TransferModel(VGG16)_whurs__aug40_SAT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amFarrouk/Lawnchair/blob/alpha/TransferModel(VGG16)_whurs__aug40_SAT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "o5U-ektid3b9"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import zipfile\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzfGg6D7WxLP",
        "outputId": "bfb2fbd7-8dfe-43c9-e780-092f887acda1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "EhL9SToEeYvi"
      },
      "outputs": [],
      "source": [
        "#!wget https://doc-08-8s-docs.googleusercontent.com/docs/securesc/78sd4ofia6ilotkrk7b0jldtpiipsguk/i69g6lef4t465jmm1s12ks60dls6iap0/1648246800000/06583234309955973519/06583234309955973519/1NXjzQcE_rmmoZZ0PgUi62oRIgvuK-YVy?e=download&ax=ACxEAsZcpTXMsN1TvbxLuR1bu5kS2sr9PinWowrlidm-hqrmNTVRTF8ZVFGam5nqTVeV1Yx08vanLNIVrfyFOeKpkKmyNlJyjm7mRAhEjwPvSRfWJBZ4TRqhiyP_y3YoiOedAukMaTN-WkSVpTGDlZeeUUslXf4myB277BHUitV3W47Qq_5H-yZD3g-X234BedDg0WL4NNU-sqjStlQwZMajVLbo-ZfQz51WKYHc2l2vva07bGvUMoo5Y0kI0V8MJXZrYOSmx7WkuVh-KW8SGPLbXAIxI4l4ubE6Gm0t6RvoQap3p1gA89EDCvNziat8a3epxVujrwldmsSzgm5TN64xzDBR-RkofBVmCmeisL2COThvjlB2oMbP7jvuUJaXKJ9P8Podc-SUsvXmrdl9baULqmqTUi_V2Oq_FiCk20KioZxZ2Pua4Lfw58o5Wf6BPxWRqU5df_idDDzhXMfvWJJV0jmmABRDymd7Rlnb4ydQ1YfTpNH-JZIDUT005ThPLwduUVJVG9J5wn1nwkzifjYNju3S8W1ooqrCkO1Ezz5msSzsddgLMZktcxYUtaMD2Rx_Vp9jdAWo7tUDuwyYByGezCc31rv2oClRBQnISoXyL28gNOX5tIAGDY3LZQo7_tDNTo_EmLQGgiyO7Pz5FQztdQe3w7CyVE7EDnpLR9v12y_Dnp-3Udj2YqH1I4LaE3f8_f8ChaB84H8-rHjBCtkBbDa2fJ9yzR6MwnJuyCvEpf1EFs_2eO-D7Zs1RSo65Aaw_dCdvVGdrwdT2ooY7khPG1PlMsGNTZd7lGPV0neWHIvClKUfx6tLm9M&authuser=2 -O ./UCMerced_LandUse.zip\n",
        "!mkdir WHURS19/\n",
        "!unzip -qq ./drive/MyDrive/MemoireM2/Scripts/dataSet_WHU19_aug40.zip -d ./WHURS19\n",
        "#!rm ./UCMerc.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "HpXdadrKeaaS"
      },
      "outputs": [],
      "source": [
        "img1=cv2.imread(\"./WHURS19/airport_24.jpg\",cv2.IMREAD_COLOR)\n",
        "img1=cv2.resize(img1,(256,256))\n",
        "img2=cv2.imread(\"./WHURS19/Farmland-06.jpg\",cv2.IMREAD_COLOR)\n",
        "img2=cv2.resize(img2,(256,256))\n",
        "img3=cv2.imread(\"./WHURS19/Mountain_01.jpg\",cv2.IMREAD_COLOR)\n",
        "img3=cv2.resize(img3,(256,256))\n",
        "img4=cv2.imread(\"./WHURS19/Park_41.jpg\",cv2.IMREAD_COLOR)\n",
        "img4=cv2.resize(img4,(256,256))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "S2YRyn8Wedif",
        "outputId": "bd4729e4-688e-4aee-fa4e-b90d338b8dd7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axarr = plt.subplots(2,2)\n",
        "fig.tight_layout()\n",
        "\n",
        "axarr[0,0].imshow(img1)\n",
        "axarr[0,0].set_xlabel(\"Airport\")\n",
        "axarr[0,1].imshow(img2)\n",
        "axarr[0,1].set_xlabel(\"Farm\")\n",
        "axarr[1,0].imshow(img3)\n",
        "axarr[1,0].set_xlabel(\"Mountain\")\n",
        "axarr[1,1].imshow(img4)\n",
        "axarr[1,1].set_xlabel(\"Park\")\n",
        "\n",
        "plt.savefig(\"whurs_sum.png\")\n",
        "pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Dz86oIkTefyz"
      },
      "outputs": [],
      "source": [
        "def load_images_names_from_folder(folder):\n",
        "    images = []\n",
        "    for filename in os.listdir(folder):\n",
        "        if filename is not None:\n",
        "            images.append(filename)\n",
        "    return images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "nXTMG4fLM8F3"
      },
      "outputs": [],
      "source": [
        "def split_lambda(x):\n",
        "  if \"_\" in x:\n",
        "    return x.split(\"_\")[0]\n",
        "  elif \"-\" in x:\n",
        "    return x.split(\"-\")[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "zHN-iEmWeieX"
      },
      "outputs": [],
      "source": [
        "whurs_list=load_images_names_from_folder(\"./WHURS19\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "hv1-QSPVekLE"
      },
      "outputs": [],
      "source": [
        "whurs_series=pd.Series(whurs_list)\n",
        "whurs_df=pd.DataFrame(whurs_series,columns=[\"img\"])\n",
        "whurs_df=whurs_df.sort_values(\"img\")\n",
        "\n",
        "whurs_df[\"label\"]=whurs_df[\"img\"].apply(lambda x : split_lambda(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "rSxjDnoMel0j"
      },
      "outputs": [],
      "source": [
        "whurs_df.to_csv(\"whurs19.csv\",header=False,index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "rBEm6WE5enY8"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "iSWD-vnzepJ6",
        "outputId": "ecd5a45b-6eb3-4db4-b0ed-6b031bc4978d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_data:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       img          label\n",
              "915   footballField_19.jpg  footballField\n",
              "1038      meadow_28gen.jpg         meadow\n",
              "723         Park_49gen.jpg           Park\n",
              "175        Farmland-42.jpg       Farmland\n",
              "480     residential_54.jpg    residential"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8522aab9-8f91-447a-bb3a-82312c39b577\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>img</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>footballField_19.jpg</td>\n",
              "      <td>footballField</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1038</th>\n",
              "      <td>meadow_28gen.jpg</td>\n",
              "      <td>meadow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>723</th>\n",
              "      <td>Park_49gen.jpg</td>\n",
              "      <td>Park</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Farmland-42.jpg</td>\n",
              "      <td>Farmland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>480</th>\n",
              "      <td>residential_54.jpg</td>\n",
              "      <td>residential</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8522aab9-8f91-447a-bb3a-82312c39b577')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8522aab9-8f91-447a-bb3a-82312c39b577 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8522aab9-8f91-447a-bb3a-82312c39b577');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "all_x,all_y=whurs_df[\"img\"],whurs_df[\"label\"]\n",
        "\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(all_x,all_y,train_size=0.8,random_state=1,shuffle=True,stratify=all_y)\n",
        "\n",
        "train_df=pd.DataFrame(xtrain,columns=[\"img\"])\n",
        "train_df[\"label\"]=ytrain\n",
        "\n",
        "print(\"train_data:\")\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "h5ZKaSoOeq_U",
        "outputId": "53745f97-5f3a-4b1b-b3d0-29a16f49ebbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_data:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     img        label\n",
              "1264     Mountain_36.jpg     Mountain\n",
              "157          Park_49.jpg         Park\n",
              "874    industrial_29.jpg   industrial\n",
              "706   residential_06.jpg  residential\n",
              "233          pond_46.jpg         pond"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66dadfa1-97f5-4cde-9d8f-76bd5953a632\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>img</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>Mountain_36.jpg</td>\n",
              "      <td>Mountain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Park_49.jpg</td>\n",
              "      <td>Park</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>874</th>\n",
              "      <td>industrial_29.jpg</td>\n",
              "      <td>industrial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>residential_06.jpg</td>\n",
              "      <td>residential</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>pond_46.jpg</td>\n",
              "      <td>pond</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66dadfa1-97f5-4cde-9d8f-76bd5953a632')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-66dadfa1-97f5-4cde-9d8f-76bd5953a632 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-66dadfa1-97f5-4cde-9d8f-76bd5953a632');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "test_df=pd.DataFrame(xtest,columns=[\"img\"])\n",
        "test_df[\"label\"]=ytest\n",
        "\n",
        "print(\"test_data:\")\n",
        "test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "GaCHQ2SFesyC"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Q_DFMh2peunW"
      },
      "outputs": [],
      "source": [
        "train_image_gen=ImageDataGenerator(rescale=1/255.0)\n",
        "test_image_gen=ImageDataGenerator(rescale=1/255.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvBGRNlpewxN",
        "outputId": "064d15b1-88e6-4920-9d13-e07ba060b3a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1125 validated image filenames belonging to 19 classes.\n"
          ]
        }
      ],
      "source": [
        "train_image_gened=train_image_gen.flow_from_dataframe(train_df,x_col=\"img\",y_col=\"label\",\n",
        "                                            directory=\"./WHURS19\",\n",
        "                                            batch_size=32,\n",
        "                                            target_size=(256,256),\n",
        "                                            color_mode='rgb',\n",
        "                                            class_mode='sparse')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzkTwxwmeyo5",
        "outputId": "8e021b6a-d56e-4740-dbb4-bce609fbd4b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 282 validated image filenames belonging to 19 classes.\n"
          ]
        }
      ],
      "source": [
        "test_image_gened=test_image_gen.flow_from_dataframe(test_df,x_col=\"img\",y_col=\"label\",\n",
        "                                            directory=\"./WHURS19\",\n",
        "                                            batch_size=32,\n",
        "                                            target_size=(256,256),\n",
        "                                            color_mode='rgb',\n",
        "                                            class_mode='sparse',\n",
        "                                            shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "B-VWdGEre0KB",
        "outputId": "7fe83f98-7d36-4606-8424-7fdb39aaa718"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.8.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "uWiY_cJXe1jC"
      },
      "outputs": [],
      "source": [
        "import tensorflow.keras.models as models\n",
        "from tensorflow.keras.layers import Flatten,Dense,Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N264737KgAX",
        "outputId": "668abb61-26e2-4417-c6a6-e6bfecab892b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n",
            "58900480/58889256 [==============================] - 0s 0us/step\n",
            "input_1 is trainable :  False\n",
            "block1_conv1 is trainable :  False\n",
            "block1_conv2 is trainable :  False\n",
            "block1_pool is trainable :  False\n",
            "block2_conv1 is trainable :  False\n",
            "block2_conv2 is trainable :  False\n",
            "block2_pool is trainable :  False\n",
            "block3_conv1 is trainable :  False\n",
            "block3_conv2 is trainable :  False\n",
            "block3_conv3 is trainable :  False\n",
            "block3_pool is trainable :  False\n",
            "block4_conv1 is trainable :  False\n",
            "block4_conv2 is trainable :  False\n",
            "block4_conv3 is trainable :  False\n",
            "block4_pool is trainable :  False\n",
            "block5_conv1 is trainable :  True\n",
            "block5_conv2 is trainable :  True\n",
            "block5_conv3 is trainable :  True\n",
            "block5_pool is trainable :  True\n"
          ]
        }
      ],
      "source": [
        "pretrained_model= tf.keras.applications.VGG16(include_top=False,weights='imagenet',classes=19,input_shape=(256,256,3))\n",
        "for layer in pretrained_model.layers[:-4]:\n",
        "        layer.trainable=False\n",
        "\n",
        "for layer in pretrained_model.layers:\n",
        "  print(layer.name,\"is trainable : \",layer.trainable)\n",
        "#pretrained_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "4lHynWTujEck"
      },
      "outputs": [],
      "source": [
        "#building costum model\n",
        "x = Flatten()(pretrained_model.output)\n",
        "x = Dense(128, activation=tf.keras.activations.selu)(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(128, activation=tf.keras.activations.selu)(x)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(19, activation=tf.keras.activations.softmax)(x)\n",
        "\n",
        "model=tf.keras.models.Model(inputs=pretrained_model.input, outputs=predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "cnyar3dxgbFw"
      },
      "outputs": [],
      "source": [
        "#Loss and Optimizer\n",
        "loss=tf.keras.losses.sparse_categorical_crossentropy\n",
        "optim=tf.keras.optimizers.Adamax(learning_rate=1e-4)\n",
        "#CallBacks\n",
        "#earlyStopCall=tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\",patience=5)\n",
        "lrCall = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4*10**(epoch/20))\n",
        "#model compilation\n",
        "model.compile(optimizer=optim, loss=loss, metrics=['accuracy'])\n",
        "\n",
        "#_=tf.keras.utils.plot_model(\n",
        "#    model, to_file='./transferModel_whurs_SAT.png', show_shapes=True, show_layer_names=False)\n",
        "#model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyqsfX4zkMB7",
        "outputId": "d41132e8-0bdd-49fa-d692-cb627432933a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "35/35 [==============================] - 12s 336ms/step - loss: 0.1469 - accuracy: 0.9652 - val_loss: 0.1350 - val_accuracy: 0.9610 - lr: 1.0000e-04\n",
            "Epoch 2/10\n",
            "35/35 [==============================] - 12s 351ms/step - loss: 0.1293 - accuracy: 0.9671 - val_loss: 0.1311 - val_accuracy: 0.9645 - lr: 1.1220e-04\n",
            "Epoch 3/10\n",
            "35/35 [==============================] - 12s 333ms/step - loss: 0.1366 - accuracy: 0.9643 - val_loss: 0.1162 - val_accuracy: 0.9752 - lr: 1.2589e-04\n",
            "Epoch 4/10\n",
