ResNet50-testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load model from .pb file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will load and return the graph.\n",
    "\n",
    "* `model_file_name`: path to a `.pb` file.\n",
    "* `node_mapping`: mapping from the name of node in the graph to a Tensorflow node in current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.platform import gfile\n",
    "def load_graph(sess, model_file_name, node_mapping={}):\n",
    "    with gfile.GFile(model_file_name, 'rb') as f:\n",
    "        graph_def = tf.GraphDef()\n",
    "        graph_def.ParseFromString(f.read())\n",
    "        tf.import_graph_def(graph_def, node_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will load the top-tagging model weights.\n",
    "\n",
    "Please change the `model_file_name` to the file name of your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    # Create a placeholder for the input\n",
    "    input_node = tf.placeholder(tf.float32, shape = [None, 224, 224, 3], name='Placeholder')\n",
    "    graph_def = load_graph(sess=sess,\n",
    "                           model_file_name='weights-floatingpoint-224x224-fixval-best/constantgraph.pb',\n",
    "                           node_mapping={'Placeholder': input_node})\n",
    "# Get the node for output\n",
    "output_node = tf.get_default_graph().get_tensor_by_name(\"import/classifier/model_1/classifier_output/Softmax:0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export to Tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will save the graph into a logging directory so that we can inspect it with Tensorboard.\n",
    "\n",
    "* `log_dir_name`: Path to the directory that is used to store the log. You can use `tensorboard --logdir <log_dir_name>` to inspect the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_to_log(log_dir_name):\n",
    "    LOGDIR='log-top-tagging-resnet50-2'\n",
    "    train_writer = tf.summary.FileWriter(log_dir_name)\n",
    "    train_writer.add_graph(sess.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will save the top-tagging model to a folder.\n",
    "\n",
    "Please change the parameter to where you want to store the Tensorboad information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_to_log('log-top-tagging-resnet50')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run an inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will run an inference on a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "with tf.Session() as sess:\n",
    "    y = sess.run(output_node, feed_dict={input_node: np.zeros((2, 224, 224, 3))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00170123 0.9982988 ]\n",
      " [0.00170123 0.9982988 ]]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those are helper functions from [util.py](https://github.com/nhanvtran/MachineLearningNotebooks/blob/nvt/bwcustomweights-validate/project-brainwave/utils.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_and_rgb(images): \n",
    "    import numpy as np\n",
    "    #normalize image to 0-255 per image.\n",
    "    image_sum = 1/np.sum(np.sum(images,axis=1),axis=-1)\n",
    "    given_axis = 0\n",
    "    # Create an array which would be used to reshape 1D array, b to have \n",
    "    # singleton dimensions except for the given axis where we would put -1 \n",
    "    # signifying to use the entire length of elements along that axis  \n",
    "    dim_array = np.ones((1,images.ndim),int).ravel()\n",
    "    dim_array[given_axis] = -1\n",
    "    # Reshape b with dim_array and perform elementwise multiplication with \n",
    "    # broadcasting along the singleton dimensions for the final output\n",
    "    image_sum_reshaped = image_sum.reshape(dim_array)\n",
    "    images = images*image_sum_reshaped*255\n",
    "\n",
    "    # make it rgb by duplicating 3 channels.\n",
    "    images = np.stack([images, images, images],axis=-1)\n",
    "    \n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_events(train_files):\n",
    "    import tables\n",
    "    n_events = 0\n",
    "    for train_file in train_files:\n",
    "        f = tables.open_file(train_file, 'r')\n",
    "        n_events += f.root.label.shape[0]\n",
    "        f.close()\n",
    "    return n_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunks(files, chunksize, max_q_size=4, shuffle=True): \n",
    "    \"\"\"Yield successive n-sized chunks from a and b.\"\"\" \n",
    "    import tables\n",
    "    import numpy as np\n",
    "    for train_file in files: \n",
    "        f = tables.open_file(train_file, 'r') \n",
    "        nrows = f.root.label.nrows\n",
    "        for istart in range(0,nrows,max_q_size*chunksize):  \n",
    "            a = np.array(f.root.img_pt[istart:istart+max_q_size*chunksize]) # Images \n",
    "            b = np.array(f.root.label[istart:istart+max_q_size*chunksize]) # Labels \n",
    "            if shuffle: \n",
    "                c = np.c_[a.reshape(len(a), -1), b.reshape(len(b), -1)] # shuffle within queue size\n",
    "                np.random.shuffle(c)\n",
    "                test_images = c[:, :a.size//len(a)].reshape(a.shape)\n",
    "                test_labels = c[:, a.size//len(a):].reshape(b.shape)\n",
    "            else:\n",
    "                test_images = a\n",
    "                test_labels = b\n",
    "            for jstart in range(0,len(test_labels),chunksize): \n",
    "                yield normalize_and_rgb(test_images[jstart:jstart+chunksize].copy()),test_labels[jstart:jstart+chunksize].copy(), len(test_labels[jstart:jstart+chunksize].copy())  \n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "# In[33]:\n",
    "test_loss_list = []\n",
    "test_acc_list = []\n",
    "def test_model(preds, in_images, test_files, chunk_size=64, shuffle=True):\n",
    "    \"\"\"Test the model\"\"\"\n",
    "    import tensorflow as tf\n",
    "    from keras import backend as K\n",
    "    from keras.objectives import binary_crossentropy \n",
    "    import numpy as np\n",
    "    from keras.metrics import categorical_accuracy\n",
    "    from tqdm import tqdm\n",
    "    \n",
    "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))\n",
    "    \n",
    "    cross_entropy = tf.reduce_mean(binary_crossentropy(in_labels, preds))\n",
    "    accuracy = tf.reduce_mean(categorical_accuracy(in_labels, preds))\n",
    "    auc = tf.metrics.auc(tf.cast(in_labels, tf.bool), preds)\n",
    "   \n",
    "    n_test_events = count_events(test_files)\n",
    "    chunk_num = int(n_test_events/chunk_size)+1\n",
    "    preds_all = []\n",
    "    label_all = []\n",
    "    \n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    \n",
    "    avg_accuracy = 0\n",
    "    avg_auc = 0\n",
    "    avg_test_loss = 0\n",
    "    epoch = 0\n",
    "    epoch_list = []\n",
    "    is_training = tf.get_default_graph().get_tensor_by_name('import/is_training:0')\n",
    "    n_current_events = 0\n",
    "    n_current_events = 0\n",
    "    roc_curve = []\n",
    "    for img_chunk, label_chunk, real_chunk_size in chunks(test_files, chunk_size, shuffle=shuffle):\n",
    "        test_loss, accuracy_result, auc_result, preds_result = sess.run([cross_entropy, accuracy, auc, preds],\n",
    "                        feed_dict={in_images: img_chunk,\n",
    "                                   in_labels: label_chunk,\n",
    "                                   K.learning_phase(): 0,\n",
    "                                   is_training: False})\n",
    "        avg_test_loss += float(test_loss * real_chunk_size / n_test_events)\n",
    "        avg_accuracy += float(accuracy_result * real_chunk_size / n_test_events)\n",
    "        avg_auc += auc_result[0]  * real_chunk_size / n_test_events  \n",
    "        preds_all.extend(preds_result)\n",
    "        label_all.extend(label_chunk)\n",
    "        n_current_events += real_chunk_size\n",
    "        test_loss_list.append( avg_test_loss*n_test_events/n_current_events)\n",
    "        test_acc_list.append( avg_accuracy*n_test_events/n_current_events )\n",
    "        roc_curve.append( avg_auc*n_test_events/n_current_events )\n",
    "        if(epoch < 157 ):\n",
    "            epoch += 1\n",
    "            epoch_list.append(epoch)\n",
    "        print(\"test_loss = \", \"{:.3f}\".format(avg_test_loss*n_test_events/n_current_events), end=\" \")\n",
    "        print(\"Test Accuracy:\", \"{:.3f}\".format(avg_accuracy*n_test_events/n_current_events), \" , Area under ROC curve:\", \"{:.3f}\".format(avg_auc*n_test_events/n_current_events))\n",
    "        print(epoch)\n",
    "    \n",
    "    import matplotlib.pyplot as plt\n",
    "    print(\"+----Confusion Matrix----+\")\n",
    "    TP = 0\n",
    "    FP = 0\n",
    "    FN = 0\n",
    "    TN = 0\n",
    "    for i in range(len(label_all)):\n",
    "        if ( (np.argmax(label_all[i],0) == 1) and (np.argmax(preds_all[i],0) == 1) ):\n",
    "            TP += 1\n",
    "        elif ( (np.argmax(label_all[i],0) == 0) and (np.argmax(preds_all[i],0) == 0)):\n",
    "            TN += 1\n",
    "        elif ( (np.argmax(label_all[i],0) == 0) and (np.argmax(preds_all[i],0) == 1)):\n",
    "            FN += 1\n",
    "        else:\n",
    "            FP += 1\n",
    "    TPR = TP/(TP+FN)\n",
    "    FPR = FP/(FP+TN)\n",
    "    print(\"TP: {0}, FP: {1}\".format(TP,FP))\n",
    "    print(\"FN: {0}, TN: {1}\".format(FN,TN))\n",
    "    print(\"+------------------------+\")\n",
    "    \n",
    "    print(\"+------TPR and FPR-------+\")\n",
    "    print(\"TPR: {0:.3f}, FPR: {1:.3f}\".format(TPR,FPR))\n",
    "    print(\"+------------------------+\")\n",
    "\n",
    "    def plot_loss(x, y):\n",
    "        plt.figure()\n",
    "        plt.plot(x,y,label='test_loss')\n",
    "        plt.xlabel('events')\n",
    "        plt.ylabel('loss')\n",
    "        plt.title('Loss per events.')\n",
    "        plt.plot()\n",
    "        #plt.savefig(figname)\n",
    "    def plot_acc(x, y):\n",
    "        plt.figure()\n",
    "        plt.plot(x,y,label='test_acc')\n",
    "        plt.xlabel('events')\n",
    "        plt.ylabel('acc')\n",
    "        plt.title('Accuarcy per events')\n",
    "        plt.plot()\n",
    "        #plt.savefig(figname)\n",
    "    def roc_plot(x,y):\n",
    "        xx = []\n",
    "        for i in range(0,157):\n",
    "            xx.append(x[i]/157)\n",
    "        plt.figure()\n",
    "        plt.plot(xx,y,label='ResNet-50(area = 0.958)')\n",
    "        plt.plot(xx,xx,'--',label='Random')\n",
    "        plt.xlabel('FPR')\n",
    "        plt.ylabel('TPR')\n",
    "        plt.legend(loc='lower right')\n",
    "        plt.title('ROC curve.')\n",
    "        plt.plot()\n",
    "        #plt.savefig(figname)\n",
    "    plot_loss(epoch_list, test_loss_list)\n",
    "    plot_acc(epoch_list, test_acc_list)\n",
    "    roc_plot(epoch_list, roc_curve)\n",
    "    return avg_test_loss, avg_accuracy, avg_auc, np.asarray(preds_all).reshape(n_test_events,2), np.asarray(label_all).reshape(n_test_events,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will test the model on a test file.\n",
    "\n",
    "Please change the parameter so that it uses the correct file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_loss =  0.459 Test Accuracy: 0.875  , Area under ROC curve: 0.000\n",
      "1\n",
      "test_loss =  0.396 Test Accuracy: 0.883  , Area under ROC curve: 0.474\n",
      "2\n",
      "test_loss =  0.323 Test Accuracy: 0.896  , Area under ROC curve: 0.632\n",
      "3\n",
      "test_loss =  0.290 Test Accuracy: 0.906  , Area under ROC curve: 0.715\n",
      "4\n",
      "test_loss =  0.288 Test Accuracy: 0.900  , Area under ROC curve: 0.765\n",
      "5\n",
      "test_loss =  0.266 Test Accuracy: 0.906  , Area under ROC curve: 0.799\n",
      "6\n",
      "test_loss =  0.267 Test Accuracy: 0.911  , Area under ROC curve: 0.824\n",
      "7\n",
      "test_loss =  0.268 Test Accuracy: 0.908  , Area under ROC curve: 0.842\n",
      "8\n",
      "test_loss =  0.287 Test Accuracy: 0.901  , Area under ROC curve: 0.857\n",
      "9\n",
      "test_loss =  0.305 Test Accuracy: 0.892  , Area under ROC curve: 0.868\n",
      "10\n",
      "test_loss =  0.281 Test Accuracy: 0.901  , Area under ROC curve: 0.877\n",
      "11\n",
      "test_loss =  0.275 Test Accuracy: 0.902  , Area under ROC curve: 0.885\n",
      "12\n",
      "test_loss =  0.260 Test Accuracy: 0.906  , Area under ROC curve: 0.892\n",
      "13\n",
      "test_loss =  0.272 Test Accuracy: 0.904  , Area under ROC curve: 0.898\n",
      "14\n",
      "test_loss =  0.272 Test Accuracy: 0.904  , Area under ROC curve: 0.902\n",
      "15\n",
      "test_loss =  0.276 Test Accuracy: 0.903  , Area under ROC curve: 0.907\n",
      "16\n",
      "test_loss =  0.274 Test Accuracy: 0.901  , Area under ROC curve: 0.911\n",
      "17\n",
      "test_loss =  0.279 Test Accuracy: 0.901  , Area under ROC curve: 0.914\n",
      "18\n",
      "test_loss =  0.278 Test Accuracy: 0.901  , Area under ROC curve: 0.917\n",
      "19\n",
      "test_loss =  0.278 Test Accuracy: 0.902  , Area under ROC curve: 0.919\n",
      "20\n",
      "test_loss =  0.282 Test Accuracy: 0.900  , Area under ROC curve: 0.922\n",
      "21\n",
      "test_loss =  0.277 Test Accuracy: 0.903  , Area under ROC curve: 0.924\n",
      "22\n",
      "test_loss =  0.283 Test Accuracy: 0.901  , Area under ROC curve: 0.926\n",
      "23\n",
      "test_loss =  0.290 Test Accuracy: 0.898  , Area under ROC curve: 0.928\n",
      "24\n",
      "test_loss =  0.298 Test Accuracy: 0.897  , Area under ROC curve: 0.929\n",
      "25\n",
      "test_loss =  0.291 Test Accuracy: 0.901  , Area under ROC curve: 0.931\n",
      "26\n",
      "test_loss =  0.295 Test Accuracy: 0.899  , Area under ROC curve: 0.932\n",
      "27\n",
      "test_loss =  0.287 Test Accuracy: 0.902  , Area under ROC curve: 0.933\n",
      "28\n",
      "test_loss =  0.289 Test Accuracy: 0.901  , Area under ROC curve: 0.934\n",
      "29\n",
      "test_loss =  0.295 Test Accuracy: 0.899  , Area under ROC curve: 0.935\n",
      "30\n",
      "test_loss =  0.294 Test Accuracy: 0.900  , Area under ROC curve: 0.936\n",
      "31\n",
      "test_loss =  0.297 Test Accuracy: 0.899  , Area under ROC curve: 0.937\n",
      "32\n",
      "test_loss =  0.302 Test Accuracy: 0.897  , Area under ROC curve: 0.938\n",
      "33\n",
      "test_loss =  0.299 Test Accuracy: 0.899  , Area under ROC curve: 0.939\n",
      "34\n",
      "test_loss =  0.306 Test Accuracy: 0.895  , Area under ROC curve: 0.940\n",
      "35\n",
      "test_loss =  0.309 Test Accuracy: 0.893  , Area under ROC curve: 0.940\n",
      "36\n",
      "test_loss =  0.314 Test Accuracy: 0.890  , Area under ROC curve: 0.941\n",
      "37\n",
      "test_loss =  0.314 Test Accuracy: 0.889  , Area under ROC curve: 0.942\n",
      "38\n",
      "test_loss =  0.313 Test Accuracy: 0.890  , Area under ROC curve: 0.942\n",
      "39\n",
      "test_loss =  0.313 Test Accuracy: 0.890  , Area under ROC curve: 0.943\n",
      "40\n",
      "test_loss =  0.314 Test Accuracy: 0.890  , Area under ROC curve: 0.943\n",
      "41\n",
      "test_loss =  0.317 Test Accuracy: 0.890  , Area under ROC curve: 0.944\n",
      "42\n",
      "test_loss =  0.315 Test Accuracy: 0.891  , Area under ROC curve: 0.944\n",
      "43\n",
      "test_loss =  0.322 Test Accuracy: 0.890  , Area under ROC curve: 0.944\n",
      "44\n",
      "test_loss =  0.319 Test Accuracy: 0.892  , Area under ROC curve: 0.945\n",
      "45\n",
      "test_loss =  0.320 Test Accuracy: 0.893  , Area under ROC curve: 0.945\n",
      "46\n",
      "test_loss =  0.322 Test Accuracy: 0.892  , Area under ROC curve: 0.946\n",
      "47\n",
      "test_loss =  0.321 Test Accuracy: 0.893  , Area under ROC curve: 0.946\n",
      "48\n",
      "test_loss =  0.323 Test Accuracy: 0.893  , Area under ROC curve: 0.946\n",
      "49\n",
      "test_loss =  0.326 Test Accuracy: 0.892  , Area under ROC curve: 0.947\n",
      "50\n",
      "test_loss =  0.329 Test Accuracy: 0.891  , Area under ROC curve: 0.947\n",
      "51\n",
      "test_loss =  0.331 Test Accuracy: 0.892  , Area under ROC curve: 0.947\n",
      "52\n",
      "test_loss =  0.329 Test Accuracy: 0.892  , Area under ROC curve: 0.947\n",
      "53\n",
      "test_loss =  0.329 Test Accuracy: 0.891  , Area under ROC curve: 0.948\n",
      "54\n",
      "test_loss =  0.331 Test Accuracy: 0.890  , Area under ROC curve: 0.948\n",
      "55\n",
      "test_loss =  0.334 Test Accuracy: 0.889  , Area under ROC curve: 0.948\n",
      "56\n",
      "test_loss =  0.331 Test Accuracy: 0.890  , Area under ROC curve: 0.948\n",
      "57\n",
      "test_loss =  0.330 Test Accuracy: 0.890  , Area under ROC curve: 0.948\n",
      "58\n",
      "test_loss =  0.328 Test Accuracy: 0.890  , Area under ROC curve: 0.949\n",
      "59\n",
      "test_loss =  0.329 Test Accuracy: 0.890  , Area under ROC curve: 0.949\n",
      "60\n",
      "test_loss =  0.328 Test Accuracy: 0.890  , Area under ROC curve: 0.949\n",
      "61\n",
      "test_loss =  0.326 Test Accuracy: 0.891  , Area under ROC curve: 0.949\n",
      "62\n",
      "test_loss =  0.325 Test Accuracy: 0.891  , Area under ROC curve: 0.949\n",
      "63\n",
      "test_loss =  0.321 Test Accuracy: 0.892  , Area under ROC curve: 0.950\n",
      "64\n",
      "test_loss =  0.320 Test Accuracy: 0.893  , Area under ROC curve: 0.950\n",
      "65\n",
      "test_loss =  0.320 Test Accuracy: 0.893  , Area under ROC curve: 0.950\n",
      "66\n",
      "test_loss =  0.318 Test Accuracy: 0.893  , Area under ROC curve: 0.950\n",
      "67\n",
      "test_loss =  0.324 Test Accuracy: 0.892  , Area under ROC curve: 0.950\n",
      "68\n",
      "test_loss =  0.322 Test Accuracy: 0.892  , Area under ROC curve: 0.951\n",
      "69\n",
      "test_loss =  0.321 Test Accuracy: 0.892  , Area under ROC curve: 0.951\n",
      "70\n",
      "test_loss =  0.322 Test Accuracy: 0.892  , Area under ROC curve: 0.951\n",
      "71\n",
      "test_loss =  0.321 Test Accuracy: 0.892  , Area under ROC curve: 0.951\n",
      "72\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.951\n",
      "73\n",
      "test_loss =  0.320 Test Accuracy: 0.893  , Area under ROC curve: 0.951\n",
      "74\n",
      "test_loss =  0.321 Test Accuracy: 0.893  , Area under ROC curve: 0.952\n",
      "75\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.952\n",
      "76\n",
      "test_loss =  0.324 Test Accuracy: 0.891  , Area under ROC curve: 0.952\n",
      "77\n",
      "test_loss =  0.323 Test Accuracy: 0.891  , Area under ROC curve: 0.952\n",
      "78\n",
      "test_loss =  0.322 Test Accuracy: 0.891  , Area under ROC curve: 0.952\n",
      "79\n",
      "test_loss =  0.323 Test Accuracy: 0.891  , Area under ROC curve: 0.952\n",
      "80\n",
      "test_loss =  0.321 Test Accuracy: 0.892  , Area under ROC curve: 0.952\n",
      "81\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.952\n",
      "82\n",
      "test_loss =  0.320 Test Accuracy: 0.893  , Area under ROC curve: 0.953\n",
      "83\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.953\n",
      "84\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.953\n",
      "85\n",
      "test_loss =  0.322 Test Accuracy: 0.892  , Area under ROC curve: 0.953\n",
      "86\n",
      "test_loss =  0.323 Test Accuracy: 0.892  , Area under ROC curve: 0.953\n",
      "87\n",
      "test_loss =  0.322 Test Accuracy: 0.892  , Area under ROC curve: 0.953\n",
      "88\n",
      "test_loss =  0.321 Test Accuracy: 0.892  , Area under ROC curve: 0.953\n",
      "89\n",
      "test_loss =  0.319 Test Accuracy: 0.893  , Area under ROC curve: 0.953\n",
      "90\n",
      "test_loss =  0.319 Test Accuracy: 0.893  , Area under ROC curve: 0.953\n",
      "91\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.954\n",
      "92\n",
      "test_loss =  0.322 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "93\n",
      "test_loss =  0.323 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "94\n",
      "test_loss =  0.324 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "95\n",
      "test_loss =  0.325 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "96\n",
      "test_loss =  0.324 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "97\n",
      "test_loss =  0.324 Test Accuracy: 0.890  , Area under ROC curve: 0.954\n",
      "98\n",
      "test_loss =  0.323 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "99\n",
      "test_loss =  0.322 Test Accuracy: 0.891  , Area under ROC curve: 0.954\n",
      "100\n",
      "test_loss =  0.320 Test Accuracy: 0.892  , Area under ROC curve: 0.954\n",
      "101\n",
      "test_loss =  0.319 Test Accuracy: 0.893  , Area under ROC curve: 0.954\n",
      "102\n",
      "test_loss =  0.317 Test Accuracy: 0.893  , Area under ROC curve: 0.954\n",
      "103\n",
      "test_loss =  0.315 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "104\n",
      "test_loss =  0.314 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "105\n",
      "test_loss =  0.312 Test Accuracy: 0.895  , Area under ROC curve: 0.955\n",
      "106\n",
      "test_loss =  0.312 Test Accuracy: 0.895  , Area under ROC curve: 0.955\n",
      "107\n",
      "test_loss =  0.312 Test Accuracy: 0.895  , Area under ROC curve: 0.955\n",
      "108\n",
      "test_loss =  0.312 Test Accuracy: 0.895  , Area under ROC curve: 0.955\n",
      "109\n",
      "test_loss =  0.312 Test Accuracy: 0.895  , Area under ROC curve: 0.955\n",
      "110\n",
      "test_loss =  0.312 Test Accuracy: 0.895  , Area under ROC curve: 0.955\n",
      "111\n",
      "test_loss =  0.315 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "112\n",
      "test_loss =  0.317 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "113\n",
      "test_loss =  0.317 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "114\n",
      "test_loss =  0.317 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "115\n",
      "test_loss =  0.317 Test Accuracy: 0.894  , Area under ROC curve: 0.955\n",
      "116\n",
      "test_loss =  0.316 Test Accuracy: 0.894  , Area under ROC curve: 0.956\n",
      "117\n",
      "test_loss =  0.314 Test Accuracy: 0.894  , Area under ROC curve: 0.956\n",
      "118\n",
      "test_loss =  0.313 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "119\n",
      "test_loss =  0.313 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "120\n",
      "test_loss =  0.311 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "121\n",
      "test_loss =  0.311 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "122\n",
      "test_loss =  0.311 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "123\n",
      "test_loss =  0.311 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "124\n",
      "test_loss =  0.311 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "125\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "126\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "127\n",
      "test_loss =  0.311 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "128\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "129\n",
      "test_loss =  0.309 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "130\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "131\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.956\n",
      "132\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "133\n",
      "test_loss =  0.309 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "134\n",
      "test_loss =  0.309 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "135\n",
