best_320x320_colab_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "best_320x320.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "Vr7JQPTq_qla",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Approach\n",
        "\n",
        "* Firstly a convolutional neural network is used to segment the image, using the bounding boxes directly as a mask. \n",
        "* Secondly connected components is used to separate multiple areas of predicted pneumonia.\n",
        "* Finally a bounding box is simply drawn around every connected component.\n",
        "\n",
        "# Network\n",
        "\n",
        "* The network consists of a number of residual blocks with convolutions and downsampling blocks with max pooling.\n",
        "* At the end of the network a single upsampling layer converts the output to the same shape as the input.\n",
        "\n",
        "As the input to the network is 256 by 256 (instead of the original 1024 by 1024) and the network downsamples a number of times without any meaningful upsampling (the final upsampling is just to match in 256 by 256 mask) the final prediction is very crude. If the network downsamples 4 times the final bounding boxes can only change with at least 16 pixels.\n",
        "\n",
        "**Edit by EAS** - Change input image size to 320x320."
      ]
    },
    {
      "metadata": {
        "id": "5jFPqYbl_tWH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# install dependencies not included by Colab\n",
        "# use pip3 to ensure compatibility w/ Google Deep Learning Images \n",
        "!pip install -q pydicom \n",
        "!pip install -q imgaug \n",
        "!pip install -q kaggle\n",
        "!pip install -q tqdm \n",
        "!pip install -q PyDrive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zcb2HG8n_qlb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import csv\n",
        "import random\n",
        "import pydicom\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from skimage import measure\n",
        "from skimage.transform import resize\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kvAZDt6q_ukM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "from googleapiclient.http import MediaFileUpload\n",
        "from googleapiclient.discovery import build\n",
        "\n",
        "auth.authenticate_user()\n",
        "\n",
        "drive_service = build('drive', 'v3')\n",
        "\n",
        "def save_file_to_drive(name, path):\n",
        "    file_metadata = {\n",
        "      'name': name,\n",
        "      'mimeType': 'application/octet-stream'\n",
        "     }\n",
        "\n",
        "    media = MediaFileUpload(path, \n",
        "                    mimetype='application/octet-stream',\n",
        "                    resumable=True)\n",
        "\n",
        "    created = drive_service.files().create(body=file_metadata,\n",
        "                                   media_body=media,\n",
        "                                   fields='id').execute()\n",
        "\n",
        "    print('File ID: {}'.format(created.get('id')))\n",
        "\n",
        "    return created"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "N7FbV28E_v4N",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# enter your Kaggle credentionals here\n",
        "os.environ['KAGGLE_USERNAME']=\"skooch\"\n",
        "os.environ['KAGGLE_KEY']=\"42f8a02ee92cc773d1dbe66565673ad3\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XCkxN_zR_xHH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "ce569fe7-a875-4823-c91c-7213b39f86fe"
      },
      "cell_type": "code",
      "source": [
        "# If you are unable to download the competition dataset, check to see if you have \n",
        "# accepted the user agreement on the competition website. \n",
        "if not os.path.exists(\"./stage_1_detailed_class_info.csv.zip\"):\n",
        "    !kaggle competitions download -c rsna-pneumonia-detection-challenge"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading GCP%20Credits%20Request%20Link%20-%20RSNA.txt to /content\n",
            "\r  0% 0.00/55.0 [00:00<?, ?B/s]\n",
            "100% 55.0/55.0 [00:00<00:00, 37.3kB/s]\n",
            "Downloading stage_1_detailed_class_info.csv.zip to /content\n",
            "  0% 0.00/561k [00:00<?, ?B/s]\n",
            "100% 561k/561k [00:00<00:00, 76.2MB/s]\n",
            "Downloading stage_1_sample_submission.csv to /content\n",
            "  0% 0.00/51.8k [00:00<?, ?B/s]\n",
            "100% 51.8k/51.8k [00:00<00:00, 40.4MB/s]\n",
            "Downloading stage_1_train_labels.csv.zip to /content\n",
            "  0% 0.00/634k [00:00<?, ?B/s]\n",
            "100% 634k/634k [00:00<00:00, 99.2MB/s]\n",
            "Downloading stage_1_test_images.zip to /content\n",
            " 89% 112M/126M [00:00<00:00, 122MB/s] \n",
            "100% 126M/126M [00:00<00:00, 168MB/s]\n",
            "Downloading stage_1_train_images.zip to /content\n",
            " 99% 3.15G/3.17G [00:24<00:00, 139MB/s]\n",
            "100% 3.17G/3.17G [00:24<00:00, 139MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f2IeRx6z_yTW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "af44b10d-f39b-4dce-8838-2f03d30bb99c"
      },
      "cell_type": "code",
      "source": [
        "# unzipping takes a few minutes\n",
        "if not os.path.exists(\"./stage_1_train_labels.csv\"):\n",
        "    print(\"Unzipping...\")\n",
        "    !unzip -q -o stage_1_test_images.zip -d stage_1_test_images\n",
        "    !unzip -q -o stage_1_train_images.zip -d stage_1_train_images\n",
        "    !unzip -q -o stage_1_train_labels.csv.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unzipping...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ltOidyEU_qle",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Load pneumonia locations\n",
        "\n",
        "Table contains [filename : pneumonia location] pairs per row. \n",
        "* If a filename contains multiple pneumonia, the table contains multiple rows with the same filename but different pneumonia locations. \n",
        "* If a filename contains no pneumonia it contains a single row with an empty pneumonia location.\n",
        "\n",
        "The code below loads the table and transforms it into a dictionary. \n",
        "* The dictionary uses the filename as key and a list of pneumonia locations in that filename as value. \n",
        "* If a filename is not present in the dictionary it means that it contains no pneumonia."
      ]
    },
    {
      "metadata": {
        "id": "Zj5d_R_sq4QA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3dc0ab91-f356-4285-c116-bd2d2a638ae1"
      },
      "cell_type": "code",
      "source": [
        "ROOT_DIR = \"./\"\n",
        "\n",
        "train_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_train_images')\n",
        "test_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_test_images')\n",
        "print(\"Train dir:\", train_dicom_dir)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train dir: ./stage_1_train_images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bJaASoWC_qle",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# empty dictionary\n",
        "pneumonia_locations = {}\n",
        "# load table\n",
        "with open(os.path.join('./stage_1_train_labels.csv'), mode='r') as infile:\n",
        "    # open reader\n",
        "    reader = csv.reader(infile)\n",
        "    # skip header\n",
        "    next(reader, None)\n",
        "    # loop through rows\n",
        "    for rows in reader:\n",
        "        # retrieve information\n",
        "        filename = rows[0]\n",
        "        location = rows[1:5]\n",
        "        pneumonia = rows[5]\n",
        "        # if row contains pneumonia add label to dictionary\n",
        "        # which contains a list of pneumonia locations per filename\n",
        "        if pneumonia == '1':\n",
        "            # convert string to float to int\n",
        "            location = [int(float(i)) for i in location]\n",
        "            # save pneumonia location in dictionary\n",
        "            if filename in pneumonia_locations:\n",
        "                pneumonia_locations[filename].append(location)\n",
        "            else:\n",
        "                pneumonia_locations[filename] = [location]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2yLhAUNl_qli",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Load filenames"
      ]
    },
    {
      "metadata": {
        "id": "mG2EOT-8_qlj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a952328b-0c44-4d6f-e02f-e744662fdc1f"
      },
      "cell_type": "code",
      "source": [
        "# load and shuffle filenames\n",
        "random.seed(1977)\n",
        "folder = './stage_1_train_images'\n",
        "filenames = os.listdir(folder)\n",
        "random.shuffle(filenames)\n",
        "# split into train and validation filenames\n",
        "n_valid_samples = 2560\n",
        "train_filenames = filenames[n_valid_samples:]\n",
        "valid_filenames = filenames[:n_valid_samples]\n",
        "print('n train samples', len(train_filenames))\n",
        "print('n valid samples', len(valid_filenames))\n",
        "n_train_samples = len(filenames) - n_valid_samples"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n train samples 23124\n",
            "n valid samples 2560\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vMmvXsFS_qlq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " # Data generator\n",
        "\n",
        "The dataset is too large to fit into memory, so we need to create a generator that loads data on the fly.\n",
        "\n",
        "* The generator takes in some filenames, batch_size and other parameters.\n",
        "\n",
        "* The generator outputs a random batch of numpy images and numpy masks.\n",
        "    "
      ]
    },
    {
      "metadata": {
        "id": "Z3SDWRfvAH4l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "IMAGE_SIZE = 320\n",
        "CHECKPOINT_PATH = \"model9_320.h5\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rSwVxQKdBB3L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6f83b324-72bd-473b-c64b-71719c40a199"
      },
      "cell_type": "code",
      "source": [
        "# upload checkpoint to GCS\n",
        "project_id = 'mammography-198911'\n",
        "bucket_name = 'pneumonia'\n",
        "\n",
        "!gcloud config set project {project_id}\n",
        "!gsutil cp gs://{bucket_name}/{CHECKPOINT_PATH} ./{CHECKPOINT_PATH} "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n",
            "CommandException: No URLs matched: gs://pneumonia/model9_320.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TKZdiz76_qlr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class generator(keras.utils.Sequence):\n",
        "    \n",
        "    def __init__(self, folder, filenames, pneumonia_locations=None, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=False, predict=False):\n",
        "        self.folder = folder\n",
        "        self.filenames = filenames\n",
        "        self.pneumonia_locations = pneumonia_locations\n",
        "        self.batch_size = batch_size\n",
        "        self.image_size = image_size\n",
        "        self.shuffle = shuffle\n",
        "        self.augment = augment\n",
        "        self.predict = predict\n",
        "        self.on_epoch_end()\n",
        "        \n",
        "    def __load__(self, filename):\n",
        "        # load dicom file as numpy array\n",
        "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
        "        # create empty mask\n",
        "        msk = np.zeros(img.shape)\n",
        "        # get filename without extension\n",
        "        filename = filename.split('.')[0]\n",
        "        # if image contains pneumonia\n",
        "        if filename in pneumonia_locations:\n",
        "            # loop through pneumonia\n",
        "            for location in pneumonia_locations[filename]:\n",
        "                # add 1's at the location of the pneumonia\n",
        "                x, y, w, h = location\n",
        "                msk[y:y+h, x:x+w] = 1\n",
        "        # if augment then horizontal flip half the time\n",
        "        if self.augment and random.random() > 0.5:\n",
        "            img = np.fliplr(img)\n",
        "            msk = np.fliplr(msk)\n",
        "            \n",
        "        # small shifts to images\n",
        "        if self.augment:\n",
        "            h_offset = np.random.randint(low=0, high=15)\n",
        "            v_offset = np.random.randint(low=0, high=15)\n",
        "            # crop the images\n",
        "            img = img[v_offset:,h_offset:]\n",
        "            msk = msk[v_offset:,h_offset:]\n",
        "            \n",
        "        # resize both image and mask\n",
        "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
        "        msk = resize(msk, (self.image_size, self.image_size), mode='reflect') > 0.5\n",
        "        # add trailing channel dimension\n",
        "        img = np.expand_dims(img, -1)\n",
        "        msk = np.expand_dims(msk, -1)\n",
        "        return img, msk\n",
        "    \n",
        "    def __loadpredict__(self, filename):\n",
        "        # load dicom file as numpy array\n",
        "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
        "        # resize image\n",
        "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
        "        # add trailing channel dimension\n",
        "        img = np.expand_dims(img, -1)\n",
        "        return img\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        # select batch\n",
        "        filenames = self.filenames[index*self.batch_size:(index+1)*self.batch_size]\n",
        "        # predict mode: return images and filenames\n",
        "        if self.predict:\n",
        "            # load files\n",
        "            imgs = [self.__loadpredict__(filename) for filename in filenames]\n",
        "            # create numpy batch\n",
        "            imgs = np.array(imgs)\n",
        "            return imgs, filenames\n",
        "        # train mode: return images and masks\n",
        "        else:\n",
        "            # load files\n",
        "            items = [self.__load__(filename) for filename in filenames]\n",
        "            # unzip images and masks\n",
        "            imgs, msks = zip(*items)\n",
        "            # create numpy batch\n",
        "            imgs = np.array(imgs)\n",
        "            msks = np.array(msks)\n",
        "            return imgs, msks\n",
        "        \n",
        "    def on_epoch_end(self):\n",
        "        if self.shuffle:\n",
        "            random.shuffle(self.filenames)\n",
        "        \n",
        "    def __len__(self):\n",
        "        if self.predict:\n",
        "            # return everything\n",
        "            return int(np.ceil(len(self.filenames) / self.batch_size))\n",