            "35/35 [==============================] - 12s 330ms/step - loss: 0.1032 - accuracy: 0.9780 - val_loss: 0.1310 - val_accuracy: 0.9610 - lr: 1.4125e-04\n",
            "Epoch 5/10\n",
            "35/35 [==============================] - 12s 326ms/step - loss: 0.0914 - accuracy: 0.9790 - val_loss: 0.1042 - val_accuracy: 0.9716 - lr: 1.5849e-04\n",
            "Epoch 6/10\n",
            "35/35 [==============================] - 12s 326ms/step - loss: 0.0958 - accuracy: 0.9707 - val_loss: 0.1263 - val_accuracy: 0.9645 - lr: 1.7783e-04\n",
            "Epoch 7/10\n",
            "35/35 [==============================] - 12s 328ms/step - loss: 0.0719 - accuracy: 0.9844 - val_loss: 0.1404 - val_accuracy: 0.9574 - lr: 1.9953e-04\n",
            "Epoch 8/10\n",
            "35/35 [==============================] - 11s 326ms/step - loss: 0.0768 - accuracy: 0.9799 - val_loss: 0.1334 - val_accuracy: 0.9610 - lr: 2.2387e-04\n",
            "Epoch 9/10\n",
            "35/35 [==============================] - 12s 326ms/step - loss: 0.0930 - accuracy: 0.9744 - val_loss: 0.2240 - val_accuracy: 0.9326 - lr: 2.5119e-04\n",
            "Epoch 10/10\n",
            "35/35 [==============================] - 11s 322ms/step - loss: 0.0841 - accuracy: 0.9808 - val_loss: 0.1285 - val_accuracy: 0.9433 - lr: 2.8184e-04\n"
          ]
        }
      ],
      "source": [
        "#defining best learing rate\n",
        "history=model.fit(train_image_gened,epochs=10,steps_per_epoch=35,validation_data=test_image_gened,callbacks=[lrCall])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "IhMCXr37ayeK",
        "outputId": "c14d72f2-8275-4d7b-8913-1025f30986dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "#plt.axis([1e-4, 3e-4, 0, 3])\n",
        "#plt.savefig(\"LR_TransferModelVGG16_whurs_aug40_SAT.png\")\n",
        "#!mv ./LR_TransferModelVGG16_whurs_aug40_SAT.png ./drive/MyDrive/MemoireM2/Scripts/screens/LR_TransferModelVGG16_whurs_aug40_SAT.png"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ior15Qc7dBYv",
        "outputId": "557df0dc-a902-448f-9efd-1a0011adc9b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 256, 256, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 256, 256, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 128, 128, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 128, 128, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 128, 128, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 64, 64, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 64, 64, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 64, 64, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 64, 64, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 32, 32, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 32, 32, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 32, 32, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 32, 32, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 16, 16, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 8, 8, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 32768)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               4194432   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 19)                2451      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18,928,083\n",
            "Trainable params: 11,292,819\n",
            "Non-trainable params: 7,635,264\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#Loss and Optimizer\n",
        "loss=tf.keras.losses.sparse_categorical_crossentropy\n",
        "optim=tf.keras.optimizers.Adamax(learning_rate=2.2387e-04)\n",
        "#CallBacks\n",
        "earlyStopCall=tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\",patience=5)\n",
        "\n",
        "bestStopCall=tf.keras.callbacks.ModelCheckpoint(filepath=r\"./checkpoint.h5\", monitor='val_accuracy',\n",
        "                                                verbose=1,\n",
        "                                                save_weights_only=True,\n",
        "                                                mode='max',\n",
        "                                                save_best_only=True)\n",
        "#lrCall = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4*10**(epoch/20))\n",
        "#model compilation\n",
        "model.compile(optimizer=optim, loss=loss, metrics=['accuracy'])\n",
        "\n",
        "#_=tf.keras.utils.plot_model(\n",
        "#    model, to_file='./transferModel_whurs_SAT.png', show_shapes=True, show_layer_names=False)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pb1sKAGPdKi0",
        "outputId": "838b1973-b154-44e0-c1b7-24f53799f116"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0204 - accuracy: 0.9945\n",
            "Epoch 1: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 327ms/step - loss: 0.0204 - accuracy: 0.9945 - val_loss: 0.1428 - val_accuracy: 0.9716\n",
            "Epoch 2/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0135 - accuracy: 0.9954\n",
            "Epoch 2: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 11s 325ms/step - loss: 0.0135 - accuracy: 0.9954 - val_loss: 0.1633 - val_accuracy: 0.9539\n",
            "Epoch 3/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0120 - accuracy: 0.9973\n",
            "Epoch 3: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 332ms/step - loss: 0.0120 - accuracy: 0.9973 - val_loss: 0.1395 - val_accuracy: 0.9645\n",
            "Epoch 4/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0246 - accuracy: 0.9927\n",
            "Epoch 4: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 354ms/step - loss: 0.0246 - accuracy: 0.9927 - val_loss: 0.1403 - val_accuracy: 0.9681\n",
            "Epoch 5/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0174 - accuracy: 0.9954\n",
            "Epoch 5: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 329ms/step - loss: 0.0174 - accuracy: 0.9954 - val_loss: 0.1479 - val_accuracy: 0.9716\n",
            "Epoch 6/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0294 - accuracy: 0.9945\n",
            "Epoch 6: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 326ms/step - loss: 0.0294 - accuracy: 0.9945 - val_loss: 0.1357 - val_accuracy: 0.9752\n",
            "Epoch 7/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0170 - accuracy: 0.9973\n",
            "Epoch 7: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 328ms/step - loss: 0.0170 - accuracy: 0.9973 - val_loss: 0.1387 - val_accuracy: 0.9645\n",
            "Epoch 8/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0103 - accuracy: 0.9982\n",
            "Epoch 8: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 327ms/step - loss: 0.0103 - accuracy: 0.9982 - val_loss: 0.1368 - val_accuracy: 0.9716\n",
            "Epoch 9/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0176 - accuracy: 0.9936\n",
            "Epoch 9: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 329ms/step - loss: 0.0176 - accuracy: 0.9936 - val_loss: 0.2263 - val_accuracy: 0.9504\n",
            "Epoch 10/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0165 - accuracy: 0.9963\n",
            "Epoch 10: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 327ms/step - loss: 0.0165 - accuracy: 0.9963 - val_loss: 0.1419 - val_accuracy: 0.9681\n",
            "Epoch 11/50\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0161 - accuracy: 0.9954\n",
            "Epoch 11: val_accuracy did not improve from 0.97518\n",
            "35/35 [==============================] - 12s 331ms/step - loss: 0.0161 - accuracy: 0.9954 - val_loss: 0.1869 - val_accuracy: 0.9645\n"
          ]
        }
      ],
      "source": [
        "history=model.fit(train_image_gened,epochs=50,steps_per_epoch=35,validation_data=test_image_gened,callbacks=[bestStopCall,earlyStopCall])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "SwP-G9hlydUh",
        "outputId": "ef2c11cf-55f5-4680-d895-71b9bc8f2aaf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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5dx8d2XY+cAXQEVgCnOPun5qZAz3dfUFkvyeApe5+vZkdDTwN/B9wOfAWcAnwFDCIEOCnAhe4+9LI81sBd0bqawK86+7/bWZfAtdGruaNmTUEVgAnuPvMRL1XIiLpyMwWAee5+z9j1jUC/gD8JLLqReAad99uZq2BJ4B8oBiYDQx192Izu4bw2d0SWA5c6O5vJ+tnkfQ2a9asRf369Vud6jrqklmzZrXu169fl9h16pol6eRnwDOR24lm1tbMMoHXgcVAF6AD8DyAmf0PMDbyvJaEVpQ1cb7W/kAr4ABgFOH/yuOR5c7AVuC+mP2fApoCBwNtgLsj6ycA58TsdzKwQiFERCRu1wGHAz8A+gEDgesj264ElgI5QFvgfwE3swOB0cBh7t6C8CXRouSWLSKVUdcsSQtmlk8IAS+6+2oz+wY4C/gIaA9cHdN96oPI/XnA7e7+SWR5QRVeshi4yd23R5a3Aq/E1PN7YErkcTvgJGA/dy+M7PJu5P5p4AYza+nuG4CRhNAiIiLxORu42N1XAZjZzcB44AZgJ9AOOCDSwv1+ZJ8ioBGQa2YF7r4oFYWLSMXUIiLp4lzgH+4ebSZ9NrKuE7C4nDEcnYBvqvl6Be7+fd9LM2tqZuPNbLGZbQDeA/aJtMh0AtbGhJDvuftyQjeu081sH0JgeaaaNYmI1EftCa3eUYsj6wD+SPiS6R9mttDMxgBEQsllhFbxVWb2vJm1R0RqFQURqfXMrAmhb/BQM1tpZisJYzf6Af8BOptZWa17S4Du5Rx2C6ErVdT+pbaXHjx1JXAgMMjdWwJHRcuLvE6rSNAoy5OE7ln/A3zo7svK2U9ERPa0nNAiHtU5sg533+juV7p7N0L32yvM7LjItmfdPdqa7oRxJiLxKi4uLo77CuFSsch7WVx6vYKIpIP/BoqAXEIf4R8ABxGa4P+bMPj7NjNrZmaNzWxI5HmPAFeZ2QALephZ9I/ZZ8BZZpZpZsOAoZXU0ILQPWtdZGD6TdEN7r4CeAN4wMz2NbOGZnZUzHP/CvQHLiWMGRERkfI1jHyWNzazxsBzwPVmlhMZnH4jodsrZnZK5LPdCDMqFgHFZnagmR0bGei+jfD5vcdJkEgFviwoKMhWGNl7xcXFVlBQkA18WXqbxohIOjgXeNzdv4tdaWb3AfcSBi7eC3xH+NbrWWCqu79kZvtFljsQBiqOJDTrX0poqbiIEBT+WkkN90SOs5rwTdydhBAUNZIwQP0rIIswfuQ9AHffamavAGcCr1b5pxcRqV8mlVq+E5gOfB5Zfgm4JfK4J2HikBygEHjA3aeYWV/gNsKXVjuBfxMmHhGJy65du85buXLlIytXruyDvrjfW8XAl7t27Tqv9AZN3yuSBJGph3u5+zmV7iwiIiJSD6hFRCTBIl25fkVoNREREREREtjUZGaPmdmqyAXdytpuZnavmS0ws8/NrH+iahFJlcgFFZcAb7j7e6muR6Qi+twWEZFkSmSftyeAYRVsP4nQt7Mnod/mgwmsRSQl3P3P7t7M3S9IdS0icXgCfW6LiEiSJCyIRL79XVvBLqcCEzz4iHBNhnaJqkdERCqmz20REUmmVM4C0IHQZSVqaWSdiIjUTvrcFhGRGpMWg9XNbBSRafeaNWs2oHfv3imuSKTqiopgxw7YubPk5g6ZmZCREe7Le2w1MIt5cXGoofR96XW1cSI9s/BeRO9jb7Hrytpem8yYMWO1u+ekuo5E02e2iNQV9eVzO1VSGUSWAZ1iljtG1u3B3R8GHgbIy8vz6dOnJ746kTi4w8aNsHx5yW3Fit2Xo7dt26r/Og0bQosW5d8aN4ZNm0ItsbfYdcVxXsqrJkJPTdrbYNSgATRpEt6jrKya+/nuvRd+/OOqPcfMFtfMq6dMXJ/b+swWkbqiDnxu12qpDCITgdFm9jwwCFgfuUK1SI3ZvBmWLIHvvoO1a2HXrnDbubPkcenlyrZt3w4rV5YEjM2b93zdZs2gQwdo3x4OPxzatQuPY2/t2oWAUTo8VHSLDRbr18PSpeHxtm3hNaPBpFUrOOCAisNLWbfmzUMLTG2zc2f4GbduDbfo47LWVbR9x46aq6ld/RwZoc9tERGpMQkLImb2HHA00NrMlgI3AQ0B3P0hwpVTTwYWAFuAXySqFqmbioth1SpYvDgEjdhbdN2aNdU7dsOG4dagQcktupyVBfvvD/37wymnlB0yWrSI/7X22y/cpHzR30dV3lepOn1ui4hIMiUsiLj7mZVsd+CiRL2+VK68b5kr+8Y5nnXFxaErTLRLTOx9PI+j9w0bhtaH0gHju+9CS0fpb7hbtAgtAZ07w6BB4T663KrVngGjrLBR28YViCSLPrdFRCSZ0mKwel1XXLznCX11uqFU9XFRUfVrjva7Ly9omIXXKCws+/W3b6/6a2ZkhNaGzp3hsMPgjDPC49jbPvtU/2cSERERkeRREEmyNWvgySfh8cfDN/pbt+5dv3WzilsWsrNDN6LKWh6qsr1x4xBE9kY0fFUWuHbsgLZtQ6tG+/ahxUJERERE0p+CSBK4w9SpMH48vPRSaA0YPBh+/vPqdVcq3XWpts1yFI+MDGjaNNykioqKwkj1wkJYty7cb9kCLVvCvvuG2z77hH5q6fiPQ0REROoFBZEEWrcOnnoqBJDZs8N54vnnw69/DX36pLq6embr1nDfpElq64i1eXOY9qqwsOQWDRYVLa9fH9/xMzNDIIkGk2hIiWc5Ozv102e5h+ax9etDTY0apbYeERERqVEKIjXMHT7+OISP558P57+HHQaPPgo//WmYYlUSbONG+OwzmDEDPv003ObODSfWRxwBxx4Lxx0XRrMns6/X9u3w0Ufwr3/B22/DtGlhTuCyNGmyezDo2BEOOaT84NCkCWzYsGd4KR1kvvuu5PHOnRXXG21hqSi0lPc4GhqKi8Pvo6x64nkc7bdoFuZD7toVunXb/b5r1zB1mWYZEBERSSsKIjVk40Z49ll46KFwDtysGYwcGVo/+vdPdXV1WGFhSdiI3r7+uuQqeO3awYABcPrp4dv1t9+Gm2+GsWPDL+moo0IoOe446Nu3Zk9mi4rCP4a33w63998PyTQjA/Ly4Oqr4eCDdz/Jj57QJ/rbf/fQnat0UIkNAqW3ff11yeMtWyo+fuPG4bZhQ8VXUszI2DPodOq0+3vRsiUUFMC338LCheG9XLZs9ysdNmoEXbrsGVKi99nZNfK2iYiISM1RENlLM2eG1o9nngkXm+vXDx58EM46K5w/SQ1atWrP0PHttyXbDzggpL6RI8P9oYeWfdW5tWvhnXdKWiauuiqsb90ajjmmpMWkR4+qjbFwh3nzSo47ZUo4aQfIzYXzzgvHHTo09dN7mYUgFr3yYlXt2FF+YIkub9tWedew6o5j2b49zOW8cGH4NxANKd9+Cx9+GGqIte++IZR07Fh28CtruXHjqtclIiIicTOP/VYxDeTl5fn06dNTWsPmzfDCCyGAfPxx6BXz05/CBRfAwIFpOD44eoL/+eeh+1J5J2ctW9b8D+deMs9vWSe0BQWhrk8/DeMponr0CGEj9lbdqwIuW1YSHt5+u+R1OnUqaS059tgwbVdpS5eWPO9f/wrHghCKos879th6exnulCksLAkosSFl2bKSf1ubNlV8jMaNKw4qZ5wRWtGqwMxmuHveXvxkaac2fGaLiFRXffzcTia1iFRBYSHceCNMmBB6nOTmwp/+FL6A33ffVFcXB3dYvnz3FoUZM0pOnisT7UYTz8Dnhg0rH3wdfVzR/MVm0Lt3aEXo3z90s/rBD2q2q02HDuGXOHJkeI++/rokXEycCE88EfY76KAQLn7wg/C+vf02zJ8ftrVuXdKSctxx4dv3tEukdUj032JF/SJ37qy8VSd2ecUKmDMnLK9fH7rVVTGIiIiISAkFkSq44AJ49dXQ+vHrX0N+/l6ca27ZEvrON2uWmBNW99B1pXToWLUqbI+e4B99dEmLQr9+IWxUNNi59HLsrE/lBYrKxgFU9K1zdvbeX7SkKsygV69w+81vwu/os89KWkweeyz87po3D+HoggtC8OjTR4Ol003DhpCTE25VVVxc8dgXERERqZSCSJz+/nd48UW45Ra47rpqHmTtWvjb3+CVV+Ctt8KJe4MGVZ9aNfYkPSMjnBB9883ugePTT0vGJ2Rmhm9vTz5599DRvHnZdWZnh65FVRHtYhUNKjt3ltSbztezyMgoec+uuir8zr75JnQN09UV66+MDAVPERGRvaQgEodNm+DCC8O5/NVXV/HJq1bBX/8KL78cBi/v2hVO8i+6KFzyvHQrw9q1oT97dLmoqPxjm4VxG0VFJf3ds7LCNK9nnBG6MfXvH5YTPfDWrOQKhWWNpagrsrJCFy0RERER2SsKInG48cZw+YWpU8N5aKWWL4e//CWEj/feCy0WPXrAlVeWBIR4Wgjcw8j4ysZYQBi30L9/GLgSV5EiIiIiIqmjIFKJGTPCgPTf/AYGD65gx+++CwNIXn4Z/v3vECIOOij04zr99DCotardk8xC96nmzcOYChERERGROkJBpAK7dsH550PbtnDrrWXssHBhGO/x8sthHl8IYy9uvjmEj9zcpNYrIiIiIpIu6kcQqezqzuV46D5YOBMmPAnZDqwjTOEZ7XY1c2bYMS8vJJXTT4eePWu0dBERERGRuqh+BJFBg+Crr6r8tNGRG+eWsfGII+DOO+G006BLl72rT0RERESknqkfQWTMmJJB3XFwh4cfDj2vxowpdbHC5s1h2DDo2LHm6xQRERERqSfqRxA5t6wmjfK98Dxc8FUYpL7vJQmqSURERESkHtMVuUopLIRLL4XDDguX+hARERERkZpXP1pEquC3v4U1a2Dy5HBBchERERERqXlqEYnx3nvwyCNwxRXh+oAiIiIiIpIYCiIR27fDqFHQtSvcdFOqqxERERERqdvUNSvi1lth3jx4801o1izV1YiIiIiI1G1qEQHmzg1B5Kyz4MQTU12NiIiIiEjdl9AgYmbDzGyemS0wszFlbD/AzN42s8/N7B0zS/rFOYqL4de/Dq0gd9+d7FcXEREREamfEhZEzCwTuB84CcgFzjSz3FK73QFMcPe+wDjg1kTVU55HH4X334c77oA2bZL96iIiIiIi9VMiW0QGAgvcfaG77wCeB04ttU8u8K/I4yllbE+olSvDdL1HHw2/+EUyX1lEREREpH5LZBDpACyJWV4aWRdrFnBa5PGPgRZmtl8Ca9rNZZfB1q0wfjyYJetVRUREREQk1YPVrwKGmtlMYCiwDCgqvZOZjTKz6WY2vaCgoEZeeNIkeOEFuO466NWrRg4pIiIiIiJxSmQQWQZ0ilnuGFn3PXdf7u6nufuhwHWRdetKH8jdH3b3PHfPy8nJ2evCNm2CCy+E3Fy45pq9PpyIiIiIiFRRIq8j8gnQ08y6EgLICOCs2B3MrDWw1t2LgWuBxxJYz/duugkWLw6D1LOykvGKIiIiIiISK2EtIu6+CxgNTAbmAi+6+2wzG2dmwyO7HQ3MM7P5QFvg94mqJ+rTT+Gee8KUvfn5iX41EREREREpS0KvrO7uk4BJpdbdGPP4ZeDlRNYQa9cuOP/8ME3vbbcl61VFRERERKS0hAaR2ubee0OLyIsvwj77pLoaEREREZH6K9WzZiXNokVwww1wyilwxhmprkZEREREpH6rF0HEPcySZQb3369rhoiIiIiIpFq96Jr14ovwxhtw993QuXOqqxERERERkTrfIrJzJ1x9NQwYABdfnOpqREREREQE6kGLSMOGoTXEHTIzU12NiIiIiIhAPQgiAAcfnOoKREREREQkVp3vmiUiIiIiIrWPgoiIiIiIiCSdgoiIiIiIiCSdgoiIiIiIiCSdgoiIiIiIiCSdgoiIiABgZsPMbJ6ZLTCzMWVs72xmU8xsppl9bmYnp6JOERGpGxREREQEM8sE7gdOAnKBM80st9Ru1wMvuvuhwAjggeRWKSIidYmCiIiIAAwEFrj7QnffATwPnFpqHwdaRh5nA8uTWJ+IiNQxCiIiIgLQAVgSs7w0si7WWOAcM1sKTAIuLutAZjbKzKab2fSCgoJE1CoiInWAgoiIiMTrTOAJd+8InAw8ZWZ7/DHvOj4AACAASURBVB1x94fdPc/d83JycpJepIiIpAcFERERAVgGdIpZ7hhZF+tXwIsA7v4h0BhonZTqRESkzlEQERERgE+AnmbW1cyyCIPRJ5ba5zvgOAAzO4gQRNT3SkREqkVBREREcPddwGhgMjCXMDvWbDMbZ2bDI7tdCZxvZrOA54Cfu7unpmIREUl3DVJdgIiI1A7uPokwCD123Y0xj+cAQ5Jdl4iI1E1qERERERERkaRTEBERERERkaRTEBERERERkaRTEBERERERkaRLaBAxs2FmNs/MFpjZmDK2dzazKWY208w+N7OTE1mPiIiIiIjUDgkLImaWCdwPnATkAmeaWW6p3a4nTBF5KGHO+gcSVY+IiIiIiNQeiWwRGQgscPeF7r4DeB44tdQ+DrSMPM4GliewHhERERERqSUSeR2RDsCSmOWlwKBS+4wF/mFmFwPNgOMTWI+IiIiIiNQSqR6sfibwhLt3BE4GnjKzPWoys1FmNt3MphcUFCS9SBERERERqVmJDCLLgE4xyx0j62L9CngRwN0/BBoDrUsfyN0fdvc8d8/LyclJULkiIiIiIpIsiQwinwA9zayrmWURBqNPLLXPd8BxAGZ2ECGIqMlDRERERKSOS1gQcfddwGhgMjCXMDvWbDMbZ2bDI7tdCZxvZrOA54Cfu7snqiYREREREakdEjlYHXefBEwqte7GmMdzgCGJrEFERERERGqfVA9WFxERERGRekhBREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREkk5BREREREREki6hQcTMhpnZPDNbYGZjyth+t5l9FrnNN7N1iaxHRERERERqhwaJOrCZZQL3AycAS4FPzGyiu8+J7uPul8fsfzFwaKLqERERERGR2iORLSIDgQXuvtDddwDPA6dWsP+ZwHMJrEdERERERGqJRAaRDsCSmOWlkXV7MLMDgK7AvxJYj4iIiIiI1BK1ZbD6COBldy8qa6OZjTKz6WY2vaCgIMmliYiIiIhITUtkEFkGdIpZ7hhZV5YRVNAty90fdvc8d8/LycmpwRJFRERERCQVEhlEPgF6mllXM8sihI2JpXcys97AvsCHCaxFRERERERqkYQFEXffBYwGJgNzgRfdfbaZjTOz4TG7jgCed3dPVC0iIlK5yqZcj+zzEzObY2azzezZZNcoIiJ1R8Km7wVw90nApFLrbiy1PDaRNYiISOXimXLdzHoC1wJD3L3QzNqkploREakLastgdRERSa14plw/H7jf3QsB3H1VkmsUEZE6REFEREQgvinXewG9zGyqmX1kZsOSVp2IiNQ5Ce2aJSIidUoDoCdwNGEmxPfM7BB3Xxe7k5mNAkYBdO7cOdk1iohImlCLiIiIQHxTri8FJrr7Tnf/FphPCCa70ZTrIiISDwURERGB+KZc/yuhNQQza03oqrUwmUWKiEjdoSAiIiLxTrk+GVhjZnOAKcDV7r4mNRWLiEi60xgREREBKp9yPXK9pysiNxERkb2iFhEREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BREREREREUk6BRERkTrGzJqZWUbkcS8zG25mDVNdl4iISCwFERGRuuc9oLGZdQD+AYwEnkhpRSIiIqVUGkRiv1mLLGeYWdPEliUiInvB3H0LcBrwgLv/D3BwimsSERHZTTwtIm8DscGjKfDPxJQjIiI1wMzsCOBs4O+RdZkprEdERGQP8QSRxu6+KboQeawWERGR2usy4FrgL+4+28y6AVNSXJOIiMhu4gkim82sf3TBzAYAW+M5uJkNM7N5ZrbAzMaUs89PzGyOmc02s2fjK1tERMrj7u+6+3B3/0Oka+1qd78k1XWJiIjEahDHPpcBL5nZcsCA/YGfVvYkM8sE7gdOAJYCn5jZRHefE7NPT8K3dkPcvdDM2lTjZxARkRiRL3UuAIqAT4CWZvYnd/9jaisTEREpUWkQcfdPzKw3cGBk1Tx33xnHsQcCC9x9IYCZPQ+cCsyJ2ed84H53L4y81qqqFC8iImXKdfcNZnY28AYwBpgBKIiIiEitEc+sWRcBzdz9S3f/EmhuZhfGcewOwJKY5aWRdbF6Ab3MbKqZfWRmw+ItXEREytUwct2Q/wYmRr488hTXJCIispt4xoic7+7roguR1ovza+j1GwA9gaOBM4E/m9k+pXcys1FmNt3MphcUFNTQS4uI1FnjgUVAM+A9MzsA2JDSikREREqJJ4hkmplFFyJjP7LieN4yoFPMcsfIulhLiXxb5+7fAvMJwWQ37v6wu+e5e15OTk4cLy0iUn+5+73u3sHdT/ZgMXBMqusSERGJFU8QeRN4wcyOM7PjgOcIfY4r8wnQ08y6mlkWMAKYWGqfvxJaQzCz1oSuWgvjrF1ERMpgZtlmdle0JdnM7iS0joiIiNQa8QSRa4B/EWZguQD4AmhS2ZPcfRcwGpgMzAVejMxnP87Mhkd2mwysMbM5hDnur3b3NVX/MUREJMZjwEbgJ5HbBuDxlFYkIiJSSjyzZhWb2TSgO+EPWmvglXgO7u6TgEml1t0Y89iBKyI3ERGpGd3d/fSY5ZvN7LOUVSMiIlKGcoOImfUiDCA/E1gNvADg7upnLCJSu201s3x3/wDAzIYQ54VoRUREkqWiFpGvgPeBU9x9AYCZXZ6UqkREZG9cAEwws+zIciFwbgrrERER2UNFY0ROA1YAU8zsz5GB6lbB/iIiUgu4+yx37wf0Bfq6+6HAsSkuS0REZDflBhF3/6u7jwB6EwaSXwa0MbMHzeyHySpQRESqx903uHv0+iEaiyciIrVKpbNmuftmd3/W3f+LcC2QmYSZtEREJH2oRVtERGqVeKbv/Z67F0YuLnhcogoSEZGE8FQXICIiEqvS6XtFRCQ9mNlGyg4cRhzXfxIREUkmBRERkTrC3VukugYREZF4ValrloiISKLdcAMcdVSqqxARkURTEBERkVolKws++ADWrUt1JSIikkgKIiIiUqsceSS4w4cfproSERFJJAURERGpVQYOhAYN4P33U12JiIgkkoKIiIjUKk2bwoABoXuWiIjUXQoiIiJS6+Tnw8cfw/btqa5EREQSRUFERERqnfz8EEJmzEh1JSIikigKIiIiUusMGRLu1T1LRKTuUhAREZFaJycHDjxQQUREpC5TEBERkVopPx+mToXi4lRXIiIiiaAgIiIiAJjZMDObZ2YLzGxMBfudbmZuZnmJrOfII2HtWvjqq0S+ioiIpIqCiIiIYGaZwP3ASUAucKaZ5ZaxXwvgUmBaomvKzw/3up6IiEjdpCAiIiIAA4EF7r7Q3XcAzwOnlrHf74A/ANsSXVC3brD//honIiJSVymIiIgIQAdgSczy0si675lZf6CTu/89GQWZhVYRBRERkbpJQURERCplZhnAXcCVcew7ysymm9n0goKCvXrd/HxYtAiWLt2rw4iISC2kICIiIgDLgE4xyx0j66JaAH2Ad8xsEXA4MLGsAevu/rC757l7Xk5Ozl4VFR0nMnXqXh1GRERqIQUREREB+AToaWZdzSwLGAFMjG509/Xu3trdu7h7F+AjYLi7T09kUf36QbNm6p4lIlIXJTSIVDYVpJn93MwKzOyzyO28RNYjIiJlc/ddwGhgMjAXeNHdZ5vZODMbnqq6GjSAI45QEBERqYsaJOrAMVNBnkAY9PiJmU109zmldn3B3Ucnqg4REYmPu08CJpVad2M5+x6djJogXE/k5pth/XrIzk7Wq4qISKIlskUk3qkgRUREypWfH66u/uGHqa5ERERqUiKDSKVTQUacbmafm9nLZtapjO0iIlKPDRoEmZnqniUiUtekerD6a0AXd+8LvAU8WdZONTkVpIiIpJdmzaB/fwUREZG6JpFBpLKpIHH3Ne6+PbL4CDCgrAPV5FSQIiKSfvLzYdo02LEj1ZWIiEhNSWQQqXAqSAAzaxezOJwwU4uIiMhu8vNh2zb49NNUVyIiIjUlYUEkzqkgLzGz2WY2C7gE+Hmi6hERkfQ1ZEi4V/csEZG6I2HT90LlU0G6+7XAtYmsQURE0l/bttCzZwgiV12V6mpERKQmpHqwuoiISFyOPDIEEfdUVyIiIjVBQURERNJCfj6sWQNffZXqSkREpCYoiIiISFrIzw/3GiciIlI3KIiIiEha6NED2rRREBERqSsUREREJC2YhVYRBRERkbpBQURERNJGfj4sXAjLl6e6EhER2VsKIiIikjai40SmTk1tHSIisvcUREREJG384AfQtKm6Z4mI1AUKIiIikjYaNoTDD4f33091JSIisrcUREREJK0ceSTMmgUbNqS6EhER2RsKIiIiklby86G4GD76KNWViIjI3lAQERGRtDJoEGRmapyIiEi6UxAREZG00qJFGLSuICIikt4UREREJO3k54euWTt3proSERGpLgURERFJO/n5sHUrzJyZ6kpERKS6FERERCTtDBkS7tU9S0QkfSmIiIhI2mnXDrp31/VERETSmYKIiIikpSOPDC0i7qmuREREqkNBRERE0lJ+PqxeDfPnp7oSERGpDgURERFJS/n54V7jRERE0pOCiIiIpKVevaB1awUREZF0pSAiIiJpySy0iiiIiIikJwURERFJW/n5sGABrFyZ6kpERKSqFERERCRtRceJTJ2a2jpERKTqFERERCRtHXooNGmi64mIiKSjhAYRMxtmZvPMbIGZjalgv9PNzM0sL5H1iIhI3ZKVBYcfrnEiIiLpKGFBxMwygfuBk4Bc4Ewzyy1jvxbApcC0RNUiIiJ1V34+zJwJGzemuhIREamKRLaIDAQWuPtCd98BPA+cWsZ+vwP+AGxLYC0iIlJH5edDcTFM09dZIiJpJZFBpAOwJGZ5aWTd98ysP9DJ3f+ewDpERCSd7NhRpWmwDj8cMjLUPUtEJN2kbLC6mWUAdwFXxrHvKDObbmbTCwoKEl+ciIikzgUXwODB8O23ce3esiX066cgIiKSbhIZRJYBnWKWO0bWRbUA+gDvmNki4HBgYlkD1t39YXfPc/e8nJycBJYsIiIpd+GFsG4dHHUUzJ8f11Py8+Gjj2DnzgTXJiIiNSaRQeQToKeZdTWzLGAEMDG60d3Xu3trd+/i7l2Aj4Dh7j49gTWJiEhtl5cH77wD27eHMPLll5U+JT8fNm+GWbMSX56IiNSMhAURd98FjAYmA3OBF919tpmNM7PhiXpdERGpA/r2hffeg8xMOProMC1WBaIXNtT1RERE0kdCx4i4+yR37+Xu3d3995F1N7r7xDL2PVqtISIi8r3evUMYadYMjjkm9L0qR/v20K2bxomIiKQTXVldRERqr+7dQzNH69ZwwgkhmJQjPz8EEfck1iciItWmICIiIrVb584hgHTsCMOGwVtvlblbfj6sWgULFiS5PhERqRYFERERqf3at4d334WePeGUU+C11/bYJTpORN2zRETSg4KIiIikhzZtYMqUMJD9tNPgpZd229y7N+y3n4KIiEi6UBAREZH00aoV/POfMGgQjBgBTz/9/SYzGDJEQUREJF0oiIiICABmNszM5pnZAjMbU8b2K8xsjpl9bmZvm9kBqaiT7Gx4880wre/PfgaPPPL9pvz8cA3EVatSUpmIiFSBgoiIiGBmmcD9wElALnCmmeWW2m0mkOfufYGXgduTW2WM5s3h9dfD4PXzz4f/+z9A40RERNKJgoiIiAAMBBa4+0J33wE8D5wau4O7T3H3LZHFj4COSa5xd02awF/+Aj/+MVxyCdx+OwMGQOPGCiIiIumgQaoLEBGRWqEDsCRmeSkwqIL9fwW8UdYGMxsFjALo3LlzTdVXtkaN4IUX4Nxz4ZpryNqyhUEDb+KDDyyxrysiIntNQURERKrEzM4B8oChZW1394eBhwHy8vISf3nBhg3hqadCU8jNNzPuiK0cO+M2Nm82mjVL+KuLiEg1KYiIiAjAMqBTzHLHyLrdmNnxwHXAUHffnqTaKpeZGQatN2nCUQ/czt1sYdqHf+LY49UDWUSkttIntIiIAHwC9DSzrmaWBYwAJsbuYGaHAuOB4e5e++alysiA++5j++gruZj72OeaX0NRUaqrEhGRciiIiIgI7r4LGA1MBuYCL7r7bDMbZ2bDI7v9EWgOvGRmn5nZxHIOlzpmNLr3jzzc5nr6f/pIGDuya1eqqxIRkTKoa5aIiADg7pOASaXW3Rjz+PikF1UdZnz+P7/j5j834aZnroN168KAdg0YERGpVdQiIiIidU5+Pozd8b8sHvMgvPEGHHssFBSkuiwREYmhICIiInVO9MKGf93/Anj1Vfj8cxg8GL75JrWFiYjI9xRERESkzunYEbp0iVzY8NRT4e23Ye3aEEamT09dYQUF4Srwap0REVEQERGRuik/PwQRd0IA+fe/w9XYjz4a3nwz+QVNnAh9+oSrwPfqBQ8+qFm9RKReUxAREZE6KT8fVq6EhQsjKw48ED78EHr2hFNOgSeeSE4hGzbAL38ZWmbatYO//Q3694cLL4RBg+Djj5NTR320eDH86lfwu9+FfwwiUqsoiIiISJ109NHh/pZbIq0iEILAu+/CMcfAL34Bv/99zMYEeOcd6NsXnnwS/vd/Q+gYPhz++U94/nlYsQIOPxxGjYI1axJXR3307LPhvX/2WbjxRujUCUaMgPffT+zvXETipiAiIiJ10oEHwg03hIaP22+P2dCyJfz973DOOXD99aFloqa7SG3dCpdfHgJPVlboI/b734fHAGbw05/CV1/BFVfAY4+F7lp//jMUF9dsLfXNunVw9tnhdsghMGcOzJ8PF18MkyfDUUdBv37w0EOwaVOqqxWp1xRERESkzrr55nC+P2ZMmDzre1lZMGFC2PDQQ3D66bBlS8286PTpoevVPffARRfBzJlwxBFl79uiBdxxB3z2WRg/MmpU2HfGjJqppb55//0QMl54AcaNCy1SXbuG7nh33QXLlsEjj0CDBvCb30D79iGgzJ2b6spF6iUFERERqbPM4PHHQ++nc84pNWGWGdx6a5jFauJEOP74vesetXMnjB0bXmzjRvjHP+C+++K7kGKfPuGk+amnwriGww4LIaawsPr1JMrGjWE65IkT4d57Q4vOj38Mhx4K++0HJ50E06Ylt6adO+G660J/vIYNYerU0BzWoNR1m5s2DWNGZswI44VOPRUefhhyc8O1Zl55JRxLRJLCPM36Sebl5fn0VE69KCJSTWY2w93zUl1HMtWWz+z//CeMC9+xIwzT6Nix1A6vvgpnnRXm/H3jjfAtelXMnQsjR4YT3HPOCSfo++5bvWLXrw9jGu67L5zY3347/OxnkJGk7w43bQph6NtvYdGicIt9vHbt7vs3bRrety5dYP/9w2D8NWtCIBk7FgYOTGy98+eHbljTp4eQcc890Lx5/M8vKIBHHw0tY4sXh1aSX/8azj8/jCmSeq0+fm4nk4KIiEiS1Mc/aLXpM/vLL8Msvt27hx48e5yrfvAB/Nd/QePGMGlS+Ia/MsXF8Kc/wbXXhgOOHx+6edWEWbPC+JV//zsUfv/98IMf1Myxi4rCdGKffw5ffBHGUUTDxurVu+/buHEIGV27lgSO6K1rV2jdOrQuRW3aFELUH/8YQsuPfhQCSV4N/9N3D92sLrss1PjnP8Npp1X/eEVF4fd+//1hLEmDBuF4F10ERx65+89YV2zeDFOmhJ/344+hTZvwH6Rbt3Dr3j38nps0SXWlKVMfP7eTKaFBxMyGAX8CMoFH3P22UtsvAC4CioBNwCh3n1PRMWvTHzURkaqoj3/Qattn9ptvhvPiH/0I/vIXyMwstcOcOTBsWOgS9eqrcMIJ5R9s0aIw89Y774QA8/DDoUWgJhUXh7Esv/1taGUYPTqMfcjOjv8YBQUhbERDx+efw+zZYUA9hJaW2JPO0qGjTZvqnYRv3Bi6vd15Zwgkp5wSAsmAAVU/VmmrV8N554XWl+OPDzMSdOiw98eNWrAgXOflscfC4Pc+fUIoPOecMK4nXbmHRP7mmyF8vP9+aCZs2jR0BywshG++CQElVvv2uweU6L+Xbt2q/++jprmH31U0UJdu0fvxj8M0zlVUHz+3kylhQcTMMoH5wAnAUuAT4MzYoGFmLd19Q+TxcOBCdx9W0XFr2x81EZF41cc/aLXxM/u++8L45CuuCOfIe1i2DE4+OYSSxx4LXa5iuYcT30svDcv33BMCSSJPxgoLwxiIhx4KJ3533BG6I8W+5rZtoebY0PHFF7tfP6NNmzCTVN++Jfe5uYn9xnvDhpJAUlgYpi++6aYwoL86Jk+Gn/88hJtbbw0tIonqtrZlS5hm+f774dNPQ3Lt1y/084veevVKXre56li7NkwXHQ0fy5eH9X36hNB94onhojuNG4f17iG8LlxYcvvmm5LHS5fufvxmzfYMKG3bhvXNmoWWwtj7Zs3COJ7qWL++7O6C0ccbNuy+f8uWJaH61FPD/9Mqqo+f28mUyCByBDDW3U+MLF8L4O63lrP/mcDP3P2kio5bG/+oiYjEoz7+Qautn9kXXxwCyfjxYaKqPaxfH75BnTIFbrsttEiYhcEm558Pr70GQ4eGQNKlS/IKnzEjfDP/8cehu9APf1gSPL7+umQa4saN4eCDdw8dhxwSThBTZf36MHbmrrvCN9ennhpaSOLtbrZtG1xzTTjGwQfDM8+EUJAM7uE9f+21MBD/449LTnqzs8M4mNhwkpOTnLrKUlQUxsu8+Wa4ffxxaFnbZ5/QwjdsWPh3s8dAqTht2xZO+ssKKQsXxjf7XFbWnuGkrMCSmQlLlpQEjdKTNzRrVhI0Ylvxoo/32WevvyCoj5/byZTIIHIGMMzdz4ssjwQGufvoUvtdBFwBZAHHuvvXFR23tv5RExGpTH38g1ZbP7N37QpfzP/jH+Fc7fjjy9hp+/bwDepzz4VxAkcfHaZ83bgxfBN/6aWp+Sa8uDgMrh4zJnzb3a3bnq0cPXqU0e+slli/PoyrueuuksB3000Vh4ovvgiTCXz5ZUiRf/hDasctFBeHa8BMm1Zy++KLkiDYrdvuweTQQ6FRo8TVs2JFaO148014663w78IsdLcaNizcDjtsz1nEapp7COurV4exQps3l9zHPi5rXVnbd+4MF6KMDRexgWO//RLeLaw+fm4nU8qDSMz+ZwEnuvu5ZWwbBYwC6Ny584DFixcnpGYRkUSqj3/QamsQgfCF9pAh4QvXDz+Egw4qY6fi4vAt/B13hOUBA8KYjdzcpNZapm3bwolauo5ZWLcudGu7++7wyzjttNBCcsghJftEJwMYMybMQvbEE+GkujbavDm0WMWGk2g3poYNQ8tPNJjk5YVgsm1buG3fXvK4rOWK9vnyy9AiBmGM0oknhvfohBPCibrslfr4uZ1MtalrVgZQ6O4VjsCrzX/UREQqUh//oNX2z+zFi0OvmubNw3lj69bl7Pjoo2Gw+OWXV79/u5StsDAEknvuCYHkjDNCC0mrVmEsyFtvhearRx5JbZen6li+fPdg8sknew4Er4qGDUO3u0aNwn3jxqHFIBo++vatHQPH65D6+LmdTIkMIg0Ig9WPA5YRBquf5e6zY/bpGe2KZWb/BdxU2S+7tv9RExEpT338g5YOn9kffRR6XR12WBjTm8geNFKBtWtD68if/hS65jRrFro63X13GMhTF06wi4rCjGWffRa6MUXDRGywKG+5UaPa292uDquPn9vJlLDOgu6+y8xGA5MJ0/c+5u6zzWwcMN3dJwKjzex4YCdQCOzRLUtERCSRDj8cnnwSRowIM8JOmFA3znnTTqtWYXrVyy8P40dmzgwzbfXunerKak5mZmi16Ns31ZWI1AoJHbXk7pOASaXW3Rjz+NJEvr6IiEg8fvrTcIHuG2+EAw+E669PdUX1WKtWcMstqa5CRJIgwdMniIiIpIfrrw9h5IYbwqUhfvKTVFckIlK31eIr8IiIiCSPWRgPPWQInHtuGFssIiKJoyAiIiIS0agR/OUv0L59mKhJs8WLiCSOgoiIiEiMnBx4/fVwmYZTTim5gLaIiNQsBREREZFSDjoIXn4Z5s4Ns2nt2pXqikRE6h4FERERkTIcfzw88AC88QZccUWqqxERqXs0a5aIiEg5Ro2CefPCZS3WrQtXXi8uDteiKy4u/1bR9oYNIT8/XAi7e/dU/4QiIqmjICIiIlKB22+HggJ45RXIyNjzZlb2+vK2bdwIzz8fjt29ewgkw4bBMceEi4mLiNQXCiIiIiIVyMwMV1ufMKHmjrlgAbz5Zrg9/jjcfz9kZcGRR4ZQcuKJ0KePrvAuInWbxoiIiIgkWY8eMHp0mJ1r7Vr45z/hkkvgP/+Bq6+Gvn2hY0f41a/gpZegsDDVFYuI1DwFERERkRRq1AiOOw7++Ef44gtYsiRcWHHwYHj11XCF99atw/K4cfDxx1BUlOqqRUT2nrl7qmuokry8PJ/+/9u792CryvOO499fDvfDRQEFDmDAwoTBCEhJpLk0M8Ea0xAxwQ5R0rENnUwzTWLbTBvTzGQmnU7HJh2TmDJtjJJaw4SxeGOaqBAx1hkilSh4waYQayMcEKiAcgd5+se7ds8+N84Gzl5ruffvM7NmrfXuffZ+NufwnPOs933XFiyzXgAADNVJREFUu2lT0WGYmZ01Sb+IiHlFx5En5+zzc+pUKjwqw7g2bUoT4ceMgauvTnNMTpzovp08eXbtAwbA3Llw5ZVpe897YOTIoj+9WfGaMW/nyXNEzMzMSmrAgNQTUukN2bsX1q2DRx9N26pVaW5JT9vAgZ3PBw+GESO6tw8aBEeOpCJnzZr0vhLMnJmKkvnz0/6yy9J8GTOz/uJCxMzM7G3ioovgxhvTFtH/k9n37089MBs3pu3BB2HFivRYa2vqKan0mlx5JbS19e/7m1lzcSFiZmb2NlSPO2pdeGG6Y9dHPpLOI+BXv0pFyVNPpf1tt6UhXgCTJ3cUJfPnp+Fdw4b1f1xm1phciJiZmVmPpHSHr2nTYOnS1HbsGGze3FGYbNwIq1enx1pa0pCuOXNg9uyO/dixxX0GMysvFyJmZmZWsyFDUu/H/PkdbXv2dBQlzz4L69fDPfd0PN7W1r04mTatfnNOjh6FXbugvT3Nq5k9Gy69tD7vZWbnzoWImZmZnZeLL4aPfzxtFfv2wZYtqfeksl+7Nt0JDNIQrssv71yczJoFw4f3/j6HD6cCo1JkVO+rjw8c6P61c+bA4sXwyU+mXhszK55v32tmlpNmvA2kc7ZVO34ctm7tXqBUCgcp3ZJ49my45JLU01JdcLzxRvfXHDQIJkxIW1tb5/2ECTB6NDz5ZFqTZcOG9DUzZqSiZPHiVKB4BXvrTTPm7Ty5EDEzy0kz/kJzzra+RKRFHLsWJ+3tMH5870VGZT96dO2FRHs7PPAA3HcfPPEEnD4NU6emXpLFi9Ok+3d4qWer0ox5O08uRMzMctKMv9Ccs62s9u2Dhx5KRclPf5ruBNbWBp/4RCpKPvjBtI5Lfzt8OPX0DBqU1nUZPtzFT5k1Y97Ok+eImJmZWdMZOxaWLUvbgQPw4x+nomTFCli+PD2+aFEqShYsSIVDb956KxU2u3enYWRn2h861P3rW1tTQTJiROetlrbBgzt6hKp7hs7U1tPjLS0wblya75P3wpUHD8L27d23HTtSr1VvMddyXNnfeCN8/ev1id/OnQsRMzMza2oXXJBuT7x0aeqxePjhNKfk3nvhrrtg1ChYuDAt6Lh3b/fiYs+eVIx0NXJkGj42fjzMm9cx1Oyii1IPzJtvdmyHDnU+37Wrc3tPBUw9tLSkOCdOTD1EEyf2fDxy5NnNrXn99Z6Lje3b079ptba2dFe1D3ygo1eqMoCneiBPX8fVbe98Z+2xWn5ciJiZmZllWlvh+uvTduxYGrZ1//1pGNfKlR09B+PHpz+Y587tKDaq9+PG9e/ijqdPpyKpa9Fy/Hh6vK8/xPt6/ORJeO012Lkzbe3tsG0b/OxnPd+FrLW1c4FSXagcPdq50Ni2Dfbv7/z1kyenYuO662D69I71ai69NL22NQcXImZmZmY9GDIk9YQsXJj+UD9wIE2Oz3voEqR5JJXhWHk7ciQVJu3tnQuVyvGGDem8UhRV4r3kklRcLFmS9pWCY+pUGDo0/89h5VPXQkTSNcB3gBbgzoi4tcvjfw78EXAK2At8JiL+p54xmZlZz2rI2YOBfwF+E/hfYElEvJJ3nGZFGDgwDalqRsOGdfRY9CYiDb/auTPNp5k6Nc1fMTuTut2nQVILsBz4KDATuEFS1yWEngXmRcQsYDXwjXrFY2ZmvasxZy8D9kfENOBbwN/lG6WZlZUEY8akRSlnzHARYrWp5w3j3gtsj4iXI+IEsApYVP2EiHg8Io5kp08Bk+oYj5mZ9a7PnJ2d350drwYWSF4KzszMzk09C5GJwKtV5zuytt4sAx6uYzxmZta7WnL2/z8nIk4BB4ExuURnZmYNpxST1SV9GpgHfKiXxz8LfDY7PSTpl+fwNmOBfecWYd04ptqULaayxQOOqVZFx9QUN5B0zs6VY6qNY6pN2WIqQzxNkbeLUs9CZCcwuep8UtbWiaSrgK8CH4qI410fB4iIO4A7zicYSZvKtjKmY6pN2WIqWzzgmGpVxphKpJacXXnODkkDgFGkSeudOGfnxzHVxjHVpmwxlS0e63/1HJr1NDBd0lRJg4BPAWuqnyDpCuB7wLURsaeOsZiZ2Zn1mbOz85uy4+uB9RHVKxGYmZnVrm49IhFxStLngUdJt4JcEREvSvprYFNErAG+CQwH/jWb7/jriLi2XjGZmVnPaszZdwH3SNoOvE4qVszMzM5JXeeIRMRPgJ90afta1fFV9Xz/Ls5rmECdOKbalC2mssUDjqlWZYypNGrI2ceA38spnDJ+rxxTbRxTbRxT38oWj/UzuVfdzMzMzMzyVs85ImZmZmZmZj1q+EJE0jWSfilpu6RbShDPZEmPS9oq6UVJNxcdU4WkFknPSvq3omMBkHSBpNWS/lPSS5J+qwQx/Vn2fXtB0o8kDSkghhWS9kh6oapttKR1krZl+wtLENM3s+/dc5IekHRB0TFVPfYlSSFpbJ4xWW2ct2vjnF1TTM7ZtcfknG25a+hCRFILsBz4KDATuEHSzGKj4hTwpYiYCcwH/qQEMVXcDLxUdBBVvgM8EhEzgNkUHJukicAXgXkR8W7ShN4iJuv+M3BNl7ZbgMciYjrwWHZedEzrgHdHxCzgv4CvlCAmJE0GrgZ+nXM8VgPn7bPinH0GztlnHZNztuWuoQsR4L3A9oh4OSJOAKuARUUGFBG7IuKZ7PhNUqI+04rzuZA0CfgYcGfRsQBIGgX8NukuPUTEiYg4UGxUQLrBw9BsDYVhQHveAUTEv5PuWFRtEXB3dnw3cF3RMUXE2mz1bYCnSOtSFBpT5lvAXwKeIFdOzts1cM6umXN2jTE5Z1sRGr0QmQi8WnW+gxL80V8haQpwBbCx2EgA+DbpP/rpogPJTAX2Aj/Ihh7cKam1yIAiYifw96SrMruAgxGxtsiYqoyLiF3Z8W5gXJHB9OAzwMNFByFpEbAzIrYUHYv1ynm7Ns7ZfXDOPi/O2ZaLRi9ESkvScOA+4E8j4o2CY1kI7ImIXxQZRxcDgLnAP0bEFcBh8u+67iQbw7uI9Au3DWiV9OkiY+pJtsBcaa4cSfoqaWjLyoLjGAb8FfC1vp5r1pOy5G3n7No4Z58b52zLU6MXIjuByVXnk7K2QkkaSPpltjIi7i86HuD9wLWSXiENg/iwpB8WGxI7gB0RUbnquJr0S65IVwH/HRF7I+IkcD/wvoJjqnhN0gSAbL+n4HgAkPQHwEJgaQlW4P4N0h8kW7Kf9UnAM5LGFxqVdeW83Tfn7No4Z58l52zLW6MXIk8D0yVNlTSINEltTZEBSRJpDO1LEXFbkbFURMRXImJSREwh/Rutj4hCrxpFxG7gVUnvypoWAFsLDAlS9/58ScOy7+MCyjNRdA1wU3Z8E/BQgbEA6c5HpKEj10bEkaLjiYjnI+LiiJiS/azvAOZmP2tWHs7bfXDOrplz9llwzrYiNHQhkk26+jzwKCn53BsRLxYbFe8Hfp90BWtztv1uwTGV1ReAlZKeA+YAf1tkMNmVvtXAM8DzpP8/ua/6KulHwM+Bd0naIWkZcCvwO5K2ka4C3lqCmP4BGAGsy37O/6kEMVnJOW+/rTln98A5+7xisgbnldXNzMzMzCx3Dd0jYmZmZmZm5eRCxMzMzMzMcudCxMzMzMzMcudCxMzMzMzMcudCxMzMzMzMcudCxBqGpLeqbq25WVK/reoraYqkF/rr9czMzHnbrNkNKDoAs350NCLmFB2EmZnVzHnbrIm5R8QanqRXJH1D0vOS/kPStKx9iqT1kp6T9JikS7L2cZIekLQl296XvVSLpO9LelHSWklDs+d/UdLW7HVWFfQxzcwahvO2WXNwIWKNZGiXLv4lVY8djIjLSSvHfjtr+y5wd0TMAlYCt2fttwNPRMRsYC5QWdV5OrA8Ii4DDgCLs/ZbgCuy1/njen04M7MG5Lxt1sS8sro1DEmHImJ4D+2vAB+OiJclDQR2R8QYSfuACRFxMmvfFRFjJe0FJkXE8arXmAKsi4jp2fmXgYER8TeSHgEOAQ8CD0bEoTp/VDOzhuC8bdbc3CNizSJ6OT4bx6uO36JjjtXHgOWkq3BPS/LcKzOz8+e8bdbgXIhYs1hStf95drwB+FR2vBR4Mjt+DPgcgKQWSaN6e1FJ7wAmR8TjwJeBUUC3q3tmZnbWnLfNGpyvAFgjGSppc9X5IxFRuRXkhZKeI10duyFr+wLwA0l/AewF/jBrvxm4Q9Iy0hW0zwG7ennPFuCH2S89AbdHxIF++0RmZo3NedusiXmOiDW8bKzxvIjYV3QsZmbWN+dts+bgoVlmZmZmZpY794iYmZmZmVnu3CNiZmZmZma5cyFiZmZmZma5cyFiZmZmZma5cyFiZmZmZma5cyFiZmZmZma5cyFiZmZmZma5+z8HTPXqaVUgggAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#----------------------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch for both models\n",
