqeaioGoeKqqEN9keKXgtDom7QzrxENLF1dfeg9GI9jKUm/FBhRVuH430vIjQABr0O\nhkQddGrWvSQi9zdiYqfVamG19m/TbzabodFonLfXrFnj/DotLQ1nz54d8ZzhNDS0Dntco1HAYhk+\nybjd2lr7R6PGog2BUmHI87zw0Gx02ntgsTRDPyUYxpKraGzuuOlrivYutNranbc72zthsTQPKr/g\nK/WBTukHq9WGvKfvwoHCS/jieA0AQCL21+FrbnUkr30F4O1dPXj2P444HtebvAFwjjpa6lpQe7XJ\nefxqXSuu1rXi1b8U4OF7YwEAX35Xg0fvi/9J36MbJbNE48XHR4B+mhr6aWpkLZ+B4vP1MJaZ8P05\nC/5xrAr/OFaFKE2g46/7eh2vWaIJqKdHxNnqRhjLTDhebna+N2rVAbjnDsdU6yhNIEfhicijjJjY\npaamYvv27cjIyEBJSQm0Wq1zSmVzczNeeOEF7NixAzKZDEVFRVi+fDl0Ot0Nz3Enk7VByFwWj/jJ\nQ0fDxsrsmP71aXfODMe5miYkz7h5EizzlQwqvt5X3iEnax4+/uo8Tpy14MkH9M771Uo5IgdMWVUF\nyrD1qYX4/NsqfHOqFoBj3d31hdWnRSiwfMEUqJVy586ijjUJg+vxAY7yBxh5YJbIrUglPpgbF4a5\ncWHo6OzGyUqrc2fNfV+fx76vz2PGlBDcEReGBQlaFkInciFRFHGhtrl3SrXJWSdWFShD+rwox+jc\nnEmwWm0jPBMRkXsaMbFLTk5GUlISMjIyIAgCcnNzsW/fPigUCtx3331IS0vDunXr4OfnB71ej/vv\nvx+CIAw5x13dmxw1bq8llfjgF6sSb3j/zOlqFJ+vR5hKPmgdXt8GMhGhgXj257OGPXfgRi3+flIE\nyH2x8f4EZ2Inl0mGFGkPU/k7dxE11TtGVK1N7ThUVD3sa/SN/hF5Ij/ZwELodnx31oLCUhPKqhpw\n5lID9hw+hxlTgpGi12H+DBZCJxovNRabs3C4pbG/cHjanAgYEnWYMSXEWdeWI3RE5MlGtcYuOzt7\n0O2EhATn1xs3bsTGjRtHPIdu3XM/n4XWjm6oAmWD1i2Opv6WYsDunH1vbAMLuPvJJFgyZxLs3T3O\n3TMHbtjSt0No8fm6YZ9fEARca+0vJP/RlxVYmhzFRejkkQLkvlg8OxKLZ0dCKvfFwaMXYCwzofxS\nI8ovNeKDQ2eRFO3YJn1uXBgLoRONMXNDK4xlZhSWmZw7P/v5SrCwt3D4TBYOJyIvxE8bbsRXKoGq\nd4OZH/tXx8ARRg9kUgkC5FI8dHesM7EbmPj11fTr7OoZ9nxbmx2fFfTX0vv820u4cOUa7N092LR6\nJkJVTPDIM4Uo5Fg6LwpL50XB2tTmLGx8qrIOpyrrIJP6YHZsGAyJWsyOCYXvDTaJIqKba2juQFGZ\nCcYyMy7UXgPgKCGUHK9BSqIWc2LDWDiciLwaEzs3FjNJOeo3sb51eDcycHfMlEQtCsvMiA7v38HT\nVyqBVCKgq7t/pDA6Qul8cx1O+aVGAMB3Z8xYljJlVO0kcmdhKn+sMEzFCsNU1Na1oLDMkeQdLzfj\neLkZ/n4SJMdpkKLXIXEqC6ETjcTWZsfxcsfI3JlLAwqHR6uRkqhDcjwLhxMR9eFvQzf226z5o35s\niMIPm36WdMNt2lvb++vTPb4yEYvnRCJpQNF0wJHcdXX3P04X4o+LtdeG7JkSGRaIK9YW521zY9uo\n20nkKSJCA/GzRdFYnToNl0w2FJY51gAdLb6Ko8WOQugLErQw6HWIjVKxEDpRr7aOLnx/zgJjqRml\nF+uduzDHRalg6F3Dqgxk4XAiousxsfMiKYm6IcfWL43Dh4fPITm+fydOP1/JkKQOcBRwLatqcN6e\npAnEn/8tDc2tndjyzrcAAFWQDL9aMxMv/8XofNzAJA8Aunt6IAgCP8iSVxAEAVPDFZgarsDau2NQ\nebkJhaVmFJWbcOT7yzjy/WWEKPyQkuhI8qbqWAidvE+nvRunKutgLHNMY7b3TvufqlPAoGfhcCKi\n0WBi5+XS50fh7jsiR7Xu51drZuJ//u8C7p4bidq6ViTHa+DjI8DfT4pwdQCu1rciUO4L+XXTQ6/U\nDa5R+NtdRrR3duO/nls0pn0hmuh8BAFxUcGIiwpGRnosyqscdba+O2PBwcJqHCyshi7EHymJOhj0\nOkSGBY78pERuylE4vAHGUkedyPbOAYXDe+tEhrNwOBHRqI0qsdu6dStOnjwJQRCQk5OD2bNnO+/7\n9ttv8eabb8LHxwfR0dH4wx/+gKKiImzevBlxcXEAgPj4eLz88su3pwd0SwRBGPVmDkH+vnikt/D4\n9VM6+0olBMil8LuubEJzayd6enfx/Ms/SmFu4NRMIomPD5Ki1UiKViNr2QwUX6iDsdSEHyqs+PTY\nRXx67CKiNEEw6LUwJOoQFuzv6iYT3bIeUcS56kbH2tMzFtjaHGVyQpVy3JM8CYZEHSZrgzhqTUT0\nE4yY2BUWFqKqqgp79uxBZWUlcnJysGfPHuf9r7zyCt5//32Eh4fj+eefxzfffAO5XI6UlBS8/fbb\nt7XxNHH0vQf7+UqGbOgiikB7RxdaO7rwbYnJebxHFDkdkwiAr9QHd8RpcEecBh2d3fi+woLCUjNO\nn6/Dx1+dx8dfnUdMpBIpeh1SErRQBbEQOrkPURRx8Wpf4XAzGpo7AADKQBmW9hYOj4lUMpkjIrpF\nIyZ2BQUFSE9PBwDExMSgqakJNpsNQUGOEZt9+/Y5v1ar1WhoaEBERMRtbDJNRD29VRB8BAFSydA3\n56aWTvzvicuDjnV0drO+F9F1/GQSLNSHY6E+HC3tdnx3xoLCMkch9Mor1/D3w+eQMCUEBr1jR0AW\nQqeJ6rLF5qw11zdTI8BPisWzI2DQ6zBjSjAkPtwZlohorIz4qdpqtSIpKcl5W61Ww2KxOJO5vv/N\nZjOOHj2KzZs34+zZs6ioqMCmTZvQ1NSEZ599FqmpqbepCzQR9BVM9xEG19hTBspwraUTv91lHHJO\nOxM7opsKlPsibU4k0uZEosnWgaJyMwrLzCirakBZVQPyD57BzGg1DHpHIXS5jPFErmVubHPUmis1\noaa3cLjM1wcGvQ6GRB1mTmfhcCKi2+VHfwro+wA/UF1dHTZt2oTc3FyEhIRg2rRpePbZZ7FixQpU\nV1fj0UcfxaFDhyCT3Xh74pCQAEhvsNZLo1H82GZ6BHfqt0/vG7Vc7juo3eGhAbjW0jnsOQFBfm7V\nRyJXUgX5IX3+ZKTPnwxrYxsKy80oLDXhZGUdTvYWQp8TGwaDXodZ00PhK+WHZxofjbYOFJWZYSwz\n4fyV/sLhd8Q5rsc5MWFD1l4TEdHYGzGx02q1sFqtzttmsxkaTf/W+DabDU8++SReeOEFLFrk2OVQ\np9Nh5cqVAIApU6YgLCwMJpMJkydPvuHrNDS0Dntco1HAYmkeXW88iLv1227vdv4/sN3KgBsn87Wm\na/C7btYmEz2ikYUF+2PlwqlYuXAqrlhbUNg7QlJUbkZRuRn+flIkxzs+VCdODeF0NxpztjY7vjtj\nhrG0v3C4IABJ00KQotdhXrwGAXJOEyYiGk8jJnapqanYvn07MjIyUFJSAq1W65x+CQDbtm3Dxo0b\nkZaW5jy2f/9+WCwWPPHEE7BYLKirq4NON7SGGnkOf7njUvL3G/xX2TDV0LpDaqUf6q91oL2je1za\nRuTJIsMCsWbxdPxsUTQumWwwlppgLDPh6OmrOHr6KhQBvpif4NhZk4XQ6Va0dXThhworjKUmlFzo\nLxweG6WCIVGH+QlaqFg4nIjIZUZM7JKTk5GUlISMjAwIgoDc3Fzs27cPCoUCixYtwieffIKqqirs\n3bsXAPDAAw9g1apVyM7OxuHDh2G32/G73/3uptMwyf3964NJ+PirSqy7N27Q8eHW0IUE9SZ2nUzs\niMbKwELo/3JPDCpqmmAsM+F4uRlHTlzGkROXoVb6ISXBUSNvio5bytPI7F3dOFVZ7ygcXmFFZ2/h\n8Cm6IBj0OixI0CJMxVIcREQTwajW2GVnZw+6nZCQ4Py6uLh42HN27tx5C80idxOqkuOp1UlDjmt7\na2/FTw7G2epGAEBw71bt7fau8WsgkRfxEQTETw5G/ORgbEiPQ1mVowj0ibMWHCi8hAOFl6BTB8CQ\nqIVBr0NEKAuhU7/unh6U9RYOP3HOgrbe2RXh6gAY9DqkJGp5zRARTUDcQo1ui4fuiUFhqRnzEzQI\nVc1DlCYQT7/5NYABiR1H7CaMrVu34uTJkxAEATk5OZg9e7arm0RjROLjg5nRoZgZHYpHl3fj9Pl6\nGEtNOFlhxf6jF7H/6EVM0QY5auQlcvTF1W4Wi8eOHcObb74JiUSASN1HAAAKZElEQVSCtLQ0PPPM\nM2P2uj2i6Bjl7V2r2V843A93z52ElESO8hIRTXRM7Oi2WGGYihWGqQCA2EmqQfcFKxzTcrnGbmIo\nLCxEVVUV9uzZg8rKSuTk5GDPnj2ubhbdBr5SCZLjNUiO16C9sws/nHOslyq+UI+9X1Zi75eViJ2k\nQkqiFgsSdVwvNc5GisXf//73ePfdd6HT6ZCZmYnly5cjNjb2J7+eKIqoMjkKhxeWDSgcHuCLpclR\nSNFrETOJ6zKJiNwFEzsad/0jdpyKOREUFBQgPT0dABATE4OmpibYbLZBmySR55HLpFiYFI6FSeGw\ntdlx4qwFxlITyqsaUHG5CR8ePofEqSFISdRh/gwtAuR8u7jdbhaL1dXVUKlUiIiIAAAsWbIEBQUF\nPymxq61rwcHvavDl8WqYeguH+/tJsai3cHgCC4cTEbklvlPTuJusDYKPIEDJ0YAJwWq1Iimpf32k\nWq2GxWJhYudFgvz7C6E39hVCLzWh9GIDSi824Mj3l5H72AJXN9Pj3SwWLRYL1Gr1oPuqq6t/9Gtc\ntrYg991C9IgiZL4+SOldZzkzmrUPiYjcHRM7GjcvPTofly02TNEp8PbmxUNKI9DEIIriTe8PCQmA\nVDr8z85b6xB6Ur81GgXiosOwYYUeV+tacPTkFYQo/Tyqj+5ipFgcjevjVaH0x9p7YzE1XAlDUjjk\nw+xc7Ol4LRORp/K+3+jkMtMjlZgeqQQATuuaQLRaLaxWq/O22WyGRqO54eMbGlqHPa7RKAYVp/cW\nntxvCYC0WeEAMGwf+QF5bN0sFq+/z2QyQavVjvicw8XrigWTndetZ165N+bJ8XozjFUi7zBhPl3f\n7JeOt/5C8tZ+0/hKTU3F9u3bkZGRgZKSEmi12ptOw2SsDuWt/aaxdbNYjIqKgs1mQ01NDcLDw3Hk\nyBHk5eWN+JyM16G8td9E5PkEcSzmehCRW8vLy8Px48chCAJyc3MH1aokovFzfSyWlpZCoVDgvvvu\nQ1FRkTOZW7ZsGZ544gkXt5aIiCYSJnZERERERERujltgERERERERuTkmdkRERERERG6OiR0RERER\nEZGbY2JHRERERETk5iZsYrd161asW7cOGRkZOHXqlKubc1ucPXsW6enp2L17NwCgtrYWWVlZ2LBh\nAzZv3ozOzk4AwP79+7F27Vo89NBD+Oijj1zZ5DHxxhtvYN26dVi7di0OHTrkNf32ZIxXz71uGa+e\nhbHqudcsY5WIIE5ARqNRfOqpp0RRFMWKigrx4YcfdnGLxl5LS4uYmZkpvvTSS2J+fr4oiqK4ZcsW\n8Z///KcoiqL4pz/9Sfzggw/ElpYWcdmyZeK1a9fEtrY2cdWqVWJDQ4Mrm35LCgoKxF/+8peiKIpi\nfX29uGTJEq/otydjvHrudct49SyMVc+9ZhmrRCSKojghR+wKCgqQnp4OAIiJiUFTUxNsNpuLWzW2\nZDIZdu3aBa1W6zxmNBqxdOlSAMA999yDgoICnDx5ErNmzYJCoYBcLkdycjJOnDjhqmbfsgULFuCt\nt94CACiVSrS1tXlFvz0Z49Vzr1vGq2dhrHruNctYJSJggk7FtFqtCAkJcd5Wq9WwWCwubNHYk0ql\nkMvlg461tbVBJpMBAEJDQ2GxWGC1WqFWq52PcffvhUQiQUBAAABg7969SEtL84p+ezLGq+det4xX\nz8JY9dxrlrFKRMAETeyuJ3phDfUb9dlTvhdffPEF9u7di1deeWXQcU/vtzfwxp+Vp1+3jFfP5I0/\nJ0+/ZhmrRN5tQiZ2Wq0WVqvVedtsNkOj0biwReMjICAA7e3tAACTyQStVjvs92LgFBN39M0332Dn\nzp3YtWsXFAqF1/TbUzFePfu6Zbx6DsaqZ1+zjFUimpCJXWpqKg4ePAgAKCkpgVarRVBQkItbdfvd\nddddzn4fOnQIixcvxpw5c3D69Glcu3YNLS0tOHHiBObPn+/ilv50zc3NeOONN/DOO+8gODgYgHf0\n25MxXj33umW8ehbGqudes4xVIgIAQZyg4/B5eXk4fvw4BEFAbm4uEhISXN2kMVVcXIw//vGPuHz5\nMqRSKXQ6HfLy8rBlyxZ0dHQgMjISr7/+Onx9fXHgwAG8++67EAQBmZmZWL16taub/5Pt2bMH27dv\nR3R0tPPYtm3b8NJLL3l0vz0d49Uzr1vGq+dhrHrmNctYJSJgAid2RERERERENDoTciomERERERER\njR4TOyIiIiIiIjfHxI6IiIiIiMjNMbEjIiIiIiJyc0zsiIiIiIiI3BwTOxc7c+YMMjMzkZmZiYcf\nfhglJSUAgKqqKmRlZeGRRx5BZmYmqqqqXNxSIu92o1gFgH379mHu3Lk4duyYC1tI5F1qamowc+ZM\nZGVlOf+tX78eRUVFMBqNWL9+/aDHd3V1YcaMGQAw7P1ERO5O6uoGeLucnBxkZ2fjzjvvxOHDh7Ft\n2zbk5+fjtddew/r167Fy5UocOnQIr776Kv7617+6urlEXutGsfrJJ5+guLjY4+qBEbkDtVqN/Px8\n5+2Kigo89thjyMvLc2GriIhcg4ndODKZTMjOzgYAtLe3Y926dXjvvfcQFBQEAAgNDUVjYyPsdjuO\nHz+OHTt2AACWLl2KF198EZ2dnZDJZC5rP5G3GG2sAkB6ejrWrFmDrKwsl7WXiBxiY2PR0dGBhoYG\nVzeFiGjccSrmOPr8888xffp05OfnY/fu3Whvb4dCoYAgCBBFEbt27cLatWtRX1+PwMBA+Pr6AgAk\nEgmUSiWsVquLe0DkHUYbqwCcyR4Rud7hw4ehVqsREhLi6qYQEY07jtiNo8WLF+Nvf/sbtmzZgiVL\nlmDdunUAALvdji1btkCpVGLjxo0wm81DzhVFEYIgjHeTibzSaGOViFyrvr7eOVp+5coVREZGYufO\nncO+jxIReTqO2I2jmJgYfPbZZ1i9ejUKCgqQlZWF7u5uPPfcc4iIiMDWrVshCAJCQ0PR2tqKzs5O\nAI4PkzabDaGhoS7uAZF3GG2sEpFr9a2xy8/Px4svvghRFDFt2jQEBgaiqalp0GPr6uqgUChc1FIi\notuPid04+vTTT3H69GncddddyM3NRW1tLd5++21ER0cjOzvb+UFRKpVi4cKFOHDgAADHtDCDwcD1\ndUTjZLSxSkQTx/Lly6FUKrF7927MmDEDoiiisLAQgGPWy/vvv48HH3zQxa0kIrp9BFEURVc3wluU\nlZUhNzcXMpkMoihixYoVyMvLQ3x8PPz8/JyPe++993D16lX85je/gd1uh0wmw9atWzFp0iQXtp7I\ne/yYWN2xYweMRiPKysoQGRkJlUqFt956C2q12oU9IPJ8NTU12LBhA77++mvnMZPJhLVr1+LDDz9E\nV1cXXn/9dbS1tcFut2PWrFn49a9/DX9/fxiNRjz99NPQ6/XOc++99148/vjjrugKEdGYYGJHRERE\nRETk5jgVk4iIiIiIyM0xsSMiIiIiInJzTOyIiIiIiIjcHBM7IiIiIiIiN8fEjoiIiIiIyM0xsSMi\nIiIiInJzTOyIiIiIiIjcHBM7IiIiIiIiN/f/3oqLpWCfcrAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 19 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vaK_71ZHMdxu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sequence_length = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "foMK1rrGMdx2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def gen_labels(id_df, seq_length, label):\n",
        "    data_array = id_df[label].values\n",
        "    num_elements = data_array.shape[0]\n",
        "    return data_array[seq_length:num_elements, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UHS2u0QFMdx8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def gen_sequence(id_df, seq_length, seq_cols):\n",
        "    data_array = id_df[seq_cols].values\n",
        "    num_elements = data_array.shape[0]\n",
