Final/Thinkful_Final_Capstone_Project_Detect_Pneumonia_using_Chest_X_Ray_Images.ipynb

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  "metadata": {
    "colab": {
      "name": "Thinkful Final Capstone Project - Detect Pneumonia using Chest X-Ray Images .ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUydoygeXnGm",
        "colab_type": "text"
      },
      "source": [
        "# Thinkful Final Capstone Project\n",
        "## Detect Pneumonia using Chest X-Ray Images\n",
        "\n",
        "According to World Health Organization (WHO), 2 million children under 5 years old killed by pneumonia each year, and WHO reported that 95% of childhood clinical pneumonia cases occurred in developing countries (Kermany, 2018). Bacterial and viral pathogens are the two leading causes of pneumonia (Mcluckie, 2009) but require different treatments. Bacterial pneumonia require urgent treatments, while viral pneumonia will be treated with supportive care. X-ray images is one of the key elements for diagnosing pneumonia, since X-ray images are able to obtained as standard care procedure, and help differentiate between different types of pneumonia. However, specialist for interpreting the images are not always available, especially in low-resource areas. Therefore, we can utilize neural network technique to act as the primary screening to detect pneumonia using chest X-ray images.\n",
        "\n",
        "> \n",
        "\n",
        "This dataset is discovered at Kaggle.com. The original dataset can be obtained from Mendaeley Data website. The link is included at the References section. The original dataset classified the images into two classes (normal and Pneumonia). The dataset split into train set and test set. The train dataset consist with 1349 Normal and 3883 Pneumonia images. The test dataset consist 234 normal and 390 pneumonia images. All images are in JEPG format, but each image have different resolutions. \n",
        "\n",
        "> \n",
        "\n",
        "Deep neural network will be utilized in this project to classify chest X-ray images and detect pneumonia. There are two possible outcome options for this model. The first option is to build a binary classifier and classified images into Normal or Pneumonia. The second option is to build a classifier that classified images into Normal, Bacterial Pneumonia, and Viral Pneumonia. \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## References\n",
        "\n",
        "\n",
        "Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, v2\n",
        "http://dx.doi.org/10.17632/rscbjbr9sj.2\n",
        "\n",
        "Identifying medical Diagnoses and Treatable Diseases by Image-Based Deep Learning\n",
        "Kermany, Daniel S. et al.\n",
        "Cell, Volume 172, Issue 5, 1122 -1131.e9\n",
        "\n",
        "Mcluckie A.\n",
        "Respiratory disease and its management. Volume 57. Springer, ; 2009\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uye-asbDho2O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# suppress duplicate keras warning messages\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "import warnings\n",
        "warnings.warn = warn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Or6tklhWsIa",
        "colab_type": "code",
        "outputId": "a002d542-a310-49e1-fc32-60352b602238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import keras\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Dropout, Flatten, ZeroPadding2D, Conv2D\n",
        "from keras.layers import MaxPooling2D, Activation, BatchNormalization\n",
        "from keras.layers import SeparableConv2D\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.utils import to_categorical\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "from keras import backend as K\n",
        "K.tensorflow_backend._get_available_gpus()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0715 18:05:18.517747 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "W0715 18:05:18.519422 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "W0715 18:05:18.525593 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "W0715 18:05:19.759586 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/job:localhost/replica:0/task:0/device:GPU:0']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9q8t9GOmHTAR",
        "colab_type": "text"
      },
      "source": [
        "## Data Input\n",
        "\n",
        "I made this dataset availabel to download from Google Drive. You can download it through my google drive or download it from the original source. If you would like to download the dataset and store it on your local machine, please modify your code below so you can work with dataset from your local machine. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUTgMQFuZivJ",
        "colab_type": "code",
        "outputId": "a4c0c620-f44a-4285-e8db-a065d734af61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Install PyDrive for downloading the dataset from google drive\n",
        "!pip install -U -q PyDrive"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25h  Building wheel for PyDrive (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xqe4rgqv3xM6",
        "colab_type": "text"
      },
      "source": [
        "### Get Authentication for Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkaskdevZkCY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rUZ2sxvS376q",
        "colab_type": "text"
      },
      "source": [
        "### Download the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kPJpNTkZpfc",
        "colab_type": "code",
        "outputId": "4f989010-d1e3-4bd3-8245-d554fae75963",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "file_id = '1X340OA2peo_48i71amfRTvsB9Y8Wjt2A' # URL id. \n",
        "downloaded = drive.CreateFile({'id': file_id})\n",
        "downloaded.GetContentFile('ChestXRay2017.zip')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0715 18:05:42.903063 139974641186688 __init__.py:44] file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/googleapiclient/discovery_cache/__init__.py\", line 36, in autodetect\n",
            "    from google.appengine.api import memcache\n",
            "ModuleNotFoundError: No module named 'google.appengine'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/googleapiclient/discovery_cache/file_cache.py\", line 33, in <module>\n",
            "    from oauth2client.contrib.locked_file import LockedFile\n",
            "ModuleNotFoundError: No module named 'oauth2client.contrib.locked_file'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/googleapiclient/discovery_cache/file_cache.py\", line 37, in <module>\n",
            "    from oauth2client.locked_file import LockedFile\n",
            "ModuleNotFoundError: No module named 'oauth2client.locked_file'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/googleapiclient/discovery_cache/__init__.py\", line 41, in autodetect\n",
            "    from . import file_cache\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/googleapiclient/discovery_cache/file_cache.py\", line 41, in <module>\n",
            "    'file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth')\n",
            "ImportError: file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTCmKq0K4DkD",
        "colab_type": "text"
      },
      "source": [
        "### Unzip the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MMtbpGRb20p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile, os\n",
        "zip_ref = zipfile.ZipFile('ChestXRay2017.zip', 'r')\n",
        "zip_ref.extractall('MODEL/')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hcv-FBXi4IRV",
        "colab_type": "text"
      },
      "source": [
        "### Read the dataset\n",
        "Below section will read each image and assign a path. These image paths will then input into a dataframe. A class label will assign to each image based on their path name as well. Two dataframe will be created below - train dataframe and test dataframe. However, these two dataframe here will not be used for the CNN because Keras takes more time to fit the model with data flow from a dataframe. These two dataframe here only used for EDA purposes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3B08fOeeY6O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from glob import glob\n",
        "from pathlib import Path\n",
        "\n",
        "#Please change directories if you use it on your local machine.\n",
        "# Training Dataset\n",
        "train_dir = '/content/MODEL/chest_xray/train'  \n",
        "train_normal_image_dir = Path('/content/MODEL/chest_xray/train/NORMAL')\n",
        "train_pneumonia_image_dir = Path('/content/MODEL/chest_xray/train/PNEUMONIA')\n",
        "\n",
        "# Read images based on their class.\n",
        "train_normal = train_normal_image_dir.glob('*.jpeg')\n",
        "train_pneumonia = train_pneumonia_image_dir.glob('*.jpeg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUGa_PhEJxsn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Testing Dataset\n",
        "test_dir = '/content/MODEL/chest_xray/test'\n",
        "test_normal_image_dir = Path('/content/MODEL/chest_xray/test/NORMAL')\n",
        "test_pneumonia_image_dir = Path('/content/MODEL/chest_xray/test/PNEUMONIA')\n",
        "\n",
        "# Read images based on their class.\n",
        "test_normal = test_normal_image_dir.glob('*.jpeg')\n",
        "test_pneumonia = test_pneumonia_image_dir.glob('*.jpeg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrmwAHhsqFGl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train image list with two classes.\n",
        "train_image = []\n",
        "\n",
        "for img in train_normal:\n",
        "    train_image.append((img,'Normal'))\n",
        "    \n",
        "for img in train_pneumonia:\n",
        "    train_image.append((img,'Pneumonia'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNq5eusdIIjq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test image list with two classes.\n",
        "test_image = []\n",
        "\n",
        "for img in test_normal:\n",
        "    test_image.append((img,'Normal'))\n",
        "    \n",
        "for img in test_pneumonia:\n",
        "    test_image.append((img,'Pneumonia'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_AgUiZ7mdEf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create train and test dataframe\n",
        "train_df = pd.DataFrame(train_image, columns=['image','label'],index=None)\n",
        "\n",
        "test_df = pd.DataFrame(test_image, columns=['image','label'],index=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZGU7ff64hEw",
        "colab_type": "text"
      },
      "source": [
        "### Check original loaded image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vg4-D3Oc04Dh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing import image\n",
        "\n",
        "normal_sample = load_img(train_df['image'][5])\n",
        "pneumonia_sample = load_img(train_df['image'][3000])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMnUd3k66FxU",
        "colab_type": "code",
        "outputId": "64b10190-348b-4c87-80dd-6082dfbc42ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "plt.subplot(1, 2, 1)\n",
        "plt.imshow(normal_sample)\n",
        "plt.title('Normal')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.imshow(pneumonia_sample)\n",
        "plt.title('Pneumonia')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZVL/f12AwMAiy1WqZkeY1rpXfGCueO9AiYzOXuczlpZPb3kD5Sg3Sgx/x+HmHUCikhYUF\ny32g2MnZYMT29vYUiUT08MMP65lnntFoNFI4HFaz2dTy8rJarZYZJxQvio8EfKvVUjgc1s7OjrH2\nYNI1Gg3dd999unbtmhKJhMF7KEcimIWFBYXDYYMHfVgvCAIlEglNJhNls1nLm/mQFko1lUrpnnvu\nsSinXC6bxw+TjWMyZkSi6+vrisViOn36tM6fP69oNKp8Pq92u21GFpIAkBdGj3sGhhuPxwZjci84\nC6FQSK1WS51OR/V6/e/kpc6cOWMQXSKRsOsLhUJaXV01w0xkCFmhWCzavRWLRctfpdNpM1A8Lxya\nVCqlSCRiuSTml/+s/blHhMqz4ju3WpxzXy/pvZrC4/97EASP3vKLmMtcbqHc9gaKXADeLTkSiAvk\nnuLxuEqlkrH2MpmMedaZTMaoz8PhUMVi0dhn5KcwDJKM6RePx+2cuVxOsVhM9XpdxWLRoMVsNqtU\nKqVaraYrV66o1WqZAUUJptNpyyVJst9EPJA+fCUJOYK8E169c07pdFrSNL+VTqd19uxZbW5uKplM\nKpfLaXFx0fIwCwsLarfb6nQ62tjYUDQatUiQ3NRkMtH+/r7OnTunwWBgyrzRaKhWq2kwGGgwGBik\nFgqFzHjG43F7RslkUoPBQIVCQaPRSL1eT0EQmDFPpVJqNBrKZDI6e/asut2url+/rle/+tUGrUlS\nPp83Gj5GXJoaQyJPnIpOpyNJZsRzuZxKpZLdRywWM7jQN6rcgx+V+/R7DBNzAsiYv2+1gXLHXVje\nrGn94Medc78XBMFnbumFzGUut1DuCAMF1DIcDmeS5SgumG/AWHjSzWZT+XxezWbTjA01S5lMRru7\nu8aK29jY0M7OjkVjUNHxuhcWFgyKq1QqisViOnfunLrdrlHgySEBcQFTpdNpFQoFRaNRI2ug6CFY\nNBoNNRoNbW9va39/X71eT/F4XPF43AwySrLf7xsNOxqNKggCy71tbm7KOWf5MnJk4XBYBwcHNnbk\nt/r9vnK5nI1LOBzWxYsXLTKVZA4ANHdeD4fDFlX5kFi327VjQQUnekwkEgbDZrNZvepVrzLnIxqN\namVlRaFQSPV63aJinjk5RQQq+WQysRwg18l9+sSHWCxmRhRjv7S0pHK5bI4P0RNG159/zJ9wOPxy\nsPisC4skOefowjI3UHN5xcptb6CIiHzm2GQyMc8YeAYISJIl+rPZrBkDDFw8HreIotFoKAgCLS4u\nqtfrWb6DfA+RC4l1WHftdtuYZNls1lhz8XjckvSDwUDJZFJra2saDoeq1WpGjIBkEAqFtL+/b3U9\n2WxWr3vd69Tr9dRqtVSr1XTjxg2NRiO7Fhh+GB7qkXK5nPb29kwZUxskHRvwWCymWCxmNPROp2Ps\nxfF4rGazaWw1jAb1ZJIMIlxfX9fS0pJyuZxSqZRFUhiByWSier2uRqOhS5cuGYwqyWqqMJqSjHKf\nSCQsl7e+vm7PESNMxMkzwQAB/YXDYV2/fl3333+/GTLmBbR7auQGg4Hq9bruu+8+HRwcWORE9Mz3\nwuGwGTzq4jBmt1ierwvL62/1RcxlLrdSbnsD5XcmwMhACZZk9TEoFZLf1MWQo6nVajO5IGkK/aHY\n8IwxTihyFGKv15sha0BOSKVSymaz2tzc1Gg0UqFQULPZNK+/UqkonU4rm81andP29rbW1tbMsHEN\nklQul632KJ1O6zWveY3G47G2t7dVLpdNkRIJxONxpVIpdbtdiyioxfKZfalUyvJavvKFQg0tnXFm\nPBmLjY0NJZNJFYtFdTodRaNRgzBjsZjR8qUphV2aQpn333+/vvIrv1I7Ozv61Kc+pUajMfOsiFh4\nLlDWib6kaaSUz+dVLpfNwGMsJdn3iJobjYZWV1fNiWDeJJNJq2Pj3nF2cICIuIgCuSfgQYgmtyvN\n3O/EIul1R6/p5G/GmNwnDoPfpYMx9olKOEg+HH2y7MB/j2fkO5cc2/+cXyvkn9sn4FDz2Ov1jAQE\nEkHuEfFh4ZPX5MvzdWh5vs/cDNv55Lj4jt1nO7b/Pf98J89/8u+TXU4kmVPJuEjTpgD+eXieQOj+\nsU/+73eZwYHnc36ahby07wz6DhyIkl+i0uv1NBqNPm+B2G1voHgAeNOwwRgkoKrxeGwKVpIpZZLz\no9FI+XxetVrNFDIdFYC3qLfxFbQkK+qMRCI22HjhrVbLmHD9fl+bm5sqFqelFP1+3/IwoVBIp06d\nUiQS0fr6uiXbyZfxOZQyxaHc98LCgh566CE9++yzFhlBlJBk0R+1Q9T7YPAYLyKJxcVFY9wFQaBm\ns2kGAwMM25Cc1mAwsCJfCAhEZ9Dlq9Wqzp07Z/VCoVBIjUZDi4uLevOb32xdPSqVinq9nrrdrtHz\nC4WCUqmU5Q/H47F6vZ7lu1ZXV1WpVIxNSHeLcDis++67Tzdu3FA+n7fFAbEhHo+r1+tZJw0U4Hg8\nVqVS0erqqnZ3d43UQRRHcTOf91s/vQzyebuwSLOdWJxzAUYH5U4UGo1GVSwWFY/HjQiTy+Vmcoyx\nWEyRSMRg2kKhoHa7PRNZRyIRLS0t2fjg7LHucC6YxzhWKDjGWpJFq5zPh2ZhToZCIZ0+fVqtVkuX\nLl0yhQe5p1Qq2f1SLwjSQnTsK3WUMvA9BoZxOBpTu36UsSSD0H1Gr996DJj7pCEnhcA89qF0HFac\n4hPP1so/UPZ0v6lWq5Jka340Gun8+fNqtVrmpIPuxGIxnTp1ykhWvi6F6SpJ73rXu/Too48ql8vN\n5Jlxir/zO79TkUhE733ve/UjP/Ij+qmf+ik9+uijete73mWs15/+6Z/W93zP96jZbOpDH/qQvu7r\nvk5//ud/bnn0zye3vYGSNJM7ot0OD5/oCUZdNps1pc0CYXIOBgN7+ByrUCjYZKnX60aoYHFyLn+S\nra+vW60QcBs5lqWlJasdAlJjUf7SL/2SGatkMqlv/MZvVCgUMo8QKBIFwrHxdi9duqRcLqeNjQ1t\nb2+bsWF8IDJ0Oh3VajW1Wi01m03zEOlakc1mzSj4rYHy+bxdiyQ9+OCD1sMO6I/oIpPJyDlnBhEl\nfubMGavZarfb2traMsXDxB4Oh2ZQ+/2+Tp8+bYuXBSAdR5NEpDAL/cJaaPl7e9MWaURgtVrNcnTj\n8VhLS0uW1+PaIpGI9vf3Z8aOZ+57qjg9LNKXqYvEC+rC8rkEB4Q6P4wN84S6M+a33xXE7/84Ho91\n