        "#----------------------------------------------------------------\n",
        "fig, (axs1, axs2) = plt.subplots(1, 2,figsize=(12,5),sharey=False)\n",
        "fig.suptitle('Training and validation graphs')\n",
        "#plt.axis(ymin=0.2,ymax=1,xmin=0)\n",
        "axs1.set_title('Accuracy')\n",
        "axs1.axis(ymin=0.2,ymax=1)\n",
        "axs1.set(xlabel=\"Epochs\",ylabel=\"Acc\")\n",
        "axs1.plot(history.epoch, history.history['accuracy'],'b',label=\"train\")\n",
        "axs1.plot(history.epoch, history.history['val_accuracy'],'r',label=\"validation\")\n",
        "\n",
        "axs2.set_title('Loss')\n",
        "axs2.axis(ymin=0,ymax=1)\n",
        "axs2.set(xlabel=\"Epochs\",ylabel=\"Loss\")\n",
        "axs2.plot(history.epoch, history.history['loss'],'b')\n",
        "axs2.plot(history.epoch, history.history['val_loss'],'r')\n",
        "\n",
        "fig.legend(loc=\"upper right\")\n",
        "plt.savefig(\"res_TransferModelVGG16_whurs_aug40_SAT.png\")\n",
        "#!mv ./res_TransferModelVGG16_whurs_aug40_SAT.png ./drive/MyDrive/MemoireM2/Scripts/screens/res_TransferModelVGG16_whurs_aug40_SAT.png\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights(\"./checkpoint.h5\")"
      ],
      "metadata": {
        "id": "9s9j-P0Mdeyo"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "eLdS2l1Ar0t-",
        "outputId": "6d58c058-d6d5-433d-85cc-db9d1df1e0a4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     img        label\n",
              "1264     Mountain_36.jpg     Mountain\n",
              "157          Park_49.jpg         Park\n",
              "874    industrial_29.jpg   industrial\n",
              "706   residential_06.jpg  residential\n",
              "233          pond_46.jpg         pond\n",
              "1220      parking_35.jpg      parking\n",
              "887     Desert_01gen.jpg       Desert\n",
              "840        bridge_09.jpg       bridge\n",
              "783   Farmland-37gen.jpg     Farmland\n",
              "1039    bridge_24gen.jpg       bridge"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <td>industrial_29.jpg</td>\n",
              "      <td>industrial</td>\n",
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              "    <tr>\n",
              "      <th>706</th>\n",
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              "      <th>887</th>\n",
              "      <td>Desert_01gen.jpg</td>\n",
              "      <td>Desert</td>\n",
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              "      <td>bridge_09.jpg</td>\n",
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              "    <tr>\n",
              "      <th>783</th>\n",
              "      <td>Farmland-37gen.jpg</td>\n",
              "      <td>Farmland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1039</th>\n",
              "      <td>bridge_24gen.jpg</td>\n",
              "      <td>bridge</td>\n",
              "    </tr>\n",
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              "          document.querySelector('#df-37769c40-d312-472b-91c0-a0e6c82e0b2d button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-37769c40-d312-472b-91c0-a0e6c82e0b2d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "rt7r0vF5daWf",
        "outputId": "f38956e9-92b7-4b16-ba89-249b681a9e6c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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sOs0Fp/NZ0czOnWpmQ0foGQGraRrXhLUqTjftrM5fFaa73R45nTGkHCB1xVxnVEh/Nhxr/g2znT1XvY9QE/PqkzOLzcdbjIO92ECT3K0s/gNSk8I+hZlYtbCgUEMGlJot4oyo1gal5GWL0FFaI8V1zBpLoX1oU8pWLSOoTnSF8QR0fC1+pQkCF0SqAKoqg0rN1KgMICUvE1ytEQ1paYlZESBFocjNQ/W66UEIBu7JRxWGulm4tkCdWsGV1UYUAgVBzIlq7juxyS3CxCCWaZZq11OS6b2UMGSZ3ycAh/1OZgsSUOcRKQ8YcsY8T7h7MWOaRozjjGmqyEkdakXqBQKe//gz3N3eYT9kMDOmcUY2m1MluERCat3CFFzwnaOxVAW5stB7lAVQMWO33yHvdyimLpk7IjC+JSLMk5gQlBiVCxKyaltFK2BBVPrJYIzjGfM0YRxHdxhmkER/1hmpZoHJNvVpTA7rc4PWgig8RgHKtCYglZCz+ioYrLJSTCrXsJBgpspAqbOaKJJPY16Jp8gsU6wgQqoAZdIHuJlQ0BgLoBOiyaKclMFLrciKXqtPK5swJhRvCzsKcmImAlWCOGS03VYvlVQWkelihUkFtoiKBYsEX4L+BiOaGp3Tk9iFx+umByEYAAi01NQHqhozmSaRGQyVh2rfJ5TCIFSknDyyDswt8ITE065YHBaqm9TWns5nTBojAEDn6gkzZQyJcLq9xd3prpvXtylWIp3DV4Y539xizhLzkFPCQBm1CkNVneMX65pgbmMHoAoficWkaUPPcNFRRWjWWjFPxZ1b9lTrRwOikkctxSEmTCsb81aohqyNMbni7nSHy8tLPHv2DBZnUOYiPpPCqKUKiEkAqxOPXNsLU1u0Irin7qpCoV+wo0RsZoLOHLAzbVKmZUCFSPasq8lGNTNIrApDIoZMucqz5qiFBkOxlC1BZfCpQWc+E15RTUPyzMl/tCYa7PfLrP4WuF+ixpkEs0DYDOskQhfi6G0WltICWcOtu0Wd5uCgpDDeb5IejGCoXNw+EhmZVXITijruTLOknMVjXAuqeWx1Hl4ApmhZC6khInVgyqyE2cSAdNd5POHudIf9fi/IgQiHwwHMFafTGbVMmOcZZdZpuAyx23TePKcEo4ycBGGIzDDtYQwhWrWqeST3hcnJJu/NFgIUXts1cy4KUqq1YhxH3NzdYRpHTPOMeS7YDcHphGYfAxI6LpGCgsLEpWtBTuymiBFxAuF42GM/HPD+++85hK61gueKWnTazWV48JhX9jrYXD2Zz4h7Iq1qAhABxdjQtC+MHlovETEoiZ2eQDq9WUUwQU0uJlTOKwYWwWeisoVHV7biqkRDKtPZ1CmYQbUZcybANRtkx/Pkw8j6PhEZUBEzjZpDtpMiLGhEftlMif5XxZSohnyz+NKiweIIjdmFASUzKfBG6cEIBteIzrDCMKVohF6SuPhSqsBNEsLN6hAjZllEZJrJYWszJQgA1+K6121FvT+dTuBdxm63R8KAaZ4xnm4wTZNEFsKmF5OE/yb4YitVfgAS6jSqANFJZC6CMJPCSSgjgEAqGEwTkGpBaYNMRxIsoo8x16J5VnCVfMex4HweUeYZu7SDrN0SAelkxzKVOM+z+hmUrG2aDg3yA4bagMPhgBfPn4tYGzLm84QdEWopUveqiIEJRIJedG6gh75mVAT/hgxVQxfCmcnrq4/AnHCCsNhr2hBWM8Nc7SoCMMHgRRIBakZ6/oGR3ezRuAnztXjEYYKYBdRiMgGoAIGLWUM9jOhoVC8DA9njJFrYsvlWfMICTdklmgVjMqNAZjXI+oytXwR1VDY0Sr5OosbBeI30IASDed19oI1gfJAkth21wpw04EbAxLUtQKoO3mBdYhqRXEKzeKeDJgIBFRVlnJFQMZ7atNaQZSEOVwnCMumck2ovCsRQGUWRSTKJb34FCkyhRCs/qU2X6aIgCwRqAiKJMHCIXpG4IiuiGqfizM7FCIw1XNiKZLRQYhaziRmgKozgWlDqklPCOI6YS8GMgsPxiHmUyMhaZ4nCrBkpDdqPWQVdQAVBABkcERitRO2yyAjX4g/89SYE1Elo/ccM50hDLtG+ZuNWNBjui6nQogmlWA28UkTZhIFmXOUdEKFScD5q3bNRGde+GwHkLr5RLppJZ9at1UmIXy4wFFnopcIVtQrTVu9jdmThi6mYUdVHYQJ+a3Xqy9KDEAwKnBCG0USu3m3J5vsb/K0aI4COmTr56AwhQiIRUJxJDT8QksroMs84qffefAkWTm1eZah33jiPZNIapnMoSTnNLjRQHDSN4zutP8WpLhaqSKofVZsJ4avIU3MipaR1MG99EmaeJhFqg5o2lZEoh25pxETWTwjIQau3Pxx0/cdOQ3wryjxjmidQHkBZFjbVWkRjRsZWaeVlkGlgKFJrmhLG2PZseA6GUrTvvYASyllR1cY3NRFWbJJaLMESnouwSCoXxN8hTmxzlEq+NsXoJqT5amrDY5Z3UQWXNaJU1oI4LvInE2ne9jvbGLE6KNtQNbrSPg0rkLBu8UvTAxEMQGxak+7RlmvE4FNQiEPeE0eTzgElmMOvu2fSWn4nZfDiMfkO9NsUk73DFp0o06EelUZNc5GNezfcTYtbe+2LA3vtDjYnZyBaWQchwV13d3fIOqtSuSIlmbr80Y9+iG9844/x9MkTfOlLX8LhuEfSOIat/uJFpaz/9vs9yumM66srAFBBGZY5b6VgLripEKY15SP4UTZhLvcfMT/LR52EvGiLvVZNUeiF+IiV2ayNNeM4gl1+piRxFBqXwLyImVBzx6eFVRgxcw/pGeK8hSgNq0IGdbNVjqUMRVMLfqOk3il9yOa3vJUmgN9MLjwQwWCIyH6Q2ItJoZv0TIXPkys11DjQqXmz5QKc6ORys6RliosDLQSQRxLqak4jG1S51WCsEwLgdjXp90H9Dsllj6xKrE6FPXEgEhXUpkxuupuE9LqSIocyV5zPZ10kVTHNBfUIjOOEb3zjj/GP/tFv48tf/jKevvsuhsMegKw/MSertKlt2tJpHYizchgG3Nx8gjwMIiy1jqUURRyKxGjQ9rflUfasYTKxpRsmUZiicqPFXHgMBgwuU8e8vmKRWk92gsPumakW6SQIdzMDajVzcMk9G8gCcFhuFGX56pcAd6IWZ7h/CQjtbHQDRb0ewGSkpwjRpnopsQe8Jeh0JEOdsWZWqL+NzF/yZulhCAZLbndzY0qIdNc+8eeWRGzMYi4omG0IwP3iZPBZ3nCtHCB9+wgdjzXTpJScQFMHAdHqwQ1SdqvfHP/1gScR1hMlDamF2rzqnaemX4mAWgvO5zPO5x0qH1Gr7Lq0PxxwfX0tsw+1BUdNk4REW0QcLzjHsFXSfmE1qfb7PS4vL/H8k890N6ziji2D5nEVZDSTDJBRYIxlP4vAhr9vzstVn9nTZoZQfCbeD7+6Im0epgmSCMuNjjZybL4LhvdbcxSTB0C54490KhTq/KtGSxWZMqJpSQyPb2GJboLNV9kMhq1rMeWXSGY9JBKd3V/RkKV+2zCzXpUejGCQgSa32UgX9FhnGtHFlWNLskkw6G6BTI3oY8CNlSdfLNKNxe5DEyCmbgzam3lgcQNkdfL8xQssTNVAo2yplprAsxqbfOhMIBv0dj2GPRGpnyURahFTZp4ndTRKHAXlhHffew//+i/+RVweLzw2AwCGYdf3eZB2jtJNKDAjU8Ll5SVu7u7w6NEjfLd+GykPYF17YeiLuQR/TCTEZm6YTu6coVB8wdWVrfWndDspNGzRDp0JxKVpaW50ITJnjQCg5fXWSw0yKnVl2VoOZ2AA4KQ+oWBK9jlpLUSvF0UG7ivSdRMWFMbml0hJL5PzspFhtZUnDPdnVGaNt2hRnrYED7B7hjrwRunBCAZpijjzUgr2uiW2JTmt0xocqw0AcAsTbRto9BJzBTshTN/mp4GeY1Q8KPXoOqFOyESmZ2ZZUg14KO6qPeG9Do5ovRsaVcepz9sUgLLcYdkboZSCnCTk25hzt9vj8eNHuLq8ks1uIKbBbrdzIbU5CIuUhwH7/Q4Ewi7L4iFzdpZadX0Bu0+mBRCFzuv6s7lfTTDAtHiAzQgsLiJ97c/wt10gBE0OY8xotgA9KUQs2IiqF+CSW6cM0FCD9WPphJW1Q/0GLGqjBXo1EWWoxyrpcSVGS4U1+C+7sDRz1xQXM6Mq+us2/2FqQuENQcODEQxN4pP7FkyjRbjbYF/TNlXX3XdCwEQtA0QWKxAGe4NJfXysSjo4Nd5jrawRCq3zZAt5XjiuokDaEhT2XAL5nhTV28Qe3gtSRKFmw263l9WTsyynTinh8uoSX/yZn8HF4YDj8ShOqtwEbtywZVkHQz9JNdpcZjCLTyHnAbUWDDAzQ30MlXQHpcaIa2fwuu8FRjd7oO1BEPqHotBFEB7o8jGBIMiHFtq772Mtzeku1o262gbcGQRZ+ze2qXGlkJ7Rovb5ss6KVqtvsrBWSuzXuVNa5mRMYA95bkav1vkNUUJMD0YwLLW3bZ9l8NlAXdIhN2hfwroDMjOE4YE0osHkCfdjrso2UwFoZGG5GqFvCIAlgWMhKEJ7sLwXhcJyAKnpVVLHEiiuspN7SWE7gTGOo09HMjMu9gfs339fTI9km6VAGdw83glE5sZaMIPaFcyMOst+F/v9Xn0cjFJk16haCkrKyJSQrc2KXym2x1YRLlF8KDoyF6sAZt14ZkuQt9ebARJb0ul8H4yePaX/EJhedpqyKfNW30YPqNxCy014K5q0ujcWBczAMKHlXWTPOORv0+0uMBdt9ohMrU5rtQbcmTJDP6JvCBgejmCIKXqW42/rALsZryvrQyRrf98GzbSNPduhCzVRCBoeSxEytt+xjl6+oZtQO31pG7JbC5ZzSPZOM1JaGQoTcyYAshcjc8U0jri7ucUH71zrKiIOW99LtKNQXm0zN1p2m15dCi5piW33lnLG5eUVrq+vMY5n7Hd73ZNBzAib11iZU8u2mZQ3Ad4JSis39qKNy0YfNjII79t3ZWpqPiuH3ToDsSVsoFreTIduIRLrVDaae9tSVCHuB9B7NmW6FBbW7ffRh21oY9PX1lW1FqfvFtPCzffk78P7+E2FAvCABIORAwdmkOvBfoQSk4vxNrg2r2umxr2dUSlALNMwjRmt6FobE0UBEH/3DaDmIEVjFBMOvujqlb3QNJRHeapqMOdsLTPqXMGlYjydME8TzLQnlqXmEvVH2KUERpEWqpYyps6y5RACCHaNZN9zShIByROePHmCq6trzNPUBAE0rkK1p/lqWFViNYEaGdc+TXBb28lCxEN/GATs3pQeNguzoUp5xvs+RByaf9LqaGPm9SDBo1v1w+I3AS2+xKuqu1c7fmlTrk24B7r0bt6m1EpA3ObQaMCmOBNkz1D5bn9Vn42SidrgvkF6MIIBQNCY4oTstDpgtNM0sa7Jd/KKHc1Nq3eMH6SMKzBsE4DX6b57Vq3gD7ERjxrRZjJs9mMzb22Xi6ogvGza0tFMSoAHGUlocy2zrHp0ZiXkNCAlXZiThDBTItSyapZMq0UJIbVw7QoA7777Lr7w8cf43ve+h9up6h6SBfvYngCxQaa5XD16O6Lm7Bg09vXSyOk8axuIS2/G9xwt+N4KPbLp3vTxCQFaNjSxfo4sX46Qotvb1l70FdN8toSD5mfTnVaGOzsBN4MYAJMI/qT9btGW6xmY10sPRjAIX2ijtOcNRjWvvw07uWZriCKcUAU4AehXdKaFwnWbEYhEYh3daVDPpfVwHKT4PT5DchMWw5AoTHUuhEQA1QJJWRjTFjmpukMmwjyLJvnoo4/wF37mS3jy5AlKkek+XX+pMw9iacpBPnJ/GHYQP6HuskQNbrLXF26WAcAwDDjsD7i6ukTWfSYSAWWa5aSqygiR1kEgeyc27Gxtcbt8SbfmV2oEvQX5l8S+YtAFcmMwlrJg5aeg8DzbbBD5+LkzWn0MKa5FYBu5XvnY2DbrOPg7vClWv76CKfxe9mmFxDckRQi2nLvtJemtwnJh++ukByMYzFnShx23jqm1YkiDNFLtxFKBWno7ytayL80RcEAGtUHMzvYN5bZ4QM3X3+DuOS10E6319q3OZ9s9pFgAACAASURBVHud4PW0gKjGPHafApWFEipjlzI+/uBD/OVf+rcwjmfcvPgxkJMwfRahQL7nAwDIEuSUdyhl8ilh03omUDwMlxjEsvHNftgDLNvcffbiOW7ubjHsD0jE2PuO0wTZWwIw/7uZLtLF3uJVP8n4SLkW7em7bAFoO0wFYb/YNYlskLhFThRm71MyYWvfaxsJi41ptkmRzXds2bchdGUwWd1rpkw0nxi2foKkx2UPUyIUblOegDiUK7gXImaKBpS2mEuDdVKMFLU2mSmjAEnKSXLC2ct8XVvpwQgGA5lLBrt37n/RWKeRjXfcZvay+u/xHgG+ghURCSzm/l8lFGLe+sICWWD7ezBJAunCNiwBA7ssm8t+77vfxTf+5b/ERx9/hEePHqEwY64Vs25hl5OVCYASmDKQMkqV8yh2g+z6HOvatJQs6skpISGjlgnn0xm3pzsM+506u6C7azHqXNQ5mvXYt1Z3KcAEQwh46swW7sZAvrQ5Is8v9Hunnk3Ad9o4iPM4GBS2b+vGQThKlmvr3pRo4wFYoJIwMOtyeJGqtuKy7c0INDOg19kqrJPuGMJtNytHw16vNkMCQxWGPph9vwXrNe8W92u04t4kPRjBsBYJLblW3YBWALp7Bsm2Pdn8WstPm1zYZuK+3n0du3sb9mdntmzUM74Tv7MWRpRkqnAu+N4Pv49P/vH/jS9/+cv4i7/4czjs9cDdWiUCtJq9bXP6TTj50u7Ulv3G+sguyVV2gcaI29tbACIIzuMZh+Nl1++y4zPaQb/Ldm0weN8vdlfrqULkVXqOlxyPwASL8WBWpk+py9cd2CmhQifGPeyYvL52BmVbV2Fb4rUdoIW7qRNEddXOVk/iEPuhPRRNCBOYFkhWfXm+9SEU5UkZmXv6sRPEcA/93pcejGCw9NIpr3htU3uj2bMbyTpyo9TW0WgwP5o1W68tHTvd1F+o46Yo0kLcUddf9kLjgTRcmwatKixKKRjHEc+fv8DVpe392BYjiWoX4qtFpxdTmK5zaN6L5lLaid7jNCHnjPfeew/7vSzGspkW373Y2wP33cS+3TIjOqewowkO75lqJLeTTYta13v5aBZ1e2cxNgEYoLsnF/00Lo652XhS61cA3eIksg18Qp6R1YMCs7JMQLczJxoZxBW1oUW6sZBAFK9n6vvVRakjG0JbJ/z66cEJhq0UnXxmD94XxWizzEvN2xOrv9jeimgkEF6OQkcedZ0WHXStfHt4ob1CnhEO2jPdlOhS08X8SXaeyrsBAxOmqeA8TThPk5oSCncpzDRwezcn3TKMC8DZ87ezPLyMsNfDkDMuLi5xff0Ih/1R2sHtKEFS4TNQbtqLdVZfy7cFYJ2YtG2aTQjYfKveY9vxmcMOycYqbCinYkuBOACJneiKYzFqDF9bwz6+5vMw5GAmV4tgdAERAphgY2nNiMUslF61kPI2uO2ToOeDNh+IbVznHjLfD7Ll67l5wRz+ff30YATDq2C4Ocv0bnfPCDmmBpnDlJFRSyCyV1kWJTKsvaUE1th/kbolfx6iAou/WJodmz6RzgBv0IEJoCyby1JKKDyjMuP6yRMwEuZS5fh33XFNTIoKJN2FKmd3Olr9Dd267NP+/L3f+z18+9vfxl/5q38VT955ilJKi35k3V0IzcxLeWiuBLIOF5Yh071duyJyKbovIi/ehaAgK8eegSgBD1ayTRW7btxmh6UZujb1IoIj41MgPMuAb2DMlqcPV49a3bmJYKqxRbbGQLzlO02eOZ7iNmsFnaIkUNvsOCqcgFDuodR704MRDMv0ul5UI4xlB68dfVj0TfAIoxHA/cW2DKIJ4TxgnxxuxLJgaKBV4775dFuboRXrZJlF0qWUUJLMQCDrKr4qW3/ZknDWLeZACidDX3VMFQVtEMi///u/j6997Wv42Z//ebzz9CkujkdcXopvofhBuVUPt4UyTu3aF5sBbhGFS18L5ax7KbZzL1q9Wod206BeDrrnepMxoLdFmbGOUek0hx7CgjbuiIOZ0Zb12WzAErkuFUHP+EQkG+SG1OhZymTvt+pCuIYNeKk9pVGdaQUPxK3y51gwbAesbKc4CPdFFVqsgxtkDPRLWTqQrtCQNu+5XXcPKHudmvdxFVtE2fJyYtog8AgfiQinkxxuu3t85RovDQk7X/MfoDWZgBI9VCsjDdn3AvCl0yzrL+5OcrQfEeHJO+/4LtpA80NUc3a6MDYm0h0FyJjEzoAwvG5spZcowfeoMAHKDNuaUprQvBKVxQTa0vguJKIk3ujrZaogcLWTnFTr2sxBkt05apFl7npYpZhUZenrkK3gkgHFpcYxxCmbJvRjD1MOTVNxcGjKZxNsTcihi8iM9BV3JHud9KAEw5YGvY95XpZH9zv8u2RqFxzGgAxAw0qX2m4ZHRnvwbTBAsa9rF7LZ1bQduGQhBJ8QyXkR93XKkxMFB1U1jbVQty0GwCZ2UC/7wMAj6YsZcYXv/gFfPObfyLEb2d8QmzjNGQXJBx8hlZ0XITU+tDarX4Qs+tBLiv86bDzcT+daF6k5suwvmrQPJQWhkF0hPZzELLrZ+B7HRhUB+lSFEjdCzOoat3BzqxSsq5h4Bi5uKYRkDoLO+gaaQL9w2Cvn1ODCy9DJx4RpNm9GVKw9HAEgw7w0rzefnQ9HXbfc+0HAGpbX3UmaXwuEHO7ps82ql/D5HXp3Z37B2ghyILtH7Ow+WhRVCSbspTmeJuLRCHWIourbClzZ16FsqqdBh3rmBJYd2cCGH/pL/0bGIYBH7z/PpgZt3c32B/2snybZZaklCKbwzADtShD6W7e0CPTjEeNXiPjQn67zWznNqrJBDJnm2XSEKDBeUsJyZlM+ju7AGjRtNr+oIQsutZKKVybQCMxLUgDmcSv0gKmfHy8HxfH57Bs9+5+RSzpxcZnQ5n4GzaOtr+EmYK2Qa09Haa1F6bNm6aHIxh0LtjnZgEZSD8DnXrJalBxAzZtOpaovWbjacQYH7By+8kuG9V7mDsQSJf3FkJZpbVkX9rglnMHpxmoc0Etsh9CKQXjNLoGaRWB26YcoClgfdYLWYPiOWd8+ctfxkcffYTLR49wdzpjnkZcX1/peovqeciiTg6wV5g6U4hIVUFBgXFbfIatZ6EGvqjr1lBtgpmEJkD6ac0FEkPf3jgi3YwVTPZr37KFVBOaFrH9nAz+c1ubYAItjqfLsEZjJp4JFr6O1g4TKpuLAGXsRCQkbKFN5rAcPKYwNq+bHpBgMMkeWNLhWZvHtuvLHYqZmz25Cq7pvMKN2IjaqRNdPitPdavfy67JFd68bnmvnt9wiFH4zYGoGpOo4NN9Fua5YNKpRd/9yupBLS8Fo25CrcwutuAaPV4vZ1xdX2GcZwy7jMurS1xdXSIlWa/Bihqa1lO/R0qODmR6v8X5Wyhv6y/5txtN0n5cobfAbFgssPO26fZoizFcev6XZmtnp7PlvyWcmtKJMxa1qe4+OSpb0LeiId9+Dej7gAMtduUvqCuilq78IKh/AnPiAQkGSSs9ueF3uC91swXBUxPIw60CS3bOZc8j8cdacPhVWuYP11qvk5axFqv2uNaKtW/3hpwxk5zUXcqMYciQE6qqnL2pOsimuUC28CdhLjMKGIP5LjriIj9IOFFCzqIhd/sBlOQQYbD4HUotq74m9wMAxTS7YHAX7+R7RljLQj9SuKZcE1cZxv6znbezbjrzJokj4/CaNtT72C0ht2lXVTMBxS0zj/0ZsvRP+RY3VsmWO1FYVyPCo0ZF2GtORFzUTYeaecIb9XtF+qkEAxF9A8BzyCrQmZm/QkTPAPwvAH4WwDcA/E1m/uR18+yndoxam9bftpsWcNEhstyj1VMhd5foXlT3ILnd6sOk9upCOtvbCkm2cQNhTX2t3i23FgzE7QFFtNX3kWRU9zbXyuBKmCbZAxKDTeECzbg3AUCoXFw7RvDj8/h6IEZRv8E0jQAzDocD8jC4k5LVpJAVnE3r2klJXNs2/wRgZgnEBDNy0LqJ0mLrM3Zob93dusiiBtueyLXHlHo4Td/fW3ECdt1Q1grEe/+QnofaRtaEQ7WrkSacdDdsfRsX6HF0Yc+KmiC7b7uCMeRk9YviFItfbXrTxiLH22+Q/iwQw7/PzD8Iv38dwG8z839NRL+uv/+rl2dBvkFp9AnUOsP2+2M0zSSMWcN3IwADf02StsSuefzKQihIuQpuvdcbsdsi22rHq+vzcXt6me7TU2Y2/ATyfxAkUciQxRi0u83eNGmj8fksEZCUGIUrxnHGeC5yzm5O0EPtpMacYM63ygUgOTo2gxTaiz4m1VRWn6RS0pi/MuPZe+/5RqRJV/60MzXafLmtcpXlwI07LfaHCO7QyyDfWUnejQjKNl9nHyoHIPo+1H/i5pMhFy4QI3S9+W1TNBrdqm0tkOPm2MfJ8gwHHZFFSury56piyWMF2uKm6rMEOobBpFPJJ2WZPWEHrqI5izsTWZ2hJpTgn7VtMacOX2OHXqi+XnrzhdqvTr8K4Df1+28C+Buv89KrpiW32tWCdXg1k9BrYKBRVfh7GeinjfvCj61cJ6jtut2Xb+9c7J8zDdnqtwhGImHglOwv6YaiGjyUBkEPUglEBGpOwbYLd+iQSDmuwNQ3kBKGYUBKCY8fP2mH1aizjVmnMIFu6/iYZVvITrpQybZVZ4/ClGrYGgyb0rTv4s+w4wjdK28CE3B9z8EhCLR1JT2JaP6mjBgI8ZnydLU/m/3pEUcc43Z6detr+7P2xE5xYKaMbrTEWl9HWeF+d9qVj1xfjqFAr4ocenKv3/y+9NMKBgbwfxLR/0NEX9VrHzLzd/T7dwF8uPUiEX2ViH6XiH53PJ0ks84h1gRF1KLr54DFqSP+nn/vBo3gm2GtttGlxR9W0M3/nKB6gXZfoNV9f1v1tfLbIJMzh5k2wpIJSbVHLYJU5mlCKaKl4qo+Y2ArqxESY+3I1cAE7qMUyzyDa8XxcGwADU17uhmykboZAYYeQqvrLexeeLVfTiUXvLvt4SDTTGhEoSr1STBfAbp+700JGVOjDet/Yzhjvo02Lfp43fD++fa9v2fvN9pYCx9s0M1W3vpoEwb3D8u96ac1JX6Fmb9FRB8A+C0i+mfxJjMzLXdRbfd+A8BvAMA7777LK3nODVLe52H232hsLFvCmbhVSa9IVp4LVqRDLMthKWyaGUGpZ9y2VZv8bjJ80Yag0WJqS2VDGQ6HQ15LRe5MlJwHaq1+JuU0T1rDhMpF4gj87eAsc8FQ3WnXtFwFSpVdh1O7ByLsDwc8fvIYn/3oM9FWIT6XiHTr/Ea8cRaA7ZnQMBOynSCwMQ/vxS60XuumGsOnMw6Ru4eMN9p6DGp5LR2E1CJL/ez0Jc1tmiaeoTMla9xFX0RASFqdcD7tKkkfOATAksuXKmXxNowF3iT9VIKBmb+ln98jor8H4K8B+FMi+piZv0NEHwP43qtzUshMrcO3GuJz3wtNpuZhl99yztqea/v0eyv8eRsa+04cwqBDZubAW6bl7ML9LdkmLCOSXtS174lo4SNhABIvPE4zpnFCJoH8lWXGIaWEUuXgExM8wuy0qCvDoHyZZ4znE/aHHXa6aCrrKssPP/wQF/uD1I3ZD7lBh0BCzxrzRt8JmQ3feieOa9dPi76KMPpVs1UmSNuPDfoiWtMJtVkZo8ltp3d7vpXZyrvXLA6CQujU+ggrzB+RXXPKb9GZ5Zv6ju2eeP30E5sSRHRFRI/sO4D/CMDXAPwDAL+mj/0agL//6txaxV3ThN9LmG4wq72tXtiwSYV8kWu92dH3vZgpCeICSwDb3wJqhj+D+WYrG1IBSz5cKeTRzIK1iYDueof5GFr3+00U16iVMc8zxmkGEsnBM4gatU2DWV4pkdukABwuA7IG4sWLF7i703USLNZ3ZcZhH7aOh4VEm2AoXq/awfBW30pBYJDMJlTIdyZxftpf5W2G7G3qXgGskYosOqosfr0KQtHvy7+5Nts+ml2yRiKtaHFZlg1fx/QLROPPde2puhjNaCKaLUJlSzO7+TwE4TlyDeWYCX6PHntp+mkQw4cA/p52zgDgf2Lm/52IfgfA/0pE/wWAPwbwN3+i3MPArBO5RI4Sejtp1zCDuc1J9/fDr4Xd74zl12O97oeXbXADlF4M2qqOprWbYbTKF1p/0fBGmIRShAlrqZjmGcBRHq0RffRl11pXjEckAuN8PoMS8PjxI9FW0M1f54JEOSxzZ9gpVUuTa4kevC2Bj4CI4lpfOdPZ5cZtVipgHiBnzKZUQtd3eS6diM2UMz9DRSlaJLdyrA9ixVdjajYLGtLwOlM/Bl372fox0ofR31oQ6VvB/3WfEdJ1yxuln1gwMPO/APCXN67/EMB/8Ga5NUnnNq3/A4ei3Y5BAGwfRPJB7SW0E1h3ElVjlCB0NyVqN30G2Wk53lvuAbHlD7kvuTmErO0MSEIyc63vAsOIjRrSsDJrET/DNM0aU9B8EW4JkQXULHYgJPJTvW0MDodDO4hXx2SeC0qpeO/994H/95/roTcs12txv0nSbeFgzIhAttxrY4PRSQ8C6jQstQCmUNlg9rXfHeNEcyN8l+eCYOHgCzAUQu3ESyaEcGgdI7ALjF4o2GO6xsMG0hFbO/tkO62dikvBY13R+1GgYeYhorJ7Qer1prLhgUQ+tp2ANqEXNmTiglho4x3NqMvP4tvaINSFbRcFRyBg10oR5pmwQVu3zz5EkmUgXH/ntYZJAmdsQ492rKGaI/qUTD1WzKVgHCeUyrKrMUhOLQLpBh69wEwp6/tNy1ra7/d48uQJbCqA9XDF3TBgt8948uSJw19bRMWVQSmgAbPdVSIl688F9HeUgBY41rSz1Tl+X49xnOXpnJChn5baVgRX7+VvTs9tDRyGfXVbxnj1yuJ96R/3UZFSQ0/KG+VvmJHM7thsSmDxLLUyX4YottKDEAwmzYGFNEQc0CUSeHme0ekVOHnR5RwuRMHRI4417I91a/Pg4DZ1Z49GqW82X9QOsb1L/4rJqHjdPNTVEIPmNU0TzudRD7qdUWqB8n63X4WdN5ESoc4yv55Sr1JSSri4uACXgmHImJkxDIOsn9gd5HTtnH2HaK5V0BQtGTC0A/AYh04EUxSTJhywxQstu3v8DtE5LSdtpe6MGqta9KcEydEJbF4wUxQ6EWWIknldV50pHW2nCUq2GZumlGzKUrajry5MlgLO/g3qrH0zSKynwb9JehCCYZmaXR+vrsX02nTQJ12DyBCsPLgBzkYCiV7fsO5W4XibH1g5ndCmtUzDy/1+n+NGuLUTBk7UtWlTBod2tPp7EIu3hlBJiL3obkql2m4/EqvZQVL7YHFYOrFbWwEXNikDiTKoFqSUUQuQMiOnQfdmkHxK8FVQbrZ0P9OyQF5tEF1AtHq0XY/9WkiOvRaCNLbF/U92Le7K1KnoBvVJbbRoJrQ2bGvwVyupbW1vG+WIQDXKij6ZoHTQWQWL3Zh6YbtUXHYI8hsChocjGFbeZMQON0GxbYP1TN5rfHs/MvHLyrdHtuB+FAr9FFIUML3sjtkYsuAgmBoEhmoPtMmMjWTTj4kS3MdCBGh0XNVjy4iShCgjnkrVFuMw2pkQrX7N/JGysu8UnUrFbrdDHgb83M/9nPh7CEi6vVvhikGrbb6XOIvELngXY6hXVvY69bRgDy+9+8bMDV7ZLfJ3WpE9vQTrriGERb8b6uyUSxCukirACeie6U3OeJ3M/gwVXPfNYiYsBPF15S76rk+ab8UiDufV6fMIiX7jZAzXMVlgnFfhIHk36d/rd8DWVGhfbl8/AHJWwwJxRLOjAmAPqn952ZaiwNArLV/ohBXb9ubVNaxtA08Kg0tt75QiNnRkAsveQqJzDsNvkD4ImygkAGC32wEMPH78uNVZaivxDNxPHzq/vqL9MXm7K0I4t23awv0fGLKySRiajDelIVrQy8uUqUjRxSbOWzzGOhamy9svtL9XtcvaY/8uTZat91ov3J9v76TcUohvlh4EYhBCj4whjDAgd8xisxIGE2vQEJZE++h3zdTs8faE5NegZ0c94sk25UGmsWp/MK1KcIPHxqS+m7WeIchA82KrUzAthZER5qItyZmT3XwwYnVdyiIspjLiPI+ozD57gCHrTEb1vG0GYkgJcyldX1WugiJUINiW767Vq0yFjucz9scDTqczMjNsd9LCjFRsdoK8XU17Nli8Reb2XGRHCfvuGbGLD71X8MCFLZH1FWBzJLahiY2xRLNInMVKoHsmkfmCOYgeLfgzaHmZebb0mdUGVxCnO+MzLtC3GtkhE4ifggxxS53lzIs3wwAPAjEA6KAjAIjHOCEupl2ZDabRVFA023AhZfUZUyf2nz8RynaFoKjF1lnMtaCqJ140mQVGWR0XzE7m3U5oZEdeht11pRxkRCLSJcleGT/N2N6KsyREskfjOE04zxNO44yUh+6sROtTCbzSZc66hZvXiQIj653lrs4EwuF4xJOnTwEwEhjkodW23X5Q32rmNHSTVjwUy2JlYHauoh75hB5sfyrcA5Lr4brRji6QZg4jJ6FtGcJUtlJx2S9Oalrj5thTUvH1FJH2mlMWaYlYtO7qw3LBgehbUwFow7csG4t+ArUsGyF7375JehCIAQAW63i63YhE2jGAEiA/OumaAI8ca1aBSuUYb6ACx5yL8Xl/xKb3gmlj0LluOA6bd3oDvgT97lQUCLp7lPpmGVEwWM5mieZNsN8TgELANE24O51k3YQKO0LvRwCAAtv6XKcjmUGoGFJC4YCCXGoZqpOdnZ49e4bdbocmuowZRRi4l557rWkIon2uhjH0ZVvjEX0NgR9bPywysalKK1GyUlQWyg4939AYcXe/18Z+VT8afSx9UtHZK7EPJG6IcH/DUDHefolx0Zexes54IAUF+hOkhyEYgqTvJD5iw1Tbq1S1gXRtVgO1hEFaSfGqQS60Xv8fPeMLXbuu8JKxN23LZS7cMQkphG38ElZGOPKIDMFKiy2CL5oC0zRhniXA6Xw+43B5RPTMA7SIh5A6DSmhlAJGASgvlmVb/wB5GFCmGdfX17i5uXGoU4seAFsrht3BWucnUovPwDOzCggii229r6u1H2SGRcN/F/RhXdiP9zJfeb+1qQkFy7/1jSGzjSp1F6OfqqGuLuON1b9W4ZVQYZM3zW8lNLFtiqzr1tezKbU/j4iB4I5DO2y1T8YYgh5cphNksw8G7gtKAdBtI1YDXXYlhDKXjkFmIHE7ooxZpox6gSOZUrCHjbxM8+fgl7AaO7pRYpCfetCp7Z1oxGLCwwVpI2SL6EspC3T0HaTXuN3sT2aWLd7qDGbyjVaYGXkYELW1McA0jWBU7Pd7z6eUgnmeMcCsf4PD2gNB6JPnFkaL+50QAAV0zGtj9zXpuxOINgCwZd4VRDngtqaM1ooo5MlibrimpiVOWPY1OUZpPorI2FuNWVzbQCP9vX6MW1xI9KGt5dKr0oPwMRCC086ldCAh7ufi3eZT8loKEubmWdYL/j0liQQk76714Gx5el9OkRF6LiR0sPMbTO2f9+fYNGz0irc2JxVwiWS1o89IpN7HUUvx6SnXOhsawxZAiUav+PTHP8ann36KeZ43jvyrmOcJp/MJd+czPv74Y1/kxAxM84xEyRdYCU/HfjYBFs2gQK7xa8eNAW/x9sKqbbRG3ccyWf/G/SCaj6NHetb3tlGu3VpnzR1qiWMnVelNr+3q3T+z4bIulh0QowvXDpWuY2FeJz0MxICXD64pj76h8oQ5IeP8MG8xnn13LK3brxmUD8n8Cf31XirHZztfA9UmtFKri2kwg4UpeLLv1RsOwQM6sCpT8x5Y4O9cCqZpwjTPvj9DKaVpnI12MkTQvLi7xT/9J/8EFYRf/uVfxrNnzzAMtvRYXqysW8KlhOvra13VWXDY2f3GED7VytXXsnj7qWnn2Je2TsbHAA1ZtWutKat+CwKdFIUC1s3N7BLN30wPgHt6AlZ1k7GNyqnVYzm1HWu5Imtt1lrxtAeiCbeQmPdaXdZmYQej6YYY3jQ9CMQAALVg5YA0zW/fI2KIh9z2nuDQ4bYjjy6/Xi6ljqI32oaihdeS1sptZTUi2BJspt0JAHSqNWoT37loI7nm8Z2b+sHyPGrFrBGPp/NZll+fzzifxmByWOwC/HvKGcyMaRx9zcPXv/51/OEf/iFubm66BWtVZxxkS/mE/XGPeZ6lP6jtFGUb01qMxZKRrT9T2l7CvLqGpmXbGNynRBaCgtsY24yEMd3KVPSR3ECPaLQXGbaNI1r+iz2JmjkVyqS+TUvFxYvrvQDppyZjLbdRgazOfE3rq0sPBjFIotXYLBnT/rrB2ehE5ig0m+273DdvVb5+GPjbes61S/xhdaWERHp8OjXNhNQ2JI2xAcvEaBvWuvAyrUnt3VhmSgQq7JumnMczpnFCSgk575ADtDS/gHX0OE3O2JQTMK/PODQEvRsG3N7ddmdYyqlXcnJTrTMy9osepVZv7gk4/t4031zqYkUXXR/EzyVzBM3ZmLtFGsr4RNMhMmo/RstqGHyPlejbQQ7xegTC23lYf7mfpmtZ1/qGygwVhHdWXbCJr16aHo5gsMNMgXukX3i0UtcpQID/HHYEptgh1DFdeHEBBcPgNfGN2NscBo9ZjiAzZJDYhE8/+HLGZNU6mBaRY9VsaJ2wN4QRW32MkcI9Zy4QxnHGNEqAU62yCGq5NsPqLsuzlZiJ8PM///OYC+PR48dBq7eWGAogInz80UdyJoVv1FpC3gprqe0swYvw3Qb5t9ZPtNFwpjYkuKk41oTvcNoRhuEtFXxMIUxYF4N1pkhAnVER3JcYwIYPaevBhmq3TsuwdtmCPOtBE3z2RO1IxcaxOR9bX7+pfwF4QILBbT79vbQ3jSikrVFaRhtMMnAprESxdqSx2r3bsQfNe87dikAnP27EGouO+cvD7YBRfLt20gAAIABJREFU01ZRxjuD++BaIM0aatp0FYi6fLpdf2rFOE4YdjvsD3shLG7amuWH13e/36PMM6ZSsNvt8G/+0i+BkfD48WOdESHtP/FX1Fpl45Za8YWPPgKlhAzZfr2UWRZWcUVOOx1Tajs5cfUBXgq2rhP828Kbv+Q1bm3pEIELkuWYNMaxOvRjt0B+dm2ZSTAXmgB5Pcbje34FihCatLawoQD42IdKrvrNW2H0obRT+dWrkZfpwQiGKPWjduu1Sdtii9XsYN+LdPFskPIc8vHO8ocsKgLdfShvywYmPaOab8Dq1/YHbIRK7QUph8OB9GS2X9PY8j5A1QgBnqfMoqy7itlekjUaNiMw7HbIKTujLE0YY5CcM6ZpQqkV+/0Bz957FwSNY1CfhLWPIcL6eNjLFnIMHHY71HFufTMXkK71jvjKz5lc3Gn3F30flARtvOGdFXMx+3yTT9fja4I/IoL7Zm+af4A2rq9r57S2vGs+D22dybYomDZya1dZxEaUlbFX3UQNCpL7Ql47PRjnY5T6vQ0q91bOKWW2zZyY9fi06k40/2ON+pOelBeI/LcUJ1y37YfAipCjEwqGDII9a0nqr1vXw6L2JX5ANliRw2Mq1xbCumhi56wiRVbUtIqdOclgn960spdtsN2RSPNg7Yu4GC3C05QSapHDdkopuDwefb9HZpYdqp2G2wxE7LVVPbh/3vtq0XQON9wxGcYvOvcQyr2P3VpmsS5r7ds5Rhe1i7Njr8N4MXdDvi97JmYcsRAzowZh25Akr/L48z1dGYlj4UyMn+vOXwRELexzRmNSv1YrOJEfJho7lPR5hH0R+mg4+ZTw6zYoS7vONK1e6DSSQf8KhoW/WF62SMtOWs4KF1nhiwsgFSaSvfSB7cfgR9bZfWLdUkwqIRuzkMN8Pw6eSLQ9RwFWBXmYhquyyIor49Gja+z2e9mBmouilRaM03q8IUHTcls02oh6I8JvS0B3nG80E3GGv9206T2Cfouuujw26a9v56Zj0a7fR75NbHdXV07Y1WvrsvrEi57gfgOh10gPQjAwLH6/6qyiQGPSz9mn7AhIZrMuOo8s1JncRkuG4mvTfkys2pTaiWImEBSD9dOgFpcgge7ZkVkvMLwaLiR6bzMDPvVYWdtZ2z7T8kwLdbbwfkEiIvC4g67ynWVNtpqcBXfnE+apbdgiy4MYqFKbgbIwOID9MKDMssJyyFlambLOYghsLdDj+Eh3ftoNuHtxi3fffQ+nccScxFSgUuQYHyagZnCWfqo8y/qLxSYTrPY6Ueo2rLV7Pm+ji8dkiLsJW+8FgMWRWEK/cG9+yYa18bi43oQLI9ieIUA32HOkYihQdhSP9SWQb/tnechUuSELKYdU+KYeURLMsPQTutgPRTThwq78SI/dM0wY9xVqPWS09eaGwYMQDCbxO83eOZfik6Z1eojO6m1eh1T3c+NLh6OBDBMmZBcRQlENBcDYpc3fM0vocqfVDOp2BNbIJejQvmEkuWfXmBTutbIRCQYAIYOooNaiu0QXnEfb4j27aQQikJpShHZeqMVXJEVDsb8qEwpXgMhjH87jiIuLC+wPB8mLK2qdhPz0FGzZOOYeneZEbD2xdCPbtz6GZQnmm14E/PSsThz0/iMdmsUzcFQmDlxqzy0UenOCk14Lz2LbFFk1PdD5si42E+L0EYRM55A2hGSyM5bE7pZ2xLK1ovVV6cH4GOIg9xp78Vy4ZtOA9mn3u2knNLgPyHLj5Vy6MUf0QchgeEFdmSb+PQ80BjAM0MjdBnmLQO0HNPZBP8mEg8FR7yQliCqCkAsI7CdGEZGYE7WiloK5Vj+fQV63A2vY6+oMsdXXWrkYZ2Bh1PM848mTx3rylNRLQqmLt9vHa5n1gkir7srSeeFFRSti4tb2ZR35fui9zQx9MNVq3j9Kb6wR4ZL+AhRY3V/w6/Yz7er29ZAHLehu62HLYVPRvkF6IIgBykkNsvnlyORkHbwtmTedLEGimqBgs9tj/rZNOFE41nIdNVaDkSxVjtNqXmnZtMTumUZCHDDy9pgwAKJQaGgD6ltIsTxrA4kmzUQYS8V4PqOW4gRknmppXr8p6HILNg7+EO8X6B6D3CIhd7sd5vmMx4+fhLEDylz8x1LwmT1AK2Zh/07WMMT9GpcjsMAWThOLpzh68KOvid00WFQw0M5iFijkGevQfA9rU2gbL2wnhu77YcTZ3xS0+BLG7vwbRrdREN0ndF6SHo5gQCQOs+f6qbZGBHHZMXtA0fLciZBzExpcG1TzsUv6jO0jIG5B11ZopkYN8NSOHW+BP5aT/Ikz0WqwGFyzS/VmZJguGMlMAKspad0UMpLjAGn/6XzCrCemBLS56N8Ww8Hc9q9kioQP57o2M0F+6vXhcIDNfDBsbQLaCdFWb/8L4xoEh3cJ9X1taMH73vunCeX7Esfq+7WgPV3oRHOmy8EwYzcmnRNyg+9f5jR8GZS3OrRxWvZ/KH+jvjY+Tdn0yNviI94kPQjBsGSa5XfpjwQ70GSxQ3uT0HHjDJLfcisSBRpRwKhZNIgIi7ZUlkUhg0iOMLNUDJpzrKPU07ZDAzOyQWEnMxnUHBCHCYVIa22rQ27P2SUlkMoVPutCBFAFJYHltRRM44R5nsC8a4hKvbGjHnybKYnJMc8gna0QYWcEZQQpkZMJhH0eMM8Tctojp4y2E7XUd55npLx35UVo4eFxzBx1cdTUAZ7H50xAcnjGtWMbA7sQead9jb24Ng/arMaag6OAaUI7CI4Npn9ljMNC4C9r26p83xPRkXq/1GmOz9dHMMADEQxA32H2PS46ilqvl/ahWyiB0MJ/LQgq5t93pTJBZMCgwqLp0VNaU/Frf0iPcrK96vInMEGokNeLIVOqUXhYbYMG97wUklRS3wjLisfzOGKeGaDkG6YQS4Ti3ekErhWHnZxDaSdNORZSZq21ImdhfrHz5fo8TjhcX+L9994DkfoI5lnjIgBQ2LJcGbrJevZW+RU3rZbEGzTpli9gyUjxJpnQWdxZMOPKH4I1E9n4N5PWqbGrympKdDHeS/TSNcekmQkD7u8tTUv/vhSqoaRe4L1ZehDOx6b14fsMAH1Ht+/rlWSGOFZw6Z7+aMyMiFE7odQ+4QPBWJN1JJbmAQ7OUP2L0ZF90MyaUBnwMyIEhQSHphFFIiC3yMla2adDx3EUhg6w22IWSpnxyQ9+iG9/65uYpgmH3R4ZesBtaJm1wU6cst+1Vjx6JOdZfvDBB7Lzk96vdcJhGJB1Ks5OezKmv09rvUybLZ3J4a2XvvPSfDv0stbsSwfjCtEsy9PP1VmgHcIhF6wtzyUakGtsgnRBv80HE+luWae1AHtzsfBAEENX8Y2BeCkUWgxEdDSaqo6D5bELAR82XqPV9YZJuJfwAGz60BGFajVq+6e5TjEzJThKWkZKNHa1qJATZGDHmocgKqubXtDNowAwpmmSmYEadtXWo+Nszvvi8ohhl7Hb7cDMHhRlDbbZhzbrA9g0cE4JwzDgfHvWYCmdTyeAi5TnptICmRszbNm726MbBDIvdjIyO8/ukYWuq7BVoWvteZO0dH7HT7tvdrsJdnf0dmWxD3XzqYR8/Eb/TucEfcVU4xoRRAEakcafQ1OiZ7j7pbdLUpea1Q8tbc9EY2FNEAZb2ex/7onLpLqjYiK0U4PgRAc0uNVK6qnAPOMAfMMV32tpaV+amaD3q9XJ2scM2MKyAKvF9gcqKalygWsdFQ6JCOAqa/lSwtOnT1GrnCh1Po9d/9r2+CvmYInEnFmEDxQRXF1c4vnNCxd2pVSkMoNoALL1FUN2p7a2YCUJ7FKnAJZCtOuvRc8v0ARLY+7VlqTvW/2WxWwig8jUtn6FW3SJZRzU2sttezd3rPVsltf2w4s7LvwWdewRU9oQWK9OD0IwADpFlxISJV241C9zchTltBU6QO9t2YtAIwK5npyoDIlsdVqKKCUKCkMhCLRJYS9JNGLkRZ1In2sAL0TEsVcUBLSoTTsazwiOyEsxorCt3ixcucyznzgFjQBs+14SdkkOjuEijNcOFG5Q2LWwmWgKUWqtOJ/P2OUjrq4uZe/H5+2AW3bTQpei23Lzbl1LxD5NWHsPknVemzIF2njYeN7vaY9IcTmz5Tp+VZ/mjO7LW37fLNEY0gSFE+z9iq5dk2fbIvVNldblcX/d4uyd0vdLa76dHohgaFrR+jPqfSiRMfX741PXAzGbnhis4zuP92KgV78B0dabz1OP5Rf3e8jfGMydeG6uLJ1ZgfgB2DHtNnWKcA1O3ipmSITreTzjxYvnmKcZpQizZtVuFtVIRKi6sUs3NUr9atHeTm1aNeeMcRzx9Okz/w0SU0LGKnksRhOS7IMZkHJAatvORxfcymNtyNmHYf18zGuZp4mTpVuwEd3WeNyvcQmrat+TVug0lNEsimU5rO2Jfbimm9YiE0yvqvfL0wMRDH0SLWHhsD1c6hyHC7tfbtw/mPdNica8my4RG5bMd4CWZ2P0VnrqBBG3KU37U7Ngs06WTRKkYEzMFCA2DLUstFD4WmvBdB5xd3uHWqv6GyqAASm1wKlxnIAKDHkn/oLxjFIKdnY89qKvrRgTGvvdDj/4/vdxfX2NJ0+eYCozxlHWIszzjKEWJB78HV/AowhqZW/7OgryMWgMsGznWp8u+zRYWhuoIorTNjZbbd7Ke/XMhlBYOhfvQ6VbPNsQUsSVTTj0zelp0vakbE760Mg3TA9jVkK1FfnxaG3AjHFsB180GgLQ2rwmji0n5v2ifen5doZeFohAdSaAyMKMJA6gmJ3IgjqS+wqkDbafQ2MMg+voqMXhOAFE4uBzcmmvApDFS0MacHV1jcurS+Qho1RxKuYsZkQpBWBGThlzmfGDH/0At3d3vv17Z57F/mJAbFXzcQCn8YQ8ZHzwwYe4OF6CANRScL67xTSdUcrcFgOxLpCzIOyFgL5vSq1n6h7my89t9OdVxjJfWj2xrE/s0y6nzm7ful9W96M/aov0lteYa1hApeWx5uRDYXEmcWfwmNd2X25KsJekVwoGIvq7RPQ9IvpauPaMiH6LiP4//Xyq14mI/hsi+iMi+qdE9FdepxIMaJAO6V4Jas8CfjRb3EfB5vhlbcGC4WmrU7Ai+OZxp+4Zhk0NykYqFYRaZbmybj0gwX1abztw1r4XPTEqpYycEnZJ/AHJhBtJm2TfhVa/BNLj8FTLQtASs7YTQCYgmYAII8cgJMr44N2P8Cv/7q/gK1/5Ci6vLvwEbIKghYGyrlBMuLm9wbe++y28uHmOYdgJMzBDDp2RtRiNYZLnQ5B+e/TkMQpXUMq4eXEjzlWuAM/uW5A+1YAwE/42XqbdwtFu8rsuBIV1ivXLclqatH4WbGXHvaEpDzZmsvJhtiLsABori/3goqhu+9Bx11xOT1HQiSnj0+AszsUlSrID8nJuTF9tYtr9ICpcvJ1tJ7B4h61PqMIWk1l/hC56o/Q6j//3AP764tqvA/htZv4FAL+tvwHgPwbwC/r3VQD/7etWxG0oXkM7n37a0BhrrfFqVGDTeKWUTmDY2galGfjeTSslwV1+rAwPIMyHaj7UziQwEGtlpSSCLVFfgJ+cqJWp1kGGkReIBQBKYYzjhDRkpJyQUgalHBZQAXEZ8PF4xHvvvSfbuOmS6r4f21vRwEo5Yb/f4/LqCiDCOI4YzzJLkXPG7rDH/nBAHrK2hTuQtRoi6sfW7GQr30+e2ni/63+RqB3q69qw0s73a9WGlAzZYSPfPv/7mhfr2eWNRov+ngrUpeJazjyIgFuwrp2ruoGcX9sJEtIrBQMz/18AfrS4/KsAflO//yaAvxGu/w8s6R8DeIeIPn5VGdHqM21gnRZh1YoQlvksieylHdKEUCeIYKAjQPsl1FyWGe+Z8DGtv7gvpoUhAEKmhBxWfNqeC4x+IYxr3NQOQHUYWysqz/js+Wf4gz/4Gv7Z17+O25OsmSi6AW2sHwAcj0d88MEHuLq6dkHZmW/eG9YX6vCC7BJ1Gk+YS8Xd6YSqvgWkhJyH3tYN8gzMq/4KDVQixgYjLJ6kDUXRahq+xV/k7d9y3HlbXUAteuBeGuA149PL6xj9D1sNMOTq/ic1Je1YxT5Lcubf9I9w+P4G6Sd1Pn7IzN/R798F8KF+/yKAPwnPfVOvfQcvSabTKC2IgtlhNMFWCEK+uZOmNXrTU7saGFth2K7bRms2DiBeWSQexUBpdXJw89yv2yX59QvCDD1kSGhCMQgNRaB2FgAJZCX/DP3l7Wc1cyrGqeBPv/99DEPG+8+e4PIi27yr7AXpAlNqlnPSruw116ofyaBzC7w6n084XuxxeX0pEFj3YWCubSNfzSf5++tEm5AMCyEV7SYdiSD840wPhQycuTZKsDY2x+CigtGSCOWsBZsI6MZ4HPJsiHfpBI2zPS3/roIq2qrnF31Arf7r+sV8uFHhG6Wf2vnI93mOXpGI6KtE9LtE9Lvj6RRsT7iWNgLorCwzOdD/xQ5r3wEj5lBwkOboqi62u8J8gv/FzreMKZQVJfyWh9uZHGGbebThsu8BQPeIiS2cuboQcC1v0iwRhp2sfDyNI25PJ5TC4ArU0g9PSgnDIHtDppT9jIiuzgtCbHJCvhwOB1xcXOALX/iCRDsOcjzdNM/aAtm7Ut4nPeMiBGjZ2DpastwNNUTmxyqt+psbA0SFHIXpJsJ4qSbtbaDOXF0g0x4h9ObYEvHaVRMirR4BbaTgA+uy7EPXYzvWY9UQ7cvR8zr9pILhT81E0M/v6fVvAfhSeO5n9NoqMfNvMPNXmPkr++MRrqJVKDTmkS3HtKxOgFCCvHePw9G1UVwPUfu/9myYKtPfaTHgpJAu+gW2bHIbIJmhaNvQ9W6k9TSsQ25zNIG6Vcwx7w4tmT2aMyhnMAPjWDCVitM0oW2dopvkFrHbS5FApGEYvO+7jWw2utW00vF4xIubF068vm8luBNYxC1+Yqm5eqZcmBIbTGeDei+RN7kdylh+wWa71g2Vf+4VHF0ZvelljBjbvHR+hyIgm9To1nMcA7tjWVF49vn3wqUvw154U1PiJxUM/wDAr+n3XwPw98P1v02S/h0AnwaT494kCq9nErmujjv9i06ZtHS+UMORa2LqGbZLYQ6dX0I8S1Jc+jK2bcpFuXFgsUAczGFWYquXwpv/P3XvFmrdtqUHfa2POdflv+1/X86uVOpuOFWkBC2lkIAvgShiXkpftPKgQQIlWIKCL+qLvgTy4AVECCRETEAiAQXzkHrQoBHBC0FCtKoS6lSV5amiUnVSp845+9//WnOOMXrzod37GHP9a51UZFXf/HvNOeYYffRLu3yt9dZbH95tGNpWb5a1ox0OaO2AZVmxzCuWtWPtK4gs25MdkDuVNo/mmb/D75Hry7Lg/u4evXfc3t46wljXFeuYoJe22j36vc2f4Uu3F8Z8hPPjuJf+FATJ4dQd+injHoNvBojD+DQepu1HlOp92ZFbZQihws9AzsDYYgdZtvBYpfE2ehvG+veF6RNlAoDHLVf+ZQD/K4AfI6JfJ6I/BeDPAPhnieiXAPwz+h0A/hqAXwHwNQB/HsC/8eiGEJXlxxHK7rRrkJS8GZx0t38qE2x0sMeMW0kAI4YwZ/bqHIXQ1rZk/Ze1YCy7sjNOrjMbTuKRrs93JUymOMiWAfBqG330xCuXPrapSt5laegzwVOrg5Chf2sNb9++xUcffYSrq2tVfHqW5roKyrFs24O5sFfG3/bmPjvTxnGvbQSyho3rFRHu0tajIfdl5JLRXRU8vt5kd25e6e0nk6kkvjcieA5MFZwhMEJojAJ9nx8+XD7ofGTmP3Hhpz+2cy8D+NkntQCA2Q2N9yWcQdNRsxVPMJMzA1CJJg9J1xc0BCxmvUakzME9mNC1TIbWZjem4/DY/d7+VvvJdCJRTq8hJoAtz5EKRk5IyAiABjTjpgoQgDMR07KuOM1ydiVTk7iKqQ0h3vJQ7x2HwwG9L7vmkTOQr48L4nhxc4OlMz5688YDrziZKpJNuwpvH6pdG/mxPMkY9lnG4KZqdVQ3jLGHLEzY22i6gPZ2q2N6gwY32gNWy9YRbkxL2lQ1tXosde9q+mpPqFLaMvpIH668nigQrDybkGiyHoMwrqkXuJ3QRIHqfHkHWR5ES702jDqYZXmRetmZ4IyrFSV1sAPnYIzktZbf