        "        else:\n",
        "            # return full batches only\n",
        "            return int(len(self.filenames) / self.batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zuDIxMEW_qlt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Network"
      ]
    },
    {
      "metadata": {
        "id": "2cENEbF7_qlu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_downsample(channels, inputs):\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(channels, 1, padding='same', use_bias=False)(x)\n",
        "    x = keras.layers.MaxPool2D(2)(x)\n",
        "    return x\n",
        "\n",
        "def create_resblock(channels, inputs):\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
        "    return keras.layers.add([x, inputs])\n",
        "\n",
        "def create_network(input_size, channels, n_blocks=2, depth=4):\n",
        "    # input\n",
        "    inputs = keras.Input(shape=(input_size, input_size, 1))\n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(inputs)\n",
        "    # residual blocks\n",
        "    for d in range(depth):\n",
        "        channels = channels * 2\n",
        "        x = create_downsample(channels, x)\n",
        "        for b in range(n_blocks):\n",
        "            x = create_resblock(channels, x)\n",
        "    # output\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(1, 1, activation='sigmoid')(x)\n",
        "    outputs = keras.layers.UpSampling2D(2**depth)(x)\n",
        "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NCUyPB3i_qlx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Train network\n"
      ]
    },
    {
      "metadata": {
        "id": "QkzK5IBY_qly",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2941
        },
        "outputId": "780b9538-d287-410a-9e1d-4b16107f05ca"
      },
      "cell_type": "code",
      "source": [
        "# define iou or jaccard loss function\n",
        "def iou_loss(y_true, y_pred):\n",
        "    y_true = tf.reshape(y_true, [-1])\n",
        "    y_pred = tf.reshape(y_pred, [-1])\n",
        "    intersection = tf.reduce_sum(y_true * y_pred)\n",
        "    score = (intersection + 1.) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection + 1.)\n",
        "    return 1 - score\n",
        "\n",
        "# combine bce loss and iou loss\n",
        "def iou_bce_loss(y_true, y_pred):\n",
        "    return 0.5 * keras.losses.binary_crossentropy(y_true, y_pred) + 0.5 * iou_loss(y_true, y_pred)\n",
        "\n",
        "# mean iou as a metric\n",
        "def mean_iou(y_true, y_pred):\n",
        "    y_pred = tf.round(y_pred)\n",
        "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
        "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
        "    smooth = tf.ones(tf.shape(intersect))\n",
        "    return tf.reduce_mean((intersect + smooth) / (union - intersect + smooth))\n",
        "\n",
        "# mean iou with any image with no truth counted as 0\n",
        "def adj_mean_iou(y_true, y_pred):\n",
        "    y_pred = tf.round(y_pred)\n",
        "    y_true_pos = tf.reduce_max(y_true, axis=[1, 2, 3])\n",
        "    \n",
        "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
        "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
        "    smooth = tf.ones(tf.shape(intersect))\n",
        "    \n",
        "    return tf.reduce_mean((y_true_pos * (intersect + smooth)) / (union - intersect + smooth))      \n",
        "  \n",
        "# create network and compiler\n",
        "model = create_network(input_size=320, channels=32, n_blocks=2, depth=4)\n",
        "model.compile(optimizer='adam',\n",
        "              loss=iou_bce_loss,\n",
        "              metrics=['accuracy', mean_iou, adj_mean_iou])\n",
        "\n",
        "# cosine learning rate annealing\n",
        "def cosine_annealing(x):\n",
        "    lr = 0.002\n",
        "    epochs = 40\n",
        "    return lr*(np.cos(np.pi*x/epochs)+1.)/2\n",
        "  \n",
        "learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)\n",
        "checkpoint = keras.callbacks.ModelCheckpoint(CHECKPOINT_PATH, monitor='val_loss', verbose=1, save_best_only=False, save_weights_only=True, mode='auto', period=1)\n",
        "\n",
        "# create train and validation generators\n",
        "folder = './stage_1_train_images'\n",
        "train_gen = generator(folder, train_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=True, predict=False)\n",
        "valid_gen = generator(folder, valid_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=False, predict=False)\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            (None, 320, 320, 1)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 320, 320, 32) 288         input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 320, 320, 32) 128         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_42 (LeakyReLU)      (None, 320, 320, 32) 0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 320, 320, 64) 2048        leaky_re_lu_42[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2D)  (None, 160, 160, 64) 0           conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 160, 160, 64) 256         max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_43 (LeakyReLU)      (None, 160, 160, 64) 0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 160, 160, 64) 36864       leaky_re_lu_43[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 160, 160, 64) 256         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_44 (LeakyReLU)      (None, 160, 160, 64) 0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 160, 160, 64) 36864       leaky_re_lu_44[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 160, 160, 64) 0           conv2d_47[0][0]                  \n",
            "                                                                 max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 160, 160, 64) 256         add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_45 (LeakyReLU)      (None, 160, 160, 64) 0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 160, 160, 64) 36864       leaky_re_lu_45[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 160, 160, 64) 256         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_46 (LeakyReLU)      (None, 160, 160, 64) 0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 160, 160, 64) 36864       leaky_re_lu_46[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_17 (Add)                    (None, 160, 160, 64) 0           conv2d_49[0][0]                  \n",
            "                                                                 add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 160, 160, 64) 256         add_17[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_47 (LeakyReLU)      (None, 160, 160, 64) 0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 160, 160, 128 8192        leaky_re_lu_47[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, 80, 80, 128)  0           conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 80, 80, 128)  512         max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_48 (LeakyReLU)      (None, 80, 80, 128)  0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 80, 80, 128)  147456      leaky_re_lu_48[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 80, 80, 128)  512         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_49 (LeakyReLU)      (None, 80, 80, 128)  0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 80, 80, 128)  147456      leaky_re_lu_49[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_18 (Add)                    (None, 80, 80, 128)  0           conv2d_52[0][0]                  \n",
            "                                                                 max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 80, 80, 128)  512         add_18[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_50 (LeakyReLU)      (None, 80, 80, 128)  0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 80, 80, 128)  147456      leaky_re_lu_50[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 80, 80, 128)  512         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_51 (LeakyReLU)      (None, 80, 80, 128)  0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 80, 80, 128)  147456      leaky_re_lu_51[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_19 (Add)                    (None, 80, 80, 128)  0           conv2d_54[0][0]                  \n",
            "                                                                 add_18[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 80, 80, 128)  512         add_19[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_52 (LeakyReLU)      (None, 80, 80, 128)  0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 80, 80, 256)  32768       leaky_re_lu_52[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 40, 40, 256)  0           conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 40, 40, 256)  1024        max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_53 (LeakyReLU)      (None, 40, 40, 256)  0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 40, 40, 256)  589824      leaky_re_lu_53[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 40, 40, 256)  1024        conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_54 (LeakyReLU)      (None, 40, 40, 256)  0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 40, 40, 256)  589824      leaky_re_lu_54[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_20 (Add)                    (None, 40, 40, 256)  0           conv2d_57[0][0]                  \n",
            "                                                                 max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 40, 40, 256)  1024        add_20[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_55 (LeakyReLU)      (None, 40, 40, 256)  0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 40, 40, 256)  589824      leaky_re_lu_55[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 40, 40, 256)  1024        conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_56 (LeakyReLU)      (None, 40, 40, 256)  0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 40, 40, 256)  589824      leaky_re_lu_56[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_21 (Add)                    (None, 40, 40, 256)  0           conv2d_59[0][0]                  \n",
            "                                                                 add_20[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 40, 40, 256)  1024        add_21[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_57 (LeakyReLU)      (None, 40, 40, 256)  0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 40, 40, 512)  131072      leaky_re_lu_57[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling2D) (None, 20, 20, 512)  0           conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 20, 20, 512)  2048        max_pooling2d_11[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_58 (LeakyReLU)      (None, 20, 20, 512)  0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 20, 20, 512)  2359296     leaky_re_lu_58[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 20, 20, 512)  2048        conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_59 (LeakyReLU)      (None, 20, 20, 512)  0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 20, 20, 512)  2359296     leaky_re_lu_59[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_22 (Add)                    (None, 20, 20, 512)  0           conv2d_62[0][0]                  \n",
            "                                                                 max_pooling2d_11[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 20, 20, 512)  2048        add_22[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_60 (LeakyReLU)      (None, 20, 20, 512)  0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 20, 20, 512)  2359296     leaky_re_lu_60[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 20, 20, 512)  2048        conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_61 (LeakyReLU)      (None, 20, 20, 512)  0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 20, 20, 512)  2359296     leaky_re_lu_61[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_23 (Add)                    (None, 20, 20, 512)  0           conv2d_64[0][0]                  \n",
            "                                                                 add_22[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 20, 20, 512)  2048        add_23[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_62 (LeakyReLU)      (None, 20, 20, 512)  0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 20, 20, 1)    513         leaky_re_lu_62[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2D)  (None, 320, 320, 1)  0           conv2d_65[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 12,727,969\n",
            "Trainable params: 12,718,305\n",
            "Non-trainable params: 9,664\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "p9Bs2ajKlKZj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights(CHECKPOINT_PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G369kKMh_ql2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1057
        },
        "outputId": "53ca1a04-779a-47cd-e192-140dcb1866ac"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=15, shuffle=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4960 - acc: 0.9615 - mean_iou: 0.5782 - adj_mean_iou: 0.0365\n",
            "Epoch 00001: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1408s 975ms/step - loss: 0.4959 - acc: 0.9615 - mean_iou: 0.5783 - adj_mean_iou: 0.0365 - val_loss: 0.4950 - val_acc: 0.9435 - val_mean_iou: 0.4947 - val_adj_mean_iou: 0.0625\n",
            "Epoch 2/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4615 - acc: 0.9651 - mean_iou: 0.6349 - adj_mean_iou: 0.0522\n",
            "Epoch 00002: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1396s 966ms/step - loss: 0.4615 - acc: 0.9651 - mean_iou: 0.6349 - adj_mean_iou: 0.0522 - val_loss: 0.4756 - val_acc: 0.9693 - val_mean_iou: 0.6858 - val_adj_mean_iou: 0.0385\n",
            "Epoch 3/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4466 - acc: 0.9671 - mean_iou: 0.6592 - adj_mean_iou: 0.0565\n",
            "Epoch 00003: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1398s 967ms/step - loss: 0.4465 - acc: 0.9671 - mean_iou: 0.6593 - adj_mean_iou: 0.0565 - val_loss: 0.4491 - val_acc: 0.9729 - val_mean_iou: 0.6954 - val_adj_mean_iou: 0.0477\n",
            "Epoch 4/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4374 - acc: 0.9678 - mean_iou: 0.6677 - adj_mean_iou: 0.0607\n",
            "Epoch 00004: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1416s 980ms/step - loss: 0.4375 - acc: 0.9678 - mean_iou: 0.6677 - adj_mean_iou: 0.0607 - val_loss: 0.4385 - val_acc: 0.9657 - val_mean_iou: 0.6467 - val_adj_mean_iou: 0.0650\n",
            "Epoch 5/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4284 - acc: 0.9688 - mean_iou: 0.6773 - adj_mean_iou: 0.0627\n",