6tQp9Xo9pdNpizJpm0VJA3OHPCqsWiL0drttiAVeuaSZ6Agno9FoaH9/X8ViUa997Ws1Ho+tLKLb\n7WpnZ8e6zsB2pcsKjsvJSIG1JsnOjU7wmbwcg7FA70DWodDfj+xIK7CGnXMW7TFeJ6NFnEGfHEU+\nFANOLpv5ilPRbrfVbrd19epVc1RxZLPZrCEQnAekgu47oA4gQ+FwWPV6XT/3cz8nSXr3u9+td77z\nnXrPe96jX/mVX9E73vEOSbLcP/Pl3e9+t97znvcon8/rd37nd/T1X//1GgwGeuyxx8xI3ozcEQaK\nPFS/37e8EpMNrxbrXywWzeuVZAaGnAxeHDkmPBAgQEm2YPlsv9/X4uKiRTt+jgJYKQgC7e/vm7dR\nKBS0urqqbrerT3/603r66afNi6U90Ic//GG9+tWvnvGyUOKcF6NA8a0k1Wo1ixSIKIrFotLptPb2\n9pRMJnVwcKDRaKRWq6VIJKJisWhGQpIODg7MgBaLRa2sTHtaNhoNRaNRu99CoWCLEi+VRR8Khaw/\n3t7enuLxuPb29swxWFhY0D333KOdnR212221Wi3l83kr4pVkUCGGwCfAZDIZK/JlDuTzeVWrVRsX\n6Rj6iUaj5plhALPZrMF4kUjEIgA/qup0OuZU+KULKNWTDXFfDop58CK6sHjf/TtQFvdBL0ZfYeA0\nAVOjcBuNhs0ZiDYYuX6/bzlY3i+VSuYgUV5BnaEkG+9CoWDrDcPnw1aRSMSITmfOnFG1WjUUJBwO\na3l5WWtra2q327px44b29vbUbDa1trZmERhzyof+iKiY334OUprqDr9F1vP12sRpwVnmepk3lF9g\n2KUpOkJeFUgMaB09xzhgSEAUmJ9EcTjcmUzGjH21WtXy8rIODw+VSqXMsFJrCSkKRjA6gnw6eXrW\nTbfb1aOPPqrt7W1NJhP9zM/8jB2XsfnlX/5l/eiP/qj19fz5n/953bhxQ9/7vd+rnZ0d/fEf/7EW\nFhb0pje9SePx2BjEn0/uGAOFMfG9NSAmn0W2u7urlZUVWwyJRMI8g3a7bQsBJRQKhWwRAR8GXuEm\nHgaUdhYYZAomJEod77xcLuuZZ55REEy7TPiMM0lmlJ599lkrUF1YWDCYEg+MxZ9KpbS8vGyKgckc\njUbVaDQMMjt37pwODw/NaLBAgOcI90ulkpLJpGKxmBXuNhoN8/CIMLlWKOy+5821djodtVotfeIT\nn1C327VzMo7f8i3fouvXr6vZbFpvQxYmNVbkFqDG9/t97ezsqN/vW9Ti1yMRsfnFu9RN+fnCSqWi\nUqlkTkqpVNLe3p45Fnjx3KffzV3SjEePEnm5+vAFQfBHkv7oBX7HICdJ5swxZj7BCIXsR0m0liLf\nCJTNfMY58PO4wL/AguQnmLsYBaBnP1ph7uAM4FjyvBuNhorF4gyygcOayWT00EMPWauxra0tK9r2\nDY90XOuGUWR86MPI+vTLW0AC+B6wH1EN89rPAfnIA8fwj8vcI7r04WSen88s7fV6dhzWDVD47u6u\noRC9Xk9LS0uqVqvmBIxGIxWLRYXDYR0eHhppC8gRyLVerxvMur29rVOnTimRSKhYLKpQKOgd73iH\nfvZnf9YMcSKR0MLCgt73vvfpB3/wB7WwsKDv+77v0y/+4i9qa2tLiURCb3nLdIcT5s3NOnm3fauj\no9/KZDLKZDJaW1uzlkd+l3M8cSYt3RFYhJ1Ox7wPWHk8DDpIUNNE2OwPJuej0zmTBPjg4ODAJrbf\nV48J6Yf9hNkU0C4tLalQKGhjY8PYgs5N2yF1u10zmj41nZwVE5v2TkQ8/X5f+/v72t/f18HBgVKp\nlMEqiUTCIp/FxUWDHOnWjcEFivNJCSxm2k0NBgNdu3bNlEmn09H29rYZOiCON77xjWo0GjMwpCSD\nRnAE0um0RbW7u7uqVqv2frfbNbjRbwOFc8E1ttttU6QUIheLRe3s7Eg6ho7a7bZ1EBkMBjo8PDSj\nCWzbarVULpdVq9XsNxGzpNu61ZFzLuB5+VEmCpOCaMoZeO4rKysz5ASiedqLgTAQVfN5jI/fjR9n\ngRyvJHMsWKfMB4rpT8K45H2IfMmhpNNpg6DJrUlTYkC5XDbDCcwej8ft2k+2EvMN1Umo0c87cX8+\nFM7rNHT2oToaBfjrJwgCizZBDjDKfsTuH6ter5vewCBhpLvdrtrttkWHdJLBCA4GAyuE517YiWAy\nmdgapwzGj6az2awV1gPXDodDlUolLSwsWF6L9bu7u6tEImE1lKPRSNevX1ev11O1WlUul9Ply5e1\nu7urTqdz55MkpNliXRh6wF3SMVTAQ0Xxo5CWlpbMMwLawTMPgkBXrlwxr92PDPD8fG+ISeAnn5vN\npuWkMIS+l08k5E8GrvHs2bOSjjuSp9NpdbtdbW1t2eRC+fI3uZT7779fu7u71lEdSIUoBTgNL7Tb\n7SqXy1lPQJK6QJ8oKgyBc05bW1vmRePxoQyy2aw6nY5WVlYUjUa1t7endDqt++67T88995yNVbvd\n1qc+9Sk98sh052pIKxgen8wAtu5HqpVKxTB7v00T0RzwBg1zi8WisSf7/b5qtZoVCAN7+MwjYBKM\nEwqDZL7/up+fuhPkJOvLzyn5bCs+1+/3tbu7q0KhYB41kS2fY/7BoO12uwZbM1ZEFBgon/jgK39K\nNoDugeRORlY+sjAej3Xt2jXdc8891pXf73TvRy1EgcxxP4pjPHzDdZKRiKLnOvgMhofXuDeiSMbK\nz3txTJytk63EcD7RDRgsyAnMV/8YrFXyaEBs6Md7771XyWRSlUrFHAVpGinicEuynRzowMJzoskz\njQkkWUoEYwbcJ8nG3zmnlZUVNRoN05WpVMqg1/39/Zuav3eEgZKOjdRgMLAFQr6EVjpMAKAKDMve\n3p49PCx9t9u1uh86F6ytrVkxKYlNjI6f0JRkUBkTC28MeEM6Tr4yiWG8wXorlUqWuAyHp53VWTCD\nwUDNZnOGsQikSfPTJ598UqWoizAeAAAgAElEQVRSSb1eT/V6XcvLy0YHBc6s1WoG53DdPqsPbB9v\nDOMdj8etSSpQIApnPB6r3W7rypUrkmSe0blz5zSZTAuPv/Irv1Kf/vSnjXp/cHCgP/uzP9PrX/96\nFQoFHR4eqt/vz+zpxDYZfiI4mUzOtJ8C4oVp5Pco9JlbKAeIFZPJxIp/K5WKkTVQhL7nzhhAQgHz\n9xlbd4qchPhQ+r7ChLTAeyhImgejjCEe8D/wqM8sYy2Qw/IjkmQyqWazqeFwaMxV0AKcRRS9n4cZ\nj8dmgDBS0nG+tFQqaTKZNmYGJcD4sNaIOChg99c58x2WI+PlQ5cnDReQMcbBhwb9yMd3APw0gp8/\nPfmsuGdyT4w5aysUClnJBakMoMWFhQUNh0M99NBDarfbtmWQX1IgydYJRBJaiHEuDLo0jaCr1aq1\nDZNkOXvWz3g8bZItHXfI2dvbsxZtfhNqnNObkTvGQHFz/X5fzWZTsVjM6piIWMBE8WQkzXgDeHvg\nqsvLy1YgCsxGIrhYLJrHAHsII0E4HI/H7VowTjDH/A4OPuMsCALde++99qDx8j/zmc8oFotZk1Yf\nZgMqpA6BvAETCRgDpU1uKgim/emazeZMxAk8kUwmjXQA9s9YQ1kn90YCtt1um7EnuUonhk6nozNn\nzmhxcVG1Wk1f/uVfrscff3wm0f3Rj35UpVJJp0+fNugVZRUKTRvqNpvNmXwPipI8GZ42zxjlgCHh\n2eAYYLBRwigljhkKhayrvZ8jJL8A5f9lpJh/QeKz4lCaePkoKJwBogOeh888q1ar5pT5dXBs1UIi\n34e4gfDC4bA913w+P7NHlw+D+/Af5/WjFMgOwPUYSs6PomVNEmXjNGKAyGezo4DPkPNZtHyeiMaP\nSH3ojsiM68XAoIucczNsRUnmCPhKnjlHuoI8E9E7uy3wGvdEjSI6gi45GCaMKNcUiURUq9Xs3tAJ\nvmGGeMaYct3sjED0xXWgjymhoWsMTiQ6knPejNwxBopcBoPc6XSsToCapJO4NZMHRY63sri4qH6/\nr4cfftjotAsLC7YvVDabnfEAFhcXjaHGsSORiA4PD23B4+ExwfwJ7Sdkz549axHA0tKSut2ubaq3\nubmpRqNhRAMSmuC8JPXj8bg6nY4KhYJ5KEtLSzOQIrg+iwf2XTKZtLotyCHkoBqNhkGRKHKYb3Ss\n6Pf7qlQqOjw8tEUUCoW0trZm9UuDwUB33XWXKpWK3vCGN+jpp582phiG9MKFC2ZQIaHUajWLMn16\nL4sHT5/79w0KyXPnnA4PDw1yIOomqezDMP5vSTOeHXk8IkufrHGnycm8ip8nJS+E8QImYw5PJhPL\n6fmGhN9shunPO5Q1Sj6dTms0mu4rtri4aOdx7riMAqeD9eI/FxQmEZ50vIvAZDJRs9mceZ44ZkQO\n3Adzk2cMSkLkxVpmHuCUEBH5QrE6hoX5gdHyc0kQT1D+jJE023NTko0l1xkEgSqViuk1DC85dMhO\nOI04kkTAQKfMYQwHJDOusVgsql6vmxPHuXkerVbLclTAvs45Y+zGYjHbHYB7azabqtfrRqoiVQAE\neDNyxxgo6bg7OcqKiYnx8amceApMUN+TCIfDuvfee9Vutw1mI/rxJxwLAqyZJqT9fl/1et0UKQv+\n+XB3DGUikdBdd92ldDqtcDis1dVVnT17Vp/85Cd1+fJlU/pMDLYLIdcTjUbNO4JtV6/Xlc1mDXOm\nGwPesSSr7cJjIQ9FnoAIBW+TMSUyxRAMh0Ntb28bcYBFhGIpl8uGfz/yyCNaXl5WqVRSpVLRPffc\no1qtpsuXL5sCwEtFEeRyuRlWEnAelFQMJli6Lyy4RqOhXC6ndDptDCWiZqAkjL/PDGNBS8fQ4GAw\nMIyfv+/kCMr3WlkX0jGbinkH/R9HBaSCiIcIgXpBoiaUJ0YCurJzTs1mU71ezwg5HLPZbFo+meeO\nIfKjPN84YDyJkNk1GiNAnpS1BwnHd1yB9zCOdFshAsORwxj69PJoNGqRA/AxOoHokmtnTfnGRTqu\nrcMwE1WwFkBNUPREM1wHY7i6uqpMJmM5Xe47HA5bl3+/cwo6rdlsmp7E0SDK9J8zRCbWB7oXeNMP\nDHxHA6TDj1RPNh64WbmjDBTKzce/8ZLIz/iTg8gDeOv06dNKJpPa2trS3t6eDeYjjzxiCtHH6oGe\nmJCwzFhMTHwGHGiEiehfUyaTsd1iT506pWQyqUuXLhkhoFwuWySEhwr54tSpUzO4OEYVRSod5+iA\nNFqtlur1uilijAJQHnUOGGE28cPzgqBw1113qdvtqlwua3V1Vf1+X6VSSZcvXzbsfjKZ1jWdOnVK\n/X5fFy5cUBAEOn36tDKZjNUgBUFgRg7lw3j7nqu/k26lUjE2IfVPLO4gCKyLOYoCqI7zEXWSfwI2\nZQzIm0iyYzB36ATC5+60/BPCWCGMn68wef1v/uZv/s73m82mfuzHfkz7+/sWaQD7QCxCGVNTBrN1\naWlJkci0ewo1ahg15gAQG/OXiIfn5ed3fEUuTSMZdtEmz8Xxie4wPjgnPuTvw4f+82Wu+bCdv44Y\nVww/hk+SObV8RpI5sRgjIE+cP5xidBffQc/5jEIiInQAa9bPEYHEdLtd1Wo1g9v8Lhh+OoSxPWpB\nZMdiTfrOuH99hUJBu7u7xpLc399Xq9UyWNNHkLjWk/Pxc8kdZaBO0jF9XBbPgSpt2hz5ifBKpWIT\nNBQKGY765JNP6o1vfKPh7NRN+V25gUjIUTHh8Jh8mIIoDYw8kUhoeXlZuVzOYLkrV64Y3ZI9rEj0\nwpiDnOBj70RjKFE8KyYZOSc8JR/b97skQLMulUqWNCbUb7Va1i3dOaf9/X3zgIgkzp07J0nW0Baa\nOhARnnk+nzea7+LiotVosSjx1PFaWaS+0c9ms9re3pYkqwXzxx2arT8ORF9sp+LnX9j9l0VCJMgC\nYl4B2fA3i/tOERwp6bi/HcpYkkUiwF+f7f6y2ax++Zd/WZL0Qz/0QzY+OB4oZD/PmU6njYJOhM45\nicyYo8lk0uoVpVkiD9fOnOE5ch8YIFiGfF+S5VElWXkJY+DDfNw3sJpPJiAP5OeQQVTIV/m1RER/\nOJM++QBHCOKVT1ZhTE4y+zDEOF28DsEDJIfnNJlMdO3aNVtDRD7Mb9IRkCsYW3QXUaBPaOCaeW0y\nmRb7YlTz+bwZWowqcwLDyP5tvqG7GbmjDJR0PFB4bjw4vCSUrR9ZMPiNRmOmOzdJXOjrwGU7OzuW\nbAfi8uGParWqTCZjk8AP53kN6EySVlZWtLKyonQ6rY997GO6fv26gmDabSISiRhkhqcUiUSsxmAw\nGOjy5csaDAYqlUq2Pbskq+dikqRSKStM9aMKoi6iO47LRIUcAnSazWZtYZFIpT6LLc93dnYMdrz7\n7ruVSqV0eHioWq2marWq/f193bhxQ4uLi3rggQcUDod1+vRp1Wo1g+kwRnikksxrk2ReLsY5k8lo\ncXFR5XLZthzBYCeTSe3u7loXgUgkokKhoKtXrxphxGf8SZoh0zjnZvJNML46nY69fqfBez6DzFe8\njKekGYOOpytJb3jDG1QqlVQoFNRqtfS7v/u7kjRToM53OBbrL5PJKBQKGUTtR0XkvJhrhUJB/X7f\nSBrdbnfGy8aQkJjHiIGa4BDBRMWw4Lk75wwJwSnxPXjuh+NhRFCy5NNqtZrpBJiyrA2MPNeC8cYI\nSNN5DdTuR4I4hnyPMcKRRB9wrVzT2tqaMVr9Qt79/X0zSNKxcQfFwUiQdyOqxLD6BAqiqHa7PVPb\nxn2QBmAt+mUbPtkIne2jJczNzyd3nIHCOBGeM9HZ2h3r73eY8D/LQwAz5uFvbW3pDW94g1qtllZX\nV62pbKFQMDz94ODg79SNcHxJM94I54TR94EPfMAo4nigflKy3+9rdXVVOzs7euqpp8wjoRKeokTq\nqEg+4zlSOY7BZuGfpBIDfYAhE2kOBgNrpnp4eKhEIqG//du/1c7OjiaTiVqtliqVijqdjh566CGt\nra2pXC4bQ9CH+hYXF1WtVvXUU08pmUzq8ccf11d91VepWq3a2LAQWOzAcSwC7oei3LNnz1ouCE8a\nZ4Q6kbW1Na2srGhnZ0cHBwd6+umnLZeAAUKR+cXWGCXGsd/vq9vtGkTqe5J3qvg5NtYFP9IxEQGB\n2YdC+o7v+A69//3v16OPPqqf/MmfnCHkUDSOIkyn00ZYwVEi7+c/O+m4oS+oAaxWH0ZDKUP6wfhh\ncEOhkOVOaYqKw0dUjOJED/jGTTruq8daYE3DACVXhqIlX8r84zokzdSE4UQzB/08DPcF/OgX4vpt\npHwIEVgbNARHUtJMXt43es45Y8dy/dQ9MdboCtYh2/CwTkFPuH5JZrRAtdBr6CBKOnCIWO8vBIm4\nIw0UD5/JxsDAjgM+IGmIVyYd9/biofKwwM2JXoB3UOhURWN4OI5fT8MCZ6LT2+r8+fO2jfrBwYF5\n5iygXC6nZ599Vs8995wpQliLBwcHWlhYMEiBmgefCDAajYzqy5YeKG3qxSALMBl9xp1fS0Juand3\nV4eHhxZFlstlVSoVjcdjPf7448ba+Yqv+AqLyOjIwYaOGxsbZhA+8pGPaGNjQ29605use4fP8EI5\n+REpY0ky3Ic9h8Np920SuHQ6CIJAy8vLunr1qnVGZ074bClJRmxBcQI3QowAj38hHt/tLj4Tjhwm\nNTEgBJLM4AyHQ4O16fYPOgFRAngrnU5b9xY/Z9Pr9QwGOhlxMMa+ggd2w1lhfYFQ+BAlkQjKFYoz\njpkPkfv1gcB2REUYYvJeGAZy2ORlMMCMI+dkrfnsYaIPP3ICxkaR+1C9dOzkEjn5xA+UPl3nyS8D\nYfsR4kl2oG9UpOMdov30AVHWZDKxNcr4kp8/yfLj+eMEUJgbi8VUqVQMOqYkhbz8KzIHJR0n2nwa\nN14g7T7oRsAC8OnI0jG8wISLRCLGKoIdR989cklACxA0/D5i/j4pTIyFhQVrkkoLEB86IoL52Mc+\nNpML8esNYrGY9vf3ZxhS/oSLRqPa3t422DKVShmWz4JfWlpSPp9Xo9GwZrs050QB0MS10WjYfbC/\nk3NOly5dMhgS1hRtmJ544gk1Gg1ls1k98MADWltbs7wAHiJ1ZoPBQE888YTW1taMhuxDRBh5CjhZ\nvD70Qs0W7CVgJZyOWq2mq1ev6ty5c9rd3bU6MaATlBfQFsWMk8m0dIFOHNBj/dzUnS6+QkcR+hCc\nD7XiFDAHfv/3f1+DwUDf9E3fpPX1dVOeEFhwyFiPPuXcRxbI2zD+zG2fBODT//1nL8lYrUS/5GMo\nSCVn6eeT0BN8FsWK9+8rah/1wOOHNYex9nPM4XDYyjbIOfnv+5Cjz/Dze2T60SL6yH8WvBcOh607\njE9e8MlRfm5WkjlmPswLNMd4cB1AgEB/zAMMeLfbtSbMGHRQjSAIdHh4aJGU7+BLx8QRn514M3LH\nGShpdrMun/kDPZPEoySz7ihk39vpdDpaW1uziIPvM7B33323tramW+6wYOhhx3X4HSdgRqVSKWv/\nPxqNVK1WbSET8Tz11FMGD2QyGTOqRGvkwrLZrLUFIc8EdCIdbwEATMh1wpgaDoe2XUUkElE+n5+h\nmKIEOH+/31e5XFa5XLb8F22D/MQpEZ1fg/LJT35S+Xxe6+vrKhQKVn8G2wtPne4W99xzzwzLkMXC\nuRqNhkGVwC48t2QyadEr45BMJvXEE08YQ2phYUHZbNZaXuFpA6sA+UAdxhEBtvRpv3e6nDSwfj4A\nev7J6JL18uEPf1jlcln5fN666PtrCYeM7/vwKcqWFlQoaoyDT9LwiQZA574yD4fD9hr3gEPoXzc5\nE6Azv7ccz98nSlFnhzHq9/vW6snvIuHnh/yoB4PgR+AYBKIF2in5TGQgSfJgKH7fuDNexWJxpixD\nOkYXOBc7hCP+88CxxLCxZjHORL2SrIAZfQCUTr4cw0jDWYwi18X1oNtwbv1OMTcrd6SB8rFzDJPP\n9PHrNU6GvHwH74Y+b/5OuJKMFbawsKBms2nNagmHaTCK905HX/ZZ4kH4bJ1PfOITGo1GloCEDeMz\n0Ki2J6fGZ5vNps6fP69HHnnEromNEYEaMdSxWMzC/tFoZEWLwHyMDdEHnx8Ohzo4OFClUlG9XjfS\nA4sOzxjjeBI2QNk553Tt2jWtrKwYTRcoD7gsn8+rUqlYe6m9vb2Zmhq/xg0vmgW1sLBgHiMKc3Fx\nUR/5yEeMTShNa8AikYhR5jkWkKDPBB0MBqpUKrZxIx6gP9deCcIz9JEH36tG/vqv/1qSbF+tbDar\nt7zlLcZYQ8nzvPzo1D8XUQ6OEOMKC8zf8dVnm0qyKJs57RsCv9wAw+Lnd7km1joR0mg0MucNA4Kx\nBXLz67m4Ft6XZGvGzwXjHEoyJ4v3gSxR7tLxmsEIMP44vOREgcLJ9WEEIWugWyqVijnhPsMYg4TR\n80kLPkEDfYDOTCQSKpfLplNg/KFDyNWiB51zBr/XarWZbZHI1/mR283KHWugWGBER3jCDL5f88B3\nfO+k2+0a3IUXU6lUlMvlJB23ISEnA5vJp4qCo6+trRm+zUQknN3Z2bF6JyihPg5P0tDHr8l1+fuy\noFSfe+45Pfjgg+b5+RX5TIh8Pm9/A01yrX4+hUiCRUMiulwua3NzU4eHhwqCwLpMwNqLxWJGaPBr\nz8h3YbjozdVqtbS+vm51ZMvLywaljcdjrays6JFHHrHo6MaNGxoOhwaxSTIjx/gBqaysrKhYLOrK\nlSsqFAq2cSG5BsYNzxJvD48VhUkxKcaK8cMYv9LEJyqwfiAGSNKrX/1q2xHggx/8oHZ3d/W2t73N\nogbyq36OR5rdsoS5xVwOhUK2CaXfP87Pv/jFptJxzow14zPWUNo4dX4OhuJ2v+CbeY9C9ZEEmKx+\n3sU3rswTvgeU7hsQDIB0vDsuwt8UwFK6gBHBWNRqNYusfHhyMpnMRK9EPjjB5L1h5PnwKkYHR94n\nY2BYc7mcGXSMOg433fvRq7RIK5fLtoa4Joytb0jJTeMgvOIhPhT5ydwAXppPNABD90kVYNV0D6eV\nPe08mFws2lKppFqtplwuZ+2AJGljY0NnzpzRYDAwEkEQTFuTnD9/Xtvb2zNYOwQPIoPhcGi4Ln3Q\niMpOnTo1E4aDAS8vLysUmm4USPdyPEaoukQ9MH84Z6/XM2o7ESOTql6va29vTzs7O1a/sLKyYvtM\nkTvz2XMkfMnbkR9gcTCmw+FQly9fNs9zc3NTy8vLuu+++0zpAMlBCFlfX1e/39dTTz1l5xwOp62G\nVlZWZmDaaDSqK1euWL7Kr6fCUcAzx8NmrCnEBY4FqgDaudPZe4gPi0vHa4O5RYErQpQyHo9NeUsy\nj9j3wOmKncvlZlijJwlItMnh3LlcbqZDRxAERlTgmv18DJ48cBGkHhi3OHnhcFj7+/sKhUIzXdmB\nGiEH+UYDhxbnCmiOXIxf/kA0wLX5a8InSfiKWjrOffuUa3/jTtYMhBTyU+gv9BPnYH1h5DmPD0vy\nvLlO9BBOMczfk+UX5NG63a51acE4SjKGJ8ckgiaq29/fn2E+oq/9+XgzckcaKB+e4Mb9B8n71Amg\naHymHTkNOpXzgKvVqjGFgO62t7etS3IymdTS0pIWFhZsErRaLW1vb+vixYu6evWqPVQWEQ+cB8ae\nS9J0cRBZYTyi0aixYVCu0WhUCwsLFqUUCgXlcjlTEjDPlpaWLPnPcX14k6iAJrecA3p5t9u17g1g\n3+PxtM2TNCUpNBoN9Xo9a0/EOHW7XVWrVes+gRdGPcji4qKCILAk7NNPP61r167p9OnTeuCBBxQE\n0y3jz549q4sXLyoWi+nee+/VjRs3zOteWVnR+vq6Dg8P1ev1LCFOwWS73TajxXP2GZzMAZ+wgmGC\nVYkSeSXknp5PfO/bZ0z6CgRoLRQK6Wu+5mv0J3/yJ3rsscf0bd/2bep0OnYM35vnWEBc5ILpdynN\ndl8gMplMJpbDwvgRRXU6HavRA6r1a21gsNLRX5IxXFGyPkOUecrrjAGIBlEQUQZ5ExxFnEEiQ4wv\nThYKnCgIXUSkhKOIw8zYQO7AWHBu/3iID20SfYGG+HrOj6LQI0D07KJAKyj/+RBZRSLHHUDG42m3\ncnLVGCTy2v7+VESWMAxxgriuk1Dw55I70kBxcz7zh3CcKEWSeUREEnhF4NA+XR3vDo+HyVSpVGZ6\n2d17771W7Hrq1ClVKhXt7OzYgwYibLVaM94DOSFJ1gVdku6++27zNplozWbTrmdlZcVYd845VatV\n26WUcNk5p9XVVTWbTdVqNa2srNhEx0hwXxgllAR91liY1DTBFqIo+eDgQJPJRMViUXfffbe1UWm1\nWjPd0SORiBl5jLo03ZOr1WqpUCjYWBA9bW9vKwgCPffcc1pbW9Pq6qruu+8+a1XDdio0xqzVaqrV\napaDK5fLM1EbsA73Pxwed1TGSKEYqaHjPX7uxM4RNyN+JAUs7t8zgsJKJBLKZrN685vfrI9+9KN6\n3/vep7e//e1GMWed+PlfWmWBEvgOI/M1CALLVZzMUxE9+JsOMjf5zb1IMpIF/5fLZcvR1Ot1bWxs\nWH6ZllcQbDBGQHY4OziVGF9YstJsoanvCBFJEZ11Oh1ls1nbKgayEOsCPQPBRzrewh1SFX+j4zCo\nGFkMl4+UBEEw00WDaAt9QaQLGYtIVzrOK3Nd3E80GrUdddkyhUiPAMGHLYEDaSzrR4847jcjX5CB\ncs5dldSUNJY0CoLgy5xzRUm/JemspKuSvjUIgqqbWpX3SnqLpI6k7wqC4MkXc16fCYQwMHhCkowV\n41O/mcw+95/FhZLCc4FYQERy7tw5m2jpdFqXLl3S1taWms2mefR47BgFOj3QGSKfz1uLJQpBUeJU\n4FP/VC6X9ZnPfMZ60/lJ0J2dHbVaLb32ta/VcDjU7u6uNZj1a8JoLUQY7rOJoNZT5zKZTLS4uKhr\n165pY2ND29vbdv/UcWEcYAWePn3aFsnm5qb9DaXdn7xsWOZ3GodkcnBwYJAJcCOtW6AlS8fNOqE2\nYyD95CsL2e8AwSJhwQNVjUbHm9n5EIkfTbxShPmDgfChcn8zTmmq9H2WZCqV0lvf+lb94R/+of7g\nD/5Ab33rW217h5N1heQhYAaiXP1cBQgGChwHZzKZ6ODgwPJAGBo/x0xhMIqQ6/ahWZQ/URlGBaSF\nbULIR+PwEcX4RgL4F/IA50GpM7bcJxEV5/K7zqB7iFAw9OgvP3VBbonvca0+SgS136d2+7VKGAO+\ng9MH3M61M7Y4AOic/f19ZTIZJZNJHRwcWO4Qw+UjPD4hzS8VYGzJv70Q2Dz0+T/yeeVNQRB8aXC8\n9fVPSPpPQRDcJ+k/Hf0vSd8g6b6jn++X9Csv9oRMTOk4iuLB4rH5C5D3mEAoLJ+NxsP3oUJo4zB7\nmFDdblfPPvusrl+/rs3NTW1tbVmTTLywWq1mCjCZTGp9fd0o3v6OtyQnl5aWJEnXr1/X5cuXVa/X\nZ7wRvwCRRpb1el2XLl2y9wi5URIoJApn/ZwLx8RDZOHv7OxoPB7r4x//uC5cuKALFy7o8ccfV71e\nV7FY1OnTpw3iICIlF7C2tqalpSW7FyYrUQrMH4w2Rc+9Xs8M3+XLl3X58mU999xz2t3dNaMFSxJH\nAKVB/shnS1Gb4X/OTxyjfHxFwdzxySavNGEtYKhx8HB6giDQXXfdZQqcNcB6GAwG+oZv+Aabk35O\nCwPAZ4HhhsOh5UZZf1DH/eJpDBJ7i6FwyS8Nh0O7Jv8ecDAwGEQj/CaP5LMUee4+CUeSrQ9fL/hl\nFJwbZ4uIj5ZAOJo+pEetHjrIp1mTI+XzOLf85nW6bED/TiaTtu2M3xqKdko8a+6Bc7APml8kDHKD\nc4e+8tm6rVZLzWZT6XRaq6urM/1MgSb9Tj6DwcDqCIEX/fUHcetm5KWA+L5Z0huP/v6/JP2FpB8/\nev3Xg+nI/X/OuYJzbi0Igp0XeoKTC42bP0lxxfvjdcJg/3tEUH7HY4wRnuFwONTGxob6/b6uX7+u\narWqVqulzc1N9ft9q3PCKE0mEy0vL9tCh0gAs6her9tiIQF57dq1GW9oNJruROs3qgRT9ztbZDIZ\nraysGFRRKpUMsmORQIQAUvPZdxhdKNbD4dDYd5BCotGobty4oWvXrhmOL03hyZ2dHdtrSjre3uPB\nBx+0ib+zs2NwoN8It1qtKhaLqdFoWI0UYwTkQyukbDarcrksSfa8wuGwJW/pE4gSAGah6BLPHSOP\n8vRzTyednVei+BEHDhBGRdLMLs8Yb5T4aDSt03vb296mxcXFmb2AiNKJxPkN3AoMC3Tmb7HO+q3X\n62b8MFyw0ohemNdEFTxfOptAvCBZT+2WJNtwEUahdJwe4Fo4FmsEp4r75/rJMVNz6MNwRBZA/hga\noivQH7+OCSfKL3AFIUin0zo4OLC8HGvWL3Xh2RIl4YRhFKTjKI/v+RR+DFo6nVa73VYul9POzo6R\nk1KplHWkYct2rh3mIVA5raF4jTnkE8xuFUkikPSYcy6Q9L8FQfCrklY8o7MraeXo73VJN7zvbh69\n9oINlHS8CZsvPGDCYRKIhMJ4DShrwn/qoHyaKz3c2D8J5U2tkHNO29vbtplYLBZTLpez5CreOMwi\nvAYW3dbWluW4gMtIuEpTSOXy5cszVFEaQzabTTM03W5XFy9e1Jd8yZdoNBrN4Pa0NPKTknQX57h4\nR9Vq1WA8okCiHsYFBSRNWTx/9Vd/ZRBdr9cz5l2lUplp1S9JxWLRYM5IJKL9/X2LZInufNbiZDIx\nduWpU6cs6sNzZREDgXA8FC3JaBYHz5LkMsrBj7JRFH5k8FKJc25D0q9ruj4CSb8aBMF7X0qI/CSq\n4Oduj65JlUpFS0tLM8wsSaYcMRTkdDgGsBswHY2YgXpQyrxO1E5kTpT/fDkW5jvzBUeT/8PhsBEk\nKAVhzgLfplIprays2EMbRmYAACAASURBVP1gFFmjIA4YTKIenwxAVEF0iJNHNOp/l/tjnUnH+9nB\nxiO681l6PgMPI3bjxg1zqrgeGi6zRQ+pDf/7lA2gWzBm6AP0B4xYrjuRSJizxxiAtEA+oW8naAV5\nQ5wTf371ej27f9bczcoXaqC+OgiCLefcsqQ/cc49678ZBEFwZLxuWpxz368pBPi5PjNDG0eZYLT8\nCcNn8b783RyZPHgRCBCA3zKl1+tpeXlZTz75pLHcVldXVSgUVKvVDGaj3RKK1IffODYwG5gvyhni\nBhOESQELCUNFa6FWq6WFhQVjEi4uLhr27eff8GRoZYJRoV0RlFEWQbVaVbVatXb+zjnbL4q2SD4L\n6sqVK2o0Gmq1Wmo0Gjpz5ow5Bq1Wy/a48hf04uKisRIPDw9t4p5cQM45Yy2SeCXaxbvFQcC4UByM\nd+d328A7x4nw29EA7d1sAvcLlJGkfxYEwZPOuaykJ5xzfyLpuzSFyB91zv2EphD5j2sWIn+9phD5\n67+QC8Cgo0Cj0agZCdhr/jY2RFCSjDGKN46XjYMnHY+nJOs4AfTGegCSwknxcyc4hKxRUA26ONCe\