7Vvd72EoIDwQRLxZ+bD32TZn8wFwWBIieHrHihXLPIM7YZnlzAc+pkNlrP/KDLL34V7OmEgEzbHjy6GVOcWur67xxTe/KdGOp7OaJZoSXSk9mywjyrIxt84ZY478dsn5uS1VXBjiYKep7f6EUcPGi/ih16T7Ri2t9hxhw7hjcTQAAJSEZqorBJTyhEJcM6PdMbkRtEkIJr55Snk2gsFDnmFoQOAvcEk66uQxAem8geonSNrUPthgkeuUID57zL0AGqBzsSQqHpg9Iwe/V4nGUMPKGi5tRAYDT+xjElVIm4qpkgkdpI4+6d/pfsb9/T2WFwcwk6KTaCeDfA+D7Zhc5hmHdhWElDRZwibyfO9YTmcQA8fDhHvEvGRE00xLoiFvLwY0eQ1H3x5bkqgertay8ZNc/J2He2hzvfgxdkiCTUJjixTyuypKqYhmKzilKaT3O4pFTL2Ljmx//B6UZ7FXAmp/mfSVpTje7KQcnSv5t71Ka311ia/8gwmDKhSK9zjX7I4JFIGwB/eNnTjdzoh8C8zaT44lUGuBnSDl+yUuDJ+Tsy9xNSzzjPP5DKKm2ZxQNFmggsjQtEEp2GcKe+b65hqvXr3Cm9dvIHEMq5+IVZdat5mNskO2vKPy5K5vwpyERbuqafZYR8auAknT+kAlpb2XHH75/mxG5b45be5ELTqf154/LD51XHavP7E8C8QgsK+r5pcJX5VYCSgHuhRnjn54sN9ZqYPjAGp7Vu8hkoOMhCGzBFdtOUJhAyzD6+QEKfJ6oUfBc2q7kLG8e1VBkOMbAI2sbvJu2zeB0q6hj1qHbeDyNxFpboZ6jqXVcH864XQ6yX09DngZPf/2nK3wHA8HHKYDjocDrq6vfKCZO9Z1EWHDHcx2CHGIXjfB/E81JbKZmFkjv7+2C47GyrAMh9BsdqZuKsKF+3Id1uIs9O1bEhLK1eMKSwEdSsR7PhetWNFAjJdjLqVZxzx+7xZdi7J5mnB4FoKBAHWWqwNQL7bW0LX3gc6V0Lku3+RzGWRAlR118DgPIsEdcQasObUBUPu4mAHWAFuSYlgSFCMKahaFJ7KdkYQNRXJZ2VRFoOSllkzMFhNQB4eI0JIycCer/s+JTdfDl2XF+XTC4TDhfDpjXRZYPIgLMyUYyd1w1nyOK8AdRFM568p2+FlbLTEMYEcLqrDpqzgduSflPUYC2mRsGd+EhglRmaLmTGZHEIryiKmJihFWF+cf5WK2vmJ50S4GFJcptHZIPEkbzB1TFOYPc1GRhG5hUQIo74iEoQwTxoCtNmVBZnSfhb1vwfbfbDwUZyZbQ9rXsbcs/FB5FoLBShlIloHrqD4AAsoBnQMPKWEIUXeLX0BI1JzjwLXczvudIbzSNDVkE5k0hsF0jjyQa5q8IATZI9GUQYzAW1oa5ETUon3kPAsG9JzKtOyWBGBnO19CjpGzpcO1b89stH+yJfvo74pDctKYJ6GK1O+1d9y+fInrmxt0lvMwocIl9KBJM9N6eXltB35T/J6ZJBpvW7dVkdg9u9yoI0ShzbcYL9FD8kkZzeRTvDzWxmJmkpOcOceyYtP/jL/CqRg+LhEohk5tdST53bqOCVkMzx7+Mao2qhDBJfuEH0BKO+XZCIauWgFmd5FoIyQtbppOyDzsrSwMfPozIuCqtQCgU4A/CTapBON0iawrrYzaIzGdaoVOHgajGgiFoVk5rg3Zhkyr2e7fFh3XsbBbI3eFEVfv3ZHW2leszDgcj6oFQ11mR9irV6/AveP+/t5/axdpKGL+jZg/+fRTvH79WnJLtiZnWXSG7VF3oylhYNtqrVNzwUc0vjl/CiFtQjSgOwrNPKa43H8K76TqmaN/wdSXU8/ZVBDIzQRZAdr6KEo9Q/uE7jOaZTC3TbDg00SClGchGLI2MiekmRSjnHfHU7paht/UuX62SbKj5TOXu+PI7k1asrQvaXCH4fmVaP7QuMpuTUl8ISsdGo4MxHma7jcBNABKtbsJCnZRgRy1EcFA8pbWGvra0Vf27c/+s/lNlDCvr67R+4rT6YS+dhcgl8jJhk+iUeXQWxM+NtZG+YYGJH/lnsYOuOsMYYPk45c3BvnohO2cn0OZ3qEME+Ejh6CL3ExroZmyG+EVAjJXHOO3VTQu/u2FlH8db9r2xlcegoD9T5A8+VzYjzu464PlWQiGKAGrDhDIxEhuK2bP7mxwEgipaPDP7DyDU6Z9GaLI6ivD429+gA115KHVumIZU5mIRW+tvYOb7hcwexhwQRdtlIp68j34GFhfQX6eoWyeYjVFptT2AZFo35d1wfl8lo1Xa4f5do3Q5Xl18HIgjoc1bQhEZsbd3T2+PJ1xdXWNwzSBuoxGV7RQLGaGJN1BYows9geaD02Ydj+yXB9zNGyF2E58wgDzA+JvHi0XaecmMwnrO2jnU46liPnZFCbUlwZKoDwmO00ksjNIefub/XsCerLyLASDrMFTYWg7eEZpIxglCQqznC9FgfWieWJ8e2FEhOBReB/X80DHVmhDBQ5b03sZkdHJbFQyNGJCgDZzDWYTLsLoPaMUDiHI1m+WvA6BMPx2MOQ0qvM8Y14WzzglzLmFubHUViblQtFVo1UCp4gIb9++xdXVFZb7M9a+qmBwkTmU7e7AYJjM7YZAGlh9JNQMjITJRSo8DfXVshN/MNySABRc8Nk4OlPXsdkwtyKsWHLNSBSD4K/tK07BHaGxZ+aGEvC3JOGDgqifuhph5VkIBiBrFumIpyVV4WAdtSNpOyjCmvXWUUsYU4ZQ2AeAChZBAAAgAElEQVSa9lxeOix3JUKUWlowJGpyWFd4enMj+AE0mwAWGJOGpOhAWVIV25kcbme3nvcvoReQrNqc5xmn+zPu7+89U1VetMzIgaimubskEzJonqYJL16+wBUTbm6uI/NQYDdnXIs2JZA6ZPMysTFtWsYc2lBjVtxSCcTozwTnG0MXH4QPeh7//KJtX0v/Oc91FhxDxY8oG9v/AV9AMRMSrezVWQhVru6gmw+XZyMYGiRpyCQHpgEsSUxtVET2dYelIhykOEIwO4EIOaVZ2Of2c3ICcsQZEHcXRLmMcJDTQI9OzTEoqimz2n21LkMZDEYcDmuCsKV+kB5HZ8dCOn3484Bo1aaH2zLO8wwQeaLXzvCDdrOjcj7LcuU8y2G3nRkNCdm4ViXY+aDH4xE3tzd4d3fvOSKaJvPl3n3ptzXrO6UZCwG9ixZ8Tikhgbwsi03ZddpxDJQLcLBX4POuI84pgvbSsqOjxUfy2YbuKvRwM9dEsp88pQMfj1MxOQJFjr4WctqB0dvQh8eUZyEYREs3NxLEU89yTgHCziJAT10ShvOVh4FJx1UMzoJhuN9g1679pz6NsW4zE5D+ju/esyXz+2x50jV30uCRR3RkFr1GAExIprqNiSeNfjQn5LysutIzwbaHEUkQ2WlZ0EBY1xVnXeKslBRQOkS0YO91XXF/f4fj1YRpajjdnXBoE0K0pWS9WbHm8Rj7kBlp+Lv99mFyD4aLZcA8f4Xph7kLmrE/9fqHtPBoMo1CIuglzjSjuAEWJ8M+flsaHduQ6dhqbgQ9Ce3x5VkIBpOUkqxCCapBkAMAJ0lWac9tM+hWOBGXsgAyMY9PuW2pQmaEXU4sLZaB1kEA7BFIJQJpTOx4ZnCHB0TZ4bV5JcaEVa0rvyd0nX9n0xLSj/N5xmleXCtJXQDzis6ML758h/v37/Hm9Zua2s21UB0pN1qU0Cyj80dvP8I0TVjWFRMi1Zz0raOhyRZui9vws0JCA27Gm8bfwkTJV/Jze6jMkc6mnnSFAtXEu3eEezY10ns2bTXBukuiMhmOaJlhC/DNzCMGbF9Jh8VSbP0cdk82NWS5OaCVxaYkMPSo8iwEgzncDEabNs0kYHCZEYSbcIHb0Hq7TlrMTp6jWGuGTkJaarMWZdThtpv9XGHwQ5rDyd8IlKtPRBhIeFq6LU/kezwOQNvqWtjtKlZnpTzV+4qFgdP55PkXe2fQlBiud8znE87nM5i5HHB7mDp4E8xQGZIATE0Oxr25ucHhMLlQ7dyx9BWH3tFJ3pvH3uanOjzJxzW/x97sa1Fcb7CRqhA9Nzeuc5pDg+IjsyFNM+n7cjut/USAnQWcW1nGKrUjhHx6oqCjUUAG1W/8JW4KV+njy7okUajRh1HIf7g8C8EA2PkMSMyvHQo9lfSFhtxaDn8o5M9+Bf0Uutbggqju0AoKt1oVqXt+g83EWxt3ILHdbb+Yr2GyhChNIhWJ4gj6DpH23EP7yQabVdLBJyZoyS73NRIiQE+nPp0XrF0Y3mIU6Dr62KaGVy9f4vb6Fjc3N5t0+nUMOHcZZq8fDgcsy4L5POPm9la2hGvPe+fYPcqG2y6PaZo+vWoCclS9wSh5/EPQVPvfBPIuCtwggF62YgSmyeF0huxYw8gtWWuRMConEnoxZtamR3sESe1yLhGwE8qcRJ2PTV5Byb8xA53XB4LW9suzEAzMwNLD354BoyOFrMGzvMyaOEn8TGhCyCEXYnJGICulQPiEJmKlIS2tKmNXz7ltGFeYyOY/Uc1D5MwVY6CaP4sybnkgdhvLSoCp9ZBkmKsy7YLzMQbFd3I2wu3tLYAkiJTY5bAYdsG89147UGZdVxyvBDVkG3y103liUHTZMfobAlRWXTgmTMcWSSlexOZVV1/w84Tdvf0t2/rMLBHP2ee06b8Ikc2ZEMmPkZ8pKxAU94o/J9FaDJX3i5PDkcaKgfA7wWJrVp9jWbXr6OuCdngaqz+PbdeAnsxkh5gBnUkdfxGDHs4/LkSUi5ka5ScbPJMEDgvTM4mgsv04XpPn7MCYSHNWCMPf4Yey+QR6sznZw/5c83ZZPZJ4tp4p6d3S5y3ZLCszcu+Ypoa7uzu8e/clCBNsRbtBkr/KnojIncnMmM9zspstQ7ExSNbwosnWdcH18Yjv+crnOLQDVk3r3/sCXhfkhDt7jJvnM0/gZcPMtHXk6fDDfJyJKzLY+BzSbxfvGc2L+uQwj6FlmHkjQ4wmct7S/HdXM0Uj/Y+9M6O4IgAT/fm7AXDvWNX5/JTyPBCDwvtVI9pIIekE0XCN7JCVsLd7ynLE5kg3Rk+O9TJZJBK0UZMw3dwGZlBj1bYdti7Ymi0f6VKipn3LSCITCak2BnWs3LECmNAcIRjSEA2ZcyBE4FS5T3/bc4hlu5V0izazHGNnDk0GoU1HzEsPwlKEYNu5iSas64JpEmhszitr2WrIIY6LAAM4HCZcHY5oOGBeFphDrK1ysO0VGg6tqeZaozeG1hxBG0MasWdTIfEOW27L6Cu1rgjNEEXawu2miH1lZ2AggouqWWMvjF2juQ7Lg5nPqpQxz4aP1EEIVJDwgDuerdiSLtKo2x6K3m3catIio20xQyULl11v1ND5LGPVO5ZlxeFwxFPKsxAMACKJSgcW6Bo+dHmSoSnHsoGB9M8QArm2zSWIzaSrV+PXyGuAsSdA5ntIGkKJRgiWYds1WfsgXnkCUxfziAirtrvpWv9oqmgrChGGhx0uDHdND32HPoSJCJgI54WxLDOWpWNeO5aFPVVbRgDLsqCvq5s4vXesveN4mJyYXUzkLEWAnDoFwul8ws3trTXBebCRCPfwAWt/c7ftoQQV9kJ93Mk7mgqmsG1eOsNCpokOLsDz/G99RoZEfHRDiJqugMLyrgoqCXRHkEmgsreFS90yDFw2ksV82tyMHrJAkML42nKTrOnZPHKtkWx2HdDSY8qzMSWsGGMbK+fDYxfWcxoAh892dsNINEA+DFYYZkraOA+h1a9nQ0E2RZE7JJm1HRRsYYtMHensCK0nItfJTQab48gkdWGy0uUghmymXIi3YCdHGLGe5zPu5zNO5zOWdSlBX25rA/id3/kdfPOb33RTQqq0e/OdUfJyGxHhe77ncxyOk2drMgHTTULkPo2esGHuhvWm8mwdm0HIlyGtNDGOdnbUidkUUB9kDu80XsncqHtKwh5lZukv8YYWN83btGU7JGNfCbX/fh7qMC7VEft0oQA8M8EgRDNp9F7zNOt69rImBWEYuDIBkf+tvYfNzbyZwGwtW/H70jOWVKNzRKeFkIp3rWsPwtP2otHATPK37hJIbcz5JVLjtnYldoWDEa44Yc1M6TifzzidTljXtT6LINJlXfGrv/qr+MY3vgFqlGzRtEy6nSnxf6jdfH19jVdvXiviEAE0r4ugkW4jkdqtmaur72Isnp2gzNFFQlfzoawODPVt/Ub2TIy3j+gGTWLjK5BryoyUHcc2ftvh22PkbCbuOTx3fTHp2zgm+R2ihFZfvnxseTamhDGWK4CBuQV2ixU+URrwQFI72i6uu/9BFcWecKj4Fige4zRhrPkoe7fAlJQGg2TKDEHAmBVmxmS2HNtsW8OpEingIcfhHLzAAGTPSzun1nA8HMPsUm1oIKa1hk8++QRv377B9dW1pGVbV3Cboj5tB4H0kF4xK+yYu6XLCojBZkbk2RSBqwPOctq2MaR2FWqUgdJkemQoYn7Hkud17yYfb0cHe4JlZxz9lqo3W5uiOgxM3Vqp+7vV1ED0aZuD02jH+oINLdivYlkpuumyMvGU8mwEA5SRXKOpBq/3AEwMO4qxZAh7oGQfAgB3TsYEAwD5Jqr0IGBCwWErF7tuT6X2bhI+cG4SecksCa9pQMWkRQekAFRCGJEEQ5faQKBpQjscgWkCpglrN9MLsLhR6Rrhq1/9KloD3r175z0SLVj7JQgiNKFBVQmKqoIke99dClkdyO03YjehGIxBgOxKbTauXfLT9KxlkzVRiGF/DEfBuidkOd2fnX3GdqMAqL6fyLMhTF6dySq3ExLsiJRucq3pC+WcEUOj2amqbeTApfFKjr52FqQwLK0+pjwbwdC7xv6njVOXbCS3QZkzzRXIh+Ga0V/D3kbU0CYB5WwCSJkhJXpBhWsVDkaduel5IqWO7syTakzIZoCHl/wLpRexcahp7oXz+YzzeZbELb0DrUXsB5EvXS7LGcfjEWtfhjZF+/M3y11hQVGv3ryRiMd5xjTJbkvRsB6zCDMMRkgc/R0VAaspYiiHXZjlXud6fCSMdgYUivT58ljW3kqVEXlYL1dTJx8pNPavmAkMWEizMbyZVjQYna2R4TV5mLJz0q6FALPUmOF3Avr6NFPi2fgYqKu20xj8i0lDEvQeSSmbDkWbJjhv94X0DmhPdmDKqLkxEGOR1BbRZggnIvECwoYAiZiHdHis/gsCiX7s2/jVBLE3+DJZaxJ5uK5Y5gXruuB0PmFZVj81e8Q7rclp1RbNB69zaIcRIDPQWRK0AHjx4gUIFMKghxOSe4JojHLQ7d4ybNaezBw5HnYHIpBPXm2IsQbQ2H0B9q5du354f3nJpo11HkTQ76C5pNwMEVUhKJ9bkxUcc5A3qvExkwq4lnBog+2CDRoUv6eMlUWzQqNnn1Kej2DYuTZqEf+2hyKyLyI/b1jTmC0JbQyEtPFpQAlHCYyI/HBtywc6/hPxbGcqQAl3X8vkvpP9l6D4FoXkujhMFiUIr0ufPZ/PmGdxAt6fTlh6HL1nCVtM85tT7Xw+O1Nvxn8Y36k1HI9HNAA3V1e4fXHrQsEcwAXKl1nksMz8+lBcYGYPuznhKtLYdU5S+VPqvSwQqDRlByTsvwuEjcARURnmsZtw4gy02A7mDu4rOss/h8FJ6ItfR7M953FVmjSBGx+M+Oz+34emBAFo0+SaFMCgIRJK4I7GsvU64B3idwRz2PDIrjVluqaWfNqPIM/WejafmxohyQ9hnvaxL2Nde1rR+hI1hbOyIAaHmzvvovQuyvQgGnJdO+Z5xrpKkAuR7NVouruxm+CEdbHpASjh7DXOCBTUUx5aUohKuL6+xssXL/Dui+8k+9kjQpKtHujD++n/y9eTKUAxD/H/cUxDkFfHLW9MgD0lNPocHIVog8jbMT4T77ffQmhY37dogdLR5sya0dxPy84KQhfHO/m9ruS0flu2FJ20yv0k/gUxQizu9fHlWQgGAOk0+pjwurSkkpUZCwfByW9DXfqX9LnVhy2mcast2Cc+/+asy2m5iGRS4mAarZ2EOy2NGgCVSkPbkiYuKybKNLIhLEwpMmljpkbuI5F48HX8GklQS2sTWpMly2VZ3HSZWtOENNI7ZsLUJjCvoCY+B29XIkK7Bn3V1JokfpkmXN9c4dNPP8XxeAAYfrJS7x28MjCp6NPDMWQPUu7FONpW7IwE1rZWDe2fhqncwPmBQEanrc1LMWZoGzdg73JfQBEMVUC5YEiVukavzVclv2Vc27ol5KJZt41RSChQVMoKSQ0ouypNeK7rovtTGBap+djyLAQDI/IwNu+WXgdUUu6vY288ygqLgTRVxogEiRngqsn3yuj4DEldJzbrJrBBRREiluSlxPKzQsrSjWTnqPQfWcSEBiU5k6G+EbGMIXAgwjo1FQxyojWzRmeSkFNLBM4s7ZSTr5NQQmYKkQoSjSq/397eAnTCp59+WgW5CQY3G+TIGyaWhLx5bMtI2sdsRmUGjCHLjLtnAu6Xfc1Z+NTRSawmlaXR9FRdtYgPQoMVTQSGVQHbJCxb9IkJStdOgRqQnd8SM2EC2k3VJHSIVRB02y3bt0mQP1CehWAAcoLWkLg9M0H6Zw47hkraQKcPMrvVayrehEN9RqEdx+Yk2U48CCSoZgcVYmTfw2FgO7TR6I23XZniu1ChICfRiABIKjuxQLwzbRgSItER0vGYEM4rC+2ufSB1VEY7zGGog4UI5835E1TzK0S9v7/D6Tzj+upKxlLHtvsSKWNSTSuORzNXTPvVsTHh6mPosDoxDdK1/Gyaiz2lIfWNgvlSGWhJCG7/XfkWf28QcIHyZMjDSVHq5VBo5AgxCSVSQWIv8iap8CXxaTDWCMeGUuHO0X4PlWchGJwQWOCmDBaVwXSB6BIARSDkH9zpyAlmUmiV/RbITT3BN/NZ+D1sWiTpaSX4XKtkRSS35UO8Rycyyo1oPWV6JP8DB/KoIoJD4iSN2sEg6uAubZumCURN4xgAkCxlgjWVPpnwkz70bsjEzJMg0IDOdgQdY11mnE53eP/+Dq9evxbiZgbrqsiKjqYIiixVmU6mbvTOkhPQcPSY/YLJ4MyWoL2y+8afYzRjAo/IlsUHiG8TgUEgpRus68KDoVjQCJO12U+oUjpRJdQwweyypj03cbuqoBJaiiApixmpxWgzC8egUVITVK7ZsXRGP78PBQMADbyBBx+BATvHJcYnuNwOeRk1pxDCuNhiMNOW6SiC8RCOGd/XgNjlKG1LG7jMAQRBBwH3ECpAW2jLpKSwNzSVEV/zewwaBztwCAzTOonA2eGlopYkfKjJkCxrx+k0y16JZRHbnw3WhlATqMneZntPM8GwkbBi1xLJPpTj4YDrm2t8/PEn4Q+BbL/usK3nOhbKWGFTrwA1T7rDWn9GAgw7MRveX7gQMEGddqiQ9wSW16s1O9owBB18xSBpHEcVsV/CZ8WEgY24yU7iCu8JmBiIw5MydrTTxSj644K/SqtAA3GdBs1vdGdCwpyaBHZHsvRlQD8fKM9GMISzx0dEfnCmsPELht0uT9o9pkOscjixGrPt7eOJz4N0feSYGsIwm69MadoZGtUaVCYnqPJaEmE1wmIuH4waWXehwr8v64rT6R7LIisHy6JxAcyYdOMQEcE2aNqSpWdycoG2HQDbOnw8HnW/xA1evnqFw/GIPncJG+85I5SJ4ZCfY87o8buglsGIG5SfCdoQ6joPVaMMvocY95iJECKXCuVWEokTVwWd+Y08PX/uw4aetuNJF4RD+uNCKxCdCpdCaDmWAm5GruvQhg+UD8YxENF/QUS/TUT/d7r2HxLRbxDR39J/fzz99u8R0deI6O8S0T/36IakzSmFEVQgUCMnZKAKBZsUs4+dGMv4J6EDDO8J23nf1KgTae+ydpT60rULj4ejDImQGX5NYKDfXasybXjBhhbtqvalRcwRYe2M0/kkdaQ9F3aK0aTLxQB801UQfG2DWa7MjDZNmM+zbCBi+C5Oy3BkNnFMp8yjZIqiyG/gmjzmLq0DIYuHQDrDMCczotw8jHsdUxMUwxjvjK/13J9SxGLIgJk9WU5uR3aMFicpD7/1Sr91RW5UlJVHoonpnWCMm74eWx6DGP5LAP85gL80XP9Pmfk/yheI6McB/DSAfxTAHwTwPxDRj/IH1ko2upDywKimR53QPFBuD/tvUdce89DIqU4c+R0GJ4MQNwySbNroQSXgsIvI78jtd5hJ8d5yj1dCtR0po7GZTq1pwpJQagBJzoW7+3ss8wsJttXfO5sDMZaGbaNW6tAuYiIi3014vDpiYQmLXpcVDVMRfkwhYBis6qjJKgXDV4q8zWmcTDiB8+xvZzC1DEUBwJT6jsB3EgoFI6s1Nv52MG9dusxo1ua/WauS0HKRc4EpDdm4MklH7xE1iDMyZ++uzzpvWDt0jGTT1CrHLygUfWJ804cRAzP/zwC++cj6fgrAf83MJ2b+VQBfA/BPPe7RJP0Kk6fUZVzFgyGr7HmPHApVwo5f/OeMPDLx5OYME2sMWgT18D4e3zW8h1K7C81RSPvN6DBco1SILr/Z9m3f0kyE3lfMtvNxmsCspkMyYSxzEwAcj0dM0+Rj7XUP/eva2N47bm5u0Tvj9evXMJ+P+3Q4chB2dYiVYVY44Sggfd5DBgOLOs+F7V/bO4qDGnsQJSMjY7qszcu4c6UbpPZKNiap0f0QRdhVZVdp6FJb9wRLmAtxb6ovbXd32P2E8g8SEv1vEtHfVlPjY732fQC+nu75db22KUT0M0T0N4nob55Pp0QAexo9EyYnqBa/5X9lMgYkEQRX2rI1UQrzjQQajOe/FdNn3y6vxFbfn9vqixQ06KqRyjdKMG30IQDcMc+LZIgGYZllS/WUcw2mMTRzYrN6MyJz0rFEA6hhWVfM81l2WR4DhFqyFsaKjprTszCd4cEdpjPAQmhyPkXyD+QxLmM4IDxhbHuf5mwo6dbymIpj9NJeHSJdUegh/Dw9j8XKo4Pyd9kE5J9Jhg0bR+Ke2aNypCIwrQDR1+Z0wh7SbmYbtYZ22M8beql8t4LhzwL4QwB+AsBvAviPn1oBM/85Zv5JZv7J4/V1stMqzM52p8B62v2HZOtJGXVL1gLwEPItGhiI7QE70e6RSRi0SKmz9mPUhvFMZYj8Dq9rU7EmznVmMZoRX8WyLJiXBafzGfenk5+Lmccpm0QWsRijVtsQ3SODTn42xdQaXr18lZpGXt/acz0JUfi8bdOZjWNiWtauhX/IDAa/LcYZSDQyCGtOf7l8AQGerk0+2zmkQZu+JMixpyFL/KxkcpustNYk2Cz9y362KLoKl8yjaKn2r6VnAXcgS51PQwvAdykYmPm3mHllmc0/jzAXfgPAD6Rbv1+vPaJOIMRj+ozkJOPMWFVrbMF3qd3ajT1YZja2fx8gRwIpXt9WAGyl/Qbup+EeHWXZVzDes+3OWH99/0S67boRqAHzfEbvK6Z2QGvTRvhmwWDmWGtt7LT1XAAJxdwcDgccr67wvX/gD0ga+WSDuCk2aGATDLFyYf8eKAPKy2jHhEuG/nUsZRUGgwloY0CtIr1s4VVAL2M1TXIsH1HsVSgCog7a5S5ROKxHmrL2TNOku1arI7HSpfqIXGAFr4xnpjymfFeCgYi+N339FwHYisVfBfDTRHRNRD8C4KsA/o+nvyA6XWFlcmL59bBp7XPUU8NYXaPGa6LeDPELjXL5Y09upfplWOh3JChrcLY+o8+xaAjZmWduLbpAcVumEjs/QrHn+YypTZimAyzoRRgrngvt0goh5RUQh/pqqtgzr16+xDRN+OSTTzDPZ2dMyTid0qYP9p3VFzENKmxS5kxjGpQn7YMJp15/NbleohRTNs7R3LM/qnnDf5T9BFmAp/kn8mxeW4UQvdrM3HCvn0MyCE8/w9gUByeUlASjHQsYJpC0u7WGw3REa08zJT64KkFEfxnAHwXwGRH9OoD/AMAfJaKf0Db/PwD+dW3gzxPRXwHwC5Bkzz/Lj969EXY36aQHjB2U16D43VnnCtqcMFWL2297mtu/D8/J+nYIpPxztcPtYiYem0hc1BqlDueP+n7Xcdpvzl+AIblqMB0RoXmQjcD6eV5we60pypKZMBLksiy4Oh52+roVgG2acD6dMJ8W8NpDC66KCNaODsJhOrjqjTR1Vn8ehGq+levOD6YuQ7g1XdLmZBrE0i/7eG4HHH4vIU4l87wUSTBW0zUc4i0R6S7SG+mVudZHPrH+Lp83ZqdY5pRufmSKlITWuKe1hjY1TL/XgoGZ/8TO5b/wwP1/GsCfflIrIH28ZAmJhrJk8thiPJt4YgCTazXWe8SWNQ30AZBEVUta/Wajmj/BW8Z1IgNqh4ZNcy6voIQWsrVhjO/X8psgIooZ4uiKdlqdttsTkK3R06FhmfUQGna3n6KQjtyqXccXdsws7f3KLFCaGefzSYTJPOOTTz7Ft7/1HTXbRRDN8ww0YG0EmjLaSgKQTTPm8xO0vSYEOM7tNF+D5Nashx/b7dGlXk0ITgRkSoj1AF6vO7b0y1zaJAUiK76BNI6joDVhXkTSIIj9RoRQ3yvZLxUmGJJ5GKZU0DHQpkvctV+eTeSjFUMLpNx0QdHKvZB7HXJmRkEsGxWYDWTOCwnLiaiQJoaT7PEGkiKJBP8vaAxOdRgx+Xwm5rAJDE9JlX4E1hOjtZ/IhKYCSHNGdGd+xt379/jOF98R5uS4nglqa24l8wGVoO3mnFqfiHB9PGI9Lnjz0RvZ3afa1vw6Ncgmjzl5tca6pPcy2Jfd7F1hVqaDBdhGjdPcGhoZVjh2EWLpWmE4+y7zJp7brK0N+WTfRB7LELBDoaxkxjmXa/b8h/I1ujLoopjMnKApVp8u+qsulGcnGIDMYxUqWjFyMiYXTa1MjtieOqLGqJMyTzrM3tWSTiw5onIgrkEo5Ekw1mLXenYreShrEGQQ/qYeIr+v6WYbyw7EhpBYUJN1bT6f8Tvf/CbQGefzjHmZsWiatKlZtm2uBJw0UhY+tU82ZpIK7ng4YF5mHI8H3N7eSsCT1mHCaNKUb+o02NjhozYdydjMDmFCE6AicZsJsAGF2D2UmJAADyqK9yoCQAshk2jFarN2yHhUpAgfN33z2D/aWR6l2goXOpsRwaa+XK8jUCL0NQRJI0qn0zytPA/BwBFjDlShQJBOix+p+eSHWSZPkklz/00cfTJJ5vATqGraiLi75g97lxxhhnNK17bR0W3ffBEOkW7LGE11vFJQCBQTIsVcUKgZzk9GZ8m4FPv6KbWtMgDIIDacSNZ5xXpa0FaAVzln8pvf+l188slLfPTqRswOJgANq551aduv70/3YHS8wC2I4nBUcc5pZGSX3ZJtapimCXfv3+PN64/x9uO3eP32Dd69vwMTofWOSSMVGASS6CqF3oo6KIKeLNGI+NhkiW7tJsw9GBvGqI0IbH4FddSumuNA1vabCHWdrwlJqIRBMtBjNrPSSWSKQLuaNXnDjaUN2PPXVMFrR8nZ/ckBWZzlgU5UnwTy8aMam65YNDXFGOvCEdIOO3SJcPi99jH8/1NM4pnNLlddGjt8DqzgPglTHNz8OWcQJGQ56Ah51IjDJq5q6LjdvNlbEF8EvEUesqEE+93azCoEMrQ1DRaIwJBQHh4BRFVrld+9zUJch8MRN1fAP/LDP4If+qEfxNu3H2M6EqbjEWgNva84YFL6jr0TplmdoAj/FsIAACAASURBVHsHTZOgDFvVYHGArd2yOEvE5NX1FV68fInzOgtzcgf3RUdi1bwMtOmb62SF8GQCWs/3tHFyB19GFDItzsbuUNUxtO8pQbvht9oOL4yysgU7AjDm0E0NTv4qZj8BvKCuAV2qiNMx7pmUMCJjJfwYqkKT+dUcyJlFiTVIlq1pmkCQzF1PKc9DMBBEuzin2/VKRJV181X95MKCCrMY/ASHA4q0/m5Cxmz/sdZsIw6WhMF7Ins5+U2c7jOBV73RrRD3mEhjNCWiOckxaH4AjqP7DpNoz9YIV9fX+IPf+/34wz/+47i5PWCZv5Tox86g6ajaWBgfPQ4kOR7lANR1XT2jU++MZQmHlrWhrx2tEd6+fQuA8PLlS3zxxTv0TpiuJs9ObMuVrZGPxTZPVYypSUPZzWzaVeIQLOrQBk8ch/ZIk70CRGhpPb/luRkE7HiIr7WjOF0HeSbTmuoZBUB5lqJvRbE0PLRol30d2Uzi1FI3G2DCoXtHDE10sBzL8ITyLASDw/ZQHUWrOuo2YqkchTKlDN8zIKmz2K/nlQaJCxgJcxufIARB4ce4YLCZne892jijKjH6ioJLM7NX7UDWfaFQWmuCwUbGjsZjBhMwrzO+/hv/L9a+4gd/+Pvw+WdvQVPD0hlH6rr1ukY+SuDOhNVSvkNCqSW6kfDi9gWaCh85/AVoNOH29hbf/vY7EBGOxyvMs5lCk7RHz7QYhjsYsChDNRgYAHdBHD4edQxCPrAgjIysirCtkD49CKOsSkecyDFiPLIjcLuEGfdn4V3