facies_classification.ipynb

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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_8aiNu3bdsL",
        "outputId": "c33975fa-532b-47ed-a400-4f02cf4c0943",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUhde4kKbmWy"
      },
      "source": [
        "\n",
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "import seaborn as sns\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from pandas import set_option\n",
        "set_option(\"display.max_rows\", 10)\n",
        "pd.options.mode.chained_assignment = None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pDSAndfb-ZN",
        "outputId": "dab257af-e23a-4f67-89da-c5c02c2641ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "\n",
        "# read data into python as dataframe \n",
        "dataset = 'drive/My Drive/prac/Participants_Data_TGIH/Participants_Data_TGIH/Train.csv'\n",
        "training_data = pd.read_csv(dataset)\n",
        "training_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6141</td>\n",
              "      <td>1583</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>2011-05-06 16:54:00</td>\n",
              "      <td>3.75</td>\n",
              "      <td>14056.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6349</td>\n",
              "      <td>1300</td>\n",
              "      <td>3682</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-05-11 07:35:00</td>\n",
              "      <td>1.95</td>\n",
              "      <td>13098.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16783</td>\n",
              "      <td>2178</td>\n",
              "      <td>1939</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-11-20 13:20:00</td>\n",
              "      <td>5.95</td>\n",
              "      <td>15044.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16971</td>\n",
              "      <td>2115</td>\n",
              "      <td>2983</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-11-22 12:07:00</td>\n",
              "      <td>0.83</td>\n",
              "      <td>15525.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6080</td>\n",
              "      <td>1210</td>\n",
              "      <td>2886</td>\n",
              "      <td>12</td>\n",
              "      <td>2011-05-06 09:00:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>13952.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>284775</th>\n",
              "      <td>3408</td>\n",
              "      <td>1706</td>\n",
              "      <td>1081</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-03-01 12:07:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>17965.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284776</th>\n",
              "      <td>13982</td>\n",
              "      <td>2184</td>\n",
              "      <td>2947</td>\n",
              "      <td>16</td>\n",
              "      <td>2011-10-13 15:08:00</td>\n",
              "      <td>0.65</td>\n",
              "      <td>16793.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284777</th>\n",
              "      <td>6641</td>\n",
              "      <td>667</td>\n",
              "      <td>3632</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-05-16 12:59:00</td>\n",
              "      <td>8.50</td>\n",
              "      <td>14755.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284778</th>\n",
              "      <td>13820</td>\n",
              "      <td>1576</td>\n",
              "      <td>3089</td>\n",
              "      <td>48</td>\n",
              "      <td>2011-10-11 14:18:00</td>\n",
              "      <td>3.45</td>\n",
              "      <td>14866.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284779</th>\n",
              "      <td>11042</td>\n",
              "      <td>2603</td>\n",
              "      <td>1156</td>\n",
              "      <td>2</td>\n",
              "      <td>2011-08-22 13:30:00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>17585.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284780 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        InvoiceNo  StockCode  Description  ...  UnitPrice CustomerID  Country\n",
              "0            6141       1583          144  ...       3.75    14056.0       35\n",
              "1            6349       1300         3682  ...       1.95    13098.0       35\n",
              "2           16783       2178         1939  ...       5.95    15044.0       35\n",
              "3           16971       2115         2983  ...       0.83    15525.0       35\n",
              "4            6080       1210         2886  ...       1.65    13952.0       35\n",
              "...           ...        ...          ...  ...        ...        ...      ...\n",
              "284775       3408       1706         1081  ...       1.65    17965.0       35\n",
              "284776      13982       2184         2947  ...       0.65    16793.0       35\n",
              "284777       6641        667         3632  ...       8.50    14755.0       35\n",
              "284778      13820       1576         3089  ...       3.45    14866.0       35\n",
              "284779      11042       2603         1156  ...       0.75    17585.0       35\n",
              "\n",
              "[284780 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mCcMk5hcEND",
        "outputId": "2768d8cb-4adb-472a-aab7-f0588785cfba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "\n",
        "training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
        "training_data['Formation'] = training_data['Formation'].astype('category')\n",
        "training_data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SHRIMPLIN, ALEXANDER D, SHANKLE, LUKE G U, KIMZEY A, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]\n",
              "Categories (10, object): [SHRIMPLIN, ALEXANDER D, SHANKLE, LUKE G U, ..., NOLAN, Recruit F9, NEWBY,\n",
              "                          CHURCHMAN BIBLE]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6bhmafocH2t"
      },
      "source": [
        "\n",
        "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
        "       '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
        "\n",
        "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
        "                 'WS', 'D','PS', 'BS']\n",
        "#facies_color_map is a dictionary that maps facies labels to their respective colors\n",
        "facies_color_map = {}\n",
        "for ind, label in enumerate(facies_labels):\n",
        "    facies_color_map[label] = facies_colors[ind]\n",
        "\n",
        "def label_facies(row, labels):\n",
        "    return labels[ row['Facies'] -1]\n",
        "    \n",
        "training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f52H-MtcLuC",
        "outputId": "e9d8d070-3097-4a0a-bd30-14fe09811f49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "training_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284780.000000</td>\n",
              "      <td>284780.000000</td>\n",
              "      <td>284780.000000</td>\n",
              "      <td>284780.000000</td>\n",
              "      <td>284780.000000</td>\n",
              "      <td>284780.000000</td>\n",
              "      <td>284780.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9955.394083</td>\n",
              "      <td>1573.386807</td>\n",
              "      <td>2023.955573</td>\n",
              "      <td>12.028468</td>\n",
              "      <td>3.451216</td>\n",
              "      <td>15288.224278</td>\n",
              "      <td>32.823846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5551.519138</td>\n",
              "      <td>843.604991</td>\n",
              "      <td>1089.812078</td>\n",
              "      <td>295.111588</td>\n",
              "      <td>78.399541</td>\n",
              "      <td>1714.336529</td>\n",
              "      <td>6.743602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-80995.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12346.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5069.000000</td>\n",
              "      <td>939.000000</td>\n",
              "      <td>1141.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13953.000000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10310.000000</td>\n",
              "      <td>1521.000000</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.950000</td>\n",
              "      <td>15152.000000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>14657.000000</td>\n",
              "      <td>2106.000000</td>\n",
              "      <td>2945.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>16794.000000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>22188.000000</td>\n",
              "      <td>3683.000000</td>\n",
              "      <td>3895.000000</td>\n",
              "      <td>80995.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "      <td>18287.000000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           InvoiceNo      StockCode  ...     CustomerID        Country\n",
              "count  284780.000000  284780.000000  ...  284780.000000  284780.000000\n",
              "mean     9955.394083    1573.386807  ...   15288.224278      32.823846\n",
              "std      5551.519138     843.604991  ...    1714.336529       6.743602\n",
              "min         0.000000       0.000000  ...   12346.000000       0.000000\n",
              "25%      5069.000000     939.000000  ...   13953.000000      35.000000\n",
              "50%     10310.000000    1521.000000  ...   15152.000000      35.000000\n",
              "75%     14657.000000    2106.000000  ...   16794.000000      35.000000\n",
              "max     22188.000000    3683.000000  ...   18287.000000      36.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iZa6i5fmEr2",
        "outputId": "0e53336e-c787-40b6-9176-acf47483011d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(training_data['Country'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrAcaL2EvSni",
        "outputId": "e98009a0-d78b-4d2e-eb5f-b6db0155c196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "\n",
        "# Get correlation\n",
        "training_data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>InvoiceNo</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.086809</td>\n",
              "      <td>0.024804</td>\n",
              "      <td>-0.008363</td>\n",
              "      <td>0.007927</td>\n",
              "      <td>-0.007463</td>\n",
              "      <td>0.005432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockCode</th>\n",
              "      <td>0.086809</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013230</td>\n",
              "      <td>-0.000942</td>\n",
              "      <td>0.020073</td>\n",
              "      <td>0.002887</td>\n",
              "      <td>0.006242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>0.024804</td>\n",
              "      <td>-0.013230</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>-0.000424</td>\n",
              "      <td>-0.005227</td>\n",
              "      <td>-0.014791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>-0.008363</td>\n",
              "      <td>-0.000942</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000924</td>\n",
              "      <td>-0.002930</td>\n",
              "      <td>-0.008001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnitPrice</th>\n",
              "      <td>0.007927</td>\n",
              "      <td>0.020073</td>\n",
              "      <td>-0.000424</td>\n",
              "      <td>-0.000924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.004932</td>\n",
              "      <td>-0.005378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>-0.007463</td>\n",
              "      <td>0.002887</td>\n",
              "      <td>-0.005227</td>\n",
              "      <td>-0.002930</td>\n",
              "      <td>-0.004932</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.388198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <td>0.005432</td>\n",
              "      <td>0.006242</td>\n",
              "      <td>-0.014791</td>\n",
              "      <td>-0.008001</td>\n",
              "      <td>-0.005378</td>\n",
              "      <td>0.388198</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             InvoiceNo  StockCode  Description  ...  UnitPrice  CustomerID   Country\n",
              "InvoiceNo     1.000000   0.086809     0.024804  ...   0.007927   -0.007463  0.005432\n",
              "StockCode     0.086809   1.000000    -0.013230  ...   0.020073    0.002887  0.006242\n",
              "Description   0.024804  -0.013230     1.000000  ...  -0.000424   -0.005227 -0.014791\n",
              "Quantity     -0.008363  -0.000942     0.000194  ...  -0.000924   -0.002930 -0.008001\n",
              "UnitPrice     0.007927   0.020073    -0.000424  ...   1.000000   -0.004932 -0.005378\n",
              "CustomerID   -0.007463   0.002887    -0.005227  ...  -0.004932    1.000000  0.388198\n",
              "Country       0.005432   0.006242    -0.014791  ...  -0.005378    0.388198  1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "963JDAtVoE0x"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5gp2MQV19F5"
      },
      "source": [
        "correct_facies_labels = training_data['Facies'].values\n",
        "feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jx9jw7y9GCK"
      },
      "source": [
        "\n",
        "from sklearn import preprocessing\n",
        "\n",
        "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
        "scaled_features = scaler.transform(feature_vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQ6JUBcf9K3B"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "        scaled_features, correct_facies_labels, test_size=0.2, random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTmmY7Vf9O1V",
        "outputId": "0f82686a-00b7-48db-bff7-b5d0d589397e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_train.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15771"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYHw05QP9VOL",
        "outputId": "7eccc41d-607f-4735-9c50-2e428480b978",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_train.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-wwg2wb9dam",
        "outputId": "4a95e227-09b1-41dc-883f-a93066153a64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "blind.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4980"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57Bv1YBUOvCN"
      },
      "source": [
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nErPTf2ROF89"
      },
      "source": [
        "def accuracy(conf):\n",
        "    total_correct = 0.\n",
        "    nb_classes = conf.shape[0]\n",
        "    for i in np.arange(0,nb_classes):\n",
        "        total_correct += conf[i][i]\n",
        "    acc = total_correct/sum(sum(conf))\n",
        "    return acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOxjjcG1rjy4"
      },
      "source": [
        "adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
        "\n",
        "def accuracy_adjacent(conf, adjacent_facies):\n",
        "    nb_classes = conf.shape[0]\n",
        "    total_correct = 0.\n",
        "    for i in np.arange(0,nb_classes):\n",
        "        total_correct += conf[i][i]\n",
        "        for j in adjacent_facies[i]:\n",
        "            total_correct += conf[i][j]\n",
        "    return total_correct / sum(sum(conf))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfbsZ9ClvNE7"
      },
      "source": [
        "def display_cm(cm, labels, hide_zeros=False,\n",
        "                             display_metrics=False):\n",
        "    \"\"\"Display confusion matrix with labels, along with\n",
        "       metrics such as Recall, Precision and F1 score.\n",
        "       Based on Zach Guo's print_cm gist at\n",
        "       https://gist.github.com/zachguo/10296432\n",
        "    \"\"\"\n",
        "\n",
        "    precision = np.diagonal(cm)/cm.sum(axis=0).astype('float')\n",
        "    recall = np.diagonal(cm)/cm.sum(axis=1).astype('float')\n",
        "    F1 = 2 * (precision * recall) / (precision + recall)\n",
        "    \n",
        "    precision[np.isnan(precision)] = 0\n",
        "    recall[np.isnan(recall)] = 0\n",
        "    F1[np.isnan(F1)] = 0\n",
        "    \n",
        "    total_precision = np.sum(precision * cm.sum(axis=1)) / cm.sum(axis=(0,1))\n",
        "    total_recall = np.sum(recall * cm.sum(axis=1)) / cm.sum(axis=(0,1))\n",
        "    total_F1 = np.sum(F1 * cm.sum(axis=1)) / cm.sum(axis=(0,1))\n",
        "    #print total_precision\n",
        "    \n",
        "    columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length\n",
        "    empty_cell = \" \" * columnwidth\n",
        "    # Print header\n",
        "    print(\"    \" + \" Pred\", end=' ')\n",
        "    for label in labels: \n",
        "        print(\"%{0}s\".format(columnwidth) % label, end=' ')\n",
        "    print(\"%{0}s\".format(columnwidth) % 'Total')\n",
        "    print(\"    \" + \" True\")\n",
        "    # Print rows\n",
        "    for i, label1 in enumerate(labels):\n",
        "        print(\"    %{0}s\".format(columnwidth) % label1, end=' ')\n",
        "        for j in range(len(labels)): \n",
        "            cell = \"%{0}d\".format(columnwidth) % cm[i, j]\n",
        "            if hide_zeros:\n",
        "                cell = cell if float(cm[i, j]) != 0 else empty_cell\n",
        "            print(cell, end=' ')\n",
        "        print(\"%{0}d\".format(columnwidth) % sum(cm[i,:]))\n",
        "        \n",
        "    if display_metrics:\n",
        "        print()\n",
        "        print(\"Precision\", end=' ')\n",
        "        for j in range(len(labels)):\n",
        "            cell = \"%{0}.2f\".format(columnwidth) % precision[j]\n",
        "            print(cell, end=' ')\n",
        "        print(\"%{0}.2f\".format(columnwidth) % total_precision)\n",
        "        print(\"   Recall\", end=' ')\n",
        "        for j in range(len(labels)):\n",
        "            cell = \"%{0}.2f\".format(columnwidth) % recall[j]\n",
        "            print(cell, end=' ')\n",
        "        print(\"%{0}.2f\".format(columnwidth) % total_recall)\n",
        "        print(\"       F1\", end=' ')\n",
        "        for j in range(len(labels)):\n",
        "            cell = \"%{0}.2f\".format(columnwidth) % F1[j]\n",
        "            print(cell, end=' ')\n",
        "        print(\"%{0}.2f\".format(columnwidth) % total_F1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv9jrWsSr60F"
      },
      "source": [
        "def display_adj_cm(\n",
        "        cm, labels, adjacent_facies, hide_zeros=False, \n",
        "        display_metrics=False):\n",
        "    \"\"\"This function displays a confusion matrix that counts \n",
        "       adjacent facies as correct.\n",
        "    \"\"\"\n",
        "    adj_cm = np.copy(cm)\n",
        "    \n",
        "    for i in np.arange(0,cm.shape[0]):\n",
        "        for j in adjacent_facies[i]:\n",
        "            adj_cm[i][i] += adj_cm[i][j]\n",
        "            adj_cm[i][j] = 0.0\n",
        "        \n",
        "    display_cm(adj_cm, labels, hide_zeros, \n",
        "                             display_metrics)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NnHc9OQ2Ocpe"
      },
      "source": [
        "## **SVM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HMQKeDQ9e8k"
      },
      "source": [
        "\n",
        "from sklearn import svm\n",
        "SVM_model = svm.SVC(C=10, gamma=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJ7UC3ta-ntz",
        "outputId": "2a81729f-e08f-4e54-8cd6-9f4d6ddfa65f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "\n",
        "SVM_model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
              "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OwCHhGc-qCj"
      },
      "source": [
        "\n",
        "yhat_SVM = SVM_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlnNYo2RAyzU"
      },
      "source": [
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnqyQOVp-tHs",
        "outputId": "40f8dcb1-74a0-4754-ce34-52bea9e6102f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_mtx = confusion_matrix(y_test, yhat_SVM) \n",
        "print('Facies classification accuracy = %f' % accuracy(confusion_mtx))\n",
        "display_cm(confusion_mtx, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.744681\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    42    10                                              52\n",
            "     CSiS     6    83    23     1                       1         114\n",
            "     FSiS     2    19    79     2     1     1           1         105\n",
            "     SiSh                      29           6           2          37\n",
            "       MS                       3    17     5           3          28\n",
            "       WS                 2     3     7    75          11     2   100\n",
            "        D           1     2           1          22     2     1    29\n",
            "       PS                             7    12     2    53     2    76\n",
            "       BS                 1                 1           1    20    23\n",
            "\n",
            "Precision  0.84  0.73  0.74  0.76  0.52  0.75  0.92  0.72  0.80  0.75\n",
            "   Recall  0.81  0.73  0.75  0.78  0.61  0.75  0.76  0.70  0.87  0.74\n",
            "       F1  0.82  0.73  0.75  0.77  0.56  0.75  0.83  0.71  0.83  0.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQh5Xr5Dr_mZ",
        "outputId": "4415d81c-bdcf-485a-da4f-e2c3b62bb929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(confusion_mtx, adjacent_facies))\n",
        "display_adj_cm(confusion_mtx, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.927305\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    52                                                    52\n",
            "     CSiS         112           1                       1         114\n",
            "     FSiS     2          98     2     1     1           1         105\n",
            "     SiSh                      29           6           2          37\n",
            "       MS                            25                 3          28\n",
            "       WS                 2     3          93                 2   100\n",
            "        D           1     2           1          24           1    29\n",
            "       PS                             7                69          76\n",
            "       BS                 1                 1                21    23\n",
            "\n",
            "Precision  0.96  0.99  0.95  0.83  0.74  0.92  1.00  0.91  0.88  0.93\n",
            "   Recall  1.00  0.98  0.93  0.78  0.89  0.93  0.83  0.91  0.91  0.93\n",
            "       F1  0.98  0.99  0.94  0.81  0.81  0.93  0.91  0.91  0.89  0.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soQv7MVBE--8",
        "outputId": "058633ac-93bf-468c-c605-d1cb4d283c99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "from sklearn.metrics import jaccard_similarity_score\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "jSVM = jaccard_similarity_score(y_test, yhat_SVM)\n",
        "f1SVM = f1_score(y_test, yhat_SVM, average='weighted')\n",
        "print(\"SVM Jaccard index: %.3f\" % jSVM )\n",
        "print(\"SVM F1-score: %.3f\" % f1SVM  )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM Jaccard index: 0.745\n",
            "SVM F1-score: 0.746\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ot6OAl_yumw_"
      },
      "source": [
        "# **GPC**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-dM-AuIrxDo"
      },
      "source": [
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.gaussian_process.kernels import RBF\n",
        "\n",
        "GPC_model = GaussianProcessClassifier().fit(X_train, y_train)\n",
        "\n",
        "yhat_GPC = GPC_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAqZbvvkvZ4G",
        "outputId": "72475d7e-9ee9-40de-d421-668cdb4c2518",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jGPC = jaccard_similarity_score(y_test, yhat_GPC)\n",
        "f1GPC = f1_score(y_test, yhat_GPC, average='weighted')\n",
        "\n",
        "print(\"GPC Jaccard index: %.3f\" % jGPC)\n",
        "print(\"GPC F1-score: %.3f\" % jGPC )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPC Jaccard index: 0.663\n",
            "GPC F1-score: 0.663\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHC3VNmzvcRi"
      },
      "source": [
        "conf_GPC = confusion_matrix(y_test, yhat_GPC)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nr0IVpYWvl58",
        "outputId": "f0ab5349-5839-43a0-f8a0-ebc0483ad4df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf_GPC))\n",
        "display_cm(conf_GPC, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.663121\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    34    16     2                                        52\n",
            "     CSiS     8    75    30                 1                     114\n",
            "     FSiS     1    28    72     1     1     1           1         105\n",
            "     SiSh                      25          10           2          37\n",
            "       MS                            10    12     1     5          28\n",
            "       WS                 1     3     7    71          16     2   100\n",
            "        D           1                       1    20     5     2    29\n",
            "       PS                       1     2    20     2    46     5    76\n",
            "       BS                                   1           1    21    23\n",
            "\n",
            "Precision  0.79  0.62  0.69  0.83  0.50  0.61  0.87  0.61  0.70  0.67\n",
            "   Recall  0.65  0.66  0.69  0.68  0.36  0.71  0.69  0.61  0.91  0.66\n",
            "       F1  0.72  0.64  0.69  0.75  0.42  0.65  0.77  0.61  0.79  0.66\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-FjPz5Uv1FT",
        "outputId": "9b2c5a90-b7d7-4b66-896f-2b1f73083349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf_GPC, adjacent_facies))\n",
        "display_adj_cm(conf_GPC, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.930851\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    50           2                                        52\n",
            "     CSiS         113                       1                     114\n",
            "     FSiS     1         100     1     1     1           1         105\n",
            "     SiSh                      25          10           2          37\n",
            "       MS                            22           1     5          28\n",
            "       WS                 1     3          94                 2   100\n",
            "        D           1                            26           2    29\n",
            "       PS                       1     2                73          76\n",
            "       BS                                   1                22    23\n",
            "\n",
            "Precision  0.98  0.99  0.97  0.83  0.88  0.88  0.96  0.90  0.85  0.93\n",
            "   Recall  0.96  0.99  0.95  0.68  0.79  0.94  0.90  0.96  0.96  0.93\n",
            "       F1  0.97  0.99  0.96  0.75  0.83  0.91  0.93  0.93  0.90  0.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYy4PDgNwDn4"
      },
      "source": [
        "# **RANDOM FOREST CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVi0zFDxv8qc"
      },
      "source": [
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "\n",
        "RFC_model = RandomForestClassifier(max_depth=12, n_estimators=20, max_features=7).fit(X_train, y_train)\n",
        "\n",
        "\n",
        "yhat_RFC = RFC_model.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmbX7QGewK8c",
        "outputId": "2c187778-7992-4831-f128-99687be7c0ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jRFC  = jaccard_similarity_score(y_test, yhat_RFC)\n",
        "f1RFC = f1_score(y_test, yhat_RFC, average='weighted')\n",
        "\n",
        "print(\"RFC Jaccard index: %.3f\" % jRFC )\n",
        "print(\"RFC F1-score: %.3f\" % f1RFC)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RFC Jaccard index: 0.679\n",
            "RFC F1-score: 0.680\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ru9X9VwwOxz"
      },
      "source": [
        "conf_RFC = confusion_matrix(y_test, yhat_RFC)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIiz4kXkwU7z",
        "outputId": "359ef100-659c-47fe-80c8-7348682d8c51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf_RFC))\n",
        "display_cm(conf_RFC, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.679078\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    36    15     1                                        52\n",
            "     CSiS     2    83    28                 1                     114\n",
            "     FSiS     1    30    69     1     1                 3         105\n",
            "     SiSh                      25     2     7           3          37\n",
            "       MS                       3    13     6     1     5          28\n",
            "       WS                 1     2     5    73     1    15     3   100\n",
            "        D           1                 1     1    18     5     3    29\n",
            "       PS                             5    25     1    45          76\n",
            "       BS                                   1           1    21    23\n",
            "\n",
            "Precision  0.92  0.64  0.70  0.81  0.48  0.64  0.86  0.58  0.78  0.69\n",
            "   Recall  0.69  0.73  0.66  0.68  0.46  0.73  0.62  0.59  0.91  0.68\n",
            "       F1  0.79  0.68  0.68  0.74  0.47  0.68  0.72  0.59  0.84  0.68\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXJeo9wDwXX2",
        "outputId": "efb22f45-a236-482e-8fc1-a975dc3dcc31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf_RFC, adjacent_facies))\n",
        "display_adj_cm(conf_RFC, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.927305\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    51           1                                        52\n",
            "     CSiS         113                       1                     114\n",
            "     FSiS     1          99     1     1                 3         105\n",
            "     SiSh                      27           7           3          37\n",
            "       MS                            22           1     5          28\n",
            "       WS                 1     2          94                 3   100\n",
            "        D           1                 1          24           3    29\n",
            "       PS                             5                71          76\n",
            "       BS                                   1                22    23\n",
            "\n",
            "Precision  0.98  0.99  0.98  0.90  0.76  0.91  0.96  0.87  0.79  0.93\n",
            "   Recall  0.98  0.99  0.94  0.73  0.79  0.94  0.83  0.93  0.96  0.93\n",
            "       F1  0.98  0.99  0.96  0.81  0.77  0.93  0.89  0.90  0.86  0.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rz1KB0AVwlxN"
      },
      "source": [
        "# **NEURAL NETWORK CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUXR85XEwgIq"
      },
      "source": [
        "\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "\n",
        "NNC_model = MLPClassifier(alpha=0.001, max_iter=1000, learning_rate_init=0.001, \n",
        "                          solver='adam', batch_size=8, hidden_layer_sizes=200).fit(X_train, y_train)\n",
        "\n",
        "yhat_NNC = NNC_model.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNN-HQ358dhD",
        "outputId": "fe9c7120-c64d-4055-88d9-be313974fe4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jNNC  =  jaccard_similarity_score(y_test, yhat_NNC)\n",
        "f1NNC =  f1_score(y_test, yhat_NNC, average='weighted')\n",
        "\n",
        "print(\"NNC Jaccard index: %.3f\" %jNNC)\n",
        "print(\"NNC F1-score: %.3f\" %f1NNC )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NNC Jaccard index: 0.699\n",
            "NNC F1-score: 0.701\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxe1s48Uwr0E",
        "outputId": "401ef28f-5266-4e96-c8cb-2fe31322006f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jNNC  =  jaccard_similarity_score(y_test, yhat_NNC)\n",
        "f1NNC =  f1_score(y_test, yhat_NNC, average='weighted')\n",
        "\n",
        "print(\"NNC Jaccard index: %.3f\" %jNNC)\n",
        "print(\"NNC F1-score: %.3f\" %f1NNC )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NNC Jaccard index: 0.699\n",