np9vtWqsuWHq5XM7adYGKMIe5d8RXmihTDCWODMYJQ+CTDjDOvnMkaYbODozsN7zF8NEp3Sd/0c3i\n6tWrlqvz0QEc7L29PfV6Pa2srCiRSFguiVIL2pLRE9OH8HDmeeYU0y8sLOjy5cs2TuSU/IL5WCym\nhYUFXb9+3YwQ7GPGwX9+tJFD392SCCoIgq2j3/vOuf8g6csl7QHdOefWJO0ffXxL0ob39dNHr508\n5q9K+lVJ+mzGDWX6fEQJv0IbI+UbIB/SoMUPDSEnk4klNKGEc/zxeGw1OYVCYYa2Te4Dho7fiPLg\n4MAYeZPJxGqPOL6PU4Mhw3IjAiJfMhwObXH7pA/yMEQgLBCUMJ4thttnFHKv1GPkcjnzaOlQ0Ww2\ntby8rHa7bQwktteQjllUtVrNSBfAmhAx+L5fewN0Qi8zlBW1Sul0WslkUvv7+6YwMHooQUl2nGaz\naQYPDzkWi6lcLtvi9yEVFCjPivlyK2qfjlCGnaO/m865ZzRFFG4JRC7NUr4x2uQkWB+M+3g8nsnp\nALtKxzVP/neA4cjj8uzpIEHuBgVK82SiGBQnc5YIgM8DX+Ow8F4oFDJFizElqU8U7dcv4XwSDfLb\nV7LSMbzJvfJ9oHXgcn77+WOEa5dk64MIjzmIMzmZTHT+/PmZ2iX0GFEK8zQejxtzNplMzpCLMDw4\nu0SaXCdrig1BB4OBwbw8E2BPnzk8Ho8tT06ndByHWq02EzHDcPbzazcbRb1oA+WcS0sKHS2utKT/\nWtLPSPo9Sf9Y0qNHv/+fo6/8nqQfds79O009v/qLWVxH556JlvhhguB5M4l9D9D/LOEwHrZP2eR7\nkUjEcFn67O3u7pryhxlH6AvVm8QsOSg/odrpdHR4eKh4PG4LliTj1atXtbi4aAuOWqdwOKxTp06Z\nh8c+TAcHBzO1FdwnEdNJtk6hUDBPjIQlXivwxcrKitrttq5cuWLFt+TWgCioSyJPJk1hv0KhoOvX\nr+tLvuRLtL+/r2q1altzMJbFYtHGA2XC9YXDYS0vLysWi2llZcWgxo2NqW8DE9KPpnyvFu/ST8yy\nUPnxo+yTEfctip5mxDl3VtJrJf2VbgFE7q8bDDZzHUgUpwYlxudBIoCyfchMOt5sEEOA8mWsQ6HQ\nDOyMkwaMRMRMsTkKja1vgmBaskFiHpKNT21nnyYUKnkoaOon4TiiGYwxThg6ASfU/y0d6wmOJ832\nv/MjM/9ecIKIGNE1IAK+LoKqzb2e7P9H4T6GDFYr7Zy4DgxrPp+3iDcUCllhPfkjnitODBB8q9Wy\n4/j5fxod+PcIUiPNMmK5r1sVQa1I+g9HDyAi6QNBEPxH59zHJf1759z3SLom6VuPPv9HmuLnFzXF\n0L/7Czj332ESnSQ++N5XJpOZ8biZdBgoDEA4HJ4xCKFQSGtraxbljMdjy68AixG6drtdXblyxYpe\nqavgmODLeFBQLoHiiOIeeugho56yOSCh+I0bN+z6aK9E+xM8zmw2a54N7XtgGbG4UEAnlYcPjz30\n0ENmZNnHCUXGTrsbGxva2dkxMgWGYTgcam9vzzpfwIyCYdhsNrW6umoeFsV+w+HQtv0AugmHw1pd\nXbXNGcHZeV6RSMSS45zDj4ik496N3AMLlDnDIkJJ3Uoj5ZzLSPpdSe8IgqDhe5YvFUQuaUapSsdj\n5HvWwEnJZNJgocPDQ+XzeS0tLdm85LnybCigpnyB6COXy9lz9dcoxZzNZtMQBpw2v1uCJCNlQAQi\nMmJ+YERogDscHvfAI1/jnDM4nbGA+MBahihCHU8QBNZNw9/DjLQByA2GBeIODiCOEoaE87EOo9Go\n5aaABSFAXb161eorid6ITIHqWQs8R/LH5NZp58YxTiIGRK9ER37ZDnk+4F7QFXKLuVxO1Wp1BrHw\nr8WPKH1I+WbkRRuoIAguS3rN87x+KOkfPM/rgaQferHnQ1Ck/t/+Q/MTsn4xmSQLVcmp8B6RkL/d\n8dmzZ+0cvtfABHNu2t7n+vXrtgAIpflsIpGwRQZ85XeZIDo5deqUhcjgtkBZeK30RYNQgQcai8VU\nKpUsYoDSi2eKsfOhLMZrOJw2wyXSI6zPZrPKZrP60i/9Um1tbVluazSaNrpdXl7WZDLR9va2wQOS\nDH+GGIIhOzw8NDgEhXfhwgWjBbMHTbFYnKkZYwEWCgUNBgMVi0UVi0Wrk2Jh8UyIjFFcLISTkQKe\nMh42zxwl+EKSuF/gXI5qapx+MwiCDx29/JJD5IwHa8anX6P8qWGC0YaiYc75XQ6IrH0jR0EsEQy7\nVUNKYC50Oh1Vq1XLK/p5HUg8fk7MHZFcKELF60eBUlqAsYSNJ8kcrXQ6bQxPHzaDYQby4htRogW2\ngsG4Ef0QmfPDsU+2P/K7ivu/x+OxVldXjchALntzc9M2F8QQ45wRMRJ9oW+g9FPuQloCp5iiW4ge\nwINcJ/eCscVhPDg4MMYmOhhHGKedtIhPFkF/cv/oopuRO66ThI9f4pWcjKDwbqTjzsIobOm4VQie\nHVAapIiv/uqvtuNBC08kEjo4ONC1a9dm9osCWgJyAkaj7QnJxHa7bR274/G4HnzwQTUajZnEJfTR\nXC5nu9pK0tramkUo+/v7On36tEGD0HMzmYzq9boZEzxEfyNGlDdKJ5vNam9vzxQ2W3zjwSUSCS0v\nL+vKlStGd6UAOBqN6lWvepUxeZ577jkzuqVSybDpRqNhzwlIp1QqWSQJHBkEger1upE+wPYXFhZU\nLpeVTqdVKBSUyWSUSqW0tLSkT3/60+b1YnyBi2BRAUn5eS8Wjc/i8yGqWxFBuekk/j8kPRMEwS94\nb72kEPlJ5w5DhZHxmWJ+lEHrIkmWe2J8UcaSTCkR5WazWfP+iVTIV6KsaPjKdfnFn2xdQ4RCHhZy\nBrATSjocnrbVAmJkHoESQJSgQwzH9ZmysVjM1jSRDfAUShsozG+wi7FHJ0maaeZMhOfT/DEMzjmL\ntqrV6owew9EGtSmVSnZv0WhUBwcH1pBVkq0F9AdkIZ/U4tyUbr63t2ewoY80SDK6Pjsu0BAbFqPP\nOD48PLSImu2LMMZE1axDabZ10+eSO85A+fULLDA8YB9PxmIziWAZ+WE9HjdFg845ve51rzMc/IEH\nHtDVq1f11FNPaXNzU9Vq1aIP6KwQI2DyhUIh1Wo125NGmtYGnT17Vg8//LBR1PHMxuPplgNMnlOn\nTlli8vDwUPV6XRcuXNBkMu1XRj6M/oDAGNRQYXD9oj88MIwEXiOU9CAIzMPF+EL/rdfrVnhLMhsP\nlohpPB7r3LlzZvg7nc7Moi6VSkbMKJVKhtsDJ4ZCIa2urqpcLqvT6ejatWuWnyMSBcLL5/O6ceOG\nisWizp49q09+8pPmtZOgRwnS0cDPMfmJ6+dL1r4Q+OELlDdI+k5Jf+uc++TRa/9cU8P0kkLkPoFB\nOqY/+yQj0AIMPd85c+aMterCqToJ3RBt5HI5LSwsWEsdYOzr168b7EuEgXfNfJ5MJjo8PNTi4qLV\nNhFp+MqP+/HbCbVaLYP82IOKPDGtlVhjKGr0BlAXypcO5+12ewam5phE5tyDP1boHdYIUD5RF10y\niJoGg4H14OReFhcXba3UajWDGnECnXOGRPhkqslkYk4gY39wcKDl5WUjh1GrREEuTgktqDgOY8Vx\n0H04BrBwiXhZ38ClGETmF5DpzcgdZ6CkWZz3ZNGddNxMNhqNqlAoWAhOPobPQnpAUUqyNkQPPPCA\n9vf3dfHiRV25ckWSbPLjheNl8QB8mMO5aaNVEsKSbMFVq1Xt7OyY90ndCcWwKysrZjQgP6TTadu7\nSprtNk0Nw8rKipEwgLhg+gET4lmSzCT3Ql0FrL6rV6/q/Pnz2tzc1IULFwyiXF9f19mzZ5VIJHR4\neGiwwcHBgfL5vBkO56Z7QtENfXl52YqAgyBQqVSyjhjFYlGXLl2yBc+4hEIhw7vZQgRcnsJFH54i\nKub/0ei4Nx8G0ycIoHwxWCinWyFBEPylpM+GJb5kEPnRsUyhYtiBs4DvUCRskSJpRhHy3PDamYs4\negsLC+bwwNC8fv26UfsRlB3w3t7enh0HBIDuB+Q6VlZWzCH1YUTgpdXVVesSQeEoa5Oi4WKxaHoB\niIrIh3EB8mO948zhEPGaDw/70CJz+GR7L+6tUqmYroAwQUQJlIkRiMViWlpaUjabNcPC+VgvkCC4\nZ+azD7fu7OzMGH1YtuiFg4MDgzcxMqwbtjXBAAMHw45kTDBsXJ90zJ71iSM3I3ekgUL8fJB0jIXz\nOowukogYEXBbQmAewsMPP6zBYKD19XU1m03rTP7ggw/q8PBQFy5cMC9B0kyHAhhqhNp+7zn6V0Ui\nEZus5Iy63a6uXr1q3gkGjcldrVZnOggTJbIxYjwe1/33329JV58thFEh6mMhY1Dp7EAynDEEMtzY\n2DCIDhbfxYsXdfnyZS0sLKhUKuk1r3mN4vG4rly5Yt7v1taW5eQwWru7u6pWq/bcuNaVlRX1+9Pd\ndvEG2eqDhHksFtONGzcUiUT0mc98Rg8//LCWl5f1zDPP2J41eG2wo8g9MJZ+XtKHg3l+/M17Xyzi\n5wtQoqwbHBbGkoQ7iqjRaBgLk7IAIguih1wup0gkoq2tLVP0QLqck3XEOmT7GXZTpgYQA8kxgSY5\nFrA6cBJ7uNGQ+eDgwFAFv3ib65WOSyZoI8S68efGZDKxvJY0WwDNfUBegGgA6QkhsgCSbLValsPB\ngBE5QSABbiQPFY1GlclkDG6EIo8BIUeVSCRUqVQsVwxK4ufoifQYS6IsIG+cBBwaajWZH37ENB6P\njRiGA4sDwxjfrNxxBsqnkPu5KCaFNEtD93d2pC2/JJsAPi6Osjo8PFSr1bLtJ4DlaHcUj8fN0yuV\nSpaXAb+liJQ6IZLD4/G0jX0qldLBwcFM0TCECPJQLIpIJGJ7JKFogTNYyORogCwkWcKYe/Qr3/3Q\nHaPBYicPB5WXBYP3w1jl83nVajU988wzuvvuuy3vxoaFQJ2rq6u2AKmx6vV6ajQaymQyZhgxTsPh\nUIVCQdvb2xbhoriuXbum5eVlnT9/3nqCFYtFuwcfloKpyQL0i0b9xC2L0qfW4hV/MQiOG2MCK4t1\nAVmlUChYVAUklkgkbMdn5kU8Htfq6qrBdUEQaHNzU81mU5VKxfJHfuRK77xcLqe9vT0VCgWL6Ikm\niF6AFf39m+gYEolELGJjHcdiMSMBnD171iIL9AXXyFwIh8OWD8OhQZfwP4LBwjFiPPkfKI/ogwgO\nmJG532g0DPVYXFw0Y+S37AJWRR/Q6sxv35TJZKygHedUOi4v8XcZIGqUZqnooCu1Wk35fN52LSB3\nRZTp64GDg4OZayUHJcn0Dg7pC2XJ3nEGCm/FzyX4eSg8GYwTExvrTqdfoD4/lL1x44bW19dtUp9s\n5Aj7bmNjQ+vr68bI8zFVFhrKkocL5VuSdRiHkEBnZX7T/iibzer06dO6ePGiGQ1wfUgdREoktMPh\nsE0Gkqx4i+Fw2PZq4bokWSPI3d1dw5DxllgYvkcGxLC8vGzKv16va2dnx3YljsViuuuuuyzxC7mB\nbTKWl5cVBIGWl5d1eHg4w87ySSNEj8CddLYYDodaXFxUrVazqn5yYiwejNDJhCwG2qeeM7eIEF7p\nglKGuYfSYb0wj9iaAWfJ/z4eOOjAaDTS6dOnDbEIh8O6evWqgiAwFAFIiGJ3HDfWxvLyshFy6BBB\nqzEMla/4WWelUsmYnqxdHE4+X61WzbgCZ/owL+sEEgjzws8tMQ4+ScAnnkjHO1X71wgRiVKMcrls\n+SQMHcaRaA1jTu4bMgoNbIG3B4OBEUNoAJ3NZmfyZRBIiHhPIgrZbNa6yHPPbEKK3iiVSoaosNcc\n8Knv5HU6nZk2WTCTpRdOQrrjDJQveMtMDB4G0Qd47GAwsD2UmCh4fVReSzIGTTgctjqedrut5eVl\nS2DSuw5jgqeCp+V7dIT0TzzxhG2RTb87v26iVCppf39fpVLJWDPcA10ZSqWSeUmJREJra2uWqAyC\n4/6CKAAiFZQ1ihvPjYUN7ElhHVAl+Z1z585Zzz28WYzJ5uamzpw5Y8wnv56I2rFKpaJTp04ZDbVY\nLBq7b3l5WbVazQw1W5HU63XDzYFcKPilVgb4lEVLsttnDPm5AD4Dzu8bJ//Hj8xfieIrU3/d8IPS\n9plekFSIxnu9nm1SR86JaIQ5iZfu3LTMgEgMGBAaNV4880867ixSq9XUaDR0/fp1c+RYv/R+w5lh\nztHtpFAoWFsjSBXMCQwqBo/owG+m7OezfVYe+gWnlTSBT67xI3A+T32gfw0YI/QBERLOAmsJxxQl\nD/SIY44jCYEJYwy1nB8cDH/zUM4B/IeDxrqj9MUvFia/R0pic3PT5g/OMeQkSTPX6eeAb0buOAPF\nIPvFgc/3t4+pg6f63pfPavOl3+/r0qVLSiaTyufzBlvgAdKK6P8n711jI82vM7/nreKdLNaFdSGL\nd3aTfR2NZkbySA6klRMgipWF/SXe3SQIdo2FDQNJECAIYi8QIEGyCZxPieMFNjCyTqSFsXIsf/CO\nbCm2ZFgaYWasuUnTPdPTVzavxSrWlcVbk8V686H4OzxFSXbPyJbcPS9AdJMsVr2X//9cnvOc58Ci\nISopFArq6+szld++vj6DsoDmxsbGbAFQyyJTwBAcHR1pY2NDS0tLunTpkmKxmCYmJmyTQ4n3bDxo\nsaOjo4Z7b21tmdPEEbPZWHCe9QMdHfo5kF82m9XHP/5xvfrqq7Z5wzDUxsaGZXPIC+3s7JjzSqVS\nevfdd02NOpvNGsOQ+gDZGOMSgqDTW9ZutxWPx43w8eDBA4VhaLDfyMiIksmkDSwkUPGwrRftJcLD\nKfsokX+9A3uaD294PdOViJo95GnMZPfUK4GBgdi4vz7AwpFBC6coDwx9fHxs05ppvvXMVRhfjUaj\nCwaenJxUoVBQrVYzR0kGT9M6WpnFYtHW1czMjNmDcrlsLDuyI4IhPsc31XJPPPmIvedRG9aTJ05Q\nE2Y/8VmQEyRZLRqqPE4XCJ8aHJkf78lnSWeKDX6NE2AjlO2FlkFSqI+hV0g26VmdDHoE1iXAJYtM\npVLWe8b1EPz5INAzRx83CHziHBQwFv/66Nc3zLHgPAMH58ZrPZ2UB5VIJDQ9Pa1isajNzU17sEQM\npNtEM/RzMJqaSI6IJZVKdYk1AqfxkGHGgDlfuHDBHCASIpVKxbIfYDZovixaKLzJZFI7OzsaGhoy\nmimG1xts7hFQCo5taGjInAbj7a9cuWI9XycnJzY6gc1SLBYtyms2m0okEkboIDAYGxtTvV638/K1\nDyBK5v0Ui0W73pOTEyNaMM6EiLhcLmt6erpLnYOggWeKAx4ZGbGxKLCbuCcfBUiP47yBOE8UwUjB\naovH4zYihayK2imBlTeIULL9yHevdEBW5jPtjY1OvzH753wNB4YevUSsi5GREQvQRkZGjKkJmYKs\nqN3uDEGkj4eg0EOcHh73NSfuzXnj7O+ZLy9IP6h16JXCWY/VarUrSyWIgzTRarW6stb19XVrrIe5\nR13XO0juDU31/jqB3vy1URMHwWDKAvuI2hTOD/vBzwjCQXE8TR97I52x+AiGHvd44hwUxscvIlJH\nnEir1TLdLnBQ/7dI//jmwzAMdfXqVRUKBTWbTVuoe3t7JuPBwkeNnMgfFeVGo2EOj2ZRegpo0mUT\n7O/vmzLE2NiYRYIsGiJRnBwd4Tx8xrr73iNgByilkDXI9vr6+gyaIdrjXKhHkW3Snd/T02P1tj/5\nkz/R8PCw5ubmFIlEjKpLgzI9XcAYbB5U0Pv7+633KpFIKJvN2iRgPt8XWj3N16u1AxsBI/hxCcA+\nrAs2BM+Fa+J3/vDQztN+sJYI1DxjDFg4k8mYWDG/h/UGJIejol+HBlHqHsCAwLjIED148MDqOexH\nKO6gFWQ51ESB6fv7+22KAGs6k8moVqvZ+dF7RL2UgJVaZSQSsczEB0P+vkjqWt8Egud7xDzzzQfP\nBEBkRbw/zDvWOtklNoI1jgOgLkbtnPsPS7G/v1+pVErFYtFG8sBq5BwJhJm6DYLBfeF1sPvIpPx+\nYH+wRgggvE0GLiWjOt+H6B3V4xxPnIPyWK90Rgvl5uH5eei+COrrO2DtQ0NDGhsb0/j4uBl1TxXH\nCe7s7Oj+/fvmPBKJhGHHGEYMIf0E4MyItgITpNNpLSwsdNVCOJrNpkVJzEPCuN6+fVvz8/Pq7+/X\nvXv3ND8/3wXNsVCojxHZATEwXiORSKjRaFjzK59PNEq0xeKFUv6Zz3zGesLYKO12Zygbxdz+/n7F\n43GT/79586bCsCP2mUwmTR4pDEMjdgwODmpqakphGBqsAQuR10GtPT7uDIi7dOmSQRTUDXE+fkQ4\ntQciRzafj+Q8BPG0Q3wcHpLyRtEb7Hb7TDpLkjV3kmnhAFhfFPW5/7w3TLnNzU3t7OyY0T08PFSx\nWNT4+LhBcedbAFivvucPQgUB0tHRkTY3NxWLxboyNzIe+hdpZWBdgUIA3ZGBI6AKHO6zE+rYnA8O\nXpIZYOkMtcC5g1j4+h6OwQfE2Bdqsel02uB6VDCSyWTXegZ1QNUdIgT1br/HcXwwgWkn8NJvnhyG\npuHw8HDXPvNaiJOTk1pdXZUky6LYRzixD8Pgk55AByXJbsz5grYvcvtx5qTGLAAMKhsB9QZwcNh6\nzWZThUJB9Xrd5q6QpTDYDwIATLODgwNtbW1pbGzM2HsYefpI2Nx7e3va3d1VPp83QgIR4p07d9TX\n12d1l62tLTMA5XLZDPnY2JhtIArQdIGzWdgkZIGw6oDXJHWN1QZ6Q6gWx760tGQwnC/qSp1IM5fL\naXZ2VpFIR2T39ddfNxZepVIxmRTfWwFFdmtrywKKXC5nvRrQzKlfoKMGdOGZiDhyNjvnxmZi7bD5\nOA/P1nraSRJSt56lD/i8aj9BAUEE8C97BhkixtWQWXiDiBzXrVu3TCOPDAVBVpRFCBqR9AqCwESI\nMWqsT9YuwVCtVlOj0dDOzo5mZ2cNBvSq5jjJIAhMAJq6kG+9IJADEcBpA1exdghGzytJeCNMFsf3\n7EECJurQnC/kJNY59dq5uTndvXvXMkwmFUD0SqfTBuvX63WjfHuGJvuDNhYIJ0dHR0qn0xZsRqNR\ny9Y8WYO9Sj+XZxSi/edZizwvKObSWVDkqfp/3fHEOShPG/eRC4vm6OhIMzMztgBhtxGVYORJyz/z\nmc8YLAZD7ujoSLdu3dLKyooVEMMwtMImxs3PRAKLHx4e1tLSkhqNhg4PD3X58mVbdEBgqJTPz89r\nenpa6XRay8vLunHjhgYGBjQ9Pa1Lly7p0aNHWl9fV7VatawKBhyRaiqVsmgP6IKFEolELPOA/EDG\n5THjR48emfI0rCpftEUrDap8pVLRxYsXNTw8bGSLMAxVKBSUy+W0v79vUN/09LTu379vMMPo6Kgx\nklCFbrVaplqwu7urlZUVc7bj4+PKZrNKpVIaGhoy0oqnNk9MTCiVSunOnTsWlBDVcz8wZltbW7Zm\npDPVEV9PeFz44Uk+vCMmwqUHjWbp3t5eVatVIx/g+MnUWVsomHDvyTxQTGENAbn76ByEg33m1xTy\nWJ6liTHEwDOziGyAtd/X16dsNqtarabe3l4bbzMwMKCTkxOlUimr+5KxY0CDILCg08N1nDdOnevF\nHgDhkSnhULw0E3JhMPHIgsIwNMYuDgD192g0qqtXr3ZNxWUfwyIEEaKmzN7lvXEUHtonCD05OVEu\nl7N6k8/OYAYTUOJcmUIAlD4+Pq7l5eUudvD53ifu0Qc5njgHJXVHgD7F5nfQTs9TasGRgTTm5uYs\nmnv22Wf1jW98Q1//+te1u7trBXUgAElWNyLCkmQMP7IuXk+Pkpd2wWn29fVpYmJCpVLJ5tcUCgWN\nj48rk8lYEyJOkLoU7Lp8Pm8ZG9N1wfqhjHppF6Iizpsi9/HxsdG1MTYeK8dx01Tb29urbDarYrGo\n+/fvG6NxZmZGExMTVuSt1Wq6ceOGQTnj4+M24XR9fd1qhVNTU/Z6mg77+/utbtBud0a5IxY6Ozur\nfD7fxfxKJBL2/3w+r7W1NXN01OdwWMAT5zeJzyI+ChkUh4fU/BRcAgHus69RAbFSi52cnLRMFSdV\nr9fVaDRM8JgI2htrmuZ9lgKsC+sP5tje3p4WFhYsqykWi0qlUl0MVgwiz531PzIyolKpZMxE4Dve\nCwSBNcAexZjiVHHKMHR95o1RpvZ5voYDmuEzG/ZXoVDoUtKQZHYKJiS1OFRlent7NTU1ZWw/zzTE\nGfggfGtrSycnJ8rn8xbUQUuHULGxsaF8Pm+OLJfLaXV11WylvwbWCp9JXd1n5QQzBH783H//OMcT\n5aB4gJ4q7J0QwzBnVwAAIABJREFUKSlQHK/F2EGZhF566dIlG4ZXLpf1yiuvmFQHC8ar8CYSCZPt\ngKCwtbWliYkJDQ8Pm/IChp0FST2HWhR0cJhosN7W1tYUBJ0G4/fee8/qWLFYzBhLEAj8eI3x8XFJ\nMn09FofvtfKTRNmc0E6p13mcmg7wSqWikZERu7cjIyNaWlpSqVRSGIY2jHB9fd3qUES0wKXAkqOj\no3ruueds2FypVLLOd4wksBE9KRibSCSicrmscrmsTCajeDxuxfBsNqt79+5paWlJhULBWJF+UjDw\npr9GghhfN/ggBdwn9fAsLq+gEol0Gl553jh1sgtoxRjkdDpta571ViwWTYUlCDqqJ7lcTmtra2ac\n4/G4UaxBN9iXvb295nR8bYo5aJLs95HI2Shxsgn2Oj2KtC1Uq1UlEgnreSRY4bw9TMce8TU61hSO\n67zzls4aernHwMw4GQJdglbWKFCfR4EIJEE+uFfA9Zubm3bvpqenDQ4nsyLoPD4+1uzsrMGlOC0y\nMWxDu93W2tqa6YA2m03NzMzYtAOQC9+Lxs8JWqjvQyA738Drvx73eKIcFAvq/EX6iHdyctIitr6+\nvq5IwteuoHYSCS4vL+vChQtaXV21v+fhJZPJLkFFGmdzuZwNMCR682whYD8ynWKxqHK5bDIwvIbF\nmUwmFYvFtLKy0tUtH4ZnQ92gY7NhaYilaY5F52fecI1EQeD/1ITYEGR8ZI5QSXFOUkcwlOGL0MiB\n63Z2drS+vi5JphQwODioa9euWfqPc+7p6dHFixcNTlpZWZEkY2axCYiSKeT29vaqXq/r1VdfVTqd\n1vDwsK5du6a+vo7a8/Xr1/Xaa68pGo0ats+1+UDFF+o9O+mjQpLwtRLWCPVJMmvuG46JZ4WIKexM\ngq719XVtb2/bmgCKGhwcVDqd1sOHD7uUSRYWFkyTDmiMWihBJir73sAz9BC2GjCkh7tKpZIajYaG\nhoaUSqUsexgcHDSHRPsBtR7fsOp7lshwPBUdhMNn5L6+yXtQt2LcD1AXpJR4PK5isWhkBOA4iAyI\nXZPFUF8mUD08PDTtS1o7pqenTVg3kUhIkrXQQPiiB5Esmibf4+NjLSws6PDw0JwRzxy7QJbI9RHo\ngVzxOp/9+UbsD3I8UQ7Kp5A+amEjscgoHrJJuMlEWGDGjFmXOr1GlUpFV69eteI+dFD6M/L5vBKJ\nhLFq+DnySWDZFB9RRGARl0olcxbAJTS5Ilj5/vvvm7jqycmJksmknYeHHs53sPviYxAERmCAxeRp\nt5IsasXJNZtNu0dESsCRHm9G3TqXy5liBo4PVh3NlkHQGVr4/vvv69KlS+bUMEKRSEevb3R0VJcu\nXdLo6KhWVla0tbVlETwwB9CMdFZ8pi9qe3tbc3NzevjwoZFhzq8ZHC8GkszJs/d8ZvG09kZxb4A+\n/fV65huQnnTGTiPCHxoa0oULF2xdsF6r1aoZKJQkPBXbw7ORSMQETHEaZGtefw4GHj9HE5A6CSxB\negCpF6dSKVUqFYP1qG2RjZ+cnCgWi5mzIliVZP9ivD3hxkOA3EPf9O9/Jp1JqUln9U7fY9TT02N7\nyTs2yAtDQ0NddobeRdQ1cHbQ1lutlkqlkvr7+00YAAdL0AsZA5UI6YxYNDIyYhDq7u6ustmsOUAv\ndcW5c87MqqIWBdkKRwbc7tfg4xxPlIP6YbCe713I5XLGCCKakM5IFF6Wxf99uVy26Gl7e1uXLl0y\n/bvBwUFNTExYpFculxUEgfVZjY6OGl0dwgHzbjD8dN8HQWB9QIhheqfB5u/t7YzgwNESqdHjxYYE\nEjk5ObGmPhqB2Vy+uEnhFwKCbxwk+oGJSLGVPhMir76+Psu+UCtmdg2ZIpH4wcGBCoWCUqmUHjx4\nYNHoxMSEYrGYyuWyTQMm88xms8pkMhoYGFC5XDaYE302qbM55ufnrQZ28+ZNXblyxeBBImIibEld\nFPV6vd5lfDx778MUcp+0gz0AVNfT09NFOhgbG7NaC9lOLpfrymwh5UxNTanZbGp9fd3WB0EIrDxU\n7VutzlgX6h4EdFJnb1cqFVubQOQ8j1KpZJlUqVSyQBBprkQi0dWAyzNEHkySrQtvB7Ad/J3Povm5\nrzdJsnsgyQJiXk/g5Q/uM86W+pN3iBBGIKOQ/ZNlASeGYafvL5lMqtVqWQCLMybwlaTV1VWNjIx0\nkUuAEL3jRBUmCAIT2+V9gHIfPHhga8dfI46PZ+YZtAQwBKac11ML8UlndQLPFmm32xobG7NN4SVI\neMjQkX0DHYq929vbXUyj4eFh/ezP/qwODw9VKpUsPceoUzehtrK1tWXY797enkEckszwgil7CiYK\n5LlczprnzuPabBIou6T/OFzqStDnc7mcFS6pwVDYxOgAlwF/9ff3W9c5EJiXPgEvz+VypqRMFElP\nytzcnI6Pj03lnKLpxMSEqtWqDg4OtLy8rFarZbOjgAXYRHxPttPb26vFxUUdHh5qc3NTYRjq4sWL\nxojkOprNpsrlspFFpLPMGigPiJR6io9wfbGbrOJpgPp+1HVwb2C9eegzmUzq6OhImUzGsuxkMmmR\nOCzT/f19Qw42Nja0u7tr+wdSyujoqC5evKhms2mIxMzMjLa2tqw+MTAwoK2tLTNuu7u72t/fV6vV\nMnUIr9S9ubmpyclJu5Yw7IiTrq+vm5EEkurt7Qg7A+MRuHIPfPAnddexfWuGJ5PwM5ygd4iwZrmX\nMOg8ocuTNDxyceXKFVWrVXt/ygkQhgg+2b9A7dLZZGOvOA68Rk8jaAy2kAC6v79f6XTaqPZ89oMH\nD7S0tGSN9NhRSFfAgtQUJVkZJJfLGWSPnfb7zNu3v+54ohyU97xcYLvdtkLj+R4EDLUvdrJg+vr6\ntLy8rIsXLxrklMvlbGOyeJLJpI3dQKWb1HhjY8P6e1qtlhl1ohSUyX3XOGlxLpczJtPBwYHJGpFR\n+OuJx+O2idAH7O3tNadM6l6tVpVKpbpou0B/OF5kaPhs1AEwNjhRT0Fl4wZBYHI28Xhc165d0927\nd5VKpWx8vX+PWCymvr4+i/ai0agKhUJXpIVBymQy1mBIT9rw8LDBiNevX9fo6Kgxw1jkFLDL5bJt\nPIwk64TgwjsgSBKeEepZVE/D8aOc7HlaPffR9/n4BmdPVvG1pampKes/8i0Lc3NzVt+jljI6OmrB\n14ULF1QoFPTw4UODh6vVqjlNT2wB2oZIMDg4aMSHfD5vew62KQFdNpu154mTY+/hhLADnhDCevIo\ni+/lIeAkQztf3+S++jVIpkQmQaYFEnN4eGjOH3gbmJ2gjQZcr3OIM+QL1iPX2mw2lU6nFQSBtX5E\no1FNTExYfZ0sB7LI/v6+zYOjpw0kAmKWhw35Gy9wgO2lIRmWIc+DOt7jHE+Ug2Jx+ciQvh2yC35O\nxgAkxt+wOE9OOlNq79+/r2w2azcdvJfu8na7rUQioZGRERUKBa2vr5s8ELL+QBUUd9nYdOGDnZM1\n0KfgoYJYLKatrS3byBAK0MCjf4dMbXh42Jg1frIsB+/NRgITxgmA4QOTEIni1CuVitXuIFH4DI4N\nDN11dnZWR0dHun//voaHh1UsFpVMJlUqlQyy4/lgAIms4/G41tfXlUwmlcvlTGmZe+obQScmJgzq\nxBECndCkSLTnKb5E1Ri78xsG44wjfpoPkAXIEb4NA3iGQIw1RDY+Pj5utSqIMbVaTfv7+5qbmzMn\nhsF79OiR8vm8GapqtWpwYLVaNYMGxCTJGnjpceLZ8FwLhYJGR0d1584dZbNZqythjAngWDeSbB15\nZQyyJx/R+zWAIcUQS2eTg9k7BIOsK5wbDgpDzN4530SOvcKJZTIZazBG3R9b4Qle1NMJUFH08PXd\nTCZjTGJqeltbW8bMnZiYkCSrxYNiAEdyzkdHR8pms1pbW7PnwR6Gpbu/v2+kK+6ZDwi4Tu7b4x5P\nlIPi8Pgp6TIPge/5nTfC3ohHo1GrrfT09KhSqVhzoN+gfX192tjYMGVgIj2P1UpSrVYzHStJJnE0\nPz8vSWY8gdFg9BwdHVln+/T0tNXOqtWq6vW6wQUUQ3GYbAKcx+zsrNLptC1YGmbZMOcbYmFIQdNl\nI1HsPD4+tjqZ1yyU1IWf010uybKmkZERTU1NGQV8c3PTJgsDDYVhaPAF57m9va39/X2VSiXNzMwo