      "test_loss =  0.309 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "136\n",
      "test_loss =  0.309 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "137\n",
      "test_loss =  0.308 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "138\n",
      "test_loss =  0.308 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "139\n",
      "test_loss =  0.309 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "140\n",
      "test_loss =  0.309 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "141\n",
      "test_loss =  0.308 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "142\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "143\n",
      "test_loss =  0.309 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "144\n",
      "test_loss =  0.309 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "145\n",
      "test_loss =  0.308 Test Accuracy: 0.896  , Area under ROC curve: 0.957\n",
      "146\n",
      "test_loss =  0.309 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "147\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "148\n",
      "test_loss =  0.310 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "149\n",
      "test_loss =  0.309 Test Accuracy: 0.895  , Area under ROC curve: 0.957\n",
      "150\n",
      "test_loss =  0.310 Test Accuracy: 0.894  , Area under ROC curve: 0.957\n",
      "151\n",
      "test_loss =  0.312 Test Accuracy: 0.894  , Area under ROC curve: 0.958\n",
      "152\n",
      "test_loss =  0.312 Test Accuracy: 0.894  , Area under ROC curve: 0.958\n",
      "153\n",
      "test_loss =  0.312 Test Accuracy: 0.894  , Area under ROC curve: 0.958\n",
      "154\n",
      "test_loss =  0.314 Test Accuracy: 0.893  , Area under ROC curve: 0.958\n",
      "155\n",
      "test_loss =  0.313 Test Accuracy: 0.894  , Area under ROC curve: 0.958\n",
      "156\n",
      "test_loss =  0.313 Test Accuracy: 0.894  , Area under ROC curve: 0.958\n",
      "157\n",
      "+----Confusion Matrix----+\n",
      "TP: 4088, FP: 992\n",
      "FN: 70, TN: 4850\n",
      "+------------------------+\n",
      "+------TPR and FPR-------+\n",
      "TPR: 0.983, FPR: 0.170\n",
      "+------------------------+\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAgAElEQVR4XuzdB5TUVPvH8YcmHQSkSG/SBFFBukhvdoqIoKKAiFhREQQpgoAgvoIICBaUlxewK0gXl470orA0kSZd6U3K/zzxPyGzO7OTZGbZ3cz3nuPRnclNcj83k3Py897cVFeuXLkiFAQQQAABBBBAAAEEEEAAAQQQQAABBBDwpEAqAkBP9iuNQgABBBBAAAEEEEAAAQQQQAABBBBAwBAgAORCQAABBBBAAAEEEEAAAQQQQAABBBBAwMMCBIAe7lyahgACCCCAAAIIIIAAAggggAACCCCAAAEg1wACCCCAAAIIIIAAAggggAACCCCAAAIeFiAA9HDn0jQEEEAAAQQQQAABBBBAAAEEEEAAAQQIALkGEEAAAQQQQAABBBBAAAEEEEAAAQQQ8LAAAaCHO5emIYAAAggggAACCCCAAAIIIIAAAgggQADINYAAAggggAACCCCAAAIIIIAAAggggICHBQgAPdy5NA0BBBBAAAEEEEAAAQQQQAABBBBAAAECQK4BBBBAAAEEEEAAAQQQQAABBBBAAAEEPCxAAOjhzqVpCCCAAAIIIIAAAggggAACCCCAAAIIEAByDSCAAAIIIIAAAggggAACCCCAAAIIIOBhAQJAD3cuTUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAkGsAAQQQQAABBBBAAAEEEEAAAQQQQAABDwsQAHq4c2kaAggggAACCCCAAAIIIIAAAggggAACBIBcAwgggAACCCCAAAIIIIAAAggggAACCHhYgADQw51L0xBAAAEEEEAAAQQQQAABBBBAAAEEECAA5BpAAAEEEEAAAQQQQAABBBBAAAEEEEDAwwIEgB7uXJqGAAIIIIAAAggggAACCCCAAAIIIIAAASDXAAIIIIAAAggggAACCCCAAAIIIIAAAh4WIAD0cOfSNAQQQAABBBBAAAEEEEAAAQQQQAABBAgAuQYQQAABBBBAAAEEEEAAAQQQQAABBBDwsAABoIc7l6YhgAACCCCAAAIIIIAAAggggAACCCBAAMg1gAACCCCAAAIIIIAAAggggAACCCCAgIcFCAA93Lk0DQEEEEAAAQQQQAABBBBAAAEEEEAAAQJArgEEEEAAAQQQQAABBBBAAAEEEEAAAQQ8LEAA6OHOpWkIIIAAAggggAACCCCAAAIIIIAAAggQAHINIIAAAggggAACCCCAAAIIIIAAAggg4GEBAkAPdy5NQwABBBBAAAEEEEAAAQQQQAABBBBAgACQawABBBBAAAEEEEAAAQQQQAABBBBAAAEPCxAAerhzaRoCCCCAAAIIIIAAAggggAACCCCAAAIEgFwDCCCAAAIIIIAAAggggAACCCCAAAIIeFiAANDDnUvTEEAAAQQQQAABBBBAAAEEEEAAAQQQIADkGkAAAQQQQAABBBBAAAEEEEAAAQQQQMDDAgSAHu5cmoYAAggggAACCCCAAAIIIIAAAggggAABINcAAggggAACCCCAAAIIIIAAAggggAACHhYgAPRw59I0BBBAAAEEEEAAAQQQQAABBBBAAAEECAC5BhBAAAEEEEAAAQQQQAABBBBAAAEEEPCwAAGghzuXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAyDWAAAIIIIAAAggggAACCCCAAAIIIICAhwUIAD3cuTQNAQQQQAABBBBAAAEEEEAAAQQQQAABAkCuAQQQQAABBBBAAAEEEEAAAQQQQAABBDwsQADo4c6laQgggAACCCCAAAIIIIAAAggggAACCBAAcg0ggAACCCCAAAIIIIAAAggggAACCCDgYQECQA93Lk1DAAEEEEAAAQQQQAABBBBAAAEEEECAAJBrAAEEEEAAAQQQQAABBBBAAAEEEEAAAQ8LEAB6uHNpGgIIIIAAAggggAACCCCAAAIIIIAAAgSAXAMIIIAAAggggAACCCCAAAIIIIAAAgh4WIAA0MOdS9MQQAABBBBAAAEEEEAAAQQQQAABBBAgAOQaQAABBBBAAAEEEEAAAQQQQAABBBBAwMMCBIAe7lyahgACCCCAAAIIIIAAAggggAACCCCAAAEg1wACCCCAAAIIIIAAAggggAACCCCAAAIeFiAA9HDn0jQEEEAAAQQQQAABBBBAAAEEEEAAAQQIALkGEEAAAQQQQAABBBBAAAEEEEAAAQQQ8LAAAaCHO5emIYAAAggggAACCCCAAAIIIIAAAgggQADINYAAAggggAACCCCAAAIIIIAAAggggICHBQgAPdy5NA0BBBBAAAEEEEAAAQQQQAABBBBAAAECQK4BBBBAAAEEEEAAAQQQQAABBBBAAAEEPCxAAOjhzqVpCCCAAAIIIIAAAggggAACCCCAAAIIEAByDSCAAAIIIIAAAggggAACCCCAAAIIIOBhAQJAD3cuTUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAkGsAAQQQQAABBBBAAAEEEEAAAQQQQAABDwsQAHq4c2kaAggggAACCCCAAAIIIIAAAggggAACBIBcAwgggAACCCCAAAIIIIAAAggggAACCHhYgADQw51L0xBAAAEEEEAAAQQQQAABBBBAAAEEECAA5BpAAAEEEEAAAQQQQAABBBBAAAEEEEDAwwIEgB7uXJqGAAIIIIAAAggggAACCCCAAAIIIIAAASDXAAIIIIAAAggggAACCCCAAAIIIIAAAh4WIAD0cOfSNAQQQAABBBBAAAEEEEAAAQQQQAABBAgAuQYQQAABBBBAAAEEEEAAAQQQQAABBBDwsAABoIc7l6YhgAACCCCAAAIIIIAAAggggAACCCBAAMg1gAACCCCAAAIIIIAAAggggAACCCCAgIcFCAA93Lk0DQEEEEAAAQQQQAABBBBAAAEEEEAAAQJArgEEEEAAAQRSkMDDDz8sU6dONc94+PDh0q1btxTUAk4VAQQQQAABBBBAAAEErrUAAeC1Fud4CCCAAAIIuBQ4fvy45MuXT86dO2fu4ZZbbpH169e73CPVEEAAgWsrMGHCBHniiSeMgz7++OOif1MQQAABBBBAIPEFCAAT35gjIIAAAgggEBGB8ePHy1NPPRVvX2vXrpVbb701IsdgJwgggEBiChAAJqYu+0YAAQQQQCC4AAEgVwcCCCCAAAIpRKBWrVqyZMkS42wzZMhgjgR88cUX5T//+U8KaQWniQAC0SxAABjNvU/bEUAAAQSSUoAAMCn1OTYCCCCAAAI2BXbs2CElS5Y0tk6dOrUR+L3wwgvG33ny5JF9+/ZJ2rRpbe6NzRBAAIGkESAATBp3jooAAggggAABINcAAggggAACKUCgT58+MmDAAONM69WrJzNnzpQbb7xR/vrrL+OzH374Qe69994U0BJOEQEEolmAADCae5+2I4AAAggkpQABYFLqc2wEEEAAAQRsCFy5ckWKFy8uf/zxh7H1p59+Ku3bt5cuXbrI2LFjjc9atGghX331lY29+W/y+++/y+effy7z588XHWV49OhRY4ShLjaiC4zUr19fWrdubYwyTKjowiT/+9//ZNasWbJ69Wo5fPiwMUX5+uuvl9KlS0vNmjXlwQcflKpVq8bbjbbls88+82tbQseyGyBcvnzZmDI9d+5cWb58ucTGxsqRI0dEP8+ZM6eUKVNGGjRoYLxX8YYbbghplypVKnMb7RMtugDLJ598Ij/99JP8+eef8vfff8v9998v3333XcD9ufG+7777ZNq0acb+Bg8eLD169Ah5rrrB66+/bmyv5ZFHHpFJkybZqhd3o379+kn//v2Nj/v27Sv695kzZ4zFGyZPnizbt2832q3XiPZzp06djJDaSdm8ebNMnDhR5s2bJ7t27TL2lz17duO6b9y4sTz99NOSP3/+BHdZp04dWbBggbHNzz//LPr3/v37jd+L9sfu3buN6zJr1qxy7NgxJ6cXcNuVK1ca17weS0fg6iI9el3p9d6sWTPjusqRI0fAuhrWT58+3fhu0KBB0rNnT1vn8+abbxp9oKV58+by9ddfB623Z88eo4/mzJlj/rYzZ84sRYoUMX7Xen6lSpVK8LiBfpunT582fq/a9m3bthmW2vf6ioJnn33WuAYCFeu+QjX2rrvukpiYGL/N9Df3/fffyxdffGHcY7Rv9R6jxvr71etD6+n1UqlSJeM+RkEAAQQQQACBqwIEgFwNCCCAAAIIJHMBfRCuW7eucZYZM2aUgwcPGiHG0qVLzYft6667zngg1gDCTjl//ry8/PLL8uGHH8rFixcTrJIuXTojGNRjBirffPONPP/880YIEqqMGTPGCHOsJTECwH/++UeKFStm65w0FNEgtV27dgmeftwAUIOwgQMHyqVLl/zqBQoAw/HWoMg3uvOmm26SrVu3hmI2zkmDHl+faEDpNJTzHSRuANimTRsjfNq0aVPQ89A+HTdunOi1k1BRF32HpS5wE9fRWk+v+6FDhxoBU7ASNwDUQE5Xm9Uw0Vo0WAwnANT9aciZUPimx9PwW9vVsmXLeKc8depUefjhh43Pb775Zvn1119D9qluoOGir//1d6ehetyiAbf22bBhw/xWDI+7nb4yoHv37sY1bL22E/ptaoCv/7NBA9tgRUcr+wLjYPsK1di4AaDe87Sty5YtC1XV+F5Dfw33KQgggAACCCBwVYAAkKsBAQQQQACBZC6gIYaO5NGi4YuOvPEVDYR0BJaWDz74QJ555pmQrTl16pQ0atTI72E6U6ZMxgieggULio600eBIR9lo8KdFQw8NNOKW4cOHy6uvvmrU0aJBQsWKFaVcuXKSJUsWY4ryxo0bZcuWLcb3+u5CDXysJTECQB0ZpKGRFj0PDVl0NFm2bNlEw8G9e/caowJPnDhhnsqUKVOM0Y7BijUk0TBKwxMtJUqUkCpVqoga6ihNHZH05ZdfmrsJ11uDMQ0zdUSXFh3lVrt27QT7+ccff5R77rnH2EbbrddIsJAn1AVjDQC173S6uY5k1NBZQ7fChQsb/awj4axh20MPPSQadAUrOpJMR2v5FrbR7bSdlStXNgx1XxpyW4Plt956yxjZGKhYA0DdTs9b+zpXrlyGl44SO3TokOiq2TrK0E05cOCAEaRaA7CyZcsaq3BrQK77X7x4sTHS1Pd70JGNbdu29TucXp958+Y1r79169YZv5uEio441OtMi/rouWgfWIteK3oNW8NJfVWABnc6Sk+vxV9++cUYEegrGmZqWBuoWH+baqr3GB3pqveCO++80xgprG3VEcQauPpKoN/Sf//7X3MkrgbSWnQUro5GjFv0vuZ7x6m2Sc9f70e+Ur58edF/9DzUUi10NK7+TxAtBIBurm7qIIAAAgh4XYAA0Os9TPsQQAABBFK0gE611IfskydPGu3QYEenF/qKjrTRoEOLPiRrqBWq6MgjXzCTJk0a0RE7OhpQR8JZi44k0rBpxIgRxpQ/HTllLTNmzDBCJl/4p8GIBgT6UB+37Ny505iKqWHZ448/7vd1YgSAFy5cMEYaPvbYY8YoyUAj0XT02ciRI41ASUdBapigIZsGhoGKNUDT0VPqpcHsAw884Le57jd9+vTmZ5HwtoZw2ibflOlgfa2jtHSEmBYd4dWrV69Ql0XQ763H1sBJbXV0lU4d13DJVzSI0ams7733nvmZTo/WADtQ0etA96FFrwsdhRl31JaGPxpOvfTSS6Kuer0uWrRIqlevHm+X1gBQ+0fr6pTZ1157za//4/aPXRj9Pej5adCp5fbbbzdG0GpgaS3q8Pbbbxuj4PS3odeJhuAablqLNdh/5ZVXjBF7CRUNxPR61aLTd/XYcYv1XaEa+L3//vvGCMS402H1dQEdO3Y0Qzu9H2hgG7dYf5t6TaudBt86DVkDb1/RALhVq1ZGEKglodDZ7hR+3751+rZvpKNeb99++23AVwno9r/99psxlVxHqPrCUrv9y3YIIIAAAgh4XYAA0Os9TPsQQAABBFK0gI6aefTRR402BFrtV0diaXjiKzoyKVAA5/te37HWsGFDc3t9h5tvKqITKA3MdJSO772EGgTqg7mblYgTIwB00hYNa3zv1Rs9erTxbsVAxRoA6n/r1OxQI/Ei5a3BZNGiRY33F2rwoiOddDRjoKLvuStQoIAx+k0DMx3tpn+7LdYAUPdx2223GSPzMmTIEHCXOqLso48+Mr7T97Lp8eNeFxri+ex01KmObtOgO1jR8PjJJ580vm7SpImxCE7cYg0A9btwg8+4+9dgScNXLTriT9sQLCzWbTQk0wBSi4bROv3dWnQUnC/w1P7RdxQGe2+dhpm6jU6F1bJw4UJjBJ616G9RVwrXbXU04ooVKxK8F2hY5xt9p6MYNTyLO0o07nv7NODVdxYGKnpuei/SkZ1a9H9GBHrnp9MAUMNRHWmsRadUa3BJQQABBBBAAAHnAgSAzs2ogQACCCCAwDUT0LBOQyQtOgLIOrrKdxI6ddc3jTKhB3TdvmnTpsZCHVp0qqBO1XNTrO8w0xFOGj7YWUgj0LGSOgDUaZs6HVNLQgsrWMORUNNbfe2MlLfu7+677xYddalFR8t17tw5YNdpWKKhia+Ob7EJN/2sdeIGgBoc+d5JGWif+n49DfV8QZCO4NL3IlqLjujyLZSiwVqo9y/qSDqdVq4LuWg/aJ/Fvd6sAaCGZXpNugmkgzlp8KlTdbVo+Ke/u4SKjgTUEWvqoaNndZScNeDTMFenT/umOCfkqr9ZvZa06LsddURt3LBOR0n67g+6Ynjv3r1DdrmGqbNnzza20ym2OqrRWqy/zdy5cxshZbDg13dP0UU6tOjow0DvbHQaAOpoRw3+tAS6lkI2kg0QQAABBBBAwBAgAORCQAABBBBAIJkK6Hvq9GFfgwItOkoq7nRD/VynAvoW1tDgRUdcBRpJpNP3fO/M0nr6rrJgK3aGIrFOX9QROb4H9FD1An2f2AGg+mm4oeGNmup7/3R0nLXo1GUtOnXR+n406zbWwCXYAgzW7SPprfvVFVB9043vuOMOY4RXoKLvRtPRXFrsnGeoPrMGgIUKFTKur1DvE9R33vneVRk3uPZNt9aAUAM67Q/f+xoTOhd9v6VvFJ1a6OrI1mINAHVRGp26HqmiIy59qxBruKjXkZ2i0/V9oxX1HXW6sra16Psz33nnHeOjDh06mCMn4+5bRwHraGAtOmVd38cXt1gXCNHVeXU0YKii77LUKdJa1EvdrMX629QRmB9//HGCuxwyZIi5orG+VsDXNmslpwGgjuR84403jF1oCKrvoIxksBvKiO8RQAABBBDwigABoFd6knYggAACCHhOYPDgweaCBzqtN9jKm7pYgo400sBJS7AX4OvL/6tVq2Zso9NI9aX9bh+kdcqgjsbSMmnSJHnkkUdc+ydWAKhBk45Cevfdd20HNjqt1rqYgbVR1tBLQzAdvZVQiaS3Hkfbo4GwLsKgZcOGDVKhQgW/U9Bpl7734+mUcQ2qQq3EG6rjrAGgvk/OusBJsLqjRo2S5557zvhap/rquyR9Zc2aNVKpUiXjT51CG/edkMH2qZ6rVq0yvtb9d+3a1W9TawCo70j0TdcN1T4732uQqu9V1KJBYKDVdwPtR3+LvlV7dSSmjuK0Fu1D3+IfOkpQp9Fa3x+p2+p7QHWEqi7goUVXX9bfn7XoYj3WEZEaloYKaX378r3TMNB7CK2/zUAL+MRts/6PAB2xpyXY/xhwGgDqfUYX8fH9jxB99YD+DwgNAzVQDTZt2k6/sg0CCCCAAALRJEAAGE29TVsRQAABBFKUgDVkS2j1U22UBjO+lT91OqVOq4xbdOSMbyqmjhbyBXhuUHLmzGmu+Lps2TIzWHSzr8QIADUM1RFic+bMcXRKGpr4goa4Fa2BioYyoUatRdLbdy46rdM3+ivQlHDrdEkdXaYjvMIt1gCwW7du5vvYEtqvdbSiXscaWvmKBmH33ntvWKcVaIqrNQDUqdK+KbNhHej/K1sDTbf7CzbVWUMsXSREiy7O4QsafcfRkZS+VYR1iq51NVzfNr/++mu8MNjpeQYagWj9bSa0oIvvWHbCPTvbxD13ndqs155vwSHf9xrYa+Ctfa+hrN7XKAgggAACCCAQWIAAkCsDAQQQQACBZChgHT2mwZO+80tHfwUr1sBFR/cdOHDAWAjAWnSknu9dazoCyzeayk3zdVSZjkjTouGFTjt1WxIjANRpkjqCUouOENKFTjQg0PPUEVwa3llHxlnDvbghg69ddrZJLG/ffvW9drrQgoaUuXLlMkYD6sq8WuKuGK0BbyQCEWsAqAGkhm+hinXxE52WrouY+Ir1Ogy1n2Df6wIbvtWvfdtYA0Ad1aZ/R6po6GrnnXoJHU8XMtFrPW6xLkKj16hv9WbfdtZpxDqaVd/1F7foO0BDvZMwlIWOxNRwzloS47fpJgDUc9JXFuh0YB1VGSyk15XINSyMOzI2VNv5HgEEEEAAgWgQIACMhl6mjQgggAACKU7A+r4zNycfaLROJEekJeUIQG2bjlbSEii00NF/Oh3SN2VS353mG0EVyPLkyZN+K+pGKgCMpLf1vK0LN+iCC61atTK+tgYrGgbpQhWRKJEeAagLOfim0OpqumvXro3EaRqBn2+qcaQDQA2VfMGbvodRV7yOVLG+61On/2p4r+/q1KIrOmtgrWG7ruis2wZaLVnfb6mLlGjRuvpagEiU5BQA+tpz5MgRo5/1+tZ/tO3WQFDDfQ0J3b7fNBJu7AMBBBBAAIHkKEAAmBx7hXNCAAEEEIhqgQsXLhjv9NNVQ90WDUN87/by7SOS76SzTk/WKYpt2rRxe6rSqVMnc/EDfYeYvjssoaLvItPpgFoCBYAaCuh757ToSCB9z1pCJe70yUgFgJH0tp6/TvXWKd9aGjdubK7qrG32hX7BRpu56SQ37wDUdy/6FpS48847ZeHCheahdcp4jRo1jL91CqeGVZF4j1tiBoCTJ08233Op76PTayaSRVdVjomJMXY5btw44zehxerYqFEjc8XeuMe2LlKi3+kqybpqb7glOQaAcdukgaD2z5tvvin631oSo4/CtaQ+AggggAACSS1AAJjUPcDxEUAAAQQQiCNgDXh0mqq+98tO0VFCvveD6XTV33//XYoWLWpWjeSqtJFcBVjDPA31tOgqor5VSYO1WVcj1YBLS6AAcOrUqcaUXy36bw0HEiqjR4/2W1AiUgFgJL2t568rGOsCJDpSTIMznR6uxypVqpSxmU791u90KngkijUA1Om8u3fvDrnAREKrAJ89e1Zy5MhhLloTzmrU1vYlZgCoxrpCtK/olGa1iFTR1XV9wfddd91lhoG6aI8GyVo+//xz0dWAgxV9RYD2jZZQo17tnndiBIC6QItvKnSg36/dc4u7nTVY1u90NW9rn7ndL/UQQAABBBDwigABoFd6knYggAACCHhGQBevmDZtmtGeQO8ES6ihGgJt27bN2ERHxLzxxht+m1vfJ2YnHAt2LJ162rp1a+NrXclVAxLrKqROOsM6yinUOZ07d04KFSpkjvQJFCDoKrUPPfSQcQpqqe9HDFZ06qAGrOvXrzc3iVQAqDuMlHfc8+/Zs6cRlmrp37+/aKjm+1sXAvnwww+ddEGC21oDQN1w/vz5oiPWgpVjx44Z4djp06eNTXTKr2/xGV8dq4sueqGLX4RbEjMA1HMrV66cuRL3yy+/LO+88064p2zW15WndWqvXt8a3usq0xrq6oq3WjTM1RWC9bcWrFhfG1C5cmVZsWJFyKA2VAMSIwC0BvS6eri+EzJSRd+L6Rs5vXTpUnNF7Ejtn/0ggAACCCCQkgUIAFNy73HuCCCAAAKeE7C+80sbF2hV0IQarWGQb3GEkiVLmmGgr451cQb9TEfH+UbLOcHU0Ya6fw0qtNxzzz3Ge9HSpk3rZDfGttapsqHCxO7du8uwYcPMYwQKAHUUpAYgWrJnz26co/47ULEuwOD7PpIBYKS84567jm7ScEjPVUd5alik00C1aPBzxx13OO6HYBXiBoD63j4dbZUhQ4aAVaxTunUqu45Ki3td6DvcrIt0OJmyrKMbA70HL7EDQOvINW3PzJkzpUGDBracg52ztbK+y9EXhGqYq2Gg77dsJyjTa0IXfbl06ZKx20ALpQQ72WDnlxgBoPU3YR3tGOzcdFqvnf+5oFPJ9brQVyho0f8RovcoCgIIIIAAAgj8K0AAyJWAAAIIIIBAMhKwLjag70fTUT/BgpZAp719+3Zz1JB+H2h6pY6O01FyWnRhgT59+sgrr7wSb8qojo7ToGbkyJHGAhNxQ7Qff/xR7r33XiOE0qIrcH7wwQdSpkyZeKemq9fq4h36QP7YY4/5fa/1NczSAEOLhio6wlCnifqKrnCrYYiGf7pQggZeWgIFgHreOkV23759xjYNGzY0zl8XU/AVra8ru+qKtpkzZzZHq+n3kQwAdX+R8o6Lqk4//fST38d23nno9HK3BoC64rAGLGqqgZgGfFZTHZnom86tn3/00Ufmgi1xj2sNl3TUm46q69Gjh7G6cdyi/TVr1iyjH7VfNeSMWxI7ANRgTd/DpyMgtaiFjrJ99tlnjWsobtFFaHQkr77XUtvk+80F87eu5K39qAGgbzTvjBkzpGnTpiG7Lm5Yq78PXTk30HRlbY++J1SnFmsopytKJ9RHdkJaOyv86j3NF+DqvW3Lli3G7zVY0fuKLuyh14uOHA1krSGzBs9z5swxdqP3k61bt4b0YgMEEEAAAQSiSYAAMJp6m7YigAACCCR7AV3JU1e11KLv2dPQzGmpWrWqGZDoQ7EuKmAtJ06cMAIca4iiD9W6aqZOr9UATEOWVatWydGjR42qOrrGtzKpdV9xR9BpkFOxYkXjJfw6mk+n4+kiHPqQr0XDoRdffDFek6zTdvVLDRvr169vjPzR0Um6iIROLdUQr2vXrtKrVy9jH8HeIaZhhb4r0Fe0fbrwhL4nTdukCy74VkrVKYjWVf8AOpkAACAASURBVIIjHQBG0tsKZ52G7ft8xIgR5uIbTq+bYNtbQyXtOw2qdMq3BrEauml4o/2sYZJ14ZpQU3s11NMp2r7QRo+voZqOXixRooQR+ujUWA2GN27caARiWipVqmRcm3FLYgeAejy9dvS3Y125WKfn6rv61EHPX68rDZ82bdok+r5GLaEsdBvdVgNV32/O1748efIYv0c7o2v12tX7hoazvqIhv95XNJjX36RejzoqVqe9+1bK1oDSt4CG1TUxRgDq/nWV6iVLlhiH0vuKrmytbfctBqP936VLF+N7a7/qO1HLly9vvO9S7xG6gre2Zfny5eZKwNre6dOnG/ukIIAAAggggMBVAQJArgYEEEAAAQSSiYAGZRqe+YqOytEQzGmxvlNPH5I1QIs7ilDfGffCCy8YAaNvymCw42hdXVVUF5cIVPSdXrovHdkTqlhXOI27rY6m0mmLwYpOb9QFUlauXGmEHFoSWkRAQ8JBgwYF3Z+2SwPJp59+2u9daZEOAPUEIunta5COxNORXTptXIsGcjqKK2fOnKG6wdH31gBQ+0enjOu7KWNjY4PuRxer0IUtNLBJqOi1p9PWhw8fLjrKM1TR/ek7DkeNGhVv02sRAPr6Uheu0dGNOhU+VNEgU9/FqaMjQxUNvcaOHeu3ma6mrMGuk6I+OrLXF3InVFdDex3JG+hdmYkVAK5Zs8Z4j6SGkYGKdWqwnpsGenaKhqV6j4n7zkk7ddkGAQQQQAABrwsQAHq9h2kfAggggECKEdApkO+++65xvjoaZu/eveaIGCeN0LCuQIECZjiR0Hv+NMTRKYA6lVSn6eoILh3FpMe/5ZZbjNFOuthHqFBJF3zQ/eh70XRkkYZSGu7oNF4N7nTET8uWLY2RSAkVfbecBpiLFi0yQkedBq3ThjV06tChgzGCyc40Q98xdAq0hiH6bz0nDTE1NNPRQbo/3yILGoL4SmIEgL59R8rbtz9tg2+UaKgFVJxcQ9Zt4waA+reOHNPj6ihEnXauQZOGLzrKUgM6p8G19o1vKqqOnNPRaDoiTvtfR23qlFgNjHQKaO7cuQM25VoFgL6D6+9FV9vVKcE64k9H7un0cw3ddfVZDfPVQa81bYedogtX6Ehca3H7TkcdHTdx4kSZO3eu+ZvUUZS+34CO0lUzNdWRv4FKYgWAeiwduae/dfXTFcv1mvL9zwhrAKi/Rx1xqfcofV/o5s2bjXuj3nM09NbrQe9V2g59V6Jdazv9wTYIIIAAAgh4SYAA0Eu9SVsQQAABBBBAIGoENBjRoEmDKC0a9NhdlMIJUqAA0El9tkUAAQQQQAABBBBIegECwKTvA84AAQQQQAABBBBwLGBdTVVXAtZRVNaRjI53GKQCAWCkJNkPAggggAACCCCQdAIEgElnz5ERQAABBBBAAAHXArqAhq4yq2Xw4MHGCrqJUQgAE0OVfSKAAAIIIIAAAtdWgADw2npzNAQQQAABBBBAIGyBH374wVzoQN+LqNOAdSXXxCgEgImhyj4RQAABBBBAAIFrK0AAeG29ORoCCCCAAAIIIOBYYMeOHTJmzBhjkQRdcEIXW/EtVqKr6OqKr4lVCAATS5b9IoAAAggggAAC106AAPDaWXMkBBBAAAEEEEDAlUBMTIyxCm7coivG6iqqunJzYhUCwMSSZb8IIIAAAggggMC1EyAAvHbWHAkBBBBAAAEEEHAlYA0ANewrVqyYtG7d2njvX8aMGV3t024lAkC7UmyHAAIIIIAAAggkXwECwOTbN5wZAggggAACCCCAAAIIIIAAAggggAACYQsQAIZNyA4QQAABBBBAAAEEEEAAAQQQQAABBBBIvgIEgMm3bzgzBBBAAAEEEEAAAQQQQAABBBBAAAEEwhYgAAybkB0ggAACCCCAAAIIIIAAAggggAACCCCQfAUIAJNv33BmCCCAAAIIIIAAAggggAACCCCAAAIIhC1AABg2ITtAAAEEEEAAAQQQQAABBBBAAAEEEEAg+QoQACbfvuHMEEAAAQQQQAABBBBAAAEEEEAAAQQQCFuAADBsQnbgRuDcuXOyceNGo2ru3Lklbdq0bnZDHQQQQAABBBBAAAEEEEAAAQQQSEDg4sWLcvjwYWOLChUqSIYMGfCKQgECwCjs9OTQ5JUrV0qVKlWSw6lwDggggAACCCCAAAIIIIAAAghEhcCKFSvkjjvuiIq20kh/AQJArogkESAATBJ2DooAAggggAACCCCAAAIIIBDFAgSA0dv5BIDR2/dJ2vI//vhDihUrZpyD3oBuvPHGJD0fDo4AAggggAACCCCAAAIIIICAFwX2799vzsDbuXOnFC1a1IvNpE0hBAgAuUSSRGDv3r1SqFAh49h79uyRggULJsl5cFAEEEAAAQQQQAABBBBAAAEEvCzA87eXe9d+2wgA7VuxZQQFuAFFEJNdIYAAAggggAACCCCAAAIIIBBEgOdvLg0VIADkOkgSAW5AScLOQRFAAAEEEEAAAQQQQAABBKJMgOfvKOvwIM0lAOQ6SBIBbkBJws5BEUAAAQQQQAABBBBAAAEEokyA5+8o63ACQDo8OQlwA0pOvcG5IIAAAggggAACCCCAAAIIeFWA52+v9qyzdjEC0JkXW0dIgBtQhCDZDQIIIIAAAggggAACCCCAAAIJCPD8zeWhAgSAXAdJIsANKEnYOSgCCCCAAAIIIIAAAggggECUCfD8HWUdHqS5BIBcB0kiwA0oSdg5KAIIIIAAAggggAACCCCAQJQJ8PwdZR1OAEiHJycBbkDJqTc4FwQQQAABBBBAAAEEEEAAAa8K8Pzt1Z511i5GADrzYusICXADihAku0EAAQQQQAABBBBAAAEEEEAgAQGev7k8VIAAkOsgSQS4ASUJOwdFAAEEEEAAAQQQQAABBBCIMgGev6Osw4M0lwCQ6yBJBLgBJQk7B0UAAQQQQAABBBBAAAEEEIgyAZ6/o6zDCQDp8OQkwA0oOfUG54IAAggggAACCCCAAAIIIOBVAZ6/vdqzztrFCEBnXmwdIQFuQBGCZDcIIIAAAggggAACCCCAAAIIJCDA8zeXhwoQAHIdJIkAN6AkYeegCCCAAAIIIIAAAggggAACUSbA83eUdXiQ5hIAch0kiQA3oCRh56AIIIAAAggggAACCCCAAAJRJsDzd5R1OAEgHZ6cBLgBJafe4FwQQAABBBBAAAEEEEAAAQS8KsDzt1d71lm7GAHozIutIyTADShCkOwGAQQQQAABBBBAAAEEEEAAgQQEeP7m8lABAkCugyQR4AaUJOwcFAEEEEAAAQQQQAABBBBAIMoEeP6Osg4P0lwCQK6DJBHgBpQk7BwUAQQQQAABBBBAAAEEEEAgygR4/o6yDicApMOTkwA3oOTUG5wLAggggAACCCCAAAIIIICAVwV4/vZqzzprFyMAnXmxdYQEuAFFCJLdIIAAAggggAACCCCAAAIIIJCAAM/fXB4qQADIdZAkAtyAkoSdgyKAAAIIIIAAAggggAACCESZAM/fUdbhQZpLAMh1kCQC3ICShJ2DIoAAAggggAACCCCAAAIIRJkAz99R1uEEgHR4chLgBpSceoNzQQABBBBAAAEEEEAAAQQQ8KoAz99e7Vln7WIEoDMvto6QADegCEGyGwQQQAABBBBAAAEEEEAAAQQSEOD5m8tDBQgAuQ6SRIAbUJKwc1AEEEAAAQQQQAABBBBAAIEoE+D5O8o6PEhzCQC5DpJEgBtQkrBzUAQQQAABBBBAAAEEEEAAgSgT4Pk7yjqcAJAOT04C3ICSU29wLggggAACCCCAAAIIIIAAAl4V4Pnbqz3rrF2MAHTmxdYREuAGFB7k5ctXJPbAScmV5TrJmy1DeDujNgIIIIAAAggggAACCCCAgGcFeP72bNc6ahgBoCMuNo6UADcg95Ia/rWfsFIWbj0s6dOmlvfb3CaNbs7nfofURAABBBBAAAEEEEAAAQQQ8KwAz9+e7VpHDSMAdMTFxpES4AbkXnL1rr+kxZhl5g4qFswu3z9by/0OqYkAAggggAACCCCAAAIIIOBZAZ6/Pdu1jhpGAOiIi40jJcANyL3kN2v2Srcv1ps7yJAutWx+s4mkSpXK/U6piQACCCCAAAIIIIAAAggg4EkBnr892a2OG0UA6JiMCpEQ4AbkXvHTJTul/7RNfjtY3buB5MqS3v1OqYkAAggggAACCCCAAAIIIOBJAZ6/PdmtjhtFAOiYjAqREOAG5F7xP3O3yoiftvnt4Idna8otBa93v1NqIoAAAggggAACCCCAAAIIeFKA529PdqvjRhEAWsh2794tI0eOlB9//FH0v9OnTy8lS5aUhx56SJ555hnJlCmTY+C4FXbu3GkcY+7cubJr1y65fPmyFChQQBo2bGgc4+abb07wGOfPn5e1a9fKypUrZcWKFcY/27ZtkytXrhj1fP+2e6LXos2BzoUbkN0eir9dvx9+kwlL//D7YnTb26VZhRvd75SaCCCAAAIIIIAAAggggAACnhTg+duT3eq4UQSA/0+moV/btm3l+PHjARFLly4tM2bMkOLFiztG9lUYN26cPPfcc3LhwoWA+7juuuvkvffeky5dugQ9xhNPPCETJkwI+r2TAPBatDnYiXIDcn0ZyUtT18m3a/f57aBXs7LSqbb7a9P92VATAQQQQAABBBBAAAEEEEAgOQvw/J2ce+fanRsBoIisX79eatSoIWfOnJEsWbJIz549pW7dunL27FmZMmWKjB8/3uiRMmXKGCPvdBunRffTpk0bo1r27Nnl5Zdflnr16hmjDHVE39ChQ2X79u3GQg5ffPGFtGzZMuAh2rdvL5999pnxXdasWeX222+XLVu2yIEDB4zP7AaA16LNCRlxA3J6BV3d/olPV8jPWw777aB9jaLS776ER4+6PyI1EUAAAQQQQAABBBBAAAEEUqoAz98ptecie94EgCJG2BcTEyNp06aVhQsXSvXq1f2Uhw0bJt27dzc+69+/v/Tp08dRL2iwWKxYMTl06JARHi5btkzKly/vt48TJ05IrVq1ZOPGjZIvXz4jDMycOXO840ydOtUIJu+44w4pW7aspE6dWurUqSMLFixwFAAmdptDAXEDCiUU/Pvmo5fImt3H/DZoUDaPfPT4He53Sk0EEEAAAQQQQAABBBBAAAFPCvD87cluddyoqA8AdURflSpVDLjOnTvL2LFj4yHqe/o0sNu8ebPkyJFDDh48KOnSpbON/fXXX5sj+nr16iUDBw4MWHfevHnGuwC1jBo1Srp27WrrGE4DwGvR5lAnzg0olFDw7+sNj5HfD5/226BMvqwy68Xa7ndKTQQQQAABBBBAAAEEEEAAAU8K8PztyW513KioDwA1kBs0aJABt3z5cqlatWpAxCFDhhhTg7XMmTPHDOrsiPfo0UPefvttY9PFixdLzZo1A1a7ePGiMa333Llzxqi+n3/+2c7uHY8AvBZtDnXi3IBCCQX/vtKAuXL0tP97JLNmSCsb+zV2v1NqIoAAAggggAACCCCAAAIIeFKA529PdqvjRkV9AFi7dm1ZtGiRMd322LFjxjTgQEWn7ep7ArXoFGCdCmy3PPXUU+Z7BHXFXl1ZOFjRFYH//PNP0QVBTp8+HfR8rPWdjgC8Fm0OZcMNKJRQ4O/1HY839ZopFy//u+qztazv20iyZ7Q/MtXdGVALAQQQQAABBBBAAAEEEEAgJQnw/J2SeivxzjXqA8DcuXPLkSNHpGLFirJu3bqg0n///bfkzJnT+L5Vq1bGQh12y0svvWSs7qtl1apVUqlSpYBVNdzJli2bnDp1yvhepxzrwiOhitMA8Fq0OdQ5cwMKJRT4+1PnL0r5vrMDfjnj+TulXP5s7nZMLQQQQAABBBBAAAEEEEAAAU8K8PztyW513KioDgB1qm3GjBkNtLvvvlumT5+eIKAu4KGj8qpVq2Ys5GG3jBs3zni/oJbhw4dLt27dAlZds2aNXzg4e/ZsadSoUcjDOAkAr1Wb9QaTUNm/f7/57sU9e/ZIwYIFQ7aTDUT2HTsrNYfMD0gx/rHK0rBcXpgQQAABBBBAAAEEEEAAAQQQMAUIALkYVCCqA8DDhw9Lnjx5jCuhdevWMmXKlASvirx58xor+eqCILpar92iP7bixYvLP//8IzrFV0ca3nDDDX7VdaERDSFnzZplfv7VV19JixYtQh7GSQB4rdqcKlWqkOft24AA0DaV/Pbncbl75OKAFfreW06eqFnM/s7YEgEEEEAAAQQQQAABBBBAwPMCBICe72JbDYzqAFCDp8KFCxtQjz76qHz++ecJoum2WqdEiRKyfft2W8C+jV544QUZOXKk8WepUqVk6NChUrduXeNdfxoI9uvXT3TEn/594cK/CzxMnDhR2rVrF/I4TgLAa9VmAsCQ3eZqg6Xbj8gjH/0SsG7HWsWk9z3lXO2XSggggAACCCCAAAIIIIAAAt4UIAD0Zr86bVVUB4DXajScdoqGei1btpRp06YF7SMdJdi8eXN55513jG2+++47uf/++0P2qZMA8Fq1mSnAIbvN1QYzN+6XLpPWBKzbtHw+GdMu8PslXR2MSggggAACCCCAAAIIIIAAAilegAAwxXdhRBoQ1QHgtXofnq+ndJGPzz77TEaNGiVr164Vnfar5frrrzdGIA4YMMD4R98TqGXBggWiK/aGKk4CwGvd5mDnzg0oVK8G/n7yit3S85vA088rFMgu056r5W7H1EIAAQQQQAABBBBAAAEEEPCkAM/fnuxWx42K6gBQtZJqRVxd6ffgwYPGlN/8+fNLmjRpjM7TRT/mzp1r/LeO1ov7rsBAPewkAEzKNlvPnRuQ49+qUWFMzA55e1ZswMo5M18na95o6G7H1EIAAQQQQAABBBBAAAEEEPCkAM/fnuxWx42K+gBQR9gtWrRIMmfOLMeOHZO0adMGRNRVf2vUqGF816dPH+nfv79j7FAVdJqwLkpy/PhxY9GQHTt2hKpifO80AEwObeYGZKtr4200ZGasjF0Q/LrY9GZjyXRd4GvY3RGphQACCCCAAAIIIIAAAgggkJIFeP5Oyb0XuXOP+gDw9ddfl8GDBxuiy5cvl6pVqwbUHTJkiPTs2dP4Thfr0JF6kS76zr8HH3zQ2G2PHj3M8wp1HKcBYHJoMzegUL0a+Pue32yQySv2BK0896XaclPerO52Ti0EEEAAAQQQQAABBBBAAAHPCfD87bkuddWgqA8AV6xYYYZ+nTt3lrFjx8aD1Hf1lS9fXjZv3my8r+/QoUOSLl06V+DBKl28eFEqVaokGzZsMPYdGxtrjAK0U5wGgMmhzdyA7PRs/G2embRaZmw8ELTyp0/cIXVL53G3c2ohgAACCCCAAAIIIIAAAgh4ToDnb891qasGRX0AqGq+KbE6/XfhwoVSvXp1P8xhw4ZJ9+7djc/69u0r/fr18/s+JiZG6tata3z2+OOPy4QJE+J1xpEjRyRTpkzGP3GLTv3t2LGjTJw40fiqd+/exmIgdovTADASbbZ7bsG24wbkTvCR8ctl6Y6jQSsPfKC8tKtWxN3OqYUAAggggAACCCCAAAIIIOA5AZ6/PdelrhpEAChirMhbs2ZNOXv2rGTJkkV0iqwGevr3lClTZNy4cQZuqVKlZNWqVZI1q/8USzsB4FdffSWdOnWStm3bSoMGDaRw4cJy5swZ49g66nDTpk3GMXRq8bRp04zFQQKVAwcOyKxZs/y+0unJW7ZsMT779NNP/b6rVauWlCxZMt6uwm2zq6vNUokbkDvBZiMWyab9J8zKqVKJXLlydV9P31VCejQt427n1EIAAQQQQAABBBBAAAEEEPCcAM/fnutSVw0iAPx/Ng3d2rVrJydOXA1XrKIa/v34448BwzS7AWCrVq0S7KT27dvLmDFjJEOGDEG3sx7LTo9rIKj7DVTCabOdYye0DTcgd4I1h8yXfcfOmpWL35BZfj9y2vz73or55f02t7nbObUQQAABBBBAAAEEEEAAAQQ8J8Dzt+e61FWDCAAtbLt27ZIRI0YYQZ/+QHQUno6e0+Du2WefDTh9V6vbCQAPHjxoTPGdP3++8X4//Tt16tSSP39+Y7ShhnTVqlUL2YmRDAD1YG7bHPJEQ2zADcidYPm+s+XU+Ytm5XtuuVGmb9hv/n174evlm2dquts5tRBAAAEEEEAAAQQQQAABBDwnwPO357rUVYMIAF2xUSlcAW5AzgX/uXRZbuo106/ia03KyNuzYs3P8mRNLyt6NXC+c2oggAACCCCAAAIIIIAAAgh4UoDnb092q+NGEQA6JqNCJAS4ATlXPHrqvFQaOM+v4udPVpHHPlnh99mWgU0kfdo0zg9ADQQQQAABBBBAAAEEEEAAAc8J8PztuS511SACQFdsVApXgBuQc8Edh09J/eEL/Cqu6t1AKscJBWNeqSNFb8js/ADUQAABBBBAAAEEEEAAAQQQ8JwAz9+e61JXDSIAdMVGpXAFuAE5F1yz+29pPnqpWTFjujSyeUATqdBvtpw8d/W9gP/tUFVq3XSD8wNQAwEEEEAAAQQQQAABBBBAwHMCPH97rktdNYgA0BUblcIV4AbkXPDn2EPyxISVZsUbs2eQZT3rS5P3FkrsgZPm52+3qCCt7yjs/ADUQAABBBBAAAEEEEAAAQQQ8JwAz9+e61JXDSIAdMVGpXAFuAE5F/x27V55aep6s2KZfFll1ou1peNnq2Te5oPm58/XKyndGpV2fgBqIIAAAggggAACCCCAAAIIeE6A52/PdamrBhEAumKjUrgC3ICcC05YslP6TdtkVqxaLKdM7Vxd+v3wm0xY+of5efPbCsi7rW91fgBqIIAAAggggAACCCCAAAIIeE6A52/PdamrBhEAumKjUrgC3ICcC743b6u8N2+bWbHxzXnlw0cry/iFv8tbMzabn1cpllO+6Fzd+QGogQACCCCAAAIIIIAAAggg4DkBnr8916WuGkQA6IqNSuEKcANyLhh3pN9DlQvK0JYVZebG/dJl0hpzhwWuzyhLetRzfgBqIIAAAggggAACCCCAAAIIeE6A52/PdamrBhEAumKjUrgC3ICcC3abuk6+WbvPrPhU7eLyerOysmHvMblv1BLz8zSpU8mWAU0kbZrUzg9CDQQQQAABBBBAAAEEEEAAAU8J8Pztqe503RgCQNd0VAxHgBuQc70nJ6yU+bGHzIqvNi4tXeuWlKOnzkulgfP8drj4tbpSMEcm5wehBgIIIIAAAggggAACCCCAgKcEeP72VHe6bgwBoGs6KoYjwA3IuV6LMUtl9a6/zYoDHygv7aoVkStXrki5PrPl7D+XzO+mPlVNqhbP5fwg1EAAAQQQQAABBBBAAAEEEPCUAM/fnupO140hAHRNR8VwBLgBOderPzxGdhw+bVYc9chtcs8t+Y2/G7y7QLYfOmV+N7xVRWlRqaDzg1ADAQQQQAABBBBAAAEEEEDAUwI8f3uqO103hgDQNR0VwxHgBuRcr/LAuXLk1AWz4n87VJVaN91g/P34JytkwdbD5nfdGpaS5+vf5Pwg1EAAAQQQQAABBBBAAAEEEPCUAM/fnupO140hAHRNR8VwBLgBOdPTab6les+Ufy5dMStOe7aWVCiY3fi717cbZdIvu83vWlcuJG+3vMXZQdgaAQQQQAABBBBAAAEEEEDAcwI8f3uuS101iADQFRuVwhXgBuRM8PT5i3Jz39l+lRZ1ryuFcv670MfomO0ydNYW8/uaJXPJpI7VnB2ErRFAAAEEEEAAAQQQQAABBDwnwPO357rUVYMIAF2xUSlcAW5AzgT3HTsrNYfM96u0vm8jyZ4xnfHZ9+v2yQtT1pnfF82VSWJerevsIGyNAAIIIIAAAggggAACCCDgOQGevz3Xpa4aRADoio1K4QpwA3ImuOnPE9Js5CKzUupUItvfaiap9T9EjNWBdZVgX7kuTWqJHdDE/N7Z0dgaAQQQQAABBBBAAAEEEEDAKwI8f3ulJ8NrBwFgeH7UdinADcgZ3NIdR+SR8b+YlXJkSidr+zQy/z544pxUHfST305/eb2+5M2WwdmB2BoBBBBAAAEEEEAAAQQQQMBTAjx/e6o7XTeGANA1HRXDEeAG5Exv5sb90mXSGrNS3Cm+ly9fkTJvzJILly6b23zdpYZUKpLD2YHYGgEEEEAAAQQQQAABBBBAwFMCPH97qjtdN4YA0DUdFcMR4AbkTG/Kit3S45uNZqWKha6X77vW9NtJnWE/yx9Hz5ifjWxzm9xXMb+zA7E1AggggAACCCCAAAIIIICApwR4/vZUd7puDAGgazoqhiPADciZ3tgFO2TIzFizUu1SueXzJ6v47aTtR8tlyfaj5mfdm5SWZ+qUdHYgtkYAAQQQQAABBBBAAAEEEPCUAM/fnupO140hAHRNR8VwBLgBOdN7e1asjInZYVbSkX06ws9aXvtqg0xdtcf8qG3VwvLWgxWcHYitEUAAAQQQQAABBBBAAAEEPCXA87enutN1YwgAXdNRMRwBbkDO9Hp+s1Emr9htVnqsehF58/7yfjsZPmeLvD9/u/nZ3RVulA/a3u7