        "    for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):\n",
        "        yield data_array[start:stop, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vre7RYYUMdyF",
        "colab_type": "code",
        "outputId": "247994f8-510e-44b9-e31a-266f9a71f5ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "sensor_cols = ['s' + str(i) for i in [2,3,4,6,7,8,9,11,12,13,14,15,17,20,21]]\n",
        "sequence_cols = ['setting1', 'setting2', 'cycle_norm']\n",
        "sequence_cols.extend(sensor_cols)\n",
        "\n",
        "seq_gen = (list(gen_sequence(train[train['id']==id], sequence_length, sequence_cols)) \n",
        "           for id in train['id'].unique())\n",
        "\n",
        "seq_array = np.concatenate(list(seq_gen)).astype(np.float32)\n",
        "print(seq_array.shape)\n",
        "\n",
        "label_gen = [gen_labels(train[train['id']==id], sequence_length, ['RUL']) \n",
        "             for id in train['id'].unique()]\n",
        "label_array = np.concatenate(label_gen).astype(np.float32)\n",
        "print(label_array.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(15631, 50, 18)\n",
            "(15631, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jEdXe4S-MdyQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def r2_keras(y_true, y_pred):\n",
        "    SS_res =  K.sum(K.square( y_true - y_pred ))\n",
        "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )\n",
        "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-jFsQUwgMdyY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "nb_features = seq_array.shape[2]\n",
        "nb_out = label_array.shape[1]\n",
        "model = Sequential()\n",
        "model.add(LSTM(\n",
        "         input_shape=(sequence_length, nb_features),\n",
        "         units=100,\n",
        "         return_sequences=True))\n",
        "model.add(Dropout(rate = 0.8))\n",
        "model.add(LSTM(\n",
        "          units=50,\n",
        "          return_sequences=False))\n",
        "model.add(Dropout(rate = 0.8))\n",
        "model.add(Dense(units=nb_out))\n",
        "model.add(Activation(\"linear\"))\n",
        "model.compile(loss='mean_squared_error', optimizer='rmsprop',metrics=['mae',r2_keras])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JlTzOZb4Mdye",
        "colab_type": "code",
        "outputId": "06f7bac1-6356-454d-abc0-5b9838f79f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "cell_type": "code",
      "source": [
        "print(model.summary())"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_3 (LSTM)                (None, 50, 100)           47600     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 50, 100)           0         \n",
            "_________________________________________________________________\n",
            "lstm_4 (LSTM)                (None, 50)                30200     \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 51        \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 77,851\n",
            "Trainable params: 77,851\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ktseIiPJrll3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_path = 'lstm_model.h5'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Vspa59hGMdyu",
        "colab_type": "code",
        "outputId": "044ecdcf-a751-4f67-d4c4-9e31c0d9ee77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2875
        }
      },
      "cell_type": "code",
      "source": [
        "import time\n",
        "start = time.time()\n",
        "\n",
        "history = model.fit(seq_array, label_array, epochs=100, batch_size=200, validation_split=0.05, verbose=1,\n",
        "          callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min'),\n",
        "                       keras.callbacks.ModelCheckpoint(model_path,monitor='val_loss', save_best_only=True, mode='min', verbose=0)]\n",
        "          )\n",
        "stop = time.time()\n",
        "\n",
        "model.save_weights('lstm_model.h5')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 14849 samples, validate on 782 samples\n",
            "Epoch 1/100\n",
            "14849/14849 [==============================] - 18s 1ms/step - loss: 8930.0306 - mean_absolute_error: 75.8768 - r2_keras: -1.7529 - val_loss: 8339.5272 - val_mean_absolute_error: 72.9290 - val_r2_keras: -2.5411\n",