            "NNC F1-score: 0.701\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IuFR6Jcwvug"
      },
      "source": [
        "conf_NNC = confusion_matrix(y_test, yhat_NNC)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4vJPL6vwz2q",
        "outputId": "70cee2b8-3154-4722-ca4d-48c4c7bff69d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf_NNC))\n",
        "display_cm(conf_NNC, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.698582\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    39     8     5                                        52\n",
            "     CSiS     4    77    31           1                 1         114\n",
            "     FSiS     1    16    84     1                       3         105\n",
            "     SiSh                      26     2     5           4          37\n",
            "       MS                       3    14     6           5          28\n",
            "       WS                       1     5    65          26     3   100\n",
            "        D           1                 2          18     6     2    29\n",
            "       PS                             4    18     1    50     3    76\n",
            "       BS                                   2                21    23\n",
            "\n",
            "Precision  0.89  0.75  0.70  0.84  0.50  0.68  0.95  0.53  0.72  0.71\n",
            "   Recall  0.75  0.68  0.80  0.70  0.50  0.65  0.62  0.66  0.91  0.70\n",
            "       F1  0.81  0.71  0.75  0.76  0.50  0.66  0.75  0.58  0.81  0.70\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tp7je7PVw1nt",
        "outputId": "dccf3de2-08d0-494b-bc78-0624715e7cc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf_NNC, adjacent_facies))\n",
        "display_adj_cm(conf_NNC, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.927305\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    47           5                                        52\n",
            "     CSiS         112                 1                 1         114\n",
            "     FSiS     1         100     1                       3         105\n",
            "     SiSh                      28           5           4          37\n",
            "       MS                            23                 5          28\n",
            "       WS                       1          96                 3   100\n",
            "        D           1                 2          24           2    29\n",
            "       PS                             4                72          76\n",
            "       BS                                   2                21    23\n",
            "\n",
            "Precision  0.98  0.99  0.95  0.93  0.77  0.93  1.00  0.85  0.81  0.93\n",
            "   Recall  0.90  0.98  0.95  0.76  0.82  0.96  0.83  0.95  0.91  0.93\n",
            "       F1  0.94  0.99  0.95  0.84  0.79  0.95  0.91  0.89  0.86  0.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5_zGTI0xK9w"
      },
      "source": [
        "# **K NEIGHBOUR CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zf-4X5jYxIvi",
        "outputId": "328623d5-de71-4384-c2fc-63d0f1bc5cd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "# to chose the best k value we may run in range of valus \n",
        "Ks =15\n",
        "mean_acc = np.zeros((Ks-1))\n",
        "std_acc = np.zeros((Ks-1))\n",
        "ConfusMtx = [];\n",
        "\n",
        "for n in range(1,Ks):\n",
        "    KNN_model = KNeighborsClassifier(n_neighbors=n).fit(X_train, y_train)\n",
        "    yhat = KNN_model.predict(X_test)\n",
        "    \n",
        "    mean_acc[n-1]= np.mean(yhat==y_test);\n",
        "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
        "\n",
        "k = 5    #it seems 5 is good enough\n",
        "KNN_model = KNeighborsClassifier(n_neighbors=k ,leaf_size=50, p=1,  weights='distance' ).fit(X_train, y_train)\n",
        "KNN_model\n",
        "#plot\n",
        "plt.plot(range(1,Ks),mean_acc,'g')\n",
        "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
        "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
        "plt.ylabel('Accuracy ')\n",
        "plt.xlabel('Number of Neighbours (K)')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "yhat_KNN = KNN_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVtIVH9exSiw",
        "outputId": "cc29912d-c01a-4fb8-c470-fd3523c28a73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jKNN  = jaccard_similarity_score(y_test, yhat_KNN)\n",
        "f1KNN = f1_score(y_test, yhat_KNN, average='weighted')\n",
        "print(\"KNN Jaccard index: %.3f\" % jKNN )\n",
        "print(\"KNN F1-score: %.3f\" % f1KNN  )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN Jaccard index: 0.716\n",
            "KNN F1-score: 0.717\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Wl3Mr4SyCX3"
      },
      "source": [
        "conf_KNN = confusion_matrix(y_test, yhat_KNN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InMPNrMlyGSJ",
        "outputId": "9c3232d9-e429-44ee-dc9c-a53401b28182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf_KNN))\n",
        "display_cm(conf_KNN, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.716312\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    40    10     2                                        52\n",
            "     CSiS     5    78    30     1                                 114\n",
            "     FSiS     2    21    77     1     1     1           2         105\n",
            "     SiSh                      27     1     7           2          37\n",
            "       MS                       4    15     5           4          28\n",
            "       WS                 1     3     5    73          15     3   100\n",
            "        D           1                 1          24     2     1    29\n",
            "       PS                            10    14     1    48     3    76\n",
            "       BS                                               1    22    23\n",
            "\n",
            "Precision  0.85  0.71  0.70  0.75  0.45  0.73  0.96  0.65  0.76  0.72\n",
            "   Recall  0.77  0.68  0.73  0.73  0.54  0.73  0.83  0.63  0.96  0.72\n",
            "       F1  0.81  0.70  0.72  0.74  0.49  0.73  0.89  0.64  0.85  0.72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iecvIQdtyMmm",
        "outputId": "2e097cc5-9439-4fa9-9ca5-9aa3a3c43df5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf_KNN, adjacent_facies))\n",
        "display_adj_cm(conf_KNN, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.923759\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    50           2                                        52\n",
            "     CSiS         113           1                                 114\n",
            "     FSiS     2          98     1     1     1           2         105\n",
            "     SiSh                      28           7           2          37\n",
            "       MS                            24                 4          28\n",
            "       WS                 1     3          93                 3   100\n",
            "        D           1                 1          26           1    29\n",
            "       PS                            10                66          76\n",
            "       BS                                                    23    23\n",
            "\n",
            "Precision  0.96  0.99  0.97  0.85  0.67  0.92  1.00  0.89  0.85  0.93\n",
            "   Recall  0.96  0.99  0.93  0.76  0.86  0.93  0.90  0.87  1.00  0.92\n",
            "       F1  0.96  0.99  0.95  0.80  0.75  0.93  0.95  0.88  0.92  0.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEvipRIMyWYR"
      },
      "source": [
        "# **DECISION TREE CLASSIFIFER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7E5KGvCyTSk"
      },
      "source": [
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "DT_model = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 25, min_samples_split=3 )\n",
        "DT_model.fit(X_train,y_train)\n",
        "DT_model\n",
        "\n",
        "yhat_DT = DT_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfSBFamvydTD",
        "outputId": "a11978c0-e9d1-4573-c3ee-89d7298f6e10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jDT  = jaccard_similarity_score(y_test, yhat_DT)\n",
        "f1DT = f1_score(y_test, yhat_DT, average='weighted')\n",
        "print(\"DT Jaccard index: %.3f\" % jDT )\n",
        "print(\"DT F1-score: %.3f\" % f1DT )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DT Jaccard index: 0.587\n",
            "DT F1-score: 0.592\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihB8gDpOyfnW"
      },
      "source": [
        "conf_DT = confusion_matrix(y_test, yhat_DT)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZPG1mL8yjJb",
        "outputId": "aa4bb6e5-a900-4225-df9d-748c31d11436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf_DT))\n",
        "display_cm(conf_DT, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.586879\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    35    16     1                                        52\n",
            "     CSiS     8    71    32     1     2                           114\n",
            "     FSiS     1    31    67     1     2     1           2         105\n",
            "     SiSh                      20     3     8     1     4     1    37\n",
            "       MS           1           2    13     7     1     4          28\n",
            "       WS                 2     3    18    50          22     5   100\n",
            "        D           1     1     1     3     1    16     4     2    29\n",
            "       PS                       1     6    26     2    40     1    76\n",
            "       BS                                   1           3    19    23\n",
            "\n",
            "Precision  0.80  0.59  0.65  0.69  0.28  0.53  0.80  0.51  0.68  0.60\n",
            "   Recall  0.67  0.62  0.64  0.54  0.46  0.50  0.55  0.53  0.83  0.59\n",
            "       F1  0.73  0.61  0.64  0.61  0.35  0.52  0.65  0.52  0.75  0.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTJwkMHOyoC1",
        "outputId": "ea9ce4b5-a9b4-4f98-fe5a-764849b1f975",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf_DT, adjacent_facies))\n",
        "display_adj_cm(conf_DT, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.898936\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    51           1                                        52\n",
            "     CSiS         111           1     2                           114\n",
            "     FSiS     1          98     1     2     1           2         105\n",
            "     SiSh                      23           8     1     4     1    37\n",
            "       MS           1                22           1     4          28\n",
            "       WS                 2     3          90                 5   100\n",
            "        D           1     1     1     3          21           2    29\n",
            "       PS                       1     6                69          76\n",
            "       BS                                   1                22    23\n",
            "\n",
            "Precision  0.98  0.98  0.96  0.77  0.63  0.90  0.91  0.87  0.73  0.90\n",
            "   Recall  0.98  0.97  0.93  0.62  0.79  0.90  0.72  0.91  0.96  0.90\n",
            "       F1  0.98  0.98  0.95  0.69  0.70  0.90  0.81  0.89  0.83  0.90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ow5Xtsh9ywB3"
      },
      "source": [
        "# **LOGISTIC REGRESSION CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JP44lXkLytWs"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "LR_model = LogisticRegression(C=1, max_iter=330).fit(X_train,y_train)\n",
        "LR_model\n",
        "\n",
        "yhat_LR = LR_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbFDCLHmy2Ng",
        "outputId": "e19b0e82-39c8-4ed9-88c2-929d3e36a13c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "jLR  = jaccard_similarity_score(y_test, yhat_LR)\n",
        "f1LR = f1_score(y_test, yhat_LR, average='weighted')\n",
        "print(\"LR Jaccard index: %.3f\" % jLR )\n",
        "print(\"LR F1-score: %.3f\" % f1LR )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Jaccard index: 0.589\n",
            "LR F1-score: 0.577\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LM4Z9eIez5hQ"
      },
      "source": [
        "conf_LR = confusion_matrix(y_test, yhat_LR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viNbWJMM0uPi",
        "outputId": "632cb39f-6b36-41ce-df1d-8abb06f7afa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf_LR))\n",
        "display_cm(conf_LR, facies_labels, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.588652\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    36    16                                              52\n",
            "     CSiS     8    82    22                 1           1         114\n",
            "     FSiS     1    44    53     2     1     1     1     2         105\n",
            "     SiSh                      18          18           1          37\n",
            "       MS                       3     1    18     1     5          28\n",
            "       WS                       3     2    71     3    20     1   100\n",
            "        D                 1     3           3    15     6     1    29\n",
            "       PS                       1     3    24     2    38     8    76\n",
            "       BS                                   1           4    18    23\n",
            "\n",
            "Precision  0.80  0.58  0.70  0.60  0.14  0.52  0.68  0.49  0.64  0.59\n",
            "   Recall  0.69  0.72  0.50  0.49  0.04  0.71  0.52  0.50  0.78  0.59\n",
            "       F1  0.74  0.64  0.59  0.54  0.06  0.60  0.59  0.50  0.71  0.58\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShHx7t4-0yha",
        "outputId": "b35cecaa-493d-4da8-ed88-c916b974abf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf_LR, adjacent_facies))\n",
        "display_adj_cm(conf_LR, facies_labels, adjacent_facies, hide_zeros=True, display_metrics=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adjacent facies classification accuracy = 0.913121\n",
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    52                                                    52\n",
            "     CSiS         112                       1           1         114\n",
            "     FSiS     1          97     2     1     1     1     2         105\n",
            "     SiSh                      18          18           1          37\n",
            "       MS                            22           1     5          28\n",
            "       WS                       3          96                 1   100\n",
            "        D                 1     3                24           1    29\n",
            "       PS                       1     3                72          76\n",
            "       BS                                   1                22    23\n",
            "\n",
            "Precision  0.98  1.00  0.99  0.67  0.85  0.82  0.92  0.89  0.92  0.91\n",
            "   Recall  1.00  0.98  0.92  0.49  0.79  0.96  0.83  0.95  0.96  0.91\n",
            "       F1  0.99  0.99  0.96  0.56  0.81  0.88  0.87  0.92  0.94  0.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RElIQCK79YbK"
      },
      "source": [
        "acc_LR = accuracy(conf_LR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc6UUT4f9eUb",
        "outputId": "24d61d13-a317-4697-da60-bd1755cd9a7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(acc_LR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5886524822695035\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnKG3oq408Fw"
      },
      "source": [
        "# **OBSERVATIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShJsrRnF03pC",
        "outputId": "64c3b3ee-99a4-4daf-e2d3-3d6e64db9b4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "# create dictionary with calculated errors as variables\n",
        "data_frame1 ={'Model type': ['SVM', 'GPC', 'RFC', 'NNC', 'KNN', 'DT', 'LR'],\n",
        "       'Jaccard index': [jSVM, jGPC, jRFC, jNNC, jKNN, jDT, jLR],\n",
        "       'F1-Score': [f1SVM, f1GPC, f1RFC, f1NNC, f1KNN, f1DT, f1LR],\n",
        "       'Accuracy': [accuracy(confusion_mtx), accuracy(conf_GPC), accuracy(conf_RFC), accuracy(conf_NNC), accuracy(conf_KNN), accuracy(conf_DT),\n",
        "                    accuracy(conf_LR)],\n",
        "        'Adjacent Accuracy': [accuracy_adjacent(confusion_mtx, adjacent_facies), accuracy_adjacent(conf_GPC, adjacent_facies), accuracy_adjacent(conf_RFC, adjacent_facies),\n",
        "                                     accuracy_adjacent(conf_NNC, adjacent_facies), accuracy_adjacent(conf_KNN, adjacent_facies), accuracy_adjacent(conf_DT, adjacent_facies),accuracy_adjacent(conf_LR, adjacent_facies)]\n",
        "            }\n",
        "df1 = pd.DataFrame(data_frame1, columns = ['Model type','Jaccard index','F1-Score', 'Accuracy', 'Adjacent Accuracy' ] )\n",
        "df1.round(2)"
      ],
      "execution_count": null,
      "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>Model type</th>\n",
              "      <th>Jaccard index</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Adjacent Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GPC</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RFC</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NNC</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DT</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LR</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Model type  Jaccard index  F1-Score  Accuracy  Adjacent Accuracy\n",
              "0        SVM           0.74      0.75      0.74               0.93\n",
              "1        GPC           0.66      0.66      0.66               0.93\n",
              "2        RFC           0.68      0.68      0.68               0.93\n",
              "3        NNC           0.70      0.70      0.70               0.93\n",
              "4        KNN           0.72      0.72      0.72               0.92\n",
              "5         DT           0.59      0.59      0.59               0.90\n",
              "6         LR           0.59      0.58      0.59               0.91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLuDIJNY0_fV"
      },
      "source": [
        "\n",
        "y_blind = blind['Facies'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17bYHw-G3q4v"
      },
      "source": [
        "blind_features = blind.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbtvjrWn3tCG"
      },
      "source": [
        "\n",
        "X_blind = scaler.transform(blind_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSWy-zteSD3n"
      },
      "source": [
        "#SVM\n",
        "yhat_blind_SVM = SVM_model.predict(X_blind)\n",
        "blind['SVM_Pred'] = yhat_blind_SVM\n",
        "conf_blind_SVM = confusion_matrix(y_blind, yhat_blind_SVM)\n",
        "\n",
        "#GPC\n",
        "yhat_blind_GPC = GPC_model.predict(X_blind)\n",
        "blind['GPC_Pred'] = yhat_blind_GPC\n",
        "conf_blind_GPC = confusion_matrix(y_blind, yhat_blind_GPC)\n",
        "\n",
        "#RFC\n",
        "yhat_blind_RFC = RFC_model.predict(X_blind)\n",
        "blind['RFC_Pred'] = yhat_blind_RFC\n",
        "conf_blind_RFC = confusion_matrix(y_blind, yhat_blind_RFC)\n",
        "\n",
        "#NNC\n",
        "yhat_blind_NNC = NNC_model.predict(X_blind)\n",
        "blind['NNC_Pred'] = yhat_blind_NNC\n",
        "conf_blind_NNC = confusion_matrix(y_blind, yhat_blind_NNC)\n",
        "\n",
        "#KNN\n",
        "yhat_blind_KNN = KNN_model.predict(X_blind)\n",
        "blind['KNN_Pred'] = yhat_blind_KNN\n",
        "conf_blind_KNN = confusion_matrix(y_blind, yhat_blind_KNN)\n",
        "\n",
        "#DT\n",
        "yhat_blind_DT = DT_model.predict(X_blind)\n",
        "blind['DT_Pred'] = yhat_blind_DT\n",
        "conf_blind_DT = confusion_matrix(y_blind, yhat_blind_DT)\n",
        "\n",
        "#LR\n",
        "yhat_blind_LR = LR_model.predict(X_blind)\n",
        "blind['LR_Pred'] = yhat_blind_LR\n",
        "conf_blind_LR = confusion_matrix(y_blind, yhat_blind_LR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhGWqx6V3zen",
        "outputId": "ca29fad9-6dd4-4409-cd82-6d2b80bd124e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "#SVM\n",
        "jSVM_b =  jaccard_similarity_score(y_blind, yhat_blind_SVM)\n",
        "f1SVM_b = f1_score(y_blind, yhat_blind_SVM, average='weighted')\n",
        "print(\"SVM Jaccard index for blind well prediction: %.3f\" %jSVM_b)\n",
        "print(\"SVM F1-score for blind well prediction: %.3f\" %f1SVM_b )\n",
        "\n",
        "#GPC\n",
        "jGPC_b =jaccard_similarity_score(y_blind, yhat_blind_GPC)\n",
        "f1GPC_b =f1_score(y_blind, yhat_blind_GPC, average='weighted')\n",
        "print(\"GPC Jaccard index for blind well prediction: %.3f\" %jGPC_b )\n",
        "print(\"GPC F1-score for blind well prediction: %.3f\" % f1GPC_b)\n",
        "\n",
        "#RFC\n",
        "jRFC_b =jaccard_similarity_score(y_blind, yhat_blind_RFC)\n",
        "f1RFC_b=f1_score(y_blind, yhat_blind_RFC, average='weighted')\n",
        "print(\"RFC Jaccard index for blind well prediction: %.3f\" % jRFC_b)\n",
        "print(\"RFC F1-score for blind well prediction: %.3f\" % f1RFC_b)\n",
        "\n",
        "#NNC\n",
        "jNNC_b  =jaccard_similarity_score(y_blind, yhat_blind_NNC)\n",
        "f1NNC_b =f1_score(y_blind, yhat_blind_NNC, average='weighted')\n",
        "print(\"NNC Jaccard index for blind well prediction: %.3f\" %jNNC_b )\n",
        "print(\"NNC F1-score for blind well prediction: %.3f\" %f1NNC_b )\n",
        "\n",
        "#KNN\n",
        "jKNN_b = jaccard_similarity_score(y_blind, yhat_blind_KNN)\n",
        "f1KNN_b = f1_score(y_blind, yhat_blind_KNN, average='weighted')\n",
        "print(\"KNN Jaccard index for blind well prediction: %.3f\" % jKNN_b)\n",
        "print(\"KNN F1-score for blind well prediction: %.3f\" %f1KNN_b  )\n",
        "\n",
        "#DT\n",
        "jDT_b = jaccard_similarity_score(y_blind, yhat_blind_DT)\n",
        "f1DT_b =f1_score(y_blind, yhat_blind_DT, average='weighted')\n",
        "print(\"DT Jaccard index for blind well prediction: %.3f\" % jDT_b)\n",
        "print(\"DT F1-score for blind well prediction: %.3f\" % f1DT_b )\n",
        "\n",
        "#LR\n",
        "jLR_b  = jaccard_similarity_score(y_blind, yhat_blind_LR)\n",
        "f1LR_b = f1_score(y_blind, yhat_blind_LR, average='weighted')\n",
        "print(\"LR Jaccard index for blind well prediction: %.3f\" % jLR_b)\n",
        "print(\"LR F1-score for blind well prediction: %.3f\" %f1LR_b )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM Jaccard index for blind well prediction: 0.458\n",
            "SVM F1-score for blind well prediction: 0.489\n",
            "GPC Jaccard index for blind well prediction: 0.470\n",
            "GPC F1-score for blind well prediction: 0.500\n",
            "RFC Jaccard index for blind well prediction: 0.487\n",
            "RFC F1-score for blind well prediction: 0.520\n",
            "NNC Jaccard index for blind well prediction: 0.504\n",
            "NNC F1-score for blind well prediction: 0.531\n",
            "KNN Jaccard index for blind well prediction: 0.460\n",
            "KNN F1-score for blind well prediction: 0.507\n",
            "DT Jaccard index for blind well prediction: 0.427\n",
            "DT F1-score for blind well prediction: 0.470\n",
            "LR Jaccard index for blind well prediction: 0.460\n",
            "LR F1-score for blind well prediction: 0.481\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Nii6xTg4EW2",
        "outputId": "695792d3-e4fe-4c41-af25-f0e8bee00cc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "data_frame2 ={'Model type': ['SVM', 'GPC', 'RFC', 'NNC', 'KNN', 'DT', 'LR'],\n",
        "       'Jaccard index': [jSVM_b, jGPC_b, jRFC_b, jNNC_b, jKNN_b, jDT_b, jLR_b],\n",
        "       'F1-Score': [f1SVM_b, f1GPC_b, f1RFC_b, f1NNC_b, f1KNN_b, f1DT_b, f1LR_b],\n",
        "       'Accuracy': [accuracy(conf_blind_SVM), accuracy(conf_blind_GPC), accuracy(conf_blind_RFC), accuracy(conf_blind_NNC), accuracy(conf_blind_KNN),\n",
        "                    accuracy(conf_blind_DT),accuracy(conf_blind_LR)],\n",
        "        'Adjacent Accuracy': [accuracy_adjacent(conf_blind_SVM, adjacent_facies), accuracy_adjacent(conf_blind_GPC, adjacent_facies), \n",
        "                              accuracy_adjacent(conf_blind_RFC, adjacent_facies),accuracy_adjacent(conf_blind_NNC, adjacent_facies), \n",
        "                              accuracy_adjacent(conf_blind_KNN, adjacent_facies), accuracy_adjacent(conf_blind_DT, adjacent_facies),\n",
        "                              accuracy_adjacent(conf_blind_LR, adjacent_facies)]\n",
        "            }\n",
        "df2 = pd.DataFrame(data_frame2, columns = ['Model type','Jaccard index','F1-Score', 'Accuracy', 'Adjacent Accuracy' ] )\n",
        "df2.round(2)"
      ],
      "execution_count": null,
      "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>Model type</th>\n",
              "      <th>Jaccard index</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Adjacent Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GPC</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RFC</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NNC</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DT</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LR</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Model type  Jaccard index  F1-Score  Accuracy  Adjacent Accuracy\n",
              "0        SVM           0.46      0.49      0.46               0.81\n",
              "1        GPC           0.47      0.50      0.47               0.83\n",
              "2        RFC           0.49      0.52      0.49               0.84\n",
              "3        NNC           0.50      0.53      0.50               0.86\n",
              "4        KNN           0.46      0.51      0.46               0.84\n",
              "5         DT           0.43      0.47      0.43               0.83\n",
              "6         LR           0.46      0.48      0.46               0.86"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjztkESQ4JN-"
      },
      "source": [
        "def compare_facies_plot(logs, compadre, facies_colors):\n",
        "    #make sure logs are sorted by depth\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(\n",
        "            facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    \n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    cluster2 = np.repeat(np.expand_dims(logs[compadre].values,1), 100, 1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1, ncols=7, figsize=(12, 6))\n",
        "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
        "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
        "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
        "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
        "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
        "    im1 = ax[5].imshow(cluster1, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im2 = ax[6].imshow(cluster2, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    \n",
        "    divider = make_axes_locatable(ax[6])\n",
        "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im2, cax=cax)\n",
        "    cbar.set_label((5*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-2):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=3)\n",
        "    \n",
        "    ax[0].set_xlabel(\"GR\")\n",
        "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
        "    ax[1].set_xlabel(\"ILD_log10\")\n",
        "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
        "    ax[2].set_xlabel(\"DeltaPHI\")\n",
        "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
        "    ax[3].set_xlabel(\"PHIND\")\n",
        "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
        "    ax[4].set_xlabel(\"PE\")\n",
        "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
        "    ax[5].set_xlabel('Facies')\n",
        "    ax[6].set_xlabel(compadre)\n",
        "    \n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([]); ax[6].set_yticklabels([])\n",
        "    ax[5].set_xticklabels([])\n",
        "    ax[6].set_xticklabels([])\n",
        "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7kwHz5YBsN1",
        "outputId": "b89aaa8a-8364-431c-b719-3c93d3e4cd24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "#let's plot KNN result\n",
        "compare_facies_plot(blind, 'NNC_Pred', facies_colors)\n",