nLdOB4eDM/NjUVZ/NdBGjZYgB+chEfaRzBz3RlHg26eMBg9DpezIlIluslL2AH18dcMS5E3BSmL6aG6NHPktpO2lhSwBW2XicrZN3aqHEy/thpNprvzen/bZPg706hizn31Y7SBaMpS9gAOd5xt/77d/Cu3dforPmapzk7MhVGY6ZI+ZXx0MOqGE5Un3p+KW/+zX8nV/4Rbz7zjsxL4oWBOZ59lwOV1dXork6p63EVPqzRUQ8jJWOg9rdjepkj6BbgT4wLl3mXP/pXoKdy2DLvGrj5/nQwaAdOirLxHrW6mbFI5kU4xw6baL+Zn/LM2RCrtKxmcx1FPX/5g/SCmhklg+UZ4EYALhD0LR5CPaBBFg0OBq7w8WeRxokksD2bR1JO4WyqZPDZFl2jFmtfVW/5JUCcrJOiMZNU3Yo6tNDg/zPhnJ6vHQcI/HUPtnqimEIW12Z5xnn0xnn+ezJWlghvaMnpNUcBHFO04R3797hb/yN/wnf/Oa38NP/8iu8fvNjgBG4OiOXZcHxeMSbN29wdTx6B9dVDpwhkGba1mXZBJNj9aKaczGvyrgwZBY+A097R+qGNqGrYx9LlRy0k30UxnAmR1SD766aWDvzd4r9FKXNQ8nozs1TCrRQC5dxIJsPAGQqyvriJgTHeSCQMPU2TbrTtu2+5aHybARD0RWJ+C8t39gzGVUQ1DvcYqICNcDhtt08GCEQ8qnvqwLKUEqFdEbYgPHVqDl46FMIl7FHslaeT1POmnWbL0GapazdOxoBa19xoMnjHtZ1xZfv36Pr2RLrujqs594xHQ7ovPh7LOaAE7Pe3d3jV375V/Dtb387+qtCjyEC5P37E96+fSu5HpcVQMeyzOLTIIKng79EplwZwsyrngRdjCltBakpBxIzJ0KlM53I+DWE0HdIzuyCxuvTYoilB0H4X0KNkqxdqg7ILc0N7f/AXeyGY/1sCKsPp3aZmfbUsyufhSnBgEs7JEZ0KGY3+rhRfTiVrMWtnhERxOctIiEAzYTICAUHXrbls8YcRMjbdxYTKB1NV02WEBrbdlbBEv2sDWpEgKeHl3tbY2DtmE9nNGqY78/geRVTQZe17NRuQ0vH6ys/3mw+z7i9vcFXvvI55nnG6f6MdelYlrOjqNYmXF9f4xvf+AYA+FF3NsbNohm9/dpvBfINwXiiU7ubYUR6bBtHLoo4WZP1qLzu3xvYz+2cVJvbeGxnQ5OhFLqqY20MtZ3TKLaTMRzL+ybEnlm7hy5sfi9d938MPVNEkvnaUXSZRgBRFJfafqk8C8GQi8G4bIuFdLab2BkwS4bxuxGgbAxSW3B421gKs5qAgGn4zMx2P+NDuQKjbYlAEgFGXRUlXCKOfG98r3JLF2GFkaaGm+sbrOuK83nGsq5gFsZnO1sSYS55zkdtb2fGixcv8Nlnn4EBzIusYPS+OkPf39/j6kqSvHzlK1/xmIim9Zlo8CPbkv8mxocRsQpSGlnfTGiaXc41b8UgYB3BJZ+NmIayt8M89kIT5pSOOUaai+04XzYVPsTQ1r5dQZHGYmTusX5fEUOde9bfWmtoqoT+YaV2+4deTDvv2VsVLiFBebOt9KvBeyeWJDDssco5AOyocfkx0rjDbV5PyAGCnl2f9jixn21cvc/V1LCXuTlin7kSXvYbZGGR6961X0ub/Q2wc13cabkykHc7GsGQpv5KhDtp7MLpfAIa4Uf/8I/h9uULfP8Pfj9okq3AnPp1OBzw6cef4osvvsBHH31UGsZqiwPQvAEKyzeabGBC+0shDIzVw3SbHM6baSkuA0FLBkK5V8FeZojt3JDuKJ7SJFRER/tIP/VxZPbRnIgfAEmFHjs4957dKzKm+q/HHGchTypVmX+f+hhsQvfGzlcfKN9tekCJhYBtnKtJZIulJ+RoSBcwzOnYr9omEw5xSpD9yNEKFUpGSGWL1QWCME+yCBuU+zf2yjhWGYobU/tTaolrFU2ZcJ1X3N/fo1kKN9OMzaComhFdDvOb2mQyEEyE49UVvvqjX8UP/dAP4+XLl3IUHWpAz4sXL7DOjOkgAsV8IXZOhaxkTN52aZ8se4oVZkep8T7jaf/s/2FempIAkKhCp0mnmQDdZWmCkpKm8EftuTRP+sEZDum3PWE9mnzj3IXg3xFQY91VepU7o4+BiLIpE6acMc/TRMOzEAyAkbQ0vidfQ3Rp3EptQoFdKJjUtUn3ZCKIaD7zBdj3IKp4W2c9vo3SWgNVJiaowB4g/ARCDlnZmjem7MhwnwpGrdcJdkQS2YbdQQ6clJYPmiRsWcE4nc+YlxXvvvwS87wAN9cggvoAUr+UqJZ1FadfE/h/fX2Dm5sXcpOdlqUNNnD2/v2do5BpmtCVCRksaKV1cLeTw+s0xvsBi/svCGEoNFaQkFIRDBTp4FzwIMYqr4QQ64qEXc+Du1eS72JP2++VjfB3JaPb33ggMhcIO2jKEA1kg5etXNjyaQzKUxcrn4lgECievpsk5XFYLnTvwuXwRCsVMCRsOdlnnKoIsBrPd085hrBFtdj+BGRbkWyivBXJZmybKWJFLHEPVcYZ7t25WG4O34X6MdDArWGeV3zxnS9xPADz2h0NECABYS5sRONkP0NIQFkbbyRZojLAZ5YY/dubW3z22WelzTWRKsfhQsyaITpp+nIqV+5amhn9uMeMgvDYpqIIfele01O+IzmK/d4vCQBjMK4U8hCzlZWI4fNF+cG6L0Odr/7uvVs5ArbMcb+uiviyr4MBasATj5V4HoIBgA5GEIkjBpeKWQIaRMplsANo/3RkahEYomtayIthGYpbXV0lsRGHO36MsbKESZpIXlkj5aT+nMgTieiiv2GG7ILN1LcYEyJSZjefiHjcp6lh5Y73d3f4fHoLaoR5WdAOk2aEtoAlhcwkKwldox9lLz+VNrEhBWJMEL/F4TABBHz22SdY1xlEQF9XrMuCVQXWQTonhoOl6zPMXxCvaXZLFVuF32bpMhUTOmaGchEO5K+JxekQppsjvuMl22tDW7YCoJ5P6vdnXwQNCsdYwJEjEpwcm0SxatI7eF1lOMnwN6E/FSpoeRaCwXRbaGqzNwEHiGS0Y4xrwiPFqhWGVMRLQLe4AFuaImjorCZSTf4NWfKKxZpsWRpjSxu1Hsg+BXNDEixmgmCZgo0QI3oyCNOihIV4dhaJXO1FkBSU6btrQes+66Y8gf+sG9OoMaYD4ebmBre3L2R/A8kyXucuR+2BfVmxgXB7fS1xDww0buhQhMRdD8LVI+y6nMNwAOFwnPDt7/wu7u7u1SQBOsepVu6s1EZ3wBnDEUJDXSVJdrOMewL4Fz77s/p3BAKBQPPFPtyxrbul76GwtpyXVw6mafLP4t9JS9tmrphJov4Ahu538X5w7OfgaIPFh1BjYIUKlQ4ize0JuUaT0sYTyrMQDKXoDsbsxMkMmW4DQQ4FdeHBJpHlngC6gTYIodF3CYsofAwwwsoGB+ozhaAMDgfCKO4lThqMQlsR2bmKRlSq2Rpc6+XXm6nhaENvsVOl5FZCowmMFStsQ9QkWaPnWQGrMrc5CU1uFs0n/yYQoCG2seQq25ihy2LTIRDKi9sb3L0/4Xi4kjBtZhyntGw2QHw2b+cOAW/RwSWBEGPtl9XhF76bVJ/2rSAQy6log8ouwoBCC/WNe7T00NKl1zf01H638c11lvpsHMw8G1LGm5/k6d4FKc9OMEg/E7Tb6VilhfRlZyIyjBw1wbgu7MJIEQUxvB3MqslSsE6COP4OVqPOmF5v8H7l1tjzceZB2NWxnKnVFBOLwdSNfAPFDP+Xqw1gCVSa5wXLsup+fUELBRKl9q3rmrS0irweORksJXzTA3Q7M47HI16+fIXXr1/j6uoK93fn8CvoSoURq/lugs9tHnbmPH0dYz8u3gh4qnz/lQgpDi3AmD3rB9OGABGh0VRIf+iN9mL7E4FjLa3g5L97ETAiyBTJ1ip332smi/3SbBdq+v2p5ZkJBhMG+i31Z6MZ0jLkWMMuVHSblWPiBsle/xL6cLCusZz8rNtZXbKHWs++BNNIo0AybbUvqELr5zGJ5a6ujtnU002nTRiJNl7Wji+/vENLS4b5/WPwjUHfjNwygftbzGEK2Zl5PB7x+vVrFyy2iUqiMllScpaJjZ7K/yz1nHXLsA022travSF8n699AJ0dxaErSMdMoP64k9LWb6vAHyuGz4Hl2BiRb7kH41hUqm2t+WiM9G/Kw6rruipRzoCVAUIsWT6+PJvIx4DFOx1IKMIi9cIPUaPHSAU/husX6+NBW9vUU6qwNbQ2pQgy8rZW9CvMlrMpkb/PqtuboNpOP1coCbD9rpR1AdG2pOcrdH2eSXZVano32wFpjLu3Bt9aw/F49Pus3Rsn39ScYS0g6osvvoPj8egh1QB0Y1eNqLRBNqL3q2xAniK+xJDRIMBzm4cBLH/3xm9Ei6NqNqFou1m7bWcuc8KFlvJ7aec9UORl9/d1HdLOX4igHWnGiQq+8zMLMY9hSI8+1aR4VoghzAc7+wCodmHoZoatPBqjUrqfR/CR7ErSxYiEIBIoH8fPnJQjEeW56qmG9EIAjBWaydhRhAW2yETWo9CkDbHcCGCVsfDdhOlU6oI4nKiUSLsRqkLWldHXjvvzjNP5jIU7OsRHUPqmms2cZj5GlNqumolMOCq6WJYFd3d3uLq6wkcfvcGX7+7k+XXFsixohwN6Zz+oBbBDbVYYwiFgw+yh+ba5D7Bzv6nlMQiJiICmjAT2xeNqbm7rJfI1Mf2hhZ1Szq4YUGHugzoRi7DP7Urvz7TpBsKO3yI4ArBM6I2CPvBAez5Ung1iiMHIR9XpjxQaw7IAFTYsA2s6VHPzgT3l+BiHgPQUJcJ3Te/tMiNiLPXEn4A9adIMKqe601s3mkuIWY91W5dyApcJPxUf+m45ODb3hn3wZJORONKEtE+nE9Y1a7tkPigMNQ2UhUMMtbTVfjMUZYfL3Nzc4ObmBh999FYQRV8BkhO369g/jlBHJ9z4Wx63PLaG2PbavwXW7DQXzM8bWhhqsgp3hdVuW7m2Kde/h96yjyG3VZSmCb0qTHxPUOr/d+OAfBaCgVAEbyouXuNeMagKs8gfQwx2yViTNKQ8S/Y08M7QuTXy7mpe7GuqEGiEdJ5QeYcLC4rlN+5jnEVqQSI2Pz9RCWgiTePVGaxbbDW20Dtn3c/HsjEz7k8n2RnZO5alb2xay3wFRCYn93Yr8+8tG3YVKte3N3j54uWgsXT0eNCWNm4X2b6WYmGXucxO27r/w67lGpyBiMrKU55bQwcNKDszs3/qkhl26fteCYqxC+pPGZBRAxJCG9BU0Y+SMUuEtmiQhD2fVJ6PKUFcBh4YJL7PdRCUDGAwnpVqzwe6YEhyl0aIsGXekfa1GchCIQfFeMPSc6FJ4neDwtI21nV68XajZWgU7TbilviC3LckaEqDSeuU3+2YNzvvcFlmzMuC4/FKbH1FVLFrMQSaBCsdwr7uHU1NGEMStjTauQON0A4T+MS4O58wzzOWZfG2ZS1sfdh1GmIcd5vfDzPaBroPdQkKWlNdtkoAj7DNixiXV6zCpcmpT3vFFc8HSr6jAeiU5lfNOIYFSsUTkrpQfDlZgKc7vP1PLc8CMeQSGiDWyjf3pBRqWTObXKnORCmdJfx2JJ99h9Z2voMw2f+FmUHpn9SbnUy5F6ap8kGoYyFF/pk4N05S1Y6TeaFTcpraHGX+JstYDGA+z2A9IZuRYwsqE55OJwDQ6Egu7TBm8VOVIebC/f09jsejJ5RlFqcjuKesWFVTu5+oDHj0ZY8K9uz5+pfKeOW2X/pu5kM+cXoD/Ysf6gFhVavdtDvTud80KojyDMPygY48IoLBTAmPmPtQCx8szwcxYCvls3UkxGjXORFoRgycBnhPG4Ud2ThCnYXIMTyTPz9C4qa6slDrCj8mwJk3diUq0atWyIjIbUfIeRZQtGOj1Kh5eLeXxlh18yRRs4hvRyhLn3F39yVev3jl+wUq1K7QOJs+3mDATQruDDt0lxk4HA64fXGL66sbvHr1CvM843icNPQ5TJ09Teqa0NrVQvBbewBsnsu/jYzeCOguVqiYAUCNZKzO6HrdZrKgzyEQb2NiFYi/314ZTMD3osivgUiYts+bYGXNjK3a0MxNFwys2oWDjp5Sng1iIIwDuGfTyxjmI1geU5Ju8Xr2Pdlb+OlPVgdGeXs4kUL7jdqAU/0ygXsOSb2DjCjXR0xo9luMfVOCV/q4vz/hPJ/RWsN8WtDXqqRGAr6+vvZlx7wiYWNIrWGaRLdwlwCnFy9eYllm3N7exkEorMt+XJfn8jhHu5OzeEd7X3L0ZUS1q5V9PlSz7hBQQY3p3vybm7AqaLJvy8Ywrz7tzliiF1cimjiXeZyHWCJ1NWn94iGDFVHJFD6Oz1PKsxEMIvxij8So8x06udiHa6uoAWqPBR3kQSo2uV5v+nc0PfJ7GX0z0BtE4luFZXLEJg9H2Nam3rQmfcj+CEM5CX77b8irlKHzi/ZSs0shZ2sTFmbPwuRvHDTelHwKozljk2O7QvOYreuC+/uTbtO+CeirAmblagtXyG0YK5Zyc2q1vbJFHtvVJTl8RbJASybotHkrm2aFmXXeqbsG3wgmN3OScOZ97bwrJFQ4FSN0cxsjwu7zfqLwPeUl77az3+a78TE8E1OCYevCwtAJVpFtImHfzecsTE2CYExq6yAzmRAVqSyCImnRDPXThhYrJK9J9+d2yqakqp3kn6Fhv/eCtnfsQAIXu6kJ1yK5JdqGFOxjz7dB28sZjpNs2s0wtMv4rix2BnfxN1iNzerkXoifmTUFGsEgrplK2YZtIByIMHcGOnB1c4ubly8xHQ44n04giHhaetcj4mJU3Znr82MJRpIQoiT0HlN8LIMpNr6k0A31fc6l8m6X96lsBNsDDRsdrRvTY/+hxMyU6o+NYRWpKNJqLeXCq5BoTL//ofJMBIPa277kF951wOaKQL4kBxA3gCY/VEQ0M2Km1W9Abmex2+RyNJhNid5X2V/+39WHweRBUVuBfAHaZnQD6PFsShgUkx1hzQRCCyEHwM45NIIdtTobTGAGE0FWqVqMlELeieTffD7jPC84HK7Q2kE1pkJig6ypKxarwNz9bNGsLb3/ss8Txzbh+niFl68aXrx6CVKhwn7ehQiG1e31NIombxwtSZ0g9dNkmK8CY9TMzoRxQRXD4GQE4Al+ypzFjFgf8x1Wh63M7JqaO+Xh1YnhJVJZtHGnfl3UFIHL4UXx7N5EQvdq1oyo7jHlWQiGhOjiWmI4VqTg2gpV6+SKRvs1BIYFRwHoDZ0CoRDtTKwxn3BYhJyO9LUD/dx8KbaQaseN3yTuF+1JRQjk+6w/2XnGduArLLU5eb/EvWBoA76z8urqCt327vOeXatQtTXZiTmaQEk4UMqINWnY+Dyf/H2dV0yQnA9N946IfR4C0r47UzMgSUvMtNwyiprnIWiLRi5xirvj6JPileUx3zcFRm2fnbOXll9rvR+G9eJY3BF62sryHpZdsX5MXcubpwQ1PtXpaOXZ+BhgUn1ncHlnmCJCcuf+wQFVy2AyJAdXdXTp5zb5KoK1cc95ZkKp97wsmpcXcxOibTmy0Uyese6+EwyVnXNyn+7SLIGSQ+BOa1iWBed5xnlZwowa6szvOp/PwziyM3FdurO2SwLZ6+sr3zMh6E0iNIkiNsFwkr27CB0EmipIKaGKOpyBvLSJiBUFXMDsH/xpMz55nMbPF+sf5u4hR2Buc6YFSr/LdyqCKbfP706mxu/5qgQR/QAR/Y9E9AtE9PNE9G/p9U+I6L8nol/Svx/rdSKi/4yIvkZEf5uI/smnNKjK8K2EZ+YUnJSRQbW9iLgSlA6OMdmq/+z3zeQR0JVOxXY3yMbeDmupORhzD0wTJ/KG265eLAgpGxRIz5s3/5JAQrnOPSEAWNr8hmmKLb+n0wnv3r3DsiwJ4lYG9NalAKdR6xWmg6AVT8oC3Wl5dYR5TVZzJOop2A0d4FX+5XH0EbZ2xRoUYYId7J7bIkPb3CzqPCC8wUQq8wz2echX8x+bD5v3PcRgyOVDqye1vk1jkOmn9DGXFPnLKbAp73zl9H+Rpr/HggHAAuDfYeYfB/BHAPwsEf04gH8XwF9n5q8C+Ov6HQD+eQBf1X8/A+DPPqlFAPKMSI5B0+aTE5o7WewJn6CUrOQCzL80RLzDgKaMFKC7LyI0tv4tBJH6wSEQ8mVw9/iC1AC3h0s7domEd/7prRzoIz+zLIvulVixrKscXDuMU37GwqIzkY5nFPjb7bkmsH7tcuiMoyBFRnGwUPWww/ur1wgm7fcZrey8LHBs/3MaJmeXzJwEeDr2wHtbM8r6s4dWx2scfdBB9bG9uEoxSLDC7AOyzLtffV54v/9PlAsfFgzM/JvM/H/q5y8A/CKA7wPwUwD+ot72FwH8C/r5pwD8JZbyvwF4S0Tf++H3+CdXnZQG1KBrBH3UJalUU4W9SROYJpJ0bqElH5Ty6szJuYb3LFhWqVw0a3oG6WwE0nVGgsk3aaf9nglv1ydhAlAJ2VZts0DaCBcCbFvv1fU1Tuezwnwb+61v5nA4uDmQ0YUf05ZMj66/Hw4HHI5HXF9d4/rqStCWCzgbF3jkp+n/ymjJHMAlKK5MYA7rnXmUw20yykgnYu2M6eONikeYAzrmGQVm08nG+BLdjdfq3MS1nuhkz8XoaPZha2dTnuRjIKIfBvBPAPjfAXwPM/+m/vT3AHyPfv4+AF9Pj/26XnuwuL3t37f3rOAiEE1Th28grludpf3YhskajBSmMQ2uOQ38WneoJg4efY5ZVi76WJ9J9jSZ0M1Puj02E2KsHEC9/1zaEUqHU7uUwJOGL45JonRUXS9w+nA4+Gfu+34aQwzW53yPLa+W2IqkGRsR3r59i5vbG43nNxOulznLWY3ynI3m0ofKiKpyH8b6TaxT+v1inakNu+Zmvqf8cLnOsvidhCUj/AZ2xcaUSZfcU3vbFLtot7QpJeJAnggX8IRVCSJ6BeC/AfBvM/N3BmnGRE8DK0T0MxBTA9e3L3aYuTruXPslG4wgy3QyOAAGaSx/W6GZSiy5RZFcMzOtK0f7zxkUAR2JHDa6dvagKCrvqMNkTjz/qtdUqBSmr2PCmqA1O6cYBIvzoGILSwq2xpKkZVkWgCfffEOKJrLz1d6bkYuNiycmBTtEJSIRkhDU8ObNa1xfXSkqaXK+Z4+oxj00FO8ocgw2xJdKuTddcLPEbvCxJhhCuyQctm18KFNUICivHpdRRTXFLIqSo+2AXQ3E5GMefe2JH2wnLQ/1R4OeJhwehRiI6AgRCv8VM/+3evm3zETQv7+t138DwA+kx79fr5XCzH+OmX+SmX/yeH0N15AweJTvTQrBr0fgkt0vE5G7pNphgGXyXRjQAOZEVABnYedBKDkrSwplWHSchGvLIayTMTPVLbSsz5m/QiIOVkERWOUfd8mvqO3Kdbn5oHkY2CIFzRxRO773VVcduNDEeT5jPs/y+TwXM6JqSfk+zzPO5/O+Xc0yxjYejSYcpgMOhwOurq5wdXVllA103qyCbDVwfKbynZ1xQjBWBeHPJc5xDEhb31JOd3YJ0mcBORa2Ru7JlcfyoAuufSSy7vkx9PcGgPqqKeM5mQsFUj+Iih4qj1mVIAB/AcAvMvN/kn76qwD+pH7+kwD+u3T9XyUpfwTAt5PJcaHseN0TStBbXAhg1x7MdVyasYoGDE6Sf07XdhCF/Ssmgg8+pXaN8D63wN4h6IGMcZPmx8BAm2eH9hiayL4KGYfVvxMYvMw4n+6xrouaEIIWSp5AqoQ6zzNOp9M2F0PPYxipzi1R7Ol0ws3NjZgt8pD2rTJ26R9VJGcnidFgI4/CYTNINjybi0PZmJoPa/mCpi5cf+h18dqqaNIv3vBLfbTnOClPMfv2Dq59uglh5TGmxD8N4F8B8H8R0d/Sa/8+gD8D4K8Q0Z8C8GsA/iX97a8B+OMAvgbgPYB/7cOvSPASUGheOxnaopzgkOyyrfbZE5YOhxGE4G4b/Y2N0awtICdab3EmCCbf0+BnHKYdc5khYBouQiNEQKT+NhsC3afscNPfrde1Tqsqzq2wsaBgSDVP5nnG/f09zudbeKwGNR+rkS9G88L7A9Ij3SJRq/21d758koyn3gAAIABJREFU8RJTaxJMBY3p7/tOVTMFw3xMKxQuNAdGNhNuhwG8LZsxIxj4H8H5d89GtWR0OCLP3YaS/dmVavV2Zs0fYStwuq+l2cG1IW7ErNCdsE/s3AcFAzP/L7gsA//Yzv0M4Gef1oxUsgb2S2Hv5VWJwRMJs/kL4XH8nv0ToZbY0QjzCrhPQqYqEIbc31MdwjQtSX/V/lTfYzEMlkeCTejsdt8EEWl1LMzHIQDl84hkg6FGtanrOSAi9GXB+/dfYl1eD9B+K1yJCNfX19EXmMA1wo8YDVYhQQCOhwNe3L7Ai9sXPsy2S9OCsKwDgUIwvN/6mswbwN+XV3lMZHjX3U8xrliRnxrmvd0TLCNHP1DqakEdw7EKolBmGRn7H97oQ32HkXew/KrjGYjaEu+IypNVoqDDi4lmL5RnERINJIiESLllRBgMkVHAFroFqkjPcb232KYjpDANilA2nTuIOU6zopDJvXdMchyTPKOQ2k+ISu0QotCDZRQGFm1CDHCrpgwscCgYIIhvu1TLzHFIDZSYrPvE2lZgXVZM0yQH1zKXtubxA2QFw3wMMcoDclLB10i01/XVFZiB6TAB1NBozOhtm+X2UN0DZoL9blzkHG6h5CEkrNveF87PDl1JXcqmax6Tx9jqdYyw+0xeyfFrNu4q7G1QeKcOv8axiketITYUtiSbtsL+seX5CAYAet5RSjYCgIJw2ajctT/5Eo078xJxQ2+Pl9jvclJPVwxn9rEMtrK92roTxJy206kk3Fkdl9Q02aq0c7UsOopmwrkIWEbkxpI1qbNpd82mQi3BQTi8ZIhNEbY3pfFgj59iAK1p8lbKQgRYeQUTox1kmWs6HNCmCUvvGgGq0Z1shMuOiJgZ5/MZ1AgrZJdr83R0KuzQfXPZNE3gzljmBdfXV2BiLH3BsU/Aaisu5vcIxBXopk6b/LLVdlty121FXE0ro4cG3ZJMZvywgcxEIw9D+Pz5sYJiV5EN9VwSgqIgNTmvKSWNLu3csbK6ekgEsKemI9JdrBwCbg+KPFCeiWCQpSxjBHdCsZEe64DY/UnqpqPe/Ndd+9ViAKoWl0e7a1YTEDaoBMLUWnqOJfMxS1wCMTvhNtuJaVpLnA4QCBvqkUCanFYm25L4mG0wLvzuOrgAv7Hebu88SB+aOATBC3iRunrvmFcG2iQsyh3sp2dFjbYOLjkeGcejCKh1tbiOMCkYGmauh9hy77i+vVViNkEim6pGkF9XOrZMovKwIML8rCEWUkEJfd1osvkScJqzzbscrT5e016yPEzw7CGx8fNYmZAsl34L4pS1Fj/nIlSiolbKAeP67/I53pfKMxEMcENKZCRVat/plYfX6EA6mgDvM1GpbPzdworWJCxi4uxv05le2ZKHrD5pfm8TJECWU9EO0s1tJ/K07iblyROIwA7M0DMtHtJi0o/mAsKgujo4E9TqiobOpzPev7/D+TzjdJqxrh18mHKlhdAPh4NoqGHPRGcRio2aBOBwIC1zWN7c3ACtyZ4UgzW9o0HMGqKIbGfeTnMswxqaqO7HiHnID4fno3GM7fjM/njGkJmwifdmFDoontJmU2bh8HsU2kiKoV6uO0wJ0OXoFI3q0xyxD4ZwDSE/1aR4NoJh9BXWbzrVZJ/iVypkvN9508YuSMCbA1nkU9sQThEK5ZoS5UO2MGIp0d9ECWIa2pHZVmUmQoXclsr8fUmzJrSUSFg2f8kITe0AnhjzvGA+S0aEk+6crIQaqxqyAWvC3d2dHhgzlSSvcnvlaFv6NMfY9dU13tN7Ya4LuzkpCbDMGOakYzSftw2T7hQRioCESo/wCy70R4YVBkRhNnnrQBOXIIJfHhHrZXMh3o0N+daVoOSjcV8NdK9OcpjvIa5HmD1jeRaCgSBaprPjAP/FR4tjqcl8DHlVzoN7diSymSMmkCkRWXW8QbVrDs3WOtPWa7t332Sxu2pknfG5a1YE6pFexaTrGdKhQNjETAgY4viBPZVTFkA2buR9FXnYfa/DsmyPqUuSSs0FYfJ9Rhhtn7wNnHF1dVVWNSKv4X7gjphxdexthKJn4m3P418YKw8adt5Dtj9keCY672OZDZ5CV4/gs72lbasjt3tPEW0F1lYw+FZ8NQHNiZyPCArT4+nlWQgGQJmEqAi8fY91QAuHkp3tgmzn7VBGVkFiiXNtcjeSGQ6XORFT/tsTk1ucQTw/NpTL9fF320DkZki0VOPht6ZQgyGIsKmDuE1gJuSg49O5e1wGA+7sY2ZMbYL7/RSSOQrTqg6HA66vrzGpo5V7DyE5aHeiyCB9dXWFTz75JA6tQUBcAQaCArjrLuJUT4x7KIbCMNwcFRQHnrdHTUMN+LKaTZlUs0CHbuB208BMVTj4Y44a96VE9n3UZ2rJgiL3MQSCNEavQjevFv/PQ4LKlMDv09RuZl/nOAVUm8+YKdF/QD4jesBVRnrW3uH2KmeNz+iex8Agfn353pCalLbbjJAiBqMKByCIuEDnXKcRYWLeUseAUkYTIGWqkCuaMEZWL8SJ2rHqMiXQDrpfgjsmShFXCCLtvXv04tSmzWhkhsyIjZnx8uVLRLwHwfbzBKsRuBHQDTtxsYcFGYyjr9rWhddAL6UXmTnTL0nghIxg5/Nqpe0bLh4Lk+Z9M1/DuORxDVNgu5Fs01ulC1/CTjwhZ5TY2SKB1vw9MLz1+1IwSDHILxrT7H3pkCQsAYiCOLMUtCdtsohkj4Ewt2isg24y6XoitB9MCnWCNYp3VkggKxADDCRMpsRhmukx3uzR3Lm4Vr296geLSNcq9G3pOTM9mEkPm4mF07u7OzmEdl0xLzPAAfejTnlPPqYOgI8pm2ZP6KcBYPVLrOuK0+mE6+trMRM1b8DKgKRpkV0pcjhUcwFjLlkT8uZ3l2+Zj/eYKF1TmG2XTKnku7ZmhvX7AfjN6d70/JjMJtc/Koe9ey6ZVi5A0vXO7N8bEZoeEhxzs6npcn8ulGeS2i2cUCO02t6XLQEj/0g44rIAtjwphydYvIFL2BTMFAJpeBuRHhJq7TJ4HvZe+Az2TYCxXma4t0OYS1Oy6V9j/LEwZ+Jh1ebs160dGYL6iVcMTNQwEcnR6+uK+XxGX7c+hl0zKgkGZtZdlAO5ZXSe0MbHH791rWcuFPtttTbD2q7jgXqvmY15ZJxWEvrYHXLN2cBplrO5aI860Eub3vZoctvxaI92c9OGTCv5N/P7OHgZ78v1+2upbJWXREaSzMiFYGpzU7p9qmx4VogB2NpxpBjRiMCgumhHLswCWI7prgQGoKsmtWQmSEFJrqXs3cPp1fIJtiSoqLFsd5U77J8lMM1liwbAFMTv6dLTvQOiiCerH2brOBSdKsuhzYC0nobV0bsIi76sOJ/PmJSg6gKloaFabIu2Q9/8BEPjOeTaYZpw1CQvr1+9EuXaxT/R1+6IjvTdFihuFYvfw2MEbdQ+iMZCzBvu4A1DOHOh0s2HVjqAyyiutk3bm8y/UYJmv4Vg0YcdhEQUjhjWDXHMIuxLntIhcsOka1ryfWx5doLBSxK9xoZ5XhiqXSmSltgv8bFuIJIBjedNYjv4yvDehZFNdTBmDk6Kt1q8vvkJxolI8LtgntxyheubwQjqurT0xBxOTRuQBshRavrOSdPkLetinfSw6LYrjNQkG069NtjvwiExbCONrGwNRz0YV+izY+0repcktL7sFnhB5TSjgcpBvvaKyspAtv/Dl2BzM3IkK2oZ5ppFwQD0QcGzdT5SvK5A2Sys8/2bX4u5bEiw3qvC3Qm3u7NdloZlATce6Y6i2AUj4wNd25TnIxgSvKIW5ys4M5NCQoddADy/Iyd/Q2iYpsRC+WxAUqIRLi7vbmqfcXqJO3CasbDizjTSnKs3VENbLZBRgaEP6RoNBDJqx8vuIx7aYV2MbrGc7aAwuTVC5wW9r1jWBYva/tXnASeu1hqOxyPmeY5zC8ysgCG0bUfXdY2dpkrwghhW9GXB0homZnCDHquWHXRDdUkIF3Nqb3z8Z0Ju2R5fUOl3HscRNQIbzrIX5rbmUPQd5bEpucodn4kjtxTcJD9ovo6DmhBItJltkyeihFyej2AAQlo6RK4dC4+/DYOpLYtHYLAsU6ezCWOQJLegCJNCfDZ/VH8rATme42HLsB2ssBze7rzsRipM7BqZI1Ahv6t3JEt4sDUZtjPQ6qwEvL/GHvcSCCvLM8vphPfvvgD3zwHUvIF7c5JXU7z93EFoviycfRB2z+FwwFc++4ocjYdJ+6R+H6ww49dQXUvBUtnpJpaFQGKfgTQN2eyLOdoVo2UOXQv7QUYxXtuiTlZKb0jVcRKm2ij3j5RaBtOPmNLBa97b/Wf0r4wxkg8hOcXT/Y9xhF