            "Epoch 00005: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1413s 978ms/step - loss: 0.4285 - acc: 0.9688 - mean_iou: 0.6773 - adj_mean_iou: 0.0626 - val_loss: 0.4235 - val_acc: 0.9736 - val_mean_iou: 0.7359 - val_adj_mean_iou: 0.0565\n",
            "Epoch 6/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4217 - acc: 0.9696 - mean_iou: 0.6870 - adj_mean_iou: 0.0645\n",
            "Epoch 00006: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1406s 973ms/step - loss: 0.4217 - acc: 0.9696 - mean_iou: 0.6870 - adj_mean_iou: 0.0645 - val_loss: 0.4793 - val_acc: 0.9752 - val_mean_iou: 0.7533 - val_adj_mean_iou: 0.0259\n",
            "Epoch 7/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4160 - acc: 0.9701 - mean_iou: 0.6934 - adj_mean_iou: 0.0659\n",
            "Epoch 00007: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1424s 985ms/step - loss: 0.4159 - acc: 0.9701 - mean_iou: 0.6935 - adj_mean_iou: 0.0660 - val_loss: 0.4178 - val_acc: 0.9740 - val_mean_iou: 0.7352 - val_adj_mean_iou: 0.0578\n",
            "Epoch 8/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4126 - acc: 0.9708 - mean_iou: 0.6978 - adj_mean_iou: 0.0677\n",
            "Epoch 00008: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1416s 980ms/step - loss: 0.4126 - acc: 0.9708 - mean_iou: 0.6979 - adj_mean_iou: 0.0677 - val_loss: 0.5076 - val_acc: 0.9754 - val_mean_iou: 0.7674 - val_adj_mean_iou: 0.0178\n",
            "Epoch 9/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4098 - acc: 0.9707 - mean_iou: 0.7010 - adj_mean_iou: 0.0687\n",
            "Epoch 00009: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1415s 979ms/step - loss: 0.4097 - acc: 0.9707 - mean_iou: 0.7011 - adj_mean_iou: 0.0687 - val_loss: 0.4110 - val_acc: 0.9722 - val_mean_iou: 0.7011 - val_adj_mean_iou: 0.0709\n",
            "Epoch 10/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4056 - acc: 0.9714 - mean_iou: 0.7053 - adj_mean_iou: 0.0702\n",
            "Epoch 00010: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1406s 973ms/step - loss: 0.4056 - acc: 0.9714 - mean_iou: 0.7053 - adj_mean_iou: 0.0702 - val_loss: 0.4173 - val_acc: 0.9757 - val_mean_iou: 0.7458 - val_adj_mean_iou: 0.0582\n",
            "Epoch 11/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4054 - acc: 0.9718 - mean_iou: 0.7117 - adj_mean_iou: 0.0701\n",
            "Epoch 00011: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1404s 971ms/step - loss: 0.4053 - acc: 0.9718 - mean_iou: 0.7117 - adj_mean_iou: 0.0701 - val_loss: 0.4068 - val_acc: 0.9698 - val_mean_iou: 0.6618 - val_adj_mean_iou: 0.0801\n",
            "Epoch 12/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4002 - acc: 0.9720 - mean_iou: 0.7160 - adj_mean_iou: 0.0712\n",
            "Epoch 00012: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1405s 972ms/step - loss: 0.4002 - acc: 0.9720 - mean_iou: 0.7161 - adj_mean_iou: 0.0712 - val_loss: 0.4027 - val_acc: 0.9752 - val_mean_iou: 0.7358 - val_adj_mean_iou: 0.0658\n",
            "Epoch 13/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.4021 - acc: 0.9715 - mean_iou: 0.7092 - adj_mean_iou: 0.0735\n",
            "Epoch 00013: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1398s 967ms/step - loss: 0.4021 - acc: 0.9715 - mean_iou: 0.7092 - adj_mean_iou: 0.0736 - val_loss: 0.4039 - val_acc: 0.9742 - val_mean_iou: 0.7280 - val_adj_mean_iou: 0.0674\n",
            "Epoch 14/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.3985 - acc: 0.9723 - mean_iou: 0.7185 - adj_mean_iou: 0.0729\n",
            "Epoch 00014: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1395s 966ms/step - loss: 0.3985 - acc: 0.9723 - mean_iou: 0.7185 - adj_mean_iou: 0.0729 - val_loss: 0.4135 - val_acc: 0.9766 - val_mean_iou: 0.7604 - val_adj_mean_iou: 0.0545\n",
            "Epoch 15/15\n",
            "1444/1445 [============================>.] - ETA: 0s - loss: 0.3969 - acc: 0.9721 - mean_iou: 0.7163 - adj_mean_iou: 0.0736\n",
            "Epoch 00015: saving model to model9_320.h5\n",
            "1445/1445 [==============================] - 1401s 970ms/step - loss: 0.3969 - acc: 0.9721 - mean_iou: 0.7162 - adj_mean_iou: 0.0737 - val_loss: 0.4072 - val_acc: 0.9674 - val_mean_iou: 0.6743 - val_adj_mean_iou: 0.0839\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Kvlm_ttAv8Rz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "c6c54508-be09-4f49-ba2a-9fca53faebec"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=25, shuffle=True, initial_epoch=18)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 19/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3764 - acc: 0.9741 - mean_iou: 0.7368 - adj_mean_iou: 0.0761\n",
            "Epoch 00019: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1415s 2s/step - loss: 0.3763 - acc: 0.9741 - mean_iou: 0.7367 - adj_mean_iou: 0.0761 - val_loss: 0.3882 - val_acc: 0.9739 - val_mean_iou: 0.7371 - val_adj_mean_iou: 0.0714\n",
            "Epoch 20/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3763 - acc: 0.9742 - mean_iou: 0.7383 - adj_mean_iou: 0.0762\n",
            "Epoch 00020: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1388s 2s/step - loss: 0.3764 - acc: 0.9742 - mean_iou: 0.7382 - adj_mean_iou: 0.0761 - val_loss: 0.3852 - val_acc: 0.9735 - val_mean_iou: 0.7157 - val_adj_mean_iou: 0.0762\n",
            "Epoch 21/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3754 - acc: 0.9743 - mean_iou: 0.7372 - adj_mean_iou: 0.0773\n",
            "Epoch 00021: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1383s 2s/step - loss: 0.3755 - acc: 0.9743 - mean_iou: 0.7370 - adj_mean_iou: 0.0773 - val_loss: 0.3842 - val_acc: 0.9736 - val_mean_iou: 0.7258 - val_adj_mean_iou: 0.0765\n",
            "Epoch 22/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3719 - acc: 0.9746 - mean_iou: 0.7397 - adj_mean_iou: 0.0786\n",
            "Epoch 00022: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1393s 2s/step - loss: 0.3718 - acc: 0.9746 - mean_iou: 0.7398 - adj_mean_iou: 0.0785 - val_loss: 0.3839 - val_acc: 0.9737 - val_mean_iou: 0.7244 - val_adj_mean_iou: 0.0758\n",
            "Epoch 23/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3716 - acc: 0.9747 - mean_iou: 0.7420 - adj_mean_iou: 0.0784\n",
            "Epoch 00023: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1399s 2s/step - loss: 0.3716 - acc: 0.9747 - mean_iou: 0.7420 - adj_mean_iou: 0.0784 - val_loss: 0.3826 - val_acc: 0.9727 - val_mean_iou: 0.7191 - val_adj_mean_iou: 0.0800\n",
            "Epoch 24/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3701 - acc: 0.9749 - mean_iou: 0.7435 - adj_mean_iou: 0.0788\n",
            "Epoch 00024: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1396s 2s/step - loss: 0.3700 - acc: 0.9749 - mean_iou: 0.7436 - adj_mean_iou: 0.0788 - val_loss: 0.3846 - val_acc: 0.9749 - val_mean_iou: 0.7351 - val_adj_mean_iou: 0.0722\n",
            "Epoch 25/25\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3697 - acc: 0.9750 - mean_iou: 0.7447 - adj_mean_iou: 0.0790\n",
            "Epoch 00025: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1396s 2s/step - loss: 0.3698 - acc: 0.9751 - mean_iou: 0.7447 - adj_mean_iou: 0.0790 - val_loss: 0.3836 - val_acc: 0.9747 - val_mean_iou: 0.7370 - val_adj_mean_iou: 0.0717\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XIeMcmhwy-OD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "82d2286c-f02e-49b3-f48f-51b5e28a4f8c"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=30, shuffle=True, initial_epoch=25)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 26/30\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3702 - acc: 0.9748 - mean_iou: 0.7417 - adj_mean_iou: 0.0783\n",
            "Epoch 00026: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1432s 2s/step - loss: 0.3701 - acc: 0.9748 - mean_iou: 0.7418 - adj_mean_iou: 0.0783 - val_loss: 0.3826 - val_acc: 0.9743 - val_mean_iou: 0.7312 - val_adj_mean_iou: 0.0745\n",
            "Epoch 27/30\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3711 - acc: 0.9748 - mean_iou: 0.7417 - adj_mean_iou: 0.0791\n",
            "Epoch 00027: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1392s 2s/step - loss: 0.3709 - acc: 0.9748 - mean_iou: 0.7416 - adj_mean_iou: 0.0792 - val_loss: 0.3854 - val_acc: 0.9759 - val_mean_iou: 0.7452 - val_adj_mean_iou: 0.0693\n",
            "Epoch 28/30\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3702 - acc: 0.9749 - mean_iou: 0.7416 - adj_mean_iou: 0.0797\n",
            "Epoch 00028: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1395s 2s/step - loss: 0.3703 - acc: 0.9749 - mean_iou: 0.7416 - adj_mean_iou: 0.0797 - val_loss: 0.3858 - val_acc: 0.9758 - val_mean_iou: 0.7518 - val_adj_mean_iou: 0.0682\n",
            "Epoch 29/30\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3690 - acc: 0.9751 - mean_iou: 0.7423 - adj_mean_iou: 0.0800\n",
            "Epoch 00029: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1389s 2s/step - loss: 0.3689 - acc: 0.9751 - mean_iou: 0.7423 - adj_mean_iou: 0.0800 - val_loss: 0.3852 - val_acc: 0.9736 - val_mean_iou: 0.7267 - val_adj_mean_iou: 0.0758\n",
            "Epoch 30/30\n",
            "721/722 [============================>.] - ETA: 1s - loss: 0.3665 - acc: 0.9752 - mean_iou: 0.7434 - adj_mean_iou: 0.0807\n",
            "Epoch 00030: saving model to model9_320.h5\n",
            "722/722 [==============================] - 1398s 2s/step - loss: 0.3665 - acc: 0.9752 - mean_iou: 0.7434 - adj_mean_iou: 0.0807 - val_loss: 0.3834 - val_acc: 0.9737 - val_mean_iou: 0.7326 - val_adj_mean_iou: 0.0759\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fCE_u2jq_ql4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b772c715-1aca-47a4-9c54-34353fad6619"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(131)\n",
        "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
        "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
        "plt.legend()\n",
        "plt.subplot(132)\n",
        "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
        "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
        "plt.legend()\n",
        "plt.subplot(133)\n",
        "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
        "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bcpI3q95FROTGjG0TrhSbpfeGU5ROsgrekf5COeviVvncKKYrdn4stNsNfNp0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qXSjNDQgxw+HEpsbAyrVn3FK6+8xs6dO+jWrSc7d+4A4OjRQwQGtqkVSTE8kBgbS2IsyqeoY0Ny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\nCCGEEIAkxkIIIYQQQgDw/zkX/rq2qHbbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3699eafd30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9YpxNWbO8NzN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "adb8bfef-826f-4df0-942d-fc5dcd778f44"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(131)\n",
        "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
        "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
        "plt.legend()\n",
        "plt.subplot(132)\n",
        "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
        "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
        "plt.legend()\n",
        "plt.subplot(133)\n",
        "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
        "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pYVtkCq9/c4TvtiWRW1jOwK6OvDezD3MmdKk3gAa4WpYFgIOZ/U3f+Ss78Xrv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\nqdXFNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f368c3e2748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "UeylqHbU8uWk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a602200b-6f67-40d7-df6c-bad2325a491e"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(131)\n",
        "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
        "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
        "plt.legend()\n",
        "plt.subplot(132)\n",
        "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
        "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
        "plt.legend()\n",
        "plt.subplot(133)\n",
        "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
        "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pVVAiRqLHhSEl0QOdlN98803efvttpFIpGRkZbNmyBZvNxubNm7FYLHR2drJh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yiszM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\nAABC+gRHDUR7MfoE5QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3666951dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zpfjJkeuApN9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sample Predictions"
      ]
    },
    {
      "metadata": {
        "id": "vUKPS-jCAqTN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1697
        },
        "outputId": "ea5e8d31-b83d-42a4-ce4e-b9c549459ec1"
      },
      "cell_type": "code",
      "source": [
        "THRESHOLD = 0.5\n",
        "# look at some sample predictions\n",
        "samples = np.random.choice(valid_filenames, size=8, replace=False)\n",
        "\n",
        "for filename in samples:\n",
        "    # load the image\n",
        "    img = pydicom.dcmread(os.path.join(train_dicom_dir, filename)).pixel_array\n",
        "    \n",
        "    msk = np.zeros(img.shape)\n",
        "    \n",
        "    filename = filename.split('.')[0]\n",
        "    # if image contains pneumonia\n",
        "    if filename in pneumonia_locations:\n",
        "        # loop through pneumonia\n",
        "        for location in pneumonia_locations[filename]:\n",
        "            # add 1's at the location of the pneumonia\n",
        "            x, y, w, h = location\n",
        "            msk[y:y+h, x:x+w] = 1\n",
        "        \n",
        "    # resize both image and mask\n",
        "    msk = resize(msk, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect') > 0.5\n",
        "    img = resize(img, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect')\n",
        "    \n",
        "    # predict the image\n",
        "    yhat = model.predict(img.reshape(1, IMAGE_SIZE, IMAGE_SIZE, 1))\n",
        "    yhat = yhat > THRESHOLD\n",
        "    # add trailing channel dimension\n",
        "    msk = np.expand_dims(msk, -1)\n",
        "\n",
        "    f, ax = plt.subplots(1, 3, figsize=(10, 4))\n",
        "    ax[0].imshow(img.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
        "    ax[0].set_title(\"Image\")\n",
        "    ax[1].imshow(msk.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
        "    ax[1].set_title(\"Label\")\n",
        "    ax[2].imshow(yhat.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
        "    ax[2].set_title(\"Prediction\")\n",
        "    plt.show()\n",
        "    "