sQGyNAAIIIIAAAggggAACCCDgKQGevz3Vna4bQwDomo6K4QhwA3Km98yk1TJj4wGz0nP1SsrLjUr77WR0zHYZOmuL+Vnd0rnl0yf8pwk7OypbI4AAAggggAACCCCAAAIIpHQBnr9Teg9G5vwJACPjyF4cCnADcgYW9/1+ve8uKx3vLO63kwlLdkq/aZvMz6oWyylTO1d3diC2RgABBBBAAAEEEEAAAQQQ8JQAz9+e6k7XjSEAdE1HxXAEuAE507t75CL57c8TZqV3WlWUlpUK+u3ki5V7pPvXG8zPbimYXX54tpazA7E1AggggAACCCCAAAIIIICApwR4/vZUd7puDAGgazoqhiPADciZXs0h82XfsbNmpfGPVZaG5fL67WTa+j/luclr47Vu1gAAIABJREFUzc9K5ski87rd5exAbI0AAggggAACCCCAAAIIIOApAZ6/PdWdrhtDAOiajorhCHADcqZXoe9sOXn+olnpy6eryx1Fc/rt5KfNB6XDZ6vMzwpcn1GW9Kjn7EBsjQACCCCAAAIIIIAAAggg4CkBnr891Z2uG0MA6JqOiuEIcAOyr3fx0mUp2WumX4U5L9WWUnmz+n22dMcReWT8L+ZnOTKlk7V9Gtk/EFsigAACCCCAAAIIIIAAAgh4ToDnb891qasGEQC6YqNSuALcgOwL/nX6gtw+YK5fhRWv15c82TL4fbZuzzF54IMl5mfp06aWLQOb2j8QWyKAAAIIIIAAAggggAACCHhOgOdvz3WpqwYRALpio1K4AtyA7Av+fviU1Bu+wK9C7IAmkiFdGr/Pth48KY3+s9Dvsx2Dmkma1KnsH4wtEUAAAQQQQAABBBBAAAEEPCXA87enutN1YwgAXdNRMRwBbkD29dbs/luaj15qVsiQLrXEDog/sm/PX2fkzqE/++341/6NJUv6tPYPxpYIIIAAAggggAACCCCAAAKeEuD521Pd6boxBICu6agYjgA3IPt6P285JE98utKskC9bBln+ev14Ozh66rxUGjjP7/MVvepLnqz+U4XtH5ktEUAAAQQQQAABBBBAAAEEUroAz98pvQcjc/4EgJFxZC8OBbgB2Qf7bu0+eXHqOrNC6bxZZfZLtePt4OyFS1K2zyy/zxe8WkeK5Mps/2BsiQACCCCAAAIIIIAAAggg4CkBnr891Z2uG0MA6JqOiuEIcAOyrzdhyU7pN22TWaFKsZzyRefq8XZw5coVKf76DLly5epXM1+4U8remM3+wdgSAQQQQAABBBBAAAEEEEDAUwI8f3uqO103hgDQNR0VwxHgBmRfb8S8bfKfeVvNCo3K5ZVxj1UOuINyfWbJmQuXzO++eaaG3F44h/2DsSUCCCCAAAIIIIAAAggggICnBHj+9lR3um4MAaBrOiqGI8ANyL5e/2m/yadL/jArtKpUUIa1qhhwB5UHzpUjpy6Y303qWFVqlrzB/sHYEgEEEEAAAQQQQAABBBBAwFMCPH97qjtdN4YA0DUdFcMR4AZkX6/bF+vkmzX7zAqd7iwmve4uF3AHdw6dL3v+Omt+N/6xytKwXF77B2NLBBBAAAEEEEAAAQQQQAABTwnw/O2p7nTdGAJA13RUDEeAG5B9vQ4TVspPsYfMCq82Li1d65YMuIPG/1koWw6eNL8b8fCtcv+tBewfjC0RQAABBBBAAAEEEEAAAQQ8JcDzt6e603VjCABd01ExHAFuQPb1WoxZKqt3/W1WGPBAeXm0WpGAO3jggyWybs8x87shzSvIw1UK2z8YWyKAAAIIIIAAAggggAACCHhKgOdvT3Wn68YQALqmo2I4AtyA7Os1eHeBbD90yqzwfpvb5N6K+QPu4JHxy2XpjqPmd33uKSdP1ipm/2BsiQACCCCAAAIIIIAAAggg4CkBnr891Z2uG0MA6JqOiuEIcAOyr1d54Dw5cuq8WeHzJ6tI7VK5A+6g42crZd5me9OF7Z8BWyKAAAIIIIAAAggggAACCKRUAZ6/U2rPRfa8CQAj68nebApwA7IHdeXKFSnVe6b8c+mKWeGHZ2vKLQWvD7iD5yavlWnr/zS/61q3hLzauIy9g7EVAggggAACCCCAAAIIIICA5wR4/vZcl7pqEAGgKzYqhSvADcie4JkLF6Vcn9l+Gy98ta4UzpUp4A5e+2qDTF21x/zuiZpFpe+9N9s7GFshgAACCCCAAAIIIIAAAgh4ToDnb891qasGEQC6YqNSuALcgOwJ/nnsrNQYMt9v4/V9Gkn2TOkC7qDfD7/JhKV/mN89fEchGdLiFnsHYysEEEAAAQQQQAABBBBAAAHPCfD87bkuddUgAkBXbFQKV4AbkD3BzftPSNMRi8yNU6US2fFWM0mdOlXAHQydFSujY3aY391XMb+MbHObvYOxFQIIIIAAAggggAACCCCAgOcEeP72XJe6ahABoCs2KoUrwA3InuCyHUelzfjl5sbZM6aT9X0bBa08av42eWfOVvP7BmXzykePV7Z3MLZCAAEEEEAAAQQQQAABBBDwnADP357rUlcNIgB0xUalcAW4AdkTnPXrfnn6v2vMjYvkyiQLXq0btPLHi3fKgOmbzO9rlswlkzpWs3cwtkIAAQQQQAABBBBAAAEEEPCcAM/fnutSVw0iAHTFRqVwBbgB2ROcunK3vPb1RnPjigWzy/fP1gpaefKK3dLzm6vb31b4evn2mZr2DsZWCCCAAAIIIIAAAggggAACnhPg+dtzXeqqQQSArtioFK4ANyB7gh8u2CGDZ8aaG9950w0ysUPVoJW/X7dPXpiyzvy+TL6sMuvF2vYOxlYIIIAAAggggAACCCCAAAKeE+D523Nd6qpBBICu2KgUrgA3IHuCb8+KlTGWRT3urZhf3k9gUY85vx2QpyauNndeOGcmWdg9+JRhe2fBVggggAACCCCAAAIIIIAAAilVgOfvlNpzkT1vAsDIerI3mwLcgOxBvf7tRvnfL7vNjR+tVkQGPFA+aOXF245Iu49/Mb+/IUt6WdW7gb2DsRUCCCCAAAIIIIAAAggggIDnBHj+9lyXumoQAaArNiqFK8ANyJ5g10lr5MeN+82Nn61bUl5pXDpo5dW7/pYWY5aa32e+Lo389mYTewdjKwQQQAABBBBAAAEEEEAAAc8J8PztuS511SACQFdsVApXgBuQPcF2H/0ii7cfMTfufXdZ6Xhn8aCVN+8/IU1HLDK/T5VK5PdBzSSV/gcFAQQQQAABBBBAAAEEEEAg6gR4/o66Lg/YYAJAroMkEeAGZI/9nvcXya/7TpgbD215izxUuVDQyruOnpa7hsX4fR87oIlkSJfG3gHZCgEEEEAAAQQQQAABBBBAwFMCPH97qjtdN4YA0DUdFcMR4AZkT6/W2/Nl799nzY3HPVpJGt2cL2jlQyfPSZW3fvL7fs0bDSVn5uvsHZCtEEAAAQQQQAABBBBAAAEEPCXA87enutN1YwgAXdNRMRyBaL4BHT/7j+z9+4yUyJ0l5Mi8Cv1my8lzF03qLzpXlyrFcgalP3nuH6nQb47f94tfqysFc2QKp7sSrHvi3D+y68gZKZkni2S8jpGGiQbNjhFAAAEEEEAAAQQQQAABFwLR/PztgsuzVQgAPdu1ybth0XoD2nbwpDzy0S9y+OR5KZ47s3zZubrkypI+YGddvHRZSvaa6ffd7BdrS+l8WYN27qXLV6TE6zP8vp/7Um25KW/wOuFcKb8fPiVtxi+XgyfOS6GcGeXrLjUkT9YM4eySuggggAACCCCAAAIIIIAAAhEUiNbn7wgSemJXBICe6MaU14hovQG9OW2TfLJkp9lhfe8tJ0/ULBawA/86fUFuHzDX77vlPetLvuwJB2yles+UCxcvm/W+71pTKha6PlEukrd+3CTjF11tz8sNS8lz9W9KlGOxUwQQQAABBBBAAAEEEEAAAecC0fr87VzK2zUIAL3dv8m2ddF6A+ry39Uy89cDZr90qFVM3rinXMB+2vPXGblz6M9+3/3Wv7FkTp82wX699c05cuzMP+Y2kztVk+olciXKtdDp81Uyd9NBc993V7hRPmh7e6Ici50igAACCCCAAAIIIIAAAgg4F4jW52/nUt6uQQDo7f5Ntq2L1hvQkxNWyvzYQ2a/tKlSSAY3vyVgP23ef0Kajlhkfpcqlcjvg5pJKv2PBEqNwT/Jn8fPmVt80r6y1CuTN1GuhZZjlsqqXX+b+y5fIJtMf+7ORDkWO0UAAQQQQAABBBBAAAEEEHAuEK3P386lvF2DANDb/ZtsWxetN6C2Hy2XJduPmv1yb8X88n6b2wL20+pdf0mLMcvM77KkTyu/9m8csk/rD4+RHYdPm9uNeuQ2ueeW/CHrudmg3vAY+d1yrKwZ0sqGvo1ChpRujkUdBBBAAAEEEEAAAQQQQAAB5wLR+vztXMrbNQgAvd2/ybZ10XoDajV2qaz84+qIuXpl8sgn7e8I2E8Lth6Wxz9ZYX6XJ2t6WdGrQcg+vff9xbJx33Fzu6Etb5GHKhcKWc/NBre9OUf+tkw31n2sfaOh5Mh8nZvdUQcBBBBAAAEEEEAAAQQQQCDCAtH6/B1hxhS/OwLAFN+FKbMB0XoDum/UYtmw92o4V7VYTpnauXrATpy5cb90mbTG/K74DZll/it1Qnb4Qx8ukxU7/zK363/fzfJ4jaIh6zndQFccLtlrhly54l/zu6415dZEWnTE6TmyPQIIIIAAAggggAACCCAQ7QLR+vwd7f0et/0EgFwRSSIQrTegJu8tlNgDJ03zhN6Z9+WqPfLqVxtsbWvtxPafrpCYLYfNj15rUka61CkR8X4+euq8VBo4L95+Rzx8q9x/a4GIH48dIoAAAggggAACCCCAAAIIOBeI1udv51LerkEA6O3+Tbati9YbUN13YmTnkavv50toVN+EJTul37RNZh8mNFrQ2tHPTFotMzZeXWn4+XolpVuj0hG/FrYfOikN3l0Yb7/dGpaS5+vfFPHjsUMEEEAAAQQQQAABBBBAAAHnAtH6/O1cyts1CAC93b/JtnXRegOqOWS+7Dt21uyXhN7r98HP22XY7C3mtvXL5JGPg7wv0NrRL3+xXr5es9f8qGOtYtL7nnIRvxZ0mrFON45bWtxeUIY/VDHix2OHCCCAAAIIIIAAAggggAACzgWi9fnbuZS3axAAert/k23rovUGVHngXDly6oLZL5mvSyO/vdkkYD8NnRUro2N2mN8ltGKwdQdvfPerTFy+y/zokaqFZdCDFSJ+Lcz69YA8/d/V8fZ7R9Ec8uXTNSJ+PHaIAAIIIIAAAggggAACCCDgXCBan7+dS3m7BgGgt/s32bYuWm9AFfrNlpPnLpr9kiqVyI63mknq1Kni9VW/H36TCUv/MD9vU6WQDG5+S8g+HTxjs3y48HdzuwdvKyD/aX1ryHpON5i8Yrf0/GZjvGp2Vyt2ejy2RwABBBBAAAEEEEAAAQQQcC4Qrc/fzqW8XYMA0Nv9m2xbF603oFK9Z8qFi5f9+uW3/o0lc/q08frqlS/Xy1err07l7VCrmLxhYyrve/O2ynvztpn7a3xzXvnw0coRvxbiTlG2HmDTm40l03Xx2xTxk2CHCCCAAAIIIIAAAggggAACCQpE6/M3l4W/AAEgV0SSCETjDejKlStSrOeMeN4retWXPFkzxPu8y39Xy8xfLYt51L9JdIGNUGXcwh0yaEasudmdN90gEztUDVXN8fcDp2+SjxbvDFhv1ot3Spl82RzvkwoIIIAAAggggAACCCCAAAKRFYjG5+/ICnpjbwSA3ujHFNeKaLwBnb94SUr3nhWvr35+pY4UuyFzvM8f+2SFLNx62Py8Z9My0vmuEiH7Wt//p+8B9JXKRXLIV10i/06+blPXyTdr9wU8nw8frSSNb84X8lzZAAEEEEAAAQQQQAABBBBAIHEFovH5O3FFU+beCQAt/bZ7924ZOXKk/Pjjj6L/nT59eilZsqQ89NBD8swzz0imTJnC7uWdO3cax5g7d67s2rVLLl++LAUKFJCGDRsax7j55pttHePixYvy8ccfy6RJk2Tz5s1y6tQpYz8NGjSQ559/XsqVS3jV16JFixrHD1WKFCkif/xx9T10oba3+3003oBOnPtHbuk3Jx7R9OdqSfkC2eN93mLMUlm962/z84EPlJd21YqEJP569V55+cv15nblbswmM164M2Q9pxu0/3SFxGy5GlBa6/dqVlY61S7udJdsjwACCCCAAAIIIIAAAgggEGGBaHz+jjChJ3ZHAPj/3aihX9u2beX48eMBO7Z06dIyY8YMKV7cfagxbtw4ee655+TChaurwFoPdt1118l7770nXbp0SfDiOnr0qNx9993yyy+/BNxOg8vRo0fLk08+GXQ/BIDX/vd75NR5qTxwXrwDT32qmlQtnive503eWyixB06an7/X+lZ54LYCIU985sb90mXSGnM7HV2oowwjXe4ftVjW7w38e2lbtbC8lQgrD0e6DewPAQQQQAABBBBAAAEEEPC6AAGg13vYXvsIAEVk/fr1UqNGDTlz5oxkyZJFevbsKXXr1pWzZ8/KlClTZPz48YZmmTJlZOXKlcY2Tovup02bNka17Nmzy8svvyz16tUzRhmuXbtWhg4dKtu3b5dUqVLJF198IS1btgx4iEuXLhn1Fi5caHzfvHlz6dSpk+TMmdMIBAcOHCiHDh2SNGnSGCMZGzduHHA/vgDw/vvvN+oEKxpKlioV+r1zTj2i8Qa079hZqTlkfjyqT9pXlnpl8sb7/M6h82XPX2fNz8c9Wkka2ZhWG7PlkLT/dKVZL2+29PLL6w2cdlHI7eOen7VCYr13MORJsQECCCCAAAIIIIAAAggggICfQDQ+f3MJxBcgABQxwr6YmBhJmzatEaxVr17dT2rYsGHSvXt347P+/ftLnz59HF1LGiwWK1bMCOY0PFy2bJmUL1/ebx8nTpyQWrVqycaNGyVfvnxGGJg5c/z3wk2YMEGeeOIJo65OGf7ggw/89qP1KlWqJLq/m266STZt2mS0K27xBYCPP/646D6vdYnGG9DOI6el7jsx8ajfb3Ob3Fsxf7zPKw2YK0dPXx0t+r+OVaVGyRtCdtWKnX/JQx8uM7fLmiGtbOwXOAgOubMENijfd7acOn8x4BaFc2aShd3rhrN76iKAAAIIIIAAAggggAACCERAIBqfvyPA5rldRH0AqCP6qlSpYnRs586dZezYsfE6Wd/Tp4GdvmsvR44ccvDgQUmXLp3ti+Hrr782R/T16tUr6Ii7efPmGe8C1DJq1Cjp2rVrvGPoOwI11NPz0B9xoPcSDhkyxBjFqOWrr76SFi1axNsPAaDt7ovYhrEHTkiT9xbF29+Q5hXk4SqF431euvdMOX/xsvn5d11ryq2Frg95Pr/uOy73vL/Y3C5t6lSyfVCzkPWcbBBsQRPfPtKkTiWxA5pIujSpneyWbRFAAAEEEEAAAQQQQAABBCIsQAAYYdAUuruoDwA1kBs0aJDRfcuXL5eqVasG7EprqDZnzhwzqLPT7z169JC3337b2HTx4sVSs2bNgNV0YY+sWbPKuXPnpE6dOvLzzz/7bbdt2zZzOu7TTz8tY8aMCbifAwcOyI033mh898gjjxgLhcQtBIB2ei6y22zYe0zuG7Uk3k7fuKecdKhVzO/zi5cuS8leM/0+m9ettpTMkzXkSe04fErqD1/gf+281TSiYdzBE+ek6qCfEjyXBa/WkSK54o9iDdkANkAAAQQQQAABBBBAAAEEEIiYAAFgxChT9I6iPgCsXbu2LFq0yJhue+zYsYDTZbWHddquvidQi04B1qnAdstTTz1lvkdQQzxdWThY0ZV8//zzT9F3750+fdrvfD755BPp0KGDUXXy5Mny8MMPB92PLlqydetWKVy4cMDVfgkA7fZe5LZb+cdf0mrs1am5vj13a1hKnq9/k9+Bjp/9Ryr2918xeGmPepL/+owhT2j/8bNSfbD/uwbX920k2TPaH7Ua6iCb/jwhzUZeHc2YKpVIluvSyknLlODPnqwid5XKHWpXfI8AAggggAACCCCAAAIIIJCIAgSAiYibgnYd9QFg7ty55ciRI1KxYkVZt25d0K77+++/jYU2tLRq1cpYqMNueemll4zVfbWsWrXKeEdfoHLlyhXJli2bnDp1yvhapxzrwiO+8uqrr8o777xj/KkLh9x6661BT0EX9/jhhx+MRUVOnjwZ732CvgBQ302o7yXcsWOH6PHz5s1rTInWBUt0H1o/MUo03oCWbD8ibT+Kv3Jz57uKS8+mZf2Y/zx2VmrEWTDEboh3/Mw/UvFN//Bwec/6ki97hoh1Zdy25MiUTgrlzCQbLKsCD7j/Znm0etGIHZMdIYAAAggggAACCCCAAAIIOBeIxudv50rerxHVAaBOtc2Y8d8RVXfffbdMnz49wR7XoExH5VWrVs0YEWi3jBs3zni/oJbhw4dLt27dAlZds2aNXzg4e/ZsadSokbmtjvibOnWq8ffhw4flhhuCLwjx7LPPmguExMbGio4ItBZfAJhQG3Sqsh5PRyU6LXqDSajs37/ffPfinj17pGDBgk4PkeK2nx97UJ6csCreeberVlgGPlDB7/Pth05Kg3f/XenZV7a/1VTS2nin3oWLl6VUb//pw/NfvkuK53a+enUw5Gnr/5TnJq81vy6eO7OUuzGbTN+w3/ysY61i0vuecimunzhhBBBAAAEEEEAAAQQQQMBLAgSAXupN922J6gBQQ7Q8efIYeq1bt5YpU6YkKKmj43QlX10QRFfrtVv0x1a8eHH5559/jDBNRxrGDe90oRENIWfNmmXuNu4CHvr9jBkzjO/Pnj0rGTIEH9H12muvydChQ41tA406LFWqlJQtW9YIGLU92bNnN6ZAa7Cp7xbUUE6LbqOf6fdOipORg9ESAM7cuF+6TFoTj/HB2wrIf1r7j+Zct+eYPPDB1fcFpk+bWrYMbGq7C0q+PkMuXr5ibj/9uVpSvoCzPkzoYJ8v+0P6fP+buUnlIjmkavGc8sHPO8zPGpbLK+Mfq2z7nNkQAQQQQAABBBBAAAEEEEAg8gIEgJE3TYl7jOoAUIMnfUeelkcffVQ+//zzBPtQt9U6JUqUkO3btzvq7xdeeEFGjhxp1NHwTcO5unXrGu/600CwX79+oiP+9O8LFy4Y202cOFHatWtnHqd+/foyf/6/73a7dOmSpE4dfIVVfU/hgAEDjG31HYe1atXyO18N+66/PvCKsjpluGXLlqKLnWjRKczvvvuuo/YSAMbn+n7dPnlhSvxp5o3K5ZVxcYKypduPyCOW6cK5Ml8nq9/4d4VoO6VCv9ly8txFc9Mvn64udxT9dwp7JMp787bKe/O2mbvSsK9h2bzS/esN5mel82aV2S/VjsTh2AcCCCCAAAIIIIAAAggggIBLAQJAl3AeqxbVAeC1GgGo14yGehqqTZs2LeglpKMEmzdvbr7n77vvvjPew+crkRwBGOo6Pn78uBF0Hj161Hh/4F9//WWEk3YLU4DjS32xco9fQObbombJXDKpYzW/CnN+OyBPTVxtflYoZ0ZZ1L2eXX6pOmieHDxx3tw+0gty9P3+V/ls2S5z/60rF5IHby8gD49bbn6WIV1q2fxmk0R7j6RtDDZEAAEEEEAAAQQQQAABBKJYgAAwijvf0vSoDgCv1TsAfd66yMZnn30mo0aNMhbx0Gm/WnQkno5A1BF7+o++J1DLggULRFcp9pVIvgPQzuVvfY/gkiVLzFWQ7dQNtU003oAmLt8lb3z3azyaWwtdL991ren3+bdr98pLU9ebn5XJl1VmvWh/NF3dd2Jk55HTZv2x7W6XJuVvDNUttr/X9//pewB9pUudEvJ49aJSbfBPfvv45fX6kjdb5BYfsX2CbIgAAggggAACCCCAAAIIIGAIROPzN10fXyCqA0DluBarAAe68HSl34MHDxqj6vLnzy9p0qQxNtN38s2dO9f477gLfbzyyitmOBjuKsB2fgyjR4+Wrl27Gpvqqse6+nGkSjTegD5a9LsM/HFzPMKb8mSRud3u8vv8v8t3SW9LWKjv2PuqSw3b/M1GLJJN+0+Y27/7UEVpfnvkFlpp99Evsnj7EXP/vZqVlQ61iknZPrPk/MV/g23juulcXaoUi9zUY9sAbIgAAggggAACCCCAAAIIIGAIROPzN10fXyDqA0AdYafvyNNprvpevLRp0wa8TnQhjBo1/g1g9P16/fv3j/j1pNOEdVESnX6r04F37Li6oIIe7JNPPpEOHToYx508ebLoiMBgRVf93bp1q/GOw127rk7VdHLSH3zwgegoQCPIIQB0Qhdw29Ex22XorC3xvsufPYMs7Vnf7/MPF+yQwTNjzc9ql8otnz9ZxfY5tByzVFbt+tvcfuAD5aVdtSK264fasOmIRbLZEjC+06qitKxUUBq+u0C2HTplVh/W8hZpVblQqN3xPQIIIIAAAggggAACCCCAQCIJEAAmEmwK223UB4Cvv/66DB482Oi25cuXS9WqVQN24ZAhQ6Rnz57Gd7pYh47Ui3TRd/49+OCDxm579OhhnpfvOBroabCn5emnnzZW6w1UDhw4IDfe+O90zzZt2sj//vc/V6eqo/90FKCWxYsXS82a/tNUXe30/ytF4w3oP3O3yoifri6c4fPLnjGdrO/rfz29O3erjLRs26xCPhndtpJt8kc//kUWbfMfodepdnHb9UNtWG3QT3LgxDlzs0/aV5Z6ZfJKx89WyrzNh8zPn61bUl5p/O81S0EAAQQQQAABBBBAAAEEELj2AtH4/H3tlZP/EaM+AFyxYoUZ+nXu3FnGjh0br9f0XX3ly5eXzZs3G+/rO3TokKRLly6ivXvx4kWpVKmSbNiwwdh3bGysMQowbilXrpxxHjlz5jRWJM6UKVO8baxhpduRe9ZFQPQYughI+vTpI9bmaLwBvT0rVsbE+I/qVNC0qVPJtrea+i2WMWD6Jvl48U7TW0fX6Sg7u6XzxFUy+7eD5uYvNSglLzS4yW71BLfTd1mWfmOWXLBM9f32mRpyW+EcEve8762YX95vc1tEjstOEEAAAQQQQAABBBBAAAEEnAtE4/O3cyXv14j6AFC72DcNWKf/Lly4UKpXr+7X88OGDZPu3bsbn/Xt21f69evn931MTIzUrVvX+Ozxxx+XCRMmxLtyjhw5YoR1gQI7nfrbsWNHmThxolGvd+/exmIggYp1GrCO0NMFRaxFpw3ffvvtcuLECWMVXw0S405rnjVrltx1112SMWPGgMc4efKksWLxnDlzjO+fe+45GTlyZER/DdF4A3pz2ib5ZMnVUM8KumVgE0mf9t/3QGrp+c0Gmbxij/l3+xpFpd99N9vug5cxyoP4AAAgAElEQVSmrpNv1+4zt+98V3Hp2bSs7foJbXj6/EW5ue9sv00WvFpHiuTKLJ8v+0P6fP+b+V3Fgtnl+2drReS47AQBBBBAAAEEEEAAAQQQQMC5QDQ+fztX8n4NAkARY0Vend569uxZyZIli+i0YA309O8pU6bIuHHjjCuhVKlSsmrVKsmaNavflWEnAPzqq6+kU6dO0rZtW2nQoIHxbr4zZ84Yx9ZRh5s2bTL2qVOLp02bZiwOEqhcunTJCO90VV4tLVq0MPabI0cO0dGMGhzqCMXUqVPL9OnTpWnTpvF2U6dOHdm4caM0b95catWqZQSF2m59B6K+61CnFuvoQi065Xjp0qXGiMNIlmi8AfX6dqNM+mV3QMY1bzSUnJmv9nncVXafqVNCujcpY7sLXv92o/zPcqzHqheRN+8vb7t+Qhvu+euM3Dn0Z79NNvRrJNkypJOYLYek/acrze+uz5RO1vWJ/HT5iDSEnSCAAAIIIIAAAggggAACUSAQjc/fUdCtjptIAPj/ZBq6tWvXzhg5F6ho+Pfjjz9KyZIl431tNwAMtYpu+/btjfAtQ4YMCXakjiZs1qyZrFx5NWixVtDwUEcGajAYqGgAuGDBgpAXi46M1PcHFihQIOS2TjeIxhvQq1+uly9X7w1Itah7XSmU8+p07icnrJT5sVffpfdq49LStW78ay+Y+8Dpm+SjMKYQJ9SfG/Yek/tG/RtAa0mXJpVsHfjvFOadR05L3Xdi/Kqv79NIsmeK7JR5p9cb2yOAAAIIIIAAAggggAAC0SoQjc/f0drXCbWbANCio6vljhgxwgj69AeiQZoGfhrc6Wq4gabvanU7AeDBgweNKb7z5883puXq3zpKL3/+/MZoQw3/qlWrZvsa1XcGjh8/3gjo9J2Ap0+fNvZVv359+T/2zgMqqqOL43/Fir33igWxC6KIFUXBEo29l0SNsURji72XxBZjid0YE2s0sYsFsYNYERUVxd57VxT4zqwfs/uWBXaXFRbef87JOdn37ty587uPOWeud+b2798fpUtHf1xUZDF6e3trsv0uXboEEVAU2X9ifkKHKIQiioeIbEQR1PkSTY0L0A9rTmNLwF2DOL0G1IB97ozyXZtFvjh27an8Pf6r0uhSrbDRrpi1+xLm7Lsi5RuVzYP5HSoZ3T8mQf0sv5wZUsN/ZD1NF3EvYKkxXggLj5AqtvR1Rbn8mS0yNpWQAAmQAAmQAAmQAAmQAAmQAAmYRkCN+2/TCKlDmgFAdfjZ6mapxgWo118n4XX+vkFfbPzeBY6FtMesG889hHN3tNmo01uWQyunAkb7URQbEUVHIludkjnwRzdno/vHJPjvqdsYuD5AitjnzgCvATXl7xrT9uHW03fytygCIoqBsJEACZAACZAACZAACZAACZAACcQ/ATXuv+OfsvWPyACg9fsoSVqoxgWo2x/+8Ln0yKA/V37jjJolcsh3bjP2I+TxG/l7QYdK8Cybx+hv4c+j1zF2i7YYR5UiWbHuO2VxG6OV6QkuPRSCSduD5FOXotmwpqc2e7Xj0mM4fOWxfG/q8WVz7WI/EiABEiABEiABEiABEiABEiCBqATUuP/mdxCVAAOA/CoShIAaF6AOS/1w5MoTg7z1A3zOk/fi4asPUvbPb5xRSydAGJvT1p+4haEbzkqxcvkzYYuFqvFO33UR832uSt36x4v1C5C0dsqPaS3LS/mIiAh8Co9ASpvksU2D70mABEiABEiABEiABEiABEiABOJIQI377zgiS5LdGQBMkm61/kmpcQFqueAoTtx4ZtA5M1qVR0vH/PJd6TFeeBMaJn/rHxGOzcPbzt5F39WnpVixnOmxd2Ct2LoZ9X74v4FY46+tZtyxakFMalZW9l188Cqm7NAeP47MPrz97C0WHQjBptN3IK6WHNukNFrozNmowSlEAiRAAiRAAiRAAiRAAiRAAiRgEgE17r9NAqQSYQYAVeJoa5umGhegr+YdxtnbLwy6QrfIR3h4BOxG7kCEto4GdvavgVJ5tEVCYvPnvosP8M2KE1IsX+a0ODLMLbZuRr3Xv8vwB7diGFi/pOzrde4+ev19Uv7Oli4V6pbKiX9P3dFk/kW21CmS4+gwN2RLn9qocSlEAiRAAiRAAiRAAiRAAiRAAiRgOgE17r9Np5T0ezAAmPR9bJUzVOMC1ODXg7j04JVBf+jek/fmwyeUHrtLIXdoaB0UyGprtC99rz5BuyV+Uj6LbUqcHlPf6P4xCbZe5At/nQrF45o4oKtrEdkl6N5LeP52yKixJjQtjc4uxlc3NkophUiABEiABEiABEiABEiABEiABCQBNe6/6f6oBBgA5FeRIATUuADVnu6D60/eGuTdp44dhjSw17x7+Oo9nCd7K+ROjXZH1nSpjPZVwK3naDr/iJQX2XaXJnka3T8mQfdZBxD88LUU+a1tBTStkE/+NhTAjE5fhQKZsamPq0XsohISIAESIAESIAESIAESIAESIIGoBNS4/+Z3wAAgvwErIaDGBajaVG/cffHeoAe6ViuMcV+V1ry79vgN6szYr5C7ONEDaVLaGO294Aev4P7rQYX81SkNYZM8mdE6ohN0nLgHT96Eytd/feuMGsW1FYzFC6dJe/H4tbaISaSwGD9M5xiweL5vUC0UzZE+znZRAQmQAAmQAAmQAAmQAAmQAAmQAAOA/AYME2AGIL+MBCGgxgCgfuBMF3wrx/yY3upzpdxzd16g8dzD8nWK5MkQPNkTyUTlDCObKLhR/RcfhfS58Q2QPnUKIzUYFhP3ExYbuQO6Mbxt/aqjTL5Mig6D/wnAhpO35bN0qWzQ0aUQRKCz8ZzDigCi/h2CcTKQnUmABEiABEiABEiABEiABEiABBQE1Lj/5icQlQADgPwqEoSAGhegsmN34dWHTwZ5NyybG793cNS8OxbyBG0Wa+/vy5gmBc6Oa2CSn56+CUWliXsUffxH1kXODGlM0qMv/PxtKCpMUOoVhTzyZk6rEH348j0m7wjCnWfvUK1YdnSrVhhZ/n+EedyW81hx9LqUL5A1LQ4OqWNSgDNOk2BnEiABEiABEiABEiABEiABElARATXuv1XkXqOnygCg0agoaEkCalyASozcidCwcIMYa5XIgT+/cda806/gmzdTGhwdXtck/O8/hsF+tJeiz4EhtVEoWzqT9OgLhzx6DbeZBxSPgyZ4IG0q448nn739HF/N095PKJT908sFlQtnjZNt7EwCJEACJEACJEACJEACJEACJBCVgBr33/wOohJgAJBfRYIQUNsCFBERgSLDd0TL2qlQFmz4vprm/daAu+i35rSULZ4zPfYMrGWSn8R4RUfsQESEttvO/jVQKk9Gk/ToC5+88RQtFvjKx2lT2iBooodJOoVt9WYdwNVHb2S/ds4FMbV5WZP0UJgESIAESIAESIAESIAESIAESCB2Amrbf8dORJ0SDACq0+8JPmu1LUCGMvJ0nWCfOwO8BtTUPFrrfxPD/g2Ur8sXyIzNZlTKLT3GC29Cw6Sejd9Xg2OhLHHy/Z4LD9Bj5QmpI1/mtDgyzM1knfN9rmD6rkuynzjm7D+ynkmFTkwelB1IgARIgARIgARIgARIgARIQIUE1Lb/VqGLjZoyA4BGYaKQpQmobQF6+f4jyo3bHS3GglltcXBoHc37ZYevYeK2C1LWtVg2rOpe1WQX6Ffi/fvbKqhePLvJenQ7rD9+C0M3npWPyubLhK39qpus01CRkgUdKsGzbB6TdbEDCZAACZAACZAACZAACZAACZBA9ATUtv/mt2CYAAOA/DIShIDaFqBHrz6g8uS90bLOli4VTo5217yf4x2MWXsuS1l3h1xY0tnJZD/VnOaDm0/fyn6LOzmifuncJuvR7bDwwFX8vPOifFSzRA6s/P/dhaYqbrPIF8euPY3zPE0dl/IkQAIkQAIkQAIkQAIkQAIkoCYCatt/q8m3psyVAUBTaFHWYgTUtgDdef4Orj/vi5ZfmpTJcXGip+b91J1BWHQgRMp+XTEffm1TwWT2HrMP4uL9V7Lfb20roGmFfCbr0e0wZUcQFh/U2tasQl7MblvRLJ3rjt/ETxu1R51T2iSD/4h6slqwWUrZiQRIgARIgARIgARIgARIgARIQEFAbftvut8wAQYA+WUkCAG1LUCGqufqg78y2RMpbJJj1KZA/O13U77uUKUgJn9teoGMr38/gtM3n0s9osiGKLYRlzb4nwBsOHlbqujmWhhjm5Q2S6U4Fl150l58+KStjDyxaWl0cilslj52IgESIAESIAESIAESIAESIAESiEpAbftvfgMMAPIbsCICaluAgu69hOdvh2L0QMDY+siUNiV+XHcG/52+I2W/q1kUwxuWMtl7HZb64ciVJ7Lf6MYO+LZ6EZP16Hb4dsVxeF98KB8Nci+BfnWLm62zz+pT2H72nuxfsWBm/Nfb1Wx97EgCJEACJEACJEACJEACJEACJKAkoLb9N/3PACC/ASsioLYFKODWczSdfyRGD/gOd0OeTGnRc+UJ7L7wQMoOdC+BH8wIsnX/8wT2Bmn1DK5fAn3dzA/WCYP0swonNSuDjlULmf1leQc9wLd/aqsKC0U+g2ujSPZ0ZutkRxIgARIgARIgARIgARIgARIgAS0Bte2/6XsGAPkNWBEBtS1A/teeovUiX+mBdKls8CY0TOGRvQNroljODNDP3BvVqBS61yhqsvd+WHMaWwLuyn69a9thqIe9yXp0O9Se7oPrT7SFRX7vUAkN41C592NYOKpO8caTN6FyGBHsFEFPNhIgARIgARIgARIgARIgARIggbgTUNv+O+7EkqYG3gGYNP1q9bNS2wJ0OPgxOi47Jv2SI0NqvHj3EaE6999t7uOK8gUyo9n8IzhzS3t338/Ny6KtGXf3Ddt4FmuP35Jjdq1WGOO+Mu++vkgl5cbtwsv3n6TOtT2romrRbHH63sZtOY8VR69LHfa5M8BrQM046WRnEiABEiABEiABEiABEiABEiCBzwTUtv+m3w0TYACQX0aCEFDbAqR/1DV/lrR4GxqGpzqZb6u7V0G1YtnhPusAgh++ln6Z064iviqf12Q/jd96Hn8c0QbW2jgVwC8ty5msJ7KDyNYrPnKnov/uH2uiRK4MZusUHf1CnqDtYj+pI3UKURHZA8mSJYuTXnYmARIgARIgARIgARIgARIgARJgAJDfwGcCDADyS0gQAmoLAO4MvIfvV52SrIvmSAcRULv19J18tqSzE9wdcsH1532481z7fHlXJ7jZ5zLZT9N3XcR8n6uyX5PyeTG3XUWT9UR2ePjqPZwneyv6Hx9ZDyKbMS7t3ot3cJm6T6HixKh6yJ4+bnrjYhP7kgAJkAAJkAAJkAAJkAAJkEBSIaC2/XdS8Zul58EAoKWJUp9RBNS2AG06fQcD1p2RbErlyYiIiAhcvP9KPpvdpgKaVcyH8uN3a44HRzZzj9nO97mC6bsuST31SuXE0i6VjfKPIaFL91+hweyDilfBkz2R0ia52TpFx7DwCJQctROfwiOknsjj0HFSzM4kQAIkQAIkQAIkQAIkQAIkQAI8AsxvQEOAAUB+CAlCQG0BwPXHb2HoxrOStbjrL0XyZDh545l8JirqdqhSUHPMVjcYtq1fdZTJl8lkPy07fA0Tt12Q/arZZcPqHlVN1hPZwffqE7Rboj2qmzFNCpwd18Bsfbodq/+yD7efabMeF3SoBM84FBexiFFUQgIkQAIkQAIkQAIkQAIkQAJJgIDa9t9JwGVfZAoMAH4RrFQaGwG1LUB/+V7H6M3nJRbnIlkh7ro7FPxYPhvR0B5dqhVGyVFeCnw+g2ujSPZ0sSGN8n6N/00M/zdQPq9YMDP+6+1qsp7IDjsC76G3zjHmwtlssX9IHbP16XZss8gXx649lY/MrXxsEWOohARIgARIgARIgARIgARIgASSEAG17b+TkOssOhUGAC2Kk8qMJaC2BWjpoRBM2h4k8dQonh3pU6fAznP35bMf6haHqNRbaeIeBUb/EXWRM2MaY9FKuc1n7qD/Wu2x47hW1/3b7wZGbTpnsYCi7oQGrjuDf0/fkY+6uRbG2CZxq1hsMjB2IAESIAESIAESIAESIAESIIEkSEBt++8k6EKLTIkBQItgpBJTCahtATJ0H1+mtKmw8dRtia579SKaDMAa03wUOM+Pb4B0qVOYihi7z99Hz79Oyn4Fs9ri4FDzM/bmegdj5p7LUl9c7xTUndCMXZcwz+eKfNSgdC4s6uRk8pzZgQRIgARIgARIgARIgARIgARIQElAbftv+t8wAQYA+WUkCAG1LUCz9lzGHO9gybph2dzIkT41/vS9IZ+1cy6gCQB6zD6k8EnIlIZInjyZyX46HPwYHZcdk/1EVV1RXdfcNn7refxx5Lrs3soxP6a3Km+uOkU//ePKZfJlxLZ+NSyim0pIgARIgARIgARIgARIgARIQM0E1Lb/VrOvY5o7A4D8MhKEgNoWoJ93XsTCA1cl668r5kOeTGnw+37ts6/K50WXaoXQYoGvlEuXygbnJ3iY5SNRYKTFgqMW0SWUDFh7GpvO3JX6vqtZFMMbljLLNv1OBy4/Qpfl/vJx1nSpcGq0u0V0UwkJkAAJkAAJkAAJkAAJkAAJqJmA2vbfavY1A4D0vtURUNsCpJ8918apAApms8X0XZekb+ra59RkAHbWCYTlzJAa/iPNy9oLuvcSnr9pswmTJQNENmEy8T9mtE7LjimKlgzztEevWnZmaIra5crD16g364DixYUJDWCbyvSjz3E16MrDVzh+/RlE1eRC2UwvvhLX8dmfBEiABEiABEiABEiABEiABCxJQG37b0uyS0q6mAGYlLyZiOaitgVo5H+BWHXspvRQZ5dCKJo9HcZtvSCfVSmSVVME5HudSrui+q+oAmxOu/HkDWpN36/oenGiB9KktDFHHRrPPYRzd17KvtNalEPrygXM0qXf6W3oJziM2aV4vHdgLRTLmd4i+o1VEnj7BVosPIrQT+FIaZMMW/tVh33ujMZ2pxwJkAAJkAAJkAAJkAAJkAAJWB0Bte2/rc4BVmIQA4BW4gi1maG2BWjwPwHYcFJb8KNHjSIonisDhm44K11fNl8mTQagkI1scbkL7+Gr93Ce7K34tMSxWnG81pzm+vM+3Hn+TnZd0tkJ7g65zFFlsI+ofvz0Tah89+c3zqhVIofF9BujaPSmc/jLT3svo8jU/KVlOWO6UoYESIAESIAESIAESIAESIAErJKA2vbfVukEKzCKAUArcIIaTVDbAvTDmtPYEqC9P69PHTuUzpsJvXWy/URGoAgAjt1yXn4SzkWyYv13LmZ9Iq8/fEKZscqsusM/1UH+LLZm6Ss12gvvPobJvhu/d4Fjoaxm6TLUST/DcGrzsmjnXNBi+o1RpH/MOa6Vk40ZkzIkQAIkQAIkQAIkQAIkQAIk8CUJqG3//SVZJmbdDAAmZu8lYtvVtgB999cJ7Dr/QHpsoHsJlC+QWVH4IlfG1JoA4DQv7b2AbvY5sbxrZbM8HRYeAbsROxR99/xYU5N5aGp7FxqGUmO8FN32DaqFojksd0S358oT2H1By6ifWzEMql/SVFPjJF97ug+uP3mr0BGXoGmcjGFnEiABEiABEiABEiABEiABErAAAbXtvy2ALEmqYAAwSbrV+ieltgWo2x/+8Ln0SDpGFNBwKpQFLRdqK/6mT51CUwV4vo+2MnDjcnkwr30lsx1actROfPgULvtv7uOqCTya2u4+f4dqP+9TdAsYUx+ZbFOaqipaef1CKc0r5sOsNhUspj82RSJgaj96Jz6GRShEp7csh1ZOlrnrMDYb+J4ESIAESIAESIAESIAESIAELE1AbftvS/NLKvoYAEwqnkxk81DbAtR+iR+OXn0ivTS2iQOqFs0WpUpv56qF8Kev9g66tpUL4OcW5t9BV3HCbjx7+1GOu6ZHVbjYZTP5azl35wUazz0s+9kkT4bgSZ5Inty8isKGDFh6KASTtgfJV6Ioyjozjz+bPEEAhoKcQk/zSvkwq3X8BSLNsZ19SIAESIAESIAESIAESIAESCA6Amrbf/NLMEyAAUB+GQlCQG0LUIsFR3HyxjPJevLXZVCjWA7UnO6j4N+oXB5sP3tPPvu2ehGMbuxgto/0C3cs7+oEN3vTC3ccvPwInZf7Szuyp0+FE6PczbbLUMedgfcUFZDzZ0mLwz+5WXSMmJT5X3uK1ou0GZmRsnkzpcGRYW5Ilsxywc54mxQHIgESIAESIAESIAESIAESUD0Bte2/Ve/waAAwAMgvI0EIqG0BajL3MALvvJCsZ7Qqjzolc8Bx0l4Ff+fCWeF//al89oNbMQyMwz149WYdwJWHr6W+ee0ronG5vCb7fPOZO+i/9ozsVzxneuwZWMtkPTF1CLj1HE3nH5EiKZInw6VJnhDZhvHRNp68jUE6FZh1x9w/uDYKZ08XH2ZwDBIgARIgARIgARIgARIgARKwKAG17b8tCi8JKWMAMAk5MzFNRW0LUP1fD+DyA20gbk67iqjvkAv2o5WFNQpns1UUoRjuaY/vatmZ7dqv5h3G2dvawOO0luXQ2oz77Ob7XMH0XdriJFWLZsXanuZVJ45uMo9efUDlycqAqO9wN+TJlNbs+ZvScfbey5i9N9hgl4SoSGyK7ZQlARIgARIgARIgARIgARIggegIqG3/zS/BMAEGAPllJAgBtS1A+tVlF3Vy1AQAi43cCVF8IrLZprLB29Aw+XtiszLoVLWQ2T5qs8gXx65pMwrHf1VaU2nY1NZ/7WlsPnNXdutYtSAmNStrqpoY5SMiIlBytBdCdYqWbPzeBY6Fslp0nOiUDf4nABtO3jb4+qvyeSGCtmwkQAIkQAIkQAIkQAIkQAIkkNgIqG3/ndj8E1/2MgAYX6Q5joKA2hYgl6neuPfivWSwoltl1C6ZE+XG7cLL95+i/Tp+bVMeX1fMb/bXo199+CcPe3xf2/SMQo/ZB3Hx/ittYLJpaXRyMT2QGNtE6szYj2uP30gxEXQTwbf4aPrBUt0xs6dPjeMj6/IewPhwBMcgARIgARIgARIgARIgARKwKAG17b8tCi8JKWMAMAk5MzFNRW0LkOPEPXjyJlS6aHWPKqhmlx36gUF9H4pMwQalc5vt2j6rTmF7oLaoiDl3Cn4MC4fDGC98DNNmKq7rWRVVippeTTi2iXRcegyHrzyWYuYGLGMbx9B7/YIp+jJ7B9ZEsZwZzFHNPiRAAiRAAiRAAiRAAiRAAiSQYATUtv9OMNBWPjADgFbuoKRqntoWoDJjd+H1B22m38bvq8GxUBboF+nQ9/eq7lXgWiy72Z+B/rHW7tWLYJSJVYWDH7yC+68HFTacGeOOzLapzLYruo5DNwRg/QntMVxx/Fkcg/7STQQ5S47aCZ3T2BC1R3R/T/xCWY9fem7UTwIkQAIkQAIkQAIkQAIkoG4Catt/q9vb0c+eAUB+GQlCQG0LUImROxEaFi5Zb+tXHWXyZULTeYcRoFOkQ98Zm/q4okKBzGb7aMzmc1jpe0P2b1+lIKZ8bdrdfdvO3kXf1aeljlwZU+PYiHpm2xRTR/1CHG72ObG8a+UvMpau0ptP3qLmdB/FOHXtc8L74kP5zLNMbizo6PjFbeEAJEACJEACJEACJEACJEACJGBJAmrbf1uSXVLSxQBgUvJmIpqLmhag8PAIFB2xQ+GdPT/WRPFcGdB+iR+OXn0Sreci5cx17dSdQVh0IER2/7piPvzapoJJ6mbuvoS5+67IPjWKZ8df31YxSYexwv+cuIUhG85KcfvcGeA1oKax3c2WO3LlMTosPSb7Z0idAqObOGCoji1ZbFPi5Ch3JBepgWwkQAIkQAIkQAIkQAIkQAIkkEgIqGn/nUhckiBmMgCYINg5qJoWoPcfw2A/2kvh9ANDaqNQtnTosfIE9lx4EO0HcXSYG/JmTmv2B/Pb3mD8uvey7N+gdC4s6uRkkr6eK09gt46NPWoUwchGDibpMFb46JXHaK8XiAsc38DY7mbLrfW/iWH/BioCj0u7OKH6L8qswJ39a6BUnoxmj8OOJEACJEACJEACJEACJEACJBDfBNS0/45vtolpPAYAE5O3kpCtalqAXrz7iPLjdyu85ze8LnJnSoMBa09j05m70Xo2YEx9ZLJNabbnlxwMweQdQbK/Odl7taf74PqTt1LH9Jbl0MqpgNk2xdTxxpM3qDV9v0Lk7Lj6yJjGfAbGGDp910XM97kqRd0dcmFJZyfUmLYPt56+k89HN3bAt9WLGKPSIjJ3n7+D+H5EJmSyZMw8tAhUKiEBEiABEiABEiABEiABlRFQ0/5bZa41aboMAJqEi8KWIqCmBejhq/dwnuytQHdqtDuypkuFkf8FYtWxm9FiDZ7siZQ2yc3G/rffDYzadE72dyqUBRu+r2a0vnehYXAY64UIbQFgbOnrinL5zb+XMKbBP3wKQ8lRymzJ+Mi667/2NDbrBGK/cS2CMZojwMqiJPVK5YLIDIyPtvnMHYgiLqL6sriP8PeOlZA6hU18DM0xSIAESIAESIAESIAESIAEkhABNe2/k5DbLD4VBgAtjpQKjSGgpgXo9rO3UY6Snh/fAOlSp8CUHUFYfFB7R58uu1QpkuPyJE9jcEYr8++p2xi4PkC+d8iTETv61zBa59nbz/HVvCNSXiShXRjvgbSpvlwgynnyXjx89UGOuayLE+qWymW0zeYINv/9CE7dfC67jm3igG6uRbDp9B0MWHdGPs+QJgXOjKkPm3i4B9Bt5n6EPHojx27nXABTm5czZ3rsQwIkQAIkQAIkQAIkQAIkoGICatp/q9jNsU6dAcBYEVHgSxBQ0wJ09dFr1J15QIExMrNP/44+XSGRISgyBePSvM7dQ6+/T0kVRbKng8/g2kar1C/KUTibLfYPqWN0f3MEm80/gjO3tMG4iU1Lo5NLYXNUGRLBIS8AACAASURBVN2n8uS9eKQTdFza2Qn1HHLhwcv3qDJFmb25tW91lM2fyWjd5giKTEhxb6Ru5qXQIyo4i0rObCRAAiRAAiRAAiRAAiRAAiRgLAE17b+NZaJGOQYA1eh1K5izmhagoHsv4fnbIUldJI9dndJQc6fb0kMhmLRde0efrmsKZE2LQ0Pd4uStA5cfoctyf6kjV8bUODaintE6J2+/gCWHrkn5+g65sLjzlz0C22fVKWwPvCfH/K5WUQz3LGW0zYYEP4WFI3myZAYr+Boq0uI1oAbsc38u9uE2Yz9CHmsz8YZ72uO7WnZxsie2ztcfv0HtGcq7EEWflDbJsLanCxwLZYlNBd+TAAmQAAmQAAmQAAmQAAmQgIaAmvbfdHn0BBgA5NeRIATUtACJbDaR1RbZ0qa0QdBED83PNf43MVyn+qyuM0ThB68BNePkn+PXn6LVQl+pQxxhDRxnfFXdTsuO4VDwY9m/n1sxDKpfMk42xdZZ/1h0k/J5Mbddxdi6GXwfFh6BufuC8efR6xDcp7Yoh1olcihkrzx8jXqzlBmakUe0heCI/wKxWueextolc2BFN2ez7DG205Erj9FBpxqybr+cGVJjW7/qyJkxjbHqKEcCJEACJEACJEACJEACJKBiAmraf6vYzbFOnQHAWBFR4EsQUNMC5H/tKVov0gbhMqVNiYCx9TVYtwTcxQ9rThtELLK8NppQsMOQknN3XqDx3MPyVYrkyXBlSkOjXVplyl48eKm9j29e+4poXC6v0f3NEVxx5BrGbb0gu1YqmBn/9nY1WdWLtx/Rb+1pHLz8SPbNnTENjgxzU9zh53PpIbr9cVzK6B+93hpwF/10fJQulQ3OjK0fp+IssU1m3fGb+GljYLRi4ttY06MqxD2RbCRAAiRAAiRAAiRAAiRAAiQQEwE17b/5JURPgAFAfh0JQkBNC9Ch4EfotEx7DFdkcPmP/HwM1zvoAb7984RBH9Qonh1/fVslTv4JefQabnr3D4rCIsYEjp6/DUWFCXsU4+/5sSaK58oQJ5ti67znwgP0WKllIoJ2fiPqxtZN8f7S/Vfo+dcJ3HjyNko/kT1XJp/2Dr+/fK9j9ObzUq58/kzY3Le6/C3uBhR3BOo2EZj9ksdwZ+6+hLn7rsQ45w5VCmLy12VN4kJhEiABEiABEiABEiABEiAB9RFQ0/5bfd41fsYMABrPipIWJKCmBUg/yJc/S1oc/unz3X5+IU/QdrGfQbKeZXJjQUfHOFG//+I9qk5VFrEIGFMfmWxTxqpXP3NR3D93YYLHF818E0adv/sCjeZosxZF5eFLE40LWor+ovCJqHz8NjTM4BzHNHbAN9WLyHf6R44blc2D+R0qKfrWnbkfV3Uq8k5rWQ6tnQrEytBcgYHrzuDf03dk92YV8uLEjWe4/eydQuWXtsNc+9mPBEiABEiABEiABEiABEjAegioaf9tPdStzxIGAK3PJ6qwSE0L0I7Ae+i9SluJ1y5HOngP+lyJN/D2CzSZpw126Tq/pWN+zGhVPk7fw4t3H1F+/G6FDt/hbsiTKW2sevUz4yxxJ2GsgwIwlHl4aGgdFMhqG2P38PAIzNpzGfN8Ys6ca1A6FxZ10hYy6b3qJHYE3pe6DRUd0b8LcZinPXp9wUIgrRf6wv/6U2nTqEal4GKXDS0WHMX7j+Hyucgm9R1eV3Gk2RjGlCEBEiABEiABEiABEiABElAPATXtv9XjVdNnygCg6czYwwIE1LQA/Xf6Nn5cFyCplcqTETv719D8NnREN1Kwi0shjG9aJk60P4aFo/jInQod3oNqwS5H+lj1jtoUiL/9bkq5phXy4re25hXjiHUwHYGIiAiUGbsLb3Qy+MR9dyIAFl0TlXwHrD0Dr/PaQF6kbBbblHj29qPsKn6fGu2uqcIsWpO5hxF454V8P6lZGXSsWkgx1IC1p7HpzF35rEeNIhjZyMGUaZkkW22qN+6+eC/7LOzoCI8yubH5zB30X3tGoevAkNoolC2dSfopTAIkQAIkQAIkQAIkQAIkoB4Catp/q8erps+UAUDTmbGHBQioaQHSL+hQoUBmbOrzuajFw5fv4TxFeUQ3Em/v2nYY6mEfZ9rFR+7Ax7AIqUf/DrzoBtDPQhvSoCT61CkWZ3uMUeA+6wCCH76WojNblUcLx/wGu4piH91XHsfx68+ivBdZlCJTT7/Kr+5dhhUm7MZznQDhn984R6kUPGHrBSw/ck3qb14pH2a1rmDMVEyWEUHbkqN2IlzrMk3VX3FvoQiOVpy4R2Hvsi5OqFsql8njsAMJkAAJkAAJkAAJkAAJkIA6CKhp/60Oj5o3SwYAzePGXnEkoKYFaKXvdYzRKTLhXCQr1n/noiH4+sMnTbaboWapgFu5cbvw8v0nOYQYW9gQUxOBJlEARBwhjmzxGWjq+oc/9l/SVu8d5F4C/eoWj2Lynefv0HW5vyJYKIRskieDuOuvs8vnTD7Xn/cpMuois/xevf+IsuOUR6T3DaqFonoZkvN9rmD6rkty/Nolc2BFN+c4/hUY7n7r6VvUmOajeHlmjDsy26bSPGu54KjmPsDINtzTHt99wePIX2SSVEoCJEACJEACJEACJEACJBBvBNS0/443qIlwIAYAE6HTkoLJalqAlh4KwaTtQdJtutV9xb11RUfsMOjScU0c0NVVW6zCXL9XneKN+y+1x0lXdKuM2iVzxqjOUPEQY+7hM9dG/X4j/gvE6mPa48dtKxfAzy3KKcQu3n+JrsuPK+YmBDKkToFFnRxRrVh2Ka9/hPer8nkxp11FBN17Cc/fDin1TvRAmpQ2imdr/W9i2L+B8lm5/JmwRadSsKXmLfQcvfoY7ZcckyrTp06BwHH15ZHlYRvPYu3xW/J9K8f8mB7HuyItaT91kQAJkAAJkAAJkAAJkAAJWBcBNe2/rYu8dVnDAKB1+UM11qhpAdLPHqtXKheWdtEWoXAY42WwYq2lKry6zdiPkMdv5Le1oEMleJbNE+O3duDyI3RZ7i9l0qWyQeC4Bkie/PO9eV+66TPTDZqKsX2vPkHPlSfw6oM2s1E8z5UxtSYzT9yzqNtEMFEEFSObkPMbXhd7LjxAz79OKp4fG1EvyvR2n7+vkMuXOS2ODPtcydnSbf2JWxi64axUWzJXBuz6sab8rR9QrlgwM/7r/flIORsJkAAJkAAJkAAJkAAJkAAJ6BNQ0/6b3o+eAAOA/DoShICaFiBRmXaOd7Dk3KhsHszvUEn+rjx5Lx69+hDFD793qISGsQTqjHFeozmHcP7uSyka0316kUJLDoZg8g5t1qLuvYXGjBlXmU2n72DAOm2xi6I50mHf/ysni6Bdn1WnEBqmrYYrxiuWMz3E/X0iOKffrjx8HeUeQFE8Y2/QQ0zcdkGKOxXKgg3fV4vS/+SNp2ixwFc+T5MyOS5O9IzrNA32/3XPZfym873Utc+JZV0rS1mfSw/R7Y/j8neGNClwdqw2Q/CLGEWlJEACJEACJEACJEACJEACiZaAmvbfidZJ8WA4A4DxAJlDRCWgpgVo6s4gLDoQIiE0r5gPs9poC0jUnu6D60/eRoFkqBiFOd9Sq4VHFQUyJjYrg056VW719Q5aH4CNp27Lx4aO4Jpji7F9/K89RetFyoBb0AQPTcZen9WnFEVNhE4RuBNZlZH35OmPI+40FIHWx69D5SuRYSmOAP9x5Lp89nXFfPhVxzeRL649foM6M/Yr1F6Y0AC2qVIYOyWj5fTZ61eDNnRHoP+IusiZMY3RY1CQBEiABEiABEiABEiABEhAPQTUtP9Wj1dNnykDgKYzYw8LEFDTAjR+63lFkEk/mKafoReJd0MvFzgVjrlYhzGu6LTsGA4FP5aioxqVQvcaRWPs2mTuYQTeeSFlREGNb6rH/T5CY+wVMqK4hyjcodumNi+L0ZvO4ZNueVwAHqVzY3bbClHu7dMfq/eqk9gReF8+FhWCRfXfvUEP5LN+bsUwqH7JKGaKYijlxyuLhXypOxHbLPLFsWtPpQ0jG5ZCj5paf4l7I0uP3YV3H8OkzOruVRR3HhrLmXIkQAIkQAIkQAIkQAIkQAJJn4Ca9t9J35vmz5ABQPPZsWccCKhpAdIvaKGf0SUy3UTGm37b2b9GlLvszEHe/c8TiiDX4Pol0NctakXdSN1hmgCTF95/1B6xXdW9Clx1imqYY4cpfT6FhaPkaC8IW2JqbZwKYErzspqqv7G1FUeuYdxW7XHfglltYZvKBhfvv5Jdp7Uoh9aVC0RRJTIIS4zaqcg83NzHFeULZI5tWMX764/fQBzhFUVEHAsZDu5W/2Ufbj97J/sZOgquHzSe0LQ0OrsUNskWCpMACZAACZAACZAACZAACaiDgJr23+rwqHmzZADQPG7sFUcCalqA9I909qhRBCMbOUiC3f7wh8+lR1GIHhxSBwWz2caRNNB39SlsO3tP6ulbpxgGN4ia5RYpIIJUtfWOu54YVQ/Z06eOsy2mKBAZgCITMLrWzrkAJjcra3Rhkgt3X6LhHGXF3xTJkykyClf3qIJqdtrqwbpjV5myFw9eau9qXN7VCW72uYyekji622D2QVnwZUlnJ7g7KPsbCnxu6euKcvmVgcb+a09j85m7cuzOLoUwoWkZo22hIAmQAAmQAAmQAAmQAAmQgHoIqGn/rR6vmj5TBgBNZ8YeFiCgpgWo35rT2BqgDdboB+D0A3SReE+OqodsFgi6Df4nABtOau/z6169CEY11gYg9d3pde4+ev2trYybLV0qnBztbgGvm6ZC/+5C3d7tnAticrMyRgf/RF+RTVhxwm68fK+sHKyrN6ZjvZ6/HdLcGRjZprcsh1ZOUbMFo5vl4oNXMWXHRflav7KxeHH72VtU/8VHoeLUaHdkTZdK8UwUlRHFZSJbNbtsWN2jqmmAKU0CJEACJEACJEACJEACJKAKAmraf6vCoWZOkgFAM8GxW9wIqGkB6rnyBHZf0N4zN9C9BH6oqz2C+9OGs1h34lYUoBcnesR6r50xXhi1KRB/+92UoqIAiCgEEl3TDy65FM2GNT3jP7ikn+UWaW/7KgUxqalpwb/Ivt+uOA7viw8NTl0cI7400QMpbJIbfN9x6TEcvqK9S3G4pz2+q2VnjAs0MuO2nMeKo9qCI5ltU+L0aHckS6Y9vuwX8gRtF/tJnWlT2kAUG9GVES93BN5D71WnpFzODKnhP7Ke0bZQkARIgARIgARIgARIgARIQD0E1LT/Vo9XTZ8pA4CmM2MPCxBQ0wLU9Q9/7Nc54qsfOJqw9QKWH7mmoCqCUVcme0YJ/JiDftK2C1h6WKu/lWN+TG9VPlpVosrudp0jw12rFca4r0qbM3Sc+kzfdRHzfa4qdHSoUhATzQz+CUX6WXi6yvNnSYvDP7lFa7N+QPK7mkUxvGEpo+eoz1V01M843HjyNgb9EyB1Fs+ZHnsG1ooyxuUHr1D/14OK52fH1UfGNCmNtoeCJEACJEACJEACJEACJEAC6iCgpv23Ojxq3iwZADSPG3vFkYCaFqB2i/3gG/JEEhvbxAHdXLUVdWftvoQ5+64oiGZMkwJnxzWII+XP3WfsuoR5Plr9Tcrnxdx2FaPV7T7rAIIfvpbvRfVdceQ2vtu5Oy/QeO5hOWxcg39C0Zlbz9Fs/hGDU4kt01G/mrOoIjwjhkCq/iD61X3F+wUdKsGzbB4p+tveYPy6V3u0t07JHPijm3MUe0M/haPUGGWRlH97V0Olglni200cjwRIgARIgARIgARIgARIwMoJqGn/beWuSFDzGABMUPzqHVxNC1CLBUdx8sYz6ewpX5eFOMYa2RYeuIqfd2rvhhPP82RKA9/hdS3ygczbF4wZu7VBJVF4QhSgMNQ+fAqDw5hdiuq7G7+vBsdCCRNYOnr1MfZceICKBbOgSbk8cc6I/BgWjvLjd8tCHLoMWjvlx7SW0WdG6nOMLjgXndPcZu5HyKM3ite9a9thqIe9fDbknwD8o3NfY0zHtd1m7EfIY60+U+8ktMjHRSUkQAIkQAIkQAIkQAIkQAJWT0BN+2+rd0YCGsgAYALCV/PQalqAGs89hHN3tMUjZrYqjxaO+aX7//K7gdGbzik+h2I502OvgaOf5nwzSw+FYNL2INnVUPGJyJfBD17BXe9oaeC4+siQhI6Wdlp2DIeCtXf5Rc59kHsJ9NO5m1Gf9epjNzHiv0D5uHz+TNjct7rRLik3bleUAiT6vmi/xA9Hr2qzRYd52qNXNPcM9lh5QhMcjWzf1SqK4Z7GH0k22nAKkgAJkAAJkAAJkAAJkAAJJGoCatp/J2pHfWHjGQDUAXzz5k3MmTMH27dvh/j/1KlTo1ixYmjdujV69+4NW1vbOLvj2rVrmjH27NmDGzduIDw8HPny5YO7u7tmjNKljbtr7dOnT1i2bBlWrVqFoKAgvH79WqOnXr16+OGHH+DgEH2VV91JPHnyRGPPpk2bcP36dURERKBIkSJo1qyZRk+2bNniPGdDCtS0ANX/9QAuP9AeqRXHb8Ux3Mj276nbGLhee++beG5qcCkmJ/3lex2jN5+XIs6Fs2J9LxeDXU7dfIbmvx+V72xTiSIUHl/kG0gopXO9gzFTp4JupB2z21RAs4r5ojVLvzpybHcG6ip6/zEM9qO9oujWLwRSc5oPbj59K+Xmta+IxuW034qugl+8LmLBfu0difVK5cTSLpUTCivHJQESIAESIAESIAESIAESsFICatp/W6kLrMIsBgD/7wYR9OvQoQNevHhh0DElS5bEjh07ULRoUbMdt3jxYvTr1w+hoaEGdaRKlQqzZ8/G999/H+MYImjXqFEjHDt2zKCcCFz+/vvv+Oabb2LUc/z4cTRt2hT37t0zKJc3b15s3rwZTk6Gj4uaDQKAmhagWtN9cOOJNqizuJMj6pfOLfHtOn8f3/11UoGzml02rO5hmcq760/cwtANZ6X+cvkzYUs0mWviyG37JdrvKmu6VDg12j0urra6vv7XnqL1It8odm3o5QKnwlmjtffE9adouVDbz5Tg6J3n7+D68z6DuiMLgYSFR6DkqJ34FB4h5Tb1cUWFApkN9tMvGFI4my32D6ljdbxpEAmQAAmQAAmQAAmQAAmQQMISUNP+O2FJW/foDAACCAgIQLVq1fD27VukT58ew4cPR506dfDu3TusXbsWS5Ys0XjR3t4eImgmZExtQk+7du003TJlyoRBgwbBzc1Nk2V4+vRpTJs2DVeuXNHccbZ+/Xq0bNnS4BBhYWGafgcPfq4A2rx5c/To0QNZs2bVBAQnTZqEhw8fwsbGRpPJ2KCB4UISd+7cgaOjIx48eIAUKVJg4MCBaNy4sUbntm3bMGvWLIgsw1y5cuHkyZOa7EJLNjUtQFWneOP+y/cS34pulVG7ZE75+3DwY3RcpgzmxnRPn6l+2BJwFz+sOS27lciVHrt/jFpZVgjsu/gA36w4IWXzZkqDoxa6i9BUu7+UvMjGKzd+N0QhDd12bERd5MqYJtphQx69htvMA4r3QRM8kDaVTaymxlR8JLIQyL0X7+AyVRkkPD6yHnJkSG1Qf8Ct52iqU9AkeTJosjXTpIzdnlgNpgAJkAAJkAAJkAAJkAAJkECSIaCm/XeScdoXmAgDgIAm2Ld//35NIEwE1lxclMcjp0+fjqFDh2rwjx8/HmPGjDHJFSKwKI7VisCcCB76+vqiTJkyCh0vX75E9erVERgYiNy5c2uCgenSpYsyzooVK9CtWzfNc3FkeP78+QoZ0U8E9oS+4sWL48KFC5p56beuXbvizz//1DwWAcdWrVopRP755x/N0WfRxHjLly83ac6xCatpAao0cQ+evtFmfa7pURUudtqj1advPsPXOsduBbtmFfJidtvoK/XGxlf3/e7z99FTJ8OwYFZbHBxqOFNsR+A99F51SnYvmiMd9g2qbcpwiUJWZACKTMDIlipFclyc4IHkIooWTXvx9iPKT9iteHv4pzrInyX2qwHEXX3izj5DLbIQyPHrT9FKJ8MwtbBpoke0hU9ef/iEMmN3KVR6DagB+9wZE4UPaCQJkAAJkAAJkAAJkAAJkED8EFDT/jt+iCbOUVQfABQZfc7Ozhrvfffdd1i4cGEUT4p7+kTATty1lyVLFk3WXMqUKY32+MaNG2VG38iRIzVZeoba3r17NXcBijZv3jz06dMnipi4I1AE9YQd4o/Y0L2EP//8syaLUbQNGzagRYsWCj3CfpHRJ7IJRYagl1fUu8lEBw8PD+zatUuTTSgyBkU2oKWamhag0mO88CY0TKLTr6prqPBGhyoFMfnrshbBffDyI3Re7i915cqYGsdG1DOoW/8+Qoc8GbGjfw2L2GFNSmbuvoS5+65Ik4wJdIr7MYuPVB7R3dLXFeXyGz6iqzvfNf43MfxfbQER3XeRhUD+O30bP67T3gVplyMdvGMJvrpM9ca9F9rsUkN3Bl599FoT7HQpmg2Fs0f9RwVr8gttIQESIAESIAESIAESIAESsDwBNe2/LU8v6WhUfQBQBOSmTJmi8aifnx+qVKli0Lu6QbXdu3fLQJ0xn8KwYcPwyy+/aEQPHz4MV1dXg93EkdsMGTLg/fv3qF27Nnx8fBRywcHBKFGihOZZr169sGDBAoN67t+/jzx58mjetW/fXlMoRLeJI809e/bUPBJHk9u0aWNQj+6xZXF/oThqbKmmpgWo+Mgd+BimvddtW7/qKJMvk0Rp6H64njWLYkRDy1R01b/zLlPalAgYW9+gK1cdu4GR/2krEjsWygIRsExq7dydF2g897CcVseqBTGpWewBV+fJe/Hw1QfZ749ulVFH5zh3dJzmeAdjloHCI0I+shDIvH1XFMVJapXIgT+/+fyPE9E1/YrGA+oVx4B6n9cI0c7ffYGWC3zx7mMYbJInw9a+1eGQlxmCSe175nxIgARIgARIgARIgARIICYCatp/80uInoDqA4A1a9bEoUOHNMdtnz9/bvC4rMAnju2KewJFE0eAxVFgY5sItkXeIyiCeKKycHRNZObdvXsXoiDImzdvFPaIY7jffvutpuuaNWvQtm3baPWIoiWXL19GwYIFNdWGdVvnzp3x119/aR6JAiDiyLGhJt6JQiCiiT6RR4aNnTcXICA8PAJFR+xQoNjzY00Uz5VBPjN0tPTHeiXQv15xS6DG2dvP8dW8I1KXOFp6aZKnQd3LDl/DxG0X5DvXYtmwqrtlipFYZDIWVCKy8kTA0y5HeoxtUhqi4ElszWP2QVy8/0qKzWxVHi0c88fWDWM2n8NKX+XfoW4nUQhkvs8VrD1+Sz5uX6UgpsSSBTpuy3msOHpd9mlcLg/mta8kf3+z4jj2XXwofzevmA+z2lSI1V4KkAAJkAAJkAAJkAAJkAAJJB0CDAAmHV/GZSaqDwDmyJEDjx8/Rvny5XHmzJloWT579kxTaEM0cV+euDfP2Pbjjz9qqvuKduLECc0dfYaaOGKYMWNGvH79WvNaHDkWhUci25AhQzBjxgzNT1E4pEKF6Dfyorrvli1bNPeHvXr1SnGfYOXKlTV2iGIkIugZUxMy4j5B0cffX3uM1Ni5RyenlgVIFJywH608Yn1wSB0UzKa9N+5jWLjmaKluG9WoFLrXML/itK6uyw9eof6vn4vGRLZrUxsavFtOBKGm77ok5era58SyrpXj6u4k07/DUj8cufJEzmdEQ3v0rGkX6/y+//skdp67H62cKASy6thNHL7yWMoM9SiJ3rWj/8cCIfi33w2M2qTN2LTPnQFeA2pqdNx48ga1Z+xHhDb5FPkyp8WRYW6x2ksBEiABEiABEiABEiABEiCBpENALfvvpOOxLzMTVQcAxVHbtGnTasg2atRIU/02piYKeIisvKpVq2oyAo1t4visuF9QtJkzZ2oq7hpqp06dUgQHxf179etrj2qKjL9169Zpuj569AjZs2eP1oS+ffvKAiEXL16EyAiMbCLjT9wDKO4TPHdOGzwwpEzcfXj+/HlNlqDICDS2iQUmpiZ0Rd69eOvWLeTPH3sWlbFjW5Ocoew+Q9VmS4zaqahKO7V5WbRzLmiRqYhAUK3p+xW6RHEJQ9Vi9e/Ga1QuD+brZJRZxKBErKTfmtPYGnBXzuC7WkUx3DP2o9qtFh7F8evPop25KAQiCrBcf/JWysxpVxFflf+cgRtd8wt5graL/eRrUcxEVCYWx31FJqfI6NRvIgAoAoFsJEACJEACJEACJEACJEAC6iDAAKA6/BzbLFUdABRBtJw5c2oYiXvwxJ13MTVRBENU8hVBMVGt19gm/tiKFi2Kjx8/aopviExD/eCdKDQigpC6BTn0C3iI9zt2fD5O+u7dO6RJkyZaE3766SdMmzZN814/61AcdxaVicV9h+Lew5iakBGZfyL4KTIJjW0i89DYlpQDgA9fvYfzZG8FitOj3ZFF77ipfqVgY4I/xvJ98PI9qkxR2nBmjDsy20Y98jp5+wUsOaQNGrWolB8zW5c3dqgkL6d/5LaVY35MbxU7n9rTfRTBvULZbHFDJ9hXvVh2TaGO0LBwyVC/WIwhuI9ff4DTpL2KVweG1Eb29KlRdao3Xr3/FKXb7DYV0KxiviTvK06QBEiABEiABEiABEiABEjgMwEGAPklCAKqDgCKwJO4I0+0Tp06YeXKlTF+FUJW9LGzs8OVK9oKosZ8Sv3798ecOXM0oqKQhwjO1alTR3PXnwgIjhs3TlNxV/wODQ3VyIl7+jp27CjV161bF/v27dP8FhV8kydPHu3Q4p7CiRMnat6LOw6rV68uZUVVXxFwrFGjBg4eVB4N1VcYeUei6COKlBjbGAD8TOrW07eoMU1ZzOXChAawTZVCgdJ91gEEP/x89Fu01d2roFqx6DM8jfWDkDOUheg3vC5yZ4oaQB61KRB/+92U6i1ZjdgUm61Vdq53sKJQh7FHpMuM3YXXH7R/P12rFVbc3ZfKJrki+Cfm7z+iI0/PpQAAIABJREFULnJmjD7IL2TEtQEVJ+7B87cfJbJlXZxw/+V7RTEXXZ4is1RkmLKRAAmQAAmQAAmQAAmQAAmogwADgOrwc2yzVHUAML4yAIUTRFCvZcuW2Lp1a7Q+EVmCzZs3l/f8bdq0CeIuv8iWmDIAeQT4s9euPnqNujMPKHx+ZbInUtgog7dLD4Vg0vYgjVzhbLbYM7AWUurJxPbHHN17Q/cQ7h9cG4Wzp4vSZfA/AdhwUnt8u3v1IhjV2MHcoZNcP/0qyRUKZMamPoarekdO/l1oGEqNUd4DufIbZ3ReHv2dmuIo78UJHkiePPZM2hYLjuLkDe3x4uGe9th46jYuP9AGlHUdUSxneuwdWCvJ+YYTIgESIAESIAESIAESIAESMEyAAUB+GYKAqgOA8XUHYOSnJrJ1RCXdefPmaYp4iCw80TJnzqzJQBQZe+I/cU+gaAcOHIDIwItsiekOwNj+vNSyAF24+xIN5xySOMTdbFenNDSI51DwI9x78R6eZXIjQ5qUsSE0+r347kQlYt1iEF4DasA+d8YoOvqsPoXtZ7V3PfatUwyDG2jvjzR60CQq6HXuHnr9fUrOrkDWtDg0NOaiGoayQM+Oq4+a03wUmXu6yIpkTwefwbWNovjThrNYd0JbPbho9nQIefwmxr4nRtXTHBNmIwESIAESIAESIAESIAESSPoE1LL/TvqejNsMVR0AFOjiowqwIReJSr+iEIc48ps3b16II7aiiaIfe/bs0fy/fqGPwYMHy+BgXKoAOzk54eTJk6wCHLe/HaN6n775DF//flTKpk1pg6CJHkb1taSQ/eideP9Re7/c5j6uKF8gc5Qhuv95HHuDHsrnQxqURJ86MVeitaSd1q5L3NPXepG2AFC6VDY4PyFmf4rsPJGlF9lSi+y+iR6aDMBDwdqqv7pzr1E8O/76topROJYcDMHkHZ+zRw01EUwU90C+DQ2Trxd2rASPMnmM0k8hEiABEiABEiABEiABEiCBxE2AAcDE7T9LWa/6AGDkHXeiMMbz58+RIoXybrZI0KLqb7Vq1TQ/xf1648ePt5QPpB5xTFgUJXnx4oWmaMjVq1cVYyxfvhzffvut5tmaNWsgMgKja6Lq7+XLlzV3HN64cUMh1rlzZ839gqKJaryiwq+hJt6J4KRooo/IXrRUU8sCdCzkCdroVGnNbJsSZ8ZoKztbimdseipM2K3INlvXsyqqFM0WpVvHpcdw+Io2KDWqUSl0r1E0NvWqeW/oSHd0FZUjoXidu49ef5+UjPJnSYvDP7nhF6+LWLBf+TceKdTOuQCmNi9nFFefiw/RbcXxaGXHNXGA98WHimDjN65FMKYJj3YbBZhCJEACJEACJEACJEACJJDICahl/53I3fTFzVd9AHDEiBGYOnWqBrSoiCuq3hpqP//8M4YPH655JYp1iEw9Szdx59/XX3+tUTts2DBpV+Q4IqAnAnui9erVCwsWLDBowv3795Enz+fsnnbt2mH16tUKucWLF+O7777TPBOVj0UFZENNvBP9RVu0aBF69uxpsSmrZQE6ePmR4q63nBlSw39kPYtxNFZR1SnemsIQke3Pb5xRq0SOKN3175Ob1KwMOlYtZOwwSV7u+dtQVJjwOUM3sh0d5oa8mdNGO/e//W5g1KZz8n3kvYE7Au+h9yrtcWJdBaZkXho6YhypS2Qo+o2oixVHriuKl5TJlxHb+tVI8v7iBEmABEiABEiABEiABEiABFgFmN/AZwKqDwD6+/vLoJ8Iii1cuDDKtyHu6itTpgyCgoI09/U9fPgQKVNa7o42MaCosOvo6IizZ89qdF+8eFGTBajfHBwcNHZkzZpVU5HY1tY2ioxusHL9+vVo1aqVQkYECPPly6e5g7BBgwbw8lIWKIgU9vDw0AQ7RbXhO3fuRJspaM4fk1oCgHsvPED3lSckImPujDOHZ2x9ak/3wfUnb6XYok6OaFA6auZnozmHcP7uSyk3s1V5tHDMH5t61bwPD49A8VE7ERYeIee8rV91lMmXKVoGv+65jN+8g+V7d4dcWNLZyWCF6Eih2W0qoFnFfEZxFTY5jPVSHPGO7NjFpRDGNy0Dv5AnaKuTiSpqiwSMrW/RuyaNMpZCJEACJEACJEACJEACJEAC8U5ALfvveAebyAZUfQBQ+CvyGLA4/nvw4EG4uLgo3Dh9+nQMHTpU82zs2LEYN26c4v3+/ftRp04dzbMuXbpgxYoVUT6Dx48fa4J1hgJ24uhv9+7d5bHcUaNGaYqBGGq6x4D79OmjKSii28Sx4UqVKuHly5ews7PTBBINHWvWPQb8zz//aCoU6zbxrHXr1jHOKS7fuloWIFFQQxTWiGx2OdLBe5BxxR3iwle/b4NfD+LSg1fy8W9tK6BphagBproz9+PqI20BifntK6FROd4Vp8vTadJePH79QT5a0a0yapfMGa27Rv4XiFXHbsr37ZwLYmrzshDFWSpO3GOwEMiGXi5wKpzV6E+g4W+HcOGeNnAb2dF7UC3Y5UgPUQm63LjdCA3T3gMZm91GD05BEiABEiABEiABEiABEiABqyaglv23VTvBCoxjABDQVOR1dXXFu3fvkD59eohjwSKgJ36LY7DiyKxoJUqUwIkTJ5AhQwaF64wJAG7YsAE9evRAhw4dUK9ePc3dfG/fvtWMLbIOL1y4oNEpjhZv3bpVUxzEUAsLC0OtWrVw5MgRzesWLVpo9GbJkgUim1EEDkWGosja27ZtGzw9PQ3qEdmDIuNQFBoRAcJBgwahcePGGlnRT1QiFlmJokjKqVOnkD+/ZbPA1LIA/Xf6Nn5cFyB94JAnI3b0j/+jl03nHUbA7RfSjmktyqF15QJRvg3Xn/fhzvN38vnyrk5ws89lBUuV9ZigH0yd1bo8mleK/u+j58oT2H3hgZzAD3WLY6B7Cc3vTsuOGSwE4je8LnJnSmP0pH9YcxpbAu4q5PULibRccBQnbjyTMr1r22Goh73RY1CQBEiABEiABEiABEiABEggcRJQy/47cXon/qxmAPD/rEXQrWPHjprMOUNNBP+2b9+OYsWiVkQ1NgCofxRXf5yuXbtq7vVLkybmjb/IJmzYsCGOHzd88b8IHorMQBEYjKkdO3YMzZo1gzgSbKiJ4iDiXsLo7kWMy2eqlgVorf9NDPs3UKKqWDAz/uvtGhd0ZvUVlWtFBdvINqFpaXR2KRxFl+PEPXjyJlQ+X929CqoVy27WmEm1U/slfjh69YmcXmyFUpr/fgSnbj6X8hOblkan/7M3VAgkpU0yXJroieTinK6RbY53MGbtuayQXtbFCXVLaYO307wu4nedoiOVC2fBP70+FzZiIwESIAESIAESIAESIAESSLoE1LL/TroetMzMGADU4Siq5f7222+aQJ/4AxGBNBHwE4G7vn37Gjy+K7obEwB88OCB5ojvvn37NMdyxW+RpSeq7IpsQxH8q1q1qtFeFdl5S5Ys0RT4EHcCvnnzRqOrbt266N+/P0qXLm2ULhFMFHMWgb7r169r+hQpUgRNmzbFgAEDkC1b1EqxRimORUgtC9BK3+sYs/m8pFGlSFas+055xNwSPGPT0Xm5P0RBksg2oqE9eta0i9LNYYwX3oaGyecbv68Gx0JZYlOvqvd9V5/CtrP35Jy/r22Hn2LIpKsxbR9uPdVmVS7sWAkeZT4fqzZUCKRQNlscGPL5SgFjm34l4IJZbeEzuDZsdIKI+y89RNc/tP9okMomOc6Oq480KW2MHYZyJEACJEACJEACJEACJEACiZCAWvbfidA18WoyA4DxipuDRRJQywK05GAIJu8Iko6vWSIHVn7jHO8fgv4x1EHuJdCvbnGFHeJOOrsRO6BT3wI7fqgBh7wZ491eax5w7OZz+NP3hjSxtVN+TGtZ3qDJgqnDmF1491EbVNW9389QBV/XYtmwqrvx/xggBhaFQDosPQbfkCcQgT1R5dnFThm8f/X+I8qP363w79qeVVG16JcJ8luzD2kbCZAACZAACZAACZAACaiJgFr232ryqTlzZQDQHGrsE2cCalmA5u0Lxozd2qOZ9UrlwtIuTnHmZ6oC/Tvi+tSxw5AGyvvfQj+Fo8SonQrV+wbVQtEc6U0dLknL/7Y3GL/u1fVpTiztUtngnF9/+IQyY3cp3h0YUhuFsqXTPDNUCKSNUwH80rKcyQyFLlEIJH9mW2SyNVylvPHcQzh3R3vNgaFAsMkDswMJkAAJkAAJkAAJkAAJkIBVE1DL/tuqnWAFxjEAaAVOUKMJalmAZu2+hDn7rkgXi4q6orJufLehGwKw/sRtOey31YtgdGMHhRkv3n3OENNtR4e5IW/mtPFtrlWP97ffDYzadE7aGNO9jtcev0GdGfsV8zk/vgHSpU4hn+kXAhnSoCT61Il616gloIzfeh5/HPl81F80/UIhlhiDOkiABEiABEiABEiABEiABKyLgFr239ZF3fqsYQDQ+nyiCovUsgBN3RGERQdDpE+bV8yHWW0qxLuPx2w+h5U6x1Y7VCmIyV+XVdjx8OV7OE/xVjw7PdodWdIZrkgd75OwkgF3Bt7D96tOSWtiurPv+PWnaLXQV8raprLBhQkeipno6kuVIjn2/FhTZghaespe5+6h199a24U9Z8fWRwqb5JYeivpIgARIgARIgARIgARIgASshIBa9t9WgttqzWAA0Gpdk7QNU8sCNG7Leaw4qs24audcAFObm368M65fw+TtF7Dk0DWppkWl/JjZWnlv3c0nb1Fzuo9iqKAJHkibikUidKEcC3mCNov95KMMqVMgcHwDgy7SL/IRXbDwyJXHOHv7BeqWyokSuTLE1d3R9n/y+gMcJ+1VvN/cxxXlC2T+YmNSMQmQAAmQAAmQAAmQAAmQQMISUMv+O2EpW//oDABav4+SpIVqWYCG/xuINf43pQ+7uBTC+KZl4t2nM3dfwtxYjiJfuv8KDWYfVNgWMqUhkutUko13w61wwCsPX6HeLCWnS5M8kDpF1ECpfhVoUVFZVFZOyFZ35n5cffRGmjCyYSn0qFk0IU3i2CRAAiRAAiRAAiRAAiRAAl+QgFr2318QYZJQzQBgknBj4puEWhagQesDsPGU9u69njWLYkTDUvHusPk+VzB91yU5br1SUQtXBNx6jqbzj0iZ1CmS49Ikz3i31doHfPomFJUm7lGY6TvcDXkyRb0rUT/w6lE6NxZ2ckzQKeoHpROqME2CQuDgJEACJEACJEACJEACJKAiAmrZf6vIpWZNlQFAs7CxU1wJqGUB6rv6FLadvSdx9XMrhkH1S8YVn8n9lx4KwaTtQbJf9WLZ8Xf3Kgo9fiFP0FbnaGumtCkRMLa+yWMl9Q7h4REoNnIHwiO0M93WrzrK5MsUZerD/z2LNf635POOVQtiUjPl3Yvxzeu/07fx47oAOWxm25Q4NcqdmZ7x7QiORwIkQAIkQAIkQAIkQALxREAt++94wploh2EAMNG6LnEbrpYFqOfKE9h94YF01iD3EuhXt3i8O0+/cq1ToSzYoHcUdf+lh+j6x3FpW+6MaeA3om6825oYBnSatAePX4dKU1d+44yaJXJEMb37n8exN+ihfP5jvRLoXy/+/a9r2J3n7+D68z6FrbsG1ETJ3NHfPSjuMvxtbzBEsHDy12VQLOeXu6cwMfifNpIACZAACZAACZAACZBAYiKglv13YvJJQtjKAGBCUOeYUMsC1GW5Pw5cfiQ9PqKhPXrWtIv3L2DDydsY/I8266tsvkzY2q+6wg6vc/fR6++T8lnhbLbYP6ROvNuaGAas/+sBXH7wWpo6u00FNKuYL4rp4ki1OFod2UTwrEOVQgk+RREAFIHAyDa1eVm0cy5o0K7Hrz+g+i/78P5juOZ91aJZsbanS4LPgQaQAAmQAAmQAAmQAAmQAAkYR0At+2/jaKhXigFA9fo+QWeulgWo3WI/+IY8kazHNXFAV9ci8c5+29m76Lv6tBy3eM702DOwlsKOTafvYMC6M/KZfe4M8BpQM95tTQwDtl3sC7+Qp9LUUY1KoXuNqIU09ANtizs5on7p3Ak+xT6rTmF7oPZoemeXQpgQTXEa/UrGqWzE3ZAeSJYsWYLPgwaQAAmQAAmQAAmQAAmQAAnETkAt++/YSahbggFAdfs/wWavlgWo+e9HcOqmNgMspkyrL+mMvRceoPvKE3KIAlnT4tBQN8WQa/1vYti/gfJZhQKZsamP65c0K9Hq7rP6FLbr3O3Yu7YdhnrYK+YTERGBkqO9EPrpc+acaP/2roZKBbMk+Lzn7QvGjN2XpR2VC2fBP70MVyfWL2QiOl2Y0AC2qVIk+DxoAAmQAAmQAAmQAAmQAAmQQOwE1LL/jp2EuiUYAFS3/xNs9mpZgBrNOYTzd19KzjNblUcLx/zxzv1Q8CN0WuYvx82RITWOj6ynsOOPI9cwfusF+YxHPaN305jN57DS94YUaFu5AH5uUU7R4cW7jyg/frfi2aGhdVAgq228+19/QO+gB/j2T21AOEPqFDg7rr7BrL5vVxyH90XtPYZC15FhbsiXOWrV4wSfGA0gARIgARIgARIgARIgARKIQkAt+2+6PmYCDADyC0kQAmpZgNxnHUDwQ+1dcfPaV0Tjcnnjnfnx60/RaqGvHDdDmhQIHNdAYceC/Vfxi9dF+axOyRz4o5tzvNuaGAacvfcyZu8Nlqa6O+TCks5OCtOvPHyNerMOKJ5dnOiBNCltEnyK9168g8tUZSGQ6IKTLlO9ce/Fe4XN23+ojtJ5o1Y9TvCJ0QASIAESIAESIAESIAESIAEGAPkNGCTAACA/jAQhoJYAYM1pPrj59K1knFB3wAXefoEm8w5LO8Q9bpcneyp8/+uey/jNWxvU8iyTGws6OibI92Htg/7lex2jN5+XZlYqmBn/9lYel/YLeYK2i/2kjMiyCxyvDLom1DzF8eSKE/fg+duP0oRFnRzRQO9+wqdvQlFp4p4oZq7qXgWuxbInlPkclwRIgARIgARIgARIgARIwAQCatl/m4BElaIMAKrS7Qk/abUsQFWneOP+S2321J/fOKNWiRzx7oDgB6/g/utBxbhXpzSETXJtIYepO4Kw6GCIlGleMR9mtakQ77YmhgHF/X/iHsDIZqhi8taAu+i3Rlt4pWj2dNg3uLbVTE+/QM2AesUxoF4JhX1HrjxGh6XHotg8v30lNCqXx2rmQkNIgARIgARIgARIgARIgASiJ6CW/Te/gZgJMADILyRBCKhlAao4YTee6WRZre1ZFVWLZot35reevkWNaT6KcfULOYzdfA5/6txr1865IETREraoBKJk9xk4Uq1/p6Jz4axY38vFanBO2HoBy49ck/bUd8iFxXrHmJccDMHkHUFRbJ7UrAw6Vi1kNXOhISRAAiRAAiRAAiRAAiRAAgwA8htgAJDfgBUSUEsAsPQYL7wJDZMeSKgqsA9fvYfzZG/Fl3BqtDuypksln/204SzWnbglf3dzLYyxTUpb4deT8CYZyqi8PMkTqVIkl8ZN87qI3/dflb8blc2D+R0qJbzx/7fgnxO3MGTDWWmPocrQP647g/9O34li85AGJdGnTjGrmQsNIQESIAESIAESIAESIAESYACQ3wADgPwGrJCAWgKAxUbswKfwCOmBhCqe8PL9R5Qbp6xI6zvcDXkyaSu5/rDmNLYE3JW29q5th6Ee9lb49SS8SU9ef4DjpL0KQ46NqItcGdPIZ0M3BGD9idvydxeXQhjftEzCG/9/C87deYHGc7X3QorHgePqI0OalNJGj9kHcfH+qyg2d69eBKMaO1jNXGgICZAACZAACZAACZAACZAAA4D8BhgA5DdghQTUEAAMC4+A3YgdCvp7B9ZEsZwZ4t0joZ/CUWLUTsW4PoNro0j2dPJZz5UnsPvCA/l7oHsJ/FC3eLzbmhgGFL4tNnIHIrSxXegHd7v94Q+fS4/kdAbXL4G+btbD88OnMJQes0sRoP6nlwsqF86qsfn9xzCUGat8HzmZlo75MaNV+cTgKtpIAiRAAiRAAiRAAiRAAqonoIb9t+qdbAQA3gFoBCSKWJ6AGhagd6FhKDXGSwHv0NA6KJDV1vJAY9Eoqr4WHaEMWO3sXwOl8mSUPTstO4ZDwY/l7xEN7dGzpl2825pYBnScuAdP3oRKc//61hk1imsLvDSZexiBd17I9z83L4u2zgWtanoNfj2ISw+0GX4TmpZGZ5fCGhsNZQhGGl+vVE4s7VLZquZCY0iABEiABEiABEiABEiABAwTUMP+m76PnQADgLEzosQXIKCGBej521BUmLBHQU//mOgXQButylKjvfDuo/Y+wv96V0PFglmkfOuFvvC//lT+nti0NDr9PxgUn3YmlrHcZx1A8MPX0tzf2lZA0wr55G/9CtDLujihbqlcVjW9AWtPY9MZ7bHvds4FMLV5OY2N64/fwtCN2jsCdQ13KpQFG76vZlVzoTEkQAIkQAIkQAIkQAIkQAIMAPIbiJ4AA4D8OhKEgBoCgA9fvofzFGXhjTNj3JHZVlt4Iz7h61ckXtOjKlzstBWJv5p3GGdvazPWprUsh9ZOBeLTxEQ1VptFvjh2TRswHdPYAd9UL6KZQ3h4hObIte79j1v6uqJc/sxWNcdFB65i6s6L0qbyBTJjcx9Xze9xW85jxdHrBu21y5EO3oNqW9VcaAwJkAAJkAAJkAAJkAAJkAADgPwGGADkN2BlBNQQALz19C1qTPNRkL8woQFsU6VIEG+4TPXGvRfv5dh/dKuMOiVzyt/6GW1z21VEk/J5E8TWxDBo71UnsSPwvjS1b51iGNygpOb3szehqDhRmf15dJgb8mbWFl2xhjkevPwInZf7S1PSpEyO8+M9YJM8GfQzQnXtzZ4+FU6McreGKdAGEiABEiABEiABEiABEiCBWAioYf/NjyB2AswAjJ0RJb4AATUsQFcevka9WQcU9K5M9kQKm+RfgGjsKuvM2I9rj99IwYUdK8GjTB75u8a0fbj19J38vaSzE9wdrOvIauyzjD+JUZsC8bffTTmg7vHZyw9eof6vBxXGXJrkgdQpbOLPQCNGevTqAypPVlYz9h5UC0Wzp9NUjX714ZNBLSmSJ0PwZE8kS5bMiFEoQgIkQAIkQAIkQAIkQAIkkJAE1LD/Tki+iWVsBgATi6eSmJ1qWIDO332BRnMOS8+JrKqrUxommCc9Zh/Exfvagg+z21RAs4raO+ucJu3F49cfpH36RS0SzHArHfjXPZfxm3ewtK6+Qy4s7uyk+X30ymO0X3pMvsuUNiUCxta3ypno+31e+4oonz9zlOxVfeMDx9VHhjQprXJONIoESIAESIAESIAESIAESEBLQA37b/o7dgIMAMbOiBJfgIAaFqDTN5/h69+PSnq2qWxwYYLHF6BpnMpm84/gzK3nUli/Km3ZsbsUGV8bernAqXBW45SrUGql73WM2Xxezly3MMbmM3fQf+0Z+a5YzvTYO7CWVVLSr/7cp44dyubLjF5/n1R8u29DtQVkxIuEqmhtlRBpFAmQAAmQAAmQAAmQAAlYMQE17L+tGL/VmMYAoNW4Ql2GqGEB8gt5graL/aRjs9imxOkxCZcF1naxL/xCtEUrxn9VGl2qFZb2FR+5Ax/DIuTvbf2qo0y+TOr6ME2Y7bazd9F39WnZQxyb3Tf4c2GMpYdCMGl7kHxXtWhWrO3pYoL2+BOduiMIiw6GyAHd7HNq/D5HJ7uxml02HL/+VPF9bO1bHWXz8/uIP09xJBIgARIgARIgARIgARIwj4Aa9t/mkVFXLwYA1eVvq5mtGhYg/QILuTKmxrER9RLMB12W++PA5Udy/OGe9viulp3m96ewcBQbuVNhm8hYE5lrbIYJHL36GO2XaI/5ZkyTAmfHNdAIT90ZhEUHtEE1UUxFFFWxxqafrZgnUxqUzpsJe4MeSHO7Vy+CTWfuKo6Ir/zGGTVL5LDGKdEmEiABEiABEiABEiABEiABHQJq2H/T4bETYAAwdkaU+AIE1LAA7bnwAD1WnpD0Cma1xcGhdf7H3nlARXF1cfwvXRSxYENsiIoVRVRsKNh77LGX2GOMvcYeS2JJNMlnjF1jb1Fjx4YdK4K9d1FsoIg0v3PHMOwsC+zCsjs7e+85nkT2zXv3/e4w7rtzSwbQ1G7KfqvPYd+VRKfO0Hol8H294sLF7z/FouykfZKJjo/2hUsOe+0mN8NRmhp9UGMMa0sLDN8YhC0XHotUetYogknNy8iSkqZ9ZLe3xtvIGFHfee09sPDIHdx68V782YKOFdGCu0TL0qasFBNgAkyACTABJsAEmAATUCVgDudvtnjqBNgBmDojHpEBBMzhAbTr8jN8u/aCSM/YdeC+X38R2y89FfUZUKcYRjdyF/5OzT+oGYSqnPuhHpyy2maA9ZUxpSZmA+sUw8iGJdF9+VlQBGiCjGpUEgPruMly4xT9WXrSPkTHxier394htTDhnxCcvf9GHDO1ZRl0q5aYQi7LzbFSTIAJMAEmwASYABNgAkyACcAczt9s5tQJsAMwdUY8IgMImMMDaOuFxxi2MUikV8Y5G3YNrpUBNLWbcvTmy9hw7pE4WDUq7dHryCRdX69MaYgstlbaTW6Go+LiP6PC1P2IiIqV7L6rd2GhXp5qx+Wf25ZHe6+CsqXU7LdjCHkSrlE/G0sLXJnaEAPXXABFtSbIsPolMLjulwhSFibABJgAE2ACTIAJMAEmwATkS8Aczt/ypS8fzdgBKB9bmJUm5vAAWhf4EGO3Bot2rVgoO7YNrGE0O0/aHoKVpx6I63esUggzW5cT/n77RQTqzQuQ6HZnRhNYWmQymr6msPCaMw8wfltIqqou71kZviXzpDrOWANGbgrCpvOJKcuqepQtkA3/flcL6mPknNZsLI68LhNgAkyACTABJsAEmAATkCMBczh/y5G73HRiB6DcLGIm+pjDA2jlyfuYtOOKaFFjd4JV7/baumJuajnUAAAgAElEQVQBzOtQQdAv+PE7NP/9uKgrRX3dnN7YTO7G9G2THL3jtgXjc2ID5SQTyr2j8rLj9zD136saQbT3csHPbT0wY/c1/KXSLVj1/kkfQb6aCTABJsAEmAATYAJMgAkwgYwkYA7n74zkp5S52QGoFEua2D7M4QH0V8AdzNh9XbQMdUylzqnGknkHbmLBwVvi8k3K5cP/OlcS/k4pq+3+PCV+5mBnheD/OtoaS19TWpfqPQ7ZcBExcZq9gGfG1UXebHay3dKpO6/QcfFpjfpNal4aPWsUxR+Hb2P2vhviGN+SubG8p/HuZ9nCZMWYABNgAkyACTABJsAEmIDMCJjD+VtmyGWpDjsAZWkW5StlDg+g3w/dwpz9N0Vj1i+dF4u7eRnNuP87chs/70104Pi558GyHpUFfY7deomuSwNF3XI72OLs+HpG09UUFz568yWo03JUjLSZRqZMwM0fv3QHlqu8i4yBx9T9GtXb0NcbVV1zYe2ZL5GOCWLslHa5smS9mAATYAJMgAkwASbABJiA3AiYw/lbbszlqA87AOVoFTPQyRweQHP338Bvh26L1mxWPj9+7+RpNOuqp3nWcMuFNb29BX32X3mOvqvPi7oVymmPgFG+RtPVVBc+d/81eq44K2kMksfBFoEm4EytPvMgnr6LSoL+8uQGyGZnjd3Bz4RGIAlS1CkLDo+oY6qmYr2ZABNgAkyACTABJsAEmIDZEDCH87fZGDMdG2UHYDrg8aVpJ2AOD6AkNfc8C2Be+y8194wh6g0rKhXOgS0Dqguq7Ah6isHrLopqlcibFfuH1jaGmia/5tWn4ei2LBBh7z8JexlQpxhGN3KX/b6+WXEWB6+/kOhZMGdmHBvlJ/zs5J0wdFp8Rvw8h701Lk5sIPt9sYJMgAkwASbABJgAE2ACTMDcCZjD+dvcbazN/tkBqA0lHqN3AubwAJq84wpWnLwvslPtuqt3oFpMuOX8YwzfFCSOLOOcDbsG1xL+vvHcI4zafFn8rLyLI3YMqqnFrDxEE4G3kdHYfukpnLLagmotZqI8YJnLnH038PvhxIhVUrdhmbxY1PVL2jo5NpssOCbughpE357eBBbcKVrmlmX1mAATYAJMgAkwASbABMydgDmcv83dxtrsnx2A2lDiMXonYA4PoLFbg0EdYhOkR/UimNyijN5ZajshNar4dm1iCmex3FlwcPiXFM5Vp+5j4vbEjsVViubExn7VtJ2axymAgPr9QVsaWq8Evq9XXNjds3cfUW3mIclOgyY2gKO9tQJ2z1tgAkyACTABJsAEmAATYALKJWAO52/lWk9/O2MHoP5Y8kw6EDCHB9CwjZew9cITkUo/H1eMbVJKB0r6HXrwWii+WXlOnLRA9sw4MeZLeqfcOhbrd+c8mzYE7r58D7+5RyVDqWkNNa8h+Rgdh1IT90o+PzKiDoo4ZdFmeh7DBJgAE2ACTIAJMAEmwASYgJEImMP520hoTWpZdgCalLmUo6w5PIAo2o6iqhLkOz83DG9Q0mhGPHE7DJ2XJNZwc8pqg3M/1Bf0me9/C7/4J3YsVk39NJrCvLBBCcTHf0aVGQfF2oVWFplwcowf8mSzE/T4/PkzSk7Yi+jYxC7H/3xbAxUKZjeonrwYE2ACTIAJMAEmwASYABNgAroRMIfzt25EzHM0OwDN0+5G37U5PID6rDqHA1dDRdYjGpTAIL8v6ZTGkPMPXqPNwlPi0lltrRAypaHw95/2XsfCI3fEz1pWcMb8rysaQ01e04gEqBnM6M2XER0Xj2H1S+BbXzeJNlVn+CM0/EtzE5IVPSujTsk8RtSYl2YCTIAJMAEmwASYABNgAkwgNQLmcP5OjQF/DrADkO8CoxAwhwcQdYINuPlS5DuuiTv6+hQzCm9aNOTJOzT77bi4PkV43Z7RRPj7lJ1XsPxEYsOSDl4F8VPb8kbTlRc2HoHI6FjExX+Gg13S2n4NfwnAjdAIUblfO1TAVxULGE9ZXpkJMAEmwASYABNgAkyACTCBVAmYw/k7VQg8gB2AfA8Yh4A5PIC+/usUTt99LQKe0qIMulcvYhzgAG6/eI9686Q13m5PbwwrSwuM3XoZ6wIfiboZu2GJ0SDxwikS6LDoFM7cS7ynJzcvjR41ijI1JsAEmAATYAJMgAkwASbABGRMwBzO3zLGLxvVOAJQNqYwL0XM4QHU6n8ncPHhW9GwM1uXQ8cqhYxm6EevI1Hr58OS9a9MaYgstlYYuuEStl1UaVhS2xVjGxuvYYnRIPHCKRLot/oc9l1JTGv/vm5xDK1fgqkxASbABJgAE2ACTIAJMAEmIGMC5nD+ljF+2ajGDkDZmMK8FDGHB1DTBcdw5Wm4aNh57T3Q2tPFaIZ+GfEJlaf7S9Y//0M95Mpqi/6rz2PvlefiZ0PqFceQeuzYMZqxZLrwmC2Xsf4sR4rK1DysFhNgAkyACTABJsAEmAAT0EjAHM7fbPrUCbADMHVGPCIDCJjDA4jSbSntNkH+6OSJpuXzZwBN7aaMiIpBucn7JYNPjPFDgeyZ0WN5II7cSKxXOKaxO/rXNl69Qu12xKMMTWDmnmtYdPSuuCw3izG0BXg9JsAEmAATYAJMgAkwASagOwFzOH/rTsX8rmAHoPnZXBY7NocHkM/Ph/HwdaTIe3E3L9Qvnddo/GPi4lF8/B7J+geH10ax3FmhXq+Qa7sZzUyyXvjPo3cwa891UcfaJXJjZa8qstaZlWMCTIAJMAEmwASYABNgAuZOwBzO3+ZuY232zw5AbSjxGL0TMIcHUNUZ/ggN/ySyW9WrCnxK5NY7S10mLDZut9DhNUF2Da6JMs6OaPnHCQQ9SqxX+FObcuhQ2Xj1CnXZE481HIH1gQ8xZmuwuKCHiyO2D6ppOAV4JSbABJgAE2ACTIAJMAEmwAR0JmAO52+doZjhBewANEOjy2HL5vAAqjB1P95Gxoi41/f1hrdrLqPiLzNxLz5Ex4k6bBlQHZUK50CjXwNw/XmE+PP5X1dAywoFjKorLy4/AntDnqP/3+dFxQrnssfRkb7yU5Q1YgJMgAkwASbABJgAE2ACTEAkYA7nbzZ36gTYAZg6Ix6RAQTM4QFUeuJeRKo427YNrI6KhXJkAE3tp6w07QBefYgWL1jbuyqquzmhzuzDuP8qMV35zy6V0KhsPu0n5pFmQeD03Vf4+q/T4l6z2Vnh8uSGZrF33iQTYAJMgAkwASbABJgAEzBVAuZw/jZV2xhSb3YAGpI2r2VWbyCSS7c15m1QY9YhPHn7UVRhWQ8v+LnnhXq6MtV1o/puLExAlcCN5xFo+GuABMqdGU1gaZGJQTEBJsAEmAATYAJMgAkwASYgUwLsAJSpYQysFjsADQycl/tCQOkPoNi4eLipNdzwH1YbbnmyGvUW8JtzBHfDPog6LOzsicbl8sNjyn68+5iYrryhrzeqGjld2aigeHGNBELDo1B1xkHJZxcm1EfOLDZMjAkwASbABJgAE2ACTIAJMAGZElD6+Vum2GWnFjsAZWcS81BI6Q+gyOhYlJ64T2LMY6N8UTCnvVEN3Hj+MVx7Fi7q8EsHD7Sq6IISP+xBdGy8+PMdg2qgvEt2o+rKi8uPQFRMHNwn7JUodmh4bbjmNq5jW36kWCMmwASYABNgAkyACTABJiAfAko/f8uHtLw1YQegvO2jWO2U/gB6GxmNClMPSOwXOK4u8mSzM6pNW/3vBC4+TOz2O7N1OXTwKgjXcbsleu0f6oMSeR2MqisvLk8CpSbsxceYpI1k5Kkta8UEmAATYAJMgAkwASbABJiA0s/fbGHtCLADUDtOPErPBJT+ANKUKhk0sQEc7a31TFK36Tr+dRqn7r4SL5rUvDS+rlwIpSZKo7oCRvqiUC7jRivqtjMebSgC1WcexNN3UeJyCXUkDbU+r8MEmAATYAJMgAkwASbABJiAbgSUfv7WjYb5jmYHoPna3qg7V/oD6NHrSNT6+bCE8bWpjZDZxtKo3HsuD8ThGy9FHUY3ckeHygXhOU0tWnF8XeRxMG60olFB8eLJElBPI5/bzgNtKrkwMSbABJgAE2ACTIAJMAEmwARkSkDp52+ZYpedWuwAlJ1JzEMhpT+Abr+IQL158uuWOuDv89gT8ly8yb6vW1xwAFafdUhy412e3ADZ7IwbrWgevwmmt8tOi0/j5J3EKNIJzUrjm5pFTW8jrDETYAJMgAkwASbABJgAEzATAko/f5uJGdO9TXYAphshT5AWAkp/AF15+g5NFxwX0VhZZMLtGU3Sgkqv1wzdcAnbLj4R5+xX21WoAeg396hknVvTG8Pa0kKva/NkyiAwcM157A5OdCJ/5+eG4Q1KKmNzvAsmwASYABNgAkyACTABJqBAAko/fyvQZBmyJXYAZghWnjQ1Akp/AF14+Aat/3dSxJDFxhJXpjZKDUuGfz5262WsC3wkrtOjehG083KROCstLTLhjgyclRkOgxdIE4Fx24Kx9sxD8dqu3oUx7auyaZqLL2ICTIAJMAEmwASYABNgAkwg4wko/fyd8QSVsQI7AJVhR5PbhdIfQKfuvELHxadFu+Swt8bFiQ2MbqfJO65gxcn7oh5fVy6Idl4F0Wah/JyVRofFCmgk8PPe6/jfkTviZ83K58fvnTyZFhNgAkyACTABJsAEmAATYAIyJaD087dMsctOLXYAys4k5qGQ0h9AR2++RPdlgaIx82Wzw+lxdY1u3Jl7rmHR0buiHq0qFkDbSi7ovOSM+DOnrDY490N9o+vKCsiTwOKAu5i++5qoXK3iTlj9TVV5KstaMQEmwASYABNgAkyACTABJgCln7/ZxNoRYAegdpx4lJ4JKP0BtP/Kc/RdfV6kViinPQJG+eqZou7T/XLgJuYfvCVe2LhsPsEB+M3Kc+LPCmTPjBNj/HSfnK8wCwIbzz3CqM2Xxb2WLZAN/35Xyyz2zptkAkyACTABJsAEmAATYAKmSEDp529TtIkxdGYHoDGo85qKfwPx7+WnGLT2omjp4nmy4sCw2ka3/MIjd/DT3uuiHr4lc6NtpYL4du0F8WduebLCXwa6Gh0WK6CRwIGroeizKtFh7JIjM46PZocx3y5MgAkwASbABJgAE2ACTECuBNgBKFfLGFYvdgAaljev9h8BpT+Atpx/jOGbgkR7l3HOhl2DjR8ltfzEPUzZeVXUq5prLrSp5IIRKrpyRBf/mqZE4Oz912j35ylxSFZbK4RMacjQmAATYAJMgAkwASbABJgAE5ApAaWfv2WKXXZqsQNQdiYxD4WU/gBaF/gQY7cGi8b0LJQdWwfWMLpx1fWqWCg72ni64Id/QkTdvArnwOYB1Y2uKysgTwK3QiNQ/5cAiXK3pjeGtaWFPBVmrZgAE2ACTIAJMAEmwASYgJkTUPr528zNq/X22QGoNSoeqE8CSn8ArThxD5NVIu28XXNifd9q+kSYprm2XXyMoRsSIxNL5c+GNp4F8OMubuqQJqBmeNHLiE+oPN1fsvOz4+sht4OtGdLgLTMBJsAEmAATYAJMwPwIXHj4BlRb/O7LD0I20ZC6xWFhkcn8QJjQjpV+/jYhUxhVVXYAGhW/+S6u9AeQeqdUnxK5sapXFaMbfE/wMwxYk1jvz9UpC1p7FsCc/TdF3eqVyosl3b2MrisrIE8C0bHxKPHDHoly/sN84JbHQZ4Ks1ZMgAkwASbABJgAEzAhAnHxn3HtWTiCHr8VtHbOnhnUpI/+S6VXtJWYuHgcuv4CoeFRaFouP3JlTf/L2hcRUfhpzw1sufBYosbAOsUwqpG7tqrxOCMQUPr52whITXJJdgCapNlMX2mlP4D+OHwbs/fdUHGq5cGS7pWNbrhD10PRa0ViAwdnRzu09nTB74dvi7o1K58fv3fyNLqurIB8CZSdtA/vP8WKCm7qXw2Vi+SUr8KsGRNgAkyACTABJsAEZEogweF3+u4r0J8z914jIirxe5aq2tnsrARHYNWiOdGrZlEUzpVF465O3XmFCdtDcPvFe+Fzx8zWWNjZE9XdnNJEgV4Arzh5DwsO3pZ8B1SdbHqrsuhctXCa5ueLMp6A0s/fGU9QGSuwA1AZdjS5XSj9AfSr/0386n9LtEvjsvmwsEslo9vp5O0wdFpyRtQjZxYbtKpYAEuP3xN/1q6SC2a38zC6rqyAfAnUmHUIT95+FBVc3M0L9Uvnla/CrBkTYAJMgAkwASbABGRGgBx/a848wIKDtxD2Plpn7SjjtrmHMwbWcUPJfF8yMV6ER2H67mvYfulpkvksLTJhcvPS6FqtiNZrPX4TCXImLjx6R0j3TUlIn6XdK8PXPY/W8/NAwxFQ+vnbcCRNeyV2AJq2/UxWe6U/gGbvu44/Dt8R7dPCwxkLOlY0ur3OP3iDNgtPinpksbHEVxULYM2Zh+LPunoXxrSvyhpdV1ZAvgSa/XYMIU/CRQV/blse7b0Kyldh1owJMAEmwASYABNgAmkkQKm0B66GCpFvzo6UimsnROHZWVumcUbgytN3GLc1GEGP36V5DtULqYSPh4sjFgXcTTZCL2F8F+9CmNS8TJIGbp8/f8a9sA9CBGLgf39UX/iqK5rZ2hIfY+IkP7a3scSGvtVQzsVRL/viSfRHQOnnb/2RUvZM7ABUtn1luzulP4Bm7L6GvwLuivzbVnLBHBlE1dGXjaYLjot60ZvAryoUkNTx6OvjinFNSsn23mHFjE+gy5IzOH47TFRkfJNS6OPjanzFWAMmwASYABNgAkyACaRAgJxcN0PfC99jKFquYdl88CyUI9krIqJi0H1ZIC48/FKPT1WcstoIjkC6vkf1IijipDkdV/WaD59iQZlCy07cB0UApiSFc9kLqbtP335MU4RgSnNXc82F3ztVxIuIT6Kzjxx/Ye8/aXX/0NlmVKOSWHXygaSUEF1MjeG2DawOlxz2Ws3FgwxDQOnnb8NQNP1V2AGoYsOHDx9iwYIF2LVrF+j/bW1t4ebmhvbt22PgwIGwt0//Q+zBgwdYuHAhDhw4gDt37uDDhw9wcHCAu7s7GjdujP79+yN37typ3ln//vsvVqxYgdOnT+Ply5fCHMWLF0fbtm0xYMCAVHXNlEm7Lk21a9fGkSNHUtVH1wFKfwBN3nEFK07eF7F0rFIIM1uX0xWT3sffefkedecelczbsExe7LsSKv5ssJ8bhjUoqfe1eULlEBi09gL+vfxM3BAXflaObXknTIAJMAEmwASURoAcfeTwO34r7IvjLyLRyUVHoonNSqNnjaJJtk3Oum7LAkEZNKkJpb/SS/Vv/dxQLHdWjXMdvvECM3dfl5RRUR1Y1CkLvF1zwts1F6oWzYV8jnbix1ExcXj2LkpwBh658ULI3omMlkbfqS9K9QJHNiyJ5+FRksykhHGkcyo+yCT7oCjDyS3KoOJ/TlNyqA7bGIRtF59IxrrlyYot/avD0d46NXT8uYEIKP38bSCMJr8MOwD/MyE5/Tp37ox37zSHYZcsWRK7d++Gq2vao1zWrl2LPn36IDIyMtkbJ1euXNi4cSP8/Pw0jomIiBD03LlzZ7JzkCNwx44dglMxOWEHYMb+7o7fFixJq+1erTCmtDR+Wi2F8VP9NlWpUjSn8OYvQeiLwre+bhkLiGc3aQI//BOMv08npo13qloIM1oZ38Ft0lBZeSbABJgAE2ACTEAvBD5Gx+HMvVc4Rg6/W2G4ERqR6rx9ahXF2MalYEFeMQA0R4/lgUI6rC5CDsVm5Z3Rv7ar0Mjj5J1XOHUnDBcfvkVsMt42V6cs+LFVWVQvpn2DjjcforHy1H0sP3Ef7z7GJFGxjacLxjZxh9N/nX+3X3qCUZsv41NsvC7bEcbmsLcWmr01LZ8fzcs7i4wSJqIGIRQleeruK8nc5Mxc2asKbK3Sniqts7J8QbIE2AHINwcRYAcggKCgIFSvXl1wzGXNmhVjx46Fr68vPn78iPXr12Px4sXC3UIOtbNnzwpjdJVTp06hVq1aiIuLg4WFBbp3746WLVvC2dlZiDZcuXKl6NTLkiULQkJCUKSItEArvWFp2rQp9uzZIyxfqVIlDB06VNCLHIPkxPztt9/w6dMn4dpz586BHIqaJMEBSNGCFN2YnJAuRYsmfSOm6/7Vxyv9ATRqcxA2nnssbrt3zaL4oVnp9GJL9/Wv3n9CpR/9JfOUzOsg+WJEb0GpqxgLE0iOwNz9N/DbocTO0U3K5cP/Ohu/yQ1bjAkwASbABJgAEzBPAlSfb83pBzhy46UQsRcdp7ujq1n5/Jjb3gOfPwO9V56TlDshqlQ728rSQqPDLS3UbSwtMNC3GAbUKZZmJxnte92Zh1hy/C5Cwz+hbIFsQn0/ctipS9Cjt+iz6pwkAlKT3nkcbFHVNRcoSIC6DbvlzprE6ad+HTkh2y48iVv/dR1O+LxlBWf82qECtA0+SQtHvkY7Ako/f2tHgUexAxAQnH2U5mplZYWAgABUq1ZNcmfMnj0bo0aNEn42ZcoUTJw4Uec7p3nz5qC0XZI//vhDo9Nt+PDhmDdvnjDmu+++E9KRVWXz5s1o166d8KP69esL89nY2EjG+Pv7o1GjRoKjUdMcCYMTHsKTJk3C5MmTdd5Pei9Q+gNo6IZLklB4+od9dKPkIzLTy1Pb6ymVocykfZLhebPZCl8YEoQiuSiii4UJJEdgybG7+HHXNfHj6sVyYW0fbwbGBJgAE2ACTIAJmAgBcpKtPHlfSDMdULsYcmSRnilMZBuCmpTh0v7PU8mm1mrai/r334Qx5PSi5hZHb76UXJYziw3W9/VGibwOQpONZ28/CutRhN/fpx+kmo6rrgNFx01vVU5junBa2FOgyNvImFTtGBoehb6rzkmajxTMmRlVilDacU7B6Ue1B9PisKOOwa3+dxIvVVKsaS/f+hbDyIbGPwelhauSrlH6+VtJtsrIvZi9A5Ai+qpUqSIw7tevH/78888kvOPj41G2bFlcu3YNOXLkQGhoKKytdatnkDNnTrx580aIyAsLSyyer7oYpR9nz55d+BFF91EEn6qoOhFv3rwp1PzTJF26dMGaNWsE5+Dz588FndWFHYAZ+WsFfLv2Anap1EgbXLc4htUvkbGLajF7bFw83MZ/iSBNEBsrC1DofoL80sEDrSq6aDEbDzFXAlvOP8bwTUHi9kvlz4Y939cyVxy8bybABJgAE2ACJkXgxvMINP/9uPj9r1juLFjT21tSc85UNkRNK8j5dzfsQ4oqO9hawbtYLtQq7oSabk6gentbLzzB6C2Xk03NTZiQGnGs6+ON0s7ZNK7x+kM0lh6/i5UnH6TYgdfKIhMqFsqOzlULgyLj0uJk04dd6Dxw+MZLUHfjCgWzC41M9CUhT96h/aJTSRyiHGCgL8Jpn4cdgGlnp6Qrzd4BOH78eMyYMUOwKTXUqFq1qkb7zpo1S0gNJtm/f78QgaeLUNowNfzw8vIS0oiTE2oAQg5CcjgGBwdLhuXLl09wPlJjklu3biU7x99//42uXbsKn1OjEEo3Vhd2AOpiPd3H0pu1/VcTG2uMaFACg/w0O2x1nz19V7iN253iF52FnT3RuFz+9C3CVyuawMFrofhmZeILCmdHO5wcW1fRe+bNMQEmwASYABNQAgHqPNv2z5NCTTpVKZTTHmt6V0XBnOlvemgoTpR22vGv07j6LDzJkpbkbCuYHTWLOwlOPw+X7EL6rrocu/USA/6+kKzjzsHOCmt7e6Oci2Oq23obGS10911+4p5Q/4/qAZZxzibU9qNsCUrLzWJrleo8pj6Amp1QCrVql2Oyx5LuXvAtmcfUt2ey+rMD0GRNp1fFzd4B6OPjg2PHjoFq3b19+1ZIA9YkVMOP6gSSUAowpQLrIp6enrh48WKKEYDh4eFwdPzyj0vr1q2xZcsWyRIU0RcTE4MaNWrg+PHjyS5PHYYbNGggfN6zZ08sW7YsyVh2AOpiPd3HUtFgqkGSIGMbu6Nf7WK6T5QBV5SdtC/Ft5PLe1SGrzv/45wB6BUzJaUNtVl4UtyPvY0lrk5tpJj98UaYABNgAkyACSiVAKX9TtpxReP28jvaCU5AVw1dbPXBgzJOrjx9B5cc9sjtYJuuKalJR7dlZ3D2vrRDb/E8WTGqkbvQTdfBTruMLdKp5/KzSWrjZbW1wupvqogdb7VVODI6FjdD36NwTvtUU3K1ndPUxq098xDjtkmDWej74sZ+1VC2QOrOVFPbrynoyw5AU7BSxuto9g7AhIg7Dw8PXLp0KVnilL5LabwkVIePOvXqIosWLUL//v2FSxYuXCj+v+ocI0eOxJw5c4Qf7du3T3TiJYyhVF5yUmqKDlSdR7VWIEU0UmSjuiQ4AEuXLo3Y2FihEQk5PynKkBydPXr0EGojZpQo/QHUeclpnLid2AlLTo01vH48gLD30cmallIcqhXT3Dwmo+4Hnte0CNx5+R515x6VKH3jx0ZpLmBtWrtnbZkAE2ACTIAJmCaBp28/ov68o/gQHZfsBqhrLDkBS+Zz0OsmI6Ji0GnxGQQ/eQdqtEtdaqlETloiDsmR2Hf1OcnLdlKWatlt7l8debPZ6aw71fOjTra3/2tiQc4q6mCrqZmGzpOb6QWz913HH4fvSHZPjt/F3byE1GMWwxJQ+vnbsDRNdzWzdgBGRUUhc+YvNQ+ou25Ck47kzJmQxuvt7Q2KCNRFqCkHpeJSbT7qAtyrVy+0aNEC+fPnF5xvlLa7bds2YcrRo0eDUo7VhRxztK6lpSWePXsGcl5qksGDBwvdgElo/qdPnyYZpk3Nia+++kpIIU6IStRlv/SASUlI/4Tai48ePYKLi7JqzlEtksD7r0UE074qi67ehXVBmGFja8w6lGKR5G0Dq+v8pjPDlOWJZUmAat14Tjsg0S1wXF3kScMXbllukJViAkyACTABJqAwAtQkgtIyD15/IdkZpf4+fB0p+Vl2e2v81fWLk4ZqRetDxm69jHWBjyRTWVtmQofKBSQRfogAACAASURBVDHIt7jW9QcprfT79Rfxr0qtbZqUHEub+1dD4VxZ0qxueFQMlh+/D6or2K1aYRTPq18naJoVM9EL6Z4bsuEStl9KehatXCQHetUoivql80pSs8m+156F48y910KjFb9SeYQUapb0E2AHYPoZKmEGs3YAvnz5EnnyfEl17NChA9avX5+iTfPmzYsXL16kGoGX0iQUOUjOPUoHVheKuBszZkySyL+EcT/99JPwOUnfvn1BUYXqQrUBKd34/fv3wkfktIyIiEgyjlKeyQFZt25duLu7C+OIx9GjR4VGKK9efYleq127NiilWNemJ9o4GBOUUqID8Ks/TuDSo8TaKj+1KYcOleXRWbfu3CO48zL5QsnUzIGaOrAwgeQIaGoms2+Ij96jBdgCTIAJMAEmwASYgHYEqB7ezN3X8OhNJBqVzY+vKxeEtUrNu38vP8WgtdLzRwevghjbxB3dlgXi8uN3SRaiGnYUEUhNIqjeb37HzHDObif8ndKF6b+5s9rCgkL6UpDjt8LQZemZZEeQk7FL1cIYUKdYiqnB5FAa/08IKL1UVahJx4Z+3nDPx99ftbtbDDfqU2wcui0NFBx6mqRA9syCs5WExpy9/1qon5ggdGtRdlJVV85OSq/V2AGYXoLKuN6sHYDkeCpU6ItThppmrFq1KkWr0li6plixYrh9+7bOd8D169cFB96uXbuEtFt1sbOzQ5s2bTB79mwhck9dqEZgmTJlkBBdR91+KW2YHHjk5Nu9ezdGjRoldP6leoHR0dFCtKCmtSiVOKHjsPo61GikcePGopNy/vz5oKhCXcTcHYBNFxzDlaeJBYnntfdAa095RDmq66Zu1yMj6qCIU9rfnupyn/BY0yVQfvI+hKt8QdvQl7+cma41WXMmwASYABMwZQLkGOu6NBDHb4eJ2yiSyx4jG7qjSbl8CP8Yi7rzjgqRbQlCjr2Dw2rD0d4aFPnWa/lZnHsgraenDROK4iuR1wGDfN00NpF7/ykWDX8JSDH7JGEdcuT91bVSss6en/Zex8Ij0pRSStX9u3dVeBbKoY26PMYIBN5FxqDDX6dw/XnSoBRt1KlSJCc29q+mzVAekwIBdgDy7UEEzNoBaMgIQGo0QhF35HgrXLgwfvzxR6GTMNUVJIfbjh078MMPP4BqDVI6LHUaLlWqVJK7lCIHmzRpIjj5kpOBAwciICAAISEhgpOP5tRV7t69K6xPTsTUug5rmtvcU4Ab/HJUKP6bIL91rIjmHs66miFDxlPzBmrikJycHltX6zSMDFGQJzUJAj4/H5akDP3ZpRIalc1nErqzkkyACTABJsAENBH48CkW/tdCQRFrFJU2pF6JdDerMATpfy4+EVItNYmHi6MQxaee+vt7p4poVj7xuyk1ruiz6pykhrWuuo9qVBIDaheDaiDAD/8E4+/T0og9nxK5cfruK1AtP3Uh7gu+rpjkO8WfR+9g1p7rkuE2lhZY1qOy0OmXRd4E6P5afuI+Vp26j9DwREe0tlpTerdXkS/1+FnSRoAdgGnjprSrzNoBaKgagJ8+fRKiBp88eSI02SAnHv1XXa5cuQIvLy+QXvTfs2fParzfKA155syZ2LBhg1ALMEHKlSsnRARSNKOTk5OQxlu0aFGQMy8t0qxZMyFakYR0d3bWnwNL6Q8g3zlHcC8sMc1WTs4R9QYl6vdG0MQGwttgFiaQEoGWvx9HkEq6kJzS3NlyTIAJMAEmwAS0JRAVE4cjN15gZ9AzHLweiqiYRKeUS47MoNIo2naT1XZNfY6j6Kq6846k2OBNfb267nmwpLuXxFFHY4jFLwduYl3gQ0mUvy769vVxxdjG7sLcJ2+HodMSaeovdedd29tb6Lj7x+HbWH/2IWLiPkuWoLRPqp/dueqX1FDSZ+xWaUdZGvO/zp5CujOL6RAgp++ekGdYevyexrTzhJ1YWmQC1QNMED/3PIKzlyXtBJR+/k47GfO60qwdgGRqQ3QB3r59O6ihBsn06dMxbty4ZO+yPn36YMmSJcLn1JWYuhOnJOQMpKhC2gd1CSYhp2CCs06b5ibJzU/pxJSOTBIYGIjKlfX30FX6A0i90cayHl7wc88ri6dLrxVncUitALSqYtenNYKdtaUsdGUl5EuA6gUF3HwpKjimsTv61y4mX4VZMybABJgAE2AC/xEgJ8SJ22HYGfQU+6+GgtJUkxOqpTerTXnZshu3LThJTbyUlM1iY4kDw2oL9fuSE0opprThp+8+4tm7j3jyNkpoyPDsXZSQyks/e/4uKonjLmG+9l4u+KFZaVDZmUevP4rLZLa2xN4htSSNOh69jsSkHVc0fjcdWq8EiuXJgu/WXcRnqY8QP7cpj/aVC8rWLqxYygToHrvw8A2WnbiP4MfvhIjbKkVzoqrwJxeoZuWPu65JJuE65em7q5R+/k4fHfO52uwdgD4+PqD0XGqKQY40Kysrjdan7rvUhZdk4sSJmDJlitZ3CTX9GDt2rDB+z549aNSoUbLXUgOOAQMGCJ9TUxJqTqKrUKORhOumTp2KCRMm6DqFMJ6iCefMmSP8PzsAdUNYZbq/8GYzQVZ/UwW1imvu2qzbzOkfPXDNeewO1pxCTsWe785okuSNcPpX5RmURoA68Kl2dSPnHzkBWZgAE2ACTIAJyJEARROdufsKOy8/xZ6Q53gbGaO1mit6Vkadkl8aB8pJqKQLlXZRFeqq2rpiAfy874YkGyVhzJQWZdC9epF0byM+/rNQU/CfS08wY7c0NZcmz+NgK/kuTD+b1Lw0etYommRtai42YXtIki7BySn5Q9NS6F3LNd174AnkS4DS8Wv+dAhvVH5PW3g4Y0HHivJVWuaasQNQ5gYykHpm7wCkaDxKpyU5ffo0qlatqhG9qhNv3759yXbq1XQxOdHImUayc+dOUGptcvLbb7+JDTc2b94sNAXRVSjakKIOSa5duyY0CUmLUPQgNRYhoQdGgQIF0jKNxmuU/gCqOHW/5B+s9X294S2T7lXDNlzC1otPNNqF3sxem5a8g1pvNwBPZPIEJm0PwcpTD8R9yD1CwuSB8waYABNgAmZKgLqIvoz4JNTis7XSLUOBnFQUZUSRfruCn0uaYCSH08oiEywyZUJ0XGIqcL5sdtg31AfUpEIuQk6zZr8dlzRWoIYYFN1HnVVj4uKx4ewj/Op/S9x3TTcnrOxVBZReqU/Zcv4xRm25LEnZVJ+fGjnQ9+HkOgZTRBilHy84lHKjxe/83DC8QUl9qs9zyZTAfP9b+MX/pqgd3baHR9SRRJDKVHVZqqX087csoctQKbN3AFJkW4LTr1+/fqAIPHWJj49H2bJlBWcaNdWgtFtra+2/AGzZsgVt27YVpqW02p9++inZW4HG0XiS8+fPw9PTU6fb5syZM6hWrRroH9G6devC399fp+sTBlPdQHIcxsTEwNXVFXfuSDtupWlSlYuU/gAqO2mfJJ1ky4DqqFRYHt3JqIYK1VLRJDnsrXFxYoP0mpevNwMC9CV9/sFb4k4blsmLRV29zGDnvEUmwASYABMwFIEdQU8xfOMlIdWUshRyZ7VFgRyZhfRVl+yZhf8nZxf9nf4/m5218B045Em4EOn3b9BTPH0Xlaq6NLd30VxCwzZqaEU1ysZvC5Fc18bTBXPbp1yaJ9WF9DhgybG7SVIkxzcphT4+0si4hMYmFAHZpFz+DCvzcuBqKL5de0FjYw9bKwvsHeKDok5ZUiWw+vQDTNwekiTlly7sVq0wKIJRtclIqhPyAJMl8DYyGlRW6UN0nLiHTlULYUarcia7J2MqrvTztzHZmtLaZu8AJGMlpAFT+i91zyUHmqpQHTxy3JFMmjQJkydPlnx+5MgR+Pr6Cj/r3r07VqxYIfmcUospei4yMhIODg44ceIEqGGHulB6MEUHksORxj98+BAWFhaSYfSzQoUKabzHbt++LehBv9w2NjZCs5HSpUsnGUtRiI0bN0423Zm6EtPndD3J3LlzMWzYML3e10p/AJX4YY/kC9DOQTVRzsVRrwzTOtmUnVeELlyaxNnRDifH1k3r1HydGRFYfeo+Jmy/Iu64bIFs+Pe7WmZEgLfKBJgAE2ACGUng6duPoKZqnzR0ik1uXQc7K1AUnLZdRunlbPPy+QXHWJ5sduK05ESkWrfHboVJllrczQuUYmtsoTp89ecdRaSKY8Q9nwN2flcT1pbSs4MhdaXOvr1XnktSU1HXlN09wc/w/fpLkijMryo4Y177CslGEBpyn7yW4QhM33UVi4/dExekzs/HR/tKfl8Np41pr6T087dpW8dw2rMDEBAcXTVq1MDHjx+RNWtWoUkHOdLo71SH76+//hIsUqJECZw7d05w4qlKag5AGjtt2jShdiAJrfHdd9+hfv36QuMOcrhRyu7ixYsRG/ulCPHq1avRpUuXJHcCOQgfPHiAbt26CZ2CEyISKS150aJFgpOR3ootXboUPXv21HgnFSlSRIjso/RicnbS3zNnzoywsDDQXigKkjoIk9SsWVOIIrS1tdXrXankBxB9aXQdt1vy5pIKHrvny6ZXhmmd7Ke917HwiOaITlenLDg0ok5ap+brzIiAemc/O2sLXJ3SiL+Ym9E9wFtlAkyACWQkgeEbg7DlwmO9L0EvrJqXd0bT8vnhksM+2fnJAdnwlwBEqDQIoTTk/UN8kCOLjd710mXCvqvOCc1LVEUu2SYhT96h+7JAvPoQLahHXX/X9PbWOe2YnImjt1zGg1eRoKgvivwzpnNTF/vwWP0RCA2PQq2fDkucwf2o03STUvpbxExmUvL520xMqJdtsgPwP4wUFUcOt/DwcI1gyfm3a9cuuLm5JflcGwcgOYUoim7+/PlCakJyQqnFM2bMwIgRIzQOIQcg6ZGc5MyZE7///js6duyY7Bhy+JETMTUhByF1JCYno75FyQ8gqsniNn6PBNnB4bVRLHdWfWNM03zq9TRUJymVPxuowxYLE0iNANVjqjxdWmIgYKQvCuVK/jCV2pz8ORNgAkyACTABInD1aTia/nZMYxpoWgi55ckKaiDQrHx+uOrwfWzjuUcYtfmyZMmWFZwx/2vjNSKgVNs+q85JdOpYpRBmtpZPWuSL8CisOfNQiMakhiN21rrVbkzYHNVwpFOTvmsWpuUe4muMR2Ds1suSBjHUyfrkmLpwtNe+JJfxtJfPyko+f8uHsvw1YQegio3IKUYOOnKwJaTRksOvXbt2GDRoEOztNR9stXEAJixDdf3IqXb8+HHBCUcRexQRSOvUrl0bVIeQnI3JCaUPb926Vehc/OjRIyFSjxx0xYoVQ4sWLdC7d2/kzp1yt9mjR4+C/lBnY6r1R5F/5PgkPQoWLCh0O6ZUZvVUaH3ezkp+AH2MjkOpiXsluI6N8kXBnPJwjCw6egcz9yTt1kYKexbKjq0Da+jT1DyXQgnQi4yK0w5Iuigu6eaFejJIjVIoct4WE2ACTMBsCHRdekaSfksH/hW9quBdZAwo/ZWi8x6//Ygnb778/4uIT0nYFMppj+Ye+YW6fiXzOqSpbhz9W9drxVkcvvFSMv/8ryugZQX9NcfT1rCR0bGoPy9AYJAgubLY4NDwOuwM0RYijzM5AvfDPsBv7hHEq8TQDK9fAt/VLW5yezGmwko+fxuTq6mtzQ5AU7OYQvRV8gPo3ccYeEzZL7HU6bF1kc8xsbaMMc244sQ9TN55VaMK1Yvlwto+3sZUj9c2IQLt/zyFwPuvRY1HN3LHgDrFTGgHrCoTYAJMgAnIjcDRmy+FFFJVSe2wT52Cn72NEpyBlHpaJFcWUKqvPppFUAoi1dsLj/pSpoeEOgX/r7MnGpTJZ1B8M3dfw6KAu5I1f+nggVYVXQyqBy/GBAxNYNDaC/j38jNx2ZxZbEABFllsrQytismup+Tzt8kaxQiKswPQCNB5SQgRlhRtSEKRjC4uyvniEvb+E7x+lKZGnv+hHnJl1W8dxbTeR+sDH2LM1mCNl9d1z4OlPSqndWq+zswIjNsWjLVnEjtKt65YAPM6VDAzCrxdJsAEmAAT0BcB6lTbdMExXH8eIU6ZN5stjozwRWabtKWR6kO3bRcfY+iGIMlU1paZsLBzJYNFvl9/Ho6mC46DGCVINVd6cVtVL45OfXDiOZhARhG48vSdcP+ryuC6xTGsfvKZcxmli6nOq+Tzt6naxBh6swPQGNR5TUU7AJ+9+4hqMw9JrBw8uQEc7ORRp+Kfi08wZMMljXdh03L58UdnT75DmYBWBNSjScsVcBQ6ELIwASbABJgAE0gLAU01935uUx7tK395aWwsoVTgH/4JEeraqQp1JP2zqyf83DO2MzDVwmu36BTOP3gjLk9r7xlSSzY1po1lG17XfAhQOv6h6y/EDdtaWQjNCwtkz2w+ENKxU3YApgOegi5lB6CCjGlKW1HyA+jhq0j4zD4sMcf1aY3SXABZ33bdG/IM/f++oHHa1p4FMK89R3Dpm7lS5ztxOwydl5wRt5fZ2hJXpjTkTsBKNTjviwkwASaQgQSohnKdOYcRGp5Yz889nwN2Da4liyYQ5IQbuzUYG849SuIEXNStEnxL5skwOusCHwprq8pgPzcMa1Ayw9bkiZmA3AjceB6BxvMDJLUAjd2UR26MUtJHyedvU7KDsXVlB6CxLWCm6yv5AXT7xXvUm3dUYtk7M5rI4ssrKXX4xgv0XH5W453XuWohTG8lny5yZvrrYTLbfhERhSrTD0r05U7AJmM+VpQJMAEmICsCvx+6hTn7b0p0WtGzMupkoGNNVwDkBBy95TI2nX8sdQJaWeCvrpUyRFcqLVN37lFQjekEKZzLHvuG+Mjm5bKuHHk8E0grgQn/hGD16QeSy7cMqI5KhXOkdUqzuU7J52+zMaIeNsoOQD1A5Cl0J6DkB9C1Z+FoPP+YCMXSIhPIASgXOXXnFTouPq1RnW9qFsWEZqXloirrIXMCmjoBL+3uhbqlMjYVSuZYWD0mwASYABPQkcDLiE+oM/swPkTHiVfWdHPC6m+qyK6+HTkBR26+jC0XkjoB1/Xx1rsjYtjGS9h64YmE6KpeVeBTIreOlHk4EzB9Aq8/RKP27MOIUGnKU6FgdmwdUJ0zUFIxr5LP36Z/ZxtuB+wANBxrXkmFgJIfQJcfv0WL30+Iu7WztsD1aY1lY/+LD9+g1f9OatTnW99iGNnQXTa6siLyJ9Duz5M4ez+xJhF3Apa/zVhDJsAEmICcCNCBvs+qc5L6dpkyAf9+VxNlnB3lpKqoCzXiGLkpCFsvSh1zhXLaY/f3tZBVT51JT94JQ6fFiaU2SIHmHs74rWNFWXJhpZiAIQgsOXYXP+66Jlnq1w4V8FXFAoZY3mTXUPL522SNYgTF2QFoBOi8pLK7AJ9/8BptFp4SzexgZ4XgyQ1lY3b1CEVVxUY0KIFBfsVloysrIn8CSToBcx1J+RuNNWQCTIAJyITA/bAP6LniLO6FfZBoZAo1ickJOHzjJfxz6alE968rF8SsNuXTTTgqJg5NFhzD3ZeJbBxsrXBweG3kyWaX7vl5AiZgqgSiY+PR8NcAyXMjXzY7HBpRG/Y2Vqa6rQzXmx2AGY7YJBZgB6BJmEl5Sir5AaSeYpsriw3OT6gvGyPeffkefnOlNQoTlPuhaSn0ruUqG11ZEfkTWH7iHqbsvCoqyp2A5W8z1pAJMAEmIAcC1NGWIv8oAlBVsttbY8/3tZDfUf6dPWPj4vH1X6dxTqU7L+1lcTcv1C+dtBzGu8gYrDp1HxGfYtHVuzAK5rTXaAoqsTF8Y9IIw6kty6BbtSJyMB/rwASMSsD/aih6rzon0WFIveIYUq+EUfWS8+JKPn/LmbvcdGMHoNwsYib6KPkBdPTmS3RfFihakt5InR5XVzaWffr2I6rPOqRRnx+/Kosu3oVloysrIn8C3AlY/jZiDZkAE2ACciOwJ/gZhmy4hE+x8RLV8jvaYXnPynDPl01uKierz8NXkUJnUtX6hfTyd99QHzhltRWvu/o0HP3+PodHrz8KP3PMbA2q5edRMHuSuRcH3MX03dIURw8XR2wdWEM2TeVMxkCsqCIJkJO869JAHL8dJu6Pyi4dHlEHhnp58Oh1JJaduIexjUvBxspC9pyVfP6WPXwZKcgOQBkZw5xUUfIDSP2NVMGcmXFslJ9szEtv2j2nHdCoz5x2HmhbyUU2urIi8ifwIjwKVWZIOwEfG+WbbFSD/HfEGjIBJsAEmEBGEqD6XeTc+vxZukqp/NmwvEdl5HM0vfTWDWcfYvSWYMmG6pXKi8XdKglNTHYEPcWozUGIipE6PCmld0WvyqhUOKd47ZEbL9BrxVnEq/DJYmOJbd/WQIm8DhlpGp6bCZgUgevPw9Fk/jHJ70qx3FnQo3oRtKxYANnsrDNkPxTJ+/vhW1h58gGi4+IxuXlp9KhRNEPW0uekSj5/65OT0ucyuAPw0aNH6N69u/CP4erVq+Hs7Jwi4ydPnqBbt27CmHXr1iFPnjxKt4lZ7E/JDyB6qz1gzQXRjq65s+DQ8DqysWtkdCxKT9ynUZ/fO1VEs/Ip/07KZiOsiCwI0BvYClMP4N3HGFEf7gQsC9OwEkyACTAB2RHYG/Ic/f8+n0Sv2iVy44/OnnprnmHojdO/hX1WnYf/tVDJ0tNblQVFCC4KuJusSvY2lljavTKqFcuFOy/f46s/Tkg6nNKFf3WthAZl8hl6W7weE5A9gfHbgrHmzMMkelI0YPPyzuhYtRAqFsyul27iVHtw9ekHWHDwluR7bw57axwZ6StE9cpZlHz+ljN3uelmcAfgvHnzMGLECNSsWRMBAQFa8ahduzaOHz+O+fPnY9CgQVpdw4PkTUDJD6Dtl57g+/WXRAO453PA3iE+sjFIfPxnuI7brVEfdtzIxkwmpYh6J+Axjd3Rv3Yxk9oDK8sEmAATYAIZSyAmLh715x3F/VeRkoU6VimIqS3LwtpS/il0KREKe/8JjX4NQNh7aU1DbajaWllgXvsKmLv/Bu6qNUQZXr8EvqvLDdq04chjzI/Aq/ef4DvnCMKjYpPdfMm8DqDnTKuKLnC0191JRw7+3cHP8dPe63j4Wvr8Slj0W99iGNnQXdYGUPL5W9bgZaacwR2Afn5+OHr0KObMmYOhQ4dqhYMcfzS2Xr162L9/v1bX8CB5E1DyA2jz+ccYsSlINIAcmyKUGL9HCFlXlzW9q6KGm5O8bx7WTnYExm4NxrrAxLevptC9UXYQWSEmwASYgMIJrDnzAOO3hUh2OYycW35ueonOkQM+TY0J1PUiR8SL8E84eP1Fqio3LZcflJ1BmVMsTIAJaCYQ8uQdBq65kKxzLuEqcrQ3LZ8fnaoUQqXCObT6vaLmiXSuu/DwrcbFrS0zoat3EeE5liOLjaxNpOTzt6zBy0w5gzsAXV1d8eDBAxw6dAgU2aeNkMPQ19cXxYoVw61bt7S5hMfInICSH0DkCCGHSIJ4FsouFG2Wk5SbvC9Jegnpt2VAdeEfRBYmoAsB9U7A5V0csWNQTV2m4LFMgAkwASagYAJRMXGoPfswQsM/ibuk5hf/DKyu1SHclNCM3XoZ6wIfJVGZHAVTWpRFp6qFQKmEg9ddxN4rz5PdWhnnbNjUvxrsbaxMafusKxMwCoG4+M84dusl1gc+woFroaC/pyRuebKiZ40i6Fi5ECwsNDvYgx69RY/lgXgTmVjmRnVOctCPalQShXNlMcqedV1UyedvXVmY83iDOwAzZ86M6OhoXLhwAR4eHlqxDwoKQsWKFUHXfvjwQatreJC8CSj5AbTq1H1M3H5FNEDVojmxoV81WRmk8nR/vIxI/BKeoNyuwTVRxtlRVrqyMvIncPxWGLosPSMqmtnaElemNEz2C5X8d8QaMgEmwASYgD4JLDp6BzP3XJdMubZ3VVRXYNbBh0+xaDz/mCQaKY+DLRZ2qSR5yRobF49hG4OEBiHq4pTVBtsH1USB7Jn1aQaeiwmYBQFqULfp/GOsP/tQ7Lqd3MZruOXC3HYVkjQfOnE7DH1XnZN0906Yg4I7xjctJWneYwpglXz+NgX+ctHR4A5AJycnvHnzBgcOHAClA2sjFC1I6b+Ojo7CtSymT0DJDyDqbvfjrmuikWoVd8Lqb6rKymg1fzqEx28+JtHp4PDaKJY7q6x0ZWXkT4A7AcvfRqwhE2ACTMBYBMKjYuDz82G8VYmiqenmhL97y+u7kT75XHsWjm9WnMXTd1Gg74Fz2nkgb7ak3Y0pSmn0lsug8jEJQpGCa/t4o3KRxM7A+tSN52IC5kKA6p6fvPNKKFOz/+pzxMRpjgqkJh4/tSkvNtqhho5Uz129XFKhnPagOteNy+YzychlJZ+/zeWe1sc+De4A9PLywsWLFzF+/HhMnTpVqz1MmDAB06dPR7ly5UDRgCymT0DJD6CFR+4IRWITxM89D5b1qCwro9WbdxS3X7xPotPJMX5w5rfNsrKVKShDxZE9puyXFGBe1sMLfu55TUF91pEJJEuAOv1tu/gElNY+vVU5k+1QyiZmAsYkQI0tfjt0W6LC9m9rgFKAlSzkfCDnZ3b7lOuC0bjZ+2+AXiDbWVni57bl0bhcfiWj4b0xAYMToCY9W84/FpyB6o2IEpTp4l0IJfI6YPKOK1DPICZH/p9dKiGLremm5Cv5/G3wG8qEFzS4A3DMmDH4+eefkSNHDoSEhCB//pT/gXvy5Ing+Hv37h2GDBmCuXPnmjBuVj2BgJIfQHRgnHfgpmjshmXyYlFXL1kZv9lvxxDyJDyJThcm1EdOmRewlRVIVkYk0HbhSZx7kBihzZ2A+eYwdQL7rzxH39XnxW309XHFuCalTH1brD8TMCgBKjdCtf8io+PEdRuVyYc/u1YyqB6msNjH6DhYWWYy+W7IpsCadTRfAvTS+sjNlxi3NRjP3kVpBYJq/c3r4AFbK0utxst1kJLP33JlLke9DO4ApAYgJUqUQGxsLEqWj8pgqAAAIABJREFULIn169ejfPnyGtlQtN/XX3+NGzduwNraGlevXhUagbCYPgElP4DU33Q3K08d3DxlZTR1Z02CctemNkJmG9P+x01WoM1IGfVOwG08XTC3vXZ1Xs0IE2/VhAj0XnkW/tcSu3RSLa7jo31NMu3HhLCzqgojQJE0K07eF3dFtfb3D/WBWx4Hhe2Ut8MEmIApEXgbGY0xW4JTbMRD+6GmPdNaloVlMo1CTGnPSj5/m5IdjK2rwR2AtOE5c+Zg1KhRwpdo+kPdgH18fIRoQPr706dPERAQAOr+S156khkzZmD06NHG5sXr64mAkh9AM/dcw6Kjd0VSrSsWwLwOFfRETj/TdFlyBsdvhyWZ7O6MJty4QT+IzW6WZcfvYeq/V8V9cydgs7sFFLXhdx9j4PXjgST1ggJG+qJQLntF7ZU3wwQyisCj15Hwm3tE8nvUrpILZrfjl0MZxZznZQJMQHsC5GfYcPYRpuy8io8xiVHKCTN85+eGYfVLKObFn5LP39pbnUcaxQFI2KdNm4YpU6YgPj4+2V8q+qW0sLAQxlHNQBblEFDyA2jqzqtYduKeaKwOXgXxU1vNUa7Gsqh6ZAvpYWNlgZs/NjaWSryuiRNQ7wRsb2OJkMncCdjEzWq26m869wgjN19Osv9Zrcvh6yqFzJYLb5wJ6EJg+MYgbLmQ2NzCxtICh0fW4c62ukDksUyACWQ4AaqL/v36i7jyNLE80oRmpfFNzaIZvrYhF1Dy+duQHE19LaM5AAkcNQOheoD79u3D27dvJSypRmCTJk0wYsQIeHjwm0JTv9HU9VfyA2jCPyFYffqBuGUqKPvjV+VkZcJv117ArsvPJDo5ZrZG0KQGstKTlTEdAqHhUag646BE4WOjfFEwJ0dLmY4VWdMEAt2XBeLozZdJgLTwcMaCjhUZFBNgAqkQuBUagYa/BkgK6fesUQSTmpdhdkyACTAB2RH4FBuHNacf4vrzcLTwKICaxZ1kp2N6FVLy+Tu9bMzpeqM6ABNAU6TfvXv3EBb2JSXRyckJRYsWVUy4rTndUNruVckPoDFbLmP92UciCjl+4VV/K0/K5s1mizPj6mlrQh7HBCQENHUCXt6jMnzd8zApJmBSBN58iEbl6f6IVW8BSN9Pstri7Pi6/P3EpCzKyhqDQL/V57DvSqi4NEWFB4zyFX6HWJgAE2ACTMDwBJR8/jY8TdNdURYOQNPFx5qnlYCSH0DDNl7C1gtPRDT9fFwxVmadI8dvC8aaMw8l5iucyx5HR/qm1aR8HRNAm4UncV6lE/DYxu7oV5sbN/GtYVoE1gU+BDW1SU4ODPVB8bzcwMC0rMraGpLAxYdv0Op/JyVLDq5bXKilxcIEmAATYALGIaDk87dxiJrmquwANE27mbzWSn4AfbfuInYGPRVtNMjXDSMalpSVzdTrFJJy7vkcsHeIj6z0ZGVMi8DYrZexLjAx+pU7AZuW/VjbLwQ6LzmNE7dfJYtjSosy6F69CONiAkwgGQKdFp/GyTuJv0M57K2F6D8HO2tmxgSYABNgAkYioOTzt5GQmuSyBncARkRE4JdffhFg9e3bF/ny5UsR3LNnz7B48WJhzMiRI5E5c2aTBM1KSwko+QHUf/V5SUv5ofVK4Pt6xWV1C/y89zr+d+SORCePgtmx/dsastKTlTEtAuqdgD1cHLF9UE3T2gRra9YEXkZ8QtUZ/pK6ZbkdbEE/T5CGZfJiUVcvs+bEm2cCyRFQbwhF48Y3KYU+Pq4MjQkwASbABIxIQMnnbyNiNbmlDe4AXL16Nbp3747ixYvjxo0bqQKjulLu7u64ffs21q1bh/bt26d6DQ+QPwElP4C+WXEWB6+/EI0wupE7BtSRVxrkgoO3MO/ATcmN4u2aE+v7VpP/zcMaypbAsVsv0XVpoKgfdwJO3VSxcfG4+iwcxXJnRRZbq9Qv4BEZSmD1qfuYsP2K5B4e16QUfvgnRPwZNUy6MKE+LC0yZaguPDkTMDUC9J39qz9OIOjxO1H1fNnscGRkHdhZW5radlhfJsAEmICiCCj5/K0oQ2XwZgzuAGzVqhV27NiBcePGYdq0aVptb9KkScLYNm3aYNOmTVpdw4PkTUDJD6CuS8/g2K0vDW1IfmhaCr1ryevN918BdzBj93XJTVKnZG6s6FlF3jcOaydrApo6AR8f7QuXHNwJWJPhQp68Q/+/z+Pxm4/ImcUGG/tVg1uerLK2sdKVa7/oFALvvRa3SV1/xzR2R/VZhyRb//e7mihbwFHpOHh/TEAnAntDnqH/3xck18xqXQ5fVymk0zw8mAkwASbABPRPQMnnb/3TUu6MBncAlipVCjdv3sS///6Lxo0ba0V27969aNKkCejaK1cS38xrdTEPkiUBJT+Avv7rFE7fTTxATm1ZBt2qyate1KpT9zFRJcqFbpJGZfLhz66VZHm/sFKmQYCiP8pP2Y+IqFhRYe4ErNl2J2+Hoe/q83j/KZFVTTcn/N27qmkY20S1PHv/tdDgIyomDoP9iqN95YLiTsiB7T3zID5/TtzcX10roUGZfPCdcwT3wj6IH4xr4o6+PvKK7DZRk7DaCiFA0cyN5h/D7RfvxR25OmXB/qE+sLK0UMgueRtMgAkwAdMloOTzt+laxfCaG9wB6ODggMjISJw/fx4VKlTQaseXLl2Cp6cnsmXLhrdv32p1DQ+SNwElP4DUO6HObF0OHWX29nvj2UcYteWy5CZpVbEAfumg3e+kvO8u1s6YBNTvf3aUJLXG7uBnGLL+EqLj4pN8yJFlGXf3Pn37EQ1/CUCEitO1r48rxjRyh4VFJiw/cQ9Tdl4VFXCwtcLZH+oJqYvjtgVjrUrn9NolcmNlL+NHTJ+++wpjtlwWHMl9arkKf2gvLBlH4MGrDwi4+RJx8Z+RI4sNstvbgJpc5LC3gVNWW2S2Mc9U143nHmHUZun3it87VUSz8s4ZZwyemQkwASbABLQmoOTzt9YQeCAM7gB0dHTE+/fvcezYMVSvXl0rE5w8eRI1a9aEvb29cC2L6RNQ8gOoxe/HcVml/s2cdh5oW8lFVkbbfukJvl9/SaJTxyoFMbN1eVnpycqYHoGRm4Kw6fxjUfGu3oUx7auypreRVDSOj/+MdWcf4smbj+hUtZDWac6rTz/AxO0hkigz1aVaVnDG/K8rKo6XsTdE0andlgVKyjMk6NS0fH7MbeeBzkvO4PyDN6KqrSsWwLz/Xor8e/kpBq29KH5G9S0vTWwAGyvjRTbdefkeLX47jg/RcaJeviVzCy9yyCnFon8Cd1++F2rchatEOauuYmWRCa09C2BW6/Jm5Yj9FBsHvzlH8eTtRxFHGeds2Dmopllx0P8dxzMyASbABPRHQMnnb/1RUv5MBncAUkOPW7duCZ2ABw8erBXhBQsWYMiQIXB1dRWagbCYPgElP4Aa/RqA688jRCPN/7oCWlYoICuj7bvyHP1Wn5fo1LNGEUxqXkZWerIypkfgV/+b+NX/lqh4Xfc8WNqjsultJBWNp++6isXH7gmjyBm0qX81lHFOviYcOaB+8b8FasCTklBjiaMj62jtUFQc2Aza0JozDzB+W2IjD/VlqGO1auMC+nxZDy/4uecVhr56/wmVfvSXXLa5fzV4FcmZQRqnPG1kdKzgiLoZmvSlaIHsmbGwiyfKu2Q3im5KXvS7dRexM+hpqluU44u/VJVOxwD1DvA01YqelVGnZJ50zMqXMgEmwASYgD4JKPn8rU9OSp/L4A7APn36YOnSpShRogSCg4NhbW2dIuOYmBiUK1dOcBp27doVK1asULpNzGJ/Sn4A+c09grsvE2tFLezsicbl8svKrkduvECP5WclOlGnYupYzMIE0kNAPQ3MPZ8D9g7xSc+Usrv28ZtI1Jl9BLHxicXiyOmyfVANIQVQXShVkKL+1qikkCaMaVfJBbuCnyFSJYqLnfH6Nfmj15Fo+GuAhHFqK1Cn37Pj60ki/NRf7gyrXwKD6xZPbSq9f07O5GEbg7Dt4pNk57axtMCkFqXRqUohZMrEKcH6MALdR3XmHBFSf1OTaq65sK6vd2rDFPH5y4hPaPDLUbyJjBH3U7VoTqzv6833niIszJtgAkxAKQSUfP5Wio0MsQ+DOwAT0nnpC2nr1q2xcuVKIbVXk1CtwG7dumHr1q3Cl4hDhw6hdu3ahuDCa2QwASU/gGr9fAiPXiemwSzp5oV6pb9EkchFqG7U13+dlqgztF4JfF/P8IdZuTBhPfRDgJpbdFpyRpzMwc4KwZMb6mdymcwyaXsIVp56kESbKkVyCk08VNNCY+LiMWJTELZfSho1NKRecXxft7hQd27FyfvifJmtLXFqrB+ncerB3pSq3XHxaZxR6exL05LjjqKWVJuwqC7X3ssFP7f1kGgwdedVLDvxJeqTxNuVnBzV9KClblP8ffoBfvgn+WhG1dkal82HSoVzCF2mE/7kymoLZ0c7WTpnqL7e6lMPEB4Vg961XFEir4NucDJwtLr96fe8cE57wfH1NjJa8kKAfK6nxtRFPke7DNTI+FOTM7rPqnPwv/ZCosyWAdWF+46FCTABJsAE5ENAyedv+VCWvyYGdwASkk6dOmH9+vXCl88CBQqAogJ9fHyQP39+4WdPnz5FQEAAlixZArpRSdq2bYsNGzbInyhrqBUBJT+AvGccxPPwKJEDFYqngvFykkuP3grpY6rCzRrkZCHT1eV+2AchSkZVLk9ugGx2KUd7m8qOw95/Qo1Zh/ApNmkDD9oDNfyZ0aqs8G8ZdZqllMEDV0Ml2yPnwLSWZdHFu7Dwc02RRSMalMAgP3bIp/e+UG/s8cVGX+qdXnsWjp7Lz0qe1wnrrepVBT5qz23/q6HoveqcqBJF2dG9TU1CDCWXH79F24WnJA1kqFnJ4u5emLPvBs6p1DBMSSeKzKXUfIpclYNQivVvh26DUrVj4r5E2NHLgx2DaqKoUxajq/guMgbVZh1MNlKXftcr/+gvaTAzvkkp9PFxNbruGanAhrMPMXpLsGQJcjov7FIpI5fluZkAE2ACTCANBJR8/k4DDrO9xCgOwKioKLRo0QL+/v4pvoGmN4sk9evXx/bt22Fnp+w3qeZ0Fyr5AVRp2gG8+hAtmnNtn6qoXsxJVua9/jwcjX49JtFpassy6FatiKz0ZGVMjwAdhN0n7JUovndILbjny2Z6m9Gg8ex91/HH4Tsp7mVayzJoU8kFfVedx/HbYZKx1paZhCYfTdTKAgxedxE7VGqLOWW1wfHRfgZ1LinCQCqboIYNTRYcQ1RMorOWHF77hvogq62VMPLZu4+CE1C1bmuuLDY4M64urCylDT4oKq3ClP1QzQBd07sqargZ5vn+5kM0mv12XNJogfbwZxdPNCqbHxRt+tOe61hyPDFKMSWbUvOTPzp5GtXsH6PjhKjKhUfuaIzGJEfl1oHVYW/zxV7GEtLvp73XxeWp0fLRkb4omDMxg0W9AVLZAtnw73e1jKVyhq/78FUkGs8PkDShoecWlXzQVAohwxXiBZgAE2ACTCBFAko+f7PptSdgFAcgqUfOPWruMWfOHDx5ormOTcGCBTFy5Eh8++23skxV0R4zj1QnoOQHULnJ+xCh0iHQmIXik7vzNEVp/dymPNpXLsg3KxNIN4HK0/1BdaESRLWZQronN+IEEVExqD7rkOT3m9JAKaJW1clEjTyK58kqcSqR2nbWFljU1UtjRHDIk3eCc0dVZrQqJ3QYZtGdANVpa/fnSVx4+FZysaYXMuTYG7YhCP7XQkFdXP/o7ImGZfJpXLTlHycQ9Chxzm99i2Fkw4yvnUqpzL1WnsWRGy8levX1ccW4JqUkP9sd/AyjNl9ONr05YTA1r7kwob7RnMzbLj7GrD3XERqe+KzQBL0VdWNu72G074HRsfGg0h6qejYrnx+/qzlPj98KQ5elieUPaC/+w2rDLU9W3W9gmV9Bv19f/3UKZ+8nds0mlRd380J9mZU8kTlKVo8JMAEmYDACSj5/GwyiAhYymgMwgR05Ai9duoSLFy8iLOxLpISTkxM8PT3h4WG8L3wKsK2st6DkB5D7hD0SZ8D2b2vAo6C8ujE+fxcF75kHJffIgo4V0cLDWdb3DStnGgTUnSTTviqLrv+lu5rGDjRrqR4FRKm8B4bWFlJJKdU3JaE0TUq5rFI0+a6xXZackUQMujplERwIFhRuxKITAU2pv92rFcaUlmWTnYcimnJmtRGjAzUNpCgwug8SpGKh7Ng2sIZOuqVl8O+HbmHO/puSS6nmJDk01SMVadCTtx+x4ewj3Hn5HhQ5+Pq/Py9UHPM0zlglKnSpY0h6GjNCfcv5xxi+KUjCfsegGkm6LJNTjP5dVX35MdjPDcMalEyLyWV9zZ9H7wjOW1XRVDdT1ptg5ZgAE2ACZkZAyedvMzNlurZrdAdgurTni02WgJIfQMXG7ZZ0Cdw9uBZKO8sr/ZEKlleYekBy//zVtRIaJBP1YrI3GituFAID15zH7uDn4tr9axfDmMYZHyWVkZul1OaaPx0G1QBMENVaVymlBuewt8aqXlVRzsUxRRWP3XqJrksDJWMWda2UbDRaRu7XlOemF4u+c47g/qtIcRtFctlj9/e10p1Kqm4jiva8NLE+HDKwxiU1beq0+LQk9ZhSLHcProk82XQrjdL6fyckUZGpOUUz4j6gen/URTviU6xkenKof1WhANp4umDA3+cln1PqPDVcMXRjCbqXGs8/JonmpQ63G/ppbv6i3iikcC57HBlRx2jRixlhP3rh0fL3E5I6lC45MmPP97Uy9PcgI/bCczIBJsAEzImAks/f5mTH9O6VHYDpJcjXp4mAUh9AFAFADkBV8R/mA7c88ulkSLppqtO2+psqqFVcXs1K0nRz8UVGJzB911UsPpZYh4wiSynC1JRl9an7mLD9imQLOwfVFJ16lKLZd3XSbph5HGyFzsDadDMlZ0PTBcdx9Vm4uI5noezYaoAIM1O2jbrummqcru1dFdX1UKuPatZ5TNkvcX5QJ2fq6EyNXzQJReGtD3wISwsL9KpZBHkctHfakcO56YJjkvRTCghd09sb1Yrl0tlsfxy+jdn7bojXkePm2ChfgzqoJu+4Iul6TcrUKu6E0Y3cUbbAFyf53pDn6P/3ecn+8mWzw7+Daxq0vlzAzZfotkzqlF/a3Qt1S+XVyJ7SwykCWlX++bYGKsgsC0DnG+e/Cz7FxgnOP9WamXTbb+hbLcXo5rSux9cxASbABJiA/ggo9fytP0LmMZNRHYAJ6b9BQUFC+u/Hjx+F2oApycSJE83DMgrfpVIfQJoca0dH1kHhXMbvYqh6S5GzwlXNUbmpfzVULpJ8eqLCb0nenh4JqKdfehXOgc0DqutxBcNOFRsXL3Q2fvzmo7gwOSxWf1NVogjVCKQOrTdCI4SfU8MJStHU5fd/+6Un+H79Jcm8WwZUN3jkk2EJ63e1X/1v4lf/W+KkZIfjo/Xn5Oqw6BTO3HstUZrq1M1sXU5ST4/uG3KE/+J/E1RHjoTKQWwbUF2rtG56TndfHohjt6SNZEY2LIlvfd3SBI2ityiiTVUODPVB8byGeUl1L+wD6s87iliVTipfVXDGr18nfUFAKaaUaqoq1VxzCTUPqUNwVjsr4b+2VhnXhbnr0jMS/q65s8B/aPJp+ZqiT3vWKIJJzcukyV5yu2ju/htCt2ZV6efjirFqdSjlpjfrwwSYABNgAoBSz99sW90IGM0BuHLlSkyZMgUPHjzQSeO4uDidxvNgeRJQ6gOIHADlJu+XQD85xg/O2TPLzhAVp+7Hm8gYUS+lFiuXHXgzUGjflefotzoxeie/ox1Oja1rsjunZgVDN0hrgCXX3fv9p1isOnUf9C6rc9VCyG5vo9O+qZMrpUdSDbcEMUaapk5Ky2wwObjI0ZUg+nbA7A15hv5/X0iy6/IujqCU7fyOmXEzNALUFTbo8bsk45Z080I9LRolqEfr0UTkeF7Zs4pWDkRNZiEHVY1Zh/D0XZT4MaXnU5q+IaT/6vPYeyWxPICNlQUODa8NlxyJ3XQT9CAHKqXEn7r7KkXVbK0shOgz1QhCfezl6tNwoYu0qpCTt2OVlBvz/HLgJuYfTHRAU7r26bF+Gms16kNPQ83x4VMsqs44KGkuQ12atw+qkaFOWEPtj9dhAkyACSidgFLP30q3m773ZxQH4Pjx4zFr1qxUo/1os5RSoxoVGB//5S06i2kTUOoDiAqte06T1tY7O74ecjvYys5gM3Zfw18BdwW9KM2QooySS2GTnfKskKwJqHe0pZTFGz82hrWlhaz11qQcRWE1mh+Am6HvxY8pnW/bwIz7ffnt4C3MPZDY8CFvNlucGlM3zU4fk4OeDoWpkYfP7MOSGTb09UZVV93TZVNSQ5NzjsaTs6dVRWesPPlAkiasOldKNeQSxgXeey10WVUJlAOlk1MdQ1ojPTJ+WzDWnHkoTkHNRDb211zTLj3rqF977v5rtP3zlOTH/Wq7YmxjaRdj1QGUAt1swXE8D090WCanE9Vj7FPLVUjHtrNOf1TgsA2XsPXiE3E5p6w2OD7aL9W5KeW77tyjEjWVUGJjXeBDjN0aLO6Lnuv/fie/Gsf6vGd5LibABJiAkggo9fytJBsZYi8GdwCeOXMG1apVExwN9erVw+zZs0FOPer6Sz+LjY3FmzdvcO7cOSxcuBDbt29HzZo1sWnTJuTNq7nmiiFA8Rr6JaDUB1BoeJTwhlxVgiY2gKO9tX4B6mE2cqz7X3uB8I8xaFo+f6qHGj0syVOYCQHqOlpRzRFOKZiaonzkjsT/aih6rzonUTOjG+bcfhGBevMCJGtyGrB2d8rigLuYvvuaODhXFhsEjq8Hcg7pWw5cDcXQDZckEVHarqGpi2zCtdQkgyLPQsMTG86Q+mv7eMNbD47MQ9dD0WtF4j1NbM7/UE/naFVt90rj6N+b/7N3F1BWVW0Dxx86hu7u7oahu0RRBAxEQCUkBRElFSkl5ENpURARUQEF6e7u7pbubr61D++cuXeYYebGzLln3/9e613wzpzY+/fc2bif2fHmmHVOB5Cow3FWfFZFEsd7+b+P205dE7Xs+tGTl28RE1SfrCkC5JsGBT1K+qqDV9Sp3I5LlbvUyCUdq+WMULNf/WGN7P4vePZnw+IZZGijwhG611cvem3kGtnlMKO1Wp5UxsnmFAQQQAABewjoOv62h77v1DLKE4DNmzeXyZMnS5YsWeTQoUMSM2ZM2bt3rxQsWNBIAIZc4quSgO3atZPChQuLSh7Gju3acirfoaYmjgK6dkCnr96VCoOdZ5/s+7qWxydP8ulBwE4CarCfr89CufcoeMuGyJiFFRUmIWcB5UqdQBZ0qhjps/Gqf7dSjlwMnnXYskJW6flKvqhosq3foZJMW09eM9vwdsmM8s2bhSKtTYcv3JKWk7c4nTgc8mVq37ib9x47nSD9auF08kMoB+OoGafNJ20WdfiEY/m0Ri7pEMHkU3iNVXvVFvl6kdx/FLyiYsTbRaR+kfTh3er29+ftPidtf3NeNt2nXj75oHzWCD1z+YGL8t3iQ8bSeLXVRkSSgWoJ/vuBWSQgTgwJiB1T4seJIbFjRA93prta+qsSjo6nFMeNFV3WfVFNkgVE7L9BJ6w+Jv3nBieiE8SJKVt6VbftL9pCzupWQXvZYSgRCioXIYAAAghEqYCu4+8oRdTgZVGeAMyVK5ccPXpUhg0bJp988olB+LIEoPp+o0aNZObMmU73aGDv103QtQM6dum2VA2x9OfwAHsuffTrDyiN91ggZAJr+FuF5Y2iGTx+blQ/oPb/rXI68dKVpIUndQ252b4Vp7V6Un8r7r2oZmAPWmrsvxhUJjYvKVXypIrU6ty4+0ja/77thcM61Kw9tSS1c41c8tOa406n76pZd6u6VTEOinEs6tALdfiFY1H7/k1qUcqrsxg/mLRZlh24aL6mfpF0MiKUgzi8AacOQKkxfKWcvHLXfFzm5PFlcedKovYAdLWoXzA8ePxUbt5/JLfvP5Z/d56TkcsPRygpGDN6NEmdKK40KJZe2lbOIfFiOy8VVr/EazBmnVy6FTz7UtWvXZXs8lmtPBGuqloNUCbEZ3F0k2JSt2DaCD/Dly5US3/VEuCgovZ1VadHx7Thtg6+5EpdEEAAgagU0HX8HZWGOrwryhOACRMmlLt378qCBQukRo0ahuH+/fslf/78xm9l79+/L7FiOS8HmT17trz++utSunRpWb/eef8YHYLgj23QtQM6eP6W1Po/56V7xwfVDXfGgT9+Bmiz3gLv/7zJaRaTJyeXWiWlDuTI12eBU2IhqmYy7j17Q175fo1T0+d0KC8F0ieOMIdKYqhlqjlSJTAOSdC9TNlwUnr9s8dsppp1tbV39Sg5oEAdWDF44UFRM7/Uvn3ZUwbIkEaFpVimpEZ9rt99KGW/WSZ3HwbPiv2ofFbpVS94Vuf2U9ek0dj1TstO1f6x8zpW8Po+siGt1DJctQw4MhI6IU8FVx6j3i1mbD3hraIOXfl8xi7Zfup6hB+ZMVk86Ve/gFTO/TxBrJZeqz0K1UnFjqVW/tQyuklxlxOw7/64QdYdDT7ARD1nXNMSEa6fr1yoDjYqPWCJ3HH47Kp9Fj+pnstXqkg9EEAAAQQiIKDr+DsCTecSB4EoTwDGjRtXHj16JNu2bTOW9apy+vRpyZw5s5EkUX9Ply6dU5C2b98uxYsXl+TJk8ulS87LYoimPQV07YBCLpNRy40ODahjzyBRawQ8EOg+c5f8vum0+QR1cqY6QdNORSUVag53TuhH1Z6eapZTpSEr5NTV4FlT7avkkK61ckeIUO1hppam3rr/2Li+bWU1gym31r+MaPrTRqdZeGEts40QoJsXnb9xXy7eui/50yV+IWH01ey9MmndCfPJKkG5rntVSRQ3ljGb7ZXvV8vpq8GnP0eLJvLbR6WlbPb92qosAAAgAElEQVQUbtYm7NvOXr9nJCQdy19tAqVkFu8mim/ceySVhyx3OnG+aKYkMjMSDp168vSZcQL34AUHnbYfCA+vXqG00rVmbuk4bbvTHnfqPpU4n/xBKbeW7v6x+ZR8PiP40Az13wOzO5STPGkShVcln/r+1I2npMffzod/rP2iqqjTrikIIIAAAvYR0HX8bZ8I+EZNozwBmDFjRjl79qysWLFCKlSoYCg8fPhQAgICjMNAFi1aJNWqVXPSmTdvntSrV8/Y/0/NEKTYX0DXDkhtVt5g9DozQAGxY8jer2vbP2C0AAEXBUYuOyxDFwWfZFspV0r55YNSLj7F2stn7zwrHX/fblYiTaK4sqGH879PkVlDx5O61XvUrLKln1YO95UL9pyTjtN2iFp66VhUAvHTmrm0TAKqZbjF+y92mj3n7Vlm4cKHc4E6objy0OVOJ/v2qJvHWCbcadoOUZ83x9KhqopXxBK+7tStzojVsv/cTfPWNpWyyxd1Ir7MNbx3qoRcq8lbZKnDUmN1z/Q2gVLCy4lGx7qoZbxqNuaKgxfNBHh4dQ3t+3nSJJQ/WgeGe0hJWM9Wyc+S/Zc4nQat+pCZbctKuhBLv92pX1TdU++H1bLnv+DPSfW8qWVCM/vNZIwqL96DAAII+KqAruNvX/X21XpFeQKwdu3asnjxYhk7dqy0bNnSdClSpIjs3r1bPvzwQxk/fryTV5MmTeT33383Dg45duyYr1pSLxcEdO2ANh67Im+N32BKJIkfS3b0qemCDJcioIfAzG1npMufO83G5EyVQBZ3qWSrxg1ZeEBGLT9q1jmqk5ghf6GgKrK4c0XJmTphmI6/bjgpfWbtcdoHz/HiTtVyGnvS6Vb+3n5GOv8R/HlTe8tt711DAuLE9Kmmfjxlq8zfc96sk9pLTZ0sq/ZYcyzFMycVtdw8MpbkBr0n5OdbHXCzqLP3fkYHLzggo1cE//yo99bOn0bGNi0eZTFRh6rcffRE7j54bCxhVftEDlt0SDaduPrSOqi9GVWiTu0X6Enp9c9umbIheO889Szl/FfrspI4/stPP/bkvd66d/eZG/LqSOetCH5uXkKq5kntrVfwHAQQQACBKBLQdfwdRXzavCbKE4ADBgyQ3r17y9tvvy1Tp041IQcNGiQ9e/aU6NGjS48ePeStt94y9gr85ZdfZPTo0caMhTZt2sioUaO0wffnhujaAa05fFne+2mjGVq1f9PmntX9OdS03U8F1BLUtx2S4fHVbNi+tWw1++yjXzbLkv3BByW0rphNutfNG2URVckLtUzz/M3gme9dauQyEkYhi1oyPHzJYfl+6eFw6xfWM8K90YcvaP3rFlm494JZw+p5U8mEZiV9rsahJXVDVjJR3Jgyr1MFyZA0fqTWX52WrE5NdizqYIeMyTx/b8jZs+od6gTdfzuUf+Hgk0htZCgPVz9X07eekQHz9ouapReyqHqqWYrZUibwuGrqxOUmEzY6nUytHurJ0mKPK+XCA0Ju5aASo+rwGnWIDQUBBBBAwF4Cuo6/7RUF62sb5QnAoBN/EyRIIOpDmCjR871QVLKvQIECcuLEiRcGiGpgkyxZMtmxY4dkyGC/UyStD7Pv1UDXDmj5gYvSYtJmE1z9x7LaK4eCgL8JqGV4FQYvd2r2jj41JEn82LahKP/tMjlzLXhPtu8aF5YGxaL236CQ+8blTZtI5nd6vn1GUFEHUPSetdfplM6g75XNntzpIIKgr9vxUJawPjj3Hj6Rov0Wyf1HwUueBzcsJI1LZPTJz5pKuqnkW1glqk6LVUt0Sw5YIlfvPDSr8nX9/PJ+YBaP3NReuA3HrnOKhzp9d8pHpaVMtuQePdubN1++/UAGzt0vM7f/Zz5W/aJiassyUiRjEq+96tqdh4bH0UvOh4vULZhGRr5TTKL7aDLt1v1HUnrgUqeDa3T85YHXAs2DEEAAAR8X0HX87ePsPle9KE8AKoGVK1fK48ePpWjRokZiL6icPHlS3nvvPVm7dq0TlEoM/vrrr+ahIT6nSIVcFtC1A1q497y0/nWr6ZE1RYAs7xr+nl0uA3IDAj4uoE7QzdVrvtNS1LkdyxuHI9ihqMFvwa8WOVXVivqHnElp/Bv6WWXJnDzAqNuDx0+MfQodZ78FVVot91WndarDWBw38Q/6/ue188jHlbPbIRwvreOCPeelzZTgflfNTlIzr9VMLl8sao/GNlO2hVq1qD4sp8sfO5wSYJ4uc1cnT782co2cu+G8X3O/1wtI0zKZfTEcsvbIZflx9TFRCVG156I3k39BDT5z7a6xP/DFWw+cDJqXzSJfvprPJ2dG/7bxpPT8O/hUbfVztfbzqpImsWfLon3yQ0ClEEAAAT8Q0HX87Qeh82oTLUkAhteCgwcPipopqJKEOXPmNBKFFL0EdO2A5uw6K+2nBh8a4O09lfT6FNAa3QXKDFzqtHx1fNPiUjN/Gls0e+vJq/LmmPVmXdXgd9/XtSROzBhRWn+VlCg1YIlccZil1b1OHmldKbvcffhYWk3eKmuOXHaqkzo9tl/9AvKeQ8JF7Q3Y+5/gwXzQDb+3LCOB2X1nVpY7uCGTWIHZksvvrcq486gouUfFtOqwFXLySvAJz+rFap/M2e3LS7zYUfcZC/lvlto7Uc3UjR/75Xsn3nnw2EjyxYkZXeLEim6ckhsjWjRp9vMm2RJiduO7pTPJwDfsdQJ4ZHwQ9p69IW+N2yC3Hzw/mTuoqINgWlX0rUS8WnlT74c1svds8OEfNfKllh/f5/CPyPhs8EwEEEAgKgR0HX9HhZ1O7/DJBKBOwLQldAFdO6CQG9HnT5dI5nZ0Xq7HZwIBfxFoMHqtbDt13WzuV6/mk+blstqi+SFnv1h5iEnIfbjUDCV1onKLiZucfBVs7BjRZcTbRaROwbQvOE9ae1y++nef09frF0knI9627y/Z1EzT4v0Wy837wUmVvq/ll2ZlPVvGGtkf0snrT0ifWXvN16hE2qz25SRPmufbokRVUXvgKb/HT5+Zr2xcIoN89Vr+UJOAajnrN/MPyMztZ+TRk+B7wqpvqSzJjKW/KrFIEWO2YfOJm16wUz+z9Yuk9xminaevS/1RzqtxJrYoKVVyp/KZOlIRBBBAAAHXBHQdf7umwNUkAPkMWCKgawf05+bT0m3GLtNUDdT/aVfOEmNeioDVAh1+3y7/7jxrVqNVxWzSIwoP0fCk/eok3cnrT5qPqFcorYx8t5gnj3T73pWHLhkzqxyLSkgevnjb6WsJ4sQ0Zui8bEbfsEUH5YdlR8z7UiSIbSyXVQdt2bGsPnxJmv7kbLO+e1VJmzieTzdHHQ6hDsnZcfq6qP3xhjQqJG8Ujdr9JYOA3hm/QdYfu+LklTl5fFF7XhbP/HybFjUjTB2cMXDefrl298WDM0LDVnvgqqRmigRxfDoWUV25f7b/J5/8scPptbFiRJNJLUpJuRwporo6L7xPxfrDX7bIsgPBByBx+IflYaECCCCAgMcCuo6/PYbxsweQAPSzgPtKc3XtgKZsOCm9HJbZqdkPf7YJ9BV26oFAlAoMmr9fxq08Zr7zlYJpZVQTa5Jorja88bj1sun4VfO2rjVzSfuqL56+6+pz3bn+4eOnUqK/8yy3kM9JEj+WTP6glBTK8PLDC05cviOVh65wun3BJxWifOaZOw6h3dPlzx0yc1vwIQ6FMySWWe3Le+vxkfocdXiLmiGbNnFcr5y8625lQ0tIqWepsynUUvNXC6WTvv/ulY0OPw/hvSterBgy/eNA2+z5GV57vP39cSuPyqD5B5wemzBOTPmjdaDkSxe1s0BDti20z4OV/Z+37XkeAggg4K8Cuo6//TWe7rabBKC7ctznkYCuHdDPa47L13OCl9iVy5FcfvvId/ei8iiI3IxAOAIhlznaZUasmgFT5OvFopZHBhU1s07tgWVVCZnocqxHqoRxjGWWuVInDLd6qm3lv10u/10PPt241yt55aMK2cK919cuUMtRSw9aKipBGlS+qJNH2lTyrf3UfM0ttPqoJe/95+yXe4+eeFxddZLuD+8UlWp5rft58bgRkfwA9XPY9999MmndCac3qZ/lmW3LSoak8SO5BqE/Xp2MXOO7lU6zPNUMzqVdKkni+LEsqRMvRQABBBDwjoCu42/v6PjPU0gA+k+sfaqlunZAIX+rXzl3SmNZDwUBfxRYuv+CsZQsqKjB7aae1X2e4vyN+1Jm0FKneq7uVsXSWVqL9p6XVg4njAdVLmOyePLbh2UkU/KIJwy+mLFLpm0+bbbPrv3U+FVHZeC84FlUahnl+u7VWHLq5k+Ymh2qEs2O+3aG9ag6BdJIr3r5JGn8WHL/0VPjNGr1p9qTMWPS+FF6kImbzbX8NnUYTIfft8m83eed6pIjVQKZ3iZQksSP+lOs203dJnN3nXOqz9j3ikntAi/uKWo5IBVAAAEEEHBJQNfxt0sIXCwkAPkQWCKgawc0ctlhGbrokGnKqXmWfLx4qY8I7D93U+qMWO1Um4P9a0f5Sbqucqw4eFGaT9xs3hYQO4bs/qqWRFdrIi0qas84dVjDnYfBM7TUPoBq5l/qRHFdqpXal1HtzxhU1HLNnV/WtNVBDU+fPpMqIU7StfuBJi4FMZIuVkmpcauOyvDFh0I95CND0njydf38UjUPs/u8EQL1c/3+T5tk04ng7QbUc0tkTmr8bKvTlaOqLNx7XlqH+CVD3YJpZHST4lFVBd6DAAIIIBCJArqOvyORTMtHkwDUMqy+3yhdO6DvFh+S75ceNgNgpz3PfP9TQw3tJqCW0Bbuu8ip2is/qyyZkwf4dFNCzuQtmimJ/N3W+sN8HE8mLp45qXHgR7IA12cJXbn9QIr3X+IUgz9alZHS2ZL7dFwcKxfawShq1lSJLM8PraB4JrDv7E1jNuCB87eMB6mDStQy8U7VcjK7zzPaF+6+cfeRNBy77oVDfdQvEMc0KSYxY0T+CcqqDtWHr5RLtx6Y9VP7ii7uXElSJuQQFy+HnMchgAAClgjoOv62BNPGLyUBaOPg2bnqunZA3y44IGNWHDVD83qRdPJ/bxe1c6ioOwIeCRT8cqHcevDYfMbUj0pLWR846fJljeryxw6ZuT34YIl3SmWUQQ0KeeTgjZvVvmGnrt41BukqAejJyb2vfL9a9p69aVarY9Uc0qVmbm9UM0qe8dEvW2TJ/gvmu/KkSSjzO1XwyCRKKm6jl6hlvfN2n5Oz1+9LrfxpRC1NpUSOgNqTs8HotXLhZnACTr2pUfEMMrhhoUj/XHf9a6dxyrNjGf5WYctOpo4cZZ6KAAII+LeAruNv/46q660nAei6GXd4QUDXDqj/nH0yYc1xU0j9x/uQRoW9IMYjELCnQK3hq+TgheeziFQZ0rCQNCqR0acbU3fEatl3Ljg59tWr+aR5uaw+XWdXKzdo3n4Ztyr4hGZfmeUYkXaoZEmFb5fJ02fBV/d/vYC8VyZzRG7nGgR8UuDA+ZvSaMx6p1+YqIq2rphNutfN+0Kd1S8Edp25YSby1URB9UuBGOp/0aNJ+qTxpFimpMbfX1ZCm01bJXdK+bl5yUhPPPpkIKgUAgggoKmAruNvTcMVac0iARhptDz4ZQK6dkBfztojv6w/aTb93dKZZOAbBfkwIOC3Ai0mbpLlBy+Z7e9cPZd0qp7TZz0eP3kq+foslIdPgk+W/b1lGQnMbp/lsRHBXXXokrz/8ybzUpUj2PFlTUkU1/dP+hy68KCMXH7ErHuCODFlQ49qov6kIGBngY3Hrhg/lw8cTrZW7Ql5uvXWk1dl8IKDsvG4896BIdter1Ba40TmsGYL337wWNQvaRxPBVc/R4s6V5R0SeLZmZK6I4AAAgiEENB1/E2gXRMgAeiaF1d7SUDXDqj7zN3y+6ZTplLzslnkq9fye0mNxyBgP4Fe/+yWKRuCfyYal1BL2nx3VuzhC7ekxvBVTtDbe9eQpG7stefL0VKHDxTqu0geOiQaxjctLjXzp/Hlahv1LfvNUrl8+6FZz/cDM8vX9Qv4dL2pHAIRFVi874K0mbJV1IEsjmXwm4WkQPrEMnTRQVl24GJEH/fSWdd9Zu2RyQ6/tFQPVb+0VL+8pCCAAAII6CWg6/hbryhFfmtIAEa+MW8IRUDXDijkPjotK2SVnq/k4zOAgN8KjF5xxJipElTK50hhnG7pqyXkCbmpE8WRjT2q+2p1ParXuz9ukHVHr5jPaBaYWfr6eCJt9s6z0tHhBGNVeTVbKVfqhB5ZcDMCviSg9uNT/z3hWNQs3RA5wQhVOXG8WLKky4uHeajZhm+N3+D0jMBsyWVqy9Is/Y2QLBchgAAC9hLQdfxtryhYX1sSgNbHwC9roGsH1Gnadpm146wZ07aVs0u32nn8MsY0GgElMGvHf9Jp2g4TI1uKAFnWtbLP4oRcXloxV0qZ/EEpn62vJxUbtfyIDFkYnJzNljJAln3qu7FRbW08br1sclj2WCprMvmzdaAnDNyLgE8KTFh9TPrP3R9u3VIljCMZksYzkoNPnz2TR0+eyX6HPUzVA14tnM5YChxU1AzgOiNWy/HLd8yvxY0VXRZ+UtHnT2kPF4QLEEAAAQRCFdB1/E24XRMgAeiaF1d7SUDXDqjtb1tl3u7zplKnajmlc41cXlLjMQjYT2DLiavScOx6s+JxYkaXA/1q++wMk5Cny7aqmE16hLIBv/0i8WKNd56+LvVHrXX6xrovqvrs3l8Hz9+SWv/nvDxbJTVUcoOCgI4C3y44IGNWHA21ackDYkv7qjnknVKZJG6sGE7XtJ+6TebsOuf0tZ+bl5CqeVIbXwt5CJD6Wq9X8spHFbLpyEibEEAAAQRERNfxN8F1TYAEoGteXO0lAV07oJDJg89q5ZZ2VXJ4SY3HIGA/gbPX70nZb5Y5VXxLr+qSIkEcyxqjTs/cf+6WJIwbUzImi+9UjwqDl8npq/fMrw1rVFjeLJ7BsrpG5ovVHmPF+i2WG/cema/x5VOae/+zR37dEHzIkvoMqYRl7JjRI5OJZyNgmYDqq9TewtM2nzbroPotdTJwi3JZJSCMg28u3Xog1b9b6fSznS5xXFnUpZIcvXhb3hi91mk5sToFfHqbsuGeGGwZBC9GAAEEEPBYQNfxt8cwfvYAEoB+FnBfaa6uHVCznzfJykPBJ572qJtHWlXM7ivs1AOBKBdQp+rm7r3AaUP72e3LSaEMSaK8LuqFakDd+Y8d8s+OsxItmkjXmsFJenUiZoEvFzrVa06H8sbG+7qWj6dslfl7gmctv14knfzf28FLBX2l3So2ZQYuFfVnUGlfJYd0rZXbV6pIPRCIFAGVqFeHi604eFEKpk8izcpmliTxY4f7rj+3nJZu03c5XdekdCbZcuKaHLxwy/x67BjRZV6n8pIjFftohovKBQgggICNBXQdf9s4JJZUnQSgJey8VNcOKOSm+l++ms/4LT0FAX8WKPfNMvnvevCsujFNikmdgmktIdlw7Iq8HWLj+2/fLChvlcwk205dkwaj15n1ihE9muztW+uF5XWWVDySXjplw0np9c8e8+lqVt3mntV8bol2yKWQ6kCE1Z9XlfRJ4kWSDI9FwN4C6pcdTSZsdDroJ7QWda2ZS9pXzWnvxlJ7BBBAAIFwBXQdf4fbcC5wEiAByAfCEgFdO6BGY9fJ5hPXTNP+rxeQ98pktsSYlyLgKwKNx66XTSeumtWxcq+pdlO3ydwQe2OpRN+kFiXlzLV7xnK7oJI9ZYAs9fFDMTyN8ckrd6TSkBVOj1nwSQXJkyaRp4/22v1HL92W2v+3yjjcIKjUzJdaxr9fwmvv4EEI6Chw4vIdY9/MB4+fhtq8fGkTyaz25SRWDJbR6xh/2oQAAgg4Cug6/ibKrgmQAHTNi6u9JKBrB6Q21Fcb6weVwW8WksYlM3pJjccgYE+BT6ZtN5bcBpUPymWVPq/mi/LGXLx1X8oOWiaP1XGZIUrCODGlSKYksvrwZfM7rxRMK6OaFIvyekblC9UsoQqDlxvJz6BiZYI2ZNtV/d7/eZNTXGLFiCbzO1WUHKkSRCUV70LAlgLqEBE1gzZkUb/4mNWunNZbHNgyYFQaAQQQiCQBXcffkcSl7WNJADqE9tSpU/L999/L3LlzRf09Tpw4kiNHDmncuLG0bdtW4sd33izenU/FyZMnZcyYMbJ48WI5evSo3LlzRxImTCh58uSROnXqSJs2bSRlypThPnrOnDkyadIk2bBhg1y6dMl4Rs6cOaVhw4by8ccfR7iuUdHm0BqjawdUd8Rq2Xfuptnk4W8VljeK6nmAQLgfUi5A4H8CQxYekFHLg0+yrJU/tYxrGvWzt35YeliGLT4U4bh0qZFLOlbTf2ncFzN2OR0yUCV3SpnYolSEnSJy4bU7D+XIpdtSIF1iiRfb+cTSl90/f/c5+fi3bU6XtK6UTbrXyRuR13INAn4v8OjJU3lt5FrZ7/DfJgqlXZXs8lmtPH7vAwACCCDgLwK6jr/9JX7eaicJwP9JqqRfkyZN5MaNG6Ha5s6dW+bNmyfZsmVz237q1KnSsmVLuXv3bpjPSJ48ufz5559StWrVUK+5deuWUc9///03zGeoRODs2bONpOLLSlS0Oaz369oB1fhupRy+eNts9sh3i0q9Qunc/sxwIwI6CPy28aT0/Dt4n7mC6RPLvx3KR2nT1GEkaqbbuRv3zffGjx1D7j58EmY9xjUtLrXyp4nSelrxsjm7zkr7qdudXHb0qem103XV3orvTdhoWGdLESB/tA6UlAnDPwX67sPHUm3YSqeYpUkUV5Z+WinM00+t8OOdCPi6wK4z1+X1UcEn/6rtDeZ2rKD1/qa+HhPqhwACCES1gK7j76h2tPv7SACKyM6dO6Vs2bJGYi5BggTSvXt3qVKlity7d0+mTZsmP/74oxFnlVDbvHmzcY2rZf369VKhQgV58uSJRI8eXZo1ayb169eXdOnSGbMNf/nlFzOpFxAQIHv27JEsWbI4vUYthXrllVdk/vz5xteLFy8unTt3NuqlEoMqoffDDz/IgwcPjHu3bNkiKqEYWomKNr/MSNcOqPKQ5XLiSnCCd3zT4lLTDxIIrv48cL1/CSw/eFFaTNxsNjp5QGzZ2rtGlCIs2nteWv261emdf7UJlEHz9su2U8HL9h0vWPlZZcmcPCBK62nFy67eeSjF+i12evW0VmWkTLbQ//1wpY5q9pHav+/opTvmbW8UTS/D3yoS7mNCHvyhbuCXKuGycQECoQqsPnxJvl96WJIFxJZ+9QtIqkRxkUIAAQQQ8CMBXcfffhRCrzSVBKCIkexbsWKFxIwZU1atWiWBgYFOuEOGDJFu3boZX+vbt6/06dPHZfxXX31V1LJdVUaNGmUsKQ5ZPv30U/nuu++ML3fo0MFYjuxYpk+fLo0aNTK+VKNGDeN5sWPHdrpmyZIlUrt2bSPRGNozgi6Oija/DEnXDijkaacTW5SUKrlTufx54QYEdBI4fOGW1Bi+yqlJ+7+u7dJSUE89mv600WkfucIZkxj7X125/UAajFknJx0S9+pdanbgnq9qSXR13KwflFe+Xy17zwZvX+Ct5YGT15+QPrP2viD4Z+tAKZU1WZiyoR38US5HcpnyYWmfO6HYDz4eNBEBBBBAAAEEbC6g6/jb5mGJ8ur7fQJQzegrVer5XketW7eWsWPHvhCEp0+fSoECBWT//v2SNGlSuXDhgsSKFculYCVLlkyuXbtmzMi7fDl4k3nHh6jlx0mSJDG+pGb3qRl8jsUxiXjo0CFjz7/QynvvvSe//fabkRw8f/68UWfHElVtfhmQrh1QyQFL5NKtB2bTf/uotJTLkcKlzwoXI6CbwO0Hj6XAlwudmqWWcWZP6fpsandsjl++I1WGOp90O6RhIWlU4vkBPccu3TaSgNfvPjIfXyRjEvmnXTl3XmfLe76Zf0DGrgzep7FQhsQyu71ny7Rv3H0klYcul2sOrkE4edIklDkdykvMUE4fDe3gj5jRo4k6nThHqoS29KXSCCCAAAIIIICAlQK6jr+tNLXju/0+AdizZ08ZOHCgETt1oEbp0qVDjeM333xjLA1WZdGiRcYMPFeKWjasDvwoUaKEsYw4rKIOAFEJQpVw3L17t9NladKkMZKP6mCSw4cPh/mMKVOmSNOmTY3vq4NC1HJjxxJVbX6Zj64dUJGvFzklEcKb5eLKZ4hrEbCzQOG+i+TGveAE24i3i0j9IumjpEkD5u6TH1cfN9+VOF4s2dijmtP+V5tPXDX2qXvw+Klx3aAGBeWdUpmipH6+8JJ1Ry/Luz9uNKsSLZrI1l41jOWC7pb+c/bJhDXB7iGf0/e1/NKsrPNWF+qaebvPSVsO/nCXnfsQQAABBBBAAIEXBHQdfxNq1wT8PgFYsWJFWb16tah9965fv24sAw6tqD381D6BqqglwGopsCulWLFisn379pfOALx586YkTpzYeGyDBg1kxowZTq9QM/oePXok5cqVkzVr1oT5enXCcM2aNY3vt2jRQn7++Wena6OqzS/z0bUDyt9ngdxxOFTg77ZlpWgm5xmYrnxuuBYBXQRCLsGtlCul/PKBd0+aDc3q/qMnUnrgUqfkY8sKWaXnK/leuFzNFJy766zkTpNIqudN5VdLTR8+firqFxiOh6J4kqQ9cfmO1Bi+Uh49eRbmRzhR3JiyrGtlSZEg+ECQLSeuykeTtzj9IoWDP3TpBWgHAggggAACCFgloOv42ypPu77X7xOAQTPuChcuLDt27Agzjmr5rlrGq4rah0+d1OtKGTdunLRp08a4ZcyYMebfHZ/x2WefydChQ40vLVy40EziBV2jlvKqJGVoswMdn+O4V6Ca0ahmNjqWqGrzy3x07YBy9ZwvD588n0GkilriViD986QuBQF/Fpix9Yx8+tdOk0Btrbfui2qSJnHkbkT/15bT8tn0XU70K7pWliwp9D/cw9XP22dkvywAACAASURBVIeTNsvSAxfN294slkGGNS7s6mOM61v/ukUW7r1g3hs7RnQZ8EaBF2LRuEQGGdzw+Tvm7z4nnf7YISoZ6Vg4+MOtEHATAggggAACCCBgCug6/ibErgn4dQLw/v37Ei9ePENMna4bdEhHWIRBy3jLlCkjakagK0UdyqGW4qq9+dQpwB988IG89tprkjZtWuMUYLVs9++//zYe+fnnn4tachyyqBmI6r0xYsSQc+fOiUrkhVY6duxonAasinr+2bNnzcuiqs2qg3lZUfUP2nvx9OnTkiFDBlc4ffJatW9V1u7znOq2qHNFyZWaPat8MmBUKkoF7j58LKUGLBW1H2BQ+axWbmlXJUek1qP+qLWy83TwKb8VcqaQXz8MfauHSK2IDR4+ae1x+erffWZNUyWMYyyVjqbWA7tQ1h+9Iu/86PyLp1YVs0mPunnlk2nb5Z8dwf8mqceqmdJbT16TAfP2y7MQEwYr504pE5uXdLkOLlSXSxFAAAEEEEAAAe0FSABqH+IINdCvE4CXLl2SVKmen9D61ltvybRp016Kljp1arl48WK4M/Be9hA1c1Al99Ry4JBFncz7xRdfvDDzL+i6b7/91vi+Kq1atRI1qzBkUXsDquXGt2/fNr6lkpa3bt0yL4uqNrsyYNQlAahmreTqNd8pJMu7VpaszDSKUGfERfoLfD59l/yx5bTZUPWzsezTSl5N7jx5+kxu338sN+8/kn3nbkrrX7c6wY5vWlxq5k+jP7YbLVSHoVQdttLpTnXwRp40iSL8NOX/2sg1TicKq30EV3xWWRLFjSUXbt6XqkNXOG2VoPZkdNwfMuhlVXKnlJHvFpOAOKFvzRHhSnEhAggggAACCCDg5wIkAP38A/C/5vt1AlAlnjJler7Juzo0Y/LkyS/9VKhr1T3Zs2eXI0eOuPwJOnDggJHAmzt3rjx+HDwLJuhBcePGlTfffFOGDBlizNwLWdQegfnz55eg2XXqtF+1bDhPnjxGkm/evHnSrVs34+RftV/gw4cPjdmCju+Kqjb7YwLwzoPHkj/ESadrPq8iGZLGd/mzwg0I6Cig9ndrONZ59vT0NoFSIsvz7RUci5pRe+zyHblw476RzLt573lS7+a9R3JTJfiMP4P/fut/X7vlMMMw5DPTJY4rq7pVCfXkWR29XW2TMq8weLmcuXbPvLVH3TzSqmL2CD1K3f/X1jPSLcSS636vF5CmZTKbzxi/6qgMnHfgpc9UB7D0q5+fWEVInosQQAABBBBAAIGXC5AA5BOiBPw6ARhVs+EUtDpoRC35VXv4Zc6cWfr372+cJKz2FVQn+86ePVt69eolaq9BtRxWnTScN2/eFz6lauZg3bp1jSRfWKVt27ayatUq2bNnjyRJksR4ZlCJqjb74xLg63cfSpGvFzuFZVOPapIqUeTucUZXhoBdBFSCqNqwlUZiL6i8VSKjfNuwkFMT1Gzaj6dsddqPzhtt7Fozl7SvmtMbj9L2GT3+3i1TN54y21c+RwqZ8tGLS6ZXH74k41Yek7PX7xkHh9x5+Nj4U80AdCw5UyWQ+Z0qOCXyVHzrjFglRy8Ffw4c7+lWO7d8XCm7V2eGahswGoYAAggggAACCERAgARgBJD84BK/TgBG1X54Dx48MGYN/vfff5ImTRpj+a/6M2TZu3evlChRQlS91J+bN28O9SOoliEPGjRI/vjjD2MvwKBSsGBBY0agms2YIkUKuXLlimTNmlWOHTtmXhNVbQ7vZ0fHDujirfvGHmeOZXvvGpI0IHZ4HHwfAb8RGLX8iAxZeNBsb0DsGLK5V3WJHzt4mefgBQdk9IqjXjVR71n+WWVJlZCE/MtgF+w5J22mbDMviR0zuuzsU1PixY5hfk0tFa49YvULh3WE9txJLUpK5dzPt9pwLCqB2PSnTU5fixUjmgxtVFjqF0nv1djzMAQQQAABBBBAwN8FdBx/+3tM3Wm/XycAFVhUnIg7a9Ysef311434DBgwQHr06BFmrFq2bCkTJkwwvq9OJVanE7+sqGSgmlWo2qFOCVZFJQXTpUtn/D20w02ios3hfRh17ID+u35Pyn2zzKnpe/rWkgTsXxXex4Hv+5HA+Rv3pew3S8VxotiwRoXlzeLPDwLacOz5ARIhD4NwhyhG9GiSKG5MyZw8QDrXyCWVcoV+cJI7z9b1HrWsuujXi51m8oVM4rX9bavM2x32LPQgG+X9ywelwqT69M+dMmPb8wOjVJzGNS0hgdmT60pLuxBAAAEEEEAAAcsEdBx/W4Zp4xf7fQKwYsWKxvLcgIAAI5EWM2bom42r03fVKbyq9OnTR/r27RvhsKtDP7p3725cP3/+fKldu3aY944dO1Y+/vhj4/vqUBJ1OImrRR00EnTf119/Lb1793Z6RFS0Obw669gBnbh8RyoPXeHU9IP9a0ucmMEzZ8Jz4fsI+INAs583ycpDl8ymBmZLLr+3KiM37j4yloaevXHf/J5K4mVMGk8SxYtlHCKRKF7M//2p/n9M4+sJ1Z/G95yviR87BstI3fhANRyzTracDN464oNyWaXPq/mMJ+04fV1eH7U23KfmS5tIfm5eUtIkDnvGpVou/NeW03L59gNpWDzjS68N94VcgAACCCCAAAIIIBCmgI7jb8LtuoDfJwDVbDy1nFaVDRs2SOnSL+51pL7nmMRbuHBhmCf1hhaCoUOHGktzVfn333+lXr16YUbqhx9+kI4dOxrfnz59unEoiKtFzTZUsw5V2b9/v3FIiGOJijaHV2cdO6DDF25JjeGrnJp+bGBdiR49WngcfB8BvxKYs+ustJ/qfBL66m5V5JsFB2TuruBtDRSKK4dQ+BViJDb2+6WH5bvFh8w35EiVQJZ0qSRqD8d3f9wo649dMb+nTvAd/lZhSRg3lqiEq1rKrWY9p0wYJxJryKMRQAABBBBAAAEEXBHQcfztSvu59rmA3ycAN23aZCb9WrduLWoGXsjy9OlTKVCggJFMU4dqqGW3sWLFivBnaMaMGdKwYUPjenVK77fffhvmveo6db0qW7dulWLFikX4PerCjRs3SmBgoDFQq1atmixZsuSF+6OizeFVWscOaO/ZG/LK92vMpseMHk2ODKwbHgXfR8DvBO4/eiKlBy6VG/cemW0vnCGx7Dxzw8mibPbkMuXD0iTRo/gTEtosv3VfVJXDF2+Lmr3pWEjQRnFweB0CCCCAAAIIIOCGgI7jbzcY/P4Wv08Aqk9A0JJYtfxXnZ6rEmiOZciQIUbiTpUvv/xSvvrqK6fvr1ixQqpUqWJ8rVmzZjJp0iSn76ulxenTp5e7d+9KwoQJZe3ataIO7AhZ1PJgNTtQJRzV9adOnZLo0aM7Xaa+lilTplA/uEeOHDHqoX64Y8eObRw2ki/f82VbIYunbfb0J0fHDijkoDlerBiyv1/Yy709NeR+BOws0GfWHpm8/mSYTVAzyxZ8UkHSJo5n52basu5qaW7x/ovl+t3gBO2gBgWNeO0/d9NsU9rEcWV518oSNxbbHNgy0FQaAQQQQAABBPxGQMfxt98Ez4sNJQEoYiTKypUrJ/fu3ZMECRIYh3SoRJr6/2ofvvHjxxvkuXLlki1bthhJPMcSXgJQXduvXz9j70BV1Ds6dOggNWrUMA7uuHDhgrFk98cff5THjx8b1/z666/y3nvvvRBqlSA8efKkvP/++8ZJwUEzEtWy5HHjxhlJxmjRoslPP/0kLVq0CPOj4mmbPf0M6tgBbT5xVRqNXW/SqP3Jdn1Vy1Mq7kdAS4HdZ27IqyODZ8yGbOSYJsWkTsG0WrbdDo1qN3Wb03LsFAniGHv1OZbBDQtJ4xIZ7dAc6ogAAggggAACCPi1gI7jb78OqJuNJwH4Pzi1N59KuN28GTy7wdFUJf/mzp0rOXLkeIE6IglAtSS3S5cuMmLECGN5blhFLS0eOHCgdO3aNdRLVAJQ1SOskixZMhk5cqS888474X4kPGlzuA8P5wIdO6B1Ry7LuxM2mi1PkSC2bOlVw1Mq7kdASwHVD9YZsVoOnL/1Qvsal8gggxu+/AR0LVF8qFF/bj4t3WbsCrNGOVMlkAWfVBR1SAsFAQQQQAABBBBAwLcFdBx/+7a4b9aOBKBDXNTMOpWgUwm2oGW0KuHXqFEjad++vcSPHz/UKEYkARh0o9rXb8KECbJmzRpjJp+asadmBKr3VKpUSdQ+hCrZGFZRy4dnzpxpnFx8+vRpuXLlijELMHv27PLaa6/JRx99JClTpozwp83dNkf4BWFcqGMHtOLgRWk+cbPZYrU8bn33ap5ScT8C2gpMWH1M+s/d79S+LMnjy9yOFSQgTugnsmuL4WMNO3fjngQOWhZmrcY3LS4186fxsVpTHQQQQAABBBBAAIHQBHQcfxNp1wVIALpuxh1eENCxA1q874K0nLzF1MmULL6s6vZ8b0gKAgi8KHDl9gMJ/GaZPHz81Pimmk024+OyUiRjErh8QKDGdyuNgz9CluKZk8r0NoHGdhMUBBBAAAEEEEAAAd8X0HH87fvqvldDEoC+FxO/qJGOHdC83eek7W/bzPhlTxkgSz+t7BfxpJEIuCvw15bT8vW/++Tps2cy4I2C8nrR9O4+ivu8LNBvzj75ac3xF576V5tAKZklmZffxuMQQAABBBBAAAEEIktAx/F3ZFnp/FwSgDpH14fbpmMHNGvHf9Jp2g5TPU+ahMYeWRQEEHi5gJoBGDN6NInOfnI+9VFZeeiSNPt5k1OdquVJJT81L+lT9aQyCCCAAAIIIIAAAi8X0HH8TcxdFyAB6LoZd3hBQMcOSM1k+mx68Kb5hTIkltnty3tBi0cggAACUS9w/9ETKTlgidy6//x0erXid0GnipI7TcKorwxvRAABBBBAAAEEEHBbQMfxt9sYfnwjCUA/Dr6VTdexA5q68ZT0+Hu3yar2yVL7mVEQQAABuwqomc29/t4jDx4/ld718krTwCx2bQr1RgABBBBAAAEE/FZAx/G33wbTg4aTAPQAj1vdF9CxA/pl3Qn5cvZeE6VMtmQyrVWg+0jciQACCPiAwL2HTyRmjGgSK0Z0H6gNVUAAAQQQQAABBBBwVUDH8berBlwvQgKQT4ElAjp2QBNWH5P+c/ebnhVzpZTJH5SyxJeXIoAAAggggAACCCCAAAIIIKAEdBx/E1nXBUgAum7GHV4Q0LEDGr3iiAxecNDUqZ43lUxoxmb5Xvi48AgEEEAAAQQQQAABBBBAAAE3BXQcf7tJ4de3kQD06/Bb13gdO6ARSw7L8CWHTNQ6BdLImPeKW4fMmxFAAAEEEEAAAQQQQAABBPxeQMfxt98H1Q0AEoBuoHGL5wI6dkBDFx6UkcuPmDivFU4n379T1HMsnoAAAggggAACCCCAAAIIIICAmwI6jr/dpPDr20gA+nX4rWu8jh3QoHn7ZdyqYybqm8UyyLDGha1D5s0IIIAAAggggAACCCCAAAJ+L6Dj+Nvvg+oGAAlAN9C4xXMBHTugr2bvlUnrTpg475TKKIMaFPIciycggAACCCCAAAIIIIAAAggg4KaAjuNvNyn8+jYSgH4dfusar2MH1PPv3fLbxlMm6vuBmeXr+gWsQ+bNCCCAAAIIIIAAAggggAACfi+g4/jb74PqBgAJQDfQuMVzAR07oG7Td8qfW86YOB+Wzyq96+XzHIsnIIAAAggggAACCCCAAAIIIOCmgI7jbzcp/Po2EoB+HX7rGq9jB9T5jx3y9/b/TNQ2lbLLF3XyWIfMmxFAAAEEEEAAAQQQQAABBPxeQMfxt98H1Q0AEoBuoHGL5wI6dkDtpm6TubvOmTgdq+aQLjVze47FExBAAAEEEEAAAQQQQAABBBBwU0DH8bebFH59GwlAvw6/dY3XsQNqNXmLLNp3wUT9tEYu6VAtp3XIvBkBBBBAAAEEEEAAAQQQQMDvBXQcf/t9UN0AIAHoBhq3eC6gYwfUYuImWX7wkomjlv+qZcAUBBBAAAEEEEAAAQQQQAABBKwS0HH8bZWlnd9LAtDO0bNx3XXsgN6bsFHWHLlsRkUdAKIOAqEggAACCCCAAAIIIIAAAgggYJWAjuNvqyzt/F4SgHaOno3rrmMH1Hjcetl0/KoZlX7180vTwCw2jhJVRwABBBBAAAEEEEAAAQQQsLuAjuNvu8fEivqTALRCnXeKjh3QG6PXyvZT183oftOgoLxdKhPRRgABBBBAAAEEEEAAAQQQQMAyAR3H35Zh2vjFJABtHDw7V13HDqjeD6tlz383zbAMa1RY3iyewc5hou4IIIAAAggggAACCCCAAAI2F9Bx/G3zkFhSfRKAlrDzUh07oFrDV8nBC7fM4H7/TlF5rXA6go0AAggggAACCCCAAAIIIICAZQI6jr8tw7Txi0kA2jh4dq66jh1Q1aEr5NjlO2ZYxr5XTGoXSGvnMFF3BBBAAAEEEEAAAQQQQAABmwvoOP62eUgsqT4JQEvYeamOHVD5b5fJmWv3zOD+1KyEVMubmmAjgAACCCCAAAIIIIAAAgggYJmAjuNvyzBt/GISgDYOnp2rrmMHVHrgErlw84EZlskflJKKuVLaOUzUHQEEEEAAAQQQQAABBBBAwOYCOo6/bR4SS6pPAtASdl6qYwdUrN9iuXrnoRnc31uWkcDsyQk2AggggAACCCCAAAIIIIAAApYJ6Dj+tgzTxi8mAWjj4Nm56jp2QAW/XCi3Hjw2wzLj40ApnjmZncNE3RFAAAEEEEAAAQQQQAABBGwuoOP42+YhsaT6JAAtYeelOnZAuXvNlwePn5rBnd2+nBTKkIRgI4AAAggggAACCCCAAAIIIGCZgI7jb8swbfxiEoA2Dp6dq65jB5St+1x5+iw4KvM7VZC8aRPZOUzUHQEEEEAAAQQQQAABBBBAwOYCOo6/bR4SS6pPAtASdl6qWwf05Okzyd5jnlNgl3SpJDlSJSDYCCCAAAIIIIAAAggggAACCFgmoNv42zJIm7+YBKDNA2jX6uvWAd1/9ETy9F7gFI7V3apIxmTx7Roi6o0AAggggAACCCCAAAIIIKCBgG7jbw1CYkkTSABaws5LdeuAbt5/JIW+WuQU2A3dq0maxHEJNgIIIIAAAggggAACCCCAAAKWCeg2/rYM0uYvJgFo8wDatfq6dUBXbj+Q4v2XOIVja6/qkjxBHLuGiHojgAACCCCAAAIIIIAAAghoIKDb+FuDkFjSBBKAlrDzUt06oPM37kuZQUudArvrq5qSKG4sgo0AAggggAACCCCAAAIIIICAZQK6jb