        "#plt.savefig(\"drive/My Drive/seminar/NNC_comp.png\", dpi=400)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N8Z5UBio1UMrpG4gDrSh9Y8iflRramqhqrWV6qhqwZCho9Hh5+M3tLNtTweRhkbz0A5VkB5vX6+WNN97g4MGDPdanpqZy8803Bymq0GBJT8OSngZAR3MLFV8sp+CfryG9XsoWfzLkE+3o6Gi8Xi/XXHMNLpeL66+/njvuuIPs7Oxgh6YoigKEQKLtMNnIcqbzSd5KRkZnMi5WnYAHq7yKJu5ftJWDVc38Yn4Wt587jDDtwLvwIdS0t7f3SLJHjhzJ9OnTcamRCs+I9vp6il5/i8ovluNvbyc8NYWYOecTPeO8YIfW75KTk1mzZg2ffPIJ//znP/nzn//Mc889x2WXXcZbb72lRhNVFCXohnyiDfDjqbfym69e4L+/fJ47Jl7P+enndNVuKwNfdXMbL688yMK1BUQYw/jHLZM5N9MZ7LBCXm1tLRs2bGDXrl091p999tnExcUFKarQ4i4rZ8vd94JGQ8z5s4mdNxdzelpIXWCq1+u57LLLuOyyy6iqquKFF17gySef5MILL+S5555j7Njjd4OoKIrybYQQ4YBXSuntXB4BLAAKpJTv9nY/IZFoO82R/PaCX/C7VS/xv5sX8dbuj5kQP4Yrs+erkSAHqA6fn5W5Vby1uYgv9lbil5KrJiTy8/lZOC2G796B0u/effddSkpKAJg0aRJTp04lKkp9ns6kyuWdA/z4/Qithqa8/XgqK9Hb7ejsNnR2O1qTKWQS7+joaJ544gkSEhJ45JFHGDduHLfddhsvv/yyqt9WFOVkLQVuA/YLITKAtcBrwMVCiLOklI/0Zichc+bRh+n5+bk/Yl3xVlYXbmJ5/mpqWmv55YwfBzs0pZuKRg8L1x7mzU3FVDW14bToufXcYVx3VhLp0aHdP+hAc+mll/LWW29RXV3Npk2b2LRpE+Hh4URHRxMREYHBYOiajEbjt86rrtdOTeKVl2N0RVO7aQuVK7/C7/Ecs41Gr0dns6Kz29HZ7ZgS4glPSiI8OYnwpES0Q3AQlzvvvJOMjAzOP/98/va3v/Hoo4+SlpYW7LAUpV95a/yUL2oOdhhB5a3x9+XuHFLK/Z3zNwP/llLeL4TQA5sBlWgf0dbRzoHaw+RV57O/5hC51YF6UodRDcU7ULjbffzhs1z+seYwHX7J+Vkurp2UxKwsFzpVhz0guVwu7rnnHhobG6murqaqqqprKi0txePx0NbWdky/2scTFhaGwWDAZrNht9u7bh0OR9etapE8ltZkIuaCOcRcMAfp8+FtbMRb30B7fT3e+vpv5hsa8dbX01ZZSf227Uivt2sfBlc04UmJmI4k38nJmFMHdxeZ//rXv7jxxhuJiori1VdfVUm2EhJU7y993ntL9xHWZgPPAEgp24UQvc7oh+x/rnp3AysOrWVHxV5yq/Pp8AdGR4uPiGFyYg4XpE8nM2pYkKNUjvjPD3bx1uZirpuUxD2z0kmJMgc7JKUXhBDYbDZsNtsJh0jv6Oigra2Ntra2ruT76Pm2tjbcbjeNjY1UVFSQm5vbI0HXaDREREQQHh6O2Wxm7ty5REdHn6mXOSgIrRa9w4He4eDbPj3S58NTUUFrYVFgKirGXVRE/Y5dXQl49MwZMH5w1jZLKfn3v/8NwIEDB7DbVYOKoiinZIcQ4lmgBMgAlgEIIU7qpDJkE+139nzCpwe+xGG0MT9zJqNcw8mMGkaEQZUfDERbCuuYmx3D764enP/clRMLCwsjLCwMs/nbvzy1t7f3aBUvKyujsLAQn8+H3++noaGhazAcj8fDbbfddibCH3KEVospPh5TfDxRU6d0rW+vr6ds8ScUv/k2poTBOdhNa2srr776KkuWLOFXv/qVSrIVRTkddwAPAKnAXClla+f6bODZ3u5kyCbaV2bPZ3dlHhUt1ei0OlwWp0qyB7D6Vi+NHi8dPr/qsm+Iamtro7GxsWtqaGjomq+pqaG+vr5rW41Gg9VqJT4+noiIiK7JarWi1WpVKcBp8nu9tBYU0pyfT8vBfJry9tNy6DBIiT4ykpg553Nwx/Zgh3lSnnrqKZ544gk6OjoYOXIkjz76aLBDUhRlEJNSuoHfdl8nhHACa6WUa3q7nyGbaDtMNh6f/R+8uP5V3tu7lHf3fMKkhHH8eMoPMeqMwQ5POcoV4xP466pDXPjcV1w7KYlLc+KJsw29i7RCRXV1NSUlJZSVlVFeXk5FRQWe41yoZzabsVqtJCQkkJOTg8vlIjo6msjISDQa9YWrL/ja2mg9XEDzwXyaD+bTkp9Pa2ERsiNQTqcND8eSnkby96/DNm4slox0NIOwHn7dunV0dHSwePFi5s6dq2r6FUU5LUKIqQQS7Vrg18A/ASegEULcJKVc2pv9DOkzkdVg4Rfn3Uudu4Hl+at5Y9dHvL1nCTeMuzLYoSlHeezibCakOPjr1/k8/ck+nv5kH2nRZs5Oj2LKsChykuwkOkKnm7LBqry8nM8++4z8/HwgUDYSExPDqFGjiIyMxGq1dk0RERGqt5E+5Pd68VRU0lZZGRiS/UhSXVwC/sB1O2EREVjS04i/7BIs6WmY09IwxrgQQ+BLzY9//GMWL15MTU2NSrIVRekLLwCPAjZgOTBfSrlOCJEF/JtA93/fKSTORg6TjXmZM3l7zxIaPE3BDkc5gQVj4lgwJo6DVc18sbeCtQdreG9LCf9aVwiA06JnXKKdcUl2cpLsjEu0YwtXI78NFD6fj0WLFuHz+ZgzZw7Dhw8nKipKtUz3A095OfXbd9CUux9PeTme8graa2tBfnORvM7hwJKeRuTUKVgy0rGkpaF3Rg25L6uNjY0sW7aMF154AQjUaStKSDJIZGqfdm83+Bjkd2/Te2FSyiMXQD4ppVwHIKXcdzLn0ZBItEubKnjs82fw+X2MjRkZ7HCU75AebSE92sKd56Xj9fnZV9bEtuJ6thfVs62onuW5lV35RJrTzIwR0SwYE8fEZEdwAw9xlZWVNDU1MX/+fCZPnhzscIYU6fPRuC+XmjVrqd2wibbKSgB0NhumhHhsY8dgjI3BGOPCGBuLMS4WfQhcCFhUVITNZgMgKiqKF198kTvuuCPIUSmKMkR0/9biPuq+Xmf0IZFo67U69FodWqGhurWWto52DGH6YIel9IJOq2FMoo0xiTZunBro27fR42VXcQPbiuvZdLiO19YX8n+rD3NWqoO7hvfpt1nlJKxYERilsKmpCb/fr1qy+9CBF1+m8vPlADjOmkTC5ZdgGzcWU0LCkGuhPhndS0RmzZrF/Pnz1d+doih9ZZwQohEQgKlzns7lXl/sFxJnJGd4JM9e+BhnJebw750fcOv7D/P0Vy/w1eH1wQ5NOQVWo46zM5zcMzODv//wLN66axoAGw/XkVcX4j+bBdHkyZOx2WysWrWK559/nrVr11JdXY2U6svP6bKNGQ0ERnk0p6YQMXJkyCfZAHFxcVRVVfHYY4/x8ccfM2zYMIQQLF++PNihKYoyyEkptVJKq5QyQkoZ1jl/ZLnXdash0aINYDGYeXDa7exOz2VTyQ62lO3ihfX/INJkY3RMVrDDU06Cu93H5oI61uZX88XeSvaVN6HVCO6dmU6WrjTY4YWsjIwMHnjgAXJzc1m9ejXLli1j2bJlWCwWkpOTsdlsPbrqOzLpdKrO/ru4Zs5AHxlJ6YcfU/z2uxS/9Q76yEiso0dhdEVjiI5G74zC4HRicDrRmsNDJgn3+/0cOHCgR682v//975k9e3YQo1KUM08Nwd7nQ7D3iZBJtCEwit3omCyyojNxWZz8Y+tb5NcVqkR7gPN4fWwprGPdwRrW5tewrager0+i1QgmJNt5/JJs5o+JI8ZqZOXKsmCHG9KEEGRlZTFixAjq6uo4dOgQhw8fpqSkhLy8PDo6u5TrzmAwYLVacTgcx0yRkZGqZ5JO9rFjsI8dQ3tdHfVbt1G7aQtNe/dSvWp1V68iR2iMRkxxcYGLIDPTsWRmYE5JQQzB9/KnP/0pr7/+OlOnTuX6669nypQp5OTkBDssRVEUIMQSbYA6dwO//frPHKorYpg9ibOTJwU7JOUoHT4/WwrrWXOwmnX5NWwprKe9w49GwJgEG7eeO4ypaVGclRqJxRByf8KDghCCyMhIIiMjmThxIhAYGtvj8dDU1HTM1NjY2JWYezuHAQdITEzklltuUXW33egdDlyzZ+GaPQsIXCjZXldPW3U17dXVtFVV01Zdjbu4hJq166j47HMAbOPGMurxXw2Jrvy6mzFjBgsXLmTjxo04nU6GDRuGXq+uwVFCjy5KQ+z1oT0wn+7jgXd++84sRQiRBCwEYghcZfkXKeUfhRA5wMsECsI7gHuklBuEED8FftBt/yOBaCllrRDiMNAE+IAOKeUZz3Jf3vgvDtUV8eDZtzM1cULI/Lw6GOwrb+RvXx/ii32V1La0oxEwKt7GzdNSAon1sEisRlVmMFgJITCZTJhMJlwu13G3kVLS2tpKXV0dW7ZsYevWrezfv58RI0ac4WgHD6HVYnBGYXBGAT3fJyklnvIKttx9Lw3bd7D++puwZGZgyczA4HSid9jR2QOT3m5Daxp8g0TdeuutzJ8/nz/96U+8+uqrXHLJJbz55ptcc801wQ4tZHibfZStafzuDZUT8jb7gh2C0k960xzYATwkpdwihIgANgshPgN+DzwhpfxECLGgc3mmlPIZ4BkAIcQlwINSytpu+5slpazu25fRexXNVQAk2eJVkj2AvL25mIff2k6EIYzzR7qYOyqWczKc2EwqsQ4lQgjMZjNms5nKykq2bt3K66+/TlRUFE6nE51Oh06nQ6/X97g93rrutx0dHSHZE4oQAlNcLGf931+p37adprw8mnLzKHnvg2PKTSBQcqK329HZbWiB0qZmLBkZmIelojUO3BF14+LiePrpp3nyySeZMmUKN954I01NTdxyyy3qPH8G5HoimJE7I9hhDGqVnuLT30mbQBwOrXPcMdoG3uf9OxNtKWUZUNY53ySE2AskEGjdtnZuZgOOdxXa9wmMnjNgRIU7KG2qwOs7tlZUCZ7C2sAgE7OyXPxkznBSneYgR6QE24QJE0hLSyMvL4/9+/dTX19Pe3s7Xq8Xt9uNz3dyLUCrV68O2RED9ZEOXLNn4po9EwiUm3gbG/HWN9BeV4e3vp72+ga8dXW01dTiKS1Fc+gwh/bldu1j2O23EH/JxUGJv7d0Oh3Lli3j6quv5rbbbuPtt99m4cKFOJ3OYIc2pI12trLqlq3BDmNQO3eXGmhpqDqp/zpCiFRgPLAe+AnwqRDiWQLdBJ591LbhwDzgvm6rJbBMCCGBV6SUfznlyE9RfEQMOyv2sacyj1hLNCbdwG2lCSV3z0ijrN7N+9tK+HB7KWelOrh8fAJzRsYQY1XHaLCSUuLz+ejo6OiavF7vCZc9Hg8ej4e2trZj5js6Omhvb6etre07k2whBEajEYPBgNFoxO12ExcXh8FgOEOvPLik309HUxPehoZAAl3fQEdLM76WVjpaW/G1tHxz29JKR0sLvtZWOlpa8XfrvaM7S0bGGX4Vp8bpdLJ8+XJefPFFHnroIeLj45k+fXrXQEpZWVlER0erlu4+pEpHTl9flI6o4zAwS3B6nWgLISzAO8BPpJSNQoinCJSFvCOEuBb4GzCn20MuAVYfVTZyrpSyRAjhAj4TQuyTUn51nOe6E7gTICYmhpUrV570CzuRFOkkWR/Hq9veZuG2dzAIHRfYppFkiO2xXXNzc58+7+kIVixHH4f+juOiaDhnupGvSzpYU1rPL9+r45fv7SIpQsOoKC0pVg1JERpizYIwzfH/SQ6k49ZX+vPzcCJHv49er5eioqKuEgyfz4ff7+/VdCo0Gg1hYWFotVrCwsK6JqvVesy6oyetVotWq+2RSDU3N2OxnN5FQmfyOHS9/1KC1wueNmhrQ7QFbgPz7YF5TxvC7YbWVkRrK7S6we1GnKD/cqnRgMEABj3SYAC9PrBstSINnfN6PR4k+qQkcEaBEGypKIeK8n57zb3V2+MwevRoXnzxRT777DM2bNjAT3/60677rFYrSUlJJCcn43Q6sVqtREREYLVasdlshIWF0dDQgMViOWO93gTj3NXc3DddwanSkdPXF6UjLf5E1jf9vg+iGbxa/BavEmEAACAASURBVD8KdgjH6FWiLYTQEUiyX5NSvtu5+mbggc75t4C/HvWw73FU2YiUsqTztlII8R4wGTgm0e5s6f4LwKRJk+TMmTN7E2avjW0Zz/9teYPNpTvxyHa0cUZmju35HCtXrqSvn/dUBSuWo4+DxWI5I3FcHnhu9pU3sSK3kpW5VXxRWIfXF0gc9FoNadFmUqPMJDhMJNhNJDhMJDpMtOzewowZM4ZUa1V/fx6O5+i/uXfffZfi4mLMZjNhYWHodDoMBsMxSa5Op0Or1aLT6Y6bBB9vffd1R1qh+7rEoy8+Q311HKSU+FpaaKuppb2mhvaamh7z7fUNhFVXo/P76WhpPW4tdXcaoxGdzRqorY6NRWe3obPZAhc52mzo7TZ0Nithlgi05nA0en2vPh8D6RzY3ckch5kzZ3LbbbcBUFJSws6dO9m3b1/XtGXLFqqqqk74hVAIgcPhICoqipiYGC644AKuvPJKRo0a1efnmGC830OtUUJRBqLe9DoiCLRW75VS/qHbXaXADGAlMBvY3+0xts77bui2zgxoOuu8zcBc4Mk+eA0n5f29n7Jox/sADLMncUX2PKYkjj/TYSjfQQjByDgrI+Os3DMzg/YOP/nVzewra2JveSO55U3sr2xiZV4lHm/Pf5Lmrz8lxmrEZTUQYzUG5iO+mY+zGYm1GdFpQ/yikV7w+/3k5eWxc+dOAO6//37VddopkH4/DTt2Ur1mHbXrN+Ctrz9mG53djj4qEr3djtTpcKYNI8xsJsxsRms2E2Yx91w2mwkzhw/JvrH7Q0JCAgkJCcybN6/Her/fT0NDAzU1NV3T6tWrcblc1NTUUFtbS01NDYcOHeLxxx/nv/7rv3C5XIwcOZKRI0eSnZ3NyJEjmTBhApGRkUF6dcGVqLfxVPxFwQ5jUHtM/3GwQxhyEmU8T7U/flKP+QHn93kcvWk2Oge4EdgphNjWue5R4A7gj0KIMMBD5095na4AlkkpW7qtiwHe62wFCAMWSSmXnmb8J8Xv9/PmrsAf88joTC4ZcT4jnOlDqvVzqNKHaciKtZIVa+VyErrWSympbWmnpN5NcZ2bLzftwuxMoKLJQ2Wjh62F9VQ0emjr6JmMCwExEUYSHCbi7Sbi7UYSHeFkxUaQFRtBhOpGkIaGBt5+++2uluyzzjpLjeJ4Chp27Sb/f/9G6+ECNEYjjonjiRg+HH1UoEs+fWQk+kgHmm7v7cqVK0kfgK3JQ5FGo+kaICmjsw49PDz8uK3LZWVlfPjhh2zYsIG9e/fy+uuvU9/tS9OoUaOYPn063//+9znvvPPO1EtQFGUA602vI6uAE2WiE0/wmH8A/zhqXT4w7uTC61sajYbHZvyY5fmr2Viynd+vernrviuz5/O9MZcGMTrlVAghiLIYiLIYGJtoJ7wml5kzs3tsI6Wk0d1BRZOHikYPZfUeiuvdlNa7Kalzs6O4nk93eWj3fZOMJ0WayI6zctWERC7IjgnJL2M7d+6kuLiYyMhIFixYQGJiYki+D6er4J+v0Xq4gLQ7b8c1ZzbaELkgcyiKi4vjrrvu4q677gIC55by8nL27NnDunXr+Pjjj3n55ZdZv349W7ZsCXK0iqIMBCHX11W2K5P4CBcT4kfzyf6V5FYfBGB98VauG32JSiSGICEEtnAdtnAdw2MijruN3y8pb/Swr7yRvWVN7ClrZEtBHZ/uruCK8Qn8z3WhN6TzpEmT2LdvHyUlJfzrX/8CAj06xMbG4nK5iImJISYmBqvVqj433yJuwXya8vZT/M57OCZNQBsTE+yQlD7gdrvZt28fe/bsYc+ePezevZv9+wMVlPfdd993PHrw635Rqs4ew++SQi6d6FPl+lM7h3Y/Dk7L8QcCU4JrSH8y6twN7K3aT1lTZedUQVlzFc3tgYoWrdCQHpnClMTxXDR8tkoWQphGI4i3m4izGbEadRTXtdLS1oEQkBIVHuzwgsJoNHL77bfjdrspKSmhpKSE0tJSioqK2LVrV9d2LpeLs846i5ycnJDtp/rbRM+YjjEult2PP8nW+x/EkpGOJTODiOGZgREaVVdzA5bf76eyspKCggLy8vK6Euo9e/aQn5+P7OzZJSwsjMzMTObMmcPPfvYzJkyYEOTI+1/3i1LDE7OO38WN0u+6H4c01wh1HAagIflf0S/9vLHzIz7O+wKvzwtAlMlBXISLaUkTiIuIISMylTRHEvowdWGXAu0dfhbvLOVvqw6xq6SRcL2WeaNiufnsVMYl2YMdXlCZTCYyMjK66lcBPB4PlZWVlJaWsn37dhYvXsyOHTu46qqrsNlsQYx2YIoYnsmoJ/6LqpVf0rz/AGWLP6H0/Q8B0NmsWDIDSbcpIQFTXGyg2z6l37W1tVFcXExBQQGFhYUUFBRQUFDA9u3baWxspKioiLZux0Kn0zF8+HAmTJjAjTfeSHZ2NtnZ2WRmZob0RcIdzfXUrP8g2GEMah3Nx14grQwNQzLR3lK6k/f2LmV4VBq3TriWBGscBpVQK8dR39rOa+sLWbj2MBWNbWS4LPzmijFcPj6ecP2Q/Hj0CaPRSHJyMsnJyUyZMoXdu3fz/vvv8/zzzzNixAjsdjs2mw2bzdbVN7HJZArpltuIzAwiMgNfVvxeL60FhTTt309z3gGaDxygbvOWQL/ZgA7Y8NrrGGPjMKcPI3LyWdhGj+pxwaRy8qSUPP744yxbtoyCggLKy8u7WqWPiIuLw+FwMHHiRK644gpSUlJISUkhPT2djIwMdUHwcYRZ7ERNuSzYYQxqtZsWBzsEpZ8MyUwiXGcCIK8mH0OYQSXZShcpJfnVLazaX83X+6tZdaAKj9fP9Ewnv7tqLOdlRqM5wWA4yvEJIRg9ejRJSUl89dVXHDp0iNzc3GNGbwwLCyM1NZXLLrvstAePGew0Ol2ghCQjHeYH1vna2vCUV+ApLWP36tXEmEy4S8uo/GIF5UuWojWbGfPfT2IelhrM0Ae1NWvW8OSTTzJu3Djmz59PcnIyKSkpXbeJiYkYDIYB24f4QBVRk8vMRWrAmtNRXtMH+9CLkK+VP9Va9/40JI+IMSxwVb/NEIHNcPyL35TQUNPcxo7iBrYV1bOtqJ7txfXUtwbKiZIjw7lmYhI/mJpMVqw1yJEOfjabjUsuuYSOjg4aGxspKyujsLCQgwcPUlNTQ0dHBwcOHODdd9/lpptuCna4A47WYMCckow5JRl/m5uMzkSvvb6eLffcj6+lhfa6OpVonwavN/DZnzBhAn/729+CHI2iKKFgSCbauyrzALh85IW0d9ZoK6Gh1uPnn+sK2HColm1FdRTVuoFAv9nDXRFcmB3LuCQ752REkRJlDnK0g19hYSHvvvsuFosFn89HY2Mjra2tPbbRaDRER0cTHR2N1WplzJgxQYp28Cl+932K334HX0srcRfNxz5ubLBDGtSWLFkCBEY6ffTRR3tcd6AoitIfhmSiPTN1Kp8f/JpXt73Nq9veJi7CxVXZCzgvdUqwQ1P6yfaien71wS52FLuBXcTZjIxPtnPDlBTGJdkZnWDDYhiSf+5BlZeXR0NDAw0NDT3WG41GoqOjcblcxMXFYTabMRgMGI1GjEYjbrcbg8GARqNG5/w2FZ99ga+lFYPLBRoN5cs+wxQfjykhAX1UZEjXvJ+K888/n2eeeYaGhga2bNmiEu0+0hQ1gpXX/yXYYQxqTX++87s3UgalIZl5WI0RPLfgcQrrS9hVmcuyA1/xwvp/YNaHMzFetaYNNbUt7Xz/f9fR2u7jmuE67rp4GunRFpWEnAFz5sxh7Nix7Nq1i4KCAjweT9dUVFREUVHRtz5er9djNBp7JOHdl4+33mQyYbFYMBgMQ/4Y5/zPM5R++DE1a9ZRsexz/N16wNAYDJji4zHGx2FKiO+cEjDFxxFmVr/WHNHY2MiKFStYs2YNv//97wF47bXXuPbaa4McmaIooWBIJtoAAsGeqv18dXg95c1VxFqiSbbFBzsspR9EGMNIj7aws6QBTwckR5qHfAI2kLhcLmbPnn3Mer/fT1tbGx6Pp+u2+9R93ZH55uZmqquru5b9fv9xnjFAq9VisVgwm81YLIEvVl6vF6PRyIIFCzAPgWRTazSSdO3VJF17NVJK2mtqcZeW4i4pwV1Shqe0hJaD+dSsXQfd3iud3Y4pPg6d3Y7Qakj94c0YnFFBfCXBc/nll7NixQp0Oh2TJ09m1qxZXHHFFcEOa0hRF0Oevr64GFIZmIZsol3RUs0/tr4FgFkfzoLhs6lqqUFKSaTJTph2yL70kKPTanj11sk89fEe3t1aQvlf1/F/t0xWpSJBptFoMJlMmEymU3q8lBKPx0NdXR21tbXU1tZSV1dHdXU1VVVVtLW1HbdsBSA+Pp5zzjnndF/CgCKEwOCMwuCMwj625y9zfq8Xd3EJNes3ULN6Da2FRXjrv+mX1+BykXrTDWc65AGhuroagIcffpisrCyGDRtGdXU18fHxaLXaIEc3NESk5zDzzS+DHcagtvia0/+ior7w9O0XFiFEClAvpWzoXJ4FXA4UAC9IKdt7s58hm4nEWqK5b8oPeW3He9S5G/j7lje67hMI7CYrUSYHdpMNu9GK3WjFYbRhNwXmm3wteH1edFrVZ+pgEGnW84frcnD5q3llRx3/+cEunrl6HFrVVd+AIaXE7XbT2tp6zNTS0nLMfS0tLbS3n/g8ZjQasVqtOBwO7HY7DocDm82G2WwmMTHxDL6y/id9PtrrG2ivraW9to722lq8dXVd823V1bhLSpEdHQBo9HoswzOxjRlNRGYG9pxxQX4FwbNw4UKuu+46nn766R7rw8LCSE5OJikpibi4OOLj47umioqKrvlQ74pSUULYm8AVQIMQIgd4C3gaGAe8CNzem50M2UQb4LzUKZyXOoX2jnaq3XVUt9RS3VpHdWst1a211LbWU9lcTV71QRrbmo95/L/eXkxchItUeyIp9kQSrLFMShiLRqgLuAaqqXFh6J3J/OmL/ewqaeCaiUlcOCqWpMjQHiylv7W3t1NTU0NjYyNNTU00NTXR3NxMc3Nzj/mjBwc5QqfTER4e3jVFRkb2WD7eFAoXUvq9XsoWf0LxW+/Q0XzUOUqjQWezoo+MxOCKxjFpIubUVMxpqZji4hCqtRaAnJwccnNz8Xg8FBYWcvjwYQ4fPsyhQ4c4fPgwJSUlbNy4kdLSUtxud9fjHnzwQQAsFktX0t09IY+LiyMxMZGpU6eqQWyUAcEcMZwp018KdhhB9VHFj/pydyYpZWnn/A3A36WU/08IoQG29XYnQzrRPkIfpic+Iob4iJgTbtPh99HoaaLe00C9p5F12zfiTIqmsL6UNUWbWVO0GYCfTLuds5MnnqnQlVPwHxcMJz3azD/WHOa/l+zlv5fsJdKsZ1yijXFJdsYl2clJtOMwq4GMToaUkpaWFqqrq7vKN2pqaqiurj5u+caR2umIiAhiYmKIiIjAbDYfN2lWicqx6rZu48Cf/kx7bS32CeOJnHwW+shI9JGOwK3dppLpk2A0Ghk+fDjDhw8/7v1SShobGyktLWXJkiXExMRQWlpKaWkpZWVllJaWsn79ekpLS/F4PF2PS0hI4IEHHuChhx4KiS9/x+OvbMX9/KZghzGo+Stbv3sj5Uzr3jo3G3gEQErpP5mGu5BItHsjTKMlMtxOZLgdgMa8GmaOnsnGku1sKNlGtDmK6SmTmZyYE+RIld64LCeBy3ISyK9qZm1+DduL6tle1MDKvP1HRrkmJSqccYl2cjqT71HxVow6lbgc7euvv2b58uUYjcYeCYZOp8PpdJKcnIzT6cTpdGK327suUFT1r6dn/x+fx1tXT/q9dxM794JghzPkCSGw2WzYbDYqKipOODKklJKGhgZKS0vZu3cvL730Ej/72c/YtWsX06dPZ+TIkWRnZ+NwOM7sC1AUpa8tF0K8CZQBDmA5gBAiDuhVfTaoRPs7vb9nKQC/m/sIFv3g78Ug1KRFW0iLtvCDKSkANLd1sLO4ge3F9WwrrGfj4Vo+3B74ZShMI5iY4uDGaSlcOCoWnTY0W6eOVlBQAIDH42HevHldSbXValXlOP1E7NiFty5wMWNEZmaQo1G6E0Jgt9ux2+1kZ2dz+eWXc+211/Lmm2+ycOHCHttu3bqVnBzVOKMog9RPgOuAOOBcKeWRERBjgV/2dicq0T6OqpYadrUeYN/GYvbXHgbg/7a8yf1TbwluYMppsxjCmJYexbT0b7o6q2j0dA3R/vGOUu5btJXzs1z89eZJKpEErr/+epYuXcrGjRtxuVwMGzYs2CENaY37ctGuWgNAwhWXqSHXBzitVss777yDz+fj17/+NU888UTXfZWVlUGM7Mwp1wt+l6TSidNRrlf/awYaKaUUQngALTAaKOlcv/Vk9qOa7I7i9Xn5z+X/j6+aNrOxeBtjY0YyO+0cLhkxJ9ihKf0kxmrkwlGx/HxeFisfnsX9szP4Yl8l+dUtwQ5tQNBoNMydOxeNRkNubm6wwxnSvE1N7Pz5o4i2NlyzZxF38UXBDknpJa1Wy3333ceVV15JZuevEHfeqUb7U5TBSgjxEvAgEAX8Wgjxq1PZT0h/BfX5fVS0VFPaWE5JYwUlTeVsK9tNvaeRDEMS/33ZI6pFM8RoNaKrZMSk6rW7hIWFER0dzebNm4mPj2fMmDHqs9GH2uvqKP3wY8qWBErVOuaeT+a99wQ5KuVkOZ1O3nnnHQDmz5/P0qVLeeWVV5g2bRqjRo0astctxLZLfl7UEewwBrXH2o/fI5MSVNOBcVJKnxAiHPga+PXJ7mRIJ9o+v486dwOVLTVUt9YGbltqqGqtoaqllqrWWnx+X9f2DqON9MgU5mXOpHZvhUokQkxRbSsvf3mQRRsKmTUimnj7qQ20MlR973vf449//CPvvfcepaWlzJs3L9ghDXqe8nKK3/2AyuUrkD4fzrOnkXDVFWwqLAh2aMppmjt3LuvWrePuu+8GAr3wjBgxgoyMDDIzM8nMzGTYsGGcddZZpzyok6J0p0p4+rwEp11K6QOQUraKU0wKB/0R8fl9FDWUcqiu6JsEuqWGqtZaalrr8MueQzjbjVaiwyNJcyQzNWkCCRGxJFhjiY+IIVz/zclu5b7QqK0LZV6fn+1F9XyVV8UX+yrZXdqITiv4wZRkfrkgO9jhBZ2Ukrq6OgoKCjhw4AAHDx7suk/VaZ8+6fez7ScP4/d6cZ0/i4QrLsMUFxe4UyXag96DDz7IT37yE/Lz81m7di0bN24kLy+PzZs38+abb3Ztd8011/RYHoxUgnf6VI32gJQlhNjROS+A9M5lQaCEe2xvdjIoPxker4elB75ke/keDtQW0NbRBgRGfIw02XGaIxnhTCc6PJJocxTR5sCtMzwSvRrpMWT5/JLdpQ2sPVjDmoM1bDxcS2u7D42AiSkOfjZvBFeMTyDOFtqtSyUlJaxatYovv/xmSOWIiAiys7MZMWIEmZmZIdtfcF8SGg0iLAzHmNEMu+0WtAZDsENS+pgQgrS0NJxOJ1OnTqWqqorq6mouvfTSrm2uvfbaIEbYN1TpyOnri9KRDm8JVRWP9UE0g1eHt6QvdzeyL3Yy6BLtbWV7eGnDQuo8DaQ5kpmVOo3hzmGkRabgCo8iTDvoXpLSj5o8Xpbvq+TT3eV8vb+aJk/gn0GGy8LVExOZlhbF1LQoNXhNN8XFxfh835RU3X333bhcLlVK1Q8ip5xF5efL2XTbXdjGjCY8KZHw5CSorsHv9aJRA/kMKlJKNm7cyN///nfWrl3blVh7vd4TPubb7lMUJXiklH3y0+KgyUrr3Q28vusjVuSvIdEWx0Pn3MlwZ1qww1IGoIZWL5/vreCTXWV8tb+a9g4/0REGFoyO4+yMKKalReGyGoMd5oA1ZcoU6urqWL9+PQDvvPMOdru9azAPm81GZGQk8fHxKvk+TRn33YNr1kzKl35K84GD1KxbD34/OmDdojcIT0nGkpEemDIzMKekqNEgB5CCggK+/vprtm/fzo4dO9i+fTsVFRWEh4czc+ZMJk2aRHR0dNfkdDp7LIeHhw+Jz5AqHTl9qnRk6BoUn4zWdjePffEMNe56FgyfzffGXIohTLVAKt+oaW5j2Z4KXtvkYd+yz+jwS+JtRm6YksL8MbFMTHag0agTWW8ZjUZuv/12duzYQUNDAw0NDRQUFNDe/s1gWN///vdPOJy10jtCCGyjR2EbPQoAX1sbntIyNn36KSnhZloO5lOzZh0Vyz4HQGsyEZE1AtvoUcRfejEavToPBouUkmnTplFWVoZer2fUqFHMnz+fc845h2uvvRar1RrsEJUQkyjjear98WCHEVSPyR8FO4RjDIpE+2BdAZUtNVwyYg435lwV7HCUAaKi0cOnu8tZsrOMDYdq8UtwhQtumz6M+aPjGJdoGxKtRcHg8/kICwsjJSWF2tpa9Ho9NTU1AMTExDBhwgQyMjKCHOXQozUYMA9LRWaNILVzCHApJW0VFTTl7qdxzx4adu2h4J+v4fd6Sf7+dUGNN5QJIXj00Ue5//77ufLKK1m4cCG6EC31UTXap68varSLRSkP6x8//WAGsWpRGuwQjjEoEu3RrhFMSRzP4rzlTE+ZQqojMdghKUHi9fn5dHc5/1xbwPpDtUCg3vreWRnMHx1HRe5mZs3qk+sXQlJubi6vv/46AKtWrepabzabGT16NBMnTlQlI2eYEAJjbCzG2FjCU1Nor2/AXVysSkgGgPvuu4/m5mYeeeQR9u/fz+23385VV11FdHR0sENTFGWAGBSJthCCWydcx/rireypylOJdoiqaPTww//byN6yRpIjw/mPC4azYEwsGa6Irm0q81QCeDrCw8N7LJtMJm666SZiY2ODFJEC4PN4yH32f6jbuAmN0UjyDdeTePWVwQ5LAX7xi1+QlJTEAw88wI9+9CPuu+8+qqqqcDgcwQ5NUZQBYND00RWhN6MRGooayoIdihIkf15xgL1ljbz4gwmsfHgmPz4/s0eSrZyeI+UiF198MRaLBQC3281nn30W5MiUyi9WULdxEwBag57WggJKP/iIlkOHkVKNKBcsO3bs4P777+fpp5/uKq264447sNvtQY5MUZSBYlC0aAN4OtrQacL4In8V05ImMDZWlQeEGldEoJ/h9g6/urCxD+3fv59Fixb1WKfT6cjOziYxMZHRo0cHKTLliJi5c9BHRdJyuICW/EM05eZR/fVqAHQOB85zzyblhuvRGlVvOmfKn/70Jx544AEAxo8fz9NPP828efPIyckJcmSKogwk39miLYRIEkKsEELsEULsFkI80Ll+nBBirRBipxDiIyGEtdtjHhFCHBBC5AohLuy2fl7nugNCiF+cTKAWg5nfzn2EJFs8v1/1Es+ufoXFuV+QX1vQYxh1Zei6fXoaE5Lt/OSNbdy3aAtlDe5ghzQkNDc391hOTEwkISGB0aNHk5GRcUw5iXLmaXQ6oqZOIfl71zLy0Z8z5re/IeP+ewhPTsJbV0fZR4vZ9djjwQ4zpDQ0NHTNb926lc8//1x9KVUU5Ri9adHuAB6SUm4RQkQAm4UQnwF/BR6WUn4phLgV+CnwKyFENvA9YBQQD3wuhDjSB9ifgQuAYmCjEOJDKeWe3gabYI3lsRk/5t87PmB3VR4bircBYAwzMMKZRpYzg0kJY0mxqxruocio0/LGXdN4aeVB/rziAMv3VXLvrAxuPjsVi2HQ/Dgz4IwfP560tDQ2btxIWVkZ1dXVNDY2cvjwYQA0Gg0Oh4Po6GiioqJwuVyMHj1ajQ55Bvna2qjdsImGHTto2LELT3l51306hx0hNESfd24QIxyapJQ0NjZSXV1NdXU1VVVVlJaWUlhYSGFhIeeccw6rVwd+Wfjiiy/4+OOPufzyy4MctaIoA8l3ZidSyjKgrHO+SQixF0gAhgNfdW72GfAp8CvgMuB1KWUbcEgIcQCY3LndASllPoAQ4vXObXudaAM4TDbumXITADWtdeytOsC+qgPsrT7AG7s+4o1dH3HxiDncOO5K1TPCEKTTavjx+ZlcMT6Bpxbv4ZlPc3lxxQEuzUlgbnYM7R2qXvVU2Gw25syZ07X8+eefk52d3TWy3ZFp3759QKC0ZORIVb7V33xtbRQs/BeVy1fia21Faw7HNmoUcRfNx5w+jPCkZHRWdZ3C6fB4PCxatIjNmzd3/b0XFBTgdrtPOKqjVqslISGB5ORkrr/+epKTk0lNTWXevHlBeAWKogxkJ9UMKIRIBcYD64HdBBLl94FrgKTOzRKAdd0eVty5DqDoqPVTTjbg7qLCHZybchbnppwFQK27nrs/fISPcz/n2tEXYwwznM7ulQEsKTKcV26cxJbCOl5bV8h7W4v594ZCtAImHlzLxBQHOUl2xifZ1SiQpyAsLIz4+Hji4+N7rP/DH/6A0WhUA9X0N6+XmrXrKV/2GfVbthI94zxi5s7BOjJLdevXR2pra3nppZd4/vnnqaiowOFw4HK5cDqdxMfHk5WV1TWSo9Pp7Jri4+OJjY0lLEz9inaEGhny9KmRIYeuXn8yhBAW4B3gJ1LKxs5ykT8JIX4F/H/23js+jurc/3/P9r6rXa16r+7dxjHFGLBpCSRAGhBKAslNI8nNDbmkkdyQ5Mb5OTfhmxCKIdxwQxKCCQEMsWnGNsaWMTbuVu9d2qLtbX5/rLSSLBlkW/ZK2nm/Xuc1M2fOHD2zo935zJnnPM8LQOgDOzgNBEH4IvBFiCfH2LZt24SOC4sRspQ2OsN93P3cvaQrLNiUZmwKCzaFBavCjEKY2E3K4/FM+O+ea5Jly8nXYSp9JiP5WAZceamGGkeM/R1+6vqcPNrYT3RwcNuiFsg1COQaZImSoZdhVDIt3nqc6ffhbDjVtQ4GgwwMDPDQQw9hMBjQ6/Xo9Xo0Gg3yKSwAJ+N/97xcB6cT2b79KI4c43g0iqhWE7twJe1LItfIQAAAIABJREFUFtLe2wM7eib/b56Cqfp9n4zr4HA4+NznPofX6wXgkksu4VOf+hSzZ89GJpPh8XgSkXdG4vP5qK2tpba29qzOYTyS8XmfPD/jTIl4nPTt+eek9JWqRDzOZJsw45Bl6NB+fdnpHfSHybdjQkJbEAQlcZH9Z1EUnwMQRfE4sG5wfwVw7WDzNoZHtwHyBuv4gPpRiKL4KPAowLJly8RLBzOkTYQrxMs52HWM3S37aXa2csLVRNBXA4BMkJFtzKDQkkehOZdyWxEV6aWo5GOzeW3bto3T+bvnkmTZcvJ1MBgMU+YzGY91DH9WgXCUI+0u9jc7Odruprp7gO3tHgLh4exlOpWcvDQteWk68geXhTYdpRkGCq06FPKp4YN8Nt+HM+VU/3PZ2dkcPnyYrq4uWlpaRoWW0+v1WCyWUUWv16PT6RJFq9Umxbd7Mr5D5/I6xCIRGv/4v3S8/C8EmYzo7ErmffpTmObMRpakkdOp9Bs4ksm4DsFgkH//939n165d7N+/n+3bt7N9+3Z+8pOf8KMf/Sgp556svzkZ5KnMPJBz7Yc3lDglP1C9dNZ9SCnYp2kKdiE+5Pc4cEwUxV+PqM8QRbFbEAQZ8APg4cFdLwBPC4Lwa+KTIcuBKkAAygVBKCYusD8D3DyZJzNoFwuz5rAwaw4AsViMLm8vTc5WmpxtNDlbqelrYFfzYExamRyDSs89K+9kfuasyTZHIklolHKWFlpZWmhN1EVjIq0OHzVdHpr7fbQ6/LQ44su9Df0MBIdFuFIuUGjTU2rXsyDPwuoKO3OyTSkfVrCyspLKykoAIpFIYoKY0+nE4XDgdDppb2/n2LFjxGKxcfvQarWjxLder6ewsJDy8nK0Wu35PJ0pg7ehkY6XXgag5Et3c1ytxLJgfpKtmrmo1Wr+67/+C4hPeGxpaaGwsJD777+f+++/nyeffDK5Bk4zvAPV7NlxebLNmNZ4B86+D8mFZ2q64EzkilwIfA44JAjCgcG67xEXzV8d3H4O+COAKIpHBEF4hvgkxwjwVVEUowCCIHyN+KRJOfCEKIpHJu1MToFMFh/FzjZmsDJ/CTExRsdAN4e6jvNy9Rt0enpwBdzU9DVIQnuGI5fFxXOhTT9mnyiKuPxhGnq91PV4qevxUNftoabLw5YjXfxqywl0KjmP376cj5TakmD91EOhUJCVlTVu1shYLIbH48Hr9eLz+RKlr6+P7u5uuru7Ewk+AN577z0A5s+fzw03pFbGQ1EUUVksZF65lq4tr1L3h0eQV1bAmjXJNi0lqK+vp6ysbFTd1q1bueqqq8jIyJgW7mUSEhJTl4lEHdlJfDR6PH57imN+BvxsnPqXgZdPx8DJQhRFvGEfP932Wxoc8TmZarmKyvRSriy7hAsLlifDLIkpgiAIWHQqFheoWFwwOnVy90CA+zYd4vXj3fR5g0mycHohk8kwmUyYTCbC4TA+n4/NmzdTU1Mzpq1CoSASib9NyMjION+mJg1RFIkMeKh7+BH63n4nXimToc3JwWOV0nefLzQaDWq1mmBw+Lv99NNP8/TTT3P//ffz4x//OHnGSUhITHtm3DuGmBijrr+Jo901dHt76fU56PX20ePrJxCJ/5Cmacx8f/XXyTVlIZdN3QlcEucXURTpdAeo6fJQ0+2htnsgse7yx0N8zc0xJ9nK5BKLxfD7/aNGqX0+X2Lk2u/3jxnFPjk82o033ojBYMBoNGIwGFCrZ150oFgoRMjhINTXT7Cvn1B/H6G+/njpHyoOYqHhOeTzf/lz9EWFyDWaKTkJcaaSm5tLIBAAIBwO09jYyKZNm7jvvvv4yU9+gsfjYcmSJeTk5JCdnU1OTg5GoxRSUUJCYmLMKKH91IFNvHjitcS2XqXDrrOSacxgXuYs7Hor6TorczMqMKrHziiXSD1iMZHfvF7D9uoears9eEb4aafplJRnGLl2QTblGQbm55opsqVOlsSOjg4effTRCbdXqVSj/K7tdvsoX2ydTkdubi4mk+nDO5tm9L+7j2M//fkHtpGpVKhsVlRWK4aKclRWK2qbDZXNiqG8DE0KjeZPVZRKJeXl5axcuZKjR4/yk5/8hA0bNnzgMStWrGDnzp0olWMn1c9kRkZ/UVoy2XbzM0m2aHoz8PsvntFxJ18HianHjBLaHZ7Roa+0Cg1apQa1XIlCJkcmxKMddAx0E4qGSdOaE3USqckf3qrjwddrWF6Uxo1LcinLNFKeYaA8w4DNMPNGWk+HodBnH0RaWhp33HEHOp0upeMKh3r7PnC/ymZDaTEnIoiEXW6iXi+Bjg4EhYK+3VXIFAoEpQJBLkfW2UljUwuCQhGvVygQFPLhdXl8KRtsP7rd0PqIenm87/i6PNEuFf2PI5EInZ2ddHd309PTM26pra0lFArR0/Ph4RSrqqpOOfF3JjMy+ktJRqX43ZbIhxwh8UH8IHRmydZGXgdd3iwpY9sUZEbdGe+96N8IRkLUO5qo6Wuk0dFCn99JbX8T/b4DhGOjfwjUchU5pkxyTdnkmbIoSStgYdaclLz5pCq13fE4shtvW45Zl1ojUqfC4XBw/Phxent7qaiowOVy0dXVNW7bRYsWzcgR6tMl66p1ZF21jrB7AH9bG/62dvxtbYT6+xEjUWKRCOJgiUUixIJBor5ofDscQYwO7xMjUWTBAO2HjiBGIiCeu3vnSNEtU4wQ4HIFimCQAy9sjgv/MSL+LIX/OX4oczgc1NfXj1uampqIRqNjjpHL5djtdux2OyqVinnz5iW2xytWq3VKx46XSD1KIy085/z3ZJuRVD4eafnwRueZGSW0AdQKFbPt5cy2l4+qF0URT8hLn89Jv99Jr6+fjoFuWt0dHO2uZmdTFQBfXHYzV5RenAzTJc4T4WiMAy1O/nmgjecPtLGq1IZRM+O+CmfM888/T1dX1ynF9XXXXceiRYukB9JxUJqMKE2zMM0+uwhGI2Mqi9FBoR4dK8zj4jw6SsSPXo8Orw+J+fBwX2OPGa73dXaiSrMgDraPhcOIfv/gg0M43m6wPuoPEBv0cz7fOBwONmzYwIkTJxJi2ukcnfwjPT2dkpISVqxYwWc+8xkKCgrIyMgYJZwtFksixvtUjSE+VdHLWrnAeG+yzZjW6GWtyTZB4hyRMupCEASMagNGtYGitDx6vH2IokiHpzsxSRLA4Xcl0UqJc8FAIMzBVhfvNTnY1+ygqqEfXyiKTIBbLyjku1fPSvn42CP59Kc/zfPPP09lZSUej4eqqip8Pl9i/wsvvEA4HEar1Y4parU6KQlpZjKCXH7aI6eiKMaFczBELBQiGgwSCwaJhUKJZfSk7VhwdB39KmQqNTEExFgMRBExEh17XCh0TkfdP4zq6mp+9atfEQqNTU48e/ZsbrvtNj760Y8yZ84c6X9TQkLivJMyQhug09PDO8372NWyjyZn/OkxU5/OR/KXMttexmx7GXa9FCN5JvHIW3X84pXjie2yDAM3LsnjwjIbK0tsWHSqJFo3NdHpdOTk5LB06VIAbDYbmzZtGtXmlVdeOeXxWq02EdpvqJjN5lHLVPbnnkzEaBRPfQOemlo8NTUM1NQS7OmNi98z8RsWBGQqFTK1GkGM4XO5kKnUyNQqZCoVCqMRmUqFXK1Gph6ul49Yl6nVw3UjtkfVq1Wgmpzv3gUXXIDL5aKxsZG6urpEqa+vZ//+/dx3333cd999WCwW5s+fz7x585g7dy75+fmsW7cOjUYzKXZISEhIjMeMvtuJosi+9oO823aQw90n6PbGJyxV2Er43MIbWZozj2xjpvQKfAZT1xP3wb774mK+dlk5Zq3kh326zJs3j3nz5iW2o9Eofr8/UQKBwKhtr9fLwMAAbreb1tZW/H7/mD51Ol1CdA+VrKwsSktLpe/jBBioqaXzX1vpr9pLxO0GQGk2Y6goI23xokERPI7gPVkYn9RGUCoTn/+2bdtYMk3cJzQaDbNmzWLWrNEuO6Io0tjYyPr163n44YfZsWMHO3bsSOz/whe+wMaNG8+3uRISEinEjBba3rCP9TsfRqvUMC+jkmsrLmdZ7gJp1DqF+Ppl5fxjfxtbjnTx/WvnJNucGYFcLsdgMGAwTCxEZjgcxu1243K5Ri3dbjcOh4PGxsZEspDvfve70gjjKRCjUfxtbXhq66j57e8ASL/kIqzLl2OcVYHabpceUk5CEASKi4vZsmVLom7NmjXo9XpUKhU/+MEPkmjdzEFpkJO9SpoUfTYoN0kTa6cqgiBYgKGJf9WiKJ6Wj/GMFtp6pQ6tQoNdZ6M4rQC73oYgCIiiKN2QUoBINMZf9zYTjooUp49Nuy5xflAqldhsNmy2Uz/g/vOf/+TAgQMcOnSI5culLK0jcR05QvP//QVPbV0iwY1MrWbWf36HtCWLk2zd1CcWi5GdnU1DQwOXXnopb7zxRrJNkpA4JyhtMrJuTu0cIcqXJm8ehiAIauAR4ONAA/Es6YWCIPwD+DdRFMdODBmHGS20BUHgullr2d60h78ffgmR+IQds9pIRXoJlemlVKaXUJJWgFIuuRTMFMLRGP/Y38bjOxo40TXAp5fl8+Pr5ibbLIlT0NnZyeHDh4G4S4nEMK5Dhzn8g/tH1ZnmzMY0by7Bnh76q/aitFhQGPTIdXoUeh2yFEuc8kH4/X62bNnCrl27AMjPz0+yRRIS55CggNCY4hN+g5M6iPp9QAnki6I4ACAIghH4PfDDwfKhzGihDXDj3Gu4ce41BMIBmlxtNDhaqO1v5ERvPXvb3gdAIVNQmlZApb2U+ZmzmJtRmWSrJc4UURT592fe58X326nMNPL7m5dw7YLsZJslcRL19fX89a9/HZWe/Z577iEtLS2JVk09dIUFZFxxOVG/j6jXR8jhwNfahvvosVMeIyiVKPR65DodCr1ucHny9nDd0P74vvhSmMbxoUVR5MiRI/ztb3/j4Ycfpre3F4PBQF5eHrfddluyzZOQkJg+3ACsEEUxEXZLFMUBQRC+AuxGEtqj0Sg1gyPYpYk6Z8BNdW89J3rrONFbz8vVb/LC8VcBWKSbxaVcmiRrJc6UYBReOdTBiiIrf/vSSslFaIry5ptvjhLZAL/73e9QKpUJf+3Vq1enfCxjpclE+de/MqY+Fg4TdrkJORyEHQ4ivrgQj3i9RH2+Mdu+vv54vdcbD933IcjUahQKBe/9/R8JcS7X6VBZzKgG08arbTZU6TZUVitydfKzqHZ2dvKLX/yCf/zjH7S0tMTfaF53HV/96ldZvXo1qkmKciIxlrAnSscud7LNmNaEPWOTKEkkndhIkT2EKIoeQRAmHNM0ZYT2eFg0JlbkLWJF3iL6/U6qWg/wxHt/A6A51CH5ck9DNAqBr1xayoNv1PKrLSf4zpWV0jWcgtx+++04HA48Hg/V1dXs37+fYDCYENkANTU1KS+0T4VMqUSdbkOdfvoTu2ORCFGfn6jPS2SEGI8L8eHt1ro6dCYzEa+XiMeDv72dYE/vuGEDFUYDaUuXUvGteybj9E6bF154gVtvvZVAIMC1117Lj370I66++mpyc3OTYo+ERDKQHngm/YFFFAQhjbhv9slMOH5qygrtZmcbR7qrqelroKavgS5vLwA2XRoXFiwns98oCbRpyjevqKDHE+ShbXW8XdfHFy4qZt2cTDTK6fs6fDoSCATYt28fCoWCYDCIx+PB5/Ph9XrxeDx4vd5E6D9BEMjPz6e4uJj8/Hzy8vKk6COThCiK8cQygQDRQJBYML6MBgJE/X4iHk989NvjiYvqwXUcTvwDHiIeb3wkfJzMj8Kg4FfZbOjy85JwdnE/7Ouvvx6Aj3/843zlK19h9uzZZGdLLmMSEhJnhRnYx/hCWxrRPhW7mt/l8ff+xkAwHl85TWum3FbM2rKLWZQ1l3xzDoIgsG3btuQaKnHGyGQCP//EfBbkWXjkrTru+ct+jBoFV83NYt3cLD5SasOgTrl//fPOq6++ynvvvTemPisrC7vdTlFREXq9HpvNRmlpqTQR8iREUSTq9RF2uwi7Bwi73EQG16M+XzxDYyBINBiILwMBYsGRy+H1iWZulOt0KAx6FHo9CAKarKy4/7ZBj1yvR2kyobLZUNttqNPTURiTPyCh0Wh47LHHePLJJ9m8eTPPP/88EE+cVFpaSllZGeXl5Vx00UVceeWVqKeAm8tMQwrvd/ZI4f2mHqIoFk1GPymnNv588HkGgh6yjRn88NJvkK6zJtskiXOAIAh8dkUBn16Wz47aXl440M4rhzv5+75WlHKBpYVpLC+ysijfwqJ8CzaDdPOdbNauXYvBYKCtrY3m5uaET3ZnZydr1qyhoqIiyRYmj2gwSLCnl2BPT7x0x5ehfgdhtxtFTy/v/L8/IEYi43cgk8WTzGjUyNWaEUsNSosZmVqDXKNGronXxdvG64b2ydTq4UmQej0K3ehJkNu2bWP2NHDdEQSBu+66i7vuuouBgQGqqqqoqamhtraWmpoaqqureeWVV/jVr34FwAMPPMD3v//9JFstITH5SA88U/OBJaWE9ut1O+kZzA65Kn+ZJLJTAJlMYHWFndUVdn4emce+JgdvVfews6aXh7bVEY3FR/qK0/VcXJ7OJeV2LixLR6uael/WqYooijidTvr7+3E4HGOWJ096BEb5YqcSvuYWjv3sFwQ6u0bvkMlQWa2obVbUdjsevZ6cygqUZjNKkwml2YTCZIqvm4zINJqkjyRPRYxGI5dffjmXX375qPqqqipuueUWamtr2bdvX5Ksk5CQSEVSQmg3OFr4v/c3cajrBAsyZ3PH4k+SZ5b891INtULOqtJ0VpWmw9XgC0U41OriQIuTPQ39/P3dVv70ThPZZg0bb1/G3Bxzsk2ekgwMDFBXV0dHRwednZ10dnYSCg3H7ZfL5aSlpWG1WikuLsZqtSa2LRYL8mkcOu5safvHPwm7Byi45bOo7emoM+yo7XbUNtuY0eSiaTCaPB3Yvn07q1evBuDmm2/mwQcfTLJFEhISqURKCO2H9z5Fg6OFOxZ/kqvKLkUmS/GA7hIA6FQKLiixcUGJjS+tLsUXivCLl4/z1O4mfv9mLQ/dsjTZJk4ZYrEY+/fv58CBA7S2tgLxjI+ZmZksWLCArKwsbDYbVqsV4xTw252qhAcG0GRmkv+pm5JtSsoQGDGJc9GiRVit0ptMCQmJ80dKCO1iSz4trg5W5C6SRLbEKCLRGFUN/Ww92sXmQx30DATJNmv4zpWzkm3alCAWi1FdXc327dvp6OggMzOTNWvWUFlZid1ul75Pp4nSZMJ9+Agtf/s75gXzMZSXIVOkxM9w0li7di1btmxhw4YN3HvvvaSnp3PnnXcm2ywJCYkUISV+4T8x5yreaNjF/W9s4OqKNazIW0yG/vTjz0pMfyLRGMc7B9jT0M/u+j721PfhDkRQK2SsrrBz49I81lRmoFJIAhLg2Wef5dixYygUCm644QbmzZsnjVafBbk3XI+3sYnmv/wNnv4rMrWapY/8HpWUEfOcIQgC69at491332Xr1q0opAcbCQmJ80hK/OJkGuzctfSzvFq7nT8d2MSfDmxifuYsLi+5iBW5C1HIU+JjSEnC0Rg7a3vZXd/H/mYnh1pd+MPxgPaFNh1XzcvislkZXFJhR6eS/g9OZs6cObS3t+NyuXjrrbfw+XxkZGRgNpsxm80p7W99Jujy8lj06/WE3QPUP/oYvTveZu+dd5N+0SpMc+eizc5CnWGHU0UbkThjmpubAdi4cSMf+9jHsFgsSbZIQkIiFUgZZbGu7BLWlV1Cp6eHnU17eaP+bX7zzkZMagOri1ZyeelF5Bgzk22mxCRxtN3Nn/c0sflQB05fGKVcYE6OmU8vz2dxgYUVxVayzdpkmznlmTdvHnPmzOHo0aPs2rWLf/3rX6P2KxQKIoOi8I477qCwsDAZZk47lCYj5d/4OpZFi3AdPITz4CF6d7w9vB+oeuov8YmSGemoLJZ4jGu9Hrleh2IwJbpCr0+kR1fodcjUaumNA3G/7NraWo4fP87x48d5//332bRpExCfHPnSSy9x6623JtlKCQmJVCBlhPYQWQY7N829hhtmX8XBrmO8VreTzdVv8OKJ11iUNYevrLgNi1aKNjEdCUdjVHVGeOjhd6hq7EejlLFuThbXLczhovJ0KTPkGSKTyZg3bx5z587F6XTidDrp7Oxk69atCZEN0NHRIQnt00CmVJJ5xWVkXnEZoijGY2l3dxPs6eF41V4y9AaCPT14GxpxutxE/f5x05+PRJDLkeu0yHU6lCYzhrJSjJXlGGdVosnKmrEiXBRFdu/ezVNPPcXWrVtpaGggNuKzKi4u5rOf/SwXXnghF154IQsWLEiitRIS5wYpBfukp2CfFFJOaA8hk8lYlD2XRdlzcfhdvNmwi+eOvsJ3tvyMO5d8KtnmSZwm0ZjITX/YxfutQfKtMr5/zWw+uSwPi06VbNNmDIIgkJaWRlpaGjk5ObS1tdHQ0IDP5wPgrbfeoqenhzlz5qDX61Gr1ahUKlQqFQqFYsaKvMlAEAQ0mRloMjMAOCoTKD8pvJ8oikT9gXhWSF88VXrU5xte+nxEvcP1of5+ut/cRucr8bcQCqMRY2VFvFSUo87MQGW1Ip8BmRJ/+tOfcv/99ye2CwoKuO2227jhhhuoqKhAr9cn0ToJCYlUJmWF9kjStGZumHM1y3IW8B9bHuA37zzOrekfTbZZEqfBkXYX77e6APjp9fO4tDIjyRbNbNRqNTfddFMiWU1rayu1tbUcOnRo3LTrgiCMEt6nWpfL5eTl5aV01shTIQgCCp0WhU4LTGwytxiN4mtpZaC6moET8eJ4d3TCFrlej9pmRWW1orLZUFnTUJrNCE1N9CqU8YySWg1yjXY406Q2nm1SmCI++jfeeCMvv/wyfr+furo6mpubeeCBB3jggQdQqVS89NJLrF27NtlmSkicU6TMkFJmyClDJBbF4XfS53PS5++nz+ek3+eguq8BgHJbMUaZLslWSpwO83PNbPjkQtZvPsjnn9zLt66o4NaVhaTppRHtc8nIUe758+dzzTXX0NraSjAYJBQKEQqFRq2fvO31egmFQjidzkSfJpNJEtqThCCXoy8qRF9USNa6uNCMeLx46usJ9fYS6ncQ7Osj1NdPqL8fX0sLIYcTYjEUwIlXX//A/mUqVVx0a7WDKd41hJ1OIj4/c3/yIwwlxefhLGHu3Lns3r0bh8PBs88+y4YNGzhx4gQAoVCIX/ziF5LQPodILgtnz1R0eZCYHGaU0I7FYriCAzj8LhwBV3zpd5607cIZcCMijjpWo1AnopNcWvwRdo2YmCQx9REEgRuX5mFw1PD3NiMbXq3md2/WsqLYyuJ8C4sKLCzOT5OE9zlGrVaTm5uL1+vF7/cTCATGFLlcjlwuRxCEuDtENJpYNxqN3HSTlMzlXKIw6LEsmD+mPhaJEAsEifp9BHv72L9zJxU5uYT6+wk5nYQdDkIOJyGHg7Aj/mAUC4WIhUKEXWNFVtjhAM6P0AZ4+umnueWWWwDIzc3l9ttvZ8WKFSxfvpyFCxeeNzskJJKF9MAzNR9YppXQDkZCHO+tpcfbHxfQAfcoIe0MuBFFccxxJrWBNK2FNI2JQkseNl0aNq0lvtSlYdOmoVNJEShmAmqFwMbbl3G8081fq1qoaujnd2/WEhv8t5ifa+aSinQuLEtnUb5FCuk3yezbt4/NmzeP+z2E+NwIrVaLRqNJFLPZzJw5c5g1axbZ2dmSL/cHIEajcV9sn3/QL9tL1OcnFgwSDQTjy2B8mVgPDC5DQWLBENFAYHB/iGgwQCwYIhYMIkZH36AUQP0p7JCp1ci12mGXEm3cnUSm0aLNySb72qvPe2zwoqKixHpbWxvPP/88b7/9Nna7fdySl5fH0qVLMZlS+1X7ZOCN5bFnYH2yzZjWeGNfPus+JNcRyXXkjHmv/TCv1m3nYNdxwtFwov5kAZ2mNZOmMceXg8WiNklxslOQWVkmfnzdXAC8wQiH2lzsbehne00PD79Vz+/frEMuE5iVZWRxgYVSu4Fss5Zci5ZsiwabXiUJvtMgFArR2NjISy+9REFBAcuWLRslpoeKNClymFgkQqivj2BPL8GeHoI9vUQGBoj4fMibmjny5vb4xEb/4IRHv5/YiHTiH4ZMpUKmVsdFsVoVd+1Qq1HodMgGJ0HK1Cpkak18v1od98FWq5BrtRytrWXR8uXDbiFDwnoK+WaPZNWqVQQCAf7+97/T0NBAT09PojQ2NrJ37156e3sJh4fvIYIgMGfOHFauXMk3vvEN5s8fO9IvITFdcLqzeX7LD5JtRlJxus/+gWWy+VAFKghCPvAnIBMQgUdFUfytIAgLgYcBA9AI3CKKolsQhLXAfwMqIAR8RxTFNwb72gZkA/7B7teJotj9QX/fGXDz3zt+T7rOyhUlF7E0Zz45pkxJQEtMGL1awcoSGytLbHz98nLcgTD7Gh281+xgf7OT5/e34wmOThCiUsjIMWvINmvJsWjJsQyta8ixaMk2azBqlEk6o+QSi8Xo6emho6ODjo4O2tra6OjoIBaLIQgCq1evpqSkJNlmThli4TDexiY8tXV4auvwt7QS7O0h1O+Ak0b+ZRoNCp0OAZGIIKDQ61BnpCPXxuNky3XDMbTlOm18fVAAy9SaQbGsRqZSIcjOLrupGItinj/vrPo436jV6g+Mjy2KIm63m56eHurr69mzZw+7d+/m8ccf5/HHH2fDhg3Mnz8ftzv+dlR6KJSQmL60O/38+PkDyTZjQiPaEeDboii+JwiCEdgnCMKrwEbgP0RRfEsQhM8D3wF+CPQCHxNFsV0QhHnAFiB3RH+3iKL47kQNjMTirzM/Wnk5V5evkX74JM4ak0bJmlkZrJkVj0wiiiL93hDtzgDtLj8dTj/trgDtTj8drgC76nrpcgcS7ifD/SgoyzBQkWmkPNNIRWajc1zuAAAgAElEQVR8PcM4c5OGtLS08MQTTyS2lUol2dnZrFq1ioKCAvLz89FoNEm0MLmIooivqRlPbS2emlo8tXV4G5sQB+ONK4xG9EWFWBYuRJ1hR21PR52ejtpuR5VuS4Ta27ZtGwtPCu8ncfYIgpDIalpWVsa6desA+OY3v8lf//pXvv3tbyfa2mw25s+fP6rMmTNHcjWRkJA4LT5UaIui2AF0DK4PCIJwjLhwrgC2DzZ7lbig/qEoivtHHH4E0AqCoBZFMXgmBlq1ZhZkzubJ/X/n7aa93LXsZorT8s+kKwmJcREEAZtBjc2gZn7e+MmKItEY3QNB2gdFeIfTT4vDR02Xh61Hu/jr3pZEW5NGMUp8Ly+yMjdn+t+cfT4ff/nLX4B4avY1a9ZgtVqRneXI6UxAFEX6d++h5e/P4a2rA0Cu12EoLSXnuo9iKCvDUFaKOsM+Yx/CpjO/+c1v+M1vfkNPTw+HDh3iueeeIxgMcujQIZ544gm8Xm+ibU5ODrNnz2bWrFnMnj2b8vJySkpKKCwsRKlMzbdcnSqBX+ZLb5jPhk6V9LswUzmtb4YgCEXAYmAPcRF9PfA88ElgPPV7I/DeSSL7j4IgRIFNwAPiOLOmBEH4IvBFgMzMTC4U55NuMvKO8yA/e/233GK79pzfrDweD9u2bTunf2OiJMuWk6/DVPpMTsX5sNE0WCotcIUFqFTiDipo88SGi9vFC+0OvOEP6WwCnHwdzsc1GO9zbG1txe/3J/YfPnz4nNsxWUzG/8Upr0MgiPyFl5C1tSNaLMQuu5RYQT5hi5mAINALEA7CsaNw7PzZe7ZMBRvG41x/H2QyGVdddRUGgwGIu0p1dnZSX19Pc3Mzzc3NNDU18c477ySSNQ0dl5GRgUqlwmaz8b3vfY/09PQJ/91kfN4ej2dS+skKiXy3JfLhDSVOyQ9C408gl5j+TFhoC4JgIC6Ovznoi/154EFBEH4IvEDcH3tk+7nAL4F1I6pvEUWxbdAFZRPwOeL+36MQRfFR4FGAZcuWiWvWrGENML95L7995wlCeTKuLF99Wid6umzbto1Lp8ir22TZcvJ1MBgMU+YzORVT6br1e0Ns3FHPQ9vqzqqfk6/D+Ti/kz/HWCxGW1sboVCIlpYW+vr6uPPOO8+5HZPFZPxfnOo6eBsaOdCxEYXJxKJf/wq1zXqW1k6N/+OpYMN4nI/vw0TOXRRFOjo6qK2tpb6+nrq6OtavX08oFKK5uZmcnBxWrVo1qX9zspmKD1ISEjONCQltQRCUxIXxn0VRfA5AFMXjDIpoQRAqgGtHtM8D/gHcJopiQmWIotg2uBwQBOFpYAXjCO1TsSp/Gf+qeYtnjryEVqnh4sIV0mtYiSmHLxThwddreXxnPeGoSGWmkdtWFXLrL5Nt2enR2dlJbW0tTU1NNDc3EwrFn6XT09OnpABLFvriImZ//z85sX4D733l6xhKitEXF6MvKcZQVoqusED6nZrGDE3+bWtro7u7m+7ubnp6esZdhkIhNBoNR48epbj4/MUQl5CQmLpMJOqIADwOHBNF8dcj6jNEUewWBEEG/IB4BBIEQbAAm4H/FEXx7RHtFYBFFMXeQeH+UeC10zFWEATuXvpZHqr6E7/b8yTOgIvrZq378AMlJM4TgXCU2x6v4t0mBzcuyeMLFxUzZ9A/+9SxEKYeoVCIRx55ZFSdIAiUl5eTm5tLLBajvb0dm82GenACXypjXbaU+f/9M7q2voa3oYGu198gtjkeik+bm4N99SXkfPy6xGRHiamDKIq0t7fz/vvvc/z4cVpbW9m/fz/f//73aWtro729fVRIwCFUKhV2u52MjAzsdjvl5eV84hOf4Oqrr5ZEtkRSkHzlp6av+0SuyIXEXTwOCYIwFCfle0C5IAhfHdx+Dvjj4PrXgDLgR4Ig/Giwbh3gBbYMimw5cZH92OkaXGDJ5edrv8uv336MPx98HovGzCVFF5xuNxIS54T9zU7ebXLwxUtK+N41s5NtzhmjUqm488476erqwul04nK5cDqdtLW1UV1dPe4xy5cv55prrjnPlk4dDCXFGP7tbgDEWIxAZyeuw0eo+/3DND/9V3RFhdguWJFkKyWGeOaZZ/j0pz89pl6n02Gz2SgrK+OSSy4hNzc3UbKyshIJb0wmk/SmYpDWkIt72zcn24xpTXfIlWwTJE5CEITlQIsoip2D27cRn3vYBPxYFMX+ifQzkagjO4FT/Zr8dpz2DwAPnKL90okY9WHIBBlfX3kn63c+xB/2PoUv7Gdt6cXIZVMviYJEalGeaSBNp+TPu5u4YnYmK4rP3l83WRQUFFBQUIAoini9Xvr7+3E4HHR3d1NfX09nZ+eo9idOnEhpoQ2DArujk4GaGjzVtbiPnwBArtejzc5OsnUSIzl48OCobZlMxtq1a5k7dy5+v59LLrmEnJwccnJyyM3NRauVsgdLSKQYjwBXAAiCcAnxHDFfBxYRnydy00Q6mbbvGNQKFd9adTf/s2sjT7z3N9oHuvj8krGjExIS55N0g5rN91zMx/7fTv5z00He+I9Lk23SGdPW1sahQ4eorq7G4XCM2mc2mykqKsJut1NYWEhhYWEiSkOqIEajeOob8NTW4WtsxNvYhLexKZG9UabRYCgtoejzt5O5di0KnSTUphIPPPAA3/jGN3j55Zd59dVXaWlpoba2lu3bt+P3+/nDH/6QaCuTyaisrGTRokUsXrw4UWw2WxLPILmMjP4i1xqTbE3qMvI6KC2ZSbZmxiEfMWr9aeIJGzcBm0Z4eHwo01ZoA3QO9CAMDrZbNNM/TrHEzMATjBAVRdKN09cfNxAIsHHjRgDKyspYsWIFNpsNq9WK2WxGoZjWPx1nTaCri6rbv0BkYACIj1jriwrJvPwy9CVFGMrL0eXlTslU5RLD2O12br/9dm6//fZEnSiKbN68mZKSEtrb22lvb6euro4DBw7w6quvJmLJA+Tn57N48WJWrlzJt771rZRK1jQy+ovKkiXFpksSI6+DLm+WdB0mF7kgCApRFCPA5Qw+0Awy4ZvgtLxbxsQYD77zBLta9mFUG7ht0Y1cVXZpss2SSHEGAmFePtTBr7ZUo5LL+OWNC5Jt0hkTiQzHxF21apU0ueskIgMexEiEojtvJ/3Cj6BKT5f8dac50WiUuro6jhw5wosvvojf76ejo4O+vj76+vro7+8nGBydd62lpYWWlhZee+01brrpJsrLy5NkfXJRGCzYLrg+2WZMa/rflXzcpyB/Ad4SBKEX8AM7AARBKAMm7FQ/LYU2gCPgBiAcDVPVeoA2dxe5pizyTFnkmbOxadOkG5/EOUMURTpcAQ62uni/1cmBZif7WxwEwjHKMwz84dYlFKfrk23mGWMwGLj77rt5/vnnefrpp/nc5z5HQUFBss2aUkT9fhr/+L+0//NFDBXlGMvLMFSUoy8uQmEwSL8/U4z6+npaW1sTo9Tt7e10dHQk1pubmwkMuv0AFBcXk5eXR1lZGRdccAFWqxWbzZZ4szO0PrSdytF3pIQ1Z4+UsGbqIYrizwRBeB3IBraOSLAoI+6rPSGmpdCWCTJ+vOZb1PQ1sL1xDy3udqpa9zMQGk6Ta1YbKbUVUW4tosxWRKm1EINq+gofieQQCEdp7PNS1+2lvsdDXY+Hup74ujcUBUApF5idbeIzywu4blEOi/MtM0Jk5eTkcMcdd7Bx40b++Mc/YrFYyMrKIisri4yMDIxGIwaDAb1en3Kppw2lJSxY/wsGamrxVNcwUF1N/+49if0yjQZNhh213Y46w446IwO13Y7KmobKakVlTZNC/Z1Hnn32WT75yU+OqtNoNOTk5JCdnc3ChQv52Mc+xrx585g7dy69vb1cffXVSbJWQuLMkB54JveBRRCE3wC7gLdFUUwITFEUxw+9dQqmpdCGeEzfivQSKtJLEnXuwACt7k5aXO3U9TdR29/I/vbDiMQ/+GxDBmW2IhZlzeWCvEWoFKpkmS8xxRBFkeZ+HwdanBxsdQ0Kag+tDj/iiO9trkVLiV3PJ5flU2rXMy/XzOxsExrlzPTF1el03HHHHRw8eJDOzk46Ojo4fvz4mHZqtRqDwZAoer1+3G29Xo98JvgtCwLGygqMlRWJqrB7AE9tLb6WVoLd3QR7egh29zJwoprIOKmu5XodqjTroPhOQ5WWhsJkQmkyIjQ14c7MQmE0ojSZUBj0CDLZ+TzDGYHD4eDBBx/k2WefTdTdddddrF+/Hovl1A/EUsbE08Ni6uDjV54q2JjERPj/tnacdR96WSsXGO+dBGumL3pZ62R2Vwt8HFg/+Fuxa7C8DbwvimJsIp1MW6E9HiaNkTkaI3Myhv3kfCE/9Y4mavubqOlr4FDXcXY0VfHEe1oWZs+lJK2AkrR8itMK0Kt0SbRe4nwjiiLv1PXx5z3NvFPfR783nvlQrZBRajewKD+NG5fkUWI3UGrXU5yuR6eaUV+ZCWEymbjooosS28FgkP7+fjwez6ji9XrxeDx0dnbi8XjG+LMOodPp0Ov1GI1GzGYzFotlVDEajdPyjYDSZCRtyWLSliwesy/i8xHs6SXU30/Y4SDUP1T6CTkcuI8eJ+RwIA4mRlEAh14Y4bMpk6EwGFAaDQkxrjAOLeNifOS6wmREoU9dcR6JRHjkkUf40Y9+hMPhYOXKldx3332sXr2aK664YmY87ElISJxTRFH8HfA7AEEQcoBVg+WbQAYwoSgcM1416FRa5mXOYl7mLCA+kfJodw1vNuziWE8tu5rfTbQtsuSxIm8xqwomJdy3xBRmIBDm7j+9y+76ftJ0Si6fncniAguL89OoyDSgkKemQJkIarWa7AnEhI5EIuMKcY/Hg8vloq2tjfr6+nGP/cpXvoLdbp9s05OGQqdDUViAvvDUfu6iKBILBgm73ex+cxsLKyqIuAcIu92E3W4iAwOE3QNEBgYIdHUTqakj7HYjRk79qthQVkrFt7+JNifnXJzWlOWOO+7gz3/+MxUVFWzdupWlS6XfdAkJidNnMDv6fOIC+0JgDvGR7qcm2seMF9onIxNkzMusZF5mJRB3N2lwtlDX38Q7zft45vCLPHP4RT5v/3iSLZU4F/R6gmza18pjOxpw+kL81/Vz+dSy/Bnr+pEsIpEIDoeDvr4+HA4Hbrcbl8uVWHrGcaUYIj8/H5Mp9cJ1CoKAXKNBrtFAZgZpixeNaSOKIrFAgIjHQ3jAQ8TjIdTbi6+1DW99A976BsKu4cnwnto6TqzfwKLfbDifp5J0PvGJT7B582aqq6tZuXIlJSUlVFRUUFFRQWVlJcuXL2f+/PkpH6ZSQkLi1AiC8CrxUesDwG7g56IoHjvdflL2VyYai9LobOVodw01fQ00OVvp9PQA8RteSEztCQUzCXcgzJbDnbzwfju76vqIxkQuKkvnW2srWFqYlmzzZgx79+5ly5YtGI1GXC4X4gjndqVSidlsxmQyUV5ejslkSriNWK3WlE9nHYtECPX2EujsItDVhaxqL9X7DxLxeIgMeIh4B5ceD2I0On4nMhlqmxXTnNmo0tNR29NRGAzjurLMdG688UZWr16dENtD5fXXX8fv9wOg1+tZvnw5119/PV/96ldTbkKvxMxDaZCTvSr1BilGotw0qYNm9cACoBzoA3oFQegRRbH3dDpJKaEtiiIneuv4V+1b7G8/jD8SD+WUobdRlJbPxUUXUGTJo8RawME9E076IzFFOdzm4rEd9bxyuJNQJEa+Vcu/rS7huoW5VGZJmcwmm+PHjxONRnE6nUDcF7u4uJiioiKysrIwm80YpLB3o+jd+TYnfvXrMfUymQx3ug2FMe5rrUsvQGEwojDoh321DYPFaEBhMKJKs0gJckaQnp4+KhENQCwWo6mpiT179rBr1y527tzJt771LZ566imqqqok320JCYkEoih+CUAQBBOwkrj7yFcFQbADh0VRvP2Djh8ipYT25urX+dOBTQCsLlrJwqw5zLGXY9VZkmyZxGQSi4n84pVjPLajAYNawc0rCrh+UQ6LZkjYvanKLbfcwoEDBzh69CgulwuXy8WRI0c4cuRIoo1CocBms2G320lPTyc9PZ20tLSZFZHkNJCNk0lQYTIRMhkx5Och1xsS4lph0KMY2tbrkWs1yLVaZJr4UhLZH45MJhsVH9tgMLB//34aGxuTbdq0JuyJ0rHLnWwzpjVhzyneVElMBYKAj3jSmiCQB0w4bF1KCe0l2fP42+GXCEaC7G7djzfsxxf2UWErJd+cjVwm3ahmArvr+3hsRwMfX5TDT66fh1krvRI+H8hkMpYsWcKSJUuA+BukQCAwyje7v7+f3t5eWltbOXz48Jg+dDrdmPCARqORrKwssrOzZ1yKa+uypSzcsJ7+ve/G3UQ8XiJeD31t7fha24h4vES9XmKh0If2JSiVyLXauAAfFN+jikaTEOfxkXDj6EglRgOyGeg+EYvFqK6uZu/evezdu5eqqioOHDhAMBhELpdz5ZVX8t3vfjflHvIkJCQ+GEEQ/of4KHY5sB94B3gYuF0URedE+0kpoZ1jyuJ/b/g1BzqO8F77YfZ3HObdtvcBUCvUlFkLWZazgNVFK5NsqcTZMDRqvXZOliSyk4ggCGi1WrRaLVlZWWP2h8Nh+vr6cDqd40YmaWlpwePxjEoHb7PZKC8vZ926dTPm7YShrBRDWemoum3btrHk0ksT27FQiIjXGxfiHg9Rn4+o3z9YAkQDgRHbw/URj4dgT298OxCvIzah0K8ogcbGZoruuG0Sz/b88a9//Yv169ezb98+3O74aKter2fp0qV87WtfY/ny5axZs4aMjIwkWyohITFFaQD+DzggiuIZv3JIKaEN8agjS3LmsyRnPqIo0u3tpaavgf0dR9jRVMWR7mr+98CzXGu5ONmmSpwh5ZkGABr7vB/SUiKZCIKARqMhLS0NrVaLyWQiEAgQDAZHLZ1OJ/X19YiiSF9fH319fVx88cXodKkT916mUqFSqVClnd7kXVEUifp8hF0uwi43YaeTQHc3/tY2/K1t+FpaiQwMnPJ4T23d2ZqeFN57771EZscvf/nLLF++nOXLlzN79mxp5FpCQmJCiKL44GT0k3JCG+I3n3jEkWrq+puo62+iw9Od2K+UKzHIU+cmPtN45t0WAC6bJY1UTQVEUcThcNDW1kZ3dze9vb309PTQ398/KjLJycjlctRqNRqNhqysLDQaDWq1GqvVilarPY9nMHXx1Nbha2lJiOi4oHYRcg5vnyrOtsJoQGk2oyvIR2k2o063oc3NRZubgyYnh3cOvs+8NWvO8xlNDkOuRh0dHbz22mtEo1GMRiOzZ89OtmkSEhIpRkoJ7X6/kx2NVWxv3E2LO57u1KZNo8RawOrilcy2l1FuLUYhV0gpeKcxakV8xMpmmPBcBYlzQEtLC8899xzBYDARUk0mk2G1WsnIyGDu3LmYzWY0Gk1CRI9cSjGOhxFFMe4G0t1DsLuHQHc3/bv34D46HNJVUChQWiwozWZUFjP6wgKUFjNKs3l4mSgmZB/2+U5j15ycnBxqamp4/PHHee2113jsscd49NFH+fa3v81Pf/pT6UFNQkLivJESd7JYLMavdz3G3rb3ERGptJVw99KbWZa7gDStOdnmSUwyK4qsyAS45bE93HlhMVfNy8Kql0T3+UIURTweDy+++GIi1F9paSlr164lPT1denX/AcRCIXytbQjHTtDY3IqvuYVgVxeB7h5igcCotuqMDIrv/gJpixehtJiR63Qzxm99MtDr9dxzzz3cc889uN1u8vPz2bBhAw899BCf+tSn2Lhxo/QwN0kobTKybjYk24xpjfIlKRvxTCUlfmUiYpSqtgMYVXoeuOJeso2SS8FMZn6emSfvXMGPXzjC9/5xiB/+8zBXzc3i31aXMj9PerA6F/T09FBbW0tbWxuNjY14vaP94xcvXkxmZmaSrJuaiLEYzgPv429rw1PXgLeuDl9rG8RiKIB2uTzuxpGdhXnhAtR2O5qMDNQZdtQZdhRSTPIJYzKZ6O7u5q233uKZZ57h8ccfJzMzk1/+8pfJNk1CQmKGkxJCe8gPdCDkZUdTFZcWf4QMvS3JVkmcSy6psPP6t1dzrGOA5w+0sXFHPZsPdfDi1y6SxPYkEwqFeOihh8bdt27dOj7ykY+cZ4umB51bXqX+4UfH1FuWLKZPqWD2xRcj16gRFAoEuRyZUomgUIAgEHY4iXg88XpFvF5QyJENtk31mNrRaJTu7m7a29tpb2+nsbExkR3yxIkTAKxfv557770Xm026F0hISJw7UkJoqxUq/uPCL7G1djubjrzMs0c2Mzejgk/N+yiz7eXJNk/iHCEIAnNyTMzJMfGRUht3/nEvH/vdTq6YnclV87K4tNJOukGdbDOnLKIoEg6HCQQCBAIB/H5/Yn3ktsfjOWUfW7duZenSpahUkuvOydhXX4yvuZmwy0UsEMTf3k6gqxvne/uRA9V79p555zLZsOhWKBAUCmSKkeuKxHpcrCtO2idH3tNL3bET8fjcajUytRq5RoNMo0auHlqq4wlzNGpk6qFlvP58iP1IJMLTTz/Nzp07aWtrSwjrzs5OYieFMTSZTFRUVHDhhRdy2223sXLlSklkTxLhvhidT5/6d0Diwwn3TSzs5gf2ISUOmpKJf1JCaAOsyFvEirxF9Hr72da4m2cOv8j9b/ya/7jwS6zIW5Rs8yTOMWsqM3jxaxfxzwNtvHiwndeOdSEIcEm5nUc+txSNcvqPAEajUcLhMKFQaFQ5ue5U20PLQCCA2+1mx44dY8TKyahUKgwGA4WFhRiNxlFFp9NhtVolkX0KFDodpV+6e1RdLBwm1NfH7rd3sXzJEmKRCOJgiUUiiNEoYnhofbA+PFg/sl1ifbA+OnJfdEy7iM83pg/B66WvtY1YOEwsGESMnt4NTFAqRwlwuU5H4a03Y1m4YNI+w/fff59bbrkFs9lMYWEhOTk5LFiwgNzcXHJychIlPz+fjIwMydVGQkLivDMjhbYoivjDARwBF30+Bz3ePnp8/fR6+weXfYm2GoU0opkqzM8zMz/PzPeumc3RDjf3/GU/b1X3cLxzgEX5lmSbNyE8Hg9bt24dNars8Xhwu92EJpA9cAhBEFCpVCiVynh85sGi0WiwWCyoVCpKSkrQarWJqCAajWbMtkwmTeCZTGRKJZqsLLBZ0RcXTVq/YiyWEMyxYJBoMEgsGCIWChELh+PiOhSKi/ZwmFgkTPXRo+QWFcePC4WIBeIJcMIDnngWy8RyYFwRLobDRMJhGBge6az