            "Epoch 2/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 8259.8923 - mean_absolute_error: 71.9752 - r2_keras: -1.5448 - val_loss: 7817.3657 - val_mean_absolute_error: 69.8918 - val_r2_keras: -2.2925\n",
            "Epoch 3/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 7761.1406 - mean_absolute_error: 69.0612 - r2_keras: -1.3904 - val_loss: 7322.5185 - val_mean_absolute_error: 67.0299 - val_r2_keras: -2.0588\n",
            "Epoch 4/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 7284.9973 - mean_absolute_error: 66.3629 - r2_keras: -1.2430 - val_loss: 6858.4836 - val_mean_absolute_error: 64.3620 - val_r2_keras: -1.8414\n",
            "Epoch 5/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 6847.4134 - mean_absolute_error: 63.9202 - r2_keras: -1.1104 - val_loss: 6421.0218 - val_mean_absolute_error: 61.8638 - val_r2_keras: -1.6383\n",
            "Epoch 6/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 6454.5279 - mean_absolute_error: 61.6472 - r2_keras: -0.9840 - val_loss: 6015.2472 - val_mean_absolute_error: 59.5677 - val_r2_keras: -1.4521\n",
            "Epoch 7/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 6035.7394 - mean_absolute_error: 59.6057 - r2_keras: -0.8600 - val_loss: 5633.0399 - val_mean_absolute_error: 57.4237 - val_r2_keras: -1.2788\n",
            "Epoch 8/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 5691.0248 - mean_absolute_error: 57.1936 - r2_keras: -0.7518 - val_loss: 5213.1413 - val_mean_absolute_error: 53.7963 - val_r2_keras: -1.0836\n",
            "Epoch 9/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 5278.1831 - mean_absolute_error: 53.1124 - r2_keras: -0.6246 - val_loss: 4819.6730 - val_mean_absolute_error: 50.1757 - val_r2_keras: -0.9040\n",
            "Epoch 10/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 4922.6250 - mean_absolute_error: 50.4164 - r2_keras: -0.5153 - val_loss: 4500.1331 - val_mean_absolute_error: 48.2084 - val_r2_keras: -0.7551\n",
            "Epoch 11/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 4620.7380 - mean_absolute_error: 48.1389 - r2_keras: -0.4192 - val_loss: 4140.8743 - val_mean_absolute_error: 44.6200 - val_r2_keras: -0.5989\n",
            "Epoch 12/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 4288.5006 - mean_absolute_error: 45.5346 - r2_keras: -0.3172 - val_loss: 3913.0847 - val_mean_absolute_error: 45.1448 - val_r2_keras: -0.5132\n",
            "Epoch 13/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 4016.5839 - mean_absolute_error: 43.8070 - r2_keras: -0.2336 - val_loss: 3574.9502 - val_mean_absolute_error: 40.9095 - val_r2_keras: -0.3581\n",
            "Epoch 14/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 3774.3864 - mean_absolute_error: 41.9466 - r2_keras: -0.1583 - val_loss: 3286.3362 - val_mean_absolute_error: 37.9871 - val_r2_keras: -0.2288\n",
            "Epoch 15/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 3507.4192 - mean_absolute_error: 40.0075 - r2_keras: -0.0783 - val_loss: 3075.9447 - val_mean_absolute_error: 37.0325 - val_r2_keras: -0.1332\n",
            "Epoch 16/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 3297.8751 - mean_absolute_error: 38.4659 - r2_keras: -0.0118 - val_loss: 2827.1537 - val_mean_absolute_error: 34.3472 - val_r2_keras: -0.0364\n",
            "Epoch 17/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 3151.0336 - mean_absolute_error: 37.2251 - r2_keras: 0.0323 - val_loss: 2598.2571 - val_mean_absolute_error: 32.4413 - val_r2_keras: 0.0549\n",
            "Epoch 18/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2924.0284 - mean_absolute_error: 35.7134 - r2_keras: 0.1039 - val_loss: 2645.8925 - val_mean_absolute_error: 34.2647 - val_r2_keras: 0.0401\n",
            "Epoch 19/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2755.6462 - mean_absolute_error: 34.5413 - r2_keras: 0.1561 - val_loss: 2212.3979 - val_mean_absolute_error: 29.0789 - val_r2_keras: 0.2109\n",
            "Epoch 20/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2564.8065 - mean_absolute_error: 33.0497 - r2_keras: 0.2145 - val_loss: 2107.7932 - val_mean_absolute_error: 29.4699 - val_r2_keras: 0.2269\n",
            "Epoch 21/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2444.9914 - mean_absolute_error: 32.2674 - r2_keras: 0.2509 - val_loss: 1898.5928 - val_mean_absolute_error: 27.1178 - val_r2_keras: 0.3288\n",