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZZyEej6NWqyGfz/sG6ZIQkBxwAdXdZVRK9ALO95BYkRwAkABQqho6V0mXDKPR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fQSHn/8cQDA3NwcqtUqLrvsMjzzzDMAgGeffRaXX345du3ahddeew2lUgmVSgX79u3DJZdcsnYD\npPxE4sOJn/9YTmIOEfft04R1JUqa+K3eH9+rpERLCYsmtCvZUlKhK/788qDvNdK/2Wff5+PnSelr\nfpmO0ON4XT2PRiloWdAnPhwPHRd+3o8W4DFst+ZBsU9KpvxSpG+Q1/wnJbRArwmdUPLsRy3ouHDs\nlfzyXBriqXsZrhen+pkwGDYT7HkwGFYQ6qyhu9ZqNXz3u9/F9PQ0arUavvnNb+Kiiy7CnXfeiXq9\njm3btuGee+5BNBrFT3/6U/z4xz9GKBTCTTfdhC9/+ctrNuChhx5Cs9nE4uIiqtUqgiBAq9VCEAQI\nh8POOBwEAZaXu1vJ0HysQZ/6nhIDTsJKvPgZ9S2pZ2s1k7KuONTsJU7GpVIJuVyuxwOlapESBlWF\nCCWFSmJWU8RWe41QtUmJmG7foqvblNDw3OVyGUNDQz0eL12lSBO/xiDwGvzb92Txb7ZDiTON4nxf\nFS+qUCTRVKzi8bhTofgaz6OrCB999FHs2bMHyWQS6XQakUgEqVQKyWQSmUwGuVwO8XgcuVxuXf/H\nfKqfCYNhM8GeB4NhBWsSqlONv/mbvwGwUmIKgqCHUC0vL6NWqyEIAmf65so4bonC1WDAyoMdCoV6\nSoEan6AESPeAo4FZFRiNASC07OebsYEuofKHVMt/JDSqoujffpnRV158IvZhqpUSSqC3vOZvMK3k\nj68vLS0hm806UuZHJ/iGbyVjGlegBIvtVwWQYJSFKo+MP+DejLFYDKFQyJX6uBghGo061Yl9ozk+\nFovhRz/6Efbs2YNYLIZMJoNEIuH+kWTlcjkMDg5i165dMBgMBoNho+h7UjonQp24dbNikhCuLgO6\nilO9Xv9ItUeX8+vr6qfyFSAtw3HSXi0iQRUe/amlL4Ln9LOvfKWMxEzLdnqcltoIJWsKJVZUfljy\nUjKpRFLLbQB6/FN+nIIqW3r/+B4/oyVTtsHv/2qrCXkOHReGm6oHi0oZvzMataArATUxne/551kt\n+sJgMBgMhvWg74QqFos5nw7JCA3Kuu0KJzxN2I5EIo5o+dvU6ITsl890Al0t4JNkgBlFBM/Ha6qv\nivC9SAznBNBDwpiHpB4s3wukZEkJk3qhfHKi1+fvfhzAaunpSi75ORIZKnF+GU/N7orVlC+eV8kc\nj+VxGkOh91CDQdvtNmq1Wg+J5H1SQz+vr8omP6thpLpxtm5wbTAYDAbDRtB3QlWv13u8NLFY7Dhy\no8RFtyZhaY7EhVBSoGoFj9fSn6+E+WoPCQA/7+/b50MN6/7xqrawzyQqqqyogqZmcl/t0RIlX/eP\n5XUJNcPr9itK9nylRs39Oq68Lu+Bjinvo59XpaZ0bZNPAPma7pXIrXFI5jiW2h+SKo6njpF+H1gu\n5rX83CqDwWAwGDaCvhMqYEVJiEajPUnbOhlSAVFy1Wg0eiZdJRk+4dFz+kqPTsq+L8nfakWjBqia\n8Ry+CqalQ1WA/PR2VY9IQNTjpARKoZ/3FSgldT4J4vskdOznaqVTn2AwLFXfWy1FPhQKHUdw/fP4\nZMdfociATypLvodL++wvFlhtOxmeS/14fklyo8GeBoPBYDAQ6w72PFWYnJxEs9lEvV5HrVZz+VKN\nRgO1Ws15qrScQ5WJpb4gCAB0FS0lSfzp+4T8khc/S+hEuxpJI/xJ+Atf+MKqBEg9ViQLPqFR75Wv\nlGkfVKHTdvmf0fP77yvpUCKpJPLyyy/vIVu+sZ/XJqHVbCq+5l9HPVQar8D2sq1aJvRXAtLbpntA\nqpLFUjCVMgD41Kc+5bKrkskkYrEY4vG4+z0WizmvmcFgMBgMG0XfV/kZDAaDwWAwbHacFiU/g8Fg\nMBgMhs0MI1QGg8FgMBgMJwkjVAaDwWAwGAwnCSNUBoPBYDAYDCcJI1QGg8FgMBgMJwkjVAaDwWAw\nGAwnib7lUP3VX/0V9u/fj1AohO985zu4+OKL+9WUDeGtt97Cbbfdht/7vd/DTTfdhOnpaezZswft\ndhvj4+O47777EIvF8NRTT+GJJ55AOBzGjTfeiBtuuKHfTT8Oe/fuxcsvv4xWq4Vbb70VO3fu3JR9\nCYIAd911F+bn51Gv13Hbbbfhwgsv3HR9sWei/7Bn4vTqiz0T/Yc9ExvoS6cPePHFFzt/9Ed/1Ol0\nOp2DBw92brzxxn40Y8OoVCqdm266qXP33Xd3/umf/qnT6XQ6d911V+fpp5/udDqdzgMPPND5l3/5\nl06lUulcc801nVKp1AmCoPNbv/VbnYWFhX42/Ti88MILnT/8wz/sdDqdTqFQ6HzhC1/YtH35j//4\nj86jjz7a6XQ6ncOHD3euueaaTdcXeyb6D3smTq++2DPRf9gzsbG+9KXk98ILL+A3f/M3AQDnn38+\nFhcXsbS01I+mbAixWAyPPfYYJiYm3GsvvvgirrrqKgDAlVdeiRdeeAH79+/Hzp07kc1mkUgksHv3\nbuzbt69fzV4Vl156Kb7//e8DAHK5HIIg2LR9+dKXvoRbbrkFADA9PY0tW7Zsur7YM9F/2DNxevXF\nnon+w56JjfWlL4Rqbm4Ow8PD7u+RkRHk8/l+NGVDiEQiSCQSPa8FQeC2NxkdHUU+n8fc3BxGRkbc\nMadj/wYGBpBKpQAAU1NTuOKKKzZtX4ivfvWr+LM/+zN85zvf2XR9sWei/7Bn4vTqiz0T/Yc9Exvr\nS9/38gOw6t53mxEf1o/TuX/PPfccpqam8Pjjj+Oaa65xr2/GvvzkJz/Bm2++iTvuuKOnnZuxL6dz\n2zaCzTj29kycnjid27YRbMaxt2difeiLQjUxMYG5uTn39+zsLMbHx/vRlJNGKpVCrVYDAMzMzGBi\nYmLV/qn8e7rg+eefx8MPP4zHHnsM2Wx20/bl9ddfx/T0NABgx44daLfbSKfTm6ov9kycHrBn4vTp\niz0TpwfsmVh/X/pCqD7/+c/jmWeeAQAcOHAAExMTyGQy/WjKSeOyyy5zfXn22Wdx+eWXY9euXXjt\ntddQKpVQqVSwb98+XHLJJX1uaS/K5TL27t2LRx55BENDQwA2b19eeuklPP744wBWygTVanXT9cWe\nif7DnonTqy/2TPQf9kxsrC+hTp+0ufvvvx8vvfQSQqEQ/vzP/xwXXnhhP5qxIbz++uv467/+axw5\ncgSRSARbtmzB/fffj7vuugv1eh3btm3DPffcg2g0ip/+9Kf48Y9/jFAohJtuuglf/vKX+938Hjz5\n5JP4u7/7O5x77rnutXvvvRd33333putLrVbDd7/7XUxPT6NWq+Gb3/wmLrroItx5552bqi/2TPQX\n9kycfn2xZ6K/sGdiY33pG6EyGAwGg8Fg+L8CS0o3GAwGg8FgOEkYoTIYDAaDwWA4SRihMhgMBoPB\nYDhJGKEyGAwGg8FgOEkYoTIYDAaDwWA4SRihMhgMBoPBYDhJGKEyGAwGg8FgOEkYoTIYDAaDwWA4\nSfw/G6POvkQ8wBEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cff51898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nn38etVoNw8PD6HQ6GB8fR39/v9wvZmfpVZA8Vq1WE5LMmAx+d0jUOZb0jVUq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ZIRRVFsBIzt5rNDh6U42bGqkvUBkT/F6xArG4NgiYQlS5AZO18PX1I6nQ5rRbHCmA6BgkRZ47zt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\npJ/SfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cfe4d048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xf3q9HsbGxiLmbf5mv790Oi3Khs6PIpnSIZw8ll5xV61WRU2xbTtSCqQhG4C0\nQ9H7HAwG8H1f2tkwDJPbFAoFpFIpNBoNMdrT+K5brfT7fWkaTa+YVmVYbiSJoHpF4qSJFQkojdok\ndKPRSMgIADQajUhwqe/7ouTQ48Yx6nkjeWM/xDVr1iAMQ+zatQvpdFr8VUxtBxBRk3guiUQCGzdu\nBABRKWu1GoIgQKPRQC6Xi7TrYQCr7tHH1+LQqyYJlnlt2zamdAMDAwODp4VVJ1RUMYBoWY6lPxID\nlsS4DZUpPq7X62g0Gjj66KNFbdAZRQQJD0FDPEmNbrfCpfbAQqYSVRC2buGKOcYjAAurEgFEVsex\nrxwVEhIVqkcso7F8qBsq53I5MVuzPKfVGJa8qMzpVjFxMqXng2qVbkNDcqTH2e124bqurM5LpRYa\nSFNto7qo1UPO//j4OJrNppRmtQKkW8o8+OCDUnZdt24djjnmGDkPKmGO44h5n6Z1rqiMl/N4fjx/\njk8vetDzXi6XJSrCwMDAwMBgOVh1QkVlIJFYTMHWpTDtmwEgN0GqEiRIg8EAGzduFLVEl9S0kqVT\nyLnEPwgCSfLmSjKSOPbHYw+/fr8P3/dFbdK5TgBQKBSknEjPFWMU2JuPni+SQhrY+be++esefCRW\nJEPD4RDValWIFnvskbywrKd9VNrEblmWGP2pwOm5tywLe/bswdjYmKhLg8FA/FYkds1mUxpNc76D\nIJCsrqmpKSFf7HNI5Y1zBQDr1q3DUUcdJSb+wWCAWq0m88uYiCAIsHbt2kg5mOocCZRW7/hZaG8V\nv3tUuWZnZ03rGQMDAwODZWNZhOrBBx/EFVdcgeOOOw4AcPzxx+Pd7343PvShD2E4HGJiYgKf/OQn\nI/lM+4LOSNKr1ZgpxXIgDdokLlRsPM9DqVRCpVKJECiSKJI0YLEsx/dyeyoemUwGjuOgUChgbm4O\nvV5PxsEVg2vXrhW/EG/eutVJp9ORrCRNOsbGxhCGoZQCAQhBotLVbDYjZnTt2dIp4CReTCHnCkcA\nkZKYnmPuh4/7/X4kCwqAlCepttGEXq/XxctVLpfltVwuB9d1hWhypaZe6dfr9ZDL5aT59OzsLMIw\nxM6dO0Vt63a7ePGLXywElGbxfr+PdruNQqEQUZUmJydldaIuf2oytdRqP73SMR4rodXI5WAlrwkD\ngyMB5poweK5h2QrVK17xCtxyyy3y+MMf/jAuuuginHPOOfjrv/5r3HXXXbjooov2ux/t+SG50IoD\nsOiX4Q06CALxN01NTQnJ4g1dK1I6BJIqCQ3WvCHrRsS+74vRmmWuZDKJQqEg5bx4Crcuk5XLZbiu\nKzd/rQ5ls1lRZBgDwZV5jA09WB7FAAAgAElEQVTQpTrOAUmUXk1ItYhzpH1het7icQNUt5hyrldZ\nskTJc2H0AZshJ5NJNJtNOR779w0GAzQaDSGA5XJZ3kcv2nA4RKFQkKiKsbExWJYFx3HgOI74mNrt\ntnjHSBoBiNG/XC5LaZZqYT6fl3MlgY0He2qFU7fh4Wv5fF7yt5aLlbomDAyOFJhrwuC5hBXLoXrw\nwQdxxhlnAAA2bdqEBx544IDeF/cwUaVgSYvqCtudULVwHAfFYhEAImoO30NSwdVg9Gq5rotut4t8\nPh/JruIxMpkMut2upKePj4+jWCwKqSERIfkolUooFAqi6rDHHZPQebx6vY5f/OIX2LlzJxqNBubn\n5zEzMyPnQ38SS5J62T+N6yyLchsGdLIEyXDQIAjQ6/XQ7XYjv/k8zfLaUM/9aY8ZvVwsd+bzeRSL\nRUxOTmLdunVCdmzbRq1Wk9WSvu9j165dEUJH8loul1EulyOkiWGnAOQzofJGwktCyoUA9JzZti3h\nrHrxAt9L6JWgunTK7wq/SyuJ5V4TBgZHKsw1YXAkY9kK1a9+9Su85z3vQbPZxHvf+174vi/Sba1W\nk0iB/YFZUKVSSYgBPUA0Jety0mg0wtjYmPhxNMmJp2cT3Fej0UAQBGJmpombZI2KULfbjZieqUhR\nIaPy1Ov1MD09Ld4rANizZw+AxbIjyVEYhqKqNZtN5PN5SRgnCdL+Jvqi2K5GN3fW/f6ouPGxJkm6\nxEdvGJU4ero0odQr7/iYc2pZlpAiGtF938fk5KQQ2lwuhyAI4Ps+MpkM9uzZI1lUwGL6PCMfLMuK\nzEU6nZZVmvFx8D3aaM7XKpUKWq2WhJNqw78ud3L+OIe6pJpMJoUULxcrdU0YGBwpMNeEwXMJyyJU\nxxxzDN773vfinHPOwbZt23DJJZdEVnAdzPLzD33oQ8sZwiGL888/f7WHsGJ4xzvesdpDWDGccsop\n+93m3nvvXfb+V/KaMHh2cTCfjfkcDxxH0jWx0mM9VM99OeM6VM9lNbAsQjU1NYXXv/71AIANGzZg\nfHwcP/3pT9HtdmHbNvbs2YPJyckD2tdnP/tZWZFHZSIMF1qizM3NYffu3ajVaigUCshkMgiCALt3\n78bU1JSoK7ZtiyeJHiOuomMJiuW3Wq0WKSnSm0SfFEttLNul02lMTExIfpTv+9i+fbvEJvC42WwW\nb3rTm3DfffdJvzz6pah6MaeJpnOW47rdrqhfVLV0gCXPh6qTNltns1kxtfM9+jEAUaJYSgMWc750\nRpb2Z334wx/GDTfcID3v+DM5OQnP85DJZGSVo+/7skqRBvJms4ndu3fL6kBGNJTLZQRBgE6nI6ob\nU9aZMF+tViNp9IlEAuPj4xLLwPPS3joqe57noVKpRFrunHzyyfjJT36yl2qo/Xucy+ViJa8Jg2cX\nB5qSr72Iq43D4SZ2JF0TK/m5L/d79Gx85gc7rtW4Jg7l7/6yPFT33HMPPve5zwEAZmZmMDc3h/PO\nOw/33XcfAOA73/kOXvva1x7QvnSuEm9uvV4Pjz/+OHbt2oWNGzfKCrG5uTns3LlTyBQJD99PT45e\nFk9PEMnUaDSSmzmJCckBV7+RiAVBgFKphOFwiB07duDXv/613PwBiKdIB3yyFJhMJlGpVIR0jI+P\no1wuy01fkxeGaZLckUjp3Cua9UkESALpseLqwkRiod1NPp+Xn3K5LKTPtm1pBkwiqpPp6dcCFkmW\n67qYnp7GE088gcceewytVgu9Xk9+SqUSLMtCv99HvV7H9PS0lPJYjiWxIokkEWKURLfbjaS96/nU\nRJuv6xV7/LzT6TRKpZL0AoyHfepVmZqY8Tdb7SwHK3lNGBgcCTDXhMFzDctSqE4//XR84AMfwPe+\n9z30+3189KMfxQknnIA///M/x5133ol169bh3HPPPaB9kbzwhjc7O4tHH30URx99NNavXw/LsjA/\nP4+tW7fCtm2sXbtWbsx6qTtVECowBA3LJGvMrKL/KJvNRlaCWZYlnqByuQzf92UFHlezMUKA5COV\nSmFiYgIAJIiTKhhVG30cxjEkEgns3LlTCA3VKBqz6e2i2sJsKWZTcXtmaOljaBWH/jBNIjln3Dab\nzcqcMp+L56ZDR5vNJtrtNmq1GiYmJiJkj9twjplxVSwWI/38GELKc+G8M26h2WyiVCqJtyufz0fI\nUTyTjM+RXDELrFQqRYI9dUYZv3dU9bS/bDlYyWvCwOBIgLkmDJ5rWBahKhQK+Lu/+7u9nv/85z9/\n0PvS2UEzMzOYnZ1FsVjErl27cPTRR8P3fQldnJycFHWCpSWtdMTzlrgajqZqKhcA4DiOkAsdnmnb\nNgaDAUqlElzXlfIcCQsN6OPj43AcZ6/z6ff7EbM7S3C+78NxHFndxiX6jB2o1+uiVOlVfjqni6Zt\nEkGaq2lsZ0lPkws9L4yl0Cvi4o2BdRYTt9GEiftiuCdLnyRuJJAkTZzXfD6PUqkkJtR0Oi3ETRMq\nEkiSXvZAJCnWKp0+D+4nHp3AMp5Wsfh+TeaB6EKGg8VKXhMGBkcCzDVh8FzDqielp1IpNJtNWeXV\n6XSQSCTw6le/Grt370Y+n8cLXvAC+L6PPXv2YM2aNUgmk0JQgMXSlCYf2isDQFamcZWgvkE7jiNE\nguU63uzb7bZ4engTZ2J3r9dDoVCQMh2wqOrwvEjY6BsgaaBfCIC01eH49JJ+rRZx9RxX/+lQS6pz\nVGCoDmlCRf8WCZI+Fs+Bj4HFlXFU03gcxi/0+33Yto1WqyWZURs3boRlWZidnY0EbzLioVqtYmZm\nRs6dn1O87RDnmgSIpFmPT0OTqzAMkcvl0Ol0hFBRidLeMu5H53YZGBgYGBgsB6t+ByFhyGQyGB8f\nF6LT6XSwdu1a/PznP0e/38exxx4rN0eSFq0CxXvV0YvF8l08yJGg0Vn7sIBFb1MulxMVjKU17fVy\nXRe+70v5TedmMcPJ8zyMRiPU63XMzc2JaZxkixlZPAeW+nS2FkkHn1/KDMh4BUY0dDodyfjS6eE8\nZ71/zouOniBJ4XGoSHF+WQ5lOS+VSqHb7UppkuU/HV7KNj7aA6Z7CMbLkZxL5nBp39S+ks3j58lz\n0aSLOVx8nj8GBgbPPcT/HTD/LhgsB6uuUNF4zhs8CdYvfvELVKtVvOhFL5JSYD6fR7Vaheu6GAwG\nopLodimaEDD1nGZnlvLi4Y70FnU6HdRqNbkJM02dLVoAYHx8POJt6vV60poGWPBsDQYD2LaNmZkZ\nZLNZIVQ0uLfbbVm1pxsek2gxa0k3HwYgyhIJJcdB8kGypVUyAOJNAhZVp2w2K0ZyQitZACKEhH4j\nGsw5Htd1JRSz2+2i0Wig1WohCAJs2LBBQku53yAI0G63MTExAdd1RXki+eWx4z0cqaLpFHdNCrW6\nRNJExZPH1r0T9T45r+YfTgMDAwOD5WLVCdXGjRuFFLTbbTExZzIZzM/Po16v40UvehHm5uawfv16\nOI4jKgwVBxILy7LkxkliwjKZ53lS6tNeILaxARZ761mWhXq9Liv1uDrP9300Gg1JGtd99difjyGk\n9XpdSnb0VAHYyxxPEkWjO7C4FJVGaZbp6FUCFhUwDU0U+Do9VSROuoSmAy7Z4sa2bZkPkhaqabpJ\nM8t9fJ7qU7PZhGVZyOVy2LFjB0qlkpA5ni9LqlwAQGWNKhqVOK1I6bljWU/37+N4CT7Pz4Wtbegh\n4/ZLte8xMDAwMDA4WKw6odI+n0QigcnJSQRBgNnZWSFHTzzxBI4//ni5OVJZ0VlDXN5PBYaeI5aa\nqtWqlKKY+6R79bGkRzXJtm3pSUdztO43CCzc4BnJQGVNEwPXdYUYaP8RVRmWu4bDIVqtVsTXRaIC\nLDYzZmlzKaKjyYTeDljM5OK+gMWymPYPUWnTZFOXGllG02MgSNziqy673W6k3EbixvEwO4xqFQ33\nejWihlaRtKqkx0MjO0uueg6WSkjnfD2dVX4GBgYGBs9tHBKEijc5lqHCMMTU1BSSySTWrVsHINoW\nxXEcyS3SsQD05nDFHG/8bHTMm3cYhmJ+HwwGQppoFm80GpIh5TiO3OipXqVSKcmSYm6VBpUlTaJ0\nUCVJV9yfxBWBYRhGVqexvKjb75DcUGXRhux4/Z++MIKkk4ocgCVLiFTY+BnR9E4yxm2y2axENtDv\nxFWA3W5XyAyjFOhJ45g5/1xtyVJrPHhTxxzE1TySJZIkljVJwuPj1mGnBgYGBgYGTxerTqi0/2c0\nGmFqaiqyao5qglZJAKBSqWBubi6yryAIJLRydnZWwjmr1aqQtnQ6Dc/z5MbM9HM29tXL8FkiYsq6\n7ivoOI6s9guCQHxEeiUZy2OO44h6Rv+WJkU8V6pjLNHF/T0kV5wHjlWrOTo6gASLZFRv7ziOqHUk\npcy2IgmJh6XqRHGSJ9u25TUSWL2Kjo/ZA5CKFeMjODe5XA6ZTEbS5KnekQgDiJyjJqI6Q0rPOxss\nAwv+rmKxuBe5jH8PDQwMDAwMloNVJ1QAJFiRf+sbMcmJzg5iyxfe8OOlHxInqjqe54kKQn8QlRgm\nho+NjUlrm36/j5mZGXk8Go3QarWQzWZlTGy/wm1I/DZs2IDhcCgKjV7q3263I+oKf+uWOZZlCXGL\nl/L4Hr36jqQpvo3OntI+JJIilihJdrTfSoOPed6JREJM/iSvJD7cThNA5k1RfdSlPQZwJpNJIWm6\nHEeyp7OolirLaZWRYwCiyef5fD5CRHWOFefHxCYYGBgYGCwXq06otG8oThRIPjQB4Q03CAI4jiM+\nH5brGObJ1X2FQgHAApGYm5sTEhCGIQqFgpSWuKqsUCjI6r12uy1EiqUs3rS5Mu2YY45BtVqNnI9W\nYVzXFQLI95NAUj3p9XrwfV/mQTcQBRAJs+Qx+FgHVfJ9JB8kpXHCqVvrsGTHudXqjzb465gDEhyS\nT5IxlhH5OrCgujERnVEUXCTASAfHcYQoFQoFaVvT7XaldQ09TjrwVKfB61iIRGIhK4zbAZAEdj6v\ny606dsPAwMDAwGA5WHVCBUTbz+iVVjqdm+UunZINLJqhtbnYdV3xYvF9o9FI1B/XdSMp5yQdVLOA\nhUBP3UePpAqA7Cufz2PXrl0AgLGxMQCQVYpBEKDRaMiY6AvisajyANESFrfXZnGe81I3fV2S00Zz\nXSLUiox+TnustLKnGyjbti0hojyWJr/8nLg/Eh/f9yOlQpYuK5WKxDhQgSJxJrGzLCvSMoj+Jz1u\nXf7jvJFkMdGe58DtWMrk8/FYBgMDAwODQxv7+7d6Ne0bq06oeFPTxm6SCd28ljddqk960qj66ARw\nvR0VIh6vWCxKKcr3fViWhUqlImRnbm4OvV4P+XxeevPR0zQcDsX87nme9Itj3tH09LQ0AKbfimZs\nkhbGE1D54fNUyYBFT1l8RZ2OE4i3UeE5a8UvrvppAqHLgpxH3RZHK0IApN8fiRVXUHLVpVa/tHpE\nY/poNML8/Lz4rviZkLBls1lRsLgd4xX4WXKOSNyoYHJMeuFAPJuK2WDFYjFivNdmdgMDAwMDg+Vg\n1QmVJlJ6RVp8JZkmB9xWL+Wnh0dHCpBk8PnhcIhCoSAkhGWqcrmMIAgwMzMjBm7e9FluowmduVZU\ns6i2cFzsqUfyQTLYbDYBIJJfpY3iJBza26WVKp6DJlFUunhuS/mp4g2EgcWVlWy5o/shapM8CY8u\nKVLl00Z0KnA6I4s9CXke/FyYDaazwKiIeZ4n2VScn06ng0KhECFQ/Ey4MpTHJanmOcZJI1d86nnQ\n3z2jUhkYGBgYLBeHBKGKEyftFaKxXBvUAUQIBBUkALKNJgrcRy6Xk+gCpqt3u100m02MRiMUi0W0\n221RenK5nBApXY7SMQ26FEgwwR2AeKh4jrpsSA8VCUSz2Yx4mzgnmoxwP1SESFx0SW+pnChNqkhG\nfN+X1/le3ddQN43Wc0zliuGeOjSUyhUVNpJK+tJ4LKp2bIrM8mqn05HntS+ORE+vVOS56jZEulwZ\nJ0n0tc3Pz8NxnEiavCauBgYGBgYGB4tVJ1Ta38OVeSQ9BNUR3S9PK0IAhFh4niekgYbp4XCIsbEx\niT5gnhKDJ1kOYm86y7KE7NTrdVmRpk3VqVQqQiZIlLh6jkoTl+4zMoDnQ3LH5zUB1Jlbet/ajA5E\n1T0SL/5okqpVK76mAztJatmLj9DtdfjZkCTpz4vQxIefF7dh82tGIHC+gyBAuVxGs9kU/xY/J00k\n9TmR6DmOI2SP3yX9HYm3meFvHfBJ0mUUKgMDAwODp4NVJ1S84ZJ8ULnhDZU9+zQp0D3qNMHQZZ7B\nYCD74U28XC5LbzmWquINkHWJKJ1Oo1QqyYoz13Vh2zYKhQKq1WqkDDU9PQ1ggVT4vo9+v49yuYxM\nJiMEhKSJKpBWsriKkCQrHiNA6BWJuv0L50X70fi39l/FFa+4f0hngFHB4oo+kg7Oq467CMMw4nXi\n50diS+Wo2+3KvNCz1e12kcvl0Ol0hOCR2FJd5DH4u9FoyCrO0WgkvzWp1J4xzkcymUSpVJIWQvp5\nQ6gMDAwMDJaLVSdUOr2cLUdIPnR7FhIIlqt0hhCASL4TQYLiOI4Y02u1mpijucRft4cpFAqiCJVK\nJWSzWfFNsblxs9nEtm3b5DHJEQBRvNhQmWrQcDhELpfDaDSKRAEww4qqi1altIeL88H5ifvK4hET\nPLYuBZI0MGyU4wUW092pSgGLhDWbzcrx6KGiwqWzorTSQ/M+Pxu9H2Z48T1Ui/L5PFqtVsSsns/n\nRTnTgaOFQgHpdBq+7yObzcJ1XVSr1ciKSK1karJkzOcGBgYGBiuNVSdUhPYIkejQe6RVHPbeAxYN\n11qZoJoCIHJTp5JE8lIulyVwkmSDfqi4d4vlP+3RKRaLonR1Oh0hAez/R+UlmUxKUvuOHTsi5Igr\nB1mu1CGl/Dsei6BLpAAiipo2YWsypUkV90lClM/nZS5ZauV8E9qszvfy2DSq6zEEQRApEZLo8e8w\nDOG6rgSl0iSfTCZRq9UkboKfAUkczyuVSqFUKknJkHPVbrdRKpUi44srVPxdLBYjTavjSqCBgYGB\ngcHB4ID+q/7444/jzDPPxB133AEA2LVrFy6++GJcdNFFuOKKK4TA3HPPPTj//PNxwQUX4Gtf+9oB\nDUCnW/OmyhszyQ6Vh3iJTzdV5s1SK0700fCGnUgkhATpYxcKBQkA5Qo/KiCWZYl6ZNs2Wq2WRAdY\nlgXHcVCr1YSYNJtN8UQVCgUpJzIRHFhQhXRSuSYLeuUflR8qOQAi5JFzQMLEv7XnKO4hArDXnGlP\nmG5czG11Y2WWJAeDgcQd8NiaqPFcdbYYyaiOkCBx0p6sYrEo568bUVMFJIrFoihYJMuNRiPSj1BD\nnzNbDsWjFQ4Uz+Q1YWBwOOJwvibiHtT4j4HBgWC/dxDP83DdddfhVa96lTx3yy234KKLLsI//uM/\nYuPGjbjrrrvgeR5uvfVW/MM//AO+9KUv4Qtf+AIajcZ+B6DLfJpQ8SeVSmFmZkZKdnHSAUTb1QB7\nt1vRZIqEjKSATX95/FwuJySIHh9mTc3NzQmh4A2cqg3JQq1WA7Cwus/3ffi+j1arJR4kjkkTASC6\nmg9YOrwsvlKPpS2Smfg/ADqEU5fB9L64Hc+D80XEfWkkR3w/j6uVJnqrdOgq9xsvXVIZ0p42PU4q\nlHoRARtja+VMK2Gu6+41d1qd2pdqdaB4pq8JA4PDDeaaMDA4AEKVzWZx++23Y3JyUp578MEHccYZ\nZwAANm3ahAceeACPPvooTjrpJBSLRdi2jVNOOQUPP/zwfgegy05aZeLNcW5uDp1OR/53w7Ym+sas\nvVSaTNA8zTJW/H8auVwu0tyXXinHcVCpVGBZFnK5HFqtFmZnZ2WfLGuxTMa4AWBBNanVahgfH0er\n1cLOnTvRarXEO8WVaSRl2n8UPw+tMHF+4kQq/j8orq6jb4umcO4zTrC4n7iHC0AkJFO/l5+PXk3J\nKAqtpJGUFotFaROk/XFU6diehmXZbDYrfjNN0Djn9F8BiPytV/DpeAo9f5pQ0S93sKb0Z/qaMDA4\n3GCuCQODA/BQseSkQSMwsKDIzMzMYHZ2VtqvAAutWGZmZvY7gFe+8pUHO+ZDGuedd95qD2HFcMMN\nN6z2EFYMuu3MvsA2QvvDM31NGDy7OJiSjin/LI0j/ZpY6c/9UP0eLWdch+q5rAaetil9X5N5oJP8\n85//HLOzs7KCzHEcWJaF8fFxzM3N4X/+53+ktOb7fmQpPo/R7/elBMSQTsYmUHVgC5nhcIharQbf\n9+E4jqgtQRDA8zykUilMTU3BdV1pe8JyVzKZRKVSQRiGGB8fRzqdRrvdFoXm3HPPxd133y0tbajA\nOI4jve04hvn5+ciqReY96b56hC4xcm6pzgGLyotepcdtqdpoBUyX5+idAiDp5WEY4pprrsE111wj\n6pkuR7JkGm/GnEwmUSgURFVi8+lsNitzEQSBNEumUmVZViSgtFgsygpIz/OkVUw+n5c4CvrbqKDp\ngNhkMonjjjtOFMBMJiPH49zxZ35+HrlcTjKzVgJP95oweHZxoMpkfJHDauJw+y4d7tfESn7uy/0e\nPRtzdbDjOpSuCWI1v1PLWj+ey+XkBrlnzx5MTk5icnISs7Ozss309HRE/t0X6LXhjZEr+5LJJHbu\n3Ck+HN5s+/0+er0egiCIEBKCJSWSA5qkgyBAr9cTszkDN/VSfpINtokplUoolUpysw7DUMpWvV4v\nYlDnfPR6PSGA3LfrukJUut0uOp0ObNtGqVSC4zjSTJger7ivRye/a/LC1zXR4nzo0mG8HKrLhnw/\nCZMuM5J48TXbtiX3iq+xBKuPzTn3PA+NRiMSXMpsqURiIbWc+VGcN8uy0G63xcfFFYQA5DPTqzj1\ncTVJ1CVPYNFnx3nlHJbLZSHET4dQreQ1YWBwJMBcEwbPNSyLUL361a/GfffdBwD4zne+g9e+9rU4\n+eST8dOf/hStVguu6+Lhhx/Gqaeeuv8BJJPI5/NCgNLpNKampgAslmlIpOLmaBIcreqQcOgIBP7O\n5/ORHnPAYuo2b6psaMyfZrMpS/fZhiYMQ7RaLRl/3PuUTC40S6b3i68zFZ3Nk/UKPbaxob9K+5qW\nWrUWz1kCIP4mprmTgOj2KtrwTWJDtYYxBFrd4jF0SKpOgSc4P9xnr9cTwup5HoIgkH2HYSjnmslk\nUCwWRXHjZ9npdJDNZuE4DrrdrsRh6FY/JKhU8HhOHBe/KwAkQT9uSk8mFxtuPx2s5DVhYHAkwFwT\nBs817Pe/5D/72c9w0003YceOHUin07jvvvvwV3/1V7jqqqtw5513Yt26dTj33HORyWTw/ve/H5de\neikSiQQuv/xyFIvFAxoEFQ7btlEsFkUlYYlHZzLxbyCa6h1f4Ua1SRuQGYGQSqUkXJNqDUs+JAos\nmzHLiuTA933Yto25uTn4vo9yuQxgUU2i0qRN9CQSus2M7ndHpUerQfzNVYUaVLHihnStLsWVLhId\nnUelTd9a5SIhCYIAlmXJWIGF1Yu6vBgvH2rDPlvWMICT++bYcrmcjJEhqYyj4Llz+2QyGTGv65Lu\nYDCA7/uiJupVioTneaKy6XmIh5IeCJ6Na8LA4HCCuSYMDIBEuMpF7F/96lcIw4WMKfqnKpUK+v0+\ntm7dil/84heibugyny71kHAMh0Np50JCRAWsWCxGyAMbHw8GA7RaLVFMqGjwRsuee8lkMtIyptvt\nRtLTXdfFpZdeiptvvlkIiV5BByw