+n9+y4snHJ+28hhgYGKC+vp729vZE1JqREWxOd72xsZGOjo4x+/R6PWazGYvFgslkkib2SkhIfCjTTmiLoshA0EOnp4dOTw99PgeOgAun3z24dOEIuAhFw6OOEwQBmzYNu97KLHsZqw12rii9CKt2eggsiclDJhNo7PPS5vQzJ9tEZaYx2SZNGLfbzTvvvDPh9hqNBr1enyhDKc61Wm1CZI9cDq3v3buXiy++GKVSKYnpJBINBuPh/Lq6CPb0EPUH4qJ3lGCOi+aR67FQkGhwuN1EUrifjBxoZHtiW1AokKlUyJQKBIUSmVKJwmhAZU2L+48r43XxdcWgT/lQnQJkMowVk+uqt3DhQr70pS/R0tKSSHA0VEZuB4NB3G73uPWBQOC0HlJHYjQaE8L7VMvCwkIuu+wy7Hb7pJ67hMTJeGN57BlYn2wzkoo39uVkmzCGKS+0Y2KM1+t28n7XMboG4uLaHxkd5kqn1JKmMWPRmii3FWPRmuPbGhNWnQW73oZVa0Ehk0YfUp1INMZtT1Sxq66PLJOG/7vrArSq6fN/YTAYuPzyy1GpVEQikVFuH0Nl5PbQel9fHx0dHYTD4Q//I4MMCXq5XD5GiAuCQG9vL6FQiLKyMj7zmc9Io3uTQDQY5PAP7kdZXcPb//P/TtlOplIhU6uQqYZ9oofqFEZjwqdaplYNr6tUg21Vw9sq1TgiOb58p6qKiy5dnZiEORXdLhQKBV/+8tnfWGOxGMFgMCG8t23bxuLFi8cIco/Hg8vlwuVy4XQ6Ry1dLhcdHR0cP34cp9M5bkKmTZs2ccMNN5y1vVMNSeCdPVNRIEpMDlNeaDc5W3nk3T+TrrOSb86mMr2ULKOdTIOdLIOddJ0VtULyAZWYGJGYSKcr/qDW6Q7wyYd3UZFppMSupyTdQIldT2mGAZNGmWRLx8dkMnHRRRed8fGiKI4r0If8tZ1OJ06nk5qaGlQqFU6nE5/Ph9/vTySdOZna2lqi0agktCcBMRzBU10z7j7z/HlYFi3ENHcOhrJSZMpz/D+q06JIkTT3MpkMrVabSGSTlZVFZWXlaffT19fHk08+yZ/+9CfcbveYB9uhiCcSEhKpw5QX2iLwX5d9m8r00ik5oiIxvdAo5bz+7dXU9Xh543gXVQ39nOgcYOvRLqKx4dGnWVlGVpbYWFli47JZGagUM8N9QhCExMi0TqcjFApx6NAhGhoaqK+vP6WY1mq16HS6hPtJeno6eXl55OXlodfrz/NZzFwUBj0X/nMT27ZuZb45jf6qvTj27iXscuM6dBjXocMApC1bypwffi/J1qYuPp+P9evX09bWhs/no7W19f9n783jo6rv/f/nmX2fycxksi8QEvYdZFVQkLoWLVpLxdt7veq1t7e/X63WttrWam1726u1tnazbq24tBarVlTcABFkX4UkECD7nklm3+d8/5hkTCBAkITJkPN8PD6Pzzmfs8z7nDMz53U+5/15v6mqqqKxsRGABQsW8O1vf5vS0lLKysooLS0lKytLuodJSIxAhr3QBqhyVjMuc0yqzZC4QBAEgTEOA2McBu64pASASCxOrdPPsTYfFU1utlc7+duOOp7bUk2RTcf3rhjHFZOyL7gb5fbt2/nggw8A0Ol0zJ8/P5m+ev78+eh0OnQ6neSnPX3SbKQAACAASURBVASI8TjhDieBpiaCTU0EGhN1sKkZRUMjFb0GGMpUKjTZWchUKqJ+P3lfui6Flks89thjPPjgg6dcnpeXx/3334/RmD7jPyQkLjSi3i46tr2eajOGv9DWKNS8Xv4ul41agE6lTbU5EhcoSrmMkkwDJZkGLp+QyGAYjsbZdKSNX75Tyddf2M1Fo6w8/bVZGIepW8nnYfbs2dTV1XH48GH8fj9btmxBpVKhVqsZN24cDoeURXUwEWMxqn7/R7xHqgg2NfcZpCgolWiys9Dm5uDJtFN20UVocnPQ5uSgslkRpIedYcM999xDZmYmoVAIl8vF6tWr+7iF/P3vf+e73/0uM2bMSKGVEhISw4FhL7StWjOukIfn9rzCrTO+jEapSbVJEiMElULGkvFZLCrL5KXttfzw9YO8tL022Qt+IaBWq1m5ciWBQIC2tjZaW1tpbW1lx44d/O1vf6O4uBiTyZSMjW0ymZLzOp0OhWLY/4WkHDEWw1dTg/tQBc6t23Ad+BRBoSDn6ivR5OSgzU0Ulc2WFNMtGzaQvXhxag2XOCUqlYqVK1dy/Phx1q1blxTZ3//+91mwYAHz5s3DarWm2EoJCYnhwBnvkoIgFAB/BbJIuEw/KYri44IgTAX+CBiAauBmURTdgiAUA+VAz+P9VlEU7+ze10zgOUALvAX8/+KJw7JPQKPQcNmo+Xx4fAvb6vcwJ386y8Zcwhhb8dkeq4TE50Ihl3HLvGLWHmjiuc3V3LpgFAr5hdW7qNVqKSwspLCwEEjE65bJZLjdbmpra3G73f0mr1GpVEn3kv6K0WjEbrdjsVhGjPuJGI/jqTxM1779eMorcFdUEg8mBuCqM+1kLrqEon9bhdouZSUcjoiiyCeffMKuXbvw+/20tbXR3t5+Uh0KhZLbTJkyhTvuuINvfOMbKbRcQkJiODKQ7qgocLcoirsFQTACuwRBeA94CrhHFMWNgiDcCnwH+GH3NkdFUewv3eIfgNuBbSSE9hXA22cy4M6LbmFJyULeO7qJbfV7+Lh2B79cdh/55pwBmC8hMTjctnA0t/11J6/tbeSGmfmpNmdIcTgcLO7VoyqKIn6/H7fbjcfjwePx4Pf7Tyrt7e34fL6Toi3I5XJEUSQej3PDDTcwceLE83xE54fGf71J/Zp/EunsAkFAX1yE49LFmCaMwzR+HGoplvKwp729nfvu6zvQtGfgb0FBAdOnTyczMxO73U5OTg6LFy8mP//C/j+QSA+aVQK/KBjZbxmbVcNvHNUZr4goik1AU/e0RxCEciAPKINkNoP3gHV8JrRPQhCEHMAkiuLW7vm/AtcxAKENUGobhV6lIxqL8nHtDurdTZLQljivLBnvoCzLwOqtNayYkXfBDYw8HYIgJJPe5OT0/7uLx+PJOMPt7e00NTVRV1dHc3MzsV4D+/bv33/hCu3X/0WkswtNdjZF/7YKy/RpKHTS2JJ0IjMzk5///Oc8/fTTVFVVAdDa2kpeXh46nQ6LxYLdbk+md/f5fHg8Hmngo4SERL+c1aNPt1vIdBI90geB5cBrwI1AQa9VRwmCsAdwAz8QRXETCXFe32ud+u62M+KPBHhuzytsOP4Jcpmcq8uWMCd/+tmYLiFxzgiCwM1zinjgjYOsO9jMFZNG7oNeV1cXTU1Nffy6Ozo6TnIv0Wq1ZGdn43A4KCoqori4mIyMjBRZPfSUfftbHPvz0/iqa6j85SMgk2GZOoXsL1yOdc5F0oDGNGHu3Ll873vfo7W1lS1btrBlyxZ27NjB7t27efPNN/H5fCdtY7VaWbBgAYsWLWLRokVMnToV5VDHOpeQ6EV2WOS7ddFUm5FSfhA+rTdyShiw0BYEwQCsAb7V7Yt9K/AbQRB+CLwB9AyfbwIKRVHs6PbJfk0QhLPqvhIE4Q7gDgBHbhZff+0+gvEQ03XjmKobi86lYePGjWezy7PG6/WyYcOGIf2MgZIqW3pfh6ysrGF1Tk7FUNuYHxcpNMq46+XdHJikZnb20L+mO/E6nI9r0N95jMVi1NXVJd1DeuhJ856Xl4dGo0GtVqPRaNBoNH2S2Ljdbvbv3z/ktvfHYHwvBnwdll8Dfj+yg+XI9h+ga89euvbsJXrlMsRxA0uCMhx+a8PBhv44H7+H3sdusVi46qqruOqqq5LLe9ykOjo6knVdXR27d+/mX//6FwBKpZKSkhLy8vJYvHjxGRNNpeJ8e73e8/p5EhIjkQGpBEEQlCRE9guiKL4KIIpiBbCse3kZcHV3ewgIdU/vEgThKAk3kwagtyNbfnfbSYii+CTwJEDBuCIxEA9yxZjF/MeML5+31/UbNmzo46OaSlJlS+/rMGvWLNFgMAybc3Iqzse5Gjvdzzde3MPv9nZx85wsvn15GTaDesg+78TrcD6uQX/n8eDBg3z88ccALFu2jMLCQjIzM1Gphn9m1sH4XpzqOoixGN5jx3EfPITn8GH8NbUEGpugu3dfk5NN1uVLybnmKuTqgX1PhsP/z3CwoT/Ox+/hXI69sbGRTZs2sXPnTn7zm99QUVHBBx98QCQSOW2UnlSc7+H4ICUhcaExkKgjAvA0UC6K4q96tTtEUWwVBEEG/IBEBBIEQcgEnKIoxgRBGA2UAsdEUXQKguAWBGEuCdeTfwN+e6bPt+utjLWN5p2qDTR5W/jPmSvJNkgDiiRSR36Gjlf+ax6/eKeCZzcf5597GrhxZj43zS5kQq4p1eadM/F4nGAwSG1tLS6XC7fbjcvlwuVyATBjxgzmzZuXYiuHB/WvvkbNX55PzquzHOiLi7HNn4e+qBBdURHavFzJZWQEIIoira2t1NXVAWC32/u4Up1JaKcz0iC8c2c4DuIb6QiCMBuoE0WxuXv+34AVQA3wY1EUnQPZz0B+GQuAW4ADgiDs7W67DygVBKEnltGrwLPd05cADwmCEAHiwJ29jPlvPgvv9zYDGAgpF+Q8eNndrKvayOp9r/K9d3/O76/5qZS8RiKlqBQyfnjNBFZeVMDv1h/lpR11/OWTGlbNLeQnyyel1UDJWCxGRUUFDQ0N1NfX09jYSCwWY9u2bcl11Go1ZrOZ0tJSLrroohRaO7xQmj57sFJZrUz71SMoDFJK+pHIvffeyyOPPNKnzWq14nQ6WblypeSvLSGRfvwJWAogCMIlwP8C3wSmkXirdsNAdjKQqCMfA6dSDY/3s/4aEm4m/e1rJzBpIIb1RiaTcWXZpXQF3fyz/B2cgS5JaEsMC3LMWm6cmY9GKeel7bWs3lrLfy8eQ64lfb6fu3bt4u23E8+8Go2G2bNn43Q6mT17NmazGZPJhHqALg8jjayll6HJyeLT+35E2OmkbeNGcq6+6swbSlxwrFy5so/Q/vOf/8xtt92WQoskJCTOEXmvjuKbSOSRWQOs6dXxfEbS6l1PJJ4YTfudd3/KxMwyco1ZZGjNnxVNojao9GnVoyiRHoiiSJ0zwK5aJ7truthV00lFs5u4CDIBloxzcPPcwrQS2QDTpk3j6NGjHD58mGAwSGVlJWq1mtbWVmKxGDKZDIVC0WdgowTEAgEq/vf/6Nq7D7leR97yL+JYuiTVZkmcZ0RRpLOzE5lMxuuvv86+ffv40Y9+xO23386iRYsoLS1NtYlDQu9BqXaDY8RHuzhXPm+0jN7XQWnJGvEuPIPsgiMXBEEhimIUWEL3ee5mwCc6ra7IqqnXMyd/Gjsa9rGv6RBHndX4IoGT1lPIFGRoTJg1JkwaIxa1EZPGiFltxKwxYe6etmhMGNUGSZRLnEQkFudYm4/yJjflzW7KmzwcanTR7k0E19Gr5EwvzOB/Lh3D9KIMZhRkYNal56vhnnTSLpeLw4cPc+TIEaqrq3nvvfeS68hkMiwWSzIV+4nFZDKh1+tRKpUj5vcUj0QJtbcDEPP5adv4Ea5PD6KyWlHZrKisVtTdtcpqRWEyDngwpETq8Hg81NfXs23bNpxO50mlo6Ojz3xjY2O/4f7sdvtJiZsuJHoPSp2crRfnGO9NsUXpjV5Wf+aV+qH3ddDljxt+se3Sm5eAjYIgtAMBYBOAIAhjANdAd5JWQlsmyBhrL2GsvYRVU78EQDgapjPoojPgwhlw0RVM1J2BLtwhL05/J8c7a3EHPcTEk1NIq+UqHAY7Dr2NLL0dh8FOnimbM2SGl7iA8IWi7Kvv4lBjQlCXN7mpavUSjiW+Lyq5jNIsA4vHOpheaGFGYQZlWUbksgtLUJrNZmbPns3s2bPZsGEDc+bMSYYva29vp6urC4/HQ0NDAx6Ph2j05B4suVyOVqtNFp1O1++82WwmIyMDrTa9ev97ozQZmf7E4/hr63Bu247v2HHCTifugwcJOzsReyXp6UGmUqEwGlAYDCiMxkRtMKDsaTMl2oT6BgJNTagyMpBrNCk4upGB0+lkw4YN7N27l3379rF//36qq6tPub7FYsFqtSZLcXEx11xzDQUFBRQWFiZLZmYmMmkArIREWiOK4k8FQfgAyAHeFT8ThjISvtoDIq2Edn+oFCqyDJlknSESiSiK+CJ+XEEPrqAHd8hDZ8BFq6+DVl87rd52DrYeJhgNAeBQWBnnmUiO0XE+DkPiPCGKIrVOP7trO09y/wBwGNWMyzFxcZmdCTkmxmWbGJ2pRykfeTdNrVabTD19IqIoEgwGk+nY3W43fr+fQCCQrAOBAE6nMzkf60d4qtVqMjIyyMjIwGKx4HA4yM7OJjMzMy1cVQRBQF9UiL6osE+7GI8TcXsIOzsIOzsJd3QQ9XiJeDxEvV6iHi9Rr5dgUxMRt4eox4PY68FFAex+5VUA5FotKmsGyowMVNYMVBkZaHKy0RUWoCsoRGmSMhKeDaIo8uSTT7J69Wq2bNlCPB5HJpMxduxY5s6dyx133IHX62XBggVJQW2z2bBYLGnxnUwFSoOcnPnpH3EplSjXnPt3qyRax6td3x4Ea9KX66J1g7YvQRCswOHuohYEoeeVZHt3GRBpL7QHiiAIGFR6DCo9eabsftcRRRFP2MfOhv38ccfzbKndyYqJ0sCmdCceF/m4qp2/76xj67GOU7p/TMkzD2k87AsJQRCSvdQOx5kfRkVRJBKJJIW4y+Wis7Mz+eq9vLy83+1WrVpFSUnJYJs/5AgyGSqLGZXFDKPPvL4oisTD4YQA93jY+dFHjC8oSIj0zkSJdHbhPVxFuLOTeCj02WcpFGTMmknZt76JPI3fEAw2brebxsbGk8rRo0d58803mTJlCvfddx9XXnkl06dP7/N2ZbjGEJeQOB3SA8/gPLD0op1EFvOeXpDer7FFBvTvPoKE9kAQBAGT2kCWwQ6ATindtNKdaCzOTU9uZVdNJxk6JZeOczCjMIOZRRem+8dwQhRFOjo6cLvdyZ7vE4vX6+23p7uH0y27kBAEAblajVytRm23IdZU4ziF0BNFkXCHE39tLb5jx6l5/gWcW7chiv9zfo0eRnR0dLBx40a2bdvG1q1b2bNnDx6P56T1DAYDubm53HnnnTzxxBNSD7WEhMTp+A1wKbCZhL/2x+Ln8CuWhHY/5BgdKAQFle1HubLs0lSbI3EObDnawa6aTr57xThuXViMWiHdWIeSQCDAnj17qKmpoba2lmAw2Ge5Wq1ODqAsKirqd2ClwWC4YBN7DAaCIKC221DbbcnBmIU3r0Sh06XYstSxZMkS9u3bl5wvKSnh+9//PoWFheTm5iaL0Si52UhISAwMURS/1Z20cTGJfDK/FQThXeAPoigeH+h+pLvZCYSiYfY1HSIqRglEg2feQCItsBlUksgeYkRR5KWXXqKurg6r1cr48eMpLCzsE60kHdK1D3fEWIymtW/TvuUTPOUVAMQv4OgWA+Gpp55izZo1bN68mR07dnD06FF+//vfs3LlSiKRyIBdnCQkJCR6092DvV4QhD3AV4CfAEeAPw90H5LQJiEQjjpr+PD4FjbX7iAQCWJXWLh95ldTbZrEObJgjJ2p+Wa+u2Y/lc0e/r8lpZi16RmGb7gTi8Xo6OjAbDbzpS99iby8vFSbdEERaGqmc8dO2jZ+hLfqKPqSEvJWXI9lymRMkyam2ryUMmvWLGbNmgVAOBzm7bff5oknnuCxxx47KTrOqlWreP7551NhpoSERBohCIIeWE4iWU0miSzoM0VRrD2b/UhCG3jpwOu8Vr4OlVzJ3IIZXDZqAS0H67Hrrak2TeIckcsEXrpjLg+vLeeZzcd5dXc9d11exsqLCkdkJJGhJBaLMX36dDZv3sxTTz3FbbfdJontQSAeiXD8qWdofuddALQF+ZR+65s4Ll2cWsOGKSqViuXLl7N8+XIikQjHjx+nsrKSN954g6eeeorVq1eTl5fHwoULWbZsmfSWRUJC4lS0kui9frm7FoFZgiDMAhBF8dWB7GTECu1oLMre5oN8VLOdrXW7AXjyi79IpnZvPdSQSvMkBhGdSsHPrp/MqjlFPLz2ED96/SDPbanmoS9OYmGpPdXmpS2xWIzy8nKOHz9OfX09ra2tQCK5zfjx47HZbCm2MI0Jhahf809c+w/gPlROPBwm+8oryLvuWjTZ/UdNkjgZpVJJWVkZZWVlLFu2DKvVyrvvvsuvfvUrfvGLXzB16lS2b98uiW0JCYn+eIWEuB7bXXojkujhPiMjTmj7IwFe+XQtG6u34g37MKkNXFG6mMtLLk6KbIkLkwm5Jl64bQ4flLfys7fK+dqz2/nTqpksnZCVatPSjurqal599VU8Hg8ajYa8vDzGjx9PQUEBxcXFUjSHcyDY0opi9cvUuN3oigrJWrYUy/RpZMycMWKybg4FoVCIK6+8EofDwTvvvMP777/Pvn37qK2tZcyYMak2T0JCYpghiuK/D8Z+RpTQDkXDfPPNH+IJ+8gyZPI/c77GlOwJKGSSKBgpCILA0glZzCuxccXjH/HCthpJaH8O6urqkuHTpk6dit1uTxYpI9654a+rQ3C7UZrNjLr13zFPnoQgPbh8brZu3codd9zBgQMHkm1FRUXce++93HrrrZLIlpCQGFJGlNCWy+RMz5nERzXbaPG28YcdqxlnL6HUVszcgpk49NKr7pGCXq1gfLaJqjZvqk1JS2bPno3T6aSpqYldu3b1GXCm0+nIyckhOzubnJwcysrKUCqlAagDxTJ1CvFJE4hVHePgAw+hMBqxzrmIzIsXYJ46RerVPgva2tq47LLLcDgc/OQnP2HWrFnMmDFDikAiISFx3hhRQlshk/M/c/+d/5jxZbbX72Vv8yE+qdvFtvo97Go8wIOX3Z1qEyXOA8FIjH/ta2RzVTtzRksPV58HjUbD8uXLgUTUHrfbTXt7O+3t7TQ3N9Pc3MzmzZsBuOyyy7j44otTaW5aIVMqiV2+hPk/+gFdu/fSvuUTOjZvofX9D7DNm0PZ3Xchkx5cBsRLL71EIBDgySefZNmyZak2R0JCYgQyooQ2QFyM0+RppdHTwqG2I0AiQc2ds29JsWUSQ0koGuOTox28X97Cm/ub6PJHmJpv5qfXT0q1aWmPIAiYzWbMZjMlJSW0traydu3a5HIpScjnQ65WY5s3B9u8OXgqD/PpDx6gY9sOQq1taPNyU23esCccDvPrX/+ahQsXcvnll6faHAkJiRHKiBLa8Xicu9f9hAZ3M3JBxpTsCVwzdgmTHGOl17EXGKIoUt3hZ9ORNj463M4nR9vxhWNolXKWjHfwldmFzC+xIZNSsA8qoVCIP/zhD8n5BQsWkJWVRSwWkwZIfk7CXV3s/979EI8z6acPSSJ7ABw5coTbbruN48eP8/DDD0v/7xIjA7WIWBxPtRWpRX3WGdKHnBEjtD0hL29UvEeDuxmAP1/3SwwqfYqtkhhMIrE4e1qjvPvPA2w60kadMwBAgVXLddPzWDo+MQhSo5QE31ChVCqZN28eBw8eJBAIsHnzZjZv3oxarWbUqFGUlJRQUFBAZmamNGhygCj0esyTJuLaf4BDD/0UQ8loDGWlGLuLym6XhGQ3oijy85//nIceegiNRsPzzz/PV78qJR6TkJBIHSNGaD+7++98XLuDmbmTuX78FZLIvoDwhaI8+dExXtxeS5snhEHdyLwSG3dcPJpLyjIpsknX+nwhk8lYtmwZy5YtIx6P09nZSVNTE8ePH+fo0aNUVFT0Wb+wsJCvfvWrqNXqFFk8/JEplUx86AGc27bjOvAp3iNVNL35Fo3dA1CVGRaMpaVkzJpB1tIlIzpCycaNG7n//vsBOHjwICUlJSm2SEJCYqQzYoT28c46AOYXzJKii1xA7Krp5Jsv7qbRFWTJOAeTdS6+seIyKevjMEAmk2Gz2bDZbEyaNImjR4+yevXqPuvU1tbS1NREUVGR1Ct7GgRBwDZ3Dra5c4BEtkhfdQ3ew0fwHD6Cp7IS5/YdNPzzdfTFRShMZpRmE0qTqVdtRmEyoTQZL9jBlHq9HrVaTSgUYsKECSxatIi77rqLK664Qvp+SUhIpIQRI7TnF87k7SMb+O22ZwFw6G0UmHMpMOdSaM5ltLWIXKMUTzmdaHUH+eqft5Jt1rDm6/OYWWRlw4YNksgeppxK6PzlL39BLpdjNBpPKiaTCaPRmBxsKbmbJJAplRhLx2AsHUPO1VciiiLObdtpWvs2/voGou5yIh4vxPv315TrdQnx3S3IFT2CvJcop7UNMRZLqx7ynrCTH3/8MW+99RZPPPEE7733Ht/+9rd59NFHU22ehMSQEumI0/ziyA5ZG+kYfj7qI0Zo3zjpGlZMvIrqznoOtFRwvKuOOlcje5sOEhMTF+YLYxZx64ybpJ6PNMGoUaJTySl1GJhZZE21ORJnYPTo0TzwwANAIiKEy+Wiq6uLrq4uXC4XHo8Ht9tNS0sLR44cIRKJ9LsfpVKJRqMhIyODFStWYDKZzudhDEtO7PEGEGMxoj4fEZebiNuVqF1uom53n7ZgayuRI1VEXK4+wlwJbHnhZSY++CMs06am4Kg+HzqdjmXLlnH06FFisRgA2dnZuFwuzGZziq2TkJAYaYwIoR2Px/GGfbhCHvyRAJl6K2qFihyDg1ZLO5tqtgOwrmojX550DUa1IcUWSwwErUrOrGIrO6qdqTZFYoDE43ECgQCBQIBgMIgoiqhUKvR6PTKZDJVKhcFgwGw209XVhdN58rWNRCJEIhE8Hg+7d+9m8eLF5/9AhiFiLEYsECDq9xPz+4n6/MQCAWI+P1G/j5g/kGyP+nzdy4PEgyHESOSUvd91f/9HWgntHm644QYef/xxKisruffee7n33nvJzMyktLSUMWPG4HA4sFqtfUpGRgZWqxWfz4coilKnywCJeGM0bXGn2oy0JuKNpdoEiSEi7YW2PxKgw99Jh78LZ6CTdn8nTn8nHYEunP5OXCEPnnDiT/NEBEHApDIwK28qM3ImMj1nkiSy04hOX5j1Fa2smluUalMk+iEWi1FRUcHu3btxOp0EAgFCodAp1xcEAY1Gg06nQ6vVYrPZyM/PR6vV9lsUCsWIzPAX9Xrp2ruPzl278Ryp6hbSfuLB4Bm3FeRy5DodCr0ehdGIwmhAk5ON0mhAYTCgSNZGDhw+zOxLLkaTpuc4MzOTgwcPUl5ezpEjR/qUDz/8kI6ODgKBwCm3l8vlSQFeUlLC7Nmzueiii5g9ezaZmZnn8UiGP5VBI4sqF6XajLSmNVh/zvvwxfPZ5vnlIFiTvvjiX0+1CSeRFkJbFEUq24/R6Gmm2dtGq7edFm87zb42fGF/n3UFBCwaE1adhWyjg7GZYzCrjZg1RkxqA6buabPaiEGll3w+0xiFXECjlLOzxsnRNi8lmdJDUqqJxWK43W7q6upYv349XV1dWCwWCgsLTymYe4S1Wq2WehDPQLjLxa7/+u+kqFaaTZinTkFlsSDX61HodMh12oSY1umQdxeFPlHLVKoBn2PR60GXnz+UhzPkyOVyJk2axKRJ/SemCgQCdHZ24nQ6cTqdyekdO3aQkZGB0+mko6ODQ4cO8fbbbyc7bJ5//nlWrVp1Pg9FQuKMNKsEflGQFrJuyGhWffb/lhFqYcXRX53V9r8ZbINIA6EdjUf58frHKO/O4igXZNj1NrINdhZYZ+Ew2LDpMrBprdh0FjI0ZhTyYX9YEoOAUaPk1zdN466/7WXJoxspydQzRhdCntfGRaOsqBXpM4grnRBFkY6ODurr62lvb8flciX9rT0eT3K9rKwsbrrpJsrKyqQH2kFCaTRQuPImnNu246k8TMTlpmPzJ6izslDbrIz55jfQZKVnD3Qq6HnYy83tmwRo1KhRJ7kkeTwedu7cyWWXXcYtt9zCb3/7W773ve9x/fXXn0eLhyf5KjMP516dajPSmh+o3jznfWSHRb5bFx0Ea9KXH4SlhDVnTaOnhdquem6buZJp2ROw6TKQyyQBJZFg6YQs3vv2It460MT6ylY+OOpj3dPb0ankzC+xs3xaLpdPyJKS1AwCHo+HtWvXUltbm3zlLpPJMJlMWCwWRo8enYwOYrVaKSwslAT2ICPI5eRcfSWW6dPwVB6m+Z138R09SrCxkWBjI87tO8i9VhI8Q4FMJsNgMHDPPffwyCOPsH37dkloS0hInJFhL7Sj8Rj3LLyTiY6yVJsiMUzJNmu4deEobl04inXvr0eZP4H1FW28X97C++UtmDQKbppdwNfmF5OfoUu1uWmLy+WisrKSgoICpk2bRn5+Pna7XRLTQ4goioQ7nLgPHcJ9qBz3oXL8dfWfDVyUydDk5mKdPRP7xQsxjJEStAw24XCYuXPnsmfPnmSbSqVi9uzZPPvssym0TEJCIh0Y9kIbYJSlINUmSKQJaoXA4nFZXDYuix9/cSKfHO3g5R21PLO5mqc+Ps7cUTaum57LjTMLkMkkf+DTEQqFaG5upqmpKVkDWCwWZsyYkWLrLkwCTc14ysuRbfqYTz/YgK+6hqg7Ec1BrtViHDcW6+xZ6AoL0RUWoMnNQS5l1RxS5HI5U6dOZe/evYiiJsedLgAAIABJREFUyI033sijjz5KQYF0X5KQkDgzaSG0f/Tho/zPnK9RnCH9sUkMHLlMYGGpnYWldhq6AvxtRx1PbTrGJ8c6kAkCN86Svk8n0hMlpL6+no0bNybb9Xo9OTk5jB07lsmTJ6fQwguHWChEoL4Bf00N3qqjdO7eQ7CpGQCZXE5sVDHWi2ajLy7CNH4c+lHFaZU85kJBLpfz7LPPcs899/DjH/+YV155hVdeeQWAf/zjH6xYsSLFFkpISAxnhr3QzjE46PA7uffdnzE9ZyLXj7+CcZljUm2WRBrR6g6yuaqdIy0eIrHEK/fhN1xieLBlyxbq6uqS8zabjeLiYux2O2q1Go1Gg8fjIRKJJOfVajUKxbD/Kxk2NPzzdaqf+2ufNplKhXnyJHKvvRrz5MlsrzrC1MsuS5GFI5t4PI7b7aaiooLW1tZkaWtrw+FwMHnyZA4cOADAM888IwltCQmJ03LGu6MgCAXAX4EsEvrkSVEUHxcEYSrwR8AAVAM3i6LoFgThZuA7vXYxBZghiuJeQRA2ADlAT/DSZaIotp7u87VKDU9c8zDrqjay9vCH/OjDR7HrrGQbMskyZJJlsCeK3k6m3oZBpZdChI1w4nGR7dVONh5uY0NlG+VNiVfvDqOam+cUcfOcQkqzjCm2cniyfPly3nrrLZqbm5HL5bjdbnbt2nXG7eRyOVqtFr1ej06nS9b9TZvNZlQq1Xk4muFJ1H9CSFK5HMOYEmRqNf7aOsJdLmQtLbTKFChNRpQmUyK+td6AXKdFkHziz5oeN6jm5mba2tr6COgTS1tbG9Fo/5EbbDYbDoeDa6+9lv/8z//k6qulgacSEhc6giCUkdC1RfTSzaIoDqg3ZCDdUFHgblEUdwuCYAR2CYLwHvAUcI8oihsFQbi124gfiqL4AvBCt3GTgddEUdzba383i6K4cyDG9aBX6fjShCu5umwJG45/QmXHMVq8bexs2Icr5OmzrlquwqbLwK6zYtdlYNdbsWkTtV1nxaa1oFKM3Jv8hU40LvLfL+zmnYPNKGQCM4sy+O4V41hUlsn4HKP0EHYGbDYbt9xyCxs2bEiGN4vH44RCIYLB4GnrQCCA3+/H5/PR2NiIz+c7ZYIavV6PRqNBoVCgVCoZO3YsCxYsGBHXp+jmleReew3tH2/GV12dSInu9uCvrSPqcRPxeJHH4xzZuOnkjQUhERfboE8knTEYUOj1iRjavdqUFjO6/Hw0uTnIRsjbhvr6et5++23q6+tpbGykoaEhWbe3t/e7jV6vx+Fw4HA4KCwsZNasWTgcDtxuN/Pnz08uczgc2Gw2lErleT4qCYmBUy80co/qx6k2I6W0C41DsdtXSHQs/xk46xSeZ/wHFkWxCWjqnvYIglAO5AFlwEfdq70HrAN+eMLmK4GXz9aoU6FWqPhC6SK+UPpZBqpAJEirL5HAps3XQYc/kR2y3e9kd1MDXcGT08Ka1AbsOitmjYnxmWNYPm7ZiLjBjwSOdsV552AzV0/O4X9XTMaokW6M54pMJkvGGx4o4XAYp9NJW1tbUvC0tLQQDocB8Pl8+Hy+5Pr19fVMmjQJi8Uy6PYPR5QmIzlXXdHvMjEeZ+O77zJn8hQi3SI86vEk0qZ7vYlMkD4vUa+PqM+Hv74+0eb1Eu8+v/0x6WcPYZ44cagOKaX8+Mc/5sEHH0zOZ2dnk5ubS0FBAXPnziU3N5fc3Fyys7OTwjkzMxO9Xt/v/no/aEqcGYupieu+8HCqzUhrHnm36Zz3MVaI85raf+YVL2CuE+JDsduoKIp/+Lwbn1VXhyAIxcB0YBtwEFgOvAbcCPQ3suym7nV686wgCDFgDfCw2E9udEEQ7gDugETSiw0bNgzIPj1y9NgpxA5KwAwxUwxvLIA37scT8+GN+WmLdnKssxaAPU2fYm4+OVua1+sd8OcONamy5cTrMJzOyYmIokiFM86L5QFAoFTpZNfWzak2a1D4vL+Hc+F011oURcLhMKFQqN8SCASIRCJ9tlEoFKjVavR6PSqVqk9RKpWo1Wr27t3b7+cN9fEMlPN5HbzRKNuOHP6sQS6AWgkxDQIiyBIuJ6iUoFYj6HRg0CO4PQhdXf3uc9/b64i3tQ3chmH6e+/vOqxZsya5XKPRkJubS1FREQUFBRQVFVFUVITVakUQBPx+P9XV1VRXV5/yM1Jx7Kn6TAkJif4RBMHaPfkvQRD+G/gnkHxNK4qicyD7GbDQFgTBQEIcf6vbF/tW4DeCIPwQeAMIn7D+HMAviuKnvZpvFkWxodsFZQ1wCwn/7z6Iovgk8CTArFmzxHPtWQhGQ+xu/JSK9ipa2xqp8SVeLWTp7dwybQUX5U87aZvh1KORKltOvA4Gg2HYnJMeRFFk3cFm/rDhKPvqXZhUAr++aRrXTc9LtWmDxmD/HgbCqb5zoijy61//Gre775sihUKByWTCZrNhsViwWq1kZGQka41GM+Q2n47B+A2dj+sQcbnwHKni0z37yDcYCDa3EGxuJtjSinjCw0sPcp0OpcmE0mxCmZODOtOOOjMzUdsT00qL+ax9u4fTf2Bv+rsO+/fvp7a2lk2bNrFp0yb27t3L+++/3ydTqc1mY+7cucydO5dZs2bxhS984ZRvMlNx7Kn6TIkLB6VBTs58U6rNSCnKNYMamWkXibGJPX8UvccfisDogexkQEJbEAQlCWH8giiKrwKIolgBLOteXgacOCrkK8BLvRtEUWzorj2CILwIXEQ/Qnuw+fuBf/Hm4Q8AmJw1lhsnXc2s3KkUWfIkl5E0Z9ORdu5cvRuAh6+bhMN3jGUXkMgebgiCgE6nS/qwTp48GZPJhFarlX5L50CovYPaF16i7aNNiNEocqBFo0GTnYWuIJ+MWTNR22woegS12YTSbEZpMiGT/IYRBCHZc71q1Sog8VDY2NhIRUUF5eXl7Nmzh2eeeYa1a9cC8MYbb3Dttdem0mwJCYlhjCiKowZjPwOJOiIATwPloij+qle7QxTFVkEQZMAPSDiK9yyTAV8GLu7VpgAsoii2dwv3a4D3B+MgzsTcghm8efgDVHIlheZ8ii352HUZkjC4ANCrP3t6ffKjY4zWhWnR1zAhx8jYbBMG9cgYCHY+WbVqFU888QRbtmxBp9NhsVgwm81YLBb0einqz+eh+e13aP1wPYJcTvGtX+OIAIuuvVY6l+eAIAjk5eWRl5fHkiVLCAQC/OMf/8DtdnPvvfdy5ZVXptpECYnBRS0iFg+Jj3L6oB684L2CIMwG6kRRbO6e/zdgBVAD/HgwXUcWkHDxOCAIQo8T5X1AqSAI3+iefxXonYv2km7jjvVqUwPrukW2nITI/vNAjDxXyuyjefSKH/LygTe6wwQmercLzLmMt49hvGMM4zNLsWpHxkCsC4mZRVY23XspGypbWV/ZxidVrWx47TNvpUKrjvE5RsbnmBiXbWJCjokCq9T7ei7o9XpmzpzJzp07ef/9vs/KPS4kRqMRg8GAwWA4adpoNJ7VwMqRgGHMGOR6HTGfn+pn/oJCENj7/nr0o0djKBmFfvQo9KNGodDpUm1qWiGKIrt37+af//wnL7zwAm63mz/84Q/ceeedqTZNQmLwCQkI1SM8/GdoUO/tfwKWAgiCcAnwv8A3gWkk3NduGMhOBhJ15GM+8085kcdPsc0GYO4JbT5g5kCMGgoKzLl8Z+GdhKNhqpw1lLcdoaK9io9qtvHu0UTwlBUTruKmydKrxHSjwKrjlnnF3DKvmPXr11M2fS7ljW7Km9xUNHsob3Lz7qEWeobd5pg1LB2fxRWTsplfYpNE9+dg6dKlLF26lGAwiMvloqurq0/t9XppampKJrc5Eb1eT1ZWVjIRzrx580a0+LbNm4N17kWEWlvxHTvOwQ/Xo47F6dq7l7b1G5LraXKy0Y8eja4gH9PECVimSFk6T8ef/vQnvv71rwNw6aWX8tRTT7FkyZIUWyUhIZEmyHv1Wt9EIo/MGmBNr47nMzLi3qurFComOEqZ4CjFHw5wqO0Ir5evo7LjGGsOvcU1Y5egV0m9RumKIAjkWbTkWbQsnZCVbPeHo1Q2ezjY6GbTkTb+saue57fWcOWkbB67aRoapZTa+vOg0WjQaDRkZWX1uzwej+NyuWhubqalpYWWlhaamppwuVwcO3aMY8cSL738fj/XXHPN+TR92CEIApqsLDRZWcRDQSZ0D4wLOzvxVB6mbdMmOjZ/QrCpmY7ubS56/lmUppE9+Ol0vPjiiwA88sgj3H333Sm25sKid/SXgkx1iq0ZuUjXYUiRC4KgEEUxCiyh+zx3M2D9POKEdiga5u0j69lcu5PargZEROQyOfMKZnJF6SJJZF+g6FQKphdmML0wg1VziwhGYjy89hCrt9ZyxyVuphdmpNrEtCQajeJ0Ouno6KC9vZ2Ojg5cLlcyTrbf339MV4VCQU5ODjk5OZhMJmbNmnWeLR9eiKJIzOcj2NpKqKUN2e49HKs6RrC5hUBDA8HmFognfC8FpRLLlEnkXH2VJLJPQygUoqKiAoCXX36ZqVOnsnTp0hRbdeHQO/rLjDGmwXOMlTgrpOswpLwEbBQEoZ1ERvNNAIIgjAFcA93JiBLa4ViEb7/zEG2+DsZnjuGGiVcxLnMMpbZRaBTSk+BIQqOUY1AnojWMzjSk2Jr0obW1lbq6Ourr66mvr6ejo4PeofCNRiMWiwWbzUZhYSF6vb7fotFokI3gVOKiKOKpPIzrwKe49h/AW3WUWK+HkmTUkSwH+uJiMi9eiLagAF1BPtq8XCnSyABQq9VUVlby3HPP8fjjj3P55ZfzjW98gyeeeCLVpklISKQBoij+VBCED4Ac4N1eeV9kJHy1B8SIEtqRWCQpsh+8THqNONKZO9rKHzce5cVttXx9cUmqzRn2VFVV8cILLwCg0+nIz89nwoQJ2O12bDYbNpsNtVp6YB0ItatfpP4frwKgKy4ic9ElaHKy0DgcqB0Odh45zKLTxHmWGBgZGRncddddXHXVVYwbN47f/e53fOc736GoqCjVpklISAxzBEEwiaK4tTtxjVoQhJ4bXHt3GRAjSmjrVTqWllzM+0c38XHNDhYWzU61SRIpIBiJsae2iw2Vbcl5iTOTm5ubnJ44cSKLFi06ZQpridPTuXsPANr8fHKvvQb7gnnIew8GrauVRPYg8Morr/DLX/6SnTt3olAouO+++ygsLEy1WRISEunBiyRCUZ+YuAYGO2HNhcS/T7+Ruq4GfrP1GbbU7WJW7mSmZI3HrreeeWOJtEMURWqdfvbVu9hX18W+ui7217sIx+IIAnx5Vj7/tWhAv5URj06n4+6772bjxo3s3LmTffv2sWLFCsrKylJtWtox4Uf30/rBelo+WE/Vb3/HsT8/Tc5VV1C06quJ1OoS54zH4+HLX/4yAF/96lf56U9/SnFxcWqNkpCQSBtEUbymuz6nxDUjTmir5Ep+dOm3WHPoLdYf/4SdDfsAyDZkMj6zlAmZpYx3lPbxO5VIL+qcfjYcbuOTo+1sPebE6QsDoFbImJhr4j8WFjNnlJWZRVbMWsnX9WwwGAxcffXVzJkzh5deeom1a9eSk5ODVqtFoRhxfyefG1VGBvk3fIm8Fdfjqaik+e11NLz6Gp2795Bz1RUQ8BP1epHrdGedOl0igVwu5wtf+ALr1q3jxRdfJB6P89JLL515QwkJCQlAEIQioEsURVf3/KXAdUA18DtRFMMD2c+IvDMq5Uq+Mnk5N036IvXuJvY1l3Oo7QjbG/ay/vgWAAwyHQe2VjMtewILi2YjE6SbXTpQ447x779cD0CuWcPisZnMLrYyJd9MWZYRpVy6joOB3W6npKSEHTt28KtfJRLGKhQKNBoNWq22T91f24m1coQO7hMEAdP4cZjGjyNj1kzq17zK0d//CSWw7dnnQSZDaTSgMBpRGI0oTd210YjCZEJh0CNTqZApVcjUKmRKZWI+WZR9lgsKxYhxSVGr1Tz55JPs3LmTFStWcPDgwVSbJCEhkV78HbgecAmCMA14Bfg5iYQ1vwduG8hORqTQ7kEQBArMuRSYc7lm7BLiYpx6VxM7G/fz5sH3+bhmOx/XbMcb9nFV2WWpNldiAOxrS/hbj7Lr+eutF1FglcI1DhWLFy+msLCQQCBAMBhM1j3TbreblpYWgsEgoVDotPs6lUhXq9XJolKp+syf2KZUKtNaRGZeshD7xQvwHqli9/vvMyYvn6jbTcTjJerxEHG7CTa3ED1ylIjbjRiNnv2HCMIJYjwxLShVyNUqhOQyJXJnJ1WHKpFrNcjVamQaDXKNBrmm97QGmUadqNWa5LqpcH9pbW3l/vvv58iRI9TU1FBfX0+01zm6/PLLz7tNEhISaY1WFMXG7ulVwDOiKD4qCIIMkBLW9EYURYLREO6QB0/IhzvkwR3y9iqJeU/IS5uvg66gu8/24djJme0khidXjVIyvnQMj71/mMsf28g3LyvltotHoVZIfq+DjU6nY9KkSQNaNx6P9xHhJ4rzE0W60+nE5XL1m1XyVAiCkBTeGo2Gq6++Ou0GvgmCgLGsFLGxgbzuhDX9IYoi8WCQqNdHPBImHg4TD0e66zDxSIR4KNz/snAYMRIhFkrU8XDos+XBEFG3h3gkjOB242xsJBYMET/Dg9KZmPLLn2McO7S+/GvXruWpp55Kzn/pS19i6dKljB49mqKiImkswRAS8cZo2uI+84oSpyTiPfdB+dJ1GJzz2IvePTeXAd8HEEUxfjadOmkrtOPxOM5gF+0+J66QB3fQ2y2k+wroHmEdifff+6OQKTCpDd3FyLScieSbsnHWtHHF/MvJ1NuQyySRli4oZAK3XzKaa6bm8JM3D/F/6yp5cVst/7GgmC/PLsCkGZkuCoNFLBYjFAolRXEoFCISiRCNRs9Yn9h2uvUGSm9xrVKpkqWnN/xCRRAE5Fpt30gl/RCPRomHQsRDYWKhUPd0KDEdDifEeHI+1Gs+jLf6OBabLTEfCBLxeIi43ETdbuLhAbkmJpHrhv7N0oIFC1i6dCnbtm3D4/Hw6quv8uqrr6LVajEajRiNRgwGw4Cma2pqiEQi/S4fqW5Op6MyaGRR5aJUm5HWtAbrz3kfSoOcnPkjO4mVcs2g6rUPBUH4O9AEZAAfAgiCkAMM+E8wLYR2V9DNhuOf0OJtp9XXTquvg3a/k1j85CcXrVKDSZUQzlZdBsUZBX2EtEltwNhrXqNQ9/u6eUPzBrKNjvNxeBJDQI5Zy+9vnslHh9t4Yn0VD68t59fvH+GGmfksHGNnXI6RPIs2rV0NBpNwOMwbb7xBY2MjKpWKQCBAXV0d4XA4KaiDweBZ9TBDwiVEoVCgVCpPqrVabXK+v3VOFM/9zcvl8rS/hvFIhFgg8FnxBxCOV9Ou2EwsECDqD/QSyOFuQdx7PtxXQPcSzz3ZJM8GQaFIuI8AnrZ2ZGo1MpUauVaLymJGplIn2tRq5GpV93IVqgwLKrsdtc2Gym5HoTv9Q8BgU1ZWxnvvvUcsFqOiooJt27ZRV1eHx+PB6/X2qdva2jh+/DgejyfZHh/guVKpVAMW7oWFhUyaNIlx48ah0WiG+AykjnyVmYdzr061GWnND1RvnvM+utw5vLbuB4NgTfrS5f76YO7uW8BNJBLWLBRFsecGmA3cP9CdDHuhHYgEuevtB/GF/ZjUBhx6OyUZhczNn45DbydTb8WiMWFSGzGq9SjlUm+DxGdcUpbJJWWZHKh38fTHx1i9tYbntlQDYFQrGJdjZLTdQK5FS65FQ55FS65FS7ZZg0Y5ct5ktLW1nTRYzO3u+wpy2rRpWCyW5ADHnl7jUwlpxQgaeHc6YqEQNX9ZTaCp6SRBHQsE+vW1VgCVJ7QJcnm3wFUlfKa7BbBMrUJpMiJT2fsVwIn5xHqfTfdepkpuJ1OpkHVHj9mwYQMzT+O+MlyRy+VMnDiRiRMnDngbURQJBAJJ0b1+/XrGjh2bFOa9RXp/0y6Xi/r6+j7t/b2ZmT59Ops2bZLiz0tIpAHdmSBf7qd9z9nsZ9gL7TZ/Bxa1iZ8suYd8U06qzZFIUybnm/n1V6bz8PWTqWz2UNHspqLJQ3mTmw8rW2nznOyDajeoybVoyDVrTxLiuRYtdoPqghKSBoMBr9d7UrtSqcRms3HppZdiMo3s15Kfh7qX/07T2rcwjClBrtOhNBm73T50icGDWi1y3WfTCp2OfeXlzF4wv3sdLTKNOimAJQYfQRDQ6XTodDqysrKoq6vj4osv/tz7E0WRYDDICy+8wO23355s37NnD4FAQBLaEhIjiGH/zy2KEIgGcQU95BmzLyhhI3H+MagVzCzKYGZRRp/2UDRGiytEQ1eAxp7iCtDQFaSqzcvGw20ETsggqVPJKbTqKLLpKLLpKbTqKMk0MDnfjEE97H9aSURR7DOIrAe73U5eXh52ux2DwUBrayuhUAiTyXRB+z8PNqHWNpDJmProLwe8jdjViS7NBnKORLq6uti6dSstLS04nU46OztxOp3Jsnv37pO2eeihh/jNb36TAmslJCRSwbBXAw69DYAH1z+GTZfBnPzpjM8cQ6l1FFadJcXWSVwoqBVyCm06Cm39D9oSRRFXINItxIPUd/qpdfqp7fBT1eplfWUb4WjCx1MQYJRNj1mn5GfXT2Z8zvDuBRYEga985StJn2yv10tDQwPxeJzy8nLC/Qx8U6vVmM1mTCYTJpOJzMxMxo0bh8Ui/SZPJOJ2QzzOvnu+S971y7EvmJ9qkyTOgWg0ys9+9jP+9a9/sXv37j6+3YIgkJGRgdVqJSMjgxkzZmC1WrFarfj9ftrb2/nmN7+ZQuslJCTON8NeaGuVGh678gG21+/lk/rdvFv1EW8d/hCADK2ZqVkT+MqUL2LVSjd4iaFDEAQsOhUWnYqJuWYAIrE4bZ4Qze4gTV1B9td3sfFwGxXNHo61+wDYdKRt2AttgLFjxzJ27Njk/IYNG1jc7Z8bDofx+Xx4PB5cLhdutztZu91uqqqqAFi3bh0A3/rWtzCbzef9GIYrZXffReuH66n5y/Mcf/o5SWinOW+88QYPPPBAcn7RokVMnz6dMWPGMGrUKDIzM7HZbNhsNkwm04h4C1sfdnFv49pUm5HWtIZd57yPZpXALwqGvawbUppVw+/3lhZXRKvUsGjUXBaNmks4FqG6s44qZzVVHdVsrt3B9oa9fGfhnUx0SHFSJc6OSCyOJxjFFYjgDkQSdbC7DkRPmO8u3et3+sOIYt/9KeUCeRYtRTYdt108ikvHpn/kmp4oHxkZn7nbNDQ0cOjQITo6Ok5af6DRGy5k4tEoHVu2EmxqwldTi/fwYQAyZs1MsWUS58ro0aOZPXs2ra2tuFwuNm7cyMaNG0+5/u23386TTz55Hi2UGKlEIw20tYzsqCPRSEOqTTiJtBDavVHJlZTZR1NmHw1As6eVu95+kAfXP8bT1/0fRrUhxRZKnE9EUSQQieHqFsmVzhiRQy2nFc3uXqLaFz59cHulXMCsVWLSKDFplZh1KgptekwaBTaDmmyThmyzmiyThmyThgydCpls+D1RDyZut/skn+6srCwmTZpEdnY2kBDbMtnITXdf/8oa6l7++0ntka4uqn7/J5RmEyqLBblen4j6oUpkZZSr1QhKJTg7Cba2JqKK9GRvvABCGaaKUChEU1MTjY2NNDY24nK5TooH3zMdDAapqanhj3/840nLek+Hw2FkMhkqlapf96oe5s+X3mBISIxk0k5on0i20cGU7AnsafqU/3ztO2TqrIyyFlJoziXHkEWO0UG2MRODShrlPdyJxuI4/WGcvjAd3jAdvjAd3lBi3hfuJZ6jfcRyNH5Ct/L2nX1mDWoFZq0SoyZRF1h1SfFs1ioxaxUJEa1VflZ3L9MoZZK4OQGj0cjy5ctpaGggGAzS0dFBR0cHH3zwQXIduVyO1WrFZrNhNpv7pFXvXXra0j19+onkXH0lUY8HQaFAjItEXF1EulwEm5vxVFQQcXs46XVIL5TArr+s7tsokyXC7/VKk56o1b2mu2ulKhHOr0+69dOlXFedsE8VhMLEo9G0EPjRaJRjx45x8OBBDh48yLFjx5KiurGxsd83LyfSk1G0J951z/e2p91oNJKZmdlnvd7LT5zWaDRMmTKFqVOnDvXhpxyFwYJtzvJUm5HWOHeeu+vNJDts+o+R28EBcPF7qbbgZNJeaAPcNf82jnQc55izluOdtRzrrGVH/T5EPruRGdUGii15LBl9MXPyp0nZHs8joijS5Y/Q6ArQ7ArS6ArS1NUzHaDNE6LDF6bL338yFJlAwj9aq8TYLYQLMrTdIrmvOK4+fIiL585Mzhs1ChTykf3HM9gIgsC0adOYNm1ask0URXw+X1J095T29naOHTt22h4/AJlMlhQnJpOJK6+8Eocjfd1ulCYTo++47ZTLxViMiMdD1OvrToMe7lM+3bePcSUln6VGj0QSSWmS656QOj0SOXU69nD4cyWuUQKf/P5PpxT4cq0WbW4O2rw8tHm5aPPy0ORkIzuPmRPD4XC/DwG5ubnk5eUxevRoFi5cSO7/Y++8w+Oozv3/Odv7rqRVb5ZkyZZ77+AKmBoCCSRAQkJuSJzcdEr4AQkJ5JIQWggJgQsk3ISYYgLYFBuDLWxw791ykWT1rl2ttu/O749drSUXLNuyV2U+z3OeOXNm5uy7Ozuz3z3znvfNyIiV9PR0EhISuolijaZ7qM6ucxRkzkxBsJL/tP083mb0a64PVp53H4HmMHX/PjlE62Ai0Nz3XBcHhNDWqbSMTh3O6NThsTZ/KECDq4n9jYfZWLWdXfX72V1/kN31B5mSNY67Zn4vjhYPTNrcfg43uDja1EFFcwflTW7KovUTXTRUCkGqRUe6VcewNDNJRi1JJg1JRg1JJi2JRg12k4ZEoxabXt1jd4ySloOMyZInxl5shBCYTCZMJhO5ubknbQ+Hw90ezXs8nm7OtPySAAAgAElEQVTrnW0+n4+ysjKef/55rFYrNpstVhISErDZbBiNRgwGA1rtqbO69geEUonGZkNzmigtks9Lai8KvXAwGBPfUsBPyOc/hcDvFO2R1OyHDuwnLzvntNuDHR207dhFw6qSbq818X+fQ3eR/iSd6vzPnz+fpUuXYrgIad9lZGT6LilWAz++qvisjnlmz9Zet2NACO1OXP4OVh1dR5WjlmpnLdXt9bgDntj2NFMyErAg/9wTEchAOCxxuNHF9mOt7K9t51BDO6X1rm5JX1QKQXY0xvSUvESyEw1kWHWkWXXRZC9alAPcl1nmOAqFIpYQ5Ew4HA62bt1Ka2srra2tlJaW0tHR8YV9nlhsNhtjxoxBqZSfXAEoVKpIwpuzSIt+0KgnuwdiP+h2U/rk07RujvxAaRITz9XMs0atViNJEoFAgF27drFs2TJ++9vfMnr0aJ555hmuvlpOCy4jIxNfBozQliSJxz97nn2Nh0jQW8k0p3FJ7hQyLWlkWdLIS8jBqJFHOM6VUFhiZUWAl17ayI5jbbT7IumFDRolhSkmZhclU5RqojDFTJ7dSGaCHrXssiFzDlitVubNm9etze/309bWhsPhwO1209HRgdvtjpWGhgbKy8u7HZORkUFqaupFtHxwojIYKPjed9m6fSdSMMiGm25h4gvPobUnXTQb1Go1EydOZOLEicyZM4dFixZxzTXXcO211/LHP/6xW+hKGZmBitqkJH1G3w8neyFRv9X3BlcGlNBu7Ggm2ZDIn695GIWQRV5vsr/WSet+P0WpXq4bl8H4nAQm5NgYkmQc8FE2ZC4sgUAgFtHB5/Ph9/tPWe9cP7HN6XR28wEXQpCRkYHdbo/ju+rfSOEweDx4qmsIulzR0tGl3r0t0N6OFIz8+ZZCIYKu9osqtLsyZ84cdu7cydNPP83DDz/MyJEjmTp1Kvn5+RQUFJCfnx8rqamp8lMPGRmZC8qAENrN7lY+OrwGl99NujkFgSz8ehuNSsFvZui4/brZ8TZFph8QCoXo6OjA5XKdVE5sP9NESYiIZ61Wi1arRaPRxCayWSwW8vPzYxFOOqOcDObQgj1BCodxHTmKp7oGf0sL/uZm/C2t+Jtb8Lc0429tQx0McnIC8QgKnQ6VyYTKZERlMqHPzCRp+jQSJ03ENLQAEWfxqtFouOeee/jWt77F448/zubNm1mzZg2vvvoqUpdoLwqFArvdTmpqarfS0dHBsWPHSE1NJSkpKfb9GiwJaM6WBoebZz7ofd/WwUTD+eerAa2ENKTvTQa8qGhPH80pXvRLoS1JEtXOOjZV72Bz1U6OtFYgEEzJGsdtY78s3wgvAL5gmL/u8NFmOMSVo9MoTDHJn/Mgx+l0UlpaSnNz80ni2ePxnPIYnU4XmzSZnp6OyWTCaDSi1+tPEtJd6yqVSv6+9QKemhrKXn4FV+khAo7jv+wKnQ5tUiKapCQsI0eiSUrkWHMzw8ePiwrqTlFtRmU0XNTIIudDSkoKjz32WGzd5/NRUVHB0aNHOXr0KLW1tdTX18fK4cOHqa+vx+Px8Pzzz5/UX2fYyk7h3fUPXkZGBldeeSXFxcWD7rtqNBcx9ZLn4m1Gv2ZZ/aLz7qOtMYN3n/9VL1jTf2lrPP/PsbfpV0K72lnH2oqNrD+2jVpXAwCFiUO4Zcz1TMsaT5q5/4YD6+tkJehJ1Ame+riUpz4uJTfJwOUjUrlsRBoTcxPkiY2DiPr6et5++23q6+sBUKlUmM1mTCYTdrud3NzcmJjuLEajEZPJhErVr245A47Wrdtp3RyJM5+yYD6ZX7oWjd2O6hSTJMtLSkiZM7CeYGm1WoqKiigqOn0WYUmSWL58OUVFRdTX19Pc3ExLS0ssZGXXelVVFTt37qS5uRm3281dd91FYWEhv/71r7n11lsv4juTkZHpq/SbX71/bH+TD0pXIYRgVMowrh42j0mZY0nUy6HcLgYJBg33TtFTPGEaH++v56O99byyroL/XVuG3aTh1qm5fGvGEBKMmnibKnOBaWpqor6+nmHDhjF//nzsdvugG8Hrr6RfcxXhQICqJW/R8PEnuA4dYtxTj8fbrD6FEAK9Xk9BQQEFBQU9Pq66upqlS5fy0ksvcdttt5GVlcXs2QPrj4qMzGBCCDEZqJQkqS66/k3gRqACeEiSpJae9NNvhPbnx7ZgVOt54spfyeI6jqRadNw6NZdbp+bS7g2wprSJt7dX8adPDvHujmo+/vlsOUHMAMPtdnPkyBFqa2upra2NRfeYMGECycnJ8TVO5qwQQpB1w/WkX30lZS/9nfoVKwkHAvKEwF4gMzOTRYsWccMNN5CWlsacOXPYunUrEyZMiLdpMjIy58bzwAIAIcSlwO+BHwHjgBeAr/SkkzMKbSGEDlgDaKP7L5Ek6ddCiDzgNSAJ2Ap8Q5IkvxBCC/wfMBFoBm6WJKk82td9wHeAEPBjSZJW9PTdGtQ6ar1O7vvo94xNG8Hc/OkMtw+VR9LiiFmn5uox6Vw9Jp0/f3KIJ1aWUt3mITdJTnff3wmHw2zbto1du3Zx7NgxJElCqVSSmprKhAkTYln3ZPonSq0WVTSmua+pCUNWVpwt6t+Ew2HKy8vZtWsX27dvj7X/4he/YPXq1XG0TEZG5jxQdhm1vhl4QZKkt4C3hBA7etpJT0a0fcA8SZJcQgg18JkQ4kPg58BTkiS9JoT4GxEB/Vx02SpJ0lAhxNeAPwA3CyFGAF8DRgIZwMdCiCJJkkKnetETeXjeXWyr3cOO2r1srt5BSfl68hKyGZUyjKFJQyhMzCPJkCAL7zjh9EbSpze5/LLQ7uc4HA7Wrl0bW581axbFxcVyKLQBgr+1lbIX/07TZ5+DQkHY6zvzQTLdCAaDbNq0ieXLl7Nq1Sp27dpFe3s7EHlqMG3aNG655RZuueWWOFsqIyNzHiiFECpJkoLAfODOLtt67BFyxh2lSCwkV3RVHS0SMA/ovIu8AjxERGh/KVoHWAI8KyLq90vAa5Ik+YAyIcRhYAqwvieGWnRm5uRNZ07edHxBP5+Wr+fTsg18eKiE4MFI/FarzsLQxFwKk/IYmjiEwqQ89GpdT7qXOU++NC6Tt7fXcONz6xibZWX2sBTmDEtmTKZVdiXphyiVSkKhyH/g3bt3M3nyZFlkDwDCgQDbfvBjQm43mTd+mfSrr0KbdPEyOQ4U1KeIujJu3DiuueYarrjiCtLS0jAYDKhUKgKBwCn3l5GR6fMsBj4VQjQBHmAtgBBiKNDjgIw9UuRCCCUR95ChwF+AI0BbVOUDVAGZ0XomUAkgSVJQCOEg4l6SCWzo0m3XY84KrUrD5UNnc/nQ2QRCASraqjncUs7h5nIOt5SztWY3AEqhoMiez5jUYq4YOhuTVh5pvVCMyrTy6d1z+NeGClbsrePZVYd45pNDGDRKJuYmMDUvkUuLkhmVYZUT3PRxrFYrs2bNYsqUKRw5coRly5axYsUKvvrVr8bbNJkeIIVCBJxOAg5nbOlrbKSjrIyOo2WE3G4Asm/+KkqtNs7W9k8effRRHn74YYqKimhvb6eyspIdO3awY8cOHnnkkZP2V6vVGAwGjEYjRqMxVu/a5nA4eOedd07aptPp0Ov16HS605au2+XIPjIyvYMkSb8TQnwCpAMfSceD8CuI+Gr3iB5dkVH3jnFCCBvwNjD8LO09K4QQdxIdok9NTaWkpOSMx+iAUQxhlGEIPp2f+kAL1f56jrXW8nrjYY6VH2O8sedmu1yuHr3uxSBetpx4HnpixzBg2AhwDTWwtznEwdYQpbXNrD3UxOMflWLRCMalKLk0S0WBVdHrrj596bz1FudyPZwvLpeLTZs2AWCz2di3bx9/+tOfMJvNsfjWXUtfH+3uje/FRTsPkoTL4aDk/Q/A70P4/OD3g88H/gDC54us+/2RuscLHg/C4wG3J9J2qm7NJqTkZKSpk5Gys1i7/osfJvbVa+linIczvfdp06bx/vvvx9bD4TBtbW3U19fT1NSE1+vF4/HEMp56vd5TFofDEdvP6/VSUlKCx+MhHD73pCMKhQKNRoNCoSAQCBAIBM65L5n+RZWo4S7NQ/E2I640iZpe60sIYZEkaYMQIhHQRucgAjRFS484q7++kiS1CSFWA9MBWxfflSygOrpbNZANVAkhVICVyKTIzvZOuh5z4uu8QGRGJ5MmTZLmzJlzNmae2Bc/fO8BDgTL0eoNZFnTybamk23J+EKf7pKSEs7ndXuTeNly4nkwmUxnZcc1XerNLh9rDjXyyf4GVh9oYE2Vl1GZFn5z3Ugm5vbeo+u+dN56i968HnpK189xxowZbN26lc2bN1NdXX1KEaDVamMxs081Wtd1Xa/XX/TMjb3xveiN8xB0ufA2NOBrbMLX0IivsTGybGoi2N5OsMNNyOMhIRg8Y18KrRalwYDaYkadlITKYkFttaC2WlF31juXCQmozeazsrWvXksX43qIx3svKSlh6tSpOJ1OWlpaqKuro76+nsbGxlhpamqK1RsaGmhqOvm3PhwO4/V6L6rtMn0DlTqT5NSTn6gMJtrUd555p57zbyJSZisRl+muglECehQRoCdRR5KBQFRk64HLiExwXE0ktMlrwO3Au9FDlkbX10e3r5IkSRJCLAX+LYR4kshkyEJgU0+MPB+EEPx0+nd4fc8ydtTtpaT8+CiOXqUjy5JGljUjIr6tGeQn5GDWmi60WYOOJJOWL4/P4svjs3D5gizdUcNfVh/mK39bz21Tc7l74TAsOtmPsS+i0WiYPn0606dPR5Ik3G43LpeL9vb2bsvO9OqNjY1UVFTgjroonIgQArPZfFLR6XRotVpGjx49ICc1N2/YyIFHHzupXWkwYMzPw5yRgdJgQGU0UFFbS+HoUSj1kXWlQY/KYERp0KM0GFDq9ShkF4F+SSgUora2lrKyMg4cOMDevXvZt28f27dvP6VwPhVarRaz2UxeXl4sWVTndWSz2bBarSctT2wzm82sXbuWuXPnXuB3LCPTP5Ek6ZroMu98+unJnTodeCXqp60A3pAk6T0hxD7gNSHEI8B24KXo/i8B/4xOdmwhEmkESZL2CiHeAPYBQeCHPY04cr4U2fN5cM5PAHD5Oqh01lDpqKXKUUuls4ZtNbtZXbYutn+aKRlz0ID3EAxNHMIQWxYqpfyj1luYtCpumZrDl8Zl8MRHpfxjXRkf7avjqZvHMaPAHm/zZL4AIURshDo1NfUL9w2Hw3g8Hjo6OroVh8NBZWUlVVVVpzxOqVQycuTIC2F+XLEUD8eYn0fH0bJu7SG3G+eevSj1etRWCyqzBRH049JqI6PUFgtqixmVxRoZvQ6GEEIgDAbERX4yINMzJEmisrKSLVu2sGfPHsrLyykvL6eiooJjx44R7PLEwmAwUFxczPjx45k9ezYJCQkniecT6/LkSplTkeaXuLfyzE/DBjIP+KUz79RDhBC5ROYjOqLrc4HrgXLgL5Ik+XvST0+ijuwCxp+i/SiRqCEntnuBU86akiTpd8DvemLYhcKkNVKcXEhxcmG3dqfPxbEukyr31B7g5W2vA6BWqMhLyCHHmkGKyU6KMYkUY2Rp1poG5OjbxUCnVnLFyFT21jjYWNbC65srZaHdzwkEAt1GuLsWh8NBS0sLDoeD43NKwGKxYLfbMZlMpKenU1xcHMd3cOFQW62xLIwhn4+Oo2X4W1sJOtsjkxajExeDTieipo22XXsIOp2E/ae5lysUXUR4xGVEn56GLiMdfUYG+swMVGazfH+6iNTU1LBo0SLWrFlDW1tbrD0jI4Pc3FymTJnCTTfdRG5uLrm5uQwfPpzc3FwUCkWfddWRkRnEvAF8GXAIIcYBbwKPEklY81fgv3rSiTxMG8WiNTEqdRijUocBsHr1akZPHcvh5nIONZdxuKWcTdU7cPpc3Y7TqbRkWdIpsudTlJRPYdIQko1J8XgL/YJgKMymshY+2FPL8j31NLl8aFUKvjk9l19eeUHn2Mr0IpIk0dzcTFVVFVVVVVRXV9PS0oL/FKKwcxTcbDaTlZXFmDFjSEpKIjExkaSkJPR6fRzeQXxRarVYik//fS8pKWFWVHSFvF4CTucpBXmgvT1W7zh6lJYNG5FCxx8UqkwmkmZMI/vmm9Da5fvShaampoalS5fG1q+99lpuvPFGxo4dy9ChQzGZBo5bYtdJqXZTSpytGbzI5+GCopckqXN25W3Ay5IkPSGEUAC9mrBmUCKEwG5IxG5IZFr28RS63oCXho5mGjqa2FG7j8+ObY6MgreU8wGrAHj2mkdIkcV2NzYebeadHTV8tLeO5g4/erWSecNTuHJ0GnOHpWDUyl/F/kBzczNbt25l//79sRE7rVZLZmYmOTk5sUfcnRMjOydHXuzJjwMJpU6HUqeDlDP/iIaDQXwNDXhqavHW1NKwajX1H31My8bNTPm/ly+CtYObiRMn8oc//IEVK1ZQWlrKsmXLWLZsWWx7RkYGRUVFsTJ16lRmzZoVR4vPna6TUvNThvXe83qZs0I+DxeUro8D5wH3AUiSFD6bJ4WyuukBkiTR7G6lyllLpaOWQ81l7G88hMMXyQSmUarJMKciEEzMHINdnxBni/sO7d4Av3hjJx/tq8eoUTKvOJWrR6cxuygFvaZvh4ST6U44HObll1/G7XaTmJjINddcQ05ODna7XXZP6CMoVKqI20hGBpIkUf1OZHQ1ZcG8OFs2OBBCcM8993DPPfdQU1PDu+++y5tvvsm6devw+XzU1NRQU1MTCx2oVCppaWnBYrHE13AZGZlTsSo6t7AWSIDIaKoQIh3okX82yEL7C9lTf5DFu96h0lmLN3g8Nm2SIYExacUUJxcy3F5AhjlVHrE7Df/acIyP9tVzz8Jh3DEzD51aFtf9FUmSYt/zlpYWVq5cSUZGBpmZmbGlWfYJ7jMIISj6xU848OgfqX77XUIdHWTe+GV0PRgZlzkzwWCQ2tpaqqurYy5UnW5Ue/bsYc+ePQCYTCby8vLIysqKlczMTLKysiguLpZFtoxM3+WnwM1EgoLMkiSpMyh9GnB/TzuRhfYJ+IN+ttXuYZVjI+Wf1WLWmpgzZDpZ1nSyLOlkWdOxyOH/ekySSQNARZObUFh+qtWfUSqV/OxnP6OxsZHq6urY6Ny6detisbWHDBnC7bffHmdLZTqxjhzJhOf+TNlL/6Bu+UfUr/yEUY/8BsuIgTnh9GJx77338thjJ4dq1Ol0ZGVlUVBQwDe/+U0uv/xyxowZI//5lJHpn3xZkqTXAIQQMVcFSZK2n00nstA+gRe3vkZJ+XoUKJiRM5GvjfmS7G99Hnx1YhblTR089+kRPt5fz+0zhnDTpGzSrLp4myZzDigUClJTU0lNTSUhIQGFQoHX66W1tRWAoUOHxtlCma40b9hIw6oS2rZH5u1YR49Cl5YWZ6v6P3Pnzo0JbYvFwtq1a8nMzCQxMVEW1TIyA4cHgP9E658AE75g39MiC+0TMGoMAIQJ0+p1sPxQCRatCYvWhEljxKI1YY4Wk1qe5HUmhBDcs3A4C0ak8uyqwzy5spSnPi5lypBErhiZxqVFdgqS5RCJ/Qm/38+xY8d49dVXASgoKGD8+PGMHDmSxMTey/Ipc350VByLJchJv/oqkufOxlwo/xHqDRYuXEg4HI7d/ysqKjCZTCQknD7b8ECmyu/gnpr3z7yjzGlp8DvibYLMyYjT1M8KWWifwK1jv8yEjFEs27SCVr+LFYc/JRAKnHJfgcCkMWDSGrFojgtws9aERWvEpIksY20aEwaNHoUYfOJ8Qk4CL39rMkcbXSzdWcOynTX89r19AKRZdEzJS2RCjo0JuQkUp1tQKwffZ9QfeP311zlw4AAQ+RN1/fXXM2bMmDhbJQMRH3pPZRWO3Xtw7N5N88bNCKWSEb+6H9u4sfE2b8AhhGDmzJl8/vnnXHfddbH2wsJCNm/ejNVqjaN1MjIyvYBeCDGeSLJGXbQeE9ySJG3rSSey0D4BlULJ6NThNJvrYskDfEE/7T4XTp8Ll78Dp89Fu89Fu98VrXfQ7nPR6G7haOsxnD4XwfDpszPNz5/FHRNuQq0cfNm98pNN/HRBET9dUERli5vPDjfx2eEmNpW1sHRnJFylRqWgKNXE8DQLxekWitPMFKdbSDBq4my9TGc2x9tuu43s7Gw0GvmcxJOQz0f7wVJaNm6ief1G/M3NAGiT7aRfuZDML1+HNjk5zlYOXD777DOOHDnCPffcw3/+E3nCXFFRIT/plJEZGNQCT0brdV3qABKRkH9nRBbaPUCr0qBVJZJosOEN+vAEvLgDntgyUrx0+N20+ztw+Vw0uVupaa+nyd1yUn+fHP2MIbYsriicHYd303fITjTw9Sk5fH1KDgA1bR62HWtlZ2UbB+raKTnYwJKtx9N0p1l0DE+PiO7haWZGpFvIsxtRyaPfF5yqqirWrl2Ly+UiLy+PgoKCeJs0KJEkicbVn+LYvRvX4SO4q6ohHEah0WAbP5aEr92EbcwotKmpg9KFIR7cfffdvP322wDccccdvPDCCyiVcnQlGZn+jiRJc3ujn0EttCVJot3fQVNHC03uaOloocXroKq1mk8+3ow7eFxUewJeJL44coZKocKsMWLSGkk2JpGfkINJa8SsMWKOupPoVBomZciP208kw6Ynw6bnmjEZsbbGdh/7a50cqHOyv7ad/bVOPj/cRCAUOQ+do9+jMqzMHGpH+OXIJr2Nx+PhX//6Fz6fj5kzZzJt2rR4mzQokcJhat/7gLKX/o7aasU0tICk6dMwDS3AOnoUykGYYbMv8NRTT1FQUMA///lPXnnlFQoLC/nJT34yqDKeZmmsPJJxdbzN6Nc8oHkv3ibIXCAGpdAua63kxa2LOdZWjS/UPea4RqkmUW8jFA5iUVpI11nQq3UY1HoMan23uiFa72wzqvVoVVp5JKkXSTZrSTYnc2nR8cff/mCYI42uqACPiO8Pdtfy2uZKBPB2zWb+9PXxmORsk2eNJEk4nU6amppYtWoVtbW1VFRUEAgEmDFjBgsWLIi3iYOGkMdDR3kFriNH6ThyBOf+A3hr6zAXD2f0736LkEdN40owGKS8vJyKigqKi4u59dZbefLJJ7nvvvu47777aG1txWazxdtMmUFElajhLs1D8TYjrjSJmjPvdJEZdErEH/Rz70f/E1tXCgUTM8cwIX00Y1KHk2SIzBovKSmJ+WjL9C00KkXEdzv9eKKHUFhiZ1Ubf31vEx8faOAPHx7g4etHxdHK/oEkSZSWllJVVUVtbS21tbW43W4gMtkrOTmZMWPGMHnyZFJTU+Ns7cDFXVmFc/9+PJVVuKuqUR06zIan/hzbrrZaMRbkk/P1r2G/ZCZC9gGOG62trTz11FM8//zzNDQ0xNqFEGRmZlJdXc0NN9yATjd4QpjWaQR/yB50cqJXqdOc/wCdSp1JcuojvWBN/6VNfWe8TTiJQXdlqJVqfjjldjbX7KSpo4Vjjho2Ve1gU1UkzmyyIZEMSxpSewBrrZ1xaSPlEep+gFIhGJtlo6YjjEohuHK0HCu4J5SVlfHaa6+d1G6xWBg/fjwGgwG1Wk1jYyNtbW2o1eqTikajQa1Wo1Ao5GvlHKha8h8q/vlqbF0olUgZ6eRcfSWmgnyMBQVoEgdn2Li+yO9//3see+wxMjIyeOmll8jLyyM3N5esrCx5crCMjMxJDDqhLYRgdt40ZudF/