            "Epoch 22/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2325.9096 - mean_absolute_error: 31.5035 - r2_keras: 0.2876 - val_loss: 1856.6034 - val_mean_absolute_error: 28.0562 - val_r2_keras: 0.3052\n",
            "Epoch 23/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2228.1449 - mean_absolute_error: 30.6112 - r2_keras: 0.3171 - val_loss: 1879.0843 - val_mean_absolute_error: 25.5806 - val_r2_keras: 0.3626\n",
            "Epoch 24/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 2112.9587 - mean_absolute_error: 29.8886 - r2_keras: 0.3523 - val_loss: 1520.0781 - val_mean_absolute_error: 24.2430 - val_r2_keras: 0.4737\n",
            "Epoch 25/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1997.4574 - mean_absolute_error: 29.0526 - r2_keras: 0.3889 - val_loss: 1426.0222 - val_mean_absolute_error: 21.3386 - val_r2_keras: 0.5267\n",
            "Epoch 26/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1913.8356 - mean_absolute_error: 28.4470 - r2_keras: 0.4124 - val_loss: 1416.3067 - val_mean_absolute_error: 25.0801 - val_r2_keras: 0.4707\n",
            "Epoch 27/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1991.2443 - mean_absolute_error: 29.3637 - r2_keras: 0.3888 - val_loss: 1564.5571 - val_mean_absolute_error: 29.0425 - val_r2_keras: 0.3243\n",
            "Epoch 28/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1862.8718 - mean_absolute_error: 28.2667 - r2_keras: 0.4302 - val_loss: 1250.5381 - val_mean_absolute_error: 23.5139 - val_r2_keras: 0.5546\n",
            "Epoch 29/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1900.9938 - mean_absolute_error: 29.2060 - r2_keras: 0.4161 - val_loss: 1147.0751 - val_mean_absolute_error: 21.5950 - val_r2_keras: 0.5860\n",
            "Epoch 30/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1894.4516 - mean_absolute_error: 29.0014 - r2_keras: 0.4181 - val_loss: 1852.1091 - val_mean_absolute_error: 27.4071 - val_r2_keras: 0.3687\n",
            "Epoch 31/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1821.2491 - mean_absolute_error: 28.3911 - r2_keras: 0.4429 - val_loss: 1730.3205 - val_mean_absolute_error: 25.1209 - val_r2_keras: 0.4194\n",
            "Epoch 32/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1744.5483 - mean_absolute_error: 27.8973 - r2_keras: 0.4654 - val_loss: 1020.5833 - val_mean_absolute_error: 19.6167 - val_r2_keras: 0.6443\n",
            "Epoch 33/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1673.9186 - mean_absolute_error: 27.2632 - r2_keras: 0.4869 - val_loss: 1063.3209 - val_mean_absolute_error: 21.4882 - val_r2_keras: 0.5564\n",
            "Epoch 34/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1653.1649 - mean_absolute_error: 27.0588 - r2_keras: 0.4954 - val_loss: 1288.4844 - val_mean_absolute_error: 25.9381 - val_r2_keras: 0.3765\n",
            "Epoch 35/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1869.5905 - mean_absolute_error: 28.7225 - r2_keras: 0.4283 - val_loss: 1026.4704 - val_mean_absolute_error: 22.2690 - val_r2_keras: 0.5615\n",
            "Epoch 36/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1752.9990 - mean_absolute_error: 28.4263 - r2_keras: 0.4589 - val_loss: 1018.7033 - val_mean_absolute_error: 22.2366 - val_r2_keras: 0.5608\n",
            "Epoch 37/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1692.5953 - mean_absolute_error: 28.1607 - r2_keras: 0.4787 - val_loss: 1391.4914 - val_mean_absolute_error: 26.0668 - val_r2_keras: 0.4129\n",
            "Epoch 38/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1994.8410 - mean_absolute_error: 31.4666 - r2_keras: 0.3843 - val_loss: 1362.1188 - val_mean_absolute_error: 26.7553 - val_r2_keras: 0.4924\n",
            "Epoch 39/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1920.5053 - mean_absolute_error: 30.1774 - r2_keras: 0.4093 - val_loss: 2169.8440 - val_mean_absolute_error: 38.5761 - val_r2_keras: -0.2368\n",
            "Epoch 40/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1790.9299 - mean_absolute_error: 28.7954 - r2_keras: 0.4515 - val_loss: 1265.9338 - val_mean_absolute_error: 22.1532 - val_r2_keras: 0.5741\n",
            "Epoch 41/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1730.8591 - mean_absolute_error: 28.1016 - r2_keras: 0.4672 - val_loss: 1102.0793 - val_mean_absolute_error: 24.4544 - val_r2_keras: 0.5126\n",
            "Epoch 42/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1644.0109 - mean_absolute_error: 27.5090 - r2_keras: 0.4956 - val_loss: 906.7154 - val_mean_absolute_error: 19.8870 - val_r2_keras: 0.6079\n",