2D+bKNPqmqAy5riulKl3G5GOWsuIqE9UVGkCBRYKnYxn4PMt0\nJDQkdros9vGPfxwf+chHhHyRULJZMeeB8+I4ToQEkkTqHDDOC31bWpnj59loNISkcWxMSQ/DENVq\nVYhttVqN9PHTCufY2JgQplwuh927dwNAJKWen9FoNEK73UYul8PExMTKfbkNDgsYD5XB/mA8VEvj\nULomiNW8Ng6JpPR8Pi+kgSGeqVRKmuSy/KYN17qco9Ukbk9yQTMzsNgHj02NWbZzXVdupJrspNNp\ndDodKUlReQrDEI7joNfrSU85en10yxwa37PZLGzbxnA4hOd5Qkh4DtrTpBU4rUzp2AISQz6nW/PQ\nRA8sXhzx4FAdJUGiNBqN0O12hZQAUSLH8bDsxpIlc6BIYkli8vm8fD5MTifol2JLIJK0MAwxNjaG\ndrsdKaXqcFDOrWVZkk0Wzy3rdDoSiRH3jXmeJ983/Y+BPk8DAwMDA4ODxaoTKpIn3sxIFkhM4oZz\n7YnSq9m4L+4DWLhJTkxMRLxXVHmorvDG7nkeLMsS5YQEiKZlYIFstdtt2LaNTqcTaY9CFYqlLtu2\nUalUJN273W4jkUhI1hUJIski1StNenizJ3HSQZ26ZKXJJRsxk1zEvV16/1qloSF8KS+RztuiuZ/E\ni0qV9lvR+6aN5TwGzd9cxQdASCeVM8dxsHv37r3Kd1Ss+v2+NFOOt71hqyCOicSa56sDXfk94Gdr\nYGBgYGCwXKx6l1gSAhqotZeFKoQub5E4AIveIL0vrdbwJpnNZuWmTjJD43S73QYAWYEWD4IsFAqi\ncDGB2/d9AIurzizLgud5cix6eaj60Divj6tLmFROljKea1LJMpr+IUHQc0BFRx9bB3nqeSMp5P7j\n7V10SZBEJP78voJVSVBJokiaSHRIjpvNpqzmJCYmJuQcuZ2OcGDGDc3mJJxaKeP7AERKpb7vS+mT\n6lwikcDc3NxTfVUNDAwMDAz2iVVXqKj+OI4TyVYCELnJagVEl7Q0AdOKDmMLstlsRAWiOZnPFQoF\nKcnRbE2fDQkdlZR+vy+lPh6D+8rlcgAWk987nY6QJJYLNQnhODlu7YvSrVmARaUnnuatyV9c0SJh\niBvvteJDBUv33yPB4/zrlZPxhHp+HjynTCYD27YlMZ0lWq2K6RgMqmiJRALNZlO8U47jIJFIoFwu\no16vR8p9ehXjcDhEpzsa/coAACAASURBVNORMi1Jmv4ekSySeFKxcl1XVpfati1GegMDAwMDg+Vg\n1QkVb4JcWadJkuM4mJycxNatWyNNhON5SyQQ3W5XQjp1BAK9PjpvSAdGav+Nzj3isnyu3GNAJF/z\nPE9apFBForeIN3QSlUKhgGazKcSEXikSGK6kowpE8sFzXKoEqFvvAIuEQxM0PZ8kM1wEwFV02iyu\nV0YCUQURgKhxOmdLEyvOk/48OQeanGk1ML4iMJVKybnQ76bniuVJbt9ut1EulyMlU5I8TUZTqRRa\nrZbkaVE942ulUumZ+ZIbGBgYGBzxWHVC5bruXkQGgHh0isUi8vk8XNeVEpD2FAGL/hjtqaL6wpsv\nb7bD4RDtdhtTU1N7KS68+XL/3W4X7XYbnU5HVBUSguFwKKnplmWJ8lEsFqV0xpWAJCOO40g2lW6W\nTE8RCQdLeyxfxqFJpDaCa5M+t+NvjpmkicQLWFQJSUTZl1Cv0uM49W+On/tjSZMEtFQqSUlNE1fO\nlfY6aWLY6XRQKBTQ7/dRKpVk/rWPTGd4aY8cvw9L+e90NIbv+xgMBqjVahGlzMDAwMDAYDlYdULF\nUp/neUI8tKKSTCblpqqVEG6nfUd6NRsQ9fPwva7rYnJyMtKWhr6bIAhEYfJ9X0pWAKQUyG2p7tCv\ntNTNnDd8Ej4msLP8xPdSSeNr+jz0+S2VuxUnR/GSniZcumSqV0LGCQ3TjXleLIEutSiAx9G+KBIl\nhm5STSJp1Q2NSd663a5sk06n4bqurNLLZDLodrsSl8DvAMka4y90NAbVSB1qSkJFRZBKnfbsGRgY\nGBgYLAerTqjYnoAGb02W6IFihAFJS5wwAYvL3nmz5M3f93254Xc6HTGMA4uEgQqI4zhIpVJy0yYZ\n0unfVJC4PJ83fpIQ5h2RULD1ilZQtMoVz1HSpnISMq1G8b0kNSRW3Icuc8VN7vr9PI5u9cN5Jngc\n+s4YkUAiFy+5ct+Mi4gTl1wuFzm+Lsc5joNWqxWZA84bCRW3JSmiWsZFCCTA/M4wpT4+F5p0drtd\n6eUXD1A1MDAwMDA4UKw6oWJOlDZis7Sj/TTAotJExPOJ4uSDN3x6cGia5oovKij06gAQAqZLcgzn\nJGkjieCNmI2UAYjnitvrjCSmfusIByCqROnS3VJGfH3u3Fd8hR3fG59nvkefg86RsixLlDceQ6+i\n5EpGTcC0ukUFLZ1OC0lmU2iqVuwFSI8Y95dOp1GpVNBqtWSFJn1wACJzpUmnNszrEinHxVWL8fKp\nJqs6XsLAwMAgjpX+9+FQ/fdmf+M6FGwRh+rcAYcAoaLiwB9NLqjScGWXJkxUj4DFnCQqVCRNJFk6\nY6rRaMg2bExMAsByG2/UjDro9XoRn1a325XjafM1QTKgjefay6VbtuiASd3+hc8TmnDqUE89D9yO\nBIFGd+0PIkHS5Ir9/pgNRRJDksi4A84X55Yqky47ajJMdYolVKbB5/N51Go1OY72dzHYk89zBSLP\nhZ9nuVyWuQcW+j4yCJbH14qnNvXHS6r7aldjYGBgYGBwoFh1QkV1Qa+2AiCqBvv3Me+p2+1GSIQm\nCtpcrtvRcBuW5/QxRqMR6vW6rN7j9vRTMexSv4flSSpO2kPFJHW98k8HjS7l+dJ/630tle2k1SW+\ntlSiuFb2dDyD3hfnTY9bRwtQgdP79n1fDONMOqfJmwb8QqEgZTmSNO2J8zxPlEDGLLANTiqVQrVa\nxdzcXOSzzuVyaDQae0U76PIuCZTOr+LcF4tFNBoNmQ9d2mTZz8DAwMDAYLlYdUJF9YSr5ai+6PYm\nOkVcrybT4GskYATVI13KInHwfV8IAY3xjALIZDLSn06b0wn6gjTJASBBmiSJmljpsXIfWnHiePX+\nNOnSj+MlL02adN7UUuqUPlYQBKKo6ZRzADJnmuTRI6X9ZTSGk9RSYSJpoVKlP0OOPZlc7Gdo2zZ8\n38fY2FikJMhYBV0i1OdC4kQCWK/XMTk5uRc5TCaTmJ2djaz241iWSog3MDAwMDA4UBwShIpG4nht\ndDAYSLgjPTi6OTKwSCC0V0mX5wBEyon0Cvm+D9/3I+SCffDimVckZFoV0qUkrYJpMqVLlJoIxb1i\nOrWc+1oqd0qX+aga8X2cC24TJ2JxPxZXyZEIcT+aWHA+44oY/UjM/dIqEX1vDErlOeXzeSkXUg3j\nZ6lb/vT7fczNzSGXy4n/imNjOyAqkLrkSALG+WVDZe2xy2azmJiYwNzcnPQk5H54vgYGBgYGBsvB\nqhMqqhhafUilUmLuzuVyKJVKGI1GaLfbSKfT0lw3vnJL5xSRQMR7xHmeh1arJU1ygcWoAioWmjzE\ny4s0XscN8nocuvQWL/HpVWzcp/ZEabLG93JMHI9+X9xorcuQ+n3cH4kJA0kHg0GkH178N0kPV8xx\nLvU50KBPL5Uu4fKzDYJAFEAemwZ8qnqZTAa5XA7VahXtdlsWAySTSRSLRemfmM1m4bquEGC+l3PL\nUqReGcqYikQigVKpJHOx1IpLAwMDAwODg8Wq30HofdHQJm+91D+fz6PVakViA/TyfW1IHo1GKBQK\nQjwGgwHm5+fRbrfhOE6kdKhXfFGx0MqMVjn0cbTJeakVe8RSahqhiaEuDep97oucaRVKEzIqNnqV\nojaK60wmzgFN9BwnACmzkYzq6AR9XMuyZOy6ybAuh1qWJXlUNOlrwuu6rqzII4GiZ47hr1SmOH5N\nSvk+/ma5lnPY6/Xk+CR+upWNzvIyMDAwMDA4WBwShGopZYDqA309zBbSxnTerKk+8HneSHWit+d5\n6PV6YnQHFkmJJi4kI1RQSFJIPHRgJ6HVGK1qxQ3z/FvvT5f9SA65TTz5nNCKVjy/Sm9PMziVJV1u\nZLioPl/tR+JnEC9R6td1iZHnGgSBkJY44SQRy+VyKBQKCIJAAltt20Y6nYbv+6JOkVQlk0nU63Xp\nuZfP5yM+Mx3hQHUwl8thdnYW69atA7DYl3A4HEbIll5wYAiVgYGBgcFycUCmkccffxxnnnkm7rjj\nDgDAVVddhTe+8Y24+OKLcfHFF+Pf//3fAQD33HMPzj//fFxwwQX42te+dkADWEoZ0Ddulmh0WGMm\nk4n04SNB0IoEb5oA0G63JR6A21O54W/6erRKpMmUfgxEc410gjmJiSY9wOLNmhlY+n1aRYoTLE1o\n9BjiZUOdVUUfli4d6h+teDHQUqtOPJb2gumypy79AZCkd/4w1JS+M+1xYlgry3vr169HtVqVz8tx\nHORyOQwGA7iui2QyiUqlEllxSNIZX+nJ+efzpVIJvu8DgKwmZMaYHocuox4onslrwuDZRfz62NfP\nwWz7TP8cijDXxJGPQ+GaOJSxX4XK8zxcd911eNWrXhV5/n3vex82bdoU2e7WW2/FXXfdhUwmgze/\n+c0466yzUKlUnnoASkWKI5vNCoHSoZj03rDVCc3lVIJ0TEGr1ZKbtVaYgEUlh8SD79Up5Nw+Hq4Z\n9zXFSQr/1vvXKpcuDWqlCcA+b/Da5K1zk3QJTBMvlt+oTulz1PvkmKkW6m1JAJkHxW34mjb8a+LH\n1ZY8d7Z4qVQqyGQykTiKdDoNy7Lgui5KpVKk9Q/J8MTEBHbu3Il0Oo1ms4lisSjjJ8nT5w4sqFL1\nel2eSyQSYn7XChXVxAMlVM/0NWFgcLjBXBMGBgegUGWzWdx+++2YnJx8yu0effRRnHTSSSgWi7Bt\nG6eccgoefvjh/Q5Ar5DTCMNQsomobrCfG7CobLENDBD1XgVBELnx6pKWLhFpksRjjUYjKYnFWTjf\nr0mJZs66fBZXhXTpTt/E9cpEDW1Y16oRV6dpAsYfvT39TyRsmsjoselxhGEY6eXHcefzeTGekwTZ\nti37zOVyyOVy8pjPscRarVYxNTUln2O328Xu3bvFs+Y4DiYmJtDpdJDJZES16vf72LVrFxqNhhAg\n9v9jfAM/e46Nn00mk8HY2Nhec0rfFM93qbl/KjzT14SBweEGc00YGBwAodJtWTTuuOMOXHLJJfiz\nP/szzM/PY3Z2NnLzGhsbw8zMzH4HwBtanNiwrER4ngfHcfbq7ReGi4ngNKszY4plIu3l4b75mMfj\nWPQNlsoPlRZNPjTRWooUcl98ry716SX+JAnad6SJly4r8vgkNDxXfT5LbR8neTrqQBMrPUZCNzbW\nY9PEUkdEMHWd4+RYfd9Ht9tFKpVCqVSCZVmYnJyUBPUdO3YgCAJMTk6K9yuXy6FYLCKfz6PRaGA4\nHEo/RfqymHkVH5dWoYBFhUyTX5JMkvQDJVXP9DVhYHC4wVwTBgbLNKX/wR/8ASqVCk444QR89rOf\nxac//Wm89KUvjWxzoLXOpS5CYPFmd/TRRwMAjj322OUM9VnHZz7zmdUeworh2muvXe0hrBj4PXoq\n3HvvvTjnnHOWtf+VvCYMDI4EmGvC4LmGZREqXSc//fTT8dGPfhSbN2/G7OysPD89PY2XvOQl+90X\ns4nivqEwXMg+ajQaePLJJyXYc+vWrZibm5NGuzRBdzoduK6LMAwl0JHlJCoPVDaWMtXpNHA+F/9b\nK1PxEiKwQKb++I//GACWNE7rVG4docC/9THj0QR6TljK056qfr8v5cBEIiFtc1ji4/t1rAGPz1BV\nnRt1/fXX45prrpFtqWoxNoHH16VF9jbUx6JqyBR8ni8lf91HkMdnBhnb27AMyXY1LAtOTEygUCgg\nlUrB8zysXbtWxsGFDGEYolarYevWrbAsC47jRHK0eMxms4lTTz11v9/XfWElrwkDgyMB5poweK5h\nWdHQf/qnf4pt27YBAB588EEcd9xxOPnkk/HTn/4UrVYLruvi4YcfPqAbFMnMUkgkEmi32yiXy2J+\nzuVycpNkKYtEaDgcRnxX8dRv7ZWJk5elVozpEmD8PdrXpMevYxX083Eypbcn4g16lyqDWpYlYZg6\nKJR+JjZ0JqnkMTkXXLHI4EuWVUmOGFehj6vjHRjCydd0cCobEnOlHklkLpdDr9eLeLmYes/VfJ7n\nod1uo9Vqwfd99Ho99Ho9bN++Ha1WC91uV47PY+/evRvbt2/HzMwMBoMBHn/8cfFb+b4fmetyuQzP\n8yJEUJN4z/P28lsdDFbymjAwOBJgrgmD5xr2q1D97Gc/w0033YQdO3YgnU7jvvvuw9ve9jZceeWV\ncuP82Mc+Btu28f73vx+XXnopEokELr/8cjGELwf6Jk6/UDK50PetUqmIqsPIA97cdWNkbpPJZCL9\n/zRB0h4jveJLB2MulQPF9xNLxS1wDFoZi6+y43v5nPb3xFUqEgSSqsFgECmZ0mfGlYr6fLTXjGqW\nJnYcpx5L3HPG56hCkcRpTxZXP/KzsCwL/X4fpVJJgju73a5kULElDXOo6Msql8tIJBJoNpvYs2cP\narWavN9xHAyHQ4yPj0vEQjabxcaNGyUENAxDuK6LdruNsbExuK4r6hqVM02qDibYc7WuCQODQxXm\nmjAwABLhKhexqRpopYRoNBrwPE9UEc/zsGfPHoxGI9TrdczPz8tN07IsCZXUKkrcSE4ju45E0MeM\nq2U6BkFjKUP77bffjne/+91Lrr5baizx89XH1OPie3Q6OMelwz+z2ayUDnmsdDotq+K4H5YNtdmc\n+yCxYsmP5T2SM5YQHceB4zgolUoyH1SrqH7x8wUWSJQmYjomIp1Oo1AoRMgrCV8QBNi5cyeGwyEq\nlQpc14XjOAiCAJVKBf1+HxMTE5IvBSwQrvHxcdRqNQBAsVhEu91Gr9fDb3/7W0xOTqJarUbG4fs+\nCoXCXuTZwMDAwMDgQLDqd494dIGGbnochiHa7TZKpZIoGVSV9Iq5eCJ5/AYZL+nFfVFLbRdPQtdk\nSK+gix8nXt5bKqaBYyX0arWlPFW6BKlXslF9iZ/TUl6xeGxC/HhxAhjPviKR4gpAeqF0WbLX68F1\nXfE9lctllMtlFItFIbWaKHKeNeHk/E1MTMBxnEj5Un/+3DaXy8FxHAwGAzQajcj8p1Ip5HI5PO95\nz0Mul0On00Gn05FVmNpbZmBgYGBgcLBY9dYz+mYfV2toeNbm7TVr1qDZbGJ+fh7D4RCu6wKItjfR\nN2S9f+5Tm8D5WPuR9Pb8O14mjHu/4moSt1nqb00G4r4uPX5NgLSSpFulUJXSJIUkS8+p/luX93S7\nGk1e9XklEguBmJOTkxiNRsjn8zLmTqeDQqEguViNRkPM8JZloVgs7nWujuOIx4lE2PM8ybvicVnK\nJdlptVool8sYDocyBn4/qJ7RID8cDjEzMxPJxUkkEigUCvA8D6VSSYJg6cNjk2gDAwMDA4ODxaor\nVCxdxRUeqgYsW/m+L+oUk9N7vR5qtZqQFKoocXLD/el8KZKKp1JmljKdx0lfXAHTRG4pMqNLiPq8\ndbmNiI+P22nSp/Or4uW7eE89Eq14iU+TTr3SEVgI7Fu/fj2OOuooUYFYXqQXql6vS5lvYmIC5XJZ\nFCudtD4cDmHbNlKplKzO0+Xe0WgUyZnS5b+1a9eKEqbLoclkEp1OR8bPc6nVasjn83jyySflc+P5\n0rDP+bZtWxY7GBgYGBgYLAerrlAFQRAhCQQVJ2BhRVq73cbzn/98uTFns1nk83npz0Y1h+U/27Yj\nyo1uzhv3TcXLTLqMpxUlPbY4ydHEKF520woVgEhpEoiGm+pWN9x2X3EKJCJxg7lOYY8rUXEyotW5\neJmyVCqhUqnAcRwhU/1+H67rIggCOI4j888xxw33JMT6fNlGiJ+tJkIkVDwnmtyBBS/UzMwMisWi\nKFw0veuyJM+lWCxKaGez2USlUhGi6fu+qHv0khn/lIGBgYHBcrHqdxD2g4t7fnSZKwxDrFu3Do7j\nSLnO931UKhW58fLGyX1Q+YiTNU1WllKH4qXBpcCb777Kg3o7rZbp3xpLZU7pcpguS/I1Xfajx4u/\n2fSXpbJ4GZLH0aSLjx3HkVU3NGlz5eTMzAx6vR6KxSIqlYqU2Ehcu90ums2mmMOBvUkf88GohDFL\njPMQhqEoX5roJRIJKdPxMfehlTy+h/PDVZDVahVBEMhqT64U1N6pg2k/Y2BgYGBgoLHqhGqp1X1a\nLQGAdrstpSJgQdXqdrsoFotS1qIKE4aLPQC5f63OaNO3PoYmXfr5uPKkPU5Lbb+UDyz+nC71hWEo\n5m6SQN1yR49BB6A+FeKkKW56JymN51gx6kCT0yAI0Gq1pJxH1YctW8IwlIbDnKNWq4VWqyUxB1SP\nCM4hz5NqoiaKWrnSnrBqtSrzSkJF75NeXBA3mfu+L5ESXFn6VKVcAwMDAwODg8Gql/wGg0FEbdFG\nbE1SuPS/3++j2+3i+c9/PtrtNlKpFHbv3i1Nklkq1NEBPE68HLeUcTtevuONW5vY9b73pUBp4zmh\nU9o14Vsq7ylueudjqnlLrRbUc6jLd7qMx9d43EwmIz0SSXB835f9lUqlyOfD39rv1e/3pXzW7XYl\n64oZYfRLJRIJycDiZ8JzsSxLcsU4Zs/zkMvlIuesM8bi49FKHku/BEkbCSs/C536bjxUBgYGBgbL\nxaorVInEQhNcDV126/f7GB8fF9Wk3+/jt7/9rRiy6ZWhL8eyLLkhU53iSjGWxDTJWMrEvpSKFU8V\n1yRjX0b1/9/etYdYWt7n55w598tcnTFuSsS6CS64WSsKjUVbk7qQFIopKiK2NLFWcGuRWo0k2tAS\nclmTULGKxmaLbUIjzD8VKtWIhVowFl0wmkuNqaVrupeZnZlz/8716x/D887zvfOdnZkdN2em/h44\nzMw53+V9v7Of3+Pze37Pq34iJXpKpuLKcQAcudAYCF8FU6+UziHObK7b8G/1oCkJZaedZlDxb/5k\n2CpJo+Z++SXQer3uiJIuU8MAUs5Pl8nhXJrNppsLS4HFYjFC0EiEdax+ZyS/OxJpvrTLMi5vzGAw\nGAyGzWDkhIrlojj43WthuBrAeNlllzmCQbWBpSiWrXwyQ1XG9ybpw1gfwH6EAD8jseIyK0ScqV47\nCwE4/w7JoELJG31QJG7aBaddgn7OlX88JUJ8saTH8hdJSavVQr/fR7lcjqS2s4POP1av13PnUH8U\nPVW8rlyTr9FoOGLF8WUyGUxOTrq09DAM3TXldWc3J8dwwQUXuGsJrAaGkthxPJlMZh2hSiQSbj1B\nkj6WWXl8K/sZDAaD4WwxckLlt/UD0Qcb1Su+x/KSHwLJ/bkALrdhqU+JGc3UJEaqWvHll5E4Bn8b\nHoMkJM6TBSBSYgIQ6UDk2PU8WvaMW+qG79G8Tejx1CPFseZyORSLRUdc0uk0pqamXFDniRMnHOmZ\nmZlBGIY4deqUSzzXkifJHRsL0uk0stksSqWS6+wj8ex2u+h0Oi7dnj4uDW9ldtXU1JT7HjmflZUV\nrKysRAgv0Ww2I4SKnirfI0fC1ul0IsSOpM0IlcFgMBjOFiP3UA0zcwNruUgkValUCu+88w4OHDgQ\n8VrlcjlHLFQBorqjhnU+bFmC4nF0G5IYXQTY9yz5kQjqu+LYtWtQSc9GqdwkVyxh+d4qv8NPvV9+\nuVJJJEkUVSolpCw/fuADH4iU7wqFArLZLGq1GrrdLiYnJ4d+f5wXCRxT0knGWq0WyuWy67bzy6Ys\n39H0TkLG9/fs2eMWfk6n06hUKkilUiiXyyiXy5iamoqob8OubSaTcSGhnDtDRH3l0GAwGAyGzWDk\nChWwPnZAFSv1CXW7XVx11VWRwMpkMunKRcViEYnEWvcXy0JaGlOFyX8B0eVkdExxxG8YCVTzOpUY\nnluJjL+/Kj9xyhXPqfv66pUqYUqmqMQVCgUUi0WXIaWkjGMmMaK6l0wmMT09jVQqhVOnTqFara4b\n92AwcKSWREi79zi25eVlAKu5UJVKBY1GA91u15XdgLUuyG6367ZJJBJYWlpyxI3fe6FQwNTUlDOv\nDyNEvuFcF2sOwxD5fB6VSiV2X4PBYDAYNsLI/3f8TJ1VGkJJxaFYLLqOPkIX3aWCEwSBUxxIsKjQ\n8CFPHxBBZcrvxCNUUfFb7v319ZRMaSlPiRbPqcfR45FUUKlSgqPXzf9bz61kk2ROgyx5vThOetIA\nuO46LZdx2ZiTJ0+iXC670E9eY+2GpEIYBIE7Ry6Xc38zyoBIpVL43//9X7fY9czMDFZWVjA1NYV+\nv+9yyDjHbrfrSCFLjxoaOgwcXyaTcV2miUTCLaZsMBgMBsNWMXJCdSb4MQbsytLW/263i2QyiVKp\nhF6v5x7sJEtaIiNhUjO6Pvy1DKg/1Yfjq1eElvr40NfSWZwSFOez4rHoT1JFikQIWN9dyL85Py2H\n0t+kZInz4jIsJG2aUM8Ue553YWEBS0tL+JVf+RXMzc0hCAKsrKy4MaufjMfK5/ORZPIgCNw1yefz\nWF5exrvvvotEYnVJmImJich6fOeddx6ANdKsShbN6JVKxf070EyqYZ4onaN/TQwGg8FgOBuMnFAp\nSQDWK1ZKOrLZrMtIGqYSUflIJFYX1yXpInFQ1cv3RflEC4iSHCU3fsCnb6iPI0w8jk/m/G2VMOqx\n/Pn6ipeSCPqMeAyNctBzkQipB4y/s1TJRoC5uTnMzs6i2+3i5z//uVOaLrjgAud3q1QqTrXy4xhI\nfNS7VigUUCgUXHmQga2lUilSwg2CAOl0Gp1Ox5ErfredTscFv2q5dBg0z4vXRH10BoPBYDBsFSMn\nVP4DzDeA+0oDlQt6ghhNkEgkMD09jaWlJWdi5gNWy0qqRPkltrg1/uJCMf318HxiNkzBivNXcR+f\nGHH8PomK+0x9V5q2TvUlLmHdJ3EkXryeAFygKQkM98lms7jooovc+Xq9nlvfr9/vo1qtrltPkOVK\n+rlUcSuXy47YXXjhhU5l0k7O06dPOwJGUpXNZp0qyTDRRqOxLu/Lx7B/Y8OM7AaDwWAwbISREyof\nPtnwP9OMJL6nD29VjqiCMH+IGEZs/M49/u6P7UzH0HNo6neckhV3bl8p0rlwv2Gf6aLCvtHeX2TZ\n947FKWXchmRF08c5N6o9ExMTkeujHZIrKyuOmJFYpdNppyql02mMj48jk8mgUCg4Ase59Pt9R6Q4\nFs2a4vvtdjtCXA0Gg8Fg+GVhU4Tq8OHDeO2119Dr9XDHHXdg//79uO+++9Dv9zE7O4uHHnoImUwG\nzzzzDJ566ikkk0ncdNNNuPHGGzc1iDOpCX7Hm4Z2ch8+1AGgXC6j2Ww6csCSERUbDaT0oxPifFNK\nKog4gqTjJYalqSsh0nPElQCHkSDdXuMV+CL54bk5fr8EqNdQ583PeP2p+lEZVEXLD/Pk9xEEAWq1\nmlv3j9eEsQ1jY2MoFouYmppycQz+MkQkXPl8HsePH3deK16XdDqNd99915UT8/k8SqVSxCflY1hZ\nbysk7FzfEwbDboPdE4b3OzYkVD/4wQ/ws5/9DE8//TSWl5fx6U9/Gh/72Mdwyy234JOf/CS++c1v\nYn5+Htdffz0effRRzM/PI51O44YbbsB11123LrdoM/DLMT6pGB8fR71edyWtVquFyclJdLtd1Go1\nt64cCQAQzZ1SFYiEyy/d+ecfVsrb6px89cs3iceRSzWZkxyRKFHNIbFSckOVTEmUkjZC50XvE38n\n0dLuPQ3kDMMQ7XbbnY/7NJtNF5ZJEtXv992CyiRJMzMzSKfTjgxxLnrNSKzy+TwajQaSySTa7TYG\ngwGq1SqCIEAQBNi7dy/y+bwr3w5TDreLUdwTBsNOht0TBsMmcqiuvPJKPPzwwwBWs3tarRZeeeUV\nfOITnwAAXHvttXj55Zfx+uuvY//+/SiXy8jlcrj88stx9OjRTQ3iTA89f609AC5zSBWZRCLhlklR\nhUfVIRInzSoiOaGCw4cwiYDvtYrzfMWVAgn1YPnbaqnSJ3okJroYMKMB+De74dLptFOkeIxutxuJ\nhWBwZVypUV/qoVKPlh6HoadaauP77XYbtVptXfMA4wm0a3B8fNx9B/1+H61WK7JMjpJNlm7px/rP\n//xP/M///A9W043qOgAAIABJREFUVlZw/vnnR9blo6cqTvWMK+X6n22EX8Y9YTDsJtg9YTBsglCN\njY25pPL5+Xlcc801rgUeWF2eZGFhAYuLi5iennb7TU9PY2FhYcMBxPld9GHHh69PUlgy4rn4MNfM\nJCVOWvpSxUkf2iRMWgbz4RvXlYz44/eJlL78z+NS0dVYrgoVCZSmwfNakHgUi0XkcrmI14gxA+ov\nUzWH8/EDUcNwtYOPxInr86kRntsEQeCCOPUYeh3S6TTK5XKE/HQ6HQRBgHa7jXa7HRkriSW37ff7\nGBsbwwc+8AEkEgmcPHkS1Wo1EtkwrITsq19ng3N9TxgMuw12TxgMWzClv/DCC5ifn8eRI0dw8OBB\n9/6wB9NmH1hxPhd2eQ1LvU6lUti7d++mjv/LxmOPPTbqIbxn+OxnPzvqIbxn2EwcwlZJ1rm6JwyG\n3Qq7JwzvZ2yKUL300kt4/PHH8bd/+7col8soFAoIggC5XA4nT57E3Nwc5ubmsLi46PY5deoULrvs\nsg2PreqIKkMsP8V1oHU6HaysrOD06dNotVqo1Wr4+c9/jkwmg2azGUn9pvrBUpb/UxUmqkCq0vgl\nsbhyGMf/yCOP4NChQ5HxqgdLy236UzvwVBFS7xRLehwzS56EHpOeJao1LF2mUqlIbIGqSAAicQO3\n3347nnzySafoMYGcxyH6/T5qtZobE1+FQiGiqPH4mUzGhXf6fjCOg0vK8PiDwQC1Wg3tdhvdbhfv\nvPOOM7vv3bsXb7/9NgaDAfbt24fZ2VnMzs5icnIysrC2/71tN2/qXN4TBsNuhN0Thvc7Nnyq1Go1\nHD58GE888YQzDl511VV47rnnAADPP/88rr76ahw4cABvvPEGqtUqGo0Gjh49iiuuuGJTg/C74Hyf\nj5bmOp0OALiyF0t/WkIiMSDp0DJQ3DH98wJYR1h0nL6x2y9Jxs1Hofupf4pEiiUtAC67iS+SjVwu\nh3w+79bky2azyOVyKJVKyOfzmJiYQLlcxsTEBIrFIorFYiRFXMkhx6PGfWAth4oEjCSI2U9BEKBa\nrSIMQzcerhXI66d+tPHxcZRKJYyNjSGbzbrwTi7czJIdAz55HVg64Jp/9GGRXO3ZswfJZBI//OEP\n8f3vfx9LS0tn/D/fYeXAzZKsX8Y9YTDsJtg9YTBsQqF69tlnsby8jLvvvtu999WvfhUPPPAAnn76\naezZswfXX3890uk07rnnHtx2221IJBI4dOgQyuXyWQ1KlRlgdbkRkgG+r4nfyeTq4r3vvPOOOwZV\nGZIrzYTikirqG2KMAv/WscR5vNSHNSzaYJhh3c/SUkJFEkKvFpduob9KYwW4rSaE83xc75BkSK+n\nzsFXB6lIAWsmfp0LiQx9VMnk6oLLJFw0jpfLZUcQScQ4h8nJSfd98vtVNbLb7bpkdCV56XQazWYT\nxWIRvV4P3W4Xg8EA//3f/40PfehD2Lt3L4rFIsrlMmq1GorF4tB/W9sJ8RzFPWEw7GTYPWEwAIlw\nxEXsuE4yAO5h7S/9wi63IAiwtLSERqOBarWKZrOJH/7whygWi+h2u+4mpWrFchF/aq6ULjmi3W5x\nSo6auZUIJRIJPPLII/jTP/3TWBLlG9m5jZrN/fdyuRwSidXcJV6TbrfrSm6qYgFrnYAkISzvaemL\nxCZuX86z3+/jrrvuwl//9V8DgDsOx6Cp8tls1pFTVaG0aUDJay6Xc4SK4xgMBqjX6xGSFwQBSqUS\nEokEGo2Gm1ez2USj0cCJEyfQarVw4YUXotPp4EMf+hCmp6eRyWScClYqldzclDwOW0TZVxYNBoPB\nYNgsdkxSuv8gU4KjHiONUchms5EFfGdmZhzpYImIfimWkghdaJnnGVaaU2JHwuMvpBy3r9/dBqwR\nSIaNqlLCz+h14nmoNvEzJSqco/qrut2umy/LdvRh8bhxJVbuy+vE4zPziypUKpVCJpNBOp2O+N2o\npDHYU7vt6OkqFAqOHPF9AMhkMk4R43p/S0tLKBaLjvzwHPl8HpOTkyiXy0ilUm4BaO0KpH8uTona\nTp6YwWAwGAxx2DGEiogjMVqOIqFSgzTLfzSlsxxENSaVSrmcJB5PyZTmPul7HA8JiK9MKSnR8iNL\nd3HL3bDcpKU4P0aBihLnrOvpJZNJ9Ho9tz4fyYZGIJCsUIkhwdDzkaj6hJPxBMBqKQ5YIyCFQsGl\nm/PcVMx0DUGSH4JjZXkSWPXCsexHNY6lRAZ3apk2lUqh3W47okyzOpUz39PG/Xw1KpFIRM5tMBgM\nBsN7gR1HqJSkqCqkD2hup3lUiUTCpXBzey6ey2PGGcc1XJPvq2rFB7cSLB5zWLU0zmOlqs2wbcMw\nxMTERMQ7pj4izpNkQPclKaSKw7X3SB5YQiXRZHkQWCt5MqyT5ycZKZVKOO+889yYWarL5/MYHx+P\nNAqQuPFahWHojOQkRqpktdttpybxO282m26+VNt0vsy24Vj8kp6WYv3oDe7vq3QGg8FgMGwHIydU\ncaU2/zPf3BwEAYA1ZYkEoVQqod1u49SpU5EHuRrB/dBHKiqET6iUeHF/JSzA+hKSlvXUzO3Pjech\nYZucnIyUN0kc/PBR/9roeNWTpR4l/iTp0nKhGuLV75RIJFAoFDA5ORlZP69UKjnyxa5D9VZREfIJ\nHL8TAJGFkpWwZrNZF