\nHo+b0gDGBPbiwcGBZZB+dhebE/iC4nu9XpekrgCHLIoMy6+dj8JBJsA+IeqempoyBwOlm8Aum81a\nTx73mjYHDNbe3p4FZFDK19bWrIBPdp7JZAz9IJhcXFy0rKZer5vkD+dSKpWs5iTJ+qLGxsaszovx\nh3F2eHho9VfWBoxArp8M0e8VMmzuj2d7cj/Ow6iwaH0djEzCZ2wExNimgYHO8E0MPpkcjiKVStnn\nov2JA0in01aGQOmBEUFHR0dWWz88PLTM6Pj4WPfv31cul7OSBefd19cR3+Xe4PDD8EylBvsXBIGN\nFSKgfPTokTKZjDY2NrpknT7M8UQ5KO+FiXJYKCxKFpFvpvPjnDFCV65csShge3vbipS+wXVzc9N+\nT9SUSqXMCVEMhmZLyn758mUrhEoywVbfe5DP51WpVKypt1QqaXt72xg+AwMDtnC8KjPXRmZDUy79\nV2QUe3t7arfb5jRZaJArSO3p0UKFGqIDm4E+Ls9sisfjNg4gEonYuZNZ4tiOj49N+BNJJuBTn5Fl\nMhmLunCwRNhjY2OamJjQ+Pi4Go2GNQuSjcHY2tvbU7lcVi6Xs7EfwIW+bYCZVEz+9Swzot6n2UmR\nLUnqunaf8WxsbGhmZkZjY2P2es/gkmQwEJlYs9k040UDdbvd1p07dyTJalS8PpvN2hoC2oKgQ7DJ\nPLeJiQl7hvl83uAtD0uxZ2ljQM6HYPPk5MQo71w/BAyfnflaCfU56qG+1uTXjA/esC9BEFgDsXRm\nu9h3/AspotVqGStVktXNBgYGtL+/r2QyaeeFo5c6AVetVjP5NmwOZCz2JESs7e1tg+KgiA8PD2ty\ncrKrFSWTyRjjmOAe0ozUCUwhN9FjhSOidk2NHdvJ+T7VJAku0GdQYLBcOBkHlGkWFhkIkRbyR+vr\n64pEOh3rECNo+OTvPNONiAZpIXB3Uvj19XX19fWZIGu5XLbIDKeysrKiarUqSTYKHSiFc/RK4ywA\nzpO+DiAa1CF8PaGvr8+cC9+PjIxY5IzD9DUY6mWQHDwU6bX6dnZ27Jq4FwQCQRBoe3tbkoyMsLy8\nrLm5OS0tLWlzc9PGHxDNoj+GigAsL0kqlUpaXl7W1atX7TrY8IODg6rX6xoYGLCaBBuF6/OFY0kG\niWIgOOePwsF1cv0YQjIMGG/UexAdxhlwT6lNgTTAZkVi5+2337bpyuxR2GaoPSSTSUkyQVjWmIet\nKMBD3qjVahoZGbFgTOoY89HRUevrgYru+wihtnOd2BCcD9k3gZgnzgDv8XP2CRmWX28+e4J4gH3i\n9QjCQvKhbsbgUkoNvv+oWCx2ZTbYomw2awGCp5NDDKI2TIBA6QPIU+qgO/v7+1paWjKVELI7HCS1\nMOplwHysBxw7GRj1LtYc9vmDBn9PlIPyBUgiGf71KaqkrqY8FiYstueee846rTGKq6ur+ta3vmWj\nNfL5vIaGhiwtJkMhKsfRvPDCCyZWCXmCiA3JmFQqpXK5bM7mwYMHxnjDCLMRcKpE+DgQJJYgX/hC\nbyKRUBAEFj2yMHgNTXl+kZCJUQDnmsgqqAF5tpsXe4VFBMnj1q1b6unp0ezsrA4ODjQ1NaXNzU2L\nWCcmJnR0dGTj7BcXF60pGbIG741gKYEGm/u9995TX1+frl69apR/cHqcztzcnN555x1JssywUql0\nKQB4aRmcsI+cf5LOKgiCqKQ3JG2EYfj3gyCYl/RlSWOS3pT0n4VheBQEQb+kL0l6QVJF0j8Mw/Dh\nB/wsSd0N75IsWAF+4lngEGB/EQBhxJCognDT29ur9957T+Vy2eAe2gzYu8DFrGsibuo46P6x5yj4\nQy9n/QK5Q4nHIQHrkcGjTsI1e3YsxB3fo0MDrVea8FkR0Pp5qrlv2aAmBhkCtivrGeeKjSKDok8v\nCAKbFs3+JCgmI3nw4IHNX5uYmDCquh81AgzIPLpKpWJZLIgMDnB/f183btxQPp/X3NycZV/YJMSx\nvYKEv5eQOghiyLAIksl0P+jxRDko35+A0SKDIK0Ev2UxIADLa6hpSB1qdKFQ0Fe/+lUVCgVLeVut\nlu7evavx8XHFYjETnAzDUJlMxiRYqCORMkNZJ6sKgsCyJ863Xq9b9IOiw/DwsKanp1Wv101pIplM\nWnTaaDTMOU1MTNjsHRYD6bR0hgMDv0my/jBgL5Qo2OxkarFYzM6R+0mNSDqb1QQzC6cQBIE1ON+7\nd8/mPvGsstmspfkHBwe20aSO8gcQD5RWz2CCtUckt7W1pVu3bmlubk6Li4td0T3ByrVr1/TGG29Y\nFEidjMj5+LgzzbVQKFiww734KfRA/VeSbkkaPf3+f5X0v4Vh+OUgCP5PSf9U0r88/bcWhuHFIAj+\n0enr/uGH/VBPoSYj4nuCJFADeqHGx8fNEbCHfKPsd77zHYuugVbJ+Onvq9Vq9v60MKDoAJzEOke6\nh3WYSqVMZ3FwcNCUYlAUGRgYsEnMGFLvFIGsfUTPs2e9k1FxbT5g+2EwsM8IyOypdRE48z0BJ44H\nlALYH9i+0WjYfSBT4XN7e3utb5PP3NjYsBptLpez9gF+xuRhnNje3p4qlYqhPDAemRW3trammZkZ\nQ5/IpLg+nA33kS8//4trQ/kGh855P+7xRDkoSV1ZAA+d1JNoHe/u1RdYMM8//7wZ6eHhYf3BH/yB\njYb26Xu73dbm5qYGBgb07LPPKpfLmXPh4ZycnKhWq9nAtEgkomQyqa2tLbXbnSmeRB8UNGkUhYE3\nMjJiHd/j4+Oq1+sGc0xNTRlLinlVExMT1pxIxIvjkWRFYSASMiPPOgJbhtrq6dqPHj1SNptVtVq1\n5r94PG4Nlxj6ZrOpK1euGFzI7Jp3331XqVRKKysrJjt1cHCgeDxuMM7q6qopznMvUZPY29vTnTt3\n9OjRo67mXjB3ZKE2Nze1v7+vZ555xrJg7pEkpdNp6//wZACeg8+8fXvCT5IoEQTBlKT/UNL/LOm/\nDjph/L8r6T85fckXJf0P6jioXzz9vyR9RdK/CIIgCD/gyXJ9GC/gIIIWsgY/0gEjDtng5OTEWhD6\n+vp09+5d3bt3z1oYCIbI2tvttur1ur0nxBwUxSHrLCwsdJ0nDlM6aydZX183VAPo1tdxgMAIXHC0\nOGLP8PSUcV8S4Lq8c/Dndf5+guT43/vaFnuUPefrVmR/2DNge6j7BJKzs7NG9gEWTKfTXbR/Rneg\n9EKDeyQSsXE2BIC8FySWmZmZrpr52tqaPvaxj1ltnv7PUqlkz4y+LfYw9w77wP4CbXnqIT7pzEFJ\nZ5kU48aJMDwxggfOxiRKmp6e1p07d7S9vW00ZSIFqM8vvvii1tfXrfH0PGMNTHhnZ8eMNFg7RdDB\nwUGlUimjVEtnvRXZbLZrxAfNee12p/dpZWXFpIV2d3fNmdGP5VNnWHsnJx0JFwgfvgPdb4Z2u224\nPtR4NhpGhfcBmgzDjogkDJ7NzU2b6kn0CvU1n8+rVCoZxIEBoSGZSa30y7Tbba2vrxuEyRTh1dVV\nM4YwBHEue3t7+v73v69yuazPfvaz9vwfPXqkq1ev2nDFgYEBYxr19fVZrSOZTKpYLP5AE+FP8Pjf\nJf23kmKn349JqodhSJi5Lomu1ElJa6fn2AqCoHH6+vIH/VCMMxAwUlJkEQR5sMDYO8gbAfO1Wi3d\nuHFD1WrViD5Aw41Gw+4l64oayMTEhCKRiBEkWO/UXU6vsavXB+IMARPnlc1mTcLI11pxbhhXrttL\nArHvCGrOt35ASuJfz/70DGHO19czfQbDuZI94fQYeQHTlnOCBAULFZju0aNHqtVqlrUCpeFYhoaG\njEi0tramRqNhIgCse+DXTCajyclJra+vG+PWE48kmcI57TOxWMxEgD1BhTobjGLOi3rwj4NM/LUO\nKgiC35X09yWVwjC8fvqzlKTflzQn6aGkfxCGYe00AvwtSV+QtC/pn4Rh+Nbp3/xjSf/d6dv+8zAM\nv/g4J+i9LhvHR7/cABr2fD8CzBWvGYWx7O/vV6FQsI3D6yYnJ419srOzY42x1WpVOzs7SqVSymaz\nZvQYEc9nsjhJkSORSFczKht/bGzMSAJsGnoXWq2zkfU0+vb09GhqasqiHxb14eGhJiYmzElzjdS2\nfHSHs+YzgcB4L08qkGT4OEV0HIBXPt/a2rJ7nkwmdfHixS4layLmo6MjJZNJI1XQVDg4OGisP5yb\njzbz+bwpRZCtEmig5lwsFvW1r31N6XRan/jEJ/Td737X+rkwEO1229TViVZ3d3fNMLDWflJHEATs\nqTeDIPjc3+D7/qqkX/0Rv7N/gYy47+wzHATZD7Wp69evm0PjPb7//e9bER5Gnt9PFP+BeIG2MHA8\nYyBCap7+efB8qFGlUin7HbA9PxsaGrKGc1hnPgPyNSjIEFC8QSP4Asoiw8Jp+YzIrxf2gP8d8BpO\nDNUXPoNg1L8P547jZM16JpyfA9dut3X79m1DQoDUsF+MkZ877VGDfs6zvnbtmr0W1iMlinK5bEEA\n5zAxMWH9UL5WCzTMdWFjsc08c47H3WuPk0H9P5L+hToFWo7fkPTNMAx/MwiC3zj9/tcl/bykxdOv\nF9WBJl48dWj/vaRPSAolvRkEwb8Nw7D2OCfp0+rzrD16n4D7Go2GYrGYGXVuuG/cpe9pa2tLCwsL\nqlQqGh8ftw1SrVatTkSkQoZWqVS0ubmpoaEhFYvFrshoYGDAsi1gp+npaROuxCkgUjkwMKB0Oq2x\nsTFJnWIrDEBGb0AXvXDhgm0OSAT9/f1WNwI6TKVSVusB50cglXvgmw0pmFJIJSP1RVLuO83Lk5OT\n9hxwgCzAiYkJJRIJbW9vK5lMmgGC/DA7O2u1u42NDaPP4rQk2RBI6mpDQ0OamJgwaC8ej5vDIVMO\nw1Bf/epX9alPfcqc0vPPP69vf/vbptEHNu4NsqSuQMdnU3/Lx78j6ReCIPiCpAF1alC/JSkRBEHP\naRY1JWnj9PUbkqYlrQdB0CMprg5ZousIw/B3JP2OJAVB8CMvhOAGnTaeIaLFZAfsI9Yuxfq3337b\n1g73bHFxUe+//74psDACAtYY7NBYLKZUKmVzz6gZAcHBKtvb2zP4Df04HCYEmFQqZdE9wwDplQPa\n5vx5L58VcbAvyHw84sLaYH0AqXtnJamLYcoa4rpoFflhcCB2jJlpg4ODmp+f19ramtWSwzBUuVw2\nUVyyqnK5bGw9kCHQGpwINd52u62pqSkjFgEXxuNxEwrAidOqcnBwYNAg6ujU13FI3DtqYzxPP0yS\nmXk+63yc4691UGEYfjsIgrlzP/5FSZ87/f8XJf2FOg7qFyV96RQXfy0IgkQQBBOnr/2zMAyrkhQE\nwZ9J+g8k/ZvHOUlvSIiozhfGpTNNKhpSa7WabR6cFLg1mnnxeFyf+9zndPPmTXtfOrC9agIRhu9+\nR2U8OGUMMQHU18JokGM8wcLCgmUXGH9UnaGecv7b29tqNpt65plnDNICHvOSSmygWCxmG4GN5BlE\nRINENpApcDxEqj9sQ3m5JAqibD4IHWtra6aw4Zss2+220um08vm83njjDTMwkEKob4VhqKWlJRMg\npUgMfTgMQ127ds2yKQwqNT7GpiwsLOjhw4eG30OegVXkR2ywWTxU85NwUGEY/jNJ/0ySTjOo/yYM\nw/80CII/kPQfqcPk+8eS/uj0T/7t6fevnv7+zz9o/YkggnuJMfXZDeQdIOJoNGpR98HBgSYnJ1Wv\n19VsNlWr1bqy1Gg0qqWlpS7VBk9Xpi5Ftg4UfvPmza5eRgIuf3msWUmW0cfjcaXTaZs8GwSBDQoF\nLYCsAezP9WP0MbBeUsn3VOLopG77w9/7DA8Hy2sJAAmUWW8EiDgm6YxJiRal1EEwaNcA/h8fH1c0\nGlWpVFIqleoS3+3p6bF5bNFoVMlk0pAKyCYovLRaZ8LQOA5UQra3t014YHV1VY1GQ5/85CdtovbY\n2JjVn7hWdAGxK15Pk0DjvEN/nOPD1qByYRgWTv+/JSl3+n/DyU8PMPQf9fPHOrig87UUNhcP31NE\n+/r6jDnkWTd7e3umlJ3P5y1SGx0dtbS+2WwapPXOO++YLA/puR9PfnR0pFwuZ+9LWg7ckE6nTUXi\n6OjIqKDg7kNDQ6YO3tvbq/n5eRtdXi6XNTs729Wk2263DWqEIEIdyxd2WehEhQwBJPJLp9M6Ojoy\n4U5JttDJNphH45lwRLDUqvx7An3G43GlUim9/vrrBrP29fXp7bffViQSMWyb6bxEhvR7ZLNZPfPM\nMwrD0IRE/Xwiut/v3btnvzs8PLQeK+APojmgKiBBjvMZ4AeJ7P4Wj1+X9OUgCP65pLcl/avTn/8r\nSf86CIJ7kqqS/tGH/QDPWvQtBTgSjDoG2889Ozk5sfVJHTQIApvGGolEVCwWVa1WFY1GTbonOGWJ\ntVod3URqf4lEwuqnZGisXfYgxJhWq2XqIqjiZzIZe53f+1wfAaMk2xs4CQ+9+TqQD+C4Bz6rgt0H\nasPn8IUDZn0RKHoYnYwUhwl8ztHf3698Pm+BX29vrynfDAwMGJM4n8+rXC5bkIsdxPFcuXLFoPOH\nDx9KkqnBP/vss7Z3CBJ7enqUyWRUKpXMCdEekkgkVKvVNDMzYzaNaxkeHla1Wu3KHgl2eJ0PAh/3\n+LFJEmEYhn8VlPBBj78KQ+fwzCw2GAacxeCxZEkWIUKD9EKrQ0NDWlxc1K1bt+x9Kf767IsNzQBD\nGGqbm5tWNB4bGzP1BpzM4uKiVldXLVtB1ZmHSs/A8vKyxsbGVCgUVK/XrfhLhzzNhMAQXOejR4+U\nSCQsm5Ki/6WIAAAgAElEQVTO5tbwe0a6E8GACxNFnYcdaLIk2wDS5JxGRkaMreMhk2g0auwv6hL0\ntlDH6+3t1fLyskGxGDoM5Pb2tl5//XUlEgktLS1ZJorxAY68fv26RfPJZNKUK8bGxuxafTb5w4zJ\n6RruYvL9JGtRp5/3F+qgEArD8IGkn/khrzmU9Es/zudwvT57YI0QpBGpE3hgZBEn3traMmWV/f19\no4NTFH/06JFSqVRXzwvN1KVSScVisauRFjIFJJqenh41Gg1TsYDQ4ms29BBmMpkufUf06Xjevt8I\nB8w1URsCzvL3h9oWPUJkiRhfbAzBD2uJ+hkOUpK1UwAvY4PIEsm+2Lc4SUoAONeNjQ2rzcHiIzPs\n6+uzKQZ+L6HeIp0pqQNxp9Npqx+jlYm0GqUAyC6UGlCxT6fTXYQkAmBIVz5Dh7EMioOzetzjwzqo\nYhAEE2EYFk4hvNLpz8HJOcDQN3QGCfLzv/hhb3weQz9fiDt9jUXtfO8LlJ5Awe/JvIApoD3G43EV\nCgX19PRofn5eX/7yl83Q+WiIxddut1WpVKyukkgktLi4aO9HJHdycmKMGZpYIUewKdbW1nRwcGDp\n9+HhoaXkiK7SE+FlQ4gqWcyofh8fH5viN8YfuMU36Pk+Ju+AYEpRbIVWTPbB+6FCDeZOfwzGgFod\nateJRELHx8caHx/X4eGh7t+/L0k2IoPpnx5243O4/hdffNGiWGZVoYoxNTVlMAq4+Xn6uIf2PMxA\nwZjX/hT6oH5iB8YqPCU74MQ9ow11CArxs7OzNmMsDEMzYnt7e7p48aJF2BhdGHQXL17U6uqq3nnn\nHSMZpdNpIxdBCNrd3bUMCYYgzFAgMxACAqnR0VElEgnt7+/bOiAjkc5YdED2OBFsBpkRhw/mJHUF\na6wLH9D4zNxn4UDeIAu8B0xWDDeZ/M7OThfEDIJBeYBnw7n7AYTlcll9fX1GMOE8CAqRPSIbrlQq\ntg99HQ0ptwsXLhgdfH193bKx6elpbW9v23kB3eKgcDrDw8PWA8r6oS4G/EvA8Ddag/oRB3j4b+oH\ncfL/IgiCL6tDkmicOrH/T9L/EgRB8vR1/75O8fcPe/gsKgxDM6I8XB4q3dRsnnQ6rUql0tVDMTg4\nqFdeeUWPHj0yBYQwDK1Bl4XNZ2WzWc3MzFiqj/AlBpRmRJoJyRIymYy2t7d1dHSktbU1zc7Omvgl\nmcju7q6q1ap6enoMwgAKhB3DNYFvw4jCQAOB+HrSeYoskB+bhY1D1COdjSdB1wyjDhWXxbq+vm6w\n4sjIiJ1rLBbT22+/rZ2dHau57ezsWDMhAQO1Pr/ZcX5An9/61reUTqf14osv2tyZk5MT0xEDcuXn\nYRiqVCp1rQW/MXx27RlGf0dgvr/xA+fP4fXlCMR4pj7LHhkZUa1W0+HhoWX6ECVoTpdk+wsl+zt3\n7mh0dFTJZNLWB03rOENf4yHCPzg46OppZD/X63VNTEyYIwRqBnpm/ZLxeMo5mRcOAKfBQS2bfe6h\nOe/I/H3EIWFwvSNkL3J/z/ebEez69/IOgPPg+oeGhrSwsKA7d+5YLZDGfpAV1jP7JZVKmb04ODhQ\nNpvter44DIK5W7dumU2jf1KSsW65dhx8KpXS5uamtaOgF8haIYtjLXmE62+0BhUEwb9RJ/tJB0Gw\nrg4b7zcl/b9BEPxTSSuS/sHpy/9EHYr5PXVo5r8sSWEYVoMg+J8kvX76uv8RwsSHPXio/MuNJwoi\noj79fEWjUX3qU5/qUlve39/Xzs6OXn755S7WUD6f1+rqqoaHh3V0dGTOhjkqRGhsYiJLMiQyFIw/\nPRpEeI1GwyTz0Z3DGdI7ND4+blN1+ayhoSElk8kux0KXvm+4ZfN6kVggLkgT6BHCOEQOCEfjiSBk\nHr29vZa5AQ8mk8kurBpqazKZ1MDAgKamprSxsdGl3UeNq9U6mwhKDxQiltx3ImOysq9+9at6/vnn\nNT8/r/39fU1OTloxf2FhQdvb2zaZFVYSRop1wxeGguyar6f5wEBgMFnL3pB6KAiiDEb+8PBQmUzG\naqGxWMzEXOv1ut544w3LNkADkOJivQ0MDBjTjJ4fegmRRaIHT5LRo6PRjugw9RJJBkWdnJwYfEyw\nw3XBAPT1EQIXSXat3ohSj+ae8X7AjZ4J6pEc3rvdbpv6i689se58iYI1CFxJMCzJnA5Ixdramtrt\ntt59911JslYY1ngqlTKnCYxP/Zb7z2uBDHd2dlSv17W5uamxsTEtLCx0Qb/RaNRYlwSPZNSgGQQv\nZGxkmNw/T3T7G61BhWH4H/+IX/17P+S1oaT//Ee8z+9K+t3HPrPHOHBQ0lmKTNMYhpeHgOoBmUmt\nVtOf/umfmlPC6CLTQ5FyaGhI9XpdYRhqfHzciqcUHlmgtVrNZGFKpZJlI0zBRLao3W7b0LXd3V3N\nz89raGioSwY/Gu10bdP13d/fGfGMInp/f79lHZ6kwN/CKPSK5yxyGHTNZtMaftH9AlcHl97a2uoq\nLGPMgBnAm3G+kD2ohzF7B6VrjMnly5dtRAZTUwcGBmwO0MOHD+06iNB4vicnJ3rrrbd048YN/cqv\n/IrRiomqGV1CfZH1gQNHqNN39P80608/jcNfK0Qb6jPUC7j/NLATUbOndnZ2TAh2Z2dHt27dsoAN\nuSEK9p5Rx7pnsiyOgHpJEARKJpMaHBy0ptTe3l7r8SNTwGnyewwxzpeA1ctdnXcmvnAvnWUI2AKf\nUflaDsgJ53s+Q/AkDFimPnNgD7GeqY9B5vEamFJn7UK8yuVypp0HQQH0hZoxup0+OwXWHRkZ6WL1\neUYu9b/3339fzz//vN0/7hMQoA9WmIKAQ+Q++MnWPK8Ps7eeOCUJf/io1zNHWHQwclqtliYnJ1Uq\nlTQ0NKR0Oq3f/u3fVrVa7TJoNJVeunTJsg30qsbHx1Wr1YzdhJOIRqPa3Nw0B9RsNs1Ywm6Bwcc4\niHK53MWKo+G33W5bUZK6kKflgr/jEJB78VAE54zyA5vWQwmSLDMikkZGn0IzFHxJthDr9boRRIAT\neY3UYR6ReRF9k/lQ35A6U4NxTPR0EP1K0uLiorUB0L9BHQSDc+nSJf3RH/2Rfv7nf94o5EATs7Oz\nevvtty1qbjQaNhreF8Y9LOMhv6f18BGtdDYLiTql7+3h9XxRv6D+yUymcrmsmzdvSjqbsdZqtazv\nDzIR7w+0yz6CcUlARxsD6571SpbUbrctO/e1Iq+/54kGkux3ODQC2fP3hCCI+wPRAgcoyTIyfx8l\ndZ0riId39gRHOAqcPsgGh2f34WRZo8fHxxakEmTR+4VGX7lctl5EX0MDwkUWjmsfGRmx+0LNeXd3\nV9///vf1Mz/zM4pEIpbVgszQY0kwz7WTCWPzvC4fNtpzAx7neKIdlCRLj0kjPWwhqasfKAxDTU5O\nam1tTc1mU8fHx3rvvfc0NTWl4+POyHcaXYmgYrGYpqenVS6XjZlCZH7//n0dHx9ramrKtOt6e3uV\nTCZ1cHCger1uToTND1zGGAyUKGiMnZubUxAEGhkZsUInhWvSZ5qHyTDoMUkkEsbCInsBhuDc2bSI\nr56cnFjNiUiOuVAeDoxEIorFYpbtABWMjo5ahEhDH6yo4eFhFQoFPfPMM/rmN79pzvPGjRtmyIAT\nMRSxWMzqdJFIxBhDOPi+vj7Nz88bHfo73/mOFhcX9elPf9okmY6Pj7W4uKjbt2939WdgOCg++wzK\nO++n9fAZA3UaIF0ieth83uDQe0N2RAP7q6++qpWVFUnS9PS0Wq2WsSipg+KIWC9A28jvgBIQhAVB\nYBH54OCgiTsDhWUyGdVqNSMhsGbZ71ybX/O+roKTBcIC1cC5eSfHXqE+dP7gdb5+yuGRHZwTWR/Q\nMoGoh9Db7bb1pxEkSzL0o16vm84kChDj4+M6OTlRLpczGG57e1sjIyMmDSZ1HPXKyordT9RqmLsF\nMYrz+/M//3N94QtfsFoeTErOmayVpICSAvA/gTpr78McT7yD8lGeT6Px1v39/frEJz6hWq1mxvUb\n3/hGV+H84OBACwsLXZkPsAdQFRF/qVTS4eGhRQ49PT02cReZEQq9FPELhYJFRLBuRkZGjG47Njam\nnp6ertlN6XTatLh6e3ttkdOjQRQEa4fNjSoEzgkjQQMjG8VHnxhq7g99FJKMPswGSafTXZh5EAQm\nIkukTH8YQxwZ4Y5RWFtbUyKRULFYNIPF/cYwpdNplUolg1Gnp6c1NzdnG2NnZ8doraVSSe+8846u\nXbtmumGeGEON6TzcwJcvMj/tBw4ag+JrBWTPnhSE0gf3Fef16quvmvp8EATa2tqyGm2pVDIaOnDZ\n2NiYkX3IrCRZDQOCjYe3BwcHVSqVLBiTzsSCMejSGSxLAOIFX33zL7UxMigyNxwNRtQTazz1nPXC\n5x8fH3cFNT5j8dkXmQmwIlC634/UpwkcvAPD+dJ/ube3Z4oR7XbbCEgEshC7ms2m1X5zuZypyTQa\nDdtHw8PD1gaDkguq5iMjI7p3756uX78uSQYDp1IpLS8vW0M3ATg2DtUIX6/j/njb8zjHE++ggMnI\nkjA2wEz0TIBn7+7uqlwuG0xx4cIFK7CCnfMggB0ODg7UaDSsnlQulw2KQMCRwi84O9lMsVg05Qdo\nuYwGIIKjwAxc2N/fr2w2q3w+b8XjZrNpmwNH5wuqbETPBmRzkW2cnJxY8zJODmoqfwdl9PyoBBYb\nEAFF6p6eHuufIRvDMLCh9/f39elPf1rf+ta3JHUW+sZGR8GHsRz7+/saGxuzAWzr6+vW+EvQAZuw\n3e6oY1OcbbVaunfvnjKZjKampmxDMALEOyLPoGKd8LOPwuFbMnzWQSYAFEdAQqbia5Hvv/++Fc/J\nnFEaZ8YaTK+xsTEzuFD6cQDUQ1ivBCnsjyAIDBIncJM6qgrAdggI42xwVryvr5H6dgLWp4c0Oc5D\nv779wau7k21xX3kf/u/p58B81Pk8MQWH5UlJ0hn6g4PmPJAQ8r2PQNk+EwSOPT7uzOWKx+OanZ21\nFhkCiJ6ejl5iq9VSoVCwTO/ChQs6OjrSzZs39dxzz5lNQKIK1Ad7QH8Wtob7hN3ya/BxjyfeQflI\nmA3jf+ejZeksXb9w4YItFjKtZrNpjLienh6tra1ZXw3G/Pj4WLFYzBwWQxH39/eNlMAiy2QyVlT+\n2Mc+ZucUjUYNm5dkDLhcriPI4R1hf3+/NfgODAxYzYZCLRkCUB/G5Xzxld8T7ZCV8TuyP/pSaBSk\nGEo9CRza17ckmdPjM5hUTEQIVRUj5DvxiUTX19e1s7OjxcVFZbNZlUolg6BoEsSxo1t2cHBgsM1L\nL72kX/u1X9Pw8LBKpZLy+XwXhu83CtfN75929h6HzyI9a9ETGWiPWFpaMiMHGnD37l2j/7NugXmR\nr8pms5qamjKDxrqg9QCjTbbj/1bq7BEkfliDy8vLthaAf1lrQLZkyb4mzWf4GpR/9vwt69Q7Gf9e\nrB3sBwExB9kO5yCpK3j0tS5Ph+d1vCdOiCzQTx9otVrWwwXjFadGbxSkKN+TNTQ0pFqt1gX7j4+P\nWyDB0FEUKjgf4NkwDHXz5k298MILRpxBdaLdbluAyn3q7e21/Q0acx6xedzjiXdQ0hnl3LNqiCR8\nz4TU6ci+du2abt++bdkSMBZQw7e//W1jjpEykxITdff19VnDL3TOnZ0dE2NsNpu2cHAcFE4Ro+3p\n6VGxWFSz2dT+/r6pR/jZOTTwssmls0JtT0+Pms2mCc6S8lP0ls5wch/p+T4WSV21LJTFWbTc12g0\narIyMHeIlPzoBi8PMzs7q3K5bD0T169f1+rqql07RV4yvL6+PlWrVb388suamppSJpNRPp+3BmZY\nY9CCx8bGtLu7q1qtZqPAb968qc9//vPGNEOhAKiTTMkTAbxBe1oPompveFnfkozliVFlTdTrdfX2\n9poAcKVSMYMJxMugOoZpehYlgdTAwIDVLhFHpobCGvfsVCBa1CmgsjMunmvxChQ4HaBLbAFMNu6D\nX6sc9FDiqDh/z/Lk3p2H6SR19Tp58gnIBqoLPkgiGGAv+1oV5+SV4nFC2BHqeti6eDxuTghG3tFR\nZwAi9T/ErJvNppaWlmy+HUSLhYUFCxhptCWbXV9fN3m4gYEBGzUE+sO68Y6WYMLbzo+cg5JkC9UT\nJqSzKIYbNTY21lXfYHPE43G9//77ev3119Xb22tyKywyFgUwIf0hw8PDBneRXW1tbWlwcNAYhEB8\nnOfo6Ki2t7dN0wzdPmSBWFhkQxSRfW0Ilh+fweRZz+hjs3APPNsJp0o6j/An1GDPkORzfXEaAgiv\nJ5PxPTP7+/uKx+NWN7h06ZLu379vFFmcojeWZKQ0Eu/t7Wl8fNwES7PZrNW4JJlyAUbpnXfe0S/9\n0i8Z1f7ixYt66623uuqTOGuckzdUT/vh0QZfiEchHkOZy+XMYBNw3bt3TwMDA6pUKsrn89re3jZF\ncaJ5SQY7IeqKswDGu3fvnq2RaDRqIyE+9rGPWQMqcPHBwYHtQ5wDOpJeLxKnhoQRRIOhoSETHvaN\n7p656NcsUKcv6vss+7wj9OvK1/dwoJ6CDgIB3dv3DElnrEoPQYJ0QF5ANQOHCFni+PjY2HV7e3va\n2NhQLBYzxjETsyuVimWT3/ve99RqtfTss89qdHTU9D1h942MjNhojXa7rWKxqJmZGUWjHaXy6elp\nvfnmm2bbUKbhnpFxeijzgyIVT4WDIsKRZGmwdDaAjOh5cXFRpVLJxl2gjuCJE5Ks+F6pVIxmSZ2C\nERdhGJqUD3ADignRaNR6ElBbJhqFsVcoFBSNRk2zjim2Dx48MIiQqIlMLwxDjY2N2TmwIFnUbE4w\nethLQIak7JBDfAOhZ7dhXIAgqRuw6YvFouLxuI0CwZmzafv6OhNBgUaJeEdGRgyeGxwctLHTnHur\n1ZFtymQyFq1SdG82m7p27Zph7TwrxqsgtQJESf0xmUx2NUj6pk0y7I8COcIfPnjDOOLgaQ+Ympqy\nHsHe3l5rFSBLoPZDtE2ggZEl8Orv71elUjFot1KpqNFomOEKw9CIFr7WQhBC8EFwNTY2pnK5bH1R\nvsfnPPtOUtckVw8rc778zMN9fM9eYW/4mhIwuj8IZM/XwD1L1AcGOC7OG6fkgyYPQeKgcYLDw8P2\nc+rlZLhzc3Oq1+vmxLARwIfUvVutjsL53NycBb+UKdrtto3B4dk2Gg2NjIyYTung4KAODg5sIChO\nn+vn/D1J5YMcT4WD4gbwACRZkVDq1HQQn8RhQbH8yle+YlBBT0+PiSZ6TJrR7cBczIYCt02lUiqX\ny8pkMhZlwGB78OCB9WAhvROLxSy1Hxoa0oULF0zJgiKkV3+ADoyTobm23e70p3j4Eee4s7NjODmk\nByIc4A0iXQyVF9Rl03MO1KhweqT5GPpisWgOHCakJBto19PTmS68sLBgc7SSyaSpEtArhhMHvgOr\nj0Qieu211zQ4OKi/9/f+nm7duiXpbPBcJBIxlWXfjEl/GtGudBbp0qvxUTlwTMAsZE8YTQIFMnGe\nA0MhyVoYPLm1tWVOn9650dFRpdNpra+vq1gsmqNCKcWPdqEnEXFfGK61Wk2tVku5XM4y6lqtZoMr\nj487mpOjo6PWAiF1q0NAPuKZs6/Y1z6I9T1VHgL0ToG6EdmRJ2H44JjsRJKN2cFRUbPz99v3OHL/\nPQPRIyIeZvxhEO3g4KAuXrxoDmV3d7dLzHl/f98UyXd3d61dpdFo6Pbt2xofHzehZ0k2OXt4eNgU\n6EdGRmw2HcQpAmJkldhvZFG+DvWRhPg8S8unlkRKKysreuaZZ1StVi0impmZ0Re/+EVbJB7ugilG\nBpHP523THh8fm0IvPT9kMvV63RQL0NTDkQHj8XsK1c1mU+Pj43rllVcsA6LQjAPo7+9XOp22vhEc\nKgad9+rp6THxWaIkL0sC1Aa0JskgG4gbPT091rBMFMniAirAcbORWXgsUAri0M+Jnnp7e3Xp0iVz\nZChfj46Odk03jkQi5mCJiDn3nZ0dfelLX9LnP/95m2QMs3BgYECXLl2yjJGNT7RLBsAGetqzJ2CV\n84evEfgaLWvo0qVLqtfr9syq1aqRerLZrGXCjOGgHrGwsKC33npLKysrtm7CMDTpK9YVZAecIAM9\nCaBKpZJGR0fVaDQUj8etDkXLB+wySVZTRMW71eqM5CBIYW/7ejKZCoGK743i/sCg4/+RSKSLkMQX\nWb5/rc/CyDDI3Dxhx2d11NFwqCAorVbLAleeDyQWnD3sXOwKdaWrV6/qzp07qlQq1ipCuYHaFY25\ne3t7Ojo6UiwW08zMjEH2nilMYEpZ4fDwUJOTk1ZHJBhh75I5nkctWJuPczwVDkrqVmr2kXIQBJqc\nnLSIanR0VPv7+/ra176m+fl53bp1y6KSdrtt9SVJevbZZ83Y8b4eC282m4rH4ya6uLm5aeMicFpX\nrlwxQz45OalaraYwPBsD0d/fr5WVFSMPAHOBMS8uLholl78BS8fAJBIJK0APDw8bvMb9gB3lHQuL\nHTq6v2cUZumhIAJithN4tocMcUjValXHx8fmgGgCZWPyWRTPmewJI/LChQsKgsC64oF5pDO4pr+/\nX1//+tc1Ozur+fl5w8yDINDU1JQKhYJlTxg36ayPw9eenmZyxI/C+z3KQEaMAjXfM+yzv79f6+vr\nOjw81PT0tJGHiPY//elPq1AoaHt7W3/5l39p9HNIK346c7vdNi1J32tDfw5BEXqWUKqHhoaM0UmU\n7+sZBDb0/0CK8f2DGGVfa+JgrXCAvrDnvaMHgsMx+ZqTfz/QHK9Y4V/HuvNZFXubvc+5sO+pRUky\nSBy1GfaIJ0709/draWnJxmowe82znYMgsPEoJycnqtVqKpfLevbZZ+1cGV7IfvIkLWwW1+ORFW9r\nOL8fxoD8q46nxkERhfgsCgM+PT1tmG0kEtErr7zyAxES0czAwIAmJiYsw6DOhLpCsVi0kdJ9fX26\nc+eODSGkpgXUQBYVj8eNLo7Kw97enun3VSqVrqm3U1NTmpqa0tzcnG0qYDeukwZDtOgwHBhw/mVD\nslFZUKTx0Ii9LAyOJpVKGRORzQ+8gTFjU6FXCFxCVuNVIeiNYXP4rnpIEDyHkZERpVIpbW9vm9y/\nx/IHBwe1tramk5MTG8rW39+viYkJiwqPjo4sG+ZvPTzyUaGWc3hnTJ3A3wcP9cLqYr0hystaQFpq\nf39fd+/etWfB2iLKhsosyeoa7XbbHE48HrdallcwJ6iKRqPG8GNuEagE9VvULYDOenp6TB3Bk5sI\nynxtyTPuPKznMyxPhDh/eMawN/pSt+ahrwEBxXMf6RMDbsT4e2RIkpGy2E9klJK6mvoJDiF/RaNR\n3b1712qCjIiH8k/WhU08PDzUjRs3dPXqVd2/f9/QCFpP2H/+cwkCJZkME31ROFvs0wdBLZ4aB0V0\n7FPwk5MTXb9+3TqqL1++rN///d+3ngCpIyL79ttvK51OK5FI2ALlYTOcjYdJdLe8vGwPDed08eJF\nVSoVKxrncjl7mI1GQ5VKRZK0trZm1NxotDMAcW5uzvqK6PQmq/CSNKTqpPfDw8NWxCbDYUH4KNQr\nJVMnAorw7D2iJtQpWOS8tlarSZI5IBYqmoVg/Ht7e3aNPJ9IJGIFcqj229vbFvGNjY1Z5E5WyvkC\nHRCNS7JZNvV6XV/4wheM3BKLxRSPx1UsFm10CufPufgN9VE9fETv52v19/dbvxmQ2dbWlqLRqCYm\nJrSysqKxsTHdv3/fggdmesViMR0dHSmRSBiZBeMM09bPHGs2m2o2m8rn8xoZGZHUUTJIJpMKgsCe\n+/z8vNVdGSWB2sT5+qoP6FhbOCjPvvP2wqtS4Ky4R8BVPush0OFf/hZnBKxHAEetj/eUfpC1Bw2d\nKbe8lvPmcz30hrJEq9UyZi2kLT6TWjN7VepIU8HcBSb3qMyjR4/06quv6hOf+IRBhCcnnfl3OHdJ\n5tAIZBhU6ZmMZJ0f5niqHBQPxItDptNpm1tDcy0LlI3y2c9+VsVi0TYXdaJqtdo1iK3VamljY8P6\nj9rttg0RQzhzd3dX2WxWmUxG5XLZRmtAByW7mZmZUSTSGdoWiUS0vLxshAgUgz1jB5ou0ZPvJ/GO\nhsyGbI5Mj4wKOI6sEWweWG5/f982IlEcjlGSsfW2tra6OvcxbkjYUMuKx+NaXV21zQVBBCr5eUYU\njlSStre37fxHR0dtLAj0XN+o+eabb+qXf/mXJcn0yahxSGcNgp5QgyP/qByeMCDpB4rvPtgBlh4d\nHdXm5qYFMdD+X3vtNYPmCISy2azW1tYUiUS0trZmjeXHx8fmaEAEenp6TKG/r6/P+t0wpr4JHPgL\n9iz6kO12W9Vq1TIwn5F7Nh/O0UsBeXvBvSGw9JCe/72vu+JUfJbkSSgYZ+6xdzIezvOkAQ+DgeAQ\nxPK5vpEamBUokXowzoR9PzAwoIWFBVUqFbXbbeXz+a5SQk9Pj9WRYPlVKhUFQaBbt27p+vXrRvAi\nC/MZHvVGb0uks/7K82jFB4HVP5yC39/Bw8MVXpaFTXXx4kX93u/9nsFpu7u7mpqa0szMjNFXYY3t\n7u5aNgAEsbW1pdu3b5ua99DQkMbHx63ACgttcHBQ8/PzljE1m01bZBSYP/7xj1u0QRbgz5lR1icn\nnTHpNEg2Gg3LbBgMyGYhI2LReww4DENzxpLsb0ZHRw2/xqltbW3ZAseI4/ToyYIZ6EkfmUzGNAXr\n9brJsUDaQAAUHBuYhrqfV6iA3UhGm8/nDX6lX8MXXln8EDhwQAyR5HUYGUldQpwf1QNj3tvbq3g8\nbrAaAqr097GGqHO+/PLLXXPBcDiHh4fKZrPGsjsfXScSCaVSKTNyGDCcETp/BwcHJoFFwzpNuhhU\n4O1qtWpK+OhI4pg8WQFDipPgNbBjPbJw/jVAclK3w/JOC+IJ68vD2LyHz+Zw+J5tTO1bUtfoCs8m\n5HgaTSQAACAASURBVAtyC5kK7ErQFuBWasLZbNakxKgd4wjJmoHleLZSh8n35ptvKp1Om+IHjhzi\nCoGed9ze/voM9IPWfJ+aDMrjtdzEmZkZS2NxBFCgJyYm1NfX19XcCqwGNn54eKjV1VUjC8RiMVOL\n2N3dVaFQsD4jz16DDp1KpZRMJhWLxazpEOl5Ijlw9Hq9rrt379rnYGhRfiYrZMPiqOixoghMgyvN\ndJ75x+ajdwrmG6xA5PGBR1jAbCAK3ZJMi9BrtUUiEU1MTFjmhsoGjrFQKCiXy9lCnp+fV6VSsewS\nTcNEIqFEIqFqtapkMmnSKu12ZzbU4eGh5ubmtLq6qng8rp6ejpbYW2+9pV/4hV+w64TyykbxzC+M\n40fx8Ewq6MLAcgQqvb2dqazAcoxc+OM//mPrU6PRnXooWfj09LSq1aoymYwZJ4RmYaTFYjHLEjgP\nAkSyn0qlYtMAMMrUbKC9Q7QhaicbZ0+TeUhnpBGyFtQjuCee9o0T4XPJiljLQM2+r8fXv33dqbe3\n1zJDzzZmXbK3wzA08hF2iUDVE5xwqjxHkBF/L8kmQVWKxaJBh9gpAnUYuATn1PfIVo+Pj1Uqlawh\nGvLMycmJQXrAmtQpIV1wv/29+SDHU+egPOkhk8lI6nj2ZrNpfQEvvPCCRQgYM7IlIqFisWibwGO6\n0GZJraFEZ7NZixaOjo40NTVli6xcLqtaraq3t1fpdFqZTEY7Ozu6efOm7t27Z/1GdMxLHWMO/OE3\nOVEiAwmJmiQZnIXxRQeQBQe9/OjoSMlk0uBLFitZBZsU4wT8MTk52SWDgjQRQpGNRsMyK98TNT4+\nbuKw1WpVpVJJg4ODKhQKunDhgiKRzviRRqOh4+OOuO/zzz9vne2e2MCGjUajmpmZ0draml544QUd\nHBwolUrpvffe0+XLlw0O8QYIiA8D9lE9fKFeOqNi4wDa7Y5sjofF+vv79dJLL1mwFY/HuwwPe2Rg\nYED5fF75fN5YeWEYmkzV5OSkWq2WjZOJxWLWdF4ulw0qRhG7Uqkok8mo2WwqkUhY7ZiWEQhHGHcy\nPdYw6AY0afr8cEaePs5+hvRATcYjEeczKaA1Al/uJbAbf8/+4709zV/6QbFZHCSvpb6GY+Qz+B4n\nhcMkMOP6yEBZ+/wOe1IqldRud/oqs9msaR/SRL28vKyf+7mfs/va29sZK/Tw4cMuej6ZH/eT68NZ\nf2QzKM8UITJhRAZUVuCiUqmkxcVFtdttg76AOlqtlm7fvm0QlSRjK8GcAa9HWp6Gw1qtpkgkYsMI\nSZn7+/tttMbOzo4ajYa2trYsEoEl48UqgdbIUnAU/iFTl6IW5Os6ZF9+Wirkg6GhITUaDdsEaNux\nifzANKLJoaEhgyJh/aHSQCRL1BSNdlSmGUZXrVYtqqIR8TwrEvV3VD9ee+01DQ8Pdylee+YlmeGF\nCxfMMT569EiFQkGf/OQntbOzY0oe9FBRrP0oMvgwXOdhlvMEGQrm9DqhsffSSy/Z3qJOyR7gWVJX\nrdfrGh4e1tzcnAqFgkn7oD6Qy+WsbntycqKtrS2DgSE7VKtVRSIRU4wABkQHErLA2NiYzQBDpNiT\nGJhV5iW1uA9e+YG9hyHlNf7AKfHZHiLzmnhBcDba3rPWyDI5X++ocF5kkJ5pyPMBiidbYU9Q0yJb\n4hogvpCZws6tVqs6ODiwERzValVDQ0PKZDJm5y5fvqzl5WUjbmDrpqenu2yCn5rA9XMAteMsP2iT\nrvQU1aB8BuWpofTHbG5uWuGeGU3AXmQLzES5fPmypLOGRlJl2DKk0IxNJypkweRyOQ0NDRnjqNFo\nqFwu67XXXtONGzes6TEejyuTydiIa+ALD4/19PSY4+OBNxoN+x1KDeiNgQmDEXs6se8DkWR1MPD2\nRqNh98RTW9nM+/v7BlVEIhHrf0EYV5KJzJbLZTMkw8PDyufzWlxcNDkooiug0p2dHaMRU5MqFova\n3t62zI9NC9FiZmZGg4ODyufzBjcghglDampqyq6fDPHDYOF/G0cQBIkgCL4SBMH7QRDcCoLg00EQ\npIIg+LMgCO6e/ps8fW0QBMH/EQTBvSAI3gmC4PkP85meyUjgQj0I2nIqlepSLG+326bQ4RlcqNRP\nTk4qn8+b4Y7H4xb1I8sF/BuJRLS5ualarabl5WVVq1Wtra1ZO4ZXZ4nFYrY+MaAYY5AFZHZ4L89+\nI+MGyWBvkQl5IVTgdg/Fse597cevG5/Zwx6UZMxdanxAyjgRzo1szTtUGqZ9H+MPgwcJIuiPpN8Q\n5+fPs7e3V7lcTnt7ewblNxoN3bhxQzdu3FA0GtX6+rokaXR01BqvU6mUBRdoJHLeICkcoCo4JJ4T\nTvXDHk+Ng+LwDJ7j42M1m01blDMzM5bSHh0d6e7du2q3Oz0Zt2/fNgp1oVDQpUuX1NfXZ9Rt+j6G\nhoY0MjKiixcvamlpybIEWC+Li4tmSI+OjvT222+rv79fm5ub1q19cHCg+fl5yw6AKqC/oyztjfnh\n4aESiYQtRHp8mO2CgjECkWQYLGTweKjjbBborZVKxSI3qVvtm3oWiw4YE4d5eHiotbU1FYtF65MY\nHh420VuKqbu7u5qdnTU1CSLQMAxVrVYNXozH411DEHFUFO0bjYbS6bRBl0NDQ0omk+bgX3rpJU1M\nTKher3f1erDBOe+/A8dvSfp6GIaXJT0r6Zak35D0zTAMFyV98/R7Sfp5SYunX78q6V9+mA8EFvJG\ng6bynp4eM05+5EqhULD1wxrx9Q9fA8FI0jxL718sFtP9+/ctwkc66+HDh9rf39f4+LixU6PRjqRY\nKpWyUfORSERbW1vWasC65Do2Nja0tbVlUDWZCMEKNSlJXVkN89bQjSSrgsLN9zggHIhnI7IvyJ5w\n6jggzxxlv+Ao+NzzhALsjmce+sCAfU2NmvP1DECUKLgPZLdMIoDRfO/ePV24cKGLDEVNcnZ21ma0\nRSKdNhFq3AcHB7p69ardU58xemaiF2P+yNagpLOeBaAcPHgQBMrn83r33Xe7FLcl6Y033jB4CLIC\nRmx2dla3b9824dcw7Ex2JUrxEdT09LRFFWC9SAfduHGja8otI+HJsoATfbNdqVRSpVJRPB7X/Py8\nxsbGbAESkeLsyEYQ9/SMITIpsHIo4x7igGFVKpW6eiJwLr5jnvfjXlH/4neIinqF95OTzswaZP/j\n8bhdy8rKihkaNioMQzZxGHam8D569EiZTEaXL1+282cDY4AePnxotTL6tDh/3x/203ZQQRDEJX1W\n0j+RpDAMjyQdBUHwi5I+d/qyL0r6C0m/LukXJX0p7Oz8106zr4kwDAsf4DN/AL7yNP/h4WEjoZDJ\n1Go1VatVg36J8qempnR0dGREFtYzMPHcqVjpnTt3zGABfQdBYP1riUTC1j+Eo+PjY5XLZTOuEIYk\nGQzI9YRhaPUqAjr2EQGch85AOch6vKoC1+idDrCfJyHgsOil8/R2X8MC1vICAr5eQ2bF+2G/pDOG\nJVC0rzmF4VkvF8EBPV04UFCU859RKBRMIoys+fj4WN/4xje0tLRkepZ8XjqdliR7XbFYNJJMsVjU\nxMSEfTaZqaf6Q6Lguj6yEJ+kLkNEBkVajn4e9NeDgwO9++672t/f187Ojt046jYoPkxOTioejyuV\nSunZZ5+VJIO6oJBT7B0ZGVG9XlexWNT6+rotdqRkgiDQlStX1Gw2jc6Jlhm1pv7+ftVqNR0cHCif\nz2t6etoaJ6lXEfVJMhiDLIloC1jg4ODA6mQwr4AEgiAw7B9GltRplIzFYpJkDCgWea1Ws+wJx00P\nBZnW/v6+1tfXtb29bcKwwIf01gBhzM7OGtsLNuHu7q4xyQYHB7W7u2uZbqvVUqlUUm9vr/XCgIlT\nD2Hze0iGiJbg5e8AxDcvaVvS/x0EwdtBEPxfQRAMS8o5p7MlKXf6/0lJa+7v109/1nUEQfCrQRC8\nEQTBG/7nHgLnIBPyzNfLly/bPUylUrpz54729/e1tLQkSUqlUkqlUsaQlNQVePC7lZUVFYtFHRwc\nWAMpNPZKpaKNjQ2rZ5JxESiyFvf39603i/3hazDAY6iFBEGnyZ0ZYfRV4Zw8rElGKJ2panj6NwEu\nDsqjC3594QzOQ6fcb+A2Hzx72B0kwate+KyD9yNY3N3dtQwMQoI/2P+UNqLRqLWS+KncfIHIRKNR\nra6u6g//8A8VhqHde+6Vl1M6XwPHzp2cnFim6ZGKH6fm+1RlUNx8Fh9MHVgpo6OjpsD8ve99z9g9\nkowdxEPlmJqa0vz8vA4ODrS8vNy1uEinYf2xeIgMNzc3bYjY8PCw4vG44vG47t69a3UUSSqVSsZ4\nOz4+1tTUlFHEGafMBoIe62tHRKc+giqXy5ZdELnieNnsHjvf2dnpwut7enqUSqUUjUZN9BZSBz0j\nQKJSZ3Q7GxjnjiNnk1MghiWWy+X0zjvvKBaLaXx8XHt7e6YcX6vVzCgQtWcyGWuWPjw81OzsrF1P\nT0+Ptre3NTMzY60C0WjUYEbv0Ilqf8pHj6TnJf2XYRj+ZRAEv6UzOE+SFIZhGATBB/KkYRj+jqTf\nkST/t94onTdS3B/W097enmUb1JLS6bTm5+cNyqnVatamAWT3/vvvWyaE4j6QIdRyivc8y2q1allO\noVAwY805ra+va2RkxPqrCCapexKckA3BBASe89m/h+/oXyTqJ7v03/t75x2c1I3WAOlhoAkOfQ+e\nr93i4EA4cHA+S/K9Tr6eQ+ANXE8dHFshdTMMCSJAJJLJpNkCRASAFHE0L7/8sj7/+c8bKiLJGJi3\nbt1Ss9m0uuXh4aHGx8dNuZ7AhWv09bwPU/t9qjIoFhULgYYzNkoqldLg4KC++93vdnWKQ6cdHBzU\n4OCgcrmc4vG4nnvuOc3MzBjclcvlTO6DRdNqtSxrgkGH8CWL4dKlSyac+c477ygIAsPSkbNPpVK6\nfv26MpmMsejY2MBskizikWSZUH9/v2VybGK0yMiQcKwnJydKJpN2n4hOwbFRhPaZB87ENwI2m021\nWi3VajXbVF51WpJtsLW1Nd2+fVsHBwcqlUpqNBrW59Lb26tyuWzY+IsvvtgFzx4cHGhhYcHgUGCN\narWq9fV12+grKyum4Ue9zDOIPLTjoZMfZ539mMe6pPUwDP/y9PuvqOOwiv9/e+ceG/d13fnvJYcj\niuQMZ8jh+yFLsuTU8kPWpknTNkV3s01TY7HZArtFGmAbtOmmfySb7aILNG3/SJGkQFu0DbBIN2i7\nDbJ9JRt0WzTYzaZxvUFTo2niuHZsy5VlWZJlkSLF18zwKZIzd//4zefwzFh2RJGSKGYOQJCc1+/O\n/d17Ht/zPeeGEIZq1xmSdLX2/ISkMff+0dpj2xL2BvODgm1paTHng9wR9wqY9vDhw/Z3X1+fxsbG\nNDw8rMnJSX3961+39by0tKS5ubm6MgeKbDEgxWJRxWJRvb29qlaTc6IoKuUamUzG1jYd1YvFomZn\nZzU1NaUrV65YdJ3P5xVjtGanQNUxRiMSSPXsPWnLYSG6xkCyB8iZ8Rr+JieEApbqu0V4kpFPNXhj\n5IvFea/fd9LWaeFA/+wNvnelUjHSFNcBEiUao3iefoa5XM7agXlSFgb46NGjevrpp+07cvI29Yfl\nctly+dVq1SA/xozRxQHg+zFf25F9FUF5ryaEoOnpab3tbW/TK6+8ohCC8vm8PvvZz74mSc5CnpiY\nMMrz6OioeVm+sv3QoUNaXV3V1NSUJFkeiCPJ8Sw8W4iNRO4mlUpZ8vfQoUNWnAeODruIpGO5XNaV\nK1d08OBBW1yDg4N1jSElGWECeASvElyYxC55Ns76AStmQRFdsdCJgugGX6lUDEKBqcV3LhaLVkRM\nrczc3JyRQOiCQd6J01HxmOfm5nT48GEtLy/r6tWrZqTBulEc5PpKpZL18ztw4IBWV1fttSgK7x3v\nRv5pN+DBGONUCOHVEMJ9McYXJb1D0gu1n/dJ+vXa77+qveWLkj4UQvi8pLdKKm0n/9QoeLe+MwNI\nATnES5cuGWmFuiUKppnTJ554wtp1UY+0urpq99XnXmGckqti3dPVnB57GDqgZPKIrIMrV67o3nvv\nVS6Xs9ZkvlwCxKJSqRi93JOEcFakLWdjdXXVoEYcQL+GMCBEYo0Ojn8Pn+sjLhxM2KQ+SmM8GARI\nGzznIUr2ALlf6PfklanPBO4PIemk09HRYY4C0VlPT48djUKbtaGhIU1NTSmTyej555/XQw89ZPnh\nnp4ey3OjS3AAvBFGf/Fan/vcruwrA8WioabBez3ValV/93d/Z8Wp3CQ2KguX3nrUUKDoCd2LxaLm\n5+e1sLBgRwHMz8/XKXOgRKAPz3AhjD548KBGRkYsyUu4DMQyPz+vK1euaHl5Wb29vUa7RUEvLS1p\naGjIvC66FFNPBd5Mo1c8Z06ghTl34MAB9fT0SJLRT6lNgk23trZmsI8kI1UQrZVKJYv68K58Jfnq\n6qrVXuCpX7582VoXkWS/cuWKHRFfrVb1tre9TZcuXdL8/LzVzuABg8c/8cQTOnXqlDGbmHMOyGPj\nojg8hX4PyH+U9KchhLSk85J+Wgmq8YUQwvslvSLpJ2qv/ZKkRyWdk7RSe+0NifdcPYyFt88+6e3t\ntYJymKn5fL6uASvHNzz55JNqb29XOp3WwsKCoRAcarmxsWHNYvn8o0ePWud98pHUv4UQ6oqD6dqN\ns4VCT6fTOnbsmBUVz83NWaEq0N7GxoYWFhbqOppjiFHmfMdGSE2SGbTXy2FKW4WniI9GMULUOvnI\niGuiBzB26Cn2GQeasnbp8oLh8mQJoHlyUxgIX7CPrsrn83VMQjqxQLaYmZlRLpeTlOyVZ599Vg8/\n/LBFbXNzc3rTm95k9wWjjk710ajPy92s7CsDJamOn3/o0CFbmOVy2XBzPBEiBTwzqtqBxQYHB63N\nkSQrUOSxjo6OutYtlUpFV69eNWPFtShQpR8WGw3iBnUKZ86cscXW39+vgwcPKp/P22bZ3Ny0A8eI\nnHzxLXg1kABQGpgznqUkI2aQM6PjOxRjf3ZMS0uLdZfm+1B/QuI6lUoZVfzq1atWpLy4uGgbnpxU\nsVjUyMiIRT8dHR3q7u7W4OCgbSTqcPL5vAYGBvTKK68YbXZ+fl6pVEqvvvqqOjo6dPr0ab397W+3\nvAS5DjxPX8PioYg7LTHGZyS9+TpPveM6r42SPriT63ll3NKy1fmBbgusse7ubknSyMiIHQtOaUII\noc6I4aGDGBBhLSwsqFAo6MSJEwZFE82TMyWqKBQK9nnQ0iFNQIbwLDfWIQaS+q3GGifQAVh2fD8U\nO/lW/5lcw5NsIFt5GnnDvZFU3ySW74pwLZxFiuuBwmAQAtM35kk9Q9C3IoMk4kkWkowRy7lu165d\nsxpQGl0PDg6azsDRoO4QndLS0qLTp0/r5MmTmpyctE4cAwMDddGpT5UA9XtijodCtyP7ykB5z5Bo\niAjo61//utbX1zUyMqKXXnpJkqw2oK+vT5lMRq2trSoWixYtnTt3ToODg+YhkluhLY+nv5ZKJVsw\n4MFEMxynQfcFcF9C5lKppP7+fqvMZ6PHGOuOs4BYEUKwOolcLmdHN2OIfY9BaatPFr3HfFU9J2fC\n9OP1HurY2NjQ5cuXLSobHR21uSZntbmZHJ7W09OjhYUFlUolcwpijJqZmbHNJCVMLjbj3Nyc5ubm\nNDY2pv7+fpXLZUkyZTk/P6/h4WErBCTXAMSwurqqJ554Qm9/+9vV1tZmBb++cazPSXEfv1vFk1a8\n509TY5S4P+yRaOSFF14w+HZyctI66vM+chgjIyPW+V5KYPTe3t66+4FBgU3m8y5AfN6IcM6XPy2a\n+wyE76N9mJ3AZ+RaMTgwdnEmK5WKEQsgHIGuYFCIwHypiiQjMODwkudkX6ILcBKYB/I1KHP0Fwoe\nuBPqPz03pS0nk/H4iBjDDZIRagzc+fl59ff3a35+XpLMAZ6amlI6ndbRo0cth7e0tGRs3pdeekn9\n/f12jA4MR19YL8kYld44edbfdo3UvjJQJA29V8SEAgOw6egfNzo6ahTxhYUF5fN5Y6RVq1VduHDB\nWgMRhdDpHG8I729pacmgpgcffFCDg4NWT0IvMqCIjY0NzczMWDPZdDptHo1f5GDMRE1EiBThwhgE\nyqJxJmwqKVH0wIPg33i0bDw2CkltPG0S2FBPY4y6ePGipqendezYMVMaKIPFxUUdOnRIXV1devLJ\nJw0q5eA0vns6nVahUDAqcozRjmogfyFJg4ODduZQd3e3stmszpw5YwxFinOZLxhOIQTrXejLD/ZS\nBHW7hPmQ6k80hVre0tJife5QzORDpUQRlstlnT17VpJMoRcKBc3Pz9tnPvjgg0qn0xoZGTHYDi+c\nYxgwROSlvMJCyeJYkJMh0uesMCAy7jmeOxA5kQoFqePj40bbDiHYHvF5KKBj9h66hMf4joyLeW2M\nzn0Rsc9Z4XhyTc+SxCHmcRwvjCtIgM8rY+AaITWu5/PxQHFra2taWVnRzMyMurq6DBXhHpIbHBgY\nMIe7XC7bkSaFQsFO1t3Y2FBfX58dYYRuxXDz3Xyu7mb23nc0UCGEz0j6V5KuxhgfqD32q5L+g5I6\nDkn65Rjjl2rP/ZKk90uqSPpwjPGva4+/S0nlfKuk/x5j/PVtjfQGxCcioWoPDAzoa1/7Wp3yGhoa\n0qVLlzQ+Pm43aXJy0hKJkgzempqaUj6fNxp6qVRiXsyTHBoaUqVS0eDgoFpbWzUyMmL4OsqYbs4t\nLclZOZlMxrxPOnwDO7LxuNkYJxYinhkkBwrrWLDUfwB1YFiJoPgMf6YS/7Px8Q5pHQS1nc8qFApG\n+R0eHjZCBP+Xy2U9+OCDunjxoqampvTqq6+qr69PExMTdYbqwIEDGhkZMWZgW1ubzp07p1wup3Q6\nrRdeeMGotURrRFUwlfBSn376ab33ve81WDfGaPdtN9h7CMp+N8gSd0pQpLCxOOKFHm0zMzOanZ1V\nNpvV4OCgHn/8cVs7vK+lpUVdXV3q6+vT+Pi4nTS9vr6uQqFgChanzXfmRvkTUQCPUcpAXpM96yEt\nmKsYHZR/a2vSET2bzZrTRD6ao8hx9pgDGp+iOInmyBERDWCYPDNtfX297jth3Ni/7GlPiCByZB2y\nXz013BOUyH+D1ICsoAOILD2LFz3AnPr2R9SYTU1NaXl5WcePH7cefZR4QD5JpVJ68cUXzQl44okn\n9K53vctyjblcTjMzM3Uki/b2diNhYSR3IjcSQX1W0qck/VHD45+MMf6WfyCEcL+k90g6IWlY0t+E\nEI7Xnv5dST+ihF77ZAjhizHGF3Yw9tcI3pUko6/6gtTV1VXz1sfGxjQ+Pm7FnyyIlZUVTU5O2mdB\nJkin05qdndXAwIAGBgYMpuM3Z92gHOnkQIJycHDQQmcSzz09Pca6wSCtrKzY54ANe0o7h5UBcxFh\n+eQrhgjCAIocaANsH4MCNMhGxNvCWOXzebW2tlqXASJQ7xVLW+1OUCIw+o4cOaLu7m6Vy2WNj48b\nPs4ZUjAce3p61NnZaedskUPjFF48shCCBgYGrN+f38Aomo2NDQ0PD+urX/2qRYZs3p2Kj0juJsEY\neLYm8BU0bA8nATk/+eSTpgA5C61cLuvQoUPWfYLIhAQ7kQHRGrkclDxRiM/LQOn2x837rgw+b+pr\n7siXeHIBcBrRIOwyIg0cGJQ4n8199cYTJc99x7hQawUUjr7wxbk+mmFe2FfcB64tqS5C8+sdsgPf\njddgSMide6NOXpG6xNXVVS0sLKhaTbqWl8tlffvb31ZXV5fuu+8+SdLS0pJmZpK4gzKA9fV1lctl\na3Lg+3zmcjmrLQWalLYOmUS8A7Adx+47GqgY49dCCPfc4Oe9W9LnY4zXJF0IIZyT9Jbac+dijOdr\ng/x87bW7aqBYKEBdxWJRFy5c0NmzZ+swbJq7lkqlOggK6jgbCo+9s7NTJ06cMMVL7VK1WrVO5e3t\n7QYVouiJSDKZjDHmJFlnCDYQMCHKgj5YbEYfXbEQfFJXkuWRenp6LFzf2NiwHoLXrl1TLpezY0co\nwPPthPAePbuR3n7UQkDqwLAhHPXBOTF4U1TADw8PK5vN2tlZKEA2HiQPGF4YxkwmY+d4cY9hCfb3\n91v/NfIWzzzzjB599FEztMAjbIo9UKB7R8UrCpRmW1ubORCjo6O2rrq7u/X3f//3KpVKOnr0qDFT\nNzY2dPjwYXPWgPCIAohKgJal+iMkfA0WRslDbBgGjAokB99ayCfefe4KhUwpQyOxwedDQCW8seRz\n+C6sFz8OHw35eiciPWA6aNbsB19fhfGBQSfJokLgdQwr12Ht+9dA9/ZlJOyV1tZWax0lyTrLk0MP\nNeZkCEFnz561Li8LCwvWoBonhfnmtGoMIGUg6EwgWh/1+rndruwkB/WhEMJPSfqWpF+IMS4oab3y\nD+41vh1LY5uWt17vQ0MIH1DSDHPbwmJh8oaHh40cQfPS48ePG4xw5swZpdNpdXV16dKlS5ZjYXFR\na8HZToTUmUzGqLc+b8Q5TxgSzrmJMVq3b7wbIAdJBsUBPaBYMRhsfn/MgSSDFXydE6wrvJeDBw9q\nbm7OPFMiH+YILJ7Ijw0JDOO9P7w06q+oRkfxsAGBLMDefXI4m80aNZ3CUPIFfB/6s8UYjdY6Pz+v\nF198sQ6eofgTnJ56DJLzvk0TytA3rtyJ3I3wHvcRA8Qa9fVKrAkMAmuePo0dHR06duyYOWQ062UN\nedTCdyGAVckYOHeKtYjBYr2xh3yTVFhq3EPPCMRoESkTFaLkWcvt7e3WN5DoQ9o6DRal6hmgPorz\nht0bPxQ044K9ynX57j5Hzvg5mQA4kPETcWKIcA4k1V0PFMHvV9Z/R0eH5ubmVCwWLQdFWzSPOF27\ndk0TExO6du2aTp06pdnZWaO2UwtHvrezs9Pam1WrVSOlEJE2GmK/V24XSeLTkj4uKdZ+/7akoaqh\nVAAAIABJREFUn7nJz6qT+DqtWm5EuDHkS+6//35jkXnojHoCPHjwVxTjwMCATpw4ob6+PoMtKpWK\nstmsheREQSwcihk52hzqKxAbEBwwhI+goJhCWpC2vClOzMUwwbbCIywUCnU0Vb+wWcR4s75tkF8o\nbFTexwIjx+WJC5wBxfu7u7sNSiAPxSKVZAWTbD5yZwcPHtTCwoL1X0O58LmcXoyS6+np0cmTJ3Xm\nzBl7TWdnp9V/ETHSVeLIkSNaWFiwuYetuFsR1M0wkvaKsAd8tEtUBCssl8vpb//2byXJPOeHHnpI\nw8PD5mgUCgVTcN7ZkmRrzRemsm5QbB52JoeE8uZeMUYMCUw8xi3pNZ/B3sJJQnnyGycKYhCPey8f\nx8kbsMbXYHi9E8f3xZiAdLBW2LfAfIzTU9p9xMRridxwZLku1yYnRX8+T3PPZDJGZvD9Dn3ky+dO\nT0/rscce0zvf+U5duHDBvlO1mjQAhjzDvYIdTFmIZ0WSC0NuJoq6KQMVY5x2F/0DSf+79u8btWPZ\ncZuW7yTc6L6+PoOt8Kg4lAtDAKb6zDPPaGpqyhKrJ06cUDab1ejoqFpbWy3Hww3v7Ow0sgPHkaNw\nr169avmuarVq3p9nm1FMCqPPGxHqUvBIKFrlZq+trdlR9LTZh6nHZ/P9UECSzFOFFEG1PV4yG5Hc\ngKeX02KoUqmYYoD+66E4kq+8l35tzJ3PRxCFZrNZayLL9WFetba2qre319hZ3Ivv/d7v1XPPPWeO\nQKVSsY3Dxn711Vd133332X3loDyvKL6bBKXv17F3ViRZSzCi3rNnz9bBW+QWW1tbVSgUjBxAtLSx\nsWHOGPPsiQ+sKxQvrC/q7oD3vGL1pwYQUeFcYtyA1DEkXvGjdFlPkurIB/xmrHxGo0Hkb1/CwtwQ\nufnIi3FgPBgDaxBj4/NvvjhYkkWuMBR9dOIdVJxeCql5HY4GRcmVSnJOHY7zxMSEtYeiRRvQbAhB\nX/nKV3TixAm7n9Q+bW5u6tKlS7rvvvuswUChUNCZM2fU2tr6msJ4H6XdjNyUgQr1bf5/XNLztb+/\nKOnPQgi/o4QkcUzSNyUFScdCCIeVGKb3SHrvTY/6+mOSlISQtOORtnpZjYyM2EahtcrFixdtkQ4N\nDVkhrY9wwKhhz1CQi/cFBEenZiAwmjOykIhaSIjSxHVzc1OLi4sWSqdSKSvmlWQ5KQwCtRsdHR3W\nKgZGjS/w29zcNOiRqJKu50R2LGYgOxLVtFQih+X7joGDk6hlE7IJML4YURLajMFHVBsbG3UUeRQU\nirBSqVhtC/nCzs5OnTx5UqdPn7ZjTTyLC7gFJlKhUND58+d3Fd67GwkSiIexfFQxPj5uJRghBINP\nNzY21N/fb4cFDgwMmFJmvXqiDJEDBsBHEzQ0Zs3R1Z61x/hwpvx4IVdUKpW6qJ096B1CDBSF+BST\noziBuX2UgvHEgcEwse4bX8vrJRljFgFF8Pkq/xy5bAgPPp/kIUo+B2FPS6rrrkE5CmOsVJLDO3FI\nZmZmbM+zJ3O5nGKMdt4ccC46rlwu68yZM7r33nvV1tamkydPWu3iyMiIOTbMp2c6Np43RxPbm5Eb\noZl/Tsn5NIUQwmVJH5X0wyGEk0ogvouSfk6SYoynQwhfUEJ+2JT0wRhjpfY5H5L010po5p+JMZ6+\n6VFfR1hYMOrwNtgMra1bTTA5JgPP+q1vfav1GvPV9RgD/vZGBo+O4wW4MXhBHFnAIsIw+RYu5MUw\nVkB8Hg4jF+QhN3I3KHU2J4QKIgs8N/qM+cQtn93Z2WnFeigWPDaSyL5ol3wSBpr6Kv9dUTLlctkY\ng8ydT1bz2rGxMb366qtGwPD5ELw0jvYAzjty5IgmJydVLBaVz+c1Ojpqh0JWKhU7doGo0sOHuyF3\nE8TXqFj5m4Q767lUKqm9vV1nzpyxNULxdXd3t0ZGRiw6Rxl6wgyKiL8xEKxPnB1PKGBvetjYw17M\nM5Ab3wGDiBDRwaz1rE1PbsCh8vknb4A8+YLxcH0/Pkl1UZnPfwPxe2PsWxuxJ32dl7TVqYY9T/kH\n34Pv4vO1PA8b2Ru4arWq2dlZXbx4UZVKRYcOHTKylp8jDA9joh3aysqKLly4oFOnTlm5xvr6urXA\nSqfTunr1qrLZrHp7e3XlyhVbWx42vaURVIzxJ6/z8B++wet/TdKvXefxLynpJ3bLhM3nvSWiC+CI\nq1evKpVK6fLly1pdXdUjjzyigYEB88KIZPAQOI+GSMnXKvEeFLiPgmrf2SKWEII1w8QzApYaGxsz\nIgNwGOdUofSho2MweByD4VlqGCeau+KheoYPY4atxXtQ8ESKbBZgR2AAaYtK6uFKj58DMfpCRO+9\n834MDrRxOmPgGHhlyXz39/crhKCxsTFNTU3VQZSSDNYB12cz7lTuJsOEeKXrxw+8PDo6ap05YL+R\nIKeLPXVOvBclx2fTdguj55U894ZIG4Xs6e68h0ic/LAkM2S+RIBcKXteku1bjELje1hTGBY8fBQo\n0Qrj9Z+N0mXOgOQalS+vwSgR4XjnzZOavMPG+gQG9HoMWBMjz3OgFd3d3SoWiyqVStbEua2tTTMz\nM1YecunSpbqTGKTkROHh4WFzLkBzgHs7Ozs1MzNjr6EV2eLionWpmJ+fV6FQMBY0c+sh/ZuVfdVJ\nAgXuceB8Pm8LmTOUnn8+QSTvv/9+a3MEGYJ+fXiH3d3ddfVEeOR4hTQ25WwakoSNVeCSTMmjeMlp\n0Scrk8lobm7OapIwLH5BYWiAMIja8Kq4Dpudo7s50ZYojOJhSXXnPRGpsanYTAMDA8bkwuD4Ikjm\nhfF5VqD3uNmg0pbnCcyAUWR8RGgoL3Jo5XLZMHHPmELRQj3HI/Sw1k7Fe9F3o6GStpwEPO1Tp04Z\ns+38+fMGp/b09GhoaEjDw8PWcYT1jbHi3nHfcSBAHrgezpdXsrDqcFYwLsDHrBdvQIig2BesK67h\ndQDRnbTVocFHYZ645A0t+SLG7w08cybJ/sYIe2VMtOPRB59raix94L2Midw3BpuaK2/0+d7ZbNaM\nSXd3t2ZnZ+1EbpxgSmM2NzftnkFBZ3/TgxS9wP1eWVnR008/rTe96U1WzlKtJqchUF+Vy+Xs++F4\nMIc7cQz3lYFi0cG4o6klC6ulpUVnz57V0tKSHnjgAfX39yufz1tHhxij+vr67G/aCXkKLBuM5CIG\nBuMFfsvC8N6Yzyvh4fnCxcnJybpaEbwwDB4wAQQDEtSSjKQATZtFQmQHLIeXzEFnV69ete8Frs1h\nh4VCwTbvxsaGenp6LKEdY0KP7enpMfKJ95BZqG1tbXbENNBHa2urQQqSzLASGXHuDwqIjZBKpQw2\n5HPa2tp077336ty5c+Z1rq6u1kESQB8ez9+J3G3GycMsKEEQgNbWVus63tXVZW2txsfHzaEBGWA9\nE73AskQRYaDS6XRdCcT16NtAWaAAnl6OE+SjfG/gPIqAIvTGl+id13n4mbXmHUDewxr2xtYbPr6P\nNyaNa8qPk2twPT4bo8bnAM/7MhP0De+FpOJZqB5C5LPa29vV19dXxzImApuYmKgjlMA4pOn12tqa\nxsfH9fLLL1saARhyc3NTV65cMaIENH6Ygb5Ehc/ejT2yrwwUiwYMnEnb2Eh601HwOTw8rHw+b4Wg\nnoCAF0dNDcoUmMHXb+Ax+A3kIxnGBOGCzwWuIgxfW1uz1kqeOedxeajSYMSFQsF65sFWIurxtG6S\nsCh2KQm/STTTGxDoDoVSrVY1NTVlncYx8h0dHUbcAIrEoHoCCAlTatAw2GD/4NySzLh7ggN94Tz5\nIZVKmWfZ3d2t1dVV6zhPI0s2DXAEBnO3NszdRpDw40XJonxDSA7FnJ6e1vj4uHUgX1lZUW9vr5F1\nYFBKCWV5YWFBxWLRPHIULnvEr1mUqO+9hwHwJRHeiDYaAe4/6wMoz9O4Jdn/vNY7ht5BlGQkDhQ2\nChlygTdO/sfnMjGePpeEQ8jeTKfThqxAQEIvkSfiO3t2JfcLx8A7fUSBIBfkqGKM1gwZ1rB3Lo4d\nO6aJiYm6mkHGgbPojwEChcBQ8Xc+n9f8/LzGx8d1/PhxXb58uS7vjB7xsLJ3kLazD/eVgeImQ4bI\nZrOSEmu+vLys8+fPq7u7W0ePHlVfX18dltvZ2WkLgiOU8bx9Ue3q6qq1dmFjklBk07BxPdMFT4ok\nKTmS+fl5UxZ4mD70Z/FD/2UcHI1BwhVPCg/TU20hZzAPnnpbLpetRohODp4CvL6+roWFBfPIfJ6H\nDYyhamlpMY8Kwoe0lQ/CWFCDxrHxeGB4arQpmp+fN4cAWITPRbFdvXpVhUJBw8PD+uY3v2m9FX3O\nzdeM7FQa8zh3k/jyhoMHD6qtrU2PPPKItaqZnZ1Vb2+vMfYgpkiyNTQ3N1fXpYP1yG/m3V+TyB2n\nxrPtMFysX0l1+RcfBRF94eQ0wraerNDS0mKdz32nCNYYa8pHdpIMwmxkCOIoEa0wJ6xbDBGf53Nd\nGCLmCKNGBANLmLXf1dVlY+S7oCM8ZIihIy/ma/3oi8j3xSAfOnTIUIWpqSkVCgWri4Imji4cHR21\nk6pbW5PC+LNnz+p7vud77Fw95gUEB5agJ6btaM3u6N17TBrzDUQsm5ubunjxokIIOn78uHK5nG1S\nDAynhXI6aAjBNjFQCIlKEsRcM5VK2fth4/niWKIONls+nzeWmW/jz+fh1QGl+BvNc0B/0M3ZuPyg\nKNhALGJJ5mF5Gjp5NsbhC+/wphAfMVFLQQTU1dWlEIItWDYa3lU6nTbMnAgHJ8Bv6o2NDTuaXpLN\nX29vrykWzwA7ePCgjh49aq2biKSIcPFAd0vupkjKe7Ak3fHGWQfUE6EwUbh4whgnf+Aj0Tsevvf2\nJVlUggHo6uqye8Z9lmRRh49UWJdAXcBrQIM8j2Hz0Q7r3dcBesq2z/807hMeY5/h4LW0tNje8sbD\nRz2M1eecPCzJ8+wLDJPvjuJbGfn0AP8fOHCg7iwtzwD0ERf3zLM20VOkCI4ePapCoaAYo44fP66B\ngQHrvgFS09fXZ7k5mJB0L6fe05/kDcyHAWvcJ9t17PZNBAWWKyU3a3h42Ly+SqWixcVF3XPPPcpm\ns+rq6tLg4KAtYBY1feCIiFpaktYuHC3e3d1d54WzEPC4yE0BNZALkmQeWkdHhx38JckYS77PmE8M\nYwC58ZAbSATDSmSD8/fy8rIZLqImT1bA6KTT6briYOaSx9ksRGucmMrncAghRbwYTww610qlUsrn\n85aEZqwoSxQKHit1LoODg5qamlIulzOmWKVSUaFQsK7tHFHd1dWltbU1jYyMWG0OjWl3K+LxCfO7\nTRgzcDZFn9THAHV3dXXVMVorlYo1EIXE4NtXNXYm8ew44D3+91ELitfnmphfT/v26xYjg7HyiXlp\nyyhgAPx+9WQKonAiJsbvoSlpq51RIxmD10oyOJ3vgGMKSYpxE5VhxD3E6I0ZusIbTPYD//tUBgYc\nRwMIHuYsaQYfGZLbJe+2vLxsMC8ID9+nr6/PHM7z589bP1McFFrF+T6eoC2eCXkzsm8MlE/+LSws\nKJvNanMzaWny3HPPaXNzU0NDQ+rp6VEulzOygCS7CXRrlmQKF48NqrpPehK1AImxmUJIDk+DkeaN\nZ6lUMrgNBe1JGIybiILiPKIT2E1EbPROW19ft9Ca486pjcBjpWcZRuHAgQPGzPPFjB7vp4iXeQDa\nIU8G44pNDQZNvgwlsri4aOMAH2cBo2CoaPf5qFQqpXvvvdcYR0Sn/O296o6ODh09etSUkmds7VbE\nc7caJg974sygtNra2jQ9PW3eOUaIyAIjI21RsT0BB0VGlAOkxTr1Ct8n9ImQUJqNChiDAIzkkQbW\nhn8O6Ix16x09acvYeJQCI4ER8pEa8+WZuF4aSRo+V+QNnq/BQg/4SI8xstd9hOkNMK/FEcawcR+k\nrb6Tm5ub1i0GxxLji1PBfS4UCnYeXl9fn9LptF5++WVzUqGxP/vss1Y288orr6hQKNi9Y94xxMC5\nGMCblX0F8XnMmgWCwXjggQeMkYQClBJDRE0NRaC+5ojqemAPYAWUPDeGaAUjA0kDaGppack2HSG/\nb8tCOM7plCh5vE8iE9/V20MVYM9sAp73RwLwfp6nBokFjnFgLmEOMUafZAc6Y37xGvGceFyqNxTL\ny8vG/PMkFJ/UjjFpAMpZReTzmCOUiDfwU1NTxmD0OTXu124Zlp16hLdbUEje6+bn2LFjlnecmJiw\n6NjDgUStQHOZTEbd3d11a4u5BfoGquIe8Rj7g/XoHRtPTPCGiLEj7LdGmI7P417znL/3jTkoFKpH\nQRgP18Wg+ejO56JwhoiIJNVFlz6axMjx/f1rPDECA+Mhez9HrEGfo+Y7svbRLV5PkIOenZ21E6+Z\nu87OThUKBS0uLiqTyejYsWPa3Ny0rueTk5MaHBy073H58mUjPc3Ozqq/v/81hBRSDX4tblfurt32\nHcTj1yyey5cva3h4WPfcc48ymYx1TUAxg+dyQzE21Av4DYViJC+FAmUBHzx4UP39/QYtsrCq1a2j\npqWt5pa0CPGYt2cLAcNIW7BbOp1Wb2+vnVDb29urAwcOaHh4WCMjI3U5Kb9RiHr4fEk2Rx5WwNDA\nwPMYuocwSqWSLXafcPatoLxyJOpJpVLWogj4EgHvx0h7ZYnB8l4nERdGDGiDxK8ka7tyPQ/4ZmQ3\niBa3W3xiHeUNmYVcKz3ViJyo8YGkgjOCgoQRSmds1jLOTiPs7JWztGUsfFGvN0r+3vv8FOtTqt/v\nPtfiITUUOIIjxH3EOPG3j7b9mBqNDJ/FGvdCFFOpVCyK4bXsJZ9f4lqeqcfzvH51ddXgPD9uHGUM\nKE4lqAcRDKcIo0NoLE3OtqWlRZlMxppCZzIZHTp0SGtrayqXy0boWF5erusQD6GMfLHPO++GU7iv\nDBQbKZ/Pa3Fx0W76ww8/rI6ODoMbPKSAd8dxx9euXdPi4qJCCKbsUYxsOuAtX2BHBT7/s6Ebw3+/\n6KTXdlaQtjwjz+DD08I7ZSPi2VLZ7Y9A9787OzuNxOF/s9Cg2PMdiGzwhIEzmCf6qhGZSVuHx0Hf\n7+7ulrR1SBvfhTwTUR8MpsachsfrUTh09qARrfcgn3rqKXV0dCiXyxlUWiwW6/IGuyF3E8zXqEC5\nB7SqwUnz3QNWV1fV3t5ujXyBAz2EjUPmC7ZhY0pbRATWl1Tf/VvaOjre53C4l74TiFfKfK7fN1zD\nw9Pc70Yat7TlmPE5OGSN88T1+Z+fxs9mjLzWG8tGR9HnlXie+WLPM0byxJ7MxLUgLfm8LHpN2moe\nTRqDhssQq0AwqtXkFIRisWhUc3Li3d3dOn78uEqlklZXV1UoFDQ+Pq4Qkq44RFEYYoweDsNu5KD2\nnYGSpL6+Pk1OTiqdTquvr8+ME942cF1vb29drgkIjAXRGMJ7iA98Fu+RxUxUgIffyByqVqvGbuIz\nYQuiAHx+CuNCjdHw8LBt3sXFRSuCxRBR0CttdXYm6bm8vKz29naDGzc3k64RLFy+D8oIIyJtJVqB\nP1mMRKOcist3pQGsdwqYezB07gmdyZeWlrSysqKlpSUtLS2Z9yltRULAk4wJYwqFN5VKqb+/3yBK\nCnZ9EeFOZbfyWSGE/xxCOB1CeD6E8LkQQnsI4XAI4RshhHMhhP8ZQkjXXnug9v+52vP33Oh1PF0Z\nJ4SkN/M5NDRkjE0id/KqeOfMIcoH5ZROp43O7Rlr3vECFkfR+qiCvK43Aj4fKqnOKKHY+V4+QvO5\nH35Q+h6mY4z+cT9PkuqMHnvXG1gfbTUiDH4O/Jw0GrFGI8eceSPHd2Qvcw3KS1ZWVurGyx4jWiLN\nwF6uVCp2LAe5KW9kq9WqNaLOZrMaHx9Xe3u7pqenjRTR0tJiaQzg9PHxcVsb1DLi5BClE3nfqOwr\nA8UGYtNxcmxXV5d6enpsE7W2ttrxxT5shnWDMfBwDsoQb0+SHbXBwvOdHYDHyAlB30WpE5l4mA1P\nlptIexKUColMoBESo9RlgQnznfxm5rrAa2wmIiv637HpgC8lWdSIIfB92CCi+GQpBYnUoVF17tsP\nMdc4A/770+VdkjECvfIi8mOzQoFtaWnR7OysGSaUDhtxN8SPY4efMyLpw5LeHGN8QEkT5fdI+g1J\nn4wx3itpQdL7a295v6SF2uOfrL1uW2P2eVkYjn7d4CCR5Pb0Z5wdjAw/3mP2ytWz7XwE5YkR7Cn/\nWn43wn4+KvJGjD3mCRHeEDQaHpzCxs9Duft7i8HFOHjiQmMuGljPfwbRKU5UI3QJouIhPZ9f8pAn\nc+Ih0kqlYs4e6YTGgn3GRCkNr8XJbIzGcIopLp6amtKRI0eUz+dNj5EH58Twjo4O/eIv/qL+5E/+\nRI899pgkaXZ2to7YVCwWbZ1sZy/uKwPFDSEiKJVK6urqMtgHGq0k5XI5U/zcEPI0fnMcPHjQIh4I\nCyyGQqFgmDhwU7lclpQsbnr8kbNiE9Flvb29vW5ceJMsUl8zwjiBDLgmitwTP3xYz3xkMhlj8RHh\nQFqgbRKR4OZmck4UPbt8Zww2COMEGmSDrays2ObiszFQ/PBd/Mm7GMNyuWy5PyI/KP+MC7ycjY/H\nuLGxocnJSXNOiILJJ+6WeAW4Q0lJOhhCSEnqkHRF0r+Q9Oe15/+HpH9T+/vdtf9Ve/4d4QasZKPC\n5C3kDlgfHrqbnZ2tS7SjfFtaWuoiYg9b+RyLj1D8e6V60obfr7zeG7Trsdka4Tm+G3uZfejJQcyD\n9NpaSQ/BS/U1U8wBipvxsn89EcOP0xtq5sQblsbIzuefSBFcD7JkbnBAfUqAqHRlZcUiJa5BtImT\nS/TFeMhxMXbqCFdWVqy338MPP2wdZYaGhpROp7WwsKD19XV9+MMfVltbmz7xiU9obm5OuVxOZ86c\n0UMPPSQp2dMU0FcqyUkDNyr70kBxxszExESdgcBg9Pb21i1cogAWpadmE3EAi5CHSaWSnnh4Y21t\nbZa0J0IhjA4hmEfSmFfiesBdQCyZTEY9PT222fB4Cc993RD1LLDYlpeXLXIiQqGQD0Pc2NuLTY6B\nRhkRWTG3sIvwhiCAsPG8p1mtVq3VEcWBRGd4lxgyPFvuFwYQ7xhlgAIERweDRwnQBcF7j7sIyV33\n75uRGOOEpN+SdEmJYSpJekpSMcYIHnlZ0kjt7xFJr9beu1l7fe8NXqsud0IURP813/JK2kIgfDKf\nCFySIQCeNOGvhaEBbvKPo7y9YfBQm8/FetjLK2TvELLm8NQZpzcoPlriOtJWjqhxH3hYj789FAeL\nlX3ux4vgdPF5rG/G5OfNG0rWO1R45gRHi72Isvd5NYy3h1GJkvlZX1+3LhM42jiU6+vrxuyjew7o\nyszMjB544AHTC+zf+fl5jY2NaWNjQ8ViUT/7sz+rL3/5y0qlUlpYWNDQ0JA6Ojqs9If6zBuVfWOg\nfChP5ECylxtCBMTZMSjQ5eVlgzTwNohOgLbYOCy6XC5X18mcQlcKHvFcvDcD4cJvbvIybW1tdiaV\nJPX29tqiJhrESGaz2TrWFMa0o6PDGDi8plAomNKHdeNDdTw3NjYkEyI8aYs2C6TmcwbQ4RkH38sn\ntlHmqVRKPT09ViBM/Rj3BQixkQHEHKIwgBi8wcewj46OGkaPotqlaKdOdvqZIYS8kqjosJLDPTsl\nvWun4wohfCCE8K0Qwrd4zBsKlHUIQdls1hifKLoLFy7Y/KEYG9/H/UdBYvxQeBz3cL1I0xs+xiRt\n5VgwDI2RA5H2G73OX8v3oeNvcqvUe/F44/iuM6d1OSYvPi/n38trmXugSPY/z3tixvWEKNcbLIwT\n95bvFUJy0CRRnoc+uc8Yd5Ab6qTS6bR1tM9msxZh4wS3t7drfn5eDz30kIrFokWRpVJJn/rUp/Sx\nj31MMSYnG8zMzOj7v//76zpiDA4OGlGqUCi84Xeuux83/Mo9Ltw8ICGUImeWsCmGh4fryA54RIS2\nMUaDoaBm8r/33n3EMD09XUcLJXwm2QhM2OghkrROpbZqnFg8165dUyaT0ezsrObm5iTJorClpSXL\n82Bk8M7IN/H9iKxSqZQd+IfxwnPyHaSBG1H+RCMQFHyhJRRhD2Ow6TB8ECuYG4/LS7JWSdJWRT4M\nQXJNGDIgPpSK9+ApGCYilVTHttyD8i8lXYgxzsQYNyT9haQfkJSrQX6SNKrkBGrVfo9JUu35bklz\njR8aY/z9GOObY4xvrr22zuniHkOUGR8ft3n3h1l6uA3DxJpnnflcDk6Ez6sgjAFngt+8FkPl15KH\nCaWtvJQ/gqbRgOGoMI7G3BN/Ax3jpHoozEOOrCv/PHsCCJHnPQzPa/jNd/J5N17vr8PrMOw42ZVK\nxaBvol9o5+xN9gKoDGhQa2urOQygFxhIIs6WlhaVy2WLOoHg+L4tLVuF/EeOHNF9991n+a+xsTFJ\nso74IQS9+93vVrlctntF6mN4eFiSttWfb98YKL84PSMN754NSq7FbywKFNkYbAQ8Hp+wzOVydnOB\nBoiySA5j2MDruTbXYEOyUDw7jegqnU5renq6ztuim4WkuqPeyRt5XBxDC2yGYfX4N8eil8tlS2h2\ndXWpvb3d4EXCfaIRj3H7CIh58sZ+fX1di4uLam9vt8QoyqWrq6uOek5tGFFpW1ubdfXAGAKF+tNQ\nM5mMbbTh4WFVKklbK99mabfEf9YuwIaXJH1fCKGjlkt6h5KTqL8q6d/WXvM+SX9V+/uLtf9Ve/7/\nxRv4co25FiIjYFQMeG9vrzUFBvZB+aIsWb8oQ/YFStMLyspHHh5i9JGPVE8H9wrd5ze9ofR5GvYE\nDiD1dqw1T+LwKIg32t6gNkJ7Hj6TtuC0xoiJeWPcfD+MAGPBmZbqEQYcL3QC65j5hcREXRL3FaMk\nyfLIOL3kkT1EzzWIyiA+0eGmpaXFCnkhUqETi8WiDh8+bBEWxgyyxejoaB3ZZXl52aKWSue5AAAN\nXUlEQVS5ycnJ77RcXyP7qtURC8B7zDFGy8kMDQ3VbZZSqWQRB943EQRsMxKFm5ub1ouPzcOiwYh4\nmBFDxGKH3dbd3W1NUg8cOGDHknujt76+Xsd6YRHhIcUYre8cXi9GgM2HoSyXy7ah/LHvxWKxLuIh\nqllYWLD6Je89kaNCUD6tra12bD3KyrORUqmUdV5nPjB6QEUexsCpYH5I6GLE2HAYKxQUjWH5v1gs\nWkEikOBOxRulnRq+GOM3Qgh/LukfJW1KelrS70v6P5I+H0L4RO0xTq/+Q0l/HEI4J2leCePvO8lS\ntVp9cXl52ej209PTevnllyVJjz/++I6+ww6kIGn2Tl38DaQ5rhuXnY7p0I28aN8YKKk+GYy3gWHI\n5XLm8ZGY9xg4YTIGBe8Sxc3xEMB3RA7eK8IILC0tWaEocAhKlXOQVlZWDIqkoS3X9l4LynltbU09\nPT11JAkMFcdidHd3a21tra6LA01e/VHeQJkoLbxFWjrRpRjYkPcRcXoque84QA4O6isGhjopD++1\nt7ero6PDDncEgiXH5Ws2fGTr6fqS7DcnBVNwyHfDA97N9bVbEmP8qKSPNjx8XtJbrvPaNUn/bpuX\neBGoby9JCOFbzXHduOzFcd2uMe0bA+VpnkBEhKXt7e0WFZA3aYwqaEXUiKHj9WPMstmsZmdnjSEH\nJk+EUa0mFfbLy8vWBsZDJpLqDlOkczQwHSy7RrgCBR5jNMMhJbVYxWKxrg9fqVQyQwBFm7H6/Bhd\nHIgyPFZPzzwiIoglHiZgbMyptFVLQS0TMARwEWPkfZAsMpnMaxhH0lbymYgMI8H8VyoVO3wRh4IG\nuL7T826IN04eUm5KU5pya2Tf5KCk+spuopu1tTXLb0iJYvGtjMj7oOx8MhdoCwXYSKv1CWOUKlXW\n/iwaYEGiIuif7e3tdVi2fy3jgNUHXCVtwYew9jC2jBfDxNk1vI/oBOXaCLHxPyfTUgVOHqDxlGHm\n0+Pr5JVI5HIPwKiXlpaMfo8xhzXoE8fkCjBm/jwtamvIPdE7kagrnU6b8ZO0axFUU5rSlNsr+8pA\n4V3D8UcxAw1Rv0CEAsulserdKzQOcstkMnWthYhmMHAoV3/UMwoUeNBX7nPwHjDk4uKiHXPOd+Hz\nMW5eCfuTTH1k5mFMWot45h31Qxg3H/34+ipJVtuFYYMqT7SHtLa22lyTaKXwj+/s55jaJe4NBrqR\nOcXzRIwYKHJ0/vgNKTFM5XLZ6tSI9r6LDdTv3+kBvI40x7U92Yvjui1j2jcQn6elQniAEVQoFMww\nccAfChfDBbXaR2GQBIDbqDGiEJg6K2Awjpr3DVtXVlasFRERE0W3GLalpaW6WhTyYSEEi0iAwjo6\nOlQoFOpo9NDYvSLnbKsQknoXIqFyuWyRHGOXtuo5MJDkjzB+UOCJcHxejYgIBlNXV5cVOBNxQkDh\nPmG4vJEmT+XZZr6gmWgRQ0XUt7y8bMxJ4EWixN2C4RrhvbtBYox7UbE1x7VN2Yvjul1j2ncGikgG\nZQYdE/qsNwIeAiLxTwQAVIeSRjlXKhUzOEQG1O3AsCMCAIqD7Qcbr1KpWO4JuIpWSBg3T1llbNVq\nVWNjY3YN39WBpo8w1mg5RHshohZ6B2azWV27dk2lUslam8DsAx4FiiQSQ+F3dXVZxOepv0Cj5NiI\n2phPKLO0lvL1LhgWmpT6s7KWlpbU1tZW5wg0HldPwTXGy1fe71YOCtltskRTmtKU68u+gfhQZp4J\n52neHA3ua3Xw0FFq/MYgQT9vaWnRwsKCGRDqgTB+dKJAUWOMvLEC9kOhUkznj3KmahvYCvgO5Z/L\n5SQlxqdUKpmxw6g1tkLCiFGLQDRULpfreqRhRIANaZ0EZFipVKw+SZLNbT6fN4NDFEqzWuBND7ny\n/RgbdVLMJcxJSdZ1A7iReSJXhdGWZPeKaJN82OLiojY3N3elULexBmovEyRCCO8KIbwYks7nH7nN\n1x4LIXw1hPBCSDq1/6fa478aQpgIITxT+3nUveeXamN9MYTwo7dwbBdDCM/Vrv+t2mM9IYTHQggv\n1X7na4+HEMJ/rY3r2RDCqVs0pvvcnDwTQiiHEH7+TsxXCOEzIYSrIYTn3WPbnp8Qwvtqr38phPC+\n613rRmXfGSiUP8wwaNZ44HjWGAtJFkkBCQL1ocQpNvX1TxguX8SL4u3o6JAkIyn4aniM0eLiosrl\nct3x5hgGH5X4z4SZl0qljDZPx4tG+M0fhsh7iOSohaKtCdElRoJaMNiFfl7Jg9G5nKiJ91DfRARL\nXo6cIJCqL9yFfo5Bh71IBORbGvEeos/l5WWtrq5qZWXFqP7UfnGY4m7moDBOezWCCiG0SvpdST8m\n6X5JPxlCuP82DmFT0i/EGO+X9H2SPuiu/8kY48naz5dq471fSU3XCSWtnv5b7TvcKvnntetDkf6I\npMdjjMckPV77X0rm71jt5wOSPn0rBhNjfJE5kfTPJK1I+sva07d7vj6r17bb2tb8hBB6lJROvFVJ\nucRHMWo3I/vGQKFQQ612CGVHg9KlpSUjGEBRDrUcD9EFeSm8//b29rpOFHRG9/RqujOEGgHBN5pt\nbU3ORSFp76njbW1txjaDIk5+qjH6wjD6nnQYAqIWokU6MJdKJatRQrlns1lVq1U7soN2KdQV0akC\nwwr9HoOzsrJiUQvRWC6Xs7olxsz1ID3QXsW3o2HcEFY4bLG1tVXd3d1W3c91yEExZ0CAs7OzNmZy\nbXQ0B97bDWOyVw3SdeQtks7FGM/HGNclfV5Jz7/bIjHGKzHGf6z9vSjpn7TV8PZ68m5Jn48xXosx\nXpB0TtepA7uF4rvEN3aP/6OYyD8oaUE1dIvH8g5JL8cYX3mD19yy+Yoxfk1JEXjj9bYzPz8q6bEY\n43yMcUHSY9pBj8m9noNakvTijbxwbW1NExMTmpiY0FNPPXWLh7UvK7tvheybMXl2pft9Q9Xwt1ms\n63lNLivxZm+7hORQxUckfUNJn8EPhRB+StK3lERZC0rG+w/ubb6D+25LlPSVEEKU9Hu1RP9AjPFK\n7fkpSQO1v683jyNKOs/fKnmPpM+5/+/0fEnbn5/Xe/ymZK8bqGYl/A1Kc0w3JntxTPtRQghdkv6X\npJ+PMZZDCJ+W9HElRuLjkn5b0s/c5mH9YIxxIoTQL+mxEMIZ/2SMMdaM122XkJyc/K8l/VLtob0w\nX3VyJ+Zn30B8TWlKUyS5ruc18R3Rb4uEENqUGKc/jTH+hSTFGKdjjJUYY1XSH2gLlrpt443JGVyK\nMV5Vkud5i6RpoLva76u3e1w1+TFJ/xhjnK6N8Y7PV022Oz+7Or6mgWpKU/aXPCnpWAjhcM0rf4+S\nTui3RUKSrPtDSf8UY/wd97jP3/y4JJhiX5T0nhDCgRDCYSVJ92/egnF1hhAy/C3pnbUx+C7xjd3j\nf6rGVvs+SSUHdd0K+Uk5eO9Oz5eT7c7PX0t6ZwghXyNHvLP22E3JXof49lyBWk324riaY7ox2Ytj\n2jWJMW6GED6kRCm0SvpMjPH0bRzCD0j695KeCyE8U3vsl5WwCU8qgawuSvq52nhPhxC+oOSokU1J\nH4wx3orWHwOS/rJGdklJ+rMY45dDCE9K+kII4f2SXpH0E7XXf0nSo0pICCuSfvoWjEmSGcwfUW1O\navKbt3u+Qgifk/TDkgohhMtK2Hi/rm3MT4xxPoTwcSWOkiR9LMbYSLy48THt5XqOpjSlKU1pynev\nNCG+pjSlKU1pyp6UPWugwp2thr/jFedhD1Z1v86Ytl3xvpv3Nrx+54I7OldNaUpTdkEomtxLP0qw\n85clHZGUlvRtSfffxutflFRoeOw3JX2k9vdHJP1G7e9HJf1fSUFJ5fw3dmkMPyTplKTnb3YMknqU\nHIDXIylf+zu/y2P6VUn/5Tqvvb923w5IOly7n627fW8lDUk6Vfs7I+ls7dp3dK6aP82f5s/Of/Zq\nBHVHq+FfR25rxXncg1XdrzOm15PXq3jf1XsbX79zwR2dq6Y0pSk7l71qoHa1GvkmhIrzp0IIH6g9\ntt2K6lshd7Sq+w3kQzW47DNhq+/WbR9TqO9csFfnqilNacoNyl41UHdafjDGeEpJ8dwHQwg/5J+M\nMUYlRuyOyV4YQ00+LemopJNK2sD89p0YRGjoXOCf20Nz1ZSmNGUbslcN1B2tho97t+L8jlZ1X0/i\n9ived31M4TqdC7QH56opTWnK9mSvGqg7Vg0f9nbF+R2t6r6ehO1XvO/qvQ3h+p0LtAfnqilNaco2\n5U6zNF7vRwnb6qwSxtev3MbrHlHCLPu2pNNcW1KvkvNQXpL0N5J6ao8HJefvvCzpOUlv3qVxfE4J\nZLahJB/y/psZg5IGk+dqPz99C8b0x7VrPqtE+Q+51/9KbUwvSvqxW3FvJf2gEvjuWUnP1H4evdNz\n1fxp/jR/dv7T7CTRlKY0pSlN2ZOyVyG+pjSlKU1pyne5NA1UU5rSlKY0ZU9K00A1pSlNaUpT9qQ0\nDVRTmtKUpjRlT0rTQDWlKU1pSlP2pDQNVFOa0pSmNGVPStNANaUpTWlKU/akNA1UU5rSlKY0ZU/K\n/weO7hbt4lk7IAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyJPCXpKnW7n",