EyD4RA1zjqqnHXUtNdR7ayjxllPpbuGXWv+Qq4ti2+P/yojUoribLnMmWh1+9nfHGZqfiIzCuzxNqdfkJeXx7e//W0aGxtpaWmhpaWF5uZmmpqa+PTTT8+qLyHESeK7a1GpVLFyNutftG0giM+mz9d1W5dCIRSVVVS+/iYqkwm11YLaakVtMaMym1GZTKjMZtRmEyqTGZUlujSZUFvMKNSDL5pRbxIOh3G5XFRUVNDW1tattLa2UlpaCkBNTQ3f+c532Lx5M/n5+XG2Or6k+SXurTx9pC2ZM/NAL8wvMjcfZM6/B3eQhbrmeFtwMoNOaJ+ISqEkx5ZJji2zW/snqz/hqLmelUfW8tDqp7hr5veYkjUuTlbK9ITWDj8KgRyD+ywQQpCTk0NOTk639tWrVzNr1iwCgQCBQAC/3x+rn6503ScYDHZrc7lcBIPBWHtnPRg8vx9npVKJSqVCr9eTkZFBZmYmxcXFJCQknPngPsLYJx4j6Oog4HQQcDgItDnZt2UzQ1JSCTic0bY23FXVBF0ugu0upC/43BRaLWqbFW1ycrTYoyU5tlRqtRfxHcYXj8dDTU0NjY2NNDU1feGyubkZh8OBJPVc9Hg8ngtoff/AqKhiqvmeeJvRrzEqqs680xlIsRr48VXFvWBN/2XVK/vjbcJJDHqhfSJtHgfrKrfyTvMK2hraAcg0p5FpkV0R+iJOb4APd9fy9vZqNpa1YFDBHbPy4m1Wv6fr6PSFIBwO4/F46OjowOl04nQ66ejowOPx4Ha7Y8vO+unETCgUIhQK4fP5aGtrY9++faxcuZJf/epX/Wa0WygUqC1m1BYzZGUBEA74yDnNHBFJkgh7vQTa22PCO9jeTqDdFV1vx9/ahq+xEcfuPfhbWiAc7taHymKJCW+VQU/I5yN1/jwSJp5ThuG443K5ePPNN6mpqaGqqipWKisraW4+9RCXVqslOTkZu91OcnIyeXl5JCUlkZCQQFNTE5MmTcJms5GQkIDNZosVi8Uih++T6ZOoTUrSZ1jOvOMARv1W37s2ZaEd5fNjm1my9wOqnXUApKoT+cHEbzI5c2wsLbtM30CSJNYfbebVDcdYub8efzBMnt3IT+cXkReuYu6wlHibKBOlsrKSTZs2EQqFcLvddHR0xAT0mVCpVGg0GjQaDUajMeaO0nXZuU/nn4LW1lYKCgr6jcg+F4QQKPX6SDi/lDN/18PBIP6WFnz1DbRu207T2s/wNTYRdCg5A/0AACAASURBVDrpOHIktl/z5+uZ+e5bF9L0C8aSJUu44447YuvJyclMmTKFadOmkZWVRWZmJikpKd2EtdFoPO33RJ4ML9MfCbhC1K5zxtuMuBJwheJtwknIQjvKqqOfx0T2rWO+jL5WMCdvepytkjkRhyfAN1/exM7KNhIMar4+OZvrx2cyLtsWjRZTHW8TZbpw4MAB9uzZc1K7SqUiMTGRpKQkEhMTSUxMjI0Wms1mNBrNgBbLF4Og24OrtBTngYO0HzhIe2kpoY7IHxyV2YQ+Kwt9Zib6zAw0NiuWkSPibPG5c+utt/Lee+/x1luRPwqNjY1s2bIFr9cbc18KhUKo1WrMZjNCCPn7JSMzwOkrI/yy0I6yaPI3eW7zP9nfeJhXd72NAsGez8u5smgOw+1D5ZtyH2H1gQZ2Vrbx/64azjenD0Gn7nuPiWSOM3fuXLxeL3V1dXi93lgJBoM0NDR0C4/WlYyMDKZMmUJhYSEGg/xE6Wxp27GTvb95pJvLSMKkidhnzcA8fBi6tLQBdU9Tq9UsWbIEl8vFzp072bp1K9u2bWP//v18+OGH1NXVneR3/f3vf5/nnnsuThbLyPQ+fUVYxhPZdaQPYzcm8uCcn+AL+iltPsrSTR+yu+EAG6q2kW1J58aRVzMjZ2K8zRz0pFkjsWmzEwyyyO4HqFQqrr322m5tkiQRCATwer14PJ6Y+O6su91u9u/fzzvvvAOAyWQiOTmSsGjUqFGMHz9+QInEC4EhJ4e0hZfjOnSYjrJypGCQ1i1b8VRXYx4+nPzv3oHKaIy3mb2OyWRi5syZzJw5s1u73++nrKyMBx98kDfffBOASy+9NB4mysjIDDJkoX0CWpWG0anDaTbXcdesH1JStp6Xtr3G0+tfZFr2eBRCjmgRTzojioTOIiqATN9CCBHzvbZYTj36MnfuXI4dO0ZlZSWffPIJLpcLiMT9ttvtJCUlYTAYZMF9GjSJCRR877sAhAMBOsrK2XX3L/HW1uGtrSNhwjiSL70kzlZeHMLhMEuWLOH++++nvLycSy65hIceeoh58+bF2zQZGZlBgCy0vwCtSsOEjFG8tC2yXuOsJ8uaHl+jBjmjM61kWHU8+VEpV4xMk0P5DVCEELEkIKtXrybcxQXi73//e7d9b7jhBkaPHn2xTew3KNRqDLk5CJUKKRhEbbXirqyicc3aqJ92xoAN9xcIBJg3bx6fffYZ48aN46OPPuKyyy6Lt1kyMjKDCFlonwZf2M9ru5fyQekqAKxaM3r14Emp21fRqBT8+rqRfO+fW3mu5Ag/mif7zw9klEolDz74IOFwmPb2durr61m8eHG3ffR6fZys6z8otVrGPf0EFf/6N+6KCqqW/Oe4/7YQaFOSMWRloc/KRJuSgm3smPga3Eu89957fPbZZzz11FP8+Mc/RiGnrpeRkbnIyEL7BILhEFuqd/Ja83I6Gj1Mz57ItcMWMDRpSLxNk4lyWXEq14xJ58mVpaw60MAvLi9i1lC7LLj7OeFwmF27dtHU1BQLAdg1JKDX6+22/5133kl6uvyEqacYsrMovi+SVCTs9+OpqcVTVYW7qpqOsnJaNmykdeu24wf89L/jZGnvUVlZCTBgsojKyMj0P2Sh3YUNldt4edvrtHmd2JRm/mfBvbLA7oMoFII/fW08M4fa+fMnh/jGS5uYkpfILy4rirdpMueI3+9n8eLFlJeXo1AoMBgMGAwGjEYj6enp3dYNBgOJiYmyyD4Hwn4/3voGvLW1eGrr8NbV4S6vwHX4eDxtfVYm+Xf+FztaW+Joae/w7W9/m/fff58f/ehHLF68mAULFjBixAhGjhxJYWEh2gHqMiMzOJHjaF/4ONpCiASgTTqL9LGy0I7S7G7lH9vfRJIk7pn1fZylzbLI7sMoFYKvT8nhhgmZvLapkmdXH+bmFzYwMkmBJb+VCTn9JwW3DKxbt47y8nISExO56aabSElJkUcge5Ggy8X2H/8Mf3N38aw0GNBnZZK28HIsI4oxFw9HY7NFNpaUXHxDexmz2cyHH37Iiy++yBNPPMEjjzwS8/dXKpVcfvnlvPvuuxcsA6qMjEz/RQjxK+ANSZIOCCG0wHJgLBAUQtwiSdLHPelHFtqAP+jnoVVP4g54uPeSHzAypYiSQyXxNkumB2hVSm6fMYSbJmXzrw0VPLNyPzf8dR3zhqfw88uKGJVpjbeJMj0gJyeH5ORkGhsb+dvf/kZCQgJFRUUUFBSQlJSEzWaT/WvPA4VWGxPZ6ddeTfIls9Clp6GKJm8ZyCgUCu68807uvPNOPB4PpaWl7Nu3j3Xr1vHss89SVFTEsGHDePTRRxk/fny8ze2XyPGbz5++GP9ZhpuBh6P126PLZKAIeAWQhfaZaPM42NNwkHf2f0R9RxMPzP4xI1Nk94P+iF6j5LuX5pPtr+CIMpsX1hzlmj9/xsKRadx/dTHZiXLSk75Mfn4+P/jBD2hvb+fgwYMcPHiQLVu2sHHjRiAiloxGIz6fD61Wy6233kpqamqcre4/SKEQiVMn07JxM42friX3tltQ6gbf5G69Xs/YsWMZO3Ys+fn5PPvss5SXl1NeXk5ZWRkHDx6Mt4kyMjJ9B38XF5ErgNckSQoB+4UQPdbPg1JoH2g8zP9uXUylowaAJH0C917yA8akFcfZMpnzRacS/HDOUL4xPZeX1pbx4tqjXPmnJp7/xkRmDrXH2zyZM2A2m5k0aRIjRoygqqqKnTt3cvDgQUKhEO3t7cDx5COy0D6ZcDCIp7oGd3kFHeXldJRX4KmsxNfYFNsn6HTirW/AmJsTR0vjz0MPPQTATTfdxPTp0/nqV78aX4P6MbJv8PlzoX2LZc4JnxBiFFAPzAXu6rKtx6N3ZxTaQggdsAbQRvdfIknSr4UQecBrQBKwFfiGJEl+IcTPgf8CgkAjcIckSRXRvkLA7mjXxyRJuq6nhvYmr+56B5evg1vGXM+Y1OEMsWXLj6UHGBadmp9dVsRXJmbxrb9v4p4lu1hzz1yUioH9mLy/0NHRQWNjI06nE6fTicPhoL29PVZ3u92xfXU6HZmZmWRlZZGdnU12djbGAZjV8GwJ+Xy0HzhIR3kF7vJyOsoqcFdWIgWDAAiVCkN2NpYRI9BnZWLIzkKflYUuLRWF7JPM5MmTWb58Obt27WLMmDE4HA6Sk5PRaDTxNq3fIbuOnD+94Toin4ded8H5KbCEiLvIU5IklQEIIa4Ctve0k56MaPuAeZIkuYQQauAzIcSHwM+jL/yaEOJvwHeA56IvPkmSJLcQYhHwGBE/FwCPJEnjemrchcKkMdCmVHN98RXxNkXmApOdaOCnC4r40eLtvLW1ipsmZ8fbpEGPz+fjT3/6E4FAINam0+mwWCxYLBbS09NJSkoiJSWFlJQUzIPAj/hcOPq3F2hYVQKAOsGGccgQMsaNwTBkCMa8IegzM1CoBuVDyx7x29/+lrFjx/L000/zwAMP8MADDyCEIDMzk4SEBMaOHUteXh7Dhw/npptuQiV/ljIygwpJkjYAw0/R/gHwQU/7OeOdI+qf4oquqqNFAuYBt0TbXwEeAp6TJGl1l8M3ALf11JiLhV6lo83rxBPwykloBgFXjkpj8pAEHltxkK9OypJFWxwpLS1l2bJlBAIBLr30UkaPHo3FYpFHEc8SSZJoP1gKwORXXkZjkyf9ngs33ngjN954IxUVFaxevZqysjL27t3LO++8w+7du2P7jRkzhlGjRsXRUhkZmYuNEOI2SZL+FfXUOAlJkp7sST898pcQQiiFEDuABmAlcIRIHMFgdJcqIPMUh34H+LDLuk4IsUUIsUEIcX1PXru38YcCrKvcii/kx+lrj4cJMhcZlVLB6Ewb7d4APY98KXMhWL58OS6Xi3HjxjFlyhTsdrssss8BKeqLDRGfa5lzR5IkvF4vbrebPXv2sHz5ckKhEJmZmdx1111s375dFtkyMoOTTh9F82lKj+jRs7DoLMtxQggb8DanGEo/ESHEbcAkYHaX5lxJkqqFEPnAKiHEbkmSjpzi2DuBOwFSU1Mp6cV4rkEpRIY6mSp/Pfcvf4ybEi9HozjZX9HlcvXq654P8bLlxPPQlz6T03EqG8OSxMpdXrKMsGbNp/Ex7Dy4kNfD6bhQ59pgMOB0OtmxYwc7duxArVaj1+vR6XTodLpY3Wq19uqTh954PxfzPJzJXsWmLXR6Im7avg2OnnQbveA2xIvePg/PPfccb7zxBgAWi4W5c+cyY8YMpk6dikKhoK2trV9fc2d6TRkZmVMjSdLz0eVvzqefs3I6kySpTQixGpgO2IQQquiodhZQ3bmfEGIBcD8wW5IkX5fjq6PLo0KIEmA8kdHxE1/nBeAFgEmTJklz5sw5y7f1xSxgPttq9vCHtX9li/IAd836Phpld7FdUlJCb7/uuRIvW048DyaTqc98JqfjxM+qod3LL9/aTWW7mz9+ZQxzJvU/H+0LfT2cigv1nZszZw7hcJi6ujrKyspobm6mra2N1tZWGhsb6YykdM011zBx4sRee93eeD8X8zycyV5XTi4HDh/BV99Adl0DeVdf3ev+2H3pHtiV3j4PWq02JrSdTifz5s1j/PjxF/29x+Pz7ot/pGRk+gpCiO8CJZIkHRKRkZ+XgBuBCuB2SZJ6NCGyJ1FHkoFAVGTrgcuAPwCrga8QiTxyO/BudP/xwPPAQkmSGrr0kwC4JUnyCSHswEwiEyXjwoSMUdw56Rae3/IqT3z+Ar+85Aey7+4A49PSRn72+g46fEEeunYEX5mYFW+TZIjExM7IyCAjI6NbeygU4qOPPmLTpk3dJkrKnIwpP4/xzzzF/t/9nroPlpMydw7mosJ4m9Wv8Pv9PP744yxfvhyr1YrD4QDgV7/6FUuXLo2zdTIyMn2AnwD/iNa/TiQrZD6RQeJngEt60klPfLTTgdVCiF3AZmClJEnvAfcCPxdCHCYS4u+l6P5/BEzAm0KIHUKIzjtWMbBFCLGTiEj/vSRJ+3pi5IVifsEsvj76S2yv3UO1sy6epsj0IuGwxItrj3LHPzaTYtby3o9m8a2ZefIfqT5OS0sLmzZtAsButxMKyXFlvwilTod1dMR32FNdfYa9ZTpxu9387//+L+PHj+f+++/H7/dzyy238Nxzz/H5559TW1sr3ytkZGQAgpIkdY76XAP8nyRJzdHU6z2OMduTqCO7iKj3E9uPAlNO0b7gNP2sA0b31LCLxfDkAgCOOarJsqbH2RqZ86XVG+a2lzay7kgzl49I5YmbxmLWyTGD+wN6vR4hBJIk8eqrrzJq1ChuvPHGeJvVZ3HuP8CxVxcD0H6wFNPQoejS0+SQfmfgS1/6Eh9/HMmcfNlll7F48WKSkpLibJWMjEwfJCyESAdagfnA77ps0/e0k0F/Rw6EIoFTnl7/Em/seY/CpDymZ0+Is1Uy58rbhwOsq2rmsRvHyKH8+hlGozHmow2wZ88eKisrsVgsKJVK2tvbue6668jJGdwZDTsx5g0h7aqFtGzcRN2HK6j7cAVCpUKfmYE+KwtDdhaaxATUtgQ0NivqBBsamw3FII/ycv/997N9+3aam5tZuXIlDz74IH/961/jbVa/o+uk1OxkbZytGbx0PQ8ZFs2gz9DZyxk2fwVsAZTAUkmS9gIIIWYDR3vayaAX2qNTh/PQ3J9xoOkIh5rL2Fa7h0/LNzBSX8Cl0qUohJwxsj9R6wozPM0sJ6bphwgh+O///m9qamrweDw4HA42bNgQ850F2LFjhyy0oyh1Ogq+913y7/wv3BXH6Cgrw32sEndlJR1HjtC8bj2nimepNBrQ2GyobTYSJk0k8/rrEAM8M67H4+HVV19l8eLFbNu2jba2tti27373u3G0rP/SdVLqhKEWOXBqnOh6HkanGeXz0ItIkvSeECIXMEuS1Npl02aOJ2I8I4NeaAshGJFSxIiUItp9LrbX7uXZjf9gr+cIKw+v5YrC2WfuRKbP0OyVSNJItHT4STQO7pG7vkJra2tMLPv9fnw+30nlVO1ut5twOIzdbmfkyJEUFRWRni67d52IEALjkFyMQ3K7tYcDAQIOJ/7WVgIOB4HWVvytbXiqqmlc+xme6hqce/cR9nrJueVrcbL+wrNixQq+8Y1v0NjYSHFxMTfffDMTJkxgwoQJjBo1Cp1OTlomMzCQU7D3bgr2aJhqIUnSP0/YdCMQAv7dk34GvdDupN3n4jvv3A2AXq3DipFh9vw4WyVztlxXoOZf+zuY+j8fMzUviTnDkpk3PIU8u1F2I4kD7e3tPPPMM2d1jF6vJy8vD61WS15eHqNGjZLP3TmgUKvR2pPQ2o/7H3f16wbQZ2ViGTUyHuZdFDweDwsXLgRg5MiRbN68Gb2+x66VMjL9ioArJLuO9K7ryI+I+GafyH+ANchC+zihcAinz0Wb10mb10Gbx0mb10mzu5Xq9jqqHLU4umSJfPn6x1m7Zi1DEmT3g/7GnGw1X79sGv/ZVsWqAw088v5+Hnl/P7lJBuYUJTNxSCLjsmxkJ+pl8XYRMJlMLFiwgNLSUsLhMJIkEQ6HY/VQKITX68Xj8RAOh4GIOLruuuvQamW/z95CkiRCHg9Hn3+xW7unqpq9Dz4UWx//56cx5Ayc+94nn3wSq+/du5f333+fr3zlK3G0aGAiC7zzp5cFokzvoJYk6aSsTpIkdQghehxlod8KbUmS6Ai4I+I5KpyPFweOLu1OnwuJk12XjGo9mZZ0JmaMJsuaTpYlnaGJQ1Aqeu/Rg8zFZ1iamfuuKua+q4qpbHFTcrCBVQcaeH1LJa+srwAgwaBmTJaNsdk2xmVbGZtlI8kkC7veoFNA+/1+AoEARUVF5OXlxdYDgUCs3nW5fv36WB+///3vAVi0aBEpKSnxeit9AkmSCPv9hNxugh0dhDrcBN1uQh0d0WW03e0m2KXeuX+ww03I44HoH5kvwnX4yIAS2h988EGsLoRg5cqVKBQK5s+fj9VqjaNlA4uOcBYb2+OWFmNA0BFedN59yK4jves6AuiFEEZJkjq6NgohzECPfVP7hdAOhUMcaamgvK2S8tYqytoqqXTU4A+dnNRCpVBh01mw6SwkG5MoTMrDprdE26yRpd6KTWtGo5J9eAc62YkGvjF9CN+YPoRAKExpfTs7Kx3srGxjZ1Ubz646RDj6HywrQR8R3lEBPirTgkHTLy6RuCJJEi6Xi4qKCrZv3055eXlsdLqnqFQqDAYD4XAYr9cbax8oTx2Crg7cVVURAezxRIrbExHKsXU3IXekrmpsZMu/X4+tS8HgF7+AQoHKYEBpMKAyGlEaDWiTkzEMGRJpN0bbDQZUXepKgwGVyYjKYBiQ0Uj++te/ctddd7FixQpWrFjB4sWLeeGFF1AoFKSkpJCamhpbdi21tbWkpqZSXFwc77cgI9Nj2pzpvLPigXibEVfanOf/h6ULLwFLhBDflySpAkAIMQT4C8dzx5yRPq8imtytfH/pfTHXDqPGQJ4tmwUFl2A3JMZEdaeYNqoNA+bHWaZ3USsVjMywMjLDyi1TI5ErOnxB9lQ72FnVxs5KBzuOtfH+rloAFAKKUs2My7YxKtNKbpKB7AQDGTY9GtXAjtJwKiRJorq6mmPHjtHa2kpbW1usBKNC0Gq1MmXKFAwGA2q1Go1G06PlQL9mDzz2OI6du065TaHVotTrURr0KPUGVAY9ksWMJTsbpd6AUq+LiefjYtmIymhAaYgKZr1uwH+G50p+fj6LFi1i0aJFBAIB1q9fz+rVq6mqqqK+vp76+noOHTpEfX09Ho8ndtwvf/lLysvLyc3N/YLeZWRkBiqSJD0uhHABa4QQpmizi0jCxed62k+fF9rtvnaKkwuZlj2BoqQ8kgwJ8g+KTK9h1KqYmp/E1PzjE8Ya233sqmpjZ2UbO6ocfLinjtc2V8a2CwFpFh1ZCXqyEw0MTzMzKtPKqEwrlgGSHEeSJFpbW2lubqa5uZnGxkYOHTqE0xnxw9TpdNhsNpKTkyksLMRms5GSkkJOTg6KAR4q7lzIuO4aHDt3YRiSy9AfLkJts0ZGmvV6hPLkR50lJSUUzZlz8Q0d4KjVai699FIuvfTSk7Z1PpmxWI4/ep84cSKjR49m3LhxTJkyhalTp5KXJ2eZlZEZLEiS9Dfgb1F3ESRJaj/DISfR54U2CK4vvpy8hBz55iZzUUg2a5lfnMr84lQg8gNc4/BS2eKmqtVzfNnqZt3hZv6z7Xj664JkIy/ePpk8e4+zs/YpfD4fTz/9NF6vlzVr1sTatVotubm5zJs3j8LCQgwGQxyt7H8kTppIyoJ5NHy8iv3/8wfGPPY/qEymMx8oc9EQQmA2m2ltbeXvf/87SqWS3bt3s3v3bp5//nmefvppAJKTk5k7dy7XXnstCxcuxG63x9lyGRmZC825COxO+oHQlvjlyt9zx4SbWVg4J97GyAxChBBk2vRk2k4dFqzZ5eO9XbX8eulejjR2sLOyrd8KbYVCgU6n6+YnPWHCBMaPH09GRoY8Wn0eFP7oh2gSE6l6Ywlhry/e5gx6JEnC4XDQ1NREc3NzzB3K4XBw4MAB7HY7Wq2WwsJCEhMT+fzzz3E6nTQ2NvLGG2/wxhtvAJE48TabLc7vRkZGpq/S54V2ri2LBL2Vl7e9Tl17A18b8yV0Kjk6hEx8CYclShva2VzWwrKdtWwqb0GjVPDl8ZnMHdZ/o2So1Wp+8pOfsGLFCsxmMwcPHmT79u1s27YNrVaLxWJh2rRpTJgwId6m9isCznb2/eZhXIePoE1JRp+dFW+TBiyBQIDy8nIOHToU871uamqKCerOektLS2xuwalQKBRYrdZYGTduXKzeeW3MmTMHs9l8Ed+djMzpqdMI/pDd52XdBaVO0/c8H/r8GVEIBQ/Pv5sle9/nw8Ml1Loa+O+p38KslR+7ylw8OnxBdla2saWilS0VrWw/1kq7N/IjnW83cs/CYXxlQhYploGRZU6r1TJjxgxmzJiB2+3m6NGjvPXWWzQ2NrJs2TKUSiWJiYkkJCRgNMrJgM5E+8GDuA4fAcDX0MjBPz6JbcxorGNHo0tLkz+/86SiooLhw4d3exLTiUqlIikpCbvdjt1uZ/jw4bF61/aEhISYkN61axcLFy6Uz0sPqfI7uKfm/Xib0a9p8DvibYLMBaLPC22AFGMSP5jyTQoT83h522v87MPf8K3xNzEzZ5J8I5S5INS0edhS0crW8ha2Hmtlf207obCEEFCUYubasRlMzElg0pAEchIHdqQbg8HAqFGjqKur4/PPPwfgnXfeiW1XqVTY7Xays7PJyckhJyen24QyGUicPIlJL/6Ntl27cezag2PXbpo/XweAJjERfVYmuvR09Bnp6NLToLmFsN8/IEPuXQhcLtdJIvvaa6/lj3/8I0VFRWd9fR46dGhAX9MyMjIXj34htDu5bOglFNnzeH7zqzyz4WXWlG/gG+NuJNuaEW/TZPo5Ll+QNaWNfHa4ic8PN1HR7AbAoFEyLtvGD+cUMCE3gfE5CVj1AyOyyNmyYMECFixYgN/vx+FwxHxaW1tbqa+vZ8eOHWzevBkhBN///vcHfaKZE9EmJ5M6fx6p8+chSRKe6hocu3bTfuAgntpamtetI9geSUKmBtb/899o7Uno0tMjIjw9DX12Fgnjx50yUslgZuTIkYTDYQ4dOsQnn3zCkiVLWLZsGR988AGTJ09m0qRJ3HnnnYwePTrepsrIyAwy+pXQhojP9iPz72bF4U9ZvPtdfrH8YbIs6UzLHs+0rAlkWzPkkQiZHrOjMpK0Zs2hJvzBMCatimn5idw+fQhT8hIZnmZGpZQnAHZFo9GQnJxMcnJyt/ZwOMwbb7zBwYMH2bZtG9OmTcNqtcrX4ykQQmDIysSQlUn6VQtj7YH2dry1dWxbtYo8WwLe2lq8tXU0r1tPsD0y6d2Qk03Bou9hGSEnU+mKEIKioiKKiopYtGgRu3bt4sEHH2Tp0qVs2LCBkpISdu/eHW8zZWQuGGl+iXsrz5DcaoDzgL9LFnCthDTk7JKnXQj6ndCGyCSVK4vmMiNnIhsqt7Ohahtv7fuQJXs/IN2cwozsSVySO5kMS1q8TZXpw/iCIe78vy00tPv49swhLByZxsTcBFlYnyMKhSIW9m/jxo1s3LgRtVqN3W7HZrNhNptPWbRarSzGo6jNZtRmM1JNNTknxNEOuly07dhJ+f/9iz0PPkTx/7uXhInypNQTkSSJZcuW8fDDD7NlyxbUajU33HADjz76aLxNk5G5oMiTIeXJkL2OVWfhisLZXFE4mzavk81VO9lQtZX/7P+Qt/Z9wFdGXsVNo66Nt5kyfRRJArNORUO7D4NGyfA0iyyyz5PrrruO+fPn09TURGNjYyzCQ2NjI0ePHsXnOzmsnUajYejQoYwYMYLi4mI5hOBpUJlM2GfNxDZuLLt+eT+HnvkLI3/7a4y5OfE2rU9x991388QTT1BUVMT/b+/Ow6Mqz8aPf+9sk2QSyB4CYQ2b7BgQFBegrQtVwWor1VKXvq6tVVvftl59K9bWtr61Sq2t/tyK9rUuRVxA0WIRAUVAgUQSCEtYAoRskH2d5P79cU7CEAmZQJIzkzyf65orZ845c84952Rm7jnzPPfz5z//meuuu87Uuu5isXUFXL3nMafDCGivmoqfPVZAJ9reosLcDIrpT1ldOUVVRzlSWcQXh780ibbRpvDQYP51+3k8+E4Wf1u9h3+s389tF6Vx04whRIb1mJdGt3O73bjd7pMOXV1bW9sy7HVBQQGFhYUcPHiQ7OxssrOzmTNnDlOnTnUgav/W1NBAfUkJdSUl1B4pQBsaaCgtZccf/pf0p550Ojy/Ul1t9a8oLy/nk08+QVW57bbbCA/vGRWBDMMILAGdTRRVlbD58Da2Hskiu3AXNZ5aBGFo7EDmjr6Y8webD2zj1OLcYTzx3cncMTONP/17J3/8IIe/f7KXTft9owAAIABJREFUWy4YxnemDCTWbao+tKWxsZGamhqqq6u/cms9v/n+ya5oBwUFkZqaSr9+/ZgwYYIDz8RZTfX11JUctRLp4hLqi4sJysxk+6ef2fdLaCg7sfRXaN++JM6aSb9LL3Yoav/10EMPMWrUKNavX89rr73G66+/zr59+3jkkUcIM1VcusQxVzJvpF3vdBgB7Vjey06HYHSRgEy0K+oquXP5/1DnsT60k90JzBg8lQnJoxmbNNLU2DY67KyUPjx3wxQ2HzjGn/6dw+9X7OBPK3dy0chEZqTFc97wBEYkRfXKtsSqSlFRETt37mTPnj2UlZW1mTQ3CwsLIzIykoiICCIjI4mPjycyMvKEec23mJgYXK6ePwhVfWkp1fv2U7V3H1X79lOdl0d9cTENZeVfWTfI5aK2XzKuhHiihqfhio8nLD4eV0I8YQnxRKSkmMojbUhISODuu+/m7rvvZvLkyfziF79g0aJFPPPMM5x//vnMmjWLWbNmkZ6eTkhIQH4E+p2QqBjip811OoyAdvRzU4e8pwrId5lgCSYyJLwl0Y6J6EtCZCxJ7njcYZEOR2cEsrMHxfLyf01nx5FyXtlwgFU5hazMLgCgT3gIZ6X04ayUPoyx/45IjiI8tGcnPGvXruWjjz46YZ6IMHDgwJbhqVsn0CaBOdGht5ex74XFLfeDwsLoM3YMUcOHW8lzfByuhAQrmY6PY+2GDUxu1RnS6Lif//zn3HLLLXz88cd89NFHrFq1ivvvvx+AKVOmsGnTJocj7Bk8laWUbHjb6TACmqey1OkQjC4SkJ+GkWER/L+5f+Bw+RHW7N/I1vwsXv3yHV798h36uqIZFjeIuaMvZkzSSKdDNQLU6H59+PXccfwayDtazfo9JWw9WMr2/HJe/zyP6vpGAIKDhGEJbiv57t+H6cPiGT+gL8FBPefK96hRo8jOziYoKAiPx8OxY8fweDzk5eWRl5dH//79WbBggWkDewqxZ0+mev8ByjIzqSsqpqm+nuoDBwhxu+k780JTqq8LxcXFcdVVVzFt2jRyc3PJysoC4Oabb3Y4MsMweoOATLSb9e/Tj/njr+TacVewsySXN7LeY+uRbLbkZ+Fp8phE2+gUA+MiGRgXyXemDgSgqUnZf7Sa7fnlLbcv9h/jnYzDAMRGhjJrdBILLx9L38jAHNzG4/FQXFxMRUUFFRUVjB8/nvLycsrLrWYORUVFLesePnyYAwcOMHKkeb21JXJgKkN/cCN1hUWUZmZS+OEqqg/kUbzuE+pLSxn/8ENOh9jjLVu2jHfftX6enzdvHkFBQXzwwQcMGTKEwYMHmy+KRsAzvyz45y8DAZtoNzU1sa0whzX7N7Dl8DYq6qsAcAWHce6gdL4/6WqHIzR6qqAgYWiCm6EJbuaMT2mZX1xZxye7i/l4ZxHvbD1MeU0Dz90QGB1y6+rq2LJlC1u3buXYsWPU19e3DLfezOVyER0dTXx8PMOHDyc+Pr7lFh0d7VDk/sdTXUP1/v1U5e6lMncvVXv3UXskn8aq6hPWCwoLI3LQQIb/6A6HIu1dbr31ViIjI/nd737Hu+++y1tvvXXC8pSUFCZMmMCMGTNwu91MnToVt9vtULSG0XGmrbx/tnUPyET7SEUhv1/zV/IrC4kMjWDKgAmMTkhjeNxQBvZNITioZ7eZNfxTQpSLuZMGMHfSAKrqPHyQVcDe4iqGJvj/h/VLL73E4cOHERHGjRtHVVUVkyZNOmFgGVOxoW21R45Q9PFajm3ZSkXOTmiyRiMLiY7GPWwoiRddiCsxkfDkJFyJibiSEgk1o2Z2KxFhwYIFLFiwgMbGRvLz89m3bx979+5l37595Obm8vnnn7Nw4UJUlccff5wDBw6Yc2QYxhkJyER76fb3ya8sBOCBmfcwLM4M2GD4lx/NGsHGvUe5dNEafjhrOHfNHu50SKc0c+ZM1q1bx4EDB8jKyiI+Pp7BgwfTp08fp0MLCLv+8jfKt2UR7I4k9VvziD5rNO6hQwiLizOJmh8KDg4mNTWV1NRUzj///BOWlZaWMnfuXNasWcPhw4cZMGCAQ1EahtETBOQQbN+bcBXfGnMpUWFufrN6EV8W7HA6JMM4wfjUvrz74wuYPTqJx1buZM2uYqdDOqURI0Zw0003ceeddzJ16lSKioq+UmnEaNvgBVYN4ab6BhrKy2mqrUWCQ0ySHSBUlfz8fFauXMnixYtbSleWlJQ4HJlhGP5ERGKlg2/sAZlo9wmPZv74uTxy8f1EhEbw29VPkF240+mwDOME/WMi+PpZyQCUVAbG+LqJiYmkp6cD9Ira1p2lz+hRTHnhWRLOn0Hx2k/I+eNjbLrhZrIWPkR9aVn7GzC6XVFREYsXL+baa68lKSmJ/v37c/HFF3PvvfeyY8cO7rrrrl45gJJhGBYReUBERtvTLhH5CNgDFIjI133dTkA2HWnmaWqkuPoo/aISSYpKcDocw2ixNa+Uv/xnF//ZUUj64FguGdvP6ZB8tmfPHgBCQ0NRVXNV1keu+DhG3nMX2ngnlXtyKd2ylYNLlrL59h+SOPMiBn7nGsLiYp0O0wCOHj1KWloaFRUVpKSk8M1vfpP09HTGjRvH2LFjyc7OZqapY24Yvd21wG/s6Rvsv4nASOBF4ENfNhLQiXZ+hTWQSH1jA5V11SRExjkckdGbVdd7eGfrYV7ecIAvD5XRJzyE/75kFP91wVBcIYHTQXfChAls2rSJdevWkZeXx9lnn83AgQOJiYkxSbcPJDiY6JEjiB45grhp55Dx059xZMX7BEeEM+SGBU6HZwBbtmyhoqICgIULFzJp0iTGjRvXUmUkOzvbyfAMw/AP9aqq9vQlwKuq2ghsFxGf8+d2VxSRcGAN4LLXX6KqC0VkKPAqEA98ASxQ1XoRuRH4I3DI3sSTqvqcva0bgP+x5/9WVV/0NdCTmZwyjrum3cQ/Mt7glx8+wp3Tvs+MQYFRTs3oGVSVjINl/OvzPN7ZepiKOg+jkqN5aO5Y5k0eQJ/wwKujHRkZybhx44iJiWHlypW8+eabALjdblJSUoiLiyM2Npa4uDji4uKIiYkxI0F60cZGKnbtpnTzFkrWf4Z6PEQOGcyAq+Y5HZphmz17Ns8//zw/+9nPuP322wGrKsnw4cOZMGEC0dHRlJWVMWHCBIYMGWK+YBoBwdTR7vQ62nUiMg4oAGYB93kt83kYcl8+HeuA2apaKSKhwDoRWQH8BHhcVV8VkaeBHwBP2Y95TVV/5L0REYkDFgJTAAW+EJF3VPWYr8G2JiJcMOQcJvY7iz99+ixPfPZ33s1ZxfC4IQyPt24pUUnmTdLodIUVtSzdfIglXxxkd2El4aFBzBmXwvXTB3H2oNiA/58TESZPnszEiRMpKipqGQWysLCQAwcOUF9ff8L6119/PcOH+3dlle7y2XcX0FR3vE1+eP/+9L/ycmoOHqQpOYmw2FgkKCC7x/QYIsLNN9/MjTfeyP79+8nIyCAzM5OMjAwyMjLYvXs3ixcvBiAhIYGrrrqK+fPnM2vWrIB/bRuG4bN7gCVYzUUeV9W9ACIyB9ji60baTbTty+aV9t1Q+6bAbOA6e/6LwIMcT7RP5hJgpaoetQNdCVwKvOJrsG3pEx7NLy64k7e2f0BO8R4+2ree93evBsAdFsnwuMGkxQ1hdEIaoxPSCA81I4AZp6fO08iiD3fxwrq91HmamDoklkeuHs+c8SlEB+DV6/YEBQWRnJxMcnIyU6ZMAayr+NXV1Tz33HOUllpXD9566y2GDh1KfHw8ycnJpKam9tpBbAYvuJ79L/0f4f2S8VRVU5ufz+4n/tqyXEJDCU9KxJWcTHhyEuHJybjsv+HJyYRE+X/d9Z4iKCiIoUOHMnToUObNO/6Lw4oVK4iNjSUzM5OVK1fy7LPP8uyzz7Jq1SpmzZrlYMSGYXQXVf0MGH2S+e8B7/m6HZ9+7xWRYKzmIcOBv2L1uixVVY+9ykHAu9jo1SJyIbATuFdV8+zleV7rtH6M9/5uBW4FSE5OZvXq1T49mRT6khJ0NhfGTeKYp5wCz1EKG0o4WJxP5pEdKNqy7iV9zyMtfGCb26qsrPR5v13NqVhanwd/OiZt6eoYl+6q5509DZzbP5i5aRH0c9dDVS5ffJbbZfs83dfDmfDlOI4ePZqCggIaGhqorq5m9+7dbNu2rWW5y+WiT58+LaNHOtm8pDP+L3w+D9Fu+OFttFzT9jRCRTlSZt0oK6eqvJyqg4eQrGyk7sSKNOpyIVFu1i57j6aRw9GRI8CBq6j++nrvjtdDY2MjO3bs4KmnniInJwewam+XlZV12TFx4nhXVla2v5IPzIiEZ64zRjSMrSvg6j2PdUI0gevVTizwJSJXAJmqut++/wBwNbAfuLv5Cnd7fPrksxt/TxKRGOBNTpLhe1kGvKKqdSJyG9bV7tm+7Mdrf88AzwBMmTJFz6T395HKIpbnfMjafRup8dS2zHcP6MPM8W1vd/Xq1X7T69ypWFqfh6ioKL85Jm3p6mP19M71jB/QyCt3nd/+yp2kM18Pvjrd4+jxeCgoKGDXrl1s3ryZoqIiioqKALjssss455xzOjlS33TG/0VXnQdPVRW1BYXUFRRQk3+E0i1bKcv8EkqOErR7D6lR0Qy+/rudsq+O8Kf3QG/d8XpYvnw5zz//PFu3bmXQoEE8+uijzJ49m/j4+E7fVzMnjrc/fpEyDD/yMDAdQEQuB74HfBeYDDyN1VKjXR26xKSqpXYdwXOBGBEJsa9qp2J3flRV7wr/zwH/a08fAmZ6LUsFVndk/x11pKKQe1b8miZt4uyUcUxKGUta3GAG9x1AWIgZTtromDpPI9vzK5g1KtHpUPyCqpKXl0dxcTHHjh074VZTU/OV9XtrU5JTaaypoXLXbsp35FCxfQcVOTtptI+dhISgHg8R/VMcjrJ38Xg8zJ8/n6qqKgCWLVtm6mm3o1+98vM8T/srGm36n3ptfyWju6mqVtvT3wKeV9UvsPoY3unrRnypOpIINNhJdgTwDeAR4CPgGqzKIzcAb9vrp6hqvv3wK4Ht9vQHwO9EpLmQ7MXA/b4GejpiwvsQGhRCXWM9m/O3kVOSS2JkHAnueJIi40h0x5MSnUS/6CSS3AmEBAVOCTaj+23IPUpZTQOXT+jvdCiOy8zMZPny5TQ0NABWW9e+ffsSFxfXUpkkKSmJpKQkoqOjTQcyL+U7cihes47yHTuo2rsPmppABPeQwSTOvIjokSPIPlrCRVfNQ4LNe1J3Wr9+Pc8++2xLkj1kyBCTZBsBwx09kmkXnKqrXM+3rOCOztyciEgUUA18Dfib1zKfO/v5ckU7BXjRbqcdBLyuqstFJBt4VUR+i9X78nl7/R+LyJWABzgK3AigqkdF5DfAJnu9h5o7RnaV8NBw/jzn1+wo3kNRVQlF1SUUVx2loLKIbQU7qPUcb8wTJEFW4h2VSFxkLOWVx6jbJcRG9CUmvE/LzVwJ772arze88MleBsRGcFZKH0fjcdKaNWtoaGggNDSUGTNmMHbsWOLj401CfQqe6mp2P/EkJes3EORyET1yBKnXfIs+Z40metRIQtzHO0Fmr15tkuxu1tTUxLXXXkteXh4zZsxgzpw5XHPNNU6HZRiGcxYBW4FyYLuqfg4gIpOB/FM90JsvVUcysdqjtJ6fC3ylwaWq3k8bV6pV9QXgBV+D6wxxkTGcNyj9ZLFQUV/FkYpC8isKya8s5EhFIUcqi9hXepCy2go+3/zVQQsiQyOIDnMTHhpORIiL8BCXPR1OeIiLiFAX4c3TIeFEhFrT1jJrPVdIGK4QF6FBISYxCSAXjkjgV5eP4dEPcrjsz2uZOiSWyyf057Lx/UiK7l2VbG666SY+/PBDtm/fzurVq1m9ejWRkZEtFRzS0tKIiYlxOky/svOxP3Ns0+cMuv679L/ycoLDe9f/jL+78cYbycuz+uufe+65zJkzx5SsPAXvTqkJUUkOR9N7mfPQdVT1BRH5AEgCMrwWHQFu8nU7vXaUCRGhjyuKPq4oRiYM+8ryVR+t4uzp6ZTWlnOstozSmuN/KxuqqW2opcZTS0V9FUVVR6nxWPdrPXUcH0io/RhcwVbSHW7/dYWE2fOOzy8qL+JwZmnLus3Lw0NcRLvcRLui6OOKJio0kiBTn7fLiAg/OH8oV589gFc25vHWlkMsfCeL3yzPZu6kAdwxcxjDk3pHO2S3283cuXO54oorKCoq4uDBg+Tl5ZGbm0tWVhYAo0aN4utf/zoJCQkOR+sfgsNdABzduImmujpip04hetRI82XbT8yePZvXX3+dxsZGHn30UR599FEeeughfvWrXzkdml/y7pQ6LGmUaWDsEO/zkBwhumHt1xyOyFlVFZ27PVU9xPEBGJvn+Xw1G3pxot2eIAkiJqIvMRF9GULbZQBbU1XqGxuspLvBSrxrPLXUNNRR66mlpqGWusZ66jz11DXWUetpnq6nzlPXMl1ZXUWdp57axjqqa6vJzsmlsanxlPsWEaLC3PQJi6JPeBTRYdYXCaNzxUSGccfMNO6Ymcauggr+ufEAr2w8wNItB7lkTD9+dcUYBsREOB1mt/Cus52eno6qkpmZybvvvktOTg5NTU1cd9117W+oF0i7/VYiUlMp3ZrBwaVvcXDJUiJSB5D67atJmnmR0+H1evHx8UycOJGNGzficrm4/vrrueGGG5wOKyBUVeyktyd4Z6qzE0TDf5hEu5OJiH01OgzC227D25yQ13nqqG3ntmN3Dv1SU6iqr6astpyyugrKais4VlNKozadsM2Kukoq6io5ZF603WJEcjQLrxjLj2YNZ/Gn+/j7J/u46q+fsOT28xgU7/MIrT1CTU0NS5YsITc3l/DwcKZNm8b06dOdDstvhERFMWj+dxg0/zt4qqrIf3cFB15+hV2PP0HfceNwJXRd6Tjj1J588knuuusuAO655x5mz57NFVdc4XBUhmH0BCbR9pGqUuOppbS2nBr7SrV1q6W2oY66xvrj8xrqTlhe11jfap518x5Apz2u3bta2nq7QlzERcQwsG9/osPcRLncRIe5iXa5iQqzbtGuqJZl/+LpLjwyBkB8lIufXjyKKyb25+qnPuW+JRm8est0goJ6T7OA5cuXs3//fr7xjW+Qnp6Oy+VyOiS/VbzuEw68bA2K605LQxwczMeAsLDjndwXLVrEiy++yJgxY0hLS2PYsGGkpaWRlpZGenr6CesahmG0x7y7A03aRGlNOUXVJRRVHeVoTSlfVmxj66e7OVZbzrGaUo7VlFHXWN/utoKDglsSYu9bXGSM1ea6A7fm9dev+5TZszo05o/hkJHJ0fzq8jH8bEkmL288wILpg50Oqcs1Njaye/dudu7cycSJEznvvPOcDsmv1RWXsG/xPyAoiMl/eZzI1FSnQ+r1br31Vm655Rays7NZtWoVK1asoLq6mlWrVvHSSy+1rDdx4kTWr19PRETvaBpmGMaZ65WJdnH1Ud7IWkFhVRGFVUcpqT6Gp+nEYvshBJMQFEdsRAzD4gYTG963pdSfOywSV3CYXXHEKzkOdhES3PmHNEhMB8dA8u30VP6xfj9LPs/r0Yl2ZWUlb7zxBvv27QOsNq4XXWTaGrenZP1nNFZXIyEhhJnKLH5DRBg7dixjx45l/PjxXHjhhezdu5dNmzaxbNky/vnPf5KRkUF2djbp6V+tZGUYTjN1tDu9jnan6FWJdkNjA9lFu3jhi9fIrywkJTqJtNhBTE+dTKLbGsAmMTKeuMgYNqz7jFmzZjkdshGARIQoVwiepqb2Vw5g5eXlLUn2nDlzmDRpEqGhoc4GFQD6XWaN2rv3hcUcevNtBi+43uGIDG81NTX85Cc/YceOHSeMcJqens7DDz9skmzDb5lOqf7ZqbRHJ9qqSk5xLlmFOWwv2k1OSS51njrCgkOZMWgKd02/qc2rxabklnG6GpuUXYWVnD+8Z3du69+/PzNnzmT16tXExMSYJNtHQSEh9L/imxz54N/UHDrsdDhGKwUFBWzZsgWAv/3tb5x99tmMHTuWqChTwckwjI7rkYl2kzax6VAGS7NWsLc0D0EY1Lc/s4acy6SUMYxLGmVGeDS6zBubD1JcWcclY/s5HUqX8Hg8FBYWkpuby7p160hMTGTw4J7bRKarmC/z/mnw4MEMGzaM3Nxc3G4306ZNczokwzACWI9LtDcf/pKXM98ir+wwKVFJ3D51AecMmEiUy93+gw3jDBVW1PLb5dlMHRLboxJtVWXp0qVs27bthPlpaWnMmzfPVGLooNKMTKoP5BF91minQ+n1PB4PO3fuJCMjg4yMDN577z1yc3NJSEhgzpw5TodnGD6riB/F6uuecToMR1X89VanQ/iKHpNoqyp/+uQZNh7aSkp0Ej+efhPnDkwnOCjY6dCMXmTh21nUepr4w9UTelRpv6amJnJyclruJyYmMn/+fGJjY82V2dNQf+wYAOVZ2XgqqwiJMhcCnHDppZfy8ccfU1tbC0BoaChTpkzh3nvv5YEHHiDGdFY1DOMMia/DhTtFRIqA/Q7sOgEodmC/J3OmsQxW1cQzCcA+D1VnGEd38Kfz1lpnnYfueD3483E8Hd7PJxDOgz8c/66OwZ/PgxPH36l9ujvhPFQAOe2u6Cx/eE2dSme8Hpw+D/5wjFuOo4i8jxVTRxSr6qWdGZDfJ9pOEZHPVXWK03GA/8TiL3GcSiDEGAh62nEMtOfjD/H6QwxOceK5B/I+A+F/JRBiPFNOP0en9++vTIFmwzAMwzAMw+gCJtE2DMMwDMMwjC5gEu22+VPXXX+JxV/iOJVAiDEQ9LTjGGjPxx/i9YcYnOLEcw/kfQbC/0ogxHimnH6OTu/fL5k22oZhGIZhGIbRBcwVbcMwDMMwDMPoAr020RaRF0SkUES2ec2LE5GVIrLL/htrzxcReUJEdotIpoic3YlxDBSRj0QkW0SyRORup2LxiulSEcmx9/GLkyx3ichr9vINIjKks2M4w/huFJEiEdlq3/6rO+MLZO0d20DQkde2P/CHeDv6PtSTiUiwiGwRkeXduM8YEVkiIjtEZLuInNsN+7zXPtfbROQVEQnv6n0aRm/UaxNtYDHQulbiL4D/qOoI4D/2fYDLgBH27VbgqU6MwwP8VFXHANOBH4rIGIdiQUSCgb/a+xkDfNeOx9sPgGOqOhx4HHikM2PohPgAXlPVSfbtue6KL5B14Nj6u8X4/tr2B4txPt6Ovg/1ZHcD27t5n38G3lfV0cDErt6/iAwAfgxMUdVxQDAwvyv3aRi9Va9NtFV1DXC01ey5wIv29IvAPK/5L6nlMyBGRFI6KY58Vd1sT1dgvcEOcCIW2znAblXNVdV64FV7n968Y1sCfE2k24YH9CU+4/T0iGPbwde24/wh3tN4H+qRRCQV+CbQbV/ORaQvcCHwPICq1qtqaTfsOgSIEJEQIBI43A37NIxep9cm2m1IVtV8e/oIkGxPDwDyvNY7aM/rVHYTjMnABgdj8WX7LeuoqgcoA+I7MYZT8fX5X203rVkiIgO7J7SA1y3/5w5p6/XkrxyL18f3oZ5qEfAzoKkb9zkUKAL+bjdZeU5E3F25Q1U9BDwKHADygTJV/XdX7tMweiuTaLdBrXIs3VaSRUSigDeAe1S13MlYeoBlwBBVnQCs5PgVOcMIuNdTd8bbm9+HRORyoFBVv+jmXYcAZwNPqepkoIoubqJjt7Wfi5Xk9wfcIvK9rtxnZxCRRq++N1s72j9IRG4Xke93TXSGcXIm0T5RQXMzDPtvoT3/EOB9VTTVntcpRCQU68PtZVVd6mQsPm6/ZR37Z8e+QEknxnAq7canqiWqWmfffQ5I76bYAl1X/285qa3Xk7/q9ng7+D7UE80ArhSRfVjNpmaLyP91w34PAgdVdYN9fwlW4t2Vvg7sVdUiVW0AlgLndfE+O0ONV9+bSaq6ryMPVtWnVfWlLootIIiIisifvO7fJyIP2tMPiki1iCR5La/0mu4nIq+KyB4R+UJE3hORkW3sZ4iI1NhfiLJF5GkROe2cU0RWi0hADu9uEu0TvQPcYE/fALztNf/7YpmO9TNb/sk20FF22+bnge2q+piTsdg2ASNEZKiIhGF1kHmn1TresV0DrNLuK8jebnyt2qxfSfd3bApUvpz7QNXW68lfdWu8p/E+1OOo6v2qmqqqQ7D+91epapdf5VXVI0CeiIyyZ30NyO7i3R4ApotIpH3uv0YAvk+KSJSI/EdENovIlyIy12vZ9+3mgxki8g973oMicp89nSYi79sJ41oRGW3P/7ZdiSVDRNY488y6VB3wLRFJaGN5MfDT1jPt/5M3gdWqmqaq6cD9nLo52R5VnQRMwOpgf0IfD/tCXc+nqr3yBryC1TatAeuKwg+w2hn/B9gFfAjE2esKVjWGPcCXWD21OyuO87F+js0Ettq3OU7E4hXTHGCnvY9f2vMeAq60p8OBfwG7gY3AsG4+d+3F93sgC8gAPgJGO/3/Fii3kx3bQLt15LXtDzd/iLej70M9/QbMBJZ34/4mAZ/bx/8tILYb9vlrYAewDfgH4HL6uPsQc6PX/+ebWM1u+tjLEuzPJAHG2u9jCfay5s/PB4H77On/ACPs6WlYX6ywP1cH2NMxTj/nLjiGlVgJ8sP2/fuAB72Oz4PAPq9jVmn/nQ2s6cB+hgDbvO7/Aav/w41YX+BXAR8DbuAFrFxiCzDXXj8C65el7fa53kAX5DvdcTMjQxqGYRiG4fdEpFJVo7zuh2KVmL0QqwPrKKx2598G+qnqL1s9/kGsRPNprA6oOV6LXap6log8DaQBrwNLVbW7mkV2C7spSH+sL3UTgVuAKFV90Ov4RALBqrqw+ZiLyI+Boap6r4/7GYL1ZXWciERiJdUPYF0B/y0wQVWPisjvgGxV/T8RicFKuCcDtwHjVPVmEZkAbAamq+rnnXQouk3vuGxvGIYXmUwNAAAHfElEQVRhGEZPcz2QCKSraoPdvt6XgXeCgFK1mjWcQFVvF5FpWGUevxCR9J6WbKtquYi8hFVLveYkqzwBbBWRR89wV2kishXr17K3VXWFiNwIrFTV5pKmF2P1jbjPvh8ODML68vSEHW+miGSeYSyOMW20DcMwDMMIRH2xKsU0iMgsYLA9fxXwbRGJB2uEU+8HqVVRZ6+IfNteLiIy0Z5OU9UNqvoA1lXvnloedhFWM7WvlJJUq477P4Efes3OouOFBfao1Wl1sqo+6DW/ymtagKv1eAfXQaoacP0FTsUk2oZhGIZhBKKXgSki8iXwfaw256hqFvAw8LGIZACPneSx1wM/sJdncXxwrj/aHSu3AZ9i9fXpcewryq9jJdsn8xhW843mlg+rAJeI3Nq8gohMEJELzjCUD4C77M6WiMhke/4a4Dp73jisDpUByTQdMQzDMAzD73m3z7bvFwPntrHui7QaQ8H7qqqq7gUuPcnjvtUZsQaIPwE/OtkCVS0WkTeBe+37KiJXAYtE5OdALVanyXvOMIbfYF1dz7TL/+0FLgeewhrEaTtWh8jurm/faUxnSMMwDMMwDMPoAqbpSAeISLKI/FNEcu3am+tF5CoRmSkiZXZh9h2d0IHAMLqFHB9pLcuuG/vT9gYVsAci2GZPTxKROT7sx/s1sl1EFnrNX95q3cUico09HbCDFHQGr/OzTUT+ZffeP2EQCfv+jSLypD3tXSt4sYgcEhGXfT/B7jDmPaDEFvucbLQ7Khmn4RTnqvVohl066qNhGP7FJNo+stsPvYVVR3KYWsXa52ONngew1u7BPBm4XERmOBSq32pODrwTtVbLF4vIXjvh2ykiL4lI6le3dMJj9knbhffbi+fbdoLZ1DqZE5H7RWS3iOSIyCWns/0A0TzS2ljgG8BlwMIOPH4SVr1lXzS/RqYA3xORrh79ridoPj/jgHrg9tPYRiNwcxvL9tgdlc7Cej+7R0RuOs1Ye7u2zlXr0Qz/4GCMhtGpRGR8qy+SW0VkQ/uP7D1Mou272UC9qj7dPENV96vqX7xXUtUarGL6A7o5vp7iv1V1IlY91C3AKrFGKewK24BvYXW6aCEiY7CSjrFYbfj+JiLBXRSD31DVQuBW4Ed2L/xgEfmjiGwSa4S127zXt8/LQ8C19pvrtSJyjv1LzxYR+VSOj3bnvZ8qrPZ2w7vjefUgazm9Y7YIuFfaGYVNVXOBn2CV/DLOzOmeK8MIKKr6ZasvkpNUdZrTcfkTk2j7bixWwfRTEpFYYAStkjejY9TyOHAE6ypru0TkJ/bPtttE5B6v+b+yr0yvE5FXmn9WV9Xtqppzkk3NBV5V1Tq7w8xu4Jwzf1b+z062goEkrN7oZao6FZgK3CIiQ73WrccagOA1+831Naxe/xeo6mR72e9a70OsklvTsXr6A1zgfTUEuLLrnmFgspPky7BGrQOIaHXMHjrFww8A64AFPuxqMzD6jILt5do7VyJyrYPhGYbRzUzVkdMkIn/FGra4HvhvrGQhAyvJXqSqR5yMrwdp/uB/+1QriUg6cBPWULoCbBCRj7H+x6/GGgEr1N5ee72XBwCfed0/SO/8heJiYEJze2msmrUjsIY2bktf4EURGYE1SEGo17ILRGQL1ghuf1DVLBGZidWk5PLmlURkcec9hYAXYSfSYF0lfd6ervEebMNuW32qtuy/x3oNvdvO/uQ04zR8PFeGYfQuJtH2XRZWwgaAqv7QbhvcPBzoWlW93L7i95mIvK6qW0+2IaNDfP3gPx94026WgIgsBS7A+tXmbVWtBWpFZFnXhNkziMgwrDa9hVjH/i5V/aDVOkNOsYnfAB+p6lX2equ9lp2QUBs+6ZQkTVV32Ungd9pZdTJWKS2j40xCbRjGV5imI75bBYSLyB1e8yJbr2Q3NfgD8PPuCqyHc+KD/xAnjgaWas/r0UQkEXgaeFKtup8fAHeISKi9fKSItB5FrAKI9rrfl+PH6saujdjooIeB+9paaH8xehT4S1vrGIZhGB1jEm0f2YnHPOAiuzLGRqxi+CdLqJ8GLmznyp9xCnZnvB8DKcD7PjxkLTBPRCLtZPAqe94nwBUiEi4iUViF8NvzDjBfRFz2LxQjgI2n9UT8X3P70SzgQ+DfwK/tZc8B2cBmu0rM/+Orv4J9BIzxanv6v8Dv7SYi5hczP2KPlte6n0ma3XF1O9YocU+o6t+7P7oerXUbbVN1xDB6ETNgjdFtRKRSVaPsLyC7gAKvxfcC3wQuAsqxfi34DLhfVQ+eYpv7gCn2KFY/4XgZs+dUdZG9zoNYQ7kWYDWJeF9VnxVrlKu/AIlAKbBVVS+xH/NLe1se4B5VXXHGB8AwDMMwjF7FJNpGjyciUapaKdYAEmuAW1W13QoyhmEYhmEYZ8L8tGv0Bs/YtbHDgRdNkm0YhmEYRncwV7SNgGCPNOVqNXuBqn55svUNwzAMwzCcZhJtwzAMwzAMw+gCpuqIYRiGYRiGYXQBk2gbhmEYhmEYRhcwibZhGIZhGIZhdAGTaBuGYRiGYRhGFzCJtmEYhmEYhmF0gf8P7bqPcds0mCUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQPuwRUpBwLf"
      },
      "source": [
        "\n",
        "def compare_all_facies(logs, Pred1, Pred2, Pred3, Pred4, Pred5, Pred6, Pred7, facies_colors):\n",
        "    #make sure logs are sorted by depth\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    cluster2 = np.repeat(np.expand_dims(logs[Pred1].values,1), 100, 1)\n",
        "    cluster3 = np.repeat(np.expand_dims(logs[Pred2].values,1), 100, 1)\n",
        "    cluster4 = np.repeat(np.expand_dims(logs[Pred3].values,1), 100, 1)\n",
        "    cluster5 = np.repeat(np.expand_dims(logs[Pred4].values,1), 100, 1)\n",
        "    cluster6 = np.repeat(np.expand_dims(logs[Pred5].values,1), 100, 1)\n",
        "    cluster7 = np.repeat(np.expand_dims(logs[Pred6].values,1), 100, 1)\n",
        "    cluster8 = np.repeat(np.expand_dims(logs[Pred7].values,1), 100, 1)\n",
        "\n",
        "   \n",
        "    f, ax = plt.subplots(nrows=1, ncols=8, figsize=(12, 6))\n",
        "\n",
        "    im1 = ax[0].imshow(cluster1, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im2 = ax[1].imshow(cluster2, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im3 = ax[2].imshow(cluster3, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im4 = ax[3].imshow(cluster4, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im5 = ax[4].imshow(cluster5, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im6 = ax[5].imshow(cluster6, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im7 = ax[6].imshow(cluster7, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im8 = ax[7].imshow(cluster8, interpolation='none', aspect='auto',\n",
        "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
        "       \n",
        "    \n",
        "    divider = make_axes_locatable(ax[7])\n",
        "    cax = divider.append_axes(\"right\", size=\"10%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im8, cax=cax)\n",
        "    cbar.set_label((5*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-8):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=2)\n",
        "    \n",
        "    ax[0].set_xlabel('Facies'); ax[1].set_xlabel(Pred1); ax[2].set_xlabel(Pred2)\n",
        "    ax[3].set_xlabel(Pred3); ax[4].set_xlabel(Pred4); ax[5].set_xlabel(Pred5)\n",
        "    ax[6].set_xlabel(Pred6); ax[7].set_xlabel(Pred7)\n",
        "    \n",
        "    #ax[0].set_yticklabels([]) ;\n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([]) \n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([]); ax[6].set_yticklabels([])\n",
        "    ax[7].set_yticklabels([])\n",
        "    \n",
        "    ax[0].set_xticklabels([]); ax[1].set_xticklabels([]); ax[2].set_xticklabels([])\n",
        "    ax[3].set_xticklabels([]); ax[4].set_xticklabels([]); ax[5].set_xticklabels([])\n",
        "    ax[6].set_xticklabels([]); ax[7].set_xticklabels([])\n",
        "\n",
        "    f.suptitle('Various model predictions in well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fELgWmHPB1FN",
        "outputId": "bb3687b2-1d81-4d05-d480-f4e76b87a88c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "\n",
        "compare_all_facies(blind,'SVM_Pred','GPC_Pred','RFC_Pred', 'NNC_Pred', 'KNN_Pred','DT_Pred', 'LR_Pred', facies_colors)\n",
        "#plt.savefig(\"drive/My Drive/seminar/Compo.png\", dpi=400)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKk-uu-hRCKU"
      },
      "source": [
        "# **NEW DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8uoMFF-B7Cu"
      },
      "source": [
        "well_data = pd.read_csv('drive/My Drive/seminar/validation_data_nofacies.csv')\n",
        "well_data['Well Name'] = well_data['Well Name'].astype('category')\n",
        "well_features = well_data.drop(['Formation', 'Well Name', 'Depth'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3uQLXrlRIld"
      },
      "source": [
        "X_unknown = scaler.transform(well_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyuZZxh4RMJ-",
        "outputId": "7fa8db3f-aa27-4167-cefe-c659a3a7d796",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "\n",
        "#predict facies of unclassified data\n",
        "y_unknown = NNC_model.predict(X_unknown)\n",
        "well_data['Facies'] = y_unknown\n",
        "well_data"
      ],
      "execution_count": null,
      "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>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "      <th>Facies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2808.0</td>\n",
              "      <td>66.276</td>\n",
              "      <td>0.630</td>\n",
              "      <td>3.300</td>\n",
              "      <td>10.650</td>\n",
              "      <td>3.591</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2808.5</td>\n",
              "      <td>77.252</td>\n",
              "      <td>0.585</td>\n",
              "      <td>6.500</td>\n",
              "      <td>11.950</td>\n",
              "      <td>3.341</td>\n",
              "      <td>1</td>\n",
              "      <td>0.978</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2809.0</td>\n",
              "      <td>82.899</td>\n",
              "      <td>0.566</td>\n",
              "      <td>9.400</td>\n",
              "      <td>13.600</td>\n",
              "      <td>3.064</td>\n",
              "      <td>1</td>\n",
              "      <td>0.956</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2809.5</td>\n",
              "      <td>80.671</td>\n",
              "      <td>0.593</td>\n",
              "      <td>9.500</td>\n",
              "      <td>13.250</td>\n",
              "      <td>2.977</td>\n",
              "      <td>1</td>\n",
              "      <td>0.933</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2810.0</td>\n",
              "      <td>75.971</td>\n",
              "      <td>0.638</td>\n",
              "      <td>8.700</td>\n",
              "      <td>12.350</td>\n",
              "      <td>3.020</td>\n",
              "      <td>1</td>\n",
              "      <td>0.911</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>825</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3158.5</td>\n",
              "      <td>86.078</td>\n",
              "      <td>0.554</td>\n",
              "      <td>5.040</td>\n",
              "      <td>16.150</td>\n",
              "      <td>3.161</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>826</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3159.0</td>\n",
              "      <td>88.855</td>\n",
              "      <td>0.539</td>\n",
              "      <td>5.560</td>\n",
              "      <td>16.750</td>\n",
              "      <td>3.118</td>\n",
              "      <td>1</td>\n",
              "      <td>0.611</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>827</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3159.5</td>\n",
              "      <td>90.490</td>\n",
              "      <td>0.530</td>\n",
              "      <td>6.360</td>\n",
              "      <td>16.780</td>\n",
              "      <td>3.168</td>\n",
              "      <td>1</td>\n",
              "      <td>0.583</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>828</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>90.975</td>\n",
              "      <td>0.522</td>\n",
              "      <td>7.035</td>\n",
              "      <td>16.995</td>\n",
              "      <td>3.154</td>\n",
              "      <td>1</td>\n",