4mgXip62uiJ43fhX1NV6BRa/uOajvz+DAaDwWDYLrYXxvMe4EwqgRIIkilC\nu940Z0kzqLRrjg9hYC19PY5A6E9NLo/r5NPSkpIjkhRNP+e2vlrGMWpHWty2JJEkDUqCuI+f36Vk\ni8SFZT2mkmvXJEmf5kYxikGjG3gNSXqoImlpNJFIOPKkRIhErNvtotPpRNQ+EqHp6ekIKeT356uC\nJJ9KQvX78bdlunsymUSj0Rj2z85gMBgMhi1j5ArVmaCRB/x9MBggk8k49YlhkSQu7XbbeZDCcDU+\ngIoEoQRCvU2+cTwuSkE9UjS/+/4d/wHP/eN8YZwnM6ZIinySEYYhms1mhLxp2KeqZFoq5Th5Tu0C\nJLHUMh3XBwTWuvuUvOVyuXVkjeVMltKAtS7NRGJ1jUWWAwGgWCw61ZHjoVqVyWTccWg0X15edh4v\nfle8bkri9DMdH8Hj9vt9Z473FUqDwWAwGM4GI1eozgQlD4R6htToTJWKD33uD6yWe0h8eExVgTQ5\nXFUtILqAMRUXKje9Xi9W3fIVJv/Yikwmg3w+7/w+/rja7fY6EzwXEabKo6RLyZXGRPAFrIWiqsJH\nY7l2UWrmFBPoeVyOl9eAiyGT+DGQs1aroV6v49SpU6hUKuh2u6hWq26xZq4NyPm0222srKyg2+2i\nUqm4/fVaM7yV3y2JmiqOccon36dy+OMf/9g6/gwGg8HwnmBHE6o4lUHVEj40SQISibXMJlWKSDK0\n/KbEgdv73ikfqozwuLVaDY1GI0IoFHocnxjFqVkkNfxd9/PLhPRzkSCo0qbkIi7PyydOLKnyM92W\nJIlKEomU5mUNBgNXRqNS6Hu6giBwCegsPbKpgO8FQeAIm8Yf6FJBVCyVnPrf10aqUyqVwsUXX4xa\nrWYGdYPBYDBsGzuCUA17mPmeGZbtVC0C1kpOyeRqYjpLRVRg6N3hviQc/rnVl6XKk6pNanhWYhKX\nd+R3ncWZyDkOdrxpblS/30e5XF5HgJRMstylSotmVZGA6Bh4rdhxx/Nls9mIAV+JGwA0Gg1UKhU0\nGo0IuaRxfHx8HP1+H41GwxEvdlued955yGazqFarTnlqNpuOQNHczuNozAMAF3tBYqXz1sYBLakO\nI1W8diz7AYgoeAaDwWAwbBUjJ1QXX3wxBoMBZmdnXTK2QskPvT+DwQA//elP3TZKahgV0Gq1nNpD\ncuWTJC39+QSLfiP9PJ/PO+UlkVjtKpycnESpVHJlp3q9HgnG1HnouQG4ZVp0DTv1CSmB8gNBSR40\nvFQzpritkgT6k3K5XCRuAliNaEgkEhHz/2AwcMbxXC4XITVKDjXGoN1uY3Jy0mVVcZFkRkAwwqFU\nKrnFUi+44AJMTU2hUCg4L1apVML09LTzXjUaDZeRxTnrddVSMP8eRqj0/enpaSwuLjpyaTAYDAbD\n2WDkT5APfvCDKBQKyOfzuP7669cpC0p+1ACuZEO76ej7KZVKkVZ/YM0PRQVKiYof+qmddARVHyU3\n1WoVyWTSLXOSy+UQBIEzXsd1nPkPfyVeWqIkeeFY1bzNn1oO9DsafdVKrymvm66TGASBI0B6vejD\nyufzLqcqnU4jn8+70h/JXavVch156XQauVwO2WzWjVFN6Or90u9Fy6csNzLok3OjL06zx/QaxpVv\nh6lWc3NzaDabkUBVg8FgMBi2gpETqn/7t3/D3r17kUwm8Z3vfCfWJEySow/66enpiHk7m80iCAIk\nk0nMzs46bwwf2tyWaot2vqmyo51i+pkqXJVKxa0VR3M6S5DsKvSVE/8hz246Eh0SIp8gMceJChST\nyeNM8Fry4jG4rZb8tIRI9YgESNc1pKepWCyuK50y5kGN5dyHqhLPTyJKUqadmmqGD8O1nKper4d6\nvY7l5eVIOdT3wfHfg15fJeGKODLF93K5XGTuBoPBYDBsBSMnVGEY4u233x76+TBzuJrPNQah0+kg\nl8uhUqng1KlTmJycdOfh9hq4qePQn4R2Ew4GAxSLRfR6PbRaLXQ6nUhKuO7jkzSeOwzDoYZqZjVR\ngVKfkgZy6piUhPF9DfCkSpTL5dxxdBsSUr0mavYHVr1TVLGYPn/ixAkUCgUkEgnnvSL5S6fTKBaL\nEeJH4kSSq9lbQRA4panX66HRaLguwHw+75ajIRlWX5ca7/3vcitIJpNotVoW9mkwGAyGs8LICdVG\n3VVxJTOW9vRhSlJFQtBsNp0nq1gsolqtIgxDtziwLrESdx71MOnCypo8rq38vnlelzdRUsV9eWzf\nE6VlPo05IAnShYJZSlMypL4vVZV8IuWXTbPZrFPB6InKZrNOGQqCwF07KkqNRsMRKZJIlvi0/Eil\nLZvNIpPJoN/vo9lsurn1ej2XsVUsFjExMeFIK68Dj0HSqb4tLQPzmg9bGPlM/86OHTuGSy65ZNP7\nGAwGg8FAjJxQbYRhEQZ+2rlGBySTSXzwgx/E8vIyGo2GM0hzeyZ7k+Sol8p/T9eR4+djY2POb6OL\nMXNcejydg76nxyPpANa6CHU8unwLt9EMKF1wWFUmvrrdLhqNhiOYSvT0b/ql1KBPAzszr2j816Vn\n/LUFSXxI6lRZY6hmEASuxEhimE6nnVmepCmZTKJWq0WS0v0OQCWsqsxtRNb1++F3ZzAYDAbD2WBH\nPEG0JBYHJRPa+q+I876QiPjxBel02mUd6fn9VHQelx4dHQ//5jp2Gsvgj01TvnWcfio6yYgu6aJE\nQc/b7XadCqPn1rwpHSuN3YVCwZFPjpPH4Ti5zE2lUsHU1BSKxSL6/T4qlYpTf0h2qK7515jnnZqa\ncqoe/WlBEKDT6Tif2/j4eOTa+D4yvVYkVqrWqe+Nv5+py88H/21deOGFm9reYDAYDAYfO4JQbaQk\n+GQGWJ+KrV1+/Mk08EajEUkF5xp2/jn0oZxIJFx5Km4sqpyp8ZplMQVLaXoMn4BwOxI4DbEEoh4t\njtUvWXIZHu2o07JlGIZOGcpkMo6c8Nw8Dn9vtVrI5/NuHbzx8XGnxNEcn81mUSwWXWI71+ljrhRL\njvS88bsjiWq32xFzOWMpeLxWq4VWq+ViFzg3NbLrtWRJ0PfIbQZbKREaDAaDwaDYFKE6fPgwXnvt\nNfR6Pdxxxx148cUX8aMf/cgZvm+77Tb81m/9Fp555hk89dRTSCaTuOmmm3DjjTdua3BaliK0G07L\neNqNR4/S1NSU68hjeUrXptPAS108l/urquKPwfdcxSk0Sno4F/X4aOdg3P6qVvk+KC3TDTO4aymU\n4ac8FrBmdif8HKt+v496vY5Wq4VSqeT8VMAqiazX6ygWi6jX607d6vV6KJfLEdVLy5n0aOmYGN3A\nciyzqNRHpnNWEq3XjaVcxmVsRaXaKkZ1TxgMOxV2Txje79iQUP3gBz/Az372Mzz99NNYXl7Gpz/9\nafz6r/86/uzP/gzXXnut267ZbOLRRx/F/Pw80uk0brjhBlx33XXuZhqGTqeDTCaDVquFer2Oubk5\ntNttZDIZdDodR6C0cw1Ya3PnA3piYsKpHSy7qa+JBIUqhBIrKiVA/NIxWvbzVQ9/eyV8wPqWfs6D\n69FpB6GvtmiZTwkgsFbO9A36fsmN56Tviufisi/AemWGpcDJyUkXddDr9VCr1ZyHqdvtolAooFKp\nuEWdqUYxvqJWq7lFn7nfYDBwBnhey0aj4T7ntUkkEk7t8nOp9JqQpPrL0ShZfq9xru8Jg2G3we4J\ng2ETSelXXnklHn74YQDA+Pg4Wq3WujIYALz++uvYv38/yuUycrkcLr/8chw9enTDAWSzWXz5y1/G\niy++iImJCXQ6HRw7dsyZn9966y0kEgn8+7//O5LJpFue5B//8R8dERgfH8df/dVfRVQbYJUMsexE\ncsLMpbi8K2AtKBRYn7atD/FhZTv+rWPxy4Q8NxUd7RbUsiP9USQMXB4lCIJIWZD78bzcnvvSNE/F\nqFAoOO9Xu912ERA+qeN1qNVqjpyGYRiJOGg2m6jX686cz0WR2+02KpUKlpaW0Gg00G633TIzPCd9\nXcyxajQaqNfrqFarWFlZQaVSQaVScdEUOk+ORbsvlVz5pPK9xLm+JwyG3Qa7JwyGTShU2tE2Pz+P\na665BmNjY/jOd76Dv/u7v8PMzAwefPBBLC4uYnp62u03PT2NhYWFDQeQSCTwwAMPRN778Ic/7B6E\nH/3oRwEA1113HYD1ilCcZ2mU4H9U/j/grrvuGvUQ3jNsJrBzs/+OzvU9YTDsNtg9YTBswZT+wgsv\nYH5+HkeOHMGbb76JyclJ7Nu3D9/61rfwN3/zN/i1X/u1yPabVQbO9BDz/UTaXdZsNl2piYpHpVJx\nykcQBGg2m07VWVlZceoIDdxUOHTNOoIqh/5flipAOnaqJg8//DDuuuuuiDqi89NjqU8rrozI95rN\nZiQCQf1iACJlP9+fRZWP14zeIlXP6H3SsmAqlcKDDz6Ib3zjG640SELCJXWCIHClQcYfcNxcloYL\nJzNugQGjDBulasicKd8T1W63ceLEiYgXrVAouPHwPJlMBtlsFoVCwb3H5YwY63AuSPe5uicMht0K\nuycM72dsap2Nl156CY8//jiefPJJlMtlfOxjH8O+ffsAAB//+Mfx1ltvYW5uDouLi26fU6dOYW5u\nbluD8yVjdu0BcL4aYC1SQIMngejSMWz9J4kAov4mvzykJIdkQz1M/s+4sceVpng+Ri1oeY7n8Mt0\n/njoF9KIBl24OM5vxY45HYOavrXbj+SDhJTHbbfbqFarLpRT1+UjqdPcKWCVABWLRczMzKBUKq1b\nyDqVSqFcLqNUKjlixGter9fd/LRzzy+3+iBp3IyH6mz/gz6qe8Jg2Kmwe8LwfseGhKpWq+Hw4cN4\n4oknnHHwrrvuwrFjxwAAr7zyCj784Q/jwIEDeOONN1CtVtFoNHD06FFcccUV2xqcT3D4dyKRcIsR\n+9vRbK4KER+sfMjr4sFqbPZ9UEB06Rm/0+9MP/3f+bfOQXOX1BjuB1bGlav8rsa4cFKdH71aJJU6\nFnq4+D6vD1U7kj8a4f05BUEQuUb0WJFEFgoFp35RQSL5ymQyTkXL5/MoFotOvVLPGU3r/ncQZ/r3\nOzA3g61sO8p7wmDYibB7wmDYRMnv2WefxfLyMu6++2733u/93u/h7rvvdqWVr3zlK8jlcrjnnntw\n2223IZFI4NChQyiXy9sanEYA+MnY+XzedebxPcLv8FLjMo3punSMPoy17MT3fILC82lJz8+Z4k+f\nqOk2JC7a2aaKmZ+sriSP59QgzziCoeW0wWCAWq2GXC7n9uMaiNrFSBO4BoYWCgXX3cfuQJ6D0QdU\nsorFoiNfxWLRld5arRYmJyeRzWZd9165XI6QYCpmqrDpWoO+yqjfjV+C3YxCpaXTzWKU94TBsBNh\n94TBACTCERex/TBN/30+ZLnoMVO39SHOLrFms+m6ziqVCtrttus+Y2mqXq+7bjQSEJbESCDUK6Xd\ndH4nHbflJXzkkUdw6NChCNnSeQx7wOvcfVWGUEWL86fq44eBatCp5jbxfRKSbDbr8p90LL1eD1/6\n0pfw4IMPunMzmZz7+CnlPAbX7ON+uVwO4+PjGBsbw/j4uBsvF1QmOeP+nU7HfYeLi4tOWeO29IJx\nPoVCwXmz6NsaHx93Y+R/zDfrodqMed1gMBgMBh879unhm8Gz2awr+dTr9ci2VEaUhHAtN38ZGD5Y\nubyKkhc/94nbK1mJ+8xXqvyxxHl+WBLjtv5+caTN31Z9UFoC5Bx4fF5PkpN2ux05t09mSXIYdKok\nb9j16XQ6CIIgQn4Y09Dr9VwH0NLSEpaXl928mDVFUsz3NRqCqpTOheegDy2OuG4mNsEnrQaDwWAw\nnA12xNIzPpTcxD3Ex8fH1+2jXWuKOGLWaDQArKohcWvrcVvfWK7EaaNykk9UNMhSiVnc5zpmJQqq\nMqnxm8RF5wkgUiajz4rlRSaSk4xpwrz6vLgdsOZP02ur+VRUt6hSsbRHT0UikcDExARyuRyWlpbQ\n7XYxOzsbKb8uLy/j9OnTbgkavq/l2LhmAo7LXzfwTPC/P59YGgwGg8GwWew4hSoMQ9x///1YWVlx\nnWDVahVf/OIXEYahW5eP2yp54MPcV4RUiaG5WVUabqfH1G45/s4HtB8cGee5ilOafHWJhJFjiFPB\nuB1JE83cSjjjOgEJGsFZatMXVSV/rL4fjNeP8+10Oq7cStLjG+AZl5DNZtFqtZyxvdlsotVqObXx\nv/7rv1Cr1VCtVlGr1dwyNABc6Kev7pEk8pyq3LFs6y/tMwxGoAwGg8HwXmDHKVSFQmHdMjCzs7P4\n2te+hnw+j0qlEvmMP0kSlGyod4hge3+pVIqoH/rTfx9YT5SoovmqDWMDlGz5nWdxrf9nKk9pOU/z\npuKgCg3XxstkMu46cG7qsWq1Wo6saTQFlSMqeWEYolgsulKbb/7nmOr1ekQVo9+r1WpFcqdSqRSm\npqawtLTkPE/0ZqXTadTrdXQ6HUcG1fdGMqnkutfrrevy3IhQGQwGg8HwXmDHKFQkDNq5d++99wIA\nXnzxRQCr2VN84ALR0pa+4ggMoQ9bzX5ScuAfXxUQvqfkQ8ttJApxuVa+7yjuXLpPHMnT8NEwDNeV\n+vQ4VL8YfMryHMkRx8qYAypP/A4YKsp8KK7n12g0nPdJs6j44vVpt9uRQFZ6sxYWFrCysoJqtYrT\np08jCAK3zAyXp9FyIpUndh/6sQh6nTiWzfin/OttMBgMBsPZYkcpVFQ1gNUH3e///u/j+eefx8GD\nB902PuFQxcInTuozUiWLpSS/1BbX1RcXe5BOpyN/+/EGiUTCpbjrg57jGJZnRZKj89LfOX5eK01E\n12PGqTNUp6hwUcnRwEwSGELJIvelAsfroOVRVdGYpL6ysoLx8fGIByqRSGBxcRHpdBrj4+Oue5OR\nDVxbkH4vLSU2Gg2Xiq7zymazke3U/zYMmyVdBoPBYDBshB1FqABEyNGBAwcwOzsbWy4jSDZUbaJK\nxePxQctuMvqn4vxO6pdSxYreK03yVjJB4qElQJqy1Y91pnKfdhz6JcswDF1+FudC4gZE2/3VO0Qi\nw2vY6/Vc/IB6s4DVEp+a0OlhU7LDMiKN8dzON/Bz3KVSCfl83o2l1+shnU5jamoKpVIpspQOl5NJ\np9MujV1VQpYz6auiEuiHmup3udm4BIPBYDAYtoMdQ6iolLTbbaRSKeRyOQDAnj17AMC15FOZUKKj\n+UwEPULclkSK6d+qfPheIJ9IhWGITCaDQqHgyAIJnN9hp/sDa4oVsOZJ8st/hJIoHodkgURNFTkS\nCn3FkRqaw31y4YdjEiz5+TEMfl4Vx89zxS35Q/LDkh/fY9K9qnL8joDV6AZdsV49UyRVlUoFg8EA\n4+PjEUXS/z7PBDOlGwwGg+G9wI7wUKkqlc1mMTY2hsceewx79+7Fr/7qr+Lxxx9HOp1GsVhcty9L\nPH65jscNw9XOQK5LF6dM8Tj8qeW+RCLhwiHjymvaQQdECRT/VvUk7m8lIzRyqzKjJnF6t/xz6HUE\nomnq/X4fQRCg3W47tUg/4wLS7MQjiVE/mp/kzjwr9XXx2mpqOr1WPHYymUS1WkW9XnffSavVcn/T\nK0UVcVgnJH9nAKiOjS8r6RkMBoPhl4WRK1T68APgHsC333477rjjDlQqFYyPj8eW+gglMfp7GIZY\nWVlBEAQRNUrJl79mnMYGMEyUa87pMQC4z6h2AYgYqf14BB1zXL4UyZQSCcYi6Jx8zxi317noudQL\npWRRU9Z5DKpZwGoTgCaga3cdz8/OPSVwLAvy+5ycnIx0KNIIz2R7qlMkbt1uF0EQuO8qnU5HiCWh\naw6eOHECe/bscd8T52oKlMFgMBh+GRg5odKHc6fTcSU0mqTpszkT+ADldslkEs1mEwsLC64zTIlQ\nnPlclSl6h4CoKqS5VTpGv1xIqOqk/i71DfkvABHfkuY/AdESFcfKspyWx7iPnynF/XQMvGadTsd1\n4nEcjUYjEjrK+WinIOemYymVShETOwlSPp93y86Mj487X9bExITzhnW7XTQaDdRqNQBwSwX5cRGc\nQzKZRBAEOH78OIrFIvbs2bMurd5gMBgMhnOJkRMqPphpdB4MBk5loE8oDkpi9OE+NjaGer2O06dP\nR0p8JD7qr9HSGx/MmUzGxSGQ0PgeKQ2y9MfC7j7dX0mYEiMSBB6f41Gju/qT4nKfqPiQRMWFkPIa\nkrTqvNQor74zYmxszPnadJHlMAxdlAJVJR4rrgsyl8shnU67CIdKpYKZmZkIKeN49XpR/VNTul/i\n5Fy63S7UfT/jAAAR50lEQVQqlYq7FroItMFgMBgM5xIjJ1TA2oOR7e98bxiZAuKXDRkMBlheXsbJ\nkycjJSO/88v3T/GhS2VIu+K4LZUuVaQ0c4pEhGOmZ4jn1JgCJUm+UqZeIBItjkfLmlSUlEwpIVIz\nuBJJZkJxe3+pFiVsVI+GlVup0CnJ4fxpLtfSJrBqNmcJlzEI/X7feee4j5ZRef1Y9tProGZ0/l6r\n1bCwsDDUd2cwGAwGw3uNkRMqEopOp4NisYgwXF0Yl2WhYVCVgkRgYWEBp06diiwrQ/VL2/q1w4/H\nABAJDeUxaZJmOjhVIy7+SzWGx+B2OnZ6h5QkaAefGtrpPVLfFAmFlvE4BqpSfpcfz6nrAhJUe1Sd\n0pRxzoXKFL1kWjZNp9OuC4/Xh2qRxlKwXKrfKY87NjaGcrnsSBWw2s3JOfkEWL979YqlUil3zTmv\nSqWCTCaDVCqF6elpM6gbDAaD4Zxi5ISKxIfeHZKHrfimqLQsLy9H1piLK+v5qhWwXung5z4pozpC\n1YcEzH/Y8yePpWPgT33A+54mJRKcox7Hvw5qXteyom6jCpeeT+fG669zUfO6fl8kfqoeAXAdiCSf\n3DadTrvsLM5JTeMkbKlUyoWuKtnk3DlunWecv4xL3XAhbCNTBoPBYDiX2BGEimqF7ynaCCQJg8EA\nr732GlZWViIqi9+VF7e/PpSp2PhlwbGxsXUZVKqGUJXyoZlIcWUzLTvGddHxPFSnhs2DpJLkiOqX\n+qr0byWBJDsaiKqlNlWytNymnjeWSbmPvz9/17KkLoMDwBEfYLXUqI0DfqlPS5g+QdPyY7VajXQ3\nWsefwWAwGM4VNiRUrVYL999/P06fPo12u40777wTl1xyCe677z70+33Mzs7ioYceQiaTwTPPPIOn\nnnoKyWQSN910E2688cYNB6ClIiBa6joT1Gv06quvotFoONWCRmmqKVQ7NEEdWDOTsxRHBYUPXxIN\nzW7iMWg+59hJIjgGLaORbPlqFgmDzkXVJF4HGsl9QqN+KF4TJR3+Is9KaMIwdKU6v4tP1UEtaVJ1\nIoGkouQTOm6nZUQqh/wsn887HxjHyGYAEkcei+nqPL7mcCnp5X5K7kjUut1ubAjr2eBc3xMGw26C\n3Q8Gwyo2JFT/+q//iksvvRS33347fvGLX+Czn/0sLr/8ctxyyy345Cc/iW9+85uYn5/H9ddfj0cf\nfRTz8/NIp9O44YYbcN1112FycvKMx1dFRskAPzsTjh07huPHj2NpacntryRCE9HVeA1gnYKlD2j1\nK1Hh0RgD/xjA2kOcypV2+XFbJT46Z1890eOqCkcCQ2LR6XQiUQe6nIyWPDXbij9TqZQzkMcpZ8Ba\n+Y/XhNeF15YKFckK92H8hZI4YC3+AFhdeJk+r7GxMeRyOUfKqFbqtdD589xqyFfFi4pWKpVy72sz\nwZmaHTaDc31PGAy7CXY/GAyr2PDJ8qlPfcr9fvz4cZx//vl45ZVX8Jd/+ZcAgGuvvRZHjhzBRRdd\nhP3796NcLgMALr/8chw9ehQf//jHz3h8PmSVUBH6oPc/q9fr+OlPf+rIlCpRfvnK98/oIsD6ACcx\n6Ha7yGazEcKgChqPQbKgrf4abcCH99jYmFubjnP2SZP+1M47PS7JBI3fupxMXEAp9yMh5OdUavSl\nBna9Xko2lXwyKiGZTLplgpQYkjAmk0m3cLGa7AG4ZYB4fVSZY1lQc8R47UikqPxxbjpnf1kdKmQk\npFqmHPZvbBjO9T1hMOwm2P1gMKxi0/+rfvPNN+PEiRN4/PHH8ZnPfMapIjMzM1hYWMDi4iKmp6fd\n9tPT01hYWNjwuMPIFBH3frfbxcLCAk6fPh3xzvAVBEGks0+9ODxmnMeKBmq28GsJicfpdDrOYK3p\n3wSPp+PRhz1JgBIhP2RTiY1vRqcBXD1Vw46p73G8uqyNrr1HUqLkkPtrJ2Cr1XLvk4ypP4rH01Ig\nj68qk87R93nxGpCEqsKmXjv9XvTfijYTUI1qNBooFAqR7327JcBzdU8YDLsRdj8Y3u/YNKH63ve+\nh5/85Ce4995715WI4rDZrio/BHIzyGaz+MhHPoKPfOQjW973XONLX/rSqIfwnuHv//7vRz2E9wwX\nXnjhhttstRPwXN0TBsNuhN0Phvc7NiRUb775JmZmZnDBBRdg37596Pf7KBaLCIIAuVwOJ0+exNzc\nHObm5rC4uOj2O3XqFC677LINB3CmDjwFS2+dTgetVgs//vGPcezYMSQSCQRB4MpHXOaE+VFaXvOX\nI9GYBCpY2WwWxWLRBYxq2z+wWjrSjjxdMPmhhx7Cfffd5zxOVEk4HjXMU21hSZB+KIJj5xh4rnQ6\n7Qzx7XbbJbtriUvnqWUtVW4GgwEKhQLK5XLE81Wr1TAYDPDd734Xt956a0QJArDud5b0tIswCAKn\n/nDxaH8bGtW1BKnfC71Y6o3iMThefdHjpWn3hUIBqVQKt9xyC/7jP/4D09PTSKfTyGQyLluLRnh/\nwekz4VzfEwbDboLdDwbDKs4c9gTg1VdfxZEjRwAAi4uLaDabuOqqq/Dcc88BAJ5//nlcffXVOHDg\nAN544w1Uq1U0Gg0cPXoUV1xxxXsySJba2u22Ix5BEESCLdX47IdCqqdKy0JKPrRsxgc785Da7Tbq\n9TpWVlZQr9cjC/jWajVUKhWsrKwAAJaWllCtVtFqtdDr9Vz5kWQqrptRDd5KIki+2OWmKexazvLj\nHzgflrbYIaf+L82I0utFQsrt9Dj60k66brfrvhv1OnFdPl2ahuehp4nQz3w/Fwlvu92OXEM1q/tz\n4LhJhFutFoIgQLvddi+Sbs5hs//HvBPuCYNhp8DuB4NhFRsqVDfffDO+8IUv4JZbbkEQBPiLv/gL\nXHrppfjc5z6Hp59+Gnv27MH111+PdDqNe+65B7fddhsSiQQOHTrkzIfbgZIpPrSZpq0t975nSRUh\nNab7f/teHmBNHfK3UUKmoZ48DwCnunAs6pHi30pK1MTNc5H80HhN87nmWvFvBmGquqLj5nUi2eBy\nMpyv5lC1Wi1HgLg/PUu6tAuVIPWVqbFdyZ1vRmfnnXZl6nXVsWusBD9T0ubnX/Ga8bydTscZ5gE4\nosh9wjB0SiSwSkx1cehhGPU9YTDsJNj9YDCsIhGOuJC9Uehmq9VyqlS73UYQBGi1Wjh58iSOHz+O\nwWCAZrPpSJBf7tPylxqY/WgAXgZ2ggFr/i41LjMvyVdKwjDEY489hs985jPrCBmJk78cjLb993o9\nRxqVHAKIPOT1/UQiEVkDT8maH3xJ5YYp79lsFplMxpELdtVRbfqnf/on3HDDDZFyoJrMScJIeNQQ\nz7Hx2vE8GiWh6pqa8v0YhHa7HTmmHpdZViyd8li5XA5jY2OufPsHf/AH+P73v49SqYRsNuvKgVT9\nuD3fNxgMBoNhqxh5Unoc+NBstVpoNpuOKLEcRdKRSCRc6cbviFOFA4iqS1pqAtaUD5I4PvD5UKcC\no/v6KoyOXZd1Adba+llaUpLDbake+Z4leqw0uoHbj42NOZ8Cz011iT4tvS7acUcSouqT31HoQ0kp\n1S96kPiZEkmOW3Oh9HpoRIJeP+1g5E/tSKRHjYRSs8L0u1blkP47Kmp63QE4f5bBYDAYDGeDHUmo\ngFVlotFoOFWKCk4QBM6ErgZl9fuoyqNlJ59cxAV/arSAlgdZatKHOaEKjfpzODaqRUp2SLC0JKgE\nK5FIoFAouOOxxKiesHa7HVGnqIwpoSPJUj8RTewkRX65lHMjoeR1IdFUz5q/1A+vh0ZUaCmu2+1G\n1Cnur+VPvZ7AGkHyvWKcvxIzXQ5HFUmSYi5q3Wg0UK1WMTMz467hdgM/DQaDwfD+xY56guiDvNls\nOvJEbw9VKvqoNCCSRKPdbq8L9gSi6gpVClV8+GK3l5IdGpZ1O334+w9ugsfw9+U5df0/LTnSa8T5\nUeXxA0QJNVSTQOn49Rzqc1LzPYmfT2RIgPz0c/5UT5Y/D4ZwUj1S1ZBESuegCqIfRgpEOwx5riAI\nnErFffn9cWwAXH4WAJcjlk6n0Wq1MBgMXCnQYDAYDIazwY4hVFoGC4IAjUYD9Xo9ok6px0e7uVSh\nUhKloEKkxCSubKfr7mm5SWMS/JKSkiPt0NMuPs5RPVvqw+IxNf6AZDIMo2GYJEv8yXIgSYgf6qmk\njsfn+FSp43G4vy4nw/FSDYpLHPdLn1q+0/mpuqXbcVzagemPn9efihb/vXDxaj8ygkb0ZrMZUdr0\ne+d5jFAZDAaD4WwxckLll3GCIEC9Xke9Xo9ED7DcF4ahU6+azaZL1Cb5oEJEBconPyzH6UNcFSSa\nk5UcaOmQx/EjC0iaarUagNVkbnbDMaaAJAiAy55SdYsqji55A6zFHCj0uEqWtHTGMp+qSlwzj74r\nLmdDj5F6kSqVSiSZnXOkH0qjH+LKZVTIuB330/woHau+9Lrzu/LN9/51orpI7xbT4QFgZWXFEfFc\nLodut+uS03UJHIPBYDAYzgYj7/IzGAwGg8Fg2O2wtiaDwWAwGAyGbcIIlcFgMBgMBsM2YYTKYDAY\nDAaDYZswQmUwGAwGg8GwTRihMhgMBoPBYNgmjFAZDAaDwWAwbBMjy6H68pe/jNdffx2JRAKf//zn\n8dGPfnRUQ9kS3nrrLdx55534wz/8Q9x66604fvw47rvvPvT7fczOzuKhhx5CJpPBM888g6eeegrJ\nZBI33XQTbrzxxlEPfR0OHz6M1157Db1eD3fccQf279+/K+fSarVw//334/Tp02i327jzzjtxySWX\n7Lq52D0xetg9sbPmYvfE6GH3xBbmEo4Ar7zySvjHf/zHYRiG4dtvvx3edNNNoxjGltFoNMJbb701\nfOCBB8J/+Id/CMMwDO+///7w2WefDcMwDL/xjW+E3/3ud8NGoxEePHgwrFarYavVCn/nd34nXF5e\nHuXQ1+Hll18O/+iP/igMwzBcWloKf/M3f3PXzuWf//mfw29961thGIbhu+++Gx48eHDXzcXuidHD\n7omdNRe7J0YPuye2NpeRlPxefvll/PZv/zYA4OKLL0alUkG9Xh/FULaETCaDJ598EnNzc+69V155\nBZ/4xCcAANdeey1efvllvP7669i/fz/K5TJyuRwuv/xyHD16dFTDjsWVV16Jhx9+GAAwPj6OVqu1\na+fyqU99CrfffjsA4Pjx4zj//PN33Vzsnhg97J7YWXOxe2L0sHtia3MZCaFaXFzE1NSU+3t6ehoL\nCwujGMqWkEql3JItRKvVQiaTAQDMzMxgYWEBi4uLmJ6edtvsxPmNjY2hUCgAAObn53HNNdfs2rkQ\nN998M/78z/8cn//853fdXOyeGD3snthZc7F7YvSwe2Jrcxn5Wn5AdFHd3Yxh89jJ83vhhRcwPz+P\nI0eO4ODBg+793TiX733ve/jJT36Ce++9NzLO3TiXnTy2rWA3Xnu7J3YmdvLYtoLdeO3tntgcRqJQ\nzc3NYXFx0f196tQpzM7OjmIo20ahUEAQBACAkydPYm5uLnZ+Kv/uFLz00kt4/PHH8eSTT6JcLu/a\nubz55ps4fvw4AGDfvn3o9/soFou7ai52T+wM2D2xc+Zi98TOgN0Tm5/LSAjVb/zGb+C5554DAPzo\nRz/C3NwcSqXSKIaybVx11VVuLs8//zyuvvpqHDhwAG+88Qaq1SoajQaOHj2KK664YsQjjaJWq+Hw\n4cN44oknMDk5CWD3zuXVV1/FkSNHAKyWCZrN5q6bi90To4fdEztrLnZPjB52T2xtLolwRNrc17/+\ndbz66qtIJBL44he/iEsuuWQUw9gS3nzzTXzta1/DL37xC6RSKZx//vn4+te/jvvvvx/tdht79uzB\nV77yFaTTafzLv/wLvv3tbyORSODWW2/F7/7u7456+BE8/fTTeOSRR3DRRRe597761a/igQce2HVz\nCYIAX/jCF3D8+HEEQYA/+ZM/waWXXorPfe5zu2oudk+MFnZP7Ly52D0xWtg9sbW5jIxQGQwGg8Fg\nMPx/gSWlGwwGg8FgMGwTRqgMBoPBYDAYtgkjVAaDwWAwGAzbhBEqg8FgMBgMhm3CCJXBYDAYDAbD\nNmGEymAwGAwGg2GbMEJlMBgMBoPBsE0YoTIYDAaDwWDYJv4PsZ8iQl/oBrEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cfdc65c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ssQCAjRs34oorrsBHP/pRdDod9Pf34wtf+IL4V/aFpcI6SXSoHDBCYHp6Gq7r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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cfd6c748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lEkqlEhqNBsLhMIaHh6UUGAwG8Zvf/Ea68VqtlgRwuq4Ly7Kwdu1aIZ25XA6F