8qz0IwMIBVCT8fy27Qu+sAx/goErBTqwgJ3ufJNbSRTIoueZLyzgDSd49r2aN2NMIaHUTmmMwTEi5JBceOeVWoUSxTwQSFtZrXDAZLq3oyj1DppbQFLBtvrJmHSRj43RfvsZzPeP/+Hdalo6/qXFQzw2WpCtx17X7oTF9XdcZO6CuDJkNLsqrQuKOTeMdvrq9ARPj0s0/CNyFwTMwTiuQw5TQx7imYUscbQ/hvRlcsormMUkXnW8VtGp0GxDWMdSmGBuyrzU5WzmkiLrPkgAxTcJjrwiQwR9TCujvX6I1aK0IhN6k25mlC4lkIBgBYqXmSMFaxLD4XCknpE0POUGJndqyqZd2xKLcF6jczIUN5f16JG0DNURDRluTcqFU7ekl6KnuViUG6i1KSyYYo6kQSKOQmx6DxuKFLlJZPeiZYButxe3pVvdSy+7b5Vm1qpJpYlgbn8wnvv/wCWFes84LzacHdadZjCix5jUaQEqF3ArcJzHK8nZkGFvfAS8d6aCBi3Wmq1yexfe/neyy94/Xr15hPsy4Hkwp5EbDT1NQHodGVcrwWzIXYYe7ZELqZ54PNwvzLXzefEyqJKQwJe1nLmigY1cfYhv06RjQWcQa5MfZ8im2AIFc32bhXGs3IqRHQc1h/QrhPBA/PRDAQDtSQ94UJUzOoyQG1bGnQABgMJUisODP5YSsxQftwjFtaanOPrXh0BTlwQpysssP3riHbgnuTLYE8OWtOVufWtFgVMTibjA6YihuXyliFYENaqtuxuQnNF6LF5yFjuywr2kR4+/GneP3mDQ7TEXoObWkiSPINUmrD/f09en/trCHmkBLp2sGNdD+n+HvmecY8z7i+vsYnn32G3/mtv4/OjHWVY/KOV0dMU8O6dkUhDbbL1TGfzsOjIfFj7uM8Uqoqkpl08SEfzTRWZlrV0XtkUxNK2nlToI8QHOSKB7KLUo8EBCUzM5ushoSzE+yR5VkIBoJlYgAyQzv8JyNwIXpWdFDu07/mjMm2Vn2XDFKzuhVmhROHa4XDx9HkuOTJD/lv/wu9UrV/Jnx2JBPrIRHvbkjEKicotIQjY5gvJBOrrd5cX1/j888/xz/+j/0EfvAHflACYqxFSuQW+m1LYlBfgR88wwAbAmqE3sXJuCwdPEkKfBv766trfOUrn0Vm6nZAX2WvRk4f72My2sYXaDlsd3IkWADdjk1QLrEJ+5jrS/ED3hDm3fZcWiW62O4Lz4zvpnTdhA8DHrZPRBKJ2pouf6raHJzYNq5PEwvPRDAIR0ecQIbJbQXscM6mC3mNCUwdTKvkRoA5G4Md5WuOr1dG281hoLeYw4vsWcS9ZBuENP6gc+xBYHmmaf2MrpmatubdSBRNNZi1egyqYQhactjpAkASpIRGAcAHZA9+eilWsCRmmVfMc8eySn29syRywSTj5woq+ScOB7x89UrHIgsHCbb59re+ha9//ev4Qz/yQ3j78VuACLc3t5h7x6tXr/DFF19gWWYcriSu/6DnVeQlxkzOo/bedabpc44Qi7mwndtyaVhKHoO6dpk9C6ChjAqZEh3uCZvs6N16kdL7mRNAlWhdy5tJjYCWVtHyCVr/AE5HK88k8jE848IeDY0UJmkMePNQaA2ndeYBwLH33OGhStCRycBq0yZYkOG6368arbXqWCRCvIsNueSItaR5EhS34vVrb0ilv23MGjVFAtZw2afP1d/MUatnXXYW7eyHjhCm6QAG8PO/8Av4xV/8O/jy/XuczmfdZdnKWAHw2PvWGq50u7UxoKGYZVnwtV/+Zfzcz/0cfu3Xfg3LsmBdFhAB9/f3ePfuvZxmxYJcLK4fCH+OjWXW2hrO5gwpAjHPEfyftJX8XwwWi1A1lJVzOwQg27M8d4vIou2N5Igw5g5qCu3SVqaHpMSsMa4EHClXweE+hjRuEbtwod1PLM8CMTADvOjsuLkgWqlzV0ccwBpTT0SYRMeJ09D9BhQ6ROGmr6QT6QkMDNgqATVf37c0YACCahyyNz+CzW08zqKl9gVeV8D60YnoVqtpxyR4csJZQwgWEDQdCCEQUp2MlFofsJgP1vVb7gv6IkT05Zdf4lvf/g7O8yqrC3vQFoAE/sgZlL13TIcpTBxS4YUV83pGOxDu7u5EeRMkcKp3dDA+//xz/O7f/5YCEp3T3oWJEcE3ZRzNrBLIpNcezrqUl1NDwOSaKaa1rEZQGktDYZR+tw8JxYxjtcN+ZXn5AZMjkFO8hgCMcTYAu5AnRbDUmobMkMy1j9Vann2qMfEsBAMQa8oEgbZYVnRIGjA71UmcaCRET4xOHEfUaT0WmmsedCE9C6BJA0QpLJbjmiRMqVpKNPyHw1ZJ6zR0Ynn2aLivHGvvmuz/a+9sYm1Lqjr+W1X73Pvefe91Q3/SYkfA4AAn2OkQEgiRiUpP0BkOhAERBxA10QEfE6YaxWhiTCCSgFGJiRo7RqNgTJwICqRpuiEICIkSbDCSpvt93Hv2ruVgrVVV+5xzv5rHu6eTszqvz7n77L1r7dpVa/3XR62Ktpr2bxdVGeXRh/BRm8YQfwTp/gvhEgIqpYwMyjRZktM0ToylIDkRbt2pKINfK96HIdRWob1i6CSlxMMPP8xPv/nNPPTAg76RrQm0nAcODg6466676/XFk5w2pV0f27f16depn8JzHld7vvs7LtL5pF5/W2EKNkFdkeKqs3IFFcadzgLqZ2HPuJW0EgGqpdsD1DciqubYimCsTM8f4iwp3z1thWAQgWHPJry9qYnYLt1qLJZOUiaDAf6hHsetnURosnh/4XEO6Jm6YwHDbJDaNu1t6Sp47YWUfJsIE05lwh1v3dqBbmA0id8G3XEJRLOYgPTXhLBqodKosRAnVzwyG+Rd2/W4IQkFUs5MExwejRwdLVmOI8vRUqQNnYFj4LXnWi6XLPb3fTBSV0w+8MAD3H///eyn7EggMSwW9dpxXNZ5E3kpYSate/bXqR/Ua/b8/MRjcfNMgND3YYUQVVn0Po1+cq9GL2a+go6v0/wKJzBWEWxDkaYYVWOFsTkfW0GceEdNuaisBFVfgM9hKwRDTsK1y4NLR9zTbVVvUdNy0+SQ072yaHLoPHktxuSQucz6oZCtaKnEgGir0cK0bHbcHIJWAeNHtDQIXYVRD1udjz65aVMYzNoqoRrWfq+QWdpg3ESzzMoVCgwVJlLx0nE5DyylsJxGluPE89dvcnhoJd5sS3Vl6GpAenyCcRzRMnGlto1FRFJif/8SIolczHxQEa9CPbFcLi3zsasVMHveqoU9WY3SJq7WN9H13cauqP114k9qLYQze3b6iqkHc9Nk4z1nKCHG0ibp1I6tOVVPhU3t/OqT8WjETBnFp+iaoJUkZ4MuHW2FYBCEvZyr4C7FB0Ny7VIGShkQ35R1mgoF6ZY/W0JMKYUyehUm1KB8mmz9v4YpkVAZDXmgMwRR6/SHsu7+19wPYuXTaX/PJ/b6NK0RlpRm5+EIpk8jRqRVO3Y61kM/s5MhsilZEUzViSfJNu71ytHL5RE3b97k8GjJVJTUjekQiiEIqymgtuBKUcThrAY8Q5DskaNsz3XlygEve9nLePrJp5jKRCrFhbc2TmMgbxy81YiaH+5kx6a5dSwG2WBzrzZ72lztofv6dZuuXhcKM5Zmvgs3B9aEkdZ+iw1sg/d140eP75gz0lYIhqLK9VtTWzseeyrixSeSh7jEkpnKZPlwY3E/gS8pVp3QhVbIZVXeGgLoN3ktEw7TXfJqm+CzqjqdUAhaDZ9t8jwHbQqBFWukapk6EMSHq2zCAO1eM60rfYTD7x5ecRGS5Cp0BGHqshcnVYbFwuo6epmw3Kcnx3Oitg0ayXw3KZGTFWOhCG0pZJxvbRwcHFAK3HPvS5kmz2MopfKw+mwVGyi0yt0r5zGfgPOems+GVbt6dV6uI7m4n278/TSKdxdszLjRxs1ZEEhtv3vnoQhb2f7WnF9BTQNcBSNrq31Ppq0RDDcOjxCs/BiqJDFYm3MCnRBRhjRUezslyB7jE81dQdR4tWrZkO5Km7TYorTJtveKRJtwJk2ECSK+12LL8JM0j+q2GHRXRaqbzM1BOIej6/Z0m9zxuSpIjtNmqyGv3pcZA8YGh9ZBWh1aWGZiztl8ATHJqlBryEn9eM4DWqb6d/xezTDtpoGP5729PZ5//maNQJgDuBXt7RO7Vqlqzt52Ruu77f0wTaDI/D2s3bUdnZszcROBFNmuq7zMTYqW3boiSGdCoeuTY4T92nOHA3HWF3Nl0Eck6gnxJRiviG+TeD2dtkIwqMbuU6a5UdNsqgrjhOpkVYiS1xFQyINBesvz6OPaYinDSZCciFJwgwzYpI+NVR1BqHX6VFrySDVRaol5c3y2DMT2AotPplij0Ws9WS0qAN0Acf0uXZ0Dv3Y1uaQmwazawRrmg7UazlGhH2DWn+FTQZScvZJTUW4dHnG4XFZ4GoK3t/OTO1+X43yxku3sFNWDurCsWu2Ho8NDBKsWfXBwwPUbt+ze2qpCxaKwuC4yW6spM+sD78INGx73f1QV0Ql062Nq+zPU1+RZXUoxD2eGpl7fXmCNkbma3vjTmkDZQE3JdLePa9K6f+s4Em/8vFbFVggGESHJ0Dn0fH1EKWixwqU2k6NzlGnpy3RdMLhYtfu5YLAEpexJSrmuq7C6BJDF7WFpEtl2vLKJNk3NpisTjFMMahtYUycodLJFTxEWRfDVh8ZTztk2Zonp7/UIrZhK0xLhkJpbnbGOFN/Ipk/5btrbd7txAZmqFm9e6kRKnjmXM8ujQ27dvOnP00rWxw1tIVaC0SpNT3XHrhCGxfuyLb0WbKu8zMQiDQx7Aw8++CB7+5e4fuOWaVGf9OFcrqnZEtBZOwm7QYPWdx3TraGr/tzQtHHrONh78ev56khE5sign8BrjtP6DuIW6sJ6g5+pO783CTfRTFD6u1wul3X3qaqUHLWEuqgOVe0e1jN+T6sStUpbIRhASGkw7Z+lwuD4kmqcf6qCAdpLLQgUdZQRhV5BpJByca06md9CzFFppkjyBTxdcxRyNmk85NjINrddmjUKsiqj7xg1TRM6mQCZRt+3wU2XqUz1vqS5ptCKALK/wzA32ks0eJ8IB11k1fXwP/owNGL9J4FuUoXKKSUmbLu/o6Mlly5dthLkqmEbGXSWYkIoqgVl3w1RY0s9rSAmwsGhaRPCIg9cuXTArcNDrl27ZlWjktSBG7tXFrd7cn0KDdbxU2fvOp4Q3w6wp1O3Z+zGTm/WVd9SX36//t4zw+z3k5vajAhO0vB9/oMQS+ztJdYMVrGapYGGpNot8/aayRrtny/JeUsEQzxQmUNB+6Hz5ofGLd3U6O7Rbd3Vx/tVPQSqimiTuklGQxM+YAP5p6S1ll5OggwKaSINFoaLPV2zFSFDPc06JeFoOVoJs8kGrk4Dk8NQc7yFtui5bxPFln/7K00x8SKJSZtWCJDkC5qSwJAH66HOkaqJDuVEirkgeWDY2+Pq1atYNuKE5MGBjNbJp8W0aM6GtGwbv4Ur9eQNuDDpytenJOQhcevZW1WLnkVTrhn43fjoIxlrm7SaBNx43+jjaGXzaW0tyhpTDkxWikKdiWZmC3NIv0lorB0LsyoEcaDhajs3nvrrK5qs/J7PmNgawRDr8cOsr3slatuNWqPYahQ58WvXIgLaljNbGLI5/4RskriEb0G90KbOBEZA2JyENCRSGpEk7gxt+f55cCel10IYFpnsm6mIzUqrKYFHArwYaplcGCg2saJYKi5Q3HxSlwIhGERB68CwTojS9uYziZRtqkNMkpCi9FCJdQ4TSmE5LhmXtrYh5+zJ6KVaZpJs0RpE6TutTsdoJ7STIQ1rd5xGxtGWXpdSuPe+e/nes8/WdGgtkc16hgHr0L/5IZiZGu2cGEw4WtLuQAWgG+4PmyZ8nci9Uo4+oLfvj49crTaz+tsmIbn6u3bKrs0Fav+JSF0VO0u+Oqcw6Gk7BEP/AjtI2mLpcywXGYCq81r+4QQEC3MGVI/y7zaZvXiKYnDfw3vqQijgsnaTdvJPGxe223B2+yP2DkwJUm5OKhEli4ddBXdcJRZunhTV2XMWDRMiMY1qsqpu51YqTyZV7DkcTnhSS7Mj1SeMJCHKXVo/JU/2MlNnGkfKVMh5QBiQSIsS0//RlyK22cz+3n7dvSqlAUpxoecCKGyXzta+fPkyz928wbW7rlkflFjY1Z6/5loQEahew7ZZW8dCjJeQCYGwQuFLGyttcHjHaHOsNrjv2qczz/rwaUNQm1PD15DrBqfgWs7DMX6MNfLxEXuBWp7IigO1E2x9Pk6M5RdC2yEYwAY/EHsXqprXPNVt1JQIt9mzhlbrMr3cHDHU1Xd81044tlwVpNgqLbLDfMKuFrfQEtrfBMhonjMk2SQzITFaifaMF1OxZlLOzWRxIWEFb7X+bZEUzxVY2AjvoyNRiKYu59b2XKoFHdVXnWKIxtizEK0orVJUtslRlMObR2Z+eNm1JC4avApb2NwJWOztcXR4i6QtRCspkXwD3vAz4NDX0FUGURaLwdOntVOb8Y4c5fng7udGC1lSBYLQT6CAyho37DT5XIvP3qcrGt1qAAAMn0lEQVTGZOz4mY3FbvIDtdDjzMnQeDyNjkuICl56P8Ya+u0cmUI41CPrcTMEMmTFOs/noFMFg4g8DHwceNBb+bCq/r6IfBD4ZeC7fur7VfXv/Jr3Ae8EJuBXVfUfTmelh31NU5YSSU+4zR2gVXys9Cmg6ghgDrUgNnWxdN1WtYkAhdVWb+/InX64VkuxsjMKlpgJEiHJiM/Hmo6KOIA0eYVaF2ZRb0LEnK01cpKEnNS2ik+w8GhKTADbOCu+N41rvgvxu0OUT4vFY7ElwVS8tHhRShGWh0egcHh4iOL7INY1KdQ8j5wSZRpt9WRKXD44oJk3/SBt/pvs1YWef/45FnsDg5eUN3ncO8Zqb/t77vbmiIm+8i6j9EAgozpWCVS3wTbwfou5KT4OVnfg3kjSfzn/RFv1HczQRF9ZeuWa3qcS6Dl5clmfuKYbrutzTezzfGbFWRDDCPyGqn5eRK4BnxORT/pvv6eqv9OfLCKvAd4G/CTwI8CnROQn1EoDn4FWk1/wQZHqQ6rb0lZ7wDVRD2dDUrqACO1gcNwrQfvbTn7fqB5dJx4K6iXkTE3ROzwV07opylFrCBC7ZyCLuplKEhilevSj0AnLhMhYHzUJ5OzaO2VSKtWmR6StJh1SDWFmMgvfxblGCEoVQ0zF3IKFzLgsZCkWgkwjox4xTQtbCxGIRCw8GTkGlkmqVqqttHLzFvxpdi7EPorWJcm3qrv77ru55557LFMyCZM7gpMki3rkflcwM5NmCT1IWwBb/9ejC6nv+CSSXiGEuSWZXqAdF5Zs9zh9gq2ZGyfi+Za/ElRd7WLKaRpHdJyQbpeyMk0eKdrcTs/nqfkXG+hUwaCq3wa+7d+fE5EvAy8/4ZK3Ap9Q1UPgGyLyNeB1wL+e2I41sCbtKywi4HOzn+qu0Wqpz33Htcy45qsIGBpOvGaKdI6w3nytdltUS2qchX3ZO+NswA3klBFVCoXsV1iY0OP89Qn8bqU9f1FHNyjCSCAofNAO2dfgm5wg5dR8GskdiDmRcqoIYiGm2VWFcaHsLRPLZWF/D45u3YKrV5hGWB4VkhZSdts5xQSFMioHl6+SWdCX0m8ayt9VSqjaoqlhGHjggQe4cXjIXXfd1TScS65kHVORWpSpi/fUsENrr74bzK8yGyfdZ5g1bYBVbE01ytvLdCGzGfLP+N5Ap03+k4TJ6lVSx7Q/q9aHIfxkKWcP8zYk0YTzvL3zIoWgc/kYROQVwE8BnwHeALxHRN4OfBZDFd/DhManu8v+mw2CRETeBbwLYP/ygcNW6mTrO9r+nrpeLCSfrNVb6xq9rVtXSG5Xe+dKSrZFGmoQONUC9NXOjmlrnc2szZokMnNg2d+lDjypAxwSKhZ7VhHIWgUIUJOVWog5PP3iG5caAxFtQG0FpEipzNVCqj4pcyomIBJeot0iJl49niGZcDkYFtx79QoHi332hyuMh4XDW5MXvzGhZssghJQHEgsu711lLCM6CpOM1gMzDWv/YqLklDg4OOD7N66TUrZds1VhKkzjVN9bq5Qlvogl3nt0cF/sX5mXi+9geEV7PZqIsxq1lOr1XytypE34E30EcYczIon1cORcqEn3d80OdWdiVQJxToPQc2fkpnbOiRjOnPUgIleBvwR+XVW/D/wR8OPAazFE8bvnaVhVP6yqj6rqo4v9/Tq4ZgJBtZuA4YSzBCWB6oix4iAhQHoICqHV4h5mOjgE82SlafQwYrenxWy7v5gg1RGkLq1L/V7N7I5fiywkdBIoGTT+DVBrUGVL/IlqPDmZIzQnyysY/F8eIGUr556yP8NAKcI0CeMIh0eFm4cTN24uuXFzyeHhyHI5sjxaMo1LyrikjCPT8hDKkiEpe3kgpwXLZaFMZh6MY0EnQUdBR+XwcMl4a2J5y0rOj8up7jTV922YIr1nfBwt9Ht0ZI5OjbTzMrUK2RqRl6b94r3mnGfa0Mdi9936vAmHOnrqdSJS91+Ie/e/nZXquTEutDu+wl/7ty406rFNWr7OdQ9N04fRU3X89pGMXiCsCoDqDzuna+RMiEFEFphQ+FNV/Stv8Jnu948Af+t/fgt4uLv8R/3YSS00TSq9dIvEmQhpzWR7NxjLfN/Geobfo7tn39Hh3Eopzzp3lTeHBc110R7cv3Q1BPwSAc9jMNifNEVxhC45teUc9G9OtW30GiXDiYGiShSTqZDbEUmghyTCkBJDSuQsiKg5OcVzM3RA3A8yTbZdXY7ICYY0QusmhMWQoUwMUza/ShZ0ni7aBrjzL0lggpzM9BiGwdGO1GXfqbs2BG4z77Tuj7FK7R2FyeVQvpoDunIeFY5vfsdno3pdBSuGEjdOxvmRld/bsRCEdUw68IynqEZU3HOm9Nbb2+RriFl0HjpLVEKAPwa+rKof6o4/5P4HgF8AnvLvjwN/JiIfwpyPrwb+7dR2vP5f9XA7VFaamSDdCkqkaZigBj2ZH+1MyzA37DYGyevka3jA5UETRlLtak+4whKKkuRZWyrmfDQLo4AWyzNImaIjzbSxzyJzlFMfQRsfuBMw53DuhdnkCVgpmblQ/Q9WPLet2beJOqmFWZfF1oHk60fcNx5xbbrB3qV7WexbanJK2cw0sWcdkjAejYzlJnnIDAtsCbWjoh7Ox3BPkinTyNHRUV1LMY5L9rIt8x4cCYRgL8lzUFYRY8yQThisKnlzFOvMjRDXzzv1mN+cYhJV2/1EodRaP87HsI4UNqOTmQ+ju1ed1FE8NwrhQI2g9uf295q1tQG1nEZnQQxvAH4J+KKIPOHH3g/8ooi8Fntt3wR+xRl7WkT+AvgSFtF491kiElXfrISPxCdSD8m0k7a1I+iqNzuFIdJ8Fx2qEM938AQlC9PNX4xdZYM6+XqGKsHxXIUqi0P7qy020mKCIQYtyddyOD/qwqVyG8u0vbWcfC2HIw5RS88WW+ORk3iZe8XCf8VCnS6Moq2pCFMgEBk49PLxR2MhXxpJiwUihbyAvUsJKZbAJcSenEKSQh6U6zef48pdB5BL9/TU+gDU92Gf4zjyzDPPcO2lL+H688/XHhWhG+zF0FSDYQQajCSwTrZvJu/3GA/HUdXCx5wSUaXTEQBxpxN+b2PzPKTdM0Mzq8Cc0pERXFyhbTKH1qITLwAgyQuFVbeTROS7wHXgfy+alzPQfbw4+IQXD687Pm8/beL1x1T1/rNcvBWCAUBEPquqj140H6fRi4VPePHwuuPz9tMPyuuWbDizox3taJtoJxh2tKMdrdE2CYYPXzQDZ6QXC5/w4uF1x+ftpx+I163xMexoRzvaHtomxLCjHe1oS+jCBYOI/JyIfEVEviYi771oflZJRL4pIl8UkSdE5LN+7B4R+aSIfNU/X3oBfH1URL4jIk91xzbyJUZ/4H38pIg8sgW8flBEvuX9+oSIPNb99j7n9Ssi8rN3kM+HReSfReRLIvK0iPyaH9+qfj2Bz9vXp/0ahTv9D8jA14FXAXvAF4DXXCRPG3j8JnDfyrHfBt7r398L/NYF8PUm4BHgqdP4Ah4D/h7LyHk98Jkt4PWDwG9uOPc1Pg72gVf6+Mh3iM+HgEf8+zXgP5yfrerXE/i8bX160YjhdcDXVPU/VfUI+AS2bHvb6a3Ax/z7x4Cfv9MMqOq/AP+3cvg4vt4KfFyNPg28REQeujOcHsvrcVSX7avqN4BYtv9DJ1X9tqp+3r8/B0SJga3q1xP4PI7O3acXLRheDvxX9/fGJdoXTAr8o4h8TmypOMCD2taJ/A9W3Wob6Di+trWf3+MQ/KOdObYVvIrIK2glBra2X1f4hNvUpxctGF4M9EZVfQR4C/BuEXlT/6MaVtu60M628tXRD7Rs/4dJsl5ioNI29esGPm9bn160YHgBS7TvLKnqt/zzO8BfYxDsmYCM/vmdi+NwRsfxtXX9rKrPqOqkVtTiIzRoe6G8yoYSA2xhv27i83b26UULhn8HXi0irxSRPaxW5OMXzFMlEbkiVucSEbkC/Ay2vPxx4B1+2juAv7kYDtfoOL4eB97uXvTXA8920PhCaMUWX122/zYR2ReRV3LGZfu3iaeNJQbYsn49js/b2qd3wot6iof1Mcyr+nXgAxfNzwpvr8K8uV8Ang7+gHuBfwK+CnwKuOcCePtzDC4uMZvxncfxhXnN/9D7+IvAo1vA6584L0/6wH2oO/8DzutXgLfcQT7fiJkJTwJP+L/Htq1fT+DztvXpLvNxRzva0RpdtCmxox3taAtpJxh2tKMdrdFOMOxoRztao51g2NGOdrRGO8Gwox3taI12gmFHO9rRGu0Ew452tKM12gmGHe1oR2v0/85EJ2Asgqf2AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "img_batch shape (1, 256, 256, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "img_path=\"./WHURS19/bridge_24gen.jpg\"\n",
        "img = cv2.imread(img_path,cv2.IMREAD_COLOR)\n",
        "img= cv2.resize(img,(256,256))\n",
        "img=img/255.0\n",
        "\n",
        "plt.imshow(img)\n",
        "plt.show()\n",
        "\n",
        "img_array = tf.keras.preprocessing.image.img_to_array(img)\n",
        "img_batch = np.expand_dims(img_array, axis=0)\n",
        "\n",
        "print(\"img_batch shape\",img_batch.shape)\n",
        "\n",
        "prediction = model.predict(img_batch)\n",
        "np.argmax(prediction,axis=1)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2hQz4_fihDT",
        "outputId": "ff998140-062c-4ad9-f9b8-3575be49693a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Desert': 0,\n",
              " 'Farmland': 1,\n",
              " 'Mountain': 2,\n",
              " 'Park': 3,\n",
              " 'airport': 4,\n",
              " 'beach': 5,\n",
              " 'bridge': 6,\n",
              " 'commercial': 7,\n",
              " 'footballField': 8,\n",
              " 'forest': 9,\n",
              " 'industrial': 10,\n",
              " 'meadow': 11,\n",
              " 'parking': 12,\n",
              " 'pond': 13,\n",
              " 'port': 14,\n",
              " 'railwayStation': 15,\n",
              " 'residential': 16,\n",
              " 'river': 17,\n",
              " 'viaduct': 18}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "test_image_gened.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "csuDtrHlAX_d"
      },
      "outputs": [],
      "source": [
        "predictions=np.argmax(model.predict(test_image_gened),axis=1)\n",
        "labels=test_df[\"label\"].apply(lambda x : test_image_gened.class_indices[x])\n",
        "labels=labels.values.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "ObTx4w84QrCt"
      },
      "outputs": [],
      "source": [
        "def show_confusion_matrix(y_true, y_pred, classes):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    \n",
        "    cm = confusion_matrix(y_true, y_pred, normalize='pred')\n",
        "\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sp = plt.subplot(1, 1, 1)\n",
        "    ctx = sp.matshow(cm)\n",
        "    plt.xticks(list(range(0, 19)), labels=classes,rotation = 90)\n",
        "    plt.yticks(list(range(0, 19)), labels=classes)\n",
        "    plt.colorbar(ctx)\n",
        "    #plt.savefig(\"mat_TransferModelVGG16_whurs_aug40_SAT.png\")\n",
        "    #!mv ./mat_TransferModelVGG16_whurs_aug40_SAT.png ./drive/MyDrive/MemoireM2/Scripts/screens/mat_TransferModelVGG16_whurs_aug40_SAT.png\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "992CsfDdRAds"
      },
      "outputs": [],
      "source": [
        "classes=[str(key) for key in test_image_gened.class_indices]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "DSsv0yx_Qtyi",
        "outputId": "c5acbbd3-20ce-422c-b1fc-7faa7ad98bbe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "show_confusion_matrix(labels, predictions, list(classes))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "C8AjLLlJoLt1"
      },
      "outputs": [],
      "source": [
        "!mv ./checkpoint.h5 ./drive/MyDrive/MemoireM2/Scripts/weights/TransferModelVGG16_whurs__aug40.h5"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "d_gl6t_Td8d-"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "TransferModel(VGG16)_whurs__aug40_SAT.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}