              "      <td>0.556</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3160.5</td>\n",
              "      <td>90.108</td>\n",
              "      <td>0.513</td>\n",
              "      <td>7.505</td>\n",
              "      <td>17.595</td>\n",
              "      <td>3.125</td>\n",
              "      <td>1</td>\n",
              "      <td>0.528</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>830 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Formation Well Name   Depth      GR  ...     PE  NM_M  RELPOS  Facies\n",
              "0       A1 SH    STUART  2808.0  66.276  ...  3.591     1   1.000       3\n",
              "1       A1 SH    STUART  2808.5  77.252  ...  3.341     1   0.978       3\n",
              "2       A1 SH    STUART  2809.0  82.899  ...  3.064     1   0.956       2\n",
              "3       A1 SH    STUART  2809.5  80.671  ...  2.977     1   0.933       3\n",
              "4       A1 SH    STUART  2810.0  75.971  ...  3.020     1   0.911       3\n",
              "..        ...       ...     ...     ...  ...    ...   ...     ...     ...\n",
              "825      C SH  CRAWFORD  3158.5  86.078  ...  3.161     1   0.639       3\n",
              "826      C SH  CRAWFORD  3159.0  88.855  ...  3.118     1   0.611       2\n",
              "827      C SH  CRAWFORD  3159.5  90.490  ...  3.168     1   0.583       2\n",
              "828      C SH  CRAWFORD  3160.0  90.975  ...  3.154     1   0.556       2\n",
              "829      C SH  CRAWFORD  3160.5  90.108  ...  3.125     1   0.528       2\n",
              "\n",
              "[830 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJkazg4MRPvQ",
        "outputId": "36d27547-2c9e-4101-8ba1-a34db26f43b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "well_data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[STUART, CRAWFORD]\n",
              "Categories (2, object): [STUART, CRAWFORD]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yPQxuOqRexK",
        "outputId": "a50f5bf3-f07b-4b49-a538-570aba8bbcef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        }
      },
      "source": [
        "make_facies_log_plot(\n",
        "    well_data[well_data['Well Name'] == 'STUART'],\n",
        "    facies_colors=facies_colors)\n",
        "#plt.savefig(\"drive/My Drive/seminar/stuart.png\", dpi=400)\n",
        "\n",
        "make_facies_log_plot(\n",
        "    well_data[well_data['Well Name'] == 'CRAWFORD'],\n",
        "    facies_colors=facies_colors)\n",
        "\n",
        "#plt.savefig(\"drive/My Drive/seminar/crawford.png\", dpi=400)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zSZs3F51NlVUcKo/Hw+233w7ANddcw/33309xcXGKo1IUZVwm2n3m5ZcPqI/tC/tp7G6hobs5uTR2NbOlcQd/3fkqS0oW8N0F30hdwMqQXTIrlz+uP8TOhi42HmxnyeRMzAbVTWQ8MhgMXHLJJZx22mlUVVVRV1dHXV0dmzdvxu/3IwfpqC+EwGazUVZWxsSJEznnnHNUn+3PQAgBbjc5h01jHYtECDa34K+pwVd1CN/BKjq3fExrxVqEVou5IB9zXi6mvDwMbjeW4iJcs1RpzsE4nU4ikQhNTU08//zzPP/889x4442qy4iipJh650gIRkN0h3oI90bQabTYDFbSTE46/J3JY4xa9bXmaGEx6FhxyyLuf3UPT71XxYubavnczBxmFbooTbdSmmkl12FSk9aMI0VFReTn5xMMBgmFQgSDQfx+P21tbbS2tuLxeJLrvhJ/mzdvZvPmzQghOO+881L9EsYcjV6PpSAfU1Ym9illRLu6iXi9dGz+iObVb+OvrsFfXTPgMXN/9ximnJwURTxyGY1GGhoaaGhoYMuWLfzLv/wLL7/8Mo8//rj6kKgoKTRu/voivRHqu5pp8Xlo8Xlo7vHQ4mujxeehI+DFHwkc8RiDVk+pq5BLys5ncnoJZ+TPTkHkyoky6rTc86UZfGFWLi98WMtrO5r4y+a6fvdrKEqzkOsyk+swkeM0kecykeM0k+c0UZJhRa9V3QdGq0gkwoYNG3jjjTcIBAIEg8Fk1ZFj0Wg05OXlUVpaSnZ2NmefffYwRzv6xaJRgg0NRH1+oj4fvX4/UZ+fXn980ezfz76PthL1++j1B+L39/QQ6e4hFgwe9bw6hwPbhFL0LifuuaerJPtTCCHIz88nPz+fG264gV/+8pds3ryZBQsWpDo0RRl1hBAWICKljCRuTwE+D1RLKVcM9TxjNtGO9kbZ1rybrU27OdBWRVVnHdFYNHm/RW8m25pBviOHWdnTcJuduE3O+DqxWPUWNWhqDJhXksa8kjR+ceUsmruDVHl8HPL4qfL0UNPup8kbZHdjF56e0IByfwathrIcG9NzHVwwLZuLZqg3+JEmFotx6NAh9u7dS0dHR3Lpa5EGsNvtzJkzB5PJhMlkwmg0JrePtk+n06m//SGIRaO0b9iIZ/37dH68lV6ff/ADNRo0BgNdTgc6ixWt1YIxMxNLSQl6hx2d3d5v7fjkts2GRq+qQp2ICy+8kCeffJLFixdz66238p3vfIeJEyemOixlKIwSWRJLdRSjh3HY6vSuAr4J7BdCTALeB54FviCEmC+l/NFQTjImE+31NZv57Yf/RzAawqg1MCGtmM+XncsEdzE5tkyybOnYDGqQzXij0QhynWZynWYWDfJ+E47GaOkO0uQNUtcRYHdTF7saunhxUx1/2VzHK99dzMx856kPXBlUfX39gLrAVqsVt9uNy+WipKQEl8uF2+1mzpw5WCyWFEY69kR9PprfeIuGf7xK2ONB73aTfuaZOE+bid7pQGexoLVakkm1xmBg7dq1zDusj7YyfJYtW0ZlZSU/+tGP+NWvfsXDDz/MggULuO222/jyl7+MVk0MpCjH4pZS7k9sfx14Xkp5qxDCAGwGxm+iXeOtJxgNUejI5T/OuZV0izvVISmjgEGnocBtocBtYV4JXEa8ysyDr+/l0bcPqG4kI0z/qiB33XUXRUVFKYxm/JBSsv0/foz/UDWOmTOYePONuOfNRagqLSNORkYGv//977n77rv585//zJNPPsk111zDnXfeyX333ce1116b6hAVZSTr31R+HvDfAFLKsBBiyF85jMn/jF+ccgFLShZQ19XErf+8m4fee4LX9r3NwfYaemO9xz6BoiSEozFau0MAZNjUYNiRJDc3l5/97GcYjUYeeughnn76ad566y127txJR0fHoNVElJMgFiPY2ITWaiHvi5fgmjNbJdkjXGFhIT/4wQ/YuXMnL730EtXV1XzrW9+it1e9HyrKp9gmhHhQCPH/gEnAGwBCCNfxnGRMtmhbDRa+u+AbXDFtGav2r+WD+o/ZWLcFAKPOyOS0EqZkTGRKxkTK0kuxGMwpjlgZCaSU1HUE2FrXybY6L1trO9lR78UX7mVmvkOVBxyBsrOzueuuu1ixYgVbt27lvffeS96n1WqTdbH718juv22xWLBYLFitViwWC0ajUfXNPgah1TLp1u9w8Ikn2XP/LxB6PZaCAizFhViKijAXFGDKycaUnaWmWR9htFotl156KRaLhUmTJqnfdUX5dP8K/BtQAlwkpewbhDIdeHCoJxmTiXafPEcOy+d+heVzv4LH184eTyX7PAfZ66lkxe7XkFIiEBQ685iSMSGRfE8gy5qh/gGNYVJKWrtDHPT4qEose5u62VbXSYc/XpXCoNUwLc/BlXMLOG9qFueUZarfiREqOzs72Ve7u7s7WeKsb0Bk39La2kpPTw/BT6lwodVqk8l3/wT80wZO9t/XV9lkrA+mzDz7LDIWLaRz6za827bjr6nBu31nfMr1fvROJ6acbIzZWWj8fhp9fgzp6RgzMjBkpKN3OFRr+Cmm1Wq55557uOOOO7jkkktYvnw5F1xwAW636mKpKP1JKQPAA/33CSEygPellOsHf9SRxnSi3V+GNY3F1jQWF88HIBAJcqD9EHs9lez1VLKu+kPerHwXgLL0Cfz8gh+mMlxlGLR2h3hy3UH+/GEtnf5PyrwZdBomZFi5cHo2swpclBe4mJJjx6BTCcBoY7fbmTJlClOmTDnqMZFIhJ6eHrq7u/H7/cnF5/MNWPv9frq6umhqaiIYDBIMBolGo0c9b5+XX34ZiA9Gu/zyy0/aaxtphFaL+/Q5uE+fk9wX7fERqK8n2NxCsLmZUGLdvXcfmlYPBzd9NPAcej32yZNwzS4nbcF8rCUlp/hVjE8/+MEPMBgM/OQnP2HVqlVoNBrOPfdcLr/8cm6++WZVdzvVQgJxSL3/DFloeBo1hBALiSfa7cC9wP8BGYBGCHGDlHLVUM4zbv+azHoTp2VP5bTsqUC8TNjLu1fx5x2vUNlRTSwWU1MwjyH7mrv52h820tod4nMzc1kwIY3SDCulGVZynWY1Pfs4otfrcbvdJ9SC19vbm0y6g8EgXV1dHDhwgP3797N//35isU/Gx7S0tJzMsEcFnc2KfUoZ9illR9xX8fbbLJozh7CnjZDHQ7itjWBTM96du6h57gVqnnuBrAvOI+fii7BNVt0ahks0GqW5uZlFixbxhz/8gb/97W8888wzrF69mtWrV5OZmcnVV1+d6jDHtUhPL43ru1IdxqgR6Rm2sQaPAv8BOIE1wOeklBuEEFOB54mX/zumcZtoH06j0XCoMz6ZyVdmflEl2aNcMNJLdZufzdUdbKxqY83uFswGLf+49Wym5zlSHZ4yQnR2duLxeAiFQse9BIPBAYMurVYrNpuN8vJyioqKmD59eopf3QgjBAaXC4PLhW3SwPqaEa+Xur+uoPHVVbS8tQa9O36c1mpFZ7Wgs1oT29bEdmKfxYLO1m+/2YxQZeuSwuEwmzZt4t133+Xdd99l8+bNNDc3HzFQWKPRMHXqVJYvX84VV1yRomgVZcTRSSn7BkD+TEq5AUBKued4GgJUot3PvLxZbKzbwnPb/saySedg0quBPKNBdzDCe/URVv9tR7LPdYM3kJx8JtNu5NypWfzw4ikUpql6ykqclJK7776bUCh01GOMRuMRi9lsxuVyYTQaycjIoKioiMLCQtxuN2vXrmWpqhV93PROJ6Xf/BcKv/Jl2jZsxLtjF70+H1Gfj2BzS2I7PsPksWgtFnRWy2GJeSJht9sx5+ZiKSqEIXQDGo2ampp44oknePvtt9m4cSOBQHzW46lTp7Js2TKKiorIy8tLLrm5uWRlZanuIopypP4l/A6fPnzIZa3UX1aClJIVu18D4DtnfF0l2aPE/75XxU9f2ZW4Vc2sAifzStyUpBdQmmFlVoGT0gyr+hpaGZTVaj1qov3oo4+iV7MSnlI6m43sC84n+4LzB71f9vbSGwh8Ms17IhmPb/v7bfuSx4RaPUQPVRPp7CQWDifPpQfee+Rxyh9+ENuE0lP0CodPa2sr119/PS+99BLhcJg5c+Zw8803s2TJEhYvXkxmZmaqQ1SU0aZcCNEFCMCc2CZxe8hJ4rhPtHtCPg521LCrdR+N3S3oNDqWlCxIdVjKEKXbjANu13cEiPZKvr10IlNzVBcR5eiEEJx11lm88sorg96/fv16CgoKyMvLw2xWJUBHAqHVorPZ0Nlsg97f+M/XaK14B6HTodHrQKOlNxQk6usZkGT3pzUZB90/2tTU1PDss88CMGXKFM444wxyc3OZPHmySrIV5QRIKU9KP7RxlWhLKWn2edjRvIcdzXvZ336IVl8bEH/TnZU9jUumnKdaP0eRL5Xn8aXyPB7961s8uClEmy9Mmy/M8xtr+OmlM1MdnjLCLVmyhLq6Ovx+P1qtllAoRHt7O16vl+eeey55nMvlIisrC4vFgtlsTpb169s2m82YzWZaWlqora1NlvxzONSHvVPp4BNPfur9BVdfhat8FpaiIt77aPOY6uaTlpbGV7/6Verq6qiuruaFF16go6ODf//3f2fu3LlMnz6dwsJCCgoKKCwsTC5ut1u9540wepuW3EXqf8dQ6V8a2eMyxlyiLaWkO+yjpcdDi89DQ3cLDd3NNHY109DdTCAar6HrNjuZmjGJiyYuYUJaEaXuQmwGa4qjV05EdzDCg5s++frfbozXMP7T+kNk2o04zfrk4jDrsRt1aFSVEQVwOBx861vfOmJ/b28vHo+HdevWsWXLFlpbW+ns7BzSOVevXp3c/s53vsOsWbNOWrzjheztJRaJEAtHiEXCxMIRZGIdiySWcBiZOCbq99Pr85Gz7CJ8VdX462rp9R3Zn7vuxb9SdM1XxmTt7tLSUh577LEB+1paWnjmmWd4+eWXWbt2LfX19UfMBmmxWAYk333bGRkZuFwu3G43LpcLl8uFw+FAqwabDrsdHgtl/zvn2AcqALR4dh37oBQa1Yl2uDdCZfshdrceoLK9muZEch2MDuxzmW5xk2/P4ZyShRQ4c5mZVUauPVt9ih8jbEYdX59uwKNNJxjppabdz/Mf1BCKxgY9XiPAbtIPSMCdlsNuD7KoJH10ikajySohfUvf7cPXfdt+v5/9+/fj8/kGnEun0yUnpzEYDAghkFISi8WQUuL3+zEajcRiMXJycpg4ceJRohp7YtEo0a5uIl1eIl3dRLxdRLu6iHR1EfF2oa08wK531xMLh4lFIvEkOZEwJxPoRPIsT3BqcI3BgNZqxZCWhq6wMNHNxIrOaiPq95M27/QxmWQfTVZWFrfffju33347EP8A2dTURG1tLXV1ddTW1iaXuro63nzzTRobGweUqTyc0+lMJt6HJ+KdnZ1s27btqPfbbDb1vjsEBQYnP8+7JNVhjBr/afhHqkP4VKM20d7TeoC71zyUvJ1nzybXnsWMrDKyrOlk2TLIsqaTbcvEpBsbffCUwQkhOLdIz9Klpyf39c3+6OkJ4w1E8AYidCXWgy0NnYHkdjR29MHEGhGvYpLjNJPjMJLjMMW3nUaK0iyUZduxm9QAulRob29n8+bN9PT00NLSQmtrK62trZ86E2R/QogBMz3OnDmT8vJySktLk/uO1ZpXUVExprojHC7q99O9Zy/BxkaCLa2EWlsJtXgItbQQ8XqP+jidzYbQ6Qj3xtDo9WgMBjQWCxqDHqE3oDHo0STX8ftF33H99h1xnz5+n9ZsRmezojEYTuFPY/TRarXk5+eTn59/1GOi0SiNjY20t7fT0dFBZ2dnchnsdmVlZfJ2d3c3f/zjH496bpPJxHnnncell17KxRdfTHFx8TC8SkUZWUZtol1R9X5yu9RVSHnudIqc+RQ588izZ6PTjtqXppwEQgiyHCayHMdXPUZKiT/cO2gy3hWI0OEP09IVoqkryMFWH+sr2+gODiwTlu8yk6EPs613P2dNyqC8wIlOO35a0VLlueeeY/v27QC43W7y8/OZNGkSDodjwLTpfduHr/V6vWptG0RvMEjzm2/hee99uvfug0Rrp9DrMWZmYMzMxD1/HsaMdPROJ3qnA53d/kyYCtMAACAASURBVMm2zYZGp6OiooLZY/hDyFih0+mS3UiO1+rVq5k9e/ZRk/Pq6mr++c9/8uqrryYfc9111/GnP/1JdUnpp8kg+K9ClcMMVZNhZP/fHrVX8oY5VzErZxq7Ww+wp/UAK/e8SUzG3wC0QkOuPZtCZx6FzjyKEkuWNUNNRKN8KiEEVqMOq1FHnmtolSZ8oShNXUEOeXzsaepmb1M3mysbefitffzyzX1YDVruWDaVry8qGd7gx7nrrruOO++8E4BbbrmFoqKiFEc0+vlratnxnz8h4vVinTiBgisuw1k+C0tBAXqXc1x1w1COTavVkp6eTlpaGj6fj5aWFsxmM6FQiIaGBrq6usjNzaWqqir5mBdffJGnnnpKJdrKmDVqE22L3syionksKpoHQKQ3QmN3CzXeBmoTy8H2at6v3Zx8jEGrp8CRS6Ezj4lpxczMnkK+PUe1YimfidWoY0KGlQyrkeJ0C3MKXeTINrRpBTyzoZruYJSfrNzJeVOz1IQ5w6i+vj65XVlZiUajIS8vT324PkEhTxtbbr0NgNMe+P9wTJua4oiUkaCrq4uamhqqq6upqamhpqaG5uZmWlpaqKysJBAI0NLSkpwop7+CggImTpzI8uXLWbhwIeeeey4TJ05U78GHifZ00rbx76kOY9SI9gxtoHqqjNpE+3B6rZ4iVz5FroF9z4KRIHVdTQMS8K1Nu1h7aAMALpODUnchBY7cZBLuNjuxG6wYdKq/33jUG5N0+MO0+8J0+iN0+sN0BiJ4/fEuJJ2B+P6+LiXtvjCt3aFBBl9WYjPquGRWLledXqCS7GE2bdo0rrzyStauXcsLL7wAgNlsJi8vj6ysLDIzM5Nrp9OJ1WrFoPr0HlWwuTm5vfcXD2HKy8WQ5saQlpZYpyfWbvROZ3z6c5UwjWpSSlpbWzlw4EAyie6fUFdXV+M9rC++TqcjOzubrKws7HY78+fPJysra8BSUlJCaWmpqkc/RGow5PFRgyFTzKQ3MSm9hEnpJcl9UkpafB62N+9lV+t+ajvr2d68l2hsYF9bg1aP3WDDZrBgM1rj20YrdoMVq8GMSWeiKnAIS93HmHTGQRe9VvX7HGk6fGG213vZ3dhFc1eINl8IT08IT3eYNl+Idl+Yo42H1AhwWQzJSiRpVgMTMqzx/uB2I5l2I1l2E1W7t/KFC85OlhpUhp9Wq+Wiiy7iwgsvxOPxUFlZSWVlJU1NTezevZv333//iMfo9XqsVis2mw2r1TpgsdvtAyosOJ3OcZWYO2dM54z/+yPtH35I58fbCHs89Ow7QLi9fdDJX4ROl+yXHV8ntl0ujBnpiIZGQp42DG4XQnUTSLlYLMbOnTt599132bp1K7t27WLXrl20t7cPOM7tdlNUVERxcTFLliyhqKgoebuoqIjs7Oxkt4+xPhhYUU7EmE+0ByOEINuWSbYtkwsmLgagN9ZLs89DnbcRb7CbnrCP7rCPnpAvuV3rbaA73ENP2J/sDw6w+r2Nn/pcRybgpqMm5slFP/jxdqMNnUa9SZ2If2xr4P5X91Df+clXmlaDlnSbkQybgaJ0C6cXu8mwGciwGUmzGnBbDLgsn5QAtBmGVt4vVKvBoaqPpIQQgszMTDIzM1m4cGFyfzgcTlYi6erqwufzDVh6enpobGykp6cHn883aIkzi8WC0+kkKyuLgoKCZM3hsTrznt5hJ/v888g+/7zkPiklvT4/4fY2wu0dhNs7EiX8vES8fWsvgfoGIl4vscQU9zpg05//ChoNxox0TNnZWEtLsJaWYp1QiqW4SH0oPUUeeeQRvve97yVvu91uZsyYwVVXXcX06dOZPHlyMpG22+0pjHR8UoMhj8/hgyELZB4/D99zQue6jvNPQkQDqSuZoNVoybNnk2fPPuaxUkqC0RChaIi1771D+dzZBKOhoyxBgpHEdm84+biuYDctUU/ymEA0NCB5/zR2gxWXyYHT5EiunSY7c3JnUOwq+Kw/ijHrzV3NyST7ijn5XLewmDmFLlUXe5wwGAzHLG3Wp68mdmdnJ16vN1k1oW+7ubmZbdu2IWX8q4+bb76Z008//RhnHRuEEPHa1DYrliEMOI36A4Q9Hj5cs4YpObnxkoAeD4H6BppWvZFsHS/++tcouOKy4Q5fgQHfzLz66qssW7ZMfchRlGGiEu0TIITArDdh1ptw6uyUuI+/DNLhpJREY9EjEnVfOEB7oAOPv50WXxuNXc3UdTdR29VIbVfjgHPsbNnHXefc+pljGatuPW8ydpOO13c2s2JLPSu21GMz6piZ72DhhHQWT8qgvNCFXpXiG/eEEMkuJH2Jud/vT9bnbmlpoa6ujo8++oiioiLKyspSHPHIpbOY0RUVIkuKyTmsW4Hs7cWzbj37fvkrot3dqQlwHLr55puZOXMmixcv5vOf/zz/+Mc/uOQS1SdYUYaDSrRPopiMEYqGCUSD8UQ5kmjRTiTNgUhwQBId6HdfstX7sP2R3sinPqdFb8ZqsGDSGvjyDPWP8tNMyrLx88tO42dfmsn+lh621XWyrc7LltoOfr16P796az+FaWae+vp8Jmerr0uVuH379vGPf/yDvXv3DtjvcrmYP38+1113nRrkdYKEVkv7pkRlKNWiekqdddZZpKen09bWxtNPP60S7RHE3raXpc+dk+owRo2mtlRH8OlUop0QkzH8kQA9YT++xNIT9tGTWPft80eCya4ewWiIju5Onv3bq/EuIb1HDhA6Gr1Gl+iL/Un/a7POhMvk6NdPO36fVW/GZrBiM1ria4MVq8GCVW9Gq/prHzeNRjAlx86UHDtfnhf/NqLTH+ad/R7uWbmTi371DudNyaIsx06u00Su00yu00SO00S61aC+Yh1DYrEYgUCAnp4e/H5/sn9239LR0cH69etxOBx84QtfoKCgIFm5ZDwNjDxZpJREe3oIt7UTbmsj1NZGqKUFAF/lwRRHN/488sgjXHvttbz44ouceeaZ3HbbbakOSVHGnHGTaHeHetjrOUh7oJOOgJeOQGdyuz3opSfkQ3L0qbcNWj1WgwWL3ow5MTjRbrVhCGopzi9KJMqfDFw0Hz6Y8bDbakDjyOKyGPhSeR4LJ6Txx/cO8cq2Bt7Z30qkd+DvhEGrwWnR4zDpcJj1OEx6HGY9dpMusR1f1zVGYW8LbouBNGt8sRi0Kkk/yWKxGNXV1XR1dREMBgmFQoRCoUG3B9sXCASS/awPJ4TAYrEwYcIEvvWtb+F0Ok/xqxs9ZG8vodZWgk3NhDu9yQGRyaWzC11zMxse+92RFUuEQO92k7F4UWqCH6c++ugjrr322uTt119/ne9+97vodOMmLRixOozZvDTxulSHMWp01D6b6hA+1Zj/i+oK9XDv27+i2vvJZBZCCFxGB26zk0xrOmUZE3Ca7Fj1lmRrcbzl2BJvOTZYMGgHryBRUVHB0nlLT9GrUYZblt3EHcumcseyqcRiEo8vRJM3SKM3mFx7AxG6gvEp2TsDEWrb/XQF4zW1+yfmv9364YBzG3Qa3BY9wUgMbyDeJeiaMwq57/LTVAJ+Avbt28fzzz9/1PsNBsOAadaNRiNWq5W0tLTkfrPZnOyLfXiJP4vFoia7OYpYNErlb35Hy+o1GDMzCLd3IHt7BxwTL/f3Sak/qcslZ8oUDGlpGDPSMaSnY0xPR+92oVHJ3SlXXFzM/Pnz+fDD+P+pVatW4Xa7WbRoERdffDFXXXWVml01RVQd7eOj6minWLu/M5lkCyH48TnfY1rmZNXlQjkmjUaQZTeRZTcxq18xl2hvjK5gNDmRTXcwSncwvvZ0h6jvDLD9YD0BYaa2w59MvsPRGM1doQHP8fwHtfz8stPQqjz7uPWfCRLiE2f01bx2uVxYrVbMZnNysVgsA2737TMYVHeg4xULh2lZvQaAUKsHAK3VSvrCBbhmz8I2eTKmnOwBP9eKigpKVY3lESM9PZ0PPvgAgKamJt555x3Wrl1LRUUF3//+9/n+97/PnXfeyf3335/iSMefurCXOxr+meowRo2WsPfYB6XQmE+0S9wFPHX5g7xzaCN/2flP7l37P8zMKuPbZ9xAhiUt1eEpI0g4GqOytYd9zd1Utvro8MUT6U5/YobIxIyQ3cHoMc9l1oHb2suEDBs2kw67SYfdpCfDZmBylp3J2TYmZdpwW1U/3xO1dOlSZs6cyZ49e6iqqsLn8xEIBAgEAlRXV+P3+wkEAoPWw+5Po9EMmoD3bdtsNnJzcyksLCQ9PV21cgM6i4UzX/oz3q3baNuwEV/VIfzVNbSsXpNMwDUmE8aMDIxZmRgzM9D4fLRbbViKijBmZiDUz3HEyMnJ4eqrr+bqq68G4OWXX+aKK67ggQce4N5771XdSRTlMxjzfz3BSJCm7lZMOhOzc6bzXs0mtjfvZWfzPs4pXXjsEyhjXocvzNm/eJue0CcJdN8MkK7ERDUZNgOTsmw4zXpcFj0usx6XxYDDHE+g+xJpu0mHzaDjnXfWqhnShln/iWnOPvvsQY+RUhKJRJJJd9/68KVvf0dHB62trXR0dBz1eefPn8+NN944XC9r1NDodLjnno57brx+uOztJdDYRKC2llCrh2BLK2FPK8EWD77KSrTeLnavW598fOa5Sym7TZUjHYkuu+wy7HY73d3d/OhHP+JrX/saU6ZMwWg0pjq0ccEdaubKyl+mOoxR44XQsY9JpTGVaPsjAXY07+VA+yFqvA3Uehto9X1S98Wg1TPBXcS8/FksKVmQwkiVkUSnFRw+Z81pBS6mZNsozbBRmmFlYqaVonQLRp3qcjRaRCIRurq66Orqwuv14vV6j6gq4vf7B9w+Vus3xFv/lCMJrRZLQT6WgnxkLBaflKa2Dn9dHf6aOpo/2oLoN7231mRKYbTKpxFC8Mc//pF/+7d/48EHH+TBBx9ECEFeXh4lJSXJpaioiLy8PHJzc8nLy6P3sH76yonpTp9CxbVPpDqMUaP7sZtSHcKnGtWJdleoh/1tVez1VLLXc5B9nkp6ZQyt0JDnyGFyeinnTziLQmcehY5csqwZ6mtf5Qh2k55t91zMtrpONh5s56Cnh4OtPt7e28qLm+qSx2kEFLgtnF7k4ofLppLvUrWTUyUWi+Hz+di3bx/t7e20tbXR1tZGe3s7nZ2dySnWB9M3KLJvwGNeXh42mw2LxZLc53A4kovdbkevH3ww9Hgne3sJNjfjr62LJ9WJJVBfTywYTB6ndzqRdhu5Cy7GXFBA2vx5mLKzUhi5cixXXHEFV1xxBbW1taxdu5bKykoOHTrEoUOHeO+993jhhReOSKw1Gg05OTnk5eUxffp0lixZwpIlS5g0aZIaB6GMW6Mu0W7p8bCm6j22NO6kqqMWAK3QUOwq4AtTLmB27gzK0kvRH6VKiKIczawCF7MKXAAEwr14ekJUeXxsOtTOuwc8bKnppKbdT027n9W7W9h2z0XqzWOYxWIxuru7ky3Sra2t7N27l3379uH3+wcc63A4SEtLIzs7m7KyMpxOZ3JxOBw4nU5sNpvqb3oSxKJRDv7u9zS/8daA/Yb0dCyFBTguPB9LYQHmggIshQXoHQ4qKiqYqLpTjTqFhYVcf/31R+yPRqM0NTXR0NBAQ0MDjY2NvP/+++j1eurr63nttdd4+umnAZgyZQrXXXcdN910E9nZ2af6JYw6OWHJv9ceeyyQEvef4aOXZh4JRtU7Tru/k7tW/zfdoR7K0kv56mlfYmrGJCamFWPUqUFlyuBC0V68/ggd/ggdfQMb/WE6+gY4+uL7PT0hPD1h2npC+MJHfgXqNOuZkmMnzWLg0tl5Ksk+CSKRCG1tbbS0tCSnN/d4PMnEuru7+4g61+np6cyZM4dwOMyiRYtIT08nLS1NtTqfQj379g9IsnUOB+7TZ2POz8eQloYhPS2+TnOjs9lSGKkyXHQ6HQUFBRQUfFKSadq0acmxKVJK9u7dy5o1a3jxxRe5++67ufvuu3n++ef56le/mqKoFWXohBDFQKeU0pu4fS5wGVANPCqlHNIshaMq0X7r4Dq8wS5+fv4PKcuYkOpwlBTojUnafCFaukK0dAdp98WT5q37wrzZsT1ZHaQjsb8zEME/SNLcx6DV4LLocVsMpNsMzC50kWEzkmE3kGEzkmkzkm4zkOMwkWk3quT6JKqqquKBBx44Yn9eXh7p6ekUFRUd0SKdlpaG2+0G4uXipk+ffqrDVgDH9GnMeeRhuvbspXv3XnoqK+nY/BGtFe8ccazGYMCYlYlWq+XArj0YMzMxZWWhd7sSNbYd6O12hFaNfxhLhBBMnTqVCRMmJLufALQkZgJVjq7JIPivwlGVnqVUk2HY3pdfBC4HvEKI2cBfgPuBcuA3wJBGxY+qK7n20AaMOiOT0kpSHYoyTKSUtPaEONDSQ2Wrj8qWHho6AzR3h2jpCtLSHaI3duTXRAJwNzclK4LkOk1My3Ukkuh4hZC+hNqVuO226DHr1WyNqZKVlUVpaSlVVVUD9vv9fubNm8fChQtJT09PUXTKsViKirAUFZFz0YXJfb2hEJGODsLtHYTa2gm3J6Zab2nFV3mQtvc3Eu3qOvJkQqCz2eJJt8MRX7tciUlt0jCkpye3tWY1NmK0OHDgAJMnTwbird0vvfQS06ZNS3FUI180Uk9r83+mOoxRIxqpP/ZBJ8YspWxIbF8PPCWlfEgIoQE+HupJRlWifVbRPP62+3V+8+HTfHfBN1IdjnKS1Lb7WbOnhXUHPHxQ1Z6cNRHAYtBS6LaQ5TAyOSuDbIeRbEd8Epksh5F0qwGXxcDmDes479xzU/gqlONltVq58847k7eDwSD79+9n9erVrFy5kpUrV1JWVsbChQs544wzVNeQUUBrNKLNycE0SGWWpooKFixdSm8wSKjVQ7ijg2hXFxFvV3ya9r7tri78dfVEduwk2t1z5HNYrVgKC7CWFGMpKcY2aRK2SRPVB+YRqK7uk8Hku3fvZvr06UyYMIFZs2Yll/nz56sZKJWRqv8/lfOAHwFIKWPH8/9mVCXa15x2Kf5wgDcq32FO7gzOKpqf6pCUz6DRG+Bnr+xi1c4mpISiNAufm5nD1Bw7k7LsTMyykuMwDekNVKPeZEc9k8nEaaedxrRp03j//ff561//yr59+9i3bx/hcJhz1QepMUFrMmEpjA+SPJbeUCjeKu5pI9TWRritnVBrK/6aWlrfXUfvqjcAsE4oZdKtt2CboLoUjiRLly4lFotRW1vL1q1b2bZtW3JZuXJlspym1WqloKCAl156iRkzZqQ4akVJWiOEeBFoBNzAGgAhRC4wpP7ZMIREWwhRCDwNZAMSeEJK+etEf5XfAiYgCtwipfxACLEU+DvQ933wCinlzxLnWgb8GtACT0opj+yg+emxcH355dR2NfI/G/4Xm8FKeY7qozkaxWKS636/kbrOAN8+ZyJfmV9Icbo11WEpKbZnzx4efvhhAPR6PeXl5cyaNYsFC1Td+/FIazRizs3FnJt7xH1SSsKeNupeWkHTa6/T8eFmlWiPQEIIioqKKCoq4otf/GJyfyAQYOfOndx7772sXLmSvXv38v7776tEWxlJbgO+AuQCi6WUfV+35wB3DfUkQykqHQW+L6WcDiwEviOEmA78AviplHI2cHfidp93pZSzE0tfkq0FHgM+B0wHrkmc57iY9CZ+tOQ7ZFszeObjFcTksSeYUEaemJS0dMenc5qUZcNuUt0ClIEWL17M2WefzaxZs1S3EWUAKSWhllZ6DlTS8dEWDOlp5Hzu4lSHpRwHs9nMvHnz+NnPfpbcd8899/DNb36TJ554gtWrV1NVVUU0qsrcKakh4yWvgsQbh2f2279FSvn6UM9zzBZtKWUj8WZzpJTdQojdQD7x1m1H4jAn0DD4GZLOAA5IKQ8CCCFeAC4Fdg012D4mnZGrZ36B/9nwv6yt2sC5ExYd7ymUFNNpNTxxw1zuenkHt7+4FSHgtHwnswqczMhzMiPPQVm2HZNeVSIYT6ZOncott9zCu+++y9q1a3n77beT9914443Mn6+6i403sXCYQEMDvoNV9Bw8hK+qCl9VFb2+eB11rdnMjJ/ejd7hOMaZlJGovLwcn8/HCy+8wKpVq1ixYgVPPfVU8n6dTkdxcTF33HEHN900smcAVMYWIcTjxBuG1wP3CiHOkFLee7znOa4+2kKIEmAOsJF4k/rrQogHibeM9892zxRCbCWefP9ASrmTeHJe2++YOuCEvw9eVDiPNyvf5YlNz9IZ7OLy6ctO9FRKiiyamMFbt5/D9nova/e2sr7Sw9+3NPDMhprkMTkOE0XpFkrSLRSnWylwm8lzmcl1msh2mNBr1UyfY015eTnl5eUEg0Huu+8+mpubAdCq8m9jkpSSiLeLYFMTwaZmQs3NBJuaCSbW4fZ2SNRS1xgMWEqKyVi8GNuEUqylJVhKitEajal9EcpnYrFYWL58OcuXL6e5uZnHH3+cRx99lLa2NqLRKD6fD7OqNqOcemcD5VLKXiGEBXgXGL5EWwhhA14CbpNSdgkhfg78PynlS0KIq4E/ABcAHwHFUsoeIcTngb8Bk48nKCHETcBNANnZ2VRUVAx63CJOw6vz8vz2vxOq6yHHkHE8T3NS9PT0HDW+kexUxD3U6whQroPyKRArM+AJ6KnuitHoi9Hsi9LS0cnehg68oYFl/QTgMgrSTAKHvpfn97xOuklDmkmQZk7sN4iUDJQcrb8XR3M81/JkOv3003nttdcAePvtt2loaMCWmABlJP6MR2JMh0vVtfR5PKx99jno6ER0dCA6OhFeL3i7EJHIgGOl1Yp0OiArEzl5ItLtQmZmgttFSKOho+/Axob4cgJGw7U6llRdy6MZys9USpmc5dXj8STXHo+H+vp6duzYQSwWIycnh6uvvpqzzz6b6dOno9FoUvb6xsLvinJCwlLKXgAppV+cYGkjcfisa4MeJIQe+AfwupTyl4l9XsAlpZSJJ/dKKY/47k4IcQiYRzzZvkdKeXFif1+ZlPs/7bnnzZsnN23adNT7W3o83PHGfdgNVh763N0YTvHU6xUVFcmZsEaTocQthNgspZx3Mp7vWNdxKHyhKA2dARq8QRo6AzQmthu9ASob2ukIC0LRgX32DVoNOU4TuU5TsiU812WmKM1CWbZtyFVNjtdI+70YadfyeGzdupXf/e539PbGJx7q60Iy0n7GcGqu+2i6lr5D1XjeXUfbhg8I9Cv1JnQ6TDk5mPNyMWZnY8pJLNnZ8cltTkEL9Uj4/RlN13IoBvuZSinZt28f69at46233mL16tW0trYOOEYIQU5ODoWFhVx00UVcccUVzJ49e8SUbDzW78rJvI4Ahmy7zLhmzsk63ZjneX4L4ebu5C/LhKwp8udXPX5C57ru8fOT11II4QcOJO4SwMTEbUG8C/esoZxzKFVHBPHW6t19SXZCA3AOUEG8vuD+xPE5QHMiAT+DeLeSNqATmCyEKAXqga8C1w4lyE+TZcvgC1Mu4MUdr1DrbWBiWvFnPaUyQlmNOiZn25mcbT/ivoqKCs455xw6/JF4Ep5IwBs6E0m5N8AHVe00dwWJ9pvwxmbUMSnLxtQcO+WFLsoLXJRl29CpLikjRnl5OY899hj33XcfNTU11NXVUVpamuqwlKMINrfQsWkznnXv0bVrN2g0OGfOoKe0mJlLz8Gcn48pK1PNBDnG1dfXc8011/DBBx8QCsUHvufm5rJs2TLmzZtHQUEB+fn55Ofnk5OTg043qqoND6sCmcfPw/ekOoxR4z/lt4fr1CdldqWh/GafBXwN2C6E6JsJ5z+AfwV+LYTQER+V2TdK4Srg20KIKBAAvpoYuRkVQnwXeJ34CM6nEn23P7NMSxoAqyvXkW3LwGZQZeLGIyEEaVYDaVYDM/Odgx7TG5N4ekIc8vjY19LDgeZu9jX3sGpnEy98GB9CYNJrOC3fSXmBi/JCFzPyHBSnW9FqRkbryngkhOD666/nN7/5DatWrWLVqlWYzWY2b95MTk4O2dnZ5OfnM2XKFDQa9SHpVAq1tdO55WO6du+ma9dugg2NAJjycin5xg1knX8ueoeDiooK0ubNTXG0yqnS2trKu+++C8DChQu59957Of/880dMK/VIpqZgPz7DNQW7lLL6ZJxnKFVH1jFwdpz+jvivKaV8FHj0KOd6FXj1eAIcisXF86nsqOb1/WvpDHZxx9nD9ulGGeW0GkG2Iz6QcsGET6b3llJS0+7n49pOttZ62VrXyf9tqObJdfFy8Ca9hinZdqbmOJiaG19Py7XjshhS9VLGneLiYh544AEaGxvZs2cPH3zwAdFolE2bNuH3xytQzJgxgyVLlpCZmUlmZiYGg7o+wyHqD9C+YQMtFe/g3bYdpERnt+GYNo2cZReTNm8u5vy8VIeppEAkEqGzsxOLxcKPf/xj7r33XjZs2MCFF16YPGbVqlVcfLEqx6iMD2PiI5NWo2X56V/BZXLwwvaVrNj1GpdNvVi1bClDJoSgON1KcbqVS2fnAxDpjbG3qZvdjV3saepmT1MXb+5u5s+bPimek+eMJ+xnTkzngmnZpFlVYjechBDk5eWRl5eHRqNh6dKlSClpamrigQceYOfOnezc+ckXZZmZmdxwww2UlZWlMOqxxbtjJzvuuhsAU042hVf//+ydd3hc1Zn/P3d6LxqNZkZdslzljo1t7ICxwaZDTAgJISybZLNLkoXspuwmm+xu8ttCNtlUEspmSbKbhRgMARJCMyAXbNxtXOSiXkbSaKSpmj5zf3+MNJZcZWN7VO7nec5zzzn33KP3au7M/d5zz3nfj2Fbfg26slIE6Td3QiKKIk1NTbS2ttLT04PH46Gnp2dE3uPx4PF4iEaj5+1v27ZtktCWmDRMCKE9xB0z1tDi7+B3B1/hvbbd3D/vo8x31kqvqiQuCqVcxuwS84hpKKIo0huOc7QrK7y3Nfbx+32d/H5fJx+Z215CXgAAIABJREFUWsj/flaKYHilEQQBl8vFT37yEyKRCB6Ph97eXjweDzt27OCnP/0pd955JzNnzswJdImLR2UryOUFhRJBoSAZCJC2F6LQ6fJomcSlIJPJ0NjYyJ49e3Jp7969BAKBEe3kcjlFRUU4HA4cDgczZswgGo0yZ84crFbrWZNacsUoMcmYUEJbIZPzN8s+x46yffzfBy/x75t/zuyi6Xxizh1MtVVJglviQ+ENxznsDnK0K0h9V5AjXUFOeMK5/bfPk16V5xudTkdlZSWVlZUAXHfddfziF79gw4YNAGg0GlwuFyaTCaPRiNFoPC1vsVjQSYLxrGhdLpY9/yyeus143n6Htv97NrtDENCWlmBbcjW25cvQV0m/ueOBxsZGduzYkRPV+/btIxgMAqBWq5k7dy6f+MQnuOqqq5g6dWpOXFut1tMeWseCJxcJibHGhBLakB3dWlq2kEXFc3mzcTMvHnmNb739fRz6QhYWz2GucyYzC2vQqSTn9xLnZ3+7n5f2dbKt0cvxnpOi2mXWMMNp5JY5LhaUW5lfZsGslcKEjzUMBgNf+9rX8Hq9NDY20tjYSG9vL319fTQ3NxMOh8lkMqcdp9FosNlsqFQqYrEY06dP55577pE8IwwiU6lwrrkB55obSAZDhBsaCJ9oIHD4CB0vvkTHhhfRuJwULr8G2/Jr0FdV5ttkiTOwZ88eFi3KeqXTaDTMmzeP+++/n4ULF3LVVVdRW1uLUin9rklIfBgm7F1DIVdwy7RVrKq6hs2tO9nrPsjGpq28duJdBEGgylLGLdNWsaJiMTJBepUsMRLfQIJvvXyIVz/oQqOUsbiygLsWlLCgzMoMpxGrNBd73CAIQm5x5NKlS0fsC4VCdHR00NbWRnt7O62trXg8HmKxGJ2dnbl2XV1drFq1CofDcaXNH/MoTUasCxdgXbiAMiAZDNL3/g68W7edFN3FLmQzppNZsQKZ9LCSd9LpNEeOHOGFF14AwOl00tbWJolqCYnLwIT/xdMoNaypuZY1NdeSSCU43tfMkd4T7OrYz2M7fs3L9W9w6/TVLCu7Cq1Sk29zJfLMiZ4Qz+1u57ndHUQSKb58w1Q+95FqDOoJ/1WZNPT19bF582YOHDhAV1fXiH2CIGCxWLBYLDidTkpLS3P+fk2m0+JxSZwBpcmEc82NONfcSDIQoO/9HXjerkP+Th3NKhVTHvrLfJs4aens7ORHP/oRzzzzzIhr/+6775ZEtoTEZWJSqQeVQsVsx3RmO6bzsdpbeK91N68cfZMndv2Wp/euZ2nZQh6cfw8GteSHe7Lx+qEufrzxBEe7Q8hlAmtrHXxhZc1Z/XFLjF/++Mc/sm3bNmw2G+vWrcNut2O1WrFYLJhMJuRSIJVLhtJsxrl2DY41N7L1oS/R/fqbaIpdlNx5R75Nm5Q8+uijPPbYY9x5552sW7eOZcuWUVNTI82lHwMIgvB5BuORGJWw8pnr8mzR+KG7L98WnJtJJbSHIxNkfKTyalZULOaYt4kXjrzK5pYd9A708Z1VX8m3eRJXkM3He/mr3+4F4N5FZXzh+ilU2KSHrYmKXp/9bPv6+pg2bZoUZfIKkI5GySxbguzFl2n9zW9xrF6NwiB9x640Q9f6/v37SSaT7Nu3j2nTplFdXY3T6cTpdFJYWCg9bOYBURSfAp4CcGgF8TzNJcYRk05oZzIZusMe2gJu2gKdNPnaOdHXTCieXei2rEyKXDbZqC02Ma/MwoF2P+t3t7N+dztOk4bKQh3lBTqsehUWrQqLTolFq8SsU54s65RolXJpRGiMEYvF8Pv9+Hy+3Nbn83Hs2DF6enoAmDZtmjTn+kOSSSZJBoMkAwGS/sDJfCBI0ucn1t1N1N1F0u/P3WycN61FrpMWo+eDL37xiyQSCfbt28fx48epq6vLBXsaQiaTUVRUlBPe50omk0n67bsMhGzTqbvvqXybMW4I/fzz52+URyak0BZFEW+kn45gFz1h72DqpSfcS/eAl2Q6CQwGvzA4uMo1h2mFVcy0T6XE5Myz9RJXGptBzctfXM7xnhDHukO0eAdo7hugxTtA3bFe/JEkifTpnimGUMllmLQK9GoFepUCg0ZBPBzjha59GNTyXJ1BPdhGrcCglmNQK9Gr5bl6g1qBWiGTblyjIBaL4Xa72bx5M36//zRRfaagGTqdjoqKCq6//nrmz5+P1WrNg+VjHzGdJubpJe7xDIrmQeF8aj4YJD0QOWMfglyO0mxG43RgvWoh2mIXjb5+rr7tVrQu1xU+I4kh1Go1f//3f58ri6KI2+2mubmZnp4euru7T0uHDh2iu7ubVCp1Wn8ajYaioiJsNhuFhYWk02lefPHFXHkoDS9rNNJaqPPhTIj8Xfvp/2+JM/OtxNh+ATAhhHYmk+FI7wl2uz+gxddOq7+DgeTJG61arqLIUIjDWMQ8Vy3l5mLKzcWUmlyoFJL3CIks0xxGpjmMp9WLokgsmcEfTeCPJPFHkgSG8tFsORhLMhBPEY6lCMdT+OMiwc4A4cG6aDI9KhsUMiEnuvVqeS4/XIwXGlSUWnWUWrWUWLUUGTXIZZNHnKdSKX70ox/R0tICZB+YTSYTVquVoqIipk2blptzPXwrhWM/M5GODrybtzLQ0kq0002suxvxVFElk6E0mVCaTSjNZgw1U1CazCgt5sF6c26f0mxGrted9sDYUFcniewxhiAIlJSUUFJScs52mUwGn893mgjv6urC6/XmUnt7O3v37sXv95+1L71ef5r4LiwsxOVyUVJSklt8XFJSkpvmNdnoVgl8r2xCyLMrQrdq5G+NrEiH9q8XXVxnj18Cg05hXH+SoijybvN21h98BV8sgFKupNJSyrLyRVRaSik3F+Mw2LFopNdbEhePIAhoVXK0Ki0u8+heeZ8auCGdERlIpBiIZ1MolmIgniY8WB5IDNVlUzieHtxm67sDsexxg+XhKOUCpVYdV1VYWV5jY0mVDZdZMyGv+VgsxpNPPklLSwtz587lk5/8JGazWZpTepHEurvZ98VHQCZDW1yMtqSYgqsXoS0pRuNwoLRYUJrNKAx6Kbz6JEYmk2Gz2bDZbNTW1p613dDvXjKZpL+/PyfA+/r6Rgjy4eWGhgZ6e3sJhUKn9WexWJg5cya33norn/nMZ3BNkgc1aUT7wpBGtC8jLx99k2c+eIkZhVN4cOE9LHTNQS2NUEuMQeQyAZNGiUnz4V1oRRNpOv1ROnyRwW2Upt4wG+t72LCnAwC7Uc3cEjPzyiysW3ju0arxgiiK/Pa3v6W+vp4HHniAZDJJQUHB+Q+UOCup8ACuW2+m9OMfQ2Wx5NsciQmCUqnMhWYfLQMDA3R2dtLW1sa2bdt4++232bx5M9u3b2f79u0888wzHD58+DJaLSFxeRjXQvvVY28z3zmLv7/2i1LQGYlJg1Ylp6bIQE2RYUR9LJlme1Mffzjg5tUPunj7qIe3j3r44VvH82TppeWNN95g165d3HbbbSxfvpy6urp8mzQhEORyEt4+lGbzhHwLIjF2SKVSeDwe3G43brebrq6uXN7tdtPe3s6JEyeIxWK5Y6qrq5k1axb/9E//lEfLJSQunnEttGOpOPu7j/CbfRtYVDKX6YVTUMklp/sS45NMRiScSBGMJglEkwSjqew2liQYTZ6sjw3W58rZtqOdBz5eicfjABw4cACAaDRKIpGQ5l5/CBQGPV1/eh33K39EZbNhXbQQ61VXYZk3B7m0aE3iAonH4+zatYsPPviAjo6OESLa7Xbj8XjIZEYuLBcEAYfDgcvloqKigjVr1jB9+nRmzJjB9OnTsdvt0gOgxLhmXAvtv13+F7zd+B4bG7fw2ol3UcmVzHfW8uVln0UhH9enJjHBOdQZ4HuvH8UXSeQEdSiWJHOOqWaCAEa1ApNWiVmbnYYyxW7ApFXkymadEqdJQ1mBjrICXS6ipfC9K3Ril5FbbrkFjUbDgQMHePXVVxFFkbq6OsrLy/n0pz993gVdEqejcTpZ/Otf0r9jF77du+ndtIWeN95CUCiwLlzA9K/9LTLpQUbiPDz22GM8/vjjHD16NCekBUGgqKgIl8tFcXExCxcuzOWHksvlwuFwoFBI92uJicu4vroXuGazwDWbWDLG4d4TPPPBS+zs3M+XXv02i4rnUmOrpNjowGEoxKQ2Sk/FEleUTEbkWE+Iw+4gbn+UrkCUTn+MLn+UE56s3/aPTC2kxm7ICuUhsaxVYtIqTikrMaoVyCaRd5FTUSqVrF27lrVr1xIOh3n55ZcRBIFNmzbx6KOPcvfddzNnzhxsNlu+TR1XKI1GHDeswnHDKjLJJMEj9bT85n/p37mLg9/8R0rvuRvbksX5NlMiT6RSKdrb22lqaqKtre20UWq3201HR3ZtyAMPPMBdd93F3LlzKS8vl8K6S0gwzoX2EBqlhquK5zCzsIY/Ht/IMW8TW1p38mbj5pNtFGocBjsOfSF2vQ27viC71WW3epUuj2cgMd5JpjM0ewc41h3iRE+ITQdjPFz3JsFhHkIKDSpcZi3Vdj3LawpZUG7hzvnSKOzFYDAYKCkpYeXKlRQXF7Np0yaeffZZnn32Wex2O3PnzuXWW2+dtO7BLhaZUoll3lxm/8t3aF//PD1vbuTovz1K1ec+g+uWmxAk7y4Tnt7eXh5//HG2bt1KU1MTra2tp/nQLigoyI1Kz5o1i+LiYu6++26CweAIb0sSEhITRGgPoVNp+fjs24Gs38+usIfuwUA1PWEvPQNeOkPdHOg+QjydGHGsVqnBrrNRqC+gaHBr1xfgNBThMhahUajzcUoSeUQURfyRJN5wnN5wHG84gTcUz5YHt95wIldODc77kMsEHFq4dW4piysLmF9modiiRaOURMrlYOXKlVx33XV0dXVx9OhRjhw5wrvvvsvWrVuprKykoqKCiooKioqKsFgsGAwGZJKrunOi0Omo+vM/o/TudXzwd9+g+ZdP0/bM71DbC1Hb7agLbagKC7PlwsJs2WZDJo1gjmtaW1uprKwEoKamhsWLF3PvvfdSXV3NlClTqKiowOVynTXojLRAWULidCaU0B6OTCajxOQ8Y6RHURQJJQboHeijd6APb6Qfz0Af3oF+eiP9HO1tIJIcGVmuUFdAiclBTUEV62bdhFJadDkuyWRE/NGsePaGsgI6K5oTg8J5MIWy5dQZJk0rZAI2g4pCgxq7Uc10pxGHSc3UomzAm2q7nvff28LKlXPzcIaTE0EQciNsq1atoqOjg02bNtHa2srbb79NOn1yoahCocBsNueC2ZjNZkwm04hkNBoxGo2Tfu6o0mRk4WM/wbdnL759+0l4+4j3egkdO07qVL/HgoDSYkZdOCTEbagslmwgG4sFobuHmMeDymKR5n2PQWKxGLt27cqV77vvPr7zne/k0aLJi17WwRLj1/NtxrhBL+vItwnnZFLeRQRBwKQ2YFIbmFJQccY2kUQUz0AfbYFOdnUeYEfHPryRfg5015PMJLl/3rorbLXExRKMJXlyUyMv7XPTE4ydUTwr5QKFBnVWPBvUzHSaKDSqc2K60KDCPrjfrFVO6rnS44HS0lI+9alPAZBMJunq6qKvrw+fz5cL1e73+2lra8Pn85FMJs/Yj06nGyG+T82bzWbKy8sn9Ai5IJdTcPViCq4eOU87HY8T7/WS8HqJe73EvX3Ee3tJePuItLXh27efzDA3bQpgz7PPASDXatG4nJhmzsRUOxPrwgXItaMLBiVx6dmxYwdLly7Nlb/61a/y1a9+NY8WTW6UBjmua0z5NmPcoHxhbL8tnpRC+0xEElE6gl10BLvpDHbRGeymI9hF70A/IllhplfpsGmt1BZNy7O1EhfC9147yv/taAPgjnnFLCi3nBTVRlVOPEuLZScmSqWS8vJyysvLz9omFosRCoUIBoO5NFQe2ra3txMMBkf4+B3ixhtv5KMf/ejlPI0xh1ytRldagq707OsM0vE4SX+AZCDA3i1bmVFeRsIfIHikHv/efQw0NdP16p9w3ryWKX/1+StovcRwXC4XBQUF9Pf3A/CDH/yAXbt28fLLL2M2m/NsnYTE+GZSC+3j3ibebNzMwZ6j+KKBXL1SpqDY6KCmoJKVVcsoMTmpspbj0BdKYmwc8vDqqSRSGZ7f08ErB9x88foapjuN+TZLYgyh0WjQaDTY7fbztk0kErS0tLB79262bNlCJpPhrbfeYs2aNVfA0vGFXK1G7ihC4yhCdHdiW7yYQ//wTww0NwNgqJlCwdIlOG5YlWdLJzclJSW88MILPPHEE6xfvx6ATZs20dDQwFVXXZVn6yYfyXCarm3BfJsxbkiGx3YMiUkrtFt87Xzr7e8DYFTpuW/uXZSaXJSanBTpCyf0q+DJhkwQ0KtPXup69dh+zSQxtshkMhw4cIDjx4/T0dFBR0cHkUgEAKPRSG1tLbfffjsmk/Sq93yEjh3PiWzj9OkUfmQ5hinVyNRScJwrjSiK/O53v2PDhg288847+P1+9Ho99913H7fffjtr167FarXm20wJibwjCIIFmDpYPC6KYuBc7U9l0grtcnMJ62bdxBsNmwklBkhlUiwqkRavTST8kQRPbm7i1++1kEhnuHdRGV9ZM40ik3RTlxgdfX19fPOb3xxRV1NTw4IFC5gxYwbFxcXSQ/kFYJ4zmylf+EsChw4TPFxP8y+fzu3TuJzoq6vQV1VhqK5CX12FShJ6lwVRFPn617/OD37wA8rKyli3bh1r166VXGJKSAxDEAQ18CRwF9AMCECFIAi/B/5KFMXEuY4fYtIKbZlMxqqq5Zzoa+Fgz1HSp4SFlRi/ZDIiP3+3gcc3NRJNprl9bjF/c+M0qgqlG4jEhSEIAmq1Ohf+HaChoYHGxkYKCgooKirC4XBQVFTEjBkz8mjp+ECmVOJcuwbn2jWIokiir5+B5mYGmlsYaGoidOwEfe9tz7Wv/c4/Ypk/L48WT0xefPFFfvCDH6DVannvvfcoKyvLt0kSEmORfwCUQJkoiiEAQRCMwM+Bbw+m8zIphXabv5PnD7/Kzs79KGQKPrPwXtbWXJdvsyQuEc19A/znW8cB+MTiMu5ZVIbLLI1iS1w4BQUF/PSnPwUgEong8XjweDz09PTktu+//35ugaTFYqGmpobS0tJ8mj2mEEWR9EAEfD6CR+pJBgIkBhdIJgMBkv4Aib4+4l4vCZ9/xLGx7p48WT2xufbaa5kzZw4HDx6kvLyc4uJilixZwo033sjatWuprq7Ot4mTGsnryIVxGb2OrAOuFkUxMlQhimJIEIQvAO8jCe0z09DXwjc3fg+Au2au5aaalRToLHm2SuJSMsVu4J9vn8Uv6hr53a52frerHblMoMyqpapQT2WhnupCPVWFBqrselwmjeSuT+K86HQ6KisrcwE9hhBFkUAgwHPPPceePXuor6+flEI7GQoROPABsR4PsR4PcU82xTy9iMkkSuDgKccojAaUJhMqmw3LvHmo7YXZQDiFNtRFdrQlUuTUy4Hdbmfv3r3s3r2bXbt2sXPnTrZu3crvf/97AKZOncpNN93EunXrpEiPEpOZzHCRPYQoimFBEE73E3wWJp3QdhrsmDUmArEgm1reRylT8LHaWyVvIhOMB5dX8eDyKroDMQ50+DnUGaDJO0Bz7wDvN/UTTZ5cpaxWyKgq1FNVqOfWuS5uneOSrgeJUZFOp2loaGD37t3s27cPvV7PokWL8m3WFUUURRp/8QQ9b27M1SmMRtRFRegqKrAuXoSqoIAGt5t5y5aiNJtRms0oTEZkkzwgUD5RKBQsXbo05z9bFEUaGhp4/fXX2bBhAz/72c/42c9+xuuvv87atWvzbK2ERF4QBUGwkp2bfSqjnm886X7lDGo9j936/9jZsZ+f7fgVzx9+lemFU5jrnJlv0yQuA06zBqfZydrakxFCRVGkJxinyRum2TtAi3eAbY19vHaom9cOdbNjaT//767ZebRaYiySTCZxu905zyMdHR20t7cTjUZRKBQsX74cu90++Tw1ZDIEPjiUK5rnzcW2dAkFSxajttly9Sfq6qT51mOUVCrFrl272LhxI2+99Rbbt5+cJx8MSm7mrjhqEbFSWjc2atSjHly+UMzAHs4stKUR7XPhDvWwtS0bajbr0s+VZ4skriSpjIgnFONYd4g9rT72tvpwB04GIVkxtTCP1knkE1EUCQaDeL3eXOrq6qKjo4Oenh4yg4umVSoVJSUlLF68mJkzZzJr1iw0Gg11dXX5PYE8IMjlLHz8ZwQPH6F38xb8+w/Q9OR/0fLr/6Hqsw/iXCv5Fx+LpFIpnnvuOZ577jneffddgsEggiCwcOFCvvKVr3DDDTewfPlytFLETolJiiiKlZein0kltDuD3aw/+Afe79iLXqXjvrl3ccvU61EpVPk2TeIK0OmP8v3Xj/L64W5iyaxgcpk1LKyw8rlyK0urbcx0GaVpI5OA7u5uuru7RwjqoXRqOHar1UppaSnz58+nrKyM0tJS7Ha75NZvGIJMhnnObMxzZiOKItGOTk785Gc0PvFfFCxejKpgko3yj1GCwSCvv/4677zzDn/6059ob2+noqKCe++9lxtvvJHrr7+ewkJpoEFC4lIyaYS2KIp8t+7H+KIBbqpZySfm3IFOJT2pTyb+8n93c6InzD2LSllabWNhuZVii3QNTCZEUeTFF1/kzTffzNVpNBoKCwtxOBzU1tZSWFiYSzabDZVKehC/EARBQFdWispqRa7RIJP+f2OGG2+8kZ07d2I0Glm5ciWPPfYYt912m/TQONaICwgt0mcyauJje3Bs0ghtQRD46MybeHrveja1vE8sFWeucyZzHTMwaaRw3JOBLn+Mjy4o4V/umpNvUyTyxB/+8AfefPPNXDRHu92OXq+X3mJcBiJt7QgyGZ0vv4Jt6RIQL9s8SolR8OSTT7Jz504g+0ZHp9Pl2SIJicnBpBHaADdNXUlt0TRern+TXe4D1LVkF3xUWcuY55zFVcVzmGqrQiZIT5ITjQZPmL6BBFMd0kPVZMZqtaJSqTh8+DD19fVYLBasVisGg4GBgQGmT5/Orbfeilx+2fyyThpm/eM/0Pj4k3RseJGO5zagUKs49O5mjFNr0BS7UJpMKIxGlEYjCqMRhV6HIP3fLzmZTIbGxkbWr18PwMMPPyyJbAmJK8ikEtoAZeZivrT0QTKZDE2+Nj7oqedAdz1/OPoWL9W/gVVj5qGrP818V22+TZX4kIiiyPGeMNsbvby4rxOAj0gLHSc1H/nIR1i4cCH79u3D6/WyZcsWGhsbc/sbGhpYtmwZdrs9j1ZODLQlxcz+l++QDATw7dnH0XffJRUO0/n7lxHT6dMPEAQUBv1I8W00ojQaTuZNxlP2G5Cr1Vf+5MY44XCY3/zmN6xfv579+/cTCoUAqKqq4pFHHsmzdRLnIxlO07VN8vYyWpLhM/yejCEmndAeQiaTUWOrpMZWybpZN9MT7uW7dT+hd6CPJl+bJLTHMQ2eMM/saOO1Q110DXoTKbFo+eHH5zFNGtGe9Oj1elasWAFkBcmWLVty++644w5JZF9ilGYzRatWckQG81euJB2Pk+j3kQqFSIVCJIe2wRCpUDhXl+jvJ9LaSjIUJhOLnbV/mUqFYlCMK41GFAYDCtOwvNGIxlGEvroKhV5/5U48j1x77bXs27cPgM9//vNcffXVLFiwgDlz5qBUKvNsnYTE5GJSCm1RFOmN9NPm76At4OZEXzP7u4+QzqS5puwqbqpZmW8TJS6CQDTJzuZ+/uJ/dgNww0wHX75hKtdMKaSsQHpVKnE6w0U2wCuvvMLGjRsxGo0YjUbMZjM1NTXMnTtX8sZwiZCr1WhdTnA5z994kEwyOSjER4rzVCg8mM8K9FQ4TKS9g1Q4Wz515FxT7EJfVYnabkfm68erVKEqKEBlK0BltSKbICL0gQceyAntX/3qV+zcuZMVK1bwzW9+E5dLcmc71pFCsF8YlzEE+yVhwgvtYCxER7CbtkAnbQE3bf5O2gNuoqmTIyQOfSE3T72elZVLKbdIIX/HGw2eEP+wNULn61lPEiqFjL++voa/Xj01z5ZJjHW+//3v43a7icViHDlyhE2bNhGJRIhEIvT09ACwZ88e1q9fz7p166QIeXlCplSithWgthUAIKbTZFIpxGSSzGASUykyyZF1qWCQWHcP4YYG/B8cJObuIubuAkAOHNu0dcTfqfj0pyj92LorfXqXnC9/+cvceeed7Ny5k/3797Nv3z6eeuopnnrqKaqrq6msrKSioiK3LS4uxmq1UlBQgNVqRafTSQuEJSQuERNCaIuiSF/ER0ewm85gF53BbjoGt6HEQK6dXqWj3FzCtZVLqLCUUG4uocxcjFapyaP1Eh+Wb7x4kM7wSY8GH7uqFJ1aweuHuim1aim1ajFrldKNQ+I0TCYTJlN25Ojxxx8/Z9tp06ZdCZPGFWI6TToeJxOLZ7fxGJl4gnQsRjoWJzNYl47FkdXX09rZNVgXJ5NMZQVyMnlSNJ+6TSaz4jk1si2Zi4yaJ5Oh0OuQ63TEIlGUQDoWQxz0nW6eO3E8ElVVVVFVVcW9994LZNcfPPnkkzQ1NdHa2sru3bvxer1nPFapVI4Q3qfmz1ZOJBJX8hQnLP6gi5fe+Fa+zRg3+IMP5duEczJuhXY4McD77fvY2bGPo95GYql4bp9BpafU5OTq0gWUmJyUmpyUmYsp0FoksTUB+fl9C3n85S2I5mIOdPh5aV8nkcTIV8Z6lZxSq44Sq5YSS1Z8l1i1lFp1VNn0mHUT45WxxIWRSCTo7u4mFApx2223sWXLFgKBwBnbPvroo3zjG9+gsrLyyho5RhBFkXBDI01P/pJYV1dWoKZSoz5eDnQIAjK1GrlahaBQIlMqEZQKZIrBrVKJXKtFoVAgUyoQlMqT+4a1ERTZ7cnjB9sqlYP9KrJtVCoUOi1yvR6FTodDjFnaAAAgAElEQVRMo8ndA+rq6lixcuXl+WeNQWpqavj+978/oi4cDtPa2kpPTw8+n4/+/n58Pt9p+e7uburr6/H5fAQCAcRzuGrUaDS4XC6+/OUv84lPfAK73S7ddyUmNeNSaHeHPHzn3R/TF/Xh0BdyXeVSyszFlJqclJicmNRSdL/JRJFJw3VlSlauzC5gFUURfyRJpz9Khy9Chy86mI/S6Yuyu6WfYGykQLDpVVQV6qkq1FNZqM+NhJdYdBQZ1chk0vU0EUgkEhw4cID6+npaW1txu925sOrDUSqV6PX6nBs0t9tNYWEhZrP5SpucV9KxGP07d+Pbs4fAwUMk+vpRWiwUXrsiG4xGrUauViPTqJGrB8uabFmmUiPXaLJltZr3du7kutWrpd/mMYTBYKC2tpba2tEv/k+n0wQCgTMK8j179mCxWNi1axePPPIIjzzyCFarlRkzZjBz5swR26qqKsmNpsSkYNwJbX80wPe3PkEineC7q77C9MIp0g+3xAgEQcCqV2HVq5hdcmZhFIplhXh7f5Rmb5hm7wCNvQO8e6wX756OEW1VchkuiwaHKZuKjGqKjOqTeZMau1GDSaOQrsUxxFCY9d7eXjweD729vTQ3NxOLxdDr9VRUVDBnzhzKy8sxmUzodLqcuJ7snhni3j46nt+A591NZOJxlGYTptm1mOfMpvCaZSgv5oFDIX0/JgJyuZyCggIKCgpO21dXV8fKlSsRRZFt27axZ88e6uvrOXr0KK+++ipPP/10rq1KpWLmzJmsXr2aW265hWuvvXbSf+8kJibjRmjHUwlePvomrx1/h1Qmzdc/8hAz7DX5NktinGLUKJnhVDLDaQIcI/ZFEik6fdkR8I7BUfFOXxRPMM4HHX48wTjR5Ol+OzVKGUXGrPgW4jHqgocpMqkpMmpwDG6LjGosOmm++OXm7bff5rnnnsuV9Xo9drudRYsWsXjxYqZNmyaFnR5Gwu+n5623CZ9oINzYRMLrRZDLKVp1Pfbrr8U0cyaC9P+SGCWCILB8+XKWL18+ot7n83H06FGOHj1KfX09e/fu5bHHHuOHP/whVVVVfP3rX+fuu++WXGxKTCjGhdCOJKJ8c+P3cId6uLpkPvfOuZ0yc3G+zZKYoOhUCqY6jGeNIimKIuF4ip5gHE8oRm8oTk8whicYxxPK1rUEM9Tv6SAcP30OqyBko1ELArzzlZVUFU4O375Xgr6+PlpbW3Mi+95772XJkiXoJ4n/5Ish3NjEgb/92og62zXLKL7zdozTpkoCW+KSYbVaWbZsGcuWLcvVhcNh3njjDf793/+dhx56iIceeojbb7+dDRs2oFKp8mithMSlYVwIbQB/LBsl6RNz7qDULPkBlcgfgiBg1CgxapTUFBly9fFUmt5QVmy/s20PRRU1NHsHONwZ5JA7kFugObSOSBRBGtf+8GQyGTZu3Mj27dtxu925ep1OR01NjSSyz4MgkyEolTnPGwB927bTt207yGQojUaUZhMKkwml2YzSPGxrGllWGAySMJcYFbFYLDe9SyaTcfvtt3Po0CHi8TiHDx9GoRg38kRC4pyMiytZp9Ly1eWf50fbfslX3/gXrq1Ywpqaa5lSUCG9gpe47MSS6cHR6hieodHrUPxk3eDWF0mOPHD/YeQygUKDiil2A47BudxFRjUzXSZumFmEQi6Jkoshk8kQCoU4ePAge/bsYfv27QB8/OMfZ8qUKZSUlEjzPUeJvqqSazb8Dsgufox7PMR6sikZCJAMBEkGAqSCQSItLSQDQVLh8Jk7O0WYqywWVAVWlFYrQk8P/gIbqgIrqoIC5Fqt9Ps9wRBFkb6+Prq7u+nq6jrjdijv9/tHHCsIAosXL+aWW27h4YcflqZ2SVw0bn+Uf35pf77NyDEuhDbAbMcMfnTzP/HCkdd4p+k96lq2U2UpY/WU5cx2zMBpsCMTpC+mxOiJJFJ4gsOEcyiOJ5ePZaeGBGOneSgBUMoF7AY1dpOGCpuOxVXWEXOx244f5JbrV1CgVyGXPJZcUkRR5KGHTvebWlpayty5czEYDNJo2EUi12jQlZejKy8/Z7tMKpWN0JgT4lkxngwGRwjzcGMjid1+MrEYCuDwYFApAE1xMdWf+3OsVy28zGclcalIpVK43W5aWlpobW0dkVpaWmhpaSGZTJ52nFarxeVy4XK5mDVrFqtXr8bpdOJyuXA6nTidTiorK8+4wHIy0q0S+F6Z9Bs2WrpVY/seO64+SZPGyJ8v/Dj3zr6drW07eatxK7/ckx2J0So0VFpLqTCXUmJyUmJyUGxyYtWYpVGTScTQ/OkzjTj3DBuV9gTjZ5w/rZLLBhcwqqmxG1g+xUaRSYN9uJcRoxqrTnVOl3913XLsRvXlPNVJza233sqrr746oq6jo4NvfSsb5EEmk6HX609LOp3urEmv16PVaqV5oaNAplCgslpRWa2jap+KRNn6+uvMr6kh6fMz0NxM5+9f5sh3/5Vrfv+8NN1kjJFKpTh48CDbtm1j165dNDc309raSkdHB+lTwto7HA4qKipYsGABCxcuZNmyZSNEtMvlwmAwSPfhC6DYouWf75qfbzPGDV/7P22+TTgn40poD6FTaVlTcx03TrmW9oCbhv5Wmn1tNPvaqWvZPiJ4jVapocTopNjkoMSY9bNdanZJI+ATBG84zmuHuqk7EucnR97jeHeIgcTZPYI4TGpmOk1cO3Wke76hvBRBcuwjCAJ33HEHJpOJlStXEggE8Hq9hMNhIpEI4XCYgYGBXAqHw/T399Pe3k4kEiEej5+zf4VCMUJ4n0ucm0wmqqqqpNfc50Gh00KBFctg5EX9lCo6f/8yAIl+H+pCWz7NkxgkHo/zyCOP8Mtf/jInqB0OB1OnTmXFihW5kO1D4dvLysrQak+KnCH3fhISEicZl0J7CEEQKLeUUG4pAa4BsiOavmiAzlA3ncFu3MEeOkNdHOo5xuaWHblj1Qo15eZiplgr+NjsWzGpDWf5KxJjmb9/4SAb63sGS34sOiXXVxUwp9TCvFIzFTYdRSYNRrXkw3eiYjabLyiQTDqdJhqNMjAwQCQSOW/q7+/n+PHjZw0vXVtby8MPP3ypTmdCkopEocdD7+atRN1u/PsPACDX65HrxvZo1GRi/fr1PPnkk7ny1VdfzZo1aygpKaG4uDiXHA6HFGxGQmKUjGuhfSYEQaBAZ6FAZ2GOY8aIfcFYiN3ug7zfvof93Uc40dfMib5mFHIFD8y/O08WS3wYvntnLYIA20/0EE6CP5Lk3WO9vHusFwCjRoHdqMaiVWIeTBadCtNQfqhel90aNQqMGiU6pVyKBjmByGQyRCKREeJ6tPmzCewhvF4vmUxm0o9qi6JIrLubaHsH0U43UbebqLuLaKebpM+HEjgOIAjoKyso++S9FK28FsVg9E2J/PPJT34SrVbLkSNHOHDgAAcOHODf/u3fToueKpPJcDqdVFRUUF1dTXV1NVOmTCEQCDBt2jScTuek/z58GEKN+6n7+OSKQvthCDXm24JzM+GENmR/8D0DXo57m2n2t+MOdtMZ6sEz4EUc8q0G2PU2nIZC5jpm5tFaiQ9DsUXLfz2wiLq6OhYtW0F3IEZPMEZ3IEZ3MJv3huMEokl6w3EaesMEIskzLnAcjiCAQa3ApFFiUCsGBXhWhBsG8yaNEotOiUWrwqrLinWrToVFpxxxnUlcPkRRpKuri2PHjtHf339WsRyLxc7Zj0qlGjGH2263j5i7fba8VqudlCN7YiZD3Osl0tZOpK2d0LHjhOrrSQaCuTZKswlNcTHWhQvQFrto9PWzaM0aNE4HcrW0fmEsolQqueeee0bUpdNpPB4PnZ2duN3uXOrs7KSlpYWtW7fy7LPP5sT4I488gkajoaqqiilTplBdXU1lZSWlpaWUlpZSUlKCy+WSvAJJTBomlNBOppP89sDv2da2m0A8BIBSpqDY6KDaWs5HKhZnF0oanbiMDtQKadHTRMKgVlBTZBjh2/pspDMioViSQDSb/JHsNhxPEYolCcdSBGMpQrHBcjyFN5yg2TtAOJ7dl0hlztq/QgYF2zZi1alwmDWUF2gpL9BRXqCnvEBHtV2PRjn5BNqlIpFIsH//fl599VWCway4Gz63WqfTYbFYKCkpOU0kn0ksSzf9syNmMkQ7Ogk3NBA63kC4oZFIezuZYQ8vGqcD61ULMc6cgb6yEm2xC4Vh5Pewoa4OfcW5vZlIjD3kcnnOY8jZSCQStLW18eKLL2I0GmlsbKSpqYmmpibq6uoIn+IOUhAEnE4nJSUlIwT4UL6oqIiZM2dO2ul+Idt06u57Kt9mjBtCP//8ZelXEITFQLsoit2D5QeAu4FW4J9FUewfTT8TSmi/cORPvHbi3Vx5SkEFS0oX4DIWYdfZKNLb0Kt0k/bLK3ESuUzAolNh0V38w1Ysmc6JdF8kgT+SxB9J4I8m+eBoI6bCIvoHEnQHY3zQ4cc/zM+2TiVnba2Tv/hINbOKTZfilCYVGzZsoL6+Ple+6aabuPPOO6XX1ZeISHsHHRteINHXT7ihkXQ0CoBcq8VQMwXHDavQlZehKytDW1qK0nTmKKoSkwOVSkVNTQ1XX331aYshRVHE7/fT0dGRS52dnbn8iRMneOmll07r8+c//zlf+MIXrtAZSEickSeBGwAEQbgWeBT4a2A+8BTwsdF0MqGE9urqFYgi+GIBukIe3KEenvlg5BdYo1Bj19uw620U6WyD+YJcnbQoUmK0aJRyNEo5DpPmtH11YjsrV84dUReIJmnvj9DaF+HX25r5/b5OgtEk//3g4itl8oRh9erVuN1uvF4vPp+P119/nRtuuAGjURJ8l4KuV/9Eb93mXLnw2hW4brkZ4/Rpkis+iQtCEASsVitWq5U5c+actj+TyYyYflVaWsrs2bP5+Mc/fiXNlJA4E/Jho9b3Ak+JovgC8IIgCKOOiDOhhLZdb+OTc+8cUReOD+AZ6MMb6ccz0EfvsFTfe4JocuTczfvnreOOGTdeSbMlJglqhYwGT5j1u9rZ1eJDrZBxx/zifJs17hBFEYVCgdPpJB6P4/P5KCoqkkT2JaTqMw+iLirC8867RDs68W7einfzVlQ2G7qKcnTlZegrKjDVzkLjKMq3uRLjkEQiwbPPPsubb54MYvSrX/2KBx98MH9GSUiMRC4IgkIUxRSwGhg+R2XU+nlCCe0zYVDrMaj1VBeMnBsYS8Y43tfMbvcHbGnZwUAy+2p0r/ugJLQlLinBWJKfbDzBc7vbCcVSlBVo+cqN07hvSTk2g7QobDT09PSwY8cOjh8/TmdnJ5FIBAC1Ws2sWbO47bbb8mzhxEKmUlG67i5K191FKhIh3NCYnZvd2kqktZ2ug4cQByMAyvV6Kj71SVy33pxnqyXGC5FIhFWrVrFjxw4cDgf33Xcf999/PzffLF1DAM6EyN+1n3vBvsRJvpW4bM4HngU2CYLgBaLAFgBBEGqAwGg7mfBC+0xsbtnBYzt+DWRfa1VaSplZWMMMe81pLgElJD4s636xjQZPmDvnF3Pv4jKWVtkk14EXwMaNG3n++eez39XKShYtWkRpaSm9vb3ceeed0kLGy4xCp8Myd04u2AyAmE4T6eik54236Hr1TwQOHpSEtsSo2bBhAzt27ODHP/4xDz/8sLRuSmJMIorivwqC8DbgAt4UT7oTk5Gdqz0qJo3QjqcS/Gbf82xt25WLHPnlZZ9jvmsWOqUUMEHi0iCKIt3BGIe9aVrea+b9pn4aPGG+dH0NX107Pd/mjUtCoawHoZkzZ7JixQqmTJmCxWKhrq5OEtlXEFEUiff0EKw/SrD+GKGjR4m0tgFQtHpVnq2TyCeiKBIOh+nt7eXw4cP4/X4CgUAuDZV7e3vZvn07R44cAeCuu+6SRLbEmEUQhB8D24D3RFEcGKoXRfH4hfRzXqEtCEIZ8D+AAxDJTgb/iSAI84AnAAPQAnxKFMXg4DHfAD4LpIGHRVF8Y7D+JuAngBz4pSiKj16IsR+GSDLKxqatufIcxwwGEhFafB2UmpyYNNL8TomTDLn/C0ZTOReAwWHuAIPRk/nsvhTBaBJPMDYsBPwRTBoFj6yeyheun5LX8xnP3HzzzSQSCbZs2ZK7QRcXF3Pdddfl2bLJQyoS5cBXvkbM3XWyUibDetUCbNcsw1RbiyiKkmiaAAyJZq/XS29vby6dWh6q6+/vJxAInBbU5lQUCgVWq5WFCxdy//33s2bNGioqKq7QWUlIXBQNwF3Afwz+tm0bTO8BB0RRPPdFP8hoRrRTwFdEUdwrCIIR2CMIwlvAL4GviqK4SRCEzwBfA74tCMIs4BNALVAMbBQEYdpgXz8HbgQ6gF2CILwiiuKR0Z3vh8OqNfP9tf/AH45upD3o5ri3iYM9R3P7jSp91se2yUWZ2UW5uZhyS6nkhWSCkM6I9IbiuANR3P4oXf4YnlBsUDSfLqbD8RTnijmjkAm5SJNDUSbLC3TYptmZUmQg3NnA3Tcux25US+LjQ6LRaLj33nuZP38+P/zhDwFwu928+eabNDY2YrfbsVgsmM1mLBYLFosFo9Eoufq7hMiUCmxLribc2ESsq4u4tw8yGXx79uHbs4+Gn/0CQalEaTKiNJlRmk0oTEaUJhMKoxGFXo/Q3k6/To9cr0cxlAx6ZBqN9B25jGQyGXw+33kF8/ByPB4/Y18qlQq73Z5L1dXV2Gw2zGYzZrOZ7u5ulixZkisPJYvFglarlT5niXGFKIqPAY8BCIJQDFwzmL4MFAGj8s17XqEtimIX0DWYDwmCUA+UANOAIf9PbwFvAN8G7gR+J4piHGgWBKEBuHqwXYMoik2DRv9usO0VEdoAFZZSvrT0QQAyYoa+iI/OYDcdwW46g910BrvY2bGPt4eNfFs1ZmqLprG0bCELXLUo5dKr6vHEc7va+ek7J+gOxEhlRipntUKGRafEpMkKZZdZwwynEdMw8WzWKjFpFCPCtJu1SrRK+TlvGnV1zRSdwe2fxMUzdepUPvOZz9DX14fP5+PYsWM0Njaya9eu0yJxymQyTCZTLhmNRoxGYy5vMpnQarWUlZWhUEyaGXQXjUyppPLBB3LlTCJBrMdDrKeHZCBAMhAkFQySDIZIBrPlWHcPyWCQ9ODCVQVQ/8ZbZ+hcdlJ4Gw0oTUYURtPgdlCsDxftBj1yrQ65Ri25GhxGOBzmv//7vzl+/DjHjx+noaEBj8dDX18f6XT6jMcYjUYKCwux2+0UFxczb9487HZ7rm54KiwsxGg0nud3r+40P9oSF45e1sES49fzbca4QS/ruGx9C9kLfg5Zgb0cmEV2pPt/R9vHBd1hBEGoBBYAO4DDZIXyS8A9QNlgsxLg/WGHdQzWAbSfUr/kQv7+pUQmyHK+s+e7anP1oigSiAVpC7hp9XfS4m9nf9dhtrbtYoFrNt+49ov5MllilKQzIm39Ebac6OUfXz7M/DILd8wrxmXRUmLR4DJrKbZoMWkU0gjLOEImk7FkycmfjKGbejqdJhgMjpgLOnyOaDAYpLOzk1AoRCo1ciX/8uXLeeCBB079UxLnQaZSoSsrRVdWet62YjpNaiDCe++8w1W1taQHBkgNREgNDJAKh4eVw6RCYZKBIJH2TpLB4Ijok2dCrtUi12mzwlunRaHTDdbpkGs1g9vBep12RJlAgGQwiFyrRTYB5vofO3aMz33uc6hUKqZMmUJNTQ3XXHPNCKF8qnDWaKTBAAmJszE4e8ME7Cera/9NFMX6cx91OqMW2oIgGIAXgC+LohgcnC7yU0EQvg28AiQu9I+f4299nkF/hQ6Hg7q6ukvV9QVhRMEcqqi1VPAn/1YOdB3mnXffQSacHEUJh8N5s+/DcCXsvhKfY180Q0c4Q1NfjKc+eIPOcAZ3OENycOaUSSXwYE0ci7obYkA39HRDzyW3ZCTj9bo4G2PlOzmcc/2Ph15ZD0cURTo7O9myZUuu7siRI/znf/4nOp0OrVabC82u1WpHBNG4FDaNFfL1WYYVcvZ0Dht5UimgwJJNZyOVhlgUojGEaHZLIo6QSEA8QTqRzOYTCYjHIRQ6WR5sc7ZHaSWw8+n/AUCUy0Glyia1CnEor1IOy2eTONhmxH6lEvR6uMIj7MM/yyE++tGPcsMNN1BdXX3G6VNDD6ANDQ2X3J7xcP1fDBP1vCTOSxMwF5gK9AFeQRB6RVH0XkgnoxLagiAoyYrs/xNF8UUAURSPAmsG908Dbh1s3snJ0W2A0sE6zlE/AlEUnyIb3pJFixaJ+X4V1Rns5tl336DE7GLV9SNX14/XV2VXwu7L+Tke7wnxmV/vosMXHawRcJoUTHMaWeMwMM1hZLrTyNQiI1rVhQumD8t4vS7Oxlj7TsLF/4/Xrl3LBx98wJEjR/B6vbS2tp42J1UQBCwWC4WFhblkt9txuVw4nU5UKtUltelKkq/PMh//G1EUycRipCJR0tHBFImQjkQ5tHcvUysqsuXcviipoXIkQtofJB2NkIpEc37Dz4Z95bVM+5tHrtCZZRn+WVZVVYkzZsxgw4YNrF+/HrVaTVVVFdXV1bk0depUZs2aRWVl5WVZwzAerv+LYaKel8S5EUXxLwEEQTABS8lOH/miIAh24JAoin82mn5G43VEAP4bqBdF8YfD6otEUfQIgiADvkXWAwlkR7efEQThh2QXQ04FdgICMFUQhCqyAvsTwH2jMfJKI4oi7QE3B7rr2e0+QH1vAxqFmvsW359v0yQGeWpzU05k1xabmGWIcu+qBUx1GDFrx/9rYInLh91uZ/Xq1axevTpXF41G8fl8+P1++vv76ezspLW1lebmZk6cOHHGfqxWK9/97nfPKrol8o8gCNmpJNrTXbiK8SjFw8STKIqkByIkfD4S/f2D26HUT7SjI+fO8FTkWi3Fd9x+uU5jVNhsNl577TV6e3t55ZVXOHr0KE1NTTQ1NbF161aCwWCurVarZcaMGaxYsYK/+7u/o6Sk5Bw9S0hMeuJAhGzQmjjZgeJR//CPZkR7OfBp4OCw2O7fJCuahyYsvwj8CkAUxcOCIDxHdpFjCviiKIppAEEQvkR20aQceFoUxcOjNfRyEkvGaPS1cdzbxIm+Zk70NROIZ333lppc3Df3LlZWLcOiGdUCU4krwL/cNZslVQVs2NPBoc4Ah91pnj++HQCjRoHDpKHIqM6mwbzdqD5Zb9JgUEuL4CSyaLVatFotTqeThx56aFTH+Hy+0xZhSlxZgvVHiff2kkkkyMQT2e1gSsfjZ6zPluMo/H52/e+zI/ZxBhd1Mo0GVYEVTVER5tmz0ZYUD6YSVIW2MbfOw26389nPfnZEnSiK9Pf3c+zYMY4cOUJ9fT2HDx/miSee4Be/+AXTpk2jtraWWbNmsWDBAu644w7Ja08eURrkuK6R9MZoUb5wed5aC4LwI7Kj2FOBfcB2soPKfyaKon+0/YzG68hWOOs0t5+c5Zh/Bf71DPV/Av40WuMuN88d+iO7Ow/QGujM3TCLjQ7mu2qZaa9hrnMmhbqCPFspcSY0Sjn3LCrjnkVlZDIiL77xLgVVtRzvCdPlj+IJxfGE4uxu9eEJxUmkTr+B6lTyQTGuocik5vZ5xaytdebhbCQuNel0mlgsNiJFo9FzlofqRssXvvAF1Gr1ZTwLiXPR8Isn6DmTFxNAkMuRqVTZpFYNy6uRadQoTEZEpQJLaenJfSoVCoMBVUEBqgIrqoIClFYrCt34D2gmCAI2m41rrrmGa665Jlff3NzM008/zcGDBzlw4AAbNmwA4L333hvRTkJiktIM/BbYPzRgfDFM2iG9/oifDYdfBWBl1TKWli5kqq0So+Q3e9whkwkUamWsnOFg1QzHaftFUSQYTeEJxfCE4nQHYuxu9bGt0UtLX4SWvqz7sT9+0MW3b5vFZ1dUXelTkLgAGhsb8Xq9HD9+HL/fTzAYHJEGBgZIJEa3NlutVqPRaNBoNGi1WtRqNfPnzx9RN5TXaDQolUrS6TR6vZ5Zs2Zd5jOVOBeJvv4z1iuMxqwfb70BhdGAwqA/Y97T1sbUu9ddYavHBqIoUl9fz8GDB1Gr1ZhMJmw2GydOnKCqqoqrr776/J1ISExwRFH86aXoZ9IKbaNaz8qqZWxqfp+65u009rUwzzmLuc5ZzLLXoFJI8y4nCoIgcLAzwEv7OznWHeKEJ0QseXKE26pTUlagA2BRhTVfZkqMAr/fz3/8x3+cdb9MJsNqtWIymTCbzSN8aQ9PBoMBtVotvR4fx8z69jcRMxkS/T5iPd3EezxE/z97Zx4fWVXm/e+tfV+Tyr53upPQ+06zNTsKyKrj+6rIwOCgvoqOoh9nVOwZHBlRFMVBBWYURXABAQHZCWs3ve9Jd7qzL5VUKrWn9rrvH1WpTnXSTbrp7spyv5/P+Zxzzz331HNTqarfPfe5zzPgJO71kgimQwfGRkYY7e4hNjKCeFRoRyXw7qOPseQn92Korc3PSZxhNm7cyPr16ydciJaXlzN//ny++MUv8s1vflOKLS8hcQqZs58mpVzJF1bfxLWNl7O1bxfb+vfy3MHXeO7gawB8ftVnuLBWunU2k4nEk2w87OaRdzp459AwVp2Ss0rN/N/VVTQUG5lXZKC2QI9FJ11UzRQsFgu33HILL774Iv39/QiCkOMnnUqlcLvduN3uE5r3Rz/6EUaj8VSbK3GaEWQy1AV21AV2OOusnH0xr48tn73lA+fw7tyNvqZm2vlanw5ee+21HJG9c+dO6uvr0el0ebRK4mjiwSQD7/k/eKAEkP57TWfmlNAOxyN4wl5Gwl5Gwj7cox56/QN0e/voDThzxjoMBXmyUuLD8Habi2d39rOnz0fbUJBkSsRhVPOvH23gs+uqUSvOfKg/iVPLmjVrWLNmDc3NzZx33nl4PB4GBwcZGhpiaGiIQ4cO0d09eXSIybDb7ShnQcISiVxkKhWakmIiA87jjuv67e/ofmDtnzkAACAASURBVOxxlGYzgkKeeTAyjkyl5Ky7voO+pvqM2HsmODpBzeOPP05NTQ1lZWWUlpZSWlpKYWHhScWQl5CQmJxZIbRTYgpv2I877MET9mWEtJeRUS+eiJeR0XRfODExy5hda6XSUsay0oVUmstYWLRAii4yQ/GOxvjMI5tz+i5rKuKTqyuosOqIxFKo5LI5sXI1ExFFkZ6enuyDjKOjoxNKKBQiHA4TCoUYGhriiSeemBD5Q6/Xs3z5choaGigsLESv12eL5C4yuxBTqXSEkUiEVCRCMhIhGYlm2xWf/ASpSDTTHyEZDhMdchEZHCTY24eQiZ8uJhLEjroLkgzB8HsbZ5XQvu222/B4PAwMDLBz505+/OMfT8iWKpfLKS4upri4GJvNhtlsxmKxHLeMuWmlJonaInHiSFFHTozTFXXkVDFjhLYoirhCbrp8ffT5nQwFhxkKuRkKDTM86iGROurLQpBh1Vqwas2Um0tYXNyITWtJF12636a1oFFIUQNmC0q5jGuWlrKzx0sgkmAkFOPl/YO8vP9IHkiFTMCqV2HXq7AbVNj06nRbr6LcpuWceQU4jFJa4nzw4osv8vTTT09pbENDA8XFxTQ2NmK1WrFYLNnaYDBIF1MziFQ8Tqizi1BHB4lgaFLBnBbSUZKRMKlolGQ4kg7f9wEp2o9mLPKIXKsBvR5DZSVyjRq5Toe6sAC1oxCNw4G6sBC1oxDFLHMnMpvNfP/7RwKCJRIJnE4n/f39OaWvrw+n04nX66Wnpwev14vP55tSVB69Xo/RaMRoNGIymbLtyfpKSkqorq6mpqaGwsJC6XObwesv4emXvp1vM2YMXv/UQrLmi2kvtH2RAHe9/mM6vb2E40e+VI1qAw69nRprJavLl+HQ27BprVkhbVIbclKlS8x+9GoF939yWXY7Ek8y5I/i9EcY8IUZDsZwB6OMhGK4Q+n2Ho8XdzBGIHrkQq2h2MgD/3cZ8xyz60d2urNu3ToOHDhAMBjMrlKnUilEUSQej2dXswGamppQq9VStrYZhphKEe4fINjWRrDtEIGDhwh1dEx4UFGm0SDPCGKZWo1co0WmUaO0mJFrNOn9GvW4dnr7SFuDbGy/WoNcm55PGOcS0dzczJI5/v+jUCgoLy+nvLx8SuOj0Sg+nw+v1zuhBAIBdu/ejd1uJxAI5JTu7u6c7chxLpBuuOGGbJjBuYQgCJ8DPgdQYHDk2RqJU8m0F9rusIdIIsp5VauptpRTZSmn3FSCVimtOkocH41STqVdR6X9gx/0iSaStA0Gufv5/WxqH+EHL7Ty359eLvl0n0HMZjNf+cpXJvS7XC62bdtGa2srBw8eJJlMYjAYiH9ASmyJ/JAMh4kMDhEZHCTiHCSaqRXt7Wx84JfZVOYyjQbDvDpKr74SQ309hnm1KM1mZCoVguTeMy1Rq9U4HA4cjsmF4FRTlbe1tfHss8/y6quv8uKLL+bs27dvH6lUas65eImi+Gvg1wC1jgVSJqxZxLQX2gAX157LpXXnSbeVJE4baoWchWVmHrppJV95YievtQ6x7gevs+Gas7hqcWm+zZuTpFIpfvGLX7B3714ASktLueCCCzj33HMpKyujubk5vwbOcVKxGKGOTgIH2wgeOkxkYICIc5C4z5czTq7ToSkuQrTbKV1/AdryMgz19ejKy3JWmCVmL06nk3fffZd33nmHl19+mf379wNp4b548WKamppoamri+uuv56yjosdISMx0pr3Q1io0PLztcfr9Tm5e/ol8myMxSxFFkZFQjLahICurbbx9aBh3KMbbB4cloZ0nRFFkeHg4uz06OkpPTw+vvfYaDoeD/v5+1q1bh0olhWc8k8T9Adp+9gCebduzKcuVViu6inJsq1ehKS5CU1yEuihdKzI+883NzVTPcVeNuUAqleK///u/2bJlCyMjI+zZs4euri4gHfXknHPO4bbbbuPyyy9n/vz5UoQTiVnPtBfaJUYHl9Sey9/bmllfczbV1op8myQxg0mlRPq8YQ4NBTk0FOSwK10fcgXxjh5xRagt1LOqysZXL52fR2vnNnK5nA0bNjA0NMTu3bvp7e3F5XLx7rvvZsdUVlZyySWX5NHKuUf/M8/i2bIVAEGhoOFb38C2ckWerZKYLrz++ut86UtfmtC/evVqLr74YhwOB1arlba2NlwuF1arNVt0Op1051pi1jHthTbAp5ZcR3PnJpo7N3GzJLQlTpBDQ0Fe3u/kzQMudvV6c7JC2vQq5hUa+MjCEuY5DNQV6llUZsZukKLRTBccDkeOmG5tbeUnP/kJAJs2baKpqYni4uI559OZL8o/fgPasjJ8e/YysnUbrffcS8mVH6HsumtQWSz5Nk8iz1x88cX89a9/pauri76+Pg4ePEhbWxsHDhxg8+bNxz1WqVRisVhyopQYDIYp1zabDbvdjtVqlbJbSkwbZsR/olKuRBRTqORSUgmJE+O53f18+fEdpERoKjHxf1ZXMr/ImBHVBmx6ye1gptHQ0MB9993HY489xp49e9iwYQMKhQKHw0FRURFFRUXYbLZssVqtaLVaaaXsFCHXaHBctB7HResJHm5n19e/Sf/TzxJzu1nw9X/Jt3kSeUYQBK699tpJ9yWTSXw+Hx6PB6/Xi8fjySljfYFAgGAwSCAQwO1209nZmd0OBAJTitdttVqx2+3ZUlBQgN1ux2g0otFo0Gq1aDSanHKsPo/Hg9/vR6PRoFQqpe8SiRNiRgjtRDKBIMjwRwL5NkVihpBKiezu8/GtJ/cwv8jIb29ZTZFJilQzW9Dr9cyfP5/rrruOAwcOMDg4yODgIP39/ezatWvSH2KTycRdd92FwWDIg8Wzi/CAk93f+BYJfzpNtFyrpey6ycWVhMQYcrk8ewF8soiiSCQSyQrv8QLc4/HgdrsZHh7OqZ1OJ3v37sXtdhMKhU7hGZ0eeoV+vq76Xr7NmDEMC/0524mgF/f7z+TJmonMCKGtU2lpKqxnp3M//kgAk0aKbyyRJhRN0OcNs8uVoGdTF22DAfb3+2l1BghGE9j1Kr57dZMksmcRqVSKYDCIx+NheHgYpVKJyZTOoqbT6bDb7fT29uLPiMAxRkdHpdvJpwiZQpEV2QAKo4GOR/4XdUEBKrsNlc2GwmhEaTKma6MBolFEUZRWAyU+FIIgoNVq0Wq1FBYWntCxiUSCQCDAyMgIw8PDuFyuHFE+Vvr7++nu7sbpdJ6ms5CYS8yYX51rGy/j+2/+nC89/13+z+JruHzeBdIX9hwhkUzRPhzi0FCQtsEgbUMBOoZD9HnDOQ8wsm0vBrWCxhIj1y8vo6nExEcXl2DSSC5HMxmfz8czzzxDZ2cnfr8/J6HN+Bi8CoUim3muqqoqJwOdw+Fg4cKFkh/3KUJdWMCyn/8U7+49xIaHibrdxIbd+FtaiY2MTEhAA6AENv7qERQGwzgRnmkb04I82zYZUBpNKEwmlGaT9F0vQSwWyybHGXMzGd8+um/su2J8iUajU349u91OY2NjNmb4woULszHEy8vLaWxspLq6GrlcLv1/ShyXGSO0FxY1cO8V3+a3O/7M/2z/I6HYKDec9dF8myVxmtnb5+Oqn7+T3RYEqLDqqCnQs6zSQplFR5lVy1B7C1dffA6FBjUymfSlNxOJRCK4XC5cLlc2DfRYpBG5XE5TUxO1tbVZAd3T08PZZ5+dFdeSH/aZRVdZga5y4sPpYipFIhgk7g+QCKRLPBDgwM6dVDmKiAcCJPzpvohzkETbYeKBQDaRzdHIdTp0FRUoDDo0JSVU3fRp5GrpYeXZgNvt5pVXXqG/v/+YwnmsHh0dPe5cSqUyG73EYrFgMpkoKyvDYDDkFL1eP6Hv6P0mkwml8sgCzVQT8UhITMaMEdoA5aYS/vX8L/GT9x7mz/uep1Bv5/zqNfk2S+I08tDb7dl2gUHFZWcVs6DISKUtnfGx3KpFrZDT7DkouYfMQPbt28fPfvazSfeNrRytXbuWVatWTchG19zcTH19/ZkwU+IEEGQylCYTyow7zxgtchlVk4gVURRJhkJEhoYI9/Qx2ttLuLePcG8vo909JEdHCRw4kBm9A8uypVI4wRlKKpViz5492YyQb7zxBslkEki7hJjNZiwWS1Ysz58/P9seq8e3x/dJF9oS05UZJbQh/WG8ffWn8b0d4IH3f4Mr5JZWtmcx/3XDYq5dWsabB11sanfz1+19hOPJnDE2vQqdEKfm8PsUGtUUGtU4jJpMrc7WBrVC+iKeZkz2YNLChQtZuXIlJSUlmM1mTCaTlNRiBiGKIqlIhLjPR9znz9Q+ZDt20nG4Y1xfpvb7J3U1AdJuJiYTuvIyzIsWYmxYgHG+dHE1U/nUpz7FE088AUB5eTl33nkn119/PfX19ZhMJsm1K0O5WMrdse/l24wZw7fFz+fbhOMy44Q2gE6p5bvr7+A/33qAP+79G1c3XCqF/pulaJRyLmxwcGFDejVTFEVcwSjd7lG6R9JlKBClpaMPfyRBuyuEKxAllpwYdUKrlOeIb7tBhVWnwqJTYdUpsepUmDO1VafEpFFKbiinmdWrV7N48WJ27tzJwYMH6erqYv/+/dm065C+uDYajVgsFsxmM1arlYKCAgYHB+nq6qKgoAC9Xp/Hs5gbiMkkcZ8/7Y89MkLMPULM7SY24smK6TEBnYrFJhwvB5waDSqLGYXJhKrAjr6uNrutNJtz2kqTEZlS+l6fTXzmM5/JCu1vfOMbkya2kZCYbcxIoQ0gl8m5bN757Bls5afvPcxXzr4VlUKKiTzbEQQBh1GDw6hhZfWREFHNzW7Wrz8HSItxXzjOUCCKK1OGApFMnd5uGwryfkcM72iMlDj5a8kEMGuVGTGuzBXl+iN9Nr2KmgI9DqNaWjE/CTQaDWvXrmXt2rVA+qGngYEBvF4vPp8vp/Z6vbS3t2dXwseyROr1epqamliyZAlLly7N8a+UOHESwSCeHbsY7e5mtCtdIkND2ZTrWWQyVFYLSosVpdmEtqICpTkjlLN1WkBv3LWL9VIWz1lLIBCgt7eXt956C6fTycDAwIS6p6cHgJUrV3LzzTfn12AJiTPEjBXaAGvKl/GZJTfwu11PcvNfv0a9vZoFBXVUW8qptJRRYnAgl0m3nOcagiBgyYji+UXHDwWZSokEIgk8ozE8ozG8o/FMO4430zfWHvBFaBnw4xmNT3BfOZoN6yR/8ZNFpVJRVVVFVVXVMceEw2FefPFFqqurGR4epq+vj71797JlyxYA7rrrLkpLS8+UybOO9ocewdX8VnZbW1ZK8RWXoauoyIbvU9vt6YggU3XrkUIrzljC4TDt7e0cPnyYw4cP09HRMUFIT+YGplAoKC4upqSkhKqqKtauXctHP/pRPvaxj0mLEhJzhhn/zXfVgoupspSxe7CFfUMHebb1FVJietVFKVNQbiqhwlJKlbmcSkspVeYyzBopXJREGplMwKxTYtYpqSbX/SCeTNHnCTPoj+AOxXAHo7iC6brPG+bQUJBeT3jSedu9H5y5TOLk0Wq1WK1Wli1blu07dOgQ9957LwBPPPEEn/zkJykqKpL8u08C86KFOUI73NdPuK8fuV6H2m5HZbOhtFpR2ayobDZUmba6IL1vyuJbIu+Ew+Fssqfe3l56e3vp6enJtru7u+nvz00IYjQaKSsro7i4mNWrV2fFtMfj4aKLLspuW61Wye9aYsYiCMIqoEcURWdm+ybgBqAL+J4oiiNTmWfGC21BEFhc3Mji4kYA4sk4fX4n3b5+ury9dPv62eNs5a3O97PHmNQGqixlVJrLaXLUs7ioEbXkdjJnSaZEWgb8bOkcoW0oSLd7lK6REP3eCMmj/EoEAaw6FQUGFRVWHUsrLJRZtVTadFTZ9FTadJRYNLz79lvHeDWJ08VYFAKPx8OBAwfYsGEDcrmcoqIilEoljY2NXH311VLSmilQdMnFOC66kJjHm46T7XIRGXIRdbnSvtkeD6O9fcQ9HsTkUXd3ZDLUBQWoHYWoCwtRFxagNJkQenoYMRhR6PWZWNoGFHq95Id9mhFFMRvpo6urC6fTmVOOTuwE6cRPFRUVlJeXc9lll1FXV5dTbDbbpItVUhg8iVnGr4BLAARBOB+4B/gSsBT4NXDjVCaZdb84SrmSamsF1dYK4EjoP380SLe3j25fH93ePrp8fbxy+C2eP/gaSrmS1WVL+MTCqykxOo49ucSMJZZI4Q5F6fOE6fGM0u1O1z0jo+zr9xOMpqMeWHRKqmw6llZYuWZJOoRgiVlDgSH98KRNp0Ihl1ZopiMFBQXcc889iKLI4OAgnZ2dDAwMZJPadHV1sW7dOoqKivJs6cxAkMlQ222o7TaMC+ZPOkZMpUgEAsRGPMRGRogODxMdchF1DRMZGsK3ew+xkREQRRRAy0uvTJhDplanhbdBj1yrRa7RINNokGs0yLUaZGp1ul+tzvSrkWu0yDTqcWPStVytRqZWI0irqIRCIe677z5++ctfZlekTSYTxcXFFBcXs3Tp0mx7rIyJa7PZLN31zSN6WS9rjN/ItxkzBr2s93RNLR+3av0PwK9FUXwSeFIQhJ1TnWTWCe1jYVIbWFi0gIVFC7J9iVSSFlcbW3p38UbnRjb17uBTi6/jqgUX59FSiZNBFEWe2dlHuyuEOxTFHYzhDsYYzrR94YnJMIpMaiqsOq5ZWsrqGhura2yUmLV5sF7iVCIIQlY4ANhsNv7whz8A8N3vfhe1Wo3JZMJsNmfDB5rNZhYuXEhFxcQELBLHRpDJsg886muqJx0jJpMkQiHefe11VjQ1kQiFSASDJALBo9pBkuEIidFRUiMjJCNRkpEIqUhk0igmx0OmVmcEe0aMjxfmGSEvcw3R3e9EptGgLS3BvnZ25GRob2/nBz/4AX/605/w+/1ceeWV3H333Vx++eXScwsSEieGXBAEhSiKCeBi4HPj9k1ZP88ZoT0ZCpmcRUUNLCpq4PqmK9jQ/FMe3fkX1levxaCWwoVNZ+LJFN0jo3S5Q3QMj/K37VF2utIXmDa9Crtehd2gorHERIFehT2zIl1qSbt5lFm0aJSSH+lc4IILLuCcc85h3759OJ1OfD4ffr8fn89Hb28vPp+PSCRCS0sL//Iv/5Jvc2cdglyeTl5jtRxzZfyDEJNJktEoqUiUZCRMMhIlFYmQzJRURpSPCfN0e+KYuNdLKholGY4gC4Xo2X5kUar8xuuxLFuKoX7ejM48+cMf/pCHH36Ym266idtvv52zzz473yZJSMxUHgfeFARhGAgDbwMIgjAP8E11kjkttMcQRZE9gwfwhH2UGovQKqWIEdOda3/xLvv6c30Li0xqHr1lDQuKjx9pRGJukUgkeOGFF/B6vWg0GkKhEKFQiHA4TDQaJZ5J/b1gwYIPmEkiXwhyOQqdDnQ6wJoW3uPFdDRCMjwmstNi/Eg7Mqkojw4NoVMoCfembzv3/uUpev/yFHKtlrVP/D6/J/whsNvtQNp1pKuri9LSUiorKyVXEAmJE0QUxe8LgvAaUAK8LIri2ENbMtK+2lNizgttX8TPQ1sfZ3PfTubba/ny2bdIIQFnAJ87v5Y7nsh1kRr0R7ni/rew61UUGNQ4TBqKjGpKLFrKLBpKzFpKLVpKLRp0qjn/rz/rEEWReDzO6OhotoRCIbZt28b7778/YfyiRYuorKzMRlBYtWpVHqyWmIz+vz2Hd9ee7OpzKpoRyuEIqWj0xFxJBCHj561BnvHllmk0oFajKy1Jr2Br1KRicUKdnZTfcP3pO7EzwNe//nV8Ph9/+ctfePLJJ4H08wsrV65k1apV2bqkpCTPlkpITG8EQbABBzNFLQjC2K2u4UyZEnNabewY2MsDm35DJBHl00uu56r5F0uhiGYI1ywt45qlZQwHo3SPjPLGxm04quozCWqOJKc54PQzFIgiHpWU5jNrq/iPaxfmx3iJU044HOYrX/nKlMdfdtll3HDDDafRIokPQ99TzxAbGcHY2JB+SLLAhkyjzfhYq488LJl9cHLswUhtVlSPPSApU6mOGSGjYRZGyLBarTzwwAPcf//9bN++na1bt7Jlyxa2bNnCyy+/TCqTdKiuro5rr72Wm2++mYULpe/C6YTSIKdknSnfZswYlE+etsXRYaAXSGS2x3+RiEDtVCaZs0JbFEUe3Pw7ArEQ31l/B4uKGvJtksRJUGBQU2BQ429XsH7t5AlOYokU339+P7/d2JXtqyuUfPBnEyqVipUrV7J169bjjrNYLNTW1nLuueeeIcskTgZ9bQ0xj4fCC86n5COX59ucGYlcLmfVqlWsWrWKz3/+80DanWTHjh1Z0f3jH/+Yhx56iKGhIdQz2C99thEPJhl4b2LYRYnJiQePn0DuQ/Az4ELgXdL+2u+Mcx+ZMnNWaAuCwCcWXsWvt/4B96gn3+ZInEZUChmPb0mn/r39gjoubXKwosr2AUdJzCTkcjm33XYbt912W05/MpnE7/fT1tbGCy+8wMDAANu3b6eiooKPfvSjebJWYgxRFEmOjmYjj4QHnIR7e9OuIaJI31N/lYT2KUSv13Puuedy7rnn8tWvfpXvfOc73H333fT391NTU5Nv8yQkphWiKH5FSN8OWw98Bvi5IAgvAw+Kotgx1XnmrNAG6PENANBUWJ9nSyRON59YWc7vN3Xz0NvtvN3mYmmFhYsbHZxXX4hSios9a0mlUni9Xh555BEAzGYzdXV1LF++PM+WzW5EUSQ6NESos4vRrm5iHg/y9nb2vflONpTfWHg/UkdlURUENEUOrCuWY1+3Nj8nMAcYGBjg7rvvprKyksrKynybIyExLcmsYL8hCMIO4JPAfwBtwENTnWNOC22lPH36Owb2cXn9BXm2RuJ08u8fW8jFDUVs7RphV4+PZ3b289j73QA896VzWVhmzrOFEqeae+65h46OI4sOdXV13HnnnVL0hdNM1+//QO+fn8zpUxgNCDI5CUchCr0eTbEDhcGIwqBPZ4g0pIumyIGmpGRGh9ebroiiSHt7O8888wxPPfUU7733HgA33ngjcrkUAGA6Iflonxiny0dbEAQ9cA3pZDWFwFPAClEUu09knjkttD+58GP0+gb47c6/UGkppVFa2Z6VxBIpOoZDhGIJBASO1lkG9Zz+GMxaCgsLs0K7pqZGEtlnCLkmNzyqprgYbUU5w7Eo9iVLUBiNKI0G5JlU7Om2AblWI70/p5hQKMSPf/xjNm3axNatW3G5XAAsWbKEDRs2cP3119PU1JRnKyUkpi1DpFevn8jUIrBSEISVAKIoPjWVSea0wlDIFXxhzWf55kv/yV2v38fa8uV8Yc1NaBTSaspMJRxLsrffx64eL7t6fezv99HpHiWZSj+/IJcJzC8yctXiEhaXW7h6SQlGjTLPVkucDm699VZuvvlmfvjDH9LR0cEjjzxCcXExJSUlFBcX43A4UCql9/5UU37j9ehrawh1dhF1pdOxx4aHkfUP0LVrzzGPE+TydIQRfTod+5gAVxj02RXvdNuY06c0m5Ap5vRP2THZvHkzd911FwAlJSXcd999fOxjH6Ouri7PlkkcF7WIWJ364HESadQn/HziVPkzaXG9IFPGI5Je4f5A5vy3k0lt4EdXfJsHN/+OTb3bKdDbuGmpFPZrJvKrNw/zg7+3ZrfLLFrOKjXxkYUl1BcZmOcwUFdokDJCziHkcjlf/vKXeeKJJzh8+DBbt25l7KFxQRAoLCykuLiYgoIC6uvrJd/tU4R1+TKsy5fl9DU3N3PemjXpBx+DmdTr2RKa0I77/IT7BtJ9oRATYnSOIZOhstnQOApRms0kw2HMixZSdt01CHPcJWL9+vXccccdPPLIIwwMDPC1r32N+++/n/r6eubNm0d9fT1lZWUUFhZSWFhIQUEBBQUF0gVonom7Uzj/EMy3GTOGuPv0XJSIonjzqZhnzgjtlJjCHwkwPOpheHQkW7tCblqHDxOIpv+pV5UtzrOlEidDKC7yg9eOiOxrlpayospKlV1PlU1HmVUrPfQ4R9Hr9dx6660AxGIxBgcHGRgYwOl04nQ6GRgYYPfu3bz++uvcd9996PVS6MfThVyrRa7Voi4snNJ4URRJRaPE/X6iQy4iAwNEnIOEB5xEnE4iA06So6PEhtOr5mN4d+6i4Lxz0BQVna5TmREIgsBPf/pT7rnnHl599VW2bt1KW1sbhw4d4o9//CMez+QRtywWCwUFBVnxbbPZsFqtWK3WnPbR2xISEhOZNUI7Eo8wHPYwHBoT0iO4x4lq96iHRCqRc4xarqJAZ2NZ8VksLFrA4uJGbFpLns5A4sOgVwq8deeF/HlbD6/sH+SlfU6e2dmf3S8I6ZjbxSYNRSY1RSZNpqgpt+qoKzRQZFJLPqJzAL1eT0lJCSaTCYfDgcViob8//b/y8MMPc8cdd+TZwtlPeGCA4KF2EsFAOvpIJrzf2Kp2PBDI9omJxDHnERQKlFYrCoMeTXExhtoadFWV6Guq57zIHo9Go+Gqq67iqquuyukfGRlhYGAAl8uFy+VieHh4Qru7u5tdu3YxMjJCMHj8VVaTyUR5eXmOi9bRdXFxMVarVfquPQ7Sw5AnxmlMWHNKmFFCO5FK0u930uHpodPbizM4lF2ZDsVGc8YKgoBNa6FAZ6POVsWa8mUU6KwU6GzZWq/SSR/2WUSlXcfXLlvA1y5bgCiKuAJROt2jdLlD9HjCDPoiDAYi9HrCbO/2MhLKTeNsUCsot2pZVW3j365slFxMphmpVIpYLEY4HCYSiTA8PMz+/fuz20fXo6OjBINBQqFQtsTj8QnzKhQKKioqSCaTXHTRRXk4s7nDwPMv0P7rRyb0yzSa7IORCoMBXXn5kWgkRmPOvnS/EYXRcMysjxJTw2azYbNNPadAPB7H6/UyMjKCx+PJlpGREUZGRti+fTtyuZyBgQHee+89BgYGiEQiE+ZRqVQ54ruqqopFixaxT6ZtNwAAIABJREFUaNEizjrrLAwGw6k8zRmH11/C0y99O99mzBi8/s/n24TjMmOE9nvd2/jvzb8llkz/UKrkSkoMDgr0NhoK6rBnRbSNAr0Vq8aMXCYJpbmKIAg4TBocJg2rayb/IYkmkgz5oxx2BfnT1h5e2OOk1Rmg1RngXy6dLwntaUIwGOTuu++e9Db3K6+8MqFPrVaj0WjQ6XTo9XoKCgqorq5Gr9fnFIPBgNFoxOFwSOHNzhCBg23Zdt3n/xnbmlUoDAZkkk/wjECpVGb9uSejubmZ9ePS2ouiiN/vz7poja+dTid9fX28+uqrhEKhnHn+7d/+jbvvvvt0noqExBljxgjtZ1peIpaMc/uqzzDfXkOpsQiZTPK5lTg54skUL+wZ4G+7BtjU7mY0lkQhE1heZeXTa6uw6lX5NlEig0qlyopsvV7PunXrqKqqoq2tjdWrV6PVatFoNGi1WtRqtSSapyujYawrVuDf30J0yIV35y6Kr7gs31ZJnEYEQcBsNmM2m6mpqWHr1q10dXXR3d1NS0sLBw4cmHTFe7xYl5CY6cwYof3xhVfyw3d+iT8aoNxckm9zJGYwrU4/t/9uG53uUSptOm5cUc559YWcXWeXYmpPQ1QqFRs2bODxxx+ntbWVV155Bbvdno2MMLbCNvbgliS0808yGiV4sI1gewehjk5C7e0ou7o5CCiMRsquu4aSq6/6wHkkZjbxeJx33nmHBx54gBdeeCErqmtqamhsbOTiiy+msbGRxsZGFi9ejNFozLPFEhKnnhmjKlaWLaHeVs2f9j5HIpWkylJGsaGQIn0BKoW0+ihxfBLJFPv6/bzeOsSv3jqMWavkoZtWckmjQ/LxnAEUFxfz1a9+Fbfbze7duzl48CAdHR1s3ryZcDicM9ZkMlFYWIjBYKCuro5LLrlEEt9nCPf7W2j9z3ty+pRWC4baGgJlpSy77loMdbVzPuzeTEYURUKhED6fL6f4/X42b97M1q1b6e7uZsuWLezYsYNoNIrNZuO2227jwgsv5Nxzzz2m64mExGxkxghtgC+u+SwPb3uCP+39W06/TWuh2FCYLkZHtl1kKESr1BxjNonZSCKZotMdot0VotMdomM4xGFXiL19PkZjSQAubSri7msXUmSS/jdmGna7nQsvvJALL7yQ5uZmLrjgAkKh0ISICQMDA+zatYtdu3axdu1azGZzvk2fEzhf+HvOtqmpEWPDAtQOB66hQRTGuf2Q20zA7/fT2tpKa2srLS0ttLa20tHRgdfrzQrqVOr4cYt1Oh0rVqzg//2//8eaNWu46qqr0Gq1Z+gMZj5OlcB/VcwoeZZXnKrpvVg2o97JUlMx373wKwRjIQaDwziDQzgDLpzBdNk2sBdfhz/nGLPGRLG+gCJjIcUGxzhBXohBJcXLnQ28uHeAx/dG+cned2hxBogljvwI2PUqagr03LC8nDW1NlZX23BIAnvWIAgCBoMBg8FATU1Ntn/79u386le/AuDPf/4zn/3sZ6UkHGeApu/+G8HD7Xh37sLf0krEOUj/s88hJhIogO1PPYNMo8Ewrw7j/Hrs687GWD8v32bPOUKhEJ2dnXR2dtLR0ZEjrMdCXUI6Ik99fT11dXUsXrw46299dDGZTJjNZvbv388VV1yBwWCQ7hR+CBLxPlyDUtSRqZKI9+XbhOMyo4T2GAaVHoNNT52tasK+cDzCYPCI+HYGXQwGXewbPMhbne9PmKfYUMjCogVcVHsOxQbpdtZMQxRFbv/99syWj0qbjmuXlbF+QSF1BQbMOklczSW2bt3KQw89lNO3ZcsWLrnkEqqrq/Nj1BxCkMsxzq/HOL8+2yemUsRGPGx68UUaiosJHmrH9eZb+Pfuo++pp6n67Gcov/7aPFo9e4lGo+zcuZMtW7awZcsWWlpa6OzsxOVy5YwzGo00NjZy6aWX0tDQQGNjIw0NDdTW1p7QBarL5ZL8rE8BC40C71w4I+VZXjj3uel9UTfr3kmtUkO1tYJqa8WEfbFEjMHQcFqAB1w4g0P0+Z083fIST7e8xD2XfotaW2UerJY4WQRB4L9uWMR9f9/LUFike2SUn73WxsNvtzO/yEhDcbpc2OCgyi7dwZjtRKPRnG2ZTMbixYtpa2sjGAxSXV0952P0nmkEmQyFQY/gHGSkp49ASwvJ0dGc/RKnh+XLl7N//34AioqKWLx4Mddddx01NTVUV1dnS1FRkbQCLSFxmph1Qvt4qBQqKsylVJhLc/of2fYELx16k2+9cg8X1pzN1Q2XUmYqzpOVEifKP6yqpCjUzup159I2GKTV6afVGeCAM8DL+wd5YksP3/vbfq5fXsa9Ny5BLpN+UGYr55xzDtXV1Wzbtg2Xy8XQ0BAHDx5k586d2TEOh4Pq6mpqa2upqamhvLwchWJOfRWecVxvvY387XcZIR11pPqWz2JdsQJtWakk8E4jd955J//4j/8IpEX3c889J4XFlZA4w0i/LsBnl32cpSVnsbFnG+92b+X1jvf44urPckHN2nybJnEC6FQKllRYWFJhyfZF4kl+9NIBHn6ng3fahvNoncSZoqysjLKyspy+YDBIX18fHR0ddHR0cODAATZv3gyk/VDLy8upqqqisrKSZcuWoddLdz9OJY4L19P2yqvIO7pIBAJ0/uZ3eLZup+C8cyk87xzk0oNyp4Wbb76ZQCDAl7/8ZV5//XVisRgajfSMisTsxhod5IbD953UsT87xbaAJLQBUMjkrChdxIrSRSwtbuJnm/6Xlw+9KQntGc6QP8L6HzUzGkuyutrGd69uklaz5ygGg4EFCxawYMECUqkUwWCQrq4udu/eza5du7IPhgE4nU5uvPHG/Bo8y5AplSSv/AjnrFuHv/UAvl276X/2OXy793D4Fw9S9/l/lpLXnCY2btwIwK233iqJbAmJPDCnhbYoihwe6eLQSCftnm46Rrrp8vVRYnDwz6s+nW/zJD4k/b5INqSfViXn9dYhfOE4SyosUmKaWYwoigwPD+N0OnG73dkyMjLCyMgIfr8fURQnHKdQKCgqKuLyyy/Pg9Wzk2Q0ymhXN+H+AWQb3+fQjl1EBgYJ9/eRisWy4/ytByShfYoRRZHf//73PP744wBs2LAhzxZJSMxN5qzaiCXj/PjdX7FjYB8AJrWBGmslF9SczeXzzkcpl6JVzHSWVlh48871/PqtdrZ0jnDfK+kn7WUCNBSbWFlt5bbzaqmw6fJsqcSHJRgMsm3bNlpaWjh8+DB+/5EwnwqFApvNhs1mY+HChdlQZCaTKadoNBrJX/gUMLJ1G6433yLU3km4vx8yMZdlgL+wAE1JCfZ1Z6OvqkJfW4O+ukpyHTkNvPTSS9x0003Z7ZaWFs4777w8WiQhMTeZs0LbE/ZmRfbl8y7gYw2XUqi359kqiVNNlV3P969bBIBvNM6OHg/buzxs7/by6MYuntnZz8tfPV9KXjODeeONN3jiiSeAdFbIxsZG6urqKC8vx263YzKZpAfAziCH//uXxNwjaEqKqfjEjeirq9GWl7H5QCsrL7kk3+bNGS699FIefPBBNm7cyKOPPsrHP/5x+vr6pCypEhJnmDn761NkKOR7F36VensNLx16ky8+922cQdcHHygxYzHrlKxf4OBfLlvAb/5xFZBe3dYopR+emYzVas22k8kkK1eupLa2FofDgdlslkT2GabgvHMBiAw4MTU2YFu7Gl1FOUiRXc4ocrmc22+/nd/85jdotVrKysqkz4KERB6Y0998TY75fP+Sb/BiWzP/s/2PfPn573KWYz4rShezonQRJUZHvk2UOIXEEil293ppPuDi+T0DAHx6bRVmreQmNJNZunQpDz74IPfeey/t7e384he/yO5TKBQYjUbsdjvXXXcd8+ZJWQhPNzX/+FlMTY20/ud/se+uf0ddWIC2rAz5aIhD+w+gMOhRGAyZoj9S6w3ItVpkKiUypRJBWnk9KcLhMPv27ePAgQO0trayZ88ewuEwn/vc5yTXKAmJPDCnhfYYV9SvZ3FRA+90b2FTzw4e3fkXHt35F75y9j+xrnJFvs2TmCKxRIpBf4RBfwSnP0KvJ0yXe5TukRBd7lH6vWFSIshlAisqrXzzigYuP6so32ZLZPD5fDz22GMkEgk0Gg0ajQa1Wo1MJiORSBCPx7N1PB7H5XKxZcsWEokEiUSCWCyGTCYjlfEJBkgkEng8HjweD/feey/33HNPzgq4xOnBvmY1K//n13i2bce7fSdRtxvBNYzbOUQiGMz6bR8XmQyZQoGgTAtvmUqJoFAiUyrSQnysP9se169QIigVyFSqdJ9CkR0rU6uQqdUIHZ34CvchU6mRazTI1CrkajUyjSZ9zAxd/V22bBkHDhzI6bv11lu59dZb82SRxAmjFhGrp/AZkUijnvhw+6lEEIT5wJ1AFeN0syiKF03l+DkttEOxUQaDwwyGXAwGh/GE/WiVR3x1W12HJKE9TUmlRN44MMST23vpco/SMxzC/+LfJ4yz6VVU2nSsqLJy/fJyGouNrJtXIK1iT0Pee+89du3aNeXxYw806vV6FAoFSqUymzJ6bHusnUgksFgsmM3m03gGEuNR2+0UX3YpxZddCkBzczPnrl+PKIokwxGSoSCJYIhEMFOHgiTDYVLxBGI8TipTxHiCVDyW0y8m4qTiCVLxOMnR0XFjj/SL8TipROKYol4B7H36b8e0X6ZWI1OrkWvUyFRH2nKNBsdF6yk495zT8Ff78Nxzzz1cd911OX2PPvoomzZtYv78+ZSWllJaWkpJSUlObbfbpRXvaULcncL5h2C+zZgxxN2n/aLkz8AvgYeA5IkePOuEtiiKBGIhvGEfnogPb9ifqX14In68ER+eTDuayE3XbFIbKDEWcX3TFSx0NNBQUJens5D4IP7j+f3877udk+6rK9TTUGKiqcREuVWLTqVArZCli1JOvzeMOxhFrZRn+1UKGSq5TPqhOQmCwSBPPvkkyWQSQRCOWyD9GU0mk9mV6PHto1ekj4fP52PDhg1opYgVMwpBEFDotCh0WtSFhSd0rJhMkkokEJNJxEQSMZFATCZIjWuLibExmXYslhb24dGssI/7Azg72jGr1CQCARKBAHF/AMaFfUxFo6SiURL+iXZ4tm1HrtNhXb7sw/45TjnXXnstoigyOjrKvn372L9/Py0tLbS0tNDa2sobb7yB1+udcJxcLsdqtWK1WrNRemw2W8621WrFYrFgMBgwGAzo9fps22AwTBo2U0JiFpAQRfHBkz14RgltURQJxUYZCg0zFHJnyjAjo168kYygjvhJpiZecGgVGixaExaNmVprJRatGZvWQrGhEIe+gCJDQc5qtsT05tqlZRwcDFBm0RKKJunoH0RQG/COxhn0RznsGuD53QMnPO94QZ5tK+SolWkhPll/tp0R7GqFjO7uOENbe445brJ+lUI2IxPqPPfcc7z33nun9TXOPvtsVCoVCoWCZDJJZ2cnOp0OtVp9Wl9XYuqIokgiECTm8RD3eIh5PMQ8XmIjHhKBAPL+flo2bs4KYDEjmFOJxJF2PHHc/ZxCIScDApB2L1GrUNms6bZKlVnBVmW35Wp1ti0o5IgpEUNd7Smz5XSg0+lYtWoVq1atmrAvHA4zMDDAwMAA/f399Pf3MzQ0hMfjycabHxoa4sCBA4yMjEwqzCdDLpdjNBonCPDx5Vj7jtc/1yKlhFLlvB/4Yb7NmDGEUp8/LfMKgmDLNP8mCMIXgL8C2RVaURRHpjLPBwptQRAqgEeBIkAEfi2K4v2CICwhvZRuADqBT4mi6BcEoRpoAcacxDaJonh7Zq4VwG8ALfACcIf4AZfAiVSSvx98gz1DB2hxtRGKjebs1yu12HRWrBozpaYirBozVq0Zi8aMVWvCqjFj0ZrRKKQf5NnEkgoLj/3Tkcydzc3NrF9/JEZsPJnCF47jHY0RjqWIJpJEE5k6niKaSBFLjO9PEY2Pa4+NS6Yy49P7fOE40Xgyc2zu8bHEUSux+3ef9Pn960cb+Nz5M+OOyo033khtbS3JZPoCN5VKZVe2xtrJZDLHx3oyn+vt27cf8zU+9alPoVTmuvs0NzdLURTyQNznw7dnL5HBISKDg0QHh4g4B4kODyMmEhPGyzQalCYTQjxGJBpFUCgQ5PK0/7VCgUKtTvtQK+QIcgWCQp4Zk+nLjE+POdI+Ms+R8YLiyLyTHTNePL/7/vtccPHFc/Iullarpba2ltraqV0sJJNJfD5fVnSHQiGCwSDBYDCnvW/fPux2+4T+gYGBnPGBQGDKd67G+OlPf8odd9xxMqcrIXGybCOte8e+JO4ct08EpvQBmsqKdgL4miiK2wVBMALbBEF4BXgY+Looim8KgnBLxoDvZI45LIri0knmehC4DXiftNC+ApjoWDuOfr+T/93xJ4r0BawpW0q5uQSHvoBCvR2H3o5eJSUbkZiIUi6jwKCmwHDmLrBSKZFIIsmQP8rf39xEce0CBnwRnL5Ith4KRBgKRD9wYe4/X2jlM2ur0aqm/0qOQqFg9erVH3qe559/nmeffXZCv1qt5rHHHmPhwoU0NDRgMBg+9GtJnDyHH/wV7o3vZ7cN9fPQ19ViP3sNKpsNpdWKymZBZbWislqzyWiam5tZtn59nqyeBIViTorsk0Eul2fdR45HesFj/QfOJ4oi0Wg0K77HBPhTTz3Fz3/+c8Lh8IRj1qxZc7LmS0icFKIo1pyKeT5QaIuiOAAMZNoBQRBagDJgPvBWZtgrwEscEdoTEAShBDCJorgps/0ocC0fILSTYvqq9/L6C7io9hx0SskfUyL/dA6H2NPn49BQkMOuIIddIZy+MN5w/IiI3pL7YJ9WKceiUzLfYcSsVWLSKjBplem2JlNrlZg0Cqrs+hkhsk8lV155JVdeeSWQ/iH2+/309fWxdetW3n33XTZu3Aikf3BvueWWfJo6pym5+qocoR11DaMtL8e2dg2mhgV5tExipiAIQjaykM1m49VXX+WFF17g/vvvRxAErrjiCubPn8+CBQtoaGjg/PPPRyHFYZc4wwiCsAroEUXRmdm+CbgB6AK+d8pcR4560WpgGekV6X3ANcDTwMeBinFDawRB2AH4gW+Lovg2aXHeO25Mb6bvuJSZimgsnMejO5/k+QOv89V1/8T8guntGycxu3EHo6z/UXN2WyWXsW6eneWVFux6FVa9isGuw5yzaglWnQqbXoVVp5pzwvnDIAgCZrMZs9lMZWUl+/fvx+PxAFBVVZVn6+YuiWAIQS6n7gv/zPA77+HbvYe414vrjWZcbzSz5vHfodBJdxklps5jjz2WTRX/kY98hJ///OfU1c0MtzmJWc+vgEsABEE4H7gH+BKwFPg1cONUJpmy0BYEwQA8CXwl44t9C/AzQRC+AzwLxDJDB4BKURTdGZ/spwVBOGuqr5N5rc8BnwMoKiriQtkKGqxVvObfxN2v388641LqNVXIhfz7ZwaDQZqbm/NtxglzJuw++n08na93Jt8HURSpMcvo8KXvtsSSKTYecuHQyTCrBYwq0JBg+O0dGFUCJpWQU2vkzLhb1mfyvRyP3+/n+eefB2DFihXU19cjCALNzc3T8rM3HW06mim9l9EYwvAwuEcQRkYQ3JkSCmWHiHI5OAoRbTZEvR6xoox3Nm8+5utOt7/NdLPnZMjX5/JYnOjfNJlM8pOf/ARIf76/8Y1v0NPTQ09Pz2my8OSYDf8rEieFfNyq9T+QfkbxSeBJQRB2TnWSKQltQRCUpEX2Y6IoPgUgimIrcFlm/3zgykx/lMxTmaIobhME4TBpN5M+oHzctOWZvgmIovhr0lcLrFy5Uhzz+bogcB4/efchXvdtZkfsAOtrzmZJcRO1tsq8Pew4VZ+06caZsPtY7+Pp4Ey/D+vXiwz6o7S7ghweDtHuCtLtHmU4FKMnGMXlTxLtj096rEouw25Ir3Tb9CrsehU6tYIhf5SvXz6fhmLTGTuPqXIm38sx4vE4fX19WaG9bds2tm3bBkBTUxNNTU3T7rM3E74Pjvdexrxedn31TmIjuXdEVQUFmJYvRV9Tg66iHF1lBerCwhPK3jjd/jbTzZ6TIR+fy+Mx/m+aSqUIBoP4/f5Jy6FDh/jzn//MoUOH2LBhA9/85jenbRSh2fC/InFSyAVBUIiimAAuJnNRm2HKC9VTiToiAI8ALaIo3jeu3yGK4pAgCDLg26QjkCAIQiEwIopiUhCEWqAeaBdFcUQQBL8gCGtJu57cBPx8qoYClBqL+OHl/8aOgb28fOgtnm55ib+2vIhMkFFlLqPE6MCus1Kgs2VqK3adFaPagGwarH5LzA6SKZF4MoVOLWeew0C5VcfqahuReJJwPEkknmTrjt2U1c6n3xumzxOmN1P3+8LEkikGMg9IHk2hUcUPrl+ch7OaOqlUCr/fTywWy0YNGWvHYrFsNJGx9lRKLBYjEokQDoeJRCJEIhESk0SwGGP//v04HI4zeNZzg5HNWyeIbIDY8DDDbw8z8v4WFAYDcr0OhV6PXKM5KkNjJjvjZBkcOztwxuLIFJksj2PHjMvaeKwMj4JMls7UKJNi3eeDVCqFx+NhcHAQp9OZLWPbg4ODeL1enE4niUQCv99PIBA47pwymYyLLrqIu+66i0996lPS+yoxHXkceFMQhGEgDLwNIAjCPMA31UmmosjPAT4D7Bm3VP6vQL0gCF/MbD8F/G+mfT7w74IgxIEUcPu4pfcvcCS839/5gAchJ0MQBJaXLmJ56SL8kQBtI520udtpc3dy2NPN5r5dJFKJCccYVHqMKj1GtSFdxtqZelnJWVi1Uta4mUIomuDttmH8kTiJpMj+rjiH3m4nnkyL4EQyReyodiKZIp5MEU+JxBPpdiIlEkuk63gylT0+fZxILHN8fFw7NdVQvjv3TOjSKGVolXI044pWKWNhmZnz6wu5YMGJJfDIBz09Pdx5550fPPA4GI1GtFptNnujUqnEarVSUlKCVqtFq9VmH5Yaa4/v12q12dVtiVNH8WWXYFu9kkQgSCKUTu6SDI2mszeGQpm+0JGsjqOjmcyNY9ka40dlcDzyXSwHDr/x1rFffKpkRLcgk6VX1DN1evuo/nH7kOWOkQcC7H39zZwxglyWO59sbP50e9Ix2fkn2oFMnnOswmhAX1uL2n786B1nkkOHDnHXXXfhcDiQyWS43e4JxePxTBqOT6VSUVxcTFFRETabDY1Gw7x58zAajZhMpuMWu90uRRCSmNaIovh9QRBeA0qAl8eFo5aR9tWeElOJOvIOR2IIHs39k4x/krSbyWRzbQUWTtW4D8KkMbKidBErShdl+1JiCn80iHvUg3vUw/DoCP5okEA0SCAWIhAN4goOczjWSSAayhHlD1z5HzgMBafKPInTxA9eaOFXb7VP3NHSkm0KQjrEn1ImoFTIctoKmYBSnk4uM9bWKuWYNIr0OLkMpVxAkWmrxrWVciFnjFYlR6OQp2ulLCue9+7cznnr1uYIa7VidqzGKZVK1Gp1NqvjyRAIBAiHw1kxrdFoEEUxG3dbLpejUqmQy+Wo1eps8gqj0YhOp5sVf8fpispiQWWxnJK5RFFETKSF+DvNb3L26tXj0qPHsyJ9Qvr0xLj065k06mIqlc4ImanJ2U7l9qeO9JOafAypFKloNNt/ZFwysz1u/kSCZCSdKXK28a1vfYu//OUvxx1TXl7OvHnzaGhoyJYFCxZQUVGRk0xGcrE4NehlvawxfiPfZswY9LLeDx50EgiCYBJFcVMmcY1aEIQx36bhTJkSsy5ejkyQYdGYsGhM1NmqiCViuEZHcGWySA6F0m1FaJhEKkkgGsweG05MvJUvMf1wBSf+2MkFWFhmpqnURJVdz0cXllBpz1/0A3+7nJoCfd5e/3RSWlrKz372s2PuH4uRGw6Hc1xBxlxDotHohPZYCQQCDA4OEgwGJ42lO8bNN9+M1+vF5XKhUqlQq9WoVCopgc00QxCErIsIOi3qAvtJzyWKYlqMx2Iko1FSsRipaCydKj0Wy5Q4qXhuPXZMKqeOQzSC0mTK6RPjMcTU2OvEs8dMlohntvDb3/6Wa665hp6eHg4ePMiePXsYGhrC6/Vm3T96e3vp7e2d8ECgIAg4HA4qKiooLy9HEAS2bNlCRUUFRUVF2O32bPxtnRSNRmLm8QfgKiYmroFTnLBm2iOKIu6wh05PL53eXvr8AwyF3LhCbrwRf85YuUxOoc6GQ1/A6rIKKswlLC5qpMxULK2SzRDu+8RS/vO6Rezu9fHuoWE2HnazuXOEXb0+dvWm3aZebx3iT/98dp4tnZuMj5FrtVpPep54PJ5NZhEIBAgE/j977x0f11Xn/b/v9D6j0UijZjVLlltc4rjFNrGdGIg3kIRAEkoCecgCeR5gYRdY+ME+QMpSnmwgLNmQ7BJIQgIxaZDmdDtxiS33FhfJkq0+mqJpmj7398eMxpIt23Jie1TO+/U6r3vOufcefa9mpPnMOd/z/QZ55JFHAPjjH/8IwCuvDPU+U6lUQ4T3yXWlUkl5eTmrV68Wf+95RpZl2p76K6GmZhRqdVY4nxDNqVgsI6QH+hOJD5WCPZdCXZ31EU8kiCVTKNQaFBo1aosJKVtX5I7qQfdosu0T53PXD7nnpHNZP/Rh32+j4D1oMBj4whe+MOy5VCpFIBCgr69v2OLz+ejq6qKtrY0jR47Q2trKc889N+xYAzGzB4vvwsJCysvLmTRp0pAy0V1K1CYlpZePvk3xoxX1MxcmdK4sy9dkjx8qcc2YFdrJVJI9PQd5r20H27v2DpmZLjIW4jQ6mFs6k2JjYS6LZLHRgU1vERsjRynptIy3P05vMEZvMIY7FMvVe0+q9/UPH9HDqldzSbmVn99wybDnBWOHAb/twWK9oaEBl8tFPB5n586d1NXV5VJD+3w+vF5vrn46tm/fztVXXy2Edr5Jp2n7y5qzimd1QQGGEiefxCnzAAAgAElEQVQaux2toxBNYSEqkxGFVpsTz8qBulYzYqG7bt065gg3hzOiVCpP+Rs8E+vWrePSSy+lra0Nl8uF1+vF4/Hk/i4Ht1966SXi8fhpx9qyZct5yTg7FjkYLWPxgZ/k24wxw5HoV85+0QdAkqQqoE+WZX+2vYJMosVW4AFZlk//Bh7EmBPaoViYx3Y/Q2P7LsKJCHq1jnlls2gorKW6oIJKazl6tS7fZgqGQZZl2n0R9nb4eb8rQLc/OkRAe8JxUsPsNNSrlRRbtBSZtNQVm1hUW0iRWZsppsyxad8Orll1BVqVSAoz1kkkEvj9fvx+P319fbnjQJrmgaPP52PTpk3DbtKCzMy6Xq/HZDJhNBpzx4ULFwoXk1GApFQy+77/R9dLr5AMBjMbLoNBEsHMccBdI+HzkTjpi5NCo0FlNqEymVCZzajNJlQmc6bPbEZlMqE2m0+5RqHR5ONRJxQWi4UZM2YwY8bw6TPC4TA7d+5k2bJlpx1jYKZbIMgza4DrAb8kSXOAvwI/I5Ow5r+A20cyyJgT2q8ceZt1LZtZWDGX5TWLmeWcilqpzrdZgrPQE4gy967XczPRSoVEsVmLw6TFadExs8x6QjyfJKKN2rO/TX3NCiGyxzi//e1v2bv31EgtkJlZGxDLJpOJkpISDAYD9fX1GI3GIecG6gaDQQjqUY6ptob6b/zvU/plWSYdi2Win4RCJILBbD1zTAwS5slgiEhHZ+6aM/lTZwS6Ga3DgcKgx4WCgrmzUVtFxKkLzcsvv8w3v/lNmpubh/S//vrrLF++XKRYH0RJXOZf28bvvoDzzY/iH9yl7CzoZVnuzNa/ADwiy/J/ZMNan9+ENaOJ+sKM77kr7EZCoivowmGwY9Do82yZ4Ey4gjGU/Qkuqyrg6ktKWTXNSUWBHoVCLN8LMgz2y6yrq+Pqq6/GZrNhs9kwGo3DLv2LCAfjE0mSUOp0KHU6tEUjjwR1QqBnZsbjXi/R7h6iXV2ZY3c30a5u4h4PSuDIzl1oHA7m//6hC/cwAnbu3Ml1113HlClTuOuuu5g+fTozZsxg8uTJQmALRjODP3RWAj8AkGU5fS6uh2PuHT6ndDqXlc9mW8dufv7uA7l+vUqHw1BAhbWMqY7JTC2qo9JahlIhZjlHA0UmLTq1gm3HfGw75uOuFw+gUSmosOlpKDGzrL6IZfUOJtnFzvSJype+9CU+9alP8a//+q+0tLTw5ptv5maphyuRSIRUKjUkvJhg/CKn05nZ7T4/Cf9ACQyqZ9vBYDbWd5j0GXyAZY0GY2kJtf/45Yv4FBOD/v5+tm3bxttvv83rr7/Oxo0b0Wq1/OUvf2HmzPMW4XdcIUnSV8hmHnSYRDKuUcJbkiStAbqAAuAtAEmSSoER+WfDGBTaAN9Z8hX6IgHc/V7c2VjZnn4fvf1ejnha2NyWSWShVqiosJZSZaugylpOfWENNQWThKtJHiix6tjwfz9Kuy9Cm6+fdm8/7b4Ix7397Grr45V93QDMmWTjpvmTuPGySSjFbPeEw2KxcNddd/HCCy/Q3d2Ny+UiFAoRjQ4fevP555/P+WGfrhQXF+N0OrFYLGID5CgmGQrj2bKFuMc7jJAOkAgEMvGvT0aSUFvMqCwW1FYrhopylEYTKpMx459tMqI6uW0ysXH7duauWHHxH3QcEI1GaW5u5tChQ7S0tHD8+HGOHz/O/v378fl8uN2ZEMOSJDFnzhzuuecevvjFLwq/6zMgy/LDwMMAtcUNF8wXQnBOfAu4iUzCmqWyLA9EYSgBfjjSQcak0FZICuwGG3aDjSnDnHeHvRx0N9Hia+NYXwc7u/azrmUzkBHfk+1VLKyYy7LqhVi0EzuM0MVEp86kLK8rHvo7l2WZ5t4Qb77v4tkdHfzg2b00tnq578Y5ebJUkE8cDge33XbbkL5kMpnbCDlQduzYQVlZ2ZA+v99PR0cHoVDolIgGer0ep9NJUVFRziWloKAgd7RarWJ2PE+41r3DkV+dyH8mKZVoncWorVZ0JSWYpzagtloHFcuJutmUyQR5rogvXadFlmXcbjetra20tLTQ2tpKa2srR48e5fDhw7S2tiIPihZjNpupqqrCbrezcuVKKisrmTFjBh/5yEc+VIjPiYqi2ID+G5fl24wxg+KdC7MSns0E+Zdh+neeyzhjUmifDYfRzlLjApZWnQgN5Iv4OeJp4aC7mf2uQzy662me3PM8V09ZwQ3TV4tIJXlEkiTqis3UFZv5xOwyLv/5WxzuCebbLMEoQqVSYbVasQ7atBYMBs/oox2PxwkEAvT09NDT00N3dzc9PT20trbS19dHInFqiEiDwTBkQ+XJEUscDgfTpk0TM+PnmfRJKxZyKkUqEkFlMiGplCj1+ozodhZjrK5G4ygUr8EF5Otf/zr/9V//NaTPbrdTVVXFggULuOWWW2hoaKChoYHJkydjy2YSFfsmBIJTGZdCezgK9FYWVMxhQUVmlvR4XweP7XqGvx98nfWtW3jwE/+OSvhz55Vuf5RPP7gJnVrBTz4xfGgogWCkaDQaHA4HDofjlFBjsiwTDodzSTcGEnAMDh149OhRwuHwKeOuWLGCm2+++WI9xoSg5OMfxfmxVcS9XkJNR+lvbSXqchHrdRNuOYavcfuw/tbTf/wjCi6dmweLxyfHjx+nqqoq177qqqv4xje+wdKlS7Hb7Xm0TCAYu0wYoQ2QTqdp8rayo2svOzv309LXBoBWqSaWjKHSiI14+aQnEKXTn5nZ2tXWh06dSWM+kvB+AsFwhMNhjh07NiQd/OC08CeniR/cPtOYgvNLKhfKL4hSr0NXUoKkVqPUGzIxsS1mwq3HTpn5jnR2CaF9Hunt7R3SfuONN3jjjTdQKpWUlJRQVlZGaWkppaWluXpZWRmVlZXU1Hyo5HkCwbhlXCuYaDLG8b4OjvqOc9jTwu6u/QTjYRSSggZHLZ+bdR2Xls5kkrVMLEOOAmZPsvF/r5nOmm1t3P