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Wlubk7Ly8v2ucXFRb3yla887wJ8NIF/uA0GA3U6Ha2urmp5eVlPP/20ms2mlpeXNTo6aqZwuuEo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\nJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cfb5f2e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MTEzINqFQCPF4HGNjY4jH47AsC5FIRLYPh8NCmtauXYunnnoKgUAA4XAY+XxeCBG3Y9kQ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hkZZJkskTEN3HVdiXWgmkbSViwWheCxnKYDO+nR4j77/T4cx0GhUBAyybWPRiO4ritxCa7rSikT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\nZTAYDAaDwXCWMEJlMBgMBoPBcJb4P8A/k2UdZS76AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cfacb3c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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28VG949z29fW5uaAvyldqSEa4oTPPRZLTbrdRr9cdqeLeeslk0p0vl8uh0+mg\nWq0in89jYGAgFGCqfi8Ajty0Wi2nmPG8zWYTpVIpFNXARHV2+fX19SEajTpljUpaKpVyyehUteLx\nONasWYNVq1a5Tsc9ifl+JwyG/RX2nTDsz5g3ofrd736Ht7zlLSgWi3jrW98aapUfHh7G2NjYTh2H\nBIBKEwCnYKg61Wg08Pjjj6NcLod8VwBC+UXsimNpbdOmTe5cVEW4IFerVbfQNhoN5HI59PX1hba2\noeLlG5ipPLFEl8/nAcyQEP7Uclur1Qp1D5LE8DpYLqPiRLWOEQWMUvBLfiybacQCVRztwuOedblc\nzpUjWT7jBsvqKaIiVqlU3Jh1/8FOp4NUKuWe5xxRkWNQaKfTQSaTQblcdgSOxNQna7wGDSMlKep2\nu8hms+6z0Ww2XWJ8Mpl0m09rNESj0XDHpXLJDDAAWLp0KX70ox/hJS95SSgVfz7YU98Jg2F/gX0n\nDAcS5kWoDjvsMLz1rW/FmWeeiQ0bNuCiiy4K/d/9rph7ly5dOp8h7LU466yzFnoIewwvf/nLF3oI\newxXXnnlDl+zbt26eR9/T34nDIb9AfadMBxomBehGh0ddYvtIYccgkWLFuGXv/ylK2tt2bIFIyMj\nO3Wsxx57zPlzVBkaHx/HL37xC+RyOafUPPHEE8jlcsjn885IrYna2hGo25Jox5ummlOJSaVSyOVy\noUgAjVhQ5aJerzuVSA3RqVQKr33ta/H1r3/d+ZOAaaWKnWo0VlO50W5DXgc3/GXZi+VN+qVYLqPK\nUy6X3Z5+vG6W3TQ5nHOUzWbdcxy7GvU5rpe97GX43ve+h4mJCadi0SSucwrMNBaUy2VXNmQXovri\nqFaxTMjXcpyE380IzJRGNViV95DXpx4o7RZ929vehg996EPu2KlUyuVx5fN5JJNJLFmyZLdKfnvy\nO2Ew7A+w74ThQMO8CNXdd9+NsbExXHzxxRgbG8O2bdvwmte8Bvfccw9e9apX4d5778WLX/zinToW\nF0n6g6LRKNatW4dNmzah2WyiWq0ik8mEFlh6odSIzMVaF296rFg6ImHTlHR2hwVB4Dw27KYjudPz\n8jzcmJdeH5KvIAiQzWaRTqdDC7zmLmm6uCac0/Dd6/VcBADLYWqoJ5nRWAHtVtSOSC3BMQdLt2bR\nLr1oNBrK56IpntdRq9UcQSMppJG+2Wwil8thYmIC2WwWnU7HleB8EpvNZlGr1dwc8jrVH0ZDP8uc\nJLl+tIYa1jnWRCKBfD6Pgw46yF3rySefPOuzwqDQQqEQukfzwZ78ThgM+wPsO2E40DAvQnXaaafh\n7/7u7/Dd734XrVYL73//+3HVAQx9AAAgAElEQVT00UfjHe94B+644w4cfPDBO136UjN6uVzGI488\ngrGxMdeJFo/HnZGZYY6RSMR5oQA4jw3N0RpLQK8QALf9Sa/XQzqddllR6XQ6lM+k4wLgFnLGH1AF\novlZNzkeGBgIZR3xdcCM2qLXrAQJgPMHsStPDftUh0i+aMbWTCuiXq+7DCxeK71THAM7BEkU6dMi\nSaOapcnu3W4XmzZtch40zhk9TIVCAZVKxREljtEPDWWXXqVSQT6fR6VSmaUwcWNpqmPZbNbdV5Jf\nJdFU3UZHRx0pUw/VXHsIAnDKn6bF7yr25HfCYNgfYN8Jw4GGSG+BC9m/+93v0G63sW7dOgRB4LZq\noerADYUPOeQQlEoll7kEwLXxk0wkEgnU6/XQIsznaD6vVquujDg0NORUCt0Tzw/+jEajqFars7ZV\nYQmt1+uhr68Pp556Kv77v/87RMq0KxBASKnxSQbJCUkAN4mu1+uzzPgkQiw/aoYVCRm3bGHnWz6f\nD+V2AdOKGgkqS2cs+T344IOzEukBuBDNcrkcKvdxzPF4HLVazRFcXpsSP03Cr9frTski2a1Wq66s\nqnliHKtGVZAcDQ8Phwh0Op1Gt9vFa1/7Wvz7v/+7m3eqXVpqTCaT6Ha7OOWUU/b8h9xgMBgM+z0W\nPCm91WrhJz/5CYIgQKFQcGSIJSUSKHZuUalR7wwwnbXExby/vz+09xw9TfQblcvlkGcmCAKXuq2d\ncsD0AkwFjOSDx0smk07d0hgAEglVPXTrFV6Dxi6o/4v5Tlzw2c3G6yOpIzkiuaCi52+IzOdJwKi4\nsZzKCAH6s9ixCMCVPX2ymUgksGjRIpRKJaeG8RycLyp5SsY4p7w/DOgMggDdbtfdH4JdhyyPklTS\nv5ZOp5HNZjE0NOTKspxXkmxgmuht2rQJ9Xo9tEdgNpvF4Ycfjl6vh4cfftgIlcFgMBjmhQUnVGvX\nrnWlnWq16ko/2pYfi8UwOTnpzNTqo6FKxaBGltj8oEgNEM1msyHTup+e7m8DQ1KmpIiKil8q5E/d\nr0/zqGjWplrGshXJjIZl0iSvBEFzungulrxUEeJrNOeJZT3OIYkM9wCMRCIoFAqhsXIu+Rq9N7FY\nDMPDw9i4cWOIvDEPS31iegyWTvVauF1Oo9EI7beofje+judmnIYmqiuJ0vv4hz/8IRQLQS9YJBLB\nunXrXInSYDAYDIb5YMEJVbFYRDqdRqFQcBsTk0RNTU25jr5arRbau46LKg3rTMamcsEFlp1sU1NT\njihQTSHR0Ewrv0TFx/zSXDabxcDAQIjYAHDnU2O4T8aoTmnpK5lMus2EATglioqXkgNd+LUEyuf0\n+VQq5cqVHFelUnHKWrfbdfcgn8+jWCy649GEDsDNMwmOms1HR0exefNmp2bRrE9FDZgptfnjJOmi\nykeDO8kbCZyqY0Q8Hkcmk3Elwkajgf7+fvdeLQvSL8bxkOixxEiSazAYDAbDfLDghGpwcBDATHkI\nALZt2+bKP8DMZsFa6tPFVctBAFwHIJWlRqPhynqLFy8OhXZqkCYVDD/JnGVAdg6Ojo664EifUDUa\njVCJTLvofLWMJIPnz2azziul18CON926hgqWbh2jXY0cMxWfSCSCqakp1Ot1LF682JVI6R9Lp9Nu\njLwPJBgsvVIp0lIr/x4dHUW328Uf/vAHpNPp0PWToJHU8H3AzPY7WuokISKh0+5L3lOOlV4xql5T\nU1MYGhoCMF3mGx8fBzBT/uQmy7yuSCSCXC7nSqoGg8FgMMwHC06omPA9MTGBZrOJLVu2hLYJyWQy\njjQACJmUgRkVRUt8JEv0YzFHafHixbOyhrjoU9nSUiIAt5cejzk8POy65tQcTzJHcqD+KD7OMphu\nm8JzJhIJl+bNKAD6hahcMaaAPih/42O9JoLjporT19eHXq+H8fFxR6ba7bYjnKpwUUFj6ZDkkioU\nr4PXHIlEcOihh+K3v/2tK4NqfIVuMK0eN95LzjlJFXO7eE6qbLoXIE34WpqdnJxEoVDAE0884VRN\njpORFySCLBX7OVYGg8FgMOwKFpxQPfHEE647LZlM4phjjnELZRAEqNVqzkjst74TXAxVnWEYJg3O\nJFP6OpIv7junyhGPqyWjbDbrCB5Ld1RVSK70/dpl6Hf5qcoDwBEw9VNxY2IqPlSJeA7t+puLcGpH\nW6VSQaFQCMUOcAyaV6VbvWjeExVEkhJ28Wn3HgnJQQcdhPXr14fiJnRbHd+Ar3NG4kTVTvPJ9Hc+\nz1IwX8t5efzxx91jel84L0r2+Lmwkp/BYDAY5osFJ1R/+qd/6jbpDYIAAJwXplAooF6vY+vWrSHF\nxt8nj4pSNBpFqVRyCyRJWT6fd8SJqo6qWar8AHD+K5bvGo0GEomEy5hqt9uYmJhAX19fKKgTmFGH\n/DBK9XmRYPkeLYJesE6ng2w26zxYmuSuBNBXp1jiI2lgRATLbZrlBcCVL5UYAnBkRJU4qlMkd5oz\npTEWIyMj2LBhQ6gxwI+QIOYy3KtaxmPS2M73Mz+L94hkjUSUChdBAkUVkD62qampkL/KYDAYDIZd\nxYKvIPl83i2G9PUwm4gKw9DQEMbHx53nSRO1tbxHEzQVqEqlEgqApKpEsNyom+1qiKa265M8AXCq\nFBdt9W9prABDNRuNhjPX87wkBaq4NBoNZLNZVKtVZ25neYv+I5IEVbhIBP3yXDKZRLvdRn9/vyMt\n0WgU/f39bg64mTDnR8tefX19TlmqVCrodDquRMjrYHmy1Wohl8sBgGsKKBQKKJVK7jp5b7R7by6F\nivPE6yBRU9+V+sVIKjl+EnM1spOYsbSq/jN+DkyhMhgMBsN8seCEqlKpuGgAqh6tViukYnQ6HfT3\n9yOZTGJqasoFS8bjcdfBBmCWQRuYDnfkwk2lR8tUXHSZocQFXH1T3K5FO/cYrMloB6o9DMjU1zP8\nk8RAW/zp3+F4MpkMIpGIi5IgaSGxURO3XoN2EmqcAsdF07nu68fzakSEKjo0/+t7q9WqI3l+NAOz\npBiEypR1klNVqDSagedW5ZCP6Th9r5WSTYXfKADMeNhIClnWVA+ZJtYbDAaDwbArWHAXLg3hXEy5\nObCqQcDM1iE0EvMxAE654fFIdPL5fCh00ycMVLtU5SCho2qRTqdDkQE6biBsQgemiQtjH6iYaNYS\nCRhVGSUKJDs0i5OosYONZAvArM43KlIkVySEjARQhUdDL6la8Vi8Hs4vx84A0KGhIQwODoZCV/2I\nChre4/E40um0mxt6skjAeG7f66T33f8ZiUxvc8O5pUIGhHOn/PvEbKxer4dSqeTmIAgCJJPJUC6Z\nwWAwGAy7igVXqJgxRZ9UIpFAoVBw5Icko91uo16vu+dZgtKyGBdTkgcu1qoCaQefEiSqGn6QJkkc\nlR4SK6pQflJ6o9FAJpNxi72fEaXdi9qJCMCRD8YkUAXj85rJpFEOnCsqVjpOlrb4fn+/Q/5NMsFS\nGgDXEcjXNZtNN6e5XA7ZbBZbtmxx/jBeTzKZRKPRcD8ZSaBxElpuVZWQP1UBU9LHsXFueL+BmSYC\nVQNJ5nRDaRJkfg64xY+fkm8wGAwGw85iwQnV448/jiAInAG81Wphy5Yt6PV66O/vd5sgF4tFp070\nej23GS8A19nHhV8DIxnYqEGO6sUh6O0BZjYOpto0VxI6SZOW9gCEFB6SMr6GpIPnYjlNYxtIDrLZ\nLMbHx50Sw8BLjoHX6CeSc/yqSpHE6CbOAJxvSElNNpsNleE0iFSVLfreRkZG0Gg0sHXrVnfNVL5K\npZIjo9yfUEuWmtCuih8VNl6LEkYlhNqxyXnSsqg2C9Anpu/3CW61Wt3dj7PBYDAYDlAseI2jVquF\nOvc0SqBSqWDbtm2OTLFrjwuoGtCp3rRaLUcUVFWaK1+Ji7K2z/v75AEIlcuazSb6+/tDXWEs8RFU\nUnhOXchJglgS1HKdhnNGo1HXfafHJliaVAKnJEPJhO4vSGLFHCeWz0j41JjOeeL18Zw8L68FAEZG\nRlAoFNz9IVnUDCnOjd/Rp2Z69THpfdBOQy0D6rVTIdN7rbEXPBfvfa1WcyVmM6UbDAaDYXew4AoV\nFRBVgLTlPxKJYGJiwnWmUT3iYs6FUNPTSdCo1Gin1/bOQWJFgsPWfd32hXvXkUywTV9LhzSg8z1c\nrKkkaamuVqvNIj0M8tQxqnKm45zLxO3nYDE/il12JI008LPEGY/HXYlOk8RJMkjEtAzbaDQccYtE\nplPLDzroIPzv//6vK2kmk0mXyF6pVELH5diVFCkZBWZKkPRpaYgniaDOPwmhetc4F1oOVS8XS8YG\ng8FgMMwXC06ouJCzrMQSDAC3kDKYknvwUfXh4q6Lsx5DPTvqq9EFnERMlRGNMuDzyWQS/f39oQBL\nqiq6GGsIqXqTNMmd506lUiG1jNlTHIOmezPSgddAY7uOg0oYCQf3AuRPkhW+l0SUqk4QBKG5oLrE\na2AGFWMJaLJX1a3VauGII47A73//+9Bc8/0kQCS5LBGyMcFX93yyyLkDELqP/CwRqoDxbxInHleP\nRzJpMBgMBsN8sOAlP6o8aj7Xsg6JRCQSca3ufI2Ww1juATCr041EhcSEZEdVHpazuPCSpDWbTeRy\nObevIMtXus8dEC5VdjodF9eg2+gQc5EULZ+RpFFB0uMq0VDCp11yGqHALjztZNQxaCefhmgC04Sl\n0WigXq+7fySJ2sVIgkfFJx6P4xnPeAbq9Xoo9kG7+3S+OR4lX77Xjc/55VtVGtVv5edq8fr5GVCP\nFj87eg8NBoPBYNgVLDih0kVMiYcuivTWaOmMaoOWzPgc3w8gVPLyc5f88hmJmPp5SIgIn9Aw5V27\nyEjidI84NX6TqNCIT9VJr52eJk1X5+NKmjgnShiUbJC06NyQAJHM8rWcQ50fRaPRQLlcdooXfVKq\n+pAcJpNJLFmyBLVazamF6mHifeH4OZ/qcVIPGuder8PPHOPf+rngtfgdnfp6/m45VAaDwWCYL3aK\nUD388MNYsWIFbr/9dgDApk2bcOGFF+KCCy7AZZdd5rrP7r77bpx99tk499xz8dWvfnWnBsDFk9lP\nVEG0rKclKP5TPxLfq4uwloOobGkJySchfJ7KFhd5pn/zWDxGvV53OUxaalKlQ7O1ADijfCqVQqFQ\ncFvi0NcEzCgu9DHxd5rWVZFrNBpOcVPvFIAQgdPgUFX//M5AXrcGZfpGcZKwSqXiCKHGHOixE4kE\nnvWsZ7nuOfWW6b3Xsqd64fjTLx36nx3ONUuXSsgU+pzeJ34GdsVH9VR+JwyGfRH2nTAc6NghoarV\narjmmmtw8sknu8duueUWXHDBBfjSl76EQw89FKtXr0atVsOtt96Kf/3Xf8UXvvAFfO5zn8PU1NQO\nB6CKBIBQ6QuYWcD5j2Zklp60fEcVREtgvmfHLysB4cBP7b4bGBgIlZ54XnaGKQkhqBxR7Wq1Wm5T\nZZa91MzOkp9GGmiOFtUxkra5wiv96yF0LkkYlGg1m01X/qJXjXlfBLe8oT+L46OqVqvVHPlT4sOI\ni263i6GhIWcqV3VJlSIt2/lme70+Nd5r/ISWLflTP1c+eVPCrf92Bk/1d8Jg2Ndg3wmDYScIVTKZ\nxGc+8xmMjIy4xx588EGcfvrpAIDly5fjgQcewLp167Bs2TIUCgWk02mccMIJeOihh3Y4ACVDDIPU\nRVUJk+8VUk+RdtXp1iz+wknM9Tj9Wgx/1NfxnCQhWmrT0hFN4alUCul0GgMDA87HpIZrv9SWTCZR\nKBRcQrrGHgBw2/LwOknuCJ98cOycMxI/jo+p58PDwxgZGcHg4CAymQwWL16MxYsXu/nQ8FESGZrS\niWKx6DoFSRBJvuLxOLLZrCNdOm4lOep/0mYCnX8lTPq54T2gQqUEXOdFPw98n0Yt7Gyw51P9nTAY\n9jXYd8Jg2IkuP81bIrhdBwAMDw9jbGwM4+PjGBoacq8ZGhrC2NjYDgfwjne8Y1fHvFdj2bJlCz2E\nPYaTTjppoYewx3DzzTfv8DVXXXXVTh3rqf5OGAz7Guw7YTDsgdiEnS0/bQ/XX3/9LIWBpTRt6dc2\nd+5v56sVqnAA019yDZT0S3csQanaAQADAwPIZrMYHBxELpdDvV5HOp0GALdfHiMHmMKey+VwzDHH\nYP369bNM3VqqqtfrToHi9SSTSRSLRfT394dCOev1OoIgwLZt21zAKYBQ6jhVPE0Aj0QiTmHL5/NI\npVLIZrNOidJ50vIYTfiRSARHHnkkfvvb3wKY9n4FQeDGRoVOzejAtH+sUqk4FaxaraLZbKJWq6HV\naqFcLmPz5s0hJYjdlHOV8VSN0+vV3DDOsxrfCZYxP/axj+GKK64AgFleKXaY8rk9gd39ThgM+xvs\nO2E4EDCvLr9sNut8Nlu2bMHIyAhGRkYwPj7uXrN169aQ/Ls96OLITi9tkfdJiRIgNS/7GUaEerTm\n8uWo50a74TiWarUaijjgmLhZcTabRV9fX6hEqF19/MkORgZpsuxGczmJmpJH+q2YJUWiwmsmtpf3\nlE6nQ0nwPK/+07gCGtB94kmCl0gkkEgkkE6nkc/nZ0UZKPGcnJwMkTYazvv6+tx99030WuLU6+Q/\nP3Fd51nfw9/p4eLnQCMy1Eu1vc/GrmBPficMhv0B9p0wHGiYF6E65ZRTcM899wAA7r33Xrz4xS/G\nsccei1/+8pcolUqoVqt46KGHcOKJJ+7U8UhiSIwoH6uCoguwKko0gfuKhW92B8JZRKpskTBQ6aH/\nhnvpzaVy5fP50MbI6s3huZSotFot191HGZzHZLegTxIBuBwoNaxzznwyoKRAyQ6N9NVq1RG7SCSC\nqamp0DX7XXE0pKsKBcDtcdjX1xcihbwW/l6v151iRZAgqo9K50sztPReauOAEltVpuYymHOuNWpC\nn1cCvjsK1Z7+ThgM+zrsO2E40LDDkt+vfvUr3HDDDXj88ccRj8dxzz334CMf+Qiuvvpq3HHHHTj4\n4INx1llnIZFI4Morr8TFF1+MSCSCSy+9FIVCYYcDYEu+lnm0FR8IJ2MrQeJ7WDaay3iuJMwnZFyM\nSagY2NntTqeG53I5twkwCVs6nXYqjCo5Goap6pSaz3luBpMyjymRSLi0eMYjADOmcN0smPAJmG4B\nw3PQZE+1ql6vo9lsujLlwMAAIpEIarWaK7lmMpnQ9jicJ84x09GpGJFUlstl97g2DPR6PUcISV6Y\ntq73ivdHiZESWN2cGUBoexwdJ+GrVizdsnWb95TxDv77nwxP9XfCYNjXYN8JgwGI9Ba4iH3ttdeG\nOuVUnQCmFz76d3TPP6LT6bjFldBLIqHhNi++z0qVj0gkgv7+fiSTSRd1wPdmMhkMDQ25c7GcpmTr\n8MMPx/r16125UP1JJIjc/w6Ay7LK5/Oui87PVqrVanj00UdDae8sG1K5UbKl/jGml3OMmUwGhx12\nGPr6+pxfiudlNyW3gTn22GPxP//zPy4ri/eEahrJaKfTweTkJIAZr9XY2JiT+judmT3/er0eSqUS\ngiBwG177xInz5KtSJGpU2Khy+aqelo9J4m666SZcffXVzm/GvQV5zzV09GMf+9jOf3gNBoPBYPj/\nsVckpfslHoKLJhd0TSzngqubH6svyE/b5qKuSofGLSjBUnM0ALdhMPOn9Dw8t8YEkAjRjE0yRKWG\ngZyMVyAx43iUFNTrdXftnCvdsNhXeXzPEJUnluXYUaNb2zCSodFohAgdr1HPpeXPXm86y4qxCLFY\nDNlsFkNDQ+6c2mhQrVZDpNYvWbLUqqoTiRTHzIR8//8D/HnjfOi1kDAyyV7fqz8NBoPBYNhVLDih\nUjWGP0kGSICUKNAYnUgkZqVz04vl5xPp+31TM03Y+h4lZZ1Ox5XGSDra7TYqlQqA2duz1Ov10Dh8\nb1Cn00EymUQ8Hkc6nQ4RKSa+c6zlchlBELjsKypJfL12q2mnHA3gfho8y4A0x3N/Pg0j5fk4VySW\njUbDdfZx3km+6BHj66LRKA4++OBZ182/I5GZBHpVHPVe+4Z1zjVLoqpK+d2B6rVST5t2cfoZZrtS\n8jMYDAaDwceCEypVpuZSqbjYsQRHD5P6plTVICnRxRSYWZS5aOsGy77awSDOdruN4eFh5xPi3nok\ndEEQuLDPWq0GYLbfi4u8musBODJDZYvjYas/iVaj0UA2m3Vzo3ELJCuEb7QnAeN4SKhqtdqszkUq\nX7rVTKvVQrFYdB4resd4XE1PZ0mV1xyNRjEyMoJkMulUOl6/H1rKa9GuSL8JgcRNSR6Ppx2Aeu81\nRoFzSqKZz+ddmVPHbTAYDAbDfLDghAoIm81JBEiSNBmdpEQ3Imb+kiaqb++4ugDzMT0HF2WWhBYt\nWhTKWtKsJ6pb1WrVlfAAuHH4kQnM1KJ6xKTgbDaLRCLhjkNSU61WQ2oUr52ER8tlfjSEvockhJss\n6xjT6bQrm+rGyCRhQRA4BSsSmc6VqlarofnQ0ijnnFlWsVgMixYtQjabdXNCUqOlOyU/mkel90/T\n73kcdmby3qmnje/XEqh+tvL5vCN3qnYZDAaDwTAf7Haw5+5CtzXRxVMXQC5+JC1cwKlGcJHlYsx2\nf74GCHesKenSvfu4EFerVYyOjroWfy1zcWzs/uN4VHniuEg4WKrKZDKuZMhONxIuGt/T6bRb5FmW\nI+FSE7fGDfAxNacroSAJJZEpFosYGBhw+/Yp+aJCBswEqPJYJGJUp6hKcQ5IVoBpL1m9Xke5XEYm\nk0GtVnMKGZ+nusf7o11+fvmO90pVTN4P3VaHXjMek+A88RjNZhN9fX0YHx93njQjVAaDwWCYLxac\nUHFhpOIDILRoZ7PZWT4bnyRpec8vuXER9Vvy9fWtVsv93Wg0MDo6ilwu50qDJF1UrvwuMwBuUdZu\nQgCuXKgeKZIbjkmVLJ4nm81icnLSlft8L5nOH48LzHTJ+eU3+qbS6bRTv4aGhpBOp0ObBWcyGbdZ\nabfbdd2C7LDTjr1areY8YbwvPCfVO97TWCwWimTQcpz655QIa7ffXPeYx+X1sqzIe+HPld5PNdnz\nMT+awmAwGAyGncWCl/zUNEySQeKQSqWc4uCHdKpCAyC0EPvdXeo/8smUr4y02+1QSz1JTrVadUZ0\nnl+VMv2dBMw3zfNxJYjaBUjSw5JeoVBw6pwa6PX8akTndZAY8H2xWAypVAq5XM7NcaVScV2H7XYb\nQRC4Lr9FixYBQCgElPeBKpdGNmjXIlGv113XX6/Xc0Z8ki+OT8mxr07N5Q/zzeh6nfTWsWtxLhKm\nf0ejUQwMDLgGBN/XZTAYDAbDzmLBVxAqCn6gJQmV+pzUAzWXF0oXWFV0NLzTL/kRNIEzo4leJ55D\nE75VNanVati2bZs7N31KWrakwR2A81PRD+WX1zhmkheGjSoR1DKcdiUCcKoLX0+iqa9rtVrYtGkT\nisUiJiYmQj6wbrcbypAqFApubCSazOIKgsDdL8ZBTE5OOgVMU9bpbVK/Fsmn3+GppTn9nPgxC3xc\niRbHybIeoY0IPJZ/LlOoDAaDwTBfLDih8hd8EiAuwABmLaB+B52qGeqxUWXHz57yu/vUY6UEBZiO\nL+Bj2lXIclehUAhtCaMmeZbGqEJxfFzIOVZ/w16qQzwmSaeOS7sH1UtFskAlSQmHxgUwF4olQ41L\nAIBareaCPOkzo7lelSu9LwBQKpVc+jiDRfWa2fnHx3w1UVU2fc6/Vz654uv0/ulGzH6UBY+lERMG\ng8FgMMwHC+6hAmZUBjU9Dw0NhciVds+pN0rN5EpqeFwArlyl6pFP4NrtNtLptPubW7RUq1UXvsnn\naKTPZrMuSdwvR3JBp+pDA7ampQ8MDAAIkysSCc4Jzd1KBOgv8xU7jZ4g8VM1htussEQ5NjaGxYsX\nu/MoqeMxaIoncaJyxvPxuLxvVKampqacEkVDPc8bjUaRSqVCexQqMVLSqoZ7VQxVwfMJGd83l0Hd\nv/csRzIawmAwGAyG+WCvIFTADJHgJsIkAkpWuICrzwoIe6G2Ryr4U7v1SIwAuLKU7h/IzYRVBeP4\nuD0NX0syQOLBxZ8GaBJCHdfWrVtRLpfR398fIoTAzKbI5XIZlUoltGmwr6z56ppv9vaDRjl/zWbT\nbbWjxIO/ZzIZxONxt9kxiRDLZ+wS5F5+iUTCESrmdHHudd7ppVJCpSRUOzbpg/LVKD/UVI/hb18D\nILRPIp/juXg/Go3GLn9uDQaDwWAA9oKSH5Ub7UojUVG/k5aDAIQWWd9MrH4aza/ic3oMQhd9lsO4\nQLNjrdPpIJ1OI5/PI5vNOpO4kj4SMy7cVNiUWDUaDZTLZZRKJYyNjWHTpk1oNpvOuwTMlEJVjSKJ\n0Y2R/VKf/qM6pl4zbgfDjCu9ByQy9Eb1ej1n8KbhmyqbEiOdbypqGlTa7XZRqVRCZnPOiXYhqs+J\nc8lxa4mX55rrfioJ1wYAbj6t79XPBbs6DQaDwWCYDxacUKn/hZ4kYEYpYUmJi7Aay6nGKNHi63RB\nVaVCz6kqlxq5y+WyIw0kHo1GIxTESRWGY6IypcqJ+pW41UytVkMikUA6nUa9XsfAwECoDMUU8na7\njVKp5Iger8kvS5FoqVdKiQFJpQaW8l+r1cLk5KQbcxAEIULDch3HSiJG4sNYBIaRssOOxJHH0BgF\nJU5+Vpgqab5ix7nVEqeSRfVOKVHWRoVUKhXK2dK55LENBoPBYJgPFrzkp1updLtd9Pf3u9KamoVp\nLFdDMd+jvipVJ5Ro+eZlVSm0DFSr1TAwMODGQFXmkEMOQSaTcYSv2+3Oypzi8bT02Gg0UKvVnKE8\nk8kAmDZuU4kjcWBsATAdO9BoNNz5SGj8vC6ek8qM3z1HEhYEgUsp19Lf5OQkMpkMut0uMpmMI0gA\n3IbHQRAgCAIXIaHXX6vVkMlkXHfk4sWLMT4+7roFOa5kMokgCFCr1UJzxDIh75mSPyVMSqABzFIF\nCX0f763ec46DnYokoitkrKIAACAASURBVEqKDQaDwWDYVSw4oeLCx1ISjcSMGvAXRF1Y1U9FZYLP\nK7kgfDXMz4Sq1+tuOxbtfDvssMNCJUYuzCR9NHUTzJYiAYpGo87cTdUrk8m4cpgeu1gsIpVKOXI1\nPj7uNlzWzZP9LjeOVbeD4XVoojnPqWRz27ZtLkC1Xq8jn8+7+fL9RoyK4PFY+iRZpJrWbDYRBIGL\nMOB80+iukQoKPq4GdJ5bCZT66viYb2rX12lpWL1efF+v13MbNhsMBoPBsKtY8JIfMEOqaBpW4qKK\ng2/G1s1wfZ8UDeh8r5aB/CgBXcQ1RLTX6+GZz3ymI1ksQ7J0RrLEiABgOmJBgyWVjChIuorFons9\noxV0zz96oegz02vU66E/iB4pzeYiceEx1ABOBa1cLrvXlctlAECxWMTk5KTbULnT6aBUKgGYKSXS\nNE9Fr9fruUDW/v5+N1YSTM4FN5PWMp2vPPq+KT7uf3YIkiNtLNCSn36mGFDqG/kNBoPBYJgPdopQ\nPfzww1ixYgVuv/12AMDVV1+NV77ylbjwwgtx4YUX4r/+678AAHfffTfOPvtsnHvuufjqV7+6UwNQ\nFYEqiZIQ33ysCyShXW5zqRjaWs/nVM3wOwRJPEZHR2ct9GrGjsViqNfrLhgTmNkKhuchGeM+ft1u\nF/l8Hvl83qWXM02c4BYxWo5UUklPEokT4RNPfb1vHKcCyE49muJpRAemFTsqUOl0Ghs3bsTk5KTb\njiabzYZ8ZfSUpVIp9PX1IZPJYHBwEOl02o2FPizOEwkQCbJf4lMVUX/fXqejr9rNlX7OOdXPkn+f\nd4Sn8jthMOyLsO+E4UDHDkt+tVoN11xzDU4++eTQ41dccQWWL18eet2tt96K1atXI5FI4JxzzsHK\nlStd1tL2wIVQ/SvqqdISn28iV4MyX68+Knpx/NfoY/7r6Hs69NBDQ+UzfxsZ+q2odBAsZ/lp4AAw\nPDyMTqfjvEfNZhMbN25EOp3GIYccgl6vF0r5ZmecqlM6F/58KTHQMfM1zG5KJpMuhZ5z3Gq1UC6X\nQ9ejmwazdDg4OOjIGHO4SMR4rfQpATMkKJfLYXx8fJa3ieXC7UViqFdKFUvC92P5yqSSJj99nflT\nmUxmu+RrLjzV3wmDYV+DfScMhp1QqJLJJD7zmc9gZGTkSV+3bt06LFu2DIVCAel0GieccAIeeuih\nHQ6AC18ymUQkEpnVNq+eIS6WGuDpkyn9Ww3sqlzw2G4SJFyy1WphYGAAnU7HmaoJkjgqUloG1Bwn\nVVwAoL+/3/lz6EGiqZtBmyzx0WfF/fdIoqh6qa+MxETLY+xK5LWQRCUSCeTz+dB2Nq1WKxR7EI/H\nUSwW3dyQUNKMPjQ0FEqeZ06Wdij2ej1ngCchS6fTSKfTGBoaCnm4eJ/Y8ac+NP18qCqlzQR+OY+l\nTE3JV9/UXMpkoVBwhHVny35P9XfCYNjXYN8Jg2EnCBXb/X3cfvvtuOiii/C3f/u3mJiYwPj4OIaG\nhtzzQ0NDGBsb2+EASDxY4uJC2OuFM4m088svf2k3GBAufenCygVfc434ev5j3hIN6jrObnc6TykI\nglC+lW6To74qBoDqXPI99XodkUgEixYtQjabdenpJDBUTUiQqHj55Tu9Lo6TBEMfY9o8/+Z1MPuK\n/i1ePzATo5BMJl2JUgNYqXCVSiVMTk6Ggj5JOv3GgeHhYVf6VHLEnyw3+n4x34Tu33s1obMsq3sl\ncu55Hs0Fy2Qys3x2T4an+jthMOxrsO+EwTDPLr9XvepVGBgYwNFHH41Pf/rT+OQnP4njjz8+9Bol\nOE+Gm266aT5D2Gtx3HHHLfQQ9hjOOOOMhR7CHsO11167w9dcdtll8z7+nvxOGAz7A+w7YTjQMC9C\npXXy0047De9///uxatUqjI+Pu8e3bt26U+TiyiuvRC6Xc+UeqhpUTvy8IVVnFOqN0u46YCZ1nEqO\n+mUikYjLg+rv70c6nUZ/f78zXScSCbTbbbcNCzvE2NbPff4ikQiWLVuGRx99NKSmqEGaxu+JiQlE\nIhEEQYBGo+EiB1jui0QiKBaL2Lp1KzZu3OgUNZbxVGWba06oSLHslkqlnPLld7txXgC4a0kkErj0\n0ktx991345BDDkGj0cDU1BT6+vrcuVutForFoitd0p9UrVbR7XZRLpdRKBRcBhbHXa1WnQm+WCyG\nPFtUjNRgrtEG+hmYK25B9xikMhiJRPCBD3wA7373u939YCo8y5Sq3s0Xe/I7YTDsD7DvhOFAw7xi\nE/7mb/4GGzZsAAA8+OCDeM5znoNjjz0Wv/zlL1EqlVCtVvHQQw/hxBNP3OGxGJeg24bMZVQGZspZ\n2tHHhZe+pbkWXpaGtEtMy2TczsU3JjebTUxNTblQTRIJkhuWljTwEpgpIdJQ3mg0UCqVUCwWUSwW\nMTY2hna77YhOo9FAtVp159BkcS3zafK6kig/WoBz5T/earVCieccO03qnFtey/j4OH7zm9+g0Wig\nr6/PER7tamT8A8/ZbreRy+WQSqUwNTWFiYkJd37da6/X67ksLt4XljzV5L+9EqYSajXi67mUdLH8\nF41GXVmTXjWWVPW8u4o9+Z0wGPYH2HfCcKBhhwrVr371K9xwww14/PHHEY/Hcc899+D1r389Lr/8\ncmQyGWSzWVx//fVIp9O48sorcfHFFyMSieDSSy9FoVDY4QBokGYoJY3U2iqvSoqSKmDGf6OmdVW0\neA7tklNwsdYEdGC6G0W9ODQuc0+4VCrl4gA0XZwb/nJTZV5Tt9t1BGJ4eNgRR461VquFjletVlGt\nVkNEQv1CSqxIGpVE8jr8LWk0rkB9X2r0J1kiwdm0aRMOPvhgR1qo2nGrGao7jEMol8uOHNLoTv8V\nvWMAXMch7z1JjR+66ndZqmrJ+0rFUO8F7z0AF26qIaPsLuVjO0uonurvhMGwr8G+EwYDEOktcBH7\nPe95D3K5nCMHzWYT5XLZKUCqxvhDVeKlREkJhm8ap3LDRHYAyGQyyOfzLumbZTKWsvgY1Y1sNuvI\nAAlfr9fD8ccfj3Xr1iGVSrkFngs1x7dx40ZnwqcqpYb4ZDKJarWKjRs3olgsupR0LvZqrNc5UeKl\n+U2JRAK5XG7WJtEkfbofoao+V1xxBT71qU9h0aJFAKYJCbeYYeBorVZDu91GpVJBu9125IrlzEql\ngkwmg2q1ilgshnK5jGQyGSJWrVYrFPLJcq9PqH0FktfM0p3uxRiJTG9QncvlEIvF8J73vAcf/vCH\nASAUQaFlYsZl7G+ePoPBYDA8PVjwpHT6doCZxHJtqddSnHZyacr1k0H3ntMFWdUhYLZ/h4RFNyaO\nxWKh7VVIWDQigD95Piou9FDxcc3T4mbJJEUkKyzx+eVN/x9Jlj6v56ECoz/ZaQjMzv3SuSNp6nQ6\n2LZtm3ucRBWA85cxmJX3TTsBGTPBedcNobWkq/fLV6I4Vp888nk/w4rj4H3RWAadP38cBoPBYDDs\nKhZ8Lz9uUkvSQpO2Qkt+Gq2g3hotd2lZSLOsCFW1WPYB4Ep7JEokRM1m02VJcZxKpvxsKC2tkZjV\n63UX1FmtVjE8POy2lGEeVbVaRbPZxOOPP+7KZxyDeoR8UsbH9HfdH5AxBRyfGtm5UXCj0UAqlQpt\nS0N1rNfrORXpd7/7HQ477DBHdJmkzrJfLpdzW9EAcD4wjp9ZWUyH5/wHQRC6L5otReWK5yQBIrGb\n6zGmzbOUS4IHhMmUKmFGqAwGg8EwXyy4QkXlSU3oQHiPN6oMLE35JIl/+0qOvo6LM4DQTyUezFCi\nkkEDN8kUfUAkVd1uN9QxBsDlSXGBpyoFzBAdAKhUKohEIk6hi0ajyGazqFQqbqwsB/o5U3p9quLw\nHPxJP5of/sn3UiUi0QiCwM01r6VWqzkTOkt1v/71r7Fp0yZ3zRMTE+4+ZbNZVy7VMXMcDCxVBZBj\n0XupnX/6edB7rVv86BxwjpnoDsAlvXM8fgODEmuDwWAwGHYVC65Q6QI5OTnpHlPPEx9Thcrv5iNZ\nUbLhl4toptataei9aTQaTpXSYxQKBXfcIAiQyWRQq9WQy+Vc0rgqVES1WsXk5CQikQjy+bwjWAwF\nrdfryOVyIfUtCAJMTk46U7cmsOvx1XiuBElN20C4+40meL6fBELfH4lEQunwJIY0jXOfvng8jk2b\nNqFWq+HQQw/F0NAQNm/e7LxnJCa5XC7U5aeqk95XprVzuxpfRdPPgh6DxJf3Eggnq3M7HWCmy88v\nGfP43N/QYDAYDIb5YMEJlXpctNykZm8qFkwLB8KKBeF3genz9EWpSqTnp8LSbDbd4js4OBjaly+T\nybi9+KhUNRqNkDeHyhM9UMlk0pmsu90utm3bhmaz6Tr+mNLdbrcxMTER2vSY4+W1AQj5qpQUkGSQ\nOKmKxteqARvArC1X2HHHOSmVSq4Lrr+/3x2H92Dz5s0IggBHHXUUCoWCy9dqtVqOfKbTaWdaJ9Ei\nUeR+gBpnUKvVQt4wJdJqTFcTOokjH9NEes4b1b50Ou28Y1THOFcL3J9hMBgMhn0YC06ouIcfPTok\nJ2oo9kMXfbVFoeUcNRurcsPX0ARPAkIC1G630d/f7xZmvxUfmCYjDMrkti18nLlOJDjpdBpBEDif\nEPfE4956rVbLlctIkujN8kuVvF6+zleodBwMuKTy4vuseP0042u8BOeZJU126vF1VHWq1Sp+9atf\nYcmSJRgYGEAQBOj1es4Dls1mkclkUCwWXbCp+t1IvHh//K1q9LWaY6XlTo2T4LXxfSRP2unJa+cc\na4SCwWAwGAzzwYITKpbZ6F/SdnaSKj/RW8tfWhryN0v2jeskIWrq9jvDms0mFi9eHDovj0nCRZWq\nXq+HFDEdGzOVotGoK/NxQ1Ci0Wg4gsJMJxIS9Yf5f2vGFDGX0ZrzwDnmGLvdrrsOlvRisRiCIAht\nlkxSxN/5XlUC6/W6aySgaZ9jLhaL7n0kbyzrATMlScYpcKwkQXr/6b8iGdIuTzXhKwHTKIhoNIrl\ny5cjkUjg9ttvd8ob1a5UKmUlP4PBYDDMGwtOqJLJJJLJJDZt2uRUDwCOjKhiof4YIBx0OVfHl/96\n+m50odXW+V6vh3w+HyIL9FDxfDxOpVIJkQ99jmU7lvtyuZwL6+S19Xo9FzMAzO52VI+Rxh34xE1f\nq3OkpVO/I1DnSskiOxMVLLWqgkUCzM5AzlU8HsfAwIAjR4VCwW01Q7AL0DfT8zx+HhavUwmTfibU\n/0X4zwHTpdh6vY7BwUFHKEkYlVQZDAaDwTAfLHiXH8thbG/3fVG+IVk9Q/o6mrj9eIS5VBuGT/rq\nTyaTcQs61aNqtYpIJOIM7ex4q9frzudUr9ed8qLJ3Ol0Gn19fa48SSWHMQy8Pl/9UeKkPim+hoRM\nO/d8Mz5fp8Z2vp/PcY60fOYTUJYEeZ2tVguNRsNdZ71edwGlzWYTpVIJwDQxLJfLTv3SbshMJuPG\nxmtnMKhfmtWuRSWPfr6Y/0/vPzC9jc7GjRvR6XQwMjKCer2O4eFh9PX1udKoeuEMBoPBYNgVLDih\nYgYSTeEkNJpbpOZsIOybomJB0kDzsSpHuuhS+eJx+LsqMTwXSRoX+nq9jnK57PKiyuUyKpWKI13A\nNKGih4r7+DF6oFarhaIgSOxisRgqlQpqtZqbCx2vb0zX69JQTypjSix170EtjalJn8cmOfPVL1XF\neD8YScBzVSoVVCoVNJtNbNu2DZVKBel02hn6qVoBcN2APoHUUqKfRaZEmu/hPz7vE0Ide7vdxtq1\na1Eul/Hc5z4XQRA4Pxc/dyT1BoPBYDDsKhb8f8m56M61B58u8mo056Lre6FoLgemF1Aa3n1/Ef04\nwMw+cfQZaRwDF/4gCFCr1VwnYjKZdCWiXq8XylxiCUyjIGgQ53Y12h3Y6XSQTqcxPj6OIAgcUaHR\nXbOWOA9+LpWSR1Woer1eaK86jY3QedHSqPrQNKZCS5pK+DTxvNPpOMKZy+Vcgrqa1Pk+eqr02FqC\n045P9UFxLuYqDXIsvncKgItF2Lx5MwYHB5FOpzExMeG21tEmCIPBYDAYdhULrlBxa5NMJhMyT3MR\n1lKWn6+kKo5PBDSDyc+I8n1HuoirCgRMh1bSuB0EgSMoaubu9XrObM7yFsuB0WjUkaxUKoWxsTEA\ncAZ0vl/fo0oTr007/lSd0t+BcNlQVStgmrBQseL16nF9dY/n1nnWUp2SO/+8LP/Ru0RCp6oSs7w4\nft4zNbYTSrg5Nv9+6hj4OdH31Ot1/OY3v0Eul3Pm93K57I5tXX4Gg8FgmC8WXKGanJx0XXOqNGmp\nT9PLNWOKOUpUeuYyM+sx+Td/Us1gHlG9Xndln16vh8nJScTjcZRKJbc5MM/HUlE6nXa/Awh1ijEK\nggs2VS4a1rdt2+aM7VNTU460kbCosV7Jil/683/XWAGW3LTUqSRI0+mplhFqiNfEehIYBn7qeKgU\nUb2iwkWiyfnUbK/JyUk3D3ydjsvv4uN7VZnjudRjpXPD+7Bx40Zs2bLFkehqtYpCoeC8XQaDwWAw\nzAcLrlBpeOTo6CiA6TKgqi6qHmnZSEuChJYNtaQ0FxnR42nnVxAEKBaLAODKiCwZkehwnFyYfY8X\nFRkeI5PJuAU/kUggkUggm81iYmICU1NTrqyoW9UoofBJJpU3/3f1hXEsejyazElGVYHidWjGl3qw\nODbdNkbPrUZ3X8Uql8suc8vvpmNivK8S+l44X7nUa/RVRx03r50RFhMTEzjmmGPcXE5NTSGbzbpG\nAYPBYDAYdhULTqi4uNOTxMU2m81iYGAAS5YscSZlLrpUL9Q87UckqMLB1/jxC0xBB2a63RqNhgva\nZBClEhcei2UjqjHa3Ua1q1QqodFoAIDzFtHnMz4+7spNpVIpZCbX0E4gHJEAzO54m+unb95WP5RG\nO6gvS8kRwflRksXyn5YIqRL1ej3XAamG9FgshqmpqZCRnuOlt8s32Pvk1/eK8XfCJ81zXUc0GsXY\n2BgGBwddB2atVkM2m7WSn8FgMBjmjQUnVMB0aYwLHpO1lyxZgsMOO8wRKC3haLmOCytVGSUAqlJp\nyWiu90Qi02ntJF0s3THWQNv+aXancZwGdmCaOE1NTaHdbrstaqLRKKrVqosGaDQargOQHYMkEBpY\nyfH726wA4f0O1QTvlwN9wqFkhkSKyhNLo9r55x9DfVvqb9PyoJIieqgymQxisZjbK1BJYSQScaVG\nXq9mXvlkyjerE9q0oI0NfC3HuX79elSr1dCxS6XSrM5Cg8FgMBh2Fgu+gnABpEKRy+Xcc319fa7T\njvAXSl+VoWrit88rCfDJh98dpqoLiRK79Gjw5rlIjLgYc9+6TqfjyAO79iqVCoIgcFlO9H5xPKpS\n+QZ5NeLP1eXmlzf1Gv1//nNKRJRAqSlej6+EVMuInA8lPLFYDI1GI7S/IANWFZpMrwRK7zl/959T\nhU0/D9uLUGCMBQAX5+DnfRkMBoPBsCvYKVP6jTfeiJ/97Gdot9v4q7/6KyxbtgxXXXUVOp0OFi9e\njJtuugnJZBJ33303Pve5zyEajeK8887Dueeeu8Nj09syOTmJRYsWuT3ter0eFi1a5DrFuBjG43G3\ngS4ws9iqEqXQhVVLflRBqFxp6jcXaCo/NKrTcF0ulx2hYedfNpsNnY+KU7lcdvvYsfxXq9WQz+fR\narXctZCYUBmj+dovf2kZUMuYvjKl86XzHI1GHXnw977z1R0qV9Ho9H6EfplRSR1VPo16oC+NCl02\nmw2VL0lQedx0Oo1isejKpr4/TUtyvIccO5VJEkLdSFmhCl82m3WfJ45vZ/FUficMhn0R9p0wHOjY\nIaH68Y9/jEceeQR33HEHJicn8epXvxonn3wyLrjgApx55pn46Ec/itWrV+Oss87CrbfeitWrVyOR\nSOCcc87BypUrMTAw8KTHp0mb5a9sNouhoSG35QuTrLlIqmlbf+oCquSDPh59nZKuRCKBIAhcLpJ2\nvmk3H/OjuI9cIpFAoVBAq9VCJpNxikc+n3fm9c2bNyMIAjdOnoNlRI5Ly4C6IbSW3tT75Rvvldz4\nCg3JmqpX2u2nx9MyIEFCwnR0+sw4nyRo2u3HOaeBn8dl1yPHwXnUMiOzvpT4agSGlmz9UqR2gvLe\n++oWfzYaDRx++OF4+OGHXbmXkRY7wlP9nTAY9jXYd8Jg2ImS3wte8AJ8/OMfBzBdgguCAA8++CBO\nP/10AMDy5cvxwAMPYN26dVi2bJnbAPiEE07AQw89tFODICEol8uo1+uo1WqOSG3bts2Rkk5nOrVc\nyQHfq4uwHwTKcwBhgzfN49vrBGS5qlQquW1X6KUiCWLrPZWlcrmMbrcb2o4lGo26fftIQrhPIFUW\nmrdJMNVDpMTR9z/xse2FfQKYRcKUoGh5zydYusUNX88sKpY8Sfy4t58a27VrkMdlZyTPqSVazaJS\n75P+rdD58H/3GxcInuMPf/iD22ORJecnnnhipz6vT8d3wmDYl2DfCYNhJwgVS3AAsHr1arzkJS8J\n7b03PDyMsbExjI+PY2hoyL1vaGhop/6Pn/4oLojNZhPJZBKNRgP9/f0oFApOFeICzTIRMEMe1Bfk\nEy5VXfQ1qnZxIadyxLId36NdYvxZq9VQLpdRq9Wc+tLpdPDHP/4RjUYDmUwGg4ODWLRoEeLxuCNV\nlUoFBx98MAqFAlKplPuPC8+rREnHrCRDy4C+R0z38FPSRMVNc6p4nVpK1N+VSPldfRyLPqb+Kh7b\n76AEZhQ4qli8HlWV/DgKvVa990qgCCVnfK3mW01NTbl4Dr6f+zHuCE/1d8Jg2Ndg3wmDYReCPb/z\nne9g9erV+OxnP4s///M/d4/PpRw82eM+3v72tz/p88cdd9zODnGvwPnnn7/QQ9hj+NKXvrTQQ9hj\nuPXWW+d8fMWKFe73G2+8cZeO+VR9JwyGfRX2nTAcyNgpQvXDH/4Qn/rUp/BP//RPKBQKyGazqNfr\nSKfT2LJlC0ZGRjAyMoLx8XH3nq1bt+4UGfrIRz4SUiharRYGBwfxvOc9z22cfOedd7r2fqpVVCCo\ntgCzt5QhtExGM3oqlXKeG/VJ8XF6eVKp/6+9b42xsyq/X2fOnOucmc6lnWmLMRHEFCgUSCGIASlI\noxgJmELQ8EG5JgUTItcAxm+CBTUGSbhIfyFihFg/iIqAiERIShUqpQUMQpRgLW1nmM7l3ObMzPv7\nMP+1Z73PvMPMtPZ/Oj+elTQz8172u/c+581eXc96np2LlUtYsmQJJicnUSqVUC6XceDAgWD23rhx\nI+67776gvnR0dMQqnheLRdTr9XBudHQ0eKcGBweDisKimwwzqjpnlTdg2gtlvWXc7kYVH2vi17lS\nE/jjjz+Or33tazHfkmZDWm8SVTRew+w5Kno0lHPTaFXO2BdWyweAcrkcU4+oqGk4lkqj+t+swpVK\npfDggw9i48aNob/cu3H16tXo7+/H/v37Y9+H+eBwvhMOx2KEvxOOjzvmDPmNjIxg06ZNePDBB4Nx\n8Mwzz8QzzzwDAHj22Wdx1llnYc2aNdi5cyeGh4dRLpexfft2rF27dl6dYGiNiyxNz/V6He+88w56\nenpie74llQDQf3re+ovUqK3EjKZznmdBT/aNW6+QIB04cAAffvhhqC3FcNHExNT2LcViEWNjYyGk\nyZpU3AB4ZGQkeMKUHLKfdrzqlVJTfVK5A+ux4nXsn51DG74jbAhN9xu0BUA1zEhiqG3TqM+iqbxO\nfVrVanXWchc6B9ZIT1O7esPsnChp59zs2bMHQ0ND4Xp+7+bC/493wuFYTPB3wuGYh0L11FNPYXBw\nEDfccEM4dvfdd+POO+/EE088gZUrV+Kiiy5CJpPBjTfeiCuvvBKpVArXXXcd2tvb5+yArbeUzWaR\nzWbR0dGB/v5+DAwMoFwuhywza1hWT5AFF9kk5Wq2Y2yP2WW8n+rF+Ph4MJxPTk7O2E6FXqtarRbK\nMLCsQqVSiRX5JAFUr5Y1V2v/lBQpUWR/eU6vtffpVjt8vs2WJJS8KHm186ftsC/sl/VBtbS0oFQq\nBYJGcqVkmPNqa0OpP077Yj1XTFRQsFaYficqlQry+XzYoFs3XP4oHO53wuFYbPB3wuEAUlGTg9j3\n3nvvjL3Z2tvbcdZZZ6FSqeCPf/xjUIO4SXJSdhcQD/MpydKMM5Y+ABAKa3Z3dweViqSt0Wigra0t\nmCqpUNHIzv6ydEJXVxeuuOIK3HfffWhtbUW5XEZXVxeA6YWexT1rtRrS6TTGx8dDhfADBw4ExapS\nqYQ+UOnhOID4NjCa0chxa6iNtZ80REYSlxQq4zW//OUvsWHDhlhWoM6vkhNbhZ7/lNSwf/l8HoVC\nAZOTk2GclqxRravVaoEc2TYZXmTbqlqxDZ776U9/imuuuSZ8ttxyKJfLYcWKFfjggw+QyWQwOjoa\nMpUcDofD4VgIjohK6Vp0MoqiIBk3Gg2MjIygv78/5o1R9QbADKXCKk5W7VElpb29PfzN1H96jyYm\nJsJ2NCyxMDY2hmq1Gko5TE5OhlpWwBRJ09ARN1UGpsjN8PAwqtXqjPCSZuixP6o28Z8WzdTzDCcC\n8VAdt77RzEEb7rPH1Julz+f8aYjNhlS1DRJlG4pk/Slu4cPPVr1fSp4+KuxnvXK8X9UsnrP9HB8f\nR7FYRD6fn7F3oMPhcDgcC8H8XbiHCapMAAjmcFYQHxgYQCaTmWGEBjCDJFnyBMTJBdvnedZ9oppF\n43S9XkexWIwRBpItLv4kgCRGWltJQ1X0SGUyGYyMjIR7WQgziqIQAtQx6Lg0JDebMqckgwoU1TUt\nK6AFL5XIsh37eejzdE/FRqMRK6RpibGqg2yjpaUlJBbw71wuF8iplnCwVd01HMnf1Tdlt63R0K39\nvun3h205mXI4HA7HoaDpCpX1v6RSqbDNyUsvvRRCNElhHatSWHVFiQd/kri0trbGNuple5lMJlTv\n5nMrlUrIxqPiwsXhbQAAIABJREFUw2dw4adCxT36Ojo6AgljG7xGK6Xv27cP5XI51ESy9aOSCA/D\nj4SOnwRSjd9APKtPTel8DlUurVquz2Qb6vfSIql6vSpKNhRIMkaywwrntj3rvbJeLiVCqsDpdZoV\nqP3T80NDQ6jVamFeHA6Hw+E4GDRdoeLCRlUjk8mgq6sLqVQK7777blCNuIiqqVoJlFUp7DP4HBuq\n0grlExMTsT3dqI6RaLS2toY2Wltbkc1mw8bHVIMqlQo6OztDaQCG4sbHxzEyMhI8XFEUYXBwMLZl\ni4altPAllRqOk+BYGo0GstlsjJCwT0oSrAmdx/hMVaCAaS+TXqvEDJj2c2nIln2gT0zJH0kUyaOq\nTBxTKpUK47ZGdJvhqM/UEKFu86N9Jwkj6RsdHY1lbzocDofDcTA4IhQqXcja29tRKpWwY8eOWO0h\nXVg1vEXoYqokS0kGj2cymXA/U/n5N8lLuVwOW8SwMrtVQHRrGWb5tbW1BUWL5KFer4eK6iQPVKvY\nrpZpoILD57J2VVLJA/qRSOA05MlMOvVPkUioEmZLIFjFySpRdu4JKng6Xxyvlizg/SSoURTFalCp\nwkVSp+TOhjtVYdRwn/YzyQ/GRAFmdDqhcjgcDsfBoumECkDw4qTTaaxYsQIA8MYbb4RsrKR0fV0U\n+VO3TNHFNImMKOnQ0BfJ1MTEBKrVajChp9PpEIrkc1KpVFC3SJBIJhqNRgj5MVOvq6sLjUYDo6Oj\nMYLBcQHT++dpbS7rDSMR0H4roSS0ZpUlpNYozj6r/4sEUvcV5HHOFZ9BQzxhn0O1jX2megVMhUmj\nKEKxWAzjJxHTz92WTNB/OnY7p/ysZiPaAEIhUofD4XA4DgZND/kB8YWvu7sbQ0NDGB0dDSn/vEaV\ni9ky/pIM3Bp2Ut+NEgm9h6Ehkhlu6KtmdJ5jaEoVEPqmcrlcqPjN542OjoYwIxUpboZs954Dpg3f\nWoOL6o4tN2FVI/Ulsa4TEPeXqUfLklSdH45fjd6Tk5MhJGrDcZpdqAZyFjvlXnr8TFjwM5PJBMKj\napZ6sfhskj2OVQmXfo4cs4YQNexrVVKHw+FwOBaKpitUtv5QNpvFnj1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VomsT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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cf9702e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pYnx8HI7jyPgXkoRyuSxlvEajIerGU089hUQiIb+HQiHE43GsWrUKAFyG+0wm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\n1cR1LTmxvEeCpF4ebf0H1r1bSuRI+HR8CrAeKkolTbsSNRZCu+xIxOh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dKhP02vCGylRx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\nkUqSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36cf8d73c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Att9nue5_ql7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Predict test images"
      ]
    },
    {
      "metadata": {
        "id": "F6-gG8q__ql7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "72e9fc4c-e433-4bc9-f82a-550c8ba2814a"
      },
      "cell_type": "code",
      "source": [
        "# load and shuffle filenames\n",
        "folder = './stage_1_test_images'\n",
        "test_filenames = os.listdir(folder)\n",
        "print('n test samples:', len(test_filenames))\n",
        "\n",
        "# create test generator with predict flag set to True\n",
        "test_gen = generator(folder, test_filenames, None, batch_size=25, image_size=320, shuffle=False, predict=True)\n",
        "\n",
        "# create submission dictionary\n",
        "submission_dict = {}\n",
        "# loop through testset\n",
        "for imgs, filenames in test_gen:\n",
        "    # predict batch of images\n",
        "    preds = model.predict(imgs)\n",
        "    # loop through batch\n",
        "    for pred, filename in zip(preds, filenames):\n",
        "        # resize predicted mask\n",
        "        pred = resize(pred, (1024, 1024), mode='reflect')\n",
        "        # threshold predicted mask\n",
        "        comp = pred[:, :, 0] > 0.5\n",
        "        # apply connected components\n",
        "        comp = measure.label(comp)\n",
        "        # apply bounding boxes\n",
        "        predictionString = ''\n",
        "        for region in measure.regionprops(comp):\n",
        "            # retrieve x, y, height and width\n",
        "            y, x, y2, x2 = region.bbox\n",
        "            height = y2 - y\n",
        "            width = x2 - x\n",
        "            # proxy for confidence score\n",
        "            conf = np.mean(pred[y:y+height, x:x+width])\n",
        "            # add to predictionString\n",
        "            predictionString += str(conf) + ' ' + str(x) + ' ' + str(y) + ' ' + str(width) + ' ' + str(height) + ' '\n",
        "        # add filename and predictionString to dictionary\n",
        "        filename = filename.split('.')[0]\n",
        "        submission_dict[filename] = predictionString\n",
        "    # stop if we've got them all\n",
        "    if len(submission_dict) >= len(test_filenames):\n",
        "        break\n",
        "\n",
        "print(\"Done predicting!\")        \n",
        "        \n",
        "# save dictionary as csv file\n",
        "sub = pd.DataFrame.from_dict(submission_dict,orient='index')\n",
        "sub.index.names = ['patientId']\n",
        "sub.columns = ['PredictionString']\n",
        "sub.to_csv('submission.csv')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n test samples: 1000\n",
            "Done predicting!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1IxkJela_ql-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cddbd63c-b848-4a58-8b8a-e67c4d697a2a"
      },
      "cell_type": "code",
      "source": [
        "!kaggle competitions submit -c rsna-pneumonia-detection-challenge -f submission.csv -m \"Colab segmentation 5 320x320 30 epochs\""
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully submitted to RSNA Pneumonia Detection Challenge"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lf8l38RrA1tN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "ffc29d5f-926d-4675-d9c1-8b048efb6a5f"
      },
      "cell_type": "code",
      "source": [
        "# upload checkpoint to GCS\n",
        "project_id = 'mammography-198911'\n",
        "bucket_name = 'pneumonia'\n",
        "\n",
        "!gcloud config set project {project_id}\n",
        "!gsutil cp ./{CHECKPOINT_PATH} gs://{bucket_name}/"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n",
            "Copying file://./model9_320.h5 [Content-Type=application/octet-stream]...\n",
            "\\\n",
            "Operation completed over 1 objects/48.7 MiB.                                     \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}