            "Epoch 43/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1605.7227 - mean_absolute_error: 27.1050 - r2_keras: 0.5083 - val_loss: 1539.6161 - val_mean_absolute_error: 24.1951 - val_r2_keras: 0.4637\n",
            "Epoch 44/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1560.8662 - mean_absolute_error: 26.6194 - r2_keras: 0.5200 - val_loss: 1021.1718 - val_mean_absolute_error: 20.2715 - val_r2_keras: 0.6422\n",
            "Epoch 45/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1545.6176 - mean_absolute_error: 26.5105 - r2_keras: 0.5238 - val_loss: 891.8558 - val_mean_absolute_error: 19.7746 - val_r2_keras: 0.6305\n",
            "Epoch 46/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1582.6917 - mean_absolute_error: 26.5540 - r2_keras: 0.5147 - val_loss: 1228.3487 - val_mean_absolute_error: 20.9522 - val_r2_keras: 0.5772\n",
            "Epoch 47/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1518.9249 - mean_absolute_error: 26.0929 - r2_keras: 0.5331 - val_loss: 1148.1289 - val_mean_absolute_error: 18.3903 - val_r2_keras: 0.6282\n",
            "Epoch 48/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1477.5736 - mean_absolute_error: 25.6706 - r2_keras: 0.5469 - val_loss: 839.8707 - val_mean_absolute_error: 19.0893 - val_r2_keras: 0.6719\n",
            "Epoch 49/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1474.2195 - mean_absolute_error: 25.5482 - r2_keras: 0.5465 - val_loss: 770.9773 - val_mean_absolute_error: 16.3166 - val_r2_keras: 0.7160\n",
            "Epoch 50/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1456.9780 - mean_absolute_error: 25.4501 - r2_keras: 0.5517 - val_loss: 893.7995 - val_mean_absolute_error: 20.8206 - val_r2_keras: 0.6054\n",
            "Epoch 51/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1519.1701 - mean_absolute_error: 25.9371 - r2_keras: 0.5338 - val_loss: 770.4187 - val_mean_absolute_error: 18.5637 - val_r2_keras: 0.6716\n",
            "Epoch 52/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1454.8718 - mean_absolute_error: 25.3073 - r2_keras: 0.5534 - val_loss: 1248.2806 - val_mean_absolute_error: 23.3265 - val_r2_keras: 0.5506\n",
            "Epoch 53/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1491.3652 - mean_absolute_error: 25.8659 - r2_keras: 0.5428 - val_loss: 798.8890 - val_mean_absolute_error: 19.3494 - val_r2_keras: 0.6372\n",
            "Epoch 54/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1448.8421 - mean_absolute_error: 25.3928 - r2_keras: 0.5546 - val_loss: 736.1124 - val_mean_absolute_error: 16.9445 - val_r2_keras: 0.6903\n",
            "Epoch 55/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1376.7711 - mean_absolute_error: 24.7548 - r2_keras: 0.5774 - val_loss: 914.7690 - val_mean_absolute_error: 20.6060 - val_r2_keras: 0.5335\n",
            "Epoch 56/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1404.5673 - mean_absolute_error: 25.2093 - r2_keras: 0.5669 - val_loss: 814.8384 - val_mean_absolute_error: 18.5152 - val_r2_keras: 0.6383\n",
            "Epoch 57/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1424.6254 - mean_absolute_error: 25.3275 - r2_keras: 0.5632 - val_loss: 997.9351 - val_mean_absolute_error: 23.4335 - val_r2_keras: 0.5057\n",
            "Epoch 58/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1410.3903 - mean_absolute_error: 25.1399 - r2_keras: 0.5671 - val_loss: 909.4105 - val_mean_absolute_error: 19.5718 - val_r2_keras: 0.5758\n",
            "Epoch 59/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1411.5295 - mean_absolute_error: 25.0376 - r2_keras: 0.5675 - val_loss: 732.8872 - val_mean_absolute_error: 15.9495 - val_r2_keras: 0.7322\n",
            "Epoch 60/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1374.6662 - mean_absolute_error: 24.6854 - r2_keras: 0.5775 - val_loss: 760.8725 - val_mean_absolute_error: 18.0336 - val_r2_keras: 0.6569\n",
            "Epoch 61/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1346.4772 - mean_absolute_error: 24.4910 - r2_keras: 0.5860 - val_loss: 832.0640 - val_mean_absolute_error: 20.2256 - val_r2_keras: 0.6015\n",
            "Epoch 62/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1354.6275 - mean_absolute_error: 24.4937 - r2_keras: 0.5824 - val_loss: 991.7448 - val_mean_absolute_error: 17.9620 - val_r2_keras: 0.6727\n",