3S+7n+YrOWaoeRmkIj5QV6nJZM7G2nRUeJRYfNIDa3Cobi8/l46qmn2LNnD6lU6pTzWq0WnU6HXq/PHa1Wa65uNpuxWCxDitlsRq0W77WRIKfTJAIB4m4PMY+HuMeLYscOjuw9kIl/PSgGdjIUOm10EIVOh8ZuR2MvwL5gPtoiB8bqaoy11ehLSz+Yb7bgtMybNw9ZlonFYrS1tXHs2LGcf3ZHRwddXV20trayadOm3IbHwVitVhoaGqipqWHq1KksWrSIhQsXCj/tcyTt6ifyn9vybcaYIe3qz7cJZ2RcCe10Os2B3iPs7NrHru4DtPu7kMls2LBqzcwtncmlZTOZVTINk8aYZ2sFw/G/ltbwv5bW0ObtZ2+HnxZ3mBZ3mFZ3mDcP9uAOnfqBrFEqsGhkqt/flBPgTouWEquOYrOOEmumbdCMq7e74Azs2bOHnTsz+1UmTZpEbW0tRUVFLFq0CKPRKBLZfEiS/REibW3E3B7inqyYHiSq417vKYljFAoFPqs1EyHEZEJXUoKqfiCrowmV5URGR3VBARq7HZVB5EfIB1qtlrq6Ourq6k57TTwep7u7m46ODo4fP87Ro0fZuHEjsViMbdu28de//jWXtbW2tpbq6moqKyuZNGkSlZWVVFZWUldXR01NjZjoEoxrxrzykGWZNn8nW9p38nbLZtz9XpQKJdOL6lhUMZeagklUF0yiUF8g/pjHEJPshmFjaseSKVyBGK5glJ5AjG5/lJ5glD2Hj4FSwftdAd4+5KI/fuosplmnys2CF1u0lORE+Qlh7jBpUSuFCBvrDPbJbmtro60t4yaWSCRYvXp1vswac6QTCWJuDzGXi1hvL5GOTvz79hNqah4Sak+h0aAptKMpLMQyfSqawkK02XamXsimXTtZunJlHp9GcD7RaDQ5wbx48WJg6GbIYDBIY2MjmzdvZs+ePbS1tfH666/T2dk5JGJJaWkpixYt4o477mDVqlX5eJRRh4g6cm5cqKgj54sxK7QTqQSvN7/LS4ffojfsAWCWcxq3zPkUc0tmoBNRRMYlWpVyWBG+Tt/D8uWLcu1gNEFPIEZPIEpPIEp3IIprkDB/rzmEKxgjmR4arlSSwGHSUmbVMbPcypxJNi6tKmBykQgDOZbo7s7EZbfZbJSVleVcQ1aIuMnDkk4mCR48RKSzk/7WY4Rbj2UyKPp8MEgUSUolpvo6Kj51HeaGKWiLi9AUFmaig5xtIkOsIkwozGYzK1euZOVJX64SiQSdnZ25uNuvvvoqzz33HE1NTezevVtMiAGdfRF+8vyIM3xPeDr7Ivk24YyMSaGdltP8csOD7O5+n2lF9Xxq2se5tOwSCvTWs98smBCYdWrMOvUpMbsHk07LeMLxnBjvCcToDkTp8UfZftzHE1uO88SW4wA8+Y8LuXzyyFNBCy48qVQKt9uN1+vF5/Phcrlob2+nvb0dt9tNUVERP/nJT0SK5xHQ8j9/oPuVtaf0G2tqMNZUY6zNHA1VlSMT1QLBaVCr1VRVVVFVVcWyZcu47bbbqK+vZ+/evZjN5tws+UCZNWsWV1xxxZDQngLBWGJMfgKF4/3s6zkEwBfn3ECtveosdwgEJ+iPJ+n2RzMlO9vdk623+yK0usOEB7meTC4yMneS2MyTD2KxGC6Xi+7u7lwc7N7eXnw+H36/nzVr1uSulSSJ4uJiqqqqWLJkCfPnzxcie4SUX/cJIh0dJMNhkqEQsR4XAOGWFsItLfDW27lrJaUSpdGYzbiYKUq9HqXBgFKvyx71KPV6VIP66HER6erKnVNoNEKwC9Bqtaxfv57nn38+l2Hy+PHj7Ny5E5cr8z5UKpVcfvnlPPXUU5SWlubZ4gtPMtFBb8+P8m3GmCGZ6BjSLrYa+ObqaR9orN/s234+TBrCmPwUMmtNfG/ZHTyw5VF+8Pov+NLcz3D1FLEkLMj47LuCMQ73BDnm6c+4jQwI6uwxGE2ecp9Zp6LEoqO8QM/8ajs1DmOuVBTohSC4wLjdbvbs2YPH48Hr9eaOweCJxEWSJGG32ykqKmLGjBkEAgHmzp2L3W7PFY1Gk8enGLvoSkqYeddPcu1UJEIiEMgK73A2UkjmmAqHc4I8GcrUYx4PqUiUVKSfVCQ6bKp0NbDjyadybUmpzAp0fU58D20bUBlO9KmMxswmyQIbmoIClHqxUXK8UFNTw7e//e0hfV6vl5/+9Kf89re/za1eDfbtFgjGCmNSaAPMLZ3Jb1bfyQNbH+UPO9fQEexmefViJturhCiaYLR5+3nmSJzfvr+JI64Q/siJjH8KCYrMmY2PtUVGLp9ciNOa2RBZMugoIpLkl+eff57GxsZc2263M2vWLBwOB06nE6fTSXFx8RAhvW7dOpYuXZoPc8c9A6L3gyDLMulYjFR/hFQkQrK/n1Qkwu7GRqbV1mYEebZvoCT7I6T6+0mGwsR6ezP9/RFS0egQH/HBZEL/FRDrdVPy8Y9R8+Uvif/944RgMMh1113Hu+++y3XXXcfXv/51Vq5cKV5fwZhkTKsLg0bPty//R/5n+595s3kDrzW9g1Gtp8RUjNNcRInJQYmpmCJjIQU6CwV6m0i1Ps7Y2OTm8/+zBYB5VfAPs0qZUmxiitNMTZGRIpMWlYgiMuq58sorOXjwYG4G2+v1snHjRn72s5+JjHRjDEmSUOp0KHU64ITLlez1UHyO6bnldJpUJEKko5NQ81GChw4TPHiQaFc36WiUaGcXAO533qHmtltBxNUeE6RSKbZu3cqRI0fo7OzMla6uLjo7O+no6CCRSPCRj3yEZ599VghswZhmTAttAJVCydfmf4EvzL6ebR17aPK00h3qpdnTyua27acsNWlVWuw6KwV6Kza9FYfBjtPowGlyUGxy4DDYRSr2MUSZ7cSsW4FBw7zKAlZMLcZuFC4EY4mamhruvffeXGze3/3ud3g8Hh588EEuu+wyKioqqKioEBuixiGyLJMMBIj2uIj2uIj19BB19WaPLmKu3lNicqsLCtA5i9EWF2OonETZNatF8ppRTiQS4fXXX+dvf/sbL7zwwpAslFarlbKyMsrKyli6dCnl5eVcd911LFy4UIhswZhnzAvtAUwaI8trFrO8ZnGuL5lO0Rv24On34o346Yv6M8eIH1/UT7P3GFvbd5FMn/gnLkkSdr0Nm9aCVWfGprNg1Q3Urdh0FiptZSLhzSihxmHkzX+5gv/3zCZ2dvTxxvs9KKRMf1WhkUq7gUq7gapCA7VFJirtBpQK8Y97tDIQm/fHP/4x77zzDuvWrePZZ5/NnbfZbCxbtowrr7wyj1YKRkIyHCbm6iXu85Hw+1Hs2Enr8XYS/gDJQICEP0Ai4Cfu6zsltbrKbEbnLMZYU03hwgVoncXonE60xcVoixwotdr8PJTgA/Hiiy/yiU98AsiI6tWrV3Pttdcyb948SktLMRrF5+lgKuQy7o7/JN9mjBl+JN+RbxPOyLgR2sOhUigpNRdTai4+7TVpOY0v4qcn5MYVdtMd6sXd78UfDeKN9NHia8MfC5KWh27uKTeXUO+owZEQ8ZXzzeQiEzdP1fBfH7mCfZ1+3nzfxaHuIMe8/Ww56hkSQUSnVlBfbKahxExtUUaITyrIxOUuMKjF7MkoQavVsmrVKlatWkU4HM6F7du9ezcvvPACbW1tTJv2wXaVC84Pyf5IJpGNKzsT7XJlZ6JdRF0uUuHwkOuVQKdSicpiQW21oLZYMNVNRm0rQOcsQlvszM1Si4yQ44v/+I//yNWXLl3KrFmzKC4uxul0CpEtGPeMa6E9EhSSgkJDAYWGAqZTP+w1aTlNKBamLxrAG+ljd/f7vHT4TTqCmaQYs92zaXBMvphmC4ZBoZCYVWFjVoUt1yfLMt5wnGPefppcIQ51BznUHWTdoV6e3t4+5H6TVkW5TU+xRUuxOZMtstispdgyUNdRZNaiU4sl6ouJ0WikoaGBhoYG5s+fz/e+9z127dpFR0cHzc3NuYgjBQUF2O12ysrKRIr184CcShHp7CLa05MTzzFXb05UJwdFhIFMdkitsxhdcTHmqQ050ayxF6C2WtiyZy9XfOyj4svsBOTRRx/lb3/7G1u2bGHr1q289NJLQGYF+bLLLuPFF1+kuPj0E2ICQT6QJGk+0CbLcne2fStwA3AM+Iksy96RjDPhhfZwpNNp/LHMjLan34c30kd30EVboJPj/i780UDuWpvSjEVrzqO1gjMhSRKFJi2FJi2XVg6NhR2KJWnz9meKL0Kbt5+OvgiuYIxml3vYzJEAVr2aYrMWp0VHsVlLkUVLmVVPVaGB6kIj5QViNu5CYbFY+Pd//3deeeUV3n//fZqbm9m2bRvpQeHkbDYbCxYsYNGiRZSXl+fR2rGFLMv4d+/Bu7WRUNNRwi0tpOPx3HlJrUZbVITOWYypbnJOSOuKi9A6naitljOL6MOHhciegESjUdRqNStXrmTu3LnceOONNDc38/LLL/PGG2/Q2NjIb37zG+6+++58mzpq6NZI/GKSkGcjpVtzwf6vPARcBSBJ0keAnwPfAOYADwOfHskgE+6VTMtpXGEP3v4+vJE+vBEf3v4+PJGBdh++iP8UVxGtUkOFtZS5pTOotJZRW1BJfWENG9/deEbXFMHoxaRVMa3UwrRSy7Dn02kZX3+cnkAMVzCKKxjDFRg4xugJRtnSEqY3GCOeOjVu8I/VLdy2pOZCP8aEw2638/nPf55169axfPly0uk0gUAAn89HT08PO3bs4I033uC1116joqKChoYGampqqK2tpbCwMN/mj1oO3/dr3O9sAMAyfRrOj30UU20NutISdE4napsVSawUTEjS6TR9fX14vd5cJtbhjkeOHEGhUAzpj0ROnx67traWm266iW984xsX8WkEghGjHDRrfRPwsCzLzwDPSJK0a6SDTDih/afdz/HioTeG9OlUWux6G4UGGzOKp2Tq+gLsBlu2bsOiM6OQxIfMeEaWZWLJNKFYklA0mTnGkoSzx1gyjSRJaJQKTDoVybQWpSShUyto7g2fMp4IK3jhkGWZRCJBb28vgUCAYDCYK3a7nfr6eg4dOpTz7R7gF7/4BTab7QwjT1wUKnWuHnj/IOHWY3g2ZZPFDGR7NBiyCWWyyWSymR8Vag2SWo1CrUKhVmfr6kF1FQSCxPv6TvSrVEK45wlZlnG73bS3t9PR0UFXVxdutxu3243H4zmleL3eMyaLMRqN2O121Go1lZWV1NfXD3HnGu5YUFCAzWYTqxzDUBKX+de2UxOrCYbnR/ELlshIKUmSSpblJHAl8JVB50asnyec0F5evWiI0DZrTVxS3EC5pYRySwll5hLKzMVoVCI83FgjnZYJRpMEogn8kRMlMLgeTeCPJAlEEjkRHYwmCccz4no4V5GT0auV2AxqbAYNNr2ahhIzC2oKsRnUFBjU1BebSXXu56pFVRfhqScGsizzxBNP0NbWRjAYJBAIkEgkePrpp896r0KhIJ1OU1lZSWtrK2q1GpVKhVKpRKVS5cpA++T+ieLvXffN/4Nj2RL6j7dlksdkk8gMJJdJ9vcTc3sy2R+zyWjOBTXQ+Ps/DumTVCokVUacKzQDAnxAoKtOEutD+yXViXty/arstUPGGiT+NercNQRDJPz+IWOPN2KxGHv27KGxsZHGxkaOHDlCR0cHnZ2dxAe5BQ1gMBgoLCzMlUmTJuFwOCgsLMRut1NYWDisaB5IJDWwyiT4cBgV7Sw0fy/fZowZjIr2s1/0wfgzsF6SJDcQAd4FkCSpDvCPdJAJJ7QrbeU8+en/pMl7jMOeoxz2HKXZe4zNbTuQyYgshaRgWlEdl5XNYln1QixaEVlktLG/08+2Vh+tnjDHPf0caOvH8/raYV04BlAqJKx6NVa9GotOhUmnotBowKRTYdJmilGrwqxTYdSohu236tUj2gy5rvvA+XxcAZlscclkErVajdVqpb+/H6VSSTKZJJlMkkqlhvhqDzDQd/z4cR588MFz/rlqtRqdTodWq80dT1dvbW2lt7eXwsJCli9fPqZEuiRJFFw6l4JL547oejmdJhWNkuqPkE4kkBMJ0skE6Xi2nkiQTiSRk5n6+3v3MaW2lnQygZxIZs8Pvnb4/mR/f65/YKx0PHtNMnlKjO2Roga2/s8fTjy/SoXSYMjM3huzR4MepcGY61Pq9ZnQggoJ51VXjmpxfuDAAWbMmJFrFxUVMX36dJYsWUJ5eTkVFRWUl5dTXl5OWVkZRUVF6HQioZtAMIAsy/dIkvQmUAq8Jp9Y1lGQ8dUeERNOaAOolCqmFk1matGJSCHxZJyukIuOQA8tvuPs6NzLo7ueZs2+F/nyvJv5SPXCPFosGMwfN7bw0xcPIMtg0CipKjRSblLwycuqKDJpc2LaqldjGVQ3aJRimXKMIkkSd9wxNFbqcLNn6XSaVCqVE98DAnzw8eT+M51LJBLEYjFisRjRaDRX7+/vx+fzEYvF8HqH33je2dlJQ0PDsCJ9oIwlIX4ykkKBKitMR8IBZEovwGynnE4jJ4cT7smTRPzQ/vf376O+pjbXn0n73k/CHyDa3UPwSNMp8b0HoykooHDR6P1cOHm1Z/r06UyZMoWSkhJKSkpwOp04nU4cDgcFBQVCZAsEJyFJkkWW5fckSbIDWkmSBgL4u7NlRExIoT0cGpWGKlsFVbYKLq+cx+dmXcdT+17g2QOvsKNrnxDao4hndnQgy/DeD67EadEiSVJWdIm4yhMdhUKBQqFAfRFnGjds2MDjjz9+Sv+7777Lu+++e8Z71Wo1FovljP6sBoNBfEE8A5JCgaTRoNCcm7vfAQWUZYV/8NBhXG+vw7NlKwlf3/A3KBQotVr0kypwrroK+8IFH9LyC8sPfvADli1bxr59+9i7dy979+7l73//Oy6Xa1h/a6vVysc+9jFuvvlmrrvuOvGeEwjgSeAaYDsgA4P/KGSgdiSDCKE9DO5+L/+x4WGafceoslXw6emr822SIEs6LXOoO8iyegclVjEDI8g/S5cuZenSpUBmln3p0qXDzoIP1xeNRvH7/Xi93mFDFZ7MWJ4BH41EOjvp/PuLdL/yKgqNhoL58zBWVaG2WjNJdazWXF1pNI4p8alWq1mxYgUrVqwY0p9MJnG73fT09NDd3U13dzc9PT2sXbuWNWvWsGbNGvbs2cMll1ySJ8sFgtGBLMvXZI8fKnyYENrD0ORppdl3jPnls/mXy78iPtxGEQqFxPQyCxua3Ly0p4t/mFWab5MEgiEMbKL8IBnv0uk0HR0d3HvvvUSHcVs4kwgXjJyE34/yby+y42gLKBSU/sNqqm75HEr9+I+Br1Kpcu4js2fPzvW/8847ADzwwAPMnDkzX+YJBKMGSZKqgD5Zlv3Z9grgOqAVeECW5VN3FA+DENpn4OP1Y2sz00Th/316Fqt+9Q6Pbm4VQlswpkkmk/zxj3+ksbHxlHOXXXYZkydPzoVCs9vtmEwmHn744TxYOj6I9/XRt3MXxx5/AqnPT+Xnbqb4qivRFtrzbVpe2bFjRy5b47Rp08bUzL1AcAFZA1wP+CVJmgP8FfgZmYQ1/wXcPpJBhNAehu5QLwBlZmeeLRGcTH88ya2PbAXgux9ryLM1AsGHIxQKDSuyAXp6enA4HEydOpWysrKLbNnYRU6liHR1E+vpIdrjyqaQ7yHS0Un/8TYAdGWlJG/+DJNu/EyerR0dvPjii7n6nXfeybJly1CphDwQTHj0six3ZutfAB6RZfk/JElSACJhzYdhd/cBNEq1COs3ClEpFFh0arr8Ub7/zB4W1RYyr6qAYCBFLJlCqzp76D2BYLRgsVj4+c9/TktLC01NTTQ3N9Pa2gpAW1sbbW1trF27lvvuu+8DuaKMd5L9EWIuFzGXi0hXF4H9B/DvO0AqfCKBlEKjyaSKL3FSdMVHsM2ZjbG2hvVZVwkB/Nu//Rs33HAD3/nOd1i7di2PPvooX/7ylx+56bUAACAASURBVPNtlkCQbwYv7awEfgAgy3L6XFZ9hNAehjKzk0Puoxz3dzLZLpKOjCY0KgVPfXURTzW2sfmoh+d3dvDEluMA3Pneq9Q4jFxaaWPl1GKW1hdh0oq3uCC/pNNpent76enpyW1Ac7lceL1e+vr6SJ4UB1qn01FcXExxcTFqtZopU6ZgGGEIvfFMOpkksG8/nve2Ejx8hJjLRTIYHHKNrsRJ4eJFWKZPRV9WJlLHjxBJkpgxYwbf//73Wbt2LX/605+E0BYI4C1JktYAXUAB8BaAJEmlwIj8s0EI7WG5un4Frze/y51v/5r/vu6XaJSjNynBRMRm0PDVKyZzy+IqDnQGeO1ADy9sb6ErLNPkCtHkCrFmWzsapYLD91ydb3MFE5zHH3+cTZs2Demrrq6mpqYm53s9OJyfcYxFt7hYND/wO1xvvQ2ArqQE+8IF6MtKM7PVzkxRW615tnLsIssyN910EwB/+MMfznK1QDAh+BZwE5mENUtlWU5k+0uAH450ECG0h+GguxmAyfYqIbJHGe5QjHteep9dbX20esIMhIOVAKdFS5lNT7FZS2dflK9eMaIQlwLBBWXZsmWnCO1jx47h9/spLCykv78fjUaD0+kUIvsMFC5emBPa0e5uoi4XWocDXYkTc8MUilcuF0L7QyBJEsuWLePpp5+msbGR6urqfJskEOSb62VZ/guAJEkFA52yLO88l0GE0B6GeWWXUKgvYJ/rEJvbtrN40rx8myTI8i9rdrP+cGaz6tQSM5+cU8aqaU6O7d/GVStXnOVugeDiU1tby0MPPURvby9utxuPx4PH48Hr9eLxeNixYwcbNmwAwOFwcOedd6JUir0GJ2NfMJ/5jz5C//HjxFwu+tva8W5txL9nL/49e2n/6zNM+c4/U7RsSb5NHZNs3bo1l00yEonk2RqBYFTwI+DZbP1N4NIPMogQ2sPQG/ZQoLfiifh491ijENqjiFsXVxGKJTnYFeBgd5CDaw9x76uHsOskprVsobrQSLXDSI3DQHWhkUl2A2ql8M8U5Jd4PH5KqvdEIkE8Hh8yg+12u3G5XJSWirCVA6TjcSKdXUQ6Ool0dBBp7yDc0kJ/ewdk44orNBr0FeWYp9Tn2dqxS319PVOnTuXgwYNs376dW2+9Nd8mCQT5RjpN/ZwQQjuLLMu833uE15reYXP7Dmw6CzfN/ASrJi/Lt2mCQVw5zcmV05wkUmkOdAZocYdpcYfZcuAo/kiC53d1EIye2FymVEjUFZlYPLmQJXUOrphShEYlhLfgwiPLMu+99x4vvPACHo9nyDmVSkVhYSGFhYVUVVXl6qWlpUJkA9EeVyYl+qbNmZB8g1KGawoLMdZUY1+0EGNNNeb6OrRFRXmzdbywc+dOjh07BmSi4Qjyh9qkpPRy8RqMFPUzF2wFUC9J0lxAAeiy9ZzglmV5x0gGEUIb6IsG+P5rP8Mb6cOoMfCJhqv41PSrMajHf5awsYpaqWD2JBuzJ9kAWKfuZPnypciyjDccp9UTpsXdT6s7zO72Pv689Th/3NSKXq3k3X9dgcOkzfMTCMYz0WiUV199lZdffpmamhqWLl2Kw+GgsLAQh8OB2WwWybBOQ7THxfav3JFr2xfMxzrrEizTpqIvL5sQ2RvzwXe/+10ikQgbNmxgyRLhfiMQkIk2cl+23j2oDiCTCfl3VoTQBrqCPXgjfQCUm0v4/KzrxYakMYokSRSatBSaMhsjFRL0BKKYtCpiyTiRRIpWd1gIbcEFY+/evfz2t78FwGg08p3vfEck/zgH4l7vkLZ3ayPerY1IKhVaRyHOj66i7NpPoBC/0/PGK6+8wo4dmcm54uLiPFsjEIwOZFk+Lxu/xH8qoNRUzOyS6ezuPoBFaxIiewwiyzJt3n52HPexpcXL5mYPLe5M0gqbQc3i2kJWTXdy5VQnVoOIJCO4cAxedg+Hw/zTP/0TZWVlVFRUUFZWRlFREQ6HA4fDgU6ny6OloxPLtKlc/uwaEv4AMY+HeLbE3B76du/l2GN/QqnTUvoPq/Nt6rjh2Wcz+72+/e1vU1NTk2drBILxxYQX2ju79vHrzb8nkUryqekf54bp4p/3WCCWTLGn3c+OYz62H/PxXlOEwKuZ0F8mrYoFNXY+v7CSxZMLmVZiQaEQX54EF4eqqioeeugh/H4/77//Pu3t7bS3t7N3795TwvyZTCbKysq47bbbsNvtebJ49CEplWjsBWjsBVBfB0D7088Sbm4euCJ/xo1DfvjDH9LW1savfvUr1q9fz8aNG8WXQIHgPDGhhXZaTvOb9/5AJBHl1jk3sLxmMWoRN3vUk07LfPI/N3KoJ5MVrqrQwAyHgtXzpzK3soCpJWZUItKIIM9YrVYWLVqUa8uyTDgcxu1258revXs5fPgwzz77LLfffnserR3dyKkUkc6uXDvS0ZlHa8Yf1dXVvPLKKyiVSnbs2MHhw4eZNWtWvs0SCMYFE1qNKCQFt86+AaPGwGO7nuF/Pfcd/mXtXTy682maPK3Ig3a6C0YPwWiSFk84165xGLFqFGhVSmLJFKFY8gx3CwQXH1mWiUajRKNRIJNm3Waz0dmZEYyNjY189atf5fnnn8+nmaMKWZbx7djJgbt/xpZbvoTrzbcA0BY5KPn4R/Ns3fhDkiRee+01CgoKmD17Nj/72c/ybZJAMC6Y0DPaACtqL2dJ5WUc9rRwyN3MQXcTa5vW8dLhNym3lPAPU65kZe3lKKQJ/Z1kVGE1qNnwvRVsPurhvaNedh730eRKsLZ1T+6aQqOGycUmJheZmFxkZEmdg2mlIlyS4MLT0dHB3r17CQQCuQQ1vb29xGKxs96bSqUugoWjFzmVwru1Ed/2nfTt3k3M1YvGbsexdAmW6dOwzpguQvldQKZMmcL111/PI488wsGDB/NtjkAwLpjwQhtAo9Iw09nATGcDAKF4mK3tu3i1aT0Pb3uCeCrO6ikjiuIiuEgUW3RcO6eca+eUA/DW229TN2shTb1Bml1hmlwhmntDvLKvi77+BEVmLVv/vyvFRlfBBSGRSOB2u+no6ODPf/5zrr+srAyHw8GUKVOw2WyYzWaMRiMmkylX9Hq9CPUHRDo72XHHN3Jt+8L5VH72JhzLlqJQC5e+C0EoFGLDhg289dZbrF27lr179wJwyy23cP/99+fZuolLIpSia1Mg32aMGRKh0T1BIYT2MJg0RlbWLmFZ1QI+//Q3eXTn0yytWoBFa8q3aYLToJAkKgsNVBYaWDk105dOyxxxhbjtD1vxRxL5NVAwpkilUqRSKRKJRC6T4+CMjslkkng8TlNTE7t376ajo+MUV7NPf/rTrFq1Kk9PMPZIBIJD2v49+0gGQ4Saj2KsqUHnLEZSq1Go1UgqFQq1KlNXq1Go1EgDbfGl5bT09vaya9cu1q1bx9tvv01jYyPJZBK1Ws3ll1/Ovffeyyc/+Unq60WGTYHgfCGE9hlQKpSsnrKSVw6/ze3Pf5cGx2S+v+x/Y9QY8m2a4CRiKZm97X6OuIIc7gmxt6OPPW1+grEkOrWCn35yhpjNniB0dnayfv36U0TyQPt0AnrwNSPdnyFJEpMnT2b16tU4nU7a2tr4+Mc/jtFoFO+3c8QytYHLn3+aaFcXwYOHCR4+QrilhZ433iKd9W0fCZJSmRXkKiRVRpgr1KqsIFflxLoyEOD9TVtOiPWsUFdotZmi0aDUanJ1hVaLctA5hVaTaWtOXDPaRL4sy9x999386le/QqPR0NPTA4BSqWT+/Pl897vfZcWKFVx++eUYjcY8WysQjE+E0B5EMp2iL+LHFfbQHXLRFXTh6fehkCRSsswhdzP+aEAI7VFCKi3z8DtHeXLrMdq9EeTXNwCgUkg0lJj55Jwy5kyysay+iBKrCFU1Udi8eTPr1q0b0bUDLht2u5158+ahUqlQqVSo1WqUSmWuPtA/+LxKpaKsrAyT6cRKVyQSGdIWnBuSJKEvK0NfVkbxyuVANuJIVzcJn490IkE6kUROJkgnEsjZdq6eTGb7Bs4nT9STA/dm+ohEiHZ3DxkvHU+QjseREx9sBUyh0eREuNpiQW2zoSmwobbZUNusKHU6kCTsCxagsVnP3y/uNCSTSX76058O8f2/8cYb+drXvsaSJUvQaDQX3AaB4GIz2lLYj3uhnU6nCcXDBGIhArEQwXiIYLbui/jxRPrw9ffhifjwR4PInJjJUkoKik0OZpdMZ2pRHVfVLsWkFd/6Rwtr93Xzi7VDN+zMry5g8WQHhUYNFr0Ki05Nm68ffySRaxs0SjHbOI6ZP38+gUCAnp4e2tvbSZxBNKXTaQDcbjdz586loaHhYpkpGCGSUomhohwqys/ruOvWrWPu8uW5tpxK5YR6KhYn1d9PMhwmGQqRCp+oJ8PhTDtbz7TD2fNh0vE4hCDu8Z72Z3f+7QUufeA35/V5hkOtVuP1ern33ntZt24du3btYs2aNaxZswadTseCBQtYtGgRDodjyL6B0xWdTif+dwoE58iYFNqyLBOMhfBE+uiL+umLBOiLBvBF/fRFA/ijmXYwFiYc7x8ingdjVOuxGwoo1NuospXn6oUGO6XmIhwGO0qF8iI/nWCkXDW9mF/ccAk9gRjv7T9KVGWiuTdMY6vvjPcpFRIWnQqLXo1Fp84J8CF1/Ul1nRqnRYvNIGaARjM+n4977rnnnO+rqKggHA5z4MAB1Gr1kFnskpKSC2CpQJZlUpEoyWBw2Bnnk2eoM/3JQfXB1yRJJ0++58RYQ+9Jkk4mUfWHee/h3+faZL90nQ8kpRK11ZoptsxRoVZnXJJkmfLrrz1vP+tsWCwW7rzzTiDzxbK1tZXt27ezadMmNm7cyH333UcyObKQqAqFYojwNhqNGI1GDAYDBoOBUCjEk08+icFgGNJ/prrZbMbhcKDVai/kr2FMEU5XsCX4y3ybMWYIp+/ItwlnZEwI7SOeFva7DtMR6KYz2ENnsIdwvP+U6wxqPTadhQK9lWrbJCxaE2atKXs0YtZk6hatGZPWiEYkpxnTaFVKbppfCcAsZQfLly8BMlkjg9EkgUiCQPYYjCYJRBPZvgSByOB2ElcglOuPJE6/g1mrUvDs/76cGWUXftlXcO6o1Wq0Wu0pofTMZjOSJBGPx3OxrAfT3t7OQw89NOyY3/rWt5g2bdoFsXe8E+3uxrt1G3GfD+WhQxx4dxMJvz9T+vyZ2d8PgaRUZjdGZjdDDvLBllSq3CZJlcFw0kZKNV29LoorKwdtqFQN8u3Ojqka7Ot9on/wOJmfO2gzpkqVsWsUzvwqFApqa2upra3lM5/5DJAR35FIhFAoNGwJBoOnPRcKhejv78fv99PV1YXH42Hfvn309/cTDofPKVyl2WymqKiI4uJiioqKcqW0tJQbb7yR0tLSC/VrEQguKKNeaB/r6+CHb2S+2RXorZSZnVw+aR5lZidFxkJsOgs2vRWb1oxGJWYbBRkBrjUpcZjOfYYklZZpcYfZ1dbHrjYfO4/3sb/zRJilWDLNE1uO8+/XX3I+TRacJ0wmE/fffz8ejyeX+ryjowOPx0MoFBpRLGsgF4Kvrq6Ourq6C2z1+CPZH6Hptw/g2fQeyHJGfOp0xJ3FqK1W9BUVqK0WNDYbKrMZhUZ9kuAdJF5VpxHCWUH7QelYt47aQa4jExWFQpGbmXY6nR9qrHXr1rF80O80Ho/T39+fE94D9cHtQCBAb2/vkHL8+HG2b99Ob28viUSCH/zgB1xzzTVMmzaNadOmUV9fz7Rp0zAYxH4pwehn1AvtlJzimilXcv30j2MW4fUE54lgNMHR3jBH3SGaXWGaezNxt1vd/cRTJ5aQC40a5lcXMKPMyowyCzPLrUxxmvNouWA40uk0Pp8Pt9vN+vXr2b59O5DZXDd4ZmxAQJvN5tzy90BdRAo5fxx7/E94Nm5GW1zE5Du+ivWSmbyzcSNzhLCdUGg0GjQaDTabLdeXTqcJhUL4/X76+vrw+/1nrG/bto2mpib++te/njK+yN4sGAuMeqGtkCRePPwmu7oPcPu8zzK9WMT3FHwwNhxx88DbTRx1h+gJnJjZVCokquwGaotMrJhanMsmWeswUWAUqySjFZfLxf3335/LvjiwTC1JEldffTWzZs2irKwMnU5EnLnYOK9ciXfrNmKuXg789G4kpRKVxcyhbTuYfMdXUYlQcuOejRs38stf/hKfzzdEQAcCgbMK5AFxbrVasdlsXHXVVVitVrRaLb29vXR1dfG9733vIj2JQPDhOKvQliRJB7wDaLPXPy3L8o8lSaoB/gIUAtuBW2RZjkuSpAUeA+YBHuAmWZZbs2P9APgykAK+Kcvyq2f7+ZXWcm6f91n+Z/uf+cnb93FV7VKmF09hQfls4SoiOCtd/givH+hhxzEfz+/qBODT8yqYXGSitsjI5CITlXYDGtXoin8rOD29vb38+c9/Zv/+/QDMmzePSy+9FIfDgcPhoKSkhIKCgjxbObEx1U1m3kMPEG5pJdLRQaStnbbX38D97kbkVJqG73z7Q7l9CEYXW7ZsYdeuXbzzzjv87ne/o7W1lS1btgCwfPlyampqsFqtOeF8trr4ciwYrUiSVAD0yeewnDKSGe0YsFKW5ZAkSWpggyRJrwD/DPxKluW/SJL0OzIC+sHs0SfLcp0kSTcDvwBukiRpOnAzMAMoA96QJGmKLMtn3C2hkBR8tO4j6FU6NhxvZGPbNt44ugG73sbX5t/CnNLpI31WwQTj7UMubvtDIwBFZi0fn1HCtXPKuPoSsalmLPPkk09y4MCBXNvv96PX6zEajcTjceLxOKlUCqUQcnlFoVJhrq/DXF+HnE5zzOVC+c4GPJs2Ezz0D1imiw2mY51Dhw5x11138cQTTwCgUqmorq6murqa22+/nauuuoqbbropz1YKBB8MSZL+L7BGluWD2UnktcBsIClJ0udkWX5jJOOcVWhnVXso21RniwysBD6X7X8U+AkZoX1ttg7wNPBbKeP4eC3wF1mWY0CLJElNwAJg80gMXVa9gGXVC0in0xzoPcyDWx/nwa2P8btP/kz4VQqG5UB2E6NKIfHjT0znmlllebZIcD64/fbb2b9/P9u3b8dkMtHd3c3u3bsJBoem8L722mtZvXp1nqwUDOb4n59C+U4modTU738X87SpebZI8GH57//+b77yla8AMGvWLB555BH6+vq48sor82yZQHDeuAm4K1v/YvZYBEwho3vPj9AGkCRJScY9pA54AGgmM3U+EHyzHRjIJlAOtAHIspyUJMlPxr2kHHhv0LCD7xkxoXiYd45tpbffi003ejL/CEYfd1wxmf54kgfebubrT+5kcpGJaaXiPTPWMRqNLFiwgFAoRFFREfv27UOtVtPS0jIkdF9lZWUerRQMxjpjOu3Z+uFf/yfmKWuxXjIT+4L5GKoqxWTJGGTOnDlYrVb8fj979uzhsssuo6CggAULFjB79mzmzJnD7NmzmTJlCirVqN8OJhAMR3yQi8jHyEwWp4D3JUka8Zt6RBdmB54jSZINeA64oNMRkiR9BfgKgNPpzKVTlmWZZ7xv4En2MV1fyyzDFNavX38hTTkroVBoxOmeRxMXw+7TvY4XgoHn6Yul2dubYp87xQFPimA2KeCSMhVdB7fTc+jifKCP1ffF6biYr+VI8Hq9rFu3jlgshkqlwmKxUFJSgtlsztXdbvdFt3MsvO75ei1DX/oCZlcvUkcnfZ1d+Pfs5fgTf0a2WknX1SLX1yGXXrzkQGPhtTob+f67fP7554nH4xw8eJDDhw9z8OBBmpubefPNN4ckwbnhhhv4+te/flFtO5+Mh/eK4AMRkyRpJtADrAC+M+jciGNLntPXTFmW+yRJehtYDNgkSVJlZ7UrgI7sZR3AJKA9q/itZDZFDvQPMPiek3/Ow8DDAJdddpk8EJezLxrgwb+t4eZLPsmnpl99LqZfME6OGzpWuBh2n+51vBD8/bW3ecVt59md7SRSMg6TllUzS1g2xcGSOgfF5ou7uWasvi9Ox8V8Lc+Gx+Ph17/+NbIsc8cddzBz5sxRM2M2Fl73fL2W69at44rrr8+1414fni1b8b63hb7tO2H7Tmb/6l5MtTUXzZ7R/lqdjdHyd/nRj34UgLfeeovp06fT1NTEq6++yt133w3AkiVLxvTvejy8VwQfiG+RcYEuIrMnsQVAkqTVwM6RDjKSqCNFQCIrsvXAKjIbHN8GPk0m8sgXgb9lb/l7tr05e/4tWZZlSZL+DjwpSdJ9ZDZD1gNbR2oogE6lRavUcMTTci63CcY50USKe96L4It18NkFldw8v5JppWaxHD0OiUQi/PKXv6Svr4+rrrqKOXPm5NskwQdEYy+g9OqPoS1y0LdrN6b6OgyTKvJtluADcOedd/L4449z7NgxEonEkHO/+93v+OpXv5onywSCD44sy+8xjAeHLMsvAy+PdJyRTAOVAo9m/bQVZHZgvihJ0gHgL5Ik3U1G2f8+e/3vgcezmx29ZCKNIMvyfkmS1gAHgCTwf84WceRkdCotq+o+wiuH38Id9uIw2s/ldsE4pTcYo6df5pJyC7curmZykUg8Ml6RJIlAILPJ1ShiMY95Ol94kZb/+QP6SRVM++H3UajV+TZJcI5Eo1Huv/9+vF4vN910E8uWLaOyspLKykqqqqqGJKsRCMYSkiR9QZblP0mS9M/DnZdl+b6RjDOSqCN7gLnD9B8lEzXk5P4o8JnTjHUPcM9IDDsdH69fzhvN7/LLDQ/yrcVfpsxy8Xz6BKOTSXYDq2vUvNzi56r71uO0aJlXVZCLlV3ryBzNOvEhPpbZuHEjL7/8Mul0JnNnX19fni0SfBCS/f34d+/BvWkz7nc2YF8wn4bv/YsQ2WMQj8fD73//e7xeL5WVlXzta18TLhaC8cTAbM6HSgc9Ohwbz4FiYyHfvvx27t/8CP+89i5mOaeyeNI85lfMxqQRM1wTlRsbNPzgM0vZ0ORmY5ObvR1+1u7rJj0opHyRWUutw0htNvPjjDIr86sLUClFsprRTjqd5rHHHsu1a2pq6OvrY8eOHTidToqKitBoRAKr0YAsy6TCYWK9bmJuN9GubiKdXSj372fbE08R6+0FWUZpNFJ+/bVUfv6zQmSPEWKxGEeOHKGxsZEnnniCt956K5flcfHixXm2TiA4v8iy/FD2+NMPM86YE9oAc0tn8uvVP+GlQ2+yqW07DzY+zsPbn2S2cxo3zvwEtXYR1msiMslu4LMLKvnsgszrH0umOO7pp7k3zFF3iKO9YY72hnhlXxd9/Sf8CL+8tIZ/u0YkPhrNSJLErbfeSlNTEy6XC5fLRUtLC7t3785dU1BQgNPpZMGCBSxZsiSP1k4M0okE/W1thJqOEj56lGhXNzG3m5jbQ3pQmEUApdEAJhPmqQ0UX7kC6yUzsEydKrJDjmICgQCNjY1s3ryZ7du3c+DAAZqbm0mlMh6ftbW1/OhHP2LVqlUsWLAArVYrInOcJ7o1Er+YNCblWV7o1lwYV1FJkv4RWCfL8pFsPpjfAzcAx4AvyrI8og2RY/aVtOksfH729Xxu1nU0e4/xXvsO1re8x//3xi/41uIvs2jSpfk2UZBntCol9U4z9c4Tqz798SQbmzy8sreLZ3dmgt68e6QXWZaFX/coRpIklixZMkRAv/766zQ0NNDT04PL5aKnp4eDBw/y2GOPsXbtWgoKCigoKMBms+XqU6ZMQa/X5/FJxj7JUIjDv/5PfNt3QNaNR2kwoC8vwzCpAtvcuWiLCtE6HGgdDnQlTlQWC+vXr6dBuBWMOqLRKIcOHeLAgQPs378/V5qamnKz1Q0NDVxyySXceOONTJ8+nRkzZnDJJZeI/5mC8c4/AX/M1j9LJitkLRl36t8Ay0YyyJgV2gNIkkRdYTV1hdXMck7j7vW/4W/vvyaEtmAIXf4Id7/4Pm8ddBFJpNCoFCyssbNyajFfvLxafGCMQdRqdW7T1QCRSIRXX30Vl8uFz+fj8OHD9PX15fy6jUYjd955JyaTKV9mj3k6nvsbvsZtuXbBvLkUX3Ul1pkzUVs+lCuj4CLx+OOP89xzz+UE9cDfh1KppL6+nlmzZvGFL3yBRYsWsWDBArGhUTBRScqyPLD8fQ3wmCzLHuANSZJ+OdJBxrzQlmWZnV37eeHQ6+x3Hcao1nPLnBvybZZglPHK3m5e2tuFQaPkT19eyGXVBejUYtl6vKHX67nuuuuG9KXTadrb27nnnnsIh8N0dXVRX1+fJwvHPhWfuQH9pEmEW1oINTXj33cA3/bMCqpt7hwavvNtVOKLzKjlT3/6E7feeisAn/rUp7jpppuYMWMGM2bMoL6+Hq1Wm2cLBYJRQ1qSpFLAB1zJ0GAeI14aHdNCu7FjN3/Z+3fa/J0U6gu4dc4NXFm7FL364iYnEYx+ltU7+P/bu+/4Kuvz/+OvK3sTkpCQIYQREmYCIkO2CipaqKPVWrEqVcvXhVZq+QlCaaVa+Vb7tShOHMUBOADbqiCylAAywwwrkJiwQsLIHp/fH+cmhggkhJzcJyfX8/E4j5zc933uvM991pX7fEanqCDSD59m8oKt3DMgnlt6xRHo26RfAqoOPDw88PD4scPrqlWrtNC+BJ5+fkQOHQxDBwOOttqn9+wlf+Mmsj7+lPW/e5DW111LWJ8rCOrQXtthu5ikJMewwCJCcnIykydP1m/0XED1WT5bRV/G1J/rHAF1NWGO05oDPg18D3gCC40x2wBEZAiwr647abJVxsqMtby0ZjZxIdE82Oc3DGjTGy/PJnt3lJMlRAXz30cH8++0HN5ctZ+nF2zj+S93MTolhp+nxNKrTUs8PPTDxl0YY8jLy2Pr1q1s2rSJ7du34+XlRXJyMqNHj7Y7nlvx8PYmpHMSIZ2TCOtzBZkfzSNr3sdkzfsYr6AgIq+5ija/us3umMrSu3dvcnNzGThwIFOmTMHf358JEybYwC53eQAAIABJREFUHavZqz7LZ0Bckpn62SabEzUd2flFTtmvNWdMWyDYGJNXbdU6oM5vak22Ml2T5fiqcuqwxwjx03aBqnaeHsKo5BhGJcew4WAe73yXwfz1Wfwr9SChAd70axfOgI7hXNkxgvYROulNU3L69Gl27NjB/v37ycrKIisri4KCAgAiIiK4/vrrGTZsGCEhITYndW9BHTvQ+ak/UnbiBPmb0zi+di3Zny2k8GAmDBlodzxlKSgoYMeOHYBjLGyl1E+JyJ2AGGPeq7HqFqACeL8u+2myhfaIjoNZn72Ff6S+ycRBD+nZbHVRerVpSa82LTldUs7XOw6zavcxvtubyxfbDgHQOsSPKzuE079DOAM6RhATqiNVuJLKykqOHTvGokWL2LZtGxkZGRhj8Pb2JiYmhpSUFOLi4khKSiI6Olr/aWpk3i1a0GrwQPyiIjm28ltOp++GQVfaHUtZ4uLimD59OlOmTOG5555j165dTJs2je7du9sdTSlX8jCOttk1fQKswN0L7R6tO/PAFXfy8tp3+b/U2YzvP/asdphK1UWQrxejU2IZnRKLMYaDxwv5dk8u3+49xvL0o1VDAPZu25Ix/dtyY48YPLWJie3mzZvH0qVLAYiKiuKGG26ga9eutG3bFk9tE+wSjixdxp6Zr+AbFUnnpyaybn+dmzQqJxMRJk6cyEMPPcSLL77IjBkzWLhwIampqVxxxRV2x1PKVXgbY07XXGiMKRCROs+y1aQr06Ht+nNT5+tIzdrA5sPb7Y6jmjgRoW14IHf0bcPMO3qx7qlr+GL8IJ68LoncglIe/XATo/65iu8zjtsdtdmrPgvdkSNH2Lp1K/v27aOsrOwCt1KNJXd1Krv/8RIhXTqTPONvBLbVScRcUXBwMJMnT2b79u34+flx7733alMSpX7kLyI/mXJcRIKBOk9F3KQLbYCRnYYR6O3PG99/wL7jB+2Oo9yIh4eQ1DqEcUM7sPT3Q/jH7Snkni7l1lmr+dVrqXy28QeKyyrsjtkstWnThttuu43f//73jBw5EnCc5Z48eTJHjx61OV3zZSoryV2dyq7n/05QpwQ6T5qoY2s3AbGxsSxYsICdO3fSu3dvTp48aXckpVzBm8B8q0MkACISD3xorauTJl9ot/ALYdLQRykuL2HmmrftjqPclIgwOiWWpU8MYcK1iWTlFzL+o030eWYJf1q0jdMl5XZHbHY8PDzo1KkTo0aNYuLEiYwfP57CwkKefvppXn75ZQ4e1H+8G0tpfj57Zr7CunvuY+ezz+MfF0vXPz2Np47J3GSkpqZSXl5ORkYGX375pd1xlLKdMWYGsABYISK5IpILLAc+N8Y8X9f9NPlCG6BDWFtGJY0g82QOe48fsDuOcmMBPl48OKwjy58Yxvv39eWqpEje/i6DO15PtTtas9e5c2cmTZrE1VdfzebNm3nmmWfYtWuX3bHcXmV5OZt//ySHv1pCi25dSRj/MN2ffQavgAC7o6mLsHz58qrrCxcutDGJUq7DGDPLGNMWiAfijTFtjTGvXMw+3KLQBhgU3weABTu/sjmJag48PIQrO0Tw4u09ubNvW7ZknaCsotLuWM1eQUEBixcvBsDPz49WrVrZnMj95W/aTOmxY7S+bgSJEx4ncthQLbKboMWLF5Oens7IkSP517/+xbhx48jJybE7llIuwRhzyhhzqj63dZtCe0XGGgCGdxhkcxLV3Hh6CIE+nnh7us3LqcmqfgZ75MiRhIWF2ZjG/eWuWcuOP0/HJyyMy3RSmiYvISGB+fPnM378eF577TXatWvHmDFjWLJkCRUV2h9Fqfpwm8rg8Olj+Hh60zEs3u4oqpk5XVKuU7m7gJ07d7Jxo2Miq5CQECIjI21O5P58QkMdPyPCq66rps3f358XXniB9PR0xo4dy6JFixg+fDjx8fFMmjSJkpISuyMq1aS4TaHdJy6Z0ooynvjyL+QW5tV+A6UayMHjhcSH/2QEINXIlixZQmZmJrfffjvTp0+nZ8+edkdye55WE5GSw4cxldp0yp106NCBmTNncujQIebOncvJkyd55pln2LRJpwZX6mK4TaHdM7obU4c9xvHCPP6962u746hmxM/bk1M66ojtevfuDTjOyHl713kuAXUJji5bDh4edH9uOqIThrmlFStW8MEHH1QN+ZeZmWlzIqWaFrd6Z+wS2Ykw/1AW713JsUKdVEQ1jtYhvmTnF9kdo9nz9/cHYPbs2aSlpdmcxv1VFBdzdMVKQjon4R8dbXcc5SR33303n376KQDPPvss119/vc2JlGpa3KrQBgjyDaSkopSTxT+ZNVOpBldQUs5X2w/Tr712urNbjx49ePzxx4mMjOStt95ixYoVHDp0SDtxOUnWx59ScuQoMaNH2R1FOVFMTAwAffr04Q9/+AOBgdpMTrk4X4OJr6zXxRncrtC+pYtjlrhX1/2LwjI9y6ica13GcfILy/hl78vsjtLsiQiJiYk89NBDREVFMWfOHKZMmcIjjzzCtGnTeOutt9i1axfGGLujuoXS446+MKZcp713Z3/9618BWLt2LZMmTdIp2pW6SG5XaPeJS+GPgx5kf34mn2tbbeVk7SOCANh3tMDmJOqMqKgo/vCHPzBx4kT69esHwA8//MCaNWv4+9//zieffGJzwqavICODI0sc76+lx49Tdqpew8uqJmD48OEsXboUDw8Ppk+fTqtWrejWrRt33XUX//jHP7RzpFK1cLtCG6BXTDd8vXxJO7SDSu0Jr5zou73HAMcENsp1iAjffPNN1bTSAQEBXHbZZURGRtK9e3e74zV5vpFRRF41FID9b8zmwHtzbM2jnGvYsGGUlJSwevVqpk6dSrt27ViyZAnjx4+nZ8+eTJgwgQMHdFZmpc7FbQf/va3bz3h303z+unIm9/W+g8jAcLsjKTezPP0of/wkjcGdWvHrvm3sjqOqKS8vJzU1FV9fX5555hmCgoIQ0X+GGoqHtxd+0dF4+PpSWVJC5NAhdkdSTubl5UW/fv2qviUCyMnJ4Y477mDGjBnMmDGD++67j6FDh9oXUikX5LaF9o2JV+Pn5cPbG+fx0OeTuKxFDD2iOpMQ3o4OYW2IDIzQD151SfYfdXS4vaPPZfh5e9qcRgGUlZUxb948vvvuOwBuvPFGgoODbU7lfg5/tYSDcz7APy6OLk8/hV+UTg7UHIWGhrJs2bKq39u3b29fGKVclNsW2gDXdBhEj9ZdSM1cz+ZDO/hqz3L+ne5oVxjoE0BcSDStAsNpFRBGq8AwIgLCaRUYRpBPAAHe/nh76li86vxu6hXH+2sPMm7OBm6/4jLGDelIm/AAu2M1a8eOHWP58uUAPProo3Tp0sXmRO6nsqyMoh+yAYi+8XotspuBvLw8duzYwZ49e9i9eze7d+8mPT39rGE0Z82aRWJioo0p3Udw7i6Gvq/fEtXVIRfvn+vWhTZAZGA4o5JGMCppBOUV5Rw8kc2+vAPsO36Q7FOH2XVsL98Vfk+l+Wlbbi8PLwK8/fD39ifAy48AH3/8vfzw9/YjwNsff28/DhXkULwbfD198PXywdfLF19Pb3w8ffDz8sXHy+fHdZ4+eHromU930cLfm0//ZwDPf7mL99ccZO73WYxKjmHi9Ul2R3NrxhgyMzPZuXMnhw8fJj8/n7y8PPLz8zlx4gQAnTt31iLbSTI/mkfOv/8DQGhKss1plDMUFhby7rvvsmrVKtauXcvu3bur1nl4eBAfH09CQgJPPPEE/fv3p1+/fkRGRp51dlsp5eD2hXZ1Xp5etA9rQ/uwNtDhx+UVlRXkFZ3gaGEuxwryKCgrpLCsiKKy4rN/lhdztCCXwvLiqmWVppLUDVvqnMHTw/Oswttx3RdfL0dx7ijUqxXnXj6O5dV+9622nY+n99kFvacPHjpDW6MJ9PVi6qiu/G5IB15fuY83V+3nRFEZd8Xbncw9bdu2jVdffZWSkhIAfH19admyJaGhoSQlJREaGkrLli2rZolUDa/1dddS9EM2ualrSJ/xAokTHsevdWu7Y6kGkJaWxty5c3nzzTfJyckhJiaGvn37cu+999K9e3cSEhKIj4/Hx8fH7qhu7VR4IsvueM3uGE3GqZn32x3hgppVoX0+nh6eRASGEREYBq3qfjtjDEuWfU2f/n0oqSijpLyEkvJSSitKKakopaTculT8+LO0anmJY7l1u9LyUk6VFJy9bXkpZZUXP7W3t4fXT86knynOHcu9OX4il/0bjlQV8n7WzxDfIBLC2xEe0PKi/25z1rqFH+1bOSZyaBngA+hwf86Ql5dXVWT37NmTBx54QPtaNDLfiHCSnnyCI8uWs3fmLDb8zyOE9etD3C03E9RB2+g2VXfddRfvvfceACNGjGDu3LkMHDjQ5lRKNX1aaF8CEcFbvGjhF+K0v1FRWUFpRZlVgJ8p5Kv9XqOgL61Z4FvblVaUUlxewonik5RUlJJXcoIdu/c7LXdztPFgPgDfHziOnC6jc69iokL8bE7lXvr3709BQQGffPIJGzdupLi4uGrqddW4IocOoUX37mQvWMjhJUvJW7eebn/5E8GJneyOpi5SUVER77///lm/z58/n/T0dHr06EGXLl0ICND+J0rVhxbaLsQYQ0VlRVWBXFpRRmn5j9erzpZXK7ZLq4rrsqqz4NXXnbVt1dn2MsrNxZ8pVxf259HdaBcRyPNf7uJALsyf/jWLHhpI97gWdkdzG56ensTFxQGQlJSkRbbNfMPDaHfv3cTdejNrx9zD1klTaH//WFpe0Ruf0FC746k68vf358CBA6xcuZLU1FRSU1N5/fXXKSwsBBztsocNG8Yvf/lLBgwYQKdOnfD21sECnEU7Q14c7QzppsoryiksL+ZkxWky8rIoLi+msKz4pz/LiiksLz6rYD5XAXxm3bk6ZdbGQzwc7bWtZiFn2nT7ePkQ6BNAWNU6R5tuXy8fcjKzSU7qTpBPIMG+QQT7BBLkG0iwT2DVaCty+6yGPmxupbLSsD3nJMvTj7Iu4zhbfzjJsdMlVeuHdGpF2wg9C9TQvvnmGwByc3NZvHgxMTExREdH07JlS21GYhPvkBA6/M8DZC/8nD3/fAWAgLZtaNGjB6EpPQjRjqkuLzY2lttvv53bb78dgIqKCvbt20daWhrr1q3j448/5oEHHgDA29ubpKQkUlJSeOqpp3S0EdVsiEhLIN8YY+p6Gy20aygsLSLn9BFyC/PIKzpBXnE+eUUnySvKJ6/oBPnFJykoK6K8etvpr/593v35efni7+VX1Vbax9MbHy8fQv1C8PGyCt+qwtj7rI6PPtUK46riuebvXj541WMkk2XHlzG0w6D6HKJm73hBKe+tPsCcNQc4cspRWCdGBTM0sRXdYkLoHteC/L2bufqqPjYndU9jxoxh8eLFrFy5kvnz51ct9/f3JykpieTkZBISEggPD9fCuxG1vnYEUSOGU7B3H/mbNpO/eQuHvviSnEWfI56eeEZFcjDnMC0v70VwpwS746paeHp6kpCQQEJCAjfffDPTp09n+/btbNy4kbS0NLZu3crChQtZuHAh8+bNY/jw4XZHdhuBwZ3oO+gVu2M0GYsOj3PKfkXkaWCuMWaniPgCXwDJQLmI3GGMWVKX/TTrQvtIQS6bcrax9/gBck4dJufUEU6UnDprGxEh1C+EML9QIgLDSAhvR6BPAP7efvh7+XFgbwY9u6dUDft3Zrm/tx9+nr46Aoib+WJrDk9+nMaJojKGJbbij8kxDO7Uiogg37O2W7ZfCzxnadGiBbfeeisRERH07t2b7OxscnJyOHjwIFu3bmXjxo2Ao/COjY2lbdu23HDDDQQGBtqc3P2JCEEdOxDUsQNxt95MRUkJp3buIn/zFjJXfUvmR/PI/HAuMaN/RtSI4QTExdodWdWRiNC1a1e6du1KYWEhy5cvx9PTk0WLFvHAAw+wb98+uyMq1dBuA/5sXf+N9bMV0Al4B9BC+1wqKytJzdrAp9u/4MCJHwBo4RtMTEhrLo/tQUxwJK2DIokICCPMvwUhvsEXLJaXZS+jb1zPRkqv7JR7uoTxH22iU1QwM36RTKconXHQbkFBQXTq1IlOnRwd8M6Msb18+XJWrVrFnj17yMjIYMCAAVpo28DT15fQ5B6EJvcgo00c/S6/nIzZ75K9YBHZCxYR0q0r3f48FdETEk1CZWUlTz75JC+99BIlJSX4+/tz991388gjj9gdza0UnEpnzcqr7Y7RZBScqn2beiqt1kTkWuBDY0wFsENE6lw/N5tCO+tkDkv2ruLbg99zovgkl4VEc1fKrfSM7kpMcJR+xaxqlZFbQHFZJY8N76RFtosSEcLCwqqmYAfHDJGxsXrm1HbGcDx1DWUnT1Yt8vT11SK7Cfnyyy+ZMWMGffv2Zdq0aQwaNEg7JDuBjqN9cZw4jnaJiHQDDgPDgCeqratzB6xmUWjPXPMOyzNS8fLwold0NwbH96V3TA9t1qEuyqLNOQBc1lI7OLqyoKAg+vXrV1VsZ2ZmVp3xVvaRzCz2fPwZAJFXX0WLbl2JGDTA5lTqYqSkpABwyy23MGLECJvTKOV044H5OJqLvGCM2Q8gIiOBjXXdSbMotA+dOoKfly//d8M0Qp045rVyX3PXZfL2dxncfWU8HSOD7I6jLqCoqKiqyL7xxhu56qqrbE6kAExAAN4tW1KWl0dRdjZxt96Ehw4R12Rs3ryZxx57DIC2bdvanEYp5zPGpAJJ51j+H+A/dd2P25/SLSkvpaCsiOLyEqj7aCxKVVm9N5f/92kagxIimHRDZ7vjqAsoKCggJyeH+Ph4AD7//HO2bNlibyjlEBFO7zdm0fGhcRRlZbHlyafI+fd/KDvlvAaW6tJVVlby3nvvMWDAANLS0njuuee49dZb7Y6llNOJyM9EpG21358Wkc0islBE2tV1P25/RrvSVJJXfAKAvXkHudy/u82JVFOy/1gB4+asJz4ikH/e0QsvT7f/37TJmjNnDitWrLA7hroADy8vooZfQ0i3rqTPeIF9r71JxtvvcdltvyD2lpu0r4wLmjJlCn/5y1/o27cvn376KdHR0XZHUqqxPAP0AxCRG4E7gV8BPYFZODpI1srtqwZ/bz8mDPgdAC+uftPmNKopyS8sZezb6/AQ4a3fXEELf/2a21UVFRWdVWQPGjSIl19+mVdffZXk5GQbk6lz8Y+OJvl//0bKi/+Ld8tQDrw3h4L9++2Opc5hxYoVxMTE8N1332mRrZobY4wptK7fDLxpjFlvjHkDR7vtOnH7QhugS2QCg+P74ikelFWU2R1HNQEl5RWM+9cGsvKKeHXM5bQJ1w6Qrszf359p06ZVzVCXl5eHp+fFT+SkGldgu3ja3/9bAE7t2m1vGHVOLVu2JDs7m9mzZ9sdRanGJiISJCIewNXA19XW+dV1J82i0DbGUFhWTGFZEbtz9ayJurBvdh7h+hdXsnpfLs/e0p0r4sPsjqRqUV5ezjvvvMOuXbuIjo7mvvvuszuSqqOWvXoSlNCRfbNeY9P4Jzi8eAkXMbuxcqLy8nIWLFgAQP/+/W1Oo1SjexHYBHwP7DDGfA8gIj2BnLrupFkU2p/v+prvf9hMn9gUklp1tDuOclFlFZWM/3Aj97y9DoDZ91zBzb3ibE6l6qKkpIQDBw4AkJOTw2uv6Ri0TYV4eNBlyiTa/fYexNODPf98hS0T/sjBD+dyes9eu+M1W4cOHSIpyTHgwpAhQ+jSpYvNiZRqXMaYt4AhwFhgZLVVh4B76roft+8MCdAq0HFGsmN4PB7SLP63UPXw0bpMPtuUzaNXJ/DgsI74eOlzpakIDAzkpZdeIisrizfeeINt27axZs0akpKSaNGihd3xVC28g4OJ+dmNRN8wkkP//YIj3ywn84OPyPxwLj2em05woo6D3theeeUV9u7dy+uvv869995rd5xmJTh3F0PfH2J3jCbjUK7z9m2M+QH4ocayOp/NhmZSaPeN60nbFrF8mLaQE8Wn+HnnEbTQ8bRVDZsz82kZ4M1jw/VDvSny8PCgTZs2PPjggzz33HO89dZbAERERBAXF0d0dDQxMTFER0cTGxurE1a5IPHwIPqGkUTfMJLCg5lsfHg8p/fs0ULbBhEREQA8+OCDpKamMnnyZB0/W6l6aBaFtogw9arHeXfTx/xn91KW7FvFyIRh/CzpGoJ8Au2Op1xAWtYJFm3J5rqure2Ooi5RVFQUzz33HJmZmezdu5d9+/aRnZ3Nli1bqKysrNrmmmuuoX///njrpCkuqbLc0XE94905BHfuTFD7Og9bqxrAww8/zNChQ3nggQd48803Wb16Ndu2bbM7llJNTrMotAECfQIY12cMo5KGM2/r53y64wu+3LOcX/e4iWs6DNTxW5upsopK3l9zkOe+2ElYgA//b6ROSOMOvL29ad++Pe3bt69aVlZWxpEjR8jIyGDZsmXMmTOHhQsX0qNHD9q1a0e7du2IiYnRM90uIjA+nvh7f0PW3I/ZPvXPJDz2CKEpyfpe3Yi6d+9OUlISq1ev5vHHH7c7jlJNUrMptM+IDWnN+Ct/y0351/Hupo95ff37VJpKrk3Q9lDNiTGGL7Ye4m9f7mL/sQKu7BDOC7elEBlS5xF7VBPj7e1NbGwssbGxXHnllaSnp7N06VI2bdrEt99+C4Cvry9t27YlPj6e5ORkOnbUztN2EQ8PYkePIiQpiZ3PPs/2qX/GKySE4MQEghMTiRw6GN9WdR7KVtXTmDFjmD17Nh988AFjx461O45STU6zK7TPaBsax1ODH2by18+zdN+3Wmg3IxsO5jH5s61syz5JQmQQb93dm2GJkXqmrBkRERITE0lMTMQYw9GjR9m/fz/79u0jLS2N9PR0vvrqK8aNG0dKSordcZu14MROXP7ayxxb9S0n0rZxatcu8tatJ2vufHrNmolvuA6/6QyHDh1i8uTJfPDBBwBcddVVNidSqmlqtoU2ODpPtQoMZ39+pt1RVCPZln2CW175DmPgf3+RzOiUGJ1WvZkTESIjI/H392f9+vXk5v7Yhb20tNTGZOoMD29vIocNJXLYUIqys9kw7mEqy8upLC2xO5pbKikpoUePHhw9epTf/va33HnnnQwePNjuWEo1Sc260Abw8vCivLLC7hiqkbQK8sVThHJjmLlsD98fyKNNWABxLf2tSwARQT56druZOX36NK+99hrp6ekATJw4kTZt2mh7bRd08P0PAejx7DP465TgTuHj48PRo0cBePTRR+nWrZvNiZRqupp9oY3WU81KZIgfS38/lMU7DrMi/Sj/3ZpDfmHZWdv4eXsQFeJHZLAvkWd+Bjt+RoX4ERniS2SwLy38vbUgb+Ly8/N5/fXX2bNnT9WyTp06ER8fb18odUHe1rjoWydPpevUyYR00Q7MDW3ChAlV17OysrTQVuoSaKGtmp024QGMHdiOsQMdw4WdLinnh7wisvIKycorIvN4IYdPlXDkZDE7sk+y7GQxBaU//dbDx8vDKsIdhXhUiC+JrUPo2SaUSp1C2iWVlZWRlZXFgQMH2L59O2lpaXh6ejJq1Cg6dOhAmzZtCAgIsDumuoD2940l8uqr2P6nv7B31mt0+8s0vEOC7Y7lNioqKqo6Bx87dozw8HCbEzU/p8ITWXaHzm5bV6dm3m93hAvSQls1e0G+XiS2Diax9fk/rAtKyjliFd9nivCjp0o4cqqEwyeL2XP0NN/uOcapEsc04H6eMKZwO0/doNMWuwJjDH/72984cOAAFRWOf5pCQ0O55pprGDhwIFFRUTYnVBcjqH07Ym8aTcbsd/h+7P20+fWviP35KLtjNXkFBQWMGDGC1NRUnnzySS2ybdK61PBkZrndMZqMSaWufWJLjIufeRORo8ABu3NcQARwzO4Q9VCX3G2NMQ0yflYjPI6u9ji4Wp5EY0yDnPZzodekqx1jaJxMTel1WZ2rPV6ukKepPpbn4wrH1Blqu18N9jgCiMgpYFdD7a8Buerje9bxF5EvcGStj2PGmOsaJpaVx9ULbVcnIt8bY3rbneNiNdXc5+Nq90fzOJ8r3idXzOQqXO3YuFoed+Cux7Sx75erHkdXzeXqtEu9UkoppZRSTqCFtlJKKaWUUk6ghfala6pdg5tq7vNxtfujeZzPFe+TK2ZyFa52bFwtjztw12Pa2PfLVY+jq+ZyadpGWymllFJKKSfQM9pKKaWUUko5gRbatRCRt0TkiIhsrbYsTEQWi8hu62dLa7mIyP+JyB4R2SIivWzKfJmIfCMi20Vkm4g82hRy10ZErhORXVbOP55j/d0iclRENlmX3zo5z0+eGzXWN+pxrUOeoSJyotrxedqZeZyltudBI+ao83tDc+Nqx+Zi3xPVxRMRTxHZKCKf252loYhIqIjMF5GdIrJDRPrbnUk1PVpo1+5toOaYin8EvjbGJABfW78DXA8kWJf7gVcaKWNN5cDvjTFdgH7AgyLSBdfPfV4i4gnMxJG1C/Ar6z7V9JExJsW6vOHkWG/z0+dGdY19XGvLA7Cy2vGZ5uQ8De4ingeN4W3q/t7Q3LyNax2bi31PVBfvUWCH3SEa2D+AL4wxSUAy7nf/VCPQQrsWxpgVwPEai0cD71jX3wF+Xm35u8YhFQgVkejGSfojY0yOMWaDdf0UjjeHWFw8dy36AHuMMfuMMaXAhzhy2+Y8z43qGvW41iGPO3CZ58FFvjc0K652bOrxnqgugojEATcAzj650WhEpAUwGHgTwBhTaozJtzeVaoq00K6fKGNMjnX9EHBm/uZYILPadlnWMtuISDzQE1hDE8p9DnXNeIvVTGO+iFzWONHOyxWPa38R2Swi/xWRrjZnqQ9XPKbVne81plzk2NTxPVFdnBeBPwCVdgdpQO2Ao8Bsq0nMGyISaHco1fRooX2JjGPYFpccukVEgoCPgfHGmJPV17ly7kuwCIg3xvQAFvPjmSo6AiFoAAAJIElEQVTlsAHHVLXJwEvAZzbncWtu+hprEHYdm2b4nuh0InIjcMQYs97uLA3MC+gFvGKM6QkU4CZNi0SkolpfnU3WP58Xc/vfichdzknnfrTQrp/DZ5oAWD+PWMt/AKqfRY2zljU6EfHG8YEyxxjzibXY5XNfQK0ZjTG5xpgS69c3gMsbKdv5uNRxNcacNMactq7/B/AWkQi78tSTSx3Tczjfa0zZfGwu8j1R1d0AYJSIZOBoynWViPzL3kgNIgvIMsassX6fj6PwdgdF1frqpBhjMi7mxsaYWcaYd52Uze1ooV0/C4HfWNd/Ayyotvwua7SJfsCJal9LNhoRERztynYYY/5ebZVL567FOiBBRNqJiA9wO47cVWq0fx6F/R1XXOq4ikhr67mBiPTB8frPtStPPdX6PLDZ+V5jysZjU4/3RFVHxpiJxpg4Y0w8jtfjUmPMnTbHumTGmENApogkWouuBrbbGMlpRCRIRL4WkQ0ikiYio6utu8tqjrlZRN6zlk0VkSes6x1E5AsRWS8iK0UkyVr+CxHZat1uhT33zEUYY/RygQvwAZADlOH4D3csEI6jh/puYAkQZm0rOEZE2AukAb1tyjwQx1egW4BN1mWkq+euw/0aCaRbOZ+ylk0DRlnX/wpsAzYD3wBJNjw3fgf8zo7jWoc8D1U7PqnAlXY/pg31PLApR53fG5rbxdWOzcW+J+ql3sd5KPC53Tka8P6kAN9bz5vPgJZ2Z2qg+1VR7XXwKY5mMiHWughgj/X51dV6r42w1p2pGaYCT1jXvwYSrOt9cfyjhfWZF2tdD7X7Ptt50ZkhlVJKKaWaCRE5bYwJqva7N/ACjlFWKoFEHJ1BfwG0NsY8VeP2U4HTwCwcHUZ3VVvta4zpLCKzgA7AXOATY0xT+/a0wXjZHUAppZRSStnm10Ar4HJjTJnV3t6vDrfzAPKNMSk1VxhjficifXEM+7heRC5vrsW2ttFWSimllGq+WuAYOaZMRIYBba3lS4FfiEg4OGZSrX4j4xi5Z7+I/MJaLyKSbF3vYIxZY4x5GsdZb7uH27WNFtpKKaWUUs3XHKC3iKQBdwE7AYwx24BngOUishn4+zlu+2tgrLV+Gz9OIPa81bFyK/Adjr5BzZK20VZKKaWUUsoJ9Iy2UkoppZRSTqCFdgMRkSgReV9E9lnjSa4WkZtEZKiInLBmX9opIjPszqpUU1FtBrNt1nisvxeRC75viUi89XUlIpIiIiPr8Heqv053iMiUass/r7Ht2yJyq3V9mYj0rv89VNUe460iMk9EAqzlp2tsd7eI/NO6Xn0c37dF5AcR8bV+j7A6c515LhSJYwrtHSKyVkTubsz7p852gce75myFbjELo1JaaDcAazKEz4AVxpj2xpjLcQzcH2dtstLqldsTuFFEBtgU1a2d+WCuXmjVWP+2iOy3CrZ0EXlXROJ+uqezbpMh9Zw90Rqwf5uIVNYsxkRkoojsEZFdInJtffbfTJyZwawrMBy4HphyEbdPwTFecl2ceZ32Bu4UEXeZBc7VnXmMuwGlOMZ+v1gVwL3nWbfXGNPTGNMZx/vyeBG5p55Z1aU73+Ndc7bCZ23MqFSD0UK7YVwFlBpjZp1ZYIw5YIx5qfpGxpgiHAPExzZyPvWjCcaYZBzjhG4ElopjhkFn2ArcDJw1K5aIdMHxgd8VuA54WUQ8nZTBbRhjjgD3Aw9Zvds9ReR5EVknjpnLHqi+vfW4TgNus86Q3SYifaxvmzaKyHfy46xv1f9OAbAe6NgY90udZSX1O+4vAo+JyAWHrDXG7AMeBx6px99QDa++j7dSTYYW2g2jK7Chto1EpCWQQI3CSzU+4/ACcAjHWdJaicjj1tedW0VkfLXlk60z06tE5IMzX2kbY3YYY3adY1ejgQ+NMSXGmP04ZuHqc+n3yv1ZhZInEIljtsETxpgrgCuA+0SkXbVtS4GngY+sM2Qf4ehNP8gY09NaN73m3xDHUFb9cPSgBxhU/SttYJTz7mHzZRXJ1+OYUQ7Av8Zxn3aBmx8EVgFj6vCnNgBJlxRWXbLaHm8Ruc3GeEo1GJ2wxglEZCaOKX9LgQk4Pqg34yiyXzTGHLIznzrLmQ/dBRfaSEQuB+7BMcWsAGtEZDmO19AtQDLgbe1vfS1/MxbHFOhnZKHfctTHCKDHmfbSOMaCTcAxZfD5tADeEZEEHFNye1dbN0hENuKYGe1ZY8w2ERmKo0nJjWc2EpG3G+4uKKwCy7q+EnjTul5UfSIMq231hdrD/xXH6/jftfw9qWdO1TDq9Hgr5S600G4Y23AUWwAYYx602vV+by1aaYy50Trblioic40xm861I9Xo6vqhOxD41GpWgIh8AgzC8a3QAmNMMVAsIoucE1MBiEh7HO1xj+B47B42xnxZY5v4C+ziz8A3xpibrO2WVVt3VkGtGk2DFFjGmN1WAffLWjbtCey41L+n6k0LatWsaNORhrEU8BORcdWWBdTcyGom8CzwZGMFU7Wy40P3B86eJSvOWqYuQERaAbOAfxrHBABfAuNExNta30lEAmvc7BQQXO33Fvx4rO92bmJlg2eAJ8630vrnagbw0vm2UUqphqSFdgOwPvR/DgyxRrVYC7zDuQvqWcDgWs66KSezOtM9AkQDX9ThJiuBn4tIgFXM3WQt+xb4mYj4iUgQUJczoguB20XE1/qWIwFYW6874v7OtNvcBiwBvgL+ZK17A9gObLBGmXmVn35L9w3QpVqbz78Bf7WaiOg3em7GmsmuZn+ZDlbn1x3AXOD/jDGzGz+dqkXNNto66ohyCzozpHIbInLaGBNk/ROzGzhcbfVjwA3AEOAkjm8cUoGJxpisC+wzA+htjDkmIo/z4xBibxhjXrS2mQrcYf29I8AXxpjXReQmHGfOWgH5wCZjzLXWbZ6y9lUOjDfG/PeSD4BSSimlXIoW2kpdIhEJMsacFsfECyuA+40xtY5Co5RSSin3pl+dKnXpXrPGxvYD3tEiWymllFKgZ7SVAkBE1gC+NRaPMcaknWt7pZRSSqnaaKGtlFJKKaWUE+ioI0oppZRSSjmBFtpKKaWUUko5gRbaSimllFJKOYEW2koppZRSSjmBFtpKKaWUUko5wf8HqXfa6At8pGUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJXKnrveRixq"
      },
      "source": [
        "#well_data.to_csv('drive/My Drive/seminar/predict_facies.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5gPEjwZShPw"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}