        "colab_type": "text"
      },
      "source": [
        "As you can see from above two images, image resolution for this dataset is not uniform. However, we will resize these images later so we don't need to worry about it now. If we look closer to both images, Normal X-Ray image have higher contrast and clear view in the center. Pneumonia image have lower contrast and more blurry view in the center. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyOgPVsl4p0b",
        "colab_type": "text"
      },
      "source": [
        "### Simple Exploratory Data Analysis\n",
        "\n",
        "We will explore our dataset briefly and check how many images we have for each class. \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukA47GUtBOc5",
        "colab_type": "code",
        "outputId": "a1405fe2-4862-4c93-ea23-368f2fcb07f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "train_label = train_df['label'].value_counts()\n",
        "print(train_label.sort_index(ascending = True))\n",
        "\n",
        "print('Normal: ',round((train_label[1]/train_label.sum())*100,2),'%')\n",
        "print('Pneumonia: ',round((train_label[0]/train_label.sum())*100,2),'%')\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.countplot(x='label', data=train_df, palette = 'Set2')\n",
        "plt.xlabel('Class Type', fontsize=12)\n",
        "plt.title('Train Dataset', fontsize=12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal       1349\n",
            "Pneumonia    3883\n",
            "Name: label, dtype: int64\n",
            "Normal:  25.78 %\n",
            "Pneumonia:  74.22 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4AfiFqjoE+AfgdW3M1t5/9wGew+A99F2buf5dwPOr6mnAb29yrjRh/FO3NEZV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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcdLFHQMHPrX",
        "colab_type": "code",
        "outputId": "cb81bb4c-024c-446c-9cf6-bf77b5b0b79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        }
      },
      "source": [
        "test_label = test_df['label'].value_counts()\n",
        "print(test_label.sort_index(ascending = True))\n",
        "\n",
        "print('Normal: ',round((test_label[1]/test_label.sum())*100,2),'%')\n",
        "print('Pneumonia: ',round((test_label[0]/test_label.sum())*100,2),'%')\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.countplot(x='label', data=test_df, palette = 'Set2')\n",
        "plt.xlabel('Class Type', fontsize=12)\n",
        "plt.title('Test Dataset', fontsize=12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal       234\n",
            "Pneumonia    390\n",
            "Name: label, dtype: int64\n",
            "Normal:  37.5 %\n",
            "Pneumonia:  62.5 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S73N9QZJ7LbIQAABY7ZY6c31gkouq6lNJbtja2N1PWkhVAACwCi01XL9ykUUAAMDtwVLv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\n/w8vtNDPhw5JgQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmhoAUXcoOyX",
        "colab_type": "text"
      },
      "source": [
        "Both training and testing dataset are imbalance. An immbanalced dataset may increase the bias of our model. Also, it will influence the accuracy score as well. For example, our training dataset contains about 75% of Pneumonia images, and our test dataset contains about 63% of Pneumonia images, then our training accuracy and validation accuracy will return 75% and 63% even our model just classify all images as Pneumonia cases. Therefore, I will add an class weight to our model. In addition, I will evaluate the precision/recall scores, confusion matrix, and ROC curve of the model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoVxrCeDbK_K",
        "colab_type": "text"
      },
      "source": [
        "### Class weight"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mj4NJcYbKff",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create class weight for our model\n",
        "from sklearn.utils import class_weight\n",
        "\n",
        "class_weights = class_weight.compute_class_weight('balanced',\n",
        "                                                 np.unique(train_df['label']),\n",
        "                                                 train_df['label'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTR8ueeBbfV3",
        "colab_type": "text"
      },
      "source": [
        "## Data Augmentation and Pre-processing\n",
        "Since we already have the directories of train and test datasets, and processing the images from a dataframe to Keras require intensive computing power, I decided to input the datasets from directories in batches. Data Augmentation will also happened at the same time for the training dataset. Data Augmentation is helpful here because we have a imbalance dataset, and sometimes X-Ray images may not always perfectly line up in all cases. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NekgUbvHM5ZV",
        "colab_type": "code",
        "outputId": "86d79215-e314-441b-9105-f5ed0f772c42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Define data generator parameter and data augmentation happened here\n",
        "train_datagen=ImageDataGenerator(horizontal_flip = True,\n",
        "                                 rotation_range = 0.1,\n",
        "                                 width_shift_range = 0.1,\n",
        "                                 zoom_range = 0.1)\n",
        "\n",
        "# Create regular training data\n",
        "# Also shuffle the training dataset here so we don't have all Normal class\n",
        "# at the beginning and then Pneumonia class\n",
        "train_data = train_datagen.flow_from_directory(train_dir,\n",
        "                                               classes = ['NORMAL','PNEUMONIA'],\n",
        "                                               class_mode=\"categorical\",\n",
        "                                               target_size=(256,256),\n",
        "#                                               color_mode = 'rgb',\n",
        "                                               shuffle=True,\n",
        "                                               seed=4)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5232 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgWtaFFuSYjJ",
        "colab_type": "code",
        "outputId": "1cb7d05f-97a1-4166-ce34-991c847ecf86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Create testing data\n",
        "test_datagen=ImageDataGenerator()\n",
        "test_data = test_datagen.flow_from_directory(test_dir,\n",
        "                                             classes = ['NORMAL','PNEUMONIA'],\n",
        "                                             class_mode=\"categorical\",\n",
        "                                             target_size=(256,256),\n",
        "                                             shuffle=False)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTSpbcrL0sD-",
        "colab_type": "text"
      },
      "source": [
        "### Check the first batch of train image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3t55tlfWisIA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retrieve one bacth of data\n",
        "x,y = train_data.next()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRR-iHt_0rkT",
        "colab_type": "code",
        "outputId": "6e12dd0c-ae75-4b5a-e3e2-e6e8551e60dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Check if our data augmentation is working or not\n",
        "plt.figure(figsize=(16,60))\n",
        "\n",
        "for i in range(0,7):\n",
        "    plt.subplot(1, 7, i+1)\n",
        "    plt.imshow(x[i].astype(int))\n",
        "    plt.tight_layout()\n",
        "    "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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33INmqt/+9rejHPZZI9/X0iyo5pkjELIDHoU3HWjwNXVe5prb29tqNBoZINDB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\nN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5Ita8XRxBaSPAKUcseYvYXTCKhOAgfQlv00Ho+1sbER5bE0qeRUTGw4epj79QbL0gwc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\n32g8HmttbS2OUQfMkKQ//uM/1ng81gcffKBKpaKNjY0I2ePoM7w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XLWXsvV5Pn332me7d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\n",
            "text/plain": [
              "<Figure size 1152x4320 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1DDsMEHdeur",
        "colab_type": "text"
      },
      "source": [
        "## Build the Model\n",
        "\n",
        "One model will be built here, but this model will fit into two different training dataset. The original dataset will be added a class weight. The balanced dataset will not add class weight since two classes are balaned already. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3f8n61Pdg5k",
        "colab_type": "code",
        "outputId": "3125bb8a-9566-43f7-beef-b8c00430afe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "# Start Building the model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32,(3,3),activation='relu',padding='same',\n",
        "                 input_shape = train_data.image_shape))\n",
        "model.add(Conv2D(32,(3,3),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(64,(3,3),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(128,(3,3),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(2, activation='softmax'))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0715 18:06:23.190103 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0715 18:06:23.264141 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "W0715 18:06:23.493140 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0715 18:06:23.612666 139974641186688 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOXWjQHJeBCa",
        "colab_type": "code",
        "outputId": "7f12fce3-805b-462e-eca3-d3108c5a59b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 256, 256, 32)      896       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 256, 256, 32)      9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 256, 256, 32)      128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 128, 128, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 128, 128, 64)      18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 128, 128, 64)      256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 64, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 64, 64, 128)       73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 64, 64, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 131072)            0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                8388672   \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 2)                 130       \n",
            "=================================================================\n",
            "Total params: 8,492,194\n",
            "Trainable params: 8,491,746\n",
            "Non-trainable params: 448\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzUXkuu7eBvc",
        "colab_type": "code",
        "outputId": "22a46dab-40df-4cd5-d3b9-e2d128c1958f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# Compile our model\n",
        "model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.Adam(lr=0.0001),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0715 18:06:23.873677 139974641186688 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7KWRlUnNRZj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set bacth size\n",
        "batch_size = 64\n",
        "# Define steps per epoch\n",
        "steps_per_epoch = np.ceil(len(train_df) / batch_size)\n",
        "\n",
        "# Define checkpoint, learning rate reduce criterias\n",
        "# Monitor validation loss here because I don't want my model to overfit too much\n",
        "from keras.callbacks import ReduceLROnPlateau , ModelCheckpoint, EarlyStopping\n",
        "\n",
        "lr_reduce = ReduceLROnPlateau(monitor='val_loss',\n",
        "                              factor=0.1,\n",
        "                              epsilon=0.001,\n",
        "                              patience=1,\n",
        "                              verbose=1)\n",
        "\n",
        "filepath=\"checkpoint.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath,\n",
        "                             monitor='val_loss',\n",
        "                             save_best_only=True,\n",
        "                             mode='min',\n",
        "                             verbose=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUDSmcRoa8zx",
        "colab_type": "code",
        "outputId": "9ee1bb43-2050-4dad-e73e-7511ca2176fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fit model with original dataset with class weight\n",
        "\n",
        "history = model.fit_generator(epochs=18,\n",
        "                              validation_data=test_data,\n",
        "                              generator=train_data,\n",
        "                              validation_steps=20,\n",
        "                              steps_per_epoch=20,\n",
        "                              class_weight=class_weights,\n",
        "                              callbacks=[checkpoint],\n",
        "                              shuffle=False,\n",
        "                              verbose=2)\n",
        "\n",
        "# After tuning the parameters manually, I found out that steps per epoch\n",
        "# and validation steps = 20 return better model performance. \n",
        "# However, Keras documents recommend that steps = sample size / batch size. \n",
        "# Please use based on your own model performance. "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0715 18:06:24.046786 139974641186688 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/18\n",
            " - 27s - loss: 1.3719 - acc: 0.7953 - val_loss: 1.4294 - val_acc: 0.7853\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.42942, saving model to checkpoint.hdf5\n",
            "Epoch 2/18\n",
            " - 21s - loss: 0.4921 - acc: 0.8875 - val_loss: 0.6035 - val_acc: 0.8494\n",