8tg7T5i0kA2jyAdq2+bh3Q6at3pcLg5U7hONCvtsSNFcOuIaLeCCCAAAIIIIAAAggggAACGgjoNv7WICSWNIEEoCXsvFS3DujIxdtS/buVToE9OrCuxIgejWAjgAACCCCAAAIIIIAAAgggYJmAbuNvyyBt/mISgDYPoF2rr1sHtP/cTakzYrUZDpX3OzboFbuGh3ojgAACCCCAAAIIIIAAAghoIqDb+FuTsER5M0gARjk5L1QCunVAu85cl9dGrjWDGydmdDnYvw7BRgABBBBAAAEEEEAAAQQQQMBSAd3G35Zi2vjlJABtHDw7V123Dmjryavy5pj1ZkgSxokpu/vWsnOIqDsCCCCAAAIIIIAAAggggIAGArqNvzUIiSVNIAFoCTsv1a0DWn/0irzz4wYzsMkCYsu23jUINAIIIIAAAggggAACCCCAAAKWCug2/rYU08YvJwFo4+DZueq6dUCrDl2S93/eZIYkdaI4srFHdTuHiLojgAACCCCAAAIIIIAAAghoIKDb+FuDkFjSBBKAlrDzUt06oKX7L8iHv2wxA5shaTxZ83lVAo0AAggggAACCCCAAAIIIICApQK6jb8txbTxy0kA2jh4dq66bh3Qgj3npM2UbWZIsqUIkGVdK9s5RNQdAQQQQAABBBBAAAEEEEBAAwHdxt8ahMSSJpAAtISdl+rWAc3eeVY6/r7dDGzu1AllYeeKBBoBBBBAAAEEEEAAAQQQQAABSwV0G39bimnjl5MAtHHw7Fx13TqgGVvPyKd/7TRDUiB9IpnToYKdQ0TdEUAAAQQQQAABBBBAAAEENBDQbfytQUgsaQIJQEvYealuHdC0Tafki5m7zcAWzZRE/m5bjkAjgAACCCCAAAIIIIAAAgggYKmAbuNvSzFt/HISgDYOnp2rrlsH9Ov6E9J71l4zJKWyJpM/WwfaOUTUHQEEEEAAAQQQQAABBBBAQAMB3cbfGoTEkiaQALSEnZfq1gH9tOa49Juzzwxs+RwpZMpHpQk0AggggAACCCCAAAIIIIAAApYK6Db+thTTxi8nAWjj4Nm56rp1QGNXHpVv5h8wQ1Ild0qZ2KKUnUNE3RFAAAEEEEAAAQQQQAABBDQQ0G38rUFILGkCCUBL2Hmpbh3QD0sPy7DFh8zA1syXWsa/X4JAI4AAAggggAACCCCAAAIIIGCpgG7jb0sxbfxyEoA2Dp6dq65bB/TdooPy/bIjZkheKZRWRr1bzM4hou4IIIAAAggggAACCCCAAAIaCOg2/tYgJJY0gQSgJey8VLcOSC3/VcuAg8obRdPL8LeKEGgEEEAAAQQQQAABBBBAAAEELBXQbfxtKaaNX04C0MbBs3PVdeuA1AEg6iCQoNK4RAYZ3LCwnUNE3RFAAAEEEEAAAQQQQAABBDQQ0G38rUFILGkCCUBL2Hmpbh1Qn1l7ZPL6k2Zgm5TOJAPeKEigEUAAAQQQQAABBBBAAAEEELBUQLfxt6WYNn45CUAbB8/OVdetA+o+c5f8vum0GZIW5bLIl6/mt3OIqDsCCCCAAAIIIIAAAggggIAGArqNvzUIiSVNIAFoCTsv1a0D+vTPnTJj2xkzsK0rZpPudfMSaAQQQAABBBBAAAEEEEAAAQQsFdBt/G0ppo1fTgLQxsGzc9V164A6/r5dZu88a4akfZUc0rVWbjuHiLojgAACCCCAAAIIIIAAAghoIKDb+FuDkFjSBBKAlrDzUt06oI+nbJX5e86bge1cPZd0qp6TQCOAAAIIIIAAAggggAACCCBgqYBu429LMW38chKANg6enauuWwf00S+bZcn+i2ZIutXOLW0r57BziKg7AggggAACCCCAAAIIIICABgK6jb81CIklTSABaAk7L9WtA2r600ZZffiyGdher+SVjypkI9AIIIAAAggggAACCCCAAAIIWCqg2/jbUkwbv5wEoI2DZ+eq69YBvT1+vWw4dtUMSd/X8kuzslnsHCLqjgACCCCAAAIIIIAAAgggoIGAbuNvDUJiSRNIAFrCzkt164DeHLNOtp68ZgZ24BsF5d3SmQg0AggggAACCCCAAAIIIIAAApYK6Db+thTTxi8nAWjj4Nm56rp1QK+NXCO7ztwwQzKkYSFpVCKjnUNE3RFAAAEEEEAAAQQQQAABBDQQ0G38rUFILGkCCUBL2Hmpbh1Q7f9bJQfO3zIDO+LtIlK/SHoCjQACCCCAAAIIIIAAAggggIClArqNvy3FtPHLSQDaOHh2rrpuHVC1YSvk6KU7ZkhGNykmdQumtXOIqDsCCCCAAAIIIIAAAggggIAGArqNvzUIiSVNIAFoCTsv1a0Dqjh4uZy6etcM7I/vl5Aa+VITaAQQQAABBBBAAAEEEEAAAQQsFdBt/G0ppo1fTgLQxsGzc9V164ACBy2VczfumyGZ1KKkVM6dys4hou4IIIAAAggggAACCCCAAAIaCOg2/tYgJJY0gQSgJey8VLcOqET/xXL59kMzsFM/Ki1lc6Qg0AgggAACCCCAAAIIIIAAAghYKqDb+NtSTBu/nASgjYNn56rr1gEV+mqh3Lz/2AzJX20CpWSWZHYOEXVHAAEEEEAAAQQQQAABBBDQQEC38bcGIbGkCSQALWHnpbp1QHl7L5B7j56Ygf2nXTkpkjEJgUYAAQQQQAABBBBAAAEEEEDAUgHdxt+WYtr45SQAbRw8O1ddtw4oR4958vjpMzMkczuWl/zpEts5RNQdAQQQQAABBBBAAAEEEEBAAwHdxt8ahMSSJpAAtISdl+rUAT19+kyy9ZjnFNTFnStKztQJCTQCCCCAAAIIIIAAAggggAAClgroNP62FNLmLycB6BDAU6dOyffffy9z584V9fc4ceJIjhw5pHHjxtK2bVuJHz++x+E+efKkjBkzRhYvXixHjx6VO3fuSMKECSVPnjxSp04dadOmjaRMmTLc9yxatEgmTpwoGzdulPPnz8vTp0+N+4oXLy7vvvuuNGrUSKJFixbqc06cOCFZs2YN9x3qgmbNmsmkSZMidK0rF+nUAT14/ERy91rg1PwVXStLlhQBrpBwLQIIIIAAAggggAACCCCAAAJeF9Bp/O11HD96IAnA/wVbJf2aNGkiN27cCDX8uXPnlnnz5km2bNnc/nhMnTpVWrZsKXfv3g3zGcmTJ5c///xTqlatGuo1Dx8+lKZNmxrXvKxUrlxZ/vnnH0mc+MVlqCQA3Q5hqDfefvBYCny50Ol7a7+oKumTxPPui3gaAggggAACCCCAAAIIIIAAAi4KkAB0EUzTy0kAisjOnTulbNmyRmIuQYIE0r17d6lSpYrcu3dPpk2bJj/++KMRfjVLb/PmzcY1rpb169dLhQoV5MmTJxI9enRjZl39+vUlXbp0xmzDX375Rf7991/jsQEBAbJnzx7JkiXLC69RMxHVDEJVUqVKJd26dZNixYpJrFixZPfu3fLtt9+KmmWoSt26dY3ZjCGLYwKwf//+Rj3CKkmTJpX06dO72txwr9epA7p256EU7bfYqc2belaTVAnjhuvABQgggAACCCCAAAIIIIAAAghEpoBO4+/IdNL92SQARYxk34oVKyRmzJiyatUqCQwMdIr7kCFDjESbKn379pU+ffq4/Ll49dVXZc6cOcZ9o0aNMpYUhyyffvqpfPfdd8aXO3ToYCxHdiwXL16UtGnTGst9VWJusidMawAAIABJREFU165dkiFDBqdrbt68KYULFxaV5FNl69atRoLQsTgmANUy4ubNm7vcHk9v0KkDunjzvpQauNSJZEefGpIkfmxPmbgfAQQQQAABBBBAAAEEEEAAAY8EdBp/ewTh5zf7fQJQzegrVaqU8TFo3bq1jB079oWPhEq4FShQQPbv328k3i5cuGDMuHOlJEuWTK5duyZqie/ly5dDvVUtP06SJInxPbWX35YtW5yumz17tjlbr0uXLjJs2LBQn6MSh506dTK+98MPP0j79u2driMB6Erkwr/2zLW7Uv7b5U4X7u1bSwLixAz/Zq5AAAEEEEAAAQQQQAABBBBAIBIFSABGIq6NHu33CcCePXvKwIEDjZBt2LBBSpcuHWr4vvnmG2NpsCrqAI4aNWq4FGa1bFgd+FGiRAljGXFYRR3koRKEKuGolvQ6lunTpxuHe6gycuRIadeuXaiPUct+69WrZ3xv6NChomYWOhYSgC6FLtyLj1++I1WGrnC67lD/OhI7ZvRw7+UCBBBAAAEEEEAAAQQQQAABBCJTgARgZOra59l+nwCsWLGirF692th37/r168Yy4NCK2sNP7ROoiloCrJYCu1LUMtzt27e/dAagWr4bdGhHgwYNZMaMGU6vUEt+1fJeVSI6A1DNGlTLj0kAuhIt1649dOGW1By+yumm44PqhnkKs2tP52oEEEAAAQQQQAABBBBAAAEE3BcgAei+nU53+n0CMGjGnUqs7dixI8zYquW7ahmvKmoWXnin8IZ80Lhx46RNmzbGl9UhHkF/d7zus88+M2bsqbJw4UKpWbPmC/VRSUiVjFR1UTME1SEijuXWrVtGkvD48ePGISIHDhyQOHHiOF3jOANQJSavXr0qZ8+elbhx4xp7CqrDSlq1avXC3oHe/ODr1AHt+e+G1PthjckTO0Z0OTSgjje5eBYCCCCAAAIIIIAAAggggAACbgnoNP52C4CbDAG/TgDev39f4sWLZ0C88sor5iEdYX02gpbxlilTxkjCuVLU6b/q5N/ffvvNOAX4gw8+kNdee8041EOdAjxlyhT5+++/jUd+/vnnopYch1ZUQq927drGSb+pU6c2TwFWMxfVycGDBw82kn9qr0E1+y9o1qLjsxwTgC9rg9oTccSIES8kECPSbtXBvKycO3fO3Hvx9OnTLxxmEpF3+Mo1209dkzdGrzOrExA7huz9uravVI96IIAAAggggAACCCCAAAII+LEACUA/Dr5D0/06AXjp0iVJlSqVwfHWW2/JtGnTXvqpUAk3dRJvaPvzRfTjpGYOquSeWg4csqjTiL/44otQZ/45Xqv2CBw9erSo04lv377t9Bh1OEnHjh2N/2XKlCnUaqkEYNGiReWNN96QypUrS86cOY3Zfyopp/Y3/Omnn8znvvvuu0bS0tUSLVq0CN9i9wTgpuNXpfG44IRwkvixZEefF2dvRhiECxFAAAEEEEAAAQQQQAABBBDwkgAJQC9B2vwxfp0AVImnoCRZ06ZNZfLkyS8Np7pW3ZM9e3Y5cuSIy6FXs/dUgk8d0vH48eMX7ldJuDfffNNI7KmZgWEVVc9vv/1W9u3bF+olmTNnlg4dOhj7BIaWiHv48KHx/vjx44d6/+HDh6V69erGzERVZs2aZcxWdKX4UwJw7ZHL0mTCRpMnZcI4srlndVe4uBYBBBBAAAEEEEAAAQQQQACBSBEgARgprLZ7qF8nAKNyBqA6aEQl0dRBIypB179/f+MkYbWX34ULF4zlur169RK116Dah0/NxMubN+8LH6iuXbvKsGHDjK+//vrrovYNVHv+xYgRQ/bv3y8//PCDTJw40fi+2qtQzWpUS45dLWvWrDH2AlRFJQMXL17s0iP8aQnw8gMXpcWk4JOd0yeJJ2u/qOqSFxcjgAACCCCAAAIIIIAAAgggEBkCJAAjQ9V+z/TrBGBU7QH44MEDY9bgf//9J2nSpDGW/6o/Q5a9e/dKiRIlRNVL/bl5c3BSSV07Z84c80Tf5s2bm4m+kM9RpxT369fP+PLIkSOlXbt2bn0y1VJnVSc1M/HOnTtuJRLDerFOHdDCveel9a9bzaZmSR5fVnxWxS1zbkIAAQQQQAABBBBAAAEEEEDAmwI6jb+96eJvz/LrBKAKdlScAqyW0KrZeqoMGDBAevToEebnrGXLljJhwgTj++pUYjW7L6ioPfv++ecf4/+qH+D06dOH+hyVQFTtUvsDFilSJNT9BiPyQW/cuLH89ddfxqVq70P1TG8VnTqgObvOSvupwXs65kyVQBZ3qeQtKp6DAAIIIIAAAggggAACCCCAgNsCOo2/3UbgRv8+BVjFv2LFiqKW5wYEBBjLc9VpuqEVdepv0Im6aoZd3759I/zxUYd+dO/e3bh+/vz5xim+YZWxY8fKxx9/bHxbLd9Vh5MElTx58sjBgweN03/Pnz//0verk4o3btxozN67d+9ehOvqeKFaQjx9+nTjSyQAwyb8e/sZ6fzHTvOCfGkTybxOz5dPUxBAAAEEEEAAAQQQQAABBBCwUoAEoJX6vvNuv58BqGbjDRo0yIjIhg0bpHTp0qFGxzGJt3DhwnBP6nV8yNChQ429+lT5999/pV69emF+AtQefuoEX1VU8k0dChJUgpbkJk+eXNRJwC8rxYsXl23btkmCBAnk1q1bbn3i8ufPbxw0EidOHLl79y5LgMNQ/HPzaek2Y5f53cIZk8isduXcMucmBBBAAAEEEEAAAQQQQAABBLwpQALQm5r2fZbfJwA3bdpkJv1at24tagZeyPL06VNRyTd1yEaSJEmM2XCxYsWKcNRnzJghDRs2NK7v1q2bcYJvWEVdp65XZevWrVKsWDHz0ldffdXYB1AVlZgL7ZAQ9b2rV68apwir034LFiwou3YFJ6ciWmnHQ0CqVasmS5YsieitEbpOpw5oyoaT0uufPWa7S2ZJKn+1KRshBy5CAAEEEEAAAQQQQAABBBBAIDIFdBp/R6aT7s/2+wSgCnDQMmC1/HfVqlUSGBjoFPchQ4YYiTtVvvzyS/nqq6+cvr9ixQqpUuX5oQ/NmjWTSZMmOX1fLS1W+/WpWXQJEyaUtWvXGom5kEUtD1azA1XCUV1/6tQpp1l348ePF5WkVKVmzZrGbMLYsWM7PUbdq+owZcoU4+tq6fHAgQOdrlH7CNavX1+iRYsW6uf7yJEjopJ+6v2qqIRkgwYNvPqzoFMHNHHtcen77z7Tp2z25DK1ZRmvevEwBBBAAAEEEEAAAQQQQAABBNwR0Gn87U77uee5AAlAEeOQjHLlyhl75akls2pZsEroqf+v9uFTiTdVcuXKJVu2bDGSeI4lvASguladyqv2DlRFvaNDhw5So0YNSZo0qVy4cEHUQSE//vijPH782Ljm119/lffee8/pPWpGnzoU5MCBA8bXVRJRPUd9LUaMGMaswDFjxojar1AVtVfgnj17JEWKFE7PUYm/HDlyGEm9UqVKSYYMGYxlvmfPnpVFixYZh5CoU39VUQeB/PHHH17/edGpAxq/6qgMnPc8JqpUypVSfvmglNfNeCACCCCAAAIIIIAAAggggAACrgroNP52te1cHyxAAvB/Fmo2nUq43bx5M9TPh0r+zZ0710ichSwRSQA+e/ZMunTpIiNGjBD197CKWlqsZux17do11EtOnjxpzN7buTP40InQLsyaNavMnDnTOAU4ZAlr5l/I69RhJMOHDzeSg94uOnVAo5YfkSELD5pE1fOmlgnNSnibjOchgAACCCCAAAIIIIAAAggg4LKATuNvlxvPDaYACUCHD4NKrqkEnUr0qR8QtbxWJfzUabjt27eX+PHjh/rRiUgCMOhGta+fmmGn9thT71PLgtWMQPWeSpUqGUt8VbLxZeXRo0fGzER1SIg66OPSpUtGUjFZsmRSqFAhef311+X99983TjYOrahkp5olqE4JVnVQB4qoGX+JEiWSbNmySYUKFeSDDz4w9j2MrKJTBzR88SEZsfSwSVW3YBoZ3aR4ZNHxXAQQQAABBBBAAAEEEEAAAQQiLKDT+DvCjebCFwRIAPKhsERApw5o8IIDMnrFUdOxfpF0MuLtopa48lIEEEAAAQQQQAABBBBAAAEEHAV0Gn8TWfcFSAC6b8edHgjo1AENmLtPflx93NRoWDyDDG1U2AMdbkUAAQQQQAABBBBAAAEEEEDAOwI6jb+9I+KfTyEB6J9xt7zVOnVAX83eK5PWnTBN3ymVSQY1ePGUZ8vRqQACCCCAAAIIIIAAAggggIDfCeg0/va74HmxwSQAvYjJoyIuoFMH1OPv3TJ14ymz8c0CM0vf+pG3f2LElbkSAQQQQAABBBBAAAEEEEDA3wV0Gn/7eyw9aT8JQE/0uNdtAZ06oM/+2il/bT1jWnxUPqv0qpfPbRtuRAABBBBAAAEEEEAAAQQQQMBbAjqNv71l4o/PIQHoj1H3gTbr1AF9Mm27/LPjrKn6ceXs8nntPD6gTBUQQAABBBBAAAEEEEAAAQT8XUCn8be/x9KT9pMA9ESPe90W0KkDavfbNpm7+5xp0bFaTulSI5fbNtyIAAIIIIAAAggggAACCCCAgLcEdBp/e8vEH59DAtAfo+4DbdapA2o5eYss3nfBVO1aM5e0r5rTB5SpAgIIIIAAAggggAACCCCAgL8L6DT+9vdYetJ+EoCe6HGv2wI6dUDNJ26SFQcvmRbd6+SR1pWyu23DjQgggAACCCCAAAIIIIAAAgh4S0Cn8be3TPzxOSQA/THqPtBmnTqgJhM2yNojV0zVPvXyyQfls/qAMlVAAAEEEEAAAQQQQAABBBDwdwGdxt/+HktP2k8C0BM97nVbQKcOqPHY9bLpxFXTot/rBaRpmcxu23AjAggggAACCCCAAAIIIIAAAt4S0Gn87S0Tf3wOCUB/jLoPtFmnDuj1UWtlx+nrpurgNwtJ45IZfUCZKiCAAAIIIIAAAggggAACCPi7gE7jb3+PpSftJwHoiR73ui2gUwf0yverZe/Zm6bF8LcKyxtFM7htw40IIIAAAggggAACCCCAAAIIeEtAp/G3t0z88TkkAP0x6j7QZp06oJrDV8qhC7dN1ZHvFpV6hdL5gDJVQAABBBBAAAEEEEAAAQQQ8HcBncbf/h5LT9pPAtATPe51W0CnDqjK0BVy/PId02Jc0+JSK38at224EQEEEEAAAQQQQAABBBBAAAFvCeg0/vaWiT8+hwSgP0bdB9qsUwdU7ptl8t/1e6bqxOYlpUqeVD6gTBUQQAABBBBAAAEEEEAAAQT8XUCn8be/x9KT9pMA9ESPe90W0KkDKjlgiVy69cC0mPJhaSmfM4XbNtyIAAIIIIAAAggggAACCCCAgLcEdBp/e8vEH59DAtAfo+4DbdapAyry9SK5fveRqfpHqzJSOltyH1CmCggggAACCCCAAAIIIIAAAv4uoNP4299j6Un7SQB6ose9bgvo1AHl77NA7jx8YlrMbFtWimVK6rYNNyKAAAIIIIAAAggggAACCCDgLQGdxt/eMvHH55AA9Meo+0CbdeqAcvWcLw+fPDVV53QoLwXSJ/YBZaqAAAIIIIAAAggggAACCCDg7wI6jb/9PZaetJ8EoCd63Ou2gC4d0LNnzyRr93lODgs/qSi50yR024YbEUAAAQQQQAABBBBAAAEEEPCWgC7jb295+OtzSAD6a+QtbrcuHdCjJ08lZ8/5TprLPq0k2VImsFiY1yOAAAIIIIAAAggggAACCCAgosv4m1h6JkAC0DM/7nZTQJcO6O7Dx5Kvz0InhdXdqkjGZPHdlOE2BBBAAAEEEEAAAQQQQAABBLwnoMv423si/vkkEoD+GXfLW61LB3Tj7iMp/PUiJ8+NPapJ6kRxLTemAggggAACCCCAAAIIIIAAAgjoMv4mkp4JkAD0zI+73RTQpQO6dOuBlBywxElhW+8akiwgtpsy3IYAAggggAACCCCAAAIIIICA9wR0GX97T8Q/n0QC0D/jbnmrdemAzl6/J2W/WebkufurmpIwbizLjakAAggggAACCCCAAAIIIIAAArqMv4mkZwIkAD3z4243BXTpgE5euSOVhqxwUjjQr7bEjRXDTRluQwABBBBAAAEEEEAAAQQQQMB7ArqMv70n4p9PIgHon3G3vNW6dEBHLt6S6t+tcvI8OrCuxIgezXJjKoAAAggggAACCCCAAAIIIICALuNvIumZAAlAz/y4200BXTqgfWdvSt3vV5sKKvGnEoAUBBBAAAEEEEAAAQQQQAABBHxBQJfxty9Y2rkOJADtHD0b112XDmjn6etSf9RaMxJxYkaXg/3r2DgyVB0BBBBAAAEEEEAAAQQQQEAnAV3G3zrFxIq2kAC0Qp13ii4d0MZjV+St8RvMiCaJH0t29KlJhBFAAAEEEEAAAQQQQAABBBDwCQFdxt8+gWnjSpAAtHHw7Fx1XTqgFQcvSvOJm81QpE0cV9Z3r2bn0FB3BBBAAAEEEEAAAQQQQAABjQR0GX9rFBJLmkIC0BJ2XqpLB7RgzzlpM2WbGdBsKQJkWdfKBBgBBBBAAAEEEEAAAQQQQAABnxDQZfztE5g2rgQJQBsHz85V16UD+nv7Gen8x04zFPnSJpJ5nSrYOTTUHQEEEEAAAQQQQAABBBBAQCMBXcbfGoXEkqaQALSEnZfq0gH9tvGk9Px7jxnQ4pmTyoyPyxJgBBBAAAEEEEAAAQQQQAABBHxCQJfxt09g2rgSJABtHDw7V12XDmjC6mPSf+5+MxTlc6SQKR+VtnNoqDsCCCCAAAIIIIAAAggggIBGArqMvzUKiSVNIQFoCTsv1aUDGrnssAxddMgMaPW8qWVCsxIEGAEEEEAAAQQQQAABBBBAAAGfENBl/O0TmDauBAlAGwfPzlXXpQMasvCAjFp+1AzFq4XTyQ/vFLVzaKg7AggggAACCCCAAAIIIICARgK6jL81CoklTSEBaAk7L9WlA/r6333y89rjZkAbl8gggxsWJsAIIIAAAggggAACCCCAAAII+ISALuNvn8C0cSVIANo4eHauui4dUPeZu+X3TafMUDQLzCx96xewc2ioOwIIIIAAAggggAACCCCAgEYCuoy/NQqJJU0hAWgJOy/VpQPq/McO+Xv7f2ZA21TKLl/UyUOAEUAAAQQQQAABBBBAAAEEEPAJAV3G3z6BaeNKkAC0cfDsXHVdOqA2v26VBXvPm6HoXD2XdKqe086hoe4IIIAAAggggAACCCCAAAIaCegy/tYoJJY0hQSgJey8VJcOqNnPm2TloUtmQHvUzSOtKmYnwAgggAACCCCAAAIIIIAAAgj4hIAu42+fwLRxJUgA2jh4dq66Lh1Q43HrZdPxq2Yo+tXPL00Ds9g5NNQdAQQQQAABBBBAAAEEEEBAIwFdxt8ahcSSppAAtISdl+rSAb02co3sOnPDDOiQhoWkUYmMBBgBBBBAAAEEEEAAAQQQQAABnxDQZfztE5g2rgQJQBsHz85V16UDqvHdSjl88bYZipHvFpV6hdLZOTTUHQEEEEAAAQQQQAABBBBAQCMBXcbfGoXEkqaQALSEnZfq0gGV/3aZnLl2zwzoT81KSLW8qQkwAggggAACCCCAAAIIIIAAAj4hoMv42ycwbVwJEoA2Dp6dq65LB1Si/2K5fPuhGYqpH5WWsjlS2Dk01B0BBBBAAAEEEEAAAQQQQEAjAV3G3xqFxJKmkAC0hJ2X6tIB5e+zQO48fGIGdGbbslIsU1ICjAACCCCAAAIIIIAAAggggIBPCOgy/vYJTBtXggSgjYNn56rr0AE9e/ZMsvWYJ8+eBUdifqcKkjdtIjuHhrojgAACCCCAAAIIIIAAAghoJKDD+FujcFjWFBKAltH794t16IDuP3oieXovcArkiq6VJUuKAP8OLq1HAAEEEEAAAQQQQAABBBDwGQEdxt8+g2njipAAtHHw7Fx1HTqg63cfSpGvFzuFYWOPapI6UVw7h4a6I4AAAggggAACCCCAAAIIaCSgw/hbo3BY1hQSgJbR+/eLdeiAzt24J4GDljkFcueXNSVxvFj+HVxajwACCCCAAAIIIIAAAggg4DMCOoy/fQbTxhUhAWjj4Nm56jp0QMcu3Zaqw1Y6heFQ/zoSO2Z0O4eGuiOAAAIIIIAAAggggAACCGgkoMP4W6NwWNYUEoCW0fv3i3XogPaevSGvfL/GDGSM6NHkyIA6Ei1aNP8OLq1HAAEEEEAAAQQQQAABBBDwGQEdxt8+g2njipAAtHHw7Fx1HTqgrSevyptj1pthSBgnpuzuW8vOYaHuCCCAAAIIIIAAAggggAACmgnoMP7WLCSWNIcEoCXsvFSHDmjN4cvy3k8bzWCmTBhHNvesTnARQAABBBBAAAEEEEAAAQQQ8BkBHcbfPoNp44qQALRx8OxcdR06oMX7LkjLyVvMMGRKFl9Wdati57BQdwQQQAABBBBAAAEEEEAAAc0EdBh/axYSS5pDAtASdl6qQwc0e+dZ6fj7djOYuVMnlIWdKxJcBBBAAAEEEEAAAQQQQAABBHxGQIfxt89g2rgiJABtHDw7V12HDujPzael24xdZhgKZ0wis9qVs3NYqDsCCCCAAAIIIIAAAggggIBmAjqMvzULiSXNIQFoCTsv1aED+mXdCfly9l4zmGWyJZNprQIJLgIIIIAAAggggAACCCCAAAI+I6DD+NtnMG1cERKANg6enauuQwc0duVR+Wb+ATMMVXKnlIktStk5LNQdAQQQQAABBBBAAAEEEEBAMwEdxt+ahcSS5pAAtISdl+rQAQ1ffEhGLD1sBrNuwTQyuklxgosAAggggAACCCCAAAIIIICAzwjoMP72GUwbV4QEoI2DZ+eq69ABDZy3X8avOmaGoUGx9PJd4yJ2Dgt1RwABBBBAAAEEEEAAAQQQ0ExAh/G3ZiGxpDkkAC1h56U6dEC9/9kjv244aQazSelMMuCNggQXAQQQQAABBBBAAAEEEEAAAZ8R0GH87TOYNq4ICUAbB8/OVdehA+r6106ZvvWMGYaPymeVXvXy2Tks1B0BBBBAAAEEEEAAAQQQQEAzAR3G35qFxJLmkAC0hJ2X6tABtZv6/+3dCbyV0/7H8R9pUEqFBhmSoihRKUklKcpQhsxERIZwM1aGkjIkl8yFQsiUIZUGJFOjIkNSKFKJJJpI/V/f9b972+e09zl7n7On9ezPer3u697bfvbzrPX+PXud/fz2Gj6xcZ8tDwez51G17Zr2+xNcBBBAAAEEEEAAAQQQQAABBLJGIAjP31mD6XFFSAB6HDyfqx6EDujCkbPs7QU/h8Nw3TH72+VtavscFuqOAAIIIIAAAggggAACCCAQMIEgPH8HLCQZaQ4JwIywc9EgdEBnDZ9uHy3+NRzMW44/wLodsQ/BRQABBBBAAAEEEEAAAQQQQCBrBILw/J01mB5XhASgx8HzuepB6IBOevhDm7t0TTgMd5zcwM5supfPYaHuCCCAAAIIIIAAAggggAACARMIwvN3wEKSkeaQAMwIOxcNQgd07H3TbMGKP8LBvP+Mg63TwTUILgIIIIAAAggggAACCCCAAAJZIxCE5++swfS4IiQAPQ6ez1UPQgd05OB37ftf14fD8Ni5je2YA6v5HBbqjgACCCCAAAIIIIAAAgggEDCBIDx/BywkGWkOCcCMsHPRIHRAzQZNsZVrN4WD+XS3ptZqv90ILgIIIIAAAggggAACCCCAAAJZIxCE5++swfS4IiQAI4K3dOlSGzp0qI0bN870v0uXLm21a9e20047zS677DIrW7ZssUO9ZMkSe+SRR2zy5Mm2ePFiW7dunZUvX97q1q1rHTp0sB49ethuuxWeRJo0aZKNGDHCZsyYYStWrLAtW7a49zVu3NjOOuss69Kli2233XaF1veLL76wBx54wKZMmWLLli2znXbayerVq2dnn322XXjhhbbDDjsUeo6iHBCEDuigfhNt7cbN4ea/1KO5HVqzclE4eA8CCCCAAAIIIIAAAggggAACKREIwvN3SmBy7KQkAP8XcCX9lPT6/fffo94C+++/v40fP95q1apV5Fvkueees+7du9v69f9OG81/sl122cVefPFFO+qoo6Je56+//rJzzz3XHVNQOfLII+21116znXfeOeZhTzzxhF1++eW2adO/o9giDz7ssMPszTffNNUp2SUIHdB+fSfYX/9sCdO82fMIq18jtneyDTkfAggggAACCCCAAAIIIIAAAoUJBOH5u7A28nrhAiQAzezTTz+1ww8/3CXmNAKud+/e1qZNG9uwYYONHj3ahg8f7iQ1Sm/WrFnumETLxx9/bC1btrR//vnHtt9+e+vatat16tTJdt99dzfa8KmnnrKxY8e605YrV84+//xzq1mz5jaX0UhEjSBUqVKlil1//fXWqFEjK1mypM2fP9/uuusu0yhDlY4dO7rRjNHKxIkT3esaOVi1alXr27evNWvWzFavXu3aO2bMGPe2Vq1a2bvvvuvqnMziewe0+Z8tVrvvhDwkU3q1ttpVEr83kunKuRBAAAEEEEAAAQQQQAABBBCIFPD9+ZtoJkeABKCZS/ZNnTrVTXedNm2aNW/ePI/u4MGDXaJNpX///nbLLbckrH/CCSe40XQqDz30kJtSnL9cc801du+997p/7tmzp5uOHFl+/vlnq169ukvaVapUyT777DPbY4898hyzdu1aa9iwoX3//ffu3+fMmeMShJFl8+bNbprvokWLrEKFCvbJJ5/Yvvvum+cYjQx8+OGH3b8pOXneeecl3OaC3uB7B/Tnps1W/9aJeZr44Y1HWY2KOybViZMhgAACCCCAAAIIIIAAAgggUBwB35+/i9N23vuvQM4nADWir2nTpk7kkksusUcffXSb+0MJt/r169tXX33lEm8rV650I+4SKZUrV7bffvvNTaf95Zdfor5V048rVqzoXtNafrNnz85z3BtvvOFGDaqzoXXfAAAgAElEQVT06tXLhgwZEvU8ShxeddVV7jWt73fFFVfkOe6ll15y6xqq3HHHHXbjjTducx6NhlRyUXVW2zW6MJnF9w7o5z82WtOBb+ch+eTmdla5XKlkMnEuBBBAAAEEEEAAAQQQQAABBIol4Pvzd7Eaz5vDAjmfANTU10GDBjmQ6dOnu2mw0cqdd97ppgaraAOOdu3aJXQbadqwNvxo0qSJm0Ycq2gjDyUIoyXdXn75Zbe5h8qDDz7o1u+LVjTt9/jjj3cv3XPPPaaRhZFFax1qPUKV5cuXW7Vq1aKeRxuSPPbYY+61hQsXWp06dRJqc0EH+94BLf11vbUa/G6eJn5127G2Y6kSSTPiRAgggAACCCCAAAIIIIAAAggUV8D35+/itp/3/79AzicAtcbd+++/79bdW7NmTcxdb7WGn9YJVNEUYE0FTqRoGu7cuXMLHAGo6buhTTtOPvlke+WVV/JcQlN+Nb1XJd4RgBo1qOnHkWWvvfayH374wbSxyYIFC2I24/nnn3c7Cqs8+eSTdsEFFyTS5AKP9b0D+nrFH3bMfdPytPHbQR1t++0L33k5aYicCAEEEEAAAQQQQAABBBBAAIFCBHx//ibAyRHI+QRgaMSdEmvz5s2LqaqpsJrGq6JReIXtwpv/RBpJpxF1KtrEI/S/I4+77rrr3Ig9FW3S0b59+23qoySkkpGqi6blahORyPLHH3+4JOF3333nNhFRgq906dLhQ/7880+37t/WrVvddGLtFByrKGEZWj9Qdbv77ruTc9eZme8d0Lwf1ljnhz4Me5Qpub0tGNAhaT6cCAEEEEAAAQQQQAABBBBAAIFkCPj+/J0MA86R4yMAN27caDvu+P+bNhx33HHhTTpi3RihabyHHXaYS8IlUrT7r3b+ffbZZ92Out26dbMTTzzRbeqhXYBHjRplr776qjvlDTfcYJpyHK0ooXfssce6nX61e29oF2BtYKKdg5WkU/JPaw1q9F9o1GLoXHq/NgBR0RRiTSWOVTQVWQlSlTPOOMM0IjDeog6moKKpx6G1FzUaMf9mJvFeJ1PHfbz4Vztz+PTw5SuVLWlzb9k2YZup+nFdBBBAAAEEEEAAAQQQQAABBCRAApD7QAI5PQJw1apVVqVKFXcnnH766TZ69OgC7wol3LQTb3E2xdDIQSX3NLouf9FuxNqQI9rIv8hjlZjTDr3anVgj+iKLNie58sor3X801Td/idz0pKBEo963YcMGK1u2rDuF1hQcO3Zs3J+a7baLfyqsjwnAdxf8bBeM/Hctx913LmMf9W4btw8HIoAAAggggAACCCCAAAIIIJAOARKA6VDO/mvkdAJQiadQkuzcc8+1p59+usCIhdbO23fffW3RokUJR1ej75Tg0yYdmzdv3ub9ZcqUsVNOOcUl9jQyMFZRPe+66y778ssvox6y9957W8+ePd06gfkTcVrvUOseqtx888122223xbyOdj8uUeL/N7Vo27atTZkyJe42Bz0BOH7+crvs2U/CHrV2K2fvXHNk3D4ciAACCCCAAAIIIIAAAggggEA6BEgApkM5+6+R0wnAdI4AVOJNU3610YgSdLfffrvbSVhr+a1cudJN173ppptMaw1qOqx2Gg5N1Y28ja699lobMmSI+6fOnTub1ubTmn9K1H311Vf2wAMP2IgRI9zrWqtQoxo15ThU0jUCMOhTgF+Z86Nd89KnYdcDd69g465smf2feGqIAAIIIIAAAggggAACCCCQUwIkAHMq3DEbm9MJwHStAbhp0ybTqMFly5ZZtWrV3PRf/Xf+8sUXX1iTJk1M9dJ/K1kXWd58883wjr7nn39+ONGX/zzapXjAgAHun7XGn9b6C5V0rQFY2MfL9w5o1PQldtNrn4ebeWjNSvZSj//fJZqCAAIIIIAAAggggAACCCCAQLYI+P78nS2OvtcjpxOACl46dgF+/fXX3Wg9lYEDB1qfPn1i3jfdu3e3xx9/3L2uXYk1ui9UTjrppPCuvfoA16hRI+p5lEBUu7Q+4MEHH5xnvUH9W/ny5d372AW46B/fx9//1m4f91X4BC3r7GrPXNis6CfknQgggAACCCCAAAIIIIAAAgikQIAEYApQPTxlzicAtR6epueWK1fOTc/VbrrRinb9De2oqxF2/fv3jzvc2vSjd+/e7vgJEya4XXxjlUcffdQuvfRS97Km72pzklCpW7euff3112733xUrVhR4fe1UPGPGDNO6gtrMI7KE1jLcf//9TSMCYxXt+nvWWWe5l5988km74IIL4m5zYQf63gE98PY3NmTywnAz2x9Q1Yad16SwZvM6AggggAACCCCAAAIIIIAAAmkV8P35O61YAb5YzicANRrvjjvucCGePn26NWsWfRRXZBJv4sSJhe7UG3nP3HPPPW6tPhXtpKsddWMVreGnHXxVXn75ZbcpSKho92FNE95ll11MOwEXVBo3bmyffPKJ7bTTTvbHH3/kOVRJPSX3VJYvXx51OrJe69Gjhz322GPuOCUe99tvv6R9FHzvgO5+a4E9PHVx2KPTwbvb/WcckjQfToQAAggggAACCCCAAAIIIIBAMgR8f/5OhgHnMMv5BODMmTPDSb9LLrnENAIvf9FuuEq+aZONihUr2s8//2wlS5aM+/555ZVX7NRTT3XHX3/99W4H31hFx+l4lTlz5lijRo3Ch55wwgmmdQBVtANwtE1C9Nrq1avdLsJ//fWXNWjQwD777LM8l3vxxRfDIwuV/NTOxPnL+vXr3WYk2pTkgAMOcInHZBbfO6D+Y7+wER9+HyY549A97c5TDkomEedCAAEEEEAAAQQQQAABBBBAoNgCvj9/FxuAEziBnE8ACiE0DVjTf6dNm2bNmzfPc3sMHjzYJe5Ubr31VuvXr1+e16dOnWpt2rRx/9a1a1cbOXJkntc1tVjr9SmppvX3PvzwQ5eYy180PVijA5Vw1PFLly7Ns4PvsGHDTElKlfbt27vRhKVKlcpzGr1XdRg1apT7d009HjRoUJ5j/v77b5c8XLx4sVWoUMGNFNQmJZFFG4c8/PDD7p+0q7A2HUlm8b0D6j3mM3t+5g9hkvMPr2n9TjwwmUScCwEEEEAAAQQQQAABBBBAAIFiC/j+/F1sAE7gBEgAmrlNMlq0aOHWytOUWU0LVkJP/1/r8CnxpqIpsLNnzw5vohG6hwpLAOo47cqrtQNVdI2ePXtau3btrFKlSrZy5UrTRiHDhw+3zZs3u2OeeeYZO+ecc/LcphrRp01BQuv2KYmo8+jfSpQo4UYFPvLII6b1ClW0VuDnn39uu+666za3+/jx492OwkoY6ribbrrJmjZt6kb8qR6hUYhHHHGEqX06fzKL7x3Q1aPn2mvzfgqTXHrkvnbDsXWTScS5EEAAAQQQQAABBBBAAAEEECi2gO/P38UG4AROgATg/24EjaZTwm3t2rVRbw0l/8aNG2e1a9fe5vV4EoBbt261Xr162f3332/637GKphZrxN61114b9ZAlS5a43Xs//fTTAm/hffbZx8aMGeN2AY5VlOi74oor3FThaEUJQbU5WgKxuJ8f3zugi5+ebZO+XBlm6NVuP7uybZ3isvB+BBBAAAEEEEAAAQQQQAABBJIq4Pvzd1IxcvhkJAAjgq/kmhJ0SnrpA6LptUr4denSxSXKypYtG/VWiScBGHqj1vV7/PHH7YMPPjBdT9OCNSJQ12ndurWb4lvYZhuawquRidokRNN3V61a5ZKKlStXtoMOOsg6d+5s5513ntvZuLCiEYJDhw61t99+23766Sf3Hk0PPvvss+2iiy6KuStyYect7HXfO6Bzn5hh73/z70YsfTvWs+6tahXWbF5HAAEEEEAAAQQQQAABBBBAIK0Cvj9/pxUrwBcjARjg4GZz03zvgLo8+pHN+v63MPGAzvXt3MP2zmZy6oYAAggggAACCCCAAAIIIJCDAr4/f+dgyFLSZBKAKWHlpIUJ+N4BHf/A+/b5sn+ni9/TpaGd2niPwprN6wgggAACCCCAAAIIIIAAAgikVcD35++0YgX4YiQAAxzcbG6a7x1Q2yFTbfGqdWHih85qZMcdVD2byakbAggggAACCCCAAAIIIIBADgr4/vydgyFLSZNJAKaElZMWJuB7B9Tiznds2ZoN4WY+eX4TO6pu1cKazesIIIAAAggggAACCCCAAAIIpFXA9+fvtGIF+GIkAAMc3Gxumu8dUKMBk231un93T36uezM7fN9ds5mcuiGAAAIIIIAAAggggAACCOSggO/P3zkYspQ0mQRgSlg5aWECvndA9W5+yzb8/U+4ma9edrgdslelwprN6wgggAACCCCAAAIIIIAAAgikVcD35++0YgX4YiQAAxzcbG6azx3Q1q1bbZ/e4/PwTry6le1frXw2k1M3BBBAAAEEEEAAAQQQQACBHBTw+fk7B8OVsiaTAEwZLScuSMDnDmjj3/9Y3ZvfytO8ade1sb12KUvQEUAAAQQQQAABBBBAAAEEEMgqAZ+fv7MK0vPKkAD0PIC+Vt/nDui3dX/ZIQMm56Gf2betVSlfxtdwUG8EEEAAAQQQQAABBBBAAIGACvj8/B3QkGSkWSQAM8LORX3ugH5as8EOv/OdPEGc36+9lS9TksAigAACCCCAAAIIIIAAAgggkFUCPj9/ZxWk55UhAeh5AH2tvs8d0OJVf1rbIe/lof9mYAcrWWJ7X8NBvRFAAAEEEEAAAQQQQAABBAIq4PPzd0BDkpFmkQDMCDsX9bkD+nzZ73b8Ax+Eg1iyxHb2zcCOBBUBBBBAAAEEEEAAAQQQQACBrBPw+fk76zA9rhAJQI+D53PVfe6AZn2/2ro8+nGYv3yZHWx+v2N8Dgd1RwABBBBAAAEEEEAAAQQQCKiAz8/fAQ1JRppFAjAj7FzU5w5o2sJVdt6TM8NBrFK+tM3sezRBRQABBBBAAAEEEEAAAQQQQCDrBHx+/s46TI8rRALQ4+D5XHWfO6CJX6ywS56ZE+bfe5ey9t51bXwOB3VHAAEEEEAAAQQQQAABBBAIqIDPz98BDUlGmkUCMCPsXNTnDuj1ecvsqtHzwkGsW628vXV1K4KKAAIIIIAAAggggAACCCCAQNYJ+Pz8nXWYHleIBKDHwfO56j53QKNnLrUbx8wP8x+8Z0V77fIWPoeDuiOAAAIIIIAAAggggAACCARUwOfn74CGJCPNIgGYEXYu6nMHNOLD76z/2C/DQWxeaxd7/uLDCCoCCCCAAAIIIIAAAggggAACWSfg8/N31mF6XCESgB4Hz+eq+9wBPTx1kd391tdh/qPqVrEnzz/U53BQdwQQQAABBBBAAAEEEEAAgYAK+Pz8HdCQZKRZJAAzws5Ffe6A7p30tQ19Z1E4iMc1qG4Pnd2IoCKAAAIIIIAAAggggAACCCCQdQI+P39nHabHFSIB6HHwfK66zx3QwHFf2vD3vwvzn9JoDxtyWkOfw0HdEUAAAQQQQAABBBBAAAEEAirg8/N3QEOSkWaRAMwIOxf1uQO66bX5Nmr60nAQzz1sbxvQuT5BRQABBBBAAAEEEEAAAQQQQCDrBHx+/s46TI8rRALQ4+D5XHWfO6BrXvzUXvnkxzD/xa1qWZ+O9XwOB3VHAAEEEEAAAQQQQAABBBAIqIDPz98BDUlGmkUCMCPsXNTnDujyZz+xcfOXh4N4Zds61qvdfgQVAQQQQAABBBBAAAEEEEAAgawT8Pn5O+swPa4QCUCPg+dz1X3ugLqNnGXvLPg5zH/DsXXt0iP39Tkc1B0BBBBAAAEEEEAAAQQQQCCgAj4/fwc0JBlpFgnAjLBzUZ87oDOHTbePv/01HMR+Jxxg57fYh6AigAACCCCAAAIIIIAAAgggkHUCPj9/Zx2mxxUiAehx8Hyuus8dUOeHPrR5P6wJ8991SgM7/dC9fA4HdUcAAQQQQAABBBBAAAEEEAiogM/P3wENSUaaRQIwI+xc1OcO6Jj/TrOvV/4RDuL9ZxxsnQ6uQVARQAABBBBAAAEEEEAAAQQQyDoBn5+/sw7T4wqRAPQ4eD5X3ecOqNXd79rS1evD/MPObWztD6zmczioOwIIIIAAAggggAACCCCAQEAFfH7+DmhIMtIsEoAZYeeiPndAhw6cYqv+2BQO4jMXNrWWdXYjqAgggAACCCCAAAIIIIAAAghknYDPz99Zh+lxhUgAehw8n6vucwfU4NaJ9semzWH+l3s0tyY1K/scDuqOAAIIIIAAAggggAACCCAQUAGfn78DGpKMNIsEYEbYuajPHVDtPuNt85at4SC+2fMIq19jZ4KKAAIIIIAAAggggAACCCCAQNYJ+Pz8nXWYHleIBKDHwfO56r52QH//s8Xq9J2Qh/7ta1rbvrvt5HM4qDsCCCCAAAIIIIAAAggggEBABXx9/g5oODLWLBKAGaPP7Qv72gGt3fi3HdRvUp7gfXTjUbZ7xR1zO6C0HgEEEEAAAQQQQAABBBBAICsFfH3+zkpMjytFAtDj4PlcdV87oJ/XbrSmg97OQz/35nZWqVwpn8NB3RFAAAEEEEAAAQQQQAABBAIq4Ovzd0DDkbFmkQDMGH1uX9jXDmjJr+us9eCpeYK3YMCxVqZkidwOKK1HAAEEEEAAAQQQQAABBBDISgFfn7+zEtPjSpEA9Dh4Plfd1w5owYq1dux974fpt9vO7NtBHW07/Q8KAggggAACCCCAAAIIIIAAAlkm4Ovzd5Yxel8dEoDeh9DPBvjaAc1d+pud9PBHYfSypUrYl7cd62cQqDUCCCCAAAIIIIAAAggggEDgBXx9/g58YNLcQBKAaQbncv8v4GsH9NHiX+ys4TPCYdylXCmbc3M7wooAAggggAACCCCAAAIIIIBAVgr4+vydlZgeV4oEoMfB87nqvnZA7yxYad1Gzg7T16i4o31441E+h4K6I4AAAggggAACCCCAAAIIBFjA1+fvAIckI00jAZgRdi7qawc07rPldvlzn4QDWLvKTjalV2sCigACCCCAAAIIIIAAAggggEBWCvj6/J2VmB5XigSgx8Hzueq+dkAvzf7Brnv5szB9gxo729ieR/gcCuqOAAIIIIAAAggggAACCCAQYAFfn78DHJKMNI0EYEbYuaivHdAzH39vN7/+RTiATWtWthd7NCegCCCAAAIIIIAAAggggAACCGSlgK/P31mJ6XGlSAB6HDyfq+5rBzRs2mIbNH5BmL7VfrvZ092a+hwK6o4AAggggAACCCCAAAIIIBBgAV+fvwMckow0jQRgRti5qK8d0P1TvrH/TlkYDuAxB1a1x85tQkARQAABBBBAAAEEEEAAAQQQyEoBX5+/sxLT40qRAPQ4eD5X3dcO6M4JC+zR9xaH6TsfvLvdd8YhPoeCuiOAAAIIIIAAAggggAACCARYwNfn7wCHJCNNIwGYEXYu6msH1O+NL2zkR9+HA3hm0z3tjpMPIqAIIIAAAggggAACCCCAAAIIZKWAr8/fWYnpcaVIAHocPJ+r7msHdMPLn9kLs38I01/QoqbdesKBPoeCuiOAAAIIIIAAAggggAACCARYwNfn7wCHJCNNIwGYEXYu6msHdOXzc+2NT38KB/CyI/e164+tS0ARQAABBBBAAAEEEEAAAQQQyEoBX5+/sxLT40qRAPQ4eD5X3dcOqPvTs23ylyvD9Ne02896tq3jcyioOwIIIIAAAggggAACCCCAQIAFfH3+DnBIMtI0EoAZYeeivnZA5z4xw97/5pdwAG86rp5d1LIWAUUAAQQQQAABBBBAAAEEEEAgKwV8ff7OSkyPK0UC0OPg+Vx1XzugUx/5yGYv+S1MP/Ck+nZ2s719DgV1RwABBBBAAAEEEEAAAQQQCLCAr8/fAQ5JRppGAjAj7FzU1w7ouKHv2xc/rQ0H8N7TGtrJjfYgoAgggAACCCCAAAIIIIAAAghkpYCvz99ZielxpUgAehw8n6vuawd01JCp9u2qdWH6R85uZB0aVPc5FNQdAQQQQAABBBBAAAEEEEAgwAK+Pn8HOCQZaRoJwIywc1FfO6DD73jbfvp9YziAIy441NrsX4WAIoAAAggggAACCCCAAAIIIJCVAr4+f2clpseVIgHocfB8rrqvHdAht02y39b/HaYfffFhdlitXXwOBXVHAAEEEEAAAQQQQAABBBAIsICvz98BDklGmkYCMCPsXNTXDmj/mybYps1bwgF8/fIW1nDPigQUAQQQQAABBBBAAAEEEEAAgawU8PX5OysxPa4UCUCPg+dz1X3sgLZs2Wq1+ozPwz7pP61sv6rlfQ4FdUcAAQQQQAABBBBAAAEEEAiwgI/P3wEOR8aaRgIwY/S5fWEfO6D1f222A26ZmCdw71/fxvasXDa3g0nrEUAAAQQQQAABBBBAAAEEslbAx+fvrMX0uGIkAD0Ons9V97ED+vXPTdb49il52Gf1Pdp2K1/a51BQdwQQQAABBBBAAAEEEEAAgQAL+Pj8HeBwZKxpJAAzRp/bF/axA/rxt/V2xF3v5gnc5/2PsZ1K75DbwaT1CCCAAAIIIIAAAggggAACWSvg4/N31mJ6XDESgB4Hz+eq+9gBLfr5Dzv63ml52BcN7GA7lNje51BQdwQQQAABBBBAAAEEEEAAgQAL+Pj8HeBwZKxpJAAzRp/bF/axA5r/4+92woMfhANXqsT2tnBgh9wOJK1HAAEEEEAAAQQQQAABBBDIagEfn7+zGtTTypEA9DRwvlfbxw5o5ner7bTHPg7TVyizg33W7xjfQ0H9EUAAAQQQQAABBBBAAAEEAizg4/N3gMORsaaRAMwYfW5f2McO6L2Fq6zrkzPDgataobTN6HN0bgeS1iOAAAIIIIAAAggggAACCGS1gI/P31kN6mnlSAB6Gjjfq+1jB/TW5yusx6g5Yfp9di1n7157pO+hoP4IIIAAAggggAACCCCAAAIBFvDx+TvA4chY00gAZow+ty/sYwf02txldvUL88KBq1e9gk24qmVuB5LWI4AAAggggAACCCCAAAIIZLWAj8/fWQ3qaeVIAHoaON+r7WMH9PzMpdZ7zPwwfaO9KtqYy1r4HgrqjwACCCCAAAIIIIAAAgggEGABH5+/AxyOjDWNBGDG6HP7wj52QE9+8J3d9uaX4cC1qL2LPXvRYbkdSFqPAAIIIIAAAggggAACCCCQ1QI+Pn9nNainlSMB6GngfK+2jx3QQ+8ussETvw7TH12vij3e9VDfQ0H9EUAAAQQQQAABBBBAAAEEAizg4/N3gMORsaaRAMwYfW5f2McO6J6JX9uD7y4KB+74g6rbg2c1yu1A0noEEEAAAQQQQAABBBBAAIGsFvDx+TurQT2tHAlATwPne7V97IAGvPmlPfHBd2H6Lo33sMFdGvoeCuqPAAIIIIAAAggggAACCCAQYAEfn78DHI6MNY0EYMboc/vCPnZAfV6db8/NWBoO3HnN97bbOtXP7UDSegQQQAABBBBAAAEEEEAAgawW8PH5O6tBPa0cCcCIwC1dutSGDh1q48aNM/3v0qVLW+3ate20006zyy67zMqWLVvsMC9ZssQeeeQRmzx5si1evNjWrVtn5cuXt7p161qHDh2sR48etttuu0W9Tr9+/ax///4J1eHWW281vS+yfP/997bPPvvEdZ6uXbvayJEj4zo2kYN87IB6vTDPxsxdFm7mJa1qWe+O9RJpNscigAACCCCAAAIIIIAAAgggkFYBH5+/0wqUIxcjAfi/QCvpd/bZZ9vvv/8eNfT777+/jR8/3mrVqlXkW+O5556z7t272/r162OeY5dddrEXX3zRjjrqqG2OKUoCUNc888wzSQAWOWr/vvHSUXNswucrwv9wVds69p92+yXhzJwCAQQQQAABBBBAAAEEEEAAgdQIkABMjatvZyUBaGaffvqpHX744S4xt9NOO1nv3r2tTZs2tmHDBhs9erQNHz7cxVWj9GbNmuWOSbR8/PHH1rJlS/vnn39s++23N42s69Spk+2+++5utOFTTz1lY8eOdactV66cff7551azZs08l/n5559N/ymo6PytWrWytWvXWoUKFWzFihW24447xkwA3n777a4esUqlSpWsRo0aiTa30ON97IDOHzHTpn69Kty2GzvUtR6t9y20rRyAAAIIIIAAAggggAACCCCAQKYEfHz+zpRVkK9LAtDMJfumTp1qO+ywg02bNs2aN2+eJ+aDBw+266+/3v2bpuDecsstCd8TJ5xwgr355pvufQ899JCbUpy/XHPNNXbvvfe6f+7Zs6ebjpxomTBhgnXs2NG97cILL7THH398m1NETgEeMWKEnX/++YleptjH+9gBnf7Yxzbju9Xhtvc/8UDrenjeJG2xYTgBAggggAACCCCAAAIIIIAAAkkU8PH5O4nN51T/E8j5BKBG9DVt2tRxXHLJJfboo49uc3Ns2bLF6tevb1999ZVpRNzKlSutZMmSCd1ElStXtt9++800xfeXX36J+l5NP65YsaJ7rXHjxjZ79uyErqGDNd1XoxZV3nvvPTcaMH8hAZgwq3tDpwc/sE9//HeK+N2nHGSnHbpn0U7GuxBAAAEEEEAAAQQQQAABBBBIgwAJwDQge3CJnE8A9u3b1wYNGuRCNX36dGvWrFnUsN15551uarDKpEmTrF27dgmFV9OGteFHkyZN3DTiWEUbgChBqITj/PnzE7qGpv1Wq1bNTV3WJh/aZGS77bYjAZiQYuyD2//3PVu48s/wAUPPPMRObLh7ks7OaRBAAAEEEEAAAQQQQAABBBBIvgAJwOSb+njGnE8AaoTc+++/79bdW7NmjZsGHK1oDT+tE6iiKcCJ7sbbqFEjmzt3boEjAJXA23nnnd01Tj75ZHvllVcSuqeeeOIJu+iiiwqtIyMAE2INH9zy7nfsh9Ubwv9/+HlNrN0BVYt2Mt6FAAIIIIAAAggggAACCCCAQBoESACmAdmDS+R8AjA04q5hw4Y2bxgzvC4AACAASURBVN68mCHT9F1N41Xp0qWL26k3kfLYY49Zjx493FseeeSR8P+OPMd1111n99xzj/uniRMnWvv27RO5hLVu3dqtYaiyaNEi23ff6BtURCYAlZhcvXq1/fTTT1amTBnbY4893GYlF198sem1VBUfO6Amt0+xX/7cFCZ59qJm1qL2rqki4rwIIIAAAggggAACCCCAAAIIFFvAx+fvYjeaE2wjkNMJwI0bN4Z3yD3uuOPCm3TEuk9C03gPO+ww04jARIp259XOv88++6zbBbhbt2524oknWvXq1d0uwKNGjbJXX33VnfKGG24wTTlOpCxZssRN+926dau1aNHCPvjgg5hvj0wAFnQNrYl4//33W+nSpROpijtWHUxBZfny5eG1F3/44QeXeMz2Uv/Wifbnps3har5y6eHWeO9K2V5t6ocAAggggAACCCCAAAIIIJDDAiQAczj4EU3P6QTgqlWrrEqVKo7j9NNPD2+eEevWqFq1qv38889FWp8vdE6NHFRyT9OB8xftRnzjjTcmPPJP5xkwYEB4d+Jhw4ZZ9+7dY97hSgAecsghdtJJJ9mRRx5pderUcaP/lJTT+oaaSvznn/+/1t1ZZ53lkpaJlmhrD8Y6hw8JQCVWa/edYP9s2RpuxvgrW9oBu1dIlIbjEUAAAQQQQAABBBBAAAEEEEibAAnAtFFn9YVyOgGoxNNee+3lAnTuuefa008/XWCwdKzeo6m1mmKbaFmwYIFL8I0bN842b/53JFnoPErCnXLKKTZ48GA3MjCRsv/++9vChQtdIm/FihXhtQSjneOvv/5y1y9btmzUS3zzzTd29NFHu5GJKq+//robrZhICVoC8K/NW2y/mybkIXj32iNtn13LJcLCsQgggAACCCCAAAIIIIAAAgikVYAEYFq5s/ZiOZ0ATOcIQG00oiSaNhrZe++97fbbb3c7CWtdwZUrV9obb7xhN910k2mtQU2H1Ui8evXqxXXjaPfi5s2bu2PjGckYz0k1hVhrAaooGTh58uR43hY+JmhTgDX199RHPrINf/9j6//6xzb+9Y9N7tXaqu1cJiEXDkYAAQQQQAABBBBAAAEEEEAgnQIkANOpnb3XyukEYLrWANy0aZMbNbhs2TKrVq2am/6r/85fvvjiC2vSpImpXvrvWbNmxXXnXHbZZW5jERWNLuzYsWNc7yvsoPr165vqpFGF69atc2sXJqvQASVLkvMggAACCCCAAAIIIIAAAgggEFuA52/uDgnkdAJQAOnYBVhTaDt37uzuuIEDB1qfPn1i3n1au+/xxx93r2tXYu1OXFDRdF5NF9ZOvlqjUEnGEiVKJOXuPu200+yll15y59Lah7JKVqEDSpYk50EAAQQQQAABBBBAAAEEEECABCD3QMECOZ8AbNWqlWl6brly5dz03B122CGqmHb9Pfzww91rt9xyi/Xv3z/ue0ubfvTu3dsdP2HCBDv22GNjvvfRRx+1Sy+91L0+evRoN6W3oDJmzBi3bqBKr169bMiQIXHXq7ADu3TpYi+//LI7jARgYVq8jgACCCCAAAIIIIAAAggggED2CTAAJ/tikoka5XwCUKPx7rjjDmevtfSaNWsWNQ6RSbyJEycmtFPvPffcY9ddd50779ixY+3444+PGesHHnjArrzySve6km+h5F6sN2hkoUYYqsQzYjCRm+zAAw+0L7/80kqXLm3r169nCnAieByLAAIIIIAAAggggAACCCCAQBYIkADMgiBkQRVyPgE4c+bMcNLvkksuMY3Ay1+2bNliWg/vq6++sooVK7rRcCVLlow7fK+88oqdeuqp7vjrr7/e7rrrrpjv1XE6XmXOnDnWqFGjmMf++uuvbvrv33//7aYKKwGYrBK5CUjbtm1typQpyTq1Ow8dUFI5ORkCCCCAAAIIIIAAAggggAACUQV4/ubGkEDOJwCFEJoGrOm/06ZNC++oG7pFBg8e7BJ3Krfeeqv169cvz90zdepUa9Omjfu3rl272siRI/O8rqnFNWrUcKPoypcvbx9++KE1aNBgmztQ04M1OlAJRx2/dOnSAkfdPfjgg9azZ093Hk391RTgeMprr71mnTp1su222y7q4YsWLTIl/XR9FSUkTz755HhOHfcxdEBxU3EgAggggAACCCCAAAIIIIAAAkUW4Pm7yHSBeiMJQDO3K2+LFi1sw4YNttNOO7lNOpTQ0//XOnzDhg1zQd9vv/1s9uzZLokXWQpLAOrYAQMGuLUDVXQNJe7atWtnlSpVspUrV7ppvMOHD7fNmze7Y5555hk755xzCrzZmjZt6nYK1qYf2vxDm4DEU5T4q127tkvq6Rx77LGHm+b7008/2aRJk9wmJNr1V0UbgbzwwgvxnDahY+iAEuLiYAQQQAABBBBAAAEEEEAAAQSKJMDzd5HYAvcmEoD/C6nW5lPCbe3atVGDrOTfuHHjXOIsf4knAbh161Y3Qu/+++83/e9YRVOLBw0aZNdee22BN9vXX39tdevWdcd07NjR1S3eEmvkX/73azOS//73vy45mOxCB5RsUc6HAAIIIIAAAggggAACCCCAwLYCPH9zV0iABGDEfbBkyRKXoFMyTR+QUqVKuYSfdsO94oorrGzZslHvmngSgKE3al0/jbDTGnu6nqYFa0SgrtO6dWvTOoRKNhZW+vbt6xKFKvHsFhx5PiU7tavxjBkzXB1++eUXN+KvQoUKVqtWLWvZsqV169bNrXuYqkIHlCpZzosAAggggAACCCCAAAIIIIDAvwI8f3M3kADkHsiYAB1Qxui5MAIIIIAAAggggAACCCCAQA4J8PydQ8EuoKmMAOQ+yIgAHVBG2LkoAggggAACCCCAAAIIIIBAjgnw/J1jAY/RXBKA3AcZEaADygg7F0UAAQQQQAABBBBAAAEEEMgxAZ6/cyzgJAAJeDYJ0AFlUzSoCwIIIIAAAggggAACCCCAQFAFeP4OamQTaxcjABPz4ugkCdABJQmS0yCAAAIIIIAAAggggAACCCBQgADP39weEiAByH2QEQE6oIywc1EEEEAAAQQQQAABBBBAAIEcE+D5O8cCHqO5JAC5DzIiQAeUEXYuigACCCCAAAIIIIAAAgggkGMCPH/nWMBJABLwbBKgA8qmaFAXBBBAAAEEEEAAAQQQQACBoArw/B3UyCbWLkYAJubF0UkSoANKEiSnQQABBBBAAAEEEEAAAQQQQKAAAZ6/uT0kQAKQ+yAjAnRAGWHnoggggAACCCCAAAIIIIAAAjkmwPN3jgU8RnNJAHIfZESADigj7FwUAQQQQAABBBBAAAEEEEAgxwR4/s6xgJMAJODZJEAHlE3RoC4IIIAAAggggAACCCCAAAJBFeD5O6iRTaxdjABMzIujkyRAB5QkSE6DAAIIIIAAAggggAACCCCAQAECPH9ze0iABCD3QUYE6IAyws5FEUAAAQQQQAABBBBAAAEEckyA5+8cC3iM5pIA5D7IiAAdUEbYuSgCCCCAAAIIIIAAAggggECOCfD8nWMBJwFIwLNJgA4om6JBXRBAAAEEEEAAAQQQQAABBIIqwPN3UCObWLsYAZiYF0cnSYAOKEmQnAYBBBBAAAEEEEAAAQQQQACBAgR4/ub2kAAJQO6DjAjQAWWEnYsigAACCCCAAAIIIIAAAgjkmADP3zkW8BjNJQHIfZARATqgjLBzUQQQQAABBBBAAAEEEEAAgRwT4Pk7xwJOApCAZ5MAHVA2RYO6IIAAAggggAACCCCAAAIIBFWA5++gRjaxdjECMDEvjk6SwPfff2/77LOPO9vMmTOtevXqSTozp0EAAQQQQAABBBBAAAEEEEAAgZDA8uXLrWnTpu7/fvfdd1azZk1wclCABGAOBj0bmjxr1qxwB5QN9aEOCCCAAAIIIIAAAggggAACCARdQANwDj300KA3k/ZFESAByG2REQESgBlh56IIIIAAAggggAACCCCAAAI5LEACMHeDTwIwd2Of0ZZv3LjR5s+f7+qw22672Q477JDR+kRePHJ4NNOTsyYsRa4I8SwyXVa+kXhmZViKVCliWSS2rH0T8cza0CRcMWKZMFlWv4F4ZnV4Eq4c8UyYLGvfkO5Ybt682VatWuU8GjRoYGXKlMlaGyqWOgESgKmz5cyeCrBAqqeBi1Ft4kk8gyUQnNbw2QxOLNUS4hmceBLL4MSSz2awYkk8gxVP+tpgxdOX1pAA9CVS1DNtAnTGaaNOy4WIZ1qY03YR4pk26pRfiFimnDitFyCeaeVO6cWIZUp5035y4pl28pRekHimlDetJyeWaeXmYv8TIAHIrYBAPgE642DdEsSTeAZLIDit4bMZnFiqJcQzOPEklsGJJZ/NYMWSeAYrnvS1wYqnL60hAehLpKhn2gTojNNGnZYLEc+0MKftIsQzbdQpvxCxTDlxWi9APNPKndKLEcuU8qb95MQz7eQpvSDxTClvWk9OLNPKzcX+J0ACkFsBgXwCdMbBuiWIJ/EMlkBwWsNnMzixVEuIZ3DiSSyDE0s+m8GKJfEMVjzpa4MVT19aQwLQl0hRz7QJ0BmnjTotFyKeaWFO20WIZ9qoU34hYply4rRegHimlTulFyOWKeVN+8mJZ9rJU3pB4plS3rSenFimlZuL/U+ABCC3AgL5BOiMg3VLEE/iGSyB4LSGz2ZwYqmWEM/gxJNYBieWfDaDFUviGax40tcGK56+tIYEoC+Rop5pE6AzTht1Wi5EPNPCnLaLEM+0Uaf8QsQy5cRpvQDxTCt3Si9GLFPKm/aTE8+0k6f0gsQzpbxpPTmxTCs3F/ufAAlAbgUEEEAAAQQQQAABBBBAAAEEEEAAAQQCLEACMMDBpWkIIIAAAggggAACCCCAAAIIIIAAAgiQAOQeQAABBBBAAAEEEEAAAQQQQAABBBBAIMACJAADHFyahgACCCCAAAIIIIAAAggggAACCCCAAAlA7gEEEEAAAQQQQAABBBBAAAEEEEAAAQQCLEACMMDBpWkIIIAAAggggAACCCCAAAIIIIAAAgiQAOQeQAABBBBAAAEEEEAAAQQQQAABBBBAIMACJAADHFyahgACCCCAAAIIIIAAAggggAACCCCAAAlA7gEEEEAAAQQQQAABBBBAAAEEEEAAAQQCLEACMMDBpWkIIIAAAggggAACCCCAAAIIIIAAAgiQAOQeQAABBBBAAAEEEEAAAQQQQAABBBBAIMACJAADHFyalrjA0qVLbejQoTZu3DjT/y5durTVrl3bTjvtNLvsssusbNmyiZ+UdyRN4JNPPrG33nrL3n//ffv888/t559/tpIlS9ruu+9uhx9+uF144YXWsmXLuK+ncw0bNsxmzpxpq1atst12282aNm1qF198sR177LFxn4cDkytw/fXX2+DBg8Mnfffdd+3II48s8CLEMrkxKM7ZfvnlF3vyySft9ddft8WLF9tvv/1mu+yyi+25557WqlUrO/nkk6158+YFXuLjjz+2hx9+2H3WV6xYYZUqVbKGDRva+eefb2eccUZxqsd7ExD466+/7JlnnrGXXnrJPv30U1u9erXrc2vUqGEtWrRwfeVhhx1W6Bn5fBZKVOQD9HdQf8P0n1mzZrn//Prrr+58Xbt2tZEjRyZ07mTEavPmzfbEE0/Ys88+a1999ZX9+eef7p45+uij7corr7QDDjggoTrl0sHJiOfGjRtt4sSJNmXKFHdffPPNN/bHH39Y+fLlbf/997djjjnGLrnkEqtevXpctOvXr7eHHnrI9QOLFi0y9Qvqz4877jgXz7322iuu8+TaQcmIZSwzxaR+/fr23XffuUP23ntv+/777wslJpaFEsU8IBXx1OdTfbS+5y5btsy2bNliVatWtbp161rbtm3t3HPPdc8msQp9bdHjmcvvJAGYy9Gn7XkElPQ7++yz7ffff48qoy9N48ePt1q1aiGXAYHWrVvbtGnTCr2y/lg+/vjjVqpUqZjHbt261Xr06OGSf7GKHmwfffRR22677Qq9JgckT0BJhiZNmpi+1IRKQQlAYpk8+2ScSQ+Il156aTgBEe2cnTp1stdeey3m5W677Tbr37+/+yIcrZxwwgn24osvWpkyZZJRZc4RQ+CHH35wD/jz588v0Og///mPDRkyJGpfyecz9bdXQX+jEkkAJitWSj7qvpkxY0bUxuuHVSX3u3XrlnocD69Q3Hh+9tlndsQRR7iEX0FFyUB9V9IP3AUV/YijeH799ddRD9t5553tueees44dO3qondoqFzeWBdXu2muvdf1uqMSTACSWxYt3MuO5adMmu+KKK9wPJep7Y5VXX33VOnfuHPVl+trixTOX300CMJejT9vDAko6aASZfhnbaaedrHfv3tamTRvbsGGDjR492oYPH+6O1S8y+nVdx1DSK6CRmPryotF+Xbp0cSP99KvzP//8YxotpC9C+vVM5cwzz3RfSGOVvn372qBBg9zLhxxyiGnE2b777uvOf/fdd9vcuXPdazru9ttvT29Dc/hqSvhoNJE+Y1WqVHEjPFUKSgASy+y5YZ5++mm74IILXOJO8VMiUA+ilStXdqP49PkaO3as6YFRicJoRQ+k3bt3dy/pM9mnTx9r0KCB/fTTT3b//fe7e0FFP9aMGjUqexofsJooAd+oUaNw8u+ggw6yXr16udFDSix88MEHrs9dt26da7n6zeuuu24bBT6fqb8xIh9KNSqrXr16NmnSJHfhRBKAyYiV/h4fddRR4R/rNNpXn2f1AUoI6u+p+vUSJUq4mRYaiUbJK1DceOqzGZoJoVG6xx9/vPtRTaOwNdNhzJgxLvGnWCkO6pM7dOgQNQwauXnooYfaggUL3OuKpUZg77jjjq4vvuOOO9zoTs2O0fcw9ROUfwWKG8tYlvqOqrhoNLb+oz65sAQgsSz+nZmseGoErZJ6EyZMcJXS5/W8885zffcOO+xgS5YscSPu9T1Jf1ujJQDpa4sfz1w+AwnAXI4+bQ8LKNk3depU1/FqlFn+6WmajqgkkYpGptxyyy3opVlAX2L1B/KUU05xX1rzF0071JfdhQsXupcUx2jTgTV9RX9k9YCrL8U6Tl9mQ0VJYI02nD17trsf9MVXiQhK6gXuu+8+02gixUdfePRwoRIrAUgsUx+TeK+gaX5KputXbX3uQom+aO/Xl99oI3TXrFlj++yzj+m/ldyfM2eO7brrruFT6AvvSSed5M6t8t5777kpxZTkC7zyyit26qmnuhPr76GmYufvdxUfvfb333+7KdpK7KjPDBU+n8mPS7Qz3nrrrS4ZoP9o6pimAepzpBJvAjBZsdJUNv0IoKJlUzRtNLLoOo0bN7a1a9danTp17Msvv8xzz6RHLLuvUtx4fvTRR+7HEp0n1lRrLc+gvlQjj/T9RlOEo41u6tevn/vOqxItya+kn/pgfZ/S9+h33nknu3HTXLvixjJadfV3sFmzZu7vo0bLawSZEkaFJQCJZfGDn6x46hlywIABrkL33HOPXXPNNTErp7+vSvLmL/S1xY9nLp+BBGAuR5+2OwGNNtK6bypaE0XTPvMXjWjRWht6yNWDzsqVK6N2yJBmVuDNN980TQ9U0bo0+hKcv1x++eVu+pGKvrxGW79q+vTp4SSwhug/8MADmW1YDlxd0w31sKJfqZWMV9Iv9OARKwFILLPnxtDaXm+//bZL2KmfjEzcxVvLyB9ann/++ahr/f34449Ws2ZNN3pFPwqEkoHxXoPj4hPQaL///ve/7uA33ngj3K/mf7dGeGmKkoqmCuvvZKjw+YzPOtlHFSUBmKxYHXjggS6pp+9J+qxGWzf5zjvvdLMsVF5++WX3ox4ltkBR4hmPpxL8SvSraH1l/YATWZR40Ehu/SCjH+W07vL222+/zam1nMpjjz3m/l0/nCrBS4kukIxY3nvvvS5hpNHYmu693377FZoAJJapuSOLEs9vv/3WzSZTTLSm8YgRI4pUOfraIrHxpv8JkADkVsh5gchpL0r86Je1aCXyS6um17Rr1y7n7bINQMkjrWujojVrlBCMLPq1W1OkNFVYf4CVqIhV9LrWvNljjz3chjCsBZjaaCtxq3iFRqxE/lodLQFILFMbj0TOrlGyekBUUdz0K3lRikbwavRKhQoV3FS1WOt4aoMeLXCvtcQ08pclGYqiXfB79MNHaPSWHvz1sBGtaNqvRjCoRD788/lMfkziPWOiD6XJipVGkSkZoaKk0COPPBK1yloOILT5xFlnneU2CqHEFkg0nvFa6vOtz7mKphqGRvyG3j958mRr3769+7/6/nvDDTdEPXXkD6ZasmHgwIHxViHnjituLDXST32xll7QaEuNutQPYoWNACSWqbnVihLPG2+80e666y73TKFlUUKjtROpIX1tIlocG02ABCD3Rc4LaPqCpjeVK1fO/dIZOYUpEkejxbROoIqGb4dGJ+U8YBYBaIdKrXOjooSSRq5EFv3yFprOG2u0Z+h4vR7aJETvK8of6SyiyeqqaEOH008/3a0TpWSSdjwrLAFILLMnpJrKEloW4YsvvghPO9Puv0rQKa6hz2WsWmtasPpgTSXTumDaiTRW0dRwPWiqhB6CskcjGDUZOnSoXXXVVa4x8YwA1MOM/n4qeavC5zNz90GiD6XJipV2/r7wwgtdw2ON4A2paPSSluvQVH8lLyixBRKNZ7yWoZFkOl4jATWaN7JETlOMNVtCx6vPrlixoktK6fu0lmagRBcobiz1w7Y2I9Rmd1pzVyWeBCCxTM0dWZR46hlEfa6WbNAOwCqaZaZ1jjUqsFq1anmWJYpWc/ra1MQzl85KAjCXok1bowoo2aCH1IYNG9q8efNiKulhVg+yKtqEQkkLSnYJaCpa6EusRqZozZrIokXHNW1QRdPbrr766pgN0OuaBqei97HDXWpiHZpepFEh2mznoosuchcqLAFILFMTj6KcNfRQos091E9qAx599jQ9KVSUQNfoTk1dijZiT4nD0PRRJZ60HmSsEvk51ygWrTVGSa6ARmBq4yWt1aaRmXqoz78GoBai1xIKSt7m33iJz2dy45HI2RJ9KE1WrCJHg+reOPjgg2NWWzuBK7GsxLE2MFDynxJdINF4xusYioGOj/zhJvR+fc/VFG0V9etK8sUq+v6s/l7fp0Obd8Vbj1w6rjix1IaE6mc1vV4/lGp6tko8CUBimZq7LNF46u9qKG5apij046mSufqMqWgQigabaN15fbeKVuhrUxPPXDorCcBcijZt3UZg48aN4V9aok0Zzf8GPbjqV0499OgXUUr2COgXNC1IH/pFTWs7apOPyKL1HbUzqUq0KS+Rx+qLr740qeh9GhFISb7AxRdf7BJ/+sKj3QtDU60LSwASy+THoqhnVHJPX4T1EKhdf/Mv/B95XiX5NH1Xu3lHFo34C+1EqbUAr7322pjV0VRT/Xquouk0oc1iilp/3hddQIlW7ba8YcMGtz6YfjDRFE8ttfDhhx+6XYCVvFGiR7sZauRCqPD5zNxdlehDabJipd1hX3jhBddwPegWtA5o5BRzJTM0IpASXSDReMbjqB1GtVaf1lLVlFJN889f9D1XOzcrOavPfEFFP6wqkayi79VanoGyrUBRY6nkkJbZ0PrjWm9R35tCJZ4EILFMzd2YaDz1Q9qRRx7pKqMp9XrO0DTgWEWb4mmkbv5CX5uaeObSWUkA5lK0aes2ApG/xmgKon5hK6hohz39uqmHWC14TskeAT2MhpIG2t1uzJgx21QucpMBPbBqLbFYRa+HRv0VtktX9ij4VRMl/DRlSCOLtAh5gwYNwg0oLAFILLMn1hr5p5FieujTLsAaKaI1ozQaV1NC1VdqCpI+UypK9mrZhcgF5ZWQP+2009zrWjtMa4jFKlq7M7S7JZv0pPY+0IYOegDRlCOtFRdZ9PdQDzF6GM0/govPZ2rjUtDZE30oTVasQiOBVTcljcuUKROzmrpvQiP02Tii4Hsl0XgWduepj9YPNXJX0Y7AJ5544jZvC20yoM+5RugXVPT9OTQrRjNqClvyobA6BvX1osZSMyO0269+5NaPL5FrUseTACSWqbmjEo1n5OyF0PclfR/Sj5j6UVPJc31P0rPM8uXLY34foq9NTTxz6awkAHMp2rR1GwHtPKo1aFQi19SIRaVj9R6t4bBo0SJEs0RAv6ppF1KtRaPh9ZqKoi+t+UvkWmXasfSoo46K2QKtLda2bVv3ut530003ZUlrg1ENTRvUyCElc6JN1y4sAUgss+c+0JQVjSRRUTJXid38u2trhK5GiYSSgPlH4D7zzDN23nnnuXPoQadbt24xGxi5ZpnWHHv88cezByNANdF6RFrrViN0Y03r00OLNn3JP1WJz2fmboREH0qTFSv9vdTfTRX1B9F2jA2pRK5Jph8DlJCiRBdINJ6FOXbv3j3cZ4Y23Yr2ntBaZdo4TRuhFVTUd6sPV9F3ZG2eRtlWoCixnDZtmhs1pr+tc+bMsYMOOijPieNJABLL1NyNicZz1KhR7lkzVDQKV9+X8v9Yok0+NOpeM840rV7rpO64447h99HXpiaeuXRWEoC5FG3auo0AIwD9vym0dk3Lli3d+hn6RU3TC1u3bh21Ycka6eC/WuZbEErwKamuUUb5RxAVlgAklpmPYagGoaUR9P81NUUbAEQrkev8aXSgFp4PFUYAZk88VRM9eGgEtB4+9eCptRsvuOACq1WrlhuloKmBt912W3javtZMDW0aovfz+cxcPBN9KE1WrBiVkpqYJxrPgmoRuYGSkg/68TTW+ouMGkt+PBONpUZrammNr7/+2vXBoR3XI2sWTwKQWCY/ljpjovGMXFpI79fSJ9r0LFqJXOcv/0Zc9LWpiWcunZUEYC5Fm7ZuI8AagH7fFN99950bOaDds/SQqiSCpv/GKsla68hvtczXXms+6UutRgHGmn5UWAKQWGY+jqEaVK9ePTxF7KmnngqP5ItWQ40MWbZsxDbqxQAAGipJREFUmeUfVcIagNkTT9VEU5C0rILKyJEj3QYu+YtGXLdv397effddN9pLGz+ERqfw+cxcPBN9KE1WrFiXKjUxTzSesWqhteNCSytozUWNvNTooliFdeOSH89EYxkaKau/l/qhNNoGWvEkAIll8mNZlASgBiiElh4qVaqUW0NX/x2tTJkyxdq1a+deuvnmm90PbqFCX5uaeObSWUkA5lK0aWtUAXYB9vPGUNJPI/80HVDroeghNTSFMFaL3nzzTTvhhBPcy+wCnLm4a0OVYcOGudFEAwcOjFoR/VIaGiGmLz+hNd8UP41YIJaZi1/+Kzdt2tS06Y5KYVPrtYbR9OnT3Whd/QATKlqEPrQGJLsAZza2WutPGzisXr3abfqh0SexitajCk3f1CYh6ldV+HxmLoaJJhmSFavIpDG7ACcv/onGM9qVNSr7nHPOMS3FsPfee7uRu4VN0z311FPDf4PZBTg58Uw0lvo7qR9KNfpaP7ZEKz179jStu6g++4EHHnCHaCmcyCVuiGVy4pf/LInGU0lcjcZUKWxqvf7u1q1b1x2r78z6oSZU6GtTE89cOisJwFyKNm2NKqBNCPRLqJIKa9ascVuwRyva9VeLtaroVzmtjUTJjIC+7Giar/6Yqjz44IN2+eWXF1qZyLXD8v9Bzf/mUJJK/673aadTSnIEzj//fNNIsaIUjfrUL97Esih6qXmPHk6UgFeZNGlS+FfraFcLJQvz7yyph5yyZcu6tcM0JUYjAmOVyGlsWnOsTZs2qWlYjp5VC/5rVKdKYZtjRY6i18iG0BqPfD4zd/Mk+lCarFhpoxityamihJNGqcQqGoG2cOFCtwaz1reixBZINJ75z6Tpg6eccopbI1mfa33f1ZpwhZXIdRr1/Tf/uq6h9+u82vhJywbo+7SmFVOiCyQay8jNPhIx1ffjqVOnht9CLBPRi//YROOpdXX13Uf/vfvuu7vZELFKZLJQzzd6zgkV+tr4Y8SR0QVIAHJn5LxAnz593A5MKhqZ0qxZs6gm2tWyd+/e7jUN4471a1zOg6YY4Pfff3e/bGrXWBXFRTsKxlM0skW/emv0oH5Z0wYUsUq9evVMU1Vr1KjhFrUu6hexeOqVa8ckIwFILLPnrhkxYkR4047CdvDVKIVff/016sgy/cCiB03tHKz1WWNNjVGiSX2wRkfouPLly2cPRgBqoh9YQlMDlTjQaNxYRVOYFC8VbfIyduxY97/5fGbuRkj0oTRZsVJCT4k9FU01VV8QrUQmmM8880x77rnnMoflwZUTjWdkkzQiW+uFaS057cyr5FxoBFJhTdePOaH1yQr6nqXvzRrZraLvyIMGDSrs1Dn7eqKxLOr3zvwJQGKZmlsu0XiqFopNaG1dPc/EWoNTf0tDu3PrMxV6/tQ56GtTE89cOisJwFyKNm2NKjBz5sxw0i/WqDBNm6hfv75LGOmXTu2IWLJkSUTTLLB+/XqXeNW0M5W+ffva7bffnlAtLrvssvCDSaxftSO/0Or4hx56KKFrcHDxBQpbA1BXIJbFd07GGZTQ08gS/aqtNWv0sBGt6OFTuxmqRNu99+677w4n82ONIPrxxx/dCFCNFNQmFePGjUtGEzhHhID+3lWqVMnWrl3rRilohFaskfGR00c1FW3o0KHhM/H5zMxtVZSH0mTFSks16HtS5cqV3Q9nGtWbv0T+mPriiy9aly5dMgPlyVWLEk817aOPPnLflzQyT0l6jZbWxh/xFo3K1lRSJSn0g6g2cYqWkFKyV+sLquj7tHYGp0QXKGosC/KMZw1AYpmaO7Io8dTfyNCGWc8++6ydddZZUSsXObMi2k7p9LWpiWmunJUEYK5EmnYWKBCaBqyHHP0yE/o1M/SmyF3ybr31VlNygpJeAX2B0fpvoeRCYeuExaqdfjnTL+CattKkSRMX7x133DF8+IYNG9w0ltmzZ7uHXg3Dr1OnTnoby9XcZyw0zV6bDIQSR5E0xDJ7bpTIBEK05J1GiulzNW/evJgPilpzTutC6oFT61TNmTPHjVoJFSX9tMlPaJQZ039TF389lIR2c9ZnUX/38hetC6b1/0JLMeQfGc/nM3XxKejMRXkoTVasIqem5Z+2pjovXrzYGjVq5JLLmoaqUfaxksuZ0cu+qxYlnupntTSClrXRCCN9Nlu0aJFw4yKnjuoHGu1MGln0I6r6dX2fyj/qLOGL5cAbihLLwljiSQDqHMSyMMnEXy9KPP/880/X92kgib7nzJgxw6pWrZrn4pq+ffTRR7sfOjX45LPPPtsm+U5fm3i8eMe/AiQAuRsQMHO7F+rLkZI/2mVL04L15Un/f/To0W7DAhUtiK7EEFPO0n/baCramDFj3IU1Bfi+++4rcFqupg8qXtGKhtJrFILKIYcc4kYd6Q+yHk7uuusudz+oMJ0l/XEOXTGeBGAoRsQyc3EKXVlTcZVQX7p0qXug16iQk08+2Y08mT9/vvtc6WFf5dJLL7WHH344aqUjd6rUZ1KjfLU5iKbt6zOvZLAKUwdTG3PFSqOFNOpaRT++aCdgJWi17p9GSSseirdK27ZtTbsW5i/0tamNk86uDR0WLVoUvpCmcIcSNfpec9FFF+WphJZgSNXfRT2wKhEUGqWvv9vdu3d3I0o1OmzAgAHuwVe7Rmv0aIcOHVIP5NkVihtPfY/RcgpyVtHGPEomFFQ00k//yV/0w436dSWIVS6++GK3tqN+NFVfrKmJSmjo/2vE4cEHH+yZdmqrW9xYxlO7eBOAxDIezYKPSVY8X3jhBfcdRssvaDOQG2+80bQ+sv62ah1dfWb1/KnvUkoGRkve09cWP565fAYSgLkcfdqeR0CjSrRLmn6ZjlaUTNJ0s9q1ayOXAYFE10LRL2v6dS5a0RQ3PZToF7RYRVMUlfjVgwol/QLxJgCJZfpjE+uKmvqnNWsikxH5j+3WrZvbza6gJRQ02kyJAn05jlY09Vc7RJcpUyZ7Gh/Amiihp4cUJZQKKvpBRusEKsmTv/D5TP2NkeiaqrE+V8mKle4XfUZDO4PnF9CPc1rQXn+DKdsKFDee2pBJ0wcTKQXNbFF/rnh+8803UU+pH3k0lVFrgFLyChQ3lvF4xpsA1LmIZTyisY9JZjy1tFCvXr3cLs/RigajjBo1yjp16hSzQvS1xYtnLr+bBGAuR5+2byOgtY7uv/9+l+jTWlP6oqqEn9aoueKKK6KuZwNjegSSmQAM1Xj8+PEuyacHFf0h1QYFWr9Ga0EyMiE9cY11lXgTgMQys3HKf3WtN6XF/5UQ0gOjRodoZIl+wdbnKt4dezWaRF+QtfbNypUr3dqrDRs2dA+2SkpR0iOg9R2feOIJNypBa4BpSqFGJVSrVs31lZoqrKRvYf0zfW3q4pXMh1LVMhmx0pTQ4cOHuw0+9MOA+gWtJ6mRolq+I96NKFKnlr1nLm48k50AlJTip/74pZdeckkkJS00ckmJQcVTP7hSthUobizjMU0kAUgs4xFNTwJQV9HfVH2uJk+e7HYELlGihBtlr43Orr76are2cmGFvrYwIV6PJkACkPsCAQQQQAABBBBAAAEEEEAAAQQQQACBAAuQAAxwcGkaAggggAACCCCAAAIIIIAAAggggAACJAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnAAAeXpiGAAAIIIIAAAggggAACCCCAAAIIIEACkHsAAQQQQAABBBBAAAEEEEAAAQQQQACBAAuQAAxwcGkaAggggAACCCCAAAIIIIAAAggggAACJAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnAAAeXpiGAAAIIIIAAAggggAACCCCAAAIIIEACkHsAAQQQQAABBBBAAAEEEEAAAQQQQACBAAuQAAxwcGkaAggggAACCCCAAAIIIIAAAggggAACJAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnAAAeXpiGAAAIIIIAAAggggAACCCCAAAIIIEACkHsAAQQQQAABBBBAAAEEEEAAAQQQQACBAAuQAAxwcGkaAggggAACCCCAAAIIIIAAAggggAACJAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnAAAeXpiGAAAIIIIAAAggggAACCCCAAAIIIEACkHsAAQQQQAABBBBAAAEEEEAAAQQQQACBAAuQAAxwcGkaAggggAACCCCAAAIIIIAAAggggAACJAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnAAAeXpiGAAAIIIIAAAj4KbLfddq7at956q/Xr18/HJlBnBBBAAAEEEEAgqwRIAGZVOKgMAggggAACCCCAAAlA7gEEEEAAAQQQQCC5AiQAk+vJ2RBAAAEEEEAAAQSKKUACsJiAvB0BBBBAAAEEEMgnQAKQWwIBBBBAAAEEEEAgqwRIAGZVOKgMAggggAACCARAgARgAIJIExBAAAEEEEAgOAIzZ8604cOH23vvvWc//fSTbd261fbcc09r27atXX311VanTp08jd13333t22+/tRYtWtgHH3xQIMSKFStsjz32sH/++cd69eplQ4YM2eb4zZs321NPPWWvvPKKzZs3z3799VcrX768HXDAAXbyySdbjx49rEyZMlGvc+SRR7p6t27d2qZOnWrLli1z13jjjTfc/y5btqw1adLEtaNDhw7bnKNmzZq2ZMmSAtvQtWtXGzlyZPiYjRs32rBhw2zMmDH2+eef2++//+7qW6VKFatVq5a1a9fOTjrpJNO5KQgggAACCCCAQK4KkADM1cjTbgQQQAABBBDIKgEl3q688kp75JFHYtarZMmS9tBDD1n37t3Dx9x88812++23m0bNKRFYUKLrvvvus//85z/uvXPmzLFGjRrludbixYvtxBNPtC+//DJmHZSAHDdu3DaJSL0hMgGoOnXu3NklEKOVwYMH27XXXpvnpUQTgMuXL7ejjz66wPrqAtdcc43dc889WRVvKoMAAggggAACCKRTgARgOrW5FgIIIIAAAgggEENAI9uefvpp96pGx5199tm23377ucSeRuIpeffFF1+41zWi7oQTTnD/e8GCBVavXj33vwcOHGh9+vSJaXzooYfa7NmzrW7duvbVV1/lOU7JtEMOOcRWrlzpRtBdfPHFLrlWtWpVN6pu0qRJdv/999v69evdyLpPPvnEdt555zznCCUAVe/Vq1fb9ttv70YaHnHEEVaqVCk3QvG2226zNWvW2A477ODadeCBB4bPsXDhQvvrr7+sQYMG7t8uvfRSu+yyy/Jco1KlSlajRg33b6eeeqobqahyzjnnuBGKu+++u5UoUcK1Q0nO1157zY466igSgHzyEEAAAQQQQCCnBUgA5nT4aTwCCCCAAAIIZIOAklhKZqlo+u9FF120TbU01fW4446zd955x43y++abb1wSTaVx48YuIadkmqbBRis6Xok5lQEDBthNN92U5zAlFN9880033VjTd5Xky1/mzp1rLVu2tHXr1rn36zyRJZQA1L/tvffe9uGHH4aTdaHjlARs1aqVm9qsEY9KKuYv8awBKI8KFSrY33//XegIPyUjK1eunA2hpg4IIIAAAggggEBGBEgAZoSdiyKAAAIIIIAAAv8KaF08jVbTWnVayy5W0ag9rcWnMnnyZDdCT+Xee+91STAVjapr2LDhNqfo16+f9e/f3/27pvpGJviUNAyNunv99dfdNOBY5YYbbrC7777bjbTTun6RJTIBGDlKMf+5mjdvbtOnT3cjDpW4zF/iSQBqfcTQSMDC6sy9hgACCCCAAAII5LoACcBcvwNoPwIIIIAAAghkVEBJNG3MofL888/bGWecUWB9dtttN/vll1/cVFqt/6ei6bs6x5YtW+z666+3u+66a5tzaPSfRgEq+fbRRx/leV1ThzWiT5t0rF271k2hjVW0/t/xxx/vXl66dKkbMRgqoQRgxYoV3dp/mgIcrVxxxRVuLUNNIdZ04PwlngTgpk2b3AhATRnW6EnZhUZEZjSgXBwBBBBAAAEEEMhCARKAWRgUqoQAAggggAACuSOg0WvaLCPRorXxlEQLFe12O2XKFJeQ0066oSSaXp81a5Y1bdrUHfrggw/a5ZdfnudyGnmotfISLdqxWOsKhkooAah/02uxyi233OKmDytBqB2J85d4EoB6z3nnnWfPPPOMe7umHJ922mluIxLtiJx/fcJE28bxCCCAAAIIIIBAkARIAAYpmrQFAQQQQAABBLwT0Jp/2nAj0XL++efbiBEjwm8bOXKkXXDBBe7/aw2/1q1bh1+7+uqr3Vp7GiGnqbMaRRhZlDDLPyownvrkv07kLsB6LVaJnI6stQDzl3gTgBqtqM0/xo4dm+cUGsGoHY6VEJStRgpSEEAAAQQQQACBXBYgAZjL0aftCCCAAAIIIJBxgUcffdTtdqvy7LPP2kEHHRRXnSJ3w9UblAyrVq2abdiwwSW9HnvsMXcejbDT9OAVK1ZYx44dTVN485fDDjvMZsyYYfvss4/bYTjeouPLlSsXPjzdCcDQhTXa8MUXX7R3333XPv300zyjCqtUqeJGN2rqMwUBBBBAAAEEEMhVARKAuRp52o0AAggggAACWSHw0ksvuZFqKs8995ydeeaZRa7X6aef7hJhSg4q4VeqVCm3WUj79u3dOZVgPOuss7Y5v3YXHj9+vFsD8Pfffy/yWnqZSgBGNuiPP/5wIyA1OvLVV191L1WvXt1tfLLjjjsW2ZY3IoAAAggggAACPguQAPQ5etQdAQQQQAABBLwXWLRokdWpU8e1I/+03kQbp9F7nTp1cm8L7Yyrcz711FNupN7KlSvzjNgLnV8bgGgjEJX803oTqUM2JAAj63vVVVfZ0KFD3T9NmjTJtE4iBQEEEEAAAQQQyEUBEoC5GHXajAACCCCAAAJZJXDggQfal19+aWXKlLGvv/7a9tprryLV7++//3bTgFevXu1GFSrxV7VqVTc9+Oyzz7ZRo0ZFPW/kJiGxpgnHU6FkJQA1Um/jxo1244032h133BHPpaMeE5kQLe7oyiJXgjcigAACCCCAAAJZIEACMAuCQBUQQAABBBBAILcFnn/++fDU3MaNG9uECRO22agjJLRp0yZ74oknrFu3bi5hmL/06NHDrf+nJJp2/L3wwgvdIZri26FDh5jQxxxzjBslp6Jdevv37x/z2O+//94+/vjjbaYrJysBWKtWLfvuu++sS5cubkpztPLtt9/aDz/8kGezk/zHXXHFFeGdklVfrXVIQQABBBBAAAEEclGABGAuRp02I4AAAggggEDWCYSm6qpiu+66q11yySUuuaUde9etW+fWsHv//fdtzJgxboSf1rrbaaedtmnHBx98YC1btnT/XrFiRVuzZo07h3b/1S7AsYpeb9KkiS1fvtwd0qxZM5dkbNCggUs0/vrrr/bZZ5/ZW2+9Ze+884517tzZXn755TynS1YCUDv7ar3C0qVLu92LtUtxKNmpHX21sYemKrdp08YOOOAAO+mkk1zda9So4eqjxOALL7wQTh4ecsghNmfOHAvtLpx1wadCCCCAAAIIIIBAigVIAKYYmNMjgAACCCCAAALxCGi33j59+tiQIUPy7GIb7b1az2/VqlVRN7XYunWr2813yZIl4bdqJNwDDzxQaDX0Ho2605TgwsoFF1xgTz75ZEoSgPPmzXOj9TTaMX/p2rWrjRw5MpwALKye9erVc6Mfa9asWdihvI4AAggggAACCARWgARgYENLwxBAAAEEEEDAR4GFCxfasGHD3Cg7TbXV+n3anVfrAh588MFuR1+NeCtfvnzM5imRGLl23kcffWTNmzePi0MJRG0gohF0M2bMcBuHaG1BjSbUZiU6z4knnuhGGeYfUZesEYCq6Ny5c23w4MH24YcfujqEkoGhBKASpkpUapdjjQZcunSpO05rB1auXNkaNmxop5xyittYRbshUxBAAAEEEEAAgVwWIAGYy9Gn7QgggAACCCCAAAIIIIAAAggggAACgRcgARj4ENNABBBAAAEEEEAAAQQQQAABBBBAAIFcFiABmMvRp+0IIIAAAggggAACCCCAAAIIIIAAAoEXIAEY+BDTQAQQQAABBBBAAAEEEEAAAQQQQACBXBYgAZjL0aftCCCAAAIIIIAAAggggAACCCCAAAKBFyABGPgQ00AEEEAAAQQQQAABBBBAAAEEEEAAgVwWIAGYy9Gn7QgggAACCCCAAAIIIIAAAggggAACgRcgARj4ENNABBBAAAEEEEAAAQQQQAABBBBAAIFcFiABmMvRp+0IIIAAAggggAACCCCAAAIIIIAAAoEXIAEY+BDTQAQQQAABBBBAAAEEEEAAAQQQQACBXBYgAZjL0aftCCCAAAIIIIAAAggggAACCCCAAAKBFyABGPgQ00AEEEAAAQQQQAABBBBAAAEEEEAAgVwWIAGYy9Gn7QgggAACCCCAAAIIIIAAAggggAACgRcgARj4ENNABBBAAAEEEEAAAQQQQAABBBBAAIFcFiABmMvRp+0IIIAAAggggAACCCCAAAIIIIAAAoEXIAEY+BDTQAQQQAABBBBAAAEEEEAAAQQQQACBXBYgAZjL0aftCCCAAAIIIIAAAggggAACCCCAAAKBFyABGPgQ00AEEEAAAQQQQAABBBBAAAEEEEAAgVwWIAGYy9Gn7QgggAACCCCAAAIIIIAAAggggAACgRcgARj4ENNABBBAAAEEEEAAAQQQQAABBBBAAIFcFiABmMvRp+0IIIAAAggggAACCCCAAAIIIIAAAoEXIAEY+BDTQAQQQAABBBBAAAEEEEAAAQQQQACBXBYgAZjL0aftCCCAAAIIIIAAAggggAACCCCAAAKBFyABGPgQ00AEEEAAAQQQQAABBBBAAAEEEEAAgVwWIAGYy9Gn7QgggAACCCCAAAIIIIAAAggggAACgRf4P5pWKT0FDBGQAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.3131051635026933,\n",
       " 0.8938000000000005,\n",
       " 0.9576883044242859,\n",
       " array([[9.9118209e-01, 8.8178487e-03],\n",
       "        [1.3005758e-03, 9.9869943e-01],\n",
       "        [9.0870954e-04, 9.9909127e-01],\n",
       "        ...,\n",
       "        [9.4619691e-01, 5.3803090e-02],\n",
       "        [9.9911457e-01, 8.8545849e-04],\n",
       "        [9.9992597e-01, 7.4013820e-05]], dtype=float32),\n",
       " array([[1., 0.],\n",
       "        [0., 1.],\n",
       "        [0., 1.],\n",
       "        ...,\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model(output_node, input_node, ['img224_all/converted/rotation_224_v1/test_file_0.h5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load model from .h5 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change `file_name` to the path of model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3831: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:167: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:183: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:192: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:199: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py:757: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:976: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:963: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 1, 1, 2048)        0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1, 1, 1024)        2098176   \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "classifier_output (Dense)    (None, 2)                 2050      \n",
      "=================================================================\n",
      "Total params: 2,100,226\n",
      "Trainable params: 2,100,226\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "file_name = 'weights-quantized-224x224-fixval-best-final/class_model_best.h5'\n",
    "new_model = keras.models.load_model(file_name)\n",
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}