            "Epoch 63/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1362.5317 - mean_absolute_error: 24.6267 - r2_keras: 0.5817 - val_loss: 784.4087 - val_mean_absolute_error: 19.2116 - val_r2_keras: 0.6851\n",
            "Epoch 64/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1330.5585 - mean_absolute_error: 24.3847 - r2_keras: 0.5895 - val_loss: 700.8028 - val_mean_absolute_error: 16.6826 - val_r2_keras: 0.7112\n",
            "Epoch 65/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1343.3822 - mean_absolute_error: 24.3556 - r2_keras: 0.5856 - val_loss: 731.8878 - val_mean_absolute_error: 16.4505 - val_r2_keras: 0.6834\n",
            "Epoch 66/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1399.4189 - mean_absolute_error: 24.9457 - r2_keras: 0.5664 - val_loss: 772.1431 - val_mean_absolute_error: 17.6307 - val_r2_keras: 0.6338\n",
            "Epoch 67/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1294.7500 - mean_absolute_error: 24.0112 - r2_keras: 0.6025 - val_loss: 832.1584 - val_mean_absolute_error: 19.4810 - val_r2_keras: 0.6633\n",
            "Epoch 68/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1328.2248 - mean_absolute_error: 24.3105 - r2_keras: 0.5913 - val_loss: 729.1465 - val_mean_absolute_error: 18.3116 - val_r2_keras: 0.6671\n",
            "Epoch 69/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1291.6644 - mean_absolute_error: 24.0605 - r2_keras: 0.6020 - val_loss: 889.6363 - val_mean_absolute_error: 20.7048 - val_r2_keras: 0.5709\n",
            "Epoch 70/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1308.4959 - mean_absolute_error: 24.1803 - r2_keras: 0.5963 - val_loss: 1020.8067 - val_mean_absolute_error: 20.1842 - val_r2_keras: 0.6239\n",
            "Epoch 71/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1296.1812 - mean_absolute_error: 23.9074 - r2_keras: 0.6027 - val_loss: 765.3112 - val_mean_absolute_error: 17.0919 - val_r2_keras: 0.6422\n",
            "Epoch 72/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1296.7777 - mean_absolute_error: 24.0051 - r2_keras: 0.6015 - val_loss: 1038.8213 - val_mean_absolute_error: 22.2634 - val_r2_keras: 0.4356\n",
            "Epoch 73/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1283.8733 - mean_absolute_error: 24.0067 - r2_keras: 0.6050 - val_loss: 806.6315 - val_mean_absolute_error: 18.2867 - val_r2_keras: 0.6180\n",
            "Epoch 74/100\n",
            "14849/14849 [==============================] - 16s 1ms/step - loss: 1274.2233 - mean_absolute_error: 23.7379 - r2_keras: 0.6094 - val_loss: 1131.9723 - val_mean_absolute_error: 19.2790 - val_r2_keras: 0.5999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9pcKy-i0rupj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "3d377224-7b45-448f-ad50-05dfca5d500f"
      },
      "cell_type": "code",
      "source": [
        "print('Processing time =', stop-start, 'seconds')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processing time = 1208.0441660881042 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x9NbCBhWMdyy",
        "colab_type": "code",
        "outputId": "a931c25c-4596-4994-9f28-fbe351f95c0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "cell_type": "code",
      "source": [
        "fig_acc = plt.figure(figsize=(10, 10))\n",
        "plt.plot(history.history['r2_keras'])\n",
        "plt.plot(history.history['val_r2_keras'])\n",
        "plt.title('model r^2')\n",
        "plt.ylabel('R^2')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "fig_acc.savefig(\"model_r2.png\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YlWiUI+eMjMUlu6i01hDgaR70x1+SJG6IXcmfjv+ddaWbeDLjkXPxFKaU3eUY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\nERER8QBqykREREQ8wL8A4EZR4UDHd1gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2apDC8wlMdy3",
        "colab_type": "code",
        "outputId": "d505c432-5855-4ac3-aee8-39ef6ddbcca2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "cell_type": "code",
      "source": [
        "fig_acc = plt.figure(figsize=(10, 10))\n",
        "plt.plot(history.history['mean_absolute_error'])\n",
        "plt.plot(history.history['val_mean_absolute_error'])\n",
        "plt.title('model MAE')\n",
        "plt.ylabel('MAE')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "fig_acc.savefig(\"model_mae.png\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BGq8Tnafl8XgkSaV5yYqymjjmBwAATFlAphR/9KMf6Ytf/KIaGhq0dOlSSdKNN96o3bt3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\n5m3FQDhIp6crpdeqlXI+c0sFn721glA4zC+ePc1jr1VPmcw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sdzahV+hGhbfOFI082ioT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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Lc-Btz3SMdy7",