            "\n",
            "Epoch 00002: val_loss improved from 1.42942 to 0.60354, saving model to checkpoint.hdf5\n",
            "Epoch 3/18\n",
            " - 21s - loss: 0.2941 - acc: 0.8687 - val_loss: 0.6096 - val_acc: 0.8253\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.60354\n",
            "Epoch 4/18\n",
            " - 22s - loss: 0.3277 - acc: 0.8828 - val_loss: 0.4900 - val_acc: 0.8494\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.60354 to 0.48999, saving model to checkpoint.hdf5\n",
            "Epoch 5/18\n",
            " - 22s - loss: 0.2722 - acc: 0.9016 - val_loss: 0.7229 - val_acc: 0.7853\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.48999\n",
            "Epoch 6/18\n",
            " - 22s - loss: 0.3141 - acc: 0.8719 - val_loss: 0.4974 - val_acc: 0.8574\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.48999\n",
            "Epoch 7/18\n",
            " - 22s - loss: 0.2911 - acc: 0.8828 - val_loss: 0.8968 - val_acc: 0.7516\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.48999\n",
            "Epoch 8/18\n",
            " - 21s - loss: 0.2487 - acc: 0.8922 - val_loss: 0.8638 - val_acc: 0.7404\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.48999\n",
            "Epoch 9/18\n",
            " - 21s - loss: 0.2340 - acc: 0.8845 - val_loss: 0.7859 - val_acc: 0.7837\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.48999\n",
            "Epoch 10/18\n",
            " - 22s - loss: 0.2713 - acc: 0.8984 - val_loss: 0.6668 - val_acc: 0.7708\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.48999\n",
            "Epoch 11/18\n",
            " - 21s - loss: 0.2172 - acc: 0.9172 - val_loss: 0.3930 - val_acc: 0.8686\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.48999 to 0.39299, saving model to checkpoint.hdf5\n",
            "Epoch 12/18\n",
            " - 21s - loss: 0.1909 - acc: 0.9312 - val_loss: 0.7127 - val_acc: 0.8205\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.39299\n",
            "Epoch 13/18\n",
            " - 21s - loss: 0.2135 - acc: 0.9125 - val_loss: 0.4823 - val_acc: 0.8574\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.39299\n",
            "Epoch 14/18\n",
            " - 22s - loss: 0.2045 - acc: 0.9156 - val_loss: 1.0143 - val_acc: 0.7885\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.39299\n",
            "Epoch 15/18\n",
            " - 21s - loss: 0.2570 - acc: 0.9047 - val_loss: 0.3956 - val_acc: 0.8590\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.39299\n",
            "Epoch 16/18\n",
            " - 21s - loss: 0.2023 - acc: 0.9141 - val_loss: 0.4037 - val_acc: 0.8590\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.39299\n",
            "Epoch 17/18\n",
            " - 21s - loss: 0.1984 - acc: 0.9187 - val_loss: 0.4254 - val_acc: 0.8670\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.39299\n",
            "Epoch 18/18\n",
            " - 22s - loss: 0.2699 - acc: 0.8953 - val_loss: 0.4288 - val_acc: 0.8670\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.39299\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AECINYBfkXmV",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate the Model\n",
        "\n",
        "The model will be evaluated in multiple ways, including accuracy/validation accuracy, precision/recall score, confusion matrix, and ROC curve. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXCm9pk9kof5",
        "colab_type": "text"
      },
      "source": [
        "### Accurcy/Loss Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6CJqkMpQBQs",
        "colab_type": "code",
        "outputId": "87d4402b-884a-4283-9686-a1501375d3f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "source": [
        "# Original Dataset with class weight\n",
        "plt.figure(figsize=(25,8))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EHPAQPeOLiIiI+I9GkdQCU6dOZerUqfTs2ZNevXqxatUq1q5dS7du3Zg2bRp33nknM2fO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\ng4uuxhBiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJEbMI3ddYpA",
        "colab_type": "text"
      },
      "source": [
        "### Precision Score/Recall Score\n",
        "Since we have an imbalanced dataset, we will need to evaluate our model with precision score and recall score. Since this is a model for detecting if an X-Ray image indicate the patient have Pneumonia or not, we would need to be very careful about False Positive, which means a patient diagnosis as Normal, but have Pneumonia. Therefore, the model should be minimize number of False Positive, which means we should maximize our precision score. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eF47cuZKmJDV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReShAg1NxqPu",
        "colab_type": "code",
        "outputId": "81c1e6bd-4b1c-4a10-cf75-27d804bde180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Get predictions from model\n",
        "\n",
        "prediction = model.predict_generator(test_data,\n",
        "                                     steps = 20,\n",
        "                                     verbose = 1)\n",
        "\n",
        "y_predict = np.argmax(prediction, axis=1)\n",
        "\n",
        "# Get y_true from our datagenerator\n",
        "y_true = test_data.classes"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20/20 [==============================] - 8s 384ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1a8rhahlajm",
        "colab_type": "code",
        "outputId": "af218319-d688-48fd-eae6-1055dde78b25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Calculate precision and recall score\n",
        "\n",
        "original_ps = precision_score(y_true,y_predict)\n",
        "original_rs = recall_score(y_true, y_predict)\n",
        "print('Original Dataset with class weight') \n",
        "print('Precision Score:', original_ps)\n",
        "print('Recall Score:', original_rs)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original Dataset with class weight\n",
            "Precision Score: 0.7852193995381063\n",
            "Recall Score: 0.8717948717948718\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGaWmWupdbxS",
        "colab_type": "text"
      },
      "source": [
        "### Confusion Matrix\n",
        "A confusion matrix can provide a direct way of how the model perform when predicting different classes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdCfAh2kJUAz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cf_test = confusion_matrix(y_true, y_predict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTv4MAKq4ClI",
        "colab_type": "code",
        "outputId": "6bb33c7f-9ba5-4c6d-ebfb-5efe2049b322",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        }
      },
      "source": [
        "# Plot the confusion matrix\n",
        "plt.figure(figsize = (15,10))\n",
        "\n",
        "ax = sns.heatmap(cf_test,annot=True,fmt='d',cmap='Paired' )\n",
        "plt.title(\"Test Dataset Confusion Matrix\",\n",
        "                   fontsize=12)\n",
        "ax.set_xlabel(\"Predicted Label\",fontsize=16)\n",
        "ax.set_ylabel(\"True Label\",fontsize=16)\n",
        "ax.set_xticklabels(['Normal', 'Pneumonia'], fontsize=12, rotation=0)\n",
        "ax.set_yticklabels(['Normal', 'Pneumonia'], fontsize=12, rotation=360)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Normal'), Text(0, 1.5, 'Pneumonia')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NkjwhyVOT7DttFQEAAMxSEwW01tqvpuj6QVXdkuRFSX44bVUBAADMQpN+UfWi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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CJ1w1QudfN7",
        "colab_type": "text"
      },
      "source": [
        "### ROC Curve\n",
        "A ROC curve tells us how well our model when distingushing between classes. A ROC curve plotted with True Positive Rate vs. False Positive Rate. If our model performs well, we will have a model with high TPR and curve above the 50% diagonal line. If our model performs poorly, then the curve will be below the 50% diagonal line. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts-W3VHaNNfB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ROC Curve\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "original_fpr, original_tpr, original_thresold = roc_curve(np.array(y_true),\n",
        "                                                          prediction[:, 1],\n",
        "                                                          pos_label=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZcuXWxE8Ru_",
        "colab_type": "code",
        "outputId": "59589f80-942e-4f0b-8941-7b2429c007e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.title('ROC Curve', fontsize=18)\n",
        "plt.plot(original_fpr, original_tpr, label='Test Dataset: {:.4f}'.format(\n",
        "    roc_auc_score(y_true, y_predict)))\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.axis([-0.01, 1, 0, 1])\n",
        "plt.xlabel('False Positive Rate', fontsize=16)\n",
        "plt.ylabel('True Positive Rate', fontsize=16)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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88HS2XrN22QBe7VTnis/buHEjX331FStXrsTLy4s+ffowa9YsqlatyvHjx9mwYQMA0dHR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\nMzNrWA56zczMzMzMrGH9B3OZg5Z9EMVOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x5R8luIXxQSd",
        "colab_type": "text"
      },
      "source": [
        "## What went wrong?\n",
        "At the end, we also want to find out how many predictions are incorrect, and number of wrong predicions for each class. Afterward, we can re-train and fine tune our model purposely to have better prediction. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mu9Taal0Ax9-",
        "colab_type": "code",
        "outputId": "e4566ac5-3070-402e-8807-e3c220ba7bd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Make a copy of test_df\n",
        "eval_df = test_df.copy()\n",
        "\n",
        "# Changed label from str to 0 and 1. \n",
        "eval_df['label'].replace('Normal',0,inplace=True)\n",
        "eval_df['label'].replace('Pneumonia',1,inplace=True)\n",
        "\n",
        "eval_df['label'].unique()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJpnYbWJA91-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Before compare true value with prediction, we need to make sure label is \n",
        "# exactly the same as our y_true\n",
        "check_true = []\n",
        "\n",
        "for i in range(0, len(eval_df)):\n",
        "    if eval_df['label'][i] == y_true[i]:\n",
        "        check_true.append(0)\n",
        "    else:\n",
        "        check_true.append(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiYsDW50BiMJ",
        "colab_type": "code",
        "outputId": "325ce11a-5552-4624-b946-3f7e8f533c6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# If our label is exactly the same as y_true, this code will return False.\n",
        "1 in check_true"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiKug31hDbrr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make a new column that specify correct and wrong prediction\n",
        "comparison = []\n",
        "\n",
        "for i in range(0, len(eval_df)):\n",
        "    if eval_df['label'][i] == y_predict[i]:\n",
        "        comparison.append('Correct')\n",
        "    else:\n",
        "        comparison.append('Wrong')\n",
        "        \n",
        "eval_df['comparison'] = comparison"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_McGQhuJEGH8",
        "colab_type": "code",
        "outputId": "d1d37cf6-828e-492b-c0cf-94a2be973a56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Check the number of wong predictions with our confusion matrix number.\n",
        "eval_df[eval_df['comparison'] == 'Wrong'].count()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "image         143\n",
              "label         143\n",
              "comparison    143\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNrHUP7JErDg",
        "colab_type": "code",
        "outputId": "433a3770-6410-4e5b-952c-8dff39f0ea2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Create a new dataframe to store all wrong predictions. \n",
        "wrong_df = pd.DataFrame()\n",
        "\n",
        "wrong_df = eval_df[eval_df['comparison'] == 'Wrong']\n",
        "\n",
        "wrong_df.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(143, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLzWqd9GH4bz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reset the index of the wrong prediction dataframe\n",
        "wrong_df.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-iIudDwFEKv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Find out the class of each wrong prediction\n",
        "text_label = []\n",
        "\n",
        "for i in range(0, len(wrong_df)):\n",
        "    if 'bacteria' in str(wrong_df['image'][i]):\n",
        "        text_label.append('Bacterial')\n",
        "    elif 'virus' in str(wrong_df['image'][i]):\n",
        "        text_label.append('Viral')\n",
        "    else:\n",
        "        text_label.append('Normal')\n",
        "        \n",
        "# Input the text_label as a column\n",
        "wrong_df['text_label'] = text_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUIMEVbzLVRO",
        "colab_type": "code",
        "outputId": "e578f48e-e3b0-4f93-ff4d-c66ff78596c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "# Print the value of each wrong prediction class and its number\n",
        "print(wrong_df['text_label'].value_counts().sort_index(ascending = True))\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.title('Number of Wrong Predictions', fontsize=18)\n",
        "sns.countplot(x='text_label', data=wrong_df, palette = 'Set2')\n",
        "plt.xlabel('Class Type', fontsize=12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Bacterial    36\n",
            "Normal       93\n",
            "Viral        14\n",
            "Name: text_label, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5PcljZ7mdnWbzDcxJnp1kvyUss0eSj8ymLg3HEK3FuRHYOcn82aycZE7G3Esr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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUgPCDc_hyBQ",
        "colab_type": "text"
      },
      "source": [
        "At the end, we can see that for false positive predictions, there are more bacterial Pneumonia are failed to predict compare to Virual Pneumonia. Therefore, we could try to work with professionals to check what are key characteristics to disgush Bacterial Pneumonia and Virual Pneumonia, and then try to reduce the number of wrong predictions for baterial Pneumonia first. However, our ultimate goal is to create a model that correct predict each case. "
      ]
    }
  ]
}