        "colab_type": "code",
        "outputId": "e6c646c0-ef2e-495c-e80c-4d57cea986ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "cell_type": "code",
      "source": [
        "fig_acc = plt.figure(figsize=(10, 10))\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "fig_acc.savefig(\"model_regression_loss.png\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3PX3ve9/T17/+df3mN79RZ2enXn/9dZ09e1YvvfSSnnzySf3sZz/Tv/3bv8nlcunxxx/X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\n04ZBqceij6bd1Tmr7b4EQbj0zVpwdvjwYcrLy3n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zPZxxubx9tLs6WGROH/MYuUxOpC6C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\nFUszNXkq+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "c464d_ZgMdy_",
        "colab_type": "code",
        "outputId": "83e44bf4-e6fc-4aac-acb7-98cc06df5371",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "cell_type": "code",
      "source": [
        "scores = model.evaluate(seq_array, label_array, verbose=1, batch_size=64)\n",
        "print('\\nMAE: {}'.format(scores[1]))\n",
        "print('\\nR^2: {}'.format(scores[2]))\n",
        "\n",
        "y_pred = model.predict(seq_array,verbose=1, batch_size=64)\n",
        "y_true = label_array\n",
        "\n",
        "test_set = pd.DataFrame(y_pred)\n",
        "test_set.to_csv('submit_train.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18631/18631 [==============================] - 20s 1ms/step\n",
            "\n",
            "MAE: 52.36285323036075\n",
            "\n",
            "R^2: -6.324139296762175\n",
            "18631/18631 [==============================] - 21s 1ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R0-f7cUYMdzF",
        "colab_type": "code",
        "outputId": "a387b29c-792d-4c50-df92-ece0e2d5e5c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "seq_array_test_last = [test[test['id']==id][sequence_cols].values[-sequence_length:] \n",
        "                       for id in test['id'].unique() if len(test[test['id']==id]) >= sequence_length]\n",
        "\n",
        "seq_array_test_last = np.asarray(seq_array_test_last).astype(np.float32)\n",
        "print(\"seq_array_test_last\")\n",
        "\n",
        "print(seq_array_test_last.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "seq_array_test_last\n",
            "(93, 50, 18)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ka4AzEvkMdzI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_mask = [len(test[test['id']==id]) >= sequence_length for id in test['id'].unique()]\n",
        "label_array_test_last = test.groupby('id')['RUL'].nth(-1)[y_mask].values\n",
        "label_array_test_last = label_array_test_last.reshape(label_array_test_last.shape[0],1).astype(np.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "11RbGCKwMdzL",
        "colab_type": "code",
        "outputId": "f81330e0-8ef7-4cd8-f77e-68759133b886",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "scores_test = model.evaluate(seq_array_test_last, label_array_test_last, verbose=2)\n",
        "print('\\nMAE: {}'.format(scores_test[1]))\n",
        "print('\\nR^2: {}'.format(scores_test[2]))\n",
        "\n",
        "y_pred_test = model.predict(seq_array_test_last)\n",
        "y_true_test = label_array_test_last\n",
        "\n",
        "test_set = pd.DataFrame(y_pred_test)\n",
        "test_set.to_csv('submit_test.csv', index = False)\n",
        "\n",
        "plt.plot(y_pred_test, color=\"red\")\n",
        "plt.plot(y_true_test, color=\"blue\")\n",
        "plt.title('prediction')\n",
        "plt.ylabel('RUL')\n",
        "plt.xlabel('Engines')\n",
        "plt.legend(['predicted', 'actual'], loc='upper left')\n",
        "plt.show()\n",
        "fig_verify.savefig(\"model_regression_verify.png\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "MAE: 14.75392355970157\n",
            "\n",
            "R^2: 0.7154991665194111\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XXoEPfegjOPLIN+OLX/wyhoeHffYzjn/4h/8HK1a8Db/5zd341a/uxD/90xcCnwsHR6Mg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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oDK4nZCuOmP0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}